ORNL--6251
DE86 010063
ORNL-6251
Dist. Category UC-11
ENVIRONMENTAL SCIENCES DIVISION
USER'S MANUAL FOR ECOLOGICAL RISK ASSESSMENT
Editors
L. W. Barnthouse c"33n-02:S0»
G. W. Suter II | 8 1 f -2.8 5
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A. E. Rosen .lit 112 1^1
ORNL Project Manager
S. G. Hildebrand
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Environmental Sciences Division § i *'' r j I ^ 3
Publication No. 2679 e-ss?^.|s!:
EPA Project Officers
A. Moghi
F. Kutz
A. A. Moghissi ° 3 f« s
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Date of Issue — March 1986 is£?8lt
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Prepared for 1'll.ff
Office of Research and Development
U.S. Environmental Protection Agency
Washington, D.C. 24060
Interagency Agreement No. DW 8993 0292-01-0
(DOE 40-740-78)
Prepared by the
OAK RIDGE NATIONAL LABORATORY
Oak Ridge, Tennessee 37831
operated by
MARTIN MARIETTA ENtRGY SYSTEMS, INC.
for the
U.S. DEPARTMENT QF ENERGY
under Contract No. DE-AC05-840R21400
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CONTENTS
Page
CONTENTS ill
LIST OF FIGURES vii
LIST OF TABLES ix
ABSTRACT xi
1. INTRODUCTION 1
1.1 Concepts and Definitions 2
1.2 Elements and Rationale for Risk Assessment Methodology . . 4
1.2.1 End Points for Environmental Risk Assessment .... 6
1.2.2 Methods for Ecological Effects Assessment 9
1.3 Organization of Users' Manual 16
REFERENCES (Section 1) 18
2. EXPOSURE ASSESSMENT 20
2.1 Surface Water Transport and Transformation 23
2.2 Atmospheric Transport, Transformation, and Deposition ... 26
REFERENCES (Section 2) 29
3. TOXICITY QUOTIENTS 31
3.1 Definition 31
3.2 Factors 31
3.3 Implementation 32
3.3.1 Matching Exposure and Effects 33
3.3.2 Benchmark Selection 36
3.4 Discussion 44
REFERENCES (Section 3) 46
///I"
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Page
4. ANALYSIS OF EXTRAPOLATION ERROR 49
4.1 Definition 49
4.2 Implementation 54
4.2.1 Risk Calculation 55
4.2.2 Extrapolation 56
4.2.3 Double Extrapolation 58
4.3 An Example: Aquatic Invertebrates and Fish 58
4.3.1 Data Sets 58
4.3.2 Extrapolation Results 60
4.3.3 A Demonstration 70
4.4 Risk Without Regression 71
4.5 Comparison of Methods 73
4.6 Discussion 76
REFERENCES (Section 4) 80
5. EXTRAPOLATION OF POPULATION RESPONSES 82
5.1 Formulation of Concentration-Response Model 83
5.2 Fitting the Logistic Model to
Concentration-Response Data 84
5.3 Extrapolation of Concentration-Response Functions and
Confidence Bands for Untested Species 87
5.3.1 Extrapolation of 6 and LC25 87
5.3.2 Calculation and Verification of Synthetic
Concentration-Response Function 88
5.4 Calculating Reduction in Reproductive Potential 89
5.5 Application of the Model to Rainbow Trout and
Largemouth Bass 92
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Page
5.5.1 Comparison of Fitted and Extrapolated
Concentration-Response Functions
and Uncertainty Bands 96
5.5.2 Comparison of Extrapolated Concentration-Response
Functions and Prediction Intervals for
Different Species 102
5.6 Discussion 106
REFERENCES (Section 5) Ill
6. ECOSYSTEM LEVEL RISK ASSESSMENT 113
6.1 Introduction 113
6.2 Ecosystem Risk Methods 114
6.2.1 Description of the Standard Water Column Model
(SWACOM) 114
6.2.2 Organizing Toxicity Data 117
6.2.3 General Stress Syndrome 119
6.2.4 Microcosm Simulation 122
6.3 Uncertainties Associated with Extrapolation 123
6.4 Results of Ecosystem Risk Assessments 124
6.4.1 Risk Assessment for Direct and Indirect
Liquefaction 125
6.4.2 Risk Assessment of Chloroparaffins 128
6.4.3 Patterns of Toxicological Effects in SWACOM .... 130
6.4.4 Using SWACOM to Extrapolate Bioassays 134
6.5 Monte Carlo Methods and Analysis 136
6.6 Discussion 139
REFERENCES (Section 6) 142
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Page
7. GENERAL DISCUSSION 145
7.1 Spatioteinporal Scale in the Integration of Exposure
and Effects 145
7.2 Interpreting Uncertainty 146
7.2.1 Inherent Variability 148
7.2.2 Parameter Uncertainty 148
7.2.3 Model Error 149
7.3 Interpreting Ecological Significance 151
7.4 Other Applications of Ecological Risk Assessment 155
7.5 Critical Research Needs 158
REFERENCES (Section 7) 162
APPENDIX A. Acute and Chronic Effects Data Used in Analysis of
Extrapolation Error 165
APPENDIX B. Concentration-Response Data Sets from
Chronic Toxicity Experiments 171
VI
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LIST OF FIGURES
Figure Page
1.1 Flow chart for ecological risk assessments
of toxic chemicals ....... .............. 5
4.1 Logarithms of LCsg values for Salvelinus
plotted against Sal mo ................... 51
4.2 Logarithms of MATC values from life-cycle or partial
life-cycle tests plotted against logarithms
of 96-h LCso values determined for the same
species and chemical in the same laboratory ........ 52
4.3 Probability density functions for a predicted
Salvelinus MATC and an expected environmental
concentration ....................... 53
5.1 Uncertainty band for the logistic model fitted to
concentration-response data ................ 86
5.2 Example of the procedure used to verify the synthetic
concentration-response modeling technique ......... 90
5.3 Fitted concentration-response function and uncertainty
band for the reduction in female reproductive potential
of brook trout (Salvelinus fontinalis) exposed
to methylmercuric chloride ................. 97
5.4 Synthetic concentration-response function and uncertainty
band for the reduction in female reproductive potential
of rainbow trout (Salmo qairdneri) exposed to methylmercuric
chloride. Chronic LC25S for the three life stages
were obtained by single-step extrapolation from an acute
for rainbow trout ................... 98
5.5 Synthetic concentration-response function and uncertainty
band for the reduction in female reproductive potential of
rainbow trout (Salmo gairdneri) exposed to methylmercuric
chloride. Chronic LC25* for the three life stages were
obtained by two-step extrapolation from an acute LCso for
fathead minnow (Pimephales promelas) ............ 100
5.6 Synthetic concentration-response function and uncertainty
band for the reduction in female reproductive potential
of rainbow trout (Salmo gairdneri) exposed to methylmercuric
chloride. Chronic LC2§s were obtained as in Fig. 5.4.
Uncertainty concerning the curvature of the function was
eliminated by setting the curvature parameter (6)
constant at its median value ................ 101
vii
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Figure Page
5.7 Synthetic concentration-response function and uncertainty
band for the reduction in female reproductive potential
of rainbow trout (Salmo gairdneri) exposed to cadmium.
Chronic 10255 were obtained by single-step extrapolation
from an acute LC$Q for rainbow trout 103
5.8 Synthetic concentration-response function and uncertainty
band for the reduction in female reproductive potential
of largemouth bass (Micropterus salmoides) exposed
to cadmium. Chronic LCgsS were obtained by two-step
extrapolation from an acute LC$Q for bluegill (Lepomis
macrochirus) 104
6.1 A schematic illustration of SWACOM (Standard Water
Column Model) 115
6.2 A typical simulation of SWACOM showing seasonal
dynamics of phytoplankton, zooplankton, and forage fish . 116
6.3 Risk estimates for naphthalene over a range
of environmental concentrations 126
6.4 Comparison of risks among direct coal liquefaction
technologies 127
6.5 Comparison of risks for two indirect coal liquefaction
technologies 129
7.1 Four applications of ecological risk functions 156
viii
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LIST OF TABLES
Table Page
4.1 Taxonomic extrapolations 61
4.2 Summary of aquatic taxonomic extrapolations 63
4.3 Acute-chronic extrapolations 66
4.4 Pooled variances of log LC$Q, £€59, and MATC values
from replicate tests 72
4.5 Comparison of methods for estimating the HATC for a
species other than fathead minnow 75
5.1 Life table for rainbow trout (Salmo gairdneri). modified
from Boreman (1978) 93
5.2 Life table for largemouth bass (Micropterus salmoides).
modified from Coomer (1976) 94
6.1 Risks of increased algal production and decreased
game fish production in systematic alteration
of the General Stress Syndrome 121
6.2 Toxicological data used in examination of patterns
of effects for cadmium 131
6.3 Comparisons of responses to different patterns
of sensitivity to cadmium 133
7.1 Contaminant classes determined to pose potentially
significant risks to fish populations by one or more
of three risk analysis methods: Quotient method (QM),
analysis of extrapolation error (AEE), and ecosystem
uncertainty analysis (EUA) 154
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ABSTRACT
BARNTHOUSE, L. W., and 6. W. SUTER II. 1986. Users' manual
for ecological risk assessment. ORNL-6251. Oak Ridge
National Laboratory, Oak Ridge, Tennessee. 220 pp.
This report presents the results of a four-year project on
environmental risk analysis of synfuels technologies, funded by the
Office of Research and Development (ORO), U.S. Environmental Protection
Agency. The overall objective of the project was to support the ORO's
synfuels research program by developing a risk assessment methodology
capable of (1) ranking the waste streams in a process by risk to the
environment, (2) estimating the change in environmental risk that would
be achieved using alternative control technology options, (3) estimating
the sensitivity of risk estimates to site-dependent variables, and
(4) identifying research problems contributing the greatest uncertainty
to risk estimates.
At the time the project was initiated, the kinds of environmental
risk analyses desired by ORD had never been performed, and proven
quantitative methods analagous to the methods used to perform human
health risk assessments or engineering safety assessments did not
exist. Consequently, methods for quantifying ecological risks had to
be developed de novo and/or borrowed from other fields. An initial
suite of five potentially useful techniques was applied in a
preliminary risk analysis of indirect coal liquefaction technologies.
As a result of this application, it was determined that two of the
original five techniques were unsuitable for synfuels risk assessments.
The remaining three were developed further and applied in a unit-release
xi
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risk assessment, a revised Indirect liquefaction risk assessment, a
direct liquefaction risk assessment, and an oil shale risk assessment.
The methodology used in the synfuels environmental risk
assessments has many potential applications, in addition to the
specific purpose for which it was developed. This users' manual is
intended to facilitate wider use of ecological risk analysis techniques
by (1) presenting the rationale for the approach developed in this
project, (2) describing the derivation and mechanics of the three
techniques used in the synfuels risk assessments, and (3) discussing
the limitations and other potential applications of ecological risk
assessment methods.
xii
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1. INTRODUCTION
L. W. Barnthouse and G. W. Suter II
This report presents the methodological results of a 4-year project
on an environmental risk assessment of synfuels technologies, funded by
the Office of Research and Development (ORD), U.S. Environmental
Protection Agency. The overall objective of the project was to support
the ORD's synfuels research program by developing a risk assessment
methodology capable of (1) ranking waste stream components in a process
by risk to the environment, (2) estimating the change in environmental
risk that would be achieved by alternative control technology options,
(3) estimating the sensitivity of risk estimates to site-dependent
variables, and (4) identifying areas of research most likely to reduce
uncertainty in the risk estimates. The methodology would be required to
address both atmospheric and aqueous releases of chemical contaminants,
but would not be required to address nonchemical effects such as
thermal pollution or habitat disturbance. In addition, the methodology
would be required to produce best estimates of environmental
risk rather than worst-case estimates, and to explicitly quantify
uncertainties concerning magnitudes of risk. The methodology would be
demonstrated by using it to perform risk assessments for three classes
of synthetic liquid fuels technologies: direct coal liquefaction,
indirect coal liquefaction, and surface oil shale retorting.
At the time the project was initiated, environmental risk
assessments of the type desired by ORD had never been performed, and
proven quantitative methods analogous to the methods used to perform
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ORNL-6251 2
human health risk assessments or engineering safety assessments did not
exist. Consequently, methods for quantifying ecological risks had to
be developed de novo or borrowed from other fields. An Initial suite
of five potentially useful techniques were described by Barnthouse et
al. (1982). These five were applied in a preliminary risk assessment
for indirect coal liquefaction technologies. As a result of this
application, 1t was determined that two of the original five
techniques, specifically fault tree analysis and the analytic hierarchy
process, were unsuitable for synfuels risk assessments. The remaining
three were further developed and applied in a unit-release risk
assessment (Barnthouse et al. 1985a), a revised Indirect coal
liquefaction risk assessment (Barnthouse et al. 1985b), a direct coal
liquefaction risk assessment (Suter et al. 1984), and an oil shale risk
assessment (Suter et al. 1986).
The methodology used in synfuels environmental risk assessments
has many potential applications in addition to the specific purpose for
which it was developed. This users' manual is intended to facilitate
wider use of ecological risk assessment techniques by (1) presenting
the rationale for the approach developed in this project, (2) describing
the derivation and mechanics of the three techniques used 1n synfuels
risk assessments, and (3) discussing the limitations and other
potential applications of ecological risk assessment methods.
1.1 CONCEPTS AND DEFINITIONS
The approach described here is based on the concepts of risk
assessment and risk management, as defined by Ruckelshaus (1983) and
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3 ORNL-6251
Moghissi (1984). The stimulus for adopting risk assessment as a
fundamental component of environmental regulation is the recognition
that (1) the cost of eliminating all environmental effects of
technology is prohibitively high, and (2) regulatory decisions must
usually be made on the basis of incomplete scientific information. The
objective of risk-based environmental regulation is to balance the
degree of risk permitted against the cost of risk reduction, against
competing risks, or against risks that are generally accepted by the
public. Scientific risk assessment has two roles in this process.
First, it provides the quantitative bases for balancing and comparing
risks. Second, it provides a systematic means of improving the
understanding of risks by comparing the relative magnitudes of
uncertainties concerning different steps in the causal chain between
initial event (e.g., release of a toxic chemical) and ultimate
consequence (cancer in humans or extinction of a bird population).
Risk assessment may be defined as the process of assigning
magnitudes and probabilities to adverse effects of human activities (or
natural catastrophes). This process involves identifying the adverse
effects to be addressed in the assessment and using mathematical or
statistical models to quantify the relationship between initiating
events and ultimate effects. Ideally, although not always in practice,
the results of a risk assessment reflect both the inherent uncertainty
of events (e.g., probabilities of pipe ruptures or frequencies of
rainstorms) and the scientific uncertainty resulting from an inadequate
understanding of cause/effect relationships.
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ORNL-6251 4
A risk-based approach to ecological effects assessment and
management differs fundamentally from conventional impact or hazard
assessment. In ecological risk assessment, uncertainties concerning
potential effects must be explicitly recognized and, if possible,
quantified. It is necessary to consider not only uncertainty regarding
the biological effects of environmental stressors, but also the
inherent variability of natural populations and ecosystems. Moreover,
ecological risk assessments used in decision making should be based, to
the greatest extent possible, on objective estimates of ecological
damage (e.g., probabilities of population extinction or reductions in
abundance of plants and animals). Such assessments require more
information about the environments and organisms potentially affected
than 1s used in current hazard assessment schemes for effluent
discharges or toxic chemical releases.
1.2 ELEMENTS AND RATIONALE FOR RISK ASSESSMENT METHODOLOGY
The ecological risk assessment scheme adopted for this project
consists of the components outlined in Fig. 1.1. First, the specific
adverse effects to be evaluated, known as "end points," are selected.
Second, the environment within which the technology being assessed is
located (the "reference environment") is described. Third, a technical
description of the facility that is the source of potential impacts is
developed, and estimates of effluent magnitudes and compositions, or
"source terms," are developed. Fourth, appropriate environmental
transport models are used to perform an "exposure assessment," i.e.,
to estimate patterns of contaminant.distribution in time and space.
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ORNL-6251
ORNL-DWG 85-17070
SELECTION OF
ENDPOINTS
DEVELOPMENT OF
SOURCE TERMS
I
DESCRIPTION OF
REFERENCE ENVIRONMENT
EXPOSURE ASSESSMENT
1
EFFECTS ASSESSMENT
3 £
RISK ASSESSMENT
Fig. 1.1. Flow chart for ecological risk assessments of toxic chemicals,
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ORNL-6251 6
Fifth, in the "effects assessment," available toxicological data are
analyzed to determine the effects of the released contaminants on the
organisms exposed. Finally, all of the previous steps are combined to
produce the final risk assessment, which expresses the ultimate effects
of the source terms on the end points in the reference environment.
The above scheme closely parallels risk assessment schemes used in
human health risk assessments. The components that are unique to
ecological risk assessment, and for which no previous guidance was
available, include the selection of (1) end points and (2) methods for
effects assessment. Rationales for the decisions made regarding these
two components are presented here.
1.2.1 End Points for Environmental Risk Assessment
There are no obvious ecological equivalents of cancer or core
meltdown, hence, there can be no standardized list of universally
applicable ecological end points for risk assessment. To be useful in
risk assessment, however, any end point should (1) have biological
relevance, (2) be of importance to society, (3) have an unambiguous
operational definition, and (4) be accessible to prediction and
measurement. For synfuels risk assessments, it was concluded that the
most appropriate end points were impacts on biological populations of
importance to society. Societal importance was emphasized because
assessments of risks to insects, zooplankton, or other organisms not
perceived by society as being valuable are not likely to influence
decision making unless they can be clearly shown to indicate risks to
fish, wildlife, crops, or forest trees. Biological populations were
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7 ORNL-6251
emphasized because (1) the death of an Individual organism 1s usually
biologically meaningless, and (2) current scientific understanding of
higher levels of organization (communities and ecosystems) is
insufficient to support the use of higher-level end points.
Specific descriptions and rationales for the five classes of end
points used in synfuels risk assessments are presented here. They were
chosen on the basis of their perceived importance and the availability
of methods for quantifying population-level effects, without regard
to any known or hypothesized vulnerability to synfuels-derived
environmental contaminants. The existence and quantity of toxicity
data relating to the end point biota were not considered.
1.2.1.1 Reductions in abundance and production of commercial or
game fish populations. Impacts on fish species harvested by man are
among the most socially important impacts on aquatic ecosystems. These
species are also Important indicators of the ecological health of
aquatic ecosystems. Many harvested fish, especially game fish, are
predators at the top of aquatic food chains; these top predators are
frequently among the first species to disappear as a result of
disturbances.
1.2.1.2 Development of algal populations that detract from water
use. Undesirable blooms of algae commonly occur as consequences of
nutrient additions to lakes or reservoirs. These blooms are a nuisance
to shoreline residents and recreational lake users; they can affect
fish populations and cause taste and odor problems in drinking water.
Although changes in the abundance and relative concentrations of
inorganic nutrients are responsible for most such blooms, they can also
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ORNL-6251 8
be caused by reductions 1n grazing pressure from zooplankton that are
sensitive to toxic chemicals, and they could, at least in theory, be
caused by species-specific differences 1n sensitivity to toxic
chemicals.
1.2.1.3 Reductions in timber yield and undesirable changes in
forest composition. Forests have direct economic, aesthetic, and
recreational values as well as indirect values. Direct economic values
are the easiest to quantify. Aesthetic and recreational values of
forests can be related to primary production because of the general
preferences for mature forests with large trees, however,
pollution-induced chlorosis and necrosis of tree leaves is also an
important aesthetic impact, even when reductions 1n yield cannot be
detected. The indirect values of forests are possibly the most
Important, but they are difficult to analyze. These values include
erosion and flood control, removal and detoxification of pollutants,
and climate moderation. Although production has been used as an index
of indirect values, community structure and composition are also
clearly important.
1.2.1.4 Reductions in agricultural production. The value of
agriculture is self-evident. For the purpose of synfuels risk
assessment, agriculture is assumed to refer only to crop production.
Livestock and poultry are considered with wildlife, because assessments
of risks to all vertebrate animals are based on the same toxicological
data base.
1.2.1.5 Reductions in wildlife populations. Wildlife is valued
as game and as an object of various forms of nondestructive
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9 ORNu-6251
appreciation. Hunting, bird watching, and other wildlife-oriented
/
forms of outdoor recreation are economically and psychologically
Important. Effects of pollutants on wildlife may result from direct
toxicity, habitat modification, or food-chain dynamics.
1.2.2 Methods for Ecological Effects Assessment
Direct information on risks to populations in nature, comparable to
human epidemiological data, is rarely available and often unobtainable
even in principle. For the case of ecological effects of toxic
chemicals, it is inevitably necessary to extrapolate risk estimates
from laboratory toxicity test data or from limited field experiments.
The quantity, quality, and applicability of available test data varies
vastly among chemicals and end point biota. In addition, extrapolations
from even the best laboratory data are compromised by incomplete
characterization of the species compositions of affected environments,
biotic interactions among the exposed populations, and interactions
with other stresses (e.g., exploitation by man) that affect the exposed
populations.
Given the diversity of end points and the variety of data types
that must be accommodated, it is clear that no single method can be
adequate for making all of the necessary extrapolations for all
chemicals and end points of interest. Moreover, confidence in the
conclusions from any risk assessment 1s Increased if similar
conclusions can be reached using several independent methods.
Consequently, at the initiation of the project, it was determined that
five distinctly different methods for assessing ecological effects of
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ORNl-6251 10
toxic chemicals for risk assessment would be Investigated. The
following subsections briefly describe the major characteristics of
the five methods and present the rationales for their choice. As
previously noted, fault tree analysis and the analytic hierarchy
process were abandoned following application in a preliminary risk
assessment for indirect coal liquefaction. To illustrate the
difficulty of applying methods borrowed from other fields to ecological
assessment problems, the reasons for failure of our applications of
these two methods are discussed.
1.2.2.1 Fault tree analysis. Fault tree analysis is a standard
method used in engineering safety assessments to Identify events and
system states that can lead to disastrous failures of complex systems
such as nuclear power plants and space shuttles. A fault tree is a
model that graphically and logically represents these events and
states. When the probabilities of each of the possible initiating
events are specified, the fault tree can be used to calculate the
probability of failure of the whole system.
There is an appealing analogy between complex engineering systems
and complex ecosystems, and it is even possible to define ecological
"failures," such as population extinctions, that are analogous to
boiler explosions or core meltdowns. Based on this analogy, fault
trees were developed for (1) recruitment failure in a fish population
and (2) local extinction of a bird population. These fault trees
proved useful in illustrating the various possible direct and indirect
pathways through which toxic chemicals can affect populations; however,
it is clearly impossible to perform quantitative analyses of ecological
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11 ORNL-6251
fault trees. One major problem is the difficulty of estimating
probabilities for the various initial states that make populations
vulnerable to additional stresses (e.g., habitat restrictions). More
fundamentally, the continuous responses and cumulative effects that
characterize responses of biological systems to stress cannot be
represented using the binary logic of fault trees. However, even
without quantification, construction of ecological fault trees can
serve important heuristic functions.
1.2.2.2 Analytic hierarchy process. The analytic hierarchy
process (Saaty 1980) is a decision-making technique developed for use
in economic planning. Its two basic components are (1) the ordering of
the elements of a decision into a hierarchy and (2) the use of expert
opinion to rank the elements of each level in the hierarchy. This
approach was intended to be used in situations where qualitatively
different attributes must be compared, quantitative measurement scales
are unavailable, and/or subjective judgments are necessary. Because
all of these characteristics are typical attributes of environmental
assessment problems, it seemed possible that the analytic hierarchy
process could be fruitfully used as an alternative to quantitative
assessment models. For example, the decision about the relative hazard
of 17 components of a complex effluent mixture can be hierarchically
ordered into comparisons of the relative importance of different fish
populations that may be exposed, the relative importance of direct and
indirect effects of chemicals on each fish population, and so forth
down to the effects of each effluent component on the exposed organisms.
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ORNL-6251 12
When this approach was applied using expert ecologists and
toxlcologlsts, Interesting results were, 1n fact, obtained. Taking
Into account Information and opinions that could not be objectified
with any of the strictly quantitative methods used 1n the preliminary
risk assessment for Indirect coal liquefaction (e.g., mlcrobial
degradation of contaminants 1n soils), both aquatic and terrestrial
experts rated organic contaminants as substantially less hazardous than
would be predicted based on toxicity alone. However, the analytic
hierarchy process proved to be prohibitively cumbersome when applied to
the synfuels risk assessment problem because of the necessity for large
numbers of pair-wise comparisons among classes of chemicals. For
example, applying the method to 17 contaminant classes requires 136
pair-wise comparisons of relative toxicity for each type of organism
exposed. Although the method appears promising, adapting Its use with
synfuels risk assessment was judged to be beyond the scope of this
project.
1.2.2.3 Quotient method. The quotient method entails a direct
comparison of the estimated concentration of a chemical in the ambient
environment with a measured toxicological benchmark concentration
(e.g., an LC5Q) for that chemical. No attempt 1s made to quantify
uncertainties or to extrapolate to population-level effects. As such,
the quotient method 1s not a quantitative risk assessment technique
according to the definition used 1n this project. However, this method
1s nonetheless an Important component of any risk assessment scheme for
toxic chemicals. There are two major reasons for this. First, the
quotient method is a valuable screening technique because environmental
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13 ORNL-6251
concentrations of chemicals that are several orders of magnitude below
concentrations that affect laboratory test organisms are unlikely to
have serious ecological consequences. Second, direct comparisons
between environmental concentrations and laboratory test data are the
basis for all existing chemical hazard assessment protocols. Thus, the
quotient method provides a means of comparing results obtained using
more sophisticated, quantitative risk assessment techniques with
results obtained using conventional procedures.
Not all toxicological benchmarks are equally useful in applying
the quotient method; moreover, substantial care must be used in
comparing toxicity test data obtained under differing experimental
conditions. These issues, as well as (1) criteria for interpreting
values of quotients and (2) procedures for evaluating complex effluents
using the toxic units approach, are discussed in detail in Section 3 of
this report.
1.2.2.4 Analysis of extrapolation error. The classical approach
to assessing potential ecological effects of toxic chemicals 1s based
on laboratory testing using one or a few standard species and life
stages. Variability among species, life stages, and exposure durations
is accounted for by using correction factors, supposedly sensitive test
species, and subjective judgment. The usual objective of this approach
is to estimate a "safe" level, below which no effects will occur. It
is not possible, using this approach, to estimate the consequences of
exceeding the safe level; moreover, it is still possible, because of
the sources of variability previously mentioned, that effects will
occur even if the safe level is not exceeded.
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ORNL-6251 14
Section 4 of this report presents a method for explicitly
quantifying uncertainty resulting from (1) Interspecies differences in
sensitivity and (2) the variable relationship between acute and chronic
effects of chemicals. The method, known as analysis of extrapolation
error, 1s based on statistical analysis of acute and chronic toxicity
test data sets collected using uniform experimental protocols. At the
time technology risk assessments for this project were performed,
adequate data sets were available only for fish.
Given a chemical and species of interest, regression equations
derived from the data base can be used to estimate a chronic effects
threshold for the species of interest from a 96-h LC5Q for either
(1) the species itself or (2) any other species that has been tested.
Residual errors from the regressions are used to estimate the prediction
error of the estimated effects threshold and, consequently, the risk
that a given environmental concentration of the chemical being assessed
exceeds the chronic effects threshold of the species of interest.
Section 5 presents an extension of analysis of extrapolation error
that enables extrapolation of individual-level effects of toxic
chemicals to effects on populations. This extrapolation involves
estimating concentration-response functions, with confidence bands, and
Unking these functions to a life-cycle model of the species of
interest. The objective of this extension of the original methodology
is to enable extrapolation to the level of ultimate end-points, that
is, reductions in valued populations. Development of the
population-level assessment model was not completed in time for use in
the four synfuels technology assessments.
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15 ORNL-6251
1.2.2.5 Ecosystem uncertainty analysis. As heretofore noted,
effects of environmental stresses on real populations depend on complex
biotic and abiotic processes that cannot be reproduced in the
laboratory. Although many stresses can be usefully studied in field
experiments, such experiments are impossible for some risk assessment
problems. Mathematical models of the biological systems of interest
provide an alternative means of incorporating environmental complexity
in risk assessments. In particular, ecological models can incorporate
biological phenomena, such as competition and predation, that can
magnify or offset the direct effects of contaminants on organisms. For
the synfuels risk assessment project, recent developments in systems
ecology were exploited to develop an assessment method known as
ecosystem uncertainty analysis.
In ecosystem uncertainty analysis, effects of stress on individual
organisms are extrapolated to net effects on populations and trophic
levels using an ecosystem simulation model. Estimates of uncertainties
associated with individual-level effects are translated into estimates
of risks of significant adverse changes in the model populations. An
existing ecosystem model, the Standard Water Column Model (SWACOH), was
used for the synfuels risk assessment, however, it was necessary to
develop a procedure for translating laboratory test results, such as
LC5Qs, into changes in model parameters, such as photosynthesis and
respiration rates.
In Section 6 of this report, the basic concepts used in ecosystem
uncertainty analysis are described, and several applications of the
method are presented and discussed. The fundamental components of the
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ORNL-6251 16
method Include (1) the Unking of toxicity data to changes in
ecological rate processes and (2) the use of efficient uncertainty
analysis techniques to extrapolate from parameter uncertainties to
ultimate risks. The specific ecological model used 1n an assessment
can be selected to meet the needs of the problem at hand. It 1s
expected that 1n many future applications SWACOM will be replaced by a
more appropriate model.
1.3 ORGANIZATION OF USERS' MANUAL
The remaining sections of this report describe the steps in an
ecological risk assessment for a synfuels facility, any other facility
producing chemical effluents, or an Individual chemical. It 1s assumed
that source terms, 1n units of mass per unit time, have been provided
to the risk assessor.
Section 2 describes the process of modeling the transport and
transformation of contaminants in air, surface water, and groundwater.
Because of the large number of existing models available for use in
exposure assessments, the emphasis 1n this section 1s on criteria for
selecting models that are properly matched to the available Information
concerning (1) the environmental chemistry of the contaminant(s)
being modeled, (2) the spatiotemporal resolution of data on the
characteristics of the reference environment, and (3) the requirements
of the effects assessment methods being used.
Sections 3 through 6 document the effects assessment methods used
in the synfuels risk assessments. Throughout these sections, the
emphasis 1s on explanation and documentation of biological assumptions,
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17 ORNL-6251
statistical/mathematical methods, and data sources. No attempt was
made to document the computer codes used by the project staff in
Implementing the methods. It 1s expected that, because of differing
computing configurations and assessment needs, the code modifications
required by most users of the risk assessment methodology would render
any such documentation effectively useless.
Section 7 discusses the Integration of exposure and effects
assessments to produce overall ecological risk assessments for toxic
chemicals. In addition, Section 7 discusses the application of the
methods documented in this report to problems other than technology
risk assessment and also outlines the project staff's views on the
research needed to increase current utility and scientific credibility
of ecological risk assessment.
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ORNL-6251 18
REFERENCES (SECTION 1)
Barnthouse, L. W., D. L. DeAngelis, R. H. Gardner, R. V. O'Neill,
C. D. Powers, G. W. Suter II, and D. S. Vaughan. 1982.
Methodology for Environmental Risk Analysis. ORNL/TM-8167.
Oak Ridge National Laboratory, Oak Ridge, Tenn.
Barnthouse, L. W., G. W. Suter II, C. F. Baes III, S. H. Bartell,
R. H. Gardner, R. E. Millemann, R. V. O'Neill, C. D. Powers,
A. E. Rosen, L. L. Sigal, and D. S. Vaughan. 1985a. Unit Release
Risk Analysis for Environmental Contaminants of Potential Concern
in Synthetic Fuels Technologies. ORNL/TH-9070. Oak Ridge
National Laboratory, Oak Ridge, Tenn.
Barnthouse, L. W., G. W. Suter II, C. F. Baes III, S. M. Bartell,
H. G. Cavendish, R. H. Gardner, R. V. O'Neill, and A. E. Rosen.
1985b. Environmental Risk Analysis for Indirect Coal
Liquefaction. ORNL/TH-9120. Oak Ridge National Laboratory,
Oak Ridge, Tenn.
Hoghissi, A. A. 1984. Risk management - practice and prospects.
Mech. Eng. 106(ll):21-23.
Ruckelshaus, W. D. 1983. Science, risk, and public policy. Science
221:1026-1028.
Saaty, T. L. 1980. The Analytic Hierarchy Process. McGraw H111, N.Y.
Suter, G. W. II, L. W. Barnthouse, C. F. Baes III, S. H. Bartell,
M. G. Cavendish, R. H. Gardner, R. V. O'Neill, and A. E. Rosen.
1984. Environmental Risk Analysis for Direct Coal Liquefaction.
ORNL/TM-9074. Oak Ridge National Laboratory, Oak Ridge, Tennessee.
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19 ORNL-6251
Suter, G. W. II, L. W. Barnthouse, S. R. Kraemer, M. E. Grismer,
0. S. Durnford, D. B. McWhorter, F. O'Donnell, and A. E. Rosen.
1986. Environmental Risk Analysis for Oil Shale Extraction
Technologies. ORNL/TM-9808. Oak Ridge National Laboratory,
Oak Ridge, Tenn.
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ORNL-6251 20
2. EXPOSURE ASSESSMENT
L. W. Barnthouse
For the purpose of risk assessments for toxic chemicals, exposure
assessment may be defined as the "determination of the concentration of
toxic materials in space and time at the Interface with target
populations" (Travis et al. 1983). Before an exposure assessment can
be performed, it 1s necessary to develop (1) source terms for the
technology (or other contaminant source) being assessed and (2) a
description of the environment Into which contaminants will be
released. The source terms are simply estimates of the quantity and
composition of contaminant releases. They may be either time
dependent, as in accidental spills or upset events, or time
independent, as in continuous routine emissions. Reference
environmental descriptions are those of (1) the biota that may be
exposed to contaminant releases and (2) the hydrological,
topographical, geological, and meteorological characteristics of the
environment that affect the transport and transformation of
contaminants. Environmental characteristics may vary in time and
space. Given source terms and a reference environment, the key step 1n
exposure assessment is the use of a model of contaminant transport and
transformation to quantify the movement of contaminants from the
source, through the environment, to the target populations.
Many atmospheric, surface water, groundwater, and multimedia
models have been developed for quantifying the environmental fate of
radionuclides and toxic contaminants. Rather than developing entirely
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21 ORNL-6251
new models for the synfuels risk assessments, existing models that
appeared appropriate were selected and, where necessary, modified.
Only general descriptions of the models are presented here; detailed
documentation is provided elsewhere (Travis et al. 1983). Only the
atmospheric and surface water pathways are discussed in this section,
because these are the primary routes of exposure for aquatic and
terrestrial biota. The particular models chosen for the synfuels risk
assessments were selected based on the following considerations:
1. Risk assessments were to be performed for technologies and
processes rather than specific plants and sites. Only
engineering judgments of routine emission compositions were
available.
2. Exposure assessments were needed for a large number of complex
effluent components, both organic and inorganic. The
environmental chemistry of most of the organic chemicals to
be assessed was poorly understood.
3. Both acute and chronic ecological effects were to be
considered.
4. For ecological effects at the screening level, near-field
exposure assessments should be sufficient. The concentrations
of toxic contaminants would be expected to decline with
decreasing distance from the source; therefore, if risks are
minimal 1n the near field, they should also be minimal in the
far field.
5. Both the Inherent variability of environmental processes and
scientific uncertainty concerning the fate of synfuels-derived
contaminants should be explicitly modeled.
6. Models used in synfuels risk assessment should rely, to the
extent appropriate, on models that have proved useful in other
types of environmental assessments.
The above considerations suggested that relatively simple but
flexible environmental transport models would be best suited for
synfuels risk assessments. Because of the lack of specificity of the
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ORNL-6251 22
source terms and the generic nature of the assessment, it was determined
that generalized site descriptions characteristic of broad regions in
which synfuels facilities might be sited, rather than detailed
descriptions of particular sites, would be used. Given the use of
generalized site descriptions, high spatiotemporal resolution in the
models would be irrelevant. Moreover, because of the large number of
chemicals involved and the poor understanding of the environmental
chemistry of most of them, it seemed prudent to limit the modeling of
chemical transformations and mass transfers to simple, first-order
rates based on direct measurements or structure-activity relationships.
Whatever information exists should be incorporated to avoid undue
conservatism (e.g., by assuming complete solubility and no degradation
of organic chemicals); however, consideration of higher-order processes
and multistep transformations could be deferred to subsequent
assessments focused on those contaminants identified in initial
assessments to be potentially hazardous.
Because of the need to consider both acute effects of
short-duration, high-level exposures and chronic effects of long-term,
low-level exposures, the models would have to operate on time scales
ranging from hours to months and years. Uncertainty and variability
are important aspects of risk analysis; therefore, it was desirable for
the models to be amenable to error analysis (Gardner et al. 1981), both
to quantify scientific uncertainty regarding transport processes and to
model hydrological and meteorological variability that affects the
transport and fate of chemicals.
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23 ORNL-6251
Because of the many similarities between the transport of
radlonuclldes from power plants and the transport of chemical
contaminants from Industrial facilities, the models used in
radiological Impact assessments performed for the U.S. Nuclear
Regulatory Commission and the U.S. Environmental Protection Agency were
taken as the starting points for choosing environmental transport
models for synfuels risk assessments.
2.1 SURFACE WATER TRANSPORT AND TRANSFORMATION
The surface water transport model used in the synfuels
environmental risk assessment project 1s a steady-state model similar
in concept to the EXAMS model (Baughman and Lassiter 1978) but simpler
in terms of process chemistry and environmental detail. This model is
also similar to the radionuclide transport model described by Niemczyk,
Adams, and Murfin (1980). It is intended as a flexible descriptor of
the transport and fate of contaminants in streams and rivers. Rivers,
rather than lakes, were chosen as model environments because the most
common proposed sites for synfuels plants are on rivers. As in EXAMS,
a river is represented as a connected series of completely mixed
reaches. Within each reach, steady-state contaminant concentrations
are estimated based on dilution and on physical/chemical removal from
the water column. The steady-state contaminant concentration (C ,)
W, I
1n the first reach downstream from a continuous effluent discharge 1s
given by
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ORNL-6251 24
where
I = contaminant Input rate (kg/s),
3
V1 = volume of first reach (m ),
3
Q, = stream discharge of first reach (m /s), and
k, . = first-order contaminant removal rate for
*• 1
the first reach.
The steady-state concentration for the n reach downstream from the
first is given by
Cw.n - [(CWfn-l/Qn-l)/Vn]/[(On/Vn) + kt§n] (2.2)
The first-order removal rate (k+ ) is equal to the sum of
T.,n
first-order rates due to volatilization, settling, direct photolysis,
and biological/chemical degradation. With the exception of
biological/chemical degradation, all of the above rates are modeled as
functions of environmental parameters and physical/chemical properties
of the contaminants. Procedures for estimating rate constants for
volatilization, settling, adsorption, and photolysis are presented in
Section 2.3.2 of Travis et al. (1983).
For the purpose of ecological risk assessment, only a 1-km stream
reach immediately downstream from the assumed contaminant release point
was modeled. In effect, the released contaminants were assumed to be
completely diluted within a "box" 1 km 1n length. This reach size was
selected on the basis of biological/social significance. It is
unlikely that adverse ecological consequences would ensue from the
killing of one fish at the end of a discharge pipe. However, the
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25 ORNL-6251
biological degradation of a 1-km river segment could significantly
reduce biological production or disrupt local fish populations (either
through direct mortality or through Indirect effects such as
interference with migration). An impact on this scale would also
likely be considered unacceptable by local residents.
The requirement to assess both short-term and long-term effects
was met by modeling the effects of stochastically varying hydrologic
parameters such as stream discharge, temperature, and sediment load.
Realistic distributions for these parameters were obtained from U.S.
Geological Survey water resources monitoring data for streams typical
of those on which synfuels plants might be sited (Travis et al. 1983,
Sect. 3). Frequency distributions for contaminant concentrations were
computed as functions of the distributions of hydrologic parameters,
according to the procedure of Gardner et al. (1981). For assessing
chronic effects, the median daily concentration was chosen as the best
estimator of the long-term average concentration to which organisms
would be exposed. For assessing acute effects, the concentration
chosen was the upper 95th percentile concentration, that is, the
concentration expected to be met or exceeded on only 5X of days.
In practice, it was found that an even simpler model would have
been sufficient for the purpose of ecological risk assessment.
Estimated water-column half-lives for contaminants of interest in
2 4
synfuels risk assessment were on the order of 10 to 10 h
(Barnthouse et al. 1985a). Processes operating at these rates have
negligible effects on water-column concentrations in the near field.
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ORNL-6251 26
Near-field concentrations suitable for ecological risk assessment
can be obtained by modeling only (1) dilution, as determined by
stochastically varying stream discharges; and (2) essentially
instantaneous chemical processes such as lonization and complexation.
.2.2 ATMOSPHERIC TRANSPORT, TRANSFORMATION, AND DEPOSITION
Many computer codes exist for calculating the transport,
transformation, and deposition of radionuclides and toxic contaminants
within 50 km of a pollutant source. Most are variants of a single
underlying model, the Gaussian plume. In its simplest form, the
Gaussian plume predicts the diffusion and dispersion of a conservative,
gaseous substance from a continuous point source elevated above the
ground, under constant wind speed and homogeneous atmospheric
conditions, and over uniformly flat terrain. The basic model can be
modified to account for such phenomena as plume buoyancy, atmospheric
stratification, contaminant degradation or decay, and wet and dry
deposition of particles and aerosols.
Because of the relative ease of application of Gaussian plume
models and the large accumulated experience with these models, a
Gaussian plume model was used to calculate atmospheric exposures for
synfuels risk assessment. The specific code chosen was AIROOS-EPA
(Moore et al. 1979). This model was chosen over five alternatives
because it (1) incorporates first-order degradation rates for
pollutants, (2) can estimate surface deposition rates, and
(3) provides output in a form suitable for calculating exposures to
human populations. The equations for estimating plume dispersion,
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27 ORNL-6251
contaminant degradation, dry deposition, and wet deposition In
AIROOS-EPA are presented 1n Section 2.2.2 of Travis et al. (1983).
The AIRDOS-EPA code calculates average ground-level atmospheric
concentrations and surface deposition rates for sixteen 22.5° sectors
surrounding the plume source.
Adverse meteorological conditions (such as inversions) can lead to
high ground-level concentrations that cause acute toxicity to exposed
plants and animals. Such conditions occur on time scales of from 8 h
to a few days. Unfortunately, Gaussian plume models are relatively
poor predictors of short-term plume behavior (Hoffman et al. 1978).
These models are much better predictors of annual average
concentrations. As a substitute for short-term exposure estimates,
annual average concentrations were calculated at 500 m intervals over
the 16 sectors modeled in AIRDOS-EPA, and the highest of these averages
was used in the synfuels risk assessments (Barnthouse et al. 1985b,
Sect. 2.3).
Deposited contaminants, when dissolved in soil water, can cause
toxic effects on exposed plant roots. To provide root exposure
estimates for ecological risk assessment, the deposition rates from
AIRDOS-EPA were used to estimate accumulation of contaminants in soil
over an assumed 35-year operational lifetime of a synfuels plant. As
with ground-level atmospheric concentrations, accumulation was
estimated at the point of greatest annual deposition. The soil
solution exposure estimates Incorporate both degradation of
contaminants in soil and partitioning of contaminants between soil
particles and solution (Barnthouse et al. 1985b, Sect. 2.3).
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ORNL-6251 28
The atmospheric exposure assessments performed using AIRDOS-EPA
did not meet all of the requirements for ecological risk assessments
described in the introduction to this section. Specifically,
short-term exposures were not addressed, only worst-case exposures were
estimated, and no error analyses were performed. These deficiencies
result in part from the use of a computer code designed for estimating
long-term exposures to human populations, however, any Gaussian plume
model would have been of uncertain utility for estimating short-term
exposures. Although other classes of models are more suitable for this
purpose, such models require far more site-specific meteorological data
than are appropriate for technology-level risk assessments. Given
necessary code modifications, error analyses of AIROOS-EPA or any other
similar code could be performed. It was not deemed necessary to
perform such analyses for the synfuels risk assessment project, because
preliminary screening using worst-case exposure estimates suggested
that the majority of synfuels-related chemicals present negligible
risks to terrestrial plants and animals (Suter et al. 1984, Barnthouse
et al. 1985b). Future ecological risk assessments could, however,
benefit from the development of atmospheric exposure assessment models
designed specifically for ecological risk assessment, with capabilities
for modeling short-duration events and incorporating error analyses.
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29 ORNL-6251
REFERENCES (SECTION 2)
Barnthouse, L. W., G. W. Suter II, C. f. Baes III, S. M. Bartell,
R. H. Gardner, R. E. Millemann, R. V. O'Neill, C. D. Powers,
A. E. Rosen, L. L. Sigal, and D. S. Vaughan. 1985a. Unit Release
Risk Analysis for Environmental Contaminants of Potential Concern
in Synthetic Fuels Technologies. ORNL/TM-9070. Oak Ridge
National Laboratory, Oak Ridge, Tenn.
Barnthouse, L. W., G. W. Suter II, C. F. Baes III, S. M. Bartell,
M. G. Cavendish, R. H. Gardner, R. V. O'Neill, and A. E. Rosen.
1985b. Environmental Risk Analysis for Indirect Coal
Liquefaction. ORNL/TM-9120. Oak Ridge National Laboratory,
Oak Ridge, Tenn.
Baughman, G. L., and R. R. Lassiter. 1978. Prediction of
environmental pollution concentration, pp. 35-44. IN J. Cairns,
K. L. Dickson, and A. W. Maki (eds.), Estimating the Hazard of
Chemical Substances to Aquatic Life. ASTM STP 657. American
Society for Testing and Materials, Philadelphia, Penn.
Gardner, R. H., R. V. O'Neill, J. B. Mankin, and J. H. Carney. 1981.
A comparison of sensitivity and error analysis based on a stream
ecosystem model. Ecological Modelling 12:173-190.
Hoffman, F. 0., D. L. Schaeffer, C. W. Miller, and C. T. Garten, Jr.
(eds.) 1978. Proceedings of a Workshop on the Evaluation of
Models Used for the Environmental Assessment of Radionuclide
Releases. CONF-770901. Oak Ridge National Laboratory,
Oak Ridge, Tenn.
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ORNL-6251 30
Moore, R. E., C. F. Baes III, L. M. McDowell-Boyer, A. P. Watson,
F. 0. Hoffman, J. C. Pleasant, and C. W. Miller. 1979.
AIRDOS-EPA: A Computerized Methodology for Estimating
Environmental Concentrations and Dose to Man from Airborne
Releases of Radlonuclides. EPA-520/1-79-009. U.S. Environmental
Protection Agency Office of Radiation Programs, Washington, D.C.
Nlemczyk, S. 3., K. G. Adams, and W. B. Murfin. 1980. Groundwater and
surface water transport and dispersion. Appendix B IN The
Consequences from Liquid Pathways After a Reactor Meltdown
Accident. NUREG/CR-1596 (SAND80-1669), Sandia National
Laboratories, Albuquerque, N.M.
Suter, G. W. II, L. W. Barnthouse, C. F. Baes III, S. M. Bartell,
M. G. Cavendish, R. H. Gardner, R. V. O'Neill, and A. E. Rosen.
1984. Environmental Risk Analysis for Direct Coal Liquefaction.
ORNL/TM-9074. Oak Ridge National Laboratory, Oak Ridge, Tenn.
Travis, C. C., C. F. Baes III, L. W. Barnthouse, E. L. Etnier,
G. A. Holton, B. D. Murphy, G. P. Thompson, G. W. Suter II, and
A. P. Watson. 1983. Exposure Assessment Methodology and
Reference Environments for Synfuels Risk Analysis. ORNL/TM-8672.
Oak Ridge National Laboratory, Oak Ridge, Tenn.
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31 ORNL-6251
3. TOXICITY QUOTIENTS
G. W. Suter II
3.1 DEFINITION
The quotient method is simply the direct arithmetic comparison of
a benchmark concentration (BC) from a toxicity test with an expected
environmental concentration (EEC). It is typically calculated as the
quotient of the ratio EEC/BC. It is the basis for nearly all
assessments of the environmental hazards of chemicals. In this basic
form, the method amounts to an assumption that the test benchmark is a
good model of the assessment end point (i.e., the level of toxic effect
that is not to be exceeded in the ambient ecosystem). This assumption
is most likely to hold when the toxicity tests have been performed for
the particular assessment, using the anticipated temporal pattern of
exposure and dilution water and organisms from the site. When it is
recognized that this assumption may not hold, multiplicative factors
are often applied to the quotients.
3.2 FACTORS
The most common method of allowing for imperfect correspondence
between the benchmark concentration and the end point is to multiply
the quotient or either of its components by factors. These are
variously referred to as safety factors, uncertainty factors, or
correction factors, depending on whether the goal is to ensure safety,
account for a recognized source of uncertainty, or correct for
proportional differences between types of data. Traditionally, a
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ORNL-6251 32
single number was used that Incorporated all of the assessor's
knowledge and beliefs about the relationship between the test result
and the anticipated effect 1n the field (Mount 1977). More recently,
1t has become common to use multiplicative strings of factors, each of
which accounts for a different correction or source of uncertainty
(e.g., EPA 1985). These multiplicative chains Imply an assumption that
everything will go wrong at once. For example, the most sensitive life
stage of the most sensitive species will be exposed to the most
concentrated effluent at low-flow conditions while debilitated by
stress, and the actual response is at the limit of our range of
uncertainty. If carried out consistently, this approach would be
extremely conservative. In actual applications, only a fraction of the
possible uncertainties and corrections are included, so that the
product of the factors will not be unacceptably large. To avoid the
problems of subjectivity and conservatism, we have used unadorned
quotients in our assessments and left the consideration of uncertainty
and data extrapolation to methods that use more appropriate statistical
models.
3.3 IMPLEMENTATION
The critical decisions in Implementing the quotient method are
(1) selection of expressions of the expected environmental concentration
that reflect the pattern of exposure in the field, (2) selection of
toxicological benchmarks that correspond to the effect of concern in the
field, and (3) matching the benchmarks and environmental concentrations
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33 ORNL-6251
so that they logically correspond. The selection and derivation of
estimates of the expected environmental concentration Is discussed In
Sect. 2. The other two decisions are discussed here.
3.3.1 Matching Exposure and Effects
If the quotient 1s to be consistent, the toxlcological benchmark
must bear a logical relationship to the expected environmental
concentration. The first major problem is ensuring that the medium
and mode of exposure are consistent. For example, the environmental
concentration that should be estimated for benthic infauna is the pore
water concentration rather than the free water concentration, and per
cutaneous toxicities should be compared with concentrations in films on
traversed surfaces rather than with bulk concentrations.
The second major problem is ensuring that the response of the
organism to the toxicant does not change the exposure. The most
conspicuous example is avoidance of polluted food or media. However,
toxicants may also reduce feeding, thereby reducing the oral dose, or
may cause aquatic organisms to lose contact with the substrate and
drift out of the area. Since behavioral data are lacking for most
chemicals, this problem is relatively seldom addressed, but it should
be kept in mind.
The third major problem is duration, which is a major source of
confusion, largely because of ambiguities concerning the terms acute
and chronic. The ambiguity arises from the use of these terms to
describe severity as well as duration. Acute exposures and
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ORNL-6251 34
toxldties are assumed to be both of shorter duration and more severe
than chronic exposures and toxldties. The Implicit model behind this
assumption 1s that chronic effects are sublethal responses that occur
because of the accumulation of the toxicant or of toxicant-Induced
Injuries over long exposures. Conversely, 1t has become clear that the
most sensitive responses in chronic toxicity tests for aquatic
organisms are typically effects on sensitive life stages or processes
that occur fairly quickly, do not require long prior exposures, and may
be quite severe (McKim 1985). As a result, duration is now often
defined both in temporal terms and in terms of the life cycle of an
organism (i.e., a chronic exposure 1s one that potentially Involves all
life stages).
The resulting confusion 1s Illustrated by the standard
toxicological benchmarks for fish. The standard acute benchmark is the
96-hour median lethal concentration (LC5Q) for adult or juvenile fish
(EPA 1982, ASTM 1984, OECD 1981). The duration of this test was
selected because most mortality in most such tests occurs 1n the first
four days; in fact, this acute benchmark is considered a good estimate
of the time-independent or incipient LC5Q (Ruesink and Smith 1975).
The standard chronic benchmark 1s the maximum acceptable toxicant
concentration (MATC), which is the threshold for significant effects on
survival, growth, or reproduction (EPA 1982, ASTM 1984). Since this
benchmark is based on only the most sensitive response, life stages
that are generally less sensitive have been dropped from chronic tests
so that those tests have been reduced from life cycle (12 to 30 months)
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35 ORNL-6251
to early life stages (28 to 60 days) (McKim 1985). Tests that expose
larvae only for 11 (Birge et al.1981) or 7 days (Norberg and Mount,
1985) have now been proposed as equivalent to the longer chronic
tests. As a result, the chronic benchmark for fish is now tied to
events of short duration (the presence and response of sensitive
larvae), whereas the acute benchmark is applicable to exposures of
indefinite duration and life stages that are continuously present.
Even the severity distinction is not clear. Although the LC5Q
clearly indicates a severe effect, the fact that the MATC is tied to a
statistical threshold rather than a specified magnitude of effect means
that it too can correspond to severe effects (e.g., failure of more
than half of the females to spawn at the MATC for chlordane in Cardwell
et al. 1977).
The solution for the assessor is to disaggregate the concept of
duration from severity when categorizing exposures. In the simplest
case the temporal pattern of exposure falls into distinct categories,
based on characteristics of the source and its interactions with the
environment. If the aqueous dilution volume is relatively constant,
exposures may be divided into those that result from spills and other
short-term upsets and those that result from routine releases.
Exposures to an atmospheric release might be divided into plume strikes
(an hour or less), stagnation events (a week or less), and the growing
season average exposure. In these cases the durations are determined
by the exposure, and the toxicological benchmarks must be selected to
match.
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ORNL-6251 36
In other cases 1t may not be possible to Identify distinct and
relatively constant categories of exposure; there may simply be a
continuous spectrum of fluctuations 1n exposure concentrations. In
such cases the biology of the toxicological responses must be used to
select durations, and the exposure must be selected to match. For
example, if the most sensitive response to a chemical is mortality of
larval fish, which begins within a day of the beginning of exposure,
then the appropriate exposure concentration could be based on dilution
of the effluent in the 24-h low flow that recurs at an average Interval
of 10 years during the months in which larval fish are present at the
site. In any case, the matching of exposure with a toxicological
benchmark should be based on an analysis of the situation being
assessed rather than on preconceptions about acute and chronic toxidty.
3.3.2 Benchmark Selection
In many cases the selection of toxicological benchmarks for an
assessment 1s largely constrained by the availability of published
data, by differences in the quality of available data, or by the need
to match the benchmark to the mode and duration of exposure. However,
when data are abundant or when testing can be prescribed by the
assessor, toxicological benchmarks should be selected on the basis of
their statistical form and their expression of the Important responses
of the organism of interest.
3.3.2.1 Statistical form. There are two statistical types
of toxicological benchmarks: (1) those that are based on a
concentration-response function and prescribe a level of effect and
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37 ORNL-6251
(2) those that are based on hypothesis testing. The first type is
obtained by fitting a function to sets of points relating the level of
response (proportion dying, mean weight, etc.) to an exposure
concentration (dose, concentration 1n water, concentration in food,
etc.). The concentration causing a particular level of effect is then
obtained by inverse regression. Examples of this type of benchmark
include the LC5Q, median lethal dose (L05Q), median effective
concentration (EC-0), and lethal threshold concentration (LC.).
The other statistical category of benchmarks consists of those
that are derived by hypothesis testing techniques. Responses at the
exposure concentrations are compared with control (unexposed) responses
to test the null hypothesis that they are the same as the control
responses. Benchmarks of this type include the no observed effect
level (NOEL), the lowest observed effect level (LOEL) and the MATC,
which is assumed to lie between the LOEL and the NOEL.
The disadvantages of benchmarks based on hypothesis testing
relative to those based on curve fitting have been discussed by Stephan
and Rogers (in press). They include (1) the use of conventional
hypothesis testing procedures (with a = 0.05 and 3 unconstrained)
Implies that it is very important to avoid declaring that a
concentration is toxic when it 1s not, but it 1s not so important to
declare that a concentration is not toxic when 1t is; (2) the threshold
for statistical significance does not correspond to a toxicological
threshold or to any particular level of effect; (3) poor testing
procedures increase the variance in response and therefore reduce the
apparent toxicity of the chemical in a hypothesis test; and (4) the
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ORNL-6251 38
results are relatively sensitive to the design of the test. The
advantages of hypothesis testing benchmarks are that they can be
calculated even when the test data are too poor or meager for curve
fitting and they allow the assessor to avoid specific decisions about
what constitutes a significant level of effect. We feel that
hypothesis testing 1s generally an Inappropriate way to calculate
benchmarks; however, 1n many cases, the use of such benchmarks by the
assessor 1s unavoidable.
3.3.2.2 Taxon-speciflc factors. We discuss here benchmarks
currently used to express toxic effects on the four end point taxa in
our risk analyses: fish, planktonlc algae, terrestrial vascular plants,
and vertebrate wildlife.
1. Fish
The most abundant toxlcologlcal benchmark for fish 1s the 96-h
LC5Q for adult or juvenile (post-larval) Individuals; for most
chemicals, it is the only type of data available. As previously
described, it is acute in terms of severity but is often applicable to
extended durations. Since it does not protect early life stages and
Implies mortality in all life stages, it can be thought of as a
benchmark for conspicuous fish kills (large numbers of large dead
fish). Although the median response was chosen for the benchmark
because of Its small variance relative to other levels of mortality, a
correction factor must be applied 1f the assessor 1s interested in
preventing low-level mortality (EPA 1985), a process that adds
considerable variance.
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39 ORNL-6251
Another problem with this benchmark is that in most cases only the
response at 96 h is reported. Many assessments involve transient
events, and the time to mortality is more important than the percent
mortality. However, despite the suggestions of Sprague (1973),
Alabaster and Lloyd (1982) and others, the time course of mortality is
seldom reported. In defense of the 96 h LC50, it might be argued
that it is only meant to be used for comparative purposes and not for
assessment of effects. However, assessments have been conducted and
criteria have been set on the basis of this benchmark because it is
available and better numbers are generally not.
The standard benchmark for chronic effects on fish is the MATC.
As previously discussed, MAICs have all of the considerable faults of
benchmarks that are derived from hypothesis tests. In this context, it
is important to reiterate that assessments based on MATCs do not
provide a consistent level of protection, and the industry that
performs the poorest tests will, on average, be the least regulated.
The most generally useful benchmarks for assessing effects on fish
by the quotient method would be a set of LC. values for each of the
life stages that will be exposed at 1, 24, 48, and 96 h (or longer if
mortality continues), plus EC., values for growth and fecundity in
suitably long exposures. Individual thresholds could then be selected
for each assessment, depending on the life stages that will be exposed
and the duration of the exposure.
If all life stages will be exposed to a relatively constant
concentration of the toxicant, then a global benchmark [one that
integrates the individual measured effects (Javitz, 1982)] may be
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ORNL-6251 40
preferred as an expression of chronic effects. The simplest such
benchmark is the standing crop of fish at the end of the test. More
commonly, the weight of young per initial female (or initial egg, in
the case of early life stage tests) is calculated as
where S is the survivorship of life stage x, M is fecundity, and W
is the weight of the final cohort (e.g., Eaton et al. 1978). A third
global benchmark (which can only be used with life-cycle results) is
the intrinsic rate of increase r which 1s calculated from:
where 1 is the proportion surviving to age x, and m is the number of
female offspring produced by a female of age x during the next interval
(e.g., Daniels and Allan 1981). The intrinsic rate of increase, r,
is a more appropriate benchmark for Invertebrates than fish, since
life-cycle tests are still routinely performed with invertebrates, and
effects on growth (which are not included in the formula for r) are
reflected in fecundity 1n invertebrate chronic tests.
The main advantage of global benchmarks is that they combine a
diversity of individual responses, some of which have little intuitive
significance, into a parameter that has the form of a population-level
response. Global responses may be more sensitive than individual
responses when a number of small toxic ^effects are combined into1 one
large global response; however, sensitivity can also be reduced if
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41 ORNL-6251
toxic effects are combined with hermetic or pseudo-hormetic effects
or (if hypothesis testing is used) with highly variable effects.
2. Algae
Benchmarks for effects on algae have been poorly standardized.
Reported responses included mortality, growth, CO- fixation, cell
numbers, chlorophyll content, and others. Durations were various, and
a variety of statistical expressions derived from both hypothesis
testing and curve fitting were used. There is now some agreement on
the use of 96-h EC5Q values for some measure of productivity.
However, there is still no agreement on whether the appropriate measure
is weight, number of cells, chlorophyll, or carbon assimilation, and
whether the benchmark should be based on the final value, the
time-integrated value, or the maximum rate of increase. The EPA calls
for the use of final cell weight, cell number, or an equivalent
indirect measurement, whereas OECO calls for the use of the maximum
growth rate based on cell number (EPA 1982 and OECO 1981). If, as is
often the case, planktonic algae are limited by nutrient availability,
then equilibrium biomass or cell numbers may be more relevant.
However, if algae are limited by herbivory, the ability of a population
to replace losses (i.e., maximum growth rate) may be more relevant.
Since the life cycles of microalgae in a rapidly growing culture
are much shorter than test durations or most effluent releases, these
test results can be used in most assessments. However, it should be
remembered that algal communities are generally nutrient limited, and,
over the course of chronic exposures, resistant algal species will tend
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ORNL-6251 42
to replace sensitive species. The Implications of these changes In
community composition depend on the effects of the algae on water
quality and their palatability to herbivores (Sect. 6).
3. Terrestrial plants
Existing toxicity data for terrestrial plants are even more
diverse and nonstandard than for aquatic algae. Although (as with
algae) production 1s measured and statistically analyzed in a variety
of ways, terrestrial plants also have long life cycles with distinct
stages and organs, and they can be exposed through the stomates, leaf
surfaces, or roots. We have confronted this chaotic situation by
limiting the benchmarks used to those such as yield, growth, or numbers
of particular organs that directly express productivity (visible injury
and changes in gas exchange rates are commonly reported responses that
do not correlate with production), and by trying to match the duration
and route of exposure in the test to the exposure being assessed.
The most common general type of phytotoxidty test 1s the seedling
growth test. This type of test can be conducted in soil or hydroponic
systems and can be adapted to test chemicals in air, sprays, soil, or
irrigation water. There is little agreement on durations or responses,
but the EPA (1982) recommends the determination of EC1Q and EC5Q
values for weight and height after 14 days. Tests for effects on seed
germination and hypocotyl elongation have been used as quicker and
less-expensive phytotoxidty tests, as well as Indicators of effects on
those particular life stages (EPA 1982); however, their relationship to
other plant responses has not been established. A definitive test
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43 ORNL-6251
would Include the entire life cycle from seed germination to germination
of daughter seeds, but such tests are rarely performed. A life-cycle
test using Arabadopsis is being developed by the EPA.
4. Wildlife
The most common benchmark available for assessing effects on
wildlife is the acute, oral, median lethal dose (LD.50) for laboratory
rodents. Avian toxicologists have followed the mammalian example by
relying largely on acute LD5Qs for adults (e.g., Hudson et al. 1984),
but subacute median lethal dietary toxicities for young birds (LCrQs)
have become more common (e.g., Hill et al. 1975) and have been adopted
by the EPA (1982) and ASTH (1984). These benchmarks are applicable to
short-term exposures such as result from application of nonpersistent
pesticides. In most such cases, the concentration in food is the
primary expression of exposure; therefore, oral LC5Qs are directly
applicable, whereas intake must be estimated to calculate doses before
LD5Qs can be used (Kenega 1973). In a few cases, notably when the
exposure results from consumption of granular pesticides or cleaning
pelt or plumage, an oral LD,-0 is more directly applicable. Since the
relative sensitivities of adults and young and the effects of exposure
duration are less well known for birds than fish (Tucker and Leitzke
1979), the comparability and usability of these benchmarks are
uncertain.
The other standard wildlife benchmark is the threshold for effects
in the avian reproduction test (EPA 1982, ASTM 1985). This test
resembles the HATC for chronic and subchronic effects on fish, in
that the benchmark is usually derived by applying hypothesis testing
statistics to an array of measured parameters. Like the MATC, it would
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ORNL-6251 44
be more useful for assessment If curve fitting were used to establish a
consistent level of effect, and 1f a global parameter (such as the
weight of young per female) were calculated along with the Individual
measured responses. The duration of exposure in this test (6-10 weeks)
can be considered to represent a chronic adult exposure for all but the
most persistent and bloaccumulated chemicals; however, since the young
are not exposed, this cannot be considered a full chronic (I.e.,
life-cycle) test.
There are very few data available for assessing the toxic effects
of nonpesticide chemicals and effluents on wildlife. It is generally
necessary to resort to the use of the health literature for such
assessments. We have used rodent LD-0 values as a relatively
consistent benchmark for comparative purposes and the lowest-reported
toxic effect as a benchmark for suggesting where hazards may exist.
3.4 DISCUSSION
The chief advantages of the quotient method are that it is quick,
easy, generally accepted, and can be applied to any data. Because the
effects benchmark is directly compared with the expected environmental
concentration, the burden of ensuring realism in the description of the
effects and their relationship to exposure falls largely on the
toxicologist rather than the assessor. As previously discussed, the
use of multiplicative factors to modify quotients amounts to treating
uncertainty 1n a deterministic manner, and this logical Inconsistency
has resulted in Incomplete and Inconsistent treatments of corrections
and uncertainties. However, without the factors, the assumptions
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45 ORNL-6251
concerning the appropriateness of the toxicologlcal benchmark and the
estimated environmental concentration are not Incorporated in the
analysis. Therefore, this method is useful when (1) a large number of
chemicals must be screened to find potential hazards, (2) the toxicity
data are unconventional, or (3) the data are believed to be completely
appropriate to the assessment, or at least cannot be improved by
available analytical techniques.
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ORNL-6251 46
REFERENCES (SECTION 3)
Alabaster, J. S., and R. LLoyd. 1982. Water Quality Criteria for
Freshwater Fish, 2nd ed. Butterworth Scientific, London.
ASTM. 1984. 1984 Book of ASTM Standards, Vol. 11.04. American Society
for Testing and Materials, Philadelphia.
Birge, W. 0., J. A. Black, and B. A. Ramney. 1981. The reproductive
toxicology of aquatic contaminants, pp. 59-110. IN J. Saxena and
F. Fisher (eds.). Hazard Assessment of Chemicals, Vol. 1.
Academic Press, New York.
Cardwell, R. D., D. G. Formeman, T. R. Payne, and D. J. Wilbur. 1977.
Acute and chronic toxicity of chlordane to fish and invertebrates,
EPA-600/3-77-019. U.S. Environmental Protection Agency,
Duluth, Minn.
Daniels, R. E., and J. D. Allan. 1981. Life table evaluation of
chronic exposure to a pesticide. Can. J. Fish. Aquat. Sci.
38:485-494.
Eaton, J. G., J. H. McKim, and G. W. Hoi combe. 1978. Metal toxicity to
embryos and larvae of seven freshwater fish species-I. Cadmium.
Bull. Environ. Contam. Toxicol. 1978:95-103.
EPA. 1982. Environmental effects test guidelines, EPA-560/6-82-002.
U.S. Environmental Protection Agency, Washington, D.C.
EPA. 1985. Technical support document for water quality-based toxics
control, U.S. Environmental Protection Agency, Washington, D.C.
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47 ORNL-6251
Hill, E. F., R. G. Heath, J. W. Spann, and J. 0. Williams. 1975.
Lethal dietary toxicities of pollutants to birds. Special
Scientific Report-Wildlife No. 191. U.S. Fish and Wildlife
Service, Washington, O.C.
Hudson, R. H., R. K. Tucker, and M. A. Heagele. 1984. Handbook of
toxicity of pesticides to wildlife. Resource Publication 153.
U.S. F1sh and Wildlife Service, Washington, D.C.
Javitz, H. S. 1982. Relationship between response parameter
hierarchies, statistical procedures, and biological judgment in
the NOEL determination, pp. 17-31. IN J. G. Pearson,
R. B.(-END-), and W. E. Bishop (eds.), Aquatic Toxicology and
Hazard Assessment, Fifth Conference, ASTM STP 766. American
Society for Testing and Materials, Philadelphia.
Kenega, E. E. 1973. Factors to be considered in the evaluation of the
toxicity of pesticides to birds in their environment,
pp. 166-181. IN F. Coulston and F. Court (eds.). Environmental
Quality and Safety, Vol. II. Academic Press, New York.
McKim, 3. M. 1985. Early life stage toxicity tests, pp. 58-97. IN
G. M. Rand and S. R. Petrocelli (eds.). Fundamentals of Aquatic
Toxicology, Hemisphere Publishing Corp., Washington, D.C.
Mount, D. I. 1977. An assessment of application factors in aquatic
toxicology, pp. 183-190. IN R. A. Tubb (ed.). Recent Advances in
Fish Toxicology, EPA-600/3-77-085. U.S. Environmental Protection
Agency, Corvallis, Washington.
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ORNL-6251 48
Norberg, T. 0., and D. I. Mount. 1985. A new fathead minnow
(Pimephales oromelas) subchronlc toxldty test. Environ. Toxlcol.
Chem. 4:711-718.
OECD. 1981. OECD guidelines for testing of chemicals, Organization for
Economic Cooperation and Development, Paris.
Rueslnk, R. G., and L. L. Smith, Jr. 1975. The relationship of the
96-hour LC5Q to the lethal threshold concentration of hexavalent
chromium, phenol, and sodium pentachlorophenate for fathead
minnows (Pimephales promelas Rafinesque). Trans. Am. Fish. Soc.
1975:567-570.
Sprague, J. B. 1973. The ABC's of pollutant bioassay using fish.
pp. 6-30. IN J. Cairns, Or., and K. L. Dickson (eds.), Biological
Methods for the Assessment of Water Quality, ASTM STP 528,
American Society for Testing and Materials, Philadelphia.
Stephan, C. E., and J. W. Rogers, in press. Advantages of using
regression analysis to calculate results of chronic toxiclty
tests. IN Aquatic Toxicity and Hazard Assessment, Eighth
Symposium, American Society for Testing and Materials,
Philadelphia.
Tucker, R. K., and J. S. Leitzke. 1979. Comparative toxicology of
Insecticides for vertebrate wildlife and fish. Pharmacol. Therap.
6:167-220.
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49 ORNL-6251
4. ANALYSIS OF EXTRAPOLATION ERROR
G. W. Suter II, A. E. Rosen, and E. Linder
4.1 DEFINITION
Analysis of extrapolation error (AEE) 1s a method of calculating
the probability of exceeding assessment end points to be used 1n those
cases where the end points can be expressed as standard toxlcologlcal
benchmarks. The method has two components: (1) the extrapolation
component that, like the factors used with the quotient method
(Sect. 3.2), is used to estimate the value of the assessment end point
from the available test data and to account for the uncertainty in the
estimate; and (2) the risk component that calculates the probability of
exceeding the assessment end point using the results of the
extrapolations. Since the extrapolation component treats extrapolation
and uncertainty in a more rigorous and conceptually appropriate manner
than does the use of chains of multiplicative factors, it can be used
in place of such factors in hazard assessment. However, 1t is the
calculation of the probability that an expected environmental
concentration will exceed the end point (rather than simply comparing
them arithmetically as in the quotient method) that makes AEE a true
risk assessment method.
In the following sections we will explain the assumptions and
statistical procedures for AEE and provide numerical examples; however,
the method can be best introduced by presenting an example
graphically. Assume that we wish to estimate the probability that the
expected environmental concentration of a chemical will exceed the
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ORNL-6251 50
threshold for life-cycle effects on survival, growth, or reproduction
of brook trout (Salvelinus fontinalis) and that we only have an LC5Q
for rainbow trout (Salmo gairdneri). In that case we must extrapolate
between the genera Salmo and Salvelinus. and we must extrapolate
between the LC5Q and the chronic threshold. The relationship between
the two genera is illustrated in Fig. 4.1. Each of the points
represents an individual chemical for which a member of both genera has
been tested using a common protocol and with the results expressed as
96-h LC5Q$. The relationship between LC5Qs and life-cycle effects
thresholds (expressed as MATCs) is shown in Fig. 4.2. The points here
represent different species-chemical combinations for which both an
LC50 and a life-cycle or partial life-cyle MATC have been determined
in the same laboratory. If we use the rainbow trout LC5Q as the x
value in the Fig. 4.1 relationship, we can estimate a brook trout
LC50 and an associated variance that can be used in the Fig. 4.2
relationship to estimate a brook trout MATC and associated variance.
The estimated MATC and Its total variance can be represented as a
probability density function, as in Fig. 4.3. The risk that the MATC
will in fact be exceeded is the probability that a realization of the
MATC, chosen at random from that probability density function, will be
less than a similarly chosen value from the probability density
function for the expected environmental concentration.
AEE differs from previous approaches to extrapolating
environmental toxicology data in Its emphasis on the uncertainty
associated with the extrapolations and the contribution of that
uncertainty to the risk. The traditional approach is to ask whether ,
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51
ORNL-6251
ORNL-DWG 85-16999
5.0
4.5 I-
4.0
3.5
3.0
2.5
o
in
y 2.0
C/5
1.5
g 1.0
w 0.5
0
-0.5
-1.0
-1.5
-2.0
-0.5
oo o_
o o oo
oo
1
1
I
0.5 1.5 2.5 3.5
log SALMO LC50 (yLtg/L)
4.5
Fig. 4.1. Logarithms of LCsp values for Salvelinus plotted against
Salmo. The line is determined by an errors-in-variables
regression; the parameters are presented in Table 4.1.
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ORNL-6251
52
ORNL-DWG 85-17000
o
<
o»
o
-1
1 2 3
log LC50 (ftg/L)
Fig. 4.2.
Logarithms of MATC values from life-cycle or partial
life-cycle tests plotted against logarithms of 96-h 1.050
values determined for the same species and chemical in the .
same laboratory. The line is derived by an
errors-in-variables regression; the parameters are presented
in line 4 of Table 4.3.
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53
ORNL-6251
ORNL-DWG 85-16995
1.0
0.8
0.6
LU
Z>
o
UJ
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ORNL-6251 54
one particular species, life stage, or test duration 1s an acceptable
surrogate for another. When this question 1s asked, 1t 1s Invariably
discovered that no two tests give Identical results, and that the
results are not consistently proportional across test chemicals. This
discovery can lead to the pessimistic conclusion that toxidty data
should not be extrapolated (Tucker and Heagele 1971), which Implies
that only tested species can be protected. However, since no test 1s
perfectly precise or accurate, even test results have associated
uncertainty that can prevent fine discrimination between effective and
Ineffective exposures. Thus, the relevant question 1s: Does a
particular benchmark, whether derived by testing alone or by testing
and extrapolation, provide sufficient accuracy so that an acceptable
level of risk can be determined?
4.2 IMPLEMENTATION
AEE consists of five steps: (1) define the end point of the risk
assessment (e.g., the probability of causing reductions in brook trout
productivity) in terms of a toxicological benchmark (e.g., the
probability of exceeding the brook trout MATC); (2) identify the
existing datum for the chemical of Interest that is most closely related
to the end point (e.g., a rainbow trout 96 h at LC_); (3) break the
relationship between the datum and the end point into logical steps
(e.g., rainbow trout to brook trout and LC50 to MATC); (4) calculate
the distribution parameters of the end point extrapolated from the
datum; and (5) calculate the risk that the expected environmental
concentration (EEC) will exceed the end point concentration. Step 1
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55 01NL-6251
1s dependent on the assessment situation and on the assessor's and
decision-maker's conceptualization of environmental values; however,
steps 1, 2, and 3 are severely constrained by the state of the science
of environmental toxicology as reflected 1n the available benchmarks
and data for the organisms 1n question (Sect. 3.3).
4.2.1 Risk Calculation
In this method, risk 1s defined as
Risk = Prob(EEC > BC) , (4.1)
where BC 1s the benchmark concentration that 1s used as the estimator
of the assessment end point. If we assume that the EEC and BC are
Independent and log-normally distributed, then
Risk = Prob(log BC - log EEC < 0) (4.2)
= Prob[Z < [0 - (vb - ue)3 / (of, + o*)1/2] (4.3)
- 4>z[Ue - vb) / (<»£ + °e)1/23 • (4'4)
2 2
where (ub, o') and (wg, og) are the mean and variance of
the log BC and log EEC, respectively and
Z = [(log BC - log EEC) - (ub - Vfi)] / (o\ + <^)1/2 • (4'5)
a standard normal random variable with 2 as its cumulative
distribution function. If 1t 1s assumed that the EEC 1s constant and
certain, then the risk calculation reduces to
Risk = Prob{Z<[(log EEC - VD) / ob]} (4.6)
= «>z[(log EEC - Vb) / ob] . (4.7)
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ORNL-6251 56
Given this definition, risk depends on the definitions of the EEC and
2 2
BC and their associated uncertainties (I.e., on u_, ufat a , and of).
For the BC, the mean and variance can be estimated by statistical
extrapolation of the toxldty data.
4.2.2 Extrapolation
The choice of extrapolation model for this method was based on the
following characteristics of toxldty data:
1. the observed values X and Y are subject to error of
measurement and to Inherent variability,
2. X 1s not a controlled variable (like settings on a
thermostat),
3. values assumed by X and Y are open-ended and non-normally
distributed.
These characteristics suggest that an ordinary least-squares model
would be Inappropriate and an errors-1n-var1ables model should be
used. Since we can estimate the value of \, the ratio of the point
variances of Y to X, a functional model provides maximum likelihood
estimators of the regression parameters.
The estimators of the slope (0) and Intercept (a) are
b = {Ey2-XEx2+ [(Ey2-XEx2) 2+ 4\(£xy)2]1/2}/2Exy and (4.8)
a = y - bi , (4.9)
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57 ORNL-6251
where x = X.-X and y = Y.-Y for 1 = l...n. The variance of a single
predicted Y-value for a given X-value (X = XQ) is given in Handel
(1983) as
var(Y|Xo) = s2{l + 1/n + [1 + (b2/X)] ^(X^ jtf/Eu2]}, where (4.10)
sf. = (b2Ex2 - 2bExy + Ey2)/(n - 2), and
Eu2 = Ex2 * 2b/\Exy + (b/\)2Ey2.
This variance is the appropriate value to use 1n calculating confidence
intervals and risk estimates because the Interest in this case 1s the
certainty concerning an individual future observation of Y, such as a
toxic threshold, for an untested species-chemical combination. This
p
variance is larger (by a factor of s ) than the variance of the
mean of a Y|X , which is in turn larger than the variance of the
regression coefficient—the number provided by most programmable
calculators. Confidence intervals calculated from this variance are
larger than those that are conventionally reported and are referred to
as prediction intervals.
For ease in using this method we reduce the variance formula to
var(Y|X0) - FT + F2(X0 - X)2 (4.11)
and provide values for FI and F2 in the tables.
All of the data used 1n our extrapolations are log transformed,
and the reported variances and prediction intervals are for the
transformed values. The log transformation was used to Increase the
homogeneity of the variances and the linearity of the relationships.
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ORNL-6251 58
4.2.3 Double Extrapolation
In some cases 1t 1s necessary to make multiple extrapolations; the
most common example 1s the combination of acute/chronic and taxonomic
extrapolations. In those cases the Y from the first extrapolation
becomes the "Independent" variable 1n the second extrapolation, and the
parameters of the second regression (z = c + dy) are determined as for
the first, that 1s substituting y for x and z for y. The total
variance for the two extrapolations 1s
Var(Z|X0) = var(Z|Y0) + d2var(Y|X0) . (4.12)
4.3 AN EXAMPLE: AQUATIC INVERTEBRATES AND FISH
4.3.1 Data Sets
The data set for the taxonomic extrapolations of LC5Qs 1s based
on an expansion of the Columbia National Fisheries Research Laboratory
data set in Johnson and Flnley (1980); the expansion was prepared by
Mayer and Ellersieck (1n press). This 1s the largest and most
taxonomically diverse set of publicly available aquatic toxlcity data
that 1s reasonably uniform with respect to test procedures. We have
created a more uniform subset of the data by limiting 1t to tests
performed in soft water (except for those organisms such as Daphnla
that are not tested in soft water), with post-larval fish weighing
between 0.4 and 2.0 g, or with Invertebrates belonging to the most
often-tested life stage. Tests with aged test solutions, results
expressed as > or < values, nonstandard temperatures or pHs, or
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59 ORNL-6251
forms of a chemical other than the most often-tested form were not
used. If, after these criteria were applied, there were still
replicate LC5Qs for a combination of species and chemical, one of the
replicates was chosen at random. This subset contains 61 species and
327 chemicals.
The data sets for the extrapolations Involving chronic effects on
fish are presented in Appendices A and B. The chronic fish data are a
compilation of published results of life cycle, partial life cycle, and
early life-stage tests of freshwater fish. The concentration-response
data for hatch of normal larvae, larval survival, early juvenile
weight, eggs produced per female, and adult survival (Appendix B) were
extracted from the tests listed in Appendix A. In Appendix B replicate
results were averaged, and relationships were not used if there was not
at least a 25X reduction in performance at the highest concentration,
if there was greater than 30X mortality in the controls, or if there
was not a significant positive slope to a fitted logit function. Since
these studies were designed for calculating MATCs rather than for curve
fitting, most of the responses did not pass these lenient criteria.
However, they are the only chronic data available for fish and they
serve to illustrate the use of benchmarks based on chronic effects
levels and population models (Sect. 5).
The invertebrate chronic data are limited to life-cycle tests with
Daphnia spp., since there are few good chronic data for any other
freshwater invertebrate. Those data are from the 1980 and 1984 EPA
ambient water quality criteria support documents and are not reproduced
here.
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ORNL-6251 60
4.3.2 Extrapolation Results
The taxonomic extrapolations of acute data are presented 1n
Table 4.1. The extrapolations were performed between taxa having the
next higher taxonomic level 1n common rather than simply matching all
possible species combinations. For example, the extrapolation between
the fathead minnow (Pimephales promelas) and largemouth bass
(Micropterus salmoides) constitutes an extrapolation between the
Cyprinlformes and Perciformes. This system allows extrapolation to
species that have rarely or never been tested by assuming that they are
represented by tested species that are members of some common higher
taxonomic level. The taxonomic hierarchy is based on the concept that
greater evolutionary distance implies greater morphological and
physiological dissimilarity, which Implies greater dissimilarity in
response to toxicants. It is the basis for preferring mammals over
nonmammals and primates over nonprimate mammals in testing for effects
on humans. It will not hold if the traits that determine sensitivity
are extremely evolutionarily labile or conservative. The concept has
been shown to hold on average for aquatic organisms (Suter et al. 1983,
Suter and Vaughan 1984, and LeBlanc 1984).
As shown in Table 4.2, most extrapolations between taxa within the
same family (i.e., between congeneric species and between confamilial
genera) can be made with fair certainty, but extrapolations between
orders of arthropods, classes of chordates or arthropods, and between
the phyla Chordata and Arthropoda are highly uncertain. We use the
prediction interval rather than the correlation coefficient (r),
-------
61
ORNL-6251
Table 4.1. Taxonomic extrapolations [units are log(v9/L)].
Level3 Taxon Xb
SPECIES
CUTTHROAT TROUT
CUTTHROAT TROUT
CUTTHROAT TROUT
RAINBOW TROUT
RAINBOW TROUT
ATLANTIC SALMON
BLACK BULLHEAD
GREEN SUNFISH
0. MAGNA
6. FASC1ATUS
GENUS
ONCORHYNCHUS
ONCORHYNCHUS
SALMO
CARASSIUS
CARASSIUS
CYPRINUS
LEPOMIS
LEPOMIS
OAPHNIA
PURONARCELLA
FAMILY
BUFON1DAE
CENTRARCHIDAE
CENTRARCHIOAE
PERLIOAE
PERLODIDAE
SALHONIDAE
PERCIDAE
AST AC I DAE
Taxon Yc
RAINBOW TROUT
ATLANTIC SALMON
BROWN TROUT
ATLANTIC SALMON
BROWN TROUT
BROWN TROUT
CHANNEL CATFISH
BLUEGILL
D. PULEX
G. LACUSTRIS
SALMO
SALVELINUS
SALVELINUS
CYPRINUS
PIMEPHALES
PIMEPHALES
MICROP1ERUS
POMOXIS
SIMOCEPHALUS
PTERONARCYS
HYL1DAE
PERCIDAE
CICHLIDAE
PTERONARCYIDAE
PTERONARCYIDAE
ESOCIDAE
CICHLIDAE
PALAEMONIDAE
Nd
18
6
B
10
15
7
12
14
9
11
56
13
56
8
19
10
30
8
51
8
6
47
6
11
9
11
5
6
IcepteS1opef
0.04
-0.25
-0.20
-0.51
-0.21
0.09
-0.11
-0.62
0.26
-0.06
-0.13
-0.47
-0.33
-0.47
-0.27
0.24
-0.20
-0.01
0.35
-0.05
1.26
-0.02
0.93
0.21
0.54
-0.49
0.15
0.27
0.98
1.00
1.02
1.20
1.09
1.01
1.00
1.09
0.81
0.84
1.02
1.09
1.10
1.05
1.03
0.93
1.05
0.82
0.92
1.03
0.56
0.95
0.40
1.11
0.75
1.40
1.43
0.54
Xbar9
2.47
2.99
2.42
2.61
2.16
2.53
2.23
2.39
0.66
1.32
2.63
2.40
2.86
3.04
2.79
2.90
2.33
1.28
1.48
1.34
2.34
1.96
0.90
0.17
1.12
1.05
1.42
1.89
Flh
0.24
0.16
0.14
0.20
0.08
0.13
0.11
0.17
0.59
0.15
0.11
0.08
0.14
0.09
0.17
0.17
0.22
0.23
0.16
0.15
0.34
0.27
0.08
0.40
0.22
0.23
0.33
1.37
F2h
0.01
0.01
0.01
0.01
0.00
0.01
0.00
0.01
0.07
0.01
0.00
0.00
0.00
0.01
0.00
0.01
0.00
0.01
0.00
0.01
0.14
0.00
0.04
0.19
0.01
0.13
0.13
0.05
Ybar1
2.45
2.74
2.26
2.62
2.15
2.65
2.13
1.99
0.81
1.05
2.56
2.15
2.82
2.73
2.61
2.95
2.24
1.04
1.71
1.33
2.58
1.85
1.29
0.39
1.39
0.99
2.19
1.29
61^
0.25
0.16
0.14
0.14
0.07
0.13
0.11
0.14
0.90
0.21
0.10
0.07
0.11
0.08
0.16
0.20
0.20
0.34
0.19
0.14
1.06
0.29
0.51
0.32
0.39
0.12
0.16
4.67
G2j
0.01
0.01
0.01
0.01
0.00
0.01
0.00
0.00
0.16
0.03
0.00
0.00
0.00
0.01
0.00
0.01
0.00
0.02
0.00
0.01
1.37
0.00
1.67
0.12
0.05
0.03
0.03
0.55
PIk
0.96
0.78
0.74
0.87
0.56
0.70
0.66
0.80
1.51
0.76
0.65
0.57
0.73
0.58
0.82
0.82
0.92
0.94
0.78
0.75
1.14
1.01
0.56
1.24
0.92
0.94
1.12
2.30
-------
ORNL-6251
62
Table 4.1. (Continued)
Level3 Taxon Xb
ORDER
SALMONI FORMES
SALMON I FORMES
SALMON I FORMES
CYPRINIFORMES
CYPRINIFORMES
SILURIFORMES
CLADOCERA
CLADOCERA
OSTRACODA
OSTRACOOA
ISOPODA
ISOPODA
AMPHIPODA
PLECOPTERA
PLECOPTERA
SALMONIFORMES
CYPRINIFORMES
SILURIFORMES
ATHERINIFORMES
OSTRACOOA
CLASS
AMPHIBIA
CRUSTACEA
Taxon Yc
CYPRINIFORMES
SILURIFORMES
PERCIFORMES
SILURIFORMES
PERCIFORHES
PERCIFORMES
OS1RACODA
AMPHIPODA
ISOPODA
AMPHIPODA
AMPHIPODA
DECAPODA
DECAPODA
ODONATA
DIPTERA
ATHERINIFORMES
ATHERINIFORMES
ATHERINIFORMES
PERCIFORMES
DECAPODA
OSTEICHTHYES
INSECTA
N«
225
203
443
111
219
190
22
105
7
14
20
5
14
13
18
6
5
5
10
9
206
373
Icept6
0.90
0.87
0.33
0.23
-0.39
-0.74
0.79
0.27
-1.10
-2.74
-0.22
-2.31
0.65
0.60
0.77
0.37
0.02
-0.48
-0.10
-1.05
-6.97
0.01
Slopef
0.87
0.85
0.94
0.93
0.99
1.08
0.62
0.91
2.05
2.30
0.45
1.85
1.67
0.53
2.46
0.66
0.74
0.85
1.03
1.37
3.34
0.83
Xbar9
2.32
2.35
2.34
2.59
2.66
2.67
1.05
1.14
1.26
1.62
1.92
2.00
0.89
0.55
0.18
0.17
0.95
0.84
0.77
1.86
2.57
1.19
F,h
0.45
0.66
0.31
0.28
0.59
0.82
0.96
0.63
1.23
2.07
0.92
4.42
2.73
0.61
3.15
0.10
0.06
0.91
0.21
1.34
3.84
1.33
F2h
0.00
0.00
0.00
0.00
0.00
0.00
0.04
0.00
0.61
0.33
0.04
2.09
0.25
0.10
1.68
0.00
0.00
0.09
0.01
0.13
0.16
0.00
Ybar1
2.92
2.86
2.53
2.63
2.24
2.15
1.44
1.31
1.49
0.99
0.66
1.39
2.14
0.89
1.22
0.48
0.72
0.23
0.70
1,51
1.63
0.99
GlJ
0.59
0.91
0.35
0.33
0.61
0.71
2.53
0.76
0.29
0.39
4.45
1.29
0.98
2.16
0.52
0.24
0.12
1.25
0.20
0.71
0.34
1.94
G2'
0.00
0.00
0.00
0.00
0.00
0.00
0.28
0.00
0.03
0.01
0.87
0.18
0.03
1.26
0.05
0.02
0.01
0.17
0.01
0.04
0.00
0.01
PIk
1.31
1.59
1.09
1.04
1.51
1.78
1.92
1.56
2.17
2.82
1.88
4.12
3.24
1.53
3.48
0.63
0.50
1.87
0.91
2.27
3.84
2.26
PHYLUM
CHORDATA
ARTHROPODA
2103 -0.55 0.77 2.35 1.76 0.00 1.27 2.94 0.00 2.60
SPECIAL
FATHEAD MINNOW
BLUEGILL
RAINBOW TROUT
FATHEAD MINNOW
BLUEGILL
RAINBOW TROU1
CYPRINIFORMES
PERC1FORMES
SALMON1FORMES
OSTEICHTHYES
OSTEICH1HYES
OS1E1CH1HYES
30
65
88
354
500
480
0.26
0.16
-0.11
-0.30
0.17
0.29
0.95
0.95
1.04
1.01
0,96
0.99
2.63
2.13
2.59
2.77
2.52
2.42
0.19
0.22
0.17
0.45
0.49
0.38
0.00
0.00
0.00
0.00
0.00
0.00
2.77
2.19
2.59
2.49
2.60
2.67
0.21
0.24
0.16
0.44
0.53
0.39
0.00
0.00
0.00
0.00
0.00
0.00
0.85
0.91
0.81
1.31
1.37
1.20
aTaxonomic level at which the extrapolation is made.
bTaxon from which values of the independent variable are drawn.
cTaxon from which values of the dependent variable are drawn.
^Number of points in the regression.
Estimated intercept (a).
fEstimated slope (b).
SMean of X.
^Factors used in calculating the variance of an Individual Y.
1Mean of Y.
^Factors used with the inverse regressions to calculate the
variance of an individual X.
klhe 95X prediction interval on the point XBAR Is YBAR + PI.
-------
63
ORNL-6I51
Table 4.2. Summary of aquatic taxonomic extrapolations
Taxonomic level
n
a
n Weighted
mean 95%
prediction
Interval
Species
Fish
Arthropods
Genera
Fish
Arthropods
Families
F1sh
Arthropods
Amphibians
Orders
Fish
Arthropods
Classes
Chordates
Arthropods
Phyla
8
2
8
2
4
3
1
10
10
0.76
1.10
0.74
0.78
0.97
1.37
1.14
1.35
2.06
3.84
2.26
2.60
aNumber of pairs of taxa at that taxonomic level
-------
ORNL-6251 64
because we are interested 1n the precision of the estimate rather than
the ability of the model to "explain" the data. In addition, the r
values for this regression model are considerably higher than those for
ordinary least squares; therefore they could not be used for comparison
with other results.
Because these extrapolations are made between identical benchmarks
(96-h LCjj0s) determined at a single laboratory, \ was set to 1.
This assumption was tested by pair-wise comparisons of the 95%
confidence intervals reported by Johnson and Finley (1980). Average
ratios of confidence interval widths on LC5Qs for pairs of taxa at
each taxonomic level were all found to be very close to 1.
Table 4.1 can be used to extrapolate between taxon X and taxon Y,
as previously explained (Sect. 4.2.1). Since we are using an
errors-in-variables model, the inverse regression (X from Y) can be
calculated as x = (y - a)/b. Variance for this inverse regression
(Mandel 1983) reduces to var (X|YQ) = 6^ G2(YQ - Y)2, with GI and
6_ provided in the table.
Four special taxonomic extrapolations are presented at the end of
Table 4.1. These are extrapolations between the three most common test
species of fish [fathead minnow, bluegill (Lepomis macrochirus). and
rainbow trout], and both the Order to which they belong and the entire
Class Osteichthyes. The extrapolations are useful for assessments in
which members of an entire higher taxon are to be protected or for
which.an appropriate lower-level extrapolation is not available. This
type of extrapolation also serves to indicate how well these species
serve as representatives for the taxa as a whole. The measure of
-------
65 ORNL-6251
predictive power provided by the prediction intervals for these
equations is a better guide to the selection of test species than
relative sensitivity, importance of the species, or its similarity to
currently used species (Suter and Vaughan 1984). By this criterion,
the three fish species are about equally good representatives, but the
rainbow trout is slightly better.
A variety of acute-chronic extrapolations are presented in
Table 4.3 for different chronic benchmarks and subsets of the data.
The values of \ for these extrapolations are estimated from the
ratios of the mean variances of benchmarks from replicate tests in
Appendix A. The choice of extrapolation depends on the input data and
on the end point desired, that is, HATC vs effects levels, all chronics
vs life-cycle, or specific categories vs all chemicals. Clearly the
extrapolations presented are only a fraction of those that could be
created from different subsets of data.
The first extrapolation in Table 4.3 relates fathead minnow MATCs
to those of all other freshwater Osteichthyes. Although the predicted
Y for this type of extrapolation is meaningless (there is no mean
fish), this relationship can be used to estimate the risk that the MATC
(for some species of fish) will be exceeded, given a fathead minnow
MATC and an expected environmental concentration. The prediction
interval for this extrapolation is similar to that for the analogous
extrapolation in Table 4.1 between fathead minnow LC5Qs and those for
all other Osteichthyes; however, the interval is slightly smaller,
possibly due to the smaller array of species that have been used in
chronic tests. One might expect that there would be greater variance
-------
Table 4.3. Acute-Chronic Extrapolations. Units are 1og(yg/L).
O
70
OBSa Xb
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
IB
FM MATC
FM MATC
FM MATC
LCso
J\t
LCtn
J\J
LCso
J V
LCso
J V
LCso
J V
LCso
Jw
LCso
J V
LC5o
LCso
LCso
J V
LCso
J V
LCso
3U
LCso
J v
LCso
JU
LCso
Yc
All Fish MATC
Salmonlformes MATC
Perclformes MATC
MATC
MATC
MATC
MATC
EC2s Mortl
EC25 Mort2
EC2s Mort2
EC2s Hatch
EC?5 Eggs
EC25 Weight
EC25 Weight
EC25 Wt of Juveniles/Egg
EC2j Wt of Juveniles/Egg
Daphnla MATC
Daphnla MATC
Cond1t1ond
All
All
All
Type - LC
All
Class - N
Class = M
Type = LC
All
Species = FM TYPE = ELS
All
Type - LC
All
Species * FM TYPE * ELS
All
Species - FM TYPE = ELS
All
Class = M
Lamda6
1.0
1.0
1.0
1.5
1.5
1.5
1.5
1.0
1.0
1.0
1.0
1.0
.0
.0
.0
.0
.3
.3
Nf
52
27
8
55
98
23
25
15
30
16
13
26
37
24
14
11
57
27
Icept9S1opeh
-0.04 0.79
-0.10 0.80
-0.26 0.93
-1.16 0.90
-1.51 1.07
-0.42 0.90
-0.70 0.73
-1.46 0.96
-1.69
-2.33
-2.24
-2.43
-2.03
-1.72
-1.88
-2.00
-1.30
.21
.33
.34
.19
.24
.18
.10
.16
.11
-1.08 0.96
Xbar1
1.80
1.87
1.97
2.75
3.13
3.87
3.25
2.71
2.98
3.35
3.40
2.83
3.40
3.70
3.20
3.18
2.73
2.44
FlJ
0.33
0.39
0.45
0.51
0.59
0.09
0.37
0.53
1.10
1.52
1.46
0.75
0.77
0.84
1.49
1.60
0.48
0.63
F2J
0.01
0.02
0.11
0.01
0.00
0.00
0.02
0.03
0.03
0.06
0.06
0.04
0.01
0.02
0.04
0.05
0.01
0.02
Ybark
1.37
1.38
1.56
1.31
1.85
3.05
1.68
1.14
1.91
2.12
2.33
0.94
2.18
2.66
1.66
1.68
1.72
1.26
PI1
1.13
1.22
1.31
1.40
1.50
0.59
1.19
1.43
2.06
2.42
2.37
1.70
1.72
1.79
2.39
2.48
1.35
1.56
a*
ro
in
LCso values for the species and chemical
aOBS « Observation number.
blndependent variable. FM MATC - MATC values for fathead minnows.
corresponding to those of the dependent variable.
Dependent variable. All F1sh MATC - values for all freshwater Hsh other than fathead minnows. Salmonlformes
MATC - values for members of the order SalmonUormes. Perdformes MATC «= values for members of the order Perclformes.
MATC - Values for fish. EC2s Mortl = a concentration estimated to cause a 25X Increase 1n mortality of parental fish.
EC?s Mort2 = a concentration estimated to cause a 25X Increase In mortality of larval fish. £035 Hatch - a
concentration estimated to cause a 25X decrease 1n normal hatches of fish eggs. EC?; Eggs - a concentration estimated
to cause a 25X decrease In the number of eggs produced per female fish. EC2S Weight - a concentration estimated to cause
a 25% decrease 1n the weight of fish at the end of the larval stage. Daphnla MATC - values for members of the genus Daphnli
dsubset of the data used In the extrapolation. All = all pairs of X and Y points are used. Type = types of tests
Included: LC •= life cycle or partial life cycle, ELS » early life stage. Species = Species of test organism: FM « fathead
minnow. Class •= Chemical class: M «• metal, N = narcotic.
eRat1o of the variances of the Y and X variables.
^Number of points 1n the regression.
9Est1mated Intercept (a).
"Estimated slope (b).
^Mean of X.
JFactors used In calculating the variance of an Individual Y.
kMean of Y.
Uhe 95X prediction Interval at the point XBAR 1s YBAR * PI.
-------
67 ORNL-6251
among species in chronic toxicity than in acute toxicity because of the
greater variety of responses potentially involved, particularly in
life-cycle tests. However, this analysis does not support that idea,
and the substitution of larval mortality or growth for life-cycle
responses in chronic tests suggests that acute and threshold chronic
responses may be equally simple; therefore the true variances may be
equal. Extrapolations 2 and 3 are analogous but extrapolate to
specific orders. There is no gain in precision by this increased
specificity. All extrapolations have negative intercepts and slopes
less than 1, indicating that fathead minnows are a little less
sensitive than most other fish in chronic tests.
The next four extrapolations in Table 4.3 predict MATCs from LC5Qs
for the same species. Extrapolations 4 and 5 include all species and
chemical types, but 4 includes only life-cycle tests (which are
somewhat more reliable than early life-stage tests), whereas 5 includes
all MATCs for which there is a corresponding LC5Q. Extrapolations 6
and 7 include all species and test types but are limited to narcotics
and metals, respectively. The chemicals identified as narcotics belong
to the classes of chemicals identified as such by Veith et al. (1983)
and Call et al. (1985). The particularly narrow prediction interval
for this extrapolation reflects the precision of the quantitative
structure-activity relationships (QSARs) for narcotics presented in
those reports, thus reinforcing the idea that the action of these
chemicals is highly predictable. In fact, the fathead minnow LC50s
and MATCs generated by the QSARs in these reports, or by any other QSAR
with precision as good as that of replicate tests, could be used in the
-------
ORNL-6251 68
extrapolations between fathead minnow benchmarks and those for other
taxa, 1f there 1s reasonable certainty that the chemical in question
belongs to the correct category. QSARs can be more precise than
Individual tests because they summarize large amounts of Information,
and because chemical measurements are generally much more precise than
biological tests (Craig and Ensleln 1981).
The next nine extrapolations (8-16) constitute an examination of
the predictability of particular levels of chronic effects (LC?5s and
EC25s) from acute LC5Qs for the same species. Mortl is mortality
of parental fish; Mort2 1s mortality from hatching to the early juvenile
stage; Hatch is the proportion of eggs failing to successfully hatch;
Eggs is the reduction in the number of eggs produced per female relative
to controls; Weight is the proportional reduction 1n the Average weight
of early juveniles relative to controls; and Wt of Juveniles/Egg is the
proportional reduction in the weight of early juveniles per initial
egg. We used a 25X reduction in performance in this exercise largely
as a matter of convenience in dealing with this data set rather than as
a proposed assessment end point, but 25X could be defended as a level
of effect that would be barely detectable in the field. These
extrapolations are more Imprecise than those from acute LC5Qs to
MATCs. This result 1s surprising since we expected that an acute
median lethal concentration would be a better predictor of a chronic
quartile lethal concentration than of a hypothesls-testlng-derlved
benchmark that 1s not indicative of any particular type or level of
effect. Limitation of the data set to only early life-stage tests with
fathead minnows does not reduce the uncertainty. The most obvious
-------
69 ORNL-6251
explanation is that the chronic LC0,s and ECocs contain much
CD £D
extraneous variance because of the poor data from which they were
derived. Nearly all of the chronic concentration-response data would
fail to pass conventional requirements for calculating acute LC5Qs
and EC5Qs because of the lack of partial kills, lack of effects
levels of 50% or greater, or high control mortality. In addition, many
of the chronic results show apparent hormesis at low concentrations,
which complicates curve fitting.
The last two extrapolations in Table 4.3 are for predicting
life-cycle HATCs for Daphnia from 48-h LC5Qs, first for all chemicals
and then for metals only. These extrapolations have about the same
uncertainty as the corresponding LC5Q to MATC extrapolations for fish
(Nos. 4 and 7 in Table 4.3). These LC5Q to HATC extrapolations for
fish and Daphnia have about the same average level of uncertainty as
the extrapolations of LC5Qs between families Of arthropods or orders
of fish (Table 4.2).
One potential source of bias in these extrapolations is the fact
that investigators will sometimes report results as being greater than
or less than some value because the highest or lowest concentration
tested was not high or low enough to allow the benchmark to be
determined. Since the true value of the benchmark is unknown, these
results cannot be used in the extrapolations. However, since these are
likely to be chemicals with extreme application factors (MATC/LC5Q
values), they would presumably increase the variance in the
extrapolations if their true values were known and included. In
addition, there may be a bias in the centroids because there are more
-------
ORNL-6251 70
< than > values for MATCs in the data set (17 vs. 6, - App. A).
However, this does not appear to be a significant problem since all but
one of the > or < estimates of the MATC fall within the 95* PI for
extrapolation 5, Table 4.3. In addition, an examination of these
studies indicates that the failure to show a statistically significant
effect at the highest concentration tested is due primarily to high
variance in the test data rather than extremely low chronic
toxicities. These observations suggest that the true application
factors for these chemicals may not be extremely high or low.
4.3.3 A Demonstration
As an example of the use of these extrapolations, consider the
estimation of the risk of exceeding the threshold for chronic effects
on brook trout beginning with a rainbow trout LC5Q of 5300 vg/L for
the chemical of concern. Substituting the log of that LC5Q into the
Salmo-Salvelinus extrapolation (Table 4.1) gives a log brook trout
LC5Q of 3.77; using Eq. (4.11), the variance is 0.14 (the second term
- o
of the variance equation, F2(XQ - X) , is trivial in this case).
Substituting 3.77 into extrapolation 4, (Table 4.3), g^lves an estimate
of 2.22 for the log brook trout life-cycle MATC, with a variance for
this extrapolation of 0.53. Using Eq. (4-12), the total variance for
the double extrapolation is 0.14 + (0.81 x 0.53) = 0.57.
If the log of the expected environmental concentration (EEC) is
2.0 with a variance of 0.5, then the probability that a realization of
the brook trout MATC is less than a realization of the EEC is determined
from Eq. (4.4), by calculating
(2.0 - 2.22) / (0.57 + 0.5)1/2 * -0.21 .
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71 ORNL-6251
The cumulative probability for this Z value (obtained from a Z table)
is 0.42. Thus, the risk that the threshold for chronic effects on
brook trout would be exceeded is 0.42, or we are 58X certain that
chronic effects would not occur.
4.4 RISK WITHOUT REGRESSION
In a few cases the assessor will have in hand the benchmark that
corresponds to his assessment end point; for example, he is interested in
chronic effects on rainbow trout and he has a rainbow trout MATC for the
chemical of concern. In that case uncertainty (as a result of the
variance between replicate tests) must be accounted for, because the
assessor will be uncertain as to the representativeness of the sample
of fish used in the test and the biases introduced by variation in
procedures and conditions. This variance is not accounted for separately
when regressions are used for extrapolation, because it contributes to
the total uncertainty in the regression estimates.
Pooled variances for particular test types and taxa are presented in
Table 4.4. These are averages of the variances of replicate benchmark
values, weighted by the degrees of freedom for each set of replicate
tests. The sets are drawn from Appendix A and the EPA ambient water
quality criteria support documents. Since we have determined the
variances to be homogeneous, this pooled variance can be applied to
unreplicated data. If we assume that an Individually measured
toxicological benchmark is the best estimate of the mean of such
benchmarks, then that benchmark and the appropriate pooled variance can
be used to estimate the risk that the benchmark will be exceeded by a
particular distribution of environmental concentrations (Sect. 4.2).
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ORNL-6251
Table 4.4.
72
Pooled variances of log 1059, £€50, and MATC
values from replicate tests
Taxon Benchmark
Osteichthyes LC5Q
MATC
Daphnia ECso
MATC
na
37/333
15/66
11/81
10/33
Pooled
variance^
0.018
0.22
0.15
0.17
aNumber of species-chemical combinations/total number of tests.
bMean variance of log values weighted by the degrees of freedom.
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73 ORNL-6251
If 1n our example the rainbow trout MATC for the chemical of
interest is 20 vg/L, then the mean and variance of the log MATC are
1.3 (log 20) and 0.22, respectively. If the environmental concentration
is known with certainty to be 10 vg/L. then the cumulative Z value
calculated from Eq. (4.7) is -0.64; the probability (risk) that this
concentration is higher than the MATC is 0.26. In other words, we are
74X certain that the environmental concentration will not exceed the
rainbow trout MATC.
We have limited ourselves to empirically derived estimates of
variance in this section, thereby implicitly assuming that the variance
in response between the laboratory and the field is no greater than the
variance between one laboratory and the next. The assessor who does
not believe that the toxicological benchmark adequately represents his
assessment end point may readily incorporate that subjective uncertainty
by adding an increment of variance before calculating the risk. It is
important to clearly document such judgments, including who made them
and on what basis, and to separate the judgment from the calculation of
end point values and risks so as to avoid the temptation to fiddle with
the conclusion.
4.5 COMPARISON OF METHODS
We examine here the efficacy of AEE by comparing its ability to
predict the MATC for particular fish species from a fathead minnow
LC50, with the ability of an untransformed fathead minnow MATC, a
fathead minnow MATC with an application factor, and LC5Qs with
acute/chronic correction factors to predict the MATC for that species.
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ORNL-6251 74
Although the double extrapolation used as an example of AEE Is not
Intended to be used if a measured MATC 1s available (one would use
extrapolations from the fathead minnow MATC to MATCs for the taxa of
interest), 1t does provide an Instructive comparison of the predictive
power of AEE using a double extrapolation to that of the quotient
method and the quotient method with factors.
The results of this comparison are presented 1n Table 4.5. All of
the numbers 1n the table are derived from data in Appendix A. The
measured fathead minnow MATC 1s 1n error by at least a factor of 2 in
71% of the cases and by a factor of 10 in 10X of the cases. The
application factor MATC [(true LC5Q/FM LC5Q) x FM MATC] 1s in error
by a factor of 2 in 57X of the cases and by a factor of 10 in 19X of
the cases. The extrapolation MATC is in error by a factor of 2 in 71X
of the cases and by a factor of 10 in 19X of the cases. In pair-wise
comparisons of the methods, the extrapolated MATC was closer to the
true MATC than the fathead minnow MATC in 44X of the cases. The
extrapolation MATC was closer than the application factor MATC in 43X
of the cases. Thus, the use of AEE with acute fathead minnow data is
approximately as accurate in predicting the chronic toxicity to a
particular species (other than the fathead minnow) as is fathead minnow
chronic data, with or without an application factor.
The use of LC5Qs with the most common acute/chronic correction
factors (1/20 and 1/100) gives somewhat worse results. When these
correction factors are applied to the fathead minnow LC5Qs, the 1/20
factor fails to predict the true MATC within a factor of 2 in BOX of
the cases and within a factor of 10 in 39X of the cases; the 1/100
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75
ORNL-6251
Table 4.5. Comparison of methods for estimating the MATC for a species other
than fathead minnow (all values are vg/L)
Chemical
Arsenic
Atrazine
Cadmium
Chromium
Copper
Hexachloro-
cyclohexane
Halathion
Methyl mercury
Toxaphene
Zinc
Species
Flagfish
Bluegill
Brook trout
Bluegill
Brook trout
Flagfish
Walleye
Channel catfish
White sucker
Small mouth bass
Northern pike
Lake trout
Coho salmon
Brown trout
Brook trout
Rainbow trout
Bluegill
Channel catfish
Lake trout
Northern pike
White sucker
Bluegil 1
Bluntnose minnow
Brook trout
Brown trout
Lake trout
Northern pike
White sucker
Channel catfish
walleye
Rainbow trout
Bluegill
Brook trout
Bluegill
Flagfish
Brook trout
Flagfish
Channel catfish
Brook trout
Rainbow trout
Flagfish
FM
"so*
14,200
15,000
15.000
6000
6000
6000
6000
6000
6000
6000
6000
6000
6000
6000
36.900
36.900
36.900
36.900
36.900
36.900
36,900
253
253
253
253
253
253
253
253
253
253
69
69
10,500
10,500
65
65
7.2
2349
2349
2349
True
<
14.400
6700
4900
21100
2500
59,000
69.000
1100
230
100
80
30
26
110
349
75
240
16.5
2000
430
1500
True
MATCC
2962
218
BB
50
2.4
5.3
15
14
7.1
7.4
7.4
7.4
7.2
6.7
265
265
765
214
143
720
395
29
8.B
13
32
31
60
21
15
17
20
10.7
12.1
5.2
9.7
0.52
0.2
0.20
652
191
36
FM
MATCd
3026
4309
4309
46
46"
469
469
469
469
469
469
469
469
469
19879
19879
19879
19879
1987h
19879
19879
25
259
25
25
25
259
25
25
25
25
14.6
14.6
341h
341h
0.099
0.099
0.0379
889
889
889
AF
MATC*
3251
192
140
1629
199
31779
3715h
1099
239
10
7.99
6.3
5.59
3.6
11.3
0.109
0.33
0.0859
75"
16"
56
Extrapolated
MATCf
62.7"
306
3389
56
54 h
239
569
1129
138"
569
549
549
549
549
255
255
214
389
255
2559
498
5.69
14.7
3.649
3.649
3.649
3.64h
14.7
12.7
5.69
3.649
1.02h
0.44"
210h
499
0.41
0.879
0.38
24h
249
149
aHeasured fathead minnow LCjg: only LCjQS from the same study as the FM MA1C determination
are used.
Measured LC^gs for the listed species; only LCjps from the same study as the HATC
determination are used.
cThe measured MA1C for the listed species. Life-cycle MATCs are preferred over early
life-stage MAKs, otherwise the geometric mean of replicate MAlCs is used.
dA measured MA1C for fathead minnows; replicates are treated as in note (c).
e(True LC50/m LC5Q) * ™ HATC.
^HAIC calculated from a fathead minnow LC5Q using taxonomic and acute/chronic
extrapolations.
9[stimates that differ from the true MA1C by a factor of 2 or greater.
"Estimates that differ from the true MA1C by a factor of 10 or greater.
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ORNL-6251 76
factor fails to predict within a factor of 2 in 76% of cases and within
a factor of 10 in 29% of cases. When applied to the true LC5Q, the
1/20 factor fails to predict the true MATC within a factor of 2 in 81%
of the cases and within a factor of 10 in 24% of the cases; the 1/100
factor fails to predict within a factor of 2 in 86% of cases and within
a factor of 10 in 38% of cases. These factors and LC^s are poorer
predictors of MATCs than the methods previously discussed, and neither
correction factor does significantly better than the other in this
exercise.
AEE has the advantage over the other methods of indicating how
inaccurate it is likely to be. In this exercise the 95% prediction
intervals (Pis) for the extrapolated MATCs includes the true MATC in
all but one of the 41 cases; therefore, using the lower 95% Pis as
standards would have prevented exceeding the true MATC in 98% of the
cases. This result suggests the reasonableness of the variance terms
used in this version of the method.
While this exercise does not constitute a validation of AEE, it
does indicate that it is a good predictive tool relative to methods
that are currently used. It also demonstrates that all of the methods
have large associated errors; therefore, it is important to explicitly
account for uncertainty in predictions, as is done with AEE.
4.6 DISCUSSION
The chief advantage of the analysis of extrapolation error method
is that it provides an objective, quantitative estimate of risk without
departing from the generally accepted practice of defining assessment
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77 ORNL-6251
end points in terms of toxicological benchmarks. Compared with the
quotient method, the extrapolation error method has the advantages of
making assumptions concerning the relationship of the data and the
end point explicit, treating the relationship as a set of quantitative
extrapolations, estimating the uncertainty in the relationship, and
producing an estimate of risk based on estimates of the end point and
of the associated uncertainty. If the data available for an assessment
are not from the needed test type and species, the quotient method
requires that one use the data available and pretend that they are
appropriate, use correction factors without considering the associated
uncertainty, or aggregate the uncertainty factors with the correction
factors and treat the assessment deterministically. Compared with
population and ecosystem models (Sects. 5 and 6), AEE has the advantage
of using as its end point the toxicological benchmarks that constitute
the end points for all existing regulatory assessment schemes and
environmental quality criteria.
The limitations of AEE are that the method (1) is limited to
end points that can correspond to standard toxicological benchmarks;
consequently, unless subjective corrections and uncertainties are used,
it cannot address effects on entities or processes that occur on
spatial or temporal scales beyond the range of toxicity testing; (2) is
computationally difficult relative to the quotient method and
conceptually opaque to decision-makers who lack statistical training;
and (3) assumes that existing data sets are representative of future
toxicity data. The problem of the representativeness of existing data
sets is characteristic of any method that attempts to extrapolate
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ORNL-6251 78
beyond the existing data. However, it is important to pay close
attention to the potential biases in available data sets and to be
aware of which sources of variability (e.g., water chemistry,
interlaboratory variability, or different strains of the test species)
are represented in the data set and which are implicit in the
assessment (e.g., should data from laboratories of unknown reliability
be used, and should the results of the assessment apply to a variety of
sites). In some cases, the extrapolations can be inappropriately
precise as the result of using a highly standardized data set. For
example, studies of the acute effects of narcotic chemicals in Lake
Superior water on the Duluth population of fathead minnows (Veith et
al. 1983) are used in QSARs that generate predicted LC5Qs that are
more precise than replicate tests in different laboratories using
different waters and fish populations. More often, there will be
sources of variance in the data sets that are extraneous to the
assessment but cannot be avoided because a more appropriate data set 1s
not available. In those cases the extraneous variance is simply part
of the uncertainty associated with performing assessments with limited
knowledge, which is similar to the uncertainty concerning future
emission rates or dilution volumes.
While the AEE method was developed to provide estimates of risk,
it has a variety of other potential uses. The regression and error
propagation portions can be used to estimate toxic effects for
population and ecosystem models and to generate the parameter
distributions used in Monte Carlo simulations. This use is described
in Sect. 5 and 6. Another potential use is.in designing testing
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79 ORNL-G251
programs. Decisions about the need for additional testing of a
chemical could be made on the basis of the expected reduction in the
total uncertainty concerning the true value of the end point, the
expected reduction in risk, or the probability that the test will cause
a change in a regulatory decision. In addition to making decisions for
testing Individual chemicals, AEE could be used to elucidate the
Implications of the decision rules 1n tiered testing schemes or to
devise new decision rules.
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ORNL-6251 80
REFERENCES (SECTION 4)
Call, 0. J., L. T. Brook, M. L. Knuth, S. H. Poirler, and M. D. Hoglund.
1985. Fish subchronic toxlclty prediction model for Industrial
organic chemicals that produce narcosis. Environ. Toxicol. Chem.
4:335-342.
Craig, P. N., and K. Enslein. 1981. Structure-activity in hazard
assessment, pp. 389-420. IN Hazard Assessment of Chemicals,
Vol 1. Academic Press, N.Y.
Johnson, W. W., and M. T. Flnley. 1980. Handbook of acute toxicity of
chemicals to fish and aquatic invertebrates. Resource Publication
137. U.S. F1sh and Wildlife Service, Washington, D.C.
LeBlanc, 6. A. 1984. Interspecies relationships in acute toxicity of
chemicals to aquatic organisms. Environ. Toxicol. Chem. 3:47-60.
Mandel, J. 1984. Fitting straight lines when both variables are
subject to error. 0. Qua!. Techno!. 16:1-14.
Mayer, F. L., Jr. and H. R. Ellersieck (in press). Manual of acute
toxicity: Interpretation and data base for 410 chemicals and
66 species of freshwater organisms. U.S. Fish and Wildlife
Service/Resource Publication, Washington, O.C.
Suter, G. W., II, D. S. Vaughan, and R. H. Gardner. 1983. Risk
assessment by analysis of extrapolation error: A demonstration
for effects of pollutants on fish. Environ. Toxicol. Chem.
2:369-378.
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81 ORNL-6251
Suter, G. W., II, and D. S. Vaughan. 1984. Extrapolation of
ecotoxicity data: Choosing tests to suit the assessment. IN
K. E. Cowser (ed.), Synthetic Fuel Technologies, Results of Health
and Environmental Studies. Butterworth Publishers, Boston.
Tucker, R. K., and H. A. Heagele. 1971. Comparative acute oral
toxicity of pesticides to six species of birds. Toxicol. Appl.
Pharmacol. 11:57-65.
Veith, G. D., D. J. Call, and L. T. Brook. 1983. Structure-toxicity
relationships for the fathead minnow, Pimephales promelas:
Narcotic industrial chemicals. Can. J.. Fish. Aquat. Sci.
40:743-748.
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ORNL-6251 82
5. EXTRAPOLATION OF POPULATION RESPONSES
L. W. Barnthouse, G. W. Suter II, A. E. Rosen,
and J. 3. Beauchamp
As noted 1n Section 1 of this report, the end points of ultimate
Interest in ecological risk assessment are effects of long-term
exposures on the persistence, abundance, and/or production of
populations. In contrast, the data available for assessing ecological
risks of toxic contaminants are nearly always restricted to effects of
contaminants on individual organisms. If assessments of ecological
effects of toxic contaminants are ever to reach the same level of
sophistication as assessments of nontoxicologlcal stresses, such as
fishing and power plants, it will be necessary to develop analytical
techniques for extrapolating from Individual-level responses to
population-level responses.
Many of the components necessary for this task already exist.
Section 4.1 of this report showed that statistical relationships
(1) among 96-h LC5Qs for different fish taxa and (2) between 96-h
LC.QS and maximum acceptable toxicant concentrations (MATCs) can be
used to extrapolate chronic effects thresholds for untested fish
species from acute LC5Qs for tested species. The literature on fish
population modeling contains a variety of techniques for estimating
population-level responses to age-specific changes in mortality,
fecundity, and growth.
In this section we describe a method of generating life-stage-
specific concentration-response functions for either tested or
untested fish species. We demonstrate the linking of the estimated
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83 ORNL-6251
concentration-response functions, together with their associated
uncertainties, to simple fish population models that have proved useful
in other problems involving anthropogenic stresses on fish populations.
Our objectives are, first, to quantify the uncertainty resulting from
extrapolation from bioassay results to population responses, and
second, to express effects of toxic contaminants in common units with
effects of other anthropogenic stresses on fish populations.
5.1 FORMULATION OF CONCENTRATION-RESPONSE MODEL
The concentration-response function used in this study is the
logistic model
, (5.1)
where
P = fractional response of the exposed .population,
X = exposure concentration, and
a.fl = fitted parameters with no biological interpretation.
When fitted to concentration-response data, the logistic function has a
sigmoid shape similar to the probit model. Because ecological risk
assessment does not involve extrapolation to extremely low doses, 1t
does not matter which model is used. The logistic model has convenient
properties that can be seen by reformulating it as
Xp = [ln[P/(l - P)] - a]/3 , (5.2)
where
Xp = concentration producing a fractional response equal to P.
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ORNL-6251 84
If a and 0 are specified, then X_ can be directly calculated
from Eq. (5.2). Alternatively, 1f Xp and 3 are specified, then a
can be calculated from
a = 1n[P/(l - P) - 3Xp] . (5.3)
In other words, the complete concentration-response function can be
obtained by specifying either a and 3 or 0 and the concentration
associated with a single response level (e.g., the LC25). The
parameter 3 specifies the curvature of the logistic function and 1s
independent of the position of the curve on the concentration axis. If
two logistic functions have different LCp.s but the same curvature,
their 3 parameters will be equal.
If a chronic concentration-response data set is available for a
species and contaminant of interest, then a logistic
concentration-response function and associated confidence bands can
be obtained by fitting the logistic model to the data. If, however,
directly applicable data are not available, a function and confidence
bands can be obtained using extrapolated values of 3 and LC.,..
The following subsections describe methods for calculating
concentration-response functions and confidence bands directly
from data and by extrapolation.
5.2 FITTING THE LOGISTIC MODEL TO CONCENTRATION-RESPONSE DATA
Concentration-response data sets can be fitted to Eq. (5.1) using
nonlinear least squares regression. This section describes the
procedure for fitting chronic concentration-response data sets from
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85 ORNL-6251
whole life cycle experiments to the logistic model. Although a variety
of test end points can be used (e.g., growth or fecundity), only the
method used to model mortality is described here. The data required
are (1) the number of replicates tested at each concentration (including
the controls), (2) the number of organisms in each replicate, and
(3) the number of organisms dying 1n each replicate (Including the
controls). As in the extrapolation models described in Section 4, test
concentrations are entered as log1Q(concentration in vg/L) so that
the units represent orders of magnitudes of concentrations. The
fraction of organisms dying in each replicate is corrected for control
mortality using Abbott's formula (Abbott 1925), as described in
Section 4. We use the SAS procedure NLIN to produce estimates of a
and 3 and a variance-covariance matrix for a and 0.
Uncertainty concerning the shape and position of the
concentration-response function, as reflected in the variances and
covariances of a and B, can be represented graphically as a
confidence band surrounding the fitted function, as illustrated in
Fig. 5.1. Brand et al. (1973) described a procedure for calculating
confidence band functions for the logistic model from the elements of
the variance-covariance matrix. Alternatively, confidence bands can be
calculated numerically by iterative random sampling (I.e., Monte Carlo
simulation) from the bivariate normal distribution defined by the
variance-covarlance matrix. Published data from full life cycle tests
for fish are commonly broken out by life stage (e.g., eggs, larvae, and
juveniles). To perform a population-level assessment using these data,
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ORNL-6251
86
ORNL-DWG 83-12457
LLJ
CO
CO
ill
CC
LU
O
CC
LU
Q.
UNCERTAINTY
BAND
CONCENTRATION
Fig. 5.1. Uncertainty band for the logistic model fitted to
concentration-response data. For any contaminant
concentration, there is a 90X probability that the fraction of
organisms responding will lie within the shaded region.
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87 ORNL-6251
concentration-response curves must be calculated separately for each
life stage and then combined. We use Monte Carlo simulation for
analysis of these data sets.
5.3 EXTRAPOLATION OF CONCENTRATION-RESPONSE FUNCTIONS AND CONFIDENCE
BANDS FOR UNTESTED SPECIES
Because full life cycle concentration-response data are rarely
available for species-contaminant combinations of Interest 1n risk
assessments, we developed a method for extrapolating logistic functions
and confidence bands using data sets presented 1n Appendix B. We used
data sets for mortality to three life stages (eggs, larvae, juveniles)
that together encompass the fish life cycle from egg to first
reproduction. The data were screened, and sets for which (1) mean
control mortality was 30X or larger or (2) the range of test
concentrations did not span the LC25 were deleted.
5.3.1 Extrapolation of 3 and
The chronic LCOC, rather than the LCer., was chosen as a
lb DU
benchmark because, in the majority of available data sets, the range of
concentrations used (usually 5-7 values per experiment, excluding
controls) did not span the LC5Q. The logistic model was fitted to
the data sets that satisfied the exclusion criteria using the procedure
described in Section 5.1. Data sets for which confidence intervals for
the fitted 3 values included zero were excluded from further
analysis. When the fitted P values for the remaining 77 data sets
were examined, they were found to fit a lognormal distribution
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ORNL-6251 88
with a median of 6.08, a 5th percentile of 1.87, and a 95th percentile
of 16.43. No significant difference was found between the distributions
of (3's for the three life stages, and no correlation was found
between the f3's and the LC?5s.
Equations for estimating chronic LC?5s (with associated
confidence intervals) from acute LC5Qs were derived using the
procedure described in Section 4. Separate equations were developed
for each of the three life stages represented 1n the chronic
concentration-response data sets.
5.3.2 Calculation and Verification of Synthetic
Concentration-Response Functions
Given extrapolated estimates of p (B*) and LC25 (LC25*),
an extrapolated estimate of a (a*) can be obtained from
a* - ln(l/3) - B*LC2s* . (5.4)
When substituted into Eq. (5.1), the extrapolated values of a* and
B* permit the calculation of the expected response associated with any
contaminant concentration. Uncertainty concerning the expected response
is quantified, using Monte Carlo simulation, from (1) the observed
distribution of fitted values of 3 and (2) the extrapolated error
around the estimated LC?5 (Sect. 4). Each distribution is sampled
1000 times, and the randomly chosen paired values of 3* and LC« * are
used to calculate a statistical distribution for the response associated
with a given contaminant concentration. When this procedure 1s repeated
for a range of concentrations, the plotted values form a confidence band
around the extrapolated concentration-response function (Fig. 5.1).
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89 ORNL-6251
Of the 77 chronic concentration-response data sets used in this
analysis, corresponding 96-h LC—s (i.e., same species, contaminant,
and experimental conditions) were available for 60. We used this subset
of 60 data sets to verify the extrapolation method. First, one data
set was arbitrarily deleted from the subset. A distribution of (3's
and a set of acute-chronic regression equations were then calculated
using the remaining 59 sets. A synthetic concentration-response
function and 90X confidence bands for the contaminant-species life-stage
combination represented in the deleted data set were then extrapolated
from the appropriate acute LC5Q. Finally, the logistic model was
fitted to the deleted data set and overlaid on the extrapolated
uncertainty band. An example is presented in Fig. 5.2.
This process was repeated for each of the 60 data sets in the
verification subset. The number of times the empirically estimated
LC.QS, LCpgS, and LC5Qs fell outside the extrapolated 90% confidence
bands were counted. There were seven "misses" at each of the three
response levels. These compare favorably with the expected number, six.
5.4 CALCULATING REDUCTION IN REPRODUCTIVE POTENTIAL
The population-level variable chosen as a response variable is the
reproductive potential of a female recruit, defined here as a 1-year-old
fish. The reproductive potential of a female recruit is defined as
the expected contribution of that female to the next generation of
recruits, taking into account her annual probability of survival at
different ages; her expected fecundity at different ages, provided that
-------
ORNL-DWG 83H6298
Q
LJ
o
<
cc
Lu
o
TO
O»
IN)
(A
o
6 8 10
CONCENTRATION (/xg/L)
12
14
F1g. 5.2.
Example of the procedure used to verify the synthetic concentration-response modeling
technique. A logistic model fitted to an actual concentration-response data set 1s over-
laid on the uncertainty band of a synthetic concentration-response model constructed for
the same chemical, species, and life stage. When many such comparisons are made, 90% of
the fitted functions should fall within the uncertainty bands of the synthetic functions.
-------
91 ORNL-6251
she survives; the probability that a spawned egg will hatch; and the
probability that a newly hatched fish will survive to age 1. The
ability of a fish population to sustain exploitation (harvesting) by
man and to persist in a variable environment is directly related to the
reproductive potential of female fish.
Models based on reproductive potential have been used to assess
the effects of fishing and of power plant cooling systems on the risk
of catastrophic declines in fish populations (Goodyear 1977). Toxic
contaminants, like fishing, reduce the reproductive potential of a
female recruit. Mortality rates for fish exposed to toxic contaminants
can be translated into changes in reproductive potential, thus allowing
comparisons between the population-level consequences of fishing and
toxic contaminants. The reproductive potential of a 1-year-old female
recruit is given by:
n
P = S0 I SiEiM1 , (5.5)
where
Sn = probability of survival of eggs from spawning to
age 1 year,
S. = probability of survival of female fish from age 1
to age i,
E. = average fecundity per mature female at age i,
M. = fraction of age i females that are sexually mature,
n = number of age classes in the population.
-------
ORNL-6251 92
Toxic contaminants may reduce the survival of fish at all ages. The
reproductive potential of a female recruit exposed to a toxic
contaminant throughout her life cycle is given by
PS = S0O-mo)lSi(l-mr)i-1HiE; (5-6)
where
m0 * probability of contaminant-induced mortality during
the first year of life, and
m = probability of contaminant-induced mortality for
1-year-old and older fish, assumed equal for all
age classes.
The fractional reduction in reproductive potential because of toxic
contaminants (R ) is given by
RS = (P - PS)/P . (5.7)
Note that natural young-of-the-year survival (SQ), for which reliable
estimates are almost never available, cancels out of Eq. (5.7) and is
not required for the assessment.
5.5 APPLICATION OF THE MODEL TO RAINBOW TROUT AND LARGEHOUTH BASS
The rainbow trout (Salmo galrdneri) and largemouth bass
(Micropterus salmoides) were chosen as examples for illustrating the
above extrapolation techniques. Tables 5.1 and 5.2 present life
tables for representative populations of these species. The
life-stage-specific mortality estimates obtained from the
-------
93 ORNL-6251
Table 5.1. Life table for rainbow trout (Salmo qairdneri). modified
Age
1
2
3
4
5
6
from Boreman
M*
0.151
0.234
0.995
1.00
1.00
1.00
(1978).
Eb
207
850
1787
2734
4685
5424
Si C
1.0
0.31
0.090
0.013
0.0020
0.00030
3Proport1on of mature females.
Fecundity per mature female.
Cumulative probability of survival from age 1 to age i.
-------
ORNL-6251 94
Table 5.2. Life table for largemouth bass (Micropterus
salmoides). modified from Coomer (1976).
Age
1
2
3
4
5
6
7
8
M*
0.0
0.17
1.00
1.00
1.00
1.00
1.00
1.00
Eb
0
5,243
10,830
16,190
24,500
29,973
36,287
42,600
Si c
1.0
0.52
0.19
0.085
0.039
0.018
0.0073
0.0029
Proportion of mature females.
Fecundity per mature female
Cumulative probability of survival from age 1 to age i.
-------
95 ORNL-6251
concentration-response model are translated Into age-specific survival
probabilities using the following equation:
(1 - mo) • (1 - me)(l - m-|)(l - mj) (5.8)
where
m = probability of mortality for the egg stage,
m, * probability of mortality for the larval stage, and
m. = probability of mortality for post-larval stages.
In the chronic toxicity tests, m. applies roughly to the period
from the end of the larval stage to the age of first reproduction. The
total duration of the egg and larval life stages is only a few months,
whereas juvenile females in both example populations do not reach
sexual maturity until two years of age. In theory, therefore, some
fraction of juvenile mortality should be allocated to older age
classes. However, if mortality due to contaminants is restricted to
prereproductive fish, then the allocation of a given fractional
mortality (1 - m.) among prereproductive age classes does not affect
the predicted population response. It is common practice in life-cycle
toxicity tests to sacrifice the test fish after one spawning; thus,
there is normally no information on the effects of toxic contaminants
on adult age classes. It can be assumed either that (1) adults suffer
the same mortality as juvenile fish; or (2) all susceptible fish are
killed during the first reproductive cycle; therefore, fish surviving
their first spawning will not suffer excess mortality for the remainder
of their lives (i.e., m =0). Assumption (2) is adopted here.
-------
ORNL-6251 96
We note that Eqs. (5.6) and (5.7) are highly sensitive to errors in
estimates of adult mortality because of the cumulative effect of
applying (1 - m ) to each successive age class.
5.5.1 Comparison of Fitted and Extrapolated Concentration-Response
Functions and Uncertainty Bands
Full life cycle toxidty data are not available for either the
rainbow trout or the largemouth bass for any chemical. However, full
life cycle toxicity data exist for brook trout (Salvelinus fontinalis)
exposed to methylmercuric chloride (Appendix B). Figure 5.3 shows a
concentration-response function and confidence bands constructed by
using the brook trout as a surrogate for rainbow trout. The logistic
model was fitted to egg, larval, and juvenile test data for brook
trout. The reproductive potential index was then calculated using the
life-table data for rainbow trout (Table 5.1). The brook trout MATC
for methylmercuric chloride, as calculated from the same data set used
to construct the concentration-response functions, is plotted on the
concentration axis. The median value of the EC,Q is 0.07 iig/L, and
the prediction interval (i.e., the 90% confidence interval around the
median) is approximately 0.03 to 0.1 vg/L. The brook trout MATC for
methylmercury, 0.53 vg/L, corresponds to a 60 to 78% (median 68%)
reduction in reproductive potential.
A methylmercuric chloride acute LC_0 1s available for rainbow
trout. Figure 5.4 shows a concentration-response function constructed
from a single-step extrapolation, from rainbow trout acute LC_0 to
chronic LC25, using the method described in Section 5.3. The median
-------
97
ORNL-6251
ORNL-DWG 85-17076
LJ
1.1
1.0
0.9
£ 0.8
> 0.7
0.6
Q
§ 0.5
CL
LU
c 0.4
z 0.3
o
o 0.2
8 0.1
0
-3
-2 -1 0
Iog10 CONCENTRATION (/ig/L)
Fig. 5.3. Fitted concentration-response function and uncertainty band
for the reduction in female reproductive potential of brook
trout (Salvelinus fontinalis) exposed to methylmercuric
chloride. The dashed line denotes the 10X effects level
(EC10).
-------
ORNL-6251
98
ORNL-DWG 85-H7071
1.1
< «'v
z 0.9
LU
O 0.8
> °'7
H
^ 0.6
§ 0.5
a.
LJ
* 0.4
z 0.3
o
o 0.2
S 0.1
0
IT
I
>MATC
M I
-4 -3-2-10 1 I
Iog10 CONCENTRATION (^g/L)
F1g. 5.4. Synthetic concentration-response function and uncertainty band
for the reduction in female reproductive potential of rainbow
trout (Salmo gairdneri) exposed to methylmereuric chloride.
Chronic LC2sS for the three life stages were obtained by
single-step extrapolation from an acute IC^Q for rainbow
trout.
-------
99 ORNL-6251
responses from the extrapolated model (Fig. 5.4) are very close to
the median responses (Fig. 5.3) from the fitted model (median
EC1Q - 0.09 ug/L for the fitted model and 0.10 ug/L for the
extraplated model). The prediction intervals, however, are much
wider. The prediction interval for the EC1Q in Fig. 5.4, for
example, ranges from 0.003 to 1.2 ug/L. The rainbow trout MATC for
methylmercuric chloride (1.2 ug/L, extrapolated from brook trout
using the method described in Section 4), corresponds to a 10-100%
reduction in reproductive potential.
If no acute LC5Q had been available for rainbow trout, it would
have been necessary to extrapolate a value from an acute LC5Q for
another species. Figure 5.5 shows a concentration-response function
constructed from a two-step extrapolation (Section 4), from fathead
minnow (Pimephales promelas) to rainbow trout acute LC5Q to chronic
LC25> The prediction interval for the EC1Q obtained from the
two-step extrapolation ranges from 0.0002-0.56 ug/L, with a median of
0.015 ug/L. Thus, compared to the single extrapolation, the two-step
extrapolation produces median effects about a factor of five lower and
prediction intervals about an order of magnitude wider.
Comparisons of Figs. 5.3, 5.4, and 5.5 suggests that, as is
true in extrapolation of MATC's (Section 4), in extrapolation of
concentration-response functions the acute-chronic extrapolation is
dominant source of uncertainty. As a means of confirming this
inference, we examined the importance of uncertainty concerning P
in determining the widths of prediction intervals obtained in the
single-step extrapolation (Fig. 5.4). Figure 5.6 presents a
-------
ORNL-6251
100
ORNL-DW6 85-47075
-5 -4-3-2-10 1 2 :
Iog10 CONCENTRATION (/ig/L)
Fig. 5.5. Synthetic concentration-response function and uncertainty band
for the reduction in female reproductive potential of rainbow
trout (Sajhmo gairdneri) exposed to raethylmercuric chloride.
Chronic LC25* for the three life stages were obtained by
two-step extrapolation from an acute LC5Q for fathead minnow
(Pimephales proroelas).
-------
101
ORNL-6251
ORNL-DWG85-<7074
LU
0
-4 -3-2-10 1 't
log<0 CONCENTRATION (/ig/L)
Fig. 5.6. Synthetic concentration-response function and uncertainty band
for the reduction in female reproductive potential of rainbow
trout (Salmo gairdneri) exposed methylmercuric chloride.
Chronic LCgsS were obtained as in Fig. 5.4. Uncertainty
concerning the curvature of the function was eliminated by
setting the curvature parameter (B) constant at its median
value.
-------
ORNL-6251 102
concentration-response function constructed similarly to F1g. 5.4, but
assuming the value of 3 to be constant at Its median value. Because
B 1s constant, the width of the prediction Interval 1n F1g. 5.6 Is
determined solely by the confidence Intervals around the extrapolated
LC25s for the three life stages. Within the effects Interval of 10
to 90X, F1gs. 5.4 and 5.6 are nearly Identical. Thus, within this
range, uncertainty accumulated 1n the acute-chronic extrapolation
dominates all other sources.
5.5.2 Comparison of Extrapolated Concentration-Response Functions
and Prediction Intervals for Different Species
Figures 5.7 and 5.8 show extrapolated concentration-response
functions and uncertainty bands for rainbow trout and largemouth bass
exposed to cadmium. For rainbow trout, a single extrapolation was
required, from rainbow trout acute LCcrt to chronic LCOC. A double
DU CD
extrapolation, including a genus-level taxonomic extrapolation from
Lepomis spp. to Hicropterus spp. and an acute-chronic extrapolation was
necessary for largemouth bass. Despite the double extrapolation, the
uncertainty band for largemouth bass 1s noticeably narrower than the
uncertainty band for rainbow trout. The explanation for this result is
the relatively high sensitivity of salmonlds to cadmium. The rainbow
trout acute LC5Q 1s near the low end of the range of LC5Qs
(Appendix A) used 1n the acute-chronic regression; as 1n all linear
regression models, prediction Intervals for extrapolated chronic
LC-cS increase in width with Increasing distance from the mean
LC. Otherwise, the two sets of bands are qualitatively similar.
-------
103
ORNL-6251
ORNL-DWG 85H7072
Q.
UJ
1.1
1.0
O 0.8
2 0.7
0.6
0.5 -
0.4
0.3
g
o 0.2
LU 0.1
CC
-5 -4 -3 -2 -1
CONCENTRATION (/ig/L)
F1g. 5.7
Synthetic concentration-response function and uncertainty band
for the reduction 1n female reproductive potential of rainbow
trout (Salmo galrdnerD exposed to cadmium. Chronic LC25*
were obtained by single-step extrapolation from an acute
for rainbow trout.
-------
ORNL-6251
104
ORNL-DWG 85-17073
0
-2
•1012
Iog10 CONCENTRATION
Fig. 5.8. Synthetic concentration-response function and uncertainty band
for the reduction 1n female reproductive potential of
largemouth bass (Mlcropterus salmoldes) exposed to cadmium.
Chronic LC25S were obtained by two-step extrapolation from
an acute LCso for blueglll (Lepomls roacrochlrus).
-------
105 ORNL-6251
For both species, the range of cadmium exposure concentrations can
be divided fairly precisely Into three segments: a region of no
significant reduction, a region of certain extinction, and a region of
Indeterminate reduction. The curves defining the upper and lower limits
of the predicted responses are quite steep. The upper limit of the
predicted response, for example, falls to near zero at concentrations
only a factor of 2 lower than the lower limit of the EC,Q. Similarly,
the lower limit of the predicted response rises to a 100* reduction
within an order of magnitude of the upper limit of the EC,0. These
limits provide useful operational definitions for qualitative
Identification of low, high, and Indeterminate impacts. For example,
based on Fig. 5.8 it might be concluded that a long-term average
cadmium exposure concentration of 0.01 ug/L would have no impact on a
largemouth bass population, because, at that level, the upper limit of
the predicted response interval is less than IX. However, no inference
could be made regarding the effect of this same concentration on
rainbow trout, because the predicted response interval at 0.01 ug/L
spans the full range from 0 to 100*.
For both species, cadmium MATCs correspond to predicted reductions
in reproductive potential ranging from 10 to 100X. In fact, for all
Figs. 5.4 through 5.8, the MATC's fall within the range of maximum
uncertainty concerning population response. In F1g. 5.3, the MATC
corresponds to a 60 to BOX reduction in female reproductive potential.
This result is especially noteworthy because the concentration-response
function and confidence bands plotted in Fig. 5.3 were obtained without
taxonomlc or acute-chronic extrapolation by fitting the logistic model
-------
ORNL-6251 106
to the same data set used to estimate the HATC for brook trout.
Although no firm conclusions are possible from the limited number of
comparisons presented here, the consistent pattern displayed suggests
that 1t may Inappropriate to Interpret the MATC, either calculated or
extrapolated, as a chronic effects threshold for fish.
5.6 DISCUSSION
Waller et al. (1971) and Wallis (1975) proposed the use of
fisheries-derived population models for quantifying the effects of
contaminants on populations, although experimental or observational data
on model applicability was not provided. We do not propose that the
methods described 1n this report can be used to directly predict the
long-term responses of fish populations to toxic contaminants. We have
noted elsewhere (Barnthouse et al. 1n press) that fisheries scientists
are still unable to predict the long-term effects of exploitation on
fish populations to an accuracy and precision that would be useful for
management decisions. However, we believe it 1s feasible to use
population-level assessment methods to perform risk assessments in
the same way that these methods are used by fisheries managers: as
Indicators of stress to be supplemented by expert judgment. We consider
three applications to be currently feasible: (1) identification of
data collection priorities, (2) setting of water quality standards, and
(3) quantitative comparison of contaminant-related risks to risks
associated with fishing or other environmental stresses.
We noted in Section 5.5.1 that the dominant source of uncertainty
in estimating reductions in female reproductive potential (due to toxic
-------
107 ORNL-6251
contaminants) 1s the uncertainty accumulated 1n extrapolating from
acute LC5Qs to chronic LC25s. This result, and the fact that only
acute data are available for most chemicals, suggests the great
Importance of obtaining a better understanding of relationships between
acute and chronic effects 1n risk assessment. The sensitivity of
population-level Indices to estimates of contaminant effects on adult
fish 1n Ueroparous species, noted 1n Section 5.4, Indicates the need
to evaluate the effects of contaminants on older fish, at least to the
extent of testing the hypothesis that mortality is restricted primarily
to early life stages.
Currently, water quality criteria are derived from MATCs, the
geometric means of no observed effects and lowest observed effects
concentrations (NOECs and LOECs). A NOEC is the highest concentration
used in a toxidty test at which no statistically significant
(conventional 95X confidence level) difference is observed between
experimental and control mortality and the LOEC is the next higher
concentration in the dilution series. As noted by Gelber et al.
(1985), NOECs have the undesirable property that the likelihood of
observing an effect at a given concentration is as much a function of
experimental design as of contaminant toxldty. In particular, NOECs
are nonconservative 1n that factors resulting 1n lower test precision
(e.g., low number of organisms per replicate, low number of replicates,
and high between-replicate variability) tend to increase the observed
NOEC and reduce the level of environmental protection afforded by water
criteria derived from the NOEC. In Section 5.5.2, it was shown that
MATCs for rainbow trout and largemouth bass are consistently greater
-------
ORNL-6251 108
than estimated population-level EC,Qs, even when the logistic model
1s fitted directly to the same concentration-response data used to
derive the MATC. It seems possible, 1f the results in Section 5.5.2
are confirmed by further research, that an approach to water quality
criteria based on concentration-response relationships would be
superior to one based on HATCs. In this connection, it 1s significant
that, when concentrations are plotted logarithmically, all of the
concentration- response functions developed in this section approximate
step functions. When uncertainty bands are considered, the plots can
be divided into nearly rectangular regions of no expected effect, high
expected effect, and Indeterminate effect. If this observation 1s
generally true of concentration-response relationships for toxic
chemicals, then the response regions could be used to define ambient
water quality criteria that reflect the degree of scientific
uncertainty concerning concentrations having adverse effects on
populations.
Expression of the effects of toxic contaminants 1n the same units
used to assess other forms of mortality permits comparison of the
effects of contaminants with the effects of exploitation by fishermen.
Many coastal fish stocks, for example, are subject both to Intense
fishing pressure and to environmental pollution. Successful management
of these populations depends on determining the relative importance of
these stresses. The reproductive potential Index used in Section 5 1s
similar to indices that have been used to compare the entrapment and
impingement by power plants to the impact of fishing (Goodyear 1977,
Dew 1981), thus, the index appears suitable for this purpose.
-------
109 ORNL-6251
The utility of comparing/combining estimates of effects of
contaminants and of exploitation depends on whether populations exposed
to toxic contaminants respond 1n a manner similar to exploited
populations. Some evidence exists that these responses are at least
qualitatively similar. In a review of the effects of exploitation on
fish populations, McFadden (1977) concluded that exploitation typically
causes Increased growth and fecundity and sometimes causes decreased
maturation time. These responses have the effect of compensating for
the Increased mortality associated with fishing, thus allowing the
populations to persist and sustain exploitation. HacFarlane and
Franzin (1978) noted these same changes 1n a population of white
suckers (Catastomus commersoni) in a metal-contaminated lake. Jensen
and Marshall (1983) noted that laboratory populations of Daphnia
galeata mendotae exhibit responses to cadmium stress that are
qualitatively similar to the responses described by McFadden. They
proposed that effects of toxic contaminants on zooplankton populations
could be quantified using models developed to describe fisheries.
At least for fish populations, population-level risk assessment
models appear to have several Important uses. We believe that the
reproductive potential Index used 1n this report is the simplest such
Index that Integrates data on effects of toxic contaminants on all life
stages; however, it 1s by no means the only possible Index that could
be used. Several authors, notably Gentile et al. (1983) and Daniels
and Allan (1981), have used the Intrinsic rate of natural Increase (r)
to Integrate data on mortality, growth, and reproduction obtained from
chronic toxicity tests for zooplankton. Models of growth could be used
-------
ORNL-6251 110
to assess the effects of contaminants on biomass production, where the
primary effect of chemicals 1s reduced growth rather than Increased
mortality. All of these approaches are applicable to Invertebrate
populations as well as to fish. The extent to which the use of
population-level risk assessment models can supplement or supplant
currently used Individual-level approaches remains to be determined.
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Ill ORNL-6251
REFERENCES (SECTION 5)
Abbott, W. S. 1925. A method of computing the effectiveness of an
Insecticide. J_. Econ. Entomol. 18:265-267.
Barnthouse, L. W., R. V. O'Neill, S. M. Bartell, and G. W. Suter II.
Population and ecosystem theory 1n ecological risk assessment.
IN Aquatic Ecology and Hazard Assessment, 9th Symposium. American
Society for Testing and Materials, Philadelphia, Penn. (1n press).
Boreman, J. 1978. Life history and population dynamics of Cayuga Inlet
rainbow trout (Salmo galrdneri Richardson). Ph.D. Dissertation,
Cornell University, Ithaca, N.Y.
Brand, R. J., D. E. Plnnock, and K. L. Jackson. 1973. Large sample
confidence bands for the logistic response curve and its Inverse.
Am. Stat. 27(4):157-160.
Coomer, E. C., Jr. 1976. Population dynamics of black bass in Center
Hill Reservoir, Tennessee. TWRA Technical Report No. 76-54.
Tennessee Technological University, Cookeville, Tenn.
Daniels, R. E., and J. D. Allan. 1981. Life table evaluation of
chronic exposure to a pesticide. Can. J_. Fish. Aquat. Sci.
38:485-494.
Dew, C. 6. 1981. Impact perspective based on reproductive value.
pp. 251-256. IN L. D. Jensen (ed.). Issues Associated with Impact
Assessment. EA Communications, Sparks, Hd.
Gelber, R. D., P. T. Lavin, C. R. Mehta, and D. A. Schoenfeld. 1985.
Statistical analysis, pp. 110-123. IN G. M. Rand and
S. R. Petrocelli (eds.), Fundamentals of Aquatic Toxicology.
Hemisphere Publishing Co., Washington, D.C.
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ORNL-6251 112
Gentile, 0. H., S. M. Gentile, and G. Hoffman. 1983. The effects of a
chronic mercury exposure on survival, reproduction and population
dynamics of Mvsidopsls bahla. Environ. Toxic. ol. and Chem. 2:61-68.
Goodyear, C. P. 1977. Assessing the Impact of power plant mortality
on the compensatory reserve of fish populations, pp. 186-195. IN
W. Van Winkle (ed.), Assessing the Effects of Power-Plant-Induced
Mortality on Fish Populations. Pergamon Press, N.Y.
Jensen, A. L., and J. S. Marshall. 1983. Toxicant-Induced fecundity
compensation: A model of population responses. Environ. Manage.
7:171-175.
McFadden, 3. T. 1977. An argument supporting the reality of
compensation in fish populations and a plea to let them exercise
it. pp. 153-183. IN W. Van Winkle, (ed.). Assessing the Effects
of Power-Plant-Induced Mortality on Fish Populations. Pergamon
Press, N.Y.
McFarlane, G. A., and W. G. Franzin. 1978. Elevated heavy metals: A
stress on a population of white suckers, Catastomus commersonl. in
Hammell Lake, Saskatchewan. J_- F1sh. Res. Board Can. 35:963-970.
Waller, W. T., M. L. Dahlberg, R. E. Sparks, and J. Cairns, Jr. 1971.
A computer simulation of the effects of superimposed mortality due
to pollutants on populations of fathead minnows (Pimephales
promelas). J_. Fish. Res. Board Can. 28:1107-1112.
WalUs, I. G. 1975. Modelling the Impact of waste on a stable fish
population. Water Res. 9:1025-1036.
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113 ORNL-6251
6. ECOSYSTEM LEVEL RISK ASSESSMENT
R. V. O'Neill, S. M. Bartell, and R. H. Gardner
6.1 INTRODUCTION
Environmental toxicology 1s 1n a period of rapid transition. The
need to predict toxic effects 1n natural ecosystems 1s pressing, yet
our ability to extrapolate from laboratory to field 1s limited by our
Inability to describe mechanisms controlling natural systems. Thus,
the science 1s experiencing rapid evolution 1n laboratory measurements
and 1n methods for extrapolation to the field.
Particularly critical 1s the need to predict higher-order effects
at concentrations well below acute toxldty (LC5Q). Synerg1st1c
effects result from blotk Interactions, such as competition and
predatlon, and abiotic constraints, such as temperature and limited
nutrients. These processes alter the response of organisms 1n the
ecosystem and cause effects that would not be anticipated from
laboratory measurements of single species.
Development of a credible predictive ability logically begins with
the extrapolation of toxlcologlcal data collected in the laboratory to
more complicated systems. O'Neill et al. (1982) Introduced ecosystem
uncertainty analysis (EUA) as one potential method for extrapolating
toxldty data 1n aquatic systems. The objective of this section 1s
(1) to review the methodology that has been developed, (2) to Illustrate
results obtained with EUA using the Standard Water Column Model
(SWACOM), and (3) to briefly discuss the methodology with regard to
future modifications and refinements.
-------
ORNL-6251 114
6.2 ECOSYSTEM RISK METHODS
Because most of our work has centered on SWACOM, it 1s convenient
to begin by describing this model. This will permit us to describe the
methods in the context 1n which they were developed and permit us to
use SWACOM to Illustrate methodological details.
6.2.1 Description of the Standard Water Column Model (SWACOM)
SWACOM was modified from an earlier model known as CLEAN (Park
et al. 1974). The model (F1g. 6.1) is designed to mimic the pelagic
portions of a lake ecosystem, Including ten phytoplankton populations,
five zooplankton populations, three planktlvorous fish, and a top
carnivore. The populations within a trophic level are described by
similar equations but with different parameter values. Thus, each
phytoplankton population is characterized by Its maximum photosynthetlc
rate, light saturation constant, Michael1s-Menten constant, temperature
optimum, and susceptibility to grazing.
The abiotic driving variables mimic the environment of a northern
dimictlc lake (Fig. 6.2). The temperature describes an annual
sinusoidal curve with lake turnover occurring at 4°C 1n the spring
and fall. Radiant energy follows a similar curve, with light greatly
reduced under ice cover. External sources add nutrients each day of
the year. Remineralized nutrients are added to the water column from
the hypolimnlon at spring and fall overturn.
Phytoplankton grow in response to light, temperature, and available
nutrients. Self-shading effects are accounted for by integrating
photosynthesis over the 10-m deep euphotlc zone. Each phytoplankton
-------
ORNL-DWG 81-10845 ESO
PHYTO-
PLANKTON
NUTRIENTS
36O
LIGHT
O 36O
DAYS
PREDATION
TEMPERATURE
FORAGE
FISH
O 360
DAYS
j
CARNIVO-
ROUS FISH
J
F1g. 6.1. A schematic Illustration of SWACOM (Standard Water Column Model). Daily levels of
nutrients, light, and temperature serve as model input. SWACOM considers the trophic
relationships of 10 phytoplankton, 5 zooplankton, 3 forage fish, and a single
carnivorous fish population (From O'Neill et al. 1982).
I
cr»
in
-------
ORNL-DWG 81-10933 ESD
30
24
in
<
-2
O
m 12
0
9
AVAILABLE
NUTRIENTS
I
STANDARD WATER COLUMN MODEL
iPHYTOPLANKTON
\
^
\
\i \ZOOPLANKTON
0
40
80 120 160 200 240
DAYS OF THE YEAR
280 320 360
Fig. 6.2. A typical simulation of SWACOM showing seasonal dynamics of phytoplankton,
zooplankton, and forage fish. Values shown on the graph are summed over the
component populations (from O'Neill et al. 1982).
o>
ro
en
cr>
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117 ORNL-6251
population has an optimal temperature at which Us photosynthetic rate
1s maximum. Total fixation of blomass 1s primarily limited by
available nutrients that are exhausted 1n periods of rapid growth.
Grazing and predatlon are described by a nonlinear Interaction
function (DeAngelis et al. 1975). This function considers both limited
food supply and competition with other grazers. The consumer
populations are limited by their individual metabolic and mortality
rates and by predatlon. Both grazing and respiration rates are
affected by temperature, with each population characterized by an
optimal temperature.
SWACOM can describe a number of higher-order effects. Effects on
one population can be altered by competition with other populations in
the same trophic level. For example, stress on one phytoplankton
population permits other phytoplankton populations to increase until
the nutrient pool limits growth. Effects of a toxicant on one trophic
level can precipitate effects elsewhere in the system. For example,
Increased mortality in the forage fishes releases zooplankton from
predatlon, which results 1n increased grazing on phytoplankton.
Effects on all populations are Influenced by seasonal variations in
light, temperature and available nutrients. All these indirect effects
are consequences of the dynamic relationships Included in SWACOM.
6.2.2 Organizing Toxicitv Data
Ecosystem uncertainty analysis was derived to extrapolate toxic
chemical effects measured on laboratory populations to likely effects
on ecological production in aquatic systems. Laboratory test species
-------
ORNL-6251 118
are not comprehensive In their representation of Inhabitants of aquatic
environments. Thus, an important aspect of performing EUA lies 1n
associating assay species with their ecological equivalents as
expressed 1n SWACOM.
The first step in Implementing EUA is to select of appropriate
toxidty data and to associate that data with specific components of
SWACOM. Toxidty data on phytoplankton are sparse. It 1s possible to
find values for green algae, such as Selenastrum capricornutum. and
these data are used for all ten algal populations 1f no other
information 1s available. If data are available on diatoms and
bluegreens, then a further division is possible based on physiological
parameters in the model and past experience with SWACOM. Like diatoms.
species 1 to 3 appear early 1n the spring and are associated with low
temperatures and high nutrient concentrations. Species 4 to 7 dominate
the spring bloom and are associated with Intermediate temperatures and
light. Species 8 to 10 appear in the summer and are tolerant of high
temperatures and low nutrient concentrations.
The identification of zooplankton is more tenuous. Based on model
behavior and physiological parameters, species 12 and 13 are identified
with Cladocerans. The ubiquitous data for Daphnia magna are used for
species 12. When data are available for Daphnia pulex. they are used
for species 13. The remaining zooplankters (species 11, 14 and 15, and
species 12 when no data were available for 0. pulex) are simply
identified as crustaceans. Of the available data, the smallest LC5Q
is assigned to 15 and the largest to 11. Species 14 (and 13 when
necessary) is assigned an intermediate value between these extremes.
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119 ORNL-6251
To assume species 15 to be the most sensitive 1s conservative. Since
an Increase 1n bluegreen algae 1s one of our end points, we assign the
greatest sensitivity to the consumer (I.e., 15), which 1s most abundant
during the summer of the simulated year.
Acute toxldty data for fathead minnow (Plmephales promelas).
bluegUl (Lepomls macrochlrus). and guppy (PoedHa retlculata) are
assigned to forage fish (species 16, 17, and 18). When data on these
species are not available, others are substituted, such as goldfish or
mosqultoflsh. The top carnivore or game fish (species 19) 1s usually
Identified as rainbow trout (Salmo qalrdnerD.
The general paucity of acute toxldty data can complicate the
assignment of SWACOM populations to assay species. Therefore, 1t has
been prudent to determine the sensitivity of risk estimates to
different patterns of assigning assay species to model populations
(O'Neill et at. 1983).
6.2.3 General Stress Syndrome
Typical toxldty data provide Information on mortality (or similar
end point) but provide little Insight on the mode of action of the
chemicals. Thus, some assumptions must be made about how the toxicant
affects the physiological processes 1n SWACOM. In an application that
focuses on a single chemical, 1t may be possible to obtain detailed
Information on modes of action. However, 1n general, such Information
1s not available, and 1t 1s necessary to make a single overall
assumption.
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ORNL-6251 120
Me assumed that organisms respond to all toxicants 1n a uniform
manner, that 1s, the General Stress Syndrome (GSS). For phytoplankton,
this Involved decreased maximum photosynthetlc rates (Ps), an Increased
Michael1s-Menten constant (Xk), Increased susceptibility to grazing
(W), and decreased light saturation (S1). For zooplankton, forage
fish, and game fish, the syndrome Involved Increased respiration (R),
decreased grazing rates (G), Increased susceptibility to predatlon (W),
and decreased assimilation (A).
The GSS defines the direction of change of each parameter 1n
SWACOM. It 1s also necessary to make an assumption about the relative
change 1n each parameter. We have assumed that all parameters are
changed by the same percentage.
To test the effects of the GSS on estimates of risk, the signs on
the growth parameters were systematically varied, and EUA was performed
for two chemicals characterized by very different patterns of
sensitivity among assay species: naphthalene and mercury. The signs
on the effects parameters for photosynthesis and consumption must be
negative or no toxic effects are possible. Results of biologically
reasonable variation 1n the remaining growth parameters showed the GSS
to be conservative 1n Its estimation of the risk of blue green algal
production (Table 6.1). Effects syndromes other than the GSS always
produced greater estimates of risk to game fish. However, these
syndromes Involved a decrease 1n optimal temperatures for growth 1n
response to toxicant exposure, for which little experimental evidence
1s likely to be available from current bloassays. If information
concerning the physiological mode of chemical action 1s available for a
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121 ORNL-6251
Table 6.1. Risks of Increased algal production and decreased game fish
production 1n systematic alteration of the General Stress
Syndrome. The optimal temperature for growth (To), prey
preference (W), assimilation efficiency (A), and grazing
rate (6) were either increased (+), decreased (-), or
unchanged (0) in the associated estimates of risk for
exposure to naphthalene (0.0468 mg/L).
To W A G
0 + - -*
0 + +
0000
.
H + + +
+ I +
+ 1 - +
H +
1 + +
^ +
^ 4-
^- _
f t -i-
+ +
<• - +
f
- •»• -f
•i-
+
Algae increase
43.6
0.4
9.4
0.2
9.4
7.0
0
42.4
0
0
0
0
11.2
14.4
0
31.6
0
0
1.8
Game fish decrease
1.6
0
.4.0
31.0
0
0.2
13.2
1.0
0
0.2
14.8
1.6
0
1.8
30.6
33.8
0
29.2
0.4
Used in the General Stress Syndrome
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ORNL-6251 122
specific toxicant, the GSS may be appropriately modified. For example,
chemicals with a narcotizing effect could be represented by decreasing
respiration 1n the GSS. Similarly, photosynthetlc enhancers or
Inhibitors can be more explicitly depicted. The development of
alternative stress syndromes 1s limited only by the basic bloenergetlc
formulation of the growth equations 1n SWACOM.
In the absence of Information that details the mode of action,
the GSS appears as a conservative choice 1n the application of EUA for
evaluating the likely effects of potentially toxic chemicals.
6.2.4 Microcosm Simulations
The key to changing parameters 1n the model 1s simulation of the
experiments used to generate toxlcity data. This involved simulating
the production dynamics of each species 1n isolation, as it might occur
in a laboratory under Ideal constant conditions. The parameters of
that species were then altered to duplicate the end point used 1n the
original experiment. Thus, for an LC5Q of 96 h, we would find the
percentage change that halved the population in 4 d.
At the conclusion of the MICROCOSM simulations, we have the
percentage change in the parameters that matches the experimental end
point; that is, we can match the response of the population to the
specific concentration that represents the LC5Q and EC5Q. We must
now make an additional assumption to arrive at the level of response to
be expected for other concentrations that He below the LC5Q or
EC5Q. We assumed a linear concentration-response relationship.
Thus, an environmental concentration one-fifth of the LC_0 would
-------
123 ORNL-6251
cause a 10X reduction in the population over the same time Interval as
the original test. MICROCOSM simulations are then repeated with this
new end point to arrive at the percentage change in the parameter
resulting in a 10£ reduction. The linear assumption can be removed 1f
a concentration-response curve is available for the toxicant. Because
most concentration-response curves are concave, our assumption should
result in choosing a level of effect larger than would actually result
if the test were conducted at that concentration. Therefore, the
linear assumption is conservative. In addition, EUA emphasizes the
Implications of interacting ecosystem components on modeling the
response of the system to toxicant exposure. It 1s not the intent to
model concentration-response relationships for individual organisms.
6.3 UNCERTAINTIES ASSOCIATED WITH EXTRAPOLATION
To implement EUA, it is necessary to know not only the percentage
change 1n parameters but also the uncertainty to be associated with
this change. Monte Carlo simulation (Sect. 6.5) is used to translate
uncertainties regarding individual parameters into uncertainty regarding
system responses. We have assumed that all parameter changes have an
associated uncertainty of plus or minus 100%. This assumption seemed
sufficiently conservative. In a specific assessment, one might wish to
adopt a more complex strategy that would combine greater information on
modes of action with statistical extrapolation procedures (Sect. 4) or
a survey of experienced researchers to arrive at more specific estimates
of uncertainty.
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ORNL-6251 124
Because of the relatively large uncertainties, the possibility
exists that risks are due to the uncertainties rather than the actual
effect of the chemicals. In such a case, the risk 1s due to our
ignorance of the system rather than the potential toxic effect of the
chemicals.
To test for the effect of large uncertainties, we analyzed the
deterministic response of the model to several toxic substances. The
deterministic response assumes no uncertainties in the parameters.
This response is approximately the average response of the system to
that level of toxicant. The response can be expressed as the percentage
change in the mean population relative to the "no toxicant" case. If
the percentage change is close to zero, then the risk can be attributed
to uncertainty alone. If the mean populations are significantly
changed, the risks are attributed to toxic effect plus uncertainty.
Analysis of the deterministic solution for nine chemicals
associated with the production of synthetic fuels from direct
(Table 3.3.2 in Suter et al. 1984) and indirect (Table 3.3.2 in
Barnthouse et al. 1985) coal liquefaction indicates that the toxicity
of mercury, cadmium, nickel, ammonia, naphthalene, and phenol
contributes significantly to estimates of risk. Risks posed by
arsenic and lead result more from uncertainties 1n extrapolation in
these particular applications.
6.4 RESULTS OF ECOSYSTEM RISK ASSESSMENTS
Having described the methods to be used in setting up EUA, we
will now present four example applications. Our primary purpose 1s to
-------
125 ORNL-6251
demonstrate the utility of the method 1n routine assessments. However,
we will also make 1t a point to show how the results of EUA differ from
population-oriented assessments.
6.4.1 Risk Assessment for Direct and Indirect Liquefaction
The results of risk assessments for real liquefaction technologies
are shown 1n F1g. 6.3 (Suter et al. 1984). Two end points were
considered: A quadrupling of the peak blomass of noxious bluegreen
algae and a 25X decrease 1n game fish blomass. These end points were
chosen as Indicative of minimal effects that could be noticed 1n the
field. Risk values I.e., probabilities of exceeding the above end
points, were calculated across a range of environmental concentrations.
The range of exposures for each technology 1s shown at the bottom of
the figure.
Results for naphthalene are shown 1n F1g. 6.3. There 1s an
upturn 1n the risk curves, showing significant risks at the higher
concentrations reached by at least one of the technologies. The
increased risk to game fish populations seems Intuitively reasonable.
However, the increasing risk of a bluegreen algal bloom with increasing
concentration is counterintuitive. This is an example of the indirect
effects that EUA is capable of showing. Even though each of the
chemicals is toxic to the algae, the reduction 1n sensitive grazing
organisms more than compensates for the direct effect on phytoplankton.
Ecosystem uncertainty analysis can be used to compare risks
estimated for different classes of chemicals for different direct
liquefaction technologies (F1g. 6.4). Here the four technologies all
-------
10
0
ORNL-DWG 83-16214
^
cr
10
,-2
NAPHTHALENE
ALGAE
.E/G.
.E/B.
CT>
ro
in
ro
10
r5
10
r4
10
r3
10
r2
CONCENTRATION (mg L~1)
Fig. 6.3.
Risk estimates for naphthalene over a range of environmental concentrations. The 5th
percentlle, mean, and 95th percent!le concentrations associated with four direct coal
liquefaction technologies are shown at the bottom of the graph. The notations /B and
/G refer to two alternative wastewater treatment options. The plotted values are the
probability of a fourfold increase in algal biomass and a 25% reduction in game fish
biomass (From Suter et al. 1984).
-------
EXXON
ORNL-DWG 83-12712
HCOAL
1.0 0.8 0.6 0.4 0.2 0 0.2 O.4 0.6 08 1.O 1 0 08 0.6 0.4 02 0 0.2 0.4 0.6 0.8 1.0
SRC I
SRCH
T n r i T i i i i i i i i i r
1.O 0.8 0.6 0.4 0.2 0 0.2 04 0.6 0.8 1.0 1.0 0.8 06 0.4 02 0 0.2 0.2 06 0.8 1.0
Fig. 6.4. Comparison of risks among direct coal liquefaction technologies. Risks at the 95th
percentlle concentration are shown first for algae and then for game fish for each
of nine contaminant categories (5 = ammonia, 12 = benzene, 14 = mono- and dlaromatic
hydrocarbons, 21 = phenols, 31 = arsenic, 32 = cadmium, 33 = nickel, 34 = mercury,
and 35 = lead; from Suter et al. 1984).
cr
ro
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ORNL-6251 128
show considerable risks of Increased algal production for chemical
class 5 (ammonia). The Exxon and H coal processes also suggest similar
risks associated with class 34 (cadmium). Other similarities and
differences among the technologies are readily apparent from these
presentations. Risks posed by chemical classes 5 and 34 are also
notable for Indirect Hquefactor technologies (F1g. 6.5).
6.4.2 Risk Assessment of Chlorooaraffins
SWACOM has also been applied (Bartell 1984) 1n an assessment of
risk for chloroparaffins (CPs). In this case, the risk of Increased
algal production 1s 14 to 33% at concentrations of 0.0001 mg/L. These
risks Increase at Intermediate exposure concentrations and then decrease
to near zero at the highest concentrations tested.
The risk of decreased production of zooplankton, forage fish, and
game fish Increase monotonically with exposure concentrations. At the
highest test concentrations, the likelihood of a 50% decrease in forage
fish and game fish approaches 1.0. The highest estimates of risk to
game fish result at exposure concentrations that lie at the upper range
of expected ambient concentrations (Zapotsky et al. 1981).
Risks of decreased game fish biomass appear to result from the
combined direct toxic effects and the effects of decreases 1n
zooplankton and forage fish biomass at Intermediate chloroparaffin
concentrations.
.The relative Importance of direct and indirect effects on the
responses of each trophic level to chloroparaffins was analyzed. The
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129
ORNL-6251
ORNL-DWG 83-12714
LURGI
1.0 0.8 0.6 0.4 0.2 0
i
0.2 0.4 0.6 0.8 1.0
KOPPERS
1.0 0.8 0.6 0.4 0.2 0 0.2 0.4 0.6 0.8 1.0
F1g. 6.5. Comparison of risks for two Indirect coal liquefaction
technologies. Risks and contaminant categories defined as in
Fig. 6-4 (from Suter et al. 1984).
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ORNL-6251 130
results Indicated that Indirect effects contribute more to risk that do
direct effects on Individual growth processes within trophic levels.
At exposure concentrations that approach the highest measured
concentrations of CPs, the risk of a 100% Increase 1n bluegreen algae
blooms ranges from 70 to 76%. At this concentration, the risks of a
50% decrease 1n forage fish or game fish might reasonably be expected.
6.4.3 Patterns of lexicological Effects 1n SHACOM
In another study (O'Neill et al. 1983), SWACOM was used to
Investigate how different aggregations of ecosystem components might
alter conclusions drawn from laboratory data. We compiled data for
cadmium, as shown 1n Table 6.2. The distribution of sensitivities 1n
the first column of Table 6.2 will be referred to as the standard or
"population" pattern.
The first step was to remove the differences in sensitivity among
populations in the same trophic level. The standard approach would be
to take the geometric means of LC,Qs; however, the data represent a
variety of test durations and end points (e.g., EC5Qs and EC2Qs).
To correct for differences 1n test conditions, we assumed a simple
mortality process described by x(t) = x(0) exp(-dt), where x(0) is
the Initial population size, x(t) is the size at time t, and d is
the mortality rate. We assume that mortality is a function of
concentration, d = aC. We know the fraction, F. = x(t)/x(0), that
survives at one concentration, C.., measured over one time period,
t,. Since In F./C.t.. - -a - lnF?/C_t2, we can then
estimate the concentration, C-, that would result In a different
-------
131
ORNL-6251
Table 6.2. Toxlcological data used 1n examination of patterns of effects for
cadmium
Model populations
Phytoplankton
Zooplankton
Forage fish
1-3
4.7
8-10
11
12
13
14
15
16
17
18
LC50/EC5
Population
pattern
0.16
0.06
0.06
0.50
0.0099
0.14
0.25
0.0035
0.63
1.9
1.6
O.vg/L
Trophlc
pattern
0.050
0.050
0.050
0.057
0.057
0.057
0.057
0.057
1.2
1.2
1.2
No pattern
0.025
0.025
0.025
0.025
0.025
0.025
0.025
0.025
0.025
0.025
0.025
Game fish
19
0.002
0.002
0.025
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ORNL-6251 132
fraction, P2, measured over a different time period, t«. By simple
rearrangement we find
C2 = (C^! lnF2)/(t2 InFT) . (6.1)
Using Eq. 6.1 we arrived at a single LC5Q for each trophic
level. The distribution of sensitivities shown in the second column of
Table 6.2 will be referred to as the "trophic" pattern. In addition,
we applied this approach once again to equate the trophic value and
arrived at a single LC_0 that removes even the trophic pattern. This
value is shown in the last column of Table 6.2 and will be referred to
as "no-pattern." By beginning with the no-pattern case, we can
progressively add elements of toxic pattern into the simulations. In
this way, we can analyze for the effect of the pattern of differential
sensitivities.
Comparing the trophic with the no-pattern case, the upper half of
Table 6.3 shows the percent difference in annual blomass of each
trophic level. The results Indicate the kind of indirect effect that
one could reasonably expect to find in the ecosystem. The game fish 1s
more sensitive than the no-pattern LC5Q would indicate. The other
trophic levels are relatively insensitive. Therefore, the toxicant
reduces game fish population and has relatively less direct effect on
other organisms. Because game fish are reduced, the forage fish
experience less predation and show an Increase. Because there are more
forage fish, there are fewer zooplankton. Because there 1s less
grazing, the phytoplankton increase.
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133 ORNL-6251
Table 6.3. Comparisons of responses to different patterns of
sensitivity to cadmium
Trophic vs no pattern Percent difference
Phytoplankton 19.
Zooplankton -19.
Forage fish 25.
Game fish -33.
Population vs trophic pattern
Phytoplankton 1.0
Zooplankton -6.0
Forage fish -4.0
Game fish -4.0
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ORNL-6251 134
The next step is to compare the trophic pattern with the full
population pattern of toxic sensitivities. The percent difference
between trophic and population response 1s shown 1n the lower portion
of Table 6.3. The average phytoplankton population 1s larger, and the
consumer trophic levels are always smaller when population-specific
patterns of toxic sensitivity are Ignored. Thus, the Interactions that
occur among differentially sensitive populations within a trophic level
can affect the way the system responds to chemical stress.
Biotlc Interactions are Important determinants of how the
ecosystem will respond to stress. The results emphasize that
predator-prey and competitive Interactions are Important determinants
of system response to toxicants. Ignoring the way ecosystem processes
Interact with toxic stress can bias estimates of environmental risk.
6.4.4 Using SWACOM to Extrapolate Bioassavs
An alternative to standard algal bloassay methods measures
short-term effects on physiological processes. Photosynthesis can be
measured simply and precisely and 1s more sensitive to low
concentrations of some toxicants than population growth. In the study
described here (Giddings et al. 1983), photosynthetic Inhibition 1n
algae was extrapolated to the ecosystem level using SWACOM to
illustrate the potential risk of photosynthetic Inhibition for the
ecosystem as a whole. We considered a toxic Impact of 7-d duration,
Introduced at various times during the year. On each date, we
simulated a toxicant that caused a 50% reduction 1n the maximum
photosynthetic rate and a 10X mortality on all consumer populations.
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135 ORNL-6251
Mortality alone had little effect on the simulated pelagic
ecosystem. When 50* Inhibition was Included 1n the deterministic
solution of the model, the effects were much more pronounced with
average changes approaching 25X 1f the stress began 1n day 170.
Thus, the model Indicates that even a temporary Inhibition of
photosynthesis can have an Important effect on other populations
1n the ecosystem. The exercise demonstrates that the
Interdependence of populations 1n an ecosystem makes 1t possible
for even temporary Inhibition of algal photosynthesis to have a
measurable Impact on other organisms, particularly 1f the other
organisms are also experiencing toxic effects.
Another Implication of the ecosystem simulation 1s that the
net effects of releasing a toxicant Into the whole ecosystem
depend on the state of the ecosystem at the time of release. The
authors also Infer that the effects on a population are, to a
large extent, functions of the ecosystem of which the populations
are a part. A single toxicological response may have a variety
of expressions, depending on the ecosystem context. For example,
the death of a fraction of a population may be Inconsequential if
the growth of the population is limited by 1ntraspec1fic
competition; reduced competition may compensate for the
additional mortality. Conversely, a slight toxic effect may lead
to complete elimination of the population by Increasing Its
vulnerability to predators or reducing its ability to compete
with other populations.
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ORNL-6251 136
6.5 MONTE CARLO METHODS AND ANALYSIS
The essential feature of the ecosystem approach to risk analysis
1s to use models such as SWACOM to extrapolate Information on toxic
substances to the ecosystem level. There are many numerical techniques
available to quantify the effect of uncertainties associated with such
extrapolations (Rose and Swartzman 1981). Monte Carlo methods are
particularly useful because they are easily Implemented, and they
provide the necessary Information to estimate confidence Intervals
(Gardner et al. 1983).
Monte Carlo methods Involve the Iterative selection of random
values for model parameters from specified frequency distributions,
simulation of the model for each set of parameters, and analysis of the
combined set of Inputs and outputs (McGrath et al. 1975, Rubinstein
1981). Systematic sampling methods are more efficient than simple
random sampling. We use quasi-orthogonal stratified random sampling
methods (referred to as Latin Hypercube sampling) because (1) the
estimates of output parameters (e.g., mean, median, and mode) are more
precise (see McKay et al. 1979), (2) low rates of spurious relationships
between randomly generated values are ensured (Iman and Conover 1982),
and (3) computer codes exist for generating values from a variety of
distributions.
We have Implemented a program, PRISM (Gardner et al. 1983),
especially written to perform Monte Carlo simulations for the
estimation of risk Indices. The program requires a FORTRAN subroutine
of the model and an input file listing model parameters and their
frequency distributions (e.g.t normal, uniform, lognormal, etc.).
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137 ORNL-6251
Multiple regression analysis of the Monte Carlo results provides
an analysis of how the Index 1s affected by assumptions required In
extrapolating from laboratory to the ecosystem level (Downing et al.
1985). The contribution of each parameter to the regression sum of
squares (I.e., the amount of the variability of y explained by a
particular parameter) divided by the total sum of squares and
multiplied by 100 forms an Index, U, representing the percent
variability of the model prediction explained by each parameter. The
values of U range from 0.0 to 1.0, thus allowing a comparison between
parameters. The adequacy of each Index can be determined by comparison
o
and by Inspection of the R statistic.
The classical sensitivity Index, S (Tomovlc 1963) analytically
examines the relationships between model predictions and model
parameters. This approach is limited by the difficulty of obtaining
an analytical solution for many models and by Us assumption of small
Instantaneous changes (Gardner et al. 1981). These difficulties have
resulted 1n the proliferation of numerical and statistical approaches
to uncertainty analysis (Hoffman and Gardner 1983).
If a single parameter 1s randomly varied from a prespedfled
probability distribution, then the slope of the regression of the model
prediction on the parameter 1s the least-squares estimate of S 1f the
parameter perturbations are very small (Gardner et al. 1981). If
several parameters are simultaneously and Independently varied, then a
multiple regression on all the parameters simultaneously estimates all
the sensitivities. The adequacy of this method of estimating linear
relationships between model predictions and parameters can be evaluated
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ORNL-6251 138
2
by Inspection of R , the ratio of regression sum of squares to total
2
sum of squares. If R 1s nearly 1.0, then linear methods are
adequate to describe the relationship between parameters and
o
predictions. The divergency of R from 1.0 Indicates that nonlinear
effects and Interactions between parameters are Important.
Any analysis that relates the Importance of an Input to a
prediction without first removing the effects of the variability of
other Inputs (e.g., simple regression or correlations) 1s not very
useful. Partial sum of squares (Draper and Smith 1966) determined
by regression techniques are particularly useful because they
quantitatively express relationships between each model Input and
output, with the effects of the variability of the remaining Inputs
statistically removed.
The partial sum of squares (PSS) represents the unique effect of
each Input on each prediction after correction of the total sum of
squares because of the variability 1n all the other Input variables.
The PSS has the property that (1) the estimated effect does not Involve
other model Inputs, (2) the estimates are Invariant to the ordering of
the calculation, and (3) the sums of squares calculated 1n this way do
not add up to the total regression sum of squares, unless the Inputs
are orthogonal to each other.
If there are a large number of Inputs, 1t 1s natural to ask 1f
these could be replaced by a smaller number of Inputs or some linear
function of them, with a minimal loss of Information 1n explaining the
output. This problem was first Investigated by Rao (1964) and termed
principal components of Instrumental variables.
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139 ORNL--6251
Principal components of Instrumental variables reduce to multiple
regression 1n the case where there 1s only one main variable to
predict. The coefficients of the multiple regression equation, when
the variables are standardized, can be looked upon as Importance
coefficients, Indicating which Input variables are most Important 1n
Influencing the output. Principal components are thus an extension of
the multiple regression techniques when more than one output 1s
examined simultaneously. The coefficients of the eigenvector Indicate
which Input variables are most Important, and the size of the eigenvalue
determines how Important that eigenvector 1s 1n explaining the variation
we observe 1n the outputs.
6.6 DISCUSSION
The physiological process formulation of the growth equations in
SWACOM provides the framework for extrapolation of acute toxldty data
to estimates of likely effects of chemicals 1n aquatic ecosystems.
Translation of mortality measurements to reductions 1n blomass
production through the use of the General Stress Syndrome permits
Investigation of the Implications of sublethal chemical effects on
population dynamics calculated 1n an ecosystem context. The role of
competitive and predator-prey Interactions 1n mitigating or amplifying
chemical effects can be examined through EUA (O'Neill et al. 1982,
1983). Statistical analyses of simulations used to estimate risk can
Identify the relative Importance of direct vs Indirect chemical effects
as components of risk. Application of the methods to date encourage
further evaluation and refinement of EUA.
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ORNL-6251 140
Several areas for Improvement 1n EUA are evident from our
results. A more comprehensive collection of acute toxlcity data could
aid in the refinement of risk estimation. An examination of the
relative contributions to risk Identifies physiological processes
that determine risk 1n specific applications. Risk estimates could
be refined 1f bloassay protocols were modified to measure effects on
physiological processes. For example, modification of acute assays for
Daphnla, fathead minnows, or bluegills to measure changes 1n oxygen
consumption during the course of the assay would provide direct data to
test the GSS and estimate corresponding effects parameters for SWACOM.
The accuracy of risks estimated with EUA is a function of the
applicability of SWACOM or other models to the systems of interest.
SWACOM was designed to mimic the behavior of a northern dimictlc lake.
As the particular system of Interest departs 1n Its characteristics
from those of a lake, SWACOM becomes less appropriate for risk
estimation. In the case of chloroparafflns (CPs), low estimates of
risk might underestimate the potential hazard of these chemicals.
The propensity of CPs to accumulate in sediments might pose potential
effects to benthlc populations. SWACOM does not directly consider
benthlc populations or sediments. Again, SWACOM can be replaced with a
more site-specific model to further refine estimates of risk. Even
though absolute magnitudes of risk might be in error when the system of
Interest deviates substantially from a dimlctic lake, SWACOM might
still be used to compare relative risks for several different chemicals.
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141 ORNL-6251
In EUA, risk 1s a function of both toxldty and the uncertainty 1n
extrapolation from bloassay to natural systems. In the cases we have
examined, the toxic effect has been more Important than the uncertainty
associated with the effects parameters (Bartell 1984). Nevertheless,
the analyses would be considerably Improved 1f more Information were
available on the field effects of toxicants. Future emphasis should
focus on reducing the uncertainties associated with extrapolation so
that attention can focus on the risks Involved 1n ecosystem effects due
directly to the toxicants.
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REFERENCES (SECTION 6)
Barnthouse, L. W., G. W. Suter II, C. F. Baes HI, S. M. Bartell,
H. G. Cavendish, R. H. Gardner, R. V. O'Neill, and A. E. Rosen.
1985. Environmental Risk Analysis for Indirect Coal
Liquefaction. ORNL/TM-9120. Oak Ridge National Laboratory,
Oak Ridge, Tenn.
Bartell, S. M. 1984. Ecosystem uncertainty analysis: Potential
effects of chloroparafflns on aquatic systems. Report to the
Office of Toxic Substances, U.S. Environmental Protection Agency,
Washington, D.C.
DeAngelis, D. W., R. A. Goldstein, and R. V. O'Neill. 1975. A model
for trophic Interaction. Ecology 56:881-892.
Downing, D. J., R. H. Gardner, and F. 0. Hoffman. 1985. An
examination of response-surface methodologies for uncertainty
analysis 1n assessment models. Technometrlcs 27:151-163.
Draper, N. R., and H. Smith. 1966. Applied regression analysis.
John Wiley and Sons, N.Y.
Gardner, R. H., R. V. O'Neill, J. B. Mankln and J. H. Carney. 1981. A
comparison of sensitivity analysis and error analysis based on a
stream ecosystem model. Ecological Modelling 12:177-194.
Gardner, R. H., B. Rojder, and U. Bergstrom. 1983. PRISM: A
systematic method for determining the effect of parameter
uncertainties on model predictions. Studsvlk Energ1tekn1k AB
report/NW-83/555, Nykoplng, Sweden.
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GidcHngs, J. M., A. J. Stewart, R. V. O'Neill, and R. H. Gardner.
1983. An efficient algal bloassay based on short-term
photosynthetlc response. Aquatic Toxicology and Hazard
Assessment: Sixth Symposium, ASTM STP 802, W. E. Bishop,
R. 0. Cardwell, and B. B. Heldolph (eds.), American Society for
Testing and Materials, Philadelphia.
Hoffman, F. 0., and R. H. Gardner. 1983. Evaluation of uncertainties
in environmental radiological assessment models, pp. 11-1 to
11-55. IN Radiological Assessment: A Textbook on Environmental
Dose Assessment, J. E. Till and H. R. Meyer (eds.), U.S. Nuclear
Regulatory Commission, Washington, D.C. NUREG/CR-3332 (ORNL-5968).
Iman, R. L., and W. J. Conover. 1982. A distribution-free approach to
Inducing rank correlation among input variables for simulation
studies. Comm. Stat.. B11(3).
McKay, M. D., W. J. Conover, and R. 0. Beckman. 1979. A comparison
of three methods for selecting values of input variables in the
analysis of output from a computer code. Technometrics.
21:239-245.
McGrath, E. G., S. L. Basin, R. W. Burton, D. C. Irving,
S. C. Jaquette, and W. R. Ketler. 1975. Techniques for efficient
Monte Carlo simulation. Vol. 1. Selected probability
distributions. ORNL/RSIC-38. Oak Ridge National Laboratory,
Oak Ridge, Tenn.
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O'Neill, R. V., R. H. Gardner, L. W. Barnthouse, G. W. Suter,
S. G. Hlldebrand, and C. W. Gehrs. 1982. Ecosystem risk analysis:
A new methodology. Environ. Toxlcol. and Chem. 1:167-177,
O'Neill, R. V., S. M. Bartell, and R. H. Gardner. 1983. Patterns of
toxlcological effects 1n ecosystems: a modeling study. Environ.
Toxlcol. and Chem. 2:451-461.
Park, R. A. and 24 others. 1974. A generalized model for simulating
lake ecosystems. Simulation 23:33-50.
Rao, C. R. (1964). The use and Interpretation of principal component
analysis 1n applied research. Sankhva A26:329-358.
Rose, K. A., and G. L. Swartzman. 1981. A review of parameter
sensitivity methods applicable to ecosystem models. NUREG/CR-2016.
U.S. Nuclear Regulatory Commission, Washington, D.C.
Rubinstein R. Y. 1981. Simulation and Monte Carlo Method. John Wiley
and Sons, N.Y.
Suter, G. W. II, L. W. Barnthouse, C. F. Baes III, S. M. Bartell,
M. G. Cavendish, R. H. Gardner, R. V. O'Neill, and A. E. Rosen.
1984. Environmental risk analysis for direct coal liquefaction.
ORNL/TM-9074. Oak Ridge National Laboratory, Oak Ridge, Tenn.
Tomovlc, R. 1963. Sensitivity Analysis of Dynamic Systems.
McGraw-Hill, N.Y.
Zapotsky, J. E., P. C. Brennan, and P. A. Benloff. 1981.
Environmental fate and ecological effects of chlorinated
paraffins. Report to the Environmental Assessments Branch, Office
of Pesticides and Toxic Substances, U.S. Environmental Protection
Agency, Washington, D.C.
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7. GENERAL DISCUSSION
L. W. Barnthouse and G. W. Suter II
Combining exposure and effects estimates and Interpreting the
results requires considerable judgment on the part of the analyst.
Among the key Issues are matching spatlotemporal scales of exposure
and effects models. Interpreting uncertainties, and Identifying
"significant" risks. We cannot provide explicit procedures for
addressing these Issues because they will vary with each application.
A discussion of how Issues were addressed 1n the synfuels risk
assessments should, however, provide some useful guidance. In addition
to discussing the application of our approach 1n technology assessment,
this section presents our views on (1) other potential applications to
regulatory and resource management problems, and (2) critical research
needs for the future development of ecological risk assessment.
7.1 SPATIOTEMPORAL SCALE IN THE INTEGRATION OF EXPOSURE AND EFFECTS
Superficially, Integrating exposure and effects models appears to
be a simple matter of estimating an environmental concentration and then
comparing 1t with a toxlcologlcal benchmark or a concentration-response
curve. However, the risk assessment may be meaningless 1f the
spatlotemporal scale of the exposure assessment 1s Improperly matched
to the scale of the ecological effects of Interest (and vice versa).
Both short-term and long-term exposure assessments were used 1n
synfuels risk assessments to address, respectively, acute effects and
chronic effects of contaminant releases. A stochastic surface water
fate model (Sect. 2) was used to estimate frequency distributions of
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contaminant concentrations as functions of dally variability 1n
Important hydrologlcal parameters. To assess risks of acute mortality
during high-concentration episodes, 96-h LC5Qs (both measured
and extrapolated) were compared with 95th percentlle contaminant
concentrations (I.e., concentrations expected to be exceeded on 5X of
days). To assess risks of chronic toxldty, MATCs and ecosystem risk
functions were compared to seasonal average contaminant concentrations.
In a site-specific assessment, seasonal dilution volumes could be
matched to chronic benchmarks for the species and life stages present
at the site.
Spatial scaling was not a significant problem 1n the synfuels risk
assessments we performed. In the absence of detailed Information on
the spatial distribution of vulnerable resources, 1t was appropriate to
use spatially homogeneous exposure and effects models. In site-specific
risk assessments, however, spatial scales of both exposure estimates
(deposition rates, surface concentrations) and effects measures (number
or fraction of organisms affected, reduction 1n system productivity)
must match the spatial resolution of distributional data for the
exposed organisms. For reasons of scale, the models used 1n the
synfuels risk assessment project may not be appropriate for
site-specific assessments.
7.2 INTERPRETING UNCERTAINTY
As noted 1n Section 1, a major objective of risk assessment 1s to
Identify and quantify the uncertainties Involved 1n extrapolating from
experimental data on the environmental chemistry and toxicology of
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contaminants to expected fate and effects 1n the field. We could not
quantify all of these uncertainties. In risk assessment, there must
always be a trade-off between uncertainties that are explicitly modeled
and uncertainties that are consigned to expert judgment. At one
extreme, it 1s possible to base assessments on simple toxldty
quotients and safety factors without explicit treatment of uncertainty
(Sect. 3). Although feasible, this approach provides no information
about either the reliability of the assessment or the feasibility of
improving it through research. At the other extreme, one can imagine
developing an explicit model of all the physicochemlcal, physiological,
and ecological processes that determine the fate and effects of a
chemical and then assigning parameter distributions to each. We have
argued elsewhere (Barnthouse et al. 1984, Suter et al. 1985, Barnthouse
et al. in press) that current scientific understanding of natural
populations and ecosystems is insufficient to support such an
approach. In the synfuels risk assessment project, we attempted to
identify the major classes of uncertainties involved in ecological risk
assessment and to develop methods of addressing them without exceeding
the limits of feasibility or scientific credibility.
We distinguish three qualitatively distinct sources of uncertainty
in ecological risk assessment: inherent variability, parameter
uncertainty, and model error. It is important to distinguish between
these three sources, because they differ with respect to (1) feasibility
of quantification and (2) degree of possible reduction through research
or environmental monitoring.
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7.2.1 Inherent Variability
Limits on the precision with which variable properties of the
environment can be quantified limit the precision with which It 1s
possible to predict the ecological effects of stress. The concentration
of a contaminant 1n air or water varies unpredlctably 1n space and time
because of essentially unpredictable variation 1n meteorological
parameters such as precipitation and wind direction. The spatlotemporal
distributions and sensitivities to stress of organisms 1n nature are
similarly variable. This variability can be quantified for many
characteristics of the physical environment that Influence the
environmental fate of contaminants. For the synfuels risk assessment
project, long-term hydrological records were used to estimate frequency
distributions of contaminant concentrations 1n rivers (Sect. 2) as
functions of daily variability 1n stream discharge, sediment load,
and temperature.
Variable biological aspects of the environment are more difficult
to quantify. Little 1s typically known, for example, about the
variability of sensitivities among individuals 1n natural populations,
and long-term records of variations 1n the abundance and distribution
of organisms are uncommon. We did not quantify biological variability
among Individual organisms for the synfuels risk assessment project.
7.2.2 Parameter Uncertainty
Errors 1n parameter estimates Introduce additional uncertainties
into ecological risk estimates. Parameter values of Interest may have
to be estimated from structure-activity relationships (e.g., Kenaga and
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Goring 1980, Velth et al. 1984) or from taxonomlc correlations (e.g.,
Suter et al. 1983, Calabrese 1984). Even direct laboratory measurements
are subject to errors (e.g., confidence limits on LCcQS and variation
between replicate tests), although these are often unreported. Major
efforts 1n the synfuels risk assessment project were devoted to
quantifying uncertainties from this source. The methods described 1n
Sections 4 and 5, for example, were specifically developed to quantify
uncertainty due to (1) variations 1n sensitivity between taxonomlc
groups of organisms and (2) the variable relationship between acute and
chronic toxldty. The ecosystem uncertainty analysis described 1n
Section 6 was designed to translate uncertainties concerning effects of
contaminants on Individual species Into uncertainties regarding
ultimate ecological effects.
Unlike Inherent variability, uncertainties due to parameter error
can be reduced by Increasing the precision of measurements or by
replacing extrapolated parameter estimates with direct measurements.
Comparisons of the relative contributions of different uncertainties to
overall risk estimates provide guidance as to which parameters should
be refined. The analyses described 1n Sections 4 and 5 show, for
example, that uncertainty accumulated 1n predicting chronic effects of
contaminants from acute LC5Qs 1s far more Important than 1s
uncertainty resulting from Interspecies extrapolation of acute LCCAs.
DU
7.2.3 Model Error
Model errors constitute the least tractable source of uncertainty
1n risk assessment. Major types of model errors that have been
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Identified Include (1) using a small number of variables to represent a
large number of complex phenomena (termed aggregation error),
(2) choosing Incorrect functional forms for Interactions among
variables, and (3) setting Inappropriate boundaries for the components
of the world to be Included 1n the model. The most serious problem
associated with model error 1s that these errors frequently Involve
systematic biases whose magnitudes and directions may be difficult to
determine. One might naively think that the solution to model error 1s
to disaggregate variables and Increase the boundaries of the system
until errors are eliminated. However, as has been noted by O'Neill
(1973), there is a trade-off between model error and parameter error
such that, the more variables and processes represented 1n a model, the
greater the cost of data aqu1s1t1on and the greater the opportunity for
parameter error. For any model, a point 1s reached where adding
additional variables and parameters reduces, rather than Increases,
the accuracy of model predictions.
Although model errors can never be completely eliminated, they can
be bounded and reduced. The most straightforward method 1s to test the
model against Independent field data. However, the data necessary to
perform such tests are difficult to collect and, when collected, are
difficult to Interpret. No matter how well a model performs for one
set of environmental conditions, 1t 1s never possible to predict with
certainty Its applicability to a new set of conditions.
Empirical testing, although crucial 1n the long run for improving
the models used 1n risk assessment (Mankln et al. 1975, National
Research Council 1981), 1s unsuitable as a routine method of assessing
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model errors. However, 1t 1s still possible to evaluate model
assumptions by comparing of different models (Gardner et al. 1980).
By comparing models that use different sets of assumptions, 1t 1s
possible to assess how assumptions alter model output. This was the
principal rationale for developing both statistical (Sects. 4 and 5)
and ecological process (Sect. 6) models for the synfuels risk
assessment project. Although this procedure does not ensure that model
results will correspond to effects 1n the field, 1t can be used to
distinguish between predictions that are robust to model assumptions
and predictions that are highly sensitive to assumptions, and therefore
susceptible to serious model errors (Levins 1966, Gardner et al.
1980). The strategy of comparing different risk models was used to
Identify potentially hazardous contaminants 1n the environmental risk
assessments for Indirect (Barnthouse et al. 1985a) and direct (Suter et
al. 1984) coal liquefaction (see Sect. 7.3).
7.3 INTERPRETING ECOLOGICAL SIGNIFICANCE
The question of how large an ecological Impact 1s significant has
statistical, ecological, and societal components (Beanlands and
Dulnker 1983). In the synfuels risk assessment project, we considered
statistical and societal components, respectively, by using
probabilistic risk models and by defining end points 1n terms of
sodetally valued environmental attributes. No generally applicable
definition of ecological significance has ever been formulated
(Beanlands and Dulnker 1983); therefore, definitions must be developed
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1n the context of particular assessment objectives. We developed
operational definitions of ecological significance based on the
primary objective of the project, that 1s, the Identification of
synfuels-related contaminant classes having the greatest potential for
adverse ecological effects. Our strategy for assessing significance
Involved (1) defining, for each effects method used, a criterion below
which risks would be considered Insignificant, (2) counting, for each
contaminant class studied, the number of methods by which 1t was judged
"significant"; and (3) explaining, where possible, the failures of the
three methods to agree.
For the quotient method (Sect. 3), the significance criterion used
was an acute-effects quotient greater than 0.01, that is, a lowest
observed LC5Q less than two orders of magnitude greater than the
estimated environmental concentration. This criterion has sometimes
been used in hazard assessments for toxic chemicals. For analysis of
extrapolation error, potential ecological effects of a contaminant were
considered significant if the risk that the environmental concentration
may exceed the MATC of one or more reference fish species is greater
than 0.1. This value was chosen to avoid (1) being overly conservative
and (2) relying on risk estimates obtained from the tails of the
probability distributions for MATCs, where the reliability of
extrapolation is most questionable. For ecosystem uncertainty analysis,
contaminants were considered to pose significant risks 1f the risk of a
25% reduction in game fish biomass is greater than 0.1. This value was
selected on the basis that risks should be at least twice as high as
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the background risk resulting from environmental variability
Incorporated 1n SWACOM (about 0.04) before they are considered
significant.
Assessments of the aquatic end points 1n Indirect coal liquefaction
(Barnthouse et al. 1985a) provide an Illustration of our procedure
(only toxldty quotients were used to assess terrestrial end points).
For the fish end point, comparisons between risk estimates obtained
from all three risk methods were possible. Using at least one of the
three methods (Table 7.1), nine contaminant categories were determined
to pose potential risks to fish populations. The nine were Identified
as the classes most appropriate for refined risk assessments and/or
further research. Four contaminant classes, all trace elements or
conventional Industrial pollutants (hydrogen sulflde and ammonia),
were found significant by two or more methods and Identified as the
contaminants of greatest concern.
For the phytoplankton end point, only nickel and cadmium were
judged significant using toxlcity quotients. However, using ecosystem
uncertainty analysis, these elements, along with three other heavy
metals, and ammonia were all judged significant This result required
explanation in that, although all of the contaminants studied are
potentially toxic to phytoplankton, the end point 1n ecosystem
uncertainty analysis 1s defined as a fourfold increase in peak
phytoplankton blomass. An inspection of the model output revealed that
Indirect effects of contaminants on fish and zooplankton, rather than
direct effects on phytoplankton, were responsible for the results.
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154
Table 7.1. Contaminant classes determined to pose potentially
significant risks to fish populations by one or more of
three risk analysis methods: quotient method (QM),
analysis of extrapolation error (AEE), and ecosystem
uncertainty analysis (EUA). Separate lists were developed
for treated aqueous waste streams from two indirect coal
liquefaction processes. From Barnthouse et al. (1985)
Lurg1/F1scher-Tropsch process
Koppers-Totzek/F1scher-Tropsch process
(add gases) - QM, AEE
(alkaline gases) - QM, AEE, EUA
(volatile carboxyllc acids) - AEE
(carboxyHc adds, excluding
volatlles) - AEE
(arsenic) - AEE
(mercury) - AEE, EUA
(nickel) - EUA
(cadmium) - QM, AEE, EUA
(add gases) - QM, AEE
(alkaline gases) - QM, AEE, EUA
(volatile carboxyllc acids) - QM, AEE
(cadmium) - QM, AEE, EUA
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7.4 OTHER APPLICATIONS OF ECOLOGICAL RISK ASSESSMENT
We have not claimed to accurately predict the magnitudes of
ecological risks associated with toxic chemicals, whether or not
associated with synfuels production. However, even without such
predictions, applications of the concept of risk and, 1n some cases,
the methods described 1n this report can substantially Improve current
approaches to environmental decision-making. By (1) emphasizing
probabilities and frequencies of events and (2) explicitly quantifying
uncertainty, risk assessment can provide a more rational basis for
decisions that may otherwise be highly subjective.
For example, frequency distributions of ambient contaminant
concentrations can be used to forecast water quality Impacts or
compliance with standards. For any given benchmark concentration
(e.g., an ambient air or water quality criterion), the probability of
exceeding the benchmark can be read from the cumulative distribution
function 1n F1g. 7.1(a). The presentation of such functions would
enhance the quality of environmental Impact assessments, which commonly
are based on worst-case analyses (e.g., 7-d, 10-year low flow) of
questionable ecological significance. If the benchmark concentration
1s an action level above which contaminant discharges are not
permitted, then F1g. 7.1(a) could be used to estimate the frequency of
days on which action would be required. Probabilistic environmental
fate models that could be used for this purpose already exist (e.g.,
Parkhurst et al. 1981, Travis et al. 1983).
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156
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Fig. 7.1. Four applications of ecological risk functions. In (a), a
cumulative frequency function is used to estimate the
frequency with which the environmental concentration of a
contaminant will exceed an "action" concentration. In (b), a
cumulative probability function for the effects threhsold of a
hypothetical organism is used to select an action
concentration with a 5X chance of exceeding the true effects
threshold. In (c), probability density functions for two
components of a risk estimate are compared to identify the
component with the greater uncertainty. In (d), the risks of
adverse effects of different magnitudes are compared for two
alternative facility designs. The expected effects of the two
alternatives are the same, but alternative B presents greater
risks of severe adverse effects.
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15? ORNL-6251
Risk estimates could also be used to set standards based on
probabilities of exceeding effects thresholds. Section 4 of this
report describes a method for calculating probability distributions for
acute LC5Qs and MATCs. Figure 7.1(b) presents such a distribution
plotted as a cumulative probability function. Using this curve, the
allowable ambient concentration of a contaminant might be set so that
the risk of exceeding the threshold level 1s 5%. Figure 7.1(b) could
also be used to define the decision points In tiered hazard assessment
schemes. In this application, the decision to perform further tests on
a chemical would be determined by the risk of exceeding an LC5Q or
HATC, and by the reduction 1n uncertainty expected to result from
acquisition of additional test data.
If the contributions to total uncertainty of different components
of a risk estimate can be compared, then research effort can be
concentrated on the component(s) contributing the greatest uncertainty.
For example, 1n F1g. 7.1(c), uncertainty about the environmental
concentration of a contaminant 1s compared with uncertainty concerning
Its effects threshold. The relative variances of the two distributions
correspond roughly to the variances estimated by Suter et al. (1983) for
largemouth bass exposed to mercury released from a hypothetical Indirect
coal liquefaction plant. Barnthouse et al. (1985b) used comparisons
between variances of MATCs and of environmental concentrations
estimated for 23 synfuels-related contaminants to argue that, 1n
general, uncertainty concerning effects thresholds for contaminants
1s much larger than uncertainty concerning environmental fate.
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ORNL-6251 158
Decisions concerning alternative plant sites and mitigating
technologies could be facilitated by using risk curves like those shown
1n F1g. 7.1(d). Such curves provide Information about both the
expected effects of an action (e.g., building a plant or licensing a
chemical) and the risk of extremely large effects. Risk curves are
commonly used to assess safety-related risks (e.g., comparing
automobile travel to airplanes or earthquakes to nuclear power plant
accidents); we see no reason why they could not also be used to assess
ecological risks.
7.5 CRITICAL RESEARCH NEEDS
Given the Immaturity of the art of risk assessment, 1t would be
possible to 11st dozens of research topics that would enhance our
capabilities. Through the application of risk assessment concepts to
synfuels technologies, we have Identified four deficiencies that we
think are especially critical: (1) Insufficient understanding of
chronic effects of toxic chemicals, (2) Insufficient data on effects of
contaminants on Invertebrates, (3) poor standardization of toxidty
test systems for aquatic and terrestrial plants, and (4) Insufficient
validation of ecological risk models.
Most exposures of organisms to toxic contaminants are chronic
rather than acute. However, most research and toxidty testing to date
has been directed at acute exposures. We have shown 1n Sections 4 and
5 of this report that, at least for fish and probably also for aquatic
Invertebrates, it 1s possible to extrapolate from acute effects to
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159 ORNL-625'I
MATCs and even to population-level effects of chronic exposures. The
uncertainties associated with this extrapolation are very large,
presumably because the relationship between effective concentrations
for acute vs chronic effects 1s highly variable. Significant
reductions 1n uncertainty could be obtained 1f more effort were devoted
to chronic toxldty testing and to understanding the physiological
mechanisms responsible for chronic toxldty. In contrast, acute
effects of contaminants on fish are well studied, and our research
(Sect. 4) has shown that acute effects of contaminants on one fish
species can be extrapolated to other fish species with a relatively low
degree of uncertainty (I.e., within an order of magnitude).
A redressing of the Imbalance 1n testing effort between fish and
Invertebrates 1s needed. Modeling studies performed using SWACOM
(Sect. 6) suggest that differences 1n sensitivity between and within
trophic levels 1n aquatic ecosystems can cause responses that are
qualitatively different from those predicted on the basis of a few
standard species. Although Invertebrates are both taxonomically and
physiologically more diverse than fish, more aquatic toxldty data 1s
available for fish than for Invertebrates. Moreover, most testing of
Invertebrate responses 1s restricted to a small set of standard
organisms (e.g., Daphnia magna).
Lack of comparability of test systems limits the possibility of
any meaningful risk assessments for plants and especially terrestrial
vegetation. Suitable test systems for phytoplankton are available, all
that 1s required 1s a standardization of end points. For terrestrial
plants, interpretabUHy 1s an even greater problem than comparability.
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Many systems are of severely limited utility for risk assessment
because of the near Impossibility of relating the test end points
(e.g., reductions 1n root elongation rates) to meaningful ecological
end points. Readily Interpretable data are available only for major
combustion products, such as ozone and SO .
Lack of validation of ecological risk models, especially ecosystem
models, 1s perhaps the greatest single limitation on the future
development of ecological risk assessment. The Standard Water Column
Model, a model of the pelagic zone of a northern d1m1ct1c lake, was
used to develop ecosystem uncertainty analysis (Sect. 6), not because
such lakes are relevant to synfuels risk assessment, but because
northern d1m1ct1c lakes are by far the best understood aquatic
ecosystems. The model Itself has not been rigorously validated, but
the functional components of the model have been validated through more
than a century of 11mnolog1cal research. Because of the great expense
and difficulty of site-specific modeling efforts, 1t 1s likely that
ecosystem-level risk assessments will always be limited primarily to
site-Independent purposes, such as Identifying particular contaminants
or contaminant classes with the potential for causing Indirect
ecological effects. Even for this more limited purpose, validation
studies are needed. At a minimum, the existing case studies on
ecological effects of toxic chemicals should be synthesized to
determine how frequently Indirect effects have been observed and to
Identify the ecological processes (e.g., prey switching or reductions
1n primary production) responsible.
-------
161 ORNL-6251
Ecological risk assessment methods Inevitably represent a
compromise between the Ideal and the possible. Ideally, we would like
to quantify effects of toxic contaminants on valued ecosystem components
1n any environment of Interest, based on an understanding of fundamental
chemical, physiological, and ecological processes. Statistical models
and generic ecosystem models, such as those described 1n this report,
would then be unnecessary. Until breakthroughs 1n fundamental
understanding are achieved, however, we believe that the most
appropriate strategy for Improving our capability 1n ecological risk
assessment 1s the strategy pursued 1n the synfuels risk assessment
project, that 1s, Incremental extension of the existing state of the
art 1n ecotoxlcology and ecology.
-------
ORNL-6251 162
REFERENCES (SECTION 7)
Barnthouse, L. W., J. Boreman, S. W. Chrlstensen, C. P. Goodyear,
W. Van Winkle, and D. S. Vaughan. 1984. Population biology 1n the
courtroom: The Hudson River controversy. BloSdence 34:14-19.
Barnthouse, L. W., G. W. Suter II, C. F. Baes III, S. M. Bartell,
M. G. Cavendish, R. H. Gardner, R. V. O'Neill, and A. E. Rosen.
1985a. Environmental Risk Analysis for Indirect Coal Liquefaction.
ORNL/TH-9120. Oak Ridge National Laboratory, Oak Ridge, Tenn.
Barnthouse, L. W., G. W. Suter II, C. F. Baes III, S. H. Bartell,
R. H. Gardner, R. E. MUlemann, R. V. O'Neill, C. D. Powers,
A. E. Rosen, L. L. S1gal, and D. S. Vaughan. 1985b. Unit Release
Risk Analysis for Environmental Contaminants of Potential Concern 1n
Synthetic Fuels Technologies. ORNL/TM-9070. Oak Ridge National
Laboratory, Oak Ridge, Tenn.
Barnthouse, L. W., R. V. O'Neill, S. M. Bartell, and G. W. Suter II.
Population and ecosystem theory 1n environmental risk assessment.
IN Proc. 9th ASTH Symposium on Aquatic Toxicology and Hazard
Assessment, American Society for Testing and Materials,
Philadelphia, Penn. (1n press).
Beanlands, G. E., and P. N. Oulnker. 1983. An ecological framework
for environmental Impact assessment In Canada. Institute for
Resources and Environmental Studies, Dalhousle University, Halifax,
Nova Scotia, Canada.
-------
163 ORNL-6251
Calab'rese, E. 0. 1984. Principles of animal extrapolation. John WHey
and Sons, N.Y.
Gardner, R. H., R. V. O'Neill, J. B. Mankln, and K. D. Kumar. 1980.
Comparative error analysis of six predator-prey models. Ecology
61:323-332.
Kenaga, E. E., and C. A. I. Goring. 1980. Relationship between water
solubility, soil sorptlon, octanol-water partitioning, and
concentrations of chemicals 1n biota, pp. 78-115. IN J. G. Eaton,
P. R. Parrlsh, and A. C. HendHcks (eds.) Aquatic Toxicology.
ASTM STP 707. American Society for Testing and Materials,
Philadelphia, Penn.
Levins, R. 1966. The strategy of model building 1n population biology.
Am. Sd. 54:421-431.
Mankln, J. B., R. V. O'Neill, H. H. Shugart, and B. W. Rust. 1975.
The Importance of validation 1n ecosystem analysis, pp. 63-72.
IN G. S. Innls (ed.), New Directions 1n the Analysis of Ecological
Systems. Simulation Councils Proc. Ser. 1(1). Simulation Councils,
Inc., La Jolla, Calif.
National Research Council. 1981. Testing for Effects of Chemicals on
Ecosystems. National Academy Press, Washington, D.C.
O'Neill, R. V. 1973. Error analysis of ecological models, pp. 898-908.
IN D. J. Nelson (ed.), Rad1onucl1des 1n Ecosystems. CONF-710501.
National Technical Information Service, Springfield, Va.
-------
ORNL-6251 164
Parkhurst, M. A., Y. On1sh1, and A. R. Olsen. 1981. A risk assessment
of toxicants to aquatic life using environmental exposure estimates
and laboratory toxldty data. pp. 59-71. IN D. R. Branson and
K. I. Dlckson (eds.). Aquatic Toxicology and Hazard Assessment.
ASTM STP 737. American Society for Testing and Materials,
Philadelphia, Penn.
Suter, G. W. II, D. S. Vaughan, and R. H. Gardner. 1983. Risk
assessment by analysis of extrapolation error, a demonstration for
effects of pollutants on fish. Environ. Toxlcol. Chem. 2:369-378.
Suter, G. W. II, L. W. Barnthouse, C. F. Baes III. S. M. Bartell,
H. G. Cavendish, R. H. Gardner, R. V. O'Neill, and A. E. Rosen.
1984. Environmental Risk Analysis for Direct Coal Liquefaction.
ORNL/TM-9074. Oak Ridge National Laboratory. Oak Ridge, Tenn.
Suter, G. W. II, L. W. Barnthouse, 0. E. Breck, R. H. Gardner, and
R. V. O'Neill. 1985. Extrapolating from the laboratory to the
field: How uncertain are you? pp. 400-413. IN R. D. Cardwell,
R. Purdy, and R. C. Bahner (eds.), Aquatic Toxicology and Hazard
Assessment: Seventh Symposium. ASTM STP 854. American Society for
Testing and Materials, Philadelphia, Penn.
Travis, C. C., C. F. Baes III, L. W. Barnthouse, E. L. Etnler,
G. A. Holton, B. D. Murphy, G. P. Thompson, G. W. Suter II, and
A. P. Watson. 1983. Exposure assessment methodology and reference
environments for synfuels risk analysis. ORNL/TM-8672. Oak Ridge
National Laboratory, Oak Ridge, Tenn.
Velth, G. D., D. J. Call, and L. T. Brook. 1983. Strueture-toxlcity
relationships for fathead minnow, Plmeohales promelas; Narcotic
Industrial chemicals. Can. J.. F1sh. Aouat. Sc1. 40:743-748.
-------
APPENDIX A
Acute and Chronic Effects Data Used 1n Analysis
of Extrapolation Error
-------
Table A.I. LCso/HATC data set (units are vg/L)
DBS CHEMICAL
1 AC 222.705
2 ACEHAPHTHENE
3 ACENAPHTHENE
4 ACROLEIN
5 AG
6 AG
7 AS SULFIOE GELL
B AG THIOSULFATE COMPLEX
9 ALACHLOR
10 ALOICARB
11 AROCLOR1242
12 AROCLOR124B
13 AROCLOR124B
14 AROCLOR12S4
15 AROCLOR1260
16 AS
17 AS
IB AS
19 ATRAZ1NE
20 ATRAZINE
21 ATRAZINE
22 BENZOPHENOME
23 BRONACIL
24 CAPTAN
25 CARBARYL
26 CO
27 CO
28 CD
29 CD
30 CO
31 CO
32 CO
33 CO
34 CO
35 CO
36 CD
37 CD
38 CD
39 CO
40 CD
41 CHLORAMINE
42 CHLOROANE
43 CHLOROANE
44 CN
45 CN
SOURCE
SPEHAR ET AL. 1983
CAIRNS AND NEBEKER 19B2
LEHKE ET AL. 1983
HACEK ET AL. 1976C
DAVIES ET AL. 1978
NEBEKER ET AL. 1983
LEBLANC ET AL. 1984
LEBLANC ET AL. 1984
CALL ET AL. 1983
PICKERING AND 61 LI AH 1982
NEBEKER ET AL. 1974
DEFOE ET AL. 1978
NEBEKER ET AL. 1974
NEBEKER ET AL. 1974
DEFOE ET AL. 1978
BIODINGER 1981
CALL ET AL. 19B3B
CALL ET AL. 19B3B
HACEK ET AL. 1976B
MACEK ET AL. 1976B
HACEK ET AL. 1976B
CALL ET AL. 19B5
CALL ET AL. 1983
HERHANUTZ ET AL. 1973
CARLSON 1971
BEHOIT ET AL. 1976
CARLSON ET AL. 1982
EATON ET AL. 1978
EATON ET AL. 1978
EATON ET AL. 1978
EATON ET AL. 1978
EATON ET AL. 1978
EATON ET AL. 1978
EATON ET AL. 1978
EATON 1974
PICKERING AND GAST 1972
SAUTER ET AL. 1976
SAU1ER ET AL. 1976
SAUTER ET AL. 1976
SPEHAR 1976
ARTHUR AND EATON 1971
CAROHELL ET AL. 1977
CAROWELL ET AL. 1977
LEDUC 1978
SMITH ET AL. 1979
SPECIES
FM
FM
FM
FM
RT
RT
FM
FN
FM
FH
FM
FM
FF
FN
FM
JM
FF
FN
BG
BT
FM
FM
FM
FM
FN
BT
FF
BNT
BT
COS
LT
NP
SB
US
BG
FM
BT
CC
WE
FF
FM
BG
BT
AS
BG
CLASS
PV
PA
PA
HC
N
H
OC
CB
OC
OC
OC
OC
OC
ON
ON
ON
N
ON
OS
N
N
N
M
N
H
N
N
H
M
N
N
H
N
H
OC
OC
TYPE
ELS
ELS
ELS
LC
ELS
ELS
ELS
ELS
ELS
ELS
LC
LC
LC
LC
LC
LC
ELS
ELS
LC
LC
LC
ELS
ELS
LC
1C
LC
LC
ELS
ELS
ELS
ELS
ELS
ELS
ELS
LC
LC
ELS
ELS
ELS
1C
1C
1C
LC
ELS
LC
LCSO
0.22
60B
B4
6.5
9.2
>240
>2BO
5000
1370
300
>33
30200
14400
14200
6700
4900
15000
14800
182000
65
9000
21100
7200
2SOO
114
59
47
120
NOEC
0.03
345
139.5
11. 4
0.09
<0.1
16000
520
78
5.4
0.1
2.2
0.52
<0.1
2500
2130
2130
95
65
213
540
<1000
16.5
210
1.7
3.3
3.8
1.1
4.1
4.4
4.2
4.3
4.2
31
37
1
11
9
4.1
16
1.22
<0.32
<0.01
<5.2
LOEC
0.07
495
274
41.7
0.17
>11000
35000
1100
156
15
0.4
5.1
1.8
5000
4120
4300
500
120
B70
990
39.5
680
3.4
7.4
11.7
3.8
12.5
12.3
12.9
12.7
12.0
80
57
3
17
25
8.1
35
2.20
MATC
0.0
413.2
195.5
21.8
0.1
23664.3
756.3
110.3
9.0
0.2
3.3
1.0
3535.5
2962.4
3026.4
217.9
88.3
430.5
731.2
25.5
377.9
2.4
4.9
6.7
2.0
7.2
7.4
7.4
7.4
7.1
49.8
45.9
1.7
13.7
15.0
5.8
23.7
1.6
en
-------
Table A.I (Continued)
OBS CHEMICAL
46 CN
47 CN
48 CNS04
49 CR
50 CR
51 CR
52 CR
53 CR
54 CR
55 CR
56 CR
57 CR
58 CR
59 CR
60 CU
61 CU
62 CU
63 CU
64 CU
65 CU
66 CU
67 CU
68 CU
69 CU
70 CU
71 CU
72 CU
73 CU
74 CU
75 CU
76 CU
77 DDT
78 OI-M-BUTYL
79 OI-M-OCm
80 OIAZINON
81 OIAZINON
82 DIAZ IKON
83 OINOSEB
84 OINOSEB
85 01URON
86 OTDHAC
87 OURSBAN
88 ENOOSULFAN
89 CNDOSULFAN
90 ENDRIN
SOURCE
SHITH ET AL. 1979
SMITH Et AL. 1979
HAZEL AND KEITH 1970
BENOIT 1976
BENOIT 1976
PICKERING 1980
SAUTER ET AL. 1976
SAUTER ET AL. 1976
SAUTER ET AL. 1976
SAUTER ET AL. 1976
SAUTER ET AL. 1976
SAUTER ET AL. 1976
SAUTER ET AL. 1976
STEVENS AND CHAPMAN 1984
BENOIT 1975
HORNING AND NEIHEISEL 1979
HCKIM AND BENOIT 1971
MCKIH AND BENOIT 1974
MCKIN ET AL. 197B
HCKIN ET AL. 1978
MCKIH ET AL. 1978
MCKIN ET AL. 1978
MCKIM ET AL. 1978
MCKIM ET AL. 1978
MOUNT AND STEPHAN 1969
MOUNT 1968
PICKERING ET AL. 1977
SAUTER ET AL. 1976
SAUTER ET AL. 1976
SAUTER ET AL. 1976
SEIM ET Al. 1984
JARVINEN ET AL. 1977
PHTHALATE MCCARTHY AND MHITMORE 1985
PHTHALATE MCCARTHY AND MHITMORE 1985
ALLISON AND HERHANUTZ 1977
ALLISON AND HERHANUTZ 1977
JARVINEN AND TANNER 1982
CALL ET AL. 1983
WOODWARD 1976
CALL ET AL. 1983
LEWIS AND WEE 1983
JARVINEN ANO TANNER 1982
CARLSON ET AL. 1982
HACEK ET AL. 1976C
CARLSON ET AL. 1982
SPECIES
BT
FM
CHS
BT
RT
FH
BG
CC
LT
NP
RT
WE
WS
RT
BG
BM
BT
BT
BNT
BT
LT
NP
RT
WS
FH
FM
FH
BT
CC
WE
RT
FM
FH
FM
BT
FM
FH
FH
LT
FH
FH
FH
FN
FH
FH
CLASS
H
M
H
H
M
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
M
H
H
H
H
H
M
OC
N
N
OP
OP
ON
ON
ON
S
OP
OC
OC
OC
TYPE
PLC
LC
ELS
LC
LC
LC
ELS
ELS
ELS
ELS
ELS
ELS
ELS
ELS
LC
LC
LC
LC
ELS
ELS
ELS
ELS
ELS
ELS
LC
LC
LC
ELS
ELS
ELS
ELS
LC
ELS
ELS
PIC
LC
ELS
ELS
NS
ELS
ELS
ELS
LC
NS
LC50
68.3
129
59000
69000
36900
4400
1100
330
TOO
75
470
460
80
48
770
7800
690
700
79
14200
140
0.86
O.B6
NOEC
5.7
12.9
<0.02
200
200
1000
522
150
105
53B
51
290
48
21
4.3
9.5
22.3
21.5
22.0
34.9
11.4
12.9
10.6
14.5
38
3
12
13
16
0.5
560
3200
<0.55
3.2
50
14.5
<0.5
33.4
53
1.6
0.2
LOEC
11.2
19.6
350
350
3950
1122
305
194
963
105
>2167
53B
89
40
18
17.4
>9.4
44.5
43.5
42.3
104.4
31.7
33.8
18.4
33
60
5
18
21
31
2.0
1000
10000
13.5
90
48.5
78
90
3.2
0.4
HATC
8.0
15.9
264.6
264.6
1987.5
765.3
213.9
142.7
719.8
73.2
395.0
65.4
29.0
8.8
12.9
31.5
30.6
30.5
60.4
19.0
20.9
14.0
21.9
47.7
3.9
14.7
16.5
22.3
1.0
748.3
5656.9
6.6
67.1
26.5
51.0
69.1
2.3
0.3
er<
IS)
CO
-------
Table A.I (Continued)
DBS CHEMICAL
SOURCE
SPECIES CLASS TYPE LC50
NOEC
LOEC
HATC
91 ENORIN
92 ENORIN
93 ETHYLBENZENE
94 fCHUROTHION
95 FONOFOS
96 FURAN
97 GUTHION
98 HEPTACHLOR
99 HEXACHLOROBUTADIENE
100 HEXACHLOROCYCLOHEXANE
101 HEXACHLOROCYCLOHEXANE
102 HEXACHLOROCYCLOHEXANE
103 HEXACHLOROETHANE
104 HEXACHLOROPEMTADIENE
105 HG
106 H6
107 ISOPHORONE
108 ISOPHORONE
109 KEL1HANE
110 KEPONE
111 LAS MIXTURE
112 LAS 11.2
113 LAS 11.7
114 LAS 13.3
115 HALATH10N
116 MALATHION
117 MALATHION
118 METHYL PARATHION
119 METHYLMERCURIC CHLORIDE
120 METHYLMERCURIC CHLORIDE
121 METHYLMERCURIC CHLORIDE
122 HIREX
123 NAPTHALENE
124 NI
125 PB
126 PB
127 PB
128 PB
129 PB
130 PB
.131 PB
132 PB
133 PB
134 PENT ACHLOROE THANE
13S PENTACHLOROPHENOL
HERHANUTZ 1978
JARVINEN AND TYO 1978
EPA 1980A
KLEINER ET AL. 1984
PICKERING AND GILIAH 1982
CALL ET AL. 1985
ADELNAN ET AL. 1976
MACEK CT AL. 1976C
BENOIT CT AL. 1982
MACEK CT AL. 1976A
MACEK ET AL. 1976*
MACEK CT AL. 1976A
AHMED ET AL. 1984
EPA 19BOB
CALL ET AL. 1983B
SNARSKI AND OLSON 1982
CAIRNS AND NEBEKER 1982
LEMKE ET AL. 1983
SPEHAR ET AL. 1982
BUCKLER ET AL. 1981
PICKERING AMD THATCHER 1970
HOLMAN AND MACCK 1980
HOLMAN AND MACEK 1980
HOLMAN AND MACEK 1980
EATON 1970
CATQN 1970
HERHANUTZ 1978
JARVINEN AND TANNER 1982
MCK1N CT AL. 1976
MCKIM 1977
MCKIM 1977
BUCKLER ET AL. 19B1
DEGRAEVC ET AL. 1982
PICKERING 1974
DAVIES ET AL. 1976
HOLCOMBE CT AL. 1976
MCKIN 1977
SAUTER ET AL. 1976
SAUTER ET AL. 1976
SAUTER ET AL. 1976
SAUTtR ET AL. 1976
SAUTER ET AL. 1976
SAU1ER ET AL. 1976
AHMED ET AL. 1984
HOLCONBE ET AL. 1982
FF
FM
FM
FM
FM
FM
FM
FN
FH
BG
BT
FM
FM
FM
FH
FN
FH
FN
FH
FH
FM
FH
FH
FH
B6
FM
FF
FH
BT
FF
FM
FM
FM
FM
RT
BT
FF
BG
CC
LT
NP
RT
MS
FM
FN
OC
OC
N
CX
OP
N
OP
OC
N
N
N
N
N
H
M
HC
HC
OC
OC
S
S
S
S
OP
OP
OP
OH
ON
ON
OC
HC
H
M
M
M
M
M
M
M
H
M
N
OC
LC
LC
ELS
ELS
ELS
ELS
LC
LC
ELS
LC
LC
LC
ELS
ELS
ELS
LC
ELS
ELS
ELS
LC
LC
ELS
LC
LC
LC
LC
LC
ELS
LC
LC
LC
LC
ELS
LC
ELS
LC
LC
ELS
ELS
ELS
ELS
ELS
ELS
ELS
ELS
0.85
45300
1090
60676
7
102
30
26
69
1510
7.0
ISO
168
145000
145000
340
4350
12300
4100
860
110
10500
349
75
240
65
750
7900
27000
1170
4100
2750
7340
0.22
<0.17
130
16
8270
0.33
0.86
6.5
9.1
e.e
9.1
69
3.7
<0.23
<0.26
56000
B53S
19
1.2
630
5100
480
110
3.6
200
8.6
310
0.29
0.17
0.07
7
450
380
4.1
58
31.2
70
75
48
253
71
119
900
44.9
0.3
>440
300
33
12200
0.51
1.84
13
12.5
16.6
23.5
207
7.3
112000
15610
39
3.1
1200
8400
490
250
7.4
580
10.9
380
0.93
0.33
0.13
13
850
730
7.6
119
62.5
120
136
83
483
146
253
1400
73.0
0.3
197.5
23.0
10044.6
0.4
1.3
9.2
10.7
12.1
14.6
119.5
5.2
79196.0
11542.6
27.2
1.9
869.5
6545.2
485.0
165.8
5.2
340.6
9.7
343.2
0.5
0.2
0.1
9.5
618.5
526.7
5.6
83.1
44.2
91.7
101.0
63.1
349.6
101.8
173.5
1122.5
57.3
<£>
O
TO
I
-------
Table A.I (Continued)
MS CHEMICAL SOURCE
136 PERNETHRIN SPEHAR ET AL. 1983
137 PHENOL OEGRAEVE ET AL. I960
138 PHENOL OEGRAEVE ET AL. 1980
139 PHENOL HOLCONBE ET AL. 1982
140 PHENOLS DAUBLE ET AL. 1983
141 PHENOLS DAUBLE ET Al. 1983
142 P1CLORAH UOODUARO 1976
143 PROPANIl CALL ET AL. 1983
144 PYDRIN SPEHAR ET AL. 198?
14S SODIUM NITRILOTR1ACETATE ARTHUR ET AL. 1974
146 T-1.2-OICHLOROCYCLOHEXAHE CALL ET AL. 198S
147 TETRACHLOROETHYLENE AHNEO ET AL. 1984
148 TETRAHYQROFURAN CALL ET Al. 1985
149 TOXAPHENE MAYER ET AL. 1975
ISO TOXAPHENE MAYER ET AL. 1977
151 TOXAPHENE MAYER ET AL. 1977
152 TRIFLURAL1N MACE* ET AL. 1976C
153 VANADIUM HOLDMAY AND SPRAGUE 1979
154 ZEOLITE. TYPE A MAKI AND HACEK 1978
1SS ZN BEN01T ANO HOLCOMBE 1978
156 ZN BRINKS 1969
157 ZN HOLCOMBE ET AL. 1979
158 ZN PIERSON 1981
159 IN SIMLEY ET AL. 1974
160 ZN SPEHAR 1976
161 1,1.2-TRICHLOROETHANE AHNEO ET AL. 1984
162 1.1.2.2-TETRACHLOROETHANE AHNEO ET AL. 1984
H3 1.2-DlCHLOROBENZENE EPA 1980C
164 1.2-DICHLOROETHANE BEHOIT ET AL. 1982
165 1 .2-DICHLOROPROPANE BEN01T ET AL. 1982
166 1.7.3,4-TETRACHLOROBENZE AHMED ET AL. 1984
167 1.2.4-TRICHLDROBENZE AHMED ET AL. 1984
168 1 ,3-OICHLOROBENZENE AHMED ET AL. 1984
169 1.3-OICHLOROPROPANE BENOIT ET AL. 1982
170 1,3-DICHLOROPROPENE EPA 19800
171 1 ,4-OICHLOROBENZENE AHNEO ET AL. 1984
172 1.4-OIKETHOXYBENZENE CALL ET AL. 1985
173 2.4'OICHLOROPHENOL HOLCOMBE ET AL. 198?
174 2,4-DIHETHYLPHENOL HOLCOHBE ET AL. 1982
175 3,4-DICHLOROTOLUENE CALL ET AL. 1985
176 4-BRONOPHENYLPHENYL ETHER EPA 1980E
177 4-KETHYL-2-PENTANONE CALL ET AL. 1985
SPECIES
FH
FN
RT
FN
FM
RT
LT
FM
FN
FH
FH
FM
FN
BT
CC
FN
FH
FF
FH
FM
FM
BT
G
RT
FF
FM
FH
FM
FM
FH
FN
FH
FH
FN
FM
FM
FH
FM
FH
FM
FM
FM
CLASS
PY
HC
HC
HC
HC
HC
CX
ON
PY
S
N
N
N
OC
OC
ON
M
M
N
N
M
N
H
N
N
N
N
N
N
N
N
N
N
N
N
OC
HC
N
N
N
TYPE
ELS
ELS
ELS
CIS
ELS.R
ELS
ELS
ELS
ELS
1C
ELS
ELS
ELS
LC
1C
1C
LC
LC
ELS
LC
LC
LC
LC
LC
LC
ELS
ELS
ELS
ELS
ELS
ELS
ELS
ELS
ELS
ELS
ELS
ELS
ELS
ELS
ELS
ELS
ELS
LC50
15.6
24900
8900
1850
8600
114000
18400
13400
2160000
10.8
16.5
7.2
115
11200
>860000
600
9200
2000
5800
430
1500
81600
20400
118000
139000
1070
2760
7790
131000
4160
117600
2910
S05000
note
0.66
750
<200
1830
130
<130
<35
0.4
.19
610
1400
216000
<0.039
0.129
0.025
1.95
80
78
30
534
<173
140
26
6000
1400
1600
29000
6000
245
499
2267
8000
180
565
16600
290
1970
78
89
57000
LOEC
1.4
2500
3570
250
0.6
.33
>54000
980
2800
367000
0.299
0.054
5.1
170
>86700
145
1BO
1360
260
51
14800
4000
2500
59000
11000
412
1001
1000
16000
330
1040
27400
460
3110
148
167
105000
MATC
1.0
1369.3
2556.0
180.3
0.5
0.3
773.2
1979.9
281552.8
0.2
0.0
3.2
1U.6
106.3
73.5
852. 2
190.8
36.4
9423.4
2366.4
2000.0
41364.2
8124.0
317.
706.
1505.
11313.
243.
766.
21327.0
365.2
2475.2
107.4
121.9
77362.8
SPECIES - Species of test organism: AS - atlantlc salmon. B6 - blueglll. BH - blunt nose Minnow, BNT - brown trout,
BT - brook trout. CC - channel catfish. CHS - cMnook salmon, COS - echo salmon. FF • flagflsh. FM - fathead
minnow. 6 - guppy. JH - Japanese medaka. LT • lake trout. NP « northern pike, RT » rainbow trout. SB - snallMuth
bass, HE - walleye, and MS - white sucker.
CLASS - Chemical class : CB » carbamate pesticide. CX - carboxylate herbicide. HC - hydrocarbon. H » metal,
N - narcotic. OC « organochlorlde. OP - organophosphate pesticide, OS - organosulfur. PA > polycycllc aromatic
hydrocarbon, and PY « pyrethyroid pesticide.
TYPE - The types of tests Included: 1C - life-cycle or partial ttfe cycle, ELS - early life stage.
LCso - A 96-h median lethal concentration determined 1n the same study as the corresponding HATC. or at least 1n
the same laboratory using the same water.
NOEC - No observed effects concentration.
LOEC - Lowest observed effects concentration.
o
•yo
cr
ro
in
-------
APPENDIX B
Concentration-Response Data Sets from
Chronic Toxldty Experiments
/7
-------
173
ORNL-6251
Table B.I Concentration-Response Data Set
DBS CHEMICAL
1 ACENAPHTHENE
2 ACENAPHTHENE
3 ACENAPHTHENE
4 ACENAPHTHENE
5 ACENAPHTHENE
6 ACENAPHTHENE
7 ACENAPHTHENE
6 ACENAPHTHENE
9 ACENAPHTHENE
10 ACENAPHTHENE
11 ACENAPHTHENE
12 ACENAPHTHENE
13 ACENAPHTHENE
14 ACENAPHTHENE
15 ACENAPHTHENE
16 ACENAPHTHENE
17 ACENAPHTHENE
16 ACENAPHTHENE
19 ACROLEIN
20 ACROLEIN
21 ACROLEIN
22 ACROLEIN
23 ACROLEIN
24 ACROLEIN
25 ACROLEIN
26 ACROLEIN
27 ACROLEIN
26 ACROLEIN
29 ACROLEIN
30 ACROLEIN
31 ACROLEIN
32 ACROLEIN
33 ACROLEIN
34 ACROLEIN
35 AC222.705
36 AC222.705
37 AC222.705
38 AC222.705
39 AC222.705
40 AC222.705
41 AC222.705
42 AC222.705
43 AC222.705
44 AC222.705
45 AC222.705
46 AC222.705
47 AC222.705
48 AC222.705
49 AC222.705
SO AC222.70S
51 AC222.705
52 AC222.705
S3 AG
54 AG
SPECIES PARAH
FH
FM
FM
FH
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FH
FH
FH
FM
FM
FH
FH
FM
FH
FM
FM
FH
FH
FM
FH
FH
FM
FM
FM
FH
FH
FH
FH
FH
FH
FH
RT
RT
HORTS
HORT5
NORT5
HORTS
HORTS
HORTS
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
HATCH
HATCH
HATCH
HATCH
HATCH
HORT1
NORT1
MORT1
HORT1
HORT1
HORT1
MORT2
MORT2
HORT2
NORT2
MORT2
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
HORT2
HORT2
HORT2
HORT2
MORT2
MORT2
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
MORT2
MORT2
DOSE NTESTEO RESPONSE EGGS WEIGHT SOURCE
0.00
197.00
345.00
509.00
682.00
1153.00
0.00
197.00
345.00
509.00
6B2.00
1153.00
0.00
69.50
139.50
274.00
533.50
1025.50
0.00
4.60
6.40
11.40
41.70
0.00
4.60
6.40
11.40
20.80
41.70
0.00
4.60
6.40
11.40
41.70
0.00
0.02
0.03
0.07
0.13
0.29
0.00
0.02
0.03
0.07
0.13
0.29
0.00
0.02
0.03
0.07
0.13
0.29
0.00
0.10
30
37
33
32
33
33
500
750
600
600
250
30
30
30
30
15
30
160
160
160
160
80
100
100
100
100
100
100
60
60
60
60
60
60
123
77
6
5
4
9
18
32
44
118
76
114
48
2
4
7
2
5
2
77
76
56
108
78
9
4
4
8
100
100
5
8
9
15
59
60
23
17
CAIRNS AND NEBEKER 19B2
CAIRNS AND NEBEKER 1982
CAIRNS AND NEBEKER 1962
CAIRNS AND NEBEKER 1982
CAIRNS AND NEBEKER 1982
CAIRNS AND NEBEKER 1962
0.02 CAIRNS AND NEBEKER 1982
0.02 CAIRNS AND NEBEKER 1982
0.02 CAIRNS AND NEBEKER 1982
0.02 CAIRNS AND NEBEKER 1982
0.01 CAIRNS AND NEBEKER 1982
0.00 CAIRNS AND NEBEKER 1982
0.20 LEMKE ET AL 1983
0.18 LEMKE ET AL 1983
0.19 LEMKE ET AL 1983
0.15 LEMKE ET AL 1983
0.13 LEMKE ET AL 1983
O.OB LEMKE ET AL 1983
MACEK ET AL 1976C
KACEK ET AL 1976C
MACEK ET AL 1976C
NACEK ET AL 1976C
HACEK ET AL 1976C
MACEK ET AL 1976C
NACEK ET AL 1976C
MACEK ET AL 1976C
NACEK ET AL 1976C
HACEK ET AL 1976C
NACEK ET AL 1976C
NACEK ET AL 1976C
NACEK ET AL 1976C
NACEK ET AL 1976C
NACEK ET AL 1976C
NACEK ET AL 1976C
SPEHAR ET AL 1983
SPEHAR ET AL 1983
SPEHAR ET AL 1983
SPEHAR ET AL 1983
SPEHAR ET AL 1983
SPEHAR ET AL 1983
SPEHAR ET AL 1983
SPEHAR ET AL 1983
SPEHAR ET AL 1983
SPEHAR ET AL 1983
SPEHAR ET AL 1983
SPEHAR ET AL 1983
0.13 SPEHAR ET AL 1983
0.13 SPEHAR ET AL 1983
0.13 SPEHAR ET AL 1983
0.13 SPEHAR ET AL 1983
0.11 SPEHAR ET AL 1983
0.00 SPEHAR ET AL 1983
HE6EKER ET AL 1983
NEBEKER ET AL 1983
-------
ORNL-6251
174
Table B.I (Continued)
DBS
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
CHEMICAL
AG
AG
AG
AG
AG
AG
AG
AG
AG
AG
AG
AG
AG
AG
AG
AG
AG
AG
AG
AG
AG THIOSULFATE
AG THIOSULFATE
AG THIOSULFATE
AG THIOSULFATE
AG THIOSULFATE
AG THIOSULFATE
AG THIOSULFATE
AG THIOSULFATE
AG THIOSULFATE
AG THIOSULFATE
AG THIOSULFATE
AG THIOSULFATE
AG THIOSULFATE
AG THIOSULFATE
AG THIOSULFATE
AG THIOSULFATE
AG THIOSULFATE
AG THIOSULFATE
ALACHLOR
ALACHLOR
ALACHLOR
ALACHLOR
ALACHLOR
ALACHLOR
ALACHLOR
ALACHLOR
ALACHLOR
ALACHLOR
ALACHLOR
ALACHLOR
ALACHLOR
ALACHLOR
ALACHLOR
ALACHLOR
SPECIES PARAM
COMPL
COMPL
COMPL
COMPL
com
COMPL
COMPL
COMPL
COMPL
COMPL
COMPL
COHPL
COMPL
COMPL
COMPL
COMPL
COMPL
COMPL
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FH
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FH
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
NORT2
MORT2
MORT2
MORT2
HORT2
MORT2
NORT2
NORT2
MORT2
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
MORT2
MORT2
MORI 2
MORT2
MORT2
MORT2
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
MORT2
MORT2
MORI 2
MORT2
MORT2
MORT2
WEIGHT
WEIGHT
WEIGHT
WEIGHT
DOSE MTESTEO RESPONSE EGGS WEIGHT SOURCE
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
1
1
1
0
10
16
35
64
140
0
10
16
35
64
140
0
10
16
35
64
140
0
60
140
260
520
1100
0
60
140
260
520
1100
0
60
140
260
.13
.20
.24
.36
.51
.70
.06
.32
.95
.00
.10
.13
.20
.24
.36
.51
.70
.06
.32
.95
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
62
52
46
39
36
44
61
33
38
120
120
120
120
120
120
BO
80
BO
80
80
80
200
200
200
200
200
200
60
60
60
60
60
60
11
5
5
13
14
21
39
33
36
13
7
6
10
12
102
5
5
5
10
58
80
58
60
68
51
48
53
11
7
4
4
1
10
NEBEKER
MEBEKER
NEBEKER
MEBEKER
NEBEKER
MEBEKER
MEBEKER
NEBEKER
NEBEKER
31.70 NEBEKER
29.50 MEBEKER
29.40 MEBEKER
30.00 NEBEKER
29.80 NEBEKER
28.60 NEBEKER
28.90 NEBEKER
28.10 NEBEKER
24.70 NEBEKER
NEBEKER
NEBEKER
LEBLANC
LEBLANC
LEBLANC
LEBLANC
LEBLANC
LEBLANC
LEBLANC
LEBLANC
LEBLANC
LEBLANC
LEBLANC
LEBLANC
0.10 LEBLANC
0.12 LEBLANC
0.12 LEBLANC
0.08 LEBLANC
0.04 LEBLANC
LEBLANC
CALL ET
CALL ET
CALL ET
CALL ET
CALL ET
CALL ET
CALL ET
CALL ET
CALL ET
CALL ET
CALL ET
CALL ET
0.48 CALL ET
0.43 CALL ET
0.42 CALL ET
0.40 CALL ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL 1983
AL 1983
AL 1983
AL 1983
AL 1983
AL 1983
AL 1983
AL 1983
AL 1983
AL 1983
AL 1983
AL 1983
AL 1983
AL 1983
AL 1983
AL 1983
AL 1983
AL 1983
AL 1983
AL 1983
AL 1984
AL 1984
AL 1984
AL 1984
AL 1984
AL 1984
AL 1984
AL 1984
AL 1984
AL 1984
AL 1984
AL 1984
AL 1984
AL 1984
AL 1984
AL 1984
AL 1984
AL 1984
1983
1983
1983
1983
1983
1983
1983
1983
1983
1983
1983
1983
1983
1983
1983
1983
-------
175
ORNL-6251
Table B.I (Continued)
DBS CHEMICAL
109 ALACHLOR
110 ALACHLOR
111 ALDICARB
112 ALDICARB
113 ALDICARB
114 ALDICARB
115 ALDICARB
116 ALDICARB
117 ALDICARB
118 ALDICARB
119 ALDICARB
120 ALOICARB
121 ALDICARB
122 ALDICARB
123 ALDICARB
124 ALDICARB
125 ALDICARB
126 ALDICARB
127 ALDICARB
126 ALDICARB
129 AROCLOR1242
130 AROCLOR1242
131 AROCLOR1242
132 AROCLOR1242
133 AROCLOR1242
134 AROCLOR1242
135 AROCLOR1242
136 AROCLOR1242
137 AROCLOR1242
138 AROCLOR1242
139 AROCLOR1242
140 AROCLOR124B
141 AROCLOR124B
142 AROCLOR1246
143 AROCLOR124B
144 AROCLOR124B
145 AROCLOR1248
146 AROCLOR1248
147 AROCLOR124B
14B AROCLOR1246
149 AROCLOR124B
150 AROCLOR124B
151 AROCLOR124B
152 AROCLOR124B
153 AROCLOR1248
154 AROCLOR1248
155 AROCLOR124B
156 AROCLOR1246
157 AROCLOR1254
158 AROCLOR12S4
159 AROCLOR1254
160 AROCLOR12S4
161 AROCLOR1254
162 AROCLOR12S4
SPECIES PARAH
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FH
FM
FM
FH
FM
FM
FM
FM
FF
FF
FF
FF
FF
FF
FF
FF
FF
FF
FF
FF
FM
FM
FM
FM
FM
FH
WEIGHT
WEIGHT
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
HORT2
MORT2
HORT2
MORT2
MORT2
MORT2
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
EGGS
EGGS
EGGS
EGGS
EGGS
MORT4
MORT4
MORT4
MORT4
MORT4
NORT4
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
HORT2
MORT2
MORT2
MORT2
HORT2
MORT2
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
EGGS
EGGS
E6GS
EGGS
EGGS
EGGS
DOSE NTESTED RESPONSE EGGS
520.00
1100.00
0.00
20.00
38.00
78.00
156.00
340.00
0.00
20.00
38.00
78.00
156.00
340.00
0.00
20.00
38.00
78.00
156.00
340.00
0.00
2.90
5.40
15.00
51.00
0.00
0.86
2.90
5.40
15.00
51.00
0.00
0.10
0.40
1.10
3.00
0.00
0.18
0.54
2.20
5.10
18.00
0.00
0.18
0.54
2.20
5.10
18.00
0.00
0.23
0.52
1.80
4.60
15.00
100
100
100
100
100
100
BO
60
80
80
80
80
20
20
20
20
20
20
20
20
20
20
20
20
5
3
4
4
3
3
7
9
8
7
47
64
442
283
152
0
0
0
2
0
3
13
20
0
2
0
3
13
20
254
222
557
107
0
0
WEIGHT SOURCE
0.42 CALL ET AL 1983
0.32 CALL ET AL 1983
PICKERING AND GILIAM 1982
PICKERING AND GILIAM 1982
PICKERING AND GILIAM 1982
PICKERING AND GILIAM 1982
PICKERING AND GILIAM 1982
PICKERING AND GILIAM 1982
PICKERING AND GILIAM 1982
PICKERING AND GILIAM 1982
PICKERING AND GILIAM 1982
PICKERING AND GILIAM 1982
PICKERING AND GILIAM 1982
PICKERING AND GILIAM 1982
0.15 PICKERING AND GILIAM 1982
0.14 PICKERING AND GILIAM 1982
0.14 PICKERING AND GILIAM 1982
0.14 PICKERING AND GILIAM 1982
0.12 PICKERING AND GILIAM 1982
0.08 PICKERING AND GILIAM 19B2
NEBEKER ET AL 1974
NEBEKER ET AL 1974
NEBEKER ET AL 1974
NEBEKER ET AL 1974
NEBEKER ET AL 1974
NEBEKER ET AL 1974
NEBEKER ET AL 1974
NEBEKER ET AL 1974
NEBEKER ET AL 1974
NEBEKER ET AL 1974
NEBEKER ET AL 1974
0.15 DEFOE ET AL 1978
0.14 DEFOE ET AL 197B
0.12 DEFOE ET AL 1978
0.11 DEFOE ET AL 1978
0.10 DEFOE ET AL 1978
NEBEKER ET AL 1974
NEBEKER ET AL 1974
NEBEKER El AL 1974
NEBEKER ET AL 1974
NEBEKER ET AL 1974
NEBEKER El AL 1974
4.33 NEBEKER El AL 1974
3.90 NEBEKER El AL 1974
4.47 NEBEKER El AL 1974
3.02 NEBEKER El AL 1974
0.60 NEBEKER ET AL 1974
NEBEKER El AL 1974
NEBEKER £1 AL 1974
NEBEKER ET AL 1974
NEBEKER El AL 1974
NEBEKER El AL 1974
NEBEKER ET AL 1974
NEBEKER El AL 1974
-------
ORNL-6251
176
Table B.I (Continued)
DBS CHEMICAL
163 AROCLOR12S4
164 AROCLOR1254
US A80CLOR1254
166 AROCLOR12S4
U7 AS
166 AS
169 AS
170 AS
171 AS
172 AS
173 AS
174 AS
175 AS
176 AS
177 AS
176 AS
179 AS
1BO AS
181 AS
1B2 AS
183 AS
184 AS
185 AS
186 AS
187 AS
188 AS
189 AS
190 AS
191 AS
192 AS
193 AS
194 AS
195 AS
196 AS
197 ATRAZINE
198 ATRA7INE
199 ATRAZINE
200 ATRAZINE
201 ATRAZINE
202 ATRA21NE
203 ATRAZINE
204 ATRAZINE
205 ATRAZINE
206 ATRAZINE
207 ATRAZINE
208 ATRAZINE
209 ATRAZINE
210 ATRAZINE
211 ATRAZINE
212 ATRAZINE
213 ATRAZINE
214 ATRAZINE
215 ATRAZINE
216 ATRAZINE
SPECIES PARAH
FH
FH
FH
FH
FF
FF
FF
FF
FF
FF
FF
FF
FF
FF
FF
FF
FH
FH
FH
FH
FH
FH
FH
FH
FH
FH
FH
FH
FH
FH
FH
FH
FH
FH
66
86
BG
BG
B6
BG
BG
BG
BG
BG
BG
BG
BG
BG
BG
BG
BG
BG
BG
BG
HATCH
HATCH
HATCH
HATCH
HORT2
HORT2
HORT2
HORT2
HORT2
HORT2
WEIGHT
HEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
MORT2
MORT2
MORT2
HORT2
MORT2
MORT2
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
EGGS
E6GS
EGGS
EGGS
EGGS
EGGS
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
HORT1
HOST)
HORT1
HORT1
NORT1
MORT1
HORT2
MORT2
DOSE NTESTED RESPONSE EGGS
0.00
0.23
0.52
l.BO
0.00
1240.00
2130.00
4120.00
7570.00
16300.00
0.00
1240.00
2130.00
4120.00
7570.00
16300.00
0.00
1060.00
2130.00
4300.00
7370.00
16500.00
0.00
1060.00
2130.00
4300.00
7370.00
16500.00
0.00
1060.00
2130.00
4300.00
7370.00
16500.00
0.00
8.00
14.00
25.00
49.00
95.00
0.00
8.00
14.00
25.00
49.00
95.00
0.00
8.00
14.00
25.00
49.00
95.00
0.00
8.00
400
272
720
350
40
40
40
40
40
40
200
200
200
200
200
200
40
40
40
40
40
40
1400
600
2400
1200
600
BOO
20
20
20
20
20
20
100
100
103
122
264
116
9
6
8
2
7
10
34
27
40
25
40
44
2
12
4
9
1
31
8735
15254
7460
5153
7331
7676
224
204
456
156
60
72
1
3
0
1
1
3
78
57
WEIGHT SOURCE
NEBEKER ET AL 1974
NEBEKER ET AL 1974
NEBEKER ET AL 1974
NEBEKER ET AL 1974
CALL ET AL 19838
CALL ET AL 1983B
CALL ET AL 1983B
CALL ET AL 1983B
CALL ET AL 1983B
CALL ET AL 1983B
0.06 CALL ET AL 1983B
0.05 CALL ET AL 1983B
0.05 CALL ET AL 19838
0.04 CALL ET AL 19838
0.03 CALL ET AL 19838
0.03 CALL ET AL 1983B
CALL ET AL 1983B
CALL ET AL 1983B
CALL ET AL 1983B
CALL ET AL 19B3B
CALL ET AL 19838
CALL ET AL 19B3B
CALL ET AL 19838
CALL ET AL 1983B
CALL ET AL 19838
CALL ET AL 1983B
CALL ET AL 19B3B
CALL ET AL 1983B
0.06 CALL ET AL 1983B
0.06 CALL ET AL 1983B
0.05 CALL ET AL 1983B
0.04 CALL ET AL 1983B
0.03 CALL ET AL 1983B
0.01 CALL ET AL 19B3B
NACEK ET AL 1976A
MACEK ET AL 1976A
NACEK ET AL 1976A
HACEK ET AL 1976A
HACEK ET AL 1976A
HACEK ET AL 1976A
HACEK ET AL 1976A
HACEK ET AL 1976A
HACEK ET AL 1976A
HACEK ET AL 1976A
HACEK ET AL 1976A
HACEK ET AL 1976A
NACEK ET AL 1976A
NACEK ET AL 1976A
NACEK ET AL 1976A
NACEK ET AL 1976A
NACEK ET AL 1976A
NACEK ET AL 1976A
NACEK ET AL 1976A
NACEK ET AL 1976A
-------
177
ORNL-6251
Table B.I (Continued)
DBS CHEMICAL
217 ATRAZlNE
218 ATRAZlNE
219 ATRAZlNE
220 ATRAZlNE
221 ATRAZlNE
222 ATRAZlNE
223 ATRAZlNE
224 ATRAZlNE
225 ATRAZlNE
226 ATRAZlNE
227 ATRAZlNE
22B ATRAZlNE
229 ATRAZlNE
230 ATRAZlNE
231 ATRAZlNE
232 ATRAZlNE
233 ATRAZlNE
234 ATRAZlNE
235 ATRAZlNE
236 ATRAZlNE
237 ATRAZlNE
238 ATRAZlNE
239 ATRAZlNE
240 ATRAZlNE
241 ATRAZlNE
242 ATRAZlNE
243 ATRAZlNE
244 ATRAZlNE
245 ATRAZlNE
246 ATRAZlNE
247 ATRAZlNE
248 ATRAZlNE
249 ATRAZlNE
250 ATRAZlNE
251 ATRAZlNE
252 ATRAZlNE
253 ATRAZlNE
254 ATRAZlNE
255 BROMACIL
256 BROHACIL
257 BROHAC1L
258 BROHACIL
259 BROHACIL
260 BROMACIL
261 BROMACIL
262 BROHACIL
263 BRONACU
264 BROHACIL
265 BROHACIL
266 BROHACIL
267 BROHACIL
26B BROMACIL
269 BROHACIL
270 BROHACIL
SPECIES PARAH
BG
BG
BG
BG
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
FH
FM
FH
FM
FH
FM
FH
FM
FH
FM
FM
FM
FH
FM
FM
FM
FH
FN
FM
FM
FH
FH
FH
FM
FM
FM
FH
FH
FM
FM
FH
FH
MORT2
MORT2
MORT2
MORT2
EGGS
EGGS
EGGS
EGGS
E&GS
EGGS
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
MORT2
MORT2
NORT2
MORT2
NORT2
HORT2
HATCH
HATCH
HATCH
HATCH
HATCH
MORT1
MORT1
MORT1
MORT1
NORT1
MORT1
MORT2
HORT2
MORT2
MORT2
HORT2
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
MORT2
HORT2
MORT2
NORT2
MORT2
NORT2
HEIGHT
WEIGHT
WEIGHT
WEIGHT
DOSE NTESTEO RESPONSE EGGS
14.00
25.00
49.00
95.00
0.00
65.00
120.00
240.00
450.00
720.00
0.00
65.00
120.00
240.00
450.00
720.00
0.00
65.00
120.00
240.00
450.00
720.00
0.00
15.00
54.00
112.00
213.00
0.00
15.00
33.00
54.00
112.00
213.00
0.00
15.00
54.00
112.00
213.00
0.00
1000.00
1900.00
4400.00
12000.00
29000.00
0.00
1000.00
1900.00
4400.00
12000.00
29000.00
0.00
1000.00
1900.00
4400.00
200
100
SO
50
100
100
100
100
50
100
100
100
100
100
100
100
3800
16SO
1550
2450
1600
30
30
30
30
30
30
200
240
160
240
160
200
200
200
200
200
200
60
60
60
60
60
60
130
58
40
41
327
400
389
437
168
259
49
70
30
54
26
67
49
SB
60
80
72
90
642
308
254
510
369
2
5
5
6
7
6
55
110
72
77
43
76
72
92
93
90
72
7
3
1
1
5
7
WEIGHT SOURCE
MACEK ET At 1976A
HACEK ET AL 1976A
MACEK ET AL 1976A
MACEK ET AL 1976A
MACEK ET AL 1976A
MACEK ET AL 1976A
MACEK ET AL 1976A
MACEK ET AL 1976A
MACEK ET AL 1976A
MACEK ET AL 1976A
MACEK ET AL 1976A
MACEK ET AL 1976A
MACEK ET AL 1976A
MACEK ET AL 1976A
MACEK ET AL 1976A
MACEK ET AL 1976A
MACEK ET AL 1976A
MACEK ET AL 1976A
MACEK ET AL 1976A
MACEK ET AL 1976 A
HACEK ET AL 1976A
MACCK ET AL 1976A
MACEK ET AL 1976 A
MACEK ET AL 1976A
MACEK ET AL 1976A
NACEK ET AL 1976A
MACEK ET AL 1976A
MACEK ET AL 1976A
NACEK ET AL 1976A
MACEK ET AL 1976A
MACEK ET AL 1976A
MACEK ET AL 1976A
MACEK ET AL 1976A
MACEK ET AL 1976A
MACEK ET AL 1976A
NACEK ET AL 1976A
NACEK ET AL 1976A
MACEK ET AL 1976A
CALL ET AL 1983
CALL ET AL 1983
CALL ET AL 1983
CALL ET AL 1983
CALL ET AL 1983
CALL ET AL 1983
CALL ET AL 1983
CALL ET AL 1983
CALL ET AL 1983
CALL ET AL 1983
CALL ET AL 1983
CALL ET AL 1983
0.47 CALL ET AL 1983
0.41 CALL ET AL 1983
0.42 CALL ET AL 1983
0.38 CALL ET AL 19B3
-------
ORNL-6251
178
Table B.I (Continued)
DBS
271
272
273
274
275
276
277
278
279
280
261
262
263
264
265
266
287
266
289
290
291
292
293
294
295
296
297
296
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
316
319
320
321
322
323
324
CHEMICAL
BRONAC1L
BRONACIL
CAPTAN
CAPTAN
CAPTAN
CAPTAN
CAPTAN
CAPTAN
CAPTAN
CAPTAN
CAPTAN
CAPTAN
CAPTAN
CAPTAN
CAPTAN
CAPTAN
CAPTAN
CAPTAN
CAPTAN
CAPTAN
CAPTAN
CAPTAN
CAPTAN
CAPTAN
CAPTAN
CAPTAN
CARBARYL
CARBARYL
CARBARYL
CARBARYL
CARBARYL
CARBARYL
CARBARYL
CARBARYL
CARBARYL
CARBARYL
CARBARYL
CARBARYL
CARBARYL
CARBARYL
CARBARYL
CARBARYL
CARBARYL
CARBARYL
CARBARYL
CARBARYL
CARBARYL
CARBARYL
CARBARYL
CARBARYL
CO
CD
CO
CD
SPECIES PAR AM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FH
FM
FM
FM
FM
FM
FM
FH
FM
FH
FM
FH
FH
FM
FM
FM
FH
FH
FH
FH
FH
FH
FH
FH
FH
FH
FH
FH
FH
FH
FH
FM
FM
FM
FM
BT
BT
BT
BT
WEIGHT
WEIGHT
EGGS
EGGS
EGGS
EGGS
EGGS
EGGS
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
NORT1
MORT1
MORT1
HORT1
MORT1
NORT1
HORT2
MORT2
MORT2
MORT2
MORT2
NORT2
EGGS
EGGS
EGGS
EGGS
EGGS
EGGS
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
MORT2
MORT2
HORT2
HORT2
MORT2
HORT2
MORT4
HORT4
MORT4
MORT4
MORT4
MORT4
EGGS
EGGS
EGGS
EGGS
OOSE NTESTED RESPONSE
12000
29000
0
3
7
16
39
63
0
3
7
16
39
63
0
3
7
16
39
63
0
3
7
16
39
63
0
8
17
62
210
680
0
8
17
62
210
680
0
8
17
62
210
660
0
8
17
62
210
680
0
0
0
1
.00
.00
.00
.30
.40
.80
.50
.50
.00
.30
.40
.60
.50
.50
.00
.30
.40
.80
.50
.50
.00
.30
.40
.80
.50
.50
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.06
.50
.90
.70
EGGS
WEIGHT SOURCE
0.37 CALL ET AL 1983
0.33 CALL ET AL 19B3
1900
1350
1150
800
150
400
30
30
30
30
30
30
320
320
320
320
240
320
1360
1120
1360
920
1920
320
100
100
100
100
100
100
20
20
20
20
20
20
531
347
173
95
26
125
1
1
0
1
7
30
93
128
143
118
164
320
484
553
539
348
126B
320
8
54
18
34
13
60
6
7
4
4
7
10
1653
1024
795
422
40
683
1070
624
265
723
11
502
244
454
260
HERMANUTZ ET
HERHANUTZ ET
HERMANUTZ ET
HERMANUTZ ET
HERMANUTZ ET
HERMANUTZ ET
HERMANUTZ ET
HERHANUTZ ET
HERMANUTZ ET
HERMANUTZ ET
HERMANUTZ ET
HERMANUTZ ET
HERMANUTZ ET
HERMANUTZ ET
HERMANUTZ ET
HERMANUTZ ET
HERMANUTZ ET
HERMANUTZ ET
HERMANUTZ ET
HERMANUTZ ET
HERMANUTZ ET
HERMANUTZ ET
HERMANUTZ ET
HERMANUTZ ET
CARLSON 1971
CARLSON 1971
CARLSON 1971
CARLSON 1971
CARLSON 1971
CARLSON 1971
CARLSON 1971
CARLSON 1971
CARLSON 1971
CARLSON 1971
CARLSON 1971
CARLSON 1971
CARLSON 1971
CARLSON 1971
CARLSON 1971
CARLSON 1971
CARLSON 1971
CARLSON 1971
CARLSON 1971
CARLSON 1971
CARLSON 1971
CARLSON 1971
CARLSON 1971
CARLSON 1971
BENOI1 ET AL
BENOI1 ET AL
BEN01T ET AL
BEN011 ET AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
1973
1973
1973
1973
1973
1973
1973
1973
1973
1973
1973
1973
1973
1973
1973
1973
1973
1973
1973
1973
1973
1973
1973
1973
1976
1976
1976
1976
-------
179
ORNL-6251
Table B.I (Continued)
DBS
325
326
327
326
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
34B
349
350
351
352
353
3S4
355
356
357
356
359
360
361
362
363
364
365
366
367
36B
369
370
371
372
373
374
375
376
377
378
CHEMICAL
CO
CD
CD
CD
CD
CO
CD
CO
CD
CO
CO
CD
CD
CD
CO
CO
CD
CD
CD
CO
CD
CD
CD
CD
CO
CD
CO
CD
CD
CO
CO
CO
CD
CD
CO
CD
CD
CD
CD
CO
CD
CD
CD
CO
CD
CO
CO
CD
CD
CO
CD
CD
CO
CO
SPECIES PARAM
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
FF
FF
FF
FF
FF
FF
FF
FF
FF
FF
FF
FF
FF
FF
FF
FF
FF
FF
FF
FF
FF
BG
BG
B6
BG
BG
BG
BG
BG
BG
BG
BG
BG
BG
BG
BG
BG
BG
BG
BG
FH
FH
EGGS
HORT1
MORT1
MORT1
HORT1
MORT1
MORT1
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
EGGS
EGGS
EGGS
EGGS
EGGS
MORT1
HORT1
HORT1
NORT1
MORT1
NORT1
MORT2
MORI 2
NORT2
MORT2
HORT2
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
HATCH
HATCH
HATCH
HATCH
HATCH
HORT1
HORTl
MORT1
HORTl
HORTl
HORTl
NORT2
HORT2
NORT2
HORT2
WEIGHT
WEIGHT
WEIGHT
WEIGHT
EGGS
EGGS
DOSE NTESTEO RESPONSE EGGS
3
0
0
0
1
3
6
0
0
0
1
3
0
1
3
7
15
0
1
3
7
IS
30
0
1
3
7
15
0
1
3
7
15
2
31
80
239
2140
2
31
80
239
757
2140
2
31
80
239
2
31
80
239
1
7
.40
.06
.50
.85
.65
.40
.35
.06
.50
.90
.70
.40
.00
.80
.70
.50
.00
.00
.60
.70
.50
.00
.00
.00
.80
.70
.50
.00
.00
.80
.70
.50
.00
.30
.00
.00
.00
.00
.30
.00
.00
.00
.00
.00
.30
.00
.00
.00
.30
.00
.00
.00
.00
.80
5
10
10
10
10
10
14
14
14
14
8
1
40
40
40
40
11
300
100
550
150
100
18
18
IB
IB
18
IB
100
100
100
100
98
0
0
0
0
5
10
1086
912
890
636
23
1
2
6
0
6
1
7
3
3
4
2
19
7
41
54
20
0
0
9
16
18
IB
22
40
90
100
1468
1704
WEIGHT
3.63
3.32
3.42
3.B1
1.80
17.40
2S.30
22.70
30.50
17.50
0.40
0.54
0.01
0.00
SOURCE
BENOIT ET AL 1976
BENOIT ET AL 1976
BENOIT ET AL 1976
BENOIT ET AL 1976
BENOIT ET AL 1976
BENOIT ET AL 1976
BENOIT ET AL 1976
BENOIT ET AL 1976
BENOIT ET AL 1976
BENOIT ET AL 1976
BENOIT ET AL 1976
BENOIT ET AL 1976
CARLSON ET
CARLSON ET
CARLSON ET
CARLSON ET
CARLSON ET
CARLSON ET
CARLSON ET
CARLSON ET
CARLSON ET
CARLSON ET
CARLSON ET
CARLSON ET
CARLSON ET
CARLSON ET
CARLSON ET
CARLSON ET
CARLSON ET
CARLSON ET
CARLSON ET
CARLSON ET
CARLSON ET
EATON 1974
EATON 1974
EATON 1974
EATON 1974
EATON 1974
EATON 1974
EATON 1974
EATON 1974
EATON 1974
EATON 1974
EATON 1974
EATON 1974
EATON 1974
EATON 1974
EATON 1974
EATON 1974
EA10N 1974
EATON 1974
EATON 1974
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
PICKERING AND
PICKERING AND
1982
1982
1982
1982
1982
1982
1982
1982
1982
1982
1982
1982
1982
1982
1982
1982
1982
1982
1982
1982
1982
CAST 1972
GAS1 1972
-------
ORNL-6251
180
Table B.I (Continued)
DBS CHEMICAL
379 CD
360 CD
381 CD
3B2 CO
383 CD
384 CD
385 CD
3B6 CD
3B7 CD
386 CD
389 CD
390 CD
391 CD
392 CD
393 CD
394 CD
395 CD
396 CO
397 CD
39B CD
399 CD
400 CD
401 CD
402 CD
403 CD
404 CD
405 CD
406 CD
407 CO
408 CD
409 CD
410 CD
411 CO
412 CD
413 CO
414 CO
415 CD
416 CD
417 CD
41B CD
419 CD
420 CD
421 CD
422 CD
423 CO
424 CD
425 CD
426 CD
427 CD
42B CD
429 CD
430 CD
431 CHLORAMINE
432 CHLORAHINE
SPECIES PARAM
FM
FM
FM
FN
FM
FM
FM
FM
FM
FM
FM
FM
FH
FM
FM
FM
FM
FM
FM
FM
FM
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
FF
FF
FF
FF
FF
FF
FF
FF
FF
FF
FF
FF
FF
FF
FF
FF
FF
FH
FH
EGGS
E6GS
EEGS
EGGS
HATCH
HATCH
HATCH
HATCH
HATCH
MORT1
HORT1
HORT1
MORT1
HORT1
MORT1
MORT2
MORT2
MORT2
MORT2
MORT2
MORT2
MORT2
HORT2
MORT2
MORT2
MORT2
MORT2
MORT2
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
EGGS
EGGS
EGGS
EGGS
EGGS
EGGS
HATCH
HATCH
HATCH
HATCH
HATCH
MORT1
MORT1
HORT1
MORT1
MORT1
MORT1
MORT1
MORT1
DOSE NTESTED RESPONSE EGGS
14.00
27.00
57.00
110.00
1.00
7.80
14.00
27.00
57.00
1.00
7.80
14.00
27.00
57.00
110.00
1.20
6. BO
15.00
29.00
57.00
110.00
0.00
1.00
3.00
6.00
10.00
24.00
47.00
0.00
1.00
3.00
6.00
10.00
24.00
47.00
0.11
0.17
4.10
B.10
16.00
31.00
0.11
1.70
4.10
8.10
16.00
0.11
1.70
4.10
8. 10
16.00
31.00
0.00
6.60
100
100
100
100
100
eo
BO
80
BO
BO
80
50
50
SO
50
50
50
400
400
400
400
400
400
400
40
40
40
40
40
60
60
60
60
60
60
10
10
4606
1448
962
403
5
4
5
6
22
24
25
33
30
30
66
17
17
2
25
16
42
0
105
82
243
320
352
392
665
768
660
283
50
0
14
14
11
14
13
2
1
6
8
14
36
3
1
WEIGHT SOURCE
PICKERING AND GAST 1972
PICKERING AND GAST 1972
PICKERING AND GAST 1972
PICKERING AND GAST 1972
PICKERING AND GAST 1972
PICKERING AND GAS1 1972
PICKERING AND GAST 1972
PICKERING AND GAST 1972
PICKERING AND GAST 1972
PICKERING AND GAST 1972
PICKERING AND GAST 1972
PICKERING AND GAST 1972
PICKERING AND GAST 1972
PICKERING AND GAST 1972
PICKERING AND GAST 1972
PICKERING AND GAST 1972
PICKERING AND GAST 1972
PICKERING AND GAST 1972
PICKERING AND GAST 1972
PICKERING AND GAST 1972
PICKERING AND GAST 1972
SAUTER ET AL 1976
SAUTER ET AL 1976
SAUTER ET AL 1976
SAUTER ET AL 1976
SAUTER ET AL 1976
SAUTER ET AL 1976
SAUTER ET AL 1976
0.24 SAUTER ET AL 1976
0.23 SAUTER ET AL 1976
0.19 SAUTER ET AL 1976
0.14 SAUTER ET AL 1976
0.13 SAUTER ET AL 1976
0.14 SAUTER ET AL 1976
0.13 SAUTER ET AL 1976
SPEHAR 1976
SPEHAR 1976
SPEHAR 1976
SPEHAR 1976
SPEHAR 1976
SPEHAR 1976
SPEHAR 1976
SPEHAR 1976
SPEHAR 1976
SPEHAR 1976
SPEHAR 1976
SPEHAR 1976
SPEHAR 1976
SPEHAR 1976
SPEHAR 1976
SPEHAR 1976
SPEHAR 1976
ARTHUR AND EATON 1971
ARTHUR AND EATON 1971
-------
181
ORNL-6251
Table 8,1 (Continued)
DBS CHEMICAL
433 CHLORAHINE
434 CHLORAHINE
435 CHLORAMINE
436 CHLORAHINE
437 CHLORAHINE
438 CHLORAHINE
439 CHLORAHINE
440 CHLORAHINE
441 CHLORAMINE
442 CHLOROANE
443 CHLORDANE
444 CHLOROANE
445 CHLOROANE
446 CHLORDANE
441 CHLORDANE
448 CHLOROANE
449 CHLOROANE
4SO CHLORDANE
451 CHLORDANE
452 CHLORDANE
453 CHLORDANE
454 CHLORDANE
455 CHLOROANE
456 CHLORDANE
457 CHLORDANE
458 CHLOROANE
459 CHLORDANE
460 CHLOROANE
461 CHLOROANE
462 CHLORDANE
463 CHLORDANE
464 CHLORDANE
465 CHLORDANE
466 CHLORDANE
467 CHLORDANE
468 CHLOROANE
469 CHLOROANE
470 CHLORDANE
471 CHLORDANE
472 CHLORDANE
473 CHLORDANE
474 CHLORDANE
475 CHLORDANE
476 CHLORDANE
477 CHLOROANE
478 CN
479 CN
480 CN
481 CN
482 CN
483 CN
484 CN
485 CN
486 CN
SPECIES PARAH
FH
FH
FM
FH
FH
FH
FH
FM
FM
BG
BG
BG
BG
BG
BG
86
BG
BG
BG
BG
BG
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
AS
AS
AS
AS
AS
AS
AS
AS
AS
HORT1
HOR11
MORT1
HORT1
MORT2
MORT2
MORT2
MORT2
MORT2
EGGS
EGGS
EGGS
EGGS
EGGS
EGGS
MORT1
HORT1
NORT1
HORT1
HORT1
NORT1
EGGS
EGGS
EGGS
EGGS
EGGS
E6GS
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
HORT1
NOHT1
MORT1
MORT1
HORT1
MORT1
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
MORT2
MORT2
MORT2
DOSE NTESTtD RESPONSE EGGS
16.00
43.00
85.00
154.00
0.00
3.80
17.00
40.00
108.00
0.00
0.2S
0.54
1.22
2.20
5.17
0.00
0.25
0.54
J.22
2.20
5.17
0.00
0.32
0.66
1.29
2.21
5.80
0.00
0.32
0.66
1.29
2.21
5.80
0.00
0.32
0.66
1.29
2.21
5.80
0.00
0.32
0.66
1.29
2.21
5.80
0.00
10.00
20.00
40.00
80.00
100.00
0.00
10.00
20.00
10
10
10
10
49
44
34
37
24
40
40
40
40
40
40
450
300
SO
50
0
0
18
IB
18
18
16
12
1827
855
915
1041
1012
978
200
100
100
0
0
7
10
14
1
8
12
15
1136
1979
2758
131
0
0
5
1
5
1
7
27
190
231
184
192
38
16
37
121
5
13
0
3
3
2
3
13
12
113
221
346
359
399
631
26
3
2
WEIGHT SOURCE
ARTHUR AND EATON 1971
ARTHUR AND EATON 1971
ARTHUR AND EATON 1971
ARTHUR AND EATON 1971
ARTHUR AND EATON 1971
ARTHUR AND EATON 1971
ARTHUR AND EATON 1971
ARTHUR AND EATON 1971
ARTHUR AND EATON 1971
CARDWELL ET AL 1977
CARDWELL ET AL 1977
CARDWELL ET AL 1977
CARDWELL ET AL 1977
CAROWELL ET AL 1977
CARDWELL ET AL 1977
CARDWELL ET AL 1977
CARDWELL ET AL 1977
CARDWELL ET AL 1971
CARDWELL ET AL 1971
CARDWELL ET AL 1977
CAROWELL ET AL 1977
CARDWELL ET AL 1977
CARDWELL ET AL 1977
CARDWELL ET AL 1977
CARDWELL El AL 1911
CARDWELL ET AL 1977
CARDWELL ET AL 1977
CARDWELL ET AL 1977
CARDWELL ET AL 1977
CARDWELL ET AL 1977
CARDWELL ET AL 1977
CAROWELL ET AL 1977
CARDWELL ET AL 1977
CARDWELL ET AL 1977
CARDWELL ET AL 1977
CARDWELL ET AL 1977
CARDWELL ET AL 1977
CAROWELL ET AL 1971
CAROWELL ET AL 1977
0.61 CAROWELL ET AL 1977
0.91 CAROWELL ET AL 1977
0.80 CAROWELL ET AL 1977
0.85 CARDWILL ET AL 1977
CARDWELL ET AL 1977
CAROWELL ET AL 1977
LEDUC 1978
LEDUC 1978
LEDUC 1978
LEDUC 1978
LEDUC 1978
LEDUC 1978
LEDUC 1978
LEDUC 1978
LEDUC 1978
-------
ORNL-6251
182
Table 8.1 (Continued)
DBS
487
488
489
490
491
492
493
494
495
496
497
498
499
500
SOI
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
5Z8
529
530
531
532
533
534
535
536
537
538
539
540
CHEMICAL
CN
CN
CN
CN
CN
CN
CN
CN
CN
CN
CN
CN
CN
CN
CN
CN
CN
CN
CN
CN
CN
CN
CN
CN
CN
CN
CN
CN
CN
CN
CN
CN
CN
CN
CN
CN
CN
CN
CN
CN
CN
CN
CN
CN
CN
CN
CN
CN
CN
CN
CN
CN
CN
CN
SPECIES PARAH
AS
AS
AS
AS
AS
AS
AS
AS
AS
B6
B&
BG
BG
BG
B6
B6
BG
BG
BG
BG
BG
BG
BG
BG
BG
BG
BG
BT
BT
BT
BT
BT
BT
BT
BT
BT
FM
FM
FM
FM
FM
FH
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FH
FM
MORT2
MORT2
MORT2
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
EGGS
EGGS
EGGS
EGGS
EGGS
EGGS
EGGS
EGGS
EGGS
MORT1
MORT1
MORT1
MORT1
MORT1
MORT1
MORT1
MORT1
MORT1
MORT2
NORT2
NORT2
MORT2
MORT2
MORT2
MORT2
MORT2
MORT2
EGGS
EGGS
EGGS
EGGS
EGGS
EGGS
EGGS
EGGS
EGGS
EGGS
EGGS
EGGS
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
DOSE NTESTED RESPONSE
40.00
80.00
100.00
0.00
10.00
20.00
40.00
BO. 00
100.
0.
5.
9.
20.
00
00
20
80
SO
100
100
100
2
5
12
EGGS HEIGHT SOURCE
LEOUC
LEDUC
LEOUC
14. BO LEDUC
16.20 LEDUC
17.20 LEDUC
16.90 LEDUC
15.50 LEOUC
13.60 LEOUC
30.00
39.70
50.20
65.
BO.
0.
5.
9.
20.
30.
39.
50.
65.
BO.
0.
5.
11.
21.
33.
43.
55.
67.
77.
0.
5.
12.
19.
27.
35.
44.
63.
72.
BO.
96.
105.
0.
5.
12.
19.
27.
35.
60
00
00
20
80
50
00
70
20
60
00
00
60
30
85
30
55
30
15
20
00
80
90
60
20
BO
20
50
80
60
10
40
00
80
90
60
30
80
30
15
15
15
IS
15
15
15
15
60
40
40
40
40
40
40
40
40
250
TOO
100
100
100
100
0
0
0
1
1
2
1
6
9
1
0
0
2
0
0
6
11
28
77
39
19
44
61
50
62
0
0
0
0
0
0
0
0
3476
2512
1845
1467
1366
1009
1124
72
318
242
0
0
SMITH
SMITH
SMITH
SMITH
SMITH
SMITH
SMITH
SMITH
SMITH
SMITH
SMITH
SMITH
SMITH
SMITH
SMITH
SMITH
SMITH
SMITH
SMITH
SMITH
SMITH
SMITH
SMITH
SMITH
SMITH
SMITH
SMITH
SMITH
SMITH
SMITH
SMITH
SMITH
SMITH
SMITH
SMITH
SMITH
SMITH
SMITH
SMITH
SMITH
SMITH
SMITH
SMITH
SMITH
SMITH
1978
1978
1978
1978
1978
1978
1978
1978
1978
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
AL 1979
AL 1979
AL 1979
AL 1979
AL 1979
AL 1979
AL 1979
AL 1979
AL 1979
AL 1979
AL 1979
AL 1979
AL 1979
AL 1979
AL 1979
AL 1979
AL 1979
AL 1979
AL 1979
AL 1979
AL 1979
AL 1979
AL 1979
AL 1979
AL 1979
AL 1979
AL 1979
AL 1979
AL 1979
AL 1979
AL 1979
AL 1979
AL 1979
AL 1979
AL 1979
AL 1979
AL 1979
AL 1979
AL 1979
AL 1979
AL 1979
AL 1979
AL 1979
AL 1979
AL 1979
-------
183
ORNL-6251
Table B.I (Continued)
OBS CHEMICAL
541 CN
542 CM
543 CN
544 CN
54S CN
546 CN
547 CN
548 CN
549 CN
SSO CN
551 CN
552 CN
553 CN
554 CN
555 CN
556 CN
557 CN
558 CN
559 CN
560 CN
561 CN
562 CN
563 CN
564 CN
565 CN
566 CN
567 CN
568 CN
569 CN
570 CN
571 CNS04
572 CNS04
573 CNS04
574 CNS04
575 CNS04
576 CNS04
577 CNS04
578 CNS04
579 CNS04
580 CNS04
581 CNS04
582 CNS04
583 CR
584 CR
585 CR
586 CR
587 CR
5B8 CR
589 CR
590 CR
591 CR
592 CR
593 CR
594 CR
SPECIES PARAM
FH
FH
FM
FH
FM
FH
FM
FM
FH
FH
FH
FH
FH
FH
FM
FH
FM
FM
FM
FH
FN
FM
FM
FM
FM
FM
FM
FM
FM
FH
CHS
CHS
CHS
CHS
CHS
CHS
CHS
CHS
CHS
CHS
CHS
CHS
FM
FM
FM
FM
FN
FM
FM
FM
FM
FM
FM
FN
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
MORT1
NORT1
MORT1
NORT1
MORT1
NORT1
MORT1
NORT1
NORT1
NORT1
NORT1
NORT1
HEIGHT
WEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HATCH
HATCH
HATCH
HATCH
NORT2
MORT2
NORT2
MORT2
HEIGHT
HEIGHT
WEIGHT
HEIGHT
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
MORT1
HORT1
NORT1
NORT1
NORT)
NORTt
DOSE NTESTED RESPONSE EGGS WEIGHT SOURCE
44.20
63.50
72.80
80.60
96.10
105.40
0.00
5.90
11,40
17.90
24.70
32.80
40.50
57.50
66.80
75.30
88.90
98.10
0.00
5.90
11.40
17.90
24.70
32.80
40.50
57.50
66.80
75.30
68.90
98.10
0.00
21.00
40.00
80.00
0.00
21.00
40.00
80.00
0.00
21.00
40.00
80.00
0.00
18.00
66.00
260.00
1000.00
3950.00
0.00
18.00
66.00
260.00
1000.00
3950.00
100
100
100
100
100
100
240
80
80
80
80
80
80
80
80
80
80
80
267
377
357
404
214
286
292
314
525
547
364
625
600
135
35
35
35
35
35
35
87
79
81
90
100
100
8B
16
33
33
39
43
33
42
46
59
68
71
53
90
65
90
49
94
276
314
26
22
25
44
30
19
0
1
1
5
2
22
SMITH ET AL 1979
SH11H ET AL 1979
SMITH ET AL 1979
SMITH ET AL 1979
SMITH ET AL 1979
SNITH ET AL 1979
SMITH ET AL 1979
SNITH ET AL 1979
SNITH ET AL 1979
SNITH ET AL 1979
SNITH ET AL 1979
SNITH ET AL 1979
SMITH ET AL 1979
SNITH ET AL 1979
SNITH ET AL 1979
SNITH ET AL 1979
SNITH ET AL 1979
SMITH ET AL 1979
0.29 SMITH ET AL 1979
0.20 SMITH ET AL 1979
0.27 SMITH ET AL 1979
0.27 SMITH CT AL 1979
0.30 SMITH ET AL 1979
0.38 SMITH ET AL 1979
0.27 SMITH ET AL 1979
0.19 SMITH ET AL 1979
0.22 SMITH ET AL 1979
0.26 SMITH ET AL 1979
0.20 SMITH ET AL 1979
0.19 SMITH ET AL 1979
HAZEL AND HEITH 1970
HAZEL AND NEITH 1970
HAZEL AND NEITH 1970
HAZEL AND NEITH 1970
HAZEL AND NEITH 1970
HAZEL AND NEITH 1970
HAZEL AND NEITH 1970
HAZEL AND NEITH 1970
0.38 HAZEL AND NEITH 1970
0.33 HAZEL AND NEITH 1970
0.30 HAZEL AND MEITH 1970
0.00 HAZEL AND MEITH 1970
PICKERING 19BO
PICKERING 19BO
PICKERING 1980
PICKERING 1980
PICKERING 1980
PICKERING 1980
PICKERING 1980
PICKERING 1980
PICKERING 1980
PICKERING 1980
PICKERING 1980
PICKERING 1980
-------
ORNL-6251
184
Table B.I. (Continued)
OBS CHtHICAL
595 CR
596 CR
597 CR
59B CR
599 CR
600 CR
601 CR
602 CR
603 CR
604 CR
605 CR
606 CR
607 CR
60S CR
609 CR
610 CR
611 CR
612 CR
613 CR
614 CR
615 CR
616 CR
617 CR
618 CR
619 CR
620 CR
621 CR
622 CR
623 CR
624 CR
625 CR
626 CR
627 CR
628 CR
629 CR
630 CR
631 CR
632 CR
633 CR
634 CR
635 CR
636 CR
637 CR
638 CR
639 CR
640 CR
641 CR
642 CR
643 CR
644 CR
645 CR
646 CR
647 CR
646 CR
SPECIES PARAM
FH
FM
FH
FN
FN
FH
BG
BG
BG
BG
BG
BG
BG
CC
CC
CC
CC
CC
CC
CC
LT
LT
LT
LT
LT
LT
LT
NP
NP
NP
NP
NP
NP
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
HORT2
HORT2
HORT2
HORT2
HORT2
HORT2
HEIGHT
WEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
WEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
WEIGHT
HEIGHT
HEIGHT
WEIGHT
WEIGHT
HEIGHT
WEIGHT
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
HORT2
HORT2
HORT2
NOR 7 2
MORT2
HORT2
HOR12
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
DOSE NTESTED RESPONSE EGGS WEIGHT SOURCE
0.00
18.00
66.00
260.00
1000.00
3950.00
0.00
57.00
70.00
140.00
265.00
522.00
1122.00
0.00
39.00
73.00
150.00
305.00
570.00
1290.00
0.00
1400.00
2900.00
6000.00
11600.00
24400.00
50700.00
0.00
123.00
290.00
538.00
963.00
1975.00
0.00
1600.00
3200.00
6100.00
12200.00
26700.00
49700.00
0.00
1600.00
3200.00
6100.00
12200.00
26700.00
49700.00
0.00
1600.00
3200.00
6100.00
12200.00
26700.00
49700.00
50
SO
50
50
50
50
400
400
400
400
400
400
400
200
200
200
200
200
200
200
14
10
9
3
1
44
94
72
126
164
338
400
400
21
1B6
200
200
200
200
200
PICKERING 1980
PICKERING 1980
PICKERING 1980
PICKERING 1980
PICKERING 1980
PICKERING 1980
0.30 SAUTER ET AL 1976
0.29 SAUTER ET AL 1976
0.25 SAUTER ET AL 1976
0.29 SAUTER ET AL 1976
0.20 SAUTER ET AL 1976
0.24 SAUTER ET AL 1976
0.13 SAUTER ET AL 1976
0.33 SAUTER ET AL 1976
0.33 SAUTER ET AL 1976
0.34 SAUTER ET AL 1976
0.27 SAUTER ET AL 1976
0.23 SAUTER ET AL 1976
0.12 SAUTER ET AL 1976
0.00 SAUTER ET AL 1976
0.21 SAUTER ET AL 1976
0.09 SAUTER ET AL 1976
0.09 SAUTER ET AL 1976
0.06 SAUTER ET AL 1976
0.09 SAUTER ET AL 1976
0.00 SAUTER ET AL 1976
0.00 SAUTER ET AL 1976
1.03 SAUTER ET AL 1976
0.88 SAUTER ET AL 1976
1.47 SAUTER ET AL 1976
0.76 SAUTER ET AL 1976
0.44 SAUTER ET AL 1976
0.34 SAUTER ET AL 1976
SAUTER ET AL 1976
SAUTER ET AL 1976
SAUTER ET AL 1976
SAUTER ET AL 1976
SAUTER El AL 1976
SAUTER ET AL 1976
SAUTER ET AL 1976
SAUTER ET AL 1976
SAUTER ET AL 1976
SAUTER ET AL 1976
SAUTER ET AL 1976
SAUTER ET AL 1976
SAUTER ET AL 1976
SAUTER ET AL 1976
0.47 SAUTER ET AL 1976
0.25 SAUTER ET AL 1976
0.00 SAUTCR ET AL 1976
0.00 SAUTCR ET AL 1976
0.00 SAUTER ET AL 1976
0.00 SAUTER ET AL 1976
0.00 SAUTER ET AL 1976
-------
185
ORNL-6251
Table B.I. (Continued)
DBS CHEMICAL
649 CR
650 CR
651 CR
652 CR
653 CR
654 CR
655 CR
656 CR
657 CR
65B CR
659 CR
660 CR
661 CR
662 CR
663 CR
664 CR
665 CR
666 CR
667 CR
668 CR
669 CR
670 CR
671 CR
672 CR
673 CR
674 CR
675 CR
676 CR
677 CR
678 CR
679 CR
680 CR
681 CR
682 CR
683 CR
6B4 CR
685 CU
686 CU
687 CU
688 CU
689 CU
690 CU
691 CU
692 CU
693 CU
694 CU
695 CU
696 CU
697 CU
698 CU
699 CU
700 CU
701 CU
702 CU
SPECIES PARAM
WS
WS
WS
WS
WS
WS
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
BG
B6
BG
BG
BG
BG
BG
BG
BG
BG
BG
BG
BG
BG
BG
BG
BG
BG
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
NORT2
HORT2
NORT2
HORT2
MORT2
HORT2
NORT2
NORT2
MORT2
MORT2
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
EGGS
EGGS
F.GGJ
EGGS
EGGS
EGGS
NORT1
HORT1
MORT1
KORT1
NORT1
MORT1
NORT2
HORT2
NORT2
NORT2
KORT2
HORT2
DOSE NTESTEO RESPONSE EGGS
0.00
123.00
290.00
538.00
963.00
1975.00
0.00
9.00
13.00
19.00
30.00
4B.OO
89.00
157.00
271.00
495.00
0.00
9.00
13.00
19.00
30.00
48.00
89.00
157.00
271.00
495.00
0.00
9.00
13.00
19.00
30.00
46.00
89.00
157.00
271.00
495.00
3.00
12.00
21.00
40.00
77.00
162.00
3.00
12.00
21.00
40.00
77.00
162.00
3.00
12.00
21.00
40.00
77.00
162.00
267
146
141
146
134
136
140
137
145
139
243
143
140
142
131
133
122
60
4
0
20
20
20
20
20
20
100
100
100
100
100
100
4
3
1
4
3
3
18
77
141
139
10
11
10
6
12
12
2
7
1
0
51906
46953
25354
4403
33300
0
1
1
1
1
4
12
61
51
56
83
91
100
WEIGHT SOURCE
0.24 SAUTER ET AL 1976
0.19 SAUTER ET AL 1976
0.22 SAUTER ET AL 1976
0.17 SAUTER ET AL 1976
0.11 SAUTER ET AL 1976
0.04 SAUTER ET AL 1976
STEVENS AND CHAPMAN 1984
STEVENS AND CHAPMAN 1984
STEVENS AND CHAPMAN 1984
STEVENS AND CHAPMAN 1984
STEVENS AND CHAPMAN 1984
STEVENS AND CHAPMAN 1984
STEVENS AHD CHAPMAN 1984
STEVENS AND CHAPMAN 1984
STEVENS AND CHAPMAN 19B4
STEVENS AND CHAPMAN 1984
STEVENS AND CHAPMAN 1984
STEVENS AND CHAPMAN 1984
STEVENS AND CHAPMAN 1984
STEVENS AND CHAPMAN 1984
STEVENS AND CHAPMAN 1984
STEVENS AND CHAPMAN 1984
STEVENS AND CHAPMAN 1984
STEVENS AND CHAPMAN 1984
STEVENS AND CHAPMAN 1984
STEVENS AND CHAPMAN 1984
0.35 STEVENS AND CHAPMAN 1984
0.33 STEVENS AND CHAPMAN 1984
0.32 STEVENS AND CHAPMAN 1984
0.38 STEVENS AND CHAPMAN 1984
0.31 STEVENS AND CHAPMAN 1984
0.30 STEVENS AND CHAPMAN 1984
0.31 STEVENS AND CHAPMAN 1984
0.32 STEVENS AND CHAPMAN V9B4
0.28 STEVENS AND CHAPMAN 1984
STEVENS AND CHAPMAN 1984
BENOIT 1975
BENOIT 1975
BENOIT 1975
BENOIT 1975
BENOIT 1915
BENOIT 1975
BENOIT 1975
BENOIT 1975
BENOIT 1975
BENOIT 1975
BENOIT 1975
BENOIT 1915
BENOIT 1975
BENOIT 1975
BENOIT 1975
BENOIT 1975
BENOIT 1975
BENOIT 1975
-------
186
ORNL-6251
Table B.I. (Continued)
DBS
703
704
70S
706
707
70S
709
710
711
712
713
714
715
716
717
71B
719
720
721
72Z
723
724
725
726
727
72B
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
CHEMICAL
CU
CU
CU
CU
CU
CU
CU
CU.
CU
CU
CU
CU
CU
CU
CU
CU
CU
CU
CU
CU
CU
CU
CU
CU
CU
CU
CU
CU
CU
CU
CU
CU
CU
CU
CU
CU
CU
CU
CU
CU
CU
CU
CU
CU
CU
CU
CU
CU
CU
CU
CU
CU
CU
CU
SPECKS PARAM
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
FH
FM
FH
FH
FH
FH
FH
FH
FH
FH
FH
FH
FH
FH
FH
FH
FM
FH
FH
FN
FM
FH
FH
FH
FH
FH
FM
FM
FM
FM
FM
EGGS
EGGS
E6GS
EGGS
EGGS
EGGS
NATCH
HATCH
HATCH
HATCH
HATCH
HATCH
MORT1
MOOT)
MORT1
MORT1
HORT1
MORT2
HORT2
MORT2
NORT2
MORT2
HORT2
EGGS
EGGS
EGGS
EGGS
EGGS
HATCH
HATCH
HATCH
HATCH
HORT1
MORT1
MORT1
MORT1
MORT1
HORT2
HORT2
MORT2
HORT2
EGGS
EGGS
EGGS
EGGS
EGGS
EGGS
EGGS
E6GS
HATCH
HATCH
HATCH
HATCH
HATCH
DOSE NTESTED RESPONSE
1
3
5
9
17
32
1
3
5
9
17
32
1
5
9
17
32
1
3
5
9
17
32
4
5
7
10
18
4
5
7
10
4
5
7
10
18
4
5
7
10
4
5
6
15
14
32
34
95
4
5
6
14
15
.90
.40
.70
.50
.40
.50
.90
.40
.70
.50
.40
.50
.90
.70
.50
.40
.50
.90
.40
.70
.50
.40
.50
.40
.00
.70
.60
.40
.40
.00
.70
.60
.40
.00
.70
.60
.40
.40
.00
.70
.60
.40
.30
.30
.00
.00
.00
.00
.00
.40
.30
.30
.00
.00
200
200
200
200
200
200
14
14
28
14
14
50
50
50
50
50
50
250
500
400
650
40
40
40
40
40
SO
SO
50
SO
200
200
200
200
200
38
2
30
4
10
148
1
4
4
3
8
4
4
10
11
50
50
80
175
212
195
8
2
2
6
20
27
3
23
28
15
35
11
11
12
EGGS HEIGHT SOURCE
328
364
296
209
315
158
584
748
186
766
0
524
397
481
201
528
0
0
0
MCKIH
MCKIH
MCKIM
MCKIH
MCKIH
MCKIH
MCKIM
MCKIH
MCKIM
NCKIM
MCKIH
MCKIH
MCKIH
MCKIM
MCKIH
MCKIH
MCKIH
MCKIM
MCKIH
MCKIM
MCKIM
MCKIH
MCKIH
MOUNT
MOUNT
MOUNT
MOUNT
MOUNT
MOUNT
MOUNT
MOUNT
MOUNT
MOUNT
MOUNT
MOUNT
MOUNT
MOUNT
MOUNT
MOUNT
MOUNT
MOUNT
MOUNT
MOUNT
MOUNT
MOUNT
MOUNT
MOUNT
MOUNT
MOUNT
MOUNT
MOUNT
MOUNT
MOUNT
MOUNT
AND BENOIT
AND BENOIT
AND BENOIT
AND BENOIT
AND BENOIT
AND BENOIT
AND BENOIT
AND BENOIT
AND BENOIT
AND BENOIT
AND BENOIT
AND BENOIT
AND BENOIT
AND BENOIT
AND BENOIT
AND BENOIT
AND BENOIT
AND BENOIT
AND BENOIT
AND BENOIT
AND BENOIT
AND BENOIT
AND BENOIT
AND STEPHAN
AND STEPHAN
AND STEPHAN
AND STEPHAN
1971
1971
1971
1971
1971
1971
1971
1971
1971
1971
1971
1971
1971
1971
1971
1971
1971
1971
1971
1971
1971
1971
1971
1969
1969
1969
1969
AND STEPHAN 1969
AND STEPHAN
AND STEPHAN
1969
1969
AND STEPHAN 1969
AND STEPHAN 1969
AND STEPHAN
AND STEPHAN
1969
1969
AND STEPHAN 1969
AND STEPHAN
AND STEPHAN
AND STEPHAN
AND STEPHAN
AND STEPHAN
AND STEPHAN
1968
1968
1968
1968
1968
1968
1968
1968
1968
1968
1968
1968
1968
1969
1969
1969
1969
1969
1969
-------
ORNL-6251
187
Table B.I. (Continued)
DBS CHEMICAL
757 CU
758 CU
759 CU
760 CU
7(1 CU
762 CU
763 CU
764 CU
765 CU
766 CU
767 CU
768 CU
769 CU
770 CU
771 CU
772 CU
773 CU
774 CU
775 CU
776 CU
777 CU
778 CU
779 CU
780 CU
761 CU
782 CU
783 CU
784 CU
785 CU
786 CU
767 CU
788 CU
789 CU
790 CU
791 CU
792 CU
793 CU
794 CU
795 CU
796 CU
797 CU
79B CU
799 CU
BOO CU
801 CU
802 CU
803 CU
804 CU
805 CU
806 CU
807 CU
SOB CU
809 CU
810 CU
SPECIES PARAM
FH
FM
FH
FM
FM
FM
FM
FM
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
CC
CC
CC
CC
CC
CC
CC
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
NORT1
MORT1
MORT1
MORT1
MORT1
MORT1
MORT1
MORT1
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
NORT2
MORT2
MORT2
MORT2
MORT2
MORT2
MORT2
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
MORT2
MORT2
MORT2
MORT2
MORT2
MORT2
MORT2
WEIGHT
WEIGHT
WEIGHT
WEIGHT
DOSE NTESTEO RESPONSE EGGS HEIGHT SOURCE
4.40
5.30
6.30
14.00
15.00
32.00
34.00
95.00
0.00
5.00
7.00
13.00
27.00
51.00
95.00
0.00
5.00
7.00
13.00
27.00
51.00
95.00
0.00
5.00
7.00
13.00
27.00
51.00
95.00
0.00
3.00
6.00
7.00
12.00
18.00
24.00
3.00
6.00
9.00
16.00
31.00
57.00
121.00
3.00
6.00
9.00
16.00
31.00
57.00
121.00
3.00
6.00
9.00
16.00
10
10
10
10
10
10
10
20
400
400
400
400
400
400
400
200
200
200
200
200
200
200
240
240
240
240
240
240
240
100
100
100
100
100
100
37
1
1
0
1
1
3
2
9
96
102
130
264
380
386
400
6
14
6
55
198
200
200
6
3
5
6
6
3
183
3
0
0
1
5
16
37
MOUNT 1968
MOUNT 1968
MOUNT 1968
MOUNT 1968
MOUNT 1968
MOUNT 196B
MOUNT 1968
MOUNT 1968
SAUTER ET AL 1976
SAUTER ET AL 1976
SAUTER ET AL 1976
SAUTER ET AL 1976
SAUTER ET AL 1976
SAUTER ET AL 1976
SAUTER ET AL 1976
SAUTER ET AL 1976
SAUTER ET AL 1976
SAUTER ET AL 1976
SAUTER ET AL 1976
SAUTER ET AL 1976
SAUTER ET AL 1976
SAUTER ET AL 1976
0.22 SAUTER ET AL 1976
0.15 SAUTER ET AL 1976
0.13 SAUTER ET AL 1976
0.11 SAUTER ET AL 1976
0.09 SAUTER ET AL 1976
0.00 SAUTER ET AL 1976
0.00 SAUTER ET AL 1976
0.37 SAUTER ET AL 1976
0.29 SAUTER ET AL 1976
0.32 SAUTER ET AL 1976
0.34 SAUTER ET AL 1976
0.32 SAUTER ET AL 1976
0.20 SAUTER ET AL 1976
0.00 SAUTER ET AL 1976
SEIM ET AL 1984
SEIM ET AL 1984
SEIM ET AL 1984
SEIM ET AL 1984
SEIM ET AL 1984
SEIM ET AL 19B4
SEIM ET AL 1984
SEIM ET AL 1984
SEIM ET AL 1984
SEIM ET AL 19B4
SEIM ET AL 1984
SEIM ET AL 1984
SEIM ET AL 1984
SEIM ET AL 1984
0.13 SEIM ET AL 1984
0.14 SEIM ET AL 1964
0.15 SEIM ET AL 1984
0.15 SEIM ET AL 1984
-------
188
ORNL-6251
Table 8.1. (Continued)
DBS
811
812
813
814
815
816
617
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
B4B
849
850
851
852
853
854
855
856
857
858
659
860
861
862
863
864
CHEMICAL
CU
CU
CU
OI-N-BUTYL
DI-N-BUTYL
DI-N-BUTYL
OI-N-BUTYL
DI-N-BUTYL
DI-N-BUTYL
DI-N-BUTYL
DI-N-BUTYL
DI-N-BUTYL
DI-N-BUTYL
OI-N-BUTYL
DI-N-BUTYL
DI-N-BUTYL
DI-N-BUTYL
OI-N-OCTYL
OI-N-OCTYL
DI-N-OCTYL
DI-N-OCTYL
DI-N-OCTYL
DI-N-OCTYL
DIAZINON
D1A2INON
DIAZINON
DIAZINON
DIAZINON
DIAZINON
DIAZINON
OIAZINON
DIAZINON
DIAZINON
DIAZINON
OIAZINON
DIAZINON
DIAZINON
OIAZINON
DIAZINON
DIAZINON
OIAZINON
DIAZINON
DIAZINON
DIAZINON
DIAZINON
DIAZINON
DIAZINON
DIAZINON
DIAZINON
DIAZINON
DIAZINON
DIAZINON
DIAZINON
DIAZINON
SPECIES PARAH
PHTHALATE
PHTHALATE
PHTHALATE
PHTHALATE
PHTHALATE
PHTHALATE
PHTHALATE
PHTHALATE
PHTHALATE
PHTHALATE
PHTHALATE
PHTHALATE
PHTHALATE
PHTHALATE
PHTHALATE
PHTHALATE
PHTHALATE
PHTHALATE
PHTHALATE
PHTHALATE
RT
RT
RT
FH
FM
FH
FH
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
FM
FM
FH
FM
FM
FM
FM
WEIGHT
HEIGHT
WEIGHT
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
MORT2
MORT2
MORT2
MORT2
MORT2
MORT2
MORT2
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
EGGS
EGGS
EGGS
EGGS
EGGS
EGGS
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
MORT1
MORT1
MORT1
MORT1
HORT1
MORT1
MORT2
MORT2
MORT2
MORI 2
MORT2
MORT2
EGGS
EGGS
EGGS
EGGS
EGGS
EGGS
HATCH
DOSE NTESTED RESPONSE
31
57
121
0
100
180
320
560
1000
1800
0
100
180
320
560
1000
1800
0
100
320
1000
3200
10000
0
0
1
2
4
9
0
0
1
2
5
11
0
0
1
2
4
9
0
0
1
2
5
11
0
3
6
13
28
60
0
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.55
.10
.40
.BO
.60
.00
.BO
.40
.70
.60
.10
.00
.55
.10
.40
.80
.60
.00
.80
.40
.70
.60
.10
.00
.20
.90
.50
.00
.30
.00
100
100
100
100
100
100
100
69
66
69
68
55
2B
0
100
100
100
100
100
100
250
300
500
200
50
250
24
24
24
24
24
24
100
100
100
93
25
75
1100
31
34
31
32
45
72
100
4
11
9
4
8
22
1
0
1
5
0
35
92
2B
145
77
26
15
0
0
0
1
1
6
8
28
23
4
9
13
88
EGGS WEIGHT SOURCE
490
334
807
593
402
220
361
505
137
76
1
0
0.11 SEIM ET AL 1984
0.05 SEIM ET AL 1984
0.00 SEIN ET AL 19B4
MCCARTHY AND WHITMORE
MCCARTHY AND WHITMORE
MCCARTHY AND WHITHORE
MCCARTHY AND WHITMORE
MCCARTHY AND WHITMORE
MCCARTHY AND WHITMORE
MCCARTHY AND WHITMORE
MCCARTHY AND WHITMORE
MCCARTHY AND WHITMORE
MCCARTHY AND WHITMORE
MCCARTHY AND WHITMORE
MCCARTHY AND WHITMORE
MCCARTHY AND WHITMORE
MCCARTHY AND WHITHORE
MCCARTHY AND WHITMORE
MCCARTHY AND WHITMORE
MCCARTHY AND WHITMORE
MCCARTHY AND WHITMORE
MCCARTHY AND WHITMORE
MCCARTHY AND WHITMORE
ALLISON AND HERMANUTZ
ALLISON AND HERHANUTZ
ALLISON AND HERMANUTZ
ALLISON AND HERMANUTZ
ALLISON AND HERMANUTZ
ALLISON AND HERMANUTZ
ALLISON AND HERMANUTZ
ALLISON AND HERMANUTZ
ALLISON AND HERMANUTZ
ALLISON AND HERMANUTZ
ALLISON AND HERHANUTZ
ALLISON AND HERMANUTZ
ALLISON AND HERMANUTZ
ALLISON AND HERMANUTZ
ALLISON AND HERMANUTZ
ALLISON AND HERMANUTZ
ALLISON AND HERMANUTZ
ALLISON AND HERMANUTZ
ALLISON AND HERMANUTZ
ALLISON AND HERMANUTZ
ALLISON AND HERMANUTZ
ALLISON AND HERMANUTZ
ALLISON AND HERMANUTZ
ALLISON AND HERMANUTZ
ALLISON AND HERMANUTZ
ALLISON AND HERMANUTZ
ALLISON AND HERMANUTZ
ALLISON AND HERMANUTZ
ALLISON AND HERMANUTZ
ALLISON AND HERMANUTZ
ALLISON AND HERMANUTZ
1984
1984
1984
1984
1984
1984
1984
1984
1984
1984
1984
1984
1984
1984
1984
1984
1984
1984
1984
1984
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
-------
ORNL-6251
189
Table B.I (Continued)
DBS CHEMICAL
865 OIAZINON
866 DIAZINON
867 DIAZINON
86B DIAZINON
869 DIAZINON
870 DIAZINON
871 DIAZINON
872 DIAZINON
873 DIAZINON
874 OIAZINON
875 OIAZINON
876 DIAZINON
877 DIAZINON
878 DIAZINON
879 DIAZINON
880 DINOSE8
881 DINOSEB
882 OINOSEB
883 DINOSEB
884 DINOSEB
BBS DINOSEB
886 DINOSEB
887 DINOSEB
888 DINOSEB
889 DINOSEB
890 DINOSEB
891 OINOSEB
892 DINOSEB
893 DINOSEB
894 DINOSEB
895 DINOSEB
896 DINOSEB
897 DINOSEB
898 DINOSEB
899 DINOSEB
900 DINOSEB
901 DINOSEB
902 DINOSEB
903 DINOSEB
904 DIURON
905 OIURON
906 DIURON
907 DIURON
906 DIURON
909 DIURON
910 OIURON
911 DIURON
912 DIURON
913 DIURON
914 DIURON
915 DIURON
916 DIURON
917 OIURON
918 DIURON
SPECIES PARAH
FH
FN
FN
FH
FH
FH
FH
FH
FH
FH
FH
FN
FH
FH
FH
FH
FH
FH
FH
FH
FH
FH
FH
FH
FH
FH
FK
FH
FH
FH
FH
FH
FH
LT
IT
LT
LT
LT
LT
FH
FH
FH
FH
FH
FM
FH
FM
FH
FH
FM
FH
FH
FM
FM
HATCH
HATCH
HATCH
HATCH
HORT1
MORT1
NORT1
HORT1
NORT1
NORT1
MORT2
HORT2
MORT2
HORT2
MORT2
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
NORT2
HORT2
MORT2
MORT2
MORT2
HORT2
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
HORT2
MORT2
HORT2
HORT2
HORT2
HORT2
WEIGHT
WEIGHT
WEIGHT
DOSE NTESTED RESPONSE EGGS WEIGHT SOURCE
3.20
6.90
28.00
60.30
0.00
3.20
6.90
13.50
28.00
60.30
0.00
3.30
6.80
28.00
62.60
0.00
0.40
1.70
4.30
14.50
48.50
0.00
0.40
1.70
4.30
14.50
48.50
0.00
0.40
1.70
4.30
14.50
48.50
0.00
0.50
1.60
2.30
4.90
10.00
0.00
2.60
6.10
14.50
33.40
78.00
0.00
2.60
6.10
14.50
33.40
78.00
0.00
2.60
6.10
900
150
200
500
100
100
100
100
100
100
400
320
40
280
320
200
200
• 200
200
200
200
60
60
60
60
60
60
200
200
200
200
200
200
60
60
60
60
60
60
288
36
12
35
26
15
36
18
34
66
134
83
18
99
77
55
31
33
46
62
43
7
13
11
8
28
55
67
45
52
61
75
88
11
7
4
17
15
45
ALLISON AND HERHANUTZ 1977
ALLISON AND HERHANUTZ 1977
ALLISON AND HERMANUTZ 1977
ALLISON AND HERHANUTZ 1977
ALLISON AND HERHANUTZ 1977
ALLISON AND HERHANUTZ 1977
ALLISON AND HERHANUTZ 1977
ALLISON AND HERHANUTZ 1977
ALLISON AND HERHANUTZ 1977
ALLISON AND HERHANUTZ 1977
ALLISON AND HERMANUTZ 1977
ALLISON AND HERMANUTZ 1977
ALLISON AND HERMANUTZ 1977
ALLISON AND HERMANUTZ 1977
ALLISON AND HERMANUTZ 1977
CALL ET AL 1983
CALL ET AL 1983
CALL ET AL 1983
CALL ET AL 1983
CALL ET AL 1963
CALL ET AL 1983
CALL ET AL 1983
CALL ET AL 1983
CALL ET AL 1983
CALL ET AL 19B3
CALL ET AL 1983
CALL ET AL 1983
0.60 CALL ET AL 19B3
0.68 CALL ET AL 19B3
0.73 CALL ET AL 19B3
0.65 CALL ET AL 1963
0.68 CALL ET AL 1983
0.52 CALL ET AL 1963
378.00 WOODWARD 1976
247.00 WOODWARD 1976
241.00 WOODWARD 1976
244.00 WOODWARD 1976
208.00 WOODWARD 1976
152.00 WOODWARD 1976
CALL ET AL 1983
CALL ET AL 19B3
CALL ET AL 1983
CALL ET AL 1983
CALL ET AL 1983
CALL ET AL 1983
CALL ET AL 19B3
CALL ET AL 1963
CALL ET AL 1963
CALL ET AL 1983
CALL ET AL 1983
CALL ET AL 1983
0.57 CALL ET AL 1983
0.57 CALL ET AL 1963
0.56 CALL ET AL 1983
-------
190
ORNL-6251
Table B.I. (Continued)
DBS CHEMICAL
919 DIURON
920 DIURON
921 DIURON
922 DTOMAC
923 DTDHAC
924 DTDMAC
92S DTDMAC
926 DTDMAC
927 DTDMAC
928 ENDOSULFAN
929 ENDOSULFAN
930 ENDOSULFAN
931 ENOOSULFAN
932 ENOOSULFAN
933 ENDOSULFAN
934 ENDOSULFAN
935 ENDOSULFAN
936 ENDOSULFAN
937 ENDOSULFAN
93B ENDOSULFAN
939 ENDOSULFAN
940 EHDOSULFAN
941 ENDOSULFAN
942 ENDOSULFAN
943 ENOOSULFAN
944 ENDOSULFAN
945 ENDRIN
946 ENDRIN
94? ENORIN
948 ENDRIN
949 ENORIN
950 CNDRIN
951 FENITROTHION
952 FENITROTHION
953 FENITROTHION
954 FENITROTHION
955 FENITROTHION
956 FENITROTHION
957 FENITROTHION
958 FENITROTHION
959 FENITROTHION
960 FENITROTHION
961 FENITROTHION
962 FENITROTHION
963 FONOFOS
964 FONOFOS
965 FONOFOS
966 FONOFOS
967 FONOFOS
966 FONOFOS
969 FONOFOS
970 FONOFOS
971 FONOFOS
972 FONOFOS
SPECIES PARAM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FH
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FH
FM
FM
FF
FF
FF
FF
FF
FF
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FH
FM
FH
FM
FM
FM
FM
FM
FM
FH
FM
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
MORT1
MORT1
MORI)
MORT1
MORT1
MORT1
MORT2
HORT2
MORT2
MORT2
MORT2
MORT2
MORT2
MORT2
MORT2
MORT2
HORT2
MORT2
MORI 2
MORT2
MORT2
MORT2
MORT2
HEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
MORT2
MDRT2
MORT2
HORT2
DOSE NTESTED RESPONSE EGGS WEIGHT SOURCE
14.50
33.40
76.00
0.00
6.00
13.00
24.00
53.00
90.00
0.00
0.04
0.06
0.10
0.20
0.40
0.00
0.04
0.06
0.10
0.20
0.40
0.00
0.04
0.06
0.10
0.20
0.00
0.04
0.07
0.15
0.30
0.60
0.00
20.00
60.00
130.00
300.00
740.00
0.00
20.00
60.00
130.00
300.00
740.00
0.00
4.90
9.20
16.00
33.00
66.00
0.00
4.90
9.20
16.00
1900
200
1850
1150
1850
150
30
30
30
30
30
15
360
80
320
320
280
90
90
90
90
90
90
60
60
60
60
60
60
100
100
100
too
100
TOO
60
60
60
60
325
28
231
161
425
148
8
18
6
5
13
15
77
21
83
73
70
1
3
4
2
12
90
15
10
17
14
24
43
6
5
3
4
7
5
5
5
4
5
0.62 CALL ET AL 1983
0.56 CALL ET AL 1983
0.50 CALL ET AL 1983
0.08 LEWIS AND WEE 1983
0.08 LEWIS AND WEE 1983
0.08 LEWIS AND WEE 1983
0.07 LEWIS AND WEE 1983
0.08 LEWIS AND WEE 1983
0.03 LEWIS AND WEE 1983
CARLSON ET AL 1982
CARLSON ET AL 1982
CARLSON ET AL 1982
CARLSON ET AL 1982
CARLSON ET AL 1982
CARLSON ET AL 1982
CARLSON ET AL 1982
CARLSON ET AL 1982
CARLSON ET AL 1982
CARLSON ET AL 1982
CARLSON ET AL 1982
CARLSON ET AL 1982
CARLSON ET AL 1982
CARLSON ET AL 1982
CARLSON ET AL 1982
CARLSON ET AL 1982
CARLSON ET AL 1982
CARLSON ET AL 1982
CARLSON ET AL 1982
CARLSON ET AL 1982
CARLSON ET AL 1982
CARLSON ET AL 1982
CARLSON ET AL 1982
KLEINER ET AL 1984
KLEINER ET AL 1984
KLEINER ET AL 1984
KLEINER ET AL 1984
KLEINER ET AL 1984
KLEINER ET AL 1984
0.14 KLEINER ET AL 1984
0.14 KLEINER ET AL 1984
0.15 KLEINER ET AL 1984
0.14 KLEINER ET AL 1984
0.10 KLEINER ET AL 1984
0.06 KLEINER ET AL 1984
PICKERING AND GILIAM 1982
PICKERING AND GILIAM 1982
PICKERING AND GILIAM 1982
PICKERING AND GILIAM 1982
PICKERING AND GILIAM 1982
PICKERING AND GILIAM 1982
PICKERING AND GILIAM 1982
PICKERING AND GILIAM 1982
PICKERING AND GILIAM 1982
PICKERING AND GILIAM 1982
-------
ORNL-6251
191
Table B.I. (Continued)
DBS CHEMICAL
SPECIES PARAM
OOSt NTESTEO RESPONSE E6GS HEIGHT SOURCE
973
974
975
976
977
976
979
960
961
962
963
964
985
986
967
986
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
1003
1004
100S
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1016
1019
1020
1021
1022
1023
1024
1025
1026
FONOFOS
FONOFOS
FONOFOS
FONOFOS
FONOFOS
FONOFOS
FONOFOS
FONOFOS
GUTHION
6UTHION
GUTHION
GUTHION
GUTHION
GUTHION
GUTHION
HEPTACHLOR
HEPTACHLOR
HEPTACHLOR
HEPTACHLOR
HEPTACHLOR
HEPTACHLOR
HEPTACHLOR
HEPTACHLOR
HEPTACHLOR
HEPTACHLOR
HEPTACHLOR
HEPTACHLOR
HEPTACHLOR
HEPTACHLOR
HEPTACHLOR
HEPTACHLOR
HEPTACHLOR
HEPTACHLOR
HEPTACHLOR
HEPTACHLOR
HEPTACHLOR
HEXACHLOR06UTADIENE
HEXACHLOROBUTAD1ENE
HEXACHLOR06UTAOIENE
HEXACHLOR06UTA01ENE
HEXACHLOROBUTADIENE
HEXACHIOROBUTAOIENE
HEXACHLOROBUTADIENE
HEXACHLOROBUTADIENE
HEXACHLOROBUTADIENE
HEXACHLOROBUTADIENE
HEXACHLOR06UTADIENE
HEXACHLOROBUTADIENE
HEXACHLOROBUTADIENE
HEXACHLOROBUTADIENE
HEXACHLOROBUTADIENE
HEXACHLOROBUTA01ENE
HEXACHLOR06UTADIENE
HEXACHLOROBUTADIENE
FM
FM
FH
FH
FH
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
MORT2
MORT2
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
EGGS
EGGS
EGGS
EGGS
EGGS
EGGS
EGGS
EGGS
EGGS
EGGS
EGGS
EGGS
EGGS
HATCH
HATCH
HATCH
HATCH
MORT1
MORT1
MORT1
MORT1
MORT1
MORT1
MORT2
MORT2
MORT2
MORT2
MORT2
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
MORT2
MORT2
MORT2
MORT2
MORT2
MORT2
WEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
33
66
0
4
9
16
33
66
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
T
0
0
0
0
.0
0
1
3
6
13
27
0
T
3
6
13
27
0
1
3
6
13
27
.00
.00
.00
.90
.20
.00
.00
.00
.04
.10
.16
.24
.33
.51
.72
.00
.11
.20
.43
.66
.84
.11
.20
.43
.86
.00
.11
.20
.43
.86
.84
.00
.11
.20
.43
.86
.06
.70
.20
.50
.00
.00
.06
.70
.20
.50
.00
.00
.06
.70
.20
.50
.00
.00
60
60
15
650
900
1550
2350
30
30
30
30
30
30
320
320
320
320
320
120
120
120
120
120
120
60
60
60
60
60
60
20
40
1691
1220
1611
1239
1718
256
782
772
365
697
733
1558
0
91
112
276
245
6
13
6
9
13
30
107
77
198
54
114
25
40
39
43
42
34
0
1
2
9
28
27
0.17
0.20
0.18
0.15
0.12
0.04
0.13
0.13
0.13
0.13
0.10
0.03
PICKERING AND GUIAM
PICKERING AND GILIAM
PICKERING AND GILIAM
PICKERING AND GILIAM
PICKERING AND GILIAH
PICKERING AND GILIAM
PICKERING AND GILIAM
PICKERING AND GILIAM
ADELMAN ET AL 1976
ADELMAN ET AL 1976
ADELHAN ET AL 1976
ADELMAN ET AL 1976
ADELMAN ET AL 1976
ADELMAN ET AL 1976
ADELHAN ET AL 1976
MACEK ET AL 1976A
NACEK ET AL 1976A
NACEK ET AL 1976A
MACEK ET AL 1976A
NACEK ET AL 1976A
NACEK ET AL 1976A
NACEK ET AL 1976A
NACEK ET AL 1976A
NACEK ET AL 1976A
NACEK ET AL 1976A
NACEK ET AL 1976A
NACEK ET AL 1976A
NACEK ET AL 1976A
NACEK ET AL 1976A
NACEK ET AL 1976A
NACEK ET AL 1976A
NACEK ET AL 1976A
NACEK ET AL 1976A
NACEK ET AL 1976A
NACEK ET AL 1976A
NACEK ET AL 1976A
BENOIT ET AL 1962
BENOIT ET AL 1962
BENOIT ET AL 1982
BENOIT ET AL 1982
BENOIT ET AL 1982
BENOIT ET AL 1962
BENOIT ET AL 1962
BENOIT ET AL 1962
BENOIT ET AL 1962
BENOIT ET AL 1982
BENOIT ET AL 1982
BENOIT ET AL 1982
BENOIT ET AL 1982
BENOIT ET AL 1962
BENOIT ET AL 1982
BENOIT ET AL 1962
BENOIT ET AL 1962
BENOIT ET AL 1982
1982
1982
1982
1982
1982
1962
1982
1982
-------
192
ORNL-6251
Table B.I (Continued)
OBS
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
103S
1039
1040
1041
1042
1043
1044
104S
1046
1047
1048
1049
1050
1051
10S2
1053
1054
1055
10S6
1057
1056
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
CHEMICAL
HEXACHLOROCYCLOHEXAN
HEXACHLOROCYCLOHEXAN
HEXACHLOROCYCLOHEXAN
HEXACHLOROCYUOHEXAN
HEXACHLOROCYCLOHEXAN
HEXACHLOROCYCLOHEXAN
HEXACHLOROCYCLOHEXAN
HEXACHLOROCYCLOHEXAN
HEXACHLOROCYCLOHEXAN
HEXACHLOROCYCLOHEXAN
HEXACHLOROCYCLOHEXAN
HEXACHLOROCYUOHEXAN
HEXACHLOROCYCLOHEXAN
HEXACHLOROCYCLOHEXAN
HEXACHLOROCYCIOHEXAN
HEXACHLOROCYCLOHEXAN
HEXACHLOROCYCLOHEXAN
HEXACHLOROCYCLOHEXAN
HEXACHLOROCYCLOHEXAN
HEXACHLOROCYCLOHEXAN
HEXACHLOROCYCLOHEXAN
HEXACHLOROCYCLOHEXAN
HEXACHLOROCYUOHEXAN
HEXACHLOROCYCLOHEXAN
HEXACHLOROCYCLOHEXAN
HEXACHLOROCYCLOHEXAN
HEXACHLOROCYCLOHEXAN
HEXACHLOROCYCLOHEXAN
HEXACHLOROCYCLOHEXAN
HEXACHLOROCYCLOHEXAN
HEXACHLOROCYCLOHEXAN
HEXACHLOROCYCLOHEXAN
HEXACHLOROCYCLOHEXAN
HEXACHLOROCYCLOHEXAN
HEXACHLOROCYCLOHEXAN
HEXACHLOROCYCLOHEXAN
HEXACHLOROCYCLOHEXAN
HEXACHLOROCYCLOHEXAN
HEXACHLOROCYCLOHEXAN
HEXACHLOROCYCLOHEXAN
HEXACHLOROCYCLOHEXAN
HEXACHLOROCYCLOHEXAN
HEXACHLOROCYCLOHEXAN
HEXACHLOROCYCLOHEXAN
HEXACHLOROETHANE
HEXACHLOROE THANE
HEXACHLOROETHANE
HEXACHLOROETHANE
HEXACHLOROETHANE
HEXACHLOROE THANE
HEXACHLOROETHANE
HEXACHLOROETHANE
HEXACHLOROETHANE
HEXACHLOROETHANE
SPECIES PARAH
BG
BG
BG
8G
BG
BG
BG
BG
BG
BG
BG
BG
BG
BG
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
FM
FH
FM
FH
FH
FM
FM
FM
FM
FM
FM
FM
FH
FM
FM
FM
FM
FM
FH
FM
FM
FM
FM
FM
FM
FM
FM
FM
HATCH
HATCH
HATCH
HATCH
MORT1
MORT1
MORT1
MORT1
MORT1
MORT1
MORT2
MORT2
MORT2
MORT2
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
MORT2
MORT2
MORT2
MORT2
HORT2
MORT2
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
MORT1
MORT1
MORT1
MORT1
MORT1
MORT1
MORT2
MORI?
MORT2
MORI 2
MORT2
MORT2
MORI 2
MORT2
MORT2
HORT2
MORT2
MORT2
WEIGHT
WEIGHT
WEIGHT
WEIGHT
DOSE NTESTED RESPONSE EGGS WEIGHT SOURCE
0
1
2
4
0
0
1
2
4
9
0
1
2
4
0
1
2
4
8
16
0
1
2
4
8
16
0
1
2
5
9
23
0
1
2
5
9
23
0
1
2
5
9
23
0
28
69
207
608
1604
0
28
69
207
.60
.10
.30
.40
.00
.60
.10
.30
.40
.10
.60
.10
.30
.40
.00
.10
.10
.10
.80
,60
.00
.10
.10
.10
.80
.60
.00
.40
.40
.60
.10
.40
.00
.40
.40
.60
.10
.50
.00
.40
.40
.60
.10
.40
.90
.00
.00
.00
.00
.00
.90
.00
.00
.00
600
200
2200
400
20
20
20
20
20
20
30
30
120
30
100
SO
200
ISO
SO
SO
SO
SO
SO
50
SO
25
200
900
1600
1600
1550
1350
15
15
15
15
15
15
40
160
160
160
80
80
120
120
120
120
120
120
60
24
770
120
3
1
3
5
4
3
30
26
49
26
75
7
6
53
12
36
23
49
25
34
39
23
26
81
192
176
186
189
1
0
0
1
1
4
10
26
48
53
24
14
15
39
30
21
12
120
MAC EK
HACEK
HACEK
MACEK
MACEK
HACEK
HACEK
MACEK
MACEK
HACEK
HACEK
HACEK
HACEK
HACEK
HACEK
HACEK
HACEK
HACEK
HACEK
HACEK
HACEK
HACEK
HACEK
HACEK
HACEK
HACEK
HACEK
HACEK
HACEK
HACEK
HACEK
HACEK
HACEK
HACEK
HACEK
HACEK
HACEK
HACEK
HACEK
MACEK
HACEK
MACEK
HACEK
HACEK
AHHED
AHHED
AHMED
AHMED
AHHED
AHMED
0.17 AHHED
0.19 AHHED
0.16 AHMED
0.12 AHHED
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
ET
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
19768
1976B
1976B
1976B
1976B
1976B
1976B
1976B
1976B
1976B
1976B
19766
1976B
19768
1976B
19768
19768
19768
1976B
1976B
19768
1976B
19766
1976B
1976B
1976B
1976B
1976B
1976B
1976B
19766
1976B
1976B
19768
1976B
1976B
1976B
1976B
1976B
1976B
1976B
19766
19768
1976B
1984
1984
1984
1984
19B4
1984
1984
1984
1984
1984
-------
ORNL-6251
193
Table B.I. (Continued)
DBS CHEMICAL
SPECIES PARAM
1081 HEXACHLOROETHANE FM
1082 HEXACHLOROETHANE FM
1083 HG
1084 HG
1085 HG
lOSb HG
1087 HG
1088 HG
1089 HG
1090 HG
1091 HG
109? HG
1093 HG
1094 HG
1095 HG
1096 HG
1091 HG
1098 HG
1099 HG
1100 HG
1101 HG
1102 HG
1103 HG
1104 HG
1105 HG
1106 HG
1107 HG
1108 HG
1109 HG
1110 HG
1111 HG
1112 HG
1113 ISOPHORONE
1114 ISOPHORONE
1115 ISOPHORONE
1116 ISOPHORONE
1117 ISOPHORONE
1118 ISOPHORONE
1119 ISOPHORONE
11 20 ISOPHORONE
1121 ISOPHORONE
1122 ISOPHORONE
1123 ISOPHORONE
1124 ISOPHORONE
1125 ISOPHORONE
1126 ISOPHORONE
1127 ISOPHORONE
1126 ISOPHORONE
1129 ISOPHORONE
1130 KELTHANE
1131 KELTHANE
1132 KELTHANE
1133 KELTHANE
1134 KELTHANE
FH
FM
FM
FN
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
HEIGHT
HEIGHT
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
MORT2
MORT2
MORT2
MORT2
MORT2
MORT2
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
EGGS
EGGS
EGGS
EGGS
EGGS
EGGS
HEIGHT
WEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
MORT5
MORT5
MORT5
MORT5
MORT5
HORT5
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
MORT2
MORT2
HORT2
MORT2
MORT2
DOSE NTESTEO RESPONSE EGGS
608.00
1604.00
0.01
0.23
0.48
1.85
0.87
0.87
0.01
0.23
0.48
0.87
1.85
3.70
0.01
0.23
0.48
0.87
1.85
3.70
0.00
0.26
0.50
1.02
2.01
3.69
0.00
0.26
0.50
1.02
2.01
3.69
0.00
11.00
19.00
30.00
56.00
112.00
0.00
11000.00
19000.00
30000.00
56000.00
0.00
2160.00
4165.00
8535.00
15610.00
25145.00
0.00
8.90
19.00
39.00
73.00
200
200
200
200
200
200
60
60
60
60
60
60
31
33
37
33
32
32
30
30
30
30
30
71
61
66
88
$4
200
0
0
0
0
26
S3
1204
557
646
0
0
0
4
5
5
6
8
29
0
6
6
16
30
WEIGHT SOURCE
0.04 AHMED ET AL 1984
0.00 AHMED ET AL 1984
CALL ET AL 1983B
CALL ET AL 1983B
CALL ET AL 19B3B
CALL ET AL 19838
CALL ET AL 1983B
CALL ET AL 19B3B
CALL ET AL 19B3B
CALL ET AL 19B3B
CALL ET AL 1983B
CALL ET AL 1983B
CALL ET AL 1983B
CALL ET AL 1983B
0.21 CALL ET AL 19B3B
0.19 CALL ET AL 1983B
0.19 CALL ET AL 1983B
CALL ET AL 1983B
CALL ET AL 19836
0.01 CALL ET AL 1983B
SNARSKI AND OLSON 1982
SNARSKI AND OLSON 1982
SNARSKI AND OLSON 1982
SNARSKI AND OLSON 1982
SNARSKI AND OLSON 1982
SNARSKI AND OLSON 1982
0.26 SNARSKI AND OLSON 1982
0.19 SNARSKI AND OLSON 1982
0.23 SNARSKI AND OLSON 1982
0.19 SNARSKI AND OLSON 1982
0.15 SNARSKI AND OLSON 1982
0.09 SNARSKI AND OLSON 1982
CAIRNS AND NEBEKER 1982
CAIRNS AND NEBEKER 1982
CAIRNS AND NEBEKER 1982
CAIRNS AND NEBEKER 1982
CAIRNS AND NEBEKER 1982
CAIRNS AND NEBEKER 1982
0.03 CAIRNS AHD NEBE.KER 1982
0.02 CAIRNS AND NEBEKER 19B2
0.02 CAIRNS AND NEBEKER 1982
0.01 CAIRNS AND NEBEKER 1982
0.01 CAIRNS AND NEBEKER 1982
0.17 LEMKE ET AL 1983
0.18 LEMKE ET AL 1983
0.17 LEMKE ET AL 1983
0.16 LEMKE ET AL 1983
0.15 LEMKE ET AL 1983
0.14 LEMKE ET AL 1983
SPEHAR ET AL 1982
SPEHAR ET AL 1962
SPEHAR ET AL 19B2
SPEHAR ET AL 1962
SPEHAR ET AL 1962
-------
194
ORNL-625T
Table B.I. (Continued)
DBS
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
CHEMICAL
KELTHANE
KEPONE
KEPONE
KEPONE
KEPONE
KEPONE
KEPONE
KEPONE
KEPONE
KEPONE
KEPONE
KEPONE
KEPONE
KEPONE
KEPONE
KEPONE
KEPONE
KEPONE
KEPONE
KEPONE
KEPONE
KEPONE
KEPONE
KEPONE
KEPONE
KEPONE
LAS MIXTURE
LAS MIXTURE
LAS MIXTURE
LAS MIXTURE
LAS MIXTURE
LAS MIXTURE
LAS MIXTURE
LAS MIXTURE
LAS MIXTURE
LAS MIXTURE
LAS MIXTURE
LAS MIXTURE
LAS MIXTURE
LAS MIXTURE
LAS MIXTURE
LAS 11.2
LAS 11.2
LAS 11.2
LAS 11.2
LAS 11.2
LAS 11.2
LAS 11.2
LAS 11.2
LAS 11.2
LAS 11.2
LAS 11.2
LAS 11.2
LAS 11.2
SPECIES PARAM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FN
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FH
FM
FM
FM
FM
FM
FH
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
HORT2
EGGS
EGGS
EGGS
EGGS
EGGS
EGGS
EGGS
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
MORT1
MORT1
MORT1
HORT1
MORT1
MORT1
MORT2
MORT2
MORT2
MORT2
MORT2
MORT2
EGGS
EGGS
EGGS
EGGS
EGGS
HATCH
HATCH
HATCH
HATCH
HATCH
MORT2
MORT2
MORT2
MORT2
MORT2
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
MORI 2
MORI 2
MORT2
MORT2
MORT2
DOSE NTESTEO RESPONSE
125
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
340
630
1200
2700
0
340
630
1200
2700
0
340
630
1200
2700
0
2500
3000
4400
5100
8400
9800
14200
0
2500
3000
4400
5100
.00
.00
.01
.07
.17
.03
.31
.31
.00
.01
.03
.07
.17
.31
.00
.01
.03
.07
.17
.31
.00
.01
.03
.07
.17
.31
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
15
2950
2750
2850
1950
2250
4200
68
71
71
62
60
66
80
80
BO
BO
BO
BO
400
400
400
400
400
400
400
400
400
400
100
100
100
100
100
100
100
100
BO
BO
80
BO
80
15
1062
625
1083
566
652
2016
4
2
0
0
7
2
19
30
IB
14
35
27
16
22
16
23
46
68
60
82
240
341
17
11
19
21
34
64
59
94
29
41
42
32
50
EGGS HEIGHT SOURCE
3B6
293
212
259
319
581
581
2496
3811
25B3
2188
1710
SPEHAR ET
BUCKLER ET
BUCKLER ET
BUCKLER ET
BUCKLER ET
BUCKLER ET
BUCKLER ET
BUCKLER ET
BUCKLER ET
BUCKLER ET
BUCKLER ET
BUCKLER ET
BUCKLER ET
BUCKLER ET
BUCKLER ET
BUCKLER ET
BUCKLER ET
BUCKLER ET
BUCKLER ET
BUCKLER ET
BUCKLER ET
BUCKLER ET
BUCKLER ET
BUCKLER ET
BUCKLER ET
BUCKLER ET
PICKERING
PICKERING
PICKERING
PICKERING
PICKERING
PICKERING
PICKERING
PICKERING
PICKERING
PICKERING
PICKERING
PICKERING
PICKERING
PICKERING
PICKERING
HOLMAN AND
HOLMAN AND
HOLMAN AND
HOLMAN AND
HOLMAN AND
HOLMAN AND
HOLMAN AND
HOLMAN AND
HOLMAN AND
HOLMAN AND
HOLMAN AND
HOLMAN AND
HOLMAN AND
AL 1982
AL 1981
AL 1981
AL 1981
AL 1981
AL 1981
AL 1981
AL 1981
AL 1981
AL 1981
AL 1981
AL 1981
AL 1981
AL 1981
AL 1981
AL 1981
AL 1981
AL 1981
AL 1981
AL 1981
AL 1981
AL 1981
AL 1981
AL 1981
AL 1981
AL 1981
AND THATCHER
AND THATCHER
AND THATCHER
AND THATCHER
AND THATCHER
AND THATCHER
AND THATCHER
AND THATCHER
AND THATCHER
AND THATCHER
AND THATCHER
AND THATCHER
AND THATCHER
AND THATCHER
AND THATCHER
MACEK 1980
MACEK 1980
MACEK 1980
MACEK 1980
HACEK 1980
MACEK 1980
MACEK 1980
MACEK 1980
MACEK 1980
MACEK 1980
MACEK 1980
MACEK 1980
MACEK 1980
1970
1970
1970
1970
1970
1970
1970
1970
1970
1970
1970
1970
1970
1970
1970
-------
ORNL-6251
195
Table B.I (Continued)
DBS CHEMICAL
1189 LAS 11.2
1190 US 11.2
1191 LAS 11.2
1192 LAS 11.7
1193 LAS 11.7
1194 LAS 11.7
1195 LAS 11.7
1196 LAS 11.7
1197 LAS 11.7
1198 LAS 11.7
1199 LAS 11.7
1200 LAS 11.7
1201 LAS 11.7
1202 LAS 11.7
1203 LAS 11.7
1204 LAS 11.7
120$ LAS 11.7
1206 LAS 11.7
1207 LAS 11.7
1206 LAS 11.7
1209 LAS 11.7
1210 LAS 11.7
1211 LAS 11.7
1212 LAS 11.7
1213 LAS 11.7
1214 LAS 13.3
121 5 LAS 13.3
1216 LAS 13.3
1217 IAS 13.3
1218 LAS 13.3
1219 LAS 13.3
1220 LAS 13.3
1221 LAS 13.3
1222 LAS 13.3
1223 LAS 13.3
1224 LAS 13.3
1225 LAS 13.3
1226 MALATHION
1227 MALATHION
1228 MALATHION
1229 MALATHION
1230 MALATHION
1231 MALATHION
1232 MALATHION
1233 MALATHION
1234 MALATHION
1235 MALATHION
1236 MALATHION
1237 MALATHION
1238 MALATHION
1239 MALATHION
1240 MALATHION
1241 MALATHION
1242 METHYLHERCURIC
SPECIES PARAM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FN
FH
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FH
FM
FM
FM
FM
FM
FF
FF
FF
FF
FF
FF
FF
FF
FF
FF
FF
FF
FF
FF
FF
FF
CHLOR 67
MORT2
MORT2
MORT2
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
MORT1
MORT1
NORT1
MORT1
NORT1
MORT1
MORT2
MORT2
MORT2
MORT2
MORT2
NORT2
MORT2
MORT2
EGGS
EGGS
EGGS
EGGS
EGGS
EGGS
HOR11
NORT1
MORT1
MORT1
MORT1
MORT1
MORT2
MORT2
MORT2
MORT2
MORT2
HORT2
MORT2
MORT2
MORT4
HORT4
MORT4
MORT4
MORT4
MORT4
MORT4
MORT4
EGGS
DOSE NTESTEO RESPONSE E6GS HEIGHT SOURCE
8400.00
9800.00
14200.00
0.00
200.00
220.00
310.00
480.00
490.00
S70.00
740.00
0.00
60.00
120.00
250.00
530.00
1090.00
0.00
200.00
220.00
310.00
480.00
490.00
570.00
740.00
0.00
20.00
33.00
56.00
106.00
252.00
0.00
20.00
33.00
56.00
106.00
252.00
0.00
5.80
8.60
10.90
15.00
19.30
24.70
31.50
0.00
5. BO
8.60
10.90
15.00
19.30
24.70
31.50
0.00
80
BO
60
ISO
ISO
ISO
ISO
ISO
ISO
150
ISO
30
30
30
30
30
30
80
80
80
80
eo
80
80
80
30
30
30
30
30
30
80
80
80
80
80
80
80
80
40
40
40
40
40
40
40
40
29
58
80
17
9
5
11
6
S
6
S
1
6
10
10
16
5
1
6
0
9
16
44
22
42
530
221
72
346
135
7
4
11
9
9
7
9
16
8
9
16
39
9
IS
47
0
0
1
2
4
S
17
14
506
HOLMAN AND MACEK 1980
HOLMAN AND MACEK 1980
HOLMAN AND MACEK 1980
HOLMAN AND MACEK 1980
HOLMAN AND MACEK 1980
HOLMAN AND MACEK 1980
HOLMAN AND MACEK 1980
HOLMAN AND MACEK 1980
HOLMAN AND MACEK 1980
HOLMAN AND MACEK 1980
HOLMAN AND MACEK 1980
HOLMAN AND MACEK 1980
HOLMAN AND MACEK 1980
HOLMAN AND MACEK 19BO
HOLMAN AND MACEK 1980
HOLMAN AND MACEK 1980
HOLMAN AND MACEK 1980
HOLMAN AND MACEK 1980
HOLMAN AND MACEK 1980
HOLMAN AND MACEK 1980
HOLMAN AND MACEK 1980
HOLMAN AND MACEK 1980
HOLMAN AND MACEK 1980
HOLMAN AND MACEK 1980
HOLMAN AND MACEK 1980
HOLMAN AND NACEK 1980
HOLMAN AND NACEK 19BO
HOLMAN AND MACEK 1980
HOLMAN AND NACEK 19BO
HOLMAN AND MACEK 1980
HOLNAN AND MACEK 1980
HOLMAN AND NACEK 1980
HOLMAN AND MACEK 1980
HOLMAN AND MACEK 1980
HOLMAN AND MACEK 1960
HOLMAN AND MACEK 1980
HOLNAN AND NACEK 1980
HERNANUTZ 1978
HERMANUTZ 1978
HERMANUTZ 1978
HERMANUTZ 1978
HERMANUTZ 1978
HERMANUTZ 1978
HERMANUTZ 1978
HERMANUTZ 1978
HERMANUTZ 1978
HERMANUTZ 1976
HERMANUTZ 197B
HERMANUTZ 1978
HERMANUTZ 1978
HERMANUTZ 1978
HERMANUTZ 1978
HERHANUTZ 1978
MCK1M ET AL 1976
-------
196
ORNL-6251
Table B.I. (Continued)
DBS
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
12B3
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
CHEMICAL
METHYLMERCUR1C
METHYLMERCURIC
METHYLMERCURIC
METHYLMERCURIC
METHYLMERCURIC
METHYLMERCURIC
METHYLMEHCURIC
METHYLMERCURIC
METHYLMERCURIC
METHYLMERCURIC
METHYLMERCURIC
METHYLMERCURIC
METHYLMERCURIC
METHYLMERCURIC
METHYLMERCURIC
METHYLMERCURIC
METHYLMERCURIC
METHYLKERCUR1C
METHYLMERCURIC
METHYLMERCURIC
MIREX
MIREX
MIREX
MIREX
MIREX
MIREX
MIREX
MIREX
MIREX
MIREX
MIREX
MIREX
MIREX
MIREX
MIREX
MIREX
MIREX
MIREX
MIREX
MIREX
MIREX
MIREX
MIREX
MIREX
NAPTHALENE
NAPTHALENE
NAPTHALENE
NAPTHALENE
NAPTHALENE
NAPTHALENE
NAPTHALENE
NAPTHALENE
NI
NI
SPECIES PARAM
CHLOR
CHLOR
CHLOR
CHLOR
CHLOR
CHLOR
CHLOR
CHLOR
CHLOR
CHLOR
CHLOR
CHLOR
CHLOR
CHLOR
CHLOR
CHLOR
CHLOR
CHLOR
CHLOR
CHLOR
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FH
FM
E66S
E6GS
EGGS
EGGS
EGGS
HATCH
HATCH
HATCH
HATCH
HATCH
MORT1
MORT1
NORT1
MORT1
MORT1
MORT2
MORT2
HORT2
MORT2
MORT2
EGGS
EGGS
EGGS
EGGS
EGGS
EGGS
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
MORT1
MORT1
MORT1
MORT1
MORT1
MORT1
HORT2
MORT2
MORT2
MORT2
MORT2
MORT2
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
EGGS
EGGS
DOSE NTESTEO RESPONSE
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
3
7
13
34
0
2
3
7
13
34
0
2
3
7
13
34
0
2
3
7
13
34
0
130
210
450
850
1840
4380
8510
0
82
.03
.09
.29
.93
.93
.00
.03
.09
.29
.93
.00
.03
.09
.29
.91
.00
.03
.09
.29
.93
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
200
200
200
100
200
12
12
12
6
6
100
100
100
100
100
2900
2400
900
2300
1050
1000
70
72
69
72
63
67
80
80
80
80
80
80
500
500
500
500
500
500
500
500
6
26
1
2
116
1
2
2
1
5
4
6
3
1
55
1015
360
117
368
284
370
4
11
7
20
13
18
9
9
IB
11
29
18
48
78
55
68
114
57
171
317
EGGS WEIGHT SOURCE
299
430
191
368
0
395
283
104
272
128
84
1603
1104
HCKIM ET AL 1976
MCKIM ET AL 1976
HCKIM ET AL 1976
MCKIM ET AL 1976
NCKIM ET AL 1976
MCKIM ET AL 1976
NCKIM ET AL 1976
MCKIM ET AL 1976
MCKIM ET AL 1976
HCKIM ET AL 1976
NCKIM ET AL 1976
NCKIM ET AL 1976
NCKIH ET AL 1976
HCKIM ET AL 1976
NCKIM ET AL 1976
NCKIM ET AL 1976
NCKIM ET AL 1976
HCKIM ET AL 1976
MCKIM ET AL 1976
NCKIM ET AL 1976
BUCKLER ET AL 1981
BUCKLER ET AL 1981
BUCKLER ET AL 1981
BUCKLER ET AL 1981
BUCKLER ET AL 1981
BUCKLER ET AL 1981
BUCKLER ET AL 1981
BUCKLER ET AL 1981
BUCKLER ET AL 1981
BUCKLER ET AL 1981
BUCKLER ET AL 1981
BUCKLER ET AL 1981
BUCKLER ET AL 1981
BUCKLER ET AL 1981
BUCKLER ET AL 1981
BUCKLER ET AL 1981
BUCKLER ET AL 1981
BUCKLER ET AL 1981
BUCKLER ET AL 1981
BUCKLER ET AL 1981
BUCKLER ET AL 1981
BUCKLER ET AL 1981
BUCKLER ET AL 1981
BUCKLER ET AL 1981
DEGRAEVE ET AL 1982
OE6RAEVE ET AL 1982
DEGRAEVE ET AL 1982
DEGRAEVE ET AL 1982
DEGRAEVE ET AL 1982
DEGRAEVE ET AL 1982
OEGRAEVE ET AL 1982
DEGRAEVE ET AL 1982
PICKERING 1974
PICKERING 1974
-------
ORNL-6251
197
Tibli B.I. (Continued)
OBS CHEMICAL
1297 HI
1296 NI
129ft NI
1300 NI
1301 NI
1302 NI
1303 NI
1304 NI
1305 NI
1306 NI
1307 NI
1306 NI
1309 NI
1310 NI
1311 PB
1312 PB
1313 PB
1314 PB
1315 PB
1316 PB
1317 PB
131B PB
1319 PB
1320 PB
1321 PB
1322 PB
1323 PB
1324 PB
132$ PB
1326 PB
1327 PB
1326 PB
1329 PB
1330 PB
1331 PB
1332 PB
1333 PB
1334 PB
1335 PB
1336 PB
1337 PB
1336 PB
1339 PB
1340 PB
1341 PB
1342 PB
1343 PB
1344 PB
1345 PB
1346 PB
1347 PB
1348 PB
1349 PB
1350 PB
SPECIES PARAH
FN
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
B6
6G
BG
B6
B6
Be
B6
CC
CC
CC
CC
CC
CC
CC
IT
LT
E6GS
EGGS
EGGS
EGGS
HATCH
HATCH
HATCH
HATCH
HATCH
MOW
MORT2
MORI 2
MORT2
MORTZ
E6GS
C6GS
E8GS
EGGS
EGGS
EGGS
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
MORT1
MORT1
MORT1
MORT1
MORT1
MORT1
MORT2
MORT2
MORT2
MORT2
MORT2
MORT2
WEIGHT
HEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
DOSE NTESTEO RESPONSE
180.00
380.00
730.00
1600.00
0.00
82.00
180.00
3BO.OO
730.00
0,00
62.00
160.00
380.00
730.00
0.85
33.40
S7.60
119.20
235.20
475.40
0.90
34.00
SB. 00
119.00
235.00
474.00
0.85
33.45
57.90
119.20
235.00
472.60
0.90
34.00
58.00
119.00
235.00
474.00
0.00
12.00
33.00
70.00
120.00
277.00
447.00
0.00
17.00
33.00
75.00
136.00
260.00
460.00
0.00
48.00
1000
1100
1200
1300
2300
50
SO
50
50
SO
724
710
250
687
792
262
10
10
5
10
10
10
200
200
150
ISO
100
50
72
45
60
75
1325
7
4
3
4
3
13
140
52
99
264
169
3
0
0
3
2
2
31
23
9
3
6
40
EGGS
1320
1398
498
36
479
497
233
460
555
183
WEIGHT SOURCE
PICKERING 1974
PICKERING 1974
PICKERING 1914
PICKERING 1974
PICKERING 1974
PICKERING 1974
PICKERING 1974
PICKERING 1974
PICKERING 1974
PICKERING 1974
PICKERING 1974
PICKERING 1974
PICKERING 1974
PICKERING 1974
HOLCOMBE ET At 1976
HOLCONBE ET AL 1976
HOLCOM8E ET AL 1976
HOLCOMBE ET AL 1976
HOLCOMBE ET AL 1976
HQLCOM8E ET AL 1976
HOLCOMBE ET AL 1976
HOLCOMBE ET AL 1976
HOLCOMBE ET AL 1976
HOLCOMBE ET AL 1976
HOLCOMBE ET AL 1976
HOLCOMBE ET AL 1976
HOLCOMBE ET AL 1976
HOLCOMBE ET AL 1976
HOLCOMBE ET AL 1976
HOLCOMBE ET AL 1976
HOLCOMBE ET AL 1976
HOLCOMBE ET AL 1976
HOLCOMBE ET AL 1976
HOLCOMBE ET AL 1976
HOLCOMBE ET AL 1976
HOLCOMBE ET AL 1976
HOLCOMBE ET AL 1976
HOLCOMBE ET AL 1976
0.38 SAUTER ET AL 1976
0.42 SAUTER ET AL 1976
0.41 SAUTER ET AL 1976
0.49 SAUTER ET AL 1976
0.25 SAUTER ET AL 1976
0.00 SAUTER ET AL 1976
0.00 SAUTER ET AL 1976
0.24 SAUTER ET AL 1976
0.23 SAUTER ET AL 1976
0.24 SAUTER ET AL 1976
0.23 SAUTER ET AL 1976
0.15 SAUTER ET AL 1976
0.00 SAUTER ET AL 1976
0.00 SAUTER ET AL 1976
0.18 SAUTER ET AL 1976
0.19 SAUTER ET AL 1976
-------
198
ORNL-6251
Tiblt B.I (Contlnutd)
DBS CHEMICAL
1351 PB
1352 PB
1353 PB
1354 PB
1355 PB
1356 PB
1357 PB
1358 PB
1359 PB
1360 PB
1361 PB
1362 PB
1363 PB
1364 PB
1365 PB
1366 PB
1367 PB
1368 PB
1369 PB
1370 PB
1371 PB
1372 PB
1373 PB
1374 PB
137$ PB
1376 PB
1377 PB
1318 PB
1379 PB
1360 PB
1381 PB
1382 PB
1383 PENTACHLOROE THANE
1384 PENTACHLOROE THANE
1385 PEN1ACHLOROETHANE
1386 PENTACHLOROETHANE
1387 PENTACHLOROETHANE
1388 PENTACHLOROETHANE
1389 PENTACHLOROETHANE
1390 PENTACHLOROETHANE
1391 PENTACHLOROETHANE
1392 PENTACHLOROETHANE
1393 PENTACHLOROETHANE
1394 PENTACHLOROETHANE
1395 PENTACHLOROPHENOt
1396 PENTACHLOROPHENOL
1397 PENTACHLOROPHENOL
1398 PENTACHLOROPHENOL
1399 PENTACHLOROPHENOL
1400 PENTACHLOROPHENOL
1401 PENTACHLOROPHENOL
1402 PENTACHLOROPHENOL
1403 PENTACHLOROPHENOL
1404 PENTACHLOROPHENOL
SPECIES PARAN
LT
LT
LT
LT
LT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
WS
MS
US
MS
MS
MS
FM
FN
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FH
FM
FM
FM
FM
FH
HEIGHT
HEIGHT
WEIGHT
WEIGHT
WEIGHT
HATCH
HATCH
HATCH
HATCH
HATCH
NATCH
HATCH
NORT2
MORT2
MORT2
MORT2
NORT2
NORT2
MORT2
HEIGHT
WEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
MORT2
NORT2
MORT2
HORT2
MORT2
MORT2
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
MORT2
HORT2
NORT2
HORT2
DOSE NTESTED RESPONSE EGGS HEIGHT SOURCE
83.00
120.00
1*8.00
404.00
483.00
0.00
49.00
71.00
146.00
250.00
443.00
672.00
0.00
49.00
71.00
146.00
250.00
443.00
677.00
0.00
49.00
71.00
146.00
250.00
443.00
672.00
0.00
33.00
67.00
119.00
253.00
483.00
10.00
900.00
1400.00
2900.00
4100.00
13900.00
10.00
900.00
1400.00
2900.00
4100.00
13900.00
0.00
27.20
44.90
73.00
128.00
223.00
0.00
27.20
44.90
73.00
400
400
400
400
400
400
400
200
200
200
200
200
200
200
120
120
120
120
120
120
200
200
200
200
200
200
100
100
100
100
62
26
46
34
50
94
286
20
24
24
109
199
200
200
18
21
27
9
66
120
73
73
65
81
74
200
6
8
8
13
0.16 SALTIER ET AL 1976
0.15 SAUTER ET AL 1976
0.13 SAUTER ET AL 1976
0.00 SAUTER ET AL 1976
0.00 SAUTER ET AL 1976
SAUTER ET AL 1976
SAUTER ET AL 1976
SAUTER ET AL 1976
SAUTER ET AL 1976
SAUTER ET AL 1976
SAUTER ET AL 1976
SAUTER ET AL 1976
SAUTER ET AL 1976
SAUTER ET AL 1976
SAUTER ET AL 1976
SAUTER ET AL 1976
SAUTER ET AL 1976
SAUTER ET AL 1976
SAUTER ET AL 1976
0.71 SAUTER ET AL 1976
0.67 SAUTER ET AL 1916
0.73 SAUTER ET AL 1976
0.70 SAUTER ET AL 1976
0.70 SAUTER ET AL 1976
0.00 SAUTER ET AL 1976
0.00 SAUTER ET AL 1976
0.19 SAUTER ET AL 1976
0.26 SAUTER ET AL 1916
0.19 SAUTER ET AL 1976
0.18 SAUTER ET AL 1976
0.07 SAUTER ET AL 1976
0.00 SAUTER ET AL 1976
AHMED ET AL 1984
AHMED ET AL 1984
AHMED ET AL 1984
AHMED ET AL 1984
AHMED ET AL 1984
AHMED ET AL 1984
0.22 AHMED ET AL 1984
0.23 AHMED ET AL 1984
0.15 AHMED ET AL 1984
0.09 AHMED ET AL 1984
0.05 AHMED ET AL 1984
0.00 AHMED ET AL 1984
HOLCOMBE ET AL 1982
HOLCOMBE ET AL 1982
HOLCOMBE ET AL 1982
HOLCOMBE ET AL 1982
HOLCOMBE ET AL 1982
HOLCOMBE ET AL 1982
HOLCOMBE ET AL 1982
HOLCOMBE El AL 1982
HOLCOMBE ET AL 1982
HOLCOMBE ET AL 1982
-------
ORNL-6251
199
liblt B.I. (Continued)
DBS CHEMICAL
1405 PENTACHLOROPHENQL
1406 PENTACHLOROPHENOL
1407 PENTACHLOROPHENOL
1408 PENTACHLOROPHENOL
1409 PENTACHLOROPHENOL
1410 PENTACHLOROPHENOL
1411 PENTACHLOROPHENOL
HI 2 PENTACHLOROPHENOL
1413 PERHETHRIN
1414 PERHETHRIN
1415 PERHETHRIN
1416 PERHETHRIN
1417 PERHETHRIN
141B PERHETHRIN
1419 PERHETHRIN
1420 PERHETHRIN
1421 PERHETHRIN
1422 PERHETHRIN
1423 PERHETHRIN
1424 PERHETHRIN
1425 PERHETHRIN
1426 PERHETHRIN
1427 PERHETHRIN
1428 PEHMETHR1N
1429 PERHETHRIN
1430 PERMETHRIN
1431 PHENOL
1432 PHENOL
1433 PHENOL
1434 PHENOL
1435 PHENOL
1436 PHENOL
1437 PHENOL
1436 PHENOL
1439 PHENOL
1440 PHENOL
1441 PHENOL
1442 PHENOL
1443 PHENOL
1444 PHENOL
144$ PHENOL
1446 PHENOL
1447 PHENOL
1448 PHENOL
1449 PHENOL
1450 PHENOL
1451 PHENOL
1452 PHENOL
1453 PHENOL
1454 PHENOL
1455 PHENOL
1456 PHENOL
1457 PHENOL
1458 PHENOL
SPECIES PAR AH
FM
FH
FM
FH
FM
FM
FH
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FH
FM
FM
FH
FM
FM
FH
FH
FM
FH
FH
FM
FM
FM
FH
FM
FM
FM
FM
FM
FM
RT
RT
RT
RT
MORT2
MORT2
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
MORT2
MORT2
MORT2
MORT2
NORT2
MORT2
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
MORTZ
MORT2
MORT2
MORTZ
MORTZ
MORT2
MORTZ
MORTZ
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
MORTZ
MORTZ
MORTZ
MORTZ
DOSE NTESTEO RESPONSE EGGS HEIGHT SOURCE
128.00
223.00
0.00
27.20
44.90
73.00
128.00
223.00
0.00
0.11
0.18
0.33
0.66
1.40
0.00
0.11
0.18
0.33
0.66
1.40
0.00
0.11
0.18
0.33
0.66
1.40
0.00
230.00
750.00
2500.00
6100.00
14500.00
33200.00
6B500.00
0.00
230.00
750.00
2500.00
6100.00
14500.00
33200.00
6B500.00
0.00
230.00
750.00
2500.00
6100.00
14500.00
33200.00
68500.00
0.00
340.00
540.00
1100.00
100
100
100
100
100
100
100
100
100
100
100
100
100
100
60
60
60
60
60
60
500
500
500
500
500
500
500
500
30
30
30
30
30
30
30
30
200
200
200
200
79
100
10
3
6
10
14
10
5
2
2
2
4
59
91
B7
93
109
114
139
111
274
14
21
17
15
16
22
30
30
19
23
14
69
NOLCOMBE ET AL 1982
HOLCOHBE ET AL 19B2
0.13 HOLCOHBE ET AL 1962
0.14 HOLCOHBE ET AL 19B2
0.13 HOLCOMBE ET AL 19B2
0.11 HOLCOMBE ET AL 1982
0.11 HOLCOHBE ET AL 1982
0.00 HOLCOHBE ET AL 1962
SPEHAR ET AL 1983
SPEHAR ET AL 1983
SPEHAR ET AL 19B3
SPEHAR ET AL 1983
SPEHAR ET AL 19B3
SPEHAR ET AL 1983
SPEHAR ET AL 1963
SPEHAR ET AL 19B3
SPEHAR ET AL 1983
SPEHAR ET AL 1983
SPEHAR ET AL 1983
SPEHAR ET AL 19B3
0.10 SPEHAR ET AL 1983
0.09 SPEHAR ET AL 19B3
0.10 SPEHAR ET AL 1983
0.09 SPEHAR ET AL 1983
0.09 SPEHAR ET AL 1983
0.11 SPEHAR ET AL 1963
DEGRAEVE ET AL 1980
OE6RAEVE ET AL I960
DEGRAEVE ET AL 1980
OEGRAEVE ET AL 1980
OEGRAEVE ET AL 1980
OEGRAEVE ET AL 1980
DEGRAEVE ET AL I960
DEGRAEVE ET AL 1980
DEGRAEVE ET AL I960
DEGRAEVE ET AL I960
OEGRAEVE ET AL I960
OEGRAEVE ET AL I960
OEGRAEVE ET AL I960
DEGRAEVE ET AL 1980
DEGRAEVE ET AL 1980
DEGRAEVE ET AL 1980
0.27 DEGRAEVE ET AL I960
0.18 DEGRAEVE ET AL 1960
0.2S DEGRAEVE ET AL I960
0.19 DEGRAEVE ET AL 1980
0.15 DEGRAEVE ET AL I960
0.18 DEGRAEVE ET AL 1960
DEGRAEVE ET AL 1980
OEGRAEVE ET AL 1960
OEGRAEVE ET AL 1980
DEGRAEVE ET AL 1980
DEGRAEVE ET AL 1980
DEGRAEVE ET AL 1980
-------
200
ORNL-6251
Tiblt B.I. (Continued)
DBS
1459
1460
1461
1462
1463
1464
146$
1466
1467
1466
1469
1470
1471
1472
1473
1474
147S
1476
1477
1478
1479
14BO
1481
1482
1483
1484
1485
I486
1487
I486
1489
1490
1491
1492
1493
1494
1495
1496
1497
1496
1499
1500
1501
1502
1503
1504
1505
1506
1507
150B
1509
1510
1511
1512
CHEMICAL
PHENOL
PHENOL
PHENOL
PHENOL
PHENOL
PHENOL
PHENOL
PHENOL
PHENOL
PHENOL
PHENOL
PHENOL
PHENOL
PHENOL
PHENOL
PHENOL
PHENOL
PHENOL
PHENOL
PHENOL
PHENOL
PHENOL
PHENOL
PHENOL
PHENOL
PHENOL
PHCNOl
PHENOL
PHENOLS
PHENOLS
PHENOLS
PHENOLS
PHENOLS
PHENOLS
PHENOLS
PHENOLS
PHENOLS
PHEHOLS
PHENOLS
PHENOLS
PIUORAM
PICLORAH
PICLORAM
PICLORAH
PICLORAM
PICLORAM
PROPANIL
PROPANIL
PROPANIL
PROPANIL
PROPANIL
PROPANIL
PROPANIL
PROPANIL
SPECIES PARAM
RT
NT
RT
HT
RT
RT
RT
RT
RT
RT
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
LT
LT
LT
LT
LT
LT
FM
FM
FM
FM
FM
FM
FM
FM
MORT2
MORT2
MORT2
WEIGHT
WEIGHT
HEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
MORT2
MORT2
MORT2
MORT2
MORT2
MORT2
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
EGGS
EGGS
EGGS
EGGS
EGGS
EGGS
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
MORT2
MORT2
DOSE NTESTEO RESPONSE
2800.
5900.
13800,
0.
340.
S40.
1100.
2800.
5900.
13800.
0.
240.
4SO.
910.
1830.
3570.
0.
240.
4SO.
910.
1830.
3570.
0.
240.
450.
910.
1830.
3570.
0.
60.
130.
250.
S60.
1210.
0.
60.
130.
250.
560.
1210.
0,
35.
75.
240.
500.
1000.
0.
0.
0.
1.
2.
3.
0.
0.
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
40
60
20
40
80
00
40
200
200
200
200
200
200
200
200
200
100
100
100
100
100
100
100
100
100
100
100
100
200
200
200
200
200
200
60
60
134
94
200
23
17
15
23
19
14
21
25
26
27
26
13
53
4B
74
85
89
161
4
16
EGGS WES6H1
1
1
1
0
0
0
0
0
0
0
0
0
270
182
91
202
50
0
20
16
23
11
13
6
373
233
154
117
.57
.31
.11
.96
.91
.46
.10
.10
.10
.10
.10
.08
.40
.80
.10
.50
.60
.80
.00
.00
.00
.00
SOURCE
DEGRAEVE ET AL 1980
DCGRAEVE ET AL 19BO
OE6RAEVE ET AL 19BO
DEGRAEVE ET AL 1980
DEGRAEVE ET AL 19BO
DEGRAEVE ET AL 1980
DEGRAEVE ET AL 1980
DEGRAEVE ET AL 1980
DEGRAEVE ET AL 1980
DE6RAEVE ET AL 19BO
HOLCOMBE ET AL 1982
HOLCOMBE ET AL 1982
HOLCOMBE ET AL 1982
HOLCOMBE ET AL 1962
HOLCOMBE ET AL 1982
HOLCOMBE ET AL 1982
HOLCOMBE ET AL 1962
HOLCOMBE ET AL 1982
HOLCOMBE ET AL 1982
HOLCOMBE ET AL 1962
HOLCOMBE CT AL 1982
HOLCOMBE ET AL 1982
HOLCOMBE ET AL 19B2
HOLCOM6E ET AL 1982
HOLCOMBE ET AL 1962
HOLCOM6E ET AL 1962
HOLCOMBE ET AL 1962
HOLCOM6E ET AL 1962
OAUBLE ET AL 1983
DAUBLE ET AL 1983
OAUBLE ET AL 1983
DAUBLE ET AL 1983
OAUBLE ET AL 1983
OAUBLE ET AL 1983
DAUBLE ET AL 1983
DAUBLE ET AL 1983
DAU8LE ET AL 1983
DAUBLE ET AL 1983
OAUBLE ET AL 1983
DAUBLE ET AL 1983
WOODWARD 1976
WOODWARD 1976
WOODWARD 1976
WOODWARD 1976
WOODWARD 1976
WOODWARD 1976
CALL ET AL 1983
CALL ET AL 1983
CALL ET AL 1983
CALL ET AL 1983
CALL ET AL 1983
CALL ET AL 1983
CALL ET AL 1983
CALL ET AL 1983
-------
ORNL-6251
201
Table B.I. (Continued)
DBS
1513
1514
1515
1516
1517
1518
1519
1520
1521
1522
1523
1524
1525
1526
1527
1528
1529
1530
1531
1532
1533
1534
1535
1536
1537
1538
1539
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551
1552
1553
1554
1555
1556
1557
1558
1559
1560
1561
1562
1563
1564
1565
1566
CHEMICAL
PROPANU
PROPANIL
PROP ANIL
PROPANIL
PROPANIL
PROPANIL
PROPANIL
PROPANIL
PROPANIL
PROPANIL
PYDRIN
PYORIN
PYDRIN
PYORIN
PYORIN
PYDRIN
TETRACHLOROETHYLENE
TETRACHLOROETHYLENE
TETRACHLOROETHYLENE
TETRACHLOROETHYLENE
TETRACHLOROETHYLENC
TETRACHLOROETHYLENE
TETRACHLOROETHYLENE
TETRACHLOROETHYLENE
TETRACHLOROETHYLENE
TETRACHLOROETHYLENE
TETRACHLOROETHYLENE
TOXAPHENE
TOXAPHENE
TOXAPHEHE
TOXAPHENE
TOXAPHENE
TOXAPHENE
TOXAPHENE
TOXAPHENE
TOXAPHENE
TOXAPHENE
TOXAPHENE
TOXAPHENE
TOXAPHENE
TOXAPHENE
TOXAPHENE
TOXAPHENE
TOXAPHENE
TOXAPHENE
TOXAPHENE
TOXAPHENE
TOXAPHENE
TOXAPHENE
TOXAPHENE
TOXAPHENE
TOXAPHENE
TOXAPHENE
TOXAPHENE
SPECIES PARAH
FN
FM
FM
FM
FM
FM
FM
FM
FM
FM
FH
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
BT
BT
8T
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
BT
CC
cc
CC
MORT2
MORT2
MORT2
MORT2
UEI6HT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
MORT2
MORT2
HORT2
MORT2
MORT2
MORT2
MORT2
MORT2
MORT2
MORT2
MORT2
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
EGGS
EGGS
EGGS
EGGS
EGGS
EGGS
MORT1
MORT1
MORT1
MORT1
MORT1
MORT1
MORT2
MORT2
MORT2
MORT2
MORT2
MORT2
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
HATCH
HATCH
HATCH
DOSE NTESTED RESPONSE EGGS
0.60
1.20
2.40
3.80
0.00
0.40
0.60
1.20
2.40
3.80
0.00
0.14
0.17
0.19
0.33
0.43
0.00
1400.00
2800.00
4100.00
8600.00
0.00
500.00
1400.00
2800.00
4100.00
8600.00
0.00
0.04
0.07
0.13
0.27
0.50
0.00
0.04
0.07
0.13
0.27
0.50
0.00
0.04
0.07
0.13
0.27
0.50
0.00
0.04
0.07
0.13
0.27
0.50
0.00
0.05
0.07
60
60
60
60
30
50
60
60
WEIGHT
0.59
30
30
30
30
30
30
120
120
120
120
120
24
24
24
24
24
24
200
200
200
200
200
200
1800
1500
1200
3
8
3
2
7
22
6
20
74
120
120
855
541
516
542
462
617
0
2
2
2
12
24
128
166
156
164
200
200
126
75
84
0
0
0.
0
0
0
0
0
0
0
0
0
0
0
0
,56
.49
.45
.26
.25
.18
.12
.00
.00
.70
.37
.51
.40
.00
.00
SOURCE
CALL ET AL 1983
CALL ET AL 1983
CALL ET AL 1983
CALL ET AL 1983
CALL ET AL 1983
CALL ET AL 1983
CALL ET AL 1983
CALL ET AL 1983
CALL ET AL 1983
CALL ET AL 1983
SPEHAR ET AL 1982
SPEHAR ET AL 1982
SPEHAR ET AL 1982
SPEHAR ET AL 1982
SPEHAR ET AL 1982
SPEHAR ET AL 1982
AHMED ET AL 1984
AHMED ET AL 1984
AHMED ET AL 1984
AHMED ET AL 1984
AHMED ET AL 1984
AHMED ET AL 1984
AHMED ET AL 1984
AHMED ET AL 1984
AHMED ET AL 1964
AHMED ET AL 1984
AHMED ET AL 1984
MAYER ET AL 1975
MAYER ET AL 1975
MAYER ET AL 1975
MAYER ET AL 1915
MAYER ET AL 1975
MAYER ET AL 1975
MAYER ET AL 1975
MAYER ET AL 1975
MAYER ET AL 1975
MAYER ET AL 1975
MAYER ET AL 1915
MAYER ET AL 1975
MAYER ET AL 1975
MAYER ET AL 1975
MAYER ET AL 1975
MAYER ET AL 1975
MAYER ET AL 1975
MAYER ET AL 1975
MAYER ET AL 1975
MAYER ET AL 1975
MAYER ET AL 1975
MAYER ET AL 1975
MAYER ET AL 1975
MAYER ET AL 1975
MAYER CT AL 1977
MAYER ET AL 1977
MAYER ET AL 1977
-------
202
ORNL-6251
Table B.I. (Continued)
DBS CHEMICAL
1567 TOXAPHENE
1568 TOXAPHENE
1569 TOXAPHENE
1S70 TOXAPHENE
1571 TOXAPHENE
1S72 TOXAPHENE
1573 TOXAPHENE
1574 TOXAPHENE
1575 TOXAPHENE
1576 TOXAPHENE
1577 TOXAPHENE
157B TOXAPHENE
1579 TOXAPHENE
15BO TOXAPHENE
1581 TOXAPHENE
1582 TOXAPHENE
1583 TOXAPHENE
15B4 TOXAPHENE
1585 TOXAPHENE
1586 TOXAPHENE
1587 TOXAPHENE
1588 TOXAPHENE
1589 TOXAPHENE
1590 TOXAPHENE
1591 TOXAPHENE
1592 TOXAPHENE
1593 TOXAPHENE
1594 TOXAPHENE
1595 TOXAPHENE
1596 TOXAPHENE
1597 TOXAPHENE
1598 TOXAPHENE
1599 TOXAPHENE
1600 TOXAPHENE
1601 TOXAPHENE
1602 TOXAPHENE
1603 TOXAPHENE
1604 TOXAPHENE
1605 TOXAPHENE
1606 TRIFLURALIN
1607 TRIFLURALIN
1608 TRIFLURALIN
1609 TRIFLURALIN
1610 TRIFLURALIN
1611 TRIFLURALIN
1612 TRIFLURALIN
1613 TRIFLURALIN
1614 TRIFLURALIN
1615 TRIFLURALIN
1616 TRIFLURALIN
1617 TRIFLURALIN
1616 VANADIUH
1619 VANADIUH
1620 VANADIUM
SPECIES PARAH
CC
CC
CC
CC
CC
CC
CC
CC
CC
CC
CC
CC
CC
CC
CC
FM
FM
FM
FM
FM
FM
FH
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FH
FH
FH
FH
FM
FM
FM
FM
FM
FM
FM
FH
FH
FH
FF
FF
FF
HATCH
HATCH
HATCH
MORT1
HOST!
NORT1
NORT1
MORT1
MORT1
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
EGGS
EGGS
EGGS
EGGS
EGGS
EGGS
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
MORT1
MORT1
MORT1
MORT1
MORT1
HORT1
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HEIGHT
HATCH
HATCH
HATCH
HORT1
MORT1
MORT1
MORT1
MORT1
HORT1
HORT2
HORT2
HORT2
HEIGHT
HEIGHT
WEIGHT
DOSE NTESTED RESPONSE EGGS
0.13
0.30
0.63
0.00
0.05
0.07
0.13
0.30
0.63
0.00
0.05
0.07
0.13
0.30
0.63
0.00
0.01
0.02
0.05
0.10
0.17
0.00
0.01
0.02
0.05
0.10
0.17
0.00
0.01
0.02
0.05
0.10
0.17
0.00
0.01
0.02
0.05
0.10
0.17
0.00
1.90
5.10
0.00
1.50
1.90
5.10
6.20
16.50
0.00
1.90
5.10
0.00
41.00
170.00
1800
1200
1200
8
8
8
8
8
8
50
50
SO
50
50
50
20
20
20
20
20
20
100
100
100
30
30
30
30
30
30
80
120
160
180
108
300
0
1
1
1
0
2
256
125
165
604
301
258
11
5
11
11
6
9
1
3
1
5
2
1
9
15
19
5
6
8
21
30
30
13
53
46
WEIGHT SOURCE
MAYER ET AL 1977
MAYER ET AL 1977
MAYER ET AL 1977
MAYER ET AL 1977
MAYER ET AL 1977
MAYER ET AL 1977
MAYER ET AL 1977
MAYER ET AL 1977
MAYER ET AL 1977
0.13 MAYER ET AL 1977
0.11 MAYER ET AL 1977
0.13 HAYER ET AL 1977
0.11 MAYER ET AL 1977
0.09 MAYER ET AL 1977
0.10 HAYER ET AL 1977
HAYER ET AL 1977
MAYER ET AL 1977
MAYER ET AL 1977
MAYER ET AL 1977
MAYER ET AL 1917
MAYER ET AL 1977
HAYER ET AL 1977
HAYER ET AL 1977
MAYER ET AL 1977
MAYER ET AL 1977
MAYER ET AL 1977
HAYER ET AL 1977
HAYER ET AL 1977
HAYER ET AL 1977
MAYER ET AL 1977
MAYER ET AL 1977
HAYER ET AL 1977
HAYER ET AL 1977
0.17 HAYER ET AL 1977
0.16 MAYER ET AL 1977
0.17 MAYER ET AL 1977
0.16 MAYER ET AL 1977
0.15 MAYER ET AL 1977
0.15 HAYER ET AL 1977
MACEK ET AL 1976C
HACEK ET AL 1916C
MACEK ET AL 1976C
HACEK ET AL 1976C
HACEK El AL 1976C
HACEK ET AL 1976C
HACEK ET AL 1976C
MACEK ET AL 1976C
MACEK ET AL 1976C
MACEK ET AL 1976C
MACEK ET AL 1976C
HACEK ET AL 1976C
0.00 HOLOHAY AND SPRAGUE 1979
0.01 HOLDWAY AND SPRAGUE 1979
0.00 HOLDWAY AND SPRAGUE 1979
-------
ORNL-6251
203
Table B.I. (Continued)
DBS CHEMICAL
1621 VANADIUM
1622 VANADIUM
1623 2N
1624 ZN
1625 ZN
1626 ZN
1627 ZN
1626 ZN
1629 ZN
1630 ZN
1631 ZN
1632 ZN
1633 ZN
1634 ZN
1635 ZN
1636 ZN
1637 ZN
163B ZN
1639 ZN
1640 ZN
1641 ZN
1642 ZN
1643 ZN
1644 ZN
1645 ZN
1646 IN
1647 ZN
164B ZN
1649 ZN
1650 ZN
1651 ZN
1652 ZN
1653 ZN
1654 2N
1655 ZN
1656 ZN
1657 ZN
165B ZN
1659 ZN
1660 ZN
1661 ZN
1662 ZN
1663 IN
1664 ZN
1665 ZN
1666 ZN
1667 ZN
1668 ZN
1669 ZN
1610 ZN
1671 ZN
1672 ZN
1673 ZN
1674 ZN
SPECIES PARAM
FF
FF
FH
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
BT
BT
BT
BT
BT
BT
6
G
6
6
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
RT
FF
FF
FF
WEIGHT
HEIGHT
HATCH
HATCH
HATCH
HATCH
HATCH
MORT2
MORT2
MORT2
HORT2
MORT2
EGGS
E6GS
E66S
EGGS
EGGS
EGGS
HATCH
HATCH
HATCH
HATCH
HATCH
MORT2
MORT2
MORT2
HORT2
MORT2
NORT2
MORT2
HORT2
MORT2
KORT2
WEIGHT
WEIGHT
WEIGHT
WEIGHT
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
MORT2
MORT2
MORT2
MORT2
MORT2
MORT2
NORT2
EGGS
EGGS
EGGS
DOSE NTESTED RESPONSE EGGS
480.00
1500.00
2.00
44.00
78.00
145.00
295.00
2.00
44.00
78.00
145.00
295.00
30.00
180.00
350.00
670.00
1300.00
2800.00
30.00
180.00
660.00
1300.00
2800.00
30.00
180.00
660.00
1300.00
2.60
39.00
69.00
144.00
266.00
534.00
0.00
173.00
328.00
607.00
2.00
11.00
36.00
71.00
140.00
260.00
547.00
2.00
11.00
36.00
11.00
140.00
260.00
547.00
10.00
28.00
47.00
16863
14341
12973
2158
694
100
100
100
100
100
442
345
425
406
475
366
318
392
381
100
100
100
100
100
100
50
48
46
48
48
48
48
48
47
46
46
46
46
46
981
620
921
455
512
2
2
2
16
82
1532
263
34
9
12
0
76
27
33
27
0
42
31
28
232
4
10
3
11
5
2
2
1
2
1
1
2
2
6
4
6
5
5
9
25
484
280
422
WEIGHT SOURCE
0.00 HOI WAY AND SPRAGUE 1979
0.00 HOLDWAY AND SPRAGUE 1979
BENOIT AND HOLCOMBE 1978
BENOIT AND HOLCOMBE 1976
BENOIT AND HOLCOMBE 1978
BENOIT AND HOLCOMBE 1976
BENOIT AND HOLCOMBE 1978
BENOIT AND HOLCOMBE 1976
BENOIT AND HOLCOMBE 1978
BENOIT AND HOLCOMBE 1978
BENOIT AND HOLCOMBE 1976
BEHOIT AND HOLCOM8E 1978
BRUNGS 1969
BRUNGS 1969
BRUNGS 1969
BRUNGS 1969
BRUNGS 1969
BRUNGS 1969
BRUNGS 1969
BRUNGS 1969
BRUNGS 1969
BRUNGS 1969
BRUNGS 1969
BRUNGS 1969
BRUNGS 1969
BRUNGS 1969
BRUNGS 1969
HOLCOMBE ET AL 1979
HOLCOMBE ET AL 1979
HOLCOMBE ET AL 1979
HOLCOMBE ET AL 1979
HOLCOMBE ET AL 1979
HOLCOMBE ET AL 1979
0.03 PIERSON 1981
0.02 PIERSON 1981
0.02 PIERSON 1981
0.01 PIERSON 1981
SINLEY ET AL 1974
SINLEY ET AL 1974
SINLEY ET AL 1974
SINLEY ET AL 1974
SINLEY ET AL 1974
SINLEY ET AL 1974
SINLEY ET AL 1974
SINLEY ET AL 1974
SINLEY ET AL 1974
SINLEY ET AL 1974
SINLEY ET AL 1974
SINLEY ET AL 1974
SINLEY ET AL 1974
SINLEY ET AL 1974
SPEHAR 1976
SPEHAR 1976
SPEHAR 1976
-------
204
ORNL-6251
Table B.I. (Continued)
OBS CHEMICAL SPECIES PAR AH
1675 ZN ff
1676 ZN FF
1677 ZN FF
1678 ZN FF
1679 ZN FF
16BO ZN FF
1681 ZN FF
1682 ZN FF
1683 ZN FF
1684 ZN FF
1685 ZN FF
1686 ZN FF
1687 ZN FF
1688 1.1.2-TR1CHLOROETHAN FM
16B9
1690
1691
1692
1693
1694
1695
1696
1697
1698
1699
1700
1701
1702
1703
1704
1705
1706
1707
1708
1709
1710
1711
1712
1713
1714
1715
1716
1717
1718
1719
1720
1721
1722
1723
1724
1725
1726
1727
1728
,1.2-TR1CHLOROETHAN FM
,1,2-TRICHLOROETHAN FM
,1.2-TRICHLOflOETHAN FM
,1.2-TRlCHLOROETHAN FM
,1.2-TRICHLOROETHAH FM
,1,2-TRICHLOROETHAN FM
,1,2-TRlCHLOROETHAN FM
,1.2-TRICHLOROETHAN FM
,1.2-TRICHLOROETHAN FM
,1.2-TRICHLOROETHAN FM
,1,2-TRICHLOROETHAN FM
,1,2,2-TETRACHLOROE FM
,1,2.2-TETRACHLOROE FM
,1,2,2-TETRACHLOROE FM
,1,2,2-TETRACHLOROE FM
,1.2,2-TETRACHLOROE FM
,1,2,2-TETRACHLOROE FM
,1,2,2-TETRACHLOROE FM
,1,2,2-TETRACHLOROE FH
,1,2,2-TETRACHLOROE FH
,1,2,2-TETRACHLOROE FM
,1,2,2-TETRACHLOROE FH
,1,2,2-TETRACHLOROE FH
,2-OICHLOROETHANE FH
,2-DICHLOROETHANE FH
,2-OICHLOROE THANE FM
,2-OICHLOROETHANE FM
,2-OICHLOROETHANE FH
,2-OICHLOROETHANE FM
,2-OICHLOROETHANE FM
,2-DICHLOROETHANE FM
,2-DICHLOROETHANE FH
,2-DICHLOROETHANE FM
,2-OICHLOROETHANE FM
,2-OICHLOROETHANE FM
,2-DICHLOROETHANE FH
,2-DICHLOROETHANE FH
,2-OICHLOROETHANE FM
,2-OICHLOROETHANE FH
,2-OICHLOROETHANE FM
EGGS
EGGS
HATCH
HATCH
HATCH
HATCH
HATCH
MORT1
MORT1
MORT1
MORT1
MORT1
MORT1
MORT2
MORT2
MORT2
MORT2
MORT2
MORT2
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
HORT2
MORT2
MORT2
MORT2
MORT2
HORT2
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
MORT2
MORT2
MORT2
MORT2
HORT2
MORT2
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
DOSE NTESTED RESPONSE EGGS WEIGHT SOURCE
75.00
139.00
10.00
28.00
47.00
75.00
139.00
10.00
28.00
47.00
75.00
139.00
267.00
50.00
2000.00
6000.00
14800.00
48000.00
147000.00
50.00
2000.00
6000.00
14800.00
46000.00
147000.00
12.00
1400.00
4000.00
6800.00
13700.00
28400.00
12.00
1400.00
4000.00
6800.00
13700.00
28400.00
300.00
4000.00
7000.00
14000.00
29000.00
59000.00
300.00
4000.00
7000.00
14000.00
29000.00
59000.00
300.00
4000.00
7000.00
14000.00
29000.00
40
40
40
40
40
60
60
60
60
60
60
120
120
120
120
120
120
120
120
120
120
120
120
120
120
120
120
120
120
60
60
60
60
60
60
296 SPEHAR 1976
36 SPEHAR 1976
12 SPEHAR 1976
10 SPEHAR 1976
11 SPEHAR 1976
16 SPEHAR 1976
11 SPEHAR 1976
6 SPEHAR 1976
B SPEHAR 1976
3 SPEHAR 1976
1 SPEHAR 1976
15 SPEHAR 1976
57 SPEHAR 1976
0
0
6
0
27
120
6
0
6
6
105
120
23
23
27
33
25
25
5
3
5
5
2
6
AHMED ET AL 1984
AHMED ET AL 19B4
AHMED ET AL 1984
AHMED ET AL 1984
AHMED ET AL 1984
AHMED ET AL 1984
0.14 AHMED ET AL 1984
0.15 AHMED ET AL 1984
0.14 AHMED ET AL 1984
0.12 AHMED ET AL 1984
0.04 AHMED ET AL 1984
0.00 AHMED ET AL 1984
AHMED ET AL 1984
AHHED ET AL 19B4
AHHED ET AL 1984
AHMED ET AL 1984
AHMED ET AL 1984
AHMED ET AL 1984
0.19 AHMED ET AL 1984
0.19 AHMED ET AL 1984
0.15 AHHED ET AL 1984
0.14 AHMED ET AL 1984
0.02 AHMED ET AL 1984
0.00 AHMED ET AL 1984
BENOIT ET AL 1982
BENOIT ET AL 1982
BENOIT ET AL 1982
BENOIT ET AL 1982
BENOIT ET AL 1982
BENOIT ET AL 1982
BENOIT ET AL 1982
BENOIT ET AL 1982
BENOIT ET AL 1982
BENOIT ET AL 1982
BENOIT ET AL 1982
BENOIT ET AL 1982
0.13 BENOIT ET AL 1982
0.13 BENOIT ET AL 1982
0.13 BENOIT ET AL 1982
0.13 BENOIT ET AL 1982
0.12 BENOIT ET AL 1982
-------
ORNL-1251
205
Table B.I. (Continued)
DBS CHEMICAL SPECIES PARAM
1729 1.2-D1CHLOROETHANE FM
1730 1.2-OICHLOROPROPANE FM
1731 1,2-OICHLOROPROPANE FM
1732 1,2-OICHLOROPROPANE FM
1733 1.2-D1CHLOROPROPANE FM
1734 1,2-OICHLOROPROPANE FM
173S 1,2-OICHLOROPROPANE FM
1736 1,2-OICHLOROPROPANE FM
1737 1.2-DICHLOROPROPANE FM
1736 1,2-OICHLOROPROPANE FM
1739 1,2-OICHLOROPROPANE FM
1740 1,2-OICHLOROPROPANE FM
1741 1.2-DICHLOROPROPANE FM
1742 1,2-OICHLOROPROPANE FM
1743 1,2-DICHLOROPROPANE FM
1744 1,2-OICHLOROPROPANE FM
1745 1,2-DICHLOROPROPANE FM
1746 1,2-OICHLOROPROPANE FM
1747 1,2-DICHLOROPROPANE FM
174B 1,2,3,4-TETRACHLOROB FM
1749 1,2,3,4-TETRACHLOROB FM
17SO 1,2,3,4-TETRACHLOROB FH
1751 1,2,3,4-TETRACHLOROB FH
1752 1.2.3,4-TETRACHLOROB FM
1753 1.2,3.4-TETRACHLOROB FH
1754 1,2,3,4-TETRACHLOROB FH
1755 1,2,3,4-TETRACHLOROB FH
1756 1,2,3,4-TETRACHLOROB FH
1757 1.2,3,4-UTRACHLOROB FH
1758 1,2,3,4-TETRACHLOROB FH
1759 1,2,3,4-TETRACHLOROB FH
1760 1,2, -TRICHLOROBENZE FH
1761 1,2. -TRICHLOROBENZE FH
1762 1,2, -TRICHLOROBENZE FH
1763 1,2, -TRICHLOROBENZE FM
1764 1.2. -TRICHLOROBENZE FM
1765 1,2. -TRICHLOROBENZE FM
1766 1,2, -TRICHLOROBENZE FM
1767 1,2. -TRICHLOROBENZE FM
1768 1.2. -TRICHLOROBENZE FH
1769 1,2, -TRICHLOROBENZE FH
1770 1,2, -TRICHLOROBENZE FM
1771 1,2, -TRICHLOROBENZE FM
1772 1.3-DICHLOROBENZENE FM
1773 1.3-DICHLOROBENZENE FH
1774 1,3-DKHLOROBENZENE FH
1775 1,3-DICHLOROBENZENE FH
1776 1.3-DICHLOROBENZENE FH
1777 1,3-DICHLOROBENZENE FM
1778 1,3-DICHLOROBENZENE FM
1779 1.3-DICHLOROBENZENE FM
1780 1.3-DICHLOROBENZENE FH
1781 1,3-DICHLOROBENZENE FH
1782 1,3-DICHLOROBENZENE FM
WEIGHT
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
MORT2
MORT2
MORT2
HORT2
MORT2
MORT2
WEIGHT
HEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
MORT2
MORT2
MORT2
HORT2
MORT2
NORT2
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
MORT2
MORT2
MORT2
HORT2
MORT2
HORT2
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
NORT2
MORT2
HORT2
HOUT2
MORT2
MORT2
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
DOSE NTESTED RESPONSE EGGS WEIGHT SOURCE
59000.00
100.00
6000.00
11000.00
25000.00
51000.00
110000.00
100.00
6000.00
11000.00
25000.00
51000.00
110000.00
100.00
6000.00
11000.00
25000.00
51000.00
110000.00
0.35
19.00
39.00
110.00
245.00
412.00
0.35
19.00
39.00
110.00
245.00
412.00
15.00
75.00
134.00
304.00
499.00
1001.00
15.00
75.00
134.00
304.00
499.00
1001.00
31.00
304.00
555.00
1000.00
2267.00
3913.00
31.00
304.00
555.00
1000.00
2267.00
120
120
120
120
120
120
60
60
60
60
60
120
120
120
120
120
120
120
120
120
120
120
120
120
120
120
120
120
120
120
4
5
3
3
43
120
3
5
3
25
44
120
10
20
12
e
22
48
10
20
10
10
14
46
4
2
4
6
8
112
0.05 6ENOIT ET AL 1982
BENOIT ET AL 1982
BENOIT ET AL 1982
BENOIT ET AL 1982
BENOIT ET AL 1982
BENOIT ET AL 1982
BENOIT ET AL 1982
BENOIT ET AL 1982
BENOIT ET AL 1982
BENOIT ET AL 1982
BENOIT ET AL 1982
BENOIT ET AL 1982
BENOIT ET AL 1982
0.14 BENOIT ET AL 1982
0.14 BENOIT ET AL 1982
0.13 BENOIT ET AL 1982
0.08 BENOIT ET AL 1982
0.02 BENOIT ET AL 1982
0.00 BENOIT ET AL 1982
AHMED ET AL 1984
AHMED ET AL 19B4
AHMED ET AL 1984
AHMED ET AL 1984
AHMED ET AL 1984
AHHED ET AL 1984
0.11 AHMED ET AL 1984
0.11 AHMED ET AL 1984
0.11 AHMED ET AL 1984
0.10 AHMED ET AL 1984
0.10 AHMED ET AL 1984
0.06 AHMED ET AL 1984
AHMED ET AL 1984
AHMED ET AL 1984
AHMED ET AL 1984
AHHED ET AL 1984
AHHED ET AL 1984
AHMED ET AL 1984
0.09 AHMED ET AL 1984
0.10 AHMED ET AL 1984
0.09 AHMED ET AL 1984
0.08 AHMED ET AL 1984
0.09 AHHED ET AL 1984
0.07 AHHED ET AL 1984
AHMED ET AL 1984
AHMED ET AL 1984
AHHED ET AL 1984
AHHED ET AL 1984
AHHED ET AL 1984
AHMED ET AL 1984
0.10 AHHED ET AL 1984
0.10 AHHED ET AL 19B4
0.10 AHHED ET AL 1964
0.10 AHMED ET AL 1964
0.07 AHMED ET AL 1984
-------
206
ORNL-6251
Table B.I. (Continued)
DBS CHEMICAL
1783 1.3-D1CHLOROBENZENE
1784 1.3-D1CHLOROPROPANE
1785
1786
1787
1768
1789
1790
1791
1792
1793
1794
179S
1796
1797
1798
1799
1800
1801
1802
1803
1804
1805
1806
1807
1808
1809
1810
1811
1812
1813
,3-DICHLOROPROPANE
,3-DICHLOROPROPANE
,3-DICHLOROPROPANE
,3-DICHLOROPROPANE
,3-OICHLOROPROPANE
,3-DICHLOROPROPANE
,3-DICHLOROPROPANE
,3-DICHLOROPROPANE
,3-DICHLOROPROPANE
,3-DICHLOROPROPANE
,3-DICHLOROPROPANE
,3-DICHLOROPROPANE
,3-DICHLOROPROPANE
,3-DICHLOROPROPANE
,3-DICHLOROPROPANE
,3-DICHLOROPROPANE
,3-DICHLOROPROPANE
,4-DICHLOROBENZENE
,4-DICHLOROBENZENE
,4-DICHLOROBENZENE
,4-DICHLOROBENZENE
,4-DICHLOROBENZENE
,4-DICHLOROBENZENE
,4-DICHLOROBENZENE
,4-DICHLOROBENZENE
,4-DICHLOROBENZENE
,4-DICHLOROBENZENE
,4-DICHLOROBENZENE
.4-DICHLOROBENZENE
1814 2.4-DICHLOROPHENOL
1815 2,4-DICHLOROPHENOL
1816 2,4-DICHLOROPHENOL
1817 2,4-DICHLOROPHENOL
1818 2,4-DICHLOROPHENOL
1819 2,4-DICHLOROPHENOL
1820 2,4-DICHLOROPHENOL
1821 2,4-DICHLOROPHENOL
1822 2, -DICHLOROPHENOL
1823 2. -DICHLOROPHENOL
1824 2, -DICHLOROPHENOL
1B2S 2, -DICHLOROPHENOL
1826 2, -DICHLOROPHENOL
1827 2,4-DICHLOROPHENOL
1828 2,4-DICHLOROPHENOL
1829 2,4-DICHLOROPHENOL
1830 2,4-DICHLOROPHENOL
1831 2, -DICHLOROPHENOL
1832 2, -DINETHYLPHENOL
1833 2, -DIHETHYLPHENOL
1834 2, -DIHETHYLPHENOL
183S 2. -DIHETHYLPHENOL
1836 2, -DIHETHYLPHENOL
SPECIES PARAH
FM
FM
FH
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FH
FH
FM
FM
FM
FM
FM
FM
FM
FM
FM
FH
FH
FM
FM
FM
FH
FM
FM
FM
FH
FH
FM
FM
FM
FM
FH
FH
FH
FH
FH
FH
FH
FH
FH
FH
FM
WEIGHT
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
MORT2
MORT2
NORT2
MORT2
HORT2
MORT2
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
HORT2
MORT2
MORT2
MORT2
NORT2
MORT2
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
HATCH
HATCH
HATCH
HATCH
HATCH
HATCH
NORT2
MORT2
MORT2
HORT2
HORT2
MORT2
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
WEIGHT
HATCH
HATCH
HATCH
HATCH
HATCH
DOSE NTESTED RESPONSE EGGS WEIGHT SOURCE
3913.00
200.00
4000.00
8000.00
16000.00
32000.00
65000.00
200.00
4000.00
8000.00
16000.00
32000.00
65000.00
200.00
4000.00
8000.00
16000.00
32000.00
65000.00
19.00
565.00
1040.00
2000.00
4090.00
8720.00
19.00
565.00
1040.00
2000.00
4090.00
8720.00
0.00
150.00
290.00
460.00
770.00
1240.00
0.00
150.00
290.00
460.00
770.00
1240.00
0.00
150.00
290.00
460.00
770.00
1240.00
0.00
900.00
1360.00
1970.00
3100.00
120
120
120
120
120
120
60
60
60
60
60
60
120
120
120
120
120
120
200
200
200
200
200
200
100
100
100
100
100
100
100
100
100
100
100
100
200
200
200
200
200
20
29
21
26
22
79
4
1
4
2
1
31
6
8
26
120
120
120
37
28
36
48
41
40
25
31
30
58
78
94
35
23
25
25
25
0.01 AHHED ET AL 1984
BENOIT ET AL 1982
BENOIT ET AL 1982
BENOIT ET AL 1982
BENOIT ET AL 1982
BENOIT ET AL 1982
BENOIT ET AL 1982
BENOIT ET AL 1982
BENOIT ET AL 1982
BENOIT ET AL 1982
BENOIT ET AL 19B2
BENOIT ET AL 1982
BENOIT ET AL 1982
0.13 BENOIT ET AL 1982
0.11 BENOIT ET AL 1982
0.11 BENOIT ET AL 1982
0.10 BENOIT ET AL 1982
0.08 BENOIT ET AL 1982
0.02 BENOIT ET AL 1982
AHHED ET AL 1984
AHHED ET AL 19B4
AHHED ET AL 1984
AHMED ET AL 1984
AHMED ET AL 1984
AHMED ET AL 1984
0.10 AHHED ET AL 1984
0.10 AHHED ET AL 1984
0.09 AHHED ET AL 1984
AHMED ET AL 1984
AHHED ET AL 1984
AHMED ET AL 1984
HOLCOHBE ET AL 1982
HOLCOHBE ET AL 1982
HOLCOMBE ET AL 1982
HOLCOHBE ET AL 1982
HOLCOHBE ET AL 1982
HOLCOHBE ET AL 1982
HOLCOHBE ET AL 1982
HOLCOMBE ET AL 1982
HOLCOMBE ET AL 1982
HOLCOMBE ET AL 1982
HOLCOMBE ET AL 1982
HOLCOMBE ET AL 1982
0.09 HOLCOMBE ET AL 1982
0.09 HOLCOHBE ET AL 1982
0.09 HOLCOMBE ET AL 1982
0.11 HOLCOMBE ET AL 19B2
0.08 HOLCOHBE ET AL 19B2
0.02 HOLCOHBE ET AL 1982
HOLCOHBE ET AL 1982
HOLCOMBE ET AL 1982
HOLCOHBE ET AL 1982
HOLCOHBE ET AL 19B2
HOLCOHBE ET AL 1982
-------
207
ORNL-6251
Title B.I. (Continued)
DBS CHEMICAL
1837 2.4-DINETHYLPHENOL
1838 2,4-DlMETHYLPHENOL
1839 2,4-OIMCTHYLPHENOL
1840 2.4-01NETHYLPHENOL
1841 2,4-OINETHYLPHENOL
1842 2.4-01NETHYLPHENOL
1843 2,4-OlNETHYLPHCNOL
1844 2.4-01NETHYLPHENOL
1845 2.4-01HETHYLPHENOL
1846 2,4-OINETHYLPHENOL
1847 2,4-OINETHYLPHENOL
1848 2.4-01NETHYLPHENOL
1849 2.4-01NETHYLPHENOL
FN
FN
FN
FM
FM
FN
FN
FN
FM
FM
FM
FM
FM
SPECIES PARAM
HATCH
NORT2
NORT2
NORT2
NORT2
NORT2
NORT2
HEIGHT
WEIGHT
HEIGHT
WEIGHT
WEIGHT
WEIGHT
DOSE NTESTED RESPONSE EGGS WEIGHT SOURCE
5130.00
0.00
100.00
1360.00
1970.00
3110.00
5130.00
0.00
900.00
1360.00
1970.00
3110.00
5130.00
200
100
100
100
100
100
100
40
10
22
22
25
27
44
HOLCONBE
MOLCOMBE
HOLCONBE
HOLCONBE
HOLCONBE
HOLCONBE
HOLCONBE
0.07 HOLCONBE
0.06 HOLCONBE
O.OB HOLCONBE
0.07 HOLCONBE
0.06 HOLCONBE
0.05 HOLCONBE
IT AL 19B2
ET AL 1982
CT AL 1982
CT AL 1982
ET AL 1982
ET AL 1982
ET AL 1982
ET AL 1982
ET AL 1982
ET AL 1982
ET AL 1982
ET AL 1982
CT AL 1982
SPECIES - Species of test organIs*: AS - etlantlc salmon, B6 - blueglll, M - bluntnose Minnow, BNT - brown
trout. BT - brook trout, CC • channel catfish, CHS • Chinook salmon, COS - coho salmon, FF - flagflsh,
FM • fathead nlnnow. G - guppy, JN • Japanese medaka. LT • lake trout. HP • northern pike. RT • rainbow
trout, SB « smaltmouth bass, WE * walleye, and WS • white sucker.
PARAM - Response parameter: MORT1 • mortality of parental fish, EGGS • number of eggs per female.
HATCH - proportion of eggs falling to produce normal larvae, NORT2 - mortality of larval fish, and
WEIGHT - mean weight of Individual fish at the end of larval exposure.
OOSE • Exposure concentration.
NTESTED • Number of test organisms per concentration.
RESPONSE - Number of organisms per concentration.
EGGS • Number of eggs per female.
WEIGHT - Nean weight of Individual fish at the end of larval exposure 1n grams.
-------
ORNL-6251 208
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-------
ORNL-6251 210
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«
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-------
2'!1 ORNL-6251
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-------
213 ORNL-6251
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-------
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Kleiner, C. F.t R. L. Anderson, and D. K. Tanner. 1984. Toxldty of
fenltrothlon to fathead minnows (Plmephales oromelas) and
alternate exposure duration studies with fenltrothlon and
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LeBlanc, G. A.. J. D. Mastone. A. P. Paradlce, B. F. Wilson,
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spedation on the toxldty of silver to fathead minnow (Plmephales
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Leduc, G. 1978. Deleterious effects of cyanide on early life stages
of atlantlc salmon (Salmo salar). J_- £iih- Res. Board Can.
35:166-174.
Lemke, A. E. , E. Duran, and T. Felhaber. 1983. Evaluation of a
fathead minnow (Plmephales promelas) embryo-larval test guideline
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Environmental Protection Agency, Duluth, H1nn.
Lewis, H. A., and V. T. Wee. 1983. Aquatic safety assessment for
cationlc surfactants. Environ. Toxlcol. Chem. 2:105-118.
Macek, K. 0., K. S. Buxton, S. K. Oerr, J. W. Dean, and S. Sauter.
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215 ORNL-6251
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Macek. K. J., M. A. Undberg, S. Sauter, K. Buxton, and P. A. Costa.
1976c. Toxldty of four pesticides to water fleas and fathead
minnows, EPA-600/3-76-099. U.S. Environmental Protection Agency,
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Mak1, A. W., and K. J. Macek. 1978. Aquatic environmental safety
assessment for a nonphosphate detergent builder. Environ. Sd.
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Mayer, F. L., Jr., P. M. Mehrle, Jr., and W. P. Dwyer. 1975.
Toxaphene effects on reproduction, growth, and mortality of brook
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Duluth, Minn.
Mayer, F. L. Jr., P. M. Mehrle, Jr., and W. P. Dwyer. 1977.
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McCarthy, J. F., and D. K. WhHmore. 1985. Chronic toxldty of
d1-n-butyl and d1-n-octyl phthalate to Daohnla magna and the
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McK1m, J. M. 1977. Evaluation of tests with early life stages of fish
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ORNL-6251 216
McK1m, J. M., and D. A. Benoit. 1971. Effects of long-term exposures
to copper on survival, growth, and reproduction of brook trout
(Salvellnus fontlnalls). J_. Fish. Res. Board Can. 28:655-662.
McK1m, J. M., and D. A. Benoit. 1974. Duration of toxldty tests for
establishing "no effect" concentrations for copper with brook
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31:449-452.
McK1m, J. M.. -3. G. Eaton, and G. W. Hoi combe. 1978. Metal toxldty
to embryos and larvae of eight freshwater fish - II: Copper.
Bull. Environ. Contam. Toxlcol. 19:608-616.
HcK1m, J. M., G. F. Olson, G. W. Hoicombe, and E. P. Hunt. 1976.
Long-term effects of methylmercurlc chloride on three generations
of brook trout (Salvellnus fontlnalls): toxldty, accumulation,
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33:2726-2739.
Merna, J. W., and P. J. Elsie. 1973. The effects of methoxychlor on
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the fathead minnow (Plmephales promelas) 1n soft water. 3.. Fish..
Res. Board Can. 26:2449-2457.
Nebeker. A. V., C. K. McAullffe, R. Mshar, and 0. G. Stevens. 1983.
Toxldty of silver to steelhead and rainbow trout, fathead
minnows, and Daphnla maqna. Environ. Toxlcol. Chem. 2:95-104.
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217 ORNL-6251
Nebeker, A. V., F. A. Pugl1s1. and D. L. DeFoe. 1974. Effect of
polychlortnated blphenyl compounds on survival and reproduction of
the fathead minnow and flagflsh. Trans. Am. F1sh. Soc.
103:562-568.
Pickering, Q. H. 1974. Chronic toxldty of nickel to the fathead
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Pickering, Q. H. 1980. Chronic toxldty of hexavalent chromium to the
fathead minnow (Plmeohales promelasK Arch. Environ. Contam.
Toxlcol. 9:405-413.
Pickering, Q., W. Brungs, and H. Gast. 1977. Effects of exposure time
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Pickering, Q. H., and H. H. Gast. 1972. Acute and chronic toxldty of
cadmium to the fathead minnow (Plmephales promelas). J. F1sh.
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Pickering, Q. H., and W. T. G1ll1am. 1982. Toxidty of aldlcarb and
fonofos to the early life-stage of the fathead minnow. Arch.
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Pickering, Q. H., and T. 0. Thatcher. 1970. The chronic toxldty of
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ORNL-6251 218
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Slnley, J. R., J. P. Goettl, Jr., and P. H. Davles. 1974. The effects
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to fish and Invertebrates, EPA-600/3-79-009. U.S. Environmental
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Snarskl, V. H., and G. F. Olson. 1982. Chronic toxldty and
bloaccumulatlon of mercuric chloride 1n the fathead minnow
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219/.2 *& ORNL-6251
Spehar, R. L., D. K. Tanner, and 0. H. Gibson. 1982. Effects of
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American Society for Testing and HateMals, Philadelphia, Penn.
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chromium to early life stages of steelhead trout. Environ.
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Woodward, D. F. 1976. Toxldty of the herbicides dlnoseb and plcloram
to cutthroat trout (Salmo clarM) and lake trout. 0.. Fish. Res.
Board Can. 33:1671-1676.
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Development, Oak Ridge Operations, P.O. Box E, Department of
Energy, Oak Ridge, TN 37831
207-409. Given distribution as shown 1n DOE/TIC-4500 under category
UC-11, Environmental Control Technology and Earth Sciences.
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