Cifras Operativas.

Using 90th percentile unitized risk estimates, and EPA’s risk management criteria (e.g., HQ of 1), screening levels were calculated for each constituent. As shown in Section 5.3.11, the calculation of SFS screening levels also allows for the adjustment of levels based on the fraction of SFS in manufactured soil. The resulting soil concentrations represent conservative estimates of SFS constituent concentrations considered protective of human health and the environment.

The remainder of this chapter is organized as follows:

 Section 5.3.1 provides an overview of the risk modeling framework implemented to perform probabilistic modeling.

 Section 5.3.2 describes the exposure scenario, including conservative screening assumptions, developed for application of SFS in home gardens.

39 _{Similar unitized approaches have been applied under previous U.S. EPA risk assessments. For example, the}

unitized approach was applied in the Risk-Based Mass Loading Limits for Solvents in Disposed Wipes and Laundry Sludges Managed in Municipal Landfills. This risk assessment and the unitized approach have been extensively reviewed, and the final rule based on this risk assessment, Solvent-Contaminated Wipes, was published July 31, 2013 (U.S. EPA, 2013a)

40 _{Appendix J describes the analysis that was performed to confirm that the unitized calculation method was}

Risk Assessment of Spent Foundry Sands in Soil-Related Applications 5-8

 Section 5.3.3 describes the receptors (both human and ecological) and the exposure pathways by which receptors could potentially be exposed to SFS constituents.

 Sections 5.3.4through5.3.10 describe the models, inputs, and outputs used in the probabilistic screening of health and ecological risk associated with SFS use in home gardens.

 Section 5.3.11 describes how the human and ecological modeling results were used to calculate SFS-specific screening levels.

 Section 5.3.12 compares the SFS constituent concentrations to the lowest human health- based SFS-specific screening levels, as well as ecological SFS screening levels.

5.3.1 Modeling Framework Overview

Unitized risk distributions were developed for this analysis using a risk modeling

framework currently used by EPA to support the Part 503 biosolids program. The risk modeling framework integrates a variety of models and input datasets facilitating site-based and national- level exposure and risk assessments. The SFS assessment modified and adopted the system to evaluate soil/produce and groundwater ingestion risks associated with the use of SFS in manufactured soils.

Under this assessment, we used a Monte Carlo approach that essentially loops over randomly selected locations within the area of economic feasibility, selecting input parameter values that correspond to each particular location. Within the looping structure, a series of modules are executed in a specific order. The modeling process can be summarized as follows:

 The source models estimate pollutant releases to the environment

 The environmental fate and transport models estimate concentrations in environmental media (e.g., soil, groundwater, ambient air) and in dietary items (e.g., fruits and

vegetables)

 The exposure models estimate the pollutant levels to which receptors are exposed

 The human risk model estimates the chemical-specific human health risk, and the ecological effects model estimates chemical-specific hazard quotients.

The major functionality of the models implemented in this risk analysis is described in

Sections 5.3.4 through 5.3.10.

As illustrated in Figure 5-3, the looping structure is comprised of four nested loops: Chemical; RunID; Human Receptor; and Ecological Receptor. The outmost loop is the chemical loop, which allows a Monte Carlo simulation to be performed on a constituent-specific basis. The next loop is the RunID loop, which controls the number of iterations performed in a given simulation and is used as the primary index to input datasets, including site location. As shown in Figure 5-3, the source, media, and food modules are executed for each Monte Carlo iteration. Outputs from the source model are used as inputs to the downstream groundwater, media and food modules to estimate concentrations that receptors can potentially be exposed to.

Within the Monte Carlo loop, the next loop in the probabilistic analysis cycles through the different types of receptors. The model considers both adult and child receptors and various ecological receptors. The receptor type determines the exposure factors used. Receptor type and exposure factors were not specific to location; as a result, any receptor (human or ecological)

could be present at any location with any applicable exposure parameter values. Receptor- specific exposure factors for humans include exposure duration, the receptor’s age when

exposure begins, dietary consumption rates, and individual body weight. A set of adult and child exposure parameters was chosen for each iteration. Exposure parameters were not correlated with each other or with geographic locations. Ecological exposure parameters included the receptor-specific health benchmarks. More detailed descriptions of human and ecological exposure modeling are found in Sections 5.3.7 and 5.3.8, respectively.

SourceID = Home Garden

Chemical (CAS) Loop

RunID Loop (Monte Carlo iterations)

Call Source Module: calculate emission rates; soil concentrations and losses due to leaching, runoff, and erosion

Call Media Module: calculate groundwater and air concentrations Call Food Module: calculate concentrations for food items Human Receptor Loop (adult, child)

Select pathways and exposure data based on human receptor type For Adult Receptor

Calculate intake over exposure duration For Child Receptor

Cohorts Loop (ages child through age cohorts) Calculate cohort intake

Next Cohort

Calculate intake over exposure duration

Call Human Risk Module: calculate risk based on human health benchmarks Next Human Receptor

Ecological Receptor Loop

Select pathways and ecological exposure data based on ecological receptor type Call Ecological Exposure Module and calculate ratios of media concentrations to

ecological concentration benchmarks Next Ecological Receptor

Next RunID Next Chemical

Figure 5-3. Basic Monte Carlo looping structure for the home garden.

The Monte Carlo simulation represents a set of individual model realizations, with each realization defined in terms of a unique set of values for the input parameters required by the model. The approach is implemented by creating input files prior to the assessment that include data that are randomly selected based on the regional setting and scenario selected for each iteration. Chemical-specific data are generally constant across all iterations and are not correlated with other input parameters. The SFS-manufactured soil concentration was also held constant under this assessment to allow the calculation of the unitized risk estimates. The input of the fixed initial soil concentration of 1 ppm wet weight (i.e., unit concentration) into this linear system allowed for the development of unitized risk estimates that reflect national variability. The unitized approach was ideal for the SFS analysis since it provided the flexibility to generate

Risk Assessment of Spent Foundry Sands in Soil-Related Applications 5-10 distributions of unitized risk estimates that could be scaled to calculate screening concentrations using a variety of recipes for SFS-manufactured soils.

Under the SFS analysis, 7,500 Monte Carlos iterations were executed. To ensure the stability of the model results and determine the appropriate number of Monte Carlo simulations, the model was run for 4 different sets of iterations: 1,000; 3,000; 5,000; and 7,500 iterations. Tolerance criteria were established at 5%; that is, the model would be considered to be stable if the mean, variance, and the 50th and 90th percentile results did not change by more than 5%. Based on previous experience, the model was expected to converge in less than 5,000 iterations. The results of the stability test are shown in Figure 5-4. The table shown in the figure presents the absolute percent changes between samples. As demonstrated by this figure, the model is stable before 5,000 iterations for the mean, variance, and at the 50th and 90th percentiles, and extending the simulation to 10,000 iterations was considered unnecessary.

Figure 5-4. Model stability. 5.3.2 Exposure Scenario—Use of SFS in Home Gardens

The modeled use of SFS in home gardens assumed that a portion of a residential yard is used for home gardening: either the yard itself is tilled or raised beds are constructed. A single application of 20 cm (approximately 8 inches) of SFS-manufactured soil is spread in the residential construction area as topsoil, or a single application of 20 cm of SFS-manufactured soil is used in the construction of raised gardening beds. SFS is generated across the United States; therefore, the evaluation used a regional approach to capture the variability across site conditions. The modeling framework used regional climate and soil data to estimate constituent- specific releases and to predict their fate and transport in the environment. For example, the source model used soil data and daily precipitation data to estimate events such as runoff, erosion, and leaching.

The SFS was assumed to be used within 50 km of the foundry (EPA, 2008c).41 This approach thereby focused the evaluation on climate and soil conditions relevant to where SFSs might reasonably be used as a component of manufactured soil. Figure 5-5 shows the areas included in the assessment.

Figure 5-5. Meteorological regions and SFS use areas.

The scenario consists of the following elements:

 Regional data for 41 climate regions. Climate regions were shaped such that climate data from a single location would represent any location within the region, taking into account geographic boundaries, such as mountains, and other parameters that differentiate

meteorological conditions (e.g., temperature and wind speed) as described in

Appendix D.

 Locations of foundries in the United States, in the form of ZIP Code boundaries extended 50 km.

 Using a geographic information system (GIS), a soil layer was overlaid with the meteorological regions to identify location-specific soil texture and characterize soil parameters as described in Appendix E.

5.3.3 Potential Release Pathways and Receptors

Chapter 3 described the conceptual models that define the sources, releases, exposure