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«February 2014 Final United States Environmental Protection Agency Office of Solid Waste and Emergency Response Office of Resource Conservation and ...»

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quality to rely upon in the current evaluation. This evaluation identified:

- One existing evaluation was found to be relevant to releases from FGD gypsum wallboard. Based on this evaluation, the evaluation did not retain releases to dust, ground water, and surface water for further consideration.

- None of the existing evaluations identified provided a sufficient rationale to eliminate COPCs from the evaluation of releases to air. The constituent associated with the FGD gypsum wallboard that may volatilize under standard environmental conditions is mercury. Therefore, mercury was retained as a COPC for this release.

- Several existing evaluations were found to be relevant to radionuclides in FGD gypsum wallboard.

The cumulative body of evidence provided by these evaluations was considered sufficient to eliminate radionuclides from further consideration.

Step 2 (Comparison of Available Data): To the extent practicable, the evaluation aggregated all of the data identified in Step 1 to allow a comparison of the releases of the one COPC, mercury, from FGD gypsum wallboard and mined gypsum wallboard. The type of comparison depended on the amount of data available. If a given COPC demonstrated the potential to be released at a higher rate from FGD gypsum wallboard than from mined gypsum wallboard, the evaluation retained that COPC for further consideration.

- A direct comparison of the range of measured mercury emanation rates was conducted for releases to air. The available data indicated the potential for mercury to be released at higher rates from FGD gypsum wallboard than from mined gypsum wallboard. Therefore, this COPC was carried forward to the next step of the evaluation.

Step 3 (Exposure Review): The evaluation reviewed releases of COPCs carried forward from Step 2 to identify any exposures that may occur. Where multiple exposure scenarios were identified, the evaluation retained the ones likely to result in the highest chronic exposures for further consideration.

- Inhalation of mercury vapor in indoor air was identified as a potentially complete exposure pathway. The evaluation identified residential receptors as HEIs in this scenario. Ecological 5-4 receptors were assumed to have negligible contact with indoor air and were not retained as potential receptors.

Step 4 (Screening Assessment): The current evaluation conducted a conservative screening assessment for the exposure scenario identified in Step 3 of the evaluation. It used conservative environmental, fate and transport, and exposure data to estimate COPC concentrations at the point of exposure. The evaluation then compared these concentrations to relevant regulatory and health-based screening benchmarks to determine if more in-depth modeling was warranted.

- The evaluation probabilistically calculated a 90th percentile indoor air mercury concentration based on a conservative scenario. This concentration was compared to the relevant screening benchmark and found to be lower. Therefore, this evaluation did not retain this exposure pathway for further consideration.

Conclusion: The evaluation eliminated mercury, the one COPC associated with the exposure scenario, by the end of Step 4. Based on these results, no further evaluation of the releases of this COPC from FGD gypsum wallboard was warranted and the evaluation did not proceed to Step 5.

5.2 Sources of Uncertainty Uncertainty results from gaps in the knowledge of the system under evaluation. Uncertainty exists to some degree in any quantitative evaluation, and may bias the calculated results higher or lower than the true value. It is important to understand both the direction and magnitude of uncertainties present in an evaluation. The direction of uncertainty is the tendency for that uncertainty to push a predicted value higher or lower than the true value, while the magnitude of uncertainty is the extent to which that uncertainty may push a predicted value away from the true value. Characterizing these uncertainties helps to ensure that the overall conclusions of the evaluation would not change with the consideration of

additional information. There are three primary types of uncertainty:

- Data variability and heterogeneity introduce uncertainty when the exact range and distribution of relevant characteristics for constituents, environmental media, or receptors are not known.

Variability and heterogeneity are a natural part of environmental systems that cannot be eliminated by further study. However, collection of additional data that better define these ranges and distributions can minimize the associated uncertainties.

- Models introduce uncertainty through the simplifying assumptions used to approximate real-world conditions, processes, and relationships. These assumptions are sometimes necessary to solve complex mathematical equations or to fill gaps in available knowledge. However, the simplification of complex systems may misrepresent real world conditions to an unknown degree.

Uncertainty can be minimized through use of the most appropriate model and by replacing any default assumptions with representative data.

- Limitations on the current state of the science may introduce uncertainty through the lack of scientific consensus or fundamental lack of knowledge of the system under evaluation. This can be the most difficult type of uncertainty to address. Neither the collection nor analysis of additional data is likely to reduce this uncertainty within the timeframe that a decision is needed.

5-5 Uncertainties in this evaluation were managed to the extent practicable by focusing the evaluation on high-end releases and exposures. In instances where the exact range of a particular variable was unknown, the evaluation relied on a conservative bounding estimate known to fall above or below the true range, as appropriate. This approach does not necessarily reduce the magnitude of uncertainties present, but does shift them in a direction that allows defensible conclusions to be drawn from the evaluation. The following subsections identify, where known, uncertainties specific to the current evaluation and the direction and magnitude of these uncertainties, as well as the potential impacts these uncertainties may have on the conclusions of the current evaluation.

5.2.1 Uncertainties for Dust Exposures The uncertainties discussed in this section pertain to releases of dust from fly ash concrete during use by the consumer, and the resulting receptor exposures.

Available Data Of the fly ashes generated through coal combustion, only a subset is suitable for beneficial use in concrete. There are requirements for silica content, loss of ignition, and other characteristics that must be met before a fly ash is considered appropriate for use (ASTM Standard C618). This type of detailed information is not available for the majority of fly ash samples. Therefore, the current evaluation considered all available fly ash data. It is unknown what portion of this dataset reflects these beneficial use specifications. However, through the use of high-end concentrations from a data set that is representative of the full range of fly ashes generated across the United States, this evaluation ensures that it also captures the subset of suitable fly ashes for beneficial use. Therefore, the fly ash dataset used in this evaluation may overestimate COPC releases. However, the magnitude of this overestimation is unknown.

Treatment of Non-detect Data Non-detect data are concentrations that are present at levels below the capacity of an analytical instrument to differentiate from background noise. The presence of non-detects in any dataset introduces some amount of uncertainty because the concentration of a COPC is not known with certainty. The quantity of non-detects present in the available fly ash data varies by COPC. To calculate 90th percentile concentrations for comparison with screening benchmarks, the current evaluation replaced non-detect values with half of the reported detection limit according to the recommendations in Risk Assessment Guidance for Superfund (RAGS) Part A (US EPA, 1989) and EPA Region 3 Guidance on Handling Chemical Concentration Data near the Detection Limit in Risk Assessments (US EPA, 1991). Because the evaluation relied on a value halfway between zero and the detection limit, the true value is equally likely to be higher or lower than the value assigned. To ascertain the impact this approach had on nondetect values, the evaluation compared the calculated 90th percentile concentrations when non-detect values are set to zero, half the detection limit, and the detection limit. Table 5-1 presents the results of this analysis.

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In most cases, there is no difference between the various 90th percentile values, regardless of the method used to address non-detect values. None of the differences identified were great enough to alter the results of the evaluation. This is because the upper percentile exposures are predominately associated with higher, detectable concentrations in the distribution rather than the lower concentrations associated with non-detect values. This comparison shows that, this uncertainty is unlikely to impact the results of the evaluation.

Constituents Not Evaluated The evaluation selected toxicity values for each constituent identified as a COPC according to the selection hierarchy detailed in the Office of Solid Waste and Emergency Response 2003 Directive 9285.7-53 (US EPA, 2003b). However, several constituents lack both human health and ecological toxicity values (i.e., calcium, chloride, magnesium, phosphate, potassium, sodium, silicon, sulfate, and sulfur). The absence of toxicity values is not necessarily equivalent to the absence of toxicity. However, in the absence of other compelling information to indicate potential adverse effects from these constituents, the evaluation did not retain these constituents as COPCs for further consideration. The lack of toxicity values for these constituents may result in an underestimation of chronic risk to some 5-7 receptors. However, the magnitude of this underestimation is likely to be small because many of the constituents that do not have toxicity values are also known to be nutrients essential for life.

This evaluation did not address dust exposure for a few of the constituents that US EPA (2010a) identified as potentially present in CCRs (i.e., cyanide, fluoride, nitrate/nitrite) because the available data for these constituents were either from CCRs other than fly ash or fly ash mixed with other CCRs.

US EPA (2010a) eliminated these constituents based on the results of a screening. A review of the data used in that screening found concentrations of these constituents in CCRs to be at least an order of magnitude below relevant screening benchmarks. While there is the potential for somewhat higher concentrations of constituents in pure fly ash, the current evaluation demonstrates that the dilution of fly ash into concrete combined with the dilution of concrete dust into surface soil reduces constituent concentrations present in the original fly ash by at least two orders of magnitude. This reduction, together with the low concentrations reported in US EPA (2010a), make the uncertainty introduced through the exclusion of these constituents small.

Portland Cement Use and Replacement Rates in Concrete This evaluation modeled fly ash concretes as having a fly ash replacement rate between 5 percent and 40 percent of the portland cement used, based on the upper limit specified in current ASTM standards for blended cements (ASTM Standard C595). In addition, this evaluation modeled concretes as containing between 7 percent and 15 percent portland cement by mass, based on typical rates reported by the Portland Cement Association (PCA, No Date a,b). These ranges may not represent the complete range of theoretical concrete mixes. However, the current evaluation focused on the range typically used in practice.

When calculating the potential fly ash contribution to concrete, the evaluation assumed that each fly ash replacement rate and cement use rate is equally likely. Furthermore, the evaluation assumed that the fly ash replacement rate selected was independent of the cement use rate. Weighting all values equally is anticipated to bias calculated results high, because studies report fly ash replacement rates around 15 percent to be more common in practice (US EPA, 2012d). This assumption is considered appropriate in the absence of detailed information on the frequency at which different replacement rates occur in practice. These conservative assumptions are likely to overestimate potential releases. However, the magnitude of this overestimation is unknown.

Comparison to Analogous Products The statistical comparison to analogous products conducted in Step 2 (Comparison of Available Data) indicated that concentrations of manganese and silver in fly ash are either less than or equal to those in portland cement. The evaluation considered the possibility that the statistical tests may be unduly influenced by a small number of extreme values present in the datasets. However, removal of the high-end values from either the fly ash or portland cement datasets did not change the results of the statistical tests. The evaluation also considered whether the data available from Eckert and Guo (1998) may overestimate COPC concentrations in portland cement because the data are from kilns co-fired with hazardous waste-derived fuels. While these data represent actual cements generated in the United States, it is unknown if the manganese concentrations measured in this relatively small number of samples are 5-8 any higher than the national distribution of portland cement. However, while there is some uncertainty associated with these data, the impact on the conclusions of the evaluation is negligible. Even if manganese had been retained through Step 2 (Comparison of Available Data), this COPC would have been screened out by a wide margin in Step 4 (Screening Assessment), when the 90th percentile exposure concentration of 1.5 mg/kg was compared to an HBN of 2,794 mg/kg and an Eco-SSL of 220 mg/kg (US EPA, 2007b). Silver concentrations were not reported in Eckert and Guo (1998).

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