vvEPA
United States
Environmental Protection
Agency
Office of Marine and
Estuarme Protection (WH-556F)
Washington, DC 20460
Office of Water Regulations
and Standards (WH-552)
Washington, DC 20460
Water
September 1989
EPA-503/8-89-002
Assessing Human Health Risks
from Chemically Contaminated
Fish and Shellfish:
A Guidance Manual
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ACKNOWLEDGMENTS
This document was prepared under EPA Contract No. 68-03-3319.
Dr. Kim Devonald of the Office of Marine and Estuarine Protection
was the Project Monitor for EPA, and also drafted the Background
section of the Introduction. John Maxted contributed the sections on
Relationship of Fisheries Risk Assessment to Water Quality Standards
and Relationship of Fisheries Risk Assessment to Monitoring under
the Clean Water Act. The EPA Office of Pesticide Programs prepared
Appendix F.
Portions of this report were based on the Guidance Manual for Health
Risk Assessment of Chemically Contaminated Seafood, a document
prepared for the Puget Sound Estuary Program in EPA Region X.
Information on quality assurance/quality control (QA/QC) plans and
documentation was taken from the Puget Sound Protocols, also
developed for the Puget Sound Estuary Program.
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.
Comments on the EPA Region X risk document were provided by
participants at a 1987 workshop on health risk assessment related to
consumption of fish and shellfish. The workshop was sponsored by
EPA Region I and the New England Interstate Water Pollution Con-
trol Commission. Valuable comments on earlier drafts of this report
were received from the following reviewers:
John R. Bagby Missouri Department of Health
Donald Barnes EPA Office of Toxic Substances
Bruce R. Barrett EPA Water Management Division, Region IV
Robert Cantilh EPA Office of Drinking Water
Richard L. Caspe EPA Water Management Division, Region II
Dave DeVault 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
Jeffery Foran National Wildlife Federation
Kevin G. Garrahan EPA Exposure Assessment Group
Rebecca Hamner EPA Deputy Assistant Administrator for Water
James E. Harrison EPA Marine and Estuarine Branch, Region IV
John L. Hesse Michigan Department of Public Health
Robert A. Kreiger Minnesota Department of Health
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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CONTENTS
Page
CHAPTER 1: Introduction 1
Objectives 1
Organization 2
Background 2
1986 EPA Risk Assessment Guidelines 3
Regulatory Roles and Coordination of Federal, State,
and Local Agencies 3
Applicability of this Guidance Manual 4
Relationship of this Manual to Other EPA Documents 5
Relationship of Fisheries Risk Assessment to Water
Quality Standards 5
Relationship of Fisheries Risk Assessment to Monitoring
Under the Clean Water Act 6
Relationship of EPA Risk Assessment Methods to FDA
Risk Assessment Methods 1
CHAPTER 2: Overview of Risk Assessment and Risk
Management 11
Major Steps in Risk Assessment 11
Need for Risk Assessment Appproach 12
Uses of Risk Assessment 13
CHAPTER 3: Hazard Identification 15
Contaminants of Concern 15
Toxicity Profiles 19
Sources of Information 22
CHAPTER 4: Dose-Response Assessment 23
Exposure and Dose 23
Dose-Response Relationships 23
Carcinogenic Potency Factors 25
Reference Doses 26
Sources of Information 27
Carcinogenic Potency Factors 27
Reference Doses 27
CHAPTERS: Exposure Assessment 29
Measurement of Contaminant Concentrations in Tissues 29
Study Objectives and General Sampling Design 32
Selection of Target Species and Size Classes 36
Sampling Station Locations 41
Time of Sampling 43
Kinds of Samples 44
Sample Replication 47
Selection of Analytical Detection Limits and Protocols 47
QA/QC Program 48
Documentation and QA Review of Chemical Data 50
Statistical Treatment of Data 51
Analysis of Sources, Transport, and Fate of Contaminants 52
Analysis of Exposed Populations 53
Comprehensive Catch/Consumption Analysis 54
Assumed Consumption Rate 57
Exposure Dose Determination 59
Single-Species Diets 59
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Page
Mixed-Species Diets 60
Sources of Information 61
CHAPTER 6: Risk Characterization 63
Carcinogenic Risk 63
Noncarcinogenic Effects 64
Chemical Mixtures 66
CHAPTER 7: Presentation and Interpretation of Results 67
Presentation Format 67
Summary Tables 67
Summary Graphics 69
Risk Comparisons 70
Summary of Assumptions 70
Uncertainty Analysis 72
Sources of Uncertainty 72
Approaches to Uncertainty Analysis 74
Supplementary Information 75
CHAPTERS: References 77
APPENDICES
A. EPA/FDA Summary Policy Statement on Chemical Residues
in Fish and Shellfish
B. Integrated Risk Information System (IRIS)
C. Sources of Information for Toxicity Profiles
D. Evaluation of the Effects of Composite Sampling on Statis-
tical Power of a Sampling Design
E. Evaluation of the Effects of Sample Replicaiton on Statistical
Power of a Sampling Design
F. Estimation of Fish/Shellfish Consumption from a National
Database
G. EPA Office of Research and Development, Environmental
Research Laboratories
H. Compilations of Legal Limits for Chemical Contaminants in
Fish and Fishery Products
LIST OF FIGURES
LIST OF TABLES
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FIGURES
Number Title Following Page
1 Overview of risk assessment and risk 11
management
Hypothetical example of dose-response 23
curves for a carcinogen and a noncar-
cinogen
Interaction between environmental fac- 35
tors and exposed population factors
4 Summary of recommended marine and es- 40
tuarine indicator species
5 General sampling station layouts for prob- 41
ability sampling in two dimensions
Conceptual structure of quantitative 63
health risk assessment model
7 Example graphic format for display of 69
quantitative risk assessment results for
hypothetical study area and reference area
Plausible-upper-limit estimate of lifetime 70
excess cancer risk vs. concentration of a
chemical contaminant in fish or shellfish
at selected ingestion rates
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TABLES
Number Title Page
1 Organic priority pollutants and 301 (h) 16
pesticides ranked according to octanol-
water partition coefficients (Kow)
2 Inorganic priority pollutants ranked 19
according to bioconcentration factor
3 Toxicity profile for mercury and PCBs 20
4 Criteria for selecting target species 37
5 Approximate range of cost per sample for 49
analyses of EPA priority pollutants in tissues
as a function of detection limits and precision
6 Example tabular format for display of quan- 68
titative risk assessment for consumption of
fish and shellfish
7 Summary of assumptions and numerical 71
estimates used in risk assessment approach
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Introduction
Contamination of aquatic resources by toxic chemicals is a well recog-
nized 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; Brown et al. 1985b; DeVault et al. 1986;
Capuzzo et al. 1987). Heavy consumption of contaminated fisheries
products by humans may pose a substantial health risk. This concern
has prompted recent studies of catch and consumption patterns for
recreational fisheries and associated health risks (e.g., Puffer et al.
1982; Humphrey 1983, 1987, 1988; Sonzogni and Swain 1984; Swain
1988).
To protect the health of consumers offish 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 Protec-
tion Agency (EPA) 1980b; Food Safety Council 1980, 1982; Connor
1984a; Tollefson and Cordle 1986]. In the present report, a stand-
ardized procedure is recommended for assessing human health risks
from consumption of chemically contaminated fish and shellfish.
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, 1987a). The objec-
tives of the guidance manual are to:
Describe the steps of a health risk assessment procedure for
consumption of contaminated fish and shellfish
Define the conceptual basis for standard lexicological vari-
ables [e.g., Carcinogenic Potency Factors or Reference Doses
(RfD) for chemicals] and criteria [e.g., U.S. Food and Drug
Objectives
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Organization
Background
Administration (FDA) action levels] related to risk assess-
ment, and information sources for updating these values
Provide guidance on presentation of risk assessment results
Summarize assumptions and uncertainties of the recom-
mended procedure for risk assessment.
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 ad-
dressed specifically in the examples provided herein, the concepts
discussed throughout the manual are relevant to risk analysis for
commercial fisheries.
This manual provides guidance only, and does not constitute a
regulatory requirement of any kind. The technical content is consistent
with approved EPA procedures for risk assessment, as published in
the Federal Register (U.S. EPA 1986a-e). The guidance manual is
intended to describe what EPA believes to be the most scientifically
defensible methods for assessing environmental health risks. These are
the methods EPA will use in conducting health risk assessments re-
quired in its statutorily mandated programs. The relationship between
these procedures and risk assessment approaches used by FDA is
described briefly in the background section below.
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 and uncertain-
ties arc summarized.
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 Coun-
cil 1983). Risk assessment provides the scientific basis for public policy
and action. In risk management, risks are interpreted in light of legis-
lative, socioeconomic, technical, and political factors, and appropriate
controls are determined. Risk management often involves evaluating
risks relative to potential benefits associated with an activity and
defining an acceptable risk level (i.e., the maximum risk considered
tolerable). For example, a risk manager might weigh the risks as-
sociated 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.
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1986 EPA Risk Assessment
Guidelines
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.
These guidelines pertain to health risk assessment for all environmen-
tal exposures [e.g., air exposure; ingestion of water or environmentally
contaminated foods; and other direct human contact with con-
taminated soils, water, sediments, or other materials (Federal Register
51 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 (Federal Register 51 No. 185, p.
33992).
While U.S. EPA's risk assessment guidelines (1986a-e) apply to all
exposure routes, they do not contain detailed information on applica-
tion 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. The risk assessment methods
recommended in this manual are consistent with the principles set
forth in U.S. EPA (1986a-e).
As described in a recent policy statement by EPA's Risk Assessment
Council and FDA (see Appendix A), FDA, EPA, and the states have
somewhat differing roles in assessing and managing risks from fish
consumption. 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 evaluations
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.
Regulatory Roles and
Coordination of Federal, State,
and Local Agencies
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Applicability of this Guidance
Manual
Section 408 of the Federal Food, Drug and Cosmetic Act authorizes
EPA to establish tolerances (maximum permissible concentrations) or
action levels for pesticides in raw agricultural commodities, including
fish and shellfish. FDA is responsible for setting action levels and
tolerances for concentrations of other chemicals in fish, shellfish, or
other foods. FDA also has responsibility for enforcing the guidelines
developed by both EPA and FDA, which may involve removal of
adulterated foods (i.e., foods contaminated in excess of an action level
or tolerance) from interstate commerce. An action level is the mini-
mum concentration of chemical in food that may be cause for FDA to
take enforcement action. An action level is promulgated when a
tolerance or exemption authorizing the presence of a substance in food
has not been established or has been revoked. Action levels are estab-
lished and revised according to criteria in the Code of Federal Regula-
tions (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 and EPA take into account both the
magnitude of the health risks to consumers and the economic impacts
of banning food from a particular source. FDA and EPA set 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 provide
national protection rather than on a regional or local basis. These
national standards protect the average consumer of a food product,
assuming the consumer eats foods from a typical "national market
basket" (U.S. FDA 1984). Action levels and tolerances are not in-
tended to protect certain local subpopulations, such as individuals
whose consumption of fish and shellfish from a given water body may
exceed the national average (Appendix A).
EPA and FDA recognized the need to coordinate their activities and
guidance in assessing health risks from contaminated fish and shellfish.
The Standing Committee on Fish Contamination has been formed to
resolve potential differences in risk assessment calculations for specific
chemicals or specific exposure situations (Appendix A). The
EPA/FDA policy statement in Appendix A provides further discussion
of the evolving coordination between EPA, FDA, other federal agen-
cies, and the states. The EPA/FDA policy statement also describes
procedures whereby states can obtain further information or assistance
pertaining to risk management in specific local situations.
EPA's nonregulatory technical guidance, including this manual and the
1986 final guidelines for risk assessment (U.S. EPA 1986a-e), is avail-
able to state and local governments responsible for fisheries manage-
ment, environmental protection, and public health. This manual is
intended for use as a handbook by those state and local agencies that
are responsible for assessing potential risks from local fish or shellfish
consumption. For example, it maybe useful in assessing risks to highly
exposed regional populations (e.g., certain fishermen or families who
may eat unusually large amounts of fish). This manual does not provide
guidance on policy issues that are beyond the scope of the technical
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risk assessment process (e.g., selection of acceptable risk levels, and
methods for performing local cost-benefit analyses).
For specific technical assistance in applying the risk assessment
methods described in this manual, users may contact EPA national
offices (see the last page of Appendix A) for updated information on
regional EPA facilities that can provide on-site assistance in applying
risk assessment techniques.
This manual is not intended as an exhaustive guide to all aspects of
sampling, statistical design, laboratory analysis, exposure assessment,
and lexicological risk analysis. Citations are provided to references
that provide details on these topics. In addition, several other EPA
documents that provide relevant information are listed below:
U.S. EPA (1987a) Integrated Risk Information System (IRIS)
Manual - A regularly updated electronic database on the
toxicity and carcinogenicity of individual chemicals (see Ap-
pendix B herein)
General guidelines on exposure and risk assessment (U.S. EPA
1986a-e).
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.
It should also be noted that the National Oceanic and Atmospheric
Administration (NOAA), FDA, and EPA have recently completed a
joint study of PCB contamination in Atlantic coast bluefish and poten-
tial human health effects (NOAA, FDA, and EPA 1986, 1987). The
design of that study, the statistical analysis of the data, and the estimates
of dietary intake of PCBs by bluefish anglers and their families provide
examples of some of the concepts illustrated in this guidance manual.
Environmental quality guidelines may be developed from risk assess-
ment models to complement available water quality standards. For
example, this manual contains recommended procedures for develop-
ing guidelines on concentrations of contaminants in edible tissues of
fish and shellfish based on risk assessment. Comparisons of data on
tissue concentrations of contaminants with such guidelines may be
used by state agencies in regulating the harvest, transportation, and
sale of fish and shellfish used for human consumption, and in develop-
ing health risk advisories. In contrast, state water quality standards are
designed to regulate discharges of contaminants to surface waters.
Relationship of this Manual to
Other EPA Documents
Relationship of Fisheries Risk
Assessment to Water Quality
Standards
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Relationship of Fisheries Risk
Assessment to Monitoring
Under the Clean Water Act
State water quality standards include two primary elements: desig-
nated uses and criteria. Recreational fishing and shellfishing are ex-
amples of designated uses that maybe applied to a water body. Criteria
are concentration levels of contaminants in surface water that provide
protection from the effects of toxic chemicals, with an ample margin
of safety. There are two basic kinds of criteria: those that protect
aquatic life and those that protect human health.
The criteria incorporated into state water quality standards are en-
forceable requirements used by the states to regulate dischargers. In
support of the state programs, and to meet the requirements of Section
304(a) of the Clean Water Act, EPA periodically issues national water
quality criteria recommendations for use by the states in setting their
enforceable standards. In developing national criteria recommenda-
tions to protect public health, EPA considers human exposure to
chemical contaminants in fish and shellfish as well as drinking water.
The Criteria and Standards Division of EPA's Office of Water Regula-
tions and Standards is responsible for developing national criteria
recommendations under Section 304(a) of the Clean Water Act. The
current 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 Docu-
ments, Availability (U.S. EPA 1980b).
The development of water quality criteria to limit human exposure to
contaminants in fish and shellfish requires the translation of the con-
taminant level not to be exceeded in the animal tissues to a level in the
water in which the animal resides. This is accomplished through the
use of the bioconcentration factors (BCF). A BCF is a measure of the
potential of a chemical to accumulate in biological tissues. A BCF value
is defined as the ratio of the concentration of a chemical in tissues of a
given aquatic species to the concentration in water. Each chemical
BCF may be estimated either directly from the results of bioassay
testing or from an octanol-water partitioning coefficient for the chemi-
cal, if test data are not available.
The calculation of a water quality criterion to protect human health
from exposure to contaminants in fish and shellfish is accomplished
through the use of the BCF and lexicological and epidemiological data
(e.g., data on the amount, or dose, of the contaminant that results in a
defined human health risk). The coefficients used in this manual to
define the critical dose or the toxic potency for each chemical (see
Dose-Response Assessment) are the same as those used to develop
water quality criteria. IRIS (U.S. EPA 1987a) is the central location
for human health-related data and information used by all EPA
programs.
States routinely conduct chemical analyses of fish and shellfish tissue
as part of their environmental monitoring programs. The results offish
contamination monitoring are documented in state reports and in the
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National Water Quality Inventory Report to Congress (as required by
Section 305(b) of the Clean Water Act). The information presented in
this guidance manual can be used to support these activities through
the identification of guidelines on levels of contaminants in tissues that
correspond to a defined risk to human health (e.g., tolerable risk
levels).
In addition to these ongoing monitoring activities, the 1987 amend-
ments to the Clean Water Act, in particular the new Section 304(1),
require states to develop lists of impaired waters, identify point source
discharges of toxic substances and the amounts of pollutants present,
and develop individual control strategies (permits) for each point
source discharger. The information in this guidance manual may be
useful in evaluating data on concentrations of chemical contaminants
in fish and shellfish tissue and associated human health risks to identify
waters impaired by toxic contamination.
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 manage-
ment. As an EPA guidance manual, this document presumes the use
of standard EPA risk assessment procedures. However, certain pro-
cedures 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 ap-
proaches concerns the methods for extrapolating the toxic potency of
chemicals in small experimental animals (e.g., rats and mice) to es-
timate 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, concur-
rent 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 the corresponding ratio of body weights as a scaling
factor. Thus, EPA uses mg of carcinogen per m 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 inter-
species 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
Relationship of EPA Risk
Assessment Methods to FDA
Risk Assessment Methods
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the most appropriate method for interspecies dosage extrapolation
may vary depending on exposure conditions and chemicals involved.
For example, one procedure may be more realistic for lipophilic
chemicals, whereas the other would be more appropriate for
hydrophilic chemicals. Differences hi target organs (i.e., primary site
of toxicity) also affect 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 mag-
nitude), 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 com-
puterized database, IRIS (U.S. EPA 1987a). IRIS is a database main-
tained by EPA to provide regularly updated lexicological data for use
in risk assessment. The use of IRIS would greatly increase the ability
of a state to perform risk assessments for chemicals of local concern
while increasing consistency among jurisdictions sharing responsibility
for common waters.
Although the conversion of EPA estimates of toxic potency to es-
timates based on equivalent dosage scales related to body weight is not
technically complex, the modified procedure should preferably be
carried out only by experienced lexicologists. The conversion factor
will vary depending on whether ihe dose-response dala were derived
from rats or from mice. Thus ihe original dala sel musl be reviewed lo
delermine an appropriate conversion factor. In general, an EPA es-
limale of carcinogenic potency would be multiplied by a factor equal
lo the ratio of surface area per unit body weighl (m /kg) of ihe
laboratory animal to that of humans. For example, if the EPA car-
cinogenic potency factor is C and Ihe surface area per unit body weight
is X for ihe laboratory animal and Y for humans, the corresponding
potency factor based on dosage scaled lo body weighl is C mulliplied
by X divided by Y. Because specific dala on surface area are often
unavailable, body weighl lo ihe Iwo-lhirds power is typically used as an
eslimale of surface area. Nole lhal some EPA carcinogenic polency
factors are derived from epidemiological sludies and Iherefore do nol
require conversion.
Olher sleps in ihe process to eslimale carcinogenic polencies may vary
somewhal among regulatory agencies. For example, different agencies
may choose differenl dala sels to derive a carcinogenic potency factor
for the same chemical. The mathemalical expression used lo model Ihe
dose-response relalionship may also differ among agencies. Hogan and
Hoel (1982) and Cothern el al. (1986) discuss various models for
exlrapolating data from high doses used in laboratory experimenls lo
ihe low doses of concern in carcinogenic risk assessmenl. Al low doses
corresponding to risks of 10"2 to 10"6 or less, different models may
produce results thai vary by as much as several orders of magnilude.
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Nevertheless, the linearized multistage procedure used by EPA (U.S.
EPA 1986a; also see below, Dose-Response Assessment) yields results
that correspond approximately (within a factor of two) to those
produced by the linear model used by FDA. The interagency Subcom-
mittee on Fish Residue Issues of the EPA Risk Assessment Council,
which included representatives from FDA, concluded that the dif-
ferences, in procedures for modeling dose-response relationships be-
tween EPA and FDA were small relative to the uncertainties in other
steps of a risk assessment. Therefore, the EPA/FDA policy statement
(Appendix A herein) 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 stand-
ardized approach for assessing carcinogenic effects on children and
fetuses. Information on perinatal carcinogenicity is presently being
developed by EPA and others.
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Overview of Risk Assessment and Risk
Management
The objective of risk assessment is to estimate the probability of
adverse health effects from exposure to a toxic agent. The elements of
the risk assessment process and their relationship to risk management
are shown in Figure 1. U.S. Office of Technology Assessment (1987)
provides a review of general policies and technical approaches of
federal agencies in assessing risks to human health associated with
exposure to chemicals. Background information on food safety evalua-
tion 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).
The following sections provide an overview of the steps in risk assess-
ment, the need for a risk assessment approach to evaluate human
health risks from chemically contaminated fisheries, and potential
applications of the results of fisheries risk assessment. The general
format for risk assessment and all definitions of terms used in this
report are consistent with those provided by National Research Coun-
cil (1983) and U.S. EPA (1986a-e, 1987a).
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 prob-
ability of an adverse health effect
Major Steps in Risk
Assessment
11
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Figure 1. Overview of risk assessment and risk management
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Need for Risk Assessment
Approach
Exposure assessment: Characterization of the populations ex-
posed 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 quantita-
tive information from the first three steps, leading to an es-
timate 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 charac-
terization step. The risk characterization includes a balanced discus-
sion of the strengths and weaknesses of the data presented.
Direct measurement of human health risks is possible in certain limited
circumstances. 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 possible in a
population of workers exposed to a 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 interpretation of such data. Mathematical
models are therefore used by EPA, FDA, the Agency for Toxic Sub-
stances and Disease Registry, states, and other regulatory agencies to
estimate human health risks from exposure information. Risk assess-
ment 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 limit exposure to toxic chemicals and reduce associated
risks.
Scientific knowledge of the effects of toxic chemicals on humans is still
rudimentary. Much of the present information is extrapolated from
results of laboratory tests on animals (e.g., rats and mice). For example,
animal test data may be used to estimate levels of chemical exposure
that are unlikely to cause toxic effects in human populations.
Toxicologists are faced with many uncertainties when estimating the
potential for human health risks associated with intake of toxic chemi-
cals. Despite these uncertainties, regulatory decisions must be made.
Many assumptions and subjective judgments may enter into an evalua-
tion 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
12
-------
chemicals to action levels or tolerances established by U.S. FDA (1982,
1984). This approach is limited for the following reasons:
FDA action levels or tolerances are available for only a few
chemicals (mercury, 12 organic pesticides or related degrada-
tion products, and PCBs).
FDA has not established regulatory limits for some of the most
potent suspected human carcinogens (e.g., 2,3,7,8-
tetrachlorodibenzo-/>-dioxin) or for some of the common con-
taminants 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 and EPA consider
economic impacts of food regulation as well as the potential
human health risks on a national basis (U.S. FDA 1984). When
using action levels or tolerances to interpret bioaccumulation
data, investigators implicitly adopt economic policies of the
federal agencies responsible for setting the limits. Thus, risk
management issues at a national level are not clearly separated
from site-specific risk assessments.
Action levels and tolerances were developed from a national
perspective. They were not intended to protect localized sub-
populations of recreational anglers that may consume con-
taminated fish or shellfish at a rate substantially above the
national per capita average.
Use of regulatory limits on toxic chemicals in food products established
by other countries (Nauen 1983) would suffer from many of the limita-
tions listed above for FDA values. Moreover, a concise review of the
basis for each of these limits is not available.
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 environ-
mental problems (e.g., Tetra Tech 1986a) and consistent policies for
reducing health risks (e.g., through reduction of toxic pollutant dis-
charges 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
Uses of Risk Assessment
13
-------
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 resources linked to human
exposure. Second, priority chemicals can be identified according to
associated health risks or indices of relative hazard (e.g., Ames et al.
1987). Finally, various fishery species and size (or weight) classes within
species can be ranked according to relative risks.
Risk assessment is an important analytical tool for developing environ-
mental criteria and guidelines. For example, water quality criteria
derived by U.S. EPA (1980b, 1986h) 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 con-
taminants 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. Further-
more, risk assessment may contribute to management decisions by
federal, state, and local agencies, which may include:
Investigating sources of pollution
Reducing exposure potential by implementing pollution con-
trols
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 maybe found in Lowrance (1976), U.S. EPA (1984b),
Lave and Menkes (1985), Ames et al. (1987), Lave (1987), Russell and
Gruber (1987), and Travis et al. (1987).
14
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Hazard Identification
The first step in the risk assessment process is to define lexicological
hazards posed by the chemical contaminants in samples of fish and
shellfish. These hazards are summarized in a toxicity profile for each
contaminant of concern. The EPA chemical database, IRIS, can be
easily accessed to obtain summaries of key lexicological data to include
in loxicily profiles. The resulls of Ihe hazard assessment influence ihe
nature and exlent of subsequenl sleps in risk analysis. For example, the
endpoint of concern in dose-response assessment may be selecled
based on the mosl severe adverse effecl idenlified in Ihe hazard
assessment In Ihe absence of quanlilalive dala for other steps in the
risk assessment process, ihe resulls of the hazard assessment constitute
the final product for a qualitative evaluation of risk.
The contaminants of concern to be included in a particular risk assess-
ment should be selected based on ihe following crileria:
High persislence in ihe aquatic environment
High bioaccumulation potenlial
High loxicity to humans (or suspecled high loxicily lo humans
based on mammalian bioassays)
Known sources of conlaminanl in areas of inlerest
High concenlralions in previous samples of fish or shellfish
from areas of inleresl.
General information on persistence, bioaccumulation polenlial, and
toxicity may be obtained from references such as Lyman et al.(1982)
and Callahan et al. (1979). Other key sources thai are periodically
updated are ihe Regislry of Toxic Effects of Chemical Subslances (e.g.,
Tatken and Lewis 1983) and Ihe Annual Reporl on Carcinogens (e.g.,
National Toxicology Program 1982,1985). Specific information that is
Contaminants of
Concern
-------
directly useful in risk assessment can be obtained for many chemicals
from IRIS (see below, Sources of Information and Appendix B).
TABLE 1. Organic Priority Pollutants and 301 (h) Pesticides
Ranked According to OctanoI-Water Partition Coefficients
(Row) (updated from Callahan et al. 1979)
Priority
Pollutant
69
83
89
79
111
__q
75
74
82
107
73
91
92
90
129
94
106
72
112
76
93
99
53
9
100
101
39
84
41
64
40
20
81
98
78
109
80
__q
52
66
68
77
67
108
8
12
1
102
Substance
di-n-octyl phthalate
indeno(l,2,3-cd)pyrene
aldrin
benzo(ghi)perylene
PCB-1260
mirex
benzo(k)fluoranene
benzo(b)fluoranene
dibezo(a,h)anthracene
PCB-1254
benzo(a)pyrene
chlordane
4,4'-DDT
dieldrin
TCDD (dioxin)
4,4'-DDD
PCB-1242
benzo(a)anthracene
PCB-1016
chrysene
4,4'-DDE
endrin aldehyde
hexachlorocyclopentadiene
hexachlorobenzene
heptachlor
heptachlor 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
acenaphthylene
butyl benzyl phthalate
PCB-1221
1,2,4-trichlorobenzene
hexachloroethane
acenaphthene
alpha-HCH
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
6.10
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
4.07
4.05
4.00
3.98
3.93
3.92
3.85
Reference
m
o
i
d
b
k
d
i
i
n
o
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
b
k
b
b
P
16
-------
Priority
Pollutant
103
104
r
7
105
21
95
96
97
49
26
25
27
55
113
38
62
22
31
28
37
58
r
60
6
42
85
11
34
87
15
47
32
86
r
14
24
50
4
51
35
36
33
30
r
23
48
56
5
13
57
54
71
Table 1 (Cont.)
Substance log(Kow)
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
l,l>l-trichloroethane
2,4-dimethylphenol
trichloroethene
1,1,2,2-tetrachloroethane
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-fra/j.y-dichloroethene
demeton
chloroform
dichlorobromomethane
nitrobenzene
benzidine
1, 1-dichloroethane
2-nitrophenol
isophorone
dimethyl phthalate
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
1.93
1.90
1.88
1.83
1.81
1.78
1.77
1.67
1.61
Reference
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
b
b
g
b
b
17
-------
Table 1 (Cont.)
Priority
Pollutant
Substance
log(Kow) Reference
16 chloroethane 1.54
59 2,4-dinitrophenol 1.53
29 1,1-dichloroethene 1.48
65 phenol 1.46 a
10 1,2-dichloroethane 1.45 b
70 diethyl phthalate 1.40 b
63 N-nitrosodipropylamine 1.31
44 dichloromethane 1.30
19 2-chloroethylvinylether 1.28 g
43 bis(2-chloroethoxy)methane 1.26 g
3 acrylonitrile 1.20 b
18 bis(2-chloroethyl)ether 1.12 b
46 bromomethane 1.00
2 acrolein 0.90 b
45 chloromethane 0.90
88 vinyl chloride 0.60
61 N-nitrosodimethylamine -0.58 g
a Veith et al. (1979a).
b Veith et al. (1980).
c Gossett et al. (1983).
d Veith et al. (1979b).
e Kenaga and Goring (1980).
f Leo, A., 20 November 1984, personal communication.
g U.S. EPA (1980b).
h Karickhoff (1981).
i Rapaport and Eisenreich (1984).
j Miller et al. (1985).
k Means et al. (1980).
1 Miller et al. (1984).
m McDuffie (1981).
n Chiou et al. (1981).
o Bnggs (1981).
p Solubilities of the various isomers of HCH indicate that they will have
similar log(Kow) values.
q Chlorinated pesticides that are not on the priority pollutant list but are
included in Section 301 (h) (Clean Water Act) monitoring programs.
r Organophosphorus pesticides that are not on the priority pollutant list
but are included in Section 301(h) (Clean Water Act) monitoring
programs.
Recommendations regarding specific contaminants of concern are
beyond the scope of this guidance manual. A general list of con-
taminants with available EPA toxicological data listed in IRIS is
provided in Appendix B. The procedures for quantitative risk assess-
ment outlined in this manual are designed for use only with chemicals
having toxicological indices [Reference Doses (RfD) or Carcinogenic
Potency Factors]. In addition to the availability of toxicological in-
dices, the relative 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
18
-------
organic compounds with a log octanol-water partition coefficient
greater than or equal to 2.3 were recommended by Tetra Tech (1985a)
for inclusion in EPA Section 301(h) (Clean Water Act) monitoring
programs. EPA priority-pollutant metals are listed in Table 2 in
descending order of bioaccumulation potential, according to their
BCF (Tetra Tech 1985a).
TABLE 2. Inorganic Priority Pollutants Ranked According to
Bioconcentration Factor (BCF)
Priority
Pollutant No.
123
123
123
120
128
115
118
122
119
119
123
124
127
114
117
121
125
126
Substance
methylmercury
phenylmercury
mercuric acetate
copper
zinc
arsenic
cadmium
lead
chromium VI
chromium III
mercury
nickel
thallium
antimony
beryllium
cyanide
selenium
silver
log BCF8
4.602
4.602
3.447
3.073
2.762
2.544
2.513
2.253
2.190
2.104
2.000
1.699
1.176
ND
ND
ND
ND
ND
BCF = Bioconcentraction Factor. The value shown is the geometric mean BCF
among studies summarized by Tetra Tech (1985a). U.S. EPA (1986h) provides
additional information on BCF values for selected chemicals.
ND = No data.
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 or-
ganisms 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 Su-
perfund 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 suffi-
cient evidence of carcinogenicity in humans should generally be con-
sidered as contaminants of concern.
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)
Toxicity Profiles
19
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Metabolic and pharmacokinetic properties (e.g., metabolic
degradation products, depuration kinetics)
lexicological effects (e.g., target organs, cytotoxicity, car-
cinogenicity, 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 assessment 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 lexicological hazards. Neither toxicity profile in Table 3 is
intended to be comprehensive.
TABLE 3. Toxicity Profile for Mercury and PCBsa
Property
CAS Number
Physical-Chemical
Molecular weight
Vapor pressure (mm Hg)
Solubility (mg/L)
LogKowd
Log Bioconcentration Factor
Carcinogenic Status
Mercury
7439-97-6
200.6 - 318.7
0.012 - 0.028
0.056 - 400,000
N/Ae
2.0 - 4.6
Noncarcinogen
PCBsc
1336-36-3
154.2 - 498.7
2.8x10-9-7.6x10-5
-5.9
4.0 - 6.9
1.9-5.2
Probable human car-
cinogen
Acute Toxicity
Human LD50 (mg/kg body wt) 29E
Mammal LD50 (mg/kg body wt)1.0 - 40.9
Group B2
- Sufficient animal
evidence
- Inadequate
human evidence
1,010 -16,000
Chronic Toxicological Effects
Humans
Mammals
Motor and sensory im-
pairment leading to
paralysis, loss of vision
and hearing, and death.
Kidney dysfunction.
Reproductive impair-
ment and teratogenic ef-
fects.
Critical endpoint for risk as-
sessment
Skin lesions, liver
dysfunctions, and
sensory neuropathy.
Possible reproduc-
tive and develop-
mental impairment.
Hepatoxicity,
fetotoxicity, skin
lesions, and hepato-
cellular carcinoma.
Reproductive and
developmental im-
pairment.
Central nervous system Hepatocellular car-
effects (e.g., ataxia and cinoma .
parathesia) .
20
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Table3(Cont.)
g
This is an example toxicity profile and is not intended to be comprehensive.
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.
Physical-chemical properties and toxicity vary according to the degree of chlorine
substitution, the number of adjacent unsubstituted carbons and steric configuration.
Bioconcentration Factors are the ratio of a chemical concentration in tissues of
marine or estuarine organisms and the concentration in water to which the organism
is exposed (Tetra Tech 1985a).
N/A = not applicable.
f U.S. EPA (1980a,b, 1986f; IARC 1978).
g
6 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).
Information in a toxicity profile is used to support the weight of
evidence classification for the likelihood of a chemical causing a given
health effect. The endpoints considered should include noncar-
cinogenic as well as carcinogenic effects. EPA has developed a weight-
of-evidence classification scheme which indicates the state of
knowledge on the 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 sup-
port a causal association between exposure to the agent and
cancer.
Group B - Probable Human Carcinogen: This group includes
agents for which the weight of evidence of human carcinogeni-
city based on epidemiologic studies is "limited." It also includes
agents for which the 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 "inade-
quate" 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.
21
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Sources of Information
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 carcinogeni-
city in at least two adequate animal tests in different species or
in both adequate epidemiologic and animal studies. The clas-
sification of an agent in Group E is based on the available
evidence and should not be interpreted as a definitive con-
clusion that the agent is not a carcinogen under any circum-
stances.
The above descriptions for the categories were taken from U.S. EPA
(1986a). At present, a weight-of-evidence classification for car-
cinogenicity is available in IRIS for each chemical assigned a Car-
cinogenic Potency Factor.
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 are available for approximately 260
chemicals (as of August 1988). 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 As-
sessment (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 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. Chemi-
cal and lexicological information can be obtained from the databases
listed in Appendix C, Table C-3. In particular, MEDLARS and its
associated databases (e.g., Toxline, RTECS, and AQUIRE) provide
extensive lexicological information.
22
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Dose-Response Assessment
After the potential hazard associated with each contaminant of con-
cern is characterized, the relationship between dose of a substance and
its biological effect is determined. Dose-response data are used to
determine the lexicological potency of a substance, a quantitative
measure of its potential to cause a specified biological effect. The
concepts of exposure, dose, dose-response relationship, and
lexicological potency are discussed in the following sections.
The concepls of exposure and dose, as defined below, are cenlral lo
risk assessment
Exposure: Conlact by an organism with a chemical or physical
agent
Dose: The amounl of chemical uplake by an organism over a
specified lime as a consequence of exposure.
The "ingesled dose," or amounl of chemical ingesled, is dislincl from
the "absorbed dose." For ihe oral roule of exposure, the absorbed dose
is the amounl of chemical assimilaled by absorplion across ihe lining
of Ihe gaslroinleslinal syslem. Exposure level or exposure concentra-
tion is used to denole Ihe concenlralion (mg/kg wel weighl) of con-
laminanl in edible lissue of fish or shellfish. As shown below, Ihe
absorbed dose is estimaled from food consumption rate, the exposure
concenlralion, and an absorplion coefficienl (see Exposure Assess-
ment).
The form of Ihe dose-response relalionship for carcinogens is assumed
lo be fundamenlally different from that for noncarcinogens (U.S.
Office of Science and Technology Policy 1985). Examples of general
dose-response relalionships are shown in Figure 2. The lack of a
demonslraled threshold in dose-response relationships for car-
Exposure and Dose
Dose-Response
Relationships
23
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V)
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LOW-DOSE
REGION OF
CONCERN
o
UJ
w w
u. }f
Z X
UJ O
3 I-
O
111
OC
Rfd
DOSE OF CARCINOGEN
-* UF
NOAEL
OBSERVED DATA POINTS
CHEMICAL A
A CHEMICALS
CHEMICAL C
MODELS
Low dose
extrapolation
^^ Models fit within
observed data range
Frequency -
RfD-
UF»
NOAEL-
Proportion of
animals tested
Reference Dose
Uncertainty Factor
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.
-------
cinogens (U.S. EPA 1980b, 1986a; U.S. Office of Science and Tech-
nology 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 lexicological 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:
Carcinogens are individually characterized by a Carcinogenic
Potency Factor, a measure of the cancer-causing potential of a
substance estimated as 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 of the daily exposure to the human population (includ-
ing 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.
The RfD is conceptually similar to an Acceptable Daily Intake (U.S.
EPA 1987a).
The data set used to define lexicological 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 re-
quired 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 contaminants are usually the administered (ingested) dose,
not the absorbed dose (i.e., uptake across the lining of the gastro-
intestinal system).
24
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Carcinogenic Potency Factors and RfD values derived by EPA are
listed in the IRIS database. At present, values for these lexicological
indices are being standardized for agency-wide use. A brief overview
of methods by which these indices are derived is presented below.
The Carcinogen Assessment Group of EPA currently uses the linear-
ized 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 carcinogenesis 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
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" ), all currently used models yield generally
similar risk estimates. Below risks on the order of 10 , 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 car-
cinogen is:
where:
R(d)
R(d) = 1 - exp [-(qid + q2d2 + ... + qkdk)]
(1)
= Excess lifetime risk of cancer (over background at
dose d) (dimensionless)
qi values = Coefficients [kg day mg" (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 calculated, which essentially defines the linear portion
of the dose-response function at low doses
Carcinogenic Potency
Factors
25
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Reference Doses
The coefficient of the maximum linear term, designated as qi *,
is set equal to the slope of the dose-response function at low
doses
The resulting estimate of qi* is used as an upper-bound es-
timate of the Carcinogenic Potency Factor (termed Slope Fac-
tor in U.S. EPA 1987a).
qi* is usually calculated as the upper 95 percent confidence limit of
the estimate of the coefficient qi in Equation 1.
The model commonly used to estimate plausible-upper-limit risk for
low levels of exposure over a lifetime is therefore:
R (d) = qi* d
(2)
where:
R (d) = Upper-bound estimate of excess lifetime risk of cancer
(dimensionless)
= Upper-bound estimate of carcinogenic potency (kg day
qi
d
mg1)
= Dose (mg kg"1 day"1).
Equation 2 represents a linear approximation of the multistage model.
Because the slope of the dose-response function at high doses could
be different from that at low doses, the use of qi* as an upper-bound
estimate of potency is not valid at high levels of exposure. Thus, qi*
should not be used as the upper-bound estimate of potency at ex-
posures corresponding to excess lifetime risks greater than ap-
proximately 10" per individual (i.e., one excess tumor per 100 exposed
individuals).
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 Pinkel (1958), Freireich et al. (1966),
Dedrick (1973), and Mantel and Schneiderman (1975). The relation-
ship between surface-area extrapolation and body-weight extrapola-
tion approaches is discussed in the Introduction above (see
Background, Relationship of EPA Risk Assessment Methods to FDA
Risk Assessment Methods).
Current methods for predicting human health effects from exposure
to noncarcinogenic 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 usual-
ly ranges from 1 to 1,000 (Dourson and Stara 1983). The relationship
between the NOAEL, the RfD, and the uncertainty factor are il-
lustrated in Figure 2 above. The uncertainty factor accounts for dif-
ferences in threshold doses among species, among intraspecies groups
differing in sensitivity, and among toxicity experiments of different
26
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duration. Dourson and Stara (1983) and U.S. EPA (1987a) discuss the
methods for deriving RfD values and the criteria for selecting uncer-
tainty 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 LO AEL in place of a NOAEL.
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 associated uncert-
ainties. Contacts for information on specific chemicals are listed in
IRIS Chemical Files.
The Carcinogenic Potency Factors calculated by the EPA Carcinogen
Assessment Group are published in IRIS and in each health assess-
ment document produced by the Office of Health and Environmental
Assessment (e.g., U.S. EPA 1985a). The EPA Carcinogen Assessment
Group develops these carcinogenic potency values and updates them
periodically. Before being entered into IRIS, Carcinogenic Potency
Factors and supporting documentation are reviewed by the Car-
cinogen 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.
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 chemi-
cals. 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-dichloro-
propane, endrin, ethylene dibromide (EDB), heptachlor and hep-
tachlor epoxide, lindane, methoxychlor, oxymyl, pentachlorophenol,
toxaphene, and 2,4,5-trichlorophenoxypropionic acid (2,4,5-TP). Of-
fice of Drinking Water Health Advisories will eventually be incor-
porated into IRIS.
Sources of Information
Carcinogenic Potency Factors
Reference Doses
27
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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 poten-
tial 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 popula-
tion is combined to construct an exposure profile, which includes
estimates of average rates of contaminant intake by exposed in-
dividuals. Key stages of an exposure assessment for contaminated fish
and shellfish are discussed in the following sections.
Measurement of
Contaminant
Concentrations in
Tissues
Guidance on development of study designs to measure concentrations
of toxic substances in edible tissues of fish and shellfish is provided in
II
29
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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 con-
cepts 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 generally
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 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, reproduc-
tive 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. Alter-
natively, 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 prac-
tices 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 assess-
ment 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 bioconcentra-
tion 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 sur-
vey" 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
30
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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:
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 dif-
ferent 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; analyti-
cal methods and detection limits; data coding; data QA/QC
steps to assess precision, accuracy, and completeness;
database management specifications; data reporting require-
ments; and performance audits.
Define data analysis steps, including statistical tests, data plots,
summary tables, and uncertainty analysis.
31
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Study Objectives and General
Sampling Design
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 num-
bers of replicate samples for each treatment (or stratum) may be
impractical 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 ap-
propriate.
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 relation-
ships between study objectives and general features of a sampling
design are addressed in the next section.
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 manage-
ment. For instance, statistical discrimination among mean con-
taminant 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 ObjectivesSome examples of objectives for exposure assess-
ments 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 recom-
mended objectives for an actual exposure assessment. In these ex-
amples, 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.
32
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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 in-
dividual organisms or composite samples would be analyzed for a large
number of compounds and the risk assessment 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 ex-
posure 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.
Bioaccumulation Design: Estimate the mean concentrations
of contaminants 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 com-
posite 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 popula-
tion.
Bioaccumulation Design: Estimate the upper bound of the 95
percent confidence interval of the mean concentration for each
of the contaminants A, B, and C in edible tissues of aquatic
33
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species L, M, N, and O combined from harvest area Z during
the season of highest contamination.
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 con-
taminants across species. To meet these objectives, samples could be
composited across species, although this is generally not recom-
mended. Multispecies composites would not provide data for assess-
ing 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 segments of an exposed population
(e.g., ethnic groups) over an annual period.
Bioaccumulation Design: Estimate the probability distribu-
tion of concentrations of contaminants A, B, and C in edible
tissues of each of aquatic species L, M, N, and O 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 popula-
tion 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 consumption 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 concentration 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.
34
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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 ap-
propriate to divide the exposed population into segments correspond-
ing 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 ex-
amples 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 con-
centrations of contaminants in tissues of aquatic organisms. Some of
the important environmental factors are:
Conventional water quality (i.e., hardness, salinity, temper-
ature, suspended solids)
Habitat location, depth, proximity to contaminant sources
Contaminant concentrations in water
Contaminant concentrations in sediments
Species available for harvest, as influenced by habitat,
migratory cycles, and fisheries management practices
Organism activity pattern, food habits, and habitat
Seasonal biological cycles (e.g., stage of sexual cycle) in rela-
tion 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 con-
sumed fish and shellfish. Therefore, at least general knowledge of
seasonal changes in contaminant concentrations and human consump-
tion patterns may be needed to design an appropriate sampling ap-
proach 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
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-------
ENVIRONMENTAL FACTORS b
CONVENTIONAL WATER QUALITY c
PROXIMITY TO CONTAMINANT SOURCES
CONTAMINATION OF WATER/SEDIMENTS
SPECIES AVAILABLE FOR HARVEST
ORGANISM ACTIVITY MODE d
SEASONAL BIOLOGICAL CYCLES e
ORGANISM SIZE
ORGANISM AGE
ORGANISM SEX
LIPID CONTENT OF TISSUE
EXPOSED-POPULATION FACTORS a
/
//
?/
(J O" //-
£ # ,{y
-e
-O
-6)
«-
-O
-O
-o
a 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
b Factors that influence contaminant concentrations in aquatic organisms
c Hardness, salinity, temperature, suspended solids
^ Degree of mobility and contact with sediments
e Reproductive, lipid storage, and growth cycles
4 Population factor affects environmental factor
O Environmental factor affects population factor
w Mutual interaction between environmental and population factors
Figure 3 Interaction between environmental factors and exposed
population factors.
-------
Selection of Target Species and
Size Classes
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.
In the first case above (homogeneous diet and contamination), the
study design could be relatively simple. Mean contaminant concen-
trations 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.
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 objec-
tives. 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 (compre-
hensive 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
36
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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
subpopulations of concern, then specific dietary information for sub-
populations 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 objec-
tive (site-specific analyses of the spatial distribution of contamination),
an 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 con-
tamination levels will not necessarily ensure that the overall purpose
of performing an exposure assessment will be met.
TABLE 4. Criteria for Selecting Target Species3
Alternative Design Objectives
Species
Characteristics
Comprehensive Typical Worst-Case Spatial Pattern
Species Exposure Species Indicator
Analysis Case Species
Harvest ranking Species forming Dominant Dominant Variable
95% of catch species In catch species in catch
Home range size Variable Variable Variable Small
Contamination level Variable Variable High High
Criteria for selecting target species to meet a given objective are shown in bold.
A full statement of each objective is given in the text.
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 ex-
posure 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 consump-
tion 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
37
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Individual humans that consume species other than the
dominant component of the diet for the entire exposed popula-
tion may not be protected when the results of the risk assess-
ment are used in risk management
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 com-
ponent of the catch maybe unidentifiable because the catch is
sometimes cleaned before being surveyed. Moreover, the loca-
tion of harvest by boat anglers often cannot be verified.
Indicator Species-The use of selected indicator species is an alterna-
tive 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 risks for specific areas within a water body).
Indicator species may include both highly mobile and relatively seden-
tary species. If small-scale discrimination of spatial patterns of con-
tamination 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 appro-
priate 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 assessment. The latter should include data on consumption
patterns and contaminant concentrations for a wider variety of har-
vested species and size classes.
The use of indicator species for exposure assessment offers the follow-
ing 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
contamination of indicator species may be available
Indicator species can be selected to represent the ayerage or
maximum level of contamination expected for all harvested
species (assuming background or pilot data are available)
38
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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 concentrations of contaminants)
The selected species may be a good indicator for some con-
taminants 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 con-
tamination of the harvested species are usually needed to select
appropriate indicator species
If a contamination problem is apparent, collection of samples
of other species and size ranges of concern may be necessary.
Phillips (1980), Tetra Tech (1985b), and Phillips and Segar (1986)
provide criteria for selecting target species for bioaccumulation sur-
veys. Important criteria to consider when choosing indicator species
for an exposure assessment are listed below. The target species should
be:
Harvested by the exposed population or be representative of
the contamination levels in the primary harvested species
Representative of a specific study area (e.g., relatively seden-
tary or restricted from migration by the presence of physical
barriers such as dams or waterfalls)
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 use-
fulness of the target species as a site-specific indicator when
contaminant composition is expected to differ among sites
Target species should integrate the effects of contaminant
uptake over time
Target species should have a high bioaccumulation potential
for the contaminants of concern, especially if a worst-case
scenario is desired.
39
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A summary of indicator species recommended by Tetra Tech (1985b)
for monitoring of chemical residues in tissues of marine and estuarine
organisms 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 concentra-
tions in demersal (bottom-dwelling) vs. pelagic (open-water) or-
ganisms 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 recrea-
tionally 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 bioac-
cumulation studies in lakes and streams has been conducted. Salmon
and trout (Salmonidae), perch (Percidae), and sunfish (Centrar-
chidae) species may be preferred for tissue analysis in many cases
because they constitute the bulk of the fisheries harvest. However,
perch and sunfish species will generally have the lowest concentrations
of organic contaminants in edible tissues because of their low lipid
content. Freshwater mussels, especiallyAnadonta spp. and Corbicula
spp., crayfish, sculpins (Cottidae), and catfishes (Ictaluridae) may be
preferred as target species for site-specific 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 con-
centrations 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. If contamination of relatively large in-
dividuals is high, sampling and analysis of all size classes typically
harvested should be performed to develop specific advisories. For
40
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FISHES
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example, when contaminant concentrations are positively correlated
with fish (or shellfish) size, frequent consumption of the smaller in-
dividuals may be acceptable even though consumption of larger in-
dividuals should be severely limited.
Two general approaches to field sampling are possible. First, the
investigator can obtain samples directly from harvesters. This ap-
proach has the advantage that the sampled population is the popula-
tion of direct interest for the exposure and risk assessments. However,
one drawback of this approach is the potential for contamination or
degradation of samples due to handling of the samples by the har-
vesters. 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 address-
es 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 concentra-
tions (and corresponding health risks) at each harvest area. Because
sampling depth or vertical position on the shore may influence con-
taminant concentration in aquatic organisms, reference station char-
acteristics 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 of a
species within a specified area has an equal chance of being selected
for measurement and that selection of one individual does not in-
fluence selection of others. A simple random sampling strategy is
appropriate 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 often be nonrandom with respect
to species and size classes because of the selective nature of the gear.
Stratified random sampling involves random sampling within nonover-
lapping strata of a population (e.g., subareas where recreational fishing
effort is concentrated or where contamination is greatest). This sam-
pling approach is appropriate when localized 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 initially be collected randomly from a given stream reach. In
Sampling Station Locations
41
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SIMPLE RANDOM
SAMPLING
STRATIFIED RANDOM
SAMPLING
STRATA
PRIMARY
UNITS
TWO-STAGE
SAMPLING
CLUSTER
SAMPLING
CLUSTERS
SYSTEMATIC GRID
SAMPLING
RANDOM SAMPLING
WITHIN BLOCKS
Reference: Gilbert (1987)
Figure 5 General sampling station layouts for probability sampling in
two dimensions.
-------
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.
Gilbert (1987) describes systematic sampling approaches for locating
"hot spots" or highly contaminated local areas. He addresses the fol-
lowing 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 ap-
plied 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 bioaccumula-
tion of toxic chemicals 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 contamina-
42
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tion 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 areas, species, and size classes. Other sampling
strategies maybe either too simple or inappropriate to meet the typical
objectives of exposure assessment studies.
The timing of bioaccumulation surveys should be based on the tem-
poral 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 con-
taminants 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 in-
fluence 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. In some species, contaminant content of edible tissues
may reach a seasonal maximum at or just before the peak of reproduc-
tive ripeness, before gametes or offspring are released. This may be
especially characteristic of species that are consumed whole (e.g.,
clams and oysters). In other species (e.g., salmonids), lipid and as-
sociated contaminants may be mobilized and transferred from muscle
tissue to eggs before they are released. In such species, the peak of
contamination may occur in edible tissue (muscle) well before spawn-
ing. Because the time of sampling should be tailored to the reproduc-
tive 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).
Time of Sampling
43
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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 representativeness of an exposure assess-
ment. The issues of composite sampling and sample preparation tech-
niques 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 sub-
sample, 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. Com-
posite sampling is a cost-effective strategy when the individual chemi-
cal 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 con-
centration. 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 com-
posite 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 popula-
tion 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 distribu-
tion of sources of contaminants.
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 the underlying population (X)
of individual samples when the variance of the composite samples (Z)
is known:
Var X = n (Var Z) (3)
44
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where:
VarX
VarZ
n
= variance of the mean of individual samples from all
composites
= variance of the mean of composite samples
= number of subsamples constituting each 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 com-
posite 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 (Tetra Tech 1986b). 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" (Phillips and Segar 1986). Space-bulking involves sam-
pling 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 loca-
tion and compositing these samples. Time-bulking over a harvest
season is especially appropriate where short-term variations in con-
taminant 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 sig-
nificant information on spatial and temporal heterogeneity may be lost.
Selection of space-bulking or time-bulking techniques should be sup-
ported by analyses of available data or data from preliminary sampling.
Tiered analyses of samples can also be used to evaluate the ap-
propriateness 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 ap-
propriate 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 concentra-
tion 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 treat-
ments) increases dramatically with the number of individual samples
in each replicate composite sample. However, the increase in power
associated with adding more individual samples to each composite
45
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eventually becomes negligible (e.g., at greater than 10 individuals per
composite at typical levels of data variability). For moderate levels of
variability in chemical residue data, 6 to 10 individual samples within
each of 5 replicate composite samples should be adequate to detect a
treatment difference equal to 100 percent of the overall mean among
treatments. 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 prepara-
tion 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 offish, not internal organs or whole fish. Because internal organs
are often more contaminated by toxic chemicals than are fillets, ex-
posure 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. In species with a
subcutaneous fat layer, this practice may also reduce the variability of
replicate data, allowing more sensitive discrimination among statistical
treatments (e.g., species or sampling locations). Within the fillet tissue,
contaminant concentrations may vary depending on the original loca-
tion of the sample on the fish's body.
The effect of cooking on the ultimate health risk from a mixture of
chemicals (including any transformation or degradation products
produced by heating) is unknown. Some studies have shown decreases
in concentrations of lipid-soluble organic compounds such as DDT
and PCBs following pan-frying, broiling, or baking offish fillets (Smith
et al. 1973; Skea et al. 1981; Puffer and Gossett 1983). For example,
cooking of fillets before chemical analysis may result in a 2 to 65 percent
decrease in the concentration of PCBs relative to the uncooked sample,
but the results vary greatly with species and cooking method. However,
cooking may also activate or transform chemicals to create carcinogens
[e.g., formation of benzo(a)pyrene during charbroiling]. Regardless of
method of tissue preparation, an adequate mass of each sample and
adequate homogenization of samples before they are analyzed are
necessary to obtain representative results (e.g., see Tetra Tech 1986e).
46
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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 Section 301(h) (Clean Water Act) 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 prepara-
tion 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.
In general, field studies to support exposure assessment should focus
on the kind of tissue that is most commonly consumed (e.g., fillet).
Analysis of raw edible tissues is recommended to provide data on the
concentrations 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.
Replicated measurements of contaminant concentrations in tissue
samples are needed to perform uncertainty analysis (e.g., charac-
terizing the precision of the estimates of mean contaminant concentra-
tions). 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 assess-
ments. Guidance on selection of a sample replication scheme is
provided in Appendix E. In most cases, at least five replicate samples
of individual fish (or shellfish) are required to provide minimal statis-
tical 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).
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 concentra-
tion associated with a specified minimum risk level defined as accept-
able 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, ben-
zidine, dieldrin, N-nitrosodimethylamine), the minimum detection
Sample Replication
Selection of Analytical
Detection Limits and Protocols
47
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QA/QC Program
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., greater than 10~5 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 pol-
lutants 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) (Clean Water Act) 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 addi-
tional guidance from the EPA Contract Laboratory Program for Or-
ganic 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, Environmental 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 detec-
tion 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 tech-
niques, laboratory experience with these techniques, and pricing
policies of laboratories account largely for the wide variation in cost.
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
(19860 describes QA/QC procedures for field and laboratory methods
used by the EPA Section 301(h) (Clean Water Act) program. Horwitz
et al. (1980) provide guidance on QA/QC in the analysis of foods for
trace contaminants. Brown et al. (1985a) describe QA guidelines
48
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followed by NOAA for chemical analysis of aquatic environmental
samples.
TABLE 5. Approximate Range of Cost per Sample for Analyses
of EPA Priority Pollutants in Tissues as a Function of Detection
Limits and Precision3
EPA Priority
Pollutant
Group
Approximate
Detection Typical
Limit Precision
Approximate
Cost Range5
Extractable
acid/base/neutrals/
PCBs/pesticides < 1-20 ppb
Volatiles < 5-20 ppb
Metals 100 ppb
$900- > $2,000
$250 - $350
$250- $300
NOTE: Range of per sample cost is based on multiple quotes compiled 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 cir-
cumstance.
Each cost range is mainly the result of laboratory differences in technique and
pricing, NOT the range in precision or detection limits shown.
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 con-
tainers, preparation, and preservation
Forms for documenting sample custody, station locations,
sample characteristics, sample analysis request, and sample
tracking during laboratory analysis
Detailed description of analytical methods
Calibration procedures for chemical measurements
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
49
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Documentation and QA
Review of Chemical Data
Procedures for QA/QC reporting and responsible federal and
state QA officers
Mechanisms for approval of alterations to the monitoring pro-
gram, 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.
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 documenta-
tion 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 each sample analyzed
by gas chromatography/mass spectrometry (GC/MS)
Mass spectra of detected target compounds for each sample
analyzed by GC/MS
Chromatograms for each sample analyzed by gas chromatog-
raphy/electron capture detection (GC/ECD) and/or gas
chromatography/flame ionization detection (GC/FID)
Raw data quantification reports for each sample
A calibration data summary reporting calibration range used
[and decafluorotriphenylphosphine (DFTPP) and
bromofluorobenzene (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)
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.
50
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All contaminant concentration data to be used in a risk assessment
should undergo a thorough QA review by a qualified chemist inde-
pendent 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 Char-
acterization). 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 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 con-
fidence 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 consump-
tion 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 treat-
ments or the Kruskal-Wallis test for multiple comparisons are recom-
mended. 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 1984 and
Gilbert 1987). Examples of trend analysis for chemical contaminants
in fish are provided by Brown et al. (1985b) 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 concentrations should be calculated twice: once
using detection limits for undetected observations and once using 0 for
undetected observations. Although alternative approaches are pos-
sible (e.g., using one-half the detection limit), the approach recom-
mended here yields more accurate, complete results by quantifying the
range of the estimated values. According to the EPA Exposure Assess-
ment Group, calculations of plausible-upper-limit risk estimates based
on detection limits should generally be avoided. However, risk es-
timates based on detection limits may occasionally be useful to indicate
that particular chemicals, species, or geographic locations are not
Statistical Treatment of Data
51
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Analysis of Sources,
Transport, and Fate of
Contaminants
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 offish
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 hi com-
bination 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 offish
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.
Exposure pathways and routes are potential mechanisms for transfer
of contaminants from a source to a target human population or sub-
population. The sources, transport, and fate of chemicals in the en-
vironment are analyzed to evaluate exposure pathways and routes. To
compensate for a limited database, this analysis often includes mathe-
matical 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:
Estimate the spatial and temporal distribution of concentra-
tions 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 con-
52
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lamination (e.g., laboratory-derived BCFs). 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 valida-
tion). 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 Cal-
lahan 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.
The second stage of the exposure assessment, analysis of exposed
populations, includes the following steps:
Identify potentially exposed human populations and map loca-
tions of fisheries harvest areas
Characterize potentially exposed populations
- Subpopulations by age, sex, and ethnic composition
- Population abundance by subpopulation
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 popula-
tion 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)
Analysis of Exposed
Populations
-------
Comprehensive
Catch/Consumption Analysis
Estimate arithmetic average consumption rate by species and
by total catch for the total exposed population and for sub-
populations. For seasonal fisheries, consumption rates maybe
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 limita-
tions. 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 ex-
posed population. In the second approach, estimates of consumption
rates are based on available values for the U.S. population (or sub-
populations) or other assumed values. Most of the available estimates
were derived from recall or diary studies (Lindsay 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 com-
mercial 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 graphi-
cally 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
may be useful for public presentations on recreational fishery resour-
ces. In this case, the risk associated with any particular subgroup within
the exposed population may be evaluated by selecting a consumption
value for the subgroup and reading the corresponding risk from the
graphic plot. Use of this approach avoids having to collect extensive
data on the exposed population. A similar approach involves selecting
an "acceptable" (tolerable) 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.
Appropriate field survey forms, data analyses, and format for presen-
tation of results for a comprehensive catch/consumption analysis of
54
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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 of analyses of catch/con-
sumption 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
Belton 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. Note that 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). Sup-
plementary 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/year [meals/year
may be calculated from g/day by assuming an average meal of fish or
shellfish equals about 150 g (0.331b) if the average meal size is un-
known]. Average consumption rate for each harvest species is calcu-
lated 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
- Multiplying the value obtained in the preceding com-
putation 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 popula-
tion.
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 direct-
ly rather than from length measurements. However, for shellfish and
55
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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:
V i
- "jkl 7jkl
(4)
where:
lijkl
Wjjkl
Pi
Hjki
Tjkl
= Mean daily consumption rate of species i for subpopula-
tion j, area k, and household 1 (kg/day)
= Number of households (successful harvest trips) for
species i, subpopulation j, and area k
= Weight of species i harvested by household 1 of subpopu-
lation j in area k (kg)
= Proportion of cleaned edible weight of species i to total
harvested weight
= Number of people in household 1 of subpopulation j in
areak
= Time elapsed since last meal by household 1 of subpopu-
lation j in area k (days).
When consumption rates (lijkl) are log-normally distributed, a
geometric mean consumption rate may be calculated by log-transform-
ing the data before applying Equation 4 to calculate a mean consump-
tion rate.
Consumption rate data may be summarized further by calculating
means across species, subpopulations, and areas. However, it should
be recognized that means of lijk across species do not represent actual
diet patterns for consumers of mixed-species diets. To calculate mean
consumption rates for mixed-species diets, all lijkl should be summed
across species within a household before determining mean consump-
tion rates across households (Ijk):
- 2
/jkl
= I
1
/ijkl
Mjk
(5)
where:
Ijkl = Mean daily consumption rate of all fishery species for
household 1, subpopulation j, and area k (kg/day)
Nik = Number of households in subpopulation j and area k
and other terms are defined above.
Landolt et al.(1985) summarized the assumptions involved in calculat-
ing mean consumption rates (Ijjki) by household as follows:
Consumption
- pi values are assumed as noted above
- Catch was distributed evenly among consumers in household
- People in household actually ate the entire cleaned catch
56
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- 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, cal-
culated consumption rates cannot be directly extrapo-
lated 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 con-
sumption 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 E of Landolt et al.
1985). These factors should be determined on a case-specific basis.
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 undoub-
tedly vary over time. Extensive long-term monitoring of catch/con-
sumption 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:
Site-specific data are unavailable
Differences among areas (or times) are expected to be small
Precise estimation of average fish or shellfish consumption is
unnecessary 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 (USDA)
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 recrea-
tionally 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 non-
Assumed Consumption Rate
57
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consumers, based on data in SRI (1980). Consumption rates for por-
tions 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 165g/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). Limitations of fisheries consumption data are discussed by SRI
(1980) and Landolt et al. (1985). The present status of data on fish
consumption in the U.S. is also reviewed by Wagstaff et al. (1986).
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 (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)
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 as-
sumption 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 recom-
mended 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 Ap-
pendix F).
58
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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.
The general model to calculate chemical intake for a single-species
diet is:
(6)
where:
Eijkm
Cjkm
lijk
W
Effective ingested dose of chemical m from fishery
species i for human subpopulation j in area k (mg
kg" day" averaged over a 70-year lifetime)
Concentration of chemical m in edible portion of
species i in area k (mg/kg)
Mean daily consumption rate of species i by subpop-
ulation j in area k (kg/day averaged over 70-year
lifetime)
Relative absorption coefficient, or the ratio of hu-
man absorption efficiency to test-animal absorption
efficiency for chemical m (dimensionless)
Average human weight (kg).
Values of subscripted terms above may be estimated means or uncer-
tainty interval bounds (e.g., 95 percent confidence intervals) depend-
ing on the exposure scenario being modeled (e.g., worst case vs.
average case vs. lower-limit case). Note that Ejjkm is analogous to the
dose "d" in Equations 1 and 2. The term "effective" ingested dose
(Eijkm) 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.
Absorption coefficients (Xm) are assumed equal to 1.0 unless data for
absorbed dose in animal bioassays used to determine lexicological
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 ap-
propriate. Toxicological indices are determined from bioassays that
usually measure administered (ingested) dose. Therefore, the es-
timated chemical intake by humans, Ejjkm, is usually the ingested dose,
not the absorbed dose. If the lexicological 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
Exposure Dose
Determination
Single-Species Diets
59
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Mixed-Species Diets
assumptions about absorption efficiencies has been incorporated into
EPA's estimates of RfDs and Carcinogenic Potency Factors. There-
fore, Xm is usually dropped from Equation 6 and Ejjkm 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 15 years 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).
Estimation of chemical exposure due to a mixed-species diet is com-
plicated 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 (Eijkm) 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:
Ehjkm = 2,
(7)
W
where:
= 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-year lifetime)
Ihijk = Average consumption rate of species i by individual h in
subpopulation j in area k (kg/day averaged over a 70-year lifetime)
and other terms are defined as above. The average exposure dose for
mixed species diets is:
(8)
Hjk
where:
= Average effective exposure dose of chemical m from
mixed-species diet for subpopulation j in area k (mg kg" day" )
= Number of persons in subpopulation j in area k.
Uncertainty estimates can be obtained by calculating 95 percent con-
fidence limits for
60
-------
Sources of Information
References to protocols for sampling and analysis of toxic chemical
residues in fish and shellfish are provided above (see Measurement of
Contaminants). For the updated status of protocols and new develop-
ments, 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 avail-
able in FDA Compliance Program Guidance Manuals (available from
FDA, Freedom of Information (HFI-35), 5600 Fishers Lane, Rock-
ville, 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., Malta 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. 1986; Sloan and Horn 1986). The Wisconsin Department
of Natural Resources (Bureau of Water Quality) maintains com-
puterized 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 As-
sumed Consumption Rate). The EPA Office of Pesticide Programs
maintains the Tolerance Assessment System (Saunders and Petersen
1987). The Tolerance Assessment System uses a USD A 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 information on the effects of food prepara-
tion methods on chemical residues in food.
61
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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 prob-
ability 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 assessment, car-
cinogens 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 em-
phasized 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 un-
certainties 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 con-
fidence 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 es-
timates 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. Ap-
proaches to presentation of summary information to be included in risk
characterization are presented in the next chapter (see below, Presen-
tation and Interpretation of Results).
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 con-
tinuous constant lifetime exposure to a unit carcinogen con-
Carcinogenic Risk
63
-------
-------
^Ha!
I Identific
1 Dose-R
| Asses
1
ard 1
alion & L
asponse 1
smenl |
Physical-Chemical
Bioaccumulation Potential
Environmental Partitioning,
Degradation, Transport
Mechanisms, and Potential
Exposure Media
Metabolism and
Pharmacokmetic Properties
Toxic Effects in Humans
and Laboratory Animals
e.g., Structure-Activity
Relationships, Kow,
Bioconcentration Factors
e.g., Air, Water, Sediments,
and Biota
e.g., Lipophihcity, Bioaclivation,
Toxification/Detoxification,
Target Organs, Elimination
e.g., Acute and Chronic
Toxicity, Carcinogenic Potency,
Epriemiologic Evidence
e.g., Dose-Response Relations,
Carcinogenic Potency
e.g., Adequacy and Quality
of Data, Likelihood of
Specific Toxic Effects
Are Data
Sufficient For a
Quantitative Risk
Assessment?
"Ce \ f~\ I
illy \ »( No ) U Stop
JS' / V-X |
Concentration of
Specific Contaminant
Body Weight of
Exposed Individual
Route - Oral
Concentration in Fish/Shellfish
e.g., In g/day of Fish/Shellfish
Consumed
e.g., Years of Exposure,
Fraction of Lifetime Exposed
e g., Assimilated
Contacted
Daily Exposure Per kg
Body Weight
Qualitative Risk Determination Based on
Toxicological Properties and Limited Exposure Data
Carcinogenic Potency
RFD
Other Standards
Probability of Tumor
Exceedance of Standard
Probability of Some Other
Adverse Health Effect
Figure 6 Conceptual structure of quantitative health risk assessment
model.
-------
Noncarcinogenic Effects
centration (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 con-
tamination 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 popula-
tion of specified size per generation).
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:
where:
R ijkm
R ijkm qi
(9)
qi*m
Plausible-upper-limit risk of cancer associated with
chemical m in fishery species i for human subpopulation
j in area k (dimensionless)
Carcinogenic Potency Factor for chemical m [(mg kg"
day" )" ] estimated as the upper 95 percent confidence
limit of the slope of a linear dose-response curve
Exposure dose of chemical m from species i for sub-
population j in area k (mg kg" day" ).
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 (Ejjkm) has replaced the dose (d), which is usually
a known quantity administered to a bioassay animal. All Eijkm are
calculated as discussed above (see Exposure Dose Determination in
Exposure Assessment). When local consumption rate data are unavail-
able, a range of Eijkm and corresponding risk estimates may be calcu-
lated based on a range of assumed consumption values. Estimates of
qi*m are available in IRIS. Note that Equation 9 is only valid for
estimated risks below 10
,-2
Estimation of upper-limit risk associated with the average mixed-
species diet follows a similar approach, except that the average effec-
tive dose (Ejkm) of chemical m from a mixed-species diet, calculated
from Equation 8 above, replaces the species-specific exposure (Eijkm)
in Equation 9. Calculation of the average effective dose was discussed
earlier (see Exposure Assessment, Exposure Dose Determination).
Noncarcinogenic risk may be evaluated by calculating the ratio of the
estimated chemical intake to the RfD as follows:
64
-------
(10)
where:
= Hazard Index of a health effect from intake of chemical
m associated with fishery species i for human subpopula
tion j in area k (dimensionless)
RfDm = Reference Dose for chemical m (mg kg"1 day"1)
and Ejjkm is defined as above. RfDm values are given in IRIS (U.S. EPA
1987a).
When all significant exposure routes and sources are taken into ac-
count, the estimated total exposure for all routes replaces Ejjkm 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 Hjjkm
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 Hykm as calculated by Equation 10 do not account for ex-
posures other than that from consumption of single fisheries species,
values of Hykm 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 contaminated 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, as-
sociated with the entire fishery. However, Hjkm could not be inter-
preted 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
65
-------
Chemical Mixtures
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 uncer-
tainties 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 noncar-
cinogenic risk. The MOE is the ratio of the NOAEL 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 MOE). 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.
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. The approach used most frequently
for multiple-chemical assessment is the additive-risk (or response-ad-
ditive) 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 interpreted as estimates of total chemical risk
associated with ingestion.
66
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Presentation and Interpretation of Results
Examples of formats for presenting the results of risk assessments to
risk managers or technical audiences are provided below. These for-
mats 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 documen-
tation on assumptions and uncertainties are also described. Interpreta-
tion of the results is largely a function of risk management. As such,
guidance on interpretation of risk estimates to support decisionmaking
is beyond the scope of this manual. Nevertheless, a brief discussion of
risk comparisons (e.g., estimated risks for various fish species; es-
timated risk vs. acceptable risk defined by policy) is provided to alert
the reader to the interface between risk assessment and risk manage-
ment. Supplementary information, such as comparisons of con-
taminant concentrations with FDA action levels, is addressed in the
final section below.
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 car-
cinogens (U.S. EPA 1986a). To guide the reader's interpretation of the
information presented, supporting text should describe assumptions,
uncertainties, and any caveats about the results. All individual and
population-level risk estimates should be interpreted as plausible-
upper-limit values for the stated assumptions and exposure conditions.
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.
Presentation Format
Summary Tables
67
-------
TABLE 6.
Example Tabular Format for Display of Quantitative Risk Assessment for
Consumption of Fish and Shellfish
F.*nosure Determination
Concen-
tration
in Medium
Substance
PCBs
PCBs
Hg
Hg
0.007
0.004
0.010
0.007
0.004
0.010
0.157
0.008
0.478
0.157
0.008
0.478
Contact
Rate
(mg/kg)a
65
65
65
20.0
20.0
20.0
65
65
65
20.0
20.0
20.0
Total
Daily
Contact
(g/day)b
4.6E-05
2.6E-05
6.5E-05
1.4E-04
8.0E-05
2.0E-04
l.OE-03
5.2E-05
3.1E-03
3.1E-03
1.6E-04
9.6E-03
Risk Determination
Carcinogens
Exposure
Duration
(mg/day)
70.0
70.0
70.0
70.0
70.0
70.0
70.0
70.0
70.0
70.0
70.0
70.0
Absorption
Coefficient
(years)
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
Body
Weight
(0-1.0)c
70
70
70
70
70
70
70
70
70
70
70
70
Exposure
Value
(kg)
6.5E-07
3.7E-07
9.3E-07
2.0E-06
1.1E-06
2.9E-06
1.5E-05
7.4E-07
4.4E-05
4.5E-05
2.3E-06
1.4E-04
Potency
Factor
(mg/kg/d)
4.34
4.34
4.34
4.34
4.34
4.34
N/A
N/A
N/A
N/A
N/A
N/A
Upper
Limit
l/(mg/kg/d)
3E-06
2E-06
4E-06
9E-06
5E-06
IE-OS
N/A
N/A
N/A
N/A
N/A
N/A
Weight
of
Risk
B2
B2
B2
B2TN/A
B2
B2
e
e
e
e
e
t
Noncarcinogens
RfD
Evidence
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
(mg/kg/d)Index
N/A
N/A
N/A
N/A
N/A
5E-02
3E-03
2E-01
2E-01
8E-03
5E-01
* Concentration of contaminant in fisheries species of concern (mg/kg = ppm by mass, wet weight).
b 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).
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.
-------
It should be emphasized that some 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-mag-
nitude basis (e.g., Carcinogenic Potency Factor). 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)] are used to estimate
potential health risk, thereby accounting for uncertainty in chemical
analyses. Also, risks are estimated for two consumption rate estimates
(6.5g/day and 20 g/day). Note that spreadsheet summaries of quantita-
tive information should be supported by a text discussion of qualitative
uncertainties such as the weight of evidence for the health effect of
concern.
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 loca-
tions or individual sampling stations
Histograms of estimated risk by fishery species, human sub-
population, 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 consump-
tion 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.
Other approaches noted above can be used to supplement plots of risk
vs. consumption. 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
Summary Graphics
69
-------
-------
cc
UJ
CD
HI
10-3-
lD 10"5-
10-6-
10
r7
STUDY AREA
N-25 BUTTER CLAMS
REFERENCE AREA
N = 25 BUTTER CLAMS
1
(2)
I
10
(25)
I
100 g/day
(250) (meals/yr)
CONSUMPTION RATE
PCBs Weight-of-evidence classification:
PROBABLE HUMAN CARCINOGEN [B2]
All cancer risks are plausible-upper-limit estimates of excess risk based on
linearized multistage procedure and assumptions summarized in the text. Solid lines
are risks associated with average PCB concentrations in butter clams. Dashed lines
are for uncertainty range (e.g., 95 percent confidence limits) for average
concentrations of PCBs, not the total uncertainty. Actual risks are likely to be
lower than those shown above and may be zero.
Figure 7 Example graphic format for display of quantitative risk
assessment results for hypothetical study area and reference
area.
-------
11 concentration for selected consumption rates and species (e.g., Figure
Risk Comparisons
Summary of Assumptions
8) aid in rapid interpretation of tissue contamination data.
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).
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 model should
be applied consistently to calculate exposure and risk. A linear ex-
trapolation 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, some perspective can be gained by examining
previous risk management decisions. For example, past regulatory
decisions by U.S. federal agencies have allowed environmental risks as
high as 10 to 10"2 when the exposed population was relatively small
(Travis et al. 1987). For exposures of the entire U.S. population, the
acceptable risk level has usually been defined as 10 .
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.
70
-------
10-2 r-
CO
£
DC
LU
o
<
o
UJ
10-3 -
C;* =TISSUE CONTAMINATION
GUIDELINE FOR 6.5g.day
10-6
.0001
.001
CHEMICAL CONCENTRATION IN
FISH OR SHELLFISH (ppm)
Figure 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.
-------
TABLE 7. Summary of Assumptions and Numerical Estimates
Used in Risk Assessment Approach
Reference
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 1986a,b
U.S. EPA 1980b,
1986a, 1987a
U.S. EPA 1987a
Assumption/Estimates Parameter
No effect on cooking.
6.5 g/day, 20 g/day, 165
g/day
1.0, Assumes efficiency of
absorption of con-
taminants is same for
humans and bioassay
animals.
70 years
Exposure Assessment:
Contaminant concentrations in
tissues of indicator species
Average consumption rate"
Gastrointestinal absorption
coefficient
Exposure duration
70kg( =
male)
average adult Human body weight
U.S. EPA 1987a
Linearized Multistage,
At risks less than 10 :
Risk = Exposure x
Potency
Potency factors are based
on low-dose extrapola-
tion from animal bioas-
say data.
Upper bound of 95 per-
cent confidence interval
on potency is used.
RfDs for noncarcinogens
compared with estimated
exposure.
Risk Characterization:
Carcinogenic risk model
Carcinogenic potency
Noncarcinogenic risk
Estimates of consumption for local population should be used in place of values
shown for U.S. population whenever possible.
Other assumptions, such as general approaches or assumptions under-
lying models that are commonly used to estimate risk, can be sum-
marized 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 ad-
verse 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 experimental 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., NOAEL) exists for noncarcinogenic
effects
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
71
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Uncertainty Analysis
Sources of Uncertainty
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 tune
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 addi-
tive. However, the total sum of individual chemical risks is not
necessarily the total risk associated with ingestion of con-
taminated fish or shellfish because some important toxic com-
pounds may not have been identified and quantified.
Uncertainty analysis is an integral part of risk assessment. 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.
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:
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 bioas-
say test results, which arise from practical limitations of
laboratory experiments and variations in extrapolation
models
3. Variance of sitespecific 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 contaminants by the human gastrointestinal system (assumed
72
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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 10" . For example, the
plausible-upper limit to lifetime cancer risk associated with 50 ,Mg/L
tetrachloroethene in drinking water ranges from about 10" 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 extrapola-
tion 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 uncertain-
ty 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 specific health effects of chemicals, including
mode of action, latency period, and target organs.
In conclusion, uncertainty ranges (e.g., 95 percent confidence inter-
vals) around estimates of mean risk may typically span at least several
orders of magnitude. The approach taken by U.S. EPA (1980b, 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 compound-
ing conservative assumptions should be evaluated to provide perspec-
tive on risk assessment results.
73
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Approaches to Uncertainty
Analysis
Analysis of uncertainty in a risk assessment should address both quan-
titative 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
uncertainties 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 devia-
tion) 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 simula-
tion
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-es-
timate analysis 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
examination 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 vari-
ables, such as contaminant concentration in fishery species and con-
sumption rate. Following U.S. EPA (1980b, 1984a, 1985a), an
74
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upper-bound estimate of the Carcinogenic Potency Factor is used in
carcinogenic risk calculations. 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 1980b, 1986a).
The U.S. EPA (1986b) guidelines on exposure assessment and Whit-
more (1985) summarize the primary methods for characterizing uncer-
tainty 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 ex-
ample, 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.
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 com-
parison, 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 corresponding 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 (refer-
ence) 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.
Supplementary
Information
75
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Appendix A
EPA/FDA Summary Policy Statement on
Chemical Residues in Fish and Shellfish
This joint EPA/FDA policy statement outlines jurisdictional under-
standings relating to the regulation and control of poisonous or
deleterious substances (chemical contaminants) in fish and shellfish
and recommends procedures for improved interaction between the
states and EPA/FDA headquarters and regional/district offices on
these matters. The statement is intentionally written on a broad policy
plane, primarily addressed to these governmental entities. More
detailed, technical aspects are treated in the main text of this guidance
manual.
The purpose of this statement is to:
Clarify the respective roles and jurisdictions of EPA and FDA
at the federal headquarters level and at the regional/district
office level relating to regulation and monitoring of con-
taminated fish and shellfish (generically, referred to as "fish"
below)
Explain the differences between federal and state respon-
sibilities and authorities in this area
Establish procedures to improve federal-state communica-
tions on fish contamination issues
Promote greater consistency between states in generating state
health advisories on the consumption of contaminated fish.
Section I contains a brief description of the recent developments that
make this statement particularly desirable at this time. Section II
articulates the specific authorities and jurisdictions of EPA, FDA, and
the states regarding contaminants in fish. Section III refers to the risk
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Background
assessment guidance on certain technical issues of toxicity and ex-
posure given in the main text of this guidance manual and other
guidance available from EPA and FDA. Section IV outlines a
proposed Standing Committee established to facilitate information
exchange, to deal with issues as they arise among the various jurisdic-
tions, and to encourage greater consistency in assessments and ad-
visories on chemical residues in fish.
The protection of human health through the regulation and control of
contaminated food stuffs is a joint federal and state responsibility. To
do this job effectively requires that each party understand and respect
the mandates and roles of the other.
The federal regulatory role is shared by FDA and EPA. FDA has direct
enforcement responsibility over all contaminated food, including fish
and shellfish that are shipped in interstate commerce. With respect to
pesticides, as a part of its registration procedure, EPA is responsible
for establishing tolerances (maximum permissible levels) for residues
of pesticide chemicals that may be anticipated to appear in fish.
Further, EPA is responsible for recommending, upon request from
FDA, the appropriate action levels on pesticides which may become
contaminants in food and for which a tolerance does not exist. FDA,
by agreement with EPA, also enforces these action levels (Federal
Register (FR) Vol. 39, No. 236,42745, December 6,1974).
Because of considerations involved in the establishment of federal
action levels, such levels may or may not be directly applicable to the
needs of the states when individual states attempt to evaluate the safety
of the local consumption of fish by sports fishermen or others. When
the states have sought federal advice from EPA or FDA offices for
more detailed information beyond the simple statement of the
tolerance or action level, that advice has not always been clear, consis-
tent, and in accord with joint EPA/FDA policy.
The issue of consistency in decisionmaking has arisen more often in
recent years as interest has broadened at both the federal and state
level in the use of risk assessment as one tool in reaching decisions
related to the consumption of contaminated fish. This trend is likely to
continue. For example, the Agency for Toxic Substances and Disease
Registry (ATSDR) is making a number of site-specific decisions,
pursuant to provisions of Superfund legislation, which deal with the
possibility of pollutants in fish. Also, EPA regional offices are under-
taking fish consumption risk assessments on a site- and area-specific
basis in order to evaluate the human health impacts of site-specific
regulatory decisions to control contamination sources. Additionally,
more states are actively addressing the need to issue fish consumption
advisories for local sports fishermen.
Therefore, it is important that federal agencies speak out as clearly as
possible regarding their respective roles and that they establish proce-
dures that will enable the states to obtain, in a timely manner, the
information and advice they need in order to make decisions that
impact on the health of their citizens.
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Authority and Jurisdiction of
EPA-FDAOver
Contaminants in Fish
Much of the substance of this section is drawn from two preambles in
the Federal Register (FR Vol. 39, No. 236,42743-42748, December 6,
1974 and FR Vol. 47, No. 139,42956-42958, September 29,1982).
The Federal Food, Drug and Cosmetics Act (FFDCA) is the principal
authority for both EPA and FDA actions directly relating to the safety
offish as a human food source. Only under this Act can federal action
be taken against contaminated fish moving in interstate commerce as
being unsafe or unfit for human consumption.
The Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA)
gives the EPA authority to deny registrations or cancel existing
registrations for pesticide chemicals whose use would (or does) cause
fish contamination to the extent that the risks of use of the pesticides
exceed the benefits. FIFRA also provides EPA with authority to collect
data on currently registered pesticides which may be causing fish
contamination. The Toxic Substances Control Act (TSCA) can be used
by EPA to regulate chemical substances to prevent such chemicals
from becoming contaminants in fish or shellfish. EPA can also take
action under RCRA or Superfund to prevent contamination of fish and
shellfish caused by the release or anticipated release of hazardous
substances.
Under the Clean Water Act (CWA), EPA publishes water quality
criteria. The criteria, based upon the best scientific information avail-
able at the time and the Agency's published risk assessment procedures
(FR Vol. 45, 79318-79379, November 28, 1980), assist the states in
establishing water quality standards. A description of the EPA risk
assessment procedures associated with contaminated fish can be found
in the main text of this guidance manual.
EPA and FDA share the federal responsibility for the regulation of
contaminants in foods that move in interstate commerce. Under the
FFDCA, FDA has the primary federal role for assuring the safety of
the food supply, including fish and shellfish. FDA is responsible for
establishing safe levels for poisonous or deleterious substances (other
than pesticide residues) that contaminate food [e.g., heavy metals, such
as lead and mercury, and organics, such as polychlorinated biphenyls
(PCBs)].
Under ideal conditions, FDA will attempt to establish a formal Section
406 tolerance limiting the extent of allowable contamination of a food.
However, when lexicological data are scanty or conflicting, when
additional data are being developed, or when other conditions are
rapidly changing, the promulgation of a Section 406 tolerance may be
inappropriate. Nevertheless, it may still be appropriate to take some
regulatory action or to control exposure to a contaminant. In such
circumstances, FDA may consider developing an action level under
Statutes
Activities
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Federal and State Distinctions
authority of Sections 306, 402(a) and 406 of the FFDCA (FR Vol 39,
No. 236,42743-42748, December 6,1974).
In practice, FDA's regulation of contaminants has proceeded more
often through the use of action levels, rather than through formal
tolerances. Existing action levels meet the same criteria as tolerances
except they are intended for interim periods and can be instituted and
changed more quickly than tolerances.
With respect to pesticide residues in food, EPA has the lead in estab-
lishing tolerances and recommending action levels. Under FIFRA, any
pesticide used in the country must be registered for specific uses
through procedures established by EPA. In addition, if the pesticide is
registered for use in the production of food, it must also have a
tolerance granted under FFDCA, limiting the amount of pesticide
residue in food. These pesticide tolerances are set by EPA and are
issued under the FFDCA for both raw agricultural commodities and
processed foods. FDA is responsible for enforcing food tolerances.
The use of action levels by EPA takes place in a somewhat different
administrative context from that of FDA. EPA can recommend to
FDA action levels on pesticide residues on foods to replace the formal
FFDCA tolerances that are revoked along with cancellation of a
pesticide registration in those cases in which the persistence of the
pesticide in the environment makes some continuing residue in food
unavoidable. EPA's criteria for establishing action levels are similar to
FDA's, but they also consider crop groupings and Codex Alimen-
tarious Commission recommendations (FR Vol. 47, No. 139, 42957,
Sept 29,1982).
Since 1971, action levels have been the result of close consultation
between the two agencies. Because action levels are similar to toleran-
ces in basis and effect and since EPA, under a 1971 Memorandum of
Understanding between FDA and EPA was delegated responsibility
for setting tolerances for pesticide contamination, EPA is responsible
for recommending the appropriate level at which a pesticide action
level is to be set. FDA is responsible for enforcing the action levels for
pesticides.
The authority of the FFDCA does not extend to fish that are not in
interstate commerce. Accordingly, tolerances and action levels are
typically established on a national basis when it is judged that a national
problem exists for a particular contaminant. Thus, the federal authority
is limited, and action levels are tailored to national needs and national
patterns of consumption. For example, consumption levels of fish on a
national per capita basis are generally considerably less than that
typical of sports fishermen, or of most lakeshore or coastal regions of
the U.S. Nonetheless, action levels or, more particularly, the toxicity
information [Reference Dose, (RfD) or Acceptable Daily Intake
(ADI)] that is considered in the setting of action levels may be useful
to the states in establishing controls or advisories on local fish con-
sumption that is outside the jurisdiction of the federal agencies. If a
potential local health threat exists, a state or locality may wish to issue
warnings or provide guidance on the quantity of contaminated fish
which may be safely consumed, based on the best available toxicity
-------
information and on assessment of the level of the contaminant found
locally and on local fish consumption patterns.
It should also be understood that FFDCA permits the consideration
of factors other than health and safety in the setting of nationally
applicable levels. Action levels are predicated not only on safety but
also on factors such as the economic impact likely to be experienced
by affected members of the food industry in complying with the estab-
lished levels. Therefore, particular risk management decisions made
by the federal agencies in managing interstate commerce may not be
in accord with, or take into consideration, the priorities of a particular
state.
In several recent revocation actions of tolerances for cancelled pes-
ticides (i.e., DDT, aldrin/dieldrin, and chlordane), a number of com-
menters raised concern about the EPA's decision not to recommend
lower action levels for such pesticide residues in fish. The Agency
concluded in its final rules revoking these tolerances that additional
data were needed before the Agency could make its final recommen-
dation on the fish action levels. It was recognized by the Agency that
some population groups may be at higher risk because of the frequency
and amount offish consumed locally. However, setting an enforcement
limit for this situation, while also satisfying the criteria for setting an
appropriate national limit, is not usually possible. This is because
action levels announced and enforced by FDA apply to fish in inter-
state commerce, and it would be very difficult, if not impossible, for
FDA to enforce and defend in court differing regional limits.
States need to understand clearly the way federal action levels are
developed if they are to adapt them to their local conditions or to
extract from them the critical scientific information which is applicable
generally and which would help to ensure a basic consistency in all state
decisions. All of this makes it important that, when action levels are
set, there be a clear distinction made between the risk assessment
components and any risk management components, e.g. economic
issues, and that there be a full explanation of any assumptions used in
deriving the final levels.
As noted above, EPA takes enforcement and permit actions to protect
against human exposure to toxic substances from specific emission
sources and hazardous waste sites which can cause localized, intrastate
impacts on public health and the environment. Analyses to support
these regulatory actions include consideration of all exposure path-
ways, including fish consumption. When EPA (sometimes assisted by
ATSDR) is the lead agency (in lieu of a state) for making site-specific
decisions based on estimates of fish consumption risks (e.g., under
CERCLA, TSCA Section 6(e), PCB Spill Cleanup Policy, etc.), the
Agency also (1) advises the public of its findings and their relevance to
EPA's regulatory actions to control specific sources, (2) notifies state
and federal agencies of identified problems, and (3) recommends that
states take action under their police powers to protect public health.
The assessment of risks associated with the consumption of con-
taminated fish involves questions of toxicity; e.g.:
Risk Assessment Issues
II
-------
Toxicity
Is the contaminant toxic? -- hazard identification
How potent is the contaminant as a toxicant? ~ dose-response
assessment and questions of exposure; e.g.:
What is the concentration of the pollutant in the fish?
What is the extent of consumption of the fish by what popula-
tions?
Much of the information relevant to the first two questions above is
available from either EPA or FDA and need not be generated by the
states. For example, information on the toxicity of pesticides is avail-
able from EPA. Further, EPA has developed its Integrated Risk
Information System (IRIS), which is a source of EPA risk assessment
information on hundreds of chemicals accessible to the states by
electronic-mail. The RfDs (ADIs) and carcinogenic potency factors
found in IRIS, represent evaluations of a wide body of scientific
literature, case-by-case judgments on difficult issues (e.g., the ade-
quacy of the studies and their relevance to humans), and general
science policy positions (e.g., the advisability of combining of benign
and malignant tumors). In many cases, the agencies' toxicity evalua-
tions and policy positions have benefited from widespread peer-review
and scientific consensus at the national and international level. Such
information should be directly useful to the states.
It should be noted that differences in approaches to risk assessment
remain at the federal level. One area of particular interest is that of
carcinogenicity risk assessment. To narrow the range of uncertainties
and inconsistencies in this area as much as possible both EPA and FDA
have officially adopted the "OSTP Cancer Principles" as the basis for
their carcinogen risk assessments ("Chemical Carcinogens: A Review
of the Science and its Associated Principles", OSTP 1985). These
general principles were developed to provide interim guidance in areas
of uncertainty until such time that additional scientific data provided
the information needed to improve estimations of risk in human
populations. The OSTP document was written in the light of the
decisionmaking processes used by EPA and FDA and should be
consulted for additional details. An application of the OSTP Principles
to use at EPA can be found in the Agency's Guidelines for Cancer Risk
Assessment (FR Vol. 51,33992-34003, September 24,1986).
Remaining differences between EPA and FDA in important risk
assessment assumptions continue to be discussed and explored, both
in discussions between staff members of the two agencies. The goal is
to move toward a common position which has a firmer scientific basis.
A detailed description of EPA's hazard identification and dose-
response assessment processes for both cancer and non-cancer effects
can be found in the main text of this guidance manual.
Exposure
The exposure information necessary to provide local answers to the
latter two questions posed at the beginning of this section is the
responsibility of federal agencies only when they have primary respon-
-------
sibility for a site-specific investigation as is the case under TSCA and
CERCLA which have not delegated substantial responsibility to the
states. Even in these instances, data on the type and extent of local fish
contamination and consumption can often best be gathered by the
states.
In gathering and using the exposure information, there would be
considerable merit in the states' using methods that are generally
consistent with those used by the federal agencies. Such an approach
would not only add to the credibility of state actions, but it also would
facilitate the generation of coherent regulations or health advisories
when different states share the same body of water.
A detailed discussion of factors to consider when planning sampling
and analysis programs and making estimates of fish consumption can
be found in the main text of this guidance manual.
Governments at both the federal and state levels are committed to
protecting public health and the environment. Within each level of
government, various agencies have been assigned selected tasks
directed toward this overall goal.
EPA has primary responsibility for identifying, correcting, and/or
preventing environmental contamination. ATSDR is playing an in-
creasingly important role in providing guidance in case-specific situa-
tions. FDA has a focused responsibility in protecting the portion of the
food supply that moves in interstate commerce. The states have the
responsibility of providing guidance or regulation at the more local
level.
In order to achieve the overall goal-protection of public health and
the environment-as expeditiously as possible, mechanisms should be
established which foster mutual assistance and communication be-
tween agencies as they go about their interrelated tasks. At the federal
level, EPA and FDA have been working together on action levels in
fish for many years. In general, this association, set out in Congressional
legislation and interagency Memoranda of Understanding over the
years, is working smoothly to set enforceable standards for con-
taminants in fish in interstate commerce. However, areas of disagree-
ment remain, and these sometime impede progress toward a common
goal. Also, the EPA regions are appropriately becoming more active
in risk assessment and in providing assistance to the states. With this
regional involvement, there is a greater possibility for differences in
interpretation and mixed responsibility when providing that assistance.
In addition, the creation of ATSDR introduces another important
participant at the federal level. At the state level, governmental agen-
cies are increasingly aware of need for consistent guidance to their
citizens. Particularly notable is the effort in the Great Lakes where, in
cooperation with federal authorities, the eight affected states and the
province of Ontario are making progress in issuing consistent health
advisories regarding fish consumption. There is a need for a
Risk Management Issues:
The Need For A Standing
Committee
-------
mechanism which can address differences between these entities in
specific cases and in general.
In addition, the federal government should do what it can to assist state
efforts to achieve rational, consistent fish consumption advisories.
Such assistance includes providing the best information available on
the hazards posed by a chemical, specifically the hazard identification
and dose-response assessment portions of the risk assessment. In
addition, the federal agencies can be available to provide whatever
advice might be sought by the states.
In order to mitigate the current difficulties which exist between the
various governmental entities involved in contaminated fish issues, it is
proposed that a Standing Committee on Fish Contamination be estab-
lished, made up of representatives of EPA, FDA, ATSDR, and the
states, possibly through the Association of State and Territorial Health
Officials (ASTHO).
The purposes of the Committee would be the following:
To work toward resolving any significant differences in the
approaches used by agencies in assessing the risk associated
with consumption of contaminated fish
To provide the states with the appropriate information on
hazard identification and dose-response assessment for con-
taminants in fish and to provide consistent interagency advice
from the federal level when requested by the states
To identify the need for federal action on contaminants found
to be present in fish in several states
To provide a forum in the instances requested by the states in
which "early warning" information could be discussed and a
coordinated response generated
To provide for a common inter-institutional base of experience
which would foster long term stability and consistency in fish
contaminant related risk assessment generated by all of the
governmental agencies involved.
EPA CONTACT ON RISK ASSESSMENT FOR FISH
CONSUMPTION
Name
Dr. Renate
Kimbrough
Organization
Office of Regional
Operations
Phone
(202) 382-4727
Subject Area
EPA Coordination on
Fish and Shell Pish
FDA/EPA Standing
Committee Chairman
Warren Office of Water (202)475-7893
Banks Regulations and
Standards
Michelle Office of Marine and (202)475-7102
Hiller Estuarine Protection
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Appendix B
Integrated Risk Information System (IRIS)
Overview of IRIS
The Integrated Risk Information System (IRIS), prepared and main-
tained by the U.S. Environmental Protection Agency (EPA), is an
electronic data base containing health risk and EPA regulatory infor-
mation on specific chemicals. IRIS was developed for EPA staff in
response to a growing demand for consistent risk information on
chemical substances for use in decision-making and regulatory ac-
tivities. Although IRIS is designed for EPA staff, it is also accessible
to state and local environmental health agencies. IRIS is available to
libraries, private citizens, and other organizations by means of DIAL-
COM, Inc.'s Electronic Mail telecommunications system. The infor-
mation in IRIS is intended for EPA staff without extensive training in
toxicology, but with some knowledge of health sciences.
The heart of the IRIS system is its collection of computer files covering
individual chemicals. These chemical files contain descriptive and
quantitative information in the following categories:
Oral and inhalation reference doses (RfDs) for chronic non-
carcinogenic health effects
Oral and inhalation slope factors and unit risks for chronic
exposures to carcinogens
Drinking water health advisories from EPA's Office of Drink-
ing water
EPA regulatory action summaries
Supplementary data on acute health hazards and physi-
cal/chemical properties
-------
Risk Assessment and Risk
Management
To aid users in accessing and understanding the data in the IRIS
chemical files, the following supportive documentation is provided:
Alphabetical list of the chemical files in IRIS and list of chemi-
cals by CAS (Chemical Abstracts Service) number.
Background documents describing the rationales and methods
used in arriving at the results shown in the chemical files.
A user's guide that represents step-by-step procedures for
using IRIS to retrieve chemical information.
An example exercise in which the use of IRIS is demonstrated.
Glossaries in which definitions are provided for the acronyms,
abbreviations, and specialized risk assessment terms used in
the chemical files and in the background documents.
The information in IRIS is intended for use in protecting public health
through risk assessment and risk management. These two processes
are briefly explained below.
Risk assessment has been defined as "the characterization of the
potential adverse health effects of human exposures to environmental
hazards" (NRC, 1983, p.18). In a risk assessment, the extent to which
a group of people has been or may be exposed to a certain chemical is
determined, and the extent of exposure is then considered in relation
to the kind and degree of hazard posed by the chemical, thereby
permitting an estimate to be made of the present or potential health
risk to the group of people involved.
Risk assessment information is used in the risk management process
in deciding how to protect public health. Examples of risk manage-
ment actions include: deciding how much of a chemical a company
may discharge into a river; determining which substances may be
stored at a hazardous waste disposal facility; deciding to what extent a
hazardous waste site must be cleaned up; setting permit levels for
discharge, storage, or transport of hazardous waste; establishing levels
for air emissions; and determining allowable levels of contamination in
drinking water.
Essentially, risk assessment provides information on the health risk,,
and risk management is the action taken based on that information.
A complete risk assessment consists of the following four steps:
1. Hazard identification,
2. Dose-response assessment,
3. Exposure assessment, and
4. Risk characterization,
with risk characterization being the transitional step to risk manage-
ment.
-------
The following discussion of the four steps of risk assessment was
excerpted from "Principles of Risk Assessment: A Nontechnical
Review" (U.S. EPA, 1985).
Hazard identification involves gathering and evaluating data on the
types of health injury or disease that maybe produced by a chemical
and on the conditions of exposure under which injury or disease is
produced. It may also involve characterization of the behavior of
a chemical within the body and the interactions it undergoes with
organs, cells, or even part of cells. Data of the latter types may be
of value in answering the ultimate question of whether the forms of
toxicity known to be produced by a substance in one population
group or in experimental settings are also likely to be produced in
humans. Hazard identification is not risk assessment; we are simply
determining whether it is scientifically correct to infer that toxic
effects observed in one setting will occur in other settings (e.g.,
whether substances found to be carcinogenic or teratogenic in
experimental animals are likely to have the same results in humans).
Dose-response assessment involves describing the quantitative
relationship between the amount of exposure to a substance and
the extent of toxic injury or disease. Data are derived from animal
studies, or less frequently, from studies in exposed populations.
There may be many different toxic effects under different condi-
tions of exposure.
The risks of a substance cannot be ascertained with any degree of
confidence unless dose-response relationships are quantified, even
if the substance is known to be toxic.
Exposure assessment involves describing the nature and size of the
population exposed to a substance and the magnitude and duration
of their exposure. The evaluation could concern past or current
exposures, or exposures anticipated in the future.
Risk characterization generally involves the integration of the as-
sessment process (hazard identification, dose-response assess-
ment, and exposure assessment) to determine the likelihood that
humans will experience any of the various forms of toxicity as-
sociated with a substance. (In cases where exposure data are not
available, hypothetical risk can be characterized by the integration
of hazard identification and dose-response assessment data alone.)
A framework to define the significance of the risk is developed, and
all of the assumptions, uncertainties, and scientific judgments of the
preceding three steps are presented.
IRIS is a tool that provides hazard identification and dose-response
assessment information, but does not provide situational information
on instances of exposure. Combined with specific exposure informa-
tion, the data in IRIS can be used for characterization of the public
The Role of IRIS in Risk
Assessment/ Risk
Management
-------
References
IRIS Questions and
Answers
health risks of a given chemical in a given situation, which can then
lead to risk management decision designed to protect public health.
The information contained in Section I (Chronic Health Hazard As-
sessment for Noncarcinogenic Effects) and Section II (Carcinogenicity
Assessment for Lifetime Exposure) of the IRIS chemical files repre-
sents a consensus judgment of EPA's Reference Dose (RfD) Work
Group or Carcinogen Risk Assessment Verification Endeavor
(CRAVE) Work Group, respectively. These two Agency-wide work
groups include high-level scientists from EPA's program offices (haz-
ardous waste, air, pesticides) and the Office of Research and Develop-
ment. Individual EPA offices have conducted comprehensive
scientific reviews of the literature available on the particular chemical,
and have performed the first two steps of risk assessment: hazard
evaluation and dose-response assessment. These assessments have
been summarized for IRIS and reviewed and revised by the ap-
propriate work group. As new information becomes available, these
work groups will re-evaluate their work and revise IRIS files accord-
ingly. For more information, contact IRIS User Support in EPA's
Environmental Criteria and Assessment Office, Cincinnati, OH
(513/569-7254 or FTS 684-7254).
NRC (National Research Council), 1983. "The Nature of Risk Assess-
ment." In: Risk Assessment in the Federal Government: Managing
the Process. National Academy Press, Washington, DC. p. 18.
U.S. EPA. 1985. "Principles of Risk Assessment: A nontechnical
review." Prepared for a risk assessment workshop. Easton, MD,
March 17-18.
1) How can I get access to IRIS?
IRIS is available on every EPA electronic mailbox. Once the EPA
electronic mail system has been accessed, simply type in 'IRIS' and hit
the return key. The IRIS menu will appear on the screen. To obtain
a copy of the IRIS User's Guide, call IRIS User Support at FTS
684-7254 or print out the identical on-line version provided in menu
option 4.
2) How can those outside the agency get access to IRIS?
Those outside EPA can obtain an IRIS account by calling Mike
McLaughlin of DIALCOM, Inc. at (202) 488-0550 or write to:
Mike McLaughlin
DIALCOM, Inc.
Federal Systems Division
600 Maryland Avenue SW
Washington, D.C. 20024
IRIS is also available through the Public Health Network (PHN) of the
Public Health Foundation. Call Paul Johnson at (202)898-5600 for
-------
more information. PHN is only available to local, state, and federal
public health officials.
IRIS will be made available on the NIH National Library of Medicine's
TOXNET system sometime during the late fall of 1989. At that time,
call (301) 496-6531 for details.
3) How much does IRIS cost?
There is no charge to EPA users and the 47 states which have EPA-
paid-for electronic mail accounts.
Those outside EPA who access IRIS through DIALCOM, Inc., must
pay only for the cost of accessing IRIS. The user will be billed by
DIALCOM, Inc. There is a $25.00 monthly minimum which is applied
against a usage fee of $25.00 per hour. In addition to the usage fee,
there is a $.05 charge per computer screen accessed. There is no EPA
charge for using IRIS.
Those eligible to access IRIS via the Public Health Network will be
charged under a different set of fees. Contact the Public Health
Foundation at (202)898-5600 for more information.
4) Who do I call if I have a question about using IRIS?
Call IRIS User Support at (513) 569-7254 or FTS 684-7254.
5) Who do I call if I have a scientific or technical question about the
reference doses?
Call the EPA Contact listed at the end of the reference dose section in
the IRIS chemical file.
6) Who do I call if I have a scientific or technical question about the
carcinogen (cancer) assessment?
Call the EPA Contact listed at the end of the carcinogen assessment
section in the IRIS chemical file.
7) Who do I call if I have a scientific or technical question about
drinking water health advisories?
Call the Safe Drinking Water Hotline at 1-800-426-4791.
8) Who do I call if I have a policy or general question about IRIS?
Call Rick Picardi at (202) 382-7315 or FTS 382-7315.
9) How can my organization get training in IRIS?
Call the IRIS contact for the appropriate EPA Region. The following
are the contacts for the EPA Regions:
EPA Region IRIS Contacts
I Boston Tom D'Avanzo
(617) 565-3222
FTS 835-3222
II New York Marian Olson
(212) 264-5682
FTS 264-5682
-------
EPA Region
III Philadelphia
IV
VI
Atlanta
Chicago
Dallas
IRIS Contacts
Roy Smith
(215) 597-9857
FTS 597-9857
Gayle Alston
(404) 347-4216
FTS 257-4216
David Dolan
(312) 886-6195
FTS 886-6195
Fred Reitman
(214) 655-2235
FTS 886-2235
Jill Lyons
(214) 655-7208
FTS 255-7208
Bob Fenemore
(913) 236-2970
FTS 255-2970
Jim Baker
(303) 293-1524
FTS 564-1524
Arnold Den
(415) 974-0906
FTS 454-0906
Dave Tetta
(206) 442-2138
FTS 399-2138
Dana Davoli
(206) 442-2135
FTS 399-2135
10) When will (chemical name) be included in IRIS? When will the
reference dose for (chemical name) be added to IRIS? When will the
carcinogen assessment for (chemical name) be added to IRIS?
Call IRIS User Support at (513) 569-7254 or FTS 684-7254.
VII
VIII
IX
Kansas City
Denver
San Francisco
Seattle
Use and Interpretation of
The Data in IRIS
Lindane; CAS No. 58-89-9
Health risk assessment information on a chemical is included in IRIS
only after a comprehensive review of chronic toxicity data by work
groups composed of U.S. EPA scientists from several Program Offices.
The summaries presented in Sections I and II represent a consensus
-------
reached in the review process. The other sections contain U.S. EPA
information which is specific to a particular EPA program and has
been subject to review procedures prescribed by that Program Office.
The regulatory actions in Section IV may not be based on the most
current risk assessment, or maybe 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
regulatory action data for a particular situation, note the date of the
regulatory action, the date of the most recent risk assessment relating
to that action, and whether technological factors were considered.
Background information and explanations of the methods used to
derive the values given in IRIS are provided in the five Background
Documents in Service Code 5.
Status of data for gamma-HexachlorocycIohexane (gamma-HCH).
Category (section) Status Last Revised
Oral RfD Assessment (I.A.) on-line 03/01/88
Inhalation RfD Assessment (I.B.) no data
Carcinogenicity Assessment (II.) pending
Drinking Water Health Advisories (III.A.)on-line 03/01/88
U.S. EPA Regulatory Actions (IV.) on-line 03/01/88
Supplementary Data (V.) on-line 01/31/87
I. CHRONIC HEALTH HAZARD ASSESSMENT FOR NONCAR-
CINOGENIC EFFECTS
Substance Name ~ gamma-Hexachlorocyclohexane (gamma-HCH)
Primary Synonym - Lindane
CASRN - 58-89-9
Last Revised - 03/01/88
The Reference Dose (RfD) is based on the assumption that thresholds
exist for certain toxic effects such as cellular necrosis, but may not exist
for other toxic effects such as carcinogenicity. In general, the RfD is
an estimate (with uncertainty spanning perhaps an order of magnitude)
of a daily exposure to the human population (including sensitive sub-
groups) that is likely to be without an appreciable risk of deleterious
effects during a lifetime. Please refer to Background Document 1 in
Service Code 5 for an elaboration of these concepts. RfDs can also be
derived for the noncarcinogenic health effects of compounds which are
also carcinogens. Therefore, it is essential to refer to other sources of
information concerning the carcinogenicity of this substance. If the
U.S. EPA has evaluated this substance for potential human car-
cinogenicity, a summary of that evaluation will be contained in Section
II of this file when a review of that evaluation is completed.
IA. REFERENCE DOSE FOR CHRONIC ORAL EXPOSURE
(RfDo)
LA.1. ORAL RfD SUMMARY
Critical Effect Experimental Doses* UF MF RfD
Liver and kidney NOAEL: 4 ppm diet 1000 1 3E-4
toxicity 3 (in/kg/day females) mg/kg/day
Rat, Subchronic Oral LOAEL: 20 ppm diet
Bioassay (1.55 mg/kg/day males)
Zoecon Corp., 1983
-------
*Dose Conversion Factors & Assumptions: Converted dose calcu-
lated from actual food consumption data
. PRINCIPAL AND SUPPORTING STUDIES (ORAL RfD)
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 (99.85%) in the diet. After 12 weeks, 15 animals/sex/group
were sacrificed. The remaining rats were fed the control diet for an
additional 6 weeks 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 distension, interstitial
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.
In a 2-year feeding study (Fitzhugh, 1950), 10 Wistar rats/sex/group
were exposed to 5, 10, 50, 100, 400, 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 mg/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).
I.AJ. UNCERTAINTY AND MODIFYING FACTORS (ORAL RfD)
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
I A.4. ADDITIONAL COMMENTS (ORAL RfD)
Data on reproductive effects of lindane are inconclusive. Most reports
indicate that hexachlorocyclohexane isomers are nonteratogenic.
I.A.5. CONFIDENCE IN THE ORAL RfD
Study: Medium
Data Base: Medium
RfD: Medium
The principal study used an adequate number of animals and measured
multiple endpoints. Since there are other reported chronic and sub-
chronic studies, confidence in the data base is medium. Medium
confidence in the RfD follows.
I A.6. EPA DOCUMENTATION AND REVIEW OF THE ORAL RfD
U.S. EPA. 1985. Drinking Water Criteria Document for Lindane.
Prepared by the Office of Health and Environmental Assessment,
Environmental Criteria and Assessment Office, Cincinnati, OH for the
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 Group Review: 01/22/86
Verification Date: 01/22/86
I .A.7. EPA CONTACTS (ORAL RfD)
Michael L. Dourson / ORD - (513)569-7544 / FTS 684-7544
Christopher T. DeRosa / ORD - (513)569-7534 / FTS 684-7534
I.E. REFERENCE DOSE FOR CHRONIC INHALATION EX-
POSURE (RfDi)
Not available at this time
II. CARCINOGENICITY ASSESSMENT FOR LIFETIME EX-
POSURE
Substance Name ~ gamma-Hexachlorocyclohexane (gamma-HCH)
Primary Synonym Lindane
CASRN - 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. HEALTH HAZARD ASSESSMENTS FOR VARIED EX-
POSURE DURATIONS
Substance Name ~ gamma-Hexachlorocyclohexane (gamma-HCH)
Primary Synonym ~ Lindane
CASRN - 58-89-9
Last Revised » 03/01/88
HI A DRINKING WATER HEALTH ADVISORIES
The Office of Drinking Water provides Drinking Water Health Ad-
visories (HAs) as technical guidance for the protection of public
health. HAs are not enforceable Federal standards. HAs are con-
centrations of a substance in drinking water estimated to have negli-
gible deleterious effects in humans, when ingested, for a specified
period of time. Exposure to the substance from other media is con-
sidered only in the derivation of the lifetime HA. Given the absence
of chemical-specific data, the assumed fraction of total intake from
drinking water is 10% for inorganic contaminants and 20% for organic
contaminants. The lifetime HA is calculated from the Drinking Water
Equivalent Level (DWEL) which, in turn, is based on the Oral Chronic
Reference Dose. Lifetime HAs are not derived for compounds which
are potentially carcinogenic for humans because of the difference in
assumptions concerning toxic threshold for carcinogenic and noncar-
cinogenic effects. A more detailed description of the assumptions and
methods used in the derivation of HAs is provided in Background
Document 3 in Service Code 5.
IILA.1. ONE-DAY HEALTH ADVISORY FOR A CHILD
Appropriate data for calculating a One-day HA are not available. It
is recommended that the Ten-day HA of 1.2 mg/L (rounded to 1 mg/L)
be used as the One-day HA.
TEN-DAY HEALTH ADVISORY FOR A CHILD
Ten-day HA - 1.2E + 0 mg/L
-------
NOAEL --12.3 mg/kg/day
UF 100 (allows for interspecies and intrahuman variability)
Assumptions - 1 L/day water consumption for a 10-kg child
Principal Study -- Muller et al., 1981
Rats were fed lindane at daily doses of 1.3,12.3, or 25.4 mg/kg bw in
the diet for 30 days. Nerve conduction delay was observed in the
animals fed a daily dose of 25.4 mg/kg but was not observed at dose
levels of 12.3 or 1.3 mg/kg. A NOAEL of 12.3 mg/kg/day was identified.
IIIA3. LONGER-TERM HEALTH ADVISORY FOR A CHILD
Longer-term (Child) HA - 3.3E-2 mg/L
NOAEL -- 0.33 mg/kg/day
UF --100 (allows for interspecies and intrahuman variability with the
use of a NOAEL from an animal study)
Assumptions --1 L/day water consumption for a 10-kg child
Principal Study -- Zoecon Corporation, 1983
Male and female rats were fed lindane at dietary levels of 0,0.2,0.8,4,
20, or 100 ppm for 84 consecutive days. Liver hypertrophy, kidney
tubular degeneration, hyaline droplets, tubular casts, tubular disten-
sion, interstitial nephritis, and basophilic tubules were observed in the
20 and 100 ppm groups. Effects were rare and very mild when noted
at 4 ppm. The NOAEL was considered to be 4 ppm in this study.
Based upon measured food consumption, the daily intake of lindane
at 4 ppm in the diet was 0.29 mg/kg in males and 0.33 mg/kg in females.
The dose of 0.33 mg/kg is identified as the NOAEL.
III.A.4. LONGER-TERM HEALTH ADVISORY FOR AN ADULT
Longer-term (Adult) HA - 1.2E-1 mg/L
NOAEL - 0.33 mg/kg/day
UF - 100 (allows for interspecies and intrahuman variability with the
use of a NOAEL from an animal study) Assumptions ~ 2 L/day water
consumption for a 70-kg adult
Principal Study - Zoecon Corporation, 1983 (study described in
III.A.3.)
III.A.5. DRINKING WATER EQUIVALENT LEVEL / LIFETIME
HEALTH ADVISORY
DWEL - 1E-2 mg/L
Assumptions - 2 L/day water consumption for a 70-kg adult
RfD Verification Date - 01/22/86 (see Section LA. of this file)
Lifetime HA - 2E-4 mg/L
Assumptions - 20% exposure by drinking water
Principal Study -- Zoecon Corporation, 1983 (This study was used in
the derivation of the chronic oral RfD; see Section I.A.2.) NOTE: A
safety factor of 10 was used in the derivation of this HA, in addition to
the UF of 1000 for the RfD, to account for the possible carcinogenicity
of this substance. The assessment for the potential human car-
cinogenicity of lindane is currently under review.
IIIA.6. ORGANOLEPTIC PROPERTIES
No data available
III.A.7. ANALYTICAL METHODS FOR DETECTION IN DRINK-
ING WATER
Determination of lindane is by a liquid-liquid extraction gas chromato-
graphic procedure.
-------
IILA& WATER TREATMENT
Treatment techniques capable of removing lindane from drinking
water include adsorption on activated carbon, air stripping, reverse
osmosis, and oxidation.
IIIA.9. DOCUMENTATION AND REVIEW OF HAs
Muller, D., H. Klepel, R.M. Macholz, HJ. Lewerenz and R. Engst.
1981.
"Electroneurophysiological studies on neurotoxic effects of hexachlor-
ocyclo-hexane isomers and gamma-pentachlorocyclohexene." Bull.
Environ. Contain. Toxicol. 27(5): 704-706. Zoecon Corporation.
1983. MRID No. 00128356.
Available from EPA. Write to FOI, EPA, Washington D.C. 20460.
U.S. EPA. 1985. Final Draft of the Drinking Water Criteria Docu-
ment on Lindane. Office of Drinking Water, Washington, DC.
EPA review of HAs in 1985.
Public review of HAs following notification of availability in October,
1985. Scientific Advisory Panel review of HAs in June, 1986.
Preparation date of this IRIS summary - 06/17/87
IIIA.10. EPA CONTACTS
Yogendra Patel / ODW -- (202)382-7585 / FTS 382-7585
Edward V. Ohanian / ODW ~ (202)382-7571 / FTS 382-7571
III.B. OTHER ASSESSMENTS
Content to be determined
IV. U.S. EPA REGULATORY ACTIONS
Substance Name ~ gamma-Hexachlorocyclohexane (gamma-HCH)
Primary Synonym - Lindane
CASRN ~ 58-89-9
Last Revised - 03/01/88
EPA risk assessments may be updated as new data are published and
as assessment methodologies evolve. Regulatory actions are frequent-
ly not updated at the same time. Compare the dates for the regulatory
actions in this section with the verification dates for the risk assess-
ments in sections I and II, as this may explain inconsistencies. Also
note that some regulatory actions consider factors not related to health
risk, such as technical or economic feasibility. Such considerations are
indicated for each action. In addition, not all of the regulatory actions
listed in this section involve enforceable federal standards. Please
direct any questions you may have concerning these regulatory actions
to the U.S. EPA contact listed for that particular action. Users are
strongly urged to read the background information on each regulatory
action in Background Document 4 in Service Code 5.
IVA. CLEAN AIR ACT (CAA)
No data available
IV.B. SAFE DRINKING WATER ACT (SDWA)
IV.B.1. MAXIMUM CONTAMINANT LEVEL GOAL (MCLG) for
Drinking Water
Value (status) -- 0.0002 mg/L (Proposed, 1985)
Considers technological or economic feasibility? - NO
-------
Discussion -- An MCLG of 0.0002 mg/L for lindane is proposed based
upon a provisional D WEL of 0.01 mg/L and an assumed drinking water
contribution of 20%. A DWEL of 0.01 mg/L was calculated from a
NOAEL of 0.3 mg/kg/day in rats (feeding study) with an uncertainty
factor of 1000 and a consumption of 2 L of water/day. Reference ~ 50
FR 46936 Part IV (11/13/85)
EPA Contact -- Criteria and Standards Division, ODW /
(202)382-7571 / FTS 382-7571; or Drinking Water Hotline / (800)426-
4791
IV.B.2. MAXIMUM CONTAMINANT LEVEL (MCL) for Drinking
Water
Value (status) -- 0.004 mg/L (Interim, 1980)
Considers technological or economic feasibility? - NO
Reference - 45 FR 57332 (08/27/80)
EPA Contact - Yogendra Patel / Criteria and Standards Division,
ODW/
(202)382-7571 / FTS 382-7571; or Drinking Water Hotline / (800)426-
4791
FV.C. CLEAN WATER ACT (CWA)
IV.C.1. AMBIENT WATER QUALITY CRITERIA, Human Health
Water and Fish Consumption: 1.86E-2 ug/L
Fish Consumption Only: 6.25E-2 ug/L
Considers technological or economic feasibility? -- NO
Discussion - For the maximum protection from the potential car-
cinogenic properties of this chemical, the ambient concentration
should be zero. However, zero may not be attainable at this time so the
criteria given represent a E-6 incremental increase in cancer risk over
a lifetime.
Reference - 45 FR 79318 (11/28/80)
EPA Contact - Criteria and Standards Division, OWRS
(202)475-7315 / FTS 475-7315
IV.C.2. AMBIENT WATER QUALITY CRITERIA, Aquatic Or-
ganisms
Freshwater:
Acute-- 2.0E + Oug/L
Chronic-- 8.0E-2ug/L
Marine:
Acute - 1.6E-1 ug/L
Chronic- None
Considers technological or economic feasibility? - NO
Discussion -- Water quality criteria for the protection of aquatic life
are derived from a minimum data base of acute and chronic tests on a
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 fre-
quency 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 3
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. The freshwater chronic
WQC is a 24-hour average.
Reference - 45 FR 79318 (11/28/80)
EPA Contact - Criteria and Standards Division, OWRS
(202)475-7315 / FTS 475-7315
IV.D. FEDERAL INSECTICIDE FUNGICIDE AND RODEN-
TICIDEACT(FIFRA)
FV.D.l. PESTICIDE ACTIVE INGREDIENT, Registration Standard
Status- Issued(1985)
Reference- Lindane Pesticide Registration Standard. Current, 1985.
EPA Contact - Registration Branch, OPP / (703)557-7760 / FTS
557-7760
IV.D.2. PESTICIDE ACTIVE INGREDIENT, Special Review
Action - Final regulatory action - PD4 (1984)
Considers technological or economic feasibility? YES
Summary of regulatory action - 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)].
Reference ~ 45 FR 48513 (10/19/83); 49 FR 26282 (06/27/84)
EPA Contact - Special Review Branch, OPP / (703)557-7400 / FTS
557-7400
IV.E. TOXIC SUBSTANCES CONTROL ACT (TSCA)
No data available
IV.F. RESOURCE CONSERVATION AND RECOVERY ACT
(RCRA)
Status ~ Listed
Reference ~ 52 FR 25942 (07/09/87)
EPA Contact ~ Jerry Carman / OSW / (202)382-4658 / FTS 382-4658
IV.G. SUPERFUND (CERCLA)
IV.G.1. REPORTABLE QUANTITY (RQ) for Release into the En-
vironment
Value (status) - 1 pound (Statutory, 1987)
Considers technological or economic feasibility? - NO
Discussion The 1-pound RQ for lindane is based on aquatic toxicity
as assigned by Section 311(b)(4) of the Clean Water Act (40 CFR
117.3). Available data indicate a 96-hour Median Threshold Limit of
ppm, which corresponds to an RQ of 1 pound.
Reference - 52 FR 8140 (03/16/87)
EPA Contact - RCRA/Superfund Hotline
(800)424-9346 / (202)382-3000 / FTS 382-3000
V. SUPPLEMENTARY DATA
Substance Name ~ gamma-Hexachlorocyclohexane (gamma-HCH)
Primary Synonym - Lindane
CASRN - 58-89-9
Last Revised - 01/31/87
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 in this section are provided in Service Code 5. The user
is urged to read Background Document 5 in Service Code 5 for further
information on the sources and limitations of the data presented here.
VA. ACUTE HEALTH HAZARD INFORMATION
Toxicity - Lindane is a stimulant of the nervous system, causing violent
convulsions that are rapid in onset and generally followed by death or
recovery within 24 hours (Hayes, 1982, p. 218). The probable human
oral lethal dose is 50-500 mg/kg, or between 1 teaspoon and 1 ounce
for a 150-lb (70 kg) person (Gosselin et al., 1984, p. 11-286).
Medical Conditions Generally Aggravated by Exposure ~ Not Found
Signs and Symptoms of Exposure ~ Contact with eyes or skin may
produce irritation (DASE, 1980, p. 529). Vomiting, faintness, tremor,
restlessness, muscle spasms, unsteady gait, and convulsions may occur
as a result of exposure. Elevated body temperature and pulmonary
edema have been 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 et al., 1984, pp. III-240,
241).
V.B. PHYSICAL-CHEMICAL PROPERTIES
Chemical Formula - C6H6C16 (Weast 1979, p. C-262)
Molecular Weight -- 290.83 (Weast 1979, p. C-262)
Boiling Point - 614F, 323.4C (Weast, 1979, p. C-262); Decomposes
(NIOSH/OSHA, 1978, p. 120)
Specific Gravity (H2O = 1) --1.9 (DASE, 1980, p. 529)
Vapor Pressure (mmHg) - 9.4 x 10-6 at 20C (Merck, 1983, p. 789)
Melting Point -- 234.5F, 112.5C (Weast, 1979, p. C-262)
Vapor Density (AIR = 1) -- Not Found
Evaporation Rate (Butyl acetate = 1) - Not Found
Solubility in Water - Insoluble (Weast, 1979, p. C-262)
Flash Point (Method Used) - Not Found
Flammable Limits:
LEL - Not Found
UEL -- Not Found
Appearance and Odor - Colorless solid with a musty odor; pure
material is odorless (NIOSH/OSHA, 1978, p. 120).
Conditions 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 (Hawley, 1981, p.
617) and scabicide (Hayes, 1982, p. 221).
VI. REFERENCES
Substance Name - gamma-Hexachlorocyclohexane (gamma-HCH)
Primary Synonym - Lindane
CASRN - 58-89-9
Not available at this time
SYNONYMS: cyclohexane, 1,2,3,4,5,6-hexachloro-, gamma-isomer;
aalindan; aficide; agrisol g-20; agrocide; agrocide 2; agrocide 7;
agrocide 6g; agrocide iii; agrocide wp; agronexit; ameisenatod;
ameisenmittel merck; aparasin; aphtiria; aplidal; arbitex; bbh; ben-
hex; bentox 10; benzene hexachloride-gamma-isomer; gamma-ben-
-------
zene hexachloride; bexol; bhc; gamma-bhc; celanex; chloresene;
codechine; dbh; detmol-extrakt; detox 25; devoran; dol granule; drill
tox-spezial aglukon; ent 7,796; entomoxan; exagama; forlin; gallogama;
gamacarbatox; gamacid; gamaphex; gamene; gamiso; gamma-col;
gammahexa; gammahexane; gammalin; gammalin 20; gammaterr;
gammex; gammexane; gammopaz; gexane; hcch; hch; gamma-hch;
heclotox; hexa; gamma-hexachlor; hexachloran; gamma-hexachloran;
hexachlorane; gamma-hexachlorane; gamma-hexachlorobenzene; 1-
alpha,2-alpha,3-beta,4-alpha,5-alpha,6-beta-hexachlorocyclohexane;
gamma-hexachlorocyclohexane; 1,2,3,4,5,6-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; kwell; lendine; lentox; lidenal; lindafor; lindagam;
lindagrain; lindagranox; lindane; gamma-lindane; lindane (acgih,dot);
lindapoudre; lindatox; lindosep; lintox; lorexane; milbol 49; mszycol;
na 2761 (dot); nci-c00204; neo-scabicidol; nexen fb; nexit; nexit-stark;
nexol-e; nicochloran; novigam; omnitox; ovadziak; owadziak; pedrac-
zak; pflanzol; quellada; rcra waste number u!29; sang gamma; silvanol;
spritz-rapidin; spruehpflanzol; streunex; tap 85; tri-6; viton
The following is a list of chemicals on IRIS. The list is in alphabetical
order by the chemical name used on IRIS. If you do not find your
chemical of interest listed here look for the Chemical Abstracts Service
Registry Number (CASRN) in the CASRN listing.
Sections Available:
RfD = Chronic noncarcinogenic assessment (Reference Dose)
CAR = Chronic carcinogenicity assessment
HA = Drinking Water Health Advisories
Chemical
Acetone
Acetonitrile
Acrylic Acid
Acrylonitrile
Alachlor
Aldicarb
Aldrin
Allyl Alcohol
Aluminum Phosphide
Amdro
Ametryn
Ammonium Sulfamate
Antimony
Apollo
Arsenic, inorganic
Atrazine
Barium
Barium Cyanide
CASRN RfD CAR HA
67-64-1
75-05-8
79-10-7
107-13-1
15972-60-8
116-06-3
309-00-2
107-18-6
20859-73-8
67485-29-4
834-12-8
7773-06-0
7440-36-0
74115-24-5
7440-38-2
1912-24-9
7440-39-3
542-62-1
List of Chemicals on
IRIS (Alphabetical
Order)
-------
Chemical
CASRN
RfD CAR HA
Cadmium
Calcium Cyanide
Captafol
Captan
Carbaryl
Carbofuran
Carbon Bisulfide
Carbon Tetrachloride
Carbosulfan
Carboxin
Chloramben
Chlordane
Chlorine Cyanide
Chloroform
Chloromethyl Methyl Ether
Chlorothalonil
Chlorpyrifos
Chlorsulfuron
Chromium(III)
Chromium(VI)
Copper cyanide
Cyanazine
Cyanide, free
Cyanogen
Cyclohexanone
Cyromazine
7440-43-9
592-01-8
2425-06-1
133-06-2
63-25-2
1563-66-2
75-15-0
56-23-5
55285-14-8
5234-68-4
133-90-4
57-74-9
506-77-4
67-66-3
(CMME) 107-30-2
1897-45-6
2921-88-2
64902-72-3
16065-83-1
7440-47-3
544-92-3
21725-46-2
57-12-5
460-19-5
108-94-1
66215-27-8
Dalapon, sodium salt 75-99-0
Danitol 39515-41-8
Decabromodiphenyl Ether (DBDPE) 1163-19-5
Demeton 8065-48-3
1,4-Dibromobenzene 106-37-6
Baygon 114-26-1
Bayleton 43121-43-3
Baythroid 68359-37-5
Benefm 1861-40-1
Benomyl 17804-35-2
Bentazon 25057-89-0
Benzene 71-43-2
Benzidine 92-87-5
Benzo[a]pyrene (BaP) 50-32-8
Beryllium 7440-41-7
Bidrin 141-66-2
1,1-Biphenyl 92-52-4
Bis(2-ethylhexyl)phthalate (BEHP) 117-81-7
Bis(chloroethyl)ether (BCEE) 111-44-4
Bromodichloromethane 75-27-4
Bromoform 75-25-2
Bromomethane 74-83-9
Bromoxynil octanoate 1689-99-2
1,3-Butadiene 106-99-0
n-Butanol 71-36-3
Butylate 2008-41-5
Butylphthalyl Butylglycolate (BPBG) 85-70-1
-------
Chemical
CASRN RfD CAR HA
Dibromochloromethane
Dibutyl phthalate
Dicamba
Dichlorodifluoromethane
p,p'-DichlorodiphenyltrichIoroethane
(DDT)
1,2-Dichloroethane
1,1-Dichloroethylene
Dichloromethane
2,4-Dichlorophenol
4-(2,4-Dichlorophenoxy)butyric acid
(2,4-DB)
2,4-DichIorophenoxyacetic acid (2,4-D)
1,3-Dichloropropene
Dichlorvos
Diethyl phthalate
Diflubenzuron
Dimethipin
Dimethoate
Dimethyl Terephthalate (DMT)
N-N-Dimethylaniline
2,4-Dinitrophenol
Dinoseb
Diphenamid
Diphenylamine
1,2-Diphenylhydrazine
Diquat
Disulfoton
Diuron
Dodine
124-48-1
84-74-2
1918-00-9
75-71-8
50-29-3
107-06-2
75-35-4
75-09-2
120-83-2
94-82-6
Endosulfan
Endothall
Epichlorohydrin
Ethion
Ethyl Acetate
S-Ethyl dipropylthiocarbamate
(EPTC)
Ethyl p-nitrophenyl phenylphos-
phorothioate (EPN)
Ethylbenzene
Ethylene Glycol
Ethylphthalyl Ethylglycolate (EPEG)
Fenamiphos
Fluometuron
Fluorine (soluble fluoride)
Fluridone
Folpet
Fonofos
Formic Acid
Fosetyl-al
Furan
Glufosinate-ammonium
Glyphosate
94-75-7
542-75-6
62-73-7
84-66-2
35367-38-5
55290-64-7
60-51-5
120-61-6
121-69-7
51-28-5
88-85-7
957-51-7
122-39-4
122-66-7
85-00-7
298-04-4
330-54-1
2439-10-3
115-29-7
145-73-3
106-89-8
563-12-2
141-78-6
759-94-4
2104-64-5
100-41-4
107-21-1
84-72-0
22224-92-6
2164-17-2
7782-41-4
59756-60-4
133-07-3
944-22-9
64-18-6
39148-24-8
110-00-9
77182-82-2
1071-83-6
-------
Chemical
CASRN
RfD CAR HA
Heptachlor 76-44-8
Heptachlor Epoxide 1024-57-3
Hexabromobenzene 87-82-1
Hexachlorobutadiene 87-68-3
alpha-Hexachlorocyclohexane 319-84-6
(alpha-HCH)
beta-Hexachlorocyclohexane 319-85-7
(beta-HCH)
delta-Hexachlorocyclohexane 319-86-8
(delta-HCH)
epsilon-Hexachlorocyclohexane 6108-10-7
(epsilon-HC)
gamma-Hexachlorocyclohexane 58-89-9
(gamma-HCH)
technical Hexachlorocyclohexane 608-73-1
(t-HCH)
Hexachlorocyclopentadiene (HCCPD) 77-47-4
Hexachlorodibenzo-p-dioxin, 19408-74-3
mixture (HxCDD)
Hexachloroethane 67-72-1
Hexazinone 51235-04-2
Hydrogen Cyanide 74-90-8
Hydrogen Sulfide 7783-06-4
Imazalil 35554-44-0
Imazaquin 81335-37-7
Isobutyl Alcohol 78-83-1
Isophorone 78-59-1
Isopropalin 33820-53-0
Lead and compounds (inorganic) 7439-92-1
Linuron 330-55-2
Londax 83055-99-6
Malathion 121-75-5
Maleic Hydrazide 123-33-1
Metalaxyl 57837-19-1
Methamidophos 10265-92-6
Methomyl 16752-77-5
Methyl Ethyl Ketone (MEK) 78-93-3
Methyl Isobutyl Ketone (MIBK) 108-10-1
Methyl Mercury 22967-92-6
Methyl Parathion 298-00-0
2-(2-Methyl-4-chlorophenoxy) 93-65-2
propionic acid (MCPP)
2-Methyl-4-chlorophenoxyacetic acid 94-74-6
(MCPA)
Metolachlor 51218-45-2
Metribuzin 21087-64-9
Mirex 2385-85-5
Naled
Nickel Carbonyl
Nickel Refinery Dust
Nickel Subsulfide
300-76-5
13463-39-3
00-02-0
12035-72-2
-------
Chemical
CASRN RID CAR HA
Nickel, soluble salts 7440-02-0
Nitrapyrin 1929-82-4
Nitrate 14797-55-8
Nitric Oxide 10102-43-9
Nitrite 14797-65-0
Nitrobenzene 98-95-3
Nitrogen Dioxide 10102-44-0
N-Nitroso-di-n-butylamine 924-16-3
N-Nitroso-N-methylethylamine 10595-95-6
N-Nitrosodi-N-propylamine 621-64-7
N-Nitrosodiethanolamine 1116-54-7
N-Nitrosodiethylamine 55-18-5
N-Nitrosodimethylamine 62-75-9
N-Nitrosodiphenylamine 86-30-6
N-Nitrosopyrrolidine 930-55-2
Norflurazon 27314-13-2
Octabromodiphenyl ether 32536-52-0
Oryzalin 19044-88-3
Oxadiazon 19666-30-9
Oxamyl 23135-22-0
Oxyfluorfen 42874-03-3
Paclobutrazol 76738-62-0
Paraquat 1910-42-5
Pentabromodiphenyl ether 32534-81-9
Thallic oxide 1314-32-5
Thallium acetate 563-68-8
Thallium carbonate 6533-73-9
Thallium chloride 7791-12-0
Thallium nitrate 10102-45-1
Thallium selenite 12039-52-0
Thallium(I) sulfate 7446-18-6
Thiobencarb 28249-77-6
Thiophanate-methyl 23564-05-8
Thiram 137-26-8
Toluene 108-88-3
Triallate 2303-17-5
1,2,4-Tribromobenzene 615-54-3
l,l,2-Trichloro-l,2,2-trifluoroethane 76-13-1
(CFC-113)
1,2,4-Trichlorobenzene 120-82-1
1,1,1-Trichloroethane 71-55-6
1,1,2-Trichloroethane 79-00-5
Trichloroethylene 79-01-6
Trichlorofluoromethane 75-69-4
2,4,5-Trichlorophenol 95-95-4
2,4,6-Trichlorophenol 88-06-2
1,2,3-TrichIoropropane 96-18-4
Tridiphane 58138-08-2
Trifluralin 1582-09-8
Uranium, natural 7440-61-1
Vanadium Pentoxide 1314-62-1
-------
Chemical
Vernam
Vinclozolin
Warfarin
Xylenes
Zinc Cyanide
Zinc Phosphide
Zineb
CASRN RfD CAR HA
1929-77-7
50471-44-8
81-81-2
1330-20-7
557-21-1
1314-84-7
12122-67-7
-------
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)
Chemical
owre
Chemical Profile
OEM Health
Effects Ae«e««ment
Acenaphthene
Acenaphthylene
Acetic acid
Acetone
Acroleln
Acrylonltrlle
Aldrln
Anthracene
Antimony
Araenlc
Aabcstoe
Barium
Benzene
Bensidlne )
Benzo(a)anthr*cene 3
Benzo(a)pyrene
Benzothlazole 3
Berjlllua ]
elpha-BHC 3
b«ta-BHC ]
f^uu-BHC (Undent) 3
delte-BHC I
Butenol ]
Butvl tcrttte )
Cedriur )
Carbon tetrachlorlde ]
clf-Chlordene 1
tr»nc*Chlordan<
Chlorine
Chlorobenzent
X
X
X
X
X
[
[
X
t
[
[
[
X
X
X
X
X
X
Chlorobenzllete
Oiloroethaoc
Chloroform
p-Chloro-»-cresol
l-Chloro-3-nltrobenzene
bl»(2-ChIoroethoxy)eth«ne
Chroclum (total)
Chroaim (hexevelent)
ChroaluB (trlvaleot)
Chrycene
Coal tare
Cobalt
Copper
Creeol
Cyenidee
Cyanuric acid
p. '-ODD
0. '-ODD
P. '-DOE
p, '-DDT
8, '-DOT
JibroBochloropropane
.2-Diehlorobenzene
,3-Dichlorobenzene
tt-Dlchlorobenzene
, l-Dlchloroethane
, 2-Dich loroe thane
.1-Dichloroethylene
,2-clc-Dlch loroethy lene
,2-tranc-Dlchloroethylene
2,4-Dichloropbenol
t.A-Dlchloropheooxyacctlc acid
1.2-Dichloropropane 1
X
X
X
X
X
X
X
t
-------
TABLE C-l. (Continued)
Cheated
1 ,3-Dlchloropropane
1 ,3-Dichloropropene
Dlcofol
Dltldrla
Dlethyl benzene
Diethylene glycol
Dlethyl phthalate
Dliaobutyl kctone
Dlawthylaalnoethyl nethacrylate
Dimethyl aniline
Dlaethylnltroiaalne
2,4-DlBethyl pentane
2.4-DlBethylphenol
o-Dloctyl phthalate
I , 4-Dloxane
Dlphenyl ethane
Endrln
Ethanol
blc(2-Chloroethyl) ether
Ether
Ethyl acetate
Ethylbenzene
Ethylene glycol
Ethyl hexanedlol
bls-2-Ethylhexyl phthalate
Ethyl toluene
Fluoranthene
Koraaldehyde
Glycol ethers
Reptachlor
Heptane
Hexachlorobenzene
Rexachlorobutadiene
Hexachlorocyclohexane
Hexachlorocyclopentadlene
He xachloroe thane
Hexjchlorophene
Rexane
Iron
Itobutyl alcohol
laopropyl benzene
I»oprop>l ether
Uad
lithlnai
Magneilun
Hanganese
Mercury
Kethacryllc acid
Met Hanoi
Methyl chloride
2-Methyl dodecane
Methylene chloride
Methyl ethyl benzene
Methyl ethyl Vetone
3-Hethyl hexane
Methyl laobutyl ketone
Methyl aethacryj.ate
Methyl parathlon
2-Methyl pentane
3-Methyl pentane
2-Methyl-l-pentene
2-Methyl tetradecane
2-Methyl trldecane
OWE
Chemical Profile
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
OEM Health
Effect* At«esaa«nt
X
X
X
X
X
X
X
X
X
X
X
-------
TABLE C-l. (Continued)
OWTE OEM B««lth
Chcalcal Chealcal Profile Effects Aese»«»ent
Monethanolaslne
Naphthalene
Ulckel
Kltrocellulose
2-Nltrophenol
Pentachlorophenol
Pentadecane
Phenanthrene
Phenol
Phenyl ether
Phosphoric acid
Phosphorus
Picric acid
Polychlorlnated blphenyla (PCBs)
Polychlorlnated dlbenzo-p-dloxln
Polycycllc aromatic hydrocarbons (PAHc)
Pyre oe
Selenlusi
Silver
Sodlus> chlorate
Sodlua cyanide
Sodlim
Stoddard solvent
Sulfurlc acid
1 ,2.4.S-Tetrachlorobcnzene
2.3,7,8-Tetrachloro-
dlbfnzc-p-dloxln (TCDD)
1 , 1 ,2,2-letrachloroethane
Tetrachloroetbylene
Tetraethyl lead
Te t rahyd ro f uran
Tftraocthyl benztne
Thai llua
Tltanlua
Toluene
Toxaphene
, ? . 3-Trlchlorobenzene
.2>4-Trichlorobenzene
. 3 . J-Trlchlorobeazene
. ,3.6-Trlchlorobeazolc acid
.1.1 -Trlchloroethaae
. 1 , 2-Trlchloro« thane
Trichloroethylene
Trlchlorofluoroaethana
2.A, 5-Trlchlorophenol
2 . * . 6-Tr Ichlorophenol
2.4.S-TTlchlorophenoxyacetlc acid
2,*lS-Trlchlorophenoxy proplonlc acid
Trlawtbylbenzene
1,3.5-Trlaethylbenzene
Ij^2j4-Trl»ethjrlben«ene
trla(2,3-Dibro«opropyl)phosphate
Undecanc
Vanadlua
Vinyl chloride
Xjrlene
-fylene
o-Xylcne
p-Xylene
Zinc
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
y
Reference: Life Systems (1985).
-------
TABLE C-2. U.S. EPA SOURCES OF TOXICITY PROFILES
Document
Availability
Description
Criteria Document - Air
Criteria Document -
Drinking Water
Criteria Document -
Ambient Water Quality
Chemical Hazard Informa-
tion Profile (CHIP)
Chemical Profile
Health Advisory
Health Assessment
Document
Office of Air Quality Summary of the latest scientific knowledge on the effects of varying quantities of a substance in the air.
Planning and Standards (OAQPS) Usually prepared for QAQPS 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 (OWPE)
ODW
Office of Health and Environ-
mental Assessment (OHEA)
Health and Environmental Office of Solid Waste (OSW)
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 environmental toxicity
levels 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
Chemline
RTECS (Registry of Toxic Effects
of Chemical Substances)
1.5 million references on environmental and toxicological
effects of chemicals.
An online chemical dictionary of 500,000 records.
Basic acute and chronic toxicity for more than 57,000 toxic
chemicals.
Contact: MEDLARS Management Section
National Library of Medicine
8600 Rockville Pike
Bethesda, MD 20209
(301) 496-6193
CIS (Chemical Information
System)
AQUIRE (Aquatic Information
Retrieval System
CESARS (Chemical Evaluation
Search and Retrieval System)
CTCP (Clinical Toxicology of
Commercial Products)
Toxicity data for 2,000 chemicals, each cross referenced by
CAS number. Lists any studies on bioaccumulation, sublethaI
effects, and environmental fate of the chemical.
Detailed toxicity and environmental fate information and
evaluation on 150 chemicals of importance to Great Lakes.
Ingredient and product information for most commercially
available nonfood items.
Contact: CIS, Inc.
Fein-Marquart Associates
7215 York Road
Baltimore, MD 21212
(800) 247-8737
Envirofate
ISHOR (Information System for
Hazardous Organics in Water)
OHMTADS (Oil 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
Columbus, OH 43210
(800) 848-6533
DOE/RECON
35 energy-related and environ-
mental databases including
Energy Database, Water Resources
Abstracts, Environmental Muta-
gens, 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
Tetra Tech (1986b) used simulation methods to make a direct com-
parison of grab and composite-sampling strategies. Simulation refers
to the use of numerical techniques 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 con-
centrations 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 com-
positing on the estimate of the population mean. Power analyses were
used in the second set of analyses to demonstrate the effect of increas-
ing 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 18.52, 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 popula-
-------
-------
Analysis 1.
Mean (p.)
Variance (a2)
18.52 Coefficient of Variation = 45.5
70.90
30 -,
25.
z
^ 20.]
O
W 15 J
!IOJ
tu
95% C.I
4 6 10 20
NUMBER OF SUBSAMPLES IN COMPOSITE
Analysis 2.
Mean(^) = 18.52 Coefficient of Variation = 101.6
Variance (02) = 354.19
40 -i
30 -
til
5
O
HI
UJ
10
4 6 10 20
NUMBER OF SUBSAMPLES IN COMPOSITE
Reference: Tetra Tech (19866)
Figure D-i Effects of increasing composite sample size on confidence
in the estimate of the mean.
-------
tion 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 concentration 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 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 increas-
ing 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 treat-
ments. At the highest level of variability analyzed, the collection of
replicate composite samples composed of 25 subsamples each is re-
quired to obtain a testing power of 0.80 (Figure D-2).
-------
-e-
i
-
Analysis
1
2
3
Coefficient
of Variation
45.5
101.6
203.5
1.0 -I
0.8-
0.6-
0.4-
0.2-
0.0
1.0 n
0.8-
0.6-
0.4-
0.2-
0.0
2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
NUMBER OF SUBSAMPLES
(a)
6 8 10 12 14 16 18 20 22 24 26 28 30
NUMBER OF SUBSAMPLES
(b)
Reference: Tetra Tech (1986b)
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.
-------
-------
Appendix E
Evaluation of the Effects of Sample
Replication on Statistical 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 treat-
ments (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 Data
-------
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 general-
ize 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 performing an
ANO VA on available data from the literature or on a preliminary data
set. If such data cannot be obtained, the average Coefficient of Varia-
tion (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.
-------
LLJ
^
LJ_
O
LJJ
O
z
LU
DC
LU
± 250-
O
LU
CQ
O
LU
LLJ
Q
550
500
450
400
350
300-
200-
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.
-------
<
LU
550-
500
450
400-
350-
300-
== 250-
200-
150-
100-
50-
UJ
O
z
LU
DC
LJJ
U_
U.
O
L1J
CO
g
111
tD
o
Data Variability
Coefficient
90
70
50
30
Number of Stations
8 16
4 6 8 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.
-------
HI
o
z
UJ
DC
UJ
U. 5*
£<
Q UJ
Ul*
-J U.
m o
<
H H
OZ
UJ UJ
H O
uj cn
Q UJ
Q.
3 REPLICATES
5 REPLICATES
7 REPLICATES
80-i
60-J
40-
20-
I I
0.05 0.1
T
0.2
0.3
0.4
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
The EPA Office of Pesticide Programs (OPP) has evaluated com-
prehensive data on dietary consumption offish and shellfish within the
conterminous United States. Selected consumption rate data for the
U.S. population were used to provide an overview of potential ex-
posure 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).
Based on sample size and relevance to recent trends in fish consump-
tion, OPP concluded that the most reliable database for average daily
consumption offish and shellfish was the U.S. Department of Agricul-
ture (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 offish 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
Development of a
National Database
-------
Estimation of Local
Consumption
average per capita consumption offish 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 offish 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 offish 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 distribu-
tion 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 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.
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 con-
sumption 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.
Dykstra, William. January 12, 1982. "Ferriamicide; Request for Con-
ditional Registration; EPA Reg. No. 38962-RR", Internal Memoran-
dum to George LaRocca.
Environ Corporation. 1985. "Fish Consumption by Recreational
Fishermen: An Example of Lake Ontario/Niagara River Region."
Prepared for USEPA Office of Enforcement and Compliance
Monitoring.
Finch, R., 1973. "Effects of Regulatory Guidelines on the Intake of
Mercury from Fish" - the MECCA Project, NMFS, Fishery Bulletin,
Vol. 71, No. 3, pp. 615-626.
Metzger, Michael., March 25, 1987. "Fish Action Level Reevalution
for Aldrin/Dieldrin, Chlordane, DDT, Heptachlor, and Mirex." No
Accession Number RCB Numbers 2058, 2062, 2063, 2064, 2065 and
2066.", Internal Memorandum to Jack Housenger.
Ontario Ministry of the Environment. 1984. Guide to Eating Ontario
Sport Fish, 1984-1985. Southern Ontario, Great Lakes.
Page, N.; Cavender, F.; Cook, B. 1985. "Carcinogenic Rish Assess-
ment for Aldrin and Dieldrin." Unpublished study prepared by
Dynamac Corp. under EPA Contract No. 68-02-4131.
Sonstegard, R., Tufts University, Boston, Massachusetts. Personal
communication.
SRI International. 1980. "Seafood Consumption Data Analysis (Final
Report)." Prepared for USEPA, under EPA Contract No. 68-01-3887.
USDA. 1984. "Consumption and Family Living," Agricultural statis-
tks., Table 697, p. 506.
USDA. 1985. "Food Consumption, Prices and Expenditures."
Statistical Bulletin 749. Table 7, p. 13.
USDA. 1985. Nutrition Monitoring Division, Human Nutrition Infor-
mation Service. Food and Nutrient Intakes: Individuals in Four
Regions, 1977-1978. Report No. 1-3.
USDA. 1986. Nationwide Food Consumption Survey Continuing
Survey of Food Intakes by Individuals, Men 19-50 Yearsr 1 Day 1985.
NFCS, CSFII Report No. 85-3.
USDA. 1986. Nationwide Food Consumption Survey Continuing
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.
References
-------
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 1986. NFCS, CSFII Report No. 86-1.
USDC. 1987. National Oceanic and Atmospheric Administration,
National Marine Fisheries Service. Fisheries of the United States,
1986, Current Fisheries Statistics No. 8385.
-------
Table 1: Fish Consumption Data Summary
Survey Data
Source
Survey
Date1
Average
Consumption
g/dav
Extreme
Consumption
g/day
Caveats
1. USDA Nationwide Food Consumption Survey
(for individuals)
note: Figure obtained from Environ 1985
1977-1978
12.0
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.
2. USDA Nationwide Food Consumption Survey
Continuing Survey of Food Intakes by
Individuals
Report 185-3
1985
21
14
(from 1977-1978
survey above)
Sample size = 658 for men 19-50 only.
day recall. Fish and shellfish.
3. USDA NFCS, CSFII
Report #86-1
1986
11
13
11
(from a CSFII
conducted in 1985)
(from 1977-1978
survey above)
Sample size = 1501 women and 509 children.
This survey included women 19-50 and their
children 1-5. 1 day recall. Fish and
shellfish.
USDA NFCS, CSFII
Report #85-2
1985
11 (women)
5 (children)
Sample size = 2,210 women, 1,314 children.
This survey included low income women 19-50
and their children 1-5. Fish and
shellfish. 1 day recall.
5. EPA Tolerance Assessment System
(computed for a 60kg individual)
a. Total
b. Freshwater finfish
note: Although the USDA survey figure
listed is for fish and shellfish, the TAS
data summary includes roe and caviar as
well. It is unclear whether the USDA
figure of 12 g/day obtained from Environ
1985 includes ore and caviar.
1977-1978
15.2
1.8
Based on USDA 19077-1978 NFCS survey. The
discrepancy between TAS's 15.2 g/day and
the USDA's 12 g/day is due to conversion of
the TAS figure from g/kg body weight/day to
g day by multiplying by 60 kg.
Those dates which reflect publication or
communication rather than the date of the
survey are enclosed in parenthesis
-------
Table 1 (cont.): Fish Consumption Data Summary
Survey Data
Source
Survey
Date
Average
Consumption
q/day
Extreme
Consumption
q/day
Caveats
6. USOA's Foods Commonly Eaten by
Individuals: Amounts Per Day and Per
Eating Session
a. 50th percent!le
b. 90th percentile
c. 95th percentile
d. 99th percentile
note: Obtained from Environ 1985
(1982)
54
38
96
132
221
Consumers of finfish other than canned,
dried or raw. Mean does not equal the
median. Sample size?
7. National Purchase Diary (analyzed by
SRI International)
a. 95th percentile
note: Obtained from SRI International. It
is unclear whether the sample size of
25,000 included nonconsumers as well as
consumers of fish.
1973-1974
14.3
41.7
Sample size = greater than 25,000. 1/12 of
the sample was surveyed each month. It
appears that this survey was for the
conterminous 48 states. The SRI analysis
and figures were only for fish consumers.
8. National Marine Fisheries Service
Market Facts Survey
a. 99th percentile
b. 99.9th percentile
note: The average value of 16.8 g/day was
derived from Environ, and the extreme
figures came form Roland Finch's article
listed in the reference section. There is
a discrepancy between the 16.8 figure and
the average figure of 14 g/day based on the
same survey cited by Finch.
1969-1970
16.8
77
165
Sample size = 4,864. Survey yielded a per
capita fish consumption figure. It is not
clear whether recreationally caught fish
are included. Representative household
completed diaries twice a month for 1 year
regarding fish consumption patterns at home
and outside the home.
9. Guide to Eating Ontario Sport Fish
1983
13.8
Sample size unknown. Self selection biases
possible. This survey is for Ontario
fishermen consumption of freshwater
finfish.
-------
Table 1 (cont.): Fish Consumption Data Summary
Survey Data
Source
Survey
Date
Average
Consumption
q/dav
Extreme
Consumption
q/day Caveats
10. Environ 1985 Estimate of Humphrey's (1976)
Lake Michigan Data
45
Estimate is extremely rough, and is for
Lake Michigan sports fishermen consumption
of Lake Michigan fish. Subjects were
selected because of how much fish they
caught.
11. Personal Communication with R.
Sonstegard concerning intensive Lake
Ontario sports fishermen.
1987
373
Intensive Lake Ontario sports fishermen.
TAS MEAT CONSUMPTION VALUES (q/day)
a. Red meat
b. Poultry
c. Fish
134
30.4
15.2
12. USDA Agricultural Statistics
note: Unclear what fish connotes.
1983
18.4
Per capita market data.
Fish.
Retail weight.
13. USDA ERS, Statistical "Food
Consumption, Prices, and Expenditures."
note: Unclear whether seafood other than
fish and shellfish included.
1985
18.0
Per capita market data.
Fish and shellfish.
Edible weight.
14. National Marine Fisheries
Current Fisheries Report
Service
Recreationally caught fish
consumption cited in 1986
Current Fisheries Report
table footnote and not
included in that table
1986
1985
1984
1960
1970
18.3
17.9
17.0
10.3
3.7-5.3
Commercial fish and shellfish per capita.
The military population is excluded and no
information on fish caught through
noncommercial activities.
15. New York State Department of
Environmental Conservation Average Fish
Consumption for Recreational Fishermen.
note: From Environ 1985.
32.4
Based on 90th percentile of nationwide fish
consumption figures. The source of the
figures is not known.
Survey
-------
MEAN
X POPULATION CONSUMPTION
POPULATION SUBGROUP; AS CONSUMERS C/KC
Table 2: Consumption of Freshwater Finfish
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/KG
U.S. POP.--48 STATES
INFANTS(<1 YEAR)
CHILDREN(1-6 YRS)
FEMALESO3+ YRS)
MALESO3* YRS)
POPULATION SUBGROUP:
U.S. POP.- -48 STATES
INFANTS(<1 YEAR)
CHILDREN(1-6 YRS)
FEMALESM3* YRS)
MALES<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
2.5524
MEAN
CONSUMPTION
G/KG
1.7510
4.5676
3.4117
1.4970
1.5181
100
100
100
100
100
Table
0
100
100
100
100
100
100 98 97 93
100 100 100 100
100 100 99 98
100 98 97 93
100 98 95 91
3: Consumption of
ESTIMATED X OF POPULATION
0.2 0.4 0.6 0.8
97 91 84 75
100 95 89 83
99 96 93 90
97 90 82 72
97 90 81 71
87
100
96
87
84
Sal
79
100
93
78
77
72
100
93
69
70
twater Fi
64
100
92
62
61
nfi
58
44
89
55
54
sh
OF CONSUMERS WITH CONSUHPTION
1.0 1.2 1.4 1.6 1.8
65
77
86
61
61
56
77
82
51
51
47
66
78
41
41
40
66
74
34
34
35
58
71
28
29
51
44
84
48
47
30
44
68
27
27
EXCEEDING
2 3
30
58
67
23
25
14
58
46
9
10
17
44
50
14
15
X. FOR
4
7
51
30
4
4
10
0
32
7
8
5
4
45
20
1
2
2
0
a
2
1
10
0
6
3
0
0
0
0
3
0
0
15
0
0
1
0
0
0
0
0
0
0
20 G/KG
0
0
0
0
0
-------
KEAN
Table 4: Consumption of Saltwater Finfish - Dried
ESTIMATED X OF POPULATION OF CONSUMERS WITH CONSUMPTION EXCEEDING X, FOR X=
POPULATION SUBGROUP;
U.S. POP. --48 STATES
INFANTS(<1 YEAR)
CHILDREN(1-6 YRS)
FEMALES<13+ YRS)
MALESO3+ YRS)
POPULATION SUBGROUP:
U.S. POP. --48 STATES
INFANTS(<1 YEAR)
CHILDREN(1-6 YRS)
FEMAtES(13» YRS)
KALESC13* YRS)
W rvw^WL-ni ivm
AS CONSUMERS
0.02
0.00
0.00
0.02
0.03
X POPULATION
AS CONSUMERS
0.01
0.00
0.00
0.01
0.00
wt*v
-------
MEAN
Table 6: Consumption of Shellfish
ESTIMATED X OF POPULATION OF CONSUMERS UITH CONSUMPTION EXCEEDING X, FOR X"
POPULATION SUBGROUP:
U.S. POP. --48 STATES
1NFANTS(<1 TEAR)
CHILOREN(1-6 TRS)
FEMALESO3* YRS)
HALESO3* YRS)
POPULATION SUBGROUP:
U.S. POP.--48 STATES
INFAMTS(<1 YEAR)
CHlLDREN(1-6 YRS)
FEKALES(13+ YRS)
MALESO3+ YRS)
* r\M~win i I^MI
AS CONSUMERS
2.61
0.11
0.98
2.98
3.10
X POPULATION
AS CONSUMERS
0.02
0.00
0.00
0.02
0.03
V vfi ^vnr i w*«
G/KG
1.3313
0.8432
2.1878
1.3156
1.2159
MEAN
CONSUMPTION
G/KG
1.6519
0.0000
0.0000
1.9775
0.9956
0 0.2
100 88
100 100
100 90
100 87
100 88
Table 7:
0.4 0.6
76 64
100 49
80 71
75 64
76 64
0.8 1.0
55 47
49 49
64 61
56 46
53 44
Consumption of
ESTIMATED X OF POPULATION OF
0 0.2 0.4 0.6 0.8 1.0
100 100
0 0
0 0
100 100
100 100
96 96
0 0
0 0
100 100
92 92
69 60
0 0
0 0
79 79
54 35
1.2 1.4 1
41 34
49 0
59 55
39 34
39 30
.6 1.8
28 24
0 0
52 49
28 24
25 21
2 3
21 9
0 0
43 22
21 9
17 7
4
4
0
13
4
4
5
2
0
11
2
2
10
0
0
1
0
0
15
0
0
0
0
0
20G/KG
0
0
0
0
0
Fish-Unspecified
CONSUMERS WITH
1.2 1.4 1
60 56
0 0
0 0
79 79
35 26
CONSUMPTION
.6 1.8
46 40
0 0
0 0
79 64
7 7
EXCEEDING
2 3
28 12
0 0
0 0
43 18
0 0
X, FOR
4
5
0
0
0
0
x=
5
0
0
0
0
0
10
0
0
0
0
0
15
0
0
0
0
0
20 G/KG
0
0
0
0
0
-------
Table 8: Fish Consumption - TAS
1. EPA TAS Average
Per capita
Fish/shellfish
g/day/kg
body weight
0.25
15
meals/year1 (assuming a meal size
of approximately 4 ounces
or 114 grams)
48
2. EPA TAS Average
Per capita
Red meat
2.2
130
420
3. EPA TAS Average
Per capita
Red Meat + Poultry + Fish
3.0
180
580
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.
-------
Appendix G
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
ODD: (401) 789-1071
Environmental Research Laboratory/ORD
Sabine Island
Gulf Breeze, FL 32561
FTS: 8-686-9011
DDD: (904) 932-5311
Environmental Research Laboratory/ORD
College Station Road
Athens, GA 30613
FTS: 8-250-3134
DDD: (404) 546-3134
Environmental Research Laboratory/ORD
6201 Congdon Boulevard
Duluth, MN 55804
FTS: 8-780-5550
DDD: (218) 720-5550
Region 4
Region 5
-------
Region 6
Region 10
Environmental Ecological and Support Laboratory/ORD
26 W. St. Clair Street
Cincinnati, OH 45268
FTS: 8-684-7301
DDD: (513) 569-7301
Center for Environmental Research Information/ORD
26 West St. Clair Street
Cincinnati, OH 45268
FTS: 8-684-7391
DDD: (513) 569-7391
Robert S. Kerr Environmental Research Laboratory/ORD
P.O. Box 1198
Ada, OK 74820
FTS: 8-743-2011
DDD: (405) 332-8800
Environmental Research Laboratory-Corvallis/ORD
200 S.W. 35th Street
Corvallis, OR 97333
FTS: 8-420-4601
DDD: (503) 757-4601
Pacific Division - Environmental Research Lab/ORD
Hatfield Marine Science Center
Marine Science Drive
FTS: 8-867-4040
DDD: (503) 867-4040
132
-------
Appendix H
Compilation of Legal Limits for Chemical
Contaminants in Fish and Fishery Products
-------
TABLE H-l. COMPILATION OF LEGAL LIMITS FOR HAZARDOUS METALS IN FISH AND FISHERY
PRODUCTS
Metals (ppm)
Country
Australia
Brazil
Canada
Chile
Denmark
Ecuador
Finland
France
Germany
Greece
Hong Kong
India
Israel
Italy
Japan
Korea
Netherlands
New Zealand
Philippines
Poland
Spain
Sweden
Switzerland
Thailand
United Kingdom
United States
U.S.S.R.
Venezuela
Zambia
Range
Minimum
Maximum
As Cd Cr Cu
1.0,1.5b 0.2-5.5 10-70
3.5
0.12,1.0 0.5 10
1.0 10
5.0
0.5
1.4-10 2.0 1.0
1.0 10
0.5-1.0
1.0 1.0 30
30
4.0 10-30
0.1
2.0 20
1.0 20
0.1 0,0.1 10
3.5-5.0 100
0.1 0 1.0 10
10 5.5 1.0 100
Hg
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
0.7°
0.3,0.4C
0.5
1.0°
0.5C
0.5
0.5
1.0C
0.5
0.5
1.0C
0.2-1.0
0.1-0.5
0.2-0.3
0.1
1.0
Pb Sb
1.5-5.5 1.5
0.5
2.0
5.0
2.0
0.5
6.0 1.0
5.0
2.0
0.5,2.0
2.0 1.0
0.5
1.0-2.0
1.0-2.0
1.0
1.0
2.0-10
2.0
0.5-10
0.5 1.0
10 1.5
Se Zn
1.0,2.0 40-1,000
0.05,0.3 100
50
2.0 40
30-50
50
100
0.05 30
2.0 1,000
a Limit varies among states.
Inorganic.
c Total.
References: Nauen (1983); U.S. Food and Drug Administration (1982,1984).
-------
TABLE H-2
. COMPILATION OF LEGAL LIMITS FOR ORGANIC PRIORITY POLLUTANTS AND PESTICIDES IN FISH AND FISHERY
PRODUCTS (ppm)
Hexa-
chloro-
Aldrin/
Chlor-
benzene PCBs TCDD Dieldrin dane
Canada
Denmark
Germany0 0.5
Iceland
Netherlands
Sweden 0.2
Switzerland
Thailand
United States
Range
Minimum 0.2
' -Maximum 0.5
2.0 20a
5.0
2.0-5.0
1.0
2.0
1.0 20"
5.0 20a
O.lb
0.5-1.0
0.1
0.1,0.3
0.3
0.1
1.0
O.lb
0.01
0.3
0.01
0.3
Heptachlor/
Heptahclor- HCH
DDT DDE ODD
5.0 5.0 5.0
2.0-5.0
5.0
5.0 5.0 5.0
2.0 5.0 5.0
5.0 5.0 5.0
DDIs
5.0
2.0-5.0
5.0
5.0
2.0
5.0
Mala-
Vinyl
Endrin epoxide Kepone (Lindane thian Mirex Parathion Toxaphene Cloride
O.lb
0.01
0.3
0.3
0.01
0.3
O.lb
0.01
0.3
0.3
0.01
0.3
O.lb O.lb
2.0
0.5
0.2
0.5
0.3-0.4
0.1 0.1
0.4 2.0
O.lb O.lb O.lb
0.01
0.6 0.2
0.1
01. 0.1 0.1
0.6 0.1 0.2
0.1 b
0.01
5.0
0.1 0.01
5.0 0.01
a ppt (parts per trillion).
Legal limit exists for agricultural chemicals in general.
c Legal limits exsit for other organic chemicals that are not priority pollutants (see references).
Reference: Nauen (1983); U.S. Food and Drug Administartion (1982,1984).
------- |