UNITED NATIONS ENVIRONMENT PROGRAMME
INTERNATIONAL LABOUR ORGANISATION
WORLD HEALTH ORGANIZATION
INTERNATIONAL PROGRAMME ON CHEMICAL SAFETY
Environmental Health Criteria 214
HUMAN EXPOSURE ASSESSMENT
This report contains the collective views of an international group of
experts and does not necessarily represent the decisions or the stated
policy of the United Nations Environment Programme, the International
Labour Organization, or the World Health Organization.
First draft prepared by Dr D. L. MacIntosh, University of Georgia,
Athens, GA, USA and Professor J. D. Spengler, Harvard University,
Boston, MA, USA
Published under the joint sponsorship of the United Nations
Environment Programme, the International Labour Organization, and the
World Health Organization, and produced within the framework of the
Inter-Organization Programme for the Sound Management of Chemicals.
World Health Organization
Geneva, 2000
The International Programme on Chemical Safety (IPCS),
established in 1980, is a joint venture of the United Nations
Environment Programme (UNEP), the International Labour Organization
(ILO), and the World Health Organization (WHO). The overall
objectives of the IPCS are to establish the scientific basis for
assessment of the risk to human health and the environment from
exposure to chemicals, through international peer review processes, as
a prerequisite for the promotion of chemical safety, and to provide
technical assistance in strengthening national capacities for the
sound management of chemicals.
The Inter-Organization Programme for the Sound Management of
Chemicals (IOMC) was established in 1995 by UNEP, ILO, the Food and
Agriculture Organization of the United Nations, WHO, the United
Nations Industrial Development Organization, the United Nations
Institute for Training and Research, and the Organisation for Economic
Co-operation and Development (Participating Organizations), following
recommendations made by the 1992 UN Conference on Environment and
Development to strengthen cooperation and increase coordination in the
field of chemical safety. The purpose of the IOMC is to promote
coordination of the policies and activities pursued by the
Participating Organizations, jointly or separately, to achieve the
sound management of chemicals in relation to human health and the
environment.
WHO Library Cataloguing-in-Publication Data
Human exposure assessment.
(Environmental health criteria ; 214)
1.Environmental monitoring - methods 2.Environmental exposure
3.Models, theoretical 4.Data collection - methods
5.Toxicity tests
I.International Programme on Chemical Safety II.Series
ISBN 92 4 157214 0 (NLM Classification: QT 162)
ISSN 0250-863X
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CONTENTS
ENVIRONMENTAL HEALTH CRITERIA FOR HUMAN EXPOSURE ASSESSMENT
PREAMBLE
ABBREVIATIONS
FOREWORD
1. DEFINING EXPOSURE
1.1. Introduction
1.2. Defining exposure
1.2.1. Exposure and exposure concentration
1.2.2. Exposure estimation by integration and averaging
1.2.3. Exposure measurements and models
1.2.4. Exposure in the context of an environmental health
paradigm
1.3. Elements of exposure assessment
1.4. Approaches to quantitative exposure assessment
1.5. Linking exposure events and dose events
1.6. Summary
2. USES OF HUMAN EXPOSURE INFORMATION
2.1. Introduction
2.2. Human exposure information in environmental epidemiology
2.3. Human exposure information in risk assessment
2.3.1. Risk allocation for population subgroups or
activities
2.3.2. Population at higher or highest risk
2.4. Human exposure information in risk management
2.5. Human exposure information in status and trend analysis
2.6. Summary
3. STRATEGIES AND DESIGN FOR EXPOSURE STUDIES
3.1. Introduction
3.2. Study design
3.3. Sampling and generalization
3.4. Types of study design
3.4.1. Comprehensive samples
3.4.2. Probability samples
3.4.3. Other sample types
3.5. Exposure assessment approaches
3.5.1. Direct approaches to exposure assessment
3.5.1.1 Personal monitoring of inhalation exposures
3.5.1.2 Personal monitoring of dietary exposures
3.5.1.3 Personal monitoring of dermal absorption
exposures
3.5.2. Indirect approaches to exposure assessment
3.5.2.1 Environmental monitoring
3.5.2.2 Models as an indirect approach to assessing
exposure
3.5.2.3 Questionnaires as an indirect approach to
assessing exposure
3.6. Summary
4. STATISTICAL METHODS IN EXPOSURE ASSESSMENT
4.1. Introduction
4.2. Descriptive statistics
4.2.1. Numerical summaries
4.2.2. Graphical summaries
4.2.2.1 Histograms
4.2.2.2 Cumulative frequency diagrams
4.2.2.3 Box plots
4.2.2.4 Quantile-quantile plots
4.2.2.5 Scatter plots
4.3. Probability distributions
4.3.1. Normal distribution
4.3.2. Lognormal distribution
4.3.3. Binomial distribution
4.3.4. Poisson distribution
4.4. Parametric inferential statistics
4.4.1. Estimation
4.4.2. Measurement error and reliability
4.4.3. Hypothesis testing and two-sample problems
4.4.4. Statistical models
4.4.4.1 Analysis of variance and linear regression
4.4.4.2 Logistic regression
4.4.5. Sample size determination
4.5. Non-parametric inferential statistics
4.6. Other topics
4.7. Summary
5. HUMAN TIME-USE PATTERNS AND EXPOSURE ASSESSMENT
5.1. Introduction
5.2. Methods
5.2.1. Activity pattern concepts
5.2.1.1 Time allocation parameters
5.2.1.2 Microenvironment parameters
5.2.1.3 Intensity of contact
5.2.2. Surrogates of time-activity patterns
5.2.3. Data collection methods
5.3. Potential limitations
5.3.1. Activity representativeness
5.3.2. Validity and reliability
5.3.3. Inter- and intra-person variability
5.4. Summary
6. HUMAN EXPOSURE AND DOSE MODELLING
6.1. Introduction
6.2. General types of exposure model
6.3. Environmental media and exposure media
6.4. Single-medium models
6.4.1. Outdoor and indoor air
6.4.2. Potable water
6.4.3. Surface waters
6.4.4. Groundwater
6.4.5. Soil
6.5. Multiple-media modelling
6.5.1. Inter-media transfer factors
6.5.1.1 Diffusive partition coefficients
6.5.1.2 Advective partition coefficients
6.5.2. Exposure factors
6.5.3. Multiple-media/multiple-pathway models
6.6. Probabilistic exposure models
6.6.1. Variability
6.6.2. Uncertainty
6.6.3. Implementing probabilistic exposure models
6.7. A generalized dose model
6.8. Physiologically based pharmacokinetic models
6.9. Validation and generalization
6.10. Summary
7. MEASURING HUMAN EXPOSURES TO CHEMICALS IN AIR, WATER AND FOOD
7.1. Introduction
7.2. Air monitoring
7.2.1. Gases and vapours
7.2.1.1 Passive samplers
7.2.1.2 Active samplers
7.2.1.3 Direct-reading instruments
7.2.2. Aerosols
7.2.3. Semivolatile compounds
7.2.4. Reactive gas monitoring
7.3. Water
7.3.1. Factors influencing water quality
7.3.2. Water quality monitoring strategies
7.3.3. Sample collection
7.4. Assessing exposures through food
7.4.1. Duplicate diet surveys
7.4.2. Market basket or total diet surveys
7.4.3. Food consumption
7.4.3.1 Food diaries
7.4.3.2 24-h recall
7.4.3.3 Food frequency questionnaires
7.4.3.4 Meal-based diet history
7.4.3.5 Food habit questionnaires
7.4.4. Contaminants in food
7.5. Summary
8. MEASURING HUMAN EXPOSURE TO CHEMICAL CONTAMINANTS IN SOIL AND
SETTLED DUST
8.1. Introduction
8.2. Selected sampling methods
8.2.1. Soil
8.2.1.1 Surface soil collection
8.2.1.2 Soil contact and intake measurements
8.2.2. Settled dust
8.2.2.1 Wipe sampling methods
8.2.2.2 Vacuum methods
8.2.2.3 Sedimentation methods
8.3. Sampling design considerations
8.3.1. Concentration and loading
8.3.2. Collection efficiency
8.4. Sampling strategies
8.5. Summary
9. MEASURING BIOLOGICAL HUMAN EXPOSURE AGENTS IN AIR AND DUST
9.1. Introduction
9.2. House dust mites
9.2.1. Air sampling for house dust mites
9.2.2. Dust sampling for house dust mites
9.2.3. Available methods of analysis for house dust mites
9.2.3.1 Mite counts
9.2.3.2 Immunochemical assays of dust mite
allergens
9.2.3.3 Guanine determination
9.2.4. Mite allergens
9.3. Allergens from pets and cockroaches
9.3.1. Air sampling for allergens from pets and cockroaches
9.3.2. Dust sampling for allergens from pets and
cockroaches
9.3.3. Available methods of analysis
9.3.4. Typical allergen concentrations
9.4. Fungi
9.4.1. Air sampling for fungi
9.4.2. Settled dust for fungi
9.4.3. Available methods of analysis for fungi in air
9.4.3.1 Total counts of viable and non-viable
fungal particles
9.4.4. General considerations for fungi
9.5. Bacteria (including actinomycetes)
9.5.1. Air sampling for bacteria
9.5.2. Dust sampling for bacteria
9.5.3. Available methods of analysis for bacteria
9.5.3.1 Total count of viable and non-viable
bacteria
9.5.3.2 Viable bacteria
9.5.3.3 Endotoxins
9.6. Pollen
9.6.1. Air sampling for pollen
9.6.2. Dust sampling for pollen
9.6.3. Available methods of analysis for pollen in air
9.6.4. General considerations for pollen sampling
9.7. Summary
10. ASSESSING EXPOSURES WITH BIOLOGICAL MARKERS
10.1. Introduction
10.2. General characteristics
10.3. Considerations for use in environmental exposure assessment
10.3.1. Toxicokinetics and toxicodynamics
10.3.2. Biological variability
10.3.3. Validation of biological markers
10.3.4. Normative data
10.4. Advantages of biological markers for exposure assessment
10.4.1. Characterizing inter-individual variability
10.4.2. Efficacy of use
10.4.3. Internal exposure sources
10.5. Limitations of biological markers for exposure assessment
10.5.1. Source identification
10.5.2. Biological variability and altered exposure response
10.5.3. Participant burden
10.5.4. Biosafety
10.6. Media available for use
10.6.1. Blood
10.6.2. Urine
10.6.3. Exhaled breath
10.6.4. Saliva
10.6.5. Keratinized tissue (hair and nails)
10.6.6. Ossified tissue
10.6.6.1 Teeth
10.6.6.2 Bone
10.6.7. Breast milk
10.6.8. Adipose tissue
10.6.9. Faeces
10.6.10. Other media
10.7. Summary
11. QUALITY ASSURANCE IN EXPOSURE STUDIES
11.1. Introduction
11.2. Quality assurance and quality control
11.3. Elements of a quality assurance programme
11.4. Quality assurance programme
11.4.1. Organization and personnel
11.4.2. Record-keeping and data recording
11.4.3. Study plan and standard operating procedures
11.4.4. Collection of samples
11.4.5. Equipment maintenance and calibration
11.4.6. Internal audit and corrective action
11.5. Quality control/quality assurance for sample measurement
11.5.1. Method selection and validation
11.5.1.1 Accuracy
11.5.1.2 Precision
11.5.1.3 Sensitivity
11.5.1.4 Detection limits
11.5.2. Internal quality control
11.5.2.1 Control charts
11.5.3. External quality control
11.5.4. Reference materials
11.6. Quality assurance and control issues in population-based
studies
11.7. Summary
12. EXAMPLES AND CASE STUDIES OF EXPOSURE STUDIES
12.1. Introduction
12.2. Exposure studies
12.3. Air pollution exposure studies
12.3.1. Particle studies
12.3.2. Carbon monoxide
12.3.3. Nitrogen dioxide
12.3.4. Ozone
12.3.5. Combined exposure studies
12.3.6. Assessing ambient pollution impacts indoors
12.3.7. Volatile organic compounds
12.3.8. Commuter exposures
12.4. Exposures and biomarkers
12.4.1. Exposure to lead and cadmium
12.4.2. Exposure to furans, dioxins and polychlorinated
biphenyls
12.4.3. Exposure to volatile organic compounds and urinary
metabolites
12.5. Exposure to contaminants in drinking-water
12.6. Exposure to microbes
12.7. Exposure studies and risk assessment
12.7.1. The German Environmental Survey
12.7.2. The National Human Exposure Assessment Survey
12.7.3. Windsor, Canada exposure and risk study
12.7.4. Pesticide exposure study
12.7.5. Czech study of air pollution impact on human health
REFERENCES
RÉSUMÉ
RESUMEN
NOTE TO READERS OF THE CRITERIA MONOGRAPHS
Every effort has been made to present information in the criteria
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Criteria monographs, readers are requested to communicate any errors
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356, 1219 Châtelaine, Geneva, Switzerland (telephone no.
+ 41 22 - 9799111, fax no. + 41 22 - 7973460, E-mail irptc@unep.ch).
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This publication was made possible by grant number
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Sciences, National Institutes of Health, USA, and by financial support
from the European Commission.
Environmental Health Criteria
PREAMBLE
Objectives
In 1973 the WHO Environmental Health Criteria Programme was
initiated with the following objectives:
(i) to assess information on the relationship between exposure to
environmental pollutants and human health, and to provide
guidelines for setting exposure limits;
(ii) to identify new or potential pollutants;
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pollutants;
(iv) to promote the harmonization of toxicological and
epidemiological methods in order to have internationally
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The first Environmental Health Criteria (EHC) monograph, on
mercury, was published in 1976 and since that time an ever-increasing
number of assessments of chemicals and of physical effects have been
produced. In addition, many EHC monographs have been devoted to
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teratogenic and nephrotoxic effects. Other publications have been
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forth.
Since its inauguration the EHC Programme has widened its scope,
and the importance of environmental effects, in addition to health
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chemicals.
The original impetus for the Programme came from World Health
Assembly resolutions and the recommendations of the 1972 UN Conference
on the Human Environment. Subsequently the work became an integral
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monographs have become widely established, used and recognized
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The recommendations of the 1992 UN Conference on Environment and
Development and the subsequent establishment of the Intergovernmental
Forum on Chemical Safety with the priorities for action in the six
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the need for EHC assessments of the risks of chemicals.
Scope
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Content
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the chemical
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JMPR
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If an EHC monograph is proposed for a chemical not on the
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WHO TASK GROUP ON HUMAN EXPOSURE ASSESSMENT
Members
Dr J. Alexander, Department of Environmental Medicine, National
Institute of Public Health, Folkehelsa, Torshov, Oslo, Norway
Dr M. Berglund, Institute of Environmental Medicine, Division of
Metals and Health, Karolinska Institute, Stockholm, Sweden
Dr M. Dellarco, US Environmental Protection Agency,
Washington, DC, USA
Mrs B. Genthe, Environmentek, CSIR, Stellenbosch, South Africa
Dr L. Gil, Department of Biochemistry, University of Chile -
Faculty of Medicine, Casilla, Santiago, Chile
Dr S. Goto, Department of Community Environmental Sciences,
Institute of Public Health, Minato-ku, Tokyo, Japan
Professor M. Jantunen, Department of Environmental Hygiene and
Toxicology, National Public Health Institute, Kuopio, Finland
Dr N. Künzli, Department of Environment and Health, Institute of
Social and Preventive Medicine, University of Basel, Basel,
Switzerland
Dr D. MacIntosh, Environmental Health Science, University of
Georgia, Athens, GA, USA
Dr M. Morandi, Environmental Sciences, Houston School of Public
Health, Houston Health Science Center, University of Texas,
Houston, TX, USA
Dr S. Pavittranon, National Institute of Health, Department of
Medical Sciences, Bamrasnaradura Hospital, Nonthari, Thailand
Dr N. Rees, Risk Assessment, Management and International
Coordination Branch, Ministry of Agriculture, Fisheries and Food,
London, United Kingdom
Dr B. Schoket, Department of Biochemistry, National Institute of
Environmental Health, "Fodor József" National Public Health
Centre, Budapest, Hungary
Dr L. Sheldon, US Environmental Protection Agency, National
Research Laboratory, Research Triangle Park, NC, USA
Professor J. D. Spengler, School of Public Health, Harvard
University, Boston, MA, USA
Dr P. Straehl, Swiss Federal Agency for Environment, Forestry and
Landscape, Swiss Department of the Interior, Bern, Switzerland
Observers
Mrs S. Munn, European Commission, European Chemicals Bureau,
Environment Institute, Ispra (VA), Italy
Secretariat
Mr C. Corvalan, Office of Global and Integrated Environmental
Health, World Health Organization, Geneva, Switzerland
Dr K. Gutschmidt, International Programme on Chemical Safety,
World Health Organization, Geneva, Switzerland
Dr M. Krzyzanowski, European Centre for Environment and
Health, World Health Organization, Regional Office for Europe,
Bilthoven Division, De Bilt, Netherlands
Dr G. Moy, Food Safety, World Health Organization, Geneva,
Switzerland
Dr H. Tamashiro, Office of Global and Integrated Environmental
Health, World Health Organization, Geneva, Switzerland
Dr M. Younes, International Programme on Chemical Safety,
World Health Organization, Geneva, Switzerland
ENVIRONMENTAL HEALTH CRITERIA FOR HUMAN EXPOSURE ASSESSMENT
A Task Group on the Environmental Health Criteria for Human
Exposure Assessment met in Glion-sur-Montreux, Switzerland, from 16 to
20 February 1998. Dr M. Younes, IPCS, welcomed the participants on
behalf of the Manager, IPCS, and the three IPCS cooperating
organizations (UNEP/ILO/WHO). The Task Group reviewed and revised the
final draft of the monograph. In preparation for the final draft a
review meeting was held at the National Institute of Health Sciences
(NIHS), Tokyo, from 17 to 19 July 1996.
The first draft was prepared by Dr D. L. MacIntosh, University of
Georgia, USA and Professor J. D. Spengler, Harvard University, USA.
Dr K. Gutschmidt was responsible officer in IPCS for the overall
scientific content of the monograph and the organization for the
meetings, and Ms K. Lyle (Sheffield, United Kingdom) was responsible
for the technical editing of the monograph.
The efforts of all who helped in the preparation and finalization
of the monograph are gratefully acknowledged.
ABBREVIATIONS
ACGIH American Conference of Governmental Industrial Hygienists
ADD average daily dose
AI acceptance intervals
ALAD Delta-aminolaevulinic acid dehydratase
AMIS Air Monitoring Information System
ANOVA analysis of variance
AOAC Association of Official Analytical Chemists
ASTM American Society for Testing of Materials
CDF chlorinated dibenzofurans; cumulative distribution function
CFU colony-forming units
CI confidence interval
DG18 dichloran 18% diglycerol agar
DVM dust vacuum method
EDTA ethylenediamine tetra-acetic acid
ELISA enzyme-linked immunosorbent assays
EPS extracellular polysaccharides
ETS environmental tobacco smoke (exposure)
EU endotoxin unit
FDA US Food and Drug Administration
FFQ food frequency questionnaire
GEMS Global Environment Monitoring System
GerES German Environmental Survey
GM geometric mean
GSD geometric standard deviation
HEAL Human Exposure Assessment Location
HPLC high-pressure liquid chromatography
HUD US Department of Housing and Urban Development
IAEA International Atomic Energy Agency
IAQ internal air quality
ISEA International Society of Exposure Analysis
ISO International Organization for Standardization
LADD lifetime average daily dose
LAL Limulus amoebocyte lysate
LOD limit of detection
LOQ limit of quantification
LWW Lioy-Weisel-Wainman
MAD maximum allowable deviations
MCS multiple chemical sensitivity
MDL method detection limit
MEA malt extract agar
NAAQS National Ambient Air Quality Standard
NHEXAS National Human Exposure Assessment Survey
NIOSH National Institute for Occupational Safety and Health
NTA nitriloacetic acid
OR odds ratio
PAH polycyclic aromatic hydrocarbons
PBPK physiologically based pharmacokinetic (method)
PCB polychlorinated biphenyls
PCDD polychlorinated dibenzo- p-dioxin
PCP pentachlorophenol
PDF probability distribution function
PEM personal exposure monitor
PMn particulate matter with aerodynamic diameter < n µm
PTEAM particle total exposure assessment methodology
QA quality assurance
QC quality control
RAST radioallergosorbent tests
RIA radioimmunoassay
RSP respirable particulate matter
SAM stationary outdoor monitor
SBS sick building syndrome
SD standard deviation
SEM scanning electron microscope
SIM stationary indoor monitor
SOP standard operating procedure
SVOC semivolatile organic compound
TCCD 2,3,7,8-tetrachloro dibenzo- p-dioxin
TDS US FDA Total Diet Study
TEQ TCCD toxic equivalents
TSP total suspended particulates
TWI tolerable weekly intake
UNEP United Nations Environment Programme
VOC volatile organic compound
XRF X-ray fluorescence
FOREWORD
The International Programme on Chemical Safety (IPCS), launched
in 1980, is a joint collaborative programme of the International Labor
Organization (ILO), the United Nations Environment Programme (UNEP),
and the World Health Organization (WHO); WHO is the Administrating
Organization of the Programme. The two main roles of the IPCS are to
establish the scientific health and environmental risk assessment
basis for safe use of chemicals (normative function) and to
strengthen national capabilities for chemical safety (technical
cooperation). In the field of methodology, the work of the IPCS aims
at promoting the development, improvement, validation, harmonization
and use of generally acceptable, scientifically sound methodologies
for the evaluation of risks to human health and the environment from
exposure to chemicals. The work encompasses the development of
Environmental Health Criteria monographs on general principles of
various areas of risk assessment covering various aspects related to
risk assessment such as, in this publication, on exposure assessment.
The WHO and the World Meteorological Organization coordinate the
assessment of climate, urban air and water pollution, and health
status of populations. These measures provide the indicator of trends
and status.
Until 1995, the basic source for internationally comparable urban
air pollution data was the Global Environment Monitoring System
(GEMS/Air) of UNEP and WHO. Started in 1974, shortly after the
Stockholm Environment Conference, GEMS had built up a system that
collected comparable ambient air pollution data in about 50 cities of
35 countries, varied in geography and income (UNEP/WHO, 1988, 1992).
Typically, sulfur dioxide and total suspended particulates (TSP) had
been monitored in three stations of each city, one each in industrial,
commercial, and residential zones. Later, GEMS also collected
monitoring data for carbon monoxide, nitrogen dioxide, and lead, and
made emissions estimates for all five pollutants. The results were
published periodically by GEMS, and also often appeared in other
periodic international data sets, such as those of the World Bank
(World Bank, 1992), the World Resources Institute (World Resources
Institute, 1992), the United Nations (UN ESCAP, 1990) and UNEP itself
(UNEP, 1991).
More recently, WHO created with the Air Management Information
System (AMIS) the successor of GEMS/Air. Like GEMS/Air, AMIS provides
air quality data for major and megacities. Data on sulfur dioxide,
nitrogen dioxide, carbon monoxide, ozone, black smoke, suspended
particulate matter, PM10, lead and others are available. AMIS also
includes information on air quality management (WHO, 1997).
Much of what is known about contaminants in food, soils, water
and air has become available through WHO and UNEP publications. For
more than 20 years WHO/UNEP has been promoting an appreciation for
improved assessments of human exposures through training sessions,
workshops, demonstration projects, and published methodologies and
reports. Through a series of WHO-sponsored studies in every populated
continent, the principles of human exposure assessment have been
illustrated for indoor and outdoor air pollutants, food contamination
and water. In 1984, after some background reports (e.g., UNEP/WHO,
1982), WHO and UNEP conducted the Human Exposure Assessment Location
(HEAL) Project, which facilitates research and information sharing
among 10-15 institutions worldwide concerned with exposure assessment
for a limited number of pollutants (Ozolins, 1989). Unfortunately,
although providing important functions, the HEAL project has not had
the mandate or anything approaching the resources required to actually
make comparable international estimates of population exposures. HEAL
projects, for the most part, have investigated exposures to
conventional inorganic air pollutants such as carbon monoxide,
nitrogen dioxide and general undifferentiated particle mass where
inhalation is the primary route of exposures. However, the HEAL
programme does offer examples of lead, cadmium and pesticide studies
which illustrate multiple exposure pathways and demonstrate the
necessity of extensive analytical training and quality programmes. An
analytical quality control programme which involved all participating
laboratories enabled reliable international comparisons of exposure
despite differences in methodologies applied by the different
laboratories.
Preceding this criteria document the UNEP, FAO and WHO have been
actively advancing the concepts and methodologies for human exposures.
GEMS/Air, GEMS/Water and GEMS/Food are establishing the uniformity
among data collected worldwide to establish national and international
status and trends. These efforts, together with others, such as the
Codex Committee on Pesticide Residues, the several Joint FAO/WHO
Consultations on food consumption, pesticide residues, veterinary
drugs, additives and chemical contaminants, have been developing the
basis of quantitative assessment of human exposures and risk. Table 38
(pg. 279) provides a listing of pertinent publications related to
assessment of air, water and food contamination.
Scope
This current criteria document on human exposure assessment
presents in one publication the concepts, rationale, and statistical
and procedural methodologies for human exposure assessment. The
underpinnings of exposure assessment are the basic environmental and
biological measurements found in the more familiar specialties of air
and water pollution and food and soil sciences. Therefore, throughout
this document readers are referred to other publications for technical
details on instrumental and laboratory methods. This criteria document
is intended for the community of scientific investigators inquiring
about the human health consequences of contaminants in our
environment. As such, this text will be of interest to physical
scientists, engineers and epidemiologists. It is intended also for
those professions involved in devising, evaluating and implementing
policy with respect to managing the quality of environmental health,
inclusive of air, water, food and soil. By necessity environment is
defined broadly to include place, media, and activities where we
humans encounter contaminants.
Of primary concern in this document are those environmental
contaminants that exist in various media as a consequence of direct or
indirect human intention. We have included some biological agents that
are "natural" but, through actions of irritation and allergy, can
contribute to or cause morbidity and mortality as a result of
inadequate building design and maintenance. We recognize that viral,
bacterial and other biological agents in air, food, soil and water
contribute significantly to the burden of disease worldwide. However,
in the context of environmental exposure assessment the focus is on
chemical contaminants and a few specific allergens that might
contribute directly to disease or, in combination with biopathogens,
alter susceptibility and expression of disease.
To say that exposure assessment of environmental contaminants is
exclusive of any population or location is, in principle, a
contradiction. There are practical considerations, however, for
identifying the industrial workplace as a separate domain.
Administratively, many nations handle occupational health and safety
concerns separately from the environment. The management of workplace
hazards through well-established industrial hygiene practices of
source control, ventilation and worker protection are widely
recognized. This separation of workplace exposures from the general
environmental exposure focus in this document is not hard and fast.
Occupationally acquired contaminants can expose family members not
working in the specific industry. Industrial control strategies that
increase ventilation can adversely affect the neighbouring community.
In many societies, commercial and residential use of property are
integrated. Family operated business along congested streets means
that contaminants generated in outdoors, indoors and workplaces are
intermingled. Even where commercial and residential property are
distinct, chemical and biological contaminants can lead to non-worker
exposures.
Information on human exposures has a well-recognized role as a
corollary to epidemiology. But it is more than this, because
understanding human exposures to environmental contaminants is
fundamental to public policy. The adequacy of environmental mitigation
strategies is predicated on improving or safeguarding human and
ecological health. The public mandate for and acceptance of controls
on emissions is first based on sensory awareness of pollution.
Irritated airways, foul-smelling exhaust, obscuring plumes, oil slicks
on water, dirty and foul-tasting water, and medical waste and debris
on beaches are readily interpreted as transgressions against us and
threaten commonly shared natural resources. As we enter the
twenty-first century, we recognize that we, humans have had profound
but often subtle impacts on the chemistry of the biosphere and
lithosphere. Metals, organic compounds, particulate matter, and
photochemically produced gases are widely dispersed, recognizing no
geographic or political boundaries. Global markets, urbanization, and
increased mobility have environmental contamination as a consequence.
Assessing the quantities and distribution of potentially harmful
contaminant exposures to human populations is a critical component of
risk management. As long as disease prevention and health promotion
are the principal tenets of public health, then assessing the levels
of contaminant exposures in environmental and biological samples will
be necessary.
This book presents the methodologies for surveying exposures,
analysing data and integrating findings with the ongoing national and
global debate defining natural limits to human behaviour. It serves
the cross-disciplinary needs of environmental managers, risk assessors
and epidemiologists to learn something about the design, conduct,
interpretation and value of human exposure studies of multimedia
environmental contaminants. For investigators considering exposure
studies, this book guides them to contemporary information on
measurement of analysis methods and strategies.
In Chapter 1 of the document the basic terms and concepts used in
exposure assessment are defined. Similar understanding of terms used
commonly among health assessors working in the different fields of
air, water, soil and food sciences is a critical starting point in
defining the emerging specialist area of exposure assessment.
Application of exposure research and routine assessments to the
information needs of risk managers, policy-makers and epidemiologists
is established in Chapter 2. Discussion of these information needs is
developed in Chapter 3, which presents the objectives for various
study designs.
Chapter 4 covers basic statistical concepts used in exposure
assessment. The intent is to inform the reader of how statistical
analysis is vital to all components of an exposure assessment. By
examples and references the reader is directed to more substantial
texts on study design, data analysis, modelling and quality control.
Chapter 5 is devoted to a component of exposure assessment
related to the collection and interpretation of human activity
patterns. Information on how, where and when people contact
potentially contaminant media is useful for data interpretation,
establishing risk scenarios and identifying activities, locations and
populations at differential risk. The emphasis here is primarily
related to air pollution exposure studies. In the conduct of total
multimedia exposure investigations or modelling analogous information
is needed for the ingestion of water and food, as well as for dermal
contact.
Chapter 6 extends the concepts of the preceding chapters in
discussing models for human exposure assessment. The data requirements
for various pathways and various modelling approaches are presented.
Chapter 7 separates the conceptual first half of the text from
the pragmatic guidelines offered in the rest of the document. The
chapter contains a discussion of air monitoring, water monitoring and
food sampling. These particular fields are rather well developed
individually, if not well integrated into multimedia studies. The
reader is referred to many other resources that can guide the
investigator to details on instruments, sampling methods and
laboratory analysis.
In Chapter 8, proportionally more emphasis is placed on soil and
settled dust sampling. Again, the laboratory methods for metals,
organics and various chemical compounds are readily available in the
published literature. This chapter, then, focuses on relatively new
sampling techniques to quantify in a standardized way the contaminant
levels in soil and settled dust.
In Chapter 9, on microbiological agents, assessment techniques
for commonly encountered allergens, mycotoxins, fungal and pollen
spores, microbiological bacteria and endotoxins are presented. These
agents have been included because of their imputed contribution to
respiratory disease and potential interactions with chemical
pollutants. There is growing recognition that exposure to these agents
in schools, homes, hospitals and office buildings constitutes a
specific risk to atopic, asthmatic and compromised individuals.
The use of biomarkers for exposure assessments is presented in
Chapter 10. Biological samples derived from human tissue or fluids
have been used as markers of both effects as well as exposure (dose)
to a variety of occupational and environmental contaminants. The
chapter describes the applications of biomarkers in exposure studies.
The quality assurance (QA) activities that should be considered
in conducting and evaluating exposure studies are addressed in Chapter
11. Contributors to this document intended to impart their experiences
to improve future exposure study. It is emphasized that QA aspects
must be considered in all components of exposure studies, to enhance
comparability and interpretation.
Chapter 12 presents brief synopses of exposure studies.
Selections illustrate a variety of study designs with different
objectives and target pollutants and populations. Relatively more
emphasis has been given to particles and passive exposure to cigarette
smoke. The evidence is that cigarette consumption has increased almost
worldwide, suggesting that greater attention be given to
characterizing and reducing exposures to non-smokers, in particular,
infants and young children. Epidemiological studies conducted over the
last 15 years indicate that ambient particulate matter is adversely
affecting human health at levels well below many of the established
standards. Exposure assessment along with toxicology and epidemiology
will be needed to answer many of the remaining unresolved issues about
ambient and indoor suspended particles.
Other studies summarized show how exposure assessment is
supportive of epidemiology and risk management. The reader should
recognize that Chapter 12 is not comprehensive but is intended to help
educate the research community and others about the application, use
and limitations of exposure assessment methodologies.
1. DEFINING EXPOSURE
1.1 Introduction
People are exposed to a variety of potentially harmful agents in
the air they breathe, the liquids they drink, the food they eat, the
surfaces they touch and the products they use. An important aspect of
public health protection is the prevention or reduction of exposures
to environmental agents that contribute, either directly or
indirectly, to increased rates of premature death, disease, discomfort
or disability. It is usually not possible, however, to measure the
effectiveness of mitigation strategies directly in terms of prevented
disease, reduced premature death, or avoided dysfunction. Instead,
measurement or estimation of actual human exposure, coupled with
appropriate assumptions about associated health effects or safety
limits (e.g., acceptable daily intake, tolerable daily intake), is the
standard method used for determining whether intervention is necessary
to protect and promote public health, which forms of intervention will
be most effective in meeting public health goals, and whether past
intervention efforts have been successful (Ott & Roberts, 1998).
The purpose of this chapter is to define the concept of exposure,
and the direct and indirect method of exposure assessment. A brief
discussion of exposure in the environmental health paradigm and its
relationship to dose is presented.
1.2 Defining exposure
Exposure is defined as contact over time and space between a
person and one or more biological, chemical or physical agents (US
NRC, 1991a). Exposure assessment is to identify and define the
exposures that occur, or are anticipated to occur, in human
populations (IPCS, 1993). This can be a complex endeavour requiring
analysis of many different aspects of the contact between people and
hazardous substances (see Table 1). Although exposure is a
well-established concept familiar to all environmental health
scientists, its meaning often varies depending on the context of the
discussion. It is important however, that exposure and related terms
be defined precisely. In the following sections, we describe and
define important exposure-related terms used in this document. The
definitions are consistent with the US EPA's Exposure Assessment
Guidelines and related WHO publications (WHO, 1987, 1996a; US EPA,
1992a; IPCS, 1994). It is important to recognize, however, that
terminology and definitions vary among organizations and nations.
Thus, the reader is advised to concentrate on the concepts, rather
than the specific terms, as they represent the crux of exposure
assessment.
Table 1. Different aspects of the contact between people and pollution
that are potentially important in exposure analysis
(Sexton et al., 1995b)
Agent(s) biological, chemical, physical, single
agent, multiple agents, mixtures
Source(s) anthropogenic/non-anthropogenic, area/point,
stationary/mobile, indoor/outdoor
Transport/carrier medium air, water, soil, dust, food, product/item
Exposure pathways(s) eating contaminated food,
breathing contaminated workplace air
touching residential surface
Exposure concentration mg/kg (food), mg/litre (water), µg/m3 (air),
µg/cm2 contaminated surface), % by weight,
fibres/m3 (air)
Exposure route(s) inhalation, dermal contact, ingestion,
multiple routes
Exposure duration seconds, minutes, hours, days, weeks,
months, years, lifetime
Exposure frequency continuous, intermittent, cyclic, random,
rare
Exposure setting(s) occupational/non-occupational,
residential/non-residential, indoors/outdoors
Exposed population general population, population subgroups,
individuals
Geographic scope site/source specific, local, regional,
national, international, global
Time frame past, present, future, trends
1.2.1 Exposure and exposure concentration
Exposure, as defined earlier, is the contact of a biological,
chemical, or physical agent with the outer part of the human body,
such as the skin, mouth or nostrils. Although there are many instances
where contact occurs with an undiluted chemical (e.g., use of
degreasing chemicals for cleaning hands), contact more often occurs
with a carrier medium (air, water, food, dust or soil) that contains
dilute amounts of the agent. "Exposure concentration" (e.g., mg/litre,
mg/kg, µg/m3) is defined as the concentration of an environmental
agent in the carrier medium at the point of contact with the body.
1.2.2 Exposure estimation by integration and averaging
A minimal description of exposure for a particular route must
include exposure concentration and the duration of contact. If the
exposure concentration is integrated over the duration of contact
(Table 2), the area under the resulting curve is the magnitude of the
exposure in units of concentration multiplied by time (e.g.,
mg/litreÊday, mg/kgÊday, µg/m3Êh). This is the method of choice to
describe and estimate short-term doses, where integration times are of
the order of minutes, hours or days.
Over periods of months, years or decades, exposures to most
environmental agents occur intermittently rather than continuously.
Yet long-term health effects, such as cancer, are customarily
evaluated based on an average dose over the period of interest
(typically years), rather than as a series of intermittent exposures.
Consequently, long-term doses are usually estimated by summing doses
across discrete exposure episodes and then calculating an average dose
for the period of interest (e.g., year, lifetime). Although the
integration approach can also be used to estimate long-term exposures
or doses, its application to time periods longer than about a week is
usually difficult and inconvenient.
1.2.3 Exposure measurements and models
Direct measurements are the only way to establish unequivocally
whether and to what extent individuals are exposed to specific
environmental agents. But it is neither affordable nor technically
feasible to measure exposures for everyone in all populations of
interest. Models, which are mathematical abstractions of physical
reality, may obviate the need for such extensive monitoring programmes
by providing estimates of population exposures (and doses) that are
based on a smaller number of representative measurements (Fig. 1). The
challenge is to develop appropriate and robust models that allow for
extrapolation from relatively few measurements to estimates of
exposures and doses for a much larger population (US NRC, 1991b).
For relatively small groups, measurements or estimates can be
made for some or all of the individuals separately, and then combined
as necessary to estimate the exposure (or dose) distribution. For
larger groups, exposure models and statistics can sometimes be used to
derive an estimate of the distribution of population exposures,
depending on the quantity and quality of existing data. Monte Carlo
and other statistical techniques are increasingly being used to
generate and analyse exposure distributions for large groups (US EPA,
1992a).
1.2.4 Exposure in the context of an environmental health paradigm
The presence of hazardous substances in our environment does not
necessarily imply a risk to human health or to the ecosystem. Exposure
is an integral and necessary component in a sequence of events having
potential health consequences. An expanded and more detailed version
of the environmental health paradigm also showing the role of exposure
is depicted in Fig. 2. The role of exposure assessment in the risk
assessment framework applied by EU and US EPA is shown in Fig. 3.
The release of an agent into the environment, its ensuing
transport, transformation and fate in various environmental media, and
its ultimate contact with people are critical events in understanding
how and why exposures occur. Definitions for key events in the
continuum are summarized below. They were compiled from three sources:
Ott (1990); US EPA (1992a); Sexton et al. (1995a).
* Sources. The point or area of origin for an environmental agent
is known as a source. Agents are released into the environment from
a wide variety of sources, which are often categorized as
primary sources including point sources (e.g., incinerator)
versus area sources (e.g., urban runoff), stationary sources (e.g.,
refinery) versus mobile sources (e.g., automobile) and
anthropogenic sources (e.g., landfill) versus non-anthropogenic
sources (e.g., natural vegetation) and secondary sources
including condensation of vapours into particles and chemical
reactions of precursors producing new pollutants.
* Exposure pathway. An exposure pathway is the physical course
taken by an agent as it moves from a source to a point of contact
with a person. The substance present in the media is quantified as
its concentration.
* Exposure concentration. As discussed in 1.2.1, exposure is the
concentration of an agent in a carrier medium at the point of
contact with the outer boundary of the human body. The
concentration is the amount (mass) of a substance or contaminant
that is present in a medium such as air, water, food or soil
expressed per volume or mass. Assessments are often not at exposure
or exposure concentration, since that information alone is not very
useful unless it is converted to dose or risk. Assessments
therefore usually estimate how much of an agent is expected to
enter the body. This transfer of an environmental agent from the
exterior to the interior of the body can occur by either or both of
two basic processes: intake and uptake.
* Exposure route. Exposure route denotes the different ways the
substance may enter the body. The route may be dermal, ingestion or
inhalation.
* Intake. Intake is associated with ingestion and inhalation routes
of exposure. The agent, which is likely to be part of a carrier
medium (air, water, soil, dust, food), enters the body by bulk
transport, usually through the nose or mouth. The amount of the
agent that crosses the boundary per unit time can be referred to as
the "intake rate", which is the product of the exposure
concentration times the rate of either ingestion or inhalation. For
inhalation, intake may be calculated for any time period. For
ingestion, intake is usually expressed as the amount of food or
water consumed times the pollutant concentration in that medium
during a certain time period.
* Uptake. Uptake is associated with the dermal route of exposure,
as well as with ingestion and inhalation after intake has occurred.
The agent, as with intake, is likely to be part of a carrier medium
(e.g., water, soil, consumer product), but enters the body by
crossing an absorption barrier, such as the skin, respiratory tract
or gastrointestinal tract. The rates of bulk transport across the
absorption barriers are generally not the same for the agent and
the carrier medium. The amount of the agent that crosses the
barrier per unit time can be referred to as the uptake rate. This
rate is a function of the exposure concentration, as well as of the
permeability and surface area of the exposed barrier. The uptake
rate is also called a flux.
* Dose. Once the agent enters the body by either intake or uptake,
it is described as a dose. Several different types of dose are
relevant to exposure estimation. All these different dose measures
are approximations of the target or biological effective dose.
- Potential (administered) dose. Potential or administered dose
is the amount of the agent that is actually ingested, inhaled or
applied to the skin. The concept of potential dose is
straightforward for inhalation and ingestion, where it is
analogous to the dose administered in a dose-response
experiment. For the dermal route, however, it is important to
keep in mind that potential (or administered) dose refers to the
amount of the agent, whether in pure form or as part of a
carrier medium, that is applied to the surface of the skin. In
cases where the agent is in diluted form as part of a carrier
medium, not all of the potential dose will actually be touching
the skin.
- Applied dose. Applied dose is the amount of the agent directly
in contact with the body's absorption barriers, such as the
skin, respiratory tract and gastrointestinal tract, and
therefore available for absorption. Information is rarely
available on applied dose, so it is calculated from potential
dose based on factors such as bioavailability (Fig. 2).
- Internal (absorbed) dose. The amount of the agent absorbed,
and therefore available to undergo metabolism, transport,
storage or elimination, is referred to as the internal or
absorbed dose (Fig. 2). Bioavailability has been used to
describe absorbed dose.
- Delivered dose. The delivered dose is the portion of the
internal (absorbed) dose that reaches a tissue of interest.
- Biologically effective (target) dose. The biologically
effective dose is the portion of the delivered dose that reaches
the site or sites of toxic action.
The link, if any, between biologically effective (target) dose
and subsequent disease or illness depends on the relationship between
dose and response (e.g., shape of the dose-response curve), underlying
pharmacodynamic mechanisms (e.g., compensation, damage, repair), and
important susceptibility factors (e.g., health status, nutrition,
stress, genetic predisposition).
* Biological effect. A measurable response to dose in a molecule,
cell or tissue is termed a biological effect. The significance of a
biological effect, whether it is an indicator or a precursor for
subsequent adverse health effects, may not be known.
* Adverse effect. A biological effect that causes change in
morphology, physiology, growth, development or life span which
results in impairment of functional capacity to compensate for
additional stress or increase in susceptibility to the harmful
effects of other environmental influences (IPCS, 1994).
1.3 Elements of exposure assessment
Assessing human exposure to an environmental agent involves the
qualitative description and the quantitative estimation of the agent's
contact with (exposure) and entry into (dose) the body. Although no
two exposure assessments are exactly the same, all have several common
elements: the number of people exposed at specific concentrations for
the time period of interest; the resulting dose; and the contribution
of important sources, pathways and behavioural factors to exposure or
dose. A list of the types of estimates that might comprise a
comprehensive exposure assessment could include the following (as
described in part by Brown (1987) and Sexton et al. (1995a)):
* Exposure
- routes, pathways and frequencies
- duration of interest (short-term, long-term, intermittent or
peak exposures)
- distribution (e.g., mean, variance, 90th percentile) --
population, important subpopulations (e.g., more exposed, more
susceptible)
- individuals -- average, upper tail of distribution, most exposed
in population.
* Dose
- link with exposures
- distribution (e.g., mean, variance, 90th percentile) --
population important subpopulations (e.g., higher doses, more
susceptible)
- individuals -- average, upper tail of distribution, highest dose
in population.
* Causes
- relative contribution of important sources
- relative contribution of important environmental media
- contribution of important exposure pathways
- relative contribution of important routes of exposure.
* Variability
- within individuals (e.g., changes in exposure from day to day
for the same person)
- between individuals (e.g., differences in exposure on the same
day for two different people)
- between groups (e.g., different socio-economic classes or
residential locations)
- over time (e.g., changes in exposure from one season to another)
- across space (e.g., changes in exposure/dose from one region of
a city, country to another).
* Uncertainty
- lack of data (e.g., statistical error in measurements, model
parameters, etc.; misidentification of hazards and causal
pathways)
- lack of understanding (e.g., mistakes in functional form of
models, misuses of proxy data from analogous contexts).
Although comprehensive exposure assessments could be considered
the ideal, they are very costly; decisions therefore need to be made
on the most important elements for inclusion. For any study, the
purpose must first be defined. Possible purposes include environmental
epidemiology, risk assessment, risk management or status and trend
analysis (see Chapter 2). The data elements and measuring approaches
that are needed for this purpose are then determined. Table 3
summarizes the basic information that is required for each study. It
should be mentioned that different elements of the exposure assessment
framework might be selected to meet different study requirements.
Table 3. Basic information needed for exposure assessments in
different contexts
Information required
Risk assessment Point estimates or distributions of
exposure and dose
Duration of exposure and dose
Risk management Pollutant source contributing to
(conducted once hazard exposure and dose
is identified) Personal activities contributing
to exposure and dose
Effectiveness of intervention measures
Status and trend Change of exposure and dose of
populations over time
Epidemiology Individual and population exposures and
doses, exposure dose categories
1.4 Approaches to quantitative exposure assessment
Quantitative estimation of exposure is often the central feature
of assessment activities. The quantitative estimation of exposure can
be approached in two general ways: direct assessment, including
point-of-contact measurements and biological indicators of exposure;
and indirect assessment, including environmental monitoring,
modelling, questionnaires (US NRC, 1991b) (see Chapter 3.5). These two
generic approaches to quantitative estimation of exposure are
independent and complementary. Each relies on different kinds of data
and has different strengths and weaknesses. It is potentially useful,
therefore, to employ multiple approaches as a way of checking the
robustness of results. Among other factors, the choice of which method
to use will depend on the purpose of the assessment and the
availability of suitable methods, measurements and models.
Direct approaches for air, water and food include personal air
monitors, measurements of water at the point of use and measurement of
the food being consumed. Indirect approaches include
microenvironmental air monitoring and measurements of the water supply
and food supply (contents of a typical food basket, for instance).
Exposure models are constructed to assess or predict personal
exposures or population exposure distributions from indirect
measurements and other relevant information.
Measures of contaminants in biological material (biomarkers)
afford a direct measure of exposure modified by and integrated over
some time in the past which depends on physiological factors that
control metabolism and excretion. Such measures give no direct
information about the exposure pathways. Examples of the type of
biomarkers measured in human material that can be used for
reconstructing internal dose and their relevance to exposure
assessment are discussed in Chapter 10.
1.5 Linking exposure events and dose events
The schematic framework in Fig. 2 shows how the
interrelationships among significant exposure- and dose-related events
in the paradigm can be conceived.
It is important to keep in mind that, although events along the
continuum are correlated, the relative position of a particular
individual within a distribution may change dramatically from one
event to the next as the agent or its metabolite/derivative moves
through the various stages from exposure concentration to biologically
effective dose.
To make realistic estimates for a specific event (e.g., an
internal dose), it is necessary to have at least one of two types of
information: measurements of the event itself (e.g., internal dose),
or measurements of an earlier (e.g., potential dose) or later (e.g.,
delivered dose) event in the continuum. It is also necessary to
understand the critical intervening mechanisms and processes (e.g.,
pharmacokinetics) that govern the relationship between the event
measured and the event of interest (e.g., internal dose). Unless such
data are on hand, extrapolating from one event to another, moving
either from exposure to dose (downwards in Fig. 2) or from dose to
exposure (upwards in Fig. 2) is problematic.
Suitable data and adequate understanding are seldom, if ever,
available to describe and estimate all of the significant events for
the groups and individuals of interest. Generally speaking,
measurement of exposure concentration and delivered dose (body
burden) is in many cases relatively straightforward, whereas
measurement of potential (administered) dose and internal (absorbed)
dose is usually possible only with substantially greater effort.
Measurement of biologically effective (target) dose may also be
possible in some cases, although it is usually impossible to measure
the applied dose.
This situation presents us with a conundrum. We would like to
have realistic estimates of exposure concentrations of an agent for
all important pathways, and the resulting biologically effective dose.
Typically, however, if relevant data are available at all, they are
related to exposure concentrations for one pathway or route of
exposure. In the few cases where data on dose are also available,
these data usually reflect delivered dose (body burden) rather than
biologically effective dose. Even if suitable measurements of both
exposure concentration and delivered or target dose are on hand, the
absence of pharmacokinetic understanding to relate these measurements
to each other, as well as to other significant events along the
continuum, seriously impairs efforts to establish the link between
exposure and dose.
We are thus left with a situation in which we can measure
specific events on either side of the body's absorption boundaries,
but we can relate them to each other only by using a series of
unsubstantiated assumptions. Yet it is this relationship between
exposure and dose that is critical to, for example, establishing cause
and effect relationships between exposure and diseases.
1.6 Summary
Exposure requires the occurrence of the presence of an
environmental toxicant at a particular point in space and time; and
the presence of a person or persons at the same location and time. In
addition, the amount which comes in contact with the outer boundary of
the human body is required.
As the intrinsic value of exposure-related information has become
recognized, "exposure analysis" has emerged as an important field of
scientific investigation, complementing such traditional public health
disciplines as epidemiology and toxicology, and is an essential
component in informed environmental health decision-making (Goldman et
al., 1992; Sexton et al., 1992, 1994; Wagener et al., 1995).
2. USES OF HUMAN EXPOSURE INFORMATION
2.1 Introduction
Exposure assessments collect data on the route magnitude,
duration, frequency and distributions of exposures to hazardous agents
for individuals and populations. Human exposure data have been used
for the evaluation and protection of environmental health in four
interrelated disciplines: epidemiology, risk assessment, risk
management, and status and trends analysis. The fundamental goal of
exposure assessment studies is to reduce the uncertainty of the
exposure estimates that are used within each discipline to make public
policy decisions or reach research conclusions.
Epidemiology is the examination of the link between human
exposures and health outcomes (Sexton et al., 1992). Risk
assessment is the estimation of the likelihood, magnitude and
uncertainty of population health risks associated with exposures. In
contrast, risk management is the determination of the source and
level of health risks and which health risks are acceptable and what
to do about them. Status and trends analysis comprises the evaluation
of historical patterns, current status and possible future changes in
human exposures.
The purpose of this chapter is to describe the disciplines from
environmental epidemiology through risk assessment. It also describes
how human exposure assessment data are used in each of these
disciplines
2.2 Human exposure information in environmental epidemiology
Epidemiology is the study of the determinants and distribution of
health status (or health-related events) in human populations.
Environmental epidemiology searches for statistical associations
between environmental exposures and adverse health effects (presumed)
to be caused by such exposures. It is a scientific tool that can
sometimes detect environmentally induced health effects in
populations, and it may offer opportunities to link actual exposures
with adverse health outcomes (US NRC, 1991c, 1994; Matanoski et al.,
1992; Beaglehole et al., 1993).
Exposure assessment methods can be used for identifying and
defining the low or high exposure groups. They can also be used for
devising more accurate exposure data from measured environmental
contaminant levels and personal questionnaire or time-activity diary
data, or estimating population exposure differences between days of
high and low pollution, or between high and low pollution in
communities using measured environmental and population behavioural
data (see also Chapters 3 and 5).
In particular, to establish long-term health effects of "low
dose" environmental exposures, epidemiological methods are the
predominant, if not only, tools at hand for health-effect assessment.
However, the excess risk of most environmentally related health
effects is small, with relative risks and odds ratios usually being
less than 2 across the observed range of exposure experienced by
populations. Furthermore, there are usually no "non-exposed"
comparison groups, and the factors contributing to the development of
diseases are numerous. As a consequence, environmental epidemiology
faces considerable methodological challenges. Adequate exposure
assessment is one key issue, as well as the need for studies conducted
with large populations.
2.3 Human exposure information in risk assessment
Risk assessment is a formalized process for estimating the
magnitude, likelihood and uncertainty of environmentally induced
health effects in populations. Exposure assessment (e.g., exposure
concentrations and related dose for specific pathways) and effects
assessment (i.e., hazard identification, dose-response evaluation) are
integral parts of the risk assessment process. The goal is to use the
best available information and knowledge to estimate health risks for
the subject population, important subgroups within the population
(e.g., children, pregnant women and the elderly), and individuals in
the middle and at the "high end" of the exposure distribution (US NRC,
1983; Graham et al., 1992; Sexton et al., 1992).
Environmental health policy decisions should be based on
established links among emission sources, human exposures and adverse
health effects. The chain of events depicted in Fig. 4 is an
"environmental health paradigm": a simplified representation of the
key steps between emission of toxic agents into the environment and
the final outcome as potential disease or dysfunction in humans. This
sequential series of events serves as a useful framework for
understanding and evaluating environmental health risks (Sexton, 1992;
Sexton et al., 1992, 1993). It is directly related to the risk
assessment process.
* Exposure assessment in the risk assessment framework focuses on
the initial portion of the environmental health paradigm: from
sources, to environmental concentrations, to exposure, to dose. The
major goal of exposure assessment is to develop a qualitative and
quantitative description of the environmental agent's contact with
(exposure) and entry into (dose) the human body. Emphasis is placed
on estimating the magnitude, duration and frequency of exposures,
as well as estimating the number of people exposed to various
concentrations of the agent in question (US NRC, 1983, 1991a;
Callahan & Bryan, 1994).
* Effects assessment examines the latter portion of the events
continuum: from dose to adverse health effects (Fig. 4). The goals
are to determine the intrinsic hazards associated with the agent
(hazard identification) and to quantify the relationship between
dose to the target tissue and related harmful outcomes
(dose-response/effect assessment). The overlap between exposure
assessment and effects assessment reflects the importance of the
exposure-dose relationship to both activities (Sexton et al.,
1992).
* Risk characterization is the last phase of the risk assessment
process. The results of the actual exposure assessment and the
effects assessments are combined to estimate the human health risks
from the exposures.
Systemic (non-cancer) toxicants are usually assumed to have
thresholds below which no effects occur. For these toxicants, safety
assessments are performed with establishment of tolerable intakes
(IPCS, 1993) or reference concentrations/doses (USEPA). From these,
guidelines are derived and standards designed to protect public
health. Ambient concentration standards, and workplace personal
exposure limits, are often established at or below threshold levels
determined as part of the risk assessment process. Although these
standards are set with safety margins, exposures that exceed these
reference levels raise concerns about potentially elevated health
risks for the exposed population (Fig. 5a).
Quantitative risk assessment for carcinogens is a well
established, albeit controversial, procedure. As part of the
guidelines developed by the WHO, it is common practice to extrapolate
from high to low dose by assuming a linear, non-threshold model for
carcinogenicity. Under this assumption, cancer risk for individuals
can be estimated directly from the exposure or dose distribution, and
the number of excess cancer cases (i.e., the increase above background
rates) in the exposed population can usually be estimated by
multiplying the average dose by both the total number of people
exposed and the dose-response slope factor (Fig. 5b). Although
individual risk is assumed to increase with increasing exposure and
dose all along the distribution, exposures of concern are typically
defined to be those above some minimal level of risk (e.g., WHO
considers this to be a 1 in 105 or 106 excess lifetime risk of
developing cancer). Unit cancer risk numbers are given in inverse
concentration units for food, water and air as (ppm)-1, (ppb)-1 or
mg-1m-3). Expressed in inverse dose units (mg kg-1day-1), the cancer
slope risk factor is multiplied by ingestion or inhalation rates and
adjusted for body weight. Individual cancer risk is calculated by
assuming a lifetime of exposure at a given level of contamination.
When exposure data are available, it is then possible to approximate
the cancer risk of the typical or average person in the population or
one who might be at maximum risk due to a greater level of exposure.
In regulatory applications of risk assessments, exposure
estimates are often constructed using existing data or single point
measurements to estimate the risk of a facility, hazardous waste site
or chemical waste site, or even the use of a chemical product. This
approach can result in large errors in the exposure assessment and
hence the risk assessment. Exposure assessment studies are used to
obtain a more accurate determination of the exposure associated with a
health impact outcome of concern. Population-based risk assessments
benefit from the use of population-based measurements derived from
surveys or models (see Chapter 3) to estimate the distribution of
health effect outcomes in the total exposed population over a
specified time period.
2.3.1 Risk allocation for population subgroups or activities
Exposure studies may also be conducted to provide more realistic
and location-specific information for use in human health risk
assessments. Measurement data on pollutant concentrations and exposure
factors, such as contact rates, can be used instead of relying on
assumed "default" values for an "averaged" or representative
individual. An example of an exposure study designed to collect data
for the purpose of allocating risk to locations, sources and
activities is the Windsor Air Quality Study conducted in Windsor,
Ontario, Canada (Bell et al., 1994).
The Windsor Air Quality Study was designed to investigate the
Windsor airshed characteristics with respect to airborne toxic
compounds and to determine personal inhalation exposures to these
compounds. Data were then used as inputs for a multimedia assessment
of risk due to total pollutant exposure. The air quality study
examined just one aspect, the inhalation route. It was designed to
separately attribute risk to several airborne contaminants by indoor
and outdoor locations. Statistical analysis and inference were used to
impute source contributions to population risk (i.e., the waste
incinerator across the river in Detroit, Michigan, USA) for selected
volatile organic compounds (VOCs), carbonyls and trace metals (see
Table 4) based on microenvironmental and personal measurements and
time activity patterns. In general, air quality was determined to be
relatively poor in recreation halls, new office buildings, cars and
garages when compared to outdoor air quality standards and criteria.
Although high contaminant concentrations were detected in various
microenvironments, population exposures (defined as the product of
concentration and time) were relatively low because the study subjects
did not spend any appreciable time in those microenvironments. This
point is illustrated in Fig. 6. For all of the VOCs, the highest
concentrations were measured during the commuting periods, with
comparable concentrations being measured indoors at the office and
home and the lowest outdoors (Table 3). When time in each
microenvironment is considered, exposure in the home accounted for
over 70% of the total exposure profile for that individual.
Table 4. Target analytes in the Windsor air quality study
Volatile organic compounds
Propane, chloromethane, 2-methylpropane, chloroethene, 1,3-butadiene, butane,
2-methylbutane, pentane, isoprene, 1,1-dichloroethene, dichloromethane, allyl chloride,
hexane trichloromethane, 1,2-dichloroethane, 1,1,1-trichloroethane, benzene,
tetrachloromethane, xylenes, styrene, o-xylene, 1,1,2,2-tetrachloroethane, nonane,
1,3,5-trimethylbenzene, 1,2,4-trimethylbenzene, 1,4-dichlorobenzene; decane,
1,2-dichlorobenzene, undecane, 1,2,4-trichlorobenzene, dodecane, tridecane
Carbonyls
Formaldehyde, acetaldehyde, acrolein, acetone, propianaldehyde, crotonaldehyde, methyl
ethyl ketone, benzaldehyde, isovaleraldehyde, 2-pentanone, valeraldehyde, o-tolualdehyde,
m-tolualdehyde, p-tolualdehyde, methyl isobutyl ketone, hexanal, 2,5-dimethylbenzaldehyde
Trace metals
Beryllium, chromium, manganese, nickel, arsenic, selenium, cadmium, lead
Results of the study emphasize the importance of exposure
assessments for policy decisions. For this community, changes in
lifestyle, consumer product formulations, cleaning of indoor air and
increased ventilation would probably have more impact on reducing
health risks from exposures to VOCs than reliance on
government-mandated abatement strategies for ambient sources.
2.3.2 Population at higher or highest risk
Risk assessment may be used to identify and evaluate those
populations, subpopulations and individuals at potentially greater
risk so that, if warranted, appropriate mitigation actions can be
implemented. Individuals and groups are deemed to be at potentially
higher risk because they are exposed to high concentrations of
hazardous pollutants (Sexton et al., 1993). Individuals and groups can
also be at increased risk because they are more susceptible to the
adverse effects of a given exposure. Among the potential causes of
enhanced susceptibility are inherent genetic variability, age, gender,
pre-existing disease (e.g., diabetes, asthma), inadequate diet,
environmental or lifestyle factors (e.g., smoking), stress and
inadequate access to health care. As far as possible, it is important
to identify these susceptible individuals and groups so that we can
understand their exposures and take account of this information in
assessing and managing risks. Exposure and risk information for
susceptible populations is critical since health standards and
regulations are often developed with the intent of protecting these
individuals.
Exposure studies provide valuable information for the risk
assessment by quantifying the distribution of exposures in a
population and identifying those subpopulations or individuals who
have the highest exposures. Information is also gathered on
characteristics of the populations and factors that could contribute
to elevated exposures. In these studies, measures of central tendency,
such as the median and average, along with expressions of variability,
such as the standard deviation, are commonly used to describe the
distribution of exposures for a population (Fig. 7). Often, the
relative position of an individual or group in the exposure
distribution is of primary interest to the exposure assessor. Among
the most frequently used descriptors for individual and subgroup
exposures are values near the middle of the distribution, values above
the 90th percentile and values at the extreme upper end, such as for
the most exposed person in the population. Exposure studies that are
targeted on susceptible populations are used with the same type of
inputs in risk assessment for these groups.
2.4 Human exposure information in risk management
Risk management decisions carried out by policy-makers are of
four basic types: priority setting, selection of the most
cost-effective method to prevent or reduce unacceptable risks, setting
and evaluating compliance with standards or guidelines, and the
evaluation of the success of risk mitigation efforts. Exposure
information is crucial to these decisions. In addition to data on
exposures and related health effects, decision-makers also must
account for the economic, engineering, legal, social and political
aspects of the problem (Burke et al., 1992; Sexton et al., 1992).
Conceptually, as shown in Fig. 8, estimating and prioritizing
health risks are seemingly straightforward. Risk is a combination of
effects estimates, where "highest" priorities can be thought of as
those that entail both "high" toxicity for the agent of interest
(adverse effects are likely to occur in humans at relatively low
exposures or doses), and "high" exposures for the population,
subpopulation or individuals of interest (exposures or doses are above
a health-based standard). Conversely, "lowest" priority risks involve
"low" toxicity and "low" exposures. "Medium" priority risks are those
for which either toxicity or exposure is "low" while the other is
"high" (Sexton, 1993). The Windsor Air Quality Study, for example,
showed that incinerator emissions contributed little to total human
exposure for VOCs. Despite the fact that the pollutants were of high
toxicity, incinerator emissions were considered to be of relatively
low risk to the population. In contrast, studies show that second-hand
smoke has both high toxicity and high human exposures, and should
therefore be identified as a high priority risk.
Risk mitigation proceeds from first determining that an exposure
is a hazard (risk assessment) to identifying and quantifying the route
and the environmental pathways for a contaminant. Where a contaminant
has multiple sources or routes of exposure, relative contributions to
individual and population risk must be determined. Exposure
assessments are crucial for developing this information, and may rely
on both measurements and modelling. Once this information is obtained,
then effort can be directed toward the most effective mitigation
strategies.
In fact, intervention studies are implicitly or explicitly
predicated on the sequence of risk assessment and mitigation.
Intervention at the source, transmission or receptor (receiving
person) is intended to reduce the effect or risk of an effect.
Prohibiting smoking in public buildings or sections of restaurants is
designed to separate sources from receptors. Specific ventilation
requirements for operating theatres or isolation rooms of infectious
patients are designed to dilute potential contaminants and pathogens.
On a larger scale, substitution of cleaner fuels (e.g., reformulated
or unleaded gasoline, cleaner coal, low-sulfur oil, natural gas)
radiation of food or ozonation of drinking-water are examples of risk
mitigation interventions based on the assumption that contaminant
reductions experienced in the environmental media will result in a
corresponding reduction in actual exposures and hence risk. It is
essential, then, to understand the efficacy of mitigation strategies
with respect to their effect on human exposures.
The combined use of total exposure assessment for air,
receptor-source modelling and economic principles can assist
environmental policy and regulation in developing risk mitigation
strategies. The hybridization of these well-developed models can be
used to assist in the identification of priority sources to target
regulatory programmes, and in the development of cost-effective
strategies for air pollution control to bring about the greatest and
earliest reduction in pollutant exposures.
Epidemiological information about the health effects of
relatively low levels of air pollutants now raises controversial
policy issues for risk management. On the one hand, the economic
consequences of these health effects may be substantial; on the other
hand, for some pollutants, control measures may become very expensive.
For pollutants such as VOCs, for example, exposure monitoring rather
than ambient air monitoring may lead to more rapid and cost-effective
risk reduction policies.
Developed countries have experimented with regulatory reforms
that include emission trading. Basically, the concept calls for
emission reduction at one source to be credited to the emission levels
at another source. These trading schemes are based on the assumption
that equal mass emission reduction of a pollutant would result in
equal health or ecological benefits. Thinking in terms of total
exposure assessment reorients the relative importance of sources and
their impacts on different populations. Accordingly, control options
for reducing exposures can be broadened (Smith, 1995).
2.5 Human exposure information in status and trend analysis
Evaluating the current status of exposures and doses in the
context of historical trends is an important tool for both risk
assessment and risk management. In many cases it requires collecting
exposure data over a relatively long period of time (e.g., 5-20
years). This can only be done through an exposure assessment study and
often when the contaminant has a long residence time in the
environment or biological tissue. If concentrations of a contaminant
exhibit high variability in environmental media, the study may require
relatively large sample sizes, the use of probability samples and/or
extensive follow-up to observe trends. Data on status and trends can
be invaluable for identifying new or emerging problems, recognizing
the relative importance of emission sources and exposure pathways,
assessing the effectiveness of pollution controls, distinguishing
opportunities for epidemiological research and predicting future
changes in exposures and effects (Goldman et al., 1992; Sexton et al.,
1992).
Exposure studies may be conducted to document the status and
trends of human exposure (e.g., Kemper, 1993; Noren, 1993). A good
example of a study designed for this purpose is the German
Environmental Survey (GerES). The nationwide representative survey was
conducted for the first time in 1985-1986, on behalf of the Federal
Ministry for the Environment, Nature Conservation and Reactor Safety.
In 1990-1991 the survey was repeated in West Germany (the FRG before
reunification) and in 1991-1992 it was extended to East Germany
(former GDR) (Krause et al., 1992; Schulz et al., 1995).
The purpose of the survey was to establish a representative
database on the body burden of the general population. Biological
monitoring was used to characterize exposure to pollutants
(predominantly heavy metals). In addition, the occurrence of a number
of pollutants in the domestic area likely to contribute to total
exposure (house dust and drinking-water) was studied. The design of
the study is summarized as follows:
* Population samples. Cross-sectional samples using a stratified
two-step random sampling procedure according to the size of the
community, gender and age. The final set included 2731 West Germans
in 1985-1986 and 4287 adults from East and West Germany in
1990-1992 (aged 25-79 years). In addition about 700 children (aged
6-14 years) living in the same households were included in
1990-1992.
* Human biomonitoring. Analysis of blood (lead, cadmium, copper,
mercury), spot urine (arsenic, cadmium, copper, chromium, mercury)
and scalp hair (aluminium, barium, cadmium, chromium, copper,
magnesium, phosphorus, lead, strontium and zinc).
* Questionnaires. Questions about social factors, smoking habits,
potential sources of exposure in the domestic, working, and general
environment, and nutritional behaviour.
* Domestic environment. Concentration of trace elements in dust
deposit indoors, in vacuum cleaner bags (pentachlorophenol [PCP],
lindane and pyrethroids) and in household tap water; determination
of VOCs in homes of a subsample of 479 participants (passive
sampling) in 1985-1986.
* Personal sampling. Determination of VOCs by personal sampling
using a subsample of 113 people in 1991.
* Dietary intake. A 24-h duplicate study in 1990-1992 with a
subsample of 318 people.
Characteristics of the frequency distributions (percentiles) and
other statistical parameters of the concentration of elements and
pollutants in the different media were calculated. As an example, the
concentrations of elements and compounds in blood and urine of the
German adult population analysed in 1990-1992 are shown in Table 5.
The 1990-1991 and 1991-1992 surveys showed differences between East
and West Germany. The mercury concentrations in blood and urine as
well as the cadmium, chromium and copper concentrations in urine were
significantly higher ( p < 0.001) in East Germany than in West
Germany. The blood lead level was identical in both study populations
(geometric mean 45 µg/litre).
The comparison of the results for the biological, personal and
microenvironmental exposure measurements taken in East Germany in
1985-1986 and in 1990-1992 permits an analysis of trends over time.
The success of abatement measures could be shown in a number of cases:
the reduction of lead concentrations in petrol and of industrial
cadmium emissions resulted in decreased lead and cadmium
concentrations in the blood of the general population. The ban on PCP
led to a decrease of PCP in house dust. The results of the GerES have
provided a useful set of reference data to characterize and to assess
exposures of the general population. They have also been useful for a
number of risk assessments, for example the role of copper in
drinking-water and liver cirrhosis in early childhood, and presence of
mercury in amalgam fillings.
2.6 Summary
The specifics of any particular exposure analysis hinge on its
intended use or uses. For example, the pertinent aspects of exposure
to be considered, the nature of the information required and the
necessary quantity and quality of the data will depend on whether the
exposure assessment is being conducted in the context of an
epidemiological investigation (Matanoski et al., 1992), risk
assessment (Graham et al., 1992), risk management (Burke et al., 1992)
or status and trend analysis (Goldman et al., 1992) (see also Chapter
1, Table 1).
Table 5. Elements and compounds in blood and urine of the German population (aged 25-69 years, 1990-1992)
(Krause et al., 1992)
QL N <QL 10 50 90 95 98 MAX AM GM CI GM
Blood
Lead (µg/litre) 15 3966 61 24.0 45.3 86.8 105.6 134.2 708.0 52.4 45.3 44.5-46.0
Cadmium (µg/litre) 0.1 3965 231 0.1 0.3 1.9 2.6 3.6 11.3 0.7 0.4 0.4-0.4
Copper (mg/litre) 0.1 3968 0 0.8 0.9 1.2 1.3 1.5 2.5 1.0 0.9 0.9-1.0
Mercury (µg/litre) 0.2 3958 632 <0.2 0.6 1.6 2.1 3.0 12.2 0.8 0.5 0.5-0.5
Urine
Arsenic (µg/litre) 0.6 4001 210 1.8 7.1 19.8 29.9 56.7 205.5 10.5 6.3 6.1-6.5
Arsenic (µg/g creatinine) 4001 1.4 4.9 15.3 24.1 40.0 147.6 7.6 4.6 4.5-4.8
Cadmium (µg/litre) 0.1 4002 150 0.1 0.3 0.9 1.3 1.7 6.9 0.4 0.3 0.3-0.3
Cadmium (µg/g creatinine) 4002 0.1 0.2 0.7 0.9 1.3 6.1 0.3 0.2 0.2-0.2
Chromium (µg/litre) 0.2 4002 1716 0.15 0.2 0.4 0.6 1.0 21.2 0.3 0.2 0.2-0.2
Chromium (µg/g creatinine) 4002 0.0 0.1 0.3 0.5 0.9 10.6 0.2 0.1 0.1-0.1
Copper (µg/litre) 1.1 4002 20 4.5 9.7 18.7 22.9 28.7 444.2 11.6 9.5 9.3-9.7
Copper (µg/g creatinine) 4002 3.5 6.7 13.1 17.7 28.5 420.7 8.9 6.9 6.8-7.1
Mercury (µg/litre) 0.2 4002 785 <0.2 0.5 2.6 3.9 6.0 53.9 1.1 0.5 0.5-0.6
Mercury (µg/g creatinine) 4002 0.1 0.4 1.6 2.2 3.2 73.5 0.7 0.4 0.4-0.4
Nicotine (µg/litre) 5 3750 1566 <5 9.3 1438 2431 3567 10 984 422 24.9 23.0-27.1
Nicotine (µg/g creatinine) 3748 1.3 7.0 1003 1636 2431 10 478 292 18.4 17.0-20.0
Cotinine (µg/litre) 5 3800 1813 <5 5.6 2037 2681 3483 6573 537 26.6 24.3-29.1
Cotinine (µg/g creatinine) 3798 1.3 4.9 1396 1940 2788 8111 388 19.6 17.9-21.4
Creatinine (mg/100 ml) 0 4002 0.7 1.5 2.5 2.9 3.2 5.7 1.5 1.4 1.3-1.4
Annotations: QL = quantification limit, N = sample size, n < QL = number of values below QL, 10, 50, 90, 95, 98 = percentiles,
MAX = maximum value, AM = arithmetic mean, GM = geometric mean.
Source: UBA, WaBoLu, Environmental Survey 1990-1992, Federal Republic of Germany.
Knowledge of human exposures to environmental contaminants is an
important component of environmental epidemiology, risk assessment,
risk management and status and trends analysis. Exposure information
provides the critical link between sources of contaminants, their
presence in the environment and potential human health effects. This
information, if used in the context of environmental management
predicated on human risk reduction, will facilitate selection and
analysis of strategies other than the traditional "command and
control" approach. Most of the environmental management structures
around the world rely directly on the measured contaminants in various
media to judge quality, infer risk and interpret compliance. Even in
these cases, exposure information can evaluate the effectiveness of
protecting segments of population more susceptible or at higher risk.
It is this direct connection that makes exposure measures
invaluable for evaluation of environmental health impacts on a local,
regional and global scale.
3. STRATEGIES AND DESIGN FOR EXPOSURE STUDIES
3.1 Introduction
Accurate estimates of human exposure to environmental
contaminants are necessary for a realistic appraisal of the risks
these pollutants pose and for the design and implementation of
strategies to control and limit those risks. Three aspects of exposure
are important for determining related health consequences:
* Magnitude: What is the pollutant concentration?
* Duration: How long does the exposure last?
* Frequency: How often do exposures occur?
The design of an exposure study specifies the procedures that will be
used to answer these three questions.
In this chapter, strategies and designs for exposure studies are
discussed with emphasis on their relative advantages and
disadvantages. The brief discussion of study design presented in
Chapter 1 is expanded upon here in terms of fundamental types of
generic study designs and approaches to assessing human exposure to
chemicals in the environment. Statistical considerations for study
design are presented in Chapter 4. The reader is referred to
subsequent chapters for details on implementing exposure study designs
through modelling (Chapter 6), monitoring of environmental media
(Chapters 7, 8 and 9) and monitoring of biological tissue (Chapter
10).
3.2 Study design
A good study design is the most important element of any exposure
study. A flow chart that includes critical elements is shown in Fig.
9. First the purpose of the study is defined: epidemiology, risk
assessment, risk management or analyses of status and trends (see also
Chapter 2). Within this context, specific study objectives are
formulated. Often studies have several objectives, which must be
prioritized to ensure that the primary objective is fulfilled. Study
parameters must be selected that are consistent with the objective. A
study design is formulated which links objectives to measurement
parameters in a cost-effective manner. Two critical and often
overlooked elements of the study design are development of a
statistical analysis plan and quality assurance (QA) objectives. For
general population studies, methods for measurement and analysis of
contaminants in collected environmental or biological samples must be
sufficiently sensitive to determine their concentration at typical
ambient levels. For multimedia studies, method detection limits must
be consistent across media. The study design is not complete until a
pilot study has been conducted to evaluate sample and field study
procedures.
3.3 Sampling and generalization
Decisions on population sampling strategies involve consideration
both of the populations that are available and of the types of
measurements needed. Of prime consideration are the people, place and
time (i.e., individuals, locations, sampling period or conditions)
from which exposure samples are to be collected. Also, it is important
to determine if the estimates to be derived from the proposed sample
could be generalized to a wider population of interest. For example,
consider an exposure assessment study from a sample population of a
small town in southwestern Australia. The many potential populations
of interest which this sample might generalize include: all people
living in that town; people living in a small southwestern Australia
town; people living in southwestern Australia; people living in
Australia; people living in any small town; people in general. In this
case, the sample population is not likely to provide a representative
sample of the latter two populations.
The appropriateness of the generalization is determined by
considering if the sample is randomly selected in such a way as to be
representative of the larger population of interest (Whitmore, 1988).
This randomization is in terms of the distribution of the collected
data. For continuous outcomes, the percentages of key attributes, such
as demographic factors, should be similar between the sample and the
population. However, when this is not possible, owing to limited
funding for example, a descriptive study (described below) can provide
credible data, although the extent to which these can be generalized
is limited.
3.4 Types of study design
Once the population is defined, then the attention shifts to
sampling strategies; in particular, comprehensive samples, probability
samples, and other types of samples. A comprehensive sample includes
all members of the selected population. In a probability sample each
member has a known likelihood of being selected. Simple random
sampling is a special case where each member of the population has
an equal probability of being selected. Other types of study groups
are selected on the basis of other characteristics, such as
availability or convenience.
3.4.1 Comprehensive samples
Complete populations can be used to collect a full picture of the
process being studied, especially when the total population is
relatively small such as families in a neighbourhood. In these cases,
an exhaustive collection of measurements is taken from every potential
subject, and the completed data describe the situation exactly. There
is no sample variability except through the methods and procedures
used for measurement and monitoring. The main reasons for studies of
this nature are either a small population size, a need for a complete
evaluation of the problem, high potential risk, high variability among
units or legal requirements. The advantages of this type of study are
that a complete description of the exposure is given, and there is no
need for generalization because all potential subjects are covered.
The disadvantage of this approach, if the population is large, lies in
the expense: all individuals in all locations must be monitored at all
times.
3.4.2 Probability samples
Surveys consist of a random sampling of subjects from the
population of interest. This approach aims to remove selection bias
and is useful for generalizing results beyond the study sample. It is
important to distinguish that "random" does not translate to
"haphazard". A truly random sample is independent of human judgement.
Every unit in the total population has a known above-zero likelihood
of being included in the sample. Effective study design allows
researchers to draw statistically valid inferences about the general
population that the sample is designed to represent (Kish, 1965). For
these studies, one needs to (Sexton & Ryan, 1988):
* choose a population for investigation
* choose an appropriate unit for sampling and analysis (e.g., person,
household, neighbourhood, city, etc.)
* stratify as appropriate
* choose a sampling strategy (e.g., simple random sampling,
multistage sampling).
The results of a probability survey can be used to make general
statements about the population under investigation. The advantages
include having results that represent the population, taking into
account the possible error due to sampling. The disadvantages of this
scheme lie in the complicated sample selection, difficulty in
maintaining compliance from participants and the potentially complex
statistical analysis. In addition, randomized surveys of insufficient
sample size may miss rare hazardous events or small populations with
high exposure or risk.
Sampling strategies for survey studies include randomization
methods for choosing subjects to enroll in the study. Simple random
sampling is a scheme in which all sampling units of the same size have
equal probability of being selected. It can be difficult to implement
but relatively easy to generalize. Simple random sampling presents
logistic and fiscal constraints when considered for exposure surveys
that are large in geographic scope. For example, a national survey of
5000 personal exposures to respirable particulate matter that utilizes
simple random sampling may result in individuals selected from 1000
cities and towns. The travel and site preparation costs of such a
design may not be feasible in many situations.
A variety of alternatives to simple random sampling exist that
may be used to provide practical and efficient samples of large
populations (Callahan et al., 1995). Stratified sampling may be used
to obtain more precise survey results if exposures are more
homogeneous within strata than between them. Possible strata include
urban, suburban and rural populations, or occupationally exposed and
non-occupationally exposed individuals.
Oversampling of target populations or contaminants also may
yield substantial increases in the precision of results. Because the
individuals anticipated to have the highest exposures to a particular
pollutant may be rare in the population being studied, oversampling
can be considered to obtain more precise estimates of exposure. Before
committing substantial resources to oversampling, special care must be
taken to ensure that assumptions or data used to support a rationale
for selecting the oversampled population are accurate; otherwise
erroneous oversampling may decrease the precision of the study results
(Callahan et al., 1995).
Multistage sampling designs utilize clusters of sampling units
thereby limiting sampling locations to manageable areas. Depending on
the scope of the study, the stages of probability sampling necessary
may include:
* selection of primary sampling unit (e.g., a city)
* selection of sample area segments (e.g., blocks within the city)
* selection of sample housing units within sample segments (e.g.,
residences within the blocks)
* selection of sample individuals within sample housing units
* selection of sample time points within the monitoring period
(Callahan et al., 1995).
The optimal degree of clustering depends on the variability of the
survey variables between and within the clusters and the costs of
fieldwork relative to sample collection and analysis costs. Although
details of this approach are beyond the scope of this chapter, it
should be recognized that cluster sampling introduces correlation
among the sample individuals that affects the validity of the survey
estimates. Thus, tradeoffs between increased sample size achieved
through clustering and loss of validity must be considered carefully.
Details of multistage and cluster sampling may be found in Hansen et
al. (1953), Kish (1965), Cochran (1977), Kalton (1983), Kollander
(1993) and Callahan et al. (1995).
One concern with survey studies is maintaining participation of
subjects who did not initially volunteer. Another issue, which is more
conceptual, is subject selection for the more complex sampling
strategies. In particular, stratification factors need to be carefully
chosen so that potential confounders can be determined and the
adjustments can be made from the resultant effects. Important
considerations for the design of population-based (e.g., national or
regional) exposure surveys, including response rates and confounders,
are discussed by Whitmore (1988) and Callahan et al. (1995).
3.4.3 Other sample types
Non-probability sample studies ("anecdotal studies") may consist
of selecting a sample based on the self-reporting of conditions, such
as complaint cases for "sick building" syndrome. Data collected in
this manner are potentially subject to biased reporting. It is
difficult to generalize results unless causal relationships are very
strong or unless there is little reason to believe that a confounder
or an unmeasured significant factor is relevant. In general, such
studies are used for description or exploration of a given situation.
In particular, they can be used to evaluate the variability of
outcomes and explore unknown situations for further explanatory study.
When choosing subjects, it is useful to focus on variability in the
expected outcome and also on the likelihood of completing the study.
It is also helpful to focus on a simple, preferably dichotomous,
hypothesis. Extensive validation will be necessary before accepting or
rejecting the hypothesis since the generalization of the results is
uncertain.
The advantages of targeted anecdotal studies are the inexpensive
and quick ways in which they aid in the design of future studies. For
example, when exploring protocols, determining stratification
variables, potential biases and confounders, and identifying the units
of analysis, the use of cooperative volunteers can simplify field
operations. The uncertainty of the results of these studies is due to
potential biases from the non-random and possibly non-representative
sample (i.e., responder bias). Since the population in such
non-probability sample studies is often made up of volunteers, there
is usually some factor present which distinguishes them from those who
do not choose to participate. This factor could influence the results;
in particular, those who participate may tend to consider themselves
strongly affected or not affected by the pollutant being studied and
may alter their responses or behaviours as a result. This phenomenon
is a special case of responder bias, often termed self-selection
bias. Also, a poorly designed study can fail to control for temporal
and spatial variability, as well as meteorological, site and source
bias. This bias is a result of a single, "random-day", or grab
sampling and single-location sampling, which decreases the potential
for generalization.
Controlled experiments are useful to examine a few factors and to
study their influence on the resulting exposure. The use of
randomization and control ensures that the effects are real and not
the result of confounding causes, incorrectly measured variables or
missing variables. Examples include chamber studies and other
situations where the investigator has control over most of the
environmental factors.
3.5 Exposure assessment approaches
As discussed in Chapter 1, strategies for assessing environmental
exposure can be categorized as one of two general approaches; direct
or indirect. Direct approaches include personal exposure monitoring
and biological markers of exposure. Indirect approaches include
environmental sampling, combined with exposure factor information,
modelling and questionnaires.
3.5.1 Direct approaches to exposure assessment
Direct measures of exposure include samples collected at the
interface between an exposure medium and the human body, e.g., at the
breathing zone in the case of air pollutant exposure, or samples of
biological tissue in which concentrations of target pollutants can be
quantitated. Measurements in food or drinking-water (duplicate
portions) which are ingested could also be viewed as a direct way of
assessing exposure through these media. Thus, direct approaches to
exposure assessment include personal exposure monitoring and
biological markers of exposure. Personal monitoring methods are
discussed below, and the subject of biomarkers of exposure is
presented in detail in Chapter 10.
Personal monitoring of exposure to environmental contaminants
refers to collection of samples at the interface between the exposure
medium and the human receptor (e.g., the breathing zone). Personal
monitoring approaches are summarized in Table 6. Personal monitors
make it possible to measure exposures for an identified subset of the
general population. Moreover, if study participants maintain records
of their activities, then locations where highest exposure
concentrations occur as well as the nature of emission sources can
often be inferred. Personal monitoring can be done for all potential
exposure media (e.g., air, water, soil, food) and pollutants of
interest. Although available, personal monitoring methods may not be
employed in a particular investigation due to study design, time or
expense considerations. The principal limitation on the use of
personal monitoring for exposure assessment is the availability of
sample collection methods that are sensitive, easy to operate, able to
provide sufficient time resolution, free from interferences and
cost-effective. Consideration should be given to the likelihood that
the inconvenience of complying with personal monitoring protocols may
alter the normal behaviour of the study participants. For example,
participants tend to wear personal air monitors on days that they do
not go to work. In duplicate portion studies, participants may not
provide equal portions of expensive or well-liked foods, leading to
underestimation of intake. Approaches to personal monitoring of
inhalation, dietary and dermal exposures are discussed below.
Table 6. Summary of personal monitoring approaches
Exposure route Media Environmental sample Biological sample
Inhalation air personal monitor breath
urine
Ingestion water tap water blood
Ingestion food duplicate portion faeces
breast milk
Dermal soil/dust dermal patch others
3.5.1.1 Personal monitoring of inhalation exposures
Personal monitoring of human exposure to air pollutants requires
that study participants transport their sample collection device with
them at all times during the assessment period. Examples include a
diffusion tube used for passive sampling of gases, such as VOCs, or a
filter with a battery-operated pump for active sampling of aerosols
and their components (ACGIH, 1995).
Personal air monitors can be grouped into two general categories:
integrated samplers that collect the pollutant over a specified time
period and then are returned to the laboratory for analysis, and
continuous samplers that use a self-contained analytical system to
measure and record the pollutant concentration on the spot.
Instruments in both categories can be either active or passive.
Active monitors use a pump and a power source to move air past a
collector or sensor. Passive monitors depend on diffusion to bring
the pollutants into contact with the collector or sensor. Additional
information may be found in Chapter 7 and ACGIH (1995).
As Wallace & Ott (1982) pointed out, the direct measurement of
exposures using personal monitors raises several methodological
issues. Personal monitoring studies are complex, expensive, time
consuming and labour intensive. Other challenges include selection and
recruitment of representative subjects; distribution, maintenance and
retrieval of many monitors; laboratory analysis of many air samples
returned from monitors in the field or calibration and validation of
many real-time monitors; and the transcription and statistical
analysis of data on pollutant concentrations and time-activity
patterns.
3.5.1.2 Personal monitoring of dietary exposures
Exposures to contaminants in food may be directly measured by
collecting meals as prepared for consumption by members of the study
population; such samples are often termed duplicate portion samples.
Duplicate portion study designs provide food samples as actually
consumed, rather than samples of unprepared, individual food items
that are typical of surveillance approaches to characterizing dietary
exposures (US NRC, 1993). This distinction is important because the
method by which food is prepared for consumption (e.g., washed, washed
and cooked, or commercially processed) can influence contaminant
residues. In addition, some pollutants can be generated during
cooking, for example, benzo [a]pyrene (Waldman et al., 1991a) and
heterocyclic amines (Skog et al., 1998). Thus, residue levels measured
in duplicate portion samples are likely to more accurately reflect
personal dietary ingestion exposures than raw agricultural commodities
and other foods collected at the producer, processor or distributor
level. Depending on the objectives of the study, water may also be
included as part of the duplicate portion sample.
Duplicate portion study designs use either collection of
individual servings or meals or composite samples. In studies of this
type, participants are often monitored over one or more days, and the
duplicate portion samples are collected daily over the monitoring
period. The former affords a detailed examination of contaminant
levels in specific commodities or foods comprised of several
commodities; however, the analytical chemistry costs associated with
this degree of temporal resolution may be prohibitive. Composite
samples provide an integrated measure of dietary exposure and provide
an efficient means for characterizing total dietary exposures. Both
collection schemes require a high level of effort from study
participants, and the complex food matrices may present analytical
chemistry challenges.
Duplicate portion studies require a high degree of participation
by the study respondents, because they are primarily responsible for
preparation and storage of an additional serving of every food or meal
consumed over the monitoring period. This burden makes it difficult to
collect representative samples of all foods consumed by the
respondent, especially when food is relatively expensive or scarce or
is consumed outside the home. Respondent burden also makes it
difficult to conduct studies of chronic dietary exposures using the
duplicate portion approach. Additional information on assessment of
dietary exposure, including both direct and indirect approaches, may
be found in Chapter 7.4 as well as WHO (1985a, 1995c); EC (1997a).
3.5.1.3 Personal monitoring of dermal absorption exposures
Personal monitoring of dermal exposure is used for those
situations where a pollutant comes in contact with the skin and intake
occurs via absorption through the skin. Dermal patches and skin wipe
samples are used to evaluate exposures for residues adhering to the
surface of the skin (US EPA 1992b; Fenske, 1993; Geno et al., 1996;
Shealy et al., 1997). These methods have typically been used for
industrial hygiene assessments where very high exposures are expected.
Dermal patches and skin wipe samples have been used to characterize
transfer of pesticide residues from soil and grass to skin as well as
spot concentrations of residues on skin (Fenske et al., 1991). Dermal
absorption can also occur during bathing, showering or swimming. In
this case, the contaminant is in the water and exposure occurs when
the water contacts the skin. Dermal exposure in this situation is
defined as the concentration of the contaminant in the water and the
duration of contact.
3.5.2 Indirect approaches to exposure assessment
Indirect measures of exposure include estimates derived from
environmental monitoring (i.e., measurements made in locations
frequented by the study participants), models and questionnaires.
3.5.2.1 Environmental monitoring
Indirect estimates of exposure may be made by combining
measurements of pollutant concentrations at fixed sites with
information on rates of contact with these media recorded in data logs
and diaries or time-activity surveys. Examples include air pollutant
concentrations in specific areas combined with time budget records
(see Chapter 5), food contaminant data combined with information on
dietary patterns (see Chapter 7.4 for details), and pollutant
concentrations on skin combined with data on frequency and duration of
hand-to-mouth contact. Although collection of environmental,
time-activity and questionnaire data needed for this exposure
assessment approach is simpler than for personal monitoring, it is
still invasive and laborious, and may lead to selection bias.
Microenvironmental monitoring is a special case of environmental
monitoring in which the location where measurements are made is
considered to be homogeneous with respect to concentrations of the
target pollutants over the averaging time of interest. The concept of
a microenvironment has been widely applied in air pollution exposure
assessments. Examples of potentially important micro-environments used
for air pollution exposure assessment are listed in Table 7. The
general form of the equation used to calculate time-weighted
integrated exposure from micro environmental monitoring data is
(3.1)
where E is the time-weighted integrated exposure (e.g., mg/m3),
C is the concentration (e.g., mg/m3), t is the unit time (e.g.,
minute), T is the total elapsed time (e.g., minutes). The subscripts
i, j and k denote the medium, the pathway and the microenvironment
respectively (Duan, 1982). The most important assumptions inherent in
this model are:
* The concentration Cj in microenvironment j is assumed to be
constant during the time that person i is there.
* The concentration Cj within microenvironment j and the time
that person i spends there are assumed to be independent events.
* The number of microenvironments necessary to characterize personal
exposure adequately is assumed to be small.
The concept of a time-weighted integrated exposure is illustrated
in Fig. 10. A unit width is indicated on the j axis for each of five
microenvironments: indoors at home, indoors at work, indoors in other
locations, in transit, and outdoors. The concentration of respirable
particles (RSP) is displayed on the y axis, and the fraction of time
that person i spends in each microenvironment over the 24-h period
is plotted on the t axis. Even though the RSP concentration was low
inside the home, it contributed significantly to the time-weighted
exposure because this person spent 18 out of 24 h there. Conversely,
the RSP concentration outdoors made only a minor contribution because
this person was outdoors less than half an hour during the 24-h
period.
Indirect monitoring of ingestion exposures via hand-to-mouth
contact may be obtained by collection of dermal wipe samples. However
as indicated above, the use of this method has been limited to date. A
drawback of the dermal wipe approach is that the integration time may
be highly variable among subjects owing to variations in frequency of
hand and body-washing, making interpretation of the results difficult
(Fenske, 1993). Information on rates of contact between the
contaminated skin and mouth is also required to complete the exposure
assessment. A discussion of these types of data may be found in
Chapter 5.
Given the diversity of microenvironments that people move through
each day (see Table 7), application of the indirect approach to
exposure assessment is not straightforward. Its utility depends on
identification of and sampling in the microenvironments with the
greatest potential to influence human exposure. The costs and
practical difficulties of monitoring in all, or even most, of the
locations where people are likely to spend their time limits the scope
of indirect measurements.
Table 7. Potentially important microenvironments for air pollution exposure assessment
Microenvironments Comments
Outdoors
Urban metropolitan areas where air pollution levels are high as a result of high density of mobile and
stationary sources
Suburban small- to medium-sized cities where air pollution levels tend to be lower than in metropolitan
areas, although transport of urban pollution can affect local air quality under certain conditions
Rural agricultural communities and small towns with few major anthropogenic sources of air pollution.
Air pollution levels tend to be low, although transport of urban and suburban pollution can affect
local air quality under certain conditions
Indoors-occupational
Industrial manufacturing and production processes, such as those in petrochemical plants, pulp mills, power
plants, and smelters
Non-industrial primarily service industries where workers are not involved in manufacturing and production
processes, such as insurance companies, law offices, and retail sales outlets
Indoors-Non-occupational
Residential single-family houses, apartments, mobile homes, condominiums
Commercial restaurants, retail stores, banks, supermarkets
Public post offices, courthouses, sports arenas, convention halls
Institutional schools, hospitals, convalescent homes
Indoors-Transportation
Private automobiles, private aeroplanes
Public buses, subways, trains, commercial aeroplanes
3.5.2.2 Models as an indirect approach to assessing exposure
The microenvironmental exposure equation describes a model
commonly used for assessment of air pollutant exposure. More
generally, models are useful tools for quantifying the relationship
between pollutant exposure and important explanatory variables, as
well as for expanding existing exposure information to estimation of
exposures of new populations and subgroups, and future exposure
scenarios. Validated exposure models reduce the need for expensive
measurement programmes. The challenge is to develop exposure databases
and models that allow maximum extrapolation from minimum measurements
or costs. Such models need to reflect the structures of the physical
environments and human activities of interest in exposure assessment.
In addition to the essentially physical (deterministic) exposure
models, physical-stochastic (probabilistic) and statistical
(regression) models are used. The former type is particularly useful
for population exposure distribution assessments, the latter requires
less supporting information but cannot be used for extrapolation
outside of the study population. Exposure models are discussed in
detail in Chapter 6.
3.5.2.3 Questionnaires as an indirect approach to assessing exposure
Questionnaires typically provide qualitative, often
retrospective, information. They may be used to categorize respondents
into two or more groups with respect to potential exposure (e.g.,
exposed or unexposed, high exposure or low exposure) and are commonly
used for this purpose in epidemiological studies. As noted earlier,
questionnaires may also be used to aid in interpretation of personal
and environmental monitoring results. A priori knowledge of the
determinants of the exposure of interest is required to develop
effective questionnaires relevant to exposure assessment (e.g., high
formaldehyde exposure for workers in a certain industry, or high
carbon monoxide and lead exposure for traffic policemen, bus drivers
and road toll collectors). Most often the information necessary to
develop questionnaires is obtained from previous studies that utilized
environmental measurements, models or biological monitoring to measure
exposure. In many cases, basic socio-demographic questionnaire data
may provide extremely valuable information as they might be strong
surrogates of exposure. It has long been known that rates of disease
differ in social strata. In addition, it is readily apparent in many
countries that the physical characteristics of one's residential
environment are linked to income level. For lead exposure, differences
in exposure among groups defined by income and social status have been
demonstrated. Phoon et al. (1990) have shown that diet and job
category were the most important predictors of blood lead levels among
men in Singapore. In the USA, elevated blood lead levels have been
linked to children who live in older, inner-city housing, particularly
properties in poor repair (MMWR, 1997). Homes in these areas are more
likely to have been painted with leaded paints (pre-1950) and have
higher concentrations of lead in soil owing to deposition of emissions
from leaded gasoline prior to the 1970s. Haan et al. (1987) found an
increased risk of death among people living in a poverty area in the
USA as compared to an adjacent non-poverty area, even after adjusting
for differences in smoking, race, baseline health status, access to
medical care, employment status, marital status, depression, sleep
patterns and body mass index. These results suggest that sociophysical
aspects of the environment, such as increased exposure to contaminants
from poorer housing, may be important contributors to the association
between socio-economic status and excess death rates.
3.6 Summary
A good study design is the most important element of any exposure
assessment. It includes the purpose and objectives of the
investigation as well as relevant methods for sampling, measurements,
statistical analyses, and quality assurance. Methods for
characterizing the magnitude, duration and time patterns of human
contact with environmental contaminants may follow a direct approach
or an indirect approach. Direct approaches to exposure assessment
include point-of-contact measurements and measures of biological
markers of exposure. Indirect approaches include environmental
monitoring, modelling and questionnaires. These approaches may be
employed in various types of exposure studies that are typified by the
manner in which the study population is selected; for example,
comprehensive studies that include all members of the study
population, descriptive studies consisting of a non-probability
sample, or surveys based on a randomly selected, representative sample
of individuals.
4. STATISTICAL METHODS IN EXPOSURE ASSESSMENT
4.1 Introduction
Statistics is a necessary and critical tool in exposure
assessment studies. Statistics can be employed at each stage of the
exposure assessment study. At the planning stage, statistics aids in
selection of study design and determination of the amount and form of
data to collect. After the data are collected, statistical description
of the results helps understanding of the basic characteristics of
exposure and its determinants. Statistics is also essential during
final analysis of the data for hypothesis testing, characterizing
exposure through various routes and media, and exploring relationships
between ideal measurements (e.g., exact lung uptake) and feasible
measurements (e.g., ambient, indoor, or personal measures).
Furthermore, statistical inference allows one to generalize the
observations derived from a sample to a wider population from which
the sample was drawn. Finally, as noted in Chapter 11, statistics play
an important role in quality assurance (QA) programmes.
Selected applications of descriptive and inferential statistics
in exposure assessment studies are discussed in the following
sections. This chapter is not a substitute for a course in statistical
methods, but is intended to provide a brief review and useful
references. Widely available statistical software for personal
computers can be used to perform data processing and necessary
calculations. One example of such packages is the statistical
programme Epi Info developed for and distributed by WHO (Dean et al.,
1995).
Throughout the chapter, data collected as part of a lead exposure
study performed in Malta and Mexico (WHO, 1985b) (Table 8) will be
used to illustrate some key statistical concepts and methods. The
purpose of this study was to investigate the relative importance of
lead exposure via different routes of exposure. Blood lead
concentrations were considered to be an indicator of lead uptake from
all exposure routes, whereas faeces lead concentrations were
considered to represent exposure via ingestion. In the course of this
study, blood lead and faeces lead measurements were obtained from 36
and 19 individuals in Malta and Mexico, respectively.
4.2 Descriptive statistics
Descriptive statistics summarize data in a simple manner to
discern key points about the collected information. We typically
assume that the collected data are a sample from a larger population
of possible measurements and that the sample is representative of the
population. The sample consists of the individual observations from
the study population, with multiple variables or covariates recorded
for each observation. Univariate methods examine the distribution of
a single variable; multivariate methods describe relationships among
two or more variables. That is, if we consider a single observation
and know the value of one variable, multivariate methods indicate what
Table 8. Blood lead (PbB) and faeces lead (PbF) data from sample
populations in Malta and Mexico. Source: WHO, 1985b
Malta Mexico
Number
PbB PbF PbF PbB
(µg/litre) (µg/g) (µg/litre) (µg/g)
1 171 2.9 239 6.3
2 270 30.5 263 4.2
3 198 5.6 198 5.7
4 122 3.8 163 5.3
5 96 16.6 217 4.3
6 385 35.5 188 4.7
7 359 49.6 190 3.3
8 267 6.8 248 5.2
9 261 8.1 225 4.5
10 301 25.6 152 3.4
11 202 7.7 177 5.9
12 222 32.3 157 3.8
13 339 10.9 297 5.3
14 156 5.7 144 3.6
15 262 18.7 257 9.8
16 290 16.5 131 4.8
17 158 4.9 187 5.1
18 343 37.8 168 3.2
19 228 9.1 112 2.8
20 256 14.1
21 270 9.9
22 245 4.9
23 337 14.3
24 362 19.2
25 155 4.9
26 194 9
27 206 6.7
28 276 12.4
29 222 11.2
30 214 21.3
31 248 7.8
32 283 17.8
33 215 10.9
34 279 14.9
35 229 8.6
36 127 17.3
Table 8. (continued)
Malta Mexico
Number
PbB PbF PbF PbB
(µg/litre) (µg/g) (µg/litre) (µg/g)
Median 246.5 11.1 188 4.7
Mean 243 14.8 195.4 4.8
Standard deviation 70.9 10.8 49.5 1.6
Standard error 11.8 1.8 11.4 0.4
Minimum 96 2.9 112 2.8
Maximum 385 49.6 297 9.8
Range 289 46.7 185 7
we can infer about the other variables. Both numerical and graphical
techniques may be used to characterize the sample and any relevant
subsets, and to obtain preliminary results from the study.
4.2.1 Numerical summaries
Numerical approaches include calculating descriptive statistics
that describe the distribution of a variable (e.g., blood lead
concentrations) in terms of central tendency and dispersion as well as
descriptions of associations between pairs of variables. Other
numerical descriptive measures can be used to describe points in the
distribution (e.g., percentiles). Each of these descriptive statistics
is described below and where appropriate the formulas used to
calculate them are provided in Table 9.
Standard measures of central tendency include the sample
median (i.e., midpoint observation) and sample mean (i.e.,
average). Referring to the lower half of Table 8, note that the median
blood lead concentration for the Maltese study population was 246.5
µg/litre, intermediate between the eighteenth and nineteenth
observations. Thus, 50% of the individuals in this sample had a blood
level less than 246.5 µg/litre and 50% had a greater blood lead
concentration. The sample mean blood lead concentration in the Maltese
population was 243 µg/litre compared to 195.4 µg/litre in Mexico,
indicating that blood lead levels were higher in the Maltese
population. Methods for assigning confidence levels to statements such
as this are described in Section 4.4. The sample mean is more precise
for estimating the average of the distribution, but it is sensitive to
measurement imprecision, errors and extreme values. Although the
sample median is less precise for estimating the average, it is more
robust with respect to errors in the data. Therefore, when outliers or
extreme values are present, or when possible errors and contamination
in the observed data are suspected, the median is likely to be a
better descriptor of central tendency than the mean.
Standard measures of dispersion include the sample variance, the
sample standard deviation and the sample range (formulas in Table 9).
These measures describe the spread of the observations. Examination of
Table 8 reveals that blood lead concentrations are more variable in
the Maltese sample population (standard deviation = 70.9 µg/litre,
range = 289 µg/litre) than that in Mexico (standard deviation = 49.5
µg/litre, range = 185 µg/litre). Measures of dispersion are useful for
characterizing the degree of variability of a given measure among the
members of a study population. As we will see later in this chapter,
dispersion is also a key component of some study design issues.
The concept of sample percentile is an important aspect of
exposure assessment. A sample percentile for a variable in a data set
is the value of the data such that at least p % are at or below this
value, and (1 - p)% are at or above this value. A percentile is
determined by first ordering the sample (i.e., rank from lowest to
highest) and then identifying the observation that corresponds with
the desired fraction of the data set. In the case of blood lead
concentrations measured in the Maltese sample population, 283 µg/litre
is the 75th percentile since it is the 27th of 36th rank-ordered
values in the data set. Graphical representation of percentiles is
discussed in the next section.
Multivariate summary statistics allow one to evaluate
relationships between or among different variables. Most of these
examine correlation (the strength of the linear relationship) between
variables, where the direction and magnitude of the relationship, or
association, is described by the correlation coefficient (p). The
correlation coefficient ranges from -1 to +1, where negative values
indicate an inverse relationship between two variables, positive
values indicate a direct relationship, and values near zero, whether
negative or positive, indicate a weak relationship. In the example
case, the correlation between blood lead and faeces lead in the
Maltese study population is 0.57, indicating these biomarkers of lead
exposure have a moderate to strong positive association.
4.2.2 Graphical summaries
Graphical summaries of data provide illustrative information
about the distribution of the observed values and associations between
variables. Graphical presentations of data can suggest the shape of
the distribution and aid in exploring hypothesized relationships
between factors included in the study. In many situations and for many
exposure analysts, graphical summaries of data convey information more
readily than numerical summaries. Fundamental graphical presentation
methods are described here. A description of advanced visualization
methods may be found in Cleveland (1993) and Tufte (1983, 1997).
4.2.2.1 Histograms
Histograms are bar charts used to illustrate the relative
frequency of values or ranges of values within an exposure metric.
Observations are assigned to ranges of the data, and the height of the
bar represents the frequency of observations in that range. It is
important to note that the choice of ranges can be arbitrary,
resulting in many possible different pictures of the results. A
histogram of the Maltese blood lead data is shown in Fig. 11. Here,
the data were grouped into bins with interval ranges of 25 µg/litre.
Blood lead concentrations between 200-225 µg/litre and 275-300
µg/litre were observed the most often. Histograms can be used to
illustrate absolute or relative frequency.
4.2.2.2 Cumulative frequency diagrams
Cumulative frequency or probability diagrams can be used to
graphically express percentiles of a distribution. A cumulative
probability chart for the Maltese blood lead data is shown in Fig. 12.
The value associated with a given percentile, or vice versa, can
easily be determined from such a figure.
4.2.2.3 Box plots
A box plot is another approach for graphically describing the
distribution of a measurements in an exposure study. Some details of
box plots vary among users; however, all of them display the sample
median, mean, 25th percentile and 75th percentile. Selected other
values, such as 10th and 90th percentiles or 5th and 95th percentiles
as well as the extremes (i.e., the minimum and maximum) of the
distribution are displayed, too. Fig. 13 shows box plots of the blood
lead measurements from the Maltese and Mexican sample populations. The
bottom and top horizontal lines of each box denote the interquartile
range (i.e., the 25th and 75th percentiles) and the solid horizontal
line across the centre indicates the sample median. The dotted line
across the box indicates the mean of the distribution. The whiskers on
the boxes in Fig. 12 extend to the 10th and 90th percentiles of the
distributions, and the open circles denote all observations beyond
those percentiles. As illustrated here, box plots are a convenient
method for displaying information on the central tendency, dispersion,
symmetry and tails of an exposure measure.
4.2.2.4 Quantile-quantile plots
Quantile-quantile plots can be used to compare the distribution
of a variable with a different sample or a known distribution.
Exposure measures are commonly compared to the normal or lognormal
distribution (see section 4.3) for purposes of evaluating whether the
normality assumptions inherent in numerous statistical inference
methods are met. While a discussion of probability distributions and
statistical inference methods is reserved for later in the chapter, a
quantile-quantile plot is shown in Fig. 14. Here, the Maltese blood
lead data are plotted against the standard normal distribution (see
section 4.3). This special form of quantile-quantile plot is known as
a normal probability chart. Data that form an approximately straight
line on such a chart are approximately normally distributed. Data that
do not form a straight line follow a non-normal probability
distribution.
4.2.2.5 Scatter plots
Scatter plots display the relationship between two exposure
variables measured from the same unit of observation (e.g., a person
or location). Scatter plots are useful for graphically illustrating
associations that are summarized numerically by correlation
coefficients. Possible results include noticeable linear or non-linear
trends, the absence of trend (a big "cloud") or a general trend with
some observations as outliers. Outliers are observations that do not
follow the trends of the rest of the data and can strongly affect
estimates of associations by masking real effects. Outliers can be the
result of measurement error, human error or a correct but abnormal
observation. Regardless, all potential outliers should be checked for
accuracy and corrected or removed if this is justifiable. Fig. 15
contains a scatter plot of blood lead and faeces lead measurements
made concurrently on the Maltese sample population. Note that the plot
indicates a positive association between the two measurements, but
that the relationship is not 1 : 1, i.e., a unit change in blood lead
levels is not accompanied by a constant change in blood faeces
concentrations. This observation is consistent with the correlation
coefficient between these measures of 0.57 that was noted in the
previous section.
4.3 Probability distributions
Most exposure measurements can be considered random variables;
that is, the different values obtained for a measurement of a given
type are a function of a set of causative variables that may or may
not be known to the analyst (Ott, 1995). Statistics allows for
analysis of random variables by incorporation of variation through
probability. This addition of variation allows for the generalization
of results to populations larger or different than the sample under
consideration.
Continuous probability distributions are described by their
probability density function (PDF), which provides the probability of
an outcome taking values in a small interval, and by their cumulative
distribution function (CDF), which describes the probability of an
outcome being less than a particular value. The PDF and CDF are
directly analogous to the concepts of a histogram and cumulative
probability distribution discussed in Section 4.2.
Probability models are used to make statements such as, "The
probability that the daily maximum ozone concentration will be greater
than 120 ng/litre today is 0.08." Such estimates can be based upon
empirical evidence (i.e., by looking at the number of observed
concentrations greater than 120 ng/litre in comparison with the total
number of observed concentrations) or by choosing a distribution and
parameters that describe the observed data. An example of the latter
would be to model the distribution of blood lead levels in Maltese
subjects as normally distributed with a mean of 243 µg/litre and
standard deviation of 70.9 µg/litre and to use the properties of the
distribution to estimate the probability. The amount of confidence in
the accuracy of the estimates is related to the amount of data
available and the sampling scheme used to collect the data, as well as
the degree to which the mathematical distribution fits the observed
data.
Two standard distributions commonly used in exposure assessment
for modelling continuous outcomes are the normal and the
lognormal distributions. The binomial and Poisson distributions
are often used in exposure studies as well. Many other probability
distributions are available which have more flexibility (Johnson &
Kotz, 1970a,b), but these four are frequently used and thus warrant
attention here.
4.3.1 Normal distribution
The normal distribution, also known as the Gaussian
distribution, is one of the most important statistical
distributions. It is characterized by a symmetric, bell-shaped
frequency distribution and is commonly used as a basis for analysis of
environmental exposure data. Usually, a random variable (X) that
follows a normal distribution with mean µ and variance rho2 is
denoted by X ~ N(µ, rho2). The probability density function of the
normal distribution with parameters µ and rho2 is given in Table 10.
Since the cumulative distribution function cannot be integrated
in a closed form, the best we can do is to numerically compute the
integral. The values µ = 0 and rho = 1 specify the standard normal
distribution. The values of the CDF for the standard normal
distribution have been tabulated and are available from most
statistical textbooks and computer packages. The capital letter Z is
usually reserved to denote a standard normal random variable, i.e.,
Z ~ N(0,1). The normal distribution ranges from positive infinity to
negative infinity and is symmetric. Equation 4.7 can be used to
transform any normal random variable X to a standard normal random
variable (Table 10). Standardized normal random variables are useful
for computing the probability of an event occurring, e.g., the
likelihood that someone in Malta has a blood lead concentration
greater than 384 µg/litre. Assuming the Maltese blood lead data
presented earlier are representative of the general population and the
blood lead concentrations are approximately normally distributed, the
standard normal distribution can be used to calculate the desired
probability.
4.3.2 Lognormal distribution
Many exposure measurements are strictly positive and right skewed
(i.e., asymmetric). Examples include the size distribution of
suspended particulate matter, personal exposures to various air
pollutants and human time-activity patterns. The lognormal
distribution is one possible model for describing data with these
characteristics. The natural log (ln) transform of a lognormally
distributed random variable has the properties of a normally
distributed random variable. In other words, the distribution defined
by the mean (µln x) and standard deviation (rholn x) of the
ln-transformed values is bell-shaped and symmetric and can be
standardized according to the procedure outlined in the previous
section. Exponentiation of µln x and rholn x gives values termed the
geometric mean (GM) and geometric standard deviation (GSD),
respectively. The GM and GSD can also be used to define a lognormally
distributed exposure measure.
A histogram of the blood faeces data from the Maltese sample
population is presented in Fig. 16a. The data depart from normality as
they are clearly right skewed. The histogram in Fig. 16b shows that
the ln-transformed values are approximately symmetric and indicates
that the data approximate a lognormal distribution rather than a
normal distribution. In this data set, µln x = 2.5 and rholn x = 0.7
with corresponding GM = e2.5 = 11.8 µg/litre and GSD = e0.7 = 2.0. The
degree to which the lognormal distribution accurately describes the
data can be evaluated by plotting the raw data on lognormal
probability paper. This procedure is identical to that described in
relation in to Fig. 13, except that the y axis is expressed on a
logarithmic scale. The Maltese faecal lead data are plotted on
lognormal probability paper in Fig. 17. The nearly straight line
formed by the faecal lead measurements displayed on a logarithmic
scale versus Z indicates that the data are approximately lognormally
distributed.
4.3.3 Binomial distribution
In some situation, the analyst may be interested in
characterizing the frequency of a binary exposure outcome (e.g.,
yes/no; true/false). The binomial distribution is useful for modelling
FIGURE 16a;V214EH21.BMP
FIGURE 16b;V214EH22.BMP
binary responses. The possible responses can be generally labelled as
success or failure. Often we are not interested in a single outcome,
but rather in the number of successes (k) and failures (n - k) for
a specific number (n) of repeated independent trials for the
outcome. The probability of exactly k successes in n independent
trials, given a probability of success (p) in a single trial, is
given by the binomial probability distribution ( Pk) in Table 10.
For example, assume daily exceedances of an ozone air quality
standard are independent events in a study of 1-year and 3-year time
periods. Let k be a random variable describing the total number of
exceedances encountered in a 1-year period ( n = 365 days). Further
assume from historical data that the expected number of exceedance
days each year is 1, thus p = 1/365 = 0.00274. The calculated
probabilities of k days of exceedance per year are shown in Table
12. Examination of the resulting probabilities in this example reveals
a right-skewed distribution with the greatest probability occurring
between k = 0 and k = 4 days.
4.3.4 Poisson distribution
Some exposure-related measurements are expressed as a rate of
discrete events, i.e., the number of times an event occurs per unit
time, such as the frequency (times per week) that a person consumes an
ocean fish containing a methylmercury concentration greater than
5.0 ppm. The Poisson distribution is used for describing potentially
unlimited counts or events that take place during a fixed period of
time (i.e., a rate), where the individual events are independent of
one and other. The Greek letter lambda is typically used to denote the
average or expected number of counts per unit time. In a Poisson
distribution, the parameter lambda also describes the variance of the
random variable. We can think about this intuitively by noting that as
the expected number of counts or events increases (i.e., the rate of
events increases), the amount of variability should increase as well.
For example, if we expect a count of 1 then it is not too difficult to
imagine observing 0 or 2. Likewise, if we expect a count of 20 000
then it is not difficult to imagine 20 100 or 19 900 as reasonable
observations. However, the variance is definitely larger in the second
case. The formula used to compute the probability of a specific number
of counts being observed over a fixed time interval is listed in Eq.
4.11 of Table 10.
For example, the Poisson distribution can be used in an exposure
model to characterize the frequency with which a person comes in
contact with a contaminant; say, the number of times per day a person
encounters benzo [a]pyrene associated with environmental tobacco
smoke. Assume that based on existing data, the expected number of
encounters is anticipated to be 2 per day. Using Eq. 4.11, with lambda
= 2, there is a 9% chance that an individual will have 4 (i.e.,
n =4) encounters with benzo [a]pyrene on a given day. Subject to
limitations associated with the independence assumption noted above,
the Poisson distribution can be a useful exposure modelling tool.
4.4 Parametric inferential statistics
Inferential statistics is the process of using the observed data
and assumptions about the distribution and variation of the data to
draw conclusions. The two complementary components of inference are
parameter estimation (either point or interval estimation) and
hypothesis testing. Only frequentist, or classical, inference will
be discussed here. However, Bayesian statistical inference, as well as
decision theory, can be valuable for incorporating other aspects such
as prior belief and loss into a statistical analysis, and they are
worth consideration. Further information on Bayesian statistics may be
found in Carlin & Louis (1996).
4.4.1 Estimation
Exposure measurement data can be used to estimate the parameters
of a model (e.g., a probability distribution), especially those that
describe the mean and variance of the variable. The two types of
common reported estimates are point estimates and interval
estimates.
Point estimation for quantities is commonly performed using
maximum likelihood, ordinary least squares or weighted least squares
methods. All estimates are chosen because they optimize (i.e., find
the maximum or minimum of) some objective function such as the
likelihood function or squared error function. One example is the
sample mean for the population mean when the data are normal, using
maximum likelihood, or for any data, using least squares.
Two different forms of interval estimation are used to
characterize variability in point estimates. The first is based on
error propagation and is the result of simulating data to see what
distribution of results might be expected under the model; the second
is the usual statistical notion of confidence intervals. This approach
focuses more on the variability of a modelled outcome due to
variability of the input, and is useful in designing studies and
determining which factors will have the greatest effect on the
variability of the exposures. These procedures are described more
fully in Chapter 6.
The second form of interval estimate, the statistical (1-alpha)%
confidence interval, gives a range of estimates, for a parameter,
which is generated in a manner such that it contains the true
parameter value (1-alpha)% of the time. For a normally distributed
random variable, a one-sided confidence interval for the estimate of
the mean is derived from the standard error and Z1-alpha, while
Z1-alpha/2 is used for a two-sided confidence interval. The standard
error (rho×) is an expression of uncertainty about the mean and is
calculated as the standard deviation divided by the square root of the
number of observations (n) (Table 9). Continuing with the example
from the Maltese study, the standard error of the blood lead sample
data is 11.8 µg/litre (Table 8). For alpha = 0.05, the two-sided 95%
confidence interval about the estimated mean is computed as 243
µg/litre ± 23.1 µg/litre, where the latter is equal to Z1-(alpha/2) ×
rho× or 1.96 × 11.8 (Table 11). Details of this procedure and
related considerations may be found in most introductory statistics
textbooks, for example Kleinbaum et al. (1988).
4.4.2 Measurement error and reliability
The term measurement error refers to the accuracy and precision
of a given sample collection and analysis methodology. Accuracy
describes the degree to which a measurement is free of bias. Bias is
systematic deviation in a measurement from the true value of the
process being measured. Precision refers to the reproducibility of a
particular measurement system. Measurement reliability is a closely
related concept in that a measurement with a high degree of accuracy
and precision can be considered to be more reliable than one with a
low degree of accuracy and precision. Additional information on
measurement error and reliability is contained in Chapter 11, where
the topic is discussed in the context of QA in exposure studies.
Methods for assessing the accuracy of an exposure measure are also
discussed in Chapter 11. Here, an approach for quantitatively
estimating the precision of an exposure measurement system is
presented.
Statistical analysis of environmental samples collected
simultaneously in space and time can be used to estimate the precision
of a measurement method. Such samples are often referred to as
duplicates and are often collected in pairs. The difference in the
measurement parameter (e.g., concentration) between a duplicate pair
Table 11. Standard normal cumulative probabilities
z p(Z < z) z p(Z < z)
-4.265 0.00001 0 0.50
-3.891 0.00005 0.126 0.55
-3.719 0.0001 0.253 0.60
-3.291 0.0005
-3.090 0.001 0.385 0.65
-2.576 0.005 0.524 0.70
-2.326 0.01 0.674 0.75
0.842 0.80
-2.054 0.02 1.036 0.85
-1.960 0.025
-1.881 0.03 1.282 0.90
-1.751 0.04 1.341 0.91
-1.645 0.05 1.405 0.92
1.476 0.93
-1.555 0.06 1.555 0.94
-1.476 0.07
-1.405 0.08 1.645 0.95
-1.341 0.09 1.751 0.96
-1.282 0.10 1.881 0.97
1.960 0.975
-1.036 0.15 2.054 0.98
-0.842 0.20
-0.674 0.25 2.326 0.99
-0.524 0.30 2.576 0.995
-0.385 0.35 3.090 0.999
3.291 0.9995
-0.253 0.40 3.719 0.9999
-0.126 0.45 3.891 0.99995
0 0.50 4.265 0.99999
is indicative of the precision of the collection and analysis
methodology. Descriptive statistics generated from a set of
differences between duplicate samples can be used to characterize the
average degree of precision as well as variability in precision.
Consider a hypothetical study of respirable particulate matter
(RSP) in outdoor air where 20 duplicate pairs of 24-h average
measurements were made. Assume the average 24-h average concentration
among the 40 measurements was 50 µg/m3. Further assume that the
distribution of differences between the 20 pairs of duplicate samples
was normally distributed with a mean and standard deviation of 5 and 1
µg/m3, respectively. On average, then, a single measurement can be
expected to be within 5 µg/m3 of the actual concentration. Utilizing
Table 12. Probability distribution for the number of exceedances, using the binomial model
with expected number of exceedances of 1.0
1-Year period 3-Year period
Number of Probability Cumulative Probability Cumulative
exceedances probability probability
k Pk {k} Fk{k} PM{k} FM {k}
0 0.36737 0.36737 0.04958 0.04958
1 0.36838 0.73576 0.14916 0.19874
2 0.18419 0.91995 0.22414 0.42288
3 0.06123 0.98118 0.22435 0.64723
4 0.01522 0.99640 0.16826 0.81549
5 0.00302 0.99942 0.10086 0.91635
6 0.000498 0.999920 0.05034 0.96670
7 0.000070 0.9999904 0.02152 0.98821
8 0.0000086 0.9999989 0.00804 0.99625
9 0.0000009 0.9999999 0.00267 0.99892
10 0.00000009 0.9999999 0.00080 0.99972
11 0.000000008 0.9999999 0.00022 0.99993
12 0.000000001 0.9999999 0.00005 0.99998
concepts presented in Chapter 4.4.1, a single measurement can be
expected to be within approximately 3-7 µg/m3 of the true
concentration 95% of the time, i.e., within ±2 standard deviations of
the average difference.
For a probability distribution, the coefficient of variation is
defined as the ratio of the standard deviation to the point estimate
of the mean. In this way, the coefficient of variation error describes
the degree of dispersion of a data set relative to a measure of its
central tendency. The coefficient of variation provides a quantitative
estimate of the relative degree of variability among the observations
in a data set. Using data from the hypothetical example described
above, the coefficient of variation among the pairs of duplicate
samples is 0.2. Thus, on average, a single measurement can be expected
to be within 20% of the actual concentration.
4.4.3 Hypothesis testing and two-sample problems
Exposure assessments are often performed to determine whether the
level of exposure to a pollutant is different between two or more
groups of people or locations or periods of time. Additional
attributes typically considered to be determinants of exposure include
any number of demographic factors (e.g., age, gender, ethnicity) and
behaviour patterns. This section describes the statistical procedure
used to address this type of study objective.
Statistical hypothesis testing is a procedure where a choice is
made between two hypotheses that are not weighed equally; the null and
the alternative. The null hypothesis typically reflects what can be
stated with confidence about a particular phenomenon on the basis of
existing information. In practice, concluding that the null hypothesis
is false indicates that the data provide strong evidence for a
departure from conventional wisdom or practice. Thus, hypothesis tests
are generally constructed such that the conclusion will lie with the
null unless the evidence strongly suggests otherwise.
Two types of errors can arise from hypothesis testing:
* concluding that the alternative hypothesis is true when it is in
fact false (false negative)
* concluding that the null hypothesis is true when in fact it is
false (false positive).
The first type of error is known as a type I error and the second
one is a type II error. The probability of a type I error is denoted
by alpha and the probability of a type II error by ß. Only alpha is
considered in the construction of the hypothesis test. However, as
described later, both type I and type II errors are considered in
sample size determinations.
The general procedure for common tests that try to determine if
some factor has an effect on the exposure outcome is as follows: a
test statistic is constructed whose value is known if the null
hypothesis is true. For example, if the null hypothesis is that the
population mean is 1 (H0: µ=1), then under the null hypothesis, × =
0, where × is the sample mean. Next, adjustments are made so that
the distribution of this test statistic is known. For example, with
s denoting the sample standard deviation and n the sample size,
the test statistic T defined by Eq. 4.12 in Table 13, where T has
a distribution which follows a t-distribution with n-1 degrees of
freedom. Now, using the known distribution of the test statistic, we
construct ranges of values for which we reject (rejection region) and
fail to reject (acceptance region) the null hypothesis. The rejection
region is any area which has probability alpha, usually chosen to
correspond to likelihoods between 0.025 and 0.05.
A large number of problems in exposure assessment involve the
comparison of two groups, for example, control and treatment; old
method and new method; or normal and abnormal. If we focus on the
location problem, where the means or the medians are compared, then
depending on the assumptions we make with regard to the data,
different tests can be performed. Assumptions typically made include:
* The data consist of a random sample from population 1 ( X1,i,
i = 1, ..., n), and a random sample from population 2 ( X2,i =
1, ..., n2)
* The two samples are independent of each other.
* Observed variables are on a continuous scale.
* Measurement scale is at least ordinal.
* Population 1 ( X1) has approximately the same distribution as
X2.
If we assume that the data follows a normal distribution and that
the data are independent, with the first group distributed N(µ,
rho21) and the second group distributed N(µ, rho22) so that the
variances are possibly different, a test can be constructed to see if
the difference (Delta) between the means for the groups is equal to a
hypothesized value (Delta0), typically set to zero. This scenario
would result in a two-sample t-test, and the test statistic is
presented in Eq. 4.13 in Table 13, where t is compared with a
t-distribution with df = min( n1-1, n2-1) degrees of freedom,
and si2 is computed as described in section 4.2.1. The possible
alternatives are that Delta > Delta0, Delta < Delta0, or the
general alternative that Delta not equal Delta0. If we are looking
for differences, we reject the null hypothesis that the groups are the
same for the respective alternative if t > Tdf,alpha, t < -
Tdf,alpha, or | t| > Tdf, alpha/2, where alpha is the prespecified type I
error for the decision to be made.
Referring once again to the blood lead example presented earlier,
the following null hypothesis may be tested: mean blood lead
concentrations in the Maltese sample population are equal to those in
the Mexican sample population. The corresponding alternative
hypothesis is: mean blood lead concentrations are not equal in the two
sample populations. As indicated in Fig. 13, the point estimates of
the respective sample means are different. Completion of the
two-sample t-test will allow for determination of whether the
difference is statistically significant with 1-alpha% confidence.
Using Eq. 4.13, the t-statistic is computed to be 3.30. Setting
alpha = 0.05, the critical t-value is 1.96. Thus, the Maltese and
Mexican sample mean blood concentrations are significantly different
at the 0.05 level.
4.4.4 Statistical models
Statistical models make explicit the potential sources of
variability to be measured. The response, exposure, is dependent upon
a combination of measured factors and background variation from
unmeasured influences. For example, in examining pesticide exposures,
one might consider methods and amounts of applications, climate
conditions and duration of potential exposure. Unmeasured factors
might include exact knowledge of individual behaviours and locations,
which may cause different levels of exposure between two individuals
who are equal with respect to other exposure characteristics. One must
consider as many of the potential relationships between the responses
as possible, as well as how the possible factors will affect each
other, before finalizing a study design.
Since no simple model will perfectly describe all relationships,
the goal is to construct a parsimonious model that describes the major
factors of the process resulting in exposure. For example, in studying
the exposure of children to lead, the presence of lead in paint, in
house dust or in water could be important factors, whereas gender and
age might have an indirect effect on exposure by influencing the
location and patterns of play. However, both types of data will be
important in determining response, even though one is only an indirect
cause. The average outcome described above could be the annual average
exposure to lead or perhaps the maximum daily exposure, depending upon
whether a cumulative or a threshold effect is the focus.
As noted in Chapter 3, by considering the statistical model
before finalizing the study design one can help ensure that most
influential factors would be accounted for, and more importantly, that
the true effects of factors can be estimated from the study data. It
is possible to design a study where some influential factors were not
accounted for. Suppose there is interest in the effects of location
and time of day on outdoor ultraviolet radiation exposures. If
measures are only taken at one site at one time of day, and then at
another site at a different time of day, then the effects of location
and time of day are not distinguishable from the collected data.
The mean, or average, outcome is the most common summary used for
modelling and testing of situations of different conditions, but other
parameters, such as the variance, the percentiles or the median, can
be used for estimation and testing. Common models and statistical
analyses, such as the multiple linear regression model, the t-test
and analysis of variance (ANOVA) use the mean for modelling and
testing. The models can be as simple as taking the sample, dividing it
into groups and comparing the means in the different groups. The
models can also be as complex as trying to construct a physical model
for the means with the addition of terms which incorporate randomness
due to unmeasured factors or other sources of variation.
4.4.4.1 Analysis of variance and linear regression
ANOVA is a technique for assessing how several nominal
independent variables affect a continuous dependent variable, and is
usually concerned with comparisons involving several group means.
Regression and ANOVA models are closely related and can be analysed
within the same framework. The major difference is that for ANOVA, all
the independent variables are treated as being nominal; whereas for
regression analysis, any mixture of measurement scales (nominal,
ordinal, or interval) is allowable for the independent variables.
Examples of ANOVA used in exposure assessment can be found in Liu et
al. (1994a), who used ANOVA models to examine the effects of wind
speed, ozone concentrations, human subject and interaction between
wind speed and concentration on the performance of an ozone passive
personal sampler.
Estimation for both ANOVA and linear regression models consists
of obtaining point estimates for the parameters that describe the mean
exposure under a certain set of conditions. Part of the estimation
procedure is to determine how well the model fits. The first
diagnostic is to examine the residual error (residual). A residual is
simply the difference between the exposure estimated by the model and
the actual exposure. By examining the residuals, one can determine for
what ranges of actual exposures or conditions the model does not fit
well, and use this to decide how to adjust the model.
The simplest design (and corresponding model) occurs when
measurements are taken while varying only one possible factor over a
finite, k, number of levels. Consider PM2.5 exposure; let the factor
be the time of day when the levels are measured. For simplicity,
divide time into three categories -- morning, afternoon, or evening --
so k = 3. If there is no known or hypothesized functional form for
the relationship, the resulting abstract model for exposure, Y,
during a particular time period, i, should be the sum of the mean
(average) during the time period i, denoted by gammai, and an
error, epsilon, which will represent the natural variation of the
measurement. It is common to assume that the variation of the outcome
is the same among all levels of the factor; this assumption is known
as homoscedasticity.
This model is referred to as the one-way ANOVA. The resulting
model for the observed data, with Yij denoting the jth PM2.5
measurement collected during the ith time period with i ranging
from 1 to 3, is Yij = gammai+ epsilonij where gammai represents
the average outcome due to the ith factor level (in this example
i ranges from 1 to 3), and epsilonij (the error term) represents
independent random variation. One common assumption is that the error
terms follow a normal distribution with variance rho2. The parameters
which need to be estimated in this model from the data are the means
of the subsamples, gammai, and the variance of the outcome, rho2.
This type of model, which compares the means of distinct groups, is
the basis for ANOVA.
Increasing the level of complexity leads to multiway or
multifactor ANOVA as well as the multiple linear regression model,
which is a more specific model for the effects of independent
variables on the dependent variable. Let Y denote the exposure level
for a particular person or location; this is the dependent variable.
Let X, ..., Xn denote n independent variables (known as
covariates) which potentially influence the exposure level Y. If the
assumption of the existence of a linear relationship between the
independent and dependent variables is reasonable, then a model for
the outcome, Y, based on the covariates Xi, can be written as
where the information not conveyed by the covariates results in the
error (epsilon), which is assumed to be normally distributed. theta0
denotes the average exposure when all the X values are zero, and
thetai denotes the change in exposure for a unit change in the ith
variable. An example would be 24-h personal exposures to nitrogen
dioxide. In this case, the factors may be distinct times and locations
(or microenvironments) where nitrogen dioxide exposure may occur; for
example, outdoors, indoors while home cooking on a gas range, and in
an automobile.
A regression model is used to evaluate the relationship of one or
more independent variables X1, X2, ..., Xk to a single,
continuous dependent variable Y. It is often used in exposure
assessment to characterize the relationship between the dependent and
independent variables (continuous and discrete) by determining the
extent, direction and strength of the association. For example, in the
particle total exposure assessment methodology (PTEAM) study, indoor
PM2.5 concentration (Y) was regressed against outdoor air
concentrations ( X1), smoking rates ( X2), cooking durations
( X3), air exchange rates ( X4) and house volumes ( X5) to
determine the major factors affecting indoor PM2.5 concentrations
(Ozkaynak et al., 1996).
Further information on ANOVA and linear regression may be found
in Ott (1995), Kleinbaum et al. (1988) and most introductory
statistics textbooks.
4.4.4.2 Logistic regression
An approach which is different from the linear or additive
relationship described above is to consider a categorical outcome for
exposures, e.g., exposure measurements grouped into ordinal levels
such as low, medium and high. When the response is binary, that is, if
an exposure is either present or absent (i.e., a threshold effect),
then a linear relationship is not appropriate. In this case, we must
use an alternative model, for example:
logit P(Y = 1) = alpha + ß1 X1 + ß2 X2 + ... + ßk Xk
where the logit function is logit (x) = ln ( x/(1- x)), the
function P(Y = 1) denotes the probability that the response variable
Y will take on the value 1 (denoting "success"), and the role of
epsilon from the previous model is taken by modelling the parameter
representing the probability that Y = 1, as opposed to Y itself.
This model is known as logistic regression. In this model, alpha
denotes the baseline odds for exposure given that the associated
factors, X ..., Xk , are zero, and ßi denotes the change in the
log-odds that the response is Y = 1 given a 1-unit increase in
Xi. This approach can be adjusted to allow for the analysis of
other types of categorical outcomes (McCullagh & Nelder, 1990). One
common parameter which describes logistic regression results is the
odds ratio. For a particular set of covariates, Xi, the odds of
the event occurring ( Y = 1), is exp(ß Xi). To compare the odds
ratio for two situations, compute the first set of odds and take its
ratio over the second odds. Usually, the situations will be identical,
except that the covariate of interest will be zero for one of the
sets. For example, if the model for the linear combination of
covariates is 1.3 +2.5 X, then the odds ratio for Y = 1, comparing
X = 1 versus X = 0, is e(1.3+2.5)/e1.3. A similar computation can be
done when X is a continuous random variable, for two different
values of X. In exposure assessment, a logistic regression model
could be used to evaluate the importance of demographic or temporal
factors on the likelihood that an individual will engage in an
activity such as applying pesticides.
4.4.5 Sample size determination
Hypothesis testing attempts to determine if the data reject or
fail to reject a particular (null) hypothesis. The test is based upon
statistical considerations, and hence just reports how likely or
unlikely the null hypothesis is. If there is minimal information, it
will be difficult to statistically reject the null hypothesis; hence,
sample size calculations are done in order to ensure that there is
sufficient information from which to make a decision. The decision is
between two unequally weighted hypotheses; the first is the null
hypothesis, H0, which is the safe hypothesis, and the second is H1,
the alternative or sceptical hypothesis, which requires sufficiently
large evidence to believe in. The specifications of the test, based
upon sceptical scientific belief or common usage, are the type I error
and the type II error (defined in section 4.4.3). Introductory
information on sample size determination is presented here; the reader
is referred to Lemeshow et al. (1990) for details.
To determine the minimum sample size required to observe the
desired outcome, one needs to determine the smallest effect that is
scientifically worth detecting (i.e., based on measurement limit or
scientific principles), and use that to collect a sample with enough
information to detect such a difference. The effect is the minimum
significant difference in exposure between two groups. The smaller the
effect, the more information is required to distinguish it. This
effect is related to the type I error of a hypothesis test. There are
two components, the type II error and the sample size, which are
unspecified. When the type II error is specified, the resulting sample
size can be determined. Once the sample size is determined, the power
(1-type II error) of a study can be computed. The smaller the
difference to be detected, the larger the sample size needed for fixed
type I and type II error probabilities.
The following describes the formula for a two-group comparison.
To compare the means of two groups with equal sample size, let Delta
represent a scientifically significant difference that we would like
to detect, if it exists. Suppose that the first group can be modelled
by Y1 = µ1+epsilon1, where µ1 is the mean and epsilon1 ~
N(0,rho2) is the error term, and the second group can be modelled
by Y2=µ2 + epsilon2 with the error term epsilon2 ~ N(0,
rho2). The only difference between the two groups is the mean
response. The groups will be considered statistically different only
if |µ2-µ1| > Delta. The minimum number of observations in each
group is given by Eq. 4.14 in Table 14 where zgamma is defined as
the value satisfying P(Z > z) = gamma, where Z follows the
distribution of a standard normal random variable, alpha is the type I
error, and ß is the type II error. This formula can also be used to
approximate the sample size needed for a difference of proportions
(e.g., for dose-response models comparing two groups), by letting
Delta represent the difference in proportions (instead of a difference
in means).
4.5 Non-parametric inferential statistics
Each of the statistical analysis methods described previously
assumes that the data can be adequately described by a probability
distribution with known parameters, and that distribution can be
transformed, if necessary, to meet the assumptions of the statistical
model (e.g., normally distributed, independence, etc.). Many
exposure-related data sets do not fit this description, however. One
reason for this is that the data may not be normally distributed or
cannot be transformed so that they are approximately normal. A more
common reason is that although the underlying distribution of the
population from which the sample is drawn may be reasonably assumed to
be approximately normal or lognormal, there are too few samples to
allow the nature of the underlying distribution to become apparent. In
exposure studies sample sizes are often small (e.g., 10 or less)
because of logistic difficulties in collecting samples and the expense
of collecting and analysing the samples. In this case, the point
estimates of the standard deviation and standard error are considered
to be highly unstable. Consequently, confidence intervals generated
using the estimation methods described above are considered to be
unreliable. Non-parametric statistical analysis methods can be used to
analyse data with these characteristics.
Non-parametric statistical methods rely on rank statistics, i.e.,
the order of observations in a data set. Glantz (1987) provides a
concise introduction to non-parametric statistical methods with regard
to health statistics. The sign test and Mann-Whitney rank sum test are
two non-parametric methods for evaluating the equivalence of the
median from two sample populations. These methods are analogous to the
two-sample t-test described in Section 4.4.3. The Kruskal-Wallis
test is analogous to the k-sample ANOVA method described in Section
4.4.4 and is used to test whether the medians of more than two sample
populations are equal. For further information on this topic, the
reader is referred to Mosteller & Rourke (1973), a classic text on
non-parametric methods, and also to Gilbert (1987) and Ott (1995) for
a discussion of statistics based on rank order in an environmental
context.
4.6 Other topics
Many new developments in statistical theory can be applied to the
analysis of exposure assessment data. These include the topics of
measurement error, missing data, spatial statistics, non-linear
models, mixed effects, generalized mixed effects models, simulation
models (e.g., Monte Carlo analysis), as well as others. Modern
computing methods such as re-sampling and the bootstrap have made
possible estimation, evaluation, and testing of complex models. In
addition, other inferential philosophies, such as Bayesian and
decision-theoretic approaches, can be useful. Recommended references
for further reading on these and related subjects are Sachs (1986),
WHO (1986), Gilbert (1987), Glantz (1987), Kleinbaum et al. (1988) and
Ott (1995).
4.7 Summary
Statistical methods are a critical tool in applied and
research-oriented exposure assessment studies. It is recommended that
a statistician be involved in all aspects of an exposure
investigation, especially during the design and data analysis stages.
Sample size determination is an important use of statistics during the
planning of an exposure assessment study. Numerical and graphical
descriptive statistics can be used to summarize exposure data and
perform preliminary analyses of relationships between and among
exposure variables. In many cases, exposure data are approximately
normally or lognormally distributed and can thus be readily
incorporated into standard parametric statistical inference methods
such as estimation and hypothesis testing. In addition, other
parametric statistical models such as ANOVA, linear regression and
logistic regression can be used to quantify associations among
exposure measures. In situations where the number of observations is
small or the data cannot be transformed to an approximately normal
distribution, non-parametric methods such as the sign, Mann-Whitney
and Kruskal-Wallis tests can be used to test hypotheses.
5. HUMAN TIME-USE PATTERNS AND EXPOSURE ASSESSMENT
5.1 Introduction
Methods for the collection and application of time-use data in
exposure studies are critically reviewed in this chapter. All methods
have their limitations. With appropriate quality assurance, however,
information on time use and activity patterns collected by
questionnaire, diary, interview, observation or technical means can be
very valuable for interpreting and modelling exposures. Although the
methodologies of time-activity data collection are universal, they
need to be applied and their vocabularies selected keeping in mind the
population and culture of concern and objectives of the study.
Accurately and reliably documenting the time-activity patterns of the
general and target populations are important components of
understanding and mitigating human exposure (see Table 15).
People's activity patterns, eating and drinking habits, and
lifestyle characteristics must be superimposed over concentrations in
environmental media before it is possible to derive realistic
estimates of actual human exposure. Too often in the past, pollutant
concentrations in a particular medium have been assumed to represent
exposure, only for it to be found later that they did not provide an
accurate picture owing to modifying factors such as the time people
spend indoors rather than outdoors, food preparation and cooking, and
use of bottled water instead of tap water. Experience has shown that
exclusive reliance on central monitoring sites (e.g., urban air
pollution monitoring sites, samples from drinking-water reservoirs)
and bulk sampling procedures (e.g., spot checks for pesticides in
food) for determining human exposures may be insufficient in many
cases.
In an exposure context, data about human time use and activity
patterns (often referred to as time-activity data) have four related
purposes.
1. Knowledge of the activities performed while a study participant
carried a personal monitor can aid in identifying the determinants of
exposure, i.e., "What did this person do that led her/him to have such
a high exposure?" and "To what extent can exposure be explained the
amount of time spent in specific activities or locations?" For
instance, several studies in which activity pattern data were
collected in conjunction with monitoring data have shown that
indicators such as commuter status, work status, cooking fuel type,
season, residential location and day of week are important in
differentiating exposure to carbon monoxide and nitrogen dioxide
(Akland et al., 1985; Ryan et al., 1990; Schwab et al., 1990; Berglund
et al., 1994a). Investigations of VOC exposure have found that people
who reported engaging in auto-related activities (e.g., exposure to
vehicle exhausts, pumping gasoline and visiting a service station)
were associated with statistically significant increases in breath and
personal exposure levels of several aromatic and aliphatic compounds;
Table 15. Features of time-activity studies aimed at exposure assessment
Location Pollutant Participant characteristics Survey characteristics Spatial and source Reference
characteristics
Cincinnati, No pollutant 487 people under age 70; March and August 1985; 28 microenvironments; Johnson, 1989
Ohio, USA representative; includes diary; minute resolution; location data; breathing
children; oversample 3-day sample; time of rate; smoking status;
asthmatics; data on age, year; day of week; time pollutant-related activity
gender, race, income, of day questionnaire
work status, health status
California, No pollutant 1780 people over age 11; October 1987-July 1988; 24-h 50 microenvironments; Wiley et al.,
USA representative of recall and questionnaire; stressed activities with 1991; Jenkins et
English-speaking households; time of year; day of week; respect to toxics exposure al., 1992
stratified by region; data time of day and high breathing rates;
on demographics and location/ region; housing
socio-economic status unit characteristics
California, No pollutant 1200 children under age April 1989-March 1990; 24-h 113 activities; 63 Wiley et al.,
USA 12; representative of recall and questionnaire; locations; proximity to 1991
English-speaking households; time of year; day of week; sources; location/region;
stratified by region; data time of day housing unit characteristics
on demographic and
socio-economic status
Kanawha No pollutant 90 children aged 9-11; July and September 1989; Home/near home/far; Schwab et al.,
Valley, West longitudinal (4 weeks); diaries; 30-min resolution school; indoor vs. outdoor; 1991, 1992
Virginia, USA stratified by gender, except travel (15 min); exertion level; housing unit
respiratory health; data on time of year; day of week; characteristics
demographic, socio-economic time of day
status and health variables
Table 15. (continued)
Location Pollutant Participant characteristics Survey characteristics Spatial and source Reference
characteristics
New York, No pollutant 1000 children aged 5-12; Mid-1990 to mid-1991; Usual commuting; frequency Silvers et al.,
New Jersey; stratified by state, 24-h recall of child's of bathing, hand washing; 1994
Pennsylvania, weekday/weekend and season; activities by adult weather conditions; clothing
Oregon; data on demographic, caregiver; 30-min resolution; type; play surface; dwelling
Washington; socio-economic status and questionnaire; time of type
California, community type year; day of week; time
USA of day
Berkeley, Ozone 168 college freshmen (aged Test-retest reliability Time spent outdoors, time Künzli et al.,
California, 17-21) raised in California; study to recall lifetime spent in physical activity 1997a,b
USA convenience sample; residential history (outdoors)
long-term ozone exposure
Athens; PM25, CO, 450 adults (aged 25-55) Mostly 1997; 48-h personal, Time spent in Jantunen et al.,
Basel; VOC, NO2 personal air sampling; indoor, outdoor and at work microenvironments (e.g., 1998
Grenoble; approximately 1200 adults monitoring; time-activity Indoors, outdoors, at
Helsinki; with time-activity diary; diary (Fig. 19); specific work); traffic categories
Milano; random population sample; tasks
Praha demographic and
socio-economic status
Washington, CO 700 adults aged 18-65; 1982-1983 (winter); diary 8 locations; transport Hartwell et al.,
USA representative; oversample and questionnaire; minute mode use; activity index; 1984; Akland et
long commutes and gas resolution; 1-day sample; smokers present; range use; al., 1985
ranges; excluded smokers; time of year; day of week in gavage; census tracts
data on age, gender, work and day for work, home, other;
status housing unit characteristics
Table 15. (continued)
Location Pollutant Participant characteristics Survey characteristics Spatial and source Reference
characteristics
Denver, CO 452 adults aged 18-65; 1982-1983 (winter); diary; 8 locations; transport Johnson, 1984
Colorado, representative; oversample questionnaire; minute mode; activity index;
USA gas ranges and long commutes; resolution; 2-day sample; smokers present; range use;
excluded smokers; data on time of year; day of week; in gavage; census tracts
age, gender, work status time of day for work, home, other;
housing unit characteristics
Elizabeth/ VOCs 355 people; representative; Fall 1981; follow-up: 157 Activities > 1 h; high Wallace et al.,
Bayonne, oversampled high-exposure in summer 1982; follow-up: exposure activities (e.g., 1985, 1986
New Jersey, occupations; data on age, 49 in early 1983; 24-h smokers, occupations,
USA gender, race, socio-economic recall diary; activity travel); proximity to
status, and proximity to questionnaire industry; housing unit
VOC sources characteristics
Portage, NO2, RSP 120 children; selected from 1987; retrospective, actual, 11 microenvironments; home Adair & Spengler,
Wisconsin larger (600) cohort; and prospective diary; zip codes; school location; 1989a,b
stratified by cooking fuel; 10-15-min resolution; time housing unit characteristics
data on gender, age, of year; day of week; time
parental education of day
Steubenville, NO2, RSP 150 winter, 250 summer; 1987; retrospective actual 11 microenvironments; home Adair & Spengler
Ohio selected from cohort of 600 and prospective diary; zip codes; school location; 1989a,b
children; stratified by 10-15-min resolution; time housing unit characteristics
cooking fuel; data on gender, of year; day of week;
age, parental education time of day.
Topeka, NO2, RSP 300 winter, 300 summer; 1988; retrospective, actual 11 microenvironment; home Adair & Spengler,
Kansas, selected from cohort of and prospective zip codes; school location; 1989a,b
USA 600 children; stratified by questionnaires; 10-15-min housing unit characteristics
cooking fuel; data on gender, resolution; time of year;
age, parental education day of week; time of day
Table 15. (continued)
Location Pollutant Participant characteristics Survey characteristics Spatial and source Reference
characteristics
California, VOCs 188 people; representative; February-March 1984; Activities >1 h; Wallace et. al.,
USA oversampled high-exposure follow-up: 52 in May-June high-exposure activities 1988, 1991a,b
occupation; data on age, 1984 and 51 in Feb and (e.g., smokers, occupations,
gender, race, socio-economic March 1987; 24-h recall travel); proximity to
status and proximity to VOC diary; activity industry; housing unit
sources questionnaire characteristics
Boston, NO2 325 (winter), 298 (summer) 1986; diary and 6 microenvironments; range Ryan et al.,
Massachusetts, ages 8 and above; questionnaire; on; near roads; combustion; 1990
USA representative; stratified 15-30-min resolution; 2-day outside home; home location;
by range type; no personal sample; time of year; day housing unit characteristics
data of week; time of day
Los Angeles NO2 620 people ages 8 and above May 1987-May 1988; diary, 17 microenvironments: Spengler et al.,
and Orange sampled two 24-h periods; 65 questionnaire; 15-min including near roads; home 1994; Schwab et
Counties; sampled eight cycles; resolution; two-day sample; zip codes; work zip codes; al., 1990
representative; data on age, time of year; day of week; climate region; housing
gender, work status time of day unit characteristics
Albuquerque, NO2 1000+ infants; stratified January 1988-December 1991; Room in house; outside Samet et al.,
New Mexico, by range type; data on every 2 months for the of house (including travel); 1992
USA child's health and parents' first 18 months of life; range use; housing unit
socio-economic and 60 min; time of year; characteristics
demographic characteristics day of week; time of day
reporting a smoker present in the home was associated with increased
indoor concentrations and personal exposures of aromatic compounds;
visiting dry cleaners, self-reports of proximity to smokers, pesticide
use, exposure to solvent, degreasing compounds, and odorous chemicals,
and employment status in certain occupations (e.g., paint, chemical or
plastics plants) were associated with increased personal exposure to
several VOCs (Wallace et al., 1985, 1986, 1988). Occupational exposure
may be an important component of total exposure for some individuals
or sub-populations.
2. Time-activity data allow modelling of human exposure to
pollutants for which personal monitors are not yet available or are
very expensive, or for which exposure is a function of multiple
pathways. Total exposure can be simulated from information on the time
spent doing various activities and/or in specific locations, coupled
with knowledge about the likely range of pollutant concentrations in
each situation. For example, the models SHAPE (Ott et al., 1988), NEM
(Johnson et al., 1990), SIMSYS (Sexton & Ryan, 1988), and REHEX (Hall
et al., 1992) are currently being used to estimate exposure to carbon
monoxide, ozone, particulates, sulfur dioxide and nitrogen dioxide.
Techniques are also being developed to allow prediction of dermal and
ingestion exposures based on assumptions about human activity patterns
(e.g., Fenske, 1993). The usefulness of all of these models is
dependent upon the accurate characterization of pollutant-relevant
time-activity patterns.
3. From an epidemiological perspective, activity patterns can be
used to assess the relationship between exposure and health status
(e.g., Armstrong, 1985). For instance, "Do those who engage in
potentially high-exposure activities experience more frequent or
severe illnesses?" or "Do sensitive individuals avoid potentially
high-exposure activities or limit them to certain times of day or
locations?" In epidemiology, time-activity data may serve four
purposes:
* They may be a surrogate of the exposure of interest. For example,
people may be asked about the hours they spend indoors with smokers
to assess health effects of environmental tobacco smoke
(Leuenberger et al., 1994).
* They may be used to improve another imperfect measure of exposure.
For example, estimates of long-term exposure to ozone may be
derived from fixed site monitor data, weighted, however, for
duration of time spent in outdoor activities (Künzli et al.,
1997a,b).
* They may be used as a surrogate for a cofactor which might confound
the association between health and some other exposure. For
example, the effect of ambient air pollution on lung function may
be thought to be confounded by environmental tobacco smoke exposure
(ETS). Time spent with smokers could thus be used to control this
potential confounding.
* The association of an exposure with some health outcome might not
be the same in subgroups of different time-activity patterns
(modified effect). In this case, time-activity data will allow the
investigator to address such interactions.
4. Another purpose of time-activity data is to describe patterns of
population behaviour. The proportion of time spent by the population
in various microenvironments or frequency of use of various facilities
(e.g., swimming pools) may provide an indication for the contribution
of each of the microenvironments or activities on total population
exposure. In such studies, the emphasis is on characteristics of
groups, and not on individual data. Therefore the precision of the
estimates may be improved by the increased sample size although the
survey tools may remain relatively simple and inexpensive.
An understanding of the frequency and duration of the activities
in which the target population engages can be used to set priorities
for public health strategies designed to reduce exposure by limiting
contact with contaminated media. Comprehensive exposure factor data
for the US population may be found in AIHC (1994) and US EPA (1996a).
Although this information is focused on the USA it may serve as a
useful model to follow in other countries.
5.2 Methods
5.2.1 Activity pattern concepts
Activity pattern data that may be useful in assessing exposure
can be divided into three categories:
* the distribution of time among activities, referred to in this
document as time allocation parameters
* the factors that influence the degree of media contamination in the
activities or locations of interest, referred to in this document
as microenvironmental parameters
* the intensity of contact while engaging in each activity.
5.2.1.1 Time allocation parameters
Time allocation parameters include the amount of time spent in a
given activity, the time of day, week and year of contact, and the
expected frequency with which the person or population engages in the
activity. The relevant spatial resolution for describing time-use
patterns, thus grouping activities for exposure assessment, depends
upon the characteristics of the pollutant, the media, the location and
the emission source(s).
The concept of microenvironment has been used to define an area
across which the concentration of an air pollutant is assumed to be
homogeneous (Duan, 1982). The most basic division of microenvironments
is whether a person is indoors or outdoors, although more refinement
is necessary for many exposure assessments. Time spent indoors is
especially important with regard to pollutants which depend on indoor
sources. Other typical microenvironments of interest in studying air
pollution are home, work or school, and modes of transportation.
Depending on the characteristics of the media and the pollutant,
a description of the actual activity may also be required to
understand exposure. General activity categories such as "socializing"
and "recreation" are less important than knowing whether the
participant is involved in specific activities that lead to contact
with environmental media in addition to or other than air. For
instance, swimming leads to water contact, and farming and gardening
lead to soil contact.
5.2.1.2 Microenvironment parameters
The distinction between people's activities and the pollutant
concentration in a microenvironment is not always clear. The use of
household appliances and consumer products that emit environmental
contaminants and/or influence pollutant fate and transport affect
microenvironmental concentrations. Thus, information on the
microenvironmental parameters, i.e., the factors affecting the
concentration in a given location, have also been included under the
rubric of time-activity data. Important microenvironmental parameters
for air pollution exposure assessment include building structure and
household characteristics (e.g., the type of heating and cooking fuel
used, the presence of parking garages and air conditioning units),
information on proximity to specific sources (e.g., heavy traffic,
cigarette smoking, cooking, solvent, pesticides), timing of emissions
for each source, indoor/outdoor air exchange rates and meteorological
and topographic factors.
5.2.1.3 Intensity of contact
In addition to time allocation measures and microenvironmental
parameters, information on the intensity of contact is needed to
assess exposure. Here the focus is on micro-level activities that
affect the rate of contact with the contaminated media while the
person is in a certain microenvironment (e.g., outdoors at home) and
performing a specific activity (e.g., cleaning). The potential for
dermal contact depends upon the surface area of exposed skin, thus
clothing type and fabric consistency as well as the size of the
person, whether the individual is sitting, crawling, kneeling or using
their hands on the contaminated surface, or otherwise handling the
contaminant. In addition, exposure for the given event depends upon
the duration and frequency of each contact between the exposed skin
and the contaminated media; e.g., 50 1-min contacts between the
person's hand and the floor while cleaning. As described in Chapter 7,
dietary factors, including the type of foods that are consumed and the
amount consumed per time period of interest, are the most obvious.
Concern also has been raised about the potential for contamination of
foods from contact with surfaces during storage, preparation and
consumption (Berry, 1992, Freeman et al., 1997). Hand-mouth and
object-mouth contact, although difficult to measure, may be one of the
most important routes of exposure to contaminants such as pesticides
and lead that reside in house dust, especially in children (Charney et
al., 1980; Rabinowitz & Bellinger, 1988; Davies et al., 1990). For
pollutants for which inhalation is the primary route of exposure, the
intensity of contact is influenced by one's level of exertion (often
referred to as "activity level"). Breathing rate or heart rate is
needed to predict dose (the amount of contaminant that enters the
body), thereby producing a more accurate estimate of the resulting
health effects.
Finally, depending on the purpose of the exposure assessment, the
required temporal resolution of the time-activity data may vary
substantially. Whereas short-term time-activity patterns may be
important for acute exposures, long-term average time-activity
patterns may be more relevant in other circumstances. If long-term
exposure is of major interest, e.g., over years or lifetime,
residential history is an important information to assign respective
ambient monitor data for the entire period of interest (Künzli et al.,
1996).
5.2.2 Surrogates of time-activity patterns
For many exposures surrogates of time-activity patterns may be
developed on the basis of generalizations about the activities of
people at a particular time, who live in a specific geographic
location or who share a specific set of living conditions. Usually the
most important time-activity surrogate is age group. Some activities
that are useful for predicting exposure to air pollutants, such as
distance and timing of travel or duration of work and its locations,
also show systematic differences in their frequency and duration by
demographic characteristics. For instance, Schwab et al. (1990)
documents how time in the kitchen, which influences exposure to
combustion products, is greater among women in the USA than among men,
even after adjusting for whether the woman works outside the home;
likewise, men spend more time in transit, regardless of their age or
employment status. It is likely that the frequency of contact with a
wide variety of toxins differs across groups defined by gender and
age, owing to traditional divisions of labour in many cultures.
Similarly, information about an individual's health condition may
be important in characterizing their time-activity pattern. For
instance, the limited data available on asthmatics suggests they may
spend more time indoors than the general population (Goldstein et al.,
1986, 1988; Lichtenstein et al., 1989; Schwab et al., 1991). As
asthmatics are particularly sensitive to air pollutants, this activity
information is important.
Socioeconomic status may influence time-activity patterns related
to, for instance, time spent travelling to work or outdoors.
Currently, however, the gap existing in time-activity databases with
respect to the activity patterns of sensitive (e.g., elderly) and
potentially high-risk (e.g., low socioeconomic status) subgroups, is a
limitation for extension of exposure models to these groups. Further
study is needed to determine the extent to which income, education and
occupation are reliable surrogates for exposure-related factors (e.g.,
housing unit size and condition).
5.2.3 Data collection methods
Sociologists pioneered studies of activity patterns (Szalai,
1972; Chapin, 1974; Robinson, 1977). These "time budget"
investigations, which have been conducted in several nations,
emphasize the purpose of activities (cooking, eating, TV watching).
Ott (1989) summarizes such studies in relation to their usefulness to
exposure assessment; a basic drawback for exposure assessment
applications is the lack of information on location, particularly
distinguishing whether the participant was indoors or outdoors. In the
1960s and 1970s, a series of time-activity studies was conducted by
geographers interested in the influence of the economic and physical
structure of cities on travel patterns, e.g., journey to work (Hanson
& Hanson, 1981), access to facilities (Fox, 1983) or shopping
behaviour (Douglas, 1973). As such, the emphasis was on collecting
information on the geographic location of trip origins and
destinations as well as timing and mode use. Finally, the US
Department of Transportation, in conjunction with the Census Bureau,
has been collecting information on the travel activities (durations
and mode use) of a representative national sample approximately every
7 years since 1969 (US Federal Highway Administration, 1986, 1992).
A variety of methods are available for collecting data about
time-activity patterns, including interviewer-administered recall
questionnaires, self-administered real-time diaries, direct
observation and video recording. The diary techniques used in the
social sciences for eliciting time-activity data have been applied to
studies of total human exposure to air pollutants (see methodological
reviews by Robinson (1988), Ott (1989), Quackenboss & Lebowitz
(1989)). Specifically, participants are asked to complete a diary or
questionnaire regarding their activities during the designated period
(usually 12-48 h). The survey instruments used in these exposure
studies, however, depart from any single type used previously. Rather
than focusing on activity purposes or transportation exclusively, the
instruments used in exposure studies probe for changes in location or
activity that might lead to changes in the level of pollution to which
the person came into contact.
Time allocation measures for assessing exposure to air pollutants
frequently have been collected using self-completed real-time diaries.
Because this approach requests that participants record all activities
over one or more 12-h or 24-h periods, it has the potential to provide
the most comprehensive information on time allocation, sequencing, and
frequency. Real-time diaries are particularly useful when it is
important to know the time of day during which each activity was
performed (e.g., the amount and location of exercise in the morning
versus the afternoon when ozone levels tend to be higher).
Two diary formats are common for collecting time-activity data:
the open-ended style requires participants to describe their exact
activity (see, for example, the instruments described in Akland et al.
(1985), Johnson (1989), and Jenkins et al. (1992)), whereas the
close-ended format (Fig. 18) involves simply checking the appropriate
microenvironment for the given time of day (see, for example, the
instruments used in EXPOLIS (Fig. 18) (Jantunen et al., 1998) or those
described by Schwab et al. (1990) and Samet et al. (1992). Several
researchers are developing electronic monitors to supplement diaries
by detecting whether a participant is indoors or outdoors, a key
parameter for assessing exposure to air pollutants (e.g., Hinton,
1990; Moschandreas & Relwani, 1991; Waldman et al., 1991b).
Interviewer-administered questionnaires that ask participants to
recall frequency and duration of time spent in specific activities
during either the previous or typical day, month, year or age-period
(i.e., usual activity patterns) also have been used to collect time
allocation measures, microenvironmental parameters and exposure
surrogates. Juster et al. (1985a) points out that data collected in
this fashion are most accurate when the survey focuses on activities
that are done frequently or on a routine basis (e.g., the daily
commute to work). Questionnaires that take the form of checklists are
also particularly useful when the researcher is only interested in
certain well-defined activities. Questions to recall activity patterns
over a long period may refer to defined age groups and/or to each
residential location lived in (see Fig. 19). In environmental exposure
studies, information on the proximity of the study participant to
local contaminant sources is typically collected via questionnaires
that ask whether or not the participant engaged in a certain activity.
For instance, studies of VOC exposure have asked about use of
household cleaners, visits to petrol stations and storage of gasoline
products indoors (Wallace et al., 1987a,b). Questionnaires are also
used to solicit information on housing unit characteristics (e.g.,
type of cooking equipment or house volume) that influence
concentrations indoors (Lebowitz et al., 1989). Surveys may request
information on a variety of parameters that affect the concentration
of combustion products to which an individual is exposed during
travel, including traffic speed, time of day, mode of transportation,
age of vehicle, trip timing and roadway used.
Researchers have experimented with a variety of methods for
collecting information on the intensity of contact. As described in
Chapter 7, a number of approaches are used for quantifying food
consumption rates. An inexpensive technique that has been used to link
breathing and activity patterns is to have participants record the
level of activity (e.g., high, medium, low) associated with each
activity entry in the diary. This method has been used in several
population-based studies (Johnson, 1989; Lichtenstein et al., 1989;
Schwab et al., 1990; Wiley et al., 1991). Others have used
questionnaires that request information about specific high-exertion
activities such as exercising and working outdoors (Goldstein et al.,
1986; Lebowitz et al., 1989). Categorical exertion-level data is not
useful for calibrating activity pattern data, however, without an
increased understanding of (1) the range of reported activities
associated with each exertion level and (2) the range of breathing
rates associated with each exertion level. A compendium on energy
expenditure, which closely relates to ventilation rate, has been
published for a variety of physical activities (Ainsworth et al.,
1993). These data may be used to categorize activity data depending on
levels of exertion (Künzli et al., 1997a,b). Data are becoming
available through the application of electronic methods of tracking
exertion levels; heart-rate and breathing-rate monitors have been used
in the field studies by Raizenne & Spengler (1989), Shamoo et al.
(1991) and Terblanche et al. (1991).
Standardized methods are not available for collecting information
on hand-mouth contact. Several researchers (Charney et al., 1980;
Brunekreef et al., 1983; Bellinger et al., 1986) have administered
questionnaires to parents of toddler-age participants in order to
qualitatively characterize the frequency with which children suck
their fingers (i.e., usually, sometimes, never). Direct observation
may be better suited to capturing micro-level activities, but such
approaches have rarely been used in large-scale field studies owing to
the expense of following more than a few participants and because of
concerns that the observation process will lead to bias or alterations
in typical patterns. Video techniques have now made it possible to
record participant activities with less interference. Davies et al.
(1990), for instance, used video methods to obtain data on the number
of times 2 year olds put their hands and objects in their mouth while
in standardized play situations. Zartarian et al. (1995) used
videotape data to collect micro-level data on four young farm children
at play inside their homes to quantify dermal and ingestion exposure
to pesticides. As Zartarian et al. point out, however, researcher
presence may still have influenced the participants' behaviour.
Observation of children's hand-mouth contact also has been performed
in clinical settings (e.g., Madden et al., 1980). All of these
methods, however, share the limitation that they cannot quantify the
full variability in factors that influence hand-mouth contact. Indeed,
capturing this variability may not even be possible, as is discussed
in a subsequent section of this chapter. In the absence of information
on hand-mouth contact, several researchers have measured mineral
levels in children's faeces to estimate typical soil ingestion rates
(Binder et al., 1986; Calabrese et al., 1989, 1990). Such estimated
ingestion rates can then be used to model exposure in areas with
measured soil contamination levels.
For dermal exposure, questionnaires are most appropriate for
collecting categorical-type information, such as whether a person
performed a certain activity during a designated activity. The US
Environmental Protection Agency 1992 report entitled Dermal
Exposure: Applications and Principles, reviews the literature
regarding methods for estimating soil and water contact (US EPA,
1992b). Hawley (1985) has used data from previous studies and
professional judgement to develop assumptions for use in estimating
outdoor soil contact time, but these estimates do not account for
indoor exposure such as soil tracked into the house or for exposure to
contaminants that reside primarily in indoor dust (e.g., pesticides)
(US EPA, 1992b). The US EPA report cites Tarshis (1981) and James &
Knuiman (1987) as sources of data on the frequency with which people
shower and bathe. Few data are available on swimming (US EPA, 1992b)
which could be important because of elevated chloroform concentrations
found within air just above the pool-water surface, or other
contaminants which can be swallowed or dermally absorbed from lakes or
river waters.
Linking activities with measurements of dermal exposure,
researchers are testing innovative approaches to assessing skin
contact with contaminated surfaces. For instance, Fenske et al.
(1986a,b) applied non-toxic fluorescent tracers to lawns in lieu of
insecticides; after participants engaged in a standard set of play
activities, video imaging was used to ascertain the amount of tracer
on the exposed skin. The degree of soil adherence to skin is a closely
related issue and has been examined by several researchers (Driver et
al., 1989; Finley et al., 1994a; Kissel et al., 1996).
5.3 Potential limitations
Time-activity data can enhance an understanding of sources and
behaviours important in assessing exposures. Inferences can be drawn
from simulations, case studies or even studies using large randomized
designs. However, all users of time-activity data should be aware of
its limitations for assessing human exposure to environmental
contaminants.
The feasibility of collecting time-activity data is often limited
by the burden which such studies impose on participating individuals.
The data collection requires constant, or regular, attention to the
fact that the subjects are participating in the study, that they have
to remember about all activities and to fill in the diaries. This is
often inconvenient and takes respondent's time. Collection of the data
by an observer, which often is a method of choice in studies involving
children, may be of limited feasibility owing to the restricted access
of the observer to the subject under study and because typical
activities may possibly be modified by the fact of being under
observation.
5.3.1 Activity representativeness
One of the uses of time-activity data is to allow
characterization of the distribution of exposure for a given
geographic, demographic or socioeconomic segment of the population.
However, the study protocol may call for certain types of days or
individuals to be excluded (e.g., travel that takes the participant
away from the home for more than the 24-h or 48-h sampling period may
lead to disqualification). Although standard techniques such as
weighting and imputation can be used to treat non-response, these
methods assume that refusal to participate is random and there is
information about the non-respondents (Kalton & Kasprzyk, 1986). In
the case of time-activity studies, however, once contacted, people may
participate or not because of the variables that the study is designed
to predict. As shown in the European multi-city study EXPOLIS, the
subjects in Basel ready to participate had lower traffic density
around their homes than non-participants (Oglesby, 1998). The
potential for misrepresenting the exposure distribution must,
therefore, be considered because there is no method for quantifying
the direction and/or extent of the bias with respect to high-exposure
behaviours.
The representativeness of the activity data collected may also be
influenced by the increased burden imposed upon participants by
exposure assessment studies. Epidemiologists and social scientists
have found that participation rates and compliance with instructions
may decline with increasing study periods, longer questionnaires, more
complicated questions and more complex tasks. Whitmore (1988)
speculated that the higher than average refusal rates experienced in
air pollution exposure studies are related to the burden associated
with carrying monitors and completing activity diaries. This has been
shown in the European multi-city EXPOLIS study in Grenoble where
participants had different time activity patterns in days with
personal exposure monitors compared to days when only time-activity
data was collected (Boudet et al., 1997).
5.3.2 Validity and reliability
Survey researchers in a number of fields have raised questions
about the validity of data collected via self-administered surveys:
i.e., is the instrument measuring what is intended (Laporte et al.,
1985). Data validity is of particular importance when trying to link
measured exposure with a given day's activity diary. The error
introduced by an inaccurate diary affects both efforts to explain the
contribution of certain activities to personal exposure and efforts to
estimate the distribution of personal exposure from time-weighted
microenvironmental measurements. The relationship between the degree
of error in the diary and the degree of error in the predictive model
depends upon the concentration in the microenvironment and the total
time spent there. Neglecting to report even short-duration activities
in high-concentration microenvironments will have more effect than
underestimating a similar amount of time in a low-concentration
microenvironment in which a large portion of the day is spent.
Scientists who use activity pattern data have raised a variety of
concerns about the effects of inadvertent and/or deliberate errors in
reporting. For instance, activity diary data may be compromised by
participants' misunderstanding of the definitions of various locations
(microenvironments). Discussions with participants have revealed the
potential for confusion about: How far is "far from home?" Is a
"parking garage" inside or outside? Is "walking" a light- or
medium-exertion activity? (Schwab et al., 1991, 1992).
To a certain extent, the quality of the data can be controlled
during data collection. Detailed instructions can improve participant
compliance. Field and laboratory pretesting of the survey instrument
and instructions, important components of the survey design process,
can yield improvements in protocol and clearer definitions of survey
terminology such as distinctions between microenvironmental categories
(Bercini, 1992). Extensive training of participants in keeping the
diary can be expensive, but detailed reference sheets and one-on-one
sessions can greatly improve data quality. One of the more
time-consuming but necessary steps is reviewing the returned diaries
for temporal completeness and clarity of responses. Ideally, this
should be done in the presence of the participant, and within 24 h of
completion of the monitoring period. Another quality assurance step
involves the use of a uniform system to code information on individual
activities into microenvironmental categories.
The validity and reliability of the diary data may be increased
by the use of study forms that are simple and easy to understand. The
language of the questions and instructions must be simple and the
method of selection of answers, or of filling in data, obvious to
minimize coding errors. The number of items on the questionnaire
should be kept to a necessary minimum. Only the information for which
there is clear use in analysis and data interpretation and which
serves directly the study objectives should be included in the diary
form.
Verifying the validity of time-activity data is extremely
difficult, if not impossible, because an absolute standard does not
exist. Several researchers have sought to assess the reliability of
self-reported data through test-retest procedures and by comparing
different methods of collecting the same type of information (Laporte
et al., 1985). The University of California at Berkeley ozone study
required college students to recall time spent in physical activities
outdoors, over years. The information was used as a surrogate to
improve long-term ozone exposure assignment in an epidemiological
study (Künzli et al., 1997b). The test-retest study revealed rather
high correlations for time spent in heavy ( r = 0.81) or moderate
( r = 0.61) activity (Künzli et al., 1997b). This level of
concordance is similar to that observed in dietary intake validation
studies where food-frequency questionnaires and diet records
correlated in the order of r = 0.6 for the intake of a variety of
nutrients (Rimm et al., 1992). Robinson (1985) found that a variety of
methods for collecting time-activity data, including 24-h recall
surveys, same-day diaries, records of the activities during 40
randomly selected moments throughout the day (signalled using a
beeper), and recall of the activities during a randomly chosen hour
yielded essentially similar sample distributions of time the sample
spent in a variety of activities. Quackenboss et al. (1986) also found
consistency between diaries and the responses to self-administered
recall questionnaires. Juster (1985b) found reasonably strong
agreement in the reports of spouses regarding whether their partner
was present at any given time throughout the day. Other comparisons of
methods show that when asked about the usual time spent in selected
activities, respondents tend to over-report time in unscheduled
activities (relative to that recorded on their diaries), but reports
are consistent for habitual activities such as commuting to work
(Robinson, 1985). Waldman et al. (1991b) showed similar results when
comparing activities recorded in electronic diaries with next-day
recall; concordance between the methods was highest for routine,
long-duration activities. Additional research, however, is necessary
to determine the extent and direction of bias for the activities and
the time frames of most concern in an exposure context (e.g., the
frequency with which a person uses household cleaning products rather
than the total time spent cleaning).
5.3.3 Inter- and intra-person variability
To be of use in exposure assessment, time-activity data must
describe the aspects of human behaviour that influence the variability
in pollutant concentrations contacted. There is both between- and
within-individual variability in people's activities, which has
implications for the use of time-activity data in exposure assessment.
At one end of the spectrum are aspects of human activity patterns
that tend to be highly regular. For instance, many people tend to
follow daily routines with respect to how long they sleep and the time
they depart for work. In addition, because basic routines are fairly
uniform across individuals, diary data from several studies has shown
that the distribution of time reported in the microenvironments that
comprise the majority of the day (i.e., inside at home and inside at
work/school) exhibit relatively little variation from year to year
within a given study population or from place to place within the USA
(Robinson, 1985; Schwab et al., 1990).
The only large time-activity study done in conjunction with a
continuous monitoring device was the Denver/Washington, DC study of CO
exposures (Akland et al., 1985); this study yielded time-weighted
concentrations in specified microenvironments. Analyses of these
results suggest that variations in activities or locational attributes
(e.g., variations in source strength) that are finer than those
captured by these simple microenvironments explain much of the
variability in exposure. Although less variability in the
concentrations of some other air pollutants may be expected, these
results confirm the concerns raised above regarding the ability to
predict variations in exposure from the time allocation measures
typically collected in diary-type studies.
At the other end of the spectrum with respect to consistency in
activity patterns are aspects of human behaviour that influence the
intensity of contact with contaminated media. By their nature, these
activities are highly variable both across individuals and across time
for a given person. First, physical and demographic characteristics
influence the frequency and duration of activities. For instance, in
the case of dermal exposure it may be hypothesized that contamination
from lying on a surface (e.g., a lawn to which a weedkiller has
recently been applied) will be greater for a heavy person than a
lighter person. Similarly, a child's standing and sitting height, in
addition to crawling activities, mean that its breathing zone is much
closer to the floor than that of an adult, raising the possibility of
dust inhalation. Children also choose play locations that typical
monitoring studies might ignore, such as stairwells and corners.
5.4 Summary
Information on people's activity patterns can be used to identify
the determinants of measured exposures, predict unmeasured or
unmeasurable exposures, assess relationships between exposure and
health status, and identify high risk exposure situations that may be
amenable to public health actions. Some of the main activity patterns
important for assessing exposures by various media that were discussed
in this chapter are summarized in Table 16.
The relative cost of field sampling and laboratory analysis for
environmental and biological measurements highlights the potential
value of time-activity data. Assessments of long-term activity
patterns (e.g., lifetime) may only be feasible using time-activity
questionnaires. Various methods are used to collect information about
human activities, including diaries and questionnaires, mechanical
devices, and observation. Methods have only recently begun to be
developed for assessing the role of time-activity patterns on dietary
and non-dietary ingestion and dermal exposure pathways. Concerns about
the ability of data collection methods to ensure activity
representativeness and data validity and about the implications of
inter- and intra-person variability in behaviour place limits on the
application of time-activity data for human exposure assessment.
However, with appropriate quality assurance programmes, information on
time use and activity patterns can be very valuable for interpreting
and modelling exposures.
Table 16. Type of information obtained from time-activity data
relevant to specific exposure pathways
Personal air
time and location spent outdoors
type of indoor location
use of sources
In the presence of sources:
ventilation and filtration of indoor location
Water
quantity of water consumed direct and indirectly
accidental ingestion from swimming (pools, rivers,
etc.)
dermal contact, time in showering/bathing
hand/body washing frequency
Food
amount and type of food products consumed
preparation methods including cleaning
preparation location (e.g., street vendors)
storage practices
Soil
amount of contact time and type of soil
(e.g., farm, garden/possible pesticide
application)
skin surface contact
frequency and duration of washing since contact
6. HUMAN EXPOSURE AND DOSE MODELLING
6.1 Introduction
An exposure model is a logical or empirical construct which
allows estimation of individual or population exposure parameters from
available input data. Such data may be measured or collected for this
purpose, or obtained from other sources. Technological, logistic and
financial constraints can make it difficult to monitor the exposure of
humans to the various environmental agents. It is, therefore, prudent
in many situations to use models to assess contaminant exposures.
Models provide an analytic structure for combining data of different
types collected from disparate studies in a manner that may make more
complete use of the existing information on a particular contaminant
than is possible from direct study methods (EC, 1997b). Exposure
models, if supported by adequate observations, can be used to estimate
group exposures (e.g., a population mean) or individual exposures
(e.g., the distribution of exposures among members of a population).
Model results also can be used to evaluate exposures at various points
of population distributions which cannot be measured directly because
of limitations of methods or resources (e.g., the upper 5% of
exposures for a population). This chapter introduces the principal
aspects of exposure modelling, including those for single and multiple
environmental media. In addition, the concepts of variability,
uncertainty and model validation are discussed.
6.2 General types of exposure model
Exposure models can be divided into three broad categories;
statistical, deterministic and practical or combinations of
statistical and deterministic models (Fig. 20). Statistical (often
regression) models are in their simplest form numerical best fits
between collected exposure measurements and potentially related
factors (e.g., demographics). In statistical models, the magnitude and
direction of association between the variables are inferred from the
observations themselves. Such models cannot be considered reliable for
predicting exposures outside the original study population and
environmental setting without first validating them for that specific
purpose. Deterministic (or physical) models are based on a logical
expression of the physical environment and human behaviour in it. Such
models need to be validated by actual exposure data, and can in
principle be used for exposure prediction of new populations and
settings. Although deterministic models can be useful for estimating
mean population exposure, input data to estimate the distribution of
exposure within a population are often not available. Probabilistic
exposure models (section 6.6.3) are normally based on deterministic
models, but because they incorporate the measured or estimated
distributions of the input variables, they produce more realistic
population exposure distributions than deterministic models. Practical
models can combine features from these different types, e.g., a
statistical model may include parts of a logical construct. Several
important types of statistical models are discussed in Chapter 4, and
deterministic and practical models are discussed here.
Using a deterministic model for a given contaminant, exposure
concentration is estimated as a concentration averaged over a given
period of time (see Eq. 3.1, p. 46).
For the inhalation and dermal exposure routes, concentrations in
the different microenvironments occupied by a person are integrated
over time. The integrated time period is usually 24 h, 1 year or a
lifetime of 70 years, although any time period may be used. The
concept of microenvironment is often unnecessary for the ingestion
route. In this case, the concentration of contaminants in the food
consumed and the amount of food and beverages consumed during a given
period of time are sufficient to determine exposure.
6.3 Environmental media and exposure media
In exposure analysis, we use human exposure assessments to
translate contaminant levels in environmental media into quantitative
estimates of the amount of contaminant that comes in contact with the
human-environment boundaries, that is, the lungs, the gastrointestinal
tract and the skin surface of individuals within a specified
population. Environmental media of principal relevance to human
exposure include air, ground-surface soil, root-zone soil, plants,
groundwater and surface water in the contaminated landscape. As
described in Chapter 2, exposure pathways define a link between an
environmental medium and an exposure medium. Important exposure media
include outdoor air, indoor air, food (commercial and homegrown),
exterior soil, interior soil or household dust, and drinking and
cooking water. Exposure then occurs by contact with contaminants in
these exposure media via inhalation, ingestion and dermal uptake. Fig.
21 illustrates the types of exposure pathways we use to carry out a
multiple-media, multiple-route, multiple-pathway exposure assessment.
Exposure assessments often rely implicitly on the assumption that
exposure can be linked by simple parameters to ambient concentrations
in air, water and soil. However, total exposure assessments that
include time-activity patterns and microenvironmental data reveal that
an exposure assessment is most valuable when it provides a
comprehensive view of exposure routes and pathways and identifies
major sources of uncertainty. Listed in Table 17 are potential
interactions among environmental media, exposure media and exposure
pathways that are addressed in this chapter.
An assessment of intake requires that we determine how much
crosses these boundaries. Thus, we see the need to address many types
of "multiples" in the quantification of human exposure, such as
multiple media (air, water, soil); multiple exposure pathways (or
scenarios); multiple routes (inhalation, ingestion, dermal); multiple
chemicals; multiple population subgroups; and multiple health
end-points. The matter is further complicated by the fact that
pollutants may have both systemic and route specific health effects.
For the compounds that have mainly systemic effects the total exposure
-- sum of all routes -- is most relevant; for other agents such as
pneumococci aerosols in the lung, dermal vs. ingestion absorption of
Table 17. Interactions among environmental media, exposure media and exposure pathways
Exposure routes Media
Air Soil Water
(gases and particles) (ground-surface soil, (surface water and groundwater)
root-zone soil)
Inhalation gases and particles in soil vapours that migrate to contaminants transferred from
outdoor air indoor air tap water
gases and particles soil particles transferred to
transferred from outdoor air indoor air
to indoor air
Ingestion fruits, vegetables, and grains soil tap water
contaminated by transfer of
atmospheric chemicals to plant fruits, vegetables, and grains irrigated fruits, vegetables, and
tissues contaminated by transfer from soil grains
meat, milk, and eggs meat, milk, and eggs contaminated meat, milk, and eggs from animals
contaminated by transfer of by transfer from soil to plants consuming contaminated water
contaminants from air to plants to animals
to animals
meat, milk and eggs contaminated meat, milk, and eggs contaminated fish and sea food
through inhalation by animals through soil ingestion by animals
mother's milk mother's milk mother's milk
Dermal contact (not included) soil baths and showers
swimming, etc.
solvents which are rapidly metabolized in the liver, or fine
particulate matter in the ambient air, the route of exposure is
crucial, and total exposure as a sum of all exposure routes may be
meaningless. Multiple media exposure models are discussed in section
6.5.
6.4 Single-medium models
Most of the transport models that have been developed for
describing the behaviour of contaminants in the environment have dealt
with specific environmental media, such as indoor and outdoor air,
surface water and sediments, groundwater and soils. These
single-medium models operate at various levels of spatial and temporal
detail, depending on the particular conditions being assessed. The
following discussion will highlight some of the more commonly used
methods for characterizing contaminant transport in environmental
media. Additional information on transport modelling for use in
exposure assessments can be found in Masters (1991).
6.4.1 Outdoor and indoor air
Substances in outdoor air are transported from sources to
receptors by atmospheric advection and dispersion. In general,
pollutant concentrations in outdoor air are directly proportional to
emission strength and inversely proportional to dispersion. The
physical relationship, e.g., lateral and vertical distance, between
sources and receptors is also an important factor. Meteorological
parameters have an overwhelming influence on the dispersion of
contaminants in the lower atmosphere. Among them, wind parameters
(direction, velocity, and turbulence) and thermal properties
(stability) are the most important. A number of models are available
for estimation of ambient concentrations of pollutants. Most of them
are founded on the Gaussian air dispersion model, an introduction to
which may be found in Wilson & Spengler (1996). Two of the seminal
works in this field are Pasquill (1961) and Gifford & Hanna (1973).
Another area of air quality models focuses on determining the
sources of pollutants in outdoor air. As discussed in Chapter 2,
information on sources of exposure is important for evaluating
alternative strategies for managing risk. These models are commonly
used for apportioning concentrations of airborne particulate matter
among its various sources (e.g., coal-fired power plants,
gasoline-powered vehicles and diesel-powered vehicles). In such source
apportionment models, profiles of element concentrations in
particulate matter emitted from different sources are combined with
sophisticated statistical methods (e.g., principal component or factor
analysis) to estimate the relative abundance of particles from each
source type. Glover et al. (1991) and Daisey et al. (1986) provide a
good introduction to source apportionment models for particulate
matter, while Edgerton & Shah (1991) describe a source apportionment
model for VOCs.
Several approaches have been used to estimate expected indoor air
pollution concentrations (for reviews see Cooke, 1991; WHO, 1997b).
These include deterministic models based on a pollutant mass balance
around a particular indoor air volume; a variety of empirical
approaches based on statistical evaluation of test data and (usually)
a least squares regression analysis; or a combination of both
approaches, empirically fitting the parameters of a deterministic
model with values statistically derived from experimental measurements
(see Chapter 4). All three approaches have advantages and weaknesses.
The deterministic model provides more generality in its application,
but the results lack accuracy and precision. Deterministic models
include single- and multiple-compartment models. The empirical models,
when applied within the range of measured conditions for which they
were fitted, provide more accurate information. An example of an
empirical model for indoor concentrations of respirable particulate
matter may be found in Chapter 12. Often the compartment of the indoor
air mass balance models that is most difficult to represent is the
role of indoor surfaces as sources or sinks for contaminants. This is
an important field of inquiry with respect to inhalation exposures to
ozone and VOCs (Reiss et al., 1995).
6.4.2 Potable water
Exposure to contaminants in water may occur via the ingestion,
dermal absorption and inhalation routes. Ingestion of water primarily
occurs via two pathways: direct ingestion via drinking or cooking and
intrinsic water intake (i.e., the water intrinsic in foods prior to
preparation). It is important to consider both routes. Drinking-water
ingestion rates have also been shown to vary according to cultural
differences and can be an important source of uncertainty about
chemical exposure when extrapolating results of epidemiological
studies from one culture to another (e.g., Mushak & Crocetti, 1995).
Lognormal distributions of drinking-water ingestion rates for
individuals comprising various age groups in the USA (Table 18) are
available in the literature (Roseberry & Burmaster, 1992). Additional
information on drinking and cooking water as exposure media may be
found in Chapter 7.
Dermal absorption of contaminants in residential water sources
may occur during bathing and other forms of washing or cleaning. There
are three principal mechanisms by which molecules can transverse the
skin and enter the body: passive transfer or diffusion, facilitated
diffusion and active transport. Passive diffusion is the mechanism
most commonly expressed in dermal exposure models. The rate of passive
diffusion is a function of the concentration gradient of the
contaminant on the surface of the skin and in the tissue immediately
below the skin and the ease with which a molecule of the contaminant
can move through the lipophilic interior of the skin membrane. Ease of
passage is a function of the partition coefficient of the contaminant
(e.g., the octanol-water partition coefficient, Kow), molecular
size, the degree of ionization and the porosity of the skin. Porosity
of the skin to VOCs present in drinking-water treated with chlorine
has been shown to be temperature dependent (Gordon et al., 1998).
Table 18. Lognormal distributions of water intake by age group in
the USA. Source: Roseberry & Burmaster (1992)
Age group Geometric mean (ml/day) Geometric standard deviation
Drinking and cooking water intake
< 1 year 267 1.85
2-11 620 1.65
12-20 786 1.72
21-65 1122 1.63
> 65 1198 1.62
Total water intake (direct + intrinsic)
< 1 year 1074 1.34
2-11 1316 1.40
12-20 1790 1.41
21-65 1926 1.49
> 65 1965 1.50
Inhalation exposures to VOCs transferred from water to air could
be as great as, or even greater than, exposures from ingestion.
Inhalation pathways include contaminants transferred to the air from
showers, baths, toilets, dishwashers, washing machines and cooking.
Several models have been proposed to explain the mass-transfer
process; in particular, a time-dependent, three-compartment model for
residential exposure (McKone et al., 1987). The three compartments
used in such a model are the shower/bath stall, the bathroom and the
remaining residential volume. Factors that affect the projected
exposure are chemical mass-transfer rates from water to air,
compartment volumes, air-exchange rates and human occupancy factors.
6.4.3 Surface waters
The transport of contaminants in surface waters is determined by
two factors: the rate of physical transport in the water system and
the chemical reactivity. Physical transport processes are dependent to
a large extent on the type of water body under consideration (e.g.,
oceans, seas, estuaries, lakes, rivers or wetlands). Schnoor (1981)
and Schnoor & MacAvoy (1981) have summarized important issues related
to surface water transport. At low concentrations, contaminants in
natural waters exist in both dissolved and sorbed phases. In rapidly
moving water systems, advection controls mass transport and dissolved
substances move at essentially the same velocity as the bulk of the
water in the water system. Contaminants that are sorbed to colloidal
materials and fine suspended solids can also be entrained in the
current, but they may undergo additional transport processes that
increase their effective residence time in surface waters. Such
processes include sedimentation, deposition, scour and resuspension.
Thus, determining the transport of contaminants in surface waters
requires that we follow both water movement and sediment movement.
A water balance is the first step in assessing surface water
transport. A water balance is established by equating gains and losses
in a water system with storage. Water can be stored within estuaries,
lakes, rivers and wetlands by change in elevation or stage. Water
gains include inflows (both runoff and stream input) and direct
precipitation. Water losses include outflows and evaporation.
6.4.4 Groundwater
In groundwater, the dilution of contaminants occurs much more
slowly than it does in surface water. After precipitation, water
infiltrates the ground surface where it travels vertically down
through the unsaturated zone until it contacts the water table, and
then flows approximately horizontally. This horizontal movement is
driven by the hydraulic gradient, which is the difference in hydraulic
head at two points divided by the distance (along the flow path)
between the points. Bear & Verruijt (1987) and Freeze & Cherry (1979)
have compiled extensive reviews on the theory and modelling of
groundwater flow and on transport of contaminants in groundwater. The
movement of contaminants in groundwater is described by two principal
mechanisms: gross fluid movement (advective flow), and dispersion.
Dispersion depends on both fluid mixing and molecular diffusion. The
transport of many chemical species in groundwater is often slowed or
"retarded" relative to the flow of the bulk fluid by sorption of the
contaminant material to soil particles or rock. As is pointed out by
Bear & Verruijt (1987), many groundwater models are available for
assessing the transport of contaminants in the subsurface environment,
ranging from simple one-dimensional hand calculations to large
three-dimensional computer programmes. The choice of an appropriate
model for any situation depends to a large extent on the information
available, the type of information needed to carry out an exposure
assessment and the tolerance of the analyst for large, complex
computer programmes.
6.4.5 Soil
Soil, the thin outer zone of the earth's crust that supports
rooted plants, is the product of climate and living organisms acting
on rock. A true soil is a mixture of air, water, mineral and organic
components (Horne, 1978). The relative mix of these components
determines to a large extent how a chemical will be transported and/or
transformed within the soil. The movement of water and contaminants in
soil is typically vertical as compared to horizontal transport in the
groundwater (i.e., saturated) zone. A chemical contaminant in soil is
partitioned between soil water, soil solids, and soil air. For
example, the rate of volatilization of an organic compound from the
soil solids or from soil water depends on the partitioning of the
compound into the soil air and on the porosity and permeability of the
soil.
Models developed for assessing the behaviour of contaminants in
soil can be categorized in terms of the transport/transformation
processes being modelled. Partition models such as the fugacity models
of Mackay (1979) and Mackay & Paterson (1981, 1982) describe the
distribution of a contaminant among the liquid, solid and water phases
of soils. Jury et al. (1983) have developed an analytical screening
model that can be used to calculate the extent to which contaminants
buried in soil evaporate to the atmosphere. The multiple-media model
GEOTOX (McKone & Layton, 1986) has been used to determine the
inventory of chemical elements and organic compounds in soil layers
following various contamination events. This model addresses
volatilization to atmosphere, runoff to surface water, and leaching to
groundwater and first-order chemical transformation processes.
6.5 Multiple-media modelling
Human beings come directly into contact with certain media via
certain routes and are exposed to the chemicals therein as depicted in
Table 19. Efforts to assess human exposure from multiple media date
back to the 1950s when the need to assess human exposure to global
fallout led rapidly to a framework that included transport both
through and among air, soil, surface, water, vegetation and food
chains (Whicker & Kirchner, 1987). Efforts to apply such a framework
to non-radioactive organic and inorganic toxic chemicals have been
more recent and have not as yet achieved such a high level of
sophistication. In response to the need for multiple-media models in
exposure assessment, a number of transport and transformation models
have recently appeared. In an early book on multiple-media transport,
Thibodeaux (1996) proposed the term "chemodynamics" to describe a set
of integrated methods for assessing the cross-media transfers of
organic chemicals. The first widely used multiple-media compartment
models for organic chemicals were the fugacity models proposed by
Mackay (1979, 1991) and Mackay & Paterson (1981, 1982). Cohen and his
co-workers introduced the concept of the multiple-media compartment
model and more recently the spatial multiple-media compartment model,
which allows for non-uniformity in some compartments (Cohen & Ryan,
1985, Cohen et al., 1990). Another multiple-media screening model,
called GEOTOX (McKone & Layton, 1986; McKone et al., 1987), was one of
the earliest to explicitly address human exposure.
The preceding models deal with inter-media transfer of
contaminants on a relatively large scale, but other models are scaled
to the residence and exposures that may occur therein. Exposure to
chemicals in consumer products such as cleaning agents and paint are
the focus of a model called CONSEXPO (van Veen, 1996).
All multiple-media exposure models have at least two features in
common, regardless of the objective for which they were designed.
First, movement of contaminants from one medium to another is
characterized. Second, the rate and/or frequency of human contact with
environmental media is modelled. The former may be referred to as
inter-media transfer factors and the latter as exposure factors.
Table 19. Potential human exposure media and routes
Environmental medium Exposure routes
Air dermal contact inhalation
Tap water dermal contact ingestion
Food and beverages ingestion
Surface soil dermal contact ingestion
Surface water dermal contact ingestion
6.5.1 Inter-media transfer factors
Transfer of contaminants between media is commonly modelled as
partitioning of a chemical between two or more media. Thus,
multiple-pathway models require the measurement or estimation of
partition coefficients of contaminants between several pairs of
environmental media. There are two general classes of partitioning
coefficients. The first class relies on basic physicochemical
properties of the compounds of interest such as aqueous solubility,
vapour pressure and dipole moment; they describe partitioning due to
diffusive processes. Coefficients in the second class describe
partitioning resulting from what may be considered advective
processes, but also implicitly include diffusive partitioning.
6.5.1.1 Diffusive partition coefficients
The class of diffusive partition coefficients includes those
between soil and water in soil (e.g., groundwater), air and plants,
soil and plants, animal intake and food, surface water and fish,
mother's uptake and breast milk, residential water and indoor air,
soil-gas and indoor air, human skin and soil, and human skin and
water. In many cases, partition coefficients developed from
laboratory-scale experiments are the basis for modelling partitioning
of a compound between environmental media (Lyman et al., 1990). For
example, the octanol-water partition coefficient is often used as a
proxy for partitioning non-polar organic compounds (e.g.,
organochlorine substances) between water and fish lipids. In this
case, n-octanol is considered a good model for fish lipids.
Similarly, the organic carbon-water partition coefficient is used to
characterize partitioning of non-polar substances between organic
matter in soil and water. Finally, Henry's constant describes
partitioning of volatile and non-volatile compounds between air and
water. Connell et al. (1997) provide a comprehensive introduction to
the use of this type of partition coefficient in environmental science
and exposure assessment.
6.5.1.2 Advective partition coefficients
The second class of partitioning coefficients jointly describe
bulk transfer of compounds from one medium to another and diffusive
partitioning. They are often used to model active uptake of
contaminants by animals, principally livestock and game such as fish
or fowl. Factors of this type are used to model transfer of
semi-volatile compounds (SVOCs) such as dioxins from air to soil, soil
to beef and soil to cow's milk (e.g., Nessel et al., 1991; Fries,
1995). Bioaccumulation of lipophilic compounds and some forms of heavy
metals (e.g., methylmercury) in fish from ingestion of contaminated
prey and diffusive uptake through respiration is also modelled using
partition coefficients such as these (e.g., MacIntosh et al., 1994).
6.5.2 Exposure factors
In constructing exposure models one needs to define the
characteristics of individuals in various age and sex categories and
the characteristics of the microenvironments in which they live or
from which they obtain water and food. The types of data needed to
carry out the exposure assessment include exposure duration and
averaging time, time-activity patterns of individuals, food
consumption patterns, household parameters, human factors such as body
weight, surface area, soil ingestion and breast milk intake, and
parameters associated with food crops and food-producing animals.
Time-activity patterns provide information on how individuals
distribute their time among a number of potential exposure media.
Time-activity pattern data describe such things as the average number
of hours spent indoors at home and in what rooms and the nature of
activity. Time-activity data also includes information on time spent
outdoors at home or spent in microenvironments, such as bathrooms
(including shower and bathing time). Exposure times are activity data
that involve the number of days per year and hours per day spent in
contact with soil during recreation and home gardening and in contact
with surface water during swimming or other water recreation.
Household factors relate to drinking-water supply and use,
room-ventilation rates, and soil and dust concentrations within homes.
Soil ingestion rates and soil contact on skin are also needed. Methods
for measuring time-activity patterns and related considerations are
discussed in detail in Chapter 5.
Input data of these types may be measured in the population under
investigation, i.e., site specific, or may be drawn from standard
references such as AIHC (1994), Finley et al. (1994b) and US EPA
(1996a). Site-specific data are preferred, in case the population of
interest may exhibit unique characteristics expected to influence
exposure. If site-specific data are not available, values observed in
other populations or estimates may be applied. Some model applications
may rely solely on estimated inputs. For example, screening models are
often used to assess exposure and health risks associated with new
products such as pesticides designed for agricultural and residential
use. In this case, model inputs may be determined in a manner such
that the model result is unlikely to underestimate the true level of
exposure experienced by the population of interest. Models such as
these are often referred to as "worst-case" models. An exposure
modelling system recently developed by the European Union contains a
suite of screening models (EC, 1996).
6.5.3 Multiple-media/multiple-pathway models
Multiple-media or so-called "total" exposure models provide
methods for integrating multiple exposure pathways from multiple
environmental media into a model system that relates concentrations of
toxic chemicals to potential total human dose at toxic substances
release sites. This type of simulation matrix is used to generate the
hypothetical histogram shown in Fig. 22. The scenarios used to develop
this particular histogram are for a representative VOC incorporated in
the top several metres of soil. Here we can see that, based on a
multiple-media and multiple-pathway assessment, we get indications of
where it is most valuable to focus our resources to more fully
characterize distributions of population exposure. In this way, we
characterize total potential dose using comprehensive, simple and
possibly stochastic models to focus efforts on those exposure
pathways, media and scenarios that require more realistic assessment
of the distribution of dose within the population. This matrix allows
us to make both pathway-to-pathway and medium-specific comparisons of
total potential doses from multiple environmental media.
6.6 Probabilistic exposure models
Variability and uncertainty are two important and related
concepts in exposure modelling, but it is important to distinguish
between them. Variability arises from true heterogeneity across
people, places or time; uncertainty represents a lack of knowledge
about factors affecting exposure (or risk). Thus, variability can
affect the precision of model estimates and the degree to which they
can be generalized, whereas uncertainty can lead to inaccurate or
biased estimates (Hoffman & Hammonds, 1994). It should be noted that
variability and uncertainty can complement or confound one another.
They may also have fundamentally different manifestations. For
example, uncertainty may force decision-makers to judge how
practicable it is that exposures have been over- or underestimated for
every member of the exposed population, whereas variability forces
them to cope with the certainty that different individuals are subject
to exposures both above and below any of the exposure levels chosen as
reference points (US NRC, 1994).
Failing to distinguish between variability and uncertainty makes
it difficult to accurately characterize the population distribution of
exposure and to make informed decisions about priorities for future
research objectives. Exposure models can allow for consideration of
both variability and uncertainty.
6.6.1 Variability
Diverse sources of environmental contaminants lead to various
contaminated media (e.g., soil, dust, water, air, food), which in turn
result in a multitude of routes and pathways of human exposure. For a
given contaminant, the magnitude and relative contribution of each
exposure route and pathway may vary among geographic regions and over
time. In addition, differences in activities among individuals lead to
disparate rates of contact with contaminated media. In aggregate,
these factors result in varying levels of personal exposure to a
particular contaminant among the members of a population, i.e., a
distribution of exposures.
Exposure model inputs expressed as distributions can be used to
model inter-individual variability of exposures. Examples of
probabilistic human exposure models that explicitly consider
variability of exposure among individuals may be found in Finley et
al. (1994a) and MacIntosh et al. (1995, 1996). Variable parameters are
those that are stochastic with respect to the reference unit of the
assessment question (IAEA, 1989) and are described by probability
distributions that reflect their intrinsic randomness. Exposure
concentrations may vary between individuals owing to the influence of
personal activities (e.g., cigarette smoking contributions to indoor
respirable particulate levels). Such differences represent true
variability of factors that affect exposure among individuals and can
determine the relative position of an individual or type of individual
within the distribution of exposures for the population.
6.6.2 Uncertainty
Several publications have stressed the importance of
distinguishing among different types of uncertainty (IAEA, 1989; US
EPA, 1992c). Explicit consideration of uncertainty in exposure and
risk assessments is important for understanding the range and
likelihood of potential outcomes and the relative influence of
different assumptions, decisions, knowledge gaps and stochastic
variability in inputs on these outcomes (Bogen & Spear, 1987; Iman &
Helton, 1988; IAEA, 1989; Morgan & Henrion, 1990; US EPA, 1992c). This
understanding can help the analyst advise the decision-maker on an
appropriate course of remedial action, decide whether it is worthwhile
to collect additional information regarding model parameters, choose
the appropriate model to use and evaluate which of these actions could
be most effective in reducing uncertainty about the outcomes (IAEA,
1989; Morgan & Henrion, 1990).
Three types of uncertainty are commonly considered: scenario
uncertainty, arising from a lack of knowledge required to fully
specify the problem; model uncertainty, arising from a lack of
knowledge required to formulate the appropriate conceptual or
computational models; and parameter uncertainty, arising from a lack
of knowledge about the true value or distribution of a model parameter
(US EPA, 1992c). In practice, scenario and model uncertainty are
commonly considered to be negligible relative to parameter
uncertainty, although in many cases they may be the largest sources of
true uncertainty.
Uncertain parameters are those for which the true value is not
known or cannot be measured. For example, the true annual mean
concentration of respirable particles in Mexico City during 1996 is
uncertain because it can only be estimated from existing data which do
not cover every day of the year nor every location of the city.
Another example, is the mean and variance of soil ingestion by
children aged 6-10 years in Taipei. Presumably, a single distribution
can be used to describe this behaviour; however, its parameters can
only be estimated.
The uncertainty about various parameters of an assessment can be
formally incorporated into exposure models to estimate uncertainty
about the prediction end-point, identify the components that influence
prediction uncertainty and prioritize future research needs (Bogen &
Spear, 1987; IAEA, 1989). Uncertainty about the true population
distributions is characterized by propagating the estimated
uncertainty about model inputs through to the distributions of the
prediction end-points.
6.6.3 Implementing probabilistic exposure models
Although probabilistic exposure models are computationally more
challenging to implement than deterministic (i.e., point estimate)
models, the advantages of being able consider population distributions
and sources and magnitude of uncertainty are often worth the
additional effort. Several tools are available for propagating input
parameter variability and uncertainty through to the assessment
end-point. Classical error propagation techniques may be convenient
for models with relatively few inputs and small coefficients of
variation (Bevington, 1969; Seiler, 1987). For more complex models,
computer-based simulation techniques are likely to be the method of
choice.
Probabilistic exposure models may be run in one or two
dimensions. One-dimensional models estimate either variability among
exposures to individuals or uncertainty about a single exposure
metric; for example, the mean 8-h average carbon monoxide exposure for
individuals in a specific area. Two-dimensional simulation models
may be used to estimate both population distributions (i.e.,
inter-individual variability) and uncertainty about the population
distribution. The IAEA (1989) has suggested a Monte Carlo simulation
approach for conducting two-dimensional simulations. In the first
phase, a single realization is obtained from the distribution of each
uncertain parameter. In the second phase, repeated realizations are
obtained from the variable parameters. The entire process of a single
sampling from the uncertain parameters, followed by repeated sampling
from the variable parameters, is referred to as a simulation. A
single model run consists of generating k simulations each composed
of i iterations, which produces a family of k predicted
distributions of population exposures. Prediction uncertainty is
represented by the distribution of individual estimates for a specific
percentile or summary statistic among the family of population
distributions. In this way, the type of plot shown in Fig. 23 contains
probabilistic information on estimates of both inter-individual
variability in the prediction end-point, and uncertainty about any
specific percentile of the population distribution.
6.7 A generalized dose model
The magnitude of exposure (dose) is the amount of agent available
at human exchange boundaries (skin, lungs, gastrointestinal tract)
where absorption takes place over a specified period of time.
Depending upon boundary assumptions, a number of dose questions may be
derived. The average daily dose (ADD) is one of the most useful
approaches, and is applied for exposure to non-carcinogenic compounds
(for carcinogens, lifetime average daily dose, LADD, is often
employed). The ADD is calculated by averaging the potential dose
( Dpot) over body weight and the appropriate averaging exposure time:
ADD = total potential dose/body weight × averaging time,
where the potential dose is a product of contaminant concentration
(C) in the exposure medium contacting the body, intake rate (IR)
and exposure duration (ED):
total potential dose = C × IR × ED.
The intake rate refers the rates of inhalation, ingestion or dermal
contact depending on the route of exposure.
The concentrations in air, water and soil used for an exposure
assessment are those measured or estimated to be available in these
environmental media at the nearest receptor point to the source (e.g.,
soil or groundwater at a hazardous waste site). When an environmental
concentration is assumed constant over a long time period, the
population-averaged potential dose (for ingestion or inhalation
pathways) or absorbed dose (for dermal contact) is expressed as an
average daily dose (ADD) in mg kg-1 day-1:
where [ Ci/ Ck] is the intermedia-transfer factor, which expresses
the ratio of contaminant concentration in the exposure medium i
(i.e., personal air, tap water, milk, soil, etc.) to the concentration
in an environmental medium k (ambient air gases or particles,
surface soil, root-zone soil, surface water and groundwater);
[ IUi / BW] is the intake or uptake factor per unit body weight
associated with the exposure medium i. For exposure through the
inhalation or ingestion pathway [ IUi / BW] is the intake rate per
unit body weight of the exposure medium such as m3(air) kg-1 day-1,
litres(milk) kg-1 day-1, or kg(soil) kg-1 day-1. For exposure
through the dermal pathway, [ IUi / BW] is replaced by UFi, the
uptake factor per unit body weight as a fraction of the initial
concentration in the applied medium with nominal units [litres(water)
kg-1 day-1 or kg(soil) kg-1 day-1]; EF is the exposure frequency
for the exposed population in days per year; ED is the exposure
duration for the exposed population in years; AT is the averaging
time for the exposed population in days; and Ck is the contaminant
concentration in environmental medium k.
The potential dose factor, PDF( k-> i), is defined as the
ratio of dose to concentration, as expressed in the following
equation:
The ADD is used to make route and route-to-route comparisons and
allows one to consider the relative significance of several exposure
routes. With the ADD, we compare inhalation, ingestion or dermal
exposures to the same medium such as tap water and compare exposures
through indirect pathways (e.g., food-chain transfers) to those from
direct pathways (e.g., inhalation or ingestion). As an example, the
ADD for the ingestion route for chloroform for a 70-kg individual
ingesting 2 litres/day of tap water containing 1 µg/litre chloroform,
365 days/year for a lifetime is 2 µg/day divided by 70 kg or 0.029
µg/kg-1 day-1. This ADD can be used as the basis for determining the
relative significance of dermal, inhalation, and other ingestion
exposures attributable to tap water.
6.8 Physiologically based pharmacokinetic models
Human exposure to contaminants results in dose to the critical
organs. A mass balance on the contaminants that enter the body
accounts for the distribution in the various organs, transformation
into by-products, and excretion via specific mechanisms. The three
major exposure routes by which contaminants enter the human body are
inhalation, dermal absorption and ingestion. The vehicle that moves
contaminants between organs is blood. Transformations include the
metabolism of specific contaminants in specific organs. Mechanisms of
excretion include exhaled air, sweat, urine and faeces.
The above processes that occur in the human body can be modelled
by using physiologically based pharmacokinetic (PBPK) principles
(Masters, 1991). These principles can be applied at differing levels
of complexity. Simple models assume steady states and total absorption
and estimate dose to critical organs in a gross manner. They can be
solved by using linear algebraic relationships. Complex models include
time dependency, assume the human body to consist of multiple
homogeneous boxes, each representing an organ or a portion thereof,
and determine the distribution of contaminants in the different boxes
as a function of time. The relationships usually end up as non-linear
ordinary differential equations that are solved by using numerical
integration techniques. Examples of PBPK models may be found in Cox
(1996) for inhalation of benzene, Bookout et al. (1997) for dermal
absorption of chemicals and Rao & Ginsberg (1997) for multiple-route
exposure to methyl tert-butyl ether. A wide array of PBPK models
have been developed for other chemicals and chemical classes and may
be found in the relevant literature.
Whatever the complexity of the model representing the human body,
the difficulty is interpreting the dose results to characterize risk.
Usually, these human models are extrapolated to parallel animal models
for which toxicological data are available.
6.9 Validation and generalization
The modelling approaches described above are mathematical
abstractions of physical reality that may or may not provide adequate
estimates of exposure. The preferred way to be sure that a model is
capable of providing useful and accurate information is by validation,
i.e., comparing model predictions with measurements independent of
these used to develop the model. Models can be validated in terms of
prediction accuracy and precision by comparing predicted values to
those measured in the field. Although measurements are preferable as
the "gold standard" in validation of models, comparison of results
from different assessment methods or modelling approaches can also be
used to evaluate validity, or at least agreement. This may be the only
option when measurements are not feasible; for example, in
retrospective assessment of exposure. Model validation is a necessary
precondition for the generalization of model results to a different or
larger population (Ott et al., 1988).
In the statistical modelling approach, data collection is an
integral part of model construction. If the data are known to be from
a statistically representative sample of the population, then there is
no need for further validation. However, validation is necessary if
the results are to be extrapolated beyond the range for which the
existing database provides a statistical description. The physical and
physical-stochastic modelling approaches must be validated with actual
data from separately conducted field studies. Care must be taken that
the data used to validate a model are not biased with respect to
crucial model parameters. The validation step is useful only to the
degree that the sample population is representative of the group to
which results will be extrapolated.
Finally, when modelling environmental-response-health processes,
and when validating such models, it is important to realize that in
principle perfect modelling is possible only for closed systems, and
the systems described in this report are very open-ended. The
practical implication of this fact is that even the best models need
to be validated for each new population and environmental setting
before application.
6.10 Summary
An exposure model is a logical or empirical construct which
allows estimation of individual or population exposure parameters from
available input data. Exposure models, if supported by adequate
observations, can be used to estimate group exposures (e.g., a
population mean) or individual exposures (e.g., the distribution of
exposures among members of a population). Models may be used to
estimate exposure via single or multiple media. The latter is
particularly useful for comparing the magnitude of exposures likely to
occur from different media and thus for priority-setting. Exposure
models may be statistical or deterministic in nature or a combination
of both. Probabilistic methods may be applied to all three types as a
means to estimate population distributions of exposure, i.e.,
variability of exposure among individuals. In addition, probabilistic
methods may be used to characterize uncertainty in model input
parameters and propagate that uncertainty through to the prediction
end-point. Evaluation of the accuracy of model results is critical
before relying on model output for decision-making.
7. MEASURING HUMAN EXPOSURES TO CHEMICALS IN AIR, WATER AND FOOD
7.1 Introduction
This chapter describes sampling methods used in environmental
exposure assessment to analyse chemical concentrations in air, water
and food. The information presented provides a general description of
available sampling methods and guidance for their selection. It is not
intended to be comprehensive and the reader should refer to the
research literature for specific details.
Assessment of human exposures to contaminants in environmental
media requires establishing measurement strategies and selecting
appropriate sampling instruments and analytical methods. Taken
together, these three elements define a monitoring programme.
Monitoring methods can be used to determine the magnitude, duration
and frequency of exposure to an environmental contaminant. Magnitude
of exposure is defined as the concentration of a specific pollutant
averaged over a predetermined time interval, such as 1 h, 24 h or a
lifetime. Different measurement methods have specific characteristics
that determine the locations in which they are feasible for use. In
the case of air, the method's sensitivity to pollutants determines the
averaging times over which it will provide reliable responses.
Therefore, a clear understanding of the concentration range
anticipated, averaging time of interest, and expected frequency of
exposure events is needed to identify appropriate field and laboratory
methods. In the absence of any prior information, pilot studies may be
performed to obtain the information needed to finalize the design of
the monitoring programme.
Selection of instruments will depend on the target population
(e.g., children or adults) and study objectives. In some situations,
understanding the distribution or the average population exposure to a
contaminant may be sufficient. In fact, most environmental monitoring
of contaminants in outdoor air, water at the point of distribution and
"market basket" surveys implicitly assumes that indicators of
population exposure are more relevant than information at the
individual level. Studies assessing individual exposures using such
surrogate measures should select sampling instruments and analysis
methods based on sensitivity, selectivity, response rate, portability,
durability and cost, among other factors. Table 20 summarizes these
concepts.
7.2 Air monitoring
Air sampling methodologies should conform to the exposure
assessment approach selected, either direct or indirect, as described
in Chapter 3.
Direct monitoring methods for exposure measurements include the
use of personal air monitors and/or analysis of human tissue and/or
biological fluids. Aspects of biomonitoring are described in
Chapter 10. Indirect air monitoring methods can include
Table 20. Selection factors for instruments and methods
Factor Comment
Sensitivity The magnitude and duration of contaminant exposure define the sensitivity required. As a general
guide, one order of magnitude below and above the concentration of interest is desired.
Reproducibility (precision) as measured by percentage relative error should be below 5%. Sensitivity
is usually inversely proportional to integration time or amount of sample collected
Selectivity Response to a specific compound or analyte without interferences. In some cases, non-selective
instruments may be appropriate if exposure situation (e.g., sources, emissions) are understood.
Specific or selective response may require more expensive equipment or more time-consuming
analytical procedures
Response rate There are two aspects of response rate: (i) time required for instrument to respond to 90% of a
step change in concentration; (ii) time required between sampling and final processing of data. The
appropriate instrument response rate depends, in part, on the relationship between the contaminant
and the health effect of interest. Acute effects may require instrument methods that can resolve
exposures over intervals of minutes. If health effects from chronic exposures are of primary concern
or the metabolic half-life is long, then rapid response is not necessary
Portability Instruments and sampling procedures should not modify behaviour of subjects. Portability includes
size, weight, noise, power, and safety considerations. Portability will influence study design and
usually involves a tradeoff with sensitivity and response rate (e.g., integrated samples rather than
continuous)
Durability Instruments used for air sampling are subjected to a broad range of conditions. Since temperature
and humidity are potentially interferents and are not easily controlled, the performance of
instruments/methods must be fully evaluated
Cost Instrument cost and analytical expenses will influence study design. It may be necessary to trade
off sample cost for accuracy, precision, and response rate. Increasing the number of samples per
subject and/or the number of subjects, or relaxing resolution requirements could compensate for the
use of less expensive methods
microenvironmental sampling in combination with questionnaires and
time-activity logs. Ambient air monitors can also be used to estimate
exposures when combined with information such as building
characteristics, indoor/outdoor contaminant ratios and time-activity
patterns.
The direct approach depends largely on the availability of
sensitive, small, quiet, lightweight and portable personal monitors.
Personal air monitors can be used for microenvironmental monitoring as
well. In addition, microenvironmental monitors with larger sampling
flows are used for indoor/outdoor sampling. Ambient monitors are
generally high-volume samplers and are not suitable for indoor use.
Suitable air monitors must also fulfil several requirements, such as
detection limits, interferences, time resolution, easy operation and
of course, cost. There are several good references on air monitoring
and analysis. The reader is referred to Air Sampling Instruments
for Evaluation of Atmospheric Contamination (ACGIH, 1995).
Additional general publications include US EPA (1994, 1996b), and
Lodge (1988). It is important, however, to refer to the published
scientific literature for the most appropriate and recent air
monitoring methods.
The following sections describe methods available for air
sampling of gases and vapours, airborne particulate matter, SVOCs and
reactive gases. The methods are classified into active and passive or
continuous monitors. A detailed list of sampling methods, air
pollutants for which they are used, sources and other pertinent
information is presented in Table 21-24. An indicator of their
suitability for personal, indoor or ambient monitoring is also
included.
7.2.1 Gases and vapours
7.2.1.1 Passive samplers
Commercial passive samplers are available for a variety of air
pollutants, including inorganic gases such as carbon monoxide,
nitrogen dioxide, sulfur dioxide and ozone, and VOCs (e.g., benzene,
toluene, xylene, etc.). Passive air samplers are probably the most
convenient tool for conducting large-scale personal exposure
assessments because they are small, inexpensive and easy to use.
However, sampling rates are of the order of 10-50 ml/min and absorbing
capacity is limited. Passive samplers operate on the principle of
molecular diffusion. The rate of diffusion is related to the diffusion
coefficient of the compound, the cross-sectional area of the absorbing
surface and the length of diffusion path. Specific information on the
calculation of sampling rates can be obtained from the manufacturers.
The collection mechanism relies either on physico-chemical absorption
or adsorption or chemical reactions. The samplers for inorganic gases
rely on reaction of the contaminant with a chemical coating on the
collection surface. The samplers for VOCs typically rely on absorption
by a liquid or adsorption by a solid collection medium. Selection and
use of passive samplers should take into consideration potential
sources of error such as wind effects, temperature, humidity and
interfering gases.
In practical applications, personal monitoring is performed by
mounting the passive sampler on a participant's collar to estimate air
pollution concentrations in the breathing zone. After collection, the
adsorbent is removed from the sampler and extracted with the
recommended solvent. The extract is then analysed by a suitable method
(e.g., spectrophotometry, gas chromatography with specific or
unspecific detectors, HPLC, etc.). As with any monitoring procedure,
measures should be taken to evaluate sample preservation and
integrity. These procedures should be described as part of the quality
assurance (QA) protocol and the standard operation procedures (SOPs)
(see Chapter 11).
7.2.1.2 Active samplers
There are many commercially available liquid-media samplers for
reactive and soluble gases, such as liquid-containing bottles, and
solid-sorbent tubes for insoluble and non-reactive gases and vapours,
such as activated charcoal, silica gel, porous polymers or other
materials. Pollutants are transported with the carrier gas (air), and
are captured by collecting media. The most frequently applied
mechanisms in the collection of air pollutants in these media are
chemical reactions (e.g., acid-base and colour-forming), and
absorption/adsorption of the pollutant molecules on collecting media.
Solid sorbent collection efficiency depends on contacting surface
area, air flow rate, temperature, humidity and presence of interfering
compounds.
The sampling rate, breakthrough volume and method limit of
detection are important parameters which need to be considered for an
accurate exposure assessment by active samplers. The identification
and quantification of collected air pollutants are usually performed
by analytical instruments, such as spectrophotometry, gas
chromatography with specific or non-specific detectors, HPLC, etc.
Although not yet used extensively, small, evacuated canister samplers
have been developed for personal monitoring (Pleil & Lindstrom, 1995).
These have the advantage of not using sorbents. Analysis is typically
done by gas chromatography following thermal desorption.
7.2.1.3 Direct-reading instruments
The concentration of gases and vapours (e.g., carbon monoxide,
sulfur dioxide) in an individual's breathing zone can also be
determined with the use of portable direct-reading instruments.
Commercially available direct-reading instruments have data logging
capabilities to store measurements at a rate of 1 s-1. Depending on
the frequency of measurements, these instruments can operate up to
2 weeks continuously. Instrument software allows for direct
calculation of concentrations with different averaging times and
statistical analysis of the data.
Table 21. Air sampling methods for inorganic gases
Carbon monoxide Manufacturer Comments Application
Continuous
Electrochemical Energetic Sciences 0-50, 0-100 ppm; portable and personal; environmental/personal
LOD ~ 2 ppm
Interscan Various ranges; LOD ~ 1 ppm environmental
Bacharah Based on the measurement of Hg vapour environmental
from a pellet oxidized by CO. Range: 0-5,
0-20 dl: 1 ppm Sample flowrate: 4.7
litre/min
Photometers Beckman Instruments Based on dual-isotope fluorescence, environmental
LOD = 0.1 ppm
Passive
Diffusion detectors Lab Safety Supply Co. Changes colour; LOD ~ 50 ppm for 8 h personal
Quantum Group Inc. Simple colour change detector personal
3M Corporation Indicates presence of CO by colour personal
change
Wilson Safety Products Dosimeter badge. Colour change is personal
proportional to CO concentration
Active MSA Air is pumped through activated charcoal personal
tubes that change colour when CO is present
Sensidyne
Table 21. (continued)
Carbon monoxide Manufacturer Comments Application
Continuous
Infrared GasTech 300-5000 ppm environmental
Rosemount Analytical Measures CO, CO2, NO and hydrocarbons environmental
SKC West Sampling frequency: 8 s to 30 min environmental
Electrochemical Devco Engineering Based on conductivity in water due to environmental
ionization of gas
Nitrogen oxides
Continuous
Electrochemical Trasducer Research LOD > 2 ppb environmental
Interscan Various ranges; LOD > 20 ppm environmental
Chemiluminescence Beckman Instruments Range: 0.1-1 ppm. Operates continuously environmental
for 7 days. Analyses NO, NO2, NOx based
on the excitation of molecules by light
Columbia Scientific Uses the chemiluminescence reaction of O3 environmental
with NO. Sampling rate: 1.2 litre/min
Rosemount Analytical Designed to monitor continuous emissions environmental
Colorimetric Phillips Electronics Set for a variety of chemicals, depending environmental
Instruments on the electrolyte. Measures concentration
based on a specific chemical reaction
Table 21. (continued)
Carbon monoxide Manufacturer Comments Application
Passive
Diffusion Env Sciences and LOD ~ 500 ppb for a 1-h exposure personal
tubes/badges Physiology
MDA Scientific Palmes sampler is an acrylic tube with personal
stainless steel grids coated with
triethanolamine placed at the bottom
RS Landauer Jr. & Co Pen-shaped badge for the collection of personal
N2O on a molecular sieve. Analysis with
IR
Active
Electrochemical MDA Scientific 2-3 ppm; measurement on a 15-min basis personal
Ozone
Continuous
Chemiluminescence Beckman Instruments Operates continuously for 7 days Based on environmental
the reaction of ozone with ethylene to
produce light Range: 0-0.0025 ppm,
DL: 0.01 ppm
Philips Electronics Operates continuously for 7 days. Based environmental
Instruments on the reaction of ozone with ethylene
to produce light
Columbia Scientific Based on the reaction of ozone with environmental
ethylene. Ranges: 0-0.1, 0-0.2, 0-0.5,
0-1.0 ppm
UV Vis photometer Dasibi Environmental Concentration is determined by detecting environmental
the absorption level of UV within a volume
of air
Table 21. (continued)
Carbon monoxide Manufacturer Comments Application
Mast Development Portable. Sampling rate 2 litre/min, environmental
measurement cycle 20 s
Passive
Diffusion monitors Ogawa Uses 2 multitube diffusion barriers environmental/
with collection on glass fibre filters personal
coated with nitrite
LOD: Level of Detection
Table 22. Air sampling methods for organic vapours
Manufacturer Comments Application
Continuous
Photo-ionization detector Thermo Environmental Instruments Based on UV light, photoionization environmental
detectors can detect a wide
Flame ionization detectors Columbia Scientific variety of chemical compounds.
Foxboro Measures hydrocarbons as methane environmental
equivalents. Sample flowrate
20 ml/min
Mainly used as a portable survey environmental
equipment. Based on hydrogen flame
ionization detection. Sample
flowrate 2 litre/min, LOD ~ 0.2 ppm
Thermal ionization Photovac International Semiquantitative response environmental
detector
Infrared photometers Foxboro Miran portable air analyser. Owing environmental
to its tunable IR wavelength, can
detect several organic compounds.
Sampling rate 28 litre/min
Infrared Industries 2 models. LOD = 25 ppm environmental
Portable gas Photovac International Portable. Can detect selected VOCs: environmental
chromatographs Benzene, C4-C8, halocarbons down
to ppb level
H-Nu Systems Portable gas chromatographs with 5 environmental
different detector options (FID,
PID, ECD, TCD, FPD)
Table 22. (continued)
Manufacturer Comments Application
Microsensor Systems Portable, isothermal gas environmental
chromatograph. Samples are
concentrated in tubes, heated and
analysed. LOD = 2 ppb
Passive
Charcoal badges 3M Single charcoal strips (300 mg). personal/
Sampling rate depends on the number environmental
SKC of windows (1 or 2): 35-70 cm3/min.
Minimum collectable sample:
Gilian Instrument 0.2 ppm/h
Perkin Elmer Require laboratory analysis
Pro-Tek
3M Two charcoal strips to avoid personal/
breakthrough and increase sample environmental
SKC amount. Sampling rate depends on
the number of windows (1 or 2):
Gilian Instrument 35-70 cm3/min. Minimum collectable
sample: 0.2 ppm/h.
Perkin Elmer
Desorption efficiency depends on
Pro-Tek the amount and type of solvent used
Require laboratory analysis
Table 22. (continued)
Manufacturer Comments Application
Active
Charcoal tubes Perkin Elmer The most commonly used adsorbent personal/
is activated charcoal. environmental
National Draeger 2 sizes of tubes are available :
100/50 mg or 200/100 mg.
SKC
Formaldehyde
Passive GMD Systems LOD > 0.2 ppm for 15 min personal
Interscan Various ranges; LOD > 20 ppm personal/
environmental
Air Quality Research LOD ~ 0.01 ppm for a 7-day exposure personal
DuPont 1.6-54 ppm up to 7 days environmental
3M LOD ~ 0.8 ppm for a 1-h exposure personal
Requires colorimetric analysis
SKC personal/
environmental
AirScan Environmental Technologies Based on crystal growth and personal
nucleation Length of stain is
proportional to concentration.
Environmental Science and Physiology LOD ~ 500 ppb for a 1-h exposure personal
Envirometrics Products Based on electric reaction personal
with a lead-acid battery
Table 22. (continued)
Manufacturer Comments Application
Gases and Vapours
Active
Solid adsorbents Barneby Cheney Large number of chemicals efficiently personal/
collected under a wide variety of environmental
Columbia Scientific Instruments conditions.
The choice of the adsorbent is
Draeger designed to maximize collection
efficiency while retaining low
Fischer Scientific selectivity.
Approximately 50 sorbent types
Perkin Elmer are available; some are chemically
treated to facilitate their
3M collection properties.
SKC Most tubes contain a primary sorbent
section and a backup bed that is
Supelco used to indicate breakthrough.
Westvaco Require laboratory analysis
Polyurethane foam Supelco Collection of pesticides and PCBs personal/
environmental
Passive
Diffusion monitors 3M In general, the sorbent used is personal/
activated charcoal protected by a environmental
Gilian Instrument screen.
SKC Some monitors have a backup layer
used to indicate breakthrough.
Supelco
Each compound has a particular
diffusion rate
Table 22. (continued)
Manufacturer Comments Application
Require laboratory analysis
Desorption efficiency will vary
with the amount of material on
the charcoal and with the amount
and type of desorber used.
Table 23. Air sampling methods for particulate matter/aerosols
Manufacturer Comments Application
Continuous
Light-scattering PPM LDL ~ 10 µm/m3 Handheld monitor environmental
photometers
Air Technique Portable. Sample rate 28.3 litre/min environmental
Suction: vacuum pump. Particles can be
collected downstream of the filter
Virtis Near-forward sampling, Sample rate 28.3 environmental
litre/min. Suction: vacuum pump.
Particles can be collected downstream
of the filter
Hund Measures respirable aerosol mass personal or
concentration by IR scattering environmental
detection. Average of 8 h
Measures fine dust (0.2-10 µm) mass environmental
concentration by IR scattering
detection. Average of 8 h
MIE Detects respirable dust. Portable. fixed point/
Sample rate 2 litre/min environmental
Averages measurement over 8 h
Miniram dust monitor. Provides personal
instantaneous or 8 h average concentration
Negretti Portable dust monitor, Range environmental
0.01-20/0.1-200 mg/m3. Particles can
be collected on a filter
Table 23. (continued)
Manufacturer Comments Application
Casella Handheld, Range 0.01-20/0.1-200 mg/m3), environmental
size >0.1 µm. Particles can be collected
on a filter
TSI Integrating nephelometer averages over mostly used
30-s periods. Has different wavelengths for visibility
depending on the aerosol characteristics
Laser photometer for particles >0.1 environmental
µm diameter. Measures aerosol
concentration in mg/m3
Belfort Instruments Integrating nephelometer. Flowrate environmental
10 litre/min
Instantaneous MIE 0.01-10 mg/m3 or 0.1-100 mg/m3 personal
aerosol
monitor
Condensation nucleus Met One 2 models. Sample flowrate 1.4 or 2.8 environmental
counters litre/min. Ultrafine particles are grown
in alcohol vapour condensation
Optical particle counters Climet Instruments 6 models. Flowrate 0.3 -1.0 litre/min. environmental
Size range: 0.3-20 µm, 5-16 size
channels. Light source: white light or
laser
Hiac/Royco 6 models. Flowrate 0.01-1.0 litre/min environmental
Range: 0.3-10 µm, 6 size channels
Light source: white light or laser
Table 23. (continued)
Manufacturer Comments Application
Met One 3 models. Flowrate 0.1-1.0 litre/min environmental
Range: 0.1-1 µm, 6 size channels
Light source: white light or laser
Particle Measuring Flowrate 0.1-1.0 litre/min; range environmental
Systems 0.05-5 µm, 4-16 size channels
Illumination source: laser
Faley International 4 models. Flowrate 0.017-1.0 litre/min environmental
Range 0.3-5 µm, 2-5 size channels.
Light source: white light
TSI Flowrate: 0.1-1.0 litre/min; range environmental
0.05-5 µm; 4-16 size channels
Piezobalance TSI Less reliable for concentrations environmental
<10 µg/m3. Difficult to calibrate
Beta gauge Wedding & Assoc The particulate collected on the filter is environmental
continuously measured by the attenuation of
gamma-radiation
Active (Total)
Open cassette SKC Air is pulled through a filter with no personal
size selection device
IOM inlet SKC Samples inhalable dust particles. Reusable personal
filter cassettes. Sampling rate 2 litre/min.
Cut point 100 µm
Active (size selective)
PM10 impactors BGI Sampling rate 28.3 litre/min environmental
Suction: pump aerosol spectrometer
Table 23. (continued)
Manufacturer Comments Application
MSP Sampling rate 4 or 10 litre/min; personal
Suction: pump
10-2.5 µm virtual impactor. environmental
Sampling rate 1130 litre/min
TSI Sampling rate 1 litre/min; personal
Suction: pump
MIE Sampling rate 2 litre/min; personal
Suction: pump
Air Diagnostics Sampling rate 4 litre/min; fixed
Suction: pump location/
environmental environmental
Graseby Andersen Virtual dichotomous. Cutpoints 10 environmental
and 2.5 µm; sampling rate 16.7 litre/min
High-volume sampler. Sampling rate environmental
1100 litre/min
SKC Personal impactor. Single stage. Suction: personal/
personal pump. Sampling rate 2, 4 or 10 environmental
litre/min
PM2.5 impactors URG Sampling rate 4 litre/min; part are personal
collected in filters and organics in a
polyurethane foam
MSP Sampling rate 4 or 10 litre/min; personal
Suction: pump
Table 23. (continued)
Manufacturer Comments Application
SKC Personal impactor. Single stage. personal/
Suction: personal pump. environmental
Sampling rate 2, 4 or 10 litre/min
Cascade impactors BGI 7 stages. Sampling rate 5 litre/min; environmental
Cut points: 32, 16, 8, 4, 2, 1 µm
Graseby Andersen 13 stages; sampling rate: 3 litre/min; environmental
Cut points: 13-0.08 µm
9 stages; sampling rate: 7 litre/min; environmental
Cut points: 18, 11, 4.4, 2.65, 1.7, 0.95,
0.53, 0.32, 0.16 µm
8 stages; sampling rate 28 litre/min; environmental
Cut points 10-0.4 µm
7 stages; sampling rate 28 litre/min; environmental
Cut points 6, 4.6, 3.3, 2.2, 1.1, 0.7, 0.4 µm
6 stages; sampling rate 0.3-20 litre/min; personal
Cut points 0.5-20 µm
5 stages; sampling rate 1132 litre/min; environmental
Cut points 7.2, 3, 1.5, 0.95, 0.49 µm
4, 6 or 8 stages; flowrate 2 litre/min personal
Cut points 20-0.6 with 8 stages, 10-0.6
µm with 6, and 20-3.5 with 4
Radial slot impactor. 6, 8 or 10 stages with environmental
an optional cyclone
Table 23. (continued)
Manufacturer Comments Application
Hauke KG Sampling rate 30 litre/min environmental
Cut points below 0.1 µm
in-Tox Products 4 models of 7 stages each. environmental
Cut points 3.1-0.33 µm @ 0.1 litre/min;
4.5-0.32 µm @ 1 litre/min; 5-0.25 µm @
2 litre/min; 5-0.0.5 µm @ 5 litre/min
MSP 8 stages, Sampling rate: 30 litre/min; environmental
Cut points: 10, 5.62, 3.16, 1.78, 1,
0.56, 0.316, 0.178, 0.1, 0.056 µm
Virtual impactors BGI 3 virtual stages, flowrate: 30 litre/min; environmental
Cut points 1.2, 4 and 14 µm
Graseby Andersen The dichotomous sampler fractions the environmental
particles in 2 sizes: 10 and 2.5 µm.
Sampling rate 17.6 litre/min
MSP Sampling rate 30 litre/min; environmental
Cut points below 0.1 µm
High-volume operates at 1130 litre/min environmental
Cut point 2.5 µm
Cyclones Mine Safety Appliances Measures respirable particles with a pre-cut personal
diameter of 3.5 µm @ 2 litre/min
Sensidyne Measures respirable particles in ambient environmental
air @ 240 litre/min
Table 23. (continued)
Manufacturer Comments Application
Measures respirable particles in ambient environmental
air @ 9 litre/min
Measures respirable particles @ personal
1.7 litre/min
SKC Measures respirable particles with a cut personal
point of 3.5 µm @ 1.9 litre/min
Elutriators Casella Horizontal elutriator that retains particles environmental
with a cut point of 3.5 µm at a flowrate
of 50 litre/min
Horizontal elutriator that retains particles personal
with a cut point of 3.5 µm at a flowrate of
2.5 litre/min
LOD: Level of detection
Table 24. Air sampling methods for reactive gases
Manufacturer Comments Application
Hydrogen sulfide/sulfur dioxide/ammonia/chloride
Continuous
Electrochemical Devco Engineering
CEA Instruments
All based on conductivity change in environmental
Sensidyne water due to ionization of gas
Teledyne
Bacharah
Colorimetric Phillips Electronics Based on the reaction of the gas with environmental
Instruments the reagent to produce a coloured
product
Each compound has a specific reagent
CEA Instruments for its detection
Potentiometric AIM Conductivity of the reagent changes in environmental
proportion to the concentration of the
Calibrated Instruments gas being sampled and is measured
by an electrode
Eitel Manufacturing
UV and visible Barringer Research Based on the correlation with the environmental
light photometers absorption spectra of SO2 in the UV
Sensitivity 2 ppm
UV and visible Beckman Instruments Based on the fluorescence of SO2 environmental
light photometers under UV light
Table 24. (continued)
Manufacturer Comments Application
Rosemount Analytical For SO2 uses a non-dispersive UV environmental
"transflectance" analysis
Columbia Scientific Uses a continuous UV source of high environmental
intensity to detect SO2
Passive
Solid adsorbents Barneby Cheney Large number of chemicals are
efficiently collected under a wide
variety of conditions
Columbia Scientific Choice of adsorbent is designed to
Instruments maximize collection efficiency while
retaining low selectivity
Approximately 50 sorbent types are
Draeger available; some are chemically treated personal
to facilitate their collection
Fischer Scientific properties
Most tubes contain a primary sorbent personal
section and a backup bed that is
Perkin Elmer used to indicate breakthrough
3M Require laboratory analysis
SKC
Supelco
Westvaco
Table 24. (continued)
Manufacturer Comments Application
Active
Annular denuders URG Different models can be used to collect personal/
only gases or gases and particles/aerosols. environmental
Especially used for acid aerosols (SO2,
H2SO4, HNO3, (NH4)2SO4, NH4HSO4, NH4NO3)
7.2.2 Aerosols
At present, active sampling is the only feasible way to perform
exposure assessments on particulates directly. Active particle
samplers operate by drawing aerosols into a sensor or on to a
collection surface (e.g., a filter) by means of a pump (Hinds, 1982;
Lehtimäki & Willeke 1993). Large stationary samplers that operate with
a standard flow rate of approximately 1000 litre/min are available
commercially and are useful for collecting large sample volumes. Small
stationary samplers that operate with flow rates in the range of 1-10
litre/min are also commercially available. Both sizes are available in
configurations that allow for sampling of total suspended particulate
matter (i.e., not size separated) or specific size fractions (e.g.,
PM2.5 or PM10). Personal aerosol samplers that allow collection of
total inhalable particulate matter of specific size fractions are also
available.
The cyclone and, particularly, the impactor are the two most
commonly used size preselectors. Cyclones can collect suspended
particulate matter of various sizes depending on the geometry of the
cyclone and the flow rate. It operates on the principle of centrifugal
forces that drive particles in the direction of the outer wall of the
cyclone (Hinds, 1982). Particles with aerodynamic diameter greater
than the cut-point of the cyclone impact upon the wall and/or the
bottom of the cyclone. Particles with aerodynamic diameter less than
the cut-point remain in the air stream and are collected on a filter
downstream.
Impactors rely on inertial forces to separate particles based on
aerodynamic diameter. Air is accelerated through a nozzle or jet and
then forced to make a 90° turn around an impaction plate before
passing through a filter and exiting the sampler. Depending on their
size, particles suspended in the air stream pass through the
acceleration nozzle and then either remain entrained in the flow or
collide and are retained on the impaction plate. The cut-point of an
impactor is determined by the flow rate, jet size and shape (e.g., the
distance between the jet and the impaction surface) (Pastuska, 1988;
Lehtimäki & Willeke, 1993). The air flow rate must be calibrated
carefully because correct size selection depends largely on precise
flow rates.
Filters are made either from fibre mats of glass, cellulose or
quartz or from synthetic membranes (e.g., Teflon). The selection of
appropriate filters depends on the pump, filter static pressure,
collection efficiency, extraction and analytical requirements, and the
potential for sampling artefacts. Filter mass is determined by
weighing the filter under controlled temperature and humidity
conditions before and after use following a conditioning period of at
least 24 h at those same conditions. The collected mass can be
extracted and analysed for chemical composition. The extraction and
analysis procedures used depend on the analytes of interest. A recent
summary of methods for extraction and analysis of components of
particulate matter may be found in Koutrakis & Sioutas (1996).
7.2.3 Semivolatile compounds
For airborne contaminants that are present in both the particle
and the vapour phase at typical environmental conditions, it is
necessary to use a combination of sampling methods. The most common
approach consists of an aerosol sampling inlet (with or without size
preselector) followed by a sorbent cartridge or tube. Examples of such
contaminants include airborne PAHs, pesticides, polychlorinated
biphenyls (PCBs), dioxins and furans. Semivolatile sampling systems
are commercially available for personal air monitoring. Extraction and
analysis of these samples are done separately for the particle and
vapour phase and then the results are combined to provide a total
concentration. An introduction to sampling and analysis methods for
VOCs in air may be found in Binkova et al. (1995), Wallace & Hites
(1996), Wallace et al. (1996) and Simonich & Hites (1997).
7.2.4 Reactive gas monitoring
Certain gases present in air may react with chemicals present in
particles. For example, sulfuric acid particles collected on filters
can be neutralized by the ammonia gas present in the sample or air
stream. The preferred sampling approach to avoid this is to use a
denuder to remove the reactive gas before it reaches the downstream
filter. In the case of sulfuric acid monitoring, a citric-acid-coated
denuder is used to remove the ammonia gas. Small denuder systems are
commercially available for personal monitors. Denuder technologies are
described in Lodge (1988) and Koutrakis & Sioutas (1996).
7.3 Water
The sampling and analysis of drinking-water characterizes the
extent to which this carrier medium represents a source of specific
chemical exposure. Contaminated drinking-water supplies contribute to
the human intake of numerous chemical contaminants, including heavy
metals, fertilizers, pesticides, aromatic hydrocarbons and
organohalogens, among others. In some cases, drinking-water may be the
primary source of human exposure. Chemical pollutants in water may
originate from one or more of a myriad of sources, as summarized in
Table 25. In the selection of measurement and sampling methods, it is
important to consider raw water sources, water treatment processes,
and distribution and service systems, all of which can either reduce
or increase the contaminant concentrations in drinking-water.
Samples collected at the end of the distribution system provide a
better measure of potential exposure to individuals than samples
collected at the source prior to any removal or treatment that might
take place. Numerous texts detail sampling and analytical techniques
specific to drinking-water, and these methods can be used to develop
comprehensive exposure assessment protocols (UNEP/WHO, 1986; WHO,
1992,1993).
Table 25. Origins of chemicals commonly occurring in drinking-water (Hickman et al., 1982)
Substances affecting the source (raw water)
"Naturally occurring" Leached from geological formation (e.g., calcium, heavy metals)
Derived from soil and sediments
Pollutants derived from point sources Domestic sewage treatment (e.g., nitriloacetic acid)
Industrial effluents (e.g., synthetic organics, metals, cyanide)
Landfill waste disposal (e.g., metals, synthetic organics)
Pollutants derived from non-point sources Agricultural run-off (e.g., fertilizers, pesticides)
Urban run-off (e.g., salt, PAHs)
Atmospheric fall-out (e.g., PAHs, chlorinated organics, heavy metals)
Substances resulting from treatment
Substances formed during disinfection Trihalomethanes, chlorophenols
Treatment chemicals Chloramines, fluorides
Treatment chemical impurities Acrylamide monomer, carbon tetrachloride
Substances arising from the distribution
and service systems
Contaminants arising from contact with Lead, vinyl chloride monomer and asbestos fibres from piping,
construction material and protective coatings cadmium from fittings, PAHs from coal tar linings
Substances arising from point-of-use devices Sodium, silver
In developing countries it is quite common for individuals not to
have access to treated water from distribution systems, so analysing
water quality solely from distribution systems may not provide a true
reflection of exposure. Even if drinking-water is obtained from piped
supplies, it may not provide an adequate indication of exposure as
many individuals are forced to store water after collection, when
gross contamination may occur. In some areas of the world, run-off
water is routinely collected from roofs for drinking and cooking
needs. Dustfall attributable to traffic, industry, or construction may
contribute to variable (potentially high) pollutant concentrations in
this source.
Exposure to contaminants in water is not limited to oral routes.
For instance, disinfection by-products and radon gas dissolved in
groundwater may be released into an indoor atmosphere providing an
inhalation route. Heating water also releases dissolved VOCs. Exposure
to contaminants may also occur through inhalation of aerosols from
irrigation sprays. During other water-based activities (e.g.,
swimming, showering and bathing), other contaminants may be absorbed
via a dermal (percutaneous) route. Although the contribution of
non-oral routes is usually much less than that of oral routes, these
pathways should not be overlooked in the selection of measurement
methods to assess exposure. Methods for modelling exposure through
these pathways are discussed in Chapter 6.
7.3.1 Factors influencing water quality
In order to select appropriate measurement and monitoring
methods, it is important to understand the following factors that
influence the quality of the water being sampled, and the resultant
exposure:
* treatment systems
* distribution networks
* storage practices
* spatial and temporal variations
* climatic and seasonal changes.
Water treatment encompasses a variety of processes, ranging from
simple screening and filtration to multi-step purification. The latter
includes methods for coagulation, aeration, de-aeration, colour
removal, softening, disinfection, fluoridation, stabilization and
demineralization. Some of these steps constitute "removal", and others
involve the "addition" of treatment chemicals to mitigate the hazards
of contaminants in water. A list of chemical additives typically used
in water treatment systems is shown in Table 26. The reaction of
treatment chemicals with other substances present in raw (untreated)
water often results in the generation of intermediate reaction
products with adverse health significance. For instance, chlorine,
accepted worldwide for disinfection and oxidation, results in the
formation of disinfection by-products such as trihalomethanes (e.g.,
chloroform).
Table 26. Water treatment chemicals
Activated alumina Sodium bicarbonate
Aluminum sulfate Sodium calcium magnesium polyphosphate
Ammonia (glassy)
Ammonium hydroxide Sodium carbonate
Bentonite clay Sodium chlorite
Calcium hydroxide Sodium fluoride
Calcium hypochlorite Sodium hydroxide
Calcium oxide Sodium metabisulfite
Carbon (activated, granular, Sodium polyphosphate (glassy)
and powder) Sodium silicate
Carbon dioxide Sodium siliconfluoride
Chlorine Sodium tripolyphosphate
Ferric chloride Sodium zinc polyphosphate (glassy)
Ferric sulfate Sodium zinc potassium polyphosphate
Ferrous sulfate (glassy)
Fluosilicic acid Sulfur dioxide
Potassium permanganate Sulfuric acid
Sodium aluminate Tetrasodium pyrophosphate
Distribution networks constitute another potential source of
chemical contaminants in drinking-water. The materials used in
distribution networks may serve as a pollutant source by leaching into
the water over time. Some examples include lead from lead-containing
solders and pipes, asbestos fibres from the surface of asbestos-cement
pipes and cadmium from metallic fittings. Other contaminants include
PAHs from coal-tar-based sealants, plasticizers, stabilizers and
solvents used in the manufacture of plastic pipes.
Water sources experience considerable variations in quality over
time and geographic location. The quality of river water may change
rapidly during heavy storms, melting snows and droughts. The quality
of water in lakes may be affected by climate, season, location or some
combination thereof. Groundwater historically has enjoyed the most
consistent quality, with relatively constant composition. However, the
vulnerability of groundwater to contamination is gaining widespread
attention, with particular emphasis on synthetic organic substances,
surface impoundments, landfills, agriculture, leaks and spills, land
disposal of wastewater, septic tanks and the petroleum/mining
production industries.
7.3.2 Water quality monitoring strategies
There are numerous considerations in the design of a monitoring
and measurement strategy for water quality assessment. The
International Organization for Standardization (ISO) has provided
guidance on a number of issues related to sampling strategies for
water quality assessment (Table 27). A sound monitoring methodology
must be followed by the appropriate sample storage and transportation,
to minimize changes in sample composition. Losses can occur due to
several physical, chemical and biological changes, such as ion
exchange, adsorption with the container material, oxidation to
precipitated forms, loss of volatiles to the vapour space and
biochemical conversions. For contaminants at low source
concentrations, these changes can introduce significant errors in the
analytical results.
Table 27. ISO standards of water quality giving guidance on sampling
ISO standard Title (water quality)
number
5667-1: 1980 Sampling - Part 1: Guidance on the design of sampling
programmes
5667-2: 1982 Sampling - Part 2: Guidance on sampling techniques
5667-3: 1985 Sampling - Part 3: Guidance on the preservation and
handling of samples
5667-4: 1987 Sampling - Part 4: Guidance on sampling from lakes,
natural and man-made
5667-5: 1985 Sampling - Part 5: Guidance on sampling of
drinking-water and water used for food and beverage
processing
5667-6: 1985 Sampling - Part 6: Guidance on sampling of rivers and
streams
The design of a water monitoring programme would be incomplete
without consideration of the demographic and socioeconomic
characteristics, and also an understanding of the historical
development, of the potentially exposed community. The evolution of
materials used in distribution systems changes the profiles of
pollutants requiring measurement. Cultural and socio-economic factors
affect usage patterns, which in turn influence the extent of exposure
to contaminants in drinking-water.
In order to ensure the representativeness and validity of water
samples, sampling techniques must be carefully selected (WHO, 1992,
1993). The first step in the design of a sampling programme is to
develop concise objectives, accounting for
* the nature of the substance to be measured
* point of exposure
* the duration of time over which measurements will be taken.
The type and magnitude of spatial and temporal variations in the
concentration of water constituents will depend upon both their
sources and their behaviour in the distribution and service systems.
Substances can be classified into two main types:
* Type 1. Substances whose concentration is unlikely to vary during
distribution. The concentration of these substances in the
distribution system is largely governed by the concentration in the
water going into the supply, and the substances do not undergo any
reaction in the distribution system. Examples of such substances
are arsenic, chloride, fluoride, hardness, pesticides, sodium and
total dissolved solids.
* Type 2. Substances whose concentrations may vary during
distribution. These include
- substances whose concentration during distribution is dependent
mainly on the concentration in the water going into the supply,
but which may participate in reactions (which change the
concentration) within the distribution system. Examples are
aluminium, chloroform, iron, manganese and hydrogen ion (pH).
- substances for which the distribution system provides the main
source, such as benzo [a]pyrene, copper, lead and zinc.
This classification applies only to piped water supplies. In all
other types of supply, water constituents should be regarded as type 1
substances. The same substance may belong to different classes in
different distribution systems.
7.3.3 Sample collection
The location, frequency and time of sampling is strongly
dependent on the spatial and temporal variations for the particular
pollutant of interest. There are many different methods to collect
water samples and measure contaminant concentrations. The choice of a
particular technique can have a profound effect on the analytical
results. Some conventional measurement methods are briefly described
below:
* Grab samples represent a "snapshot" of a situation at a
particular time and place. Using samples taken at intervals and
analysed individually, this method can characterize variations in
source composition.
* First-draw (static) samples are collections immediately following
a stagnation period (e.g., overnight). This reflects the influence
of domestic plumbing on the inorganic content of water quality.
* Flushed samples are taken after taps have been run for a
sufficient length of time to eliminate stagnant water.
* Composite samples involve regular sampling, usually over a 24-h
period, followed by pooling of samples and analysis of the
composite. This integrated method overcomes the disadvantages
inherent in first-draw sampling. Time-composite samples approximate
the potential exposure to drinking-water contaminants.
7.4 Assessing exposures through food
Exposure to chemical compounds in food can be measured directly
by analysing duplicate diets or indirectly by analysing foods or total
diets, matching food consumption data with information of chemical
concentration in the foods or, for certain chemicals, estimating the
total amount of the chemical available divided by the population of
concern (called per capita estimates). The consumption of water and
the resulting exposure should also be determined if appropriate
(FAO/WHO, 1997). The estimation of exposure to food chemicals is a
complex activity and no single approach is suited to all
circumstances. The method chosen depends on the information available,
the population group of concern, whether acute or chronic effects of
the chemical are being assessed, and the intended use of the result
(Rees & Tennant, 1993). The Intake Assessment Group which has been
added to the Joint FAO/WHO Expert Committee on Food Additives is also
examining other means of evaluating dietary exposure assessments for
food additives and contaminants.
Direct approaches tend to consider samples of food as actually
consumed because the method by which food is prepared for consumption
(e.g., washing, peeling, cooking and commercial processing) can
influence contaminant residue levels. For example, malathion
concentrations were found to decrease by 99% when raw tomatoes were
processed into canned tomatoes (Elkins, 1989). In contrast,
concentrations of ethylenethiourea, a carcinogenic degradation product
of maneb (manganese ethylene bisdithiocarbamate), rose 94% when turnip
greens were washed, blanched, frozen and subsequently sautéed (Elkins,
1989; Houeto et al., 1995). Although cooking may lead to a reduction
in the lead content of vegetables, in areas where the lead
concentrations in drinking-water are higher than average (e.g., due to
lead pipes), cooking water can be a significant source of lead intake
(UK MAFF, 1989).Therefore, preparation and processing can alter
contaminant levels present in foods, or introduce new contaminants.
For these reasons, the concentration of the target analyte in
ready-to-eat foods is the most useful measure for purposes of
dietary exposure assessment.
7.4.1 Duplicate diet surveys
Duplicate diet surveys are particularly useful because they
reflect the range of preparation habits of the study population. These
studies require that respondents save a serving of each meal or
components of each meal and store them until collection by the
research team. Following collection, the food is composited over
predetermined time intervals (e.g., by meal or by day) and analysed
for the target analytes. In duplicate diet studies, logistic and cost
constraints typically require that foods be composited. The principal
disadvantage of composite samples is that they do not allow for
identification of the contribution of individual foods to total
dietary exposure. A high degree of respondent burden is associated
with duplicate diet studies, so they are not conducive to assessing
chronic dietary exposures and may underestimate intake. Such
approaches are only suitable for chemicals that can be analysed
accurately, so direct diet methods are not traditionally used for
assessing food additives exposure, for example. A summary of dietary
exposure assessments for chemical contaminants in food using the
duplicate diet performed worldwide may be found in Thomas et al.
(1997).
There are many indirect methods for estimating exposure to food
chemicals because there are a variety of ways to collect consumption
data, to express residue levels in the foods concerned (for example,
legislative levels, manufacturer or industry use levels, predicted,
proposed or analysed levels or any combination of these) and there are
several approaches which can be used to combine the information to
assess exposure (Rees & Tennant, 1994). Some methods are better than
others, depending on the chemical; for example several countries have
found it useful to assess food additive exposure by using per
capita methods (Ito, 1993). More information on these indirect
methods is given below, but the reader is strongly advised to refer to
more comprehensive documents on dietary survey methodology and dietary
exposure assessment approaches (WHO, 1985a, 1997c; FAO/WHO, 1995a,b,
1996, 1997).
7.4.2 Market basket or total diet surveys
Market basket or total diet surveys utilize food chemical
concentrations measured in ready-to-eat foods prepared in the
laboratory linked to model diets derived from food consumption data
and standard recipe preparation for large populations, households or
individuals. Food products or food groups selected for sampling and
analysis are generally intended to be representative of those most
commonly consumed by the population of interest. Total diet studies
have been carried out since the 1960s in many countries. Market basket
surveys are often employed by regulatory agencies charged with
ensuring and monitoring the safety of a national food supply (FAO/WHO,
1995b). Initially this purpose was to estimate background exposures of
the population to pesticides residues and radioactive contaminants.
The emphasis has shifted from pesticides to toxic metals and more
recently has included a variety of trace elements and organic
contaminants.
For example, the US FDA Total Diet Study (US TDS) is a market
basket survey based on heavy metal and pesticide data measured in
samples of 234 different ready-to-eat food products selected to be
representative of over 4000 foods common in the diet of residents in
the USA, and the results of national food consumption surveys
(Pennington, 1992). However, more commonly total diet (market basket)
studies consider smaller food groups rather than individual foods (UK
MAFF, 1985). The main advantage of the total diet (market basket)
approach for estimating exposure is the ability to monitor trends
without burdening study participants. The total diet approach allows
data from separate studies of food consumption and contaminant
residues to be combined (e.g., Tomerlin et al., 1996). Moreover, this
approach allows analytical chemistry resources to be directed to the
foods that are most likely to yield the greatest exposure (e.g., the
foods consumed in greatest amounts and foods that are likely to
contain the highest residue concentrations). Such foods may be
indicated by information available from existing data such as the
GEMS/Food (WHO, 1978, 1997c) and the US TDS (Pennington & Gunderson,
1987).
However, this method cannot be used for all contaminants. This is
because the analysis of food groups may be too expensive for some
contaminants and may not be feasible for others. Analytical methods
may not be sufficiently reliable, the limit of detection may be too
high or the grouping of the foods (compositing) may decrease the
likelihood of finding the source of the contaminant. Analysis of
individual food products affords a detailed examination of contaminant
levels in specific commodities -- either raw, processed or prepared.
Sampling may be designed to characterize geographic and temporal
variability of contaminant levels that may be a result of varying
application rates of pesticides, natural levels of elements (e.g.,
heavy metals), climate and other factors. In addition, samples can be
collected at all steps in the process from field to consumer thereby
providing insight into the sources and fate of contaminants in food.
Further information on the strengthens and limitations of each of
the approaches described above have been published in the
comprehensive Guidelines for the Study of Dietary Intake of
Chemical Contaminants (WHO, 1985a).
7.4.3 Food consumption
The FAO/WHO Consultation on Food Consumption and Exposure
Assessment of Chemicals (called Exposure Consultation) reviewed
current methodology for food additives, contaminants, pesticides,
veterinary drugs and nutrients. The Exposure Consultation agreed to
expand and revise the regional diets presently used by the GEMS/Food
for pesticides and recommended that this consumption data can be used
for estimate dietary exposure to certain other chemicals. The regional
diets will be based on 1990-1994 FAO Food Balance Sheets which reflect
a country's amount of raw commodities for consumption, and may not
necessarily refer to foods in the forms people consume them. Waste at
the household or individual level is not usually considered.
Major methods for determining food consumption at the national
levels were identified as population-based, household-based and
individual-based. The report from a FAO/WHO consultation on the
preparation and use of food-based guidelines (FAO/WHO, 1996) gives
more information on food consumption study designs. The Exposure
Consultation supported the concept that an improvement in dietary
exposure assessments can be achieved by refining any combination of
the contributing elements: food consumption data, food chemical
concentration data or the method used to combine the two. This allows
the risk assessor a greater flexibility in selecting cost-effective
approaches to refine dietary exposure assessments using the resources
available (WHO, 1997c).
The five basic approaches discussed by the Exposure consultation
for describing the diet of individual people are:
* food record/diary survey
* 24-h recall
* food frequency questionnaire
* meal-based diet history
* food habit questionnaire (WHO, 1997c).
The 24-h recall is a widely used dietary assessment method and is
utilized in many exposure-related studies including the National
Health and Nutrition Examination Survey conducted by the US Centers
for Disease Control and Prevention (Witschi, 1990).
7.4.3.1 Food diaries
Food diaries are detailed descriptions of types and amounts of
foods and beverages consumed, meal by meal, over a prescribed period,
usually 3-7 days. Food diaries and recalls may be presented in
numerous formats or combined with food models and weighing procedures
to characterize serving size more accurately; however, regardless of
the specific details, dietary recording places a substantial burden on
the subject (Witschi, 1990).
7.4.3.2 24-h recall
The short-term nature of the 24-h recall and the facility to
consider meal occasions or daily consumption from diary surveys make
this method ideal for assessing exposure to substances that can give
rise to acute health effects, such as the cholinesterase-inhibiting
organophosphate and carbamate pesticides. Diary methods may be used
for assessment of long-term exposure but the underlying assumption is
that the food consumption is representative of usual habits.
Probabilistic approaches can be useful to predict consumption and
resulting exposure over longer periods of time.
7.4.3.3 Food frequency questionnaires
Food frequency questionnaires (FFQs) are a standard tool for
characterizing food intake over extended periods of time. A food
frequency questionnaire consists of two basic components: a list of
foods and a frequency response section for respondents to indicate how
often a specific serving size of each food is consumed (Table 28). The
underlying principle of the food frequency approach is that average
long-term diet, for example, intake over weeks, months or years, is
important rather than intake on a few specific days. This may not be
true for all contaminant-health effect combinations (e.g., acute and
reversible effects such as cholinesterase inhibition); however, it is
reasonable in the context of assessing health effects that may be
caused by cumulative exposure, such as cancer, or reproductive and
developmental effects that may follow a threshold dose-response curve.
Some FFQs include questions on usual food preparation methods,
trimming of meats, use of dietary supplements and identification of
the most common type or brand consumed. FFQs can be used to rank
individuals by exposure to selected chemicals. Although FFQs are not
designed to measure absolute exposure, the method may be more accurate
than other methods for estimating average exposure to chemicals having
large day-to-day variability and for which there are relatively few
food sources. FFQs have several disadvantages too: specifically, they
are less reliable in estimating consumption of rarely consumed foods
and the food lists are often designed to assess nutrients and may
require substantial revision to assess chemical exposures.
7.4.3.4 Meal-based diet history
Meal-based diet history methods are designed to assess usual
individual food consumption. It consists of a detailed listing of the
types of foods and beverages commonly consumed at each meal over a
defined time period which is often a "typical week".
7.4.3.5 Food habit questionnaires
Food habit questionnaires are designed to collect either general
or specific types of information, such as food perception and belief,
food likes and dislikes, methods of preparing foods, use of dietary
supplements and social setting surrounding eating occasions. This type
of information is frequently considered with other methods but may be
used on its own.
Although the last two methods are seldom used in dietary exposure
assessments they can contribute very useful background information and
may be the only information for specific population group issues
(e.g., organic food consumption by vegetarians). They can be targeted
to answer specific questions or prioritize issues of concern and
provide a cost-effective tool for the risk assessor.
Table 28. An example of food listing and frequency response options of an FFQ
For each food listed, fill in the Average use of the last 3 months
circle indicating how often, on
average, you have used the amount Per month Per week Per day
specified, during the past 3 months
Never or 1-3 1 2-4 5-6 1 2-3 4-5 6+
less than
once
DAIRY FOODS Skim or low-fat milk
(8 oz glass) 0 0 0 0 0 0 0 0 0
Whole milk (8 oz glass) 0 0 0 0 0 0 0 0 0
Sherbet or ice milk
(1/2 cup) 0 0 0 0 0 0 0 0 0
Ice cream (1/2 cup) 0 0 0 0 0 0 0 0 0
Food consumption data is often collected for nutritional or
economic purposes, and foods may not be described in the detail
required for exposure assessment (e.g., fish consumption may be
recorded but the contaminant of interest may be found primarily in
fatty fish or fish caught in a particular location). There are number
of difficulties using the different types of consumption data. A
report from the European Commission provides a good summary of the
practical problems in using consumption data to estimate dietary
exposure (EC, 1997a).
7.4.4 Contaminants in food
The vast majority of food that is actually consumed has undergone
some form of processing, ranging from simple washing to complete
reconstitution, as it progresses from the producer to ultimately being
ingested by a consumer (FAO/WHO, 1995b). Several factors can influence
contaminant concentrations in foods that are ready to eat. These
factors include those that may vary by season and/or geographic
region, such as food source (e.g., homegrown, locally grown by a small
producer, domestically grown by a mass producer and imported), and
former or current application of pesticides (US NRC, 1993). The form
in which food is consumed (e.g., raw apple, apple sauce, apple juice)
can be very different in different subpopulations (e.g., adults,
elderly or young children).
Residue levels measured in raw agricultural commodities collected
at the producer, processor or distribution level are unlikely to be an
accurate reflection of contaminant concentration in food as actually
consumed. With the exception of the GEMS/Food, which collects
contaminant and pesticide residue data from member countries, there
are no centrally coordinated reference databases for other food
chemicals in foods. Potential data sources at the national level may
include supervised trial data, government monitoring and surveillance
data (Pennington & Gunderson, 1987), national food composition
databases (nutrients) and industry funded surveys. A number of
analytical methods for contaminants in food have been published by the
US FDA, EOAC and US EPA (e.g., US FDA 1997a,b). Different approaches
have been used for calculating exposure when the contaminant
concentrations fall below the detection limit (e.g., assuming the
concentration is zero or some percent of the detection limit).
7.5 Summary
This chapter has introduced available sampling methodology for
chemicals in air, water, and food. Common to the selection of these
methods are considerations of detection limits, interferences, ease of
operation and cost. Personal, microenvironmental and ambient air
sampling methods are available for monitoring gases and vapours, both
passively and actively, aerosols, SVOCs and reactive gases.
Sampling considerations for assessing water quality are numerous.
An important consideration is that exposure to contaminants is not
limited to oral routes and that not all individuals have access to
treated water from distribution systems. Guidance for sampling and
monitoring programmes is provided.
There are a number of methods for measuring estimating food
consumption and contamination. The method chosen will depend on the
information available, the population group of concern, whether acute
or chronic effects of the chemical are being expressed, the intended
use of the results and available resources. The reader is strongly
advised to refer to more comprehensive documents on dietary survey
methodology and dietary exposure assessment approaches.
8. MEASURING HUMAN EXPOSURE TO CHEMICAL CONTAMINANTS IN SOIL AND
SETTLED DUST
8.1 Introduction
This chapter is intended to provide the reader with important
concepts and a basic understanding of soil and settled dust sampling
so that effective sampling strategies can be designed to meet specific
research needs. Choices in sampling methods, sampling locations,
sampling areas and the sampling time of the sample collection may be
particularly important when the results are used for exposure
assessment purposes. For these methods to be used successfully, it is
important that the investigators understand the basic concepts behind
collecting soil and settled dust and the limitations of different
methods and strategies. Because this field of research is currently
evolving rapidly, it is recommended that researchers consult the
literature for new and complete information before designing a study
to measure toxic metals, pesticides, PAHs, other products of
incomplete combustion, fibres and biological matter. The most
appropriate method for sampling soil and settled dust depends on the
living conditions of the study population and the target contaminants.
The information in this chapter is therefore intended to provide
general guidance on approaches that might be taken.
Soil is a mixture of air, water, mineral and organic components
(Horne, 1978). The relative mix of these components determines to a
large extent the capacity of a soil for containing chemical
contaminants and the potential for it to be an important source of
exposure. Settled dust, which may be found outdoors or indoors, is
often a complex mixture of material from several sources. Outdoor
settled dust is material deposited on roadways, streets and other
paved surfaces. Indoor settled dust (house dust) is material deposited
on indoor surfaces such as floors, carpets and furniture. Chemical
contaminants present in indoor dust can originate from activities in
the home or can be tracked into the home from road dust, soil or work
sites (US EPA, 1991). Material present in soil, outdoor dust and
indoor dust may include clay, sand, bacteria, viruses, allergens,
products of incomplete combustion, environmental tobacco smoke, heavy
metals, pesticides, asbestos fibres, paint fragments, solvents, flame
retardants, cleaners, and residues from synthetic fibres, building
products and many other materials and pollutants (Robert & Dickey,
1995).
Unintentional ingestion of house dust, particularly for children,
may be a significant contributor to the total human exposure to many
potentially toxic substances, depending on personal living conditions
and frequency of contact with this media. Because children spend more
time in contact with soil and indoor surfaces than adults and have a
greater dose given the same exposure, these exposure pathways are
particularly relevant to children. For example, it is likely that
children's lead exposure from settled dust is an important
contribution to total lead exposure because of the past and present
use of gasoline, lead-based paint on housing and steel structures, and
airborne emissions from industrial point sources that settle in
residential environments. In the USA, house dust is considered a major
source of lead to most children (CDC, 1991; Lanphear & Roghmann,
1997). Older homes are especially susceptible to lead dust exposure if
paint is peeling or renovations are being done (Roberts et al., 1992).
Soil and settled dust can be a significant source of exposure to
numerous other toxicants in addition to lead, including pesticides and
PAHs. Pesticides, although designed to degrade to different extents
through natural environmental processes such as sun, rain and soil
microbial activity, may accumulate in soil and dust and persist for
long periods of time. Because of the lack of these external
degradation processes, pesticides may be particularly persistent in
indoor settled dust (Simcox et al., 1995). Studies have shown that in
the general population in the USA the highest concentrations and
largest number of pesticides are found in house dust as compared to
soil, air and food (Whitmore et al., 1993; Lewis et al., 1994).
Although many pesticides in house dust come from outdoor sources, many
households use pesticides indoors. Because little or no training is
provided for users of household pesticides, unnecessary exposures
often occur. Pesticides often found in house dust include those used
for control of insects; e.g., chlordane and heptachlor in homes
treated for termites, pentachlorophenol and lindane in homes where
wood preservatives had been used, and other harmful pesticides
contained in flea and garden treatment (Roberts et al., 1992).
Hazardous substances that originate at the worksite may also find
their way (e.g., via clothes) into the homes of workers. The US
National Institute for Occupational Safety and Health compiled a
bibliography of more than 350 published and unpublished accounts of
take-home, or "para-occupational" contamination worldwide (NIOSH,
1994). The reports identified by NIOSH document the spread from
workplace to home of toxic metals (lead, beryllium, cadmium and
mercury), asbestos, and various other potentially hazardous
substances. Settled dust was a major source of familial exposure in
most of these studies.
8.2 Selected sampling methods
8.2.1 Soil
Soil constitutes a potential exposure pathway through direct
contact and ingestion or inhalation of resuspended soil particles.
Children's activities make them more likely to be affected by such
exposures. In addition, contaminated soil can be tracked inside homes,
or may infiltrate indoors when resuspended. In either case, soil may
become a component of settled indoor dust. There are no standard
collection methods for soil sampling, as discussed later for settled
dust (section 8.2.2). This limitation affects the ability to make
comparisons of results from soil sampling across studies. However,
information on soil contamination can provide insights into the
relative importance of multimedia contaminants as they may affect
exposure.
8.2.1.1 Surface soil collection
The most commonly used approaches make use of an auger or similar
sampler such that a sample is defined by cross-sectional area and
predesigned depth of the auger. Alternatively, a predetermined amount
of surface soil may be scooped with a small trowel, with less precise
definition of sampler depth. In either case, the sample is stored in a
clean, inert container and transferred to the laboratory for analysis.
8.2.1.2 Soil contact and intake measurements
Skin contact has been measured by methods similar to those used
for settled dust (e.g., self-adhesive labels, hand wipes), and
controlled application followed by recovery of the fraction of
deposited soil on the skin (Lepow et al., 1975; Roels et al., 1980;
Que Hee et al., 1985). The amount of soil that adheres to the skin
depends on a number of variables including soil properties (e.g.,
water content, particle size, carbon content), region of the body and
activity (Kissel et al., 1996). A number of studies have attempted to
estimate soil ingestion based on hand adherence estimates and
scenarios of activities, as well as analyses of soil tracers (e.g.,
concentrations of aluminium, silicon or titanium) (e.g., Calabrese et
al., 1989, 1990).
8.2.2 Settled dust
Although indoor dust is becoming recognized as a reservoir for
many toxic substances and a potentially significant source of human
exposure, there is no uniform standard for sampling settled dust. More
than 15 methods have been described in the literature to date.
Scientists do not yet agree either on the definition of settled dust
or on the methods to measure it. This issue is further complicated by
the fact that results from one settled dust sampling method may not be
directly comparable to results from others. Even with these
limitations, settled dust sampling methods have been used effectively
and provided valuable insights into the total human exposure paradigm.
Selected sampling methods are described below to give the reader
an indication of the diversity of techniques available. The list is by
no means exhaustive. Several of the methods described are simple to
use and readily available to researchers worldwide. Brief descriptions
of how to use the simpler methods are provided. Other methods require
specialized equipment that is relatively expensive and may be
difficult to obtain in some regions of the world. The methods are
distinct from one another, but most fall into three categories: wipe,
vacuum sampling and sedimentation methods. These methods are widely
used for sampling settled dust indoors; however, in principle they may
be applicable for outdoor settled dust as well. Bulk sample collection
methods, such as sweeping, are not covered here. Key features of the
various methods for collecting settled dust samples described in this
chapter are summarized in Table 29.
Table 29. Comparison of features of different methods for collected settled dust samples
Feature Common HUD Preweighed Commercial DVM Rotary HVS3 Sirchee Sedimentation
wipe wipe sample vacuum vacuum vacuum vacuum -
Spittler
vacuum
Widely available Yes Yes Yes Yes No No No No Yes
Cost Low Low Low Medium High High High Medium Medium
Simple method Yes Yes Yes Yes Yes Yes No No Yes
Loading Yes Yes Yes No Yes Yes Yes Yes Yes
Concentration No No Yes Yes Yes Yes Yes Yes Yes
Sieving possible No No No Yes No No Yes Yes Yes
Portable Yes Yes Yes No Yes Yes No Yes Yes
Samples small areas Yes Yes Yes Yes Yes Yes No Yes Yes
AC powered No No No Yes Yes Yes Yes Yes No
Size selective No No No No Yes No Yes No No
8.2.2.1 Wipe sampling methods
A common wipe sampling method uses premoistened towelettes to
wipe a measured area defined inside a template placed on the sampling
surface (Vostal et al., 1974; US HUD, 1995). Typical sampling areas
are in the range of 0.1 m2 and masking tape is commonly used as a
template. The actual surface area inside the template is not critical
as long as it is measured and recorded. However, sampling areas
greater than 0.2 m2 are not recommended because larger areas cannot
be wiped effectively with one towelette. This method has been used
extensively in the USA to measure lead amounts in settled dust, but
has also been used to ascertain levels of cadmium, chromium and
arsenic, as well as many other metals and organic compounds.
With the HUD method, the person collecting the sample should
wear a clean disposable glove on the hand that will come in contact
with the towelette. To collect a sample, the surface inside the
template is wiped with a towelette back and forth in vertical
S-strokes. The exposed side of the towelette is then folded inward,
exposing a clean portion, and the same area is wiped with horizontal
S-strokes. The towelette is folded once more, again exposing a clean
portion, and the area is wiped a final time with additional vertical
S-strokes. The towelette is then folded, exposed side in, placed into
a clean sealable plastic bag or container, and sent to a laboratory
for analysis.
Several researchers have used preweighed wipe material, such as
cotton gauze or filter paper, in order to determine the quantity of
settled dust collected (Lepow et al., 1974; Stark et al., 1982;
Rabinowitz et al., 1985; Levallois et al., 1991). The sampling
material is then reweighed in a laboratory after sample collection.
Theoretically, the weight of total dust collected can be calculated by
subtraction, and toxicant concentration could be determined after
analysis on a mass basis.
An important issue that needs to be addressed when using the
preweighed wipe methods is the potential loss of sampling material or
dust during handling in the field or laboratory. Furthermore,
Chavalitnitikul & Levin (1984) noted that filter paper tends to fall
apart when rough surfaces are wiped. Loss of sampling material in the
field would underestimate the amount of total dust collected when
final weights are obtained, which would in turn overestimate the
calculated mass concentration results. Because of water loss or gain,
changes in humidity may also significantly affect the before and after
weights of the samples. These potential sources of error must be
carefully controlled to make the results from preweighed wipe methods
reliable.
A specially designed preweighed wipe sampling method has been
developed to minimize the potential sources of error mentioned above.
This method, known as the Lioy-Weisel-Wainman (LWW) method, was
developed to quantitatively measure the toxicant concentration (mg/g)
and surface loading (mg/m2) of dust on flat surfaces (Lioy et al.,
1993). The sampling device is not made from common materials and is at
this time only available from the research group that developed it.
8.2.2.2 Vacuum methods
Many researchers have collected samples from commercial household
vacuum cleaners, which are often referred to in the refereed
literature as research dust samplers. Some researchers state that they
sampled only the fine dust that settled to the bottom of the bag.
(Kaye et al., 1987; Moffat, 1989; Davies et al., 1990; Thornton et
al., 1990; Jensen, 1992). Other researchers modified their vacuum
cleaners to hold filters (Diemel et al., 1981; Watt et al., 1983).
A settled dust vacuum method, commonly called the DVM, is
constructed from conventional industrial hygiene sampling materials
that are likely to be available to researchers worldwide (Que Hee et
al., 1985). The sampler consists of a common personal air-monitoring
pump, usually operated at 2.5-3.0 litres/min. Sampling areas with this
method are typically 25 cm × 25 cm, and often take more than 5 min to
sample completely. A three-sided template is sometimes used on bare
floors to vacuum dust that has migrated to the walls. Sampling areas
are covered three times with overlapping passes in the horizontal and
vertical directions. Que Hee et al. (1985) state that the sampler was
designed to collect only small dust particles that would most likely
stick to a child's hands, not total lead on a surface. Therefore, the
amount of dust collected by this method from a given surface is
usually less than collected by other methods. This sampler has been
used in numerous studies in the USA and elsewhere, and its use has
provided considerable information linking lead in settled dust to lead
in children (e.g., Bornschein et al., 1985).
Researchers have also used laboratory rotary vane vacuum pumps
connected to the same three-piece filter cassettes as used with the
DVM described above, but with a much higher flow rate. The filter
cassette is often used openface or with a wide diameter nozzle so
sampling areas can be covered in fewer passes than required for the
DVM, thus reducing the time spent collecting samples (Solomon &
Hartford, 1976).
Prpic-Majic et al. (1992) described another vacuum pump sampling
method that used a prescreen at its nozzle entrance to prevent coarse
particles and small objects from being collected on the membrane
filter that served as the sampling surface. Total dust measurement was
obtained from the dust particles that reached the membrane filter.
There was no mention of potential loss of fine dust trapped in the
prescreen, especially after it was loaded with fibres and debris.
A sophisticated vacuum sampling device, called the HVS3, was
designed to make dust collection efficiency from different surface
types as consistent as possible (ASTM, 1993). The HVS3 is a
high-powered vacuum cleaner equipped with a nozzle that can be
adjusted to a specific static pressure and air flow rate to allow for
consistent dust collection. The sampler uses a cyclone to separate
particles greater than about 5 mm from the air stream and collects
them in a 250 ml sample bottle screwed into the bottom of the cyclone.
Smaller particles are not collected. The HVS3 can collect large,
representative samples of settled dust from indoor surfaces, such as
rugs and bare floors, and dust from outdoor surfaces, such as streets,
sidewalks, lawns and bare, packed dirt. However, it cannot be used to
sample from small or uneven areas because of the large size of the
device. The HVS3 has been used in numerous exposure assessment studies
to measure toxic metals and pesticides in settled dust. The sampler is
not made from standard materials and is therefore relatively expensive
to buy. Interested readers should consult the ASTM standard method
(D5438-93) for more information on the specifications and availability
of the HVS3 sampling device (ASTM, 1993).
Farfel et al. (1994) modified the HVS3 by using the same cyclone
as in the HVS3 but with a commercially available handheld vacuum to
make the device smaller and more portable. These authors also used
flexible tubing as the pickup nozzle to allow small surfaces, such as
windowsills, to be sampled. This modification, called the BRM
method, does not allow control of either the sampling flow rate or
the static pressure at the pickup nozzle. The ASTM standard method for
the HVS3 does not apply to the BRM, except for its description of the
cyclone.
Another settled dust vacuum sampling method that has been used in
several research studies, the Sirchee-Spittler method, is a
hand-held, battery-powered vacuum unit (Rinehart & Yanagisawa, 1993;
Weitzman et al., 1993; Aschengrau et al., 1994). The sampler is simple
to use, highly portable and can cover large areas in a short period of
time. Unfortunately, there are not many Sirchee-Spittler sampling
devices in service and its availability to researchers worldwide is
therefore limited.
8.2.2.3 Sedimentation methods
Sedimentation methods involve measuring the amount of dust which
settles on a clean, preweighed surface over a given period of time.
Such procedures can make use of a simple collecting cup (Aurand et
al., 1983) or a flat plate (Pellizzari et al., 1995). After a
specified period of time, the sample is collected and measured, and
the dust is then analysed in a laboratory. Data from the German
Environmental Survey (Schulz et al., 1995) on domestic dust
precipitation is given in Table 30. Sedimentation methods are useful
for collecting samples over a specific period of interest (e.g., a
day, week or month). In contrast, the integration times of settled
dust samples collected using the wipe or vacuum methods described
above are not well characterized.
Table 30. Sedimentation of elements in indoor dust, Germany 1990-1992 (Schulz et al., 1995)
Element No. of No. of Percentiles Maximal Confidence
samples values value interval
>LOQ 10 50 95 (GM) (GM)
Dustfalla 3282 - 1.4 21.0 579 4.52 4.36-4.68
Arsenicb 3279 965 < 4 33 1313 5.4 5.2-5.6
Boron 2896 511 < 0.06 0.64 47.1 0.13 0.13-0.14
Cadmiumb 3282 0 5 44 833 11.7 11.4-12.0
Calcium 3277 25 17 273 2679 51.2 49.5-52.9
Chromium 3282 14 0.02 0.28 3.92 0.07 0.06-0.07
Copper 3277 1167 < 0.3 1.5 48.8 0.33 0.32-0.34
Iron 3277 74 2 41 765 7.7 7.4-8.0
Lead 3282 0 0.11 1.17 86.6 0.29 0.28-0.29
Magnesium 3277 26 2 25 342 5.2 5.0-5.3
Phosphorus 3277 1063 < 1.8 17 542 2.8 2.7-2.9
Zinc 3277 15 0.9 8.6 108 2.2 2.1-2.3
Units are µg m-2d-1 unless otherwise indicated.
a mg m-2d-1.
b ng m-2d-1.
8.3 Sampling design considerations
Section 8.2.2 describes numerous innovative methods that have
been developed and used by researchers to collect settled dust from
surfaces. Many more examples can be found in the literature. However,
there has been little standardization among the methods. Differences
in vacuum pump flow rates, nozzle shapes and sizes, and sampling
technique will affect dust-pickup characteristics of vacuum sampling
methods and will, therefore, affect the results. Differences in wipe
sampling material and technique will also affect the results from wipe
samples. Different recovery rates of dust from alternative
sedimentation methods can also have a large effect on analytical
results. These differences among methods, which are not well
documented in the literature, can make interpretations and comparisons
between studies difficult. It is important that sampling methods are
well described when results from settled dust sampling are reported.
Sampling design considerations for soil should follow the
objectives of the study and consider the particular conditions of the
site being monitored. For example, multiple soil samples can be
obtained around the perimeter of a house at a sufficient distance so
that the soil is representative of material that might be tracked into
the home. In this case, the samples might be composited. Backyard soil
might vary in the number and amounts of contaminants present, as well
as usage and specific activities by residents. The number and location
of samples to be obtained should be based on these considerations.
8.3.1 Concentration and loading
Almost all settled dust contains measurable levels of common
environmental contaminants such as heavy metals and pesticides, and
most residential surfaces, such as floors and windowsills, contain
settled dust (CDC, 1991). The actual concentration of a target analyte
in a sample of settled dust depends on the amount of dust collected
that does not contain the analyte and the amount of dust collected
that does contain the analyte.
The analyte concentration, sometimes called a mass
concentration, is usually expressed as micrograms of analyte per
gram of dust (µg/g). The amount of dust on a surface can be expressed
as grams of dust per unit area, such as per square metre, and is
usually called dust loading (g/m2). The analyte concentration,
multiplied by the dust loading on a surface, gives a analyte
loading value and is commonly expressed as micrograms of analyte per
unit area (µg/m2). The dust loading and analyte loading measurements
are both area concentrations, that is, the concentration of dust or
contaminant per unit area. In this report, "concentration" refers to
mass concentration and "loading" refers to area concentration.
The example of residential sampling for lead is used to simplify
the discussion. Common wipe sampling methods, such as the HUD method,
measure lead loading directly, without measuring lead concentration
and dust loading. Fig. 24 illustrates what common wipe samples can
measure, using realistic results collected from floors in a
hypothetical residence. Assume that each diagonal line in the figure
represents the lead loading results from one wipe sample. The diagonal
lead loading lines show the infinite number of lead concentration
( y axis) and dust loading ( x axis) combinations that might result
in the measured lead loading. As mentioned earlier, the product of the
two parameters is the lead loading (µg/g × g/m2 = µg/m2). Using a
log scale on the x and y axes ensures that the infinite number of
combinations that result in the same lead loading value fall on a
straight line. As noted in Chapter 4, the distribution of many
measures of environmental exposure is skewed right and may often be
approximated by a lognormal distribution. For lognormal distributions,
geometric relationships (e.g., factorial) exist among quantiles of the
distribution, in contrast to the linear relationships present in
measures that follow a normal distribution. As described in Chapter 4,
lognormal distributions can be "normalized" in a numerical sense by
expressing the data as the log-transformed values or in a graphical
sense by plotting data on log scales. This example assumes that lead
concentration and dust loading are lognormally distributed and
perfectly correlated with each other, i.e., lead loading in µg
lead/m2 is assumed to be constant. A scatter plot of two perfectly
correlated and lognormally distributed measures depicted on a normal
scale would exhibit a curved relationship, but appears as a straight
line when depicted on a log scale.
Because common wipe sampling measures lead loading directly, but
does not measure lead concentration and dust loading, the results from
wipe sampling cannot be used to determine which combination of lead
concentration and dust loading is present. Similarly, Davies et al.
(1990) states that for a given contaminant loading value, the
contaminant concentration can range from high where there is little
dust to, conversely, low where there is a large volume of dust. The
only way to measure both lead concentration and dust loading is to
collect a house dust sample with one of the vacuum sampling methods,
or with one of the preweighed wipe sampling methods. Common wipe
sampling methods do not measure lead concentration.
Although research studies have shown that estimates of both lead
concentration and lead loading (area concentration) correlate
significantly with children's blood lead levels, it is unclear which
measure is better at predicting the true, long-term, lead dust
exposures to children. Results from Davies et al. (1990) suggest that
the average lead loading (lead area concentration) measured in a
child's environment expressed more realistically the exposure of
children to lead than did lead concentration (lead mass concentration)
measurements. Results from the Lanphear et al. (1995) study also
suggest that lead loading measurements correlate better with
children's blood lead levels than does lead concentration. However,
Bornschein et al. (1985) showed that, for their conditions, lead
concentration and lead loading have very similar correlations with
children's blood lead levels. Laxen et al. (1987) found that blood
lead levels did not correlate better with lead dust loading than with
concentration.
8.3.2 Collection efficiency
Another important concept to understand is that the type of
surface from which the dust is sampled directly affects the efficiency
of dust collection from the surface. Furthermore, different sampling
methods recover different amounts of total dust from the same sampled
surface. These differences are due to different collection
efficiencies of the methods. Differences in collection efficiency on
different surface types and among sampling devices may influence
measurements of toxicant levels in settled dust.
Roberts et al. (1991) documented total dust recoveries that
ranged from greater than 90% by weight on a smooth painted surface to
about 30% on a carpet. Chavalitnitikul & Levin (1984) compared several
types of wipe sampling methods. They conducted a laboratory wipe
sampling experiment with wipe materials on a smooth surface (Formica)
and a rough surface (plywood). The study examined different wipe
materials, such as Whatman filters, paper towels and adhesives --
paper labels, adhesive cloth and dermal adhesive. The researchers
determined that, on smooth surfaces, all techniques were comparable,
with about 85-90% recovery with carefully prescribed protocols. On
plywood, however, recoveries dropped to less than 43%. They also noted
that the Whatman filters fell apart on the rough surface. Other
sampling method characterization studies document similar differences
(US EPA, 1995a,b).
Three commonly cited methods used to sample lead in settled dust
(the DVM, BRM, and HUD methods) may collect very different amounts of
total dust from the same surface (Lanphear et al., 1995). Assuming
that a smooth hard surface is sampled, the difference in collection
efficiency between the DVM and the other two methods may be greater
than a factor of 10, with the DVM consistently collecting less dust
than the BRM and HUD methods. The latter two methods would probably
collect similar amounts of dust on a smooth hard surface.
Since contaminant loading is directly related to total dust
collected from the sampled surface, the DVM sampler will consistently
measure lower contaminant loading values on hard surfaces than the BRM
or HUD methods. This does not imply that a high collection efficiency
is better than a low efficiency. An argument in favour of the DVM's
low collection efficiency is that it measures the more biologically
active fraction of leaded dust available to a child (Que Hee et al.,
1985). However, results from the only study to use all three methods
side by side in children's homes suggest that the BRM and HUD methods
correlate slightly better with children's blood lead levels than the
DVM method (Lanphear et al., 1995). The same study showed that the BRM
collects much more dust from carpeted surfaces than the DVM or HUD
methods. The point to note is that lead loading measurements on the
same surface differ among sampling methods. Further research is needed
to determine the importance of collection efficiency for exposure
assessment studies.
As with contaminant loading, differences in collection efficiency
on different surface types and among sampling methods may affect
measurements of contaminant concentration. Differences in the relative
recovery of contaminant-containing dust and non-contaminant-containing
dust can result in different contaminant concentration measurements.
Theoretically, however, concentration measurements are likely to vary
less among methods than are loading measurements. Results from the
Lanphear study, which collected hundreds of side-by-side lead dust
samples with the DVM and BRM methods, are consistent with this theory.
Geometric mean lead levels and the corresponding standard deviations
suggest that, on average, side-by-side lead loading measurements
differ more between the two sampling methods than do the lead
concentration measurements (Lanphear et al., 1995).
8.4 Sampling strategies
Choosing an appropriate sampling method is an important part of
designing a study to measure toxicants in house dust. However, it is
only part of designing a sampling strategy. The sampling method
specifies how to collect settled dust, whereas the sampling strategy
specifies the process of sampling. Several of the questions that need
to be answered when developing a sampling strategy are:
* What age group is targeted by the study?
* Which surfaces and substrates should be sampled?
* When and how should sampling take place?
* Should a composite sample be created?
* How will the samples be analysed?
As noted in the first section of this chapter, young children who
play on floors are likely to have higher exposure to settled dust than
adults. Children may be also routinely exposed to dust in areas of a
residence that adults do not contact. Different sampling strategies
may be appropriate for different age groups.
The potential effect of the surface type and substrate on dust
collection should be factored into the strategy because dust
collection efficiencies from different surface types can vary greatly.
For example, toxicant loading or concentration measurements may
correlate relatively well with biological measurements when dust is
collected on hard floors or on carpets. However, if the person's
relative exposure to dust from floors versus carpets differs from the
sampling method's relative collection efficiency on these surfaces,
the relationship between biological and settled dust measurements will
be different for each surface. Similar differences between a human's
exposure and a sampling method's collection efficiency may be found
between components within a room, such as between a windowsill and a
floor.
Another issue to note is that the sources of dust, its temporal
and spatial variability, and accessibility to humans, especially to
young children, may vary greatly from person to person, room to room
and house to house. However, little research has been done to examine
this variability across space and time. Interpretations of house dust
sample results may, therefore, be affected by this variation in
addition to the variation introduced by the choice of sampling method.
Short-term changes in a person's environment before sampling, possibly
influenced by sporadic house cleaning practices or by a person who has
just returned home from vacation, may offset the dust/biological
relationships owing to the timing of sample collection.
The toxicant levels in settled dust to which a person is exposed
may be thought of as a weighted average across the areas where the
person has dust contact, with weights roughly proportional to the time
a person spends in different areas. From a sampling perspective, the
average toxicant level to which a person is potentially exposed may be
estimated by collecting many individual samples of settled dust for
separate analysis and combining the results by calculating a weighted
average after analysis. Or, field composite samples can be collected
before laboratory analysis by collecting and physically combining two
or more settled dust samples from each of several areas in a dwelling.
Researchers have used both strategies for collecting dust samples
(Farfel & Rhode, 1995).
A common criticism of composite sampling is that toxicant
variation across a floor or throughout a residence cannot be
determined; toxicant "hot spots" may be missed. It must be
acknowledged, however, that any settled dust sampling strategy may
miss hot spots. The important issue is how much these hot spots
contribute to the total exposure of the average person. This question
has not been answered by scientific studies. In any case, the
statistical relationship between biological toxicant levels and
average toxicant levels in settled dust levels across large areas in
which a person may be exposed are likely to be better than the
relationship between biological levels and a potential high-dose
source of toxicant exposure for a short period of time. Davies et al.
(1990) used this assumption to design a sampling strategy that
collected settled dust "taken over all the exposed floor surface in
the rooms concerned" (thus, the average level was measured in a room)
rather than from small areas in the room, and found a relatively high
statistical relationship with children's blood lead levels
( r = 0.46).
Possibly the best measures of toxicants in settled dust for
exposure assessment purposes are averages of dust measurements taken
repeatedly over time. If one were to repeat sampling over time,
averages across space and time could be obtained. However, most
sampling strategies used in previous studies collected settled dust at
only one point in time. An obvious advantage to cross-sectional (one
time) studies is that they are less expensive than longitudinal
(repeated measures) studies, which require repeated visits to a
dwelling, greater occupant burden, and higher laboratory analysis
costs.
One possible, but untested, approach to strengthening estimates
of time-weighted average dust levels in cross-sectional studies may be
to measure exposure-weighted average levels based on the activity of
the person. This may be done by listing indoor locations where the
person spends time, then roughly estimating the percent of time spent
actively in each location, rounded to a convenient percentage. Samples
can then be composited from the specific areas by adjusting the
subsample areas to be proportional to the percent of time spent in
each area. An exposure-weighted average toxicant dust level could then
be estimated from the result.
Finally, laboratories performing the chemical analysis should be
consulted before settled dust samples are collected. This is
particularly true when collecting composite wipe samples. An excess of
towelette material may present problems during the laboratory
digestion phase of analysis, requiring more reagents and larger
beakers than normally used, and potentially reducing the toxicant
recoveries owing to matrix effects. Similarly, vacuum sampling may
collect more dust than is required for analysis. If this is the case,
techniques need to be employed by the laboratory to ensure that the
fraction of dust analysed represents the whole. Another potential
source of error in the results lies in how the dust is handled after
sampling and prior to analysis. If measurements of lead concentration
in dust are important for the objectives of the study, sampling
methods that present the dust to the laboratory in an easy-to-handle
form should be considered over alternate methods. These issues and
others should be well thought out before the commencement of a settled
dust sampling effort.
8.5 Summary
Human contact with soil and settled dust can be an important
source of exposure to chemical contaminants, especially for children.
Although many sampling methods have been developed, no single approach
has been demonstrated to be superior to the others. As a consequence,
it is difficult to compare results from studies that utilize different
sampling methods. Important factors to consider when selecting a
sampling method include collection efficiency, differences in human
activity patterns, physical variability of soil and dust levels over
space and time, surface and substrate sampled, timing of sample
collection and analytical methods used to measure toxicants in the
laboratory.
9. MEASURING BIOLOGICAL HUMAN EXPOSURE AGENTS IN AIR AND DUST
9.1 Introduction
Microbiological organisms have long played an important role in
human ecology. Fungi are critical to the production of cheese and the
fermentation of beer, and in some cases are a direct source of
nourishment. In the first half of the 20th century, Penicillium
chrysogenum colonies were discovered to inhibit growth of other
organisms. Today pharmaceutical companies, among others, are exploring
fungal enzymes for a variety of reasons including new drugs,
non-chemical pesticides, biodegradation of waste and possible
catalysis of chemical reactions.
However, natural does not mean benign. Human exposures to
microorganisms have resulted in allergic, toxic and infectious
disease. As humans have modified the environment through cultivation,
landscaping and building structures, ecological balances have been
disturbed. The distribution of moisture and nutrients has been altered
to a point where it is quite common to encounter reservoirs of fungi,
bacteria and algae, and infestations of mites and cockroaches.
Through airborne dispersion, ingestion or direct contact, humans
confront components of microorganisms continuously. We may be affected
through an immune reaction requiring sensitization. Predisposed
individuals may not experience a reaction for some time after they
have been exposed. Once an individual is sensitized, a reaction such
as an asthmatic attack might be delayed hours following the exposure
event. However, there are many infectious diseases induced by fungi
and bacteria that require no period of sensitization before illness
develops. There is yet another route whereby microorganisms can evoke
irritation and health effects: some metabolites from moulds are
carcinogenic (e.g., aflatoxin B) or immunosuppressors; some cause
dermatoxic effects; others cause annoyance and irritation by the VOCs
they release.
Table 31 provides basic categories for the microorganisms of
primary interest and some possible sources. Assessing exposures to
microorganisms is very different in some aspects from assessing
exposures to physical or chemical agents. For virtually all
microorganisms, exposure-response or dose-response information is
currently limited. Nevertheless, exposures to allergens, fungal
spores, Legionella, and tuberculosis, among many others, are being
inferred from sampling. And, particularly for assayable antigens and
endotoxin, dose-response data are accumulating rapidly. Observed
increases in tuberculosis and asthma as well as atopy have brought a
resurgence of epidemiology and expanded interest in exposure
assessment.
Table 31. Common bioaerosols, related diseases and typical sources
Bioaerosol Examples of diseases Common sources
Pollens hay fever plants, trees, grasses, ferns harvesting, cutting,
Spores allergic rhinoconjunctivitis shiploading
Plant parts asthma
upper airway irritation
Fungi asthma, allergic diseases plant material, skin, leather, oils; bird, bat and
infection animal droppings; feathers, soil nutrients,
toxicosis glues, wool
tumours
Bacteria endotoxicosis humans, birds and animals (e.g., saliva, blood,
tuberculosis dental secretions, skin, vomit, urine, faeces)
pneumonia, respiratory and wound water sprays and surf, humidifiers, hot tubes,
infections, legionellosis, Q and pools, drinking water, cooling towers
pontiac fever
Other allergen sources asthma mite excreta, insect parts (cockroach, spiders,
Arthropods dermatitis moths, midge)
Vertebrates hypersensitivity dander and saliva from cats, dogs, rabbits,
pneumonitic mice and rats, bird serum, farm animal dander
Virusesa respiratory infections, colds, infected humans, animal excreta,
measles, mumps, hepatitis A, insect vectors, protozoab
influenza, chicken pox, Hanta virus
a Viruses are included in table for completeness but are not covered in this chapter.
b Protozoa in the form of free-living amoebae can be direct acting pathogens or allergens; they can also
interact with bacteria (e.g., Legionella growth within amoebae).
Source: developed from Burge (1995).
This chapter discusses the strategy and methodology for exposure
assessment of five major categories of biological particles:
* house dust mites and their faeces
* allergens from pets and cockroaches
* allergens and/or toxins derived from
- fungi
- bacteria
- pollen
For each category information will be presented regarding
sampling methods, methods of analysis, and advantages and drawbacks of
the different methods. Seasonal variations in mite allergen and fungi
are illustrated by showing the summary results of an extensive survey
conducted in Australia. Mite and pollen antigen as well as fungal
organisms can vary substantially within homes and buildings, as
illustrated in the figures in this chapter. The reader is referred to
texts such as ACGIH (1989) and Burge (1995) for details on
instrumentations, specific information relevant to the allergenic,
infectious and toxigenic properties of many microorganisms and their
constituents and metabolic by-products.
There are three different basic approaches for the exposure
assessment of biological particles: observational sampling, reservoir
sampling (dust, surfaces, water) and air sampling.
* Observational sampling means that one uses sensory perception to
collect data about potential sources of exposure to biological
particles (e.g., visible fungal growth).
* Reservoir sampling refers to the collection of bulk material
(e.g., surface contact, bulk material, water sample or dust sample)
to estimate the potential exposure.
* Air sampling is the most likely to be representative of human
exposure.
This chapter will emphasize reservoir (primarily indoor dust) and
air sampling of bioaerosols and not gaseous metabolic products.
Designing a specific sampling programme requires consideration of
the aim of the sampling, the nature of the biological particles
(including size and expected concentrations) and parameters that
influence the actual exposure to these particles. These parameters
determine the choice of the sampling and quantification method, the
sampling strategy (e.g., location, season, duration and frequency),
and approaches for statistical analysis and interpretation of the
data. For most situations, the exposure route of interest is
inhalation. Therefore, ideally, the exposure should be assessed by
personal air monitoring. As will become clear from the remainder of
this chapter, however, no single sampler fulfils the characteristics
of the ideal sampler to measure the total exposure to biological
particles. Many of the methods used for estimating environmental
concentrations of biological particles are not truly representative of
an individual's exposure to these particles. As stated earlier, this
is, in part, because the exposure measure of biological importance is
not well understood. In addition, the field of environmental
aeromicrobiology developed from a laboratory biology base that
borrowed sampling techniques and equipment from other fields. Until
recently there had been little convention or need for uniformity of
methods. It is not surprising, therefore, to find a general lack of
data regarding the validity of the methods used to estimate the
exposure to biological particles. This situation has certainly changed
as those investigating exposure assessment aspects of aerobiology have
cooperated with environmental epidemiologists.
Useful reference texts with regard to sampling and analysis of
biological particles include those by the American Conference of
Governmental Industrial Hygienists (ACGIH, 1995), the European
Commission (EC, 1993), Hamilton et al. (1992), Pope et al. (1993),
Burge (1990, 1995), and Burge & Solomon (1987), Reponen (1994), and
Verhoeff (1994a,b).
9.2 House dust mites
House dust mites are members of the arachnid family having eight
legs and an exoskeleton. They can be up to 300 µm in length and live
off organic debris found in house dust (e.g., skin flakes, hair
follicles and fungi) (Colloff, 1991). Because mites absorb water
vapour they are critically dependent on the absolute humidity.
Survival in the adult stage requires environmental moisture conditions
be sustained not lower than 7-8 g/m3 (Korsgaard & Iversen, 1991;
Fernandes-Caldas et al., 1994). This is equivalent to a relative
humidity of about 50% at 20°C.
Mite antigen is mainly found in the faecal pellets which may be
10-20 µm in diameter and will not remain suspended for very long.
Feather et al. (1993) identified enzymes derived from the mite gut as
the source of allergens. These enzymes might remain as potent
allergenic material in bedding, mattresses, carpets and furnishings
long after the mite population has diminished, further complicating
exposure determination.
Two different approaches, the sampling of air and of settled
dust, are available to measure the presence of house dust mites and
their allergens as indicators of environmental exposure. The latter is
the most commonly used approach.
9.2.1 Air sampling for house dust mites
Several techniques exist for volumetric sampling of airborne mite
allergens, using cascade impactors or high- and low-volume samplers in
combination with membrane filters (Swanson et al., 1985; Price et al.,
1990; Sakaguchi et al., 1993; Oliver et al., 1995). These techniques
have the advantage that they sample airborne allergens and might
therefore be more representative of the true exposure than assays of
settled dust. The literature is limited, however, on the validity of
air sampling as measure of exposure to house dust mite allergens
(Swanson et al., 1985; Price et al., 1990; Sakaguchi et al., 1993),
and further research is needed.
Mites themselves are not seen in air samples. Furthermore, in
undisturbed rooms amounts of airborne mite allergens are small and
difficult to detect, even after prolonged sampling. Most of the mite
allergens bind to faecal pellets, which become airborne only as a
result of disturbance, and little allergen is associated with
particles that remain airborne for more than a few minutes. Therefore,
practical disadvantages of airborne sampling of mite allergen are the
requirements for long sampling periods (2-24 h) and very sensitive
assays (Thien et al., 1994). Price et al. (1990) used a low-volume air
sampler (2 litre/min) for 3 h to sample suspended dust mite allergen
in homes. They reported that the airborne allergen levels correlated
better with sensitization to mites among children than the levels in
dust. Further, the air and dust antigen levels were not correlated.
Although this is the only study linking atopy to airborne mite
allergens, it does suggest potential limitations of using dust
sampling as a surrogate exposure measure. In a small number of
studies, air sampling and dust sampling were carried out in parallel
(Price et al., 1990; Sakaguchi et al., 1993; Oliver et al., 1995). In
only one study were significant correlations found between the levels
of house dust mite allergens in air and dust (Oliver et al., 1995).
Allergenic responses to dust mite allergens may be induced by
short-duration high-concentration exposure events. Therefore, the
clinical importance of integrated air samples may be more relevant in
predicting prevalence of atopy to mites rather than predictive of
acute health effects.
At present no reliable information is available that will support
adoption of a standardized method for air sampling of house dust mite
allergens. According to an international workshop held in 1987
(Platts-Mills & De Weck, 1989) airborne sampling has not been shown to
be better than dust sampling to measure the level of mite infestation
in homes or schools. This was confirmed by a second international
workshop in 1990 (Platts-Mills et al., 1992). It was also stated that
there are few or no data showing a relationship between airborne
measurements and sensitization to house dust mites or symptoms. In
contrast, a relationship is apparent between the concentrations of
mite allergens in settled house dust and sensitization or symptoms.
Therefore, air sampling was not recommended (Platts-Mills et al.,
1992).
9.2.2 Dust sampling for house dust mites
Dust sampling for measurement of the level of mite infestation is
accepted and recommended as the best-validated "index of exposure" to
house dust mite allergens. The approach assumes that the quantity of
allergens released into the air is a function of what is present in
settled dust, or, conversely, that the measurement of allergen in
settled dust is related to both the long-term dose a person receives
and to the short-term airborne levels experienced during events that
raise dust.
Standardized sampling procedures to measure house dust mites and
their allergens in house dust have been proposed (Platts-Mills & De
Weck, 1989; Platts-Mills et al., 1992; EC, 1993; Dreborg et al.,
1995). Sampling sites should be consistent throughout the study and
preferably include the upper mattress surface and the floors of the
living room and bedroom. Sampling can be conducted with vacuum
cleaners equipped with a special attachment to collect dust on a paper
filter. Vacuuming 1 m2 of surface in 2 min is a commonly used
sampling method. Depending on experiences with the amount of dust
recovered in specific situations, investigators may have to modify the
sampling procedures. Samples can also be obtained from upholstered
furniture, soft toys and clothing. Alternative techniques for
collecting dust samples include shaking blankets in a plastic bag and
scraping flat surfaces higher than floor level with a piece of firm
card. However, these techniques are less effective than collection by
vacuum cleaner and not standardized. The dust samples may be sieved
before analysis to obtain a sample of fine dust that can be weighed
accurately. Unfortunately, dust samples may still vary in density
after sieving. An alternate method for sampling airborne mite
allergens is to collect settling dust on large Petri dishes over a
period of 14 days (Tovey et al., 1992; Oliver et al., 1995). Brown
(1994) developed a variation on the integrated settling method. A
100-cm2 piece of sticky tape is placed in contact with the surface
for 24 h. Under low-power magnification (36×), the trapped mites are
counted. Using an empirically derived collection efficiency of 30%,
the number of live mites per area is estimated. However, this does not
reflect the true extent of exposure to mite allergens (see section
9.2.3.1).
9.2.3 Available methods of analysis for house dust mites
There are three types of method for estimating the concentrations
of house dust mites or their allergens in (airborne) dust samples:
mite counts, immunochemical assays of mite allergen and guanine
determinations. The choice of a particular method depends on the
specific purpose of a study.
9.2.3.1 Mite counts
The prevalence of mites in settled house dust can be determined
by counting under a microscope after separation from the dust sample
by flotation or suspension. This technique permits the identification
of the predominant species and the recognition of live, dead, larval
or adult types. The disadvantages of this method include:
* the need for training and development of skill in determining
different mite species
* the failure to quantify faecal pellets and disintegrated mite
bodies and therefore to reflect the true extent of exposure to mite
allergen levels
* the unsuitability for large-scale (epidemiological) studies owing
to the time-consuming nature of the work (Platts-Mills & De Weck,
1989; EC, 1993).
A further limitation of this method is variation among the actual
extraction techniques. Bischoff et al. (1992) estimates that less than
10% of the mites are removed from the carpet by typical vacuuming
techniques, but this number varies with the type of surface, the type
of vacuum used and the vacuuming technique.
9.2.3.2 Immunochemical assays of dust mite allergens
Immunochemical assays are widely used to measure the
concentrations of house dust-mite allergens. The dust mite germ is
Dermatophogoides and allergens have been identified for three
species. The conventional labelling of these allergens are denoted by
the prefix "Der" followed by a letter indicating the species. These
assays are possible because the major allergens produced by house dust
mites, i.e., the group 1 allergens (Der p I, Der f I, Der m I) and the
group 2 allergens (Der p II, Der f II, Der m II) are well
characterized and purified. For immunochemical analysis, the dust
sample is extracted (e.g., in a buffered saline solution), and then
stored frozen until analysis.
Total mite allergen content can be assessed by
radioallergosorbent tests (RAST). This method provides a good estimate
of the relative potency of different allergen extracts, but cannot be
used for absolute quantification of mite allergen levels. An advantage
of the method is that it measures "relevant" antigenic determinants
that have elicited a response in allergic subjects, since human IgE is
used. Results vary with the composition of the extract used on the
solid phase and with the composition of the serum pool used for
detecting bound allergen. However, RAST inhibition results are
difficult to reproduce over an extended period of time.
Individual mite allergens can be measured with enzyme-linked
immunosorbent assays (ELISA) or radioimmunoassays (RIA). Sandwich
radio- or enzyme immunoassays employ either rabbit polyclonal or mouse
monoclonal antibody for capture, and a second monoclonal antibody for
detection (see Fig. 25). These assays are more sensitive than RAST.
Those using monoclonal antibodies in particular have also the great
potential advantage of long-term reproducibility. Furthermore, ELISA
assays have been shown to be highly reproducible (e.g., Munir et al.,
1993; Van Strien et al., 1994) and can quantify antigen levels to less
than 1 ng/mg dust.
Immunochemical assays are highly specific and the results
obtained with these assays can be expressed in absolute units of a
defined protein by unit weight of dust or by unit area sampled. They
are suitable for large-scale surveys because they can be automated.
However, a sophisticated laboratory is required.
9.2.3.3 Guanine determination
The third possibility is the measurement of guanine, which is a
nitrogenous excretory product of arachnids, found in house dust. Since
mites are predominant among arachnids in house dust, determination of
guanine content in the dust is an indirect method for assessing mite
allergen levels. Analysis of guanine content is based on a colour
reaction between guanine and an azo compound (Le Mao et al., 1989;
Hoyet et al., 1991). The amounts of guanine can be measured
quantitatively on a weight/weight basis using a spectrophotometer, or
semiquantitatively using a commercially available test kit (Pauli et
al., 1995). The quantitative assay has been reported to demonstrate a
good correlation with the assay of Group 1 allergens (Platts-Mills et
al., 1992), whereas the semiquantitative test was found to be less
sensitive (Lau et al., 1990).
9.2.4 Mite allergens
Sampling strategies may vary depending on objectives but most
studies collect vacuum samples using a protocol that, at least
internally, standardizes equipment, area, duration and location. Mites
are typically found in higher concentration in bedding. Typical areas
would include mattresses, pillows, blankets and bedroom floors.
Because of spatial variability, mixed floor samples can be used. Other
areas of high use include living room, upholstered chairs and couches,
and covered floors. Bischoff et al. (1992) describes an approach used
to avoid depletion of the dust reservoir during routine and repeated
sampling.
Mite-antigen levels have been shown to vary with season,
reflecting the moisture and temperature dependency controlling mite
development stages. Garrett (1996) conducted a yearlong study in 80
homes in eastern Australia. Fig. 26 reveals the temporal variation in
Der p I, the prominent allergen. The allergen levels in dust collected
from the bedroom and living room are higher during the warmer and more
humid months of the year. Garrett (1996) has shown that the allergen
level for Der p I is consistently higher in dust collected directly
from the bedding. The between-home variation is quite apparent,
ranging over almost two orders of magnitude. Examining Fig. 27 offers
an explanation for the higher levels of greater variability in the
allergen levels recovered from the bedding dust. Mites survive better
in mattresses with spring cones than in foam rubber. Presumably, less
moisture is retained in the hydrophobic foam material. Also, wool
sheets and blankets favour the growth and retention of mite antigens
more than alternative bedding material. Other studies on mites in wool
rugs suggest that the thermal properties of wool help mites to survive
fluctuations in temperature and moisture and, perhaps, inhibit their
removal.
9.3 Allergens from pets and cockroaches
For estimating the exposure to allergens derived from pets (e.g.,
cats and dogs), and cockroaches, the same approaches are available as
for house dust mites and their allergens (i.e., the sampling of air
and dust). The major allergens of the cat (Fel d I), dog (Can f I),
the German cockroach (Bla g I, Bla g II), and the American cockroach
(Per a I), have been characterized and purified (Chapman et al., 1988;
Pollart et al., 1991a; Schou et al., 1991, 1992). Research is still in
progress to further unravel the structure of the allergens derived
from pets and roaches (and house dust mites) using techniques for
allergen cloning and sequencing.
9.3.1 Air sampling for allergens from pets and cockroaches
Cockroaches are year-round inhabitants of homes. They need access
to both food and water, so they are often found in kitchens and
bathrooms. Unlike mites, where the antigen source is in faecal matter,
cockroaches are thought to secrete their allergen on to their bodies
and on to surfaces (Vailes et al., 1990). This means that body parts,
egg shells, faecal particles and saliva might contain allergens
(Lehrer et al., 1991).
Similarly, a wide range of materials derived from mammals contain
potentially allergenic material, including hair, dander, serum,
saliva, urine and faecal matter. Direct contact as well as inhalation
and ingestion can cause allergic reactions (Burge, 1995). Because of
the popularity of cats and dogs as domestic pets, they have been the
subject of much of the work on mammalian allergenic reactions. Cat
allergens from saliva and sebaceous gland secretions reside on
particles less than 2.5 µm in size. Much of the dog allergen is
believed to be associated with dander and hair, but saliva and serum
are also important sources.
There are only limited data on the size ranges for airborne
allergen particles from dogs, rabbits, rats and other animals. In
general, however, saliva sources tend to be small (<2 µm) whereas
dander and urine particles are larger (10 µm).
For the sampling of airborne allergens derived from cats, dogs
and cockroaches, the same methods can be used as for the sampling of
airborne mite allergens (see section 9.2.1). These allergens have been
sampled using cascade impactors (Luczynska et al., 1990; De Blay et
al., 1991), high-volume samplers in combination with fibreglass
filters (Swanson et al., 1985; Sakaguchi et al., 1993) and liquid
impingers (Luczynska et al., 1990).
As is the case for house dust mite allergens, only limited data
have been published on the validity of air sampling as a measure of
exposure to allergens derived from pets and cockroaches. At present
there is no reliable information to support adoption of a standard
method for air sampling of these allergens. Airborne sampling has not
yet been shown to be a better estimation of the exposure to these
allergens than dust sampling. Therefore, further research to compare
the usefulness of air and dust sampling is needed.
9.3.2 Dust sampling for allergens from pets and cockroaches
The sampling of house dust to investigate the presence of
allergens derived from pets and cockroaches can be conducted exactly
as for house dust mites and their allergens (see section 9.2.2).
9.3.3 Available methods of analysis
Immunochemical assays (ELISA) are available for detection of the
allergens derived from cats (Chapman et al., 1988), dogs (Schou et
al., 1992) and cockroaches (Pollart et al., 1991b; Schou et al., 1991)
in (airborne) dust samples. The allergens of the American and German
cockroach (i.e., Per a I and Bla g I) were demonstrated to be
immunologically cross-reactive proteins and can be measured in the
same assay (Schou et al., 1991). For immunochemical analysis, the dust
sample is extracted (e.g., in a buffered saline solution), and then
stored frozen until analysis.
The ELISA assays for Fel d I and Can f I were found to be highly
reproducible (Chapman et al., 1988; Schou et al., 1992). For the
Bla g I and Bla g II ELISA assays the intra- and interassay
variability were also found to be small (Pollart et al., 1991b).
9.3.4 Typical allergen concentrations
Cat and dog allergens have been reported more often than
allergens from other mammals. Homes with cats have dust levels of Fel
d I exceeding 10 µg/g, whereas homes without cats have typically less
than 1 µg/g. A provisional value of 8 µg/g of dust has been proposed
as indicating significant exposure. Cat antigen has been found in dust
samples collected in theatres, offices, aeroplanes, schools and homes
without a cat. Because of its small particle size, cat antigen can
stick to clothing and be transported to other locations. Dog allergens
have not been as extensively examined for non-residential sites.
Dybendal et al. (1989) has reported that dog allergen was present in
homes and schools where dogs were not kept.
9.4 Fungi
Fungi are a large and diverse class of microorganisms. They live
on organic nutrients and have no chlorophyll or internal organs. The
cells that make up fungal colonies contain complex carbohydrate
macromolecules. Fungi must produce spores or conidia for their
reproduction. Spores are usually 2-20 µm in size and oblong in shape.
In the appropriate setting, spores reproduce new organisms.
The two different approaches to assess the exposure to fungal
particles are air sampling and dust sampling. For completeness, other
approaches to "dust" sampling include lifting spores from a surface
with sticky tape or direct contact with culture agar. The most
commonly used approach is air sampling of culturable (viable) fungal
particles.
9.4.1 Air sampling for fungi
Several techniques have been described for volumetric sampling of
fungi in outdoor and indoor environments. Table 32 presents an
overview of the techniques most commonly used for the sampling of
fungal particles. Detailed information on the different sampling
devices can be found in ACGIH (1995). Some of the techniques give
total counts of all airborne particles, viable and non-viable, whereas
others only give counts of viable fungal particles (e.g., propagules
or colony forming units (CFU)). A few methods are discussed that
provide not only total counts, but also viable counts (e.g., filter
samplers). The sampling efficacy of a bioaerosol sampler is both a
physical and a biological problem. For air sampling of fungal
particles the following physical sampling principles may be
distinguished: impaction on to a solid or semi-solid surface (e.g., a
culture medium or an adhesive), centrifugal impaction, filtration and
liquid impingement.
Impaction on to a culture medium (e.g., for culturable fungi) is
the most widely used technique, particularly in non-industrial indoor
environments. This process depends on the inertial properties of the
particles, such as size, density and velocity, and on the physical
parameters of the impactor, such as inlet-nozzle dimensions and
airflow paths. Because of differences in characteristics, samplers
differ in cut-off size ( d50) (e.g., the particle size above which
50% or more of the particles are collected). As most impactors have
very sharp cut-off characteristics, almost all particles larger than
the d50 are collected and d50 is therefore assumed to be the size
above which all particles larger than that size are collected
(Nevalainen et al., 1992). No sampler collects all particles with
equal efficiency, and it is therefore not surprising that different
quantitative and qualitative results are obtained using different
sampling devices for culturable fungi (Verhoeff et al., 1990). The
choice of the collection (culture) medium also affects the kinds and
levels of fungi recovered (Verhoeff et al., 1990). No single
collection medium will enable the entire range of viable fungi in the
air to be isolated. Media which are generally accepted for
aerobiological studies include malt extract agar (MEA), V8 juice agar
and dichloran 18% glycerol agar (DG18) (EC, 1993; ACGIH, 1995). MEA
and V8 agar are broad spectrum media, whereas DG18 is intended to be a
selective medium for xerophilic fungi, but many of the common fungal
species in air can also be isolated (Verhoeff et al., 1990).
Few published data are available on the validity (accuracy and
precision) of the measurement of fungi in air as estimate of exposure.
All commonly used cultural air samplers use short sampling periods,
typically 30 seconds to several minutes (Table 32). The
reproducibility of parallel duplicate samples and sequential duplicate
samples is only moderate, both in terms of CFU/m3 and in terms of
species isolated (Verhoeff et al., 1990). More importantly, repeated
sampling within weeks has demonstrated that variation in time within
homes is much higher than the variation between homes (Verhoeff et
al., 1992). This means that a single air sample has only a low
predictive value for exposure over time. Furthermore, the use of
cultures for quantifying fungal particle concentrations in air samples
will give an underestimate of the actual particle concentrations, and
may cause significant fungal contamination to be missed altogether.
The culturable fungal particles may comprise only a few percent of the
Table 32. Overview of sampling techniques for airborne fungal particlesa
Method with examples Sampling rate and time Remarks
Non-viable, non-volumetric
- settling surface, adhesive-coated undefined, minutes to days semi-quantitative, over-representation of
larger particles, microscopic identification
Non-viable, volumetric
- rotating tape/slide impactors
Burkard trap 10 litre/min, 7 days cut-off 2.5 or 5.2 µm, depending on slot
- rotating arm impactors
Rotorod sampler 47 litre/min, intermittent cut-off unknown
- filter methods
cassette filters 1-4 litre/min, hours viable counts possible by plating washings
high-volume filters 150-2000 litre/min, hours from the filters
Viable, non-volumetric
- settlement plates undefined, hours semi-quantitative, over-representation of
larger particles
Viable, volumetric
- multiple hole impactors
Andersen 6-stage sampler 28.3 litre/min, 1-30 min cut-off 0.65-0.70 µm, size separation
Andersen 2-stage sampler 28.3 litre/min, 1-30 min cut-off 0.65-0.70 µm, size separation
Andersen 1-stage (N6) 28.3 litre/min, 1-30 min cut-off 0.65-0.70 µm
Surface Air System sampler 90/180 litre/min, 20 sec-6 min cut-off depends on number of holes and flow
Eight-stage personal impactor 2 litre/min, 5-30 min cut-off 5.2 µm, size separation
Burkard portable sampler 10/20 litre/min, 1-9 min cut-off 4.1/2.9 µm (10/20 litre/min)
- centrifugal impactors
Reuter Centrifugal sampler (RCS) ca. 40 litre/min, 20 sec-8 min cut-off 3.8 µm
Reuter Centrifugal Plus (RCS-Plus) ca. 50 litre/min, 30 sec-8 min cut-off unknown
Table 32. (continued)
Method with examples Sampling rate and time Remarks
- rotating slit-to-agar impactors
Mattson-Garvin air sampler 28 litre/min, 5-60 min cut-off 0.5 µm
- liquid impingers
single-stage all glass impingers 12.5 litre/min cut-off 0.3 µm
three-stage impingers 20 litre/min cut-off <4 µm, size separation
a For detailed information see ACGIH (1995).
total number of fungal particles (Horner et al., 1994). Thus, in order
to optimize the information available from air sampling, both types of
particle should be sampled. However, even using the best available
method, a large number of airborne spores will not grow in culture and
cannot be visually identified with available methods.
At present, there is no standardized method for the sampling of
airborne fungi, although the American Conference of Governmental
Industrial Hygienists (ACGIH, 1989) and the European Commission (EC,
1993) have given recommendations. An outline for selecting a
bioaerosol sampler is presented by the American Conference of
Governmental Industrial Hygienists (ACGIH, 1995). Selection criteria
include sampling location, form of recovered particles (intact or
dispersed), the need for size separation and the expected
concentrations of the particles.
9.4.2 Settled dust for fungi
Settled house dust can be sampled for viable fungi in exactly the
same way as for house dust mites and their allergens (see section
9.2.2). The dust samples can be stored at room temperature but the
analysis should be performed within a few days.
Few published data are available on the validity of the
measurement of culturable fungi in settled dust as estimate of
exposure. The results, both quantitatively and qualitatively, depend
on the method of inoculation of the dust and on the culture medium
used (Verhoeff et al., 1994a). The reproducibility of duplicate
analyses in terms of CFU/g dust is acceptable, but in terms of species
isolated only moderate. However, as is the case for air sampling, a
single dust sample is a poor estimate of exposure to fungi over time
(Verhoeff et al., 1994a).
9.4.3 Available methods of analysis for fungi in air
Air samples obtained with sampling devices collecting total
fungal particles can be analysed by direct examination to obtain total
counts of fungal particles. Samples collected on culture media have to
be incubated to obtain counts of viable fungal particles. Dust can be
plated either directly on to a culture medium or suspended and diluted
prior to plating. Total counts of fungal particles in dust can also be
obtained by partitioning into an aqueous two-phase system followed by
epifluorescence microscopy (Strom et al., 1987).
Samples are incubated for at least 4 days; up to 7 days is the
typical time needed for spores to generate identifiable colonies. The
temperature at which samples are incubated affects the recovery of
culturable fungi. Since most environmental fungi grow well between
20°C and 30°C, the incubation temperature is generally 25°C (EC,
1993).
Sporulating colonies are identified by colour and texture, by the
naked eye or microscopically. Non-sporulating spores might be
transferred to different agar and exposed to different lighting in an
attempt to colonize them. Fungal genera are sometimes reported and
provide important insight into sources and possible health effects.
Common outdoor fungi are Cladosporium, Alternaria, Botrytis and
Epicoccum. Penicillium, Aspergillus and Stachybotrys can be found
in higher concentrations indoors. It is difficult to generalize and
there is considerable variability over time so it is important to
simultaneously collect outdoor samples. Fig. 28 shows the distribution
of viable mould spores collected inside and outside homes in Australia
(Garrett, 1996). Outdoor viable spore counts decrease in the winter
and so do the indoor levels for homes without substantial sources of
sporulating fungi.
Immunochemical assays for fungal allergens are available for only
a few fungi, primarily because fungal allergens are poorly
characterized and purified. Alternative indicators of exposure to
fungi, to be measured in (airborne) dust, may also be considered. For
example, one can assess the levels of cell wall components such as
ß-1,3-glucan (Rylander et al., 1992), or ergosterol, a membrane
steroid (Horner et al., 1994), or extracellular polysaccharides (EPS)
(Kamphuis et al., 1991). Immunochemical assays (ELISA) are presently
being developed to measure these components in (airborne) dust.
However, in one study by Miller et al. (1988), ergosterol in house
dust correlated with CFU/m3 in the air and Saraf et al. (1997) have
shown ergosterolin in house dust correlated with fungal CFU/g in the
dust samples. In addition, Abramson et al. (1996) showed an
association between ergosterol and atopy in adults.
9.4.3.1 Total counts of viable and non-viable fungal particles
Total counts of fungal particles can be obtained by counting with
a light microscope. If more detail is required, the samples can also
be viewed with a scanning electron microscope (SEM) or a direct
epifluorescence microscope. These techniques cannot be recommended as
giving a good assessment of the composition of air spora because only
fungi with distinctive spores can be identified. It is often difficult
to identify the fungal spores to species or even genus. As indicated
above, filter methods may be used not only to give total counts (e.g.,
by direct epifluorescence microscopy), but also to obtain counts of
viable fungal particles by plating washings from the filter.
Furthermore, filter samples may be analysed for mycotoxins, EPS and
glucans or tested for toxicity. The same applies for samples of
settled dust.
9.4.4 General considerations for fungi
Since fungi vary so widely it is difficult to generalize about
the presence of fungi in outdoor and indoor air. In temperate
climates, outdoor spore counts are highest during and just following
the growing season. Tropical climates show less variation by season.
Garrett's (1996) doctoral thesis provides a useful comparison of
viable mould spores from studies conducted in different climates.
Spore counts can vary greatly indoors for several reasons
including the presence of colonizing fungi. Fig. 29 illustrates this
point. The figure shows the mean values of CFU per culture plate for
four fungal taxa and all others collected during the summer in nine
Portage, Wisconsin (USA) homes. Living room and bedroom samples
reflect the taxa found outdoors. The basement samples show
Penicillium and Aspergillus fungi which grow favourably in damp
areas (Burge, 1990).
In addition physical activity, such as vacuuming, starting an
air-conditioning fan, children playing on a carpet or changing a
filter, might raise spore concentrations by a factor of 10 or more.
Even without mechanical disruption, periodic shedding or ejection of
spores from growing fungi might similarly elevate concentrations.
9.5 Bacteria (including actinomycetes)
Bacteria are prokaryotic cells. Certain bacteria are infectious
and can be transmitted by air and contact, including ingestion. Common
contagious airborne diseases include tuberculosis and some forms of
pneumonia. Other diseases, such as legionellosis from water,
respiratory infections from Pseudomonas in humidifiers and several
others from handling animals are not transmitted from person to
person.
Sampling and identifying specific bacteria is very important in
many settings, especially in hospitals with immune-compromised and
immune-suppressed patients. There are numerous other situations where
infectious diseases have been transmitted by airborne bacteria. These
include tuberculosis transmission in aircraft, and Legionella
transmission in hospitals, in hotels, in supermarkets and on cruise
ships or even in buildings in locations affected by cooling tower
mist. The two approaches available to measure the presence of bacteria
as indicators of exposure are sampling of air and sampling of soil,
dust or water.
9.5.1 Air sampling for bacteria
Most of the air sampling devices listed in Table 32 can also be
used for bacteria. The most widely used devices for bacteria include
multiple-hole impactors, centrifugal impactors and slit-to-agar
impactors, provided with collection media suitable for viable
bacteria. For sampling airborne viable bacteria, the same limitations
apply as for viable fungal particles. Thus, the results, both
quantitatively and qualitatively, will depend on the sampling device
and collection medium used. A single air sample has only a low
predictive value for exposure over time (EC, 1993). Media that collect
viable bacteria include tryptone soya agar, tryptone yeast glucose
agar, soybean casein digest agar, and nutrient agar (EC, 1993; ACGIH,
1995). To prevent fungal growth, a suitable antimycotic may be used
(e.g., cycloheximide). For specific groups of bacteria, selective
media could be employed, such as half-strength nutrient agar for
thermophilic actinomycetes.
9.5.2 Dust sampling for bacteria
The sampling of settled house dust for bacteria can be conducted
in exactly the same way as for house dust mites and their allergens
(see section 9.2.2).
9.5.3 Available methods of analysis for bacteria
Air samples obtained with sampling devices collecting total
bacteria can be analysed by direct examination. Samples collected on
culture media have to be inoculated to obtain counts of viable
bacteria. Dust can be plated either directly on to a culture medium or
suspended and diluted prior to plating. Dust can also be analysed for
total bacteria counts. Furthermore, an assay is available to measure
endotoxin content of (airborne) dust. Gram-negative bacteria contain
endotoxins as integral components of their outer membrane. Endotoxins
are potent biological agents.
9.5.3.1 Total count of viable and non-viable bacteria
Total counts of bacteria, with some information on shape, can be
obtained from some samples, for example, water and air, using
epifluorescence microscopy (most commonly, with acridine orange). This
method becomes less reliable as the amount of debris in the sample,
both organic and inorganic, increases. More detail on bacterial shape
is obtained using a scanning electron microscope but quantitative
results are less reliable. Filter samples can also be used to obtain
counts of viable bacteria, with subsequent taxonomic differentiation
if desirable (using Gram staining and other biochemical tests). Filter
samples can be analysed for endotoxin as well (see below).
9.5.3.2 Viable bacteria
Environmental samples are usually incubated for 2-7 days at 25°C
or 37°C. For bacteria, as with fungi, the incubation temperature
affects the recovery. Most environmental bacteria grow well between
20°C and 30°C, and more species were recovered with incubation at 20°C
than at 37°C (Hyvarinen et al., 1991). Therefore, it is recommended
that plates be incubated at room temperature (20-25°C) and examined
daily for several days (EC, 1993). For isolation of human pathogenic
organisms, plates can be incubated at 37°C, and for thermophilic
actinomycetes at 55°C. After incubation the number of colonies is
counted and expressed as CFU/m3.
9.5.3.3 Endotoxins
Endotoxins are a group of lipopolysaccharide (LPS) molecules
making up the outer membrane of Gram-negative bacteria. Specific LPS
macromolecules that exist in the cells or as fragments of cell
structures are known to cause fever, malaise, respiratory distress and
a variety of biochemical changes in humans. Endotoxins are ubiquitous
in nature but occur in high concentrations in particular industrial
and agricultural settings. Cotton mills and industrial processes using
recirculating water, waste-water collectors, humidifiers and swine
barns are some locations where endotoxin contamination has been
associated with respiratory disease (Milton, 1995).
Airborne and settled dust samples can be examined for the
presence of endotoxins. The Limulus amoebocyte lysate (LAL) assay is
commonly used to quantify environmental endotoxin (Walters et al.,
1994; Douwes et al., 1995). The assessment of endotoxin exposure
depends strongly on sampling, extraction and storage procedures
(Douwes et al., 1995) and further validation studies are needed to
adopt standard methods for sampling and analysis. Variation in the LAL
reagent from lot to lot and between manufacturers may be a major cause
of variation in results within and between laboratories (Saraf et al.,
1997).
In airborne endotoxin, filter material and type of aerosol (e.g.,
cotton dust, machine oil or saline mist) will affect the binding of
endotoxin to the filter. Walters et al. (1994) showed that
polycarbonate capillary pore membrane filters were optimal. Any
conventional air sampler can be used with filter cassettes. Glass
impingers have been used as alternatives to filters. Fluids and bulk
samples can be collected directly if care is taken to ensure the use
of endotoxin-free glass or plastic ware with low binding affinity.
Samples should be analysed promptly and preferably not frozen.
Endotoxin (LPS) is removed by solubilizing in a buffer solution (after
Milton, 1992). Sonication might be necessary to dislodge particles.
Concentrations, as measured by the Limulus assay, are expressed as
standard endotoxin units (EUs) which are defined as the potency of
0.10 ng of a reference standard endotoxin (EC6, US Pharmacopoeia).
9.6 Pollen
Most people associate pollen with the common experience of hay
fever and seasonal allergic rhinitis. Although only 10% of flowering
plants shed wind-borne pollen, there are locations and times when the
ambient concentrations exceed 1000 pollen grains/m3. Pollen grains
generally are spheroidal or somewhat elongated and have a very durable
outer wall. Most airborne pollen is between 10 and 70 µm in diameter.
Larger pollen grains (>200 µm) are more likely to be transported by
insects. Weather conditions such as higher temperature and lower
relative humidity and wind promote pollen emissions (Ogden et al.,
1969; Hart et al., 1994; Burge, 1995). Plant lifecycle stage, daylight
and moisture affect the time and rate of shedding. Flowering of most
plants, trees and grasses is seasonal and therefore cyclic.
Although hay fever in association with plant pollen has been
known for 175 years, the association of natural pollen with airway
reactions and asthma has not been adequately studied. Most pollen
measurements have been conducted by independent observers using a
variety of samplers.
9.6.1 Air sampling for pollen
Several techniques exist for volumetric sampling of pollen grains
in indoor or outdoor air. Table 33 provides an overview of the devices
most commonly used for the sampling of pollen. Detailed information on
the different sampling devices has been published by the American
Conference of Governmental Industrial Hygienists (ACGIH, 1995). In all
cases, the pollen impacts a semi-solid surface (tape strip or glass
slide mounted with an adhesive). The main difference between the
devices is that the moving tape/slide impactors provide the
possibility of obtaining time-discriminated data, as opposed to the
stationary and rotating rod impactors. The sampling devices listed in
Table 33 may also be used to sample large fungal spores. Published
data on the validity of air sampling to estimate the exposure to
pollen are lacking.
9.6.2 Dust sampling for pollen
The sampling of settled dust to investigate the presence of
pollen grains or allergens derived from pollen can be conducted
exactly as for house dust mites and their allergens (see section
9.2.2) Dybendal et al. (1989) and Yli-Panula & Rantio-Lehtimäki (1995)
describe dust sampling techniques for pollens.
9.6.3 Available methods of analysis for pollen in air
Analysing air samples for pollen is commonly done by light
microscopy, but scanning electron microscopy is also used.
Immunochemical assays are not routinely used to assess the presence of
pollen allergens in air or dust samples. It should be recognized that
optically counted pollen grains may not relate to the pollen antigenic
activity because empty grains do not contain allergenic protein
material.
For light microscopic counting and identification of pollen,
staining of the sample is recommended. Staining can be done with basic
fuchsin and phenosafranin, which stain the exine of the pollen (i.e.,
the outermost portion of a pollen grain) red and dark pink
respectively. The choice of the dye depends on the type of sample to
be analysed. Pollen identification rests on the microscopic
appearance, using published keys for identification. (Faegri &
Iversen, 1989; Nilsson & Praglowski, 1992).
Immunochemical assays have been used to analyse samples of
settled house dust for the content of grass-pollen allergens
(ryegrass, Lol p I) by direct RAST (Platts-Mills et al., 1987). Birch
(Bet v I) and alder (Aln i I) pollen allergens have been analysed by
means of RAST-inhibition (Dybendal et al., 1989). Jensen et al. (1989)
have analysed outdoor air samples taken with a high volume sampler for
timothy and birch pollen allergens by means of a RAST-inhibition
assay. They found a strong correlation between the amounts of
allergens and pollen counts obtained with a Burkard trap. As indicated
above, immunochemical assays are not routinely used to measure pollen
allergens.
Table 33. Overview of sampling techniques for airborne pollen grainsa
Method Examples Sampling rate and time Remarks
Rotating tape/slide impactors Burkard trap 10 litre/min, 7 days cut-off 2.5 or 5.2 µm,
depending on slot
Lanzoni sampler 10 litre/min, 7 days cut-off 10 µm
Moving slide impactors Allergenco air sampler 15 litre/min, intermittent cut-off 2.0 µm
Stationary slide impactors Burkard portable sampler 10 litre/min, 1-9 min cut-off 5.2 µm
Rotating arm impactors Rotorod sampler 47 litre/min, intermittent cut-off unknown
a for detailed information see ACGIH (1995).
9.6.4 General considerations for pollen sampling
The source of indoor pollens can be considered as entirely
outdoors. Bringing flowers indoors might be a transient source, but
only about 10% of flowering plants and trees spread their pollen
through the air. Ventilation, footwear and clothing bring outdoor
pollens indoors. Yli-Panula & Rantio-Lehtimäki (1995) demonstrated
that antigenic activity indoors was lower, and peaked 3 weeks later
than outdoors. They inferred that transport by occupants and pets is a
more important vector for indoor pollen levels in the Finnish homes
they sampled. O'Rourke & Lebowitz (1984) sampled pollen in dust from
homes in the dry southwest of the USA (Tucson, Arizona). Higher
loadings were found in the dust samples closer to entrances. Both of
these studies, as well as others, indicate that indoor pollen
allergens may be a major cause of asthma, especially since significant
antigenicity can persist more than 2 months after pollen searches at
peak outdoors. Once indoors, if not physically removed, pollen grains
are protected from "weathering" which will denature the antigenic
proteins.
9.7 Summary
Bioaerosols includes a variety of microorganisms or their
components that can become airborne and inhaled. These include
viruses, bacteria, pollens, fungi, protozoa and algae as viable
organisms that can cause illness. Fragments or metabolic components of
bacteria and fungi along with protein structures contained in these
organisms as well as in the excreta and parts of insects, animals and
arachnids can cause allergenic reactions. This chapter focused on the
most ubiquitous bioaerosols commonly found indoors and contributing to
allergenic reactions: mites, fungi, bacteria and pollen. Methods and
strategies for sampling and analysing these agents in air and settled
dust are presented. Examples of how some of these parameters vary
indoors and outdoors across time and location are offered.
Exposure assessment for microbiologicals is as advanced, at this
time, as it is for many air contaminants. Personal samplers have not
been developed. In fact, many of the techniques for sampling
aerobiological agents have been adapted from instruments designed for
other purposes. The field is maturing as professional organizations
attempt to improve and standardize measurement methods, culturing and
analysis protocols and data reporting. These aspects are of critical
importance in comparing results reported by different investigators.
By their very nature, bioaerosols have compositions and
concentrations that are highly variable. The conditions favourable for
growth, reproduction and dispersion vary within a wide range of
temperature, moisture and nutrient conditions. These same factors
influence by interactions with human and animal activities. Mechanical
systems and machines can cause amplification and distribution of
biological aerosols. As a result personal exposures are quite
variable; this, in turn, has led many investigators to rely on area
air sampling and/or bulk sampling of materials. For example, it is
recommended that a surrogate measure of mite allergen exposure is
derived from bedding and floor dust samples. Home samples, whether air
or dust is sampled, are often the "exposure" value used in
epidemiological investigations.
Finally, the advancement of aerobiological exposure assessment to
eventual use in quantitative risk assessment will require better
understanding of relevant dose to cause sensitization and reactivity
for many different organisms and/or agents.
10. ASSESSING EXPOSURES WITH BIOLOGICAL MARKERS
10.1 Introduction
This chapter presents a summary of the major concepts,
definitions, advantages, limitations, sampling, media and uses of
biological markers as applied for exposure assessment to environmental
contaminants. These exposures are frequently characterized by low
contaminant concentrations, multiple sources and multiple types of
chemicals. Use of biological markers has been extensively reviewed
from both epidemiological and occupational monitoring perspectives as
well as their use in risk assessment (Bernard & Lauwerys, 1986; Harris
et al., 1987; US NRC, 1987; Hulka & Wilcosky, 1988; Cullen, 1989;
Griffith et al., 1989; Henderson et al., 1989; Monster & Zielhuis,
1991a,b; Schulte, 1991; Hulka & Margolin, 1992; IPCS, 1993; Aitio,
1994; Grandjean et al., 1994; Silbergeld & Davis, 1994). The reader
should refer to the research literature for a comprehensive and
detailed understanding of the complex issues relevant to the
development, validation, and application of biomarkers in studies of
human exposure. The specific issue of using biological markers in
occupational settings is beyond the scope of this chapter.
It is important to indicate that, to date, very few biomarkers
can be effectively used for quantitative estimation of prior
environmental exposure to contaminants (e.g., carbon monoxide or
lead). In most cases, there are qualitative or semi-quantitative
indicators for monitoring such exposures. However, as the field
evolves, and knowledge of the pharmacokinetics and pharmacodynamics of
xenobiotics develops, the number of quantitatively validated
biomarkers of exposure will also increase.
Biological markers present unique advantages as tools for
multimedia exposure assessment. They are highly sensitive indices of
an individual's exposure to chemicals, since they provide a measure of
the internal dose, account for all routes of exposure and integrate
over a variety of sources of exposure (Friberg, 1985; Baselt, 1988;
Sim & McNeil, 1992). Therefore, they can represent past exposure
(e.g., the presence of lead in shed teeth), recent exposure to an
external source (e.g., VOCs in exhaled breath) and even future
internal exposure sources (e.g., pesticides in adipose tissue).
Furthermore, their use may result in improved monitoring of total
population exposure, characterization of individual and population
exposures and evaluation of internal sources of exposure. These
markers are also useful surveillance tools for monitoring chemical
exposure in both individuals and populations over time.
Use of biological markers of exposure can improve the risk
assessment process by providing a critical link between chemical
exposure, internal dose and health impairment (IPCS, 1993). Biological
markers of exposure can improve clinical diagnoses where there is a
well-established relationship between biological marker and outcome.
10.2 General characteristics
Biological markers represent events or changes in human
biological systems as a result of exposure or disease (US NRC, 1991b).
They are classified as markers of exposure, effect, and susceptibility
and are considered to represent events along a theoretical continuum
from causal exposure to resulting health outcome (US NRC, 1987;
Schulte, 1989). A biological marker of exposure is defined as a
xenobiotic substance or its metabolite(s) or the product of an
interaction between a xenobiotic agent and some target molecule(s) or
cell(s) that is measured within a compartment of an organism (US NRC,
1989; IPCS, 1993). Biological markers of effect are measurable
biochemical, physiological, behavioural or other alterations within an
organism that, depending upon the magnitude, can be recognized as
associated with an established or possible health impairment or
disease (IPCS, 1993). Biological markers of susceptibility are
indicators of inherent or acquired abilities of an organism to respond
to the challenge of exposure to a specific xenobiotic substance (IPCS,
1993). Although the distinction between some biological markers of
exposure and effect may be overlapping, this chapter will focus on
those biological markers which can be applied to environmental
exposure assessment.
10.3 Considerations for use in environmental exposure assessment
The use of biological markers for exposure assessment represents
a different perspective for evaluation of human exposure to a
contaminant than traditional exposure assessment. Biological markers
of exposure are considered measures of internal dose, whereas exposure
is frequently defined as the concentration of an agent at the boundary
between an individual and the environment multiplied by time (US NRC,
1991b; IPCS, 1993). Owing to long-term storage of specific
contaminants in biological tissues (e.g., bone and fat), some
biological markers are markers of both cumulative dose and future
internal exposure.
Biological markers of exposure have been used most frequently in
industrial hygiene and occupational medicine (Elkins, 1954, 1967;
Lauwerys, 1983; Schulte, 1991). Before widespread application of
biological markers for exposure assessment of the general population
can occur, it is important to consider the situations which are best
suited for biological monitoring. Ideally, a biological marker of
exposure should be chemical-specific, detectable in trace quantities,
available by non-invasive techniques and inexpensive to assay. Also,
it must relate consistently and quantitatively to the extent of
exposure and ideally also integrate the exposure over time (Bond et
al., 1992). Currently there are very few biological markers that
possess all these characteristics. However, the use of biological
markers for exposure assessment is increasing.
Biological markers represent one type of monitoring approach
available for environmental exposure assessment. Validation of
biomarkers is a complex process that should include determination of:
specificity of the available biological marker, exposure-related
toxicokinetics and toxicodynamics, dose-response relationship,
biological variation associated with the marker, route of exposure and
type of health effect associated with exposure. In addition,
validation should consider behavioural factors that influence
exposure, participant acceptance, feasibility and cost-effectiveness
(Verberk, 1995), as well as biological variability and specificity
within a human population of interest, and generation of baseline or
normative data for the biological marker. These issues are addressed
later in the chapter.
Collection of samples from humans involves important ethical
issues. Ethical concerns may limit the extent of investigations of
chemically exposed individuals and populations (IPCS, 1993). Ensuring
confidentiality both for subjects and for the obtained individual
results is imperative (Schulte, 1992). Subjects have the right to know
the implications of their participation, the analyses to be performed,
the nature of the sampling procedure, the use of the data collected
and the possible ramifications of positive findings. Knowledge of
previous exposure or genetic predisposition may have adverse
implications for an individual; for example, individuals may be denied
health insurance on the basis of presumed future risk. Since
biological markers are a relatively new tool, interpretation of
results and subsequent health implications is difficult. For many
biological markers, little information is available to interpret the
result for the subject; this may lead to concern on the part of the
individual. For example, knowledge of the presence of pesticides in
breast milk may lead an individual to avoid breast-feeding without
consideration of its advantages (Vandenberg, 1991; Sim & McNeil,
1992).
10.3.1 Toxicokinetics and toxicodynamics
Toxicokinetics describes the absorption, distribution, metabolism
and excretion of a contaminant. Understanding the toxicokinetics and
toxicodynamics of the agent is critical for development and use of a
biological marker of exposure (Sampson et al., 1994). This information
predicts the location and form of the chemical or its metabolite and
identifies sources of biological variability in the population (Droz &
Wu, 1991; Droz, 1992, 1993). Toxicokinetic modelling may be used to
estimate the optimal time for sampling (Saltzman, 1988; Droz & Wu,
1991; Droz, 1993). Differing kinetics determine whether the biological
marker reflects recent exposure, historic exposure, or an integrated
measure of exposure over time (Bernard, 1995).
The utility of biological markers for assessing exposure can be
evaluated on the basis of timing of sampling relative to the exposure
and the biological half-life of the chemical. The parameter which best
describes the toxicokinetic behaviour of a chemical in biological
systems is the elimination half-life, which reflects both the
affinity of the chemical for the biological medium and the efficiency
of the processes of elimination (Bernard, 1995). For samples taken
immediately following exposure (e.g., solvents in exhaled air, blood
and urine), the half-life reflects elimination from the central
compartment of blood and vessel-rich tissues. For samples taken days,
weeks or even years after exposure, the half-life corresponds to the
elimination from those compartments from which chemical clearance is
much slower; for example, lead from bone or lipophilic organic
chemicals from adipose tissue (Bernard, 1995).
Toxicokinetic data from animals and humans can aid in the
determination of the utility of biological markers in assessing
individual or population exposures. Fig. 30 illustrates how
toxicokinetic data could be utilized to select biological markers of
exposure based on the timing and concentration of exposure (IPCS,
1993). In order to interpret the extent of exposure, it is critical to
collect the biological sample after the exposure has reached an
equilibrium state. This can be especially important for chemicals with
short half-lives, such as benzene in exhaled air with a half-life of
22 h (Bernard, 1995). For example, in occupational settings,
biological monitoring has no advantages over environmental monitoring
for airborne chemicals with biological half-lives less than 10 h,
since the integration time for the marker is too short to allow
accurate representation of exposure (Droz, 1993).
10.3.2 Biological variability
The use of biological markers can also be affected by biological
parameters. Variation can depict true differences in individual
exposures, as represented for example by differing microenvironments
or differing behaviour. It may also represent inherent
inter-individual differences which affect the biological response to
exposure (Droz, 1992). Sources of biological variability include
demographic factors (e.g., age and sex), anthropometric
characteristics (e.g., body size and fat distribution), behaviour
(e.g., activity level or contaminant avoidance behaviours),
biological/circadian rhythms and toxicokinetic differences due to
genotype (Kompaore & Tsuruta, 1993), previous exposure, lifestyle
factors (e.g., smoking) or dietary habits (Perera & Weinstein, 1982;
Droz, 1992). Biological variability can complicate assessment of an
individual's exposure using biological markers. However, the factors
contributing to biological variability influence the internal dose and
may ultimately be informative about potential health effects relating
to exposure. For chemicals with large inter-individual variability in
biological response to exposure, biological monitoring techniques are
more useful for groups than for individuals.
10.3.3 Validation of biological markers
Validation of biological markers of exposure occurs at three
levels.
* The first validation level involves sample collection and
determination of sample stability following collection and during
transport and storage. Sample collection and handling can influence
external contamination both during the sample collection and from
the sample collection material. Stability of the sample prior to
analysis can be affected by chemical degradation, evaporation,
biological activity and interaction between the sample and its
container or with other compounds (Bernard, 1995).
* The next level pertains to the analytical method itself and the
ability to measure the marker accurately and reproducibly at levels
relevant to environmental exposure. Low limits of detection and
high analytical sensitivity (i.e., instrument response) are
critical for evaluation of environmental exposures.
* The third validation level stems from the sensitivity and
specificity of the biological marker itself. The biological marker
must demonstrate that an exposure is occurring or has occurred, and
separate individuals on the basis of their level of exposure
(Hulka, 1991). Components of this level of validation include
understanding the temporal relevance of the marker, identifying
background variability and determining potential confounding
factors (Perera, 1987; Sato, 1993). Temporal relevance is critical
since it relates the timing of exposure to the appearance of a
measurable biological marker and to the duration of time that the
biological marker is measurable following the cessation of
exposure. This includes both toxicokinetic and toxicodynamic
considerations.
Validation occurs in the laboratory, in pilot studies and in
populations (Schulte & Talaska, 1995).
For environmental exposure assessment, chemical-specific markers
of exposure such as blood lead concentrations are preferred. However,
for some chemicals, compound-class specific markers (e.g., PAH-DNA
adducts) or non-specific markers (e.g., acetylcholinesterase
inhibition) may be available. Non-specific markers of contaminants
(i.e., some metabolites or non-chemical specific biological changes)
as indicators of exposure may be more sensitive than external measures
of exposure to predict an individual's total dose (van Welie et al.,
1991; Smith & Suk, 1994). However, since non-specific markers are
neither source- nor chemical-specific, characterization of the
variability in these markers is an important validation component
prior to use in exposure assessment. To date, few biomarkers of
environmental exposure can be utilized to estimate such exposure
quantitatively. In most cases, biomarkers of exposure provide a
semi-quantitative or qualitative indication of exposure.
10.3.4 Normative data
Currently many biological markers are being developed for
research purposes (Schulte, 1987; Kelsey, 1990; Bond et al., 1992;
Fowle & Sexton, 1992). However, collecting population baseline data on
these markers is necessary before they can be useful for monitoring
and surveillance purposes. Longitudinal and cross-sectional
information on normal ranges and correlation with environmental
exposures and demographic characteristics are required. Reference
ranges are available for some biological markers. For example, blood
concentration data for a number of pesticides and metals were
collected in a representative sample of the US population in the
National Health and Nutrition Evaluation Survey (NHANES IV) and the
German Environmental Survey (Krause et al., 1992). The ranges may vary
in different exposed populations owing to differing residual levels in
the environment (Grandjean, 1986; Alessio, 1993). Data from specimen
banks are beginning to provide some exposure information on geographic
and demographic parameters (Kemper, 1993).
10.4 Advantages of biological markers for exposure assessment
Biological markers integrate over all sources of exposure, which
allows for efficient characterization of exposure to multiple sources
and evaluation of past exposures (US NRC, 1991a; Sim & McNeil, 1992).
When contaminants are found in many environmental media, biological
monitoring can be the most effective way to determine an individual's
total exposure. Biological markers may also indicate the presence of
additional exposures; for example, biological markers used in
occupational settings have been applied to determine if
non-occupational exposures to similar agents are occurring (Lauwerys,
1983). Some biological markers (e.g., PCB concentrations in breast
milk) represent cumulative exposure to environmental contaminants.
Markers which integrate over long periods of time can be used to
evaluate past exposure in a much more accurate manner than historical
record review and exposure reconstruction (DeRosa et al., 1993;
Sampson et al., 1994). Biological markers can also be the best way to
measure recent exposures, especially those where dermal contact is the
primary route of entry. Table 34 summarizes the advantages and
limitations of using biological markers in exposure assessment.
Table 34. Advantages and limitations of biological markers for environmental
exposure assessment
Advantages Limitations
Demonstrate exposure has occurred source and route identification
Integration over all exposure routes kinetics and timing of exposure
Characterization of individual biological variability and confounding
exposure-doses
Inclusion of internal sources altered response as a result of
multiple exposure
Improved health effects investigations invasive sampling
Improved population risk availability of human samples
assessment/risk management
specificity/sensitivity
Validation of exposure models lack of normative values for comparison
10.4.1 Characterizing inter-individual variability
A fundamental issue in the quantitative aspect of exposure
assessment is the characterization of inter-individual variability in
exposure. The pattern of exposure may differ within individuals,
groups or populations. For example, workers in the same factory may
have different exposures as a result of differing work habits
(Rappaport et al., 1993); families living in very airtight houses may
have greater exposure to indoor contaminants than families living in
draughty homes. Biological markers represent one strategy to assess
inter-individual variation in exposures, when measured environmental
concentrations do not differ between individuals. Genotype, dietary
habits, body size, state of health, lifestyle habits (e.g., smoking)
and behaviour may all play a role in determining an individual's
exposure (Bernard & Lauwerys, 1986). Biological markers of
susceptibility may also be used to explore biological variation in
response to exposure. For example, phenotypic differences in
Delta-aminolaevulinic acid dehydratase (ALAD) may influence both blood
lead levels and the health effects of lead (Wetmur, 1994;
Milkovic-Kraus et al., 1997). Incorporation of biological markers of
both exposure and susceptibility into biological monitoring studies
may result in further insight into inter-individual variability.
10.4.2 Efficacy of use
In some situations, biological markers can be more efficacious
tools than external exposure measurements for monitoring human
exposure in population studies. For example, participant burden may be
lower than in traditional monitoring schemes for some activities
(e.g., motor vehicle repair) and for some subjects (e.g., children and
the elderly) wearing a personal monitor may not be a practical
strategy to monitor a subject's exposure. For some exposure routes
such as dermal exposure, there is no adequate way to determine the
extent of exposure using non-biological methods (Ward et al., 1986;
Fiserova-Bergerova, 1987; Hemminki, 1992; Levesque et al., 1994). For
example, chloroform in exhaled breath has been used as a biological
marker to evaluate dermal exposure while swimming and showering
(Levesque et al., 1994). Under certain conditions, wearing a sampling
device alters behaviours that may, in turn, affect exposures as
measured by a sampling device. Wearing a sampling device may be unduly
burdensome in certain populations. Finally, monitoring devices may not
exist to evaluate these exposures. In these cases, biological markers
could be a preferred method to evaluate exposure.
Biological markers can improve the evaluation of human health
effects associated with environmental exposure to contaminants
(Schulte, 1987; US NRC, 1987; Hulka, 1991; Hulka & Margolin, 1992).
These markers have been advocated as a means to reduce measurement
error in environmental epidemiology (Schulte, 1987; Hulka & Wilcosky,
1988; Hatch & Thomas, 1993). Since they represent internal dose, they
are anticipated to be more predictive of health effects than external
measures of exposure (US NRC, 1987; Hulka, 1991) and they can be used
to validate population exposure models (Georgopoulos & Lioy, 1994).
For risk assessment purposes, biological markers can be useful tools
in evaluation of intermediate end-points and improving the transition
from environmental exposure assessment and animal dose-response models
to actual human health outcomes (Hattis, 1986; IPCS, 1993; Mercier &
Robinson, 1993).
10.4.3 Internal exposure sources
Contaminants can be stored long-term in the body and may be
produced endogenously. These sources of exposure cannot be
characterized without biological markers. Breast milk, bone mineral
and adipose tissue as well as blood represent biological media
available to assess body burden of contaminants, especially those that
concentrate in biological tissue. Mobilization of contaminants from
internal storage can be assessed using biological markers; for
example, pesticide mobilization from adipose tissue can be measured in
blood following fasting.
10.5 Limitations of biological markers for exposure assessment
Although the use of biological markers may result in improved
exposure assessment, their use is not without limitations, since few
validated markers are currently available. Challenges associated with
the use of biological markers include source identification,
pharmacokinetics, timing of exposure, biological variability, altered
response as a result of exposure, potentially invasive sampling
procedures and ethical concerns.
10.5.1 Source identification
Although the ability to integrate over all exposure sources is an
advantage for total human exposure assessment, it limits the ability
to identify the sources that contribute to exposure. Considerations
for source identification include multiple exposure sources, multiple
exposure regimes, non-specific biological response to exposure and
endogenous production. Multiple sources of exposure can result in
multimodal excretion rates because some exposures may be constant
whereas additional exposures may be intermittent. Therefore,
performing cross-sectional studies may miss multiple exposures (Que
Hee, 1993) or changes in exposure patterns and/or concentrations. Some
organic chemicals can be produced endogenously; this may result in an
overestimate of the impact of external sources of exposure.
10.5.2 Biological variability and altered exposure response
Variability of biological response is an inherent characteristic
of biological markers. Although variability may be associated with
exposure levels, genetic differences in toxicokinetics and lifestyle
factors such as diet, smoking and alcohol consumption may also result
in biological variability. The impact of the inherent biological
variability can be reduced by stratified sampling and the use of large
sample sizes. Quantification of biological variability is difficult,
and without adequate information on sources of variation the
interpretation of results for exposure assessment purposes may be
limited. Of particular concern is uncharacterized biological
variability which may influence uptake, distribution, metabolism or
excretion of the contaminant of interest and, therefore, the internal
dose. Some authors consider that the influence of genetic variation is
limited in comparison to the impact of other susceptibility factors
(Perera & Weinstein, 1982). During validation studies, it is necessary
to gather information on potential confounding factors. Baseline data
on pre-exposure biological marker concentrations and demographic
variables should be obtained to assess inter-individual biological
variability more accurately.
Previous exposure and pathophysiological states can change the
biological response to current exposure. This is of particular concern
in cross-sectional investigations or in studies of individuals with
previous or long-term exposure. Previous or concurrent exposure to
agents that elicit similar responses to the contaminant of interest
may alter metabolic activity in response to exposure. These
alterations may lead to an increase or decrease in the rate of
metabolism or excretion, or a change in the type or amount of
metabolites formed, which thereby alters the internal dose and
potentially, the biologically effective dose. Interactions between the
exposure of interest and other environmental exposures, diet,
medication use, cigarette smoking and alcohol consumption may affect
the outcome of the exposure and the interpretation of the biological
monitoring results.
In some cases, exposure to chemicals can cause maladaptive
changes and the biological markers of exposure may represent
subclinical disease. For example, long-term exposure to metals at high
concentrations can result in kidney damage and thereby alter the
processing of metals in the kidney. This results in potentially low
excretion of metals in urine and subsequent underestimation of
exposure (Lauwerys, 1983). In some situations, clinical tests can be
conducted at the same time to address issues such as reduced kidney
function. Conversely, individuals with long-term exposure may
represent those individuals who are least susceptible to the agent's
effects; this may be problematic when using workers to validate
biological markers at high levels of exposure (Hauser et al., 1995).
10.5.3 Participant burden
Collection of biological media presents challenges different from
collection of environmental samples. At the minimum, it requires
active participation on the part of the study subject. In designing
studies, it is critical to recognize that collection of biological
samples can be invasive. Some routine medical procedures, such as
collection of a small vial of blood, may be more acceptable to
participants than more involved, yet non-invasive, sampling methods
such as collection of a 24-h urine specimen or wearing an air sampling
pump for a day. Other sampling strategies, such as fat biopsies or
collection of large volumes of blood, may be impractical for
environmental exposure assessments because of excessive participant
burden. Ethical considerations also include how participants should be
informed about their results.
10.5.4 Biosafety
Biological media can serve as vectors of infectious disease, such
as AIDS and hepatitis. Because of this hazard, special procedures must
be used when collecting, transporting, storing, analysing and
disposing of any human biological samples. All personnel must be
trained for the proper handling of biological samples and protocols
must include instructions for this.
10.6 Media available for use
Numerous biological media are available for use in environmental
exposure assessment. Selection of sampling media depends on the
contaminant of interest, the pattern of exposure, the timing of
exposure, the population studied, ease of collection and storage and
participant burden. Biological monitoring is frequently considered
invasive; however, several media are available for exposure assessment
that can be collected in a non-invasive manner. For occupationally
used chemicals, biological exposure indices and monitoring protocols
are available (Lauwerys, 1983; ACGIH, 1991; Que Hee, 1993); these may
be regarded as starting points for biological sampling in
environmental studies.
Historically, blood and urine have been the primary media for
biological exposure markers. Blood and urine, as well as exhaled
breath and saliva, can be used to document recent exposures; past
exposure can be evaluated using blood and urine as well as keratinized
tissues (hair and nails), ossified tissue (teeth and bone), adipose
tissue and breast milk. Adipose tissue and bone can also represent
future sources of internal exposure. Other media available for
biomarker studies include faeces, nasal lavage, tears, sputum, semen,
cord blood and buccal cells, which can be feasible means for
population exposure monitoring. Other media from cadaver or biopsy
specimens (e.g., liver and kidney samples) have been collected in
select populations but these cannot be used for exposure assessment of
healthy individuals. Table 35 summarizes the media available for use
in biological monitoring in environmental settings.
10.6.1 Blood
Blood has been frequently used for biological monitoring,
especially in clinical settings such as occupational medicine. Blood
can integrate all sources of exposure, including internal sources, and
provide an indication of current internal dose. Since blood transports
all agents throughout the organism, it represents an opportunity to
sample all types of contaminants, such as gases, solvents, metals and
fat-soluble compounds. Both specific (e.g., blood lead) and
non-specific (e.g., sister chromatid exchange) analyses can be
performed. Components of blood available for sampling are whole blood,
red blood cells, white blood cells, plasma, serum and blood proteins,
primarily haemoglobin and albumin.
* Whole blood consists of all the blood components and is
preferable when the distribution of the analyte between plasma and
cellular elements is unknown (Que Hee, 1993).
* Red blood cells make up a large portion of blood and their
primary role is to transport oxygen via haemoglobin throughout the
body. Mature red blood cells contain no nucleus and therefore no
DNA, and have a 120-day lifetime. Chemicals that interact with
haemoglobin, such as carbon monoxide, are found in red blood cells.
Table 35. Biological media available for sampling
Media Chemicals Type of samples Collection Storage Issues References
(examples)
Adipose lipophilic surgical specimens, invasive, minor fat samples can be good for chemicals Patterson et
tissue chemicals, fat biopsies surgical procedure archived that bioconcentrate al., 1986;
pesticides, PCBs or persist Kohlmeier &
Kohlmeier, 1995
Blood gases, metals, whole blood, red invasive, quick, for use of cells, plasma can be analysed Chase & Shields,
pesticides, blood cells, white requires trained sample must not be for lipophilic 1990; Wolff
semivolatile blood cells, plasma, phlebotomist frozen, plasma substances et al., 1991;
organics, VOCs protein samples can be frozen Bond et al.,
1992; Que Hee,
1993
Bone metals (lead) cortical bone, non-invasive, not applicable minimal radiation Rabinowitz, 1991;
trabecular bone 30-60 min collection exposure to subject Hoppin et al.,
(X-ray fluorescent time 1995; Hu et
spectra) al., 1995
Breast lipophilic fat and aqueous non-invasive samples easily fat content of milk Rogan et al.,
milk compounds phases stored in varies, good for 1980; Sim &
refrigerators chemicals that are McNeil, 1992
persistent
Breath gases, mixed air, alveolar non-invasive, quick, samples should be generally represent Wallace, 1987;
semivolatile air requires air analysed soon after recent exposure; for Que Hee, 1993;
organics, sampling apparatus collection, not airborne contaminants Levesque et al.,
volatile organics easily available should use alveolar 1994
air samples
Table 35. (continued)
Media Chemicals Type of samples Collection Storage Issues References
(examples)
Faeces high molecular faeces non-invasive, poor may provide Doi et al.,
weight organic compliance by information on 1988; Wilhelm et
compounds, subjects chemicals that were al., 1990;
lipophilic not absorbed by Kemper, 1993
substances, through the GI tract
metals
Hair cotinine, scalp, pubic hair non-invasive, samples easily stored high potential for Vahter et al.,
metals, PCBs important to and archived external contamination 1991; Bihl et
standardize al., 1993
collection location
Saliva metals, mixed saliva non-invasive, easy samples can be potential for Tomita & Nishimura,
pesticides, for participant stored at room contamination from 1982; Dabbs, 1991;
semivolatile temperature materials in mouth Nigg & Wade; 1992;
organics Silbergeld, 1993
Teeth metals shed deciduous teeth non-invasive samples easily stored useful only in Rabinowitz et
and archived children al., 1989
Toenails metals single toenail or non-invasive, sample easily stored potential for external Garland et al.,
composites requires participant and archived contamination 1993
to collect sample
Urine low molecular spot or grab samples, non-invasive, 24-h samples can be frozen standardised to Lauwerys, 1983;
weight first morning void, urine sample requires creatinine Baselt, 1988;
metabolites, 24-h urine samples motivated participant concentration, kidney Que Hee, 1993
metals, mutagens, for accurate sample damage can influence
pesticides, collection excretion rate
semivolatile
organics
* Numerous types of white blood cells are present in blood. These
cells have a half-life ranging from 18-20 days to decades
(Carrano & Natarajan, 1988). Since these circulating cells have
DNA which can itself be altered or the expression of which can be
changed as a result of exposure to a genotoxic agent, they may be
used for biomarkers of exposure to genotoxic agents (Carrano &
Natarajan, 1988; Kelsey, 1990). Interpretation of genotoxic
response is complicated because DNA damage can result in either
cell death or removal of the marker by DNA repair, or may alter
cell functions (Perera, 1987). Regardless of this, correlations
have been seen between environmental exposures and DNA adducts
and other cytogenetic responses (Perera et al., 1988; Heddle et
al., 1991; Santella et al., 1993; Yager et al., 1993).
* Plasma and serum represent the non-cellular component of
blood. Plasma is a straw-coloured aqueous solution of
electrolytes, non-electrolytes and macromolecules (including
clotting factors); serum is plasma without the clotting factors
(Que Hee, 1993). Plasma represents a component of whole blood
(approximately 60%), and it may contain the most biologically
active fraction of blood borne contaminants, since plasma is in
more immediate contact with tissues (Silbergeld, 1993). Plasma
can be used for analysis of lipophilic chemicals, thereby
avoiding the need for fat sampling.
* Blood proteins can be sensitive monitoring tools for chemicals
that bind to macromolecules including DNA (Osterman-Golkar et
al., 1976; Bond et al., 1992). Protein adducts, unlike DNA
adducts, are not repaired and may prove to be a useful dosimeter
of mutagen exposure (Grassman & Haas, 1993; Que Hee, 1993).
Haemoglobin and albumin are two proteins available for use in
exposure assessment. Haemoglobin is located in red blood cells in
high concentration and has the half-life of red blood cells (120
days); albumin is present in serum and has a half-life of 21
days. Because of their differing biological half-lives, these
proteins can be used to investigate the timing of exposure.
Collection, storage, and shipping of blood samples can be
resource-intensive. Blood sampling is invasive to the subject and
requires a trained phlebotomist. Two primary methods of blood
collection are venipuncture and finger stick. Blood from finger sticks
is more subject to surficial contamination, has a higher percentage of
red blood cells and is a smaller volume than that collected using
venipuncture (Graziano, 1994). However in some populations, such as
small children who have veins that are hard to find or who have fears
of venipuncture, finger sticks can be the collection method of choice.
The analytes of interest influence the sample collection regimen.
Concerns about external contamination, interaction between the analyte
and the sample collection vessel, and degradation, bacterial
contamination and time until processing should be considered when
designing the sampling protocol (Aitio & Järvisalo, 1984). Test tubes,
additive agents such as EDTA and sample stoppers all represent
potential contamination sources. Processing of serum samples should
occur in a timely fashion to prevent degradation of the analytes of
interest. For example, collection of serum samples for organochlorine
pesticide analysis requires that:
* all equipment be solvent-rinsed before collection and processing
* care be taken to avoid contact between the blood and the stopper of
the test tube
* the sample be centrifuged within 24 h of collection
* the serum be kept frozen prior to analysis.
Shipment of frozen samples requires special precautions to
prevent thawing during transport. For results to be valid, the
biological sample should be analysed within holding time, as required
by the specific medium and analyte.
10.6.2 Urine
Urine is another frequently collected biological medium. The
concentrations of compounds found in urine usually reflect
time-weighted averages in plasma during collection and storage in the
bladder (Que Hee, 1993). The presence of a contaminant or its
metabolite in urine generally represents recent exposure, though in
some cases it may represent release from storage within the body
(Lauwerys, 1983). Urine can be analysed for metabolites of organic
chemicals (e.g., benzene and styrene), metals (e.g., arsenic and
mercury) and pesticides as well as for mutagenic potential (Lauwerys,
1983; Baselt, 1988; Que Hee, 1993). Since collection of urine samples
is non-invasive, some investigators feel that, when validated, urine
may be a better sampling medium than blood for monitoring (Smith &
Suk, 1994).
Three types of urine samples are used for biological monitoring:
spot urine specimens, first morning voids and 24-h urine specimens
(Baselt, 1988).
* Spot urine samples are relatively easy to collect but there may
be significant variability with respect to exposure prediction as a
result of metabolism, liquid consumption and kidney function.
* First morning void samples have less variability since they are
more concentrated than spot samples, but require motivated subjects
to collect the samples.
* Twenty-four hour urine samples control much of the
intraindividual variability but require highly motivated subjects
in order to collect useful samples (Baselt, 1988).
The choice of sampling type is dependent upon the analyte of
interest, the pattern of exposure, the anticipated concentration and
the population. Urine samples should be refrigerated before analysis
to reduce biological degradation and bacterial growth. Sample
processing and storage should occur as soon as possible after
collection in the manner required for the analyte of interest.
To make the results of urine monitoring comparable between
individuals, analytical results are frequently standardized to
creatinine concentration or specific weight. Standardization reduces
some of the variability of body size and urinary output (Lauwerys,
1983; Sato, 1993). However, kidney damage can alter the creatinine
excretion and therefore standardization may not be appropriate in all
cases. Clinical data can be used to evaluate kidney function.
10.6.3 Exhaled breath
Exhaled breath analysis has been used in both occupational and
environmental settings. Breath analysis is useful for assessing recent
exposure to gases (e.g., carbon monoxide) and organic vapours and
solvents (e.g., acetone and toluene). Limited studies have been made
on the use of breath analysis for airborne particles and associated
PAHs (Sugita et al., 1997). To be useful, breath measurements must
relate both to exposure and to blood concentration (Bond et al.,
1992). Breath sample concentrations of contaminants can vary as a
function of body build, metabolism, sex, physical activity and
ventilation rate as well as the exposure route (Que Hee, 1993). Two
types of samples are available for collection: mixed exhaled breath
and alveolar air, or end-expired air (Wallace, 1987; Wallace et al.,
1991a,b; Que Hee, 1993).
* Exhaled breath can be a mixture of inhaled and exhaled air. If
the exhaled biological marker is not present in inhaled air, then
exhaled breath analysis is an effective means to measure internal
exposure. For example, when alcohol has an internal source only
(i.e., ingestion) a mixed breath sample is appropriate.
* Alveolar air provides a measure of the air that is in equilibrium
with the blood in the deep lung (Bond et al., 1992). For analytes
present in inhaled air, it is necessary to collect an alveolar air
sample.
Breath samples can be used to assess microenvironmental exposure
as well as exposure to chemicals with short biological half-lives that
enter the body through non-inhalation routes (Wallace, 1987; Levesque
et al., 1994). A number of methods are available to collect exhaled
breath samples for organic and other gaseous contaminants (Wallace,
1987a; Pellizzari et al., 1988; Periago et al., 1992). Exhaled breath
collection is quick and easy for the participant, but the actual
sample collection can involve complex air collection apparatus to
gather a sufficient sample. The sampling tools are similar to those
currently employed for sampling of air contaminants. Methods for
airborne particles and components thereof have been used to a limited
extent (Goto et al., 1997, Sugita et al., 1997). Potential sample
collection error concerns in sample collection include loss of sample
volume, sample storage prior to analysis and cross-reaction among
analytes.
10.6.4 Saliva
Glands at four locations in the mouth produce saliva; the
secretion rate varies at each location. Chemicals enter saliva via
passive diffusion from plasma. Therefore, saliva may become a useful
tool to non-invasively characterize plasma levels of contaminants
(Silbergeld, 1993). Social science research has used saliva sampling
because of its ease of collection and storage (Dabbs, 1991, 1993).
Contaminants found in saliva include cotinine, drugs, metals, organic
solvents, pesticides and steroid hormones (Tomita & Nishimura, 1982;
Nigg & Wade, 1992; Silbergeld, 1993).
Collection of saliva is relatively easy. One approach consists of
having the subject rinse the mouth and chew on a stimulant, typically
a piece of clingfilm or sugar-free gum. The subject then spits into
the sampling container. Another method uses pumps to sample from the
salivary glands. In either case, care should be taken to minimize
contamination from the mouth. Because the concentrations of chemicals
in saliva can fluctuate with circadian rhythm, the collection of
saliva samples should occur at the same time each day (Dabbs, 1991).
Saliva samples are quite stable and can be stored at room temperature
for several days (Dabbs, 1991, 1993). However, in order to reduce
viscosity from proteins, saliva samples are typically frozen prior to
chemical analysis.
10.6.5 Keratinized tissue (hair and nails)
Keratinized tissues, primarily hair and toenails, are practical
sampling media for evaluation of past exposure to metals (Bencko et
al., 1986; Bencko, 1991; Subramanian, 1991; Kemper, 1993; Bencko,
1995). Toenails are usually the medium of choice: see below. These
media integrate exposures over a period of months, contain relatively
larger concentrations of trace elements than blood or urine and are
easy to collect, store and transport (Garland et al., 1993; Kemper,
1993). Therefore, archiving specimens for future analyses is a viable
option. Since hair and toenails are no longer in contact with the
blood supply, they can provide a picture of past exposure. Because of
the ease of collection, hair and toenails have been collected in
numerous environmental studies, especially of children (DiVincenzo et
al., 1985; Bencko et al., 1986; Wilhelm et al., 1989, 1991; Sukumar &
Subramanian, 1992; Bustueva et al., 1994; Santos et al., 1994). Hair
and toenails have been collected for elemental analyses in both
environmental exposure assessment and nutritional evaluation studies
(Garland et al., 1993; MacIntosh et al., 1997).
Hair might be a useful medium to study exposure to environmental
tobacco smoke (ETS). For example, in the framework of the German
Environmental Survey (Krause et al., 1992) it was concluded that in
large population studies nicotine and continine in urine as well as
nicotine in hair are useful indicators of exposure for different
levels of active and passive smoking. Continine and nicotine
concentrations in hair have also been used to study fetal exposure by
maternal smoking (Klein et al., 1993). Hair has also successfully been
used in studies evaluating exposure to organic mercury (Suzuki et al.,
1989) or PCB (Que Hee, 1993).
One of the major concerns in the use of hair as a sampling medium
is the potential for surficial contamination, in part due to the large
surface area and the oily nature of hair. Sources of contamination
include sweat, cosmetics, dirt and clothes (Doi et al., 1988;
Raghupathy et al., 1988). Other potential disadvantages of sampling
hair include sample preparation, differing growth rates at different
locations on the body, unknown correlation with biological effects and
biological variability (Wilhelm et al., 1990; Kemper, 1993). Hair
grows approximately 1 cm/30 days (Que Hee, 1993) and can be evaluated
along the shaft to provide a profile of exposure over time. Since
growth rates of hair differ based on body location, standardization of
sampling location is crucial.
Because of concerns about contamination, exposure assessors
usually prefer toenails to hair; however, some authors prefer hair for
exposure assessment based on correlations with concentrations measured
in environmental media and ease of collection (Wilhelm et al., 1991).
When sampling nails, toenails are usually the medium of choice, since
they are thicker and less subject to contamination than fingernails.
Toenails can be collected by participants, shipped to distant research
sites and stored in paper envelopes prior to analysis. To reduce
problems associated with surficial contamination, the outer layer of
the toenail can be removed. Each toenail grows at a different rate and
therefore each nail may represent a different time period of exposure;
long-term average exposure can be evaluated using a composite of all
toenails (Garland et al., 1993).
10.6.6 Ossified tissue
10.6.6.1 Teeth
Teeth constitute a unique medium for assessment of past exposure.
Depending on the tooth type and part of the tooth, one can reconstruct
early childhood exposures to bone-seeking elements, such as lead
(Rabinowitz et al., 1989). However, sample collection opportunities
are limited to shed deciduous teeth in young children. Sample
collection and storage are easy; removing the outer layer of the tooth
minimizes external contamination.
10.6.6.2 Bone
Bone represents both past exposure to bone-seeking elements and
is a source for future internal exposure to these elements. The
concentrations of elements in bone represent long-term exposure and
storage of contaminants. For example, the half-life of lead in bone is
approximately 10-40 years (Rabinowitz, 1991). Although numerous
elements can be detected in bone tissue using destructive analyses
such as atomic absorption spectroscopy (AAS), in vivo measurement of
environmental contaminants in bone has been limited to lead (e.g.,
Somervaille et al., 1988; Hoppin et al., 1995). Lead concentration in
bone can be analysed non-invasively using a technique known as X-ray
fluorescence (XRF) (Hu et al., 1995). Bone lead concentrations in
adults correlate well with integrated measurements of blood lead
concentrations (Somervaille et al., 1988; Hu et al., 1995). Sources of
variation in bone lead measurement include the degree of bone
mineralization, the concentration of lead in bone, the overlying skin
thickness and subject movement during the measurement. Lead and other
elements can be mobilized from bone during physiological events such
as pregnancy, lactation and osteoporosis (Silbergeld, 1991).
10.6.7 Breast milk
Environmental studies have used breast milk to evaluate past
exposure to lipophilic chemicals (e.g., pesticides and PCBs) and
metals (WHO, 1996b) and to examine potential exposures for
breast-feeding infants (Niessen et al., 1984; Davies & Mes, 1987;
Sikorski et al., 1990; Sim & McNeil, 1992). Organic chemicals found in
breast milk have high lipid solubility, resistance to physical
degradation or biological metabolism and slow or absent excretion
rates (Rogan et al., 1980). Breast milk represents a major route of
excretion of lipophilic chemicals for lactating women (Rogan et al.,
1980; Sim & McNeil, 1992). Concentrations of chemicals in breast milk
are a function of parity, age, body mass, time of sampling,
nutritional status, lactation period and fat content of milk (Rogan et
al., 1986; Sim & McNeil, 1992). Breast milk results are generally
standardized to milk fat levels.
Breast milk sampling represents a non-invasive method to estimate
body burden of contaminants in adipose tissue. The correlation between
contaminant concentrations in the lipid phase of milk and adipose
tissue is good (Sim & McNeil, 1992). External contamination is a
concern for breast milk samples; all sampling equipment should be
cleaned in a manner that will prevent contamination. Although breast
milk sampling is an applicable way to estimate population exposure to
chemicals that bioconcentrate, sampling is limited to lactating women,
who may or may not be representative of the population as a whole.
10.6.8 Adipose tissue
Exposure assessment studies using adipose tissue have been
limited primarily to ecological studies comparing fat from cadavers or
surgical specimens to general pollution levels. Adipose tissue
represents a long-term reservoir of lipophilic compounds that the body
slowly metabolizes and may release into the bloodstream. Unfortunately
there is no non-invasive manner to sample fat stores directly, and
many subjects see fat sampling as exceedingly invasive. The analytical
method and detection limit requirements determine the quantity of
adipose tissue necessary. In studies measuring body burden of dioxins
in fat, a minor surgical procedure was necessary to collect a 20 g
sample from healthy potentially exposed subjects (Patterson et al.,
1986). When a smaller sample size is sufficient (200 mg or less),
needle biopsies of fat stores in the buttocks can be used (Que Hee,
1993; Kohlmeier & Kohlmeier, 1995).
10.6.9 Faeces
Faeces are a highly fat-soluble medium that provides information
on compounds of high-molecular weight that exit the body via biliary
excretion and on unabsorbed chemicals that enter the body via
ingestion. Metals may also be monitored in faeces; however, it is
unclear whether the metals found in faeces represent absorbed or
unabsorbed elements (DiVincenzo et al., 1985; Vahter et al., 1991).
Although collection of faeces is regarded as non-invasive, very few
subjects are sufficiently motivated or interested in their collection
(Bihl et al., 1993).
10.6.10 Other media
Several additional biological media have been used for
determination of biomarkers of exposure including tears (e.g.,
Ellegard, 1997), nasal lavage and nasal plugs (Steerenberg et al.,
1997), sputum (e.g., Pizzichini et al., 1997) and semen (e.g., Sram et
al., 1996). In most cases, these studies have focused on biomarkers of
inflammation as indicators of exposure to airborne oxidants.
10.7 Summary
Biological markers represent a means to monitor environmental
exposure by characterizing an individual's total dose of a contaminant
from all sources of exposure. The main advantage of this strategy is
in evaluation of an individual's total exposure using a measure which
integrates over all exposure sources and is influenced by human
behaviour. Biological markers are also believed to be more predictive
of health effects than external measures of exposure. They address
several exposure assessment needs:
* characterizing an individual's or a population's exposure
* generating population distributions of dose
* identifying the environmental and demographic determinants of
exposure.
The main disadvantage of biological markers is the difficulty in
characterizing the individual sources which contribute to the
subject's total exposure. When developing and utilizing biological
markers, understanding the toxicokinetics of the contaminant in the
system is crucial to characterize the biological variability and to
determine whether the biological marker is valid for exposure
assessment purposes at the concentration of interest. Biological
markers have been crucial in improving our understanding of human
exposure to certain contaminants, such as lead. Numerous non-invasive
methods are available for biological monitoring, and exposure
assessors should try to include these when developing environmental
monitoring protocols.
11. QUALITY ASSURANCE IN EXPOSURE STUDIES
11.1 Introduction
Human exposure studies are complex and often utilize specialized
instrumentation and management of large amounts of data. Consequently,
quality assurance (QA) should be applied to all aspects of an exposure
study, including its design, implementation and reporting, to ensure
the reliability and reproducibility of the results. A successful QA
programme will monitor occurrence of potential errors in various
components of the study and establish protocols for remedying such
errors. In this chapter, important types of potential error in
exposure results are described, and tools for identifying them are
introduced.
Exposure studies involve evaluations and comparison of exposures
over time, geographical locations and populations. It is important
that the results of the study accurately represent the exposures,
rather than reflecting bias or error introduced by the study design or
method. The reader is referred to the previous chapters on strategies
and designs for exposure studies (Chapter 3) and related statistical
concepts (Chapter 4) with respect to the principles of designing a
high quality exposure study.
The reader is also referred to Environmental Health Criteria
141: Quality Management for Chemical Safety Testing (IPCS, 1992) a
monograph which deals explicitly with the organization of a QA
programme. A prerequisite for producing data of good quality in
exposure studies is the availability of adequate facilities,
equipment, and personnel who are both well educated and trained.
Studies need to be adequately designed and planned. Field and
laboratory procedures need to be well defined, so that they can be
carried out in the most appropriate way and in a consistent and
reproducible manner. Other key elements in performing and reporting
exposure studies include the final report and, lastly, the archiving
and retention of data (IPCS, 1992)
11.2 Quality assurance and quality control
It is important to distinguish between the related concepts of QA
and quality control (QC). QA refers to the overall management and
organizational systems instituted to assess and maintain the integrity
of the study. It includes independent monitoring that assures end
users of the data that facilities, equipment, personnel, methods,
practices, records and controls conform to accepted quality management
principles. An effective QA programme provides confidence that the
overall study meets the pre-established quality standards of accuracy,
precision, completeness and clarity. QA should be integrated within
the entire study so that the results are valid and that the final
report accurately reflects these results (IPCS, 1992). Assessing data
periodically is an essential aspect of QA.
QC is a valuable QA tool that is applied to individual components
of the study. Examples of such components are selection of study
participants, collection of environmental samples, chemical analysis
and analysis of data. The quality of an analytical measurement may be
evaluated, for example, by comparing analytical results against a
known standard, determining the sensitivity, accuracy, and precision
of the analysis and ensuring that the analytical equipment has been
properly maintained. These measurements would be part of a QC system
(IPCS, 1992).
Auditing procedures, on the other hand, are used to assess the
quality of other aspects of a monitoring operation such as sampling
procedure and transport of samples, as well as recording and reporting
data. These procedures, although not necessarily quantitative, will
generally promote vigilance by the operator against possible errors
(WHO, 1986).
11.3 Elements of a quality assurance programme
All exposure studies must have a QA programme with a
corresponding quality assurance plan that describes the implementation
of the programme. The US EPA, for example, requires the development of
detailed QA project plans that contain a complete description of all
elements of the QA programme associated with the collection,
measurement, validation and reporting of data. Common elements in QA
plans are given in Table 36.
Table 36. Common elements in quality assurance plans
Organization and personnel
Record keeping and data report
Standard operating procedures
Equipment maintenance and calibration
Internal audit and corrective action
Sample handle management
A fundamental step in the QA process is the delineation of the
study objective and a description of the study design which describes
the study population, the data to be collected and the statistical
analysis to be performed to meet the objectives. The programme
management must be described (which identifies individuals in the
programme and defines their responsibility from management, field
monitoring, sample handling, sample analysis, data reduction and
reporting). All programmes must have a QA officer who is independent
of the programme and organization and who has oversight of all QA
activities. The QA plans must define data quality objectives
(accuracy, precision, detection limit, representativeness and
completeness) and demonstrate that they are adequate to meet the study
objectives. Specific procedures for generating and validating study
data are given. An important component of any exposure study is
demonstrating the feasibility of all study procedures through a pilot
study which should be included in the QA plan. For complex field
studies where methods of procedures may require modification in the
field, it is important to clearly delineate corrective action
procedures. Standard operating procedure (SOPs), detailed protocols
for all components of the study, are appended to the QA plan. A
description of these are given in section 11.4.3.
11.4 Quality assurance programme
Important elements in assuring that the outcome and data
generated from the laboratory is accurate and reliable depends on the
QA programme implemented by the laboratory. Key elements are given in
the following.
11.4.1 Organization and personnel
It is important for the competent laboratory to establish an
organization that can function to best serve the need of the testing
facilities, regardless of the specific organization structure,
personnel include management, study directors, QA coordinator and
support staff. Properly trained personnel are crucial to the conduct
of a quality study. The training requirements and procedures should
cover professional, technical and support staff, to assure that they
have adequate knowledge and skills to carry out their duties.
11.4.2 Record-keeping and data recording
Detailed records, with dates, should be kept on the introduction
of new batches of so-called useable supplies (e.g., filters, reagents,
sample containers, plasticware, pipettes, etc). Likewise, any changes
in instrumentation or personnel have to be recorded. Afterwards, when
identifying the reasons for changed analytical performance, this kind
of thorough record-keeping is of utmost importance.
The risk of introducing errors while transcribing data into
record books or entering them into computer files is quite large.
These are so-called clerical or human errors which may involve not
only numerical errors but also formatting errors, identification
errors, etc., which can lead to a serious bias in the analysis of
data. There are several procedures for spotting such errors, from
simply reviewing the data visually to computerized procedures which
flag questionable data.
Finally, data sets should be validated, that is approved for
analysis. Data validation should form part of QA, and its procedures
should be clearly defined. Data validation usually consists of two
parts: data entry procedures to identify formatting errors at the time
of entry, and acceptance procedures which are designed to compare the
reported data against specified criteria in order to judge the
reasonableness of reported values. Acceptance procedures identify
various types of anomalies in the data including impossible values,
individual and multiple outliers and entire subsets of incorrect data.
11.4.3 Study plan and standard operating procedures
QA involves the development and use of study plans and SOPs. The
study plan (which should be very explicit and detailed) is the most
important document for providing information to all participants on
all aspects of the study, including information on responsible
personnel, sample collection, sample storage and pre-analytical
treatment, analytical procedures and data analysis. Deviations from
the study plan in, for example, sampling frequency, sampling media or
sampled population, necessitate a detailed amendment to the study
plan. This is very important since in many monitoring studies sampling
occurs on a regular basis, and the data obtained may be worthless if
the present sampling scheme is not followed.
SOPs are written instructions which describe how to perform all
field and laboratory activities. These should be available to all
personnel involved in the field and laboratory work and be a component
of their training programme. SOPs ensure that all personnel associated
with study operations will be familiar with and use the same
procedures. If different individuals perform important study
functions, such as storing samples, preparing solutions, running
analytical equipment, or archiving documents, these operations should
be performed in the same manner. By standardizing procedures for the
conduct of studies, SOPs have a valuable QA function. They prevent the
introduction of many potential errors in the generation, collection
and reporting of data.
The development of SOPs includes the following aspects: who
should prepare, review and authorize SOPs; which field and laboratory
operations require SOPs; and what information the SOPs should contain.
The nature of the laboratory work being done and the training and
experience of the laboratory personnel at a particular facility should
determine exactly how extensive the content needs to be (IPCS, 1992).
11.4.4 Collection of samples
The potential for contamination is a major issue in environmental
sampling. The highly sensitive analytical instrumentation currently
available permits analysis of very small analyte quantities where the
amounts of pollutants or chemicals present in environmental media are
often quite low (e.g., parts per billion). The risk of significant
contamination is particularly great when monitoring low concentrations
of substances ubiquitous in the environment or present in the
materials and tools coming into contact with the samples.
Samples must be handled and stored in such a way that the level
of the substance to be analysed remains stable. Processes that are
most likely to decrease sample stability include precipitation,
chemical deterioration (e.g., photolysis), surface absorption and
evaporation of the analyte. Another factor that could be important is
changes in the matrix, which may affect recovery of the analyte.
11.4.5 Equipment maintenance and calibration
Adequate equipment should be available for the receipt, handling,
storage and analysis of samples. Its maintenance and calibration is
essential to QA practise. Equipment used for analysis must be tested,
calibrated and standardized.
11.4.6 Internal audit and corrective action
Internal audit and corrective action are critical activities in
QA management. Audit procedures are to verify that the procedures are
conducted the way they were planned and described in the study plan.
Non-conformance must to be corrected to attain the study objectives.
11.5 Quality control/quality assurance for sample measurement
11.5.1 Method selection and validation
A chemical compound can usually be analysed by a variety of
different methods. Some methods emphasize quality of analysis, i.e.,
accuracy and precision, whereas others are directed mainly towards
practicality and low cost. However, even the best method may give
incorrect results if improperly used. Methods are customarily
classified according to their main use in the analytical field into
definitive, reference and field routine methods. The best
approximation of the true value obtained by analysis is the
definitive value, i.e., the result obtained by the definitive
method. The definitive method is, however, generally not considered
practicable for daily laboratory use. Therefore, the purpose of a
reference method is often to serve as a basis for the determination
of the accuracy of the field methods. Field methods (or routine
methods) are those in everyday use in the laboratory. They are not
necessarily as precise and accurate as reference and definitive
methods. Their use is justified, however, because the definitive and
reference methods are often too cumbersome and expensive and may not
be available. The precision and bias relating to the method should be
known for every routine method used.
The following data quality specifications have to be validated in
method selection to meet the requirements and objectives of the
exposure assessment project.
11.5.1.1 Accuracy
Accuracy is defined as the closeness of agreement between a test
result and the accepted true value. Accuracy or validity of an
analysis is primarily determined by the specificity of the method and
the analytical recovery.
Fig. 31A illustrates the ideal case where the reported values
correspond exactly to the expected values. However, all analytical
procedures are subject to a variety of analytical inaccuracies or
biases. Fig. 31B shows the effects of a proportional bias in which
the reported values are higher than the expected values. This bias is
called proportional because the amount of bias increases in direct
proportion to the concentration of analyte in the specimen. Fig. 31C
illustrates the effect of a constant bias, in which the reported
values are higher than the expected values by a constant amount, at
all concentrations of analyte. The biases shown in Figs. 31B and 31C
are positive biases, because the reported values are greater than the
expected values. Of course, negative biases may also occur;
furthermore, the reported values may fall along a curve rather than a
straight line. Fig. 31D illustrates combined positive and negative
proportion biases. When combined biases are present, there is
frequently one concentration at which the reported value corresponds
to the expected value. This phenomenon -- that the reported value at
some analyte concentration is the same as the expected value whereas
there is disagreement at all other concentrations -- is commonly
observed and must be considered when interpreting quality control
data. Because of this, it is recommended that reference samples should
cover the range of expected measurement values.
11.5.1.2 Precision
Precision or reliability of an analysis refers to the uniformity
of the results of replicate analyses irrespective of the true
concentration of the analyte. The precision of an analysis may vary
depending on many factors, such as the skill and experience of the
analyst, the purity of the chemicals, the quality of measuring devices
and the time interval between replicate analyses. The precision is
determined by calculating the percentage relative standard deviation
among replicate analyses. In this manner precision can be defined as
the closeness of agreement between independent test results obtained
under prescribed conditions.
The effects of random variation on analyses are illustrated in
Figs. 32A and 32B, which show how laboratory's results may, on the
average, fall along some operational line even though the individual
results are distributed about the line, within certain limits of
variability. Fig. 32A illustrates limits of variability that increases
in proportion to the mean analyte concentration.
11.5.1.3 Sensitivity
Sensitivity of a test method can be defined and measured as the
limit of detection (LOD) and the limit of quantification (LOQ).
11.5.1.4 Detection limits
Appropriate determinations of detection limits is crucial to
exposure data analysis and interpretations. Since there are several
types of detection limits, each with its specific applicability, it is
important that the methods and procedures to determine such limits be
clearly presented as part of the QA/QC procedures. The term "lower
limit of detection" (LLD) may refer to very different concepts. For
example, the instrumental detection limit is defined as a multiple of
the noise level of the analytical instrument, usually a factor of 3.
However, when sampling and analysing environmental media, the
instrumental detection limit constitutes only a fraction of the true
limit of detection. The LOD takes into consideration the response of
the analytical instrument to the specific analyte in standards of
known concentration or, preferably, a matrix similar to that of the
sample. Since the response of the instrument to repeated analysis of
the same analyte concentration varies, the LOD is estimated as a
function of the standard deviation (SD) of the repeated analysis,
typically as tn-0.01 × SD, where t is the value of the Student
t distribution with n-1 degrees of freedom, alpha = 0.01 is the
type I error and n the number of repeated analyses, usually 7.
However, sampling methods can have a background of one or more of
the target analytes. For example, when using solid sorbent for air
sampling, the sorbent may contain varying levels of background
contamination across samples (e.g., varying benzene concentrations in
Tenax). The method detection limit (MDL) includes the background
contamination in the sampler. It is determined using a similar
statistical approach as the LOD, but using field and/or laboratory
blanks instead of the known analyte concentrations. The MDL can be
defined as the analyte concentration at which we have a given
certainty (e.g., 1-alpha = 0.99) that the sample concentration differ
from background. Finally, the lower quantification limit (LOQ) is
defined as 10 SD, where SD is derived from the LOD determination. LOD,
MDL and LOQ differ across laboratories using the same methods. More
importantly, they vary over time for the same laboratory.
The treatment of values below the MDL in statistical analysis of
the data can have a strong impact on estimates of exposure
distribution parameters. The reader may refer to Gilbert (1987), for
more specific information. There also are upper limits of detection,
defined by the range of linear response of the analytical method. In
that case the detector may be overloaded with the result that it is
not working in its linear detection range.
11.5.2 Internal quality control
The purpose of internal QC is to document that the method is in
statistical control and without systematic errors so that the observed
sampling results consistently fall within established control limits.
Internal QC procedures are used primarily for the control of
analytical precision (Taylor, 1988).
11.5.2.1 Control charts
Sometimes changes in the analytical performance are not abrupt
but take place gradually ("drift"). Such gradual changes are difficult
to perceive from a single central result, but many become evident with
time if the results of control samples are graphically displayed with
respect to time or sequence of measurements.
Control limits are used as criteria for signalling the need for
action, or for judging whether a set of data does or does not indicate
a state of statistical control. Control limits can be based entirely
on the data from the samples, in which case the chart illustrates
whether the method or procedure is repeatable. If the control chart
contains limits that are based on analytical standards, then the chart
is useful for discovering whether the observed measures for a sample
value differ from the adopted standard values by an amount greater
than should be expected by chance alone. Generally, control charts
consist of warning limits and action limits. Warning limits often
correspond to ± 2 SD from the mean, whereas action limits are set at
± 3 SD from the mean. Such thresholds mean that as long as the process
is in control at the centre value there is a 5% chance that a result
will exceed the warning limits and a 0.3% chance that a result will
exceed the action limit, thus erroneously signalling an "out of
control" message (WHO, 1986; ISO, 1993).
To set up a Shewhard control chart (Fig. 33), measurements of,
for instance, standard solutions, duplicates or spiked samples must be
gathered while the analytical procedure is in control. The control
parameters most frequently evaluated in Shewhard control charts
include the average or median of control measures (average or median
charts) and the range (difference) between duplicate analyses on the
same test sample (range charts) (UNEP/WHO, 1986; AOAC, 1991; ISO,
1991; Christensen et al., 1994). In a cumulative sum (cusum) chart
(Fig. 34) a reference value, usually the intended or expected value,
is subtracted from each observation. The cumulative sums of the
deviations from the reference value are formed, and these cusums are
plotted against the serial numbers of observations. The cusum chart is
usually more sensitive to small shifts in level than the Shewhard
chart and highlights persistent and recurring differences (Christensen
et al., 1994; ISO, 1997).
The Shewhard chart has six control limit lines corresponding to
±rhop, ±2rhop, and ±3rhop (rhop = rhoy/ n1/2) (rhop, appointed
standard deviation in the control chart; rhoy, estimated standard
deviation of the distribution of results; n, number of control
results) (Christensen et al., 1994).
11.5.3 External quality control
The aim of external QC is to demonstrate that analytical results
obtained are accurate and comparable with the results ascertained by
other laboratories. Usually, external QC measurements form part of
external QA schemes or proficiency testing programmes. In such a
scheme or programme, the coordinating laboratory prepares a
homogeneous reference sample and distributes portions to the
participating laboratories for analysis. Participating laboratories
are required to examine the reference samples within a specified time,
preferably together with collected samples, and submit the results of
the reference samples to the coordinating laboratory which collates
the data, performs a statistical analysis and sends an evaluation
report back to the participating laboratories. In cases of poor
performance, the laboratories may be contacted, and suggestions may
made for improving performance.
External QC schemes offer participating laboratories many
advantages. They demonstrate the reliability of laboratory results and
provide independent evidence of the quality of laboratory performance
and individual analyst proficiency. In addition, external quality
schemes allow participating laboratories to compare their own
performance with that of other laboratories. Participation in an
external QC scheme can encourage self-appraisal and minimization of
laboratory errors. It can also be used to reduce the frequency of
internal QC efforts when consistently favourable results are achieved
on external QC test samples. Furthermore, it can assist in identifying
needs for training and changes in laboratory procedures (AOAC, 1991).
Examples of existing external QC schemes are given in Table 37
(see also Christensen et al. (1994)).
A specific example for an approach used in external QC is the
UNEP/WHO regression method. The regression method is based on the QC
programmes developed in the UNEP/WHO pilot project on assessment of
human exposure to pollutants through biological monitoring for the
determination of lead in blood and cadmium in kidney cortex (Vahter,
1982). This external QC method was also applied in the UNEP/WHO Human
Exposure Assessment Location (HEAL) study. For the studies on lead and
cadmium, reference external QC samples of blood, air filter material,
dust and diet were sent to participating laboratories (Vahter &
Slorach, 1990). The same procedure was also adopted for the HEAL
nitrogen dioxide study, using standard sodium nitrite solutions and
nitrogen dioxide-exposed sample badges as QC samples (Matsushita &
Tanabe, 1991).
Table 37. External QC schemes
Scheme Country Reference
Centre de Toxicologie du Quebec Canada Weber, 1988
(CTQ)
Danish External Quality Assessment Denmark Anglov et al.,
Scheme (DEQAS) 1993
External quality control in the Germany Schaller, 1991
toxicological analyses of biological
materials, German Society of
Occupational Medicine
Japanese External Quality Japan Sugita et al.,
Assessment Scheme 1990
NIOSH Proficiency Analytical USA NIOSH, 1979
Testing Programme
United Kingdom National External United UKNEQAS, 1993;
Quality Assessment Schemes Kingdom Bullock, 1986
(UKQAS)
The accuracy and precision of the analytical results were
evaluated by using a statistical technique known as the maximum
allowable deviations (MAD) method (Vahter, 1982; WHO, 1986; UNEP/WHO,
1991). This method is based on the linear regression line of reported
versus "true" values and established acceptance criterion on how much
the line may deviate from the ideal line where the measured equals the
expected value. Acceptance criteria or the MADs have to be decided on
separately for each pollutant or for each medium. The stringency of
the MAD is primarily based on the required quality of the actual
monitoring data and the sensitivity, accuracy and precision of the
analytical methods. As in the use of control charts, decision
concerning acceptance and rejection of results is based on statistical
criteria, i.e., on the probability of making right or wrong decisions.
A laboratory may be erroneously rejected when in fact its methodology
is satisfactory, or it may be erroneously accepted when its
methodology is not satisfactory. Power analysis is done to determine
the probability of including an unsatisfactory laboratory. The concept
of statistical power is described more fully in Chapter 4.
On the basis of the power analysis, acceptance intervals (AI) can
be calculated for a laboratory. The AI will be narrower than the
interval between the MAD lines and the AI lines will be closer to the
MAD lines with increasing number of QC samples. Also, methods with
inherently smaller errors will decrease the difference between the MAD
lines and the AI. Fig. 35 shows the regression line for the reported
results of analysis of six QC samples versus the reference values. The
MAD interval is indicated by solid lines and the acceptance interval
by broken lines (Vahter, 1982). Fig. 36 shows the results of lead in
blood from one laboratory during the training phase in the global
UNEP/WHO Project on Assessment of Exposure to Lead and Cadmium through
Biological Monitoring. The results of QC 2 and QC 3 were rejected,
i.e., the regression lines fall outside the acceptance lines when
evaluated between the reference values 100 and 400 µg/litre (Lind et
al., 1987).
The experience from the QA programme of the WHO/UNEP HEAL
Programme shows that detailed protocols, instructions, and control
activities covering the whole process, from sampling to analysis and
data reporting, are essential if reliable data are to be obtained. The
results clearly demonstrated that good analytical performance for a
pollutant in one medium (e.g, lead in blood) is no guarantee of good
performance for the same pollutant in another medium (e.g., lead in
air filters). Similarly, good analytical performance at one
concentration is no guarantee of good performance at higher or lower
concentrations. It is, therefore, important that the QC samples have a
matrix similar to that of the monitoring samples and that the
concentrations of the QC samples cover the expected range of
concentrations. One, or a few, reference samples are not sufficient
for the evaluation of the evaluation of analytical performance using
rigorous MAD criteria (Vahter & Slorach, 1990).
11.5.4 Reference materials
Reference materials are important means of ensuring comparability
of analytical results across laboratories, over time, among
populations and among studies. Reference samples for internal as well
as external QA have been used in the analysis of environmental media,
such as air, water and food, as well as for biological tissues and
body fluids, such as internal organs, blood and urine. It is important
that reference materials have a matrix which is the same or very
similar to the matrix the "real" monitoring samples are comprised of.
Matrix effects may seriously invalidate analytical results. The
concentrations of a substance to be measured should also cover the
same range as is expected in the monitoring samples. For several
substances, the chemical forms in which they may exist must be taken
into consideration. For example, arsenic, mercury, tin and several
other metals occur in a number of different valence states and in
organic and inorganic forms which vary in stability.
Various types of reference samples are commercially available.
The International Atomic Energy Agency (IAEA) conducted a survey on
analytical reference materials of biological and environmental origin.
The IAEA database presently contains over 10 000 analyte values for
455 substances in 650 reference materials produced by 30 different
suppliers (IAEA, 1995, 1996). It is expected that this survey will
help analysts to select reference materials for QA purposes that match
as closely as possible, with respect to matrix type and concentrations
of the samples and analytes of interest.
The availability of certified reference materials is still
limited, however. In addition, owing to their relatively high cost,
these materials can only occasionally be used for QC. Alternatively, a
laboratory may prepare control materials calibrated against and,
therefore, traceable to certified reference materials, or a
conventional true value may be established by one or more reference
laboratories, preferably using a validated definitive method (ISO,
1994).
11.6 Quality assurance and control issues in population-based
studies
Population-based studies present specific challenges to QA/QC
programmes that are not common to more conventional exposure
investigations. A description of the potential problems and
alternative approaches to address them, avoid them and estimate the
biases that they may introduce in the results should therefore be part
of the study plan. Study design, including QA/QC procedures, should
consider methodologies that address configurations as they occur in
the field, and provide for modifications that will not compromise the
stated objectives.
Among the most important considerations in human population
studies are the potential differences in cultural, geographical or
social class framework between the exposure assessment study team and
the target population. The investigators should be cognizant of local
culture and conditions and include those considerations in the study
design. For example, perceptions and customs with regard to the
importance of time in daily life varies across cultures and even among
subpopulations within the same country. As a result, appointments for
household visits and interviews may not be kept as the field personnel
expect.
A good approach for identifying problems and alternatives is the
use of pilot studies before full implementations of the field
component of a study. In pilot studies, trained staff perform the
complete set of SOPs established for the full investigation on a small
subset of the sample population. Thus, pilot studies also have the
purpose of effectively providing final training of all field
personnel. The information collected during these studies is very
useful for adjusting the study plan and SOPs for local conditions. It
is important that all personnel participating in a field study, i.e.,
study design personnel, principal and co-investigators, laboratory
staff and field personnel require practical experience with the local
field conditions. Pilot studies provide the means to obtain such
experience.
Another potential confounder in population studies is the
Hawthorne effect; that is, the alteration of behaviour patterns that
might affect exposures as a result of participation in the study. For
example, many population-based field studies provide compensation to
individuals who agree to participate. Monetary compensation could
alter behaviour patterns that may affect exposure: for example, the
participant may decide not to go to work (a typical activity) the day
he receives the payment and instead spend the time performing an
activity that is not customary. This problem can be resolved by
offering the payment a short time after the participants have
completed the monitoring period. The presence of an environmental
monitor (e.g., an air sampling inlet and pump) may cause participants
to avoid situations in which they may feel conspicuous, such as their
place of employment. An innovative approach for assessing the
influence of this effect is to expand the study to include a subsample
of participants who engage in only a portion of the study (e.g., a
time-activity questionnaire) and to compare the results for this group
to those for the remainder of the population that completed the full
monitoring programme.
11.7 Summary
QA includes independent study monitoring that assures laboratory
management and users of data that facilities, equipment, personnel,
methods, practices, records and controls confirm to accepted quality
management principles. Errors in exposure data may be due to
analytical variation as well as changes that may take place during
sample collection and handling, preparation and storage of samples,
and data keeping and data recording. Analytical variation can be
divided into two major categories: accuracy, which refers to the
agreement between the amount of analyte measured and the amount
actually present, and precision, which refers to the random
variability or reproducibility of the method.
The study plan is the most important document for providing
information on the critical components of an investigation, e.g.,
responsible personnel, sample collection, sample storage and
pre-analytical treatment, analytical procedures and data analysis.
SOPs are appended to the study plan and contain written detailed
instructions on how to perform certain routine field and laboratory
activities. The study plan and SOPs can be seen as management
directives designed to ensure that all personnel associated with study
operations will be familiar with and use the same procedures.
QC refers specifically to the quality of the laboratory results.
It has two components. Internal QC is a set of procedures used by
staff of a laboratory for continuously assessing results as they are
produced. External QC is a system for objective checking of laboratory
performance by an independent agency. Internal QC includes displaying
results of control samples in control charts (e.g., Shewhard and cusum
charts), and use of control limits as criteria for signalling the need
for action, or for judging whether a set of data does or does not
include a state of statistical control. External QC, on the other
hand, provides independent evidence of the quality of laboratory
performance and individual analyst proficiency. Usually, a
coordinating laboratory distributes samples of known concentration to
the participating laboratories. Participating laboratories examine the
reference samples and submit the results to the coordinating
laboratory for performance evaluation.
Reference samples used in internal and external QC should have a
matrix and pollutant concentration which is similar to the real
sample. In addition, for several substances it is necessary to take
into consideration the chemical form in which they may exist.
Finally, interactions with human populations present a unique set
of study design and QA considerations that should be carefully
evaluated together with the conventional issues of sampling analysis
and procedures.
12. EXAMPLES AND CASE STUDIES OF EXPOSURE STUDIES
12.1 Introduction
Exposure studies have helped to establish a subdiscipline of
environmental science. Exposure analysis is now essential to
environmental epidemiology and quantitative risk assessment. Exposure
studies have called attention to practices, locations and populations
subjected to higher risk owing to environmental contamination.
However, the full promise of exposure analysis has not been realized.
Exposure information can influence public opinion and policy and offer
cost-effective innovative strategies for those looking for
alternatives to current regulatory approaches. Our current practice is
to partition problems into specific media (air, water, soil and food)
and structure our management as restrictive commands limiting
discharge. Exposure analysis is an integrated and more comprehensive
approach for dealing with risk management.
Kirk Smith discusses the potential of human exposure assessment
for air pollution regulation (in WHO, 1995a). He argues that for
developing countries, without the investment in monitoring, regulation
and control infrastructures, exposure reduction strategies "may
provide a much more efficient pathway of control over time than the
path followed by the currently developed countries". Structuring a
regulatory control programme that bases performance on the reduction
of human exposure while also considering cost should appeal to market
economies in both developed and developing countries. In order to
implement such strategies effectively one must have the ability to
quantitatively assess the relative contribution of various sources to
human exposure and risk by measurement or modelling. With the
development of exposure science, trading exposures among sources and
pathways results in the possibility of integrating the concept of
product stewardship, which would include extraction, manufacturing,
consumer use and disposal. If society sets the performance goal of
exposure minimization both for humans and for the ecosystem, the
commercial and governmental institutions can devise more
cost-effective responses. For example, a company might receive credit
for reformulating a product that reduces exposures to VOCs in the
home. The cost to the company might be far less in terms of exposure
reduction per capita than equipping the manufacturing site with
emissions control equipment that reduces local concentrations.
Before exposure trading across media, across pollutants or
between employees and the general public can seriously be considered,
the science of exposure assessment must mature. A great deal more must
be learned about risk-producing behaviours and activities, about
variation in individual susceptibility, and about the chronic as well
as acute implications of mixtures of pollutants. Progress has been
made in these areas, and studies are continuing. Perhaps the
contribution of exposure assessment to environmental epidemiology best
exemplifies its advancements in recent years. As environmental
epidemiology is practised today, quantification of exposure measures
play a significant role vital to study design and to interpretation.
Linet et al.'s article (1997) on residential exposures to magnetic
fields and acute lymphoblastic leukemia in children is an excellent
case in point. They measured magnetic field strength in the current
and former housing in over 1200 cases and controls. No associations
were reported for direct measures of magnetic field strength exposures
in this study, although some previous studies had found association
between childhood leukemia and surrogate indicators of exposures to
magnetic fields (power line classification schemes).
Exposure information has influenced risk management and public
policy. A well-known example involves materials containing asbestos.
In the USA, as a result of US EPA's response to actions regarding the
handling of asbestos-containing material in schools as well as
numerous legal suits on behalf of asbestos industry workers, there was
a costly and widespread effort to remove all asbestos from buildings.
Eventually the review and public dissemination of studies showing
insignificant exposures to asbestos to building occupants in the vast
majority of situations resulted in a clarification of US EPA's policy
and an end to the indiscriminate removal of all asbestos-containing
material regardless of its condition (HEI, 1991; Camus et al., 1998).
We now see the concern for protecting the public from
environmental risk requiring more information about exposures.
Recently, the US Congress passed the Food Quality and Protection Act.
For the first time legislation has explicitly recognized the problem
of multiple source exposure. The Act requires that EPA and other
agencies account for aggregate or multiple sources of exposure when
setting maximum allowable levels (i.e., tolerances) for pesticides in
food. In this case, regulations are ahead of the science as current
knowledge on the numerous pathways of pesticide exposure is not
sufficient to establish a standardized methodology.
So in recent years, we have seen exposure analysis gain status.
In 1998 the International Society of Exposure Analysis (ISEA) held its
8th annual conference in conjunction with the International Society
for Environmental Epidemiology. ISEA has a journal with more than 500
subscribers worldwide. The European Commission in 1995 published
reports on Exposure Assessment (EUR 14356EN) and Time-activity
Patterns in Exposure Assessment (EUR 15892EN) along with a report on
Study Design (EUR 15095EN) for air pollution epidemiology in which
exposure assessment was featured.
The US EPA (1996a) updated its 1989 Exposure Factors Handbook
(EPA/600/8-89/043) with a comprehensive three-volume compilation of
statistical data on various factors used in assessing exposure. The
updated document incorporates new information available from the late
1980s through the first half of the 1990s. Used by risk assessors in
conjunction with the revised Guidelines for Exposure Assessment (US
EPA, 1992a), the new Exposure Factors Handbook gives point estimates
for many parameters along with distributional information. The median
as well as the high end of individual and population risk can be
calculated using these inputs and appropriate concentration data.
As described earlier in the book, the WHO has been promoting
exposure assessment methodology and investigations for almost
20 years. The HEAL project has provided training, documents and
assistance to investigators worldwide. Notable successes of HEAL have
been the establishment of high-quality measurements for metals such as
lead and cadmium (see Foreword and Chapter 11).
A number of professional organizations have contributed to the
promotion of exposure sciences through conferences, workshops and
publications. Although a comprehensive description of all these
efforts is beyond the scope of this chapter, readers may find some of
the publications listed in Table 38 useful.
12.2 Exposure studies
Exposure studies described in this chapter serve as examples of
the variety of approaches and purposes such investigations have taken.
Exposure studies are conducted for different reasons. Some were
designed for the simple purpose of demonstrating methodology for
generating hypotheses. Other studies were components of
epidemiological studies. Still others were designed for regulatory
purposes to determine possible exposure routes and dose rates for
specific products or applications. Such studies may have participants
adhere to a certain regime. There are also examples of large and
expensive studies recruiting representative populations to provide
generalizable exposure and risk estimates. Brief descriptions of
different types of studies are presented in this chapter. Those
included illustrate a variety of design strategies. The following
discussion is not intended as a comprehensive review but may give the
reader the sense of how exposure assessment can serve a variety of
purposes.
12.3 Air pollution exposure studies
Perhaps the most numerous examples of exposure studies are in the
field of air pollution. This section briefly presents examples of air
pollution studies performed for a variety of purposes.
12.3.1 Particle studies
Particle exposures have taken a new importance in light of a
substantial and growing literature on morbidity and mortality effects
of ambient particulate matter (Wilson & Spengler, 1996). In 1986, the
US Congress mandated that the US EPA Office of Research and
Development "carry out a TEAM study of human exposure to particles."
The EPA Atmospheric Research and Exposure Assessment Laboratory joined
with California's Air Resources Board to sponsor a Particle Total
Exposure Assessment Methodology (PTEAM) study in the Los Angeles
Basin. The study was carried out primarily by the Research Triangle
Institute and the Harvard University School of Public Health. The main
goal of the study was to estimate the frequency distribution of
exposures to particles for residents of Riverside, California, a city
of approximately 250 000 inhabitants located 75 km east of downtown
Table 38. Guide to documents on exposure assessment
The Potential of Human Exposure Assessment for Air Pollution Regulation
(WHO, 1995a)
Human Exposure Assessment for Airborne Pollutants: Advances and
Opportunities (US NRC, 1991b)
Methods for Assessing Exposure of Human and Non-human Biota
(Tardiff & Goldstein, 1991)
Biological Monitoring of Metals (IPCS, 1992)
Guidelines for Exposure Assessment (US EPA, 1992a)
Dermal Exposure Assessment: Principles and Applications (US EPA, 1992b)
Methodology for Assessing Health Risks Associated with Indirect Exposure
to Combustion Emissions (US EPA, 1990)
Estimating Exposures to Dioxin-Like Compounds (US EPA, 1994)
Superfund Exposure Assessment Manual (US EPA, 1988a)
Selection Criteria for Models Used in Exposure Assessments
(US EPA, 1987, 1988b)
Standard Scenarios for Estimating Exposure to Chemical Substances During
Use of Consumer Products (US EPA, 1986a)
Pesticide Assessment Guidelines, Subdivisions U and K
(US EPA, 1984, 1986b)
Methods for Assessing Exposure to Chemical Substances, Volumes 1-13
(US EPA, 1983-1989)
Assessment of Human Exposure to Lead: Comparison between Belgium,
Malta, Mexico and Sweden (Bruaux & Svartengren, 1985)
Guidance on Survey Design for Human Exposure Assessment Locations
(HEAL) Studies (Kollander, 1993)
Air pollution in African villages and cities (Koning de, 1988)
Exposure Monitoring of Nitrogen Dioxide. An international pilot study
within the WHO/UNEP Human Exposure Assessment Location (HEAL)
Programme (Matsushita & Tanabe, 1991)
Assessment of Human Exposure to Selected Organochlorine Compounds
Through Biological Monitoring (Slorach & Vaz, 1983)
Assessment of Human Exposure to Lead and Cadmium Through Biological
Monitoring (Vahter, 1982)
Global WHO/UNEP: Environment Monitoring System. Exposure Monitoring
of Lead and Cadmium (Vahter & Slorach, 1990)
Human Exposure to Carbon Monoxide and Suspended Particulate Matter in
Zagreb, Yugoslavia (WHO, 1982a)
Human Exposure to SO2, NO2 and Suspended Particulate Matter in Toronto
(WHO, 1982b)
Human Exposure to Suspended Particulate Matter and Sulphate in Bombay
(WHO, 1984)
Human Exposure to Carbon Monoxide and Suspended Particulate Matter in
Beijing, People's Republic of China (WHO, 1985c).
Guidelines for integrated air, water, food and biological exposure
monitoring (WHO/UNEP, 1986)
Indoor Air Quality Study Maragua Area, Kenya (WHO/UNEP, 1987)
Indoor Air Quality in the Basse Area, The Gambia (WHO/UNEP, 1988)
Table 38. (continued)
An introductory guide to human exposure field studies: survey methods and
statistical sampling (WHO/UNEP, 1992a)
Endemic fluorosis: a global health issue (WHO/UNEP, 1992b)
Human Exposure to Pollutants. Report on the pilot phase of the Human
Exposure Assessment Locations Programme (UNEP/WHO, 1991)
Los Angeles. Another goal was to determine particle concentrations in
the participants' homes and immediately outside the homes.
The study had a three-stage probability sampling procedure
(Ozkaynak et al., 1996). Ultimately 178 residents of Riverside, took
part in the study in the fall of 1990. Respondents represented
139 000 ± 16 000 (SE) non-smoking Riverside residents aged 10 and
above. Their homes represented about 60 000 Riverside homes.
Each participant wore a personal exposure monitor (PEM) for two
consecutive 12-h periods. Concurrent PM10 and PM2.5 samples were
collected by a stationary indoor monitor (SIM) and stationary outdoor
monitor (SAM) at each home. The SIM and SAM were essentially identical
to the PEM. A total of 10 particle samples were collected for each
household (day and night samples from the PEM10, SIM2.5, SIM10,
SAM2.5, SAM10). Air exchange rates were also determined for each 12-h
period.
Up to 4 participants per day could be monitored, requiring
48 days to conduct the study. A central outdoor site was maintained
over the entire period (22 September-9 November 1990). The site had 2
high-volume samples with 10 µm inlets (actual cut-point about 9.0 µm),
2 dichotomous PM10 and PM2.5 samples (actual cut-point about 9.5 µm),
1 PEM10, 1 SAM10 and 1 SAM2.5.
More than 2750 particle samples were collected, about 96% of
those attempted. All filters were analysed by XRF for a suite of
40 metals. More than 1000 12-h average air exchange rate measurements
were made. A complete discussion of the quality of the data is found
in Thomas et al. (1993a). LODs, based on 3 times the standard
deviation of the blanks, were of the order of 7-10 µg/m3. All field
samples exceeded the LOD. Duplicate samples ( n = 363) showed
excellent precision for all types of particle samplers at all
locations, with median relative standard deviations ranging from 2 to
4%.
Daytime mean personal PM10 concentrations (150 µg/m3) were more
than 50% higher than either indoor or outdoor levels (95 µg/m3).
Overnight mean personal PM10 concentrations (77 µg/m3) were similar
to the indoor (63 µg/m3) and outdoor (86 µg/m3) levels. The higher
personal concentrations do not appear to be due to skin flakes or
clothing fibres; many skin flakes were found on filters (up to an
estimated 150 000 per filter) in subsequent scanning electron
microscope (SEM) analyses, but their mass does not appear to account
for more than 10% of the excess personal exposure (Mamane, 1992).
Mean PM2.5 daytime concentrations were similar indoors
(48 µg/m3) and outdoors (49 µg/m3), but indoor concentrations fell
off during the sleeping period (36 µg/m3) compared to 50 µg/m3
outdoors. Thus the fine particle contribution of PM10 concentrations
averaged about 51% during the day and 58% at night both indoors and
outdoors. Unweighted distributions are displayed in Fig. 37 for 24-h
average PM10 personal, indoor and outdoor concentrations. About 25%
of the population of Riverside was estimated to have 24-h personal
PM10 exposures exceeding the 150 µg/m3 24-h US National Ambient Air
Quality Standard (NAAQS) for ambient air. Central-site PM2.5 and PM10
concentrations agreed well with backyard concentrations. Overall, the
data strongly suggest that a single central-site monitor can represent
well the PM2.5 and PM10 concentrations throughout a wider area such
as a town or small city, at least in the Los Angeles basin.
Stepwise regressions resulted in smoking, cooking, and either air
exchange rates or house volumes being added to outdoor concentrations
as significant predictors of personal exposure. Smoking added about 30
µg/m3 to the total PM2.5 concentrations. Cooking added 13 µg/m3 to
the daytime PM2.5 concentration, but was not significant during the
overnight period. At night, an increase in air exchange of one air
change per hour resulted in a small increase of about 4.5 µg/m3 to
the PM2.5 concentration, but was not significant during the day. The
house volume was not significant at night, but was significant during
the day, with larger homes resulting in smaller PM2.5 concentrations.
Since air exchange and house volume were weakly correlated
(negatively), they were not included together in the same regression.
Following Koutrakis et al. (1992), a non-linear least squares
regression equation was used to estimate penetration factors, decay
rates and source strengths for particles and elements from both size
fractions (Ozkaynak et al.1996). Penetration factors were very close
to unity for nearly all particles and elements. The calculated decay
rate for fine particles (< PM2.5) was 0.39 ± 0.16 h-1, and for PM10
was 0.65 ± 0.28 h-1. Since PM10 contains the PM2.5 fraction, a
separate calculation was made for the coarse particles (PM10 - PM2.5)
with a resulting decay rate of 1.01 ± 0.43 h-1. Decay rates for
elements associated with the fine fraction were generally lower than
for elements associated with the coarse fraction, as would be
expected, due to their lower settling velocities. For example, sulfur,
which is associated with the fine fraction of aerosols in the form of
sulfate, had calculated decay rates of 0.16 ± 0.04 and 0.21 ± 0.04 h-1
for PM2.5 and PM10 fractions, respectively. The crustal elements
(calcium, aluminium, manganese, iron) on the other hand, had decay
rates ranging from 0.6 to 0.8 h-1. Each cigarette emitted 22 ± 8 mg of
PM10 on average, about two-thirds of which (14 ± 4 mg) was in the
fine fraction. Cooking emitted 4.1 ± 1.6 mg/min of PM10 particles, of
which about 40% (1.7 ± 0.6 mg/min) was in the fine fraction. All
elements emitted by cooking were limited almost completely to the
coarse fraction; presumably carbon or other elements not measured by
XRF were contained in the fine fraction. Sources other than cooking
and smoking emitted about 5.6 ± 3.1 mg/h of PM10, of which only about
1.1 mg/h ± 1.0 (20%) was in the fine fraction (see Figs. 38 and 39).
Based on the mass-balance model, outdoor air was the major source
of indoor particles in Riverside, providing about 75% of fine
particles and 65% of inhalable particles in the typical home. It was
also the major source for most elements, providing 70-100% of the
observed indoor concentrations for 12 of the 15 elements. Unidentified
indoor sources accounted for most of the remaining particle and
elemental mass collected on the indoor monitors. The nature of these
sources is not yet understood. They do not include smoking, other
combustion sources, cooking, dusting, vacuuming, spraying or cleaning,
since all these sources together account for less than the
unidentified sources. For example, the unidentified sources accounted
for 26% of the average indoor PM10 particles, whereas smoking
accounted for 4%, and cooking for 5%. Of the identified indoor
sources, the two most important were smoking and cooking. Smoking was
estimated to increase 12-h average indoor concentrations of PM10 and
PM2.5 by 2 and 1.5 µg/m3 per cigarette, respectively. Most of this
increase was in the fine fraction. Cooking increased indoor
concentrations of PM10 by about 6 µg/m3 per min of cooking, with
most of the increase in the coarse particles. Other household
activities such as vacuuming and dusting appeared to make smaller
contributions to indoor particle levels. An interesting finding was
that commuting and working outside the home resulted in lower daytime
particle exposures than for persons staying at home.
Multivariate calculations in two separate studies resulted in
rather similar estimates of the effect of smoking on indoor fine
particle concentrations. Spengler et al. (1981) estimated an increase
of about 20 µg/m3 per smoker, or 25 µg/m3 per smoking home, based on
55 residences monitored over a year in 6 US cities. In another study,
a smoking effect of about 32 µg/m3 was estimated for smoking homes
based on data collected in Tennessee, USA (Spengler et al., 1985).
12.3.2 Carbon monoxide
The largest personal monitoring study of carbon monoxide
exposures was carried out by US EPA in Washington, DC and Denver,
Colorado in the winter of 1982-1983 (Johnson, 1983; Hartwell et al.,
1984; Akland et al., 1985; Johnson et al., 1986). About 800 people in
DC and 450 in Denver were monitored for 24 h (48 h in Denver) using
electrochemical carbon monoxide monitors with specially designed data
loggers. The data loggers were capable of sampling the current from
the monitor about 4 times a second. They were equipped with buttons
that the subject could press when one activity ended and the next
began; at that point, the logger would average all preceding values
from the time the activity began. (There was also an automatic
averaging every hour.) The result was an extraordinarily rich
database, with approximately 1200 people averaging 40 activities per
day, each with an associated average carbon monoxide level. At the end
of the monitoring period, each subject provided a breath sample. Major
findings of the study included the following:
* Commuters had the highest exposures to carbon monoxide in general,
averaging up to 13 µg/g. Parking garages had the highest carbon
monoxide levels of any microenvironment, with churches and schools
among the lowest.
* The main indoor sources of carbon monoxide were gas stoves and
cigarettes. Gas stoves increased levels by about 2.5 µg/g when
being used; homes with smokers had increases of about 1.5 µg/g on
average.
* Personal exposures were higher than would be predicted by
measurements at fixed monitoring stations. About 10% of DC
residents appeared to exceed the 8-h standard of 9 µg/g, as
determined by their breath concentrations, although only 1 of the
11 fixed stations exceeded the standard during the monitoring
period.
A study of California homes (Wilson et al., 1993a,b, 1995; Colome
et al., 1994), each monitored for 48 h, indicated that 13 of 277 homes
(about 5%) had indoor 8-h averages exceeding 9 µg/g (the outdoor
standard). Since the outdoor standard is to be exceeded only once per
year, it is clear that the fraction of homes with 8-h indoor averages
exceeding 9 µg/g more than once per year would be larger than the 5%
observed in the single 48-h monitoring period. Homes with gas stoves
and gas furnaces had indoor source levels for carbon monoxide that
were about 3 times higher than homes without such sources. Homes with
wall furnaces had higher levels of carbon monoxide than homes with
forced-air gas furnaces. Homes with smokers ( n = 85) had levels of
carbon monoxide about 0.5 µg/g higher than homes without smokers
( n = 190). Malfunctioning gas furnaces were a major cause of
elevated concentrations of carbon monoxide. However, the homes with
the highest carbon monoxide levels also included some with electric
cooking stoves and electric heat, suggesting that other sources of
carbon monoxide were present in these homes. Such sources could
include cars idling in attached garages or unvented gas or kerosene
space heaters.
12.3.3 Nitrogen dioxide
Nitrogen dioxide is a ubiquitous respiratory irritant for which
air quality standards have been established in many countries (WHO,
1997d). It is emitted by industrial processes and mobile sources, but
also by indoor combustion appliances such as gas cooking stoves and
furnaces. Several studies in the 1970s suggested that children in
homes with gas stoves suffered more infectious disease than children
in homes with electric stoves; a possible connection with nitrogen
dioxide (in lowering resistance) was postulated (Samet & Spengler,
1991). Also, exposure is likely to be higher for those living closer
to roadways.
A study in Helsinki, Finland, explored weekly nitrogen dioxide
exposure of preschool children as well as between- and within-children
variances of repeated personal exposure measurements. The study tested
the hypothesis that exposure to the low levels of nitrogen dioxide in
Helsinki increases the risk of respiratory symptoms in preschool
children (Mukala et al., 1996).
The parents of 246 children, aged 3-6 years, returned a letter of
consent to participate in a personal nitrogen dioxide exposure study.
The children spent their days at one of three daycare centres, two
located in the downtown area and one in a suburban area. All children
carried personal Palmes tubes on outdoor clothing one week at a time
during six consecutive weeks in winter (14 January-4 March 1991) and
seven consecutive weeks in spring (8 April-27 May 1991). Weekly
concentrations of nitrogen dioxide were also measured inside and
outside each daycare centre to assess the usefulness of the stationary
measurements in estimating the variation of exposures. Ambient
concentrations of nitrogen dioxide were monitored at three fixed sites
of the Helsinki Metropolitan Area council network with
chemiluminescence monitors. The distance from each daycare centre to
the nearest monitoring site varied from 0.5 to 11 km.
The geometric mean of personal nitrogen dioxide exposure levels
of in the total 13-week period was 26.5 µg/m3 in the downtown area
and 17.5 µg/m3 in the suburban area. These exposure levels were
significantly lower than ambient air levels of nitrogen dioxide in the
same areas. Gas cooking stove or/and smoking at home significantly
increased personal exposure to nitrogen dioxide. The weekly exposures
averaged over all children in each daycare centre correlated poorly
with the fixed site ambient air levels ( r2 = 0.37), but much better
with the nitrogen dioxide levels inside and outside the daycare
centres ( r2 = 0.88 and 0.86, respectively). In the suburban and
downtown groups the between-child variances in nitrogen dioxide
exposures were only 14% and 28% of the total variances, which were
dominated by the within-child variances.
Stationary measurements at the ambient air fixed sites and inside
and outside the daycare centres explained the variations in personal
nitrogen dioxide exposures of the children well during the spring, but
not during the winter. A statistical model, where data from outside
daycare centre measurements, fixed ambient air monitors, residential
area and home characteristics (i.e., gas cooking stove, smoking inside
at home, type of dwelling) were included, explained only 32% of the
personal exposure variations in winter, but 67% in spring.
There were significantly more days with stuffed nose (26% versus
20%) and cough (18% versus 15%) in the downtown area than in the
suburban area. The observed risk of cough was highest and
statistically significantly increased compared to the levels of
personal nitrogen dioxide. Also, when using daycare centre
measurements or fixed site ambient air data for exposure assessment,
there was a positive trend between nitrogen dioxide exposure and cough
in winter these associations were, however, weaker and
non-significant.
According to the result of the study, exposure to nitrogen
dioxide should be measured using personal exposure measurements when
studying health effects of the gas in non-symptomatic children in
areas with low nitrogen dioxide levels. Even personal exposure
measurements using weekly averages, however, may not adequately
reflect all biologically relevant exposure, e.g., short-term peak
concentrations. The most significant determinants of the personal
nitrogen dioxide exposures of the children in Helsinki are living in
downtown rather than in a suburban area, gas versus electric cooking
stove and smoking in the home. However, even all risk factors together
did not increase the personal exposures of downtown children up to
suburban outdoor air levels.
12.3.4 Ozone
The UC Berkeley Ozone project (USA) is an example of an
epidemiological study addressing long-term effects of lifetime ambient
oxidant pollution on pulmonary function (Tager et al., 1998a,b). A
major purpose was to address the feasibility of improving ozone
exposure assignment by means of collecting lifetime information on
relevant time-activity patterns to exposure, in combination with fixed
site ambient air monitoring data. Individual factors considered to be
relevant for exposure were:
* lifetime residential history
* time spent outdoors in different age periods
* time spent in outdoor physical activities in each lifetime
residence.
To test the reliability of the retrospective assessment, a
test-retest design was chosen. The study included a convenience sample
of 168 non-smoking UC Berkeley college freshmen who had to be lifetime
California (USA) residents (San Francisco Bay Area or Los Angeles
Basin). It was shown that retrospective lifetime residential history
is highly reliable (Künzli et al., 1996). Using pollution monitor
data, averaged over lifetime across all respective residential
locations, may in fact improve the health effects assessment.,
compared with mere reliance on the ambient monitor data from the last
or actual residence only (Künzli et al., 1997a). Three retrospective
approaches to assess outdoor physical activity patterns have been
tested and two methods gave rather reliable overall estimates for time
spent in outdoor heavy or moderate activities, during summer. For the
activity table format (see Fig. 19), only 13% of the total variance
was attributed to reporting variability (test-retest). The
categorization into heavy and moderate activities based on published
data regarding energy expenditure (Ainsworth et al., 1993). Ambient
long-term mean daytime concentrations of ozone were weighted by the
duration and exertion level of the reported long-term average outdoor
physical activity. This "effective exposure", therefore, may be
considered a surrogate measure of ozone dose. Although the study had
some promising results regarding the feasibility of retrospectively
assessing exposure relevant surrogates over long periods of time,
validity of the time-activity data cannot be directly assessed.
Neither could the study answer the open question of whether
time-activity data may be needed in the assessment of long-term
effects of air pollution (Künzli et al., 1997a).
12.3.5 Combined exposure studies
The WHO, in a number of studies termed the Global Environmental
Monitoring System (GEMS), sponsored several studies of combustion
related air pollutants. GEMS, now renamed as the Air Management
Information System (AMIS), continues to coordinate the gathering of
data on levels of ambient air pollution in cities around the world.
GEMS also conducted a series of exposure studies to examine the
assumptions that fixed ambient monitoring accurately reflected
personal exposures. Studies were carried out in Toronto, Canada (WHO,
1982b); Beijing, China (WHO, 1985c); Zagreb, Croatia (WHO, 1982a); and
Bombay, India (WHO, 1984).
Through the GEMS, WHO and UNEP later formed the HEAL project. The
goal was to improve exposure monitoring internationally, using direct
measurements of human exposure with activity information, and provide
guidelines for techniques that could be used uniformly around the
world. Another goal was to increase the accuracy of risk assessment
studies with the goal of better protection of human health.
A study in Kenya was carried out to attempt to determine the risk
of acute respiratory infection by quantifying indoor air pollution
levels caused by home combustion sources (WHO, 1987). The study was
carried out in 36 randomly chosen households that used biomass as
fuel. It compared concentrations of particles, carbon monoxide,
nitrogen dioxide and formaldehyde inside the home with outdoor
pollution levels.
The mean 24-h concentration of respirable particles was
1400 µg/m3, and the estimated levels in areas where cooking and
heating fires were used was 3000-4000 µg/m3. Although elevated levels
of carbon dioxide and nitrogen dioxide were found in the homes, the
levels were below those found in previous studies of indoor air
pollution in developing countries. Little correlation was seen between
indoor and outdoor levels, confirming that the source of the excess
levels was combustion.
The excessive levels of particulate, which included a large
concentration of PAHs, suggested that individuals spending a
significant amount of time indoors (e.g., women and small children)
were at greater risk owing to exposure to smoke. Because the levels
observed were homogeneous across the samples, leaving no unexposed
control group, the researchers were unable to reach conclusions about
the effect of the levels on occurrence of acute respiratory
infections.
12.3.6 Assessing ambient pollution impacts indoors
Santiago, Chile, a city of 5.2 million inhabitants in 1997 (40%
of the Chilean population), has chronic high concentrations of certain
air pollutants. For example, in 1995 the Chilean air quality standards
for PM10, carbon monoxide and ozone were exceeded on more than
200 days. Annual PM10 levels surpassed 100 µg/m3 in the 1990s and
levels of 300 µg/m3 are common during the winter, especially when an
inversion layer is formed.
In many countries when pollutants in the outdoor air exceed
standards, the population is advised to remain indoors, but if
infiltration occurs and indoor sources are generating pollutants,
indoor air quality (IAQ) might be even worse than outdoors. A study
was designed to evaluate the contribution of outdoor pollution to IAQ
in Santiago and in a small rural town (Curocori). Carbon monoxide,
PM5 and PAHs were monitored simultaneously indoors and outdoors along
heavy traffic roadways in Curocori and in Santiago. The methodology
used is described in Gil & Adonis (1997).
In downtown Santiago, carbon monoxide concentrations ranged from
1.9 to 73 µg/g outdoors; indoor levels were 0.5-93 µg/g. Although
levels were slightly higher indoors (but not significant, p > 0.05)
than outdoors, changes in outdoor levels (which were always related to
vehicular traffic) simultaneously produced changes in indoor levels. A
typical carbon monoxide profile is shown in Fig. 40 for outdoor and
indoor levels in an office. Outdoor and indoor levels showed a high
correlation ( r = 0.89) with the higher values occurring during the
rush hour.
Levels of total PAHs, carcinogenic PAHs and PM5 were very high
and showed no statistically significant differences indoors and
outdoors ( p > 0.05). Highest PM5 levels were 260 and 280 µg/m3
for indoors and outdoors respectively. Levels of benzo [a]pyrene
indoors and outdoors were highly correlated ( r = 0.869). For
restaurants which usually work with open doors, the correlation
between indoor and outdoor levels was even higher ( r = 0.99). In
Curocori, levels (a town with little vehicular traffic) were almost
identical indoors and outdoors. Levels of carbon monoxide PM5, total
PAHs and carcinogenic PAHs were considerably lower in this rural town
and indoor/outdoor correlations were also much lower than those
obtained in Santiago.
These results confirm the importance of ambient pollution to
population exposures when outdoor pollution levels are high. Only when
the contribution of penetrating ambient pollution is lowered can the
indoor contributions be more readily assessed.
12.3.7 Volatile organic compounds
Human exposure to VOCs occurs mainly through inhalation, although
some VOCs are ingested as contaminants in drinking-water, food and
beverages. Some hydrocarbons are known carcinogens or mutagens (e.g.,
benzene). Almost all cause eye irritation, coughing, drowsiness,
clumsiness and loss of alertness. Acute effects from industrial
exposures at the parts per million (µg/g) level include skin
reactions, dizziness and fainting. Sick building syndrome (SBS) and
multiple chemical sensitivity (MCS) have been associated with
relatively low (ng/g, parts per billion) concentrations of VOCs.
Between 1979 and 1987, the US EPA carried out the TEAM studies to
measure personal exposures of the general public to VOCs in several
geographic areas in the USA (Pellizzari et al., 1987; Wallace et al.,
1987a). About 20 target VOCs were included in the studies, which
involved about 750 people, representing 750 000 residents of the
areas. Each participant carried a personal air quality monitor
containing 1.5 g Tenax. A small battery-powered pump pulled about
20 litres of air across the sorbent over a 12-h period. Two
consecutive 12-h personal air samples were collected for each person.
Concurrent outdoor air samples were also collected in the
participants' backyards. In the 1987 studies, fixed indoor air
samplers were also installed in the living room of their homes.
The initial TEAM pilot study (Wallace et al., 1982) in Beaumont,
Texas and Chapel Hill, North Carolina indicated that personal
exposures to about a dozen VOCs exceeded outdoor air levels, even
though Beaumont has major oil producing, refining and storage
facilities. These findings were supported by a second pilot study in
Bayonne-Elizabeth, New Jersey (another major chemical manufacturing
and petroleum refining area) and Research Triangle Park, North
Carolina (Wallace et al., 1984a). A succeeding major study of 350
people in Bayonne-Elizabeth (Wallace et al., 1984b) and an additional
50 people in a non-industrial city and a rural area (Wallace et al.,
1987a) reinforced these findings (Table 39). A second major study in
Los Angeles and in Antioch-Pittsburgh, California (Wallace et al.,
1988), with a follow-up study in Los Angeles in 1987 (Wallace et al.,
1991a,b) added a number of VOCs to the list of target chemicals with
similar results (Table 40). Major findings of these TEAM studies
included the following:
* Personal exposures exceeded median outdoor air concentrations by
factors of 2-5 for nearly all of the 11 prevalent VOCs (Fig. 41).
The difference was even larger (factors of 10 or 29) when the
maximum values were compared. This is so despite the fact that most
of the outdoor samples were collected in areas with heavy industry
(New Jersey) or heavy traffic (Los Angeles).
* Major sources are consumer products (bathroom deodorizers, moth
repellents); personal activities (smoking, driving); and building
materials (paints and adhesives). In the USA, one chemical (carbon
tetrachloride) has been banned from consumer products and exposure
is thus limited to the global background of about 0.7 µg/m3.
* Traditional sources (automobiles, industry, petrochemical plants)
contributed only 20-25% of total exposure to most of the target
VOCs (Wallace, 1991a,b). No difference in exposure was noted for
persons living close to chemical manufacturing plants or petroleum
refineries.
The results of the VOC TEAM study encouraged investigators to
explore the causes for higher personal exposures. In a study designed
to better understand the VOC contributions of specific sources,
Wallace et al. (1989) had 7 volunteers undertake about 25 activities.
A number of these activities (using bathroom deodorizers, washing
dishes, cleaning an automobile carburettor) resulted in 10-1000-fold
increases in 8-h exposures to certain VOCs.
A recent study of benzene and toluene in 293 California homes
(Wilson et al., 1993a, b; 1995; Colome et al., 1994) resulted in some
interesting differences between the two agents. For benzene, 48-h
average indoor concentrations correlated fairly well with outdoor
levels, but for toluene almost no correlation was observed. This is
probably due to the much wider use of toluene in consumer products.
Major variables associated with higher net indoor benzene levels were
presence of a gas heating furnace and having two cars parked in an
attached garage. For toluene, a particular brand of furnace had the
highest partial correlation with net indoor toluene concentrations;
apartments also had higher concentrations.
A study of 170 homes in Avon, England found mean indoor levels of
benzene to be 8 µg/m3, compared to outdoor concentrations of 5 µg/m3
(Brown & Crump, 1996). The study employed passive Tenax tubes to
collect 28-day indoor and outdoor samples. These results were in good
agreement with the levels of 10 µg/m3 indoors and 6 µg/m3 outdoors
at 50 homes in Los Angeles measured over two seasons in 1987 (Wallace
et al., 1991).
Table 39. Weighted estimates of air and breath concentrations of 11 prevalent compounds for 130 000 Elizabeth-Bayonne
residents (fall 1981); 110 000 residents (summer 1982); and 49 000 residents (winter 1983)
Compound Personal Fall Breath Personal Summer Breath Personal Winter Breath
air outdoor (300) air outdoor (110) air outdoor (49)
(n = 340) air (150) air (49) air
(86) (60) (9)
1,1,1-Trichloroethane 94a 7.0a 15b 67 12 15 45 1.7 4.0
m,p-Dichlorobenzene 45 1.7 8.1 50 1.3 6.3 71 1.2 6.2
m,p-Xylene 52 11 9.0 37 10 10 36 9.4 4.7
Tetrachloroethylene 45 6.0 13 11 6.2 10 28 4.2 11
Benzene 28 9.1 19 NCc NC NC NC NC NC
Ethylbenxene 19 4.0 4.6 9.2 3.2 4.7 12 3.8 2.1
o-Xylene 16 4.0 3.4 12 3.6 5.4 13 3.6 1.6
Trichloroethylene 13 2.2 1.8 6.3 7.8 5.9 4.6 0.4 0.6
Chloroform 8.0 1.4 3.1 4.3 13 6.3 4.0 0.3 0.3
Styrene 8.9 0.9 1.2 2.1 0.7 1.6 2.4 0.7 0.7
Carbon tetrachloride 9.3 1.1 1.34 1.0 1.0 0.4 NDd ND ND
Total 11 (compounds) 338 48 80 200 59 66 216 25 31
a Average of arithmetic means of day and night 12-h samples (µg/m3).
b Arithmetic mean.
c Not calculated-high background contamination.
d Not detected in most samples.
Another study of benzene in four New Jersey homes was focused on
the extent of contamination from attached garages (Thomas et al.,
1993b). Each home was monitored for either 6 or 10 consecutive 12-h
periods. At all four homes, garage levels of benzene were higher than
outdoors, and at three of the four homes the garage levels were higher
than in the living area. Air exchange measurements made it possible to
calculate the amount of benzene transferred from the garage to the
living area in the four homes; in the home without elevated benzene
levels in the garage, the total air flow between the garage and the
living area was extremely small. Benzene concentrations in the garages
ranged from 5 to 200 µg/m3, and the 12-h average source strength
estimates ranged from 730 to 26 000 µg/h. The mere presence of an
attached garage was not a significant factor in affecting benzene
concentrations in the living area. However, the total number of hours
the car was parked in the garage had a significant effect on
living-area benzene concentrations, as did the mass flow rate of
benzene from the garage to the home.
In 1991 a subsample of the German Environmental Survey (see
Chapter 2.6) of 113 people took part in a study to assess exposure to
VOCs by personal sampling. The subjects wore passive samplers
(OVM-3500, 3M Company) for 7 consecutive days and simultaneously
documented the length of time spent indoors, the room characteristics
and any specific exposure such as that caused by renovation
activities. Seventy-four VOCs were analysed by gas chromatography
(Ullrich, 1992).
The results of personal sampling showed, for example, that from
the various types of environments the workplace has the highest impact
on exposure to C8- and C9-aromatic hydrocarbons (Figs. 42 and 43).
Other important factors that need to be considered are renovation
activities, use of paints and lacquers and the frequent reading of
newspapers and journals (printing inks contain many VOCs). Smoking
contributes significantly to human VOC exposure. In the case of
benzene, the multivariate model contained five variables: two related
to smoking exposure indoors, two related to vehicle traffic and the
residential density (Fig. 44). The two smoking variables alone
accounted for 20% out of a total variance of 40% that could be
explained (Hoffmann et al., 1996; Ullrich et al., 1996).
Three large studies of VOCs, involving 300-800 homes, have been
carried out in the Netherlands (Lebret et al., 1986), Germany (Krause
et al., 1987) and the USA (Wallace, 1987). A small study of 15 homes
was carried out in Northern Italy (De Bortoli et al., 1986). Observed
concentrations were remarkable similar for most chemicals, indicating
similar sources in these countries. One exception is chloroform,
present at typical levels of 1-4 µg/m3 in the USA but not found in
European homes. This is to be expected, since the likely source is
volatilization from chlorinated water (Wallace et al., 1982; Andelman,
1985a,b); Germany and the Netherlands do not chlorinate their water.
12.3.8 Commuter exposures
In crowded urban areas it is not uncommon to find substantial
populations living near busy roads. Still others make their living
working among cars or vending goods along busy streets. Around the
world the routine of commuting between home and workplace exposes most
of the urban population to motor vehicle exhaust (carbon monoxide,
oxides of nitrogen, PAHs, VOCs and lead, in many cases) on a daily
basis. There have been several studies designed to assess exposures to
vehicle exhaust.
In a study conducted in Stockholm, Sweden, Bostrom et al. (1991)
demonstrated that nitrogen oxides can be used as tracers for VOCs
originating from vehicular traffic. The most important sources of VOCs
in Swedish cities are motor vehicles. Also, some 80-90% of NOx
(nitric oxide and nitrogen dioxide) in large Swedish cities originates
from motor vehicle traffic. Quantitative relationships were developed
between NOx and individual hydrocarbons, independent of traffic
intensity and time of year. For instance, a PAH/NOx ratio of 2.0 ×
10-2 was reported for Gothenburg, Sweden, and a benzene/NOx ratio of
0.16 was reported for Stockholm.
Chan et al. (1991) assessed in-vehicle levels of carbon monoxide
in Raleigh, North Carolina, USA during the summer of 1988. The ratio
of mean concentrations of carbon monoxide inside and outside the
vehicle was 1 : 1, and the ratio of mean concentrations inside the
vehicle to a fixed-site location was about 5. The ratio of in-vehicle
concentrations under three different driving conditions, urban/
interstate/rural, was roughly 3.3 : 2.8 : 1. An investigation of
carbon monoxide concentrations inside private and public transport
vehicles in Mexico City in 1993 (Fernandez-Bremauntz & Ashmore,
1995a,b) found an average ratio of in-vehicle : ambient concentrations
of 2 : 1 for the metro and 5 : 2 for cars.
Liu et al. (1994b) conducted a study of carbon monoxide exposure
among Taipei commuters (adults and students) in 1990. Roadside and
in-vehicle measurements were made at the same time that commuters'
personal exposure was assessed. Concentrations of carbon monoxide were
measured for three transportation modes (bus, private car and
motorcycle) and three times of day (morning rush hour, midday and
evening rush hour). The ratio of in-vehicle to ambient concentrations
of carbon monoxide was roughly 6 : 5, overall.
As part of their study of carbon monoxide exposure, Liu et al.
performed a survey of commuting patterns in Taipei, for students and
adults. Adults had a significantly longer average commuting time than
students (1.4 h versus 0.8 h). Students commuted typically by walking
(58%) or by riding on public buses (29%). Adults commuted to work by
motorcycle (28%), public bus (26%), or in private cars (26%).
Commuters using public buses had the longest commuting times (1.8 h
for adult workers, and 1.2 h for students).
WHO recommended guidelines for carbon monoxide are 30 mg/m3 as a
1-h mean, 60 mg/m3 for a 30-min mean, and 100 mg/m3 as a 15-min
mean. These guidelines are designed to prevent carboxy-haemoglobin
levels in the bloodstream from surpassing 2.5-3.0% in the non-smoking
population, and to protect people who are prone to heart problems.
According to the 1992 UNEP report of air pollution in megacities of
the world, the 1-h WHO guideline is routinely exceeded by a factor of
2-3 times in several cities in Asia (Amman, Bangkok, Jakarta,
Peshawar, Shanghai) and Latin America (Mexico City, Santiago, Lima)
(UNEP/WHO, 1992). Considering the exposure studies conducted in Mexico
City and Taipei, the stationary monitors are an underestimate of the
population at risk of elevated carbon monoxide levels.
12.4 Exposures and biomarkers
12.4.1 Exposure to lead and cadmium
Dose-response relationships exist for lead toxicity in children
and adults, and demonstrate that subtle effects begin at levels as low
as 1 µg/dl of lead in blood. Severe toxicity is associated with
blood-lead levels of 70 µg/dl or higher in children, and 100 µg/dl or
higher in adults. Toxicity symptoms include poisoning of the central
nervous system, causing convulsions, coma, and deep, irreversible
mental retardation. Functional changes in the peripheral nervous
system and anaemia can also occur at levels below 40 µg/dl.
Particulate lead present in gasoline (from the octane enhancer
tetraethyl lead) and bromine (from the lead scavenger ethylene
dibromide) have traditionally been used as tracers for mobile sources.
The WHO recommended ambient air quality guideline for lead is
1 µg/m3, a level routinely exceeded in many large Asian cities today
where lead is still permitted in gasoline. This guideline value is
based on the assumption that 98% of the general population will be
maintained below a blood level of 20 µg/litre, which is considered the
maximum acceptable concentration in blood.
Jimenez & Velasquez (1989) conducted a study in Manila,
Philippines to measure blood lead concentrations in children. In a
sample of 544 children, the average blood lead level was 22.8 µg/dl,
with approximately 8% of the children having levels greater than
30 µg/dl. The study also found a significant correlation between high
blood lead levels and proximity of the household to dense traffic. In
a 1990-1991 study of exposure to lead among schoolchildren in Manila,
Subida & Torres (1991) found that mean blood lead concentrations were
14 µg/dl ( n = 387), with 10% having levels over 10 µg/dl. The same
study measured blood lead levels among child street vendors. Mean
blood lead level for a sample of 101 vendors was 17.8 µg/dl, with 33%
having levels over 20 µg/dl.
Muangnoicharon (WHO, 1995b) reports on a lead exposure study of
bus drivers in Bangkok. The study was a cooperation between the WHO
HEAL Project and the Department of Medical Science, Ministry of Public
Health of Thailand and was designed to assess exposure in a high risk
group. Subjects were bus drivers assigned to traffic routes in Bangkok
where ambient lead levels exceeded 1 µg/m3. Lead was analysed in 24-h
air samples, duplicate food for each meal for 6 days and faeces for 5
days, as well as blood collected on the day 7.
Average personal air exposures were 0.117 µg/m3, which yielded
an estimated 0.936 µg/day absorption by inhalation at an estimated
absorption rate of lead via inhalation of 40% and 20 m3/day
respiratory air. Intake by food was 87.92 µg/day (27.32 SD). Thus, the
estimated average lead absorption from air, food and water was 13.325
µg/day at an estimated absorption rate via ingestion of 10%.
Researchers suggested that meals consumed from roadside restaurants
and food stands resulted in higher than expected lead levels in food.
Blood lead levels for bus drivers ranged between 5 and 12 µg/dl.
The results for the Thai study were compared to other HEAL
sponsored studies in China, Sweden and Yugoslavia (Table 41).
One of the HEAL projects investigated lead and cadmium exposure
among small groups of non-smoking women (Vahter & Slorach, 1990).
Subjects kept activity diaries. Duplicate portions of food and
corresponding faeces samples were collected along with personal air
samples. Food was found to be the main source of both lead and
cadmium. Faecal concentrations could be used for validation of the
duplicate portion samples because of the low uptake in the
Table 40. Weighted estimates of air and breath concentrations of 19 prevalent compounds for 360 000 Los Angeles
residents (February 1984); 330 000 Los Angeles residents (May 1984); and 91 000 Contra Costa residents
(June 1984)
Compound LA1 (February) LA2 (May) CC (June)
Personal Outdoor Breath Personal Outdoor Breath Personal Outdoor Breath
air air air air air air
(n = 110) (24) (110) (50) (23) (50) (76) (10) (67)
1,1,1-Trichloroethane 96a 34a 39b 44 5.9 23 16 2.8 16b
m,p-Xylene 28 24 3.5 24 9.4 2.8 11 2.2 2.5
m,p-Dichlorobenzene 18 2.2 5.0 12 0.8 2.9 5.5 0.3 3.7
Benzene 18 16 8.0 9.2 3.6 8.8 7.5 1.9 7.0
Tetrachloroethylene 16 10 12 15 2.0 9.1 5.6 0.6 8.6b
o-Xylene 13 11 1.0 7.2 2.7 0.7 4.4 0.7 0.6
Ethylbenxene 11 9.7 1.5 7.4 3.0 1.1 3.7 0.9 1.2
Trichloroethylene 7.8 0.8 1.5 6.4 0.1 1.0 3.8 0.1 0.6
n-Octane 5.8 3.9 1.0 4.3 0.7 1.2 2.3 0.5 0.6
n-Decane 5.8 3.0 0.8 3.5 0.7 0.5 2.0 3.8 1.3
n-Undecane 5.2 2.2 0.6 4.2 1.0 0.7 2.7 0.4 1.2
n-Dodecane 2.5 0.7 0.2 2.1 0.7 0.4 2.1 0.2 0.4
alpha-Pinene 4.1 0.8 1.5 6.5 0.5 1.7 2.1 0.1 1.3
Styrene 3.6 3.8 0.9 1.8 no data no data 1.0 0.4 0.7
Chloroform 1.9 0.7 0.6 1.1 0.3 0.8 0.6 0.3 0.4
a Average of arithmetic means of day and night 12-h samples (µg/m3).
b Arithmetic mean.
Table 41. Results of lead HEAL exposure pilot studies in Bangkok,
Thailand; Beijing, China; Stockholm, Sweden and Zagreb,
Croatia
Sample Unit Thailand China Sweden Croatiaa
(n = 24) (n = 10) (n = 15) (n = 17)
Breathing zone µg/m3 0.117 0.116 0.064 0.412
Food µg/day 89.72 46.0 26.0 15.0
Faeces µg/day 60.57 42.0 23.0 49.0
Blood µg/dl 8.83 7.3 2.9 5.0
gastrointestinal tract. Inhalation was found to account for a few
percent of the total exposures of cadmium. Inhalation contributed more
than 70% of total lead exposure. On the other hand, where air lead
levels were high, as in Zagreb, Croatia, inhalation contributed twice
as much to total exposure as the ingestion route (see Fig. 45).
These pilot studies illustrated that without thorough analytical
QC it was not possible to compare results between countries. The pilot
study, although expensive, identified problems in collection and
analysis. The need to exchange standards in the various media and
training in analytical methods and procedures were important
components for the success of the studies.
In a study conducted on Swedish women the bioavailability of
dietary cadmium was contrasted for different diets. Dietary intake and
uptake of cadmium were studied in non-smoking women, 20-50 years of
age, consuming a mixed diet low in shellfish ( n = 34), or with
shellfish once a week or more ( n = 17), or a vegetarian diet rich in
fibre ( n = 23) (Berglund et al., 1994b; Vahter et al., 1996). The
objectives were to identify important factors, dietary and other,
influencing cadmium exposure and dose.
Duplicate portions and corresponding faeces (using a coloured
marker to indicate start and end of duplicate portion collection) were
collected for four consecutive days (including weekdays and weekends),
for the determination of cadmium intake. Blood and 24-h urine samples
were collected for determination of total cadmium exposure. The women
kept detailed dietary records for identification of significant
sources.
The shellfish diets contained twice as much cadmium (22 µg/day)
as the mixed diets (10 µg/day). The high fibre diets were intermediate
(13 µg/day). The content in faeces were on the average 100%, 99% and
98% of intake in the shellfish group, the mixed diet group and the
fibre group, receptively, indicating a low average absorption of
dietary cadmium. Despite the differences in cadmium intake there were
no significant differences in blood cadmium (about 0.25 µg/litre) or
urine (0.1 µg/litre), indicating a lower absorption of cadmium in
shellfish and in high fibre foods compared to the mixed diet (low in
shellfish and cereals) or a difference in the kinetics. A higher
cadmium absorption in the mixed diet and the fibre diet group compared
to the shellfish group could partly be explained by lower body iron
stores (measured as serum ferritin). Low body iron stores in women of
reproductive age are very common. Serum ferritin levels were
negatively correlated with blood cadmium concentration, indicating an
increased absorption of cadmium at reduced body iron stores (defined
as serum ferritin below 30 µg/litre).
12.4.2 Exposure to furans, dioxins and polychlorinated biphenyls
Dioxins, furans and PCBs are persistent compounds found in
industrial discharges and incinerator air emissions, and as trace
contaminants in many products. These compounds accumulate in fat and
undergo amplification in marine and terrestrial food chains. People
consuming large amounts of contaminated seafood may have higher
concentrations of organochlorine compounds in their tissues than the
general population.
For 35 years a magnesium-producing factory in the inner part of a
fjord in southern Norway had discharged 50-100 kg TCDD toxic
equivalents (TEQ) to the fjord area. PCDDs/Fs and PCBs were monitored
in sediments and marine organisms in 1986, 1989-1990 and 1992. In
spite of a reduction by >98% in the discharge from 1990 to 1992,
levels were still very high. Restrictions in commercial fishing and
advice to the general public regarding consumption were established.
Some residents still catch and consume considerable amounts of local
fish and shellfish, particularly crabs, during summer and autumn. The
crabs contain high concentrations of PCDDs/Fs with a predominance of
tetra- and hexa-CDFs and PCB-209.
In the study by Johansen et al. (1996), 24 male crab consumers
were recruited non-randomly from news announcements and 10 controls
were drawn randomly from the population register. PCDDs/Fs and PCBs
were measured in blood samples. Information on crab and fish
consumption and intake of fatty food items were collected and the
fishing site reported. The study was designed to determine if
consumption of crabs from the contaminated fjord area led to increased
body burden of PCDDs/Fs and PCBs. From the patterns of PCDDs/Fs and
PCBs congeners in crabs, can congeners in blood be inferred sources?
Finally, can exposure estimates based on blood levels be predicted by
reported crab intake of location? A considerable increase of PCDDs/Fs
in blood was found upon consumption of contaminated crabs. A direct
relationship was found between blood level and the number of crabs
times contamination level. See Fig. 46 from Johansen et al. (1996).
The PCDD/F profile in the high intake group clearly reflected the
profile found in the crab hepatopancreas. PCB-209 does not appear to
be absorbed since it did not increase after crab consumption.
Using a simplified toxicokinetic calculation, good correlation
( r = 0.61) was reported between estimated yearly intake based on
blood values and intake based on reported intake and the fishing site.
The intake calculated for the controls was 9.7 pg TEQ/kg body weight
per week, in good agreement with estimated intake from food in Norway
(8-10 pg TEQ/kg body weight per week). The average exposures of the
moderate and high-intake groups were 31 (10-61) pg/kg body weight per
week and 62 (24-114) pg/kg body weight per week. Most individuals in
the high-intake group exceeded the recommended Nordic tolerable weekly
intake (TWI) of 35 pg/kg body weight per week
12.4.3 Exposure to volatile organic compounds and urinary metabolites
In Tokyo, Nakahama et al. (1997) measured personal VOC exposures
over 12 h and the metabolites in urine. Thirteen men and 17 women
participated. The VOCs 1,1,1-trichloroethane, trichloroethylene and
tetrachloroethylene were sampled with passive absorbent badges.
Trichloroethanol and trichloroacetic acid corrected for creatinine
were measured in urine. Personal exposures were well correlated
( r = 0.80) with urinary metabolites. Interestingly, women inhaled
twice as much VOCs as men, perhaps because of increased exposure to
household chemicals and cosmetics.
12.5 Exposure to contaminants in drinking-water
The health effects of exposure to natural background radiation,
specifically radon, through inhalation have been explored, but
drinking-water is another source of natural background radiation that
may contribute to cancer incidence. Elevated rates of bone cancer have
been hypothesized as potential outcomes of exposure to radium-226 and
radium-228 because of their accumulation in the bone (Bean et al.,
1982). These two isotopes are found in some deep aquifers. Previous to
the study 300 000 residents of Iowa were found to have levels of
radium-226 in their municipal drinking water that exceeded the US
Public Health Service's 1962 drinking water standard of 3 pCi/litre.
The time periods 1969-1971 and 1973-1978 were studied for all 28
municipalities in Iowa. After testing each town for radium-226 levels,
the towns were divided into three groups, with respectively 0-2, 2-5
and >5 pCi/litre of radium-226 in the water supply. In towns level
> 5.0 pCi/litre, the incidence of lung and bladder cancer in men and
lung and breast cancers among women was higher (Fig. 47). Although 77%
of the individuals in the study had been on the same water supply for
at least 10 years, misclassification due to uncertainties about past
concentrations and past residential histories create problems for the
study.
A Taiwanese study of a population that used artesian wells
suggests that there may be a link between high arsenic levels in
drinking water and the incidence of internal cancers, particularly
bladder cancer (Chiou et al., 1995). Levels above the maximal
permitted level of 50 µg/litre occur in some locations in the western
USA. In 1978 a study was done in Utah of individuals between the ages
of 21 and 84. Concentrations of arsenic in the water ranged from 0.5
to 160 µg/litre (mean 5.0 µg/litre). Two indexes of exposure were
used, both of them assuming constant past levels in the water supply:
* total cumulative exposure was calculated using the duration of time
spent in the town, the rate of water consumption, and the 1978
levels of arsenic in the drinking-water
* intake concentration was calculated using the above measurements,
as well as the total fluid intake, to approximate the arsenic
concentration in the urine to which the bladder is exposed (Bates
et al., 1995).
Overall, no association between arsenic exposure and bladder
cancer was seen with either index (Table 42). The only odds ratio
(3.32) significantly different from 1 was for smokers with a
cumulative dose greater than 53 mg. This suggests that smoking
potentiates the relationship between arsenic and bladder cancer.
That drinking-water can be a main source of exposure could be
shown in the framework of German Environmental Survey (see Chapter
2.6). Drinking-water (first draw and grab samples, see Chapter 7.3.2)
was analysed in approximately 4000 German households. A significant
correlation was observed between the lead content in drinking water
and the lead content in the blood of the population (Nöllke et al.,
1995; Becker et al., 1997).
12.6 Exposure to microbes
Examination of biological contamination involves a different
approach, as discussed in Chapter 9. Bioaerosol samples are widely
used and rely on impaction on to culture medium. The cut-off size of
the samplers limits the ability to capture all bioaerosols, and no one
culture medium and growth temperature is appropriate for all viable
bacteria in the air. Therefore, the numbers from the impactor will be
less than those actually present in the air because of limited power
of detection. Chemical assay for endotoxin is independent of the
ability to grow the bacteria, but it is sensitive to sampling and
storage procedures.
Previous studies of bioaerosols in the occupational setting have
examined levels of airborne bacteria. A study of bioaerosols at water
treatment plants was planned to go beyond previous studies and examine
concentrations of both bacteria and endotoxin (Laitinen et al., 1994).
Endotoxin (bacterial toxin not freely liberated into the surrounding
medium) can cause fever, eye inflammation, fatigue and/or respiratory
difficulties if inhaled, and may be a more reliable measure of
Table 42. Adjusted odds ratios (OR) and 90% confidence intervals (CI) for bladder cancer and arsenic exposure, restricted to subjects
enrolled into the National Bladder Cancer Study in Utah in 1978 who had spent at least 50% of their lifetimes until diagnosis
living in study towns (Bates et al., 1995)
Exposurea All subjects Never smoked Ever smoked
Cases Controls ORb 90% CI Cases Controls ORb 90% CI Cases Controls OR 90% CI
Exposure index (cumulative dose (mg)
<19 14 47 1.00 10 25 1.00 4 22 1.00
19-< 33 21 36 1.56 0.8-3.2 10 19 1.09 0.4-3.1 11 17 3.33 1.0-10.8
33-< 53 17 39 0.95 0.4-2.0 7 20 0.68 0.2-2.3 10 19 1.93 0.6-6.2
„53 19 38 1.41 0.7-2.9 4 17 0.53 0.1-1.9 15 21 3.32 1.1-10.3
Exposure index (mg/litre × year)
<33 18 42 1.00 11 19 1.00 7 23 1.00
33-< 53 16 42 0.69 0.3-1.5 3 19 0.21 0.1-0.8 13 23 1.95 0.7-5.6
53-< 74 16 40 0.54 0.3-1.2 6 24 0.25 0.1-0.9 10 16 1.21 0.4-3.7
>74 21 36 1.00 0.5-2.1 11 19 0.91 0.3-3.2 10 17 1.41 0.5-4.3
a Cut-points based on exposure quartiles of all subjects in the table.
b Adjusted for sex, age, smoking, (all subjects and smokers only), years of exposure to chlorinated surface water, history
of bladder infection, educational level, urbanization of the place of longest lifetime residence, and ever employed in a
high-risk occupation.
biological exposure because it is independent of the ability to
culture the microorganism.
The treatment area, control room and outside were sampled at nine
industrial waste water treatment plants. Bacteria were collected using
an impactor which sampled on to agar plates. After incubation for 2
days, the number of colonies of each plate were counted, and
translated into numbers of CFU/m3 of air. Endotoxin was sampled on to
sterile filters using a suction pump, and was reported as ng/m3.
Levels of bacteria and endotoxin were correlated. Concentrations of
endotoxins in the work areas of the treatment plant were assessed by
collection on to glass fibres that were than tested using the
Limulus endotoxin assay. The measured levels of endotoxin ranged
between 0.1 and 350 ng/m3, and the 8-h time-weighted average
concentration was greater than the exposure limit of 30 ng/m3 at some
of the plants. Bacterial levels were between 10 and 105 CFU/m3
(Table 43).
Sixteen male workers with a mean exposure duration of 10 years
were examined for symptoms related to exposure to bacteria and
endotoxin. The workers' occupational history and symptoms were
evaluated using written questionnaires. Symptoms were found in
6 workers; 4 reported fever, shivering and eye irritation, and 3 a
cough. Although the number of subjects was too low for epidemiological
conclusions based on the symptoms, the levels in some locations were
high. Excessive levels of contamination were restricted to certain
areas of the plant, suggesting that exposure levels should be
minimized by changing the physical layout of those work areas and
improving hygienic practices by individuals after working in those
locations.
Previous work has established that both settled and airborne
house dust contains allergens such as dust mites, animal dander, and
fungi. The connection between home dampness, fungus levels and
respiratory symptoms was investigated in 60 homes as part of a
Netherlands case-control study (Verhoeff et al., 1994b). A
relationship between dust levels and exposure to moulds was
hypothesized, as was a relationship between characteristics of homes
(e.g., dampness and type of flooring).
The level of viable fungal propagules present in settled dust was
chosen as the sampling parameter; dust was collected from the floor
and mattresses using a vacuum cleaner with a cellulose fibre filter.
The presence and number of fungal propagules was determined by plating
the dust on to agar growth medium. A checklist and a questionnaire on
the residence type and occupant behaviour were filled out for each
home.
The geometric mean of the number of dust sampled from the floor
was 8990 CFU/g and it was significantly higher in samples from
carpeted floors (12 880 CFU/g) than from smooth floors (3550 CFU/g).
The level from mattresses was 7660 CFU/g. In rooms where damp spots
Table 43. Concentrations of endotoxin and bacteria (Laitinen et al., 1994)
Phase Endotoxin Gram-negative bacteria Total culturable bacteria Number
(ng/m3) (103 CFU/m3) (103 CFU/m3) of
samples
AM MD Range AM MD Range AM MD Range
Wastewater pumping 16 18 <0.04-30 0.56 0.08 0.01-1.6 4.5 0.5 0.4-13 3
Screening 30 31 3.6-55 5.0 6.3 0.63-7.9 34 32 6.3-63 3
Wastewater entering
sedimentation basin 130 140 100-140 7.5 6.3 6.3-10 27 25 16-40 3
Sedimentation basin
(indoor) 7.1 1.6 1.0-19 0.5 0.2 0.05-1.3 1.9 0.5 0.32-5 3
Aeration basin (indoor) 73 83 17-110 11 5.6 1.0-32 19 16 16-25 4
Aeration basin (outdoor) 8.7 1.7 0.8-36 1.8 0.5 0.1-6.3 6.5 4.0 2.5-16 5
Biofilter tower 38 38 4.8-71 13 13 10-16 180 180 130-250 2
Sludge treatment 140 97 9.2-350 25 13 1.3-63 77 79 13-200 9
Control room 3.5 1.4 <0.04-13 0.17 0.2 <0.002-0.32 3.8 3.2 0.2-13 7
Outdoors 0.6 0.3 <0.04-3.0 0.02 0.01 <0.002-0.06 0.15 0.12 0.01-0.4 8
were observed the levels were higher, but not significantly, and the
number was not related to the average relative indoor humidity. The
hypothesized association between the presence of fungi in the dust and
respiratory symptoms was not observed. Home characteristic and
occupant behaviour were therefore seen as poor predictors of fungal
levels, with the exception of floor type.
12.7 Exposure studies and risk assessment
12.7.1 The German Environmental Survey
Much of the research into better exposure assessment strategies
has come from the desire to more accurately estimate risks associated
with environmental exposure in order to better protect human health:
for instance, the HEAL studies, including the development of personal
monitors and the use of biomarkers. The German Environmental Survey is
a unique study providing a database of exposures to pollutants on a
representative basis for the general population in Germany. The
results of human biomonitoring provide important reference data for
evaluating results of smaller studies addressing specific problems.
The data of the study were also used for a number of risk assessments,
for example in the case of liver cirrhosis in early childhood caused
by copper in drinking water (Becker et al., 1997).
Using the German Environmental Survey data and multiple
regression analysis it was possible to identify relevant factors that
influence the body burden of pollutants of the general population.
12.7.2 The National Human Exposure Assessment Survey
The National Human Exposure Assessment Survey (NHEXAS) was
created in order to design an exposure surveillance programme covering
the population of the USA (Sexton et al., 1995c). NHEXAS is concerned
with policy issues that include differentiation between high and low
risk exposure groups and individuals in society. Understanding
expected values in the "normal" population is essential for use in
comparing to contaminant levels for those who live in a polluted area.
NHEXAS is conducted by researchers in the academic, private and
governmental areas of science working in cooperation. The studies are
coordinated; they share a common questionnaire on activity and
sociodemographics, examine the same exposure sources and send samples
for analysis to the same laboratory. The studies are unique in the
degree of characterization of exposures of individuals. Multiple
chemicals, chemical classes, exposure pathways and routes will be
examined for each individual for each study. NHEXAS is conducted as
four projects:
* Population study in Arizona of exposures to metals, VOCs and
pesticides carried out by the University of Arizona, Batelle
Columbus and the Illinois Institute of Technology.
* A study of population exposure measurements of metals, pesticides,
PAHs and VOCs in two random populations drawn from the EPA's region
5, which includes the industrial northern states of the USA.
* The relationship between long-term and short-term exposures of
individuals to metals, PAHs, pesticides and VOCs is being studied
on a population of 50 urban and suburban residents in the
Baltimore, Maryland area.
* Parallel to the field studies is a modelling exercise, where
existing information is used in Monte Carlo simulation routines to
estimate exposure distributions. A preliminary study of the regions
in the first two projects for exposure to pesticides, metals and
VOCs is being carried out by the Harvard School of Public Health.
12.7.3 Windsor, Canada exposure and risk study
The Ontario Ministry of Environment in Canada conducted a
pioneering study to assess air pollution exposure risks to populations
living in Southern Ontario downwind of Detroit, Michigan, a large city
in the USA.
Windsor, Canada has a long history of air pollution monitoring
dating back to the 1940s. The city's environmental concerns increased
when an incinerator was built in Detroit, Michigan. At that time,
Detroit had several steel mills in operation. Concern for
transboundary transport of pollutants continues today.
The Ontario government performed a study between 1991 and 1993 in
order to determine the level of risk associated with air pollution and
to limit the exposure of residents of Windsor to airborne toxics (Bell
et al., 1994). After examination of concentrations of local airborne
toxic pollutants, 10 compounds were chosen for study due to their
persistence, bioaccumulation, and toxicity in the environment:
* metals: cadmium, chromium (VI), mercury
* VOCs: benzene, 1,3-butadiene, carbon tetrachloride,
1,4-dichlorobenzene, formaldehyde
* PAHs: benzo [a]pyrene
* SVOCs: dioxins, furans.
The study first identified the exposure levels by emissions
monitoring, fixed site ambient air monitoring, mobile air monitoring,
personal exposure measurements, and soil and garden produce survey.
Data on emissions from fixed, area, and mobile sources were placed on
to a grid with 1 km × 1 km squares, including both the Windsor and the
Detroit areas. Ambient monitors in the Windsor area showed levels for
most of the toxics to be lower than federal guidelines, with the
exception of benzo [a]pyrene. Dioxin and furan levels were below
regulatory levels, but were higher than in other Canadian cities. A
mobile monitoring system travelled to find potential hot spots
downwind of potential sources of toxins. The toxins were found to be
below regulatory levels, with the exception of hydrogen fluoride and
hydrogen chloride.
Fifty-six VOCs and 8 airborne trace metals were sampled at
residences, office, cars and recreation areas. Each personal exposure
study had a 24-h exposure profile prepared using the concentrations in
each environment and the time spent in that environment. It was found
that the period of highest exposure to VOCs was during the commute,
with outdoors the lowest. The highest exposure to heavy metals was
inside the home.
Heavy metal concentrations in garden produce and soil were below
regulatory limits, but cadmium and mercury levels were higher in the
Windsor area than in rural areas. Indoor air exposure had
disproportionately higher health risk than did outside air. Inhalation
was found to be more important than dermal absorption and ingestion
for these compounds.
The exposures to dioxins and furans were each less than one
quarter of the tolerable daily dose. Mercury was 60% of the tolerable
daily dose, and could be a health risk because of its persistence in
the environment and bioaccumulation. The additive risks for all of the
studied air pollutants were approximately 1 × 10-5.
12.7.4 Pesticide exposure study
The Non-Occupational Pesticide Exposure Study (NOPES) was
designed to examine human exposure to 32 pesticides and pesticide
degradation products in two cities in the USA: Springfield,
Massachusetts and Jacksonville, Florida (Whitemore et al., 1994). The
two goals of the project were to develop instrumentation, laboratory
procedures and surveys needed for a study of non-occupational exposure
to pesticides and to determine non-occupational exposure to
pesticides.
The NOPES study used TEAM in determining exposure levels. A
probability sampling design was used to make statistical conclusions
on the health risks of the pesticides. More participants were chosen
from high-exposure groups in order to facilitate estimation of risk
levels. Jacksonville was chosen as the high-use region and Springfield
as the low-use area. The two sites were studied during the summer of
1986 (Jacksonville only), the spring of 1987 and the winter of 1988.
The study population varied between 49 and 72 people. The study
examined skin, food and water as routes of exposure, but focused on
air as the primary route of exposure. Twenty-four-hour personal,
indoor and outdoor samples were collected on polyurethane foam and
analysed by gas chromatography/mass spectrometry and gas
chromatography/electron capture detection. Personal samples were used
as well as fixed monitors inside and outside the home. A questionnaire
was administered after the end of the 24-h period to determine the
activities of the subjects.
The lowest concentrations were found in the winter and the
highest concentrations in the summer, with the spring levels
intermediate. Readings from the indoor monitors were correlated with
personal monitors, but neither was comparable to the lower
measurements from the outdoor monitors. The relative importance of
dietary and respiratory routes of exposure varied between pesticides:
most of the chemicals had the diet as the main routes of uptake, but
pesticides used indoors were mainly taken via inhalation.
The chlorinated hydrocarbons chlordane, heptachlor, aldrin and
dieldrin were calculated to have the largest risk for health effects,
although all but heptachlor and aldrin had negligible risks.
Heptachlor and aldrin had excess lifetime cancer risks of 2 × 10-4
and 1 × 10-4 respectively in Jacksonville, despite having been banned
for many years.
12.7.5 Czech study of air pollution impact on human health
The aim of the Teplice programme has been to conduct a
multi-end-point air monitoring and human biomonitoring study to assess
the impact of air pollution on population health in the district of
Teplice, Czech Republic (Sram et al., 1996). Particulate and gaseous
air pollutants were measured in Teplice and in the control region
Prachatice. PM2.5 and PM10 composition and toxic metals as well as
concentrations of PAHs were measured daily in winter and periodically
during the spring/summer season. The concentrations of all pollutants
measured were significantly higher in winter compared to spring and
summer. Average fine particle mass in Teplice was 122 µg/m3 compared
to 44 µg/m3 in Prachatice during the winter of 1993, and 28.7 µg/m3
and 17 µg/m3 respectively, in spring/summer. Total PAH concentrations
in Teplice in winter were approximately twice as high as in Prachatice
(278 versus 163 ng/m3). Evaluation of the benzo [a]pyrene to lead
ratio in Teplice over time indicated the presence of at least two
sources of PAHs. During the summer when mobile sources are the major
contributor to benzo [a]pyrene, the ratio was about 0.01. During
winter, when the ratio was 0.05 to 0.15, emission from inefficient
combustion of brown coal in domestic heating systems is considered to
be the most likely source of PAHs.
Personal exposure and biomarkers were measured with the objective
of simultaneously evaluating personal exposure to air pollution and
internal measures of dose and genetic effects and susceptibility using
a series of biomarkers. PAHs were selected as the pollutant marker for
monitoring personal exposure. A group of 30 women working outdoors in
Teplice district was compared with a group of 30 women from the
Prachatice district. Personal exposure monitoring (PM2.5) was
conducted for the 24-h period prior to collection of blood and urine.
High correlation were observed between the mass of fine particles and
personal exposure to total carcinogenic PAHs and benzo [a]pyrene.
Significant correlations were observed between the personal exposures
to PM2.5 or carcinogenic PAHs and blood lead and blood selenium. The
urinary PAH metabolites, adjusted for creatinine content, were also
significantly correlated with PM2.5 or PAHs. Significant correlations
were found between personal exposure to carcinogenic PAHs and white
blood cell DNA adduct level.
The results consistently suggested that elevated levels of
airborne fine particle pollution could result in measurable uptake,
metabolism and cellular DNA damage in a population exposed to high
concentrations, even for a short-term winter inversion period.
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RÉSUMÉ
1. Définition de l'exposition
Pour qu'il y ait exposition, il faut qu'une substance toxique se
trouve à l'instant t en un point donné de l'environnement et qu'une ou
plusieurs personnes se trouvent également en ce point au même moment.
En outre, la quantité de substance en contact avec le tégument entre
également en ligne de compte.
Lorsqu'on a pris conscience de l'intérêt intrinsèque de cette
notion, "l'analyse de l'exposition" s'est imposée comme un aspect
important de l'investigation scientifique en santé publique, qui vient
compléter les disciplines traditionnelles que sont la toxicologie et
l'épidémiologie et constitue désormais un élément essentiel de toute
décision éclairée dans le domaine de l'hygiène de l'environnement.
2. Usage des données relatives à l'exposition humaine
Toute analyse de l'exposition se caractérise par le ou les usages
qu'on a l'intention d'en faire. Ainsi, les aspects de l'exposition qui
sont à prendre en compte, la nature de l'information nécessaire, de
même que la quantité et le niveau de qualité des données à réunir
pourront varier selon que l'analyse est effectuée dans le cadre d'une
enquête épidémiologique, d'une évaluation du risque, d'une analyse de
tendance ou encore selon que l'on cherche à caractériser le risque ou
à le maîtriser.
La connaissance de l'exposition humaine aux polluants présents
dans l'environnement est importante dans ce type de démarche ou
d'action. Elle permet d'établir le lien déterminant qui existe entre
les sources de contamination, leur présence dans l'environnement et
les effets qui peuvent en découler pour la santé humaine. Les
informations de ce genre, si elles sont utilisées en vue d'une gestion
de l'environnement visant à réduire le risque pour la santé humaine,
vont faciliter le choix et l'analyse de stratégies qui se distinguent
de l'approche traditionnelle, basée sur "le commandement et la
conduite des opérations". La plupart des structures de gestion de
l'environnement se fondent sur une exploitation directe des données
relatives à la concentration des polluants dans les divers milieux
pour juger de la qualité environnementale d'un site, évaluer le risque
et voir dans quelle mesure les normes sont respectées. Même en pareil
cas, la connaissance de l'exposition peut permettre de déterminer dans
quelle mesure on peut protéger efficacement tel ou tel groupe de
population plus sensible ou plus exposé au risque.
C'est ce lien direct qui fait que la mesure de l'exposition est
d'une aide précieuse dans les études d'impact au niveau local,
régional ou mondial.
3. Stratégies et protocoles pour l'évaluation de l'exposition
L'élément le plus important d'une évaluation de l'exposition est
constitué par le protocole de l'étude. Ce protocole doit préciser la
finalité et les objectifs de l'étude ainsi que les méthodes
d'échantillonnage, de mesure et d'analyse statistique qui seront
utilisées, sans oublier l'assurance de la qualité. La caractérisation
de l'ampleur, de la durée et de la chronologie des contacts entre
sujets humains et polluants de l'environnement peut se faire de
manière directe ou indirecte. Si l'on procède directement, on pourra
par exemple effectuer des mesures aux points de contact et évaluer les
marqueurs biologiques de l'exposition. Si l'on a recours à une méthode
indirecte, elle pourra se fonder sur la surveillance de
l'environnement, la modélisation ou l'usage de questionnaires. Toutes
ces méthodes sont susceptibles d'être utilisées dans divers types
d'études d'exposition caractérisées par la manière dont s'opère le
choix de la population à étudier. Par exemple, il pourra s'agir
d'études exhaustives portant sur la totalité de la population en
cause, d'études descriptives sur un échantillon non aléatoire ou
encore d'enquêtes basées sur un échantillons représentatif constitué
d'individus tirés au sort.
4. Les méthodes statistiques utilisées dans l'évaluation de
l'exposition
Ces méthodes constituent un outil essentiel pour les études
d'exposition à vocation heuristique ou à caractère appliqué. Il est
souhaitable qu'un statisticien intervienne à tous les stades de
l'étude et notamment lors de la mise au point du protocole et de
l'analyse des données. L'un des aspects importants de l'utilisation
des méthodes statistiques est la détermination de la taille de
l'échantillon à laquelle on procède lors du stade de planification de
l'étude. On peut également récapituler les données d'exposition sous
la forme de statistiques descriptives numériques ou graphiques et
effectuer une analyse préliminaire des relations entre les diverses
variables. Les données d'exposition ont souvent une distribution
normale ou log-normale et on peut donc les soumettre immédiatement à
une analyse statistique paramétrique avec estimation et tests
d'hypothèses. On peut également avoir recours à d'autres modèles
statistiques paramétriques tels que l'analyse de la variance, la
régression linéaire et la régression logistique afin de quantifier les
associations entre les diverses mesures de l'exposition. Lorsque le
nombre d'observations est faible ou que les données ne peuvent pas
être transformées pour leur faire adopter une distribution
sensiblement normale, on peut utiliser des méthodes non paramétriques
comme le test des signes, le test de Mann-Whitney ou celui de
Kruskal-Wallis pour vérifier les hypothèses.
5. Profils temps-activités des sujets humains et évaluation de
l'exposition
La connaissance du type d'activité des individus permet de mettre
en évidence les déterminants de l'exposition mesurée, de déterminer
a priori une exposition non mesurée ou non mesurable, d'établir les
relations susceptibles d'exister entre exposition et état de santé ou
encore de reconnaître les situations à haut risque justiciables d'une
intervention par les autorités en charge de la santé publique.
Le coût relatif des prélèvements sur le terrain et des analyses
en laboratoire dans le cas d'échantillons environnementaux ou
biologiques fait ressortir l'intérêt potentiel des données
temps-activités. Pour une évaluation à très long terme (par ex. sur
toute la durée de la vie), on n'a parfois pas d'autre possibilité que
l'utilisation de questionnaires temps-activités. Il existe diverses
méthodes pour recueillir des informations sur les activités humaines:
agendas, questionnaires, dispositifs mécaniques ou observation. Plus
récemment, on a commencé à élaborer des méthodes permettant d'évaluer
le rôle du profil temps-activités sur les voies d'exposition
alimentaire, non alimentaire ou cutanée. On s'interroge cependant sur
le point de savoir si les méthodes de collecte des données
garantissent la représentativité de l'activité effective et permettent
de recueillir des informations valables, sans parler du problème des
variations inter- et intraindividuelles, ce qui fait entrevoir les
limites de l'utilisation des données temps-activités pour l'évaluation
de l'exposition humaine. Cependant, au prix d'un bon programme
d'assurance de la qualité, les profils temps-activités peuvent se
révéler très précieux pour l'interprétation et la modélisation de
l'exposition.
6. Exposition humaine et modélisation de la dose
Un modèle d'exposition est une représentation logique ou
expérimentale qui permet l'estimation des paramètres d'exposition d'un
individu ou d'une population à partir d'un certain nombre de données
initiales. Les modèles d'exposition qui s'appuient sur de bonnes
observations peuvent être utilisés pour évaluer l'exposition
collective (moyenne pour une population par ex.) ou individuelle (par
ex. la distribution de l'exposition parmi les membres d'une
population). Ces modèles peuvent également servir à estimer
l'exposition par le canal d'un seul ou de plusieurs milieux. Cette
dernière possibilité est particulièrement utile pour comparer
l'intensité de l'exposition imputable à divers milieux et fixer des
priorités. Les modèles d'exposition peuvent être de nature
déterministe ou statistique ou tenir des deux. Des méthodes
probabilistes sont applicables aux trois types de modèles pour obtenir
la distribution estimative de l'exposition dans une population,
c'est-à-dire pour se faire une idée de sa variabilité selon les
individus. En outre, ces méthodes peuvent être utilisées pour
caractériser l'incertitude des paramètres initiaux d'un modèle en
conservant ce caractère incertain jusqu'au point d'aboutissement
prévisible. Il est capital d'évaluer le degré d'exactitude d'un modèle
avant de prendre une décision sur la base de ses résultats.
7. Mesure de l'exposition humaine aux substances chimiques présentes
dans l'air, l'eau ou les aliments
Qu'il s'agisse de l'air, de l'eau ou des aliments, les mêmes
considérations relatives aux limites de détection, aux interférences,
à la commodité de mise en oeuvre et au coût se retrouvent lors du
choix d'une méthode d'échantillonnage. Pour la surveillance passive ou
active des gaz, des vapeurs, des aérosols, des produits organiques
semi-volatils ou des gaz réactifs, on peut faire des prélèvements
individuels, microenvironnementaux ou dans l'air ambiant.
Lorsqu'on se propose de déterminer la qualité d'une eau,
plusieurs aspects sont à prendre en considération. Il y en a un
notamment, qui a son importance - à savoir le fait que l'exposition à
d'éventuels contaminants ne se limite pas à la voie digestive et que
tout le monde n'a pas accès à un réseau de distribution délivrant de
l'eau traitée. Le document fournit des indications relatives aux
programmes d'échantillonnage et de surveillance.
Il existe un certain nombre de méthodes pour évaluer la
consommation de nourriture et le degré de contamination des aliments.
La méthode à adopter sera fonction des données disponibles, du groupe
de population en cause, des effets - aigus ou chroniques- qui se
manifestent, de l'utilisation que l'on se propose de faire des
résultats et des ressources disponibles.
8. Mesure de l'exposition humaine à des contaminants chimiques
présents dans le sol et la poussière déposée
Les contacts avec le sol et la poussière déposée peuvent
constituer une source importante d'exposition humaine à des
contaminants chimiques, notamment chez l'enfant. On a mis au point de
nombreuses méthodes d'échantillonnage mais aucune d'entre elles ne
s'est révélée supérieure aux autres. Il est donc difficile, dans ces
conditions, de comparer les résultats d'études qui font appel à des
techniques d'échantillonnage différentes. Lorsqu'on choisit une
méthode d'échantillonnage, un certain nombre de facteurs importants
sont à prendre en considération: l'efficacité de la collecte, les
différences dans la nature des activités humaines, la variabilité des
caractéristiques physiques du sol, l'évolution du taux de poussière
dans le temps et l'espace, la surface et le substrat sur lesquels on
effectue les prélèvements, le moment de la collecte et les méthodes
d'analyse qui seront utilisées pour le dosage des produits toxiques en
laboratoire.
9. Dosage des agents biologiques présents dans l'air et l'eau et
auxquels l'Homme est exposé
Les aérosols biologiques contiennent divers microorganismes ou
constituants de microorganismes aéroportés qui sont susceptibles
d'être inhalés. Il s'agit notamment de virus, de bactéries, de
pollens, de champignons, de protozoaires ou d'algues qui, lorsqu'ils
sont viables, peuvent être la cause de maladies. Des fragments de
bactéries ou de champignons, de même que les produits de leur
métabolisme ou les structures protéiques que ces microorganismes
contiennent ou encore des déjections ou des parties du corps d'animaux
divers, comme des insectes ou des arachnides, sont capables de
déclencher des réactions allergiques. Les aérosols biologiques les
plus répandus que l'on trouve communément à l'intérieur des
habitations contiennent des acariens, des champignons, des bactéries
et des pollens.
L'évaluation de l'exposition microbiologique en est actuellement
au même stade que celle de nombreux autres polluants atmosphériques.
On n'a pas encore mis au point d'échantillonneurs individuels. De
fait, nombre de techniques d'échantillonnage utilisées pour
l'évaluation de l'exposition aux agents biologiques aéroportés sont
des adaptations de techniques employées à d'autres fins. C'est un
domaine qui progresse, maintenant que des organismes professionnels
s'efforcent d'améliorer et de normaliser les méthodes de mesure et de
culture, les protocoles d'analyse et la présentation des comptes
rendus. Tous ces points sont d'une importance capitale pour la
comparaison des résultats obtenus par les différents chercheurs.
De par leur nature même, les aérosols biologiques sont de
composition et de concentration très variables. La croissance, la
reproduction et la dispersion des microorganismes varient largement en
fonction de la température, du degré d'humidité et de la présence de
nutriments. Leurs interactions avec les activités humaines ou animales
dépendent d'ailleurs de ces mêmes facteurs. Certains dispositifs
mécaniques ou machines peuvent amplifier et disperser les aérosols
biologiques. Il en résulte que l'exposition individuelle est
passablement variable; dans ces conditions, de nombreux chercheurs ont
été amenés à s'en remettre à des prélèvements d'air dans un secteur
donné ou à un échantillonnage général. Par exemple, plutôt que de
chercher à mesurer l'exposition individuelle aux allergènes des
acariens, il est recommandé de s'en tenir à des prélèvements sur la
literie ou dans la poussière du sol. La "mesure" de l'exposition
utilisée dans les enquêtes épidémiologiques est souvent effectuée sur
des échantillons intradomiciliaires constitués de prélèvements d'air
ou de poussière.
En définitive, si l'on veut que l'évaluation de l'exposition
aérobiologique progresse suffisamment pour qu'on puisse l'utiliser en
vue d'une estimation quantitative du risque, il va falloir déterminer
avec une plus grande précision et pour de nombreux agents ou
microorganismes, la dose nécessaire à la sensibilisation et à
l'apparition de réactions.
10. Evaluation de l'exposition à partir de marqueurs biologiques
Les marqueurs biologiques constituent un moyen de mesurer
l'exposition environnementale en caractérisant la dose totale de
contaminant reçue par un sujet à partir de l'ensemble des sources
d'exposition. Le principal avantage de cette stratégie réside dans le
fait que l'on obtient une évaluation de l'exposition individuelle
totale par une mesure qui intègre la contribution de toutes les
sources d'exposition et qui dépend du comportement humain. On estime
également que les marqueurs biologiques permettent une meilleure
prévision des effets sur la santé que les mesures externes de
l'exposition. Ils permettent aussi de répondre à plusieurs des
exigences de l'évaluation:
* caractérisation de l'exposition d'un individu ou d'une population
* obtention de la distribution de la dose à l'intérieur de la
population
* mise en évidence des déterminants environnementaux et
démographiques de l'exposition
Le principal inconvénient des marqueurs biologiques tient à la
difficulté de caractériser individuellement les sources de pollution
qui contribuent à l'exposition totale du sujet. Lorsqu'on met au point
et que l'on utilise de tels marqueurs, il est capital de connaître la
toxicocinétique du contaminant dans le système pour caractériser la
variabilité biologique et déterminer si tel ou tel marqueur permet de
déterminer valablement l'exposition à la substance en cause pour la
concentration toxicologiquement intéressante. Les marqueurs
biologiques ont joué un rôle capital dans la détermination de
l'exposition humaine à certains polluants, comme le plomb. Il existe
un grand nombre de méthodes non effractives pour la surveillance
biologique et il faut s'efforcer, lorsque l'on se propose d'évaluer
l'exposition à telle ou telle substance, de les inscrire dans les
protocoles de surveillance de l'environnement élaborés à cet effet.
11. Assurance de la qualité des études d'exposition
L'assurance de la qualité consiste en un suivi indépendant du
déroulement de l'étude destiné à faire en sorte que les responsables
du laboratoire et les utilisateurs des données aient l'assurance que
les installations, le matériel, le personnel, les méthodes, les
pratiques, les dossiers et les contrôles sont bien conformes aux
critères de qualité en la matière. Certaines erreurs dans l'évaluation
de l'exposition s'expliquent par des variations dans les résultats des
analyses ou par des changements qui se produisent lors du prélèvement
et de la manipulation des échantillons, de leur préparation et de leur
conservation ou encore dans la tenue et l'enregistrement des données.
Les variations dans les résultats des analyses peuvent se répartir en
deux grandes catégories: celles qui concernent l'exactitude,
c'est-à-dire la concordance entre le résultat du dosage et la quantité
de substance effectivement présente dans la prise d'essai et celles
qui concernent la précision, c'est-à-dire la variabilité aléatoire ou
la reproductibilité de la méthode.
Le plan de l'étude est le document le plus important à consulter
pour obtenir des informations sur les éléments essentiels d'une
enquête, c'est-à-dire le personnel responsable, le mode de collecte
des données, la conservation des échantillons et leur traitement
préliminaire, les méthodes d'analyse et l'analyse des résultats. Un
mode opératoire normalisé est annexé au plan de l'étude. Il comporte
des instructions détaillées sur la manière d'effectuer certaines
tâches sur le terrain ou au laboratoire. On peut considérer le plan de
l'étude et le mode opératoire normalisé comme des directives de
gestion visant à faire en sorte que l'ensemble du personnel qui
participe à l'étude se familiarise avec les diverses procédures et
n'en utilise pas d'autres.
Le contrôle de qualité vise expressément la qualité des résultats
de laboratoire. Il comporte deux volets. Le contrôle interne consiste
en un ensemble de procédures utilisées par le personnel du laboratoire
pour évaluer en continu les résultats qu'il obtient. Le contrôle de
qualité externe est un système de vérification objective de la bonne
exécution des analyses par un organisme indépendant. Dans le contrôle
interne, on affiche les résultats sur des cartes de contrôle (par ex.
cartes de contrôle de Sheward ou à sommes cumulées) et on se base sur
les limites de contrôle pour prendre des mesures le cas échéant ou
juger si l'ensemble de données comporte ou non un contrôle
statistique. Le contrôle externe, par contre, donne des indications
indépendantes sur la qualité du travail effectué par le laboratoire et
sur la compétence de tel ou tel opérateur. Généralement, un
laboratoire coordinateur distribue aux laboratoires participants des
échantillons contenant le produit à doser à une concentration connue.
Les laboratoires participants effectuent leurs dosages sur ces
échantillons et soumettent leurs résultats au laboratoire coordinateur
qui vérifie alors la bonne exécution des analyses. Dans les
échantillons de référence utilisés pour les contrôles de qualité
interne et externe la matrice et le polluant doivent être à la même
concentration que dans l'échantillon réel. En outre, pour certaines
substances, il peut être nécessaire de prendre en considération la
forme chimique sous laquelle elles sont susceptibles de se trouver.
Enfin, les interactions avec la population humaine constituent un
ensemble spécifique d'éléments à prendre en considération au niveau de
la conception de l'étude et de l'assurance de la qualité, éléments qui
doivent être soigneusement évalués en même temps que les questions
plus traditionnelles relatives à l'échantillonnage, à l'analyse et aux
modes opératoires.
RESUMEN
1. Definición de la exposición
El concepto de exposición implica la presencia de una sustancia
tóxica ambiental en un determinado punto del espacio y el tiempo, y la
presencia simultánea de una persona o personas en el mismo lugar.
Además, es necesario precisar la cantidad de la sustancia que entra en
contacto con la superficie externa del cuerpo humano.
Tras reconocerse el valor intrínseco de la información
relacionada con la exposición, el "análisis de la exposición" se ha
convertido en un importante campo de investigación científica, que
complementa las disciplinas tradicionales de la salud pública, como
son la epidemiología y la toxicología, y constituye un componente
esencial de la adopción de decisiones fundamentadas en materia de
higiene del medio.
2. Usos de la información sobre la exposición humana
Las particularidades de un determinado análisis de la exposición
dependerán del uso o usos previstos. Por ejemplo, los aspectos de la
exposición que interese examinar, la naturaleza de la información
requerida y la cantidad y calidad de los datos dependerán de si la
evaluación de la exposición se inscribe en el contexto de una
investigación epidemiológica, de una evaluación del riesgo, de la
gestión de los riesgos o de un análisis de la situación y las
tendencias.
El conocimiento del nivel de exposición humana a los
contaminantes ambientales es un componente importante de la
epidemiología ambiental, la evaluación de los riesgos, la gestión de
los riesgos y el análisis de situaciones y tendencias. La información
sobre la exposición proporciona el nexo decisivo entre las fuentes de
contaminantes, su presencia en el medio y los posibles efectos para la
salud humana. Esta información, empleada en el contexto de una gestión
del medio basada en la reducción de los riesgos para el ser humano,
facilitará la selección y el análisis de otras estrategias distintas
del tradicional enfoque de "dirección y control". En todo el mundo, la
mayoría de las estructuras de gestión del medio ambiente dependen
directamente de las mediciones de los contaminantes presentes en
diversos medios para estimar la calidad del entorno, inferir los
riesgos e interpretar el grado de observancia de las normas. Incluso
en estos casos, la información sobre la exposición permite evaluar la
eficacia de las medidas de protección de los sectores de la población
más vulnerables o en situación de mayor riesgo.
Es esa relación directa lo que hace de las mediciones de la
exposición un arma inestimable a efectos de evaluación de las
repercusiones para la higiene del medio a escala local, regional y
mundial.
3. Estrategias y diseño de las evaluaciones de la exposición
El correcto diseño del estudio es el elemento más importante de
cualquier evaluación de la exposición. Se deben especificar los fines
y objetivos de la investigación, así como los métodos idóneos de
muestreo, medición, análisis estadístico y aseguramiento de la
calidad. Para caracterizar la magnitud, duración y distribución
temporal del contacto humano con los contaminantes del medio se pueden
emplear métodos directos o indirectos. Entre los primeros cabe citar
las mediciones en el punto de contacto y las mediciones de marcadores
biológicos de la exposición. Los métodos indirectos incluyen la
vigilancia ambiental, las modelizaciones y los cuestionarios. Estos
métodos pueden emplearse en estudios de la exposición basados en
distintas maneras de seleccionar a la población estudiada; se
distinguen así, por ejemplo, los estudios amplios que abarcan a todos
los miembros de la población analizada, los estudios descriptivos de
una muestra no probabilística, o los estudios basados en muestras
representativas de individuos, seleccionados al azar.
4. Métodos estadísticos de evaluación de la exposición
Los métodos estadísticos son un instrumento crítico de los
estudios -- aplicados o de investigación -- de evaluación de la
exposición. Se recomienda que participe siempre un estadístico en
todos los aspectos de la investigación de la exposición, en especial
durante las fases de diseño y de análisis de los datos. La
determinación del tamaño de la muestra es una aplicación importante de
la estadística durante la planificación de esos estudios. La
estadística descriptiva numérica y gráfica permite resumir los datos
de exposición y llevar a cabo análisis preliminares de las relaciones
entre las variables determinantes de la exposición. En muchos casos
los datos sobre ésta adoptan una distribución aproximadamente normal o
log-normal, y se prestan pues fácilmente a ser analizados mediante los
métodos paramétricos habituales de inferencia estadística, como las
estimaciones y la verificación de hipótesis. Se pueden emplear además
otros modelos estadísticos paramétricos, como el análisis de la
varianza (ANOVA), la regresión lineal y la regresión logística, para
cuantificar la relación entre los niveles de exposición medidos.
Cuando el número de observaciones es pequeño o no es posible
transformar los datos en una distribución aproximadamente normal, se
pueden utilizar métodos no paramétricos, como las pruebas de signo de
Mann-Whitney y de Kruskal-Wallis, para verificar las hipótesis.
5. Patrones temporales de la actividad humana y evaluación de la
exposición
Se puede utilizar información sobre el perfil de actividades de
la gente para identificar los determinantes de los niveles de
exposición detectados, predecir exposiciones no medidas o no medibles,
evaluar la relación entre la exposición y el estado de salud, e
identificar situaciones de exposición de alto riesgo que puedan
afrontarse con medidas de salud pública.
El costo relativo del muestreo sobre el terreno y de los análisis
de laboratorio en las mediciones ambientales y biológicas subraya el
valor potencial de los datos tiempo-actividad. La evaluación de los
perfiles de actividad a largo plazo (p. ej., toda la vida) sólo puede
hacerse a veces empleando cuestionarios de análisis de la relación
tiempo-actividad. Para reunir información sobre las actividades
humanas se emplean diversos métodos, entre ellos diarios y
cuestionarios, dispositivos mecánicos y medidas de observación. Sólo
recientemente se han empezado a desarrollar métodos para evaluar la
influencia de los perfiles de tiempo-actividad en la ingestión
alimentaria y no alimentaria y en la exposición cutánea. Las dudas
albergadas respecto a la capacidad de los métodos de acopio de datos
para reflejar fielmente las actividades y garantizar la validez de la
información, así como respecto a las repercusiones de la variabilidad
ínter e intrapersonal del comportamiento, imponen límites a la
aplicación de los datos de tiempo-actividad a la evaluación de la
exposición humana. Sin embargo, con unos programas adecuados de
aseguramiento de la calidad, la información sobre los patrones
temporales de la actividad puede ser de enorme utilidad para
interpretar y modelizar la exposición.
6. Modelización de la exposición humana y de las dosis
Un modelo de exposición es un esquema lógico o empírico que
permite estimar los parámetros de la exposición individual o
poblacional a partir de una serie de datos. Los modelos de la
exposición, cuando están basados en observaciones adecuadas, pueden
emplearse para estimar exposiciones colectivas (p. ej., la media de
una población) o individuales (p. ej., la distribución de la
exposición entre los miembros de una población). Mediante esos modelos
se puede estimar la exposición sufrida a través de uno o de varios
medios. Esto último es especialmente útil para poder comparar la
magnitud de las exposiciones previsibles a través de distintos medios,
y por tanto para establecer las prioridades. Los modelos pueden ser
estadísticos, deterministas, o una combinación de ambos. En los tres
casos se pueden aplicar métodos probabilísticos para estimar la
distribución de la exposición en la población, esto es, la
variabilidad de la exposición entre los individuos. Además, los
métodos probabilísticos pueden utilizarse para definir la
incertidumbre de los parámetros introducidos en el modelo y propagar
esa incertidumbre hasta la variable de evaluación sometida a
predicción. La evaluación de la exactitud de los resultados del modelo
es una condición fundamental para pasar a utilizarlos con fines de
adopción de decisiones.
7. Medición de la exposición humana a productos químicos presentes en
el aire, el agua y los alimentos
En la selección de los métodos de muestreo de productos químicos
presentes en el aire, el agua y los alimentos intervienen
consideraciones comunes relacionadas con los límites de detección, los
factores de interferencia, la facilidad de manejo y los costos. Se
dispone de métodos de muestreo para personas, aire de microambientes y
aire ambiente, para la vigilancia de gases y vapores -- tanto pasiva
como activamente--, aerosoles, compuestos orgánicos semivolátiles y
gases reactantes.
La evaluación de la calidad del agua obliga a considerar
numerosos factores en relación con el muestreo. Una consideración
importante es que la exposición a los contaminantes no se limita a las
vías orales y que no todos los individuos tienen acceso al agua
tratada que circula por los sistemas de distribución. Se proporciona
orientación respecto al muestreo y los programas de vigilancia.
Existen varios métodos para estimar el consumo de alimentos y la
contaminación de éstos. El método elegido dependerá de la información
disponible, del grupo de población de interés, de que lo expresado
sean los efectos agudos o crónicos del producto químico, del uso
previsto de los resultados y de los recursos disponibles.
8. Medición de la exposición humana a los contaminantes químicos
presentes en el suelo y en el polvo depositado
El contacto humano con el suelo y el polvo depositado puede ser
una importante fuente de exposición a contaminantes químicos, sobre
todo en los niños. Aunque se han desarrollado muchos métodos de
muestreo, no se ha demostrado que ninguno de ellos sea superior a los
otros, por lo que resulta difícil comparar los resultados de estudios
realizados con diferentes métodos. Entre los factores que deben
tenerse en cuenta para seleccionar un método de muestreo cabe citar la
eficiencia de la recogida de muestras, las diferencias entre los
perfiles de actividad de las personas, la variabilidad física del
suelo y de los niveles de polvo en el espacio y en el tiempo, las
superficies y sustratos de muestra, el momento elegido para obtener
las muestras y los métodos analíticos utilizados para medir las
sustancias tóxicas en el laboratorio.
9. Medición de la exposición humana a agentes biológicos presentes en
el aire y el polvo
Se consideran bioaerosoles diversos microorganismos, o
componentes de los mismos, que pueden ser transportados por el aire e
inhalados. Comprenden virus, bacterias, pólenes, hongos, protozoos y
algas en forma de microorganismos viables potencialmente causantes de
enfermedades. Diversos fragmentos o componentes metabólicos de las
bacterias y los hongos, al igual que las estructuras proteicas
presentes en esos microorganismos, así como en los excrementos y en
distintas partes de los insectos, arácnidos y otros animales, pueden
provocar reacciones alérgicas. Los bioaerosoles más extendidos en
interiores y más implicados en las reacciones alérgicas son los
ácaros, los hongos, las bacterias y el polen.
La evaluación de la exposición a agentes microbiológicos está tan
avanzada en estos momentos como la de muchos contaminantes del aire.
No se han desarrollado muestreadores de personas. De hecho, muchas de
las técnicas disponibles para obtener muestras de agentes
aerobiológicos son adaptaciones de instrumentos diseñados con otros
fines. Sin embargo, se observan progresos en el sector, a medida que
diversas organizaciones profesionales intentan mejorar y normalizar
sus métodos de medición, sus protocolos de cultivo y análisis y sus
sistemas de notificación de datos, aspectos todos ellos decisivos para
poder comparar los resultados de diferentes investigadores.
Por su propia naturaleza, los bioaerosoles varían mucho en lo que
respecta a su composición y concentración. Las condiciones favorables
para su crecimiento, reproducción y dispersión varían dentro de un
amplio margen de condiciones de temperatura, humedad y presencia de
nutrientes. Esos mismos factores se ven alterados por las actividades
del ser humano y de los animales. Los aerosoles biológicos pueden
verse difundidos y amplificados por sistemas mecánicos y máquinas de
diverso tipo. Como resultado de ello, las exposiciones personales son
bastante variables; esto, a su vez, ha llevado a muchos investigadores
a obtener muestras del aire de la zona y/o de grandes cantidades de
material. Por ejemplo, se recomienda medir de forma indirecta la
exposición a los alergenos de los ácaros a partir de muestras de polvo
de la ropa de cama y el suelo. Esas muestras domésticas, ya sea de
aire o de polvo, determinan a menudo el nivel de "exposición" empleado
en las investigaciones epidemiológicas.
Por último, para que los progresos de los métodos de evaluación
de la exposición a agentes aerobiológicos permitan aplicarlos a la
evaluación cuantitativa de los riesgos, habrá que determinar mejor las
dosis causantes de sensibilización y reactividad para muy diversos
microorganismos y/o agentes.
10. Evaluación de la exposición mediante marcadores biológicos
Los marcadores biológicos permiten vigilar la exposición
ambiental determinando la dosis total de un contaminante recibida por
un individuo a partir de todas las fuentes de exposición. La principal
ventaja de esta estrategia es que permite evaluar la exposición total
usando una medida que integra todas las fuentes de exposición y está
influida por el comportamiento humano. Se considera además que los
marcadores biológicos tienen un mayor valor predictivo de los efectos
sanitarios que las medidas externas de la exposición. Con ellos se
responde a varias exigencias de la evaluación de la exposición, como
son las siguientes:
* caracterización de la exposición de un individuo o población
* determinación de la distribución de la dosis en la población
* identificación de los determinantes ambientales y demográficos de
la exposición
El principal inconveniente de los marcadores biológicos estriba
en la dificultad para caracterizar las fuentes individuales que
contribuyen a la exposición total del sujeto. Al poner a punto y
emplear marcadores biológicos, el conocimiento de la toxicocinética
del contaminante en el sistema es fundamental para caracterizar la
variabilidad biológica y determinar si el marcador biológico es válido
para poder evaluar la exposición a la concentración de interés. Los
marcadores biológicos han sido cruciales para profundizar en el
conocimiento de la exposición humana a ciertos contaminantes, como el
plomo. Se dispone de numerosos métodos no invasivos para la vigilancia
biológica, métodos que los asesores en materia de exposición deberían
intentar incorporar al desarrollar protocolos de vigilancia del medio
ambiente.
11. Aseguramiento de la calidad de los estudios de la exposición
El aseguramiento de la calidad (AC) implica una vigilancia
independiente del estudio que garantice a los responsables de la
gestión del laboratorio y a los usuarios de los datos que las
instalaciones, el equipo, el personal, los métodos, las prácticas, los
registros y los controles se atienen a principios aceptados de gestión
de la calidad. Los errores en los datos de exposición se deben tanto a
variaciones analíticas como a los cambios que pueden afectar a la
obtención y el manejo de las muestras, la preparación y el
almacenamiento de las mismas y el registro y conservación de los
datos. La variación analítica depende de dos factores principales: la
exactitud, que refleja la concordancia entre la cantidad de analito
medido y la cantidad realmente presente, y la precisión, que refleja
la variabilidad aleatoria o la reproducibilidad del método.
El protocolo es el documento más importante para detallar los
componentes críticos de una investigación, como por ejemplo el
personal encargado, el acopio de muestras, el almacenamiento de las
muestras y el tratamiento preanalítico, los procedimientos analíticos
y el análisis de los datos. Los procedimientos normalizados de
utilización (PNU) que se añaden al protocolo contienen instrucciones
escritas detalladas sobre la manera de llevar a cabo algunas
actividades rutinarias sobre el terreno y en el laboratorio. El
protocolo y los PNU pueden considerarse como directrices de gestión
concebidas para asegurar que todo el personal que participe en las
operaciones del estudio conozca y emplee correctamente los mismos
procedimientos.
El control de la calidad (CC) atañe específicamente a la calidad
de los resultados de laboratorio, y presenta dos componentes. El CC
interno es el conjunto de procedimientos que emplea el personal de un
laboratorio para evaluar continuamente los resultados a medida que se
obtienen. El CC externo es un sistema de control objetivo del
desempeño de un laboratorio por un organismo independiente. El CC
interno comprende la presentación de los resultados correspondientes a
muestras de control en gráficos especiales (por ejemplo los gráficos
de Shewhard y las sumas acumuladas) y el uso de límites de control
como criterio para intervenir, o para juzgar si un conjunto de datos
incluye o no algún tipo de control estadístico. El CC externo, por
otra parte, aporta pruebas independientes de la calidad del desempeño
de los laboratorios y de la competencia de los analistas. Por lo
general, un laboratorio coordinador distribuye entre los laboratorios
participantes muestras de una concentración conocida. Los laboratorios
analizan las muestras de referencia y envían los resultados al
laboratorio coordinador para que evalúe el desempeño.
Las muestras de referencia de los CC interno y externo deben
poseer una matriz y una concentración de contaminante similares a las
de la muestra real. Además, a veces es necesario tener en cuenta las
formas químicas que puede adoptar la sustancia.
Por último, las interacciones con las poblaciones humanas
plantean una serie de consideraciones peculiares respecto al diseño y
el CC del estudio, que deben ser detenidamente evaluadas junto con los
aspectos tradicionales concernientes al muestreo, los análisis y los
procedimientos.