As noted earlier under the description of the Exposure Decision Tree, several factors must be considered when evaluating data adequacy for allocating the RfD among media. One of the main factors to consider is the number of samples in the data set being used to describe a particular exposure medium. Although there are no universal rules about adequate sample sizes, the proposed rules of thumb discussed earlier on page 146 are used here. For estimating a 90th percentile value using a non parametric method, 45 samples are needed, of which at least five must be above detection limits to determine the 90th percentile value. Fewer samples are usually adequate for estimating mean and median values. In addition to evaluation of sample size, the other aforementioned factors should be assessed for a full evaluation of data adequacy [ i. e., representativeness of the sample, the accuracy in the analytical procedures, and the sensitivity of the measurement relative to the environmental levels of concern ( i. e., whether detection limits are low enough such that concentrations can be detected in most samples within a data set)].

Intake of Drinking Water from Raw Surface Water Sources For this analysis, the two most recent studies were used to represent central and high end estimates of concentrations. The central tendency estimate was determined using the most recent data previously indicated. The number of data points that made up this value was five. Because the high end estimate of 15.6 ng/ L was not determined as a 90th percentile value, the sample size used to determine the value was not evaluated in the context of the number needed to determine a 90th percentile. 152 It is important to consider several factors in determining whether the data are adequate. First, both samples are current and thus more representative of Chemical X concentrations than older data. However, the data are from two lakes only, and thus, do not represent a national distribution of data. In addition, the data used for the average and the high end values are taken from two surveys, and thus, differences exist between these studies such as the number of chemical analogues generally detected. Neither study reports the detection limits, or whether any of the values were below detection.

Based on the lack of information regarding detection limits, limited geographic representation of the data, and low sample sizes, it was determined that data are inadequate to obtain central tendency and high end estimates of exposure. Although such estimates are presented, they represent only crude numbers.

Freshwater/ Estuarine Fish Intake The most recent data appear adequate to use in estimating typical and high end exposures from contaminants in fish. The purpose of the national study used was to determine the geographical extent of chemical contamination. Thus, data were collected from all watersheds in the United States and were collected in 1984. The sample size of this study seems large enough to adequately represent the geometric mean value. As noted above, the minimum number of samples needed to adequately represent the 90th percentile of a given exposure ( using non parametric methods to estimate acceptable sample size) was determined to be 45. The sample size needed to adequately determine the median value would be smaller than the size needed to determine the 90th percentile. Because the geometric mean may be assumed to be equivalent to the median, it is assumed that the number of samples used in the study is adequate to estimate the geometric mean. In addition to a minimum sample size needed to estimate the geometric mean, a minimum number of positive values is also needed to determine the median ( or geometric mean). Although it is not known how many samples are above detection, 91 percent of the stations sampled had Chemical X concentrations above detection. Thus, for purposes of this example, it was assumed that a majority of samples had Chemical X concentrations above detection.

For the estimates of fish consumption rates, the large sample size and national representation of the CSFII survey make it a useful survey for measurement of fish consumption, if the assumption is made that the consumption rates from the CSFII study ( which measured consumption of both sport caught and commercial fish) apply to consumption of freshwater/ estuarine fish.

Intake of Treated Drinking Water from Surface Water Sources None of the studies of Chemical X concentrations in drinking water from surface water sources is ideal for estimating exposure through drinking water from surface water sources. The first national study reviewed may offer the best information to use in estimating exposure because Chemical X concentrations from surface water sources were taken from many cities across the country and because the survey reported detection limits. However, because of the large number of 153 nondetected samples, central exposure values cannot be determined without making assumptions about the concentrations in the undetected values. In addition, the data are older and, therefore, may not represent current concentrations. The more recent national study did not detect any Chemical X concentrations and did not report the detection limits. Without knowing the detection limit, it is impossible to make any assumption about the concentrations in the undetected samples, unless it is assumed that the concentrations are zero. Because of these problems, these data are considered inadequate for estimating exposure from surface water supplies of drinking water.

Intake of Treated Drinking Water from Ground Water Sources Data on exposures from ground water sources of drinking water are difficult to use because few detected samples have been found. As with the surface water sources, it is impossible to make assumptions about undetected samples measured in the study chosen. For data from the first national study, the number of undetected samples and the time period during which the study was conducted make it difficult to estimate central estimates of exposure from this study with any level of confidence. The state study chosen collected about 160 samples, of which 30 had concentrations above the detection limit. However, this study was done at the time that uses of Chemical X stopped and the concentrations were measured in industrialized settings within this one state. Because some studies reported so many nondetected values, and others used only regional data, these data are considered inadequate for estimating reliable central estimates of exposure to Chemical X in ground water sources of drinking water.

Sources of Food Intake The limited number of positive samples found in the 44 diet collections make estimating exposure using these data difficult. Without knowing exact detection limits, it is difficult to make assumptions about concentrations for the undetected samples. In addition, for any given food item, generally only one value was above the quantifiable limit for Chemical X. ( Two positive samples were found for one food item.) Thus, neither adequate central or high end concentrations could be estimated. Because of these problems, it was determined that the data are not adequate for estimating national distributions of Chemical X concentrations in food.

Sources of Air Intake These data are too limited for adequately estimating national exposure to Chemical X from air. Several of the studies were performed in the 1970s when companies still manufactured Chemical X. In addition, more recent nationally representative exposure estimates are not available. Finally, sample size was not available for many of these studies. Thus, it was determined that these data are inadequate to estimate exposure from air sources of Chemical X. 154 Setting AWQC Under the Exposure Decision Tree Approach, either the available exposure data or default values are evaluated against the toxicological dose that should result in no adverse health effects from exposure to Chemical X. The toxicological dose used to evaluate the exposure and the method of allocation are described below.

The toxicological dose is determined first, as it is the parameter to which the other factors are applied. Chemical X has been shown to cause more than one type of toxicity. Two chronic RfDs have been established for Chemical X, based on the oral route. The lowest value is 2.0 x 10 5 mg/ kgday and is based on clinical and immunological studies performed on monkeys. The adverse health effects found in this study include decreased antibody response to injected sheep red blood cells by three principal cells of the immune system, exudate from the eye, inflammation of eyelid glands, and changes in finger and toe nails. Because non water exposures are considered for cases in which pollutants cause threshold effects, this example uses the chronic RfD value of 2.0 x 10 5 mg/ kg day to evaluate chronic toxicity effects.

In addition to these chronic effects, Chemical X has been shown to result in adverse health effects based on results from short duration studies. EPA recommends that where such effects have been identified, these should be considered and compared with exposure estimates which use intake rates that may plausibly be ingested within a short time. A literature review shows an acute study with a LOAEL of 244 mg/ kg day for developmental ( fetal) effects. Assuming uncertainty factors of 10 for animal to human, intrahuman, and LOAEL to NOAELs results in a value of 0.244 mg/ kg day. To determine whether to evaluate the chronic or developmental effects, the differences between short term exposures and the " RfD DT" were compared with differences between chronic exposure and the chronic RfD.

Specifically, health effects and relevant intake assumptions for a target population of pregnant women were considered for this example due to the fetal developmental effects indicated above. It was assumed that a pregnant woman might ingest a one time high dose or multiple high doses of fish within a short time period. Data show that such doses may be much higher than average fish ingestion. However, it is unlikely that high end intakes from other media would occur simultaneously. Thus, the comparison used high intake assumptions for fish intake only. The information on " acute" fish intake rates ( as defined in Section <IP_ADDRESS>, Preference # 3) available from the CSFII includes assumptions for women of childbearing age ( ages 15 44 years old). The 90th percentile value of " acute" intake ( see Section <IP_ADDRESS> for a discussion of these intake values determined in the CSFII) is 148.8 g/ day, which was used in the comparison. The body weight value of 65 kilograms from Ershow and Cantor ( 1989) was used and is applicable to pregnant women.

Comparing differences between this shorter term exposure and the developmental effect RfD " RfD DT" with the differences between chronic exposure and the chronic RfD indicated that shorterterm exposure compared with the developmental RfD is lower than the chronic exposure versus the chronic RfD. Chronic exposures using intake from public drinking waters rather than ambient water 155 ( 1.1 x 10 4 mg/ kg day) are about five and a half times higher than the chronic RfD ( 2 x 10 5 mg/ kgday whereas the shorter term exposures ( 1.6 x 10 3 mg/ kg day) are much lower than the developmental " RfD DT" ( 0.244 mg/ kg day). Thus, only chronic exposures are considered in this example.

Using the process to set AWQC outlined in Exhibit 2.3.4, the available exposure data ( although limited) are used to determine allowable doses for each medium. This allocation is performed because there is more than one source/ use of the chemical ( Box 8) and because there is some information available to characterize all sources of exposure ( Box 10A). Because the exposure estimates are determined not to be adequate enough to represent central and high end estimates, the allowable dose for any one source can only be as high as 50 percent of the total allowable dose. The allowable dose is also limited by the floor 20 percent of the allowable dose ( Box 10C). Total exposures compared with the RfD are included in Table 2.3.26. Each exposure source as a percent of total exposure, as well as the default values ( also as percentages) of these exposures used in criteria allocation, are included in Table 2.3.27. These allowable doses from each medium are determined for three types of individuals discussed earlier ( average individuals from the general population, sportfishers, and subsistence fishers). Crude contaminant concentration values are available for high exposure estimates and for central tendency estimates. Thus, a decision must be made about whether to use the high end or central value when setting standards for a given environmental medium. Guidance is currently being developed to address the use of central tendency versus high end values. For this example, central estimates of contaminant concentrations are used for the general population, and high end concentration estimates of fish are used for sport and subsistence fishers. [ Because the variability in the exposure estimates is based predominantly on the variability of concentrations in the exposure media, the high end values do not reflect use of high end versus central tendency consumption rates. Rather, differences in consumption rates ( which apply only to fish consumption) are reflective of the defaults used for the different populations of fishers.] States may wish to use different percentile values for the general population and other fishers based on concentrations of contaminants in their area.

For the three groups of fish consumers being evaluated ( subsistence fishers, sportfishers, and individuals from the general population), two criteria are relevant: the AWQC and health tolerances set for pesticide use. Thus, an allocation based on the percentage approach ( and allocating the free space) is done. Because total exposure is greater than the RfD, there is no free space to be allocated. Thus, the percentage approach was used without allocating free space. 20 These estimates were made using high end values of fish exposure ( i. e., the reported high end contaminant concentration, along with the default consumption rates) for these populations. High end values were also assumed for the estimates made with ambient water data ( i. e., high end contaminant concentrations). If central tendency contaminant concentrations had been used, the subsistence fisher percent of total exposure attributable to fish and water, and fish only would be 99.3% and 98.6%, respectively. If central tendency contaminant concentrations had been used for the sportfisher, the estimates would equal those for the general population.

156 Table 2.3.26: Total Exposure Compared with the RfD Total Exposures with Ambient water ( mg/ kg day) Total Exposures with Drinking water ( mg/ kg day)

General population 1.0 x 10 4 1.1 x 10 4 Sportfisher20 1.7 x 10 3 1.7 x 10 3 Subsistence fisher20 8.3 x 10 3 8.3 x 10 3 RfD 2.0 x 10 5 2.0 x 10 5 Table 2.3.27: Exposure Information  Percent of Total Exposures and Default Exposure Percentages for Three Types of Individuals Fish and Water Criterion Fish Only Criterion Exposure as a Percent of Total Exposure Default Value Exposure as a Percent of Total Exposure Default Value Subsist. Sport Gen. Subsist. Sport Gen.

Fish 99.9 99.8 96.6 50% 99.9 99.6 93.5 50%

Water 0.04 0.2 3.2 20%

Diet 0.01 0.07 1.1 20% 0.01 0.07 1.1 20%

Air 0.03 0.1 2.2 20% 0.03 0.1 2.2 20%

Total exposure compared with the RfD is included in Table 2.3.26. All exposures are greater than the RfD. For both criteria and all three types of individuals, the percent of exposure from eating either ( 1) fish and water or ( 2) fish only is very high (> 90 percent of the fisher's total exposure). The ceiling of 50 percent is used for the RSC allocation for the ambient water quality criterion.

The value of 50 percent is then multiplied by the total allowable dose ( 2.0 x 10 5 mg/ kg day) to determine allowable doses for all three types of individuals, as noted in Table 2.3.28. For each of the criteria, the allowable dose of 1 x 10 5 mg/ kg day is used to determine the AWQC. Criteria based on ( 1) fish and water intake, and ( 2) fish intake only are calculated and presented in this table. For 21 If the alternate default intake assumption of 86.3 g/ day was used instead, the subsistence fisher AWQC estimates for fish and water, and fish only, would be 1.5 x 10 7 mg/ L 157 each type of fisher, the fish and water criterion values do not differ from the fish only criterion values. Other exposure factors from the equation used in the calculation are: body weight= 70 kg; drinking water intake= 2 L/ day ( or incidental ingestion of 0.01 L/ day); fish intake rates of 0.01780 kg/ day for the general and sportfisher populations and 0.08630 kg/ day for subsistence fishers; and a bioaccumulation factor of 120,000.

Table 2.3.28: AWQC for Three Types of Individuals Fish and Water Criterion Fish Only Criterion Default Allowable Dose ( 50% of RfD) 1.0 x 10 5 mg/ kg day 1.0 x 10 5 mg/ kg day AWQC: Subsistence Fisher21 Sportfisher General Population 6.8 x 10 8 mg/ L 3.3 x 10 7 mg/ L 3.3 x 10 7 mg/ L 6.8 x 10 8 mg/ L 3.3 x 10 7 mg/ L 3.3 x 10 7 mg/ L Presenting Information to Risk Managers Although the above example utilizes the 20 percent floor for the other sources of exposure, it is clear that a combination of allocations of the RfD, if used, would exceed the 80 percent ceiling and in the case of the fish only criterion, would exceed 100 percent of the RfD. This example also illustrates the potential need for flexibility to lower the floor ( perhaps to zero?) due to exposures in exceedance of the RfD.

Because total exposures from all environmental media are greater than the dose without an appreciable risk of toxicological effects, several pieces of information can be presented to risk managers for their review in deciding how to apportion the dose of 2.0 x 10 5 mg/ kg day among exposure sources. These data include information about the toxicity of the chemical ( including uncertainty in the estimate), information about exposures, and issues involving control of Chemical X. Because exposures and uncertainties in these estimates were discussed in previous sections, they will not be discussed again here. Additionally, control technology issues will not be discussed here, as they are not directly related to estimating exposure via the Exposure Decision Tree Approach. However, additional toxicity information is presented below. 158 The toxicity data supporting the RfD of 2.0 x 10 5 mg/ kg day should be described in order to give risk managers an idea about the confidence in the value. Some evaluative information is available from the Integrated Risk Information System. As noted above, the critical endpoints upon which the RfD is based include decreased antibody response to injected sheep red blood cells, exudate from the eye, inflammation of eyelid glands, and changes in finger and toe nails. A total uncertainty factor of 300 is applied to the Lowest Observed Adverse Effect Level ( LOAEL) from the critical study. The total number of uncertainty factors account for: sensitive individuals within the population ( a 10 fold factor); extrapolation from monkeys to humans ( a 3 fold factor); use of a LOAEL instead of a NOAEL ( a partial factor); and use of a subchronic rather than a chronic study ( a 3 fold factor). The overall confidence in the RfD is medium because the confidence in the principal study based, in turn, on the confidence in the data base are also considered medium.

In addition to toxicity data, information on ways to control exposure in different environmental media can be presented to risk managers to aid them in determining the relative ease of controlling exposure from different environmental media. Ideally, this information would include expected incremental costs of treatment needed per unit decrease of Chemical X and other feasibility issues associated with controlling Chemical X in different environmental media.

Exposure from consuming freshwater/ estuarine fish represents the single largest exposure to Chemical X. Other sources of exposure represent smaller percentages of total exposure, but all highend exposures except air exposures also exceed the RfD individually.

<IP_ADDRESS> Bioavailability of Substances from Different Routes of Exposure For many chemicals, the rate of absorption can differ substantially from ingestion compared to inhalation. There is also available information for some chemicals which demonstrates appreciable differences in gastrointestinal absorption depending on whether the chemical is ingested from water, soil, or food. For some contaminants, plant and animal food products may also have appreciably different absorption rates. Regardless of the allocation approach used, EPA proposes using existing data on differences in bioavailability between water, air, soils, and different foods for estimating total exposure and in allocating the RfD. The Agency has developed such exposure estimates for cadmium ( USEPA, 1994b). In the absence of data, EPA will assume equal rates of absorption from different routes and sources of exposure.

Information on absorption rates for Chemical X is not available. Available studies only generally describe varying fractions of the chemical in different media due to varying rates of volatilization, solubility, and adsorption. Discussions about chemical transformations in the environment and by human metabolism are similarly vague. In the absence of such data, it is assumed for this example that Chemical X is fully absorbed and that the rates of absorption from different routes and sources of exposure are equal. 159 2.3.5 References Alabama Department of Environmental Management. 1993. Estimation of Daily Per Capita Freshwater Fish Consumption of Alabama Anglers. Prepared by Fishery Information Management Systems, Inc., and the Department of Fisheries and Allied Aquacultures, Auburn University.

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USEPA. 1995b. Mercury Study Report to Congress. Volume II: Inventory of Anthropogenic Mercury Emissions in the United States.

USEPA. 1996. Asian and Pacific Island Seafood Consumption Study. Exposure Information Obtained Through a Community Centered Approach. Risk Evaluation Unit, Office of Environmental Assessment, USEPA Region 10. EPA 910/ R 96 007. August.

USEPA. 1997a. Exposure Factors Handbook. National Center for Environmental Assessment, Office of Research and Development. Washington, DC. EPA/ 600/ P 95/ 002Fa. August.

USEPA. 1997b. Guidance for Conducting Fish and Wildlife Consumption Surveys. September.

USEPA. 1998. Daily Average Per Capita Fish Consumption Estimates Based on the Combined USDA 1989, 1990, 1991 Continuing Survey of Food Intakes by Individuals ( CSFII). Volume I: Uncooked Fish Consumption National Estimates; Volume II: As Consumed Fish Consumption National Estimates. Prepared by SAIC under Contract # 68 C4 0046. March.

Wells, P. 1990. Michigan Department of Natural Resources ( MDNR), Recreation Division, Personal Communication to Gary Hurlburt, Surface Water Quality Division, MDNR.

West, P. C., et al. 1989. Michigan Sport Anglers Fish Consumption Survey: A Report to the Michigan Toxic Substance Control Committee, University of Michigan Natural Resource Sociology Research Lab. Technical Report # 1, Ann Arbor, Michigan, MDMB Contract # 87 20141.

West, P., J. M. Fly, R. Marans, F. Larkin and D. Rosenblatt. 1993. The 1991 92 Michigan Sport Anglers Fish Consumption Study: Final Report to the Michigan Great Lakes Protection Fund, Michigan Dept. of Natural Resources. University of Michigan, School of Natural Resources, the Natural Resource Sociology Research Lab. Technical Report # 6. May. 22According to the 1980 AWQC National Guidelines, laboratory measured or predicted bioconcentration factors were used when field measured bioconcentration factors ( equivalent to what are now called field measured BAFs) were not available.

166 Wheatley, B. 1996. Environment Canada. Personal communication with Abt Associates. March 26.

Wolfe, R. J. and R. J. Walker. 1987. Subsistence Economics in Alaska: Productivity, Geography, and Development Impacts. Arctic Anthropol. 24( 2): 56 81.

Young, P. 1994. USEPA Region 9. Personal communication with Abt Associates. July 28.

Zein Eldin, Z. P. and M. L. Renaud. 1986. Inshore Environmental Effects on Brown Shrimp, Penaeus aztecus, and White Shrimp, P. setiferus, Populations in Coastal Waters, Particularly Texas. Marine Fisheries Review. 48( 3): 9 16.

2.4. Use of BAFs in the Derivation of AWQC 2.4.1 Introduction Aquatic organisms are known to accumulate certain types of chemicals in their bodies. Uptake of these chemicals may occur from exposure to contaminated water, consumption of contaminated food, and exposure to other sources such as contaminated bottom sediment. This chemical uptake process is called bioaccumulation. For some chemicals, such as certain highly hydrophobic chemicals, uptake through the food chain can be the most important route of exposure. As organisms in higher trophic levels feed on organisms in lower trophic levels, tissue concentrations of these chemicals increase through the trophic levels so that the concentrations in the highest trophic level organisms may be many orders of magnitude higher than levels in the environment. The trophiclevel increase in contaminated concentrations is called biomagnification, and may result in serious adverse health effects for consumers of the highest trophic levels of fish.

To protect humans from harmful exposures to bioaccumulative chemicals, EPA proposes to use bioaccumulation factors ( BAFs) in deriving AWQC. These BAFs are ratios of the contaminant concentration in tissue to the concentration in water, taking into account uptake through contaminated food, sediment, and water. Chemicals with larger BAFs reflect greater accumulation in fish tissues compared to chemicals with lower BAFs. The BAFs may be of such large magnitude that the resulting ambient water quality criterion will be strongly influenced by the BAF.

In contrast to the current guidelines, the 1980 AWQC National Guidelines for deriving human health criteria relied on an alternate type of ratio, the biconcentration factor ( BCF), to derive AWQC. 22 In contrast to the BAF, the BCF measures uptake of chemicals into fish that have been exposed only through water ( not through food or sediment). Because BAFs account for uptake from all sources of waterborne exposure of a chemical to an organism ( through food, water, and sediment), EPA believes the use of the BAF to be superior to the BCF for deriving human health AWQC. 167 <IP_ADDRESS> Bioaccumulation and Bioconcentration Concepts Bioaccumulation reflects the uptake and retention of a chemical by an aquatic organism from all surrounding media ( e. g., water, food, sediment). Bioconcentration refers to the uptake and retention of a chemical by an aquatic organism from water only. Both bioaccumulation and bioconcentration can be viewed simply as the result of competing rates of chemical uptake and depuration ( chemical loss) by an aquatic organism. However, the rates of uptake and depuration can be affected by numerous factors including the physical and chemical properties of the chemical, the physiology and biology of the organism, environmental conditions, ecological factors such as food web structure, and the amount and source of the chemical. When the rates of chemical uptake and depuration are equal, the distribution of the chemical between the organism and its source( s) is said to be at equilibrium or at steady state. For a constant chemical exposure, the time required to achieve steady state conditions varies according to the properties of the chemical and other factors. For example, some chemicals require a long time to reach steady state conditions between environmental compartments ( e. g., many months for certain highly hydrophobic chemicals) while others reach steady state relatively quickly ( e. g., hours to days for certain hydrophilic chemicals).

The concept of steady state or equilibrium conditions is very important when assessing or evaluating bioaccumulation or bioconcentration and applying these principles in real world situations, such as the derivation of ambient water quality criteria. For some chemicals and organisms that require relatively long time periods to reach steady state, changes in water column chemical concentrations may occur on a much more rapid time scale compared to the corresponding changes in an organism's tissue concentrations. Thus, if the system departs substantially from steady state conditions, the ratio of the tissue concentration to the water concentration may have little resemblance to the steady state ratio and have little predictive value of long term bioaccumulation potential.

For highly bioaccumulative pollutants in dynamic systems, reliable BAFs can be determined only if, among other factors, water column concentrations are averaged over a sufficient period of time ( e. g., a duration approximating the amount of time predicted for the pollutant to reach steadystate In addition, adequate spatial averaging of both tissue and water column concentrations is required to develop reliable BAFs for use in deriving human health ambient water quality criteria.

For this reason, a bioaccumulation factor is defined in this guidance as representing the ratio ( in L/ kg) of a concentration of a substance in tissue to its concentration in the surrounding water in situations where the organism and its food are exposed and the ratio does not change substantially over time. A bioconcentration factor is considered to represent the uptake and retention of a substance by an aquatic organism from the surrounding water only, through gill membranes or other external body surfaces, in situations where the tissue to water ratio does not change substantially over time.

This chapter provides the technical basis and rationale for EPA's proposed procedures for determining BAFs for toxic substances. Section 2.4.2 lists pertinent definitions used throughout the 168 BAF ' C t C w ( Equation 2.4.1) chapter, Sections 2.4.3 through 2.4.5 describe issues and procedures relevant to estimating BAFs for nonpolar organic chemicals, Section 2.4.6 describes procedures relevant to the derivation of BAFs for inorganic chemicals, and Section 2.4.7 discusses the derivation of BAFs for two example chemicals. Issues associated with applying fish consumption rate information to trophic level specific BAFs are discussed in Section 2.4.8.

2.4.2 Definitions Baseline BAF ( BAF R f d). For organic chemicals, a BAF ( in L/ kg lipid) that is based on the concentration of freely dissolved chemical in the ambient water and the lipid normalized concentration in tissue; for inorganic chemicals, a BAF that is based on the wet weight of the tissue.

Baseline BCF ( BCF R f d). For organic chemicals, a BCF ( in L/ kg lipid) that is based on the concentration of freely dissolved chemical in the ambient water and the lipid normalized concentration in tissue; for inorganic chemicals, a BCF that is based on the wet weight of the tissue.

Bioaccumulation. The net accumulation of a substance by an organism as a result of uptake from all environmental sources.

Bioaccumulation Factor ( BAF). The ratio ( in L/ kg tissue) of the concentration of a substance in tissue to its concentration in the ambient water, in situations where both the organism and its food are exposed and the ratio does not change substantially over time. The BAF is calculated as:

where: C t = Concentration of the chemical in the wet tissue ( either whole organism or specified tissue) C w = Concentration of chemical in water Bioconcentration. The net accumulation of a substance by an aquatic organism as a result of uptake directly from the ambient water, through gill membranes or other external body surfaces.

Bioconcentration Factor ( BCF). The ratio ( in L/ kg tissue) of the concentration of a substance in tissue of an aquatic organism to its concentration in the ambient water, in situations where the organism is exposed through the water only and the ratio does not change substantially over time. The BCF is calculated as: 169 BCF ' C t C w ( Equation 2.4.2)

BSAF ' C R C soc ( Equation 2.4.3)

BMF( TL, n) ' C R ( TL, n)

C R ( TL, n & 1) where: C t = Concentration of the chemical in the wet tissue ( either whole organism or specified tissue) C w = Concentration of chemical in water Biota Sediment Accumulation Factor ( BSAF). The ratio ( kg of sediment organic carbon per kg of lipid) of the lipid normalized concentration of a substance in tissue of an aquatic organism to its organic carbon normalized concentration in surface sediment, in situations where the ratio does not change substantially over time, both the organism and its food are exposed, and the surface sediment is representative of average surface sediment in the vicinity of the organism. The BSAF is defined as:

where: C R = The lipid normalized concentration of the chemical in tissues of the biota ( F g/ g lipid) C soc = The organic carbon normalized concentration of the chemical in the surface sediment ( F g/ g sediment organic carbon)

Biomagnification. The increase in tissue concentration of poorly depurated materials in organisms along a series of predator prey associations, primarily through the mechanism of dietary accumulation.

Biomagnification Factor ( BMF). The ratio ( unitless) of the tissue concentration of a predator organism at a particular trophic level to the tissue concentration in its prey organism at the next lowest trophic level, for a given waterbody and chemical exposure. For organic chemicals, a BMF can be calculated using lipid normalized concentrations in the tissue of organisms at two successive trophic levels as: 170 BMF( TL, n) ' C t ( TL, n)

C t ( TL, n & 1)

C fd w ' ( f fd) @ ( C t w)

( Equation 2.4.4) where: C R ( TL, n) = Lipid normalized concentration in appropriate tissue of predator organism at trophic level " n"

C R ( TL, n 1) = Lipid normalized concentration in appropriate tissue of prey organism at the next lowest trophic level from the predator.

For inorganic chemicals, a BMF can be calculated using chemical concentrations in the tissue of organisms at two successive trophic levels as:

where: C t ( TL, n) = Concentration in appropriate tissue of predator organism at trophic level " n" ( may be either wet weight or dry weight concentration so long as both the predator and prey concentrations are expressed in the same manner)

C t ( TL, n 1) = Concentration in appropriate tissue of prey organism at the next lowest trophic level from the predator ( may be either wet weight or dry weight concentration so long as both the predator and prey concentrations are expressed in the same manner)

As explained in the TSD, BMFs can also be related to ( and calculated from) FCMs and baseline BAFs.

Depuration. The loss of a substance from an organism as a result of any active or passive process.

Food Chain Multiplier ( FCM). The ratio of a baseline BAF for an organism of a particular trophic level to the baseline BCF ( usually determined for organisms in trophic level one).

Freely Dissolved Concentration. For hydrophobic organic chemicals, the concentration of the chemical that is dissolved in ambient water, excluding the portion sorbed onto particulate or dissolved organic carbon. The freely dissolved concentration is considered to represent the most bioavailable form of an organic chemical in water and, thus, is the form that best predicts bioaccumulation. The freely dissolved concentration can be determined as: 171 BAF R ' C R C w ( Equation 2.4.5)

BCF R ' C R C w ( Equation 2.4.6) where: C w f d = Freely dissolved concentration of the organic chemical in ambient water C w t = Total concentration of the organic chemical in ambient water f fd = Fraction of the total chemical in ambient water that is freely dissolved Lipid normalized Bioaccumulation Factor ( BAF R ) . The ratio ( in L/ kg lipid) of a substance's lipid normalized concentration in tissue to its concentration in the ambient water, in situations where both the organism and its food are exposed and the ratio does not change substantially over time. The lipid normalized BAF is calculated as:

where: C R = Lipid normalized concentration of the chemical in whole organism or specified tissue C w = Concentration of chemical in water Lipid normalized Bioconcentration Factor ( BCF R ) . The ratio ( in L/ kg lipid) of a substance's lipid normalized concentration in tissue of an aquatic organism to its concentration in the ambient water, in situations where the organism is exposed through the water only and the ratio does not change substantially over time. The lipid normalized BCF is calculated as:

where: C R = Lipid normalized concentration of the chemical in whole organism or specified tissue C w = Concentration of chemical in water Lipid normalized Concentration ( C R ) . The total concentration of a contaminant in a tissue or whole organism divided by the lipid fraction in that tissue or whole organism. The lipid normalized concentration can be calculated as: 172 C R ' C t f R ( Equation 2.4.7)

C soc ' C s f oc ( Equation 2.4.8) where:

C t = Concentration of the chemical in the wet tissue ( either whole organism or specified tissue) f R = Fraction lipid content in the organism or specified tissue Octanol water Partition Coefficient ( Kow). The ratio of the concentration of a substance in the n octanol phase to its concentration in the aqueous phase in an equilibrated two phase octanolwater system. For log K ow, the log of the octanol water partition coefficient is a base 10 logarithm.

Organic Carbon normalized Concentration ( Csoc). For sediments, the total concentration of a contaminant in sediment divided by the fraction of organic carbon in sediment. The organic carbon normalized concentration can be calculated as:

where: C s = Concentration of chemical in sediment f oc = Fraction organic carbon in sediment Uptake. Acquisition by an organism of a substance from the environment as a result of any active or passive process.

2.4.3 Determining BAFs for Nonpolar Organics The calculation of a BAF for a nonpolar organic chemical ( chemicals that do not readily dissolve in water) used in the derivation of AWQC is a two step process. The first step is to calculate a baseline BAF for the chemical of interest using information from the field site or laboratory where the original data were collected ( i. e., the lipid content of the species collected and the freely dissolved fraction of the chemical in water at the site where the data were collected). If information used to estimate fish consumption rates indicates that organisms are being consumed from different trophic levels, then baseline BAFs need to be determined for each of the relevant trophic levels. 173 The second step is to calculate a BAF ( or BAFs) for the chemical that will be used in the derivation of AWQC, using information from the location where the aquatic species of interest are consumed ( i. e., the lipid content of the aquatic species consumed by humans and the freely dissolved fraction of the chemical in water at the site where the aquatic species are being consumed). The difference between a baseline BAF and a BAF used in the derivation of a AWQC is that baseline BAFs can be used for extrapolating from one species to another and from one water body to another. This is the case because baseline BAFs are lipid normalized, enabling extrapolation for organic chemicals from one species to another; and because they are based on the freely dissolved concentration of organic chemicals, enabling extrapolation from one water body to another ( the importance of these concepts is discussed below). Baseline BAFs, however, cannot be used directly in the derivation of AWQC because they may not reflect the conditions in the area of interest ( e. g., the lipid content of the aquatic species consumed in the area of interest and the freely dissolved fraction of the chemical in the area of concern).

Depending on the type of information available for a given chemical, different procedures may be used to determine the baseline BAF. The most preferred BAFs are those derived using appropriate field data. Field measured BAFs, however, have not been determined for all chemicals. Thus, EPA proposes a hierarchy of procedures to determine BAF values. The data preference for derivation of baseline BAFs for nonpolar organic substances is as follows ( in order of priority):

1. A field measured baseline BAF derived from a field study of acceptable quality.

2. A predicted baseline BAF derived from a field measured BSAFs of acceptable quality.

3. A predicted baseline BAF derived from a laboratory measured BCF of acceptable quality and a food chain multiplier ( FCM).

4. A predicted baseline BAF derived from an acceptable K ow and a food chain multiplier.

While EPA recommends the above hierarchy for determining final baseline BAF values, for comparative purposes, baseline BAFs should be determined for each chemical by as many of the four methods as available data allow. Comparing baseline BAFs derived using the different methods recommended above can provide insight for identifying and evaluating any discrepancies in the BAF determinations that might occur. The information needed to derive a baseline BAF using each of the four methods is discussed in Section 2.4.4. Section 2.4.5 discusses the information needed to derive a BAF for use in the calculation of AWQC.

2.4.4 Estimating Baseline BAFs for Nonpolar Organics All the baseline BAFs for nonpolar organic chemicals should be expressed on a freelydissolved and lipid normalized basis. The procedures for adjusting a field measured BAF, field 174 measured BSAF, or laboratory measured BCF to a freely dissolved and lipid normalized basis are discussed below.

<IP_ADDRESS> Field Measured Baseline BAF EPA's first preference for deriving a BAF for nonpolar organic substances is the use of a valid field measured BAF. Field measured BAFs are preferred to other procedures because they inherently account for the effects of metabolism, biomagnification, and other factors affecting bioaccumulation.

The calculation of a field measured baseline BAF requires information on: ( 1) a fieldmeasured BAF based on the total concentration of a chemical in the tissue of the aquatic organism sampled and the total concentration of the chemical in the ambient water; ( 2) the fraction of tissue that is lipid in the aquatic organism of interest; and ( 3) either the measured or estimated freely dissolved fraction of the total chemical in the ambient water where the aquatic species were collected ( estimating the freely dissolved fraction for a chemical requires information on the particulate and dissolved organic carbon content in the ambient water and the K ow of the chemical of interest). The equation for deriving a field measured baseline BAF expressed on a freely dissolved and lipidnormalized basis is:

Baseline BAF fd R ' Measured BAF t T f fd & 1 1 f R ( Equation 2.4.9)

where:

Baseline BAF R f d = BAF expressed on a freely dissolved and lipid normalized basis Measured BAFtT = BAF based on total concentration in tissue ( wet weight basis) and water f R = Fraction of the tissue that is lipid f fd = Fraction of the total chemical that is freely dissolved in the ambient water For the derivation of Equation 2.4.9, see Appendix C.

For each trophic level, a species mean baseline BAF is calculated as the geometric mean if more than one acceptable, measured baseline BAF is available for a given species. For each trophic level, a trophic level specific BAF is calculated as the geometric mean of the species mean measured baseline BAFs. Each of the three components for deriving the baseline BAF are described in further detail below. 175 Measured BAFtT To estimate a measured BAFtT , information is needed on the total concentration of the pollutant in the tissue of the organism and the total concentration of the chemical in ambient water at the site of sampling. The equation to derive a measured BAF tT is:

Measured BAF tT = Total concentration of chemical in tissue ( ug/ Kg wet weight) Total concentration of chemical in the ambient water ( ug/ L)

( Equation 2.4.10)

Guidance for Measuring Field Based BAFs Application of data quality assurance procedures when measuring, estimating, and applying BAFs is of primary importance. The following procedural and quality assurance requirements should be met for field measured BAFs:

C The field studies used should be limited to those that include fish at or near the top of the aquatic food chain ( i. e., in trophic levels 3 and/ or 4). In situations where consumption of lower trophic level organisms represents an important exposure route, such as certain types of shellfish at trophic level 2, the field study should also include appropriate target species at this trophic level.

C The trophic level of the fish species should be determined, taking into account the life stage( s) consumed and food web structure at the location( s) of interest.

C Collection of bioaccumulation field data at a specific site for which criteria are to be applied and with the species of concern are preferred.

C If data cannot be collected from every site for which criteria are to be derived, the site of the field study should not be so unique that the BAF cannot be extrapolated to other locations where the criteria and values will be applied.

C Samples of the appropriate resident species and the water in which they reside should be collected and analyzed using appropriate, sensitive, accurate, and precise methods to determine the concentrations of bioaccumulative chemicals present.

C For organic chemicals, the percent lipid should be either measured or reliably estimated for the tissue used in the determination of the BAF to permit the measured concentration of chemical in the organism's edible tissues to be lipid normalized. 176 C The concentration of the chemical in the water should be measured in a way that can be related to particulate organic carbon ( POC) and dissolved organic carbon ( DOC), as further described in the forthcoming section on POC and DOC concentrations ( page 14).

C For organic chemicals with log K ow greater than four, the concentrations of POC and DOC in the ambient water should be either measured or reliably estimated.

C For inorganic chemicals where lipid normalization does not apply, BAFs should be used only if they are expressed on a wet weight basis; BAFs reported on a dry weight basis can be used only if they are converted to a wet weight basis using a conversion factor that is measured or reliably estimated for the tissue used in the determination of the BAF.

EPA recommends the use of field measured BAFs as the first preferred method for determining BAFs because they incorporate numerous site specific factors that can affect bioaccumulation ( food web structure, temporal and spatial variation in contaminant levels, and metabolism of the contaminant). However, in order to ensure that the resulting BAFs accurately reflect contaminant bioaccumulation and subsequent exposure to the target human population, the measurement of field based BAFs must be performed carefully and should consider several factors that can lead to variability and uncertainty in BAF estimates. Several of these factors are summarized below. Further discussion of these and other factors is provided in USEPA ( 1995a; 1995b). EPA is developing additional guidance on performing field studies for determining BAFs and will provide this guidance for review upon its completion.

Selection of Target Species. The choice of the target species for contaminant analysis is one critical aspect in determining a valid and representative field measured BAF for establishing AWQC designed to protect human health. Selection of the target species should be made with knowledge of the key exposure route( s) involved in bioaccumulation of the contaminant of interest ( e. g., uptake from water, food, sediment/ pore water). Several important factors to consider when selecting target species for contaminant monitoring have been summarized by EPA in their document: Fish Sampling and Analysis: A Guidance Document for Issuing Fish Consumption Advisories ( USEPA, 1993), and are recommended for consideration when identifying target species in BAF studies. While the objectives of fish consumption advisory studies and BAF studies are not entirely identical, many of the principles described in USEPA ( 1993) also apply to the determination of BAFs from field studies.

It is of primary importance that the target species selected be among those species commonly consumed in the study area and those of commercial, recreational or sustenance fishing value. In addition, the potential for bioaccumulation of the contaminant( s) of interest should be considered. Knowledge of the food web structure, likely exposure routes, and contaminant properties ( e. g., K ow for organics) is important for evaluating a species' bioaccumulation potential. Species occupying 177 trophic level three ( e. g., forage fish) or four ( e. g., predator fish) are recommended for selection in BAF studies because they have consistently been among the highest bioaccumulators in the aquatic food web, particularly for highly hydrophobic chemicals. If possible, the target finfish species should include at least one species of bottom feeding fish species ( trophic level three) and one top predator species ( trophic level four). Including species with different dietary preferences will help account for the effect of food web structure on bioaccumulation, the effect of which can vary with the properties of the chemical ( i. e., in some cases, bottom feeders can have higher BAFs and in other cases lower BAFs compared to top predator ( piscivorous) species). Organisms occupying trophic level two ( e. g., clams, oysters) should also be sampled if information indicates that consumption of such organisms is likely to be an important exposure route to contaminants. In addition, the geographic distribution of the species should be considered in relation to the target human population intended for protection. Further information pertaining to the selection of target aquatic species for contaminant analysis for fish advisories is provided in USEPA ( 1993).

Choice of Sampling Sites. Selection of sampling sites and the frequency at which they are sampled should take into account numerous factors, many of which relate to the spatial and temporal variability in the contaminant concentrations in the target aquatic species and environmental media. If the proper temporal and spatial intervals are not selected, such measurements can lead to erroneous estimates of bioaccumulation. The sites should be representative of those from which the target human population are expected to be exposed. In addition, the sampling sites need to be representative of the area of movement of the target species. This is particularly important for migratory species which may only spend a portion of the time in the study area of interest.

Temporal and spatial variability can be particularly high for water concentrations of contaminants. Thus, individual water samples taken at one point in time may not adequately reflect average exposure to the target species. Water concentrations should be averaged over the approximate time it takes for the target species to reach steady state, which varies depending on the toxicokinetics of the contaminants in relation to the target organism. For example, chemicals with high K ow values are expected to reach steady state in top trophic level organisms much slower than chemicals with low K ow values, and thus, require greater temporal averaging of water column concentrations for estimating BAFs. Other factors to consider when determining the frequency of sampling include the home range of the target species, its life history, and the pattern of contaminant release ( episodic vs. continuous releases). Selection of sampling sites should also consider temporal and spatial variations in food web structure that may occur across the study area. The desired level of statistical power should also be considered when determining the number of sampling sites and replicates.

Biological Considerations. When sampling target species for BAF determinations used in deriving human health criteria, several biological attributes of the target species should also be considered. For example, the size/ age of the organism can affect the extent of bioaccumulation in the organism. Young fish can exhibit lower accumulation of some contaminants due to growth dilution. In addition, the reproductive status ( e. g., pre/ post spawning) can alter the body burden of contaminants, with significant contaminant loss observed due to maturation and release of sperm or 178 eggs. Seasonal variations in lipid content can also lead to differences in accumulation of contaminants. In general, the size of the target species should be representative of the size being consumed by the target human population. If this size range is broad, stratifying sampling strategies by size class is necessary, particularly when taking composite samples. The timing of sampling should include the period of most frequent harvesting of the species. Additional discussion of these and other attributes to consider when sampling finfish and shellfish for contaminant monitoring is provided in USEPA ( 1993).

Measurement of Other Important Parameters. For nonpolar organic chemicals, lipid content of the target species should be measured in the same tissue in which the contaminant was measured to permit lipid normalization. This will usually be fillet for finfish and edible tissue for shellfish. In addition, POC and DOC should be measured in the water samples in order to estimate the freely dissolved fraction. For inorganic chemicals, the bioavailability of various forms of the chemical should be considered when deciding upon the analyte being measured for the BAF determination. Where appropriate, BAFs should be expressed for specific forms of the contaminant. For example, methylmercury is known to be more bioavailable than inorganic forms of mercury, and the relative proportions of each can vary significantly over space and time. Thus, BAFs determined for total mercury without knowledge of the relative proportion of various organic and inorganic forms of mercury are more uncertain in their applicability to other sites and times than BAFs measured for specific forms of mercury. Other parameters such as temperature, pH, dissolved oxygen, conductivity/ salinity, total suspended sediments, and sediment grain size should also be measured, as they may alter the bioavailability and subsequent bioaccumulation of contaminants by aquatic organisms. EPA will provide additional guidance on the design and conduct of field BAF studies in its forthcoming guidance document, which is expected to undergo external review in the fall of 1998.

Freely Dissolved Fraction of the Chemical in Water ( ffd)

Nonpolar organic chemicals can exist in water in several different forms, including freely dissolved chemicals in the water column, chemicals bound to particulate matter, and chemicals bound to dissolved organic matter in the water. The form of the chemical has been shown to affect bioaccumulation, with the freely dissolved form of a chemical considered to be the best expression of the bioavailable form to aquatic organisms. Because the amount of chemical that is freely dissolved may differ among water bodies due to differences in the total organic carbon in the water, BAFs based on the concentration of freely dissolved chemical will provide the most universal BAF for organic chemicals when averaging BAFs from different studies. However, BAFs based on the total concentration of the chemical in water ( i. e., the freely dissolved plus that sorbed to particulate organic carbon and dissolved organic carbon) can often be measured more accurately than BAFs based on freely dissolved concentrations in water. Therefore, if BAFs based on total water concentrations are reported in a BAF study, information on the organic carbon content of water ( at the site from which the BAF was measured, if available) is required to predict freely dissolved concentrations used to determine the baseline BAF. Specifically, the fraction freely dissolved ( f fd) in Equation 2.4.9 must be estimated, using information on the chemical's K ow and both dissolved and 179 f fd ' 1 [ 1 % ( POC @ K ow) % ( DOC @ K ow 10 )]

( Equation 2.4.11) particulate organic carbon contents ( DOC and POC) of the water. The equation used to estimate f fd is:

where:

POC = Concentration of particulate organic carbon ( kg/ L) in the ambient water DOC = Concentration of dissolved organic carbon ( kg/ L) in the ambient water K ow = N octanol water partition coefficient for the chemical In this equation, the terms " K ow" and " K ow/ 10" are used to estimate the partition coefficients to POC and DOC, respectively, which have units of L/ kg. The scientific basis supporting the derivation of this equation for estimating the freely dissolved fraction is provided in Appendix D.

POC and DOC Concentrations. As noted above, when converting from the total concentration of a chemical to a freely dissolved concentration, the POC and DOC should be obtained from the original study that reports BAFs based on total concentrations of a chemical in water. However, if the POC and DOC concentrations are not reported in the BAF study, then reliable estimates of POC and DOC might be available from other studies of the same site used in the BAF study or closely related sites within the same water body. When using POC/ DOC data from other studies of the same water body for the same or very similar sites, care must be taken to ensure that environmental conditions that may affect POC or DOC concentrations are similar to those in the BAF study. Information on the spatial and temporal variability of POC and DOC at the site of interest ( and factors influencing this variability) should be used in evaluating the applicability of any surrogate POC or DOC data for estimating the freely dissolved fraction. For example, differences in hydrological conditions between the BAF study and the surrogate study ( e. g., high vs. low flow events, mixed vs. stratified water column, tidal cycle differences) and the degree to which such conditions influence POC and DOC concentrations should be evaluated in deciding whether surrogate data provide reliable estimates of POC and DOC for the BAF study. Similarly, differences in other factors which may influence POC and DOC concentrations, such as the sampling season, sampling depth, proximity to areas of high DOC inputs including wetlands, should be evaluated in determining the reliability of surrogate data. Additional factors besides the examples listed here may also be important in determining the reliability of surrogate POC and DOC data for estimating the baseline BAF.

Guidance on Selecting Appropriate Kow Values The conversion of total chemical concentrations in water to freely dissolved chemical concentrations, as well as other procedures discussed in this chapter ( including the BSAF method and 180 use of the food chain model) rely on the K ow for chemicals. A variety of techniques are available to estimate or predict K ow values, some of which are more or less reliable depending on the K ow of the chemical.

As discussed in USEPA 1998a, EPA is proposing and taking comment on two options on how to select a reliable K ow value. The first option is EPA's existing guidance published in the Great Lakes Water Quality Initiative ( 60 FR 15366, March 23, 1995). A second option is more detailed, draft guidance on selecting K ow values which EPA has developed and is currently undergoing external scientific peer review. The salient features of both the GLWQI K ow selection guidance ( option 1) and EPA's new, draft guidance ( option 2) are presented below. Additional details of the new draft K ow selection guidance ( option 2) are provided in Appendix F.

Guidance on selecting reliable values of K ow based on the GLWQI approach ( option 1) is as follows:

For chemicals with log K ow < 4:

Priority Technique 1 Slow stir Shake flask Generator column 2 Measured value from the CLOGP program 3 Reverse phase liquid chromatography on C 18 with extrapolation to zero percent solvent 4 Reverse phase liquid chromatography on C 18 without extrapolation to zero percent solvent 5 Calculated by the CLOGP program For chemicals with log K ow > 4:

Priority Technique 1 Slow stir Generator column 2 Reverse phase liquid chromatography on C 18 with extrapolation to zero percent solvent 181 3 Reverse phase liquid chromatography on C 18 without extrapolation to zero percent solvent 4 Shake flask 5 Measured value from the CLOGP program 6 Calculated by the CLOGP program If no measured K ow is available, the K ow must be estimated using the CLOGP program. Several general points should be kept in mind when using K ow values. Values should be used only if they were obtained from the original authors or from a critical review that supplied sufficient information. If more than one " best" K ow value is available for a chemical ( i. e., the highest priority value available), the arithmetic mean of the available log K ow s or the geometric mean of the available K ow s may be used. Because of potential interference due to radioactivity associated with impurities, values determined by measuring radioactivity in water and/ or octanol should be considered less reliable than values determined by a K ow method of the same priority that employ non radioactive techniques except when measurements of parent chemical are done. The values determined using radioactive methods should be moved down one step in the priority below the values determined using the non radioactive technique except when measurements of parent chemical are done. Because the K ow is an intermediate value in the derivation of a BAF, the value used for the K ow of a chemical should not be rounded to less than three significant digits. K ow values that are outliers compared with other values for a chemical should not be used.

The salient features of EPA's new draft methodology ( option 2) for selecting reliable values of K ow is described below.

I. Assemble/ evaluate experimental and calculated data ( e. g., CLOGP, LOGKOW, SPARC)

II. If calculated log K ow's > 8, A. Develop independent estimates of K ow using: 1. Liquid Chromatography ( LC) methods with " appropriate" standards. ( See Appendix F for guidelines for LC application). 2. Structure Activity Relationship ( SAR) estimates extrapolated from similar chemicals where " high quality" measurements are available. " High quality" SARs are defined in Appendix F of the TSD. 3. Property Reactivity Correlation ( PRC) estimates based on other measured properties ( solubility, etc.). B. If calculated data are in reasonable agreement and are supported by independent estimates described above, report the average calculated value. Guidance on determining whether K ow values are in " reasonable agreement" are presented in Appendix F of the TSD. 182 C. If calculated/ estimated data do not agree, use professional judgement to evaluate/ blend/ weight the calculated and estimated data to assign a K ow value. D. Document rationale including relevant statistics.

III. If calculated log K ow's range from 6  8, A. Look for " high quality" measurements. These will generally be slow stir measurements, the exception being certain classes of compounds where micro emulsions tend to be less of a problem ( i. e., PNA's, shake flask measurements are good to log K ow of 6.5). B. If measured data are available and are in reasonable agreement ( both measurements and calculations), report average measured value. C. If measured data are in reasonable agreement, but differ from calculated values, develop independent estimates and apply professional judgement to evaluate/ blend/ weight the measured, calculated and estimated data to assign K ow value. D. If measured data are not in reasonable agreement ( or if only one measurement is available), use II A, B, and C to produce a ` best estimate;' use this value to evaluate/ screen the measured K ow data. Report the average value of screened data. If no measurements reasonably agree with ` best estimate,' apply professional judgement to evaluate/ blend/ weight the measured, calculated and estimated data to assign K ow. E. If measured data are unavailable, proceed through II A, B, C and report the ` best estimate.' F. Document rationale including relevant statistics.

IV. If calculated log K ow's < 6, A. Proceed as in III. Slow stir is the preferred method but shake flask data can be considered for all chemicals if sufficient attention has been given to emulsion problems in the measurement.

The general operational guidelines for EPA's new draft methodology for selecting K ow values are as follows:

1. For chemicals with log K ow > 5, it is highly unlikely to find multiple " high quality" measurements. ( Note: " high quality" is data judged to be reliable based on the guidelines presented in Appendix F of the TSD).

2. " High Quality" measured data are preferred over estimates, but due to the scarcity of ` high quality' data, the use of estimates is important in assigning K ow ' s.

3. K ow measurements by slow stir are extendable to 108. Shake flask K ow measurements are extendable to 106 with sufficient attention to micro emulsion effects; for classes 183 of chemicals that are not highly sensitive to emulsion effects ( i. e., PNA's) this range may extend to 106.5.

4. What is to be considered reasonable agreement in log K ow data ( measured or estimated) depends primarily on the log K ow magnitude. The following standards for data agreement have been set for this guidance: 0.5 for log K ow > 7; 0.4 for 6 # log K ow # 7 ; 0.3 for log K ow < 6.

5. Statistical methods should be applied to data as appropriate but application is limited due to the scarcity of data, and the determinate/ methodic nature of most measurement error( s).

The various techniques are summarized as follows:

C The slow stir method requires adding the test chemical to a reaction flask which contains a water and octanol phase. The chemical partitions to these two phases under conditions of slow stirring the flask. After the phases are allowed to separate, the concentration of the test chemical in each phase is determined ( Brooke et al., 1986). This method is easy to use and can be replicated with a high degree of confidence. Emulsions, which can contaminate the aqueous phase and influence the observed K ow values, can be prevented, and high K ow values can be obtained easily ( de Bruijn et al., 1989). In general, there is reasonable agreement between the slow stir method and literature data obtained using the generator column method. For log K ow values less than 4.5, data agree well with K ow s determined based on the shake flask method ( de Bruijn et al., 1989).

C The shake flask method also involves adding the chemical to a reaction flask with a mixture of octanol and water. In this method, however, the flask is shaken to obtain partitioning of the chemical between the octanol and water phases ( OECD, 1981). Several researchers have found that the shake flask technique is acceptable only for chemicals with log K ow s less than certain values. Some researchers found that the shake flask technique has been reported to be acceptable only for chemicals which have log K ow s less than 4 ( Karickhoff et al., 1979; Konemann et al., 1979; Braumann and Grimme, 1981; Harnisch et al., 1983; Brooke et al., 1990). Others have found that the technique is acceptable for chemicals with slightly higher log K ow values. Brooke et al. ( 1986) compared techniques and decided that the shake flask technique is acceptable for chemicals with log K ow s up to 5, whereas Chessells et al. ( 1991) stated that this technique is acceptable for log K ow values up to about 5.5.

C The generator column method involves filling a column with an inert material ( silanized Chromosorb W or glass beads) that is coated with water saturated octanol and contains the test chemical. Pumping water through the column results in an aqueous solution in equilibrium with the octanol phase. The water that leaves the 184 Composite K ow ' 1 DOC 10 % POC j n i ' 1 C t w j n i ' 1 C fd w & 1 column is extracted with specifically either an organic solvent or a C 18 column that is then eluted with hexane or methanol ( DeVoe et al., 1981; Woodburn et al., 1984; Miller et al., 1984).

C The reverse phase liquid chromatography method involves adding the test chemical in a polar mobile phase ( such as water or water methanol) to a hydrophobic porous stationary phase ( the C 18 n alkanes covalently bound to a silica support). The chemical partitions between the column and the polar aqueous phase. K ow values are estimated from linear equations between the K ow and retention indices that are derived for reference chemicals ( Konemann et al., 1979; Veith et al., 1979; McDuffie, 1981; Garst and Wilson, 1984).

C The CLOGP program is a computer program that contains measured K ow values for some chemicals and can calculate K ow values for additional chemicals based on similarities in chemical structure between chemicals with measured K ow values and chemicals for which K ow s are to be determined. The method used to calculate the K ow values is described in Hansch and Leo ( 1979).

C SPARC ( SPARC Performs Automated Reasoning in Chemistry) is a mechanistic model developed at the Ecosystems Research Division of the National Exposure Research Laboratory of the Office of Research and Development of the U. S. Environmental Protection Agency by Sam Karickhoff, Lionel Carreira, and coworkers

In some situations, available data may require determination of a single K ow value for a class of chemicals or a or mixture of closely related chemicals ( e. g., when toxicity data are class or mixture specific). However, it is not possible to determine experimentally a valid K ow for a substance that is a mixture of chemicals ( e. g., PCBs, toxaphene, chlordane). For calculating the composite freely dissolved fraction used to adjust a composite field measured BAF to a composite baseline BAF, a composite K ow value of the mixture can be calculated based on the sum of the total concentration of the mixture components in water ( e. g., individual congeners for PCBs), the sum of the dissolved mixture components in water, and the DOC and POC from the site for which the BAF was measured. The equation used to derive the composite K ow for use in determining a composite baseline BAF is:

where: 185 i = 1, 2, ... n individual mixture components ( e. g., congeners for PCBs). C w t = total concentration of the mixture component in water.

C w f d = freely dissolved concentration of the mixture component in water.

Notably, calculation of a composite K ow is just one of a series of steps involved in deriving composite baseline BAFs and AWQC BAFs for chemical mixtures for which single toxicity values apply. These steps include: ( 1) first determining a composite initial total BAF ( analogous to the total field measured BAF for individual chemicals), ( 2) determining a composite baseline BAF, using the composite freely dissolved fraction and composite K ow derived using the equation above, ( 3) calculating a composite AWQC BAF from the composite baseline BAF based on the composite freely dissolved fraction at the AWQC site( s). This last step requires calculation of a second composite K ow which is used to determine the composite freely dissolved fraction at the AWQC site( s) using POC and DOC data from the AWQC site( s). Additional details of the steps required in determining a composite K ow for deriving composite baseline BAFs and composite AWQC BAFs ( including example calculations for PCBs) are provided in 62 FR 117250 ( March 12, 1997).

Lipid Normalization of Data Partitioning of organic chemicals into aquatic organisms has been shown to be a function of the lipid content of the organism ( Mackay, 1982; Connolly and Pederson, 1988; Thomann, 1989). For this reason, EPA assumes that BAFs and BCFs for lipophilic organic chemicals are directly proportional to the percent lipid in the tissue or whole body of the organism of interest. For example, an organism with two percent lipid content would accumulate twice the amount of a chemical as an organism with one percent lipid content, all else being equal. This assumption has been extensively evaluated in the literature and is generally accepted. To account for the influence of the lipid content on the BAF or BCF, EPA recommends normalizing the BAF or BCF to the percent lipid in the fish. This procedure is consistent with other EPA guidance on bioaccumulation ( Stephan et al., 1985; USEPA, 1991).

To compare BAFs and BCFs that have been measured in fish that have different lipid contents, EPA recommends that BAFs and BCFs be normalized by dividing by the mean lipid fraction of the aquatic organism. Whole body and edible tissue BAFs and BCFs are normalized using the respective whole body and edible tissue fraction lipid values. Since lipid content is known to vary from one tissue to another and from one aquatic species to another, EPA recommends the percent lipid used to normalize the BAF or BCF ( whole body or edible tissue) be obtained from the BAF or BCF study. Unless comparability can be determined across organisms, the fraction lipid should be determined for the test organism. Lipid content of the fish tissue is affected by the age, sex and diet of the fish, by the season the fish are sampled, and by differing environmental conditions. Therefore, it is generally necessary to determine an average percent lipid value for the test organisms. 186 EPA recommends using a gravimetric method for determining the percent lipid value ( USEPA, 1995a). The method is easy to use and is employed by many laboratories. It should be noted that the solvent used to determine lipid content has been shown to affect the percent lipid values measured in some studies because different solvent systems extract differing fractions of total lipids ( Lapin and Chernova, 1969; Randall et al., 1991; and Cabrini et al., 1992). These authors note that percent lipid values can vary by as much as a factor of four depending on the solvent system used. To ensure consistency among States, EPA recommends for lipid analyses that the method of Bligh and Dyer ( 1959) which uses chloroform/ methanol as an extraction solvent or the lower toxicity solvent modification of this the method by Hara and Radin ( 1978) which uses hexane/ isopropanol as an extraction solvent. Other extraction solvents for lipid analyses, e. g., hexane/ acetone and dichloromethane, might provide equivalent results when used with appropriate sample sizes and extraction times ( Honeycutt et al., 1995, de Boer, J., 1988).

In addition to the effect of the solvent on lipid analysis, additional factors may affect variability of results if they are not adequately controlled ( USEPA, 1995a). Use of alcohol as a solvent may overestimate total lipids because non lipid material may also be extracted. Several factors, including solvent contaminants, lipid decomposition from exposure to oxygen or light, and lipid degradation from changes in pH during cleanup can lead to underestimation of total lipids. Finally, high temperature may decompose lipid material. Laboratories should consider these sources of error when conducting and evaluating results of lipid analyses ( USEPA, 1995a).

<IP_ADDRESS> Baseline BAF Derived from Biota Sediment Accumulation Factors ( BSAFs)

When acceptable field measured values of the BAF are not available for a nonpolar organic chemical, EPA recommends the use of a BSAF to predict the BAF as the second procedure in the BAF data preference hierarchy. Although BSAFs may be used for measuring and predicting bioaccumulation directly from concentrations of chemicals in surface sediment, they also can be useful in estimating a BAF as noted by Cook et al. ( 1993). Because BSAFs are based on field data and incorporate effects of metabolism, biomagnification, growth, and other factors, BAFs estimated from BSAFs will incorporate the net effect of all these factors. The BSAF approach is particularly beneficial for developing water quality criteria for chemicals such as polychlorinated dibenzo pdioxins dibenzofurans, and certain biphenyl congeners. These chemicals are detectable in fish tissues and sediments but are difficult to measure in the water column and are subject to metabolism.

Predicting BAFs from BSAFs requires several steps. First, BSAFs must be measured for the chemical of interest and for one or more reference chemicals for which measured BAFs are also available. Second, the relationship between the BSAFs for the chemical of interest and the reference chemical and the relationship between the chemicals' K ow values must be determined. Finally, information on the BSAF and K ow relationships and the BAF of the reference chemical( s) should be used to determine the BAF for the chemical of interest. The following sections describe the methodology for determining BAFs from BSAFs, the data requirements, and the application and validation of this procedure for estimating BAFs using data from Lake Ontario. 187 BSAF ' C R C soc ( Equation 2.4.12)

C R ' C t f R ( Equation 2.4.13)

C soc ' C sed f oc ( Equation 2.4.14) Determination of BSAF Values As shown in the following equation, the BSAF is determined by relating lipid normalized concentrations of chemicals in an organism to organic carbon normalized concentrations of the chemicals in surface sediment samples associated with the average exposure environment of the organism.

where:

C R = Lipid normalized concentration of the chemical in tissues of biota ( F g/ g lipid)

C soc = Organic carbon normalized concentration of the chemical in the surface sediment ( F g/ g sediment organic carbon)

The lipid normalized concentration of a chemical in an organism ( C R ) is determined by:

where:

C t = Concentration of the chemical in the wet tissue ( either whole organism or specified tissue) ( F g/ g)

f R = Fraction lipid content in the organism The organic carbon normalized concentration of a chemical in sediment ( C soc) is determined by: 23Because K R and K soc are of similar magnitude and vary in proportion to one another, the BSAF at equilibrium is expected to be at or near unity.

188 BSAF . C fd b C K R C fd s C K soc ' D bs C K R K soc . D bs C 2 ( Equation 2.4.15) where:

C sed = Concentration of chemical in sediment ( F g/ g sediment)

f oc = Fraction organic carbon in sediment BSAFs are most useful when measured under steady state or near steady state conditions in which chemical concentrations in water are linked to slowly changing concentrations in sediment. However, because BSAFs are rarely measured for ecosystems which are at equilibrium, the BSAF inherently includes a measure of the " disequilibrium" of the ecosystem. This disequilibrium can be assessed for chemicals with log K ow > 3 with the following relationship:

where:

C b f d = Concentration of freely dissolved chemical ( associated with water) in the tissues of biota ( F g/ g wet tissue)

C s f d = Concentration of freely dissolved chemical ( associated with pore water) in the sediment ( F g/ g sediment organic carbon)

K R = Lipid water equilibrium partition coefficient ( C R / C b f d)

K soc = Sediment organic carbon water equilibrium partition coefficient ( C soc/ C s f d)

D bs = Disequilibrium ( fugacity) ratio between biota and sediment ( C b f d/ C s f d)

Measured BSAFs may range widely for different chemicals depending on K R , K soc, and the actual ratio of C b f d to C s f d. However, at equilibrium, the ratio between the freely dissolved chemical in the tissue water to sediment pore water ( D bs) is one. Thus, the BSAF under equilibrium conditions is equal to the ratio K R / K soc ( which is thought to range from 1 4) 23. When chemical equilibrium between sediment and biota does not exist, the BSAF will equal the disequilibrium ( fugacity) ratio between biota and sediment ( D bs = C b f d/ C s f d) times the ratio of the equilibrium partition coefficients ( approximately 2). 189 ( Baseline BAF fd R ) i ' ( Baseline BAF fd R ) r @ ( BSAF) i @ ( K ow) i ( BSAF) r @ ( K ow) r ( Equation 2.4.16) The deviation of D bs from the equilibrium value of 1.0 is determined by the net effect of all factors which contribute to the disequilibrium between sediment and aquatic organisms. A disequilibrium ratio ( D bs) greater than one can occur due to biomagnification or when surface sediment has not reached steady state with water. A disequilibrium ratio ( D bs) less than one can occur as a result of kinetic limitations for chemical transfer from sediment to water, water to food chain, and biological processes ( such as growth or biotransformation of the chemical in the animal and its food chain). BSAFs are most useful when measured under steady state conditions. BSAFs measured for systems with new chemical loadings or rapid increases in loadings may be unreliable due to underestimation of steady state C soc s.

Relationship of BAFs to BSAFs Differences between BSAFs for different organic chemicals are good measures of the relative bioaccumulation potentials of the chemicals. When calculated from a common organism sediment sample set, chemical specific differences in BSAFs primarily reflect the net effect of biomagnification, metabolism, bioenergetics, and bioavailability factors on each chemical's disequilibrium ratio between biota and sediment. Thus, the relationship between the BSAF for the test chemical i and the BSAF for the reference chemical r can be used with additional information ( K ow values for all chemicals and the BAF for the reference chemicals) to predict a BAF for chemical i. This approach is consistent with previously proposed guidance, in which ratios of BSAFs for PCDDs and PCDFs to TCDD were proposed for evaluation of TCDD toxic equivalency associated with complex mixtures of the dioxin and furan congeners ( see 60 FR 15366 for discussion of bioequivalency factors).

The calculation of the BAF from the BSAF is as follows:

where:

( Baseline BAF R f d) i = BAF expressed on a freely dissolved and lipid normalized basis for chemical of interest " i"

( Baseline BAF R f d) r = BAF expressed on a freely dissolved and lipid normalized basis for reference chemical " r"

( BSAF) i = BSAF for chemical " i"

( BSAF) r = BSAF for the reference chemical " r"

( K ow) i = octanol water partition coefficient for chemical " i" 190 ( K ow) r = octanol water partition coefficient for the reference chemical " r"

Appendix E presents the derivation of this equation using the general BAF equation relating concentration in tissue to concentration in water, a relationship between concentrations in sediment organic carbon and water, and assumptions about equilibrium between water and sediment.

Note that BAF R f ds calculated from BSAFs will incorporate any errors associated with measurement of the BAF R f d for the reference chemical and the K ow s for both the reference and unknown chemicals. Such errors can be minimized by comparing results from several reference chemicals and assuring consistent use of freely dissolved water concentration ( C w f d) values which are adjusted for dissolved organic carbon binding effects on the fraction of each chemical that is freely dissolved ( f fd) in unfiltered, filtered, or centrifuged water samples. Other errors may be introduced by using values based on non steady state external loading rates or chemicals with strongly reduced C w f d due to rapid volatilization from water. When selecting K ow values for use in estimating BAFs from BSAFs, consideration should be given to the similarity of K ow measurement techniques between the reference and target chemicals, in addition to the guidance previously described for selecting representative K ow values.

The trophic level to which the baseline BAF applies is the same as the trophic level of the organisms used in the determination of the BSAF. For each trophic level, a species mean baseline BAF is calculated as the geometric mean if more than one acceptable baseline BAF is predicted from BSAFs for a given species. For each trophic level, a trophic level specific BAF is calculated as the geometric mean of the acceptable species mean baseline BAFs derived using BSAFs.

Procedural and Quality Assurance Requirements EPA recommends certain requirements for measuring the data needed for this procedure. These requirements, described below, apply to BAF values for the reference chemicals, the measured BSAFs for all chemicals, and the K ow values used in this procedure.

The data requirements for measuring BAF values that were noted in Section <IP_ADDRESS> ( Field Measured BAFs) are also applicable to the measurement of BAF R f d values to assure reliable values for the reference chemicals. Data on several reference chemicals should be obtained for use in the analysis to ensure that predictions are more robust than those that would be obtained using only one reference chemical. The water sample analyses should approximate the average exposure of the organism and its food chain over a time period that is most appropriate for the chemical, organism, and ecosystem. It is preferable to choose at least some reference chemicals that have similar log K ow s and chemical class characteristics as the test chemicals for which the BAF is to be determined. In addition, for consistency among reference chemicals, each freely dissolved water concentration used to calculate a BAF R f d should be based on a consistent adjustment of the concentration of total chemical in water for DOC and POC using the relationship described in the section titled " Freely Dissolved Fraction of Chemical in Water." 191 For measured BSAFs, chemical concentrations in surface sediment and in biota and data on the percent organic carbon in surface sediment samples are needed. The following procedural and quality assurance requirements should be met for determining the field measured BSAFs:

C The field studies used should be limited to those conducted with fish at or near the top of the aquatic food chain ( i. e., in trophic levels 3 and/ or 4). In situations where consumption of lower trophic level organisms represents an important exposure route, such as certain types of shellfish at trophic level 2, the field study should also include appropriate target species at this trophic level.

C Samples of surface sediments ( 0 1 cm is ideal) should be from locations in which sediment is regularly deposited and is representative of average surface sediment in the vicinity of the organism.

C The K ow s used should be of acceptable quality as described in Section <IP_ADDRESS> above.

C The site of the field study should not be so unique that the resulting BAF cannot be extrapolated to other locations where the criteria and values will be applied.

C The percent lipid should be either measured or reliably estimated for the tissue used in the determination of the BAF.

Application of BSAF Procedure for Predicting Lake Ontario and Green Bay BAF R f ds To demonstrate the use of the BSAF procedure to predict BAFs, EPA has calculated BAF R f ds from BSAFs using two independent data sets from Lake Ontario and one from Green Bay. These data sets come from Oliver and Niimi ( 1988), the EPA Lake Ontario TCDD Bioaccumulation Study ( USEPA, 1990), and the EPA Green Bay/ Fox River Mass Balance Study. The first data set ( Oliver and Niimi, 1988) has been used extensively for construction of food chain models of bioaccumulation and calculation of food chain multipliers, biomagnification factors and BAF R f ds from chemical concentrations determined in organisms and water. Oliver and Niimi ( 1988) also collected surface sediment data which allows calculation of lakewide average BSAFs. These data were collected from 1981 to 1984 for PCB congeners and other chlorinated organics.

The second data set ( from the TCDD Bioaccumulation Study) includes extensive samples of fish and sediment collected in 1987 from Lake Ontario. Samples from this study were later analyzed for PCDD, PCDF, PCB congeners, and some organochlorine pesticides at EPA. Although data from the TCDD Bioaccumulation Study have not been published, they are useful to show a comparison with BAF R f ds calculated from Oliver and Niimi samples and to provide BAF R f ds for additional organic chemicals not measured by Oliver and Niimi ( 1988). 192 Four reference chemicals ( the PCB congeners 52, 105 and 118 and DDT) were used for evaluating chemicals from both Oliver and Niimi ( 1988) and the TCDD Bioaccumulation Study in order to examine the variability introduced by the choice of reference chemical.

The third study, the Green Bay/ Fox River Mass Balance Study, involved extensive sampling of water, sediment, and fish in Green Bay in 1989. Brown trout BAF R f ds were calculated from PCB BSAFs measured in the mid bay region using PCB congeners 52 and 118 as reference chemicals. The reference chemical BAF R f ds were determined using water and brown trout data from the same region.

Tables 2.4.1a and 2.4.1b present the predicted BAF R f ds from all three data sets as well as measured BAF R fds from Oliver and Niimi ( 1988) and the TCDD Bioaccumulation Study. The geometric means of the BAF R f d predicted using the Lake Ontario data ( Oliver and Niimi, 1988; TCDD Bioaccumulation Study) are reported in Table 2.4.2.

There are several assumptions and additional data used for these evaluations. First, the water analyses of Oliver and Niimi ( 1988) were adjusted for an estimated 2 mg/ L residual dissolved organic carbon concentration in the centrifuged water ( assuming no residual particulate organic carbon after centrifuging) and an estimated K doc = K ow/ 10 in order to calculate a freely dissolved water concentration from f fd ( see Section <IP_ADDRESS> on total vs. freely dissolved concentrations and Appendix D for calculation of water concentrations). Concentrations of freely dissolved PCBs from Green Bay were also calculated on the basis of dissolved organic carbon in the water samples and an assumed K doc = K ow/ 10. Log K ow values were taken from a variety of sources. Log K ow s for PCBs are those reported by Hawker and Connell ( 1988). Log K ow s for PCDDs and PCDFs are those estimated by Burkhard and Kuehl ( 1986) except for the penta, hexa, and hepta chlorinated dibenzofurans which were estimated on the basis of assumed similarity to the trends reported for the PCDDs by Burkhard and Kuehl ( 1986).

Evaluation of BAF R f ds Calculated from Lake Ontario and Green Bay BSAFs The validity of the BSAF method for predicting BAFs is evaluated in this section using several approaches: ( 1) correlating measured vs. predicted log BAF R f ds from the same lake and same study ( i. e., Lake Ontario, Oliver and Niimi, 1988), ( 2) correlating measured vs. predicted log BAF R f ds from the same lake ( Ontario) but separate studies ( Oliver and Niimi, 1988, U. S. EPA, 1990), ( 3) comparisons of predicted BAF R f ds with K ow values, and ( 4) comparisons of predicted and measured BAF R f ds from different lakes ( i. e., Lake Ontario and Green Bay, Lake Michigan). These comparisons were based on data presented in Tables 2.4.1a and 2.4.1b.

Exhibit 2.4.1 illustrates that measured log BAF R f ds calculated using water data from Oliver and Niimi ( 1988) generally agree with log BAF R f ds predicted from BSAFs determined using sediment data from the same study. The correlation coefficient ( r) for the correlation of BAFs using data from Tables 2.4.1a and 2.4.1b is 0.92 and indicates that the data are well correlated. One deviation of predicted BAFs from measured BAFs should be noted, however. For chlorinated benzenes and 193 toluenes, BAF R f ds predicted from BSAFs are underestimated compared with measured BAF R f ds. This underestimation may be due to altered water sediment fugacity gradient in response to rapid volatilization from water. The better agreement between measured and predicted BAF R f ds for PCBs, on the other hand, is facilitated by the lower volatilization of PCBs from water. In addition to the correlation shown in Exhibit 2.4.1, the ratios between the BAFs ( which indicates the magnitude of difference between the values) were plotted as a frequency distribution, as shown in Exhibit 2.4.2. The magnitude of difference between these two BAFs is within a factor of four in the majority of cases.

Exhibit 2.4.3 demonstrates the predictability of BAFs for the same chemical but based on BSAF from different studies. The predicted log BAF R f ds using data from the EPA TCDD Bioaccumulation Study ( U. S. EPA, 1990) ( collected several years after the Oliver and Niimi samples were collected) correlate equally well with the predicted log BAF R f ds calculated from Oliver and Niimi ( 1988) data. An r value of 0.94 was obtained for the correlation of BAFs using data from Tables 2.4.1a and 2.4.1b. Exhibit 2.4.4 shows that for the majority of chemicals, predicted BAFs from the TCDD Bioaccumulation Study are within a factor of two of predicted BAFs from Oliver and Niimi ( 1988); measured and predicted BAFs for all chemicals are within a factor of ten of each other.

Exhibit 2.4.5 shows the relationship of log BAF R f ds calculated from EPA BSAFs using lake trout data from the TCDD Bioaccumulation Study ( Cook et al., 1994) to log K ow s. The bioaccumulative PCDDs and PCDFs ( 2,3,7,8 chlorinated) have BAF R f ds 10 to 1,000 fold less than PCBs with similar K ow s, which is expected due to PCDD and PCDF metabolism in fish. It should be noted, however, that some of the chlordane and nonachlor BAF R f ds do not have the expected correlations with K ow. This is shown in Exhibit 2.4.5 by the BAF R f ds for five of six chlordanes and nonachlors that are much greater than those for PCBs with the same estimated log K ow. This finding is unexpected because PCBs are not metabolized in fish and would be expected to have higher BAFs than other chemicals with the same K ow values. Therefore, the log K ow values chosen here for the chlordanes and nonachlors may be significantly underestimated.

All of the above correlations were based on the BSAF procedure using the Oliver and Niimi ( 1988) Lake Ontario salmonid BAF R f d for PCB congener 52 as a reference chemical. As noted earlier, the BSAF procedure is strengthened through use of several reference chemicals with both a range of K ow s and ability to be accurately measured in water. Using additional reference chemicals ( PCB congeners 105 and 118 and DDT) results in correlations with other measured and predicted BAF R f ds from Tables 2.4.1a and 2.4.1b that are very similar to comparisons seen using PCB congener 52 as a reference chemical.

A good test for robustness of the BSAF procedure for predicting BAF R f ds is comparison of two independent data sets based on different ecosystems and conditions. Such a comparison can be made for bioaccumulation of PCBs in Lake Ontario fish and Green Bay fish. Although both ecosystems are specific to the Great Lakes area, Green Bay is a shallower, smaller, and more eutrophic body of water than Lake Ontario. The correlation between the PCB log BAF R f ds for brown trout predicted from BSAFs using Green Bay data and measured log BAF R f ds based on Oliver and Niimi ( 1988) data 194 is shown in Exhibit 2.4.6. An r value of 0.91 was obtained for the correlation of these log BAFs ( using data from Tables 2.4.1a and 2.4.1b), showing that the values are well correlated. Exhibit 2.4.7 shows that, most frequently, the BAFs differ from each other by less than a factor of two, and all chemical BAFs are within a factor of ten of each other. The correlation between predicted log BAF R f ds from Green Bay ( for brown trout) and predicted lake trout log BAFs using Oliver and Niimi ( 1988) salmonid and water measurements and lake trout BSAFs from the EPA TCDD Bioaccumulation Study is shown in Exhibit 2.4.8. The r value for the relationship between these log BAFs using data from Tables 2.4.1a and 2.4.1b is 0.90. As shown in Exhibit 2.4.9, the most frequent difference in these BAFs is less than or equal to a factor of two. However, for PCB congener 198, the difference between BAFs is 85, as indicated by the value farthest to the right in the exhibit.

Despite the complex exposures of Green Bay fish ( which result from movement and interaction of biota through gradients of decreasing PCBs, nutrients and suspended organic carbon extending from the Fox River to the outer bay and Lake Michigan), good agreement exists between Green Bay brown trout predicted log BAFs and both field measured log BAF R f ds and predicted log BAF R f ds from BSAFs from Lake Ontario, using PCB 52 as a reference chemical. Although not shown, good agreement also exists for the predicted BAFs using PCB 118 as the reference chemical. In addition to the above comparisons, correlations of predicted log BAFs with log K ow values from Green Bay show relationships that are similar to the log BAF  log K ow relationship for predicted BAF R f ds from Lake Ontario data.

Based on the above correlations and ratios, the BSAF method appears to work well not only for predicting BAFs using data from the same system ( Lake Ontario) but also for predicting BAFs between systems ( Green Bay vs. Lake Ontario). These evaluations support the use of the BSAF method for predicting BAFs. 195 Table 2.4.1a: Great Lakes Trout BAF R R f d s Calculated from Measured BSAFs/ BAFs Measured Values Predicted BAF R f d Chemical Log K ow BSAF Ol. & Niimi ( 1988) log BAF Ol. & Niimi ( 1988) BSAF EPA ( 1990) log BAF Ol. & Niimi ref PCB 52 log BAF EPA ref PCB 52 log BAF Ol. & Niimi ref PCB 105 log BAF EPA ref PCB 105 dieldrin 5.3 6.65 7.67 6.95 ddt 6.45 1.09 7.78 1.67 7.87 8.22 7.54 7.5 dde 6.76 4.14 8.35 7.7 8.76 9.19 8.43 8.47 ddd 6.06 0.28 7.00 6.90 6.56 mirex 6.89 1.43 8.13 1.31 8.43 8.55 8.09 7.84 photomirex 6.89 5.48 8.07 9.01 8.68 g chlordane 6.0 2.22 6.79 7.73 7.40 t chlordane 6.0 2.00 7.85 7.13 c chlordane 6.0 4.77 8.23 7.51 t nonachlor 6.0 10.5 8.57 7.85 c nonachlor 6.0 0.51 7.25 6.54 alpha hch 3.78 2.45 4.69 5.55 5.22 gamma hch 3.67 0.69 4.93 4.89 4.56 hcbd 4.84 ocs 6.29 0.98 8.07 7.67 7.33 hcb 5.6 0.09 6.40 5.95 5.62 pcb 5.11 0.04 5.81 5.07 4.73 1235tcb 4.56 1245tcb 4.56 1234tcb 4.59 0.01 5.07 4.11 3.78 135tcb 4.17 124tcb 3.99 123tcb 4.1 245tct 4.93 236tct 4.93 pct 6.36 Total PCB 6.14 1.85 7.81 7.79 7.46 PCB 5 4.97 PCB 6 5.06 0.36 6.16 5.44 PCB 8 5.07 Table 2.4.1a: Great Lakes Trout BAF R R f d s Calculated from Measured BSAFs/ BAFs Measured Values Predicted BAF R f d Chemical Log K ow BSAF Ol. & Niimi ( 1988) log BAF Ol. & Niimi ( 1988) BSAF EPA ( 1990) log BAF Ol. & Niimi ref PCB 52 log BAF EPA ref PCB 52 log BAF Ol. & Niimi ref PCB 105 log BAF EPA ref PCB 105 196 PCB 12 5.22 0.44 6.41 5.69 PCB 13 5.29 PCB 16 5.16 5.92 PCB 17 5.25 0.15 5.52 0.99 5.80 6.79 5.47 6.07 PCB 18 5.24 0.26 5.75 0.1 6.05 5.79 5.71 5.07 PCB 22 5.58 0.21 6.39 0.27 6.28 6.56 5.95 5.84 PCB 25 5.67 0.25 0.33 6.44 6.74 6.11 6.02 PCB 26 5.66 1.72 0.44 7.28 6.85 6.94 6.13 PCB 32 5.44 0.18 6.76 6.09 5.75 PCB 33 5.60 0.15 5.32 0.49 6.15 6.84 5.82 6.12 PCB 40 5.66 0.10 6.55 0.18 6.06 6.46 5.72 5.74 PCB 42 5.76 0.52 7.49 6.86 6.53 PCB 44 5.75 0.48 6.96 0.4 6.82 6.90 6.48 6.18 PCB 45 5.53 0.22 6.42 5.70 PCB 46 5.53 0.57 0.02 6.67 5.38 6.34 4.66 PCB 49 5.85 0.69 7.13 7.07 6.74 PCB 52 5.84 0.61 7.01 0.42 7.01 7.01 6.67 6.29 PCB 53 5.62 1.84 6.51 7.27 6.93 PCB 63 6.17 0.82 7.63 6.91 PCB 64 5.95 0.73 7.51 7.20 6.86 PCB 66 6.20 0.85 7.79 7.52 7.18 PCB 74 6.20 3.45 7.66 0.61 8.12 7.53 7.79 6.81 PCB 77 6.36 0.29 7.37 6.65 PCB 81 6.36 0.67 7.73 7.01 PCB 82 6.20 2.45 8.13 0.18 7.97 7.00 7.64 6.28 PCB 83 6.26 1.33 7.93 7.21 PCB 84 6.04 3.04 8.28 7.91 7.57 PCB 85 6.30 1.45 7.89 1.29 7.85 7.96 7.51 7.24 PCB 87 6.29 1.37 7.97 7.25 PCB 91 6.13 1.25 6.92 0.64 7.61 7.48 7.28 6.76 PCB 92 6.35 1.43 8.11 7.89 7.55 Table 2.4.1a: Great Lakes Trout BAF R R f d s Calculated from Measured BSAFs/ BAFs Measured Values Predicted BAF R f d Chemical Log K ow BSAF Ol. & Niimi ( 1988) log BAF Ol. & Niimi ( 1988) BSAF EPA ( 1990) log BAF Ol. & Niimi ref PCB 52 log BAF EPA ref PCB 52 log BAF Ol. & Niimi ref PCB 105 log BAF EPA ref PCB 105 197 PCB 95 6.13 1.40 7.25 7.66 7.33 PCB 97 6.29 0.28 7.28 6.56 PCB 99 6.39 0.68 7.39 1.51 7.61 8.12 7.27 7.40 PCB 100 6.23 1.78 8.03 7.31 PCB 101 6.38 2.45 7.45 1.06 8.15 7.95 7.82 7.23 PCB 105 6.65 2.70 8.13 4.49 8.47 8.85 8.13 8.13 PCB 110 6.48 1.53 7.79 0.82 8.05 7.94 7.71 7.22 PCB 118 6.74 4.09 8.15 1.72 8.74 8.52 8.40 7.80 PCB 119 6.58 3.83 8.71 7.99 PCB 126 6.89 3.21 8.94 8.22 PCB 128 6.74 3.61 2.78 8.68 8.73 8.35 8.01 PCB 129 6.73 1.75 1.13 8.36 8.33 8.02 7.61 PCB 130 6.8 2.15 8.68 7.96 PCB 132 6.58 0.87 7.56 7.90 7.57 PCB 136 6.22 10.87 7.37 8.64 8.30 PCB 138 6.83 4.25 8.30 8.84 8.51 PCB 141 6.82 2.75 8.32 1.74 8.64 8.61 8.31 7.89 PCB 146 6.89 3.22 8.73 1.25 8.78 8.53 8.45 7.81 PCB 149 6.67 2.33 7.99 0.93 8.42 8.19 8.09 7.47 PCB 151 6.64 3.38 8.51 1.65 8.55 8.40 8.22 7.69 PCB 153 6.92 4.22 8.32 1.91 8.93 8.75 8.59 8.03 PCB 156 7.18 3.97 9.16 8.83 PCB 158 7.02 1.52 8.75 8.03 PCB 167 7.27 0.69 8.66 7.94 PCB 171 7.11 2.71 8.93 8.59 PCB 172 7.33 1.36 9.01 8.29 PCB 174 7.11 1.54 8.74 1.25 8.68 8.75 8.35 8.03 PCB 177 7.08 3.53 9.01 1.91 9.01 8.91 8.68 8.19 PCB 178 7.14 4.48 2.76 9.18 9.13 8.84 8.41 PCB 180 7.36 3.78 8.58 3.26 9.32 9.42 8.99 8.70 PCB 183 7.20 5.62 9.03 2.68 9.33 9.17 9.00 8.46 Table 2.4.1a: Great Lakes Trout BAF R R f d s Calculated from Measured BSAFs/ BAFs Measured Values Predicted BAF R f d Chemical Log K ow BSAF Ol. & Niimi ( 1988) log BAF Ol. & Niimi ( 1988) BSAF EPA ( 1990) log BAF Ol. & Niimi ref PCB 52 log BAF EPA ref PCB 52 log BAF Ol. & Niimi ref PCB 105 log BAF EPA ref PCB 105 198 PCB 185 7.11 1.55 2.24 8.68 9.01 8.35 8.29 PCB 189 7.71 0.71 9.11 8.39 PCB 194 7.80 1.53 8.56 2.47 9.37 9.74 9.03 9.02 PCB 195 7.56 1.90 9.22 8.89 PCB 197 7.3 1.1 8.89 8.17 PCB 198 7.62 6.55 9.98 9.26 PCB 201 7.62 1.53 1.13 9.19 9.22 8.85 8.50 PCB 205 8.00 0.34 0.48 8.91 9.23 8.58 8.51 PCB 206 8.09 0.47 0.34 9.15 9.17 8.81 8.45 PCB 207 7.74 0.66 0.89 8.95 9.24 8.61 8.52 PCB 209 8.18 0.14 0.03 8.70 8.20 8.36 7.48 PCB 24+ 27 5.40 0.25 0.12 6.17 6.02 5.83 5.30 PCB 28+ 31 5.67 0.52 6.89 0.19 6.77 6.50 6.43 5.78 PCB 37+ 42 5.8 0.62 7.14 6.42 PCB 47+ 48 5.82 1.23 7.18 0.65 7.29 7.17 6.95 6.46 PCB 41+ 64+ 71 5.87 0.46 7.08 6.36 PCB 56+ 60 6.11 0.31 7.15 6.43 PCB 70+ 76 6.17 1.49 7.56 0.61 7.72 7.50 7.39 6.78 PCB 66+ 95 6.17 0.53 7.44 6.72 PCB 56+ 60+ 81 6.19 0.55 7.96 7.32 6.98 PCB 84+ 92 6.2 1.22 7.83 7.11 PCB 87+ 97 6.29 2.45 8.08 8.06 7.73 PCB 137+ 176 6.8 1.16 8.41 7.69 PCB 138+ 163 6.91 2.23 8.81 8.09 PCB 156+ 171+ 202 7.18 1.25 8.82 8.10 PCB 182+ 187 7.19 3.80 8.43 9.15 8.81 PCB 157+ 200 7.23 1.56 8.97 8.25 PCB 170+ 190 7.37 2.06 9.20 4.17 9.06 9.53 8.73 8.81 PCB 195+ 208 7.64 0.72 9.04 8.33 PCB 196+ 203 7.65 1.56 9.26 1.12 9.23 9.25 8.89 8.53 2378 TCDD 7.02 0.059 7.34 6.62 Table 2.4.1a: Great Lakes Trout BAF R R f d s Calculated from Measured BSAFs/ BAFs Measured Values Predicted BAF R f d Chemical Log K ow BSAF Ol. & Niimi ( 1988) log BAF Ol. & Niimi ( 1988) BSAF EPA ( 1990) log BAF Ol. & Niimi ref PCB 52 log BAF EPA ref PCB 52 log BAF Ol. & Niimi ref PCB 105 log BAF EPA ref PCB 105 199 12378 PeCDD 7.5 0.054 7.78 7.06 123478 HxCDD 7.8 0.018 7.60 6.88 123678 HxCDD 7.8 0.0073 7.21 6.49 123789 HxCDD 7.8 0.0081 7.26 6.54 1234678 HpCDD 8.2 0.0031 7.24 6.52 OCDD 8.6 0.00074 7.02 6.30 2378 TCDF 6.5 0.047 6.72 6.00 12378 PeCDF 7.0 0.013 6.66 5.94 23478 PeCDF 7.0 0.095 7.52 6.81 123478 HxCDF 7.5 0.0045 6.70 5.98 123678 HxCDF 7.5 0.011 7.09 6.37 123789 HxCDF 7.5 0.037 7.61 6.90 234678 HxCDF 7.5 0.04 7.65 6.93 1234678 HpCDD 8.0 0.00065 6.36 5.64 1234789 HpCDD 8.0 0.023 7.91 7.19 OCDF 8.8 0.00099 7.34 6.62 a. Oliver and Niimi ( 1988). BAF R f d calculated from measured BAFs using freely dissolved equation 2.4.11, DOC= 2.0 mg/ L, POC= 0.0 mg/ L, K doc= K ow/ 10, K poc= K ow. Predicted BAFs based on equation 2.4.16. b. U. S. EPA 1990. ( TCDD Bioaccumulation Study). Predicted BAFs based on equation 2.4.16. c. Green Bay/ Fox River Mass Balance Study ( As described in Great Lakes Water Quality Initiative Technical Support Document for the Procedure to Determine Bioaccumulation Factors. EPA 820 B 95 005. March 1995.) 200 Table 2.4.1b: Great Lakes Trout BAF R ds Calculated from Measured BSAFs/ BAFs Predicted Values Chemical Log K ow log BAF Ol. & Niimia ref DDT log BAF EPAb ref DDT log BAF Ol. & Niimia ref PCB 118 log BAF EPAb ref PCB 118 BT BSAF EPA G Bayc log BAF EPA G Bayc ref PCB 52 log BAF EPA G Bayc ref PCB 118 dieldrin 5.3 7.23 7.30 ddt 6.45 7.78 7.78 7.29 7.85 dde 6.76 8.67 8.75 8.18 8.82 ddd 6.06 6.80 6.31 mirex 6.89 8.33 8.11 7.84 8.18 photomirex 6.89 8.92 8.43 g chlordane 6 7.64 7.14 t chlordane 6 7.41 7.48 c chlordane 6 7.78 7.85 t nonachlor 6 8.13 8.20 c nonachlor 6 6.81 6.88 alpha hch 3.78 5.46 4.97 gamma hch 3.67 4.80 4.31 hcbd 4.84 ocs 6.29 7.57 7.08 hcb 5.6 5.86 5.37 pcb 5.11 4.97 4.48 1235tcb 4.56 1245tcb 4.56 1234tcb 4.59 4.02 3.53 135tcb 4.17 124tcb 3.99 123tcb 4.1 245tct 4.93 236tct 4.93 pct 6.36 Total PCB 6.14 7.70 7.21 PCB 5 4.97 0.14 4.88 5.12 PCB 6 5.06 5.72 5.79 1.7 6.05 6.29 PCB 8 5.07 0.14 4.98 5.22 Table 2.4.1b: Great Lakes Trout BAF R ds Calculated from Measured BSAFs/ BAFs Predicted Values Chemical Log K ow log BAF Ol. & Niimia ref DDT log BAF EPAb ref DDT log BAF Ol. & Niimia ref PCB 118 log BAF EPAb ref PCB 118 BT BSAF EPA G Bayc log BAF EPA G Bayc ref PCB 52 log BAF EPA G Bayc ref PCB 118 201 PCB 12 5.22 5.97 6.04 PCB 13 5.29 PCB 16 5.16 PCB 17 5.25 5.71 6.35 5.22 6.42 0.75 5.89 6.13 PCB 18 5.24 5.95 5.34 5.46 5.41 0.64 5.81 6.05 PCB 22 5.58 6.19 6.12 5.70 6.19 0.39 5.94 6.18 PCB 25 5.67 6.35 6.29 5.86 6.36 0.73 6.30 6.54 PCB 26 5.66 7.18 6.41 6.69 6.48 0.95 6.40 6.64 PCB 32 5.44 5.99 5.50 PCB 33 5.60 6.06 6.39 5.57 6.46 0.29 5.83 6.07 PCB 40 5.66 5.96 6.02 5.47 6.09 0.69 6.26 6.50 PCB 42 5.76 6.77 6.28 PCB 44 5.75 6.72 6.46 6.23 6.53 PCB 45 5.53 5.98 6.05 1.16 6.36 6.60 PCB 46 5.53 6.58 4.94 6.09 5.01 0.61 6.08 6.32 PCB 49 5.85 6.98 6.49 3.34 7.14 7.38 PCB 52 5.84 6.91 6.57 6.42 6.64 4.74 7.28 7.52 PCB 53 5.62 7.17 6.68 2.12 6.71 6.95 PCB 63 6.17 7.19 7.26 4.37 7.57 7.81 PCB 64 5.95 7.10 6.61 PCB 66 6.20 7.42 6.93 3.1 7.46 7.70 PCB 74 6.20 8.03 7.09 7.54 7.16 2.46 7.36 7.60 PCB 77 6.36 6.93 7.00 4.12 7.74 7.98 PCB 81 6.36 7.29 7.36 11.6 8.19 8.43 PCB 82 6.20 7.88 6.56 7.39 6.63 4.05 7.57 7.81 PCB 83 6.26 7.49 7.56 5.67 7.78 8.02 PCB 84 6.04 7.81 7.32 7.2 7.66 7.90 PCB 85 6.30 7.75 7.51 7.26 7.59 7.25 7.92 8.16 PCB 87 6.29 7.53 7.60 6.13 7.84 8.08 PCB 91 6.13 7.52 7.04 7.02 7.11 8.44 7.82 8.06 PCB 92 6.35 7.79 7.30 Table 2.4.1b: Great Lakes Trout BAF R ds Calculated from Measured BSAFs/ BAFs Predicted Values Chemical Log K ow log BAF Ol. & Niimia ref DDT log BAF EPAb ref DDT log BAF Ol. & Niimia ref PCB 118 log BAF EPAb ref PCB 118 BT BSAF EPA G Bayc log BAF EPA G Bayc ref PCB 52 log BAF EPA G Bayc ref PCB 118 202 PCB 95 6.13 7.57 7.08 PCB 97 6.29 6.84 6.91 6.42 7.86 8.10 PCB 99 6.39 7.51 7.67 7.02 7.74 7.18 8.01 8.25 PCB 100 6.23 7.58 7.65 1.71 7.23 7.47 PCB 101 6.38 8.06 7.51 7.57 7.58 10.01 8.14 8.38 PCB 105 6.65 8.37 8.41 7.88 8.48 5.35 8.14 8.38 PCB 110 6.48 7.95 7.50 7.46 7.57 4.15 7.86 8.10 PCB 118 6.74 8.64 8.08 8.15 8.15 4.96 8.20 8.44 PCB 119 6.58 8.27 8.34 3.03 7.83 8.07 PCB 126 6.89 8.50 8.57 PCB 128 6.74 8.59 8.29 8.10 8.36 10.21 8.51 8.75 PCB 129 6.73 8.26 7.89 7.77 7.96 PCB 130 6.8 8.24 8.31 11.21 8.61 8.85 PCB 132 6.58 7.81 7.32 PCB 136 6.22 8.55 8.05 PCB 138 6.83 8.75 8.26 PCB 141 6.82 8.55 8.16 8.06 8.24 9.30 8.55 8.79 PCB 146 6.89 8.69 8.09 8.20 8.16 10.0 8.66 8.90 PCB 149 6.67 8.33 7.74 7.84 7.81 8.7 8.37 8.61 PCB 151 6.64 8.46 7.96 7.97 8.03 9.7 8.39 8.63 PCB 153 6.92 8.84 8.31 8.34 8.38 5.35 8.41 8.65 PCB 156 7.18 9.07 8.58 PCB 158 7.02 8.31 8.38 PCB 167 7.27 8.21 8.28 16.0 9.24 9.48 PCB 171 7.11 8.83 8.34 PCB 172 7.33 8.57 8.64 PCB 174 7.11 8.59 8.31 8.10 8.38 4.46 8.52 8.76 PCB 177 7.08 8.92 8.47 8.43 8.54 8.04 8.75 8.99 PCB 178 7.14 9.08 8.69 8.59 8.76 PCB 180 7.36 9.23 8.98 8.74 9.05 10.96 9.16 9.40 PCB 183 7.20 9.24 8.73 8.75 8.80 6.5 8.78 9.02 Table 2.4.1b: Great Lakes Trout BAF R ds Calculated from Measured BSAFs/ BAFs Predicted Values Chemical Log K ow log BAF Ol. & Niimia ref DDT log BAF EPAb ref DDT log BAF Ol. & Niimia ref PCB 118 log BAF EPAb ref PCB 118 BT BSAF EPA G Bayc log BAF EPA G Bayc ref PCB 52 log BAF EPA G Bayc ref PCB 118 203 PCB 185 7.11 8.59 8.56 8.10 8.63 3.23 8.38 8.62 PCB 189 7.71 8.67 8.74 3.45 9.01 9.25 PCB 194 7.80 9.27 9.30 8.78 9.37 3.29 9.08 9.32 PCB 195 7.56 9.13 8.64 PCB 197 7.3 8.45 8.52 PCB 198 7.62 9.54 9.61 0.46 8.05 8.29 PCB 201 7.62 9.10 8.78 8.60 8.85 4.79 9.06 9.30 PCB 205 8.00 8.82 8.79 8.33 8.86 3.09 9.25 9.49 PCB 206 8.09 9.05 8.73 8.56 8.80 0.95 8.83 9.07 PCB 207 7.74 8.85 8.79 8.36 8.86 1.3 8.62 8.86 PCB 209 8.18 8.60 7.76 8.11 7.83 0.19 8.22 8.46 PCB 24+ 27 5.40 6.07 5.58 5.58 5.65 1.55 6.35 6.59 PCB 28+ 31 5.67 6.68 6.05 6.18 6.12 0.67 6.26 6.50 PCB 37+ 42 5.8 6.70 6.77 6.75 7.39 7.63 PCB 47+ 48 5.82 7.19 6.73 6.70 6.80 7.86 7.47 7.71 PCB 41+ 64+ 71 5.87 6.64 6.71 2.55 7.04 7.28 PCB 56+ 60 6.11 6.71 6.78 1.14 6.93 7.17 PCB 70+ 76 6.17 7.63 7.05 7.14 7.12 1.2 7.01 7.25 PCB 66+ 95 6.17 7.00 7.07 3.1 7.43 7.67 PCB 56+ 60+ 81 6.19 7.22 6.73 1.15 7.02 7.26 PCB 84+ 92 6.2 7.39 7.46 7.25 7.82 8.06 PCB 87+ 97 6.29 7.97 7.48 6.3 7.85 8.09 PCB 137+ 176 6.8 7.97 8.04 1.43 7.72 7.96 PCB 138+ 163 6.91 8.36 8.43 11.94 8.75 8.99 PCB 156+ 171+ 202 7.18 8.38 8.45 10.70 8.97 9.21 PCB 182+ 187 7.19 9.05 8.56 9.38 8.92 9.16 PCB 157+ 200 7.23 8.53 8.60 8.66 8.93 9.17 PCB 170+ 190 7.37 8.97 9.09 8.48 9.16 4.10 8.74 8.98 PCB 195+ 208 7.64 8.60 8.67 1.01 8.41 8.65 PCB 196+ 203 7.65 9.13 8.80 8.64 8.87 4.24 9.04 9.28 2378 TCDD 7.02 6.90 6.97 Table 2.4.1b: Great Lakes Trout BAF R ds Calculated from Measured BSAFs/ BAFs Predicted Values Chemical Log K ow log BAF Ol. & Niimia ref DDT log BAF EPAb ref DDT log BAF Ol. & Niimia ref PCB 118 log BAF EPAb ref PCB 118 BT BSAF EPA G Bayc log BAF EPA G Bayc ref PCB 52 log BAF EPA G Bayc ref PCB 118 204 12378 PeCDD 7.5 7.34 7.41 123478 HxCDD 7.8 7.16 7.23 123678 HxCDD 7.8 6.77 6.84 123789 HxCDD 7.8 6.81 6.88 123334678 Hp 8.2 6.80 6.87 OCDD 8.6 6.57 6.64 2378 TCDF 6.5 6.28 6.35 12378 PeCDF 7.0 6.22 6.29 23478 PeCDF 7.0 7.08 7.15 123478 HxCDF 7.5 6.26 6.33 123678 HxCDF 7.5 6.65 6.72 123789 HxCDF 7.5 7.17 7.24 234678 HxCDF 7.5 7.21 7.28 Table 2.4.1b: Great Lakes Trout BAF R ds Calculated from Measured BSAFs/ BAFs Predicted Values Chemical Log K ow log BAF Ol. & Niimia ref DDT log BAF EPAb ref DDT log BAF Ol. & Niimia ref PCB 118 log BAF EPAb ref PCB 118 BT BSAF EPA G Bayc log BAF EPA G Bayc ref PCB 52 log BAF EPA G Bayc ref PCB 118 205 1234678 HpCD 8.0 5.92 5.99 1234789 HpCD 8.0 7.47 7.54 OCDF 8.8 6.90 6.97 a. Oliver and Niimi ( 1988). BAF R f d calculated from measured BAFs using freely dissolved equation 2.4.11, DOC= 2.0 mg/ L, POC= 0.0 mg/ L, K doc= K ow/ 10, K poc= K ow. Predicted BAFs based on equation 2.4.16. b. U. S. EPA 1990. ( TCDD Bioaccumulation Study). Predicted BAFs based on equation 2.4.16. c. Green Bay/ Fox River Mass Balance Study ( As described in Great Lakes Water Quality Initiative Technical Support Document for the Procedure to Determine Bioaccumulation Factors. EPA 820 B 95 005. March 1995.) 206 Table 2.4.2: Mean BAF R f ds from Lake Ontario BSAFs for Salmonids Chemical log Kow Number Mean Mean BAFs log BAF R f d BAF R f d dieldrin 5.30 4 7.29 1.93e+ 07 ddt 6.45 8 7.73 5.33e+ 07 dde 6.76 8 8.66 4.56e+ 08 ddd 6.06 4 6.64 4.39e+ 06 mirex 6.89 8 8.17 1.49e+ 08 photomirex 6.89 4 8.76 5.74e+ 08 g chlordane 6.00 4 7.48 3.00e+ 07 t chlordane 6.00 4 7.46 2.91e+ 07 c chlordane 6.00 4 7.84 6.95e+ 07 t nonachlor 6.00 4 8.18 1.53e+ 08 c nonachlor 6.00 4 6.87 7.43e+ 06 alpha hch 3.78 4 5.30 2.00e+ 05 gamma hch 3.67 4 4.64 4.34e+ 04 hcbd 4.84 ocs 6.29 4 7.41 2.58e+ 07 hcb 5.60 4 5.70 5.01e+ 05 pcb 5.11 4 4.81 6.47e+ 04 1235tcb 4.56 1245tcb 4.50 1234tcb 4.59 4 3.86 7.25e+ 03 135tcb 4.17 124tcb 3.99 123tcb 4.10 245tct 4.93 236tct 4.93 pct 6.36 PCBs 5 4.97 6 5.06 4 5.78 6.02e+ 05 8 5.07 12 5.22 4 6.03 1.06e+ 06 13 5.29 16 5.16 17 5.25 8 5.98 9.52e+ 05 18 5.24 8 5.60 3.96e+ 05 22 5.58 8 6.10 1.27e+ 06 25 5.67 8 6.27 1.87e+ 06 26 5.66 8 6.75 5.57e+ 06 207 Table 2.4.2: Mean BAF R f ds from Lake Ontario BSAFs for Salmonids ( continued)