° The species most biologically relevant to humans is known; in the absence of the most biologically relevant species, the most sensitive species is chosen as the basis for RfD development. For example, study results from an animal whose pharmacokinetics and toxicokinetics match those of a human would be considered the most biologically relevant.

Minimum data suggestions for benchmark and categorical regression analyses are currently evolving. However, the examples and text provided in this TSD under the noncancer section do provide some information of the data needs of each of these analyses.

3.2.2 Cancer  Data Suggestions <IP_ADDRESS> Minimum Data A minimally acceptable data base for cancer assessment is one similar to the weight of evidence established by the 1986 Guidelines for Carcinogen Risk Assessment ( e. g., A, B, and C classifications) fully described at 51 FR 33992 and in the Federal Register notice which accompanies 280 this TSD. However, such a data base may be lacking information on mode of action, which is important for making judgments using the new Cancer Guidelines of 1996 ( 61 FR 17960). At a minimum, some information is needed to determine the mode of action; otherwise, the chemical must be treated as a linear compound.

<IP_ADDRESS> Ideal Situation The goal is to establish a complete data base which includes not only adequate tumor data from chronic studies, as described above, but data on mode of action, metabolism, pharmacokinetics, and target toxicity. The ultimate goal in any cancer assessment is to establish the mechanism by which the cancer develops. Since the number of studies for a weight of evidence is yet to be established, there is no quantitative guidance being presented today. These minimum data requirements may be established in time to incorporate them into the final TSD. However, as with all determinations based on a weight of evidence, the number of studies which demonstrate ( 1) a carcinogenic effect in a number of animal species and sexes, and ( 2) a particular mode of action, determines the confidence in the overall weight of evidence.

3.2.3 Exposure  Data Suggestions Numerous suggestions are made at the beginning of the exposure analyses section of this TSD regarding the factors used in the AWQC derivation. These factors include ( 1) body weight of the individuals exposed; ( 2) drinking water ingestion rates; ( 3) fish consumption rates; ( 4) incidental ingestion of water; and ( 5) the relative source contribution factor to account for other exposures. Body weights and fish intake assumptions are used in each criterion. The suggestions made are to help the exposure assessor locate sources of information for conducting exposure analyses and do not prescribe minimum data considerations. However, the specific approach to estimating non water sources of exposure when setting AWQC ( i. e., the Exposure Decision Tree Approach) discusses data adequacy considerations. This discussion is not repeated here; the reader is referred to the data adequacy subsection in Section <IP_ADDRESS> of this TSD.

3.3 Site Specific Criterion Calculation The 1980 AWQC National Guidelines allowed for site specific modifications to reflect local environmental conditions and human exposure patterns. The methodology stated that " local" may refer to any appropriate geographic area where common aquatic environmental or exposure patterns exist. Thus " local" may signify a Statewide, regional, river reach or entire river.

In today's proposal, site specific criteria may be developed as long as the site specific data, either toxicological or exposure related, is justifiable. For example, a State should use a site specific fish consumption rate that represents at least the central tendency ( median or mean) of the population surveyed ( either sport or subsistence, or both). If a site specific fish consumption rate for sport anglers or subsistence anglers is lower than an EPA default value, it may be used in calculating AWQC. To justify such a level ( either higher or lower than EPA defaults) the State should present 281 survey data it used in arriving at the site specific fish consumption rate. The same conditions apply to site specific calculations of BAF, percent fish lipid, or the RSC. In the case of deviations from toxicological values ( IRIS values: verified noncancer and cancer assessments), EPA recommends that the data upon which the deviation is based be presented to and approved by the Agency before a criterion is developed.

3.4 Organoleptic Criteria The 1980 AWQC National Guidelines provided for the development of organoleptic criteria if organoleptic data were available for a specific contaminant. The methodology also made a clear distinction that organoleptic criteria and toxicity based criteria are derived from completely different endpoints and that organoleptic criteria have no demonstrated relationship to potential adverse human health effects. The 1992 National Experts Workshop participants and the Great Lakes Committees of the Initiative both recommended that EPA place highest priority on setting toxicity based criteria, rather then using limited resources to set organoleptic criteria. Both efforts, the GLI and the National Experts Workshop, concluded that organoleptic effects, while significant from an aesthetic standpoint, were not a significant health concern and did not merit significant expenditures of time and effort. While it can be argued that organoleptic properties indirectly affect human health ( people may drink less water or eat less fish due to objectionable taste or odor), they have not been demonstrated to result in direct adverse effects, such as cancer or other types of toxicity.

3.5 Criteria for Chemical Classes The 1980 AWQC National Guidelines allowed for the development of criteria for chemical classes. A chemical class was defined as any group of chemical compounds which were reviewed in a single risk assessment document. The Guidelines also stated that in criterion development, isomers should be regarded as part of a chemical class rather than as a single compound. A class criterion, therefore, was an estimate of risk/ safety which applied to more than one member of a class. It involved the use of available data on one or more chemicals of a class to derive criteria for other compounds of the same class in the event that insufficient data were available to derive compoundspecific criteria. The criterion applied to each member of the class, rather than to the sum of the compounds within the class. The 1980 methodology also acknowledged that, since relatively minor structural changes within the class of compounds can have pronounced effects on their biological activities, reliance on class criteria should be minimized.

The 1980 methodology prescribed the following analysis when developing a class criterion:

° A detailed review of the chemical and physical properties of the chemicals within the group should be made. A close relationship within the class with respect to chemical activity would suggest a similar potential to reach common biological sites within tissues. Likewise, similar lipid solubilities would suggest the possibility of comparable absorption and distribution. 282 ° Qualitative and quantitative data for chemicals within the group are examined. Adequate toxicological data on a number of compounds with a group provide a more reasonable basis for extrapolation to other chemicals of the same class than minimal data on one chemical or a few chemicals within the group.

° Similarities in the nature of the toxicological response to chemicals in the class provide additional support for the prediction that the response to other members of the class may be similar. In contrast, where the biological response has been shown to differ markedly on a qualitative and quantitative basis for chemicals within a class, the extrapolation of a criterion to other members is not appropriate.

° Additional support for the validity of extrapolation of a criterion to other members of a class could be provided by evidence of similar metabolic and pharmacokinetic data for some members of the class.

The proposal in the Federal Register allows for the development of a criterion for classes of chemicals, as long as the 1980 methodology guidance is followed and a justification is provided through the analysis of mechanistic data, pharmacokinetic data, structure activity relationship data, and limited acute and chronic toxicity data. When potency differences between members of a class are great ( such as in the case of chlorinated dioxins and furans), toxicity equivalency factors ( TEFs) may be more appropriately developed than one class criterion.

3.6 Criteria for Essential Elements The 1980 AWQC National Guidelines acknowledged that developing criteria for essential elements, particularly metals, must be a balancing act between toxicity and essentiality. The 1980 guidelines state:

that the criteria must consider essentiality and cannot be established at levels which would result in deficiency of the element in the human population. The difference between the RDA and the daily doses causing a specified risk level for carcinogens or the ADIs ( now RfDs) for noncarcinogens defines the spread of daily doses from which the criterion may be derived. Because errors are inherent in defining both essential and maximum tolerable levels, the criterion is derived from the dose levels near the center of such dose ranges.

In the current proposal, EPA endorses the guidance from the 1980 methodology and adds that the process for developing criteria for essential elements should be similar to that used for any other chemical with minor modifications. The RfD represents concern for one end of the exposure spectrum ( toxicity), whereas the RDA represents the other end ( minimum essentiality). Where the RDA and RfD values might occasionally appear to be similar in magnitude to one another, it does not imply incompatibility of the two methodological approaches, nor does it imply inaccuracy or error in either calculation. Appendices A 1 Appendix A TABLE A. 1 DAILY AVERAGE PER CAPITA ESTIMATES OF FISH CONSUMPTION As Consumed Fish Individuals 18 Years of Age or Older in the U. S. Population  Finfish and Shellfish Grams/ person/ day 90% Interval* Habitat Statistic Estimate Lower Bound Upper Bound Fresh/ Estuarine Mean 5.59 4.91 6.28 50th % 0.00 0.00 0.00 90th % 17.80 14.89 20.63 95th % 39.04 36.13 42.16 99th % 86.30 81.99 96.67 Marine Mean 12.42 11.55 13.29 50th % 0.00 0.00 0.00 90th % 45.98 44.48 48.34 95th % 64.08 61.61 68.05 99th % 111.38 101.94 120.49 All Fish Mean 18.01 16.85 19.17 50th % 0.00 0.00 0.00 90th % 60.64 57.06 64.63 95th % 86.25 80.29 91.00 99th % 142.96 134.23 154.15 * Percentile intervals were estimated using the percentile bootstrap method with 1,000 bootstrap replications.

Note: Estimates are projected from a sample of 8,478 individuals to the U. S. population of 177,807,000 using 3 year combined survey weights.

Source of individual consumption data: Combined 1989, 1990, and 1991 USDA Continuing Survey of Food Intakes by Individuals ( CSFII).

The fish component of foods containing fish was calculated using data from the recipe file for release 7 of the USDA's Nutrient Data Base for Individual Food Intake Surveys. A 2 TABLE A. 2 DAILY AVERAGE PER CAPITA ESTIMATES OF FISH CONSUMPTION As Consumed Fish Individuals 18 Years of Age or Older in the U. S. Population  Finfish and Shellfish Milligrams/ kilogram/ person/ day 90% Interval* Habitat Statistic Estimate Lower Bound Upper Bound Fresh/ Estuarine Mean 75.56 66.37 84.75 50th % 0.00 0.00 0.00 90th % 242.49 205.05 277.26 95th % 547.61 493.47 587.37 99th % 1,171.84 1,123.52 1,252.78 Marine Mean 172.86 160.73 184.99 50th % 0.00 0.00 0.00 90th % 624.83 598.84 670.34 95th % 911.05 877.29 952.66 99th % 1,573.20 1,468.43 1,713.17 All Fish Mean 248.42 232.19 264.64 50th % 0.00 0.00 0.00 90th % 829.02 791.06 872.61 95th % 1,197.36 1,133.18 1,264.74 99th % 2,014.67 1,839.55 2,180.87 * Percentile intervals were estimated using the percentile bootstrap method with 1,000 bootstrap replications.

Note: Estimates are projected from a sample of 8,478 individuals to the U. S. population of 177,807,000 using 3 year combined survey weights.

Source of individual consumption data: Combined 1989, 1990, and 1991 USDA Continuing Survey of Food Intakes by Individuals ( CSFII).

The fish component of foods containing fish was calculated using data from the recipe file for release 7 of the USDA's Nutrient Data Base for Individual Food Intake Surveys. A 3 TABLE A. 3 DAILY AVERAGE PER CAPITA ESTIMATES OF FISH CONSUMPTION As Consumed Fish Individuals of Age 14 and Younger in the Acute Consumers+  Finfish and Shellfish Grams/ person/ day Habitat Statistic Estimate Fresh/ Estuarine Mean 45.73 n = 295 50th % 28.35 N = 6,267,000 90th % 108.36 95th % 136.24 99th % 214.62 Marine Mean 73.62 n = 663 50th % 56.00 N = 13,190,000 90th % 153.20 95th % 176.90 99th % 337.24 All Fish Mean 74.80 n = 807 50th % 56.49 N = 16,159,000 90th % 153.70 95th % 178.08 99th % 337.46 + Note: Acute consumer = Individual who consumed fish at least once during the 3 day reporting period. n = sample size N = population size Estimates are projected from a sample of acute consumers of age 14 and younger to the population of acute consumers of age 14 and younger using 3 year combined survey weights. The population for this survey consisted of individuals in the 48 conterminous states.

Source of individual consumption data: Combined 1989, 1990, and 1991 USDA Continuing Survey of Food Intakes by Individuals ( CSFII).

The fish component of foods containing fish was calculated using data from the recipe file for release 7 of the USDA's Nutrient Data Base for Individual Food Intake Surveys. A 4 TABLE A. 4 DAILY AVERAGE PER CAPITA ESTIMATES OF FISH CONSUMPTION As Consumed Fish Individuals of Age 14 and Younger in the Acute Consumers+  Finfish and Shellfish Milligrams/ kilogram/ person/ day Habitat Statistic Estimate Fresh/ Estuarine Mean 1,721.99 n = 295 50th % 1,271.12 N = 6,267,000 90th % 3,760.67 95th % 4,208.18 99th % 9,789.49 Marine Mean 2,532.95 n = 663 50th % 2,107.05 N = 13,190,000 90th % 5,068.69 95th % 6,376.47 99th % 8,749.02 All Fish Mean 2,624.35 n = 807 50th % 2,172.61 N = 16,159,000 90th % 5,020.14 95th % 6,904.83 99th % 10,384.82 + Note: Acute consumer = Individual who consumed fish at least once during the 3 day reporting period. n = sample size N = population size Estimates are projected from a sample of acute consumers of age 14 and younger to the population of acute consumers of age 14 and younger using 3 year combined survey weights. The population for this survey consisted of individuals in the 48 conterminous states.

Source of individual consumption data: Combined 1989, 1990, and 1991 USDA Continuing Survey of Food Intakes by Individuals ( CSFII).

The fish component of foods containing fish was calculated using data from the recipe file for release 7 of the USDA's Nutrient Data Base for Individual Food Intake Surveys. A 5 TABLE A. 5 DAILY AVERAGE PER CAPITA ESTIMATES OF FISH CONSUMPTION As Consumed Fish Females of Age 15 to 44 in the Acute Consumers+  Finfish and Shellfish Grams/ person/ day Habitat Statistic Estimate Fresh/ Estuarine Mean 61.40 n = 445 50th % 35.22 N = 10,853,000 90th % 148.83 95th % 185.44 99th % 363.56 Marine Mean 76.53 n = 774 50th % 62.96 N = 17,967,000 90th % 149.78 95th % 178.74 99th % 271.06 All Fish Mean 88.80 n = 952 50th % 69.95 N = 21,924,000 90th % 170.01 95th % 212.56 99th % 361.04 + Note: Acute consumer = Individual who consumed fish at least once during the 3 day reporting period. n = sample size N = population size Estimates are projected from a sample of female acute consumers of age 15 to 44 to the population of female acute consumers of age 15 to 44 using 3 year combined survey weights. The population for this survey consisted of individuals in the 48 conterminous states.

Source of individual consumption data: Combined 1989, 1990, and 1991 USDA Continuing Survey of Food Intakes by Individuals ( CSFII).

The fish component of foods containing fish was calculated using data from the recipe file for release 7 of the USDA's Nutrient Data Base for Individual Food Intake Surveys. A 6 TABLE A. 6 DAILY AVERAGE PER CAPITA ESTIMATES OF FISH CONSUMPTION As Consumed Fish Females of Age 15 to 44 in the Acute Consumers+  Finfish and Shellfish Milligrams/ kilogram/ person/ day Habitat Statistic Estimate Fresh/ Estuarine Mean 961.58 n = 445 50th % 533.18 N = 10,853,000 90th % 2,578.81 95th % 3,403.75 99th % 6,167.24 Marine Mean 1,227.41 n = 774 50th % 986.25 N = 17,967,000 90th % 2,469.67 95th % 3,007.98 99th % 4,800.68 All Fish Mean 1,414.54 n = 952 50th % 1,100.44 N = 21,924,000 90th % 2,726.46 95th % 3,740.83 99th % 6,703.25 + Note: Acute consumer = Individual who consumed fish at least once during the 3 day reporting period. n = sample size N = population size Estimates are projected from a sample acute consumers of females of age 14 and younger to the population of acute consumers of females of age 14 and younger using 3 year combined survey weights. The population for this survey consisted of individuals in the 48 conterminous states.

Source of individual consumption data: Combined 1989, 1990, and 1991 USDA Continuing Survey of Food Intakes by Individuals ( CSFII).

The fish component of foods containing fish was calculated using data from the recipe file for release 7 of the USDA's Nutrient Data Base for Individual Food Intake Surveys. A 7 TABLE A. 7 DAILY AVERAGE PER CAPITA ESTIMATES OF FISH CONSUMPTION As Consumed Fish Individuals 18 Years of Age or Older in the Acute Consumers+  Finfish and Shellfish Grams/ person/ day 90% Interval* Habitat Statistic Estimate Lower Bound Upper Bound Fresh/ Estuarine Mean 70.91 64.16 77.65 n = 1,541 50th % 42.45 37.24 46.91 N = 37,166,000 90th % 176.58 165.08 193.26 95th % 230.41 224.00 255.55 99th % 402.56 358.58 518.41 Marine Mean 91.49 87.35 95.64 n = 2,432 50th % 77.56 74.89 78.52 N = 57,830,000 90th % 172.29 168.00 182.00 95th % 215.62 201.99 225.63 99th % 313.05 292.80 324.81 All Fish Mean 106.39 102.37 110.41 n = 3,007 50th % 85.36 84.00 87.36 N = 70,949,000 90th % 206.76 197.84 213.00 95th % 258.22 241.00 266.86 99th % 399.26 336.50 423.56 * Percentile intervals were estimated using the percentile bootstrap method with 1,000 bootstrap replications.

+ Note: Acute consumer = Individual who consumed fish at least once during the 3 day reporting period. n = sample size N = population size Estimates are projected from a sample of acute consumers 18 years of age or older to the population of acute consumers 18 years of age or older using 3 year combined survey weights. The population for this survey consisted of individuals in the 48 conterminous states.

Source of individual consumption data: Combined 1989, 1990, and 1991 USDA Continuing Survey of Food Intakes by Individuals ( CSFII).

The fish component of foods containing fish was calculated using data from the recipe file for release 7 of the USDA's Nutrient Data Base for Individual Food Intake Surveys. A 8 TABLE A. 8 DAILY AVERAGE PER CAPITA ESTIMATES OF FISH CONSUMPTION As Consumed Fish Individuals 18 Years of Age or Older in the Acute Consumers+  Finfish and Shellfish Milligrams/ kilogram/ person/ day 90% Interval* Habitat Statistic Estimate Lower Bound Upper Bound Fresh/ Estuarine Mean 959.15 867.58 1,050.72 n = 1,541 50th % 601.88 532.31 656.86 N = 37,166,000 90th % 2,442.97 2,233.16 2,606.66 95th % 3,116.28 2,839.90 3,303.96 99th % 5,151.98 4,432.30 6,931.61 Marine Mean 1,270.78 1,214.65 1,326.90 n = 2,432 50th % 1,062.93 1,019.60 1,087.06 N = 57,830,000 90th % 2,467.68 2,331.88 2,585.09 95th % 3,116.74 2,906.16 3,264.98 99th % 4,250.22 4,037.74 4,387.96 All Fish Mean 1,461.71 1,406.34 1,517.09 n = 3,007 50th % 1,189.29 1,156.77 1,225.43 N = 70,949,000 90th % 2,802.28 2,685.81 2,868.73 95th % 3,588.11 3,308.93 3,798.54 99th % 5,355.90 5,095.58 5,766.99 * Percentile intervals were estimated using the percentile bootstrap method with 1,000 bootstrap replications.

+ Note: Acute consumer = Individual who consumed fish at least once during the 3 day reporting period. n = sample size N = population size Estimates are projected from a sample of acute consumers 18 years of age or older to the population of acute consumers 18 years of age or older using 3 year combined survey weights. The population for this survey consisted of individuals in the 48 conterminous states.

Source of individual consumption data: Combined 1989, 1990, and 1991 USDA Continuing Survey of Food Intakes by Individuals ( CSFII).

The fish component of foods containing fish was calculated using data from the recipe file for release 7 of the USDA's Nutrient Data Base for Individual Food Intake Surveys. A 9 TABLE A. 9 DAILY AVERAGE PER CAPITA ESTIMATES OF FISH CONSUMPTION As Consumed Fish U. S. Population  Finfish and Shellfish Grams/ person/ day 90% Interval* Habitat Statistic Estimate Lower Bound Upper Bound Fresh/ Estuarine Mean 4.71 4.17 5.25 50th % 0.00 0.00 0.00 90th % 12.62 10.91 13.98 95th % 32.16 29.81 35.15 99th % 82.45 77.17 86.40 Marine Mean 10.94 10.14 11.73 50th % 0.00 0.00 0.00 90th % 39.51 37.29 42.91 95th % 59.62 57.03 61.84 99th % 106.84 104.59 114.55 All Fish Mean 15.65 14.67 16.63 50th % 0.00 0.00 0.00 90th % 55.02 51.38 56.00 95th % 78.34 75.21 80.56 99th % 133.46 125.27 140.21 * Percentile intervals were estimated using the percentile bootstrap method with 1,000 bootstrap replications.

Note: Estimates are projected from a sample of 11,912 individuals to the U. S. population of 242,707,000 using 3 year combined survey weights.

Source of individual consumption data: Combined 1989, 1990, and 1991 USDA Continuing Survey of Food Intakes by Individuals ( CSFII).

The fish component of foods containing fish was calculated using data from the recipe file for release 7 of the USDA's Nutrient Data Base for Individual Food Intake Surveys. A 10 TABLE A. 10 DAILY AVERAGE PER CAPITA ESTIMATES OF FISH CONSUMPTION As Consumed Fish U. S. Population  Finfish and Shellfish Milligrams/ kilogram/ person/ day 90% Interval* Habitat Statistic Estimate Lower Bound Upper Bound Fresh/ Estuarine Mean 74.16 65.74 82.57 50th % 0.00 0.00 0.00 90th % 204.00 177.97 225.16 95th % 547.64 505.10 565.37 99th % 1,274.55 1,197.29 1,324.90 Marine Mean 186.06 170.81 201.31 50th % 0.00 0.00 0.00 90th % 663.00 627.39 717.18 95th % 991.96 960.40 1,044.69 99th % 1,942.17 1,815.48 2,042.99 All Fish Mean 260.22 242.60 277.83 50th % 0.00 0.00 0.00 90th % 880.47 844.35 918.79 95th % 1,308.54 1,267.15 1,346.71 99th % 2,356.54 2,224.54 2,556.68 * Percentile intervals were estimated using the percentile bootstrap method with 1,000 bootstrap replications.

Note: Estimates are projected from a sample of 11,912 individuals to the U. S. population of 242,707,000 using 3 year combined survey weights.

Source of individual consumption data: Combined 1989, 1990, and 1991 USDA Continuing Survey of Food Intakes by Individuals ( CSFII).

The fish component of foods containing fish was calculated using data from the recipe file for release 7 of the USDA's Nutrient Data Base for Individual Food Intake Surveys. A 11 TABLE A. 11 DAILY AVERAGE PER CAPITA ESTIMATES OF FISH CONSUMPTION As Consumed Fish U. S. Population, Acute Consumers+  Finfish and Shellfish Grams/ person/ day 90% Interval* Habitat Statistic Estimate Lower Bound Upper Bound Fresh/ Estuarine Mean 68.00 61.92 74.07 n = 1,892 50th % 39.52 36.16 44.68 N = 44,946,000 90th % 170.84 158.74 181.79 95th % 224.78 212.91 245.98 99th % 374.74 336.50 431.34 Marine Mean 87.77 83.74 91.80 n = 3,184 50th % 71.77 69.73 74.23 N = 73,100,000 90th % 169.39 167.00 173.65 95th % 209.50 198.11 221.73 99th % 320.41 292.80 341.88 All Fish Mean 100.63 96.66 104.60 n = 3,927 50th % 80.79 79.29 83.90 N = 89,800,000 90th % 197.44 188.74 205.12 95th % 253.38 231.51 264.45 99th % 371.59 359.29 401.61 * Percentile intervals were estimated using the percentile bootstrap method with 1,000 bootstrap replications.

+ Note: Acute consumer = Individual who consumed fish at least once during the 3 day reporting period. n = sample size N = population size Estimates are projected from the sample to the population of acute consumers in the 48 conterminous states, using 3 year combined survey weights.

Source of individual consumption data: Combined 1989, 1990, and 1991 USDA Continuing Survey of Food Intakes by Individuals ( CSFII).

The fish component of foods containing fish was calculated using data from the recipe file for release 7 of the USDA's Nutrient Data Base for Individual Food Intake Surveys. A 12 TABLE A. 12 DAILY AVERAGE PER CAPITA ESTIMATES OF FISH CONSUMPTION As Consumed Fish U. S. Population, Acute Consumers+  Finfish and Shellfish Milligrams/ kilogram/ person/ day 90% Interval*

Habitat Statistic Estimate Lower Bound Upper Bound Fresh/ Estuarine Mean 1,076.80 980.00 1,173.61 n = 1,892 50th % 656.62 588.84 709.37 N = 44,946,000 90th % 2,695.81 2,546.77 2,819.33 95th % 3,399.46 3,132.65 3,839.47 99th % 6,526.10 5,270.61 6,931.61 Marine Mean 1,495.37 1,422.63 1,568.12 n = 3,184 50th % 1,151.58 1,120.00 1,181.14 N = 73,100,000 90th % 2,956.38 2,838.46 3,083.70 95th % 3,887.52 3,770.65 4,113.22 99th % 6,510.73 5,772.57 6,852.01 All Fish Mean 1,674.31 1,606.79 1,741.83 n = 3,927 50th % 1,307.30 1,267.12 1,339.46 N = 89,800,000 90th % 3,299.54 3,133.69 3,462.35 95th % 4,258.69 4,065.32 4,483.83 99th % 7,126.90 6,644.11 7,794.41 * Percentile intervals were estimated using the percentile bootstrap method with 1,000 bootstrap replications.

+ Note: Acute consumer = Individual who consumed fish at least once during the 3 day reporting period. n = sample size N = population size Estimates are projected from the sample to the population of acute consumers in the 48 conterminous states, using 3 year combined survey weights.

Source of individual consumption data: Combined 1989, 1990, and 1991 USDA Continuing Survey of Food Intakes by Individuals ( CSFII).

The fish component of foods containing fish was calculated using data from the recipe file for release 7 of the USDA's Nutrient Data Base for Individual Food Intake Surveys. A 13 TABLE A. 13 DAILY AVERAGE PER CAPITA ESTIMATES OF FISH CONSUMPTION As Consumed Fish New England Region  Finfish and Shellfish Grams/ person/ day 90% Interval* Habitat Statistic Estimate Lower Bound Upper Bound Fresh/ Estuarine Mean 4.94 3.87 6.01 50th % 0.00 0.00 0.00 90th % 17.58 6.92 25.76 95th % 30.11 25.76 57.86 99th % 72.04 64.61 74.67 Marine Mean 16.96 15.57 18.35 50th % 0.00 0.00 0.00 90th % 56.95 49.00 66.28 95th % 74.59 73.91 83.81 99th % 124.64 99.13 178.05 All Fish Mean 21.90 19.95 23.85 50th % 0.00 0.00 0.00 90th % 73.56 65.07 74.68 95th % 91.33 83.81 96.30 99th % 145.86 138.94 178.05 * Percentile intervals were estimated using the percentile bootstrap method with 1,000 bootstrap replications.

Note: Estimates are projected from a sample of 595 individuals to the regional population of 12,769,000 using survey weights.

Source of individual consumption data: Combined 1989, 1990, and 1991 USDA Continuing Survey of Food Intakes by Individuals ( CSFII).

The fish component of foods containing fish was calculated using data from the recipe file for release 7 of the USDA's Nutrient Data Base for Individual Food Intake Surveys. A 14 TABLE A. 14 DAILY AVERAGE PER CAPITA ESTIMATES OF FISH CONSUMPTION As Consumed Fish Middle Atlantic Region  Finfish and Shellfish Grams/ person/ day 90% Interval* Habitat Statistic Estimate Lower Bound Upper Bound Fresh/ Estuarine Mean 3.79 2.62 4.96 50th % 0.00 0.00 0.00 90th % 12.13 9.28 13.71 95th % 25.29 19.32 31.42 99th % 61.72 48.00 84.21 Marine Mean 14.89 12.76 17.02 50th % 0.00 0.00 0.00 90th % 52.01 46.27 55.67 95th % 67.21 60.67 74.63 99th % 148.91 113.21 154.15 All Fish Mean 18.68 15.59 21.77 50th % 0.00 0.00 0.00 90th % 61.26 55.67 64.74 95th % 80.54 69.72 94.85 99th % 153.23 149.28 171.37 * Percentile intervals were estimated using the percentile bootstrap method with 1,000 bootstrap replications.

Note: Estimates are projected from a sample of 1,585 individuals to the regional population of 37,330,000 using survey weights.

Source of individual consumption data: Combined 1989, 1990, and 1991 USDA Continuing Survey of Food Intakes by Individuals ( CSFII).

The fish component of foods containing fish was calculated using data from the recipe file for release 7 of the USDA's Nutrient Data Base for Individual Food Intake Surveys. A 15 TABLE A. 15 DAILY AVERAGE PER CAPITA ESTIMATES OF FISH CONSUMPTION As Consumed Fish South Atlantic Region  Finfish and Shellfish Grams/ person/ day 90% Interval* Habitat Statistic Estimate Lower Bound Upper Bound Fresh/ Estuarine Mean 4.92 3.93 5.91 50th % 0.00 0.00 0.00 90th % 16.72 8.63 20.77 95th % 30.45 28.10 39.25 99th % 77.54 72.35 86.41 Marine Mean 11.41 9.72 13.11 50th % 0.00 0.00 0.00 90th % 44.56 36.43 49.55 95th % 63.37 57.88 65.33 99th % 102.78 91.51 107.98 All Fish Mean 16.33 15.10 17.57 50th % 0.00 0.00 0.00 90th % 57.62 54.65 65.74 95th % 83.39 77.30 92.88 99th % 130.78 122.02 139.45 * Percentile intervals were estimated using the percentile bootstrap method with 1,000 bootstrap replications.

Note: Estimates are projected from a sample of 2,245 individuals to the regional population of 42,307,000 using survey weights.

Source of individual consumption data: Combined 1989, 1990, and 1991 USDA Continuing Survey of Food Intakes by Individuals ( CSFII).

The fish component of foods containing fish was calculated using data from the recipe file for release 7 of the USDA's Nutrient Data Base for Individual Food Intake Surveys. A 16 TABLE A. 16 DAILY AVERAGE PER CAPITA ESTIMATES OF FISH CONSUMPTION As Consumed Fish East North Central Region  Finfish and Shellfish Grams/ person/ day 90% Interval* Habitat Statistic Estimate Lower Bound Upper Bound Fresh/ Estuarine Mean 2.88 1.99 3.77 50th % 0.00 0.00 0.00 90th % 5.10 1.71 5.94 95th % 18.24 14.49 26.02 99th % 58.24 56.50 66.00 Marine Mean 10.33 8.33 12.33 50th % 0.00 0.00 0.00 90th % 38.82 37.33 42.96 95th % 56.88 52.63 65.03 99th % 113.83 104.59 132.39 All Fish Mean 13.21 11.07 15.35 50th % 0.00 0.00 0.00 90th % 47.50 43.87 51.55 95th % 72.05 65.87 80.51 99th % 114.31 106.60 132.39 * Percentile intervals were estimated using the percentile bootstrap method with 1,000 bootstrap replications.

Note: Estimates are projected from a sample of 2,222 individuals to the regional population of 41,565,000 using survey weights.

Source of individual consumption data: Combined 1989, 1990, and 1991 USDA Continuing Survey of Food Intakes by Individuals ( CSFII).

The fish component of foods containing fish was calculated using data from the recipe file for release 7 of the USDA's Nutrient Data Base for Individual Food Intake Surveys. A 17 TABLE A. 17 DAILY AVERAGE PER CAPITA ESTIMATES OF FISH CONSUMPTION As Consumed Fish East South Central Region  Finfish and Shellfish Grams/ person/ day 90% Interval* Habitat Statistic Estimate Lower Bound Upper Bound Fresh/ Estuarine Mean 10.66 5.84 15.49 50th % 0.00 0.00 0.00 90th % 37.64 28.11 49.93 95th % 58.41 51.53 72.36 99th % 165.12 86.40 224.33 Marine Mean 5.97 4.41 7.54 50th % 0.00 0.00 0.00 90th % 23.87 18.54 32.27 95th % 37.29 32.27 47.13 99th % 61.16 56.00 76.45 All Fish Mean 16.63 12.03 21.24 50th % 0.00 0.00 0.00 90th % 52.26 45.44 60.67 95th % 69.94 66.77 86.40 99th % 165.12 119.53 224.33 * Percentile intervals were estimated using the percentile bootstrap method with 1,000 bootstrap replications.

Note: Estimates are projected from a sample of 671 individuals to the regional population of 15,113,000 using survey weights.

Source of individual consumption data: Combined 1989, 1990, and 1991 USDA Continuing Survey of Food Intakes by Individuals ( CSFII).

The fish component of foods containing fish was calculated using data from the recipe file for release 7 of the USDA's Nutrient Data Base for Individual Food Intake Surveys. A 18 TABLE A. 18 DAILY AVERAGE PER CAPITA ESTIMATES OF FISH CONSUMPTION As Consumed Fish West North Central Region  Finfish and Shellfish Grams/ person/ day 90% Interval* Habitat Statistic Estimate Lower Bound Upper Bound Fresh/ Estuarine Mean 4.48 2.22 6.74 50th % 0.00 0.00 0.00 90th % 4.41 0.31 9.19 95th % 25.84 14.42 47.73 99th % 104.32 58.05 178.33 Marine Mean 8.37 5.71 11.03 50th % 0.00 0.00 0.00 90th % 31.50 27.83 39.88 95th % 49.00 40.36 56.00 99th % 99.62 66.20 103.24 All Fish Mean 12.85 8.06 17.64 50th % 0.00 0.00 0.00 90th % 42.99 36.39 54.78 95th % 63.05 55.03 86.40 99th % 141.07 112.00 178.33 * Percentile intervals were estimated using the percentile bootstrap method with 1,000 bootstrap replications.

Note: Estimates are projected from a sample of 785 individuals to the regional population of 17,720,000 using survey weights.

Source of individual consumption data: Combined 1989, 1990, and 1991 USDA Continuing Survey of Food Intakes by Individuals ( CSFII).

The fish component of foods containing fish was calculated using data from the recipe file for release 7 of the USDA's Nutrient Data Base for Individual Food Intake Surveys. A 19 TABLE A. 19 DAILY AVERAGE PER CAPITA ESTIMATES OF FISH CONSUMPTION As Consumed Fish West South Central Region  Finfish and Shellfish Grams/ person/ day 90% Interval* Habitat Statistic Estimate Lower Bound Upper Bound Fresh/ Estuarine Mean 7.04 4.82 9.26 50th % 0.00 0.00 0.00 90th % 23.85 17.18 37.48 95th % 55.46 47.71 74.95 99th % 112.68 77.17 115.75 Marine Mean 6.02 4.71 7.33 50th % 0.00 0.00 0.00 90th % 22.23 18.72 27.67 95th % 33.75 28.38 41.99 99th % 92.12 55.67 100.20 All Fish Mean 13.06 10.04 16.08 50th % 0.00 0.00 0.00 90th % 49.69 37.48 55.92 95th % 74.77 57.88 85.33 99th % 114.22 100.74 115.75 * Percentile intervals were estimated using the percentile bootstrap method with 1,000 bootstrap replications.

Note: Estimates are projected from a sample of 1,287 individuals to the regional population of 26,321,000 using survey weights.

Source of individual consumption data: Combined 1989, 1990, and 1991 USDA Continuing Survey of Food Intakes by Individuals ( CSFII).

The fish component of foods containing fish was calculated using data from the recipe file for release 7 of the USDA's Nutrient Data Base for Individual Food Intake Surveys. A 20 TABLE A. 20 DAILY AVERAGE PER CAPITA ESTIMATES OF FISH CONSUMPTION As Consumed Fish Mountain Region  Finfish and Shellfish Grams/ person/ day 90% Interval* Habitat Statistic Estimate Lower Bound Upper Bound Fresh/ Estuarine Mean 3.23 1.86 4.60 50th % 0.00 0.00 0.00 90th % 0.48 0.00 5.25 95th % 20.90 10.51 36.00 99th % 78.60 55.50 96.67 Marine Mean 7.97 5.64 10.29 50th % 0.00 0.00 0.00 90th % 30.96 27.83 34.86 95th % 52.68 37.80 58.73 99th % 89.62 63.71 91.00 All Fish Mean 11.20 8.26 14.13 50th % 0.00 0.00 0.00 90th % 39.32 36.89 48.20 95th % 58.55 55.67 68.37 99th % 95.84 91.00 136.75 * Percentile intervals were estimated using the percentile bootstrap method with 1,000 bootstrap replications.

Note: Estimates are projected from a sample of 889 individuals to the regional population of 13,385,000 using survey weights.

Source of individual consumption data: Combined 1989, 1990, and 1991 USDA Continuing Survey of Food Intakes by Individuals ( CSFII).

The fish component of foods containing fish was calculated using data from the recipe file for release 7 of the USDA's Nutrient Data Base for Individual Food Intake Surveys. A 21 TABLE A. 21 DAILY AVERAGE PER CAPITA ESTIMATES OF FISH CONSUMPTION As Consumed Fish Pacific Region  Finfish and Shellfish Grams/ person/ day 90% Interval* Habitat Statistic Estimate Lower Bound Upper Bound Fresh/ Estuarine Mean 3.93 2.78 5.07 50th % 0.00 0.00 0.00 90th % 10.16 8.53 12.08 95th % 26.46 22.72 29.79 99th % 68.74 51.53 91.02 Marine Mean 12.88 10.18 15.58 50th % 0.00 0.00 0.00 90th % 50.78 43.68 54.65 95th % 69.74 60.67 79.57 99th % 111.49 106.93 121.52 All Fish Mean 16.81 14.32 19.29 50th % 0.00 0.00 0.00 90th % 55.87 51.53 59.76 95th % 83.44 71.74 95.95 99th % 122.64 116.93 169.54 * Percentile intervals were estimated using the percentile bootstrap method with 1,000 bootstrap replications.

Note: Estimates are projected from a sample of 1,633 individuals to the regional population of 36,197,000 using survey weights.

Source of individual consumption data: Combined 1989, 1990, and 1991 USDA Continuing Survey of Food Intakes by Individuals ( CSFII).

The fish component of foods containing fish was calculated using data from the recipe file for release 7 of the USDA's Nutrient Data Base for Individual Food Intake Surveys. A 22 TABLE A. 22 DAILY AVERAGE PER CAPITA ESTIMATES OF FISH CONSUMPTION As Consumed Fish New England Region  Finfish and Shellfish Milligrams/ kilogram/ person/ day 90% Interval* Habitat Statistic Estimate Lower Bound Upper Bound Fresh/ Estuarine Mean 72.50 59.10 85.89 50th % 0.00 0.00 0.00 90th % 271.02 103.78 363.88 95th % 503.29 366.45 723.60 99th % 1,031.43 755.19 1,240.52 Marine Mean 283.20 251.27 315.14 50th % 0.00 0.00 0.00 90th % 964.92 852.34 1,041.69 95th % 1,321.16 1,163.41 1,547.33 99th % 2,083.17 1,789.78 2,631.23 All Fish Mean 355.70 315.28 396.12 50th % 0.00 0.00 0.00 90th % 1,059.55 1,019.19 1,269.56 95th % 1,536.32 1,323.53 1,568.72 99th % 2,362.63 2,120.35 3,014.33 * Percentile intervals were estimated using the percentile bootstrap method with 1,000 bootstrap replications.

Note: Estimates are projected from a sample of 595 individuals to the regional population of 12,769,000 using survey weights.

Source of individual consumption data: Combined 1989, 1990, and 1991 USDA Continuing Survey of Food Intakes by Individuals ( CSFII).

The fish component of foods containing fish was calculated using data from the recipe file for release 7 of the USDA's Nutrient Data Base for Individual Food Intake Surveys. A 23 TABLE A. 23 DAILY AVERAGE PER CAPITA ESTIMATES OF FISH CONSUMPTION As Consumed Fish Middle Atlantic Region  Finfish and Shellfish Milligrams/ kilogram/ person/ day 90% Interval* Habitat Statistic Estimate Lower Bound Upper Bound Fresh/ Estuarine Mean 63.05 45.79 80.31 50th % 0.00 0.00 0.00 90th % 204.00 130.89 260.42 95th % 413.18 320.99 597.44 99th % 1,223.45 759.78 1,324.90 Marine Mean 239.72 211.16 268.29 50th % 0.00 0.00 0.00 90th % 768.83 706.68 834.11 95th % 1,128.64 930.83 1,365.60 99th % 2,047.28 1,697.69 2,352.51 All Fish Mean 302.77 262.82 342.72 50th % 0.00 0.00 0.00 90th % 941.32 803.12 1,160.53 95th % 1,416.61 1,209.29 1,561.31 99th % 2,510.55 2,302.98 2,673.55 * Percentile intervals were estimated using the percentile bootstrap method with 1,000 bootstrap replications.

Note: Estimates are projected from a sample of 1,585 individuals to the regional population of 37,330,000 using survey weights.

Source of individual consumption data: Combined 1989, 1990, and 1991 USDA Continuing Survey of Food Intakes by Individuals ( CSFII).

The fish component of foods containing fish was calculated using data from the recipe file for release 7 of the USDA's Nutrient Data Base for Individual Food Intake Surveys. A 24 TABLE A. 24 DAILY AVERAGE PER CAPITA ESTIMATES OF FISH CONSUMPTION As Consumed Fish South Atlantic Region  Finfish and Shellfish Milligrams/ kilogram/ person/ day 90% Interval* Habitat Statistic Estimate Lower Bound Upper Bound Fresh/ Estuarine Mean 73.39 60.04 86.73 50th % 0.00 0.00 0.00 90th % 256.50 150.60 290.43 95th % 509.78 451.81 596.27 99th % 1,216.10 1,092.18 1,353.36 Marine Mean 178.44 154.11 202.77 50th % 0.00 0.00 0.00 90th % 641.43 604.63 690.25 95th % 977.52 913.81 1,071.87 99th % 1,690.98 1,331.69 1,943.82 All Fish Mean 251.83 234.98 268.67 50th % 0.00 0.00 0.00 90th % 858.43 795.11 943.87 95th % 1,297.53 1,192.41 1,411.80 99th % 2,175.84 1,943.82 2,323.75 * Percentile intervals were estimated using the percentile bootstrap method with 1,000 bootstrap replications.

Note: Estimates are projected from a sample of 2,245 individuals to the regional population of 42,307,000 using survey weights.

Source of individual consumption data: Combined 1989, 1990, and 1991 USDA Continuing Survey of Food Intakes by Individuals ( CSFII).

The fish component of foods containing fish was calculated using data from the recipe file for release 7 of the USDA's Nutrient Data Base for Individual Food Intake Surveys. A 25 TABLE A. 25 DAILY AVERAGE PER CAPITA ESTIMATES OF FISH CONSUMPTION As Consumed Fish East North Central Region  Finfish and Shellfish Milligrams/ kilogram/ person/ day 90% Interval* Habitat Statistic Estimate Lower Bound Upper Bound Fresh/ Estuarine Mean 44.34 29.23 59.46 50th % 0.00 0.00 0.00 90th % 75.18 25.97 100.90 95th % 316.64 222.48 408.50 99th % 858.02 808.03 1,001.91 Marine Mean 197.24 155.33 239.14 50th % 0.00 0.00 0.00 90th % 682.31 624.84 747.86 95th % 1,114.31 943.16 1,333.60 99th % 2,128.19 1,923.34 3,430.60 All Fish Mean 241.58 198.48 284.68 50th % 0.00 0.00 0.00 90th % 827.02 731.33 943.16 95th % 1,388.72 1,241.40 1,513.97 99th % 2,376.91 1,930.85 3,430.60 * Percentile intervals were estimated using the percentile bootstrap method with 1,000 bootstrap replications.

Note: Estimates are projected from a sample of 2,222 individuals to the regional population of 41,565,000 using survey weights.

Source of individual consumption data: Combined 1989, 1990, and 1991 USDA Continuing Survey of Food Intakes by Individuals ( CSFII).

The fish component of foods containing fish was calculated using data from the recipe file for release 7 of the USDA's Nutrient Data Base for Individual Food Intake Surveys. A 26 TABLE A. 26 DAILY AVERAGE PER CAPITA ESTIMATES OF FISH CONSUMPTION As Consumed Fish East South Central Region  Finfish and Shellfish Milligrams/ kilogram/ person/ day 90% Interval* Habitat Statistic Estimate Lower Bound Upper Bound Fresh/ Estuarine Mean 199.53 106.27 292.79 50th % 0.00 0.00 0.00 90th % 807.10 573.22 1,021.39 95th % 1,086.94 1,021.39 1,355.11 99th % 2,552.93 2,257.50 3,044.97 Marine Mean 94.04 70.81 117.28 50th % 0.00 0.00 0.00 90th % 340.21 258.49 425.71 95th % 569.69 435.77 690.12 99th % 1,370.45 880.38 1,644.42 All Fish Mean 293.57 209.10 378.05 50th % 0.00 0.00 0.00 90th % 977.86 898.05 1,023.86 95th % 1,342.17 1,035.12 1,585.92 99th % 2,786.16 2,257.50 3,044.97 * Percentile intervals were estimated using the percentile bootstrap method with 1,000 bootstrap replications.

Note: Estimates are projected from a sample of 671 individuals to the regional population of 15,113,000 using survey weights.

Source of individual consumption data: Combined 1989, 1990, and 1991 USDA Continuing Survey of Food Intakes by Individuals ( CSFII).

The fish component of foods containing fish was calculated using data from the recipe file for release 7 of the USDA's Nutrient Data Base for Individual Food Intake Surveys. A 27 TABLE A. 27 DAILY AVERAGE PER CAPITA ESTIMATES OF FISH CONSUMPTION As Consumed Fish West North Central Region  Finfish and Shellfish Milligrams/ kilogram/ person/ day 90% Interval* Habitat Statistic Estimate Lower Bound Upper Bound Fresh/ Estuarine Mean 68.68 31.08 106.29 50th % 0.00 0.00 0.00 90th % 44.73 5.23 99.14 95th % 428.01 129.96 904.27 99th % 1,819.78 929.24 2,310.54 Marine Mean 165.41 101.17 229.64 50th % 0.00 0.00 0.00 90th % 546.17 438.28 711.48 95th % 839.85 762.30 981.18 99th % 2,354.93 1,809.91 2,890.40 All Fish Mean 234.09 137.32 330.86 50th % 0.00 0.00 0.00 90th % 756.81 578.06 829.11 95th % 1,222.04 941.54 1,653.86 99th % 2,988.90 2,224.38 3,733.22 * Percentile intervals were estimated using the percentile bootstrap method with 1,000 bootstrap replications.

Note: Estimates are projected from a sample of 785 individuals to the regional population of 17,720,000 using survey weights.

Source of individual consumption data: Combined 1989, 1990, and 1991 USDA Continuing Survey of Food Intakes by Individuals ( CSFII).

The fish component of foods containing fish was calculated using data from the recipe file for release 7 of the USDA's Nutrient Data Base for Individual Food Intake Surveys. A 28 TABLE A. 28 DAILY AVERAGE PER CAPITA ESTIMATES OF FISH CONSUMPTION As Consumed Fish West South Central Region  Finfish and Shellfish Milligrams/ kilogram/ person/ day 90% Interval* Habitat Statistic Estimate Lower Bound Upper Bound Fresh/ Estuarine Mean 106.38 74.14 138.63 50th % 0.00 0.00 0.00 90th % 428.38 297.04 565.37 95th % 863.50 717.62 1,038.97 99th % 1,398.61 1,112.73 1,416.47 Marine Mean 96.30 71.85 120.75 50th % 0.00 0.00 0.00 90th % 354.59 291.60 396.92 95th % 655.36 548.14 747.56 99th % 1,189.90 1,088.33 1,697.71 All Fish Mean 202.68 154.13 251.23 50th % 0.00 0.00 0.00 90th % 840.80 698.95 887.76 95th % 1,085.60 920.62 1,133.18 99th % 1,725.49 1,416.47 1,755.07 * Percentile intervals were estimated using the percentile bootstrap method with 1,000 bootstrap replications.

Note: Estimates are projected from a sample of 1,287 individuals to the regional population of 26,321,000 using survey weights.

Source of individual consumption data: Combined 1989, 1990, and 1991 USDA Continuing Survey of Food Intakes by Individuals ( CSFII).

The fish component of foods containing fish was calculated using data from the recipe file for release 7 of the USDA's Nutrient Data Base for Individual Food Intake Surveys. A 29 TABLE A. 29 DAILY AVERAGE PER CAPITA ESTIMATES OF FISH CONSUMPTION As Consumed Fish Mountain Region  Finfish and Shellfish Milligrams/ kilogram/ person/ day 90% Interval* Habitat Statistic Estimate Lower Bound Upper Bound Fresh/ Estuarine Mean 43.34 27.12 59.55 50th % 0.00 0.00 0.00 90th % 7.32 0.00 64.26 95th % 295.74 145.18 458.98 99th % 1,028.60 750.63 1,115.58 Marine Mean 132.52 88.63 176.42 50th % 0.00 0.00 0.00 90th % 458.07 421.93 583.88 95th % 751.69 680.90 902.64 99th % 1,535.18 1,344.97 1,713.17 All Fish Mean 175.86 128.71 223.01 50th % 0.00 0.00 0.00 90th % 641.64 542.17 729.72 95th % 957.82 817.21 1,075.34 99th % 1,702.77 1,421.80 1,786.53 * Percentile intervals were estimated using the percentile bootstrap method with 1,000 bootstrap replications.

Note: Estimates are projected from a sample of 889 individuals to the regional population of 13,385,000 using survey weights.

Source of individual consumption data: Combined 1989, 1990, and 1991 USDA Continuing Survey of Food Intakes by Individuals ( CSFII).

The fish component of foods containing fish was calculated using data from the recipe file for release 7 of the USDA's Nutrient Data Base for Individual Food Intake Surveys. A 30 TABLE A. 30 DAILY AVERAGE PER CAPITA ESTIMATES OF FISH CONSUMPTION As Consumed Fish Pacific Region  Finfish and Shellfish Milligrams/ kilogram/ person/ day 90% Interval* Habitat Statistic Estimate Lower Bound Upper Bound Fresh/ Estuarine Mean 59.58 44.67 74.50 50th % 0.00 0.00 0.00 90th % 182.83 125.87 221.07 95th % 416.82 345.98 472.91 99th % 1,018.21 861.13 1,114.52 Marine Mean 226.16 162.61 289.71 50th % 0.00 0.00 0.00 90th % 799.42 760.71 945.97 95th % 1,159.26 1,096.99 1,369.73 99th % 2,173.89 1,995.09 2,598.14 All Fish Mean 285.75 222.88 348.61 50th % 0.00 0.00 0.00 90th % 946.55 873.95 1,054.94 95th % 1,413.34 1,295.67 1,489.91 99th % 2,589.08 2,181.37 2,661.40 * Percentile intervals were estimated using the percentile bootstrap method with 1,000 bootstrap replications.

Note: Estimates are projected from a sample of 1,633 individuals to the regional population of 36,197,000 using survey weights.

Source of individual consumption data: Combined 1989, 1990, and 1991 USDA Continuing Survey of Food Intakes by Individuals ( CSFII).

The fish component of foods containing fish was calculated using data from the recipe file for release 7 of the USDA's Nutrient Data Base for Individual Food Intake Surveys. A 31 TABLE A. 31 DAILY AVERAGE PER CAPITA ESTIMATES OF FISH CONSUMPTION As Consumed Fish Individuals 18 Years of Age or Older in the U. S. Population  Mean Consumption by Species within Habitat Estimated Mean Habitat Species ( grams/ person/ day)

Estuarine Shrimp 1.72959 Perch 0.60368 Flatfish ( Estuarine) 0.52735 Crab ( Estuarine) 0.37126 Flounder 0.29941 Oyster 0.22555 Mullet 0.08756 Croaker 0.06749 Herring 0.03925 Smelts 0.03753 Clam ( Estuarine) 0.03146 Scallop ( Estuarine) 0.00322 Anchovy 0.00292 Scup 0.00068 Sturgeon 0.00054 Freshwater Catfish 1.18227 Trout 0.44946 Carp 0.05727 Pike 0.02337 Salmon ( Freshwater) 0.01096 Marine Tuna 4.71788 Flatfish ( Marine) 1.28921 Cod 1.26813 Salmon ( Marine) 0.91786 Haddock 0.61729 Notes: Estimates are projected from a sample of 8,478 individuals 18 years of age or older to the population of 177,807,000 individuals 18 years of age or older using 3 year combined survey weights. The population for this survey consisted of individuals in the 48 conterminous states.

Source of individual consumption data: USDA Combined 1989, 1990, and 1991 Continuing Survey of Food Intakes by Individuals ( CSFII).

The fish component of foods containing fish was calculated using data from the recipe file for release 7 of the USDA's Nutrient Data Base for Individual Food Intake Surveys. A 32 TABLE A. 31 ( continued) DAILY AVERAGE PER CAPITA ESTIMATES OF FISH CONSUMPTION As Consumed Fish Individuals 18 Years of Age or Older in the U. S. Population  Mean Consumption by Species within Habitat Estimated Mean Habitat Species ( grams/ person/ day)

Marine ( Con't.) Crab ( Marine) 0.43234 Pollock 0.37254 Clam ( Marine) 0.35788 Ocean Perch 0.30679 Porgy 0.30502 Scallop ( Marine) 0.28389 Sea Bass 0.25467 Lobster 0.25446 Swordfish 0.17743 Sardine 0.13812 Squid 0.12760 Pompano 0.10485 Sole 0.10096 Mackerel 0.07188 Whiting 0.06481 Shark 0.02596 Halibut 0.02396 Mussels 0.01911 Whitefish 0.00888 Snapper 0.00735 Octopus 0.00512 Barracuda 0.00151 Abalone 0.00103 Seafood 0.00057 Unknown Fish 0.00077 Notes: Estimates are projected from a sample of 8,478 individuals 18 years of age or older to the population of 177,807,000 individuals 18 years of age or older using 3 year combined survey weights. The population for this survey consisted of individuals in the 48 conterminous states.

Source of individual consumption data: USDA Combined 1989, 1990, and 1991 Continuing Survey of Food Intakes by Individuals ( CSFII).

The fish component of foods containing fish was calculated using data from the recipe file for release 7 of the USDA's Nutrient Data Base for Individual Food Intake Surveys. A 33 TABLE A. 31 ( continued) DAILY AVERAGE PER CAPITA ESTIMATES OF FISH CONSUMPTION As Consumed Fish Individuals 18 Years of Age or Older in the U. S. Population  Mean Consumption by Species within Habitat Estimated Mean Habitat Species ( grams/ person/ day)

All Species Tuna 4.71788 Shrimp 1.72959 Flatfish ( Marine) 1.28921 Cod 1.26813 Catfish 1.18227 Salmon ( Marine) 0.91786 Haddock 0.61729 Perch 0.60368 Flatfish ( Estuarine) 0.52735 Trout 0.44946 Crab ( Marine) 0.43234 Pollock 0.37254 Crab ( Estuarine) 0.37126 Clam ( Marine) 0.35788 Ocean Perch 0.30679 Porgy 0.30502 Flounder 0.29941 Scallop ( Marine) 0.28389 Sea Bass 0.25467 Lobster 0.25446 Oyster 0.22555 Swordfish 0.17743 Sardine 0.13812 Squid 0.12760 Pompano 0.10485 Sole 0.10096 Notes: Estimates are projected from a sample of 8,478 individuals 18 years of age or older to the population of 177,807,000 individuals 18 years of age or older using 3 year combined survey weights. The population for this survey consisted of individuals in the 48 conterminous states.

Source of individual consumption data: USDA Combined 1989, 1990, and 1991 Continuing Survey of Food Intakes by Individuals ( CSFII).

The fish component of foods containing fish was calculated using data from the recipe file for release 7 of the USDA's Nutrient Data Base for Individual Food Intake Surveys. A 34 TABLE A. 31 ( continued) DAILY AVERAGE PER CAPITA ESTIMATES OF FISH CONSUMPTION As Consumed Fish Individuals 18 Years of Age or Older in the U. S. Population  Mean Consumption by Species within Habitat Estimated Mean Habitat Species ( grams/ person/ day)

All Species ( Con't.) Mullet 0.08756 Mackerel 0.07188 Croaker 0.06749 Whiting 0.06481 Carp 0.05727 Herring 0.03925 Smelts 0.03753 Clam ( Estuarine) 0.03146 Shark 0.02596 Halibut 0.02396 Pike 0.02337 Mussels 0.01911 Salmon ( Freshwater) 0.01096 Whitefish 0.00888 Snapper 0.00735 Octopus 0.00512 Scallop ( Estuarine) 0.00322 Anchovy 0.00292 Barracuda 0.00151 Abalone 0.00103 Fish 0.00077 Scup 0.00068 Seafood 0.00057 Sturgeon 0.00054 Notes: Estimates are projected from a sample of 8,478 individuals 18 years of age or older to the population of 177,807,000 individuals 18 years of age or older using 3 year combined survey weights. The population for this survey consisted of individuals in the 48 conterminous states.

Source of individual consumption data: USDA Combined 1989, 1990, and 1991 Continuing Survey of Food Intakes by Individuals ( CSFII).

The fish component of foods containing fish was calculated using data from the recipe file for release 7 of the USDA's Nutrient Data Base for Individual Food Intake Surveys. A 35 TABLE A. 32 DAILY AVERAGE PER CAPITA ESTIMATES OF FISH CONSUMPTION As Consumed Fish Individuals 14 Years of Age and Younger in the U. S. Population  Mean Consumption by Species within Habitat Estimated Mean Habitat Species ( grams/ person/ day)

Estuarine Shrimp 0.36872 Perch 0.28899 Flatfish ( Estuarine) 0.17291 Flounder 0.08613 Oyster 0.03172 Crab ( Estuarine) 0.02880 Mullet 0.02396 Clam ( Estuarine) 0.01273 Croaker 0.00335 Smelts 0.00080 Anchovy 0.00061 Scollop ( Estuarine) 0.00034 Freshwater Catfish 0.47501 Trout 0.34732 Carp 0.02862 Pike 0.01170 Salmon ( Freshwater) 0.00321 Marine Tuna 2.82208 Cod 1.13423 Pollock 0.64386 Flatfish ( Marine) 0.42271 Ocean Perch 0.38098 Porgy 0.32992 Salmon ( Marine) 0.26894 Notes: Estimates are projected from a sample of 2,977 individuals of age 14 and younger to the population of 55,163,000 individuals of age 14 and younger using 3 years combined survey weights.

Source of individual consumption data: USDA Combined 1989, 1990, and 1991 Continuing Survey of Food Intakes by Individuals ( CSFII).

The fish component of foods containing fish was calculated using data from the recipe file for release 7 of the USDA's Nutrient Data Base for Individual Food Intake Surveys. A 36 TABLE A. 32 ( continued) DAILY AVERAGE PER CAPITA ESTIMATES OF FISH CONSUMPTION As Consumed Fish Individuals 14 Years of Age and Younger in the U. S. Population  Mean Consumption by Species within Habitat Estimated Mean Habitat Species ( grams/ person/ day)

Marine ( Con't.) Haddock 0.24788 Clam ( Marine) 0.14478 Squid 0.12532 Sea Bass 0.07716 Pompano 0.06221 Lobster 0.05980 Mackerel 0.04899 Mussels 0.03597 Crab ( Marine) 0.03354 Scallop ( Marine) 0.02981 Whiting 0.02808 Whitefish 0.01170 Halibut 0.00995 Sardine 0.00765 Seafood 0.00005 Unknown Fish 0.00568 All Species Tuna 2.82208 Cod 1.13423 Pollock 0.64386 Catfish 0.47501 Flatfish ( Marine) 0.42271 Ocean Perch 0.38098 Shrimp 0.36872 Notes: Estimates are projected from a sample of 2,977 individuals of age 14 and younger to the population of 55,163,000 individuals of age 14 and younger using 3 years combined survey weights.

Source of individual consumption data: USDA Combined 1989, 1990, and 1991 Continuing Survey of Food Intakes by Individuals ( CSFII).

The fish component of foods containing fish was calculated using data from the recipe file for release 7 of the USDA's Nutrient Data Base for Individual Food Intake Surveys. A 37 TABLE A. 32 ( continued) DAILY AVERAGE PER CAPITA ESTIMATES OF FISH CONSUMPTION As Consumed Fish Individuals 14 Years of Age and Younger in the U. S. Population  Mean Consumption by Species within Habitat Estimated Mean Habitat Species ( grams/ person/ day)

All Species ( Con't.) Haddock 0.24788 Trout 0.34732 Porgy 0.32992 Perch 0.28899 Salmon ( Marine) 0.26894 Flatfish ( Estuarine) 0.17291 Clam ( Marine) 0.14478 Squid 0.12532 Flounder 0.08613 Sea Bass 0.07716 Pompano 0.06221 Lobster 0.05980 Mackerel 0.04899 Mussels 0.03597 Crab ( Marine) 0.03354 Oyster 0.03172 Scallop ( Marine) 0.02981 Crab ( Estuarine) 0.02880 Carp 0.02862 Whiting 0.02808 Mullet 0.02396 Clam ( Estuarine) 0.01273 Pike 0.01170 Whitefish 0.01170 Halibut 0.00995 Sardine 0.00765 Fish 0.00568 Croaker 0.00335 Notes: Estimates are projected from a sample of 2,977 individuals of age 14 and younger to the population of 55,163,000 individuals of age 14 and younger using 3 years combined survey weights.

Source of individual consumption data: USDA Combined 1989, 1990, and 1991 Continuing Survey of Food Intakes by Individuals ( CSFII).

The fish component of foods containing fish was calculated using data from the recipe file for release 7 of the USDA's Nutrient Data Base for Individual Food Intake Surveys. TABLE A. 32 ( continued) DAILY AVERAGE PER CAPITA ESTIMATES OF FISH CONSUMPTION As Consumed Fish Individuals 14 Years of Age and Younger in the U. S. Population  Mean Consumption by Species within Habitat Estimated Mean Habitat Species ( grams/ person/ day)

A 38 Salmon ( Freshwater) 0.00321 Smelts 0.00080 Anchovy 0.00061 Scallop ( Estuarine) 0.00034 Seafood 0.00005 Notes: Estimates are projected from a sample of 2,977 individuals of age 14 and younger to the population of 55,163,000 individuals of age 14 and younger using 3 years combined survey weights.

Source of individual consumption data: USDA Combined 1989, 1990, and 1991 Continuing Survey of Food Intakes by Individuals ( CSFII).

The fish component of foods containing fish was calculated using data from the recipe file for release 7 of the USDA's Nutrient Data Base for Individual Food Intake Surveys. A 39 TABLE A. 33 DAILY AVERAGE PER CAPITA ESTIMATES OF FISH CONSUMPTION As Consumed Fish Females of Age 15 to 44 in the U. S. Population  Mean Consumption by Species within Habitat Estimated Mean Habitat Species ( grams/ person/ day)

Estuarine Shrimp 1.52145 Perch 0.55348 Flatfish ( Estuarine) 0.45313 Flounder 0.23224 Crab ( Estuarine) 0.22766 Mullet 0.05635 Oyster 0.02736 Croaker 0.02672 Clam ( Estuarine) 0.01925 Herring 0.01112 Scallop ( Estuarine) 0.00225 Anchovy 0.00018 Freshwater Catfish 0.81492 Trout 0.34703 Carp 0.07291 Pike 0.00756 Salmon ( Freshwater) 0.00479 Marine Tuna 4.41949 Flatfish ( Marine) 1.10778 Cod 1.04468 Pollock 0.48699 Haddock 0.43548 Salmon ( Marine) 0.40098 Crab ( Marine) 0.26511 Notes: Estimates are projected from a sample of 2,891 females of age 15 to 44 to the population of 58,750,000 females of age 15 to 44 using 3 year combined survey weights.

Source of individual consumption data: USDA Combined 1989, 1990, and 1991 Continuing Survey of Food Intakes by Individuals ( CSFII).

The fish component of foods containing fish was calculated using data from the recipe file for release 7 of the USDA's Nutrient Data Base for Individual Food Intake Surveys. A 40 TABLE A. 33 ( continued) DAILY AVERAGE PER CAPITA ESTIMATES OF FISH CONSUMPTION As Consumed Fish Females of Age 15 to 44 in the U. S. Population  Mean Consumption by Species within Habitat Estimated Mean Habitat Species ( grams/ person/ day)

Marine ( Con't.) Porgy 0.23300 Clam ( Marine) 0.21896 Lobster 0.20638 Scallop ( Marine) 0.19840 Sea Bass 0.18125 Ocean Perch 0.17925 Swordfish 0.17758 Squid 0.06367 Pompano 0.06076 Mackerel 0.04620 Sole 0.03611 Halibut 0.03495 Sardine 0.03436 Whiting 0.02711 Whitefish 0.00756 Octopus 0.00318 Abalone 0.00312 Mussels 0.00280 Seafood 0.00046 Shark 0.00034 All Species Tuna 4.41949 Shrimp 1.52145 Flatfish ( Marine) 1.10778 Cod 1.04468 Catfish 0.81492 Perch 0.55348 Pollock 0.48699 Notes: Estimates are projected from a sample of 2,891 females of age 15 to 44 to the population of 58,750,000 females of age 15 to 44 using 3 year combined survey weights.

Source of individual consumption data: USDA Combined 1989, 1990, and 1991 Continuing Survey of Food Intakes by Individuals ( CSFII).

The fish component of foods containing fish was calculated using data from the recipe file for release 7 of the USDA's Nutrient Data Base for Individual Food Intake Surveys. A 41 TABLE A. 33 ( continued) DAILY AVERAGE PER CAPITA ESTIMATES OF FISH CONSUMPTION As Consumed Fish Females of Age 15 to 44 in the U. S. Population  Mean Consumption by Species within Habitat Estimated Mean Habitat Species ( grams/ person/ day)

All Species ( Con't.) Flatfish ( Estuarine) 0.45313 Haddock 0.43548 Salmon ( Marine) 0.40098 Trout 0.34703 Crab ( Marine) 0.26511 Porgy 0.23300 Flounder 0.23224 Crab ( Estuarine) 0.22766 Clam ( Marine) 0.21896 Lobster 0.20638 Scallop ( Marine) 0.19840 Sea Bass 0.18125 Ocean Perch 0.17925 Swordfish 0.17758 Carp 0.07291 Squid 0.06367 Pompano 0.06076 Mullet 0.05635 Mackerel 0.04620 Sole 0.03611 Halibut 0.03495 Sardine 0.03436 Oyster 0.02736 Whiting 0.02711 Croaker 0.02672 Clam ( Estuarine) 0.01925 Herring 0.01112 Notes: Estimates are projected from a sample of 2,891 females of age 15 to 44 to the population of 58,750,000 females of age 15 to 44 using 3 year combined survey weights.

Source of individual consumption data: USDA Combined 1989, 1990, and 1991 Continuing Survey of Food Intakes by Individuals ( CSFII).

The fish component of foods containing fish was calculated using data from the recipe file for release 7 of the USDA's Nutrient Data Base for Individual Food Intake Surveys. A 42 TABLE A. 33 ( continued) DAILY AVERAGE PER CAPITA ESTIMATES OF FISH CONSUMPTION As Consumed Fish Females of Age 15 to 44 in the U. S. Population  Mean Consumption by Species within Habitat Estimated Mean Habitat Species ( grams/ person/ day)

All Species ( Con't.) Pike 0.00756 Whitefish 0.00756 Salmon ( Freshwater) 0.00479 Octopus 0.00318 Abalone 0.00312 Mussels 0.00280 Scallop ( Estuarine) 0.00225 Seafood 0.00046 Shark 0.00034 Anchovy 0.00018 Notes: Estimates are projected from a sample of 2,891 females of age 15 to 44 to the population of 58,750,000 females of age 15 to 44 using 3 year combined survey weights.

Source of individual consumption data: USDA Combined 1989, 1990, and 1991 Continuing Survey of Food Intakes by Individuals ( CSFII).

The fish component of foods containing fish was calculated using data from the recipe file for release 7 of the USDA's Nutrient Data Base for Individual Food Intake Surveys. A 43 TABLE A. 34 DAILY AVERAGE PER CAPITA ESTIMATES OF FISH CONSUMPTION As Consumed Fish U. S. Population  Mean Consumption by Species within Habitat Estimated Mean Habitat Species ( grams/ person/ day)

Estuarine Shrimp 1.37241 Perch 0.52580 Flatfish ( Estuarine) 0.43485 Crab ( Estuarine) 0.29086 Flounder 0.24590 Oyster 0.17419 Mullet 0.07089 Croaker 0.05021 Herring 0.02937 Smelts 0.02768 Clam ( Estuarine) 0.02691 Scallop ( Estuarine) 0.00247 Anchovy 0.00228 Scup 0.00050 Sturgeon 0.00040 Freshwater Catfish 1.06776 Trout 0.43050 Carp 0.04846 Pike 0.01978 Salmon ( Freshwater) 0.00881 Marine Tuna 4.19998 Cod 1.22827 Flatfish ( Marine) 1.06307 Salmon ( Marine) 0.73778 Notes: Estimates are projected from a sample of 8,478 individuals 18 years of age or older to the population of 177,807,000 individuals 18 years of age or older using 3 year combined survey weights. The population for this survey consisted of individuals in the 48 conterminous states.

Source of individual consumption data: USDA Combined 1989, 1990, and 1991 Continuing Survey of Food Intakes by Individuals ( CSFII).

The fish component of foods containing fish was calculated using data from the recipe file for release 7 of the USDA's Nutrient Data Base for Individual Food Intake Surveys. A 44 TABLE A. 34 ( continued) DAILY AVERAGE PER CAPITA ESTIMATES OF FISH CONSUMPTION As Consumed Fish U. S. Population  Mean Consumption by Species within Habitat Estimated Mean Habitat Species ( grams/ person/ day)

Marine ( Con't.) Haddock 0.51533 Pollock 0.44970 Crab ( Marine) 0.33870 Ocean Perch 0.31878 Clam ( Marine) 0.30617 Porgy 0.29844 Scallop ( Marine) 0.21805 Sea Bass 0.20794 Lobster 0.20001 Swordfish 0.13879 Squid 0.12196 Sardine 0.10313 Pompano 0.09131 Sole 0.07396 Mackerel 0.06379 Whiting 0.05498 Halibut 0.02463 Mussels 0.02217 Shark 0.01901 Whitefish 0.00916 Snapper 0.00539 Octopus 0.00375 Barracuda 0.00111 Abalone 0.00075 Seafood 0.00043 Notes: Estimates are projected from a sample of 8,478 individuals 18 years of age or older to the population of 177,807,000 individuals 18 years of age or older using 3 year combined survey weights. The population for this survey consisted of individuals in the 48 conterminous states.

Source of individual consumption data: USDA Combined 1989, 1990, and 1991 Continuing Survey of Food Intakes by Individuals ( CSFII).

The fish component of foods containing fish was calculated using data from the recipe file for release 7 of the USDA's Nutrient Data Base for Individual Food Intake Surveys. TABLE A. 34 ( continued) DAILY AVERAGE PER CAPITA ESTIMATES OF FISH CONSUMPTION As Consumed Fish U. S. Population  Mean Consumption by Species within Habitat Estimated Mean Habitat Species ( grams/ person/ day)

A 45 Unknown Fish 0.00186 All Species Tuna 4.19998 Shrimp 1.37241 Cod 1.22827 Catfish 1.06776 Flatfish ( Marine) 1.06307 Salmon ( Marine) 0.73778 Perch 0.52580 Haddock 0.51533 Pollock 0.44970 Flatfish ( Estuarine) 0.43485 Trout 0.43050 Crab ( Marine) 0.33870 Ocean Perch 0.31878 Clam ( Marine) 0.30617 Porgy 0.29844 Crab ( Estuarine) 0.29086 Flounder 0.24590 Scallop ( Marine) 0.21805 Sea Bass 0.20794 Lobster 0.20001 Oyster 0.17419 Swordfish 0.13879 Squid 0.12196 Sardine 0.10313 Notes: Estimates are projected from a sample of 8,478 individuals 18 years of age or older to the population of 177,807,000 individuals 18 years of age or older using 3 year combined survey weights. The population for this survey consisted of individuals in the 48 conterminous states.

Source of individual consumption data: USDA Combined 1989, 1990, and 1991 Continuing Survey of Food Intakes by Individuals ( CSFII).

The fish component of foods containing fish was calculated using data from the recipe file for release 7 of the USDA's Nutrient Data Base for Individual Food Intake Surveys. TABLE A. 34 ( continued) DAILY AVERAGE PER CAPITA ESTIMATES OF FISH CONSUMPTION As Consumed Fish U. S. Population  Mean Consumption by Species within Habitat Estimated Mean Habitat Species ( grams/ person/ day)

A 46 Pompano 0.09131 Sole 0.07396 Mullet 0.07089 Mackerel 0.06379 Whiting 0.05498 Croaker 0.05021 Carp 0.04846 Herring 0.02937 Smelts 0.02768 Clam ( Estuarine) 0.02691 Halibut 0.02463 Mussels 0.02217 Pike 0.01978 Shark 0.01901 Whitefish 0.00916 Salmon ( Freshwater) 0.00881 Snapper 0.00539 Octopus 0.00375 Scallop ( Estuarine) 0.00247 Anchovy 0.00228 Fish 0.00186 Barracuda 0.00111 Abalone 0.00075 Scup 0.00050 Seafood 0.00043 Sturgeon 0.00040 Notes: Estimates are projected from a sample of 8,478 individuals 18 years of age or older to the population of 177,807,000 individuals 18 years of age or older using 3 year combined survey weights. The population for this survey consisted of individuals in the 48 conterminous states.

Source of individual consumption data: USDA Combined 1989, 1990, and 1991 Continuing Survey of Food Intakes by Individuals ( CSFII).

The fish component of foods containing fish was calculated using data from the recipe file for release 7 of the USDA's Nutrient Data Base for Individual Food Intake Surveys. B 1 Appendix B Evaluation of the Quality of Data Set( s) for Use in Deriving an RfD The derivation of RfDs begins with a thorough review and assessment of the toxicological data base to identify the type and magnitude of possible adverse health effects associated with a chemical. This evaluation should include an examination of the full range of possible health effects, including acute, short term ( 14 to 28 days), subchronic, reproductive/ developmental, and chronic effects.

To be useful for supporting the derivation of an RfD, a study must meet certain standards with regard to experimental design, conduct and data reporting. This appendix provides general guidance on criteria for appropriate study design for a variety of types of toxicity studies. These guidelines provide the assessor with a means to evaluate the quality and adequacy of data. Appropriate studies are used both for the evaluation of potential hazard of the chemical and for the derivation of the RfD.

Acute Toxicity Determination Studies of acute exposure ( one dose or multiple dose exposure occurring within a short time ( e. g. less than 24 hours)) are widely available for many chemicals. Acute toxicity [ often expressed in terms of the lethal dose ( or concentration) to 50 percent of the population ( LD 50 or LC 50)] is usually the initial step in experimental assessment and evaluation of a chemical's toxic characteristics. Such studies are used in establishing a dosage regimen in subchronic and other studies and may provide initial information on the mode of toxic action of a substance. Because LD 50 or LC 50 studies are of short duration, inexpensive and easy to conduct, they are commonly used in hazard classification systems.

Acute lethality studies are of limited use, however, in the derivation of chronic criteria, since the establishment of chronic criteria should never be based on exposures that approach acutely lethal levels. However, the data from such studies do provide information on health hazards likely to arise from individual short term exposures. Such studies provide high dose effects data from which to evaluate potential effects from exposures which may temporarily exceed the acceptable chronic exposure level. An evaluation of the data should include the incidence and severity of all abnormalities, the reversibility of abnormalities observed other than lethality, gross lesions, body weight changes, effects on mortality, and any other toxic effects.

In recent years guidelines have been established to improve quality and provide uniformity in test conditions. Unfortunately, many published LD 50 or LC 50 tests were not conducted in accordance with current EPA or OECD guidelines ( USEPA, 1985; OECD, 1987) since they were conducted prior to establishment of those guidelines. For this reason, it becomes necessary to examine each test or study to determine if the study was conducted in an adequate manner.

The following is a list of ideal conditions compiled from various testing guidelines which may be used for determination of adequacy of acute toxicity data. Many published studies do not report B 2 details of test conditions making such determinations difficult. However, test conditions guidelines that might be considered ideal may include:

General:

C Animal age and species identified.

C Minimum of 5 animals per sex per dose group ( both sexes should be used).

C 14 day or longer observation period following dosing.

C Minimum of 3 dose levels appropriately spaced ( most statistical methods require at least 3 dose levels).

C Identification of purity or grade of test material used ( particularly important in older studies).

C If a vehicle used, the selected vehicle is known to be non toxic.

C Gross necropsy results for test animals.

C Acclimation period for test animals before initiating study.

Specific conditions for oral LD 50:

C Dosing by gavage or capsule.

C Total volume of vehicle plus test material remain constant for all dose levels.

C Animals were fasted before dosing.

Specific conditions for dermal LD 50:

C Exposure on intact, clipped skin and involve approximately 10 percent of body surface.

C Animals prevented from oral access to test material by restraining or covering test site.

Specific conditions for inhalation LC 50:

C Duration of exposure at least 4 hours.

C If an aerosol ( mist or particulate), the particle size ( median diameter and deviation) should be reported. B 3 Although the above listed conditions would be included in an ideally conducted study, not all of these conditions need to be included in an adequately conducted study. Therefore, some discretion is required on the part of the individual reviewing these studies ( USEPA, 1985; OECD, 1987).

Short Term Toxicity Studies ( 14 Day or 28 Day Repeated Dose Toxicity)

Short term exposure generally refers to multiple or continuous exposure usually occurring over a 14 day to 28 day time period. The purpose of short term repeated dose studies is to provide information on possible adverse health effects from repeated exposures over a limited time period.

The following guidelines were derived using the OECD Guidelines for Testing of Chemicals ( OECD, 1987) for determining the design and quality of a repeated dose short term toxicity study:

C Minimum of 3 dose levels administered and an adequate control group used.

C Minimum of 10 animals per sex, per dose group ( both sexes should be used).

C The highest dose level should ideally elicit some signs of toxicity without inducing excessive lethality and the lowest dose should ideally produce no signs of toxicity.

C Ideal dosing regimes include 7 days per week for a period of 14 days or 28 days.

C All animals should be dosed by the same method during the entire experiment period.

C Animals should be observed daily for signs of toxicity during the treatment period ( i. e., 14 or 28 days). Animals that die during the study are necropsied and all survivors in the treatment groups are sacrificed and necropsied at the end of the study period.

C All observed results, quantitative and incidental, should be evaluated by an appropriate statistical method.

C Clinical examinations should include hematology and clinical biochemistry, urinalysis may be required when expected to provide an indication of toxicity. Pathological examination should include gross necropsy and histopathology.

The findings of short term repeated dose toxicity studies should be considered in terms of the observed toxic effects and the necropsy and histopathological findings. The evaluation will include the incidence and severity of abnormalities, gross lesions, body weight changes, effects on mortality, and other general or specific toxic effects ( OECD, 1987).

These guidelines represent ideal conditions and studies will not be expected to meet all standards in order to be considered to be adequate. For example, the National Toxicology Program's cancer bioassay program has generated a substantial data base of short term repeated dose studies. The study periods for these range from 14 days to 20 days with 12 to 15 doses administered generally B 4 for 5 dose levels and a control. Since the quality of this data is good, it is desirable to consider these study results even though they do not always identically follow the protocol.

Subchronic and Chronic Toxicity Studies involving subchronic exposure ( occurring usually over 3 months) and chronic exposure ( those involving an extended period of time, or a significant fraction of the subject's lifetime) are designed to permit a determination of no observed effect levels ( NOEL) and toxic effects associated with continuous or repeated exposure to a chemical. Subchronic studies provide information on health hazards likely to arise from repeated exposure over a limited period of time. They provide information on target organs, the possibilities of accumulation, and, with the appropriate uncertainty factors, may be used in establishing water quality criteria for human health. Chronic studies provide information on potential effects following prolonged and repeated exposure. Such effects might require a long latency period or are cumulative in nature before manifesting disease. The design and conduct of such tests should allow for detection of general toxic effects including neurological, physiological, biochemical, and hematological effects and exposure related pathological effects.

The following guidelines were derived using the EPA Health Effects Testing Guidelines ( USEPA, 1985), for determining the quality of a subchronic or chronic ( long term) study. Additional detailed guidance may be found in that document. These guidelines represent ideal conditions and studies will not be expected to meet all standards in order to be considered for use as the basis for RfD derivation. Ideally, a subchronic/ chronic study should include:

C Minimum of 3 dose levels administered and an adequate control group used.

C Minimum of 10 animals for subchronic, 20 animals for chronic studies per sex, per dose group ( both sexes should be used).

C The highest dose level should elicit some signs of toxicity without inducing excessive lethality and the lowest dose should ideally produce no signs of toxicity.

C Ideal dosing regimes include dosing for 5 7 days per week for 13 weeks or greater ( 90 days or greater) for subchronic, and at least 12 months or greater for chronic studies in rodents. For other species, repeated dosing should ideally occur over 10 percent or greater of animal's lifespan for subchronic studies and 50 percent or greater of the animal's lifespan for chronic studies.

C All animals should be dosed by the same method during the entire experimental period.

C Animals should be observed daily during the treatment period ( i. e., 90 days or greater). B 5 C Animals that die during the study are necropsied and, at the conclusion of the study, surviving animals are sacrificed and necropsied and appropriate histopathological examinations carried out.

C Results should be evaluated by an appropriate statistical method selected during experimental design.

C Such toxicity tests should evaluate the relationship between the dose of the test substance and the presence, incidence and severity of abnormalities ( including behavioral and clinical abnormalities), gross lesions, identified target organs, body weight changes, effects on mortality, and any other toxic effects noted in USEPA ( 1985).

Developmental Toxicity Guidelines for reproductive and developmental toxicity studies have been developed by EPA ( USEPA, 1985 and OECD, 1987). Developmental toxicity can be evaluated via a relatively shortterm study in which the compound is administered during the period of organogenesis. Based on the EPA Health Effects Testing Guidelines ( USEPA, 1985), ideal studies should include:

C Minimum of 20 young, adult, pregnant rats, mice, or hamsters or 12 young, adult, pregnant rabbits recommended per dose group.

C Minimum of 3 dose levels with an adequate control group used.

C The highest dose should induce some slight maternal toxicity but no more than 10 percent mortality. The lowest dose should not produce grossly observable effects in dams or fetuses. The middle dose level, in an ideal situation, will produce minimal observable toxic effects.

C Dose period should cover the major period of organogenesis ( days 6 to 15 gestation for rat and mouse, 6 to 14 for hamster, and 6 to 18 for rabbit).

C Dams should be observed daily; weekly food consumption and body weight measurements should be taken.

C Necropsy should include both gross and microscopic examination of the dams; the uterus should be examined so that the number of embryonic or fetal deaths and the number of viable fetuses can be counted; fetuses should be weighted.

C One third to one half of each litter should be prepared and examined for skeletal anomalies and the remaining animals prepared and examined for soft tissue anomalies.

As with any other type of study, the appropriate statistical analyses must be performed on the data for a study to qualify as a good quality study. In addition, developmental studies are unique in B 6 the sense that they yield two potential experimental units for statistical analysis, the litter and the individual fetus. The EPA testing guidelines do not provide any recommendation on which unit to use, but the Guidelines for the Developmental Toxicity Risk Assessment ( USEPA, 1991) states that " since the litter is generally considered the experimental unit in most developmental toxicity studies . . ., the statistical analyses should be designed to analyze the relevant data based on incidence per litter or on the number of litters with a particular endpoint." Others have also identified the litter as the preferred experimental unit ( Palmer, 1981 and Madson et al., 1982).

Information on maternal toxicity is very important when evaluating developmental effects because it helps determine if differential susceptibility exists for the offspring and mothers. Since the conceptus relies on its mother for certain physiological processes, interruption of maternal homeostasis could result in abnormal prenatal development. Substances which affect prenatal development without compromising the dam are considered to be a greater developmental hazard than chemicals which cause developmental effects at maternally toxic doses. Unfortunately, maternal toxicity information has not been routinely presented in earlier studies and has become a standard practice in studies only recently. In an attempt to use whatever data are available, maternal toxicity information may not be required if developmental effects are serious enough to warrant consideration regardless of the presence of maternal toxicity.

Reproductive Toxicity The EPA Health Effects Testing Guidelines ( USEPA, 1985) include guidelines for both reproduction and fertility studies and developmental studies. These EPA guidelines can serve as the ideal experimental situation with which to compare study quality. Studies being evaluated do not need to match precisely but rather should be similar enough that one can be assured that the chemical was adequately tested and that the results are a reliable estimate of the true reproductive or developmental toxicity of the chemical.

These guidelines also recommend a two generation reproduction study to provide information on the ability of a chemical to impact gonadal function, conception, parturition and the growth and development of the offspring. Additional information concerning the effects of a test compound on neonatal morbidity, mortality, and developmental toxicity may also be provided. The recommendations for reproductive testing are lengthy and quite detailed and may be reviewed further in the EPA Health Effects Testing Guidelines. In general, the test compound is administered to the parental ( P) animals ( at least 20 males and enough females to yield 20 pregnant females) at least 10 weeks before mating, through the resulting pregnancies and through weaning of their offspring ( F1 or first generation). The compound is then administered to the F1 generation similarly through the production of the second generation ( or F2) offspring until weaning. Recommendations for numbers of dose groups and dose levels are similar to those reported for developmental studies. Details should also be provided on mating procedures, standardization of litter sizes ( if possible, 4 males and 4 females from each litter are randomly selected), observation, gross necropsy and histopathology. Full histopathology is recommended on the following organs of all high dose and control P and F1 animals used in mating: vagina, uterus, testes, epididymides, seminal vesicles, prostate, pituitary gland, and target organs. Organs of animals from other dose groups should be examined when pathology has been demonstrated in high dose animals ( USEPA, 1985). B 7 References Interagency Regulatory Liaison Group ( IRLG)

Madson, J. M. et al. 1982. Teratology Test Methods for Laboratory Animals. In: Principles and Methods of Toxicology. Hayes, A. W. ( ed). New York: Raven Press.

Organization for Economic Cooperation and Development ( OECD), 1987. Guidelines for Testing of Chemicals. Paris, France.

Palmer, A. K., 1981. Regulatory Requirements for Reproductive Toxicology: Theory and Practice. In: Developmental Toxicology. Kimmel, C. A. and J. Buelke Sam ( eds). New York: Raven Press.

USEPA. 1985. Health Effects Testing Guidelines. 40 CFR Part 798. Federal Register Vol. 50. September 27.

USEPA. 1991. Final Guidelines for Development Toxicity Risk Assessment. Federal Register 56: 63798 63826. December 5. C 1 Appendix C Derivation of Basic Equations Concerning Bioconcentration and Bioaccumulation of Organic Chemicals Introduction Most work dealing with the bioconcentration and bioaccumulation of organic chemicals has concerned chemicals whose log K ow s are greater than 3. The purpose of this appendix is to explain why modifications of the equations generally used with such chemicals are necessary so that the equations also are appropriate for chemicals whose K ow s, BCFs, or BAFs are less than 1,000, and to derive all of the appropriate equations that are used in the calculation of BAFs for the final Guidance.

Background BCFs were originally defined as:

BCF t T ' C t B C t W ( 1) where:

= Total BCF ( i. e., a BCF that is based on the total concentrations of the chemical in BCF t T the water and in the aquatic biota)

= Total concentration of the chemical in the aquatic biota, based on the wet weight C t B of the aquatic biota = Total concentration of the chemical in the water around the aquatic biota C t W This is not the nomenclature that was used originally, but it is used here for clarity.

It was subsequently realized that extrapolation of BCFs for organic chemicals from one species to another would be more accurate if the BCFs were normalized on the basis of the amount of lipid in the aquatic biota. It was also realized that extrapolation of BCFs for organic chemicals from one water to another would be more accurate if the BCFs were calculated on the basis of the freely dissolved concentration of the organic chemical in the water around the aquatic biota. Thus, two additional BCFs were defined and used:

BCF t R ' C R C t W ( 2) C 2 BCF fd R ' C R C fd W ( 3) where:

= Lipid normalized total BCF ( i. e., normalized to 100 percent lipid and based on BCF t R the total concentration of the chemical in the water around the biota)

C R = Lipid normalized concentration of the chemical in the aquatic biota = Lipid normalized, freely dissolved BCF BCF fd R = Freely dissolved concentration of chemical in the water around the aquatic biota C fd W The experimental definition of C R is:

C R = the total amount of chemical in the aquatic biota the amount of lipid in the aquatic biota = ( 4) ( B)( C t B )

L ' ( B)( C t B )

( f R ) ( B) ' C t B f R where:

B = Wet weight of the aquatic biota.

L = Weight of the lipid in the aquatic biota.

f R = Fraction of the aquatic biota that is lipid = L/ B Using Equation 4 to substitute for C R in Equation 2 and then using Equation 1:

BCF t R ' C t B ( C t W) ( f R ) ' BCF t T f R ( 5)

If f fd = the fraction of the chemical in the water around the aquatic biota that is freely dissolved, then: C 3 f fd ' C fd W C t W ( 6)

Using Equations 4 and 6 to substitute for C R and in Equation 3 and then using Equation 1: C fd W BCF fd R ' C t B ( f R ) ( C t W) ( f fd ) ' BCF t T ( f R ) ( f fd )

( 7)

Equations 1, 5, and 7 show the relationships between the three different BCFs.

Theoretical justification for use of both lipid normalization and the freely dissolved concentration of the organic chemical in the ambient water is based on the concept of equilibrium partitioning, whereas practical justification is provided by the general similarity of the value of BCF fd R for an organic chemical across both species and waters. It will be demonstrated, however, that a more complete application of equilibrium partition theory shows that extrapolates well only BCF fd R for chemicals whose K ow s are greater than 1,000, whereas a different BCF extrapolates well for organic chemicals whose K ow s are greater than 1,000 as well as for chemicals whose K ow s are less than 1,000.

Partition Theory and Bioconcentration Equilibrium partition theory provides the understanding necessary to ensure proper use of K ow s, BCFs, and BAFs in the derivation of water quality criteria for organic chemicals. For the purpose of applying partition theory, aquatic biota can be modeled as consisting of water, lipid, and non lipid organic matter ( Barber et al., 1991). In this model, an organic chemical in aquatic biota exists in three forms:

1. Chemical that is freely dissolved in the water that is in the biota.

2. Chemical that is partitioned to the lipid that is in the biota.

3. Chemical that is partitioned to non lipid organic matter in the biota. The total concentration of chemical in the water inside the biota includes chemical that is partitioned to lipid and non lipid organic matter in the water.

According to this model:

C t B ' ( f W)( C fd WB) % ( f R ) ( C L) % ( f N)( C N )

( 8) C 4 where:

f W = Fraction of the aquatic biota that is water = Freely dissolved concentration of the organic chemical in the water in the C fd WB aquatic biota f R = Fraction of the aquatic biota that is lipid C L = Concentration of the organic chemical in the lipid f N = Fraction of the aquatic biota that is non lipid organic matter C N = Concentration of the organic chemical in the non lipid organic matter in the aquatic biota The most important partitioning of the organic chemical within the aquatic biota is between the lipid and the water, which is described by the following equation:

K LW ' C L C fd WB ( 9) where:

K LW = the lipid water partition coefficient.

" K LW" ( Gobas 1993) is used herein because it is more descriptive than " K L," which is used by DiToro et al., ( 1991). This partition coefficient is central to the equilibrium partition approach that is used to derive sediment quality criteria ( DiToro et al., 1991), the Gobas model that is used to derive Food Chain Multipliers for the final Guidance, and the equations given here that are used to derive BCFs and BAFs for the final Guidance.

In order for Equations 8 and 9 to be correct, partition theory requires that the concentration of the organic chemical in the lipid, C L, be defined as:

C L ' the amount of chemical partitioned to lipid in aquatic biota the amount of lipid in the aquatic biota It is difficult to determine C L experimentally because it is not easy to measure only the chemical that is partitioned to the lipid ( i. e., it is not easy to separate the three different kinds of chemical that, according to the model, exist in aquatic biota). Because all of the organic chemical in the biota is measured when C R is determined, C R can be determined easily, and C R is higher than C L.

It is useful to define another BCF as: C 5 BCF fd L ' C L C fd W ( 10)

Because C L is lower than C R , < . BCF fd L BCF fd R The only difference between K LW and is that the denominator in K LW is , whereas BCF fd L C fd WB the denominator in is . When partition theory applies, however, all phases are in BCF fd L C fd W equilibrium and so:

C fd W ' C fd WB ( 11)

Therefore, when the organic chemical is not metabolized by the aquatic biota and when growth dilution is negligible:

BCF fd L ' K LW ( 12)

Because octanol is a useful surrogate for lipid, a reasonable approximation is that:

K LW ' K ow ( 13) where:

K ow = the octanol water partition coefficient.

Thus:

predicted BCF fd L ' K LW ' K ow ( 14)

By using Equations 9 and 11 to substitute for C L and in Equation 8: C fd WB C t B ' ( f W)( C fd W ) % ( f R ) ( BCF fd L )( C fd W ) % ( f N )( C N) ( 15)

By using Equation 6 to substitute for in Equation 15: C fd W C t B ' ( f W)( f fd )( C t W) % ( f R ) ( BCF fd L )( f fd )( C t W) % ( f N )( C N )

( 16) C 6 Dividing by gives: C t W C t B C t W ' ( f W)( f fd ) % ( f R ) ( BCF fd L )( f fd ) % ( f N )( C N)

C t W ( 17)

Using Equation 1 and rearranging gives:

BCF t T ' ( f fd ) [ f W % ( f R ) ( BCF fd L ) % ( f N )( C N )

( f fd )( C t W) ]

( 18)

Using Equation 6:

BCF t T ' ( f fd ) [ f W % ( f R ) ( BCF fd L ) % ( f N )( C N )

C fd W ]

( 19)

Substituting and rearranging gives: x ' f W % ( f N)( C N C fd W )

BCF t T ' ( f fd ) [ x % ( f R ) ( BCF fd L ) ]

( 20)

The term " " accounts for the amount of organic chemical that is partitioned to the lipid ( f R ) ( BCF fd L )

in the biota, whereas in " x," the term " f W" accounts for the amount of organic chemical that is freely dissolved in the water in the biota and the term " " accounts for the amount of organic ( f N)( C N C fd W )

chemical that is partitioned to non lipid organic matter in the biota. The relative magnitudes of these three terms depend on the following:

C Because of bones and other inorganic matter, the sum of f W + f R + f N must be less than 1.

C f W is usually about 0.7 to 0.9.

C Because f R must be measured if the BAF or BCF is to be useful, f R is known for the aquatic biota; it is usually between 0.03 and 0.15. C 7 ( BCF t T f fd & 1 ) ( 1 f R ) C The term " " is similar to ( see Equation 10) and is therefore probably related ( C N C fd W ) BCF fd L to K ow ( see Equation 14), although the affinity of the chemical for non lipid organic matter is probably much less than its affinity for lipid.

Although such considerations aid in understanding " x," the magnitude of " x" in Equation 20 is important only for chemicals whose log K ow s are in the range of 1 to 3. For organic chemicals whose log K ow s are about 1, f fd is about 1. In addition, such chemicals distribute themselves so as to have similar concentrations in water and in the different organic phases in the aquatic biota, which means that will be approximately 1 if both metabolism and growth dilution are negligible. An organic BCF t T chemical whose log K ow is less than 1 will also have a on the order of 1 because water is the BCF t T predominant component in aquatic biota. Setting " x" equal to 1 is about right in the range of log K ow s in which it is not negligible ( see also McCarty et al., 1992).

Substituting x = 1 into Equation 20:

BCF t T ' ( f fd ) [ 1 % ( f R ) ( BCF fd L ) ]

( 21)

Rearranging gives:

BCF fd L ' ( BCF t T f fd & 1 ) ( 1 f R )

( 22)

can be called the " baseline BCF" because it is the most useful BCF for extrapolating from one BCF fd L species to another and from one water to another for organic chemicals with both high and low K ow s. The baseline BCF is intended to reference bioconcentration of organic chemicals to partitioning between lipid and water.

Equations 12, 13, and 22 demonstrate that both K ow and are useful approximations of the baseline BCFs. It will probably be possible to improve both approximations within a few years, but such improvements might not affect the BCFs substantially and probably will not require changes in the rest of the equations or the terminology. C 8 When is greater than 1,000, the " 1" in Equation 22 is negligible and so this equation BCF t T becomes equivalent to Equation 7 ( i. e., when is large, is a useful approximation of the BCF t T BCF fd R baseline BCF).

Bioaccumulation By analogy with Equations 21 and 22:

BAF t T ' ( f fd ) [ 1 % ( f R ) ( BAF fd L ) ]

( 23)

BAF fd L ' ( BAF t T f fd & 1 ) ( 1 f R )

( 24)

can be called the " baseline BAF" because it is the most useful BAF for extrapolating from one BAF fd L species to another and from one water to another for chemicals with both high and low K ow s.

It is convenient to define a food chain multiplier ( FCM) as:

FCM ' baseline BAF baseline BCF ' BAF fd L BCF fd L ( 25)

Some of the consequences of Equation 25 are:

1. Substituting Equations 22 and 24 into Equation 25:

FCM ' BAF t T & f fd BCF t T & f fd ( 26)

Therefore, only when f fd is much less than and . BAF t T ' ( FCM)( BCF t T ) BAF t T BCF t T 2. When FCM = 1 ( as for trophic level 2 in the Gobas model):

baseline BAF ' baseline BCF ( 27) C 9 3. Predicted baseline BAFs can be obtained using FCMs and the following rearrangement of Equation 25:

predicted baseline BAF = ( FCM)( baseline BCF) ( 28)

a. Using a laboratory measured BCF in Equation 22:

predicted baseline BAF ( 29) ' ( FCM) ( measured BCF fd L )

( 30) ' ( FCM) ( BCF t T f fd & 1 ) ( 1 f R )

b. Using a predicted BCF in Equation 14:

predicted baseline BAF ( 31) ' ( FCM) ( predicted BCF fd L )

( 32) ' ( FCM) ( K OW)

The FCMs used to calculate predicted baseline BAFs must be appropriate for the trophic level of the aquatic biota for which the predicted baseline BAF is intended to apply.

Although BAFs can be related to BCFs using FCMs, BAFs, and BCFs can also be related using Biomagnification Factors ( BMFs). The two systems are entirely compatible, but confusion can result if the terms are not used consistently and clearly. Because both systems are used in the final Guidance and elsewhere, it is appropriate to explain the relation between the two here. The basic difference is that FCMs always relate back to trophic level one, whereas BMFs always relate back to the next trophic level. In the FCM system:

BAF TL1 = BCF BAF TL2 = ( FCM TL2)( BAF TL1)

BAF TL3 = ( FCM TL3)( BAF TL1)

BAF TL4 = ( FCM TL4)( BAF TL1)

In the BMF system:

BAF TL1 = BCF BAF TL2 = ( BMF TL2)( BAF TL1)

BAF TL3 = ( BMF TL3)( BAF TL2) C 10 BAF TL4 = ( BMF TL4)( BAF TL3)

Therefore:

BMF TL2 = FCM TL2 BMF TL3 = ( FCM TL3)/( FCM TL2)

BMF TL4 = ( FCM TL4)/( FCM TL3)

Both metabolism and growth dilution can cause BMFs to be less than 1.

Calculation of Criteria Baseline BCFs and BAFs can be extrapolated between species and waters, but they cannot be used directly in the calculation of criteria that are based on the total concentration of the chemical in the water. The BCFs and BAFs that are needed to calculate such criteria can be calculated from measured and predicted baseline BCFs and BAFs using the following equations, which are derived from Equations 21 and 23:

BCF t T ' [ 1 % ( baseline BCF)( f R ) ] ( f fd )

( 33)

BAF t T ' [ 1 % ( baseline BAF)( f R ) ] ( f fd )

( 34)

References Barber, M. C., L. A. Suarez, and R. R. Lassiter. 1991. Modeling Bioaccumulation of Organic Pollutants in Fish with an Application to PCBs in Lake Ontario Salmonids. Can. J. Fish. Aquat. Sci. 48: 318 337.

DiToro, D. M., C. S. Zarba, D. J. Hansen, W. J. Berry, R. C. Swartz, C. E. Cowan, S. P. Pavlou, H. E. Allen, N. A. Thomas, and P. R. Paquin. 1991. Technical Basis for Establishing Sediment Quality Criteria for Nonionic Organic Chemicals Using Equilibrium Partitioning. Environ. Toxicol. Chem. 10: 1541 1583.

Gobas, F. A. P. C. 1993. A Model for Predicting the Bioaccumulation of Hydrophobic Organic Chemicals in Aquatic Food Webs: Application to Lake Ontario. Ecological Modeling 69: 1 17.

McCarty, L. S., D. Mackay, A. D. Smith, G. W. Ozburn, and D. G. Dixon. 1992. Residue Based Interpretation of Toxicity and Bioconcentration QSARs from Aquatic Bioassays: Neutral Narcotic Organics. Environ. Toxicol. Chem. 11: 917 930. C 11 D 1 C t w ' C fd w % POC @ C poc % DOC @ C doc ( 1)

C t w ' C fd w @ ( 1 % POC @ K poc % DOC @ K doc)

( 2) Appendix D Derivation of the Equation Defining ffd Experimental investigations have shown that hydrophobic organic chemicals exist in water in three phases, ( 1) the freely dissolved phase; ( 2) sorbed to suspended solids ( particulate organic carbon); and ( 3) sorbed to dissolved organic matter ( Hassett and Anderson, 1979; Carter and Suffet, 1982; Landrum et al., 1984; Gschwend and Wu, 1985; McCarthy and Jimenez, 1985; Eadie et al., 1990, 1992). The total concentration of the chemical in water is the sum of the concentrations of the sorbed chemical and the freely dissolved chemical ( Gschwend and Wu, 1985; Cook et al., 1993):

where:

Cfw d = Concentration of freely dissolved chemical in the ambient water ( kg of chemical/ L of water)

Ctw = Total concentration of the chemical in the ambient water ( kg of chemical/ L of water)

C poc = Concentration of chemical sorbed to the particulate organic carbon in the ambient water ( kg of chemical/ kg of organic carbon)

C doc = Concentration of chemical sorbed to the dissolved organic carbon in the water ( kg of chemical/ kg of organic carbon)

POC = Concentration of particulate organic carbon in the ambient water ( kg of organic carbon/ L of water)

DOC = Concentration of dissolved organic carbon in the ambient water ( kg of organic carbon/ L of water)

The above equation can also be expressed using partitioning relationships as:

where: D 2 f fd ' C fd w C t w ( 3)

f fd ' 1 ( 1 % POC @ K poc % DOC @ K doc)

( 4)

K doc . K ow 10 ( 5) K poc = C poc / C fw d and K doc = C doc / C fw d K poc = equilibrium partition coefficient of the chemical between POC and the freely dissolved phase in the ambient water K doc = equilibrium partition coefficient of the chemical between DOC and the freely dissolved phase in the ambient water From Equation 2, the fraction of the chemical which is freely dissolved in the water can be calculated using the following equations:

Experimental investigations by Eadie et al. ( 1990, 1992), Landrum et al. ( 1984), Yin and Hassett ( 1986, 1989), Chin and Gschwend ( 1992), and Herbert et al. ( 1993) have shown that K doc is directly proportional to the K ow of the chemical and is less than the K ow. The K doc can be estimated using the following equation:

The above equation is based upon the results of Yin and Hassett ( 1986, 1989), Chin and Gschwend ( 1992), and Herbert et al. ( 1993). These investigations were done using unbiased methods, such as the dynamic headspace gas partitioning ( sparging) and the fluorescence methods, for determining the K doc.

Experimental investigations by Eadie at al. ( 1990, 1992) and Dean et al. ( 1993) have shown that K poc is approximately equal to the K ow of the chemical. The K poc can be estimated using the following equation: D 3 K poc . K ow ( 6)

f fd ' 1 ( 1 % POC @ K ow % ( DOC @ K ow 10 ))

( 7) By substituting Equations 5 and 6 into Equation 4, the following equation is obtained:

The utility in using the freely dissolved equation described above to derive baseline BAFs applicable to multiple sites has been evaluated recently in a study conducted by Burkhard et al. ( 1997). In their study, Burkhard et al. measured BAFs for various chlorinated butadienes, chlorinated benzenes and hexachloroethane for three species of forage fish and blue crab in Bayou d'Inde of the Calcasieu River system, Louisiana. Using the freely dissolved equation, Burkhard et al. adjusted their field measured BAFs to baseline BAFs ( BAF R f d) and compared these to baseline BAFs determined for other trophic level three species in two other field studies ( Pereria et al., 1988; Oliver and Niimi, 1988). The field study by Pereria et al. ( 1988) was conducted in different sites within the Calcasieu River system and that of Oliver and Niimi ( 1988) in Lake Ontario. Burkhard et al. found no significant difference between BAF R f d determined in their study and those determined by Pereria et al. ( 1988) ( Tukey's, " = .05). However, for one chemical ( HCBD) about an order of magnitude difference was observed in the measured BAF R f d between the two studies. Burkhard et al. further noted their baseline BAFs were not substantially different than those derived for Lake Ontario, suggesting broader applicability of properly derived baseline BAFs.

References Carter, C. W. and I. H. Suffet. 1982. Binding of DDT to Dissolved Humic Materials. Environ. Sci. Technol. 16: 735 740.

Chin, Y., and P. M. Gschwend. 1992. Partitioning of Polycyclic Aromatic Hydrocarbons to Marine Porewater Organic Colloids. Environ. Sci. Technol. 26: 1621 1626.

Cook, P. M., R. J. Erickson. R. L. Spehar, S. P. Bradbury and G. T. Ankley. 1993. Interim Report on Data and Methods for Assessment of 2,3,7,8 tetrachlorodibenzo p dioxin Risks to Aquatic Life and Associated Wildlife. Duluth, MN: USEPA, Environmental Research Laboratory. EPA/ 600/ R 93/ 055. D 4 Dean, K. E., M. M. Shafer, and D. E. Armstrong. 1993. Particle Mediated Transport and Fate of a Hydrophobic Organic Contaminant in Southern Lake Michigan: the Role of Major Water Column Particle Species. J. Great Lakes Res. 19: 480 496.

Eadie, B. J., N. R. Morehead and P. F. Landrum. 1990. Three Phase Partitioning of Hydrophobic Organic Compounds in Great Lake Waters. Chemosphere. 20: 161 178.

Eadie, B. J., N. R. Morehead, J. Val Klump and P. F. Landrum. 1992. Distribution of Hydrophobic Organic Compounds between Dissolved and Particulate Organic Matter in Green Bay Waters. J. Great Lakes Res. 18: 91 97.

Gschwend. P. M. and S. Wu. 1985. On the Constancy of Sediment Water Partition Coefficients of Hydrophobic Organic Pollutants. Environ. Sci. Technol. 19: 90 96.

Hassett, J. P. and M. A. Anderson. 1979. Association of Hydrophobic Organic Compounds with Dissolved Organic Matter in Aquatic Systems. Environ. Sci. Technol. 13: 1526 1529.

Herbert, B. E., P. M. Bertsch and J. M. Novak. 1993. Pyrene Sorption by Water Soluble Organic Carbon. Environ. Sci. Technol. 27: 398 403.

Landrum, P. F., S. R. Nihart, B. J. Eadie and W. S. Gardner. 1984. Reverse Phase Separation Method for Determining Pollutant Binding to Aldrich Humic and Dissolved Organic Carbon of Natural Waters. Environ. Sci. Technol. 18: 187 192.

McCarthy, J. F. and B. D. Jimenez. 1985. Interaction Between Polycyclic Aromatic Hydrocarbons and Dissolved Humic Material: Binding and Dissociation. Environ. Sci. Technol. 19: 1072 1076.

Yin, C. and J. P. Hassett. 1986. Gas Partitioning Approach for Laboratory and Field Studies of Mirex Fugacity in Water. Environ. Sci. Technol. 20: 1213 1217.

Yin, C. and J. P. Hassett. 1989. Fugacity and Phase Distribution of Mirex in Oswego River and Lake Ontario Waters. Chemosphere. 19: 1289 1296. E 1 ( A soc) i ' ( C soc) i ( C fd w ) i ( 2)

( BAF fd R ) i ( BAF fd R ) r ' ( BSAF) i ( A soc) i ( BSAF) r ( A soc) r ( 3)

( BAF fd R ) i ' ( BAF fd R ) r · ( BSAF) i ( K ow) i ( BSAF) r ( K ow) r ( 5) Appendix E Derivation of the Equation to Predict BAF from the BSAF Several steps are involved in the derivation of the equation to predict the BAF for a chemical from the BSAF. First, in the basic equation for BAF for a given chemical, BSAF and C soc can be substituted for C R for a given chemical i as follows:

( BAF fd R ) i ' ( BSAF) i @ ( C soc) i ( C fd w ) i ( 1)

The chemical concentration quotient between sediment organic carbon and a freely dissolved state in overlying water may be symbolized by A soc, as follows:

Thus the ratio of BAF R f ds for chemical i and a reference chemical r may be expressed as:

If both chemicals have similar fugacity ratios between water and sediment, the following assumption can be made: ( A soc) i ( A soc) r ' ( K ow) i ( K ow) r ( 4)

therefore:

The assumption of equal or similar fugacity ratios between water and sediment for each chemical is equivalent to assuming that for all chemicals used in BAF R f d calculations: ( 1) the concentration ratios between sediment and suspended solids in the water, and ( 2) the degree of equilibrium between E 2 suspended solids and C w f d are the same. Thus, errors could be introduced by inclusion of chemicals with non steady state external loading rates or chemicals with strongly reduced C w f d due to rapid volatilization from the water. F 1 Appendix F EPA New Draft Protocol for Determining Octanol Water Partition Coefficients ( K ow) For Compounds with Log Kow Values > 5 1. Introduction The octanol water partition coefficient ( K ow) is one of the most widely used chemical parameters. The K ow of a chemical has been found to be representative of a chemical's propensity to partition into biotic and abiotic components of the environment as well as a chemical's propensity to accumulate in living organisms. Because of these associations, the K ow is widely used to predict a chemical's behavior in the environment and to evaluate a chemical's impact on human health.

The octanol water partition coefficient ( K ow) is a unitless measure and is defined as the ratio of the equilibrium concentrations, C, of a chemical in the two phases of a system consisting of n  octanol and water at standard temperature and pressure ( STP, 25o C, 1 atm):

K ow = C oct/ C w where C oct represents the concentration in the n  octanol phase, and C w represents the concentration in the water. The concentrations in the respective phases are expressed in the same volume referenced units ( i. e., mg/ ml, mole/ L, etc.), therefore, the K ow is a unitless property. Since the value of the partition coefficient spans orders of magnitude, it is frequently expressed on a log scale ( base ten) such that a given chemical has a log K ow value which may range from 1 to > 8. This parameter is also called the log P value.

Some specific applications of the K ow within the U. S. EPA include: evaluation of a chemical's potential to bioaccumulate in aquatic life, wildlife and humans; modeling the fate, transport and distribution of a chemical in the environment; prediction of the distribution of a contaminant in a living organism; classification of persistent bioaccumulators for regulatory actions; derivation of soil screening levels; calculation of water quality benchmarks; and derivation of Sediment Quality Criteria.

Although a seemingly simple experimental determination, K ow measurement is beset with difficulties. The appropriateness and accuracy of laboratory methods to directly measure a K ow are influenced by a number of factors which include the magnitude of the value itself. For chemicals with log K ow values at or exceeding 5, common sources of measurement error include: ( 1) failure to achieve equilibrium; ( 2) incomplete phase separation or interphase mixing during sampling; ( 3) emulsion effects derived from ? excessive" mixing or induced by contaminants; ( 4) propensity of the chemical to self associate, tautomerize or form hydrates; and ( 5) the presence of small quantities of contaminants with a lower K ow value. These errors tend not to be random, but to give measured numbers lower than the true value, frequently by an order of magnitude or more. The likelihood and degree of error increases with increasing K ow and also seems to be more prevalent for certain classes of chemicals ( such as halogenated compounds or phthalate esters). F 2 As a result, in addition to direct experimental measurement methods, techniques to indirectly experimentally measure or estimate K ow values have been developed.

Direct experimental measurement techniques include the shake flask approach, generator column, and slow stir methods. The shake flask method is the classical approach and fairly straight forward for chemicals with log K ow values below 5. For chemicals with higher log K ow values, the shake flask approach requires large volumes of water and formation of emulsions becomes a significant impediment to accurate measurements. The generator column approach was developed to measure the partition coefficients of more hydrophobic chemicals ( those with larger log K ow values). This is a laborious method which results in more reliable data than the shake flask approach for chemicals with higher log K ow values, but some discontinuities in the data for higher chlorinated PCB congeners have been observed. A third direct measurement technique is the slow stir method. In this method, careful stirring and close temperature control can prevent or limit the formation of emulsions and reliable very high partition coefficients can be obtained relatively easily.

Because of the difficulty of directly and accurately measuring K ow values, estimation methods have been developed. These methods can be divided into two types: those requiring a training set of chemicals with measured K ow s and those based upon fundamental chemical thermodynamics. Those methods requiring a training set of chemicals use Quantitative Structure Property Relationships ( QSPRs) or Quantitative Structure Activity Relationships ( QSARs) to derive K ow s. In QSPRs, K ow values are correlated with the values for other chemical parameters either measured or calculated using data available from the training set of chemicals. In QSARs, K ow values are derived from fragment constants obtained from the training set of chemicals.

One application of QSPRs is estimating K ow s indirectly from other experimental measurements. In this approach, the K ow is correlated with another measured property. These techniques include the use of reversed phase high performance liquid chromatography ( HPLC) and reversed phase thin layer chromatography ( TLC). In applying these approaches, K ow s are estimated from linear equations relating retention times on the reversed phase column to the K ow values. The equations are developed based on a set of reference chemicals for which K ow values are well established. These are relatively efficient methods because they do not require quantification of concentrations, but the linear equations can not be extrapolated beyond the K ow range represented by the reference chemicals from which the equation was derived. In application, values for the reference chemicals are usually shake flask values obtained from the literature, resulting in unreliable K ow estimates for chemicals with higher log K ow values.

In addition to direct and indirect measurement methods, QSPRs are also used to establish correlations between the K ow and calculated properties. For example, Hawker and Connell ( 1988) developed a correlative relationship between log K ow and molecular surface area using approximately two dozen PCBs. They then estimated log K ow s for the remaining PCBs by inputting the molecular surface area of each PCB. This technique is limited to estimating K ow s for chemicals which are similar to the chemicals used in developing the relationship. 1CLOGP is a molecular fragment based model developed at Pomona College by Albert Loe, Corwin Hanch, and ( See Hansch and Leo, 1995, for model description and performance data.)

2LOGKOW is essentially an expanded CLOGP with more recent training data and additional fragment constants. Research Corporation. ( See Meylan and Howard, 1994, for model details and performance information.)

3SPARC ( SPARC Performs Automated Reasoning in Chemistry) is a mechanistic model developed at the Ecosystems Research and Development of the U. S. Environmental Protection Agency by Sam Karickhoff, Lionel Carreira, and co workers. The model complements the aforementioned models because development, training, and testing were done away from K OW data. ( See F 3 In QSARs, hydrophobic fragment values are derived from a large data base of measured K ow s. These fragment constants are used to estimate K ow in two ways. One approach is to estimate the K ow by adding up the values for all the fragments composing the chemical, either by atom or by functional group. The other approach is to start with a measured K ow value for a structurally similar compound and add or subtract the fragment constants for functional groups or atoms to estimate the K ow for the specific compound. In both these cases, the calculated K ow value must also be corrected for proximity effects between structurally close substituent groups, and the K ow value derived is only as good as the data associated with the training set of chemicals. This method is also limited to predicting K ow s for chemicals with structures similar to those within the training set. Computer based models exist which apply QSAR approaches to estimate K ow s. CLOGP1 and LOGKOW2 data bases are both applications of this approach.

Other computer methods are based on fundamental chemical structure theory and are not limited by nor require a training set of chemicals with measured K ow s. For example, the SPARC3 model consists of a set of core models describing intra and inter molecular interactions. These models are linked by appropriate thermodynamic relationships to provide estimates of reactivity parameters under desired conditions ( e. g., temperature, pressure, solvent).

Given the numerous techniques available to determine the K ow and its numerous and important applications across the Agency, the U. S. EPA has formed an Agency K ow Work Group to draft the following guidance for selecting reliable values of K ow and ultimately for developing a data base of reliable K ow values. In determining these recommended K ow values, the preferable option would be to recommend actual measured values. For chemicals with log K ow values below 5, the classical shake flask approach is adequate to obtain these measurements. Although recent advances in measurement technology ( development of the slow stir method and increased awareness of, and compensation for, determinate errors) have significantly improved the quality of data available for chemicals with higher log K ow values, there remains a serious shortage of reliable measured data for compounds with higher log K ow values ( log K ow > 5). Unfortunately, it is frequently these chemicals that exhibit a propensity to accumulate in living tissues or bind to soils and sediments.

2. Protocol For Determining Recommended Kow Values F 4 Measured data are preferable for determining recommended K ow values. However, the absence or scarcity of reliable data necessitates the use of estimation methods in evaluating data and in assigning K ow s. K ow estimates used in this exercise include: ( 1) QSARs ( e. g., CLOGP, LOGKOW, and fragment additions or subtractions); ( 2) QSPRs ( e. g., HPLC and TLC methods) and ( 3) estimation methods based on fundamental chemical structure theory ( e. g. SPARC). All of these approaches except the last one listed ( the SPARC model) require measured K ow values for a training set of chemicals.

Assigning a K ow from these data will necessarily involve scientific judgement in evaluating not only the reliability of all data inputs but also the accretion/ concretion of evidence in support of the recommended K ow value. Supporting rationale will be provided for each recommended value.

2.1 Operational Guidelines for Kow Selection Protocol ° 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 Section 3)

° " 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.

° 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 of chemicals that are not highly sensitive to emulsion effects ( i. e., PNAs) this range may extend to 106.5.

C 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.

C 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).

2.2 Tiered Procedure for Setting KOW Values 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 Section 3 for guidelines for LC application.) F 5 2. Structure Activity Relationship ( SAR) estimates extrapolated from similar chemicals where " high quality" measurements are available. " High quality" SARs are described in Section 3. 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 average calculated value. 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.

C. If calculated/ estimated data do not agree, use professional judgement to evaluate/ blend/ weight 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., PNAs, shake flask measurements are good to 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 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 measured data; report average value of screened data. If no measurements reasonably agree with ` best estimate,' apply professional judgement to evaluate/ blend/ weight 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, F 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.

3. Guidelines for Evaluating Measured and Liquid Chromatography estimated KOWs 3.1 Assessment of Measured Kow Values 3.1.1 Molecular Speciation. In order to interpret measured data, it is necessary to understand the molecular species present in both the octanol and water phases including ionization, self association, tautomerization, and hydrate formation. For these reasons, it is difficult to conduct or interpret such measurements for mixtures of unknown composition or for single molecules of unknown structure. Solutes composed of more than one molecular species may also show substantial temperature dependence of K ow reflecting relative change in speciation in the octanol and water phases.

° Ionization. For weakly ionizable molecules, shake flask measurements are conducted in solutions of a stable, non extractable buffer to suppress ionization.

° Self Association  For molecules able to associate through hydrogen bonding ( e. g., amines, carboxylic acids, phenols, especially if cyclic dimers can form), measurement needs to be conducted at a sufficiently low concentration that K ow reflects only unassociated form of the molecule in both water and octanol phases. Measurements at several concentrations with no change in K ow provide an indication that this is the case.

° Tautomerization  If the molecule is likely to exist in more than one tautomeric form, the ratio of tautomers may be different in the octanol and water phases. If that is the case, the measured K ow may not be a very meaningful number. Sometimes molecules exhibit both ionization and tautomerization, leading to further complications.

° Hydrate Formation  Similar to the case of tautomerization, hydrates may exist to different degrees in the water and octanol phases thus confounding the interpretation of the measured value. A comparison of estimated and measured K ow for chloral hydrate suggests that such hydration may be much less important in the octanol than water phases, making the compound more lipophilic than would be expected from the hydrate structure alone.

3.1.2 Shake Flask or Slow Stirring Considerations. ( 1) Water and octanol phases should be free of impurities; ( 2) mixing should be of sufficient duration ( e. g., 7 days for dioctyl phthalate) to reach steady state equilibrium, particularly for very hydrophobic chemicals; ( 3) analytical measurement of both phases is particularly important when using volatile solutes; ( 4) Avoid formation of emulsions during mixing and centrifuge before measuring; ( 5) experimental protocol should be particularly scrutinized for K ow measurements 4 6; ( 6) F 7 ratio of octanol to water should be reduced for high K ow chemicals; and ( 7) sorption to glass ( e. g., for pyrethroids) during workup can be a problem.

3.1.3 General Considerations. Solute should be stable to hydrolysis during the course of the experiment. Solutes should be of high purity as the presence of a less lipophilic impurity exerts a dominant effect in the measured K ow value. Mixtures such as chlorinated paraffins ( containing thousands of isomers, congeners, and degrees of chlorination) therefore cannot be determined except by chromatographic methods.

3.1.4 Indicators of Potential Concern. Inconsistency with other measured values, with estimated value, or inconsistency among estimated values. The importance of professional judgement and knowledge of chemistry cannot be overemphasized in making the best K ow assignments. For example, inconsistency between measured and predicted may reflect only problems in the training set used based upon poor experimental values when better data have since become available.

3.2 Assessment of Kow Values Estimated using Liquid Chromatographic Techniques An estimated K ow value would be considered " appropriate" provided the following experimental conditions existed during its determination:

3.2.1 K ow s used for the reference compounds consist of " high quality" slow stir measurements.

° Better estimates for K ow s are obtained when reference and test chemicals are similar.

3.2.2 A minimum of five chemicals are used in developing the log capacity factor ( k') log K ow calibration relationship. The K ow s of the reference chemicals should be evenly distributed and should span 3 to 4 orders of magnitude.

3.2.3 The log k'  log K ow calibration curve is linear and has a correlation coefficient greater than 0.95.

3.2.4 The K ow estimated for the test chemical is within the range of K ow s for the reference compounds or does not exceed the upper end of the range of K ow s for the reference compounds beyond 0.5 log units without adequate justification.

3.2.5 Chemical speciation must be accounted for in performing the measurements. For example, with ionizable chemicals, measurements must be performed on the unionized form by using an appropriate buffer with a pH below the pK for an acid and above the pK for a base.

3.2.6 Reference and test chemicals are of known purity and structure. Independent confirmation of the identity and purity of the reference and test chemicals is required or highly desirable. F 8   ( C2 ' log K OW ( pyrene) log K OW ( phenanthrene) . 0.50   $ C3H ' 0.5 log K OW ( pyrene) log K OW ( naphthalene) . 0.85   " C4H2 ' log K OW ( anthracene) log K OW ( naphthalene) . 1.20 A S ' log K OW R S log K OW R H 3.2.7 Chemical mixtures can be used as the source of test chemicals provided accurate identities can be assigned to individual chromatographic components.