Draft Report C737-01
GUIDANCE MANUAL FOR ASSESSING HUMAN HEALTH RISKS
FROM CHEMICALLY CONTAMINATED
FISH AND SHELLFISH
Submitted to:
Battelle New England Marine Research Laboratory
Duxbury, Massachusetts
For:
U.S. Environmental Protection Agency
Office of Marine ind Estuarine Protection
Washington, DC
December 1987
By:
PTI Environmental Services, Inc.
13231 SE 36th Street, Suite 200
Bellevue, Washington 98006
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CONTENTS
Page
LIST OF FIGURES
LIST OF TABLES
ACKNOWLEDGMENTS
INTRODUCTION 1
OBJECTIVES 1
ORGANIZATION 2
BACKGROUND 2
Applicability of this Guidance Manual 4
Relationship of this Manual to Other EPA Documents 4
Relationship of Fisheries Risk Assessment to Water Quality
Criteria and Standards 5
Relationship of EPA Risk Assessment Methods to FDA Risk
Assessment Methods 6
OVERVIEW OF RISK ASSESSMENT AND RISK MANAGEMENT 8
MAJOR STEPS IN RISK ASSESSMENT 8
NEED FOR RISK ASSESSMENT APPROACH 8
USES OF RISK ASSESSMENT 10
HAZARD IDENTIFICATION 12
CONTAMINANTS OF CONCERN 12
TOXICITY PROFILES 13
SOURCES OF INFORMATION 14
DOSE-RESPONSE ASSESSMENT 16
EXPOSURE AND DOSE 16
DOSE-RESPONSE RELATIONSHIPS 16
CARCINOGENIC POTENCY FACTORS 17
REFERENCE DOSES 19
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SOURCES OF INFORMATION ]9
Carcinogenic Potency Factors 19
Reference Doses 20
EXPOSURE ASSESSMENT 21
TISSUE CONCENTRATIONS OF CONTAMINANTS 21
Study Objectives and General Sampling Design 24
Selection of Target Species and Size Classes 28
Sampling Station Locations 32
Time of Sampling 34
Kinds of Samples 35
Sample Replication 38
Selection of Analytical Detection Limits and Protocols 38
QA/QC Program 39
Documentation and QA Review of Chemical Data 40
Statistical Treatment of Data 41
ANALYSIS OF SOURCES, TRANSPORT, AND FATE OF CONTAMINANTS 42
EXPOSED POPULATION ANALYSIS 43
Comprehensive Catch/Consumption Analysis 45
Assumed Consumption Rate 47
EXPOSURE DOSE DETERMINATION 49
Single-species Diets 49
Mixed-species Diets 50
SOURCES OF INFORMATION 51
RISK CHARACTERIZATION 52
CARCINOGENIC RISK 52
NONCARCINOGENIC EFFECTS 53
CHEMICAL MIXTURES 55
PRESENTATION AND INTERPRETATION OF RESULTS 56
PRESENTATION FORMAT 56
Summary Tables 56
Summary Graphics 57
RISK COMPARISONS 57
SUMMARY OF ASSUMPTIONS 58
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UNCERTAINTY ANALYSIS 59
Sources of Uncertainty 59
Approaches to Uncertainty Analysis 6]
SUPPLEMENTARY INFORMATION 62
REFERENCES 64
APPENDIX A - EPA OFFICE OF WATER CONTACTS ON RISK ASSESSMENT FOR FISH
CONSUMPTION
APPENDIX B - INTEGRATED RISK INFORMATION SYSTEM (IRIS)
APPENDIX C - SOURCES OF INFORMATION FOR TOXICITY PROFILES
APPENDIX D - EVALUATION OF THE EFFECTS OF COMPOSITE SAMPLING ON
STATISTICAL POWER OF A SAMPLING DESIGN
APPENDIX E - EVALUATION OF THE EFFECTS OF SAMPLE REPLICATION ON
STATISTICAL POWER OF A SAMPLING DESIGN
APPENDIX F - ESTIMATION OF FISH/SHELLFISH CONSUMPTION FROM A NATIONAL
DATABASE
APPENDIX G - EPA OFFICE OF RESEARCH AND DEVELOPMENT, ENVIRONMENTAL
RESEARCH LABORATORIES
APPENDIX H - EPA REGIONAL NETWORK FOR RISK ASSESSMENT AND RISK MAN-
AGEMENT ISSUES
APPENDIX I - COMPILATION OF LEGAL LIMITS FOR CHEMICAL CONTAMINANTS IN
FISH AND FISHERY PRODUCTS
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LIST OF FIGURES
Following
Number Page
1 Overview of risk assessment and risk management g
2 Hypothetical example of dose-response curves for a
carcinogen and a noncarcinogen 16
3 Interaction between environmental factors and exposed
population factors 27
4 Summary of recommended marine and estuarine indicator
species 31
5 General sampling station layouts for probability
sampling in two dimensions 32
6 Conceptual structure of quantitative health risk
assessment model 52
7 Example graphic format for display of quantitative
risk assessment results for hypothetical study area
and reference area 57
8 Plausible-upper-limit estimate of lifetime excess
cancer risk vs. concentration of a chemical contaminant
in fish or shellfish (ppm wet wt.) at selected ingestion
rates 57
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LIST OF TABLES
Following
Number page
1 Organic priority pollutants and 301(h) pesticides
ranked according to octanol-water partition
coefficients (Kow) 12
2 Inorganic priority pollutants ranked according to
bioconcentration factor 13
3 Toxicity profile for mercury and PCBs 13
4 Criteria for selecting target species 28
5 Approximate range of cost per sample for analyses of
EPA priority pollutants in tissues as a function of
detection limits and precision 39
6 Example tabular format for display of quantitative risk
assessment for consumption of fish and shellfish 56
7 Summary of assumptions and numerical estimates used in
risk assessment approach 58
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ACKNOWLEDGMENTS
This document was prepared by PTI Environmental Services, Inc., under the direction
of Dr. Robert Pastorok, for the U.S. Environmental Protection Agency (EPA) in partial
fulfillment of Battelle Contract No. L3198(8873)-018D to PTI Environmental Services,
and EPA Contract No. 68-03-3319 to Battelle. Dr. Kim Devonald of the Office of
Marine and Estuarine Protection was the Project Monitor for EPA. Dr. Michael Connor
was the Technical Monitor for Battelle.
The primary author of this report is Dr. Robert A. Pastorok. Dr. Kim Devonald
prepared the initial draft of the background section in the introduction. The EPA
Office of Pesticide Programs prepared Appendix F. Mr. Pieter Booth and Ms. Carol
Newlin of PTI Environmental Services and Dr. Kim Devonald contributed to the Executive
Summary (under separate binding). Dr. Thomas C. Ginn provided a technical review
and quality assurance for PTI Environmental Services. Ms. Carol Newlin and Ms.
Sharon Hinton provided editorial and production support. Portions of this report were
based on a document prepared for the EPA Region X Puget Sound Estuary Program.
That document, entitled Guidance Manual for Health Risk Assessment of Chemically
Contaminated Seafood, was prepared while Dr. Pastorok was an employee of Tetra
Tech, Inc. Dr. Leslie Williams and Mr. Jonathan Shields provided valuable technical
assistance in preparing the guidance manual for EPA Region X. Dr. John Armstrong of
EPA Region X Office of Puget Sound was instrumental in developing the original
concept of a guidance manual for assessing human health risk from contaminated fish
and shellfish. Dr. Pastorok also benefited from comments on the EPA Region X document
from participants at a workshop on health risk assessment related to consumption of
fish and shellfish. The workshop, held in Westbrook, Connecticut on January 15-16,
1987, was sponsored by EPA Region I and the New England Interstate Water Pollution
Control Commission.
Valuable comments on earlier drafts of this report were received from the following
reviewers:
Name Affiliation
Donald Barnes EPA Office of Toxic Substances
Bruce R. Barrett EPA Water Management Division, Region IV
Robert Cantilli EPA Office of Drinking Water
Richard L. Caspe EPA Water Management Division, Region II
Michael Connor Battelle Memorial Institute
Dave DeVauIt EPA Great Lakes National Program Office, Region V
Kim Devonald EPA Office of Marine and Estuarine Protection
Carol Finch EPA Great Lakes National Program Office, Region V
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Kevin G. Garrahan EPA Exposure Assessment Group
A.R. Malcolm EPA Physiological Effects Branch
Alvin R. Morris EPA Water Management Division, Region III
Edward V. Ohanian EPA Health Effects Branch, Office of Drinking Water
Kenneth Orloff EPA Water Management Division, Region IV
Gerald Pollock California Department of Health Services
Vacys J, Saulys EPA Great Lakes National Program Office, Region V
Malcolm Shute Connecticut Department of Health Services
Paul E. Stacey Connecticut Department of Environmental
Protection
Brian Toal Connecticut Department of Health Services
Paul White EPA Exposure Assessment Group
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INTRODUCTION
Contamination of aquatic resources by toxic chemicals is a well recognized problem
in many parts of the U.S. High concentrations of potentially toxic chemicals have
been found in sediments and in aquatic organisms from Puget Sound, the Southern
California Bight, northeast Atlantic coastal waters, the Hudson River, the Great Lakes,
and elsewhere (Malins et al. 1984; Tetra Tech 1985b,d, 1986c; Brown et al. 1985; DeVault
et al. 1986; Rideout and Bender 1986). Heavy consumption of contaminated fisheries
products by humans may pose a substantial health risk (e.g., Sonzogni and Swain 1984;
Swain 1986; Capuzzo et al. 1987). This concern has prompted recent studies of catch
and consumption patterns for recreational fisheries and associated health risks (e.g.,
Puffer et al. 1982; Conner 1984; Landolt et al. 1985, 1987; Versar 1985; Swain 1986).
To protect the health of consumers of fish and shellfish, information is needed on
relative health risks associated with various edible aquatic species, geographic locations,
and consumption rates. In the past, diverse models have been used to estimate human
health risks from exposure to toxic substances in food [e.g., Cordle et al. 1978; U.S. Office
of Technology Assessment 1979; U.S. Environmental Protection Agency (EPA) 1980b;
Food Safety Council 1980, 1982; Connor 1984; Tollefson and Cordle 1986). For consistency
among EPA regions and programs, a standardized procedure is recommended here for
assessing human health risks from consumption of chemically contaminated fish and
shellfish.
OBJECTIVES
The purpose of this manual is to provide guidance for health risk assessment
related to chemically contaminated fisheries based on EPA approaches (e.g., U.S. EPA
1980b, 1986a-e, J987a). The objectives of the guidance manual are to:
• Describe the steps of a health risk assessment procedure for consumption
of contaminated fish and shellfish
• Provide guidance on presentation of risk assessment results
• Summarize assumptions and uncertainties of the recommended procedure
for risk assessment
• Summarize standard model variables (e.g., Carcinogenic Potency Factors
or Reference Doses for chemicals) and criteria [e.g., U.S. Food and
Drug Administration (FDA) action levels] related to risk assessment, and
information sources for updating these values.
The guidance provided in this manual is directed primarily at risk assessment related to
recreational fisheries. Although assessment of human health risks from commercial
fisheries products is not addressed specifically in the examples provided herein, many
of the concepts discussed throughout the manual are relevant to risk analysis of commercial
fisheries.
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This manual provides guidance only, and does not constitute a regulatory requirement
of any kind. The technical content is entirely consistent with approved EPA procedures
for risk assessment, as published in the Federal Register (U.S. EPA 1986a-e). The
relationship between these procedures and risk assessment approaches used by FDA is
described briefly in the background section below.
ORGANIZATION
Background information on available health risk assessment guidance and use of
this manual is provided in the remainder of this introduction. An overview of risk
assessment is provided in the following section, including a discussion of the distinction
between risk assessment and risk management, and a review of their possible uses.
The major steps of the risk assessment process recommended herein are described in
subsequent sections. Guidance is provided on mathematical models used to estimate
chemical exposure and risk. Sources of information on toxic chemicals and model
variables are noted. Finally, suggestions for presentation of risk assessment results are
provided. Uncertainties and assumptions of the assessment approach described in this
manual are summarized.
BACKGROUND
Risk analysis encompasses both risk assessment and risk management. Risk assessment
is a scientifically based procedure to estimate the probability of adverse health effects
from a specific exposure to a toxic agent. Risk assessment differs from risk management,
although both are elements of regulatory decision-making (National Research Council
1983). Risk assessment provides the scientific basis for public policy and action. In
risk management, risks are interpreted in light of legislative, socioeconomic, technical,
and political factors, and appropriate controls are determined. Risk management often
involves defining an acceptable risk level, i.e., the maximum risk considered tolerable.
Risk management often involves evaluation of risks in light of potential benefits associated
with an activity. For example, a risk manager might weigh the risks associated with
chemical contamination of fish and shellfish against the health benefits (e.g., decreased
risk of heart disease) associated with consumption of fish and shellfish in place of red
meat.
In September 1986, EPA published final guidelines for assessing health risks
related to environmental pollutants. The guidelines are in five parts (U.S. EPA 1986a-e):
• Carcinogen Risk Assessment
• Exposure Assessment
• Mutagenicity Risk Assessment
• Health Assessment of Suspect Developmental Toxicants
• Health Risk Assessment of Chemical Mixtures.
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These guidelines pertain to health risk assessment for all environmental exposures [e.g.,
air exposure; ingestion of water or environmentally contaminated foods; and other
direct human contact with contaminated soils, water, sediments, or other materials
(FR51 No. 185, p. 34049)]. The guidelines were developed through a 2-year process
that included contributions and review by the larger scientific community; full Agency
consideration of public comments in response to proposed guidelines on November 23,
1984; and review and approval by the EPA Science Advisory Board (FR51 No. 185, p
33992).
This guidance is in the form of a policy statement and does not constitute a
regulatory requirement. The guidance is intended simply to describe what EPA believes
to be the most scientifically defensible methods for assessing environmental health
risks, and to inform the public that these are the methods EPA will use in conducting
the health risk assessments required in its statutorily mandated programs.
While U.S. EPA's (1986a-e) risk assessment guidelines apply to all exposure routes,
they do not contain detailed information on application of the basic principles for each
exposure route. This guidance manual provides such step-by-step assistance for assessing
health risks from exposure through consumption of chemically contaminated aquatic
organisms. The guidance is applicable to freshwater, brackish water, and saltwater fish
and shellfish. It is based entirely on the principles set forth in U.S. EPA (1986a-e).
As described in a recent report by EPA's Risk Assessment Council and FDA (U.S.
EPA and U.S. FDA 1987), FDA, EPA, and the states have somewhat differing roles in
assessing and managing risks from fish consumption. These roles are summarized
below.
FDA has the lead responsibility for risk management of foods in interstate commerce
or other products of national importance including fish and shellfish. For some chemicals
in foods (specifically pesticides), EPA assists FDA in performing the technical risk
assessments that support risk management decisions. The federal government is not
directly responsible for managing risks to individuals who consume unusually large
amounts of foods not in interstate commerce or foods harvested from locally contaminated
areas (e.g., some recreational fisheries). Environmental agencies and health departments
at the state and local levels have responsibility for protecting consumers of local
fisheries products. These agencies are responsible for issuing public health advisories
and regulations related to local fisheries.
Only the FDA has federal responsibility for setting action levels and tolerances
for concentrations of specific chemicals in fish or other foodstuffs that constitute
sufficient health hazards to the general public to require that the foodstuffs be removed
from interstate commerce. Action levels are established and revised according to
criteria in the code of Federal Regulations (21 CFR 109 and 509). An action level is
revoked when a formal tolerance for the same substance is established. In developing
action levels and tolerances, FDA takes into account both the magnitude of the health
risks to consumers and the economic impacts of banning a foodstuff from a particular
source. FDA sets limits on chemical contaminants in fisheries products to achieve an
optimal balance of health protection and minimization of economic impacts on food-
producing and harvesting industries (e.g., commercial fisheries and fish marketers).
All action levels and tolerances to date have been developed to be protective
nationally, rather than on a regional or local basis. These national standards protect
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the average consumer of a foodstuff, assuming the consumer eats foods from a typical
"national market basket" (U.S. FDA 1984). For these reasons, it has been stated by
FDA that action levels and tolerances are not intended to protect certain consumers of
local fish and shellfish, such as local recreational fishermen whose consumption of fish
from a given water body may exceed the national average (Taylor, J., October 1986,
personal communication).
EPA and FDA recognized the need to coordinate their activities and guidance in
assessing health risks from contaminated fish and shellfish. A formal interagency
mechanism is being created to resolve potential differences in risk assessment calculations
for specific chemicals or specific exposure situations. U.S. EPA and U.S. FDA (1987)
provides a detailed discussion of the evolving FDA/EPA coordination and procedures
whereby states can obtain further information or assistance pertaining to risk management
in specific local situations.
Applicability of this Guidance Manual
EPA's nonregulatory technical guidance, including this manual and the 1986 final
guidelines for risk assessment (U.S. EPA 1986a-e), is available to state and local govern-
ments responsible for fisheries management. This manual is intended for use as a
handbook by state and local agencies responsible for assessing potential risks from
local fish or shellfish consumption. For example, it may be useful in assessing risks to
highly exposed regional populations (e.g., certain fishermen or families who may eat
unusually large amounts of fish) and in cases where a national action level or tolerance
has not been defined for a chemical that is a local pollution problem. This manual
does not provide guidance on policy issues that are beyond the scope of the technical
risk assessment process (e.g., selection of acceptable risk levels, and methods for
performing local cost-benefit analyses). Such risk management decisions at state and
local levels are not ordinarily within the scope of federal regulatory authority.
For specific technical assistance in applying the risk assessment methods described
in this manual, users may call the EPA Office of Water (see Appendix A) for updated
information on regional EPA facilities that can provide on-site assistance in applying
risk assessment techniques.
Relationship of this Manual to Other EPA Documents
This manual is not intended as an exhaustive, technically-detailed guide to all
aspects of sampling, statistical design, laboratory analysis, exposure assessment, and
toxicological risk analysis. Citations are provided to references that provide details on
these topics. In addition, several other EPA documents are available that provide
relevant information:
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U.S. EPA (1987a) Integrated Risk Information System (IRIS) Manual -
A regularly updated electronic database on the toxicity and carcinogenicity
of individual chemicals (see Appendix B herein)
Contaminants in Fish: The Regulation and Control of Residues for
Human Safety [Report by the EPA Risk Assessment Council Subcommittee
on Fish Residue Issues (U.S. EPA and U.S. FDA 1987)]. This report
includes a discussion of the relationships of EPA, FDA, and state
responsibilities for risk assessment and risk management.
General guidelines on exposure and risk assessment (U.S. EPA 1986a-e)
discussed earlier.
Guidance documents on risk assessment approaches for specific chemicals
[e.g., dioxins and dibenzofurans (Bellin and Barnes 1986)].
Superfund Risk Assessment Information Directory (U.S. EPA 1986g).
Risk Assessment, Management, Communication: A Guide to Selected
Sources (U.S. EPA 1987b) - A general bibliography which is updated
periodically.
Relationship of Fisheries Risk Assessment to Water Quality Criteria and Standards
The Criteria and Standards Division of EPA's Office of Water Regulations and
Standards is responsible for developing water quality criteria for the protection of
aquatic life and human health. Section 304(a) of the Clean Water Act requires EPA to
develop recommendations for criteria to be used by the states in setting water quality
standards. These criteria are summarized in "Quality Criteria for Water - 1986" (U.S.
EPA 1986h). The technical procedures for deriving human health criteria for water are
described in "Water Quality Criteria Documents; Availability" (U.S. EPA 1980b).
Water quality criteria and standards are established as guidelines or legal measures
for acceptable concentrations of contaminants in water. State agencies and EPA use
water quality criteria and standards to regulate discharges of contaminants to surface
waters. In contrast, the risk assessment approach described in this document may be
used to develop guidelines on concentrations of chemical contaminants in tissues of fish
and shellfish. Data on tissue concentrations of contaminants are often used by state
health departments in regulating human exposure to contaminated fish and shellfish, or
in developing health risk advisories that allow sport fishermen and other frequent fish
eaters to adjust their own consumption rates. The risk assessment methods described
in this manual are consistent with those used by EPA to develop water quality criteria.
Moreover, the toxicological and epidemiological data used to establish guidelines for
concentrations of contaminants in fish and shellfish are the same as those used to
develop acceptable levels in water. In developing water quality criteria, U.S. EPA
(1980b, 1986h) considered human health risks from consumption of chemically contaminated
fish and shellfish. For each chemical, a bioconcentration factor was used to establish
the relationship between contaminant concentrations in fish and shellfish associated
with three "reference" (i.e., benchmark) levels of health risk (1(T5, 10'6, 1(T7) and
corresponding concentrations of contaminants in water. A bioconcentration factor is
an empirically derived measure of the potential for a chemical to accumulate in tissues
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of aquatic organisms. Bioconcentration factors are usually expressed simply as the
ratio of the chemical concentration in tissue to the concentration in water.
Relationship of EPA Risk Assessment Methods to FDA Risk Assessment Methods
Because of differences in legislative and regulatory responsibilities among EPA,
FDA, and state and local governments, these entities have developed differing procedures
for risk assessment and risk management. As an EPA guidance manual, this document
presumes the use of standard EPA risk assessment procedures. However, certain procedures
recommended in this manual can be modified to make the risk assessment compatible
with alternative approaches used by FDA and some states. This section explains how
conversion factors can be used to make risk assessment procedures recommended herein
compatible with certain assumptions used in FDA risk assessments.
A major difference between EPA and FDA risk assessment approaches concerns
the methods for extrapolating the toxic potency of chemicals in small experimental
animals (e.g., rats and mice) to estimate potential effects in humans. U.S. EPA (1986a)
pointed out several species-specific factors that may influence the response to a carcinogen,
including life span, body size, genetic variability, concurrent diseases, and the rates
and products of metabolism and excretion. To account for at least some of the differences
between experimental animals and humans, the estimate of exposure in laboratory
animals is multiplied by a scaling factor to obtain an estimate of equivalent dosage in
humans. EPA uses the ratio of animal-to-human surface area, whereas FDA uses a
corresponding ratio of body weights as a scaling factor. Thus, EPA uses mg of carcinogen
per m2 body surface area per day as a standardized scale for expressing dosages,
whereas FDA uses mg carcinogen per kg body weight per day. This difference in
interspecies extrapolation factors results in approximately a five- to ten-fold difference
in estimates of carcinogenic potency (and risk) derived by the two agencies.
In recognition of the difficulties that differences in interspecies extrapolation
procedures between EPA and FDA may pose for state agencies and others who rely on
federal guidance on risk assessment, EPA's Risk Assessment Council and FDA reviewed
the pros and cons of their respective methods for dosage scaling. They concluded that
the most appropriate method for interspecies dosage extrapolation may vary depending
on exposure conditions. For example, one procedure may be more realistic for lipophilic
chemicals, whereas the other would be more appropriate for hydrophilic chemicals.
Differences in target organs (i.e., primary site of toxicity) among carcinogens also
affects the preferred extrapolation procedure.
Because the EPA extrapolation procedure results in a higher estimate of risk than
the FDA procedure (by approximately an order of magnitude), the former is considered
more protective. For most EPA assessments, the surface-area based extrapolation is
appropriate. The technical basis for EPA's approach relies primarily on a demonstrated
relationship between pharmacological effects (e.g., balance of rates of metabolism and
excretion) and body surface area (Pinkel 1958; Freireich et al. 1966; Dedrick 1973). If
state or local policymakers decide that the body-weight based extrapolation is more
appropriate for local risk management needs, then procedures recommended in this
manual can be modified by converting EPA's dose-response data using a ratio of human
body weight to surface area. This would allow the risk assessor to use carcinogenic
potency factors in EPA's computerized database, IRIS (U.S. EPA 1987a). IRIS is a
database maintained by EPA to provide regularly updated toxicological data for use in
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risk assessment. The use of IRIS would greatly increase the ability of a state to
perform risk assessments for chemicals of local concern.
Although the conversion of EPA estimates of toxic potency to estimates based on
equivalent dosage scales related to body weight is not technically complex, the modified
procedure should preferably be carried out only by experienced toxicologists. The
conversion factor will vary depending on whether the dose-response data were derived
from rats or from mice. Thus the original data set must be reviewed to determine an
appropriate conversion factor. In general, an EPA estimate of carcinogenic potency
would be multiplied by a factor equal to the ratio of surface area per unit body weight
(m2/kg) of the laboratory animal to that of humans. For example, if the EPA carcinogenic
potency factor is C and the surface area per unit body weight is X for the laboratory
animal and Y for humans, the corresponding potency factor based on dosage scaled to
body weight is C multiplied by X divided by Y. Because specific data on surface area
are often unavailable, the body weight to the two-thirds power is typically used as an
estimate of surface area. Note that some EPA carcinogenic potency factors are derived
from epidemiological studies and therefore do not require conversion.
Other steps in the process to estimate carcinogenic potencies may vary somewhat
among regulatory agencies. For example, different agencies may choose different data
sets to derive a carcinogenic potency factor for the same chemical. The mathematical
expression used to model the dose-response relationship may also differ among agencies.
Hogan and Hoel (1982) and Cothern et al. (1986) discuss various models for extrapolating
data from high doses used in laboratory experiments to the low doses of concern in
carcinogenic risk assessment. At low doses corresponding to risks of 10"2 to 10"6 or
less, different models may produce results that vary by as much as several orders of
magnitude. Nevertheless, the linearized multistage procedure used by EPA (U.S. EPA
1986a; also see below, Dose-Response Assessment) yields results that correspond approx-
imately (within a factor of two) to those produced by the linear model used by FDA.
The interagency Subcommittee on Fish Residue Issues of the EPA Risk Assessment
Council, which includes representatives from FDA, concluded that the differences in
procedures for modeling dose-response relationships between EPA and FDA were small
relative to the uncertainties in other steps of a risk assessment. Therefore, U.S. EPA
(1987b) does not discuss procedures to reconcile these differences.
A final distinction between EPA's risk assessment procedures and other potential
approaches is that EPA does not yet provide a standardized approach for assessing
carcinogenic effects on children and fetuses. Information on peri-natal carcinogenicity
is presently being developed by EPA and others.
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OVERVIEW OF RISK ASSESSMENT AND RISK MANAGEMENT
As defined earlier, the objective of risk assessment is to estimate the probability
of adverse health effects from exposure to a toxic agent. The elements of risk assessment
and their relationship to risk management are shown in Figure 1. The following sections
provide an overview of the steps in risk assessment, the need for risk assessment, and
the potential uses of risk assessment of chemically contaminated fisheries. The general
format for risk assessment and all definitions of terms used in this report are consistent
with those provided by National Research Council (1983) and U.S. EPA (1984a-e, 1987a).
Background information on food safety evaluation by federal and state agencies is
provided by U.S. Office of Technology Assessment (1979) and Food Safety Council
(1980, 1982). Examples of approaches used by FDA to assess human health risks from
toxic chemical exposures are described in Cordle et al. (1978) and Flamm and Winbush
(1984).
MAJOR STEPS IN RISK ASSESSMENT
A complete risk assessment includes the following steps:
• Hazard identification: Qualitative evaluation of the potential for a
substance to cause adverse health effects (e.g., birth defects, cancer) in
animals or in humans
• Dose-response assessment: Quantitative estimation of the relationship
between the dose of a substance and the probability of an adverse
health effect
• Exposure assessment: Characterization of the populations exposed to
the toxic chemicals of concern; the environmental transport and fate
pathways; and the magnitude, frequency, and duration of exposure
• Risk characterization: Integration of qualitative and quantitative information
from the first three steps, leading to an estimate of risk for the health
effect of concern.
Because uncertainties are pervasive in risk assessment, uncertainty analysis is a key
element of each stage of the assessment process. Assumptions and uncertainties are
summarized in the risk characterization step. The risk characterization includes a
balanced discussion of the strengths and weaknesses of the data presented.
NEED FOR RISK ASSESSMENT APPROACH
Direct measurement of human health risks is possible in certain limited circum-
stances. Such circumstances generally involve a single high exposure or repeated
moderate exposures of humans to a specific chemical with a clear adverse effect. For
example, direct measurement of the incidence of chloracne (a skin disorder) might be
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Figure 1. Overview of risk assessment and risk management
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possible in a population of workers exposed to a polychlorinated biphenyl (PCB) spill.
In contrast, it is virtually impossible to directly measure the incidence of cancer
associated with consumption of chemically contaminated fish or shellfish. The long
latency period for cancer, the potential for contamination of fisheries by multiple
chemicals, and confounding exposures through other routes would complicate the inter-
pretation of such data. Mathematical models are therefore used by EPA, FDA, the
Agency for Toxic Substances and Disease Registry, states and, other regulatory agencies
to estimate human health risks from exposure information. Risk assessment procedures
discussed in this manual focus on estimating potential health risks from long-term
exposure to relatively low levels of contamination. This prospective approach is also
useful for developing regulations to prevent exposure to toxic chemicals and associated
risks.
Scientific knowledge of the effects of toxic chemicals on humans is still rudimen-
tary. Much of the present information is extrapolated from results of laboratory tests
on animals (e.g., rats and mice). Animal test data are used to estimate levels of
chemical exposure that are unlikely to cause toxic effects in human populations.
Toxicologists are thus faced with many uncertainties when estimating the potential for
human health risks associated with intake of toxic chemicals. Despite these uncertainties,
regulatory decisions must be made. Many assumptions and subjective judgments may
enter into an evaluation of human health risk. The risk assessment approach provides
a framework for consistent, systematic estimation of health risks, with clear statements
of assumptions and uncertainties.
The risk assessment framework offers an alternative to some common approaches
to evaluation of data on chemical residues in fish and shellfish. As noted by Kneip
(1983) and Peddicord (1984), many investigators have evaluated chemical residue data in
light of human health concerns simply by comparing tissue concentrations of selected
chemicals to action levels or tolerances established by U.S. FDA (1982, 1984). This
approach is limited for the following reasons:
• FDA limits are available for only a few chemicals (mercury and approxi-
mately 13 organic compounds).
• FDA has not established regulatory limits for some of the most potent
suspected human carcinogens (e.g., 2,3,7,8-tetrachlorodibenzodioxin) or
for some of the common contaminants in surface waters (e.g., polynuclear
aromatic hydrocarbons and most heavy metals).
• Action levels and tolerances were intended to be used only for regulation
of interstate commerce of food products.
• When setting regulatory limits, FDA considers economic impacts of food
regulation as well as the potential human health risks on a national
basis (U.S. FDA 1984). When using FDA limits to interpret bioaccumulation
data, investigators implicitly adopt economic policies of FDA. Thus,
risk management issues are not clearly separated from risk assessments.
-------
Use of regulatory limits on toxic chemicals in food products established by other
countries (Nauen 1983) would suffer from many of the limitations listed above for FDA
values. Moreover, a concise review of the basis for each of these limits is not available.
USES OF RISK ASSESSMENT
Risk assessment may be applied to data on chemical residues in fish and shellfish
for the following purposes:
• Identify and rank toxic chemical problems in specific locations
• Develop environmental criteria or guidelines at the national, state,
regional, or local level
• Develop public information and advisories.
The first two applications fall within the general category of regulatory decision-
making. In this context, one goal of EPA is to define, identify, and set priorities for
reducing unacceptable risks. Risk assessment and management provide a framework for
balanced analysis of environmental problems and consistent policies for reducing health
risks (e.g., through reduction of toxic pollutant discharges and cleanup of polluted
areas).
Risk assessment can be used to identify and rank environmental problems in
several ways. First, contaminated sites can be ranked according to the relative risks
associated with consuming fish and shellfish harvested nearby (e.g., Versar 1985). Site
rankings may be used to establish priorities for investigation of contaminant sources
and for cleanup. Maps of chemical residue data or risk estimates provide a geographic
overview of the condition of harvested resources. Second, priority chemicals can be
identified according to associated health risks (e.g., Ames et al. 1987). Finally, various
fishery species and weight classes within species can be ranked according to relative
risks.
Risk assessment is an important analytical tool for developing environmental
criteria and guidelines. For example, water quality criteria derived by U.S. EPA (1980b)
are based in part on human health risk assessment. FDA uses quantitative risk assessment
to estimate potential human health risks, which are considered together with economic
factors in developing action levels for chemical contaminants in fishery products (U.S.
FDA 1984). Risk assessment models can be used to develop guidelines on maximum
advisable contaminant concentrations in recreationally harvested species. Such guidelines
can contribute to development of target cleanup criteria established to develop remedial
actions for contaminated sites.
The results of risk assessments may be used to inform the public about the relative
health risks of various fishery species and geographic locations. Providing the recreational
public with such information allows for individual choice in determining harvest area,
target species, consumption rates, and other factors based on relative risk. Furthermore,
risk assessment may contribute to management decisions by federal, state, and local
agencies, which may include:
10
-------
• Investigating sources of pollution
• Reducing exposure potential by implementing pollution controls
• Restricting fishery harvests by geographic area or by species
• Issuing public advisories or controls to limit:
Geographic area of harvesting
Harvest season
Harvest methods
Species harvested
Catch number
Size range harvested
Consumption rate.
Further information on the relationship between risk assessment and risk management
may be found in U.S. EPA (1984), Ames et al. (1987), Lave (1987), Russell and Gruber
(1987), and Travis et al. (1987).
11
-------
HAZARD IDENTIFICATION
The first step in the risk assessment process is to define toxicological hazards
posed by the chemical contaminants in samples of fish and shellfish. These hazards
are summarized in a toxicity profile or IRIS chemical file for each contaminant of
concern. The results of the hazard assessment influence the nature and extent of
subsequent steps in risk analysis. For example, the endpoint of concern in dose-response
assessment may be selected based on the most severe adverse effect identified in the
hazard assessment. In the absence of quantitative data for other steps in the risk
assessment process, the hazard assessment constitutes the final product for a qualitative
evaluation of risk.
CONTAMINANTS OF CONCERN
The contaminants of concern to be included in a particular risk assessment should
be selected based on the following criteria:
• High persistence in the aquatic environment
• High bioaccumulation potential
• High toxicity to humans (or suspected high toxicity to humans based on
mammalian bioassays)
• Known sources of contaminant in areas of interest
• High concentrations in previous samples of fish or shellfish from areas
of interest.
General information on persistence, bioaccumulation potential, and toxicity may be
obtained from references such as Lyman et al. (1982) and Callahan et al. (1979).
Other key sources that are periodically updated are the Registry of Toxic Effects of
Chemical Substances (e.g., Tatken and Lewis 1983) and the Annual Report on Carcino-
gens (e.g., National Toxicology Program 1982). Specific information that is directly
useful in risk assessment should be obtained from IRIS (see below, Sources of Informa-
tion and Appendix B).
Recommendations regarding specific contaminants of concern are beyond the scope
of this guidance manual. A general list of contaminants with available EPA toxicological
indices [Reference Dose (RfD) or Carcinogenic Potency Factor] listed in IRIS is provided
in Appendix B. The procedures for quantitative risk assessment outlined in this manual
are designed for use only with chemicals having toxicological indices. The bioaccumulation
potential of various chemicals is a key consideration in selecting contaminants of
concern. EPA priority-pollutant organic chemicals and selected pesticides are listed in
Table 1 in descending order of bioaccumulation potential, according to their octanol-
water partition coefficients (Tetra Tech 1985a). Note that organic compounds with a
log octanol-water partition coefficient greater than or equal to 2.3 were recommended
12
-------
TABLE 1. ORGANIC PRIORITY POLLUTANTS AND 30I(h) PESTICIDES RANKED
ACCORDING TO OCTANOL-WATER PARTITION COEFFICIENTS (Kow)
(updated from Callahan et al. 1979)
Priority
Pollutant No.
69
83
89
79
111
__r
75
74
82
107
73
91
92
90
110
129
94
106
72
112
76
93
99
53
9
100
101
39
84
41
64
40
20
81
98
78
109
80
__r
52
66
68
Substance
di-n-octyl phthalate
indeno( 1 ,2,3-cd)pyrene
aldrin
benzo(ghi)perylene
PCB-1260
mirex
benzo(k)fluoranthene
benzo(b)fluoranthene
dibenzo(a,h)anthracene
PCB-1254
benzo(a)pyrene
chlordane
4,4'-DDT
dieldrin
PCB-1248
TCDD (dioxin)
4,4'-DDD
PCB-1242
benzo(a)anthracene
PCB-1016
chrysene
4,4'-DDE
endrin aldehyde
hexachlorocyclopentadiene
hexachlorobenzene
heptachlor
heptachior expoxide
fluoranthene
pyrene
4-bromophenyl phenyl ether
pentachlorophenol
4-chlorophenyl phenyl ether
2-chloronaphthalene
phenanthrene
endrin
anthracene
PCB-1232
fluorene
methoxychlor
hexachlorobutadiene
bis(2-ethylhexyl)phthalate
di-n-butyl phthalate
log(Kow)
8.06
7.66
7.40
7.05
6.91
6.89
6.85
6.60
6.50
6.48
6.42
6.42
6.36
6.20
6.11
6.10
6.02
6.00
5.91
5.88
5.79
5.69
5.60
5.51
5.47
5.44
5.40
5.22
5.18
5.08
5.00
4.92
4.72
4.57
4.56
4.54
4.48
4.38
4.30
4.28
4.20
4.13
Reference
m
0
i
d
b
k
d
i
i
n
0
d
i
i
a
j
d
j
h
d
k
d
d
j
h
g
d
g
g
h
d
h
d
b
f
d
m
-------
TABLE 1. (Continued)
77
67
108
8
12
1
102
103
104
--•
7
105
21
95
96
97
49
26
25
27
55
113
38
62
22
31
28
37
58
--•
60
6
42
85
11
34
87
15
47
32
86
--•
14
24
50
4
51
35
36
33
30
acenaphthylene
butyl benzyl phthalate
PCB-1221
1 ,2,4-trichlorobenzene
hexachloroethane
acenaphthene
alpha-HCH
beta-HCH
delta-hexachlorocyclohexane
parathion
chlorobenzene
gamma-HCH
2,4,6-trichlorophenol
alpha-endosulfan
beta-endosulfan
endosulfan sulfate
fluorotrichloromethane (removed)
1 ,3-dichlorobenzene
1 ,2-dichlorobenzene
1 ,4-dichlorobenzene
naphthalene
toxaphene
ethylbenzene
N-nitrosodiphenylamine
para-chloro-meta cresol
2,4-dichlorophenol
3,3'-dichlorobenzidine
1 ,2-diphenylhydrazine
4-nitrophenol
malathion
4,6-dinitro-o-cresol
tetrachloromethane
bis(2-chloroisopropyl)ether
tetrachloroethene
1,1,1- trichloroethane
2,4-dimethylphenol
trichloroethene
1 , 1 ,2,2- tetrachloroe thane
bromoform
1 ,2-dichloropropane
toluene
guthion
1,1, 2- trichloroethane
2-chlorophenol
dichlorodifluoromethane (removed)
benzene
chlorodibromomethane
2,4-dinitrotoluene
2,6-dinitrotoluene
1 ,3-dichloropropene
1 ,2-trans-dichloroethene
4.07
4.05
4.00
3.98
3.93
3.92
3.85
3.85
3.85
3.81
3.79
3.72
3.69
3.60
3.60
3.60
3.53
3.48
3.38
3.38
3.36
3.30
3.15
3.13
3.10
3.08
3.02
2.94
2.91
2.89
2.85
2.64
2.58
2.53
2.47
2.42
2.42
2.39
2.30
2.28
2.21
2.18
2.18
2.16
2.16
2.11
2.08
2.00
2.00
1.98
1.97
b
k
b
b
P
P
h
e
d
h
c
c
k
k
k
h
b
a
a
g
d
e
d
g
b
b
b
b
b
b
b
c
d
c
-------
TABLE 1. (Continued)
--•
23
48
56
5
13
57
54
71
16
59
29
65
10
70
63
44
19
43
3
18
46
2
45
88
61
demeton
chloroform
dichlorobromomethane
nitrobenzene
benzidine
1,1-dichloroe thane
2-nitrophenol
isophorone
dimethyl phthalate
chloroethane
2,4-dinitrophenol
1,1-dichloroethene
phenol
1,2-dichIoroethane
diethyl phthalate
N-nitrosodipropylamine
dichloromethane
2-chloroethylvinylether
bis(2-chloroethoxy)methane
acrylonitrile
bis(2-chloroethyl)ether
bromomethane
acrolein (
chloromethane 1
vinyl chloride
N-nitrosodimethylamine
.93
.90
.88
.83
.81
.78
.77
.67
.61
.54
.53
.48
.46
.45
.40
.31
.30
.28
.26
1.20
1.12
1.00
3.90
3.90
3.60
3.58
b
b
g
b
b
a
b
b
g
g
b
b
b
t
g
• Veith et al. (1979a).
b Veith et al. (1980).
c Gossett et al. (1983).
d Veith et al. (1979b).
' Kenaga and Goring (1980).
f Leo, A., 20 November 1984, personal communication.
« U.S. EPA (1980).
h Karickhoff (1981).
' Rapport and Eisenreich (1984).
J Miller et al. (1985).
k Means et al. (1980).
1 Miller et al. (1984).
mMcDuffie (1981).
n Chiou et al. (1981).
0 Briggs (1981).
P Solubilities of the various isomers of HCH indicate that they will have
similar log(Kow) values.
*> Estimated according to the procedure described by Chiou et al. (1982).
r Chlorinated 301(h) pesticides that are not on the priority pollutant
list.
• Organophosphorus 301(h) pesticides that are not on the priority
pollutant list.
NA » not applicable.
-------
by Tetra Tech (1985a) for inclusion in EPA Section 301(h) monitoring programs. EPA
priority-pollutant metals are listed in Table 2 in descending order of bioaccumulation
potential, according to bioconcentration factor (Tetra Tech 1985a).
Screening of potential contaminants of concern should be done on a case-by-case
basis during preparation of risk assessments. When data on concentrations of contaminants
in edible tissues of fishery organisms are available, preliminary calculations of potential
risks may be made to rank chemicals by relative priority for detailed evaluation. If
contaminant concentration data are available for soils, air, and water (at a hazardous
waste site, for example), U.S. EPA (1986f) methods for selecting indicator chemicals for
public health evaluations at Superfund sites may be used to gain perspective on contaminants
of concern. For potential carcinogens, the qualitative weight of evidence for carcinogenicity
should be considered. Those chemicals with sufficient evidence of carcinogenicity in humans
should generally be considered as contaminants of concern.
TOXICITY PROFILES
Toxicity profiles are summaries of the following information for the selected
chemicals of concern:
• Physical-chemical properties (e.g., vapor pressure, octanol-water partition
coefficients)
• Metabolic and pharmacokinetic properties (e.g., metabolic degradation
products, depuration kinetics)
• Toxicological effects (e.g., target organs, cytotoxicity, carcinogenicity,
mutagenicity) according to specific uptake route of concern (i.e., ingestion).
A toxicity profile may consist of an IRIS chemical file. An example file taken- from
IRIS is provided in Appendix B.
The key elements of a hazard identification should be summarized in a concise
tabular format. The examples shown in Table 3 and in the first two sections of the
IRIS file (Chronic Systemic Toxicity; Risk Estimates for Carcinogens) in Appendix B
illustrate the kinds of information used to evaluate toxicological hazards. Neither
toxicity profile in Table 3 is intended to be comprehensive.
Information in a toxicity profile is used to support the weight of evidence classifi-
cation for the likelihood of a chemical causing a given health effect. The endpoints
considered should include noncarcinogenic as well as carcinogenic effects. EPA has
developed a weight-of-evidence classification scheme which indicates the potential
carcinogenicity of chemicals (U.S. EPA 1986a; 1987a). It includes the following categories:
• Group A - Human Carcinogen: This group is used only when there is
sufficient evidence from epidemiologic studies to support a causal
association between exposure to the agents and cancer.
• Group B - Probable Human Carcinogen: This group includes agents for
which the weight of evidence of human carcinogenicity based on epi-
demiologic studies is "limited." It also includes agents for which the
13
-------
ALPHABETIC LISTING OF CHEMICALS ON IRIS (03/31/87)
67
79
15972
116
3O9
107-
2O859-
7773
7440
74115-
7440-
542
43121-
68359-
1861-
17804-
25057-
92
50
7440
92
117
111
74
1689
106
71
85
7440-
592
133
63
56
55285-
5234-
133
57
506
67
1897
2921
64902-
16065
7440
544-
-64-1
-10-7
-60-8
-O6-3
-00-2
-18-6
-73-8
-06-0
•36-0
•24-5
•39-3
62-1
43-3
•37-5
-40-1
-35-2
-89-0
-87-5
-32-8
-41-7
-52-4
-81-7
-44-4
-83-9
-99-2
-99-0
-36-3
•70-1
•43-9
-01-8
06-2
•25-2
23-5
14-8
-88-4
-90-4
-74-9
-77-4
-66-3
-45-6
•88-2
-72-3
83-1
47-3
92-3
21725-46-2
57-12-5
460-19-5
127-20-8
39515-41-8
94-82-6
50-29-3
1163 19-5
Acetone
Acrrlic Acid
Alachlor
Aldlcarb
Aldrin
Allyl Alcohol
Aluminum Phosphide
Ammonium Sulfamate
Antimony
Apollo
Barium
Barium Cyanide
Bayletoo
Bajrthroid
BeoefIn
Benomyl
Bentazon
Benzldiae
Beozo[a]pyrene (BaP)
Beryllium
1.1-Blphenyl
Bi3(2-ethrlhexyl)phthlate (BEHP)
Bl3(chloroethyl)ether (BOS)
Bromomethane
Bromoxynll Octanoate
1.3-Butadiene
n-Butanol
Butylphthalyl Butylglycolata (BPBG)
Cada>luai
Calcium Cyanide
Captan
Carbaryl
Carbon Tetrachlorlde
Carbosulfan
Carboxln
Chloramben
Chlordane
Chlorine Cyanide
Chloroform
Chlorothaloall
Chlorprrlfoa
Chlorsulfuron
Chroeiluai(III)
Chromlum(VI)
Copper Cyanide
Cyanazlne
Cyanide, free
Cyanogen
Dalapon
Danltol
2,4 DB
DDT
Decabromodlphenyl Ether (DODPE)
8065-48-3
106-37-6
84-74-2
924-16-3
75-71-8
107-06-2
75-35-4
120-83-2
94-75-7
62-73-7
55-18-5
55290-64-7
60-51-5
120-61-6
987B5-43-2
62-75-9
12345-67-8
51-28-5
88-85-7
127-39-4
122-66-7
85-00-T
298-04-4
145-73-3
106-89-8
563-12-2
141-78-6
100-41-4
84-72-0
16984-48-8
59756-60-4
944-22-9
64-18-6
39148-24-8
110-00-9
1071-83-8
1024-57-3
87-82-1
87-68-3
319-84-6
319-86-8
6108-10-7
no CAS No.
77-47-4
19408-74-3
67-72-1
74-90-8
7783-06-4
35554-44-0
81335-37-7
78-83-1
78-59-1
33820-53-0
58-89-9
330-55-2
no CAS No.
123-33-1
Dave ton
1,4 -DlbroBobenzene
Dlbutyl Phthalate
DSbuty Inl troaajnlne
Dich1orod1f1uoro»«thane
1,2-Dlchloroethane
1.1-Dlchloroethylene
2,4-Dichlorophenol
2,4.-Dlchlorophenoxyacetlc acid (2,4-D)
Dlchlorvos
Dlethylnltroaaaine
DlMthipln
DlMthoata
Dlaethyl Tarephthalate
DiMthyl doorknob (DMDK)
Dimethylnitroaaaiine
Dinitrochlckenwlre (DNCH)
2,4-Dlnltrophenol
Dlnoseb
Diphenylaaine
1,2-DlphenyIhydrazioe
Dlquat
Dlsulfoton
Endothall
Eplchlorohydrln
Ethlon
Ethyl Acetate
Ethylbencene
Ethylphthalyl Ethylglycolate (EPEQ)
Fluoride
Flurldone
Fonofon
Formic Acid
rosetyl-Al
Furan
Glyphoaata
Beptachlor EpoxIda
Hexabroarabenzane
Rexachlorobutadlene
alpha-Bexachlorocyclohexaae (alpha-BCB)
delta-Hexachlorocyclohexane (delta-BCB)
epsllon-Baxachlorocyclohexane (epslloo-BCH)
Bexachlorocyclohexane, technical (t-BCH)
Bexachlorocrclopentadlene (HCCPD)
Bexachlorodlbenzo-p-dloxln (UxCDD)
Bexachloroethane
Hydrogen Cyanide
Hydrogen Sulflde
iMzalll
laiazaquln
Isobutyl Alcohol
Isophorone
Isopropalln
Llndane
Llnuron
Londajt
Halelc Hydrazlde
94-74-6 MCPA
93-65-2
57837-19-1
16752-77-5
75-09-2
78-93-3
108-10-1
22967-92-6
298-00-0
S1Z18-45-2
21087-64-9
3OO-76-5
1929-82-4
14797-55-a
10102-43-9
14797-65-0
98-95-3
10102-44-O
86-30-6
621-64-7
10595-95-8
930-55-2
27314-13-2
32536-52-0
19044-88-3
23135-22-0
42874-03-3
76738-62-0
1910-42-5
32534-81-9
608-93-5
87-86-5
52645-53-1
108-95-2
108-45-2
62-38-4
732-11-6
7803-51-2
151-50-8
506-61-6
1610-18-0
23950-58-5
1918-16-7
709-98-8
139-40-2
51630-58-1
13593-03-8
7783-OO-8
630-10-4
74051-80-2
7440-22-4
506-64-9
26G28-22-8
143-33 9
148-18-5
57-24 9
MCPP
Metalaxyl
He thorny 1
Methylena Chloride ,
Methyl Ethyl Kxtooe (HER)
Methyl laobutyl Katone (MIDK)
Methyl Mercury
Methyl Parathlon
Metolachlor
Metrlbuzln
Haled
Nltrapyrlo
Nitrate
Nitric Oxide
Nitrite
Nitrobenzene
Nitrogen Dioxide
N-NltrosodlphenylaaUne'
H-Nltro9odl-N-propyla»lne
N-Nltrosoawthylethrlaaiine
N-Nltrosopyrrolldlna
Norflurazon
Octabroaxxiipheayl ether
Oryzalln
Oxaayl
Oxyfluorfen
Paclobutrazol
Paraquat
PentabroaKxliphenyl ether
Pentachlorobenzene
Pentachlorophenol
Perawthrln
Phenol
•-Phenylanedlaailne
Phenyl Mercuric Acetate
PhosMt
Phosphlne
Potasslua Cyanide
Potasaluai Silver Cyanide
Proveton
Pronaalde
Propachlor
Propan11
Propazine
Pydrln
Oulnalphoa
Selenioua Acid
Selenourea
Sethoxydla
Silver
Silver Cyanide
Sodiua> Azlda
Sodium Cyanide
Sodium DtnthyldlthlocarbaMte (Dlthlocarb)
Strychnine
-------
TABLE 2. INORGANIC PRIORITY POLLUTANTS RANKED
ACCORDING TO BIOCONCENTRATION FACTOR (BCF)
Priority
Pollutant No.
115
118
119
119
123
124
127
114
117
121
124
125
126
Substance
arsenic
cadmium
chromium VI
chromium III
mercury
nickel
thallium
antimony
beryllium
cyanide
nickel (subsulfide, refinery dust)
selenium
silver
Log BCF*
2.544
2.513
2.190
2.104
2.000
1.699
1.176
ND
ND
ND
ND
ND
ND
11 BCF = Bioconcentraction Factor (U.S. EPA 1980b; Tetra Tech
1985a).
ND •= No data.
-------
TABLE 3. TOXICITY PROFILE FOR MERCURY AND PCBsa
Property
Mercury13
PCBsc
CAS Number
Physical-Chemical
Molecular Weight
Vapor Pressure (mm Hg)
Solubility (mg/L)
Log Kowd
Log Bioconcentration Factord
Carcinogenic Status
Acute Toxicity
Human (mg/kg body wt) LD50
Mammal (mg/kg body wt) LDSO
Aquatic (mg/L) LC50
Chronic Toxicological Effects
Humans
Mammals
Aquatic Organisms
Critical end point for
risk assessment
7439-97-6
200.6-318.7
0.012-0.028
0.056-400,000
N/Ae
2.0-4.6
Noncarcinogen
29«
1.0-40.9
0.015-32.0
Motor and sensory impairment
leading to paralysis, loss
of vision and hearing, and
death. Kidney dysfunction.
Reproductive impairment and
teratogenic effects.
Developmental and structural
anomalies, suppression of
growth and reproduction,
impairment of behavior.
Central nervous system
effects (e.g., ataxia and
parathesia)h
1336-36-3
154.2-498.7
2.8 x lO'9 - 7.6 x lO'5
<0.001-5.9
4.0-6.9
1.9-5.2
Probable human carcinogenf
Group B2
— Sufficient animal evidence
-- Inadequate human evidence
1,010-16,000
0.001-61.0
Skin lesions, liver dysfunctions,
and sensory neuropathy.
Hepatotoxicity, fetotoxicity, skin
lesions, and hepatocellular carcinoma
Reproductive and developmental
impairment.
Hepatocellular carcinomaf
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TABLE 3. (Continued)
* This is an example toxicity profile and is not intended to be comprehensive.
b Mercury may occur in its elemental form, as inorganic salts, or as organic complexes.
Consequently, the chemical and toxicological properties vary tremendously depending on
the degree of complexation or metal speciation.
c Physical-chemical properties and toxicity vary according to the degree of chlorine
substitution, the number of adjacent unsubstituted carbons and steric configuration.
d Tetra Tech (1985a).
' N/A » not applicable.
f EPA (1980a,b 1985a); IARC (1978).
* For mercury (II) choride via oral route of exposure (Tatken and Lewis 1983). Relevance
to consumption of mercury (primarily methylated) in fish is questionable.
h Clarkson et al. (1973).
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weight of evidence of carcinogenicity based on animal studies is "sufficient."
The group is divided into two subgroups. Usually, Group Bl is reserved
for agents for which there is limited evidence of carcinogenicity from
epidemiologic studies. It is reasonable, for practical purposes, to regard
an agent for which there is "sufficient" evidence of carcinogenicity in
animals as presenting a carcinogenic risk to humans. Therefore, agents
for which there is "sufficient" evidence from animal studies and for
which there is "inadequate" evidence or "no data" from epidemiologic
studies would usually be categorized under Group B2.
• Group C - Possible Human Carcinogen: This group is used for agents
with limited evidence of carcinogenicity in animals in the absence of data
on humans. It includes a wide variety of evidence; e.g., (a) a malignant
tumor response in a single, well-conducted experiment that does not
meet conditions for sufficient evidence; (b) tumor responses of marginal
statistical significance in studies having inadequate design or reporting;
(c) benign but not malignant tumors with an agent showing no response
in a variety of short-term tests for mutagenicity; and (d) response of
marginal statistical significance in a tissue known to have a high or
variable background rate.
• Group D - Not Classifiable as to Human Carcinogenicity: This group is
generally used for agents with inadequate human and animal evidence of
carcinogenicity or for which no data are available.
• Group E - Evidence of Noncarcinogenicity for Humans: This group is
used for agents that show no evidence for carcinogenicity in at least two
adequate animal tests in different species or in both adequate epidemiologic
and animal studies. The classification of an agent in Group £ is based
on the available evidence and should not be interpreted as a definitive
conclusion that the agent is not a carcinogen under any circumstances.
The above descriptions for the categories were taken from U.S. EPA (1986a). At
present, a weight-of-evidence classification for carcinogenicity is available in IRIS for
each chemical assigned a Carcinogenic Potency Factor.
SOURCES OF INFORMATION
In many cases, EPA regions and others may rely on toxicity profiles generated
previously. IRIS is a key source of chemical toxicity data, including information from
critical studies and weight-of-evidence classifications for carcinogens. The first step
in a hazard assessment should be to consult IRIS chemical files for potential contaminants
of concern. IRIS chemical files will be available for approximately 270 chemicals by
November 1987. Further information on IRIS is provided in Appendix B.
The primary sources of toxicity profiles are the EPA Office of Waste Programs
Enforcement and Office of Health and Environmental Assessment (e.g., Appendix C,
Table C-l). EPA toxicity profiles are available for approximately 195 chemicals.
Additional sources are shown in Appendix C, Table C-2. Under the Superfund Amendments
and Reauthorization Act of 1986, EPA and the Agency for Toxic Substances and Disease
14
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Registry are preparing toxicity profiles for 100 hazardous substances considered as high
priority contaminants at Superfund sites.
Supplementary information on the toxicity of contaminants of concern may be
obtained from bibliographic or chemical/toxicological databases. DIALOG, a comprehensive
bibliographic database system (Dialog Information Services, Inc., 3460 Hillview Avenue,
Palo Alto, CA 94304), offers access to databases such as Pollution Abstracts, National
Technical Information Service, and ENVIROLINE. Chemical and toxicological information
can be obtained from the databases listed in Appendix C, Table C-3.
15
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DOSE-RESPONSE ASSESSMENT
After the potential hazard associated with each contaminant of concern is charac-
terized, the relationship between dose of a substance and its biological effect is deter-
mined. Dose-response data are used to determine the toxicological potency of a sub-
stance, a quantitative measure of its potential to cause a specified biological effect.
The concepts of exposure, dose, dose-response relationship, and toxicological potency are
discussed in the following sections.
EXPOSURE AND DOSE
The concepts of exposure and dose, as defined below, are central to risk assessment:
• Exposure: Contact by an organism with a chemical or physical agent
• Dose: The amount of chemical uptake by an organism over a specified
time as a consequence of exposure.
The "ingested dose," or amount of chemical ingested, is distinct from the "absorbed
dose." For the oral route of exposure, the absorbed dose is the amount of chemical
assimilated by absorption across the lining of the gastrointestinal system. Exposure level
or exposure concentration is used to denote the concentration (mg/kg wet weight) of
contaminant in edible tissue of fish or shellfish. As shown below, the absorbed dose is
estimated from food consumption rate, the exposure concentration, and an absorption
coefficient (see Exposure Assessment).
DOSE-RESPONSE RELATIONSHIPS
The form of the dose-response relationship for carcinogens is assumed to be
fundamentally different from that for noncarcinogens (U.S. Office of Science and
Technology Policy 1985). Examples of general dose-response relationships are shown in
Figure 2. The lack of a demonstrated threshold in dose-response relationships for car-
cinogens (U.S. EPA 1980b, 1986a; U.S. Office of Science and Technology Policy 1985)
implies a finite risk of cancer even at very low doses of the carcinogen.
For noncarcinogenic effects, there is usually a threshold dose, i.e., a dose below which
no adverse biological effects are observed in the animal bioassay. The threshold dose
is termed the No-Observed-Adverse-Effect-Level (NOAEL), as shown in Figure 2. Note
that a nonzero mean response may be a NOAEL if that mean response is not significantly
different from zero as determined by a statistical test. The Lowest-Observed-Adverse-
Effect-Level (LOAEL) is the lowest concentration that results in a statistically significant
health effect in the test population.
A measure of toxicological potency is derived from an empirical dose-response
relationship for the chemical of interest. Toxicological potency indices for two broad
categories of toxicants are defined as follows:
16
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Crt
oe
o
o
o
Ul
o
UJ
oc
LOW-DOSE
REGION Of
CONCERN
DOSE OF CARCINOGEN
OBSERVED DATA POINTS
• CHEMICAL A
A CHEMICAL B
• CHEMICAL C
MODELS
— —- — Low dose
extrapolation
—— Models fit within
observed data range
U
LU
Z X
o
u
oc
Rfd
•»••• UF
NOAEL
Frequency - Proportion of
animals tested
RfD • Reference Dose
UF. Uncertainty Factor
NOAEL . No Observed
Adverse Effects
Level
Dose • Ingested Dose
DOSE OF NONCARCINOGEN
Figure 2 Hypothetical example of dose-response curves for a
carcinogen and a noncarcinogen.
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• Carcinogens are individually characterized by a Carcinogenic Potency
Factor, a measure of the cancer-causing potential of a substance estimated
by the upper 95 percent confidence limit of the slope of a straight line
calculated by the linearized multistage procedure or another appropriate
model
• Noncarcinogens are individually characterized by an RfD, an estimate
(with uncertainty spanning perhaps an order of magnitude) of the daily
exposure to the human population (including sensitive subpopulations)
that is unlikely to produce an appreciable risk of adverse health effects
during a lifetime.
Carcinogenic Potency Factors are also referred to as Slope Factors. RfDs are conceptually
similar to Acceptable Daily Intakes (U.S. EPA 1987a).
The data set used to define toxicological indices is determined by the quality of
available data, its relevance to modes of human exposure, the similarity of the species
to humans, and other technical factors. Adequate data from clinical or epidemiological
studies of humans are preferred over animal data. If adequate human data are not
available, a data set for the animal species most similar to humans or for the most sensitive
species is used in the dose-response assessment. Data are evaluated by EPA to ensure
high quality (e.g., U.S. EPA 1980b, 1985a).
The main source of dose-response data for deriving Carcinogenic Potency Factors
and RfDs is lifetime cancer bioassays performed on rats or mice. Because most of these
experiments are designed to be cost-effective, doses in bioassays may be orders of
magnitude above those encountered in the human environment. High doses are used in
laboratory bioassays for several reasons: 1) to reduce the time required to produce a
response and thus overcome problems related to latency period (i.e., length of time
between exposure and appearance of health effects), 2) to obtain sufficient statistical
power to detect health effects, and 3) to decrease the absolute number of animals
required and thereby reduce costs. Doses in animal bioassays for oral uptake of con-
taminants are usually the administered (ingested) dose, not the absorbed dose (i.e.,
uptake across the lining of the gastrointestinal system).
Carcinogenic Potency Factors and RfD values derived by EPA are listed in the
IRIS database. At present, values for these toxicological indices are being standardized
for agency-wide use. A brief overview of methods by which these indices are derived
is presented below.
CARCINOGENIC POTENCY FACTORS
The Carcinogen Assessment Group of EPA currently uses the linearized multistage
procedure to derive Carcinogenic Potency Factors (U.S. EPA 1980b, 1985a, 1986a,
1987a). The multistage model assumes that carcinogenesis results from a series of
interactions between the carcinogenic chemical and DNA, with the rate of interactions
linearly related to dose. For example, a chemical may induce a mutation in the DNA
of a cell to initiate carcinogenesis. However, a series of further interactions between
DNA and the same chemical (or another one) may be necessary to promote carcino-
genesis and induce a tumor. The multistage model is the model most frequently used
by EPA when there is no convincing biological evidence to support application of an
17
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alternative model. Other models include the logit, probit, single-hit, and Weibull
models (Food Safety Council 1980, 1982; Hogan and Hoel 1982; Cothern et al. 1986). At
high doses (corresponding to lifetime risks greater than about 10~2), all currently used
models yield generally similar risk estimates. Below risks on the order of 10'2, the
models diverge increasingly as dose declines. In the low-dose range, the linearized
multistage model generally predicts risks similar to the single-hit (i.e., linear) model.
For many data sets, both of these models yield higher estimates of low-dose risk than
do other models (U.S. EPA 1980b; Hogan and Hoel 1982; U.S. Office of Science and
Technology Policy 1985). The mathematical form of the multistage model for a specified
carcinogen is:
R(d) « 1 - exp [-(Qid + q2d2 + ... + qkdk)] (1)
where:
R(d) - Excess lifetime risk of cancer (over background at dose d)
(dimensionless)
q{ values = Coefficients [kg day mg"1 (i.e., the inverse of dose units)]
d * Dose (mg kg'1 day'1)
k » Degree of the polynomial used in the multistage model.
U.S. EPA (1987a) described the linearized multistage procedure as follows:
• The multistage model is fitted to the data on tumor incidence vs. dose
• The maximum linear term consistent with the dose-response data is cal-
culated, which essentially defines the linear portion of the dose-response
function at low doses
• The coefficient of the maximum linear term, designated as qt*, is set
equal to the slope of the dose-response function at low doses
• The resulting estimate of q/ is used as an upper-bound estimate of the
Carcinogenic Potency Factor (termed Slope Factor in U.S. EPA 1987a)
qx* is usually calculated as the upper 95 percent confidence limit of the estimate of
the coefficient qx in Equation 1. Because the slope of the dose-response function at
high doses could be different from that at low doses, the use of qt* as an upper-bound
estimate of potency is not valid at high exposures. In general, ql should not be used
as the upper-bound estimate of potency at exposures corresponding to excess lifetime risks
greater than approximately 10~2 per individual (i.e., one excess tumor per 100 exposed
individuals).
The model commonly used to estimate plausible-upper-limit risk for low levels of
exposure over a lifetime is therefore:
R'(d) - QJ* d (2)
18
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where:
R*(d^ - Upper-bound estimate of excess lifetime risk of cancer (dimensionless)
QI - Upper-bound estimate of carcinogenic potency (kg day mg'1)
d - Dose (mg kg'1 day'1).
Equation 2 represents a linear approximation of the multistage model.
If a potency factor is derived from nonhuman data, as is usually the case, it must
be extrapolated to humans. Before being applied to humans, Carcinogenic Potency
Factors derived from animal data are corrected using surface-area differences between
bioassay animals and humans (U.S. EPA 1980b, 1986a). The rationale for using surface-
area extrapolations is detailed in Mantel and Schneiderman (1975). The relationship between
surface-area extrapolation and body-weight extrapolation approaches is discussed in the
Introduction above (see Background, Relationship of EPA Risk Assessment Methods to FDA
Risk Assessment Methods).
REFERENCE DOSES
Current methods for predicting human health effects from exposure to noncarcino-
genic chemicals rely primarily on the concept of an RfD (U.S. EPA 1987a). The RfD is
derived from an observed threshold dose (e.g., NOAEL or LOAEL if the NOAEL is
indeterminate) in a chronic animal bioassay by applying an uncertainty factor, which
usually ranges from 1 to 1,000 (Dourson and Stara 1983). The relationship between the
NOAEL, the RfD, and the uncertainty factor are illustrated in Figure 2 above. The
uncertainty factor accounts for differences in threshold doses among species, among
intraspecies groups differing in sensitivity, and among toxicity experiments of different
duration. Dourson and Stara (1983) and U.S. EPA (1987a) discuss the methods for
deriving RfDs and the criteria for selecting uncertainty factors. In brief, an uncertainty
factor of 1000 is based on combining a factor of 10 to account for animal-to-human
extrapolation, a factor of 10 to protect sensitive individuals, and a factor of 10 to
account for use of a LOAEL in place of a NOAEL.
SOURCES OF INFORMATION
In many cases, EPA regions and other agencies will be able to rely on dose-
response assessments generated previously. Current values for Carcinogenic Potency
Factors and RfDs are given in IRIS (U.S. EPA 1987a; e.g., see Appendix B). Before
using these values, investigators should consult the IRIS database and current EPA
health assessment documents for information on their derivation and uncertainties for
each chemical. Contacts for information on specific chemicals are listed in IRIS Chemical
Files.
Carcinogenic Potency Factors
The Carcinogenic Potency Factors calculated by the EPA Carcinogen Assessment
Group are published in IRIS and in each health assessment document produced by the
Office of Health and Environmental Assessment (e.g., U.S. EPA 1985a). The EPA
Carcinogen Assessment Group determines these carcinogenic potency values and updates
19
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them periodically. Before being entered into IRIS, Carcinogenic Potency Factors and
supporting documentation are reviewed by the Carcinogen Risk Assessment Verification
Endeavor (CRAVE) work group. The list of Carcinogenic Potency Factors published in
each health assessment document is intended only to provide comparative information
for various chemicals. IRIS should be used as the primary source of Carcinogenic Potency
Factors for risk assessment.
Reference Doses
IRIS is the primary source of RfD values. An example of an IRIS data sheet for
the pesticide lindane is shown in Appendix B. The data sheet provides information on
the RfD, the endpoints (biological effects) of concern, experimental data sets, doses,
uncertainty factors, additional modifying factors, confidence in the RfD, reference
documentation, and dates of agency RfD reviews.
Individual program offices within EPA may need to be consulted for information
on chemicals not yet incorporated into IRIS. For example, the Office of Drinking
Water is a source of RfDs for selected chemicals. In May 1987, the Office of Drinking
Water released draft Health Advisories containing RfDs and guidelines for short-term
effects for 16 pesticides: alachlor, chlordane, l,2-dibromo-3-chloropropane (DBCP), 2,4-
dichlorophenoxyacetic acid (2,4-D), 1,2-dichloropropane, endrin, ethylene dibromide
(EDB), heptachlor and heptachlor epoxide, lindane, methoxychlor, oxymyl, pentachlorophenol,
toxaphene, and 2,4,5-trichlorophenoxypropionic acid (2,4,5-TP). Office of Drinking
Water Health Advisories will eventually be incorporated into IRIS.
20
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EXPOSURE ASSESSMENT
Exposure assessment is the process of characterizing the human populations exposed
to the chemicals of concern, the environmental transport and fate pathways of those
chemicals, and the frequency, magnitude, and duration of the exposure dose (U.S. EPA
1986b). For exposure assessment of contaminated fish and shellfish, the following
factors should be considered:
• Concentrations of contaminants in aquatic biota of concern
• Potential environmental transfer of contaminants from sources through
aquatic species to humans
• Fisheries harvest activities, diet, and other characteristics of exposed
human populations
• Numerical variables (e.g., food consumption rate, contaminant absorption
efficiency) used in models to estimate exposure
• Purpose of the exposure assessment (e.g., assessment of potential closure
of sport or commercial fishery; documentation of health risk from local
contaminant sources such as hazardous waste site or wastewater discharges;
development of sportfish consumption advisories).
Information on contaminant concentrations and the exposed population is combined to
construct an exposure profile, which includes estimates of average rates of contaminant
intake by exposed individuals. Key stages of an exposure assessment for contaminated
fish and shellfish are discussed in the following sections.
TISSUE CONCENTRATIONS OF CONTAMINANTS
Guidance on development of study designs to measure concentrations of toxic
substances in edible tissues of fish and shellfish is provided in this section. The
guidance provided below focuses primarily on field surveys or monitoring programs
involving the collection of samples directly from aquatic environments, or from harvesters
when the specific geographic origin of samples is known. Such guidance is directly relevant
to analysis of recreational fisheries. The present document does not specifically address
approaches to marketplace sampling of commercial fisheries products, although some of
the concepts discussed below apply to marketplace surveys. Sampling designs for
collection of fisheries products from the marketplace are available in FDA Compliance
Program Guidance Manuals (e.g., U.S. FDA 1986). Sampling of commercial fisheries
directly at the source is preferred over marketplace sampling because the former often
allows documentation of the sampling location.
If the exposure assessment is designed to include contaminant intake from consumption
of commercial fish and shellfish, samples may be obtained in two ways. First, samples
of target species can be obtained directly from commercial fishermen. In this case, a
21
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strict quality assurance/quality control (QA/QC) program should be implemented to
ensure proper handling, storage, and documentation of samples. Documentation should
include sampling location, species name, size (length, carapace width, or shell height/width),
weight, sex, reproductive condition, time and date of sampling, and preservation technique.
In most cases, a technician or observer should be on board the fishing vessel to maintain
proper sample handling and documentation. Alternatively, samples may be collected by
monitoring program personnel using vessels other than commercial fishing boats. In
this case, samples should be collected in a way that simulates commercial fishing
practices as closely as possible (e.g., same species, size classes, season, fishing area,
sampling method, and water depth). Regardless of the general approach to sampling,
the organisms collected should be placed directly in temporary storage on board the
sampling vessel. Upon return to shore, resection of samples should be accomplished as
quickly as possible using an adequate clean-room. If an extended sampling cruise
necessitates resectioning on board, an adequate clean-space should be set aside to
ensure that samples are not contaminated.
Analysis of chemical residues in tissue to support an exposure assessment is one
kind of bioaccumulation study. Bioaccumulation is defined here as the uptake and
retention of a contaminant (e.g., a potentially toxic substance) by an organism. The term
bioconcentration refers to any case of bioaccumulation wherein the concentration of
contaminant in tissue exceeds its concentration in the surrounding medium (i.e., water
or sediment). The phrase "bioaccumulation survey" will be used below to refer to
measurement of chemical residues in tissue samples from fish and shellfish collected in
the field.
The elements of a study design for analysis of chemical residues in tissue include:
• Objectives
• Target species and size (age) class
• Sampling station locations
• Target contaminants
• Sampling times
• Kind of sample (e.g., composite vs. grab, cooked vs. raw; fillet vs.
whole organism)
• Sample replication strategy
• Analytical protocols, including detection limits
• Statistical treatment of data.
Because the complexity and specific features of a sampling design will depend on
the objectives of the exposure assessment, no single design can be recommended here.
Nevertheless, some basic steps in the study design process can be summarized as follows:
22
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• Define concise objectives of the study and any hypotheses to be tested.
• Define spatial and temporal characteristics of fisheries relative to
harvesting activities (e.g., seasonality, catch or consumption rates,
species composition, size ranges, demersal vs. pelagic species).
• Define harvesting activities and methods of preparing food for consumption
that potentially affect exposure to contaminants.
• Define kinds of samples to be collected (species, type of tissue, mode
of preparation) and variables to be measured, based on a preliminary
exposure analysis.
• Evaluate alternative statistical models for testing hypotheses about
spatial and temporal changes in measured variables. Select an appropriate
model.
• When possible, use stratified random sampling for each fish and shellfish
species, where the different strata represent different habitat types or
kinds of harvest areas that may influence the degree of tissue contamination.
• When practical, specify equal numbers of randomly allocated samples for
each stratum/treatment combination (e.g., habitat type in combination
with species or season).
• Include samples from a relatively uncontaminated reference or control
area to help define local contamination problems.
• Perform preliminary sampling or analyze available data to evaluate the
adequacy of alternative sampling strategies (e.g., composite samples vs.
tissue from individual organisms) and statistical power as a function of
the number of replicate samples.
• Develop a QA/QC program that covers: sample collection and handling;
chain of custody; data quality specifications; analytical methods and
detection limits; data coding; data QA/QC steps to assess precision,
accuracy, and completeness; database management specifications; data
reporting requirements; and performance audits.
• Define data analysis steps, including statistical tests, data plots, summary
tables, and uncertainty analysis.
Note that the second and third steps above depend on information developed as part of
the characterization of the exposed population (see Exposed Population Analysis below).
Also, practical limitations of field sampling may dictate compromises in the sampling
design. For example, use of equal sample sizes is generally recommended because
statistical analysis of data sets with unequal sample sizes may be difficult or unnecessarily
complex. However, collection of equal numbers of replicate samples for each treatment
(or stratum) may be undesirable if both dominant and rare species are to be sampled at
a series of harvest locations with a broad range of harvest yields. Depending on the
specific objectives and corresponding study design, a series of statistical analyses
rather than a single test may be appropriate.
23
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Detailed guidance on sampling strategies is provided by Phillips (1980), Green
(1979), Tetra Tech (1985b,c; 1986b), Phillips and Segar (1986), and Gilbert (1987). Much
of the guidance provided in the following sections incorporates the suggestions of these
authors.
The statement of objectives is a critical step in the study design process, since
specification of other design elements depends on the survey objectives. The study
objectives must in turn relate to the objectives of the exposure assessment in which
the data will be used. The relationships between study objectives and general features
of a sampling design are addressed in the next section.
Study Objectives and General Sampling Design
Specific objectives of a chemical residue study should be defined to ensure collection
of appropriate data for the exposure assessment. Different objectives may require
radically different sampling designs. Although the primary objective of a field study
may be to estimate the mean concentrations of specified chemical contaminants in
edible tissues of harvested species, it may be necessary to specify additional objectives
to meet the needs of exposure assessment or risk management. For instance, statistical
discrimination among mean contaminant concentrations in samples from different harvest
areas, seasons, or species may be desired. Such information might be needed to manage
relative risks among harvest areas and to impose fisheries closures on a site-specific basis.
Example Objectives--
Some examples of objectives for exposure assessments paired with appropriate
bioaccumulation survey objectives are given below. These objectives are provided to
illustrate the ways in which the elements of a bioaccumulation study design depend on
the exposure assessment objectives. They are not intended to be recommended objectives
for an actual exposure assessment. In these examples, the bioaccumulation study design
involves specifically the measurement of chemical residues in edible tissues of fishery
species. Information on the exposed population, including an analysis of their dietary
habits (e.g., fisheries species consumed, food preparation method, and consumption
rate), is discussed later (see Exposed Population Analysis). Such information may
influence the objectives of the exposure assessment and the bioaccumulation survey.
Example 1:
• Exposure Assessment Estimate the worst-case exposure for a wide
range of contaminants over a predefined geographical area.
• Bioaccumulation Design: Estimate mean concentrations of contaminants
in edible tissues of a selected narrow size range of individuals of the
most contaminated species during the season of peak contaminant
concentrations.
Example 1 represents a screening survey to evaluate the need for further work.
Edible portions of a limited number (e.g., 3-5) of individual organisms or composite
samples would be analyzed for a large number of compounds and the risk assessment
24
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conducted assuming moderate or high (but plausible) consumption rates. The species
and size range selected would be the ones most likely to accumulate high concentrations
of contaminants. Typically, the target species for a screening survey would be the
largest individuals of a bottom dwelling species associated with soft sediments.
Example 2:
• Exposure Assessment: Estimate the long-term average exposure to each
of the contaminants A, B, and C through consumption of aquatic species
L, M, N, and O combined from harvest area Z for the average person
in the exposed human population.
• Bioaccurnulation Design: Estimate the mean concentrations of contami-
nants A, B, and C in edible tissues of aquatic species L, M, N, and O
combined from harvest area Z over an annual period.
Example 2 illustrates a simple case involving the consumption of multiple species
from a single harvest location. Individual or composite samples of each species would
be analyzed separately during different seasons or during a single season expected to
represent the annual average. If samples are analyzed separately during different
seasons (e.g., see discussion of Example 4 below), the mean annual exposure for all species
could still be calculated from the seasonal data. In general, highly composited samples
are not recommended because information on different factors (e.g., species, seasons)
that affect contaminant concentrations is lost.
Example 3:
• Exposure Assessment: Estimate a plausible-upper-limit of exposure to
each of the contaminants A, B, and C through consumption of aquatic
species L, M, N, and O combined from harvest area Z for a seasonal
harvester in the exposed population.
• Bioaccurnulation Design: Estimate the upper bound of the 95 percent
confidence interval of the mean concentration for each of the con-
taminants A, B, and C in edible tissues of aquatic species L, M, N, and
O combined from harvest area Z during the season of highest contamina-
tion.
The general sampling design for the objectives of Example 3 would require replicate
composite samples to estimate upper bounds of 95 percent confidence intervals for the
mean concentrations of contaminants across species. To meet these objectives, samples
could be composited across species, although this is generally not recommended.
Multispecies composites would not provide data for assessing exposures corresponding
to different dietary habits. To obtain an upper-limit estimate of exposure, it might be
sufficient to analyze samples from only one season if available information on seasonal
variation was sufficient to select one season as the expected worst case.
Example 4:
• Exposure Assessment: Estimate the probability distribution of exposure
to each of the contaminants A, B, and C through consumption of each
of aquatic species L, M, N, and O from harvest area Z for various
25
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segments of an exposed population (e.g., ethnic groups) over an annual
period.
• Bioaccumulation Design: Estimate the probability distribution of concen-
trations of contaminants A, B, and C in edible tissues of each of
aquatic species L, M, N, and 0 from harvest area Z over an annual period.
To accomplish the objectives of Example 4, extensive seasonal data on the dietary
composition of several subgroups of the exposed population must be available. Separate
replicate composite samples of each harvested species could be analyzed for each
season. During each season, the species analyzed would correspond to those represented
to a significant extent in the diet. Probability (frequency) distributions and means of
contaminant concentrations would be derived for each species during each season. By
combining data from different species, the probability distribution of exposure and the
mean exposure weighted by species representation in the diet could be calculated for
each population segment. Note that data to support the analyses required by Example
4 are seldom available before a specially designed study is conducted.
Example 5:
• Exposure Assessment: Estimate an average and a plausible-upper-limit
of exposure to each of the contaminants A, B, and C through consump-
tion of each of aquatic species L, M, N, and O from each of the harvest
areas X, Y, and Z over an annual period.
• Bioaccumulation Design: Estimate the mean concentration and the
upper bound of the 95 percent confidence interval of the mean con-
centration for each of the contaminants A, B, and C in edible tissues
of each of species L, M, N, and O from each of the harvest areas X,
Y, and Z during each of the harvest seasons.
The sampling strategy appropriate for Example 5 is complicated by the occurrence
of discrete harvest areas. Replicate composite samples of a given species would generally
be required for each season and area in which the species is harvested. Because the
characteristics of the exposed population may differ among harvest areas, it may be
appropriate to divide the exposed population into segments corresponding to geographic
areas, ethnic groups, age classes or other factors. The seasonal and total annual
exposure for each segment of the exposed population would be calculated for each
species as in Example 4 above.
Influence of Environmental and Population Factors—
The four examples just given illustrate the variety of general study designs that
may be needed to meet diverse objectives. The specific design of a chemical residue
study will depend on the interplay between dietary patterns of the exposed population
and environmental factors that influence concentrations of contaminants in tissues of
aquatic organisms. Some of the important environmental factors are:
26
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• Conventional water quality (i.e., hardness, salinity, temperature, suspended
solids)
• Habitat location, depth, proximity to contaminant sources
• Contaminant concentrations in water
• Contaminant concentrations in sediments
• Species available for harvest and migratory cycles
• Organism activity pattern, food habits, and habitat
• Seasonal biological cycles (e.g., stage of sexual cycle) in relation to the
frequency and seasonality of contaminant inputs (e.g., industrial discharges,
waste dumps, dredging)
• Organism size (or weight), age, and sex
• Lipid content of tissue analyzed (where lipophilic organic contaminants
are of concern).
Examples of the interaction between these factors and parameters of the exposed
population are given in Figure 3.
Seasonal variation in environmental factors or activities of the exposed population
may correlate with contaminant concentrations in consumed fish and shellfish. Therefore,
at least general knowledge of seasonal changes in contaminant concentrations and
human consumption patterns may be needed to design an appropriate sampling approach
for estimating long-term exposure. Two extreme examples of contamination and diet
patterns are provided below:
Homogeneous Diet and Contamination:
• Each of the species is present in the harvest area all year
• There is no seasonal variation in contaminant concentrations
• Contaminant concentrations do not vary among species
• Species are equally represented in the diet.
Heterogeneous Diet and Contamination:
• Some species are absent from the harvest area during one or more seasons
• Contaminant concentrations vary among species and among seasons
• Some species are eaten more than others, and diet composition varies
seasonally.
27
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EXPOSED-POPULATION FACTORS •
ENVIRONMENTAL FACTORS b
CONVENTIONAL WATER QUALITY c
PROXIMfTY TO CONTAMINANT SOURCES
CONTAMINATION OF WATER/SEDIMENTS
SPECIES AVAILABLE FOR HARVEST
ORGANISM ACTIVITY MODE d
SEASONAL BIOLOGICAL CYCLES •
ORGANISM SIZE
ORGANISM AGE
ORGANISM SEX
LIPID CONTENT OF TISSUE
-O
-O
-O
8 Harvest activities and dietary patterns of exposed population:
Mode of harvest refers to fishing technique (e.g., trap.net, or pole)
Mode of preparation refers to trimming and cooking technique
0 Factors that influence contaminant concentrations in aquatic organisms
c Hardness, salinity, temperature, suspended solids
d Degree of mobility and contact with sediments
6 Reproductive, lipid storage, and growth cycles
•4 Population factor affects environmental factor
O Environmental factor affects population factor
V Mutual interaction between environmental and population factors
Figure 3 Interaction between environmental factors and exposed
population factors.
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In the first case above (homogeneous diet and contamination), the study design could
be relatively simple. Mean contaminant concentrations could be estimated from analyses
of a single composite sample of one of the species collected at one time of year from
each harvest area. If previous data were available to verify the lack of variation in
chemical concentrations among species and among seasons, it would be appropriate to
extrapolate the results from a single composite sample to the entire diet composed of
several species. However, this is an unrealistic case. It is more likely that both
contaminant concentration and diet composition will vary seasonally, and that contaminant
concentrations will vary among species. Analyses of contaminant concentrations in
each species during different seasons is generally recommended here to meet the diverse
objectives of a typical exposure assessment.
Selection of Target Species and Size Classes
Ideally, the set of species selected for contaminant analysis would include all
harvested species. Because available data and funds for collecting new data are often
limited, only one or a few target species may be used for human health risk assessment.
The particular species selected for an exposure assessment will depend on the study
objectives. Examples of approaches and guidance on selection of target species are
given below.
Four alternative objectives that affect the choice of target species are:
• Perform a comprehensive analysis of harvested species
• Characterize the typical exposure case represented by the dominant
harvested species
• Characterize exposure for the worst-case species (e.g., heavily consumed
species expected to be highly contaminated)
• Characterize the spatial distribution of contamination using an indicator
species.
The criteria for selecting species for chemical analyses to meet each of these objectives
are shown in Table 4. For the first objective (comprehensive species analysis), all of
the harvested species do not necessarily need to be analyzed, but some criterion is
required to select species for analysis (e.g., the most important species in the harvest
that together comprise greater than 95 percent of the catch by weight). For the second
objective (typical exposure), a few of the dominant species (by weight) in the harvest
may be selected to represent a typical exposure level. However, this approach has the
major disadvantage that highly contaminated species may be overlooked (see Dominant
Harvested Species below). For the third objective (worst-case species analysis), the
target species should be among the most contaminated species in the harvest. If the
worst-case assessment is species-specific (i.e., the consumption rate for a single species
is used to estimate exposure), then the target species should also be one of the dominant
species in the harvest. When the dominant component of the diet differs among subpopu-
lations of concern, then specific dietary information for subpopulations should be used
to select the worst-case target species. The target species may be the most contaminated
species regardless of its status in the diet of the entire exposed population. For the
last objective (site-specific analyses of the spatial distribution of contamination), an
28
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TABLE 4. CRITERIA FOR SELECTING TARGET SPECIES'
Species Characteristics
Comprehensive
Species Analysis
Alternative Design Objectives'3
Typical Worst-Case
Exposure Case Species
Spatial Pattern
Indicator Specie
Harvest ranking
Home range size
Contamination level
Species forming
>95°/o of catch
Variable
Variable
Dominant species
in catch
Variable
Variable
Dominant species
in catch
Variable
High
Variable
Small
High
* Criteria for selecting target species to meet a given objective are shown in bold.
b A full statement of each objective is given in the text.
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indicator species with a small home range that is expected to have high concentrations
of contaminants in edible tissue would be selected. Note that an indicator species
could be a species that is relatively rare in the harvest. Although home range size
and degree of contamination of species may not constrain the selection of species to
meet the first two objectives listed above, selecting species without regard to contamination
levels will not necessarily ensure that the overall purpose of performing an exposure
assessment will be met.
Dominant Harvested Species--
If available, data on fisheries catches or consumption from field surveys (e.g.,
Finch 1973; Puffer et al. 1982; Landolt et al. 1987; McCallum 1985) can be used to
select species for analysis that are dominant members of the catch on a wet-weight
basis. The advantages of choosing the dominant harvested species for exposure assessment
are that:
• Exposure estimates will be based on realistic conditions in terms of
relative contribution of species to the diet, providing that catch data
reflect consumption patterns or that consumption data are used for the
selection of species
• Adequate numbers of organisms for chemical analyses should be relatively
easy to obtain.
The disadvantages of this approach are that:
• Species that are minor components of the diet by weight but that are
highly contaminated may be overlooked
• Exposure of human subpopulations that consume species other than the
dominant component of the diet overall may not be protected
• Which species are dominant often varies spatially, making it difficult to
compare risk estimates for different sites
• Extensive species-specific data on catch, consumption, and contamination
patterns are needed to select target species (these data are costly to
obtain if not already available)
• If samples are obtained directly from harvesters, a major component of
the catch may be unidentifiable because the catch is sometimes cleaned
before being surveyed.
Indicator Species—
The use of selected indicator species is an alternative to the use of dominant
harvested species. Indicator species can be chosen to represent the average (or maximum)
contamination levels in the harvest, as determined from available data or from a pilot
survey. Use of indicator species may be appropriate for investigations with multiple
objectives (e.g., assessment of bioaccumulation in fishery species and human health
29
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risks for specific areas within a water body). Indicator species may include both
highly mobile and relatively sedentary species. If small-scale discrimination of spatial
patterns of contamination is a concern, indicator species should include nonmigratory biota
or species that exhibit minimal movement within the aquatic habitat (e.g., bivalve
molluscs and English sole in nearshore marine areas; mussels and sculpins in streams).
The use of a few indicator species for exposure assessment is appropriate for
initial screening of geographic areas before more detailed exposure assessments are
conducted. If no potential health problems are identified in an initial risk analysis,
further data collection may not be warranted, unless long-term monitoring is desired.
If, on the other hand, analysis of tissues from indicator species reveals substantial
health risks, further field surveys may be needed to perform a detailed exposure assess-
ment, using consumption patterns and contaminant concentrations for a wider variety
of harvested species.
The use of indicator species for exposure assessment offers the following advantages:
• Field surveys based on indicator species are cost-effective because
efforts can be focused on collecting large sample sizes of one or a few
species rather than minimally adequate sample sizes of many species
• Background information on the distribution, abundance, and contamina-
tion of indicator species may be available
• Indicator species can be selected to represent the average or maximum
level of contamination expected for all harvested species (assuming
background or pilot data are available)
• Because the indicator species does not have to be a dominant species in
the harvest, extensive data on catch and consumption patterns may not
be needed.
The disadvantages of the indicator species approach are that:
• The exposure estimate may be biased if the indicator species does not
truly represent the case of interest (e.g., average- or worst-case concen-
trations of contaminants)
• The selected species may be a good indicator for some contaminants of
concern but not for others
• If the selected indicator species are not major components of the
harvest, the exposure assessment may appear unrealistic
• Background data on the distribution, abundance, and contamination of
the harvested species are usually needed to select appropriate indicator
species.
Phillips (1980), Tetra Tech (1985b), and Phillips and Segar (1986) provide criteria
for selecting target species for bioaccumulation surveys. Important criteria to consider
when choosing indicator species for an exposure assessment are listed below. The
target species should be:
30
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• Harvested by the exposed population or be representative of the contami-
nation levels in the primary harvested species
• Relatively sedentary to be representative of a specific study area
• Easy to sample and abundant enough to obtain adequate samples
• Large enough to yield an adequate sample size for chemical analysis.
Some additional criteria for target species to be used as indicators of contaminant
concentrations in the environment are:
• Contaminant concentrations in the target organisms should be related to
those in the environment
• Metabolic regulation of contaminant concentrations by the target species
should be absent or weak
• Contaminant interactions should not greatly diminish the usefulness of
the target species as a site-specific indicator when contaminant compo-
sition is expected to differ among sites
• Target species should integrate the effects of contaminant uptake over
time.
A summary of indicator species recommended by Tetra Tech (1985b) for monitoring
of chemical residues in marine and estuarine species is shown in Figure 4. Many of
the recommended indicator species are associated with soft-sediment substrates. Contact
with sediments by such species may lead to body burdens of contaminants that are high
relative to those in pelagic organisms of similar lipid content and size. However, the
relationship of contaminant concentrations in demersal (bottom-dwelling) vs. pelagic
(open-water) organisms is difficult to predict without extensive data. As shown by
Tetra Tech (1986c), English sole may be used as an indicator of the order-of-magnitude
contaminant concentrations that would be expected in edible tissues of pelagic fish
species in Puget Sound, WA. However, relative contamination among species may vary
among bays within Puget Sound. For example, in Commencement Bay, the average PCB
concentration in muscle of English sole was about twice that in recreationally harvested
pelagic species (Pacific cod, Pacific hake, Pacific tomcod, rockfish, walleye pollock, and
white-spotted greenling; based on data from Gahler et al. 1982). In Elliott Bay, the
average PCB concentration in angler-caught English sole was about 0.4 times that in
harvested pelagic species (sablefish, squid, Pacific cod, Pacific hake, Pacific tomcod,
rock sole, and rockfish; based on data from Landolt et al. 1985). Site-specific data are
needed to evaluate contamination of potential indicator species relative to contamination
in other species of interest.
Apparently, no comprehensive review of target species for bioaccumulation studies
in lakes and streams has been conducted. However, it is clear that salmon and trout
(Salmonidae), perch (Percidae), and sunfish (Centrarchidae) species will be preferred for
tissue analysis in many cases because they constitute the bulk of the fisheries harvest.
Freshwater mussels, especially Anadonta spp. and Corbicula spp., crayfish, sculpins
31
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FISHES
~7
INVERTEBRATES
LOCATION
t/////////////////
MASSACHUSETTS/
RHODE ISLAND
NEW JERSEY/
VIRGINIA
FLDRIOMJSW _.
\
1 • 1
1
PUERTO RICOMAIMWI
Note: See Appendix Table D-1 for scientific names of recommended target species.
Reference: Tetra Tech (1985b)
"7
Figure 4 Summary of recommended marine and estuarine indicator species.
-------
(Cottidae), and catfishes (Ictaluridae) may be preferred as target species for site-speci-
fic analyses.
Size Classes--
A study design for analysis of chemical residues should incorporate stratified
random sampling of a selected size class or various size classes within each target
species. Stratification by size is extremely important, since both lipid content and
contaminant concentrations can vary greatly among different sized organisms of the
same species (Phillips 1980). Moreover, the nature of the relationship between body
size and contaminant concentration varies among chemicals, among species, and possibly
among sampling stations and seasons (Phillips 1980; Strong and Luoma 1981; Sloan et al.
1985; Johnson 1987). The size classes of each species selected for analysis should be
representative of those in the diet of the potentially exposed human population. For
persistent chlorinated organic compounds and organic mercury complexes, the largest
(i.e., oldest) individuals within an aquatic species are expected to be the most contaminated.
If organic compounds are of concern and a limited analysis is planned, the study should
focus on the largest individuals likely to be harvested by the exposed human population.
Sampling Station Locations
Two general approaches to field sampling are possible. First, the investigator can
obtain samples directly from harvesters. This approach has the advantage that the
sampled population is the population of direct interest for the exposure and risk assess-
ments. However, one drawback of this approach is the potential for contamination or
degradation of samples due to handling of the samples by the harvesters. Moreover,
the precise sampling locations may be unknown if samples are collected at dockside
from recreational or commercial fishing boats. The second approach is to obtain
samples independent of the normal harvesting efforts, allowing standard sample handling
practices to be implemented. Independent sampling also facilitates the collection of
adequate samples for stratification by organism size, habitat, or some other variable.
The remainder of this section addresses a sampling effort that is independent of normal
harvesting activities.
Sampling stations should generally be located in known harvest areas. However,
additional stations in relatively uncontaminated reference or control areas should also
be sampled. By comparing results among harvest areas and between each harvest area
and the reference station, one can establish not only the degree of spatial heterogeneity
but also the magnitude of elevation above reference of contaminant concentrations (and
corresponding health risks) at each harvest area. Because sampling depth or vertical
position on the shore may influence contaminant concentration in aquatic organisms,
reference station characteristics should be closely matched to those for the harvest areas.
Sampling stations may be located within a study area according to one of several
probability sampling designs (Figure 5). Gilbert (1987) provides a concise summary of
conditions under which each sampling design is preferred.
Simple random sampling implies that each individual organism within a species has
an equal chance of being selected for measurement and that selection of one individual
does not influence selection of others. A simple random sampling strategy is appro-
32
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SIMPLE RANDOM
SAMPLING
• '• .'•
STRATIFIED RANDOM
SAMPLING
— STRATA
PRIMARY —
UNITS
TWO-STAGE
SAMPLING
CLUSTER
SAMPLING
— CLUSTERS
SYSTEMATIC GRID
SAMPLING
e
e
•
e
•
e
e
e
e
e
e
•
RANDOM SAMPLING
WITHIN BLOCKS
Reference: Gilbert (1987)
Figure 5 General sampling station layouts for probability sampling in
two dimensions.
-------
priate if there are no major trends or patterns of contamination in the study area.
Note that sampling of fish or shellfish with sampling gear (e.g., hook and line, nets) will
always be nonrandom to some extent because of the selective nature of the gear.
Stratified random sampling involves random sampling within nonoverlapping strata
of a population (e.g., subareas based on concentrations of fishing effort). This sampling
approach is appropriate when geographic areas within a harvest region are heterogeneous
relative to the kind or degree of contamination.
Two-stage sampling involves random or systematic subsampling of primary units
selected by a random sampling technique. For example, fish could be collected randomly
from a given stream reach. In the second stage of sampling, subsamples of fillet from
each fish could be selected randomly for chemical analyses. Multistage sampling is an
extension of two-stage sampling.
Cluster sampling involves choosing groups of individual organisms at random, then
measuring contaminant concentrations in all individuals within each cluster. Cluster
sampling is sometimes used to estimate means if clusters of sampling units (e.g., individual
organisms in a clump) can be selected randomly more easily than can individual units.
Systematic sampling consists of sampling at locations and/or times according to a
pattern. For example, samples may be collected at equidistant points on a spatial grid
or at equally spaced time intervals. Systematic sampling is generally preferred for
mapping patterns of contamination. As such, it is more appropriate for soil or sediment
sampling than for bioaccumulation studies. The random-sampling-within-blocks strategy
shown in Figure 5 combines systematic and random sampling. Such procedures produce
more uniform coverage than does simple random sampling.
i
Gilbert (1987) describes systematic sampling approaches for locating "hot spots" or
highly contaminated local areas. He addresses the following questions:
• "What grid spacing is needed to hit a hot spot with specified confidence?"
• "For a given grid spacing, what is the probability of hitting a hot spot
of specified size?"
• "What is the probability that a hot spot exists when no hot spots were
found by sampling on a grid?"
If grid sampling is to be applied to a bioaccumulation study, the target species must
exhibit limited mobility. Grid sampling can also be applied to collection of aquatic
sediment samples. Gilbert (1987) provides guidance on spacing of grid samples.
Grid sampling is especially appropriate for identifying environmental contamination
associated with discrete sources of pollution such as industrial discharges, storm drains,
and combined sewer overflows. The use of caged mussels is a promising approach for
identifying sources through chemical residue analysis. As part of the Long Island
Sound Estuary Program, EPA Region I is using caged mussels to monitor chemical
contaminants entering the Sound from tributaries. The California mussel watch program
(e.g., Ladd et al. 1984), the U.S mussel watch (Goldberg et al. 1978, 1983; Farrington et
al. 1983), and the NOAA status and trends program (Boehm 1984) illustrate the use of
both resident and caged transplant mussels to monitor bioaccumulation of toxic chemicals
33
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over space and time. Toxic chemical residues in mussels are excellent indicators of point
source discharges as well as pollution gradients (Phillips 1976; Popham et al. 1980;
Phelps et al. 1981). U.S. EPA (1982) described recommended protocols for caged mussel
studies.
A combination of two-stage and stratified- random (or stratified-grid) sampling is
recommended here for most studies of fisheries contamination to support exposure
assessment. The two stages correspond to an individual organism and edible tissue.
Samples of individual organisms may or may not be composited depending on specific
study objectives (see below, Kinds of Samples, Composite Sampling). Sampling strata
may include harvest area, species, and size classes. Other sampling strategies may be
either too simple or inappropriate to meet the typical objectives of exposure assessment
studies.
Time of Sampling
The timing of bioaccumulation surveys should be based on the temporal distribution
of harvest seasons and inherent biological cycles of target species. The timing of
harvest periods depends on the availability of fishery resources, which may be influenced
by the migratory patterns and feeding cycles of target species. Biological cycles that
influence an organism's susceptibility to bioaccumulation should also be considered
when choosing a sampling period. The most important of these is the reproductive
cycle, which is discussed further below. In crustaceans (e.g., crab and shrimp), the
molting cycle also determines the potential for bioaccumulation of toxic chemicals. The
rate of uptake of contaminants by crustaceans is highest just after molting, before
hardening of the integument limits its permeability.
The reproductive cycles of aquatic organisms may exert a major influence on
tissue concentrations of many contaminants, especially organic compounds (Phillips
1980). If a worst-case analysis is desired, the target species should be sampled at a
time during the harvest period when tissue contaminant concentrations are expected to
be at their highest levels. An effort should be made to sample at or just before the
peak of reproductive ripeness, before gametes or offspring are released. At this stage
of the reproductive cycle for a given species, lipid content and concentrations of
organic contaminants in tissues should be at their highest levels. Because the time of
sampling should be tailored to the reproductive characteristics of the target species,
sampling periods may vary among species. However, once a sampling period is chosen,
it should remain constant over time if an ongoing monitoring program is planned.
An alternative approach is to sample throughout the harvest season for each
target species. In this way, representative values can be obtained for estimating means
within sampling periods and for detecting seasonal or long-term trends. In most cases,
exposure assessments will be performed over relatively short periods of time (e.g., a
year), and multiyear sampling may not be required. Within a harvest -season, however,
sufficient samples should be collected to estimate the mean concentrations of contaminants
during the harvest period. To estimate temporal variation or to obtain worst-case
estimates, replicate samples will be needed at several times within the harvest season.
The frequency of sampling should be related to the expected rate of change in tissue
concentrations of contaminants. For an extensive review of temporal changes in
bioaccumulation and body burdens of contaminants in aquatic organisms, the reader
should consult Phillips (1980).
34
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Kinds of Samples
The kind of tissue sampled and the sampling unit (i.e., individual organisms vs.
composites of several organisms) greatly influence the sensitivity, precision, and repre-
sentativeness of an exposure assessment. The issues of composite sampling and sample
preparation techniques are addressed in the following sections.
Composite Sampling--
An alternative to the analysis of tissue from individual organisms is the analysis
of composite samples. Composite tissue sampling consists of mixing tissue samples,
each called a subsample, from two or more individual organisms typically of a single
species collected at a particular site and time period. The mixture is then analyzed as
a single sample. The analysis of a composite sample therefore provides an estimate of
an average tissue concentration for the individual organisms that make up the composite
sample. Composite sampling is a cost-effective strategy when the individual chemical
analyses are expensive but the cost of collecting individual samples is relatively small.
The collection of composite samples is required in cases where the tissue mass of an
individual organism is insufficient for the analytical protocol.
Bioaccumulation surveys designed to support exposure assessments may use a
composite sampling strategy. Current risk assessment models used by EPA are based
on estimates of long-term average exposure. Estimates of the mean concentrations of
contaminants in edible tissue samples from harvested organisms are used as estimates
of the exposure concentrations for human consumers of fish and shellfish. Composite
sampling of the tissue from selected organisms is a method for preparing a sample that
will represent an average concentration. The collection of replicate composite tissue
samples at specified sampling locations will result in a more efficient estimate of the
mean (i.e., the variance of the mean obtained with replicate composite samples is
smaller than that obtained with the collection of replicate samples of individual organisms).
One major disadvantage of composite sampling is the inability to directly estimate
the range and the variance of the underlying population of individual samples. Such
information is extremely useful in bioaccumulation monitoring programs as an early warning
signal of increasing levels of contamination. For example, only a few individuals
within a sample may contain high contaminant concentrations. Mixing these individuals
with less contaminated organisms in a composite sample at a given station may dilute the
contaminants and mask a potential problem. In exposure assessment, the patchy distribution
of highly contaminated fish or shellfish may indicate the spatial distribution of sources
of contaminants. Also, a lack of data on individual samples may mask the potential for
short-term health effects (e.g., learning disabilities, neurological malfunctions) in
sensitive individuals or in those who consume excessive amounts of highly contaminated
organisms over a short period of time. In some cases, however, preliminary exposure
and risk assessment calculations could be performed to justify focusing on chronic
effects (e.g., carcinogenesis).
The benefits of compositing individual samples from a single station within a
given sampling period often outweigh the disadvantages just discussed. In such cases,
Rohde (1976) and Tetra Tech (1986b) provide a method for calculating the variance of
35
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the underlying population of individual samples when the variance of the composite
samples is known:
Var X - n (Var Z) (3)
where:
Var X «= variance of the mean of individual samples
Var Z » variance of the mean of composite samples
n - number of subsamples constituting the composite sample.
This equation assumes that replicate observations from individual and composite samples
are normally distributed. Also, the composites must each consist of subsamples of
equal mass (i.e., the same mass of tissue is taken from each organism). For unequal
proportions of composite subsamples (i.e., tissue mass), the variance of the series of
composite samples increases and, in extreme cases, exceeds the variance of grab samples.
Thus, it is recommended here that the same mass of tissue be taken from each organism
contributing to a composite sample of a single species. For the analyses presented
below, it was assumed that the composite samples consist of subsamples of equal proportions.
Two special cases of composite sampling are "space-bulking" and "time-bulking"
(cf. Phillips and Segar 19S6). Space-bulking involves sampling of individual organisms
from several locations and combining tissue samples into one or more composite samples
for analysis. Time-bulking involves taking multiple samples over time from a single
location and compositing these samples. Time-bulking over a harvest season is especially
appropriate where short-term variations in contaminant concentrations in tissue samples
are significant and budget constraints preclude repeated analyses over time.
The adoption of space-bulking or time-bulking strategies ultimately relates to the
objectives of the exposure assessment. Because exposure concentrations are typically
averaged over time in risk assessment models, time-bulking may be more justified than
space-bulking. In any case, one should use these strategies with extreme caution since
significant information on spatial and temporal heterogeneity may be lost. Selection of
space-bulking or time-bulking techniques should be supported by analyses of available
data or data from preliminary sampling. Tiered analyses of samples can also be used
to evaluate the appropriateness of compositing strategies. For example, individual
samples may be stored separately over the entire harvest season. At the end of sample
collection, preliminary analyses of individual tissue samples from a selected series of
sites and times could be performed to evaluate temporal and spatial heterogeneity and
to devise an appropriate compositing strategy.
Tetra Tech (1986b) evaluated the effects of composite sampling on the statistical
power of a sampling design (see Appendix D). Their results demonstrate that the
confidence in the estimate of the mean concentration of contaminant in tissue increases
as the number of individual samples in the composite increases. The statistical power
(i.e., the probability of detecting a specified minimum difference among treatments)
increases dramatically with the number of individual samples in each replicate composite
sample. However, the benefit of adding more individual samples to each composite
eventually decreases with each successive increase in the number of individual samples
per composite. For moderate levels of variability in chemical residue data, 6-10 individual
samples within each of 5 replicate composite samples may be adequate to detect a
treatment difference equal to 100 percent of the overall mean among treatments.
36
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Rohde (1976), Schaeffer et al. (1980), Brumelle et al. (1984), and Gilbert (1987) also
discuss statistical aspects of composite sampling.
Sample Preparation--
Tissue samples should be removed from target organisms and prepared for analysis
according to a well-defined protocol. Tissue preparation methods can greatly affect
the results of bioaccumulation analyses (Smith et al. 1973; Skea et al. 1981; Puffer and
Gossett 1983; Landolt et al. 1987). In specifying a tissue preparation protocol, the
following issues should be addressed:
• Type of tissue (e.g., muscle fillet, whole body, internal organs)
• Location of tissue in organisms' body
• Removal of any or all of shells, scales, skin, and subcutaneous fat
• Raw vs. cooked samples and cooking method
• Homogenization method
• Minimum sample mass for each kind of analysis.
The kind and location of tissue analyzed may influence the realism of the exposure
assessment. For example, most humans consume only fillets of fish, not internal organs
or whole fish. Because internal organs are often more contaminated by toxic chemicals
than are fillets, exposure estimates based on chemical analyses of organs or whole fish
could be unrealistically high. Removal of skin and subcutaneous fat from samples
before chemical analysis generally reduces the mean concentrations of chlorinated
organic compounds. However, this practice may also reduce the variance of measure-
ments, allowing more sensitive discrimination among statistical treatments (e.g., species
or sampling locations). Even within the fillet tissue, contaminant concentrations may
vary depending on the original location of the sample on the fish's body. Cooking of
fillets before chemical analysis may result in a 2-64 percent decrease in the concentra-
tion of PCBs relative to the uncooked sample, but the results vary greatly with species
and cooking method (Smith et al. 1973; Skea et al. 1981). However, cooking may also
activate or transform chemicals to create carcinogens (e.g., creation of benzo(a)pyrene
during char-broiling). Finally, adequate homogenization of samples before they are
analyzed is necessary to obtain representative results.
Because information on the effects of tissue preparation methods on the results of
chemical residue analyses is limited, it is recommended that a pilot survey be performed
to establish consistent, reliable methods. Relevant protocols for sample storage and
preparation are available in a bioaccumulation monitoring guidance document issued by
the EPA 301(h) program (Tetra Tech 1986e) and in the EPA Interim Methods for the
Sampling and Analysis of Priority Pollutants in Sediments and Fish Tissue (U.S. EPA
1981). Because many decisions about sample preparation depend on the specific objectives
of the study, no single protocol for sample preparation covers all of the possible
approaches. For example, samples are usually blotted dry before being weighed to
obtain an estimate of wet weight. However, when bivalve molluscs are being prepared
for analysis, it may be desirable to retain excess water for later analysis.
37
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Analysis of raw edible tissues is recommended to provide data on the concentra-
tions of contaminants initially present in tissues that are normally consumed. Eventually,
it may be possible to mathematically account for cooking effects in the exposure
assessment. At present, however, data on cooking effects are highly variable.
Sample Replication
Replicated measurements of contaminant concentrations in tissue samples are
needed to perform uncertainty analysis (e.g., characterizing the precision of the estimates
of mean contaminant concentrations). Replicated data are also needed for many statistical
tests of spatial and temporal trends. Sample replication is recommended here for all
bioaccumulation measurements to be used in exposure assessments. Guidance on selection
of a sample replication scheme is provided in Appendix £. In most cases, at least five
replicate samples of individual fish (or shellfish) are required to provide minimal statistical
power (e.g., ability to discriminate a treatment difference equal to 200 percent of the
overall mean among treatments). Increases in sample replication beyond about 10
individual replicates clearly do not provide sufficient benefits in statistical power to
justify added costs of sampling and analysis (Appendix E). Greater power can be
achieved in a cost-effective manner by composite sampling if information on contamination
of individual organisms is not needed (Appendix D).
Selection of Analytical Detection Limits and Protocols
Criteria for selection of method detection limits for analytical protocols may be
based on risk assessment models explained below (see Risk Characterization). For
example, the analytical chemistry methods may be chosen to enable detection of a chemical
concentration associated with a specified minimum risk level defined as acceptable by
risk managers. Other factors may dictate choice of a lower detection limit. For
example, routine analytical methods may attain much lower limits than required by the
specified minimum detectable risk level. Also, lower detection limits may be desired if
an objective of the study is to develop baseline bioaccumulation data as well as health
risk data. In some cases (e.g., 2,3,7,8-tetrachlorodibenzo-p-dioxin, benzidine, dieldrin,
N-nitrosodimethylamine), the minimum detection limit that can be achieved with current
technologies corresponds to a plausible-upper-limit risk that is substantially above risk
levels of potential concern (e.g., 10"6 to 10"6). Tetra Tech (1985c) provides further
guidance on detection limits for bioaccumulation surveys.
Approved routine EPA methods for sampling and full-scan analysis of chemical
contaminants in tissues are not available. U.S. EPA (1981) published interim methods
for sampling and analysis of priority pollutants in tissues. EPA-approved protocols for
chemical analysis of water samples were adapted for application to tissue samples as
part of the Section 301(h) marine discharge waiver program of the Office of Marine
and Estuarine Protection [see Tetra Tech 1986e for 301(h) sampling and analysis protocols}.
Specifically, 301(h) analytical methods for extractable organic compounds were adapted
from Method 1625 Revision B (U.S. EPA 1984a) and additional guidance from the EPA
Contract Laboratory Program for Organic Analysis (U.S. EPA 1984c). When applicable,
the 301(h) protocols incorporate established EPA advisory limits for precision, accuracy,
and method performance (U.S. EPA 1984c). The EPA Office of Acid Deposition, Environ-
38
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mental Monitoring, and Quality Assurance is developing further guidance on sampling
and analysis methods to support exposure assessments.
Other available methods for analysis of chemical contaminants in tissue samples
include those used by U.S. FDA (1978), NOAA (MacLeod et al. 1984), and Ozretich and
Schroeder (1985). These analytical protocols are designed to apply to specific subsets
of the EPA priority pollutants. U.S. FDA (1978) methods, as described in the Pesticide
Analysis Manual, include variations in procedures for tissues differing in lipid content.
The choice of an analytical protocol may be influenced by available financial
resources. Chemical analysis of samples is often the most costly portion of a sampling
and analysis program. Higher analytical costs may be required to achieve greater
sensitivity (i.e., lower detection limits). Examples of analytical costs are shown in
Table 5. At a given level of sensitivity, a wide range of precision is encountered among
diverse organic compounds. For example, the low end of the range of variation shown
for extractable compounds in Table 5 can usually be achieved for hydrocarbon analyses,
whereas substantially more variability is common for analyses of phthalates and some
organic acid compounds. A wide range of analytical costs is also encountered at a
given level of sensitivity (Table 5). Differences in analytical techniques, laboratory
experience with these techniques, and pricing policies of laboratories account largely
for the wide variation in cost.
QA/OC Program
An adequate QA/QC program is essential for any sampling and analysis effort to
support exposure assessment. U.S. EPA (1984c, 1985c) provides guidance on QA/QC for
chemical analysis. Tetra Tech (1986f) describes QA/QC procedures for field and laboratory
methods used by the Section 301(h) program. Horwitz et al. (1980) provide guidance on
QA/QC in the analysis of foods for trace contaminants. Brown et al. (1985) describe
QA guidelines followed by NOAA for chemical analysis of aquatic environmental samples.
A QA/QC plan should be developed as part of the study design for sampling and
analysis of chemical residues. The QA/QC plan should include the following information:
• Project objectives
• Project organization and personnel
• QA objectives for precision, accuracy, and completeness for each kind
of measurement
• Summary of sampling procedures, including sample containers, prepara-
tion, and preservation
• Forms for documenting sample custody, station locations, sample charac-
teristics, sample analysis request, and sample tracking during laboratory
analysis
• Detailed description of analytical methods
• Calibration procedures for chemical measurements
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TABLE 5. APPROXIMATE RANGE OF COST PER SAMPLE FOR
ANALYSES OF EPA PRIORITY POLLUTANTS IN TISSUES
AS A FUNCTION OF DETECTION LIMITS AND PRECISION'
EPA Priority
Pollutant
Group
Approximate
Detection
Limit
Typical
Precision
Approximate
Cost Rangeb
Extractable
acid/base/neutrals/
PCBs/pesticides <1-20 ppb
Volatiles <5-20 ppb
Metals 100 ppb
<±5% - >±100% $900->$2,000
<±IO% - >±100% $250-5350
<±10% - >±30% $250-$300
* NOTE: Range of per sample cost is based on multiple quotes compiled in
1986 for specific applications and >5 samples. The actual costs may vary from
the range shown. This information is provided solely for perspective on relative
differences in cost and should not be interpreted as a recommendation of appropriate
costs for any given circumstance.
b Each cost range is mainly the result of laboratory differences in technique
and pricing, NOT the range in precision or detection limits shown.
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• Internal QC checks for analytical laboratories
• Performance and system audits for sampling and analysis operations
• Preventive maintenance for equipment
• Procedures for data management, data QA review, and data reporting
for each kind of measurement
• Corrective actions
• Procedures for QA/QC reporting and responsible federal and state QA
officers
• Mechanisms for approval of alterations to the monitoring program, for
suspending sample analyses, and for stopping sample analyses within a
tiered design.
Relevant portions of the QA plan should be incorporated in the statement of work for
each contract laboratory involved in sample analyses.
Documentation and OA Review of Chemical Data
Adequate documentation of the results of chemical analyses are needed to ensure
proper interpretation of the data. If a contract laboratory is performing the sample
analyses, such documentation should be specified in the original statement of work.
The documentation listed below is recommended for chemical residue data:
• A cover letter discussing analytical problems (if any) and referencing or
describing the procedure used
• Reconstructed ion chromatograms for GC/MS analyses for each sample
• Mass spectra of detected target compounds (GC/MS) for each sample
• GC/ECD and/or GC/FID chromatograms for each sample
• Raw data quantification reports for each sample
• A calibration data summary reporting calibration range used (and DFTPP
and BFB spectra and quantification report for GC/MS analyses)
• Final dilution volumes, sample size, wet-to-dry ratios, and instrument
detection limit
• Analyte concentrations with reporting units identified (to two significant
figures unless otherwise justified)
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• Quantification of all analytes in method blanks (ng/sample)
• Method blanks associated with each sample
• Tentatively identified compounds (if requested) and methods of quantification
(include spectra)
• Recovery assessments and a replicate sample summary (laboratories
should report all surrogate spike recovery data for each sample; a
statement of the range of recoveries should be included in reports
using these data)
• Data qualification codes and their definitions.
The data reporting forms for the EPA Contract Laboratory Program illustrate an appropriate
format for documentation of chemical data.
All contaminant concentration data to be used in a risk assessment should undergo
a thorough QA review by a qualified chemist independent of the laboratory that analyzed
the samples. In some cases, the analytical laboratory may provide a QA review that is
simply checked by an independent chemist. The purpose of the QA review is to evaluate
the data relative to data quality objectives (e.g., precision and accuracy) and performance
limits established in the QA plan. In many cases, qualifiers are necessary for selected
data values. These qualifiers should be added to the database. A summary of data
limitations should always be included in the risk characterization (see below, Risk
Characterization). The EPA Office of Acid Deposition, Environmental Monitoring, and
Quality Assurance is developing guidelines for quality assurance of chemical data to support
exposure assessments.
Statistical Treatment of Data
Statistical analyses of data will depend on specific study objectives. For each
species, statistical summaries of tissue concentration data should include sample size,
estimates of arithmetic mean concentration, range, and a measure of variance (standard
error or 95 percent confidence limits). Geometric mean concentrations are appropriate
measures of central tendency when only estimates of tissue burden of contaminants or
exposure dose are desired. However, arithmetic means are needed to compare exposure
estimates with RfDs and to calculate health risk for chronic effects because long-term
consumption is an averaging process. Mean tissue concentrations and variances may be
calculated for mixed-species diets if data are available on the proportion of each
species in the diet.
The one-way ANOVA model discussed earlier or multifactor ANOVA models are
appropriate for testing for differences in mean contaminant concentrations among
species, among sampling stations, or among time periods (Schmitt 1981; also see Tetra
Tech 1986b,d). For small sample sizes and data that do not satisfy the assumptions of
ANOVA, nonparametric tests such as the Wilcoxon rank sum test for two treatments or
the Kruskal-Wallis test for multiple comparisons are recommended. These tests have
the added advantage of being relatively insensitive to a few missing data points or
undetected observations (Gilbert 1987). Long-term data sets may be tested for trends
by time series analysis (for reviews, see Montgomery and Reckhow 1985 and Gilbert
41
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1987). Examples of trend analysis for chemical contaminants in fish are provided by
Brown et al. (1985) for PCBs in striped bass of the Hudson River and by DeVault et al.
(1986) for PCBs and DDT in lake trout from the upper Great Lakes.
Data on concentrations of contaminants of concern in tissue samples will often
contain observations below detection limits. Means and variances for tissue concentra-
tions should be calculated twice: once using detection limits for undetected observa-
tions and once using 0 for undetected observations. Although alternative approaches
are possible (e.g., using one-half the detection limit), the approach recommended here yields
more accurate, complete results by quantifying the range of the estimated values.
According to the EPA Exposure Assessment Group, calculations of plausible-upper-limit
risk estimates based on detection limits should generally be avoided. However, risk
estimates based on detection limits may occasionally be useful to indicate that particular
chemicals, species, or geographic locations are not problems, even assuming undetected
contaminants are present at concentrations just below their respective detection limits.
The choice of contaminant concentration values to use in subsequent calculations
to estimate exposure (and ultimately risk) is partly a risk management decision. Exposure
estimates are commonly based on arithmetic mean concentrations of contaminants in
edible tissue of fish or shellfish. Use of the upper 90 or 95 percent confidence limit
in place of the mean would provide a conservatively high estimate of exposure. Calculation
of conservative estimates for exposure is an appropriate step in uncertainty analysis.
However, U.S. EPA (1986b) guidelines on exposure assessment discourage the use of
worst-case assessments. Use of upper confidence limits for chemical concentrations in
combination with a plausible-upper-limit estimate for the Carcinogenic Potency Factor
may lead to an unrealistic (i.e., highly unlikely) estimate of upper-bound risk, especially
if a conservatively high estimate of fish consumption is also adopted. In most cases,
the best estimate of exposure based on mean contaminant concentrations should be
used to develop risk estimates. If upper confidence limits for chemical concentrations
are used to develop risk estimates, the effects of compounding conservative assumptions
should be evaluated.
ANALYSIS OF SOURCES, TRANSPORT, AND FATE OF CONTAMINANTS
Exposure pathways and routes are potential mechanisms for transfer of contaminants
from a source to a target human population or subpopulation. The sources, transport,
and fate of chemicals in the environment are analyzed to evaluate exposure pathways
and routes. To compensate for a limited database, this analysis often includes mathematical
modeling of contaminant transport and fate. The modeling of exposure pathways
focuses on transfer of contaminants from source to target fishery species, since the
transfer step from fishery to humans can be based on knowledge of fishery harvest
activities (see below. Exposed Population Analysis). When extensive data on contamination
of a fishery is available and source-tracing is not an objective, modeling of chemical
transport and fate may be unnecessary.
Although the specific uses of modeling in exposure assessment are diverse, several
broad objectives may be outlined as follows:
42
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• Estimate the spatial and temporal distribution of concentrations of
chemical contaminants in edible tissues of fish and shellfish
• Identify potential sources of contaminants
• Evaluate alternative source controls or remedial actions.
Estimation of contaminant concentrations in fish and shellfish by mathematical modeling
is especially useful when available data on tissue contaminants are limited. If the
distribution of contaminants in sediments or water can be estimated from available data
or model predictions, estimates of chemical residues in fishery species can be based on
relationships of tissue contamination to environmental contamination (e.g., laboratory-
derived bioconcentration factors). Spatial characterization is important for identifying
areas of high contamination resulting from heterogeneous transport and deposition of
contaminants. Temporal characterization is important for defining time-dependent
changes in contaminant concentrations that may mitigate future exposure and risk.
Predictions of spatial trends in chemical residues may also aid in identifying and
controlling sources of pollutants. For example, when data on sources, sediments, and
tissues are available, modeling of chemical transport and transformation processes may
help to link the patterns of chemical contaminants observed in the environment with
specific individual sources. Information on differential degradation of contaminants and
compositional relationships for complex mixtures can be used to support the model
analysis (e.g., calibration and validation). Finally, modeling of contaminant releases in
combination with chemical residues in fisheries may aid in evaluating alternative source
controls or remedial actions for waste sites. The results of modeling can indicate the
level of source control or remedial action needed to achieve a desired level of environmental
quality.
In the exposure assessment guidelines, U.S. EPA (1986b) describes general approaches
for characterizing sources, exposure pathways, and environmental fate of chemicals.
Analysis of chemical transport and fate is a major endeavor, which cannot be addressed
in detail here. For additional information, the interested reader should consult Callahan
et al. (1979), Burns et al. (1981), Jensen et al. (1982), Mills et al. (1983), Games (1983),
Connor (1984b), Thomann and Connolly (1984), Onishi (1985a,b), U.S. EPA (1986b),
Pastorok (1986), and references therein.
EXPOSED POPULATION ANALYSIS
The second stage of the exposure assessment, analysis of exposed populations,
includes the following steps:
• Identify potentially exposed human populations and map locations of
fisheries harvest areas
• Characterize potentially exposed populations
- Subpopulations by age, sex, and ethnic composition
- Population abundance by subpopulation
43
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• Analyze population activities
- Harvest trip frequency
- Seasonal and diel patterns of harvest trips
- Time per harvest trip
- General activity (e.g., clamming, crabbing, fishing)
• Analyze catch/consumption patterns by total exposed population and
subpopulation
- Proportion of successful trips
- Catch by numbers and weight according to species
- Time since last meal of locally harvested organisms
- Number of consumers sharing catch
- Parts of organisms eaten
- Method of food preparation (e.g., raw, broiled, baked)
• Estimate arithmetic average consumption rate by species and by total
catch for the total exposed population and for subpopulations. For
seasonal fisheries, consumption rates may be estimated on an annual
and a seasonal basis.
Only selected steps may be performed in a given exposure assessment, depending
on data availability, study objectives, and funding limitations. Note that many of the
steps to characterize harvest activities and consumption rates apply only to analyses of
recreational fisheries. When estimating consumption of fish and shellfish of commercial
origin, harvest activities are irrelevant. Also, the specific geographic origin of commercial
fisheries products is often unknown.
Two approaches to estimating consumption rates are outlined below. In the first
approach, a comprehensive analysis of a recreational fishery is performed based on extensive
catch/consumption data for the exposed population. In the second approach, estimates
of consumption rates are based on available values for the U.S. population (or subpopu-
lations) or other assumed values. Most of the available estimates were derived from
recall or diary studies (Lindsey 1986) and include commercial fisheries products. It is
recommended here that local or regional assessments of fishery consumption be performed
whenever possible to avoid possible errors inherent in extrapolating standard values for
the U.S. population to distinct subpopulations. Moreover, extrapolation of standard
consumption estimates that include commercial fisheries products to recreational fisheries
should generally be avoided.
In developing a profile of the exposed population, there is no single "correct"
estimate of consumption rate. Because consumption rates are highly variable, use of a
range of values or a probability distribution for consumption rate estimates is recommended.
This approach may also be followed when estimating consumption rates for subpopulations
of interest.
An alternative to the typical practice of basing risk estimates on selected consumption
rates involves presenting risk estimates graphically for a wide range of consumption
rates that essentially includes all possible realistic values (see below, Presentation and
Interpretation of Results). For example, plots of estimated risk vs. consumption rate
are useful for public advisories on recreational fishery resources. In this case, each
44
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individual may evaluate risks by selecting a consumption value based on his or her diet.
Use of this approach avoids having to collect extensive data on the exposed population.
A similar approach involves selecting an "acceptable" risk level and providing advice on
levels of consumption, such that the "acceptable" risk is not exceeded. The advantage
of both of these approaches is that consumption rates need not be determined or
assumed. While both may provide excellent formats for advising sports fishermen, they
may not be appropriate in cases where involuntary exposures are likely (e.g., commercial
fisheries).
Comprehensive Catch/Consumption Analysis
Appropriate field survey forms, data analyses, and format for presentation of
results for a comprehensive catch/consumption analysis of fisheries are provided by
Landolt et al. (1985), McCallum (1985), and National Marine Fisheries Service (1986).
Details of methods will not be presented here, except to emphasize some important
considerations for calculating consumption rates. Examples analyses of catch/consump-
tion data can be found in Puffer et al. (1982) for coastal waters of southern California,
in Landolt et al. 1985, 1987) for Puget Sound, in Helton et al. (1986) for New York Bay
and Newark Bay, and in National Marine Fisheries Service (1986) and companion documents
for other areas of the U.S.
Lindsay (1986) reviewed alternatives to field survey methods, including use of food
diaries and dietary recall. Gartrell et al. (1986a,b) described methods used by FDA in
their total diet studies to estimate rates of consumption of various foods. However,
the results of the FDA total diet studies are of limited use in the present context
because fish are grouped with meat and poultry. Estimates of seafood consumption
used by FDA to calculate average intake of methylmercury for exposed portions of the
U.S. population were based on a diary survey sponsored by the Tuna Research Foundation
(Tollefson and Cordle 1986). Supplementary information on analysis of fisheries consumption
data can be found in SRI (1980).
The average rate of consumption of fish or shellfish is the key exposure variable
for use in subsequent steps of risk assessment. Consumption rates should be expressed
in terms of g/day and meals/yr [meals/yr may be calculated from g/day by assuming an
average meal of fish or shellfish equals about 150 g (0.33 Ib) if the average meal size
is unknown]. Average consumption rate for each harvest species is calculated from
field data according to the following steps:
• For each successful angler trip, calculate the weight of harvest by
species based on number and total weight harvested per household
• Calculate mean harvest weight consumed per person per time by
- Dividing the total harvest weight for each species by the .number of
consumers in household and by the days elapsed since last meal from
the same area
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- Multiplying the value obtained in the preceding computation by a
factor to account for the proportion of cleaned weight to total
weight [according to Landolt et al. (1985), this factor equals about 0.5
for squid and crabs, 0.3 for fish, and 1.0 for shucked clams; these
estimates should be verified or replaced by local data]
• Calculate mean consumption rate per person by geographic harvest area,
by subpopulation, and by total exposed population.
Note that the above method (cf. Landolt et al. 1985, 1987) may provide a biased estimate
of average consumption rate due to its dependence on a short-term observation (i.e.,
time since last meal). Averaging of data over a longer time period might be preferable,
but such data may be more susceptible to biases from inaccurate recall of consumers
(interviewees). Harvest weights should generally be determined directly rather than
from length measurements. However, for shellfish and crabs, it may be necessary to
establish tissue weights from weight-length regression analysis.
The model for calculating mean daily consumption rate (Ijjk) for fishery species i,
human subpopulation j, and area k is therefore:
wijkl Pi
(4)
*jkl Tjkl
where:
I,jkl = Mean daily consumption rate of species i for subpopulation j, area k, and
household 1 (kg/day)
N;jk = Number of households (successful harvest trips) for species i, subpopula-
tion j, and area k
wijki " Weight of species i harvested by household 1 of subpopulation j in area k
(kg)
Pi = Proportion of cleaned edible weight of species i to total harvested weight
Hjkl « Number of people in household 1 of subpopulation j in area k
Tjkl = Time elapsed since last meal by household 1 of subpopulation j in area k
(days).
When consumption rates (lyy) are log-normally distributed, a geometric mean consumption
rate may be calculated by log-transforming the data before applying Equation 4 to
calculate a mean consumption rate.
Consumption rate data may be summarized further by calculating means across
species, subpopulations, and areas. However, it should be recognized that means of Iijk
across species do not represent actual diet patterns for consumers of mixed -species
diets. To calculate mean consumption rates for mixed-species diets, all Ijjkl should be
summed across species within a household before determining mean consumption rates
across households (Ijk):
/o
(5)
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Where:
Ijk] - Mean daily consumption rate of all fishery species for household 1, subpopu-
lation j, and area k (kg/day)
Njk » Number of households in subpopulation j and area k
and other terms are defined above.
Landolt et al, (1985) summarized the assumptions involved in calculating mean
consumption rates (Ijjkl) by household as follows:
• Consumption
PI values are assumed as noted above
Catch was distributed evenly among consumers in house- hold
People in household actually ate the entire cleaned catch
Personal harvest consumption was distributed evenly over the time
interval since the last successful trip
• Fishing interval
Fishing frequency (days) is related to seasonal fisheries; that is,
interviewees did not report average time interval for entire year
but only for recent past. Therefore, calculated consumption rates
cannot be directly extrapolated to a yearly basis. Fishing interval
was set to 1 day if unreported (Landolt et al. 1985).
Despite the limitation noted in the last item above, calculated consumption rates
can be extrapolated to an annual average rate by multiplying the Ijjkl by 365 days and
by a species-specific factor equal to the fraction of the year a fishery is available.
Determination of this species-specific factor is somewhat subjective because of large
seasonal fluctuations of the harvest (e.g., Appendix £ of Landolt et al. 1985). These
factors should be determined on a case-specific basis.
Assumed Consumption Rate
In many cases, comprehensive data on fisheries catch and consumption patterns
are not available. For some risk assessment problems (e.g., ranking of potential problem
chemicals in aquatic organisms or development of consumption advisories) extensive
catch/consumption data are not needed. Moreover, catch/consumption patterns undoubtedly
vary over time. Extensive long-term monitoring of catch/consumption for all areas of
interest within a large water body may not be warranted. Despite its obvious limitations,
extrapolating consumption data from one area (or time) to another may be a suitable
approach when:
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• Site-specific data are unavailable
• Differences among areas (or times) are expected to be small
• Precise estimation of average fish or shellfish consumption is unneces-
sary to meet the study objectives.
In the past, many risk analysts have simply assumed standard values for food
consumption rates based on previous analyses of dietary patterns of the U.S. population
(U.S. EPA 1980b; SRI 1980). Average values for fish and shellfish consumption for the
U.S. population generally range from 6.5 to 20.4 g/day (Nash 1971; National Marine
Fisheries Service 1976, 1984; SRI 1980; U.S. Department of Agriculture 1984; also see
Appendix F). Most estimates include fish and shellfish (molluscs, crustaceans) in
marine, estuarine, and fresh waters, but saltwater species form the bulk of consumed
items. Most estimates also include commercially harvested fisheries products. Also,
estimates of average U.S. consumption do not account for subpopulations in areas such
as the Great Lakes that consume large quantities (>20 g/day) of locally caught sport fish.
An estimate of 6.5 g/day for consumption of commercially and recreationally
harvested fish and shellfish from estuarine and fresh waters was used by U.S. EPA
(1980b) to develop water quality criteria based on human health guidelines. The value
of 6.5 g/day is an average per-capita consumption rate for the U.S. population, including
nonconsumers, based on data in SRI (1980). Consumption rates for portions of the
U.S. population (e.g., by region, age, race, and sex) show that average consumption of
fisheries organisms may vary from about 6 to 100 g/day (e.g., Suta 1978; SRI 1980;
Puffer et al. 1982). Finch (1973) determined that approximately 0.1 percent (i.e., the 99.9th
percentile) of the U.S. population consumes 165 g/day of commercially harvested fish
and shellfish. Pao et al. (1982) provided estimates of 48 g/day for the average and 128
g/day for the 95th percentile consumption rates by U.S. consumers of fish and shellfish.
Rupp (1980) presented estimates of average daily consumption of freshwater fish,
saltwater fish, and all shellfish according to age group within the U.S. population. SRI
(1980) presents average and 95th percentile rates of consumption of all fish and shellfish
according to age group, race, region and other demographic variables. Estimates of
food consumption rates for specific subpopulations in the U.S. are also available from a
database maintained by the EPA Office of Pesticide Programs (see Appendix F). Limita-
tions of fisheries consumption data are discussed by SRI (1980) and Landolt et al. (1985).
One or more of the following values of average consumption rate may be assumed
when site-specific data are unavailable:
• 6.5 g/day to represent an estimate of average consumption of fish and
shellfish from estuarine and fresh waters by the U.S. population (U.S. EPA
1980b)
• 20 g/day to represent an estimate of the average consumption of fish
and shellfish from marine, estuarine, and fresh waters by the U.S.
population [U.S. Department of Agriculture (USDA) 1984]
• 165 g/day to represent average consumption of fish and shellfish from
marine, estuarine, and fresh waters by the 99.9th percentile of the
U.S. population (Finch 1973)
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• 180 g/day to represent a "reasonable worst case" based on the assumption
that some individuals would consume fish at a rate equal to the combined
consumption of red meat, poultry, fish, and shellfish in the U. S. (EPA
Risk Assessment Council assumption based on data from the USDA
Nationwide Food Consumption Survey of 1977-1978; see Appendix F).
Extrapolation of these values to local populations and recreational fisheries should
generally be avoided. Limited estimates of average consumption rates for recreational
fisheries are given in SRI (1980). Whenever possible, data on local consumption patterns
should be collected or obtained from a current database. Alternatively, risk estimates
may be expressed on a unit consumption basis (i.e., per unit weight of fish/shellfish
consumed). This latter approach is used by some states in issuing sportfishing advisories.
If average consumption values listed above are assumed for local risk assessment, it is
recommended that a range of values be used. The references cited earlier should be
consulted for consumption rate data for fish and shellfish separately, or for individual
species (also see references cited in Appendix F).
EXPOSURE DOSE DETERMINATION
In the next step of the exposure analysis, information on estimated contaminant
concentrations and rate of consumption of fish and shellfish are combined to estimate
chemical intake by exposed humans. Analyses of single-species diets and mixed-species
diets are discussed separately in the following sections.
Single-species Diets
The general model to calculate chemical intake for a single-species diet is:
Qkm *ijk Xm
Eijkm " y/ ^
where:
Eijkm * Elective ingested dose of chemical m from fishery species i for human
subpopulation j in area k (mg kg'1 day"1 averaged over a 70-yr lifetime)
Cikm « Concentration of chemical m in edible portion of species i in area k (mg/kg)
Ijjk « Mean daily consumption rate of species i by subpopulation j in area k
(kg/day averaged over 70-yr lifetime)
Xm * Relative absorption coefficient, or the ratio of human absorption efficiency
to test-animal absorption efficiency for chemical m (dimensionless).
W » Average human weight (kg).
Values of subscripted terms above may be estimated means or uncertainty interval
bounds (e.g., 95 percent confidence intervals) depending on the exposure scenario being
modeled (e.g., worst case vs. average case vs. lower-limit case). Note that Egkm is
analogous to the dose "d" in Equations 1 and 2. The term "effective" ingested dose
(Ejjkm) is introduced to emphasize that estimates of chemical intake (i.e., ingested dose)
may be modified by the term Xm to account for differential absorption of contaminants
by humans and bioassay animals.
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Absorption coefficients (Xm) are assumed equal to 1.0 unless data for absorbed
dose in animal bioassays used to determine toxicological indices (carcinogenic potency
or RfD) are available and the human absorption coefficient differs from that of the
animal used in the bioassay. Assuming that Xm is equal to 1.0 is equivalent to assuming
that the human absorption efficiency is equal to that of the animal used in the bioassay.
In the absence of data to the contrary, this is appropriate. Toxicological indices are
determined from bioassays that usually measure administered (ingested) dose. Therefore,
the estimated chemical intake by humans, Ej:km, is usually the ingested dose, not the
absorbed dose. If the toxicological index used to estimate risk is based on the absorbed
dose, then an estimate of human absorption efficiency for the chemical of concern may
take the place of the term Xm in Equation 6 above. In most cases, however, information
or assumptions about absorption efficiencies has been incorporated into EPA's estimates
of RfDs and Carcinogenic Potency Factors. Therefore, Xm is usually dropped from
Equation 6 and Eljkm becomes simply the ingested dose.
W is usually assumed to be 70 kg for the "reference man" (U.S. EPA 1980b).
Assuming other average values to account for growth from a child's body weight to
adult weight over a lifetime would not change the results of carcinogen risk assessment
substantially. Concerns about exposures over a time period of less than about IS yr
may require modeling of early childhood exposures. Standard values for age-specific
body weight and other factors used in exposure assessment are provided by Anderson
et al. (1985).
Mixed-species Diets
Estimation of chemical exposure due to a mixed-species diet is complicated by
variation in the dietary habits of individuals. The various diets of individual humans
may differ from one another in the kinds and relative proportions of fishery species
consumed. The sum of species-specific exposures (Ejjkm) is not equivalent to total
exposure for a mixed-species diet. In a diverse fishery, each individual consumer is
likely to consume only a subset of the total available species. Thus, the sum of
species-specific exposures might overestimate the average consumption rate for mixed-
species diets.
To estimate average chemical exposure resulting from a mixed-species diet, an
exposure dose should first be estimated for each individual in a subpopulation as follows:
EMkro
«j.»
where:
Ehjkm * Effective exposure dose of chemical m from a mixed-species diet eaten by
individual human h in subpopulation j in area k (mg kg'1 day'1 averaged
over a 70-yr lifetime)
Ihijk • Average consumption rate of species i by individual h in subpopulation j
in area k (kg/day averaged over a 70-yr lifetime)
and other terms are defined as above. The average exposure dose for mixed species
diets is:
50
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where:
Ejkm * Average effective exposure dose of chemical m from mixed-species diet
for subpopulation j in area k (mg kg"1 day1)
Hjk - Number of persons in subpopulation j in area k.
Uncertainty estimates can be obtained by calculating 95 percent confidence limits for E:km.
SOURCES OF INFORMATION
References to protocols for sampling and analysis of toxic chemical residues in
fish and shellfish are provided above (see Tissue Concentrations of Contaminants). For
the updated status of protocols and new developments, contact a representative of the
EPA Office of Water (Appendix A) or one of the EPA Office of Research and Development
Laboratories (Appendix G). Information on sampling and analysis of commercial fisheries
products collected from the marketplace is available in FDA Compliance Program Guidance
Manuals (available from FDA, Freedom of Information (HFI-35), 5600 Fishers Lane,
Rockville, MD 20857).
Compilations of data on concentrations of chemical contaminants in fish and
shellfish are available in the EPA Ocean Data Evaluation System (ODES), reports of the
NOAA Status and Trends Program (e.g., Matta et al. 1986), Tetra Tech (1985b), and
Capuzzo et al. (1987). For current local information, contact a member of the EPA
Regional Network for Risk Assessment/Risk Management Issues (Appendix H). Many
state health and environmental agencies maintain regional databases on chemical residues
in fish and shellfish. For example, the New York State Department of Environmental
Conservation and the New Jersey Department of Environmental Protection publish
periodic reports on contaminants levels in fish (e.g., Armstrong and Sloan 1980; Belton
et al. 1983; Sloan and Horn 1986). The Wisconsin Department of Natural Resources
(Bureau of Water Quality) maintains computerized records of long-term data on PCB
concentrations in fish of the Great Lakes.
Summaries of data on contaminant concentrations in a variety of foods are available
in Grasso and O'Hare (1976), Lo and Sandi (1978), Stich (1982), U.S. FDA (1982), and
Vaessen et al. (1984). FDA is developing a data system called FOODCONTAM for pesticide
and industrial contaminant residues in foods.
References containing estimates of the rates of consumption of fish and shellfish
by the U.S. population were presented above (see Assumed Consumption Rate). The
EPA Office of Pesticide Programs maintains the Tolerance Assessment System (Saunders
and Petersen 1987). The Tolerance Assessment System uses a U.S. Department of
Agriculture database (based on a 1977-1978 survey) to generate estimates of consumption
of various foods stratified by specific subpopulations (e.g., infants, children, and adults
in the northeastern U.S.). The Office of Pesticide Programs is also developing informa-
tion on the effects of food preparation methods on chemical residues in food.
51
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RISK CHARACTERIZATION
In the risk characterization stage, results of the hazard, exposure and the dose-
response assessments are combined to estimate the probability and extent of adverse
effects associated with consumption of contaminated fish or shellfish. An overview of
the risk characterization process is shown in Figure 6. In human health risk assess-
ment, carcinogens and noncarcinogens are treated separately. Indices of risk for these
different categories of toxicants are based on different dose-response models (see
above, Dose-Response Assessment).
The procedures for generating quantitative estimates of risk are emphasized in the
following sections. However, it is critical that numerical estimates of risk not be
presented in isolation from the assumptions and uncertainties inherent in the process
of risk assessment. The risk characterization should include a discussion of assumptions
and uncertainties and their potential impact on numerical estimates of risk; i.e., the degree
to which the numerical estimates are likely to reflect the actual magnitude of risk to
humans. For example, if upper confidence limits for mean chemical concentrations are
used to develop risk estimates, the effects of compounding assumptions of upper-bound
estimates of carcinogenic potency and conservatively high estimates of consumption
rate should be evaluated. A risk characterization should include a summary of the
preceding steps of the risk assessment: hazard assessment, dose-response assessment,
and exposure assessment. The weight-of-evidence classification and other supporting
information should be summarized concisely. Approaches to presentation of summary
information to be included in risk characterization are presented in the next chapter
(see below, Presentation and Interpretation of Results).
CARCINOGENIC RISK
Numerical estimates of carcinogenic risk can be presented in one or more of the
following ways (U.S. EPA 1986a):
• Unit risk - the excess lifetime risk corresponding to a continuous
constant lifetime exposure to a unit carcinogen concentration (e.g., 1 mg/kg
carcinogen in edible tissue of fish or shellfish)
• Dose or concentration corresponding to a specified level of risk - for
example, a guideline for maximum allowable contamination of a specified
medium may be derived from a maximum allowable risk value established
by risk managers
• Individual and population risks - upper-limit estimates of excess lifetime
cancer risk may be expressed for individuals (as a probability estimate)
or for the exposed population (as an estimate of the number of cancers
produced within a population of specified size per generation).
52
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1 Ha.
1 Identrti
lOOM-R
1 Asses
i
urti 1
-anon 4 L
esponsel^
smenl |
p
Physical-Chemical
BioaccumulatBn Potential
Environmental Partitioning,
Degradation. Transport
Mechanisms, and Potential
Exposure Media
Metabolism and
Pharmacokineuc Properties
Tone Effects in Humans
and Laboratory Animal*
e.g., Structure-Activity
Relttionsnips. K^.
Bioconcentration Factors
e.g., Air, Water, Sediment,
and Biota
e.g., Upopnilicrty. aoactivalnn,
Tonlication/Oetoiification,
Target Organs. Ekminaicn
e.g.. Acute and Chronic
Toiicrty, Carcinogenic Potency,
Ep()erniologic Evidence
e.g., Dose-Response Relations,
Carcinogenic Potency
e.g., Adequacy and Quality
of Data, bkelinood of
Specific Tone Effects
Are Data
Sufficient For a
Quantitative Risk
Assessment?
Is Substance
Potentially
Hazardous'
Select Route of Exposure
Concentration of
Specific Contaminant
oQoy WttQhf of
Exposed Indvidual
e.g., in g/day of nsrvSneilfisn
Consumed
e.g., Vea/1 of Exposure,
Fraction o« lifeline Exposed
e.a.. Assimilated
Contacted
Daily Exposure Per kg
BodyWeignt
Qualitative Risk Determination Based on
Toxieoteggal Properties and LrnHed Exposure Data
• Carcinogenic Potency
•HFD
• Other Standards
• ProoaWty of Tumor
• CxMSdanne of Standard
• Procaoikty of Some Other
Adverse Health Effect
Figure 6 Conceptual structure of quantitative health risk assessment
model.
-------
Regardless of the option chosen for expressing risk, final numerical estimates should be
presented as one significant digit only, followed by the EPA classification of the
weight of evidence for carcinogenicity in brackets (U.S. EPA 1986a).
The general model for estimating a plausible upper limit to excess lifetime risk of
cancer at low doses for a single-species diet is:
R ijkm * fll m Eijkm (9)
where:
R'ijkm • Plausible-upper-limit risk of cancer associated with chemical m in fishery
species i for human subpopulation j in area k (dimensionless)
q/n, « Carcinogenic Potency Factor for chemical m [(mg kg'1 day'1)"1] estimated
as the upper 95 percent confidence limit of the slope of a linear dose-
response curve
Eijkm * Exposure dose of chemical m from species i for subpopulation j in area k
(mg kg'1 day'1)
The actual risk is likely to be below the estimated upper-limit value calculated from
Equation 9, and may be zero in some instances. Equation 9 corresponds to Equation 2
above, except that an estimate of human exposure (E^) has replaced the dose (d),
which is usually a known quantity administered to a bioassay animal. All Ejjkm are
calculated as discussed above (see Exposure Dose Determination in Exposure Assessment).
When local consumption rate data are unavailable, a range of E^km and corresponding
risk estimates may be calculated based on a range of assumed consumption values.
Estimates of q,*m are available in IRIS. Note that Equation 9 is only valid for estimated
risks below 10.
Estimation of upper-limit risk associated with the average mixed-species diet
follows a similar approach, except that the average effective dose (Ejkm) of chemical
m from a mixed-species diet, calculated from Equation 8 above, replaces the species-
specific exposure (Ei;km) in Equation 9. Calculation of the average effective dose was
discussed earlier (see Exposure Assessment, Exposure Dose Determination).
NONCARCINOGENIC EFFECTS
Noncarcinogenic risk may be evaluated by calculating the ratio of the estimated
chemical intake to the RfD as follows:
where:
Hijkm- 575- do)
Hijkm " Hazard Index of a health effect from intake of chemical m associated
with fishery species i for human subpopulation j in area k (dimensionless)
RfDm » Reference Dose for chemical m (mg kg"1 day'1)
and Eijkm is defined as above. RfDm values are given in IRIS (U.S. EPA 1987a).
53
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When all significant exposure routes and sources are taken into account, the
estimated total exposure for all routes replaces Eijkm in the numerator of Equation 10
and the resulting hazard index is compared to a value of 1.0 to evaluate the chemical
hazard (Stara et al. 1983; U.S. EPA 1985b). Values of the hazard index for total
exposure or of H;jkm that are above 1.0 indicate that the estimated exposure is potentially
of concern. Above 1.0, increasing values of either hazard index indicate increasing
hazard. However, the hazard index does not define a dose response relationship, and
its numerical value should not be regarded as a direct estimate of risk.
Because Hijkrn as calculated by Equation 10 do not account for exposures other
than that from consumption of single fisheries species, values of Hjjkm substantially
below 1.0 do not necessarily indicate a lack of significant risk overall. Although
species-specific hazard indices are useful for evaluating whether contamination of any
single species is of concern, two problems remain:
• How can hazards from mixed-species diets of fish and shellfish be evaluated?
• How should exposures from sources other than consumption of contami-
nated fish and shellfish (either single-species or mixed-species diets) be
taken into account?
To address the first question above, one approach would be to sum Hjjkm values
across all species to obtain a hazard index, Hjkm, associated with the entire fishery.
However, Hjkn, could not be interpreted as representative of actual hazard to individuals,
since the sum of estimated exposures across species will not be the same as exposures
associated with the mixed-species diets of individuals (see above, Exposure Assessment,
Exposure Dose Determination, Mixed-species Diet). An alternative approach recommended
here is to use the average effective dose (Ejkm) for mixed-species diets to calculate a
hazard index. This hazard index for mixed-species diets still does not account for
exposures due to other sources.
To address the second question above, the sum of exposures from all sources
should be compared to the RfD to evaluate total hazard. Guidance on estimation of
exposures due to other sources is available in U.S. EPA (1986b,f). If exposure estimates
for sources other than the fishery are not available, then some relatively small fraction
of the RfD (e.g., 0.1) could be assigned to intake from consumption of fish and shellfish.
This fractional RfD would then replace the RfD in the denominator of the hazard
index. The index would be compared to a value of 1.0 to evaluate the potential for
concern. However, the uncertainties associated with such an approach should be clearly
stated. Further research on this problem is clearly needed.
The margin of exposure (MOE) is an alternative indicator of noncarcinogenic risk.
The MOE is the ratio of the No-Observed-Adverse-Effect-Level to an estimated exposure
dose. When the MOE is equal to or greater than the product of the uncertainty
factor and the modifying factor used to derive the RfD, the level of regulatory concern
is usually low (see U.S. EPA 1987a for details of the derivation of RfDs). Concerns
about mixed-species diets and exposures from non-fishery sources, as discussed above
for hazard indices, also apply to MOE for exposure to contaminated fisheries.
54
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CHEMICAL MIXTURES
U.S. EPA (1986d) discussed various models for assessment of the upper limit to
risk from chemical mixtures. Because of present data limitations and the complexity of
possible contaminant interactions, it is virtually impossible at present to predict synergistic
or antagonistic effects of most chemical mixtures. Moreover, such effects may be
unlikely at low environmental concentrations of contaminants. The approach used most
frequently for multiple-chemical assessment is the additive-risk (or response-additive)
model. Thus, total upper-limit risk for a chemical mixture is usually estimated as the
sum of upper-limit risks for carcinogens or of hazard indices for noncarcinogens. A
sum of noncarcinogenic hazard indices should be calculated only for a group of chemicals
acting on the same target organ (Stara et al. 1983). The numerical estimates obtained
using the response-additive model are useful in terms of relative comparisons (e.g.,
among fishing areas or among fishery species). However, risk estimates for chemical
mixtures should be regarded only as very rough measures of absolute risk (U.S. EPA
1986d). Because technological limitations preclude analyzing fishery samples for all
potentially toxic chemicals, risk estimates for chemical mixtures should not be inter-
preted as estimates of total chemical risk associated with ingestion.
55
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PRESENTATION AND INTERPRETATION OF RESULTS
Examples of formats for presenting the results of risk assessments are provided
below. These formats are adaptable to any level of summary analysis (e.g., subpopula-
tion vs. total exposed population, individual fishery species vs. average across species).
Approaches to presentation of supporting documentation on assumptions and uncertain-
ties are also described. Interpretation of the results is largely a function of risk
management. As such, guidance on interpretation of risk estimates to support decision -
making is beyond the scope of this manual. Nevertheless, a brief discussion of risk
comparisons (e.g., estimated risks for various fish species; estimated risk vs. acceptable
risk defined by policy) is provided to alert the reader to the interface between risk
assessment and risk management. Supplementary information, such as comparisons of
contaminant concentrations with FDA action levels, is addressed in the final section below.
PRESENTATION FORMAT
The results of risk assessment may be summarized in both tabular and graphic
format. All final estimates of risk should be rounded to one significant digit (or an
order of magnitude if appropriate). The EPA classification of the qualitative weight of
evidence for carcinogenicity should be shown in brackets adjacent to final risk estimates
for carcinogens (U.S. EPA J986a). To guide the reader's interpretation of the information
presented, supporting text should describe assumptions, uncertainties, and any caveats
about the results All risk estimates should be interpreted as plausible-upper-limit values
for the stated assumptions and exposure conditions.
Summary Tables
An example format for summarizing an exposure analysis is shown in Table 6.
The table format allows storage of quantitative information in a computer spreadsheet.
Columns of notes containing references to sources of information can easily be added
to the spreadsheet to further document the exposure analysis.
It should be emphasized that some of exposure variables are capable of being
measured relatively precisely (e.g., contaminant concentrations in fish tissue), whereas
others may only be estimated on an order-of-magnitude basis (e.g., consumption rate).
The precision and accuracy of the final risk estimates are directly related to the
precision and accuracy of the variables incorporated into the equations used to calculate
exposure and risk.
Quantitative uncertainty analyses such as sensitivity analysis are easily performed
with a spreadsheet by calculating exposure estimates for low, mid, and high values of
key variables within their respective plausible ranges. Specification of probability
distributions for key variables is an alternative method of uncertainty analysis requiring
graphical models (see below, Uncertainty Analysis). In the example shown in Table 6,
the average, minimum, and maximum concentrations of each contaminant [PCBs and
mercury (Hg)J are used to estimate potential health risk, thereby accounting for uncertainty
56
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TABLE 6. EXAMPLE TABULAR FORMAT FOR DISPLAY OF QUANTITATIVE RISK ASSESSMENT FOR CONSUMPTION OF FISH AND SHELLFISH
Substance
PCBs
PCBs
Mg
Hg
r
Concen-
tration
in Medftn
(mg/kg>8
0.007
O.OM
0.010
0.007
0.004
0.010
0.157
0.008
0.478
0.157
0.008
0.478
Contact
Rate
|
None arc inogens
RfD
(mg/kg/d)
N/Ad
N/A
N/A
N/A
N/A
N/A
2.9E-04
2.9E-04
2.9E-04
2.9E-04
2.9E-04
2.9E-04
Hazard
Index
N/A
N/A
N/A
N/A
N/A
N/A
5E-02
3E-03
2E-01
2E-01
8E-03
5E-01
8 Concentration of contaminant in fisheries species of concern (ing/kg = ppn by mss, wet weight).
k Amount of fish/shellfish ingested per day, prior to accounting for absorption efficiency, etc.
c Ratio of g of contaminant absorbed per g of contaminant ingested, or correction factor to account for differential absorption by humans and
bioassay animals (see text. Exposure Assessment. Exposure Dose Determination).
d N/A = not applicable.
e Carcinogenicity of methyl Hg has not been evaluated by EPA Carcinogen Assessment Group. Hg is typically treated as a noncarcinogen in risk
assessment.
-------
in chemical analyses. Also, risks are estimated for two consumption rate estimates
(6.5 g/day and 20 g/day). Note that spreadsheet summaries of quantitative information
should be supported by a text discussion of qualitative uncertainties such as the weight
of evidence for the health effect of concern.
Summary Graphics
Presentation of risk assessment results in graphic form may include:
• Plots of estimated risk vs. consumption rate
• Plots of estimated risk vs. contaminant concentration in edible tissue of
fish or shellfish
• Summary maps of risk estimates for different geographic locations or
individual sampling stations
• Histograms of estimated risk by fishery species, human subpopulation,
or geographic location.
Because estimated risk for a given area and fishery species varies with consumption
rate and because consumption rates vary greatly among individual humans, the first
approach above is recommended as a primary means of presenting risk assessment
results. Actual consumption patterns of the exposed population may or may not be
estimated (see above, Exposure Assessment). If they are, estimates of average consumption
rate (and 95 percent confidence limits) can be identified in a footnote (e.g., Figure
7). Uncertainty in chemical measurements can be illustrated by plotting lines corresponding
to the minimum and maximum (or 95 percent confidence limit) values of contaminant
concentrations in fishery species, as well as the mean concentration (e.g., each solid
line in Figure 7). As an interpretive aid, risk assessment results for a reference area
can be presented along with those for the study area. Coupled with information on
comparative risks (see below, Risk Comparisons), Figure 7 is an appropriate format for
graphic display of results to lay public.
Other approaches noted above can be used to supplement plots of risk vs. consump-
tion. Summary maps and histograms may be especially useful for presentation of
detailed results of spatial analyses by human subpopulation or by fishery species. Plots
of risk vs. contaminant concentration for selected consumption rates and species (e.g.,
Figure 8) aid in rapid interpretation of tissue contamination data.
RISK COMPARISONS
Interpretation of carcinogenic risk assessment results may be based on comparison
of estimated health risks for the study area with:
• Estimated health risks for consumption of fishery species from a reference
area
• Estimated health risks for consumption of alternative foods (e.g., charcoal-
broiled steak, marketplace foods).
57
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cr
cr
111
CD
LU
s
LL
10-3-
10"*-
10'5-
10-6-
10
•7
STUDY AREA
N . 25 BUTTER CUMS
REFERENCE AREA
N - 25 BUTTER CLAMS
1
(2)
10
(25)
I
100 g/day
(250) (m«al*/yr)
CONSUMPTION RATE
PCBs Weight-of-evidence elMsifiettion:
PROBABLE HUHAM CARCINOGEN [82]
All canctr ri*ks are pt«usiblt-hpper-limit tttiMttc of txctsc ritk btctd on
linearized nultittage procedure and assumptions sumarized in the text. Solid lines
are risks associated with average PCS concentrations in butter clams. Dashed lines
are for uncertainty range (e.g., 95 percent confidence Knits) for average
concentrations of PCBs, not the total uncertainty. Actual risks are likely to be
lower than those shown above and ny be zero.
Figure 7 Example graphic format for display of quantitative risk
assessment results for hypothetical study area and reference
area.
-------
10-* r-
cc
LLI
o
z
<
o
UJ
: R;*. REFERENCE
C" -TISSUE CONTAMINATION
GUIDELINE FOR S.Sg.day
11 I I t t i I i t I I I i i i i i i I
CHEMICAL CONCENTRATION IN
FISH OR SHELLFISH (ppm)
Figure 8 Plausibfe-upper-limit estimate of lifetime excess cancer n'sk
vs. concentration of a chemical contaminant in fish or shellfish
(ppm wet wt.) at selected ingestion rates.
-------
An example of comparison with reference-area risk estimates is shown in Figure 7
above. Comparative risks for alternative foods can be summarized in a table or histogram.
Wilson and Crouch (1987) point out the importance of comparing the results of risk
assessments with similar assessments of common activities to provide perspective for
interpretation of the results by risk managers and the general public.
Risk comparisons should be based on consistent exposure analysis and risk extrapolation
models. Analogous exposure scenarios should be used for each risk estimate being compared
(i.e., either worst case, plausible-upper limit, average, or lower limit). A single mode!
should be applied consistently to calculate exposure and risk. A linear extrapolation
model, such as Equations 2 and 6 above, is justified in general if the excess risk
attributed to the contaminant of concern is regarded as a marginal risk, added to a
background of relatively high cancer incidence from all other causes not being modeled
(Crump et al. 1976; Omenn 1985).
When interpreting the results of risk assessments, risk managers may define an
acceptable level of risk to provide a criterion for judging the significance of potential
health effects. The term "acceptable risk" is used to denote the maximum risk considered
tolerable by an individual or a regulatory agency. An acceptable risk level has not been
strictly determined by EPA. Although acceptable risk levels must be defined on a
case-specific basis, past regulatory decisions that apply to the U.S. population have
generally set allowable levels for environmental risks on the order of 10"5 to 10"6
(Travis et al. 1987).
SUMMARY OF ASSUMPTIONS
Assumptions underlying the risk assessment model and estimates of model variables
should be summarized in a concise format (see Table 7 for summary of some assumptions
and numerical estimates used in the approach presented in this manual). Specific
assumptions adopted on a case-by-case basis should be summarized in a similar fashion.
Other assumptions, such as general approaches or assumptions underlying models
that are commonly used to estimate risk, can be summarized in the text of a risk
assessment document. Some additional assumptions involved in applying the risk assessment
approach described in this manual include the following:
• Adverse effects in experimental animals are indicative of adverse effects
in humans (e.g., lifetime incidence of cancer in humans is the same as
that in animals receiving an equivalent dose in units of mg per surface
area)
• Dose-response models can be extrapolated beyond the range of experi-
mental observations to yield plausible-upper-bound estimates of risk at
low doses
• A threshold dose does not exist for carcinogenesis
• A threshold dose (e.g., No-Observed-Adverse-Effect-Level) exists for
noncarcinogenic effects
58
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TABLE 7. SUMMARY OF ASSUMPTIONS AND NUMERICAL
ESTIMATES USED IN RISK ASSESSMENT APPROACH
Parameter
Assumptions/Estimates
Reference
Exposure Assessment-
Contaminant concentrations
in tissues of indicator
species
Average consumption rate*
Gastrointestinal absorption
coefficient
Exposure duration
Human body weight
Risk Characterization:
Carcinogenic risk model
Carcinogenic potency
Noncarcinogenic risk
No effect of cooking
6.5 g/day
20 g/day
165 g/day
1.0
Assumes efficiency of absorp-
tion of contaminants is same
for humans and bioassay animals
70 yr
70 kg (« avg. adult male)
Linearized Multistage
At risks less than 10"2:
Risk * Exposure x Potency
Potency factors are based on
low-dose extrapolation from
animal bioassay data
Upper bound of 95 percent
confidence interval on potency
is used
RfDs for noncarcinogens
compared with estimated
exposure
Worst case for parent
compounds. Net effect
on risk is uncertain.
Low, moderate, and
high values for U.S.
population (see text).
U.S. EPA 1980b; 1986a,b
U.S. EPA 1980b; 1986a,b
U.S. EPA I986a,b
U.S. EPA 1980b, 1986a,
1987a
U.S. EPA 1987a
U.S. EPA 1987a
• Estimates of consumption for local population should be used in place of values shown
for U.S. population whenever possible.
-------
The most sensitive animal species is appropriate to represent the response
of humans
Cumulative incidence of cancer increases in proportion to the third
power of age (this assumption is used to estimate lifetime incidence
when data are available only from less-than-lifetime experiments)
For carcinogens, average doses are an appropriate measure of exposure
dose, even if dose rates vary over time
In the absence of pharmacokinetic data, the effective (or target organ)
dose is assumed to be proportional to the administered dose
Risks from multiple exposures in time are additive
For each chemical, the absorption efficiency of humans is equal to that
of the experimental animal
If available, human data are preferable to animal data for risk estimation
For chemical mixtures, risks for individual chemicals are additive.
However, the total sum of individual chemical risks is not necessarily the
total risk associated with ingestion of contaminated fish or shellfish
because some important toxic compounds may not have been identified and
quantified.
UNCERTAINTY ANALYSIS
Uncertainty analysis is an integral part of risk assessment. A general discussion
of uncertainties present in the risk assessment approach described in this manual is
presented in the next section. The EPA guidelines on exposure assessment describe
general approaches for characterizing uncertainty (U.S. EPA 1986b). Methods for
uncertainty analysis are discussed by Cox and Baybutt (1981), Morgan (1984), and
Whitmore (1985). A detailed discussion of procedures is beyond the scope of the
present effort. General approaches to uncertainty analysis will be described briefly
after a discussion of sources of uncertainty.
Sources of Uncertainty
Uncertainties in the risk assessment approach presented in this manual arise from
the following factors:
1. Uncertainties in the determination of the weight-of-evidence classification
for potential carcinogens.
2. Uncertainties in estimating Carcinogenic Potency Factors or RfDs,
resulting from:
59
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• Uncertainties in extrapolating toxicologic data obtained from
laboratory animals to humans
• Limitations in quality of animal study
• Uncertainties in high- to low-dose extrapolation of bioassay test
results, which arise from practical limitations of laboratory
experiments and variations in extrapolation models
3. Variance of site-specific consumption rates and contaminant concentrations
4. Uncertainties in the selection of 6.5 g/day, 20 g/day, and 165 g/day as
assumed consumption rates when site-specific data are not available
5. Uncertainties in the efficiency of assimilation (or absorption) of contami-
nants by the human gastrointestinal system (assumed to be the same as
assimilation efficiency of the experimental animal in the bioassay used
to determine a Carcinogenic Potency Factor or RfD)
6. Variation of exposure factors among individuals, such as:
• Variation in fishery species composition of the diet among individuals
• Variation in food preparation methods and associated changes in
chemical composition and concentrations due to cooking.
Variance in estimates of carcinogenic potency or RfDs (#1 above) account for one
major uncertainty component in most risk assessments. Chemical potencies are estimated
only on an order-of-magnitude basis, whereas analytical chemistry of tissues is relatively
precise (on the order of ±20 percent). The choice of a low-dose extrapolation model
greatly influences estimates of the Carcinogenic Potency Factor and calculated risks. This
uncertainty contributed by the model is substantial when predicting risks below J0~2.
For example, the plausible-upper limit to lifetime cancer risk associated with 50 ug/L tetra-
chloroethene in drinking water ranges from about 10"6 for the probit model to 10"2 for
the Weibull model (Cothern et al. 1986). Model uncertainty is important when considering
absolute risk estimates (e.g., Cothern et al. 1986), but less important for relative risk
comparisons.
Uncertainty analysis conducted by previous researchers illustrates the variability
of risk estimates and potency factors for a given extrapolation model. For example,
the coefficient of variation for the mean value of potency generally ranged from 2 to
105 percent for each drinking water contaminant studied by Crouch et al. (1983). This
uncertainty arose mainly from error associated with experimental bioassay data for a
single animal species. Among bioassay species, the potency of a given chemical may
vary only slightly or up to approximately 1,000-fold, depending on the chemical in
question (Clayson et al. 1983). Thus, the uncertainty associated with extrapolating potency
factors from laboratory animals to humans may be much greater than the uncertainty
associated with animal bioassay techniques. By comparison, the range of potencies
among carcinogens covers 7-9 orders of magnitude (Clayson et al. 1983; U.S. EPA
1985a). Relative risk comparisons among chemicals can be made more confidently when
the range of potency factors is broad. Note that such comparisons should also include
consideration of the qualitative uncertainty (e.g., weight of evidence) in assessing the
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specific health effects of chemicals, including mode of action, latency period, and
target organs.
In conclusion, uncertainty ranges (e.g., 95 percent confidence intervals) around
estimates of mean risk may typically span at least several orders of magnitude. The
approach taken by U.S. EPA (I980b, 1985a, 1986a) and followed herein is to estimate a
plausible-upper limit to risk. In this way, it is unlikely that risk will be underestimated
substantially. Moreover, the plausible-upper-limit estimate serves as a consistent basis
for relative risk comparisons. However, the effects of compounding conservative
assumptions should be evaluated to provide perspective on risk assessment results.
Approaches to Uncertainty Analysis
Analysis of uncertainty in a risk assessment should address both quantitative and
qualitative uncertainty. Quantitative uncertainty analysis deals primarily with variation
in numerical estimates of exposure and risk that results from changing the values of
variables in mathematical models used to calculate the estimates (e.g., low-dose extrapolation
models). Characterization of variability in chemical measurements, food consumption
rates, and Carcinogenic Potency Factors (or RfDs) and its effect on estimates of
exposure and risk is an example of quantitative uncertainty analysis. A qualitative
uncertainty analysis includes primarily a summary of limitations of the data and the
weight of evidence for toxic effects of concern. A discussion of qualitative uncer-
tainties should present information from IRIS on the level of confidence that EPA
places in each Carcinogenic Potency Factor and RfD.
General approaches to treatment of uncertainty in variables used in risk analysis
models include the following (Morgan 1984):
• Perform analysis using single-value-best-estimates for model variables
without uncertainty analysis
• Perform single-value-best-estimate analysis, with sensitivity calculations
and appropriate discussion of uncertainty
• Estimate some measure of uncertainty (e.g., standard deviation) for each
model variable and use error propagation methods to estimate uncertainty
of final exposure or risk value
• Characterize subjectively the probability distribution of each model
variable and propagate error through stochastic simulation
• Characterize important model variables using a parametric model and
perform risk analysis using various plausible values of each of the variables
• Determine upper and lower bounds on model variables to yield order-of-
magnitude estimates and range of possible answers.
Morgan (1984) refers to the first two approaches as "single-value-best-estimate analysis,"
to the second two as "probabilistic analysis," and to the final two as "parametric/bounding
analysis." The analytical strategies listed above are in roughly descending order, based
on the amount of uncertainty in the model variables. Single-value-best-estimate analysis
61
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is appropriate when model variables are precisely known. Bounding analysis is most
appropriate when values of model variables are not well-known. The techniques listed
above do not address model uncertainty, which must be handled by exploratory examina-
tion of outcomes based on alternative model equations.
The choice of a method for uncertainty analysis will depend on the amount and
quality of exposure data and on the study objectives. Quantitative uncertainty analysis
is applied mainly to exposure variables, such as contaminant concentration in fishery
species and consumption rate. Following U.S. EPA (1980b, I984a, 1985a), an upper-
bound estimate of the Carcinogenic Potency Factor is used in carcinogenic risk calcula-
tions. Substitution of the mean estimate or the lower bound of the 95 percent confidence
interval for the potency factor in the risk calculations is generally not done because of
the instability of these estimates (U.S. EPA I980b, 1986a).
The U.S. EPA (J986b) guidelines on exposure assessment and Whitmore (1985)
summarize the primary methods for characterizing uncertainty in exposure estimates in
relation to attributes of the exposed population and the exposure data. In many cases,
data will be sufficient only to use parametric/bounding analysis, as described above. In
any case, a discussion of qualitative uncertainties in the analysis should always accompany
presentation of risk assessment results. For example, limitations of data related to
inadequate survey design or insensitive analytical chemistry methods should be described.
The extent of chemical data for geographic locations of interest should be summarized.
Insufficient information on characteristics of the exposed population should be noted.
The level of confidence in data used to develop RfDs, Carcinogenic Potency Factors,
and weight-of-evidence classifications based on IRIS Chemical Files should be indicated.
SUPPLEMENTARY INFORMATION
Additional information to support risk assessment of contaminated fish and shellfish
consumption may include:
• Comparisons of tissue concentrations of contaminants with FDA action
(or tolerance) levels
• Statistical comparisons of mean contaminant concentrations among
fishery species and among locations
• Statistical comparisons of mean contaminant concentrations in fishery
species with those in other foods.
FDA limits on contaminants in fishery products are shown in Appendix I. Limitations
to use of these values for assessing health risk were discussed earlier (see above.
Overview of Risk Assessment). For comparison, legal limits on fishery contaminants
established by other countries are also provided in Appendix I.
Some resource management agencies have developed advisories based simply on
comparisons between contaminant concentrations in fishery species and those in corres-
ponding species from reference or control areas. For example, the Northeast Shellfish
Sanitation Commission has established "alert levels" for metals in shellfish as the
concentration equal to one standard deviation above the mean background (reference)
62
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concentration. These alert levels are not based on health effects, but assume that the
level of concern is related to an elevation above average background conditions.
63
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Sonzogni, W.C., and W.R. Swain. 1984. Perspectives on human health concerns from
Great Lakes contaminants. pp. 1-29. In: Toxic Contaminants in the Great Lakes.
J.O. Nriagu and M.S. Simmons (eds). Adv. in Environ. Sci. Technol. Series. No. 14.
John Wiley and Sons, New York, NY.
SRI. 1980. Seafood consumption data analysis. Final Report. Prepared for U.S. En-
vironmental Protection Agency, Office of Water Regulations and Standards, Washington,
DC. SRI Internatinal, Menlo Park, CA.. 44 pp.
Stara, J.F., R.C. Hertzberg, R.J.F. Bruins, M.L. Dourson, P.R. Durkin, L.S. Erdreich, and
W.E. Pepelko. 1983. Approaches to risk assessment of chemical mixtures. Report
presented at the Second International Conference on Safety Evaluation and Regulation,
Cambridge, MA. 23 pp.
Stern, R.M. 1986. Analysis of the decision making process in chemical safety. Sci.
Total Environ. 51:27-62.
Stich, H.F. (ed). 1982. Carcinogens and mutagens in the environment. Vol. I. Food
products. CRC Press, Boca Raton, FL.
Strong, C.R., and S.N. Luoma. 1981. Variations in the correlation of body size with
concentrations of Cu and Ag in the bivalve Macoma balthica. Can. J. Fish. Aquat. Sci.
38:1059-1064.
Suta, B.E. 1978. Human exposures to mirex and kepone. EPA-600/1-78-045. U.S. En-
vironmental Protection Agency, Washington, DC.
Swain, W.R. 1986. Toxic xenobiotic chemicals in fish in relation to human health.
University of Amsterdam, The Netherlands. 127 pp.
Tatken, R.L., and R.J. Lewis (eds). 1983. Registry of toxic effects of chemical substances
1981-1982 edition. 3 volumes. U.S. Department of Health and Human Services, National
Institute for Occupational Safety and Health, Cincinnati, OH.
Taylor, J. October 1986. Personal Communication (letter to J. Moore, Assistant Admini-
strator of U.S. Environmental Protection Agency). Associate Commissioner of U.S.
Food and Drug Administration.
Tetra Tech. 1985a. Bioaccumulation monitoring guidance: 1. estimating the potential
for bioaccumulation of priority pollutants and 301(h) pesticides discharged into marine
and estuarine waters. Final Report. Prepared for Office of Marine and Estuarine
Protection, U.S. Environmental Protection Agency, Washington, DC. Tetra Tech, Inc.,
Bellevue, WA. 61 pp.
Tetra Tech. 1985b. Bioaccumulation monitoring guidance: 2. selection of target
species and review of available bioaccumulation data. Final Report. Prepared for
U.S. Environmental Protection Agency, Office of Marine and Estuarine Protection,
Washington, DC. Tetra Tech, Inc., Bellevue, WA. 52 pp. + 5 appendices.
72
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Tetra Tech. 1985c. Bioaccumulation monitoring guidance: 3. recommended analytical
detection limits. Final Report. Prepared for U.S. Environmental Protection Agency,
Office of Marine and Estuarine Protection, Washington, DC. Tetra Tech, Inc., Bellevue,
WA. 23 pp.
Tetra Tech. 1985d. Commencement Bay nearshore/tideflats remedial investigation.
Vol. 1. Final Report. EPA-910/9-85-134b. Prepared for the Washington Department
of Ecology and U.S. Environmental Protection Agency. Tetra Tech, Inc., Bellevue, WA.
Tetra Tech. 1986a. A framework for comparative risk analysis of dredged material
disposal options. Final Report. Prepared for Resource Planning Associates for U.S. Army
Corps of Engineers, Seattle District. Tetra Tech, Inc., Bellevue, WA. 94 pp. + 5 appendices.
Tetra Tech. 1986b. Bioaccumulation monitoring guidance: 5. strategies for sample
replication and compositing. Final Report. Prepared for U.S. Environmental Protection
Agency, Office of Marine and Estuarine Protection, Washington, DC. Tetra Tech, Inc.,
Bellevue, WA. 46 pp.
Tetra Tech. 1986c. Elliott Bay toxics action program: initial data summaries and
problem identification. Final Report. Prepared for the U.S. Environmental Protection
Agency, Region 10. Tetra Tech, Inc., Bellevue, WA. 181 pp. + 8 appendices and maps.
Tetra Tech. 1986d. Technical support document for ODES statistical power analysis.
Draft Report. Prepared for U.S. Environmental Protection Agency, Office of Marine
and Estuarine Protection, Washington, DC. Tetra Tech, Inc., Bellevue, WA. 28 pp.
Tetra Tech. 1986e. Bioaccumulation monitoring guidance: 4. analytical methods for
U.S. EPA priority pollutants and 301 (h) pesticides in tissues from estuarine and marine
organisms. Final Report. Prepared for U.S. Environmental Protection Agency, Office
of Marine and Estuarine Protection, Washington, DC. Tetra Tech, Inc., Bellevue, WA.
Tetra Tech. 1986f. Quality assurance and quality control (QA/QC) for 301(h) monitor-
ing programs guidance on field and laboratory methods. Final Report. Prepared for
Marine Operations Division, Office of Marine and Estuarine Protection, U.S. Environ-
mental Protection Agency, Washington, DC. Tetra Tech, Inc., Bellevue, WA. 267 pp. +
appendices.
Thomann, R.V., and J.P. Connolly. 1984. Model of PCB in the Lake Michigan lake
trout food chain. Environ. Sci. Technol. 18:65-71.
Thomas, L.M. 1984. U.S. EPA memorandum on determining acceptable risk levels for
carcinogens in setting alternate concentration levels under RCRA. Published November
23, 1984 by Bureau of National Affairs, Inc., Washington, DC.
Tollefson, L., and F. Cordle. 1986. Methylmercury in fish: a review of residue levels,
fish consumption and regulatory action in the United States. Environ. Health Perspec-
tives 68:203-208.
Travis, C.C., S.A. Richter, E.A.C. Crouch, R. Wilson, and E.D. Klema. 1987. Cancer
risk management. A review of 132 federal regulatory decisions. Environ. Sci. Technol.
21:415-420.
73
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U.S. Department of Agriculture. 1984. Agricultural statistics. U.S. Department of
Agriculture, Washington, DC. p. 506.
U.S. Environmental Protection Agency. 1980a. Ambient water quality criteria for
polychlorinated biphenyls. U.S. Environmental Protection Agency, Criteria and Standards
Division, Washington, DC. 200 pp.
U.S. Environmental Protection Agency. 1980b. Water quality criteria documents;
availability. U.S. EPA, Washington, DC. Federal Register, Vol. 45, No. 231, Part V.
pp. 79318-79379.
U.S. Environmental Protection Agency. 1981. Interim methods for the sampling and
analysis of priority pollutants in sediments and fish tissue. EPA 600/4-81-055. U.S.
Environmental Protection Agency, Environmental Monitoring and Support Laboratory,
Cincinnati, OH.
U.S. Environmental Protection Agency. 1982. Method for use of caged mussels to
monitor for bioaccumulation and selected biological responses of toxic substances in
municipal wastewater discharges to marine waters. Draft. U.S. Environmental Protection
Agency Env. Monitoring and Support Lab, Cincinnati, OH.
U.S. Environmental Protection Agency. 8 August 1984. Personal Communication (letter
to Dr. Robert Pastorok). U.S. Environmental Protection Agency, Environmental Criteria
and Assessment Office, Cincinnati, OH.
U.S. Environmental Protection Agency. 1984a. Method 1625 Revision B. Semivolatile
organic compounds by isotope dilution GC/MS. Federal Register Vol. 49, No. 209.
October 26, 1984. pp. 43416-43429.
U.S. Environmental Protection Agency. 1984b. Risk assessment and management:
framework for decision making. EPA 600/9-85-002. U.S. Environmental Protection
Agency, Washington, DC. 35 pp.
U.S. Environmental Protection Agency. 1984c (revised January, 1985). U.S. EPA contract
laboratory program statement of work for organics analysis, multi-media, multi-concentration.
IFB WA 85-T176, T177, T178. U.S. Environmental Protection Agency, Washington, DC.
U.S. Environmental Protection Agency. 1985a. Health assessment document for 1,2-di-
chloroethane (ethylene dichloride). EPA/600/8-84/006F. Final Report. Office of
Health and Environmental Assessment, U.S. Environmental Protection Agency, Washington,
DC. Table 9-66, pp. 9-253 to 9-256.
U.S. Environmental Protection Agency. 1985b. National primary drinking water regulations;
synthetic organic chemicals, inorganic chemicals and microorganisms; proposed rule.
U.S. Environmental Protection Agency, Washington, DC. Federal Register, Vol. 50,
No. 219, pp. 46936-47022.
U.S. Environmental Protection Agency. 1985c. Contract laboratory program statement
of work (SOW), inorganic analysis, multi-media, multi-concentration. SOW No. 785.
U.S. Environmental Protection Agency, Washington, DC.
74
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U.S. Environmental Protection Agency. 1986a. Guidelines for carcinogen risk assess-
ment. U.S. Environmental Protection Agency, Washington, DC. Federal Register
Vol. 51, No. 185. pp. 33992-34003.
U.S. Environmental Protection Agency. 1986b. Guidelines for exposure assessment.
U.S. Environmental Protection Agency, Washington, DC. Federal Register, Vol. 51,
No. 185. pp. 34042-34054.
U.S. Environmental Protection Agency. 1986c. Guidelines for the health assessment of
suspect developmental toxicants. U.S. Environmental Protection Agency, Washington,
DC. Federal Register, Vol. 51, No. 185. pp. 34028-34040.
U.S. Environmental Protection Agency. 1986d. Guidelines for the health risk assess-
ment of chemical mixtures. U.S. Environmental Protection Agency, Washington, DC.
Federal Register, Vol. 51, No. 185. pp. 34014-34025.
U.S. Environmental Protection Agency. 1986e. Guidelines for mutagenicity risk assessment.
U.S. Environmental Protection Agency, Washington, DC. Federal Register, Vol. 51, No.
185. pp. 34006-34012.
U.S. Environmental Protection Agency. 1986f. Superfund public health evaluation
manual. EPA 540/1-86-060. U.S. Environmental Protection Agency, Office of Emer-
gency and Remedial Response, Washington, DC. 145 pp. + appendices.
U.S. Environmental Protection Agency. 1986g. Superfund Risk Assessment Information
Directory. EPA 540/1-86/061. U.S. Environmental Protection Agency, Office of Emergency
and Remedial Response, Washington, DC.
U.S. Environmental Protection Agency. 1987a. Integrated Risk Information System
(IRIS). Vol. I - Supportive Documentation EPA 600/8-86/032a, and Vol. II - Chemical
Files EPA 600/8-86/032b. U.S. Environmental Protection Agency, Office of Health and
Environmental Assessment, Washington, DC.
U.S. Environmental Protection Agency. 1987b. Risk assessment, management, com-
munication. A guide to selected sources. EPA IMSD/87-002. [Also First Update (EPA
IMSD/87-002-a) and Second Update (EPA IMSD/87-002-5)]. U.S. Environmental Protection
Agency, Office of Information Resources Management and Office of Toxic Substances,
Washington, DC.
U.S. Environmental Protection Agency and U.S. Food and Drug Administration. 1987.
Contaminants in fish: The regulation and control of residues for human safety. Draft
Report. Prepared by U.S. Environmental Protection Agency Risk Assessment Council
Subcommittee on Fish Residue Issues, Washington, DC and U.S. Food and Drug Ad-
ministration, Office of Toxicological Studies, Washington, DC.
U.S. Fish and Wildlife Service. 1986. Type B technical information document recom-
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Procedures Work Group, Fort Collins, CO. 45 pp.
75
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U.S. Food and Drug Administration. J978. Pesticides analytical manual. Methods
which detect multiple residues: foods and feeds. U.S. Food and Drug Administration,
Washington, DC.
U.S. Food and Drug Administration. 1982. Levels for poisonous or deleterious substances
in human food and animal feed. U.S. Food and Drug Administration, Washington, DC.
13 pp.
U.S. Food and Drug Administration. 1984. Polychlorinated biphenyls (PCBs) in fish
and shellfish; reduction of tolerances; final decision. U.S. Food and Drug Administration,
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U.S. Food and Drug Administration. 1986. Pesticides and industrial chemicals in
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U.S. Office of Science and Technology Policy. 1985. Chemical carcinogens; a review
of the science and its associated principles. Federal Register, Vol. 50. pp. 10372-10442.
U.S. Office of Technology Assessment. 1979. Environmental contaminants in food.
U.S. Office of Technology Assessment, Washington, DC. 229 pp.
Vaessen, H.A.M.G., P.L. Schuller, A.A. Jekel, and A.A.M.M. Wibers. 1984. Polycyclic
aromatic hydrocarbons in selected foods: analysis and occurrence. Toxicol. Environ.
Chem. 7:297-324.
Versar, Inc. 1985. Assessment of human health risk from ingesting fish and crab from
Commencement Bay. EPA 910/9-85-129. Prepared for U.S. Environmental Protection
Agency, Office of Solid Waste and Emergency Response. Versar, Inc., Springfield, VA.
Whitmore, R.W. 1985. Methodology for characterization of uncertainty in exposure
assessments. Final Report. OHEA-E-160. Office of Health and Environmental Assessment,
Washington, DC. 44 pp. + appendices.
Whittemore, A.S. 1983. Facts and values in risk analysis for environmental toxicants.
Risk Analysis 3:23-33.
Wilson, R., and E.A.C. Crouch. 1987. Risk assessment and comparisons: an intro-
duction. Science 236:267-270.
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APPENDIX A
EPA OFFICE OF WATER CONTACTS ON RISK ASSESSMENT FOR FISH CONSUMPTION
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APPENDIX A
EPA OFFICE OF WATER CONTACTS ON RISK ASSESSMENT FOR FISH CONSUMPTION
Name Organization Phone Subject Area
Kim Devonaid Office of Marine and (202) 475-7114 EPA Coordination on
Estuarine Protection Fish Health Risk
Assessment
Frank Gostomski Office of Water (202) 475-7321 Derivation of Refer-
Regulations and ence Doses for
Standards Toxic Chemicals
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APPENDIX B
INTEGRATED RISK INFORMATION SYSTEM (IRIS)
(excerpt from U.S. EPA 1987a)
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INTRODUCTION TO IRIS
OVERVIEW
IRIS is a computer-housed, electronically communicated catalogue of Agency risk assessment and risk
management information for chemical substances. This system is designed especially for federal,
state, and local environmental health agencies as a source of the latest information about Agency
health assessments and regulatory decisions for specific chemicals.
The development of IRIS is a response to repeated requests for Agency risk assessment information
to deal with various environmental issues and a response to the need for consistency and quality in
EPA risk assessment and risk management decisions. IRIS is intended to introduce the user to Agency
information which may be useful for building the database necessary to make a risk assessment.
IRIS is not a primary toxicologic data base or a conclusive risk resource; rather, it is an introduction to
EPA's risk information, and should be used with an understanding of its capabilities as well as its
limitations and constraints (see background documents in Service Code 4). Supportive
documentation included in the system provides instruction and explanation for the risk information
presented. The information contained in IRIS is intended for users without extensive training in
toxicology, but with some knowledge of health science.
The risk assessment information contained in IRIS, except as specifically noted, has been reviewed
and agreed upon by intra-agency review groups, representing an Agency consensus. An intra-agency
work group has been responsible for the development of IRIS.
As intra-agency review groups continue to review and verify risk assessment values, additional
chemicals and data components will be added to IRIS. Although IRIS is available in hardcopy, it is also
available through Dialcom, Inc.'s Electronic Mail, the computer-based electronic communications
system to which the EPA subscribes. Designed as an electronic loose-leaf notebook, IRIS provides
users with the ability to access, copy, and print information from the data base. IRIS harcfcopy, which
will be available in the future through the National Technical Information Service (NTIS), is provided
initially to help users get started. This material can then be expanded and updated by users through
electronic retrieval of new and revised data.
SYSTEM STRUCTURE
The information contained within IRIS is divided into two major components: the chemical files,
which form the heart of the system, and the supportive documentation, which provides instruction
and explanation in support of the system and the chemical files. This information is distributed
among six Service Codes, with the chemical files (the functional files in IRIS) contained in one Service
Code and the supporting documentation contained in the remaining five. The Service Codes and
their functions are as follows:
Service Code 1 Chemical Files: This is the heart of the system. It is within this file that the actual
chemical-specific data have been compiled. A detailed presentation of the
content and format of this Service Code will be provided later in this Introduction
and in the Chemical Fife Structure description in Service Code 4.
Service Code 2 List of Chemicals on IRIS: A simple alphabetical and Chemical Abstract System
(CAS) number listing of all the chemicals contained in IRIS.
Chemical File Update Information: The chemical files which have been recently
updated are listed here. Chemical name, CAS No. and date of revision are given.
Service Code 3 Chemical File Revision History: This Service Code contains a running record of
specific revisions to each chemical file. The information is more specific than that
found in Service Code 2, which is just a list of updated fifes. The specific file
sections that have been changed or modified are given and the type of change is
indicated (e.g., "Oral RfD: UF text modified", "Risk Estimates for Carcinogens:
slope factor corrected", "Risk Management Section added", etc.). The date of the
change is also given.
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Service Code 4 Introduction to IRIS (this document): a brief overview of IRIS.
Chemical File Structure: General background information is provided on each of
the data elements contained in the chemical files. This section is intended to help
the user understand the information contained in the chemical files. In addition.
there is some discussion of the general limitations, restrictions, and qualifications
placed on the EPA data so as to minimize misinterpretation of the data
presented.
Background Documents (Appendices): Concept papers are provided for the
categories of information contained in the chemical files (oral RfD,
carcinogenicity assessment, risk management actions, and supplementary data).
As background documents are prepared for other information categories, such as
inhalation RfDs or Drinking Water Health Advisories, they will be added to the
system.
EPA Chemical Profile Database References: List of references cited in the
Supplementary Data section of the chemical files is provided at the end of the
background document for that section.
Service Code 5
Service Code 6
Glossary: A glossary of terms and abbreviations used in the chemical files and
supportive documentation is provided for user reference.
User's Guide: An operations manual is provided which describes how to use the
system and lists commands, procedures, helpful hints, and a series of examples for
illustration.
Case Study: A case study is included to provide an example of a situation to which
IRIS can be applied and how the information it contains might be used.
1. Chemical files.
2. List of chemicals in IRIS.
3. Revision information.
4. Background information.
5. Glossary.
6. User's guide and case study.
Service Codes in IRIS
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CHEMICAL FILE FORMAT
The chemical files are intended to assist the user in developing risk assessments which can be used in
making management decisions for specific situations. For reference, agency risk management
information is also included. One is cautioned, however, that the EPA risk management data have
been developed for conditions and with constraints which may have little applicability to a given
user's specific situation.
Each chemical file begins with a short introductory paragraph followed by a status table indicating
the availability of various components of the chemical file. The information contained within the
chemical file includes risk assessment and risk management information. The specific chemical file
content is outlined below:
IRIS CHEMICAL FILE STRUCTURE
INTRODUCTION AND STATUS
I. CHRONIC SYSTEMIC TOXICITY (NON-CARCINOGENIC HEALTH EFFECTS)
A. REFERENCE DOSE (RfD) FOR ORAL EXPOSURE
1. REFERENCE DOSE SUMMARY TABLE
2. PRINCIPAL AND SUPPORTING STUDIES
3. UNCERTAINTY AND MODIFYING FACTORS
4. ADDITIONAL COMMENTS
5. CONFIDENCE IN THE RfD
6. DOCUMENTATION AND REVIEW
7. U.S. EPA CONTACTS
B. REFERENCE DOSE (RfD) FOR INHALATION EXPOSURE
(same format as for oral exposure)
II. RISK ESTIMATES FOR CARCINOGENS
A. U.S. EPA CLASSIFICATION AND BASIS
1. HUMAN DATA
2. ANIMAL DATA
3. SUPPORTING DATA
B. ORAL QUANTITATIVE ESTIMATE
1. UNIT RISK SUMMARY TABLE
2. DOSE RESPONSE DATA
3. ADDITIONAL COMMENTS
4. STATEMENT OF CONFIDENCE
C. INHALATION QUANTITATIVE ESTIMATE
1. UNIT RISK SUMMARY TABLE
2. DOSE RESPONSE DATA
3. ADDITIONAL COMMENTS
4. STATEMENT OF CONFIDENCE
D. DOCUMENTATION AND REVIEW
1. REFERENCES
2. REVIEW
3. U.S. EPA CONTACTS
III. DRINKING WATER HEALTH ADVISORIES
(format in preparation)
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IV. RISK MANAGEMENT SUMMARIES
A. RISK MANAGEMENT ACTIONS
B. RISK MANAGEMENT RATIONALE
V. SUPPLEMENTARY DATA
A. ACUTE HEALTH HAZARD INFORMATION
B. PHYSICAL-CHEMICAL PROPERTIES
SYNONYMS
Each section consists of a data and rationale summary of two or three pages in length. In addition,
EPA contacts who are familiar with the chemical are provided in each section (except for the
Supplementary Information section).
Unavailability of data for a section will be indicated, and, if known, other information pertaining to
the status of the data will be provided. A more detailed description of each of these sections is
provided in the Chemical File Structure document following this Introduction.
ELECTRONIC REPRESENTATION OF SPECIAL CHARACTERS
The use of a computerized telecommunication system for IRIS imposes limits on the number and
types of nonalphanumeric characters that can be represented. Special characters such as degree
symbols or Greek letters, and print codes such as superscripts and subscripts cannot be reproduced on
most display terminals. Therefore, very small numbers are given in scientific notation using the "E"
format. That is, a number such as 0.0006 is expressed as 6E-4, which is equivalent to saying "6 times
10 to the power of-4." Large numbers are given in "E" format in some instances, for consistency (for
example, 2E2 for the number, 200). Some other substitutions for notations generally represented by
superscripts or subscripts are: "cu. m" for cubic meter, "**" for exponentiation in formulas (for
example, "Y = X"2" represents "Y equals X squared"), and Ca(CN)2 for the chemical formula of
calcium cyanide (chemical formula subscripts are subscripted one full line in other instances). Upper
case "L" is occasionally used as the abbreviation for liter in those cases where the lower case "I" may
be misinterpreted as the number, one.
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IRIS CHEMICAL FILE STRUCTURE
PREFACE
The user is directed to Service Code 6 for instructions on how to call up information on specific
chemicals. The discussion below supplements the introduction under Service Code 4 by describing in
detail the information displayed in each of the chemical-specific files. The Appendices are
background documents which provide more detailed information on risk assessments and risk
management concepts and terms.
When one calls up a chemical, sections of information are displayed in the following order:
INTRODUCTION & STATUS, CHRONIC SYSTEMIC TOXICITY: NONCARCINOGENIC HEALTH EFFECTS,
RISK ESTIMATES FOR CARCINOGENS, DRINKING WATER HEALTH ADVISORIES, RISK MANAGEMENT
SUMMARIES, SUPPLEMENTARY DATA, and SYNONYMS. Each numbered section (all sections except
the Introduction and Synonyms) begin with a heading with the following information:
Chemical: The chemical name of the agent is given, with the common name in parentheses
where appropriate.
CAS No.: The Chemical Abstract Service number unique to the compound.
Preparation date: The date of the most recent revision of the summary sheet.
The subsections and data entries found in each of the sections are discussed below.
INTRODUCTION AND STATUS
The chemical name and Chemical Abstracts Service (CAS) number which uniquely identifies this
substance is given, along with the latest revision date for the chemical file. An introductory
statement is included in each file, followed by a status table indicating the availability of each
section. A status of "review pending" means that a chemical is currently under review, or is
scheduled for review by an EPA work group.
/. CHRONIC SYSTEMIC TOXICITY: NON-CARCINOGENIC HEALTH EFFECTS
Risk assessors are often faced with the task of interpreting the significance of long-term exposure
to chemicals which might produce toxic effects other than cancer. These effects are sometimes
referred to as the "systemic toxicity" of the compound. Traditionally, these effects have been
assessed by identifying the lowest No Observed Effect Level (NOEL) and reducing this amount by
some factor (Safety Factor or Uncertainty Factor) to estimate a level which is judged to be without
significant toxicologic concern to humans.
The CHRONIC SYSTEMIC TOXICITY section contains chemical-specific information couched in terms
of a Reference Dose (RfD), a concept which is discussed in greater detail in Appendix A. The RfD is
related to a formerly used notion of "acceptable daily intake (ADI)" but has been tailored to the
risk assessment/risk management approach used at EPA.
A. REFERENCE DOSE (RfD) FOR ORAL EXPOSURE
Chemical name, CAS No., and preparation date are given.
1. REFERENCE DOSE SUMMARY TABLE
This table summarizes the data used in the derivation of the reference dose.
Critical Effect
This first column lists the critical effect, the species and type of study, and the reference.
Experimental Doses
The second column is a summary of the information on the highest level at which no
adverse effects were found (i.e.. the No Observed Adverse Effect Level [NOAEL]) and/or
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the lowest level tested at which adverse effects were found (i.e., the Lowest Observed
Adverse Effect Level [LOAEL]). The dose levels are usually given in the units presented in
the original study and in units of milligrams per kilogram body weight per day (mg/kg/day
or mg/kg-day).
UF
The Uncertainty Factor which contributes as a divisor to the NOAEL (or LOAEL) in
calculating the Reference Dose is given. In most instances, these uncertainty factors are
standardized, based on the particular data set available. See the paper on the Reference
Dose in Appendix A for a more complete description.
MF
The Modifying Factor which also contributes as a divisor to the NOAEL in calculating the
Reference Dose is given, in most cases, this factor is 1; however, in certain instances, the
review group uses its collective professional judgment to adjust the RfD through the use
of a Modifying Factor. In such cases, explanations are provided in the text following the
table.
RfD
The RfD is an estimate (uncertainty spanning perhaps an order of magnitude) of a daily
exposure to the human population (including sensitive subgroups) that is likely to be
without an appreciable risk of deleterious effects during a liftime. The RfD is expressed in
units of milligrams per kilogram body weight per day (mg/kg/day or mg/kg-day). See
Appendix A for a full discussion of the concept and its use in risk assessment and risk
management.
Doie Conversion Factors And Assumptions
The factors used to convert the dose to mg/kg-day are listed, as well as any assumptions
made. These factors include food and water consumption, and, in some cases, inhalation-
to-oral conversion factors.
2. PRINCIPAL AND SUPPORTING STUDIES
An elaboration of the material in the summary table immediately above is presented,
providing descriptions of the critical study and other germane studies.
3. UNCERTAINTY AND MODIFYING FACTORS
An explicit presentation of the individual Uncertainty Factors contributing to the overall
Uncertainty Factor is given. The UFs are:
10-fold factor for extrapolation from animal to human (lOa)
10-fold factor for variability in the human population (10h)
10-fold factor for use of a less-than-chronic study (10s)
1 to 10-fold factor for extrapolation from a LOAEL (1 -> I0e)
See Appendix A for a more complete discussion.
An explicit explanation of the selection of any Modifying Factor is also presented.
4. ADDITIONAL COMMENTS
Ancillary information is given which may be of use or interest, e.g., other approaches taken
to establishing an RfD and why EPA prefers its approach.
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a. CONFIDENCE IN THE RfD
This entry provides a qualitative estimate, expressed in both summary and narrative form, of
the confidence that the EPA review group had in the quality of the critical study, the
supporting data base, and the RfD. A "Low" designation for the RfD suggests that the value
is likely to change as new data are generated.
6. DOCUMENTATION AND REVIEW
The EPA document(s) in which the RfD (ADI) was originally derived, and the level of review of
that document, are given. The dates of the RfD work group meetings at which the chemical
was discussed are also given.
7. U.S. EPA CONTACTS
Persons to contact for additional details on the technical issues associated with the RfD of
this chemical are listed.
B. REFERENCE DOSE (RfD) FOR INHALATION EXPOSURE
Inhalation RfD methods are under development.
//. RISK ESTIMATES FOR CARCINOGENS
A. U.S. EPA CLASSIFICATION AND BASIS
Classification
The EPA weight-of-evidence classification of the agent, as described in the Hazard
Identification section (HA) of appendix B.
1. HUMAN DATA
A description of the human evidence leading to the classification. Difficulties in determining
the final classification are also given where necessary.
2. ANIMAL DATA
A description of the experimental animal evidence leading to the classification. Difficulties in
determining the final classification are given where necessary.
3. SUPPORTING DATA
A description of data fending support to the classification, such as genotoxicity.
B. ORAL QUANTITATIVE ESTIMATE
Slope Factor
The upper-bound incremental lifetime cancer risk estimated to result from a continuous orally
absorbed dose of 1 mg per kg body weight per day. Since the oral absorption fraction is usually
assumed to be 100%, the same oral slope factor is used for continuous oral intake.
1. UNIT RISK SUMMARY TABLE
Water concentration producing risk levels of E-4, E-5, E-S
The concentration of the agent (micrograms per liter) in drinking water estimated to result in
upper-bound incremental lifetime cancer risk of E-4, E-5, E-6, if 2 liters of water which is
contaminated with the agent were ingested per day continuously for a lifetime.
-------
Unit Risk
The upper-bound incremental lifetime cancer risk estimated to result from ingestion of 2
liters of water per day of drinking water contaminated with the agent at a concentration of
one microgram per liter.
Model
The abbreviation for the dose extrapolation model used to estimate cancer risk at low doses
from experimental observations at higher doses. M is the multistage procedure, W is Weibull,
P is probit, LO is logit, OH is one-hit, GM is gamma multi-hit.
2. DOSE-RESPONSE DATA
This table shows the animal data set from which the risk parameters were estimated. The
table shows the species and strain of the animals used, the tumor type or types used for the
estimate, the dose administered in the experiment, the lifetime tumor incidence observed, a
code for the literature citation of the report where the data was published, and the route of
administration used in the experiment. The table is modified when human data are used for
the estimation of risk parameters.
3. ADDITIONAL COMMENTS
An explanation of the assumptions used in deriving the risk estimate. For each agent the
following information is presented:
method of selecting the data set,
animal-to-human equivalent dose assumption,
statement of whether the administered animal dose or a pharmaco-kinetically-derived
effective metabolized dose was used, and
relevant non-cancer toxicity.
Other comments describing the estimation procedure for the agent are included. A
statement is also made that the risk estimate should not be used if the water concentration is
larger than x ug/l and the air concentration is larger than y ug/cu.m. In this statement the
values of x and y are the concentrations above which the risk exceeds 1.0%.
4. STATEMENT OF CONFIDENCE
A high, medium, or low rating based on the factors enumerated in section II of appendix B. A
description of the main factors leading to this rating is included.
C INHALA TION QUANTITA TIVE ESTIMA TE
The entries in this subsection are analogous to those in the Oral Quantitative Estimate
subsection above.
D. DOCUMENTATION REVIEW
1. REFERENCES
Literature citations for the major papers used in the classification of the agent and in
quantitative estimates.
2. REVIEW
Description of the review procedure received by the EPA evaluation document which is
summarized by these sheets:
-------
Agency CRAVE Work Group Review
Dates on which the Agency review committee met to review data on the agent.
Verification Date
Date on which the Agency review committee agreed that the information is accurate.
3. U.S. EPA CONTACTS
The person or persons at EPA who can explain the origin of the items on the summary sheet.
///. DRINKING WATER HEALTH ADVISORIES
Health advisories are still under development.
IV. RISK MANAGEMENT SUMMARIES
INTERPRET A TION OF RISK MANAGEMENT DATA
A cautionary statement is presented concerning the interpretation of the data.
A. RISK MANAGEMENT ACTIONS
A table summarizing the risk management actions taken by the U.S. EPA is given. This table
includes the following categories:
Risk Management Action
The type of action (i.e., official name)
Status
Current status of this action
Date
Date of the action
Risk Management Value
The numeric risk management value. Some values are specific for duration of exposure, and
are so indicated. Values that vary according to a given set of conditions (e.g., site-specific
values) will not be listed here. Call the EPA Contact for specific information.
Considers EconlTefh Feasibility
Indicates whether or not the economical or technical feasibility of the risk management
action has been considered prior to setting the value.
Reference
The document in which the value was published.
B. RISK MANAGEMENT RATIONALE
The chemical-specific information underlying each of the risk management actions is described.
U.S. EPA contacts are also given.
-------
V. SUPPLEMENTAR Y DA TA
A. ACUTE HEALTH HAZARD INFORMATION
In response to concerns raised following the tragic release of toxic substances from a chemical
plant in Bhopal, India in 1985, EPA has generated a list of chemicals which could conceivably
pose acute hazards to people living in the neighborhood of production or storage facilities. The
list includes a range of chemical-specific information which would be useful in assessing the
significance of levels determined in the environment.
B. PHYSICAL-CHEMICAL PROPERTIES
The chemical and physical properties of the compound are listed and other properties of the
substance are presented.
SYNONYMS
A listing of synonyms for the chemical as extracted from a number of sources is given.
-------
100-42-5 Etrrene
62476-59-9 Tackle
59O2-51-2 Terbacll
95-94-3 1.2.4,S-Tetrachlorobencene
79-34-5 1,1,2.2-Tetrachloroethane
127-18-4 Tetrachloroethrlene
58-90-2 2.3.4.6-Tetrachlorophenol
961-11-5 Tetrachlorovlnphos
78-00-2 Tetraethyl Lead
1314-32-5 Thalllc O*lde
563-68-8 TtuUllua Acetate
6533-73-9 Thallium Carbonate
7791-12-0 Thalllua Chloride
10102-45-1 Thalllua Nitrate
12039-52-0 Thai HUB Selenlte
7446-18-6 Thalllua(I) Sulfate
235C4-05-B Thlophanate-Mthrl
108-88-3 Toluene
2303-17-5 Triallate
615-54-3 1.2.4-Trlbroawbencene
120-82-1 1.2.4-Trlchlorobencene
71-55-6 1.1.1-Trlchloroethane
79-00-5 1.1,2-Trlchloroethane
79-01-6 TrlchloroethrlwM
75-69-4 Trlchloroaonof luoroaMthaae
95-95-4 2.4.5-Trlchlorophenol
88-OC-2 2.4.6-Trlchloropbeaol
96-18-4 1.2.3-TrlcMoropropana
76-13-1 1.1.2-Trlchloro-1.2.2-trlfluoroetbai>e (H-113)
no CAS No. Trlxllphane
1314-62-1 Taaadlua Pentorlde
1929-77-7 Vernaa
50471-44-8 Tlnclocolio
81-81-2 Warfarin
557-21-1 Zinc Cyanide
1314-84-7 Zinc Phosphide
12122-67-7 Zlneb
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Lindane: page 2 of 7
I. CHRONIC SYSTEMIC TOXICITY: NONCARCINOGENIC HEALTH EFFECTS
INTERPRETATION OF CHRONIC SYSTEMIC TOXICITY DATA
The Reference Dose (RfD) is based on the assumption that thresholds may exist
for certain toxic effects such as cellular necrosis, but may not exist for
other toxic effects such as carcinogenicity. The RfD is considered to be the
level unlikely to cause significant adverse health effects associated with a
threshold mechanism of action in humans exposed for a lifetime. RfDs can
also be derived for the noncarcinogenic health effects of compounds which are
also carcinogens. Therefore, it is essential to refer to section II, and
other sources as well, for risk assessment information pertaining to the
carcinogenicity of this compound. Please refer to the Background Document on
the RfD (Appendix A) in Service Code 4 for an elaboration of these concepts.
A. REFERENCE DOSE (RfD) FOR ORAL EXPOSURE
Chemical: Lindane
CAS No.: 58-89-9 Preparation Date: 04/28/86
1. REFERENCE DOSE SUMMARY TABLE
Critical Effect Experimental Doses * UF MF RfD
Liver and kidney 4 ppm diet 0.3 mg/kg 1000 1 3E-4
toxicity bw/day (females) mg/kg/day
(NOAEL)
Rat, subchronic oral
bioassay 20 ppm diet 1.55
mg/kg bw/day (males)
RCC (1983) (LOAEL)
* Dose Conversion Factors & Assumptions: none
2. PRINCIPAL AND SUPPORTING STUDIES
Research and Consulting Co., Ltd. 1983. Ace. Nos. 250340-250342.
Available from EPA. Write to FOI, EPA, Washington D.C. 20460.
Twenty male and 20 female Wistar KFM-Han (outbred) SPF rats/treatment
group were administered 0, 0.2, 0.8, 4, 20 or 100 ppm lindane (99.851) in the
diet. After 12 weeks, 15 animala/sex/group were aacrificed. The remaining
rats were fed the control diet for an additional 6 veeks before sacrifice.
No treatment-related effects were noted on mortality, hematology, clinical
chemistry or urinalysis. Rats receiving 20 and 100 ppm lindane were observed
to have greater-than-control incidence of the following: liver hypertrophy,
kidney tubular degeneration, hyaline droplets, tubular diatension, inter-
stitial nephritis and basophilic tubules. Since these effects were mild or
rare in animals receiving 4 ppm, this represents a NOAEL. The reviewers of
the study calculated the dose to be 0.29 mg/kg/day for males and 0.33
mg/kg/day for females, based on measured food intake.
-------
Llndane: page 3 of 7
In a 2-year feeding study (Fitzhugh, 1950), 10 Wistar rats/sex/group were
exposed to 5, 10, 50, 100, AGO, 800 or 1600 ppm lindane. Slight liver and
kidney damage and increased liver weights were noted at the 100 ppm level.
If a food intake equal to 5% body weight is assumed, a NOAEL of 2.5 tng/kg
bw/day (50 ppm) can be determined from this assay. In a 2-year bioassay
(Rivett et al., 1978), four beagle dogs/sex/group were administered 0, 25, 50
or 100 ppm lindane in the diet. Treatment-related effects noted in the
animals of the 100 ppm group were increased serum alkaline phosphatase and
enlarged dark friable livers. A NOAEL was determined to be 50 ppm (1.6 mg/kg
bw/day).
Use of the NOAEL derived from the RCC (1983) study is most appropriate, in
keeping with the practice of utilizing data from the most sensitive species
(or strain) as a surrogate for humans when human data are lacking.
3. UNCERTAINTY AND MODIFYING FACTORS
UF - 1000. A factor of 10 each was employed for use of a subchronic vs. a
lifetime assay, to account for interspecies variation and to protect
sensitive human subpopulations.
MF - 1
4. ADDITIONAL COMMENTS
Data on reproductive effects of lindane are inconclusive. Most reports
Indicate that hexachlorocyclohexane iaomers are nonteratogenic.
5. CONFIDENCE IN THE RfD
Study: Medium Data Base: Medium RfD: Medium
The RCC (1983) study used an adequate number of animals and measured mul-
tiple end points. Since there are other reported chronic and subchronic
studies, confidence in the study, data base and RfD is considered medium.
6. DOCUMENTATION AND REVIEW
U.S. EPA. 1985. Drinking Water Criteria Document for Lindane. Office of
Drinking Water, Washington, DC.
The RfD in the Drinking Water Criteria Document has been extensively reviewed
by U.S. EPA scientists and selected outside experts.
Agency RfD Work Croup Review: 01/22/86
Verification Date: 01/22/86
7. U.S. EPA CONTACTS
Primary: M.L. Dourson FTS/684-7544 or 513/569-7544
Office of Research and Development
Secondary: C.T. DeRosa FTS/684-7534 or 513/569-7534
Office of Research and Development
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Lindane : page 4 of 7
B. REFERENCE DOSE (RfD) FOR INHALATION EXPOSURE
Chemical: Lindane
CAS No.: 58-89-9
Information is not available at this time.
Chemical:
CAS No.:
II. RISK ESTIMATES FOR CARCINOGENS
Lindane
58-89-9
This chemical is among those substances evaluated by the U.S. EPA for
evidence of human carcinogenic potential. This does not imply that this
chemical is necessarily a carcinogen. The evaluation for this chemical is
under review by an inter-office Agency work group. A risk assessment summary
will be included on IRIS when the review has been completed.
III. DRINKING WATER HEALTH ADVISORIES
Chemical:
CAS No.:
Lindane
58-89-9
Information is not available at this time.
IV. RISK MANAGEMENT SUMMARIES
Chemical :
CAS No.:
Lindane
58-89-9
Preparation Date: 09/30/86
INTERPRETATION OF RISK MANAGEMENT DATA
EPA risk assessments may be continuously updated as new data are published
and as assessment methodologies evolve. Risk management (RM) decisions are
frequently not updated at the same time. Carefully read the dates for the
risk management actions (in this section) and the verification dates for the
risk assessments (in sections I & II), as this may explain apparent inconsis-
tencies. Also note that some risk management decisions consider- factors not
related to health risk, such as technical or economic feasibility. Such
considerations are indicated in the table below (Considers Econ/Tech
Feasibility). Please direct any questions you may have concerning the use of
risk assessment information in making a risk management decision to the
contact listed in Part B of this section (Risk Management Rationale). Users
are strongly urged to read the background information on each RM action in
Appendix E in Service Code 4.
-------
INTEGRATED RISK INFORMATION SYSTEM: Chemical Files
Lindane; CAS No. 58-89-9 (Revised 11/16/1986)
USE AND INTERPRETATION OF THE DATA IN IRIS
Health risk assessment information on chemicals is included in IRIS only
after a comprehensive review of chronic toxiclty data by work groups
composed of U.S. EPA scientists from several Agency Program Offices. The
summaries presented in Sections I and II represent * consensus reached in
those reviews. The conceptual bases of these risk assessments are described
in Appendices A & B in Service Code 4. The other sections are supplementary
information which may be useful in particular risk management situations, but
have not yet undergone comprehensive U.S. EPA review. The risk management
numbers (Section V) may not be based on the most current risk assessment, or
may be based on a current, but unreviewed, risk assessment, and may take into
account factors other than health effects (e.g., treatment technology). When
considering the use of risk management numbers for a particular situation,
note the date of their development, the date of the most recent risk
assessment, and whether technological factors were considered. For a more
detailed description of procedures used in these assessments and the
development of risk management numbers, see Appendix E in Service Code A.
STATUS OF DATA FOR Lindane
I. Chronic Systemic Toxicity: Noncarcinogenic Health Effects
A. Oral RfD: available
B. Inhalation RfD: none
II. Risk Estimates for Carcinogens: review pending
III. Drinking Water Health Advisories: none
IV. Risk Management Summaries: available
V. Supplementary Data: available
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LIndane: page 5 of 7
A. RISK MANAGEMENT ACTIONS
Risk
Management
Action
Reportable
Quantity (RQ)
Status
Date
Statutory
1980
Risk
Management
Value
1 Ib
Considers
Econ/Tech
Feasibility
no
Reference
50 FR 13456
04/04/85
Water Quality
Criteria (WQC):
a. Human Health
b. Aquatic Toxicity
1) Freshwater
18.6 ng/1 no
2) Marine
Pesticide Active
Ingredient:
a. Registration
Standard
b. Special
Review
Final Acute no
1980 2.0 ug/1
Chronic
0.080 ug/1
Final Acute no
1980 0.16 ug/1
Chronic
none
Current
1985
various
Termination P.D. 1
of RPAR
1983
no
no
B. RISK MANAGEMENT RATIONALE
RQ
45 FR 79318
11/28/80
45 FR 79318
11/28/80
ibid.
Reg. Std.
Sept. 1985
42 FR 9816
02/18/77
P.D.
P.D.
2/3
4
yes
yes
45 FR 45362
07/03/80
48 FR 48512
09/30/83
The statutory RQ of 1 pound established pursuant to CERCLA Section
102(b) is retained until the assessment of potential carcinogenicity is
complete.
Contact: Office of Emergency and Remedial Response
202\382-2180 or FTS\382-2180
WQC
Contact: Office of Water Regulations and Standards
202-382-5400 or FTS-382-5400
a. Human health: The WQC of 18.6 ng/1 represents a cancer risk level of
1E-6 based on consumption of contaminated organisms and water. A WQC of
62.5 ng/1 has been established based on consumtion of contaminated aquatic
organisms alone.
b. Aquatic toxicity: Water quality criteria for the protection of aquatic
life are derived from a minimum data base of acute and chronic tests on a
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Llndane: page 6 of 7
variety of aquatic organisms. The data are assumed to be statistically
representative and are used to calculate concentrations which will not have
significant short or long term effects on 95% of the organisms exposed.
Recent criteria (1985 and later) contain duration and frequency stipulations:
the acute criteria maximum concentration is a 1-hour average and the chronic
criteria continuous concentration is a 4-day average which are not to be
exceeded more than once every three years, on the average (see Stephen et al.
1985). Earlier criteria (1980-1984) contained instantaneous acute and
24-hour average chronic concentrations which were not to be exceeded. (FR 45:
79318: November 28, 1980). The freshwater chronic WQC is a 24-hour average.
Pesticide Active Ingredient
a. Regulation Standard: Lindane Pesticide Registration Standard.
September 1985. Registration Support and Emergency Response Branch.
Office of Pesticide Programs.
Contact: Office of Pesticides Programs
202/557-7760 or FTS/557-7760
b. Special Review: Negotiated settlements have been made for Lindane in dog
dips [49 FR 26282 (06/27/84)] and in smoke bombs [50 FR 5424 (02/08/85)].
Contact: Office of Pesticides Programs, Special Review Branch
202/557-7420 or FTS/557-7420
V. SUPPLEMENTARY DATA
Chemical: Lindane
CAS No.: 58-89-9 Preparation Date: 11/07/86
USE AND INTERPRETATION OF SUPPLEMENTARY DATA
The information contained in this section (subsections A and B) has been
extracted from the EPA Chemical Profiles Database, which has been compiled
from a number of secondary sources and has not undergone formal Agency
review. The complete reference listings for the citations below are provided
in Service Code 4. The user is urged to read the background document for
this section (Appendix E in Service Code 4) for further information on the
sources and limitations of the data presented here.
A. ACUTE HEALTH HAZARD INFORMATION
Lindane is a stimulant of the nervous system, causing violent convulsions
that are rapid in onset and generally followed by death or recovery with 24
hours (Hayes, 1982, p. 218). The probable human oral lethal dose is 50-500
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Lindane: page 7 of 7
reported in children. Coma, respiratory failure and death can result.
Exposure to vapors of this compound, or its thermal decomposition products,
may lead to headache, nausea, vomiting, and irritation of the eyes, nose, and
throat (Gosselin, 1984, pp. III-240, 241).
B. PHYSICAL-CHEMICAL PROPERTIES
Chemical Formula: C H Cl
666
Molecular Weight: 290.83
Boiling Point: 614F, 323.4C; Decomposes
Specific Gravity (H20-1): 1.9
Vapor Pressure (mmHg): 9.4 x 10-6 at 20C
Melting Point: 234.5F, 112.5C
Vapor Density (AIR-1): Not Found
Evaporation Rate (Butyl acetate-1): Not Found
Solubility in Water: Insoluble
Flash Point [Method Used]: Not Found
Flammable Limits: Not Found
Appearance and Odor: Colorless solid with a musty odor; pure material is
odorless (NIOSH/OSHA. 1978, p. 120).
Conditions or Materials to Avoid: Not Found
Hazardous Decomposition or Byproducts: Thermal decomposition products may
include chlorine, hydrochloric acid, and phosgene (Sax, 1984, p. 366).
Use: Lindane is used as a pesticide (Havley, 1981, p. 617) and scabicide
(Hayes, 1982, p. 221).
Synonyms: (NIOSH/RTECS 1983 Synonyms, Volume 1, p. 1,000): Cyclohexane,
1,2,3,4,5,6-Hexachloro-, Gamma-Isoraer; Aalindan; Aficide; Agrisol G-20;
Agrocide; Agrocide 2; Agrocide 7; Agrocide 6G; Agrocide III; Agrocide VF;
Agronexit; Ameisenatod; Ameisanmittel Merck; Aparasin; Aphtiria; Aplidal;
Arbitex; BBH; Ben-Hex; Bentox 10; Benzene Hexachloride-gamma-isomer;
gamma-Benzene Hexachloride; Bexol; BHC; gamma-BHC; Celanex; Chloreaene;
Codechine; DBH; Detmol-Extrakt; Detox 25; Devoran; Ool Granule; Drill
Tox-Spezial Aglukon; Ent 7,796; Entomoxan; Exagama; For1in; Gallogama; Gamacid
Gamaphex; Gamene; Gammahexa; Gammahexane; Cammalin; Gamma1in 20; Gammaterr;
Gammex; Cammexane; Gammopar; Gexane; HCCH; HCH; gamma-HCH; Heclotox; Hexa;
Hexachloran; gamma-Hexachloran; Hexachlorane; gamma- Hexachlorane;
gamma-Hexachlorobenzene; 1-alpha,2-alpha,3-beta,4-alpha,
5-alpha,6-beta-Hexachlorocyclohexane; gamma-Hexachlorocyclohexane; gamma-
1,2,3,4,5,6-Hexachlorocyclohexane; Hexachlorocyclohexane, gamma-Isomer;
1,2,3.4.5,6-Hexachlorocyclohexane. gamma-Isomer,; Hexatox; Hexaverm; Hexicide;
Hexyclan; HGI; Hortex; Inexit; Isotox; Jacutin; Kokotine; Xvell; Lendine;
Lentox; Lidenal; Lindafor; Lindagara; Lindagrain; Lindagranox; gamma-Lindane;
Lindane (DOT); Lindapoudre; Lindatox; Llndosep; Lintox; Lorexane; Milbol 49;
Mszychol; NCI-C00204; NEO-Scabicidol; Nexen FB; Nexit; Nexit-Stark; Nexol-E;
Nicochloran; Novigam; Omnitox; Ovadziak; Owadzlak; Pedraczak; Pflanzol;
Quellada; Sang gamma; Silvanol; Spritz-Rapidin; Spruehpflanzol; Streunex; Tap
85; TRI-6; Viton
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APPENDIX C
SOURCES OF INFORMATION FOR TOXICITY PROFILES
-------
TABLE C-l. TOXICITY PROFILES AVAILABLE FROM U.S. EPA OFFICE
OF WASTE PROGRAMS ENFORCEMENT (OWPE) AND OFFICE OF
EMERGENCY AND REMEDIAL RESPONSE (OERR)
ov
CS«aical Cheated
Acta«pbtb«n*
Actfuphtbylcnr
Aettlc arid
4ft tent
At r»l«ln
tue rrlonlcrlle
Aldrla
Aatbraccnt
Aatlaony
Arttnic
AjWtto*
Urlua
»«m«n«
haiidln*
l*nto(*)inehr*c«n(
l«nio(a)pyrcnt
••asotblazolt
Wrjlllu.
• Iplu-IHC
b«t«-»HC
fUU-BHC (Undine)
ttltt-lHC
BuCinp)
•u(v] «crtit>
U4rlur
Ccrbon t«cr»ehlorid*
cl>-Chlord«nr
tr»n»-Chlord»n«
Chlorlnt
CMorobtnztnr
Chlorob«n« ilatc
Cblore«tb«nf
Chlorofor*
p— Chloro-»-crt«o]
l-dloro-3-nttrebcBtcn*
kli (2-ChJor«>«tho»y)«th«Df
Chraviua (total)
ChrMiu* (h«««*«l*at)
OirovluB (triviltot)
OtryMOt
Co«l t«rt
Cob* It
topper
CrtMl
Cy«nld«f
Cycnurle acid
• ,• -BOD
«,p -DDD
• ,. -MI
-,» -CDT
e,s -DOT
Olbroaochloropropan*
,2-Dlch]oreb«nitnf
, )*Dich]orob«nittt«
,*-Dlchlorob«ni«n«
, l-Dichloro«th«n«
2-DlcMorotthant
,i-Pichloro*th]rl*Bt
. ?-cl»-DlchIcr«>«thyl«nt
, 7-tr«ni-OJehlorot thy lent
2,4-Dlchloropb«ool
Z,4-PJehlorophtDory«cetle acid •
1.2-Dtcbleropropanc :
n OEM Itealth
Profile Cffteti A««*«*Mnt
I
Z
I
Z
Z
Z
Z
X
Z
Z
Z
Z
Z
X
X
X
X
X
X
X
X
[
[
-------
TABLE C-l. (Continued)
oari oexi i*«uh
Qir»lc«: frofllt ttftett A«»ti«wnt
1 ,J-Clchloroprop«nf
1 ,3-Cichloroprepaoa
Dleofol
Dialdrla
Di*thyl bcaxanr
Dltthyltn* glycol
Dltthyl phthaJatr
Dlliobutyl kcton*
DlB«thyUslno«thr] »eth*cryl»t«
Ctjittbyl •allla*
Cl»ethjUth*nol
Jtatbyl ehlorldt
2-Krthyl dodceant
Mubyitnt chlorld*
Ktthyl ctbyl b«nxtnc
Krthyl ethyl ktton*
3-Kfthy) b«>ant
H«thyl laebutyl katena
Kathyl itcehaerylata
Hathyl parathlon
2-M*thyl pantana
J— H*thy) pantant
2-Hathyl-l-pantaoa
2-Mathyl tatradccana
2-Hathyl Crldacana
Z
Z
Z
Z
Z
Z
Z
Z
Z
Z
Z
-------
TABLE C-l. (Continued)
Otf
Oi«»lc»l Ch««lcil
Hone th« no 1 ••ine
IUphtlvil«n«
•ick«]
KUrocelluloie
2-NitrophtDol
Ftntachlorophcnol
Pent«decanf
Ph«Mnthreo«
Phenol
f bitty] ether
fboiphoric acid
Phoiphorwi
Merle acid
Polycblorlnated biphenyl* (Kit)
Folych]orln«t«4 dib«nte-p-4iozln
folycyellc tromttlc bydroccrbon* (FAHt)
Pyrta*
Scltnlua
Silver
todlua chlorite
todlia eycnid*
Sodiu»
ttexidird «ol»«nt
Sulfuric Trlehlore«thaBt
Trlehlere«tbyl«a«
Trichlerefluora««th«a*
2,i,5-Triehleroph«ael
2.4,t-TrichlorepbtBol
2>4(S-Ttlehlereph«B«s7«ettic acid
2,4,3-Tricblorephtnozr proploolc »cld
Trl»«tb7lb«a(«n«
I.3,S-7rlMtbylV«Bi«a«
1 , J,*-Trl»«thTlb«niene
trlf (2,3-Dlbro»opropyJ)phofptutt
Cnd«c«n«
V*n«dlu«
Vinyl chlorldr
Xyltnt
•-lyltn*
e-Xylent
p-Zyltoc
Zinc
ri OCU lUiltb
Profile tffecti Aiiete««nt
I
X
X
s
X
I
X
X
X
X
X
X
X
X
X
X
X
1
Reference: Life Systems (1985).
-------
TABLE C-2. U.S. EPA SOURCES OF TOXICITY PROFILES
Document
Availability
Description
Criteria Document - Air
Criteria Docunent -
Drinking Water
Criteria Document -
Ambient Water Quality
Chemical Hazard Informa-
tion Profile (CHIP)
Chemical Profile
Health Advisory
Health Assessment
Document
Office of Air Quality Simrnry of the latest scientific knowledge on the effects of varying quantities of a substance in the air.
Planning and Standards (OAOPS) Usually prepared for (MOPS by the Office of Health and Environmental Assessment (OHEA).
Office of Drinking Water (ODW) Summary of important experimental results from the literature relevant to the chemistry and health effects
of a specific drinking water contaminant. Serves as a foundation to support regulatory standards or guide-
lines for the acceptable concentration of the contaminant in the drinking water.
Office of Water Regulations
and Standards (OURS)
Office of Toxic Substances
(OTS)
Office of Waste Programs
Enforcement (OUPE)
CPU
Office of Health and Environ-
mental Assessment (OHEA)
Health and Environmental Office of Solid Waste (OSU)
Effects Profile
Health Effects
Assessments
Office of Emergency and
Remedial Response (OERR)
Information on the type and extent of identifiable toxic effects on health and welfare expected from the
presence of pollutants in any body of water. Objective of document is to protect most species in a balanced
and healthy aquatic community and/or to protect human health.
Summary of readily available information concerning the health and environmental effects and potential
exposure to a chemical.
Brief summary of the chemical/physical properties, fate and transport, health effects and environmntal toxicity
level* for 202 chemicals identified at hazardous waste sites. Currently 183 of the planned Chemical Profiles
are available in draft form.
Develops toxicological analyses to establish an acceptable level in drinking water for unregulated contaminants
for various exposure durations.
Inventories the scientific literature and evaluates key studies. Discusses dose-response relationships so that
the nature of the adverse health response is evaluated in perspective with observed environmental levels.
Usually prepared by OHEA for another office.
Profiles are "mini-" criteria documents prepared usually as summaries of existing water quality criteria
documents. They serve as a support for the listing of hazardous wastes in the RCRA program.
Summary of the pertinent health effects information on 58 chemicals found most often at hazardous waste sites.
Developed by the Environmental Criteria and Assessment Office (ECAO) for OERR.
Address for all offices listed above; U.S. Environmental Protection Agency. 401 M Street S.W., Washington, DC 20460 (202) 382-2090
Reference: Life Systems (1985).
-------
TABLE C-3. SELECTED CHEMICAL AND TOXICOLOGICAL DATABASES
Database vendor
Database Name
Database Contents
Access Procedures
MEDLARS (National Library
of Medicine)
Toxline
1.5 million references on environmental and toxicological
effects of chemicals.
Contact: MEDLARS Management Section
National Library of Medicine
8600 Rockville Pike
Bethesda, MO 20209
(301) 496-6193
CheMline
An online chemical dictionary of 500,000 records.
RTECS (Registry of Toxic Effects
of Chemical Substances)
Basic acute and chronic toxicity for more than 57,000 toxic
chemicals.
CIS (Chemical Information
System)
AQOIRE (Aquatic Information
Retrieval System
CESARS (Chemical Evaluation
Search and Retrieval System)
Toxicity data for 2,000 chemicals, each cross referenced by
CAS nuifcer. Lists any studies on bioaccunulation, sublethaI
effects, and environmental fate of the chemical.
Detailed toxicity and environmental fate information and
evaluation on 150 chemicals of importance to Great Lakes.
Contact: CIS, Inc.
Fefn-Marquart Associates
7215 York Road
Baltimore, HD 21212
(800) 247-8737
CTO> (Clinical Toxicology of
Coamercial Products)
Ingredient and product information for most commercially
available nonfood items.
Envirofate
ISHOR (Information System for
Hazardous Organics in Water)
OHMTADS (OiI and Hazardous
Materials Technical Assistance
Data System)
Information on the environmental fate of approximately 500
chemicals.
Physical and chemical.properties of 14,000 organic compounds
and associated aquatic toxicity data.
Created by U.S. EPA Superfund. Includes information on
environmental effects of 11,000+ hazardous substances.
-------
TABLE C-3. (Continued)
CAS Online
(Chemical Abstracts)
Chemical Abstracts
Physical and chemical properties on 6 million chemical
substances.
Contact: Chemical Abstracts Office
Customer Service
P.O. Box 3012
Colunbus, OH 43210
(BOO) 848-6533
DOE/RECON
35 energy-related and environ-
mental databases including
Energy Database, Water Resources
Abstracts, Environmental Nuta-
gera, and Environmental
Teratology.
Contact: Technical Information Center
U.S. Department of Energy
P.O. Box 62
Oak Ridge, TN 37380
(615) 575-1272
Reference: U.S. Fish and Wildlife Service (1986).
-------
APPENDIX D
EVALUATION OF THE EFFECTS OF COMPOSITE SAMPLING ON
STATISTICAL POWER OF A SAMPLING DESIGN
-------
APPENDIX D: EVALUATION OF THE EFFECTS OF COMPOSITE SAMPLING ON STATIST-
ICAL POWER OF A SAMPLING DESIGN
Tetra Tech (1986b) used simulation methods to make a direct comparison of grab
and composite-sampling strategies. Simulation refers to the use of numerical tech-
niques to generate random variables with specified statistical properties. For the
analyses described below, Tetra Tech (1986b) developed computer programs to 1) produce
individual random samples from populations with normally distributed concentrations of
contaminants, and other statistical properties similar to those of historical bioaccumulation
data sets described in Tetra Tech (1986b), 2) construct composite samples, and 3)
calculate statistical power of sampling designs using individual or composite samples.
Two sets of analyses were performed by Tetra Tech (1986b). In the first set,
simulation methods were used to show the effect of sample compositing on the estimate
of the population mean. Power analyses were used in the second set of analyses to
demonstrate the effect of increasing the number of subsamples in a composite sample
on the probability of detecting specified levels of differences among stations.
The first set of analyses demonstrated that the confidence in the estimate of the
mean increases as the number of subsamples in the composite increases (Figure D-l).
The simulated sampling consisted of randomly selecting 10,000 composite samples from
two populations exhibiting two different levels of variability in the sampling environ-
ment. The mean value in both populations was fixed at J8.52, but the population
variances were set at 70.90 or 354.19, corresponding to coefficients of variation of 45.5
and 101.6, respectively. These population characteristics were selected as representative
of the range of values for the coefficient of variation observed in the historical data
sets for selected metals and organic compounds in marine organisms (Tetra Tech 1986b).
For a series of individual fish samples taken from the corresponding populations used
in Analysis 1, the 95 percent confidence intervals would range from 1.7 to 35.4 concen-
tration units (e.g., ppm).
To demonstrate the effect of sample compositing on the power of the statistical
test of significance, Tetra Tech (1986b) performed statistical power analyses using a
one-way Analysis of Variance (ANOVA) model. In these analyses (Figure D-2), the
number of stations (5), number of replicate composite samples at each station (5),
significance level of the test (0.05), residual error variance level, and level of minimum
detectable difference (100 percent of overall mean) were fixed. The power of the test
(i.e., the probability of detecting the specified minimum difference) was then calculated
as a function of the number of subsamples constituting each replicate composite sample.
Power analyses were conducted for three levels of sample variability. All design
parameters except the residual error variance were identical in each set of analyses.
Values of the residual error variance were selected to represent the range of values
found in the historical data sets described by Tetra Tech (1986b). The coefficients of
variation selected for these three sets of analyses were 45.5, 101.6, and 203.5.
As shown in Figure D-2, the probability of statistically detecting a difference
equal to the overall sample mean among stations increases with the collection of
replicate composite samples at each station and as the number of subsamples constituting
the composite increases. The results of both sets of analyses shown in Figure D-2 also
demonstrate the phenomenon of diminishing returns for continued increases in the
number of subsamples per composite. In Analysis Set 1, for example, virtually no
-------
Analysis 1. Mean(u) . 18.52 Coefficient of Variation .455
Variance (o*) • 70.90
25.
Z
i •
o
V 15.
95% C.I.
4 • 10 20
NUMBER OF SUBSAMPLES IN COMPOSITE
Analysis 2. Mean(^)
Variance (
. 18.52 Coefficient of Variation « 101.6
. 354.19
40 •
< »
Ul
5
o
Ul jo -
H
<
•g
in 10 •
Ul
a-
4 • 10 ao
NUMBER OF SUBSAMPLES IN COMPOSITE
Reference: Tetra Tech (1986b)
Figure D-i Effects of increasing composite sample size on confidence
in the estimate of the mean.
-------
Analysis
1
2
3
Coefficient
of Variation
45.5
101.6
203.5
i.o
o.s
0.6
0.4-
0.0
01 2 3 4 S 6 7 ( 9 10 11 12 13 14 IS 16
NUMBER OF SUBSAMPLES
(a)
1.0
o.t
0.6-
0.4-
0.0
0 2 4 6 I 10 12 14 1C 1» 20 22 24 2t it M
NUMBER OF SUBSAMPLES
(b)
Reference: Tetra Tech (19866)
Figure D-2 Power of statistical tests vs. number of subsamples in composite
replicate samples. Fixed design parameters: number of stations
5, number of replicates = 5, significance level = 0.05, minimum
detectable difference = 100 percent of overall mean value.
-------
increase in the power of the statistical test was achieved with increasing the subsample
size above three. In the second analysis set, substantial increases in statistical power
were achieved by increasing the number of subsamples in each composite from 2 to 10.
However, with each successive increase in subsample size, the relative benefit was
reduced until very little was gained by increasing the subsample size above 10. For
moderate levels of variability, 6-10 subsamples within each of 5 replicate composite
samples may be adequate to detect a treatment difference equal to 100 percent of the
mean among treatments. At the highest level of variability analyzed, the collection of
replicate composite samples composed of 25 subsamples each is required to obtain a
testing power of 0.80 (Figure D-2).
-------
APPENDIX E
EVALUATION OF THE EFFECTS OF SAMPLE REPLICATION ON
STATISTICAL POWER OF A SAMPLING DESIGN
-------
APPENDIX E: EVALUATION OF THE EFFECTS OF SAMPLE REPLICATION ON STATISTI-
CAL POWER OF A SAMPLING DESIGN
Statistical power analysis can be used to evaluate alternative sampling designs
with varying levels of replication (Cohen 1977; Gordon et al. 1980; Tetra Tech 1986b).
In statistical power analysis, relationships among the following study design parameters
are evaluated:
• Power - Probability of detecting a real difference among treatments
(e.g., species, stations, times)
• Type I error (a) - Probability of wrongly concluding that there are
differences among treatments
• Minimum detectable difference - Magnitude of the smallest difference
that can be detected for given power and Type I error
• Residual error - Natural variability
• Number of stations
• Number of replicate samples.
The analyses presented below were conducted with the objective of providing guidance
in selecting levels of sampling replication. This objective was addressed by determining
the magnitudes of difference among variables that can be reliably detected with varying
levels of sampling effort.
A one-way ANOVA model was used to evaluate statistical sensitivity relative to
level of sample replication. Tetra Tech (1986b,d) provides details of the ANOVA model
and results of the analyses. All power analyses were conducted using the Ocean
Discharge Evaluation System (ODES) maintained by EPA's Office of Marine and Estuarine
Protection (Tetra Tech 1986d). The measure used to evaluate the statistical sensitivity
of the monitoring design was the minimum detectable difference between two mean
values. To generalize the results of the power analysis, the minimum detectable difference
was expressed as a percentage of the grand mean among treatments. The power of the
test was fixed at 0.80.
Predicted values of minimum detectable difference are shown for various levels of
sample replication in Figures E-l and E-2. For these analyses, the Type I error was
fixed at 0.05. Minimum detectable difference was plotted vs. number of replicate
samples for the following cases:
• Number of stations (or sampling times) equal to 4, 6, 8, and 16 stations
(or times)
• Data Variability Coefficient (across treatments) equal to 30, 50, 70, and
90 percent.
The Data Variability Coefficient is equal to the within-groups mean square divided by
the grand mean among groups (and multiplied by 100 to convert to a percentage). In
designing a bioaccumulation study, the Data Variability Coefficient can be estimated by
-------
<
ID
LL
o
o
LU
rr
LU
5 25H
LU
CD
< 200-
LU
O
550
500
450
400
350-
300-
150-
100-
50-
Data Variability
Coefficient
90
70
50
30
Number of Stations
4 6
4 6 8 10 12
NUMBER OF REPLICATES
14
16
Reference: Tetra Tech (1986b)
Figure E-I Minimum detectable difference versus number of replicates
at selected levels of unexplained variance for 4 and 6
stations. Power of test = 0.80, significance level = 0.05.
-------
z
UJ
LU
o
LU
cr
HI
LL
LL
O
LU
00
o
LU
LU
O
550
500
450
400
350
300
250-
200-
150-
i
z 100
50-
Data Variability
Coefficient
90
70
50
30
Number of Stations
e 16
46 S 10 12
NUMBER OF REPLICATES
14
16
Reference: Tetra Tech (1986b)
Figure E-2 Minimum detectable difference versus number of replicates
at selected levels of unexplained variance for 8 and 16
stations. Power of test = 0.80, significance level = 0.05.
-------
performing an ANOVA on available data from the literature or on a preliminary data
set. If such data cannot be obtained, the average Coefficient of Variation (within
groups) can be used as a rough estimate of the Data Variability Coefficient.
The effect of setting a different value for Type I error is shown in Figure E-3.
The effect of changes in Type I error is greater for higher levels of data variability.
Note that substantial increases in sensitivity (i.e., decreases in minimum detectable
difference) are achieved only for the case of three replicate samples in Figure E-3.
-------
UJ
o
z
UJ
tr
UJ ^
u. £"
5 uj
-I U.
m o
OZ
UJ U
t- o
UJ ff
O UJ
fi.
s~
s
3 REPLICATES
5 REPLICATES
7 REPLICATES
80-
60-
40-
20'
I
0.05
I
0.1
I
02
\
0.3
0.4
I
0.5
TYPE I ERROR (a)
Figure E-3 Minimum detectable difference versus Type I Error for
one-way ANOVA design with 3, 5, and 7 replicate samples.
-------
APPENDIX F
ESTIMATION OF FISH/SHELLFISH CONSUMPTION
FROM A NATIONAL DATABASE
(by U.S. EPA Office of Pesticide Programs)
-------
APPENDIX F: ESTIMATION OF FISH/SHELLFISH CONSUMPTION
FROM A NATIONAL DATABASE
The EPA Office of Pesticide Programs (OPP) has evaluated comprehensive data on
dietary consumption of fish and shellfish within the conterminous United States.
Selected consumption rate data for the U.S. population were used to provide an overview
of potential exposure of humans to toxic chemicals associated with the consumption of
contaminated fish and shellfish. Many surveys and reports were examined to determine
probable sources for data on patterns of fish and shellfish consumption. Some economic
reports are useful only for estimating average fish and seafood consumption. In contrast,
polls have the potential to provide estimates of individual consumption trends by
consumer, ethnic, or geographical subgroup (Table 1).
DEVELOPMENT OF A NATIONAL DATABASE
Based on sample size and relevance to recent trends in fish consumption, OPP
concluded that the most reliable database for average daily consumption of fish and
shellfish was the U.S. Department of Agriculture (USDA) Nationwide Food Consumption
Survey of 1977-1978. In addition to being relatively recent, the USDA survey had a
weighted sample size of 36,000 individuals. The consumption values listed in this
survey are based on 3 days of individual consumption (from a 1-day recall and a 2-day
diary) gathered by interviewers over the course of 1 year. Although the USDA 1977-
1978 National Food Consumption Survey is an excellent source of fish consumption
data, this survey was conducted 9-10 years ago. Fish consumption in the United States
has been rising slowly for several years. Based upon the USDA 1977-1978 survey and
their National Food Consumption Survey CSFII Report No. 85-3, the U.S. National
Marine Fisheries Services estimated that average per capita consumption of fish and
shellfish increased from 13 g/day in 1960 to 21 g/day in 1986. Because of the nature
of these surveys and limitations of polls in terms of duration of individual records and
numbers of people surveyed, precise statistical distributions for life-time fish consumption
cannot be obtained with existing data.
Consumption values derived from the 1977-1978 USDA study were used to develop
EPA's Tolerance Assessment System (TAS). Mean and percentiles of fish and shellfish
consumption rates are provided in TAS for the U.S. population in the 48 conterminous
states and various population subgroups (Tables 2-7). These estimates are for "acute"
consumption (i.e., the amount of fish eaten in a single day). The average per capita
fish/shellfish consumption rate of 15 g/day in TAS (No. 4 of Table 1) is generally
consistent with the per capita consumption values listed for other surveys and reports.
The distribution of consumption provided in Tables 2-7 is the distribution among
fish or shellfish eaters only, and is not a distribution for the entire population. The
column titled "% Population as Consumers" provides the percentage of each population
subgroup that is estimated to be a consumer of each category of fish/shellfish on any
given day. The mean consumption estimates shown in Tables 2-7 are also for eaters
only, and should not be confused with the mean per capita consumption estimates that
are more commonly used in TAS analyses. These numbers provide valid estimates of
the amounts of fish eaten in a single day. However, because of the way the data were
derived, the frequency of fish consumption and, hence, annual consumption applies only
-------
Table 1 ; Fish Consumption Data Summary
Survey Da t a
Source
1. USDA Nationwide
Food Consumption Surwr/
(for individuals)
note; Figure obtained from
Environ 1985.
2. USDA Nationwide Food
Consumption Survey
Continuinq Survey of
Food Intakes by Individuals
Report I 85-3
3. USDA NFCS, CSFII
Report I H6-1 -
Survey
1977-1978
Averaqe
Consumpt ion
n/day
12.0
Extreme
Consumpt ion
q/dciy
1985
1986
4. USDA NFCS, CSFII
Report I 85-2
1985
21
14 (from 1977-1978
survey above)
11
13 (from a CSFII
concucted in 1985)
11 (from 1977-1978
survey above)
11 (women)
5 (children)
*Those dates which reflect publication or
communication rather than the date of the survey
are enclosed in parenthesis.
Caveats
Sample size > 36,000
(weighted). Fish and
shellfish in the
conterminous 48 states.
Based on a three day
survey that included
a 1 day recall and a
2 day diary.
Sample size = 658
for men 19-50 only.
1 day recall.
Fish and shellfish.
Sample size =
1501 women and 509
children. This
survey included
women 19-50 and
their children
1-5. 1 day recall.
Fish and shellfish.
Sample size =
2,210 women, 1,314
children. This
survey included
low income women
19-50 and their
children 1-5.
Fish and shellfish.
"--.al1
-------
Table 1 cont.
Source
Survey Date
Averaqe
Consumpt ion
q/day
Rxtreme
Consumpt ion
q/day
Caveats
5. EPA Tolerance Assessment System
(computed for a 60kq individual)
1977-1978
a. Total
h. Freshwater finfish
15.2
• 1.8
note; Althouqh the USDA survey fiqure listed is for Pish and
shellfish/ the TAS data summary includes roe and caviar as well,
It is unclear whether the USDA fiqure of 12 q/day obtained from
Environ 19fl5.includes roe and caviar.
Based on USDA 1977-
1978 NFCS survey. The
discrepency between
TAS's 15.2 o/day and the
USDA's 12 q/day is due to
conversion of the TAS fiqi
from q/kq body weiqht/day
to q/day by multiplyinq
by 60 kq.
6.
USDA's Foods Commonly Eaten (1982) 54
by Individuals: Amounts Per
Day and Per Eatinq Session
901
Consumers of
finfish other
than canned,
dried or raw.
Mean does not
equal the median.
Sample size ?
note: Obtained from Environ 1985.
7. National Purchase niary
(analyzed by SRI International)
a. 95th percentile
1973-1974
14.3
41.7
note; Obtained from SRI International.
It is unclear whether the sample
size of 25,000 included nonconsumers
as well as consumers of fish.
Sample size =
qreater than 25,000.
1/12 of the sample
was surveyed each
month. It appears
that this survey was
for the conterminous
48 St-
-------
Table i cont.
Source
Survey Hate
Averarje
g/day
Extreme
q/day
Caveats
8. National Marine Fisheries
Service Market Facts Survey
1969-1970
16.8
a. 99th percentile --
h. 99.9th nercentile
- 77
165
note; The average value of 16.8 q/day was derived from
Environ, and the extreme finures came from Roland
Finch's article listed in the reference section.
There is'a discrepancy between the 16.8 fiqure and
the averaqe figure of 14q/day based on the same survey
ci ted by - Pinch.
Sample size =
4,864. Survey
yielded a per
capita fish
consumption figure.
It is not clear
whether recreationally
cauqht fish are
included. Representative
households completed
diaries twice a
month for I year
reqardinq fish
consumption patterns
at home and outside
the home.
9. Guide to Eatinq Ontario
Sport Fish
1983
13.8
Sample size
unknown. Self
selection biases
possible. This
survey is for
Ontario fishermen
consumption of
freshwater finfish.
-------
Table f cont.
Source
Survey
Average
n/day
Rxtreme
q/day
Caveats
10. Rnviron 19R5
Estimate of Humphrey's
Lake Michigan Data
(1976)
45
11. Personal Communication
with R. Sonsteqard
concerninn intensive
Lake Ontario sports
fishermen.
1187
373
Rst ima te is
extremely rough,
and is for Lake
Michigan snorts
fishermen consumption
of Lake Michiqan fish.
Rubiects were selected
because of how much
fish they caught.
Intensive Lake Ontario
snorts fishermen.
TAS MEAT CONSUMPTION VALUES (q/day)
a. Red meat
b. Poultry -
c. Fish
134
30.4
15.2
-------
Table i cont.
Market Data
Source
Date
Average
q/day
Extreme
q/day
Caveats
12. USDA Agricultural Statistics
note: Unclear what fish
connotes.
13. USDA, ERS, Statistical
"Food Consumption, Prices,
and Expenditures."
note; Unclear whether seafood
other than fish and
shellfish included.
14. National Marine Fisheries
Service Current fisheries
Report
a. Recreationally cauoht
fish consumption cited
in 1986 Current Fisheries
Report table footnote and
not included in that tablet
15. New York State Department
of Environmental Conservation
Average Fish Consumption for
Recreational Fishermen.
note: From Environ 1985.
1983
18.4
1985
1986
1985
1984
1960
1970
18.0
18.1
17.9
17.0
10.3
3.7-5.3
32.4
Per capita
market data.
Retail weight.
Fish.
Per capita
market data.
Edible weight.
Fish and shellfish.
Commercial fish
and shellfish per
capita. The military
population is
excluded and no
information on fish
caught through non-
commercial activities.
Based on 90th
percentile of
nationwide fish
consumption figures.
The source of the
fif ?s .,
-------
TABU 2 CONSUMPTION Of FRCSHUATCR FINFISH
MEAN
X POPULATION CONSUMPTION
POPULATION SUBCROUP: AS CONSUMERS C/KC
ESTIMATED X OF POPULATION Of CONSUMERS VIM CONSUMPTION EXCEEDING X, FOR X>
0 0.? 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2 3 4 5
U.S. POP. --48 STATES
INFANTS(<1 TEA!)
CHILDIENd 6 TRS)
FENALES(13» TRS)
NAtES(13» f«$)
POPULATION SUBGROUP;
U.S. POP.- -48 STATES
INFAMTS(<1 TEAR)
CHI10REN(1-6 TRS)
FEMALESO3* TRS)
NAIES(13» TRS)
1.10
0.11
0.62
1.14
1.37
X POPULATION
AS CONSUMERS
10.73
0.93
9.36
11.69
10.32
2.7038
2.6724
4.8498
2.4986
2.SS24
MEAN
CONSUMPTION
c/rc
1.7510
4.5676
3.4117
1.4970
1.5181
100
100
100
100
100
0
too
100
100
100
100
100
100
100
too
too
TABLE
98
100
100
98
98
_3
ESTIMATED X
0.2 0.4
97
too
99
97
97
91
95
96
90
90
97
100
99
97
95
93
100
98
93
91
CONSUMPTION Of
Of POPULATION
0.6 0.8
84
89
93
82
81
75
83
90
72
71
87 79
100 100
96 93
87 78
04 77
SALTWATER
72
100
93
69
70
64
100
92
62
61
58
44
89
55
54
51
44
84
48
47
30
44
68
27
27
17 10 2
44 0 0
50 32 8
14 7 2
15 8 1
0 0
0 0
3 0
0 0
0 0
flNFISH
OF CONSUMERS WITH
1.0 1.2 1.4
65 56
77 77
86 82
61 51
61 51
47
66
78
41
41
CONSUMPTION
1.6 1.8
40
U
74
34
34
35
58
71
28
29
EXCEEDING
2 3
30
58
67
23
25
14
58
46
9
10
X. FOR X>
4 5 10
740
51 45 6
30 20 3
4 1 0
470
1$ 20 c/rc
0 0
0 0
1 0
0 0
0 0
-------
TABLE 4 CONSUMPTION OF SALTUATER FINFISH--DRIED
ME AM
X POPULATION CONSUMPTION
POPULATION SUBGROUP; AS CONSUMERS C/KC
ESTIMATED X Of POPULATION Of CONSUMERS WITH CONSUMPTION EXCEEDING X. FOR X*
_0.. 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2 3 4 5 10 15 20 C/KC
U.S. POP.--48 STATES
INFANTS(<1 TEAR)
CMlLDREN(1-6 TRS)
FEHALES(13* TRS)
MALES(13» TRS)
POPULATION SUBGROUP;
U.S. POP.- -48 STATES
INFANTS(<1 TEAR)
CHILDREN! 1-6 TRS)
FEMALES! 13* TRS)
NALES(1)» TRS)
0.02
0.00
0.00
0.02
0.03
X POPULATION
AS CONSUMERS
0.01
0.00
0.00
0.01
0.00
0.4758
0.0000
0.0000
0.4511
0.5016
MEAN
CONSUMPTION
C/KG
2.3449
0.0000
0.0000
2.S632
0.7346
100 26
0 0
0 0
100 25
100 27
TABLE
26 26
0 0
0 0
25 25
27 27
26
0
0
25
27
26
0
0
25
27
26
0
0
25
27
5 CONSUMPTION OF FISH-ROE
ESTIMATED X Of POPULATION
0 0.2 0.4 0.6 0.8
100 100
0 0
0 0
100 100
100 100
100 100
0 0
0 0
100 100
100 100
85
0
0
100
. 0
OF
1.0
85
0
0
100
0
26
0
0
25
27
.CAVIAR
CONSUMERS WITH
1.2 1.4
85
0
0
100
0
69
0
0
100
0
6
0
0
13
0
0
0
0
0
0
CONSUMPTION
1.6 1.8
69
0
0
100
0
69
0
0
100
0
0
0
0
0
0
0
0
0
0
0
EXCEED INC
2 3
69
0
0
100
0
38
0
0
42
0
00000
00000
00000
00000
00000
X, FOR X*
4 5 10 15 20 GAG
15 0 0 0 0
00000
00000
00000
00000
-------
MBit 6 CONSUMPTION Of SHELLFISH
MEAN
X POPULATION CONSUMPTION
ESTIMATED X OF POPULATION OF CONSUMERS WITH CONSUMPTION EXCEEDING X, FOR **
4
U.S. POP.- -48 STATES
iNFAJtrs(«EN(1-6 T(S)
FCHALES(13» TBS)
MALES(I3» TRS)
POPULATION SUBCHOUP:
U.S. POP.- 44 STATES
INfANTS(
-------
to the "average" person. It is not possible to predict from that survey the population
distribution for frequency of consumption and range in annual consumption.
ESTIMATION OF LOCAL CONSUMPTION
Since the estimates of fish consumption just discussed are national averages, they
are not predictive of all subgroups and regions on a scale fine enough to address local
situations of potential concern. If local fish consumption information is not available,
the Fish Contamination Subcommittee of the Risk Assessment Council suggests that
other estimates of extreme consumption can be made by assuming that fish consumption
by some subgroups would be equal to the average consumption of red meat (130 g/day)
and, as a "reasonable" worst case, that some people would consume fish at levels equal
to the combined TAS average consumption of red meat, poultry, and fish/shellfish (180
g/day) (Table 8). Conceivably, these values could be exceeded locally, especially when
economically disadvantaged people rely on fishing to survive. Adding on an additional
equivalent for egg consumption would bring the average estimate up to 215 g/day, and
this might not be unreasonable for special situations. The above values are based on
consumption by an average 60-kg individual.
Based on 114 g (0.25 pound) for a single serving of fish/shellfish, an average
annual consumption of 18 g/day (e.g., see Data Source Nos. 12 and 13 of Table 1)
corresponds to approximately 1 meal per week of fish or shellfish. Using the TAS
estimate of 180 g/day for total meat protein consumption (consisting of red meat,
poultry, and fish/shellfish), and an estimate of 114 g for an average single serving, the
total average meat consumption corresponds to about 11 meals per week.
-------
Table 8
Fi sh Cbnsumpt ion - TAS
g/day/kg body weight g/dayl meals/year! (assuming a meal size
of approximately 4
ounces or 114 grams)
1. EPA TAS Average 0.25 15 48
Per capita
Fish/shellfish
2. EPA TAS Average 2.2 130 420
Per capita
Red meat
3.0 180 580
3. EPA TAS Average
Per capita
Red Meat + Poultry + Fish
1 Based on TAS values for average consumption presented in g/day/kg body weight and adjusted to g/day
for a 60 kg individual.
-------
References
Dykstra, William. January 12, 1982. "Ferriamicide; Request
for Conditional Reaistration; EPA Req. No. 38962-RR",
Internal Memorandum to Georqe LaRocc*.
Environ Corporation. 19^5. Fish Consumption by Recreational
Fishermen: An Example of Lake Ontario/Niaara River Reaion.
Prepared for US EPA Office of Enforcement and Compliance
Mon i to r i na.
Pinch, R., 1973. Effects of Reoulatorv Guidelines on the Tntak<=> of
Mercury from Fish - the MECCA Proiect, N'1FS, Fishery
Bulletin, Vol. 71, No. .3, pn. 615-626.
Metzqer, Michael., March 25, 19R7. "Fish Action Level Reevaluation
for Aldrin/Dieldrin, Chlordane, DOT, Heptachlor, and Mirex. o
Accession Number RCR Numbers 205ft, 20fi2, 2063, 2064,
2065 and 2066.", Internal Memorandum to Jack Housenaer.
Ontario Ministry of the Environment. 19R4. Guide to Eatinq Ontari
Snort Fish, 1984-1985. Southern Ontario, Great Lakes.
Page, N.; Cavender, F.; Cook, R. 1985. Carcinoaenic Risk Assess™ i
for Aldrin and Dieldrin. Unpublished study prepared by Dynaiua<
Corp. under EPA Contract No. 68-02-4131.
Sonsteqard, R. , Tufts University, Boston, Massachusets. Personal
communicat ion.
SRI International. 1980. Seafood Consumntion Data Analysis
(Final Report). Prepared for USEPA, under EPA Contract No.
68-01-3887.
USOA. 1984. Agricultural Statistics* Consumption and Family
Li vino i Table 697, p"I 506.
USDA. 1985. ERS, Statistical Bulletin 749. Food Consumption,
Prices and Expenditures. Table 7, p. 13.
fJSDA. 1985. Nutrition Monitorina Oivision, Hunan Nutrition Informat
Service. FOOD ANO MtJTRIF.NT INTAKES: INDIVIDUALS IN FOrjR
1977-1978. Report No. 1-3.
USDA. 1936. Nationwide Food Consumption Survey Continuina
Survey of Food Intakes by Individuals, Men 19-50 Years,
1 Day 19R5. NFCS, CSFH Report No. 85-3.
-------
USDA. 1986. Nationwide Food Consumption Survey Conf.inuina Survey
of Food Intakes by Individuals, Low-Income Women 19-50 Years
and Their Children 1-5 Years, 1 Day 1985. NFCS, CSFII report
No. 85-2.
USDA. 1987. Nationwide Food Consumption Survey Continuing Survey
of Food Intakes by Individuals, Women 19-50 Years and Their
Children 1-5 Years, 1 Day 1,986. NFCS, CSFII Renort No. 86-1.
USDC. 1987. National Oceanic and Atmosoheric Administration,
National Marine Fisheries Service. Fisheries of the United
States, 19*5, Current Fisheries Statistics No. 83«5.
-------
APPENDIX G
EPA OFFICE OF RESEARCH AND DEVELOPMENT,
ENVIRONMENTAL RESEARCH LABORATORIES
-------
EPA OFFICE OF RESEARCH AND DEVELOPMENT,
ENVIRONMENTAL RESEARCH LABORATORIES
Region 1 Environmental Research
Laboratory/ORD
South Ferry Road
Narragansett, RI 02882
FTS: 8-838-5087
DDD:(401) 789-1071
Region 4 Environmental Research Lab/ORD
Laboratory/ORD
Sabine Island
Gulf Breeze, FL 32561
FTS: 8-686-9011
DDD:(904) 932-5311
Region 5 Environmental Research Lab/ORD
Laboratory/ORD
6201 Congdon Boulevard
Duluth, MN 55804
FTS: 8-780-5550
DDD:(218) 720-5550
Environmental Ecological and
Support Laboratory/ORD
26 W. St. Clair Street
Cincinnati, OH 45268
FTS: 8-684-7301
DDD:(5J3) 569-7301
Region 6 Robert S. Kerr Environmental
Research Laboratory/ORD
P.O. Box 1198
Ada, OK 74820
FTS: 8-743-2011
DDD:(405) 332-8800
Region 10 Environmental Research
Laboratory—Corvallis/ORD
200 S.W. 35th Street
Corvallis, OR 97333
FTS: 8-420-4601
ODD:(503) 757-4601
Environmental Research
Laboratory/ORD
College Station Road
Athens, GA 30613
FTS: 8-250-3134
DDD:(404) 546-3134
Center for Environmental
Research Information/ORD
26 West St. Clair Street
Cincinnati, OH 45268
FTS: 8-684-7391
DDD:(513) 569-7391
Pacific Division - Environ-
mental Research Lab/ORD
Hatfield Marine Science
Marine Science Drive
FTS: 8-867-4040
DDD:(503) 867-4040
-------
APPENDIX H
EPA REGIONAL NETWORK FOR RISK ASSESSMENT AND
RISK MANAGEMENT ISSUES
-------
EPA REGIONAL NETWORK FOR
RISK ASSESSMENT/RISK MANAGEMENT ISSUES
Region
Senior Contact
Staff
Other Memberships
I Paul Keough
Deputy Regional Admin.
J.F. Kennedy Federal Bldg.
Room 2203
Boston, MA 02203
(FTS) 835-3402
E-Mail EPA9102
II Alice Jenik, Acting Chief
Policy & Program
Integration Branch
26 Federal Plaza
New York, NY 10278
(FTS) 264-4296
E-Mail EPA9243
III Greene Jones, Director
Environmental Services Div.
841 Chestnut Bldg.
Philadelphia, PA 19107
(FTS) 597-4532
E-Mail EPA9380
IV Lee DeHihns3'*
Deputy Reg. Admin.
345 Courtland St., N.E.
Atlanta, GA 30365
(FTS) 257-4727
E-Mail EPA9400
V Bill Sanders, Director
Environmental Services Div.
230 South Dearborn St.
Chicago, IL 60604
(FTS) 353-3808
E-Mail EPA9580
Tom D'Avanzo, Chair
Toxics Coord. Comm.
(FTS) 835-3222
E-Mail EPA9136
Maria Pavlova
Office of Emergency &
Remedial Response
(FTS) 264-1918
E-Mail EPA9231
Roy Smith
Environmental Scientist
Environmental Serv. Div.
(FTS) 597-9857
E-Mail EPA9381
Barbara Beck1
Toxicologist
Air Management Div.
Harley Laing5
Director
Planning & Mgmt. Div.
Bill Muzynski2
Deputy Regional
Administrator
Stan Laskowski3
Deputy Regional
Administrator
Steve Wassersug2
Director, Hazardous
Waste Management
Susan Deihl
Risk Assessment Coord.
Office of the Regional
Administrator
(FTS) 256-3776
E-Mail EPA9400
David Dolan (5S-PTSB-7)
Environmental Scientist
Pesticides & Toxic Substances Branch
(FTS) 886-5518
E-Mail EPA9575
Milt Clark (5HT)
Chairman, Health Effects Forum
Pesticides & Toxic Substances Branch
(FTS) 886-3388
E-Mail EPA9575
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VI
VII
VIII
IX
Allyn M. Davis, Director
Hazardous Waste
Management Division
120J Elm Street
Dallas, TX 75270
(FTS) 729-2730
E-Mail EPA9650
William W. Rice
Deputy Region. Admin.
726 Minnesota Avenue
Kansas City, KS 66101
(FTS) 757-2800
E-Mail EPA9703
Alexandra Smith3
Deputy Region. Admin.
One Denver Place
Denver, CO 80202-2413
(FTS) 564-2413
E-Mail EPA9802
Jill Lyons
Toxics Coordinator
Air Programs Branch
(FTS) 729-9187
E-Mail EPA966J
Bob Fenemore
Air & Toxics Div.
(FTS) 757-2835
E-Mail EPA9761
Jim Baker
Waste Management Div.
(FTS) 564-1518
E-Mail EPA9873
Suzanne Wuerthele
Air & Toxics Div.
(FTS) 564-1743
E-Mail EPA9850
Frances Phillips4
Acting Regional
Administrator
Art Spratlin6
Director
Air & Toxics Div.
Arnold Den
Senior Science Advisor
Office of the Regional Administrator
215 Fremont Street
San Francisco, CA 94105
(FTS) 454-0906
E-Mail EPA9900
Randy Smith5, Chief
Hazardous Waste Policy
Branch
1200 6th Avenue
Seattle, WA 98101
(FTS) 399-1261
E-Mail EPA9401
Elaine Somers
Program Analyst
Management Division
(FTS) 399-2966
E-Mail EPA9021
David Tetta
Environmental Engineer
Environmental Services Div.
(FTS) 399-1597
E-Mail EPA9051
JRisk Assessment Forum
'Risk Assessment Council
'Risk Management Council
^Agency for Toxic Substances Disease Registry
Comparative Risk Task Force
Reference: U.S. EPA 1987b.
-------
APPENDIX I
COMPILATION OF LEGAL LIMITS FOR CHEMICAL CONTAMINANTS IN
FISH AND FISHERY PRODUCTS
-------
TABLE 1-1. COMPILATION OF LEGAL LIMITS FOR HAZARDOUS
METALS IN FISH AND FISHERY PRODUCTS
Metals (DOW)
Country
Austral ia*
Brazil
Canada
Chile
Denmark
Ecuador
Finland
France
Germany
Greece
Hong Kong
India
Israel
Italy
Japan
Korea
Netherlands
New Zealand
Phil Ippines
Poland
Spa In
Sweden
Switzerland
Thailand
United Kingdom
United States
U.S.S.R.
Venezuela
Zambia
Range
Minimum
Max imum
As Cd Cr
1.0,1.5" 0.2-5.5
3.5
0.12,1.0 0.5
1.0
5.0
0.5
1.4-10 2.0 1.0
1.0
0.05-1.0
1.0 1.0
3.0
4.0
0.1
2.0
1.0
0.1 0,0.1
3.5-5.0
0.1 0 1.0
10 5.5 1.0
Cu
10-70
10
10
10
30
10-30
20
20
10
100
10
100
H9
0.5,1.0
0.5C
0.5
0.5
1.0
1.0
0.5,0.7
1.0
0.7
0.5
0.5C
0.5
0.7C
0.3.0.4C
0.5
l.OC
0.5C
0.5
0.5
l.OC
0.5
0.5
l.OC
0.2-1.0
0.1-0.5
0.2-0.3
0.1
1.0
Pb
1.5-5.5
0.5
2.0
5.0
2.0
0.5
6.0
5.0
2.0
0.5,2.0
2.0
0.5
1.0-2.0
1.0-2.0
1.0
1.0
2.0-10
2.0
0.5-10
0.5
10
Sb Se Zn
1.5 1.0,2.0 40-1,000
0.05,0.3 100
1.0
50
1.0 2.0 40
30-50
50
100
1.0 0.05 30
1.5 2.0 1,000
• Limit varies among states.
b Inorganic.
C Total.
References: Nauen (1983); U.S. Food and Drug Administration (1982, 1984).
-------
TABLE 1-2. COMPILATION OF LEGAL LIMITS FOR ORGANIC PRIORITY POLLUTANTS
AND PESTICIDES IN FISH AND FISHERY PRODUCTS (ppm)
HeiKhloro-
btnitnt Kit ROD
CM*d* 2.0 20*
OvMWk
C«r««i/< O.S
Inland
tetkcrltmlt 4.0
SMdM 0.2 2.0-4.0
fellicrlMd 1.0
ItUllMd
••ng*
Mtitodi O.S 4.0 20*
HrpUcklor/
Aldrl*/ Hrpl«hlor- HCH
Oleldrta CDIordwr 001 OOC 000 OOlt fndrki rpo> id« (rponr (1 lC l»r (Iktr •ffwfe (lMllc
------- |