Hazard Ranking System Issue Analysis:
Classification of Hazardous Substances
for Potential to Accumulate in the Food Chain
MITRE
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Hazard Ranking System Issue Analysis:
Classification of Hazardous Substances
for Potential to Accumulate in the Food Chain
Stephen V. McBrien
Alan S. Goldfarb
Sharon D. Saari
June 1987
MTR-86W114
SPONSOR:
U.S. Environmental Protection Agency
CONTRACT NO.:
EPA-68-01-7054
The MITRE Corporation
Civil Systems Division
7525 Colshire Drive
McLean, Virginia 22102-3481
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Department Approval:
i: "m
ii
MITRE Project Approval:
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ABSTRACT
This report, prepared for the Office of Emergency and Remedial
Response (OERR) of the Environmental Protection Agency, contains an
option for rating the relative potential of hazardous substances to
bioaccumulate. This rating scheme is intended as a component of a
human food chain exposure methodology which is being separately
developed for possible inclusion in the EPA Hazard Ranking System
(HRS). This rating scheme makes use of available data on
bioconcentration factors, n-octanol-water partition coefficients,
and water solubility. In addition, the rating scheme makes use of
available information on potential biomagnification of hazardous
substances. The system makes use of data in order of its
reliability and its availability, as well as on its ability to
describe, quantitatively, the potential of a substance to
bioconcentrate.
Suggested Keywords: Superfund, Hazard ranking, Hazardous waste,
Bioconcentration, Bioconcentration factor surrogates.
iii
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TABLE OF CONTENTS
Page
LIST OF FIGURES vii
LIST OF TABLES vii
1.0 INTRODUCTION 1
1.1 Background 1
1.2 Issue Description 3
1.3 Scope 4
1.4 Approach 6
1.5 Organization of Report 9
2.0 MEASURES OF BIOACCUMULATION POTENTIAL 11
2.1 Biomagnification 12
2.1.1 Definition 12
2.1.2 Discussion 13
2.2 Bioconcentration 17
2.2.1 Definition 17
2.2.2 Bioconcentration Factor 18
2.2.3 Potential Surrogates for Bioconcentration Factor 19
3.0 THE PROPOSED RANKING SCHEME 27
3.1 Overview of the Ranking Scheme 27
3.1.1 Classification Based on Bioconcentration Factor 29
3.1.2 Classification Based on Logarithm of the 30
n-Octanol-Water Partition Coefficient
3.1.3 Water Solubility 32
3.2 Decision Procedures for Ranking Hazardous Substances 33
3.3 Illustration of Proposed Ranking Scheme 35
4.0 BIBLIOGRAPHY AND REFERENCES 37
APPENDIX A - CLASSIFICATION OF HAZARDOUS SUBSTANCES FOR 53
POTENTIAL TO BID-ACCUMULATE
APPENDIX B - SUMMARY OF DATA COLLECTED ON HAZARDOUS SUBSTANCES 55
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TABLE OF CONTENTS (Concluded)
Page
APPENDIX C - ECOLOGICAL FACTORS WHICH AFFECT BIOACCUMULATION 87
APPENDIX D - CHEMICAL/PHYSICAL FACTORS WHICH AFFECT 99
BIOACCUMULATION
APPENDIX E - GLOSSARY OF TERMS USED 109
APPENDIX F - OTHER METHODS REVIEWED 115
vi
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LIST OF FIGURES
Figure Number
1
2
3
4
Biomagnification of DDE, PCBs and Toxaphene
in Lake Providence, Louisiana
Lake Powell, Arizona Trophic Levels with Mean
Parts per Billion Mercury
Example Correlations of Log BCF versus Log P
for Bioaccumulation
Decision Tree for Ranking Substances for
Potential to Bioaccumulate in the Food Chain
Page
14
16
22
28
Table Number
LIST OF TABLES
Summary of Values Found in the Literature
For the Constants a and b in the
Equation log BCF = a log Poct + b
Page
23
vii
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1.0 INTRODUCTION
1.1 Background
The Comprehensive Environmental Response, Compensation, and
Liability Act of 1980 (CERCLA) (PL 96-510) requires the President to
identify national priorities for remedial action among releases or
threatened releases of hazardous substances. These releases are to
be identified based on criteria promulgated in the National
Contingency Plan (NCP). On July 16, 1982, EPA promulgated the
Hazard Ranking System (HRS) as Appendix A to the NCP (40 CFR 300;
47 FR 31180). The HRS comprises the criteria required under CERCLA
and is used by EPA to estimate the relative potential hazard posed
by releases or threatened releases of hazardous substances.
The HRS is a means for applying uniform technical judgment
regarding the potential hazards presented by a release relative to
other releases. The HRS is used in identifying releases as national
priorities for further investigation and possible remedial action by
assigning numerical values (according to prescribed guidelines) to
factors that characterize the potential of any given release to
cause harm. The values are manipulated mathematically to yield a
single score that is designed to indicate the potential hazard posed
by each release relative to other releases. This score is one of
the criteria used by EPA in determining whether the release should
be placed on the National Priorities List (NPL).
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During the original NCP rulemaking process and the subsequent
application of the HRS to specific releases, a number of technical
issues have been raised regarding the HRS. These issues concern the
desire for modifications to the HRS to further improve its
capability to estimate the relative potential hazard of releases.
The issues include:
• Review of other existing ranking systems suitable for
ranking hazardous waste sites for the NFL.
• Feasibility of considering ground water flow direction and
distance, as well as defining "aquifer of concern," in
determining potentially affected targets.
• Development of a human food chain exposure evaluation
methodology.
• Development of a potential for air release factor category
in the HRS air pathway.
• Review of the adequacy of the target distance specified in
the air pathway.
• Feasibility of considering the accumulation of hazardous
substances in indoor environments.
• Feasibility of developing factors to account for
environmental attenuation of hazardous substances in ground
and surface water.
• Feasibility of developing a more discriminating toxiclty
factor.
• Refinement of the definition of "significance" as it relates
to observed releases.
• Suitability of the current HRS default value for an unknown
waste quantity.
• Feasibility of determining and using hazardous substance
concentration data.
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• Feasibility of evaluating waste quantity on a hazardous
constituent basis.
• Review of the adequacy of the target distance specified in
the surface water pathway.
• Development of a sensitive environment evaluation
methodology.
• Feasibility of revising the containment factors to increase
discrimination among facilities.
• Review of the potential for future changes in laboratory
detection limits to affect the types of sites considered for
the NPL.
Each technical issue is the subject of one or more separate but
related reports. These reports, although providing background,
analysis, conclusions and recommendations regarding the technical
issue, will not directly affect the HRS. Rather, these reports will
be used by an EPA working group that will assess and integrate the
results and prepare recommendations to EPA management regarding
future changes to the HRS. Any changes will then be proposed in
Federal notice and comment rulemaking as formal changes to the NCP.
The following section describes the specific issue that is the
subject of this report.
1.2 Issue Description
Tbe U.S. Congress, EPA personnel, and the public have expressed
the concern that public health and welfare are potentially
threatened by exposure through the human food chain to hazardous
substances released from CERCLA sites, and that this route of
exposure is not adequately considered in the HRS. In order to
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evaluate the potential exposure of humans to hazardous substances
through the food chain, it is necessary to evaluate those substances
that have the capability to accumulate in parts of the food chain at
levels substantially higher than in the surrounding environment.
This capability, often referred to as bioaccumulation, has been
demonstrated most clearly in the aquatic food chain, although there
is reason to believe it also exists in the terrestrial food chain.
This paper addresses the issue of how to evaluate those substances
which may pose a substantial danger due to their capability to
concentrate in the food chain.
1.3 Scope
This paper presents a methodology for classifying substances in
order to identify those of primary concern should they enter the
human food chain. Specifically, the paper presents a classification
scheme based on the potential of the substances to accumulate in the
human food chain. This paper is restricted to consideration of the
accumulation of hazardous substances in the human food chain; it
does not address the relative toxicity of these substances. DeSesso
et al. (1986) addresses the toxicity of these substances.
The likely availability (and cost) of the required data was a
major consideration in the development of this classification
methodology. The methodology presented here is based on the
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assumption that the level of data available will continue to be
those data obtained during a CERCLA site inspection. This paper is
restricted to the potential of substances to accumulate in the food
chain. It is not intended to be used to establish a comprehensive
human food chain rating methodology for the HRS. Saari (1986)
addresses the broader questions of developing a comprehensive human
food chain rating methodology for use in the HRS.
In addition, the focus of this paper is primarily on the
accumulation of hazardous substances In the surface water pathway
component of the food chain. This focus is largely a function of
available data. Although a review of the research reported in the
literature revealed that aquatic and terrestrial ecosystems both
exhibit bioconcentratlon and, in some cases, biomagnlfication, there
are many references to the potential threat to humans through the
aquatic food chain, but none indicating a serious threat via the
terrestrial food chain. As a result, it was possible to more
readily identify aquatic human food chain impacts, and, therefore,
the scope of this paper was largely restricted to the surface water
pathway.
In this paper, common definitions are used for processes
related to the uptake of hazardous substances (Appendix E contains a
comprehensive glossary for all terms in this report). The following
four definitions are particularly important:
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• Bioconcentration; The process whereby chemical substances
enter aquatic organisms through the gills or epithelial
tissue directly from water, and are accumulated at levels
that exceed the concentration of the substance In the
surrounding environment.
• Bioaccumulatlon; A broader term referring to a process
which includes bioconcentration but also any uptake of
chemical residues from dietary sources.
• Biomagniflcation; A process by which the tissue
concentrations of bioaccumulated chemical residues increase
as these materials run up through the food chain through two
or more trophic levels. Historically implicit in the use of
this term is the connotation that residue concentrations at
successively higher trophic levels increase by integral
multiples (e.g., by factors of 3, 4, 5, etc.).
• n-Octanol-Water Partition Coefficient: A measure of the
preferential partitioning of a substance between n-octanol
and water.
• Solubility; A property of a substance by virtue of which it
forms chemically and physically homogeneous mixtures with
other substances.
1.4 Approach
In brief, the approach taken to examine the question of which
hazardous substances are of most concern in the human food chain
incorporated four steps. These steps were: (1) a literature search
for indicators which could be used to identify substances with the
potential to accumulate in the human food chain, (2) a review of
those methods found in the literature search for classifying
hazardous substances as to their potential to accumulate in the food
chain, (3) the development of a classification scheme suitable for
hazardous substances found at waste sites, and (4) the application
of the scheme to Illustrate the classification of several substances
typically found at NPL facilities.
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As a result of the literature search and the review of other
methods of ranking hazardous substances, several measures were
identified as indicators of the potential for a substance to
accumulate in food chain organisms. The most important of these are:
• Biomagnification factor
• Bioconcentration factor
• n-octanol-water partition coefficient
• Solubility
As mentioned in Section 2, the occurrence of biomagnification
far in excess of reported bioconcentration levels has been documented
in the literature for a few substances (e.g., methylmercury, DDT,
PCBs). For other substances, however, there is some evidence that
the process of biomagnification of chemical residues in the aquatic
food chain is quantitatively insignificant when compared to the
process of bioconcentration (Macek, 1979).
Bioconcentration factors (BCF) are well documented in the
literature for a number of substances. The U.S. Environmental
Protection Agency, for example, reports BCFs (when available) in
their water quality criteria documents. Reported BCFs range from
less than one up to a million. A BCF of 1,000 or more for a given
substance indicates a potential for significant bioaccumulation
(Trabalka and Garten, 1982), that could cause substantial human
exposure to that substance, even if relatively small amounts of food
in which the substance is concentrated are ingested.
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Unfortunately, BCFs are not available for all of the hundreds
of substances found at CERCLA sites. For those substances for which
BCFs are not available, it is necessary to estimate BCFs.
The logarithm of the n-octanol-water partition coefficient (log
Pow) has been used as a surrogate measurement for the BCF. While
the direct measurement of bioconcentratlon from the environment is
preferred, the log Pow commonly is recommended as a surrogate in
evaluating the bioconcentratlon potential of hazardous substances in
the absence of direct measurement (e.g., Van Gestel et al., 1985).
The water solubility of a substance is an important character-
istic for establishing that substance's potential environmental
movement and distribution. In the absence of log Pow data, it is
possible to make use of water solubility data to determine the
extent to which a substance is inclined to bioconcentrate in an
organism (Verschueren, 1983).
Of these four types of data, data on the biomagnification of
substances are the least available; BCFs are more commonly
available; and log Pow and/or water solubility data are most readily
available for a majority of the hazardous substances of concern
under CERCLA. A classification scheme using all four types of data
is proposed in this report. The scheme uses a hierarchy based on
data reliability and availability. Using this hierarchy of data,
selected hazardous substances are classified for their potential to
bioaccumulate.
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Results presented in this report have been used in developing
a proposed methodology for incorporating the potential for exposure
to hazardous substances via the human food chain in the HRS (Saari,
1986).
1.5 Organization of Report
This report presents a proposed scheme developed to classify the
bioaccumulation potential of hazardous substances. The scientific
basis of the proposed method is documented and the results of
applying the scheme to selected hazardous substances are presented.
The concepts of biomagnification and bioconcentration factors
are discussed in Section 2. Examples from field and laboratory
studies are provided to support the use of these factors in the
classification scheme.
A classification scheme which could be used in the HRS is
presented in Section 3. The scheme incorporates a decision tree
based on the availability of biomagnification, BCF, log Pow, or
solubility data. Using this decision tree, several substances were
classified; the results are reported in Appendix A.
Section 4 contains a bibliography of references.
Appendix B provides, for several hundred substances, BCF and
log Pow data obtained from the literature and other available data
bases.
Appendices C and D provide background material applicable to
the proposed ranking scheme. These appendices provide an
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explanation for why the reported BCF data have such wide
variations. Ecological factors which affect bioaccumulation are
explained in Appendix C and chemical factors are explained in
Appendix D.
Appendix E is a glossary of ecological terms, acronyms, and
other technical terms used in this paper.
Appendix F contains a summary description of other existing
methods used to rank hazardous substances and which were reviewed in
preparing this report.
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2.0 MEASURES OF BIOACCUMULATION POTENTIAL
Many hazardous substances may be concentrated in the aquatic
food chain from low ambient levels in water to much higher levels in
various species of fish.* For example, fish and shellfish are able
to bioaccumulate some organic compounds to levels thousands of times
greater than the concentration in the water in which they live.
Thus, even substances with very low solubility in water have the
potential to achieve high concentrations in aquatic organisms.
The process of bioaccumulation is an especially important
consideration in evaluating whether a fish population has
accumulated a substance in concentrations which might pose a health
hazard to humans eating the fish.
Unfortunately, data providing direct measurement of the ability
of a given substance to bioaccumulate in a given species of fish are
very limited. Indeed, the very large number of fish species, coupled
with the extremely large number of potentially hazardous substances,
makes any comprehensive compilation of such measurements
impractical. As a result, scientists have developed several methods
for estimating the potential of hazardous substances to
bioaccumulate in aquatic life. While these methods have their
inadequacies, they do provide an indication of the relative hazard
*The term fish as used throughout this report includes fish,
shellfish and other aquatic human food chain organisms.
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posed by the potential bioaccumulation of hazardous substances,
based on data which are readily available.
This section provides information on the processes and chemical
characteristics which can be used to describe, in a quantitative
manner, the potential for bioaccumulation. These are
biomagnification, bioconcentration, the n-octanol-water partition
coefficient, and solubility. This section provides technical
definitions for each of these, and illustrates how data on each
could be used in a hazardous substance food chain impact
classification scheme.
Several studies were reviewed to determine possible approaches
to estimating bioaccumulation potential. Summary discussions of
these studies may be found in Wolfinger and Haus (1986).
2.1 Biomagnification
2.1.1 Definition
Biomagnification is a biological process whereby a substance
becomes increasingly more concentrated in the tissue of different
species as the substance moves up the food chain through two or more
trophic levels. For example, the environment (e.g., a specific body
of water) may have a very low concentration of a substance, but
aquatic insects living in it have higher concentrations. The small
fish that eat those insects may concentrate the substance even
further. Near the top of the food chain, large predator fish may
have very high concentrations of that substance.
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Figure 1 (from Niethammer et al., 1984) illustrates the classic
biomagnification process as it occurs in freshwater lakes of
Louisiana. The vertical axes represent the concentrations (in parts
per million) of DDE, PCBs, and toxaphene. The horizontal axes,
reading from left to right, represent the trophic levels of the
animals sampled. For example, on the left are lower order crayfish
and frogs which show very low residues. In the middle are small
consumers such as bluegills and shiners, which have low
concentrations. As one moves up the food chain (to the right),
predatory fish, such as the gar and largemouth bass, have higher
concentrations of these residues.
2.1.2 Discussion
There are a number of studies (see Appendix B) which report
biomagnification of substances in the aquatic ecosystem. It is not
so clear that this process occurs in terrestrial ecosystems (Garten
and Trabalka, 1983; Walton and Edwards, 1986). In those cases where
there is some indication of biomagnification in the terrestrial
ecosystem (e.g., the biomagnification of pesticides in birds of
prey), very often the top predator is a fish eater.
Measurement of a hazardous substance in all parts of the
ecosystem and at all trophic levels of the food chain is both
difficult and expensive. While not all research results agree, it
is clear that some substances do biomagnify. For example, published
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1 -
DDE
(ppm)
15 -
10
-2i
A^
/
^
**
y
Toxaphene
(ppm)
15
10
18.8
—••••illl
0<'
V
^
/
/
Species
Source: Niethammer et al., 1984.
FIGURE 1
BIOMAGNIFICATION OF ODE, PCBs
AND TOXAPHENE IN LAKE PROVIDENCE, LOUISIANA
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research almost universally supports the concept of biomagnlfication
of DDT and its metabolites, PCBs, and mercury. Numerous studies
show that DDT biomagnifies in the food chain (Carey et al., 1973;
Daly, 1984; Domsch, 1984; Hatfull, 1983; Keene, 1983; Kenaga, 1980;
Kodric-Smit et al., 1980; Niethammer et al., 1984; Niewiadowska and
Sosyniak, 1983; Ofstad and Martinsen, 1983; U.S. Environmental
Protection Agency, 1983). However, Kay (1984) concludes that while
there appears to be biomagnification potential for PCBs,
methylmercury, kepone, mirex, benzo-a-pyrene, and naphthalenes,
"compounds which probably do not biomagnify include DDT and its
derivatives."
Kay (1984) describes the biomagnification (Figure 2) of mercury
in Lake Powell, Arizona (from Potter et al., 1975). In this case,
the water and sediments had relatively low concentrations; small
consumers and producers (plants) had slightly higher levels, and the
top predator fish (walleye and bass) had the highest concentrations.
In countries where fish are a major diet component, methylmercury
poisoning of human populations is possible (Ditri and Ditri, 1976).
PCBs have also been demonstrated to biomagnify, especially in
large predatory fish (Ray et al., 1984). O'Connor (1984) reports
that while fish can take PCBs directly from water (bioconcentration),
for striped bass at least, diet appears to be the most significant
source of PCBs. Both Larsson (1984) and Rubenstein et al. (1984)
conducted laboratory experiments with fish and found food uptake of
15
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jj
c.
Q.
T3 i»
£ °
E
w
£••2
CO
•p a)
in
o>
DC
Walleye
427 ppb
Bass
314 ppb
Trout
84ppb
Crappie
204 ppb
1 1
lad Bluegill
ppb 19 ppb
ppb 85 ppb
! l
Flannelmouth
Sucker *—
99 ppb
I
252
113
I
rp
ppb
ppb
Catfish
126 ppb
Invertebrates
10 ppb
I
^
»
Young Fish
Phytoplankton
Periphyton
32 ppb
I
I
Water
0.01 ppb
Terrestrial
Plants
19 ppb
I
I
Bottom
Sediments
30 ppb
*-»•
Torres
Subst
10 p
Plant
Debris
148 ppb
Source: (Modified from Kay, 1984)
FIGURE 2
LAKE POWELL, ARIZONA TROPHIC LEVELS
WITH MEAN PARTS PER BILLION MERCURY
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PCBs to be the most important route of exposure. In the example of
FCB bioaccumulation at the top of the food chain (e.g., lake trout),
the older and larger individuals usually have the greatest
concentrations of the substance (Thomann and Connolly, 1984). Black
(1983) compared the human consumption rates for drinking water and
eating fish from the Great Lakes, and he concluded "the relative
importance of the fish versus drinking water in this situation can
be a little better appreciated if one considers that given a fish
contaminated with PCS at 5 ppm, a human would have to drink Great
Lakes water for about 1,000 years in order to equal the amount of
PCB that you get in a single one pound serving of these contaminated
fish."
2.2 Bioconcentration
2.2.1 Definition
Bioconcentration refers to a process through which an organism
accumulates a substance to levels that exceed the concentration of
the substance in its environment (U.S. Environmental Protection
Agency, 1982b). This can also be regarded as the first step in
biomagnification. Bioconcentration is most often expressed in terms
of a bioconcentration factor (BCF), the ratio of the concentration
of the substance in an organism, for example in fish tissue, to the
concentration of the same substance in the ambient water. Another,
less common measure of bioconcentration is the ratio of the
concentration of a substance in plant tissue to its concentration in
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the soil the plant grows In. A little used measure of
bioconcentration is the ratio of the concentration in animal tissue
(e.g., beef) to the concentration in feed.
2.2.2 Bioconcentration Factor
There is a large volume of scientific literature in which the
BCFs for a number of substances are reported. Data have been
reported on bioconcentration of radionuclides since the 1950s, of
pesticides since the 1960s, and of heavy metals since the 1970s.
More recently, in the 1980s, ecologists have been reporting
measurements of complex organics in food chains. The reports on
complex organics include uptake rates and modes of transfer from the
environment to biota via food, and directly from water or air.
Research results are available from controlled laboratory
experiments as well as from measurements of biota in the field.
While some food chain studies may report on only a single segment of
the food chain (e.g., uptake from sediment by worms), others report
on the entire food chain from producers to consumers, to first order
predators, and finally to top predators. Some studies show uptake
of residues in a matter of days, while others report uptake only
after a year or more of exposure. In some instances where actual
measured concentrations in the field or laboratory are unavailable,
it may be possible to calculate the BCF by one of several methods
described in Moriarty (1983).
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Table B-l of Appendix B summarizes bioconcentration factor data
from EPA criteria documents and the peer reviewed literature for over
130 substances reported to be present at NPL sites. Most of these
data are reported for aquatic ecosystems, and the BCF values were
calculated as the ratio of the concentration of the chemical in fish
tissue to the concentration of the chemical in the aquatic habitat
of the fish. In many cases, different authors report different BCF
values for the same substance (even for the same species), and
opinions vary as to the specific value which should be used for the
bioconcentration factor.
The wide range in BCFs reported in the literature (see
Appendix B) can be explained by a number of ecological and chemical
factors. They include:
• Mobility and availability of a particular compound
• Species role or niche in the environment
• Physiological differences among species
• Testing and measurement protocols
These factors are discussed in more detail in Appendices C and D.
2.2.3 Potential Surrogates for Bioconcentration Factor
A number of different factors which might indicate the potential
of a substance to bioconcentrate have been considered as surrogates
for the BCF. These factors have included the n-octanol-water partition
coefficient, solubility, and the soil adsorption coefficient. Each of
these has been found to correlate with the bioconcentration factor and
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may, therefore, be useful as surrogates when direct bloconcentration
measurements are not available (Geyer et al., 1984; Lyman et al., 1982;
Veith et al., 1980). For the purposes of classifying bioconcentration
potential in the HRS, the n-octanol-water partition coefficient (Pow)
and solubility factors have been included in the ranking scheme as
surrogates for the BCF for reasons discussed below. The soil
adsorption coefficient has not been used for reasons also discussed
below. Appendix D discusses other potential surrogates that were
considered and rejected because they have not been found to correlate
with bioconcentration.
2.2.3.1 n-Octanol-Water Partition Coefficient. When n-octanol,
water, and an organic substance are mixed together until equilibrium
has been reached, and then allowed to stand, the n-octanol and water
will separate into two distinct phases, each of which contains some of
the organic substance. The n-octanol-water partition coefficient (Pow)
is the ratio of the concentration of the organic substance in the
n-octanol to its concentration in water when the system is in
equilibrium. In addition to the direct measurement of the Pow, a
method of calculating the Pow from information on the chemical
structure of a substance has been developed by Lyman et al. (1982).
In general, higher values of the logarithm of the Pow for a
substance correlate with higher values of its BCF in fish, and
consequently, its bioaccumulation potential. For example, compounds
such as DDT and some PCBs which have high BCF values in fish have log
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Pow values above 5. Compounds such as monochlorobenzene and
tetrachloroethylene which have low BCFs also have log Pow values of
less than 3. Garten and Trabalka (1983) suggested that for screening
purposes, substances with a log Pow of 3.5 or more should be considered
as having the potential to bioaccumulate in mammals or birds.
Many researchers have stated that the logarithm of the Pow has
a statistically significant linear correlation with the logarithm of
the bioconcentration factor of organic chemical compounds (e.g., Chiou,
1985; Geyer et al., 1984; Lyman et al., 1982; Veith et al., 1980).
This relationship is illustrated in Figure 3. Table 1 presents a
range of values which various researchers have presented as indicators
of this relationship.
This linear relationship occurs because many organic substances
bioconcentrate by incorporation into fatty tissue, and the n-octanol-
water partition coefficient provides a measure of the preferential
partitioning of substances between water and organic or fatty tissue.
Based on these relationships and the correlations indicated above,
many researchers (e.g., Trabalka and Garten, 1982) have concluded that
the log Pow is a highly satisfactory indication of bioaccumulation
potential in aquatic systems.
The log Pow cannot, however, be used as a predictor of
bioconcentration for inorganic substances, because the mechanism by
which inorganic substances accumulate in food chain organisms is
different from that for organic substances. Where metals are involved,
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a (Geyer et al., 1984)
b (Veith et al., 1980)
o
8
LU
c
g
31
|
I *
o
en
O)
o
o
8
c
o
c 3
g
o 2
o
o
1.0 2.0 3.0 4.0 5.0
Log Pow
6.0
234
Log Pow
c (Lyman et al. 1982)
O
CO
C
O
•H
4-1
rt
o
a
cfl
o
•H
PQ
* = Radio-tagged chemicals
FIGURE 3
EXAMPLE CORRELATIONS OF LOG BCF
VERSUS LOG P FOR BIOACCUMULATION
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TABLE 1
SUMMARY OF VALUES FOUND IN THE LITERATURE
FOR THE CONSTANTS a AND b IN THE
EQUATION log BCF - a log Poct + b
Value of
b" r * n Reference
0.76
0.85
0.858
0.542
1.1587
0.6335
0.935
1.53
1.022
0.997
0.84
0.86
0.79
-0.23
-0,70
-0.808
0.124
-0.7504
0.7285
-1.495
-3.03
-0.632
-0.869
-0.057
-0.333
-0.40
0.907
0.947
0,955
0.948
0.9771
0.7879
0.87
0.843
0.993
0.993
0.976
0.984
0.928
84
59
16
8
9
11
26
15
n
11
12
12
122
Veith et al., 1980
Veith et al. 1979
Geyer et al. 1982
Neeley et al. , 1974
Metcalf et al., 1975
Lu and Metcalf, 1975
Kenaga and Goring, 1980
Kanazawa, 1981
Oliver and Niimi, 1983
Oliver amd Niimi, 1983
Oliver and Niimi, 1983
Oliver and Niimi, 1983
Veith and Kosian, 1983
Note: BCF is based on wet weight concentrations.
r « correlation coefficient; n • number of chemicals tested.
Source: van Gestal et al., 1985.
23
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the BCF typically is available from direct measurements (Hildebrand
and Cushman, 1976).
For a variety of reasons discussed in Appendices C and D, the
relationships indicated in Table 1 do not always provide reliable
estimates of the BCF. In addition to being inapplicable for metallic
substances, log Pow is generally considered a poor indicator of BCF
in those instances where it is greater than 6 (Hawker and Connell,
1985). The table illustrates that different authors have developed
different regression equations relating BCF to the Pow. Figure 3
illustrates the potential for outliers. The number of substances
used to develop the correlation, the biological organism used, and
the accuracy of the measurement technique appear to contribute to
the differences in results.
As a result of these and other difficulties, many researchers
have warned against indiscriminate use of the Pow. Several authors
(e.g., Chiou, 1985; Garten and Trabalka, 1982) have noted, for
example, that the bioaccumulation of organic substances that bind to
proteins (e.g., methylmercury) is not adequately predicted by
generalized relationships based on chemical characteristics such as
the Pow. Some substances with a low log Pow (e.g., organometallic
compounds), will, in fact, have a high BCF, while other substances
with a high log Pow may not show much evidence of bioaccumulation,
either because they are not absorbed by the organism, are present in
an unavailable form, or are readily metabolized and excreted. These
24
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possibilities are graphically illustrated by the fact that about
25 percent of 68 substances studied by Garten and Trabalka (1983) were
classified on the basis of their log Pow values as having a higher
bioconcentration potential than their actual measured bioconcentration
factors. Additional considerations are generally necessary to
establish the potential for bioaccumulation of a substance in the food
chain. Only chronic feeding tests can adequately predict the
bioaccumulation of a substance. Such tests, however, are both
time-consuming and expensive.
As a result, although problems remain in using the n-octanol-water
partition coefficient to predict bioconcentration (most notably, log
Pow is not applicable for inorganics), it is widely used to rapidly
screen organic substances to identify those with potential for
bioconcentration.
2.2.3.2 Other Chemical Parameters. Besides the n-octanol-water
partition coefficient, water solubility and soil adsorption
coefficients are the properties most commonly used to predict the
bioconcentration of organic compounds in food chain organisms.
Substances with low water solubility are likely to bioconcentrate.
Substances with high soil adsorption are also likely to bioconcentrate.
Both measures have been used to predict BCFs in a manner similar to
the Pow. Both are well correlated with log Pow; however, because
solubility data are more readily available than soil adsorption
coefficients, the solubility data have been adopted for use in this
classification scheme.
25
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The measurement of water solubility does not usually impose
particularly complicated demands on standard chemical techniques,
although measurement for some barely soluble substances can require
specialized equipment. However, since the design of many chemical
and environmental tests requires precise information on water
solubility, these tests are standard for the chemical industry, and
data are widely available.
Unfortunately, there are many variables which can affect the
»
solubility of a substance in water, including other chemicals in the
water, the temperature, and the molecular structure of a substance
and the associated purity of the substance. In addition, there are
some difficulties in conducting measurements of low solubility
substances. As a result of these factors, specific values reported
for water solubility may be suspect (Verschueren, 1983).
Chiou et al. (1977) and others have noted nonetheless that
there is a good correlation between the logarithm of water
solubility of organic compounds and the logarithm of their
n-octanol-water partition coefficient. Furthermore, this
correlation has been extended to determine a correlation between the
BCF and the solubility of a substance. Overall, while there are a
variety of factors which make the correlation between solubility and
BCF less certain than the correlation between log Pow and BCF, the
2
correlation is quite strong (R greater than .66), and these
solubility values can be used effectively when Pow data are not
available.
26
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3.0 THE PROPOSED RANKING SCHEME
This chapter contains a scheme for classifying the potential of
substances to bioaccumulate in the food chain. The proposed
methodology meets the following conditions:
• Supported by published scientific literature.
• Uses readily available data.
• Able to differentiate among different levels of potential
bioaccumulation.
• Easily calculable by, or available to, a nonchemist.
• Easily applied to a large number of substances.
3.1 Overview of the Ranking Scheme
The proposed scheme is illustrated as a decision tree in Figure 4.
The guiding principle of this scheme is that it is adaptable to the
data available for a substance. Within this context, data obtained
from direct measurements of bioconcentration factors are considered to
be most reliable, and should be used first. Within this category,
field measurements should be used before laboratory measurements. If
BCF data are not available, data on n-octanol-water partition
coefficient should be used, if these data are not available
information on solubility should be used. Although information on
biomagnification is considered somewhat controversial, available data
on this process are included in the scoring system to slightly elevate
the rating for those substances which may biomagnify. If none of
these data are available, the substance is given a score of zero. If
available data show that the substance is not found in food or biota,
27
-------
Bioconcentration
Factor (BCF) Known'
n-Octanol Water
Coefficient (Log Row)
Available and
< 6.00
BCF
> 10,000
> 1,000-9,999
> 100-999
>10-99
1-9
Assigned
Value (V)
6
5
4
3
2
1
NO
00
Value = 0
Value = V +
Value = V
Yesb .
LogPow
5.5-6.0
4.5-5.49
3.2-4.49
2-3.19
0.8-1.99
<0.8
Assigned
Value (V)
6
5
4
3
2
1
k-t
Value = V +
Value = V
Yes
Water
Solubility
>1500mg/1
501-1500 mg/1
25-500 mg/1
< 25 mg/1
Assigned
Value (V)
1
4
5
6
^ — ^^Xw Yes
I No ^
Value = V + 1C
Value -V
Use EPA bioconcentration values provided in EPA Water Quality Criteria documents if available, otherwise use maximum value found in literature.
__ Either as reported from published literature; or calculated by Leo's Fragment Constant Method; or from Log P Data Base.
'If V = 6, then final score is 6, regardless of biomagnification.
FIGURE 4
DECISION TREE FOR RANKING SUBSTANCES FOR POTENTIAL
TO BIOACCUMULATE IN THE FOOD CHAIN
-------
the substance is assumed to not bioaccumulate, and is assigned a
score of one in the evaluation scheme.
Within each of the data classes (i.e., BCF, Pow, solubility),
evaluation classes of 1 to 6 are used to indicate the relative
potential of substances to bioaccumulate. On this relative scale, a
value of 1 means that the substance probably does not bioaccumulate
in tissues, while a value of 6 indicates a high bioaccumulation
potential. A hazardous substance would receive a value of 6 because
available data indicates that it has a very high bioconcentration
factor. If the literature indicates that a substance may biomagnify,
then an enhancement factor of +1 is added to the value obtained from
either the BCF, Pow, or solubility data. In no case is a value
greater than 6 assigned to a substance. That is, if the BCF of a
substance is high enough that the substance is given a score of 6,
and the literature indicates that the substance may biomagnify, the
final score for the substance under this scheme would be 6.
3.1.1 Classification Based on Bioconcentration Factor
Bioconcentration factors reported in the literature range from
less than one (no bioconcentration) to a million. For ranking
purposes within the HRS, the scheme presented in this paper uses
powers of 10 to create classes, with a BCF of 10,000 set as the
upper level (i.e., substances with a BCF of 10,000 or greater
receive a score of 6). Ihis ranking reflects the fact that
substances such as DDT, dioxin, and PCBs, known to present high risk
29
-------
to human health, have a BCF of 10,000 or higher. For purposes of
ranking In the HRS, an assumption is made that the higher the
bioconcentration factor, the greater the potential for humans to
ingest, or for seafood to accumulate, significant amounts of a
substance, and the greater the potential threat of significant
exposure to humans via the food chain.
3.1.2 Classification Based on Logarithm of the
n-Octanol-Water Partition Coefficient
If bioconcentration data are not available for a substance, the
first choice for a surrogate is log Pow data. While log Pow data
allow a good approximation of log BCF, they must be used with care.
For example, Veith (1979) has noted that several researchers
have suggested the relationship presented in Section 2 be used with
caution for chemicals with a molecular weight greater than 600.
Spacie and Hamerlink (1985) have suggested that there are
limitations in using log Pow data which are outside the 2-5 range.
Hawker and Connell (1985) have suggested that for compounds with log
Pow greater than 6, the log BCF/log Pow relationship breaks down due
to the lengthy time required to reach equilibrium. Others (e.g.,
Van Gestal et al., 1985) have suggested that substances with very
high values for log Pow (e.g., greater than 6.0) may not tend to
bioconcentrate due to their (potentially) large molecular size. In
addition to these factors, it has been reported that it is extremely
difficult to accurately measure substances with log Pow greater than
6.0 (MacKay, 1982).
30
-------
Based on these considerations, substances for which log Pow
data are available, and whose log Pow is greater than 5.5 and less
than 6.0 are assigned a ranking value of 6 (see Figure 4). This
practice is consistent with both the Michigan Site Assessment System
(1984) and Veith et al. (1980), who use a log Pow of 6.0 as the
highest (worst case) score for log Pow. It also is consistent with
the assignment of the value of 6 to compounds with a BCF greater
than 10,000. Furthermore, this approach will flag those substances
whose log Pow values are high enough to suggest that this value may
not be a good surrogate for the BCF of a substance. While using a
log Pow value of 6.0 may include some substances whose BCF is lower
than might be predicted from the log Pow Information (Garten and
Trabalka, 1982), the value of 6.0 has been selected to make maximum
use of available data in the MRS screening process. Substances for
which actual BCF data are not available and whose log Pow in greater
than 6.0 should be ranked based on solubility data (see below). The
remaining assigned values presented in Figure 4 are derived from
Veith's (1979) regression equation and the assigned values based on
BCF data.
In summary, this scheme would assign the highest rating values
(on the 1-6 scale) to those substances with a log Pow from 5.5
to 6.0. The lowest value (1) would be assigned to substances with
log Pow less than 0.8. Overall, this ranking scheme matches the six
classification categories established using bioconcentration factors
and provides a reliable indicator of potential bioaccumulation.
31
-------
Using the log Pow value for organic substances tends to err on
the conservative side; i.e., it may predict an organic substance
will bioaccumulate when in fact it does not. For example, as noted
earlier, Garten and Trabalka (1983) found that, using log Pow data
to predict bioconcentration, 25 percent of the organic compounds
reviewed were "false positive" classifications (i.e., overrated with
regard to their bioconcentration factor). As a result, use of the
log Pow is recommended only when BCF data are not available. It
should also be remembered that log Pow data cannot be used for
inorganic substances.
3.1.3 Water Solubility
In those instances in which neither BCF nor log Pow data are
available (or where log Pow exceed 6.0), data on water solubility
(S) can be used as a surrogate for BCF data. In general, water
solubility is inversely related to BCF, following the general
equation (Garten and Trabalka, 1982):
log BCF - -0.48 log S + 4.42 R2 - 0.66
Using this relationship, BCF is projected to be less than 100
if S is greater than about 25 mg/liter. Although the constants in
the above equation have been subject to some dispute, van Gestal
et al. (1985), while recognizing the uncertainty in the regression,
have concluded that log Pow would be less than 3 and BCF less than
100, if S is greater than 25 mg/liter.
32
-------
Similarly, the BCF of a substance is expected to be less than
10 if the solubility of that substance is greater than about 1,500
mg/liter.
Once again, in order to be conservative, in this scheme a rating
value of 4 is assigned to those substances with solubility greater
than or equal to 25 mg/liter. Again, to provide for a conservative
measure, a rating value of 1 is assigned to substances with an
S value greater than or equal to 1,500 mg/liter.
Unfortunately, data on the S value for various organic
substances does not reliably discriminate among those substances
which tend to most strongly bioconcentrate. In order to provide as
conservative a screening mechanism as is reasonable in this scheme,
a rating value of 6 has been assigned to substances with S values
less than 25 mg/liter. The literature does not support further
subdivision based on solubility, so none has been attempted.
Furthermore, it has been noted that it is extremely difficult to
measure S below 10 mg/liter, so concentrations below this level may
be suspect. It should be emphasized that under this scheme
solubility data would be used only if BCF and log Pow data are not
available.
3.2 Decision Procedures for Ranking Hazardous Substances
This section presents the procedures for using the proposed
classification methodology. These procedures were used to evaluate
33
-------
several substances reported present at NPL facilities as an
illustration of the classification scheme.
With reference to Figure 4, the first question in the decision
tree is to determine whether information on bioconcentration for a
specific substance is available. If this data is available, the
substance is ranked according to the scheme presented above (e.g.,
if the BCF is greater than 10,000, the substance is scored as 6).
Similarly, if information on BCF is not available, the
substance is ranked on the n-octanol-water partition coefficient, if
data for it is available and log Pow is less than 6.0. If data on
the n-octanol-water partition coefficient are not available or if
the log Pow is greater than 6.0, the substance is ranked on water
solubility.
At each stage in the decision tree, it is necessary to ask a
second question after a "yes" response is obtained and an initial
estimate of a score for a specific substance is generated. That is,
it is necessary to determine if the substance being considered has
been shown to blomagnify through the food chain. If the substance
has been shown to biomagnlfy, and if the initial score obtained from
using BCF, Pow, or S data is less than 6, than 1 should be added to
the initial score to elevate the rating value because of the
biomagnification potential. In no instance should a substance be
scored higher than 6.
34
-------
For those substances for which there is no information on BCF,
Pow, or S, a score of zero would be assigned under this scheme. The
general rationale for assigning a "zero" in this instance is that in
employing a screening mechanism, it is not desirable to impute
knowledge concerning a substance when there is none. Finally, it is
possible to "tag" substances for which there are no data available
for further investigation. This investigation would allow, at
least, a laboratory analysis of the water solubility of the
substance, and could lead to a preliminary score for that substance.
The scheme for ranking substances for which only solubility
data are available reflects the fact that this information is less
certain than either the BCF or Pow data. Specifically, solubility
data provide a good indication of those substances which are not
likely to bloaccumulate, but information on the extent to which a
substance may bioaccumulate is not easy to determine from solubility
data alone. If a more refined screening is desired for those
substances for which only S data is available, a detailed laboratory
analysis of either the Pow or the BCF of the substance is required.
3.3 Illustration of Proposed Ranking Scheme
Appendix A contains a series of examples of how this scheme
would be used to rank substances.
Table B-l gives the measured BCFs for over 130 hazardous
substances. It also indicates substances believed to biomagnify.
Tables B-2 and B-3 present data on the log Pow. Table B-2 is taken
35
-------
from a computerized data base which is maintained by and available
from Technical Database Services, Inc. (1985). Table B-3 relies on
either values reported in the open literature or on calculations
performed by The MITRE Corporation. Data on water solubility are
available in publications such as Verschueren (1983). Using these
data, a. series of substances were ranked to illustrate the use of
the decision tree in Figure 4. This illustration is contained in
Appendix A.
In this ranking scheme, hazardous substances with a high
potential to bioaccumulate in the food chain would have one or more
of the following characteristics:
• BCF of 10,000 or more
• Log Pow of greater than 5.5 to 6.0
• S less than 25 mg/liter
This classification of a substance in terms of its potential for
bioaccumulation in the food chain is intended to be factored into a
methodology for rating human food chain exposure in the HRS. This
rating methodology is explained separately (Saari, 1986).
36
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52
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APPENDIX A
CLASSIFICATION OF HAZARDOUS SUBSTANCES
FOR POTENTIAL TO BIOACCUMULATE
This appendix illustrates the use of the decision tree presented
in Figure 4 (Section 3). The substances included in this appendix
were selected for their ability to illustrate the use of the decision
tree rather than for any particular importance of the individual
substances. Fifteen substances were chosen for this illustration.
These substances include:
1. Acenaphthene
2. Benzo(a)pyrene
3. Cadmium
4. DDT
5. Dichlorome thane
6. Dichlorobiphenyl
7. Copper
8. Vinyl chloride
9. Xylene
10. N-Pentane
11. Lorsban
12. Dichlorobiphenyl
13. Lead
14. Thiourea
15. Cyclohexanone
The process for following the decision tree presented in Section 3
is illustrated in Table A-l. In all instances, the BCF data should be
used first, then the log Pow data, and if neither of these is
available, the S data. For Benzo(a)pyrene, for example, the score
based on the BCF would be 5. Since the substance has been reported to
bioaccumulate, 1 is added to this factor for a final score of 6. For
DDT, the score based on the BCF would be 6, the maximum allowable
regardless of the fact the substance bioaccumulates.
53
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TABLE A-l
BIOACCUMULATION RATING BASED ON PROPOSED RATING SCHEME
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
Substance
Acenaphthene
Benzo( a)pyrene
Cadmium
DDT
Dichloromethane
DI chlorobisphenyl
Copper
Vinyl chloride
Xylene
N-Pentane
Lorsban
Di chlorobiphenyl
Lead
Thiourea
Cyclohexanone
BCF
387
2,177
1,000-3,999
10,000
5
214
1,000
1.2
21-23
—
—
215
924
—
—
Log Pow S Biomagnify
— * — No
— 0.003 mg/1 Yes
Yes
0.0031 mg/1 Yes
20,000 mg/1 No
— No
— — No
— 1,100 mg/1 No
3.15 198 mg/1 No
3.39 — No
4.96 — No
— No
No
— 91.8 mg/1 No
3.44 55 mg/1 No
Rating
Value
4
6
6
6
1
4
5
2
3
3
5
4
4
4
3
*Indicates data not readily available for inclusion in table.
54
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APPENDIX B
SUMMARY OF DATA COLLECTED ON HAZARDOUS SUBSTANCES
Table B-l presents the findings from a literature review to
identify substances which may bioaccumulate in food chains. The
automated EPA NPL technical data base was queried to identify
substances found at NPL sites. Most of the substances subsequentially
researched were found at five or more NPL sites. The collected
literature was scanned to determine which of these substances have
been reported to bioaccumulate in biota. Most of the tabulated BCF
data are for fish, but several reflect measurements of plants or
animals. Some BCFs contained in Table B-l are generic values as
presented in the referenced literature. BCFs are for edible tissue
(e.g., meat or whole fish) not for liver concentrations.
Table B-2 presents a range of log Pow values from a computerized
database (Technical Database Services, Inc., 1985).
For a limited number of substances found at waste sites, and
which are not included in the computerized data base, MITRE calculated
log Pow values or found log Pow values in the literature. These
values are reported in Table B-3.
55
-------
TABLE B-l
DATA COLLECTED ON HAZARDOUS SUBSTANCES AND POTENTIAL BIOACCUMULATION
Substance Name
Acenaphthene
Acrolein
Acrylonitrile
Aldrin
Aluminum and
compounds
Aniline
Anthracene
Antimony and
compounds , NOS
Antimony trioxide
Arsenic and
compounds
CAS Number
00083-32-9
0107-02-8
00107-13-1
00309-00-2
07429-70-5
0062-53-3
00120-12-7
07440-36-0
1309-64-4
07440-38-2
Bioconcentration
Factor Biomagnifies
387 fish
242
344 fish
215
48 fish
30
greater than 4000
13,390 clam
2-3.5 cattle
3,000-11,000 fish
4,600-6,300
10,000-15,000 yes
10 in fish
6 fish
0.5
917
100-10,000
greater than 4,000
less than 1
40 fish
16,000 invertebrates
700-999
1-3 plants
10,000 fish
0-4 fish
333 fish
44 fish
Source
Number
3/22
42
3/22
42
3/22
42
4
31
11/38
11
22
10
39
26
42
25
32
4
42/22
32
32
4
9
17
22
39
42
56
-------
TABLE B-l (Continued)
Substance Name
Atrazine
Barium
Benzene
Benzidine
Benzoic acid
Benzo(a)pyrene
Beryllium and
Biphenyl
Bis (2-chloroethyl)
ether
Boron and compounds
Bromine
Butylbenzyl
phthalate
CAS Number
01912-24-9
07440-39-3
0071-43-2
0092-87-5
00065-85-0
00050-32-8
7440-41-7
00092-52-4
0011-44-4
07440-42-8
07726-95-6
00085-68-7
Bioconcentration
Factor Biomagnifies
2-4 no
less than 8
3
0.6-5.2
4
3.5 eels
less than 10 fish
5.2
38-44
83
87 fish
21 fish no
37 mosquito
2,177 snail
930 fish yes
less than 1 plant
30
2-100 fish no
19
100
340
437
11 fish
6.9 fish
0.22 in fish
0.015-420 fish
772 fish
279
Source
Number
17
25
33
30
35
21
33
42
22
33
28
26
20
20
20
38
42
20/39
22
32
24
25
3
28
39
23
3
22
57
-------
TABLE B-l (Continued)
Substance Name
Cadmium
Cap tan
Carbon
tetrachloride
Cerium
Chlordane
Chlorobenzene
Chlorobiphenyl
Chloroform
2-Chlorophenol
CAS Number
074409-43-9
00133-06-2
0056-23-5
07440-45-1
00057-74-9
00108-90-7
02051-62-9
00067-66-3
00095-57-8
Bioconcentration
Factor Biomagnifies
1,000-3,999 yes
1,100-2,400 fish
30,000 algae no
7.5 fish
8.9 muscle
649 freshwater fish
226 saltwater fish
1,220-2,040 oyster
64-81 fish
less than 300
30 fish
17-39 fish
30
18
1-10 fish
0.1-0.5 cattle
8,000-11,400 fish
4,702
4,810 clam
14,000
645 fish
1,000-10,000
12
10
650
490
6 fish
214 fish
Source
Number
4
13
17
17
17
22
22
22
42/28
4
3
13
22
42
23
11
11
22
31
42
12
32
24
42
26
24
3/22
3/22
58
-------
TABLE B-l (Continued)
Substance Name
Chromium and
compounds
Cobalt and
compounds
Copper and
compounds
Cyanides (NOS)
DDD
DDE,p,p'
DDT,p,p'
1, 2-Dichlorobenzene
1 , 3-W.chlorobenzene
1 , 4-Dichlorobenzene
CAS Number
07440-47-3
07440-48-4
07440-50-8
00057-12-5
72-54-8
00072-55-9
00050-29-3
00095-50-1
00541-73-1
00106-46-7
Bioconcentration
Factor Biomagnifies
8,500 algae
300 algae no
less than 1-2.5 fish
125-200 salt oyster
6-10 plants
6,760 fish yes
320 fish
1 plant
1,000-5,000 marine
1-290 fish
9,960 shellfish no
200-320 in fish
36
none
2.3
80 x 103
1,000-100,000 yes
62 x 102-103 . yes
51,000 fish
10,000-100,000
greater than 4,000
0.9 cattle
61,600-84,500 fish
17,870
16,950 yes
54,000 fish
56-89 fish
60-215
66 fish
37-60 fish
89-215
Source
Number
22
10
22/17
22
9
34
39
9
10
22
22
39
42
22/43
36/38
10
32
18
28
32
4
11
11
22
26/18
28
42/3
22/24
3
42/3
22/24
59
-------
TABLE B-l (Continued)
Substance Name
3, 3-Dlchlorobenzene
Dichlorobiphenyl
1 , 2-Dichloroe thane
1 , 1-Dichloroethylene
Dichlorome thane
2 , 4-Dichlorophenol
2 , 4-Dichlorophenoxy-
acetic acid (2-4-D)
1 , 3-Dichloropropene
Dieldrin
Diethyl phthalate
Diethylhexyl
phthalate
Di-N-butyl
phthalate
Di-N-octyl phthalate
Dimethyl phthalate
2 , 4-Dimethylphenol
Dinitrotoluene
CAS Number
00107-06-2
00075-35-4
00075-09-2
00120-83-2
00094-75-7
00542-75-6
00060-57-1
00084-66-2
00117-81-7
00084-74-2
00117-84-0
00131-11-3
00105-67-9
2532-114-6
Bioconcentration
Factor Biomagnifies
312 fish
215
2 fish
1.2
5.6 fish
5 fish
41 fish
17-45xlO~5 cattle no
20 fish
1.9 fish
greater than 4,000 yes
1.6-3 cattle no
4,400-5,800 fish
1,557
3,540 clam
117 fish
38
14
9,400
57 fish
150 fish
3.8 fish
Source
Number
28
24
3
42
28
28
28
11
11
28
4
11
11
22
31
3
22
22
22
3
3/22
28
60
-------
TABLE B-l (Continued)
Substance Name
Dioxin
Diphenylamine , N,N
1 , 2-Diphenylhydrazine
Endosulfan
Endrin
Ethylbenzene
Fluor anthene
Fluorene
Heptachlor
Heptachlor
epoxide
CAS Number
01746-01-6
00122-39-4
00122-66-7
00115-29-7
00072-20-8
00100-41-4
00206-44-0
00086-73-7
00076-44-8
01024-57-3
Bloconcentratlon
Factor Biomagnifies
10,000
5,800
greater than 4,000
5,000 fish
130-9,000 plants
30 fish
25 fish
50-30,000 no
greater than 4,000
1.2-1.3 cattle
1,400-4,050 fish yes
0.4 lobster
1,324
37.5 fish
1,150 fish
100-10,000
1,300 fish
10,630 clam
15,700 fish
greater than 4,000
0.4-0.6 cattle
2,000-17,400 fish
9,500 freshwater fish
7,500 saltwater fish
102-10*
2,330 clam
14,400 fish
Source
Number
32
42
4
28
32
28
28
32
4
11
11
15
22
28
28
32
28
31
28
4
11
11
22
22
32
31
28/22/27
61
-------
TABLE B-l (Continued)
Substance Name CAS Number
Hexachlorobenzene 00118-74-1
Hexachlorobutadiene 00087-68-3
Hexachlorocyclo- 00608-73-1
hexane (NOS)
Hexachlorocyclo- 00077-47-4
pentadiene
Hexachloroethane 00067-72-1
Iron and compounds 07439-89-6
Isophorone 00078-59-1
Kepone 00143-50-0
Bioconcentration
Factor Biomagnifies
1,166 fish yes
8,600
22,000
1,166
5,500-21,900
19
3-4.3
1000
10-500
480 fish
130 oyster
352 fish
300-699
448 fish
11
300-2,000
139 fish
87
1,000-5,000 fish
10,000-100,000
invertebrates
100 fish
7 fish
7-16 fish
5,000-7,000 oyster
440-1,060 crab
4,500-11,700 shrimp yes
3.9-10.5 yes
8,400 (estimate from
water solubility)
2,600-7,600
Source
Number
12/43
24
22
24
27
22
42/28
32
20
20
20
22
4
20
22
32
3
42
10/38
10
39
22/3
36
40
40
35/40
17
24
29
62
-------
TABLE B-l (Continued)
Substance Name
Lead
Lindane
Magnesium and
compounds
Manganese and
compounds
Mercury
Mercuric chloride
Methoxychlor
Bioconcentration
CAS Number Factor Biomagnifies
07439-92-1 0.1 plants
17 snail
0.2-2 plants
0.1-0.3 fish
42-45 fish
924 bivalves
300 fish
49
00058-89-9 0.4-0.7 cattle
2,610
325-560 fish
130-170 shellfish
352 fish
754
07439-95-4 50-93
07439-96-5 0.2-0.3 fish
660 fish no
10045-94-0 greater than 4,000- yes
5,500
13,000 yes
1,000-10,000
07487-94-7 1,800-4,994 fish
10,000 oyster
00072-43-5 less than 300
0 cattle
185-1,550 fish
8,300
Source
Number
14
22
9
14
22
22
39
42
11
31
11
20
22
33
39
14
39
4/28
42
10/22
22
22
4
11
11
25
Methyl mercuric
chloride
Metfiyl parathion
11,000-85,700 fish
45 fish
yes
22
28
63
-------
TABLE B-l (Continued)
Substance Name
Mirex
Molybdenum
Naphthalene
Nickel and
compounds
Nitrobenzene
4-Nitrophenol
N-Nitrosodi-
phenylamine
Parathion
Pentachlorobenzene
CAS Number
02385-85-6
07439-98-7
00091-20-3
07440-02-0
00098-95-3
00100-02-7
00086-30-6
00056-38-2
00608-93-5
Bioconcentration
Factor Biomagnifies
84 fish yes
44-50 plants
10 fish
20-100 marine
100
131
427
5,000 copepods yes
10
greater than 4,000
425-467 plants
100-200
300 fish no
30 fish
84 oyster
47
29 fish
3
57
3
217 fish
24
3,400 fish
5,000
3,400
2,125 fish
Source
Number
15
9
39
10
20
25
25
22/17
42
4
9
10
17
22
22
42
26
42
33
42
3
42
1
24
22
28
Pentachloroethane
00076-01-7
67 fish
64
-------
TABLE B-l (Continued)
Substance Name
Pentachlorophenol
Phenanthrene
Phenol
Plutonium
Plutonium-238
Plutonium-239
Polychlorinated
biphenyls
Pyrene
Radium and
compounds
Bioconcentration
CAS Number Factor Biomagnifies
00087-86-5 greater than 4,000
296 fish
770
200 (approximately)
1,050 fish
31
00085-01-8 325
2,630
100-10,000
00108-95-2 1.4
350
1-15 fish
230-520 shellfish
15117-48-3 6 x 10~5 land
1,000 marine yes
2-15 fish muscle
1,100 fish gut
01336-36-3 4 x 10^
greater than 4,000
2,500 shrimp
105-106
10,400 yes
31,200 yes
00129-00-0 2,700
2,800 (log Pow
estimate)
10,000
07440-14-4 0.9 x 103
5-16 plants
Source
Number
4
12/26
25
22
33
33
25
28
32
42
34
1
1
5
13
13
13
2
4
2
16
22
42
25
22
32
6
9
65
-------
TABLE B-l (Continued)
Substance Name
Selenium
Silver
Sodium
Strontium
Sulfur
1,2,4, 5-Te trachloro-
benzene
1,1,2, 2-Tetrachlor o-
ethane
1,1,2, 2-Tetrachlor o-
ethene
2,3,4,6 -Tetrachlor o-
phenol
Thallium
CAS Number
07782-49-2
07440-22-4
07440-23-5
07440-24-6
07704-34-9
00095-94-3
00079-34-5
00127-18-4
00058-90-2
07440-28-0
Bioconcentration
Factor Biomagnifies
1-40
16 fish
78-104 plants
8-20 fish yes
167 fish no
10-3,000 fish
3,080 fish
28 fish
0.067-100 fish
171 yes
1-10 soft tissue
1,000 bone
200
2-5 marine
1,800 fish
1,125 fish
4,500
5-8 fish
42 fish
100 no
greater than 100 in
liver
240 fish
100,000
116
Source
Number
8
28
9
22/41
39/17
20
28
22
23
6
10
10
34
10
3
28
24
3/42
28
32/20
32/20
28
32
42
66
-------
TABLE B-l (Continued)
Substance Name
Tin and compounds
Titanium and
compounds
Toluene
Toxaphene
Trichlorobenzene
1,2,3-Trichloro-
benzene
1,2,4-Trichloro-
benzene
1,1,1-Trichloro-
e thane
Bioconcentration
CAS Number Factor Biomagnifies
07440-31-5 0.5 fish
3.5 invertebrates
3,000 fish
1,000
07440-32-6 40-1,000 marine
00108-88-3 13.2 eel
11
08001-35-2 greater than 4,000
10,000 fish yes
4,372-6,150
26,400
13,100
00050-31-7 1,700 in fish
00087-61-6 182 fish
890-2,300
00120-82-1 100-1,000
2,800 fish
491
183 fish
890-2,300
00071-55-6 9 fish
11
5-5.6 fish
Source
Number
17
17
39
39
10
21
42
4
18
22
24
42
39
22
24/33
32
28
24/33
17
27
3
42
28
1,1,2-Trichloro-
ethane
00079-00-5
4.5
3/42
67
-------
TABLE B-l (Continued)
Substance Name
Trichloroethylene
Trichlorophenol
Trichlorophenoxy
acetic acid (2,4,5-T)
Tris
Uranium and
compounds
Vanadium and
compounds
Vinyl chloride
Xylene
CAS Number
00079-01-6
25167-82-2
00093-76-5
00126-72-7
07440-61-1
07440-62-2
00075-01-4
01330-20-7
Bioconcentration
Factor Biomagnifies
88 fish
11-17
2.7
110-150 fish
4-19x10"* cattle no
25-43 fish
2.7
3-6 plants
10 fish
less than 0.03 animals
7-11 plants
20-100 marine
2-28 fish
1.2
21.4-23.6 eel
Source
Number
33
42/22
23
28
11
11
23
9
9/39
9
9
10
13
42
21
Zinc and compounds 07440-66-6
Zirconium
07440-67-7
less than 300
47
1,000-5,000
0.33-0.48
60-100
3.3-200
no
yes
4
42
10
14/37
22
23
68
-------
TABLE B-l (Concluded)
Sources:
(1) National Research Council, 1975
(2) Thomann & Connolly, 1984
(3) Veith et al., 1980
(4) Michigan Water Resources Commission, 1984
(5) Garten & Dahlman, 1978
(6) Lemons, 1975
(7) Dawson et al., 1983
(8) Robberecht et al., 1983
(9) Dreesen & Williams, 1982
(10) Wilber, 1969 (in Brown, 1985)
(11) Kenaga, 1980
(12) U.S. EPA, 1977
(13) Holdway et al., 1983
(14) Drifmeyer & Odum, 1975
(15) Reish et al., 1982
(16) Weaver, 1984
(17) Kay, 1984
(18) Niethammer et al., 1984
(19) Oliver and Nicol, 1982
(20) Callahan et al., 1979
(21) Ogata & Miyake, 1978
(22) U.S. EPA, 1980 (updated with 1985 final)
(23) U.S. NRC, 1977
(24) Kenaga & Goring (in Lyman, 1982)
(25) Southworth et al., (in Lyman, 1982)
(26) Lu & Metcalf, 1975
(27) Vieth et al., 1979
(28) ICF, Inc., 1985
(29) Reish et al., 1983
(30) Guthrie & Davis, 1979
(31) Hartley and Johnson, 1983
(32) Ghisalba, 1983
(33) Klein et al., 1984
(34) Oakes et al., 1982
(35) Banner et al., 1983
(36) U.S. EPA, 1979
(37) Guthrie et al., 1979
(38) U.S. EPA, 1985
(39) Hildebrand and Cushman, 1976
(40) Macek et al., 1979
(41) Wiedemeyer, 1986
(42) ICF, Inc., 1985b
(43) Oliver and Niimi, 1983
69
-------
TABLE B-2
LOGARITHM N-OCTANOL-WATER COEFFICIENTS
Substance Name
1,1,1-TRICHLOROETHANE /BPA/
1,1,2, 2 -TETRACHLOROETHANE
1,1, 2 -TRICHLOROTRIFLUOROETHANE
1,1-DICHLOROETHANE /BPA/
1 , 1-DICHLOROETHYLENE/VINYLIDINE CHLORIDE/
1 , 2 , 3-TRICHLOROBENZENE
1,2, 3 -TRIMETHYLBENZENE
1,2,4, 5 -TETRACHLOROBENZENE
1 , 2 , 4-TRICHLOROBENZENE
1,2,4- TRIMETHYLBENZENE
1,2,5 , 6-DIBENZANTHRACENE
1,2-DIBROMOETHANE
1,2-DICHLOROETHANE /BPA/
1,2-DICHLOROETHYLENE -CIS
1,2-DICHLOROETHYLENE -TR
1,3, 5 -TRIMETHYLBENZENE/MESITYLENE/
1,3, 5 -TRINITROBENZENE
1,3-BUTADIENE /BPA/
1,3-DICHLOROBENZENE
1 , 4-NAPHTHOQUINONE
(Source: Technical Database Services, Inc., 1985)
70
CAS Number
71-55-6
79-34-5
76-13-1
75-34-3
75-35-4
87-61-6
526-73-8
95-94-3
120-82-1
95-63-6
53-70-3
106-93-4
107-06-2
156-59-2
156-60-5
108-67-8
99-35-4
106-99-0
541-73-1
130-15-4
Log Pow
2.49
2.39
3.16
1.79
2.13
3.99
3.66
4.82
4.12
3.78
6.50
1.96
1.48
1.86
2.09
3.42
1.18
1.99
3.38
1.78
-------
TABLE B-2 (Continued)
Substance Name
1-BUTENE /BPA/
1-CHLOROBUTANE /BPA/
1- ETHYL- 1-NITROSOUREA/ENU/
1 - ETHYL- 2 -METHYLBENZENE
L-METHYL-1-NITROSOUREA
1-PROPENE.l-PHENYL
2,2' -DICHLOROETHYLETHER
2,2,2- TRICHLORO -1,1- ETHANEDIOL/CHLORALHYDRATE
2,3,4, 5 -TETRACHLOROPHENOL
2,3,4,6- TETRACHLOROPHENOL
2 , 3 , 4-TRICHLOROPHENOL
2 , 3 , 4-TRICHLOROPHENOL
2,3,5, 6 -TETRACHLOROPHENOL
2,3, 5 -TRICHLOROPHENOL
2,3, 6 -TRICHLOROPHENOL
2,3-DICHLOROPHENOL
2,4,5- TRICHLOROPHENOL
2,4, 6 -TRICHLOROPHENOL
2,4, 6 -TRINITROTOLUENE
2 , 4-DICHLOROPHENOL
CAS Number
106-98-9
109-69-3
759-73-9
611-14-3
684-93-5
637-50-3
111-44-4
302-17-0
4901-51-3
58-90-2
15950-66-0
15950-66-0
935-95-5
933-78-8
933-75-5
576-24-9
95-95-4
88-06-2
118-96-7
120-83-2
Log Pow
2.40
2.64
-0.15
3.53
-0.16
3.35
1.29
1.61
5.05
4.10
3.51
3.51
4.88
4.56
3.46
2.52
3.72
3.62
1.60
3.30
71
-------
TABLE B-2 (Continued)
Substance Name
2,4- DIMETHYLPHENOL
2,4-DINITROPHENOL
2,4- DINITROTOLUENE
2,5-DICHLOROPHENOL
2 , 6-DICHLOROPHENOL
2-BUTANONE
2-HEXANONE
2-NITROGUANIDINE
2-PENTANONE
2 -PICOLINE/2 -METHYL PYRIDINE/
3,3' -DICHLOROBENZIDINE
3,4,5- TRICHLOROPHENOL
3 , 4-DICHLOROPHENOL
3-METHIO-4-AMINO-6-T-BU-l,2,4-TRIAZINE-5-ONE
3-METHIO-4-AMINO-6-T-BU-l,2,4-TRIAZINE-5-ONE
4 , 4 ' - 1 - PROPYLIDENE-DIPHENOL/DIPHENYLOLPROPANE
4,4'-PCB
4,4'-STILBENEDIOL,A,A'-DIETHYL/DES/
4-AMINOPYRIDINE
4 - NITROQUINOLINE - 1 - OXIDE
CAS Number
105-67-9
51-28-5
121-14-2
583-78-8
87-65-0
78-93-3
591-78-6
556-88-7
107-87-9
109-06-8
91-94-1
609-19-8
95-77-2
21087-64-9
21087-64-9
80-05-7
2050-68-2
56-53-1
504-24-5
56-57-5
Log Pow
2.30
1.50
1.98
3.20
2.34
0.29
1.38
-0.89
0.91
1.11
3.51
4.01
2.86
1.70
1.70
3.32
5.58
5.07
0.26
1.02
72
-------
TABLE B-2 (Continued)
Substance Name
6-AMINOCHRYSENE
7 , 12 -DIMETHYLBENZ( A) ANTHRACENE
A , A , A- TRICHLOROTOLUENE
A-CHLOROTOLUENE
A-NAPHTHYLAMINE
A- NAPHTHYLTHIOUREA/ANTU/
ACENAPHTHENE
ACETANILIDE , 4 - ETHOXY/PHENACETIN/
ACETIC ACID
ACETIC ACID, ETHYL ESTER
ACETIC ACID, METHYL ESTER
ACETIC ACID .BUTYL ESTER
ACETIC ACID.PROPYL ESTER
ACETONE
ACETONITRILE
ACETOPHENONE
ACETYLENE /BPA/
ACRIDINE
ACRYLAMIDE
ACRYLIC ACID, BUTYL ESTER
CAS Number
218-01-9
57-97-6
98-07-7
100-44-7
134-32-7
86-88-4
83-32-9
62-44-2
64-19-7
141-78-6
79-20-9
123-86-4
109-60-4
67-64-1
75-05-8
98-86-2
74-86-2
260-94-6
79-06-1
141-32-2
Log Pow
4.98
5.80
2.92
2.30
2.25
1.66
3.92
1.58
-0.17
0.73
0.18
1.82
1.24
-0.24
-0.34
1.73
0.37
3.40
-0.67
2.36
73
-------
TABLE B-2 (Continued)
Substance Name
ACRYLIC ACID, METHYL ESTER
ACRYLIC ACID, ETHYL ESTER
ACRYLONITRILE
ADIPIC ACID
ALACHLOR/LASSO/
ALACHLOR/LASSO/
ALDICARB/TEMIK/
ALLYL ALCOHOL
ANILINE
ANILINE ,N -METHYL
ANTHRACENE
ARGON /BPA/
AZOBENZENE , 4 - DIMETHYLAMINO
B-NAPHTHYLAMINE
BENZALDEHYDE
BENZENE
BENZIDINE
BENZO(A)PYRENE
BENZOIC ACID
BENZONITRILE
CAS Number
96-33-3
140-88-5
107-13-1
124-04-9
15972-60-8
15972-60-8
116-06-3
107-18-6
62-53-3
100-61-8
120-12-7
7440-37-1
60-11-7
91-59-8
100-52-7
71-43-2
92-87-5
50-32-8
65-85-0
100-47-0
Log Pow
0.80
1.32
-0.92
0.08
3.52
3.52
0.70
0.17
0.90
1.82
4.45
0.74
4.58
2.28
1.48
2.13
1.34
5.97
1.87
1.56
74
-------
TABLE B-2 (Continued)
Substance Name
BENZOPHENONE
BENZOTHIAZOLE
BIPHENYL
BROMOBENZENE
BROHOCHLOROMETHANE
BUTANE /BPA/
BUTANOL
BUTOXYETHANOL
BUTYL BENZOATE
BUTYLAMINE
BUTYLBENZENE
BUTYLBENZYLPHTHALATE
BUTYRALDEHYDE
CAPTAN
CARBOFURAN
CARBON TETRACHLORIDE /BPA/
CHLORAMBUCIL/NCS 3088/
CHLOROBENZENE
CHLORODIFLUOROMETHANE/FREON - 2 2/ BPA/
CHLOROFORM
CAS Number
119-61-9
95-16-9
92-52-4
108-86-1
74-97-5
106-97-8
71-36-3
111-76-2
136-60-7
109-73-9
104-51-8
85-68-7
123-72-8
133-06-2
1563-66-2
56-23-5
305-03-3
108-90-7
75-45-6
67-66-3
Log Pow
3.18
2.01
3.95
2.99
1.41
2.89
0.88
0.83
4.21
0.88
4.26
3.97
0.88
2.35
2.32
2.83
1.70
2.84
1.08
1.97
75
-------
TABLE B-2 (Continued)
Substance Name
CHLOROTRIFLUOROMETHANE/FREON 13/BPA/
CYCLOHEXANE /BPA/
CYCLOHEXANOL
CYCLOHEXANONE
CYCLOHEXYLAMINE
CYCLOPROPYLBENZENE
CYTOXAN/CYCLOPHOSPHAMIDE/
DDE
DDT
DECANE
DEMETONTHIOL
DI - ( P - AMINOPHENYL) METHANE
DI - 2 - ETHYLHEXYLPHTHALATE
DI - I - PROPANOLAMINE
DIBENZOFURAN
DIBUTYL ETHER
DICHLORODIFLUOROMETHANE/FREON - 12/BPA/
DICHLOROFLUOROMETHANE/FREON - 2 1/ BPA/
DICOFOL
DIETHANOLAMINE
CAS Number
75-72-9
110-82-7
108-93-0
108-94-1
108-91-8
873-49-4
50-18-0
72-55-9
50-29-3
124-18-5
298-04-4
101-77-9
117-81-7
110-97-4
132-64-9
142-96-1
75-71-8
75-43-4
115-32-2
111-42-2
Log Pow
1.65
3.44
1.23
0.81
1.49
3.27
0.63
4.87
3.98
5.01
1.93
1.59
3.98
-0.82
4.12
3.21
2.16
1.55
3.54
-1.43
76
-------
TABLE B-2 (Continued)
Substance Name
DIETHYLAMINE
DI ETHYLPHTHALATE
DIMETHOATE
DIMETHOXYMETHANE
DIMETHYLAMINE
DIMETHYLFORMAMIDE
DIMETHYLPHTHALATE
DINOSEB
DIOXANE
DIPHENYLAMINE
DIPHENYLNITROSAMINE
DIPROPYLAMINE
DIPROPYLNITROSAMINE
DODECANOIC ACID/LAURIC ACID/
ETHANE /BPA/
ETHANE -1,2- DIOL/ETHYLENE GLYCOL/
ETHANOLAMINE
ETHION
ETHYL CHLORIDE/BPA/
ETHYL ETHER
CAS Number
109-89-7
84-66-2
60-51-5
109-87-5
124-40-3
68-12-2
131-11-3
88-85-7
123-91-1
122-39-4
86-30-6
142-84-7
621-64-7
143-07-7
74-84-0
107-21-1
141-43-5
563-12-2
75-00-3
60-29-7
Log Pow
0.57
2.47
0.50
0.00
-0.38
1.01
1.56
2.30
-0.42
3.34
3.13
1.73
1.36
4.20
1.81
-1.93
-1.31
5.07
1.43
0.77
77
-------
TABLE B-2 (Continued)
Substance Name
ETHYLAMINE
ETHYLBENZENE
ETHYLENE
ETHYLENE OXIDE /BPA/
FLUORANTHENE
FLUORENE
FLUOROACETAMIDE
FLUOROFORM/BPA/
FORMALDEHYDE
FORMALDEHYDE
FORMIC ACID
FURAN /BPA/
FURFURAL
GLYCEROL /BPA/
GLYCERYL TRINITRATE
HEPTANE
HEXACHLORO -1,3- BUTADI ENE
HEXACHLOROBENZENE
HEXACHLOROCYCLOHEXANE , ALPHA ISOMER//124/356/
HEXACHLOROCYCLOHEXANE , BETA ISOMER//135/246/
CAS Number
75-04-7
100-41-4
74-85-1
75-21-8
206-44-0
86-73-7
640-19-7
75-46-7
50-00-0
50-00-0
64-18-6
110-00-9
98-01-1
56-81-5
55-63-0
142-82-5
87-68-3
118-74-1
319-84-6
319-85-7
Log Pow
-0.13
3.15
1.13
-0.30
5.20
4.18
-1.05
0.64
0.35
0.35
-0.54
1.34
0.41
-1.76
1.62
4.66
4.74
4.13
3.80
3.78
78
-------
TABLE B-2 (Continued)
Substance Name
HEXACHLOROCYCLOHEXANE/BHC/ GAMMA ISOMER
HEXACHLOROCYCLOPENTADIENE
HEXACHLOROETHANE
HEXACHLOROPHENE /PKA2=11.33/
HEXANE
HYDRAZINE
HYDRAZOBENZENE
HYDROCYANIC ACID /BPA/
I-BUTANOL
I-PROPANOL
I-PROPYLAMINE
IMIDAZOLIDONE , 2 -THIO/ETHYLENETHIOUREA/
INDENE
ISOPROPYLBENZENE
M-CHLOROPHENOL
M-DIHYDROXYBENZENE/RESORCINOL/
M-DINITROBENZENE
M-XYLENE
MALATHION
MALEIC ACID HYDRAZIDE /3 , 6-DIHYDROXYPYRIDAZIN
CAS Number
58-89-9
77-47-4
67-72-1
70-30-4
110-54-3
302-01-2
122-66-7
74-90-8
78-83-1
67-63-0
75-31-0
96-45-7
95-13-6
98-82-8
108-43-0
108-46-3
99-65-0
108-38-3
121-75-5
123-33-1
Log Pow
3.61
5.04
3.82
2.62
3.90
-2.07
2.94
-0.25
0.76
0.05
-0.03
-0.66
2.92
3.66
2.50
0.80
1.49
3.20
2.89
-0.84
79
-------
TABLE B-2 (Continued)
Substance Name
METHACRYLIC ACID, ETHYL ESTER
METHACRYLIC ACID, METHYL ESTER
METHACRYLONITRILE
METHANE /BPA/
METHANOL
METHOMYL
METHOMYL
METHOXYCHLOR
METHYL BROMIDE /BPA/
METHYL CHLORIDE/BPA/
METHYL IODIDE
METHYLAMINE
METHYLHYDRAZINE
METOLACHLOR
METOLACHLOR
MORPHOLINE
MUSCIMOL
N , N - DIMETHYLANILINE
N - METHYLCARBAMATE , 1 - NAPHTHYL
N-NITROSODIBUTYLAMINE
CAS Number
97-63-2
80-62-6
126-98-7
74-82-8
67-56-1
16752-77-5
16752-77-5
72-43-5
74-83-9
74-87-3
74-88-4
74-89-5
60-34-4
51218-45-2
51218-45-2
110-91-8
2763-96-4
121-69-7
63-25-2
924-16-3
Log Pow
1.94
1.38
0.68
1.09
-0.64
1.08
1.08
3.31
1.19
0.91
1.69
-0.57
-1.05
3.13
3.13
-1.08
-2.39
2.31
2.36
1.92
80
-------
TABLE B-2 (Continued)
Substance Name
N-NITROSODIETHYLAMINE
N-NITROSODIMETHYLAMINE
N-NITROSOPIPERIDINE
N-NITROSOPYRROLIDINE
NAPHTHALENE
NITROBENZENE
NITROETHANE
NITROMETHANE
NONANE
0-AMINOPHENOL
0-CHLOROPHENOL
0-CHLOROTOLUENE
0-DIBUTYLPHTHALATE
0 - DICHLOROBENZENE
0-DINITROBENZENE
0 - DIOCTYLPHTHALATE
0- ETHYL CARBAMATE/URETHANE/
0-HYDROXYBENZOIC ACID/SALICYLIC ACID/
0-METHYLBENZENESULFONAMIDE
0-NITROPHENOL
CAS Number
55-18-5
62-75-9
100-75-4
930-55-2
91-20-3
98-95-3
79-24-3
75-52-5
111-84-2
95-55-6
95-57-8
95-49-8
84-74-2
95-50-1
528-29-0
117-84-0
51-79-6
69-72-7
88-19-7
88-75-5
Log Pow
0.48
-0.57
0.63
-0.19
3.59
1.85
0.18
-0.33
4.51
0.62
2.17
3.42
4.72
3.38
1.58
5.22
-0.15
2.26
0.84
1.26
81
-------
TABLE B-2 (Continued)
Substance Name
0-PHTHALIC ACID
0-TOLIDINE
0-XYLENE
OCTANE
OCTANOL
P-AMINOPHENOL
P-CHLOROANILINE
P-CHLOROBIPHENYL
P-CHLOROPHENOL
P-DICHLOROBENZENE
P - DIHYDROXYBENZENE/HYDROQUINONE/
P-DINITROBENZENE
P-NITROANILINE
P-NITROPHENOL
P-NITROTOLUENE
P-XYLENE
PARALDEHYDE
PARAOXON
PARATHION
PENTACHLOROBENZENE
CAS Number
88-99-3
119-93-7
95-47-6
111-65-9
111-87-5
123-30-8
106-47-8
2051-62-9
106-48-9
106-46-7
123-31-9
100-25-4
100-01-6
100-02-7
99-99-0
106-42-3
123-63-7
311-45-5
56-38-2
608-93-5
Log Pow
0.73
2.34
2.77
5.18
3.15
0.04
1.83
4.90
2.35
3.39
0.59
1.46
1.39
0.76
2.37
3.15
0.67
1.69
2.15
5.52
82
-------
TABLE B-2 (Continued)
Substance Name
PENTACHLOROETHANE
PENTACHLORONITROBENZENE/QUINTOZENE/
PENTACHLOROPHENOL
PENTANE /BPA/
PENTANOL
PHENANTHRENE
PHENOL
PHENOL , 4 - CHLORO , 3 - METHYL
PHENOXYACETIC ACID, 2 ,4, 5-TRICHLORO
PHENOXYACETIC ACID , 2 , 4 - DICHLORO
PHENTERMINE
PHENYLARSONIC ACID /PKA2-8.48/
PHENYLMERCURIC ACETATE
PHENYLTHIOUREA
PHORATE/THIMET/
PHOSPHINE SULFIDE,TRIS-(1-AZIRIDINYL)/NSC 639
PHOSPHORIC ACID
PHTHALIC ANHYDRIDE
PIPERAZINE
PROARGYL ALCOHOL/2 -PROPYN-1-OL/
CAS Number
76-01-7
82-68-8
87-86-5
109-66-0
71-41-0
85-01-8
108-95-2
59-50-7
93-76-5
94-75-7
122-09-8
98-05-5
62-38-4
103-85-5
298-02-2
52-24-4
7664-38-2
85-44-9
110-85-0
107-19-7
Log Pow
3.05
4.22
5.01
3.23
1.40
4.46
1.48
3.10
3.13
2.81
1.90
0.06
0.71
0.73
3.56
0.53
-1.86
1.60
-1.17
-0.38
83
-------
TABLE B-2 (Continued)
Substance Name
PROPANOL
PROPENAL/ACROLEIN/
PROPIONALDEHYDE
PROPIONIC ACID
PROPIONITRILE
PROPYLAMINE
PROPYLENE /BPA/
PROPYLENE OXIDE
PYRENE
PYRIDINE
QUINOLINE
QUINONE
STYRENE
TEREPHTHALIC ACID
TETRACHLOROETHYLENE
TETRAFLUOROMETHANE /BPA/
TETRAHYDROFURAN /BPA/
THIOPHENOL
THIOUREA
TOLUENE
CAS Number
71-23-8
107-02-8
123-38-6
79-09-4
107-12-0
107-10-8
115-07-1
75-56-9
129-00-0
110-86-1
91-22-5
106-51-4
100-42-5
100-21-0
127-18-4
75-73-0
109-99-9
108-98-5
62-56-6
108-88-3
Log Pow
0.30
-0.01
0.59
0.33
0.16
0.48
1.77
0.03
4.88
0.62
2.02
0.20
2.95
2.00
3.40
1.18
0.46
2.52
-0.98
2.69
84
-------
TABLE B-2 (Concluded)
Substance Name
TRI CHLOROETHYLENE
TRICHLOROFLUOROMETHANE/FREON - 11/BPA/
TRIETHYLAMINE
TRI ETHYLPHOS PHATE
TRIFLURALIN
TRIMETHYL ORTHOFORMATE
TRIS- (2 , 3-DIBROMOPROPYL) -PHOSPHATE
UREA
VINYL ACETATE
WARFARIN
CAS Number
79-01-6
75-69-4
121-44-8
78-40-0
1582-09-8
149-73-5
126-72-7
57-13-6
108-05-4
81-81-2
Log Pow
2.29
2.53
1.44
0.80
3.06
0.25
3.71
-1.09
0.73
0.05
85
-------
TABLE B-3
LOGARITHM OF N-OCTANOL-WATER COEFFICIENTS (LOG POW) FOR
SELECTED ORGANIC CHEMICALS FOUND AT NATIONAL PRIORITIES LIST SITES
Substance Name
Log Pow
Source
Mr- ,
Acenaphthalene
Benz (A) an thr acene
Benzo (B ) f luoranthene
Benzo(K) f luoranthene
1 , 12-Benzoperylene
Creosote (coal tar)
Chrysene
Dibenz(A,H)acridine
m-Dichlorobenzene
Beta hexachlorocyclohexane (Beta BHC)
Delta hexachlorocyclohexane (Delta BHC)
3-Methylcholanthrene
1-Me thylphenanthr ene
Methylnaphthalene
Napthol
2-Pentanone (Methyl propyl ketone)
2 , 3-Phenylene pyrene
1,2,3 ,4-Tetrachlorobenzene
Tribromome thane (Bromoform)
Trlmethyl benzene
2,3, 4-Trinitro toluene (TNT)
2,4,5-Trinitrotoluene (TNT)
3.74
5.61
6.06
6.06
6.51
3.98
4.98
5.73
3.44
3.80
4.14
6.97
5.00
4.22
2.84
0.84
6.51
4.60
2.39
4.04
2.01
2.01
U.S. EPA, 1981
U.S. EPA, 1981
U.S. EPA, 1981
U.S. EPA, 1981
U.S. EPA, 1981
Callahan et al. ,
Leo et al., 1971
U.S. EPA, 1981
1979
Veith et al. , 1980
Callahan et al. ,
Callahan et al.,
U.S. EPA, 1981
U.S. EPA, 1981
MITRE*
Leo et al., 1971
MITRE*
U.S. EPA, 1981
Chiou, 1985
MITRE*
MITRE*
MITRE*
MITRE*
1979
1979
*Calculated by Leo's Fragment Constant Method as specified in Lyman et al.,
1982.
86
-------
APPENDIX C
ECOLOGICAL FACTORS WHICH AFFECT BIOACCUMULATION
The biological processes and ecological interactions within
aquatic and terrestrial systems are complex and not completely
understood. Figure C-l (Whelan and Steelman, 1984) is a simple
schematic which shows the various environmental factors that can affect
the transport of a chemical through the environment to eventually
reach man. As Figure C-l illustrates, the ecosystem involves complex
interactions among the biotic and abiotic (non-living) components of
the environment. The food chain is only one of those interconnected
links. The physical/chemical fate of pollutants in the ecosystem
influences, and is influenced by, the biological processes.
Because of the complexity of ecosystems and the diversity of
organisms, there is no easy method to evaluate the availability of
substances to either aquatic or terrestrial plants and animals or to
man (Jenne and Luoma, 1977; Fraser and Lum, 1983). Nonetheless, a
large body of scientific information, reflected in the tables in
Appendix B, points out the potential for substances to bioaccumulate
from the environment to human food organisms. Some substances clearly
are found in biotic tissues at concentrations thousands of times
greater than in the environment.
The preferred method of assessing the potential for a substance
to enter the food chain is the direct measurement of the
87
-------
Figure C-1
Schematic Diagram Illustrating the Interactions Between Media and the Movement
of Contaminants to Human Targets
oo
00
Groundwaler Environment
Source: Whelan and Steelman, 1984.
-------
bioconcentration factor and/or the biomagnification of the substance
in food organisms. However, as Table B-l illustrates, there can be
a wide diversity of bioconcentration factors measured for a specific
substance reported in the literature. The reasons for such wide
diversity are discussed below.
C.I Ecological Factors
The ecological factors which affect the behavior of pollutants
through the food chain include diversity, competition, niche, uptake
modes, mobility, availability, and trophic level. It is the latter
which is most important, especially for those substances which
biomagnify in the food chain. If, at each increasing trophic level
from producer (plants) to consumer (grazer) to predator (carnivore),
the substance is concentrated at levels higher than in the food
source or the surrounding environment, and if man is at the top of
the food chain, then the threat to man is of concern.
Not all hazardous substances show consistent results for
measurements of bioconcentration and subsequent magnification.
Furthermore, some substances act differently in terrestrial
ecosystems than they do in aquatic environments. For example,
plutonium has a reported bioconcentration factor of over 1,000 in
the marine ecosystem, but it is virtually unavailable to biota in
terrestrial ecosystems (because it remains in the soil) (Corey and
Boni, 1977; Garten and Dahlman, 1978; Hanson, 1975; National
Research Council, 1975).
89
-------
Ecosystems can change the form of the hazardous substance from
that which was released into the environment to another form in
tissue. For example, antimony may be released into the water in its
elemental form or in any of a variety of compounds, but it is found
in fish as antimony trioxide. Another example is mercury, which
when converted to methylmercury in the environment, becomes more
toxic (see Appendix D). The organism itself may take compound X and
convert it to compound Y. For example, fish convert benzo(a)pyrene,
a pollutant reported at many NPL sites, into metabolites strongly
suspected of being carcinogenic and which have been found in edible
portions of fish (Harshbarger, 1983; Melius, 1981).
C.2 Species and Individual Variability
There are distinctive characteristics of species which
complicate the uptake process even further. Separate species have
different niches in the environment; they eat different foods and
live in widely varied habitats. Sole or flounder, for example, are
bottom feeders and are more likely to be exposed to toxic substances
found in sediments. Salmon, on the other hand, are anadromous
(spawn in rivers) and may pass through a contaminated bay only once
or twice in a lifetime. Menhaden are found in estuaries mainly in
the juvenile stage, and so would only be exposed to coastal
pollution as a juvenile, not as an adult. Various species are found
on different trophic levels, and some, mullet for example, can feed
at several trophic levels (Odum, 1970). A fish in a natural
90
-------
environment may alter its feeding habits, eating vegetation one
season, insects during another, and juvenile fish when available.
Within one species, there may also be wide individual
variations which influence the levels of substances found in
tissues. Physiological factors such as size, age, sex, condition,
and metabolism all affect the results reported in the literature.
Many hazardous substances concentrate in fatty tissue. Therefore, a
female or an animal in good condition may store these substances. A
starving individual, however, may be forced to use its fatty tissue
and release the stored substances. In the case of FCB
bioaccumulation in lake trout, older and larger individuals would be
expected to have the greatest body burden because they are the top
predator (Thomann and Connolly, 1984). Some individuals may
metabolize a chemical, change it to another form, or excrete it
rapidly (McKim et al., 1976).
Another factor which influences the results of biotic
measurements is the tissue analyzed. Some authors report results
from analyses of whole fish, some from fatty tissue, and some from
edible meat, while others report the results from liver tissue. One
study (Versar, 1985) reported that the worst-case scenario from a
risk analysis for the Commencement Bay (Washington) food chain was
for ingestion of fish liver. Although the population that eats fish
liver is believed to be small, no data were available for the amount
of liver consumed (Versar, 1985). Liver concentrations of hazardous
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substances are usually high, because that is where those substances
are metabolized. It is also easily measured. However, as mentioned
above, fish liver is rarely ingestedj therefore, the majority of
results reported in Appendix B are for concentrations found in
edible tissue or muscle.
C.3 Experimental Methods Used in Ecology
Bioconcentration factors reported in the literature also vary
widely based on whether the data were collected in the laboratory or
collected in the field. In the field, the uptake into the food web
is influenced by numerous natural phenonmena including water
quality, weather, trophic level, population competition, and
availability of other food. In a laboratory experiment, the
researcher is able to control many of the variables, including the
water quality (pH, temperature, salinity) as well as the number of
species present. He may, for example, set up a simple food chain
from water to Daphnia to bluegills.
One important environmental variable which affects results is
the duration of exposure. Die exposure can be controlled in the
laboratory where fish for instance, may be held in the experimental
tank for as little as a few hours or days. In the field the
duration is generally unknown, but similar fish could be exposed to
the pollutant over a lifetime. Duke (1982) cautions scientists
about the risks of extrapolating the results from laboratory
experiments to the environment. Fish, when removed from a
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contaminated environment, may be able to depurate or to purify their
tissues when placed in clean water. Ihis protective feature
complicates field data, where the fish may live only a portion of
their entire life cycle in a polluted environment.
Bourquin and Pritchard (1983) reported that certain organics
(e.g., phthalates) are known to degrade biologically in the
laboratory; however, in the field they tend to accumulate in the
environment, even within ecological niches which appear to be
optimal for degradation. In addition, the degradative processes in
estuarine and marine environments may differ significantly from
those in other aquatic environments.
Another variable, which makes comparisons of direct
measurements difficult, is that the sampling and analysis methods
used are quite often unique to a given study. One researcher might
collect samples from local fishermen, while another uses random
sampling from an entire bay. One researcher might collect samples
over a four-day period, while another might collect his samples over
a four-season period. One study may have results from 10 samples,
while another reports results from 100 samples. All of these
variations in sampling protocol may yield different results.
After sampling, additional variables are introduced by the
preparation of the sample and by the analytical method used, further
diversifying results. For some of the newer organic chemicals
introduced into the environment (xenobiotics), there may not be an
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analytical method of detection yet developed, there may be only one
technique known, or the analysis may be very expensive. Hie results
reported in the literature may, therefore, be very limited. On the
other hand, data on many pesticides and metals are relatively easy
to obtain, so there are a number of published studies available for
these substances. For example, there are dozens to hundreds of
published research reports on the bioaccumulation of DDT and
cadmium, but very few on dioxins (TCDD), and none were found on
asbestos. Substances, such as pesticides, which biologists have
known about for decades, have a large body of research data published
to support the conclusion that they do or do not biomagnify in the
food chain. For other hazardous substances, data are limited and
their biomagnification potentials are unknown at this time.
C.4 Metals in Sediments and Their Availability to Biota
Metals are persistent, remaining in the ecosystem for a very
long time. Scientific debates continue as to whether or not those
metals are available to biota for uptake into the food chain.
Sinderman et al. (1982) report that all three media (i.e., the
settled particles, interstitial waters, and water immediately
overlying the bottom) can provide major sources of metals to benthos
in their natural habitat. Metals which are dissolved or adsorbed to
particles are both potentially available, since filter feeders take
in both sediments and water while feeding. The result is that the
filter feeding organisms taken from contaminated zones have, on
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average, ten times more metals in them than those biota found in
other regions (Sinderman et al., 1982). The same result was
reported by Taymaz et al. (1984) whose data suggest that a
relationship exists between heavy metal content in shoreline
sediments and in benthic fish.
The heavy metals may be either in dissolved form or adsorbed to
particles. If dissolved, then the likely route of exposure in fish
is across gill membranes or by direct ingestion. When water is
ingested, both suspended solids and dissolved metals can be
ingested. When food is ingested, particularly for bottom feeding
fish, the adsorbed metals are ingested along with the benthos. Some
benthic organisms, for example filter feeders and detritis feeders,
ingest the sediments themselves. When an animal takes in the slurry
mixture at the interface between sediments and water, the slurry
contains the solid sediments, the organic detritis, as well as the
interstitial waters between the particles. Metals may be found in
all of those components, and since the sediment layer acts as a
"sink" for heavy metals, this sediment interface becomes an
important consideration (Taymaz et al., 1984).
The persistence of metals in sediments is related to the
presence of organic matter and clay fractions since these affect
sorption (Drifmeyer and Odum, 1975; Sinderman et al., 1982). This
would be particularly important in the food chain of detritis
feeders (e.g., shrimp and crabs). Even sandy sediments have a
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proportion of silt and clay and retain metals, though in lesser
amounts. Clay and silty bottom sediments may constitute a
considerable reservoir of heavy metals in the ecosystem. In their
food chain studies, Drifmeyer and Odum (1975) found there was a
decrease in metal content within sediments as the particle size
increased, which is consistent with the metals being adsorbed on the
surface of the particles.
As noted above and discussed further below, a number of
researchers have shown that adsorbed metals are available and are
incorporated into biotic tissue. This is particularly true for
bottom feeders, filter feeders, and immobile shellfish. However,
the U.S. Army Corps of Engineers studies on dredged material (Lee
and Jones, 1977; Montgomery and Palermo, 1983) report metals are not
released to water, but instead remain tightly bound to the sediments.
Hardy et al. (1981) reported sediments serve as a "sink" for
removal of dissolved cadmium from water. Bioaccumulation was
+2
related to the soluble Cd ion concentration. The interstitial
waters in sediments are high in dissolved organics which may complex
with the cadmium and thus reduce availability.
Drifmeyer and Odum (1975) studied dredged spoil pond ecosystems
and the uptake of lead, zinc and manganese via the detritis food
chain. Detritus was a significant factor in the bioaccumulation of
lead in that one-third to one-half of the lead was passed on via the
detritis food chain. Tne grass shrimp, for example, eat
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particulates with a high lead content, and thus, serve as a source
of heavy metals to the mummichog fish. Ihe mummichogs, in turn, had
high levels of lead in those areas where sediments were contaminated.
Guthrie and Davis (1979) reported results from water, sediments,
and benthos in the Gulf of Mexico, Texas. Data from the Gulf
revealed that concentrations of 9 out of the 10 heavy metals studied
were higher in the sediments than in the water column. Furthermore,
zinc was present in higher concentrations in bottom organisms (e.g.,
worms and oysters) than in sediments. Oysters had significantly
higher concentrations of zinc, copper, and arsenic than were present
in the water samples. Clams had significantly higher concentrations
of arsenic when compared to water samples taken from the same
locations. Crabs had high levels of both arsenic and copper, which
were believed to have come from both water and sediment sources.
O'Connor and Rachlin (1982) report different results for the
Atlantic coast. The sediment concentrations did not correlate with
metals measured in exposed organisms. However, these authors did
indicate the geographic trends in metals in benthos of the Atlantic
coast tend to correspond with the general trends for metals in those
coastal sediments. O'Connor and Rachlin (1982) further suggest that
increasing levels of metals in the environment would result in
increased levels of metals in organisms.
These illustrations serve to explain why such diverse results
are found in Appendix B, where the bioconcentration factors are
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listed for both organic and inorganic hazardous substances. While
not an invariant paramater for a given substance, the
bioconcentration factor is well documented by published research
papers, and it is an accepted indicator of which hazardous
substances are relatively more important in the food chain.
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APPENDIX D
CHEMICAL/PHYSICAL FACTORS WHICH AFFECT BIOACCUMULATION
As discussed in Appendix C, direct measurement of the
bioaccumulation of hazardous substances in food chain organisms is
complex, expensive and not always consistent. Furthermore, data are
not available for all substances. In Section D.I, physical/chemical
characteristics which may be used as indicators of the potential for
an organic substance to bioaccumulate in the food chain are
examined. Such characteristics could be used for assessing the
potential for a substance to enter the food chain in the absence of
the more direct measurements described in Section 2 of the main body
of this report. These alternative characteristics could also be
used to confirm direct measurement data in cases where there may be
some question concerning the validity of those measurements.
Bioaccumulation information for inorganic chemicals is often
reported in terms of a particular element present (usually a metal
cation such as mercury, copper, etc.) and not in terms of a
particular chemical species. Tne uptake and accumulation of the
metal elements usually depends on the particular chemical species
present (i.e., whether it is a hydrated or unhydrated ion, a
covalent compound such as methylmercury, or a soluble ionic compound
such as mercury chloride). However, as discussed in Section D.2,
once a substance is released into the environment, it is likely to
be physically and/or chemically and/or biologically altered by
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complex natural processes. Therefore, the particular chemical
species initially present at a site is not necessarily of importance
in an initial ranking of its potential to accumulate in the food
chain.
D.I Organic Chemicals
A literature review indicated that for organic chemicals, the
characteristics which may affect their potential to bioaccumulate
include biodegradability, volatility, solubility, soil adsorption
coefficient, and n-octanol-water partition coefficient. Bie first
two influence the persistence of the substance in the environment;
the more persistent substances have a greater opportunity to
bioaccumulate. These two characteristics are discussed in the
following two sections. Ihe latter three characteristics are
discussed in Section 2.
D.I.I Biodegradability
Biodegradability refers to the propensity of a chemical to be
degraded by microorganisms in the environment. It is an important
characteristic to be considered in evaluating the fate of a chemical
because if a chemical is biodegradable in a few hours or days, then
the likelihood of its causing harm to public health through the food
chain is reduced in comparison with chemicals that persist for
months or years (Wood, 1982). If a substance is not biodegraded,
then it is more likely to be persistent and more available for
biotic uptake.
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For a variety of reasons, however, biodegradation does not
appear to be useful as a measure of the accumulation of a substance
in food chain organisms. Laboratory biodegradation study results
may not reflect conditions in the field where oxygen, oxidation
reduction potential, temperature, pH, competing organisms, and
chemical concentrations all affect the biodegradation process
(Kobayashi and Rittmann, 1982). Residual concentrations of a
substance left after the bulk of the substance has been degraded
may still be subject to bioconcentration and could accumulate in the
food chain to harmful levels. Finally, no published correlations
relating bioaccumulation to biodegradability were identified.
D.I.2 Volatility
The volatility of a chemical substance is its propensity to
vaporize and enter the atmosphere and is recognized as another
indicator of the environmental fate of that substance (Mackay et al.,
1982; Gillett, 1983; Suffet, 1975; Filov et al., 1979). Volatility
could also be considered as a measure of persistence in the local
environment. Substances which volatilize and dissipate quickly are
not as readily available to biota for uptake.
The fundamental physical property of a chemical substance,
which is an indicator of its tendency to volatilize, is its vapor
pressure. However, vapor pressure data are not always readily
available for substances or for mixtures of substances. Further,
Mackay et al. (1982) report that many of the published vapor
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pressure data may be erroneous. The limited availability of
volatility or vapor pressure data and the lack of any reported
consistent relationship between volatility and accumulation in food
chain organisms limits the usefulness of volatility as a measure of
the potential of a substance to accumulate in the food chain.
Thus, volatility and biodegradation were judged not to be
useful as indicators of the bioaccumulation potential of hazardous
substances in food chain organisms. They are considered to be more
a measure of environmental persistence than of bioaccumulation, but
they could be useful for fine tuning the food chain hazard
classification of a chemical as suggested by Gillett (1983). For
example, if two or more substances have similar bioconcentration
factors, consideration of their environmental persistence based on
biodegradeability or volatility could be used to establish a hazard
ranking relative to each other. More persistent substances are
usually those which are more likely to be taken up into the food
chain. Thus, substances which volatilize or which degrade in the
environment are not as potentially available to man via the food
chain as are more persistent substances. Note however, that the
current HRS includes a persistence factor and the use of persistence
to rank substances in the proposed bioaccumulation ranking factor
might be undesirable if the current HRS persistence factor were to
remain unchanged.
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D.2 Inorganic Chemicals
Among the elements identified at NPL sites are: aluminum,
antimony, arsenic, boron, cadmium, chromium, cobalt, copper, iron,
lead, manganese, mercury, molybdenum, nickel, plutonium, radium,
selenium, silver, strontium, tin, titanium, uranium, vanadium, and
zinc. The uptake and accumulation in the food chain of such
elements may depend on the particular chemical species present,
i.e., whether it is the pure element or a compound such as
methyMercury, mercury chloride, copper sulphate, etc. However,
once a substance is placed in a waste site or subsequently released
into the environment, it is likely to be physically and/or
chemically and/or biologically altered by natural processes into
other forms that may be more or less available for uptake and
accumulation in the food chain. Thus, even if a substance is
initially present in a waste site in a form that is not readily
biologically available, there is potential for it to be transformed
to one that is readily available, and vice versa.
For example, natural waters are complex systems containing a
variety of organic and inorganic matter from the natural decay and
degradation of plants and animals, from soil erosion, and from human
activities. The chemical and physical interaction of released
chemical elements and their compounds with the organic and inorganic
matter affects the behavior of the substances and their impact on
the environment. Humic and fulvic acids, which constitute the major
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portion of natural organic matter present in natural waters, reduce
the biological availability of many metals by forming complexes with
them (Neubecker and Allen, 1983; Morel et al., 1974).
Oily organic pollutants can concentrate at the surface of a
body of water. The elements iron, lead, copper, and nickel have
been found to concentrate in this organic surface layer (Andelman,
1973). Once concentrated, the metal and organic pollutants can
enter the food chain when surface feeders ingest the hazardous
substances along with food.
Natural waters also contain a number of inorganic compounds
which can react with another inorganic substance that is released
into the water. Chlorides, sulfates, fluorides, sulfides,
phosphates, carbonates, and bicarbonates are among the more common
species of inorganic compounds found in natural waters. A wide
variety of soluble and insoluble compounds of released metals, for
instance, can be formed depending on the inorganic compounds present
in the water and on such factors as the acidity or alkalinity of the
water and the oxidation-reduction environment. In a reducing
environment, for example, toxic hexavalent chromium is reduced to
less harmful trivalent chromium. In an acidic environment with
sulfide present, mercuric ions combine with sulfides to form
insoluble mercuric sulfide. If the water is alkaline, soluble
mercury-sulfide complexes can form (Leckie and James, 1974).
Insoluble substances tend generally to be less biologically
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available than soluble substances. However, If they remain
suspended in the water (e.g., in a fast moving stream) they can be
ingested by fish or adsorbed onto the fish gills. In quiescient
water, insoluble substances will settle in the sediment, where they
may be taken up by benthic organisms and bottom feeding fish.
Physical adsorption of metal ions on suspended clay particles
is another important process that can affect the bioavailability of
the metal as discussed in Appendix C. Metal uptake varies with clay
characteristics, the concentration and chemical form of the metal
species, and the solution pH (Beveridge and Pickering, 1983).
Microorganisms present in the environment can also alter the
form and availability of inorganic chemicals introduced into the
environment. Bacteria are involved in the immobilization of iron by
transforming the ferrous form of iron to the ferric form which
precipitates as ferric hydroxide (Berthelin and Dommergues, 1976).
Microbial immobilization of aluminum and silicon also occurs
(Berthelin and Dommergues, 1976). Bacteria are also implicated in
the conversion of mercury to methylmercury. Microorganisms are also
present in the environment that can convert methylmercury into
inorganic mercury forms (Cooley and McCarty, 1977).
The physical form in which a potentially hazardous substance is
released to the environment is another factor that must be
considered in evaluating its fate and effect in that environment.
Its initial form may inhibit or delay the conversion process;
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conversely, it may enhance the likelihood of transport and rapid
conversion in the environment. For example, solid massive forms of
metals such as iron, aluminum, copper, or zinc will slowly corrode
from exposure to air and other chemicals in the natural environment.
Leachate containing the metals can subsequently enter the soil and
nearby waterways. Aqueous solutions of inorganic chemicals, if
accidentally released, can move relatively quickly into the soil
and/or nearby waterway. Once in the environment, the fate of the
substance will be affected by the local environmental conditions as
well as its initial chemical form.
In natural waters, the behavior and bioavailability of released
inorganic compounds will be controlled by the chemical, physical,
and biological characteristics of the particular body of water.
These characteristics can be different for the same body of water at
different times of the year. They will be different for lakes,
estuaries, and oceans. They will also differ among aquatic
ecosystems and soil types in different geographical locations
(Andelman, 1973; Troup and Bricker, 1975).
D.3 Summary
Characteristics of a chemical substance which might be used in
the absence of bioconcentratlon factor data to assess the potential
of the substance to accumulate in the food chain were examined.
Characteristics examined included biodegradability, volatility,
n-octanol-water coefficient, solubility, and soil adsorption
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coefficient. For organic substances, the logarithm of the n-octanol-
water partition coefficient (log Pow) provides a satisfactory
alternative measurement, because a significant correlation has been
found between it and the bioconcentration factor. Ihe correlation
shows that, in general, as the bioconcentration factor Increases the
log Pow increases.
Biodegradation and volatility are measures of the persistence
of a substance in the environment and may affect the potential of a
substance to bioaccumulate, in that more persistent chemicals have a
greater opportunity to bioaccumulate. However, the persistence of a
substance in the environment is already considered in the HRS, and
therefore, use of these characteristics in a bioaccumulation ranking
factor was considered redundant.
As discussed in Section 2, the properties of water solubility
and soil adsorption partition coefficients have been shown to have
significant correlations with the bioconcentration factor,
comparable to those of the log Pow.
Bioaccumulation information for inorganic compounds is usually
reported in terms of a particular element present, usually the metal
cation (e.g., mercury, copper, etc.), and not in terms of the
particular chemical species. Uptake and accumulation of the metals
generally depends on the particular species present, i.e., whether
the pure element or a particular compound of the element is
present. However, once a substance is released into the environment
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it is likely to be physically, chemically, or biologically altered
by natural processes into other forms. Even if a substance is
initially present at a wastes site in a biologically unavailable
form, it is possible for it to be transformed to another form which
is biologically available. For this reason, it is considered
unnecessary to consider the specific chemical species present at
a hazardous wastes site in an initial ranking of the potential for
an inorganic substance to accumulate in the food chain. Biis
methodology is for use as a screening tool when very little data is
available at a site.
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APPENDIX E
GLOSSARY OF TERMS USED
accumulation
anadromous
- Uptake of a substance from the environment
- Fish which spawn in rivers, but live in the
ocean
annual production - Amount or rate of energy storage by plants over
a year
availability
BCF
benthic
benthos
bioaccumulation
- Being present in a physical or chemical form
that allows the substance to be taken up by
biota
- Bioconcentration factor
- Bottom dwelling
- Bottom dwelling organisms
- Uptake of a substance from the environment
(including the food chain) via a biological
process to be incorporated into tissue at
concentrations higher than those found in the
surrounding environment
bioconcentration - The process by which substances present in
solution enter aquatic organisms directly
through the gills or epithelial tissue. This
process causes an increase in concentration of
the substance in biota above that in its
ambient environment (e.g., fish have higher
concentrations than water; worms have higher
concentrations than soil)
bioconcentration
factor (BCF)
biodegradability
biodegradation
Ratio of the concentration of a substance
biota to its concentration in the ambient
environment
in
Extent to which a substance can be decomposed
by organisms
Decomposition of an organic substance by living
organisms
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biological
half-life
biomagnification
body burden
chemical index
competition
complexation
consumers
default value
diversity
food web
log Pow
magnification
methylated metal
mobility
- Time it takes an animal (organism) to degrade,
metabolize, convert or excrete 50% of a
substance
- Process whereby the tissue concentration of a
bioaccumulated substance increases at each step
in the food chain, as the substance moves
through two or more trophic levels
- The total amount of pollutant measured in all
tissues
- Score developed by NOAA which compares the
toxicity of a compound to its persistence
- The struggle by individuals or populations for
resources in short supply
- Loosely defined as the reaction of two soluble
species to form a third. In this report, it
refers to the combination of metal cations with
molecules or anions containing free pairs of
electrons
- Grazers or herbivores in the food chain
- A value to assign when no other data are
available
- Number of species (richness) compared to the
numbers of individuals of each species present
(evenness) in a community
- Interconnected food chains in a complex
ecosystem
- Logarithm of the n-octanol-water partition
coefficient
- Increase in concentration of a substance in
higher tropic levels of the food chain, also
known as biomagnification
- An organometallic compound in which the organic
molecular group is a methyl radical, i.e., CH3
- Ability of a substance to be transported through
the environment
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net production
niche
n-octanol-water
partition
coefficient
nonessential
element
Automated EPA
NPL technical
data base
organometallic
Total biomass produced and stored by all plants
and animals over an area during a specified
time, usually over a year
The role of a species role in the environment,
i.e., where it lives and what it eats
This partitition is a measure of the
preferential partitioning of a substance
between n-octanol and water (measured in a
laboratory test or calculated by one of
several methods)
An element that is not necessary for nutrition
in biota
The automated EPA National Priorities List
technical data base contains information on
information on sites evaluated with the HRS
Referring to a class of compounds made up of
the combination of an organic molecular group
and a metallic atom where the metallic atom is
bonded to the carbon atom of the organic
molecular group, e.g., tetraethyl lead
In the HRS, a possible migration route (i.e.,
ground water, surface water, or air)
Long lasting in the environment, not easily
degraded
An oxidation reaction activated by light energy
n-octanol-water partition coefficient
Organisms (e.g., plants) which can synthesize
organic compounds from inorganic compounds.
The source of energy is either light or
chemical energy derived from the oxidation of
inorganic compounds
primary production - The organic material produced by the primary
producers
pathway
persistent
photoxidation
Pow
primary producers
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protocol
secondary
production
soil adsorption
coefficient
solubility
species
standing crop
steric effects
targets
translocation
trophic level
uptake
unavailable
waste
charact eri s t i cs
score
- Series of specified steps followed in scientific
procedure from the gathering of samples to
analytical technique used and recording of
results
- The production of herbivorous animals
A ratio of the concentration of a substance on
the soil adsorbent to the concentration of the
substance in the environment surrounding the
soil
A property of a substance by virtue of which it
forms mixtures with other substances which are
chemically and physically homogeneous throughout
May refer to a subcategory of classification of
organisms, lower than genus; or may refer to a
specific chemical compound
Total amount of biomass of all aquatic species
within an area
The size and the shape of a molecule, affecting
its uptake, e.g., ability to pass through a
membrane
Population potentially affected by releases of
hazardous substances
Movement of materials in solution in plants
from one location to another
Position in the food chain; e.g., plants
(producers), herbivores (consumers), predators
(carnivores)
Absorbing or incorporating substance into
living tissue
Being present in a physical or chemical form
that does not allow the substance to be taken
up by biota
In the HRS, a point score currently based upon
rating factors such as waste quantity, toxicity
and persistence
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xenobiotics - Exogenously derived substances with no
nutritive value to organisms, especially the
synthetic organic chemicals (Walton and
Edwards, 1986)
yield - Part of the biotic production which is removed
or expected to be harvested by man
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APPENDIX F
OTHER METHODS REVIEWED
In examining the human food chain issue, a review of the technical
literature was made to determine if there was an existing method of
assessing the potential threat to humans due to exposure to hazardous
substances in the human food chain. This appendix discusses why and
how some existing methodologies incorporate bioaccumulation as a factor
in assessing or ranking hazardous substances. It also presents a
brief discussion of sources which discuss the use of bioaccumulation
in hazard ranking systems.
F.I Hazard Assessment Rating Methodology II (HARM II)
HARM II (Barnthouse et al., 1986) is an extension of the HARM
system, intended to "permit the use of site-specific monitoring data
to refine priorities for further study" at U.S. Air Force waste
sites. Bioaccumulation is considered in order to estimate the total
human intake of hazardous substances from the site per day.
Bioaccumulation is defined using three terms:
Concentration Factor - The ratio of concentration of a substance in
fish to the concentration in water.
Bioaccumulation Factor - The multiplier when an organism's pathways
of exposure include both direct uptake from
water and uptake from contaminated food.
Bioconcentration Factor - The multiplier used when the organism's
pathway of exposure is only direct uptake
from water.
All three are based on the concentration in fish at steady state. A
hierarchy is established in HARM II for determining the
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bioaccumulation factor to be used in scoring a site. Concentration
factors based on field data are most preferable. Bioaccumulation
factor or bioconcentration factor data measured in the laboratory may
be used if concentration factors based on field data are not available.
Finally, if no other data are available, a regression equation and log
n-octanol-water partition coefficient data are used.
The bioaccumulation factor (BAF) determined above is then used to
determine a final contaminant hazard score. This is done differently
depending on whether or not an observed release of contaminants has
occurred at the site. If an observed release has occurred, the BAF is
used to calculate the estimated human daily food intake of contaminated
food each hazardous substance presents at the site. Ffciman health
hazard quotients for all substances are eventually summed, indexed,
normalized, and multiplied by a waste quantity factor to determine a
final contaminant hazard score.
If an observed release has not occurred, each substance at the
site is evaluated independently. The logarithm of the health effects
benchmark and the BAF are indexed. The index values are then summed
for each substance. Tne sum for the highest scoring substance is
normalized and multiplied by a persistence factor value and a waste
quantity multiplier to determine the final contaminant hazard score.
Principal differences between the HARM II and proposed scheme
presented in Figure 4 are:
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• HARM II does not use biomagnification data.
• HARM II states that a log Bow of less than 5 will not be
important in food chains. Figure 4 assumes a log Pow of 3.2
or greater is important.
• HARM II uses log Pow to calculate the log BCF using a
regression equation. Figure 4 uses the log Pow directly.
F.2 Remedial Action Priority System (RAPS)
RAPS is a computer-based methodology to integrate and analyze
complex processes on DOE mixed waste sites. It was developed
by Whelan et al. (1986) at Pacific Northwest Laboratory to prioritize
hazardous and radioactive waste sites for further site investigation.
When first presented in 1984, the RAPS did not consider the food
chain. However, in the 1986 version of the model, RAPS uses the
concentration of the contaminant in the media of exposure, daily
ingestion rates, and a conversion factor to determine an individual
risk factor. Instead of a bioconcentration factor, RAPS uses a
"transfer factor" (e.g., water-feed-meat) for agricultural systems,
as presented in U. S. Nuclear Regulatory Commission (1977).
The data used as a basis for the transfer factor data are from
1968 and 1972 reports on radioactive material transfer, and only
data on elements are included. NRG (1977) reports bioaccumulation
factors (using 1972 data) as calculated from concentrations of
elements in fish compared to concentrations of those elements in
invertebrates. These factors are to be used in absence of
site-specific data. This is not the way BCFs are generally
calculated today, as it disregards direct uptake from water.
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The NRC does use a more standard bioaccumulation factor to
calculate doses to man via liquid effluent pathways. This
bioaccumulation factor is for nuclides in the pathway, expressed
"as the ratio of the concentration in biota (in pGL/kg) to the
radionuclide concentration in water (in pCi/liter) in liters/kg"
(NRC, 1977).
RAPS calculates the dose to man from aquatic food chains also.
Average daily intake is estimated using bioconcentration factors and
the average daily ingestion rates for aquatic foods. One must have
the concentration data for the contaminant in the food (e.g., mg/kg
or pCi/kg) to complete the calculated risk factor.
In Whelan et al. (1986), it appears the NRC transfer factors are
used to determine concentrations in meat and milk. Bioconcentration
factors are used to the aquatic food chain to estimate average daily
intake of substances. Whelan et al. (1986) does not state how to
determine bioaccumulation, but other "standard parameter values" in
RAPS are from NRC (1977), so we assume RAPS uses the NRC data on
bioaccumulation factors. RAPS does not address the individual
differences between substances which have biomagnification potential
or those with high bioconcentration factors.
F.3 Food Chain Model
Dixon and Holton (1984) of Oak Ridge National Laboratory
developed a model called FOODCHAIN, a Monte Carlo computer model for
estimating exposure to airborne pollutants via the food chain
pathway. The model calculates regression equations for ingestion of
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milk and beef based on the log Pow correlations with bioconcentration
factors from Kenaga and Goring (1980).
Garten and Trabalka (1983) and Trabalka and Garten (1982) raised
several questions concerning practices used by Kenaga (1980) in his
correlations for 'substances in terrestrial systems. In fact, such
dependence upon the Pow for a terrestrial methodology may be the
weakest point in the FOODCHAIN model. Furthermore, this model
requires data on the concentration of substances in foods. The
Monte Carlo modeling then generates 1,000 exposure results through
random sampling of food concentrations. The results are applicable
to individual exposures to pollutants. This model requires site
specific data in order to be able to rank sites. It does not address
the real differences between substances known to biomagnify and does
not use bioconcentration factors as measured in the field or
laboratory.
F.4 Action Alert System
The "Action Alert System" (Fiksel and Segal, 1982) was
developed to rank environmental pollutants for further study. For
the calculation of ingestion risk, Fiksel and Segal depend upon the
correlation of solubility with bioconcentration factors based on
Kenaga (1980). While food chain effects is beyond the scope of this
system, the authors state it is possible to derive a bioconcentration
factor for fish. They multiply the BCF with ambient water
concentrations to estimate the contamination levels in fish. This
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is then used with average consumption rates to estimate the amount of
the pollutant ingested in food. There are two weaknesses in this
approach: (1) one needs to know ambient water concentrations, and (2)
dependence solely upon water solubility to estimate the BCF. No use
is made of log Pow data, measured BCF data, or the biomagnification
process.
An optional module to the Action Alert System was designed to
assess food residues based upon the bioaccumulation factor and ambient
water concentration for estimating fish contamination. These data
then were used to predict amount of residue ingested in food to
estimate per capita risk. Action Alert uses an experimentally-derived
bioaccumulation factor (BF), which measures the ratio in equilibrium
of pollutant concentration in fish to the concentration in the
surrounding aquatic medium. The BF multiplied by the water
concentration would give the fish contamination (ppm). This is then
multiplied by per capita fish consumption to give the amount of
contaminant ingested in fish per day. This is added to terrestrial
residues consumed in foods to estimate risk.
To estimate risk from pesticides on food, Action Alert depends
upon water solubility, based upon Kenega (1980) who correlated
solubility with bioconcentration. To calculate water solubility the
model estimates from regression equations using n-octanol-water
partition coefficients following correlations by Chiou et al. (1977).
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F.5 Predictive Model for Xenobiotic Bioaccumulation in Terrestrial
Ecosys terns
Trabalka and Garten (1982) provided a critical review of
.predictive models for xenobiotic bioaccumulation in terrestrial
ecosystems. This analysis of models for estimating the fate of
xenobiotics was directed toward those substances transferred from
environmental sources to terrestrial vetebrates (i.e., birds and
mammals) via the ingestion pathway (e.g., soil to plant to
consumers). This was done to determine the "necessary and sufficient
information required to predict, within reasonable limits, toxicity
and environmental fate prior to manufacture" of such toxic materials.
The review included a number of models which correlate the
bioconcentration factor to the log n-octanol-water coefficient and
water solubility.
Trabalka and Garten (1982) developed a Terrestrial Hybrid
Model, based on NRC regulatory models, which used several types of
bioconcentration factors (BF). These BFs were used to predict the
concentration of xenobiotics in carnivorous animals. Examples of
how bioconcentration factors (BF) are defined in the terrestrial
model are listed below:
Simple Compartment Model
BFpA = BF of xenobiotic in product (tissue, organ, secretion)
of herbivorous animal from consumption of plant product
BFWp = BF of xenobiotic in product of plant from uptake from
soil interstitial water
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Complex Compartment Model
BF^ = BF of xenobiotic in carnivorous animal product from
consumption of herbivorous animal product
BFpp = BF of xenobiotic in product of plant from exposure to
foliar contamination
BFg^ = BF of xenobiotic in herbivorous animal product from
incidental ingestion of soil
These are applied in estimating uptake through the terrestrial food
chain (e.g., pasture to cattle).
Trabalka and Garten (1982) then go on to analyze aquatic food
chain models and correlations. In their data base, used to
determine bioaccumulation in fish, the following BFs are defined,
with methods given for BF determination in the laboratory:
1. BF(FW) = BF in freshwater fish from water-only exposure
systems, typically flowing-water systems which maintain a
reasonably constant exposure regime and continually removing
metabolities; ratio of concentration of parent xenobiotic in
fish (wet weight)/concentration of parent xenobiotic in water.
2. BF(ME) = BF in fish from static-model ecosystems which
receive a single dose of the contaminant, which were
maintained for approximately 30 days following initial
exposure, and which contained a sand or soil substrate; ratio
of concentration of parent xenobiotic in fish (wet
weight)/concentration of parent xenobiotic in water.
3. BF(MA.) = BF in fish from static-model aquatic systems which
differed from the above in not having a sand or soil substrate
and which were exposed for a total time of three days or less;
BF calculated in the same manner.
4. BF(MP) = BF in fish from static-model ecosystems; ratio of
concentration of total xenobiotic (including metabolites) in
fish (wet weight)/concentration of parent xenobiotic in water.
5. BF(MM) = BF in fish from static-model ecosystems; ratio of
concentration of total xenobiotic (including metabolites) in
fish (wet weight)/concentration of total xenobiotic in water.
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These bioconcentration factors are then included in a number of
correlations with water solubility and log n-octanol-water coefficients.
These authors concluded the n-octanol-water partition coefficient is a
"highly satisfactory index of bioaccumulation potential in fish and
terrestrial vertebrates (uptake from diet) for xenobiotics which do not
accumulate by covalent reaction."
F.6 Michigan Site Assessment Systems (SAS)
The State of Michigan uses the SAS to rank the relative hazard
posed by release sites for purposes of assigning priority for site
evaluation and response actions (Michigan, 1983).
In scoring environmental fate, the SAS uses the following for
bioaccumulation evaluation:
Score Criteria
10 Fish Bioconcentration Factor* greater than 4,000
or
Log Pow greater than 6
5 Fish Biocentration Factor = 700-4,000
or
Log Pow = 4.5-6
0 Fish Bioconcentration Factor less than 700
or
Log Pow 4.5
This score is then used with other factors to calculate a potential
toxicity score for each chemical. This factor is then normalized and
added to other site assessment screening factors for a total score.
*No guidance is given concerning derivation of the fish
bioconcentration factor.
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F.7 U.S. Department of Agriculture (USDA)
The U.S. Department of Agriculture (USDA, 1985) uses a compound
evaluation ranking method for pesticides, animal drugs, and
"unavoidable environmental contaminants." This method applies an
A-B-C-D score to substances found in meat and poultry. Tne main
reasons for a high score of A are high toxicity and carcinogenicity.
Other criteria include: amount of actual or probable use; conditions
of use related to residues at slaughter; potential for misuse to
result in harmful residues; and the toxicity of the residue. While
bioaccumulation is not mentioned in those criteria, background
information on these substances ranked may includes statements, such
as for FCBs, concerning bioaccumulation in the food chain. A proposed
new ranking scheme, the Prototype Compound Evaluation System (CES)
will subdivide these A-B-C-D categories further based upon hazard and
exposure data. No mention is made of bioaccumulation potential.
F.8 National Academy of Sciences (NAS)
In 1975, a comprehensive review "Assessing Potential Ocean
Pollutants," was published by the NAS (National Research Council,
1975). This report summarizes a number of earlier studies and reports
biological concentration factors for many chemicals and radioisotopes.
This report also summarizes fate and effect data for many compounds in
soils and air, biodegradation processes, toxicity, and effects of the
chemicals in the marine environment.
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F.9 U.S. Food and Drug Administration (USFDA)
While the USDA monitors meat and poultry, the U.S. Food and Drug
Administration Surveillance Index (USFDA, 1983) was developed for
monitoring substances in other foods. About 160 pesticides have been
classified in categories I to V, mainly on the basis of potential
health risk and potential for occurrence as residues in foods. Use of
the FDA method would be applicable to only a portion of the food chain
problem. The FDA monitors foods other than meat and poultry, and their
ranking index only applies to pesticides in foods.
The criteria used by FDA to score pesticides include:
• Annual production
• Use on corps
• Potential for environmental contamination and entering food
chains
• Types of food containing residue
• Relative toxicity
• Chemistry, including metabolites
• Propensity to biomagnify in edible tissue
• Biological half-life
• Persistence
• Non-dietary exposure
• Per capita consumption
• Knowledge of existing incidence
• Special regulatory interests
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Using these criteria, USFDA assigns a pesticide to one of five
classes to determine priority for monitoring in human foods. Class I
represents a high health hazard based on toxicological data or
bioaccumulation potential.
Lombardo (1979) described another FDA program, the Chemical
Contaminants Program, which selects industrial chemical types for
study. Criteria used in this program included in bioaccumulation
potential and oil-water partition coefficients. FDA's "primary
indicator organism is freshwater fish, as most problem contaminants
evenually find their way into this segment of the human food chain"
(Lombardo, 1979). Using these data, FDA then establishes Action
Levels or Tolerances for deleterious substances in seafood.
F.10 Inter-Governmental Maritime Consultative Organization (IMCO)
In 1973 IMCO published Annex II of the International Conference
on Maritime Pollution. Appendix I of the Annex provides guidelines
for the categorization of noxious liquid substances into four groups
(A, B, C, and D) based upon probable tainting of seafoods and
reduction of amenities. Cateogry A substances are bioaccumulative
and are liable to produce a major health hazard.
These categories of noxious liquid substances are then used to
regulate discharges from ships to the sea. No definitions of
bioaccumulation are given, nor are methods given for determining
this factor. Substances which bioaccumulate are either categorized
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as A or B. This list of substances was used initially to indicate
which ones may be found in fish.
F.ll Other Ranking Methods
Almost 60 methods for ranking the degree of hazard of substances
are reviewed in Review and Analysis of Hazard Ranking Systems (U.S.
Environmental Protection Agency, 1984c). In those methods which
used bioaccumulation as one of the criteria for ranking hazardous
substances, it was most often used as a modifier to address the
fate of a substance in the environment. The more complex and
sophisticated models require concentration data in water, and this
is multiplied by the bioconcentration factor of the substance. Where
the bioconcentration factor is unknown, the n-octanol-water partition
coefficient is often used as a backup. One method recommended a
laboratory test to determine the potential accumulation of
substances into biota.
Hushon et al. (1983) and Hushon and Kornreich (1984) provide
an extensive list of internationally published methodologies for
ranking substances based upon the degree of hazard. A majority of
these methods rely heavily on the n-octanol-water partition
coefficient (Bro-Rasmussen and Christiansen, 1984; Mackay, 1981 and
1982; Wood, 1981; Hushon et al., 1983; Lu and Metcalf, 1975; Veith
et al., 1979). The following methods use bioaccumulation as a
scoring factor in ranking the degree of hazard of substances:
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1. Ranking for European Economic Community Water Pollutants:
to select chemicals for further study.
2. American Society for Testing and Materials Committee (d-19):
to determine impacts of chemicals an aquatic life.
3. EPA Action Alert: previously discussed.
4. MITRE (for Federal Republic of Germany): to select
chemicals for environmental trends monitoring.
5. Office of Technology Assessment: to identify possible food
contaminants.
6. Michigan Critical Materials Register: to score chemicals
of concern.
7. EPA Office of Toxic Substances: to select chemicals
presenting environmental risk under TSCA.
Many other methods, however, were developed to rank substances based
upon toxicity criteria but not bioaccumulation.
The reportable quantities support documents (EPA, 1985; ICF,
1985) provide substantial data on the CERCLA hazardous substances,
including their toxicity and other factors which influence the fate
of the substances in the environment. The documents consider the
bioaccumulation potential, but do not quantify or apply the data.
The tables only list substances which bioaccumulate in biota.
Some ranking methods, such as the "reportable quantities"
documents (ICF, 1985) or those reviewed by Hushon and Kornreich
(1984), use bioaccumulation as a "yes it does" or "no it does not"
factor, and instead place more emphasis on toxicity to distinguish
among substances.
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In 1975, a comprehensive review "Assessing Potential Ocean
Pollutants," was published by the MAS (National Research Council,
1975). This report summarizes a number of earlier studies and reports
biological concentration factors as reported in the literature for
many chemicals and radiolsotopes. The report also summarizes fate
and effect data for many compounds in soils and air biodegradation
processes, toxicity, and effects of the chemicals in the marine
environment.
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