## APPENDIX 5: Technical Support Document for the TRIM

- Based Mu ltipathway Tiered Screening Methodology for RTR

### [This page intentionally left blank.]

## Technical Support Document for the TRIM-Based Multipathway Tiered Screening Methodology for RTR

December 2013

Prepared For:

U.S. Environmental Protection Agency Office of Air Quality Planning and Standards Research Triangle Park, NC 27711

Prepared By:

ICF International 2635 Meridian Parkway Suite 200 Durham, NC 27713[This page intentionally left blank.]TRIM-Based Tiered Screening Methodology for RTR



## CONTENTS

1. Introduction and Background ................................................................................................ 1

2. Tier 1

............................................................................................................................... ...... 2

2.1 Chemicals of Concern ................................................................................................. 2

2.2 Development of Emission Thresholds ......................................................................... 4 2.2.1 Modeling Fate and Transport ........................................................................ 5 2.2.2 Modeling Transfer and Uptake ...................................................................... 6 2.2.3 Estimating Ingestion Exposure ..................................................................... 6 2.2.4 Calculating Lifetime Cancer Risk and Non-Cancer HQs .............................. 7 2.2.5 Determining Threshold Emission Rates ........................................................ 7

3. Tier 2

............................................................................................................................... ...... 7

4. Refined Multipathway Assessment ....................................................................................... 9

5. References .................................................................................................................... ....... 9

Attachment A. Tier 1 Screening Methodology .................................................................. A-1

A.1 Introduction .............................................................................................................. A-7

A.2 Summary of Approach ............................................................................................. A-7 A.2.1 Overview ................................................................................................... A-7 A.2.2 Chemicals of Potential Concern .............................................................. A-10 A.2.3 Conceptual Exposure Scenario .............................................................. A-11 A.2.4 Approach to Development of the Tier 1 Scenario ................................... A-13 A.2.5 Fate and Transport Modeling (TRIM.FaTE) ............................................ A-15 A.2.6 Exposure Modeling and Risk Characterization (MIRC) .......................... A-16 A.2.7 Implementation of Risk-based Emission Scaling Factors for POM and Dioxin Emissions .................................................................... A-17

A.3 Description of Environmental Modeling Scenario .................................................. A-21 A.3.1 Chemical Properties ................................................................................ A-21 A.3.2 Spatial Layout ......................................................................................... A-21 A.3.3 Watershed and Water Body Parameterization ........................................ A-23 A.3.4 Meteorology ............................................................................................ A-25 A.3.5 Aquatic Food Web .................................................................................. A-27 A.3.6 Using TRIM.FaTE Media Concentrations ............................................... A-28

A.4 Description of Exposure and Risk Modeling Scenario .......................................... A-29 A.4.1 Calculating Concentrations in Farm Food Chain Media ......................... A-29 A.4.2 Ingestion Exposure ................................................................................. A-30 A.4.3 Calculating Risk ...................................................................................... A-33 A.4.4 Summary of Tier 1 Assumptions ............................................................. A-34

A.5 Evaluation of Screening Scenario ......................................................................... A-38 A.5.1 Introduction ............................................................................................. A-38 A.5.2 Cadmium Compounds ............................................................................ A-38 A.5.3 Mercury Compounds ............................................................................... A-40 A.5.4 Dioxins A-42 A.5.5 Polycyclic Aromatic Hydrocarbons .......................................................... A-43 A.5.6 Summary ................................................................................................. A-45TRIM-Based Tiered Screening Methodology for RTR



### A.6 References ............................................................................................................ A-46

Addendum 1. TRIM.FaTE Inputs ..................................................................................... 1-1

Addendum 2. Description of Multimedia Ingestion Risk Calculator (MIRC) Used for RTR Exposure and Risk Estimates ..................................................................... 2-1

Addendum 3. Dermal Risk Screening .............................................................................. 3-1

Attachment B. Tier 2 Screening Methodology .................................................................. B-1

B.1 Overview of Approach ............................................................................................. B-5

B.2 Estimation of Adjustment Factors for Selected Site-Specific Parameters.............................................................................................................. B-8 B.2.1 Selection Values for Variables of Interest ................................................. B-9 B.2.2 Estimation of Adjustment Factors ........................................................... B-12

B.3 Preparing National Databases of Lake and Meteorological Data .......................... B-16 B.3.1 Processing Lake Data for Tier 2 Analysis ............................................... B-16 B.3.2 Processing Meteorological Data for Tier 2 Analysis ............................... B-20

B.4 Implementation of Tier 2 Analysis ......................................................................... B-23 B.4.1 Facility List for Tier 2 Screen (Step 3) ..................................................... B-25 B.4.2 Facility/Lake Distance Table (Step 4) ..................................................... B-26 B.4.3 Matching Facilities to Meteorology Data (Step 5) ................................... B-27 B.4.4 Assembling Threshold Adjustment Factors (Step 6) ............................... B-28 B.4.5 Assembling Results (Step 7) ................................................................... B-30

B.5 References ............................................................................................................ B-34

Addendum 1. Summary of TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Analysis .................................................................................................. . 1

Addendum 2. Analysis of Lake Size and Sustainable Fish Population ........................... 2-1TRIM-Based Tiered Screening Methodology for RTR

Overview 1 December 2013 

## 1. Introduction and Background

Section 112 of the Clean Air Act (CAA) directs the U.S. Environmental Protection Agency (EPA) to assess the risk remaining (i.e., residual risk) from emissions of hazardous air pollutants (HAPs) following the implementation of maximum achievable control technology (MACT) standards for emission sources. This risk assessment is a major component of EPA’s Risk and Technology Review (RTR) program. As part of this program, EPA must consider additional emission standards for a source category if the current emission standards—with MACT regulations in place—do not provide an “ample margin of safety” for human health. One aspect of human health that EPA must consider under RTR is the potential for health effects resulting from exposures to persistent and bioaccumulative HAPs (PB-HAPs) via non-inhalation pathways, namely ingestion and dermal exposur e. EPA’s assessment for RTR focuses on specific PB-HAPs that the Office of Air Quality Planning and Standards (OAQPS) has identified as candidates for multipathway risk assessments (selection of the PB-HAPs is discussed in Attachment A, Section A.2.2). These non-inhalation human health risks are considered in combination with estimated inhalation human health risks, potential ecological impacts, and other factors to support decisions about residual risk for RTR source categories. For PB-HAPs, exposures via ingestion are anticipated to be significantly higher than any dermal exposures that might occur as a result of the same emissions (see below and Addendum 3 to Attachment A). Consequently, a methodol ogy has been developed to evaluate ingestion exposure and risk for PB-HAPs efficiently in the context of EPA’s RTR program.

To evaluate ingestion exposures and human health risks for RTR on a source category basis, an iterative approach was developed that enables EPA to confidently screen out PB-HAP emissions unlikely to pose health risks above levels of concern (i.e., a cancer risk of 1 in 1 million or a noncancer hazard of 1.0) and to focus additional resources on sources of greater concern within the category. To estimate exposure and risk, two models are used: the Fate, Transport, and Ecological Exposure module of EPA’s Total Risk Integrated Methodology (TRIM.FaTE) to model the fate and transport of pollutants released to the environment and the Multimedia Ingestion Risk Calculator (MIRC) to estimate transfer and uptake into the food chain and exposure to receptors consuming contaminated food products and soil. This approach is divided into three tiers of increasing refinement, as follows.

 

### Tier 1 of the approach begins by identifying the facility-level emissions of PB-HAPs within a source category and comparing them to risk-based emission thresholds. The risk-based thresholds are derived using the aforementioned models applied for a hypothetical environmental and exposure scenario , assuming ingestion of locally caught fish, locally grown produce and livestock, and local soil. This hypothetical “screening scenario” is intended to represent a situation in which the ingestion exposure is unlikely to be exceeded at any actual facility evaluated through the RTR program. The thresholds for Tier 1 are derived by estimating the emission rate that corresponds to a lifetime cancer risk of 1 in 1 million or a chronic non-cancer hazard quotient (HQ) of 1 for an individual exposed according to the char acteristics associated with the screening scenario. For a facility, if the emission rate of each PB-HAP is less than the Tier 1 threshold emission rate, risks are assumed to be low and no additional multipathway screening is done. If, however, the emission rate of any PB-HAP exceeds the Tier 1 threshold emission rate, the facility can be evaluated further in Tier 2.

 

### In Tier 2, the actual location of the facility emitting PB-HAPs is used to refine some of the assumptions associated with the environmental scenario while maintaining the Tier 1 ingestion exposure scenario assumptions. The environmental scenario assumptions are refined by incorporating binned site-specific meteorological data and and locations of fishable lakes near the facility (see below). The risk-based threshold for each PB-HAP isTRIM-Based Tiered Screening Methodology for RTR

Overview 2 December 2013 

### then adjusted for that facility based on an understanding of how exposure concentrationsestimated for the screening scenario change with meteorology and lake location. PBHAP emissions that do not exceed the adjusted threshold are assumed to pose risks below levels of concern and no additional multipathway assessment for RTR is required. Facilities having emissions that exceed the adjusted thresholds for Tier 2 may require additional analysis.

 

### For facilities emitting PB-HAPs at levels that cannot be ruled out as being above levels of concern based on the screening analyses, a refined, site-specific, multipathway risk assessment can be conducted. Such an assessment would incorporate location- or facility-specific characteristics regarding the environment to which PB-HAPs are emitted, relevant exposure pathways, ingestion ra tes or other exposure factors, and other parameters. A range of exposure scenarios c ould be evaluated as part of a site-specific assessment, resulting in a range of risk estimates.

The key processes and decisions that make up this approach are summarized in Exhibit 1. In the remainder of this overview, each of the tiers in the multipathway assessment approach is described in additional detail. Attachments to this appendix provide a comprehensive record of the characteristics and methods associated with Tier 1 (Attachment A) and Tier 2 (Attachment B). If a site-specific analysis is conducted, a separate report detailing that analysis will be prepared.



## 2. Tier 1

The methods used in Tier 1 are intended to ena ble EPA to evaluate PB-HAP emissions from multiple sources in a particular category quickly and efficiently and to remove from consideration those that are unlikely to pose risks above levels of concern, while also minimizing the possibility of EPA’s failing to identify risks that exceed levels of concern. The hypothetical scenario used to estimate Tier 1 thresholds is designed to be health-protective in estimating exposures and risks; specifically, it is intended to avoid underestimating exposures to PB-HAPS that might be encountered for any location throughout the United States. The scenario also is intended to avoid grossly overestimating risk to the point where no emissions screen out (i.e., overprotective, resulting in too many “false positives”).



### 2.1 Chemicals of Concern

The assessment of risk from multipathway exposures begins with a review of data for sources in a particular category to determine if emissions of any of the following PB-HAPs are reported:

 

### Cadmium compounds,

 

### Chlorinated dibenzodioxins and furans (dioxins),

 

### Mercury compounds, and

 

### Polycyclic organic matter (POM).TRIM-Based Tiered Screening Methodology for RTR

Overview 3 December 2013 

### Exhibit 1. Conceptual Decision Tree for Evaluating Non-Inhalation Exposures for PB-HAPsTRIM-Based Tiered Screening Methodology for RTR

Overview 4 December 2013 

### Based on current emissions, bioaccumulation potentia l, and toxicity considerations, emissions of these four PB-HAPs are expected to pose the majority of the non-inhalation risks to humans from air emissions at sources subject to residual risk provisions of the CAA 1 

### Thus, although EPA has identified nine other PB-HAPs that should be evaluated as part of residual risk assessments, the methods for multipathway assessment described here encompass only these four at this time.

If emissions of any of the four PB-HAPs listed above are reported for a facility, the emission rate for that PB-HAP is compared to the threshold emissions rate derived for that chemical using the hypothetical TRIM-based screening scenario. This threshold is the emission rate that, when input to the models used in evaluating multipathw ay risk for RTR, results in a specified cancer risk or non-cancer HQ threshold level of concern. For the screening scenario, threshold emission rates were calculated for a cancer risk of 1 in 1 million or an HQ of 1.0, depending on the more sensitive health effect of the PB-HAP. However, due to the hypothetical nature of the screening scenario, exceeding the threshold emissions rate by 60 times, for dioxins for example, does not imply a resulting cancer risk of 60 in 1 million. Rather, exceeding the threshold emissions rate by 60 times for dioxin im plies that it is highly unlikely that the actual risk would exceed 60 in 1 million.

Important to note for dioxins and POM is that the screening methodology assesses individual congeners, taking into account differences in both the fate and transport and the toxicity among the various congeners. The details of the methods for assessing dioxins and POM are provided in Attachment A to this appendix.


### 2.2 Development of Emission Thresholds

Generally, the approach used to assess ingestion exposures and resulting risks for RTR has four components (Exhibit 2):

1. Fate and transport modeling of PB-HAPs emitted to air that partition into soil, water, and other environmental media (including fish uptake);

2. Modeling of uptake of PB-HAPs by farm food chain media from soil and air;

3. Estimating ingestion exposures in terms of average daily dose for consumption of farm food items by a hypothetical exposed human; and

4. Calculating lifetime cancer risk estimates or chronic non-cancer HQs for each HAP and corresponding screening threshold emission rates.

The TRIM.FaTE model is used in the first component, and the MIRC model is used to conduct calculations for the other three components. 2 

### To derive the emission thresholds used in Tier 1, these models are used to estimate the emission rate corresponding to a cancer risk of 1 in

1 Potential impacts on human healt h from non-inhalation exposures to lead are evaluated for RTR using the National Ambient Air Quality Standard for lead, which takes into account multipathway risks.

2 EPA’s TRIM methodology was conceived as a comprehe nsive modeling framework for evaluating risks from air toxics. It was designed to address each of the four steps in screening ingestion risk; however, only the fate and transport module currently is available for use. For the RTR screening scenario, the Multimedia Ingestion Risk Calculator (MIRC) was constructed to complete the calcul ations required for estimati ng PB-HAP concentrations in farm food chain media, average daily ingestion doses, and canc er risks and chronic non-cancer HQs. The framework is conceptually identical to the ingestion exposure and ri sk analyses that TRIM intended to cover. Information about the current status of TRIM modules and document ation of modules developed thus far can be accessed on EPA’s Technology Transfer Network (TTN) on the Fate, Exposure, and Risk Analysis website (http://www.epa.gov/ttn/fera/).TRIM-Based Tiered Screening Methodology for RTR

Overview 5 December 2013 

### 1 million or a noncancer HQ of 1 (depending on the PB-HAP) for each PB-HAP within the hypothetical environmental scenario, as described in more detail in the following sections.

Exhibit 2. Overview of Ingestion Exposure and Risk Screening Evaluation Method

2.2.1 Modeling Fate and Transport

To model chemical fate and transport in the env ironment when deriving emission thresholds for Tier 1, the TRIM.FaTE module of the TRIM system was used. 3 

### The two main components of the fate and transport modeling are (1) the modeled domain, including the meteorological data and (2) the environmental and chemical-specific properties associated with fate and transport through the environment. The hypothetical modeled domain includes a farm homestead and a fishable lake near (i.e., 2 km) an emissions source, which are assumed to be the primary food sources for exposed individuals. The spatial la yout and other physical aspects of the modeled domain configuration are designed to be health-protective, which results in an ingestion exposure situation that is unlikely to be exceeded at any actual facility evaluated under the RTR program. The environmental and chemical-specific properties governing fate and transport of PB-HAPs are parameterized with either conservati ve (i.e., health protective) values or centraltendency values. The mix of health protec tive and central-tendency assumptions and parameterization is expected to result in a scenario configuration that, on average, is likely to overpredict environmental concentrations of PB-HAPs in media of interest for this evaluation. The inclusion of central-tendency values where warranted is intended to minimize the number of false positives. (See Attachment A and Addenda 1 and 2 for additional discussion on parameter values and their selection.)

Based on sensitivity analyses and model testing it is generally recognized that the spatial layout of the modeled domain (e.g, distance to a fishable lake) and the meteorological data used (or a combination of these two factors) are more influential than physical/chemical parameters in dictating the resulting chemical concentrations in air, soil, water, sediment, and fish within TRIM.FaTE. The Tier 1 assumptions about these two components of fate and transport modeling are refined with relatively more site-specific data in subsequent tiers. The spatial

3 http://www.epa.gov/ttn/fera/trim_fate.html 

### Chemical Emissions to Air

Cancer Risk Hazard Quotient Chemical fate and transport: Physical environment and aquatic ecosystem Uptake and transfer:

Produce and livestock Human Ingestion Exposure Risk and Hazard EstimationTRIM-Based Tiered Screening Methodology for RTR

Overview 6 December 2013 

### layout used to develop the threshold emission levels in Tier 1 and other details of the Tier 1 methodology are presented in Attachment A to this appendix.

2.2.2 Modeling Transfer and Uptake

MIRC was developed to conduct the required calculations involving farm food chain transfer, ingestion exposure, and risk. TRIM.FaTE outputs that are used as inputs to MIRC include:

 

### PB-HAP concentrations in air,

 

### Air-to-surface deposition rates for PB-HAPs in both particle and vapor phases,

 

### PB-HAP concentrations in fish tissue for fish consumed, and

 

### PB-HAP concentrations in surface soil and root zone soil.

From these inputs, MIRC calculates the transfer and uptake of PB-HAPs through the farm food chain using algorithms based on those included in EPA’s Human Health Risk Assessment Protocol for Hazardous Waste Combustion Facilities (EPA 2005) and biotransfer factors (e.g., soil-to-plant factors, which are the ratios of the concentrations in plants to concentrations in soil). The outputs of MIRC are PB-HAP concentrations in contaminated food items.

2.2.3 Estimating Ingestion Exposure

MIRC is also used to estimate exposure in te rms of average daily doses (ADDs), normalized to body weight for the following exposure pathways:

 

### Incidental ingestion of soil

 

### Ingestion of homegrown produce

 

### Ingestion of homegrown beef

 

### Ingestion of milk from homegrown

cows  

### Ingestion of homegrown poultry/eggs

 

### Ingestion of homegrown pork

 

### Ingestion of fish

 

### Ingestion of breast milk (children <1 year old; dioxins only) 4



### Chemicals are modeled separately to evaluate the potential for risks, with exposures (in terms of ADD) for each PB-HAP summed across all in gestion exposure pathways. For the screening scenario used in Tiers 1 and 2 of this analysis, exposure characteristics were selected that result in a highly health protective estimate of total exposure. The ingestion rate for each exposure pathway listed above was set (as feasible) equal to an upper percentile value (99 th 

### percentile for fish and 90 th 

### percentile for all other food types) based on EPA’s Exposure Factors Handbook (EPA 2011a) or other sources as appropr iate. All media were assumed to be obtained from a location impacted by the modeled source. This approach results in an overestimate of total chemical exposure for a hypothetical exposure scenario. For example, the resulting total food ingestion rate is extremely high for a hypothetical consumer, with ingestion rates at the 99 th 

### percentile for fish and the 90 th 

### percentile for every other farm food type. These health protective exposure assumptions can be replaced in a site-specific assessment as appropriate (e.g., with distributions of the data for key exposure factors).

4 Breast milk ingestion is an important exposure pathway for lipophilic compounds like dioxins and has been shown not to contribute meaningfully for exposures to mercury, cadmium, and POM. See Sect ion A.4.2.3 of Attachment A and Section 3.4 of Addendum 2 for full discussions of infant exposures via breast milk ingestion.TRIM-Based Tiered Screening Methodology for RTR

Overview 7 December 2013 

### Dermal absorption of chemicals that are original ly airborne is generally relatively minor and this pathway was not included in the scenario used to ca lculate Tier 1 emission thresholds (this topic is discussed further in Attachment A and Addendum 3).

2.2.4 Calculating Lifetime Cancer Risk and Non-Cancer HQs

Lifetime cancer risks and the potential for chronic non-cancer effects are estimated using chemical-specific oral cancer slope factors and oral reference doses. Lifetime cancer risk estimates are calculated separately for each PB-HAP. As provided in Section 5.1 of Addendum 2, age-group specific cancer risk estimates are calculated and the estimated lifetime cancer risk equals the sum of these age-group spec ific risks. Similarly, HQs are calculated separately for each PB-HAP and for each age group. However, as detailed in Section 5.2 of Addendum 2, the HQ for the most sensitive age group is used to determine the screening threshold emission rate.

2.2.5 Determining Threshold Emission Rates

Tier 1 emission thresholds were calculated by conducting iterative model simulations in TRIM.FaTE and MIRC using the screening scenario described above to determine emission rates for cadmium, mercury, dioxins, and POM that correspond to a cancer risk of 1 in 1 million or a chronic non-cancer HQ of 1. Given the generally health protective nature of the scenario inputs, these thresholds are assumed to be appr opriate for screening facilities emitting these PB-HAPs.



## 3. Tier 2

The Tier 1 screening approach is, by design, generic and health protective. It was constructed for quick application to a large number of facilities in a source category with the least chance of returning false negatives for risk. Once the initial screen is complete, however, facilities that “fail” for any PB-HAPs can be scrutinized further . Based on screening analyses conducted for RTR to date, many facilities could “fail” the Tier 1 screen for some source categories. However, conducting a full site-specific analysis of all facilities that cannot be screened out in Tier 1 would not be practical.

Site-specific values for some influential vari ables, however, can be determined without intensive effort during the assessment. The use of these site-specific values instead of the genericvalues used in Tier 1 can be used to justify adjusting the screening threshold for a given PBHAP at that facility, potentially eliminating the facility from concern while maintaining a high degree of confidence that risks above levels of concern have not been overlooked. Specifically, for Tier 2, location-specific data on two types of variables are taken into account:

 

### Meteorological characteristics, including the fraction of time the wind blows toward the farm and lake (using wind direction), wind speed, precipitation rate, and mixing height; and

 

### Location of the nearest fishable lake(s) relative to the facility 5 

### (including the absence of a fishable lake).

These variables affect the PB-HAP concentrations in environmental media estimated by TRIM.FaTE, but they are not related to specific exposure assumptions. The exposure

5 The lake size also was changed for each lake distance allowing for a constant ratio between watershed and erosion area compared with lake area within the TRIM modeling structure.TRIM-Based Tiered Screening Methodology for RTR

Overview 8 December 2013 

### assumptions, such as ingestion rate and fraction of diet derived from the lake and farm remain at fixed, health-protective values in Tier 2. In selecting the fate and transport variables to include in Tier 2, a balance was struck betw een the degree of impact on the risk estimate, the ease of implementation in TRIM.FaTE, and the ease of obtaining relatively certain site-specific values for all facilities that might be evaluated under the RTR program.

For efficient Tier 2 evaluation of the impacts these parameters could have on specific facilities, a series of TRIM.FaTE simulations was performed that systematically varied the values used in the screening scenario for four of the five selected variables (lake location, wind speed, precipitation rate, and mixing height). Wind direction affects only whether the chemical mass advects toward the farm and lake, so the effect of site-specific wind directions can be evaluated outside TRIM.FaTE simulations. The values of each of the four variables were changed, independent of any other changes. The values (four to six for each variable, including the original screening scenario values) were selected using statistics on U.S. meteorological data or professional judgment to capture the expected range in the facility data. Four to six values were selected to result in a total number of runs that was reasonable. This set of values was used to develop “bins” for each variable.

Based on the TRIM.FaTE results of these simulations (and the subsequent exposure and risk characterization, conducted using MIRC), threshold adjustment factors were calculated for each unique combination of the five parameters, for each PB-HAP. These adjustment factors represent the ratio between the risk metric (i.e., cancer risk or HQ) obtained using the baseline Tier 1 screening scenario and the risk metric obtained from the adjusted run. For a given facility and PB-HAP, an adjusted Tier 2 emission threshold can be estimated by multiplying the Tier 1 emission threshold by the adjustment factor that best corresponds to the meteorological conditions present at the site and the presence and location of lakes at the site.

To facilitate the implementation of this approach without requiring facility-specific data searches for each new source category evaluated, databases of the relevant U.S. meteorological and lake data were created that could be accessed readily during a Tier 2 evaluation. These databases are described in more detail in Attachment B. The meteorological database is based on the same hourly meteorology data used for RTR inhalation assessments. The meteorology database includes annual summary statistics on wind direction, wind speed, precipitation, and mixing heights for more than 800 surface stations located throughout the United States and is paired with their closest upper-air station with available data (data available from the National Oceanic and Atmospheric Administration). The lake database, based on U.S. Geological Survey (USGS) data and including location and size information, consisted of hundreds of thousands of water bodies classified as “Lake/Pond” or “Reservoir” but not designated for disposal, evaporation, or treatment. To focus on lakes that can support angling of upper trophic level fish, only lakes greater than 100 acres were included. Very large lakes and bays (i.e., those larger than 100,000 acres) are not included because their watersheds are too large and their lake dynamics are too complex to realisti cally model in the TRIM.FaTE system. Lakes and bays larger than 100,000 acres include the Great Lakes, the Great Salt Lake, Lake Okeechobee, Lake Pontchartrain, Lake Champlain, Green Bay, and Galveston Bay. These databases are described in more detail in Attachment B.

When the Tier 2 screening is conducted, three additional processing steps are completed for each facility and PB-HAP that will be analyzed in Tier 2 before looking up the appropriate adjustment factors. First, using GIS software, each relevant lake within a 50-km of the facility is identified and matched to its respective directional “octant” relative to the facility. For the purposes of Tier 2, a “relevant” lake meets t he size and designation criteria discussed in the previous paragraph. Second, the lakes are manually screened to remove lakes whose names indicate uses related to disposal, evaporation, or treatment (sometimes the name indicates oneTRIM-Based Tiered Screening Methodology for RTR

Overview 9 December 2013 

### of these uses while the USGS designations do not). Third, the lakes around the facility that remain after the first two processing steps are screened to include only the closest lake for each octant.

To access these databases, a Microsoft® ExcelTM tool was created that merges the TRIM.FaTE Tier 2 adjustment factors with the lake and meteorology information relevant to a specific facility from the databases. In the tool, each facility is matched with the same meteorology station used in RTR inhalation assessments, and the values for the four relevant meteorological parameters at that station are recorded. The distances from the facility to the nearest lakes estimated using GIS are also imported. These fi ve values become the set of facility-specific parameters. Then, the adjustment factors for each chemical for the combination of these five variables are determined. As described above, the Tier 1 screening emission threshold is then multiplied by the appropriate adjustment factor to obtain an updated, Tier 2 emission threshold for that PB-HAP. More information about Tier 2 assessment methods can be found in Attachment B. 

## 4. Refined Multipathway Assessment

If, based on results of the screening analyses, a risk assessor concludes that there is a reasonable probability that individuals could be adversely affected by the facility emissions, a refined site-specific multipathway analysis might be performed. Examples of recent refined multipathway analyses include residual risk asse ssments of a petroleum refinery facility ( EPA 2013), two secondary lead smelting facilities (EPA 2011b), assessments of two coal-fired electric utility units conducted in support of EPA’s utility rule (EPA 2011c), and a case study evaluation of a Portland cement facility included with other RTR materials presented to the Science Advisory Board for review (Appendix I of EPA 2009).

Whereas a Tier 2 analysis incorporates some binned site-specific and regional information on meteorology and water bodies, a refined multipathway analysis uses detailed site-specific data to parameterize more accurately (to the extent possible) each important parameter that affects pollutant fate and transport. These site-specific properties are incorporated into model scenarios configured in TRIM.FaTE and MIRC. Impor tant site-specific properties likely would include emission release height and plume buoyanc y, hourly meteorology (e.g., wind flow, temperature, mixing height, and precipitation), surface compartments based on watershed and terrain data, local farms and water bodies, land use, soil, erosion, runoff, surface water and sediment, water transfer, and aquatic ecosystems.

The outputs from the site-specific run of TRIM.FaTE (i.e., chemical concentrations in environmental media and fish) are used in MIRC to produce estimates of exposure and health risk (i.e., risk and/or HQ values). Additional analyses of the media concentrations, exposure estimates, and risk estimates for the various ingested media using a range of ingestion rates for each modeled PB-HAP allows the risk assessor to understand, based on TRIM.FaTE and MIRC, the sources and pathways of possible human health risk from emissions of PB-HAPs.



## 5. References

EPA (U.S. Environmental Protection Agency) ( 2005) Human Health Risk Assessment Protocol for Hazardous Waste Combustion Facilities. EPA Office of Solid Waste. EPA-530/R-05-006. 09/2005.

EPA. (2009) Risk and Technology Review (RTR) Risk Assessment Methodologies: For Review by the EPA’s Science Advisory Board with Case Studies – MACT I Petroleum RefiningTRIM-Based Tiered Screening Methodology for RTR

Overview 10 December 2013 

### Sources and Portland Cement Manufacturing. EPA Office of Air Quality Planning and Standards. EPA-452/R-09-006. 06/2009.

EPA. (2011a) Exposure Factors Handbook: 2011 edi tion. National Center for Environmental Assessment, Washington, DC; EPA/600/R-09/052F. Available from the National Technical Information Service, Springfield, VA, and online at http://www.epa.gov/ncea/efh.

EPA. (2011b) Human Health Multipathway Residual Risk Assessment for the Secondary Lead Smelting Source Category. Draft Report. Prepared by ICF International for EPA Risk and Exposure Assessment Group. 02/15/2011.

EPA. (2011c) Technical Support Document: Case Study Analyses of Potential Local-scaleHuman Health Risks Associated with Mercury Emissions from Electric Utility Steamgenerating Units. Draft. Prepared by ICF International for EPA Office of Air Quality Planning and Standards. 02/17/2011.

EPA. (2013) Technical Support Document: Human Health Multipathway Residual Risk Assessment for the Petroleum Refineries Sect or. Draft. Prepared by ICF International for EPA Office of Air Quality Planning and Standards. 04/10/2013.TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-1 December 2013 

## Attachment A. Tier 1 Screening Methodology[This page intentionally left blank.]TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-3 December 2013 

## CONTENTS, ATTACHMENT A

Attachment A. Tier 1 Screening Methodology .................................................................. A-1

A.1 Introduction .................................................................................................................. ..... A-7

A.2 Summary of Approach ...................................................................................................... A-7

A.2.1 Overview ................................................................................................................. A-7

A.2.2 Chemicals of Potential Concern ............................................................................ A-10

A.2.3 Conceptual Exposure Scenario ............................................................................. A-11

A.2.4 Approach to Development of the Tier 1 Scenario ................................................. A-13 A.2.4.1 Modeling Framework .............................................................................. A-13 A.2.4.2 Model Configuration and Parameterization ............................................. A-15

A.2.5 Fate and Transport Modeling (TRIM.FaTE) .......................................................... A-15

A.2.6 Exposure Modeling and Risk Characterization (MIRC) ......................................... A-16

A.2.7 Implementation of Risk-based Emission Scaling Factors for POM and Dioxin Emissions ................................................................................................... A-17 A.2.7.1 Calculation of Scaling Factors for POM Congeners ............................... A-18 A.2.7.2 Calculation of Scaling Factors for Dioxin Congeners .............................. A-20

A.3 Description of Environmental Modeling Scenario ........................................................... A-21

A.3.1 Chemical Properties .............................................................................................. A-21

A.3.2 Spatial Layout ........................................................................................................ A-21

A.3.3 Watershed and Water Body Parameterization ...................................................... A-23 A.3.3.1 Water Balance ........................................................................................ A-23 A.3.3.2 Sediment Balance ................................................................................... A-24

A.3.4 Meteorology ........................................................................................................... A-25

A.3.5 Aquatic Food Web ................................................................................................. A-27

A.3.6 Using TRIM.FaTE Media Concentrations .............................................................. A-28

A.4 Description of Exposure and Risk Modeling Scenario .................................................... A-29

A.4.1 Calculating Concentrations in Farm Food Chain Media ........................................ A-29

A.4.2 Ingestion Exposure ................................................................................................ A-30 A.4.2.1 Exposure Scenarios and Corresponding Inputs ..................................... A-30 A.4.2.2 Calculating Average Daily Doses ............................................................ A-32 A.4.2.3 Infant Ingestion of Breast Milk ................................................................. A-33

A.4.3 Calculating Risk ..................................................................................................... A-33

A.4.4 Summary of Tier 1 Assumptions ........................................................................... A-34

A.5 Evaluation of Screening Scenario ................................................................................... A-38

A.5.1 Introduction ............................................................................................................ A-38

A.5.2 Cadmium Compounds ........................................................................................... A-38 A.5.2.1 Behavior in the Environment ................................................................... A-38TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-4 December 2013 

### A.5.2.2 Concentrations in Ingestible Products .................................................... A-38 A.5.2.3 Average Daily Dose (ADD) ..................................................................... A-39

A.5.3 Mercury Compounds ............................................................................................. A-40 A.5.3.1 Behavior in the Environment ................................................................... A-40 A.5.3.2 Concentrations in Ingestible Products .................................................... A-40 A.5.3.3 Average Daily Dose ................................................................................ A-41

A.5.4 Dioxins ................................................................................................................... A-42 A.5.4.1 Behavior in the Environment ................................................................... A-42 A.5.4.2 Concentrations in Ingestible Products .................................................... A-42 A.5.4.3 Lifetime Average Daily Dose (LADD) ...................................................... A-43

A.5.5 Polycyclic Aromatic Hydrocarbons ........................................................................ A-43 A.5.5.1 Behavior in the Environment ................................................................... A-44 A.5.5.2 Concentrations in Ingestible Products .................................................... A-45 A.5.5.3 Lifetime Average Daily Dose ................................................................... A-45

A.5.6 Summary ............................................................................................................... A-45

A.6 References .................................................................................................................... . A-46

Addendum 1. TRIM.FaTE Inputs ..................................................................................... 1-1

Addendum 2. Description of Multimedia Ingestion Risk Calculator (MIRC) Used for RTR Exposure and Risk Estimates ..................................................................... 2-1

Addendum 3. Dermal Risk Screening .............................................................................. 3-1TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-5 December 2013 

### EXHIBITS, ATTACHMENT A Exhibit_Att A-1. Conceptual Decision Tree for Evaluation of Non-Inhalation Exposures of PB-HAPs ...................................................................................... A-8
Exhibit_Att A-2. Emission Thresholds for Screening of Multipathway Exposures ................... A-10 Exhibit_Att A-3. OAQPS PB-HAP Compounds ...................................................................... A-11 Exhibit_Att A-4. Overview of Ingestion Exposure and Risk Screening Evaluation Method ............................................................................................................. A-14 Exhibit_Att A-5. Overview of Process Carried Out in the Multimedia Ingestion Risk Calculator ................................................................................................. A-17 Exhibit_Att A-6. Exposure, Toxicity, and Risk Equivalency Factors Relative to BaP for POM Congeners Currently Evaluated in Tiers 1 and 2 Analyses ........................................................................................................... A-19
Exhibit_Att A-7. Relationship between Exposure and K ow 

### for POM Congeners ................... A-20 Exhibit_Att A-8. Exposure and Toxicity Equivalency Factors Relative to TCDD for Modeled Dioxin Congeners ......................................................................... A-20 Exhibit_Att A-9. TRIM.FaTE Surface Parcel Layout .............................................................. A-22 Exhibit_Att A-10. Summary of Key Meteorological Inputs ..................................................... A-26 Exhibit_Att A-11. Aquatic Biota Parameters for the TRIM.FaTE Screening Scenario ........................................................................................................... A-28 Exhibit_Att A-12. Spatial Considerations – TRIM.FaTE Results Selected for Calculating Farm Food Chain Media Concentrations and Receptor Exposures ........................................................................................................ A-29 Exhibit_Att A-13. Summary of Ingestion Exposure Pathways and Routes of Uptake .............................................................................................................. A-31 Exhibit_Att A-14. Overview of Exposu re Factors Used for RTR Multipathway Screening a,b 

### ...................................................................................................... A-32 Exhibit_Att A-15. Dose-response Values for PB-HAPs Addressed by the Screening Scenario .......................................................................................... A-34 Exhibit_Att A-16. Summary of RTR Tier 1 Screening Scenario Assumptions ....................... A-34 Exhibit_Att A-17. Estimated Contributions of Modeled Food Types to Cadmium Ingestion Exposures and Hazard Quotients ..................................................... A-39 Exhibit_Att A-18. Estimated Contributions of Modeled Food Types to Methyl Mercury Ingestion Exposures ........................................................................... A-41 Exhibit_Att A-19. Estimated Contributions of Modeled Food Types to Dioxin Ingestion Exposures ......................................................................................... A-44 Exhibit_Att A-20. Estimated Contributions of Modeled Food Types to PAH Ingestion Exposures ......................................................................................... A-46TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-6 December 2013 

### [This page intentionally left blank.]TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-7 December 2013 

## A.1 Introduction
As discussed in the Overview Document, the U.S. Environmental Protection Agency (EPA) will implement a tiered approach to evaluate multi pathway exposures and human health risks for the Risk and Technology Review (RTR) program. EPA’s assessment for RTR focuses on persistent and bioaccumulative hazardous air pollutants (PB-HAPs) that the Office of Air Quality Planning and Standards (OAQPS) has identified as candidates for multipathway risk assessments (selection of the PB-HAPs is discussed in Section A.2.2). In the first tier, a screen is conducted that focuses on the identity and magnitude of emissions of PB-HAPs,from a given facility to determine whether a facility passes certain human health risk-based criteria. Sources that are “screened out” in the Tier 1 analysis are assumed to pose no risks to human health above levels of concern and and are not considered in further analyses. For sources that do not pass the Tier 1 screen, more refined assessments, up to and including site-specific multipathway assessments, can be conducted as appropriate.

This Attachment describes the technical basis for the first, screening-level tier of EPA’s multipathway human health evaluation of PB-HAP emissions from RTR sources. Specifically, the scenarios, models, configurations, and inputs used to derive screening threshold emission rates in the first tier of the approach are described in detail in the following sections.

 

### Section A.2 presents an overview of how screeni ng is conducted in Tier 1, the chemicals and exposure scenario evaluated in Tier 1, and the models and methods used to conduct the screen.

 

### Sections A.3 and A.4 present technical descriptions of the hypothetical environmental setting and the exposure modeling scenario used in Tier 1 as well as the models used in the screen.

 

### Section A.5 provides a brief discussion of the screening threshold emissions for each of the chemicals assessed. References cited in this report are listed in Section A.6.

The Tier 2 screen is discussed separately in Attachment B.


## A.2 Summary of Approach

A.2.1 Overview The Tier 1 approach for evaluating non-inhalation, multipathway exposures to PB-HAPs for RTR is diagrammed in Exhibit_Att A-1. Air toxics emitted by a source under consideration are reviewed to determine, first, whether emissions are reported for any of the four PB-HAPs of concern for non-inhalation pathways. If such emissions are reported, the emission rates are compared to Tier 1 threshold-screening emission rates that have been derived using the TRIMbased Tier 1 scenario described in this document (see Exhibit_Att A-2 for threshold screening emission rates).TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-8 December 2013 

### Exhibit_Att A-1. Conceptual Decision Tree for Evaluation of Non-Inhalation Exposures of PB-HAPsTIER 1 EVALUATIONTRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-9 December 2013 

### The TRIM-based multipathway modeling configuration, referred to in this document as the Tier 1 scenario, forms the technical basis for determining the Tier 1 emission thresholds. The term, Tier 1 scenario, is used to refer collectively to the specific TRIM.FaTE and exposure modeling configuration described herein, including the set of assumptions and input values associated with a hypothetical watershed and the exposure and risk scenarios evaluated for this watershed. The Tier 1 scenario is a static configuration, and its primary purpose is as a modeling tool to calculate the Tier 1 emission rate thresholds for PB-HAPs of concern.

The two potential outcomes of the Tier 1 evaluation are:

 

### Non-inhalation exposures are unlikely to pose a human health problem (i.e., the emissions evaluated “pass” the screen); or

 

### Risks above the levels of concern from non-inhalation exposures cannot be ruled out.

An ideal screening approach strikes a balance between being health-protective —to ensure that risks above levels of concern are identified, and being accurate —to minimize results suggesting that additional analysis is required when in fact the actual risk is low. Typically, gains in accuracy in environmental modeling are accom panied by additional resource requirements. Stated another way, a suitable approach minimizes both false negatives and false positives. False negatives (i.e., results that suggest that the risk is acceptable when in fact the actual risk is high) can lead to inappropriate and non-protective health or environmental policy decisions. False positives (i.e., results that suggest more assessment is required when in fact the actual risk is low) can result in wasted resources by leading to additional, unnecessary analysis. For the evaluation of multipathway human health exposures to PB-HAPs, the methods for screening described in this document are intended to achieve this balance.

Because the Tier 1 evaluation enables EPA to confidently eliminate from consideration those facilities where risks from non-inhalation exposures are projected to be minimal, resources can be targeted toward those facilities that do not pass the screening test. For facilities that do not pass the Tier 1 screening, in additional tiers of analysis, some of the Tier 1 parameters are reassessed, and if appropriate, are changed to more accurately reflect site-specific characteristics. With each successive tier of the assessment, additional Tier 1 assumptions are evaluated and refined to better reflect site-specific characteristics of the facility being modeled.

The Tier 1 screening evaluation for RTR compares reported air emission rates of PB-HAPs (summed by PB-HAP for each facility) to screeni ng threshold emission rates derived using the Tier 1 scenario. A threshold emission rate is the level that, when input to a risk model using emissions as a parameter, corresponds to a specified cancer risk or non-cancer hazard quotient (HQ) that, for the purposes of the evaluation bei ng conducted, is assumed to be below a level of concern. Tier 1 threshold emission rates were calculated for a cancer risk of 1 in 1 million or an HQ of 1.0 and are presented in Exhibit_Att A-2. 6 

### Conceptually, a threshold level for the RTR multipathway screening evaluation could be obtained by back-calculating the emission rate that results in the specified cancer risk or HQ le vel, taking into account the exposure and fate and transport calculations included in the model. Be cause the models used in this assessment are not designed to run “backwards,” the rates instead were derived from regression equations established following a series of TRIM.FaTE and exposure/risk model runs spanning a wide

6 For chemicals known to cause both canc er and chronic non-cancer impacts, and for which acceptable quantitative dose-response values are available for both cancer and non- cancer endpoints, the endpoint that results in the lower threshold emission level is used for screening (i.e., the threshol d will be based on the effect that occurs at the lower exposure level). For the set of PB-HAPs for which screen ing threshold levels have been derived, only chlorinated dibenzo-dioxins and -furans meet both cr iteria. Because the cancer dose-response value is lower than that for non-cancer effects, the screening threshol d value is based on the cancer endpoint.TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-10 December 2013 

### range of emission rates for each chemical. The estimated screening-level emission rates are verified by performing model runs using the estima ted threshold emission rate to confirm that the emission rates result in a cancer risk of 1 in 1 million or an HQ of 1.0.

Exhibit_Att A-2. Emission Thresholds for Screening of Multipathway Exposures

Chemical Screening Threshold Emission Rate (TPY) Basis of Threshold (Type of Health Endpoint)

POM (as benzo(a)pyrene equivalents) a 

### 2.58E-03 Cancer Dioxins (as 2,3,7,8-TCDD equivalents) a 

### 2.81E-09 Cancer

Cadmium 1.18E-02 Non-cancer Mercury (as divalent mercury emissions) 3.16E-04 Non-cancer

TPY = U.S. short tons per year

a See Section A.2.7 for a discussion on t he derivation of equivalent emissions.



### The more probable risk for each emission rate would be lower than the level corresponding to the screening threshold risk quantities in nearly all circumstances, given the health protective and hypothetical nature of the Tier 1 screening configuration. It is considered very unlikely that the estimated risk at a real site would be greater than the estimated risk for the simulated Tier 1 scenario at equivalent emission rates. This is because the Tier 1 scenario assumes, for many of the most risk influential parameters in the model, parameter values that result in high-end risk estimates. In the real world, the probability of such risk-maximizing conditions prevailing across multiple parameters is very low. For example, the Tier 1 scenario assumes a fishable lake approximately 2 km from any given facility, when in reality, a lake may be more than 50 km away. Additional conservative assumptions used in the Tier 1 screen are described in Section A.4.4 of this Attachment.

Tier 1 emission thresholds were developed individually for elemental and divalent mercury. Both were based on the lower of the thresholds associated with multipathway exposures to divalent mercury and methyl mercury. 7 

### Only speciated emissions of divalent mercury are screened because the sum of elemental mercur y emissions across all National Emission Inventory (NEI) facilities is less than the elemental mercury screening threshold level. See Section A.5.3 for a detailed discussion of mercury.



### A.2.2 Chemicals of Potential Concern EPA’s assessment of multipathway human exposures for RTR focuses on PB-HAPs 8 

### that the Office of Air Quality Planning and Standards (OAQPS) has identified as candidates for multipathway risk assessments. OAQPS deve loped a list of 14 chemicals and chemical groups that are PB-HAPs based on a two-step process taking into account the following (EPA 2004a):

7 Note that TRIM.FaTE models the transformation of mercury wi thin the environment; thus, emissions of both divalent and elemental mercury will result in multipathway exposures to elemental mercury, methyl mercury, and divalent mercury.
8 Although POM (polycyclic organic matter) is the name of the HAP listed in t he Clean Air Act, the term “polycyclic aromatic hydrocarbons” or PAHs is used in many cases. Much of the literature regarding toxicity and fate and transport of this chemical group refers to PAHs rather than POM. In addition, the individu al POM species that are of concern with respect to health risk for RTR evaluations are all PAHs (i.e., there are no POM species explicitly evaluated for RTR that do not include an aromatic ring). The terms are used interchangeably throughout this text.TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-11 December 2013  

### their presence on three existing EPA lists of persistent, bioaccumulative, and toxic substances, and

 

### a semiquantitative ranking of toxicity and bioaccumulation potential of the entire list of HAPs.

The list’s development and utility in hazard identif ication for multipathway risk assessment are explained further in Chapter 14 and Appendix D of Volume I of EPA’s Air Toxics Risk Assessment (ATRA) Reference Library (EPA 2004a). Exhibit_Att A-3 presents the 14 chemicals and groups that are PB-HAPs.

Exhibit_Att A-3. OAQPS PB-HAP Compounds

PB-HAP Compound a Addressed by Screening Scenario? Cadmium compounds Yes
Chlordane No
Chlorinated dibenzodioxins and furans Yes DDE (1,1-dichloro-2,2-bis(p-chlorophenyl) ethylene) No Heptachlor No
Hexachlorobenzene No Hexachlorocyclohexane (all isomers) No Lead compounds No Mercury compounds Yes Methoxychlor No Polychlorinated biphenyls No Polycyclic organic matter (POM) Yes Toxaphene No
Trifluralin No

a Source of list: EPA (2004a).



### The screening scenario described in this document is not configured for evaluating the risk potential for all 14 PB-HAPs on the list. Currently, the scenario can only be used to estimate exposures and risks quantitatively for 4 of the 14 PB-HAPs (as indicated in Exhibit_Att A-3). These four PB-HAPs are the focus of the current scenario because, based on current emissions, bioaccumulation potential, and toxicity considerations, they are expected to pose the vast majority of the non-inhalation risks to humans from air emissions at sources subject to residual risk provisions of the Clean Air Act. 9



### A.2.3 Conceptual Exposure Scenario A conceptual exposure scenario was developed that encompasses the specific exposure routes and pathways of interest for the four PB-HAPs that are assessed in the Tier 1 analysis. Exposure routes and pathways describe the movement of air toxics from the point of release to the point where exposure occurs and generally consist of the following elements:

 

### Release to the environment (i.e., emissions);

9 Potential impacts on human healt h from non-inhalation exposures to lead are evaluated for RTR using the National Ambient Air Quality Standard for lead, which takes into acco unt multipathway risks. Non-i nhalation exposures to the other nine PB-HAPs not addressed by the modeling scena rio discussed in this report will be evaluated on an individual facility or source category basis as appropriate.TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-12 December 2013  

### A retention medium, or a transport mechanism and subsequent retention medium in cases involving media transfer of chemicals;

 

### A point of potential human contact with the contaminated medium; and

 

### An exposure route.

An exposure route is the particular means of entry into the body. Receptors are exposed to chemicals emitted to the atmosphere via two primary routes: either directly via inhalation, or indirectly via ingestion or dermal contact with various media that have been contaminated with the emitted PB-HAPs. (Inhalation pathways are assessed separately and are not considered in the Tier 1 assessment presented here.)

PB-HAPs can persist in the environment for long periods of time and also build up in soil and in the food chain, including fish, fruits and vegetables, and animal products (e.g., meat, dairy, eggs). For this reason, ingestion of foods grown within an area impacted by RTR sources can be an important source of exposure to PB-HAPs.

To assess risks from hazardous waste combustion facilities, EPA identified several hypothetical receptor scenarios, noting that these scenarios are considered appropriate for a broad range of situations, rather than to represent any actual scenario. These scenarios are described in EPA’s Human Health Risk Assessment Protocol for Hazardous Waste Combustion Facilities, or HHRAP (EPA 2005a). In this document, EPA recommends assessment of the following hypothetical receptors: a Farmer, Farmer Child, Resident, Resident Child, Fisher, Fisher Child, Acute Receptor, and Nursing Infant. These receptors are distinguished by their pathways of exposures. EPA further notes in HHRAP that some exposure settings might warrant including additional exposure pathways; such as including exposure through fish ingestion for the farmer receptor. For the RTR screening scenario, risks are assessed for a single hypothetical receptor. Based on the guidance provided in HHRAP, a health protective exposure scenario was developed whereby the hypothetical receptor rece ives ingestion exposure via both the farm food chain and the fish ingestion pathways. The exposure scenario for the RTR Tier 1 analysis includes the following ingestion pathways:

 

### Incidental ingestion of soil,

 

### Ingestion of homegrown fruits and vegetables,

 

### Ingestion of homegrown beef,

 

### Ingestion of dairy products from homegrown cows,

 

### Ingestion of homegrown poultry and eggs,

 

### Ingestion of homegrown pork,

 

### Ingestion of locally caught fish, and

 

### Ingestion of breast milk (for children less than 1 year old and for dioxins only). 10



### As discussed in detail in Section A.4.2, exposure via these pathways is assessed for adults, various age categories for children, and nursing infants (for dioxins only).

10 Breast milk ingestion is an important exposure pathway for lipophilic compounds like dioxins and has been shown not to contribute meaningfully for exposures to mercury, cadmium, and POM. See Section A.4.2.3 of this attachment and Section 3.4 of Addendum 2 to this attachment for full disc ussions of infant exposures via breast milk ingestion.TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-13 December 2013 

### Other non-inhalation exposure routes of possi ble concern for PB-HAPs discussed in HHRAP include the use of surface waters as a drinking water source and dermal exposure. These exposure routes, however, are not evaluated in the current assessment. The drinking water exposure pathway is not likely for the modeling scenario developed for this analysis because the likelihood that humans would use a lake as a drinking water source was assumed to be low. 11 

### Dermal absorption of chemicals that are originally airborne has been shown to a relatively minor pathway of exposure com pared to other exposure pathways (EPA 2006, Cal/EPA 2000). Preliminary calculations of estimated dermal exposure and risk of PB-HAPs, presented in Addendum 3 to this attachment, showed that the dermal exposure route is not a significant risk pathway relative to ingestion exposures.



### A.2.4 Approach to Developmen t of the Tier 1 Scenario The TRIM-based Tier 1 scenario described in this document is used to provide a means toqualitatively estimate the potential for non-inhalation risks above the levels of concern for PBHAPs emissions from facilities in the context of residual risk assessments conducted as part of RTR. The Tier 1 scenario used to derive the threshold emission rates is not intended to be representative of any particular situation. Rather, it was developed for the purpose of RTR to portray a hypothetical exposure scenario that will generate emissions screening levels that are health protective for any potential exposure situation that might plausibly be encountered in the United States. A range of conditions was assessed when conceptualizing and developing the screening scenario. The final configuration was chosen so that for a given individual, any potential long-term exposure levels for a given geographic region would be reasonably unlikely to exceed those of the Tier 1 configuration. These criteria were met by constructing a hypothetical scenario that would be health-pro tective in key aspects, including spatial orientation, meteorology, types of exposures, and ingestion rates. The overall result is a scenario that is unlikely to occur at any one location but has a high likelihood of representing the upper end of all potential exposures. This latter aspect accomplishes the goal of striking a balance between health protectiveness and the level of accuracy called for in the ideal screening approach previously discussed.

The development and application of the Tier 1 scenario for residual risk evaluations considered EPA’s technical and policy guidelines presented in the Residual Risk Report to Congress (EPA 1999); Volumes I and II of the Air Toxics Risk Assessment Reference Library (EPA 2004a, 2005a); and other EPA publications. The scenario described in this appendix is the culmination of analyses completed since 2005; it provides the basis for an efficient and scientifically defensible method for screening multipathway human health risk and provides a solid baseline from which to perform Tier 2 analyses, as described in Attachment B. Nevertheless, this scenario should not be considered “final” but rather a product that can continue to evolve based on feedback from the scientific community and Agency reviewers, lessons learned as the scenario is further applied for RTR, variations in EPA’s needs and requirements, and other factors.



### A.2.4.1 Modeling Framework The approach for multipathway risk screening and evaluation for RTR can be divided into four steps:

11 An exception to this generality would be reservoirs used for drinking water supplies. This situation might be worthy of additional analysis, if warranted by the characteristics of a given assessment (e.g., to estimate PB-HAP concentrations in treated drinking water derived from reservoirs).TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-14 December 2013 

### 1. Fate and transport modeling of PB-HAPs emitted to air by the source that partition into soil, water, and other environmental media (including fish 12 

### ); 2. Modeling of transfer and uptake of PB-HAPs into farm food chain media (e.g., produce, livestock, dairy products) from soil and air; 3. Estimating exposures from ingestion of selected media and estimating average daily ingestion doses for a hypothetical human receptor; and 4. Calculating lifetime cancer risk estimates or chronic non-cancer HQs, as appropriate, for each PB-HAP and comparing these to selected evaluation criteria.

The relationship among these four processes is shown in Exhibit_Att A-4.

As shown in Exhibit_Att A-4, two models are used to evaluate the four steps outlined above. EPA’s TRIM methodology was conceived as a comprehensive modeling framework for evaluating risks from air toxics, and the TRIM system was designed to address each of the four steps involved in screening ingestion risk. 13 

### Currently, however, only one component corresponding to the first step included in Exhibit_Att A-4—the fate and transport module—is available for application in an ingestion risk assessment. EPA has completed some development activities for TRIM.Expo-Ingesti on and TRIM.Risk-Human Health, two additional modules that cover the other three steps. Modeli ng software, however, is not currently available for these modules. For the RTR screening scenario, the Multimedia Ingestion Risk Calculator (MIRC), a Microsoft ® 

### ExcelTM-based computing framework, was constructed to complete the calculations required for estimating PB-HAP conc entrations in farm food chain media, average daily ingestion doses, and cancer risks and chronic non-cancer HQs. This framework is

12 As discussed below, concentrations in fish calculated by the TRIM.FaTE model were used to estimate ingestion exposures for humans consuming fish. Modeling of fish co ncentrations is therefore di scussed herein as part of the fate and transport modeling. Uptake of PB-HAPs into all ot her biotic media assumed to be ingested is modeled in the second step of the modeling framework.

13 Information about the current status of TRIM modules and comprehensive documentation of modules developed thus far can be accessed on EPA’s Technology Transfer Network (TTN) on the Fate, Exposure, and Risk Analysis website (http://www.epa.gov/ttn/fera/). 

### Exhibit_Att A-4. Overview of Ingestion Exposure and Risk Screening Evaluation MethodTRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-15 December 2013 

### conceptually identical to the ingestion expos ure and risk analyses that TRIM is intended to

cover.


### A.2.4.2 Model Configurati on and Parameterization
The Tier 1 scenario is intended to reduce the possibility that EPA would underestimate potential multipathway human health risks. Although the health protective approach likely overestimates risk, EPA determined that this approach is appropriate for the purposes of an initial multipathway screening assessment. As was done with the preliminary multipathway screening for RTR conducted in 2006 (EPA 2006), exposures were modeled for a hypothetical farm homestead and fishable lake located adjacent to an emissions source. The hypothetical individual for which exposures were calculated was assumed to derive all potentially contaminated foodstuffs from adjacent locations, and many of the exposure/activity assumptions (e.g., amount of food consumed per day) were se lected from the upper ends of representative exposure parameter distributions.

The physical/chemical environment represented in the screening scenario was parameterized with two types of values. One type is typica l values, such as national averages. The second type is health-protective values, or values that would tend to overestimate concentrations in media driving ingestion exposures for humans, based on knowledge of exposure patterns. In general, the spatial and temporal aspects of the scenario and the components of the scenario that influence air concentrations and deposition rates (which in turn affect all other exposures) were defined to be health protective. Chemical-s pecific and non-chemical-specific properties of the environmental media were parameterized with either typical or health protective values; properties having greater uncertainty were assigned a greater level of health protective bias. The spatial layout of the Tier 1 scenario and the meteorological data (or a combination of these two factors) are generally more influential than physical/chemical parameters in dictating the screening model outcomes, taking into account the potential range of variation in possible values. For example, where and how the layout is spatially oriented relative to the dominant wind direction can dramatically affect the concentrations in air, thereby driving estimated concentrations of PB-HAPs in soil, water, and biota. In contrast, relatively large changes in soil characteristics within the range of possible values (e.g., organic carbon content, water content) typically result in relatively small changes in media concentrations.

The mix of health protective and central-tendency assumptions and parameterization is expected to result in a scenario configurati on that, on average, is likely to overpredict environmental concentrations of PB-HAPs in media of interest for this evaluation. Given the intended application of this scenario as a screening tool, this health protective bias was deliberate, because of the desire to ensure that risks above levels of potential concern are not overlooked (i.e., to minimize false negatives). Although the inclusion of central-tendency values where warranted is intended to minimize the number of false positives, some false positives are to be expected from a screening scenario. False positives are addressed in subsequent tiers of the screening evaluation for a particular source.



### A.2.5 Fate and Transport Modeling (TRIM.FaTE) The fate and transport modeling step depicted in the first box in Exhibit_Att A-4 is implemented for RTR using the Fate, Transport, and Ecological Exposure module of the TRIM modeling system (TRIM.FaTE). 14 

### In developing the Tier 1 scenario, Version 3.6.2 of TRIM.FaTE was used to model the fate and transport of emitted PB-HAPs and to estimate concentrations in

14 TRIM.FaTE is a spatially explicit, compartmental mass ba lance model that describes movement and transformation of pollutants over time, through a user-defined, bounded syst em that includes both biotic and abiotic compartments. Outputs include pollutant concentrations in multiple environmental media and biota.TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-16 December 2013 

### relevant media. Additional information about TRIM.FaTE, including support documentation, software, and the TRIM.FaTE public refe rence library, is available at http://www.epa.gov/ttn/fera/

. The algorithms used to model mercury species and polyaromatic hydrocarbons (PAHs) are described in Volume II of the TRIM.FaTE Technical Support Document (EPA 2002a). A comprehensive evaluation of the performance of TRIM.FaTE for modeling mercury was documented in Volumes I and II of the T RIM.FaTE Evaluation Report (EPA 2002b, 2005b). Algorithms specific to the fate and transport of 14 chlorinated dibenzo-dioxin and -furan congeners were added following the addition of those for mercury and PAHs. Documentation of the application of TRIM.FaTE for dioxin emissions is contained in the third volume of the TRIM.FaTE Evaluation Report (EPA 2004). More recently, the TRIM.FaTE public reference library was updated to include information on modeling for cadmium. In general, many of the algorithms and properties included in the public reference library that are used to model mercury (except for the mercury transformation algorithms) are also applicable to cadmium. Comprehensive technical documentation of TRIM.FaTE algorithms specific to cadmium has not yet been compiled; however, all chemical-specif ic properties used by TRIM.FaTE to model cadmium (as well as PAHs, mercury, and dioxins) are documented in Addendum 1 to this attachment. Based on a thorough 2011 evaluation of TRIM.FaTE performance in modeling mercury’s fate, transport, and transformation in the aquatic food web, a zooplankton compartment was added to TRIM.FaTE’s aquatic compartment to increase the resolution and accuracy of the aquatic food web modeling. Parameterization of the TRIM.FaTE scenario used for RTR screening is described in more detail in Section A.3.



### A.2.6 Exposure Modeling and Ri sk Characterization (MIRC) The algorithms included in MIRC that calculate chemical concentrations in farm food chain media and ingestion exposures for hypothetical individuals were obtained from EPA’s Human Health Risk Assessment Protocol for Hazardous Waste Combustion Facilities, or HHRAP (EPA 2005a). 15 

### These algorithms, and the required exposure factors and other parameter values, were compiled into a database. An overview of the computational processes this tool carries out and the types of input data it requires is presented in Exhibit_Att A-5. This exhibit demonstrates the general relationships between the relevant TRIM.FaTE outputs (i.e., chemical concentrations in environmental media and fish) and the ingestion exposure and risk calculations carried out using MIRC. Additional discussion of exposure and risk calculations for the Tier 1 scenario is presented in Section A.4 and Addendum 2 to this attachment, and all inputs required by these calculations are documented in Addendum 2.

15 The farm food chain calculations and ingestion exposure equations to be included in the TRIM.Expo software are expected to be very similar to those included in HHRAP.TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-17 December 2013 

### Exhibit_Att A-5. Overview of Process Carried Out in the Multimedia Ingestion Risk Calculator



### A.2.7 Implementation of Risk-based Emission Scaling Factors for POM and Dioxin Emissions Two of the four PB-HAPs for which screening emission thresholds have been developed for RTR—POM and dioxins—are chemical groups comprising numerous individual entities. The members of these categories reported in NEI include both specific chemicals and groups containing multiple chemicals. For example, for POM, emissions reported in NEI include various species, such as benz[a]anthracene, 2-methylnaphthalene, and chrysene, as well as non-specific entries, such as “PAH, total.” The constituents included in the POM and dioxin PB-HAP categories are grouped together not only because they are types of the “same” HAP, but also because members of these groups are assumed to have similar characteristics with respect to toxicity and behavior in the environment.

To facilitate a practical application of the multipathway screening methods for RTR, reported emissions of POM and dioxins are normalized or scaled to a single reference chemical for each group. The reference chemicals used in RTR for POM and dioxins are benzo[a]pyrene and 2,3,7,8-TCDD, respectively. These compounds were selected because they are relatively wellstudied among the members of the two groups and are also among the most toxic species within each group.

Derivation of appropriate scaling factors begins with an evaluation of the basic relationship used to characterize health risk:

Risk 

# ∝ (Exposure Concentration) × (Toxicity)

For a given air pollutant, the incremental exposure concentration is directly proportional to the emissions of that substance. That is, as the emissions increase, so too does the exposure toTRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-18 December 2013 

### that substance. Furthermore, toxicity is assumed to increase linearly with concentration. Consequently, emissions of one substance (e.g., chrysene) can be scaled proportional to a reference compound (e.g., benzo(a)pyrene or BaP) by applying weighting factors corresponding to the relative differences in exposure behavior and toxicity. Using the POM group as an example and BaP as the reference compound, this scaling can be expressed through an equation as follows:

Emiss PAHi:BaP 

### = Emiss PAHi 

### × EEF PAHi:BaP 

### × TEF PAHi:BaP



### where:

Emiss PAHi : BaP = Risk-weighted emissions of PAH i (weighted according to cancer risk relative to BaP for oral exposures)

Emiss PAHi = Emission rate of PAH i

EEF PAHi:BaP = Exposure equivalency (weighting) fa ctor accounting for difference in relative oral exposure between PAH i and BaP

TEF PAHi:Ba P = Toxicity equivalency (weighting) factor accounting for difference in relative toxicity via oral route between PAH i and BaP



### In combination, the product of the EEF and TEF for a given substance is considered to be a “risk equivalency factor” for the purposes of RTR evaluations that enables scaling of emissions of a given substance for a given exposure scenario.

The TEF for each PAH and dioxin species can be calculated on the basis of relative toxicities. Toxicities were not evaluated separately for RTR but are based on analyses conducted by EPA elsewhere. For PAHs, oral toxicity values for individual species have been derived following the same approach used to develop inhalation toxicity values. For dioxins, TEFs are based on the relative toxicities developed by EPA recently and are ultimately based on the values developed by the World Health Organization (van den Berg et al. 2006). Refer to Attachment B for more information on these values.

The EEFs can be calculated directly for each individual chemical that can be modeled in TRIM.FaTE and MIRC. TRIM.FaTE is configured for 14 POM congeners and 17 dioxin/furan congeners. For these substances, EEFs were calculated directly using the modeling approach and parameterization scheme for the Tier 1 scenario described in this document. Several other POM and dioxin emissions, however, are reported in the NEI. For these, exposure surrogates must be assigned after evaluating the correlation between chemical properties of the POM or dioxin congener and exposure quantified as lif etime average daily dose. The specific calculations for EEFs and exposure surrogates for each chemical group are discussed in the sections that follow.



### A.2.7.1 Calculation of Scalin g Factors for POM Congeners The calculated EEFs, TEFs, and total REFs for the 14 POM congeners that are configured in TRIM.FaTE, plus 15 others not configured in TR IM.FaTE, are shown in Exhibit_Att A-6. To determine appropriate exposure surrogates for chemicals not parameterized in TRIM.FaTE, EPA evaluated the relationships between chemical-specific properties (e.g., K ow 

### and Henry’s law constant, k h 

### ) and intermediate modeled values (e.g., deposition, soil concentration) and exposure in terms of lifetime average daily dose (LADDs) where the average daily doses (ADDs) for the youngest two age groups were adjusted by the age-dependent adjustment factors (ADAFs) to account for the mutagenic mode of action of PAHs. The correlation between K ow 

### and exposure is stronger than for any other chemical-specific property. ExposureTRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-19 December 2013 

### surrogates were thus identified for each congener by calculating Total Lifetime Average Daily Dose (Age Adjusted) for each based on the congener’s K ow 

### and the power regression of the modeled PAHs. Exhibit_Att A-7 shows that as K ow 

### increases, so too does exposure.

Exhibit_Att A-6. Exposure, Toxicity, and Risk Equivalency Factors Relative to BaP for POM Congeners Currently Evaluated in Tiers 1 and 2 Analyses

Chemical Fully Parameterized in TRIM.FaTE? Exposure Equivalency Factor (EEF) Toxicity Equivalency Factor (TEF) a Risk Equivalency Factor (REF) Dibenzo(a,i)pyrene n 27.5 16.4 452 7,12-Dimethylbenz(a)anthracene Y 5.4 34.2 186 3-Methylcholanthrene n 4.3 3.0 12.9 Dibenz(a,h)anthracene Y 8.0 0.6 4.5 Benzo(k)fluoranthene Y 18.0 0.2 3.0 Benzo(a)pyrene Y 1.0 1.00 1.00 Benzo(b)flouranthene Y 11.4 0.2 1.9 Indeno(1,2,3-cd)pyrene Y 4.5 0.2 0.7 PAH, total n 5.1 0.07 0.3 Polycyclic Organic Matter n 5.1 0.07 0.3 Benzo(g,h,i)perylene Y 4.3 0.07 0.3 Benzo(e)pyrene n 4.5 0.07 0.3
Retene n 3.7 0.07 0.3 Dibenzo(a,j)acridine n 0.78 0.2 0.1 Perylene n 1.2 0.07 0.08 Benzo(a)anthracene Y 0.09 0.16 0.01 Chrysene Y 0.25 0.02 0.004 2-Acetylaminofluorene n 0.005 1.4 0.01
Fluoranthene Y 0.04 0.07 0.003 Acenaphthylene Y 0.04 0.07 0.003 2-Chloronaphthalene n 0.03 0.07 0.002
Fluorene Y 0.03 0.07 0.002 Acenaphthene Y 0.02 0.07 0.002 1-Methylnaphthalene n 0.02 0.07 0.001 2-Methylnaphthalene Y 0.02 0.07 0.001 Carbazole n 0.01 0.003 0.00003
Anthracene n 0.06 0.000 0.00
Phenanthrene n 0.06 0.00 0.00 Pyrene n 0.15 0.00 0.00

a TEFs are calculated as the ratio of the cancer slope factor (CSF) for each specific POM congener to the CSF for benzo(a)pyrene. Dose response values, including CSFs, that are used in the screening assessment are discussed in Section 

### 4 of Addendum 2 to this attachment.



### For POMs reported as unspeciated groups (i.e., “PAH, total” and “Polycyclic Organic Matter”) EPA assigned surrogates with K ow 

### values near the upper end of the range of all of the K ow 

### values, corresponding to an exposure near the upper end of the range (log K ow 

### = 6.5). This assignment is assumed to be health protective and likely will not under predict exposure.TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-20 December 2013 

### Exhibit_Att A-7. Relationship between Exposure and K ow 

### for POM Congeners



### A.2.7.2 Calculation of Scaling Factors for Dioxin Congeners The calculated EEFs, TEFs, and REFs for the 17 dioxin congeners that are configured in TRIM.FaTE are presented in Exhibit_Att A-8. Al though there are many dioxins reported in the NEI other than the 17 configured for TRIM.FaTE , to date, none of them have been included in emissions datasets that have been screened. Therefore, no surrogate EEF’s have been developed for dioxins. In future screening assessments, if surrogate EEFs are needed, an approach similar to that used for POM will be used to develop surrogate EEFs for dioxins.

Many facilities report dioxins as “Dioxins, Total, without Individual Isomers Reported,” “Dioxins,” or as “2,3,7,8-TCDD TEQ,” and in these cases, we do not adjust or scale the emissions. That is, we assume that they behave like and possess the toxic characteristics of TCDD. This approach could be improved by obtaining information on the speciation of dioxin emissions for each facility or an average speciation profile that could be assumed to apply to all facilities in a source category.

Exhibit_Att A-8. Exposure and Toxicity Equivalency Factors Relative to TCDD for Modeled Dioxin Congeners

Chemical Exposure Equivalency Factor (EEF) Toxicity Equivalency Factor (TEF) a Risk Equivalency Factor (REF) PentaCDD, 1,2,3, 7,8- 3.8 1 3.8
TetraCDD, 2,3,7,8- 1 1 1
Dioxins, Total, w/o Indiv. Isomers Rptd. 1 1 1

Dioxins 1 1 1
2,3,7,8-TCDD TEQ 1 1 1TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-21 December 2013 Chemical Exposure Equivalency Factor (EEF) Toxicity Equivalency Factor (TEF) a Risk Equivalency Factor (REF) HexaCDD, 1,2,3,4, 7,8- 1.6 0.1 0.2
PentaCDF, 2,3,4,7, 8- 0.4 0.3 0.1
HexaCDD, 1,2,3,6, 7,8- 1.0 0.1 0.1
HexaCDF, 1,2,3,7,8,9- 0.5 0.1 0.05
HexaCDF, 1,2,3,6,7,8- 0.5 0.1 0.05
HexaCDD, 1,2,3,7,8,9 - 1.0 0.04 0.04
HexaCDF, 1,2,3,4,7,8- 0.3 0.1 0.03
HexaCDF, 2,3,4,6,7,8- 0.2 0.1 0.02
PentaCDF, 1,2,3,7,8- 0.4 0.03 0.01
TetraCDF, 2,3,7,8- 0.1 0.1 0.01
HeptaCDD, 1,2,3,4,6,7, 8- 1.0 0.01 0.01
HeptaCDF, 1,2,3,4,6,7, 8- 0.2 0.01 0.002
HeptaCDF, 1,2,3,4,7,8, 9- 0.2 0.01 0.002
OctaCDD, 1,2,3,4,6,7, 8,9- 1.1 0.0003 0.0003
OctaCDF, 1,2,3,4,6,7, 8,9- 0.2 0.0003 0.0001

a Values from Van den Berg et al. (2006), except for 1,2,3,7,8, 9-hexaCDD, which is calculated based on the ratio of the IRISbased CSF for 1,2,3,7,8,9-hexaCDD to the IRIS-based CSF for 2, 3,7,8-TCDD. Dose response val ues, including CSFs, that are used in the screening assessment are discussed in Section 4 of Addendum 2 to this attachment. 

## A.3 Description of Environmental Modeling Scenario As described in Section A.2.4.2, the physical configuration of the RTR Screening Scenario was designed to encompass the upper end of potential long-term PB-HAP exposures, and the environmental and chemical-specific properties were parameterized with either health protective or central-tendency values. Information regarding the scenario configuration and important aspects of the parameterization process, justifications for selecting particular property values, and model uncertainties is presented in the sect ions that follow. Comprehensive documentation of TRIM.FaTE property values for this scenario is provided in Addendum 1 to this attachment.



### A.3.1 Chemical Properties The general chemical/physical properties that TRIM.FaTE requires, such as Henry’s law constant, molecular weight, and melting point, were obtained from peer-reviewed and standard reference sources. Numerous other chemical-specific properties are related more specifically to a particular abiotic or biotic compartment type; these properties are discussed generally in the sections that follow and are documented in Addendum 1 to this attachment.



### A.3.2 Spatial Layout For the purpose of estimating media concentrations, the TRIM.FaTE scenario is intended to represent a farm homestead and a fishable lake (and its surrounding watershed) located near the emissions source of interest. A diagram of the surface parcel layout is presented in Exhibit_Att A-9. The source parcel is parameterized as a square with sides of 250 m, which is assumed to be a fair estimation for the size of a relatively small-to-medium facility at the fence line. With a predominant wind direction toward the east, the modeled layout is generallyTRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-22 December 2013 

### symmetric about an east-west line and is wedge-shaped to reflect Gaussian dispersion of the emission plume.

A lateral, downwind distance of 10 km was established for the watershed included in the scenario. Based on the results of dispersion modeling, the location of the maximum air concentration and deposition rate is expected to occur relatively close to the facility (probably within a few hundred meters, with the exact location varying with stack height and other parameters) and well within a 10-km radius. A dditionally, deposition rates for the PB-HAPs for which this screening scenario is applicable is expected to decrease by about two orders of magnitude relative to the predicted maximum rate within a 10-km radius. Extending the modeling layout beyond a 10-km downwind dist ance would increase the amount of deposition “captured” by the modeled watershed, but the incremental chemical mass expected to accumulate in the watershed diminishes rapidly with distance. In addition, the impact of this additional deposited mass on ingestion exposures is expected to be negligible. 16 

### Given these conditions, a downwind length of 10 km was determined to be appropriate for the screening scenario.

Exhibit_Att A-9. TRIM.FaTE Surface Parcel Layout

The north-south width of the wedge-shaped watershed was set based on the observed behavior of chemicals emitted to the ambient air. If meteorological stability is known or can be assumed, the lateral spread of the plume ( σ y 

### , measured from the centerline) at a certain distance from the source can be estimated using the Pasquill-Gifford curves. Turner (1970) derived the equations for these curves, which can be found in the Industrial Source Complex 3 Dispersion Model Manual (among other sources). 17 

### For a relatively neutral atmosphere (stability class D), σ at 10km is about 550 m using this estimation. In a Gaussian distribution, about 99.6 percent of the plume spread area is contained within 3 σ of the median line. Therefore, the plume σ was set at 3 times 550 m, or approximately 1.75 km from th e centerline at a distance of 10 km. The plume

16 Mass deposited at the outer e dge of the watershed is expected to result in only a very small increase in estimated exposure via fish consumption by increasing the chemical mass transported to the lake through erosion and runoff. The distance from these more distant locations to the la ke would attenuate transport of chemical mass by erosion and runoff, dampening the effect of including additional de position beyond 10 km. (Other exposure pathways largely would be unaffected; the soil concentrations used to calculate exposures for the farm food scenario are derived from soil parcels located close to the source and unaffected by deposition to the far reaches of the watershed.)

17 http://www.epa.gov/scram001/userg/regmod/isc3v2.pdfTRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-23 December 2013 

### width for these conditions is expected to be about twice this distance, or 3.5 km. These dimensions were used to define the dimensions of the overall air and surface parcel layouts for the screening scenario.

The surface (land and surface water) modeling area was initially divided into five pairs of parcels the areas of which increase with distance from the source, which approximately corresponds to the spatial gradient that is expected in the downwind direction from the source. The second north parcel from the source was divided further into two parts; one of them tilled soil (Parcel N6) to represent agricultural conditions and the other (Parcel N7) untilled to represent pasture.

The depth of the surface soil compartments was set to 1 cm, except for Parcel N6, for which the depth was set to 20 cm to simulate the effect of tillage. Characteristics of the soil layers (e.g., organic carbon content, air and water content, and subsoil depth) generally were set to represent typical or national averages as summarized by McKone et al. (2001).

The overall shape and boundaries of the air parcel layout mirror those of the surface parcel layout. A single air parcel (N2) overlies surface Parcels N6 and N7, and the air over the lake is divided into air Parcels S2 and S3 (mirroring the analogous parcels on the north side of the lake).



### A.3.3 Watershed and Water Body Parameterization Properties associated with the watershed soil and lake determine how pollutants in the system are transported through and accumulate in various media compartments. These properties describe the physical characteristics of the environmental media included in the modeled region, as well as the assumed connections and relationships between media types and modeled spatial components that in turn affect chemical transport via water runoff, ground infiltration, deposition of suspended sediments in the water column, and other processes. This section presents the justification for setting the key properties of the soil, water, and sediment compartments. Also discussed are some of the chemical properties related to watershed and waterbody processes (chemical-specific com partment properties in TRIM.FaTE) and the configuration of terrestrial plants included in the scenario.



### A.3.3.1 Water Balance Water-related properties of the lake and related watershed characteristics (e.g., runoff rates from each surface soil compartment) were set so that a simplified water balance is achieved. Although TRIM.FaTE maintains a chemical mass balance, the model does not calculate or maintain media mass balances (e.g., for water) except where specified in certain formulas. For the Tier 1 scenario, the parameters were set to satisfy two equations relating water volume. The first equation maintains a balance of water entering and leaving the terrestrial portion of the scenario:

[ total precipitation] = [evapotranspiration] + [total runoff]

In this equation, total runoff is equal to the sum of overland runoff to the lake and seepage to the lake via groundwater.

The second equation describes the volumetric balance of transfers of water to and from the lake:

[total runoff] + [direct precipitation to the lake] = [evaporation from the lake surface] + [outflow from the lake]TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-24 December 2013 

### Note that TRIM.FaTE actually uses only some of these properties (e.g., precipitation rate and surface runoff, but not evapotranspiration). The water characteristics assumed for the Tier 1 scenario are meant to represent a relatively wet and moderately warm location in the United States (USGS 1987). Following are the assumptions for this scenario:

 

### 35 percent of the total precipitation leaves the scenario through evapotranspiration.

 

### 25 percent of total precipitation infiltrates into the groundwater and eventually flows into the lake.

 

### 40 percent of total precipitation contributes to overland runoff.

For these calculations, the source parcel was considered to be outside the watershed and therefore was not included in the water balance. The evaporation rate from the lake was assumed to be 700 mm/yr based on data reported by Morton (1986) for various lakes. The runoff rate was defined to be spatially constant and temporally constant (i.e., it is not linked to precipitation events) throughout the modeled domain. Based on these assumptions, the outflow of water from the lake is about 18 million m 3 

### /yr, which translates to a volumetric turnover rate of about 12.2 lake volumes per year.

Other quantitative water body and watershed characteristics TRIM.FaTE uses are listed in Addendum 1 to this attachment.



### A.3.3.2 Sediment Balance A simplified balance of sediment transfers between the watershed and the lake was also maintained for the screening scenario via the parameterization of sediment-related properties. As with water, the model does not internally balance sediment mass; these calculations were performed externally for the purposes of setti ng parameter values. The sediment balance maintained is described by the following equation, where terms represent mass of sediment:

[total surface soil transfers to the lake via erosion] = [removal of sediment from the water column via outflow] + [sediment burial]

where the second term (removal of sediment from the water column via outflow) is accounted for in TRIM.FaTE by lake flushing rate and the th ird term (sediment burial) is the transfer of sediment from the unconsolidated benthic sediment compartment to the consolidated sediment layer.

To maintain the sediment balance, erosion rates were calculated for each surface soil compartment using the universal soil loss equation (USLE, Wischmeier and Smith 1978), assuming a relatively high rate of erosion. The total suspended sediment concentration is assumed to remain constant in TRIM.FaTE, and the flushing rate of the lake (calculated via the water balance approach described above) was then used to estimate the removal of sediment from the modeling domain via lake water outflow. The difference between these sediment fluxes was taken to be the sediment burial rate. The sediment burial rate is the rate at which sediment particles in the unconsolidated benthic sediment layer are transported to the consolidated sediment, where the particles can no longer freely interact with the water column.

In TRIM.FaTE, the consolidated sediment layer is represented with a sediment sink; as with all sinks in TRIM.FaTE, chemical mass sorbed to buried sediment that is transported to the sink cannot be returned to the modeling domain. The burial rate is a formula property calculated by the model according to the difference between user-specified values for sediment deposition velocity (from the water column to the benthic sediment) and sediment resuspension velocityTRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-25 December 2013 

### (back into the water column from the benthic sediments). These formula properties assume a constant volume of particles in the sediment layer (because the densities for benthic and suspended sediment particles were defined to the same value, the mass of particles in the sediment is also constant).

For the Tier 1 scenario described here, the average sediment delivery rate (i.e., transfer of sediment mass from watershed surface soil to the lake due to erosion) for the entire watershed was estimated to be about 0.0036 kg/m 2 

### -day, based on calculations using the USLE. The HHRAP documentation notes that using the USLE to calculate sediment load to a lake from the surrounding watershed sometimes leads to overestimates (EPA 2005a). For the Tier 1 scenario, however, this approach was considered to be appropriate in that health protective assumptions are a goal of the screening scenario. 18 

### Surface soil compartments adjacent to the lake are linked directly to the lake for the purposes of estimating erosion and runoff transfers (see layout in Exhibit_Att A-9). Erosion and runoff from the source parcel are linked directly to a sink and do not enter the Tier 1 scenario lake. The transport of sediment to the lake via overland is thus assumed to be efficient. Note that erosion from parcels not directly adjacent to the lake is assumed to be somewhat attenuated, effected by using a lower sediment delivery ratio in the USLE.

Using the calculated surface soil erosion rates for the scenario, the total average daily sediment load to the lake from the watershed is about 16,600 kg/day. About 15 percent of this load is removed from the lake via outflow of suspended sediments (based on a calculated flush rate of 12.2 volume turnovers per year) with the remainder of the sediment input to the lake transferred to the sediment burial sink.



### A.3.4 Meteorology Meteorological properties used in TRIM.FaTE algorithms include air temperature, mixing height, wind speed and direction, and precipitation rate. These properties, which can vary significantly among geographic locations, and seasonally and hourly for a single location, greatly influence the chemical concentrations predicted in media of interest. Because the screening scenario is intended to be generally applicable for any U.S. location, and to minimize the frequency of false negatives, a health protective configuration was used. The meteorology of the screening scenario was defined to ensure that (when used in combination with the selected spatial layout) the maximum exposures that might be encountered for the scenarios of interest would be encompassed (i.e., consumption of home-grown fa rm foodstuffs and self-caught fish, with all farm foods and fish obtained from locations im pacted by chemicals emitted from the local source). Ensuring that the meteorological parameters were not overly protective of health, such

as always having the wind blow toward the location of interest, however, was also important to avoid too many false positives.

The meteorological data for the screening scenario are intended to be representative of a location with a low wind speed, a wind direction that strongly favors the watershed, and a relatively high amount of total precipitation falling on the watershed. The values used were based on actual data trends for U.S. locations as specified in Exhibit_Att A-10 but an artificial data set was compiled for this analysis (for example, temporally variable meteorological parameters were made to vary only on a daily basis). This simplified approach allowed for greater control (relative to selecting a data set for an actual location) so that desired trends or outcomes could be specified. Also, using a meteorological data set with values varying on a

18 Based on sensitivity analysis, a higher erosion rate will both increase surface water concentrations and decrease surface soil concentrations; the relative impact on resultin g concentrations, however, will be proportionally greater in the waterbody.TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-26 December 2013 

### daily basis rather than a shorter period (such as hour ly, which is the typical temporal interval for meteorological measurements) reduced required model run time. Meteorological inputs are summarized in Exhibit_Att A-10.

The sensitivity of modeled PB-HAPs to changes in these meteorological variables was tested. Lower wind speeds and mixing heights affected concentrations the most. This sensitivity is not unexpected because lower wind speeds should increase pollutant deposition onto the soil and lower mixing heights should reduce the volume through which pollutants disperse. The wind speed used for the screening scenario was 2.8 m/s, the 5 th 

### percentile of the annual averages among 239 stations; by comparison, the mean annual average wind speed is approximately 4 m/s in the contiguous United States). The mixing height (mean heights from 4 states) used was 710 m (the 5 th 

### percentile of annual averages among all 40 states in the SCRAM database).

Exhibit_Att A-10. Summary of Key Meteorological Inputs

Meteorological Property Selected Value Justification

Air temperature Constant at 2 98 Kelvin Recommended default value listed in HHRAP (EPA 2005a). Value is similar to the mean daily June temperature in much of the U.S. Southern Plains and Southeast. a Mixing height Constant at 710 m Value is 5 th percentile of annual average mixing heights for 75 U.S. locations, using data obtained from EPA’s SCRAM Web site. b Value is the approximate U.S. median for periods without precipitation, based on data compiled by Holzworth (1972). Value is conservative compared to the 1- to 2-km typical mid-latitude daytime value (Stull 1988).
Wind direction Blows from source parcel into scenario domain (west to east) 3 days per week; during other times does not blow into domain A wind direction that favors the loca tion of interest (for example, a watershed downwind of a source of concern) will tend to result in more emitted mass accumulating in the location of interest. For much of the U.S. mid-Atlantic and western regions, the wind tends to favor the eastward direction. a Among the NCDC 1981–2010 normalized wind vector data, the average wind direction had a strong eastward component at ov er one-third of the stations. c For the hypothetical RTR scenario, a more extreme example of this pattern is represented by conditions in Yakima, Washington, where the wind blows eastward approximately 40 percent of the time based on a review of wind direction data compiled by the National Weather Service (NCDC 1995). This pattern was approximated in the RTR scenario with a configuration in which the modeled domain is downwind of the source 3 out of 7 days.
Horizontal wind speed Constant at 2.8 m/sec Set to 5 th percentile of annual average speed for 239 stations across the contiguous United States (about 50 years of data per station). Value is similar to the annual average wind speeds of many areas of the U.S. east coast and west coast. a Precipitation frequency Precipitation occurs 3 days per week; wind direction blows into domain 2 of these days This value was selected so that tw o-thirds of the total precipitation occurs when the domain is downwind of the modeled source. This pattern approximates that for rainy U.S. locations, where precipitation occurs 35–40% of the time (Holzworth 1972). These locations include parts of the U.S. Northeast and Northwest. aTRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-27 December 2013 Meteorological Property Selected Value Justification

Total Precipitation 1.5 m/yr Assumed to represent rainy c onditions for the United States. This annual precipitation amount is experienced in parts of the U.S. Deep South and parts of the U.S. northwest coast. a Conditional precipitation rate (rainfall rate when precipitation is occurring) is 9.59 mm/d, which is similar to conditions in many areas along the U.S. east coast and in the Midwest and Plains. a

a National Climatic Data Center CliMaps (NCDC-CliMaps) (2007). http://cdo.ncdc.noaa.gov/cg i-bin/climaps/climaps.pl

. b Support Center for Regulatory Atmospheric Modeling; http://www.epa.gov/ttn/scram/

.
c National Climatic Data Center 1981-2010 Climate Normals; http://www.ncdc.noaa.gov/oa/climate/normals/usnormals.html



### A.3.5 Aquatic Food Web The aquatic food web is an important part of the screening scenario because the chemical concentrations modeled in fish are used to calculate human ingestion exposure and risks associated with eating contaminated local fish. A biokinetic approach to modeling bioaccumulation in fish is used in the RTR screening scenario. The primary producers (first trophic level) in the TRIM.FaTE aquatic ecosystems are algae and macrophytes in the water column and detritus in the sediments (the latter simulated as sediment particles). Zooplankton feed on algae in the water column, while benthic invertebrates, represented as a single compartment, consume detritus that settles to th e sediment compartment. In the water column, small young-of-the-year fish and minnows f eed on zooplankton and phytopolankton. The small fish are in turn consumed by larger or “pan” fish (e.g., bluegills, white perch), which are in turn consumed by the top consumers (e.g., gar, pickerel ). The invertebrates in the sediments of the benthic environment support small bottom-feeding fish (young-of-the-year fish for many species), which in turn are consumed by larger bottom-feeding fish (e.g., catfish). For TRIM.FaTE to provide reasonable predictions of t he distribution of a chemical across biotic and abiotic compartments in aquatic systems, the bi omass of the aquatic biotic compartments must represent all biota in the system and the dist ribution of biomass among the trophic levels and groups must be as realistic as possible.

To support the development of a relatively generic freshwater aquatic ecosystem in which to model bioaccumulation in fish, a literature search, review, and analysis was conducted (ICF 2005). This research demonstrated that the diversity of species and food webs across U.S. aquatic ecosystems is substantial, reflecting the wide range of sizes, locations, and physical/chemical attributes of both flowing (rivers, streams) and low-flow water bodies (ponds, lakes, reservoirs). In general, lentic bodies of water (lakes and ponds) can accumulate higher levels of contaminants in both sediments and biot a than lotic systems (rivers, streams). Also, that initial research (ICF 2005) suggests that a lake of at least 60 hectares (ha) or 150 acres could support higher trophic level predatory fish, with some fraction of their diet comprising smaller fish.

The RTR Tier 1 scenario includes a generic aquatic ecosystem with a 47-hectare (116-acre) lake. Although slightly smaller than the size suggested by the previous review (ICF 2005), a 47- ha lake is large enough to support higher trophic level fish given appropriate conditions (e.g., high productivity given a sufficient nutrient bas e and average temperature and growing season). Also, this size was compatible with the overall size of the defined watershed in the screening scenario. The fish types, biomass, diet fractions, and body weights recommended for fish compartments for the Tier 1 scenario are listed in Exhibit_Att A-11. Biomass is based on an assumption that the total fish biomass (wet-weight) for the aquatic ecosystem is 5.7 grams per square meter (g w 

### /m 2 

### , ICF 2005). That assumption yields health protective (i.e., higher) estimates of chemical concentrations in fish than would the assumption of higher standing biomass and fish productivity.TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-28 December 2013 

### In general, the food web implemented in the Tier 1 scenario is consistent with aquatic food webs that support trophic level 4 fish (to maximize bioaccumulation), and is intended to be generally health protective.

Exhibit_Att A-11. Aquatic Biota Parameters for the TRIM.FaTE Screening Scenario

TRIM.FaTE Compartment Type Organisms Represented by

Compartment Biomass

Diet Average Body Weight (kg) Areal density (g w /m 2 ) Fraction of Total Fish Biomass

Algae green algae,diatoms, bluegreen algae 7.95 – Autotrophic –

Zooplankton crustaceans, rotifers, protozoans 6.36 – 100% algae a 5.7 E

− 8

Macrophyte hydrilla, milfoil 500 – – – Water column planktivore young-of-theyear, minnows 2.0 35.1% 100% zooplankton 0.025

Water column omnivore bluegill, white perch 0.5 8.8% 100% water column planktivore 0.25

Water column carnivore largemouth bass, walleye 0.2 3.5% 100% water column omnivore 2.0

Benthic invertebrate aquatic insect larvae, crustaceans, mollusks b 20 – detritus in sediments 0.000255

Benthic omnivore small catfish, rock bass 2.0 35.1% 100% benthic invert. 0.25

Benthic carnivore large catfish, sculpins 1.0 17.5% 50% benthic invert. 50% benthic omniv. 2.0

Total Fish Biomass c 5.7

a Algae is modeled as a phase of surface water in TRIM.FaTE.

b Benthic invertebrates include aquatic insects (e.g., nymphs of mayflies, caddisflies, dragonflies, and other species that emerg e from the water when they become adults), cr ustaceans (e.g., amphipods, crayfish), and mollusks (e.g., snails, mussels).

c Total fish biomass does not include algae, macrop hytes, zooplankton, or benthic invertebrates.


### A.3.6 Using TRIM.FaTE Media Concentrations The Tier 1 scenario outputs include average PB-HAP concentrations and deposition rates for each year and for each parcel of the model scenario. In each surface parcel, deposition rates to the soil are provided as are soil concentrations for the surface, root, and vadose zones and grass or leaf concentrations as appropriate for the plants. For each air parcel, air concentrations are provided. For the lake, surface water concentrations and concentrations in the various levels of the aquatic food chain are provided. For the ingestion exposure calculations, some concentrations are used to calculate direct exposure (e.g., soil ingestion), and some are used to perform the farm food chain concentration calculations in the various media that humans can ingest (see Exhibit_Att A-4).

The locations that determine exposures were selected to be health protective. Decisions regarding which TRIM.FaTE outputs to use in calculating exposures for the Tier 1 scenario assume exposure at locations very close to the modeled source. These locations are predictedTRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-29 December 2013 

### to have amongst the highest media concentrations consistent with the specified spatial layout, thereby resulting in higher exposures to the emitted chemicals. These assumptions are summarized in Exhibit_Att A-12.

Exhibit_Att A-12. Spatial Considerations – TRIM.FaTE Results Selected for Calculating Farm Food Chain Media Concentrations and Receptor Exposures

TRIM.FaTE Output Used in Exposure Calculations Representative Compartment

Concentration in air, for uptake by plants via vapor transfer Air compartment in air Parcel N2 (air over tilled soil)

Deposition rates, for uptake by farm produce De position to surface soil compartment in surface Parcel N6 (tilled soil) Concentration in surface soil, for incidental ingestion by humans and farm animals Surface soil compartment in surface Parcel N1 (untilled soil, closest to facility) Concentration in soil, for uptake by farm produce and animal feed Surface soil compartment in surface Parcel N6 (tilled soil) Concentration in fish consumed by angler Water column carnivore compartment in lake (50% of fish consumed) and benthic carnivore in lake (50% of fish consumed)



### TRIM.FaTE can output instantaneous chemical concentrations for a user-specified time step and also can be configured to calculate temporal averages (e.g., annual averages). For the Tier 1 scenario, the model is set up to output results on a daily basis, largely because daily is the smallest time step over which input data change (i.e., wind direction and precipitation rate). Daily concentration results were averaged to obtain annual average concentrations. The default assumption is annual average concentrations for media during the fiftieth year of emissions.

For the chemicals modeled in this scenario, long-term concentrations in environmental media will be relatively constant at 50 years. Aside from mercury, chemicals modeled for RTR approach steady state before 50 years. And, although mercury concentrations do not achieve steady state after 50 years in the modeled screening scenario configuration, the rate of change in mercury concentrations shows a decreasing trend.


## A.4 Description of Exposure a nd Risk Modeling Scenario This section describes the approach for modeling chemical concentrations in farm food chain (FFC) media (Section A.4.1); estimating human exposures associated with ingestion of FFC media, incidental ingestion of soil, ingestion of fish, and infant consumption of breast milk (Section A.4.2); and calculating human health screening risk metrics associated with these exposure pathways (Section A.4.3). All of these calculations are conducted using MIRC. For this multipathway screening evaluation, partitioni ng of PB-HAPs into FFC media is modeled with MIRC, not as a part of the TRIM.FaTE modeling. Consequently, processes and inputs related to estimating chemical levels in FFC media are summarized in this section and discussed in detail in Addendum 2 to this attachment.



### A.4.1 Calculating Concentrat ions in Farm Food Chain Media As was shown in Exhibit_Att A-5, MIRC was compiled to calculate concentrations of PB-HAPs in foodstuffs that are part of the farm food chain. The FFC media included in this screening scenario include:

 

### exposed and protected fruit,

 

### exposed and protected vegetables,TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-30 December 2013  

### root vegetables,

 

### beef,

 

### dairy products,

 

### pork, and

 

### poultry and eggs.

The algorithms used in MIRC were obtained from EPA’s Human Health Risk Assessment Protocol for Hazardous Waste Combustion Facilities (HHRAP; EPA 2005a). These algorithms model the transfer of concentrations of PB-HAPs in FFC media using biotransfer factors. Environmental media concentrations (i.e., the chem ical source terms in these algorithms) are obtained from TRIM.FaTE. As noted in Section 2.2.2, the TRIM.FaTE outputs included as inputs to MIRC are the following:

 

### PB-HAP concentrations in air;

 

### air-to-surface deposition rates for PB-HAPs in both particle and vapor phases;

 

### PB-HAP concentrations in fish tissue for water column carnivores and benthic carnivores; and

 

### PB-HAP concentrations in surface soil and root zone soil. In general, plant- and animal-specific parameter va lues, including chemical-specific transfer factors for FFC media, were obtained from the Hazardous Waste Companion Database included in HHRAP (EPA 2005a). Addendum 2 to this attachment provides parameter values used in MIRC for the Tier 1 assessment.



### A.4.2 Ingestion Exposure MIRC was used to estimate ingestion rates as ADDs, normalized to body weight for a range of exposure pathways. Exposure pathways included ar e incidental ingestion of soil, consumption of fish, produce, farm animals and related products, and ingestion of breast milk by infants. The ingestion exposure pathways included in the screening evaluation and the environmental media through which these exposures occur are summarized in Exhibit_Att A-13.



### A.4.2.1 Exposure Scenario s and Corresponding Inputs Specific exposure scenarios are developed by defining the ingestion activity patterns (i.e., estimating how much of each medium is consumed and the fraction of the consumed medium that is grown in or obtained from contaminated areas) and the characteristics of the hypothetical human exposed (e.g., age and body weight). MIRC computes exposure doses and risks for each ingestion pathway separately, enabling the pathway(s) of interest for each PB-HAP to be determined. Data related to exposure factors and receptor characteristics were obtained primarily from EPA’s Exposure Factors Handbook (EPA 2011).TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-31 December 2013 

### Exhibit_Att A-13. Summary of Ingestion Exposure Pathways and Routes of Uptake

Ingestion Exposure Pathway Medium Ingested Intermediate Exposure Pathway – Farm Animals a Environmental Uptake Route

Medium Process b

Incidental ingestion of soil Untilled surface soil N/A Surface soil Deposition; transfer via erosion and runoff c

Consumption of fish Fish from local water body N/A Fish tissue Direct uptake from water and consumption of food compartments modeled in TRIM.FaTE c

Consumption of breast milk d Breast milk N/A Breast milk Contaminant ingested by mother partitions to breast milk

Consumption of produce Aboveground produce, exposed fruits and vegetables N/A Air Air Soil Deposition to leaves/plants Vapor transfer Root uptake Aboveground produce, protected fruits and vegetables N/A Soil Root uptake

Belowground produce N/A Soil Root uptake

Consumption of farm animals and related food products Beef Ingestion of forage Air Air Soil Direct deposition to plant Vapor transfer to plant Root uptake Ingestion of silage
Ingestion of grain Soil Root uptake Ingestion of soil Soil Ingestion from surface

Dairy (milk) Ingestion of forage Air Air Soil Direct deposition to plant Vapor transfer to plant Root uptake Ingestion of silage
Ingestion of grain Soil Root uptake Ingestion of soil Soil Ingestion from surface

Pork Ingestion of silage Air Air Soil Direct deposition to plant Vapor transfer to plant Root uptake Ingestion of grain Soil Root uptake Ingestion of soil Soil Ingestion from surface

Poultry Ingestion of grain Soil Root uptake Ingestion of soil Soil Ingestion from surface

Eggs Ingestion of grain Soil Root uptake Ingestion of soil Soil Ingestion from surface

a Calculation of intermediate exposure conc entrations were required only for the farm animal/animal product ingestion pathways.

b Process by which HAP enters medium ingested by humans.

c Modeled in TRIM.FaTE.

d The consumption of breast milk exposure sc enario is discussed in Section A.4.2.3.



### For the Tier 1 scenario described here, exposure c haracteristics that would result in a highly health protective estimate of total exposure were selected. The ingestion rate for each medium was set at high-end values (equal to the 90 th 

### percentile values for all food types except for fish, which was set at 99 th 

### percentile values). All media were assumed to be obtained from locations impacted by the modeled source. Although this approach could result in an overestimate ofTRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-32 December 2013 

### total chemical exposure for a hy pothetical exposure scenario (for example, note that the total food ingestion rate that results is extremely high for a hypothetical consumer with ingestion rates in the upper percentile for every food type), it was selected to avoid underestimating exposure for any single farm food type. The expos ure characteristics selected for the Tier 1 scenario are summarized in Exhibit_Att A-14.

Exhibit_Att A-14. Overview of Exposure Factors Used for RTR Multipathway Screening a,b

Exposure Factor Selection for Screening Assessment Age group evaluated Infants under 1 year (breast milk only) Children 1–2 years of age Children 3–5 years of age Children 6–11 years of age Children 12–19 years of age Adult (20–70 years) Body weight (BW; varies by age) We ighted mean of national distribution or recommended value Ingestion rate (IR) for farm produce and animal products other than fish (varies by age and medium) 90 th percentile of distribution of consumers who produce own food Ingestion rate for fish For adults, 99 th percentile as-prepared ingestion rate representative of subsistence fisher woman. For children, based on 99 th percentile, asprepared , consumer-only, national ingestion rates – adjusted. Exposure frequency (EF) 365 days/year Exposure duration Lifetime, for estimating cancer risk; varies by chemical for chronic non-cancer evaluation Fraction contaminated (FC) (varies by media consumed) c 1

Cooking loss d Assumed to be “typical”; varies depending on food product (see Addendum 2 to this attachment). Cooking losses were not considered for fish consumption because intake rates represent “as prepared” values. Food preparation/cooking adjustment factor for fish e Mercury = 1.5 Cadmium = 1.5 Dioxin = 0.7 PAH = 1.0

a Data for exposure characteristics are pr esented in Addendum 2 to this attachment. Exposure parameter values were based on data obtained primarily from the Exposure Factors Handbook (EPA 2011). See Addendum 2 to this attachment for details.

b Exposure factor inputs are used in calculating ADD estimates for each exposure pathway. ADD equations for each pathway evaluated in this screening assessm ent are provided in Addendum 2.

c Fraction contaminated represents the fraction of food product that is derived from the environment included in the screening scenario (e.g., produce grown on soil impacted by PB-HAPs). This parameter is defined s eparately for each FFC medium; however, for the Tier 1 scenario, all inges ted media are assumed to be impacted.

d Cooking loss inputs were included to simulate the amount of a food product that is not ingested due to loss during preparation or cooking, or after cooking.

e Because “as consumed,” fish cons umption rates are used with whole-fish conc entrations, adjustments might be appropriate to adjust the fish tissue concentra tions to reflect concentrations after food preparation. See Addendum 2, Section 6.4.4 for addit ional discussion.


### A.4.2.2 Calculating Average Daily Doses MIRC calculates chemical-specific ADDs norma lized to body weight (mg PB-HAP per kg of body weight per day). Equations used to ca lculate ADDs were adapted from the algorithms provided in the technical documentation of EPA’s Multimedia, Multipathway, and Multireceptor Risk Assessment (3MRA) Modeling System (EPA 2003), which derived much of its input dataTRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-33 December 2013 

### from the Exposure Factors Handbook (EPA 2011). The ingestion exposure modeling approach embodied by 3MRA is conceptually similar to that presented in HHRAP. A discussion of exposure dose estimation and the equations to calculate ADDs for each ingestion pathway are provided in Addendum 2 to this attachment.



### A.4.2.3 Infant Ingest ion of Breast Milk A nursing mother exposed to contaminants by any ingestion pathway described above can pass the contaminants to her infant through breast milk (ATSDR 1998). The nursing infant’s exposure can be estimated from the levels of chemical concentrations in the breast milk, which in turn can be estimated based on the mother’s chemical intake.

Reports of bioaccumulation of lipophilic compoun ds such as polychlorinated biphenyls (PCBs), polychlorinated dibenzofurans (PCDFs), and dioxins are prevalent in the scientific literature. Due to their high lipophilicity, these compounds parti tion almost exclusively to the milk fat of breast milk rather than the aqueous phase (EPA 1998). PCBs, PCDFs, and PCDDs are the most documented groups of contaminants found in breast milk. Other compounds with lower octanol-water partition coefficients, such as phenol, benzene, halobenzenes, and PAHs, are found in both the milk fat and the aqueous phase of br east milk. Heavy metals such as arsenic, lead, cadmium, and mercury have been found in the aqueous phase of the breast milk. However, given their chemical and physical char acteristics (and the impact such qualities have on partitioning within the body and pharmacokinetics), substances that do not partition as strongly to the lipophilic phase of breast milk tend to be of lower concern with regard to exposures to nursing infants. Because of the greater concern with regard to dioxins for this exposure pathway, it is the only PB-HAP included in the breast milk exposure pathway for RTR at this time. This approach is consistent with the risk assessment procedures included in EPA’s Human Health Risk Assessment Protocol (EPA 2005a).

Exposure via the breast milk consumption pathway is estimated in MIRC for dioxins only. This pathway is included in computing total exposure for developing the screening threshold for dioxins. In the absence of congener-specific data, dioxin congeners were assumed to manifest the same tendency to accumulate in breast milk as 2,3,7,8-TCDD.



### A.4.3 Calculating Risk MIRC was used to calculate excess lifetime cancer risk and non-cancer hazard (expressed as the hazard quotient or HQ) using the calculated ADDs and ingestion dose-response values. Chemical dose-response data include cancer slope factors (CSFs) for ingestion and non-cancer oral RfDs. The CSFs and RfDs for the PB-HAPs included in the Tier 1 scenario are presented in Exhibit_Att A-15 and are discussed in more detail in Addendum 2 to this attachment. Equations used to estimate cancer risk and non-cancer hazard also are provided in Addendum 2 to this attachment.TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-34 December 2013 

### Exhibit_Att A-15. Dose-response Values for PB-HAPs Addressed by the Screening Scenario

PB-HAP CSF ([mg/kg-day] -1 ) Source RfD (mg/kg-day) Source

Inorganics Cadmium compounds (as Cd) not available 1E

− 3 IRIS Elemental mercury not available not available Divalent mercury not available 3E

− 4 IRIS Methyl mercury not available 1E

− 4 IRIS Organics Benzo[a]pyrene a 7.3 IRIS not available 2,3,7,8-TCDD 1.5E+5 EPA ORD 7E

− 10 IRIS
Source: EPA (2007). CSF = cancer slope factor; RfD = reference dose; IRIS = EPA’s Integrated Risk Information System; Cal/EPA = California Environmental Protection Agency; EPA ORD = EPA ’s Office of Research and Development

a For consistency with the overall approach fo r dose-response assessment of PAHs, the CS F listed in IRIS for benzo[a]pyrene ([7.3 mg/kg-day] -1 ) was adjusted due to its mutagenic mode of action as discu ssed below (see also Addendum 2 to this attachment).



### Estimated individual cancer risks for the PAHs included in the screening scenario were adjusted upward to account for the mutagenic cancer potency of these compounds during childhood, as specified by EPA in supplemental guidance for cancer risk assessment (EPA 2005c). Specifically, cancer potency for PAHs is assumed to be tenfold greater for the first 2 years of life and threefold greater for the next 14 years. These factors were incorporated into a timeweighted total increase in potency over a lifetime of 70 years. The cancer potency adjustment for chemicals with a mutagenic mode of action is discussed in Addendum 2 to this attachment.



### A.4.4 Summary of Tier 1 Assumptions As emphasized previously, the screening scena rio created for evaluating PB-HAP emissions from RTR facilities is intended to be health protective to prevent underestimating risk. The scenario also is intended to avoid grossly over estimating risk to the point where no emissions screen. The overall degree to which the scenario is health protective is the sum of the multiple assumptions that affect the outputs of the fate and transport, exposure, and risk modeling. Exhibit_Att A-16 summarizes important char acteristics that influence exposure and risk estimates for this scenario and indicates the general degree of health protectiveness associated with the values for each assumption. Although this summary does not provide a quantitative estimate of the output uncertainty or the degree to which exposures and risks estimated using the scenario would be overestimated, it does demonstrate qualitatively that the scenario generally overestimates exposure and thus favors a health-protective risk output.

Exhibit_Att A-16. Summary of RTR Tier 1 Screening Scenario Assumptions

Characteristic Value Neutral or Health Protective? Comments on Assumptions

General Spatial Attributes
Farm location 375 m from source; generally downwind Health Protective Location dictates soil and air concentrations and deposition rates used to calculate chemical levels in farm produce.
Lake location 375 m from source; generally downwind Health Protective Location dictates where impacted fish population is located.TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-35 December 2013 Characteristic Value Neutral or Health Protective? Comments on Assumptions

Surface soil properties Typical values or national averages Neutral Based on existing EPA documentation and other references. Size of farm parcel About 4 ha Health Protective Relatively small parcel size results in higher chemical concentration. Size of lake 47 ha; about 3 m average depth Health Protective Lake is large enough to support an aquatic ecosystem with higher trophic-level fish, but is relatively small and shallow (thus increasing surface area-to-volume ratio). Meteorological Inputs Total precipitation 1.5 m/yr Health Protective Intended to represent rainy U.S. location; set to highest state-wide average for the contiguous United States. Precipitation frequency (with respect to impacted farm/lake) 2/3 of total precipitation fall on farm/lake and watershed Health Protective Most of total precipitation occurs when the farm/lake are downwind of the source.

Wind direction Farm/lake are downwind 40% of the time Health Protective Farm/lake located in the predominantly downwind direction. Temporal dominance of wind direction based on data from Yakima, Washington, where wind is predominantly from the west. Wind speed 2.8 m/sec Health Protective Low wind speed (5 th percentile of long-term averages for contiguous United States); increases net deposition to lake/watershed. Air temperature 298 K Neutral Typical for summer temperatures in central and southern United States. Mixing height 710 m Health Protective Relatively low long-term average mixing height (5 th percentile of long-term averages for contiguous United States); increases estimated air concentration. Watershed and Water Body Characteristics Evaporation of lake surface water 700 mm/yr Neutral Based on sens itivity analyses, value is not expected to under- or overestimate concentration in surface water.
Surface runoff into lake Equal to 40% of total precipitation Health Protective Based on typical water flow in wetter U.S. locations; higher runoff results in greater transfer of chemical to lake.
Surface water turnover rate in lake About 12 turnovers per year Neutral Consistent with calculated water balance; reasonable in light of published values for small lakes. Might overestimate flushing rate if water inputs are also overestimated. Note that after evapotranspiration, remaining water volume added via precipitation is assumed to flow into or through lake.TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-36 December 2013 Characteristic Value Neutral or Health Protective? Comments on Assumptions

Soil erosion from surface soil into lake Varies by parcel; ranges from 0.002 to 0.01 kg/m 2 -day Neutral Erosion rates calculated using the universal soil loss equation (USLE); inputs to USLE were selected to be generally conservative with regard to concentration in the pond (i.e., higher erosion rates were favored). Might underestimate erosion for locations susceptible to high erosion rates. Note that higher erosion increases concentration in lake (and fish) but decreases levels in surface soil (and farm products). Aquatic food web structure and components Multilevel; includes large, upper trophic-level fish Health Protective Inclusion of upper trophic-level fish and absence of large-bodied herbivore/detritivore fish favor higher concentrations of bioaccumulative chemicals and result in higher concentrations in consumed fish. Linear food-chain maximizes concentration of bioaccumulative chemicals in higher trophic-level fish. Parameters for Estimating Concentrations in Farm Food Chain Media Fraction of plants and soil ingested by farm animals that is contaminated 1.0 (all food and soil from contaminated areas) Health Protective Assumes livestock feed sources (including grains and silage) are derived from most highly impacted locations.

Soil- and air-to-plant transfer factors for produce and related parameters Typical (see Addendum 2 to this attachment for details) Neutral Obtained from peer-reviewed and standard EPA reference sources.

Biotransfer factors for efficiency of uptake by animal of chemical in food/soil Typical (see Addendum 2 to this attachment for details) Neutral Obtained from peer-reviewed and standard EPA reference sources.

Bioavailability of chemicals in soil (for soil ingested by animals) 1.0 (relative to bioavailability of chemical in plant matter) Health Protective Probably overestimates bioavailability in soil; many chemicals are less bioavailable in soil than in plants.TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-37 December 2013 

### Exhibit_Att A-16. Summary of RTR Screening Scenario Assumptions

Characteristic Value Neutral or Health Protective? Comments on Assumptions

Ingestion Exposure Assumptions Ingestion rates for all farm produce/livestock types Person obtains all food sources from local farm; ingestion rate is 90 th percentile ofrates for homeproduced food items Health Protective All food derived from impacted farm; total food ingestion rate would exceed expected body weight-normalized ingestion rates (prevents underestimating any individual food type).

Fish ingestion rate Adult: 373 g/day

Child age groups: 1 to 2: 108 g/day 3 to 5: 159 g/day 6 to 11: 268 g/day 12 to 19: 331 g/day Health Protective The adult rate is the 99 th percentile value for adult females from Burger (2002) and is considered representative of subsistence anglers. Rates for children are based on the 99 th percentile, consumer-only fish ingestion rates from EPA 2002. Rates were adjusted to be representative of the age groups used in the screening scenario. See Addendum 2 to this attachment for a detailed discussion. Exposure frequency Consumption of contaminated food items occurs 365 days/yr Health Protective All meals from local farm products.

Body weight Mean of national distribution Neutral Note that this does not affect the bodyweight-normalized rates for produce and animal products. Other Chemical-Specific Characteristics
General chemical properties used in fate and transport modeling (Henry’s law, K ow , etc.) Varies Neutral Obtained from peer-reviewed sources; intended to be representative of typical behavior and characteristics.

"General" physical properties (plant matter density, aquatic life biomass, algal growth rate, etc.) Varies Neutral Obtained from peer-reviewed sources; intended to be representative of typical behavior and characteristics.

Dose-response values Varies Neutral to Health Protective Values used are those determined to be appropriate for risk asse ssment by OAQPS; values are developed to be health protective.TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-38 December 2013 

## A.5 Evaluation of Screening Scenario

A.5.1 Introduction The screening scenario developed for assessing mult ipathway human health risk for EPA’s Risk and Technology Review has been subjected to a series of evaluations. The major PB-HAP categories of concern for this analysis are cadmium compounds (Section A.5.2), mercury compounds (Section A.5.3), dioxins (Section A.5.4), and POM (Section A.5.5). The scenario evaluations were focused primarily on assessing the behavior of these HAP categories in the environment, the accumulation of these chemicals in ingestible food products, and the predominant pathways of human exposure.



### A.5.2 Cadmium Compounds Some of the largest anthropogenic sources of cadmium to air are facilities that process, mine, or smelt cadmium-zinc ores or cadmium-zinc-lead ores, coal- and oil-fired boilers, other urban and industrial facilities, phosphate fertilizer manufacturing facilities, road dust, and municipal sewage sludge incinerators (ATSDR 2008).



### A.5.2.1 Behavior in the Environment Once emitted into the environment, airborne cadmium particles can be transported over long distances before being deposited. Cadmium has been observed to partition primarily to soil when released to the environment (ATSDR 2008). The mobility of cadmium in soil depends strongly on soil pH, clay content, and availability of organic matter—factors that determine whether the cadmium is dissolved or sorbed in surf ace soil. In general, cadmium adsorbs to soil particles in the surface layers of the soil profile, but to a lesser degree than many other heavy metals (HSDB 2005). Cadmium also binds strongly to organic matter, rendering the metal relatively immobile in some soils. Nonetheless, some plants still can take up cadmium efficiently, thus providing an entry point for cadmium into the food chain (ATSDR 2008).

Cadmium also enters surface waters, which can occur via atmospheric deposition, runoff and erosion, or wastewater streams. Most cadmium compounds entering the water column are quickly removed through adsorption to organic matter in sediment or to other suspended compounds. Cadmium that remains in the water co lumn is expected to exist primarily in the dissolved state where it is available for uptake by aquatic organisms.

Freshwater fish accumulate cadmium primarily through direct uptake of the dissolved form through the gills and secondarily through the diet, wh ich plays a variable role in total cadmium uptake (Reinfelder et al. 1998; Chen et al. 2000; Saiki et al. 1995). Although some biomagnification of cadmium has been reported for aquatic food chains in saltwater systems, biomagnification in freshwater systems appears to be present only at lower trophic levels (Chen et al. 2000) and in narrowly defined niches (e.g., plankton/macroinvertebrate food chains; Croteau et al. 2005). Biomagnification factor s (BMFs) of less than 1 generally have been reported for fish at higher trophic levels, indicating that cadmium concentrations generally biodiminish from lower to higher trophic levels (Chen et al. 2000; Mason et al. 2000).

For the RTR screening scenario, the partitioning behavior modeled in TRIM.FaTE was consistent with the behavior of cadmium expected in the natural environment.



### A.5.2.2 Concentrations in Ingestible Products Most non-inhalation exposure to cadmium outside of occupational settings is through dietary intake. Available data indicate that cadmium accumulates in plants, aquatic organisms, and terrestrial animals, offering multiple inges tion exposure pathways (ATSDR 2008). Actual cadmium levels in ingestible products, however, varies based on type of food, agricultural andTRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-39 December 2013 

### cultivation practices, atmospheric deposition rate s, characteristics of environmental media, and presence of other anthropogenic pollutants. Meat and fish generally contain lower amounts of cadmium overall, but cadmium can be highly concentrated in certain organ meats, such as kidney and liver (ATSDR 2008). In a study of cadmium concentrations in 14 food groups (including prepared foods), meat, cheese, and fr uits generally contained low levels of cadmium (ATSDR 2008).

For the RTR screening scenario, the cadmium concent rations output by MIRC were consistent with reported values in all ingestible media products. The products with higher reported cadmium levels in the literature, including soil, plants, and fish, also contained the higher modeled concentrations.



### A.5.2.3 Average Daily Dose (ADD) To determine the media most relevant to exposure and risk, the ingestion exposure factors must be considered in addition to the estimated media concentrations (i.e., a higher concentration for a particular medium does not necessarily mean higher risk). In Exhibit_Att A-17, the contributions of ingestion exposure pathways to the average daily dose (ADD) (and thus the HQ) for the different age categories are presented. As shown in the exhibit, fish ingestion is the dominant exposure pathway across all age categories , accounting for nearly 100 percent of the ADD for all groups. The combined contribution fr om all other exposure pathways accounts for less than 0.7 percent of the total ADD for all age groups. Most of the additional exposure was from ingestion of fruits and vegetables. The highest ADD corresponds to children aged 1–2 years; thus, the exposure corresponding to this group was used to determine the emission threshold for cadmium. In other words, the threshold emissions rate for cadmium is set at the level where the HQ for this age category is equal to 1.0.

Exhibit_Att A-17. Estimated Contributions of Modeled Food Types to Cadmium Ingestion Exposures and Hazard QuotientsTRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-40 December 2013 

### A.5.3 Mercury Compounds Some of the largest anthropogenic sources of mercury to air are facilities that process, mine, or smelt mercury ores; industrial/commercial boilers; fossil fuel combustion activities (primarily coal); cement production facilities; other urban and industrial facilities; and medical and municipal waste incinerators (ATSDR 1999). These fa cilities can emit a mixture of elemental and divalent mercury, mostly in the gaseous phase, with some divalent forms in particle-bound phases (EPA 1997).



### A.5.3.1 Behavior in the Environment Once emitted into the environment, mercury undergoes changes in form and species as it moves through environmental media. Elemental mercury is the most prevalent species of mercury in the atmosphere. Due to the long residence time of elemental mercury in the atmosphere, this compound is relatively well distributed, even on a global scale.

Divalent mercury is removed from the atmosphere at a faster rate than elemental mercury, and it can be transferred to the surface near the emission source via wet or dry deposition where it appears to adsorb tightly to soil particles (EPA 1997) or dissolved organic carbon. Divalent mercury in soil also can be methylated by microbes or reduced to elemental mercury and revolatilized back into the atmosphere. Most diva lent mercury from atmospheric deposition will remain in the soil profile, however, in the form of inorganic compounds bound to soil organic matter. Although this complexing behavior with organic matter significantly limits mercury transport, the ability of mercury to form these complexes greatly depends on soil conditions such as pH, temperature, and soil humic content. For example, mercury strongly adsorbs to humic materials and sesquioxides in soil at pH > 4 and in soils with high iron and aluminum content (ATSDR 1999). Small amounts of mercury in soil can be transported to surface water via runoff or leaching.

Mercury could also enter the water column through atmospheric fallout. Once in the water body, divalent mercury can be methylated through microbial activity. In addition, divalent and methyl mercury can be further reduced to elemental mercury, which can volatilize and reenter the atmosphere. Solid forms of inorganic mercury compounds could adsorb to particulates in the water column or partition to the sediment bed (EPA 1997).

The solubility of mercury in water depends on the species and form of mercury present as well as properties of the water such as water pH and chloride ion concentration (ATSDR 1999). Low pH favors the methylation of mercury in the water column, typically performed by sulfur-reducing bacteria in anaerobic conditions. Methyl mercur y is typically of greatest concern because it readily bioaccumulates and efficiently biomagnifies in aquatic organisms. A considerable amount (25–60 percent) of both divalent mercury compounds and methyl mercury is strongly bound to particulates in the water column (EPA 1997). The remaining mercury is dissolved. Most of the elemental mercury produced as a result of reduction of divalent mercury volatilizes back into the atmosphere.

For the screening scenario, the partitioning behavior modeled in TRIM.FaTE generally was consistent with trends noted in the literature. Divalent mercury was the most prevalent species in modeled surface soil, surface water, and sediment compartments, while methyl mercury was the dominant species in fish.



### A.5.3.2 Concentrations in Ingestible Products Available data indicate that mercury bioaccumulates in plants, aquatic organisms, and terrestrial animals, providing multiple ingestion exposure pathways (EPA 1997; ATSDR 1999). Low levels of mercury are found in plants, with leafy vegetables containing higher concentrations thanTRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-41 December 2013 

### potatoes, grains, legumes, and other vegetables and fruits (ATSDR 1999; EPA 1997). Cattle demethylate mercury in the rumen and, therefore, store very little of the mercury they ingest by foraging or consuming silage or grain. Thus, mercury content in meat and cow’s milk is low (ATSDR 1999). Concentrations of methyl mercury in fish are generally highest in larger, older specimens at the higher trophic levels (EPA 1997).

Although data on mercury in foods other than fish are not abundant in the literature, total mercury concentrations output by MIRC were generally consistent with the reported values that were available. The exposure pathways that most influenced the mercury HQs in the model are presented in Exhibit_Att A-18. As shown, the dominant exposure pathway for all age groups is ingestion of fish. Relative to divalent mercury, me thyl mercury concentrations in fish were very high (approximately 95 percent of total mercury).

Exhibit_Att A-18. Estimated Contributions of Modeled Food Types to Methyl Mercury Ingestion Exposures



### A.5.3.3 Average Daily Dose In Exhibit_Att A-18, the contributions of ingestion exposure pathways to the ADD (and thus the HQ) for methyl mercury across the different age categories are presented. As shown, fish is the dominant exposure pathway across all age categories , accounting for nearly 100 percent of the ADD for each group. The combined contribution of all other exposure pathways accounts for less than 1 percent of the total ADD for all age groups. The high degree of exposure to methyl mercury through fish ingestion is attributed to the ease with which this compound bioaccumulates and biomagnifies in fish and to the health protective ingestion assumptions used in the screening scenario. The highest ADD corresponds to children aged 1–2 years;TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-42 December 2013 

### thus, the exposure corresponding to this group was used to determine the emission threshold for mercury.



### A.5.4 Dioxins Incineration and combustion processes are believed to be the primary emission sources for chlorinated dioxins (ATSDR 1998). The five stationary source categories that generate the vast majority of 2,3,7,8-TCDD emissions in the Unit ed States are municipal waste incineration, medical waste incineration, hazardous waste kilns from Portland cement manufacturing, secondary aluminum smelting, and biological incineration.



### A.5.4.1 Behavior in the Environment Dioxins emitted to the atmosphere can be transpo rted long distances in vapor form or bound to particulates, depositing in soils and water bodies in otherwise pristine locations far from the source. Although airborne dioxins are susceptible to wet and dry deposition, most dioxins emitted to the atmosphere through incineration/combustion processes are not deposited close to the source (ATSDR 1998).

In soil, dioxins strongly adsorb to organic matter and show very little vertical movement, particularly in soils with a high organic carbon content (ATSDR 1998). Most dioxins deposited in soil are expected to remain buried in the soil profile, with erosion of contaminated soil particles the only significant mechanism for transport to water bodies.

The dry deposition of dioxins from the atmosphere to water bodies is another important transport process. Because of the hydrophobic nat ure of dioxins, most dioxins entering the water column are expected to adsorb to suspended organic particles or partition to bed sediment, which appears to be the primary envir onmental sink for this chemical group (EPA 2004c). Although dioxins bound to aquatic sediment primarily become buried in the sediment compartment, some resuspension and remobilizat ion of congeners can occur if sediments are disturbed by benthic organisms (ATSDR 1998).

Bioaccumulation factors (BAFs) in fish are high as a result of the lipophilic nature of chlorinated dioxins. Although the processes by which freshwater fish accumulate dioxins are not well understood, both fish and invertebrates bioaccumulate congeners that have partitioned to sediment or have become suspended in water (EPA 2004c). Because most dioxins in the aquatic environment are adsorbed to suspended particles, however, direct uptake from the water is unlikely to be the primary route of exposure for most aquatic organisms at higher trophic levels (ATSDR 1998). At lower trophic levels, the primary route of exposure appears to be through uptake of water in contaminated sediment pores, and the primary route of exposure in the higher trophic levels appears to be through food chain transfer. Following ingestion, some fish can slowly metabolize certain congen ers, such as 2,3,7,8-TCDD, and release the polar metabolites in bile. This process ultimately might limit bioaccumulation at higher trophic levels (ATSDR 1998).

For the RTR screening scenario, the partitioning behavior modeled in TRIM.FaTE was consistent with the behavior of 2,3,7,8-TCDD expected in the natural environment. Also of note is that dioxins readily partition into breast milk due to the lipophilic nature of these compounds.



### A.5.4.2 Concentrations in Ingestible Products The primary source of non-inhalation exposure to dioxins outside of occupational settings is through dietary intake, which accounts for more than 90 percent of daily dioxin exposure (ATSDR 1998). Available data indicate that diox ins concentrate in plants, aquatic organisms, and animals, offering multiple ingestion exposure pathways. Actual congener levels inTRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-43 December 2013 

### ingestible products, however, can vary based on type of food, agricultural and cultivation practices, atmospheric deposition rates, charac teristics of environmental media, and presence of other anthropogenic pollutants. Dioxins appear to enter the terrestrial food chain primarily through vapor-phase deposition onto surfaces of plants, which are then consumed by larger animals. Another major source of exposure to dioxins is through ingestion of contaminated soil by animals.

Observed trends indicate that meat, dairy, and fish consumption are the dominant exposure pathways, comprising 90 percent of dioxin dietary intake (ATSDR 1998). Consistent with the literature, the modeled concentration of 2,3,7, 8-TCDD in the fish compartment for the screening scenario was at least one order of magnitude greater than concentrations in the other compartments. Among the compartments with the lowest concentrations were fruits and vegetables, which do not readily accumulate 2,3,7,8-TCDD.

Ingestion of breast milk during infancy and fish ingestion contribute to over 97 percent of lifetime dioxin exposure for 2,3,7,8-TCDD in the screening scenario. Daily intakes of 2,3,7,8-TCDD from cow’s milk, produce, and fish have been reported in the literature to comprise 27 percent, 11 percent, and 10 percent, respectively, of the to tal daily intake in the general population. Some studies note that specific subpopulations, such as subsistence farmers and anglers, however, might have very different exposure pr ofiles in which fish, meat, and dairy drive congener exposure (ATSDR 1998). Given the subsistence diet modeled in the RTR screening scenario, the high exposure from consumption of fish is appropriate within the context of this analysis.



### A.5.4.3 Lifetime Average Daily Dose (LADD) The contributions of ingestion exposure pathwa ys to the lifetime average daily dose (LADD) (and thus lifetime cancer risk) for the modeled dioxin congeners are presented in Exhibit_Att A-19. Based on the modeling methodology and assumptions used, exposures via the breast milk pathway consistently account for approximat ely 30 percent of the lifetime exposure for all congeners, while exposure via fish, soil, and the various farm food chain pathways is highly variable across congeners. This variability can be explained in part by differences in the physiochemical properties that drive the envir onmental transport processes of these congeners (e.g., K ow 

### , molecular weight). The differences are also likely attributed to differences in the congener-specific half-life in abiotic media and the degree to which the congener is metabolized in biotic media.



### A.5.5 Polycyclic Aromatic Hydrocarbons PAHs can enter the atmosphere as a result of a variety of combustion processes, both natural and anthropogenic. Stationary emission sources account for approximately 80 percent of total annual PAH emissions. Although the primary source of stationary source PAH emissions is thought to be residential wood burning, other processes such as power generation; incineration; coal tar, coke, and asphalt production; and petroleum catalytic cracking are also major contributors (ATSDR 1995).TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-44 December 2013 

### Exhibit_Att A-19. Estimated Contributions of Modeled Food Types to Dioxin Ingestion Exposures



### A.5.5.1 Behavior in the Environment PAHs emitted to the atmosphere can travel long distances in vapor form or attached to particles, or they can deposit relatively close to an emission source via wet or dry deposition onto water, soil, and vegetation. In the atmosphere, PAHs are found primarily in the particle-bound phase, and atmospheric residence time and transport distances are highly influenced by climatic conditions and the size of the particles to which they are bound (ATSDR 1995).

As a result of sustained input from anthropogenic sources, PAHs are ubiquitous in soil. High molecular weight PAHs, such as benzo(a)pyrene, strongly adsorb to organic carbon in soil, indicating that adsorption to soil particles will limit the mobility of these compounds following deposition to soil (ATSDR 1995).

Most PAHs enter the water column directly through atmospheric deposition (ATSDR 1995). Following deposition onto surface waters, approximat ely two-thirds of PAHs adsorb strongly to sediment and suspended particles, while only small amounts revolatilize back to the atmosphere (ATSDR 1995). Aquatic organisms can accumulate PAHs via uptake from water, sediment, or food. Although fish and other organisms readily take up PAHs from contaminated food (e.g., aquatic insects, other benthic inver tegrates, smaller fish), biomagnification generally does not occur because many organisms can rapidly metabolize PAHs (ATSDR 1995). As a result, concentrations of PAHs have generally been observed to decrease with increasing trophic levels (ATSDR 1995). Due to this ability to metabolize PAHs, BAFs in fish are not expected to be especially high. Sediment-dwelling organisms can experience increasedTRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-45 December 2013 

### exposure to PAHs through association (e.g., direct uptake, consumption) with contaminated sediment (ATSDR 1995).

For the screening scenario, the partitioning behavior of benzo(a)pyrene is generally consistent with trends reported in the literature.



### A.5.5.2 Concentrations in Ingestible Products The primary source of non-inhalation exposure to benzo(a)pyrene outside of occupational settings is through dietary intake. Exposure can depend on the origin of the food (higher values are often recorded at contaminated sites) and the method of food preparation (higher values have been reported for food that is smoked or grilled). PAHs have been observed to bioaccumulate in aquatic organisms and terrestrial animals through uptake of contaminated water, soil, and food. These compounds are readily metabolized by higher trophic level organisms, including humans, however, so biomagnifi cation is not considered to be significant (ATSDR 1995). Plants accumulate PAHs primarily through atmospheric deposition, but chemical concentrations tend to be below detec tion levels. PAHs in meat have been observed at concentrations below detection levels up to higher concentrations when the meat is smoked. Similar concentrations have been reported for fish, with smoked fish concentrations sometimes quadruple those found in terrestrial animals. Because PAH concentrations are highest in products that are smoked or grilled, most of the available data for benzo(a)pyrene in food is for products that have been prepared using these processes. As a result, reported values might be significantly higher than those output by MIRC.

For the RTR screening scenario, concentrations output by MIRC were generally lower than the reported ranges for benzo(a)pyrene in ingestible products. This trend is likely the result of background exposure in reported measurements and available data that are skewed toward concentrations in highly contaminated products. Considering these mitigating factors, the RTR screening scenario output concentrations are within the range of plausible values for PAHs in ingestible products.



### A.5.5.3 Lifetime Average Daily Dose The contributions of ingestion exposure pathways to the LADD (and thus lifetime cancer risk) for various PAHs are presented in Exhibit_Att A-20. As shown, the variability in the driving exposure pathways across PAHs is significant, with fish, beef, dairy, fruits, and vegetables comprising between 90 and 99 percent of exposure for different PAHs.

This variability can be accounted for in part by differences in the physiochemical properties that drive the environmental fate and transport processes of these PAHs (e.g., K ow 

### , molecular weight, chemical structure), differences in the PAH-specific half-life in abiotic media, and the degree to which the PAHs are metabolized in bi otic media. The variability in exposure pathways is consistent with information provided in the literature.



### A.5.6 Summary This analysis provides a summary of the fate and transport processes and the major routes of exposure for the PB-HAP categories of interest to EPA’s RTR Program, as modeled in TRIM.FaTE. In general, the modeled behavior of t he compounds is consistent with data found in the literature.

This analysis reveals that fish ingestion is a major route of exposure for cadmium, mercury, dioxins, and PAHs. For organics (i.e., dioxins and PAHs), the farm-food-chain also is a major route of exposure, with beef and dairy contributing significantly to the LADD.TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-46 December 2013 

### Exhibit_Att A-20. Estimated Contributions of Modeled Food Types to PAH Ingestion Exposures



## A.6 References ATSDR (Agency for Toxic Substances and Disease Registry). 1995. Toxicological profile for polycyclic aromatic hydrocarbons. Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service.

ATSDR. 1998. Toxicological profile for chlorinated dibenzo-p-dioxins. Atlanta, GA: U.S. Department of Health and Human Se rvices, Public Health Service.

ATSDR. 1999. Toxicological profile for Mercury. Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service.

ATSDR. 2008. Toxicological profile for Cadmium. Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service.

Burger, J. 2002. Daily consumption of wild fish and game: Exposures of high end recreationists. International Journal of Environmental Health Research 12:343-354.

Cal/EPA (California Environmental Protection Agency) Office of Environmental Health Hazard Assessment (OEHHA). 2000. Air Toxics Hot Spots Program Risk Assessment Guidelines; Part IV, Exposure Assessment and Stochastic Analysis Technical Support Document. Section 6, Dermal Exposure Assessment. September. Available at: http://www.oehha.ca.gov/air/hot_spots/pdf/chap6.pdf.TRIM-Based Tiered Screening Methodology for RTR

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### Chen, C.Y., R.S. Stemberger, B. Klaue, J.D. Blum, P.C. Pickhardt, and C.L. Folt. 2000. Accumulation of heavy metals in food web components across a gradient of lakes. Limnology and Oceanography 45(7): 1525-1536.

Croteau, M., S.N. Luoma, and A.R. Stewart. 2005. Trophic transfer of metals along freshwater food webs: Evidence of cadmium biomagnification in nature. Limnology and Oceanography 50(5):1511-1519.

Hazardous Substances Data Bank (HSDB). 2005. Bethesda, MD: National Library of Medicine, U.S. [Last Revision Date 06/23/2005]. Cadmium Compounds; Hazardous Substances Databank Number: 6922. Available at: http://tox net.nlm.nih.gov/cgi-bin/sis/htmlgen?HSDB

Holzworth, G.C. 1972. Mixing Heights, Wind Speeds, and Potential for Urban Air Pollution Throughout the Contiguous United States,” AP-101, January 1972, U.S. Environmental Protection Agency, Office of Air Programs, Research Triangle Park, North Carolina.

ICF (ICF International). 2005. Memorandum: TRIM.FaTE Screening Scenario: Aquatic Food Web Analysis; submitted to Deirdre Murphy and Terri Hollingsworth, U.S. EPA, from Margaret McVey and Rebecca Kauffman, ICF Consulting. October 18.

Mason R.P., J. Laporte, and S. Andres. 2000. Factors controlling the bioaccumulation of mercury, methylmercury, arsenic, selenium , and cadmium by freshwater invertebrates and fish. Archives for Environmental C ontamination and Toxicology 38(3):283-97.

McKone, T.E., A. Bodnar, and E. Hertwich. 2001. Development and evaluation of state-specific landscape data sets for multimedia source-to-dose models. University of California at Berkeley. Supported by U.S. Environmental Protection Agency (Sustainable Technology Division, National Risk Management Research Laboratory) and Environmental Defense Fund. July. LBNL-43722.

Morton, F.I. 1986. Practical estimates of lake evaporation. Journal of Climate and Applied Meteorology 25:371-387.

NCDC (National Climatic Data Center). 1995. Hourly United States Weather Observations (HUSWO) 1990-1995.

NCDC. 2007. National Climatic Data Center CliMaps. Available online at: http://cdo.ncdc.noaa.gov/cgi-bin/climaps/climaps.pl.

Reinfelder, J.R., N.S. Fisher, S.N. Luoma, J.W. Nichols, and W.-X. Wang. 1998. Trace element trophic transfer in aquatic organisms: A critique of the kinetic model approach. The Science of the Total Environment 219: 117-135.

Saiki, M.K., D.T. Castleberry, T.W. May, B.A. Martin, and F.N. Bullard. 1995. Copper, cadmium, and zinc concentrations in aquatic food chains from the upper Sacramento River (California) and selected tributaries. Arch. Environ. Contam. Toxicol. 29:484-491.

Stull, R.B. 1988. An Introduction to Boundary Layer Meteorology. Kluwer Academic Publishers, The Netherlands. 452 pp.

Turner, D.B. 1970. Workbook of Atmospheric Dispersion Estimates. PHS Publication No. 999- AP-26. U.S. Department of Health, Education, and Welfare, National Air Pollution Control Administration, Cincinnati, Ohio.TRIM-Based Tiered Screening Methodology for RTR

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### EPA. 1997. Mercury Study Report to Congress. Volume III: Fate and Transport of Mercury in the Environment. EPA-452/R-97-005. Office of Air Quality Planning and Standards and Office of Research and Development. December.

EPA. 1998. Methodology for Assessing Health Risks Associated with Multiple Pathways of Exposure to Combustor Emissions. National Center for Environmental Assessment. Cincinnati, OH. EPA-600/6-90/003. December.

EPA. 1999. Short Sheet: IEUBK Model Soil/Dust Ingestion Rates. EPA-540-F-00-007; OSWER9285.7-33. Washington, D.C.: Office of Solid Waste and Emergency Response; December. Available at: http://www.epa.gov/superfund/lead/products/ssircolo.pdf.

EPA. 2002a. Total Risk Integrated Methodology: TRIM.FaTE Technical Support Document. Volume II: Description of Chemical Transpor t and Transformation Algorithms. EPA-453/R02-011b. Office of Air Quality Planning and Standards: Research Triangle Park, NC. September.

EPA. 2002b. Evaluation of TRIM.FaTE, Volume I: Approach and Initial Findings. EPA-453/R-02- 0012. Office of Air Quality and Planning Standards: Research Triangle Park, NC. September.

EPA. 2003. Multimedia, Multipathway, and Multir eceptor Risk Assessment (3MRA) ModelingSystem, Volume II: Site-based, Regional, and National Data. SAB Review Draft. EP-530/D03-001b. Office of Research and Development, Athens, GA, and Research Triangle Park, NC, and Office of Solid Waste, Washington, DC. July. Available at: http://www.epa.gov/epaoswer/hazwaste/id/hwirwste/risk03.htm.

EPA. 2004a. Air Toxics Risk Assessment Reference Library; Volume 1 – Technical Resource Document, Part III, Human Health Risk Assessm ent: Multipathway Chapter 14, Overview and Getting Started: Planning and Scoping the Multipathway Risk Assessment. Office of Air Quality Planning and Standards, Research Triangle Park, NC. April. Available at: http://www.epa.gov/ttn/fera/data/risk/vol_1/chapter_14.pdf.

EPA. 2004b. Evaluation of TRIM.FaTE Volume I II: Model Comparison Focusing on Dioxin Test Case. EPA-453/R-04-002. Available at: http://www.epa.gov/ttn/fera/data/trim/eval_rept_vol3_2005.pdf

EPA. 2004c. Exposure and Human Health Reasse ssment of 2,3,7,8-Tetrachlorodibenzo-pDioxin (TCDD) and Related Compounds. Volume 2: Properties, Environmental Levels, and Background Exposures. Dioxin Reassessment, NAS Review Draft. U.S. Environmental Protection Agency, Washington, D.C., EPA/600/P-00/001Cb. Available at: http://www.epa.gov/ncea/pdfs/dioxin/nas-review/

EPA. 2005a. Human Health Risk Assessment Protocol for Hazardous Waste Combustion Facilities (including the Hazardous Waste Companion Database of chemical-specific parameter values). U.S. Environmental Protection Agency, Office of Solid Waste and Emergency Response, Washington, DC. EPA-530-R-05-006. September.

EPA. 2005b. Evaluation of TRIM.FaTE, Volume II: Model Comparison Focusing on Mercury Test Case. EPA-453/R-05-002. Office of Air Quality and Planning Standards: Research Triangle Park, NC. July.

EPA. 2005c. Supplemental Guidance for Assessing Su sceptibility from Early-Life Exposure to Carcinogens. EPA-630/R-03-003F. Risk Assessment Forum: Washington, DC. March.TRIM-Based Tiered Screening Methodology for RTR

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### EPA. 2006. Risk and Technology Review (RTR) Asse ssment Plan. Office of Air and Radiation. November 20, 2006. Available at: http://www.epa.gov/sab/panels/consul_r isk_and_tech_assessment_plan.htm.

EPA. 2007. Prioritized Chronic Dose-Response Values for Screening Risk Assessments (Table 1). Office of Air Quality Planning and Standards; June 12, 2007. Available at: http://www.epa.gov/ttn/atw/toxsource/summary.html.

EPA. 2011. Exposure Factors Handbook: 2011 Edition. Office of Research and Development, Washington, D.C. EPA/600/R-090/052F. September. Available at: http://cfpub.epa.gov/ncea/risk/recordisplay.cfm?deid=236252

. USGS (U.S. Geological Survey). 1987. National Water Summary 1987 – Hydrologic Events and Water Supply and Use. USGS Water-Supply Paper 2350. J.E. Carr, E.B. Chase, R.W. Paulson, and D.W. Moody, Compilers.

Van den Berg, M., L.S. Birnbaum, M. Denison, M. DeVito, W. Farlans, M. Feeley, H. Fiedler, H. Hakansson, A. Hanberg, L.. Haws, M. Rose, S. Safe, D. Schrenk, C. Tohyama, A. Tritscher, J. Tuomisto, M. Tysklind, N. Walker, and R.E. Peterson. 2006. The 2005 World Health Organization reevaluation of human and mammalian toxic equivalency factors for dioxins and dioxin-like compounds. Toxicol Sci. 93(2): 223-41.

Wischmeier, W.H., and D. Smith. 1978. Predicting Rainfall Erosion Losses: A Guide to Conservation Planning. USDA-ARS Agriculture Handbook No. 537, Washington, D.C. 58
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Attachment A, Addendum 1 1-3 December 2013 TRIM.FaTE Inputs 

### Exhibits, Addendum 1

Exhibit_Add A1-1. TRIM.FaTE Simulation Parameters for the TRIM.FaTE Screening Scenario ............................................................................................. 1-5 Exhibit_Add A1-2. Meteorological Inputs for the TRIM.FaTE Screening Scenario ................... 1-6 Exhibit_Add A1-3. Air Parameters for the TRIM.FaTE Screening Scenario ............................. 1-7 Exhibit_Add A1-4. Soil and Groundwater Parameters for the TRIM.FaTE Screening Scenario ............................................................................................. 1-8 Exhibit_Add A1-5. Runoff Assumptions for the TRIM.FaTE Screening Scenario ................... 1-10 Exhibit_Add A1-6. USLE Erosion Parameters for the TRIM.FaTE Screening Scenario ............................................................................................................ 1-11 Exhibit_Add A1-7. Terrestrial Plant Placement for the TRIM.FaTE Screening Scenario ............................................................................................................ 1-12 Exhibit_Add A1-8. Terrestrial Plant Parameters for the TRIM.FaTE Screening Scenario ............................................................................................................ 1-13 Exhibit_Add A1-9. Surface Water Parameters for the TRIM.FaTE Screening Scenario ............................................................................................................ 1-15 Exhibit_Add A1-10. Sediment Parameters for the TRIM.FaTE Screening Scenario ............................................................................................................ 1-16 Exhibit_Add A1-11. Aquatic Animals Food Chain, Density, and Mass for the TRIM.FaTE Screening Scenario ....................................................................... 1-17 Exhibit_Add A1-12. Cadmium Chemical-Specific Parameters for the TRIM.FaTE Screening Scenario ........................................................................................... 1-18 Exhibit_Add A1-13. Mercury Chemical-Specific Parameters for the TRIM.FaTE Screening Scenario ........................................................................................... 1-19 Exhibit_Add A1-14. PAH Chemical-Specific Parameters for the TRIM.FaTE Screening Scenario ........................................................................................... 1-20 Exhibit_Add A1-15. Dioxin Chemical-Specific Parameters for the TRIM.FaTE Screening Scenario ........................................................................................... 1-22 Exhibit_Add A1-16. Cadmium Chemical-Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario .................................... 1-24 Exhibit_Add A1-17. Mercury Chemical-Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario ..................................... 1-25 Exhibit_Add A1-18. PAH Chemical-Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario ..................................... 1-29 Exhibit_Add A1-19. Dioxin Chemical-Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario ..................................... 1-32 Exhibit_Add A1-20. Cadmium Chemical-Specific Parameters for Plant Compartments in the TRIM.FaTE Screening Scenario ..................................... 1-36 Exhibit_Add A1-21. Mercury Chemical-Specific Parameters for Plant Compartments in the TRIM.FaTE Screening Scenario ..................................... 1-37TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-4 December 2013 TRIM.FaTE Inputs 

### Exhibit_Add A1-22. PAH Chemical-Specific Parameters for Plant Compartments in the TRIM.FaTE Screening Scenario .............................................................. 1-38 Exhibit_Add A1-23. PAH Chemical-Specific Parameters for Plant Compartments in the TRIM.FaTE Screening Scenario .............................................................. 1-39 Exhibit_Add A1-24. Cadmium Chemical-Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario ................................................ 1-41 Exhibit_Add A1-25. Mercury Chemical-Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario .............................................................. 1-43 Exhibit_Add A1-26. PAH Chemical-Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario ................................................................. 1-44 Exhibit_Add A1-27. Dioxin Chemical-Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario ................................................................. 1-47TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-5 December 2013 TRIM.FaTE Inputs 

### This attachment provides tables of the modeling inputs for the TRIM.FaTE screening scenario. Exhibit_Add A1-1 presents runtime settings fo r TRIM.FaTE. Exhibit_Add A1-2 and Exhibit_Add A1-3 present meteorological and air parameters, respectively, entered into the model. Exhibit_Add A1-4, Exhibit_Add A1-5, and Exhibit_Add A1-6 present the parameters for soil and groundwater, runoff assumptions, and the USLE (universal soil loss equation) erosion parameters, respectively, for the screening scenario. Exhibit_Add A1-7 and Exhibit_Add A1-8 present terrestrial parameters. Exhibit_Add A1-9 through Exhibit 1-11 present lake parameters, and Exhibit_Add A1-12 through Exhibit 1-27 pres ent parameters specific to the chemicals modeled in the scenario.

Exhibit_Add A1-1. TRIM.FaTE Simulation Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units Value Used Reference Start of simulation date/time 1/1/1990, midnight Consistent with met data. End of simulation date/time 1/1/2040, midnight Consistent with met data set; selected to provide a 50-year modeling period. Simulation time step hr 1 Selected value. Output time stepa hr 4 Selected value.

a Output time step is set in TRIM.FaTE using the scenario prope rties "simulationStepsPerOutputS tep" and "simulationTimeStep."



### .TRIM-Based Tiered Screening Methodology for RTRAttachment A, Addendum 1 1-6 December 2013TRIM.FaTE InputsExhibit_Add A1-2. Meteorological Inputs for the TRIM.FaTE Screening ScenarioParameter NameUnitsValue UsedReferenceMeteorological InputsAir temperaturedegrees K 298 USEPA 2005.Horizontal wind speed m/sec 2.85th percentile annual average value for contiguous United States, calculated from30 yrs of annual normal temperature values.Vertical wind speed m/sec 0.0Assumption; vertical wind speed not used by any of the algorithms in the versionof the TRIM.FaTE library used for screening.Wind directiondegreesclockwise fromN (blowingfrom)3-days-on4-days-offOn is defined as time during which wind is blowing into the model domain. Aconservative estimate of time during which wind should blow into the modelingdomain was determined by evaluating HUSWO; it was concluded that aconservative estimate would be approximately 42% of the time.Rainfall ratem3[rain]/m2[surface area]-dayvaries daily 1.5 m/yr is the maximum statewide 30-year (1971-2000) average for thecontiguous United States, excluding Rhode Island because of extreme weatherconditions on Mt. Washington. Data obtained from the National Climatic DataCenter at http://www.ncdc.noaa.gov/oa/climate/online/ccd/nrmpcp.txt. Theprecipitation frequency was 3-days-on:4-days-off based on data from Holzworth,1972.Mixing height (used to set airVE property named “top”)m 710 5th percentile annual average mixing heights (calculated from daily morning andafternoon values), for all stationson SCRM (40 state, 70 stations).isDay_SteadyState_forAir unitless --Value not used in current dynamic runs (would need to be reevaluated if steadystate runs are needed).isDay_SteadyState_forOther unitless --TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-7 December 2013 TRIM.FaTE Inputs 

### Exhibit_Add A1-3. Air Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units Value Used Reference Atmospheric dust load kg[dust]/m 3 [air] 6.15E-08 Bidleman 1988 Density of air g/cm 3 0.0012 USEPA 1997b Dust density kg[dust]/m 3 [dust] 1,400 Bidleman 1988 Fraction organic matter on particulates unitless 0.2 Harner and Bidleman 1998TRIM-Based Tiered Screening Methodology for RTRAttachment A, Addendum 1 1-8 December 2013TRIM.FaTE InputsExhibit_Add A1-4. Soil and Groundwater Parameters for the TRIM.FaTE Screening ScenarioParameter NameUnitsValue UsedReferenceSurface Soil Compartment TypeAir contentvolume[air]/volume[compartment] 0.28 McKone et al. 2001.Average vertical velocity of water(percolation)m/day 8.22E-04Assumed to be 0.2 times average precipitationfor site.Boundary layer thickness abovesurface soilm 0.005 Thibodeaux 1996; McKone et al. 2001 (Table3).Density of soil solids (dry weight)kg[soil]/m3[soil]2600 Default in McKone et al. 2001 (Table 3).Thickness - untilledam0.01 McKone et al. 2001 (p. 30).Thickness - tilledam0.20USEPA2005.Erosion fractionunitlessvariesbSee Exhibit 5.Fraction of area available forerosionm2[area available]/m2[total]1 Assumption ; area assumed rural.Fraction of area available for runoff m2[area available]/m2[total]1 Assumption ; area assumed rural.Fraction of area available for verticaldiffusionm2[area available]/m2[total]1 Assumption ; area assumed rural.Fraction sandunitless0.25 Assumption.Organic carbon fractionunitless0.008 U.S. average in McKone et al. 2001 (Table 16and A-3).pH unitless6.8Assumption.Runoff fractionunitlessvariesbSee Exhibit_Add A1-5.Total erosion ratekg [soil]/m2/day variesbSee Exhibit_Add A1-6.Total runoff ratem3[water]/m2/day1.64E-03 Calculated using scenario-specificprecipitation rate and assumptions associatedwith water balance.Water contentvolume[water]/volume[compartment] 0.15 McKone et al. 2001Root Zone Soil Compartment TypeAir contentvolume[air]/volume[compartment] 0.25 McKone et al 2001 (Table 16).Average vertical velocity of water(percolation)m/day 8.22E-04 Assumed as 0.2 times average precipitationfor New England in McKone et al. 2001.Density of soil solids (dry weight)kg[soil]/m3[soil]2,600 McKone et al. 2001 (Table 3).Fraction sandunitless0.25 Assumption.TRIM-Based Tiered Screening Methodology for RTRAttachment A, Addendum 1 1-9 December 2013TRIM.FaTE InputsExhibit_Add A1-4. Soil and Groundwater Parameters for the TRIM.FaTE Screening ScenarioParameter NameUnitsValue UsedReferenceRoot Zone Soil Compartment Type, continuedThickness - untilledam0.79 McKone et al. 2001 (Table 16 - U.S. average).Thickness - tilledam0.6 Adjusted from McKone et al. 2001 (Table 16).Organic carbon fractionunitless0.008 McKone et al. 2001 (Tables 16 and A-3, U.S.average).pH unitless6.8Assumption.Water contentvolume[water]/volume[compartment] 0.15 McKone et al. 2001Vadose Zone Soil Compartment TypeAir contentvolume[air]/volume[compartment] 0.22 McKone et al. 2001 (Table 17).Average vertical velocity of water(percolation)m/day 8.22E-04 Assumed as 0.2 times average precipitationfor New England in McKone et al. 2001.Density of soil solids (dry weight)kg[soil]/m3[soil]2,600 Default in McKone et al. 2001 (Table 3).Fraction sandunitless0.35 Assumption.Thicknessam1.4 McKone et al. 2001 (Table 17).Organic carbon fractionunitless0.003 McKone et al. 2001 (Tables 16 and A-3, U.S.average).pH unitless6.8Assumption.Water contentvolume[water]/volume[compartment] 0.21 McKone et al. 2001 (Table 17 - nationalaverage).Groundwater Compartment TypeThicknessam3 McKone et al. 2001 (Table 3).Fraction sandunitless0.4 Assumption.Organic carbon fractionunitless0.004 Assumption.pH unitless6.8Assumption.Porosity volume[totalporespace]/volume[compartment]0.2 Default in McKone et al. 2001 (Table 3).Density of solid material in aquiferkg[soil]/m3[soil]2,600 Default in McKone et al. 2001 (Table 3).aSet using the volume element properties file.bSee separate tables (Exhibit_Add A1-5 and Exhibit_Add A1-6) for erosion/runoff fractions and total erosion rates.TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-10 December 2013 TRIM.FaTE Inputs 

### Exhibit_Add A1-5. Runoff Assumptions for the TRIM.FaTE Screening Scenario
Originating Compartment Destination Compartment Runoff/Erosion Fraction SurfSoil_Source SurfSoil_N1 0.0 SurfSoil_S1 0.0 sink 1.0 SurfSoil_N1 SW_Pond 1.0 SurfSoil_Source 0.0

SurfSoil_N6 0.0 SurfSoil_S1 0.0 sink 0.0 SurfSoil_S1 SW_Pond 1.0 SurfSoil_Source 0.0

SurfSoil_N1 0.0 sink 0.0 SurfSoil_N6 SW_Pond 1.0 SurfSoil_N1 0.0 SurfSoil_N7 0.0 sink 0.0 SurfSoil_N7 SW_Pond 1.0 SurfSoil_N6 0.0 SurfSoil_N3 0.0 sink 0.0 SurfSoil_N3 SW_Pond 1.0 SurfSoil_N7 0.0 SurfSoil_N4 0.0 sink 0.0 SurfSoil_N4 SW_Pond 1.0 SurfSoil_N3 0.0 SurfSoil_N5 0.0 SurfSoil_S4 0.0 sink 0.0 SurfSoil_S4 SW_Pond 1.0 SurfSoil_N4 0.0 SurfSoil_S5 0.0 sink 0.0 SurfSoil_N5 a SW_Pond 0.0 SurfSoil_N4 0.5 SurfSoil_S5 0.5 sink 0.0 SurfSoil_S5 a SW_Pond 0.0 SurfSoil_N5 0.0 SurfSoil_S4 1.0 sink 0.0

a Assumes that N5 is higher ground than S5, and half of the runoff flows into N4, and the other half into S5. Assumes all runoff from S5 flows into S4.TRIM-Based Tiered Screening Methodology for RTRAttachment A, Addendum 1 1-11 December 2013TRIM.FaTE InputsExhibit_Add A1-6. USLE Erosion Parameters for the TRIM.FaTE Screening ScenarioSoilParcelAreaRainfall/ErosivityIndexSoilErodibilityIndexLength-SlopeFactorLandUseCoverMgmtFactorSupportingPracticesFactorUnit Soil LossSedimentDeliveryRatioaCalculated(Adjusted)ErosionRateCode m2R (100 ft-ton/ac)K(ton/ac/(100ft-ton/acre))LS(USCS)typeC(USCS)PA(ton/ac/yr)A(kg/m2/d)SDRakg/m2/dN1 5.8E+04 300 0.39 1.5 grass0.1 1 17.55 0.010779 0.533 0.005740N6 4.1E+04 300 0.39 1.5 crops0.2 1 35.1 0.021557 0.557 0.012014N7 7.3E+04 300 0.39 1.5 grass0.1 1 17.55 0.010779 0.518 0.005580N3 3.5E+05 300 0.39 1.5 grass0.1 1 17.55 0.010779 0.385 0.004151N4 2.0E+06 300 0.39 1.5 forest0.1 1 17.55 0.010779 0.309 0.003331N5 6.7E+06 300 0.39 1.5 forest0.1 1 17.55 0.010779 0.196 0.002116S1 5.8E+04 300 0.39 1.5 grass0.1 1 17.55 0.010779 0.533 0.005740S4 2.0E+06 300 0.39 1.5 forest0.1 1 17.55 0.010779 0.309 0.003331S5 6.7E+06 300 0.39 1.5 forest0.1 1 17.55 0.010779 0.196 0.002116aCalculated using SDR =a* (AL)-b;whereais the empirical intercept coefficient(based on the size of the watershed), AL isthe total watershed area receiving deposition (m2), andbis theempirical slope coefficient (always 0.125).TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-12 December 2013 TRIM.FaTE Inputs 

### Exhibit_Add A1-7. Terrestrial Plant Placement for the TRIM.FaTE Screening Scenario

Surface Soil Volume Element Surface Soil Depth (m) Coniferous Forest Grasses/ Herbs None

Source 0.01 x

N1 0.01 x
N6 0.20 (tilled) x
N7 0.01 x
N3 0.01 x
N4 0.01 x
N5 0.01 x
S1 0.01 x
S4 0.01 x
S5 0.01 xTRIM-Based Tiered Screening Methodology for RTRAttachment A, Addendum 1 1-13 December 2013TRIM.FaTE InputsExhibit_Add A1-8. Terrestrial Plant Parameters for the TRIM.FaTE Screening ScenarioParameter NameUnitsConiferousaGrass/HerbaValueUsedReferenceValueUsedReferenceLeaf Compartment TypeAllow exchange1=yes, 0=no 1-Seasonalb-Average leaf area indexm2[leaf]/m2[area]5.0 Harvard Forest, dom. red oak andred maple, CDIAC website5.0 Mid-range of 4-6 for old fields, R.J.Luxmoore, ORNL.Calculate wet depositioninterception fraction (Boolean)1=yes, 0=no 0 Assumption. 0 Assumption.Correction exponent, octanol tolipidunitless 0.76 From roots, Trapp 1995.0.76 From roots, Trapp 1995.Degree stomatal openingunitless 1 Assumed value of 1 for daytime(stomatal diffusion is turned off atnight using a different property,IsDay).1 Assumed value of 1 for daytime(stomatal diffusion is turned off at nightusing a different property, IsDay).Density of wet leafkg/m3820 Paterson et al. 1991.820 Paterson et al. 1991.Leaf wetting factorm 3.00E-04 1E-04 to 6E-04 for different cropsand elements, Muller and Prohl1993.3.00E-04 1E-04 to 6E-04 for different crops andelements, Muller and Prohl 1993.Length of leafm0.01 Assumption.0.05 Assumption.Lipid contentkg/kg wet weight 0.00224 European beech, Riederer 1995. 0.00224 European beech, Riederer 1995.Litter fall rate1/day 0.0021 value assumes 1st-orderrelationship and that 99% of leavesfall over 6 yearsSeasonalc-Stomatal area normalizedeffective diffusion path length1/m 200 Wilmer and Fricker 1996. 200 Wilmer and Fricker 1996.Vegetation attenuation factor m2/kg 2.9 Grass/hay, Baes et al. 1984.2.9 Grass/hay, Baes et al. 1984.Water contentunitless 0.8 Paterson et al. 1991.0.8 Paterson et al. 1991.Wet deposition interceptionfractionunitless 0.2 Calculated based on 5 years oflocal met data, 1987-1991.0.2 Calculated based on 5 years of local metdata, 1987-1991.Wet mass of leaf per soil area kg[freshleaf]/m2[area]2.0 Calculated from leaf area index,leaf thickness (Simonich and Hites,1994), density of wet foliage.0.6 Calculated from leaf area index and Leith1975.Particle on Leaf Compartment TypeAllow exchange1=yes, 0=no 1-Seasonalb-TRIM-Based Tiered Screening Methodology for RTRAttachment A, Addendum 1 1-14 December 2013TRIM.FaTE InputsExhibit_Add A1-8. Terrestrial Plant Parameters for the TRIM.FaTE Screening ScenarioParameter NameUnitsConiferousaGrass/HerbaValueUsedReferenceValueUsedReferenceParticle on Leaf Compartment Type, continuedVolume particle per area leaf m3[leafparticles]/m2[leaf]1.00E-09 Based on particle density and sizedistribution for atmosphericparticles measured on an adhesivesurface, Coe and Lindberg 1987.1.00E-09 Based on particle density and sizedistribution for atmospheric particlesmeasured on an adhesive surface, Coeand Lindberg 1987.Root Compartment Type – Nonwoody OnlyAllow exchange1=yes, 0=noSeasonalb-Correction exponent, octanol tolipidunitless 0.76 Trapp 1995.Lipid content of rootkg/kg wet weight0.011 Calculated.Water content of rootkg/kg wet weight0.8 Assumption.Wet density of rootkg/m3820 Soybean, Paterson et al. 1991.Wet mass per soil areakg/m21.4 Temperate grassland, Jackson et al.1996.Stem Compartment Type – Nonwoody OnlyAllow exchange1=yes, 0=noSeasonalb-Correction exponent, octanol tolipidunitless 0.76 Trapp 1995.Density of phloem fluidkg/m31,000 Assumption.Density of xylem fluidkg/cm3900 Assumption.Flow rate of transpired water perleaf aream3[water]/m2[leaf]0.0048 Crank et al. 1981.Fraction of transpiration flowrate that is phloem rateunitless 0.05 Paterson et al. 1991.Lipid content of stemkg/kg wet weight0.00224 Leaves of European beech, Riederer1995.Water content of stemunitless0.8 Paterson et al. 1991Wet density of stemkg/m3830 Assumption.Wet mass per soil areakg/m20.24 Calculated from leaf and root biomassdensity.aSee Exhibit_Add A1-7 for assignment of plant types to surface soil compartments.bBegins March 9 (set to 1), ends November7 (set to 0). Nationwide 80th percentile.cBegins November 7, ends December 6; rate = 0.15/day during this time (value assumes 99 percentof leaves fall in 30 days).TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-15 December 2013 TRIM.FaTE Inputs 

### Exhibit_Add A1-9. Surface Water Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units Value Used Reference Algae carbon content (fraction) unitless 0.465 APHA 1995.

Algae density in water column g[algae]/L[water] 0.0025 Millard et al. 1996 as cited in ICF 2005. Algae growth rate 1/day 0.7 Hudson et al. 1994 as cited in Mason et al. 1995b. Algae radius um 2.5 Mason et al. 1995b. Algae water content (fraction) unitless 0.9 APHA 1995. Average algae cell density (per volume cell, not water) g[algae]/m 3 [algae] 1,000,000 Mason et al. 1995b, Mason et al. 1996. Boundary layer thickness above sediment m 0.02 Cal EPA 1993.

Chloride concentration mg/L 8.0 Kaushal et al. 2005. Chlorophyll concentration mg/L 0.0029 ICF 2005. Depth a m 3.18 WI DNR 2007 - calculation based on relationship between drainage basin and lake area size.
Dimensionless viscous sublayer thickness unitless 4 Ambrose et al. 1995.

Drag coefficient for water body unitless 0.0011 Ambrose et al. 1995.

Flush rate 1/year 12.17 Calculated based on pond dimensions and flow calculations.
Fraction Sand unitless 0.25 Assumption. Organic carbon fraction in suspended sediments unitless 0.02 Assumption.

pH unitless 7.3 Assumption. Suspended sediment deposition velocity m/day 2 USEPA 1997b.

Total suspended sediment concentration kg[sediment]/m 3 [water column] 0.05 USEPA 2005.

Water temperature degr ees K 298 USEPA 2005.

a Set using the volume element properties named "top" and "bottom."TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-16 December 2013 TRIM.FaTE Inputs 

### Exhibit_Add A1-10. Sediment Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units Value Used Reference Depth a m 0.05 McKone et al. 2001 (Table 3).
Fraction sand unitless 0.25 Assumption. Organic carbon fraction unitless 0.02 McKone et al. 2001 (Table 3). Porosity of the sediment

zone volume[total pore space]/volume[sediment compartment] 0.6 USEPA 2005.

Solid material density in sediment kg[sediment]/m 3 [sediment] 2,600 McKone et al. 2001 (Table 3). pH unitless 7.3 Assumption. Sediment resuspension velocity m/day 6.69E-05 Calculated from water balance model.

a Set using the volume element properties named "top" and "bottom."TRIM-Based Tiered Screening Methodology for RTRAttachment A, Addendum 1 1-17 December 2013TRIM.FaTE InputsExhibit_Add A1-11. Aquatic Animals Food Chain,Density, and Mass for the TRIM.FaTE Screening ScenarioAquatic Biota(ConsumingOrganism)Fraction DietBiomass(kg/m2)BodyWeight(kg)Reference

Algae Zooplankto n Benthic Invertebrate Water Column Herbivore Benthic Omnivore Water Column Omnivore Benthic Carnivore Water Column CarnivoreBenthicInvertebrate0% 0% 0% 0% 0% 0% 0%0% 0.020 2.55E-04 Assumption.Water ColumnHerbivore0% 100% 0% 0% 0% 0% 0%0% 0.002 0.025 Assumption.Benthic Omnivore 0% 0% 100% 0% 0%0% 0% 0% 0.002 2.50E-01 Assumption.Water ColumnOmnivore0% 0% 0% 100% 0% 0% 0%0% 0.001 0.25 Assumption.Benthic Carnivore 0% 0% 50% 0% 50%0% 0% 0% 0.001 2.0 Assumption.Water ColumnCarnivore0% 0% 0% 0% 0% 100% 0%0% 0.0002 2.0 Assumption.Zooplankton 100% 0% 0% 0% 0% 0%0% 0% 0.0064 5.70E-08 Assumption.TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-18 December 2013 TRIM.FaTE Inputs 

### Exhibit_Add A1-12. Cadmium Chemical-Specific Parameters for the TRIM.FaTE Screening Scenario

Parameter Name a Units Value Reference

CAS number b unitless 7440-43-9 - Diffusion coefficient in pure air m 2 [air]/day 0.71 USEPA 1999 (Table A-2- 35). Diffusion coefficient in pure water m 2 [water]/day 8.16E-05 USEPA 1999 (Table A-2- 35). Henry's Law constant Pa-m 3 /mol 1.00E-37 USEPA 1999 (Table A-2- 35; assumed to be zero). Melting point degrees K 594 ATSDR 1999. Molecular weight g/mol 112.41 ATSDR 1999. Octanol-air partition coefficient (K oa ) m 3 [air]/m 3 [octanol] - -

Octanol-carbon partition coefficient (K oc ) - -

Octanol-water partition coefficient (K ow ) L[water]/kg[octanol] - -

a All parameters in this table ar e TRIM.FaTE chemical properties.

b This CAS numbers applies to elemental Cd; howeve r, the cations of cadmium are being modeled.TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-19 December 2013 TRIM.FaTE Inputs 

### Exhibit_Add A1-13. Mercury Chemical-Specific Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference Hg(0) b Hg(2) b MHg b

CAS number unitless 7439-97-6 14302-87-5 22967-92-6 -
Diffusion coefficient in pure air m 2 [air]/day 0.478 0.478 0.456 USEPA 1997b.

Diffusion coefficient in pure water m 2 [water]/day 5.54E-05 5.54E -05 5.28E-05 USEPA 1997b.

Henry's Law constant Pa-m 3 /mol 719 7.19E-05 0.0477 USEPA 1997b.

Melting point degrees K 234 5.50E+02 443 CARB 1994. Molecular weight g/mol 201 201 216 USEPA 1997b.
Octanol-water partition coefficient (K ow ) L[water]/kg[octanol] 4.15 3.33 1.7 Mason et al. 1996.

Vapor washout ratio m 3 [air]/m 3 [rain] 1,200 1.6E+06 0 USEPA 1997b, based on Petersen et al. 1995.

a All parameters in this table ar e TRIM.FaTE chemical properties.

b On this and all following tables, Hg(0) = elemental mercury, Hg(2) = divalent mercury, and MHg = methyl mercury.TRIM-Based Tiered Screening Methodology for RTRAttachment A, Addendum 1 1-20 December 2013TRIM.FaTE InputsExhibit_Add A1-14. PAH Chemical-Specific Parameters for the TRIM.FaTE Screening ScenarioParameter NameUnitsValue2Methyl712DMBAcenaphtheneAcenaphthyleneBaABaPBbFBghiPCAS numberunitless91-57-6 57-97-6 83-32-9 208-96-8 56-55-3 50-32-8 205-99-2 191-24-2Diffusion coefficientin pure airm2/day0.451 0.691 0.009 0.388 0.441 0.372 0.009 0.190Diffusion coefficientin pure waterm2/day6.70E-05 6.91E-05 8.64E-05 6.03E-05 7.78E-05 7.78E-05 8.64E-05 4.54E-05Henry's LawconstantPa-m3/mol50.56 0.20 18.50 12.70 1.22 0.07 0.05 0.03Melting pointdegrees K307.75 396.65 366.15 365.65 433 452 441 550.15Molecular weight g/mol142.20 256.35 154.21 152.20228.29 252.32 252.32 276.34Octanol-waterpartition coefficient(Kow)L[water]/L[octanol]7.24E+03 6.31E+05 8.32E+03 1.00E+04 6.17E+05 9.33E+05 6.03E+05 4.27E+06Parameter NameUnitsValueBkFChrDahAFluorantheneFluoreneIcdPCAS numberunitless207-08-9 218-01-9 53-70-3 206-44-0 86-73-7 193-39-5Diffusion coefficientin pure airm2/day0.009 0.009 0.009 0.009 0.009 0.009Diffusion coefficientin pure waterm2/day8.64E-05 8.64E-05 8.64E-058.64E-05 8.64E-05 8.64E-05Henry's LawconstantPa-m3/mol0.04 0.53 0.01 1.96 9.81 0.03Melting pointdegrees K490 531 539 383.15 383.15 437Molecular weight g/mol252.32 228.29 278.33 202.26 166.20 276.34Octanol-waterpartition coefficient(Kow)L[water]/L[octanol]8.71E+05 5.37E+05 3.16E+061.45E+05 1.51E+04 5.25E+06TRIM-Based Tiered Screening Methodology for RTRAttachment A, Addendum 1 1-21 December 2013TRIM.FaTE InputsExhibit_Add A1-14. PAH Chemical-Specific Parameters for the TRIM.FaTE Screening ScenarioParameter NameUnitsReferenceCAS numberunitless -Diffusion coefficient in pureairm2/day USEPA 2005. Exceptions include USEPA1997a (7,12-Dimethylbenz(a)anthracene), andUSEPA 2007 (2-Methylnaphthalene, Acenaphthylene, and Benzo(g,h,i)perylene)Diffusion coefficient in purewaterm2/day USEPA 2005. Exceptions include USEPA1997a (7,12-Dimethylbenz(a)anthracene), andUSEPA 2007 (2-Methylnaphthalene, Acenaphthylene, and Benzo(g,h,i)perylene)Henry's Law constantPa-m3/mol USEPA 2005. Exceptions include USEPA 2003 (2-Methylnaphthalene) HSDB 2001a(7,12-Dimethylbenz(a)anthracene), HSDB 2001b (Acenaphthylene), and HSDB 2001c(Benzo(g,h,i)perylene)Melting pointdegrees K Budavari 1996. Exceptionsinclude USEPA 2003 (2-Methylnaphthalene), HSDB 2001a(7,12-Dimethylbenz(a)anthracene), HSDB 2001b (Acenaphthylene), HSDB 2001c(Benzo(g,h,i)perylene), and USEPA 2005 (Acenaphthene, Fluoranthene, and Fluorene)Molecular weightg/mol Budavari 1996. Exceptionsinclude USEPA 2003 (2-Methylnaphthalene), HSDB 2001a(7,12-Dimethylbenz(a)anthracene), HSDB 2001b (Acenaphthylene), HSDB 2001c(Benzo(g,h,i)perylene), and USEPA 2005 (Acenaphthene, Fluoranthene, and Fluorene)Octanol-water partitioncoefficient (Kow)L[water]/L[octanol] Hansch et al. 1995. Exceptions include Passivirta et al. 1999 (Acenaphthylene,Benzo(k)fluoranthene, and Indeno(1,2,3-cd)pyrene), and Sangster 1993(Benzo(b)fluoranthene)TRIM-Based Tiered Screening Methodology for RTRAttachment A, Addendum 1 1-22 December 2013TRIM.FaTE InputsExhibit_Add A1-15. Dioxin Chemical-Specific Parameters forthe TRIM.FaTE Screening ScenarioParameter NameUnitsValue

1,2,3,4,6,7,8,9- OCDD 1,2,3,4,6,7,8,9- OCDF 1,2,3,4,6,7,8- HpCDD 1,2,3,4,6,7,8- HpCDF 1,2,3,4,7,8,9- HpCDF 1,2,3,4,7,8- HxCDD 1,2,3,4,7,8- HxCDFCAS numberunitless 3268-87-9 39001-02-0 35822-46-9 67562-39-4 55673-89-7 39227-28-6 70648-26-9Diffusion coefficient inpure airm2/day 0.751 0.168 0.7820.176 0.176 0.816 0.183Diffusion coefficient inpure waterm2/day 6.91E-05 6.91E-05 6.91E-05 6.91E-05 6.91E-05 6.91E-05 6.91E-05Henry's Law constantPa-m3/mol 0.684 0.19 1.22 1.43 1.42 1.08 1.45Melting pointdegrees K 603.0259.0 538.0 236.5 222.0 546.0 499.0Molecular weight g/mol 460 443.76 425.2 409.31 409.31 391 374.87Octanol-water partitioncoefficient (Kow)L[water]/L[octanol] 1.58E+08 1.00E+08 1.00E+08 2.51E+07 7.94E+06 6.31E+07 1.00E+07Parameter NameUnitsValue

1,2,3,6,7,8- HxCDD 1,2,3,6,7,8- HxCDF 1,2,3,7,8,9- HxCDD 1,2,3,7,8,9- HxCDF 1,2,3,7,8- PeCDD 1,2,3,7,8- PeCDF 2,3,4,6,7,8- HxCDFCAS numberunitless 57653-85-7 57117-44-9 19408-74-3 72918-21-9 40321-76-4 57117-41-6 60851-34-5Diffusion coefficient inpure airm2/day 0.816 0.183 0.8160.183 0.854 0.192 0.183Diffusion coefficient inpure waterm2/day 6.91E-05 6.91E-05 6.91E-05 6.91E-05 6.91E-05 6.91E-05 6.91E-05Henry's Law constantPa-m3/mol 1.11 0.741 1.11 1.11 0.263 0.507 1.11Melting pointdegrees K 558.0506.0 517.0 509.0 513.0 499.0 512.5Molecular weightg/mol 390.84 374.87 390.84 374.87 356.4 340.42 374.87Octanol-water partitioncoefficient (Kow)L[water]/L[octanol] 1.62E+08 8.24E+07 1.62E+08 3.80E+07 1.86E+07 6.17E+06 8.31E+07TRIM-Based Tiered Screening Methodology for RTRAttachment A, Addendum 1 1-23 December 2013TRIM.FaTE InputsExhibit_Add A1-15. Dioxin Chemical-Specific Parameters forthe TRIM.FaTE Screening ScenarioParameter NameUnitsValueReference

2,3,4,7,8- PeCDF 2,3,7,8- TCDD 2,3,7,8- TCDFCAS numberunitless 57117-31-4 1746-01-6 51207-31-9 -Diffusion coefficient inpure airm2/day 0.192 0.8990.203 US EPA 2005Diffusion coefficient inpure waterm2/day 6.91E-05 4.84E-05 5.19E-05 US EPA 2005Henry's Law constant Pa-m3/mol 0.505 3.33 1.46 US EPA 2005Melting pointdegrees K 469.3 578.0 500.0 Mackayet al. 2000. Exceptions include USEPA 2000a(1,2,3,6,7,8-HxCDD, 1,2,3,7,8,9-HxCDF, and 1,2,3,7,8-PeCDD), ATSDR 1998 (1,2,3,6,7,8-HxCDF, 1,2,3,7,8-PeCDF, and 2,3,4,6,7,8-HxCDF), and NLM 2002(1,2,3,7,8,9-HxCDD)Molecular weightg/mol 340.42 322 306 Mackay et al. 2000. Exceptions include ATSDR 1998(1,2,3,6,7,8-HxCDF, 1,2,3,7,8,9-HxCDF, 1,2,3,7,8-PeCDD,1,2,3,7,8-PeCDF, and 2,3,4,6,7,8-HxCDF) and NLM 2002(1,2,3,6,7,8-HxCDD and 1,2,3,7,8,9-HxCDD)Octanol-water partitioncoefficient (Kow)L[water]/L[octanol] 3.16E+06 6.31E+06 1.26E+06 Mackay et al. 1992 ascited in USEPA 2000b. Exceptionsinclude Mackay et al. 2000 (1,2,3,4,7,8,9-HpCDF), USEPA2000a (1,2,3,6,7,8-HxCDD, 1,2,3,6,7,8-HxCDF, 1,2,3,7,8,9-HxCDD, 1,2,3,7,8,9-HxCDF, and 2,3,4,6,7,8-HxCDF), andSijm et al. 1989 as cited in USEPA 2000b (1,2,3,7,8-PeCDD)TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-24 December 2013 TRIM.FaTE Inputs



### Exhibit_Add A1-16. Cadmium Chemical-Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference

Air Compartment Type Particle dry deposition velocity m/day 260 Calculated from Muhlbaier and Tissue 1980.
Washout ratio m 3 [air]/m 3 [rain] 200,000 MacKay et al. 1986. Surface Soil Compartment Type Use input characteristic depth (Boolean) 0 = no, Else = yes 0 Assumption.

Root Zone Soil Compartment Type Use input characteristic depth (Boolean) 0 = no, Else = yes 0 Assumption.

Vadose Zone Soil Compartment Type Use input characteristic depth (Boolean) 0 = no, Else = yes 0 Assumption.

Surface Water Compartment Type
Ratio of concentration in water to concentration in algae to concentration dissolved in water L[water]/g[algae wet wt] 1.87 McGeer et al. 2003.TRIM-Based Tiered Screening Methodology for RTRAttachment A, Addendum 1 1-25 December 2013TRIM.FaTE InputsExhibit_Add A1-17. Mercury Chemical-Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening ScenarioParameter NameUnitsValueReferenceHg(0)Hg(2)MHgAir Compartment TypeParticle dry deposition velocitym/day500 500500 CalTOX value cited in McKone et al. 2001.Demethylation rate1/dayN/A N/A 0 Assumption.Methylation rate1/day0 0 0 Assumption.Oxidation rate1/day 0.00385 0 0 Low end of half-life range (6 months to 2 years) inUSEPA 1997b.Reduction rate1/day0 0 0 Assumption.Washout ratiom3[air]/m3[rain] 200,000 200,000 200,000 Assumption.Surface Soil Compartment TypeUse input characteristic depth(Boolean)0 = no, Else = yes 0 0 0 Assumption.Soil-water partition coefficientL[water]/kg[soilwet wt]1,000 58,000 7,000 USEPA 1997b.Vapor dry deposition velocitym/day50 2500 0 Hg(0) - from Lindberg et al. 1992; Hg(2) - estimateby USEPA using the Industrial Source Complex(ISC) Model - [See Vol. III, App. A of the MercuryStudy Report (USEPA 1997b)].Demethylation rate1/dayN/A N/A 0.06 Range reported in Porvari and Verta 1995 is 3E-2to 6E-2 /day; value is average maximum potentialdemethylation rate constant under anaerobicconditions.Methylation rate1/day0 0.001 0 Range reported in Porvari and Verta 1995 is 2E-4to 1E-3 /day; value is average maximum potentialmethylation rate constant under anaerobicconditions.Oxidation rate1/day0 0 0Value assumed in USEPA 1997b.Reduction rate1/day0 1.25E-05 0 Valueused for untilled surface soil (2cm), 10%moisture content, in USEPA 1997b; general rangeis (0.0013/day)*moisture content to(0.0001/day)*moisture content for forested region(Lindberg 1996; Carpi and Lindberg 1997).TRIM-Based Tiered Screening Methodology for RTRAttachment A, Addendum 1 1-26 December 2013TRIM.FaTE InputsExhibit_Add A1-17. Mercury Chemical-Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening ScenarioParameter NameUnitsValueReferenceHg(0)Hg(2)MHgRoot Zone Soil Compartment TypeUse input characteristic depth (Boolean)0 = no, Else = yes 0 0 0 Assumption.Soil-water partition coefficientL[water]/kg[soilwet wt]1,000 58,000 7,000 USEPA 1997b.Demethylation rate1/dayN/A N/A 0.06 Range reported in Porvari and Verta 1995 is 3E-2to 6E-2 /day; value is average maximum potentialdemethylation rate constant under anaerobicconditions.Methylation rate1/day0 0.001 0 Range reported in Porvari and Verta 1995 is 2E-4to 1E-3 /day; value is average maximum potentialmethylation rate constant under anaerobicconditions.Oxidation rate1/day0 0 0Value assumed in USEPA 1997b.Reduction rate1/day0 3.25E-06 0 Value used for tilled surface soil (20cm), 10%moisture content, in USEPA 1997b (Lindberg1996; Carpi and Lindberg 1997).Vadose Zone Soil Compartment TypeUse input characteristic depth (Boolean)0 = no, Else = yes 0 0 0 Assumption.Soil-water partition coefficientL[water]/kg[soilwet wt]1,000 58,000 7,000 USEPA 1997b.Demethylation rate1/dayN/A N/A 0.06 Range reported in Porvari and Verta 1995 is 3E-2to 6E-2 /day; value is average maximum potentialdemethylation rate constant under anaerobicconditions.Methylation rate1/day0 0.001 0 Range reported in Porvari and Verta 1995 is 2E-4to 1E-3 /day; value is average maximum potentialmethylation rate constant under anaerobicconditions.Oxidation rate1/day0 0 0Value assumed in USEPA 1997b.Reduction rate1/day0 3.25E-06 0 Value used for tilled surface soil (20cm), 10%moisture content, in USEPA 1997b (Lindberg1996; Carpi and Lindberg 1997).TRIM-Based Tiered Screening Methodology for RTRAttachment A, Addendum 1 1-27 December 2013TRIM.FaTE InputsExhibit_Add A1-17. Mercury Chemical-Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening ScenarioParameter NameUnitsValueReferenceHg(0)Hg(2)MHgGroundwater Compartment TypeSoil-water partition coefficientL[water]/kg[soilwet wt]1,000 58,000 7,000 USEPA 1997b.Demethylation rate1/dayN/A N/A 0.06 Range reported in Porvari and Verta 1995 is 3E-2to 6E-2 /day; value is average maximum potentialdemethylation rate constant under anaerobicconditions.Methylation rate1/day0 0.001 0 Range reported in Porvari and Verta 1995 is 2E-4to 1E-3 /day; value is average maximum potentialmethylation rate constant under anaerobicconditions.Oxidation rate1/day 1.00E-08 0 0 Smalldefault nonzero value (0 assumed in USEPA1997b).Reduction rate1/day0 3.25E-06 0 Value used for tilled surface soil (20cm), 10%moisture content, in USEPA 1997b (Lindberg1996; Carpi and Lindberg 1997).Surface Water Compartment TypeAlgal surface area-specific uptake rateconstantnmol/[μm2-day-nmol] 0 2.04E-10 3.60E-10 Assumes radius = 2.5mm, Mason et al. 1995b,Mason et al. 1996; Hg(0) assumed same as Hg(2).Dow("overall Kow")L[water]/kg[octanol] 0 -a -b Mason et al. 1996.Solids-water partition coefficientL[water]/kg[solidswet wt]1,000 100,000 100,000 USEPA 1997b.Vapor dry deposition velocitym/dayN/A 2500 USEPA 1997b (Vol. III, App. A).Demethylation rate1/dayN/A N/A 0.013 Average range of 1E-3 to 2.5E-2/day from Gilmourand Henry 1991.Methylation rate1/day0 0.001 0 Value used in EPA 1997; range is 1E-4 to 3E4/day (Gilmour and Henry 1991).Oxidation rate1/day0 0 0 Assumption.Reduction rate1/day0 0.0075 0 Value used in USEPA 1997b; reported valuesrange from less than 5E-3/day for depths greaterthan 17m, up to 3.5/day (Xiao et al. 1995; Vandalet al. 1995; Mason et al. 1995a; Amyot et al.1997).TRIM-Based Tiered Screening Methodology for RTRAttachment A, Addendum 1 1-28 December 2013TRIM.FaTE InputsExhibit_Add A1-17. Mercury Chemical-Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening ScenarioParameter NameUnitsValueReferenceHg(0)Hg(2)MHgSediment Compartment TypeSolids-water partition coefficientL[water]/kg[solidswet wt]3,000 50,000 3,000 USEPA 1997b.Demethylation rate1/dayN/A N/A 0.0501 Average range of 2E-4 to 1E-1/day from Gilmourand Henry 1991.Methylation rate1/day0 1.00E-04 0 Value used in EPA 1997b; range is 1E-5 to1E-3/day, Gilmour and Henry 1991.Oxidation rate1/day 0 0 0 Assumption.Reduction rate1/day0 1.00E-06 0 Inferred value based on presence of Hg(0) insediment porewater (USEPA 1997b; Vandal et al.1995).aTRIM.FaTE Formula Property, which varies from 0.025 to 1.625 depending on pH and chloride concentration.bTRIM.FaTE Formula Property, which varies from 0.075to 1.7 depending on pH and chloride concentration.TRIM-Based Tiered Screening Methodology for RTRAttachment A, Addendum 1 1-29 December 2013TRIM.FaTE InputsExhibit_Add A1-18. PAH Chemical-Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening ScenarioParameter NameUnitsValue2Methyl712DMBAcenaph-theneAcenaph-thyleneBaABaPBbFBghiPBkFAir Compartment TypeParticle dry deposition velocity m/day 500 500 500 500 500 500 500 500 500Half-life day0.1540.0920.30.2080.125 0.046 0.596 0.215 0.458Washout ratio200000 200000 200000 200000 200000 200000 200000 200000 200000Surface Soil Compartment TypeUser input characteristic depth(Boolean)0 = No, Else = Yes 0 0 0 0 0 0 0 0 0Half-lifeday 18 24 56 66.5 680 530 610 415 2140Root Zone Soil Compartment TypeUser input characteristic depth(Boolean)0 = No, Else = Yes 0 0 0 0 0 0 0 0 0Half-lifeday 18 24 56 66.5 680 530 610 415 2140Vadose Zone Soil Compartment TypeUser input characteristic depth(Boolean)0 = No, Else = Yes 0 0 0 0 0 0 0 0 0Half-life day36481121331360106012208304280Groundwater Compartment TypeHalf-life day36481121331360106012208304280Surface Water Compartment TypeRatio Of Conc In Algae ToConc Dissolved In Water(g[chem]/kg[algae]) /(g[chem]/L[water])2.6 333.4 3 3.7 325 510 317 1539 473Half-life day78216251840.3750.13890167062.4Sediment Compartment TypeHalf-life day229022902290229022902290229022902290TRIM-Based Tiered Screening Methodology for RTRAttachment A, Addendum 1 1-30 December 2013TRIM.FaTE InputsExhibit_Add A1-18. PAH Chemical-Specific Parameters forAbiotic Compartments in the TRIM.FaTE Screening ScenarioParameter NameUnitsValueReferenceChrDahAFluoran-theneFluor-eneIcdPAir Compartment TypeParticle dry deposition velocity m/day 500 500 500 500 500 McKone et al. 2001.Half-life day0.3340.1780.460.460.262Howard et al. 1991 / upper bound measured orestimated value. Exceptions include ATSDR 2005 (2-Methylnaphthalene), USEPA 1998 (7,12-Dimethylbenz(a)anthracene,Benzo(g,h,i)perylene,and Fluoranthene) / average of range, HSDB 2001d(Acenaphthene), HSDB 2001b (Acenaphthylene), andSpero et al. 2000 (Fluorene).Washout ratio200000 200000 200000 200000 200000 Mackay et al. 1986.Surface Soil Compartment TypeUser input characteristic depth(Boolean)0 = No,Else = Yes0 0 0 0 0 Assumption.Half-life day100094027533730MacKayetal. 2000 / average of range. Exceptionsinclude ATSDR 2005 (2-Methylnaphthalene), USEPA1998 (7,12-Dimethylbenz(a)anthracene,Benzo(g,h,i)perylene, andFluoranthene) / average ofrange, HSDB 2001d (Acenaphthene), HSDB 2001b(Acenaphthylene), and HSDB 2001e (Fluorene).Root Zone Soil Compartment TypeUser input characteristic depth(Boolean)0 = No,Else = Yes0 0 0 0 0 Assumption.Half-lifeday 1000 940 275 33 730 Howard et al. 1991 / upper bound measured orestimated value. Exceptions include ATSDR 2005 (2-Methylnaphthalene), USEPA 1998 (7,12-Dimethylbenz(a)anthracene,Benzo(g,h,i)perylene,and Fluoranthene) / average of range, HSDB 2001d(Acenaphthene), HSDB 2001b (Acenaphthylene), andHSDB 2001e (Fluorene).Vadose Zone Soil Compartment TypeUser input characteristic depth(Boolean)0 = No, Else =Yes0 0 0 0 0 Assumption.TRIM-Based Tiered Screening Methodology for RTRAttachment A, Addendum 1 1-31 December 2013TRIM.FaTE InputsExhibit_Add A1-18. PAH Chemical-Specific Parameters forAbiotic Compartments in the TRIM.FaTE Screening ScenarioParameter NameUnitsValueReferenceChrDahAFluoran-theneFluor-eneIcdPVadose Zone Soil Compartment Type, continuedHalf-lifeday 2000 1880 550 66 1460 Howard et al. 1991 / upper bound measured orestimated value. Exceptions include ATSDR 2005 (2-Methylnaphthalene), USEPA 1998 (7,12-Dimethylbenz(a)anthracene,Benzo(g,h,i)perylene, andFluoranthene) / twice average of range, HSDB 2001d(Acenaphthene) / multiplied by 2, HSDB 2001b(Acenaphthylene) / multiplied by 2, and HSDB 2001e(Fluorene) / multiplied by 2.Groundwater Compartment TypeHalf-lifeday 2000 1880 550 66 1460 Howard et al. 1991 / upper bound measured orestimated value. Exceptions include ATSDR 2005 (2-Methylnaphthalene), USEPA 1998 (7,12-Dimethylbenz(a)anthracene,Benzo(g,h,i)perylene, andFluoranthene) / twice average of range, HSDB 2001d(Acenaphthene) / multiplied by 2, HSDB 2001b(Acenaphthylene) / multiplied by 2, and HSDB 2001e(Fluorene) / multiplied by 2.Surface Water Compartment TypeRatioOfConcInAlgaeToConcDissolvedInWater(g[chem]/kg[algae])/(g[chem]/L[water])280 1388 67.4 5.8 1653 Kowfrom Del Vento and Dachs 2002.Half-lifeday 1.626 97.8 160 8.5 750 Howard et al. 1991 / upper bound measured orestimated value. Exceptions include HSDB 2005 (2-Methylnaphthalene), HSDB 2001a (7-12Dimethylbenz(a)anthracene), HSDB 2001d(Acenaphthene), HSDB 2001b (Acenaphthylene), andHSDB 2001c (Benzo(g,h,i)perylene), Montgomery 2000(Fluoranthene), and Boyle 1985 (Fluorene).Sediment Compartment TypeHalf-lifeday 2290 2290 2290 2290 2290 Mackay et al. 1992 / PAH values are the mean half-lifeof the log class that Mackay et al. assigned forsediment, except for BbF and IcdP, which were not inTable 2.3 of Mackay et. al.TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-32 December 2013 TRIM.FaTE Inputs 

### Exhibit_Add A1-19. Dioxin Chemical-Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value1,2,3,4,6,7,8,9-OCDD1,2,3,4,6,7,8,9-OCDF1,2,3,4,6,7,8-HpCDD1,2,3,4,6,7,8-HpCDF1,2,3,4,7,8,9-HpCDF1,2,3,4,7,8-HxCDD

Air Compartment Type Deposition Velocity m/day 500 500 500 500 500 500

Half-life day 162 321 64 137 122 42
Washout Ratio m 3 [air]/m 3 [rain] 91000 22000 64000 32000 32000 9000 Surface Soil Compartment Type Input characteristic depth m 0.08 0.08 0.08 0.08 0.08 0.08 Use input characteristic depth (Boolean) 0 = No, Else = Yes 0 0 0 0 0 0

Halflife day 3650 3650 3650 3650 3650 3650 Root Zone Soil Compartment Type Input characteristic depth m 0.08 0.08 0.08 0.08 0.08 0.08 Use input characteristic depth 0 = No, Else = Yes 0 0 0 0 0 0

Half-life day 3650 3650 3650 3650 3650 3650 Vadose Zone Soil Compartment Type Input characteristic depth m 0.08 0.08 0.08 0.08 0.08 0.08 Use input characteristic depth (Boolean) 0 = No, Else = Yes 0 0 0 0 0 0

Half-life day 1008 1008 1008 1008 1008 1008 Groundwater Compartment Type
Half-life day 1008 1008 1008 1008 1008 1008 Surface Water Compartment Type Ratio Of Conc In Algae To Conc Dissolved In Water (g[chem]/g[algae])/ (g[chem]/L[water]) 5.31 4.54 4.54 2.83 1.9 3.88

Half-life day 0.67 0.58 47 0.58 0.58 6.3 Sediment Compartment Type
Half-life day 1095 1095 1095 1095 1095 1095TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-33 December 2013 TRIM.FaTE Inputs 

### Exhibit_Add A1-19. Dioxin Chemical-Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value1,2,3,4,7,8-HxCDF1,2,3,6,7,8-HxCDD1,2,3,6,7,8-HxCDF1,2,3,7,8,9-HxCDD1,2,3,7,8,9-HxCDF1,2,3,7,8-PeCDD

Air Compartment Type Deposition velocity m/day 500 500 500 500 500 500
Half-life day 78 28 55 28 51 18
Washout ratio m 3 [air]/m 3 [rain] 10000 9000 10000 9000 10000 18000 Surface Soil Compartment Type Input characteristic depth m 0.08 0.08 0.08 0.08 0.08 0.08 Use input characteristic depth (Boolean) 0 = No, Else = Yes 0 0 0 0 0 0

Half-life day 3650 3650 3650 3650 3650 3650 Root Zone Soil Compartment Type Input characteristic depth m 0.08 0.08 0.08 0.08 0.08 0.08 Use input characteristic depth 0 = No, Else = Yes 0 0 0 0 0 0

Half-life day 3650 3650 3650 3650 3650 3650 Vadose Zone Soil Compartment Type Input characteristic depth m 0.08 0.08 0.08 0.08 0.08 0.08 Use input characteristic depth (Boolean) 0 = No, Else = Yes 0 0 0 0 0 0

Half-life day 1008 1008 1008 1008 1008 1008 Groundwater Compartment Type
Half-life day 1008 1008 1008 1008 1008 1008 Surface Water Compartment Type Ratio Of Conc In Algae To Conc Dissolved In Water (g[chem]/g[algae])/ (g[chem]/L[water]) 2.06 5.36 4.25 5.36 3.26 1.55

Half-life day 0.58 6. 3 0.58 6.3 0.58 2.7 Sediment Compartment Type
Half-life day 1095 1095 1095 1095 1095 1095TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-34 December 2013 TRIM.FaTE Inputs 

### Exhibit_Add A1-19. Dioxin Chemical-Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value1,2,3,7,8-PeCDF2,3,4,6,7,8-HxCDF2,3,4,7,8-PeCDF2,3,7,8-TCDD2,3,7,8-TCDF

Air Compartment Type Deposition velocity m/day 500 500 500 500 500
Half-life day 31 59 33 12 19
Washout ratio m 3 [air]/m 3 [rain] 13000 10000 14000 18000 19000 Surface Soil Compartment Type Input characteristic depth m 0. 08 0.08 0.08 0.08 0.08 Use input characteristic depth (Boolean) 0 = No, Else = Yes 0 0 0 0 0

Half-life day 3650 3650 3650 3650 3650 Root Zone Soil Compartment Type Input characteristic depth m 0. 08 0.08 0.08 0.08 0.08 Use input characteristic depth 0 = No, Else = Yes 0 0 0 0 0
Half-life day 3650 3650 3650 3650 3650 Vadose Zone Soil Compartment Type Input characteristic depth m 0. 08 0.08 0.08 0.08 0.08 Use input characteristic depth (Boolean) 0 = No, Else = Yes 0 0 0 0 0

Half-life day 1008 1008 1008 1008 1008 Groundwater Compartment Type
Half-life day 1008 1008 1008 1008 1008 Surface Water Compartment Type Ratio Of Conc In Algae To Conc Dissolved In Water (g[chem]/g[algae])/ (g[chem]/L[water]) 1.75 4.26 1.39 1.76 0.71

Half-life day 0.19 0.58 0.19 2.7 0.18 Sediment Compartment Type
Half-life day 1095 1095 1095 1095 1095TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-35 December 2013 TRIM.FaTE Inputs 

### Exhibit_Add A1-19. Dioxin Chemical-Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Reference

Air Compartment Type Deposition velocity McKone et al. 2001.
Half-life Atkinson 1996 as ci ted in USEPA 2000b; vapor phase reaction with hydroxyl radical.
Washout ratio Vulykh et al. 2001. Surface Soil Compartment Type Input characteristic depth Not used (model set to calculate value). Use input characteristic de pth (Boolean) Assumption.
Half-life Mackay et al. 2000; the degradation rate was cited by multiple authors, value is for 2,3,7,8-TCDD. Root Zone Soil Compartment Type Input characteristic depth Not used (model set to calculate value). Use input characteristic depth Assumption.
Half-life Mackay et al. 2000; the degradation rate was cited by multiple authors, value is for 2,3,7,8-TCDD. Vadose Zone Soil Compartment Type Input characteristic depth Not used (model set to calculate value). Use input characteristic de pth (Boolean) Assumption.
Half-life Average value of the range presented in Mackay et al. 2000; based on estimated unacclimated aerobic biodegradation half-life, value is for 2,3,7,8-TCDD. Groundwater Compartment Type
Half-life Average value of the range presented in Mackay et al. 2000; based on estimated unacclimated aerobic biodegradation half-life, value is for 2,3,7,8-TCDD. Surface Water Compartment Type Ratio Of Conc In Algae To Conc Dissolved In Water Estimated from K ow value using model from DelVento and Dachs 2002
Half-life Kim and O’Keefe 1998, as cited in USEPA 2000b. Sediment Compartment Type
Half-life Estimation based on Adriaens and Grbic-Galic 1992,1993 and Adriaens et al. 1995 as cited in USEPA 2000b.TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-36 December 2013 TRIM.FaTE Inputs 

### Exhibit_Add A1-20. Cadmium Chemical-Specific Parameters for Plant Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference

Leaf Compartment Type Transfer factor to leaf particle 1/day 0.002 Assumption. Particle on Leaf Compartment Type
Transfer factor to leaf 1/day 0.200 Assumption. Root Compartment Type - Grasses and Herbs a

Root to Root Soil PartitionAlpha of Steady State unitless 0.95 Henning et al. 2001.

Root to Root Soil PartitionPartitioning Coefficient m 3 [bulk root soil]/m 3 [root] 0.23 Nriagu 1980; based on average value calculated from various agricultural plant species.
Root to Root Soil PartitionTime to Reach Alpha day 28 Henning et al. 2001.

Stem Compartment Type - Grasses and Herbs a Transpiration stream concentration factor (TSCF) m 3 [soil pore water]/m 3 [xylem fluid] 0.45 Tsiros et al. 1999.

a Roots and stems are not modeled for deciduous or conifer ous forest in the current version of TRIM.FaTE.TRIM-Based Tiered Screening Methodology for RTRAttachment A, Addendum 1 1-37 December 2013TRIM.FaTE InputsExhibit_Add A1-21. Mercury Chemical-Specific Parameters for Plant Compartments in the TRIM.FaTE Screening ScenarioParameter NameUnitsValueReferenceHg(0)Hg(2)MHgLeaf Compartment TypeTransfer factor to leaf particle1/day0.002 0.0020.002 Assumed based on 1%of transfer factorfrom leaf particle to leaf.Demethylation rate1/dayN/A N/A 0.03 Calculated from Bache et al. 1973.Methylation rate1/day0 0 0 Assumed from Gay 1975, Bache et al.1973.Oxidation rate1/day1.0E+06 0 0 Assumed to be nearly instantaneousReduction rate1/day 0 0 0 Assumption.Particle on Leaf Compartment TypeTransfer factor to leaf1/day0.2 0.2 0.2 Assumption.Demethylation rate1/dayN/A N/A 0 Assumption.Methylation rate1/day 0 0 0 Assumption.Oxidation rate1/day 0 0 0 Assumption.Reduction rate1/day 0 0 0 Assumption.Root Compartment Type - Grasses and HerbsaAlpha for root-root zone bulk soilunitless0.95 0.95 0.95 Selected value.Root/root-zone-soil-water partitioncoefficientm3[bulk root soil]/ m3[root] 0 0.18 1.2 Hg2-geometric mean Leonard et al. 1998,John 1972, Hogg et al. 1978; MHgassumed, based on Hogg et al. 1978.t-alpha for root-root zone bulk soilday21 21 21 Assumption.Demethylation rate1/dayN/A N/A 0 Assumption.Methylation rate1/day 0 0 0 Assumption.Oxidation rate1/day 0 0 0 Assumption.Reduction rate1/day 0 0 0 Assumption.Stem Compartment Type - Grasses and HerbsaTranspiration stream concentrationfactor (TSCF)m3[soil porewater]/m3[xylem fluid]0 0.5 0.2 Calculation fromNorway spruce, Scotspine, Bishop et al. 1998.Demethylation rate1/dayN/A N/A 0.03 Calculated from Bache et al. 1973.Methylation rate1/day 0 0 0 Assumption.Oxidation rate1/day 0 0 0 Assumption.Reduction rate1/day 0 0 0 Assumption.aRoots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE.TRIM-Based Tiered Screening Methodology for RTRAttachment A, Addendum 1 1-38 December 2013TRIM.FaTE InputsExhibit_Add A1-22. PAH Chemical-Specific Parameters for Plant Compartments in the TRIM.FaTE Screening ScenarioParameter NameUnitsValue2Methyl712DMBAcenaph-theneAcenaph-thyleneBaABaPBbFBghiPBkFLeaf Compartment TypeTransfer factor toleaf particle1/day 1.00E-04 1.00E-04 1.00E-04 1.00E-04 1.00E-04 1.00E-04 1.00E-04 1.00E-04 1.00E-04Half-life day 3.50 3.50 3.50 3.50 3.50 3.50 3.50 3.50 3.50Particle on Leaf Compartment TypeTransfer factor toleaf1/day 1.00E-04 1.00E-04 1.00E-04 1.00E-04 1.00E-04 1.00E-04 1.00E-04 1.00E-04 1.00E-04Half-life day 2.31 2.31 2.31 2.31 1.84 2.31 3.56 2.31 17.80Root Compartment Type - Grasses and HerbsaHalf-life day 34.60 34.60 34.60 34.60 34.60 34.60 34.60 34.60 34.60Root soil-waterinteraction - alphaunitless 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95Stem Compartment Type - Grasses and HerbsaHalf-life day 3.50 3.50 3.50 3.50 3.50 3.50 3.50 3.50 3.50aRoots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE.TRIM-Based Tiered Screening Methodology for RTRAttachment A, Addendum 1 1-39 December 2013TRIM.FaTE InputsExhibit_Add A1-23. PAH Chemical-Specific Parameters for Plant Compartments in the TRIM.FaTE Screening ScenarioParameter NameUnitsValueReferenceChrDahAFluoran-theneFluoreneIcdPLeaf Compartment TypeTransfer factor toleaf particle1/day 1.00E-04 1.00E-04 1.00E-041.00E-04 1.00E-04 Assumption.Half-life day 3.50 3.50 3.50 3.50 3.50 Edwards 1988 (calculated from metabolic rateconstant).Particle on Leaf Compartment TypeTransfer factor toleaf1/day 1.00E-04 1.00E-04 1.00E-041.00E-04 1.00E-04 Assumption.Half-life day 4.12 17.80 2.312.31 17.80 Calculated as 2times the measured photolysishalf-life from Mackay et al. 1992. Exceptionsinclude values that have been set equal toBenzo(a)pyrene (2-Methylnaphthalene; 7,12-Dimethylbenz(a)anthracene; Acenaphthene;Acenaphthylene; Benzo(ghi)perylene;Fluoranthene; and Fluorene).Root Compartment Type - Grasses and HerbsaHalf-life day 34.60 34.60 34.60 34.60 34.60 Edwards 1988 (calculated from metabolic rateconstant).Root soil waterinteraction - alphaunitless 0.95 0.95 0.95 0.95 0.95 Assumption.Stem Compartment Type - Grasses and HerbsaHalf-life day 3.50 3.50 3.50 3.50 3.50 Edwards 1988 (calculated from metabolic rateconstant).aRoots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE.TRIM-Based Tiered Screening Methodology for RTRAttachment A, Addendum 1 1-40 December 2013TRIM.FaTE InputsExhibit_Add A1-23. Dioxin Chemical-Specific Parameters for Plant Compartments in the TRIM.FaTE Screening ScenarioParameter NameUnitsValueReferenceAll DioxinsLeaf Compartment TypeTransfer factor to leaf particle1/day 0.003 Calculated as1 percent of transfer factor to leaf; highly uncertain.Half-lifeday70 Arjmand and Sandermann 1985, as cited in Komoba et al. 1995;soybean root cell culture metabolism test data for DDE.Particle on Leaf Compartment TypeTransfer factor to leaf1/day0.3 Assumption based on USEPA 2000b (an estimate for mercury) andTrapp 1995; highly uncertain.Half-lifeday4.4 McCrady and Maggard 1993; photodegradation sorbed to grassfoliage in sunlight; assumed 10% sunlight per day.Root Compartment Type - Grasses and HerbsaHalf-lifeday70 Arjmand and Sandermann 1985, as cited in Komoba et al. 1995;soybean root cell culture metabolism test data for DDE.Root soil-water interaction - alpha unitless 0.95 Assumption.Stem Compartment Type - Grasses and HerbsaHalf-lifeday70 Arjmand and Sandermann 1985, as cited in Komoba et al. 1995;soybean root cell culture metabolism test data for DDE.aRoots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE.TRIM-Based Tiered Screening Methodology for RTRAttachment A, Addendum 1 1-41 December 2013TRIM.FaTE InputsExhibit_Add A1-24. Cadmium Chemical-Specific Parametersfor Aquatic Species in the TRIM.FaTE Screening ScenarioParameter NameUnitsValueReferenceZooplankton Compartment TypeAbsorption rate constantL[water]/kg[fish wet wt]-day 1500 Goulet 2007.Assimilation efficiency from algaeunitless0.5 Goulet 2007.Elimination rate constant1/day0.03 Goulet 2007.Benthic Invertebrate Compartment TypeSediment partitioning - alpha ofequilibriumunitless 0.95 Assumption.Sediment partitioning - partitioncoefficientkg[bulk sed/kg[invertebrate wet wt] 0.27 Assumption.Sediment partitioning - time to reachalpha of equilibriumday 21 Hare et al. 2001.Benthic Omnivore Compartment TypeAssimilation efficiency from foodunitless0.1 Assumption based on Yan and Wang 2002.Absorption rate constantunitless1.23 Calculated based on body weight from regressionin Hendriks and Heikens 2001.Elimination rate constantunitless1.73E-02 Assumption.Benthic Carnivore Compartment TypeAssimilation efficiency from foodunitless0.1 Assumption based on Yan and Wang 2002.Absorption rate constantunitless0.66 Calculated based on body weight from regressionin Hendriks and Heikens 2001.Elimination rate constantunitless1.68E-03 Assumption.Water-column Herbivore Compartment TypeAssimilation efficiency from foodunitless0.1 Assumed value based on Yan and Wang 2002.Assimilation efficiency from plantsunitless0.1 Assumed value based on Yan and Wang 2002.Absorption rate constantunitless2.46 Calculated based on body weight from regressionin Hendriks and Heikens 2001.Elimination rate constantunitless1.73E-02 Assumption.TRIM-Based Tiered Screening Methodology for RTRAttachment A, Addendum 1 1-42 December 2013TRIM.FaTE InputsExhibit_Add A1-24. Cadmium Chemical-Specific Parametersfor Aquatic Species in the TRIM.FaTE Screening ScenarioParameter NameUnitsValueReferenceWater-column Omnivore Compartment TypeAssimilation efficiency from foodunitless0.1 Assumption based on Yan and Wang 2002.Assimilation efficiency from plantsunitless0.1 Assumption based on Yan and Wang 2002.Absorption rate constantunitless1.23 Calculated based on body weight from regressionin Hendriks and Heikens 2001.Elimination rate constantunitless1.73E-02 Assumption.Water-column Carnivore Compartment TypeAssimilation efficiency from foodunitless0.1 Assumption based on Yan and Wang 2002.Absorption rate constantunitless0.66 Calculated based on body weight from regressionin Hendriks and Heikens 2001.Elimination rate constantunitless1.73E-02 AssumptionTRIM-Based Tiered Screening Methodology for RTRAttachment A, Addendum 1 1-43 December 2013TRIM.FaTE InputsExhibit_Add A1-25. Mercury Chemical-Specific Parameters for Aquatic Species in the TRIM.FaTE Screening ScenarioParameter NameUnitsValueReferenceHg(0)Hg(2)MHgZooplankton Compartment TypeAssimilation Efficiency From Algaeunitless0.2 0.015 0.5 Environment Canada 2002.Half-life day1.0E+091.0E+09 1.0E+09 Assumption.How Much Faster Hg Elimination Is ThanFor MHgunitless 3 3 1 Assumption.Methylation rate1/day 0 0 0 Assumption.Oxidation rate1/day01.0E+06 0 Assumption.Reduction rate1/day 0 0 0 Assumption.Benthic Invertebrate Compartment TypeAlpha of equilibrium for sedimentpartitioningunitless 0.95 0.95 0.95 Selected value (i.e., proportion ofequilibrium achieved by time “t”).Benthic invertebrate-bulksediment partitioncoefficientkg[bulksediment]/kg[invertebratewet wt]0.0824 0.0824 5.04 Hg(0) - assumed based on Hg(2)value; Hg(2) and MHg - Saouteret al. 1991.t-alpha for equilibrium for sedimentpartitioningday 14 14 14 Experiment duration fromSaouter et al. 1991.All Fish Compartments TypesaElimination adjustment factorunitless3 3 1 Trudel and Rasmussen 1997.Assimilation efficiency from foodunitless0.06 0.06 0.5 Williams et al. 2010.Demethylation rate1/dayN/A N/A 0 Assumption.Methylation rate1/day 0 0 0 Assumption.Oxidation rate1/day1.0E+06 0 0 Assumption.Reduction rate1/day 0 0 0 Assumption.Water-column Herbivore Compartment TypeAssimilation efficiency from planktonunitless0.06 0.06 0.5 Williams et al. 2010.aScreening scenario includes: Benthic Omnivore, Benthic Carnivore, Water-column Herbivore, Water-column Omnivore, and Water-column Carnivore.TRIM-Based Tiered Screening Methodology for RTRAttachment A, Addendum 1 1-44 December 2013TRIM.FaTE InputsExhibit_Add A1-26. PAH Chemical-Specific Parameters for Aquatic Species in the TRIM.FaTE Screening ScenarioParameterNameUnitsValue2Methyl712DMBAcenaph-theneAcenaph-thyleneBaABaPBbFBghiPBkFZooplankton Compartment TypeAbsorption rateconstantL[water]/kg[fishwet wt]-day790 42650.94 42231 42302.18 42650.81 42652.78 42650.68 42655.77 42652.5Assimilationefficiency fromalgaeunitless 0.5 0.25 0.5 0.5 0.46 0.25 0.25 0.25 0.25Eliminationrate constant1/day 169.68 2.03 148.07 123.442.073 1.3864 2.12 0.33 1.48Half-life day 0.007788 17 0.00239 0.00239 1.284 16.5 17 17 17Benthic Invertebrate Compartment TypeClearanceconstantunitless 100.6 100.6 100.6 100.6 100.6 100.6 100.6 100.6 100.6Vd(ratio ofconcentrationin benthicinvertebratestoconcentrationin water)ml/g 7235 7235 7235 7235 7235 7235 7235 7235 7235Half-life day 0.722 17 0.7220.722 1.284 16.5 17 17 17All Fish Compartment TypesaGamma fish unitless 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2Assimilationefficiency fromfoodunitless 0.5 0.15 0.5 0.32 0.15 0.15 0.15 0.15 0.15Half-life day 0.2 2 0.20.2 0.408 1.925 2 2 2aScreening scenario includes: Benthic Omnivore, Benthic Carnivore, Water-column Herbivore, Water-column Omnivore, and Water-column Carnivore.TRIM-Based Tiered Screening Methodology for RTRAttachment A, Addendum 1 1-45 December 2013TRIM.FaTE InputsExhibit_Add A1-26. PAH Chemical-Specific Parameters forAquatic Species in the TRIM.FaTE Screening ScenarioParameter NameUnitsValueReferenceChrDahAFluoran-theneFluoreneIcdPZooplankton Compartment TypeAbsorption rateconstantL[water]/kg[fishwet wt]-day42649.95 42655.48 142000 15000 42655.93 Kowfrom Arnot et al. 2004.Exception is Berrojalbiz et al. 2009(2-Methylnaphthalene,Fluoranthene, and Fluorene).Assimilationefficiency from algaeunitless 0.46 0.25 0.49 0.5 0.25 Kowfrom Arnot et al. 2004.Exception is maximum value fromWang and Wang 2006 (7,12-Dimethylbenz(a)anthracene,Benzo(a)pyrene,Benzo(b)fluoranthene,Benzo(g,h,i)perylene,Benzo(k)fluoranthene,Dibenz(a,h)anthracene, andIndeno(1,2,3-cd)pyrene).Elimination rateconstant1/day 2.3746 0.4331 8.678 81.87 0.269 Kowfrom Arnot et al. 2004.Half-life day 0.495170.002390.000247617 McElroy 1990. Exceptions includeBerrojalbiz et al. 2009 (2-Methylnaphthalene, Fluoranthene,and Fluorene) and Moermond et al.2007 (Benz(a)anthracene andBenzo(a)pyrene).Benthic Invertebrate Compartment TypeClearance constant unitless 100.6 100.6 100.6 100.6 100.6 Stehly et al. 1990.Vd(ratio ofconcentrationin benthicinvertebrates toconcentration inwater)ml/g 7235 7235 7235 7235 7235 Stehly et al. 1990.Half-lifeday 0.495 17 0.722 0.722 17 Moermond et al. 2007.TRIM-Based Tiered Screening Methodology for RTRAttachment A, Addendum 1 1-46 December 2013TRIM.FaTE InputsExhibit_Add A1-26. PAH Chemical-Specific Parameters forAquatic Species in the TRIM.FaTE Screening ScenarioParameter NameUnitsValueReferenceChrDahAFluoran-theneFluoreneIcdPAll Fish Compartment TypesaGamma fishunitless 0.2 0.2 0.2 0.2 0.2 Thomann 1989.Assimilationefficiency from foodunitless 0.15 0.15 0.14 0.14 0.15 Lemair et al. 1992. Exceptionsinclude Barber 2008 (2-Methylnaphthalene andAcenaphthene) and Niimi andPalazzo 1986 (Acenaphthylene,Fluoranthene, and Fluorene).Half-lifeday 0.533 2 0.165 0.2 2 Moermond et al. 2007.aScreening scenario includes: Benthic Omnivore, Benthic Carnivore, Water-column Herbivore, Water-column Omnivore, and Water-column Carnivore.TRIM-Based Tiered Screening Methodology for RTRAttachment A, Addendum 1 1-47 December 2013TRIM.FaTE InputsExhibit_Add A1-27. Dioxin Chemical-Specific Parameters forAquatic Species in the TRIM.FaTE Screening ScenarioParameter NameUnitsValue

1,2,3,4,6,7,8,9- OCDD 1,2,3,4,6,7,8,9- OCDF 1,2,3,4,6,7,8- HpCDD 1,2,3,4,6,7,8- HpCDF 1,2,3,4,7,8,9- HpCDF 1,2,3,4,7,8- HxCDD 1,2,3,4,7,8- HxCDF 1,2,3,6,7,8- HxCDD 1,2,3,6,7,8- HxCDFZooplankton CompartmentAbsorption rate constantL[water]/kg[fishwet wt]-day8640 8640 8640 8640 8640 8640 8640 8640 8640Assimilation efficiency from algaeunitless 0.080.05 0.21 0.09 0.2 0.31 0.31 0.31 0.31Elimination rate constant1/day 0.01020.016 0.016 0.0616 0.1829 0.0252 0.1474 0.0099 0.0194Half-lifeday 7E+06 7E+06 7E+06 7E+06 7E+06 7E+06 7E+06 7E+06 7E+06Benthic Invertebrate CompartmentClearance constantunitless 0 0 0 0 0 0 0 0 0Sediment partitioning partitioncoefficientkg/kg 0.0013 0.0017 0.0055 0.0012 0.042 0.033 0.0081 0.013 0.02Sediment partitioning alpha ofequilibriumunitless 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95Sediment partitioning time toreach alpha of equilibriumdays 120 42 120 42 42 120 42 120 42Vd(ratio of concentrationin benthic invertebrates toconcentration in water)ml/g 0 0 0 0 0 0 0 0 0Half-life day5776.2 5776.2 5776.25776.2 5776.2 5776.2 5776.2 5776.2 5776.2All Fish CompartmentsaAssimilation efficiency from foodunitless 0.080.05 0.21 0.09 0.2 0.37 0.31 0.31 0.31Chemical uptake rate via gillL[water]/kg[fishwet wt]-day11 6 56 25 50 102 200 300 200Half-lifeday 70 70 70 70 70 70 70 70 70TRIM-Based Tiered Screening Methodology for RTRAttachment A, Addendum 1 1-48 December 2013TRIM.FaTE InputsExhibit_Add A1-27. Dioxin Chemical-Specific Parameters forAquatic Species in the TRIM.FaTE Screening ScenarioParameter NameUnitsValue

1,2,3,7,8,9- HxCDD 1,2,3,7,8,9- HxCDF 1,2,3,7,8- PeCDD 1,2,3,7,8- PeCDF 2,3,4,6,7,8- HxCDF 2,3,4,7,8- PeCDF 2,3,7,8-TCDD 2,3,7,8-TCDFZooplankton CompartmentAbsorption Rate ConstantL[water]/kg[fish wetwt]-day8640 8640 8640 8640 8640 8640 8640 8640Assimilation Efficiency from Algaeunitless 0.310.31 0.42 0.42 0.31 0.42 0.41 0.51Elimination Rate Constant1/day 0.0099 0.0413 0.0819 0.2316 0.0192 0.4331 0.2268 1.0375Half-life day7E+067E+067E+067E+06 7E+08 7E+08 7E+06 7E+08Benthic Invertebrate CompartmentClearance Constantunitless 0 0 0 0 0 0 0 0Sediment Partitioning PartitionCoefficientkg/kg 0.015 0.067 0.098 0.024 0.072 0.17 0.205 0.056Sediment Partitioning Alpha ofEquilibriumunitless 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95Sediment Partitioning Time toReach Alpha of Equilibriumdays 120 42 120 42 42 42 120 42Vd(ratio of concentrationin benthic invertebrates toconcentration in water)ml/g 0 0 0 0 0 0 0 0Half-life day5776.25776.25776.2 5776.2 5776.2 5776.2 5776.2 5776.2All Fish CompartmentsaAssimilation efficiency from foodunitless 0.310.31 0.42 0.42 0.31 0.42 0.41 0.51Chemical uptake rate via gillL[water]/kg[fish wetwt]-day300 200 700 300 200 400 600 400Half-lifeday 70 70 70 70 70 70 70 70TRIM-Based Tiered Screening Methodology for RTRAttachment A, Addendum 1 1-49 December 2013TRIM.FaTE InputsExhibit_Add A1-27. Dioxin Chemical-Specific Parameters forAquatic Species in the TRIM.FaTE Screening ScenarioParameter NameUnitsReferenceZooplankton CompartmentAbsorption rate constantL[water]/kg[fishwet wt]-dayZhang et al. 2011; used copepod kuvalue.Assimilation efficiency from algae unitless Morrison et al. 1999. Exceptions include Niimi and Oliver 1986 (1,2,3,4,6,7,8,9-OCDD, 1,2,3,4,6,7,8,9-OCDF), Berntssen et al. 2007 (1,2,3,4,6,7,8-HpCDD,1,2,3,4,6,7,8-HpCDF), and 1,2,3,4,7,8,9-HpCDF set conservatively as approximatelinear interpolation between values for 1,2,3,4,7,8-HxCDD and 1,2,3,4,6,7,8-HpCDD /1,2,3,4,6,7,8-HpCDF (i.e., 0.3 to 0.1–0.2).Elimination rate constant1/day Arnot and Gobas 2004; used Kowvalue.Half-lifeday Morrison et al. 1999; usedmetabolic rates for invertebrates.Benthic Invertebrate CompartmentClearance constantunitless Assumption.Sediment partitioning partitioncoefficientkg/kg Rubinstein et al. 1990; used TCDD data for sandworm.Sediment partitioning alpha ofequilibriumunitless Rubinstein et al. 1990.Sediment Partitioning Time to ReachAlpha of Equilibriumdays Rubinstein et al. 1990.Vd(ratio of concentration in benthicinvertebrates to concentration in water)ml/g Assumption.Half-lifeday Rubinstein et al. 1990; used TCDD data for sandworm.All Fish CompartmentsaAssimilation Efficiency from Foodunitless Morrison et al. 1999. Exceptions include Niimi and Oliver 1996 (1,2,3,4,6,7,8,9-OCDD, 1,2,3,4,6,7,8,9-OCDF), Van denBerg et al. 1994 (1,2,3,4,6,7,8-HpCDD),Berntssen et al. 2007 ( 1,2,3,4,6,7,8-HpCDF), and 1,2,3,4,7,8,9-HpCDF setconservatively as approximate linear interpolation between values for 1,2,3,4,7,8-HxCDD and 1,2,3,4,6,7,8-HpCDD / 1,2,3,4,6,7,8-HpCDF(i.e., 0.3 to 0.1–0.2).Chemical Uptake Rate Via Gill L[water]/kg[fishwet wt]-dayMuir et al. 1985. Exception is Opperhuizen et al. 1986 (1,2,3,7,8,9-HxCDF, 1,2,3,7,8-PeCDD, 1,2,3,7,8-PeCDF,2,3,4,7,8-PeCDF, 2,3,7,8-TCDD, 2,3,7,8-TCDF).Half-lifeday Berntssen et al. 2007.aScreening scenario includes: Benthic Omnivore, Benthic Carnivore, Water-column Herbivore, Water-column Omnivore, and Water-column Carnivore.TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-50 December 2013 TRIM.FaTE Inputs 

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Attachment A, Addendum 1 1-55 December 2013 TRIM.FaTE Inputs 

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Attachment A, Addendum 2 2-1 December 2013 Description of MIRC 

## Addendum 2. Description of Multimedia Ingestion Risk Calculator (MIRC) Used for RTR Exposure and Risk Estimates[This page intentionally left blank.]TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-3 December 2013 Description of MIRC 

## CONTENTS, ADDENDUM 2

1. Introduction .................................................................................................................. ...... 2-9

1.1. Purpose and Overview ............................................................................................. 2-9

1.2. Scope of MIRC ......................................................................................................... 2-9

1.3. Use in EPA’s Air Toxics Program ........................................................................... 2-10

1.4. MIRC Highlights ..................................................................................................... 2-10

1.5. Organization of This Addendum ............................................................................. 2-11

2. MIRC Overview ................................................................................................................ 2-11

2.1. Software ................................................................................................................. 2-12

2.2. Exposure Pathways ................................................................................................ 2-14

2.3. Receptor Groups .................................................................................................... 2-15

3. Exposure Algorithms ........................................................................................................ 2-17

3.1. Farm Food Chain Algorithms ................................................................................. 2-17 3.1.1. Estimating Chemical Concentrations in Produce ..................................... 2-18 3.1.2. Estimating Chemical Concentrations in Animal Products ........................ 2-24

3.2. Chemical Intake Calculations for Adults and Non-Infant Children .......................... 2-27 3.2.1. Chemical Intake from Soil Ingestion ........................................................ 2-29 3.2.2. Chemical Intake from Fish Ingestion ........................................................ 2-29 3.2.3. Chemical Intake from Fruit Ingestion ....................................................... 2-31 3.2.4. Chemical Intake from Vegetable Ingestion .............................................. 2-31 3.2.5. Chemical Intake from Animal Product Ingestion ...................................... 2-33 3.2.6. Chemical Intake from Drinking Water Ingestion ....................................... 2-35

3.3. Total Chemical Intake ............................................................................................. 2-35

3.4. Chemical Intake Calculations for Nursing Infants ................................................... 2-36 3.4.1. Infant Avera ge Daily Absorbed Dose ....................................................... 2-36 3.4.2. Chemical Concentration in Breast Milk Fat .............................................. 2-37 3.4.3. Chemical Concentration in Aqueous Phase of Breast Milk ...................... 2-41 3.4.4. Alternative Model for Infant Intake of Methyl Mercury .............................. 2-43

4. Dose-Response Values Used for Assessment ................................................................ 2-44

4.1. Cadmium ................................................................................................................ 2-47

4.2. Dioxins (2,3,7,8-TCDD) .......................................................................................... 2-47

4.3. Mercury .................................................................................................................. 2-48

4.4. Polycyclic Organic Matter ....................................................................................... 2-48

5. Risk Estimation ............................................................................................................... . 2-50

5.1. Cancer Risks .......................................................................................................... 2-50

5.2. Non-cancer Hazard Quotients ................................................................................ 2-52 5.2.1. Hazard Quotients for Chemicals with a Chronic RfD ............................... 2-53TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-4 December 2013 Description of MIRC 

### 5.2.2. Hazard Quotients for Chemicals with RfD Based on Developmental Effects ............................................................................. 2-53 5.2.3. Hazard Index for Chemicals with RfDs .................................................... 2-53

6. Model Input Options ......................................................................................................... 2-54

6.1. Environmental Concentrations ............................................................................... 2-55

6.2. Farm-Food-Chain Parameter Values ..................................................................... 2-55 6.2.1. List of Farm-Food-Chain (FFC) Parameters ............................................ 2-55 6.2.2. Produce Parameter Values ...................................................................... 2-57 6.2.3. Animal Product Parameter Values ........................................................... 2-70

6.3. Adult and Non-Infant Exposure Parameter Values ................................................ 2-72 6.3.1. Body Weights ........................................................................................... 2-73 6.3.2. Water Ingestion Rates ............................................................................. 2-74 6.3.3. Local Food Ingestion Rates ..................................................................... 2-75 6.3.4. Local Fish Ingestion Rates ....................................................................... 2-79 6.3.5. Soil Ingestion Rates ................................................................................. 2-85 6.3.6. Total Food Ingestion Rates ...................................................................... 2-85

6.4. Other Exposure Factor Values ............................................................................... 2-86 6.4.1. Exposure Frequency ................................................................................ 2-86 6.4.2. Fraction Contaminated ............................................................................. 2-87 6.4.3. Preparation and Cooking Losses ............................................................. 2-87 6.4.4. Food Preparation/Cooking Adjustment Factor (FPCAF) .......................... 2-89

6.5. Breast-Milk Infant Exposure Pathway Parameter Values ....................................... 2-90 6.5.1. Receptor-specific Parameters .................................................................. 2-90 6.5.2. Chemical-Specific Parameter Values ...................................................... 2-94

7. Summary of MIRC Default Exposure Parameter Settings ............................................... 2-97

7.1. Default Ingestion Rates .......................................................................................... 2-97 7.1.1. Fish Ingestion Rates ................................................................................ 2-98 7.1.2. Farm Food Chain Ingestion ..................................................................... 2-98

7.2. Default Screening-Level Population-Specific Parameter Values ......................... 2-100

7.3. Default Chemical-Specific Parameter Values for Screening Analysis .................. 2-101

7.4. Screening-Level Parameter Values for Nursing Infant Exposure ......................... 2-102 7.4.1. Dioxins 2-102 7.4.2. Methyl Mercury ...................................................................................... 2-103

8. References .................................................................................................................... 2-104TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-5 December 2013 Description of MIRC 

### Exhibits, Addendum 2

Exhibit_Add A2-1. Overview of MIRC Software Application for Performing FarmFood-Chain Ingestion Exposure and Risk Calculations .................................... 2-13 Exhibit_Add A2-2. Transfer Pathways for Modeled Farm Food Chain (FFC) Media ................................................................................................................. 2-15 Exhibit_Add A2-3. Chemical Transfer Pathways for Produce ................................................. 2-18 Exhibit_Add A2-4. Estimating Chemical Concentration in Aboveground Produce ................... 2-18 Exhibit_Add A2-5. Chemical Transfer Pathways for Animal Products ..................................... 2-24 Exhibit_Add A2-6. Oral Dose-response Values Used to Calculate RTR Screening Threshold Emission Rates for PB-HAP Chemicals a 

### .......................................... 2-45 Exhibit_Add A2-7. WHO 2005 Toxic Equivalency Factors (TEFs) for Dioxin Congeners ......................................................................................................... 2-48 Exhibit_Add A2-8. Oral Dose-response Values for Polycyclic Organic Matter (POM) Groups a 

### .................................................................................................. 2-49 Exhibit_Add A2-9. MIRC Parameters Used to Estimate Chemical Concentrations in Farm Foods ................................................................................................... 2-56 Exhibit_Add A2-10. Chemical-Specific Inputs for Produce Parameters for Chemicals Included in MIRC ............................................................................. 2-57 Exhibit_Add A2-11. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC ................................................................................................................. 2-59 Exhibit_Add A2-12. Non-Chemical-Specific Produce Inputs .................................................. 2-69 Exhibit_Add A2-13. Animal Product C hemical-specific Inputs for Chemicals Included in MIRC ............................................................................................... 2-70 Exhibit_Add A2-14. Soil and Plant Ingestion Rates for Animals ............................................. 2-72 Exhibit_Add A2-15. Mean and Percentile Body Weight Estimates for Adults and Children ............................................................................................................. 2-73 Exhibit_Add A2-16. Estimated Daily Per Capita Mean and Percentile Water Ingestion Rates for Children and Adults a 

### ........................................................... 2-74 Exhibit_Add A2-17. Summary of Age-Group Specific Food Ingestion Rates for Farm Food Items ............................................................................................... 2-75 Exhibit_Add A2-18. Fish Ingestion Rates Used in Screening Analysis ................................... 2-80 Exhibit_Add A2-19. Daily Mean and Percentile Consumer-Only Fish Ingestion Rates for Children and Adults ( IR CO,y 

### ) a 

### .............................................................. 2-82 Exhibit_Add A2-20. Fraction of Population Consuming Freshwater/Estuarine Fish on a Single Day ( F PC,y 

### ) ............................................................................... 2-83 Exhibit_Add A2-21. Calculated Long-term Mean and Percentile per capita Fish Ingestion Rates for Children and Adults ( IR PC,y 

### ) ................................................ 2-83 Exhibit_Add A2-22. Calculated Mean and 90 th 

### Percentile Per capita Fish Ingestion Rates for Populations of Recreational Fishers (IR PC,y 

### ) ...................... 2-84TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-6 December 2013 Description of MIRC 

### Exhibit_Add A2-23. Daily Mean and Percentile Soil Ingestion Rates for Children and Adults ......................................................................................................... 2-85 Exhibit_Add A2-24. Daily Mean and Percentile Per Capita Total Food Intake for Children and Adults ........................................................................................... 2-86 Exhibit_Add A2-25. Fraction Weight Losses from Preparation of Various Foods ................... 2-88 Exhibit_Add A2-26. Scenario- and Receptor-Specific Input Parameter Values Used to Estimate Infant Exposures via Breast Milk ........................................... 2-91 Exhibit_Add A2-27. Average Body Weight for Infants ............................................................ 2-91 Exhibit_Add A2-28. Time-weighted Average Body Weight for Mothers .................................. 2-92 Exhibit_Add A2-29. Infant Breast Milk Intake Rates ............................................................... 2-94 Exhibit_Add A2-30. Chemical-specific Input Parameter Values for Breast Milk Exposure Pathway ............................................................................................ 2-95 Exhibit_Add A2-31. Farm Food Category Ingestion Rates for Health Protective Screening Scenario for Farming Households .................................................... 2-99 Exhibit_Add A2-32. Mean Body Weight Estimates for Adults and Children a 

### ........................ 2-100 Exhibit_Add A2-33. Chemical-Specific Parameter Values for Input to MIRC a 

### ...................... 2-101 Exhibit_Add A2-34. Chemical and Animal-T ype Specific Biotransfer Factor (Ba) Values for Input to MIRC ................................................................................. 2-102TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-7 December 2013 Description of MIRC 

### Equations, Addendum 2

Equation 2-1. Chemical Concentration in Aboveground Produce ............................................ 2-19 Equation 2-2. Chemical Concentrati on in Aboveground Produce Due to Root Uptake ............................................................................................................... 2-19 Equation 2-3. Chemical Concentra tion in Aboveground Produce Due to Deposition of Particle-phase Chemical ............................................................. 2-20Equation 2-4. Chemical Concentration in Aboveground Produce Due to Air-toPlant Transfer of Vapor-phase Chemical .......................................................... 2-20 Equation 2-5. Conversion of Aboveground Produce Chemical Concentration from Dry- to Wet-Weight Basis .................................................................................. 2-21 Equation 2-6. Chemical Concentration in Belowground Produce: Nonionic Organic Chemicals ............................................................................................ 2-22 Equation 2-7. Chemical Concentration in Belowground Produce: Inorganic Chemicals .......................................................................................................... 2-23 Equation 2-8. Conversion of Belowground Produce Chemical Concentration from Dry- to Wet-Weight Basis .................................................................................. 2-23 Equation 2-9. Chemical Concentration in Beef, Pork, or Total Dairy ....................................... 2-24 Equation 2-10. Chemical Concentration in Poultry or Eggs ..................................................... 2-25 Equation 2-11. Incidental Ingestion of Chemical in Soil by Livestock ...................................... 2-25 Equation 2-12. Ingestion of Chemical in Feed by Livestock .................................................... 2-26 Equation 2-13. Chemical Concentration in Livestock Feed (All Aboveground)........................ 2-26 Equation 2-14. Chemical Concentration in Livestock Feed Due to Root Uptake .................... 2-27 Equation 2-15. Average Daily Dose for Specified Age Group and Food Type ....................... 2-27 Equation 2-16. Chemical Intake from Soil Ingestion ............................................................... 2-29 Equation 2-17. Chemical Intake from Fish Ingestion .............................................................. 2-30 Equation 2-18. Consumption-weighted Chemical Concentration in Fish ................................ 2-30 Equation 2-19. Chemical Intake from Consumption of Exposed Fruits .................................. 2-31 Equation 2-20. Chemical Intake from Consumption of Protected Fruits ................................. 2-31 Equation 2-21. Chemical Intake from Exposed Vegetables .................................................... 2-32 Equation 2-22. Chemical Intake from Protected Vegetables .................................................. 2-32 Equation 2-23. Chemical Intake from Root Vegetables .......................................................... 2-32 Equation 2-24. Chemical Intake from Ingestion of Beef .......................................................... 2-33 Equation 2-25. Chemical Intake from Dairy Ingestion ............................................................. 2-33 Equation 2-26. Chemical Intake from Pork Ingestion .............................................................. 2-33 Equation 2-27. Chemical Intake from Poultry Ingestion .......................................................... 2-34 Equation 2-28. Chemical Intake from Egg Ingestion ............................................................... 2-34 Equation 2-29. Chemical Intake from Drinking Water Ingestion ............................................. 2-35TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-8 December 2013 Description of MIRC 

### Equations 2-30 to 2-35. Total Average Daily Dose of a Chemical for Different Age Groups ....................................................................................................... 2-35
Equation 2-31. .......................................................................................................................... 2-35 Equation 2-32. .......................................................................................................................... 2-35
Equation 2-33. .......................................................................................................................... 2-35
Equation 2-34. .......................................................................................................................... 2-36
Equation 2-35. .......................................................................................................................... 2-36 Equation 2-36. Lifetime Average Daily Dose (LADD) ............................................................. 2-36 Equation 2-37. Average Daily Dose of Chemical to the Nursing Infant .................................. 2-37 Equation 2-38. Chemical Concentration in Breast Milk Fat .................................................... 2-38 Equation 2-39. Daily Maternal Absorbed Intake ..................................................................... 2-39Equation 2-40. Biological Elimination Rate Constant for Chemicals for Nonlactating Women ................................................................................................ 2-40 Equation 2-41. Biological Elimination Constant for Lipophilic Chemicals for Lactating Women .............................................................................................. 2-40 Equation 2-42. Chemical Concentration in Aqueous Phase of Breast Milk ............................ 2-41 Equation 2-43. Fraction of Total Chemical in Body in the Blood Plasma Compartment ..................................................................................................... 2-42 Equation 2-44. Biological Elimination Rate Constant for Hydrophilic Chemicals .................... 2-43 Equation 2-45. Calculation of Infant Average Daily Absorbed Dose of Methyl Mercury ............................................................................................................. 2-44 Equation 2-46. Calculation of Excess Lifetime Cancer Risk ................................................... 2-51 Equations 2-47 to 2-53. Lifetime Cancer Risk: Chemicals with a Mutagenic MOA for Cancer .......................................................................................................... 2-52
Equation 2-48. .......................................................................................................................... 2-52 Equation 2-49. .......................................................................................................................... 2-52
Equation 2-50. .......................................................................................................................... 2-52
Equation 2-51. .......................................................................................................................... 2-52
Equation 2-52. .......................................................................................................................... 2-52
Equation 2-53. .......................................................................................................................... 2-52 Equation 2-54. Hazard Quotient for Chemicals with a Chronic RfD ....................................... 2-53 Equation 2-55. Hazard Index Calculation ............................................................................... 2-54 Equation 2-56. Calculation of Age-Group-Specific and Food-Specific Ingestion Rates ................................................................................................................. 2-78 Equation 2-57. Calculation of Alternative Age-Group-Specific Fish Ingestion Rates ................................................................................................................. 2-81TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-9 December 2013 Description of MIRC 

## 1. Introduction
1.1. Purpose and Overview
This document provides a detailed description of the Multimedia Ingestion Risk Calculator (MIRC), a modeling tool and database designed to assist in estimating risks via multiple ingestion pathways, particularly for food products grown or raised at home or on a farm. 19 

### MIRC was designed to estimate risks to humans from ingestion of produce or animal products, fish, and water in the vicinity of a source of chemical emissions to air. The user can evaluate either generalized (e.g., health protective default) or more site-specific scenarios using the same tool. MIRC includes a database of exposure parameter values, offering the user the option of selecting mean, median, and upper percentile val ues for many parameters, data permitting. Generally health protective default values were assigned to each parameter in the tool and the default configuration is used for initial risk scr eening efforts by EPA’s Office of Air Quality Planning and Standards’ (OAQPS) for Risk and Technology Review (RTR) multimedia risk assessments. MIRC also allows the user to define the farm food chain (FFC) parameter values and receptor characteristics to better represent a site-specific scenario.

With user-input concentrations for one or more chemicals in air and soil and air-to-surface deposition rates, MIRC calculates the chemical’s concentrations in home- or farm-grown produce and animal food products using FFC algorithms adapted from EPA’s Human Health Risk Assessment Protocol for Hazardous Waste Combustion Facilities (hereafter referred to as HHRAP; EPA 2005a). MIRC uses these calculated concentrations, along with user-input chemical concentrations for fish and drinking water, to estimate chemical intake rates, as average daily doses (ADDs), for adults, children, and nursing infants. Users can obtain chemical input concentrations and deposition rates from measurements at an actual site or from a transport and fate model, such as TRIM.FaTE, as is done for RTR risk assessment.

For a specified set of chemical concentrations and MIRC parameter options, MIRC calculates ADDs separately for adults, four age groups of children, and infants to reflect differences in food ingestion rates and diet at different lifestages. MIRC estimates age-specific hazard quotients (HQs) as the ratio of age-specific ADDs to t he reference dose (RfD) for a chemical. The most appropriate HQ for a chemical depends on its toxic mode of action and the duration of exposure required to produce an effect. MIRC also estimates average lifetime ADDs and compares those to cancer slope factors (CSFs) to estimate cancer risks. A breast milk ingestion pathway is available to estimate exposure and risks to nursing infants.

MIRC was developed to be a flexible, transparent application. The tool includes chemical transfer and ingestion exposure algorithms and a database of parameter values, many with several options, used by these equations. The MIRC database includes values for the relevant physiochemical properties and toxicity reference values for more than 500 chemicals, including approximately 60 inorganics taken primarily from a database developed for HHRAP (EPA 2005a). Although designed for OAQPS’ RTR assessments for sources of hazardous air pollutants (HAPs), the tool is flexible in its design and can be used to assess risks in many other contexts where soil and air concentrations are predicted or measured.


### 1.2. Scope of MIRC
For persistent and bioaccumulative (PB) chemicals, risks from direct inhalation of the chemical can be much less than risks from ingestion of the chemical in water, fish, and food products

19 Fully functional versions of MIRC have been developed in both Access TM-based and ExcelTM-based formats; however, MIRC currently is not publicly available.TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-10 December 2013 Description of MIRC 

### grown in an area of chemical deposition. Vegetables and fruits in such areas can become contaminated directly by deposition of the airbor ne chemical to foliage, fruits, and vegetables or indirectly by root uptake of the chemical deposited to soils. Livestock can be exposed to the PB chemicals via ingestion of contaminated forage and in cidental ingestion of contaminated soils.

For PB chemicals, evaluation of the inhalation pathway for air pollutants may reveal only a portion of the risk to individuals in such populations. Households that consume high quantities of self-caught fish or locally grown produce and animal products may be particularly susceptible to ingestion of chemicals transferred from air in the vicinity of an air emissions source. For PB chemicals in particular, therefore, EPA developed methods of estimating risk from indirect exposure pathways associated with the deposition of airborne chemicals to gardens and farms, as described in HHRAP (EPA 2005a).


### 1.3. Use in EPA’s Air Toxics Program
For PB-HAPs, indirect exposure pathways, such as ingestion, might contribute more to total risk than the inhalation pathway. EPA therefore developed several computer software tools to assist in evaluating exposure and risk from non-inhalation pathways. EPA developed the Total Risk Integrated Methodology (TRIM) Environmental Fate, Transport, and Ecological Exposure (TRIM.FaTE) computer program to simulate the release, transport, and fate of HAPs from a specific source throughout the area in which local (non-source) chemical deposition is likely to be a concern. TRIM.FaTE models the transport of individual chemicals from the source through air by advection (wind) of particle- and vapo r-phase chemical and deposition of the chemical from air to terrestrial and aquatic ecosystems by wet and dry deposition. Movement of the chemical through a watershed via erosion and runoff, uptake by plants, and other abiotic and biotic transfer processes also are simulated. For the chemical that reaches surface waters, TRIM.FaTE models uptake and bioaccumulation to trophic level (TL) 3 and 4 fish (i.e., pan fish and game fish, respectively).

MIRC was developed to process TRIM.FaTE results, in particular, air deposition rates and the concentrations of a chemical, after a specified duration of emissions, in several spatially explicit environmental compartments, including air, surface and root-zone soils, surface and ground waters, and fish. MIRC uses those results to calculate exposure to the chemical through ingestion of locally grown foods, including various types of fruits and vegetables, poultry, swine, and dairy (and beef) cattle. MIRC also calculates the associated risks for individuals who consume those foods. MIRC was designed to use specific TRIM.FaTE results to estimate FFC concentrations, ingestion exposures, and human health risks for OAQPS’ RTR assessments. It uses the same approach that OAQPS intends to impl ement directly in its TRIM system via three modules beyond TRIM.FaTE: TRIM Farm Food Chain, TRIM.Expo Ingestion 

### , and TRIM.Risk. 20



### 1.4. MIRC Highlights
Although designed to assist EPA OAQPS in its RTR assessments, MIRC is a stand-alone software application that can be used in other contexts. A user can supply either measured or estimated chemical concentrations for soil, air, water, and fish, and also can provide air deposition rates likely for the location(s) of interest based on local meteorology. The user can accept the default values for many exposure parameters and screen for small possibilities of risk, or the user can select other options or overwrite parameter values to tailor the estimates to a specific scenario or location.

20 General information about the TRIM system is available at http://www.epa. gov/ttn/fera/trim_gen.html.TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-11 December 2013 Description of MIRC 

### MIRC complies with EPA’s latest guidelines for exposure and risk assessment, including HHRAP; the Agency’s 2005 Guidelines for Carcinogen Risk Assessment (Cancer Guidelines)

, Supplemental Guidance for Assessing Susceptibility from Early-Life Exposure to Carcinogens (Supplemental Guidance), and Guidance on Selecting Age Groups for Monitoring and Assessing Childhood Exposures to Environmental Contaminants (EPA 2005b,c,d); and its 2008 Child-Specific Exposure Factors Handbook (EPA 2008a). In particular, MIRC provides several important capabilities:

 

### When provided air and soil concentrations, the MIRC software package allows rapid calculation of screening-level exposures and risks associated with household consumption of locally grown/raised foods.

 

### MIRC can calculate exposures and risks associated with incidental ingestion of surface soils, fish consumption, and drinking water.

 

### The tool calculates ADDs (i.e., chemical intake rates) for six “built-in” age groups to allow use of age-group-specific body weights, ingestion rates, food preferences, and susceptibility to toxic effects.

 

### Its database of chemical information covers plant- and animal-specific transfer factors and other inputs that determine concentrations in farm food stuffs.

 

### Value options for receptor characteristics in the database include the mean and 50 th



### , 90 th 

### , 95 th 

### , and 99 th 

### percentile values where data permit.

 

### For carcinogens with a mutagenic mode of action, MIRC estimates a lifetime ADD using the three lifestages and potency adjustment factors recommended in EPA’s 2005 Cancer Guidelines and Supplemental Guidance.

 

### The data for exposure parameters in the tool have been updated to include the latestrecommended values for children issued September 30, 2008, in the Agency’s ChildSpecific Exposure Factors Handbook (CSEFH) (EPA 2008a).



### 1.5. Organization of This Addendum
Sections 2 through 5 of this addendum describe the exposure and risk models implemented in MIRC. Section 2 provides an overview of the FFC exposure scenario and indicates options available to a user to tailor the scenario to specific applications. Section 3 describes the exposure algorithms used in MIRC, including how ADDs are calculated. Section 4 presents the toxicity reference values included in MIRC to calculate risks. Section 5 describes the risk characterization algorithms in MIRC. Section 6 of this document describes data input options for the model. Section 7 describes the default parameterization of MIRC for application to health protective risk screening assessments, and Section 8 provides the references.

Note that the default parameterization described in Section 7 was used to estimate Tier 1 screening threshold emission rates of PB-HAPs from RTR facilities. These emissions levels are assumed to pose negligible risk to subsistence communities in the vicinity of a facility emitting the PB-HAPs to air. Users of MIRC can modify the default values for many of the parameters to better represent a specific exposure scenario.



## 2. MIRC Overview

The Multimedia Ingestion Risk Calculator (MIRC) software package is designed to allow rapid calculation of screening-level exposures and risks associated with subsistence and recreational farmer/fisher populations in the vicinity of a source of chemical emissions to air. The tool allows a user to assess human exposures via ingestion pathways, including drinking waterTRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-12 December 2013 Description of MIRC 

### consumption, incidental soil ingestion, fish ingestion, and ingestion of ten types of farm food chain (FFC) products: exposed fruits, protected fruits, exposed vegetables, protected vegetables, root vegetables, beef, total dairy, pork, poultry, and eggs. The tool also includes a breast milk ingestion and risk module for nursing infants. For fruits and vegetables, the terms “exposed” and “protected” refer to whether the edible portion of the plant is exposed to the atmosphere.

The remainder of this overview consists of three sections. The first (Section 2.1 of this addendum) provides an overview of the MIRC software package. The second and third sections summarize the ingestion exposure pathways included in the tool and the “built-in” receptor age categories, respectively (Sections 2.2 and 2.3 of this addendum).



### 2.1. Software
The MIRC application includes the following components:

 

### A graphical user interface through which the user locates and accesses various input and output tables.

 

### Input tables in which the user can enter environmental concentrations of a chemical estimated for air, soil, drinking water, and fish tissue.

 

### Internal chemical transfer and exposure algorithms and database of options for FFC algorithm parameter value, chemical-specific inputs, and exposure factors.

 

### Tabulated outputs of calculated chemical concentrations in the various farm food products (e.g., fruits, vegetables, beef, eggs) and ADDs for those foods and for water and fish ingestion for each receptor category.

 

### Output tables with estimated cancer risks and non-cancer hazard estimates associated with total ingestion exposure to each chemical for each receptor category.

Exhibit_Add A2-1 provides a flowchart displaying the types of required and optional inputs and the general flow of calculations carried out by the tool.TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-13 December 2013 Description of MIRC 

### Exhibit_Add A2-1. Overview of MIRC Software Application for Performing Farm-Food-Chain Ingestion Exposure and Risk Calculations

User Selects Receptor Characteristics
From Options or Over-write Body Weight User Specifies Environmental Concentrations for Chemical of Concern

Air Root-zone and Surface Soils Fish Drinking Water

User Option to Add Breast Milk Pathway yes

no User Selects BMP Parameter Values Uptake by foliage / Uptake by roots:
Vegetables, Fruits Grains Hay, Grass Animal Products

Home Grown Food Product Ingestion Rates Fish and Water Ingestion Rates

Duration Breast Feeding; maternal and infant characteristics Average Daily Dose (ADD) for Age Group y; y =1 to 5

Chemical Intake with Food/ Medium Type i; i = 1 to 10

Lifetime Average Daily Dose

Risk Characterization Module Farm Food Chain Biotransfer Calculations

Breast Milk Exposure Module

ADD Maternal
[C] in milk

Infant Dose Adult ADD x absorption efficiency

Exposure Module

Chemical Toxicity Reference

Values:

SF and RfDs Age-specific & Lifetime Exposure Doses

Lifetime Cancer Risks
Age-Specific Hazard Quotients User Selects Chemical(s) of Concern: For Each, MIRC Calculates RisksTRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-14 December 2013 Description of MIRC 

### A form within the graphical user interface enables the user to construct specific scenarios by choosing ingestion sources, receptor ages, and other input choices (e.g., diet composition, body weight percentiles). This feature facilitates the analysis of various exposure scenarios. To begin an analysis, the user must supply values for the following chemical-specific parameters for the scenario being evaluated:

 

### Air concentration of total chemical,

 

### Fraction chemical in air in vapor-phase,

 

### Wet and dry deposition rates for particle-phase chemical,

 

### Drinking water concentration,

 

### Chemical concentration in surface soils (two locations; can be tilled and untilled),

 

### Chemical concentration in root-zone soils (two locations; can be tilled and untilled), and

 

### Chemical concentrations in pan fish and in game fish.

Users can input measured values or values estimated by TRIM.FaTE or other models for these parameters.

The MIRC application uses the input data and a variety of empirical transfer factor values (included in its database) to estimate chemical concentrations in nine categories of FFC food types (see Section 2.2 of this addendum). The FFC algorithms and transfer factor values included in MIRC are based on those presented in Chapter 5 of EPA’s HHRAP (EPA 2005a).

For outputs, MIRC is designed to calculate individual cancer risk and non-cancer hazard quotients for one chemical at a time. It is up to the risk assessor to determine if cancer risks or hazard quotients may be additive across two or more chemicals (i.e., if they cause toxic effects in the same target organ by the same mode of action, such as multiple polycyclic aromatic hydrocarbons (PAHs) that are carcinogenic by a mutagenic mode of action).

The tool assumes that an individual is exposed via all of the pathways specified (e.g, fruits and vegetables, animal products, soil, etc.). The tool therefore is useful in estimating risk to the maximally exposed individuals in a risk assessment. To evaluate other receptor populations, the user must specify the each exposure scenario separately.


### 2.2. Exposure Pathways
MIRC estimates the concentrations of chemicals in FFC food categories grown in an area of airborne chemical deposition using algorithms and parameter values provided in HHRAP (EPA 2005a). FFC foods are evaluated in ten categories: exposed fruit, protected fruit, exposed vegetables, protected vegetables, root vegetables, beef, total dairy, pork, poultry, and eggs. Exhibit_Add A2-2 summarizes the pathways by which chemicals are transferred to these food media. Note that for a general Tier 1 screening-level assessment, all of the pathways can be modeled, as is the case for EPA’s Risk and Technology Review (RTR) calculation of screening threshold emission rates for persistent and bioaccumulative hazardous air pollutants (PB-HAPs) (EPA 2008b).TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-15 December 2013 Description of MIRC 

### Exhibit_Add A2-2. Transfer Pathways for Modeled Farm Food Chain (FFC) Media

Farm Food Media Chemical Transfer Pathways Exposed fruit and vegetables  Direct deposition from air of particle-bound chemical

 Air-to-plant transfer of vapor phase chemical

 Root uptake from soil Protected fruit and vegetables (including root vegetables)  Root uptake from soil

Beef and total dairy (including milk)  Ingestion of forage, silage, and grain a

 Soil ingestion
Pork  Ingestion of silage and grain a

 Soil ingestion Poultry and eggs  Ingestion of grain a

 Soil ingestion

a Chemical concentrations in forage, silage, and grain are estimated via in termediate calculations analogous to those used for aboveground produce.



### Produce types included in the FFC can accumulate a chemical directly from air and/or soil. For exposed produce, chemical mass is assumed to be transferred to plants from the air in two ways. First, particle-bound chemical can deposit directly on the plant surface. Second, the uptake of vapor-phase chemicals by plants thro ugh their foliage can occur. For both exposed and protected produce, the concentration in the plant derived from exposure to the chemical in soil is estimated using an empirical bioconcentration factor (BCF) that relates the concentration in the plant to the concentration present in the soil. For belowground root vegetables, a root concentration factor is applied. The algorithms used to estimate produce concentrations are presented in Section 3.1.1 of this addendum.

Chemical concentrations in animal products are estimated based on the amount of chemical consumed through the diet, including incidental ingestion of soil while grazing. The diet options for farm animals in MIRC include forage (plants grown on-site for animal grazing, such as grass), silage (wet forage grasses, fresh-cut hay, or other fresh plant material that has been stored and fermented), and feed grain products grown on the farm (e.g., corn, soybeans). All three animal feed products are assumed to accumulate chemical via root uptake from the soil. Forage and silage also can accumulate chemical via direct deposition of particle-bound chemical and vapor transfer.

The algorithms in MIRC are based on the assumptions that beef and dairy cattle consume all three feed products, while pigs consume only silage and grain and chickens consume only grain. The incidental ingestion of the chemical in soils during grazing or consumption of foods placed on the ground is estimated using empirical soil ingestion values. For secondary animal products (dairy products and eggs), chemical concentrations are estimated by applying a biotransfer factor to the estimated concentration in the “source” animal (cows and chickens, respectively). The algorithms used to estimate animal product concentrations are described in Section 3.1.2 of this addendum.


### 2.3. Receptor Groups
As noted in EPA risk assessment guidelines (EPA 2005b,c,d, 2008a), exposures of children are expected to differ from exposures of adults due to differences in body weights, ingestion rates, dietary preferences, and other factors. It is important, therefore, to evaluate the contribution of exposures during childhood to total lifetime risk using appropriate exposure factor values.TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-16 December 2013 Description of MIRC 

### EPA’s HHRAP (Chapter 4, EPA 2005a) recommends assessing exposures for children and adults separately, but considers all non-infant children in one category. Specifically, HHRAP recommends eight categories of receptor: farmer, child farmer, resident, child resident, fisher, child fisher, acute receptor, and nursing infant. Over time, different EPA programs have used different child age groupings to evaluate body wei ghts, ingestion rates, and other parameter values needed to estimate chemical exposures and risks to children.

To improve the match between age groups used to estimate values across exposure parameters, in 2005, EPA recommended a standard set of child age categories for exposure and risk assessments (EPA 2005b). EPA recommended four age groups for infants: birth to < 1 month; 1 to < 3 months; 3 to < 6 months; and 6 to < 12 months. For young children, EPA recommended an additional four age groups: 1 to < 2 years; 2 to < 3 years; 3 to < 6 years; and 6 to < 11 years. Two age groupings were recommended for teenagers and young adults: 11 to < 16 years; and 16 to < 21 years. These age groupings correspond to different developmental stages and reflect different food ingestion rates per unit body weight, with the highest ingestion rates occurring for the youngest, most rapidly growing, age groups.

For purposes of RTR assessment using MIRC, the selection of age categories is limited by the categories for which most of the FFC food ingestion rates have been calculated. In Chapter 13 of both its Exposure Factors Handbook (EFH; EPA 2011a) and its Child-Specific Exposure Factors Handbook (CSEFH; EPA 2008a), EPA summarized home-grown/raised food ingestion rates for four children’s age groups: 1 to < 3 years; 3 to < 6 years; 6 to < 12 years; and 12 to < 20 years. Intake rates were not calculated for children younger than 1 year because infants are unlikely to consume those foods. They are more likely to be nursing or to be fed formula and other commercial baby-food products.

Although the age groupings used to estimate FFC ingestion rates do not match precisely the groupings that EPA recommended in 2005 for Agency exposure assessments (EPA 2005b), they are the only age-groupings for which such data are available. The U.S. Department of Agriculture’s (USDA’s) 1987-1988 Nationwide Food Consumption Survey (USDA 1992, 1993, 1994a) remains the most recent survey of ingestion rates for home-grown foods, and EPA’s analysis of those data, published in its 2011 EFH, remains the most recently published major analysis of those data. Because ingestion of home-grown produce and animal products are the primary exposure pathways for which MIRC was developed, those are the age groupings used for all child parameter values used to estimate exposure and risk in MIRC.

Thus, in MIRC, values for each exposure parameter were estimated for adults (20 to 70 years) and five children’s age groups:

 

### infants under 1 year (i.e., 0 to < 1 year);

 

### children ages 1 through 2 years (i.e., 1 to < 3 years);

 

### children ages 3 through 5 years (i.e., 3 to < 6 years);

 

### children ages 6 through 11 years (i.e., 6 to < 12 years); and

 

### children ages 12 through 19 years (i.e., 12 to < 20 years).

See Sections 5.1 and 5.2 for descriptions of the risk characterization algorithms used to calculate cancer and non-cancer effects, respectively, for the above age groupings. Exposure and risks to infants under 1 year of age are estimated only for the breast-milk-ingestion pathway.TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-17 December 2013 Description of MIRC 

### For assessment of cancer risks from early-life exposure, EPA recognizes that infants and children may be more sensitive to a carcinogenic chemical than adults, with cancers appearing earlier in life or with lower doses experienced during childhood (EPA 2005c, d). Thus, the “potency” of a carcinogen might be higher for infants and children than for adults. To date, however, data by which to evaluate the relative sensitivity of children and adults to the same daily dose of a carcinogen remain limited. Based on analyses of radioactive and other carcinogenic chemicals, EPA recommends evaluating two lifestages for children separately from adults for chemicals that cause cancer by a mu tagenic mode of action (MOA): from birth to < 2 years and from 2 to < 16 years (EPA 2005c,d). EPA also suggests that, as data become available regarding carcinogens with a mutagenic MOA, further refinements of these age groupings may be considered.

For assessing risks from exposures to carcinogenic chemicals that act via a mutagenic MOA, the two early lifestages recommended by EPA (EPA 2005c,d) also are included in MIRC:

 

### children under the age of 2 years (i.e., 0 to < 2 years); and

 

### children from 2 through 15 years (i.e., 2 to < 16 years).

Different age groupings are needed for the assessment of risks from carcinogenic chemicals with a mutagenic MOA and other carcinogens with other or unknown MOAs. Currently in MIRC, the only PB-HAPs with a mutagenic mode of carcinogenesis are the carcinogenic PAHs. See Section 5.1 for a description of the age-dependent adjustment factors (ADAFs) that are used to calculate cancer risks for chemicals with a mutagenic MOA. 

## 3. Exposure Algorithms
The exposure algorithms in MIRC are described below in four sections. Section 3.1 of this addendum presents the algorithms used to estimate chemical concentrations in FFC foods from chemical concentrations in soil and air. Pathway-specific algorithms used to estimate chemical intakes by adults and non-infant children are described in Section 3.2, and total chemical intake calculations are described in Section 3.3. Finally, the sets of algorithms used to estimate chemical intake via consumption of breast milk by nursing infants are described in Section 3.4. As noted previously, the exposure algorithms used in MIRC are based on those presented in HHRAP (EPA 2005b). Any differences betw een MIRC and HHRAP are explained in this section.


### 3.1. Farm Food Chain Algorithms
The algorithms and parameters used to estimate chemical concentrations in produce and animal products are described in Sections 3.1.1 and 3.1.2 of this addendum, respectively. Discussions of the parameter value options and the values selected as defaults in MIRC for RTR risk assessment are provided in Section 6.2. The use of TRIM.FaTE to model chemical fate and transport in the environment prior to FFC calculations drives the most significant difference between the FFC algorithms included in HHRAP and the equations used for RTR. The approach in HHRAP uses estimated ambient ai r concentrations and deposition rates from dispersion model simulations that use unit emis sion rates. Chemical-specific emission rates (adjusted for vapor and particle-bound fractions) are then incorporated into some of the HHRAP FFC algorithms to calculate concentrations in FFC media. Soil concentrations are calculated using a similar approach in HHRAP. For assessm ent of multipathway exposures for RTR, TRIM.FaTE is used to estimate air concentrations, air-to-surface deposition rates, and soil concentrations, and these outputs are used in the FFC algorithms.TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-18 December 2013 Description of MIRC 

### 3.1.1. Estimating Chemical Concentrations in Produce
Produce (vegetables and fruits) can become cont aminated directly by deposition of airborne chemicals to foliage and fruits or indirectly by uptake of chemicals deposited to the soil. Given these two contamination processes, produce is divided into two main groups: aboveground and belowground produce. Aboveground produce is divided into fruits and vegetables. These groups are further subdivided into “exposed” and “protected” depending on whether the edible portion of the plant is exposed to the atmosphere or is protected by a husk, hull, or other outer covering.

Exhibit_Add A2-3 lists the pathways by whic h chemicals are transferred to the FFC produce categories. Note that for a general screening-level assessment, all of the pathways can bemodeled, as was done for EPA’s calculation of Tier 1 screening threshold emission rates for PBHAPs in its RTR assessments (EPA 2008b), and as described in the Technical Support Document. Sections 3.1.1.1 and 3.1.1.2 below describe the transfer pathways and algorithms for aboveground and belowground produce, respectively.

Exhibit_Add A2-3. Chemical Transfer Pathways for Produce

Farm Food Media Chemical Transfer Pathways Aboveground Produce Exposed fruits and vegetables Direct deposition from air of particle-bound chemical Air-to-plant transfer of vapor phase chemical Root uptake from soil
Protected fruits and vegetables Root uptake from soil

Belowground Produce Root vegetables Root uptake from soil



### 3.1.1.1. Aboveground Produce
For aboveground exposed produce, chemical mass is assumed to be transferred to plants from the air in three ways, as illustrated in Exhibit_Add A2-4. First, particle-bound chemical can deposit directly on the plant surface via deposition ( Pd ). The amount of chemical accumulated is estimated based on the areal fraction of chemical deposition intercepted by the plant surface, minus a loss factor that is intended to account for removal of deposited chemical by wind and rain and changes in concentration due to growth dilution. Second, for chemical present in air in the vapor phase, the concentration of chemical accumulated by the plant’s foliage is estimated using an empirical air-to-plant biotransfer factor ( Pv ). Third, the chemical concentration in the plant due to root uptake from the soil ( Pr AG-produce 

### ) is estimated using an empirical bioconcentration factor ( Br AG-produce 

### ) that relates the chemical Exhibit_Add A2-4. Estimating Chemical Concentration in Aboveground Produce

Deposition of Particles ( Pd ) Root Uptake from Soil ( Pr AG-produce ) Vapor Transfer

( Pv )

Chemical Concentration in

Aboveground ProduceTRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-19 December 2013 Description of MIRC 

### concentration in the plant to the average chemical concentration in the soil at the root-zone depth in the produce-growing area ( Cs root-zone_produce 

### ).

The edible portions of aboveground protected produce are not subject to contamination via particle deposition ( Pd ) or vapor transfer (Pv ). Therefore, root uptake of chemicals is the primary mechanism through which aboveground protected produce becomes contaminated. As shown below, the chemical concentration in the aboveground plant due to root uptake from soil ( Pr AG-produce- DW 

### ) is estimated using an empirical bioconcentration factor ( Br AG-produce-DW 

### ) that relates the chemical concentration in the plant to the average chemical concentration in the soil at the root-zone depth in the produce-growing area ( Cs root-zone_produce 

### ). These equations all assume measurements on a dry-weight (DW) basis.

Equation 2-1. Chemical Concentration in Aboveground Produce

where:

C

AG-produce-DW(i) = Concentration of chemical in edible portion of aboveground produce type i, exposed or protected, on a dry-weig ht (DW) basis (mg/kg produce DW)

Pd (i) = Chemical concentration in edible portion of aboveground produce type i due to deposition of particles (mg/kg produ ce DW); for protected aboveground produce, Pd equals zero

Pr

AG-produce-DW(i) = Chemical concentration in edible portion of aboveground produce type i, exposed or protected, due to root uptake from soil at the root-zone depth of the produce growing area (mg/kg produce DW)

Pv (i) = Chemical concentration in edible portion of aboveground produce type i due to air-to-plant transfer ( μ g/g [or mg/kg] produce DW); for protected aboveground produce, Pv equals zero



### Equation 2-2. Chemical Concentration in Aboveground Produce Due to Root Uptake

where:

Pr

AG-produce-DW(i) = Concentration of chemical in edibl e portion of aboveground produce type i

, exposed or protected , due to root uptake from soil at root-zone depth in the produce-growing area, on a dry-weight (DW) basis (mg/kg produce DW)

Cs root-zone_produce = Average chemical concentration in soil at root-zone depth in produce-growing area (mg/kg soil DW)

Br

AG-produce-DW(i) = Chemical-specific plant/soil chemical bioconcentration factor for edible portion of aboveground produce type i

, exposed or protected (g soil DW / g produce DW) )()()( )( 

### P i iiDW produce AG iDW produce AG 

### PvPd r C   

 

)( ce zone_produ -root )( 

### P iDW produce AG iDW produce AG 

### Br Cs r

  

###  TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-20 December 2013 Description of MIRC 

### Equation 2-3. Chemical Concentration in Aboveground Produce Due to Deposition of Particle-phase Chemical



### where:

Pd (i) = Chemical concentration in aboveground produce type i on a dry-weight (DW) basis due to particle deposition (mg/kg produce DW); set equal to zero for protected aboveground produce
Drdp = Average annual dry deposition of particle-phase chemical (g/m 2 -yr)

Fw = Fraction of wet deposition that adheres to plant surfaces; 0.2 for anions, 0.6 for cations and most organics (unitless)
Drwp = Average annual wet deposition of particle-phase chemical (g/m 2 -yr)

Rp (i) = Interception fraction of the edible portion of plant type i (unitless)
kp (i) = Plant surface loss coefficient for plant type i (yr -1 )

Tp (i) = Length of exposure to deposition in the fi eld per harvest of the edible portion of plant type i (yr)

Yp (i) = Yield or standing crop biomass of the edi ble portion of plant type i (kg produce DW/m 2 )



### Note that Equation 2-3 differs from Equation 5- 14 in HHRAP, from which it is derived. In HHRAP, Equation 5-14 includes the term Q x ( 1 – Fv ) to indicate the emissions rate, in g/sec, of chemical from the source and the proportion of the chemical that remains in, or partitions to, the particle-phase in the air. Also in HHRAP, the dry and wet particle phase deposition rates, Dydp and Dywp, respectively, are normalized to the emission rate and are expressed in units of sec/m 2 

### -yr.
With MIRC, the user inputs both the dry and wet particle-phase deposition rates, Drdp and Drwp , respectively, in units of g/m 2 

### -yr for a specific location relative to an emissions source. Those deposition rates might be values measured near that location or estimated using a fate and transport model, such as TRIM.FaTE, in conjunction with local meteorological information and emissions rate data. The chemic al emissions term used in HHRAP, Q , therefore, is not used in MIRC’s Equation 2-3. In addition, in MIRC, Drdp and Drwp , the average annual dryand wet-particle-phase deposition rates, respectively, are in units of g/m 2 

### -yr. Users of TRIM.FaTE should note that the dry- and wet-particle-deposition rates output from TRIM.FaTE are in units of g/m 2 

### -day; therefore, users must adjust the TRIM.FaTE output values to units of g/m 2 

### -yr (i.e., multiply by 365 days/yr) before inputting values for Drdp and Drwp into MIRC.

Equation 2-4. Chemical Concentration in Aboveground Produce Due to Air-to-Plant Transfer of Vapor-phase Chemical



### where: )()( ))(*)(( )( )( 

### ) 1()) ((000,1

ii iTpikp i i 

### kpYp e Rp DrwpFwDrdp Pd

 

 

a iAG iAG i 

### VG BvFvCa Pv  )( )( )( 

###   

TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-21 December 2013 Description of MIRC Pv (i) = Concentration of chemical in edibl e portion of aboveground produce type i from air-to-plant transfer of vapor-phase chemical on a dry-weight (DW) basis ( μ g/g produce DW); set equal to zero for protected aboveground produce

Ca = Average annual total chemical concentration in air (g/m 3 )

Fv = Fraction of airborne chemical in vapor phase (unitless)

Bv AG(i) = Air-to-plant biotransfer factor for aboveground produce type i for vapor-phase chemical in air ([mg/g produce DW] / [mg/g air], i.e., g air/ g produce DW)

VG AG(i) = Empirical correction factor for aboveground exposed produce type i to address possible overestimate of the diffusive tran sfer of chemical from the outside to the inside of bulky produce, such as fruit (unitless)

ρ a = Density of air (g/m 3 )



### Note that Equation 2-4 differs from Equation 5- 18 in HHRAP, from which it is derived. In HHRAP, Equation 5-18 includes the term Q x Fv to indicate the emissions rate, in g/sec, of chemical from the source and the fraction of th e chemical in vapor phase in the air. HHRAP also includes the parameter Cyv , or the unitized yearly average air concentration of vaporphase chemical in units of μ g-sec/g-m 3 

### . For MIRC, the user inputs the average annual total air concentration of the chemical, Ca , for a specific location relative to the source in units of g/m 3 

### ; MIRC includes a chemical-specific default value for Fv for chemicals included in its database. The air concentration might be a value measured near that location or a value estimated by a fate and transport model such as TRIM.FaTE. Users of TRIM.FaTE should note that the average annual concentration of the total chemical in air (i.e., total of both vapor and particulate phases), Ca , output from TRIM.FaTE is in units μg/m 3 

### ; therefore, the user must adjust the value to units of g/m 3 

### (i.e., divide by 1,000 μ g/g) before entering it in MIRC.

The calculations of chemical concentration in aboveground produce, ( C AG-produce-DW 

### ), shown above, are on a dry-weight (DW) basis. The family FFC food ingestion rates, on the other hand, are on a fresh- or wet-weight (WW) basis. MIRC therefore calculates the concentration in aboveground produce on a wet-weight basis, C AG-produce-WW 

### , using Equation 2-5 and the moisture content ( MAF ) of the FFC food category.

Equation 2-5. Conversion of Aboveground Produce Chemical Concentration from Dry- to Wet-Weight Basis



### where:

C

AG-produce-WW(i) = Chemical concentration in edible portion of aboveground produce type i on a wetweight (WW) basis (mg/kg produce WW)

C

AG-produce-DW(i) = Chemical concentration in edible portion of aboveground produce type i on a dryweight (DW) basis (mg/kg produce DW)

MAF (i) = Moisture adjustment factor for aboveground produce type i to convert the chemical concentration estimated for dry-weight produce to the corresponding chemical concentration for full-weight fresh produce (percent water) 

###        

  

  

### 100 ) 100( )( )( )( i iDW produce AG iWW produce AG 

### MAF C CTRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-22 December 2013 Description of MIRC 

### 3.1.1.2. Belowground Produce
The equations by which chemical concentrations are estimated in belowground produce are different for nonionic organic chemicals t han for inorganic chemicals and ionic organic chemicals.



### 3.1.1.2.1. Nonionic Organic Chemicals
For belowground produce, the nonionic organic chemical concentration in the tuber or root vegetable is derived from exposure to the chemical in soil and is estimated using an empirical root concentration factor ( RCF ) and the average chemical concentration in the soil at the rootzone depth in the produce-growing area ( Cs root-zone_produce 

### ), as shown in Equation 2-6. The RCF relates the chemical concentration in the plant on a wet-weight basis to the average chemical concentration in the root-zone soil ( Cs root-zone_produce 

### ) on a dry-weight basis. Belowground produce (i.e., tubers or root vegetables) are protected from the deposition and vapor transfer by being covered by soil. Therefore, root uptake of chemicals is the primary mechanism through which belowground produce becomes contaminated.

Equation 2-6. Chemical Concentration in Belowground Produce: Nonionic Organic Chemicals



### where:

C BG-produce-WW = Concentration of chemical in belowground (BG) produce (i.e., tuber or root vegetable) on a wet-weight (WW) ba sis (mg chemical/kg produce WW)*

Cs root-zone_produce = Average chemical concentration in soil at root-zone depth in produce-growing area, on a dry-weight (DW) basis (mg chemical/kg soil DW)

RCF = Chemical-specific root concentration factor for tubers and root produce (L soil pore water/kg root WW) *

VG rootveg = Empirical correction factor for belowground produce (i.e., tuber or root vegetable) to account for possible overestimate of the diffusive transfer of chemicals from the outside to the insi de of bulky tubers or roots (based on carrots and potatoes) (unitless) *

Kds = Chemical-specific soil/water partition coefficient (L soil pore water/kg soil DW)

UCF = Units conversion factor of 1 kg/L

*Note that there is only one type of BG produce; hence there are no plant-type-specific subscripts.



### The RCF, as developed by Briggs et al . (1982), is the ratio of the chemical concentration in the edible root on a wet-weight basis to its concentration in the soil pore water. RCFs are based on experiments with growth solutions (hydroponic) instead of soils; therefore, it is necessary to divide the soil concentration by the chemical-specific soil/water partition coefficient ( Kds ). There is no conversion of chemical concentrations in belowground produce from DW to WW because the values are already on a WW basis.

For nonionic organic chemicals, it is possible to predict RCF values and Kds values (for a specified soil organic carbon content) from an estimate of the chemical’s K ow 

### from empirically UCFKds VGRCF Cs rootveg produce zoneroot



###   

 

_ WW-produce-BG 

### CTRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-23 December 2013 Description of MIRC 

### derived regression models. Those models are shown in HHRAP Appendix A-2, Equations A-2-14 and A-2-15 (RCF) and in Equations A-29 and A-2-10 (Kds). The RCF and Kds values so calculated for many of the chemicals in HHRAP are included in the MIRC database (including the values for PAHs and dioxins).


### 3.1.1.2.2. Inorganic and Ionic Organic Chemicals
For inorganic chemicals and ionized organic chemicals, it is not possible to predict RCF or Kds values from K ow 

### . For inorganic chemicals, chemical specific empirical values for the root/soil bioconcentration factor must be used. The root/soil bioconcentration factor, now specified as Br BG-produce-DW 

### , must be obtained from the literature for each inorganic chemical on a DW basis. For inorganic chemicals, therefore, Equation 2-7 is used instead of Equation 2-6.

Equation 2-7. Chemical Concentration in Belowground Produce: Inorganic Chemicals



### where:

C BG-produce-DW = Concentration of chemical in edible portion of aboveground produce, due to root uptake from soil at root-zone depth in the produce-growing area, on a dryweight (DW) basis (mg/kg produce DW)

Cs root-zone_produce = Average chemical concentration in soil at root-zone depth in produce-growing area (mg/kg soil DW)

Br

BG-produce-DW = Chemical-specific root/soil chemical bioconcentration factor for edible portion of belowground produce (g soil DW / g produce DW)

VG rootveg = Empirical correction factor for belowground produce (as in Equation 2-6) (unitless)



### As for the aboveground produce, the DW estimate of concentration of chemical in the root vegetables must be transformed to a WW estimate, as shown in Equation 2-8.

Equation 2-8. Conversion of Belowground Produce Chemical Concentration from Dry- to Wet-Weight Basis



### where:

C BG-produce-WW = Chemical concentration in edible portion of belowground produce on a weightweight (WW) basis (mg/kg produce WW)

C BG-produce-DW = Concentration of chemical in edible portion of aboveground produce, due to rootuptake from soil at root-zone depth in the produce-growing area, on a dryweight (DW) basis (mg/kg produce DW)

MAF (BG) = Moisture adjustment factor (as in Equation 2-5, but single value for below ground produce) (percent water) 

### 1 _ rootveg DW produce BG produce zoneroot -DW BG-produce 

### VG Br Cs C  

  



###      

  

  

### 100 ) 100( BG DW produce BG WW produce BG 

### MAF C CTRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-24 December 2013 Description of MIRC 

### 3.1.2. Estimating Chemical Con centrations in Animal Products
Chemical concentrations in animal products are estimated based on the amount of chemical consumed by each animal group m through each plant feed type i ( Plant Ch-Intake(i,m) 

### ) and incidental ingestion of soil for ground-foraging animals ( Soil Ch-Intake(m) 

### ). Exhibit_Add A2-5 summarizes the pathways by which chemicals are transferred to these home- or farm-raised animal food products. Note that for a general screening-level assessment, all of the pathways can be modeled, as is done for EPA’s RTR calcul ation of screening threshold emission rates for PB-HAPs (EPA 2008b).

The feed options for farm animals in MIRC include forage (plants grown on-site for animal grazing, such as grass), silage (wet forage grasses, fresh-cut hay, or other fresh plant material that has been stored and fermented), and grain products grown on the farm. As seen in Exhibit_Add A2-5, the algorithms in MIRC for chemical intake with plant feeds ( Plant Ch-Intake(i,m) 

### ) are based on the assumptions that beef and dairy cattle consume all three plant feed products, while pigs consume only silage and grain, and chickens consume only grain.

Exhibit_Add A2-5. Chemical Transfer Pathways for Animal Products

Farm Food Media Chemical Transfer Pathways Animal Products Beef and total dairy (including milk)  Ingestion of forage, silage, and grain a

 Incidental soil ingestion

Pork  Ingestion of silage and grain a

 Incidental soil ingestion Poultry and eggs  Ingestion of grain a

 Incidental soil ingestion

a Chemical concentrations in pl ant feed (i.e., forage, silage, and grain) are estimated via intermediate calculations (see Equation 2-13, Equation 2-14, Equation 2-3, and Equation 2-4).



### Forage and silage are exposed to the air and can accumulate chemicals via direct deposition of particle-bound chemical and transfer of vapor-phas e chemical, while all animal feed grains are assumed to be protected from the air by a husk or pod (e.g., corn, soybeans). All three animal feed products are assumed to accumulate chemical via root uptake.

Chemical concentrations are estimated for animal feeds using algorithms analogous to those for aboveground farm produce described above. MIRC uses Equation 2-9 to calculate the concentration of chemical in beef, pork, or total dairy and Equation 2-10 to calculate the concentration of chemical in poultry or eggs. The chemical concentration in mammalian farm animals (i.e., beef and pigs) is adjusted using a metabolism factor ( MF ) that accounts for endogenous degradation of the chemical (see Equation 2-9). MF is set to 1.0 for chemicals that are not metabolized and for chemicals for which the metabolic degradation rate is unknown. Although other vertebrates, including birds, are likely to have similar metabolic pathways for most chemicals, the health protective assumpti on is that birds do not metabolize any chemicals; therefore, the MF is omitted from Equation 2-10 for poultry and eggs.

Equation 2-9. Chemical Concentration in Beef, Pork, or Total Dairy



###        

  

# 

   n

i miIntakeCh mIntakeCh m m mammal 

### Plant SoilMFBa C

1 ),( )( )()(TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-25 December 2013 Description of MIRC 

### where:

C mammal(m) = Concentration of chemical in mammalian animal product m , where m = beef, pork, or total dairy (mg chemical/kg animal product WW)

Ba (m) = Chemical-specific biotransfer factor for chemical in diet to chemical in animal food product m , where m = beef, pork, or total dairy ([mg chemical/kg animal product WW] / [mg chemical intake/day] or day/kg WW)

MF = Chemical-specific mammalian metabolism factor that accounts for endogenous degradation of the chemical (unitless)

Soil Ch-Intake(m) = Incidental ingestion of chemical in surface soils by livestock type m during grazing or consumption of foods placed on the ground (mg/day); see Equation 2-11 below

Plant Ch-Intake(i,m) = For livestock (animal product) type m , ingestion of chemical from plant feed type i (mg chemical/kg livestock WW); see Equation 2-12 below (If m = beef or total dairy, then n = 3 and i = forage, silage, and grain; m = pork, then n = 2 and i = silage and grain; m = poultry, then n = 1 and I = grain.)



### Equation 2-10. Chemical Concentration in Poultry or Eggs



### where:

C poultry(m) = Concentration of chemical in food product m , where m = poultry or eggs (mg chemical/kg animal product WW)

Ba (m) = Chemical-specific biotransfer factor for food product m , where m = poultry or eggs (day/kg animal product WW)

Soil Ch-Intake(m) = Incidental ingestion of chemical in surf ace soils by consumption of food on the ground (mg chemical/day) where m = poultry; see Equation 2-11

Plant Ch-Intake(i,m) = For poultry (and eggs), animal m , ingestion of the chemical in plant feed type i (mg chemical/day), which for poultry is limited to grain; see Equation 2-12



### In MIRC, the incidental ingestion of the chemic al in soils by livest ock during grazing or consumption of feed placed on the ground ( Soil Ch-Intake(m) 

### ) is estimated using empirical soil ingestion rates ( Qs ) and a soil bioavailability factor for livestock ( Bs ), as shown in Equation 2-11. At this time, the default value for Bs in MIRC for all chemicals is 1.0 (i.e., the chemical in soil is assumed to be 100 percent bioavailable to the animal). This assumption may be reasonably accurate for the soil surface to which airborne chemical is deposited. MIRC allows the user to enter a surface soil concentration for areas where livestock forage, Cs S-livestock 

### , that is distinct from the surface soil concentration input for areas where produce may be grown and where humans might incidentally ingest soil s (see Section 6.1 of this addendum).

Equation 2-11. Incidental Ingestion of Chemical in Soil by Livestock 

#   ),( )( )()( miIntakeCh mIntakeCh m mpoultry 

### Plant Soil Ba C

  

###   

Bs CsQs Soil livestock s mIntakeCh 

###   

  )(TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-26 December 2013 Description of MIRC 

### where:

Soil Ch-Intake(m) = Incidental ingestion of the chemical in surface soils by livestock type m during grazing or consumption of foods plac ed on the ground (mg chemical/day)

Qs (m) = Quantity of soil eaten by animal type m each day (kg soil DW/day)

Cs s-livestock = Chemical concentration in surface soil in contaminated area where livestock feed (mg chemical/kg soil DW)

Bs = Soil bioavailability factor for livestock (unitless) (assumed to be the same for birds and mammals)



### Animal ingestion of the chemical in feed is ca lculated for each type of livestock based on their assumed diets. For m = beef and dairy cattle, chemical intake is estimated for all three feed types: i = forage, silage, and grain. For pork, chemical intake is estimated only for silage and grain. The chemical intake for poultry is based on grain consumption only. The intake of chemical with each feed type, i

, Plant Ch-Intake(i,m) 

### , is calculated separately according to Equation 2-12. Note that the animal feed ingestion rates are on a dry-weight (DW) basis; hence, no DW to wet weight (WW) conversion is needed.

Equation 2-12. Ingestion of Chemical in Feed by Livestock



### where:

Plant Ch-Intake(i,m) = Ingestion of chemical in plant feed type i (mg chemical/day), where i = forage, silage, or grain, for livestock type m

F (i,m) = Fraction of plant feed type i obtained from contaminated area used to grow animal feed, where I = forage, silage, or grain (unitless) for livestock type m

Qp (i,m) = Quantity of plant feed type i consumed per animal per day (kg plant feed DW/day), where i = forage, silage, or grain, for livestock type m

C feed(i) = Concentration of chemical in ingested plant feed type i (mg chemical/kg plant feed DW), where i = forage, silage, or grain



### The concentrations of chemical in the three different types of plant feeds for livestock are calculated according to Equation 2-13. The equation is the same as that for aboveground produce in Equation 2-1, with the exception that the concentrations are for plants used as animal feeds (not produce consumed by humans) and all types of plant feed (i.e., forage, silage, and grain) are aboveground.

Equation 2-13. Chemical Concentration in Livestock Feed (All Aboveground)



### where:

C feed(i) = Concentration of chemical in plant feed type i on a dry-weight (DW) basis (mg chemical/kg plant feed DW), where i = forage, silage, or grain )( ),(),(),( ifeed mi mimiIntakeCh 

### CQpF Plant   



(i) (i) feed(i) feed(i) 

### Pv PdPrC   TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-27 December 2013 Description of MIRC Pr

feed(i) = Concentration of chemical in plant feed type i due to root uptake from soil (mg/kg DW), where i = forage, silage, or grain; see Equation 2-14 below

Pd (i) = Concentration of chemical in plant feed type i due to wet and dry deposition of particle-phase chemical (mg/kg DW), where i = forage, silage, or grain; when i = grain, the Pd term equals zero

Pv (i) = Concentration of chemical in plant feed type i due to air-to-plant transfer of vapor-phase chemical ( μ g/g [or mg/kg] DW) where i = forage, silage, or grain; when i = grain, the Pd term equals zero



### MIRC calculates the chemical concentration in animal feed due to root uptake from the soil using Equation 2-14. The equation is the same as Equation 2-2, except that a Br value appropriate to grasses is used and MIRC allows for different soil concentrations in the area used to grow animal feed than in the area used to grow produce for human consumption (see Section 6.1 of this addendum, user inputs). Note that for feed type i = grains, the Pd and Pv terms do not apply (are set to zero), because the feed products (i.e., corn kernels, soy beans) are protected from the air (i.e., by husks, pods).

Equation 2-14. Chemical Concentration in Livestock Feed Due to Root Uptake



### where:

Pr feed(i) = Concentration of chemical in plant feed type i due to root uptake from soil on a dry-weight (DW) basis (mg chem ical/kg plant feed DW), where i = forage, silage, or grain

Cs root-zone_feed(i) = Average chemical concentration in soil at root-zone depth in area used to grow plant feed type i (mg chemical/kg soil DW), where i = forage, silage, or grain

Br

feed(i) = Chemical-specific plant-soil biocon centration factor for plant feed type i (kg soil DW/kg plant feed DW), where i = forage, silage, or grain



### The algorithms used to calculate Pd (i) 

### and Pv (i) 

### when plant feed type i = forage and silage are identical to those used to calculate Pd (i) 

### and Pv (i) 

### for aboveground exposed produce (i.e., Equation 2-3 and Equation 2-4, respectively).

There are no conversions of DW feed to WW feed, because all feed ingestion rates for livestock are based on DW feed.



### 3.2. Chemical Intake Calculations for Adults and Non-Infant Children
MIRC calculates human chemical intake rates from the ingestion of home-grown foods as average daily doses (ADDs) normalized to body weight for each age group, chemical, and food type separately. ADDs, calculated using Equation 2-15, are expressed in milligrams of chemical per kilogram of receptor body weight per day (mg/kg-day).

Equation 2-15. Average Daily Dose for Specified Age Group and Food Type )( )(_ )( 

### P ifeed ifeed zoneroot ifeed 

### Br Cs r  





###                

 

 days 365 (y)

(y) (y) (y) (i) (y,i) (i) (y,i) 

### EF

ATBW EDFCIRC ADDTRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-28 December 2013 Description of MIRC 

### where:

A DD (y,i) = Average daily dose for age group y from food type or ingestion medium i (mg chemical/kg body weight-day)

C (i) = Concentration of chemical in food type i harvested from the contaminated area (mg chemical/kg food or mg food/L water)

IR (y,i) = Ingestion rate for age group y of food type i (kg/day or L/day)

FC (i) = Fraction of food type i that was harvested from contaminated area (unitless)

ED (y) = Exposure duration for age group y (years)

BW (y) = Body weight for age group y (kg)

AT (y) = Averaging time for calculation of daily dose (years) for age group y , set equal to ED in MIRC

EF (y) = Annual exposure frequency for age group y (days)



### Equation 2-15 takes into account the chemical concentration in each food type i (or in water), the quantity of food brought into the home for consumption, the loss of some of the mass of the foods due to preparation and cooking, how much of the food is consumed per year, the amount of the food obtained from contaminated areas, and the consumer’s body weight (EPA 2011a, 2003a). In MIRC, ADDs are calculated separately for each chemical, home-grown food type, and consumer age group.

ADD values, expressed as intakes, not absorbed doses, are appropriate for comparison with RfDs and for use with cancer slope factors (CSFs) to estimate risk, as discussed in Section 5 of this addendum. An exception is for the breast- milk exposure pathway, where calculating the dose available to and absorbed by the nursing infant is related to the dose absorbed by the mother as discussed in Section 3.4 of this addendum.

MIRC evaluates only one exposure scenario at a time. For screening-level assessments, all components of this equation are assumed to remain constant for consumers in a given age group over time (e.g., seasonal and annual variations in diet are not explicitly taken into account). To calculate an ADD (y,i) 

### from the contaminated area for food group i over an entire lifetime of exposure, age-group-specific ingestion rates and body weights are used for the age groups described in Section 2.3 of this addendum. In MIRC, the averaging time used to calculate the daily dose for an age group ( AT y 

### ) is equal to the exposure duration for that group ( ED y 

### ); therefore these variables drop out of Equation 2-15.

For each chemical included in a screening scenario, total average daily exposure for age group y ( ADD (y) 

### ) is estimated as the sum of chemical intake from all ingestion pathways combined: Note that the last exposure pathway is limited to infants.

 

### Incidental soil ingestion;

 

### Ingestion of fish;

 

### Ingestion of homegrown fruits (exposed and protected);

 

### Ingestion of homegrown vegetables (exposed, protected, and root);

 

### Ingestion of animal products from home-raised animals:

– 

### Milk and other dairy products from cows,TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-29 December 2013 Description of MIRC 

### – 

### Beef products,

– 

### Pork products, and

– 

### Poultry and eggs;

 

### Ingestion of drinking water from specified source; and

 

### Ingestion of breast milk by infants.

The algorithms for the first six exposure pathwa ys listed above are described in Sections 3.2.1 through 3.2.6 of this addendum. The algorithm s for the breast-milk ingestion pathway are described in Section 3.4.



### 3.2.1. Chemical Int ake from Soil Ingestion
Equation 2-16 shows the equation used to estimate chemical intake through incidental ingestion of soil.

Equation 2-16. Chemical Intake from Soil Ingestion



### where:

ADD Soil(y) = Average daily chemical intake from incidental ingestion of soil or ingestion by child in age group y (mg chemical/kg body weight-day)

C Soil = Concentration of chemical in soil from cont aminated area on a dry-weight (DW) basis (μg/g soil DW)

IR Soil(y) = Soil ingestion rate for age group y (g DW/day)

FC Soil = Fraction of soil ingested that is from contaminated area (unitless)

BW (y) = Body weight for age group y (kg)

EF = Exposure frequency; number of days per year of exposure for family(ies) as specified for scenario (  365 days)

Note: MIRC saves soil ingestion rates in units of mg/day (not g/day); therefore, there is an additional 0.001 g/mg conversion u nit in the actual MIRC algorithm than shown here.



### 3.2.2. Chemical Intake from Fish Ingestion
Ingestion of locally caught fish is included as a possible exposure pathway in MIRC (Equation 2-17). Two types of fish are included in the exposure algorithm: trophic level 3.5 (abbreviated as TL3) fish, equivalent to benthic carnivores such as catfish and trophic level 4 (TL4) fish in the water column, equivalent to game fish such as lake trout and walleye. The chemical concentration in fish in Equation 2-17 is estimated as the consumption-weighted chemical concentration using Equation 2-18. 

###        

   

 

   

 



 days 365 EF
BW μg mg 0.001 FC IRC
ADD

(y) Soil Soil(y) Soil

(y) SoilTRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-30 December 2013 Description of MIRC 

### Equation 2-17. Chemical Intake from Fish Ingestion

Equation 2-18. Consumption-weighted Chemical Concentration in Fish



### C F୧ୱ୦ 

### ൌ ሺ C F୧ୱ୦TLଷ 

### ൈ F TLଷ 

### ሻ ൅ ሺ C F୧ୱ୦TLସ 

### ൈ F TLସ 

### ሻ

where:

ADD Fish(y) = Average daily chemical intake from ingestion of local fish for age group y (mg/kgday)

L1 Fish * = Weight of fish brought into home that is discarded during preparation (e.g., head, bones, liver, other viscera, belly fat, skin with fat) (unitless)

L2 Fish * = Loss of weight during cooking, such as evaporation and loss of fluids into pan (unitless)

C FishTL3 = Chemical concentration in whole fish for trophic level 3.5 (TL3) fish on a wetweight (WW) basis (mg/kg WW)

C FishTL4 = Chemical concentration in whole fish for trophic level 4 (TL4) fish on a wet-weight (WW) basis (mg/kg WW)

F TL3 = Fraction of fish intake that is from TL3 (unitless)

F TL4 = Fraction of fish intake that is from TL4 (unitless)

C Fish = Consumption-weighted mean chemical concentration in total fish (i.e., as specified by Equation 2-18) (mg/kg WW)

FC Fish = Fraction of local fish consumed derived from contaminated area (unitless)

BW (y) = Body weight for age y (kg)

IR Fish(y) * = Local fish ingestion rate for age y (g WW/day)

EF = Exposure frequency; number of days per y ear of exposure for family(ies) as specified for scenario (  365 days)

*Parameter values must be internally consistent. In contrast to the tables included in MI RC for ingestion rate options for homegrown food products, which are based on the products as brought into the home from the field (see Section 6.3.3 of this addendum), the tables of fish i ngestion rate options included in MIRC are on an “as consumed” basis (i.e., after preparation and cooking losses), and L1 and L2 therefore are set e qual to zero. If the user wishes to enter local fish ingestion rates on an “as harvested” basis, t he user also should enter L1 and L2 values as specified in Section 6.4.3 of this addendum.



### When whole fish are prepared for cooking, it is usual for the viscera, head, and fins to be removed, particularly for larger fish. Many persons also remove (or do not eat) the skin, bones, and belly fat. EPA has, therefore, estimated the proportion of the weight of whole fish that tends to be lost during preparation and cooking across a variety of fish species (EFH; EPA 2011a) and included those losses in its HHRAP algorithms for chemical intake from fish (L1 Fish 

### and L2 Fish 

### in Equation 2-17). 

##    days365 g kg 0010
2111        

        

 

 EF
BW FC .IRC
L L ADD

(y) Fish Fish(y) Fish

Fish Fish Fish(y)TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-31 December 2013 Description of MIRC 

### 3.2.3. Chemical Intake from Fruit Ingestion
Average daily doses of a chemical from homegrown exposed fruits are calculated separately for exposed and protected fruits (Equation 2-19 and Equation 2-20, respectively).

Equation 2-19. Chemical Intake from Consumption of Exposed Fruits

Equation 2-20. Chemical Intake from Consumption of Protected Fruits



### where:

A DD ExpFruit(y) ADD ProFruit(y) = Average daily chemical intake from ingestion of exposed fruit or protected fruit (depending on subscript) (mg chemical/kg body weight-day)

L1 ExpFruit = Mean reduction in fruit weight resulting from removal of skin or peel, core or pit, stems or caps, seeds and defects, and from draining liquids from canned or frozen forms (unitless)

L1 ProFruit = Mean reduction in fruit weight that re sults from paring or other preparation techniques for protected fruits (unitless)

L2 ExpFruit = Mean reduction in fruit weight that results from draining liquids from cooked forms of the fruit (unitless)

C ExpFruit C ProFruit = Chemical concentration in whole exposed fruits or whole protected fruits (depending on subscript) on a wet-weig ht (WW) basis (mg chemical/kg exposed fruit WW)

EF = Exposure frequency; number of days per y ear of exposure for family(ies) as specified for scenario (  365 days)

FC ExpFruit FC ProFruit = Fraction of exposed fruits or protected fruits (depending on subscript) obtained from contaminated area (unitless)

IR ExpFruit(y) IR ProFruit(y) = Ingestion rate of home-grown exposed fruits or protected fruits (depending on subscript) for age y (g WW/kg body weight-day)



### Fruit ingestion rates in the survey were based on weights of unprepared fruits (e.g., one apple; one pear) or the weight of a can of fruit (e.g., 8 oz can). The weight of the fruit ingested is less than the initial weight owing to common preparation actions ( L1 ExpFruit 

### and L1 ProFruit 

### ; e.g., coring apples and pears; peeling apples; pitting cherries). Cooking of exposed fruit (e.g., berries, apples, peaches) often results in further weight loss that results from liquids lost during cooking and drained from the cooking vessel ( L2 ExpFruit 

### ). EPA has assumed that cooking of protected fruit results in no loss of weight for the fruit.



### 3.2.4. Chemical Intake fr om Vegetable Ingestion
MIRC includes three separate algorithms for homegrown vegetables adapted from EPA’s HHRAP Modeling System (EPA 2005a): one for exposed vegetables such as asparagus, broccoli, lettuce, and tomatoes (although they are actually a fruit); one for protected vegetables 

#           

        

     days 365 g kg 0010 2111 EF FC

. IR C L L ADD ExpFruit )ExpFruit(y ExpFruit ExpFruit ExpFruit )ExpFruit(y



##          

        

    days 365 g kg 0010 11 EF FC

. IR C L ADD ProFruit )ProFruit(y ProFruit ProFruit )ProFruit(yTRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-32 December 2013 Description of MIRC 

### such as corn, cabbage, soybeans, and peas; and one for root vegetables such as carrots, beets, and potatoes (see Equation 2-21, Equation 2-22, and Equation 2-23, respectively).

Equation 2-21. Chemical Intake from Exposed Vegetables

Equation 2-22. Chemical Intake from Protected Vegetables

Equation 2-23. Chemical Intake from Root Vegetables



### where:

ADD ExpVeg(y) ADD ProVeg(y) A DD RootVeg(y) = Average chemical intake from ingest ion of exposed vegetables, protected vegetables, or root vegetables (depending on subscript) for age group y (mg chemical/kg body weight-day)

L1 ExpVeg = Mean net preparation and cooking weight loss for exposed vegetables (unitless); includes removing stalks, paring skins, discarding damaged leaves

L1 ProVeg = Mean net cooking weight loss for protected vegetables (unitless); includes removing husks, discarding pods of beans and peas, removal of outer leaves

L1 RootVeg = Mean net cooking weight loss for root vegetables (unitless); includes losses from removal of tops and paring skins

L2 RootVeg = Mean net post cooking weight loss for root vegetables from draining cooking liquids and removal of skin after cooking (unitless)

C ExpVeg C ProVeg C RootVeg = Chemical concentration in exposed veget ables, protected vegetables, or root vegetables (depending on subscript) on a wet-weight (WW) basis (mg chemical/kg vegetable WW)

EF = Exposure frequency; number of days per y ear of exposure for family(ies) as specified for scenario (  365 days)
FC ExpVeg FC ProVeg FC RootVe g = Fraction of exposed vegetables, protec ted vegetables, or root vegetables (depending on subscript) obtained from contaminated area (unitless)

IR ExpVeg(y) IR ProVeg(y) IR RootVeg(y) = Ingestion rate of exposed vegetables, protected vegetables, or root vegetables (depending on subscript) for age group y (g vegetable WW/kg body weight-day) 

#          

        

   days 365 g kg 0010 11 EF FC

. IRC L ADD ExpVeg ExpVeg(y) ExpVeg ExpVeg ExpVeg(y)



#          

        

  days 365 g kg 0010 11 EF FC

. IRC L ADD ProVeg ProVeg(y) ProVeg ProVeg ProVeg(y)



#           

        

     days 365 g kg 0010 2111 EF FC

. IR C L L ADD RootVeg RootVeg(y) RootVeg RootVeg RootVeg RootVeg(y)TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-33 December 2013 Description of MIRC 

### 3.2.5. Chemical Intake from Animal Product Ingestion

Calculations of chemical intake from the consum ption of farm animals and related food products are provided below in Equation 2-24 through Equation 2-28 for homegrown beef, dairy (milk), pork, poultry, and eggs, respectively.

Equation 2-24. Chemical Intake from Ingestion of Beef



### where:

A DD Beef(y) = Average daily chemical intake from ingestion of beef for age group y (mg/kgday)

L1 Beef = Mean net cooking loss for beef (unitless)

L2 Beef = Mean net post cooking loss for beef (unitless)

C Beef = Concentration of contaminant in beef (mg/kg WW))

EF = Exposure frequency; number of days per y ear of exposure for family(ies) as specified for scenario (  365 days)

IR Beef(y) = Ingestion rate of contaminated beef for age group y (g WW/kg-day)

FCBeef = Fraction of beef consumed raised on co ntaminated area or fed contaminated silage and grains (unitless)



### Equation 2-25. Chemical Intake from Dairy Ingestion



### where:

A DD Dairy(y) = Average daily chemical intake from ingestion of total dairy for age group y (mg/kg-day)

C Dairy = Average concentration of contaminant in total dairy (mg/kg WW)

EF = Exposure frequency; number of days per y ear of exposure for family(ies) as specified for scenario (  365 days)

IR Dairy(y) = Ingestion rate of contaminated total dairy for age group y (g WW/kg-day)

FC Dairy = Fraction of total dairy products fr om contaminated area (unitless)



### Equation 2-26. Chemical Intake from Pork Ingestion 

##           

        

 days 365 g kg 0010 2111 EF FC .IRCL L ADD Beef Beef(y) Beef Beef Beef Beef(y)



###        

        

 days 365 g kg 0010 EF FC .IRC ADD Dairy Dairy(y) Dairy Dairy(y)



##           

        

 days 365 g kg 0010 2111 EF FC .IRCL L ADD Pork Pork(y) Pork Pork Pork Pork(y)TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-34 December 2013 Description of MIRC 

### where:

ADD Pork(y) = Average daily chemical intake from ingestion of pork for age group y (mg/kgday)

L1 Pork = Mean net cooking loss for pork (unitless); includes dripping and volatile losses during cooking; averaged over various cuts and preparation methods

L2 Pork = Mean net post cooking loss for pork (uni tless); includes losses from cutting, shrinkage, excess fat, bones, scraps, and juices; averaged over various cuts and preparation methods

C Pork = Concentration of contaminant in pork (mg/kg WW)

EF = Exposure frequency; number of days per y ear of exposure for family(ies) as specified for scenario (  365 days)

IR Pork(y) = Ingestion rate of contaminated pork for age y (g WW/kg-day)

FC Pork = Fraction of pork obtained from contaminated area (unitless)



### Equation 2-27. Chemical Intake from Poultry Ingestion



### where:

ADDPoultry(y) = Average daily dose (chemical intake) from ingestion of poultry (mg/kg-day)
L1Poultry = Mean net cooking loss for poultry (unitless)
L2Poultry = Mean net post cooking loss for poultry (unitless)

CPoultry = Concentration of chemical in poultry (mg/kg WW)

EF = Exposure frequency; number of days per y ear of exposure for family(ies) as specified for scenario (  365 days)
IRPoultry(y) = Ingestion rate of poultry for age group y (g WW/kg-day)

FC Poultry = Fraction of poultry from contaminated area or fed contaminated grains (unitless)



### The reduction in the weight of beef, pork, and poultry during and after cooking may correlate with an increase or decrease in the concentration of the chemical in the food as consumed depending on the chemical and depending on the cooking method.

Equation 2-28. Chemical Intake from Egg Ingestion



### where:

ADD Egg(y) = Average daily chemical intake from ingestion of eggs for age group y (mg/kgday) 

#           

        

  days 365 g kg 0010 2111 EF FC

. IRC L L ADD Poultry Poultry(y) Poultry Poultry Poultry Poultry(y)



###        

        

 days 365 g kg 0010 EF FC .IRC ADD Egg Egg(y) Egg Egg(y)TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-35 December 2013 Description of MIRC C Egg = Concentration of contaminant in eggs (mg/kg WW)

EF = Exposure frequency; number of days per y ear of exposure for family(ies) as specified for scenario (  365 days)

IR Egg(y) = Ingestion rate of contaminated eggs for age group y (g WW/kg-day)

FC Egg = Fraction of eggs obtained from contaminated area (unitless)



### 3.2.6. Chemical Intake from Drinking Water Ingestion
If the user chooses to evaluate chemical inges tion via drinking water, the user specifies a chemical concentration in g/L (equivalent to mg/mL) based on their particular scenario. The chemical concentration could represent water fr om groundwater wells, community water, nearby surface waters, or other source. For this exposure pathway, ingestion rates are in units of milliliters of water per day (mL/day) (see Equation 2-29).

Equation 2-29. Chemical Intake from Drinking Water Ingestion



### where:

ADD DW(y) = Average daily chemical intake from ingestion of drinking water from local residential water source for age group y (mg/kg-day)

C DW = Concentration of contaminant in drinking water (g/L)

IR DW(y) = Drinking water ingestion rate for age group y (mL/day)

FC DW = Fraction of drinking water obtained from contaminated area (unitless)

BW (y) = Body weight of age group y (kg)

EF = Exposure frequency; number of days per y ear of exposure for family(ies) as specified for scenario (  365 days)



### 3.3. Total Chemical Intake
To estimate the total ADD, or intake of a chemical from all of the exposure media that a singleindividual in each age group is expected to contact (e.g., soil, local fish, five types of homegrown produce, and five types of home-raised anima ls or animal products), the media-specific chemical intakes are summed for each age group. Total average daily exposure for a particular age group y ( ADD (y) 

### ) is estimated as the sum of chemical intake from all ingestion pathways combined, as illustrated in Equations 2-30 through Equation 2-35 below.

Equations 2-30 to 2-35. Total Average Daily Dose of a Chemical for Different Age Groups Equation 2-30.

Equation 2-31.

Equation 2-32.

Equation 2-33.        

         

 days 365 EF
BW FC IRC ADD

(y) DW DW(y) DW DW(y)

breastmilk 

### ADD ADD   )1( 

# 

 

 

###  n

i i 

### ADD ADD 1 ),21( )21( 

# 

 

 

###  n

i i 

### ADD ADD 1 ),53( )53( 

# 

 

 

###  n

i i 

### ADD ADD 1 ),116( )116(TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-36 December 2013 Description of MIRC 

### Equation 2-34.

Equation 2-35.



### where i represents the i th 

### food type or ingestion medium and n equals the total number of food types or ingestion media, and ADD parameters are defined below:

ADD (<1) = Total average daily dose of chemical for infants less than one year from ingestion of breast milk (mg/kg-day)

ADD (1–2) = Total average daily dose of chemical from all ingestion sources for children ages 1 through 2 years (mg/kg-day)

ADD (3–5) = Total average daily dose for children ages 3 through 5 years (mg/kg-day)

ADD (6–11) = Total average daily dose for children ages 6 through 11 years (mg/kg-day)

ADD (12–19) = Total average daily dose for children ages 12 through 19 years (mg/kg-day)

ADD (adult) = Total average daily dose for adult age 20 up to 70 years (mg/kg-day)



### The lifetime average daily dose ( LADD ) is calculated as the time-weight average of the ADD values for each age group (Equation 2-36).

Equation 2-36. Lifetime Average Daily Dose (LADD)



### The time-weighting factors simply equal the duration of exposure for the specified age category in years divided by the total lifespan, assumed to be 70 years.


### 3.4. Chemical Intake Calculations for Nursing Infants
The scientific literature indicates that infants can be exposed to some chemicals via their mothers’ breast milk. The magnitude of the ex posure can be estimated from information on the mother’s exposure, data on the partitioning of the chemical into various compartments of the mother’s body and into breast milk, and information on the infant’s consumption of milk and absorption of the chemical. To add this exposure pathway to the MIRC application, we adapted exposure algorithms and default assumptions from EPA’s Methodology for Assessing Health Risks Associated with Multiple Pathways of Exposure to Combustor Emissions (EPA 1998), hereafter referred to as MPE, as explained below.

Note that this pathway generally is of most concern for lipophilic bioaccumulative chemicals (e.g., dioxins) that can cause developmental effects. The period of concern for the more hydrophilic chemicals that cause developmental effe cts generally is earlier, that is, from conception to birth. Hydrophilic chemicals generally exchange well between the maternal and fetal blood supplies at the placenta.


### 3.4.1. Infant Average Daily Absorbed Dose
The average daily dose of chemical absorbed by the infant ( DAI inf



### ) is estimated in MIRC with Equation 2-37. This basic exposure equation relies on the concentration of the chemical in the breast milk, the infant’s breast-milk ingestion rate ( IR milk 

### ), the absorption efficiency of the 

# 

   

###  n

i i 

### ADD ADD 1 ),1912( )1912( 

# 

 

###  n

i iadult adult 

### ADD ADD 1 ),( )(



###      
      
      
      
      
      

  

### 70 50
70 8
70 6
70 3
70 2
70

1
(adult) (12-19) (6-11) (3-5) (1-2) 1)( 

### ADD ADD ADD ADD ADD ADD LADDTRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-37 December 2013 Description of MIRC 

### chemical by the oral route of exposure ( AE inf



### ), the bodyweight of the infant ( BW inf



### ), and the duration of breast feeding ( ED ). Equation 2-37 is EPA’s (EPA 1998) modification of an average daily dose for the infant model first published by Smith (1987) and includes variables for both the concentration of the chemical in the breast milk fat ( C milkfat 

### ) and the concentration of the chemical in the aqueous phase of breast milk ( C aqueous 

### ). The remainder of the DAI inf



### -associated equations assume that most chemicals of concern will partition either to the lipid phase or to the aqueous phase of breast milk, although some c hemicals may partition significantly to both phases of milk. Thus, the remaining equations in MIRC assume that either C milkfat 

### or C aqueous 

### is equal to zero and hence drops out of the equation.

For the parameters in Equation 2-37 (and the equations that follow) that are not calculated from another equation, an EPA default value and options for other values available in MIRC for the infant breast-milk-exposure pathway are described in Section 6.4 of this addendum. The user also can overwrite those parameter values with a different value from the literature as appropriate.

Equation 2-37. Average Daily Dose of Chemical to the Nursing Infant



### where:

DAI inf = Average daily dose of chemical absorb ed by infant (mg chemical/kg body weight-day)

C milkfat = Concentration of chemical in lipid phase of maternal milk (mg chemical/kg milk lipid; calculated using Equation 2-38)

f mbm = Fraction of fat in breast milk (unitless)

C aqueous = Concentration of chemical in aqueous phase of maternal milk (mg chemical/kg aqueous phase milk; calculated using Equation 2-42)

IR milk = Infant milk ingestion rate over the duration of nursi ng (kg milk/day)

AE inf = Absorption efficiency of the chemical by the oral route of exposure (i.e., chemical-specific fraction of ingested chem ical that is absorbed by the infant) (unitless)

ED = Exposure duration, i.e., duration of breast feeding (days)

BW inf = Body weight of infant averaged ov er the duration of nursing (kg)

AT = Averaging time associated with exposure of interest; equal to ED (days)



### As mentioned above, Equation 2-37 includes terms for the chemical in both the lipid- and nonlipid phases of milk. The remaining equations, however, assume that a chemical of concern will partition to the lipid or aqueous phase of breast milk, but not to both. Different models are used to estimate C milkfat 

### (described in Section 3.4.2 of this addendum) and C aqueous 

### (described in Section 3.4.3 of this addendum).



### 3.4.2. Chemical Concentration in Breast Milk Fat
When developing the Methodology for Assessing Health Risks Associated with Multiple Pathways of Exposure to Combustor Emissions (MPE) (EPA 1998), EPA reviewed three firstorder kinetics models for estimating chemical concentration in breast milk fat. The model 

#     

#   

#  

ATBW EDAEIRf CfC DAI

inf inf milk mbm aqueous mbm milkfat inf 

###    

   

 1TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-38 December 2013 Description of MIRC 

### selected for use in MIRC is the model selected in MPE. The other two models were not considered in MPE because one used a biotransfer factor (BTF) approach considered more of a screening model than a predictive tool (Travis et al. 1988) and the other assumed that the contaminant concentration in the maternal fat compartment is at steady state and that the concentration in breast milk fat is the same as in maternal body fat (Smith 1987). The model in MIRC is a changing-concentration model that EPA adapted from a model by Sullivan et al

. (1991). The model, shown in Equation 2-38, esti mates the average chemical concentration in the breast milk over the entire period of breast feeding by reference to a maximum theoretical steady-state concentration. Studies of lipophilic chemicals such as dioxins suggest that concentrations in the maternal milk are highest during the first few weeks of breast feeding and then decrease over time (ATSDR 1998). Equation 2-38 accounts for the changing concentration in breast milk fat, but estimates one average value to represent the concentration over the entire duration of breast feeding. The model is dependent on the maternal body burden of the chemical and assumes that the chemical concentration in breast milk fat is the same as the concentration in general maternal body fat. According to reviewers of the model, this assumption warrants further investigation because milk fat appears to be synthesized in the mammary glands and may have lower chemical c oncentrations than general body fat stores (EPA 2001a).

Equation 2-38. Chemical Concentration in Breast Milk Fat



### where:

C milkfat = Concentration of chemical in lipid phase of maternal milk (mg chemical/kg lipid)

DAI mat = Daily absorbed maternal chemical dose (mg chemical/kg maternal body weightday; calculated using Equation 2-39)

f f = Fraction of total maternal body burden of c hemical that is stored in maternal fat (mg chemical in body fat / mg total chemical in whole body; value from literature or EPA default - see Sect ion 6.5 of this addendum)

k

elim = Chemical-specific total elimination rate c onstant for elimination of the chemical by non-lactating women (per day; e.g., via urine, bile to feces, exhalation; value from literature or calculated using Equation 2-40)

f fm = Fraction of maternal body weight that is fat stores (unitless)

k

fat_elac = Chemical-specific rate constant for tota l elimination of chemical in the lipid phase of milk during nursing (per day; value from literature or calculated using Equation 2-41)

t bf = Duration of breast feeding (days)

t pn = Duration of mother’s exposure prior to parturition and initiation of breast feeding (days)



### Equation 2-38 relies on the daily maternal absorbed intake ( DAI mat 

### ) to determine the concentration of the chemical in the breast milk fat. DAI mat 

### is multiplied by the fraction of the chemical that is stored in maternal fat ( f f 

### ) to determine the amount (i.e., mass) of chemical in the fat. This product, divided by the chemical-specific elimination rate constant ( k elim 

### ) for nonlactating adult women and the fraction of the mother’s weight that is fat (f fm 

### ), represents the



#      
   

        

     

   bfelacfat

pnelim tk

elacfat

elim tk

bfelacfat elacfat

elim

fmelim

fmat milkfat 

### e k k e tkk

k
fk fDAI C

_ 

### 1 1 1

_ _ _TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-39 December 2013 Description of MIRC 

### maximum theoretical steady-state concentration of the chemical in an adult woman. If used alone to estimate the chemical concentration in breast milk fat, the equation as explained thus far is likely to overestimate the chemical conc entration in milk fat because it does not account for losses due to breast feeding. Alone, this term ( DAI mat 

### f f 

### / k elim 

### f fm 

### ) also assumes that the biological half-life of the chemical in the mother’s breast milk fat is small relative to the duration of the mother’s exposure. However, for chemicals with half-lives that are longer than the exposure duration, which are the chemicals of concern in the applications of MIRC to date, an additional term is needed to determine the average concentration in the milk fat over the duration of her exposure.

To account for breast feeding losses and longer chemical half-lives in the mother than the exposure duration, an additional term is included in Equation 2-38. This term includes a fraction dependent on two rate constants, k elim 

### and the elimination constant for a lipophilic chemical in lactating women via the lipid phase of breast milk ( k fat



### _ elac 

### ), the duration of the mother’s chemical exposure prior to nursing ( t pn 

### ), and the duration of breast feeding ( t bf



### ). The whole body concentration ( DAI mat 

### f f 

### / k elim 

### f fm 

### ), the maximum theoretical steady-state concentration, is multiplied by the rate of elimination averaged over the duration of the mother’s exposure, including her exposure prior to and during lactation. To review the derivation of Equation 2-38, see Appendix B of MPE (EPA 1998).

To estimate an average daily dose absorbed by an infant’s mother, or DAI mat 

### , the average daily dose ( ADD ) (in mg/kg-day) for the chemical from all sources that MIRC calculates for adults ( ADD (adult) 

### , described in Section 3.3 of this addendum, Equation 2-35), is multiplied by an absorption efficiency ( AE mat 

### ) or fraction of the chemical absorbed by the oral route of exposure, as shown in Equation 2-39. The value for AE mat 

### can be estimated from absorption efficiencies for adults in general. Available data for some chemicals, in particular some inorganic compounds, indicate AE values for ingestion expos ures of substantially less than 100 percent. For a few of these chemicals, data also indicate lower AEs for the chemical when ingested in food or in soil than when ingested in water (e.g., cadmium). For a screening level assessment, however, it is reasonable to either assume 100 percent for the AE mat 

### or to use the higher AE mat 

### of the food and water AE mat 

### values if available; hence, a single AE mat 

### parameter is included in Equation 2-39.

Equation 2-39. Daily Maternal Absorbed Intake



### where:

DAI mat = Daily maternal dose of chemical absorbed from medium i (mg/kg-day)

A DD (adult) = Average daily dose to the mother (mg/kg-day) (calculated by MIRC – see Section 3.3 of this addendum, Equation 2-35)

AE mat = Absorption efficiency of the chemical by the oral route of exposure (i.e., chemical-specific fraction of ingested chem ical that is absorbed) by the mother (unitless) (value from literature or EPA default – see Section 6.4 of this addendum)



### Equation 2-35, used to calculate ADD (adult), 

### is based on many medium-specific ingestion rates that are normalized to body weight. The adult body weights to which the homegrown food ingestion rates are normalized are the body weights of the consumers in the original USDA survey (see Section 6.3.3 of this addendum), which included both males and females. An mat (adult) mat 

### AE ADD DAI  TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-40 December 2013 Description of MIRC 

### assumption in the breast-milk exposure pathway is that those ingestion rates also are applicable to nursing mothers. The original data for ingestion rates for soil, drinking water, and fish are on a per person basis for males and females combined. MIRC divides those chemical intakes by an adult body weight for males and females combined as specified by the user (e.g., 71.4 kg mean value) to estimate the ADD normalized to body weight from those sources. If the user finds that those exposure media contribute the ma jority of the chemical intake for the risk scenario under consideration, the user may use alternative ingestion rates for those media and alternative body weights for nursing women, as described in Section 6.5 of this addendum.

Elimination rates for chemicals often are report ed as the half-life of the chemical in the body following a known dose of chemical. Many chem icals exhibit a two-phase elimination process, the first being more rapid than the second. For screening risks for persistent and bioaccumulative chemicals, the half-life of the slower phase of elimination, presumably from non-blood compartments of the body, is the more important of the two. Assuming first-order kinetics, Equation 2-40 is used to convert a measured half-life for elimination of a chemical for adults or non-lactating women to an eliminatio n rate constant (EPA 1998). The equation can be used to estimate any kind of chemical loss rate constant from a measured chemical half-life.

Equation 2-40. Biological Elimination Rate Constant for Chemicals for Non-lactating Women



### where:

k elim = Chemical-specific elimination rate const ant for elimination of the chemical for non-lactating women (per day; e.g., via urine, bile to feces, exhalation)

ln2 = Natural log of 2 (unitless constant)

h = Chemical-specific biological half-life of c hemical for non-lactating women (days)



### For chemicals transferred from the body of lactati ng women to breast milk, the rate of chemical elimination is augmented by the rate of chemical loss via the milk through breast feeding. The total elimination rate for lactating women sometimes is measured directly and reported in the literature. Where direct measurements are not available, and for chemicals that partition predominantly to the lipid-phase of milk, EPA has used Equation 2-41 to estimate the total chemical elimination rate for lactating women, k fat



### _ elac 

### (EPA 1998).

Equation 2-41. Biological Elimination Constant for Lipophilic Chemicals for Lactating Women



### where:

k

fat_elac = Rate constant for total elimination of chemical during nursing (per day); accounts for both elimination by adults in general and the additional chemical elimination via the lipid phase of milk in nursing women 

### h k elim 

### 2ln



mat fm mbmfmilk elim elacfat 

### BWf ffIR kk

   

_TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-41 December 2013 Description of MIRC k

elim = Elimination rate constant for chemical from adults, including non-lactating women (per day; e.g., via urine, bile to feces, exhalation; chemical-specific; value from literature or calculated from half-life using Equation 2-40)

IR milk = Infant milk ingestion rate ov er the duration of nursing (kg/d)

f f = Fraction of total maternal body burden of c hemical that is stored in maternal fat (mg chemical in body fat / mg chemical total in body; value from literature or EPA default)

f mbm = Fraction of fat in breast milk (unitless)

f fm = Fraction of maternal body weight that is fat stores (unitless)

BW mat = Maternal body weight over the entire dura tion of the mother’s exposure to the chemical including during pregnancy and lactation (kg)



### Equation 2-41 is based on a model from Smith (1987) and accounts for the additional elimination pathway for lipophilic chemical s via the breast milk fat. The term K fat



### _ elac 

### is estimated by adding an estimate of the first-order elimination constant for breast feeding losses to k elim 

### , which is the chemical-specific total elimination rate constant for non-lactating women. The breast feeding losses are estimated from the infant’s intake rate of breast milk ( IR milk 

### ), the fraction of the total maternal body burden of the chemical that is stored in maternal body fat ( f f 

### ), the fraction of the mother’s breast milk that consists of fat (lipids) ( f mbm 

### ), the mother’s body weight ( BW mat 

### ), and the fraction of the mother’s weight that is body fat ( f fm 

### ). In Equation 2-41, the value for the mother’s body weight should be specific to women of child-bearing age, as opposed to a body weight value for both males and females that is used to estimate an adult average daily dose and the mother’s absorbed daily intake in Equation 2-39. Body weight values for the mother are described in Section 6.5 of this addendum. Smith’s (1987) model assumes that the chemical partitions to the lipid-phase of breast milk to the same degree that it partitions into the mother’s body fat. For highly lipophilic compounds, losses from breast feeding can be larger than losses by all other pathways (EPA 1998).



### 3.4.3. Chemical Concentration in Aqueous Phase of Breast Milk
When developing MPE (EPA 1998), EPA also cons idered models to estimate chemical concentrations in the aqueous phase of breast milk (C aqueous 

### ). EPA adapted Smith’s (1987) steady state concentration model for estimating C milkfat 

### and developed the C aqueous 

### model shown in Equation 2-42 (EPA 1998). Chemicals that would partition to the aqueous phase of human milk include water-soluble chemicals, such as salts of metals, and other hydrophilic chemicals that may be in equilibrium with bound forms of the chemical in different tissues. The C aqueous 

### equation assumes that the chemical concentrati on in the aqueous phase of milk is directly proportional to the chemical concentration in the mother’s blood plasma. The portion of chemical sequestered in red blood cells (e.g., bound to RBC proteins) is assumed to be unavailable for direct transfer to breast milk.

Equation 2-42. Chemical Concentration in Aqueous Phase of Breast Milk

pm elacaq bm plmat
aqueous 

### fk PcfDAI C

  



_TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-42 December 2013 Description of MIRC 

### where:

C aqueous = Concentration of chemical in aqueous phase of maternal milk (mg/kg)

DAI mat = Daily absorbed maternal chemical dose (mg/kg-day; calculated by Equation 2-39)

f pl = Fraction of chemical in the body (based on absorbed intake) that is in the blood plasma compartment (unitless; value from literature or calculated by Equation 2-43)

Pc bm = Partition coefficient for chemical betwe en the plasma and breast milk in the aqueous phase (unitless); assumed to equal 1.0

k

aq_elac = Chemical-specific rate constant for tota l elimination of chemical in the aqueous phase of milk during nursing (per day; value from literature or calculated in Equation 2-44)

f pm = Fraction of maternal weight that is blood plasma (unitless)



### Equation 2-42 is a steady-state concentrati on model that, like the Equation 2-38 for C milkfat 

### , is dependent on the maternal absorbed daily intake ( DAI mat 

### ). In Equation 2-42, DAI mat 

### is multiplied by the fraction of the absorbed chemical that is circulating in the blood plasma compartment ( f pl 

### ) and a partitioning coefficient for the chemical between plasma and the aqueous phase of breast milk ( Pc bm 

### ). For highly water-soluble chemicals that are not transported via special carrier molecules, the chemical is assumed to diffuse passively from the mother’s blood serum to the aqueous phase of her milk, in which case Pc bm 

### would equal 1.0. The denominator includes the biological elimination constant for the chemical in the aqueous phase of breast milk in lactating women ( k aq_elac 

### ) and the fraction of the mother’s weight that is plasma ( f pl 

### ). Because the model assumes steady-state, it does not account for chemical species with long half-lives in the body or for body burden losses due to lactation. These factors are important for highly lipophilic chemicals and for non-lipophilic chemicals such as methyl mercury, lead, and cadmium that partition into body compartments such as red blood cells and bone. While these latter chemicals or forms of these chemicals are wa ter-soluble when free, they have relatively long half-lives because they are in equilibrium with the chemical bound to macromolecules in some tissue compartments. Lead is of particular concern because it can be released from the bone into the blood during lactation, and thus into t he breast milk (EPA 2001a). Due to this limitation, the model may over- or underestimate exposure to the infant.

Because Equation 2-42 is based on the relationshi p between the chemical concentrations in the aqueous phase of breast milk and the blood plasma, a value for the fraction of the chemical in the mother’s blood plasma ( f pl 

### ) is required. Ideally, an empirical value for f pl



### should be used. If empirical values are not available, f pl



### can be estimated from Equation 2-43, provided that an empirical value can be found for the fraction of the chemical in the body that is in the mother’s whole blood compartment ( f bl 

### ; EPA 1998).

Equation 2-43. Fraction of Total Chemical in Body in the Blood Plasma Compartment



### where:

f pl = Fraction of chemical in body (based on absorbed intake) that is in the blood plasma compartment (unitless); chemical-specific 

#   bp RBC bp bpbl pl 

### fPcf ff f
 

 1TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-43 December 2013 Description of MIRC f bl = Fraction of chemical in body (based on absorbed intake) in the whole blood compartment (unitless); chemical-specific

f bp = Fraction of whole blood that is plasma (unitless)

PcRBC = Partition coefficient for chemical between red blood cells and plasma (unitless); chemical-specific



### If the fraction of the total chemical in the body that is in the whole blood compartment ( f bl 

### ) is known for a given chemical, then the fraction of that chemical that is in blood plasma depends only on the partition coefficient for the chemical between the red blood cells and the plasma ( Pc RBC 

### ) and the fraction of whole blood that is plasma ( f bp 

### ).

Another parameter for which a value is needed to solve Equation 2-42 is the total chemical elimination rate for lactating women for hydrophilic chemicals, k aq_elac 

### . As for k fat_elac 

### for lipophilic chemicals, k aq_elac 

### for hydrophilic chemicals would be equal to k elim 

### plus the loss rate for the chemical in the aqueous phase of breast-milk during lactation. In the case of hydrophilic chemicals, EPA has yet to propose a term for t he additional elimination of a chemical in the aqueous phase of milk from breast feeding. Giv en basic physiological mechanisms, we assume that chemical loss rates via urine are likely to be significantly higher than loss rates from nursing, however. This is because the counter-current anatomy of kidney tubules allows substantial concentration of chemicals in the t ubules for elimination in urine compared with the concentration in circulating blood and because of active secretion of some chemicals into urine. Therefore, the best estimation of elimination of hydrophilic chemicals by lactating women is simply k elim 

### , the elimination of the chemical from a non-lactating woman, as shown in Equation 2-40. The extent to which k elim 

### is an underestimate of k aq_elac 

### for a given chemical will determine the extent of health protective bias in k aq_elac



### . Equation 2-44. Biological Elimination Rate Constant for Hydrophilic Chemicals



### where:

k aq_elac = Chemical-specific rate constant for tota l elimination of chemical by lactating women for hydrophilic chemicals (per day)

k

elim = Chemical-specific rate constant for total elimination of chemical by non-lactating women (per day; e.g., via urine, bile to feces, exhalation; value from literature or calculated from half-life using Equation 2-40)



### 3.4.4. Alternative Model for In fant Intake of Methyl Mercury
In this version of MIRC, we were unable to fully parameterize the aqueous model for mercury. In particular, no empirical value could be found for the steady-state fraction of total hydrophilic chemical body burden in the mother that is in the blood plasma (f pl 

### , see Exhibit_Add A2-29). This parameter could be estimated using Equation 2-43 if a suitable chemical-specific fraction of chemical in the body that is in the whole blood (f bl 

### ) could be found. However, the value found for f bl 

### is based on a single-dose study and is not considered reliable for use in chronic exposure calculations.

We therefore conducted a literature search to identify existing physiologically based toxicokinetic (PBTK) models of lactational transfer of methylmercury (MeHg) in humans. Most PBTK models that we identified focused on gestational transfer of mercury between mother and elim elacaq 

### kk 

_TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-44 December 2013 Description of MIRC 

### fetus, including a PBTK dynamic compartmental model for gestational transfer of MeHg in humans developed by Gearhart et al . (1995, 1996), and reparameterized by Clewell et al

. (1999).

We did find, however, that Byczkowski and Lipscomb (2001) had added a lactational transfer module to the Clewell et al . (1999) model. Byczkowski and Li pscomb compared their model’s predictions to epidemiological data from mother-nursing-infant pairs obtained following an accidental high-dose poisoning in Iraq (Amin-Zaki et al . 1976) and from 34 mother-nursinginfant pairs examined in a low-dose, chroni c exposure environment (Fujita and Takabatake 1977). Using data from the Iraq incident, Byczkowski and Lipscomb (2001) found good agreement between their model’s predictions and t he clinical data relating MeHg concentrations in breast milk to MeHg concentrations in infant’s blood with time following the poisoning. To compare their model’s predictions to data fr om chronic exposure to low doses of MeHg, Byczkowski and Lipscomb (2001) simulated MeHg intake for 500 days prior to conception, continued through gestation, and 6.5 months (200 days) of lactation. Their model’s predictions were consistent with Fujita and Takabatake’s (1977) study, although use of hair/blood partition coefficients based on the results of the 1977 study precluded use of this comparison as model validation. Both the model predictions and the mean values from the 1977 data indicated that the concentration of MeHg in the blood of nursing infants was close to the MeHg concentration in their mothers’ blood (approximately 0.025 to 0.027 mg/L, Figure 4 of report). At those blood concentrations, the PBTK model estimated the average maternal intake of MeHg to be 0.68 ± 0.33 (SD) μ g/kg-day and the average infant intake of MeHg to be 0.80 ± 0.38 μ g/kg-day. Therefore, for purposes of MIRC, the DAI inf 

### of MeHg is estimated to be the same as the maternal intake per unit body weight (Equation 2-42).

Equation 2-45. Calculation of Infant Average Daily Absorbed Dose of Methyl Mercury



### where:

DAI inf_MeHg = Average daily dose of MeHg absorbed by infant from breast milk (mg/kg-day)

DAI mat_MeHg = Average daily dose of methyl mercury absorbed by the mother, predominantly from fish (mg/kg-day)


## 4. Dose-Response Values Used for Assessment
Chemical dose-response values included in MIRC include carcinogenic potency slope factorsfor ingestion and non-cancer oral reference doses (RfDs) for chronic exposures. The doseresponse values currently used in MIRC for RTR assessments are shown in Exhibit_Add A2-6 For some chemicals, OAQPS has identified dos e-response values for use in RTR (EPA 2007a), and these dose-response values are used in MIRC for RTR assessments. In general, OAQPS chose these values based on the following hi erarchy of sources: EPA’s Integrated Risk Information System (IRIS); the Centers for Di sease Control’s Agency for Toxic Substances and Disease Registry (ATSDR); and the California En vironmental Protection Agency’s (CalEPA’s) Office of Environmental Health Hazard Assessm ent (OEHHA) Toxicity Criteria Database. ForPB-HAP chemicals that are currently evaluated in MIRC but do not currently have doseresponse values identified by OAQPS for RTR, alternative methods for deriving values were used (see Sections 4.2 and 4.4 of this addendum). mat_MeHg inf_MeHg 

### DAI DAI TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-45 December 2013 Description of MIRC 

### Exhibit_Add A2-6. Oral Dose-response Values Used to Calculate RTR Screening Threshold Emission Rates for PB-HAP Chemicals a

Chemical CAS No. Cancer Slope Factor Reference Dose

Value (mg/kg-day) -1 Source Value(mg/kgday) Source

Inorganics Cadmium compounds in food b 7440439 not available 1.0E-03 IRIS Mercury (elemental) 7439976 not available not available
Mercuric chloride 7487947 not available 3.0E-04 IRIS Methyl mercury (MeHg) 22967926 not available 1.0E-04 IRIS
Dioxins 1,2,3,4,6,7,8- Heptachlorodibenzo-p-dioxin 35822469 1.50E+03 Derived from WHO 2005 TEFs c not available

1,2,3,4,6,7,8- Heptachlorodibenzofuran 67562394 1.50E+03 Derived from WHO 2005 TEFs c not available

1,2,3,4,7,8,9- Heptachlorodibenzofuran 55673897 1.50E+03 Derived from WHO 2005 TEFs c not available

1,2,3,4,7,8- Hexachlorodibenzo-p-dioxin 39227286 1.50E+04 Derived from WHO 2005 TEFs c not available

1,2,3,4,7,8- Hexachlorodibenzofuran 70648269 1.50E+04 Derived from WHO 2005 TEFs c not available

1,2,3,6,7,8- Hexachlorodibenzo-p-dioxin 57653857 1.50E+04 Derived from WHO 2005 TEFs c not available

1,2,3,6,7,8- Hexachlorodibenzofuran 57117449 1.50E+04 Derived from WHO 2005 TEFs c not available

1,2,3,7,8,9- Hexachlorodibenzo-p-dioxin 19408743 6.20E+03 IRIS not available

1,2,3,7,8,9- Hexachlorodibenzofuran 72918219 1.50E+04 Derived from WHO 2005 TEFs c not available

2,3,4,6,7,8- Hexachlorodibenzofuran 60851345 1.50E+04 Derived from WHO 2005 TEFs c not available

1,2,3,4,6,7,8,9- Octachlorodibenzo-p-dioxin 3268879 4.50E+01 Derived from WHO 2005 TEFs c not available

1,2,3,4,6,7,8,9- Octachlorodibenzofuran 39001020 4.50E+01 Derived from WHO 2005 TEFs c not available

1,2,3,7,8- Pentachlorodibenzo-p-dioxin 40321764 1.50E+05 Derived from WHO 2005 TEFs c not available

1,2,3,7,8- Pentachlorodibenzofuran 57117416 4.50E+03 Derived from WHO 2005 TEFs c not available

2,3,4,7,8- Pentachlorodibenzofuran 57117314 4.50E+04 Derived from WHO 2005 TEFs c not available

2,3,7,8- Tetrachlorodibenzo-p-dioxin 1746016 1.50E+05 EPA ORD 7E-10 IRIS

2,3,7,8- Tetrachlorodibenzofuran 51207319 1.50E+04 Derived from WHO 2005 TEFs c not availableTRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-46 December 2013 Description of MIRC 

### Exhibit_Add A2-6. Oral Dose-response Values Used to Calculate RTR Screening Threshold Emission Rates for PB-HAP Chemicals a

Chemical CAS No. Cancer Slope Factor Reference Dose

Value (mg/kg-day) -1 Source Value(mg/kgday) Source

Polycyclic Organic Matter

1-Methylnaphthalene 90120 5.0E-01 EPA 1999, POM Group 72002 d 7.0E-02 ATSDR

2-Acetylaminofluorene 53963 1.0E+01 EPA 1999, POM Group 75002 d not available

2-Chloronaphthalene 91587 5.0E-01 EPA 1999, POM Group 72002 d not available

2-Methylnaphthalene 91576 5.0E-01 EPA 1999, POM Group 72002 d 5.0E-02 ATSDR

3-Methylcholanthrene 56495 2. 2E+1 CalEPA not available 7,12-Dimethylbenz(a)anthracene 579 76 2.5E+02 CalEPA not available

Acenaphthene 83329 5.0E-01 EPA 1999, POM Group 72002 d 6.0E-02 IRIS

Acenaphthylene 208968 5.0E-01 EPA 1999, POM Group 72002 d not available

Anthracene 120127 0 IRIS 3.0E-01 IRIS Benz(a)anthracene 56553 1.2E+00 CalEPA not available Benzo(a)pyrene 50328 7.3E+00 IRIS not available Benzo(b)fluoranthene 205992 1. 2E+00 CalEPA not available

Benzo(e)pyrene 192972 5.0E-01 EPA 1999, POM Group 72002 d not available

Benzo(g,h,i)perylene 191242 5.0E-01 EPA 1999, POM Group 72002 d not available

Benzo(k)fluoranthene 207089 1. 2E+00 CalEPA not available
Carbazole 86748 2.0E-02 HEAST not available Chrysene 218019 1.2E-01 CalEPA not available Dibenzo(a,h)anthracene 53703 4. 1E+00 CalEPA not available Dibenzo(a,i)pyrene 189559 1.2E+03 CalEPA not available Dibenzo(a,j)acridine 224420 1.2E+00 CalEPA not available

Fluoranthene 206440 5.0E-01 EPA 1999, POM Group 72002 d 4.0E-02 IRIS

Fluorene 86737 5.0E-01 EPA 1999, POM Group 72002 d 4.0E-02 IRIS

Indeno(1,2,3-c,d)pyrene 193395 1.2E+00 CalEPA not available

PAH, total 234 5.0E-01 EPA 1999, POM Group 71002 d not available

Perylene 198550 5.0E-01 EPA 1999, POM Group 72002 d not available

Phenanthrene 85018 0 IRIS 0 IRIS

Polycyclic Organic Matter 246 5.0E-01 EPA 1999, POM Group 71002 d not availableTRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-47 December 2013 Description of MIRC 

### Exhibit_Add A2-6. Oral Dose-response Values Used to Calculate RTR Screening Threshold Emission Rates for PB-HAP Chemicals a

Chemical CAS No. Cancer Slope Factor Reference Dose

Value (mg/kg-day) -1 Source Value(mg/kgday) Source

Pyrene 129000 0 IRIS 3.0E-02 IRIS

Retene 483658 5.0E-01 EPA 1999, POM Group 72002 d not available

ATSDR = Agency for Toxic Substances and Disease Registry, IRIS = Integrated Risk Information System, EPA OAQPS = EPA’s Office of Air Quality Planning and Standards, CalEPA = Califor nia EPA, EPA ORD = EPA’s Office of Research and Development, HEAST = EPA Health Effects Assessment T ables, TEF = toxic equivalency factor

a Values as of June 2012; these values may be updated as newer ones become available.

b There are RfDs for both water ingestion and food i ngestion for cadmium – the RfD for food is used.

c Dose-response values for these dioxin congeners are not available from EPA sources. CSFs for these congeners were derived as discussed in Section 4.2 of this addendum.

d The method to assign oral cancer slope factors to polycyclic or ganic matter (POM) without CSFs available from other EPA sources is the same as that used in the 1999 National Air Toxics Assessment (EPA 1999). A complete description of the methodology is available at: http://www.epa.gov/ttn/atw/nata1999/99pdfs/pomapproach jan.pdf and is summarized in Section 4.4 of this addendum.



### 4.1. Cadmium
EPA has developed two chronic RfDs for cadmiu m (Cd), one for food and one for water, based on data in IRIS indicating a lower absorption efficiency of cadmium from food than from water. The default RfD set in MIRC is the higher RfD for Cd compounds in food (as described in Section A.2.3, the drinking water exposure pathway is not modeled in the screening scenario because the likelihood that humans would use a lake as a drinking water source is assumed to be low). Users of MIRC who assess exposures via drinking water would need to use the RfD for Cd compounds in water (i.e., 5.0E-04 mg/kg-day).


### 4.2. Dioxins (2,3,7,8-TCDD)
For chemicals for which the critical health effect is developmental, either in utero and/or during the first months or years of life, the exposur e duration and timing of exposure for comparison with the RfD (or comparable values) require special consideration. The most sensitive health endpoints for both mercury and 2,3,7,8-TCDD are neurological effects during development that have long-lasting effects on learning and social behaviors. To ensure a protective risk characterization for these chemicals, it is im portant to use the shortest exposure duration appropriate, at the appropriate life stage, for comparison with the toxicity reference values. This approach avoids “dilution” of an estimated avera ge ADD that would result from averaging the lower daily chemical intake rates normalized to body weight for older children and adults with the potentially higher daily intake rates of infants over a longer exposure averaging period.

The convention for assessing risk from mixtures of dioxins is by application of toxic equivalency factors (TEFs) to dioxin concentrations, which are then expressed as toxic equivalents (TEQs). Of the dioxin congeners, 2,3,7,8-TCDD is the most widely studied and considered to be one of the most toxic congeners. It is therefore assigned a TEF of 1, with the other dioxin congener TEQ concentrations scaled relative to 2,3,7,8-TCDD concentrations on the basis of toxicity. For risk assessment of dioxins for RTR (with one ex ception), the World Health Organization (WHO) 2005 TEFs presented in Exhibit_Add A2-7 were used to derive the CSFs (shown in Exhibit_Add A2-6) for dioxin congeners without available EPA dose response values. The one exception is 1,2,3,7,8,9-Hexachlorodibenzo-p-dioxin, whose TEF was based on data from IRIS.TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-48 December 2013 Description of MIRC 

### Exhibit_Add A2-7. WHO 2005 Toxic Equivalency Factors (TEFs) for Dioxin Congeners

Dioxin Congener CAS No. WHO 2005 Toxic Equivalency Factor a 1,2,3,4,6,7,8-Heptachlorodi benzo-p-dioxin 35822469 0.01 1,2,3,4,6,7,8-Heptachlor odibenzofuran 67562394 0.01 1,2,3,4,7,8,9-Heptachlor odibenzofuran 55673897 0.01 1,2,3,4,7,8-Hexachlorodib enzo-p-dioxin 39227286 0.1 1,2,3,4,7,8-Hexachloro dibenzofuran 70648269 0.1 1,2,3,6,7,8-Hexachlorodib enzo-p-dioxin 57653857 0.1 1,2,3,6,7,8-Hexachloro dibenzofuran 57117449 0.1 1,2,3,7,8,9-Hexachlorodibenzo-p-dioxin b 19408743 0.1 1,2,3,7,8,9-Hexachloro dibenzofuran 72918219 0.1 2,3,4,6,7,8-Hexachloro dibenzofuran 60851345 0.1 1,2,3,4,6,7,8,9-Octachlorodi benzo-p-dioxin 3268879 3E-04 1,2,3,4,6,7,8,9-Octachloro dibenzofuran 39001020 3E-04 1,2,3,7,8-Pentachlorodibenzo-p-dioxin 40321764 1 1,2,3,7,8-Pentachlorodibenzofuran 57117416 0.03 2,3,4,7,8-Pentachlorodibenzofuran 57117314 0.3 2,3,7,8-Tetrachlorodibenzo-p-dioxin 1746016 1 2,3,7,8-Tetrachlorodibenzofuran 51207319 0.1

a Source: van den Berg et al. 2006, with the one exception in the next footnote.

b For 1,2,3,7,8,9-HexCDD, OAQPS identified an oral c ancer slope factor from IRIS. For the purposes of these multipathway analyses, EPA uses the TEF der ived from this IRIS oral CSF (6200 1/mg/kd/d, equaling a TEF of 0.041) rather than the WHO 2005 TEF of 0.1.



### 4.3. Mercury
The RfD applies to the pregnant mother as well as young children. EPA has not specified the minimum exposure duration at the RfD level of exposure that is appropriate to use in characterizing risk; we assume 10 years for women of childbearing age and 1 year for infants. We note that human exposures to MeHg are pr imarily through the consumption of fish and shellfish (EPA 2001b). EPA found that, on average, approximately 76 percent of the exposure to MeHg for women of childbearing age could be attributed to ingestion of mercury in freshwater and estuarine fish and shellfish, with the remaining 24 percent derived from marine fish and shellfish. Other sources accounted for less than 0.06 percent of total exposures (EPA 2001b).


### 4.4. Polycyclic Organic Matter
Dose-response values for some of the POM species that are included in the screening analysis were not identified by OAQPS; for these POM species, an alternative methodology for identifying CSFs was needed. Previously, for risk assessment of inhalation exposures to polycyclic organic matter (POM) for EPA’s Nati onal-Scale Air Toxics Assessments (NATA) and for RTR, OAQPS developed an approach for charac terizing risks associated with the individual POM species and POM groups reported in the National Emissions Inventory (NEI). Individual POMs were assigned to one of eight POM groups according to cancer potencies derived by EPA for IRIS and by CalEPA, and based on assumpti ons regarding relative carcinogenicity. OAQPS then estimated an inhalation CSF for each POM group. The same approach was used to derive oral CSFs for POMs without availabl e CSFs. Each POM group (with all its memberTRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-49 December 2013 Description of MIRC 

### POM species reported in NEI, not just the species currently evaluated in this analysis) and the corresponding CSFs using this methodology are presented in Exhibit_Add A2-8. These group CSFs are used only when OAQPS has not, for the pur poses of RTR, identified a CSF specific to the individual chemical.

Exhibit_Add A2-8. Oral Dose-response Values for Polycyclic Organic Matter (POM) Groups a

Individual POM or POM Group CAS No. Cancer Slope Factor b (mg/kg-day) -1

POM Group 71002 Benz(a)anthracene/chrysene (7-PAH) 103

0.5 Total PAH 234 Polycyclic organic matter 246
16-PAH 40
16-PAH–7-PAH 75040 POM Group 72002
Anthracene 120127

0.5 Pyrene 129000 Benzo(g,h,i) perylene 191242 Benzo(e)pyrene 192972 Benzo(c)phenanthrene 195197 Perylene 198550 Benzo(g,h,i)fluoranthene 203123 Benzo(a)fluoranthene 203338
Fluoranthene 206440 Acenaphthylene 208968 1-Methylpyrene 2381217 12-Methylbenz(a)anthracene 2422794 Methylbenzopyrenes 247 Methylchrysene 248 Methylanthracene 26914181
Benzofluoranthenes 56832736 9-Methylbenz(a)anthracene 779022 1-Methylphenanthrene 832699 Acenaphthene 83329
Phenanthrene 85018
Fluorene 86737 2-Methylnaphthalene 91576 2-Chloronaphthalene 91587 POM Group 73002 7,12-Dimethylbenz(a)anthracene 57976 1000 POM Group 74002 Dibenzo(a,i)pyrene 189559 100 Dibenzo(a,h)pyrene 189640TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-50 December 2013 Description of MIRC 

### Exhibit_Add A2-8. Oral Dose-response Values for Polycyclic Organic Matter (POM) Groups a

Individual POM or POM Group CAS No. Cancer Slope Factor b (mg/kg-day) -1

POM Group 75002 3-Methylcholanthrene 56495

10 Dibenzo(a,e)pyrene 192654 5-Methylchrysene 3697243 Benzo(a)pyrene 50328 Dibenzo(a,h)anthracene 53703 POM Group 76002 Benzo(b+k)fluoranthene 102

1 Indeno(1,2,3-c,d)pyrene 193395 Benzo(j)fluoranthene 205823 Benzo(b)fluoranthene 205992 Benzo(k)fluoranthene 207089 Dibenzo(a,j)acridine 224420 Benz(a)anthracene 56553 POM Group 77002 Chrysene 218019 0.1 POM Group 78002
7-PAH 75 0.5

a These group CSFs are used only when OAQPS has not, for t he purposes of RTR, identified a CSF specific to the individual chemical.

b The method to assign oral cancer slope factors to POM groups was the same as that used in the 1999 National Air Toxics Assessment (EPA 1999). A complete description of the methodology is available at: http://www.epa.gov/ttn/atw/nata1999/99pdfs/pomapproachjan.pdf.



## 5. Risk Estimation
For persistent and bioaccumulative hazardous air pollutants (PB-HAPs), risks from inhalation of a chemical directly from air generally will be neglig ible compared with risks from ingestion of the chemical from foodstuffs grown in an area subject to air deposition of the chemical. Risk characterization for carcinogens with a linear mode of action at low doses is described in Section 5.1 of this addendum. Risk characterization for chemicals likely to exhibit a threshold for response (e.g., non-cancer hazards) is described in Section 5.2.



### 5.1. Cancer Risks
The estimated risk of developing cancer from expos ure to a chemical from a specified source is characterized as the excess lifetime cancer risk (ELCR). The ELCR represents the incremental probability of an individual developing cancer over a lifetime as a result of lifetime exposure to the chemical. For a known or suspected carcinogen with a low-dose linear mode of action, the estimated ELCR is calculated as the product of the lifetime average daily dose (LADD) and the cancer slope factor (CSF):TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-51 December 2013 Description of MIRC 

### Equation 2-46. Calculation of Excess Lifetime Cancer Risk



### ELCR = LADD  

### CSF

where:

ELCR = Estimated excess lifetime cancer risk from a chemical summed across all exposure pathways and media (unitless)

LADD = Lifetime average total daily dose from all exposure pathways and media (mg/kgday)

CSF = Oral carcinogenic potency slope factor for chemical (per mg/kg-day)



### As described in Section 3.3 of this addendum, the LADD (in mg/kg-day) for a chemical is calculated to reflect age-related differences in exposure rates that are experienced by a hypothetical individual throughout his or her lifetime of exposure. The total chemical intake is normalized to a lifetime, which for the purposes of this assessment is assumed to be 70 years.

EPA considers the possibility that children might be more sensitive than adults to toxic chemicals, including chemical carcinogens (EPA 2005b,c). Where data allow, EPA recommends development of lifestage-specific cancer potency CSFs. To date, EPA has developed a separate slope factor for early lifest age exposure for only one chemical (i.e., 1,1,1- trichloroethane; EPA 2007b), and current data availability for most chemicals preclude this approach. EPA has, therefore, examined options for default adjustments of the CSF to protect children. To date, the only mode of action (MOA) for carcinogenesis for which EPA has adequate data to develop a reasonable quantit ative default approach is mutagenesis (EPA 2005b,c). For carcinogens with a mutageni c MOA for cancer, EPA concluded that the carcinogenic potency of a chemical may be approximat ely tenfold greater for the first 2 years of life (i.e., birth up to second birthday) and threefold greater for the next 14 years of life (i.e., ages 2 through 15) than for adults (EPA 2005c). These conclusions ar e represented by agedependent adjustment factors (ADAFs) of 10, 3, and 1 for the first two lifestages and for adults, respectively.

These three lifestages do not match the age categories for the home-grown food ingestion rates, the age categories in MIRC. As a consequence, ADAFs for the age groups in MIRC are adapted as time-weighted average values as follows:

To estimate total lifetime risk from a lifetime of exposure to such a chemical, EPA recommends estimating the cancer risk for each of the three lifestages separately and then adding the risks for i = 1 to 6 age groups. 

##   

3 56 2 yr13 yr110 10

53 21



###   

  

)-( )-( 1)(



### ADAF . ADAF ADAF 

##   

1 2 8 yrs41yrs43 3

1912 116



###   

 

(adult) )-( )-(



### ADAF ADAF ADAFTRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-52 December 2013 Description of MIRC 

### Equations 2-47 to 2-53. Lifetime Cancer Risk: Chemicals with a Mutagenic MOA for Cancer Equation 2-47. 

### Risk (<1) 

### = ADD (0–<1) 

### × 10 × CSF × (1 yr/70 yr) Equation 2-48. 

### Risk (1–2) 

### = ADD (1–2) 

### × 6.5 × CSF × (2 yr/70 yr) Equation 2-49. 

### Risk (3–5) 

### = ADD (3–5) 

### × 3 × CSF × (3 yr/70 yr) Equation 2-50. 

### Risk (6–11) 

### = ADD (6–11) 

### × 3 × CSF × (6 yr/70 yr) Equation 2-51. 

### Risk (12–19) 

### = ADD (12–19) 

### × 2 × CSF × (8 yr/70 yr) Equation 2-52. 

### Risk (adult) 

### = ADD (adult) 

### × 1 × CSF × (50 yr/70 yr)
Equation 2-53. ELCR



### In other words, Equation 2-53 indicates that the total excess lifetime cancer risk (ELCR) equals the sum of the age-group-specific risks estimated by Equations 2-47 through Equation 2-52, where:

Risk (<1) = Risk from chemical ingestion in first year of life

Risk (1-2) = Risk from chemical ingestion fr om first birthday through age 2 years

Risk (3-5) = Risk from chemical ingestion from age 3 through 5 years of age

Risk (6-11) = Risk from chemical ingestion from age 6 through 11 years of age

Risk (12-19) = Risk from chemical ingestion from age 12 through 19 years of age

Risk (adult) = Risk from chemical ingestion from age 20 to 70 years age

ADD (<1) = Average daily dose for infants und er one year of age (mg/kg-day)

ADD (1-2) = Average daily dose from first birthday through age 2 years of age (mg/kg-day)

ADD (3-5) = Average daily dose from age 3 th rough 5 years of age (mg/kg-day)

ADD (6-11) = Average daily dose from age 6 thr ough 11 years of age (mg/kg-day)

A DD (12-19) = Average daily dose from age 12 th rough 19 years of age (mg/kg-day)

ADD (adult) = Average daily dose for adults age 20 to 70 years of age (mg/kg-day)

CSF = Oral carcinogenic potency slope factor for chemical (per mg/kg-day)

Risk (i) = Risk from chemical ingestion for the i th age group
ELCR = Total extra lifetime cancer risk (incremental or extra risk)

n = Number of age groups (i.e., 6)



### 5.2. Non-cancer Hazard Quotients
Non-cancer risks are presented as hazard quotients (HQs), that is, the ratio of the estimated daily intake (i.e., ADD) to the reference dose (e.g., chronic RfD). If the HQ for a chemical is equal to or less than 1, EPA believes that there is no appreciable risk that non-cancer health effects will occur. If the HQ is greater than 1 then there is at least some possibility for an adverse health effect. The larger the HQ value, the more likely it is that an adverse health effect may occur. 

# 

 

###  n

i i 

### Risk 1 )(TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-53 December 2013 Description of MIRC 

### 5.2.1. Hazard Quotients for Chemicals with a Chronic RfD
For chemicals with a chronic RfD, MIRC calc ulates an HQ for each age group separately using Equation 2-54 to indicate the potential for adverse health effects associated with chronic exposure via ingestion pathways. The HQ is the ratio of a long-term, daily average exposure normalized to the receptor's body weight (i.e., ADD) to the RfD for that chemical. HQs are threshold effects and are not additive across age groups.

Equation 2-54. Hazard Quotient for Chemicals with a Chronic RfD



### where:

HQ = Hazard quotient for chemical (unitless)

A DD = Average daily ingested dose of chemical (mg/kg-day) from all food types and ingested media for the age group

RfD = Chronic oral reference dose for chemical (mg/kg-day)



### 5.2.2. Hazard Quotients for Chemi cals with RfD Based on Developmental Effects
For chemicals for which the toxicity reference value is an RfD based on developmental effects, a shorter exposure duration (ED) and averaging time (AT) may be required. For this type of chemical (e.g., methylmercury, 2,3,7,8-TCDD), the appropriate ED/AT and sensitive lifestage for exposure may need to be estimated from the inform ation provided in the critical developmental study(ies) from which the RfD was derived (e.g., in consultation with the RfD documentation in EPA’s IRIS or in a toxicological profile developed for the chemical). For screening-level risk assessments, however, a health protective appr oach is to compare the highest ADD from among the child age categories provided in MIRC to the RfD, as is done for all PB-HAPs. This approach ensures that the highest exposure from among the various age groups evaluated is taken into consideration, regardless of which age group might be most relevant to the health effect of interest (i.e., the age group on which the RfD is based).



### 5.2.3. Hazard Index for Chemicals with RfDs
When conducting screening-level assessments for multiple chemicals, it can be informative to calculate a hazard index (HI) for toxicologically similar chemicals (EPA 2000). The HI is the sum of HQs across chemicals (not age groups) as shown in Equation 2-55. As with the HQ, if the HI value is less than 1, adverse health effects are not expected for that suite of chemicals. If the screening level HI exceeds 1, however, the risk assessor may in some instances, evaluate the assumptions of the screening-level assessment to determine if more realistic local values are available for parameters that drive risk. In addition, the risk assessor may need to examine the mode of action (MOA) and target organ(s) for the chemicals with the highest HQs to develop an appropriate approach to assessing their potential joint action. 

### RfD ADD HQ TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-54 December 2013 Description of MIRC 

### Equation 2-55. Hazard Index Calculation



### HI = HQ 1 

### + HQ 2 

### ... HQ n



### where:

HI = Hazard index (unitless)

HQ 1 = Hazard Quotient for chemical 1 (unitless)

HQ 2 = Hazard Quotient for chemical 2 (unitless)

HQ n = Hazard Quotient for chemical n (unitless)



### The HI approach can be appropriate for chemicals with the same MOA and same target organ; however, MOA often is difficult to determine. An HI usually is “developed for each exposure route of interest, and for a single toxic effect or for toxicity to a single target organ” (EPA 2000; p 79). If a receptor is exposed to multiple chemicals that affect different target organs or that operate by different MOAs, and if more than one HQ is close to 1, the risk assessor in some circumstances, may consider whether chemical intera ctions play a role in chemical toxicity (EPA 2000). Exposures to more than one chemical can result in a greater or lesser toxic response than might be predicted on the basis of one or the other chemical acting alone (toxicologically independent) or acting in concert (toxicologically similar chemicals). Users are referred to EPA’s Supplementary Guidance for Conducting Health Risk Assessment of Chemical Mixtures for approaches to assessing the potential for adverse health effects from exposure to multiple chemicals (EPA 2000).

Note that users of MIRC are responsible for determining how to interpret HQs for multiple chemicals. 

## 6. Model Input Options
This section describes the input options currently included in MIRC. Required user inputs for environmental media concentrations and air deposit ion rates, such as those predicted by(output of) TRIM.FaTE, are described in Section 6.1 of this addendum. Values for farm-foodchain (FFC) parameters for specific types of produce and animal products are discussed in Section 6.2. Options for parameterizing receptor characteristics are described in Section 6.3, including age-group-specific values for body wei ght, water ingestion, and food ingestion by food type. Options for other exposure parameter values in MIRC, such as exposure frequency and loss of chemical during food preparation and cooking, are provided in Section 6.4.

Where values for chemical-specific parameters are presented, values are presented only for PB-HAP chemicals currently evaluated using the TRIM-based RTR screening scenario. The database included with MIRC contains chemical-specif ic parameter values for a large number of chemicals (more than 500), because all of the chemical-specific input data compiled by EPA for use in HHRAP were uploaded into MIRC. Howe ver, only chemicals that are PB-HAPs evaluated for RTR are discussed in this document (see Sections 2.1 and A.2.2), and the HHRAP inputs provided for other chemicals have not been reviewed or verified. The data presented in this chapter were reviewed and used to develop the set of modeling defaults used to calculate screening threshold emission rates for RTR. Note that the default values used to estimate RTR screening thresholds, and the justification for selecting a specific value from the data sets described in this chapter, are discussed in Chapter 7.TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-55 December 2013 Description of MIRC 

### 6.1. Environmental Concentrations
As noted in Section 2 of this addendum, MIRC is intended to estimate exposures and risks to self-sufficient farming and fishing families from ingestion of FFC media in an area of airborne chemical deposition. The tool analyzes one ex posure scenario at a time (e.g., adult farmer exposed to dioxin from ingestion of beef); therefore, it is best used to evaluate a maximally exposed individual (MEI) or family when MI RC is used to screen for possible risks.

The following values specific to the air pollutant of concern are required inputs to MIRC:

 

### a single air concentration (in g/m 3 

### );

 

### the fraction of chemical in the air that is in the vapor phase;

 

### air-to-surface deposition rates for both vapor- and particle-phase chemical in the air (in g/m 2 

### -yr);

 

### two fish tissue concentrations, one each for forage and game fish (i.e., fish in TL 3 and TL 4) (in mg/kg wet weight);

 

### concentrations in drinking water (in g/L); and

 

### four chemical concentrations in soil (in μ g/g dry weight), one each for:

– 

### surface soil in produce growing area,

– 

### surface soil where livestock feed,

– 

### root-zone soil in produce growing area, and

– 

### root-zone soil in livestock feed growing area.

The MIRC software is configured to estimate ingestion exposures via drinking water for a specified chemical concentration in the drinking water source (e.g., groundwater well). However, no exposure via drinking water is assumed to occur when calculating the Tier 1 screening thresholds. As discussed in Section A.2.3, the drinking water exposure pathway is not modeled for the scenario developed for the Tier 1 analysis because the likelihood that humans would use a lake as a drinking water source is assumed to be low.

The user must provide the inputs listed above; no default values are included for these parameters in MIRC. Media concentrations output by TRIM.FaTE can be entered into the tool manually from model output files or can be imported directly from the TRIM.FaTE output files. For RTR evaluations, a tool to facilitate this process was developed using a Microsoft ® 

### ExcelTM routine written in Visual Basic.



### 6.2. Farm-Food-Chain Parameter Values
Using the chemical information specified in Section 6.1 above as inputs, MIRC calculates chemical concentrations in foods that are co mmonly grown or raised on family farms: exposed and protected fruits; exposed and protected vegetables; root vegetables; beef; total dairy products; pork; and poultry and eggs.


### 6.2.1. List of Farm-Food-Chain (FFC) Parameters
MIRC estimates chemical concentrations in the produce identified above using algorithms from HHRAP (EPA 2005a) as described in Section 3.2 of this addendum. Parameter values required for these HHRAP algorithms, including chemical-s pecific media transfer factors (e.g., soil-to-TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-56 December 2013 Description of MIRC 

### plant transfer coefficients) and plant- and animal-specific properties (e.g., plant interception fraction, quantity of forage consumed by cattle), are included in tables in MIRC. As described in Section 7 of this addendum, the HHRAP-recommended parameter values are the default values in MIRC; however, these and other inputs in MIRC can be revised as needed. describes the parameters that are included in the algorithms used to estimate chemical concentrations in the farm food categories. The parameter names and symbols are referenced in this section for plants/produce and animal products.

Exhibit_Add A2-9. MIRC Parameters Used to Estimate Chemical Concentrations in Farm Foods

Parameter Description Units
Plants/Produce
Br

AG-produceDW(i) Chemical-specific plant/soil chemical bioconcentration factor for edible portion of aboveground produce type i, exposed or protected Unitless (g soil DW / g produce DW)

Bv AG(i) Chemical-specific air-to-plant biotransfer factor for aboveground produce type i for vapor-phase chemical in air Unitless ([mg chemical / g DW plant] / [mg chemical / g air])
Fw Fraction of wet deposition that adheres to plant surfaces; 0.2 for anions, 0.6 for cations and most organics Unitless

Kds Chemical-specific soil/water partition coefficient L soil pore water / kg soil DW kp (i) Plant-specific surface loss coe fficient for aboveground exposed produce and animal forage and silage yr - 1

MAF (i) Moisture adjustment factor for aboveground produce type i to convert the chemical concentra tion estimated for dry-weightproduce to the corresponding chemical concentration for fullweight fresh produce Percent water

RCF Chemical-specific root concentration factor for tubers and root produce on a wet-weight (WW) basis L soil pore water/ kg root WW Rp (i) Plant-specific interception fraction for the edible portion of aboveground exposed produce or animal forage and silage Unitless

Tp (i) Length of plant exposure to deposition per harvest of the edible portion of aboveground exposed produce or animal forage and silage Year

VG AG(i) Empirical correction factor for aboveground exposed produce type i to address possible overestimate of the diffusive transfer of chemical from the outside to the inside of bulky produce, such as fruit Unitless

VG rootveg Empirical correction factor for belowground produce (i.e., tuber or root vegetable) to account for possible overestimate of the diffusive transfer of chemicals from the outside to the inside of bulky tubers or roots ( based on carrots and potatoes) Unitless

Yp (i) Plant-specific yield or standing cr op biomass of the edible portion of produce or animal feed kg produce DW/m 2

Animal Products
Bs Soil bioavailability fact or for livestock Unitless
MF Chemical-specific mammalian metabolism factor that accounts for endogenous degradation of the chemical UnitlessTRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-57 December 2013 Description of MIRC 

### Exhibit_Add A2-9. MIRC Parameters Used to Estimate Chemical Concentrations in Farm Foods

Parameter Description Units
Ba (beef) Chemical-specific biotransfer factor for chemical in diet of cow to chemical in beef on a fresh-wet (FW; equivalent to WW) basis mg chemical/kg FW tissue/mg chemical/day or day/kg FW tissue
Ba ( dair y ) Biotransfer factor in dairy day/kg FW tissue
Ba (p ork ) Biotransfer factor in pork day/kg FW tissue
Ba (p oultr y ) Biotransfer factor in poultry day/kg FW tissue
Ba ( e gg s ) Biotransfer factor in eggs day/kg FW tissue
Qs ( m ) Quantity of soil eaten by animal type m each day kg/day Qp (i,m) Quantity of plant feed type i consumed per animal type m each day kg/day

Source: EPA Source: EPA 2005a DW = dry weight; FW = fresh weight; WW = wet weight



### 6.2.2. Produce Parameter Values
Exhibit_Add A2-10 and Exhibit_Add A2-11 provide the chemical-specific input values that are the current defaults for produce FFC food types in MIRC. Exhibit_Add A2-12 presents additional non-chemical-specific input values for parameters used in the algorithms that calculate chemical concentrations in produce. Unless otherwise noted, the default parameter values were obtained from HHRAP. Options for other parameter values are not included in MIRC at this time; however, the user can overwr ite values if appropriate. Refer to HHRAP (EPA 2005a, Chapter 5 and associated appendices) for detailed descriptions of these parameters and documentation of input values.

Exhibit_Add A2-10. Chemical-Specific Inputs for Produce Parameters for Chemicals Included in MIRC

Chemical Fraction of Wet Deposition ( Fw ) (unitless) a Root Concentration Factor ( RCF ) (belowground) (L/kg) b Soil-Water Partition Coefficient ( Kds ) (L/kg) c Chemical Air-toPlant Biotransfer Factor ( Bv AG(i) ) (unitless) d Inorganics Cadmium compounds 0.6 NA 7.5E+01 NA e Mercury (elemental) 0.6 NA 1.0E+03 0 f

Mercuric chloride 0.6 NA 5.8E+04 1.8E+03 Methyl mercury 0.6 NA 7.0E+03 0 f

PAHs 2-Methylnaphthalene 0.6 2.2E+02 5.0E+01 1.4E+00 7,12- Dimethylbenz(a)anthrace

ne 0.6 6.8E+03 4.0E+03 4.2E+04

Acenaphthene 0.6 2.4E+02 3.9E+01 4.6E+00 Acenaphthylene 0.6 2.8E +02 6.8E+01 8.1E+00 Benz(a)anthracene 0.6 6. 7E+03 2.9E+03 6.8E+03 Benzo(a)pyrene 0.6 9.2E+03 7.8E+03 1.7E+05TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-58 December 2013 Description of MIRC 

### Exhibit_Add A2-10. Chemical-Specific Inputs for Produce Parameters for Chemicals Included in MIRC

Chemical Fraction of Wet Deposition ( Fw ) (unitless) a Root Concentration Factor ( RCF ) (belowground) (L/kg) b Soil-Water Partition Coefficient ( Kds ) (L/kg) c Chemical Air-toPlant Biotransfer Factor ( Bv AG(i) ) (unitless) d Benzo(b)fluoranthene 0.6 6.6E+03 3.8E+03 1.7E+05 Benzo(g,h,i)perylene 0.6 3.0E+04 2.6E+04 2.3E+06 Benzo(k)fluoranthene 0.6 8.7E+03 5.5E+03 2.8E+05 Chrysene 0.6 6.0E+03 3.4E+03 1.4E+04 Dibenz(a,h)anthracene 0.6 2.3E+04 1.4E+04 6.2E+06
Fluoranthene 0.6 2.2E +03 3.9E+02 9.0E+02
Fluorene 0.6 3.8E+0 2 6.2E+01 1.6E+01 Indeno(1,2,3-cd)pyrene 0.6 3.5E+04 3.2E+04 2.8E+06
Dioxins OctaCDD, 1,2,3,4,6,7,8,9- 0.6 4.8E+05 7.8E+05 2.4E+06

OctaCDF, 1,2,3,4,6,7,8,9- 0.6 3.4E+05 4.9E+05 2.3E+06 HeptaCDD, 1,2,3,4,6,7,8- 0. 6 3.4E+05 4.9E+05 9.1E+05 HeptaCDF, 1,2,3,4,6,7,8- 0. 6 1.2E+05 1.2E+05 8.3E+05 HeptaCDF, 1,2,3,4,7,8,9- 0. 6 4.8E+04 3.9E+04 8.3E+05 HexaCDD, 1,2,3,4,7,8- 0. 6 2.4E+05 3.1E+05 5.2E+05 HexaCDF, 1,2,3,4,7,8- 0.6 5.7E+04 4.9E+04 1.6E+05 HexaCDD, 1,2,3,6,7,8- 0. 6 4.9E+05 8.0E+05 5.2E+05 HexaCDF, 1,2,3,6,7,8- 0.6 2.9E+05 4.1E+05 1.6E+05 HexaCDD, 1,2,3,7,8,9 - 0. 6 4.9E+05 8.0E+05 5.2E+05 HexaCDF, 1,2,3,7,8,9- 0.6 1.6E+05 1.9E+05 1.6E+05 HexaCDF, 2,3,4,6,7,8- 0.6 2.9E+05 4.1E+05 1.6E+05 PentaCDD, 1,2,3,7,8- 0.6 9.2E+04 9.2E+04 2.4E+05 PentaCDF, 1,2,3,7,8- 0.6 3.9E+04 3.0E+04 9.8E+04 PentaCDF, 2,3,4,7,8- 0.6 2.3E+04 1.6E+04 9.8E+04TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-59 December 2013 Description of MIRC 

### Exhibit_Add A2-10. Chemical-Specific Inputs for Produce Parameters for Chemicals Included in MIRC

Chemical Fraction of Wet Deposition ( Fw ) (unitless) a Root Concentration Factor ( RCF ) (belowground) (L/kg) b Soil-Water Partition Coefficient ( Kds ) (L/kg) c Chemical Air-toPlant Biotransfer Factor ( Bv AG(i) ) (unitless) d
TetraCDD, 2,3,7,8- 0.6 4.0E+04 3.1E+04 6.6E+04 TetraCDF, 2,3,7,8- 0.6 1.2E+04 6.2E+03 4.6E+04
Source: EPA 2005a. NA = not applicable.

a 6E-01 is the value for cations and mo st organic chemicals. As described in HHRAP (EPA 2005a), Appendix B (available at http://www.epa.gov/osw/hazard/tsd/td/c ombust/finalmact/ssra/05hhrapapb.pdf), EPA estimated this value (EPA 1994a, 1995a) from a study by Hoffman et al. (1992) in which soluble gamma -emitting radionuclides and insolubl e particles tagged with gammaemitting radionuclides were deposit ed onto pasture grass via simulated rain. Note that the values developed experimentally for pasture grass may not accurately represent all aboveground pr oduce-specific values. Also note that values based on the behavior of insoluble particles tagged with radionuclides may not accurately repres ent the behavior of organic compounds under site-specific conditions.

b For nonionic organic chemicals, as described in HHRAP (EPA 2005a), Appendix A (available at http://www.epa.gov/osw/hazard/tsd/td/com bust/finalmact/ssra/05hhrapapa.pdf ), RCF is used to calculate the below-ground transfer of contaminants from soil to a root vegetable on a wet-we ight basis as shown in Equation 2-6. EPA estimated chemicalspecific values for RCF from empirical regression equations developed by Briggs et al. (1982) based on their experiments measuring uptake of compounds into barley roots from growth so lution. Briggs’ regression equations allow calculation of RCF values from log Kow. For metals and merc uric compounds, empirical values for soil to root vegetable transfer on a dry-weight basis are available in the literature, thus the RCF was not needed.

c As discussed in HHRAP (EPA 2005a), Appendix A, Kds descri bes the partitioning of a compound between soil pore-water and soil particles and strongly influences the release and movement of a compound into the subsurface soils and underlying aquifer. Kds values for mercuric compounds were obtained from EPA (1997b). Kds for cadmium compounds were obtained from EPA 1996. For all PAHs and dioxins, Kds was calculated by multip lying Koc times the screening sc enario’s fraction organic carbon content (0.008). Empirical information for Koc was av ailable for acenaphthene, benz(a) anthracene, benzo(a)pyrene, dibenz(a,h)anthracene, fluoranthene, and fluor ene in USEAP 1996. For all other organic compounds, the Koc was calculated using the correlation equations presented in USEAP 2005a.

d As discussed in HHRAP (EPA 2005a), Appendix A, the value fo r mercuric chloride was obtained from EPA 1997b. BvAG(i) values for PAHs were calculated using the correlation equation der ived for azalea leaves as cit ed in Bacci et al. (1992), then reducing this value by a factor of 100, as suggested by Lorber (1995), who concluded that the Bacci factor reduced by a factor of 100 was similar to his own observations in various studies. The values for dioxin s were obtained from Lorber and Pinsky (2000)

. e It is assumed that metals, with the exception of vapor-phase elemental mercury, do not transfer significantly from air into lea ves. fSpeciation and fate and transport of mercury from emissions suggest that BvAG(i) val ues for elemental and methyl mercury are likely to be zero (EPA 2005a).



### Exhibit_Add A2-11. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant-Soil BioConcentration Factor ( Br AG-produce-DW(i) ) (unitless) a Empirical Correction Factor- Belowground Produce ( VG rootveg ) (unitless) b EmpiricalCorrection FactorAboveground Produce ( VG A G ( i ) ) (unitless) c Inorganics

Cadmium compounds Exp. Fruit 1.3E-01 - 1.0E+00 Exp. Veg. 1.3E-01 - 1.0E+00 Forage 3.6E-01 - 1.0E+00
Grain 6.2E-02 - -
Prot. Fruit 1.3E-01 - - Prot. Veg. 1.3E-01 - - Root 6.4E-02 1.0E+00 - Silage 3.6E-01 - 5.0E-01TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-60 December 2013 Description of MIRC 

### Exhibit_Add A2-11. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant-Soil BioConcentration Factor ( Br AG-produce-DW(i) ) (unitless) a Empirical Correction Factor- Belowground Produce ( VG rootveg ) (unitless) b EmpiricalCorrection FactorAboveground Produce ( VG A G ( i ) ) (unitless) c

Mercury (elemental) Exp. Fruit - - 1.0E+00 Exp. Veg. - - 1.0E+00 Forage - - 1.0E+00
Grain - - -
Prot. Fruit - - - Prot. Veg. - - - Root - 1.0E+00 - Silage - - 5.0E-01

Mercuric chloride Exp. Fruit 1.5E-02 - 1.0E+00 Exp. Veg. 1.5E-02 - 1.0E+00 Forage 0.0E+00 - 1.0E+00
Grain 9.3E-03 - -
Prot. Fruit 1.5E-02 - - Prot. Veg. 1.5E-02 - - Root 3.6E-02 1.0E+00 - Silage 0.0E+00 - 5.0E-01

Methyl mercury Exp. Fruit 2.9E-02 - 1.0E-02 Exp. Veg. 2.9E-02 - 1.0E-02 Forage 0.0E+00 - 1.0E+00
Grain 1.9E-02 - -
Prot. Fruit 2.9E-02 - - Prot. Veg. 2.9E-02 - - Root 9.9E-02 1.0E-02 - Silage 0.0E+00 - 5.0E-01
PAHs

2-Methylnaphthalene Exp. Fruit 2.3E-01 - 1.0E+00 Exp. Veg. 2.3E-01 - 1.0E+00 Forage 2.3E-01 - 1.0E+00
Grain 2.3E-01 - -
Prot. Fruit 2.3E-01 - - Prot. Veg. 2.3E-01 - - Root 4.4E+00 1.0E+00 - Silage 2.3E-01 - 5.0E-01

7,12- Dimethylbenz(a)anthra

cene Exp. Fruit 1.7E-02 - 1.0E-02 Exp. Veg. 1.7E-02 - 1.0E-02 Forage 1.7E-02 - 1.0E+00
Grain 1.7E-02 - -
Prot. Fruit 1.7E-02 - - Prot. Veg. 1.7E-02 - - Root 1.7E+00 1.0E-02 - Silage 1.7E-02 - 5.0E-01TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-61 December 2013 Description of MIRC 

### Exhibit_Add A2-11. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant-Soil BioConcentration Factor ( Br AG-produce-DW(i) ) (unitless) a Empirical Correction Factor- Belowground Produce ( VG rootveg ) (unitless) b EmpiricalCorrection FactorAboveground Produce ( VG A G ( i ) ) (unitless) c

Acenaphthene Exp. Fruit 2.1E-01 - 1.0E+00 Exp. Veg. 2.1E-01 - 1.0E+00 Forage 2.1E-01 - 1.0E+00
Grain 2.1E-01 - -
Prot. Fruit 2.1E-01 - - Prot. Veg. 2.1E-01 - - Root 6.2E+00 1.0E+00 - Silage 2.1E-01 - 5.0E-01

Acenaphthylene Exp. Fruit 1.9E-01 - 1.0E-02 Exp. Veg. 1.9E-01 - 1.0E-02 Forage 1.9E-01 - 1.0E+00
Grain 1.9E-01 - -
Prot. Fruit 1.9E-01 - - Prot. Veg. 1.9E-01 - - Root 4.1E+00 1.0E-02 - Silage 1.9E-01 - 5.0E-01

Benz(a)anthracene Exp. Fruit 1.7E-02 - 1.0E-02 Exp. Veg. 1.7E-02 - 1.0E-02 Forage 1.7E-02 - 1.0E+00
Grain 1.7E-02 - -
Prot. Fruit 1.7E-02 - - Prot. Veg. 1.7E-02 - - Root 2.3E+00 1.0E-02 - Silage 1.7E-02 - 5.0E-01

Benzo(a)pyrene Exp. Fruit 1.4E-02 - 1.0E-02 Exp. Veg. 1.4E-02 - 1.0E-02 Forage 1.4E-02 - 1.0E+00
Grain 1.4E-02 - -
Prot. Fruit 1.4E-02 - - Prot. Veg. 1.4E-02 - - Root 1.2E+00 1.0E-02 - Silage 1.4E-02 - 5.0E-01TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-62 December 2013 Description of MIRC 

### Exhibit_Add A2-11. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant-Soil BioConcentration Factor ( Br AG-produce-DW(i) ) (unitless) a Empirical Correction Factor- Belowground Produce ( VG rootveg ) (unitless) b EmpiricalCorrection FactorAboveground Produce ( VG A G ( i ) ) (unitless) c

Benzo(b)fluoranthene Exp. Fruit 1.8E-02 - 1.0E-02 Exp. Veg. 1.8E-02 - 1.0E-02 Forage 1.8E-02 - 1.0E+00
Grain 1.8E-02 - -
Prot. Fruit 1.8E-02 - - Prot. Veg. 1.8E-02 - - Root 1.7E+00 1.0E-02 - Silage 1.8E-02 - 5.0E-01

Benzo(g,h,i)perylene Exp. Fruit 5.7E-03 - 1.0E-02 Exp. Veg. 5.7E-03 - 1.0E-02 Forage 5.7E-03 - 1.0E+00
Grain 5.7E-03 - -
Prot. Fruit 5.7E-03 - - Prot. Veg. 5.7E-03 - - Root 1.1E+00 1.0E-02 - Silage 5.7E-03 - 5.0E-01

Benzo(k)fluoranthene Exp. Fruit 1.4E-02 - 1.0E-02 Exp. Veg. 1.4E-02 - 1.0E-02 Forage 1.4E-02 - 1.0E+00
Grain 1.4E-02 - -
Prot. Fruit 1.4E-02 - - Prot. Veg. 1.4E-02 - - Root 1.6E+00 1.0E-02 - Silage 1.4E-02 - 5.0E-01

Chrysene Exp. Fruit 1.9E-02 - 1.0E-02 Exp. Veg. 1.9E-02 - 1.0E-02 Forage 1.9E-02 - 1.0E+00
Grain 1.9E-02 - -
Prot. Fruit 1.9E-02 - - Prot. Veg. 1.9E-02 - - Root 1.7E+00 1.0E-02 - Silage 1.9E-02 - 5.0E-01TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-63 December 2013 Description of MIRC 

### Exhibit_Add A2-11. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant-Soil BioConcentration Factor ( Br AG-produce-DW(i) ) (unitless) a Empirical Correction Factor- Belowground Produce ( VG rootveg ) (unitless) b EmpiricalCorrection FactorAboveground Produce ( VG A G ( i ) ) (unitless) c

Dibenz(a,h)anthracene Exp. Fruit 6.8E-03 - 1.0E-02 Exp. Veg. 6.8E-03 - 1.0E-02 Forage 6.8E-03 - 1.0E+00
Grain 6.8E-03 - -
Prot. Fruit 6.8E-03 - - Prot. Veg. 6.8E-03 - - Root 1.6E+00 1.0E-02 - Silage 6.8E-03 - 5.0E-01

Fluoranthene Exp. Fruit 4.0E-02 - 1.0E-02 Exp. Veg. 4.0E-02 - 1.0E-02 Forage 4.0E-02 - 1.0E+00
Grain 4.0E-02 - -
Prot. Fruit 4.0E-02 - - Prot. Veg. 4.0E-02 - - Root 5.6E+00 1.0E-02 - Silage 4.0E-02 - 5.0E-01

Fluorene Exp. Fruit 1.5E-01 - 1.0E-02 Exp. Veg. 1.5E-01 - 1.0E-02 Forage 1.5E-01 - 1.0E+00
Grain 1.5E-01 - -
Prot. Fruit 1.5E-01 - - Prot. Veg. 1.5E-01 - - Root 6.2E+00 1.0E-02 - Silage 1.5E-01 - 5.0E-01

Indeno(1,2,3- cd)pyrene Exp. Fruit 5.1E-03 - 1.0E-02 Exp. Veg. 5.1E-03 - 1.0E-02 Forage 5.1E-03 - 1.0E+00
Grain 5.1E-03 - -
Prot. Fruit 5.1E-03 - - Prot. Veg. 5.1E-03 - - Root 1.1E+00 1.0E-02 - Silage 5.1E-03 - 5.0E-01TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-64 December 2013 Description of MIRC 

### Exhibit_Add A2-11. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant-Soil BioConcentration Factor ( Br AG-produce-DW(i) ) (unitless) a Empirical Correction Factor- Belowground Produce ( VG rootveg ) (unitless) b EmpiricalCorrection FactorAboveground Produce ( VG A G ( i ) ) (unitless) c
Dioxins

OctaCDD, 1,2,3,4,6,7,8,9- Exp. Fruit 7.1E-04 - 1.0E-02 Exp. Veg. 7.1E-04 - 1.0E-02 Forage 7.1E-04 - 1.0E+00
Grain 7.1E-04 - -
Prot. Fruit 7.1E-04 - - Prot. Veg. 7.1E-04 - - Root 6.1E-01 1.0E-02 - Silage 7.1E-04 - 5.0E-01

OctaCDF, 1,2,3,4,6,7,8,9- Exp. Fruit 9.2E-04 - 1.0E-02 Exp. Veg. 9.2E-04 - 1.0E-02 Forage 9.2E-04 - 1.0E+00
Grain 9.2E-04 - -
Prot. Fruit 9.2E-04 - - Prot. Veg. 9.2E-04 - - Root 6.8E-01 1.0E-02 - Silage 9.2E-04 - 5.0E-01

HeptaCDD, 1,2,3,4,6,7,8- Exp. Fruit 9.2E-04 - 1.0E-02 Exp. Veg. 9.2E-04 - 1.0E-02 Forage 9.2E-04 - 1.0E+00
Grain 9.2E-04 - -
Prot. Fruit 9.2E-04 - - Prot. Veg. 9.2E-04 - - Root 6.8E-01 1.0E-02 - Silage 9.2E-04 - 5.0E-01

HeptaCDF, 1,2,3,4,6,7,8- Exp. Fruit 2.0E-03 - 1.0E-02 Exp. Veg. 2.0E-03 - 1.0E-02 Forage 2.0E-03 - 1.0E+00
Grain 2.0E-03 - -
Prot. Fruit 2.0E-03 - - Prot. Veg. 2.0E-03 - - Root 9.4E-01 1.0E-02 - Silage 2.0E-03 - 5.0E-01TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-65 December 2013 Description of MIRC 

### Exhibit_Add A2-11. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant-Soil BioConcentration Factor ( Br AG-produce-DW(i) ) (unitless) a Empirical Correction Factor- Belowground Produce ( VG rootveg ) (unitless) b EmpiricalCorrection FactorAboveground Produce ( VG A G ( i ) ) (unitless) c

HeptaCDF, 1,2,3,4,7,8,9- Exp. Fruit 4.0E-03 - 1.0E-02 Exp. Veg. 4.0E-03 - 1.0E-02 Forage 4.0E-03 - 1.0E+00
Grain 4.0E-03 - -
Prot. Fruit 4.0E-03 - - Prot. Veg. 4.0E-03 - - Root 1.2E+00 1.0E-02 - Silage 4.0E-03 - 5.0E-01

HexaCDD, 1,2,3,4,7,8- Exp. Fruit 1.2E-03 - 1.0E-02 Exp. Veg. 1.2E-03 - 1.0E-02 Forage 1.2E-03 - 1.0E+00
Grain 1.2E-03 - -
Prot. Fruit 1.2E-03 - - Prot. Veg. 1.2E-03 - - Root 7.6E-01 1.0E-02 - Silage 1.2E-03 - 5.0E-01

HexaCDF, 1,2,3,4,7,8- Exp. Fruit 3.5E-03 - 1.0E-02 Exp. Veg. 3.5E-03 - 1.0E-02 Forage 3.5E-03 - 1.0E+00
Grain 3.5E-03 - -
Prot. Fruit 3.5E-03 - - Prot. Veg. 3.5E-03 - - Root 1.2E+00 1.0E-02 - Silage 3.5E-03 - 5.0E-01

HexaCDD, 1,2,3,6,7,8- Exp. Fruit 7.0E-04 - 1.0E-02 Exp. Veg. 7.0E-04 - 1.0E-02 Forage 7.0E-04 - 1.0E+00
Grain 7.0E-04 - -
Prot. Fruit 7.0E-04 - - Prot. Veg. 7.0E-04 - - Root 6.1E-01 1.0E-02 - Silage 7.0E-04 - 5.0E-01TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-66 December 2013 Description of MIRC 

### Exhibit_Add A2-11. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant-Soil BioConcentration Factor ( Br AG-produce-DW(i) ) (unitless) a Empirical Correction Factor- Belowground Produce ( VG rootveg ) (unitless) b EmpiricalCorrection FactorAboveground Produce ( VG A G ( i ) ) (unitless) c

HexaCDF, 1,2,3,6,7,8- Exp. Fruit 1.0E-03 - 1.0E-02 Exp. Veg. 1.0E-03 - 1.0E-02 Forage 1.0E-03 - 1.0E+00
Grain 1.0E-03 - -
Prot. Fruit 1.0E-03 - - Prot. Veg. 1.0E-03 - - Root 7.1E-01 1.0E-02 - Silage 1.0E-03 - 5.0E-01

HexaCDD, 1,2,3,7,8,9- Exp. Fruit 7.0E-04 - 1.0E-02 Exp. Veg. 7.0E-04 - 1.0E-02 Forage 7.0E-04 - 1.0E+00
Grain 7.0E-04 - -
Prot. Fruit 7.0E-04 - - Prot. Veg. 7.0E-04 - - Root 6.1E-01 1.0E-02 - Silage 7.0E-04 - 5.0E-01

HexaCDF, 1,2,3,7,8,9- Exp. Fruit 1.6E-03 - 1.0E-02 Exp. Veg. 1.6E-03 - 1.0E-02 Forage 1.6E-03 - 1.0E+00
Grain 1.6E-03 - -
Prot. Fruit 1.6E-03 - - Prot. Veg. 1.6E-03 - - Root 8.5E-01 1.0E-02 - Silage 1.6E-03 - 5.0E-01

HexaCDF, 2,3,4,6,7,8- Exp. Fruit 1.0E-03 - 1.0E-02 Exp. Veg. 1.0E-03 - 1.0E-02 Forage 1.0E-03 - 1.0E+00
Grain 1.0E-03 - -
Prot. Fruit 1.0E-03 - - Prot. Veg. 1.0E-03 - - Root 7.1E-01 1.0E-02 - Silage 1.0E-03 - 5.0E-01TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-67 December 2013 Description of MIRC 

### Exhibit_Add A2-11. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant-Soil BioConcentration Factor ( Br AG-produce-DW(i) ) (unitless) a Empirical Correction Factor- Belowground Produce ( VG rootveg ) (unitless) b EmpiricalCorrection FactorAboveground Produce ( VG A G ( i ) ) (unitless) c

PentaCDD, 1,2,3,7,8- Exp. Fruit 2.4E-03 - 1.0E-02 Exp. Veg. 2.4E-03 - 1.0E-02 Forage 2.4E-03 - 1.0E+00
Grain 2.4E-03 - -
Prot. Fruit 2.4E-03 - - Prot. Veg. 2.4E-03 - - Root 1.0E+00 1.0E-02 - Silage 2.4E-03 - 5.0E-01

PentaCDF, 1,2,3,7,8- Exp. Fruit 4.6E-03 - 1.0E-02 Exp. Veg. 4.6E-03 - 1.0E-02 Forage 4.6E-03 - 1.0E+00
Grain 4.6E-03 - -
Prot. Fruit 4.6E-03 - - Prot. Veg. 4.6E-03 - - Root 1.3E+00 1.0E-02 - Silage 4.6E-03 - 5.0E-01

PentaCDF, 2,3,4,7,8- Exp. Fruit 6.8E-03 - 1.0E-02 Exp. Veg. 6.8E-03 - 1.0E-02 Forage 6.8E-03 - 1.0E+00
Grain 6.8E-03 - -
Prot. Fruit 6.8E-03 - - Prot. Veg. 6.8E-03 - - Root 1.5E+00 1.0E-02 - Silage 6.8E-03 - 5.0E-01

TetraCDD, 2,3,7,8- Exp. Fruit 4.5E-03 - 1.0E-02 Exp. Veg. 4.5E-03 - 1.0E-02 Forage 4.5E-03 - 1.0E+00
Grain 4.5E-03 - -
Prot. Fruit 4.5E-03 - - Prot. Veg. 4.5E-03 - - Root 1.3E+00 1.0E-02 - Silage 4.5E-03 - 5.0E-01TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-68 December 2013 Description of MIRC 

### Exhibit_Add A2-11. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant-Soil BioConcentration Factor ( Br AG-produce-DW(i) ) (unitless) a Empirical Correction Factor- Belowground Produce ( VG rootveg ) (unitless) b EmpiricalCorrection FactorAboveground Produce ( VG A G ( i ) ) (unitless) c

TetraCDF, 2,3,7,8- Exp. Fruit 1.2E-02 - 1.0E-02 Exp. Veg. 1.2E-02 - 1.0E-02 Forage 1.2E-02 - 1.0E+00
Grain 1.2E-02 - -
Prot. Fruit 1.2E-02 - - Prot. Veg. 1.2E-02 - - Root 1.9E+00 1.0E-02 - Silage 1.2E-02 - 5.0E-01

a As discussed in HHRAP (EPA 2005a), the Br AG-produce-DW(i) for aboveground produce and forage accounts for the uptake from soil and the subsequent transport of contaminants through the roots to the aboveground plant parts. For organics, correlation equations to calculate values for Br on a dry weight basis were obtained from Travis and Arms (1988). For cadmium, Br values were derived from uptake slope factors provided in EPA 1992. Uptake slope is the ratio of contamin ant concentration in dry wei ght plant tissue to the mass of contaminant applied per hectare soil. Br aboveground values for mercuric chloride and methyl mercury were calculated using methodology and data from Baes, et al. (1984). Br forage values for mercuric chloride and methyl mercury (on a dry weight basis) were obtained from EPA 1997b. T he HHRAP methodology assumes that elemental mercury doesn’t deposit onto soils. Therefore, it’s assumed t hat there is no plant uptake through the soil.

b As discussed in HHRAP (EPA 2005a), Appendix B, VG rootveg represents an empirical correction factor that reduces produce concentration. Because of the protective outer skin, size, and shape of bulky produce, transfer of lipophilic chemicals (i.e., log K ow greater than 4) to the center of the produce is not likely. In addition, typical preparation te chniques, such as washing, peel ing, and cooking, further reduce the conc entration of the chemical in the vegetable as consumed by removing t he high concentration of chemical on and in the outer skin, leaving the flesh with a lower concentration than would be the case if the entire vegetable were pureed without washing. For belowground produce, HHRAP (EPA 2005a) recommends using a VG rootveg value of 0.01 for PB-HAP with a log K ow greater than 4 and a value of 1.0 for PB-HAP with a log K ow less than 4 based on information provided in EPA 1994b. In developing these values, EPA (1994b) assumed that the density of the skin and the whole vegetable are equal (potentially overestimating the concentration of PB- HAP in belowground produce due to root uptake).

c As discussed in HHRAP (EPA 2005a), Appendix B, VG ag represents an empirical correction factor that reduces aboveground produce concentration and was developed to estimate the transfer of PB-HAP into leafy veget ation versus bulkier aboveground produce (e.g., apples). Because of the protective outer skin, size, and shape of bulky produce, transfer of lipophilic PB-HAP (log K ow greater than 4) to the center of the produce is not likely. In addition, typical preparation techniques, such as washing, pee ling, and cooking, further reduces residues. For aboveg round produce, HHRAP (EPA 2005a) recommends using a VG ag value of 0.01 for PB-HAP with a log K ow greater than 4 and a value of 1.0 for PB-HAP with a log K ow less than 4 based on information provided in EPA 1994b. In developing these values, EPA (1994b) assumed th e following: (1) translocation of compounds deposited on the surface of aboveground vegetation to inner parts of aboveground produce would be insignificant (potentially underestimating the concentration of PB-HAP in aboveground produce due to air-to-plant transfer); (2) the density of the skin and the whole vegetab le are equal (potentially overestimating the concentration of PB-HAP in aboveground produc e due to air-to-plant transfer); and (3) the thickness of vegetable skin and broadleaf tree skin are equal (effects on the concentration of PB-HAP in aboveground produce due to air-toplant transfer unknown). For forage, HHRAP recommends a VG ag value of 1.0, also based on information provided in EPA 1994b. A VG ag value for silage is not provided in EPA 1994b; the VGag va lue for silage of 0.5 was obtained from NC DEHNR (1997); however, NC DEHNR does not present a spec ific rationale for this recommendation. Depending on the composition of the sitespecific silage, this value may under- or overestimate the actual value.TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-69 December 2013 Description of MIRC 

### Exhibit_Add A2-12. Non-Chemical-Specific Produce Inputs

Plant Part Interception Fraction

( Rp (i) ) (unitless) a Plant Surface

Loss Coefficient ( kp (i) ) (1/year) b Length of Plant Exposure to Deposition ( Tp (i) ) (year) c Yield or Standing

Crop Biomass

( Yp (i) ) (kg/m 2 ) d Plant TissueSpecific Moisture Adjustment Factor ( MAF (i) ) (percent) e Exposed Vegetable 0.982 18 0.16 5.66 92
Protected Fruit NA NA NA NA 90
Protected Vegetable NA NA NA NA 80

Forage (animal feed) 0.5 18 0.12 0.24 92

Exposed Fruit 0.053 18 0.16 0.25 85 Root Vegetables NA NA NA NA 87 Silage (animal feed) 0.46 18 0.16 0.8 92 Grain (animal feed) NA NA NA NA 90
Source: EPA 2005a. NA = not applicable.

a Baes et al . (1984) used an empirical relationship developed by Chamberlain (1970) to identify a correlation between initial Rp values and pasture grass producti vity (standing crop biomass [ Yp ]) to calculate Rp values for exposed veget ables, exposed fruits, forage, and silage. Two key uncertainties are associated with using these values for Rp : (1) Chamberlain’s (1970) empirical relationship developed for pasture grass may not accurately represent aboveground produce. (2) The empirical constants developed by Baes et al . (1984) for use in the empirical relationship dev eloped by Chamberlain (1970) may not accurately represent the site-specific mixes of abovegr ound produce consumed by humans or the si te-specific mixes of forage or silage consumed by livestock.

b The term kp is a measure of the amount of chemical that is lost to natural physical processes (e.g., wind, water) over time. The HHRAP-recommended value of 18 yr -1 (also recommended by EPA 1994a and 1998) represents the midpoint of a range of values reported by Miller and Hoffman (1983). There are two key uncertainties associated with using these values for kp : (1) The recommended equation for calculating kp includes a health protective bias in that it does not consider chemical degradation processes. (2) Given the reported range of kp values from 7.44 to 90.36 yr -1 , plant concentrations could range from about 1.8 times higher to about 5 times lower than the plant conc entrations estimated in FFC media using the midpoint kp value of 18.

c HHRAP (EPA 2005a) recommends using a Tp value of 0.16 years for aboveground produce and cattle silage. This is consistent with earlier reports by EPA (1994a, 1998) and NC DEHNR (1997), which recommended treating Tp as a constant based on the average period between successive hay harvests. Belcher and Travis (1989) estimated this period at 60 days. Tp is calculated as 60 days ÷ 365 days/year = 0.16 years. For forage, the av erage of the average period between successive hay harvests (60 days) and the average period between successive graz ing (30 days) is used (that is, 45 days), and Tp is calculated as (60 days + 30 days)/ 2 ÷ 365 days/yr = 0.12 yr. Two key uncertain ties are associated with use of these values for Tp : (1) The average period between successive hay harvests (60 days) may not reflect the length of the growing season or the length between successive harvests for site-specific aboveground produce crops. The concentration of chemical in aboveground produce due to direct (wet and dry) deposition ( Pd ) will be underestimated if the site-specific value of Tp is less than 60 days, or overestimated if the sitespecific value of Tp is more than 60 days.

d Yp values for aboveground produce and forage were calculated using an equation presented in Baes et al . (1984) and Shor et al

. (1982): Yp = Y hi / A hi , where Y hi = Harvest yield of i th crop (kg DW) and A hi = Area planted to i th crop (m2), and using values for Y h and A h from USDA (1994b and 1994c). A production-weighted U.S. average Yp of 0.8 kg DW/m2 for silage was obtained from Shor et al . 1982.

e MAF represents the plant tissue-specific mois ture adjustment factor to convert dry- weight concentrations into wet-weight concentrations (which are lower owing to the dilution by water compared with dry-weight concent rations). Values obtained from Chapter 10 of EPA's 2003 SAB Review materials for 3MRA Modeli ng System, Volume II, “Farm Food Chain and Terrestrial Food Web Data” (EPA 2003a), which references EPA 1997c. Note that the value for grain used as animal feed is based on corn and soybeans, not seed grains such as barley, oats, or wheat.TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-70 December 2013 Description of MIRC 

### 6.2.3. Animal Product Parameter Values
MIRC also requires chemical-specific inputs for many of the animal product algorithms. The relevant values are shown in Exhibit_Add A2-13 for the chemicals included in MIRC to date. The HHRAP algorithms require additional inputs for the animal products calculations that are not specific to PB-HAPs, but are specific to the animal and animal product type. The soil and plant ingestion rates recommended in HHRAP for beef cattle, dairy cattle, swine, and chicken are provided in Exhibit_Add A2-14.

Exhibit_Add A2-13. Animal Product Chemical-specific Inputs for Chemicals Included in MIRC

Compound Name Soil BioAvailability Factor ( Bs ) (unitless) Biotransfer Factors (Ba m ) (day/kg FW tissue) a and Metabolism Factors (MF) (unitless) b

Mammal Non-mammal
Beef ( Ba bee f ) Dairy ( Ba dair y ) Pork ( Ba p or k ) MF Eggs ( Ba e gg s ) Poultry ( Ba p oultr y ) MF

Inorganics Cadmium compounds 1 1.2E-04 6.5E-0 6 1.9E-04 1 2.5E-03 1.1E-01 NA Mercury (elemental) 1 0 0 0 1 0 0 NA
Mercuric chloride 1 1.1E-04 1.4E -06 3.4E-05 1 2.4E-02 2.4E-02 NA Methyl mercury 1 1.2E-03 1.7E-0 5 5.1E-06 1 3.6E-03 3.6E-03 NA
PAHs 2-Methylnaphthalene 1 2.4E-02 5.0E-0 3 2.9E-02 0.01 1.0E-02 1.7E-02 NA 7,12- Dimethylbenz(a)anthra

cene 1 3.9E-02 8.3E-03 4.8E-02 0.01 1.7E-02 2.9E-02 NA

Acenaphthene 1 2.5E-02 5.2E-03 3. 0E-02 0.01 1.0E-02 1.8E-02 NA Acenaphthylene 1 2.6E-02 5.5E-03 3. 1E-02 0.01 1.1E-02 1.9E-02 NA Benz(a)anthracene 1 3.9E -02 8.3E-03 4.8E-02 0.01 1.7E-02 2.9E-02 NA Benzo(a)pyrene 1 3.8E-02 8.0E-03 4.6E-02 0.01 1.6E-02 2.8E-02 NA Benzo(b)fluoranthene 1 3.9E-02 8.3E-0 3 4.8E-02 0.01 1.7E-02 2.9E-02 NA Benzo(g,h,i)perylene 1 2. 9E-02 6.1E-03 3.5E-02 0. 01 1.2E-02 2.1E-02 NA Benzo(k)fluoranthene 1 3.8E-02 8.0E-0 3 4.6E-02 0.01 1.6E-02 2.8E-02 NA Chrysene 1 4.0E-02 8.4E-03 4.8E -02 0.01 1.7E-02 2.9E-02 NA Dibenz(a,h)anthracen

e 1 3.1E-02 6.5E-03 3.8E-02 0.01 1.3E-02 2.3E-02 NA

Fluoranthene 1 4.0E-02 8.5E-03 4. 9E-02 0.01 1.7E-02 3.0E-02 NA
Fluorene 1 2.9E-02 6.1E-03 3.5E -02 0.01 1.2E-02 2.1E-02 NA Indeno(1,2,3- cd)pyrene 1 2.7E-02 5.8E-03 3.3E-02 0.01 1.2E-02 2.0E-02 NA

Dioxins OctaCDD, 1,2,3,4,6,7,8,9- 1 6.9E-03 1.4E-03 8.3E-0 3 1 2.9E-03 5.1E-03 NA

OctaCDF, 1,2,3,4,6,7,8,9- 1 8.8E-03 1.8E-03 1.1E-0 2 1 3.7E-03 6.5E-03 NA

HeptaCDD, 1,2,3,4,6,7,8- 1 8.8E-03 1.8E-03 1.1E-0 2 1 3.7E-03 6.5E-03 NATRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-71 December 2013 Description of MIRC 

### Exhibit_Add A2-13. Animal Product Chemical-specific Inputs for Chemicals Included in MIRC

Compound Name Soil BioAvailability Factor ( Bs ) (unitless) Biotransfer Factors (Ba m ) (day/kg FW tissue) a and Metabolism Factors (MF) (unitless) b

Mammal Non-mammal
Beef ( Ba bee f ) Dairy ( Ba dair y ) Pork ( Ba p or k ) MF Eggs ( Ba e gg s ) Poultry ( Ba p oultr y ) MF

HeptaCDF, 1,2,3,4,6,7,8- 1 1.6E-02 3.5E-03 2.0E-0 2 1 6.9E-03 1.2E-02 NA

HeptaCDF, 1,2,3,4,7,8,9- 1 2.4E-02 5.1E-03 3.0E-0 2 1 1.0E-02 1.8E-02 NA

HexaCDD, 1,2,3,4,7,8- 1 1.1E-02 2.3E-03 1.3E-0 2 1 4.6E-03 8.1E-03 NA HexaCDF, 1,2,3,4,7,8- 1 2.3E-02 4.8E -03 2.8E-02 1 9.6E-03 1.7E-02 NA HexaCDD, 1,2,3,6,7,8- 1 6.8E-03 1.4E-03 8.2E-0 3 1 2.9E-03 5.0E-03 NA HexaCDF, 1,2,3,6,7,8- 1 9.7E-03 2.0E -03 1.2E-02 1 4.1E-03 7.1E-03 NA HexaCDD, 1,2,3,7,8,9

- 1 6.8E-03 1.4E-03 8.2E-0 3 1 2.9E-03 5.0E-03 NA

HexaCDF, 1,2,3,7,8,9- 1 1.4E-02 2.9E -03 1.7E-02 1 5.8E-03 1.0E-02 NA HexaCDF, 2,3,4,6,7,8- 1 9.6E-03 2.0E -03 1.2E-02 1 4.1E-03 7.1E-03 NA PentaCDD, 1,2,3,7,8- 1 1.8E-02 3.9E-03 2.2E-02 1 7.8E-03 1.4E-02 NA PentaCDF, 1,2,3,7,8- 1 2.6E-02 5.5E-03 3.2E-02 1 1.1E-02 1.9E-02 NA PentaCDF, 2,3,4,7,8- 1 3.1E-02 6.5E-03 3.8E-02 1 1.3E-02 2.3E-02 NA TetraCDD, 2,3,7,8- 1 2.6E-02 5.5E -03 3.2E-02 1 1.1E-02 1.9E-02 NA
TetraCDF, 2,3,7,8- 1 3.6E-02 7.7E-03 4.4E-0 2 1 1.5E-02 2.7E-02 NA
Source: EPA 2005a, unless otherwise indicated. NA = not applicable.

a As discussed in HHRAP (EPA 2005a), Appendix A, biotransfer factor s for mercury compounds were obtained from EPA 1997b. Conside ring speciation, fate, and transport of mercury fr om emission sources, elemental mercury is assumed to be vapor-phase and hence is a ssumed not to deposit to soil or transfer into aboveground plant parts. As a consequence, there is no transfer of elemental mercury into ani mal tissues. Biotransfer factors for cadmium compounds were obtained from EP A 1995b. Biotransfer factors for dioxins and PAHs were calculat ed from chemical octanol-water par titioning coefficients (K ow values) using the correlation equation from RTI (2005) and assuming the following fat contents: milk - 4%; beef - 19%; pork - 23%; poultry -14%; and eggs - 8%.

b As discussed in HHRAP (EPA 2005a), EPA (1995c) recommends using a metabolism factor (MF) to account for metabolism of PAHs by m ammals to offset the amount of bioaccumulation suggested by biotrans fer factors. EPA has recommended an MF of 0.01 for bis(2-ethylhexyl)phthalate (BEHP) and 1.0 for all other chemicals (EPA 1995d). For MIRC, an MF of 0.01 is also used to calc ulate concentrations of PAHs i n food products from mammalian species based on the work of Hofelt et al. (2001). This factor takes into account the P450-mediated metabolism of PAHs in mammals; applying this factor in our approach reduced the concent rations of chemicals in beef, pork, and dairy by two orders of magnitude.TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-72 December 2013 Description of MIRC 

### Exhibit_Add A2-14. Soil and Plant Ingestion Rates for Animals

Animal Soil Ingestion Rate – Qs ( m

) (kg/day) a Plant Part Consumed by Animal Plant Ingestion Rate – Qp ( I , m

) (kg/day)

Beef cattle b 0.5 Silage 2.5 Forage 8.8
Grain 0.47

Dairy cattle c 0.4 Silage 4.1 Forage 13.2
Grain 3.0

Swine d 0.37 Silage 1.4
Grain 3.3 Chicken (eggs) e 0.022 Grain 0.2
Source: EPA 2005a HHRAP (Chapter 5).

a Beef cattle : NC DEHNR (1997) and EPA (1994b) recommended a soil ingestion rate for subsistence beef cattle of 0.5 kg/day based on Fries (1994) and NAS (1987). As discussed in HHRAP, Frie s (1994) reported soil ingestion to be 4 percent of the total dry matter intake. NAS (1987) cited an average beef cattle weight of 590 kg, and a daily dry matter intake rate (non-lactating cows) of 2 percent of body weight. This results in a daily dry matter intake rate of 11.8 kg DW/day and a daily soil ingestion rate of about 0.5 kg/day. Dairy cattle : NC DEHNR (1997) and EPA (1994b) recommended a soil ingestion rate for dairy cattle of 0.4 kg/day based on Fries (1994) and NAS (1987). As discussed in HHRAP, Fries (1994) reported soil ingestion to be 2 percent of the total dry matter intake. NAS (1987) cited an average beef cattle weight of 630 kg and a daily dry matter intake rate (non-lactating cows) of 3. 2 percent of body weight. This resulted in a daily dry matter in take rate of 20 kg/day DW, and a daily soil ingestion rate of approximately 0.4 kg/day

. Uncertainties associated with Qs include the lack of current empirical data to support soil ingestion rates for dairy cattle and the assumption of unifo rm contamination of soil ingested by cattle. Swine : NC DEHNR (1997) recommended a soil ingestion rate for swine of 0.37, estimated by assuming a soil intake that is 8% of the plant ingestion rate of 4.3 kg DW/day. Uncertainties in clude the lack of current empirical data to support soil ingestion rates and the assumption of uniform contamination of the soil ingested by swine. Chicken : HHRAP (EPA 2005a) assumes that chickens consume 10 percent of their total diet (which is approximately 0.2 kg/day grain) as soil, a percentage that is consistent with the study from Stephens et al . (1995). Uncertainties include the lack of current empirical data to support soil ingestion rates for chicken and the assumption of uniform contamination of soil ingested by chic ken.

b The beef cattle ingestion rates of forage, s ilage, and grain are based on the total daily intake rate of about 12 kg DW/day (ba sed on NAS [1987] reporting a daily dry matter intake that is 2 percent of an average beef cattle body weight of 590 kg) and are supported by NC DEHNR (1997), EPA (1994b and 1990), and Boone et al . (1981). The principal uncertainty associated with these Qp values is the variability between forage, s ilage, and grain ingestion rates for cattle.

c The dairy cattle ingestion rates of forage, s ilage, and grain are based on the total daily intake rate of about 20 kg DW/day (N AS 1987; EPA 1992) as recommended by NC DEHNR (1997). Uncertainties include the proportion of each food type in the diet, which varies from location to location. Assuming uniform contamination of plant materials consumed by cattle also introduces uncertainty.

d Swine are not grazing animals and are assumed not to eat forage (EPA 1998). EPA (1994b and 1998) and NC DEHNR (1997) recommended including only silage and grains in the diet of swine. EPA (1995c) recommended an ingestion rate of 4.7 kg DW/day for a swine, referencing NAS (1987). Assuming a diet of 70 percent grain and 30 percent silage (EPA 1990), HHRAP estimated ingestion rates of 3.3 kg DW/day (grain) and 1. 4 kg DW/day (silage). Uncertainties associated with Qp include variability of the proportion of grain and silage in the diet, which varies from location to location.

e Chickens consume grain provided by the farmer. The daily quantity of grain feed consumed by chicken is assumed to be 0.2 kg/day (Ensminger (1980), Fries (1982), and NAS (1987). Uncert ainties associated with this variable include the variability of actual grain ingestion rates from site to site. In addition, assuming uniform contamin ation of plant materials consumed by chi cken introduces some uncertainty.


### 6.3. Adult and Non-Infant Exposure Parameter Values
The exposure parameters included in MIRC and their default and other value options are summarized in the following subsections. The default values were selected to result in a highly health protective screening scenario. Parameter value options were primarily obtained or estimated from EPA’s Exposure Factors Handbook (EFH; EPA 2011a) and Child-Specific Exposure Factors Handbook (CSEFH; EPA 2008a). Where values were reported for agegroupings other than those used in MIRC (see Section 2.3 above for MIRC age groups), timeweighted average values were estimated for the MIRC age groups from the available data.TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-73 December 2013 Description of MIRC 

### In MIRC, ingestion rates for home-produced farm food items are included for exposed fruit, protected fruit, exposed vegetables, protected vegetables, root vegetables, beef, total dairy, pork, poultry, and eggs. Those ingestion rates are already normalized to body weight (i.e., g wet
weight 

### /kg-day) (EPA 2011a). The body weight parameter values presented in Exhibit_Add A2-15, therefore, are not applied in the chemical intake (ADD) equations for these food types.

In MIRC, ingestion rates also are included for drinking water (mL/day), soil (mg/day), and fish (g/day). These ingestion rates, however, are on a per person basis (i.e., not normalized for body weight). The body weight parameter values presented in Exhibit_Add A2-15, therefore, are applied in the chemical intake (ADD) equations for these media.


### 6.3.1. Body Weights
Body weight (BW) options included in MIRC include mean, 5 th 

### , 10 th 

### , 50 th 

### , 90 th 

### , and 95 th 

### percentile values for adults and the five children’s age groups: <1 year; 1–2 years; 3–5 years; 6–11 years; and 12–19 years. For its default screening assessment, EPA uses the mean BW for each age group. The BWs currently in the MIRC database are listed in Exhibit_Add A2-15.

In general, BW values for the five children’s age groups were calculated from the summary data provided in Table 8-3 of EPA’s 2008 CSEFH. For purposes of comparison, alternative BW values for children ages 12 through 19 years also were estimated using data from Portier et al

. (2007). These values are listed in the last row of Exhibit_Add A2-15, but are not included in MIRC. The means calculated using the two methods for children ages 12 through 19 years were essentially identical at 64 kg. The other percentile values for this age group using the two methods differed by approximately 10 percent or less.

Exhibit_Add A2-15. Mean and Percentile Body Weight Estimates for Adults and Children

Lifestage

(years) Duration (years) Body Weight (kg)
Mean 5 th 10 th 50 th 90 th 95 th

Adult a (20-70) 50 80.0 a 52.9 56 69.3 89.7 97.6
Child < 1 b 1 7.83 6.03 6.38 7.76 9.24 9.66
Child 1-2 c 2 12.6 9.9 10.4 12.5 14.9 15.6
Child 3-5 d 3 18.6 13.5 14.4 17.8 23.6 26.2
Child 6-11 e 6 36.0 22.1 24.0 33.5 51.2 58.6
Child 12-19 f 8 64.2 39.5 45 64.2 83.5 89 [Child 12-19 g 8 64.3 41.1 44.6 60.9 88.5 98.4]

a BW represents the recommended body weight from EPAs 2011 EF H. Although the 18 to 74 year age category in EPA’s EFH does not match exactly the age 20 to 70 year categorization of adults in MIRC, the magnitude of error in the mean and percentil e body weights is likely to be very small (i.e., less than 1%).

b Each BW represents a time-weighted average of body weights fo r age groups birth to <1 month, 1 to <3 months, 3 to <6 months, and 6 to <12 months from Table 8-3 of the 2008 CSEFH. Original sample sizes for each of these age groups can also be found in Table 8-3.

c Each BW represents a time-weighted average of body weights for age groups 1 to <2 years and 2 to <3 years from Table 8-3 of the 2008 CSEFH. Original sample sizes for each of these age groups can also be found in Table 8-3.

d BWs obtained directly from Table 8-3 of the 2008 CSEFH (age group 3 to <6 years).

e Each BW represents a time-weighted average of body weights fo r age groups 6 to <11 years and 11 to <16 years from Table 8- 3 of the 2008 CSEFH. Original sample sizes for eac h of these age groups can also be found in Table 8-3.

f Mean BW estimated using Table 8-22 of the 2008 CSEFH, which is based on NHANES IV data as presented in Portier et al. (2007). This estimate was calculated as the average of the 8 single-year age groups from 12 to 13 years through 19 to 20 years. Values for the other percentiles were estimated using Portier et al., 2007.

g Each BW represents a time-weighted average of body weights fo r age groups 11 to <16 years and 16 to <21 years from Table 8-3 of the 2008 CSEFH. Estimated values include 11-year-old s and individuals through age 20, whic h contributes to uncertainty in the estimates for 12 to 19 years. Those values are provided for comparison purposes only and are not included in MIRC.TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-74 December 2013 Description of MIRC 

### 6.3.2. Water Ingestion Rates
MIRC also includes the option of calculating ch emical ingestion via drinking water obtained from surface-water sources or from wells (i.e., from groundwater) in the contaminated area. Users have the option in MIRC to set drinking water ingestion rates to zero or to revise the drinking water ingestion rates in MIRC to better reflect site-specific water uses. The 2008 CSEFH recommends values for drinking water ingestion rates for children based on a study reported by Kahn and Stralka (2008). Table 3-4 of the CSEFH provides per capita estimates of community water ingestion rates by age categories. Community water ingestion includes both direct and indirect ingestion of water from the tap. Direct ingestion is defined as direct consumption of water as a beverage, while indirect ingestion includes water added during food or beverage preparation. The source of these data is the 1994-1996 and 1998 U.S. Department of Agriculture’s (USDA’s) Continuing Survey of Food Intakes by Individuals (CSFII) (USDA 2000). Exhibit_Add A2-16 includes the drinking water ingestion rates for children that are included in MIRC.

Mean and percentile adult drinking water i ngestion rates were obtained from EPA (2004b), which presents estimated per capita water ingestion rates for various age categories based on data collected by the USDA’s 1994–1996 and 1998 CSFII (USDA 2000). Adult ingestion rates, presented in Exhibit_Add A2-16, represent community water ingestion, both direct and indirect as defined above, for males and females combined, ages 20 years and older.

Exhibit_Add A2-16. Estimated Daily Per Capita Mean and Percentile Water Ingestion Rates for Children and Adults a

Lifestage (years) Ingestion Rates, Community Water (mL/day)
Mean 50 th 90 th 95 th 99 th

Child <1 b 504 482 969 1113 1440
Child 1-2 c 332 255 687 903 1318
Child 3-5 d 382 316 778 999 1592
Child 6-11 e 532 417 1149 1499 2274
Child 12-19 f 698 473 1641 2163 3467
Adult g 1219 981 2534 3087 4567

Sources: EPA 2004, 2008a *The sample size does not meet minimum reporting requirem ents as described in EPA 2008a. For some of these MIRC age groupings, the values are based on the time -weighted average value for 2 or more age ranges from CSEFH Table 3-4. One or more age ranges within the group may not meet the minimum reporting requirements, but not necessarily all of them fall within t his category.

a Source is Kahn and Stralka 2008, also presented in the CSEFH (EPA 2008a).

b Each IR represents a time-weighted average of ingestion rates for age groups birth to <1 m onth, 1 to <3 months, 3 to <6 months, and 6 to <12 months from Table 3-4 of the 2008 CSEFH.

c Each IR represents a time-weighted average of ingestion rates for age groups 1 to <2 years and 2 to <3 years from Table 3-4 of the 2008 CSEFH.

d Each IR represents the ingestion rate for age group 3 to <6 years from Table 3-4 of the 2008 CSEFH.

e Each IR represents the ingestion rate for age group 6 to <11 y ears from Table 3-4 of the 2008 CSEFH. This value represents a health protective (i.e., slightly low) esti mate of IR for ages 6 through 11 years since 11-year olds are not included in this C SEFH age group. f Each IR represents a time-weighted average of ingestion rates fo r age groups 11 to <16 years, 16 to <18, and 18 to <21 years from Table 3-4 of the 2008 CSEFH. Note that estimated values include 11-year-olds and individuals through age 20, which contributes to uncertainty in the estimates for 12 to 19 years.

g Adult drinking water ingestion rates were obtained from EPA (20 04b), Appendix E, Part I, Table A1 for community water, both sexes (ages 20+), direct pl us indirect water ingestion.TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-75 December 2013 Description of MIRC 

### 6.3.3. Local Food Ingestion Rates
MIRC includes mean, median, 90 th 

### , 95 th 

### , and 99 th 

### percentile food-specific ingestion rates (IRs) for consumers-only of farm food chain (FFC) media for adults and children. The mean and percentile values are from EPA’s analysis of data from the USDA’s 1987 to 1988 Nationwide Food Consumption Survey (NFCS) (USDA 1993), as presented in Chapter 13 of the Agency’s Exposure Factors Handbook (i.e., Intake Rates for Various Home Produced Food Items) (EPA 2011a). Consumers-only means that individuals who did not report eating a specified type of food during the three-day period covered by the food ingestion part of the survey were not included in the analysis of ingestion rates for that food type. The questionnaire included the options for a household to self-identify in one or more of five categories: as a household that gardens, raises animals, hunts, fishes, or farms. As of September 2008, that survey was the most recent NFCS available (EPA 2008a, CSEFH), and we are not aware of any that might be more recent. 21



### For the adult age group in MIRC, data were compiled on food-specific IRs separately for two types of households as indicated in the “Res ponse to Questionnaire” (EPA 2011a, Chapter 13): (1) households that farm (F) and (2) households that garden or raise animals (HG for homegrown). This division reflects EPA’s data analysis. EPA tabulated IRs for fruits and vegetables separately for households that farm and households that garden. Similarly, EPA tabulated IRs for animals and animal products for households that farm and for households that raise animals. Thus, the first type of household, F, represents farmers who may both grow crops and raise animals and who are likely to consume more homegrown/raised foods than the second type of household. The second type of household, HG, represents the non-farming households that may consume lower amounts of home-grown or raised foods (i.e., HG encompasses both households that garden and households that raise animals).

The food-specific ingestion rates are based on the amount of each food type that households that farm (F) or households that garden and raise animals (HG) produced and brought into their homes for consumption and the number of perso ns consuming the food. EPA averaged the actual consumption rate for home-grown f oods over the 1-week survey period.

The default food-specific ingestion rates in MIRC for adults are those for farming households (F) in Exhibit_Add A2-17. The user can specif y use of the non-farming household (HG) ingestion rates if they are more appropriate for the user’s exposure scenario.

Exhibit_Add A2-17. Summary of Age-Group Specific Food Ingestion Rates for Farm Food Items

Product Child (age in yr) Adult (20–70

yrs) <1 1–2 3–5 6–11 12–19
Mean ingestion rates (g/kg-day)
Beef a N/A 4.14 4.00 3.77 1.72 1.93 Dairy b N/A 91.6 50.9 27.4 13.6 2.96 Eggs a N/A 2.46 1.42 0.86 0.588 0.606 Exposed Fruit a N/A 6.14 2.60 2.52 1.33 1.19

21 Note that EPA’s 2008 CSEFH does not distinguish betwe en exposed and protected fruits and vegetables when recommending food ingestion rates based on the same dat a set for the same age categories. EPA’s 1997 analysis for its EFH therefore remains the most appr opriate data source for use in MIRC.TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-76 December 2013 Description of MIRC 

### Exhibit_Add A2-17. Summary of Age-Group Specific Food Ingestion Rates for Farm Food Items

Product Child (age in yr) Adult (20–70

yrs) <1 1–2 3–5 6–11 12–19
Exposed Vegetable a N/A 3.48 1.74 1.39 1.07 1.38
Pork a N/A 2.23 2.15 1.50 1.28 1.10 Poultry a N/A 3.57 3.35 2.14 1.50 1.37
Protected Fruit a N/A 16.6 12.4 8.50 2.96 5.19 Protected Vegetable a N/A 2.46 1.30 1.10 0.78 0.862 Root Vegetable a N/A 2.52 1.28 1.32 0.94 1.03 Water (mL/day) c N/A 332 382 532 698 1218 Median ingestion rates (g/kg-day)
Beef a N/A 2.51 2.49 2.11 1.51 1.55 Dairy b N/A 125 66.0 34.4 15.5 2.58 Eggs a N/A 1.51 0.83 0.561 0.435 0.474 Exposed Fruit a N/A 1.82 1.11 0.61 0.62 0.593 Exposed Vegetable a N/A 1.89 1.16 0.64 0.66 0.812
Pork a N/A 1.80 1.49 1.04 0.89 0.802 Poultry a N/A 3.01 2.90 1.48 1.30 0.922
Protected Fruit a N/A 7.59 5.94 3.63 1.23 2.08 Protected Vegetable a N/A 1.94 1.04 0.79 0.58 0.564 Root Vegetable a N/A 0.46 0.52 0.57 0.56 0.59 Water (mL/day) c N/A 255 316 417 473 981
90 th percentile ingestion rates (g/kg-day) d

Beef a N/A 9.49 8.83 11.4 3.53 4.41 Dairy b N/A 185 92.5 57.4 30.9 6.16 Eggs a N/A 4.90 3.06 1.90 1.30 1.31 Exposed Fruit a N/A 12.7 5.41 6.98 3.41 2.37 Exposed Vegetable a N/A 10.7 3.47 3.22 2.35 3.09
Pork a N/A 4.90 4.83 3.72 3.69 2.23 Poultry a N/A 7.17 6.52 4.51 3.13 2.69
Protected Fruit a N/A 44.8 32.0 23.3 7.44 15.1 Protected Vegetable a N/A 3.88 2.51 2.14 1.85 1.81 Root Vegetable a N/A 7.25 4.26 3.83 2.26 2.49 Water (mL/day) c N/A 687 778 1149 1640 2534
95 th percentile ingestion rates (g/kg-day)
Beef a N/A 12.9 12.5 12.5 3.57 5.83 Dairy b N/A 167 89.9 56.0 32.3 7.80 Eggs a N/A 5.38 3.62 2.37 1.43 1.59 Exposed Fruit a N/A 14.6 6.07 11.7 4.78 3.38 Exposed Vegetable a N/A 11.9 6.29 5.47 3.78 4.46
Pork a N/A 6.52 6.12 4.73 6.39 2.60 Poultry a N/A 8.10 7.06 5.07 3.51 3.93TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-77 December 2013 Description of MIRC 

### Exhibit_Add A2-17. Summary of Age-Group Specific Food Ingestion Rates for Farm Food Items

Product Child (age in yr) Adult (20–70

yrs) <1 1–2 3–5 6–11 12–19

Protected Fruit a N/A 48.3 35.1 26.9 11.4 19.2 Protected Vegetable a N/A 9.42 5.10 3.12 2.20 2.83 Root Vegetable a N/A 10.4 4.73 5.59 3.32 3.37 Water (mL/day) c N/A 903 999 1499 2163 3087
99 th percentile ingestion rates (g/kg-day)
Beef a N/A 20.9 19.8 13.3 4.28 6.84 Dairy b N/A 180 87.2 54.8 34.7 9.20 Eggs a N/A 16.2 11.2 8.19 4.77 1.83 Exposed Fruit a N/A 25.2 32.5 15.7 5.9 13.0 Exposed Vegetable a N/A 12.1 7.36 13.3 5.67 8.42
Pork a N/A 8.71 9.74 6.61 4.29 3.87 Poultry a N/A 9.63 10.24 6.12 4.60 4.93
Protected Fruit a N/A 109 71.2 58.2 19.1 34.4 Protected Vegetable a N/A 9.42 5.31 5.40 2.69 5.56 Root Vegetable a N/A 10.4 4.73 7.47 5.13 7.57 Water (mL/day) c N/A 1318 1592 2274 3467 4567

a Primary source for values was the 1987–1988 NFCS survey; compiled results are presented in Chapter 13 of 2011 Exposure Factors Handbook (EPA, 2011a). When data were unavailable for a particular age group, intake rate for all age groups was used multiplied by the age-specific ratio of intake based on national population intake rates from CSFII.

b Primary source for values was 1987–1988 NFCS survey, compiled results presented in Chapter 13 of 2011 Exposure Factors Handbook (EPA, 2011a). When data were unavailable for a particul ar age group, intake rate for all age groups was used multipli ed by the age-specific ratio of intake based on national populati on intake rates from an NHANES 2003–2006 analysis in Chapter 11 o f the Exposure Factors Handbook.

c Primary source for children less than 3 years of age was a Kahn and Stralka (2008) analysis of CSFII data, and from EPA’s analysis of NHANES 2003–2006 data for children and adults greater than th ree. All data tables that were used and justification s for data sources are presented in Chapter 3 of the 2011 Exposure Factors’ Handbook.

d Default ingestion rate percentile us ed in MIRC for Tier 1 assessments and chemical threshold calculations.



### For children, EPA estimated food-specific IRs for four age categories (EPA 2011a): 1–2 years, 3–5 years, 6–11 years, and 12–19 years. Sample size s were insufficient to distinguish IRs for children in different types of households; hence, for children, a single IR value represents both F and HG households for a given food type and age category (Exhibit_Add A2-17). For some food types and age categories, there were insuffi cient data for EPA to provide consumer-only intake rates (i.e., data set for the subpopulation consisted of fewer than 20 observations). The HHRAP methodology, Section 6.2.2.2, recommends a method by which to calculate the “missing” age-specific consumer-only ingestion ra tes, as explained below. Food-specific intake rates (IRs) for those child age groups and food items not included in Chapter 13 of the 2011 EFH, that is IR age_group_x 

### , were derived using the following information:

 

### Mean or percentile-specific consumer-only inta ke of the farm food item, as brought into the home, for the total NFCS survey population (from EFH Chapter 13) – IRCO_total;

 

### Mean or percentile-specific per capita intake of the food type from all sources, as consumed, for the specific child age group, from Chapter 3 of the CSFII Analysis of Food Intake Distributions (EPA 2003c) – IRPC, age_group_x; andTRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-78 December 2013 Description of MIRC  

### Mean or percentile-specific per capita intake of the farm food item for the total CSFII survey population (from Chapter 3 of EPA 2003c) – IRPC_total.

The ratio of IR PC, age_group_x 

### to IR PC_total 

### from the CSFII data shows the consumption rate of a particular food type by a specific age group relative to the consumption rate for that food type for the population as a whole. The ratio of IR CO, age_group_x 

### to IR CO_total 

### , that is the consumption rate of a particular food type by a specific age group (consumers only) relative to the consumption rate for that food type for the NFCS survey population as a whole (consumers only), should be approximately the same. Given the assumption that the two ratios are equal, Equation 2-56 was used to calculate the “missing” age-specific consumer-only IRs :
Equation 2-56. Calculation of Age-Group-Specific and Food-Specific Ingestion Rates



### where:

IR CO, age_group_x = Mean or percentile-specific consumer-only intake of the food type from all sources, as consumed, for the specific child age group X

IR CO_total = Mean or percentile-specific consumer-only intake of the farm food item, as brought into the home, for the total NFCS survey population

IR PC, age_group_x = Mean or percentile-specific per capita intake of the food type from all sources, as consumed, for the specific child age group X from the CSFII

IR PC_total = Mean or percentile-specific per capita intake of the farm food item for the total CSFII survey population



### In this discussion, per capita (as opposed to consumer-only ) indicates the intake rates are based on the entire population rather than the subset of the population that ingests the particular food category (i.e., consumers). Here, the use of per capita ingestion rates are recommended by the HHRAP methodology becaus e no consumer-only percentile-specific intakes are provided for the different age groups.

The above calculation implicitly assumes that the distribution of the consumption rate for a food type for a specific age group (consumers only) has the same shape as the distribution of the consumption rate for a food type for a specific age group in the general population ( per capita ). Otherwise, the separate calculation of each percentile might yield intake estimates that decrease as the percentile increases. This calculation artifact could occur if the shapes of the two distributions differ in the upper percentiles (or “tails”) of the distributions.

In the instances where the above calculations were used to fill data gaps in the above exhibit, only the dairy child-specific age group intake estimates are not strictly increasing with increasing percentile. The distributions likely track better (and thus the above assumption of equal ratios is more reasonable) for lower percentiles, with deviations occurring due to outlier ingestion rates based on only a few respondents in the tails of the distributions. The MIRC defaults use the 90 th 

### percentile ingestion estimates, which are likely more reliable than the 95 th 

### or 99 th 

### percentile estimates in this particular calculation. 

### IR IR IR IR

PC_total oup_x PC, age_gr CO_total
oup_x CO, age_gr 

### 

TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-79 December 2013 Description of MIRC 

### 6.3.4. Local Fish Ingestion Rates 6.3.4.1. Screening Scenario

The USDA’s 1987 to 1988 Nationwide Food Cons umption Survey (NFCS) (USDA 1993, 1994a), as presented in Chapter 13 of the Agency’s Exposure Factors Handbook (i.e., Intake Rates for Various Home Produced Food Items) (EPA 2011a), includes family-caught fish ingestion rates by age category. There are several disadvantages, however, to using that data source to estimate fish ingestion rates. First, due to inadequate sample sizes, EPA did not report fish ingestion rates for children less than 6 years of age. Second, the NFCS data were collected more than two decades ago. Third, the reported fish ingestion rates are for ages 6 to 11 and 12 to 19 and are based on 29 and 21 individuals in each age category, respectively (EPA 2011a, Table 13-20). Finally, the ingestion rates from NFCS data are based on total weight of fish as brought into the home, and do not include losses from preparation of the fish (i.e., removal of inedible parts and, possibly, the skin). Estimates of preparation losses for fish intended to apply to the NFCS fish ingestion rate data are very uncertain and are based on a wide variety of freshwater, estuarine, and marine fish, and squi d (EPA 2011a, Table 13-69). Additionally, when considering the multipathway screening methodology, it is important that potential health effects to those individuals who are most likely to have the greatest PB-HAP exposure are not underestimated and, therefore, ingestion rates that are reflective of subsistence fisher ingestion rates are desired. Therefore, a more recent survey was sought that included larger sample sizes, data for children younger than six years, ingestion rates for the parts of fish actually consumed, and ingestion rates reflective of subsistence fisher ingestion rates.

Taking all of these issues into consideration, the default adult fish ingestion rate selected for use in MIRC is 373 g/day, which is the the estimated 99 th 

### percentile of fish ingestion rates for woman anglers as reported by Burger (2002). This rate is based upon ingestion of “wild-caught” fish, which includes freshwater, estuarine, and marine species, while our screening scenarios focus only on freshwater fish from lakes. This is notable because a number of studies indicate that fish ingestion rates are limited by species and habitat (i.e., lake, river, estuary, and ocean) and that the majority of the fish consumed in the United States are from river, marine and estuarine habitats versus lakes. Thus, although the fish ingestion rate for this group of subsistence fishers is not the highest fish ingestio n rate available for use by EPA, we do believe it strikes the appropriate balance between being health protective and having screening scenarios so conservative that they are of limited use in the decision making process. This high-end fish ingestion rate is appropriate in the context of the conservative screening scenario used in the RTR process. This methodology is particularly applicable for national rulemakings given that it is very likely that subsistence woman fishers of child bearing age are located throughout the United States. Finally, we note that using a high-end subsistence fish ingestion rate is consistent with section 112 of the CAA, which focuses on risks associated with maximally exposed individuals.

Because Burger (2002) did not estimate fish ingestion rates for children, another data source was needed to develop ingestion rates for the child age categories that are used in MIRC. The child ingestion rates need to be consistent with the Burger adult ingestion rate, reflective of subsistence fisher ingestion rates, and based on adequate sample sizes. To satisfy these requirements, data on child ingestion rates from EPA’s Estimated Per Capita Fish Consumption in the United States (EPA 2002) were selected for use. Specifically, the estimated 99 th 

### percentile of as-prepared

, consumer-only ingestion rates for finfish and shellfish were selected (see Section 4.2.1.1 Table 5 of EPA 2002).The original data were collected as part of the 1994– 96 and 1998 USDA Continuing Survey of Intake s by Individuals (CSFII; USDA 2000). Values reflect “as prepared” ingestion rates for which cooking and preparation losses ( L1 and L2 ) didTRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-80 December 2013 Description of MIRC 

### not need to be considered. “Total” fish as reported in this source represents consumption of finfish plus shellfish.

Because the child age categories used in MIRC differ from the CSFII age categories presented in EPA 2002, the CSFII data were adjusted for use in MIRC. The CSFII data did not provide ingestion rates for the 1–2 year age category. To estimate ingestion rates for this age group, EPA used the ingestion rate for the 3–5 year age category scaled downward by the ratio of the mean body weight of the 1–2 year age group to the mean body weight of the 3–5 year age group. Because MIRC uses a 3–5 year age category, no adjustment was needed for CSFII data from that age category. For the 6–11 and 12–19 age categories, time-weighted average ingestion rates were calculated based on the CSFII ingestion rates. Exhibit_Add A2-18 provides the fish ingestion rates used in the screening analysis.

Exhibit_Add A2-18. Fish Ingestion Rates Used in Screening Analysis

Ingestion Rates (g/day)
Infants

<1 yr Child 1–2 yrs Child 3–5 yrs Child 6–11 yrs Child 12–19 yrs Adult 20–70 yrs
NA 107.7 a 159.0 b 268.2 c 331.0 c 373 d

a A fish ingestion rate for ages 1-2 years wa s not available. The value represents th e consumer-only fish ingestion rate for ages 3 to 5 from EPA (2002) (Section 4.2.1.1 Table 5 [freshwat er/estuarine habitat]), scaled down by the ratio of the mean Child 1-2 body weight to the mean Child 3-5 body weight.

b This value represents the consumer-only fish ingestion rate for ages 3 to 5 from EPA (2002), Section 4.2.1.1 Table 5 (freshwater/estuarine habitat) rounded to the nearest full number.

c These values represent time-weighted average consumer-only fi sh ingestion rates based on ingestion rates from EPA (2002), Section 4.2.1.1 Table 5 (freshwater/estuarine habitat).

d This value represents the 99 th percentile ingestion rate of wild caught fi sh for women as reported by Burger (2002).



### 6.3.4.2. Other values
EPA’s (2002) analysis of freshwater and estuar ine fish consumption from the USDA’s Continuing Survey of Food Intake by Individuals (CSFII) for 1994-96 and 1998 was chosen to provide fish ingestion rate options by age category in MIRC. Although the fish consumption rates reported in the CSFII include all sources, commercial and self-caught, for purposes of screening level risk assessments, it was assum ed that all freshwater and estuarine fish consumed are self-caught. The inclusion of commercially obtained and estuarine fish will overestimate locally caught freshwater fish ingestion rates for most populations in the United States; however, it also might underestimate locally caught fish ingestion rates for some populations (e.g., Native Americans, Asian and Pacific Island communities, rural African American communities). Because consumption of lo cally caught fish varies substantially from region to region in the United States and from one population or ethnic group to the next, users of MIRC are encouraged to use more locally relevant data whenever available.

For children, MIRC also includes values for the mean and the 90th, 95th, and 99th percentile fish per-capita ingestion rates (freshwater and estuarine fish only) based on EPA’s analysis of 1994-96 and 1998 CSFII data (EPA 2002, 2008a). Those rates include individuals who eat fish and those who do not eat fish. As shown in EPA’s 2008 CSEFH, Table 10-7, the 90 th 

### percentile per capita ingestion rates estimated from the two-day CSFII recall period are zero for some child age groups. Although not presented in CSEFH Table 10-7, median ingestion rates for all child age groups would be zero (considering the “consumer only” sample sizes [CSEFH Table 10-9] relative to the “ per-capita ” sample sizes in Table 10-7).

The high percentile fish ingestion rates that are zero result from the short duration of the CSFII recall period (two days) compared with the averaging time of interest (a year) and the relativelyTRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-81 December 2013 Description of MIRC 

### infrequent consumption of fish (e.g., on the order of once a week to once a month or less) compared with the near daily ingestion of other types of food products (e.g., dairy, produce, meat). Use of zero for fish ingestion rates, however, is not useful in MIRC. As a result, an alternative method was used to estimate fish ingestion rates for children and adults that could provide reasonable, non-zero values for all age groups and percentiles.

The alternative, age-group-specific fish ingestion rates were derived using values for each age group, y :

 

### Mean or other appropriate percentile consumer-only fish ingestion rates for age group y , IRCO, y , from EPA’s Estimated Per Capita Fish Consumption in the United States (EPA 2002), Section 5.2.1.1, Table 5, for freshwater/estuarine habitat. 22

 

### Fraction of the population consuming freshwater/estuarine fish, FPC, y , calculated as consumer-only sample size / U.S. population sample for age group y . The data to calculate those fractions are ava ilable in the 2008 CSEFH and EPA 2002.

Equation 2-57 was used to calculate the alternative, per capita fish ingestion rates by age group 

### ( IR PC,y 

### ):

Equation 2-57. Calculation of Alternative Age-Group-Specific Fish Ingestion Rates



### where:

IR PC,y = Per capita fish ingestion rate for age group y (g/day)

IR CO,y = Consumer-only fish ingestion rates for age group y (g/day) (EPA 2002, Section 5.2.1.1, Table 5, for freshwater/estuarine habitat)

F

PC,y = Fraction of the population consuming freshwater/estuarine fish, calculated as consumer-only sample size / total U.S. population sample size for age group y (unitless) (2008 CSEFH, EPA 2002)



### In the above, per capita (as opposed to consumer-only ) indicates the intake rates are based on the entire population rather than the subset of the population that ingests the particular food category. Here, per capita ingestions are recommended by the HHRAP methodology because no consumer-only percentile-specific intakes are provided for the different age groups.

The mean and percentile consumer-only fish ingestion rates for children and adults and the fraction of the population consuming freshwater/estuarine fish used in calculating long-term per capita fish ingestion rates by age group are present ed in Exhibit_Add A2-19 and Exhibit_Add A2-20. The mean and percentile per capita fish ingestion rates estimated using this methodology are summarized in Exhibit_Add A2-21 and are available in MIRC. The fish ingestion rates provided in Exhibit_Add A2-21 and included in MIRC are intended to represent the harvest and consumption of fish in surface waters in a hypothetical depositional area. For site-specific application of this tool, users should consider using more localized survey data to estimate more appropriate fish ingestion rates. The fishing season varies substantially across

22 Most of these data also are provided in Table 10-9 of the CSEFH; the median values, however, are not presented in the CSEFH, and values for the mean and all other per centiles are slightly different due to rounding. yPC,yCO, yPC, 

### FIR IR  TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-82 December 2013 Description of MIRC 

### the United States by latitude, and fish consumpt ion patterns also vary by type of water body (e.g., ponds, lakes, rivers, streams, estuaries, coastal marine), cultural heritage, and general geographic area. Therefore, use of more localized information is encouraged. Note that as indicated in Exhibit_Att A-14 and Exhibit_Att A-16, in developing the screening threshold emission rates, health protective fish ingestion rates for child and adult fish consumers that more closely represent exposures of a high-end recreational fisher were used.

As noted in Section 6.4.3 of this addendum, if the user overwrites the fish IRs shown in Exhibit_Add A2-21 with fresh-weight as caught values (e.g., values obtained from a local creel survey), the user is advised to set non-zero values for the preparation and cooking loss factors L1 and L2 in Equation 2-15. Suggested values are presented in Section 6.4.3.

Exhibit_Add A2-19. Daily Mean and Percentile Consumer-Only Fish Ingestion Rates for Children and Adults ( IR CO,y 

### ) a

Lifestage (years) Ingestion Rates, All Fish (g/day)
Mean 50th 90 th 95 th 99 th

Child <1 NA NA NA NA NA
Child 1-2 b 27.31 15.61 64.46 87.60 138.76*
Child 3-5 c 40.31 23.04 95.16 129.31 204.84*
Child 6-11 d 61.49 28.46 156.86* 247.69* 385.64*
Child 12-19 e 79.07 43.18 181.40* 211.15* 423.38*
Adult f 81.08 47.39 199.62* 278.91 505.65*

Sources: EPA 2002, 2008a NA = not applicable; it is assumed that children < 1 year of age do not consume fish. *Indicates that the sample size does not meet minimum reporting requirements as described in EPA 2002. Owing to the small sample sizes, these upper percentiles values are highly uncertain.

a Per capita fish ingestion (FI) rates for children by age group are ava ilable from Chapter 10 of the CSEFH (EPA 2008a); however, all 50th and some 90th percentile ingestion rates are zero. Per capita FI rates were therefore estimated as described in Equation 2-57 to provide reasonable, non-zero val ues for all age groups and percentiles.

b A fish IR for ages 1-2 years was not available. The value repr esents the consumer-only fish i ngestion rate for ages 3 to 5 fro m EPA (2002) (Section 5.2.1.1 Table 5 [freshwat er/estuarine habitat]), scaled down by the ratio of the mean Child 1-2 body weight to the mean Child 3-5 body weight.

c These values represent the consumer-onl y fish ingestion rate for ages 3 to 5 from EPA (2002), Section 5.2.1.1 Table 5 (freshwater/estuarine habitat). Sample size = 442.

d These values represent the consumer-only fish ingestion rate for ages 6 to 10 from EPA (2002), Section 5.2.1.1 Table 5 (freshwater/estuarine habitat). Sample size = 147.

e These values represent the time-weighted average per capita fish ingestion rate for ages 11 to 15 and 16 to 17 years from EPA (2002), Section 5.1.1.1 Table 5 (freshwater/estuarine habitat); th e value may underestimate ingestion rate for ages 12 to 19 ye ars. Sample size = 135.

f These values represent the consumer-onl y fish ingestion rate for individuals 18 ye ars and older from EPA (2002), Section 5.2.1. 1 Table 4 (freshwater/estuarine habitat). Sample size = 1,633.TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-83 December 2013 Description of MIRC



### Exhibit_Add A2-20. Fraction of Population Consuming Freshwater/Estuarine Fish on a Single Day ( F PC,y 

### )

Lifestage (years) Fraction Consuming Fish
Child 3-5 0.0503 a

Child 6-11 0.0440 b

Child 12-19 0.0493 c

Adult 0.08509 d

Sources: EPA 2002, 2008a

a This value was calculated using the ages 3 to 5 sample size for consumers only divided by the sample size for the U.S. population divided by 2 to represent the prop ortion consuming fish on a single day (the consumers-only gro up includes individua ls who consumed fish on at least one of two surv ey days) to match the one-day ingestion rate.

b As in footnote a, the value was calculated using the ages 6 to 10 sample size for consumers only divided by the sample size for U.S. population divided by 2.

c The value was calculated by summing the ages 11 to 15 and 16 to 17 sample sizes for consumers only and dividing by both by the sum of the sample sizes for U.S. population and by a factor of 2.

d The value was calculated using the ages 18 and older sample size for consumers only divided by the sample size for U.S. population from Section 5.1.1.1 Table 4. The result was divided by 2 to represent a one-day sampling period in order to match the one-day ingestion rate.



### Exhibit_Add A2-21. Calculated Long-term Mean and Percentile per capita Fish Ingestion Rates for Children and Adults ( IR PC,y 

### )

Lifestage (years) Ingestion Rates, All Fish (g/day)
Mean 50th 90 th 95 th 99 th

Child <1 NA NA NA NA NA
Child 1-2 a 1.37 0.79 3.24 4.41 6.98
Child 3-5 b 2.03 1.16 4.79 6.51 10.3
Child 6-11 c 2.71 1.25 6.90 10.9 17.0
Child 12-19 d 3.90 2.13 8.95 10.4 20.9
Adult e 6.90 4.03 16.99 23.73 43.02

Sources: EPA 2002, 2008a NA = not applicable; it is assumed that children < 1 year of age do not consume fish.

a Values were calculated as (consumer-onl y IR for Child 1-2) x (fraction of popul ation consuming fish for Child 3-5).

b Values were calculated as (consumer-onl y IR for Child 3-5) x (fraction of popul ation consuming fish for Child 3-5).

c Values were calculated as (consumer-onl y IR for Child 6-11) x (fraction of popul ation consuming fish for Child 6-11).

d Values were calculated as (cons umer-only IR estimated for Child 12-19) x (fract ion of population estimat ed to consume fish for Child 12-19).

e Values were calculated as (consumer- only IR for Adults) x (fraction of population consuming fish for Adults).TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-84 December 2013 Description of MIRC 

### MIRC also includes values for the mean and the 90th percentile fish ingestion rates for recreational anglers, black and female recreational anglers, and anglers of Hispanic, Laotian, and Vietnamese descent which are shown in Exhibit_Add A2-22. These latter three populations are culturally or economically disposed to higher rates of fish ingestion than the general population. Recreational angler values are from the EFH (EPA, 2011a). Black and female recreational anglers ingestion rates are presented in Burger (2002). The fish ingestion rates for Hispanic, Laotian, and Vietnamese populations were derived from a study by Shilling et al

. (2010) of contaminated fish consumption in California’s Central Valley Delta. Shilling et al

. (2010) reported mean and 95 th 

### percentile ingestion rates for each subpopulation. In part due to the low sample size in the Shilling study (n of 30 to 45), 95 th 

### percentile values were believed to be unrealistically high. The 90 th 

### percentile ingestion rate estimates presented in Exhibit_Add A2-22 for Hispanic, Laotian, and Vietnamese fishers were derived by EPA using information from Shilling et al . (2010; EPA, 2010).

Exhibit_Add A2-22. Calculated Mean and 90 th 

### Percentile Per capita Fish Ingestion Rates for Populations of Recreational Fishers (IR PC,y 

### )

Subpopulation

Percentile Units Recreational Fisher a FemaleRecreational Fisher b BlackRecreational Fisher b HispanicRecreational Fisher c LaotianRecreational Fisher c VietnameseRecreational Fisher c

Ingestion of Fish

Mean g/day 8 39.1 171 25.8 47.2 27.1 90th g/day 11 123 446 98 144.8 99.1

a 1997 Exposure Factors Handbook (USEPA, 1997a)

b Burger (2002) weights are “as consumed” for locally caught fish.

c Shilling, F., A. White, L. Lippert, and M. Lubell. 2010. Contam inated fish consumption in California’s Central Valley Delta. Environmental Research 110:334-344.



### Applications to date of MIRC have used whole fish concentrations estimated by TRIM.FaTE. The proportion of lipid in TL3 and TL4 fish in TRIM.FaTE is assumed to be 5.7 percent (by weight) for the whole fish, based on information provided by Thomann (1989). The lipid content of the part(s) of the fish normally consumed is likely to be less than 5.7 percent. For example, EPA estimated a consumption-weighted mean lipid value for fillets of fish from TL3 to be 2.6 percent and from TL4 to be 3.0 percent (Table 6-9 in EPA 2003b). If a user of MIRC wishes to account for reduced chemical concentration in fillet compared with whole fish for lipophilic chemicals, the user can specify a “preparation” loss of chemical (see Section 6.4 of this addendum).

For lipophilic chemicals (e.g., log K ow 

### greater than 4), which partition primarily into the fatty tissues of fish, much of the higher concentration tissues might be stripped from the fish during preparation (e.g., belly fat, viscera which includes fat in liver, etc., fat under skin). The degree to which the concentration of chemical in a fillet is less than the average total concentration in the whole fish is chemical specific. Assuming the chemical concentration in the fillet is the same as in the whole fish may result in a health protective bias for highly lipophilic chemicals. For persons who prefer to consume fillets with the skin on and do not discard belly fat, assuming the same concentration of chemical in the fish cons umed as in the whole fish also is protective.TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-85 December 2013 Description of MIRC 

### 6.3.5. Soil Ingestion Rates
Adult gardeners and farmers may incidentally ingest soils from gardening activities, and gardening and farming families might ingest soil particles that adhere to exposed fruits and exposed and belowground vegetables. Soils that are re-suspended in the air by wind can resettle on exposed fruits and vegetables. Children may incidentally ingest soils in those ways, but in addition, children playing outdoors may ingest soils directly or by hand-to-mouth activities during play. MIRC includes soil ingestion rate options by age group for these types of exposures. MIRC does not include options for children who may exhibit pica, or the recurrent ingestion of unusually high amounts of soil (i.e., on the order of 1,000 - 5,000 mg/day or more) (EPA 2008a).

Data on soil ingestion rates are sparse; the soil ingestion rates listed in Exhibit_Add A2-23 and included in MIRC are based on very limited data, as is evident from the values listed. The studies evaluated by EPA for children generally focused on children between the ages of 1 and 3 to 6 years and were not specific to families that garden or farm. The default ingestion rates in MIRC are the 90 th 

### percentile values, as for other ingestion rate parameters.

Exhibit_Add A2-23. Daily Mean and Percentile Soil Ingestion Rates for Children and Adults

Age Group

(years) Soil Ingestion Rate (mg/day)
Mean a 50 th a 90 th 95 th 99 th

Child < 1 NA
Child 1-2 50 50 200 b 200 b 200 b

Child 3-5 50 50 200 b 200 b 200 b

Child 6-11 50 50 201 c 331 d 331 d

Child 12-19 50 50 201 c 331 d 331 d

Adult 20-70 20 20 201 c 331 d 331 d

Sources: EPA 2008a, EPA 2011a

a For mean and 50 th percentile soil ingestion rates for children, value represents a “central tendency” estimate from EPA’s 2008 CSEFH, Table 5-1. For adul ts, value is the recommended mean value for adults from EPA’s 2011 EFH, Chapter 5, Table 5-1.

b Values are the recommended “upper percentile” value for children from EPA’s 2011 EFH, Chapter 4, Table 4-23. The 2008 CSEFH and 2011 EFH included a high-end value associated with pica only, but this value has not been used.

c Values are 90 th percentile adult ingestion rates calculated in Stanek et al. 1997; used to represent older children and adults.

d Values are 95 th percentile adult ingestion rates calculated in Stanek et al. 1997; used to represent older children and adults.



### 6.3.6. Total Food Ingestion Rates
Although not included in MIRC for deterministic screening-level exposure and risk assessments, total food ingestion rates would be included in any probabilistic module developed for MIRC. The total food ingestion rates presented in Exhibit_Add A2-24 will be used to normalize or to truncate the sum of food-specific ingestion rates to reasonable values. This procedure is particularly important when chemical intake fr om multiple upper-percentile food ingestion rates for different types of food are added together. Individuals representing the upper percentile ingestion rate for one food category might not be the same individuals who reported high percentile ingestion rates for one or any of the other food categories.TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-86 December 2013 Description of MIRC 

### Exhibit_Add A2-24. Daily Mean and Percentile Per Capita Total Food Intake for Children and Adults

Lifestage (years) Percent of Group Consuming Food Mean 50th 90 th 95 th 99 th

Total Food Intake (g/day, as consumed)
Child < 1 a 67.0% - 99.7% h 322 270 599 779 1152
Child 1-2 b 100% 1,032 996 1537 1703 2143
Child 3-5 c 100% 1,066 1,020 1,548 1,746 2,168
Child 6-11 d 100% 1,118 1,052 1,642 1,825 2,218
Child 12-19 e 100% 1,197 1,093 1,872 2,231 2,975
Adult f 100% 1,100 1,034 1,738 2,002 2,736 Total Food Intake (g/kg-day, as consumed)
Child < 1 a 67.0% - 99.7% h 39 34 72 95 147
Child 1-2 b 100% 82 79 125 144 177
Child 3-5 c 100% 61 57 91 102 132
Child 6-11 d 100% 40 38 61 70 88
Child 12-19 e 100% 21 19 34 40 51
Adult g 100% 14.8 13.9 23.7 27.6 35.5

Sources: EPA 2005e, 2008a

a These values represent a time-w eighted average for age groups birth to <1 month (N=88), 1 to <3 months (N=245), 3 to <6 months (N=411), and 6 to <12 months (N= 678) from Table 14-3 of the 2008 CSEFH. These values represent a time-weighted aver age for age groups 1 to <2 years (N=1,002) and 2 to <3 years (N=994) from Table 14-3 of the 2008 CSEFH.

c These values were obtained from Table 14-3 of the 2008 CSEFH (age group 3 to <6 years, N=4,112).

d These values were obtained from Table 14-3 of the 2008 CSEFH ( age group 6 to <11 years, N=1,553). These values represents a health protective (i.e., slightly low) es timate for ages 6 through 11 years since 11- year olds are not included in this CSEFH age group.

e These values represent a time-weighted av erage for age groups 11 to <16 years (N=975) and 16 to <21 (N=743) years from Table 14-3 of the 2008 CSEFH. Note that estimated val ues include 11-year-olds and individuals through age 20, which contributes to uncertainty in the estimates.

f These values represent a time-weighted average for age groups 20 to 39 years (N=2,950) and 40 to 69 years (N=4,818) from Table 5B of the 2005 EPA analysis of CSFII.

g These values represent a time-weighted average for age groups 20 to 39 years (N=2,950) and 40 to 69 years (N=4,818) from Table 5A of the 2005 EPA analysis of CSFII.

h Percents consuming foods from Table 14-3 of the 2008 CSEFH include: 67.0% (birth to <1 month); 74.7% (1 to <3 months); 93.7% (3 to <6 months); and 99.7% (6 to <12 months). Infants under the age of 1 that consume breast milk are classified as “no nconsumers” of food.



### 6.4. Other Exposure Factor Values
The other exposure parameters included in the MIRC algorithms are exposure frequency (Section 6.4.1), fraction of the food type obtained from the contaminated area (Section 6.4.2), and reduction in the weight of the food types during preparation and cooking (Section 6.4.3). For the breast milk ingestion pathway, additional exposure parameters are included in the FFC algorithms (Section 6.5).


### 6.4.1. Exposure Frequency
The exposure frequency (EF) represents the number of days per year that an individual consumes home-produced food items that are contaminated with the chemical being evaluated. In MIRC, the default value for EF is 365 days/year for all exposure sources and all potential receptors. This assumption is consistent with the food ingestion rates used in MIRC (i.e., daily intake rates equivalent to annual totals divided by 365 days) and does not imply that residents necessarily consume home-produced food pr oducts every day of the year.TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-87 December 2013 Description of MIRC 

### If the user wishes to evaluate daily intake rates based on shorter averaging times, the user can overwrite both the food-specific ingestion rates and the EF for each home-grown food product. Users of MIRC might want to specify a lower EF values for various food types where residents obtain some of their diet from commercial sources and where consumption of home grown produce is seasonal.



### 6.4.2. Fraction Contaminated
The fraction contaminated (FC) represents the fraction of each food product consumed that is contaminated by the chemical at a level consis tent with environmental concentrations in the area of concern (e.g., area with maximum deposition rates). Obviously, the most health protective assumption is that all food products consumed (i.e., 100 percent) are from the location represented by the chemical concentrations input into MIRC.

For non-infant children and the adult age cohorts, MIRC includes the default FC of 1, assuming that 100 percent of the food product consumed is produced by households that farm, garden, or raise animals. The user can vary this default FC value for individual food products to tailor the assessment to a particular exposure scenario.


### 6.4.3. Preparation and Cooking Losses
Food preparation and cooking losses are included in the FFC exposure calculations to account for the amount of a food product as brought into the home that is not ingested due to loss during preparation, cooking, or post-cooking. These losses need to be accounted for in the ADD equations because the food ingestion rates calculated from the USDA 1987 to 1988 NFCS are based on the weight of home grown produce and animal products brought from the field into the house prior to any type of preparation. Not all of the produce or products were eventually ingested. In general, some parts of the produce and products are discarded during preparation while other parts might not be consumed even after cooking (e.g., bones). Thus, the actual food ingested is generally less than the amount brought into the home.

Three distinct types of preparation and cooking losses are included in the ingestion exposure algorithms in MIRC: (1) loss of parts of the food type from paring (i.e., removing the skin from vegetables and fruits) or other types of preparation (e.g., removing pits, coring, deboning), (2) additional loss of weight for the food type during cooking (e.g., evaporation of water), and (3) post-cooking losses (e.g., non-consumption of bones , draining cooking liquid [e.g., spinach]). MIRC includes mean values for these three types of preparation and cooking losses for exposed fruit, protected fruit, exposed vegetables , protected vegetables, root vegetables, beef, pork, poultry, and fish. Different types of losses apply to different types of foods. Therefore, the losses can be represented by only two parameters, L1 and L2 , the definitions of which vary according to the food type as explained in the endnotes in Exhibit_Add A2-25. All preparation and cooking loss parameter values were estimated as specified in the exhibit’s endnotes from data presented in Chapter 13 of the EPA’s 1997 and 2011 EFH (EPA 1997a and 2011a).TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-88 December 2013 Description of MIRC 

### Exhibit_Add A2-25. Fraction Weight Losses from Preparation of Various Foods

Product Mean Cooking, Paring, or Preparation Loss (Cooking Loss Type 1 [L1]) (unitless) a Mean Net Post Cooking (Cooking Loss Type 2 [L2]) (unitless) b

Exposed Fruit c 0.244 0.305 Exposed Vegetable 0.162 d NA
Protected Fruit 0.29 e NA Protected Vegetable 0.088 f NA Root Vegetable g 0.075 0.22
Beef 0.27 0.24
Pork 0.28 0.36 Poultry 0.32 0.295 h

Fish i 0.0 0.0

Source: EPA 1997a and 2011a NA = Not Available

a For fruits , includes losses from draining cooked forms. For vegetables , includes losses due to paring, trimming, flowering the stalk, thawing, draining, scraping, shelli ng, slicing, husking, chopping, and dicing and gains from the addition of water, fat, or other ingredients. For meats , includes dripping and volatile losses during cooking.

b For fruits , includes losses from removal of skin or peel, core or pi t, stems or caps, seeds and defects; may also include losses from removal of drained liquids from canned or frozen forms. For vegetables , includes losses from draining or removal of skin. For meats , includes losses from cutting, shrink age, excess fat, bones, scraps, and juices.

c These values represent averages of means for all fr uits with available data (except oranges) (Table 13-6).

d This value represents an average of means for all exposed vegetables with available data (Table 13-7). Exposed vegetables include asparagus, broccoli, cabbage, cucumber, lettuce, okra, peppers, snap beans, and tomatoes.

e This value was set equal to the value for oranges (Table 13-6).

f This value represents an average of means for all protected v egetables with available data (Table 13-7). Protected vegetables include pumpkin, corn, peas, and lima beans.

g These values represent averages of means for all root vegetables with available data (Table 13-7). Root vegetables include beets, carrots, onions, and potatoes.

h This value represents an average of m eans for chicken and turkey (Table 13-5).

i If the user changes fish ingestion rates to match a survey of t he whole weight of fish brought into the home from the field (di vided by the consumers of the fish), an appropriate value for L1 would be 0.31 and an appropriate L2 would be 0.11 (EPA 2011a).



### There are substantial uncertainties associated with the L1 and L2 parameters, including the wide variation in values across produce types that were averaged together to recommend a central tendency value for each. For example, the L2 factor does not distinguish between weight loss during cooking by water evaporation, which might leave the chemical in the food (chemical not lost), and pouring the cooking liqui d down the drain (chemical lost) or using the liquid to create a sauce (chemical not lost). In addition, the concentration of chemical might be highest in the skin, which often is discarded, an d lower in the consumed portion of many bulky fruits and vegetables. Finally, the data EPA used to evaluate L1 included negative losses (i.e., weight gains) due to hydration of dried veget ables (e.g., peas and lima beans), which increases the range of L1 values across different vegetables.

Note that the default L1 and L2 values for fish are set to zero. That is because the data source for the fish ingestion rates is not the USDA’s 1987 to 1988 NFCS (USDA 1993, 1994a) as reported in EPA’s EFH, which reported food as brought into the home, as is the case for the other food categories. Instead, the fish IR data included in MIRC are based on parts actually consumed, and so no loss processes for preparation are needed.

If the user manually changes fish ingestion rates to match a local survey of the whole weight of fish brought into the home from the field (divided by number of persons consuming the fish), the user should also set the L1 and L2 parameter to non-zero values.TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-89 December 2013 Description of MIRC 

### 6.4.4. Food Preparation/Cooking Adju stment Factor (FPCAF) for Fish
In addition to estimating the weight of the food that is lost to preparation and cooking, there also can be changes in the chemical concentrations due to cooking. Because the fish consumption rates are “as consumed” and the fish concentration is based on uncooked fish, adjustments should be made to reflect the chemical concentrations in fish after cooking. In order to account for this phenomenon, an FPCAF can be applied to the uncooked fish concentration to estimatea concentration in cooked fish. The following sections discuss FPCAFs for each of the 4 PBHAPs.


### 6.4.4.1. Mercury
In the U.S. EPA Revised Technical Support Docu ment: National-Scale Assessment of Mercury Risk to Populations with High Consumption of Self-caught Freshwater Fish (EPA 2011b), a food preparation/cooking adjustment factor (FPCAF) of 1.5 was used to adjust methyl-mercury (MeHg) concentrations in consumed fish (i.e., a 50% increase in MeHg concentration due to cooking). Cooking fish typically increases MeHg levels per unit fish (as consumed) because MeHg concentrates in the muscle, while preparation involves removal primarily of non-muscle elements of the fish. The value is based on a study by Morgan et al. (1997).



### 6.4.4.2. Cadmium
Similar to mercury, cadmium will bind to muscle and will be retained during the cooking process. As such, the same FPCAF of 1.5 that is used for mercury is assumed for cadmium.



### 6.4.4.3. Dioxin
Dioxins are lipophilic and have been demonstrated to be lost during cooking. Based on a literature review, an FPCAF of 0.7 to is applied to account for these losses during the cooking process. A brief summary of supporting literature follows.

 

### Schecter et al. (1998) found that the mass of PCDD and PCDF in fresh catfish fillet (skin on) decreased by about 50 percent per serving portion during cooking. Given the simultaneous losses of moisture/fats during broiling of the catfish, the PCDDs and PCDFs concentrations decreased by 33 percent (i.e., multiply uncooked concentration in fresh fish by a factor of 0.66 = 0.70 to one significant digit).

 

### Reinert et al. (1972) reported higher losses of another highly lipophilic chemical, DDT, from cooking fish fillets of bloaters, yellow perch, lake trout, and coho salmon. Concentrations of DDT in fish fillet portions for lake trout and coho salmon, top predators, were reduced by 64 to 72 percent by frying or broiling, primarily through preferential loss of fat (and lipophilic DDT) during cooking. The investigators did not report skin on or off; however, they used steak cuts instead of flat fillets, which provide a smaller ratio of skin to muscle than is the case for fillets that constitute one side of the fish.

 

### Zabik and Zabik (1995) quantified the reduction in TCDD concentration of cooked, with the skin off, fillets compared with uncooked fillet with skin for fish harvested from the Great Lakes. Concentrations in the cooked fish with the skin off were reduced relative to the raw fillet with the skin on by approximately 44 percent for walleye, 80 percent for white bass, and 61 percent for lake trout. Comparing losses of TCDD for fillets cooked with the skin on versus fillets that were both skinned and cooked, Zabik and Zabik (1995) found reductions in TCDD concentrations of approximately 43 percent for Chinook Salmon cooked with the skin on and 57 percent for chinook salmon cooked withTRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-90 December 2013 Description of MIRC 

### the skin off. They found a 37 percent reduction of TCDD concentration for carp fillets cooked with the skin on and 54 percent reduction if the skin was removed.

The three studies listed above indicate that the 0.7 factor is not likely to overestimate loss of PCDD/PCDFs from fish during cooking (pan fryi ng, broiling, grilling). Reductions in TCDD concentrations could be much higher with skin removal and trimming of fat.



### 6.4.4.4. PAHs
While it is reasonable to assume that there might be losses of lipophilic PAHs during the cooking process, there is insufficient information to distinguish what the net loss (or gain) during cooking might be because cooking can create PAHs from proteins in the tissue. The literature acknowledges these competing forces, but does not provide information to disentangle the gain and loss mechanisms. As such, a neutral approach was taken, which is to assume an adjustment factor of 1.0 (i.e., no adjustment) for PAHs.


### 6.5. Breast-Milk Infant Exposure Pathway Parameter Values
Values used for parameters in the breast-milk exposure pathway algorithms (see Section 3.4 of this addendum) can be scenario-specific, recepto r-specific, and/or chemical-specific and might be empirically derived or estimated by an appropria te model. For parameters that are scenariospecific or for which empirical values are required, the default values provided in MIRC are listed. For parameters for which MIRC calculates values, the appropriate equation is listed.Scenario- and receptor-specific parameters are discussed in Section 6.5.1 and chemicalspecific parameters are discussed in Section 6.5.2.


### 6.5.1. Receptor-specific Parameters
Receptor-specific values are needed for parameters that describe the characteristics or activities of the exposed individual. In this cont ext, there are two relevant receptors: the mother and the infant. Exhibit_Add A2-26 lists the parameters and their default values. The text that follows describes the input value or value options for each exposure parameter required by MIRC to calculate the infant absorbed chemical intake rate, or DAI inf 

### . For parameter values that can be estimated when empirical values are not available, see the equation description in Section 3.4 of this addendum.TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-91 December 2013 Description of MIRC 

### Exhibit_Add A2-26. Scenario- and Receptor-Specific Input Parameter Values Used to Estimate Infant Exposures via Breast Milk

Parameter Description Default Value

AT Averaging time for infant’s exposure via breast milk, i.e., duration of nursing (days) =ED

BW inf Body weight of infant (kg) averag ed over duration of nursing exposure 7.8

BW mat Body weight of mother (kg) averaged over duration of mother’s exposure 66

DAI mat Daily absorbed intake of chemical by mother (mg/kg-day) Equation 2-36 ED Exposure duration for infant, i.e., duration of breast feeding (days) =AT AT/ED Averaging time divided by exposure duration 1.0
f b p Fraction of mother’s whole blood t hat is plasma (unitless) 0.65
f fm Fraction of mother’s body weight that is fat (unitless) 0.30
f mbm Fraction of fat in mother’s breast milk (unitless) 0.04
f p m Fraction of mother’s body weight that is plasma (unitless) 0.046
IR milk Mean infant milk ingestion rate ov er duration of nursing (kg/day) 0.709

t bf Duration of breast feeding (days) 365
t p n Duration of maternal chemical exposure prior to nursing (days) 3285



### Averaging time ( AT ) and exposure duration ( ED ). AT refers to the time over which the infant’s exposure to the chemical of concern is averaged. ED refers to the duration of the infant’s exposure. For the exposure scenario considered for this age group, both AT and ED equal the duration of the nursing period, and they therefore cancel each other out in the infant average daily dose equation.

Infant body weight ( BW inf



### ). The user selects a value for BW inf 

### , the time-weighted average body weight of the infant over the entire duration of breast feeding, based on the age at which the infant stops breast feeding. For example, if the infant breast feeds for one year, the user should select the body weight for an infant that is aver aged from birth to the first birthday. Similarly, if an infant breast feeds for 6 months, the user should select the body weight for an infant that is averaged from birth to six months. Because the default breast feeding duration ( t bf



### ) is one year (i.e., 365 days), the default infant body weight is 7.8 kg, which is the time-weighted average for the mean infant body weight between birt h and the first birthday from EPA’s 2008 Child Specific Exposure Factors Handbook (CSEFH; EPA 2008a). Exhibit_Add A2-27 presents additional values for the infant body weight parameter that the user can select instead of the MIRC default.

Exhibit_Add A2-27. Average Body Weight for Infants

Statistic 0 to < 6 months (kg) 0 to < 12 months (kg) 0 to < 18 months (kg) 0 to < 24 months (kg)
Mean 6.5 7.8 a 9.0 9.6
5 th percentile 5.0 6.0 7.0 7.5
10 th percentile 5.3 6.4 7.4 7.8
15 th percentile 5.5 6.7 7.7 8.2
25 th percentile 5.8 7.0 8.1 8.7
50 th percentile 6.4 7.8 8.9 9.5
75 th percentile 7.1 8.6 9.9 10.5
85 th percentile 7.4 9.0 10.3 11.0TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-92 December 2013 Description of MIRC 

### Exhibit_Add A2-27. Average Body Weight for Infants

Statistic 0 to < 6 months (kg) 0 to < 12 months (kg) 0 to < 18 months (kg) 0 to < 24 months (kg)
Mean 6.5 7.8 a 9.0 9.6
90 th percentile 7.7 9.2 10.6 11.3
95 th percentile 8.0 9.7 11.1 11.8
Source: EPA (2008a); each value is the time-weighted average fr om the data summaries presented in the CSEFH, Table 8-3.

a MIRC default



### Maternal body weight ( BW mat 

### ). This parameter represents the body weight of the mother averaged over the entire duration of the mother’s exposure to the chemical of concern. The maternal body weight is needed to calculate the bi ological elimination constant for the lipophilic chemical in lactating women ( k fat_elac 

### ). MIRC assumes that the mother will be pregnant for 9 months (i.e., 0.75 year) and will be lactating for 1 year. The MIRC default maternal body weight also assumes that the mother has been exposed for 10 years total. For 8.25 years, she is not pregnant or lactating, for 0.75 year she is pregnant, and for 1 year she is lactating. The MIRC default BW mat 

### of 66 kg is based on CSFII data compiled by EPA for non-lactating and nonpregnant women between the ages of 15 and 44 (i.e., women of child-bearing age), lactating women, and pregnant women (EPA 2004). Exhibit_Add A2-28 presents additional values for the maternal body weight parameter which the user may choose to use instead of the MIRC default. The BW mat 

### value is not the value that MIRC uses to estimate the mother’s absorbed daily intake ( DAI mat 

### ). The daily ingestion rates for home-grown/raised food products in MIRC are for men and women combined, with the rates normalized to body weight. The ingestion rates for soil, water, and fish included in MIRC are not normalized to body weight but are based on both men and women. For those ingestion rates, MIRC uses an average body weight value for males and females to estimate the average daily dose (intake) of the chemical in mg/kg-day. These values are subject to the assumption that the body-weight normalized ingestion rates and resulting ADD values are applicable to nursing mothers.

Exhibit_Add A2-28. Time-weighted Average Body Weight for Mothers

Statistic Weight (kg)

Mean 66.0 a

5 th 47.1
10 th 50.2
25 th 54.3
50 th 62.0
75 th 72.0
90 th 85.7
95 th 97.0

Source: EPA 2004

a MIRC default value



### Exposure duration ( ED ). See discussion of AT and ED above.
Fraction of mother’s whole blood that is plasma ( f bp 

### ). Steinbeck (1954) reported that plasma volume accounts for approximately 60 percent of the total blood volume in non-lactating human females (EPA 1998). Harrison (1967) and Ueland (1976) reported plasma volumes between 63 to 70 percent in postpartum women (EPA 1998). The default value in MIRC of 65 percent (0.65)TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-93 December 2013 Description of MIRC 

### is the value recommended by EPA in its Methodology for Assessing Health Risks Associated with Multiple Pathways of Exposure to Combustor Emissions (MPE, EPA 1998).

Fraction of mother’s body weight that is fat ( f f m 

### ). A limitation of using a steady-state, instead of a dynamic partitioning, model for lactational transfer of chemicals is that several key parameters change over the course of exposure. For example, Equation 2-38, used to estimate the concentration of a lipophilic chemical in breast mi lk fat, assumes that the mother’s body fat will remain constant over the entire duration of breast feeding (t bf 

### ), which is unlikely to be true (EPA 2001a). Another limitation of the single analytic m odel is that chemical transfer rates from blood to milk are unlikely to be the same as the rate of mobilization of the chemical from fat stores tothe blood (EPA 2001a). Studies cited in ATSDR’s toxicological profile for chlorinated dibenzo-pdioxins show a correlation between percent body fat and the elimination rate of dioxins, with longer half-lives for dioxins in individuals with a higher proportion of fat in their bodies (ATSDR 1998). In the context of a screening model, how ever, EPA recommends a default value for the fraction of a mother’s body comprised of fat of 0.3 based on data and discussions presented by Smith (1987) and Sullivan et al . (1991) (EPA 1998). A fraction of 0.3 indicates that 30 percent of the mother’s body weight is fat, which is a health protective value (EPA 2001a). To establish a health protective screening scenario, the MIRC default value for f fm 

### is 0.30

. 

### Fraction of fat in mother’s breast milk ( f mbm 

### ). The C milkfat 

### model (Equation 2-38) assumes that a constant fraction of breast milk is fat, even though there is evidence that indicates variation in the fat content of breast milk throughout lactation (Sim and McNeil 1992). Different studies suggest a fat content of breast milk in humans of between 1 and 5 percent (Jensen 1987, Schecter et al . 1994, Hong et al . 1994, McLachlan 1993, Bates et al . 1994, NAS 1991, Butte et al . 1984, Maxwell and Burmaster 1993, EPA 2011a, Smith 1987, Sullivan et al . 1991). The MIRC default value for f mbm 

### of 0.04 (i.e., 4 percent) is the value EPA recommended for MPE (EPA 1998).

Fraction of maternal weight that is plasma ( f pm 

### ) . Altmann and Dittmer (1964) estimated that plasma volume for adult women ranged from 37 to 60 mL/kg of body weight and averaged about 45 mL/kg. Ueland (1976) observed that the average plasma volume of women 6 weeks postpartum was 45 mL/kg of body weight. Using a value of 1.026 for the specific gravity of plasma from Conley (1974), EPA estimated a value of 0.046 for the fraction of maternal weight that is plasma (EPA 1998). The MIRC default for f pm 

### therefore is 0.046.

Infant breast milk ingestion rate ( IR milk 

### ) . Milk ingestion rates vary with several factors, including the age and size of the infant and use of other foods such as formula. Based on its review of a several studies, EPA recommended time-weighted average and upper percentile milk ingestion rates for infants that nurse for six and for twelve months (EPA 2011a, Table 15-3). To estimate an “average” value, EPA first estimated study-sample-size weighted average values for 1 through 12 months of age and then developed time-weighted average milk ingestion rates from those (EPA 2011a). EPA estimated an upper percentile (upper bound) value as the mean plus two standard deviations. MIRC converts the inges tion rates measured volumetrically (mL/day) to mass-based estimates (kg/day) assuming the density of human milk to be 1.03 g/mL (reported by NAS 1991 and recommended by EPA 2011a). The resulting values are shown in the first two rows of Exhibit_Add A2-29. The MIRC screening-level default of 980 mL/day is an upper-bound estimate based on a one-year nursing period.

Exhibit_Add A2-29 also includes the recommended values for four non-overlapping age categories from the CSEFH (EPA 2008a, Table 15-1). The values demonstrate that although infants grow substantially from birth to one year of age, the “upper bound” estimates of their milk ingestion rates are very close to 1 liter per day at all stages of development in the first year.TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-94 December 2013 Description of MIRC 

### Exhibit_Add A2-29. Infant Breast Milk Intake Rates

Age Category Average (mL/d) Average (kg/d) “Upper Bound” (mL/d) “Upper Bound” (kg/d) Reference

1 to 6 months 742 0. 764 1,033 1.064 EPA 2011a †

0 to < 12 months 688 0.709 980 a 1.01 a EPA 2011a †

0 to < 1 month 510 0.525 950 0.979 EPA 2008a ††

1 to < 3 months 690 0.711 980 1.01 EPA 2008a †

3 to < 6 months 770 0.793 1,000 1.03 EPA 2008a †

6 to < 12 months 620 0.639 1,000 1.03 EPA 2008a †

a MIRC default; † Based on review of multiple studies; †† Based on a single study



### Duration of breast feeding ( t bf



### ). This parameter is equal to the infant’s exposure duration ( ED ) and the infant’s averaging time ( AT ). In its MPE Methodology, EPA asserts a health protective value for the duration of breast feeding of 1 year (i.e., 365 days) and a central tendency estimate of 6 months (180 days) (EPA 1998). Reviewers of MPE noted that 365 days may be overly health protective, given that only 20 percent of infants are breast fed for 6 months, at which point alternative foods are introduced, at least in addition to breast milk (EPA 2001a). Nonetheless, to establish a health protective screening scenario, the MIRC default for t bf



### is 365 days.

Duration of the mother’s exposure to the chemical of concern prior to nursing ( t pn 

### ). The model shown as Equation 2-38 includes this parameter to reduce the over-estimate of chemical concentration in milk fat that occurs if the m odel is applied to a chemical with a long biological half-life (e.g., many years). The factor is needed for applications of the model to scenarios with a brief exposure duration (e.g., beginning a few months prior to the start of nursing) relative to the chemical half-life. As the duration of an exposure scenario increases to meet and exceed the chemical half-life, however, the overestimate that occurs without this parameter is reduced. For example, assume a chemical biological half-l ife of 8 years and a nursing period of 1 year. If exposure of the mother starts at the beginning of nursing, using Equation 2-38 without the t pn 

### term results in an over-estimate of the concentration of the chemical in breast milk by a factor of 28.1 compared with the prediction using Equation 2-38 with the t pn 

### term (EPA 1998, Table 9-6). However, at longer pre-natal exposures of the mother, the magnitude of the over-estimate is reduced: for a 10-year exposure, the magnitude of the overestimate without the t pn 

### term is 2.28, and for a 30-year exposure, the overestimate is reduced to 1.39.

For purposes of the screening-level assessment, we assume an exposure duration equal to the MIRC default half-life for dioxins, or 10 years. Only 3,285 days of that period are pre-natal (i.e., 3650 minus 365 days, assuming 1 year lactation period). Although longer exposure periods are possible for the screening scenario, there is sufficient uncertainty in the model to merit accepting a health protective bias for this parameter value.


### 6.5.2. Chemical-Specific Parameter Values
The chemical-specific parameters in the breast-m ilk pathway in MIRC are listed in Exhibit_Add A2-30. Note that the parameters for which values are needed are different for the lipophilic chemicals (i.e., dioxins), for which lactational transfer is assumed to occur via milk fat, and inorganic chemicals, for which the transfer is assumed to occur via the aqueous phase of breast milk (i.e., mercury). All dioxin congeners were a ssumed to manifest identical values as TCDD in regard to breast milk-related parameters.TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-95 December 2013 Description of MIRC 

### Exhibit_Add A2-30. Chemical-specific Input Parameter Values for Breast Milk Exposure Pathway

Parameter and Description 2,3,7,8- TCDD MeHg

AE inf Infant absorption efficiency of the chemical by the oral route of exposure (i.e., fraction of ingested chemical that is absorbed by the infant; unitless) 1.0 (default) 1.0 (default)

AE mat Maternal absorption efficiency of the chemical by the oral route of exposure (i.e., fraction of ingested chemical that is absorbed by the mother; unitless) 1.0 (default) 1.0 (default)

f bl Fraction of steady-state total body burden of hydrophilic chemical in mother that is in the mother’s whole blood compartment (unitless) NA 0.059 (Kershaw et al . 1980) a

f f Fraction of steady-state lipophilic chemical body burden in mother that is stored in body fat (unitless) ≥ 0.90 (ATSDR 1992) NA

f pl Fraction of steady-state total hydrophilic chemical body burden in mother that is in the blood plasma compartment (unitless) NA Not yet identified b

h Biological half-life for chemical in non-lactating women (days) 3650 (EPA 1994c) 50 (Sherlock et al

. 1984)
k aq_elac Rate constant for total elimination of hydrophilic chemicals by lactating women (per day) NA = k elim

k elim Rate constant for elimination of chemical for nonlactating women (per day; related to chemical halflife) 1.9E-04 b 1.4E-02 c

k fat_elac Rate constant for total elimination of lipophilic chemicals by lactating women (per day) Est. using Equation 2-41 NA

Pc bm Partition coefficient for hydrophilic chemical between maternal blood plasma and aqueous phase of breast milk (g milk/g plasma; model assumption) NA 1.0 (model assumption)

Pc RBC Partition coefficient for hydrophilic or protein-bound chemical between red blood cells (RBC) and plasma in maternal blood (mL whole blood/mL RBC) NA 40 (Hollins et al

. 1975)

NA = not applicable. ND = not yet determined from literature.

a This value is based on a single-dose study and may not be appropriate for a chronic exposure model.

b An empirical value for this variable is currently missing for application of model.

c This value was calculated fr om biological half-life ( h ) using Equation 2-40. 

### Absorption efficiency of the chemical by the oral route of exposure for the infant ( AE inf



### ). The models included in MIRC assume that the AE inf



### from the lipid phase of breast milk is equal to the AE inf



### from the aqueous phase of the milk. Reviewers of the model stated that this assumption may not be valid and that ideally, the equation DAI inf



### would include variables for the AE inf



### from the breast milk fat and the AE inf



### from the aqueous phase of breast milk (EPA 2001a). However, since the MIRC assumption is that chemicals will partition to either the lipid or aqueous phase of milk, it is not necessary at this time to have multiple AE inf



### values for a given chemical. If data on the AE from the mother or an adult but not for the infant are available, data for the adult may be used for AE inf 

### . Reviewers also recommended that chemic al-specific values come from studies that account for absorption of the chemical fr om milk, because absorption from other matrices (e.g., solid foods) may not be relevant (EPA 2001a) . If chemical-specific data are not available for adults or infants, a health protective default value for AE inf



### for a screening level assessment is 1.0, which assumes 100 percent absorption (EPA 1998).TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-96 December 2013 Description of MIRC 

### The default value for AE inf



### in MIRC for both MeHg and dioxin is 1.0. For ingested lipophilic chemicals, it is reasonable to assume that absorption will be high (EPA 2004c). ATSDR (1998) reported that dioxins are well absorbed by the oral route of exposure, with one human experiment indicating more than 86 percent absorption. It is EPA policy to assume 100 percent absorption for chemicals with reported AEs of 50 percent or higher (EPA 2004c). MeHg also is well absorbed, with measured values as high as 95 percent, and so a value of 100 percent is used in MIRC (EPA 2001b).

Absorption efficiency of the chemical by the oral route of exposure for the mother ( AE mat 

### ) . The default value for both dioxins and MeHg is 1.0, as described in the previous paragraph.

Fraction of total maternal chemical body burden that is in the whole blood ( f bl 

### ). The default value for MeHg in MIRC, 0.059, is from Kershaw et al . (1980), which reported kinetics of blood deposition and clearance of MeHg in humans. Individuals consumed one meal of fish that contained between 18 and 22 μg Hg/kg body weight. The fraction of the dose deposited in the blood volume after mercury was fully distributed in tissues was 5.9 percent or 0.059. This study used a single-dose and thus may not be appropriate for a chronic exposure analysis.

Fraction of total maternal chemical body burden that is in body fat ( f f 

### ). Based on ATSDR’s Toxicological Profile for Selected PCBs (ATSDR 1992) and Sullivan et al . (1991), EPA concluded that the “fraction of ingested contam inant stored in fat may be >90%” for lipophilic chemicals such as PCBs and dioxins (EPA 1998). This statement was interpreted to mean that 90 percent of the maternal body burden of chemical at “steady state” is located in body fat for dioxins at steady state.

Fraction of total maternal chemical body burden that is in blood plasma ( f pl 

### ). For hydrophilic chemicals, this parameter represents the steady-sta te fraction of the total chemical in the body that is circulating in the blood plasma. Values for f pl



### may be available for some chemicals in the scientific literature. No value for this parameter for methyl mercury has been identified from the literature at this time. A value can be calculated using Equation 2-43. However, this equation requires a reliable value for f bl 

### , and the value found for mercury may not be appropriate for a chronic exposure analysis (see above).

Chemical half-life in non-lactating women ( h ) . In general, highly lipophilic chemicals tend to have relatively long biological half-lives. EPA estimates that the half-life for dioxins is between 7 and 10 years (EPA 1994a). ATSDR estimates that the half-life for 2,3,7,8-TCDD in particular may be as long as 12 years (ATSDR 1998). To establish a health protective screening scenario, the MIRC default half-life for dioxins is set to 10 years or 3650 days.

The half-life for methylmercury is on the order of weeks, not years. Greenwood et al . (1978) measured blood clearance rates for MeHg in lactating Iraqi women exposed accidentally to MeHg via bread prepared from wheat treated with a fungicide that contained MeHg. The data indicated a mean half-life for MeHg of approximately 42 days. Sherlock et al . (1984) reported an average measured half-life for MeHg of 50 days with a range of 42-70 days. The MIRC default for MeHg is set to the longer average half-life of 50 days.

Chemical elimination rate constant for lactating women – aqueous ( k aq_elac 

### ). The parameter k

aq_elac 

### is equal to k elim 

### plus the loss rate for the chemical in the aqueous phase of breast-milk during lactation. EPA has yet to propose a term for the additional elimination of a chemical in the aqueous phase of milk from breast feeding. In the absence of empirical values, a reasonable assumption for water soluble chemicals is that k aq_elac 

### is equal to k elim 

### as discussed for Equation 2-43. The extent to which k elim 

### is an underestimate of k aq_elac 

### for a given chemical will determine the extent of health protective bias in k aq_elac



### .TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-97 December 2013 Description of MIRC 

### Chemical elimination rate constant for non-lactating women ( k e lim 

### ). Although values for this parameter often are reported directly in the literature, MIRC estimates k elim 

### from chemical halflife assuming first-order kinetics as shown in E quation 2-40. For example, for a biological halflife of 3,650 days for dioxins, k elim 

### is estimated to be 1.9E-04 per day. Assuming a biological half-life of 50 days for MeHg, the value for k elim 

### is estimated to be 0.014 per day.

Rate constant for total elimination of lipophilic chemicals by lactating women ( k fat_elac 

### ). Although values for this parameter might be found in the scientific literature for some chemicals, in MIRC, k

fat_elac 

### for dioxins is calculated from Equation 2-41. When the parameters in that equation are set to the default values in MIRC for dioxins, MIRC estimates a value of 0.0015 per day for k

fat_elac



### . Partition coefficient for chemical between maternal blood plasma and aqueous phase of breast milk ( Pc bm 

### ). The aqueous model, presented in Equation 2-42, assumes that the concentrations in the plasma and aqueous phase of breast milk are directly proportional (EPA 1998). Therefore, the default value for this parameter for MeHg in MIRC is 1.0.

Partition coefficient for chemical between red blood cells and plasma in maternal blood ( Pc RBC 

### ). Chemical-specific values for this parameter should be located in the scientific literature. If chemical-specific values are unavailable and it is assumed that there is equal distribution of the chemical in the plasma and red blood cells, EPA suggests a default value of 1.0 (EPA 1998). For MeHg, MIRC includes a value of 40 based on Hollins et al . (1975) study of cats exposed to MeHg, which reported a ratio of radio-labeled mercury in red blood cells to plasma of 97.7 to 2.3 (i.e., ratio of 42.5). 

## 7. Summary of MIRC Default Exposure Parameter Settings
The default settings included in MIRC are intended to be characteristic of a health protective (but plausible) exposure scenario that results in a negligible or extremely low chance of underestimating risk. These default parameter values were used to derive the screening threshold emission rates used for screening emissions of PB-HAPs from sources included in RTR risk assessments. These values are the default, or initial setting, for parameter values in MIRC as described in Section 6 of this addendum. This section summarizes the default parameter values used to calculate screening thresholds.

This chapter is organized to present the chemical- and scenario-specific inputs to MIRC by data type. The screening-level analysis uses 90 th 

### percentile ingestion rates for soil, breast milk, and farm food items and 99 th 

### percentile ingestion rates for fish (presented in Section 7.1) and population-specific characteristic assumptions (presented in Section 7.2), that are generally health protective in nature. Screening thresholds were derived for five RTR chemical species: benzo(a)pyrene, cadmium, mercuric chloride, me thyl mercury, and 2,3,7,8-TCDD; Section 7.3 presents chemical-specific parameter inputs for these five chemicals. Finally, Section 7.4 presents default parameter values for the nursing infant exposure scenario, which applied only to dioxin and methyl mercury as discussed in Section 3.4.


### 7.1. Default Ingestion Rates
The screening-level (or default) values for ingestion rates for soil, breast milk, and for each farm food item are equal to the 90 th 

### percentile of the distribution of national data for that ingestion medium. In general, these values were obtained from the 2011 Exposure Factors Handbook or the 2008 Child-Specific Exposure Factors Handbook (see Exhibit_Add A2-31). Fish ingestion rates are also available from these sources; however, as described in Section 6.3.4, these sources were not used to obtain fish ingestion rates.TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-98 December 2013 Description of MIRC 

### 7.1.1. Fish Ingestion Rates
The adult fish ingestion rate was obtained from Burger (2002), a study that examined daily consumption of wild-caught fish for high-end recreationalists (white, black and female) in South Carolina. For female high-end consumers of wild-caught fish, Burger identified average and higher-percentile consumption rates as follows: 39.1 g/day (mean), 123 g/day (90 th 

### percentile), 172 g/day (95 th 

### percentile), and 373 g/day (99 th 

### percentile). As shown in Exhibit_Add A2-31 and discussed in Section 6.3.4, for adults, the rate of fish ingestion assumed in the screening scenario is 373 g/day, which corresponds to the 99 th 

### percentile value estimated by Burger for adult females. This value was selected to be representative of subsistence fishers.

For the child age groups, as discussed in Section 6.3.4, the baseline fish ingestion rates for the screening scenario are based on “as prepared” total freshwater/estuarine fish ingestion rates at the 99 th 

### percentile of the distribution for the consumer-only population (i.e., inclusive only ofpeople who consume fish, rather than per-captita rates, which include both consumers and nonconsumers), as estimated in EPA (2002), Section 4.2.1.1. Some adjustments were necessary because the age groups evaluated for RTR (which correspond to the age groups for which farm food ingestion rates are available) do not all di rectly correspond to t he age groups in the EPA(2002) report. As described in Section 6.3.4, these adjustments convert the available agespecific data on fish ingestion rates to the age-specific values needed for MIRC.

For the screening-level fish ingestion exposure scenario, the consumer evaluated is an individual who regularly consumes a large amount of fish that he or she has caught locally over the course of a 70-year lifetime. Modeled exposures are intended to encompass those of a subsistance fisher whose diet comprises a substantial proportion of fish. The scenario is not, however, intended to represent the maximum possibl e exposure an individual subsistence fisher might experience.

Although the fish ingestion rates presented here are representative of the 99 th 

### percentile of the evaluated data set, the use of these inputs (compared with 90 th 

### percentile values used for other food types) is not considered to be inconsistent. This is due to the idiosyncrasies of the survey data on fish consumption, the fact that the data sets for homegrown foods and fish are not parallel, and the consideration of rates appropriate for subsistence fishers, as described above.

As discussed above, EPA believes that use of these fish ingestion rates strikes the appropriate balance between being health protective and having screening scenarios so conservative that they are of limited use in the decision making process. This high-end fish ingestion rate is appropriate in the context of the conservative screening scenario used in the RTR process and is applicable for national rulemakings given that it is very likely that subsistence woman anglers of child bearing age are located throughout the United States. Using a high-end subsistence fish ingestion rate also is consistent with section 112 of the CAA, which focuses on risks associated with maximally exposed individuals.


### 7.1.2. Farm Food Chain Ingestion
The default settings assume that all food types are obtained from the area of chemical deposition specified by the user (i.e., fraction of food from contaminated area = 1.0).

For estimates of screening threshold emission rates for PB-HAPS, environmental concentrations and air deposition rates were estimated using TRIM.FaTE for the area of maximal deposition in the vicinity of a hypothetical facility, and thus represent risks estimated for a maximally exposed individual/farm/family.TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-99 December 2013 Description of MIRC 

### Exhibit_Add A2-31 also includes a sum of the 90th percentile ingestion rates for homegrown food categories and 99th percentile fish ingestion to show the implied total food ingestion rate associated with setting multiple food-type-specific ingestion rates at upper percentiles. Because these upper percentile values for each farm food category are likely to reflect different individuals, it is likely that addition of multiple upper percentile intake values will exceed the total food ingestion rates expected for the general population. This sum is shown on the third row from the bottom (Total Food: Homegrown Only).

The second row from the bottom presents the 90 th 

### percentile of the distribution of individual total food ingestion rates from the USDA’s 1994-96 and 1998 Continuing Survey of Food Intakes by Individuals (CSFII) (USDA 2000) data sets, as analyzed by EPA (EPA 2005e). The total ingestion rate for the farming households (third row from bottom) takes into account the cooking losses typical of each food category to provide a better comparison with the 90 th 

### percentile individual total food ingestion rates from CSFII (which are based on consumption of prepared foods). The final row of Exhibit_Add A2-31 s hows the likely magnitude of the overestimates by age category by presenting the ratio of the two preceding rows. The values in this row demonstrate the potential for overestimating intake by using upper percentile values for all food groups. This bias may be considered when ev aluating the results estimated by MIRC.

Exhibit_Add A2-31. Farm Food Category Ingestion Rates for Health Protective Screening Scenario for Farming Households

Product Screening-Level Consumer Ingestion Rate Units Infants

<1 yr Child 1–2 yrs Child 3–5 yrs Child 6–11 yrs Child 12–19 yrs Adult 20–70 yrs

Farm Food Item

Beef a NA 9.49 8.83 11.4 3.53 4.41 g/kg-day Dairy b NA 185 92.5 57.4 30.9 6.16 g/kg-day Eggs a NA 4.90 3.06 1.90 1.30 1.31 g/kg-day Exposed Fruit a NA 12.7 5.41 6.98 3.41 2.37 g/kg-day Exposed Vegetable a NA 10.7 3.47 3.22 2.35 3.09 g/kg-day
Pork a NA 4.90 4.83 3.72 3.69 2.23 g/kg-day Poultry a NA 7.17 6.52 4.51 3.13 2.69 g/kg-day
Protected Fruit a NA 44.8 32.0 23.3 7.44 15.1 g/kg-day Protected Vegetable a NA 3.88 2.51 2.14 1.85 1.81 g/kg-day Root Vegetable a NA 7.25 4.26 3.83 2.26 2.49 g/kg-day

Other

Breast milk c 1.01 NA NA NA NA NA kg/day Soil (dry) NA 200 d 200 d 201 e 201 e 201 e mg/day Fish (per individual) f NA 107.7 g 159.0 g 268.2 h 331.0 h 373 g/dayTRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-100 December 2013 Description of MIRC 

### Exhibit_Add A2-31. Farm Food Category Ingestion Rates for Health Protective Screening Scenario for Farming Households

Product Screening-Level Consumer Ingestion Rate Units Infants

<1 yr Child 1–2 yrs Child 3–5 yrs Child 6–11 yrs Child 12–19 yrs Adult 20–70 yrs Total Food Ingestion Rates for Comparison Only (not in MIRC; excludes soil and water) Total Food: Homegrown only i NA 259 142 99 51 35.5 g/kg-day

Total Food: All Sources j NA 125 91 61 34 23.7 g/kg-day Overestimate (ratio of Homegrown/Total) NA 2.1 1.6 1.6 1.5 1.3 (unitless)

Sources: EPA 2011a, EPA 2008a, unless otherwise noted. NA = not applicable

a Primary source for values was the 1987–1988 NFCS survey; comp iled results are presented in Chapter 13 of the 2011 Exposure Factors Handbook (EPA, 2011a). When data were unavailable for a particular age group, the intake rate for all age groups was multiplied by the age-specific ratio of intake based on national population intake rates from CSFII.

b Primary source for values was 1987–1988 NFCS survey, compiled resu lts are presented in Chapter 13 of the 2011 Exposure Factors Handbook (EPA, 2011a). When data were unavailable for a particul ar age group, the intake rate for all age groups was multiplie d by the age-specific ratio of intake based on nati onal population intake rates from an NHANES 2003–2006 analysis in Chapter 11 of the Exposure Factors Handbook.

c Infants are assumed to consume only breast milk for one year.

d These values are the recommended “upper percentile” value for children from EPA’s 2011 EFH, Chapter 4, Table 4-23. The 2008 CSEFH and 2011 EFH included a high-end value associated wi th pica only, but this value has not been used.

e These values are 90th percentile adult ingesti on rates calculated in Stanek et al. 1997, and they are used to represent older c hildren and adults.

f The ingestion rate for adults was obtained from Burger (2002) and is the 99 th percentile value for adult females considered high-end recreationists; this value is believed to be re presentative of subsiste nce fishers. The 99 th percentile values for children were derived based on EPA’s Estimated Per Capita Fish Cons umption in the United States (2002)—Section 4.2.1.1 Table 5 (for child age categor ies) adjusted and scaled. Values reflec t “as prepared” ingestion rates.

g The fish ingestion rate for children aged 3–5 years was obtained directly from Section 4.2.1.1, Table 5 in the EPA (2002) report (value presented is rounded); for these children, the RTR age-group ran ge matches the EPA (2002) age cat egory. Fish ingestion rates fo r children less than 3 years old, however, were not provided. Th erefore, for children aged 1–2 years, the fish ingestion rate was calculated using the ingestion rate for children aged 3–5 years scaled downwar d by the ratio of the mean body weight of children aged 1–2 years to the mean body weight of children aged 3–5-years.

h Time-weighted average ingestion rates were ca lculated using the EPA (2002) fish ingestion estimates in order to adjust for the differences between the age group ranges used for the RT R screening and those presented in the 2002 EPA report.

i Sum of post-cooking food ingestion rates. This estimate is calculated by multiplying the food ingestion rates on previous rows (excluding soil and water) by (1-L 1 )x(1-L 2 ), where L 1 and L 2 are the loss rates from Exhibit_Add A2-25. The rows are then summed to get the total post-cooking ingestion rate. j 90 th percentile total food intake rates from EPA 2008a and 2005e based on CSFII data 1994-96 and 1998; see Section 6.3.6 of this document.



### 7.2. Default Screening-Level Population-Specific Parameter Values
The screening-level values for body weights (BWs) for the RTR screening threshold analysis, which serve as the default values in MIRC, are mean values and are presented in Exhibit_Add A2-32. As stated in Section 6 of this addendum, EPA recommends using the mean BW for each age group when using upper percentile values for medium ingestion rates. Use of the mean body weights introduces no bias toward over- or underestimating risk.

Exhibit_Add A2-32. Mean Body Weight Estimates for Adults and Children a

Lifestage (years) Duration (years) Mean Body Weight (kg)
Adult b (20-70) 50 80.0
Child < 1 c 1 7.83
Child 1-2 c 2 12.6
Child 3-5 d 3 18.6TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-101 December 2013 Description of MIRC 

### Exhibit_Add A2-32. Mean Body Weight Estimates for Adults and Children a

Lifestage (years) Duration (years) Mean Body Weight (kg)
Child 6-11 e 6 36.0
Child 12-19 f 8 64.2

a Sources: EPA 1997, 2008a

b EPA-recommended value (EPA 2011a).

d These values were obtained directly from Table 8-3 of the 2008 CSEFH.

e Each BW represents a time-weighted average of BWs for age groups 6 to <11 years and 11 to <16 years from Table 8-3 of the 2008 CSEFH. Original sample sizes for each of these age groups can also be found in Table 8-3.

f These values were calculated as time-wei ghted average BW for age groups 11 to <16 years and 16 to <21 years from Table 8-3 of the 2008 CSEFH. The direction of the po ssible bias is unknown. The values match the estimate based on Table 8-22 of the NHANES IV data as presented by Portier et al. (2007).



### 7.3. Default Chemical-Specific Parame ter Values for Screening Analysis
Exhibit_Add A2-33 presents chemical-specific parameter values for input to MIRC for the screening-level analysis. Values for bioavailability when ingested in soil ( Bs ), mammalian metabolism factors ( MF ), correction factors for belowground produce ( VG rootveg 

### ), wet deposition fractions ( Fw ), air to plant transfer factors ( Bv AG 

### ), root concentration factors ( RCF ), and soilwater partition coefficient ( Kds ) are presented in Exhibit_Add A2-33.

Exhibit_Add A2-33. Chemical-Specific Parameter Values for Input to MIRC a

Parameter Description Benzo(a)- pyrene Cadmium Mercuric chloride Methyl mercury 2,3,7,8- TCDD Units

Bs Soil bioavailability factor for livestock 1 1 1 1 1 unitless

MF Mammalian metabolism factor 0.01 1 1 1 1 unitless

VGrootveg Empirical correction factor for belowground produce, i.e., tuber or root vegetable, to account for possible overestimate of the transfer of chemicals from the outside to the inside of bulky tubers or roots (based on carrots and potatoes) 0.01 1 1 0.01 0.01 unitless

Fw Fraction of wet deposition that adheres to plant surfaces; 0.2 for anions, 0.6 for cations and most organics 0.6 0.6 0.6 0.6 0.6 unitlessTRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-102 December 2013 Description of MIRC 

### Exhibit_Add A2-33. Chemical-Specific Parameter Values for Input to MIRC a

Parameter Description Benzo(a)- pyrene Cadmium Mercuric chloride Methyl mercury 2,3,7,8- TCDD Units

BvAG Air-to-plant biotransfer factor for abovegroundproduce for vaporphase chemical in air 124,742 0 1,800 0 65,500 [mg/g produce DW]/[mg/g air]

RCF Chemical-specific root concentration factor for tubers and root produce 9,684 0 0 0 39,999 L soil pore water/kg root WW

Kds Chemical-specific soil/water partition coefficient 160,000 75 58,000 7,000 38,904. 51 L soil pore water/kg soil DW

a Values presented in this exhibit are also presented in previous exhi bits; however exact values us ed in the analysis are present ed here, rather than values restricted by signi ficant figures. In addition, only values for those chem icals that are specifically used in the screening analysis are provided here.



### Only single estimates were developed for eac h of these parameters for HHRAP (EPA 2005a), and the potential direction and magnitude of bias toward over- or underestimating risks were not investigated in this analysis. The inputs that are both chemical-specific and plant-type-specific, as presented in Exhibit_Add A2-11, are not repeated here. Again, only single estimates weredeveloped for these parameters and the potential direction and magnitude of bias toward overor underestimating risks were not investigated. Finally, Exhibit_Add A2-34 presents biotransfer factors for each of the chemicals and animal types for which screening threshold emissions were calculated.

Exhibit_Add A2-34. Chemical and Animal-Type Specific Biotransfer Factor (Ba) Values for Input to MIRC
([mg chemical/kg WW tissue or dairy] / [mg ch emical intake/day] = day/kg WW tissue or dairy)
Chemical Beef Dairy Pork Eggs Poultry Benzo(a)pyrene 3.8E-02 8.0E -03 4.6E-02 1.6E-02 2.8E-02
Cadmium 1.2E-04 6.5E-06 1.9E-04 2.5E-03 1.1E-01
Mercuric chloride 1.1E-04 1. 4E-06 3.4E-05 2.4E-02 2.4E-02 Methyl mercury 1.2E-03 1.7E -05 5.1E-06 3.6E-03 3.6E-03 2,3,7,8-TCDD 3.6E-02 7.7E-0 3 4.4E-02 1.5E-02 2.7E-02



### 7.4. Screening-Level Parameter Values for Nursing Infant Exposure
MIRC is configured to evaluate risk to nursing infants exposed to dioxins and to methylmercury (MeHg) in their mother’s milk for a family farm ing and catching fish in the area of maximal air deposition of chemical. Input values were summarized in Section 6.5 of this addendum.



### 7.4.1. Dioxins
For dioxins, chemical intake via breast milk by nursing infants was estimated using the model presented in EPA’s Methodology for Assessing Health Risks Associated with Multiple Pathways of Exposure to Combustor Emissions (MPE, EPA 1998). The assumption that lactational transfer of dioxins to the infant occurs via the lipid-phase of milk appears reasonable. TheTRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-103 December 2013 Description of MIRC

### following screening-level assumptions used in that model should bias the results toward healthprotective estimates of risks.

 

### Duration of nursing is a full year and no other foods or liquids are consumed by the infant; a more “typical” value would be six months.

 

### Absorption efficiency of dioxin in food or milk by mother and infant are 100 percent; this assumption might overestimate absorption but probably by no more than 15 percent (see Section 6.5.2).

 

### The fat content of human milk is assumed to be 4 percent, a value toward the high end of the reported range of values (1 to 5 percent).

 

### The maternal chemical intake is estimated using upper percentile ingestion rates for the different homegrown foods (see discussion fo r Exhibit_Add A2-31); this assumption might overestimate total ingestion of homegrown foods by a factor of more than 2 (see Exhibit_Add A2-31).

 

### If the fraction of the maternal body burden of dioxin that is in the body fat compartment is greater than 90 percent, as suggested by ATSDR (1998), then actual exposures of the infant may be less than estimated.

There also are parameter values and model assumptions for the lipid-phase breast-milk pathway for which possible bias is unknown.

 

### The accuracy of the model is unknown; it has not been verified or validated with empirical data.

 

### Using a half-life of 10 years for dioxins may over- or under-estimate risks.

Finally, there is one assumption that might possi bly introduce some bias toward underestimating risks. The model results are sensitive to the biological half-life of the chemical in the mother relative to the length of her exposure prior to the lactation period. Using an exposure duration for the mother equal to the assumed half-life for dioxins, 10 years, may underestimate the duration of exposure of the mother.


### 7.4.2. Methyl Mercury
For MeHg, empirical data from a single human study (Fujita and Takabatake 1977) was used in conjunction with a physiologically based pharmacoki netic (PBPK) model of lactational transfer of MeHg developed and partially validated by Byczkow ski and Lipscomb (2001) to support a very simple predictive model. Both the human data and the PBPK model indicated that for relatively low MeHg exposures, the concentration of MeHg in the nursing infant’s blood is similar to its concentration in the mother’s blood. The PBPK model suggested in addition that the average daily dose of MeHg absorbed from milk by the nursing infant ( DAI inf



### ) is indistinguishable from the dose of MeHg absorbed by its mother from her food ( DAI mat 

### ). The data are limited, and the model includes various assumptions; however, there is no known directional bias in the estimates.TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-104 December 2013 Description of MIRC 

## 8. References
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Attachment A, Addendum 2 2-107 December 2013 Description of MIRC 

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### USDA. 1993. Food and Nutrient Intakes by Individuals in the United States, 1 Day, 1987-88. Nationwide Food Consumption Survey 1987-88, NFCS Report No. 87-I-1. (As cited in EPA 1997)

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### EPA. 1996. Soil Screening Guidance: User’s Gu ide. Office of Solid Waste and Emergency Response, Washington D.C. EPA/540/R-96/018, April 1996.

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Attachment A, Addendum 2 2-110 December 2013 Description of MIRC 

### 00-001. October. Available at: http://www.epa.gov/waterscience/crit eria/drinking/percapita/2004.pdf

EPA. 2004c. Risk Assessment Guidance for Superfund. Volume I: Human Health Evaluation Manual (Part E, Supplemental Guidance for De rmal Risk Assessment), Final. Office of Superfund Remediation and Technology Innovation, Washington, D.C. EPA/540/R/99/005; OSWER 9285.7-02EP; NTIS PB99-963312. July.

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EPA. 2005b. Guidance on Selecting Age Groups for Monitoring and Assessing Childhood Exposures to Environmental Contaminants. Risk Assessment Forum. Washington, DC. November. EPA/630/P-03/003F. Available at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=146583.

EPA. 2005c. Guidelines for Carcinogen Risk Assessment. Risk Assessment Forum, Washington, DC. EPA/630/P-03/001F. March. Available from: http://www.epa.gov/IRIS/cancer032505-final.pdf.

EPA. 2005d. Supplemental Guidance for Assessing Susceptibility from Early-Life Exposure to Carcinogens. Risk Assessment Forum: Washington, D.C. EPA-630/R-03-003F. March. Available at: http://www.epa.gov/ttn/atw /childrens_supplement_final.pdf.

EPA. 2005e. Analysis of Total Food Intake and Co mposition of Individual's Diet Based on the U.S. Department of Agriculture's 1994-96, 1998 Continuing Survey of Food Intakes By Individuals (CSFII) (Final). Office of Research and Development, National Center for Environmental Assessment, Washington, D.C. EPA/600/R-05/062F. Available at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=132173.

EPA. 2007a. Prioritized Chronic Dose-Response Values for Screening Risk Assessments (Table 1). Office of Air Quality Planning and Standards; June 12, 2007. Available at: http://www.epa.gov/ttn/atw/toxsource/summary.html .

EPA. 2007b. Toxicological Review of 1,1,1-Trichloroethane (CAS No. 71-55-6) In Support of Summary Information on the Integrated Risk Information System (IRIS). Office of Research and Development, Washington, DC. EPA/635/R-03/006. August. Available at: http://www.epa.gov/iris

EPA. 2008a. Child-Specific Exposure Factors Handbook. Office of Research and Development, Washington, D.C. EPA/600/R-06/096F. September. Available at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=199243 .

EPA. 2008b. Draft Report on EPA OAQPS Risk and Technology Review Methodologies: For Review by the EPA Science Advisory Board; Case Studies – MACT I Petroleum Refining Sources, Portland Cement Manufacturing. Office of Air Quality Planning and Standards, Office of Air and Radiation, Research Triangle Park, NC. July 14, 2008.

EPA. 2011a. Exposure Factors Handbook: 2011 Edition. Office of Research and Development, Washington, D.C. EPA/600/R-090/052F. September. Available at: http://cfpub.epa.gov/ncea/risk/recordisplay.cfm?deid=236252

.TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-111 December 2013 Description of MIRC 

### EPA. 2011b. Revised Technical Support Document: National-Scale Assessment of Mercury Risk to Populations with High Consumption of Self-caught Freshwater Fish; In Support of the Appropriate and Necessary Finding for Coal- and Oil-Fired Electric Generating Units. Office of Air Quality Planning and Standards, Research Triangle Park, NC. EPA-452/R-11- 009. December.

Van den Berg, M., L.S. Birnbaum, M. Denison, M. De vito, W. Farlans, M. Feeley, H. Fiedler, H. Hakansson, A. Hanberg, L.. Haws, M. Rose, S. Safe, D. Schrenk, C. Tohyama, A. Tritscher, J. tuomisto, M. Tysklind, N. Walker, and R.E. Peterson. 2006. The 2005 World Health Organization reevaluation of human and mammalian toxic equivalency factors for dioxins and dioxin-like compounds. Toxicol Sci. 93(2): 223-41.

Zabik, ME; Zabik, MJ. 1995. Tetra-chlorodibenzo-p-dioxin residue reduction by cooking/processing of fish fillets harvested from the Great Lakes. Bulletin of Environmental Contamination and Toxicology. 55:264-269.[This page intentionally left blank.]TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 3 3-1 December 2013 Dermal Risk Screening 

## Addendum 3. Dermal Risk Screening[This page intentionally left blank.]TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 3 3-3 December 2013 Dermal Risk Screening 

## CONTENTS, ADDENDUM 3

Addendum 3. Dermal Risk Screening .............................................................................. 3-1

1. Hazard Identification and Dose Response Assessment .................................................... 3-5

2. Dermal Exposure Estimation ............................................................................................. 3-6

2.1. Equations for Estimating Dermal Exposure .............................................................. 3-6

2.2. Exposure Factors and Assumptions ......................................................................... 3-7

2.3. Receptor-Specific Parameters ................................................................................. 3-8

2.4. Scenario-Specific Parameters .................................................................................. 3-8

2.5. Chemical-Specific Parameters ................................................................................. 3-9

3. Screening-Level Cancer Risks and Non-Cancer Hazard Quotients ................................ 3-10

3.1. Dermal Cancer Risk ............................................................................................... 3-10

3.2. Dermal Hazard Quotient ......................................................................................... 3-11

4. Dermal Screening Results ............................................................................................... 3-11

5. References .................................................................................................................... .. 3-14TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 3 3-4 December 2013 Dermal Risk Screening 

### Exhibits, Addendum 3



### Exhibit_Add A3-1. Cancer Slope Factors and Reference Doses Based on Absorbed Dose .................................................................................................... 3-6 Exhibit_Add A3-2. Receptor-Specific Body Surface Area Assumed to be Exposed to Chemicals ......................................................................................... 3-8 Exhibit_Add A3-3. Scenario-Specific Exposure Values for Water and Soil Contact ................................................................................................................ 3-9 Exhibit_Add A3-4. Chemical-Specific Dermal Exposure Values for Water and Soil Contact ....................................................................................................... 3-10 Exhibit_Add A3-5. Summary of Dermal Non-Cancer Hazards ............................................... 3-12 Exhibit_Add A3-6. Summary of Dermal Cancer Risks ............................................................ 3-13TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 3 3-5 December 2013 Dermal Risk Screening 

### Non-inhalation exposure to PB-HAPs can occur by way of the dermal pathway through contact with PB-HAP-contaminated soil and water. However, dermal absorption of chemicals that are originally airborne is generally a relatively minor pathway of exposure compared to other exposure pathways (EPA 2006, Cal/EPA 2000). This section demonstrates that for the conservative tierd screening scenario developed fo r RTR multipathway evaluation, the dermal exposure route is not a significant risk pathway when compared to the ingestion pathway. In general, the RTR multipathway assessment followed the protocol for evaluating a reasonable maximum exposure as described in EPA’s Risk Assessment Guidance for Superfund (RAGS), Volume I: Human Health Evaluation Model, Part E, Supplemental Guidance for Dermal Risk Assessment (EPA 2004).



## 1. Hazard Identification and Dose Response Assessment
To assess the potential contribution of dermal exposure to non-inhalation exposure, we evaluated the potential for cancer and chronic non-cancer effects for the four PB-HAPs currently assessed in the multipathway screening evaluation for RTR: cadmium, divalent mercury, 2,3,7,8-TCDD, and benzo(a)pyrene. EPA has not developed carcinogenic potency slope factors (CSFs) and non-cancer reference doses (RfDs) specifically for evaluating potential human health concerns associated with dermal ex posure to PB-HAPs. Instead, dermal toxicity values can be derived from oral toxicity values via route-to-route extrapolation by adjusting for gastrointestinal (GI) absorption. EPA reco mmends making this adjustment only when GI absorption of the chemical is significantly less than 100% (i.e., less than 50 percent). Otherwise, a default value of complete (100 percent) oral absorption is assumed, and no adjustment is made (EPA 2004).

The absorbed cancer slope factor (CSF ABS 

### ) is based on the oral cancer slope factor (CSF O 

### ) and the fraction of the contaminant absorbed in the gastrointestinal track (ABS GI 

### ), as follows:

where:

CSF ABS = Absorbed slope factor (mg/kg-day) -1

CSF o = Oral slope factor (mg/kg-day) -1

ABS GI = Fraction of chemical absorbed in gastrointestinal tract (unitless)



### The absorbed reference dose (RfD ABS 

### ) is based on the oral reference dose (RFD O 

### ) and the fraction of the contaminant absorbed in the gastrointestinal tract (ABS GI 

### ), as shown below.

where:

RfD ABS = Absorbed reference dose (mg/kg-day)

RfD o = Oral reference dose (mg/kg-day)

ABS GI = Fraction of chemical absorbed in gastrointestinal tract (unitless) GI o ABS 

### ABS CSF CSF 

GI o ABS 

### ABS RfD RfD  TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 3 3-6 December 2013 Dermal Risk Screening 

### The GI absorptions for 2,3,7,8-TCDD and all pol ycyclic aromatic hydrocarbons (PAHs) (which includes benzo[a]pyrene) were estimated to be greater than 50 percent. Therefore, as shown in Exhibit_Add A3-1, no adjustments to the available oral CSFs were required. For cadmium and divalent mercury, adjustments were made based on absorption data provided in RAGS Part E, Exhibit 4-1. The RfDs for dermal exposure to cadmium and divalent mercury are also shown in Exhibit_Add A3-1.

Exhibit_Add A3-1. Cancer Slope Factors and Reference Doses Based on Absorbed Dose

PB-HAP Fraction of Contaminant Absorbed in GI Tract (ABS GI ) (unitless) Absorbed Cancer Slope Factor (CSF ABS ) a (mg/kg-day) -1 Absorbed Reference Dose (RfD ABS ) a

(mg/kg-day)
Cadmium Compounds 0.05 NA 2.5E-05

Divalent Mercury 0.07 NA 2.1E-05

2,3,7,8-TCDD No adjustment required b 1.5E+05 1.0E-09

Benzo[a]pyrene No adjustment required b 1.0E+01 NA

NA = Not applicable

a Oral dose response values are presented in Appendix 2. Only the resulting adjust ed dose response values are presented in this table.

b According to RAGS Part E, Exhibit 4-1, GI absorption is expected to be greater than 50%.


## 2. Dermal Exposure Estimation
Dermal exposures and risks resulting from absorption of the chemical through the skin from contact with contaminated water and soil were evaluated for the RTR screening scenario. Individuals were assumed to be exposed on a fraction of their bodies (i.e., their head, forearms, hands, lower legs, and feet) to contaminated soil from the TRIM.FaTE surface soil parcel with the highest concentration (N1) on a daily basis. For the water evaluation, individuals were assumed to be exposed to contaminated surface water with the same PB-HAP concentration as the TRIM.FaTE screening scenario lake over their entire bodies on a daily basis.



### 2.1. Equations for Estimating Dermal Exposure
The general equation used to estimate dermal absorbed dose (DAD) for water or soil is shown below, and is expressed in milligrams of PB-HAP per kilogram of receptor body weight per day (mg/kg-day). DADs are calculated separately for the water and soil pathways and then added together for each age group.

where:

DA event = Absorbed dose per event; chemical-specific; equation for DA event also differs depending on water or soil contact (mg/cm 2 -event)

EV = Event frequency (events/day)

ED = Exposure duration (years) 

### ATBW SAEFEDEV DA DAD event



###     

TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 3 3-7 December 2013 Dermal Risk Screening EF = Exposure frequency (days/year)

SA = Skin surface area available for contact (cm 2 )

BW = Body weight (kg)

AT = Averaging time; for non-cancer effects, equals ED x 365 days/year; for cancer effects, equals 70 years x 365 days/year (days)



### DA event 

### is estimated to be the total dose absorbed through the skin at the end of exposure and the equation for calculation is different for organic and inorganic chemicals in water and for soil. The equations for calculating these chemical-specific DA event 

### values for water contact are provided in RAGS Part E, Chapter 3 (see Equations 3.2 – 3.4). For soil, the equation for calculating these chemical-specific DA event 

### values is provided in RAGS Part E, Chapter 3 (see Equation 3.12).

Water – Organic Chemicals:

Water – Inorganic Chemicals:

Soil – All Chemicals:



### where:

DA event = Absorbed dose per event (mg/cm 2 -event)
C w C s = Chemical concentration in water (mg/cm 3 ) or soil (mg/kg)

K

p = Chemical-specific dermal permeability coefficient of compound in water (cm/hr)

FA = Chemical-specific fraction absorbed; accounts for loss due to the regular shedding of skin cells of some chemical originally dissolved in to skin (unitless)



# τ

event = Chemical-specific lag time per event (hr/event)

t event = Receptor-specific event duration (hr/event)

AF = Receptor- and activity-specific adherence factor of soil to skin (mg/cm 2 -event)

ABS = Chemical-specific dermal absorption fraction (unitless)

CF = Conversion factor (10 -6 kg/mg)



### 2.2. Exposure Factors and Assumptions
The exposure parameters included in this assess ment and their default and other value options are summarized in this subsection. Default values were selected to result in a highly conservative estimate of exposure (i.e., exposures are likely overestimated). Parameter values were primarily obtained or estimated from RAGS Part E (EPA 2004) and the Child-Specific Exposure Factors Handbook (CSEFH, EPA 2008). Receptor-and scenario-specific exposure assumptions are discussed first, and a discussi on of chemical-specific parameters values follows. Estimated water and soil exposure concentrations are presented at the end of this subsection. 

###   event

event p w event 

### t6 KFA2CDA  

event pw event 

### tKCDA   



### CFABSAFCDA s event 

###    TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 3 3-8 December 2013 Dermal Risk Screening



### 2.3. Receptor-Specific Parameters
Dermal exposures and risks were estimated for the same age groups used in the ingestion exposure assessment: adults (ages 20 to 70 years) and five child age groups: <1 year; 1 to 2 years; 3 to 5 years; 6 to 11 years; and 12 to 19 years. The body weight values used in the ingestion exposure assessment were used in the dermal exposure assessment.

Body surface areas (SAs) for water and soil exposures for adults were calculated using Appendix C, Exhibit C-1, of RAGS Part E. For children, SAs for water and soil exposures for the five children’s age groups were estimated using Tables 7-1 and 7-2 of the CSEFH, respectively. For SA (water), individuals were assumed to shower or bathe in the water with 100 percent of their body exposed. For SA (soil), it was assumed that individuals were exposed on a fraction of their total body, specifically their head, forearms, hands, lower legs, and feet. Based on information provided in RAGS Part E, the SA for forearms was calculated using the SA for arms and assuming a forearm-to-arm ratio of 0.45, and the SA for lower legs was estimated using the SA for legs and assuming a lower leg-to-leg ratio of 0.4.

Values for body SA by age group are summarized in Exhibit_Add A3-2.

Exhibit_Add A3-2. Receptor-Specific Body Surface Area Assumed to be Exposed to Chemicals

Age Group a (years) Surface Area for Water Exposure (cm 2 ) Surface Area for Soil Exposure (cm 2 )
Adult 20-70 18,150 g 6,878 h

Child <1 b 3,992 1,772
Child 1-2 c 5,700 2,405
Child 3-5 d 7,600 3,354
Child 6-11 e 10,800 4,501
Child 12-19 f 17,150 6,906

a Sources for the child groups included Table 7-1 (tot al body surface area for SA-Water), and Table 7-2 (fraction of total body surface area for SA-Soil) of the 2008 CSEFH.

b Represents a time-weighted average for age groups birth to <1 month, 1 to <3 months, 3 to <6 months, and 6 to <12 months.

c Represents a time-weighted average for age groups 1 to <2 years and 2 to <3 years.

d Values for age group 3 to <6 years in the 2008 CSEFH.

e Values for age group 6 to <11 years in the 2008 CSEFH. Represents a conservative (i.e., slightly low) estimate for ages 6 through 11 years since 11-year olds are not included in this CSEFH age group.

f Represents a time-weighted average for age groups 11 to <16 years and 16 to <21 years. Note that estimated values include 11-year-olds and individuals th rough age 20, which contributes to uncertainty in the estimates for 12 to 19 years.

g Represents the average total surface area of ad ults from Table C-1 of RAGS Part E.

h Represents the average surface area of adults for head, forearms, hands, lower legs, and feet from Table C-1 of RAGS Part E.



### 2.4. Scenario-Specific Parameters
Exhibit_Add A3-3 summarizes the exposure values related to frequency and duration of contact. In general, these are the recommended defaults for calculating a reasonable maximum exposure (RME) for a residential scenario as pro posed by EPA in RAGS Part E, Chapter 3.TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 3 3-9 December 2013 Dermal Risk Screening 

### Exhibit_Add A3-3. Scenario-Specific Exposure Values for Water and Soil Contact

Exposure Parameter Receptor Value Source
Water Contact Event Duration (t event ) (hr/event) Child 1 Reasonable maximum exposure scenario for showering/bathing from RAGS Part E, Exhibit 3-2 Adult 0.58

Soil Contact Soil Adherence Factor (AF) (mg/cm 2 ) Child 0.2 For children, value is geometric mean value for children playing (wet soil) and for adults, value is geometric mean value for an adult farmer from RAGS Part E, Exhibit 3-3 Adult 0.1

Both Media Event Frequency (EV) (events/day) All 1 Reasonable maximum exposure scenario from RAGS Part E, Exhibits 3-2 and 3-5. Exposure Frequency (EF) (days/year) All 350

Exposure Duration (ED) (years) Child <1 1 Represents the number of years included in the age group; also used in ingestion exposure calculations. Child 1-2 2
Child 3-5 3
Child 6-11 6
Child 12-19 8
Adult 20-70 50 Averaging Time (AT) (days) For cancer assessm ent, an AT equal to a lifetime (70 years) × 365 days/year is used. Same value used in ingestion exposure calculations. For non-cancer assessment, an AT equal to the exposure duration (ED) × 365 days/year is used, so AT will va ry by receptor group. Same value used in ingestion exposure calculations.



### 2.5. Chemical-Specific Parameters
The chemical-specific parameters required to quantitatively evaluate dermal pathway exposures are listed in Exhibit_Add A3-4. For the water concentration in the dermal analysis, the modeled TRIM.FaTE chemical concentration in the screening scenario pond at the screening threshold emission rate was used. For the soil c oncentration, the modeled TRIM.FaTE chemical concentration in surface soil in parcel N1 (untilled soil, closest to facility) of the screening scenario at threshold emission rate was used. This same soil concentration was also used in ingestion exposure calculations for soil ingestion.

Dermal absorption of chemicals in water is based on the use of a dermal permeability coefficient (K p 

### ), which measures the rate that a chemical penetrates the skin. Dermal absorption of soilbound chemicals is based on the use of a dermal absorption fraction (ABS), which is a measure of how much of a chemical the skin absorbs through contact with soil.TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 3 3-10 December 2013 Dermal Risk Screening 

### Exhibit_Add A3-4. Chemical-Specific Dermal Exposure Values for Water and Soil Contact

PB-HAP Cadmium Divalent Mercury 2,3,7,8- TCDD Benzo[a] pyrene Source

Chemical concentration in Water (Cw) (mg/cm 3 ) 2.4E-08 1.9E-09 2.6E-18 2.1E-13 TRIM.FaTE modeled concentration in screening scenario pond
Chemical concentration in Soil (Cs) (mg/kg) 6.9E-02 6.3E-02 2.2E-10 1.4E-04 TRIM.FaTE modeled concentration in surface soil in parcel N1 in screening scenario Permeability coefficient in water (Kp) (cm/hour) 0.001 0.001 0.81 0.7 Values from RAGS Part E,Exhibits B-3 (organics) and B4 (inorganics)
Fraction absorbed water (FA) (unitless) NA NA 0.5 1.00 Values from RAGS Part E, Exhibits B-3; only used for organic chemicals Lag time per event (event) (hr/event) NA NA 6.82 2.69 Values from RAGS Part E, Exhibits B-3; only used for organic chemicals Dermal absorption fraction (ABS) from soil (unitless) 0.001 0.045 a 0.03 0.13 Values from RAGS Part E, Exhibit 3-4, unless otherwise noted

a Value obtained from Bioavailability in Envir onmental Risk Assessment (Hrudey et al. 1996). 

## 3. Screening-Level Cancer Risks and Non-Cancer Hazard Quotients
Toxicity values were used in conjunction with exposure information to evaluate the potential for cancer risks and non-cancer health hazards. Risk estimation methods are presented below.



### 3.1. Dermal Cancer Risk
Cancer risk for the dermal route was calculated as the product of the age-specific 

### DAD s and the absorbed CSF for each chemical, as follows:



### Dermal Cancer Risk = DAD × CSF ABS



### where:

DAD = Dermal Absorbed Dose (mg/kg-day)

CSF ABS = Absorbed cancer slope factor (mg/kg-day) -1



### Lifetime dermal cancer risks were calculated for 2,3,7,8-TCDD and benzo[a]pyrene. The total risk accounts for dermal exposures that an indi vidual might receive from these PB-HAPs in water plus soil over his or her lifetime (70 years).TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 3 3-11 December 2013 Dermal Risk Screening



### 3.2. Dermal Hazard Quotient
Dermal hazard quotient (HQ) was estimated as the ratio of age-specific 

### DAD s to the absorbed RfD for each chemical, as shown below:



### Dermal HQ = DAD / RfD ABS



### where:

DAD = Dermal Absorbed Dose (mg/kg-day)

RfD ABS = Absorbed reference dose (mg/kg-day)



### The aggregate HQ accounts for exposures that an individual in a receptor group may receive from the PB-HAP in water and soil over the exposure duration. Non-cancer hazard is not additive 

### across the age groups evaluated here. 

## 4. Dermal Screening Results
Exhibit_Add A3-5 presents a summary of estimated dermal non-cancer hazards by age group. A summary of estimated lifetime dermal cancer risks is provided in Exhibit_Add A3-6. The highest HQ value was 0.006 (representing divalent mercury exposure for children less than 1 year of age) or less. This is approximately 170 times less than the potential ingestion hazard quotients associated with the screening scenario (i.e. emissions of divalent mercury in the screening scenario resulted in an ingestion hazard quotient of 1). The highest estimated individual lifetime cancer risk associated with potential dermal exposures was 4.1E-09 for benzo[a]pyrene; this value is approximately 240 times smaller than the ingestion risk (i.e., 1E-06) estimated for the same screening threshold emission rate.TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 3 3-12 December 2013 Dermal Risk Screening 

### Exhibit_Add A3-5. Summary of Dermal Non-Cancer Hazards

0 0.0000005 0.000001 0.0000015 0.000002 0.0000025 0.000003 0.0000035 0.000004 0.0000045

Child <1 Child 1-2 Child 3-5 Child 6-11 Child 12-19 Adult 20-70Hazard Quotient

Receptor 2,3,7,8-TCDD
Water

Soil

Total

0 0.0001 0.0002 0.0003 0.0004 0.0005 0.0006 0.0007

Child <1 Child 1-2 Child 3-5 Child 6-11 Child 12-19 Adult 20-70Hazard Quotient

Receptor Cadmium
Water

Soil

Total

0 0.001 0.002 0.003 0.004 0.005 0.006 0.007

Child <1 Child 1-2 Child 3-5 Child 6-11 Child 12-19 Adult 20-70Hazard Quotient

Receptor Divalent Mercury
Water

Soil

TotalTRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 3 3-13 December 2013 Dermal Risk Screening 

### Exhibit_Add A3-6. Summary of Dermal Cancer Risks

PB-HAP Dermal Lifetime Cancer Risk Magnitude of Difference a

2,3,7,8-TCDD Water 2.64E-10 >3,700 Soil 1.49E-11 >67,300
Total 2.79E-10 >3,500 Benzo[a]pyrene
Water 1.50E-09 >600
Soil 2.63E-09 >300
Total 4.12E-09 >200

a Represents the magnitude of differenc e between the estimated dermal risk and the potential ingestion risk associated with the screening scenario.



### Based on these results and taking into consideration the extremely conservative nature of the dermal exposure calculations, it was assumed that it is not necessary to incorporate dermal exposures in calculating multipathway screening threshold levels. Specifically, the daily exposure durations of 0.58 hour for adults and 1 hour for children used to calculate dermal exposure from water are highly conservative and assume that the individual is bathing in surface water taken directly from a contaminated lake or is swimming in the lake for 350 days of the year. The exposure frequency of 350 days and corresponding skin surface area available for contact with contaminated soils (i.e., head, hands, arms, legs, and feet) likely also grossly overestimates dermal exposure to soil.TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 3 3-14 December 2013 Dermal Risk Screening 

## 5. References
Cal/EPA (California Environmental Protection Agenc y) Office of Environmental Health Hazard Assessment (OEHHA). 2000. Air Toxics Hot S pots Program Risk Assessment Guidelines; Part IV, Exposure Assessment and Stochastic Analysis Technical Support Document. Section 6, Dermal Exposure Assessment. September. Available at: http://www.oehha.ca.gov/air/hot_spots/pdf/chap6.pdf.

Hrudey, S.E., W. Chen, and C.G. Roussex, 1996. Bioavailability in environmental risk assessment. CRC Press, Inc, Lewis publishers.

EPA (Environmental Protection Agency). 2004. Risk Assessment Guidance for Superfund Volume 1: Human Health Evaluation Manual (Part E, Supplemental Guidance for Dermal Risk Assessment). EPA/540/R99/005. Available at: http://www.epa.gov/oswer/riskassessment/ragse/index.htm

EPA. 2006. Risk and Technology Review (RTR) Asse ssment Plan. Office of Air and Radiation. November 20. Available at: http://www.epa.gov/sab/panels/consul_risk_and_tech_assessment_plan.htm.

EPA. 2008. Child-Specific Exposure Factors Handbook. Office of Research and Development, Washington, D.C. EPA/600/R-06/096F. September. Available at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=199243 .TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-1 December 2013 

## Attachment B. Tier 2 Screening Methodology[This page intentionally left blank.]TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-3 December 2013 

## CONTENTS, ATTACHMENT B

Attachment B. Tier 2 Screening Methodology .................................................................. B-1

B.1 Overview of Approach ...................................................................................................... B-5

B.2 Estimation of Adjustment Factors for Selected Site-Specific Parameters ........................ B-8

B.2.1 Selection Values for Variables of Interest ............................................................... B-9

B.2.2 Estimation of Adjustment Factors .......................................................................... B-12 B.2.2.1 General Approach ................................................................................... B-12 B.2.2.2 Incorporation of the Risk Equivalency Approach .................................... B-14

B.3 Preparing National Databases of Lake and Meteorological Data ................................... B-16

B.3.1 Processing Lake Data for Tier 2 Analysis ............................................................. B-16

B.3.2 Processing Meteorological Data for Tier 2 Analysis .............................................. B-20 B.3.2.1 Sources of Meteorological Data .............................................................. B-20 B.3.2.2 Coverage of Meteorological Stations Compared with Facility Locations ................................................................................................ B-20 B.3.2.3 Data Processing ...................................................................................... B-21

B.4 Implementation of Tier 2 Analysis ................................................................................... B-23

B.4.1 Facility List for Tier 2 Screen (Step 3) ................................................................... B-25

B.4.2 Facility/Lake Distance Table (Step 4) .................................................................... B-26

B.4.3 Matching Facilities to Meteorology Data (Step 5) .................................................. B-27

B.4.4 Assembling Threshold Adjustment Factors (Step 6) ............................................. B-28

B.4.5 Assembling Results (Step 7) ................................................................................. B-30

B.5 References .................................................................................................................... . B-34

Addendum 1. Summary of TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Analysis .................................................................................................. . 1

Addendum 2. Analysis of Lake Size and Sustainable Fish Population ........................... 2-1TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-4 December 2013 

## EXHIBITS, ATTACHMENT B

Exhibit_Att B-1. Basic Implementation Process for Implementing Tier 2 ................................. B-6 Exhibit_Att B-2. Lake Distance and Meteorological Parameter Values for which Adjustment Factors were Developed in Tier 2 a,b 

### .............................................. B-10 Exhibit_Att B-3. Layouts for Tier 2 TRIM.FaTE Simulations Using Alternate Distances Between the Facility and the Fishable Lake a 

### .................................. B-13 Exhibit_Att B-4. Maximum Fish Ingestion Rate (g/day) Associated with Sustainable Fishing a 

### ......................................................................................... B-19 Exhibit_Att B-5. The Locations of Meteorological Stations and Point Source Facilities a 

### .......................................................................................................... B-22 Exhibit_Att B-6. Example of the Dashboard To Conduct the Tier 2 Analysis ........................ B-24 Exhibit_Att B-7. Example of Global Inputs Used in the Tier 2 Analysis a 

### ................................ B-25 Exhibit_Att B-8. Example of the Facility Input Data Required To Conduct the Tier 2 Analysis a 

### ................................................................................................. B-26 Exhibit_Att B-9. Example of the Lake Distance Data Required To Conduct the Tier 2 Analysis a 

### ................................................................................................. B-27 Exhibit_Att B-10. Example Results of the Meteorological Station Matching Required To Conduct the Tier 2 Analysis a 

### ....................................................... B-28 Exhibit_Att B-11. Example Results of the Octant Analysis Required To Conduct the Tier 2 Analysis (shown in 2 pieces due to size) a 

### ........................................ B-29 Exhibit_Att B-12. Example of the TRIM.FaTE Matrix Results Required To Conduct the Tier 2 Analysis ............................................................................. B-30 Exhibit_Att B-13. Example of Summary Output Table Created by the Tier 2 Tool a 

### ............... B-31 Exhibit_Att B-14. Example of Detailed Output Table Created by the Tier 2 Tool a 

### ................. B-32 Exhibit_Att B-15. Example of Individual Ou tput Table Created by the Tier 2 Tool................. B-33TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-5 December 2013 

## B.1 Overview of Approach The Tier 1 screening scenario is, by design, generic and health-protective. It was constructed to allow for quick application to a large number of facilities in a source category with a minimal chance of returning false negatives for risk. Based on screening analyses conducted for RTR to date, many facilities can “fail” the Tier 1 screen. Because the Tier 1 screen uses “worst case” assumptions, the analysis must be refined to determine whether the failing facility is actually expected to pose a risk.

One way to refine the risk estimates is to c onduct a site-specific assessment where the Tier 1 model values and layout are replaced with site-specific values wherever possible. However, this approach would not be feasible given the resource intensive nature of a site-specific assessment and the number of facilities that tend to fail the Tier 1 screen in the different source categories.

As an intermediate approach, we instead refine our Tier 1 screening estimates to Tier 2 screening estimates. This is done by replacing some of the worst-case assumptions in a Tier 1 screen with more site-appropriate values. Specifically, for Tier 2, the following variable values are varied from their Tier 1 values:

 

### Meteorological characteristics, including the fraction of time the wind blows in the direction of the farm and lake (using wind direction), the wind speed, the precipitation rate, and the mixing height; and

 

### Location of the nearest fishable lake(s) relative to the facility. 23



### In selecting the fate and transport variables listed above to include in Tier 2 adjustments, a balance was struck between: 1) the degree of impact on the potential risk estimate; 2) the ease of implementation in TRIM.FaTE; and 3) the ease of obtaining site-specific values on a facilityby-facility basis. Because of the expected variability in exposure parameters (such as fish or food ingestion rates) amongst any population living around a given facility, only the fate and transport parameters were candidates to be varied in the Tier 2 analysis, and the exposure parameters remain at their health-protective Tier 1 values.

Tier 2 screening assessments are performed for those facilities that fail the Tier 1 screening assessment. The overall implementation of Tier 2 is shown in Exhibit_Att B-1. The starting point (shown in green) is the ratio of the facility emission rate for the PB-HAP of concern to the Tier 1 threshold for that PB-HAP. Next, the facility-spec ific estimates of the Tier 2 meteorological and lake-location parameters listed above must be gathered for each facility (shown in red). Then, the associated TRIM.FaTE and MIRC estimates of risk must be estimated (shown in orange). Because of the volume of facilities that need to be evaluated in Tier 2, the implementation focuses on estimating refined risk using pre-calculated databases (discussed below) rather than gathering the input data and performing TRIM.F aTE and MIRC modeling speartely for each facility.

23 The lake size was also changed for each new facility lake distance. This change allowed the simulations to maintain a constant ratio between watershed and eros ion area compared with the lake area.TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-6 December 2013 

### Exhibit_Att B-1. Basic Implementation Process for Implementing Tier 2

First, databases of the relevant U.S. meteorological and lake data were created that can be accessed during a Tier 2 evaluation (shown in red in the figure). These meteorological and lakedata are discussed in more detail in Section B.4. The meteorological database includes annualaverage summary statistics on wind direction, wind speed, and precipitation for more than 800 surface stations paired with their closest upper-air stations located throughout the United States. These surface data cover year 2011 and are the sameAERMOD-ready data used by EPA OAQPS for RTR inhalation modeling. As discussed below, in most cases the 2011 precipitation data were not used, and instead, the 30-year average annual precipitation data for each station were used. The database of lakes, available from ESRI® and based on U.S. Geological Survey data, includes information on the location, size, use or type designation, and name (if available) of all lakes in the United States. To focus on lakes that can support angling of upper trophic level fish, lakes used for disposal, evaporation, or treatment were excluded, and only lakes greater than 100 acres in area are included. Lakes larger than 100,000 acres in area are not included because the sizes of their watersheds and the complexity of their lake dynamics are not feasible to model with the TRIM.FaTE modeling system.

In parallel to the meterological and lake data collection, a series of TRIM.FaTE simulations was performed that systematically varied the values fo r four of the five selected fate and transport variables (shown in orange in the figure, consisting of lake location, wind speed, precipitation rate, and mixing height). Wind direction affects only whether the chemical mass advects toward the farm and lake, so the effect of site-specific wind directions can be evaluated outside TRIM.FaTE simulations. These simulations do not simulate specific facilities; instead, four or five alternative values for each of the four variables were selected using statistics on U.S. meteorological data or professional judgment to capture the expected range in the facility data. TRIM.FaTE simulations were performed for ev ery possible combination of these variable values to enable the estimation of appropriate site-specific threshold adjustment factors for scenarios with the corresponding characteristics. Based on the TRIM.FaTE results of these simulations (and the subsequent exposure and risk characteriza tion, conducted using MIRC), a matrix ofTRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-7 December 2013 

### Tier 2 threshold adjustment factors was calculated, with each element of the matrix corresponding to a unique combination of values from each of the selected variables.

These TRIM.FaTE and MIRC simulations are used to estimate part of the Tier 2 threshold adjustment factors. Wind direction is the other part of the Tier 2 threshold adjustment factors. The wind direction adjustment for a given facility is the ratio of the frequency that winds blow toward the Tier 1 farm and lake (43 percent of the time) and the frequency that winds blow toward the facility-specific farm and lake in Tier 2. These Tier 2 threshold adjustment factors are multiplied by each other and represent the ratio between the risk metric (i.e., cancer risk or HQ) obtained using the baseline Tier 1 screening scenario and the risk metric obtained from the Tier 2 TRIM.FaTE runs. For a given facility, an adj usted Tier 2 ratio (emissions compared to the emission threshold) can be estimated by dividing the Tier 1 emission ratio (the output of the Tier 1 screen) by the adjustment factor that best corresponds to the meteorological conditions present at the site and the presence and location of lakes at the site:



### Tier 2 Ratio = Tier 1 Ratio ൊ 

### Tier 2 Adjustment Factor

Matrices of threshold adjustment factors from the TRIM.FaTE and MIRC simulations were separately developed for the four PB-HAPs that currently have screening emission thresholds in the Tier 1 process: benzo(a)pyrene (BaP, representative of PAHs), cadmium, divalent mercury and 2,3,7,8-Tetrachlorodibenzo-p-Dioxin (TCDD, representative of dioxins). The wind direction threshold adjustment factors were irrespective of chemical (specific only to the facility). In implementing the Tier 2 process, a risk equivalency approach was used to convert speciated emissions of PAHs and dioxins into BaP and TCDD equivalents, respectively, similar to the Tier 1 screening approach.

Finally, to implement the Tier 2 screen, a Micros oft® ExcelTM tool was created that includes the database of meteorological data and lake data as well as the Tier 2 adjustment factors for the combinations of variables simulated in the Tier 2 TRIM.FaTE simulations. In the tool, each facility is matched with the closest surface meteorological station, and the values for the four relevant parameters at that station are recorded (wind speed, wind direction, precipitation rate, and mixing height). The distance from the facility to the nearest qualifying lake in each octant around the facility are estimated using GIS and are also imported. These five values become the set of facility-specific parameters. The threshold adjustment factor corresponding to this set of site-specific data is then estimated using the matrix of adjustment factors and the wind direction adjustment Wind direction values are used as-is with no rounding or binning. If one of the four facility matrix variables (wind speed, precipitation rate, mixing height, or lake location) is between two of the computed levels available for that variable in the simulation matrix, the more health-protective of the two levels is selected (i.e., the level that resulted in the smalleradjustment to the baseline Tier 1 exposure). The Tier 1 screening emission threshold for a PBHAP is then multiplied by the appropriate adjustment factors to obtain an updated Tier 2 emission threshold for that PB-HAP at that facility. A facility then passes the Tier 2 screen if the emission is below the Tier 2 threshold. Selectio n of Site-Specific Characteristics to Include in the Tier 2 Analysis

The screening scenario used to derive Tier 1 thresholds incorporates assumptions regarding meteorological conditions, the spatial configuration of the hypothetical exposure setting, physical parameters of the environment, and chemical-specific parameters that result in generally health-protective results. In Tier 2, selected assumptions used in the fate and transport modeling conducted using TRIM.FaTE are modified to reflect more site-specificTRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-8 December 2013 

### information for the facilities being evaluated. 24 

### To determine which scenario characteristics should be incorporated into the Tier 2 analysis, the following issues were considered for TRIM.FaTE model parameters:

 

### How sensitive are the modeled risks to a spec ific user-input model parameter (e.g., wind direction, wind speed)?

 

### Do the plausible values for a given parameter span a large range when comparing different RTR facilty locations?

 

### Which site-specific characteristics can be found easily and reliably for facilities with emissions exceeding Tier 1 thresholds?

 

### In general, is the uncertainty associated with the parameter high or low?

 

### How complicated or time-consuming is the incorporation of a given parameter (e.g., wind speed) into the Tier 1 screening scenario set-up?

Addendum 1 to this attachment provides an ex hibit showing all the TRIM.FaTE variables considered for the Tier 2 analysis. These va riables were evaluated qualitatively using the criteria above to determine whether the variable was of high, medium, or low priority. The following five “high priority” variables were selected for implementation in the current Tier 2 analysis:

 

### Wind direction (the percent of time the wind blows toward the lake and farm),

 

### Wind speed,

 

### Precipitation,

 

### Mixing height, and

 

### Location of the nearest fishable lake relative to the facility.

These parameters were judged to represent a balance between range of potential variability,ease of implementation within the modeling scheme used for RTR, and ease of obtaining sitespecific values with a relatively high level of confidence.


## B.2 Estimation of Adjustment F actors for Selected Site-Specific Parameters The purpose of including site-specific detail for a facility evaluated in Tier 2 is to develop a more realistic estimate of risk associated with facility emissions. This purpose is achieved within the analysis by generating revised emission thresholds of potential concern specific to a given PB-HAP on a facility-by-facility basis. However, instead of performing full-fledged, site-specific model runs for each facility that does not “screen out” in Tier 1, a set of generally applicable threshold adjustment factors for each PB-HAP was developed based on a set of model runs. For each PB-HAP, these runs corresponded to unique combinations of values for wind speed, precipitation rate, mixing height, and lake location (wind direction is assessed separately). These adjustment factors were based on a set of runs in which the values for these parameters were varied systematically. The wind direction adjustment factor is the ratio of the frequency

24 Only TRIM.FaTE parameters were considered for inclusi on in Tier 2 adjustments because of the difficulty in identifying substantial location-related differences in values for exposure factors (and other inputs to MIRC). The exposure characteristics used in MIRC are considered to be generally consistent across different locations and facilities.TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-9 December 2013 

### that winds blow toward the lake and farm in the Tier 1 screening scenario (43 percent) and the frequency that winds blow toward the facility’s farm and lake in Tier 2. The appropriate adjustment factors are then applied to the Tier 1 threshold for that PB-HAP when evaluating a facility in Tier 2.

The analyses conducted to select the parameters to derive the threshold adjustment factors are described in this section. Section B.2.2 also describes the development of appropriate “bins” for the selected parameters. These bins correspond to the subset of parameter values for which adjustment factors were calculated, based on the anticipated range of plausible values for facilities evaluated in RTR.



### B.2.1 Selection Values for Variables of Interest For each site-specific parameter that is assessed in the Tier 2 analysis, other than wind direction, adjustment factors were estimated that correspond to a set of four to six particular values for the parameter. The adjustment factor for wind direction directly relates Tier 1 and site-specific Tier 2 wind direction frequencies, with respect to the directions of the lake and farm. These individual adjustment factors can the be multiplied for a particular variable combination to get an overall adjustment factor. To facilitate this, bins were created for each parameter of interest and a relevant range of values, with the exception of wind direction (as described below, representative bins were not necessary for this parameter). The rationale for selecting the range for each bin for each parameter of interest is described below.

Wind Direction: 

### Within the context of the hypothetical exposure scenario used in Tier 1 (and under actual conditions), when the frequency with which the wind blows towards the modeled domain (i.e., where the hypothetical farm and lake are located) increases, greater pollutant deposition will occur over and around the farm and lake. The percentage of time the wind blows toward the farm and lake is therefore positively correlated with ingestion exposure and risk. In the screening scenario used to estimate Tier 1 thresholds, the wind is assumed to blow toward the modeled domain 3 days a week, or 43 percent of the time. This assumption is intended to approximate an unusually consistent long-term wind pattern and is representative of wind direction patterns in Yakima, Washington (where the wind blows eastward approximately 40 percent of the time, based on a review of wind direction data compiled by the National Weather Service).

In the model runs to develop the Tier 2 bins, this factor was changed to 1, 2, and 4 days per week, corresponding to 14 percent, 29 percent, and 57 percent of the time. This range of values was chosen to obtain a good understanding of the impact of wind direction on risk for the range of wind direction patterns likely to be present at real facilities. Review of these results indicated that, within this modeling scenario, es timated ingestion exposure varies directly with percentage of time the wind blows toward the farm and lake. Given the exactly linear nature of the relationship observed in model results obt ained from these runs, the adjustment of the threshold for wind direction in Tier 2 is a direct, linear adjustment using the actual site value 

### rather than an incremental, bin-based approach. In other words, the Tier 1 threshold is adjusted for wind direction in direct proportion to the difference between conditions for the actual facility location and the wind direction parameters included in the Tier 1 screening scenario (i.e., blowing toward the lake/farm 43 percent of the time on average).

Wind Speed: 

### Although the impact of wind speed on non-inhalation risks also is likely to depend on configurational parameters such as the location of farms and lakes, in general it is reasonable to assume that higher wind speeds lead to more rapid chemical transfer out of the modeled domain, allowing less time for chemical deposition and, therefore, less total near-field deposition and a lower exposure and risk. The Tier 1 screening analysis assumed a windTRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-10 December 2013 

### speed of 2.8 m/s, corresponding to the 5 th 

### percentile (i.e., slower) of annual average speed for the contiguous United States (distribution was based on data from a climate publication from the National Oceanic and Atmospheric Administration [NOAA], which used data from over 200 stations nationwide). 25 

### This value is similar to the annual average wind speeds of the U.S. Deep South. 26 

### In the model runs to develop the Tier 2 threshold adjustment factors, we calculated the change to exposure resulting from increasing this value to 3.5 m/s, 4 m/s, and 5 m/s (values also shown in Exhibit_Att B-2; 5 m/s is the 88 th 

### percentile in the NOAA data). Based on these values, the bins used to classify wind speed are: 2.8–3.5 m/s, 3.5–4 m/s, 4–5 m/s and above 5 m/s. In all modeled cases, increasing the wind speed while maintaining constant lake location, wind direction, mixing height, and precipitation led to Tier 2 estimations of high-end risk or hazard that were smaller than that of Tier 1. To ensure that the approach is health-protective, a facility was assigned the lower end value of the bin into which it was placed. Facilities with wind speeds less than 2.8 m/s were assumed to have a wind speed of 2.8 m/s.

Exhibit_Att B-2. Lake Distance and Meteorological Parameter Values for which Adjustment Factors were Developed in Tier 2 a,b

Parameter Value

Wind Speed (m/s) 2.8
3.5
4
5

Precipitation (mm/yr) 512
924 1,187 1,500

Mixing Height (m) 710
865 1,097 1,537

Lake Distance (km) No lake
2
5
10
20
40

a Bold indicates the value is equal to the value used in Tier 1.

b Wind direction is not shown here because its effect on modeled exposure and risk in TRIM.FaTE is linear.



### Precipitation: 

### Higher levels of precipitation over the modeled domain are expected to increase non-inhalation risks by increasing particulate and gaseous wet deposition near-field to the source. The screening scenario used in Tier 1 analysis assumed an annual precipitation rate of

25 http://ols.nndc.noaa.gov/plolstore/plsql/olsto re.prodspecific?prodnum =C00095-PUB-A0001#TABLES – this website is updated every year, so the data it currently shows is not the exact data used to develop the wind speeds for screening analyses.

26 National Climatic Data Center CliMaps (NCDC-CliMaps) (2007). http://cdo.ncdc.noaa.gov/cgibin/climaps/climaps.plTRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-11 December 2013 

### 1,500 mm/year. This level of annual precipitation was estimated to represent rainy conditions in the U.S., such as parts of the U.S. Deep South and parts of the U.S. Northwest Coast 27 

### . Though the rate was an estimate, it does correspond to approximately the 95 th 

### percentile (i.e., higher rate) precipitation in the National Climatic Data Center’s (NCDC) 30-year (1981–2010) data from U.S. stations. 28 

### To estimate adjustment factors in the Tier 2 analysis, model simulations were conducted with the parameter value set to three lower values (i.e., 1,187 mm/year, 924 mm/year and 512 mm/year; values also shown in Exhibit_Att B-2), corresponding to the 75 th



### , 50 th 

### and 25 th 

### percentiles, respectively, of the NCDC data. Locations with lower precipitation levels were assumed to have a minimum precipit ation of 512 mm/year. Based on these levels, the bins used to classify precipitation were: 0-512 mm/year, 512-924 mm/year, 924-1,187 mm/year, 1,187-1,500 mm/year and above 1,500 mm/year. In nearly all modeled cases, decreasing the precipitation rate while maintaining constant lake location, wind speed, wind direction, and mixing height led to Tier 2 estimations of high-end risk or hazard that were smaller than that of Tier 1. To be health-protective, a facility was assigned the upper end value of the bin in which it was placed. Facilities with precipitation levels above 1,500 mm/year were assumed to experience precipitation of 1,500 mm/year.

Mixing Height: 

### Greater mixing heights can dilute the concentration of pollutants in air, resulting in lower deposition and other transfer rates from air to surfaces and consequently also lower ingestion exposures. The Tier 1 screening anal ysis assumed a mixing height of 710 meters. This value is the 5 th 

### percentile (i.e., lower) of annual average mixing heights for 463 U.S. locations, based on data obtained from EPA’s SCRAM Web site. 29 

### To estimate adjustment factors in the Tier 2 analysis, mdoel simulations were conducted with the parameter value set to three larger values (i.e., 865 m, 1,079 m, and 1,537 m; values also shown in Exhibit_Att B-2). These values correspond to North Little Rock, Arkansas, Boise, Idaho, and Tucson, Arizona and are intended to encompass the range of annual average mixing heights experienced in different parts of the United States. Based on these levels, the following bins were selected for categorization of mixing height: 710–865 m, 865–1,079 m, 1,079–1,537 m, and above 1,537. In all modeled cases, increasing the mixing heights while maintaining constant lake location, wind direction, wind speed, and precipitation rate led to Tier 2 estimations of high-end risk or hazard that were smaller than that of Tier 1. To be health-protective, a facility was assigned the lower end value of the bin into which it was placed. Facilities with mixing heights above 1,537 m and those below 710 m were assumed to have mixing heights of 1,537 m and 710 m, respectively.

Lake Location: 

### Moving the lake included in the hypothetical Tier 1 scenario to a location farther from the actual source in the modeled domain will reduce modeled (TRIM.FaTE) deposition to the lake and its watershed and consequently reduce exposures associated with the fish consumption pathway, which is an important pathway of exposure for several chemicals (for example, in the case of methyl mercury, it is by far the predominant exposure pathway). For the scenario modeled in Tier 1, the center of the lake was situated approximately 2 km from the source. To estimate lake location adjustment factors for use in Tier 2, we completed a series of model runs in which the lake was located 5 km, 10 km, 20 km, and 40 km from the source, as well as runs with no lake (values also shown in Exhibit_Att B-2). Accordingly, the bins used to classify lake location relative to the facility are: no lake, 2–5 km, 5–10 km, 10–20 km, 20–40 km, and 40–50 km (lakes farther than 50 km are not considered). To be health-

27 National Climatic Data Center Historical Climate Series (NCDC-HCS) (2007).

28 http://www.ncdc.noaa.gov/oa/climate/normals/usnormals.html

29 Support Center for Regulatory Atmospheric Modeling; http://www.epa.gov/scram001/tt24.htmTRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-12 December 2013 

### protective, a facility was assigned the lower end value of the bin into which it was placed. A facility with a lake closer than 2 km was assumed to have a lake at 2 km.

In resituating the lake at these alternative locations, we maintained ratios consistent with those included in the Tier 1 screening scenario for (1) lake area to total land area in the modeled domain, (2) runoff watershed area to lake area, and (3) erosion watershed area to lake area. Exhibit_Att B-3 provides a diagram of the TRIM .FaTE layout in each of the alternate lake location simulations except no-lake. We used a “thin” lake shape (i.e., downwind width much smaller than the cross-wind length) that minimized the potential effect of declining deposition with distance from stack that might affect lakes that are long in the downwind direction. By controlling for these potentially confounding effects, we could isolate the effect of lake location on risk appropriately. Moving the lakes to increasing distances from the stack required expansion of the modeled domain. Maintaining the same overall ratio of land area to lake area in each domain resulted in scenarios with increasingly large lakes, with surface area increasing with distance from the source. This approach also was taken for reasons of modeling convenience (i.e., taking into account resource requirements associated with reconfiguring the TRIM.FaTE spatial layout). The changes in lake size between these four runs are not expected to have a substantial independent effect on exposure and risk because the effect of increased lake size is offset by greater total deposition and runoff. Furthermore, the lake depth was not changed, which might be as important a parameter as lake surface area in determining the chemical concentrations in the water column and sediment. As noted above, we set up the configurations to ensure that the lakes in the different scenarios received runoff and erosion from equivalent watersheds on a per surface area basis.



### B.2.2 Estimation of Adjustment Factors Adjustment factors were estimated for each variable described above and applied as multipliers to the Tier 1 emissions thresholds. The resulting Tier 2 emissions thresholds are used to assess whether facilities with corresponding configurations pose multipathway risks. Notably, facilities substantially exceeding a Tier 2 emission threshold carry some potential for significant multipathway health risk.



### B.2.2.1 General Approach The core principle in the estimation of adjustment factors is the assumption of direct proportionality of risk and emissions in the modeling approach used for RTR involving TRIM.FaTE and MIRC. Although not strictly present across all variations due to feedback mechanisms and other processes encompassed by the TRIM.FaTE model, a generally linear relationship between risk and emissions has been observed across model simulations conducted for RTR. This suggests that the ratio of total estimated exposures (and consequently risks) obtained for the screening scenario and any alternative configuration could be used as an adjustment factor to scale emissions for that specific alte rnative configuration. The risk in the alternative configuration following such a scaling of emissions would be equal to the risk in the screening scenario (which in Tier 1 was set at a risk level of 1E

− 6 incremental lifetime cancer risk or an incremental hazard quotient of 1, depending on the toxic effect of the chemical in question).

.TRIM-Based Tiered Screening Methodology for RTRAttachment B – Tier 2 B-13 December 2013Exhibit_Att B-3. Layouts for Tier 2 TRIM.FaTE Simulations Using Alternate DistancesBetween the Facility and the Fishable LakeaaThe no-lake scenario is not shown.TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-14 December 2013 

### To account for potential interactions between the chosen Tier 2 variables, matrix adjustment factors were estimated by performing TRIM.FaTE runs for each unique combination of the specified variable values (that is, all permutati ons of the selected values for the wind speed, precipitation, mixing height, and lake location). Adjustment factors for each configuration were then estimated as the ratio of risks in the Tier 1 analysis to the estimated risk for the particular TRIM.FaTE configuration. This approach results in a large matrix of adjustment factors and has the advantage of accounting for all possible interaction effects between the variables.

The adjustment factor for wind direction is handl ed separately. Based on TRIM.FaTE test runs, the fraction of the time the wind blows in the direction of the farm and lake was observed to have a direct, linear effect on the resulting risk estimates. For this reason, it was not necessary to include this variable in the TRIM runs conducted to create the matrix of adjustment factors, as described above. Instead, the adjustment factor is calculated with a linear factor that divides the Tier 1 value (0.43, or 43% of the time in the direction of the farm and lake) by the site-specific facility value, as follows:



### Adjustment Factor Wind Direction 

### = 0.43 / (fraction of time wind blows towards domain)

The adjustment factor for wind direction is then multiplied by the matrix adjustment factor discussed above to obtain a consolidated threshold adjustment factor that accounts for all the five variables considered in Tier 2.



### B.2.2.2 Incorporation of th e Risk Equivalency Approach The adoption of a risk-equivalency approach to convert speciated emissions of PAHs and dioxins to BaP and TCDD equivalents, respectively, in Tier 1 required the development of risk equivalency factors (REFs) for each reported species in these groups. The REFs for PAHs and dioxins represent the ratio of the risk posed by a particular species to the risk posed by BaP and TCDD, respectively, at equivalent emissions rates in a given scenario.

The REFs can be represented as the product of exposure equivalency factors (EEFs) and toxicity equivalency factors (TEFs). For the PAHs, this can be expressed as:



### REF PAH 

### = EEF PAH 

### × TEF PAH


### The EEFs for PAHs represent the ratio of the exposure to a particular species to the exposure to BaP (and similarly for dioxins and TCDD) at equi valent emission rates. These ratios are thus specific to the TRIM.FaTE layout and input assumptions. The TEFs for PAHs and dioxins represent the ratio of the oral cancer slope factor (CSF) for a particular species to the CSF for BaP and TCDD, respectively, and are the same in Tier 1 and in Tier 2.

The EEFs depend on the TRIM.FaTE configuration, including layout and meteorlogical input values. For example, the exposure profile (i.e ., how the different ingestion pathways contribute to total exposure and risk) is different for each PA H. For a PAH where fish is a dominant driver of risk, moving the lake will have a large effect on the overall risk; however, if produce is the dominant driver of risk, moving the lake will have a much smaller effect on overall risk. Thus, for Tier 2, EEFs were recomputed for each of the representative scenarios modeled in Tier 2 separately for the PAH species currently evaluated (some based on direct TRIM.FaTE modeling, others 15 based on K ow 

### -based regression estimates) and the dioxin species currently evaluated (most based on direct TRIM.FaTE mo deling, and a small number assumed to behave like TCDD).TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-15 December 2013 

### The following mathematical formulas demonstr ate how the Tier 2 adjustment factors are estimated for the PAH and dioxin species. The formulas presented below are for a representative PAH species, but they are also applied to dioxin species.

For a given PAH species emitted at a rate E PAH 

### at a facility, the risk-equivalent BaP emission level can be expressed as:



### E

BAP EQIV 

### = E PAH SPECIES 

### × EEF PAH SPECIES 

### × TEF PAH SPECIES


### Then, the Tier 1 risk ratio is estimated by comp aring the risk-equivalent BaP emissions to the BaP emission threshold:



### Ratio TIER 1_PAH SPECIES 

### = E TIER 1_BAP EQUIV 

### Threshold TIER 1_BAP


### If the ratio is less than 1, the facility “screens out” of the Tier 1 analysis. Similarly, for Tier 2, the ratio of risk-equivalent BaP emissions to t he Tier 2 BaP threshold may be expressed as:



### Ratio TIER 2_PAH SPECIES 

### = E TIER 2_BAP EQUIV 

### Threshold TIER 2_BAP


### Using the definition of the risk-equivalent BaP emissions, this can be re-expressed for a given PAH species as:



### Ratio TIER 2_PAH SPECIES 

### = E PAH SPECIES 

### × EEF TIER 2_PAH SPECIES 

### × TEF PAH SPECIES 

### Threshold TIER 2_BAP


### This expression may be further reconfigured, after some algebraic rearrangement, in terms of the Tier 1 ratio as:



### Ratio TIER 2_PAH SPECIES 

### = Ratio TIER 1_PAH SPECIES 

### × Threshold TIER 1_BAP 

### Threshold TIER 2_BAP 

### × EEF TIER 2_PAH SPECIES 

### EEF TIER 1_PAH SPECIES



### ݎ݋ݐܽܿܨ ݐ݉݁݊ݐݏݑ݆݀ܣ 2 ݎܶ݅݁ ூாௌ஼ா௉ௌ ு௉஺ 

### = Threshold TIER 1_BAP 

### Threshold TIER 2_BAP 

### × EEF TIER 2_PAH SPECIES 

### EEF TIER 1_PAH SPECIES


### These equations simply state that to adjust the Tier 1 threshold to a Tier 2 threshold for a particular PAH species, the adjustment factor must include the ratio of the BaP Tier 1 and 2 thresholds (as is true for cadmium, me rcury, and TCDD, as discussed above) 

### and the ratio of the EEFs for the particular PAH species in Tier 2 and Tier 1. This additional EEF factor is needed to account for the fact that the EEFs are different for each Tier 2 TRIM.FaTE configuration.

Finally, the ratio of total BaP equivalents contributed by all PAH species at a given facility to the BaP Tier 2 threshold may be expressed, by summing the above expression, as:TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-16 December 2013 

### Ratio TIER 2_ALL PAHs 

### = ෍ 

### Ratio TIER 1_PAH SPECIES 

### × Threshold TIER 1_BAP 

### Threshold TIER 2_BAP 

### × EEF TIER 2_PAH SPECIES 

### EEF TIER 1_PAH SPECIES ALL PAHs



### If the ratio of total BaP equivalents contributed by all PAH species to the BaP Tier 2 threshold is greater than 1, the facility would be deemed to have failed the Tier 2 screen for the PAH group.


## B.3 Preparing National Databases of Lake and Meteorological Data To facilitate the effective application of the Tier 2 screening procedures, databases were prepared containing national-scale information about lakes (locations and sizes) and meteorological data at available surface stations (including wind direction, wind speed, precipitation, and estimated mixing height). The development and content of these two databases are discussed in the following sections.



### B.3.1 Processing Lake Data for Tier 2 Analysis The lake database was built using a geospatial file (U.S. Water Bodies) provided by ESRI® for their ArcGISTM products. 30 

### Because this geospatial file excluded water bodies in Alaska, Puerto Rico, and the U.S. Virgin Islands, it was augmented with water body information (directly from the USGS National Hydrography Dataset) for th ose other locations. The data generally have an estimated horizontal accuracy of 50 m. For the Tier 2 analysis, we focused on the hundreds of thousands of water bodies classified as “Lake/Pond” or “Reservoir” but not designated for disposal, evaporation, or treatment. We refer to these water bodies simply as “lakes” in the remainder of this document. The approximately 100,000 other water bodies (classified as canal/ditch, ice mass, inundation area, playa, stream/river, swamp marsh, or unclassified) were not included. In a more subjective step during the processing of the lake database for a specific source category (when Tier 2 is run “operationally”), the lake names (when provided) are scrutinized manually, and lakes are removed fr om the analysis when their names suggest industrial or treatment use (e.g., wastewater treatment ponds, sludge ponds, fly ash ponds, paper mill ponds, sewage pools, etc.). In this respect, the lake dataset is never truly final; lake names are scrutinized each time a new set of facilities is assessed using the Tier 2 methods and tools, which can lead to the permanent removal of some lakes from the dataset.

Early in the process of compiling this database, we encountered the question: “What size water body qualifies as a ‘lake’ for the purposes of this assessment?” The Tier 2 analysis must focus on lakes large enough to support relatively intensive angling pressure to be compatible with the assumed exposure scenario. To estimate the re lationship between high fish consumption rates, harvest rates, and lake size, the following five key assumptions were made. Information and citations to peer-reviewed literature that support these assumptions are provided in Addendum 2 to this attachment. Note that in the TRIM.FaTE model screening scenario, water-column carnivores (WCCs) are modeled as trophic level 4 (TL4) fish (e.g., pickerel, largemouth bass), with all of their diet consisting of smaller “prey” or “pan” fish in the water column that are simulated as trophic level 3 (TL3). The benthic carnivores (BCs) in TRIM.FaTE are modeled to represent an intermediate trophic level between 3 and 4, i.e., TL3.5. The BCs (e.g., catfish) obtain half of their diet from TL2 (benthic invertebrates that feed on detritus at the sediment surface) and half from TL3 fish in the benthic environment. For the screening scenario, we assume that anglers consume fish biomass in a ratio of 50:50 from the BC and WCC

30 Specifically, the geospatial file in the ESRI® Data & Maps 2009 Data Update for ArcGIS TM version 9.3.1. It was derived by the United States Geological Survey (USGS), EPA, and ESRI® from the USGS National Hydrography Dataset (USGS 2012).TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-17 December 2013 

### compartments, respectively. Together, these two fish compartments are referred to as piscivorous fish.

1. Piscivorous fish, when present, comprise approximately 21 percent of the standing biomass of fish. BC fish represent 17.5 percent of the standing fish biomass in natural lakes; WCC fish account for 3.5 percent of this total fish biomass. Thus, WCC (or TL4) fish represent the limiting compartment for angler fish harvesting and consumption. 2. Humans can harvest 10 percent of any single fish compartment without threatening the population due to overharvesting. 3. The minimum viable effective population size for a single fish species is at least 50 adult fish for a local population to survive over the short term (e.g., more than a decade). 4. Only 33 percent of the fish caught for cons umption is edible fillet muscle. A 0.33 edible fraction is used to estimate total fish biomass associated with human consumption. 5. A total fish standing biomass of 40 g wet weight/m 2 

### represents an approximate upper bound for natural ponds and lakes in the United States.

Using the above assumptions and a series of equations (see Addendum 2 to this attachment), the maximum fish ingestion rates as a function of standing biomass and lake size were estimated. Exhibit_Att B-4 presents these findings, where the grey shading indicating when WCC fish would probably not be present, the white (unshaded) cells represent combinations of lake size and productivity that could sustain the listed fish ingestion rates over some time, and the yellow cells represent likely long-term sustainability associated with more than 500 adult WCC fish in the lake (see Exhibit notes).

The Tier 2 analysis is intended to retain most of the health-protective attributes of the screening scenario used in Tier 1 so that no facilities of potential concern erroneously “pass” the screen. For a given facility, the smaller the lake size threshold, the greater the number of lakes and the greater the probability that a lake is closer to the facility. Lakes closer to a facility will result in higher chemical concentrations in fish compared with lakes farther from the facility. Thus, Exhibit_Att B-4 was used to determine the smallest lake size that would support a TL4 population. At the assumed upper-limit standing fish biomass of 40 g ww/m 2 

### , this corresponds to 25 acres (the first “white” box when moving from smaller to larger lakes).

The fish ingestion rate supported by a 25-acre lake is not as high as the adult ingestion rate used in the Tier 1 and Tier 2 analyses (i.e., 373 g ww fillet per day). Even a 100-acre lake is unlikely to sustain harvesting of piscivorous fish to support the 373 g ww fillet/day ingestion rate assumed for a subsistence angler. Exhibit_Att B-4 indicates that at a total fish biomass productivity of 40 g ww/m 2 

### , the maximum sustainable fish harvest from the WCC compartment would correspond to an ingestion rate of only 103 g/day (with half that from the WCC and half from BC). However, to be health protective and to ensure that small lakes that might be more highly contaminated than estimated by the TRIM.FaTE screens were not eliminated, we selected 100 acres as the “cutoff” for the minimum size for an actual lake near a facility to be included in the Tier 2 analysis. In addition, larger lakes (larger than 100,000 acres) were not considered since they cannot be readily modeled in TRIM.FaTE. For the purposes of proximity matching lakes to emitting facilities (as described in Section B.4.2), the location of each lake is identified as the geographic centroid inside the lake.

Lakes smaller than 100 acres could be stocked annually at a rate adequate to support the assumed fish ingestion rate. For stocked fish, however, we would have to assume that when introduced to the lake, the fish were uncontaminated by the chemicals of interest. Moreover, the period over which accumulation of chemical from the lake could occur would be roughlyTRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-18 December 2013 

### three to six months (fishing season) for the majority of the fish stocked as adults (i.e., at approximately 2 kg), instead of several years for fish hatched in the lake.

To place an upper bound on the radial distance from the source up to which lake-derived risks need to be assessed, we took into account the limitations of TRIM.FaTE. We limited the lake analysis to an outward radial bound of 50 km from the stack. For facilities with no lakes within 50 km, lake-derived risk is assumed 0.TRIM-Based Tiered Screening Methodology for RTRAttachment B – Tier 2 B-19 December 2013Exhibit_Att B-4. Maximum Fish Ingestion Rate (g/day) Associated with Sustainable FishingaStandingBiomass(g ww/m2)bSize of Pond or Lake (acres)123457.510152535507510015020040020000001112345810 20300000111234681215 3140000111234581015 20 415.7000111124571115 22 29 5810011112346913 19 26 38 51 10215011223461013 19 29 38 58 77 154201122345813 18 26 38 51 77 10220530122346812 19 27 38 58 77 115 15430735123447913 22 31 45 67 90 134179359401234581015 26 36 51 77 102 15420541050134561013 19 32 45 64 96 12819225651260235681215 23 38 54 77 115 154231307615702457913 18 27 45 63 90 1341792693597178024681015 20 31 51 72 102 1542053074108209025791217 23 35 58 81 1151732313464619221003581013 19 26 38 64 90 12819225638451210251103681114 21 28 42 70 99 14121128242356311271203691215 23 31 46 77 108 154231307461615122913037101317 25 33 50 83 1171662503334996661332aDark gray shading indicates insufficient population size for TL4 (WCC) fish (<50 adults) to be sustainable for more than a decade; yellow-shaded cells indicate the likelihood to provide long-termsustainable fish populations with at least 500 TL4 adult fish present; white area indicates medium-term sustainability.bRepresents the standing biomass of TL4 fish. At the upper-limit standing biomass of 40 g ww/ m2estimated for natural lakes, 25 acres couldsupport a water-column TL4 fish population, butwould provide for no more than 26 grams of fillet per day for a single angler over a full year.TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-20 December 2013 

### B.3.2 Processing Meteorological Data for Tier 2 Analysis In addition to the lake database, a database of U.S. surface stations with complete data was created, so that each source category facility can be paired with the closest meteorological station data. This process of pairing dozens or hundreds of facilities with meteorological data is not unprecedented. In their report to the Science Advisory Board (SAB) on the 1996 NATA, EPA described pairing each facility with the clos est meteorological station in an inventory of over 350 meteorological stations nationwide, cr eating an average facility-to-station distance of less than 50 km in the 1996 NATA (EPA 2001b). In a separate 2009 report to the SAB on the RTR program, EPA described using 158 meteorological stations to choose from nationwide, with a standard practice of selecting the station nearest to each facility unless the facility provides onsite meteorological data (EPA 2009). Using 156 petroleum refineries as a sample data set, the average facility-to-station distance was 72 km. In both instances, the SAB accepted this matching as standard practice when modeling large numbers of sources, although they recommended providing high-level siting maps (e.g., meteorological stations overlaid with terrain gradients or regional climate regimes) to qualify some of the uncertainties related to meteorological data in air dispersion modeling (EPA 2001a; EPA 2010). The current effort builds on this practice but increases the number of available meteorological stations as described below.



### B.3.2.1 Sources of Meteorological Data To construct a database of meteorological statistics for all available U.S. surface stations for use in multipathway screening assessments, EPA start ed with the same U.S. meteorology dataset used in RTR inhalation assessments. RTR inhalation assessments use data from 824 ASOS (Automated Surface Observing System) stat ions that record hourly and sub-hourly measurements. These data represent the year 2011, and the surface stations are paired with their closest, regularly-reporting upper-air stations. This number of stations is far greater than the 350 and 158 stations, respectively, used in the 1996 NATA report (EPA 2001b) and the 2009 RTR report (EPA 2009).

The 2011 precipitation measurements reflect 2011 weather conditions, and, like any other year, some areas of the country experienced rainfall that was significantly less than normal, and some areas received much more rainfall than normal. To reduce this bias in precipitation data, we used average annual precipitation data from the 1981

− 2010 National Climatic Data Center 30- year normal dataset wherever possible. If 30-year normal precipitation data were not available for a station, as was the case for a few stations in the dataset, we used the ASOS precipitation data as-is.



### B.3.2.2 Coverage of Meteorol ogical Stations Compared with Facility Locations Exhibit_Att B-5 shows the proximity of the evaluat ed meteorology sites to the locations of U.S. point source facilities from the 2005 NATA. Generally, the spatial density of the surface meteorological stations in this dataset was similar to the spatial density of the 2005 NATA facilities. That is, the density tends to be greatest in the Great Lakes region, along the East and West Coasts, and in the Southern Plains, and tends to be lowest in the Rockies (except Colorado) and Northern Plains.TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-21 December 2013 

### B.3.2.3 Data Processing To facilitate application of the meteorological data to the Tier 2 analysis, EPA gathered wind information in directional octants that could be linked to the direction of the closest lake (see Introduction and Section B.4). EPA divided the periphery around a meteorological station into eight octants representing the direction toward which the wind was blowing:

N: >337.5 to 22.5 degrees NE: >22.5 to 67.5 degrees E: >67.5 to 112.5 degrees SE: >112.5 to 157.5 degrees S: >157.5 to 202.5 degrees SW: >202.5 to 247.5 degrees W: >247.5 to 292.5 degrees NW: >292.5 to 337.5 degreesTRIM-Based Tiered Screening Methodology for RTRAttachment B – Tier 2 B-22 December 2013Exhibit_Att B-5. The Locations of Meteorological Stations and Point Source FacilitiesaaThe 2005 NATA was the most recent, comprehensive, finalized datasetof nationwide point source emitters of hazardous air pollutants, and it is used here only for illustrativepurposes. The 2005 NATA used a meteorological dataset different from the one used in this report.TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-23 December 2013 

### A software program was developed to calculate the following statistics for each of these stations:

 

### Number of hourly observations,

 

### Number of hours with calm winds or missing winds,

 

### Percentage of time the wind blows into each octant (after excluding missing wind hours),

 

### Median wind speed blowing into each octant,

 

### Median mixing height used (if heat flux > 0, convective mixing height was used, otherwise mechanical mixing height was used), and

 

### Average annual precipitation (irrespective of wind octant and preferring 30-year normal data if available).

Total annual precipitation data in the 2011 meteorological data included anomalies (relative to normal conditions) in areas that experienced extreme drought conditions or large rainfall surplusses. To address this, the 30-year (1981-2010) average annual precipitation was used instead. 31



### The choice of using median values for wind speed and mixing height was based on a comparison of median and mean values. For nearly all stations, the median value was smaller than the mean value; because smaller values are more health protective, we selected the median statistic for all stations..


## B.4 Implementation of Tier 2 Analysis The Tier 2 screening analysis is conducted using a Microsoft® ExcelTM tool. The tool was created so that all facilities in multiple source categories can be screened concurrently, if desired. The tool is controlled by a dashboard control panel (see Exhibit_Att B-6), where each of 7 sequential steps are controlled by a button on the panel. From the dashboard, the user is prompted to enter data on four worksheets.

Steps 1 and 2 are basic user input steps to prepare the tool for the analysis. In Step 1, the user verifies that the Tier 2 matrix results developed using the TRIM.FaTE model are current; this matrix remains static unless the methods used to derive the matrix must be updated or the chemicals currently in the matrix must be augmented. In Step 2, the user enters basic “global inputs” that include the source categories included in the analysis (Exhibit_Att B-7). Steps 1 and 2 on the dashboard should be performed prior to the start of any analysis to ensure that the user is using the most current information.

The remaining steps implementing the Tier 2 analysis are described in the below sections. Sections B.4.1 and B.4.2 contain discussions on Steps 3 and 4, where data for two additional input tables are entered into the Tier 2 analysis, after having been created using the Tier 1 Microsoft® AccessTM screening tool and the ArcGISTM lake database. Section B.4.3 contains a discussion on Step 5, where facilities in the analysis are linked with meteorology statistics from the same meteorology data used for the facilities in the RTR inhalation modeling. After all these input data have been supplied, Sections B.4.4 and B.4.5 respectively contain discussions on Step 6 (conducting the Tier 2 analysis) and Step 7 (producing output tables that summarize the results).

31 30-year average annual precipitation was obtain ed from the National Climatic Data Center (NCDC). http://www.ncdc.noaa.gov/oa/climate/normals/usnormals.html

.TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-24 December 2013 

### To facilitate explanation of the Tier 2 analysis implementation, example source categories with hypothetical PB-HAP emissions were run through the tool and screen shots, including the results, are provided to illustrate the overall 7-step process. These screen shots and accompanying descriptions represent the tool at one point in time (over a year into its development). While the tool is enhanced and im proved periodically (e.g, to fix “bugs” and add/change features), these modifications generally do not significantly alter the look, feel, and purpose of the tool.

Exhibit_Att B-6. Example of the Dashboard To Conduct the Tier 2 AnalysisTRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-25 December 2013 

### Exhibit_Att B-7. Example of Global Inputs Used in the Tier 2 Analysis a

a The screen shot examples shown in this section are for actual fa cilities; however their source ca tegories, NEI IDs, and coordin ates have been altered or masked so the data are not linked to specific facilities. Only a portion of the table might be shown.


### B.4.1 Facility List for Tier 2 Screen (Step 3) After clicking Button 3 on the dashboard (see Exhibit_Att B-6), the user is brought to the facility input sheet to enter a list of all facilities in a source category (or multiple source categories) with emissions of any PB-HAP. Included in this list are the average latitude and longitude of the emission sources at the facility, the list of chemicals emitted (and their respective PB-HAP groups), the emissions of those chemicals, the Tier 1 exposure factors and screening thresholds, and the ratio of the emission rate (including REF-adjusted emission rate if applicable) to the Tier 1 screening threshold for each evaluated chemical. This table is generated using the Tier 1 Microsoft® AccessTM screening tool, and it is pasted into the “Facility Inputs” sheet of the ExcelTM tool, as shown in Exhibit_Att B-8.TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-26 December 2013 

### Exhibit_Att B-8. Example of the Facility Input Data Required To Conduct the Tier 2 Analysis a

a The screen shot examples shown in this section are for actual fa cilities; however, their source categories, NEI IDs, and coordi nates have been altered or masked so the data are not linked to specific facilities. Only a portion of the table might be shown.


### B.4.2 Facility/Lake Distance Table (Step 4) After clicking Button 4 on the dashboard (see Exhibit_Att B-6), the user is brought to the lake distance sheet to enter details on the closest lake to each facility in each of the directional octants (N, NE, E, SE, S, SW, W, and NW). These lake data are assembled outside of this ExcelTM tool using the following steps. First, the location of each facility is imported into ArcGISTM along with the final database of lake centroids (Section B.2.1). Then, from within the ArcGISTM software, all lakes with centroids within 50 km of each facility are identified. A table is created that shows the lake name, location relative to the facility (octant and distance), and size (note: the lake size is informational only, it is not used as part of Tier 2 except to be sure it is not smaller than 100 acres or larger than 100,000 acres). The table also specifies if there is no qualifying lake. The lake names (where available) are scrutinized to subjectively remove industrial and treatment water bodies, as discussed in Section B.2.1. This table is pasted in the “LakeDistance” sheet as shown in Exhibit_Att B-9.TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-27 December 2013 

### Exhibit_Att B-9. Example of the Lake Distance Data Required To Conduct the Tier 2 Analysis a

a The screen shot examples shown in this section are for actual facilities; however, their their source categories, NEI IDs, and coordinates have been altered or masked so t he data are not linked to specific facilities. Only a portion of the table might be shown.


### B.4.3 Matching Facilities to Meteorology Data (Step 5) The last required input data step is to assign a meteorological station to each of the facilities. By clicking Button 5, the user is brought to the “MetStationMatch” tab. On this tab, the user provides a list of facilities and the associated meteorological station WBAN ID that should be used in the analysis. These station WBANs must already be present in the tool with the wind, mixing height, and precipitation statistics used in the Tier 2 methods (i.e., the meteorology stations used in RTR inhalation modeling; in a separate, hidden sheet). Clicking the return button will check that all input stations are acceptable. An example is provided in Exhibit_Att B-10.TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-28 December 2013 

### Exhibit_Att B-10. Example Results of the Meteorological Station Matching Required To Conduct the Tier 2 Analysis a

a The screen shot examples shown in this section are for actual facilities; however, their source categories, NEI IDs, and coord inates have been altered or masked so the data are not linked to specific facilities. Only a portion of the table might be shown.


### B.4.4 Assembling Threshold Adju stment Factors (Step 6) Next, the tool calculates the facility-specific, octant-specific threshold adjustment factor for each chemical. When Button 6 is clicked (see Exhibi t_Att B-6), the tool uses the meteorological parameters and lake locations to find the correct adjustment factor for each of the Tier 2 characteristics, as discussed in Section B.2. See example screen shot in Exhibit_Att B-11. The completed analysis is shown in the “OctantAnalysis” sheet. For each octant associated with a facility, the tool pairs its lake and meteorology parameters (lake location, wind speed, mixing height, and precipitation) with the closest matches in the Tier 2 matrix of EEF and screening threshold adjustment factors (Section B.2 describes this matrix, and a sample of the matrix is shown in Exhibit_Att B-12). The selection of adjustment factors is done in a health-protective manner, taking note of the direction of the correlation of the variable with risk. For example, smaller mixing heights are associated with larger risks, so a facility’s mixing height is matched with the closest evaluated Tier 2 value that is 

### smaller than the facility’s value. Each octant also is associated with a wind direction adjustment factor, where the frequency of winds blowing into the Tier 1 screening scenario (43 percent) is ratioed to the observed frequency of winds blowing into the octant. The octant’s total adjustment factor is the product of the EEF, screening scearnio, and wind direction adjustment factors.

The math to go from the Tier 1 results to the Tier 2 results can be performed a number of different ways. To improve transparency and understanding of the Tier 2 analysis, the current tool normalizes the raw emissions of PAH and dioxin congeners to their surrogate congeners (BaP and TCDD, respectively) using the Tier 2 EEF adjustment factor (Emissions Surrogate 

### = Emissions × TEF × (EEF Tier1 

### × EEFAdjFact Tier2 

### )). Then, the adjusted emissions (or raw emissions in the case of mercury and cadmium) are ratioed to the Tier 2 emission screening thresholds (Result Tier2 

### = Emissions Surrogate 

### ÷ (Threshold Tier1 

### × ThresholdAdjFactor Tier2 

### ) ÷ (WindDirectionAdjFactor Tier2 

### )).TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-29 December 2013 

### This evaluation occurs for each chemical in each octant of each facility, and for PAHs and dioxins the emissions are converted to Ba P- and TCDD-equivalents using REFs. For PAHs and dioxins, the species-specific Tier 2 ratios ( equivalent emissions/threshold) at a facility are summed to create the total PAH and total dioxin Tier 2 ratios. To maintain a health- protective focus in the analysis, the octant analysis then identifies the octant with the largest Tier 2 ratio for each PB-HAP at each facility.

Exhibit_Att B-11. Example Results of the Octant Analysis Required To Conduct the Tier 2 Analysis (shown in 2 pieces due to size) a

a The screen shot examples shown in this section are for actual fa cilities; however, their source categories, NEI IDs, and coordi nates have been altered or masked so the data are not linked to specific facilities. Only a portion of the table might be shown.TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-30 December 2013 

### Exhibit_Att B-12. Example of the TRIM.FaTE Matrix Results Required To Conduct the Tier 2 Analysis



### B.4.5 Assembling Results (Step 7) By clicking Button 7, the ExcelTM tool creates three separate results tables, shown in the sheets “SummaryOutput,” “DetailedOutput,” and “IndividualOutput.”

The summary table shows the Tiers 1 and 2 human health multipathway screening results at the level of source category and PB-HAP group (cadmium, mercury, total PAH, and total dioxin; see example screen shot in Exhibit_Att B-13). Cells with red highlighting call attention to cases where facilities exceeded the Tier 1 or Tier 2 thresholds. 32 

### Using Source Category A as an example – there were 10 facilities in the category. For this source category, four, eight, and five facilities emitted dioxins, mercury, and PAHs, respectively, at levels exceeding the Tier 1 threshold (there were no emissions of cadmium). The table indicates which facility’s emissions

32 The tool shading schemes have been revised so that only ratios of at least 1.5 indi cate an exceedance of the threshold. The 1.1 value in the fourth data row would not be shaded red, and its value for number of facilities exceeding would go from 1 to 0.TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-31 December 2013 

### most exceeded each threshold and by how much. The Tier 2 adjustment factors caused many but not all of these facilities to screen out at Tier 2, leaving three, five, and three facilities respectively exceeding the Tier 2 screening thresholds for dioxins, mercury, and PAHs, respectively. The largest Tier 2 exceedance ratios were far smaller than the largest Tier 1 exceedance ratios.

Exhibit_Att B-13. Example of Summary Output Table Created by the Tier 2 Tool a

a The screen shot examples shown in this section are for actual fa cilities; however, their source categories, NEI IDs, and coordi nates have been altered or masked so the data are not linked to specific facilities. Only a portion of the table might be shown.



### The detailed summary table shows much of the same information as the summary table, but the screening results for each facility are shown (see example screen shot in Exhibit_Att B-14). This detailed table is helpful because it shows whic h facilities exceeded the thresholds and by how much. It also shows the “worst” octant per facility and PB-HAP group as well as the lake analyzed in that octant. Green shading in Tier 2 columns call attention to cases where the Tier 1 screening threshold was exceeded but the Tier 2 threshold was not.

The individual summary table is similar to the det ailed summary table, but it adds the details of the screening results for each chemical (not just PB-HAP groups) and the Tier 2 adjustment factors (see example screen shot in Exhibit_Att B-15). The Tier 2 adjustment factors are specific to each chemical and each facility. The ratios to screening level by chemical are summed at the facility level to produce the ratios to screening level by PB-HAP group. The shading is specific to the PB-HAP-total ratios rather than the chemical ratios.TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-32 December 2013 

### Exhibit_Att B-14. Example of Detailed Output Table Created by the Tier 2 Tool a

a The screen shot examples shown in this section are for actual fa cilities; however, their source categories, NEI IDs, and coordi nates have been altered or masked so the data are not linked to specific facilities. Only a portion of the table might be shown.TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-33 December 2013 

### Exhibit_Att B-15. Example of Individual Output Table Created by the Tier 2 Tool

a The screen shot examples shown in this section are for actual fa cilities; however, their source categories, NEI IDs, and coordi nates have been altered or masked so the data are not linked to specific facilities. Only a portion of the table might be shown.TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-34 December 2013 

## B.5 References National Climatic Data Center (NCDC). (2012) Quality Controlled Local Climatological Data. Available online at http://cdo.ncdc.noaa.gov/qclcd/QCLCD?prior=N

National Climatic Data Center. (2007). Cimate M aps of the United States (CLIMAPS). Available online at http://cdo.ncdc.noaa.gov/cgi-bin/climaps/climaps.pl

EPA (U.S. Environmental Protection Agency). (2001a) NATA – Evaluating the National-scale Air Toxics Assessment, 1996 DATA – An SAB Adviso ry. U.S EPA Science Advisory Board. EPA-SAB-EC-ADV-02-001. 12/2001.

EPA. (2001b) National-scale Air Toxics Assessment for 1996, Draft for EPA Science Advisory Board Review. EPA Office of Air Quality Planning and Standards. EPA-453/R-01-003. 01/18/2001.

EPA. (2009) Risk and Technology Review (RTR) Risk Assessment Methodologies: For Review by the EPA’s Science Advisory Board with Case Studies – MACT I Petroleum Refining Sources and Portland Cement Manufacturing. EPA Office of Air Quality Planning and Standards. EPA-452/R-09-006. 06/2009.

EPA. (2010) Review of EPA’s draft entitled, “Risk and Technology Review (RTR) Risk Assessment Methodologies: For Review by the EPA’s Science Advisory Board with Case Studies – MACT I Petroleum Refining Sour ces and Portland Cement Manufacturing.” EPA Science Advisory Board. EPA-SAB-10-007. 05/07/2010.

USGS (U.S. Geological Survey). (2012) Nati onal Hydrography Dataset. Available online at http://nhd.usgs.gov/

.TRIM-Based Tiered Screening Methodology for RTR

Attachment B, Addendum 1 1-1 December 2013 TRIM.FaTE Parameters 

## Addendum 1. Summary of TRIM.F aTE Parameters Considered for Inclusion in Tier 2 Analysis[This page intentionally left blank.]TRIM-Based Tiered Screening Methodology for RTR

Attachment B, Addendum 1 1-3 December 2013 TRIM.FaTE Parameters 

### Exhibits, Addendum 1



### Exhibit Add B1-1. TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Analysis ............................................................................................................... 1-5[This page intentionally left blank.]TRIM-Based Tiered Screening Methodology for RTR

Attachment B, Addendum 1 1-5 December 2013 TRIM.FaTE Parameters 

### Exhibit Add B1-1. TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Analysis

Parameter Mechanism of Potential Influence in TRIM Assessment of Parameter Significance and Ease in Implementation Uncertainty in Site-Specific Data for Facilities Priority for Inclusion

Meteorological Parameters

Wind direction (% of time wind blows toward the lake and farm) In previous runs, direct deposition accounted for the bulk of chemical input onto farms and into lakes. Because wind direction is strongly correlated to direct deposition in a given location, media concentrations are potentially highly sensitive to this parameter. Also, because the percentage of time the prevailing wind blows in the direction of lakes and farms can vary considerably across locations, differences in this parameter might also result in significant changes in important environmental concentrations. Highly Significant : Previous sensitivity analyses have confirmed this to be a very sensitive parameter in the Tier 1 Screening modeling set-up. Changing the fraction of time the wind blows toward the lake and farm by a factor of two corresponds to a change in the risk by a factor of two.

Low Effort to Implement: This variable is relatively straightforward to vary in the Tier 2 screening scenarios. Low to Moderate: The average fraction of time the wind blows in a given direction can be estimated for any surface meteorological station. Then, facilities can be linked to the closest surface meteorological station. High

Wind speed Wind speed can affect the location of the “peak” concentration and deposition patterns in a given model configuration, as well as the risk-distance profile. Highly Significant : Previous sensitivity analyses have confirmed this to be a very sensitive parameter. However, wind speed does not vary widely across U.S. locations which could reduce its potential influence.

Low Effort to Implement: 

### This variable is relatively straightforward to vary in the Tier 2 screening scenarios. Low to Moderate:

### The annuallyaveraged wind speed can be estimated for any surface meteorological station. Then, facilities can be linked to the closest surface meteorological station. HighTRIM-Based Tiered Screening Methodology for RTR

Attachment B, Addendum 1 1-6 December 2013 TRIM.FaTE Parameters 

### Exhibit Add B1-1. TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Analysis

Parameter Mechanism of Potential Influence in TRIM Assessment of Parameter Significance and Ease in Implementation Uncertainty in Site-Specific Data for Facilities Priority for Inclusion

Precipitation Chemicals for which wet vapor or wet particle deposition processes are important are likely to be sensitive to the assumed level of precipitation. Highly Significant : Previous sensitivity analyses have indicated a relatively high sensitivity of risk toprecipitation for most PBHAPs (PAHs, cadmium, and mercury).

Moderate Effort to Implement: In implementing changes in precipitation in TRIM, care must be taken to also preserve the overall water balance in the model. Low to Moderate: Theannuallyaveraged precipitation rate can be estimated for the subset of surface meteorological stations that capture rainfall data. Then, facilities can be linked to the closest surface meteorological station with available data. HighTRIM-Based Tiered Screening Methodology for RTR

Attachment B, Addendum 1 1-7 December 2013 TRIM.FaTE Parameters 

### Exhibit Add B1-1. TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Analysis

Parameter Mechanism of Potential Influence in TRIM Assessment of Parameter Significance and Ease in Implementation Uncertainty in Site-Specific Data for Facilities Priority for Inclusion

Mixing height Greater mixing heights increase the dispersion of pollutants in the atmosphere and consequently reduce deposition to the ground in the areas around the stack. This is likely to be a highly sensitive parameter if there is a sizeable variation in mixing heights between facilities. Highly Significant : Previous sensitivity analyses have shown risk to be very sensitive to mixing height.

Low Effort to Implement: This variable is relatively straightforward to vary in the Tier 2 screening scenarios. Moderate to High: Mixing height estimates are available for upper air meteorological stations, and this set of stations is more limited than the set of surface meteorological stations. Each surface station can be linked to the closest upper air station to estimate the average mixing height. Then, facilities can be linked to the closest surface meteorological station. The relative uncertainty in mixing height for a given facility is high, given diurnal variations in mixing height and the smaller number of upper air stations. HighTRIM-Based Tiered Screening Methodology for RTR

Attachment B, Addendum 1 1-8 December 2013 TRIM.FaTE Parameters 

### Exhibit Add B1-1. TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Analysis

Parameter Mechanism of Potential Influence in TRIM Assessment of Parameter Significance and Ease in Implementation Uncertainty in Site-Specific Data for Facilities Priority for Inclusion

Configurational Parameters
Distance of lake from stack Deposition is known to decrease with distance from stack, although this relationship also depends on meteorological parameters such as wind speed and wind direction. Significance Difficult to Determine: Limited results from previous TRIM model runs show an inconclusive relationship between risk and distance from stack, possibly as a result of limited statistical power. Some studies in the literature show a definite decreasing risk gradient with distance but others report too many confounding factors to isolate the precise relationship.

Moderate Effort to Implement: This variable requires updates to the layout coordinates and requires more effort to vary in the Tier 2 screening scenarios than the meteorological parameters. Low: The lakes within a given radius of each facility can be found using ArcGISTM (see section 4). High

Distance of farm from stack Deposition is known to decrease with distance from stack, although this relationship also depends on meteorological parameters such as wind speed and wind direction. Significance Difficult to Determine: Limited results from previous TRIM model runs show an inconclusive relationship between risk and distance from stack, possibly as a result of limited statistical power. Some studies in the literature show a definite decreasing risk gradient with distance but others report too many confounding factors to isolate the precise relationship.

Moderate Effort to Implement: This variable requires updates to the layout coordinates and requires more effort to vary in the Tier 2 screening scenarios than the meteorological parameters. High: Although the distance to the farm will likely affect risk, it is difficult to determine the precise land parcels near each facility that are actually used for farming now or in the future. MediumTRIM-Based Tiered Screening Methodology for RTR

Attachment B, Addendum 1 1-9 December 2013 TRIM.FaTE Parameters 

### Exhibit Add B1-1. TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Analysis

Parameter Mechanism of Potential Influence in TRIM Assessment of Parameter Significance and Ease in Implementation Uncertainty in Site-Specific Data for Facilities Priority for Inclusion

Watershed: lake area ratio A higher watershed:lake area ratio potentially increases the chemical inputof water-soluble or particleattached chemicals into the lake. But the associated higher flush rate will likely reduce this effect. Significance Difficult to Determine : Changes in the watershed to lake ratio affect risk, but the interaction depends on other variables involved in the water balance.

Moderate Effort to Implement: In implementing changes in the watershed:lake ratios in TRIM, care must be taken to also preserve the overall water balance in the model. High: The portion of land serving as a watershed to a particular lake is difficult to determine. Medium

Area and depth of lake A higher lake area would capture more deposition but this effect might be counterbalanced by the ensuing larger volume of water, which reduces chemical concentration. Similarly, a deeper lake would also reduce concentrations, but this effect might be counterbalanced by the ensuing lower flush rates at a constant level of precipitation/runoff. Significance Difficult to Determine : The impact of these parameters is inconclusive based on current studies using the TRIM model.

Moderate Effort to Implement: The lake area variable requires updates to the layout coordinates and requires more effort to vary in the Tier 2 screening scenarios than the meteorological parameters. In implementing changes in these variables in TRIM, care must be taken to also preserve the overall water balance in the model. High: While the area of lakes near a facility can be determined using GIS, the depth cannot. MediumTRIM-Based Tiered Screening Methodology for RTR

Attachment B, Addendum 1 1-10 December 2013 TRIM.FaTE Parameters 

### Exhibit Add B1-1. TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Analysis

Parameter Mechanism of Potential Influence in TRIM Assessment of Parameter Significance and Ease in Implementation Uncertainty in Site-Specific Data for Facilities Priority for Inclusion

Physical Parameters
Flush rate A higher flush rate out of the lake would result in a higher rate of chemical output from the lake, assuming constant inflow and volume. Significance Difficult to Determine : The impact of this parameter is inconclusive based on current studies using the TRIM model.

Moderate Effort to Implement: In implementing changes in the flush rate in TRIM, care must be taken to also preserve the overall water balance in the model. High: The flush rate of a lake cannot be determined easily for any lake found near a facility. In addition, erosion rates, watershed information, and lake depth needed to estimate the flushing rate are not readily available. Medium

Runoff rate and fraction A higher runoff rate (or fraction) would likely result in greater chemical input into the lake for some chemicals but also potentially a higher flush rate out of the lake. Significance Difficult to Determine : The impact of this parameter is inconclusive based on current studies using the TRIM model.

Moderate Effort to Implement: In implementing changes in the runoff rate and fraction in TRIM, care must be taken to also preserve the overall water balance in the model. High: As with the flush rate, the runoff rate and fraction for any lake near a facility cannot be readily determined. Medium

Erosion rate and fraction A higher erosion rate would likely result in greater chemical input into the lake for particle-bound chemicals. It would also result in greater chemical transport onto farmlands, but this might be counterbalanced by equally greater erosion off farmland. Highly Significant : Previous analyses have shown risk to be sensitive to this parameter for some chemicals.

Moderate Effort to Implement: In implementing changes in the erosion rate and fraction in TRIM, care must be taken to also preserve the overall water balance in the model. High: As with the flush rate, the erosion rate and fraction for any lake near a facility cannot be readily determined. MediumTRIM-Based Tiered Screening Methodology for RTR

Attachment B, Addendum 1 1-11 December 2013 TRIM.FaTE Parameters 

### Exhibit Add B1-1. TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Analysis

Parameter Mechanism of Potential Influence in TRIM Assessment of Parameter Significance and Ease in Implementation Uncertainty in Site-Specific Data for Facilities Priority for Inclusion

Chemical Parameters Methylation/ demethylation rates (Hg) For Hg, methylation and demethylation rates in lake sediment and surface water are potentially sensitive parameters affecting risk. A literature survey has indicated a relatively high range for rate constants describing these processes. Highly Significant : Previous analyses run in TRIM have confirmed the high sensitivity of these parameters for Hg.

Low Effort to Implement: This variable is relatively straightforward to vary in the Tier 2 screening scenarios. High: The specific methylation / demethylation rates for mercury in the vicinity of a specific facility cannot be readily determined. Low

Total phosphorus levels in the lake The total phosphorus content of a lake is used as part of the TRIM.FaTE parameterization process to estimate the biomass content of different trophic levels. These biomass levels affect the biomagnification of chemicals up the food chain and potentially risk to human consumers of fish. Not Significant : Previous analyses have shown limited sensitivity to total phosphorus levels. This is likely because the empirical equations predicting biomass in each trophic level depend in similar ways on the level of total phosphorus. So changes in total phosphorus do not significantly affect the ratio of biomass between the different trophic levels.

Low Effort to Implement: This variable is relatively straightforward to vary in the Tier 2 screening scenarios. High: The total phosphorus levels in lakes near a specific facility cannot be readily determined. Low[This page intentionally left blank.]TRIM-Based Tiered Screening Methodology for RTR

Attachment B, Addendum 2 2-1 December 2013 TRIM.FaTE Parameters 

## Addendum 2. Analysis of Lake Size and Sustainable Fish Population[This page intentionally left blank.]TRIM-Based Tiered Screening Methodology for RTR

Attachment B, Addendum 2 2-3 December 2013 Lake Size Analysis 

## CONTENTS, ADDENDUM 2

1. Introduction .................................................................................................................. ...... 2-5

2. Angler Behavior ............................................................................................................... .. 2-5

3. Fish Biology .................................................................................................................. ..... 2-6

3.1. Lake Productivity ...................................................................................................... 2-6

3.2. Proportion of Fish Biomass by Trophic Level ........................................................... 2-8

3.3. Minimum Viable Population Size .............................................................................. 2-9

4. Summary of Assumptions for the Lake Size Analysis ..................................................... 2-10

5. Equations Used to Determine Lake Fish Populations ..................................................... 2-10

6. References .................................................................................................................... .. 2-12TRIM-Based Tiered Screening Methodology for RTR

Attachment B, Addendum 2 2-4 December 2013 Lake Size Analysis [This page intentionally left blank.]TRIM-Based Tiered Screening Methodology for RTR

Attachment B, Addendum 2 2-5 December 2013 Lake Size Analysis 

## 1. Introduction
Identifying the smallest size of a lake that might maintain self-sustaining populations of fish from trophic levels (TL) 3 and 4 and is sufficient to support at least one angler at a specified fish ingestion rate requires consideration of many factors. Some factors depend on assumptions about the behavior of anglers who consume fish from a lake (see Section 2 below). Other factors depend upon the general biology of fish populations in North American ecoregions (see Section 3). Based on evaluation of these factors, a set of assumptions was developed to support the estimation of minimum lake sizes that are needed to sustain a particular total human ingestion rate in grams/day (see Section 4). Then, equations were developed (see Section 5) that were used to create Exhibit_Att B-4, which was used to determine the threshold lake size of 100 acres. 

## 2. Angler Behavior
Several assumptions regarding angler behavior are important for estimating a minimum lake size that is fishable. The first is a conservative assumption that anglers (and their family members) consume about 50:50 top carnivore fi sh from both the benthos and from the water column. Benthic carnivores (BC), such as catfish and chub that consume benthic invertebrates and small benthic fish, can grow to large sizes (e.g., 2 kg). Assuming a diet of 50 percent benthic invertebrates and 50 percent small benthic fish (e.g., young of the year that feed on algae or detritus), the BC fish category would represent trophic level (TL) 3.5. Pelagic piscivores—water column carnivores (WCC)—include species such as largemouth bass, lake trout, pickerel, and walleye. The WCC are modeled as TL 4, with 100 percent of their diet comprised of water column omnivores (WCO, e.g., pan fish such as bluegill and white perch), and the diet of the WCO is assumed to be 100 percent minnow-sized fish that feed on zooplankton and algae . The assumption of 50:50 WCC and BC consumption is conservative, because smaller, pan fish, are not included. Although anglers might prefer to catch and consume the pelagic (TL4) game fish species, their generally lower abundance compared with fish that also consume benthic invertebrates dictates that anglers will more often capture the benthic (TL3.5) carnivores. Some TL2 (herbivorous) fish such as carp attain “catchable” size, but they generally are not popular fish for consumption and are not considered here. .

A second assumption is that anglers and their family members consume only the fillet portion of a fish. According to Ebert et al. (1993), the edible fraction of fish as a proportion of total fresh body weight is 0.4 for salmon, 0.78 for smelt, and 0.3 for all other species. EPA recommends use of 0.30 for the consumable fraction of fish (USEPA, 1989). For this analysis, a 0.33 edible fraction for TL4 fish was assumed. That factor is roughly one-third, which we considered preferable to 0.30 to account for some proportion of salmon likely in the diet. This factor is used in the lake size analysis to estimate total fish biomass required to support specified human fish consumption rates.

A third assumption relates to ingestion rates of the angler or angler family. Fish ingestion rates used for the purpose of the Tier 2 analysis are the same as those in Tier 1 and are consistent with subsistence angler ingestion rates (see Ex hibit_Att A-16. Summary of RTR Tier 1 Screening Scenario Assumptions).

The final assumption is that the lake must support a sustainable fishery despite fish harvesting by humans; in other words, the lake shouldn’t be “fished out” by the harvest rate required to meet the subsistence angler fish ingestion rate. The productivity of any particular fishery (local population of a species of fish) and the proportion of adult fish that can be harvested for human consumption are difficult values to estimate, and models to predict sustainable harvests of different fisheries are numerous and complex. Species-specific parameters of key to suchTRIM-Based Tiered Screening Methodology for RTR

Attachment B, Addendum 2 2-6 December 2013 Lake Size Analysis 

### models include fecundity with age and size; survivorship of eggs, fry, and juveniles to sexual maturity (recruitment); natural predation pressures; and temporal variation in food availability. For the purpose of this analysis, simplifying assumptions are required. In the analysis by Håkanson and Boulion (2004), which included a survey of 122 lakes, the authors noted that a typical loss from fishing by birds, mammals , and humans would be approximately 10 percent of the fish biomass in the prey fish compartm ent (TL3) and 10 percent of the biomass in the predator fish compartment (TL4). The authors al so found that as overall lake productivity increased, the biomass of prey (TL3) fish increased more rapidly than the biomass of predator (TL4) fish. For our lake size analysis, we assumed that anglers can harvest 10 percent of the biomass of pelagic WCC fish without diminishing the fish population size. 

## 3. Fish Biology
Fish life histories also are key to estimating the minimum surface area of a pond or lake that could support a sustainable fish population of WCC. The productivity and trophic structure of fish communities in ponds and lakes across the United States are varied. Thus, any set of assumptions is unlikely to all hold true at any given location. Nonetheless, three factors are important to any estimate of a minimum lake surface area for sustainable pelagic TL4 fishing: the general productivity of a lake (expressed as grams of fish wet weight per meter squared, g ww/m 2 

### ); the maximum likely proportion of the total fish biomass in a lake that is comprised of the top trophic level fish; and the minimum viable population (MVP) size required for the fish species to be self-sustaining in the short term (for at least a few decades).


### 3.1. Lake Productivity
The general productivity of a lake depends on m any factors, including latitude, seasonal temperatures, and nutrients supporting the base of the food web. For lakes at approximately the same latitude in the same climate, nutrients play a key role in the total fish biomass that a lake might support. In a regression analysis of data on total phosphorus (TP) and fish biomass for 31 lakes across North America, Europe, and Russia, Nürnberg (1996) summarized the “limits” among three TP-defined lake trophic status categories with respect to total fish wet weight biomass per unit area:

Oligo-meso (TP = 10 μ g/L) = 1.9 g ww/m 2
Meso-eutro (TP = 30 μ g/L) = 3.7 g ww/m 2

Eutro-hypereutro (TP = 100 μ g/L) = 8.5 g ww/m 2



### Nürnberg (1996) also summarized total fish biomass limits from Bachmann et al. (1996) for the same lake trophic status categories based on a sample of 60 lakes in Florida:

Oligo-meso (TP = 10 μ g/L) = 7.4 g ww/m 2
Meso-eutro (TP = 30 μ g/L) = 10.6 g ww/m 2

Eutro-hypereutro (TP = 100 μ g/L) = 15.6 g ww/m 2



### As might be expected, for the same TP concentrations, standing fish biomass per unit area in the Florida lakes is two to three times higher than standing fish biomass for more northerly lakes with shorter growing seasons.

Hanson and Legget (1982) estimated the relationship between TP and standing stock of fish using a regression model based on samples from 21 lakes ranging in surface area from 0.1–25,000 hectares (~0.25–62,000 acres) and located between 0° and 56° N latitude andTRIM-Based Tiered Screening Methodology for RTR

Attachment B, Addendum 2 2-7 December 2013 Lake Size Analysis 

### 121° E to 122° W longitude. Their linear regression relating TP to total fish standing biomass (B) had a correlation coefficient (r 2 

### ) of 0.84:



### B = 0.792 + 0.072 (TP)

where:

B = total fish biomass (kg/hectare)

T

P = total phosphorous ( μ g/L)



### The regression model of Hanson and Legget (1982) predicted total fish biomass densities in lakes of 3.0–9.5 g ww/m 2 

### for TP concentrations ranging from 10–50 μ g/L for oligo-mesotrophic to mid-range eutrophic lakes. Another regression model from Hoyer and Canfield (1991) predicted fish biomass densities in streams of 2.6–6.6 g ww/m 2 

### over the same range of TP concentrations.

In general, for very small lakes, relatively low fish productivity is likely. For example, Demers et al. (2001) found fish standing biomass values of 2.73 and 3.81 g ww/m 2 

### in two lakes of 27 and 22 acres (11 and 9 hectares), respectively, in south-central Ontario. Brönmark and Weisner (1996) reported on aquatic communities from a sample of 44 small ponds in southern Sweden (most were less than 5 hectares ≈ 12 acres). They found no fish in 5 of the smaller ponds (mean surface area of 0.20 ± 0.097 acres)—which also exhibited lower TP concentrations than the larger ponds—and no piscivorous fish in another 11 of the 44 ponds (mean surface area of 0.46 ± 0.27 acres). For the 28 ponds with piscivorous (TL4) fish present, the mean pond surface area was 1.4 (±1.3 SD) acres.

Scientists have also examined the relationship bet ween TP and total fish biomass in reservoirs. Yurk and Ney (1989) examined the relationship between TP and standing stock of fish in 22 reservoirs in southern Appalachia sampled in 1973. Their logarithmic regression relating TP to total fish standing biomass (B) used the following equation and had an r 2 

### of 0.75:



### Log 10 

### (B) = 1.07 + 1.14 × Log 10 

### (TP)

Use of the equations from Hanson and Legget (1982) and Yurk and Ney (1989) yielded similar predications of total fish biomass at low to intermediate TP concentrations. At low TP (e.g., 10 μg/L), predictions of total fish biomass were 3.0 g ww/m 2 

### (Yurk and Ney, 1989) and 1.6 g ww/m 2 

### (Hanson and Legget, 1982); at high TP (e.g., 100 μg/L), fish biomass predicted by the two models were 15.5 and 22.4 g ww/m 2 

### , respectively.

Ideally, one would have data indicating TP levels in lakes in the vicinity of facilities for a Tier 2 analysis. Such data, however, are rarely readily available. For purposes of the screening assessment, therefore, we assume that fish productivity per unit area is independent of lake size over a wide range of lake sizes and that TP levels are unknown.

Leidy and Jenkins (1977) reported analyses of several large data sets to support modeling of fish productivity and carrying capacity in reservoirs across the United States for the National Reservoir Research Program. The analyses included studies of fish standing biomass by species in 61 reservoirs across the midwestern and eastern United States sampled at different times between 1952 and 1975. Only reservoirs of at least 500 acres (202 hectares) in size were included, with some exceeding 65,000 acres (in the Missouri drainage basin). Considering all 61 reservoirs, the mean biomass density of fish was 41.3 (± 30.4 SD) g ww/m 2 

### . The minimumTRIM-Based Tiered Screening Methodology for RTR

Attachment B, Addendum 2 2-8 December 2013 Lake Size Analysis 

### and maximum total fish biomass densities were 3.2 and 133.2 g ww/m 2 

### , respectively, and the median value was 30.9 g ww/m 2 

### . Reservoirs typically have large drainage basins, which in some areas can receive excess TP from large expanses of agricultural areas.

In summary, the fish productivity in lakes and reservoirs can vary by more than three orders of magnitude. The reservoirs surv eyed by Leidy and Jenkins (1977) in general were much larger (and were often more shallow and nutrient rich) than the natural lakes surveyed by others discussed above. The mean standing fish biomass of approximately 41.3 g ww/m2 from the reservoir survey is likely to be higher than a mean value for representative samples of natural lakes in the United States. For the purpose of estimating the minimum lake size that would support a sustainable WCC fishery, we rounded that value down to a single significant digit of 40 g ww/m 2 

### as the upper limit for total fish biomass in a lake. That standing biomass is higher than predicted by the regression models of Hanson and Legget (1982), Yurk and Ney (1989), and Nürnberg (1996) at a high total phosphorus of 100 μg/L (where phosphorous is the limiting nutrient). Less productive lakes would support fewer fish per unit area, and, therefore, would have to be larger to support a specified fish ingestion rate.


### 3.2. Proportion of Fish Biomass by Trophic Level
As indicated previously, for the Tier 2 analysis, the proportion of fish in an angler’s diet that consists of WCC (TL4) and BC (TL3.5) is assu med to be 50:50 by biomass (not numbers) for lakes that support the four trophic levels. In smaller lakes, TL4 fish are likely to be missing or rare, with TL3 fish in the water column (e.g., sunfish) being the highest trophic level supported by the primary productivity (algal/plant production) in some lakes. As a “rule of thumb” in ecology, 10 percent or less of the energy produced at one trophic level usually can be converted to biomass in the next trophic level (i.e., approx imately 90 percent loss of energy) per trophic step. However, with different species having different energy assimilation efficiencies and with smaller species generally having higher turnover rates than larger species, the 10 percent energy rule does not necessarily translate into a standing biomass pyramid of similar proportions. In this section, the proportion of fish (based on biomass) that might be expected in the WCC and the BC fish compartments relative to total standing fish biomass are examined assuming that the lake is large enough to support WCC (pelagic TL4 fish).

Examination of several studies of fish biomass by trophic level indicated that top trophic level fish might comprise up to 20 percent of the standing fish biomass in many locations. Ploskey and Jenkins (1982) estimated that piscivorous fish, both those that are generally free-swimming or pelagic (e.g., pike, gar, walleye) and those that rest and forage primarily in the benthos (e.g., various species of catfish, suckers) comprise 22 percent of the total fish biomass in DeGray Lake, Arkansas (averaged across several years). Leidy and Jenkins (1977) estimated that 18 percent of the fish biomass across the 61 rese rvoirs they examined wa s piscivorous (minimum of 14 percent and maximum 24 percent). Demers et al. (2001) categorized 2 percent and 15 percent of the total fish biomass in two small lakes of 27 and 22 acres in size, respectively, as piscivorous/benthivorous fish (e.g., largemouth bass, creek chub); primary benthivores (e.g., catfish, suckers) dominated at >70 percent in both lakes.

One of the more recent food web models for freshwater lakes is that of Håkanson and Boulion (2004). They designed their model to predict productivity and standing crop of prey and predatory fishes in lakes of northern Europe. The authors acknowledged that fish feeding patterns are complicated by the fact that fish change their feeding preferences as they age. Some fish species consume zooplankton or benthic invertebrates in their first year, and switch to small fish and then to larger fish as they mature and grow in size.TRIM-Based Tiered Screening Methodology for RTR

Attachment B, Addendum 2 2-9 December 2013 Lake Size Analysis 

### Håkanson and Boulion (2004) created a “distribution coefficient” to indicate what proportion of the total fish biomass in a lake is prey versus predatory fish. Based on data from 122 lakes in Europe and North America, they concluded that 27 percent by biomass is a “normal” portion of predatory fish in a balanced system. They noted further, however, that for eutrophic lakes with TP levels >100 μ g/L, the proportion of fish represented by piscivores declines to less than 20 percent. The piscivores included both benthic and pelagic species. We note that most benthic piscivores also consume benthic macroinvertebrates.

Based on the information above, the top trophic level fish are assumed to comprise 21 percent of the total fish biomass. With the bulk of pr oductivity in lakes originating from detritus in the benthos, the total biomass of strictly pelagic gam e fish is expected to be less than that of benthic fish. Therefore, for purpose of this lake size analysis, the piscivorous/benthivorous fish were separated into two compartments, with 17.5 percent of the total fish biomass in a benthic carnivore (TL3.5 or BC) compartment and 3.5 perc ent of the total in a pelagic piscivore (WCC) compartment. Thus, the TL4 fish, when present, represent the limiting compartment for angler fish harvesting and consumption.



### 3.3. Minimum Viable Population Size
The final step in estimating the minimum lake size that can support sustainable fishing of its WCC fish species is to invoke the concept of minimum viable population (MVP) size. MVP is a concept used frequently in conservation biology for animals and is defined as the smallest population that will persist for a specified duration (e.g., 100 years) with a given probability (e.g., 95 percent). MVP for any given species and location depends on many attributes of the species biology (e.g., body size, reproductive rate, home range size, corridors between populations, variability in environmental characteristics that impact fecundity and survival). At lower numbers, the likelihood of population extinc tion increases due to environmental and demographic stochasticity (Menzie et al., 2008). As for fisheries biology, entire text books have been dedicated to applied population ecology with population simulations incorporating demographic and life-history characteristics, spatial separation of habitat patches and metapopulations, the probability of local catastrophes, genetic variation (e.g., drift), and other factors with predictions of time-to-extinction or probability of extinction within specified timeperiods (e.g., Soulé, 1987; Akçakaya et al., 1999). Consideration of such models in populationlevel ecological risk assessment has begun, but faces many challenges (Barnthouse et al., 2008). Moreover, that level of effort is beyond the resources available for screening-level analyses.

Much of the initial work on MVP investigated the genetic minima required for short-term survival, continuing adaptation to environmental change, a nd ultimately evolution. Inbreeding has been considered the primary threat to short-term survival and genetic drift the principal threat to losing the genetic variation required for adaptation (Shaffer, 1987). Several analyses (Senner, 1980; Franklin, 1980; Soulé, 1980; Frankel and Soulé, 1981; Lande and Barrowclough, 1987) have led to the conclusion that minimum effective population sizes on the order of 50 are required for short-term survival (e.g., several generations, decades), while effective population sizes on the order of 500 are necessary to provide adequate genetic variation for continuing adaptation over the long term (e.g., tens of generations, centuries for some animals) (Shaffer, 1981; 1987). Effective population size, N e 

### , is a measure of the rate of genetic drift (loss of genetic diversity or inbreeding), and its definition generally depends on the population in question (Rieman and Allendorft, 2001). N e 

### can be estimated mathematically based on stochastic behavior of gene frequencies in a diploid population. Simple models assume a fixed population size, constant fecundity, specified sex ratio, and no overlap between generations (see studies cited in NRC, 1986). For animals with 50:50 sex ratios, the effe ctive population size is essentially the same as the actual breeding adult population size (Ewens et al., 1987). One of the most extensiveTRIM-Based Tiered Screening Methodology for RTR

Attachment B, Addendum 2 2-10 December 2013 Lake Size Analysis 

### population viability analyses in the United States has been conducted on the spotted owl (Boyce, 1993). Given the number and complexity of factors that influence MVP, however, including the definitions of time horizon (e.g., 100 years) and probability of survival (e.g., 95 percent), population biologists caution against using a “rule of thumb” for MVP across circumstances (Ewens et al., 1987).

Note that the MVP is appropriate for a single species of fish, not for generic categories of fish such as WCC or TL4. For this Tier 2 analysis, the MVP of 50 associated with short-term population survival was assumed for a TL4 fish species isolated in a lake. In reality, short-term extirpations from a lake can be countered by purposeful introductions from other lakes or during flooding events. Thus, an MVP of 500 was not c onsidered necessary for game fish in lakes. 

## 4. Summary of Assumptions for the Lake Size Analysis
The following assumptions were used in processing lake data for the Tier 2 analysis and in estimating the relationship between fish ingestion and sustainable harvest rates and lake size (see Section B.3.1 of Attachment B).

1. Piscivorous fish (WCC and BC), when present, comprise approximately 21 percent of the standing biomass of fish (ignoring seasonal changes). The BC fish represent 17.5 percent of the standing fish biomass; WCCs account for 3.5 percent of the total fish biomass. Thus, WCC fish, when present, represent the limiti ng compartment for angler fish harvesting and consumption. 2. Humans can harvest 10 percent of the biomass of a fish compartment without threatening the population due to overharvesting. 3. The MVP size for a single WCC species is at least 50 adult fish for a local population to survive over the short term (more than a decade). 4. Only 33 percent of the fish is edible fillet muscle.


## 5. Equations Used to Determine Lake Fish Populations
The standing biomass of WCC (TL4) fish supported in Lake X can be calculated as the total standing biomass of fish (Total SB) multiplied by 0.035, based on the assumption that TL4 fish represent approximately 3.5 percent of the standing biomass in Lake X.

ܥܥܹ ݊݋݅ݐܽܿݎܨ ݔ ܤܵ ݈ܽݐ݋ܶ ൌ ܤܵ ܥܥܹ 

### (1 Equation (

where:

WCC SB = Standing biomass of WCC fish (g wet weight [ww]/m 2 ) in Lake X

Total SB = Total standing biomass of fish (g ww/m 2 ) in Lake X

Fraction WC C = Fraction of WCC fish in Lake X (i.e., 0.035)



### Using WCC SB and the size of Lake X (Lake Size), the total number of WCC fish supported in Lake X can be calculated using Equation 2 below.

ܰ݋. ܹܥܥ ൌ ௅௔௞௘ ௌ௜௭௘ ௫ ௐ஼஼ ௌ஻ ௫ ஼ி

஻ௐ ೌ 

### (Equation 2)

where:TRIM-Based Tiered Screening Methodology for RTR

Attachment B, Addendum 2 2-11 December 2013 Lake Size Analysis No. WC C = Total number of WCC fish in Lake X

Lake Size = Size of Lake X (acres)

WCC SB = Standing biomass of WCC fish (g ww/m 2 ; from Equation 1)

C F = 4047 (unit conversion factor m 2 /acre)

BW a = Body weight of adult TL4 fish (2000 g; assumed)



### The likely annual productivity of WCC fish (kg/year) in Lake X can be estimated using .

ܲ݋ݎ݀ݑܿݐ݅ݒ݅ݕݐ ܹܥܥ ൌ ௅௔௞௘ ௌ௜௭௘ ௫ ௐ஼஼ ௌ஻ ௫ ஼ிଵ

஼ிଶ (Equation 3)



### where:

Productivity WCC= Likely annual productivi ty of WCC fish in Lake X (kg/year)

Lake Size = Size of the Lake X (acres)

WCC SB = Standing biomass of WCC fish (g ww/m 2 ; from Equation 1)

CF1 = 4047 (unit conversion factor 1, m 2 /acre)

CF 2 = 1000 (unit conversion factor 2, g/kg)



### The maximum fish ingestion rate (g/day) for WCCs plus BCs associated with sustainable fishing can be predicted using Equation 4. It assumes that the anglers consume 50 percent WCC and 50 percent BC, represented by the factor of 2 in Equation 4.

ܯܽݔ ܵݐݏݑܽ݅݊ ܫܴ ሺ ܥܤ ൅ ܹܥܥ ሻ ൌ ଶ ௫ ௉௥௢ௗ௨௖௧௜௩௜௧௬ ௐ஼஼ ௫ ிி ௫ ுி ௫ ஼ிଵ

஼ிଶ 

### (Equation 4)

where:

Max Sustain IR (BC + WCC) = Predicted maximum sustainable ingestion rate for BC and WCCfish (g/day)
Productivity WC C = Likely annual productivity of WCC fish in Lake X (kg/year; from )

F

F = Fillet fraction; represents the assumed ed ible portion of fish (0.33; unitless)

H F = Annual harvest fraction (0.10; unitless)

CF1 = 1000 (unit conversion factor 1, g/kg)

CF 2 = 365 (unit conversion factor 2, days/year)TRIM-Based Tiered Screening Methodology for RTR

Attachment B, Addendum 2 2-12 December 2013 Lake Size Analysis 

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Attachment B, Addendum 2 2-13 December 2013 Lake Size Analysis 

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