UFD Use an additional 1, 3, or 10 fold factor when deriving an RfD from an " incomplete" data base. Missing studies, e. g., reproductive, are often encountered with chemicals. This factor is meant to account for the inability of any study to consider all toxic endpoints. The intermediate factor of 3 ( ½ log unit) is often used when there is a single data gap exclusive of chronic data. It is often designated as UFD.

Modifying Factor Use professional judgment to determine the MF, which is an additional uncertainty factor that is greater than zero and less than or equal to 10. The magnitude of the MF depends upon the professional assessment of scientific uncertainties of the study and data base not explicitly treated above ( e. g., the number of species tested). The default value for the MF is 1.

Note: With each UF or MF assignment, it is recognized that professional scientific judgment must be used. 46 The Agency's reasoning in its use of the MF is that the areas of scientific uncertainty labeled H, A, S, L, or D do not represent all of the uncertainties in the estimation of an RfD. For example, the fewer the number of animals used in a dosing group, the more likely it is that no adverse effect will be observed at a dose point which may have had an effect in a larger population. Such a case might argue for modifying the usual 10 fold factors   a 100 fold UF might be raised to 250 if too few animals were used in a chronic study. While this increase is scientifically reasonable, it introduces two difficulties: the adjustments applied could differ between risk assessors, and the applied precision of the result might not be justified by the data. For example, a UF of 250 has an implied precision of 2 digits and is not appropriate in relation to the variability of the biological response. The Agency intends to avoid these difficulties through limiting the options for the modifying factor ( 1, 3, 10).

In practice, the magnitude of the overall UF is dependent on professional judgment as to the total uncertainty in all areas. When uncertainties exist in one, two or three areas, the Agency generally uses 10, 100, and 1,000 fold UF respectively. When uncertainties exist in four areas, the Agency generally uses an UF no greater than 3,000. It is the Agency's opinion that toxicity data bases that are weaker and would result in UFs in excess of 3,000 are too uncertain as a basis for quantification. In such cases, the Agency does not estimate an RfD, and additional toxicity data are sought or awaited. For a few chemicals, an UF of 10,000 was applied. However, in such cases, the risk assessment was completed before current policies for the maximum UF were in place.

The Agency occasionally uses a factor of less than 10 or even a factor of 1, if the existing data reduce or obviate the need to account for a particular area of uncertainty. For example, the use of a 1 year rat study as the basis of an RfD may suggest the use of a 3 fold, rather than 10 fold, factor to account for subchronic to chronic extrapolation, since it can be empirically demonstrated that 1 year rat NOAELs are generally closer in magnitude to chronic values than are 3 month NOAELs ( Swartout, 1990). Lewis et al. ( 1990) more fully investigate this concept of variable uncertainty factors through an analysis of expected values.

The modification of UFs from their standard values should follow the general guidelines for composite UFs and the overall precision of one digit for UFs. The composite uncertainty factor to use with a given data base is again strictly a case by case judgment by experts. It should be flexible enough to account for each of the applicable five areas of uncertainty and any nuances in the available data that might change the magnitude of any factor. The Agency describes its choice for the composite UF and sub components for individual RfDs on its Integrated Risk Information System ( IRIS). Because of the high degree of judgment involved in the selection of uncertainty and MFs, the risk assessment justification should include a detailed discussion of the selection of uncertainty factors, along with the data to which they are applied.

<IP_ADDRESS> Confidence in NOAEL/ LOAEL Based RfD As stated previously, when available, adequate data from acceptable human studies should be used as the basis for the RfD. Use of good epidemiology studies generally give the highest 11A RfD for developmental toxicity ( RfD DT) is discussed in USEPA ( 1991a).

47 confidence in RfDs. In the absence of such data, RfDs are estimated from studies in experimental animals.

The Agency generally considers a " complete" data base for calculating a chronic RfD for noncancer health effects to include the following:

C Two adequate mammalian chronic toxicity studies, by the appropriate route in different species, one of which must be a rodent.

C One adequate mammalian multi generation reproductive toxicity study by an appropriate route.

C Two adequate mammalian developmental toxicity studies by an appropriate route in different species.

For a " complete" data base, the likelihood that additional toxicity data may change the RfD is low. Thus, the Agency usually has confidence in such an RfD because additional toxicity data are not likely to change the value.

The Agency considers a NOAEL from a well conducted, mammalian subchronic ( 90 day) study by the appropriate route as a minimum data base for estimating an RfD. However, for such a data base, additional toxicity data may change the RfD. Thus the Agency generally has less confidence in such an RfD.

For some chemicals, an acute health hazard is the critical effect of concern. These could include neurotoxic or immunotoxic effects of acute exposures at environmental levels of contaminant. In such cases, longer term studies ( subchronic or chronic) that would typically be included in a review of the toxicity literature may not capture the critical endpoint. Under such circumstances, greater emphasis should be placed on characterizing the acute threshold as opposed to the potential chronic effects.

Developmental toxicity data, if they constitute the sole source of information, are not considered an adequate basis for chronic RfD estimation. This is because such data are often generated from short term chemical exposures, and, thus, are of limited relevance in predicting possible adverse effects from chronic exposures. However, if a developmental toxicity endpoint is the critical effect established from a " complete" data base, a chronic RfD can be derived from such data, applying the uncertainty and MFs normally required. Developmental data are the basis for developmental reference doses ( RfD DT). 11 The term RfD DT is used to distinguish the developmental value from the chronic RfD which refers to chronic exposure situations. Uncertainty factors for developmental toxicity include a 10 fold factor for interspecies variation and a 10 fold factor for 48 intraspecies variation; in general, an uncertainty factor is not applied to account for duration of exposure. In some cases, additional factors may be applied due to a variety of uncertainties that exist in the data base. For example, the standard study design for developmental toxicity study calls for a low dose that demonstrates a NOAEL, but there may be circumstances where a risk assessment must be based on the results of a study in which a NOAEL for developmental toxicity was not identified. For details regarding risk assessment for developmental toxicants, refer to EPA risk assessment guidelines ( USEPA, 1991b).

<IP_ADDRESS> Presenting the RfD as a Single Point or as a Range Although the RfD has traditionally been presented and used as a single point estimate, its definition contains the phrase ". . . an estimate ( with uncertainty spanning perhaps an order of magnitude) . . ." ( USEPA, 1988). Underlying this concept is the reasoning that during the derivation of the RfD, the selection of the critical effect and of the total uncertainty factor is based on the " best" scientific judgment of the Agency Work Group and that other groups of competent scientists examining the same database would reach a similar conclusion, within an order of magnitude. For example, although EPA recently verified a single number as the RfD for arsenic ( 0.3 µ g/ kg day), there was not a clear consensus on the oral RfD. Applying the Agency's RfD methodology, " strong scientific arguments can be made for various values within a factor of 2 or 3 of the currently recommended RfD value, i. e., 0.1 to 0.8 µ g/ kg/ day" ( USEPA, 1993).

Presenting the RfD as a range may be more appropriate than expressing it as a point estimate because rarely are sufficient data available to precisely determine a lifetime threshold for a human. Even when there are good, reliable data, the variability of response in the human population argues for expressing the RfD as a range. However, although EPA supports the use of a range that spans one order of magnitude for most RfDs, there are a number of potential interpretations of the term " order of magnitude" as described below:

C Range = x to 10x. ( where point estimate of RfD= x). This view is supported by those who believe that the risk assessment process is so inherently conservative that the RfD should be considered to be the lowest estimate, with the range of imprecision all resting above this point estimate.

C Range = 0.3x to 3x. This view is held by many EPA scientists who have developed RfDs. The RfD point estimate, x, is the midpoint of a range that spans an order of magnitude.

C Range = 0.1x to x. This is the view held by many risk managers. Regulatory decisions ( e. g., setting of standards or cleanup levels) are made based on the assumption that standards or cleanup levels are protective as long as they do not exceed the RfD. 49 Table 2.2.2: Some Scientific Factors to Consider When Using the RfD Range Use point estimate RfD  Default position  Total UF/ MF product is 100 or less  Essential nutrient Use lower range of RfD  Increased bioavailability from medium  The seriousness of the effect and whether or not it is reversible  A shallow dose response curve in the range of observation  Exposed group contains a sensitive population ( e. g., children or fetuses)

Use upper range of RfD  Decreased bioavailability with humans  RfD based on minimal LOAEL and a UF/ MF of 1,000 or greater  A steep dose response curve in the range of observation  No sensitive populations identified C Range = 0.1x to 10x. This range represents the assumption that the order of magnitude range could be on either side of the point estimate x.

The Agency is considering a risk management approach where the upper and lower bounds of the range are correlated to the uncertainty. Because the uncertainty around the dose response relationship increases as extrapolation below the observed data increases, the use of a range for the RfD may be more appropriate in characterizing risk than the use of a point estimate. Therefore, as a matter of risk management policy, it is proposed that if the product of the UFs and MF used to derive the RfD is 100 or less, there would be no consideration of a range. When greater than 100 but less than 1,000, the maximum range that could be considered would be one half of a log 10 ( 3 fold) or a number ranging from the point estimate divided by 1.5 to the point estimate multiplied by 1.5. At 1,000 and above the maximum range would be a log 10 ( 10 fold) or a number ranging from the point estimate divided by 3 to the point estimate multiplied by 3.

EPA advocates the use of the point estimate of the RfD as the default to derive the AWQC. The use of another number within the range defined by the uncertainty would then have to be justified. As used in this document, justification means that there are scientific data which indicate that some value in the range other than the point estimate may be more appropriate than the point estimate, based on human health or environmental fate considerations. Table 2.2.2 gives examples of some factors to consider when determining whether to use the point estimate of the RfD or values higher or lower than the point estimate. The factors presented in Table 2.2.2 should be considered in making the decision as to whether or not to use a value other than the point estimate within a range; the uncertainty will influence the magnitude of the range. 50 The use of an order of magnitude may not be appropriate for all chemicals. There are many factors that can affect the degree of " precision" of the RfD, and thereby affect the magnitude of the RfD range. The completeness of the data base plays a major role. Observing the same effects in several animal species, including humans, can increase confidence in the RfD point estimate and thereby narrow the range of uncertainty. Other factors that can affect the precision are the slope of the dose response curve, seriousness of the observed effect, spacing of doses, and the route of exposure. For example, a steep dose response curve indicates that relatively large differences in effect occur with a given change in dose; thus, there will be a greater chance that the data will allow scientists to distinguish clearly ( i. e., statistically) between doses that produce an effect and those that do not. For a situation where the RfD is derived from a LOAEL for a serious effect, an additional uncertainty factor is often used in the RfD derivation to protect against less serious effects that could have occurred at lower doses had lower doses been evaluated. Dose spacing and the size of the study groups used in the experiment can also affect the confidence in the RfD. The " true" NOAEL can fall anywhere between the experimentally determined NOAEL ( the highest dose administered without an adverse effect) and the LOAEL ( the lowest dose administered causing an observable adverse effect). The wider the dose spacing, the greater the margin of uncertainty about where the " true" NOAEL may fall. Finally, for some RfDs, the route of exposure in the experiment may not match the route of exposure in the environment, and interroute extrapolation may be considered using assumptions about differences in absorption rates between routes.

There are cases when a range should not be used. For example, the RfD for zinc ( USEPA, 1992) is based on consideration of nutritional data, a minimal LOAEL, and a UF of 3. If the factor of 3 were used to bound the RfD for zinc, then the upper bound level would approach the minimal LOAEL. This situation must be avoided, since it is unacceptable to set a standard at levels that may cause an adverse effect. The risk manager must be informed of those specific cases when it is not scientifically correct to use the RfD range. Table 2.2.2 provides managers with guidelines on the scientific basis for using the range.

2.2.6 Deriving an RfD Using a Benchmark Dose Approach A number of issues have been raised regarding the development of the RfD based on the traditional NOAEL/ LOAEL approach. These concerns include the following:

C The traditional approach does not incorporate information on the shape of the doseresponse curve, but focuses only on a single point ( the NOAEL or LOAEL).

C The value of the NOAEL depends on the number and spacing of the doses in the experiment. The possible NOAEL values are limited to the discrete values of the experimental doses. Theoretically, the experimental no adverse effect level could be any value between the experimental NOAEL and the LOAEL, and typically the true NOAEL is below the observed NOAEL. 51 C Data variability is not directly taken into account. For example, studies based on a larger number of animals may detect effects at lower doses than studies with fewer animals; as a result, the NOAEL from a small study may be higher than the NOAEL from a similar but larger study in the same species. The traditional approach does not have a mechanism to account for such data variability.

C The determination of the NOAEL is dependent on the background incidence of the effect in control animals; therefore, statistically significant differences between the dose groups and the control group are more difficult to detect if background incidence is relatively high, even if biologically significant effects occur.

C In conjunction with exposure data, the NOAEL based RfD can be used to estimate the size of the population at risk, but not the magnitude of the risk.

In response to these concerns, alternative approaches have been developed that attempt to address some of these shortcomings. One such alternative, the BMD approach, has been the subject of extensive research over the past decade ( Crump 1984, 1995; Gaylor, 1983, 1989; Dourson et al., 1985; Brown and Erdreich, 1989; Kimmel, 1990; Faustman et al., 1994; Allen et al., 1994a, 1994b). The following discussion presents the general methods for calculation of a RfD using the BMD approach; for more extensive discussion, the reader is referred to Crump et al. ( 1995). To date, the Agency has used the BMD approach for deriving the RfD for methyl mercury ( USEPA, 1994a) and the RfC for several compounds.

<IP_ADDRESS> Overview of the Benchmark Dose Approach A benchmark dose ( BMD) or concentration ( BMC) is defined as a statistical lower confidence limit on the dose producing a predetermined level of change in response ( the benchmark response BMR) relative to controls The BMD/ BMC is intended to be used as an alternative to the NOAEL in deriving a point of departure for low dose extrapolations. The BMD/ BMC is a dose corresponding to some change in the level of response relative to background and is not dependent on the doses used in the study. The BMR is based on a biologically significant level of response or on the response level at the lower detection limit of the observable dose range for a particular endpoint in a standard study design. The BMD/ BMC approach does not reduce uncertainty inherent in extrapolating from animal data to humans ( except for that in the LOAEL to NOAEL extrapolation), and does not require that a study identify a NOAEL, only that at least one dose be near the range of the response level for the BMD/ BMC.

The central step in deriving a BMD is to calculate lower bounds directly on the dose estimate. This modeling process is limited to the experimental range and no attempt is made to extrapolate to doses far below the experimental range. Generally, the models used in the BMD approach are statistical rather than biologically based models; thus, they cannot be reliably used to extrapolate to low doses without incorporating detailed information on the mechanisms through which the toxic agent causes the particular effect being modeled. 52 Once a mathematical dose response curve and its corresponding curve of confidence limits are established, the assessor selects a point on the lower confidence dose curve corresponding to the chosen BMR ( e. g., 1 percent, 5 percent, or 10 percent increase in the incidence of an effect). This point on the lower confidence curve is the lower confidence bound of the effective dose for that BMR ( denoted as the BMD) ( see Exhibit 2.2.1). A BMD may be calculated for each agent for which there is an adequate data base.

The BMD approach offers a number of advantages over the traditional approach for the derivation of the RfD from the NOAEL/ LOAEL divided by uncertainty factors. The advantages of the BMD approach are that it considers the dose response curve, including its shape; better accounts for statistical variability in the data; is not overly sensitive to dose spacing and, thus, is not limited to experimental doses for determining the effect level. In addition, studies with small group sizes and evaluation of a limited number of endpoints, which may identify artificially high NOAELs, will tend to yield lower BMD values because the confidence bands will be wider. The BMD analyses for developmental effects shows that the NOAELs from studies are actually at about a 5 percent response level ( Faustman et al., 1994). Therefore, the BMD approach provides an incentive to conduct more robust studies, since better studies give narrower confidence bands.

Derivation of RfD Using BMD Approach 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

0 50 100 150 200 250 300 Dose Response ED10 LED10 RfD UFs Dose Response Modeling uses animal or human data Exhibit 2.2.1 53 Table 2.2.3: Steps and Decisions Required in the BMD Approach Step Decisions 1. Selection Study/ Response 1. Experiments to include 2. Responses to model 2. Model dose response 1. Format of data 2. Mathematical model( s) 3. Handling model fit 4. Measure of altered response 3. Select BMD( s) 1. Critical BMR 2. Confidence limit calculation 4. Calculate RfD 1. Uncertainty factors Source: Crump et al., 1995 <IP_ADDRESS> Calculation of the RfD Using the Benchmark Dose Method The determination of an RfD using the BMD approach involves four basic steps. The first step involves the selection of the experiments and responses that will be used for modeling the BMD. Second, BMDs are calculated for the selected responses; BMD values should be calculated for all endpoints that have the potential for yielding the critical BMD. Third, a single BMD is selected from among those calculated. Finally, the RfD is calculated by dividing the chosen BMD by appropriate uncertainty factors. The decision points associated with these steps are outlined in Table 2.2.3. The discussion below summarizes the critical issues unique to the BMD approach. The following discussion of the issues largely incorporates the information from Crump et al. ( 1995).

Selection of Response Data to Model The selection of experiments and responses suitable for BMD modeling involves considerations similar to those for identifying the appropriate studies upon which to base a NOAEL. There may be several appropriate studies and relevant health effects that could be modeled for a chemical. Ideally, BMD calculations would be performed for the complete set of relevant effects. However, utilizing all relevant responses for the calculation of BMDs may be resource intensive. Further, it is difficult to interpret results from a large number of dose response analyses. When selecting the data to model it is often considered appropriate to limit attention to those responses for 12This is due to the fact that an effect seen only at doses above the LOAEL but having a shallow dose response could produce a lower BMD than an effect seen at the LOAEL, which has a steeper dose response.

54 which there is evidence of a dose response relationship. Statistically, such a relationship may be indicated by significant trends ( either increasing or decreasing) in the response as dose level increases. Considerations of biological significance may also be warranted. Another alternative is to focus efforts on modeling the most critical effects as seen at the LOAEL. However, limiting the number of responses modeled may potentially misrepresent the minimum BMD. 12 Use of Categorical Versus Continuous Data A central issue in the selection of data to model concerns the form of the data used. Categorical data, particularly quantal data, are relatively straightforward to use in the BMD approach, since the data are expressed as the number ( or percent) of subjects exhibiting a defined response at a given dose. Data may also be of the continuous form, where results are expressed as the measure of a continuous biological endpoint, such as a change in organ weight or serum enzyme level. With continuous data the results are generally presented in terms of means and standard deviation for dose groups but are most valuable when data for individual animals are available. To perform doseresponse modeling of such data, the way the data are expressed must be decided. Continuous data can be modeled by looking at the mean response for each dose group as a fraction of the mean response of the control group or as the percentage of animals showing an adverse response at each dose level. ( Gaylor and Slikker, 1990; Crump, 1995). Such approaches take advantage of the continuous nature of the response data, but express the results in terms that are directly comparable to those derived from analysis of categorical data, i. e., in terms of additional or extra risk, rather than in terms of changes in mean response. In particular, Crump ( 1995) has extended those considerations so that the model used for analysis of a continuous endpoint yields the same model type as used for analysis of any quantal endpoints. Such developments have enhanced the consistency of results across different endpoints for any particular chemical. In any case, application of the BMD approach to continuous data requires professional judgment in order to determine what level or category of response constitutes an abnormal ( adverse) effect. The BMD approach is not recommended for routine use but may be used when data are available and justify the extensive analyses required.

Choice of Mathematical Model Various mathematical approaches have been proposed for determining the BMD. Table 2.2.4 shows a number of dose response models that may be used for estimating the BMD with quantal or continuous data.

Information generally required for application of dose response models for categorical ( including quantal) data includes the experimental doses, the total number of animals in each dose group, and the number of these whose responses are in each of the categories of response. For continuous data, the experimental doses, number of animals in each dose group, mean response in each group, and sample variance of response in each dose group are needed. 55 The BMD approach should not be applied to data sets with only two experimental groups ( a control and one positive dose). In such cases, much of the advantage of the BMD approach with respect to consideration of the dose response shape will be lost; such data supply little information about the shape of the dose response curve. The more doses available, especially at lower doses, the greater the expected benefit of the BMD approach as compared to the NOAEL based approach. 56 Table 2.2.4: Dose Response Models Proposed for Estimating BMDs Model Formula Quantal Data Quantal linear regression ( QLR)

Quantal quadratic regression ( QQR)

Quantal polynomial regression ( QPR)

Quantal Weibull ( QW)

Log normal ( LN) P( d) = c + ( 1 c){ 1 exp[ q 1( d d 0)]}

P( d) = c + ( 1 c){ 1 exp[ q 1( d d o) 2]}

P( d) = c + ( 1 c){ 1 exp[ q 1 d 1... q k dk]}

P( d) = c + ( 1 c){ 1 exp[ q 1 dk]}

P( d) = c + ( 1 c) N( a+ b log d)

Continuous data Continuous linear regression ( CLR)

Continuous quadratic regression ( CQR)

Continuous linear quadratic regression ( CLQR)

Continuous polynomial regression ( CPR)

Continuous power ( CP) m( d) = c + q 1( d d 0)

m( d) = c + q 1( d d 0) 2 m( d) = c + q 1 d+ q 2 d2 m( d) = c + q 1 d+...+ q k dk m( d) = c + q 1( d) k Notes: P( d) is the probability of a response at the dose, d; m( d) is the mean response at the dose, d. In all models, c, q1,... qk, and do are parameters estimated from the data. For the quantal models, 0 # c # 1 and qi $ 0. For the CPR model proposed by Crump ( 1984), all the qi have the same sign. In the CLQR model discussed by Gaylor and Slikker ( 1990), q1 and q2 were not constrained to have the same sign. For all models, d0 $ 0, k $ 1. N( x) denotes the normal cumulative distribution function.

SOURCE: Crump et al., 1995. Handling Model Fit 57 Fitting the models to experimental data gives estimates of the parameters that describe each model. This fitting, usually accomplished through maximum likelihood methods, estimates the probability of response ( for quantal data) or the mean response ( for continuous data) for each dose level. Goodness of fit tests can be used to determine if a model adequately describes the doseresponse data.

In many cases, several models may fit the data well. In these cases, other considerations can be used to select an appropriate model. For example, the statistical assumptions underlying the model should be reasonable for the given data. Quantal results, for example, are assumed to follow a binomial distribution around a dose dependent expected value. This assumption requires that each subject responds independently and all have an equal probability of responding. Continuous responses for each dose level are assumed to follow a normal distribution and are also assumed to be independent. When biological factors may be important ( e. g., intralitter correlation for developmental toxicity data) they may also be used to select appropriate models. Another biological consideration may be whether or not a threshold is assumed to exist. If a threshold is expected for the given effect, then a model that allows for a threshold dose may be chosen for modeling. The biological plausibility of the dose response curve shape should also be a consideration in model selection.

Even with these considerations, several different models may often adequately describe the data. In these cases, the choice of the model may not be critical, especially since the estimation will be confined to the observed dose range. Thus, any model that suitably fits the empirical data is likely to provide a reasonable estimate of a BMD.

In certain data sets, none of the standard models may provide a reasonable fit to the data. Fit is assessed statistically by comparing the model predictions to the observations. Goodness of fit statistics formalize that comparison and provide p values, ranging between 0 and 1, as a measure of fit. When using a P 2 statistic, larger p values are indicative of good fit; smaller p values of poorer fit. Sufficiently small p values ( e. g., less than 0.01 or 0.05) are typically viewed as an indication that the model was not adequate for describing the observed dose response pattern.

Poor fit is often due to reduced responses at higher doses that are inconsistent with the doseresponse trend for lower doses, perhaps due to competing toxic processes or saturation of metabolic systems related to the toxic response of interest. Several procedures can be used to adjust the modeling process in these circumstances. For example, responses at the highest doses could be eliminated, since those doses are usually least informative of responses in the lower dose region of interest. In the case of saturated metabolic pathways, pharmacokinetic data can be used to estimate delivered dose to the organ of interest. The BMD modeling can then be conducted on the delivered dose. ( Andersen et al., 1987, 1993; Gehring et al., 1978).

A particularly valuable exercise with respect to all of these fit issues is a visual ( graphical) examination of the model predictions in relation to the observations. This supplements the formal 58 statistical assessment of fit and may, in fact, be equally or more informative. The statistical test assesses overall fit. For the purposes of BMD estimation, fit is most crucial in and around the response level used to define the BMD ( i. e., the BMR). Thus, for example, models that have similar fits to the entire data set may differ with respect to their predictions near the BMR, and it may be possible to select one over another on the basis of that more local behavior.

Measure of Altered Response Crump ( 1984) proposed two measures of increased response for quantal data. These are additional risk and extra risk. Additional risk is simply the probability of response at dose d, P( d), minus the probability of response at zero dose ( control response), P( 0). It describes the additional proportion of animals that respond in the presence of a dose. Extra risk is additional risk divided by [ 1 P( 0)]. It describes the additional proportion of animals that respond in the presence of a dose, divided by the proportion of animals that would not respond under control conditions. These measures are distinguished in the way they account for control responses. For example, if a dose increases a response from 0 to 1 percent, both the additional risk and the extra risk is 1 percent. However, if a dose increases risk from 90 to 91 percent, the additional risk is still 1 percent, but the extra risk is 10 percent. The choice of extra risk versus additional risk is based to some extent on assumptions about whether an agent is adding to the background risk. Extra risk is viewed as the default because it is more conservative.

Analogous measures of risk have been proposed for continuous data ( Crump, 1984). First, altered response can be expressed as the difference between the mean response to dose d minus the mean control response. The second measure is simply the difference between dose and control means divided by ( i. e., normalized by) the control mean response. The second measure expresses change as a fraction of the control response rather than as an absolute change.

More recent consideration of BMDs for continuous endpoints have suggested other alternatives. Allen et al. ( 1994a, 1994b) and Kavlock et al. ( 1995) determined that normalizing changes in mean responses by a multiple of the background standard deviation produced BMDs that were comparable, on average to NOAELs. For the developmental endpoints that those investigators studied, the preferred multiple for the standard deviation was 0.5.

It is not clear when measures of risk expressed relative to the background ( e. g., extra risk) are preferable to measures expressed as absolute changes. Additional research is required to provide guidance regarding the measure of altered response that is most appropriate in particular circumstances. 59 Selection of the BMR A critical decision for deriving the BMD is the selection of the Benchmark level of risk ( BMR). Since the BMD is used like a NOAEL in the derivation of the RfD, the BMR should be selected near the low end of the range of increased risks that can be detected in a bioassay of typical size. The ED 10 is frequently chosen as the BMR. The ED 10 is the dose predicted to cause a 10 percent increase in the incidence of the effect in the test population. For some data, it may be possible to adequately estimate the ED 05 or ED 01, which are closer to a true no effect dose. Levels between the ED 01 and the ED 10 are usually the lowest levels of risk that can be estimated adequately for binomial endpoints from standard toxicity studies ( Crump, 1984). Another consideration is the goal of model independence. Different dose response models may fit the data equally well yet give very different estimates of risk far below the observable range ( Crump, 1984). This argues for use of a BMR close to the range of responses that can be reliably measured in typical studies.

During a BMD Workshop, sponsored by EPA, participants generally agreed that the appropriate BMR should either be 5 percent or 10 percent, but acknowledged that future research might demonstrate the advisability of selecting one value over another ( ILSI, 1993). Research by Allen et al. ( 1994a, 1994b) and Faustman et al. ( 1994) indicates that BMDs defined in terms of 10 percent increases in probability of response tend to be, on average, similar to corresponding NOAELs for quantal developmental toxicity studies. For the purposes of water quality criteria derivation, EPA recommends the use of the ED 05 or ED 10 when deriving a BMD.

Calculating the Confidence Interval The BMD is defined to be the lower confidence bound on the dose corresponding to the selected BMR. A statistical lower confidence limit is used rather than a maximum likelihood estimate ( MLE) for several reasons. The use of confidence limits accounts for the sample size of the experiment; the fact that NOAELs do not account for sample size is a major criticism of NOAELbased derivation of the RfD. Furthermore, a lower confidence limit is more stable to minor changes in data and, rarely, may be estimable where a MLE is not.

To calculate the upper confidence bound on response, and subsequently, the lower bound on effective dose, decisions must be made regarding the selection of the procedure for calculating confidence limits and the size of the confidence limits.

The recommended method used to calculate the confidence bounds on the curve relies on maximum likelihood theory. This approach is the same one used by EPA in the computer program for cancer dose response modeling. The approach can be applied to BMD modeling using commercially available software. A detailed explanation of theory supporting this approach is found in Crump ( 1984).

By convention, the size of the statistical confidence limits can range from 90 to 99 percent. The methods of confidence limit calculation and choice of confidence limits are critical. The Agency 60 recommends the use of one sided 95th percentile confidence limits for BMD modeling. This is consistent with the size of the confidence limits used in cancer dose response modeling.

Selection of the BMD As the Basis for the RfD An important decision is the choice of the appropriate BMD to use in the RfD calculation when multiple BMDs are calculated. Multiple BMDs can be calculated when different models fit the response data for a single study, when more than one response is modeled in a single study, and when there are different BMDs from different studies. When multiple BMDs exist because several models fit a single data set, the analyst may select the smallest BMD or combine BMDs by using the geometric average. When multiple BMDs are calculated due to different responses or different studies that examine the same endpoint, the choice among BMDs may also involve the selection of the " critical effect" and the most appropriate species, sex, or other relevant feature of experimental design.

Use of Uncertainty Factors with BMD Approach Once a single or averaged BMD is selected, the RfD can be calculated by dividing the BMD by one or more uncertainty factors. It is still necessary to use uncertainty factors with a BMD, because the BMD can miss sensitive subpopulations and is still subject to interspecies extrapolation uncertainties. As a default, all applicable uncertainty factors used in the traditional NOAEL based RfD approach, except for the LOAEL NOAEL extrapolation factor, should be retained. Other factors, such as the size of the BMR and confidence bounds, biological considerations ( such as the possibility of a threshold), severity of the modeled effect, and the slope of the dose response curve, may affect the choice and magnitude of uncertainty factors ( see Crump et al., 1995, for more detailed discussion).

<IP_ADDRESS> Limitations of the BMD Approach The BMD approach has been proposed as an alternative procedure that can be used until biologically motivated approaches are available for some or all effects. It provides specific improvements over NOAEL based approaches, but by no means does it resolve all issues or difficulties associated with noncancer risk assessment. The BMD approach allows for objective extrapolation of animal response data to human exposures across the different study designs encountered in noncancer risk assessment.

<IP_ADDRESS> Example of the Application of the BMD Approach The following provides a simple example of the application of the BMD approach to quantal toxicity data. The example given is taken from Crump et al. ( 1995) for acrylamide. The purpose of presenting this example is to illustrate the method only; no actual risk value nor AWQC for acrylamide is derived. 61 Selection of Data to Model This example takes the approach of identifying a critical study rather than modeling all endpoints seen in valid studies. For this example, a 6 month dietary study of neurological effects in rats is used as the critical study for acrylamide ( Johnson et al., 1986, as cited in Crump et al., 1995). The endpoint examined in this study was tibial nerve degeneration. The researchers recorded the occurrence of nerve degeneration in two categories: none or mild, and moderate or severe. Since mild nerve degeneration occurs spontaneously in older rats, and because mild degeneration showed no dose response relationship, only moderate and severe degeneration were recorded as responses. The data are presented in quantal form, with no or mild degeneration considered " no response," and moderate to severe degeneration recorded as a response. The dose levels and number of animals responding in each dose group are shown in Table 2.2.5.

Table 2.2.5: Rats Experiencing Moderate or Severe Nerve Degeneration in Response to Acrylamide Dose Dose ( mg/ kg day) Number affected Number tested 0 9 60 0.01 6 60 0.1 12 60 0.5 13 60 2.0 16 60 Choice of Mathematical Model From Table 2.2.4, we can select from among the various models available for quantal data. Fitting is accomplished through the use of maximum likelihood estimation to estimate the probability of a response at each dose level. The actual fitting exercise is done through the use of computer software.

Results of Information Above All of the models can be tried to see which achieves the best fit. The following Exhibits illustrate the best fit modeling of the study data for the Weibull model ( Exhibit 2.2.2) and the quadratic model ( Exhibit 2.2.3). Table 2.2.6 provides the best fit model parameters for the two equations. 62  0.10 0.00 0.10 0.20 0.30 0.40 0.50 0 0.5 1 1.5 2 2.5 Dose ( µ g/ kg d) P( d) P( d) Modeled P( d) Observed P( d) 99th P( d) 95th P( d) 90th LED10 Exhibit 2.2.2 Quantal Weibull Regression  Extra Risk  0.10 0.00 0.10 0.20 0.30 0.40 0.50 0 0.5 1 1.5 2 2.5 Dose ( µ g/ kg d) P( d) P( d) Modeled P( d) Observed P( d) 99th P( d) 95th P( d) 90th LED 10 Exhibit 2.2.3 Quantal Quadratic Regression  Extra Risk 63 ER( d) ' [ P( d) & ( P( 0)]/[ 1 & P( 0)]

( Equation 2.2.2) Table 2.2.6: Best Fit Model Parameters from Modeling of the Acrylamide Data Model Background rate q1 k chi square p value Quantal Weibull 0.15 0.08 1 0.48 Quantal quadratic 0.16 0.034  0.34 Note that in example given here, the measure of altered response is extra risk, which is defined as:

Extra risk is the fraction of animals that respond when exposed to a dose, d, among animals who otherwise would not respond.

Both models fit the data adequately as shown in Table 2.2.6. In both cases the chi squared goodness of fit yields P values greater than 0.05. Therefore, either model can be used for derivation of BMD. Neither model, as fitted to this data set, suggests a threshold for this response. However, both models do indicate a background rate in the absence of exposure to acrylamide.

Selection of the BMR For the data set discussed above, the BMDs were calculated using the quantal Weibull and the quantal quadratic models for 1, 5, and 10 percent extra risk ( Table 2.2.7 estimates are in units of mg/ kg day): 64 Table 2.2.7: BMD Values Calculated Using Quantal Weibull and Quadratic Models Model BMR BMD ( mg/ kg day) for Confidence Limit:

90th percentile 95th percentile 99th percentile Weibull 10 0.73 0.64 0.52 5 0.35 0.31 0.25 1 0.07 0.06 0.05 Quadratic 10 1.28 1.19 1.06 5 0.89 0.83 0.74 1 0.39 0.37 0.33 The calculated BMDs are about a factor of two apart for the BMD 10 values, but are about a factor of six apart for the BMD 1. This demonstrates the model dependence of the BMD values when low BMR levels are selected.

Calculating the Confidence Interval As shown in Table 2.2.7, the BMDs were calculated for 90th, 95th, and 99th percentile confidence limits. The effect of the confidence limit on the estimated BMD was slightly less for the quantal quadratic than for the quantal Weibull. Model results were most comparable for the 90th and 95th percentile confidence limits and least comparable for the 99th percentile confidence limits. These results demonstrate that the BMD tends to be more model dependent for wider ( higher percentile) confidence intervals. For the remainder of the example, the 95th percentile confidence limit estimate is used.

Selection of the BMD as the Basis for the RfD The example above yields different 95th percentile BMD 10 values based on the two models. Since there is no basis upon which to eliminate one of the BMDs ( i. e., goodness of fit, statistical assumptions and biological considerations), both must be considered. Either the smaller estimate may be used, or a geometric average may be used. In this case, the selection of which BMD to use is a risk management decision. In the example, the lower of the two BMDs ( 0.64) was chosen for the RfD calculation. 13Note that the logistic regression could be used to estimate the response to exposures greater than the RfD. BMD models could be used similarly, but caution is warranted when doing so in either case.

65 Use of Uncertainty Factors with BMD Approach Once the BMD is chosen, the RfD is derived by dividing the BMD by uncertainty factors. The same uncertainty factors applied to a NOAEL are used. In this case a factor of 10 was selected for interspecies extrapolation and a factor of 10 for human interspecies variability. Using an total UF of 100 and applying it to the 95th percentile confidence limit BMD for 10 percent response derived with the quantal Weibull model yields an RfD of 0.006 mg/ kg day.

2.2.7 Categorical Regression <IP_ADDRESS> Summary of the Method Categorical regression is another method under investigation to estimate risks associated with systemic toxicity ( Dourson et al., 1997; Guth et al., 1997). In this approach, health effects are grouped into ordered severity categories ( ranging from no effect to severe effect). This simplification allows for the incorporation into the analysis of both quantal and continuous data, as well as data that are reported qualitatively rather than quantitatively. Furthermore, information on many health effects can be considered together. Logistic regression analysis techniques are then applied to the data: the cumulative odds of falling into severity categories is the dependent variable, and exposure concentration, exposure duration, and other parameters are the independent variables. Using the regression results, the RfD is then specified as the dose at which the probability of adverse effects is sufficiently small at some level of confidence, modified, as in the NOAEL and BMD approaches, by appropriate uncertainty factors. For example, the dose of interest, D, might be defined as that dose for which one could conclude with 95 percent certainty that the probability of an adverse effect was less than 0.01. The value D would then be adjusted by uncertainty factors to derive the RfD. 13 <IP_ADDRESS> Steps in Applying Categorical Regression The categorical regression approach begins with a review of the toxicological data base for the chemical. For each valid study, the toxic responses observed are assigned to one of several ordered severity categories, based on biological and statistical considerations. For example, responses may be grouped into four categories: ( 1) no effect; ( 2) no adverse effect; ( 3) mild tomoderate adverse effect; and ( 4) severe or lethal effect. These correspond to the dose categories used in setting the RfD, namely the No Observed Effect Level ( NOEL), NOAEL, LOAEL, and Frank Effect Level ( FEL), respectively.

Since all response data are used in categorical regression analysis, there is no need to specify the lowest dose showing " mild to moderate" adverse effects. Accordingly, a more general term, adverse effect level ( AEL), is generally used in categorical regression in place of the term LOAEL to describe mild to moderate effects. 66 The probability of observing a response in a category at a given dose level is estimated by dividing the number of responses observed in that severity category divided by the total number of observations recorded for that dose level. Sufficient numbers of dose groups in each of several categories are required for the categorical regression. Judgment is required to define the types of effects that correspond to the severity categories.

The log odds for each dose and severity level is calculated, and then regressed against dose. The resulting regression equation can be used to calculate the probability of an effect of given severity for any dose.

Several model structures ( logistic, Weibull, or others) may be used to perform the categorical regression. Logistic regression on the ordered categories ( Harrell, 1986; Hertzberg, 1989) allows the dependent variable ( e. g., severity parameter) to be categorical and the independent variables to be either categorical or continuous.

The goodness of the fit of the model to the data can be judged using several statistical measures, including the overall P 2, model parameter standard errors and their P 2 significance levels, concordance statistics and correlation coefficients for the overall model, and the model covariates ( Hertzberg and Wymer, 1991). A variety of criteria are currently being investigated.

Some advantages of using the categorical regression to derive the RfD include the following: data concerning different health effects can be incorporated; it allows for refinement through improved data and statistical methodology; and several indicators of uncertainty in the estimates are provided. In addition, the categorical regression approach can be used to evaluate likely responses to exposures above the RfD.

2.2.8 Chronic, Practical Nonthreshold Effects Noncarcinogens are generally assumed to exhibit a threshold below which adverse effects are unlikely to occur. There are, however, exceptions to this general rule. Of particular concern are teratogenic and reproductive toxicants that may act through a genetic mechanism. EPA has recognized the potential for genotoxic teratogens and germline mutagens and discussed this issue in the 1991 Amendments to Agency Guidelines for Health Assessments of Suspect Developmental Toxicants ( USEPA, 1991a) and in the 1986 Guidelines for Mutagenicity Risk Assessment ( USEPA, 1986a). Various statements within these guidelines raise concern for the potential for future generations inheriting chemically induced germline mutations or suffering from mutational events occurring in utero. An awareness of the potential for such teratogenic/ mutagenic effects should be established in order to deal with such data. At this time, genotoxic teratogens and germline mutagens should be considered an exception and the traditional uncertainty factor approach the rule for calculating criteria or values for chemicals demonstrating developmental/ reproductive effects. In the absence of adequate data to support a genetic or mutational basis for developmental or reproductive effects, the default becomes an uncertainty factor approach. For such chemicals, this guidance recommends the procedures described above for noncarcinogens assumed to have a threshold. 67 Where evidence for a genetic or mutational basis does exist, a nonthreshold mechanism shall be assumed for genotoxic teratogens and germline mutagens. Since there is no well established mechanism for calculating criteria protective of human health from the effects of these agents, criteria will be established on a case by case basis.

2.2.9 Acute, Short Term Effects States may choose to derive criteria that correspond to acute or short term exposures. These criteria should correspond to a level of exposure that is " without appreciable risk of deleterious effects during some relatively short period of time" ( USEPA, 1991c). The derivation of such values follows the same general approaches described above for criteria based on chronic effects. The primary difference lies in the type of toxicity data used as the basis for the evaluation. Generally, studies that mimic the exposure pattern and duration of interest will be considered more relevant to the development of acute or short term criteria. This is especially important where acute or shortterm effects are of a substantially different nature than low level chronic effects. Where toxicity data do not match the exposure of interest, professional judgment is required to evaluate the relevance of the available data. Factors such as the pharmacokinetics, potential recovery periods, and potential for bioaccumulation should be considered in judging the relevance of the data.

The Office of Water has established procedures for deriving Health Advisories ( HAs) for one day, ten days, and longer term. In general HAs are developed by using NOAELs or LOAELs from studies with similar duration to the exposure period of concern, though there is some flexibility in this regard. Studies used for HAs should provide information on the critical endpoint. Studies that identify only frank toxic responses should not be used since these levels are far above the protective level targeted by HAs. More information on the derivation of HAs is given in Ware ( 1988).

2.2.10 Mixtures Exposures to multiple noncarcinogens may occur simultaneously. Possible interactions among chemicals in a mixture are usually placed in one of three categories:

° Antagonistic, where the chemical mixture exhibits less toxicity than is suggested by the sum of the toxic effects of the components.

° Synergistic, where the chemical mixture exhibits greater toxicity than is suggested by the sum of the toxic effects of the components.

° Additive, where the toxicity of the chemical mixture is equal to the sum of the toxicities of the components.

In only a few instances have the interactive effects of chemical mixtures been specifically studied. Where data on the effects of chemical mixtures exist, they should be used to characterize risk. Using the available data is especially important in cases where the resulting toxic effect from the 68 HI mix ' j n m ' 1 E m RfD m ( Equation 2.2.3) mixture has been demonstrated to be greater than the sum of the individual effects. Where specific data are not available on the interactive effects of particular chemical mixtures, the methods described below can be used by states to characterize risks from chemical mixtures. When risks from multiple chemicals are added, the quality of experimental evidence that supports the assumption of dose addition should be stated clearly ( USEPA, 1986b).

In cases where the chemicals in the mixture induce the same effect by similar modes of action, contaminants may be assumed to contribute additively to risk ( USEPA, 1986b), unless specific data indicate otherwise. To characterize risks from multiple chemical exposure to noncarcinogens, the dose for each chemical with a similar effect first is expressed as a fraction of its RfD. These ratios are added for all chemicals to obtain the chemical mixture hazard index:

where HI mix is the hazard index of the mixture ( unitless), E m is the exposure to chemical m, RfD m is the reference dose for chemical m, and n is the number of chemicals in the mixture. A hazard index greater than one implies that the individual is at some risk of the non carcinogenic effect, and the concern is the same as if exposure from an individual chemical exceeded the acceptable level by the same amount ( USEPA, 1986b). However, the numerical value of the hazard index does not indicate the magnitude and severity of the risk.

Some chemical mixtures may contain chemicals that cause dissimilar health effects. Methods currently do not exist for combining dissimilar health effects to characterize overall health concerns from chemical mixtures. Instead, States should characterize and present the risks from these contaminants separately.

2.2.11 References Allen, B. C., R. J. Kavlock, C. A. Kimmel and E. M. Faustman. 1994a. Dose Response Assessments for Developmental Toxicity: II. Comparison of Generic Benchmark Dose Estimates with NOAELs. Fundam. Appl. Toxicol. 23: 487 495.

Allen, B. C., R. J. Kavlock, C. A. Kimmel and E. M. Faustman. 1994b. Dose Response Assessments for Developmental Toxicity: III. Statistical Models. Fundam. Appl. Toxicol. 23: 496 509. 69 Andersen M., H. Clewell, M. Gargas, F. A. Smith, R. H. Ritz. 1987. Physiologically Based Pharmacokinetics and Risk Assessment Process for Methylene Chloride. Toxicol. Appl. Pharmacol. 87: 185 205.

Andersen, M., J. Mills, M. Gargas, L. Kedderis, L. Birnbaum, D. Neubert, and W. Greenlee. 1993. Modeling Receptor mediated Process with Dioxin: Implications for Pharmacokinetics and Risk Assessment. Risk Analysis 13: 25 26.

Brown, K. G. and L. S. Erdreich. 1989. Statistical Uncertainty in the No Observed Adverse Effect Level. Fund. Appl. Toxicol. 13( 2): 235 244.

Calabrese, E. 1985. Uncertainty Factors and Interindividual Variation. Regulatory Toxicology and Pharmacology. 5: 190 196.

Crump, K. S. 1984. A New Method for Determining Allowable Daily Intakes. Fund. Appl. Toxicol. 4: 854 871.

Crump, K. 1995. Calculation of Benchmark Doses from Continuous Data. Risk Analysis. 15: 79 89.

Crump, K. S., Allen, B., and E. Faustman. 1995. The Use of the Benchmark Dose Approach in Health Risk Assessment. Prepared for USEPA Risk Assessment Forum. EPA/ 630/ R 94 007.

Dourson, M. L. and J. Stara. 1983. Regulatory History and Experimental Support of Uncertainty ( Safety) Factors. Regulatory Toxicology and Pharmacology 3: 224 239.

Dourson, M. L., R. C. Hertzberg, R. Hartung and K. Blackburn. 1985. Novel Approaches for the Estimation of Acceptable Daily Intake. Toxicol. Ind. Health 1( 4): 23 41.

Dourson, M. L., Knauf, L. A. and J. C. Swartout. 1992. On Reference Dose ( RfD) and its Underlying Toxicity Data Base. Toxicol. Ind. Health 8( 3): 171 189.

Dourson, M. L., L. K. Teuschler, P. R. Durkin, and W. M. Stiteler. 1997. Categorical Regression of Toxicity Data, A Case Study Using Aldicarb. Regulatory Toxicity and Pharmacology 25: 121 129.

Faustman, E. M., B. C. Allen, R. J. Kavlock and C. A. Kimmel. 1994. Dose Response Assessment for Developmental Toxicity: I. Characterization of Data Base and Determination of NOAELs. Fundam. Appl. Toxicol. 23: 478 486.

Gaylor, D. W. 1983. The Use of Safety Factors for Controlling Risk. J. Toxicol. Environ. Health 11: 329 336. 70 Gaylor, D. W. 1989. Quantitative Risk Analysis for Quantal Reproductive and Developmental Effects. Environ. Health Perspect. 79: 243 246.

Gaylor, D. W. and W. Slikker, Jr. 1990. Risk Assessment for Neurotoxic Effects. Neurotoxicol. 11: 211 218.

Gehring, P. J., P. J. Watanabe, and C. N. Park. 1978. Resolution of Dose Response Toxicity Data for Chemicals Requiring Metabolic Activation: Example  Vinyl Chloride. Toxicol. Appl. Pharmacol. 44: 581 591.

Guth, D. J., R. J. Carroll, D. G. Simpson, and H. Zhou. 1997. Categorical Regression Analysis of Acute Exposure to Tetrachloroethylene. Risk Analysis 17( 3): 321 332.

Harrell, F. 1986. The Logist Procedure. SUGI Supplemental Library Users Guide, Ver. 5th. Ed. Cary, NC: SAS Institute.

Hartley, W. R. and E. V. Ohanian. 1988. The Use of Short term Toxicity Data for Prediction of Long term Health Effects. In: Trace Substances in Environmental Health  XXII. D. D. Hemphil, ( ed). University of Missouri, May 23 26, pp. 3 12.

Hartung, R. and P. R. Durkin. 1986. Ranking the Severity of Toxic Effects: Potential Applications to Risk Assessment. Comments on Toxicology. 1( 1): 49 63.

Hattis, D., L. Erdreich and M. Ballew. 1987. Human Variability in Susceptibility to Toxic Chemicals  A Preliminary Analysis of Pharmacokinetics Data from Normal Volunteers. Risk Analysis. 7( 4): 415 426.

Hertzberg. 1989. Fitting a Model to Categorical Response Data with Applications to Species Extrapolation of Toxicity. Health Physics 57: 405 409.

Hertzberg, R. C. and M. E. Miller. 1985. A Statistical Model for Species Extrapolation using Categorical Response Data. Toxicol. Ind. Health 1( 4): 43 63.

Hertzberg, R. C. and L. Wymer. 1991. Modeling the Severity of Toxic Effects. Presentation at the 84th Annual Meeting of the Air and Waste Management Association. June 16 21, 1991.

International Life Sciences Institute ( ILSI). 1993. Report on the Benchmark Dose Workshop. Washington, DC: International Life Sciences Institute, Risk Science Institute.

Kavlock, R. J., B. C. Allen, E. M. Faustman and C. A. Kimmel. 1995. Dose Response Assessment for Developmental Toxicity: IV. Benchmark Doses for Fetal Weight Changes. Fundam. Appl. Toxicol. 26: 211 222. 71 Kimmel, C. A. 1990. Quantitative Approaches to Human Risk Assessment for Noncancer Health Effects. Neurotoxicology 11: 189 198.

Lewis, S. C., J. R. Lynch and A. I. Nikiforov. 1990. A New Approach for Deriving Community Exposure Guidelines from No Observed Adverse Effect Levels. Reg. Toxicol. Pharmacol. 11: 314 330.

Swartout. 1990. Personal Communication to M. L. Dourson of the Office of Technology Transfer and Regulatory Support on January 12 . Washington, DC.

USEPA. 1986b. Guidelines for the Health Risk Assessment of Chemical Mixtures. Federal Register 51: 34014 34025. September 24.

USEPA. 1988. Reference Dose ( RfD): Description and Use in Health Risk Assessments. Integrated Risk Information System ( IRIS). Online. Intra Agency Reference Dose ( RfD) Work Group. Cincinnati, OH: Office of Health and Environmental Assessment, Environmental Criteria and Assessment Office. February.

USEPA. 1989. Interim Methods for Development of Inhalation Reference Doses. Office of Health and Environmental Assessment. EPA/ 600/ 8 88 066F.

USEPA. 1991a. Amendments to Agency Guidelines for Health Assessments of Suspect Developmental Toxicants. Federal Register 56: 63798 63826. December 5.

USEPA. 1991b. Final Guidelines for Developmental Toxicity Risk Assessment. Federal Register 56: 63798 63826. December 5.

USEPA. 1991c. General Quantitative Risk Assessment Guidelines for Noncancer Health Effects. Technical Panel for Development of Risk Assessment Guidelines for Noncancer Health Effects. Second External Review Draft. ECAO CIN 538.

USEPA. 1992. Reference Dose ( RfD) for Oral Exposure for Inorganic Zinc. Integrated Risk Information System ( IRIS). Online. ( Verification date 10/ 1/ 92). Cincinnati, OH: Office of Health and Environmental Assessment, Environmental Criteria and Assessment Office.

USEPA. 1993. Reference Dose ( RfD) for Oral Exposure for Inorganic Arsenic. Integrated Risk Information System ( IRIS). Online. ( Verification date 2/ 01/ 93). Cincinnati, OH: Office of Health and Environmental Assessment, Environmental Criteria and Assessment Office.

USEPA. 1994a. Reference Dose ( RfD) for Oral Exposure for Methylmercury. Integrated Risk Information System ( IRIS). Online. ( Verification date 11/ 23/ 94). Cincinnati, OH: Office of Health and Environmental Assessment, Environmental Criteria and Assessment Office. 72 USEPA. 1994b. Guidelines for Reproductive Toxicity Risk Assessment. External Review Draft. EPA/ 600/ AP 94/ 001. February.

USEPA. 1995. RQ Document for Solid Waste. Report on the Benchmark Dose Peer Consultation Workshop: Risk Assessment Forum. Office of Research and Development. EPA/ 630/ R96/ 011. November.

USEPA. 1998. Notice of Draft Revisions to the Methodology for Deriving Ambient Water Quality Criteria for the Protection of Human Health. Federal Register Notice.

Ware, G. W. ( ed). 1988. Reviews of Environmental Contamination and Toxicology: U. S. Environmental Protection Agency Office of Drinking Water Health Advisories. Vol. 104. New York: Springer Verlag, Inc.

Zielhuis, R. L. and F. W. van der Kreek. 1979. The Use of a Safety Factor in Setting Health Based Permissible Levels for Occupational Exposure. Int. Arch. Occup. Environ. Health. 42: 191 201.

2.3 Exposure Analyses 2.3.1 Role of Exposure Data in Setting AWQC The AWQC are primarily established to protect individuals from adverse health effects caused by pollutants in United States' inland and estuarine waters. To achieve this goal, exposure factors representative of the population to be protected should be used in the equations to derive the criteria ( see Section <IP_ADDRESS> for the equations to derive criteria). In addition, exposures from other non water sources such as air and food should be taken into account so that the criteria are protective of individuals who may be exposed to a particular pollutant from multiple exposure routes.

The following sections describe data available to determine exposure factors and discuss methods for incorporating non water sources of exposure, including EPA's recommended default values and methods. In addition, Table 2.3.1 presents sources of exposure related information. While not intended to be a comprehensive list, Table 2.3.1 does indicate some of the readily available sources for contaminant concentration data and exposure intake parameters.

2.3.2 Exposure Factors in AWQC Algorithms Several exposure factors are included in the equations to derive AWQC. 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. 73 The uses of and values for these factors differ based on several considerations. One consideration in the choice of values for a specific exposure parameter depends on whether the water body has been designated as a drinking water supply source or as a recreational, non potable source. The drinking water ingestion rate is recommended for use for those waters designated as public drinking water supply sources. This rate represents the amount of water an individual drinks per day ( see Section <IP_ADDRESS> for further discussion of the general policy to include a drinking water ingestion rate when setting AWQC). For waters that are used for recreational purposes, an individual may incidentally swallow water when swimming or waterskiing. Thus, an incidental ingestion rate would be applied in these circumstances. Although it is possible that individuals would incidentally ingest water from drinking water sources, incidental ingestion is not included for these sources of intake because the incidental ingestion rate is negligible when the assumed daily drinking water ingestion rate is utilized.

Another consideration in determining the exposure factors used is whether health effects result from chronic exposure or whether developmental health effects are being evaluated. For example, if a chemical causes both developmental and chronic health effects, a State or Tribe may wish to evaluate the chemical using relevant chronic or developmental exposure factors associated with those health effects, respectively, to determine whether to set criteria based on chronic or developmental effects. For chronic health effects, intake rates and body weights of adults or rates relevant to lifetime exposures are the most applicable because the health effects are associated with a long period of exposure. However, for pollutants that may cause health effects after shorter term exposure to a chemical, exposure factors for children may be most useful when setting criteria for RfDs based on health effects in children, because children often have a higher intake per body weight than adults. In addition, children may be more susceptible to certain contaminants than adults, and may have less capability to detoxify contaminants ( USEPA, 1994a). Thus, for such potential situations, EPA default values include intake rates for children.

Table 2.3.1: Sources of Contaminant Concentration and Exposure Intake Information Name of Source Type of Information Agency/ Author Aerometric Information Retrieval System ( AIRS) An updated air data base of many different sites ( from rural to urban/ industrial) that includes Federally required information, as well as data submitted voluntarily by States. Office of Air Quality Planning and Standards, EPA Continuing Survey of Food Intake by Individuals ( CSFII) A national food consumption survey conducted approximately annually. U. S. Department of Agriculture ( USDA)

Exposure Factors Handbook Summarization of studies and data bases to provide statistical data on factors used in assessing exposure. National Center for Environmental Assessment, EPA Table 2.3.1: Sources of Contaminant Concentration and Exposure Intake Information Name of Source Type of Information Agency/ Author 74 Inventory of Exposure Related Data Systems Sponsored by Federal Agencies Compilation of information on Federally managed data systems that contain exposure information. Agency for Toxic Substances and Disease Registry; Centers for Disease Control; EPA National Food Consumption Survey ( NFCS) A national food consumption survey conducted each decade by USDA. The last survey was conducted in 1987 88. USDA National Health and Nutrition Examination Survey ( NHANES) A national health and nutrition survey conducted each decade. Based on a probability sample of noninstitutionalized people residing in the U. S. National Center for Health Statistics National Inorganics and Radionuclides Survey ( NIRS)/ National Pesticides Survey ( NPS) Most recent Federal surveys ( mid to late 1980s) conducted to characterize occurrence of a series of inorganic and radionuclide chemicals ( NIRS) and pesticides ( NPS) in public drinking water supplies from ground water sources ( and rural domestic wells with the NPS). EPA National Sediment Inventory ( NSI) Compilation of available data bases on sediment contamination/ sediment chemistry data. These include data on fish tissue residues of chemical contaminants. EPA Safe Drinking Water Information System ( SDWIS) Compiled data that includes monitoring required and provided under the program for unregulated contaminants ( Section 1445 of the Safe Drinking Water Act). EPA Total Diet Study ( TDS)  also known as Market Basket Survey Contaminant concentrations in foods purchased from supermarkets or grocery stores throughout the U. S. four to five times a year. Food items in the TDS are of similar type included in the NFCS and the second NHANES ( both described above). Food and Drug Administration Total Water and Tap Water Intake in the United States: Population Based Estimates of Quantities and Sources Presents estimates of total water ( includes water intrinsic to foods) and tap water intake in the population of the continental U. S. Data used are based on the NFCS ( described above). Ershow and Cantor ( NCI/ NIH)

Shorter term exposures may also pose risks to other people with special susceptibilities due to illness ( e. g., persons with kidney, liver, or other diseases may be especially vulnerable to toxins which attack those systems). When States and Tribes assess intake from pollutants that cause toxicity resulting from such exposures, they may wish to investigate intakes for these other population groups that may also have a high intake per body weight, and/ or may be highly subject to adverse effects 75 from these toxicants. It may be appropriate to calculate criteria using developmental and chronic toxicity and exposure assumptions to see which criterion is more stringent.

Developmental effects resulting from prenatal exposure to contaminants have become an area of significant concern ( USEPA, 1994a). Thus, in addition to considering use of exposure factors specific to adults or children, States and Tribes may wish to use exposure factors specific to women of childbearing age in cases where developmental health effects may be of concern. These exposure factors are described below.

Fish consumption intake rates may also differ based on the target population to be protected. Some states may have a large population of recreational fishers who may fish a few times a year or during a fishing vacation. Other States may have populations that subsist on fish for a large portion of a year. Thus, the fish intake exposure factor may differ depending on the population that is to be protected. Different types of fishers are discussed in greater detail, below, in the section that describes fish intake rates.

When setting AWQC, it is preferable to use exposure information reflective of individuals who actually use the water body for which AWQC are to be determined. When dealing with such diverse populations as those throughout the United States, extreme ranges of behaviors and activities are likely. Therefore, EPA explicitly recommends that, for certain exposure factors that may be highly variable ( e. g., fish intake rates), States use available local data. These data should be used especially in cases that result in AWQC that are more stringent than criteria derived using default exposure assumptions suggested by EPA. In many situations, local exposure data may not be available. Therefore, EPA also recommends default values for each of the exposure values discussed below.

The following sections discuss available data and describe some of the above issues in greater detail. In addition, the sections discuss EPA's recommendations for use of the exposure factors and present EPA's suggested default values. The incorporation of these exposure factors into equations to derive criteria are described in detail in Section <IP_ADDRESS>.

<IP_ADDRESS> Body Weight The 1980 AWQC National Guidelines used a body weight of 70 kg in the derivation of AWQC, which represents EPA's Agency wide adult body weight assumption used in its risk assessments and approximates the average adult body weight of 71.8 kg from an analysis of the National Health and Nutrition Examination Survey ( NHANES II), as reported in the Exposure Factors Handbook ( USEPA, 1997a). In the current, updated guidance, EPA recommends several default body weight values, depending on whether chronic effects or acute effects are being evaluated. The use of these data in equations to derive AWQC are described in Section <IP_ADDRESS>. Chronic Exposure Scenarios For chemicals that cause chronic effects, EPA recommends using a default body weight of 70 kilograms. This value approximates the mean for adults from two sets of data. The first set of data comes from NHANES II, which was conducted from 1976 through 1980 and for which information on a variety of health and nutritional characteristics of individuals were collected ( adapted from NCHS, 1987). The National Center for Health Statistics compiled body weight data from NHANES II for over 20,000 individuals aged 6 months to 74 years. Weighted mean body weights were determined from this data. The mean body weight value for men and women ages 18 to 74 years old, using data from NHANES II, is 71.8 kg. The median body weights for men and women from this study are 76.9 and 62.4 kilograms, respectively. Table 2.3.2 includes a distribution of mean and median adult body weights, by age group, based on NHANES II data. Body weights are presented for men, women, and both sexes combined.

The second set of body weight data come from Ershow and Cantor ( 1989). These authors used data collected during the 1977 1978 Nationwide Food Consumption Survey ( NFCS), which surveyed 30,770 individuals who constituted a stratified random sample designed to represent the noninstitutionalized U. S. population living in households ( USDA, 1988). Body weights were selfreported by participants. The mean value for body weight for adults ages 20  64 years old is 70.5 kg. Means and percentile values of body weight from Ershow and Cantor ( 1989) listed by sex and age are presented in Table 2.3.3. The revised EPA Exposure Factors Handbook ( USEPA, 1997a) recommends a value of 71.8 kg for adults, based on the NHANES II data. However, the Handbook also acknowledges the 70 kg value commonly used in EPA risk assessments and cautions assessors on the use of values other than 70 kg. Specifically, the point is made that the 70 kg value is used in the derivation of cancer slope factors and unit risks that appear on IRIS. Consistency is advocated between the dose response relationship and exposure factors assumed. 78 Table 2.3.2: Body Weight ( in kilograms) of Adults from NHANES II Age Men Women Men and Women Mean Median Mean Median Mean 18  24 years 73.7 72.0 60.6 58.0 67.2 25  34 years 78.7 77.5 64.2 60.9 71.5 35  44 years 80.9 79.9 67.1 63.4 74.0 45  54 years 80.9 79.0 68.0 65.5 74.5 55  64 years 78.8 77.7 67.9 65.2 73.4 65 74 years 74.8 74.2 66.6 64.8 70.7 Overall: 18  74 years 78.1 76.9 65.4 62.4 71.8 Source: Adapted from NCHS ( 1987)

Developmental Effects Exposure Scenarios In certain cases, pregnant women may represent a more appropriate target population for consideration when setting water quality criteria than all adults in cases where developmental effects may be of concern ( USEPA, 1994b). In these types of cases, body weights representative of women of childbearing age may be appropriate to adequately protect offspring from such health effects. For example, in the Great Lakes Water Quality Initiative, EPA chose women of childbearing age as the target population for development of the mercury criterion ( USEPA, 1995). To determine a mean body weight value appropriate to this population, separate body weight values for women in individual age groups within the range of 15 to 44 years old, taken from NHANES II ( adapted from NCHS, 1987), were combined and weighted by current population percentages ( U. S. Bureau of the Census, 1996) to obtain a value applicable to the current population. The resulting mean body weight value for this age group is 63.8 kg.

Ershow and Cantor ( 1989) also present data on mean and median body weights for pregnant women, of 65.8 and 64.4 kilograms, respectively. Based on these data, States may wish to use a value of 65 kilograms in combination with relevant developmental toxicity data when assessing risks for pregnant women and for setting AWQC.

Likewise, for some contaminants, RfDs based on health effects in children may be of primary concern. As stated in the Federal Register notice, because children generally eat more fish and drink more water per body weight than adults, higher intake rates per body weight may be more appropriate in the derivation of AWQC to provide adequate protection for these individuals. In 79 addition, because children may be more susceptible to the effects of some pollutants than adults ( USEPA, 1994b), they should be especially considered when assessing adverse effects that occur following such exposures. Information on children's body weights ( from NHANES II) are included in Tables 2.3.3 and 2.3.4.

To protect children against health effects from water and fish intake when RfDs are based on health effects in children, EPA recommends a default body weight of 28 kilograms, which represents a mean body weight for children 0 to 14 years old. This is only recommended for chemicals for which adverse effects for children are the most critical endpoint in the chemical's toxicological profile. This body weight can be used with fish intake rates for children in the same age group when deriving criteria for protection against such health effects from eating fish. The default recommendation is made, in part, due to the limitations of the default fish consumption data. Specifically, the limited sampling base prohibits the use of finer age group divisions due to unacceptable confidence intervals with such finer fish intake divisions. However, finer age divisions are provided in Tables 2.3.3 and 2.3.4 for States and Tribes to consider using along with more robust fish consumption data. As with other recommended body weight values, the default estimate is based on information from analyses of NHANES II data ( adapted from NCHS, 1987). Current population estimates ( U. S. Bureau of the Census, 1996) were used to weight information on body weights for individuals in several age groups up to age 14 years, using body weight information from NCHS ( 1987) to represent values applicable to the current population. This calculation resulted in weighted mean body weight values of 28 kg for this age group. A similar analysis using body weights for separate age groups within the 0 14 year range from Ershow and Cantor ( 1989), and weighting by current population estimates also resulted in a mean body weight of 28 kilograms.

If States wish to specifically evaluate infants and toddlers, EPA recommends a lower default body weight of 10 kilograms, as has been used in previous water program guidance and regulations. The body weight is representative of children up to three years old. EPA recommends using data from this particular age group because these children may be particularly susceptible to acute effects from water based formula intake ( e. g., nitrate). Data used to determine this body weight value come from Ershow and Cantor ( 1989) and the analysis of NHANES II data ( adapted from NCHS, 1987). The analysis of NHANES II data indicate 10th, 25th, and 50th percentile values for children less than three years old as 8.5, 9.6, and 11.3 kilograms for females, and 9.1, 10.3, and 11.8 kilograms for males, respectively. Mean body weights from NHANES II are 9.1 for children ages 6 11 months, 11.3 for 1 year olds, and 13.3 for 2 year olds ( adapted from NCHS, 1987). From the Ershow and Cantor study, the 10th, 25th, and 50th percentile values for children 1 3 years old are 10.4, 11.8, and 13.6 kg, respectively, with a mean value of 14.1 kg ( Ershow and Cantor 1989).

States and Tribes may instead wish to consider certain general developmental ages ( e. g., preschool pre adolescent, adolescent, etc.) or certain specific developmental landmarks ( e. g., neurological development in the first four years, etc.) depending on the chemical of concern. EPA encourages States and Tribes to use Tables 2.3.3 and 2.3.4 to choose a body weight intake, if they believe a particular age subgroup is more appropriate due to these developmental ages or landmarks. 80 Table 2.3.3: Self Reported Body Weight ( kilograms) for Both Sexes from Ershow and Cantor ( 1989) ab Sex Age ( yr) Mean Standard Deviation Percentile Distribution 1 5 10 25 50 Both < 0.5 5.8 1.8 c 3.2 3.6 4.5 5.4 0.5 0.9 9.2 2.0 c 6.8 7.3 8.2 9.1 1 3 14.1 3.2 8.6 10.0 10.4 11.8 13.6 4 6 20.3 4.6 12.7 13.6 15.4 17.2 20.0 7 10 30.6 7.8 18.1 20.4 22.7 24.9 29.5 11 19 55.2 13.4 28.6 34.0 38.6 45.4 54.4 20 64 70.5 15.2 44.5 49.9 52.2 59.0 68.0 65+ 68.6 13.1 40.8 48.5 52.2 59.0 68.0 All 59.3 22.6 9.1 15.9 22.7 48.5 61.2 Males < 0.5 6.2 1.8 c 3.6 3.6 5.0 6.4 0.5 0.9 9.6 2.1 c 6.8 7.3 8.2 9.1 1 3 14.4 3.3 9.1 10.0 10.9 12.2 13.6 4 6 20.5 4.5 13.6 14.5 15.9 18.1 20.4 7 10 31.0 7.9 18.1 20.9 22.7 24.9 29.5 11 19 58.3 14.9 29.0 34.0 38.6 47.2 59.0 20 64 79.4 13.0 54.4 61.2 64.0 70.3 78.0 65+ 74.4 11.5 49.9 56.7 61.2 67.1 73.9 All 63.8 25.3 9.1 15.4 20.9 49.9 70.3 Females < 0.5 5.5 1.8 c 2.7 3.6 4.1 5.4 0.5 0.9 8.8 1.7 c 6.8 6.8 7.7 8.6 1 3 13.7 3.0 8.6 9.5 10.0 11.3 13.6 4 6 20.0 4.6 12.7 13.6 15.0 17.2 19.1 7 10 30.2 7.6 18.1 20.4 21.8 24.9 29.5 11 19 52.1 11.0 28.1 34.0 38.6 45.4 52.2 20 64 64.1 13.4 43.1 47.6 49.9 54.4 61.2 65+ 64.5 12.6 39.9 45.4 49.9 55.8 63.5 All 55.7 19.5 9.1 15.9 24.9 48.1 56.7 a Does not include pregnant women, lactating women, or breast fed children. b Individual values may not add to totals due to rounding. c Value not reported due to insufficient number of observations. 81 Table 2.3.4: Mean Body Weights ( kilograms) of Children from NHANES II Age Boys Girls Boys and Girls 6 11 months 9.4 8.8 9.1 1 year 11.8 10.8 11.3 2 years 13.6 13.0 13.3 3 years 15.7 14.9 15.3 4 years 17.8 17.0 17.4 5 years 19.8 19.6 19.7 6 years 23.0 22.1 22.6 7 years 25.1 24.7 24.9 8 years 28.2 27.9 28.1 9 years 31.1 31.9 31.5 10 years 36.4 36.1 36.3 11 years 40.3 41.8 41.1 12 years 44.2 46.4 45.3 13 years 49.9 50.9 50.4 14 years 57.1 54.8 56.0 Source: Adapted from NCHS ( 1987)

<IP_ADDRESS> Drinking Water Intake The 1980 AWQC National Guidelines used a value of 2 liters/ day to represent the drinking water intake of an individual. In these updated guidelines, EPA recommends the same value when setting chronic criteria. In addition, EPA recommends a drinking water intake specific to children for protecting against certain health effects ( with those chemicals for which the critical effect of concern is based on children) because young children may intake a large amount of water per body weight. Chronic Exposure Scenarios To protect against health effects due to chronic exposure, EPA recommends an adult specific drinking water intake of 2 liters per day. This value has been used as a nationwide estimate of adult daily water consumption in the drinking water program for setting Maximum Contaminant Level Goals ( MCLGs) and Maximum Contaminant Levels, to be protective of a majority of the population over the course of a lifetime. The value is also suggested by the Exposure Factors Handbook to be supported by studies analyzed in the Handbook for use as an upper percentile intake rate ( USEPA, 1997a). In addition, the value was recommended in the Technical Support Document for setting water quality criteria for human health in the Great Lakes Region ( USEPA, 1995). Based on the study data from Ershow and Cantor ( 1989, summarized below), EPA also recommends a 2 liters per day intake for women of childbearing age.

The value of 2 liters/ day has been estimated as being somewhere between the 75th and the 100th percentiles, as reported by different studies of drinking water intake. Thus, using this higher than average value in combination with recommended default body weights and fish intake rates would protect most individuals in the population. However, certain individuals who work or exercise in hot climates may consume water at rates significantly higher than 2 liters/ day. Some of the most highly exposed individuals, such as migrant workers, may not be captured in national surveys of drinking water intake. Several studies that have estimated drinking water intake are described below.

One study by the National Cancer Institute ( NCI) estimated intake from tap water ( which includes water directly from the tap and tap water added to foods and beverages during preparation) using data from the NFCS ( Ershow and Canter, 1989). For 11,700 adults ages 20  64 years old, this study reports 50th, 75th, and 90th percentile tap water intakes of 1.3, 1.7, and 2.3 liters/ day, respectively. Table 2.3.5 includes the distribution of intake values by age from this study.

NCI determined drinking water intake values from a study in which 9,000 individuals were questioned in a population based, case control study investigating a possible relationship between bladder cancer and drinking water ( Cantor et al., 1987). This study estimated an overall average tap water consumption rate of 1.39 liters of water per day. The 100th percentile consumption rate was estimated to be about 1.96 liters per day as shown in Table 2.3.6.

A survey of drinking water literature by the National Academy of Sciences ( NAS) has calculated the average per capita water consumption to be 1.64 liters per day. NAS estimates that daily water consumption may vary with physical exercise and fluctuations in temperature and humidity. It is reasonable to assume those living in arid, hot climates will consume higher levels of water. However, NAS adopted the 2 liters/ day volume to represent the intake of the majority of water consumers ( NAS, 1977). In another survey, the Food and Drug Administration's ( FDA) Total Diet Study estimated rates for water and water used to make drinks and soups for two groups of adults to be 1.04 and 1.26 liters per day with an average of 1.15 liters per day. Finally, EPA estimates based on the U. S. Department of Agriculture's ( USDA) 1977 78 Nationwide Food Table 2.3.5: Tap Water Intake ( g/ day) for Both Sexes from Ershow and Cantor ( 1989) ab Sex Age ( yr) Mean Standard Deviation Percentile Distribution 50 75 90 95 99 Both Sexes < 0.5 272 247 240 332 640 800 c 0.5 0.9 328 265 268 480 688 764 c 1 3 646 390 567 820 1162 1419 1899 4 6 742 406 660 972 1302 1520 1932 7 10 787 417 731 1016 1338 1556 1998 11 19 965 562 867 1246 1701 2026 2748 20 64 1366 728 1252 1737 2268 2707 3780 65+ 1459 643 1367 1806 2287 2636 3338 All 1193 702 1081 1561 2092 2477 3415 Males < 0.5 250 232 240 320 569 757 c 0.5 0.9 322 249 264 408 634 871 c 1 3 683 406 606 867 1228 1464 2061 4 6 773 414 693 1033 1336 1530 1900 7 10 802 437 738 1046 1391 1609 2055 11 19 1050 605 942 1364 1856 2179 2967 20 64 1460 798 1339 1841 2485 2949 4083 65+ 1570 704 1448 1952 2460 2790 3712 All 1250 759 1123 1634 2205 2673 3760 Females < 0.5 293 259 240 358 672 800 c 0.5 0.9 333 281 278 500 712 759 c 1 3 606 368 532 783 1114 1339 1806 4 6 709 395 622 930 1231 1491 1932 7 10 772 395 726 992 1299 1475 1888 11 19 882 503 799 1147 1540 1825 2424 20 64 1297 664 1207 1655 2147 2491 3359 65+ 1382 584 1309 1687 2167 2472 3071 All 1147 648 1049 1505 1988 2316 3097 a Does not include pregnant women, lactating women, or breast fed children. b Individual values may not add to totals due to rounding. c Value not reported due to insufficient number of observations. 85 Consumption Survey identified daily beverage intakes ranging from 1.48 to 1.73 liters per day. Both the FDA and USDA studies were cited in USEPA ( 1997a). Based on these studies, EPA estimated an average adult drinking water consumption rate to be 1.41 liters per day and the 90th percentile value to be 2.35 liters per day ( USEPA, 1997a).

Table 2.3.6: Frequency Distribution of Tap Water Consumption Rates*

Consumption Rate ( L/ day) Cumulative Frequency (%)

0.80 19.2 0.81  1.12 39.6 1.13  1.33 59.7 1.45  1.95 79.9 1.96 100.0 * Represents consumption in a " typical" week. Source: Cantor et al. ( 1987)

Developmental Effects Exposure Scenarios As noted above, for some contaminants, RfDs based on health effects in children are of primary concern. Because infants and small children have a higher water consumption per body weight compared to adults, a higher water consumption rate per body weight may be needed for comparison with doses from relevant toxicity studies. Use of these higher water consumption rates when setting criteria based on health effects associated with children should result in adequate protection for infants and children. Estimating a mean drinking water intake for children ages 0 14 years old, which combines drinking water intake for five age groups within the larger age group of 0 14 years from Ershow and Cantor ( 1989) and weighting by current population estimates ( from U. S. Bureau of the Census, 1996) results in a drinking water intake of approximately 750 ml. As a slightly more protective measure than using 750 ml, EPA proposes a drinking water intake of 1 liter. This value is equivalent to about the 75th percentile value, of 960 ml for children ages 1 10 years old ( Ershow and Cantor, 1989). The distribution of drinking water intakes for age groups within the 0 14 year old group from Ershow and Cantor ( 1989) is included in Table 2.3.5. This value is also appropriate to use for evaluating smaller children ages 1 3 years old and has been used as a default by EPA's water program office for small children in past regulatory efforts.

Inhalation and Dermal Exposure A number of water contaminants are volatile and thus diffuse from water into the air where they may be inhaled. In addition, drinking water is used for bathing and ambient waters for swimming 86 and thus, there is at least the possibility that some contaminants in water may be dermally absorbed.

Dermal absorption and the inhalation of volatilized drinking water contaminants may be responsible for significant increases in exposure over and above that due to ingestion. However, this issue is quite complicated. A significant fraction of the water that is ingested is either boiled or allowed to stand prior to ingestion. In both cases, it is reasonable to assume that volatilization will decrease the concentration of volatile contaminants in the water that is actually ingested. In addition, because volatilization can decrease the concentration of volatile contaminants in the water that comes in contact with the skin, it follows that volatilization can decrease the extent of dermal absorption.

Thus, volatilization may increase exposure via inhalation and decrease exposure via ingestion. The net effect of volatilization and dermal absorption upon total exposure to volatile contaminants in water is unclear. Although several approaches can be found in the literature, including various models that have been used by EPA, the Agency currently does not have a proposed methodology for explicitly incorporating inhalation ( i. e., from volatilization) and dermal absorption exposures from household water uses in the derivation of health based criteria ( i. e., MCLGs or AWQC). The Agency is currently exploring the effect of volatilization and dermal absorption upon exposure to drinking water contaminants. For example, the Agency has a joint agreement with the International Life Sciences Institute ( ILSI) to develop guidance on estimating exposures of inhalation and dermal absorption from contaminants in water. It is anticipated that this guidance would be incorporated into this methodology when it is available.

<IP_ADDRESS> Fish Intake Rates Fish intake rates ( expressed in grams/ day) are used in the equations to derive AWQC. Throughout this section, the terms " fish intake" or " fish consumption" are used. They generally refer to the consumption of finfish and shellfish, and the national survey described in this section ( the CSFII) includes both. States and Tribes should ensure that when selecting local or regionally specific studies, both types are included when the population exposed are consumers of both types. If the population of concern are also believed to consume aquatic plants from the water body, this source should be accounted for with the estimate of other exposures ( i. e., the relative source contribution analysis). Ideally, fish intake rates should be representative of individuals who eat fish from a given water body for which AWQC are to be set. In addition, priority should be given to identifying and protecting the most highly exposed fish eaters in the area. Although highly exposed populations cannot be precisely defined and may differ depending on the water body to be protected, such fish eaters may generally be separated into two groups: ( 1) sportfishers, defined generally as the group of anglers who eat the fish they catch recreationally; and ( 2) subsistence fishers, individuals who rely on fish for a large part of their protein intake. A more detailed description, as well as examples, of these highly exposed groups follows. 87 Sportfishers may vary widely in their catch and consumption rates. Some may eat fish for short periods throughout the year or during certain fishing seasons. Others may fish for much longer periods during a year. Although sportfishers may primarily fish recreationally and only supplement their regular diets with the fish they catch, some sportfishers may eat large amounts of fish throughout the year.

Populations which have been identified as eating a larger portion of sport caught fish than the general population ( e. g., Native Americans) yet are not recreational fishers are distinguished from the above group of sportfishers. Such fishers ( called subsistence fishers here) may rely on catching and eating fish to meet nutritional needs or because of cultural traditions. Subsistence fishers may catch fish year round ( CRITFC, 1994) or preserve fish to eat throughout the year. Some of these fishers, such as Asian Americans, often consume portions of the fish that recreational fishers do not often consume ( including liver, kidneys, brains, and eyes). In addition, fish may be prepared whole, providing greater exposure to contaminants ( e. g., organs and remains are often used as soup stock) ( Pestana, 1994; Shubat, 1994; Allbright, 1994; Cung, 1994; Nehls Lowe, 1994; University of Wisconsin SeaGrant, 1994; Den, 1994; Young, 1994; Lorenzana, 1994). Subsistence fishers are often ( although not always) low income individuals, and may reside in either urban or rural areas.

Several ethnic groups have been identified as having members who subsist on fish. Several specific groups of Native American fishers have been identified in the Northwest and the Great Lakes Region ( Kmiecik, 1994; CRITFC, 1994; Den, 1994; Young, 1994; Eng, 1994). Asian American fishers are a group that includes numerous populations such as Laotian, Hmong, Cambodian, and Vietnamese, each with differing consumption patterns and cultural traditions. Asian American fishers in particular may eat a larger portion of the fish than generally recommended, including consumption of additional organs or the whole fish ( Pestana, 1994; Shubat, 1994; Allbright, 1994; Cung, 1994; Nehls Lowe, 1994; University of Wisconsin SeaGrant, 1994; Den, 1994; Young, 1994; Lorenzana, 1994).

When estimating fish intake for the population of concern, EPA recommends that central tendency values ( i. e., median or mean values) or higher percentile values from studies of fish consumption relevant to the identified group be used in the derivation of criteria. Values lower than the median or mean should not be used because the identified populations would not be adequately protected. Furthermore, when considering median values from fish consumption studies, States need to ensure that the distribution is based on survey respondents who reported consuming fish because surveys based on both consumers and non consumers typically result in median values of zero.

Because fish consumption habits may vary among different types of populations and among States, EPA prefers that States use information on fish consumption rates directly relevant to the population being addressed. However, such information may not always be available. Thus, EPA proposes the hierarchy of preferences for consideration of fish consumption data described below. Although Preferences # 1 and # 2 are likely to result in higher intake rates than the default recommendations of Preference # 4, if the converse is true ( i. e., if site specific or similar geographic/ population studies indicate lower intake rates than the recommended defaults), States 88 may choose the lower intake rates determined from the first two preferences. However, if a State chooses values ( whether central tendency or high end) that particularly target highly exposed consumers, they should be compared to high end fish intake rates for the general population to make sure that the highly exposed consumers within the general population would also be protected by the chosen intake rates. As discussed in the Federal Register presentation of the Methodology, it is recommended that cooked weight values of intakes be used.

Preference # 1: Use of Local Information Once a State has identified the particular population, which, if a protected subgroup, will also afford acceptable protection to the entire population, EPA recommends that States use results from fish intake surveys conducted in the geographic area where the State is located to estimate fish intake rates ( measured in grams/ day) that are likely to most closely represent the defined populations being addressed. Generally, the more specific the data are to the individuals who use the water body of interest, the better the data are considered to be for estimating accurate fish intake rates.

Information on local fish consumption habits may not be already available to States. Thus, if time and money permit, States are encouraged to conduct their own surveys in order to obtain estimates of fish consumption ( in grams/ day) and to characterize fisher populations within the State, and specifically, the locality of interest. The EPA guidance manual entitled Guidance for Conducting Fish and Wildlife Consumption Surveys ( USEPA, 1997b) may be useful in planning and conducting surveys. This guidance document reviews five methods of obtaining fish consumption data:

° Recalled information collected by telephone.

° Recalled information collected by in person interviews.

° Recalled information from self administered mailed questionnaires.

° Diaries maintained by anglers.

° On site creel censuses ( obtaining harvest data collected on site from single anglers). 89 The advantages and disadvantages of each method are addressed, and suggestions about procedures to solve problems associated with each survey method are given.

In addition, Consumption Surveys for Fish and Shellfish lists several suggestions regarding the type of information to collect when conducting these surveys. Examples of this information include: ( 1) sociodemographic characteristics such as age of the angler, number of household members, and pregnancy or lactation status of women in the household; ( 2) fishing activities including seasonal and temporal distribution of fishing activities, whether the angler fishes for sport or consumption, and the type of fish captured ( whether bottom feeders or pelagic); and ( 3) preparation and consumption patterns including portions of the fish consumed, methods of preparation prior to cooking, and procedures for cooking. Such information can be used to investigate patterns of consumption among high risk groups, and can aid in most accurately characterizing consumption rate information using as few assumptions as possible.

The document presents a variety of guidelines for conducting surveys, and is intended to provide methods for efficiently and cost effectively collecting information necessary for valid statistical analyses of risks to subsistence and recreational anglers. States should refer to USEPA ( 1997b) for more detailed information on methods of conducting fish consumption surveys.

A couple of issues not addressed in detail in USEPA ( 1997b) should be emphasized when planning a fish consumption survey. The first issue involves identification of subsistence fisher populations. Because it may be difficult to identify subsistence fisher populations solely through traditional approaches such as mail or phone surveys, it may be necessary for surveyors to use other methods to target these populations. A couple of methods may be of use. One method involves contact with community organizations that represent these populations ( e. g., Indian tribal organizations) that have already established a relationship with community members. In addition, creel clerks ( those who interview fishers at specific fishing locations) may be good sources of information on fisher demographics because they have direct contact with individuals at fishing sites ( Shubat, 1993). It is important to anticipate cultural and language requirements of each ethnic group and to try and follow the community based approach indicated above. Asians and Pacific Islanders are currently the fastest growing minority population in the U. S. For many first and second generation immigrants and refugees, survey methods which utilize creel, mail in, telephone or doorto door techniques are ineffective in obtaining reliable data characterizing fish and seafood consumption patterns ( Nakano, 1996; USEPA, 1996). Informal studies indicate a preference for bottom dwelling fish; therefore, Asian and Pacific Islander surveys should include an appropriate species list ( Soukhaphonh et al., 1996).

A second issue important to emphasize is that, if States intend to consider health effects resulting from acute exposures when setting AWQC, surveyors may wish to obtain information regarding maximum amounts of fish that may be eaten at a meal. Because many surveys are designed to obtain information simply on the number of fish meals eaten by an individual over a specific time period, rather than the size of the fish meals, maximum meal sizes may not generally be obtained by a fish consumption survey. As noted above, such large acute exposures may be especially 90 problematic to children, people with special susceptibilities, and pregnant women. In addition, such high doses may be more likely to occur at specific times during the year, such as periods when certain types of fish are available or during specific events ( e. g., summer vacation, Native American religious festivals, or fishing tournaments). Thus, surveyors may wish to consider obtaining information on such maximum intake rates and determine whether these rates are likely to occur during specific times during the year.

Preference # 2: Use of Surveys from Similar Geographic Areas and Population Groups For those States and Tribes that do not have resources available to conduct a survey of consumption rates of local populations and when such information is otherwise not available, EPA's second preference in determining fish intake rates is for States and Tribes to use results from existing fish intake surveys that reflect similar geography and/ or population groups. For instance, States or Tribes with subsistence fisher populations may wish to use consumption rates from studies that focus specifically on these groups, or, at minimum, use rates that represent high end values from studies that measured consumption rates for a range of types of fishers ( e. g., recreational/ sport fishers, subsistence, minority populations). A State or Tribe in a particular region of the country may consider using rates from studies that surveyed the same region; for example, a State or Tribe that has a climate that allows year round fishing may underestimate consumption if rates are used from studies taken in regions where individuals fish for only one or two seasons per year. A State or Tribe that has a high percentage of a particular age group ( such as elderly individuals, who have been shown to have higher rates in certain surveys) may wish to use age specific consumption rates, which are available from some surveys.

Fish intake rates estimated from available surveys that have investigated the fish consumption habits of individuals are described below and presented in Tables 2.3.7 and 2.3.9. These surveys are divided into the two previously identified groups of highly exposed fisher populations ( sportfishers and subsistence fishers) described above. Although the surveys are divided into these two categories for ease of presentation, it should be noted that these two categories cannot always be strictly delineated. In particular, there may be individuals included in the sportfisher surveys that exhibit habits indicative of subsistence fishers ( i. e., eating fish as a large part of their diet). Also, some members of identified subsistence populations may not subsist on fish as a major portion of their diets.

These surveys use a variety of methods. The methods used in the surveys to estimate the fish intake rates are included in Tables 2.3.8 and 2.3.10. A few points should be made about the methods used in these studies to estimate the consumption rates. One major issue regarding these rates is that although they are presented as grams per day in Tables 2.3.7 and 2.3.9, they should be considered to be approximations of actual gram/ day amounts only. For example, the estimates are generally obtained by memory recall, not strict daily log keeping of grams eaten per day. In addition, surveys generally ask respondents to estimate the number of meals they have eaten over a given period of 91 time. Although some surveys include questions about approximate size of the meals, others do not ask any questions about the actual size of the meals eaten during that time and, instead, assume all meals are a given size ( most often 227 grams, or a half pound).

A second major issue to be addressed is that the estimates of fish intake may vary across surveys for reasons which depend on the type of fish included in the survey. For instance, surveys may report consumption of only certain types of fish. Some surveys have focused primarily on either freshwater or saltwater fish, whereas others have collected information on both types. In addition, some surveys have queried individuals about whether they have eaten recreational fish only, whereas others have questioned respondents about intake of commercial fish, or both.

Methods of averaging fish consumption information also differ among studies. Some studies average the consumption rates over all individuals, regardless of whether they ate fish, while other surveys average the information only for those individuals who reported eating fish. For example, Cox, Vaillancourt, and Hayton ( 1993) report consumption rates averaged for the fish eating population, whereas the Alabama Department of Environmental Management ( 1993) report a rate averaged for both individuals who eat fish and those who do not eat fish.

As discussed in the Federal Register notice, fish consumption surveys also vary in terms of whether reported rate values are for cooked fish, uncooked fish, or whether the study is unclear as to which is reported. States and Tribes should check to see if the survey study clearly identifies whether weights represent cooked or uncooked fish.

Many of the differences in survey methods are highlighted in the text and accompanying Tables 2.3.8 and 2.3.10. However, States should consult the individual surveys to obtain the most complete descriptions of the study and resulting consumption rates. USEPA's Guidance for Conducting Fish and Wildlife Consumption Surveys ( USEPA, 1997b) includes detailed descriptions of the various methods for conducting surveys, including their strengths and limitations.

Sportfishers As noted above, sportfishers differ with respect to their catch and consumption habits. Surveys of the general sportfishing population may include those who primarily fish for recreational purposes or eat fish for a small portion of the year but may also include some individuals who eat fish as a main staple in their diets. Results of sportfisher surveys are described in the following paragraphs, and are included in Tables 2.3.7 and 2.3.8.

Alabama Fishers. The Alabama Department of Environmental Management ( 1993) conducted a survey from August 1992 to July 1993 on site at various fishing locations. In this survey, 1,586 individuals were interviewed and asked about the number of fish that were caught and kept for consumption. Demographic information on age, gender, income, and region was also collected. Two survey methods, which differed in determining meal size of the fish catch that was to be eaten, were used to estimate consumption rates. Mean and 95th percentile consumption rates 92 for the harvest method were 45.8 and 50.7 grams/ day, respectively, and the rates for the serving method were 43.1 and 50.9 grams/ day, respectively. Both were averaged over a year. Although consumption rates were not found to vary across major ethnic groups, some specific subpopulations had higher than mean consumption as a function of age and income. Black anglers with incomes less than $ 15,000 ate a mean of 63 grams/ day, and anglers over 50 years old consumed a mean of 76 grams/ day of sport caught fish ( Alabama DEM, 1993).

California Fishers. The Santa Monica Bay Restoration Project contracted with the Southern California Coastal Water Research Project and MBC Applied Environmental Sciences to conduct a seafood consumption study from September 1991 to August 1992 ( SCCWRP and MBC, 1994). The purpose of the study was to characterize recreational anglers fishing in the Santa Monica Bay, including identifying ethnic subgroups of the population with the highest consumption. Information on household income was also evaluated. The survey form included a census and a questionnaire. Twenty nine sites were surveyed on 99 days of sampling, with 2,376 anglers included in the census and over 1,200 interviews ( 71%). Of these, 555 anglers ( 45%) provided enough information to be used to derive consumption rates. The overall median and mean consumption rates were 21.4 and 49.6 grams/ day, respectively, with the highest median and mean consumption rates ( 85.7 and 137.3 grams/ day) in the Other category ( i. e., primarily Pacific Island origin). Among ethnic groups, the 90th percentile rates ranged from 64.3 to 173.6 grams/ day, with the Hispanic category having the lowest and the Other category having the highest rates. With respect to income, the study showed that the lowest income group (<$ 5,000/ year) had the highest median consumption rate ( 32.1 grams/ day) but the highest income group (>$ 50,000/ year) had the highest mean consumption rate ( 58.9 grams/ day) and the highest 90th percentile ( 128.6 grams/ day). However, it should be noted that two thirds of the survey population was comprised of higher income anglers.

Louisiana Fishers. The Louisiana Department of Environmental Quality conducted a seafood consumption survey in 1993 ( Dellenbarger, et al., 1993). A telephone survey was conducted of 1,100 households in Houma, LA, a coastal community. Households sampled were stratified by ethnic characteristics; however, the households were otherwise randomly selected. Other high end consumers were individuals over 50 years old, who consumed a mean value of 40 grams/ day. Rates for all types of fish consumed were 65 grams/ day, comprised of 17 grams/ day of fresh water fish, 15 grams/ day of saltwater fish, and 33 grams/ day of shellfish. These rates include both sport caught and commercial fish and are averaged for only those people who ate fish and seafood.

New York Fishers. Based on a survey of 4,530 anglers, the New York State Department of Environmental Conservation ( Connelly, et al., 1990) estimated that consumption of fish ( all types) by New York State anglers averaged about 45.2 meals per year, or 28.1 grams per day ( assuming 227 grams per meal). Averages are also listed by age of the angler, income group, and the area within New York State where the angler lives. The highest average was recorded for the two Long Island counties of Nassau and Suffolk, whose populations consumed a combined mean value of 37 grams/ day of fish. Other high end consumers were individuals over 50 years old, who consumed a mean value of 40 grams/ day. 93 Table 2.3.7: Sportfishersa Fish Intake Data Fisher Groupb Fish Intake Rates ( g/ day) Fish Typec mean median 80% ile 90% ile 95% ile Alabama fishers1 45.8 50.7 F+ S R+ C Louisiana ( coastal) fishers2 65 F+ S R+ C New York fishers3 28.1 F R New York ( Hudson River) fishers4 23 ( typical)

Michigan fishers5 14.5 30 62 80 F+ S R Michigan fishers6 18.3 50 ( approx.) 70 ( approx.) F+ S R+ C Michigan fishers7 44.7 F R Wisconsin fishers ( 10 counties) 8 12.3 37.3 F R Wisconsin fishers ( 10 counties) 8 26.1 63.4 F R+ C Ontario fishers9 22.5 F R Ontario fishers10 31 ( average)

Los Angeles Harbor fishers11 37 120.8 225 338.8 Washington State ( Commencement Bay) fishers12 23 54 S R Washington State ( Columbia River) fishers13 7.7 F+ S R+ C Maine fishers ( inland waters) 14 6.4 2.0 13 26 Washington State ( Columbia River) fishers15 1.8 NOTES: aSportfishers may include individuals who eat fish as a large portion of their diets bFisher groups refer to the same headings as those that appear in the text cFish Type: F= Freshwater, S = Saltwater ( may indicate either estuarine or marine waters), R = Recreationally caught SOURCES: 1AL Dept. Env. Mgt., 1993 2Dellenbarger, et al., 1993 3Connelley, et al., 1990 4Barclay, 1993 5West, et al., 1993 6West, et al., 1989 7Humphrey, 1976 8Fiore, et al., 1989 9Cox, Vaillancourt, and Hayton, 1993 10Sonstegard, 1985 11Puffer, et al., 1982 12Pierce, et al., 1981 13Honstead, et al., 1971 14Ebert et al., 1993 15Soldat, 1970 94 Table 2.3.8: Sportfishersa Survey Methods Fisher Groupb Methods ( See Key) c Number Surveyed Contact Method Instrument Reporting Method Catch vs.

Consumption Individual vs. Household Data Available Duration Alabama fishers1 1,586 on site int log catch individual age, eth, inc, reg, sex 12 mos Louisiana ( coastal) fishers2 1,100 randomd tele recall consumption household age, edu, eth, inc, oth 1 mos New York fishers3 4,530 license mail/ follow up by tele recall catch individual age, inc, reg 12 mos New York ( Hudson River) fishers4 336 on site int recall consumption Michigan fishers5 2,684 license mail recall consumption household age, edu, eth, inc, reg, sex 12 mos Michigan fishers6 1,104 license mail recall consumption household age, edu, eth, inc, reg, sex 6 mos Michigan fishers7 182 license log catch individual 24 mos Wisconsin fishers ( 10 counties) 8 801 license mail recall consumption individual age, edu, eth, reg, sex Wisconsin fishers ( 10 counties) 8 801 license mail recall consumption individual age, edu, eth, reg, sex Ontario fishers9 494 license mail recall consumption individual age, reg, sex summer, fall Ontario fishers10 Los Angeles Harbor fishers11 1,059 on site int recall catch individual age, eth 12 mos Washington State ( Commencement Bay) fishers12 508 license int/ follow up by tele recall catch individual summer, fall Table 2.3.8: Sportfishersa Survey Methods Fisher Groupb Methods ( See Key) c Number Surveyed Contact Method Instrument Reporting Method Catch vs.

Consumption Individual vs. Household Data Available Duration 95 Washington State ( Columbia River) fishers13 10,900 license int recall consumption household 12 mos Maine fishers ( inland waters) 14, e Washington State ( Columbia River) fishers15, e KEY: Contact Method: Census/ Random/ Fish Licenses/ On Site/ Tribal Members Instrument: Personal Interview/ Mail Survey/ Telephone Survey Log/ Recall: Respondents recorded consumption information in a log or recalled consumption information during interview Catch/ Consumption: Catch: Original data from catch rates extrapolated to consumption rates Consumption: Data obtained on consumption patterns Individual/ Household: Consumption information obtained either for individuals or for households Data Available: Study may have data on: Age/ Education/ Ethnicity/ Income/ Region/ Sex/ Other NOTES: aSportfishers may include some individuals who eat fish as a large portion of their diets. bFisher groups refer to the same headings as those that appear in the text. c Blank cells indicate information is not available. dA " stratified random" approach was used to obtain information with adequate representation of the population of interest. eData available only from draft documents. Consequently, detailed information was not available at the time of publications. Additional information will be provided in future revisions of this document.

SOURCES: 1AL Dept Env Mgt, 1993 2Dellenbarger, et al., 1993 3Connelley, et al., 1990 4Barclay, 1993 5West, et al., 1993 6West, et al., 1989 7Humphrey, 1976 8Fiore, et al., 1989 9Cox, Vaillancourt, and Hayton, 1993 10Sonstegard, 1985 11Puffer, et al., 1982 12Pierce, et al., 1981 13Honstead, et al, 1971 14Ebert et al., 1993 15Soldat, 1970 96 Barclay ( 1993) conducted direct interviews with 336 shore based anglers on the Hudson River at sites including the upper Hudson, mid Hudson, and lower Hudson sites, at both urban and rural sites. These surveys were conducted between June and November of 1991 and April and July of 1992. Because the survey did not reach anglers in boats or all river areas, the authors of the survey note that the results cannot be directly extrapolated to the entire population of Hudson River anglers. Over 58 percent of the individuals eat their catch. The survey reports that the average frequency of fish consumption reported was 3 meals over the previous month, but did not ask respondents about the size of their fish meals. Assuming 227 grams ( 8 ounces) of fish would be eaten per meal and assuming 4.3 weeks per month, the results translate to an average fish consumption rate of 23 grams/ day.

Michigan Fishers. West et al. ( 1993) completed a survey of Michigan fishers over a one year period. For this survey, 2,684 individuals who purchased fishing licenses responded to mailed surveys. Consumption of commercial and sport caught fish was estimated through a 7 day recall, and data were separated demographically by age, education, ethnicity, income, region and gender. Mean consumption was estimated to be 14.5 grams/ day. The 80th percentile was 30 grams/ day, 90th percentile consumption rate was 62 grams/ day and the 95th percentile rate was 80 grams/ day. Several specific subpopulations surveyed in this study had higher than average consumption rates. Minority fishers ( primarily black and non reservation Native Americans) with annual incomes less than $ 25,000 averaged the highest consumption rate of all Michigan angler groups surveyed, consuming a mean of 43.1 grams/ day of sport caught fish.

An older survey by West et al. ( 1989) evaluated Michigan fishers as part of a revision of exposure pathways for the Michigan Toxic Substance Control Commission. This earlier study was only conducted over a six month period, but its results were corroborated by the more recent survey data. The population studied was sport anglers, and consumption of both self caught and commercial fish was considered. The survey relied on seven day recall in order to estimate mean fish consumption; the percentage of respondents consuming no fish was high ( 56.6 percent). The study concluded that mean fish consumption for Michigan sport anglers and their families is 16.1 grams/ day, after adjustment for non response bias. The 90th percentile consumption is approximately 50 grams/ day, the 95th percentile is about 70 grams/ day, and the maximum reported fish consumption is over 200 grams/ day.

The Michigan Department of Natural Resources conducted a survey of 381,000 sport fishers in 1974 ( Humphrey, 1976, as cited in Rupp et al., 1979). This survey obtained mean catch of 36 lbs. of fish per year ( 44.7 grams per day) for consumption.

Wisconsin Fishers. In a survey of anglers in Wisconsin, the annual mean number of sportcaught meals was 18. Using the assumed fish meal size of 8 ounces ( 227 grams) from this survey, the estimated mean daily consumption of sport caught fish in Wisconsin is about 11 grams/ day. When respondents who consumed no sport caught fish were excluded, the mean daily sport caught fish intake was 12 grams/ day ( Fiore et al., 1989). The 95th percentile value, counting only those individuals who consumed any sport caught fish, was determined to be 37 grams/ day. 97 Ontario Fishers. Another study was completed by Ontario sports fishers in 1992 ( Cox, Vaillancourt, and Hayton, 1993). Questionnaires were inserted randomly into 10,000 copies of 1992 Guide to Eating Ontario Sports Fish, and 494 replies were received. Questions regarding fish preferences and catch rate, consumption rate and portion sizes, and use of consumption advisories were asked. A mean daily consumption of 22.5 grams/ day was calculated based on estimated average meal size and frequency of eating sport caught sportfish. Anecdotal evidence provided by one researcher studying the Ontario sportfishers during an earlier survey from 1985 ( Sonstegard, 1985 as cited in Kleiman, 1985) found that an average sportfisher consumed a mean of 31 grams/ day, while a high end consumer ate 62 grams/ day ( the percentile value was not specified). The maximum amount consumed was over 310 grams/ day.

Idaho Fishers. One study was conducted in the Lake Coeur d'Alene region in Idaho ( West, 1993; Richter and Rondinelli, 1989). 933 individuals were surveyed, including Native Americans living both on and off reservations, individuals selected randomly from individuals with fishing licenses in Idaho, and volunteers who were recruited for the study. Tribal members were surveyed in person, while others were surveyed primarily by telephone. All respondents were asked to recall fish consumption patterns. This study was conducted over a period of three months, so data must be extrapolated to the rest of the year. Consumption rates for the licensed fisher population ranged from 16 to 27 grams/ day.

Los Angeles Harbor Fishers. From January to December of 1980, 1059 interviews with sportfishers were conducted in several fishing areas of the Los Angeles Harbor area ( Puffer et al., 1982). No fisher was sampled more than once. Data was collected on the following: amount of fish caught on the day of the interview, the primary use of the fish ( whether eaten by the fisher's family, given away, thrown back, etc.), frequency of fishing, and other variables. Based on this data and assuming that only an edible portion ( 1/ 4 to 1/ 2) of the caught fish would be eaten, median and 90th percentile consumption rates of 37 grams per day and 225 grams per day were determined. The 95th percentile was 338.8 grams/ day. Consumption rates were also estimated by age, race, and species caught. This study indicates that median consumption rates for Orientals/ Samoans are 71 grams/ day and 113 grams/ day for individuals over 65 years old.

Washington State Fishers. Interviews were conducted with fishers in Commencement Bay, Washington from July to late November; 304 interviews were conducted in summer and 204 were conducted in the fall ( Pierce, 1981). Data were collected on size and amount of specific species caught, size of the fishers' families, frequency of fishing, and planned use of the fish. The fishers were later called about whether the fish had been eaten. USEPA ( 1989a) used these data to estimate a median consumption rate value of 23 grams per day and a 90th percentile of 54 grams per day ( also reported in USEPA, 1997a). The authors note that although a survey of night/ dawn fishing was conducted only once, fish caught at this time could represent a significant part of the total fish caught from the bay. Therefore, these values may underestimate fish consumption.

Honstead et al. ( 1971, as cited in Rupp et al., 1979) conducted a study of the consumption patterns of sportfishers on the Columbia River in the Tri City area of Hanford, Washington. This 98 survey monitored 10,900 persons, each of which were members of households where a Columbia River angler resides. The surveyors required respondents to recall the number of fish meals consumed over a 12 month period and, using an estimate of 200 grams per meal, calculated the mean annual consumption to be 2.8 kg per year ( 7.7 grams/ day).