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United SKIM Offto* of Wrt*r
Environmental Protection Agency Washington, D.C. 20460
EPA 503/3-90-001
S*pt*mb*H965
BIOACCUMULATION
MONITORING
1. ESTIMATING THE POTENTIAL U=OR
BIOACCUMULATION OF PRIORITY
POLLUTANTS AND 301 (h) PESTICIDES
DISCHARGED INTO MARINE AND
ESTUARINE WATERS
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EPA503/3-90-001
September 1S85
BIOACCUMULATION
MONITORING GUIDANCE:
ESTIMATING THE POTENTIAL FOR
BIOACCUMULATION OF PRIORITY
POLLUTANTS AND 301 (h) PESTICIDES
DISCHARGED INTO MARINE AND
ESTUARINE WATERS
Prepared by:
Tetra Tech, Inc.
11820 Northup Way, Suite 100
Bellevue, Washington 98005
'-JFAHOUARTERS LIBRARY
o'V'WKMFiiWL PROTECTION AGENCY
Prepared for:
Marine Operations Division: 301 (h) Program
Office of Marine and Estuarine Protection
U.S. Environmental Protection Agency
401 M Street SW
Washington, D.C. 20460
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PREFACE
This report is one element of the Bioaccumulation Monitoring Guidance
Series. The purpose of this series is to provide guidance for monitoring
of priority pollutant residues in tissues of resident marine organisms.
These guidance documents were prepared for the 301(h) sewage discharge
permit program under the U.S. EPA Office of Marine and Estuarine Protection,
Marine Operations Division. Two kinds of monitoring guidance are provided
in this series: recommendations for sampling and analysis designs, and
aids for interpretation of monitoring data.
Some basic assumptions were made in developing the guidance presented
in these documents: 1) each bioaccumulation monitoring program will be
designed to meet the requirements of the 301(h) regulations, 2) t-issue
samples will be collected from appropriate locations near the sewage discharge
anc from an unpolluted reference site, 3) the initial chemicals of concern
are the U.S. EPA priority pollutants and 301(h) pesticides, and 4) the
monitoring data should be suitable for a meaningful evaluation of the potential
hazards to living marine resources as well as human health. It should
be recognized that the design of a monitoring program reflects the site-
specific characteristics of the pollutant discharge and the receiving environ-
ment. Thus, site-specific considerations may lead to a modification of
the generic recommendations herein. Finally, although these guidance documents
were prepared specifically for monitoring of sewage discharges under the
301(h) program, their potential use extends to assessment and monitoring
of bioaccumul ation resulting from other kinds of pollutant discharges into
marine and estuarine environments.
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CONTENTS
LIST OF FIGURES
LIST OF TABLES
ACKNOWLEDGEMENTS '
INTRODUCTION
SIOACCUML'LATION OVERVIEW
REVIEW OF PAST APPROACHES
Empirical Measures of Bioaccumulation and Bioconcentration
Structure-Activity Relationships
Equilibrium Partitioning Models
Metabolism and Detoxification Models
EMPIRICAL BCF VS. Kow MODEL FOR MARINE ORGANISMS
ESTIMATION OF BIOACCUMULATION POTENTIAL FROM FIELD STUDIES
PROPOSED RANKING OF BIOACCUMULATION POTENTIAL
RECOMMENDATIONS FOR SITE-SPECIFIC MONITORING
SUMMARY
REFERENCES
APPENDIX A. BIOCONCENTRATION FACTORS FOR PRIORITY POLLUTANTS
AND 301{h) PESTICIDES IN MARINE AND ESTUARINE
ORGANISMS AS MODIFIED FROM U.S. EPA (1980) WATER
QUALITY CRITERIA DOCUMENTS AND ADDITIONAL INFORMATION
PUBLISHED FROM JANUARY, 1980, TO AUGUST, 1984
APPENDIX B. PRIORITY POLLUTANTS AND 301(h) PESTICIDES SORTED
BY STRUCTURAL COMPOUND CLASS (TABLE B-l), PRIORITY
POLLUTANT NUMBER (TABLE B-2) , AND BY ALPHANUMERIC
ORDER (TABLE B-3)
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FIGURES
Environmental partitioning of chemical contaminants, and
pathways of exposure for pelagic and benthic organisms
Bioconcentration factors for priority pollutants in marine and
estuarine organisms vs. octanol-water partition coefficients
Mean contaminant concentration in fish-liver lipids vs.
concentration in sediments for five species of fish
Liver-effluent bioconcentration factors normalized to lipid
fraction vs. octanol-water partition coefficients for five
species of fishes from the PaTos Verdes shelf
Ratio of contaminant concentration in Dover sole liver to
effluent concentration as a function of the octanol-water
partition coefficient
Pac
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Number*
TABLES
Summary of information on bioconcentration factors for priority
pollutants in marine and estuarine organisms as modified from
U.S. EPA (1980) water quality criteria documents and additional
information published from January, 1980, to August, 1984 21
Octanol-water partition coefficients (
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ACKNOWLEDGEMENTS
This document has been reviewed by the 301(h) Task Force of the Environ-
mental Protection Agency, which includes representatives from the Water
Management Divisions of U.S. EPA Regions I, II, III, IV, IX, and X; the
Office of Research and Development - Environmental Research Laboratory -
Narragansett (located in Narragansett, RI and Newport, OR), and the Marine
Operations Division in the Office of Marine and Estuarine Protection, Office
of Water.
This technical guidance document was produced for the U.S. Environ-
mental Protection Agency under the 301(h) post-decision technical support
contract No. 68-01-6938, Allison J. Duryee, Project Officer. This report
was prepared by Tetra Tech, Inc., under the direction of Dr. Thomas C. Ginn.
The primary authors were Dr. Leslie G. Williams and Dr. Robert A. Pastorok.
Ms. Marcy B. Brooks-McAuliffe performed technical editing and supervised
report production.
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INTRODUCTION'
The accumulat ion of toxic substances in marine organises that may
lead to adverse biological effects or affect commercial or recreational
fishes is one of the major concerns in evaluating the effects of sewage
discharges into marine and estuarine waters (Tetra Tech 1982). Accumulation
of chemical contaminants in marine and estuarine organisms may: 1) cause
significant mortality in susceptible organisms, 2) produce either a lethal
or chronic toxic response at later stages of the life cycle or under conditions
of stress, or 3) be tolerated but result in transfer of toxic pollutants
to higher trophic level organisms, including humans (Davies and Dobbs 1984).
The 301(h) regulations state that "biological monitoring shall include
to the extent practicable: periodic determinations of the accumulation
of toxic pollutants and pesticides..." [40 CFR 125.62(b)(1)(1i)]. Therefore,
characterization of toxic substances in tissues of marine organisms will
be an important feature in many 301(h) monitoring programs.
The objectives of this report are to:
• Provide an overview of important environmental, biological,
and chemical processes that affect bioaccumulation of chemical
contaminants in marine and estuarine animals
• Review predictive and empirical approaches used to determine
the bioaccumulation potential of toxic chemicals
• Develop a method for ranking U.S. EPA priority pollutants
and 301(h) pesticides in terms of bioaccumulation potential
• Provide guidance for selection of pollutants to analyze
in 301(h) monitoring programs.
Functional relationships between bioaccumulation and various environmental
and chemical variables are shown in the studies reviewed in this document.
The quantitative relationships between contaminant concentrations in various
exposure media and bioaccumulation in animal tissues are emphasized. Where
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appropriate, the limitations and uncertainties of the available data are
notec! and discussed with resdect to their irnpl ications for 301(h) monitoring
programs. A quantitative index is provided for ranking the bioaccumulation
potential of contaminants that may be present in sewage effluent.
The review and monitoring guidance recommendations are based on the
best available bioaccumulation data, regardless of target organ or tissue
type. The liver in fishes and the hepatopancreas in invertebrates are
fatty tissues in which most hydrophobic organic contaminants are concentrated,
stored, and transformed metabolically. Thus, liver concentrations of contami-
nants are highly relevant to determining bioaccumulation potential, and
many of the bioaccumulation studies reviewed in this report focus on liver
tissues. In general, lipid content of muscle tissue is less than that
of liver tissue. Therefore, as indicated in this report, the concentrations
of lipophilic organic contaminants in muscle tissue tend to be less than
those in liver tissue. Nevertheless, selection of muscle tissue (i.e.,
the edible portion of seafood) as a target tissue for monitoring programs
may be important for human exposure assessments and quantitative health
risk determinations. As indicated below, lipid normalization of tissue
contaminant concentrations eliminates much of the variation in bioaccumulation
data due to tissue type and should be incorporated in 301(h) monitoring.
The guidance provided for evaluation of bioaccumulation potential
of 301(h) priority pollutants and pesticides is expected to result in well-
designed monitoring studies that generate useful information needed to
safeguard enviornmental and public health. This monitoring program information
should also dispel many of the uncertainties and limitations noted above
and provide a quantitative basis for re-evaluation of generalizations and
guidelines provided herein.
BIOACCUMULATION OVERVIEW
Bioaccumulation is the overall process of biological uptake and retention
of chemical contaminants obtained from food, water, contact with sediments,
or any combination of exposure pathways. Factors important in determining
bioaccumulation potential of a substance are environmental influences on
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"its bioavai i abi 1 i ty , physiological mechanisms of uptake and elimination,
che^ica1 Drocei-*.ies of t^e substance.
p"ocesses of dispersion, sedimentation, phys icochemical transformation,
and biodegracation interact to render toxic substances more, or less, bio-
available than when first discharged. Toxic substances introduced into
the marine environment through sewage outfalls are partitioned among water,
suspended particulates , sediments, and biota. The effect of this partitioning
is to provide numerous routes of exposure to benthic and pelagic organisms
(Figure 1). Most organic and trace metal contaminants are associated primarily
with the particulate phase of sewage effluent and are rapidly incorporated
into sediments in the vicinity of the discharge (Morel and Schiff 1983).
Therefore, benthic organisms (e.g., benthic infauna and demersal fishes)
are most likely to accumulate contaminants because they are directly associated
with the sediments.
Biological processes affecting contaminant bioaccumulation are membrane
permeability and absorption, translocation from absorption sites to other
tissues and organs, enzymatic transformations to metabolic daughter compounds,
and excretion of either the untransformed contaminant or its metabolites.
Once the contaminant is absorbed, the degree of contaminant bioaccumulation
is largely determined by the efficiency of metabolic and excretory processes
which may vary considerably among species. Because some contaminants are
easily metabol i zed by certain species (e.g., PAH in fish), they do not
usually accumulate in tissues of those species. However, rapidly metabolized
substances may result in the bioactivation of highly toxic daughter compounds.
Consequently, low body burden of easily metabolized substances does not
necessarily indicate low potential hazard to either marine fauna or humans.
Some metabolized substances (e.g., trace metals) may be sequestered in
the organism because their solubility or ionization characteristics preclude
active excretion. Finally, contaminants that are not easily metabolized
(e.g., high molecular weight chlorinated compounds) tend to bioaccumlate
in most species. In summary, bioaccumulation is a consequence of the physio-
logical limitations inherent in an organism's ability to transform and
excrete invading chemical substances. These limitations are often a direct
reflection of the chemical properties of the accumulated substances.
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crenica1 properties that affect both bioavailability and
of contaminants are hydrophobicity (i.e., 1 ipophil icity) ,
che-nical structure (e.g., molecular size, steric configuration, degree
ana nature o* ciilorination) , and ionization state (i.e., pKa) at physiological
and environmental pH, In general, non-ionized, hydrophobic substances
a^e readily absorbed since they are relatively nonpolar and membrane-permeable.
Hydroohobicity is also a key factor in determining soil sorption and sediment
partitioning behavior of chemical contaminants. Thus, properties describing
hydrophobicity and ionization state of chemical contaminants have often
been used to develop predictive relationships between contaminant concentrations
in various environmental media and their bioaccumulation in exposed organisms.
REVIEW OF PAST APPROACHES
Several approaches that have been used to estimate the bioaccumulation
potential of toxic substances are evaluated in the sections below. Past
approaches can be categorized as:
t Empirical measures of bioaccumulation and bioconcentration
• Structure-activity relationships
• Equilibrium-partitioning models
• Metabolic half-life and detoxification models.
The approaches vary in experimental sophistication and are not necessarily
independent of one another. For example, understanding of metabolic half-
life and detoxification is useful in determining bioconcentration factors
(discussed below), particularly when contaminant concentrations in tissues
have not reached equilibrium with exposure concentrations. Also structure-
activity relationships are useful in predicting bioconcentration factors
for some substances.
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Measures of Bioaccumulation and Bioconcentration
Approaches to measure bioaccumul ation of chemical substances may be
categorized as simple laboratory two-compartment systems, laboratory multi-
compartment systems, or field observations. All three approaches require
direct measurement of tissue residues, but vary in the extent to which
contaminant concentrations are measured in other environmental media.
Tissue residue alone is not a convenient index of bioaccumulation potential
because the effects of exposure concentration and metabolic efficiency
are not considered. Marine and estuarine organisms can sequester, transform,
mobilize, and eliminate many chemical contaminants. Effective transformation
and elimination are homeostatically controlled, and will lead toward steady-
state concentrations of toxic substances in tissues, assuming equilibrium
in the partitioning of the substance among aqueous, particulate, and biotic
phases (discussed in detail below). The physiological mechanisms necessary
to achieve steady-state are described for many substances in simple two-compart-
ment systems and have led to the development of bioconcentration factors
(BCFs). Bioconcentration refers to steady-state bioaccumulation of chemicals
from a specific medium, usually water (cf., Brungs and Mount 1978; Macek
et al. 1979; U.S. EPA 1980; and Taylor 1983).
Two-Compartment Systems and Bioconcentration--
In two-compartment systems, a single species is exposed to a toxic
substance dissolved in water at concentrations less than those that produce
a chronic toxic effect. Under such conditions, many substances show first-
order uptake and depuration kinetics such that tissue concentrations increase
to a maximum over time and remain constant thereafter (i.e., are at steady-
state). At steady-state, the relationship between tissue and water concen-
trations can be expressed according to the following equation (cf., Esser
and Moser 1982; Connell and Miller 1984):
Klcw =
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and
3CF = Ct/Cw = K1/K2
(1
wnere:
BCF
= bioconcentration factor
= uptake rate from the surrounding medium
= depuration rate from exposed organism
= contaminant concentration in water
= tissue contaminant concentration in the exposed organism.
Equation (1) states that the bioconcentration factor (BCF) can be determined
either from the ratio of contaminant concentration in tissue to that in
water, or from the ratio of contaminant uptake rate to depuration rate.
Determination of BCFs from steady-state tissue and water concentrations
is the traditional approach to estimating bioaccumulation potential, and
was recommended by the U.S. EPA (1980) in development of water quality
criteria.
The foremost limitation to this approach is the assumption of steady-
state or equilibrium partitioning of contaminants. Because some bioaccumulated
substances are not easily transformed or eliminated, their tissue concentrations
may increase during exposure without ever reaching steady-state. In the
past, the U.S. EPA (1980) indicated that BCFs may be calculated from tissue
and water residues existing at the end of a 28-day exposure period if steady-
state conditions were not met. Although this approach leads to consistent
definitions of bioaccumulation potential for problematic substances, enormous
variability in the accuracy of steady-state BCFs estimated by this approach
may be encountered. In such circumstances, BCFs should be determined by
measuring the kinetics of both uptake and depuration (Veith et al. 1979b,
1980; Bishop and Maki 1980; Kosian et al. 1981; Banerjee et al. 1984).
However, this approach may prove experimentally difficult where substances
equilibrate slowly or where the depuration rate is much smaller than the
uptake rate. In the latter case, curvilinear models used in calculations
of BCFs cannot be fit with any confidence (Kosian et al. 1981). In addition,
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depuration may follow second-order rather than first-oraer kinetics, and
causing slight variations in derived bioconcentration factors (Ellgehausen
et al . 1980; Esser ana Moser 1982). Inconsistency among methods of exposure
(e.g., static equilibrium, flow-through equilibrium, kinetic, and pharmoco-
kinetic "lethods) and calculation of results may also affect accuracy of
BCFs. For instance, Kosian et al . (1981) found that numerous methods of
measuring BCFs offered reasonable precision {i.e., were reproducible),
but that different methods of calculating the final bioconcentration factor
produced results differing by as much as a factor of three.
MuHicornpartnent and Field Estimated-BCFs--
Methods to determine BCFs as an index of bioaccumulation potential
have been extended to more complex multicompartment laboratory studies
and field studies. However, there are many technical and interpretive
difficulties associated with BCFs estimated from these kinds of studies.
Hence, BCFs generated in multicompartment and field studies will be qualified
as "estimated-BCFs" to distinguish them from those measured in more tightly
controlled, and more theoretically tractable, two-compartment experimental
systems.
In multicompartment systems, one or more species is exposed to one
or more contaminants in sediments, water, or food (e.g., Augenfeld et al. 1982;
Rubinstein et al. 1983). Multiple species exposures (e.g., Rubinstein et al.
1983) are designed to assess the bioavailability of contaminants partitioned
among water, sediments, and several trophic levels of biota. In field
studies, estimated BCFs are determined from measurements of contaminant
concentrations in tissues of natural or caged populations of organisms
and ambient contaminant concentrations in all environmental media of water,
sediments, and food (Mackay 1982; U.S. EPA 1980). The two major problems
encountered in deriving estimated BCFs from these types of studies are
1) satisfying assumptions of steady-state and equilibrium partitioning
and 2} integrating the relative contributions of various exposure media
(i.e., food, water, and sediments) to total body burden.
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Steaay-state conditions In laboratory rnul ticompartment systems rnsy
be verified empirically as they are in the two-compartment systems. Steady-
state conditions for f i el G-CO llecteG specimens are difficult to quantify.
When field observations or field experiments are used to determine estimated
BCFs, it is assumed that the concentration of the contaminant is constant
over both time and the range of the organism. However, spatial gradients
in contaminant concentrations are typical of discharges and these assumptions
are difficult to verify, except perhaps in massively contaminated areas
or for caging experiments. An additional assumption is that steady-state
concentrations of tissue residues may be approximated for the relatively
short time intervals over which a bioaccumulation study is conducted (U.S. EPA
1980). Again, this assumption is difficult to verify and may be valid
for only those species that are either sedentary (e.g., bivalves) or show
extreme die! and seasonal stability in migratory behavior.
Assuming that steady-state conditions are reasonably approximated
in multicompartment and field studies, determination of bioaccumulation
potential from estimated-BCFs remains problematic. Estimated-BCFs determined
from the ratio of contaminant concentration in tissues to that In water
vary considerably from those determined in simple laboratory two-compartment
systems. For a given contaminant concentration in water, tissue residues
derived from all media in field studies may be higher than those derived
from water alone in laboratory studies (cf., Thomann and Connolly 1984).
Although water may be the dominant route of exposure for some organisms
in nature, additional uncertainty exists regarding bioavailability of
contaminants partitioned among microparticulate, colloidal, and aquaeous
phases of natural waters (cf., Carter and Suffet 1982; Chiou et al. 1984;
Gschwemd and Wu 1985).
Although multicompartment and field estimated-BCFs present certain
technical and interpretive difficulties in comparison with two compartment
systems, they nevertheless provide meaningful information regarding bioaccumu-
lation potential of chemical contaminants. In two-compartment experiments,
the principal exposure route is through the integument or respiratory surfaces,
and not through ingesticn of food or contact with sediments. However,
many studies indicate the relative importance of uptake from sediments
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or food in the field (Karickhoff et al. 1979; Genest and Hatch 1981; Morel
and Schiff 1983; McFa^and 1983). Multicomoartment systems offer a means I
to assess the relative contributions of various exposure pathways. However,
they are experimentally complex and not conducted routinely to estimate •
bioconcentration of individual substances. Multicompartment systems containing *
more than one contaminant are further limited because synergism and antagonism «
of the test substances are difficult to document and may therefore confound •
attempts to develop indices of bioaccumulation potential (cf., Brown et
al, 1984c,d). Thus, in development of guidelines for water quality criteria, |
the U.S. EPA (1980) recommended discarding bioaccumulation data that were
based on exposure to "formulated" mixtures. I
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Estimated-BCFs derived from field studies require extensive spatial B
and temporal characterization of contaminated organisms and the habitat
from which they are collected. Therefore, field-estimated BCFs are more
time-consuming and less controlled than are those derived from laboratory
experiments. They require monitoring or historical documentation of the _
type and extent of contamination, and may be confounded by the presence |
of interacting and possibly synergistic substances. Nevertheless, properly
conducted field studies provide a more realistic assessment of actual bioaccumu- J
lation of contaminants because they integrate the effects of environmental
partitioning and multiple routes of exposure. Schnoor (1982) found that 8
field estimated-BCFs for five priority pollutants and their metabolites
(aldrin and dieldrin; DOT, DDE, ODD; PCBs; chlordane; and heptachlor) measured •
in lipids of freshwater fishes exceeded the laboratory BCFs for the same ™
substances by a factor of 1-4. When estimated-BCF values from field experiments —
are consistently higher or lower than those from laboratory studies, U.S. EPA |
(1980) recommended that only field-derived values be used in the development
of water quality criteria. |
Summary-- •
In summary, there are three principal approaches to empirical determination •
of bioaccumulation potential. The approaches vary in experimental complexity •
from controlled two-compartment laboratory systems to highly variable field _
studies. Bioconcentration factors as an indices of bioaccumulation potential |
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•nay be estimated from data generated in each type of approach. However,
exoerinental co^olexities, uncertainties regarding actual exposure concen-
trations, and difficulties in verifying steady-state tissue residues indicate
that estinated-BCFs generated in multicompartment and field studies may
vary considerably in comparison with BCFs generated in well-controlled,
two-compartment studies. Although estimated-BCFs in field studies may
exceed laboratory values by a factor of 4, they integrate the effects of
numerous routes of exposure and provide a realistic assessment of bioaccumu-
1 at ion potential. A final major limitation to empirical measurements of
bioaccunulation is the time required to determine bioconcentration factors
for thousands of potential chemical contaminants. Therefore, most laboratory
investigations of bioaccumulation potential focus on predictive relationships
between bioconcentration factors and the chemical properties of various
classes of contaminants.
Structure-Activity Relationships
In structure-activity relationships, bioconcentration and toxic effects
are predicted from the physicochemical properties of organic substances
(Hopfinger et al. 1981), particularly their electronic, steric, and hydrophobic
properties (Hansch and Leo 1979). Hydrophobicity is perhaps the most important
property with respect to biological uptake and concentration of substances,
but not necessarily with respect to specific toxic activity. A practical
model system measuring hydrophobic partitioning of a substance between
octanol and water was developed to quantitatively predict partitioning
among polar and nonpolar (i.e., principally lipids) biological compartments
(Leo 1981). The model predicts that equilibria! partitioning of nonionic
organic contaminants between biota and water will be proportional to the
octanol-water partition coefficient, which is defined as:
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where:
KOW = the partition coefficient
C0 = chemical concentration in _n_-octano1 B
Cw = chemical concentration in water.
There are many limitations to use of the octanol-water partitioning •
model for predicting bioaccumulation of organic compounds. First, the
octanol-water partition coefficient (Kow) measures only the hydrophobicity |
of a chemical compound and therefore ignores other properties that may
affect bioaccumulation of a substance (e.g., latent heat of solution, hydroly- I
sis, ionization, and vapor pressure). Also, there are numerous techniques
(e.g., shake flask, HPLC, TLC, solubility) for measurement of Kow, each •
of which has its particular set of advantages, problems, and limitations
(Esser and Moser 1982). Measured Kow values may be affected by impurities, •
temperature, pH, low solubility, volatility, and degree of ionization. m
However, log Kow can be closely approximated from chemical properties of ^
the molecule (Hansch and Leo 1979). |
Application of the log Kow model to partitioning of chemical contaminants •
between the aquatic environment and fish tissues was initially described
by Neely et al . (1974), further developed by Veith et al. (1979b, 1980), •
and embellished by Mackay (1982). Veith et al . (1980) showed that the
log of the bioconcentration factor for 84 organic compounds in three species
of freshwater fishes is a linear function of log Kow, as approximated by
the regression function: ^
log BCF = 0.76 log Kow-0.23 (R=0.907, P<0.001, N=84) (2)
This relationship agrees with Veith et al.'s (1979b) earlier equation,
which was used by the U.S. EPA (1980) to predict BCFs for which there were •
no empirical values.
Although the log Kow vs. log BCF model as developed by Veith et al. (1980) •
is based on 84 substances representing 18 classes of priority pollutants, _
there are several limitations. First, the model is based on a two-compartment |
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experimental approach, which Units assessment to organic substances aissouec
in W3ter. Second, the model assumes that bioconcentration of organic substances
•s cepenaent an tissue lipid content, although other nonpolar molecular
components may affect uptake. It is widely recognized that lipid content
of exoerimental organisms affects bioconcentration of organic substances.
However, the regression function described above uses empirically determined
bioconcentration factors that are not normalized to percent lipids. Schnoor
(1982) showed that lipid normalization eliminated 60 to 90 percent of the
variance of of estimated-BCFs measured in four species of freshwater fishes.
In practice, lipid content of tissues is rarely measured in conjunction
with determination of BCFs, although it is often estimated from values
reported in the literature. Furthermore, qualitative differences in lipid
content among organisms may affect bioaccumulation but are not well studied
(Varanassi and Mai ins 1977; Phillips 1980; Brown et al. 1982, 1983). Never-
theless, properly designed bioaccumulation studies should measure both
organic contaminant tissue residues and tissue lipid concentrations and
should express data in units of contaminant concentration per gram of lipid.
For unknown reasons, some substances such as hexachloropentadiene
may have low bioconcentration factors despite comparatively large Kow values
(Veith et al . 1979b). Log Kow estimated BCFs for polycyclic aromatic hydro-
carbons (PAH) may also be overestimated because they are partitioned almost
exclusively into the particulate phase, require an extremely long time
to reach steady-state with biological tissues, and are rapidly metabolized
by many aquatic organisms. Also, cell membranes may be relatively impermeable
to high molecular weight PAH (Mackay 1982; Connell and Miller 1984). Similarly,
PCB-1260 has a high log Kow (6.91) that may overestimate log BCF because
of poor membrane permeability brought about by its steric configuration
(Connell and Miller 1984; Shaw and Connell 1984). Other substances such
as 301(h) organophosphate pesticides have Kow values that range from 1.93
to 3.81, but have a low bioaccumulation potential because they are rapidly
degraded and easily metabolized (Brown 1978). Finally, field estimated
BCFs based on lipid normalized data may be four times greater than those
predicted by log Kow values when the principal route of exposure is through
the food rather than water (Thomann and Conolly 1984).
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In summary, log Kow values provide an order of magnitude estimate
of bioacc-nulation ootential of organic substances. Numerous factors contribute I
to this range of variation, including: properties of chemical contaminants,
analytical methods, experimental conditions, and biological variability •
of experimental organisms (Esser and Moser 1982).
Equi1ibrium Partitioning Models ™
Kenaga and Goring (1980) attempted to quantify the effects of environmental |
partitioning of organic substances on bioavailabil ity in order to arrive
at a more realistic assessment of bioaccumulation potential. They examined •
the relationships among water solubility, soil sorption, octanol-water
partitioning, and concentrations of chemicals in biota. Data summarized H
from the literature for 170 chemicals showed significant correlations among
the logarithms of water solubility, Kow, BCFs, and soil sorption coefficients •
normalized to percent organic carbon (Koc), principally for freshwater
fishes. Note that Koc values may be derived empirically in the same fashion
as K:
where:
carbon
Cw s Equilibrial contaminant concentration in water.
I
•
Koc = The organic carbon soil sorption partition coefficient •
= Equilibria! contaminant concentration in sediment organic *
I
Soil sorption coefficients and Koc values can also be derived theroetically |
from the chemical potential (i.e., fugacity) of nonideal solutes at thermo-
dynamic equl ibrium (cf., Mackay 1982; Karickhoff 1984; Connell and Miller •
1984 for detailed discussion of this approach).
In general log BCF, log KOWt and log Koc are inversely proportional "
to the log of water solubility, whereas log BCF and log Koc are directly «
proportional to log Kow. Kenaga and Goring (1980) concluded that Koc seemed •
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to be t'ne best predictor o* the other parameters, and that ?CF and Kow
2""9 i-gy -i-v;-ic2tor*s for the behavior o* chenicals in the environment. Howeve"",
recent advances in measurement, calculation, and development of a computerized
database of octanol-water partition coefficients make Kow useful in predicting
both Koc and bioaccumulation potential (Hansch and Leo 1979; Veith et al. 1979a;
Leo, A., 20 November 1984, personal communication).
Kenaga and Goring's (1980) review has provided the 'impetus for recent
research into estimating bioaccumulation potential of chemicals present
in sediments. The hypothesis is that bioconcentration of hydrophobic chemicals
from sediments into organisms can be predicted on the basis of equilibrium
partitioning among sediments, water, and biota. The major assumptions
(McFarland 1983; Karickhoff, S., 20 November 1984, personal communication)
to this approach are that:
• Maximum bioconcentration potential is reached when all three
compartments are at thermodynamic equilibrium
• The solubilities of organic contaminants in organic carbon
of sediments are about equal to their solubilities in organic
carbon or lipid of tissues
i Equilibrium concentrations in sediments and tissues will
be approximately equal if normalized to organic carbon or
lipid content
• Concentrations in the water phase affect rates of uptake
but are unnecessary to determine partitioning of substances
between sediments and tissues under equilibrium conditions.
The obvious limitation to this approach is that equilibrium conditions
are unlikely in natural environments because of the dynamics of both physical
and biological processes. For instance, Connor (1983) has shown that the
ratio of the PCB concentration in fish tissues to that in marine sediments
from a number of locations is proportional to flushing time. Also, rates
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of uptake from sediments may be profoundly affected by ingestion of sediments _
or sediment-associated orey by benthic or bottom-feeding organisms. ||
Other biological factors that may affect the equilibrium partitioning •
approach include:
• Improbable routes of exposure (e.g., exposure of pelagic
fishes to contaminated sediments) •
t Limited time of exposure due to the mobility of exposed _
organisms •
• Metabolic pathways that quickly mobilize and eliminate contain- •
inants
t Growth and age of exposed organisms (e.g., weight-specific
uptake of contaminated food may be much less for older indi- tt
viduals than for younger faster-growing individuals within •
a species) ' «
• Unusual periods of lipid utilization and consequent concentration
of toxic substances in the remaining lipid pool (Karickhoff, |
S., 20 November 1984, personal communication).
In summary, equilibrium partitioning of nonionic chemical substances
among water, sediments, and biota at thermodynamic equilibrium may provide •
an indication of maximum bioaccumulation potential (McFarland 1983). Hawever, *
this approach is highly theoretical at present and requires empirical sub- _
stantiation (Karickhoff, S., 20 November 1984, personal communication). •
Nevertheless, it provides a framework for unifying environmental partitioning,
biological uptake, and chemical variables that affect bioaccumulation of |
nonionic organic contaminants. It also indicates the importance of normalizing
tissue residue data to lipid content and of normalizing sediment residue •
data to organic carbon content.
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Metabolism and Detoxification Models
Two approaches have been used to assess the role of metabolism in
bioconcentration of organic and trace Tietal contaminants. The first is
an index of depuration of a substance, which measures declining tissue
concentrations of a contaminant following removal of the exposed organisms
to a clean, contaminant-free environment. The metabolic half-life, the
amount of time required for tissue burdens of a parent compound to decline
by 50 percent, is calculated from experimental data in order to arrive
at a measure of biological persistence of the parent compound. This approach
is subject to many of the same limitations as the kinetic approach for
calculating bioconcentration factors. Calculation of metabolic half-life
may be further hampered by a multiphasic decline in tissue concentrations
(Bryan 1976; Hardy and Roesijadi 1982). In addition, the half-life approach
does not consider toxic potential and persistence of daughter compounds
of organic contaminants, or of various bound forms of trace metals. The
rates of accumulation, the metabolic conversion of accumulated substances,
and the relative proportions of intermediate metabolites can vary among
closely related species (Frazier and George 1983; Reichert et al. 1985).
Furthermore, there is a great deal of variation in the capacity of marine
organisms to produce mixed function oxidases (MFOs) required to metabolize
organic contaminants. Mai ins et al. (1979) reviewed metabolism of aromatic
hydrocarbons in marine organisms and reported a 600-fold variation in enzymatic
activity among various species of teleosts. Finally, determination of
metabolic half-life may be subject to enormous variation because of the
variety of metabolic compartments involved in storage and elimination of
contaminants and their relative importance under different exposure conditions
(George 1982).
Despite such limitations, metabolic half-life has been used as an
index of depuration for both organic substances and trace metals. Veith
et al . (1980) reported tissue half-life for 25 organic contaminants in
bluegill sunfish (Lepomis macrochirus). Half-lives ranged from less than
1 day for 15 of the compounds to more than 7 days for acrolein. In general,
metals appeared to be more persistent than organic contaminants. Bryan
(1976) reviewed biological half-lives of some radio-label led trace metals
17
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[mercury(11), metnylmercury, mercury-protein complex, zinc, and manganese]
in a variety of marine and estuarine organisms. Half-lives ranged fron
11 days for manganese in the lobster Homarus gammarus to 1,200 days for
methyl-mercury in the flounder Platichthys flesus.
The second approach to assessing the role of metabolism in bioaccumulation
is to measure saturation of detoxification pathways for both organic and
trace metal contaminants. Brown et al. (1984a) proposed that partitioning
of contaminants between intracellular sites of detoxification and toxic
action could provide an index of the accumulative capacity of an organism.
Both metals and organic contaminants were measured in the high molecular
weight (>20,000 daltons) enzyme-containing, medium molecular weight (3,000-
20,000 daltons) metallothionein-containing, and Tow molecular weight (<3,000
daltons) glutathione fractions of cytosol obtained from a variety of marine
fishes and invertebrates. Appearance of organic contaminants in either
the enzyme-containing or metallothionein fractions indicates that bioaccumu-
lation has exceeded normal metabolic capabilities of the MFO-glutathione
system (Brown et al. 1984a,b,c). Similarly, appearance of trace metal
contaminants in either the enzyme-containing or glutathione fraction indicates
that bioaccumul ation has exceeded normal metabolic capabilities of the
metallothionein and lysosomal-vacuolar systems (George 1982; Jenkins et
al. 1982; Brown et al. 1984a,b,c). Using this approach, Brown et al. (1984a)
indicated that Dover sole (Microstomui pacificus) with fin erosion contained
a greater proportion of oxygenated metabolites of DOT in enzyme-containing
and metal! othi one in pools than did conspecifics without fin erosion. According
to Brown et al. (1984a), these results suggest that high concentrations
of DDT metabolites in cytosolic pools other than the low molecular weight
glutathione fraction are related to toxic pathological effects such as
fin erosion lesions in fishes. Other studies of the cytosol distribution
of organic and trace metal contaminants have shown that high levels of
exposure to organic contaminants may interfere with normal trace metal
metabolism (Brown et al. 1984d; in press).
The approach of Brown et al . (1984c) may not be as widely applicable
to marine organisms as originally hypothesized. Frazier and George (1983)
reported a wide range in concentrations of cadmium-induced metallothionein-
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like protein in two species of oysters (_C_rassostrea gigas and Ostrea eduTis).
Alsc, induction of the meta'Hothionein-like protein in £. edul is was dependent
on geographic location of the sample population. A further limitation
to this recent approach is that it has not been assessed for the wide range
of compouncs needed to develop predictive relationships.
EMPIRICAL BCF VS. Kow MODEL FOR MARINE ORGANISMS
The foregoing review of past approaches to assessing the bioaccunulation
potential of chemical contaminants indicates that log Kow values may provide
a quantitative index for determining the rank order of bioaccumulation
potential of organic contaminants in marine and estuarine organisms. However,
previous workers have developed log Kow (octanol-water partition coefficent)
vs. log BCF (bioconcentration factor) models primarily for freshwater and
marine species combined or for freshwater species only. Davies and Oobbs
(1984) suggested that empirically derived BCFs for saltwater species are
lower than those for freshwater species. Thus, previous models (e.g.,
Veith et al. 1979b, 1980) may not be applicable to the marine environment.
In this section, empirical relationships between log Kow and log BCF are
exanined for marine and estuarine organisms.
The U.S. EPA (1980) collected, reviewed, and screened available data
on bioconcentration factors for priority pollutants. Both freshwater and
saltwater organisms were used in development of U.S. EPA's (1980) Water
Quality Criteria. For the purposes of this review, empirical bioconcentration
factors for saltwater organisms that met U.S. EPA's screening criteria
were tabulated for four major animal taxa: polychaetes, molluscs, crustaceans,
and fishes. A computer search of Oceanic Abstracts, NTIS, BIOSIS, and
Enviroline abstracting services was then conducted for additional new
information concerning bioconcentration factors published since 1979.
The data characteristics used to select recently published BCF values were
adapted from the procedures established in the Water Quality Criteria Guidelines
(U.S. EPA 1980). Data were rejected according to the following guidelines:
• Species were not resident in marine or estuarine waters
of North America
19
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• In aDDroor i a te t5xa: Oily BCFs for poly:haetes, molluscs,
crustaceans, and fishes were accepted
• Unpublished report: Data in letters, memos, or personal
communications were unacceptable
9 Inadequate controls were used in either field studies or
lab experiments
• Signs of stress, disease, or mortality in experimental organisms
were apparent
• Chemical substances examined were formulated mixtures or
emulsifiable concentrates
• Steady-state was not obtained, experiment was shorter than
28 days, or inappropriate kinetic model was used to determine
bioconcentration factor,
Results of this review show that empirically determined BCFs for marine
and estuarine organisms are available for 14 organic substances and 9 trace
metals on the priority pollutant list (Appendix A). These data are based
on 24 studies of 44 species of polychaetes, molluscs, crustaceans, and
fishes (Table 1). Most of the U.S. EPA (1980) Water Quality Criteria for
marine and estuarine organisms are based on extrapolation from BCF measurements
on freshwater organisms or on structure-activity models that are based
primarily on freshwater studies. Note that the existing model used by
U.S. EPA to predict bioconcentration factors from octanol-water partition
coefficients is based on carefully controlled studies of freshwater fishes
conducted by a single investigator (Veith et al . 1979a,b; 1980) and on
a wide range of contaminants, many of which are not priority pollutants.
To derive a KOW-BCF model for marine and estuarine organisms, the
geometric mean of the log BCF values for each of the 14 priority pollutant
compounds summarized in Appendix A was plotted against the log Kow value
20
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o
IL.
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derived from the literature (Table 2). Correlation analysis indicated
a ooor f-it of t^e linear regression -node! to these data (R*0.243, PX3.Q5,
N = i4). Part of the reason for the poor correlation may be that the data
collected were biased towards high log Kow values, in the range of 3.25
to 6.5. Also, it should be noted that empirically derived BCFs for 4 of
the 14 substances deviated by more than an order of magnitude from those
predicted by Veith et al. (1980) (Figure 2). Bioconcentration of naphthalene
and toxaphene were much higher than predicted, whereas bioconcentration
of pentachlorophenol and benzo(a)pyrene were much lower than predicted.
Plots of log BCF vs. log Kow for two of the major taxa (fishes and molluscs)
considered individually indicated a similar lack of agreement, with correlation
coefficients less than 0.5 in each case. However, BCFs for fish correlated
with those for molluscs (R=0.89, P<0.05, N=7), In contrast to these results,
Zaroogian et al. (1985) found that freshwater models of the log Kow vs.
log BCF relationship provide a reasonable order-of-magnitude estimate of
bioconcentration factors in marine species. Their review focused on laboratory
studies of marine fishes [the sheepshead minnow (Cyprinodon variegatus)
and the pinfish (Lagodon rhomboidesj] and two species of bivalves [mussels
(Mytilus edulis) and oysters (Crassostrea vlrginica) ], and considered a
range of 15 priority pollutant and 6 non-priority pollutant compounds.
The fact that the present review includes BCFs derived from a greater diversity
of organisms (molluscs, crustaceans, polychaetes, and fishes) that were
studied under field as well as laboratory conditions may explain conflicting
results between the present analyses and those of Zaroogian et al. (1985).
These discrepancies indicate the need for a single study to characterize
bioaccumulation of a range of priority pollutants in marine organisms under
natural conditions. Consequently, remaining sections of this report focus
on recent field studies that quantify the relationship between log Kow
and bioaccumulation of organic contaminants in marine and estuarine fauna.
ESTIMATION OF BIOACCUMULATION POTENTIAL FROM FIELD STUDIES
The Palos Verdes Shelf in southern California is perhaps one of the
best studied systems with respect to compartmental characterization of
chemical contaminants in the vicinity of a sewage discharge into marine
waters. Gossett et al . (1983b) measured contaminant concentrations in
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LE 2. OCTANOL-WATER PARTITION COEFFICIENTS (<0>(,
FOR PRIORITY POLLUTANTS AND 301(h) PESTICIDES
AS MODIFIED FROM CALLAHAN ET AL. (1979)
•>pf
05
21
U
Ji
57
30
by
6U
04
b
35
J6
37
56
61
bj
ba
77
si
bU
39
It
i:
n
?t
o;
e
2:
2t
it
Pol lutant
nneoois
pnenol
'
Substituted Hnenols
2,4,b-tricnloropnenol
para-cnioro-meta cresjl
^,4-oicnloropneno"i
2-ni troynenoi
4-nurosnenoi
2,4-dinuropnenol
4,b-dinitro-o-creso!
pentacn 1 oropnenol
Uryanonitroyen Compounds
oenziaine
3,3' -dicnlorooenzidine
2,4-dinitrotoluene
2,b-dinitrotoluene
1,2-oipfienylnyarazine
nitrobenzene
N-nltrosodimetfiy lamine
N-ni trosodi pneny 1 ami ne
N-nitrosodipropy lamine
Low Molecular Keiyru Aromatic
Hydrocaroons
acenapntnene
napntnalene
acenapntny tene
antnracene
pftenantnrene
f i yorene
Hivn Molecular ueiynt PAH
f luorantnene
oenzotajantnracene
benzotajpyrene
oenzoibK luorantnene
benzo(u )f luorantnene
cnrysene
benzol^fii Jperylene
oiDenzou.njantnracene
indenoil ,2, J-cd jpyrene
pyrene
Cnlorinated Aromatic Hydrocarbons
1 ,2,4-tricntoronenzene
nexacnl orooenzeie
2-cn loronapntna 1 ene
1 ,2-oicfitorobenzene
1,3-aicnlorobenzene
1 ,4-dicnlorooenzene
1og(Ko«j
1.46 a
2.42 b
3.69 c
3.10 a
2.16 b
3 .US a
1.77
T.53
2.B5
5. DO d
l.al y
3.U2
2.00
2.UU
2.94 y
1.83 b
-0.5B y
3.13 0
1.31
l:lil
4.UB
4.34 d
4.46 d
4.38 d
b.53
b.61 d
6.00
• 6.6u
b!60
7.00
6.00
7.7U
4.23 d
b.23 d
4.72 9
3.40 b
3.44 b
3.b3 d
PW
b2
12
53
Id
4U
41
4J
bb
t>7
70
71
Ub
107
lUti
110
111
112
129
!>4
dy
9U
91
»2
93
94
as
96
97
V8
99
100
101
102
103
104
105
Pollutant
Cnlorinated Aliphatic rtydrocaroons
nexacnlorobatadiene
nexacnloroetnane
Dexacn 1 orocyc 1 opentadiene
Halouenated Ethers
bts(2-cnloroetnyl (etner
4-cntoropneny 1 pneny i etner
4-oromopneny 1 pneny I etner
bis(2-cnloroisopropyl jetner
b i s ( 2-cri 1 oroetnoxy Jraetnane
Pntnalates
bis(2-etnylnexyl)pntnalate
Duty) benzyl pntnalate
ai-n-butyl pntnalate
di-n-octyt pntnalate
aietnyl pntnaUte
dimetnyl pntnalate
PCB's
PCB-1242
PCB-12S4
PCB-1221
HCB-1232
PCB-12bO
PCb-1016
Miscellaneous Oxygenated Compounds
TCOU (dioxin)
isopnorone
Pesticides
alarm
dieldnn
cnloroane
4,4'-OOT
4,4'-00£
alpna-enaosulfan
Deta-enaosulfan
endosulfan sulfate
endrin
endrtn aldehyde
neptachlor
heptacnlor epoxide
a Ipna-hexachl orocyc lohexane
beta-htxachlorocydohexane
deH a -hexachl orocyc lonexane
gamma-nexachlorocyclohexane
*».,
4.2s f
3.93 b
5 .31 C
1.12 D
5.U8 y
1.26 y
4 .20 0
4.U5 b
5.15 a
9.20
1.4U b
1.61 b
6.UU a
6.46 d
4.00
4.48
6.11 a
6.91 d
6 »tf8 d
6.10 i
1 .67 b
3.UU c
5.40 d
6.UU d
b-J . J
./ b a
i #6 y 0
6 .UU d
3.6u
3ieu
b.bU
5.45 d
6.40 d
3. as h
3.33 H
3.85 n
3.c5 a
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"ABLE 2.
pp»
?clTutant
log(Ko)
PP*
Pollutant
113
--J
"j
••K
"K
--K
--k
6
lu
11
13
14
15
16
23
32
44
45
46
47
48
51
49
50
29
30
33
65
B7
B8
4
38
86
.
toxapnene
m rex
metnoxycMor
paratni on
ma 1 atfnon
9'jtni on
detneton
Volatile Halogenated AUanes
tetracnlorome thane
1 ,2-dichloroethane
1 ,1,1-trichloroethane
1 ,1-dichloroetnane
1,1 ,2-trichloroetnane
1,1,2,2-tetrachloroetnane
chloroethane
chloroform
1,2-dicnlorooropane
dichloromethane
chloroniethane
bromometnane
bromof orm
di chlorobromomethane
chlorodibromome thane
f luorot ri ch 1 oronethane ! Removed )
dichloroaif lourometnane (Removed)
Volatile Halogenated Alkenes
1 ,1-dichloroethylene
1 ,2-trans-dicnloroethylene
trans-l,3-dichloropropene
cts-1 ,3-dicnloropropene
tetracnlcroetnene
trichloroethene
vinyl chloride
Volatile Aromatic Myarocaroons
benzene
etny) benzene
toluene
3.30
6.69 r>
4.30 b
3.81 e
2.89 e
2.18
1.93
2.64 d
1.45 b
2.47 b
1.78
2.18
2.39 b
1.54
1.90 f>
2.28
1.30
0.90
1.00
2.30
1.88
2.08
3.53 c
2.16 c
1.48
1.97 c
1.98
1.98
2.88 d
2.42 b
0.60
2.11 d
3.15
2.21 b
7
2
3
19
114
115
117
118
119
119
120
122
123
123
123
123
124
125
126
127
128
121
116
UrtiA*i\'ifl '~hlri("'inji1*of* 4rn[~i»(*l'"
¥ Q ( 0 „ I itr 1- ' ' 1 y f iiiQLtrL **' UFMQ L, >\_
Hydrocarbons
chlorobenzene
volatile Unsaiurated Carbor/1
Compounds
acrolein
aery lonitri le
Volatile Ethers
2-chloroethyl viny 3 ether
Metals
antimony
arsenic
beryl 1mm
cadmium
cnromi um (tri va'ent )
chromium (hexavalent)
copper
lead
mercury
methyl mercury
pneny] mercury
mercuric acetate
nicxel
selenium
Si Iver
thallium
Zinc
Miscel laneous
cyanide
asbestos
2 _ -i; ~
C.4C ;
\.n D
1.23 5
NA
NA
NA
NA
SA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
SA
NA
a veith et al . 1979a.
b Velth et al . 1980.
c Gossett et al . 1983.
d Venn et al . 1979t>.
e Kenaga and Goring 1980.
f Leo, A., 20 November 1984, personal communication.
g U.S. EPA (1980).
fi Solubilities of the various isomers of KCH indicate that they will have
simlar log(Kow) values.
i Estimated according to the procedure described by Chiou et al . (1982).
j Chlorinated 301(h) pesticides that are not on the priority pollutant list.
k Organopnospnorus 301(h) pesticides tnat are not on the priority pollutant list.
NA « not applicable. "4
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-------
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
I
•
1
6 -
t .
ji "*
U.
CJ
£.
a
o
j 3 -
2 -
i -
0 -
1107, • /
/
-V2:./'
, 92-94, •>"
• ••13. /' •'9-'
• '55' /
'« i • % .,,00,
• S |Q: •
. _/' .»•
i95-97i X
/
/
X • • A
/• l8' .fi. ,^
x^
X
/
/4( PREDICTED BY vEiTHetai ii980!
X
01 23456^
LOG (Kow)
Note: Priority pollutant number in parenthesis. See Appendix B for
a key to priority pollutant numbers.
Figure 2. Bioconcentration factors for priority pollutants
in marine and estuarine organisms vs. octanol-
water partition coefficients.
25
-------
sewage effluent, fish liver tissues, and sediments. Data from this study
were used i-. t".is review to develop & predictive relationship between fielj
reasons :
t
I
I
I
bioaccumul ation and octanol-water partition coefficients for the following
• The study was conducted on samples collected in the vicinity •
of a marine discharge and therefore circumvented possible
artifacts inherent in the octanol-water model developed •
for freshwater fishes in the laboratory. •
• Concentrations of contaminants in effluent are based on . •
both the aqueous and particulate phases of the sample.
Therefore, liver-effluent estimated-BCFs represent the three J
major compartments of water, particulates, and biota discussed
in the equilibrium partitioning approach to bioaccumulation. •
The study has an extensive database, with 27 contaminants •
(22 of which are priority pollutants) analyzed in effluent, •
sediment, and tissue samples collected concurrently'. •
t The investigation was conducted in an area about 6 km (3.7 _
mi) northwest of the Whites Point outfall, where the level |
of contamination of surficial sediments in recent years
appears to have been relatively constant both temporally •
and spatially (Tetra Tech 1984; Gossett, R.W., 24 September
1984, personal communication). •
• Lipid content of liver tissues was measured and thereby •
permits lipid normalization of calculated bioconcentration •
factors. m
• The study focused on demersal or benthic-feeding fishes:
California halibut (Parallchthys californicus), Pacific •
sanddab (Citharichthys xanthostigma), Dover sole (Microstomus
pacificus), scorpionfish (Scorpaena guttata)» and white •
croaker (Genyonemus ^ineatus) (Allen 1982).
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• Finally, the iretaoolic toxification and detoxification
casaDiiities of the test organisms are well-studied (Jenkins
et al . 1982; Brown et al . 1982, 1983, 1984a,b,c,d).
Gossett et al . (1983b) established significant (P<0.05) positive rank
correlations between sediment concentration and tissue concentration (Rho = 0.77
to 0.95), log Kow and tissue concentration (Rho = 0.63 to 0.75), and log
Kow and sediment concentration (Rho = 0.74) for the various compounds studied.
However, effluent concentration was negatively correlated with sediment
(Rho = -0.55) and tissue (Rho = -0.4 to -0.69) concentrations.
Inspection of Gossett et al.'s (1983b) data showed that, depending
on species of fish examined, 4-12 of the compounds that were analyzed (principal-
ly the volatiles) were below analytical detection limits. However, the
detection limits of these compounds were included in the original rank
correlation analysis (Gossett, R.W., 24 September 1984, personal communi-
cation). In the present reanalysis of Gossett et al.'s (1983b) data, values
that were below detection limits were discarded, and tissue concentrations
of contaminants were normalized to lipid fraction (lipid fraction = percent
1ipids/lOO). Reanalysis of the reduced data set showed highly positive
Pearson product-moment correlations (R>0.99) between sediment and lipid-
normalized liver concentrations of the various contaminants for each of
the five species of fish studied (Figure 3), However, log Kow was not
significantly correlated (P>0.05, N=60) with the log of fish liver-sediment
estimated-BCFs. The field-derived BCF in this case was calculated as the
ratio of lipid normalized contaminant concentration in fish liver to the
concentration in sediments.
A much better correlation was apparent between the ratio of liver
tissue concentration to effluent concentration of contaminant and the cor-
responding Kow value (Figure 4):
log (Ci/Ce) = -2.568 + 1.123 log Kow (R=0,837, P<0.001, N=76)
(4)
27
-------
lOc
(93)
105 -
Y = -2236 + 17 9 X
(R . 0.811. N = 60;
(107),
(92)
a
JC
1 103
rr
>
j
i
52 102
(21)
•
10- -
100 J
TETRACHtOROPHENOL—7 <55>
7,
(38)
10-' 100 10i 1Q2 103 10* 105
SEDIMENTS (ug/kg dry wt)
Note: Priority pollutant number in parenthesis. See Appendix B for
a key to priority pollutant numbers.
Regression function based on individual values for each species
of fish.
Figure 3. Mean contaminant concentration in fish-liver lipids
vs. concentration in sediments for five species of
fish. Data from Gossett et al. (1983b).
28
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7 1
6^
5 H
± 3-1
Q
Q.
o
CD
0-
•1 .
2 J
C PACIFIC SANDDAB
• HALIBUT
O SCORPIONFISH
A DOVER SOLE
• WHITE CROAKER
i
A
LOG BCF - -2568 * 1.123 LOG (KQW)
(R » 0 837 N - 76)
950/0 CONFIDENCE LIMITS
LOG
Figure 4. Liver-effluent bioconcentration factors normalized to
lipid fraction vs. octanol-water partition coefficients
for five species of fishes from the Palos Verdes shelf.
Data from Gossett et al. (1983b).
29
-------
where:
CT = 1 ipid-normal ized liver tissue concentration for each of the five
species of fish studied (ug/kg tipid wet wt)
Ce = concentration in effluent (ug/L)
KOW = octanol-water partition coefficient.
This method seems to provide a reasonable estimate of bioconcentration
potential in the vicinity of sewage discharges. For instance, Young and
Gossett (1980) measured chlorinated benzenes (p-dichlorobenzene, 1,2,4-trichloro-
benzene, hexachlorobenzene), PCBs (1242 and 1254), and total DDTs in effluent
and in liver tissues of Dover sole collected in the area of the Hyperion
7-mi outfall in Santa Monica Bay and in a reference area. Although not
normalized to lipid fraction, their data indicate that the ratio of contaminant
concentration in fish liver to that in the effluent is a function of log
V
0 W •
log (Cf/Ce) - -4.255 + 1.223 log Kow (R=0.859, 0.02
-------
.»
o
o
o
o
o -
•2 -
•3 J
WHILES =C'\"r OUTFA;.!.
PALOSVERGES SHELF
Y « -3 826 » 1 204 X |R * 0 881. N = I9i
HYPERION OUTFALL.
SANTA MONICA BAY
Y m .4 255 + 1 233 X (R - 0.859. N • 6)
TETRACHLOROPHEWX
1861
Note:
LOG
Priority pollutant number in parenthesis. See Appendix B for
a key to priority pollutant numbers.
Figure 5. Ratio of contaminant concentration in Dover sole liver
to effluent concentration as a function of the octanol-
water partition coefficient. Data from Gossett et al.
(1983b) and Young and Gossett (1980).
31
-------
partition coefficient {Figures 4 and 5). Furthermore, the extent of tissue
contamination will be prooortional t: the contaminant concentration in
sediments (Figure 3; Connor 1983). Finally, it should be recognized tnat
these data are highly site-specific and may only be applicable to the Southern
Ca'Mfornia S^ght, Thus, quantitative predictions of contaminant tissue
residues for other discharges and other regions are not possible. However,
these studies indicate that log Kow is a reasonable index for determining
the rank order of bioaccumulation potential of organic contaminants discharged
through marine sewage outfalls.
PROPOSED RANKING OF BIOACCUMULATION POTENTIAL
The results of the foregoing review indicate that the octanol-water
partition coefficient provides the best available index for potential bioac-
cumulation of organic contaminants in marine and estuarine organisms because:
t It can provide an order-of-magnitude estimate of the bioconcen-
tration of discharged substances in fish liver (Figure 4)
• It is a reasonable model for partitioning between water
and biological tissues
• It is useful for predicting soil sorption coefficients and
is thereby implicated in equilibrium partitioning among
sediments, water, and biota.
A list of organic priority pollutants and 301(h) pesticides and their proposed
ranking of bioaccumulation potential based on the octanol-water partition
coefficient is given in Table 3. A list of trace metals and their proposed
ranking of bioaccumulation potential based on empirically determined BCFs
is given in Table 4. Trace metals are ranked separately from organic contami-
nants because chemical indices, such as log Kow, that predict bioaccumulation
potential have not yet been developed for trace metals.
Calculation of fish liver-effluent estimated-BCFs from Kow is proposed
as a second element in this ranking procedure because it provides a basis
32
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TABLE 3. RANK ORDER OF ORGANIC PRIORITY POLLUTANTS AND 3Ql(h
PESTICIDES BASED ON EMPIRICAL BCFs FOR WATER AND SP^AGE
EFFLUENT, AND ON OCTANOL-UATER PARTITION COEFFICIENTS (K0y)
69 di-o-octyl phthalate
83 1ndeno(l,2,3-cd)pyrene
79 benzofgfn )perylene
111 PCB-1260
— ml rex d
75
74
107 PC8-12S4
110 PCB-1248
129 TCDD (dioxin)
73 benzc(a)pyrene
91 chlordane
106 PCB-1242
94 4,4'.000
82 dibeizo{a,h)anthracene
112 PCB-1016
92 4,4'-DDT
93 4,4'-ODE
72 benzo(a)anthracene
76 chrysene
99 endrin aldehyde
39 fluoranthene
S3 hexachlorocyclopentadiene
90 dieldnn
100 heotacnlor
101 heptathlon epoxide
9 hexacnlorobenzene
68 di-n-butyl phthalat*
41 4-bromopheiyl phenyl ether
64 pentathlorophenol
40 4-chlorophenyl phenyl ether
84 pyrene
20 2-ch1oronaphtha1ene
98 endrin
109 PC8-1232
81 pr-eianthrene
80 fluorene
78 anthracene
— methoxychlor d
52 hexachlorobutadiene
8 l,2,4-tricfiloroneize*e
66 bl4(2-ethylhexy1jphtnalate
77 acenaphthylene
67 butyl benzyl phthalate
108 PC8-1221
12 hexachloroethane
'9- ttnp^rtat
jg BCF a
NO
ND
NO
ND
ND
ND
ND
4.481
ND
ND
2.423
4.104
ND
ND
ND
4.322
4.286 e
ND
ND
ND
ND
ND
ND
3.388
3.441
ND
3.480
NO
ND
2.037
NO
NO
NO
3.396
NO
NO
NO
ND
NO
ND
2.114
ND
ND
ND
ND
NO
gr _
Rank.
--
•-
—
-'
--
••
--
1
--
••
13
4
-•
•••
--
2
3
••
•-
--
—
•-
— *
11
8
••
j
••
—
1
•
-
-
1
4
ND
ND
NO
ND
NO
ND
ND
5.340
ND
NO
NO
NO
3.255
3.576
ND
NO
4.463
5.853
ND
NO
NO
ND
ND
ND
ND
NO
3.531
ND
NO
1.718
NO
ND
ND
ND
ND
ND
NO
NO
NO
ND
1.976
NO
ND
NO
NO
NO
9.20
7.70
7.00
6.91
6.89
6.85
6.60
6.48
6.11
6.10
6.00
6.00
6.00
6.00
6.00
5.88
5.75
5.69
5.61
5.60
S.60
5.53
5.51
5.48
5.45
5.40
5.23
5.15
s.oa
3 5.00
4.92
.88
.72
.56
.48
.46
.38
.34
.30
.28
9 .23
.20
.08
.05
.00
3.93
7.764
6.079
5.293
5.192
5.169
5.125
.844
.709
.294
.282
.170
.170
.170
.170
4.170
4.035
3.889
3.822
3.732
3.721
3.721
3.642
3.620
3.586
3.552
3.496
3.305
3.215
3.137
3.047
2.957
2.912
2.733
2.553
2.463
2.441
2.351
2.306
2.261
2.238
2.182
2.149
2.014
1.980
1.924
1.845
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
33
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TABLE 3.
EmpJ_r1.c8l Geometric Mean BCFs
Oetanol-water
?f>t Pollutant
1 aceiaphtrtene
102 alpna-hexacnlorocydohexane
104 delta-hexacnlorocydonexafle
103 beta-hexachlorocydohexane
105 ganma-hexachlorocydohexane
-- paratnion d
7 cMorobenzene
21 2,4,6-trlcnloropneno!
96 beta-enaosulfar\
97 enaosulfan sulfate
95 alpha-enaosulfan
S5 napntnalene
49 f luorctnchloromethane (Removed)
27 1 ,4-dicnlorobenzene
26 1,3-dichlorobenzene
25 1,2-dicnlorobenzene
113 toxapnene
38 ethylbenzene
62 N-nitrosoaiphenylamine
22 para-chloro-meta cresol
31 2,4-dichlorophenol
28 3,3'-d1chlorobenzidine
89 alarin
37 l,2-dipne"ylhydrazine
58 4-nitrophenol
— malatnion d
85 tetracn.loroethene
60 4 ,6-dinitro-o-cresol
6 tetrachlorometnane
42 bis(2-chloroisopropyl )ether
11 1,1,1-trichloroethane
87 trichloroethene
34 2,4-dimethylphenol
15 1,1,2,2-tetrachloroethane
47 bromofortn
32 1,2-dichloropropane
86 toluene
14 1,1 ,2-trichloroethane
— guthion d
50 dichlorodif louromethane (Removed)
24 2-chlorophenol
4 benzene
51 chlorodibromomethane
35 2,4-dinitrotolyene
36 2,6-dinitrotoluene
33 trans-l,3-dichloropropene
33 cis-l,3-d»chloropropene
30 l,2-trans-d\ch!oroethylene
— demeton d
23 chloroform
48 dichlorobromomethane
56 nitrobenzene
S benzidine
Tissue-*ater
log BCF a Ran*
ND
ND
NO
ND
ND
NO
ND
ND
NO
3.415 9
2.516 f 12
3.699 6
ND
ND
ND
NO
4.082 5
ND
ND
ND
ND
NO
ND
ND
ND
ND
ND
ND
NO
ND
ND
ND
NO
ND
NO
ND
ND
ND
ND
NO
NO
ND
ND
ND
ND
NO
NO
ND
ND
NO
NO
ND
ND
Liver-eff
log BCF b
NO
NO
ND
NO
NO
ND
ND
2.512
ND
NO
NO
1.104
ND
NO
ND
2.094
NO
0.012
ND
NO
ND
ND
NO
NO
ND
ND
0.672
NO
NO
ND
-0.322
0.115
ND
NO
NO
ND
-0.505
NO
ND
ND
ND
-0.011
NO
ND
ND
ND
ND
ND
NO
ND
ND
ND
NO
luent
Rank
— —
—
* •
.-
--
...
..
7
..
..
._
11
.-
..
.-
8
--
14
--
».
--
__
_.
*••>
..
»»
12
*.
*•
..
16
13
..
..
--
^m
17
._
..
._
._
15
._
..
._
_-
..
_.
„
vv
_.
—
Partition
log KOM
3.92
3.85
3.85
3.85
3.85
3.81
3.79
3.69
3.60
3.60
3.60
3.59
3.53
3.53
3.44
3.40
3.30
3.15
3.13
3.10
3.08
3.02
3.00
2.94
2.91
2.89
2.88
2.85
2.64
2.58
2.47
2.42
2.42
2.39
2.30
2.28
2.21
2.18
2.18
2.16
2.16
2.11
2.08
2.00
2.00
1.98
1.98
1.97
1.93
1,90
1.88
1.83
1.81
Coefficien
log BCF c
1.834
1.756
1.756
1.756
1.756
1.711
1.688
1.576
1.475
1.475
1.475
1.464
1.396
1.396
1.295
1.250
1.138
0.969
0.947
0.913
0.891
0.823 '
0.801
0.734
0.700
0.677
0.666
0.633
0.397
0.329
0.206
0.150
0.150
0.116
0.015
-0.008
-0.086
-0.120
-0.120
-0.142
-0.142
-0.196
-0.232
-0.322
-0.322
-0.344
-0.344
-0.356
-0.401
-0.434
-0.457
-0.513
-0.535
is
Rant
47
4B
49
50
51
52
53
54
ii
55
57
56
59
60
61
62
63
64
65
66
67
68
69
70
7!
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
96
99
I
1
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TABLE 3. [Continued;
Geometric Mean BCF
Octanol-water
pp* Pollutant
1: 1 ,1-Cicnloroetnane
57 2-iitroDnenol
54 isoonorone
71 ainetiyl pnthalate
1£ cnloroetnane
55 2,4-dini tropnenol
2= Ll-dichloroefyler-.e
6£ phenol
1C 1,2-dicnloroethane
7C diethyl phtnalate
6j N-nitrosodiDropylamine
4* aichlorometnane
Is 2-chloroetnyl vinyletner
4; bis(2-chloroethoxy jmetnane
; acrylonitri le
15 bis!2-chloroethyl jether
4f bromometnane
e acrolein
4! chloromethane
8t vinyl cnloride
61 N-nitrosodimethylamine
Tissue-water
log BCF a Rank
ND
ND
ND
ND
ND
NJ
ND
ND
ND
NO
ND
ND
NO
ND
NO
ND
ND
ND
ND
ND
ND
Li ver-«f fluent
log BCF 6 Rank
ND
ND
NO
NO
ND
NO
ND
NO
ND
NO
ND
NO
ND
ND
ND
ND
NO
ND
NO
NO
NO
Partitio
log Kow
1.78
1.77
1.67
1.61
1.54
1.53
1.48
1.46
1.45
1.40
1.31
1.30
1.28
1.26
1.20
1.12
1.00
0.90
0.90
0.60
-0.58
n Coe'fic
log BCF
-0.569
-0.53C
-0.693
-0.760
-0.839
-0.650
-0.906
-0.928
-0.940
-0.996
-1.097
-1.108
-1.131
-1.153
-1.220
-1.310
-1.445
-1.557
-1.557
-1.894
-3.219
ients
c *ai*
ICC
I V *
102
1C3
1C4
105
1C6
107
1C?
1C9
110
111
112
113
114
115
116
117
118
119
120
a L;.S. EPA Water Quality Criteria and new data from Appendix A.
b Data from Gossett et al . (1983).
c fcCFs normalized to lipid fraction predicted from Gossett et al.'s (1983) data (see Figure 4).
d :i01(n) pesticides not on the priority pollutant list.
e includes DDT, DDE, and ODD.
f Both alpha and beta isomers.
NA • not applicable.
ND » no data.
35
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rA3LE :. nAN< ORDER OF TRACE VETAL PRIORITY POLLUTANTS
BASED ON EMPIRICAL GEOMETRIC MEAN BCFs
PP* Pollutant
123 methyl mercury
123 phenyl mercury
123 mercuric acetate
120 copper
128 zinc
115 arsenic
118 cadmium
122 lead
119 chromium IV
119 chromium III
123 mercury
124 nickel
127 thallium
114 antimony
121 cyanide
116 asbestos
126 silver
125 selenium
117 beryl! ium
log BCF a
4.602
4.602
3.447
3.073
2.762
2.544
2.513
2.253
2.190
2.104
2.000
1.699
1.176
ND b
ND
ND
ND
ND
ND
Rank
1
2
3
4
5
6
7
8
9
10
11
12
13
--
--
—
--
--
~ ™
a U.S. EPA Water Quality Criteria and new data from Appendix A.
b ND = no data.
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for comparison of organic an£ trace metal contaminants, provided that trace
metals and organic suostances are measured in comparable fashions (i.e.,
~ieasjrec in tissues and in effluent). For example, recent studies of metal
accumulation ?n mussels (Mytil'j^ cal i form' an us ) conducted within the Zone
of Initial Dilution (ZID) and in farfield areas of the Whites Point and
Point loma effluent discharges in the Southern California Bight indicate
that such comparisons are feasible (Martin et al . 1984). However, calculation
of mussel-effluent ratios for trace metals is not presently possible because
trace metals were not measured in the effluent at the time of the study
(Norton, J., 27 September 1984, personal communication).
Justification of the proposed ranking procedure for organic substances
can be obtained by comparing the different rankings based on the three
potential indices of bioaccumul ation: octano1 -water partition coefficients
(Table 3), empirical BCFs obtained in laboratory and field studies (Appendix
A), and fish liver effluent estimated-BCFs calculated from the data of
Gossett et al . (1983b) (Table 3). There are seven organic priority pollutants
with sufficient data for application of all three approaches: PCB 1254,
ODTs (including ODD and ODE), heptachlor, HCB, pentachTorophenol , 1,2,4-tri-
chlorobenzene, and naphthalene (Table 5). Friedman's nonparametric analyses of
variance by ranks shows that the rank order of bioaccumulation is not the same
for all three indices (P<0.001). Furthermore, individual two-way comparisons
using Spearman's rank correlation procedure showed that correlations of the
empirical BCFs obtained from the literature with either KQW or liver-effluent
BCFs were not significant (P>0.05). However, a significant (0.01
-------
TABLE 5. RANK ORDER OF OCTANOL-WATER PARTITION COEFFICIENT (K0w) ,
EMPIRICAL 3CFs, AND FISH LIVER-EFFLUENT BIOCONCENTRATION
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FACTORS (C]/Ce) FOR SEVEN PRIORITY POLLUTANTS •
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Substance
PCS 1254
OOTsb
Heptach] or
HCB
Pentachlorophenol
1,2,4-trichlorobenzene
Naphthalene
w
1
2
3
4
5
6
7
Rank
BCF
1
2
5
4
7
6
3
Ci/ce
i
2
4
3
6
5
7
a Spearman's Rho»0.9Z9 (0.010.05).
b DDT, ODE, ODD.
Reference: Table 3.
1
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in Indigenous organisms exposed to sewage effluent. However, log Kow vs. BCF
models indicate structural compound classes comprising compounds with a
^gn Dioaccunul ation potential. Estimated BCFs greater than 1.0 indicate
that contaninant concentrations in tissues are greater than those in the
exposure medium. As seen in equation 4 and Figure 4, organic substances
with log Kow values greater than 2.3 have predicted liver-effluent BCFs
{normalized by lipid fraction in liver) greater than 1.0 (i.e., log 8CF>0).
As shown in Tables 2 and 3, structural compound classes in which all priority
pollutants have log Kow values greater than 2.3 are:
t Low molecular weight aromatic hydrocarbons
• High molecular weight polycyclic aromatic hydrocarbons
t Chlorinated aromatic hydrocarbons
• Chlorinated aliphatic hydrocarbons
• Volatile chlorinated aromatic hydrocarbons
PCBs
• Priority pollutant pesticides.
The priority pollutants with log Kow values less than 2.3 are in the following
structural compound classes:
• Phenols (1 substance)
t Substituted phenols (3 substances)
• Organonitrogen compounds {6 substances)
• Halogenated ethers (2 substances)
Phthalates (2 substances)
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• Miscellaneous oxygenated compounds (1 substance) tt
• 301(h) pesticides (2 organophosphates) M
• Volatile halogenated alkanes (12 substances)
• Volatile halogenated alkenes (4 substances)
t Volatile aromatic hydrocarbons (2 substances)
• Volatile unsaturated carbonyl compounds (2 substances) *
• Volatile ethers (1 substance). •
Eighteen of the twenty structural compound classes identified in Table 2 |
contain at least one substance with a relatively high bioaccumulation potential
(i.e., log Kow > 2.3). The only two compound classes with consistently •
low bioaccumulation potential are unsaturated carbonyl compounds and volatile
ethers. These two compound groups are extracted and analyzed with other flj
volatile compounds that have a higher bioaccumulation potential. Since *
analytical methods encompass a wide range of compounds that generally fall •
within a structural compound class, it is not practical at this time to •
eliminate any of the organic 301(h) priority pollutants or pesticides from
monitoring based on their low bioaccumulation potential (i.e., log Kow |
values less than.2.3).
Our analysis also indicates that all trace metals detected in sewage
effluent should be monitored routinely. There are several reasons for •
recommending this approach. There is a wealth of information concerning
bioaccumulation and bioconcentration of trace metals, particularly in bivalve
molluscs. However, there is not yet a good predictive relationship between
physicochemical characteristics and bioaccumulation potential of trace
metals comparable to the octanol-water partition coefficient and BCF model |
for organic substances. Second, some trace metals are not easily metabolized,
do not reach steady-state in tissues, and are slow to depurate. Thus, •
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empirical 3CFs for trace metals such as mercury are nominal values ana
~i3y be greatly exceeded under conditions of prolonged exposure. Third,
a ~iaxi~j-i o* 10 or 12 trace metals would require monitoring. Witn trie
exception of mercury, which requires separate sample preparation, trace
metal analyses can be performed on aliquots of a single sample using the
same analytical procedure. Therefore, given the analytical sophistication
available, the relatively low cost compared to organic analyses, and the
potential for bioaccumulation, any reduction in the monitoring of trace
metals is not recommended.
Although structure-activity models of bioaccumulation potential cannot
be used a priori to eliminate structural compound classes from 301 (h) monitoring
programs, they provide information useful in designing and managing such
monitoring programs. Monitoring the bioaccumulation of toxic pollutants
in natural and caged populations of indigenous organisms can be considered
as a means of integrating water quality conditions over longer periods
of time than can be accurately predicted from one-time or short-term (e.g.,
one day) composited effluent samples. For example, priority pollutants
and 301{h) pesticides may be present in the effluent below current detection
limits, but may still bioaccumulate in marine organisms. Thus, compliance
with the requirement for monitoring bioaccumulation of priority pollutants
and 301{h) pesticides may dispel uncertainties concerning effluent contaminant
concentrations and possible biological impacts. Site-specific bioaccumulation
data that demonstrates the absence of contaminants in effluent, tissues,
and sediments may provide justification for eliminating entire structural
compound classes from the monitoring program. For example, most volatile
compounds have log Kow values less than 2.3. This group could be eliminated
if there is no evidence of their occurrence in effluent or accumulation
in sediments or tissues. In general, the volatile compound classes are
relatively soluble, are degraded by a variety of environmental processes,
and have a low potential for bioaccumulation (Edwards 1977; Morley 1977;
Callahan et al. 1979; Connell and Miller 1984). Toxic organic contaminants
other than the priority pollutants and 301(h) pesticides may occur in sewage
effluent and bioaccumulate in marine organisms. These substances may be
identified and quantified from the GC/MS data generated during analysis
of priority pollutants and 301(h) pesticides in effluent, tissue, and sediment
41
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sanpies. Therefore, it Is recommenaed that non-priority pollutants ana
sestiriies be ^rcorcorated i^to 301 (h) monitoring programs when they occur ||
™
in sewage effluent ana sediments, and are shown to bioaccumjlate in Ti
organises. Typically, such analyses would include tentative identification «
and quantification of a limited number (e.g., 5-10) of the highest GC/MS 8
reconstructed ion chromatogram peaks.
It is recognized that these guidelines need to be tempered by other
considerations such as volume of the discharge, receiving water characteristics, •
history of biological impacts on sensitive communities (e.g. , benthic infauna) ,
specific knowledge of the behavior, toxicity of chemical contaminants, •
and requirements in selection of target organisms, sampling methods and ™
analytical detection limits. Many of these details are discussed in other —
volumes of the Bioaccumul ation Monitoring Guidance Report series (Tetra I
Tech 1985a,b) .
In conclusion, review of the bioaccumulation potential of toxic contami-
nants indicates that all priority pollutants and 301(h) pesticides should •
be included in design of 301(h) biological and water quality monitoring
programs. However, well designed bioaccumulation studies should provide •
site-specific information useful in program management and evaluation, ™
and may result in eliminating some compound groups from continued monitoring.
SUMMARY
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I
Toxic substances introduced into the marine environment
through sewage outfalls are partitioned among environmental |
compartments of water, suspended particulates, sediments,
and biota. Most organic and trace metal contaminants are •
associated with the participate phase of sewage effluent
and are therefore rapidly incorporated into sediments in •
the vicinity of the discharge. *
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Approaches used in the past to evaluate bioaccumulaticn
potential of toxic substances are empirical bioconcentration
'actors (3CFs), structure-activity relationsnips, equilibrium
oartitioning models, and indices based on metabolisn.
Based on U.S. EPA's (1980) Water Quality Guidelines, empirically
determined BCFs for four major classes of marine and estuarine
organisms (polychaetes, molluscs, crustaceans, and fishes)
exist for only 14 organic substances and 9 trace metals
on the priority pollutant list.
Although the relationship between the octanol-water partition
coefficient (Kow) and BCF is a reasonable predictor of BCFs
in laboratory studies of fishes and bivalves, existing data
are too limited to apply the model directly to marine and
estuarine organisms in nature.
A structure-activity model for bioaccumulation potential
was developed from compartmental characterization of chemical
contaminants in fish liver, effluent, and sediments in the
vicinity of a large sewage discharge into waters of the
Palos Verdes Shelf in southern California. The data show
that bioaccumulation potential of organic contaminants was
correlated with the octanol-water partition coefficient
and that the extent of biological contamination was propor-
tional to contaminant concentration in the sediments.
Studies conducted since 1980 indicate that tissue concentrations
of organic contaminants should be normalized to percent
lipids to aid in the interpretation of bioaccumulation data.
Therefore, data on lipid concentrations should be included
to the extent practicable in bioaccumulation monitoring
programs.
43
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9.
10.
The proposed ranking of bioaccumulation potential of organic
cor t s-iinants is based on log Kow. The prooosed ranking
of bioaccjmulat ion potential of trace metals is based on
empirically determined tissue-water BCFs (Table 4).
Tissue-effluent BCFs may provide a basis for comparing relative
bioaccunulation potentials of organic and trace metal contami-
nants when more comprehensive data become available. Also,
further development of tissue-effluent BCF vs. Kow models
should incorporate the effects of distance of the sampling
site from trie discharge site.
Review of the bioaccumulation potential of toxic contaminants
indicates that all priority pollutants and 301(h) pesticides
should be included in design of 301(h) biological and water
quality monitoring programs.
Site-specific bioaccumulation data will be useful in management
and evaluation of monitoring programs. The failure to detect
related contaminants in effluent, tissue, and sediments
may provide justification for eliminating entire structural
compound classes from the monitoring program.
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Zi*ko V and W.V. Carson. 1975. Accumulation of thalliin In cU-ns anc
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APPENDIX A. BIOCONCENTRATION FACTORS FOR PRIORITY POLLUTANTS AND 301(h)
PESTICIDES IN MARINE AND ESTUARINE ORGANISMS AS MODIFIED FROM U.S. EPA (1980}
WATER QUALITY CRITERIA DOCUMENTS AND ADDITIONAL INFORMATION
PUBLISHED FROM JANUARY, 1980, TO AUGUST, 1984
57
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pp* Pollutant Tanon Species Tissue a Llplds BCF Exposure Studies References
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- APPENDIX B. PRIORITY POLLUTANTS AND 301(h) PESTICIDES SORTED BY STRUCTURAL
™ COMPOUND CLASS (TABLE 8-1), PRIORITY POLLUTANT NUMBER (TABLE 8-2} , AND BY
• ALPHANUMERIC ORDER (TABLE B-3)
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TABLE B-l. PRIORITY POLLUTANTS AND 301(h) PESTICIDES LISTED
ACCORDING TO STRUCTURAL COMPOUND CLASS
StruCtjral CorWOufld
CUSS
PPI
Pollutant
Structural Concound
Class
Pollutant
Phenols
Substituted Phenols
Organonitrcge" Compounds
lo* Nolecu'ar Heigft
Aromatic Hyflrocaroons
High Koiecular Height
PAH
Chlorinated Aromatic
Hydrocarbons
Chlorinated Aliphatic
NydrocarDons
Hatogenatee Ethers
65
34
21
22
24
31
57
58
59
60
64
5
28
35
36
37
56
61
62
63
1
55
77
78
81
80
39
72
73
74
75
76
79
82
83
84
8
9
20
25
26
27
52
12
53
IB
40
41
42
43
phenol
2,4-d1methylpheno1
2,4,6-triehlorophenol
para-chloro-met* cresol
2-chlorophenol
2,4-dichlorophenol
2-nitropnenol
4-nitrooheno)
2,4-en?id1ne
3,3'-d1ehIorobenzid1ne
2,4-dinltrotoluene
2,6-dinitrotoiuene
l,2-d1phenylhydrazine
nitrobenzene
N-ni trosodi methyl amlne
N-nitrosodiphenylamine
N-nftrosodipropylamine
acenaphthene
naphthalene
acenapnthylene
anthracene
pftenanthrene
f luorene
fluoranthene
beniota lanthrjcene
benzo(a)pyrene
benio(b (fluoranthene
benio(k)fluoranthene
chrysene
beniotgni )perytene
d t benzo(a.h) anthracene
i ndeno (1,2 ,3-cd )py rene
pyrene
1 , 2 ,4-t rl Chi orobeniene
hexachlorobeniene
2-chlorbniphthalene
l,2-d1chlorobenzene
1,3-dichlorobenzene
1,4-dichlorobenzene
he»ach)orooutad1en«
hexachloroethane
hextchlorocyclopentadiene
bi»(2-ehloro*thyl )ether
4-chlorophenyl etner
4-bromopheny! ether
b1s(2-chloroijopropy1 Jether
b1s(2-chloroethoi>)nethane
Pnthalates 66
67
68
69
70
71
PCBS 106
107
108
109
no
in
112
Miscellaneous Oiygenated
Compounds 129
54
Pesticides 89
90
91
92
95
98
99
100
101
102
103
104
105
113
Volatile Hilogenated
AUanes 6
10
11
13
14
15
16
23
32
44
45
46
47
48
49
50
51
6is(Z-ethylhe«yl )pntnala;e
butyl benzyl phthalate
di-n-butyl phthalate
di-n-octyl phthalate
diethyl pnthilate
dimethyl phthalate
PCB-1242
PCS- 1 254
PCB-1221
PC8-1232
PC6-1248
PCB-1260
PCB-1016
TCDD (dioiin)
isophorone
ildrln
dieldrin
chlordane
DDT (a)
endosuHan (b)
endrin
endrin aldehyde
heptachior
heptachlor epoilde
•Ipha-hexach1orocycloheach 1 orocycl oneiane
deUi-heiaehlorocyclohexane
giinM-hexachlorocyclonenane
toxaphene
mires (c)
methoxychlor (c!
pirithlon (d)
malathion (d )
guthion (0)
demeton' (d)
tetrachloronethane
,2-dlchloroetfttne
,1,1-tnchloroethane
,l*d1chloroethane
. 1 ,2-t riehl oroethane
,1,2,2-tetracnloroetnane
chloroethane
chloroform
1 ,2-d1ch)oropropane
dichloromethane
cnloromethane
brofflomethine
bromof ortfi
dlchlorobromomethane
fluorotrlchloronethane (Removed)
dfchlorodif luoromethane (Removed)
chlorodibronome thane
66
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.nO-1 --.
Structural Compound
Class
Pollutant
Volatile
AUeies
Volatile Aromatic
Hydrocaroons
Volatile Chlorinated
Aromatic Hyarocaroons
29 1,1-d1. cMoroetnyleie
33 1,2-lrans-dicnloroethylene
33 trans-l,3-dichloropropene
33 cii-1,3-dichlorooropene
85 tetrachloroetnene
87 trienloroetnene
88 vinyl ehloriae
4 benzene
38 etnyloe.izene
86 toluer.e
ehlorobeirene
Volatile Unsitura'ec
Carbonyi Compounds
Volatile Ethers
Metals
ttlscel laneous
i
i
2
3
19
114
US
117
118
119
120
122
123
124
125
126
127
U8
m
116
iCrnlein
acryloni tri le
2-chloroethyl vinylether
bis(chlorometny! Jether (Removed)
antimony
arsenic
beryllium
ddmlum
Chromium
copper
lead
mercury
nickel
selenium
Silver
thallium
line
cyanide
asbestos
< Includes DOT, 003, ana DOC.
b Includes alpha-endosulfan, beta-endosulfan, and endosuKan sulfate.
c Chlorinated 301(h) pesticides that are not on the priority pollutant list.
d Organophosphorus 301(h) pesticides that are not on the priority pollutant list.
67
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PPI
E
5
1C
11
12
1:
14
15
1£
IS
IS
IS
2C
21
22
23
24
25
2£
27
2£
29
3C
31
32
33
33
34
35
36
37
33
39
40
41
42
43
44
45
rABL£ 5-2. PRIORITY POLLUTANTS AND 301{h} PESTICIDES LISTED
BY EPA PRIORITY POLLUTANT NUMBER
Pollutant
PP»
Pollutant
acenapnthene
acrolein
acrylonitri 1e
benzene
benziaine
tetrachloromethane
cfUorobenzene
1,2,4-trichlorobenzene
hexacnlorobenzene
1,2-dichloroethane
1,1,1-trichloroethane
hexachloroethane
1,1-dichloroethane
1 ,1,2-triehloroethane
1,1,2,2-tetrachloroethane
chloroethane
bis(2-chloroethyl (ether
bisfchloromethyl )ether (Remo
2-chloroethyl vinyTether
2-chloronaphthalene
2,4.6-trichlorophenol
para-chloro-meta cresol
chloroform
2-chloropnenol
1,2-dichlorobenzene
1,3-dichlorobenzene
1,4-dichlorooenzene
3,3'-dichlorobenzidine
1.1-dichloroethylene
1,2-trans-dichloroethylene
2,4-dichloropnenol
1 ,2-dichloropropane
ci s-1 ,3-dichloropropene
ved)
2, 4-di metnyl phenol
2,4-dinitrotoluene
2,6-dinitrotoluene
l,2-dipheny1hydrazine
ethyl benzene
fluoranthene
4-chlorophenyl ether
4-bromophenyl ether
b1s{2-chloroisopropyl >ether
Dis(2-ch1oroetho*y)m«thBne
dichloromethane
chloromethane
46 bromoraethane
47 bromoform
48 dichlorobromomethane
49 fluorotrichloronethane (Removed)
50 dichlorodiflouromethane (Removed)
51 chlorodlbromomethane
52 hexachlorobutadiene
53 hexacnlorocyclopentadiene
54 Isophorone
55 naphthalene
56 nitrobenzene
57 2-nitrophenol
58 4-nitrophenol
59 2,4-dlnitrophenol
60 4,6-dinltro-o-cresol
61 N-nitrosodiinethylainlne
62 N-nitrosodlphenylamine
63 N-nitrosodipropylamine
64 pentachloropnenol
65 phenol
66 b1s(2-ethy1hexylIphthalate
67 butyl benzyl phthalate
68 di-n-butyl phthalate
69 di-n-octyl phthalate
70 diethyl phthalate
71 dimethyl phthalate
72 benzo(a)anthracene
73 benzo(a)pyrene
74 benzo(b)fluoranthene
75 benzo(k)f1uoranthene
76 chrysene
77 acenaphthylene
78 anthracene
79 benzotghi )perylene
80 fluorene
81 phenanthrene
82 d1benzo(a,h}anthracene
83 1ndeno(l,2,3-cd)pyrene
84 pyrene
85 tetrachloroethene
86 toluene
87 trlchloroethene
88 vinyl chloride
89 aldrin
90 dleldrin
PPI
Pollutant
91 chlordane
92 DOT (a)
95 endosulfan (b)
98 endrin
99 endrin aldehyde
100 heptachlor
101 heptachlor epoxide
102 alpha-hexachlorocyclohexane
103 beta-hexach)orocyclohexane
104 delta-hexachtorocyclofiexane
105 garnma-hexachlorocyclohexane
106 PCB-1242
107 PCB-1254
108 PCB-1221
109 PC8-1232
110 PCB-1248
111 PCB-1260
112 PCB-1016
113 toxaphene
114 antimony
115 arsenic
116 asbestos
117 beryllium
118 cadmium
119 chromium
120 copper
121 cyanide
122 lead
123 mercury
124 nickel
125 selenium
126 silver
127 thallium
128 zinc
129 TCOD (dioxin)
mi rex (c)
methoxychlor (c)
parathion (d)
ma lathion (d)
guthion (d)
demeton (d)
a includes DOT, ODD, and ODE.
b Includes alpha-endosulfan, beta-endosulfan, and endosulfan sulfate.
c Chlorinated 301(h) pesticides that are not on the priority pollutant 11st.
d Organophosphorus 301(h) pesticides that are not on the priority pollutant list.
68
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PP»
TABLE B-3. PRIORITY POLLUTANTS AND 301{h) PESTICIDES
LISTED IN ALPHANUMERIC ORDER
Pollutant
PPI
Pollutant
PPl
Pollutant
11 1,1,1-trichloroethane
15 1,1,2,2-tetrachJoroetnane
14 1,1,2-tricftloroethane
13 l.Ndichloroetnane
29 1,1-dichloroethylene
8 1,2,4-tricnlorobenzene
25 1,2-dicnlorobenzene
10 1,2-dichloroetnane
32 1,2-dichloropropane
37 1,2-diphenylhydrazine
30 1,2-trans-dichloroetnylene
26 1,3-dicrtlorobenzene
27 1.4-dicMorobenzene
21 2.4,6-trichlorophenol
31 2,4-dichlorophemjl
34 2,4-dimethyl phenol
59 2,4-dinitropnenol
35 2,4-dinitrotoluene
36 2,6-dinitrotolgene
19 2-Chloroethylvinyletfter
20 2-chloronapnthalene
24 2-ehloropnenol
57 2-nitroohenol
28 3,3'-dicnlorobenzidine
60 4,6-dinitro-o-cresol
41 4-t>romophe«y1 ether
40 4-chlorophenyl ether
58 4-nitrophenol
92 DOT (a)
61 N-nltrosodimethylamine
62 N-nitrosodiphenylamine
63 N-nitrosodipropylamine
112 PCB-1016
108 PC8-1221
109 PC8-1232
106 PC8-1242
110 PC8-1248
107 PCB-1254
111 PCB-1260
129 TCOD (dioxin)
1 acenaphthene
77 acenaphthylene
2 acrolein
3 acrylonitrile
89 aldrtn
102 alpha-hexachlorocyclohexane
78 anthracene
114 antimony
115 arsenic
116 asbestos
4 benzene
5 benzidine
72 benzo(a)anthracene
73 benzo(a)pyrene
74 benzo(b)fluoranthene
79 benzotghi)perylene
75 benzo(k)fluoranthene
117 beryllium
103 beta-hexachlorocyclonexane
43 bis(2-chloroethoxy)methane
18 bis(2-cMoroethyl )ether
42 bis(2-chloroisopropyl )ether
66 bis(2-ethylhexylJphthalate
19 bis(ch1oromethy1 Jether (Removed)
47 bromoforn
46 bromomethane
67 butyl benzyl phthalate
118 cadmiun
91 cnlordane
7 chlorobenzene
51 chlorodlbromomethane
16 chloroethane
23 chloroform
45 chlorometnane
119 chromium
76 chrysene
33 cis-l,3-dich1oropropene
120 copper
121 cyanide
104 delta-hexachlorocyclohexane
demeton (b)
68 dl-n-butyl phthalate
69 di-n-octyl phthalate
82 dibenzo(a,h)anthracene
48 • dlchlorobromomethane
SO dicMorodiflouromethane (Removed)
44 dlchloromethjne
90 dieldHn
70 diethyl phthalate
71 dimethyl phthalate
95 endosulfan (c)
98 endrin
99 endrin aldehyde
38 ethyl benzene
39 fluoranthene
80 fluorene
49 fluorotrichlorometnane (le^c
105 gamma-hexachlorocyclohexane
guthion (b)
100 heptachlor
101 heptachlor epoxide
9 hexachlorobenzene
52 hexachlorobutadiene
53 hexachlorocyclopentadiene
12 hexacnloroethane
83 indeno(l,2,3-cd)pyrene
54 Isophorone
122 lead
malathion (b)
123 mercury
methoxychlor (d)
mi rex (d)
55 naphthalene
124 nickel
56 nitrobenzene
parathion (b)
22 para-chloro-meta cresol
64 pentachloropnenol
81 phenanthrene
65 phenol
84 pyrene
125 selenium
126 silver
85 tetrachloroethene
6 tetrachloromethane
127 thallium
86 toluene
113 toxaphene
33 trans-l,3-dichloropropene
87 trlchloroethene
88 vinyl chloride
126 zinc
a Includes DDT, ODD, and DOE.
b Organophospnorus 301(h) pesticides that are not on the priority pollutant list.
c Includes alpha-endosulfan, beta-endosulfan, and endosulfan sulfate.
d Chlorinated 301{h) pesticides that are not on the priority pollutant list.
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