Exposure and Risk Modeling Variables

Exposure parameters and benchmarks for human and ecological risk also contribute to parameter variability and uncertainty.

Human Exposure Factors. Individual physical characteristics, activities, and behavior are quite different, and thus the exposure factors that influence the exposure of an individual, including ingestion rate, body weight, and exposure duration, are quite variable. Exposure

modeling relies heavily on default assumptions concerning population activity patterns, mobility, dietary habits, body weights, and other factors. The probabilistic assessment for the adult and child exposure scenario addressed the possible variability in the exposure modeling by using statistical distributions for these variables for each receptor in the assessment: adult and child resident and adult and child recreational fisher. Data on fish consumption rates were not available for children of recreational anglers; thus the adult recreational angler data were used for children in this analysis, which could overestimate risk from this pathway for children. For all exposure factors varied, a single exposure factor distribution was used for adults for both males and females. For child exposures, one age (age 1) was used to represent the age at the start of exposure, because this age group was considered to be most sensitive for most health effects.

The Exposure Factors Handbook (U.S. EPA, 1997c,d,e) provides the current state of the science concerning exposure assumptions and represents EPA’s current guidance on exposure data, and it was used throughout this assessment to establish statistical distributions of values for each exposure parameter for each receptor. The Exposure Factors Handbook has been carefully reviewed and evaluated for quality. EPA’s evaluation criteria included peer review,

validity of the approach, representativeness of the population, characterization of the variability, lack of bias in study design, and measurement error. There are some uncertainties, however, in the data that were used.

Site-specific fish consumption rate data were not available, but the Maine study data, where anglers fished from streams, rivers, and ponds, were consistent with the modeling scenarios used in this risk analysis and provided the detailed percentile data required for a probabilistic analysis. However, applying Maine angler consumption rates to other parts of the country may under- or overestimate exposures.

EPA’s child-specific exposure guidance has been recently finalized (U.S. EPA, 2008b) but was not used in the risk assessment because the water consumption rates and body weights provided in the Child-Specific Exposure Factors Handbook (U.S. EPA, 2008b) do not differ significantly from those found in the 1997 Exposure Factors Handbook and would not have changed the results, but the use of the 1997 values may contribute some parameter uncertainty. One exception is the distribution of child fish consumption rates used. Here, U.S. EPA (2008b) consumption rates are higher than the 1997 rates used in the analysis. This introduces uncertainty into the analysis, and likely underestimates risks in the fish consumption pathway.

As is customary for EPA’s RCRA risk assessments, human exposure factor data were not correlated (i.e., for each modeling run, each exposure factor was selected from its distribution independently), introducing some uncertainty because it is possible to select, for example, a high drinking water rate with a small body weight. However, although a specific modeling run may have had an unrealistic combination of exposure factors, the large number of Monte Carlo iterations performed (10,000) ensures that this is unlikely to significantly affect the risk assessment results.

Diet Assumptions for Ecological Receptors. National-scale assessments often assume maximum intake of contaminated prey in the diets of primary and secondary consumers (i.e., 100 percent of the diet originates from the contaminated area). Under field conditions, many

receptors are opportunistic feeders with substantial variability in both the type of food items consumed and the geospatial patterns of feeding and foraging. The actual proportion of wildlife receptors’ diets that would be contaminated depends on a number of factors such as the species’ foraging range, quality of food source, season, intra- and interspecies competition. Consequently, the exclusive diet of contaminated food items tends to provide a very high-end estimate of

potential risks.

Human Health Benchmarks. The uncertainties generally associated with human health benchmarks are discussed in detail in EPA’s Guidelines for Carcinogen Risk Assessment (U.S. EPA, 2005), and IRIS (U.S. EPA, 2009a). EPA defines the RfD as “an estimate (with uncertainty spanning perhaps an order of magnitude) of a daily exposure to the human population (including sensitive subgroups) that is likely to be without appreciable risk of deleterious effects during a lifetime” (U.S. EPA, 1994, 2009a). RfDs are based on an assumption of lifetime exposure and may not be appropriate when applied to less-than-lifetime exposure situations (U.S. EPA,

2009a). The CSF is an upper-bound estimate of the human cancer risk per mg of chemical per kg body weight per day. Because exposures were often less than lifetime, some uncertainty was introduced in the noncancer hazard and cancer risk estimates.

EPA routinely accounts for uncertainty in their development of RfDs and other human health benchmarks. Uncertainty and variability in the toxicological and epidemiological data from which RfDs were derived are accounted for by applying uncertainty factors. Some of these uncertainties include those associated with extrapolation from animals to humans, from LOAELs to NOAELs, and from subchronic to chronic data, and to account for sensitive subpopulations. If certain toxicological data are missing from the overall toxicological database (e.g., reproductive data), EPA accounts for this by applying an uncertainty factor.

Table 4-31 presents IRIS uncertainty factors for the RfDs for the CCW constituents that showed HQs greater than 1 in the risk assessment, along with the highest HQ observed and the disposal scenario for which this HQ was observed. IRIS defines uncertainty factors as follows:

“Uncertainty factors (UFs) are one of several, generally 10-fold, default factors used in operationally deriving the RfD from experimental data. The factors are intended to account for (1) variation in susceptibility among the members of the human population (i.e., inter-individual or intraspecies variability); (2)

uncertainty in extrapolating animal data to humans (i.e., interspecies uncertainty); (3) uncertainty in extrapolating from data obtained in a study with less-than- lifetime exposure (i.e., extrapolating from subchronic to chronic exposure); (4) uncertainty in extrapolating from a LOAEL rather than from a NOAEL; and (5) uncertainty associated with extrapolation when the database is incomplete.”12

The constituent-specific uncertainty factors for the CCW constituents in Table 4-31 are discussed further in the source documents (e.g., IRIS) for the individual human health

benchmarks used in the analysis, which are referenced in Appendix G. In general, EPA human health benchmarks are derived using a health-protective approach. These uncertainty factors can be considered when evaluating the constituent-specific risks presented in this document, but only in the context of the above definitions and the information presented in IRIS for each chemical.

The hierarchy of data sources that was implemented for this analysis was based largely on the rigor of review that a benchmark has received. Methodologies evolve over time, with improvements in existing methods and the development of new health benchmark practices (e.g., benchmark dose methodology). As a result, the magnitude of a given benchmark can either increase or decrease, or a given benchmark can appear or disappear in a toxicity benchmark database. An example of the latter situation, disappearance of a toxicity benchmark, occurred during the development of this report. The human health benchmark for thallium was withdrawn from IRIS in late September 2009. The modeling results, including the noncancer human health effects estimates, were retained in this document to reflect the potential for thallium releases from CCW WMUs. EPA has decided to retain these estimates, in light of the National Academy of Sciences’ (NAS’s) 2008 report entitled Science and Decisions: Advancing Risk Assessment (NAS, 2008). In that report’s recommendations, the authors noted that absence of certain information from a risk characterization can result in the missing information being overlooked during the decision making process. Evidence that relatively small quantities of thallium can be

fatal to humans13 leads EPA to conclude that omitting the thallium results from this report might cause thallium’s existence in coal combustion residues to be overlooked during the risk

management decision making, and thus EPA has chosen to retain those modeling results in this report.

Table 4-31. RfD Uncertainty Factors for and Benchmark Confidence for CCW Constituents with HQs Over 1

Constituent RfD (mg/kg-day) Source Uncertainty Factor Benchmark Confidence Highest CCW HQ CCW Scenario for Highest HQ

Antimony 4.0E-04 IRIS 1,000 low 3 GW-DW, FBC wastes,

clay-lined landfills

Boron 2.0E-01 IRIS 66 high 7 GW-DW, Conventional

CCW, unlined SIs

Cadmium 5.0E-04 IRIS 10 high 9 GW-DW, Codisposed

CCW, unlined SIs

Cobalt 3.0E-04 PPRTV 1,000 low 500 GW-DW, Codisposed

CCW, unlined SIs

Molybdenum 5.0E-03 IRIS 30 medium 8 GW-DW, Conventional

CCW, unlined SIs

Selenium 5.0E-03 IRIS 3 high 3 GW-SW, Conventional

CCW, unlined SIs

Thallium 8.0E-05 IRIS 3,000 low 4 GW-DW, FBC wastes,

clay-lined landfills

Most health benchmarks used in this analysis were from IRIS. Human health benchmarks in IRIS have been subjected to rigorous internal and external reviews and represent Agency-wide consensus human health risk information. However, some benchmarks in IRIS are quite dated. Provisional human health benchmarks derived by the Superfund Technical Support Center have been peer reviewed and are used where there is no IRIS value.

Chemical-specific health benchmarks were used for all constituents assessed in the analyses. However, the RfD for fluoride was based on fluorine; the RfDs for mercuric chloride and methyl mercury were used as surrogates for elemental mercury from food, soil, and water ingestion, and fish ingestion, respectively; and the RfD for thallium was based on thallium chloride. The use of these surrogate data is not thought to have introduced any significant uncertainty. Human health benchmarks are not age-specific, and therefore, were applied to both child and adult receptors, thereby introducing some uncertainty.

EPA used the drinking water MCL for lead to estimate risks from drinking water exposure. The IEUBK model may better quantify risk for a young child exposed to lead; therefore, use of the MCL may introduce some uncertainty. However, risks from lead exposure were relatively low, well below the risk criterion for landfills and at or slightly above the risk criterion for surface impoundments, and did not drive the risk assessment conclusions.

13 _{“Temporary hair loss, vomiting, and diarrhea can also occur and death may result after exposure to large amounts} of thallium for short periods. Thallium can be fatal from a dose as low as 1 gram.” (ATSDR, 1992)

Ecological Criteria. CSCLs were developed for constituents when sufficient data were available. In many cases, sufficient data were unavailable for a receptor/constituent combination, and therefore, the potential risk to a receptor could not be assessed. In particular, insufficient data were available to derive chronic effects CSCLs for amphibians. Because the risk results can only be interpreted within the context of available data, the absence of data cannot be construed to mean that adverse ecological effects will not occur.

In addition to the effects of data gaps on ecological benchmarks, the ecological criteria tend to be fairly conservative because the overall approach is based on “no effects” or “lowest effects” study data. In site-specific assessments, a de minimis effects approach is often replaced with an effects level similar to natural population variability (e.g., sometimes as high as a 20 percent effects level). As a result, the CSCLs used in this analysis are likely to overestimate risks for representative species and communities assumed to live in surface waters impacted by CCW WMUs. Because the difference between a LOAEL and a NOAEL is often about a factor of 10, an HQ exceedance of roughly 10 may not be ecologically significant. In contrast, CSCLs based on no effects data that are developed for the protection of threatened and endangered species are presumed to be protective.