APPENDIX 5: Technical Support Document for the TRIM-Based

Multipathway Tiered Screening Methodology for RTR
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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 
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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-45
TRIM-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 ........................... 2-1
TRIM-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 exposure. 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 methodology 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 characteristics 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 is 
TRIM-Based Tiered Screening Methodology for RTR

Overview 2 December 2013 

then adjusted for that facility based on an understanding of how exposure concentrations 

estimated 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 rates or other exposure factors, and other 

parameters. A range of exposure scenarios could 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 enable 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-HAPs 
TRIM-Based Tiered Screening Methodology for RTR

Overview 4 December 2013 

Based on current emissions, bioaccumulation potential, 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 CAA1

 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 multipathway 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 implies 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 health 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 comprehensive 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 calculations required for estimating PB-HAP concentrations in 

farm food chain media, average daily ingestion doses, and cancer risks and chronic non-cancer HQs. The framework 

is conceptually identical to the ingestion exposure and risk analyses that TRIM intended to cover. Information about 

the current status of TRIM modules and 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/). 
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 environment 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 layout 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 conservative (i.e., health protective) values or centraltendency values. The mix of health protective 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 Emissionsto Air

Cancer Risk

HazardQuotient

Chemical fate

and transport:

Physical environment

andaquatic ecosystem

Uptake and

transfer:

Produce and livestock

Human

Ingestion

Exposure

Risk and

Hazard

Estimation
TRIM-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 terms 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 ingestion 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 (99th

percentile for fish and 90th percentile for all other food types) based on EPA’s Exposure Factors 

Handbook (EPA 2011a) or other sources as appropriate. 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 99th percentile for fish and the 90th 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 Section 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 originally airborne is generally relatively minor and this 

pathway was not included in the scenario used to calculate 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 specific 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 appropriate 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 variables, however, can be determined without intensive 

effort during the assessment. The use of these site-specific values instead of the generic 

values 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 facility5 (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 between 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 realistically 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 the 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 one 
TRIM-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 five 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 assessments 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. Important site-specific properties likely would 

include emission release height and plume buoyancy, 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 Refining 
TRIM-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 edition. 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-scale 

Human 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 Sector. 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 
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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-38
TRIM-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 .............................................................................. 3-1
TRIM-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 Kow 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 Exposure Factors Used for RTR Multipathway 

Screeninga,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-46

 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-6 December 2013 

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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 multipathway 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 screening 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-HAPs 

TIER 1 EVALUATION
TRIM-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 accompanied 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 screening 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 being 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 level, taking into account the exposure and fate and 

transport calculations included in the model. Because 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 cancer 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 threshold will be based on the effect that occurs at the lower 

exposure level). For the set of PB-HAPs for which screening threshold levels have been derived, only chlorinated 

dibenzo-dioxins and -furans meet both criteria. Because the cancer dose-response value is lower than that for 

non-cancer effects, the screening threshold 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 estimated 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 the 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 mercury 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 developed 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 within 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 the 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 individual 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 identification 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 Compounda 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 health 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. Non-inhalation exposures to the 

other nine PB-HAPs not addressed by the modeling scenario 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 receives 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 discussions 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 possible 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 compared 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 Development of the Tier 1 Scenario 

The TRIM-based Tier 1 scenario described in this document is used to provide a means to 

qualitatively 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-protective 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: 

 

11An 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 fish12); 

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-Ingestion and TRIM.Risk-Human Health, two additional 

modules that cover the other three steps. Modeling 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 concentrations in farm food chain media, average 

daily ingestion doses, and cancer risks and chronic non-cancer HQs. This framework is 

 

12As discussed below, concentrations in fish calculated by the TRIM.FaTE model were used to estimate ingestion 

exposures for humans consuming fish. Modeling of fish concentrations is therefore discussed herein as part of the 

fate and transport modeling. Uptake of PB-HAPs into all other biotic media assumed to be ingested is modeled in the 

second step of the modeling framework. 

13Information 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 Method 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-15 December 2013 

conceptually identical to the ingestion exposure and risk analyses that TRIM is intended to 

cover. 

A.2.4.2 Model Configuration 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 selected 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 typical 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-specific 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 configuration 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 

 

14TRIM.FaTE is a spatially explicit, compartmental mass balance model that describes movement and transformation 

of pollutants over time, through a user-defined, bounded system 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 reference 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 TRIM.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-specific 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 Risk 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. 

 

15The 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 to 
TRIM-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: 

EmissPAHi:BaP = EmissPAHi × EEFPAHi:BaP × TEFPAHi:BaP

where: 

EmissPAHi:BaP = Risk-weighted emissions of PAHi

 (weighted according to cancer risk 

relative to BaP for oral exposures) 

EmissPAHi = Emission rate of PAHi

EEFPAHi:BaP = Exposure equivalency (weighting) factor accounting for difference in 

relative oral exposure between PAHi

 and BaP 

TEFPAHi:BaP = 

Toxicity equivalency (weighting) factor accounting for difference in relative toxicity via 

oral route between PAHi

 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 lifetime 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 Scaling 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 TRIM.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., Kow and Henry’s 

law constant, kh) 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 

Kow and exposure is stronger than for any other chemical-specific property. Exposure 
TRIM-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 Kow and the power regression of the 

modeled PAHs. Exhibit_Att A-7 shows that as Kow 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 Kow values near the upper end of the range of all of the Kow

values, corresponding to an exposure near the upper end of the range (log Kow = 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 Kow 

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. Although 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 1 
TRIM-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 values, 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 sections 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 generally 
TRIM-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. Additionally, 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 distance 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 (σy, 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 3σ of the median line. Therefore, the plume σ was set at 

3 times 550 m, or approximately 1.75 km from the centerline at a distance of 10 km. The plume 

 

16Mass deposited at the outer edge 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 lake would attenuate transport of chemical mass by erosion 

and runoff, dampening the effect of including additional deposition 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.) 

17http://www.epa.gov/scram001/userg/regmod/isc3v2.pdf 
TRIM-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 compartment 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 m3

/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 setting 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 third 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 velocity 
TRIM-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/m2

-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 farm foodstuffs and self-caught fish, with all 

farm foods and fish obtained from locations impacted 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 

 

18Based on sensitivity analysis, a higher erosion rate will both increase surface water concentrations and decrease 

surface soil concentrations; the relative impact on resulting 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 hourly, 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 5th 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 5th 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 298 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 5th 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 location 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 over 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 5th 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 two-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.a
TRIM-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 conditions 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/cgi-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 the sediment compartment. In the water column, 

small young-of-the-year fish and minnows feed 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 the distribution of a chemical across biotic and 

abiotic compartments in aquatic systems, the biomass of the aquatic biotic compartments must 

represent all biota in the system and the distribution 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 biota 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 base 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 (gw/m2

, 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 

(gw/m2) 

Fraction of 

Total Fish 

Biomass 

Algae 

green algae, 

diatoms, bluegreen algae 7.95 – Autotrophic – 

Zooplankton 

crustaceans, 

rotifers, 

protozoans 

6.36 – 100% algaea 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, 

mollusksb

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 Biomassc 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 emerge from 

the water when they become adults), crustaceans (e.g., amphipods, crayfish), and mollusks (e.g., snails, mussels). c

Total fish biomass does not include algae, macrophytes, 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 predicted 
TRIM-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 Deposition 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 and 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, partitioning 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 Concentrations 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 chemical 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 values, 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 are 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 Scenarios 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 

Animalsa

Environmental Uptake Route 

Medium Processb

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.FaTEc

Consumption of 

breast milkd 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 concentrations 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 scenario is discussed in Section A.4.2.3.

For the Tier 1 scenario described here, exposure characteristics 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 90th percentile values for all food types except for fish, 

which was set at 99th percentile values). All media were assumed to be obtained from locations 

impacted by the modeled source. Although this approach could result in an overestimate of 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-32 December 2013 

total chemical exposure for a hypothetical 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 exposure 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 Screeninga,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) Weighted mean of national distribution or 

recommended value 

Ingestion rate (IR) for farm produce and animal 

products other than fish (varies by age and medium) 

90th percentile of distribution of consumers who 

produce own food 

Ingestion rate for fish For adults, 99th percentile as-prepared ingestion 

rate representative of subsistence fisher woman. 

For children, based on 99th 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 lossd 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 fishe Mercury = 1.5 

Cadmium = 1.5 

Dioxin = 0.7 

PAH = 1.0 a

Data for exposure characteristics are presented 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 assessment 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 separately for each FFC medium; 

however, for the Tier 1 scenario, all ingested 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 consumption rates are used with whole-fish concentrations, adjustments might be appropriate to 

adjust the fish tissue concentrations to reflect concentrations after food preparation. See Addendum 2, Section 6.4.4 for additional 

discussion. 

A.4.2.2 Calculating Average Daily Doses 

MIRC calculates chemical-specific ADDs normalized to body weight (mg PB-HAP per kg of 

body weight per day). Equations used to calculate 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 data 
TRIM-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 Ingestion 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 compounds such as polychlorinated biphenyls (PCBs), 

polychlorinated dibenzofurans (PCDFs), and dioxins are prevalent in the scientific literature. 

Due to their high lipophilicity, these compounds partition 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 breast 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 characteristics (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]pyrenea 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 for dose-response assessment of PAHs, the CSF listed in IRIS for benzo[a]pyrene ([7.3

mg/kg-day]-1) was adjusted due to its mutagenic mode of action as discussed 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 scenario 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 overestimating 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 characteristics 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 (5th 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 (5th 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 sensitivity 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/m2

-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 

90th percentile of 

rates 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 99th percentile value 

for adult females from Burger (2002) and is 

considered representative of subsistence 

anglers. 

Rates for children are based on the 99th

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, Kow, 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 assessment 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 multipathway 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 surface 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 column 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, which 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 factors (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 ingestion exposure pathways (ATSDR 2008). Actual 

cadmium levels in ingestible products, however, varies based on type of food, agricultural and 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-39 December 2013 

cultivation practices, atmospheric deposition rates, 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 fruits generally contained low levels of cadmium 

(ATSDR 2008). 

For the RTR screening scenario, the cadmium concentrations 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 from 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 Quotients 
TRIM-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 facilities 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 divalent 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 mercury 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 than 
TRIM-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, methyl 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 United 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 transported 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 nature 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 environmental sink for this chemical group (EPA 

2004c). Although dioxins bound to aquatic sediment primarily become buried in the sediment 

compartment, some resuspension and remobilization 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 congeners, 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 dioxins concentrate in plants, aquatic organisms, 

and animals, offering multiple ingestion exposure pathways. Actual congener levels in 
TRIM-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, characteristics 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 total daily intake in the general population. 

Some studies note that specific subpopulations, such as subsistence farmers and anglers, 

however, might have very different exposure profiles 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 pathways 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 approximately 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 environmental transport processes of these congeners 

(e.g., Kow, 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, approximately 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 invertegrates, 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 increased 
TRIM-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 biomagnification is not considered to be significant 

(ATSDR 1995). Plants accumulate PAHs primarily through atmospheric deposition, but 

chemical concentrations tend to be below detection 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., Kow, molecular 

weight, chemical structure), differences in the PAH-specific half-life in abiotic media, and the 

degree to which the PAHs are metabolized in biotic 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 the 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 Services, 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

Attachment A – Tier 1 A-47 December 2013 

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://toxnet.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 Contamination 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

Attachment A – Tier 1 A-48 December 2013 

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 Transport 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 Multireceptor Risk Assessment (3MRA) Modeling 

System, 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 Assessment: 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 III: 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 Reassessment 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 Susceptibility from Early-Life Exposure to 

Carcinogens. EPA-630/R-03-003F. Risk Assessment Forum: Washington, DC. March. 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-49 December 2013 

EPA. 2006. Risk and Technology Review (RTR) Assessment Plan. Office of Air and Radiation. 

November 20, 2006. Available at: 

http://www.epa.gov/sab/panels/consul_risk_and_tech_assessment_plan.htm. 

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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 

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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 

pp. 
[This page intentionally left blank.]
Attachment A, Addendum 1 1-1 December 2013 

TRIM.FaTE Inputs 

Addendum 1. TRIM.FaTE Inputs 
[This page intentionally left blank.]
TRIM-Based Tiered Screening Methodology for RTR 

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 ..................................... 1-37
TRIM-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 ................................................................. 1-47
TRIM-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 for 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 present 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 properties "simulationStepsPerOutputStep" and "simulationTimeStep." 

.
TRIM-Based Tiered Screening Methodology for RTR 

Attachment A, Addendum 1 1-6 TRIM.FaTE Inputs 

Exhibit_Add A1-2. Meteorological Inputs for the TRIM.FaTE Screening Scenario 

Parameter Name Units Value Used Reference 

Meteorological Inputs Air temperature degrees K 298 USEPA 2005. Horizontal wind speed m/sec 2.8 5th percentile annual average value for contiguous United 

30 yrs of annual normal temperature values. 

Vertical wind speed m/sec 0.0 Assumption; vertical wind speed not used by any of the alg

of the TRIM.FaTE library used for screening. 

Wind direction degrees 

clockwise from N (blowing from) 

3-days-on 4-days-off 

On is defined as time during which wind is blowing into the conservative estimate of time during which wind should blo

domain was determined by evaluating HUSWO; it was conconservative estimate would be approximately 42% of the 

Rainfall rate m3[rain]/m2

[surface area]-day 

varies daily 1.5 m/yr is the maximum statewide 30-year (1971-2000) a

contiguous United States, excluding Rhode Island becausconditions on Mt. Washington. Data obtained from the NaCenter at http://www.ncdc.noaa.gov/oa/climate/online/ccd/

precipitation frequency was 3-days-on:4-days-off based on 

1972. 

Mixing height (used to set air VE property named “top”) 

m 710 5th percentile annual average mixing heights (calculated f

afternoon values), for all stations on SCRM (40 state, 70 s

isDay_SteadyState_forAir unitless -- Value not used in current dynamic runs (would need to be 

state 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]/m3[air] 6.15E-08 Bidleman 1988 

Density of air g/cm3 0.0012 USEPA 1997b 

Dust density kg[dust]/m3[dust] 1,400 Bidleman 1988 

Fraction organic matter 

on particulates 

unitless 0.2 Harner and Bidleman 1998 
TRIM-Based Tiered Screening Methodology for RTR 

Attachment A, Addendum 1 1-8 TRIM.FaTE Inputs 

Exhibit_Add A1-4. Soil and Groundwater Parameters for the TRIM.FaTE Screening Scen

Parameter Name Units Value Used Refe

Surface Soil Compartment Type Air content volume[air]/volume[compartment] 0.28 McKone et al. 2001. Average vertical velocity of water (percolation) 

m/day 8.22E-04 Assumed to be 0.2 time

for site. 

Boundary layer thickness above surface soil 

m 0.005 Thibodeaux 1996; McK

3). 

Density of soil solids (dry weight) kg[soil]/m3[soil] 2600 Default in McKone et aThickness - untilleda m 0.01 McKone et al. 2001 (p. Thickness - tilleda m 0.20 USEPA 2005. Erosion fraction unitless variesb See Exhibit 5. Fraction of area available for erosion 

m2[area available]/m2[total] 1 Assumption ; area assu

Fraction of area available for runoff m2[area available]/m2[total] 1 Assumption ; area assuFraction of area available for vertical diffusion 

m2[area available]/m2[total] 1 Assumption ; area assu

Fraction sand unitless 0.25 Assumption. Organic carbon fraction unitless 0.008 U.S. average in McKon

and A-3). 

pH unitless 6.8 Assumption. Runoff fraction unitless variesb See Exhibit_Add A1-5. Total erosion rate kg [soil]/m2/day variesb See Exhibit_Add A1-6. Total runoff rate m3[water]/m2/day 1.64E-03 Calculated using scena

precipitation rate and awith water balance. 

Water content volume[water]/volume[compartment] 0.15 McKone et al. 2001 Root Zone Soil Compartment Type Air content volume[air]/volume[compartment] 0.25 McKone et al 2001 (TaAverage vertical velocity of water (percolation) 

m/day 8.22E-04 Assumed as 0.2 times 

for New England in Mc

Density of soil solids (dry weight) kg[soil]/m3[soil] 2,600 McKone et al. 2001 (TaFraction sand unitless 0.25 Assumption. 
TRIM-Based Tiered Screening Methodology for RTR 

Attachment A, Addendum 1 1-9 TRIM.FaTE Inputs 

Exhibit_Add A1-4. Soil and Groundwater Parameters for the TRIM.FaTE Screening Scen

Parameter Name Units Value Used Refe

Root Zone Soil Compartment Type, continued Thickness - untilleda m 0.79 McKone et al. 2001 (TaThickness - tilleda m 0.6 Adjusted from McKone Organic carbon fraction unitless 0.008 McKone et al. 2001 (Ta

average). 

pH unitless 6.8 Assumption. Water content volume[water]/volume[compartment] 0.15 McKone et al. 2001 Vadose Zone Soil Compartment Type Air content volume[air]/volume[compartment] 0.22 McKone et al. 2001 (TaAverage vertical velocity of water (percolation) 

m/day 8.22E-04 Assumed as 0.2 times 

for New England in Mc

Density of soil solids (dry weight) kg[soil]/m3[soil] 2,600 Default in McKone et aFraction sand unitless 0.35 Assumption. Thicknessa m 1.4 McKone et al. 2001 (TaOrganic carbon fraction unitless 0.003 McKone et al. 2001 (Ta

average). 

pH unitless 6.8 Assumption. Water content volume[water]/volume[compartment] 0.21 McKone et al. 2001 (Ta

average). 

Groundwater Compartment Type Thicknessa m 3 McKone et al. 2001 (TaFraction sand unitless 0.4 Assumption. Organic carbon fraction unitless 0.004 Assumption. pH unitless 6.8 Assumption. Porosity volume[total pore 

space]/volume[compartment] 

0.2 Default in McKone et a

Density of solid material in aquifer kg[soil]/m3[soil] 2,600 Default in McKone et aaSet 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_N5a SW_Pond 0.0 

SurfSoil_N4 0.5 

SurfSoil_S5 0.5 

sink 0.0 

SurfSoil_S5a 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 RTR 

Attachment A, Addendum 1 1-11 TRIM.FaTE Inputs 

Exhibit_Add A1-6. USLE Erosion Parameters for the TRIM.FaTE Screening Scenario 

Soil 

Parcel Area Rainfall/Erosivity Index Soil Erodibility Index Length- Slope Factor Land Use Cover Mgmt Factor Supporting Practices Factor Unit Soil Loss 

Code m2 R (100 ft-ton/ac) 

K 

(ton/ac/(100 

ft-ton/acre)) 

LS 

(USCS) type C (USCS) P A (ton/ac/yr) A (kg/m2/d) 

N1 5.8E+04 300 0.39 1.5 grass 0.1 1 17.55 0.010779 N6 4.1E+04 300 0.39 1.5 crops 0.2 1 35.1 0.021557 N7 7.3E+04 300 0.39 1.5 grass 0.1 1 17.55 0.010779 N3 3.5E+05 300 0.39 1.5 grass 0.1 1 17.55 0.010779 N4 2.0E+06 300 0.39 1.5 forest 0.1 1 17.55 0.010779 N5 6.7E+06 300 0.39 1.5 forest 0.1 1 17.55 0.010779 

S1 5.8E+04 300 0.39 1.5 grass 0.1 1 17.55 0.010779 S4 2.0E+06 300 0.39 1.5 forest 0.1 1 17.55 0.010779 S5 6.7E+06 300 0.39 1.5 forest 0.1 1 17.55 0.010779 

aCalculated using SDR = a * (AL)-b; where a is the empirical intercept coefficient (based on the size of the watershed), AL is the total watershed area receiving 

empirical 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 x 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-13 TRIM.FaTE Inputs 

Exhibit_Add A1-8. Terrestrial Plant Parameters for the TRIM.FaTE Screening Scenari

Parameter Name Units 

Coniferousa Gras

Value 

Used Reference Value Used 

Leaf Compartment Type Allow exchange 1=yes, 0=no 1 - Seasonalb - Average leaf area index m2[leaf]/ 

m2[area] 

5.0 Harvard Forest, dom. red oak and 

red maple, CDIAC website 

5.0 Mid-range of 

Luxmoore, O

Calculate wet deposition interception fraction (Boolean) 

1=yes, 0=no 0 Assumption. 0 Assumption. 

Correction exponent, octanol to lipid 

unitless 0.76 From roots, Trapp 1995. 0.76 From roots, T

Degree stomatal opening unitless 1 Assumed value of 1 for daytime 

(stomatal diffusion is turned off at night using a different property, IsDay). 

1 Assumed va

(stomatal diffusing a differ

Density of wet leaf kg/m3 820 Paterson et al. 1991. 820 Paterson et aLeaf wetting factor m 3.00E-04 1E-04 to 6E-04 for different crops 

and elements, Muller and Prohl 1993. 

3.00E-04 1E-04 to 6E-

elements, Mu

Length of leaf m 0.01 Assumption. 0.05 Assumption. Lipid content kg/kg wet weight 0.00224 European beech, Riederer 1995. 0.00224 European beLitter fall rate 1/day 0.0021 value assumes 1st-order 

relationship and that 99% of leaves fall over 6 years 

Seasonalc - 

Stomatal area normalized effective diffusion path length 

1/m 200 Wilmer and Fricker 1996. 200 Wilmer and F

Vegetation attenuation factor m2/kg 2.9 Grass/hay, Baes et al. 1984. 2.9 Grass/hay, BWater content unitless 0.8 Paterson et al. 1991. 0.8 Paterson et aWet deposition interception fraction 

unitless 0.2 Calculated based on 5 years of 

local met data, 1987-1991. 

0.2 Calculated b

data, 1987-1

Wet mass of leaf per soil area kg[fresh 

leaf]/m2[area] 

2.0 Calculated from leaf area index, 

leaf thickness (Simonich and Hites, 1994), density of wet foliage. 

0.6 Calculated fr

1975. 

Particle on Leaf Compartment Type Allow exchange 1=yes, 0=no 1 - Seasonalb - 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-14 TRIM.FaTE Inputs 

Exhibit_Add A1-8. Terrestrial Plant Parameters for the TRIM.FaTE Screening Scenari

Parameter Name Units 

Coniferousa Gras

Value 

Used Reference Value Used 

Particle on Leaf Compartment Type, continued Volume particle per area leaf m3[leaf 

particles]/m2[leaf] 1.00E-09 Based on particle density and size distribution for atmospheric 

particles measured on an adhesive surface, Coe and Lindberg 1987. 

1.00E-09 Based on pa

distribution fomeasured on and Lindberg 

Root Compartment Type – Nonwoody Only Allow exchange 1=yes, 0=no Seasonalb - Correction exponent, octanol to lipid 

unitless 0.76 Trapp 1995. 

Lipid content of root kg/kg wet weight 0.011 Calculated. Water content of root kg/kg wet weight 0.8 Assumption. Wet density of root kg/m3 820 Soybean, PaWet mass per soil area kg/m2 1.4 Temperate g

1996. 

Stem Compartment Type – Nonwoody Only Allow exchange 1=yes, 0=no Seasonalb - Correction exponent, octanol to lipid 

unitless 0.76 Trapp 1995. 

Density of phloem fluid kg/m3 1,000 Assumption. Density of xylem fluid kg/cm3 900 Assumption. Flow rate of transpired water per leaf area 

m3[water]/m2[leaf

] 

0.0048 Crank et al. 1

Fraction of transpiration flow rate that is phloem rate 

unitless 0.05 Paterson et a

Lipid content of stem kg/kg wet weight 0.00224 Leaves of Eu

1995. 

Water content of stem unitless 0.8 Paterson et aWet density of stem kg/m3 830 Assumption. Wet mass per soil area kg/m2 0.24 Calculated fr

density. 

aSee Exhibit_Add A1-7 for assignment of plant types to surface soil compartments. bBegins March 9 (set to 1), ends November 7 (set to 0). Nationwide 80th percentile. cBegins November 7, ends December 6; rate = 0.15/day during this time (value assumes 99 percent of 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]/m3[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. 

Deptha 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]/m3[water 

column] 

0.05 USEPA 2005. 

Water temperature degrees 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 

Deptha 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]/m3[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 RTR

Attachment A, Addendum 1 1-17 TRIM.FaTE Inputs 

Exhibit_Add A1-11. Aquatic Animals Food Chain, Density, and Mass for the TRIM.FaTE Screeni

Aquatic Biota 

(Consuming 

Organism) 

Fraction Diet 

Biomass

(kg/m2) 

B

W

Algae 

Zooplankto

n 

Benthic 

Invertebrate 

Water 

Column 

Herbivore 

Benthic 

Omnivore 

Water 

Column 

Omnivore 

Benthic 

Carnivore 

Water 

Column 

Carnivore 

Benthic Invertebrate 

0% 0% 0% 0% 0% 0% 0% 0% 0.020 2.5

Water Column Herbivore 

0% 100% 0% 0% 0% 0% 0% 0% 0.002 0

Benthic Omnivore 0% 0% 100% 0% 0% 0% 0% 0% 0.002 2.5Water Column Omnivore 

0% 0% 0% 100% 0% 0% 0% 0% 0.001 

Benthic Carnivore 0% 0% 50% 0% 50% 0% 0% 0% 0.001 Water Column Carnivore 

0% 0% 0% 0% 0% 100% 0% 0% 0.0002 

Zooplankton 100% 0% 0% 0% 0% 0% 0% 0% 0.0064 5.7
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 Namea Units Value Reference 

CAS numberb unitless 7440-43-9 - 

Diffusion coefficient in pure 

air 

m2[air]/day 0.71 USEPA 1999 (Table A-2-

35). 

Diffusion coefficient in pure 

water 

m2[water]/day 8.16E-05 USEPA 1999 (Table A-2-

35). 

Henry's Law constant Pa-m3/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 (Koa) 

m3

[air]/m3[octanol] - - 

Octanol-carbon partition 

coefficient (Koc) 

 - - 

Octanol-water partition 

coefficient (Kow) 

L[water]/kg[octanol] - - 

a

All parameters in this table are TRIM.FaTE chemical properties. b

This CAS numbers applies to elemental Cd; however, 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 MHgb

CAS number unitless 7439-97-6 14302-87-5 22967-92-6 - 

Diffusion coefficient 

in pure air 

m2[air]/day 0.478 0.478 0.456 USEPA 1997b. 

Diffusion coefficient 

in pure water 

m2[water]/day 5.54E-05 5.54E-05 5.28E-05 USEPA 1997b. 

Henry's Law 

constant 

Pa-m3/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 

(Kow) 

L[water]/kg[octanol] 4.15 3.33 1.7 Mason et al. 1996.

Vapor washout ratio m3[air]/m3[rain] 1,200 1.6E+06 0 USEPA 1997b, 

based on Petersen 

et al. 1995. a

All parameters in this table are 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 RTR

Attachment A, Addendum 1 1-20 TRIM.FaTE Inputs 

Exhibit_Add A1-14. PAH Chemical-Specific Parameters for the TRIM.FaTE Screening Sce

Parameter Name Units 

Value 

2Methyl 712DMB Acenaphthene Acenaphthylene BaA BaP 

CAS number unitless 91-57-6 57-97-6 83-32-9 208-96-8 56-55-3 50-32-8 Diffusion coefficient in pure air 

m2/day 0.451 0.691 0.009 0.388 0.441 0.372 

Diffusion coefficient in pure water 

m2/day 6.70E-05 6.91E-05 8.64E-05 6.03E-05 7.78E-05 7.78E-05 

Henry's Law constant 

Pa-m3/mol 50.56 0.20 18.50 12.70 1.22 0.07 

Melting point degrees K 307.75 396.65 366.15 365.65 433 452 Molecular weight g/mol 142.20 256.35 154.21 152.20 228.29 252.32 Octanol-water partition coefficient (Kow) 

L[water]/L[octanol] 7.24E+03 6.31E+05 8.32E+03 1.00E+04 6.17E+05 9.33E+05 

Parameter Name Units 

Value 

BkF Chr DahA Fluoranthene Fluorene IcdP 

CAS number unitless 207-08-9 218-01-9 53-70-3 206-44-0 86-73-7 193-39-5Diffusion coefficient in pure air 

m2/day 0.009 0.009 0.009 0.009 0.009 0.009 

Diffusion coefficient in pure water 

m2/day 8.64E-05 8.64E-05 8.64E-05 8.64E-05 8.64E-05 8.64E-05

Henry's Law constant 

Pa-m3/mol 0.04 0.53 0.01 1.96 9.81 0.03 

Melting point degrees K 490 531 539 383.15 383.15 437 Molecular weight g/mol 252.32 228.29 278.33 202.26 166.20 276.34 Octanol-water partition coefficient (Kow) 

L[water]/L[octanol] 8.71E+05 5.37E+05 3.16E+06 1.45E+05 1.51E+04 5.25E+0

6 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-21 TRIM.FaTE Inputs 

Exhibit_Add A1-14. PAH Chemical-Specific Parameters for the TRIM.FaTE Screening Sce

Parameter Name Units Reference 

CAS number unitless - Diffusion coefficient in pure air 

m2/day USEPA 2005. Exceptions include USEPA 1997a (7,12-Dimethylbe

USEPA 2007 (2-Methylnaphthalene, Acenaphthylene, and Benzo(

Diffusion coefficient in pure water 

m2/day USEPA 2005. Exceptions include USEPA 1997a (7,12-Dimethylbe

USEPA 2007 (2-Methylnaphthalene, Acenaphthylene, and Benzo(

Henry's Law constant Pa-m3/mol USEPA 2005. Exceptions include USEPA 2003 (2-Methylnaphthal

(7,12-Dimethylbenz(a)anthracene), HSDB 2001b (Acenaphthylene(Benzo(g,h,i)perylene) 

Melting point degrees K Budavari 1996. Exceptions include USEPA 2003 (2-Methylnaphtha

(7,12-Dimethylbenz(a)anthracene), HSDB 2001b (Acenaphthylene(Benzo(g,h,i)perylene), and USEPA 2005 (Acenaphthene, Fluoran

Molecular weight g/mol Budavari 1996. Exceptions include USEPA 2003 (2-Methylnaphtha

(7,12-Dimethylbenz(a)anthracene), HSDB 2001b (Acenaphthylene(Benzo(g,h,i)perylene), and USEPA 2005 (Acenaphthene, Fluoran

Octanol-water partition coefficient (Kow) 

L[water]/L[octanol] Hansch et al. 1995. Exceptions include Passivirta et al. 1999 (Ace

Benzo(k)fluoranthene, and Indeno(1,2,3-cd)pyrene), and Sangster (Benzo(b)fluoranthene) 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-22 TRIM.FaTE Inputs 

Exhibit_Add A1-15. Dioxin Chemical-Specific Parameters for the TRIM.FaTE Screening Sc

Parameter Name Units 

Value 

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 

CAS number unitless 3268-87-9 39001-02-0 35822-46-9 67562-39-4 55673-89-7 39Diffusion coefficient in pure air 

m2/day 0.751 0.168 0.782 0.176 0.176 

Diffusion coefficient in pure water 

m2/day 6.91E-05 6.91E-05 6.91E-05 6.91E-05 6.91E-05 6

Henry's Law constant Pa-m3/mol 0.684 0.19 1.22 1.43 1.42 Melting point degrees K 603.0 259.0 538.0 236.5 222.0 Molecular weight g/mol 460 443.76 425.2 409.31 409.31 Octanol-water partition coefficient (Kow) 

L[water]/L[octanol] 1.58E+08 1.00E+08 1.00E+08 2.51E+07 7.94E+06 6

Parameter Name Units 

Value 

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 

CAS number unitless 57653-85-7 57117-44-9 19408-74-3 72918-21-9 40321-76-4 57Diffusion coefficient in pure air 

m2/day 0.816 0.183 0.816 0.183 0.854 

Diffusion coefficient in pure water 

m2/day 6.91E-05 6.91E-05 6.91E-05 6.91E-05 6.91E-05 6

Henry's Law constant Pa-m3/mol 1.11 0.741 1.11 1.11 0.263 Melting point degrees K 558.0 506.0 517.0 509.0 513.0 Molecular weight g/mol 390.84 374.87 390.84 374.87 356.4 Octanol-water partition coefficient (Kow) 

L[water]/L[octanol] 1.62E+08 8.24E+07 1.62E+08 3.80E+07 1.86E+07 6
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-23 TRIM.FaTE Inputs 

Exhibit_Add A1-15. Dioxin Chemical-Specific Parameters for the TRIM.FaTE Screening Sc

Parameter Name Units 

Value 

Reference 

2,3,4,7,8- 

PeCDF 

2,3,7,8- 

TCDD 

2,3,7,8- 

TCDF 

CAS number unitless 57117-31-4 1746-01-6 51207-31-9 - Diffusion coefficient in pure air 

m2/day 0.192 0.899 0.203 US EPA 2005 

Diffusion coefficient in pure water 

m2/day 6.91E-05 4.84E-05 5.19E-05 US EPA 2005 

Henry's Law constant Pa-m3/mol 0.505 3.33 1.46 US EPA 2005 Melting point degrees K 469.3 578.0 500.0 Mackay et al. 2000. Exceptions includ

(1,2,3,6,7,8-HxCDD, 1,2,3,7,8,9-HxCPeCDD), ATSDR 1998 (1,2,3,6,7,8-HPeCDF, and 2,3,4,6,7,8-HxCDF), and (1,2,3,7,8,9-HxCDD) 

Molecular weight g/mol 340.42 322 306 Mackay et al. 2000. Exceptions includ

(1,2,3,6,7,8-HxCDF, 1,2,3,7,8,9-HxCD1,2,3,7,8-PeCDF, and 2,3,4,6,7,8-HxC(1,2,3,6,7,8-HxCDD and 1,2,3,7,8,9-H

Octanol-water partition coefficient (Kow) 

L[water]/L[octanol] 3.16E+06 6.31E+06 1.26E+06 Mackay et al. 1992 as cited in USEPA 

include Mackay et al. 2000 (1,2,3,4,72000a (1,2,3,6,7,8-HxCDD, 1,2,3,6,7HxCDD, 1,2,3,7,8,9-HxCDF, and 2,3,Sijm et al. 1989 as cited in USEPA 20PeCDD) 
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 m3[air]/m3[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 RTR

Attachment A, Addendum 1 1-25 TRIM.FaTE Inputs 

Exhibit_Add A1-17. Mercury Chemical-Specific Parameters for Abiotic Compartments in the TRIM.FaTE 

Parameter Name Units 

Value Refer

Hg(0) Hg(2) MHg 

Air Compartment Type Particle dry deposition velocity m/day 500 500 500 CalTOX value cited in McDemethylation rate 1/day N/A N/A 0 Assumption. Methylation rate 1/day 0 0 0 Assumption. Oxidation rate 1/day 0.00385 0 0 Low end of half-life range 

USEPA 1997b. 

Reduction rate 1/day 0 0 0 Assumption. Washout ratio m3[air]/m3[rain] 200,000 200,000 200,000 Assumption. Surface Soil Compartment Type Use input characteristic depth (Boolean) 

0 = no, Else = yes 0 0 0 Assumption. 

Soil-water partition coefficient L[water]/kg[soil 

wet wt] 

1,000 58,000 7,000 USEPA 1997b. 

Vapor dry deposition velocity m/day 50 2500 0 Hg(0) - from Lindberg et a

by USEPA using the Indu(ISC) Model - [See Vol. IIStudy Report (USEPA 19

Demethylation rate 1/day N/A N/A 0.06 Range reported in Porvar

to 6E-2 /day; value is avedemethylation rate constaconditions. 

Methylation rate 1/day 0 0.001 0 Range reported in Porvar

to 1E-3 /day; value is avemethylation rate constant conditions. 

Oxidation rate 1/day 0 0 0 Value assumed in USEPA Reduction rate 1/day 0 1.25E-05 0 Value used for untilled su

moisture content, in USEPis (0.0013/day)*moisture (0.0001/day)*moisture co(Lindberg 1996; Carpi and 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-26 TRIM.FaTE Inputs 

Exhibit_Add A1-17. Mercury Chemical-Specific Parameters for Abiotic Compartments in the TRIM.FaTE 

Parameter Name Units 

Value Refe

Hg(0) Hg(2) MHg 

Root Zone Soil Compartment Type Use input characteristic depth (Boolean) 0 = no, Else = yes 0 0 0 Assumption. Soil-water partition coefficient L[water]/kg[soil 

wet wt] 

1,000 58,000 7,000 USEPA 1997b. 

Demethylation rate 1/day N/A N/A 0.06 Range reported in Porva

to 6E-2 /day; value is avdemethylation rate constconditions. 

Methylation rate 1/day 0 0.001 0 Range reported in Porva

to 1E-3 /day; value is avmethylation rate constanconditions. 

Oxidation rate 1/day 0 0 0 Value assumed in USEPReduction rate 1/day 0 3.25E-06 0 Value used for tilled surf

moisture content, in USE1996; Carpi and Lindber

Vadose Zone Soil Compartment Type Use input characteristic depth (Boolean) 0 = no, Else = yes 0 0 0 Assumption. Soil-water partition coefficient L[water]/kg[soil 

wet wt] 

1,000 58,000 7,000 USEPA 1997b. 

Demethylation rate 1/day N/A N/A 0.06 Range reported in Porva

to 6E-2 /day; value is avdemethylation rate constconditions. 

Methylation rate 1/day 0 0.001 0 Range reported in Porva

to 1E-3 /day; value is avmethylation rate constanconditions. 

Oxidation rate 1/day 0 0 0 Value assumed in USEPReduction rate 1/day 0 3.25E-06 0 Value used for tilled surf

moisture content, in USE1996; Carpi and Lindber
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-27 TRIM.FaTE Inputs 

Exhibit_Add A1-17. Mercury Chemical-Specific Parameters for Abiotic Compartments in the TRIM.FaTE 

Parameter Name Units 

Value Refe

Hg(0) Hg(2) MHg 

Groundwater Compartment Type Soil-water partition coefficient L[water]/kg[soil 

wet wt] 

1,000 58,000 7,000 USEPA 1997b. 

Demethylation rate 1/day N/A N/A 0.06 Range reported in Porva

to 6E-2 /day; value is avdemethylation rate constconditions. 

Methylation rate 1/day 0 0.001 0 Range reported in Porva

to 1E-3 /day; value is avmethylation rate constanconditions. 

Oxidation rate 1/day 1.00E-08 0 0 Small default nonzero va

1997b). 

Reduction rate 1/day 0 3.25E-06 0 Value used for tilled surf

moisture content, in USE1996; Carpi and Lindber

Surface Water Compartment Type Algal surface area-specific uptake rate constant 

nmol/[μm2-day-nmol] 0 2.04E-10 3.60E-10 Assumes radius = 2.5mm

Mason et al. 1996; Hg(0

Dow ("overall Kow") L[water]/kg[octanol] 0 -a -b Mason et al. 1996. Solids-water partition coefficient L[water]/kg[solids 

wet wt] 

1,000 100,000 100,000 USEPA 1997b. 

Vapor dry deposition velocity m/day N/A 2500 USEPA 1997b (Vol. III, ADemethylation rate 1/day N/A N/A 0.013 Average range of 1E-3 to 

and Henry 1991. 

Methylation rate 1/day 0 0.001 0 Value used in EPA 1997

4/day (Gilmour and Hen

Oxidation rate 1/day 0 0 0 Assumption. Reduction rate 1/day 0 0.0075 0 Value used in USEPA 19

range from less than 5E-than 17m, up to 3.5/day et al. 1995; Mason et al. 1997). 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-28 TRIM.FaTE Inputs 

Exhibit_Add A1-17. Mercury Chemical-Specific Parameters for Abiotic Compartments in the TRIM.FaTE 

Parameter Name Units 

Value Refe

Hg(0) Hg(2) MHg 

Sediment Compartment Type Solids-water partition coefficient L[water]/kg[solids 

wet wt] 

3,000 50,000 3,000 USEPA 1997b. 

Demethylation rate 1/day N/A N/A 0.0501 Average range of 2E-4 to 

and Henry 1991. 

Methylation rate 1/day 0 1.00E-04 0 Value used in EPA 1997

1E-3/day, Gilmour and H

Oxidation rate 1/day 0 0 0 Assumption. Reduction rate 1/day 0 1.00E-06 0 Inferred value based on 

sediment porewater (US1995). 

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 0.075 to 1.7 depending on pH and chloride concentration. 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-29 TRIM.FaTE Inputs 

Exhibit_Add A1-18. PAH Chemical-Specific Parameters for Abiotic Compartments in the TRIM.FaTE S

Parameter Name Units 

Value 

2Methyl 712DMB Acenaph- thene Acenaph- thylene BaA BaP 

Air Compartment Type Particle dry deposition velocity m/day 500 500 500 500 500 500 Half-life day 0.154 0.092 0.3 0.208 0.125 0.046 Washout ratio 200000 200000 200000 200000 200000 200000 2Surface Soil Compartment Type User input characteristic depth (Boolean) 

0 = No, Else = Yes 0 0 0 0 0 0 

Half-life day 18 24 56 66.5 680 530 Root Zone Soil Compartment Type User input characteristic depth (Boolean) 

0 = No, Else = Yes 0 0 0 0 0 0 

Half-life day 18 24 56 66.5 680 530 Vadose Zone Soil Compartment Type User input characteristic depth (Boolean) 

0 = No, Else = Yes 0 0 0 0 0 0 

Half-life day 36 48 112 133 1360 1060 Groundwater Compartment Type Half-life day 36 48 112 133 1360 1060 Surface Water Compartment Type Ratio Of Conc In Algae To Conc Dissolved In Water 

(g[chem]/kg[algae]) / 

(g[chem]/L[water]) 

2.6 333.4 3 3.7 325 510 

Half-life day 78 216 25 184 0.375 0.138 Sediment Compartment Type Half-life day 2290 2290 2290 2290 2290 2290 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-30 TRIM.FaTE Inputs 

Exhibit_Add A1-18. PAH Chemical-Specific Parameters for Abiotic Compartments in the TRIM.FaTE S

Parameter Name Units 

Value 

Ref

Chr DahA Fluoran- thene Fluor- ene IcdP 

Air Compartment Type Particle dry deposition velocity m/day 500 500 500 500 500 McKone et al. 2001. Half-life day 0.334 0.178 0.46 0.46 0.262 Howard et al. 1991 / uppe

estimated value. ExceptioMethylnaphthalene), USEDimethylbenz(a)anthraceand Fluoranthene) / aver(Acenaphthene), HSDB 2Spero et al. 2000 (Fluore

Washout ratio 200000 200000 200000 200000 200000 Mackay et al. 1986. Surface Soil Compartment Type User input characteristic depth (Boolean) 

0 = No, 

Else = Yes 

0 0 0 0 0 Assumption. 

Half-life day 1000 940 275 33 730 MacKay et al. 2000 / ave

include ATSDR 2005 (2-M1998 (7,12-DimethylbenzBenzo(g,h,i)perylene, and range, HSDB 2001d (Ace(Acenaphthylene), and H

Root Zone Soil Compartment Type User input characteristic depth (Boolean) 

0 = No, 

Else = Yes 

0 0 0 0 0 Assumption. 

Half-life day 1000 940 275 33 730 Howard et al. 1991 / uppe

estimated value. ExceptioMethylnaphthalene), USEDimethylbenz(a)anthraceand Fluoranthene) / aver(Acenaphthene), HSDB 2HSDB 2001e (Fluorene). 

Vadose Zone Soil Compartment Type User input characteristic depth (Boolean) 

0 = No, Else = 

Yes 

0 0 0 0 0 Assumption. 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-31 TRIM.FaTE Inputs 

Exhibit_Add A1-18. PAH Chemical-Specific Parameters for Abiotic Compartments in the TRIM.FaTE S

Parameter Name Units 

Value 

Refe

Chr DahA Fluora n-thene Fluor- ene IcdP 

Vadose Zone Soil Compartment Type, continued Half-life day 2000 1880 550 66 1460 Howard et al. 1991 / upper 

estimated value. ExceptionMethylnaphthalene), USEPDimethylbenz(a)anthracenFluoranthene) / twice avera(Acenaphthene) / multiplie(Acenaphthylene) / multipl(Fluorene) / multiplied by 2

Groundwater Compartment Type Half-life day 2000 1880 550 66 1460 Howard et al. 1991 / upper 

estimated value. ExceptionMethylnaphthalene), USEPDimethylbenz(a)anthracenFluoranthene) / twice avera(Acenaphthene) / multiplie(Acenaphthylene) / multipl(Fluorene) / multiplied by 2

Surface Water Compartment Type RatioOfConcInAlgaeToConcDissolvedInWater 

(g[chem]/kg[algae])/

(g[chem]/L[water]) 

280 1388 67.4 5.8 1653 Kow from Del Vento and Da

Half-life day 1.626 97.8 160 8.5 750 Howard et al. 1991 / upper 

estimated value. ExceptionMethylnaphthalene), HSDB Dimethylbenz(a)anthracen(Acenaphthene), HSDB 20HSDB 2001c (Benzo(g,h,i)(Fluoranthene), and Boyle 

Sediment Compartment Type Half-life day 2290 2290 2290 2290 2290 Mackay et al. 1992 / PAH v

of the log class that Mackasediment, except for BbF aTable 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 

Value 

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 

Air Compartment Type 

Deposition Velocity m/day 500 500 500 500 500 500 

Half-life day 162 321 64 137 122 42 

Washout Ratio m3[air]/m3[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 1095 
TRIM-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 

Value 

1,2,3,4,7,8-

HxCDF 

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 

Air Compartment Type 

Deposition velocity m/day 500 500 500 500 500 500 

Half-life day 78 28 55 28 51 18 

Washout ratio m3[air]/m3[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 1095 
TRIM-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 

Value 

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-TCDF 

Air Compartment Type 

Deposition velocity m/day 500 500 500 500 500 

Half-life day 31 59 33 12 19 

Washout ratio m3[air]/m3[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 1095 
TRIM-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 cited 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 depth (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 depth (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 Kow 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 Herbsa

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

Root to Root Soil PartitionPartitioning Coefficient m3[bulk root 

soil]/m3

[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 Herbsa

Transpiration stream 

concentration factor (TSCF) 

m3[soil pore 

water]/m3

[xylem 

fluid] 

0.45 Tsiros et al. 1999. 

a

Roots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE. 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-37 TRIM.FaTE Inputs 

Exhibit_Add A1-21. Mercury Chemical-Specific Parameters for Plant Compartments in the TRIM.FaTE S

Parameter Name Units Value R

Hg(0) Hg(2) MHg 

Leaf Compartment Type Transfer factor to leaf particle 1/day 0.002 0.002 0.002 Assumed based o

from leaf particle 

Demethylation rate 1/day N/A N/A 0.03 Calculated from BMethylation rate 1/day 0 0 0 Assumed from Ga

1973. 

Oxidation rate 1/day 1.0E+06 0 0 Assumed to be neReduction rate 1/day 0 0 0 Assumption. Particle on Leaf Compartment Type Transfer factor to leaf 1/day 0.2 0.2 0.2 Assumption. Demethylation rate 1/day N/A N/A 0 Assumption. Methylation rate 1/day 0 0 0 Assumption. Oxidation rate 1/day 0 0 0 Assumption. Reduction rate 1/day 0 0 0 Assumption. Root Compartment Type - Grasses and HerbsaAlpha for root-root zone bulk soil unitless 0.95 0.95 0.95 Selected value. Root/root-zone-soil-water partition coefficient 

m3[bulk root soil]/ m3[root] 0 0.18 1.2 Hg2- geometric m

John 1972, Hogg assumed, based 

t-alpha for root-root zone bulk soil day 21 21 21 Assumption. Demethylation rate 1/day N/A N/A 0 Assumption. Methylation rate 1/day 0 0 0 Assumption. Oxidation rate 1/day 0 0 0 Assumption. Reduction rate 1/day 0 0 0 Assumption. Stem Compartment Type - Grasses and HerbsaTranspiration stream concentration factor (TSCF) 

m3[soil pore 

water]/m3[xylem fluid] 

0 0.5 0.2 Calculation from N

pine, Bishop et al

Demethylation rate 1/day N/A N/A 0.03 Calculated from BMethylation rate 1/day 0 0 0 Assumption. Oxidation rate 1/day 0 0 0 Assumption. Reduction rate 1/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 RTR

Attachment A, Addendum 1 1-38 TRIM.FaTE Inputs 

Exhibit_Add A1-22. PAH Chemical-Specific Parameters for Plant Compartments in the TRIM.FaTE Sc

Parameter Name Units 

Value 

2Methyl 712DMB Acenaph- thene Acenaph- thylene BaA BaP BbF 

Leaf Compartment Type Transfer factor to leaf particle 

1/day 1.00E-04 1.00E-04 1.00E-04 1.00E-04 1.00E-04 1.00E-04 1.00E-04 

Half-life day 3.50 3.50 3.50 3.50 3.50 3.50 3.50 Particle on Leaf Compartment Type Transfer factor to leaf 

1/day 1.00E-04 1.00E-04 1.00E-04 1.00E-04 1.00E-04 1.00E-04 1.00E-04 

Half-life day 2.31 2.31 2.31 2.31 1.84 2.31 3.56 Root Compartment Type - Grasses and HerbsaHalf-life day 34.60 34.60 34.60 34.60 34.60 34.60 34.60 Root soil-water interaction - alpha 

unitless 0.95 0.95 0.95 0.95 0.95 0.95 0.95 

Stem Compartment Type - Grasses and HerbsaHalf-life day 3.50 3.50 3.50 3.50 3.50 3.50 3.50 aRoots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE. 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-39 TRIM.FaTE Inputs 

Exhibit_Add A1-23. PAH Chemical-Specific Parameters for Plant Compartments in the TRIM.FaTE Sc

Parameter Name Units 

Value 

Refere

Chr DahA Fluoran- thene Fluorene IcdP 

Leaf Compartment Type Transfer factor to leaf particle 

1/day 1.00E-04 1.00E-04 1.00E-04 1.00E-04 1.00E-04 Assumption. 

Half-life day 3.50 3.50 3.50 3.50 3.50 Edwards 1988 (calculate

constant). 

Particle on Leaf Compartment Type Transfer factor to leaf 

1/day 1.00E-04 1.00E-04 1.00E-04 1.00E-04 1.00E-04 Assumption. 

Half-life day 4.12 17.80 2.31 2.31 17.80 Calculated as 2 times the 

half-life from Mackay et ainclude values that have Benzo(a)pyrene (2-MethyDimethylbenz(a)anthraceAcenaphthylene; Benzo(Fluoranthene; and Fluore

Root Compartment Type - Grasses and HerbsaHalf-life day 34.60 34.60 34.60 34.60 34.60 Edwards 1988 (calculate

constant). 

Root soil water interaction - alpha 

unitless 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 (calculate

constant). 

aRoots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE. 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-40 TRIM.FaTE Inputs 

Exhibit_Add A1-23. Dioxin Chemical-Specific Parameters for Plant Compartments in the TRIM.FaTE S

Parameter Name Units 

Value 

Reference 

All Dioxins 

Leaf Compartment Type Transfer factor to leaf particle 1/day 0.003 Calculated as 1 percent of transfer factor to leaHalf-life day 70 Arjmand and Sandermann 1985, as cited in Ko

soybean root cell culture metabolism test data 

Particle on Leaf Compartment Type Transfer factor to leaf 1/day 0.3 Assumption based on USEPA 2000b (an estim

Trapp 1995; highly uncertain. 

Half-life day 4.4 McCrady and Maggard 1993; photodegradatio

foliage in sunlight; assumed 10% sunlight per d

Root Compartment Type - Grasses and HerbsaHalf-life day 70 Arjmand and Sandermann 1985, as cited in Ko

soybean root cell culture metabolism test data 

Root soil-water interaction - alpha unitless 0.95 Assumption. Stem Compartment Type - Grasses and HerbsaHalf-life day 70 Arjmand and Sandermann 1985, as cited in Ko

soybean root cell culture metabolism test data 

aRoots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE. 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-41 TRIM.FaTE Inputs 

Exhibit_Add A1-24. Cadmium Chemical-Specific Parameters for Aquatic Species in the TRIM.FaTE Sc

Parameter Name Units Value Refere

Zooplankton Compartment Type Absorption rate constant L[water]/kg[fish wet wt]-day 1500 Goulet 2007. Assimilation efficiency from algae unitless 0.5 Goulet 2007. Elimination rate constant 1/day 0.03 Goulet 2007. Benthic Invertebrate Compartment Type Sediment partitioning - alpha of equilibrium 

unitless 0.95 Assumption. 

Sediment partitioning - partition coefficient 

kg[bulk sed/kg[invertebrate wet wt] 0.27 Assumption. 

Sediment partitioning - time to reach alpha of equilibrium 

day 21 Hare et al. 2001. 

Benthic Omnivore Compartment Type Assimilation efficiency from food unitless 0.1 Assumption based on Yan Absorption rate constant unitless 1.23 Calculated based on body w

in Hendriks and Heikens 20

Elimination rate constant unitless 1.73E-02 Assumption. Benthic Carnivore Compartment Type Assimilation efficiency from food unitless 0.1 Assumption based on Yan Absorption rate constant unitless 0.66 Calculated based on body w

in Hendriks and Heikens 20

Elimination rate constant unitless 1.68E-03 Assumption. Water-column Herbivore Compartment Type Assimilation efficiency from food unitless 0.1 Assumed value based on YAssimilation efficiency from plants unitless 0.1 Assumed value based on YAbsorption rate constant unitless 2.46 Calculated based on body w

in Hendriks and Heikens 20

Elimination rate constant unitless 1.73E-02 Assumption. 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-42 TRIM.FaTE Inputs 

Exhibit_Add A1-24. Cadmium Chemical-Specific Parameters for Aquatic Species in the TRIM.FaTE Sc

Parameter Name Units Value Refere

Water-column Omnivore Compartment Type Assimilation efficiency from food unitless 0.1 Assumption based on Yan Assimilation efficiency from plants unitless 0.1 Assumption based on Yan Absorption rate constant unitless 1.23 Calculated based on body w

in Hendriks and Heikens 20

Elimination rate constant unitless 1.73E-02 Assumption. Water-column Carnivore Compartment Type Assimilation efficiency from food unitless 0.1 Assumption based on Yan Absorption rate constant unitless 0.66 Calculated based on body w

in Hendriks and Heikens 20

Elimination rate constant unitless 1.73E-02 Assumption 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-43 TRIM.FaTE Inputs 

Exhibit_Add A1-25. Mercury Chemical-Specific Parameters for Aquatic Species in the TRIM.FaTE Sc

Parameter Name Units 

Value 

Hg(0) Hg(2) MHg 

Zooplankton Compartment Type Assimilation Efficiency From Algae unitless 0.2 0.015 0.5 EnvironmeHalf-life day 1.0E+09 1.0E+09 1.0E+09 AssumptioHow Much Faster Hg Elimination Is Than For MHg 

unitless 3 3 1 Assumptio

Methylation rate 1/day 0 0 0 AssumptioOxidation rate 1/day 0 1.0E+06 0 AssumptioReduction rate 1/day 0 0 0 AssumptioBenthic Invertebrate Compartment Type Alpha of equilibrium for sediment partitioning 

unitless 0.95 0.95 0.95 Selected v

equilibrium 

Benthic invertebrate-bulk sediment partition coefficient 

kg[bulk 

sediment]/kg[invertebrate 

wet wt] 

0.0824 0.0824 5.04 Hg(0) - as

value; Hg(et al. 1991

t-alpha for equilibrium for sediment partitioning 

day 14 14 14 Experimen

Saouter et 

All Fish Compartments TypesaElimination adjustment factor unitless 3 3 1 Trudel and Assimilation efficiency from food unitless 0.06 0.06 0.5 Williams eDemethylation rate 1/day N/A N/A 0 AssumptioMethylation rate 1/day 0 0 0 AssumptioOxidation rate 1/day 1.0E+06 0 0 AssumptioReduction rate 1/day 0 0 0 AssumptioWater-column Herbivore Compartment Type Assimilation efficiency from plankton unitless 0.06 0.06 0.5 Williams eaScreening 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 1-44 TRIM.FaTE Inputs 

Exhibit_Add A1-26. PAH Chemical-Specific Parameters for Aquatic Species in the TRIM.FaTE Scre

Parameter 

Name Units 

Value 

2Methyl 712DMB Acenaph- thene Acenaph- thylene BaA BaP BbF 

Zooplankton Compartment Type Absorption rate constant 

L[water]/kg[fish 

wet wt]-day 

790 42650.94 42231 42302.18 42650.81 42652.78 42650.68 

Assimilation efficiency from algae 

unitless 0.5 0.25 0.5 0.5 0.46 0.25 0.25 

Elimination rate constant 

1/day 169.68 2.03 148.07 123.44 2.073 1.3864 2.12 

Half-life day 0.007788 17 0.00239 0.00239 1.284 16.5 17 Benthic Invertebrate Compartment Type Clearance constant 

unitless 100.6 100.6 100.6 100.6 100.6 100.6 100.6 

Vd (ratio of 

concentration in benthic invertebrates to concentration in water) 

ml/g 7235 7235 7235 7235 7235 7235 7235 

Half-life day 0.722 17 0.722 0.722 1.284 16.5 17 All Fish Compartment TypesaGamma fish unitless 0.2 0.2 0.2 0.2 0.2 0.2 0.2 Assimilation efficiency from food 

unitless 0.5 0.15 0.5 0.32 0.15 0.15 0.15 

Half-life day 0.2 2 0.2 0.2 0.408 1.925 2 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 1-45 TRIM.FaTE Inputs 

Exhibit_Add A1-26. PAH Chemical-Specific Parameters for Aquatic Species in the TRIM.FaTE Scre

Parameter Name Units 

Value 

Chr DahA Fluoran- thene Fluorene IcdP 

Zooplankton Compartment Type Absorption rate constant 

L[water]/kg[fish 

wet wt]-day 

42649.95 42655.48 142000 15000 42655.93 Kow from Arn

Exception is (2-MethylnapFluoranthene

Assimilation efficiency from algae 

unitless 0.46 0.25 0.49 0.5 0.25 Kow from Arn

Exception is Wang and WDimethylbenBenzo(a)pyreBenzo(b)fluoBenzo(g,h,i)pBenzo(k)fluoDibenz(a,h)aIndeno(1,2,3

Elimination rate constant 

1/day 2.3746 0.4331 8.678 81.87 0.269 Kow from Arn

Half-life day 0.495 17 0.00239 0.0002476 17 McElroy 199

Berrojalbiz eMethylnaphtand Fluorene2007 (Benz(aBenzo(a)pyre

Benthic Invertebrate Compartment Type Clearance constant unitless 100.6 100.6 100.6 100.6 100.6 Stehly et al. Vd (ratio of 

concentration in benthic invertebrates to concentration in water) 

ml/g 7235 7235 7235 7235 7235 Stehly et al. 

Half-life day 0.495 17 0.722 0.722 17 Moermond e
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-46 TRIM.FaTE Inputs 

Exhibit_Add A1-26. PAH Chemical-Specific Parameters for Aquatic Species in the TRIM.FaTE Scre

Parameter Name Units 

Value 

Chr DahA Fluoran- thene Fluorene IcdP 

All Fish Compartment TypesaGamma fish unitless 0.2 0.2 0.2 0.2 0.2 Thomann 19Assimilation efficiency from food 

unitless 0.15 0.15 0.14 0.14 0.15 Lemair et al. 

include BarbMethylnaphtAcenaphthenPalazzo 198Fluoranthene

Half-life day 0.533 2 0.165 0.2 2 Moermond eaScreening 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 1-47 TRIM.FaTE Inputs 

Exhibit_Add A1-27. Dioxin Chemical-Specific Parameters for Aquatic Species in the TRIM.FaTE Scr

Parameter Name Units 

Value 

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-

Zooplankton Compartment Absorption rate constant L[water]/kg[fish 

wet wt]-day 

8640 8640 8640 8640 8640 8640 86

Assimilation efficiency from algae unitless 0.08 0.05 0.21 0.09 0.2 0.31 0.Elimination rate constant 1/day 0.0102 0.016 0.016 0.0616 0.1829 0.0252 0.1Half-life day 7E+06 7E+06 7E+06 7E+06 7E+06 7E+06 7EBenthic Invertebrate Compartment Clearance constant unitless 0 0 0 0 0 0 Sediment partitioning partition coefficient 

kg/kg 0.0013 0.0017 0.0055 0.0012 0.042 0.033 0.0

Sediment partitioning alpha of equilibrium 

unitless 0.95 0.95 0.95 0.95 0.95 0.95 0.

Sediment partitioning time to reach alpha of equilibrium 

days 120 42 120 42 42 120 4

Vd (ratio of concentration 

in benthic invertebrates to concentration in water) 

ml/g 0 0 0 0 0 0 

Half-life day 5776.2 5776.2 5776.2 5776.2 5776.2 5776.2 577All Fish CompartmentsaAssimilation efficiency from food unitless 0.08 0.05 0.21 0.09 0.2 0.37 0.Chemical uptake rate via gill L[water]/kg[fish 

wet wt]-day 

11 6 56 25 50 102 2

Half-life day 70 70 70 70 70 70 7
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-48 TRIM.FaTE Inputs 

Exhibit_Add A1-27. Dioxin Chemical-Specific Parameters for Aquatic Species in the TRIM.FaTE Scr

Parameter Name Units 

Value 

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 

Zooplankton Compartment Absorption Rate Constant L[water]/kg[fish wet 

wt]-day 

8640 8640 8640 8640 8640 8640 

Assimilation Efficiency from Algae unitless 0.31 0.31 0.42 0.42 0.31 0.42 Elimination Rate Constant 1/day 0.0099 0.0413 0.0819 0.2316 0.0192 0.433Half-life day 7E+06 7E+06 7E+06 7E+06 7E+08 7E+0Benthic Invertebrate Compartment Clearance Constant unitless 0 0 0 0 0 0 Sediment Partitioning Partition Coefficient 

kg/kg 0.015 0.067 0.098 0.024 0.072 0.17 

Sediment Partitioning Alpha of Equilibrium 

unitless 0.95 0.95 0.95 0.95 0.95 0.95 

Sediment Partitioning Time to Reach Alpha of Equilibrium 

days 120 42 120 42 42 42 

Vd (ratio of concentration 

in benthic invertebrates to concentration in water) 

ml/g 0 0 0 0 0 0 

Half-life day 5776.2 5776.2 5776.2 5776.2 5776.2 5776.All Fish CompartmentsaAssimilation efficiency from food unitless 0.31 0.31 0.42 0.42 0.31 0.42 Chemical uptake rate via gill L[water]/kg[fish wet 

wt]-day 

300 200 700 300 200 400 

Half-life day 70 70 70 70 70 70 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-49 TRIM.FaTE Inputs 

Exhibit_Add A1-27. Dioxin Chemical-Specific Parameters for Aquatic Species in the TRIM.FaTE Scr

Parameter Name Units Reference 

Zooplankton Compartment Absorption rate constant L[water]/kg[fish 

wet wt]-day 

Zhang et al. 2011; used copepod ku value. 

Assimilation efficiency from algae unitless Morrison et al. 1999. Exceptions include Niimi and Oliver 1986 

OCDD, 1,2,3,4,6,7,8,9-OCDF), Berntssen et al. 2007 (1,2,3,4,61,2,3,4,6,7,8-HpCDF), and 1,2,3,4,7,8,9-HpCDF set conservatilinear interpolation between values for 1,2,3,4,7,8-HxCDD and 1,2,3,4,6,7,8-HpCDF (i.e., 0.3 to 0.1–0.2). 

Elimination rate constant 1/day Arnot and Gobas 2004; used Kow value. Half-life day Morrison et al. 1999; used metabolic rates for invertebrates. Benthic Invertebrate Compartment Clearance constant unitless Assumption. Sediment partitioning partition coefficient 

kg/kg Rubinstein et al. 1990; used TCDD data for sandworm. 

Sediment partitioning alpha of equilibrium 

unitless Rubinstein et al. 1990. 

Sediment Partitioning Time to Reach Alpha of Equilibrium 

days Rubinstein et al. 1990. 

Vd (ratio of concentration in benthic 

invertebrates to concentration in water)

ml/g Assumption. 

Half-life day Rubinstein et al. 1990; used TCDD data for sandworm. All Fish Compartmentsa Assimilation Efficiency from Food unitless Morrison et al. 1999. Exceptions include Niimi and Oliver 1996 

OCDD, 1,2,3,4,6,7,8,9-OCDF), Van den Berg et al. 1994 (1,2,3Berntssen et al. 2007 ( 1,2,3,4,6,7,8-HpCDF), and 1,2,3,4,7,8,9

conservatively as approximate linear interpolation between valuHxCDD and 1,2,3,4,6,7,8-HpCDD / 1,2,3,4,6,7,8-HpCDF (i.e., 

Chemical Uptake Rate Via Gill L[water]/kg[fish 

wet wt]-day 

Muir et al. 1985. Exception is Opperhuizen et al. 1986 (1,2,3,7,PeCDD, 1,2,3,7,8-PeCDF, 2,3,4,7,8-PeCDF, 2,3,7,8-TCDD, 2,

Half-life day 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 

References 

Adriaens, P., Q. Fu, and D. Grbic-Galic. 1995. Bioavailability and transformation of highly chlorinated 

dibenzo-p-dioxins and dibenzofurans in anaerobic soils and sediments. Environmental Science and 

Technology 29(9): 2252-2260. 

Adriaens, P., and D. Grbic-Galic. 1993. Reductive dechlorination of PCDD/F by anaerobic cultures and 

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TRIM-Based Tiered Screening Methodology for RTR

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 
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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 Average 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 ............................... 2-53
TRIM-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 .................................................................................................................... 2-104
TRIM-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 Chemicalsa

 .......................................... 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) Groupsa

 .................................................................................................. 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 Chemical-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 Adultsa

........................................................... 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 (IRCO,y)

a

 .............................................................. 2-82

Exhibit_Add A2-20. Fraction of Population Consuming Freshwater/Estuarine 

Fish on a Single Day (FPC,y) ............................................................................... 2-83

Exhibit_Add A2-21. Calculated Long-term Mean and Percentile per capita Fish 

Ingestion Rates for Children and Adults (IRPC,y) ................................................ 2-83

Exhibit_Add A2-22. Calculated Mean and 90th Percentile Per capita Fish 

Ingestion Rates for Populations of Recreational Fishers (IRPC,y) ...................... 2-84
TRIM-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 Childrena

 ........................ 2-100

Exhibit_Add A2-33. Chemical-Specific Parameter Values for Input to MIRCa

 ...................... 2-101

Exhibit_Add A2-34. Chemical and Animal-Type Specific Biotransfer Factor (Ba) 

Values for Input to MIRC ................................................................................. 2-102
TRIM-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 Concentration in Aboveground Produce Due to Root 

Uptake ............................................................................................................... 2-19

Equation 2-3. Chemical Concentration in Aboveground Produce Due to 

Deposition of Particle-phase Chemical ............................................................. 2-20

Equation 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 ............................................. 2-35
TRIM-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 ..................................................................... 2-39

Equation 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 ................................................................................................................. 2-81
TRIM-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 values 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 screening 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 the 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 

 

19Fully functional versions of MIRC have been developed in both AccessTM-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 airborne 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 incidental 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 vapor-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 implement directly in its TRIM system via three 

modules beyond TRIM.FaTE: TRIM Farm Food Chain, TRIM.ExpoIngestion, 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 50th, 

90th, 95th, and 99th 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 latest 

recommended 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 water 
TRIM-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 Risks
TRIM-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 graina

 Soil ingestion 

Pork  Ingestion of silage and graina

 Soil ingestion 

Poultry and eggs  Ingestion of graina

 Soil ingestion a

Chemical concentrations in forage, silage, and grain are estimated via intermediate 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 through 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 weights, 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 mutagenic 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 between 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 air concentrations and deposition rates from 

dispersion model simulations that use unit emission 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 assessment 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 contaminated 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 which chemicals are transferred to the FFC produce 

categories. Note that for a general screening-level assessment, all of the pathways can be 

modeled, 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 (PrAG-produce) is 

estimated using an empirical bioconcentration factor (BrAG-produce ) that relates the chemical 

Exhibit_Add A2-4. Estimating Chemical 

Concentration in Aboveground Produce 

Deposition 

of Particles 

(Pd)

Root Uptake 

from Soil 

(PrAG-produce)

Vapor 

Transfer 

(Pv) 

Chemical Concentration in 

Aboveground Produce
TRIM-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 (Csroot-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 

(PrAG-produce- DW) is estimated using an empirical bioconcentration factor (BrAG-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 (Csroot-zone_produce). These equations all 

assume measurements on a dry-weight (DW) basis. 

Equation 2-1. Chemical Concentration in Aboveground Produce 

where: 

CAG-produce-DW(i) = 

Concentration of chemical in edible portion of aboveground produce type i, 

exposed or protected, on a dry-weight (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 produce DW); for protected aboveground 

produce, Pd equals zero 

PrAG-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 (μg/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: 

PrAG-produce-DW(i) = 

Concentration of chemical in edible 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) 

Csroot-zone_produce = Average chemical concentration in soil at root-zone depth in produce-growing 

area (mg/kg soil DW) 

BrAG-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) 

AG produce iDW )( P AG produce iiDW PvPd i )()()( C   r   

P AG produce iDW )( Cs -root zone_produce BrAG produce iDW )( 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/m2

-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/m2

-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 field per harvest of the edible portion of 

plant type i (yr) 

Yp(i) = Yield or standing crop biomass of the edible portion of plant type i (kg produce 

DW/m2

) 

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/m2

-yr. 

With MIRC, the user inputs both the dry and wet particle-phase deposition rates, Drdp and 

Drwp, respectively, in units of g/m2

-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 chemical 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/m2

-yr. Users of 

TRIM.FaTE should note that the dry- and wet-particle-deposition rates output from TRIM.FaTE 

are in units of g/m2

-day; therefore, users must adjust the TRIM.FaTE output values to units of 

g/m2

-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: 

)()(

))(*)((

)(

)(

((000,1 1()) )

ii

iTpikp

i

i kpYp

DrwpFwDrdp Rp e

Pd



 



a

iAG iAG

i

BvFvCa VG

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 edible portion of aboveground produce type i

from air-to-plant transfer of vapor-phase chemical on a dry-weight (DW) basis 

(μg/g produce DW); set equal to zero for protected aboveground produce 

Ca = Average annual total chemical concentration in air (g/m3

) 

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

BvAG(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) 

VGAG(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) 

ρa = Density of air (g/m3

) 

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 the 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 μg-sec/g-m3

. 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/m3

; 

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/m3

; therefore, the user must adjust the value 

to units of g/m3 (i.e., divide by 1,000 μg/g) before entering it in MIRC. 

The calculations of chemical concentration in aboveground produce, (CAG-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, CAG-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: 

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

CAG-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

AG produce iWW AG produce iDW

MAF

C C
TRIM-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 than 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 (Csroot-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 (Csroot-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: 

CBG-produce-WW = Concentration of chemical in belowground (BG) produce (i.e., tuber or root 

vegetable) on a wet-weight (WW) basis (mg chemical/kg produce WW)* 

Csroot-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)*

VGrootveg = 

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) * 

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 Kow from empirically 

UCFKds

Cs zoneroot produce VGRCF rootveg



    _ C WW-produce-BG
TRIM-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 Kow . For inorganic chemicals, chemical specific empirical values for the root/soil 

bioconcentration factor must be used. The root/soil bioconcentration factor, now specified as 

BrBG-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: 

CBG-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) 

Csroot-zone_produce = Average chemical concentration in soil at root-zone depth in produce-growing 

area (mg/kg soil DW) 

BrBG-produce-DW = Chemical-specific root/soil chemical bioconcentration factor for edible portion 

of belowground produce (g soil DW / g produce DW) 

VGrootveg = 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: 

CBG-produce-WW = Chemical concentration in edible portion of belowground produce on a weightweight (WW) basis (mg/kg produce WW) 

CBG-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) 

MAF(BG) = Moisture adjustment factor (as in Equation 2-5, but single value for below 

ground produce) (percent water) 

1

zoneroot _ produce BG produce DW rootveg

BG-produce-DW

Cs Br VG

C

    





 



     

100

100( ) BG

BG produce WW BG produce DW

MAF C C
TRIM-Based Tiered Screening Methodology for RTR 

Attachment A, Addendum 2 2-24 December 2013 

Description of MIRC 

3.1.2. Estimating Chemical Concentrations 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 (PlantCh-Intake(i,m)) and 

incidental ingestion of soil for ground-foraging animals (SoilCh-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 calculation 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 (PlantCh-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 graina

 Incidental soil ingestion 

Pork  Ingestion of silage and graina

 Incidental soil ingestion 

Poultry and eggs  Ingestion of graina

 Incidental soil ingestion 

a

Chemical concentrations in plant 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-phase 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 assumption 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 

 



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 

 

n

i

Cmammal m m SoilMFBa mIntakeCh Plant miIntakeCh

1

)()( )( ),(
TRIM-Based Tiered Screening Methodology for RTR 

Attachment A, Addendum 2 2-25 December 2013 

Description of MIRC 

where: 

Cmammal(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) 

SoilCh-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 

PlantCh-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: 

Cpoultry(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) 

SoilCh-Intake(m) = Incidental ingestion of chemical in surface soils by consumption of food on the 

ground (mg chemical/day) where m = poultry; see Equation 2-11 

PlantCh-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 chemical in soils by livestock during grazing or 

consumption of feed placed on the ground (SoilCh-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, CsS-livestock, that is 

distinct from the surface soil concentration input for areas where produce may be grown and 

where humans might incidentally ingest soils (see Section 6.1 of this addendum). 

Equation 2-11. Incidental Ingestion of Chemical in Soil by Livestock 

  C mpoultry  Ba m)()(  Soil  mIntakeCh )(  Plant  miIntakeCh ),(

Soil CsQs Bs  mIntakeCh )(   slivestock 
TRIM-Based Tiered Screening Methodology for RTR 

Attachment A, Addendum 2 2-26 December 2013 

Description of MIRC 

where: 

SoilCh-Intake(m) = Incidental ingestion of the chemical in surface soils by livestock type m during 

grazing or consumption of foods placed on the ground (mg chemical/day) 

Qs(m) = Quantity of soil eaten by animal type m each day (kg soil DW/day) 

Css-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 calculated 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, PlantCh-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

Cfeed(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: 

Cfeed(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 

Plant   mimiIntakeCh  mi ),(),(),( CQpF ifeed )(

feed(i)  feed(i)  (i)  Pv PdPrC (i)
TRIM-Based Tiered Screening Methodology for RTR 

Attachment A, Addendum 2 2-27 December 2013 

Description of MIRC 

Prfeed(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 (μg/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: 

Prfeed(i) = 

Concentration of chemical in plant feed type i due to root uptake from soil on a 

dry-weight (DW) basis (mg chemical/kg plant feed DW), where i = forage, silage, 

or grain 

Csroot-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 

Brfeed(i) = Chemical-specific plant-soil bioconcentration 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 )( Cs zoneroot ifeed )(_ Br ifeed )( r   

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

  days 365 (y)

(y) (y)

(i) (y,i) (i) (y)

(y,i)

EF

ATBW

EDFCIRC

ADD
TRIM-Based Tiered Screening Methodology for RTR 

Attachment A, Addendum 2 2-28 December 2013 

Description of MIRC 

where: 

ADD(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 (ATy) is equal to the exposure duration for that group 

(EDy); 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 pathways listed above are described in Sections 3.2.1 

through 3.2.6 of this addendum. The algorithms for the breast-milk ingestion pathway are 

described in Section 3.4. 

3.2.1. Chemical Intake 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: 

ADDSoil(y) = Average daily chemical intake from incidental ingestion of soil or ingestion by child in 

age group y (mg chemical/kg body weight-day) 

CSoil = 

Concentration of chemical in soil from contaminated area on a dry-weight (DW) basis 

(μg/g soil DW) 

IRSoil(y) = Soil ingestion rate for age group y (g DW/day) 

FCSoil = 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 unit 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. 

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

 365 days

EF

BW

μg

mg IRC FC 0.001 

 ADD

(y)

Soil Soil(y) Soil

Soil(y)
TRIM-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 

CF୧ୱ୦ ൌ ሺCF୧ୱ୦TLଷ ൈ FTLଷሻ ൅ ሺCF୧ୱ୦TLସ ൈ FTLସሻ

where: 

ADDFish(y) = Average daily chemical intake from ingestion of local fish for age group y (mg/kgday) 

L1Fish* = Weight of fish brought into home that is discarded during preparation (e.g., head, 

bones, liver, other viscera, belly fat, skin with fat) (unitless) 

L2Fish* = Loss of weight during cooking, such as evaporation and loss of fluids into pan 

(unitless) 

CFishTL3 = Chemical concentration in whole fish for trophic level 3.5 (TL3) fish on a wetweight (WW) basis (mg/kg WW) 

CFishTL4 = Chemical concentration in whole fish for trophic level 4 (TL4) fish on a wet-weight 

(WW) basis (mg/kg WW) 

FTL3 = Fraction of fish intake that is from TL3 (unitless) 

FTL4 = Fraction of fish intake that is from TL4 (unitless) 

CFish = Consumption-weighted mean chemical concentration in total fish (i.e., as 

specified by Equation 2-18) (mg/kg WW) 

FCFish = Fraction of local fish consumed derived from contaminated area (unitless) 

BW(y) = Body weight for age y (kg) 

IRFish(y)* = Local fish ingestion rate for age y (g WW/day) 

EF = Exposure frequency; number of days per year of exposure for family(ies) as 

specified for scenario ( 365 days) 

*Parameter values must be internally consistent. In contrast to the tables included in MIRC 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 ingestion 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 equal to zero. If the user wishes to enter local fish 

ingestion rates on an “as harvested” basis, the 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 (L1Fish and L2Fish in 

Equation 2-17). 

   days365

g

kg 0010

2111 

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 



EF

BW

.IRC FC

L ADD L

(y)

Fish Fish(y) Fish

Fish(y) Fish Fish
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: 

ADDExpFruit(y) 

ADDProFruit(y)

= Average daily chemical intake from ingestion of exposed fruit or protected fruit 

(depending on subscript) (mg chemical/kg body weight-day) 

L1ExpFruit = 

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) 

L1ProFruit = Mean reduction in fruit weight that results from paring or other preparation 

techniques for protected fruits (unitless) 

L2ExpFruit = Mean reduction in fruit weight that results from draining liquids from cooked 

forms of the fruit (unitless) 

CExpFruit 

CProFruit

= 

Chemical concentration in whole exposed fruits or whole protected fruits 

(depending on subscript) on a wet-weight (WW) basis (mg chemical/kg exposed 

fruit WW) 

EF = Exposure frequency; number of days per year of exposure for family(ies) as 

specified for scenario ( 365 days) 

FCExpFruit 

FCProFruit

= Fraction of exposed fruits or protected fruits (depending on subscript) obtained 

from contaminated area (unitless) 

IRExpFruit(y) 

IRProFruit(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 (L1ExpFruit and L1ProFruit; 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 (L2ExpFruit). EPA has assumed that cooking of protected 

fruit results in no loss of weight for the fruit. 

3.2.4. Chemical Intake from 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 

   

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g days 365

kg 2111 0010 EF ADD )ExpFruit(y L ExpFruit L ExpFruit CExpFruit IR )ExpFruit(y . FCExpFruit

  

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g days 365

kg 11 0010 EF ADD )ProFruit(y L ProFruit CProFruit IR )ProFruit(y . FCProFruit
TRIM-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: 

ADDExpVeg(y) 

ADDProVeg(y) 

ADDRootVeg(y)

= 

Average chemical intake from ingestion of exposed vegetables, protected 

vegetables, or root vegetables (depending on subscript) for age group y (mg 

chemical/kg body weight-day) 

L1ExpVeg = Mean net preparation and cooking weight loss for exposed vegetables (unitless); 

includes removing stalks, paring skins, discarding damaged leaves 

L1ProVeg = Mean net cooking weight loss for protected vegetables (unitless); includes 

removing husks, discarding pods of beans and peas, removal of outer leaves 

L1RootVeg = Mean net cooking weight loss for root vegetables (unitless); includes losses from 

removal of tops and paring skins 

L2RootVeg = Mean net post cooking weight loss for root vegetables from draining cooking 

liquids and removal of skin after cooking (unitless) 

CExpVeg 

CProVeg 

CRootVeg

= 

Chemical concentration in exposed vegetables, 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 year of exposure for family(ies) as 

specified for scenario (365 days) 

FCExpVeg 

FCProVeg 

FCRootVeg

= Fraction of exposed vegetables, protected vegetables, or root vegetables 

(depending on subscript) obtained from contaminated area (unitless) 

IRExpVeg(y) 

IRProVeg(y) 

IRRootVeg(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) 

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g days 365

kg 11 0010 EF ADDExpVeg(y) L ExpVeg ExpVeg IRC ExpVeg(y) . FCExpVeg

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g days 365

kg 11 0010 EF ADDProVeg(y) L ProVeg ProVeg IRC ProVeg(y) . FCProVeg

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g days 365

kg 2111 0010 EF ADDRootVeg(y) L RootVeg L RootVeg CRootVeg IRRootVeg(y) . FCRootVeg
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 consumption 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: 

ADDBeef(y) = Average daily chemical intake from ingestion of beef for age group y (mg/kgday) 

L1Beef = Mean net cooking loss for beef (unitless) 

L2Beef = Mean net post cooking loss for beef (unitless) 

CBeef = Concentration of contaminant in beef (mg/kg WW)) 

EF = Exposure frequency; number of days per year of exposure for family(ies) as 

specified for scenario ( 365 days) 

IRBeef(y) = Ingestion rate of contaminated beef for age group y (g WW/kg-day) 

FCBeef = 

Fraction of beef consumed raised on contaminated area or fed contaminated 

silage and grains (unitless) 

Equation 2-25. Chemical Intake from Dairy Ingestion 

where: 

ADDDairy(y) = Average daily chemical intake from ingestion of total dairy for age group y

(mg/kg-day) 

CDairy = Average concentration of contaminant in total dairy (mg/kg WW) 

EF = Exposure frequency; number of days per year of exposure for family(ies) as 

specified for scenario ( 365 days) 

IRDairy(y) = Ingestion rate of contaminated total dairy for age group y (g WW/kg-day) 

FCDairy = Fraction of total dairy products from contaminated area (unitless) 

Equation 2-26. Chemical Intake from Pork Ingestion 

   

































g days 365

kg 2111 0010 EF ADDBeef(y) L Beef Beef Beef Beef(y) .IRCL FCBeef



































g days 365

kg 0010 EF ADDDairy(y) Dairy Dairy(y) .IRC FCDairy

   

































g days 365

kg 2111 0010 EF ADDPork(y) L Pork Pork Pork Pork(y) .IRCL FCPork
TRIM-Based Tiered Screening Methodology for RTR 

Attachment A, Addendum 2 2-34 December 2013 

Description of MIRC 

where: 

ADDPork(y) = Average daily chemical intake from ingestion of pork for age group y (mg/kgday) 

L1Pork = Mean net cooking loss for pork (unitless); includes dripping and volatile losses 

during cooking; averaged over various cuts and preparation methods 

L2Pork = 

Mean net post cooking loss for pork (unitless); includes losses from cutting, 

shrinkage, excess fat, bones, scraps, and juices; averaged over various cuts 

and preparation methods 

CPork = Concentration of contaminant in pork (mg/kg WW) 

EF = Exposure frequency; number of days per year of exposure for family(ies) as 

specified for scenario (365 days) 

IRPork(y) = Ingestion rate of contaminated pork for age y (g WW/kg-day) 

FCPork = 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 year 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) 

FCPoultry = 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: 

ADDEgg(y) = Average daily chemical intake from ingestion of eggs for age group y (mg/kgday) 

   































 

g days 365

kg 2111 0010 EF ADDPoultry(y) L Poultry L Poultry Poultry IRC Poultry(y) . FCPoultry



































g days 365

kg 0010 EF ADDEgg(y) Egg Egg(y) .IRC FCEgg
TRIM-Based Tiered Screening Methodology for RTR 

Attachment A, Addendum 2 2-35 December 2013 

Description of MIRC 

CEgg = Concentration of contaminant in eggs (mg/kg WW) 

EF = Exposure frequency; number of days per year of exposure for family(ies) as 

specified for scenario ( 365 days) 

IREgg(y) = Ingestion rate of contaminated eggs for age group y (g WW/kg-day) 

FCEgg = Fraction of eggs obtained from contaminated area (unitless) 

3.2.6. Chemical Intake from Drinking Water Ingestion 

If the user chooses to evaluate chemical ingestion 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 from 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: 

ADDDW(y) = Average daily chemical intake from ingestion of drinking water from local 

residential water source for age group y (mg/kg-day) 

CDW = Concentration of contaminant in drinking water (g/L) 

IRDW(y) = Drinking water ingestion rate for age group y (mL/day) 

FCDW = 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 year 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 single 

individual in each age group is expected to contact (e.g., soil, local fish, five types of homegrown produce, and five types of home-raised animals 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

IRC FC

ADD

(y)

DW DW(y) DW

DW(y)

ADD  )1(  ADDbreastmilk

   

n

i

ADD ADD i 1 )21( ),21(

   

n

i

ADD ADD i 1 )53( ),53(

   

n

i

ADD ADD i 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 exposure 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 effects 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 (DAIinf) 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 (IRmilk), the absorption efficiency of the 

   

n

i

ADD ADD i 1 )1912( ),1912(

  n

i

ADDadult ADD iadult 1 )( ),(





 



  



 



  



 



  



 



  



 



  



 



   70

50

70

8

70

6

70

3

70

2

70

1 LADD ADD 1)( ADD(1-2) ADD(3-5) ADD(6-11) ADD(12-19) ADD(adult)
TRIM-Based Tiered Screening Methodology for RTR 

Attachment A, Addendum 2 2-37 December 2013 

Description of MIRC 

chemical by the oral route of exposure (AEinf), the bodyweight of the infant (BWinf), 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 (Cmilkfat) and the concentration of the 

chemical in the aqueous phase of breast milk (Caqueous). The remainder of the DAIinf-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 chemicals may partition significantly to both 

phases of milk. Thus, the remaining equations in MIRC assume that either Cmilkfat or Caqueous 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: 

DAIinf = Average daily dose of chemical absorbed by infant (mg chemical/kg body 

weight-day) 

Cmilkfat = Concentration of chemical in lipid phase of maternal milk (mg chemical/kg milk 

lipid; calculated using Equation 2-38) 

fmbm = Fraction of fat in breast milk (unitless) 

Caqueous = Concentration of chemical in aqueous phase of maternal milk (mg chemical/kg 

aqueous phase milk; calculated using Equation 2-42) 

IRmilk = Infant milk ingestion rate over the duration of nursing (kg milk/day) 

AEinf = 

Absorption efficiency of the chemical by the oral route of exposure (i.e., 

chemical-specific fraction of ingested chemical that is absorbed by the infant) 

(unitless) 

ED = Exposure duration, i.e., duration of breast feeding (days) 

BWinf = Body weight of infant averaged over 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 Cmilkfat (described in Section 3.4.2 of this addendum) and Caqueous (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

CfC EDAEIRf

DAI

inf

milkfat mbm aqueous mbm milk inf

inf



        1
TRIM-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, estimates 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 concentrations than general body fat stores 

(EPA 2001a). 

Equation 2-38. Chemical Concentration in Breast Milk Fat 

where: 

Cmilkfat = Concentration of chemical in lipid phase of maternal milk (mg chemical/kg lipid) 

DAImat = Daily absorbed maternal chemical dose (mg chemical/kg maternal body weightday; calculated using Equation 2-39) 

ff = 

Fraction of total maternal body burden of chemical that is stored in maternal fat 

(mg chemical in body fat / mg total chemical in whole body; value from literature 

or EPA default - see Section 6.5 of this addendum) 

kelim = 

Chemical-specific total elimination rate constant 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) 

ffm = Fraction of maternal body weight that is fat stores (unitless) 

kfat_elac = 

Chemical-specific rate constant for total elimination of chemical in the lipid 

phase of milk during nursing (per day; value from literature or calculated using 

Equation 2-41) 

tbf = Duration of breast feeding (days) 

tpn = Duration of mother’s exposure prior to parturition and initiation of breast feeding 

(days) 

Equation 2-38 relies on the daily maternal absorbed intake (DAImat) to determine the 

concentration of the chemical in the breast milk fat. DAImat is multiplied by the fraction of the 

chemical that is stored in maternal fat (ff) to determine the amount (i.e., mass) of chemical in the 

fat. This product, divided by the chemical-specific elimination rate constant (kelim) for nonlactating adult women and the fraction of the mother’s weight that is fat (ffm), represents the 

 













  













  

 



   pnelim  bfelacfat tk

elacfat

tk elim

elacfat bfelacfat

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 concentration in milk fat because it does not account 

for losses due to breast feeding. Alone, this term (DAImat ff / kelim ffm) 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, kelim and the elimination constant for a lipophilic chemical in 

lactating women via the lipid phase of breast milk (kfat_elac), the duration of the mother’s 

chemical exposure prior to nursing (tpn), and the duration of breast feeding (tbf). The whole body 

concentration (DAImat ff / kelim ffm), 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 DAImat, 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 (AEmat) or fraction of the chemical absorbed by the oral route of exposure, 

as shown in Equation 2-39. The value for AEmat 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 exposures 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 AEmat or to use the higher AEmat

of the food and water AEmat values if available; hence, a single AEmat parameter is included in 

Equation 2-39. 

Equation 2-39. Daily Maternal Absorbed Intake 

where: 

DAImat = Daily maternal dose of chemical absorbed from medium i (mg/kg-day) 

ADD(adult) = Average daily dose to the mother (mg/kg-day) (calculated by MIRC – see 

Section 3.3 of this addendum, Equation 2-35) 

AEmat = 

Absorption efficiency of the chemical by the oral route of exposure (i.e., 

chemical-specific fraction of ingested chemical 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 

DAImat  ADD(adult)  AEmat
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 majority 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 reported as the half-life of the chemical in the body 

following a known dose of chemical. Many chemicals 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 elimination 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: 

kelim = Chemical-specific elimination rate constant 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 chemical for non-lactating women (days) 

For chemicals transferred from the body of lactating 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, kfat_elac (EPA 1998). 

Equation 2-41. Biological Elimination Constant for Lipophilic Chemicals 

for Lactating Women 

where: 

kfat_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

kelim

2ln 

fm mat

mbmfmilk

elacfat elim BWf

ffIR kk



  _ 
TRIM-Based Tiered Screening Methodology for RTR 

Attachment A, Addendum 2 2-41 December 2013 

Description of MIRC 

kelim = 

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) 

IRmilk = Infant milk ingestion rate over the duration of nursing (kg/d) 

ff = 

Fraction of total maternal body burden of chemical that is stored in maternal fat 

(mg chemical in body fat / mg chemical total in body; value from literature or 

EPA default) 

fmbm = Fraction of fat in breast milk (unitless) 

ffm = Fraction of maternal body weight that is fat stores (unitless) 

BWmat = Maternal body weight over the entire duration 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 chemicals via the breast milk fat. The term Kfat_elac is 

estimated by adding an estimate of the first-order elimination constant for breast feeding losses 

to kelim, 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 (IRmilk), the 

fraction of the total maternal body burden of the chemical that is stored in maternal body fat (ff), 

the fraction of the mother’s breast milk that consists of fat (lipids) (fmbm), the mother’s body 

weight (BWmat), and the fraction of the mother’s weight that is body fat (ffm). 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 considered models to estimate chemical 

concentrations in the aqueous phase of breast milk (Caqueous). EPA adapted Smith’s (1987) 

steady state concentration model for estimating Cmilkfat and developed the Caqueous 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 Caqueous

equation assumes that the chemical concentration 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 

elacaq pm

plmat bm

aqueous fk

PcfDAI

C



  

_
TRIM-Based Tiered Screening Methodology for RTR 

Attachment A, Addendum 2 2-42 December 2013 

Description of MIRC 

where: 

Caqueous = Concentration of chemical in aqueous phase of maternal milk (mg/kg) 

DAImat = Daily absorbed maternal chemical dose (mg/kg-day; calculated by Equation 2-39) 

fpl = 

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) 

Pcbm = 

Partition coefficient for chemical between the plasma and breast milk in the 

aqueous phase (unitless); assumed to equal 1.0 

kaq_elac = 

Chemical-specific rate constant for total elimination of chemical in the aqueous 

phase of milk during nursing (per day; value from literature or calculated in 

Equation 2-44) 

fpm = Fraction of maternal weight that is blood plasma (unitless) 

Equation 2-42 is a steady-state concentration model that, like the Equation 2-38 for Cmilkfat, is 

dependent on the maternal absorbed daily intake (DAImat). In Equation 2-42, DAImat is multiplied 

by the fraction of the absorbed chemical that is circulating in the blood plasma compartment (fpl) 

and a partitioning coefficient for the chemical between plasma and the aqueous phase of breast 

milk (Pcbm). 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 Pcbm would equal 1.0. The denominator includes the 

biological elimination constant for the chemical in the aqueous phase of breast milk in lactating 

women (kaq_elac) and the fraction of the mother’s weight that is plasma (fpl). 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 water-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 the 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 relationship 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 (fpl) is required. Ideally, an empirical value for fpl should be used. If 

empirical values are not available, fpl 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 (fbl; EPA 1998). 

Equation 2-43. Fraction of Total Chemical in Body in the Blood Plasma Compartment 

where: 

fpl = 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



  1
TRIM-Based Tiered Screening Methodology for RTR 

Attachment A, Addendum 2 2-43 December 2013 

Description of MIRC 

fbl = Fraction of chemical in body (based on absorbed intake) in the whole blood 

compartment (unitless); chemical-specific 

fbp = 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 (fbl) 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 

(PcRBC) and the fraction of whole blood that is plasma (fbp). 

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, kaq_elac. As for kfat_elac for lipophilic 

chemicals, kaq_elac for hydrophilic chemicals would be equal to kelim 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 the additional elimination of a chemical in the 

aqueous phase of milk from breast feeding. Given 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 tubules 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 kelim, the elimination of the chemical from a non-lactating woman, as shown in Equation 

2-40. The extent to which kelim is an underestimate of kaq_elac for a given chemical will determine 

the extent of health protective bias in kaq_elac. 

Equation 2-44. Biological Elimination Rate Constant for Hydrophilic Chemicals 

where: 

kaq_elac = Chemical-specific rate constant for total elimination of chemical by lactating 

women for hydrophilic chemicals (per day) 

kelim = 

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 Infant 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 (fpl, 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 (fbl) could be found. However, the value found for 

fbl 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 

_ elacaq  kk elim
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 Lipscomb 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, chronic 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 the 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 from 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) μg/kg-day and the average infant intake of MeHg to be 0.80 ± 0.38 μg/kg-day. 

Therefore, for purposes of MIRC, the DAIinf 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: 

DAIinf_MeHg = Average daily dose of MeHg absorbed by infant from breast milk (mg/kg-day) 

DAImat_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 factors 

for 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 dose-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 hierarchy of sources: EPA’s Integrated Risk 

Information System (IRIS); the Centers for Disease Control’s Agency for Toxic Substances and 

Disease Registry (ATSDR); and the California Environmental Protection Agency’s (CalEPA’s) 

Office of Environmental Health Hazard Assessment (OEHHA) Toxicity Criteria Database. For 

PB-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). 

DAIinf_MeHg  DAImat_MeHg
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 Chemicalsa

Chemical CAS No. 

Cancer Slope Factor Reference Dose 

Value 

(mg/kg-day)-1 Source 

Value 

(mg/kgday) Source 

Inorganics 

Cadmium compounds in foodb 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 TEFsc not available 

1,2,3,4,6,7,8- 

Heptachlorodibenzofuran 67562394 1.50E+03 Derived from WHO 2005 TEFsc not available 

1,2,3,4,7,8,9- 

Heptachlorodibenzofuran 55673897 1.50E+03 Derived from WHO 2005 TEFsc not available 

1,2,3,4,7,8- 

Hexachlorodibenzo-p-dioxin 39227286 1.50E+04 Derived from WHO 2005 TEFsc not available 

1,2,3,4,7,8- 

Hexachlorodibenzofuran 70648269 1.50E+04 Derived from WHO 2005 TEFsc not available 

1,2,3,6,7,8- 

Hexachlorodibenzo-p-dioxin 57653857 1.50E+04 Derived from WHO 2005 TEFsc not available 

1,2,3,6,7,8- 

Hexachlorodibenzofuran 57117449 1.50E+04 Derived from WHO 2005 TEFsc 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 TEFsc not available 

2,3,4,6,7,8- 

Hexachlorodibenzofuran 60851345 1.50E+04 Derived from WHO 2005 TEFsc not available 

1,2,3,4,6,7,8,9- 

Octachlorodibenzo-p-dioxin 3268879 4.50E+01 Derived from WHO 2005 TEFsc not available 

1,2,3,4,6,7,8,9- 

Octachlorodibenzofuran 39001020 4.50E+01 Derived from WHO 2005 TEFsc not available 

1,2,3,7,8- 

Pentachlorodibenzo-p-dioxin 40321764 1.50E+05 Derived from WHO 2005 TEFsc not available 

1,2,3,7,8- 

Pentachlorodibenzofuran 57117416 4.50E+03 Derived from WHO 2005 TEFsc not available 

2,3,4,7,8- 

Pentachlorodibenzofuran 57117314 4.50E+04 Derived from WHO 2005 TEFsc 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 TEFsc not available 
TRIM-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 Chemicalsa

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 72002d 7.0E-02 ATSDR 

2-Acetylaminofluorene 53963 1.0E+01 EPA 1999, POM 

Group 75002d not available 

2-Chloronaphthalene 91587 5.0E-01 EPA 1999, POM 

Group 72002d not available 

2-Methylnaphthalene 91576 5.0E-01 EPA 1999, POM 

Group 72002d 5.0E-02 ATSDR 

3-Methylcholanthrene 56495 2.2E+1 CalEPA not available 

7,12-Dimethylbenz(a)anthracene 57976 2.5E+02 CalEPA not available 

Acenaphthene 83329 5.0E-01 EPA 1999, POM 

Group 72002d 6.0E-02 IRIS 

Acenaphthylene 208968 5.0E-01 EPA 1999, POM 

Group 72002d 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 72002d not available 

Benzo(g,h,i)perylene 191242 5.0E-01 EPA 1999, POM 

Group 72002d 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 72002d 4.0E-02 IRIS 

Fluorene 86737 5.0E-01 EPA 1999, POM 

Group 72002d 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 71002d not available 

Perylene 198550 5.0E-01 EPA 1999, POM 

Group 72002d not available 

Phenanthrene 85018 0 IRIS 0 IRIS 

Polycyclic Organic Matter 246 5.0E-01 EPA 1999, POM 

Group 71002d not available 
TRIM-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 Chemicalsa

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 72002d 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 = California EPA, EPA ORD = EPA’s Office of Research and Development, 

HEAST = EPA Health Effects Assessment Tables, 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 ingestion 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 organic 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/pomapproachjan.pdf and is summarized in Section 4.4 of this addendum. 

4.1. Cadmium 

EPA has developed two chronic RfDs for cadmium (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 exposure 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 important 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 average 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 exception), 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 Factora

1,2,3,4,6,7,8-Heptachlorodibenzo-p-dioxin 35822469 0.01 

1,2,3,4,6,7,8-Heptachlorodibenzofuran 67562394 0.01 

1,2,3,4,7,8,9-Heptachlorodibenzofuran 55673897 0.01 

1,2,3,4,7,8-Hexachlorodibenzo-p-dioxin 39227286 0.1 

1,2,3,4,7,8-Hexachlorodibenzofuran 70648269 0.1 

1,2,3,6,7,8-Hexachlorodibenzo-p-dioxin 57653857 0.1 

1,2,3,6,7,8-Hexachlorodibenzofuran 57117449 0.1 

1,2,3,7,8,9-Hexachlorodibenzo-p-dioxinb 19408743 0.1 

1,2,3,7,8,9-Hexachlorodibenzofuran 72918219 0.1 

2,3,4,6,7,8-Hexachlorodibenzofuran 60851345 0.1 

1,2,3,4,6,7,8,9-Octachlorodibenzo-p-dioxin 3268879 3E-04 

1,2,3,4,6,7,8,9-Octachlorodibenzofuran 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 cancer slope factor from IRIS. For the purposes of 

these multipathway analyses, EPA uses the TEF derived 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 primarily 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 National-Scale Air Toxics Assessments (NATA) and 

for RTR, OAQPS developed an approach for characterizing 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 assumptions 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 available CSFs. Each POM group (with all its member 
TRIM-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 purposes of RTR, identified a CSF specific to 

the individual chemical. 

Exhibit_Add A2-8. Oral Dose-response Values for 

Polycyclic Organic Matter (POM) Groupsa

Individual POM or POM Group CAS No. Cancer Slope Factorb

(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 189640 
TRIM-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) Groupsa

Individual POM or POM Group CAS No. Cancer Slope Factorb

(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 the 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 negligible 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 exposure 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 lifestage 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 quantitative default approach is mutagenesis (EPA 

2005b,c). For carcinogens with a mutagenic MOA for cancer, EPA concluded that the 

carcinogenic potency of a chemical may be approximately 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 are 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

ADAF
TRIM-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 from 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 under 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 through 5 years of age (mg/kg-day) 

ADD(6-11) = Average daily dose from age 6 through 11 years of age (mg/kg-day) 

ADD(12-19) = Average daily dose from age 12 through 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 ith 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

Risk i 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 calculates 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) 

ADD = 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 Chemicals 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 information 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 approach 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 = HQ1 + HQ2 ... HQn

where: 

HI = Hazard index (unitless) 

HQ1 = Hazard Quotient for chemical 1 (unitless) 

HQ2 = Hazard Quotient for chemical 2 (unitless) 

HQn = 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 interactions 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 deposition 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 weight, 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-specific 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. However, 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 exposure 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 MIRC 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/m3); 

 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/m2

-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 μg/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 commonly 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-specific 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 

BrAG-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) 

BvAG(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 coefficient 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 concentration estimated for dry-weight 

produce 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 

VGAG(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 

VGrootveg 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 crop biomass of the edible portion 

of produce or animal feed 

kg produce DW/m2

Animal Products 

Bs Soil bioavailability factor for livestock Unitless 

MF Chemical-specific mammalian metabolism factor that accounts 

for endogenous degradation of the chemical 

Unitless 
TRIM-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(dairy) Biotransfer factor in dairy day/kg FW tissue 

Ba(pork) Biotransfer factor in pork day/kg FW tissue 

Ba(poultry) Biotransfer factor in poultry day/kg FW tissue 

Ba(eggs) 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 overwrite 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 (BvAG(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+05 
TRIM-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 (BvAG(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+02 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+04 
TRIM-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 (BvAG(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 most organic chemicals. As described in HHRAP (EPA 2005a), Appendix B (available at 

http://www.epa.gov/osw/hazard/tsd/td/combust/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 insoluble particles tagged with gammaemitting radionuclides were deposited onto pasture grass via simulated rain. Note that the values developed experimentally for

pasture grass may not accurately represent all aboveground produce-specific values. Also note that values based on the 

behavior of insoluble particles tagged with radionuclides may not accurately represent 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/combust/finalmact/ssra/05hhrapapa.pdf ), RCF is used to calculate the below-ground 

transfer of contaminants from soil to a root vegetable on a wet-weight 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 solution. Briggs’ regression equations allow calculation of RCF 

values from log Kow. For metals and mercuric 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 describes 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 multiplying Koc times the screening scenario’s fraction organic carbon 

content (0.008). Empirical information for Koc was available for acenaphthene, benz(a)anthracene, benzo(a)pyrene, 

dibenz(a,h)anthracene, fluoranthene, and fluorene 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 for mercuric chloride was obtained from EPA 1997b. BvAG(i) 

values for PAHs were calculated using the correlation equation derived for azalea leaves as cited 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 dioxins 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 leaves. 

fSpeciation and fate and transport of mercury from emissions suggest that BvAG(i) values 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 

(BrAG-produce-DW(i))

(unitless)a

Empirical Correction 

Factor- Belowground 

Produce 

(VGrootveg) (unitless)b

Empirical 

Correction FactorAboveground 

Produce 

(VGAG(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-01 
TRIM-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 

(BrAG-produce-DW(i))

(unitless)a

Empirical Correction 

Factor- Belowground 

Produce 

(VGrootveg) (unitless)b

Empirical 

Correction FactorAboveground 

Produce 

(VGAG(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-01 
TRIM-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 

(BrAG-produce-DW(i))

(unitless)a

Empirical Correction 

Factor- Belowground 

Produce 

(VGrootveg) (unitless)b

Empirical 

Correction FactorAboveground 

Produce 

(VGAG(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-01 
TRIM-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 

(BrAG-produce-DW(i))

(unitless)a

Empirical Correction 

Factor- Belowground 

Produce 

(VGrootveg) (unitless)b

Empirical 

Correction FactorAboveground 

Produce 

(VGAG(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-01 
TRIM-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 

(BrAG-produce-DW(i))

(unitless)a

Empirical Correction 

Factor- Belowground 

Produce 

(VGrootveg) (unitless)b

Empirical 

Correction FactorAboveground 

Produce 

(VGAG(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-01 
TRIM-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 

(BrAG-produce-DW(i))

(unitless)a

Empirical Correction 

Factor- Belowground 

Produce 

(VGrootveg) (unitless)b

Empirical 

Correction FactorAboveground 

Produce 

(VGAG(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-01 
TRIM-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 

(BrAG-produce-DW(i))

(unitless)a

Empirical Correction 

Factor- Belowground 

Produce 

(VGrootveg) (unitless)b

Empirical 

Correction FactorAboveground 

Produce 

(VGAG(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-01 
TRIM-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 

(BrAG-produce-DW(i))

(unitless)a

Empirical Correction 

Factor- Belowground 

Produce 

(VGrootveg) (unitless)b

Empirical 

Correction FactorAboveground 

Produce 

(VGAG(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-01 
TRIM-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 

(BrAG-produce-DW(i))

(unitless)a

Empirical Correction 

Factor- Belowground 

Produce 

(VGrootveg) (unitless)b

Empirical 

Correction FactorAboveground 

Produce 

(VGAG(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-01 
TRIM-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 

(BrAG-produce-DW(i))

(unitless)a

Empirical Correction 

Factor- Belowground 

Produce 

(VGrootveg) (unitless)b

Empirical 

Correction FactorAboveground 

Produce 

(VGAG(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 BrAG-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 contaminant concentration in dry weight 

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. The HHRAP methodology assumes that elemental mercury doesn’t 

deposit onto soils. Therefore, it’s assumed that there is no plant uptake through the soil. b

As discussed in HHRAP (EPA 2005a), Appendix B, VGrootveg 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 Kow

greater than 4) to the center of the produce is not likely. In addition, typical preparation techniques, such as washing, peeling, and 

cooking, further reduce the concentration of the chemical in the vegetable as consumed by removing the 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 VGrootveg value of 0.01 for PB-HAP 

with a log Kow greater than 4 and a value of 1.0 for PB-HAP with a log Kow 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, VGag represents an empirical correction factor that reduces aboveground 

produce concentration and was developed to estimate the transfer of PB-HAP into leafy vegetation 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 

Kow greater than 4) to the center of the produce is not likely. In addition, typical preparation techniques, such as washing, peeling, 

and cooking, further reduces residues. For aboveground produce, HHRAP (EPA 2005a) recommends using a VGag value of 0.01 

for PB-HAP with a log Kow greater than 4 and a value of 1.0 for PB-HAP with a log Kow less than 4 based on information provided in 

EPA 1994b. In developing these values, EPA (1994b) assumed the 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 vegetable 

are equal (potentially overestimating the concentration of PB-HAP in aboveground produce 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 VGag value of 1.0, also based on information provided in EPA 1994b. 

A VGag value for silage is not provided in EPA 1994b; the VGag value for silage of 0.5 was obtained from NC DEHNR (1997); 

however, NC DEHNR does not present a specific 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/m2)

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 productivity (standing crop biomass [Yp]) to calculate Rp values for exposed vegetables, 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 developed by Chamberlain (1970) may not accurately 

represent the site-specific mixes of aboveground produce consumed by humans or the site-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 concentrations 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 average of the average period between successive hay harvests (60 

days) and the average period between successive grazing (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 uncertainties 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 = Yhi / Ahi, where Yhi = Harvest yield of i

th crop (kg DW) and Ahi = Area planted to ith crop (m2), and using values for Yh

and Ah 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 moisture adjustment factor to convert dry-weight concentrations into wet-weight 

concentrations (which are lower owing to the dilution by water compared with dry-weight concentrations). Values obtained from 

Chapter 10 of EPA's 2003 SAB Review materials for 3MRA Modeling 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 (Bam) (day/kg FW tissue)a 

and Metabolism Factors (MF) (unitless)b 

Mammal Non-mammal 

Beef 

(Babeef) 

Dairy 

(Badairy) 

Pork 

(Bapork) MF Eggs (Baeggs) 

Poultry 

(Bapoultry) MF 

Inorganics 

Cadmium compounds 1 1.2E-04 6.5E-06 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-05 5.1E-06 1 3.6E-03 3.6E-03 NA 

PAHs 

2-Methylnaphthalene 1 2.4E-02 5.0E-03 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-03 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-03 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-03 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-02 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-02 1 3.7E-03 6.5E-03 NA 
TRIM-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 (Bam) (day/kg FW tissue)a 

and Metabolism Factors (MF) (unitless)b 

Mammal Non-mammal 

Beef 

(Babeef) 

Dairy 

(Badairy) 

Pork 

(Bapork) MF Eggs (Baeggs) 

Poultry 

(Bapoultry) MF 

HeptaCDF, 

1,2,3,4,6,7,8- 1 1.6E-02 3.5E-03 2.0E-02 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-02 1 1.0E-02 1.8E-02 NA 

HexaCDD, 1,2,3,4,7,8- 1 1.1E-02 2.3E-03 1.3E-02 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-03 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-03 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-02 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 factors for mercury compounds were obtained from EPA 1997b. Considering 

speciation, fate, and transport of mercury from emission sources, elemental mercury is assumed to be vapor-phase and hence is assumed not to 

deposit to soil or transfer into aboveground plant parts. As a consequence, there is no transfer of elemental mercury into animal tissues. 

Biotransfer factors for cadmium compounds were obtained from EPA 1995b. Biotransfer factors for dioxins and PAHs were calculated from 

chemical octanol-water partitioning coefficients (Kow 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 mammals 

to offset the amount of bioaccumulation suggested by biotransfer 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 calculate concentrations of PAHs in 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 concentrations 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 cattleb 0.5 

Silage 2.5 

Forage 8.8 

Grain 0.47 

Dairy cattlec 0.4 

Silage 4.1 

Forage 13.2 

Grain 3.0 

Swined 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, Fries (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 intake 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 uniform 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 include 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 chicken. b

The beef cattle ingestion rates of forage, silage, and grain are based on the total daily intake rate of about 12 kg DW/day (based 

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, silage, and grain ingestion rates for cattle. c

The dairy cattle ingestion rates of forage, silage, and grain are based on the total daily intake rate of about 20 kg DW/day (NAS 

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). Uncertainties associated with this variable include the variability of

actual grain ingestion rates from site to site. In addition, assuming uniform contamination of plant materials consumed by chicken 

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 age 

groupings 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., gwet 

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, 5th, 10th, 50th, 90th, and 95th

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 5th 10th 50th 90th 95th

Adulta (20-70) 50 80.0a 52.9 56 69.3 89.7 97.6 

Child < 1b 1 7.83 6.03 6.38 7.76 9.24 9.66 

Child 1-2c 2 12.6 9.9 10.4 12.5 14.9 15.6 

Child 3-5d 3 18.6 13.5 14.4 17.8 23.6 26.2 

Child 6-11e 6 36.0 22.1 24.0 33.5 51.2 58.6 

Child 12-19f 8 64.2 39.5 45 64.2 83.5 89 

[Child 12-19g 8 64.3 41.1 44.6 60.9 88.5 98.4] a

BW represents the recommended body weight from EPAs 2011 EFH. 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 percentile 

body weights is likely to be very small (i.e., less than 1%). 

b

Each BW represents a time-weighted average of body weights for 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 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

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 for age groups 11 to <16 years and 16 to <21 years from Table 

8-3 of the 2008 CSEFH. Estimated values include 11-year-olds and individuals through age 20, which 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 chemical 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 ingestion 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 Adultsa

Lifestage (years) Ingestion Rates, Community Water (mL/day) 

Mean 50th 90th 95th 99th

Child <1b 504 482 969 1113 1440 

Child 1-2c 332 255 687 903 1318 

Child 3-5d 382 316 778 999 1592 

Child 6-11e 532 417 1149 1499 2274 

Child 12-19f 698 473 1641 2163 3467 

Adultg 1219 981 2534 3087 4567 

Sources: EPA 2004, 2008a 

*The sample size does not meet minimum reporting requirements 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 this 

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 month, 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 years from Table 3-4 of the 2008 CSEFH. This value represents a 

health protective (i.e., slightly low) estimate of IR for ages 6 through 11 years since 11-year olds are not included in this CSEFH 

age group. 

f

Each IR represents a time-weighted average of ingestion rates for 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 (2004b), Appendix E, Part I, Table A1 for community water, both 

sexes (ages 20+), direct plus 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, 90th, 95th, and 99th 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 “Response 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 persons consuming the food. EPA averaged the 

actual consumption rate for home-grown foods 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 specify 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) 

Beefa N/A 4.14 4.00 3.77 1.72 1.93 

Dairyb N/A 91.6 50.9 27.4 13.6 2.96 

Eggsa N/A 2.46 1.42 0.86 0.588 0.606 

Exposed Fruita N/A 6.14 2.60 2.52 1.33 1.19 

 

21Note that EPA’s 2008 CSEFH does not distinguish between exposed and protected fruits and vegetables when 

recommending food ingestion rates based on the same data set for the same age categories. EPA’s 1997 analysis 

for its EFH therefore remains the most appropriate 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 Vegetablea N/A 3.48 1.74 1.39 1.07 1.38 

Porka N/A 2.23 2.15 1.50 1.28 1.10 

Poultrya N/A 3.57 3.35 2.14 1.50 1.37 

Protected Fruita N/A 16.6 12.4 8.50 2.96 5.19 

Protected Vegetablea N/A 2.46 1.30 1.10 0.78 0.862 

Root Vegetablea 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) 

Beefa N/A 2.51 2.49 2.11 1.51 1.55 

Dairyb N/A 125 66.0 34.4 15.5 2.58 

Eggsa N/A 1.51 0.83 0.561 0.435 0.474 

Exposed Fruita N/A 1.82 1.11 0.61 0.62 0.593 

Exposed Vegetablea N/A 1.89 1.16 0.64 0.66 0.812 

Porka N/A 1.80 1.49 1.04 0.89 0.802 

Poultrya N/A 3.01 2.90 1.48 1.30 0.922 

Protected Fruita N/A 7.59 5.94 3.63 1.23 2.08 

Protected Vegetablea N/A 1.94 1.04 0.79 0.58 0.564 

Root Vegetablea N/A 0.46 0.52 0.57 0.56 0.59 

Water (mL/day)c N/A 255 316 417 473 981 

90th percentile ingestion rates (g/kg-day)d

Beefa N/A 9.49 8.83 11.4 3.53 4.41 

Dairyb N/A 185 92.5 57.4 30.9 6.16 

Eggsa N/A 4.90 3.06 1.90 1.30 1.31 

Exposed Fruita N/A 12.7 5.41 6.98 3.41 2.37 

Exposed Vegetablea N/A 10.7 3.47 3.22 2.35 3.09 

Porka N/A 4.90 4.83 3.72 3.69 2.23 

Poultrya N/A 7.17 6.52 4.51 3.13 2.69 

Protected Fruita N/A 44.8 32.0 23.3 7.44 15.1 

Protected Vegetablea N/A 3.88 2.51 2.14 1.85 1.81 

Root Vegetablea N/A 7.25 4.26 3.83 2.26 2.49 

Water (mL/day)c N/A 687 778 1149 1640 2534 

95th percentile ingestion rates (g/kg-day)

Beefa N/A 12.9 12.5 12.5 3.57 5.83 

Dairyb N/A 167 89.9 56.0 32.3 7.80 

Eggsa N/A 5.38 3.62 2.37 1.43 1.59 

Exposed Fruita N/A 14.6 6.07 11.7 4.78 3.38 

Exposed Vegetablea N/A 11.9 6.29 5.47 3.78 4.46 

Porka N/A 6.52 6.12 4.73 6.39 2.60 

Poultrya N/A 8.10 7.06 5.07 3.51 3.93 
TRIM-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 Fruita N/A 48.3 35.1 26.9 11.4 19.2 

Protected Vegetablea N/A 9.42 5.10 3.12 2.20 2.83 

Root Vegetablea N/A 10.4 4.73 5.59 3.32 3.37 

Water (mL/day)c N/A 903 999 1499 2163 3087 

99th percentile ingestion rates (g/kg-day) 

Beefa N/A 20.9 19.8 13.3 4.28 6.84 

Dairyb N/A 180 87.2 54.8 34.7 9.20 

Eggsa N/A 16.2 11.2 8.19 4.77 1.83 

Exposed Fruita N/A 25.2 32.5 15.7 5.9 13.0 

Exposed Vegetablea N/A 12.1 7.36 13.3 5.67 8.42 

Porka N/A 8.71 9.74 6.61 4.29 3.87 

Poultrya N/A 9.63 10.24 6.12 4.60 4.93 

Protected Fruita N/A 109 71.2 58.2 19.1 34.4 

Protected Vegetablea N/A 9.42 5.31 5.40 2.69 5.56 

Root Vegetablea 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 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 an NHANES 2003–2006 analysis in Chapter 11 of 

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 three. All data tables that were used and justifications for 

data sources are presented in Chapter 3 of the 2011 Exposure Factors’ Handbook. 

d

Default ingestion rate percentile used 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 sizes 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 insufficient 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 rates, 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 IRage_group_x, were derived using the following information: 

 Mean or percentile-specific consumer-only intake 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; and 
TRIM-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 IRPC, age_group_x to IRPC_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 IRCO, age_group_x to IRCO_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: 

IRCO, 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 

IRCO_total = Mean or percentile-specific consumer-only intake of the farm food item, as 

brought into the home, for the total NFCS survey population 

IRPC, 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 

IRPC_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 because 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 90th

percentile ingestion estimates, which are likely more reliable than the 95th or 99th percentile 

estimates in this particular calculation. 

IR

IR IR

IR 

PC_total

CO_total PC, age_group_x

CO, age_group_x

 
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 Consumption 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 squid (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 99th 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 ingestion 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 99th

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 Intakes by Individuals (CSFII; USDA 2000). Values 

reflect “as prepared” ingestion rates for which cooking and preparation losses (L1 and L2) did 
TRIM-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 107.7a 159.0b 268.2c 331.0c 373d

a

A fish ingestion rate for ages 1-2 years was not available. The 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]), 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 fish ingestion rates based on ingestion rates from EPA 

(2002), Section 4.2.1.1 Table 5 (freshwater/estuarine habitat). 

d

This value represents the 99th percentile ingestion rate of wild caught fish for women as reported by Burger (2002). 

6.3.4.2. Other values 

EPA’s (2002) analysis of freshwater and estuarine 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 assumed 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 locally 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 90th 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 relatively 
TRIM-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 available in the 2008 CSEFH and EPA 2002. 

Equation 2-57 was used to calculate the alternative, per capita fish ingestion rates by age group 

(IRPC,y): 

Equation 2-57. Calculation of Alternative Age-Group-Specific Fish Ingestion Rates 

where: 

IRPC,y = Per capita fish ingestion rate for age group y (g/day) 

IRCO,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) 

FPC,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 presented 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 

 

22Most 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 percentiles are slightly different due to rounding. 

yPC,  FIR IR yPC,yCO,
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 consumption 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 (IRCO,y)

a 

Lifestage (years) Ingestion Rates, All Fish (g/day) 

Mean 50th 90th 95th 99th

Child <1 NA NA NA NA NA 

Child 1-2b 27.31 15.61 64.46 87.60 138.76* 

Child 3-5c 40.31 23.04 95.16 129.31 204.84* 

Child 6-11d 61.49 28.46 156.86* 247.69* 385.64* 

Child 12-19e 79.07 43.18 181.40* 211.15* 423.38* 

Adultf 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 available 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 values for all age groups and percentiles. b

A fish IR for ages 1-2 years was not available. The value represents the consumer-only fish ingestion rate for ages 3 to 5 from 

EPA (2002) (Section 5.2.1.1 Table 5 [freshwater/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-only 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); the value may underestimate ingestion rate for ages 12 to 19 years. 

Sample size = 135. 

f

These values represent the consumer-only fish ingestion rate for individuals 18 years 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 (FPC,y) 

Lifestage (years) Fraction Consuming Fish 

Child 3-5 0.0503a 

Child 6-11 0.0440b 

Child 12-19 0.0493c 

Adult 0.08509d 

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 proportion consuming fish on a single day (the consumers-only group includes individuals 

who consumed fish on at least one of two survey 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 (IRPC,y) 

Lifestage (years) Ingestion Rates, All Fish (g/day) 

Mean 50th 90th 95th 99th

Child <1 NA NA NA NA NA 

Child 1-2a 1.37 0.79 3.24 4.41 6.98 

Child 3-5b 2.03 1.16 4.79 6.51 10.3 

Child 6-11c 2.71 1.25 6.90 10.9 17.0 

Child 12-19d 3.90 2.13 8.95 10.4 20.9 

Adulte 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-only IR for Child 1-2) x (fraction of population consuming fish for Child 3-5). b

Values were calculated as (consumer-only IR for Child 3-5) x (fraction of population consuming fish for Child 3-5). c

Values were calculated as (consumer-only IR for Child 6-11) x (fraction of population consuming fish for Child 6-11). d

Values were calculated as (consumer-only IR estimated for Child 12-19) x (fraction of population estimated 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 95th percentile ingestion rates for each subpopulation. In part due to 

the low sample size in the Shilling study (n of 30 to 45), 95th percentile values were believed to 

be unrealistically high. The 90th 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 90th Percentile Per capita Fish Ingestion Rates 

for Populations of Recreational Fishers (IRPC,y) 

Subpopulation

Percentile Units 

Recreational 

Fishera

Female 

Recreational 

Fisherb

Black 

Recreational 

Fisherb

Hispanic 

Recreational 

Fisherc

Laotian 

Recreational 

Fisherc

Vietnamese 

Recreational 

Fisherc

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. Contaminated 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 Kow 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 consumed 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 90th 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) 

Meana 50th a 90th 95th 99th

Child < 1 NA 

Child 1-2 50 50 200b 200b 200b

Child 3-5 50 50 200b 200b 200b

Child 6-11 50 50 201c 331d 331d

Child 12-19 50 50 201c 331d 331d

Adult 20-70 20 20 201c 331d 331d

Sources: EPA 2008a, EPA 2011a 

a

For mean and 50th percentile soil ingestion rates for children, value represents a “central tendency” estimate from EPA’s 2008 

CSEFH, Table 5-1. For adults, 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 90th percentile adult ingestion rates calculated in Stanek et al. 1997; used to represent older children and adults. d

Values are 95th 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 from 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 90th 95th 99th

Total Food Intake (g/day, as consumed) 

Child < 1a 67.0% - 99.7% h 322 270 599 779 1152 

Child 1-2b 100% 1,032 996 1537 1703 2143 

Child 3-5c 100% 1,066 1,020 1,548 1,746 2,168 

Child 6-11d 100% 1,118 1,052 1,642 1,825 2,218 

Child 12-19e 100% 1,197 1,093 1,872 2,231 2,975 

Adultf 100% 1,100 1,034 1,738 2,002 2,736 

Total Food Intake (g/kg-day, as consumed) 

Child < 1a 67.0% - 99.7% h 39 34 72 95 147 

Child 1-2b 100% 82 79 125 144 177 

Child 3-5c 100% 61 57 91 102 132 

Child 6-11d 100% 40 38 61 70 88 

Child 12-19e 100% 21 19 34 40 51 

Adultg 100% 14.8 13.9 23.7 27.6 35.5 

Sources: EPA 2005e, 2008a 

a

These values represent a time-weighted 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 average 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) estimate 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 average 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 values 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 “nonconsumers” 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 products 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 consistent 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 Fruitc 0.244 0.305 

Exposed Vegetable 0.162d NA 

Protected Fruit 0.29e NA 

Protected Vegetable 0.088f NA 

Root Vegetableg 0.075 0.22 

Beef 0.27 0.24 

Pork 0.28 0.36 

Poultry 0.32 0.295h

Fishi 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, shelling, 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 pit, 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, shrinkage, excess fat, bones, scraps, and juices. c

These values represent averages of means for all fruits 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 vegetables 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 means for chicken and turkey (Table 13-5). i

If the user changes fish ingestion rates to match a survey of the whole weight of fish brought into the home from the field (divided 

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 liquid 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, and 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 vegetables (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 Adjustment 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 estimate 

a 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 Document: 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 with 
TRIM-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 frying, 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, receptor-specific, and/or chemical-specific and might 

be empirically derived or estimated by an appropriate 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 context, 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 DAIinf. 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 

BWinf Body weight of infant (kg) averaged over duration of nursing exposure 7.8 

BWmat

Body weight of mother (kg) averaged over duration of mother’s 

exposure 

66 

DAImat 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 

fbp Fraction of mother’s whole blood that is plasma (unitless) 0.65 

ffm Fraction of mother’s body weight that is fat (unitless) 0.30 

fmbm Fraction of fat in mother’s breast milk (unitless) 0.04 

fpm Fraction of mother’s body weight that is plasma (unitless) 0.046 

IRmilk Mean infant milk ingestion rate over duration of nursing (kg/day) 0.709 

tbf Duration of breast feeding (days) 365 

tpn 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 (BWinf). The user selects a value for BWinf , 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 averaged 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 (tbf) 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 birth 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 7.8a 9.0 9.6 

5th percentile 5.0 6.0 7.0 7.5 

10th percentile 5.3 6.4 7.4 7.8 

15th percentile 5.5 6.7 7.7 8.2 

25th percentile 5.8 7.0 8.1 8.7 

50th percentile 6.4 7.8 8.9 9.5 

75th percentile 7.1 8.6 9.9 10.5 

85th percentile 7.4 9.0 10.3 11.0 
TRIM-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 7.8a 9.0 9.6 

90th percentile 7.7 9.2 10.6 11.3 

95th percentile 8.0 9.7 11.1 11.8 

Source: EPA (2008a); each value is the time-weighted average from the data summaries presented in the CSEFH, Table 8-3. a

MIRC default

Maternal body weight (BWmat). 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 biological elimination constant for the lipophilic 

chemical in lactating women (kfat_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 BWmat 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 BWmat value is not the value that MIRC uses to estimate the mother’s absorbed 

daily intake (DAImat). 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 66.0a

5th 47.1 

10th 50.2 

25th 54.3 

50th 62.0 

75th 72.0 

90th 85.7 

95th 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 (fbp). 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 (ffm). 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 milk fat, assumes that the mother’s body fat will 

remain constant over the entire duration of breast feeding (tbf), which is unlikely to be true (EPA 

2001a). Another limitation of the single analytic model 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 to 

the 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, however, 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 ffm is 0.30.

Fraction of fat in mother’s breast milk (fmbm). The Cmilkfat 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 fmbm of 0.04 (i.e., 4 percent) is the value EPA recommended for MPE 

(EPA 1998). 

Fraction of maternal weight that is plasma (fpm). 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 fpm therefore is 0.046. 

Infant breast milk ingestion rate (IRmilk). 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 ingestion 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 980a

 1.01a 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 (tbf). 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 tbf is 365 

days. 

Duration of the mother’s exposure to the chemical of concern prior to nursing (tpn). 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 model 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-life 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 tpn

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 tpn 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 tpn 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-milk 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 assumed 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 

AEinf 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) 

AEmat 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) 

fbl 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

ff Fraction of steady-state lipophilic chemical body 

burden in mother that is stored in body fat (unitless)

≥0.90 (ATSDR 

1992) 

NA 

fpl Fraction of steady-state total hydrophilic chemical 

body burden in mother that is in the blood plasma 

compartment (unitless) 

NA Not yet identifiedb

h Biological half-life for chemical in non-lactating 

women (days) 

3650 (EPA 

1994c) 

50 (Sherlock et al. 

1984) 

kaq_elac Rate constant for total elimination of hydrophilic 

chemicals by lactating women (per day) 

NA = kelim

kelim Rate constant for elimination of chemical for nonlactating women (per day; related to chemical halflife) 

1.9E-04b

 1.4E-02 c

kfat_elac Rate constant for total elimination of lipophilic 

chemicals by lactating women (per day) 

Est. using 

Equation 2-41 

NA 

Pcbm 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) 

PcRBC 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 from biological half-life (h) using Equation 2-40. 

Absorption efficiency of the chemical by the oral route of exposure for the infant (AEinf). The 

models included in MIRC assume that the AEinf from the lipid phase of breast milk is equal to the 

AEinf from the aqueous phase of the milk. Reviewers of the model stated that this assumption 

may not be valid and that ideally, the equation DAIinf would include variables for the AEinf from 

the breast milk fat and the AEinf 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 AEinf 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 AEinf. Reviewers also recommended that chemical-specific values come from studies 

that account for absorption of the chemical from 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 AEinf 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 AEinf 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 (AEmat). 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 (fbl). 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 (ff). Based on ATSDR’s 

Toxicological Profile for Selected PCBs (ATSDR 1992) and Sullivan et al. (1991), EPA 

concluded that the “fraction of ingested contaminant 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 (fpl). For hydrophilic 

chemicals, this parameter represents the steady-state fraction of the total chemical in the body 

that is circulating in the blood plasma. Values for fpl 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 fbl, 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 (kaq_elac). The parameter 

kaq_elac is equal to kelim 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 kaq_elac is equal to kelim as discussed 

for Equation 2-43. The extent to which kelim is an underestimate of kaq_elac for a given chemical 

will determine the extent of health protective bias in kaq_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 (kelim). Although values for this 

parameter often are reported directly in the literature, MIRC estimates kelim from chemical halflife assuming first-order kinetics as shown in Equation 2-40. For example, for a biological halflife of 3,650 days for dioxins, kelim is estimated to be 1.9E-04 per day. Assuming a biological 

half-life of 50 days for MeHg, the value for kelim is estimated to be 0.014 per day. 

Rate constant for total elimination of lipophilic chemicals by lactating women (kfat_elac). Although 

values for this parameter might be found in the scientific literature for some chemicals, in MIRC, 

kfat_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 

kfat_elac. 

Partition coefficient for chemical between maternal blood plasma and aqueous phase of breast 

milk (Pcbm). 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 (PcRBC). 

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 90th percentile ingestion rates for soil, breast milk, and 

farm food items and 99th 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, methyl 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 90th 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 (90th percentile), 

172 g/day (95th percentile), and 373 g/day (99th 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 99th 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 99th percentile of the distribution for the consumer-only population (i.e., inclusive only of 

people 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 directly correspond to the 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 possible exposure an individual subsistence fisher 

might experience. 

Although the fish ingestion rates presented here are representative of the 99th percentile of the 

evaluated data set, the use of these inputs (compared with 90th 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 90th 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 90th percentile 

individual total food ingestion rates from CSFII (which are based on consumption of prepared 

foods). The final row of Exhibit_Add A2-31 shows 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 evaluating 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 

Beefa NA 9.49 8.83 11.4 3.53 4.41 g/kg-day 

Dairyb NA 185 92.5 57.4 30.9 6.16 g/kg-day 

Eggsa NA 4.90 3.06 1.90 1.30 1.31 g/kg-day 

Exposed Fruita NA 12.7 5.41 6.98 3.41 2.37 g/kg-day 

Exposed Vegetablea NA 10.7 3.47 3.22 2.35 3.09 g/kg-day 

Porka NA 4.90 4.83 3.72 3.69 2.23 g/kg-day 

Poultrya NA 7.17 6.52 4.51 3.13 2.69 g/kg-day 

Protected Fruita NA 44.8 32.0 23.3 7.44 15.1 g/kg-day 

Protected Vegetablea NA 3.88 2.51 2.14 1.85 1.81 g/kg-day 

Root Vegetablea NA 7.25 4.26 3.83 2.26 2.49 g/kg-day 

Other 

Breast milkc 1.01 NA NA NA NA NA kg/day 

Soil (dry) NA 200d 200d 201e 201e 201e mg/day 

Fish (per individual)f NA 107.7g 159.0g 268.2h 331.0h 373 g/day 
TRIM-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 

onlyi NA 259 142 99 51 35.5 g/kg-day 

Total Food: All Sourcesj 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; compiled 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 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 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 with pica only, but this value has not been used. e

These values are 90th percentile adult ingestion rates calculated in Stanek et al. 1997, and they are used to represent older children 

and adults. 

f

The ingestion rate for adults was obtained from Burger (2002) and is the 99th percentile value for adult females considered high-end 

recreationists; this value is believed to be representative of subsistence fishers. The 99th percentile values for children were derived 

based on EPA’s Estimated Per Capita Fish Consumption in the United States (2002)—Section 4.2.1.1 Table 5 (for child age categories) 

adjusted and scaled. Values reflect “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 range matches the EPA (2002) age category. Fish ingestion rates for 

children less than 3 years old, however, were not provided. Therefore, for children aged 1–2 years, the fish ingestion rate was calculated 

using the ingestion rate for children aged 3–5 years scaled downward 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 calculated using the EPA (2002) fish ingestion estimates in order to adjust for the 

differences between the age group ranges used for the RTR 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-L1)x(1-L2), where L1 and L2 are the loss rates from Exhibit_Add A2-25. The rows are then summed to get the total 

post-cooking ingestion rate. 

j

90th 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 Childrena

Lifestage (years) Duration (years) Mean Body Weight (kg) 

Adultb (20-70) 50 80.0 

Child < 1c 1 7.83 

Child 1-2c 2 12.6 

Child 3-5d 3 18.6 
TRIM-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 Childrena

Lifestage (years) Duration (years) Mean Body Weight (kg) 

Child 6-11e 6 36.0 

Child 12-19f 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-weighted 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 possible 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 Parameter 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 (VGrootveg), wet deposition 

fractions (Fw), air to plant transfer factors (BvAG), 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 MIRCa

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 unitless 
TRIM-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 MIRCa

Parameter Description Benzo(a)-

pyrene 

Cadmium Mercuric 

chloride 

Methyl 

mercury

2,3,7,8-

TCDD Units 

BvAG Air-to-plant 

biotransfer factor for 

aboveground 

produce 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 exhibits; however exact values used in the analysis are presented 

here, rather than values restricted by significant figures. In addition, only values for those chemicals that are specifically used in 

the screening analysis are provided here. 

Only single estimates were developed for each 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 were 

developed 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 chemical 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-03 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 farming 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. The 
TRIM-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 for 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 possibly 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 pharmacokinetic (PBPK) model of lactational transfer of 

MeHg developed and partially validated by Byczkowski 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 (DAIinf) is indistinguishable from the 

dose of MeHg absorbed by its mother from her food (DAImat). 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 

Altman, P.L., and D.S. Dittmer, Eds. 1964. Biology Data Book. Volume 1. Bethesda, MD. 263-

264 (As cited in EPA 1998). 

Amin-Zaki, L., S. Elhassani, M.A. Majeed, T.W. Clarkson, R.A. Doherty, M.R. Greenwood, and 

T. Giovanoli-Jakubczak. 1976. Perinatal methylmercury poisoning in Iraq. Am. J. Diseases 

in Children 130: 1070-1076 (As cited in Byczkowski and Lipscomb 2001). 

ATSDR (Agency for Toxic Substances and Disease Registry). 1992. Toxicological profile for 

selected PCBs (Arochlor- 1260, 1254, 1248, 1242, 1232, 1221, and 1016). Atlanta, GA: U.S. 

Department of Health and Human Services, Public Health Service (As cited in EPA 1998). 

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TRIM-Based Tiered Screening Methodology for RTR 

Attachment A, Addendum 2 2-107 December 2013 

Description of MIRC 

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TRIM-Based Tiered Screening Methodology for RTR 

Attachment A, Addendum 2 2-108 December 2013 

Description of MIRC 

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Volume V. External Review Draft. U.S. EPA Region 5, Chicago, Illinois. 
TRIM-Based Tiered Screening Methodology for RTR 

Attachment A, Addendum 2 2-109 December 2013 

Description of MIRC 

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TRIM-Based Tiered Screening Methodology for RTR 

Attachment A, Addendum 2 2-110 December 2013 

Description of MIRC 

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OSWER 9285.7-02EP; NTIS PB99-963312. July. 

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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: 

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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. 

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009. December. 

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cooking/processing of fish fillets harvested from the Great Lakes. Bulletin of Environmental 

Contamination and Toxicology. 55:264-269. 
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TRIM-Based Tiered Screening Methodology for RTR 

Attachment A, Addendum 3 3-1 December 2013 

Dermal Risk Screening 

Addendum 3. Dermal Risk Screening 
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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 ...................................................................................................................... 3-14
TRIM-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 ............................................................ 3-13
TRIM-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 for 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 exposure 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 recommends 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 (CSFABS) is based on the oral cancer slope factor (CSFO) and 

the fraction of the contaminant absorbed in the gastrointestinal track (ABSGI), as follows: 

where: 

CSFABS = Absorbed slope factor (mg/kg-day)-1

CSFo = Oral slope factor (mg/kg-day)-1

ABSGI = Fraction of chemical absorbed in gastrointestinal tract (unitless) 

The absorbed reference dose (RfDABS) is based on the oral reference dose (RFDO) and the 

fraction of the contaminant absorbed in the gastrointestinal tract (ABSGI), as shown below. 

where: 

RfDABS = Absorbed reference dose (mg/kg-day) 

RfDo = Oral reference dose (mg/kg-day) 

ABSGI = Fraction of chemical absorbed in gastrointestinal tract (unitless) 

GI

o

ABS ABS

CSF CSF 

RfDABS  RfDo  ABSGI
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 polycyclic 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 (ABSGI) 

(unitless) 

Absorbed Cancer 

Slope Factor (CSFABS)

a

(mg/kg-day)-1

Absorbed Reference 

Dose (RfDABS)

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 adjusted 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: 

DAevent = Absorbed dose per event; chemical-specific; equation for DAevent also differs 

depending on water or soil contact (mg/cm2

-event)

EV = Event frequency (events/day) 

ED = Exposure duration (years) 

ATBW

DA SAEFEDEV 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 (cm2

) 

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) 

DAevent 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 DAevent 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 DAevent values is provided in RAGS Part E, Chapter 3 (see 

Equation 3.12). 

Water – Organic Chemicals:

Water – Inorganic Chemicals:

Soil – All Chemicals:

where: 

DAevent = Absorbed dose per event (mg/cm2

-event)

Cw 

Cs

= Chemical concentration in water (mg/cm3

) or soil (mg/kg) 

Kp = 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 into skin (unitless) 

τevent = Chemical-specific lag time per event (hr/event) 

tevent = Receptor-specific event duration (hr/event) 

AF = Receptor- and activity-specific adherence factor of soil to skin (mg/cm2

-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 assessment 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 discussion of chemical-specific parameters values 

follows. Estimated water and soil exposure concentrations are presented at the end of this 

subsection. 



 event event

event w p

t6 KFA2CDA 



event pw event    tKCDA

event  s   CFABSAFCDA
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 Groupa

(years) 

Surface Area for 

Water Exposure (cm2) 

Surface Area for 

Soil Exposure (cm2) 

Adult 20-70 18,150g 6,878h

Child <1b 3,992 1,772 

Child 1-2c 5,700 2,405 

Child 3-5d 7,600 3,354 

Child 6-11e 10,800 4,501 

Child 12-19f 17,150 6,906 a

Sources for the child groups included Table 7-1 (total 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 through age 20, which contributes to uncertainty in the 

estimates for 12 to 19 years. 

g

Represents the average total surface area of adults 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 proposed 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 (tevent) 

(hr/event) 

Child 1 Reasonable maximum exposure 

scenario for showering/bathing Adult 0.58 from RAGS Part E, Exhibit 3-2 

Soil Contact 

Soil Adherence Factor (AF) 

(mg/cm2

) 

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 assessment, 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 vary 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 concentration, 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 

(Kp), 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/cm3

) 

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 0.045a 0.03 0.13 Values from RAGS Part E, 

Exhibit 3-4, unless otherwise 

noted a

Value obtained from Bioavailability in Environmental 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 DADs and the 

absorbed CSF for each chemical, as follows: 

Dermal Cancer Risk = DAD × CSFABS

where: 

DAD = Dermal Absorbed Dose (mg/kg-day)

CSFABS = 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 individual 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 DADs to the absorbed 

RfD for each chemical, as shown below: 

Dermal HQ = DAD / RfDABS

where: 

DAD = Dermal Absorbed Dose (mg/kg-day)

RfDABS = 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

20-70

Hazard 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

20-70

Hazard 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

20-70

Hazard Quotient

Receptor

Divalent Mercury Water

Soil

Total
TRIM-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 Differencea

 

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 difference 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 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. 

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) Assessment 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 ........................... 2-1
TRIM-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 2a,b .............................................. B-10

Exhibit_Att B-3. Layouts for Tier 2 TRIM.FaTE Simulations Using Alternate 

Distances Between the Facility and the Fishable Lakea

 .................................. B-13

Exhibit_Att B-4. Maximum Fish Ingestion Rate (g/day) Associated with 

Sustainable Fishinga

 ......................................................................................... B-19

Exhibit_Att B-5. The Locations of Meteorological Stations and Point Source 

Facilitiesa

 .......................................................................................................... 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 Analysisa ................................ B-25

Exhibit_Att B-8. Example of the Facility Input Data Required To Conduct the 

Tier 2 Analysisa

................................................................................................. B-26

Exhibit_Att B-9. Example of the Lake Distance Data Required To Conduct the 

Tier 2 Analysisa

................................................................................................. B-27

Exhibit_Att B-10. Example Results of the Meteorological Station Matching 

Required To Conduct the Tier 2 Analysisa

 ....................................................... 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 Toola ............... B-31

Exhibit_Att B-14. Example of Detailed Output Table Created by the Tier 2 Toola ................. B-32

Exhibit_Att B-15. Example of Individual Output Table Created by the Tier 2 Tool................. B-33
TRIM-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 conduct 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-specific 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.FaTE and MIRC modeling speartely for each 

facility. 

 

23The lake size was also changed for each new facility lake distance. This change allowed the simulations to maintain 

a constant ratio between watershed and erosion 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 lake 

data 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 for 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 every 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 characterization, conducted using MIRC), a matrix of 
TRIM-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 adjusted 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 Microsoft® 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 smaller 

adjustment 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. Selection 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-specific 
TRIM-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 specific 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 exhibit showing all the TRIM.FaTE variables 

considered for the Tier 2 analysis. These variables 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 Factors 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 

 

24Only TRIM.FaTE parameters were considered for inclusion 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, estimated 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 obtained 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 wind 
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-10 December 2013 

speed of 2.8 m/s, corresponding to the 5th 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 88th 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 2a,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 

 

25http://ols.nndc.noaa.gov/plolstore/plsql/olstore.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. 

26National Climatic Data Center CliMaps (NCDC-CliMaps) (2007). http://cdo.ncdc.noaa.gov/cgibin/climaps/climaps.pl
TRIM-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 Coast27. Though 

the rate was an estimate, it does correspond to approximately the 95th 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 75th, 

50th and 25th percentiles, respectively, of the NCDC data. Locations with lower precipitation 

levels were assumed to have a minimum precipitation 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 analysis assumed a mixing height of 710 meters. 

This value is the 5th 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-

 

27National Climatic Data Center Historical Climate Series (NCDC-HCS) (2007). 

28http://www.ncdc.noaa.gov/oa/climate/normals/usnormals.html

29Support Center for Regulatory Atmospheric Modeling; http://www.epa.gov/scram001/tt24.htm
TRIM-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 alternative 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 RTR

Attachment B – Tier 2 B-13 

Exhibit_Att B-3. Layouts for Tier 2 TRIM.FaTE Simulations Using Alternate Dista

Between the Facility and the Fishable Lakea

aThe 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 permutations 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 handled 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 the 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: 

REFPAH = EEFPAH × TEFPAH

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 equivalent 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 PAH. 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 Kow-based regression estimates) and the dioxin species currently 

evaluated (most based on direct TRIM.FaTE modeling, 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 demonstrate 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 EPAH at a facility, the risk-equivalent BaP emission 

level can be expressed as: 

EBAP EQIV = EPAH SPECIES × EEFPAH SPECIES × TEFPAH SPECIES

Then, the Tier 1 risk ratio is estimated by comparing the risk-equivalent BaP emissions to the 

BaP emission threshold: 

RatioTIER 1_PAH SPECIES = 

ETIER 1_BAP EQUIV

ThresholdTIER 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 the Tier 2 BaP threshold may be expressed as: 

RatioTIER 2_PAH SPECIES = 

ETIER 2_BAP EQUIV

ThresholdTIER 2_BAP

Using the definition of the risk-equivalent BaP emissions, this can be re-expressed for a given 

PAH species as: 

RatioTIER 2_PAH SPECIES = 

EPAH SPECIES × EEFTIER 2_PAH SPECIES × TEFPAH SPECIES

ThresholdTIER 2_BAP

This expression may be further reconfigured, after some algebraic rearrangement, in terms of 

the Tier 1 ratio as: 

RatioTIER 2_PAH SPECIES = RatioTIER 1_PAH SPECIES × 

ThresholdTIER 1_BAP

ThresholdTIER 2_BAP

 × 

EEFTIER 2_PAH SPECIES

EEFTIER 1_PAH SPECIES

 = ூாௌ஼ா௉ௌ ு௉஺ݎ݋ݐܽܿܨ ݐ݉݁݊ݐݏݑ݆݀ܣ 2 ݎܶ݅݁

ThresholdTIER 1_BAP

ThresholdTIER 2_BAP

 × 

EEFTIER 2_PAH SPECIES

EEFTIER 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, mercury, 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 

RatioTIER 2_ALL PAHs = ෍ RatioTIER 1_PAH SPECIES × 

ThresholdTIER 1_BAP

ThresholdTIER 2_BAP

 × 

EEFTIER 2_PAH SPECIES

EEFTIER 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 those 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 from 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 relationship 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 ArcGISTM 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 consumption 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/m2

 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/m2

, 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/m2

, 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 roughly 
TRIM-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 RTR

Attachment B – Tier 2 B-19 

Exhibit_Att B-4. Maximum Fish Ingestion Rate (g/day) Associated with Sustainable Fish

Standing 

Biomass 

(g ww/m2)b Size of Pond or Lake (acres) 

1 2 3 4 5 7.5 10 15 25 35 50 75 100 150 

2 0 0 0 0 0 0 1 1 1 2 3 4 5 8 3 0 0 0 0 0 1 1 1 2 3 4 6 8 12 4 0 0 0 0 1 1 1 2 3 4 5 8 10 15 

5.7 0 0 0 1 1 1 1 2 4 5 7 11 15 22 

10 0 1 1 1 1 2 3 4 6 9 13 19 26 38 15 0 1 1 2 2 3 4 6 10 13 19 29 38 58 20 1 1 2 2 3 4 5 8 13 18 26 38 51 77 30 1 2 2 3 4 6 8 12 19 27 38 58 77 115 35 1 2 3 4 4 7 9 13 22 31 45 67 90 134 40 1 2 3 4 5 8 10 15 26 36 51 77 102 154 50 1 3 4 5 6 10 13 19 32 45 64 96 128 192 60 2 3 5 6 8 12 15 23 38 54 77 115 154 231 70 2 4 5 7 9 13 18 27 45 63 90 134 179 269 80 2 4 6 8 10 15 20 31 51 72 102 154 205 307 90 2 5 7 9 12 17 23 35 58 81 115 173 231 346 

100 3 5 8 10 13 19 26 38 64 90 128 192 256 384 110 3 6 8 11 14 21 28 42 70 99 141 211 282 423 120 3 6 9 12 15 23 31 46 77 108 154 231 307 461 130 3 7 10 13 17 25 33 50 83 117 166 250 333 499 

aDark gray shading indicates insufficient population size for TL4 (WCC) fish (<50 adults) to be sustainable for more than a decade; yellow-shaded cells indicate 

sustainable 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 could support a water-c

would 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 closest meteorological station in an inventory of 

over 350 meteorological stations nationwide, creating 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 started with the same U.S. meteorology dataset 

used in RTR inhalation assessments. RTR inhalation assessments use data from 824 ASOS 

(Automated Surface Observing System) stations 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 Meteorological Stations Compared with Facility Locations 

Exhibit_Att B-5 shows the proximity of the evaluated 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 degrees 
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-22 

Exhibit_Att B-5. The Locations of Meteorological Stations and Point Source Facilities

aThe 2005 NATA was the most recent, comprehensive, finalized dataset of nationwide point source emitters of hazardous air pollutants, and it is used her

purposes. 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 obtained 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 improved 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 Analysis 
TRIM-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 Analysisa

a

The screen shot examples shown in this section are for actual facilities; however their source categories, NEI IDs, and coordinates 

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 Analysisa

a

The screen shot examples shown in this section are for actual facilities; however, their source categories, NEI IDs, and coordinates 

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 Analysisa

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 the 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 Analysisa

a

The screen shot examples shown in this section are for actual facilities; however, their source categories, NEI IDs, and coordinates 

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 Adjustment Factors (Step 6) 

Next, the tool calculates the facility-specific, octant-specific threshold adjustment factor for each 

chemical. When Button 6 is clicked (see Exhibit_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 (EmissionsSurrogate = 

Emissions × TEF × (EEFTier1 × EEFAdjFactTier2)). Then, the adjusted emissions (or raw 

emissions in the case of mercury and cadmium) are ratioed to the Tier 2 emission screening 

thresholds (ResultTier2 = EmissionsSurrogate ÷ (ThresholdTier1 × ThresholdAdjFactorTier2) ÷ 

(WindDirectionAdjFactorTier2)). 
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 BaP- 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 facilities; however, their source categories, NEI IDs, and coordinates 

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 indicate 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 Toola

a

The screen shot examples shown in this section are for actual facilities; however, their source categories, NEI IDs, and coordinates 

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 which 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 detailed 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 Toola

a

The screen shot examples shown in this section are for actual facilities; however, their source categories, NEI IDs, and coordinates 

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 facilities; however, their source categories, NEI IDs, and coordinates 

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 Maps 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 Advisory. 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 Sources and Portland Cement Manufacturing.” EPA 

Science Advisory Board. EPA-SAB-10-007. 05/07/2010. 

USGS (U.S. Geological Survey). (2012) National 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.FaTE 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.

High 
TRIM-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 to 

precipitation 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: The 

annuallyaveraged 

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. 

High 
TRIM-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. 

High 
TRIM-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. 

Medium 
TRIM-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 input 

of 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. 

Medium 
TRIM-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. 

Medium 
TRIM-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 ...................................................................................................................... 2-12 

 
TRIM-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 fish 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 Exhibit_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 such 
TRIM-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 compartment (TL3) and 10 percent of the biomass in the 

predator fish compartment (TL4). The authors also 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/m2

); 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 many 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 μg/L) = 1.9 g ww/m2

 

Meso-eutro (TP = 30 μg/L) = 3.7 g ww/m2

 

Eutro-hypereutro (TP = 100 μg/L) = 8.5 g ww/m2

 

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 μg/L) = 7.4 g ww/m2

 

Meso-eutro (TP = 30 μg/L) = 10.6 g ww/m2

 

Eutro-hypereutro (TP = 100 μg/L) = 15.6 g ww/m2

 

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 and 
TRIM-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 (r2

) of 0.84: 

B = 0.792 + 0.072 (TP) 

where: 

B = total fish biomass (kg/hectare) 

TP = total phosphorous (μg/L) 

The regression model of Hanson and Legget (1982) predicted total fish biomass densities in 

lakes of 3.0–9.5 g ww/m2

 for TP concentrations ranging from 10–50 μg/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/m2

 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/m2

 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 between 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 r2

 of 0.75: 

Log10 (B) = 1.07 + 1.14 × Log10 (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/m2 (Yurk and Ney, 1989) and 1.6 g ww/m2 

(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/m2

, 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/m2

. The minimum 
TRIM-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/m2, respectively, and the 

median value was 30.9 g ww/m2

. 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 surveyed 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/m2

 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 assumed 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., approximately 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 reservoirs they examined was 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 μg/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 productivity in lakes originating from detritus in the 

benthos, the total biomass of strictly pelagic game 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 percent 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 extinction 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 time 

periods (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, and 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, Ne, 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). Ne 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 effective population size is essentially the same 

as the actual breeding adult population size (Ewens et al., 1987). One of the most extensive 
TRIM-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 considered 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 limiting 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. 

 (1 Equation (ܥܥܹ ݊݋݅ݐܽܿݎܨ ݔ ܤܵ ݈ܽݐ݋ܶ ൌ ܤܵ ܥܥܹ

where: 

WCC SB = Standing biomass of WCC fish (g wet weight [ww]/m2

) in Lake X

Total SB = Total standing biomass of fish (g ww/m2

) in Lake X 

Fraction WCC = 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 ஼ி

஻ௐೌ (Equation 2)

where: 
TRIM-Based Tiered Screening Methodology for RTR

Attachment B, Addendum 2 2-11 December 2013 

Lake Size Analysis 

No. WCC = Total number of WCC fish in Lake X

Lake Size = Size of Lake X (acres) 

WCC SB = Standing biomass of WCC fish (g ww/m2

; from Equation 1) 

CF = 4047 (unit conversion factor m2

/acre) 

BWa = 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 productivity 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/m2

; from Equation 1) 

CF1 = 4047 (unit conversion factor 1, m2

/acre) 

CF2 = 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 ሺܥܤ ൅ ܹܥܥሻ ൌ 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 WCC = Likely annual productivity of WCC fish in Lake X (kg/year; from )

FF = Fillet fraction; represents the assumed edible portion of fish (0.33; unitless) 

HF = Annual harvest fraction (0.10; unitless) 

CF1 = 1000 (unit conversion factor 1, g/kg) 

CF2 = 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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