Technical Support Document

for the TRIM-Based Multipathway

Tiered Screening Methodology for RTR

March, 2013

Prepared For:

U.S. Environmental Protection Agency

Office of Air Quality Planning and Standards

Research Triangle Park, NC 27711

Prepared By:

ICF International

2222 East NC-54

Suite 480

Durham, NC 27713
[This page intentionally left blank.]
TRIM-Based Tiered Screening Methodology for RTR

Overview iii 6-29-2012

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-6

A.2 Summary of Approach ........................................................................................... A-6

A.2.1 Overview .................................................................................................A-6

A.2.2 Chemicals of Potential Concern...............................................................A-9

A.2.3 Conceptual Exposure Scenario .............................................................A-10

A.2.4 Approach to Development of the Tier 1 Scenario...................................A-12

A.2.5 Fate and Transport Modeling (TRIM.FaTE) ...........................................A-14

A.2.6 Exposure Modeling and Risk Characterization (MIRC) ..........................A-15

A.2.7 Implementation of Risk-based Emission Scaling Factors for 

POM and Dioxin Emissions...................................................................A-16

A.3 Description of Environmental Modeling Scenario................................................. A-20

A.3.1 Chemical Properties ..............................................................................A-20

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

A.3.3 Watershed and Water Body Parameterization .......................................A-22

A.3.4 Meteorology...........................................................................................A-24

A.3.5 Aquatic Food Web.................................................................................A-26

A.3.6 Using TRIM.FaTE Media Concentrations ..............................................A-27

A.4 Description of Exposure and Risk Modeling Scenario.......................................... A-28

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

A.4.2 Ingestion Exposure................................................................................A-29

A.4.3 Calculating Risk.....................................................................................A-32

A.4.4 Summary of Tier 1 Assumptions............................................................A-33

A.5 Evaluation of Screening Scenario........................................................................ A-37

A.5.1 Introduction............................................................................................A-37

A.5.2 Cadmium Compounds...........................................................................A-37

A.5.3 Mercury Compounds .............................................................................A-38

A.5.4 DioxinsA-40

A.5.5 Polycyclic Aromatic Hydrocarbons.........................................................A-42

A.5.6 Summary...............................................................................................A-44
A.6 References .......................................................................................................... A-45

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 Selection of Site-Specific Characteristics to Include in the Tier 2 

Analysis................................................................................................................. B-7

B.3 Estimation of Adjustment Factors for Selected Site-Specific 

Parameters............................................................................................................ B-8

B.3.1 Selection Values for Variables of Interest ................................................B-9

B.3.2 Estimation of Adjustment Factors ..........................................................B-12

B.4 Preparing National Databases of Lake and Meteorological Data ......................... B-16

B.4.1 Processing Lake Data for Tier 2 Analysis ..............................................B-16

B.4.2 Processing Meteorological Data for Tier 2 Analysis ...............................B-19

B.5 Implementation of Tier 2 Analysis ........................................................................ B-23

B.5.1 Facility List for Tier 2 Screen (Step 3)....................................................B-26

B.5.2 Facility/Lake Distance Table (Step 4) ....................................................B-26

B.5.3 Finding the Closest Meteorological Station (Step 5) ..............................B-27

B.5.4 Assembling Threshold Adjustment Factors (Step 6) ..............................B-29

B.5.5 Finalizing Threshold Adjustment Factors and Assembling 

Screening Results (Step 7) ...................................................................B-30

B.5.6 Assembling Results (Step 8) .................................................................B-30

B.6 References .......................................................................................................... B-33

Addendum 1. Summary of TRIM.FaTE Parameters Considered for 

Inclusion in Tier 2 Analysis ...................................................................... 1-1

Addendum 2. Analysis of Lake Size and Sustainable Fish Population.......................... 2-1
[This page intentionally left blank.]
TRIM-Based Tiered Screening Methodology for RTR

Overview 1 6-29-2012

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 

Section 5.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). 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 6-29-2012

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 Tier 4 

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 6-29-2012

Exhibit 1. Conceptual Decision Tree for Evaluating Non-Inhalation Exposures

for PB-HAPs

Diagram Key

Decision point

Outcome

Tools / Data

Tier 2 Facility-specific Adjusted Threshold Emission Rates

TIER 1 TIER 2

Facility screens out – no further analysis necessary NO

Facility proceeds to Tier 2

NO

Prioritize facilities for site- specific analysis

Does the emission rate for any PB-HAP exceed Tier 1 threshold?

Does the emission rate for any PB-HAP exceed Tier 2 adjusted threshold?

Health-protective Configuration & Meteorological Data

Emissions Inventory Data

Health Protective Exposure Assumptions

Tier 1 Configuration with Local Meteorology and Site- specific Lake Location Health Protective Exposure Assumptions Facility screens out – no further analysis necessary

Estimates of Site Specific Risk

Site-Specific

Site-specific Configuration $ Meteorological Data Vary Exposure Assumptions

Evaluate PB-HAP emissions by facility

YES

Tier 1 Threshold Emission Rates

YES

Process Risk Characterization
TRIM-Based Tiered Screening Methodology for RTR

Overview 4 6-29-2012

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 PAH for example, 

does not imply a resulting cancer risk of 60 in 1 million. Rather, exceeding the threshold 

emissions rate by 60 times for PAH 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—Technical Support Document for TRIM-Based Multipathway Tier 1 Scenario 

for RTR—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 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 6-29-2012

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 Emissions to Air

Cancer Risk

Hazard Quotient

Chemical fate 

and transport:

Physical environment 

and aquatic ecosystem

Uptake and 

transfer: 

Produce and livestock

Human 

Ingestion 

Exposure

Risk and 

Hazard 

Estimation
TRIM-Based Tiered Screening Methodology for RTR

Overview 6 6-29-2012

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 of the 

distribution of national data (99th percentile for fish and 90th percentile for all other food types) for

consumers of that food type recommended by EPA in the Exposure Factors Handbook (U.S. 

EPA 2011a) or in 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 6-29-2012

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 new facility lake distance, which allowed for the simulations to maintain a 

constant ratio between watershed and erosion area compared with lake area.
TRIM-Based Tiered Screening Methodology for RTR

Overview 8 6-29-2012

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 includes 

annual-average summary statistics on wind direction, wind speed, and precipitation for more 

than 1,500 surface stations located throughout the United States (data available from the 

National Climatic Data Center). The mixing heights for each surface station were estimated 

using the data from the upper-air station closest to the surface station. The mixing height data 

are available for more than 50 stations (data available from EPA). 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 25 acres were included. 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 witnin 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 

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. 
TRIM-Based Tiered Screening Methodology for RTR

Overview 9 6-29-2012

To access these databases, a Microsoft® Excel 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 closest meteorological station, 

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 

multipthway analyses include assessments of two coal-fired electric utility units conducted in 

support of EPA’s utility rule (U.S. EPA 2011c), residual risk assessments of two secondary lead 

smelting facilities (U.S. EPA 2011b), 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 U.S. 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

U.S. EPA (U.S. Environmental Protection Agency) (2005) Human Health Risk Assessment 

Protocol for Hazardous Waste Combustion Facilities. EPA Office of Solid Waste. EPA530/R-05-006. 09/2005.

U.S. 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.

U.S. EPA. (2011a) Exposure Factors Handbook: 2011 edition. National Center for 

Environmental Assessment, Washington, DC; EPA/600/R-09/052F. Available from the 
TRIM-Based Tiered Screening Methodology for RTR

Overview 10 6-29-2012

National Technical Information Service, Springfield, VA, and online at 

http://www.epa.gov/ncea/efh.

U.S. 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.

U.S. 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.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-1 6-29-2012

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

Attachment A – Tier 1 A-3 6-29-2012

CONTENTS, ATTACHMENT A 

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

A.1 Introduction.....................................................................................................................A-6

A.2 Summary of Approach ....................................................................................................A-6

A.2.1 Overview ............................................................................................................... A-6

A.2.2 Chemicals of Potential Concern............................................................................. A-9

A.2.3 Conceptual Exposure Scenario............................................................................ A-10

A.2.4 Approach to Development of the Tier 1 Scenario................................................. A-12

A.2.4.1 Modeling Framework .............................................................................A-13

A.2.4.2 Model Configuration and Parameterization............................................A-14

A.2.5 Fate and Transport Modeling (TRIM.FaTE) ......................................................... A-14

A.2.6 Exposure Modeling and Risk Characterization (MIRC) ........................................ A-15

A.2.7 Implementation of Risk-based Emission Scaling Factors for POM and 

Dioxin Emissions ................................................................................................. A-16

A.2.7.1 Calculation of Scaling Factors for POM Congeners ...............................A-17

A.2.7.2 Calculation of Scaling Factors for Dioxin Congeners .............................A-19

A.3 Description of Environmental Modeling Scenario ..........................................................A-20

A.3.1 Chemical Properties ............................................................................................ A-20

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

A.3.3 Watershed and Water Body Parameterization ..................................................... A-22

A.3.3.1 Water Balance.......................................................................................A-22

A.3.3.2 Sediment Balance .................................................................................A-23

A.3.4 Meteorology......................................................................................................... A-24

A.3.5 Aquatic Food Web ............................................................................................... A-25

A.3.6 Using TRIM.FaTE Media Concentrations............................................................. A-27

A.4 Description of Exposure and Risk Modeling Scenario ...................................................A-28

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

A.4.2 Ingestion Exposure.............................................................................................. A-29

A.4.2.1 Exposure Scenarios and Corresponding Inputs.....................................A-29

A.4.2.2 Calculating Average Daily Doses...........................................................A-32

A.4.2.3 Infant Ingestion of Breast Milk................................................................A-32

A.4.3 Calculating Risk................................................................................................... A-32

A.4.4 Summary of Tier 1 Assumptions .......................................................................... A-33

A.5 Evaluation of Screening Scenario .................................................................................A-37

A.5.1 Introduction.......................................................................................................... A-37

A.5.2 Cadmium Compounds ......................................................................................... A-37

A.5.2.1 Behavior in the Environment..................................................................A-37
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-4 6-29-2012

A.5.2.2 Concentrations in Ingestible Products....................................................A-37

A.5.2.3 Average Daily Dose (ADD)....................................................................A-38

A.5.3 Mercury Compounds ........................................................................................... A-38

A.5.3.1 Behavior in the Environment..................................................................A-38

A.5.3.2 Concentrations in Ingestible Products....................................................A-40

A.5.3.3 Average Daily Dose...............................................................................A-40

A.5.4 Dioxins................................................................................................................. A-40

A.5.4.1 Behavior in the Environment..................................................................A-40

A.5.4.2 Concentrations in Ingestible Products....................................................A-42

A.5.4.3 Lifetime Average Daily Dose (LADD).....................................................A-42

A.5.5 Polycyclic Aromatic Hydrocarbons....................................................................... A-42

A.5.5.1 Behavior in the Environment..................................................................A-43

A.5.5.2 Concentrations in Ingestible Products....................................................A-44

A.5.5.3 Lifetime Average Daily Dose .................................................................A-44

A.5.6 Summary ............................................................................................................. A-44

A.6 References ...................................................................................................................A-45

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 6-29-2012

EXHIBITS, ATTACHMENT A

Exhibit_Att A-1. Conceptual Decision Tree for Evaluation of Non-Inhalation 

Exposures of PB-HAPs.....................................................................................A-7

Exhibit_Att A-2. Emission Thresholds for Screening of Multipathway ExposuresError! Bookmark not defined.

Exhibit_Att A-3. OAQPS PB-HAP Compounds.....................................................................A-10

Exhibit_Att A-4. Overview of Ingestion Exposure and Risk Screening Evaluation 

Method............................................................................................................A-13

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

Risk Calculator................................................................................................A-16

Exhibit_Att A-6. Exposure, Toxicity, and Risk Equivalency Factors Relative to 

BaP for Modeled POM Congeners..................................................................A-18

Exhibit_Att A-7. Relationship between Exposure and Kow for POM Congeners...................A-19

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-21

Exhibit_Att A-10. Summary of Key Meteorological Inputs.....................................................A-25

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

Scenario .........................................................................................................A-27

Exhibit_Att A-12. Spatial Considerations – TRIM.FaTE Results Selected for 

Calculating Farm Food Chain Media Concentrations and Receptor 

Exposures.......................................................................................................A-28

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

Uptake ............................................................................................................A-30

Exhibit_Att A-14. Overview of Exposure Factors Used for RTR Multipathway 

Screeninga,b....................................................................................................A-31

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

Screening Scenario.........................................................................................A-33

Exhibit_Att A-16. Summary of RTR Screening Scenario Assumptions .................................A-33

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-43

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

Ingestion Exposures .......................................................................................A-45
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-6 6-29-2012

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 document 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. 

More refined multipathway risk screening methods, may be used for facilities that do not screen 

out of the Tier 1 analysis. 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-7 6-29-2012

Exhibit_Att A-1. Conceptual Decision Tree for Evaluation of Non-Inhalation

Exposures of PB-HAPs

Facility screens out

Non-inhalation risks not likely, but evaluate on a case-by-case basis

Facility screens out; no concern for multipathway risk (check emissions of PB-HAPs other than Cd, dioxins, Hg, POM, Pb on a case-by- case basis)

Are any PB-HAPs emitted?

Are PB-HAPs of primary concern for RTR emitted? (Cd, dioxins, Hg, POM)?

Evaluate HAP emissions by facility

YES

YES

NO

NO

NO

Does the emission rate for any PB- HAP exceed Tier 1 threshold?

Diagram Key

Outcome

Process

Decision Point

TIER 1 EVALUATION Proceed to Tier 2

YES
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-8 6-29-2012

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-9 6-29-2012

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

3.09E−03 Cancer

Dioxins (as 2,3,7,8-TCDD equivalents)a

6.38E-09 Cancer

Cadmium 7.00E−02 Non-cancer

Divalent mercury 1.94E−03 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 each 

risk influential parameter 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 greater 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 8that 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 

 

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-10 6-29-2012

that are PB-HAPs based on a two-step process taking into account the following (U.S. EPA 

2004a):

 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 (U.S. 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

aSource of list: U.S. EPA (2004a). Compounds in bold text can be evaluated using the current version of the TRIMbased screening scenario.

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. 

 

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-11 6-29-2012

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);

 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 (U.S. 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

 

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-12 6-29-2012

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).

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 (U.S. 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

(U.S. EPA 1999); Volumes I and II of the Air Toxics Risk Assessment Reference Library (U.S. 

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 and Tier 3 analyses, as described in Attachment B

and Error! Reference source not found., respectively. 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-13 6-29-2012

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 Excel-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

Chemical fate and transport: Physical environment and aquatic ecosystem TRIM.FaTE

Uptake & transfer into produce and livestock Multimedia Ingestion Risk Calculator (MIRC)Human ingestion exposure Risk & hazard estimation

Chemical Emissions to Air

Cancer Risk Hazard Quotient
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-14 6-29-2012

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 (U.S. 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-15 6-29-2012

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 (U.S. 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 (U.S. 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 (U.S. EPA 2004b). More recently (largely as part of this current

project), 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 chemicalspecific 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 (U.S. 

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-16 6-29-2012

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 

Farm Food Chain Concentration Calculator Ingestion Exposure Calculator Risk and Hazard Calculator

TRIM.FaTE outputs

Human activity/ exposure factors Plant- and animal-specific parameters Chemical- specific uptake/ transfer factors Ingestion dose-response values

FFC media concentrations Average daily doses Cancer risks and hazard quotients

Access db process Tool output Input Key to symbols: data
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-17 6-29-2012

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 der 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
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-18 6-29-2012

property. Exposure 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 22.06 16.44 362.61

7,12-Dimethylbenz(a)anthracene Y 1.91 34.25 65.31

3-Methylcholanthrene n 2.75 3.01 8.28

Dibenz(a,h)anthracene Y 4.56 0.56 2.56

Benzo(k)fluoranthene Y 7.00 0.16 1.15

Benzo(a)pyrene Y 1 1 1

Benzo(b)flouranthene Y 4.38 0.16 0.72

Indeno(1,2,3-cd)pyrene Y 3.10 0.16 0.51

PAH, total n 3.34 0.068 0.23

Polycyclic Organic Matter n 3.34 0.068 0.23

Benzo(g,h,i)perylene Y 2.91 0.068 0.20

Benzo(e)pyrene n 2.88 0.068 0.20

Retene n 2.32 0.068 0.16

Dibenzo(a,j)acridine n 0.41 0.16 0.067

Perylene n 0.64 0.068 0.044

Benzo(a)anthracene Y 0.075 0.16 0.012

Chrysene Y 0.18 0.016 0.0030

2-Acetylaminofluorene n 0.0014 1.37 0.0019

Fluoranthene Y 0.019 0.068 0.0013

Acenaphthylene Y 0.012 0.068 0.00083

2-Chloronaphthalene n 0.011 0.068 0.00075

Fluorene Y 0.010 0.068 0.00067

Acenaphthene Y 0.0087 0.068 0.00060

1-Methylnaphthalene n 0.0057 0.068 0.00039

2-Methylnaphthalene Y 0.0052 0.068 0.00036

Carbazole n 0.0040 0.0027 0.000011

Anthracene n 0.023 0 0

Phenanthrene n 0.024 0 0

Pyrene n 0.066 0 0

aTEFs 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. 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-19 6-29-2012

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.

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.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-20 6-29-2012

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- 3.96 1.0 3.96

TetraCDD, 2,3,7,8- 1.0 1.0 1.0

Dioxins, Total, w/o Indiv. Isomers Rptd. 1.0 1.0 1.0

Dioxins 1.0 1.0 1.0

2,3,7,8-TCDD TEQ 1.0 1.0 1.0

HexaCDD, 1,2,3,4,7,8- 1.65 0.10 0.17

PentaCDF, 2,3,4,7,8- 0.50 0.30 0.15

HexaCDD, 1,2,3,6,7,8- 1.10 0.1 0.11

HexaCDF, 1,2,3,7,8,9- 0.64 0.1 0.064

HexaCDF, 1,2,3,6,7,8- 0.62 0.10 0.062

HexaCDD, 1,2,3,7,8,9 - 1.10 0.041 0.046

HexaCDF, 1,2,3,4,7,8- 0.42 0.1 0.042

HexaCDF, 2,3,4,6,7,8- 0.58 0.1 0.058

PentaCDF, 1,2,3,7,8- 0.61 0.03 0.018

TetraCDF, 2,3,7,8- 0.16 0.1 0.016

HeptaCDD, 1,2,3,4,6,7,8- 1.19 0.01 0.012

HeptaCDF, 1,2,3,4,6,7,8- 0.33 0.01 0.0033

HeptaCDF, 1,2,3,4,7,8,9- 0.30 0.01 0.0030

OctaCDD, 1,2,3,4,6,7,8,9- 0.45 0.0003 0.00014

OctaCDF, 1,2,3,4,6,7,8,9- 0.37 0.0003 0.00011

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 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-21 6-29-2012

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 

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

 

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.) 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-22 6-29-2012

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 

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), for example. 

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:

 

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

Attachment A – Tier 1 A-23 6-29-2012

[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]

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.

 65 percent of total precipitation remains in the modeled system and contributes to total 

runoff.

 Total runoff is divided between overland runoff and seepage to groundwater as follows:

 40 percent of total precipitation contributes to overland runoff.

 25 percent of total precipitation infiltrates into the groundwater and eventually flows into 

the lake. 

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. 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-24 6-29-2012

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 

(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 (U.S. 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) 

 

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-25 6-29-2012

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 

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

Meteorologica

l Property Selected Value Justification

Air temperature Constant at 298 Kelvin Recommended default value listed in HHRAP (U.S. 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.bValue 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.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-26 6-29-2012

Meteorologica

l Property Selected Value Justification

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

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

aNational Climatic Data Center CliMaps (NCDC-CliMaps) (2007). http://cdo.ncdc.noaa.gov/cgi-bin/climaps/climaps.pl. 

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

cNational 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). The scenario 

includes a benthic invertebrate compartment to represent the primary invertebrate consumers 

(second trophic level) in the benthic environment, and the fish compartments represent the 

higher tropic levels in the aquatic system. 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 in 

support of developing and parameterizing aquatic biotic compartments for TRIM.FaTE (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,

the previous 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 temperature). 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 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-27 6-29-2012

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).

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 algae7.95 – Autotrophic –

Zooplankton

crustaceans, 

rotifers, 

protozoans

6.36 – 100% algaea5.7 E−8

Macrophyte hydrilla, milfoil 500 – – –

Water column 

planktivore

young-of-theyear, minnows 2.0 35.1% 100% zooplankton 0.025

Water column 

omnivore sucker, carp 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 Biomassc5.7

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

bBenthic 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). 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-28 6-29-2012

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 

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.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-29 6-29-2012

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,

 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; U.S. 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 (U.S. 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 (U.S. EPA 2011).
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-30 6-29-2012

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

aCalculation of intermediate exposure concentrations were required only for the farm animal/animal product ingestion pathways.

bProcess by which HAP enters medium ingested by humans.

cModeled 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 of the distribution of national
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-31 6-29-2012

data for for all food types except for fish, which was set at the 99th percentile). All media were 

assumed to be obtained from locations impacted by the modeled source. Although this 

approach results in an overestimate of 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 farm 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

Intake rate and ingestion rate (IR) for farm produce 

and animal products other than fish (varies by age 

and media consumed)

90th percentile of distribution of consumers who 

produce own food

Ingestion rate for fish 105.1 g/day (represents the 99

th percentile of fish 

consumption in the adult U.S. general population

including consumers and non-consumersc; this 

estimate is considered within the range of average 

values for subsistence anglersd); lower values 

were assumed for younger age groups, also based 

on the 99th percentile of the U.S. general 

population. Value reflects “as prepared” 

consumption rate so cooking and preparation 

losses did not need to be considered.

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)e

1

Cooking lossfAssumed 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.

aData for exposure characteristics are presented in Addendum 2 to this attachment. Exposure parameter values were based on 

data obtained from the Exposure Factors Handbook (U.S. EPA 2011). See Addendum 2 to this attachment for details.

bExposure 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

Fish consumption rates for adult and child consumers were derived based on Estimated Per Capita Fish Consumption in the United 

States (U.S. EPA 2002c). Consumption rates were normalized to body weights that are representative of the age groups used in 

the screening scenario.

d

The relationship equating the 99th percentile of fish consumption in the general population with the range of average fish 

consumption rates for subsistence anglers was obtained from Methodology for Deriving Ambient

Water Quality Criteria for the Protection of Human Health (U.S. EPA 2000). The 105.1-g/day consumption rate is similar to the 

“default” value of 142.4 g/day EPA’s Office of Water recommends for use in estimating exposures to subsistence fishers.

e

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. 

fCooking 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.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-32 6-29-2012

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 (U.S. EPA 2003), which derived much of its input 

data from the Exposure Factors Handbook (U.S. 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 2005).

Exposure to TCDD via breast milk consumption during the first year of life was estimated to 

account for approximately 30 percent of an adult’s lifetime ADD. The 30 percent ratio was 

established by performing multiple modeling iterations using different scenarios. In the absence 

of congener-specific data, dioxin congeners were assumed to manifest the same tendency to 

accumulate in breast milk as TCDD. As a consequence, exposure to all dioxin congeners via 

breast milk consumption during the first year of life were estimated to account for approximately 

30 percent of an adult’s lifetime exposure to those congeners. Exposure via the breast milk 

pathway during the first year of life was accounted for, in this way, in developing the threshold 

screening level for dioxins. 

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-33 6-29-2012

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

10 IRIS not available

2,3,7,8-TCDD 1.5E+5 EPA ORD 7E−10 IRIS

Source: U.S. EPA (2007).

CSF = cancer slope factor; RfD = reference dose; IRIS = 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 (U.S. 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 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-34 6-29-2012

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.

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). 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-35 6-29-2012

Characteristic Value

Neutral or 

Health 

Protective?

Comments on Assumptions

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-36 6-29-2012

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 1.47 g/kg-day for 

adults; between 

1.27 and 2.08

g/kg-day for 

children ages 1–19

years

Health Protective Rates are based on the 99th percentile of 

general population fish consumption from 

EPA’s 2002 analysis of freshwater and 

estuarine fish consumption derived from the 

1994–96 and 1998 USDA Continuing 

Survey of Food Intake by Individuals

(CSFII; USDA 2002). Consumption rates 

were normalized to body weights that are

representative of the age groups used in 

the screening scenario. These ingestion 

rates likely overestimate long-term fish 

consumption rates for the general 

population. 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-37 6-29-2012

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 2005a). 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-38 6-29-2012

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 as much as 98 percent of 

the ADD for some groups. The combined contribution from all other exposure pathways 

accounts for less than 5 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. 

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 (U.S. 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 (U.S. 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. 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-39 6-29-2012

Exhibit_Att A-17. Estimated Contributions of Modeled Food Types to 

Cadmium Ingestion Exposures and Hazard Quotients

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 (U.S. 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 (U.S. 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. 

0

0.2

0.4

0.6

0.8

1

0

0.0001

0.0002

0.0003

0.0004

0.0005

Child 1-2 Child 3-5 Child 6-11 Child 12-19 Adult Hazard Quotient ADD (mg/kg/day)

Fish Meat, Dairy, & Eggs

Fruits & Vegetables Soil
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-40 6-29-2012

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 (U.S. EPA 1997; ATSDR 1999). Low 

levels of mercury are found in plants, with leafy vegetables containing higher concentrations 

than 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 (U.S. 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). 

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; 

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 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-41 6-29-2012

Exhibit_Att A-18. Estimated Contributions of Modeled Food Types 

to Methyl Mercury Ingestion Exposures

sediment, which appears to be the primary environmental sink for this chemical group (U.S. 

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 (U.S. 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.

0

0.2

0.4

0.6

0.8

1

0

0.00002

0.00004

0.00006

0.00008

0.0001

Child 1-2 Child 3-5 Child 6-11 Child 12-19 Adult Hazard Quotient ADD (mg/kg/day)

Fish Meat, Dairy, & Eggs

Fruits & Vegetables Soil
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-42 6-29-2012

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 

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 nearly 94 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-43 6-29-2012

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 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-44 6-29-2012

expected to be especially high. Sediment-dwelling organisms can experience increased 

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 86 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 Risk and Technology Review 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-45 6-29-2012

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.

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.

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.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-46 6-29-2012

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). 2005a. 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

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. 

USDA (U.S. Department of Agriculture). 2000. 1994–96, 1998 Continuing Survey of Food 

Intakes by Individuals (CSFII). CD-ROM. Agricultural Research Service, Beltsville Human 

Nutrition Research Center, Beltsville, MD. Available from the National Technical Information 

Service, Springfield, VA, Accession Number PB-2000500027. 

U.S. EPA (United States Environmental Protection Agency). 1994. Integrated Risk Information 

System (IRIS) Summary for Benzo[a]pyrene (BaP) (CASRN 50-32-8). Office of Research 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-47 6-29-2012

and Development, Washington, DC. Last revised 11/01/1994. Available at:

http://www.epa.gov/iris.

U.S. 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. 

U.S. 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.

U.S. EPA. 1999. Short Sheet: IEUBK Model Soil/Dust Ingestion Rates. EPA-540-F-00-007; 

OSWER-9285.7-33. Washington, D.C.: Office of Solid Waste and Emergency Response; 

December. Available at: http://www.epa.gov/superfund/lead/products/ssircolo.pdf.

U.S. EPA. 2000. Methodology for Deriving Ambient Water Quality Criteria for the Protection of 

Human Health. EPA-822-B-00-004. October.

U.S. 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.

U.S. EPA. 2002b. Evaluation of TRIM.FaTE, Volume I: Approach and Initial Findings. EPA453/R-02-0012. Office of Air Quality and Planning Standards: Research Triangle Park, NC. 

September.

U.S. EPA. 2002c. Estimated Per Capita Fish Consumption in the United States. EPA-821- C02-003. Office of Water, Office of Science and Technology, Washington, D.C. August. 

U.S. EP. 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.

U.S. 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.

U.S. 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

U.S. 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/

U.S. EPA. 2005a. Human Health Risk Assessment Protocol for Hazardous Waste Combustion 

Facilities (including the Hazardous Waste Companion Database of chemical-specific 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A – Tier 1 A-48 6-29-2012

parameter values). U.S. Environmental Protection Agency, Office of Solid Waste and 

Emergency Response, Washington, DC. EPA-530-R-05-006. September.

U.S. 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.

U.S. EPA. 2005c. Supplemental Guidance for Assessing Susceptibility from Early-Life Exposure 

to Carcinogens. EPA-630/R-03-003F. Risk Assessment Forum: Washington, DC. March.

U.S. 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. 

U.S. EPA. 2007. Prioritized Chronic Dose-Response Values for Screening Risk Assessments 

(Table 1). Office of Air Quality Planning and Standards; June 12, 2007. Available at: 

http://www.epa.gov/ttn/atw/toxsource/summary.html .

U.S. EPA. 2008. Integrated Risk Information System (IRIS) Summary for Toxicological Review 

of Hexachlorodibenzo-p-dioxin (HxCDD), mixture of 1,2,3,6,7,8-HxCDD and 1,2,3,7,8,9-

HxCDD (CASRN 57653-85-7 and 19408-74-3). Office of Research and Development, 

Washington, DC. Last revised 06/30/2008. Available at: http://www.epa.gov/iris.

USGS (U.S. Geological Survey). 1987. National Water Summary 1987 – Hydrologic Events and 

Water Supply and Use. USGS Water-Supply Paper 2350. J.E. Carr, E.B. Chase, R.W. 

Paulson, and D.W. Moody, Compilers. 

Van den Berg, M., L.S. Birnbaum, M. Denison, M. DeVito, W. Farlans, M. Feeley, H. Fiedler, H. 

Hakansson, A. Hanberg, L.. Haws, M. Rose, S. Safe, D. Schrenk, C. Tohyama, A. Tritscher, 

J. Tuomisto, M. Tysklind, N. Walker, and R.E. Peterson. 2006. The 2005 World Health 

Organization reevaluation of human and mammalian toxic equivalency factors for dioxins 

and dioxin-like compounds. Toxicol Sci. 93(2): 223-41.

Wischmeier, W.H., and D. Smith. 1978. Predicting Rainfall Erosion Losses: A Guide to 

Conservation Planning. USDA-ARS Agriculture Handbook No. 537, Washington, D.C. 58 

pp.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-1 6-29-2012

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 6-29-2012

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 6-29-2012

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 6-29-2012

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.

aOutput 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 6-29-2012

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 States, calculated from 

30 yrs of annual normal temperature values.

Vertical wind speed m/sec 0.0 Professional judgment; vertical wind speed not used by any of the algorithms in 

the version 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 model domain. A 

conservative estimate of time during which wind should blow into the modeling 

domain was determined by evaluating HUSWO; it was concluded that a 

conservative estimate would be approximately 42% of the time.

Rainfall rate m

3

[rain]/m2

[surface area]-

day

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

contiguous United States, excluding Rhode Island because of extreme weather 

conditions on Mt. Washington. Data obtained from the National Climatic Data 

Center at http://www.ncdc.noaa.gov/oa/climate/online/ccd/nrmpcp.txt. The 

precipitation frequency was 3-days-on:4-days-off based on data from Holzworth, 

1972.

Mixing height (used to set air 

VE property named “top”)

m 710 5th percentile annual average mixing heights (calculated from daily morning and 

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

isDay_SteadyState_forAir unitless -- Value not used in current dynamic runs (would need to be reevaluated if steadystate runs are needed). isDay_SteadyState_forOther unitless --
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-7 6-29-2012

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/cm30.0012 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 6-29-2012

TRIM.FaTE Inputs

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

Parameter Name Units Value Used Reference

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 times average precipitation 

for site.

Boundary layer thickness above 

surface soil

m 0.005 Thibodeaux 1996; McKone et al. 2001 (Table 

3).

Density of soil solids (dry weight) kg[soil]/m3[soil] 2600 Default in McKone et al. 2001 (Table 3).

Thickness - untilleda m 0.01 McKone et al. 2001 (p. 30).

Thickness - tilleda m 0.20 USEPA 2005.

Erosion fraction unitless variesbSee Exhibit 5.

Fraction of area available for 

erosion

m

2

[area available]/m2[total] 1 Professional judgment; area assumed rural.

Fraction of area available for runoff m

2

[area available]/m2[total] 1 Professional judgment; area assumed rural.

Fraction of area available for vertical 

diffusion

m

2

[area available]/m2[total] 1 Professional judgment; area assumed rural.

Fraction sand unitless 0.25 Professional judgment.

Organic carbon fraction unitless 0.008 U.S. average in McKone et al. 2001 (Table 16 

and A-3).

pH unitless 6.8 Professional judgment.

Runoff fraction unitless variesbSee Exhibit_Add A1-5.

Total erosion rate kg [soil]/m2/day variesbSee Exhibit_Add A1-6.

Total runoff rate m

3

[water]/m2/day 1.64E-03 Calculated using scenario-specific 

precipitation rate and assumptions associated 

with 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 (Table 16).

Average vertical velocity of water 

(percolation)

m/day 8.22E-04 Assumed as 0.2 times average precipitation 

for New England in McKone et al. 2001.

Density of soil solids (dry weight) kg[soil]/m3[soil] 2,600 McKone et al. 2001 (Table 3).

Fraction sand unitless 0.25 Professional judgment.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-9 6-29-2012

TRIM.FaTE Inputs

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

Parameter Name Units Value Used Reference

Root Zone Soil Compartment Type, continued

Thickness - untilleda m 0.79 McKone et al. 2001 (Table 16 - U.S. average).

Thickness - tilleda m 0.6 Adjusted from McKone et al. 2001 (Table 16).

Organic carbon fraction unitless 0.008 McKone et al. 2001 (Tables 16 and A-3, U.S. 

average).

pH unitless 6.8 Professional judgment.

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 (Table 17).

Average vertical velocity of water 

(percolation)

m/day 8.22E-04 Assumed as 0.2 times average precipitation 

for New England in McKone et al. 2001.

Density of soil solids (dry weight) kg[soil]/m3[soil] 2,600 Default in McKone et al. 2001 (Table 3).

Fraction sand unitless 0.35 Professional judgment.

Thicknessa m 1.4 McKone et al. 2001 (Table 17).

Organic carbon fraction unitless 0.003 McKone et al. 2001 (Tables 16 and A-3, U.S. 

average).

pH unitless 6.8 Professional judgment.

Water content volume[water]/volume[compartment] 0.21 McKone et al. 2001 (Table 17 - national 

average).

Groundwater Compartment Type

Thicknessa m 3 McKone et al. 2001 (Table 3).

Fraction sand unitless 0.4 Professional judgment.

Organic carbon fraction unitless 0.004 Professional judgment.

pH unitless 6.8 Professional judgment.

Porosity volume[total pore 

space]/volume[compartment]

0.2 Default in McKone et al. 2001 (Table 3).

Density of solid material in aquifer kg[soil]/m3[soil] 2,600 Default in McKone et al. 2001 (Table 3).

aSet using the volume element properties file.

bSee separate tables (Exhibit_Add A1-5 and Exhibit_Add A1-6) for erosion/runoff fractions and total erosion rates.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-10 6-29-2012

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_N5aSW_Pond 0.0

SurfSoil_N4 0.5

SurfSoil_S5 0.5

sink 0.0

SurfSoil_S5aSW_Pond 0.0

SurfSoil_N5 0.0

SurfSoil_S4 1.0

sink 0.0

aAssumes 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 6-29-2012

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

LengthSlope 

Factor

Land 

Use

Cover 

Mgmt 

Factor

Supporting 

Practices 

Factor

Unit Soil Loss

Sediment

Delivery 

Ratioa

Calculated 

(Adjusted) 

Erosion 

Rate

Code m

2 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) SDRa

kg/m2/d

N1 5.8E+04 300 0.39 1.5 grass 0.1 1 17.55 0.010779 0.533 0.005740

N6 4.1E+04 300 0.39 1.5 crops 0.2 1 35.1 0.021557 0.557 0.012014

N7 7.3E+04 300 0.39 1.5 grass 0.1 1 17.55 0.010779 0.518 0.005580

N3 3.5E+05 300 0.39 1.5 grass 0.1 1 17.55 0.010779 0.385 0.004151

N4 2.0E+06 300 0.39 1.5 forest 0.1 1 17.55 0.010779 0.309 0.003331

N5 6.7E+06 300 0.39 1.5 forest 0.1 1 17.55 0.010779 0.196 0.002116

S1 5.8E+04 300 0.39 1.5 grass 0.1 1 17.55 0.010779 0.533 0.005740

S4 2.0E+06 300 0.39 1.5 forest 0.1 1 17.55 0.010779 0.309 0.003331

S5 6.7E+06 300 0.39 1.5 forest 0.1 1 17.55 0.010779 0.196 0.002116

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 deposition (m2), and b is the 

empirical slope coefficient (always 0.125).
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-12 6-29-2012

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 6-29-2012

TRIM.FaTE Inputs

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

Parameter Name Units

Coniferousa Grass/Herba

Value 

Used Reference Value Used Reference

Leaf Compartment Type

Allow exchange 1=yes, 0=no 1 - Seasonalb-

Average leaf area index m

2

[leaf]/ 

m

2

[area]

5.0 Harvard Forest, dom. red oak and 

red maple, CDIAC website

5.0 Mid-range of 4-6 for old fields, R.J. 

Luxmoore, ORNL.

Calculate wet deposition

interception fraction (Boolean)

1=yes, 0=no 0 Professional judgment. 0 Professional judgment.

Correction exponent, octanol to 

lipid

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

Degree stomatal opening unitless 1 Set to 1 for daytime based on 

professional judgment (stomatal 

diffusion is turned off at night using 

a different property, IsDay).

1 Set to 1 for daytime based on 

professional judgment (stomatal diffusion 

is turned off at night using a different 

property, IsDay).

Density of wet leaf kg/m3820 Paterson et al. 1991. 820 Paterson et al. 1991.

Leaf 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-04 for different crops and 

elements, Muller and Prohl 1993.

Length of leaf m 0.01 Professional judgment. 0.05 Professional judgment.

Lipid content kg/kg wet weight 0.00224 European beech, Riederer 1995. 0.00224 European beech, Riederer 1995.

Litter 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 Fricker 1996.

Vegetation attenuation factor m

2

/kg 2.9 Grass/hay, Baes et al. 1984. 2.9 Grass/hay, Baes et al. 1984.

Water content unitless 0.8 Paterson et al. 1991. 0.8 Paterson et al. 1991.

Wet deposition interception 

fraction

unitless 0.2 Calculated based on 5 years of 

local met data, 1987-1991.

0.2 Calculated based on 5 years of local met 

data, 1987-1991.

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 from leaf area index and Leith 

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 6-29-2012

TRIM.FaTE Inputs

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

Parameter Name Units

Coniferousa Grass/Herba

Value 

Used Reference Value Used Reference

Particle on Leaf Compartment Type, continued

Volume particle per area leaf m

3

[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 particle density and size 

distribution for atmospheric particles 

measured on an adhesive surface, Coe 

and Lindberg 1987.

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 Professional judgment.

Wet density of root kg/m3820 Soybean, Paterson et al. 1991.

Wet mass per soil area kg/m21.4 Temperate grassland, Jackson et al. 

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/m31,000 Professional judgment.

Density of xylem fluid kg/cm3900 Professional judgment.

Flow rate of transpired water per 

leaf area

m

3

[water]/m2[leaf

]

0.0048 Crank et al. 1981.

Fraction of transpiration flow 

rate that is phloem rate

unitless 0.05 Paterson et al. 1991.

Lipid content of stem kg/kg wet weight 0.00224 Leaves of European beech, Riederer 

1995.

Water content of stem unitless 0.8 Paterson et al. 1991

Wet density of stem kg/m3830 Professional judgment.

Wet mass per soil area kg/m20.24 Calculated from leaf and root biomass 

density based on professional judgment.

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 6-29-2012

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 Professional judgment.

Organic carbon fraction in 

suspended sediments

unitless 0.02 Professional judgment.

pH unitless 7.3 Professional judgment.

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.

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

Attachment A, Addendum 1 1-16 6-29-2012

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 Professional judgment.

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 Professional judgment.

Sediment resuspension 

velocity

m/day 6.69E-05 Calculated from water 

balance model.

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

Attachment A, Addendum 1 1-17 6-29-2012

TRIM.FaTE Inputs

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

Aquatic Biota 

(Consuming 

Organism)

Fraction Diet

Biomass

(kg/m2)

Body 

Weight 

(kg)

Reference

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 2.55E-04 Professional 

judgment.

Water Column 

Herbivore

0% 100% 0% 0% 0% 0% 0% 0% 0.002 0.025 Professional 

judgment.

Benthic Omnivore 0% 0% 100% 0% 0% 0% 0% 0% 0.002 2.50E-01 Professional 

judgment.

Water Column 

Omnivore

0% 0% 0% 100% 0% 0% 0% 0% 0.001 0.25 Professional 

judgment.

Benthic Carnivore 0% 0% 50% 0% 50% 0% 0% 0% 0.001 2.0 Professional 

judgment.

Water Column 

Carnivore

0% 0% 0% 0% 0% 100% 0% 0% 0.0002 2.0 Professional 

judgment.

Zooplankton 100% 0% 0% 0% 0% 0% 0% 0% 0.0064 5.70E-08 Professional 

judgment.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-18 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-12. Cadmium Chemical-Specific Parameters

for the TRIM.FaTE Screening Scenario

Parameter Namea Units Value Reference

CAS numberbunitless 7440-43-9 -

Diffusion coefficient in pure 

air

m

2

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

35).

Diffusion coefficient in pure 

water

m

2

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

35).

Henry's Law constant Pa-m

3

/mol 1.00E-37 USEPA 1999 (Table A-2-

35; assumed to be zero).

Melting point degrees K 594 ATSDR 1999.

Molecular weight g/mol 112.41 ATSDR 1999.

Octanol-air partition 

coefficient (Koa)

m

3

[air]/m3[octanol] - -

Octanol-carbon partition 

coefficient (Koc)

- -

Octanol-water partition 

coefficient (Kow)

L[water]/kg[octanol] - -

aAll 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 6-29-2012

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

m

2

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

Diffusion coefficient 

in pure water

m

2

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

Henry's Law 

constant

Pa-m

3

/mol 719 7.19E-05 0.0477 USEPA 1997b.

Melting point degrees K 234 5.50E+02 443 CARB 1994.

Molecular weight g/mol 201 201 216 USEPA 1997b.

Octanol-water 

partition coefficient 

(Kow)

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

Vapor washout ratio m

3

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

based on Petersen 

et al. 1995.

aAll parameters in this table are TRIM.FaTE chemical properties.

bOn 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 6-29-2012

TRIM.FaTE Inputs

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

Parameter Name Units

Value

2Methyl 712DMB Acenaphthene Acenaphthylene BaA BaP BbF BghiP

CAS number unitless 91-57-6 57-97-6 83-32-9 208-96-8 56-55-3 50-32-8 205-99-2 191-24-2

Diffusion coefficient 

in pure air

m

2

/day 0.451 0.691 0.009 0.388 0.441 0.372 0.009 0.190

Diffusion coefficient 

in pure water

m

2

/day 6.70E-05 6.91E-05 8.64E-05 6.03E-05 7.78E-05 7.78E-05 8.64E-05 4.54E-05

Henry's Law 

constant

Pa-m

3

/mol 50.56 0.20 18.50 12.70 1.22 0.07 0.05 0.03

Melting point degrees K 307.75 396.65 366.15 365.65 433 452 441 550.15

Molecular weight g/mol 142.20 256.35 154.21 152.20 228.29 252.32 252.32 276.34

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 6.03E+05 4.27E+06

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 86-73-7 193-39-5

Diffusion coefficient 

in pure air

m

2

/day 0.009 0.009 0.009 0.009 0.009 0.009

Diffusion coefficient 

in pure water

m

2

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

Henry's Law 

constant

Pa-m

3

/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 6-29-2012

TRIM.FaTE Inputs

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

Parameter Name Units Reference

CAS number unitless -

Diffusion coefficient in pure 

air

m

2

/day USEPA 2005. Exceptions include USEPA 1997a (7,12-Dimethylbenz(a)anthracene), and 

USEPA 2007 (2-Methylnaphthalene, Acenaphthylene, and Benzo(g,h,i)perylene)

Diffusion coefficient in pure 

water

m

2

/day USEPA 2005. Exceptions include USEPA 1997a (7,12-Dimethylbenz(a)anthracene), and 

USEPA 2007 (2-Methylnaphthalene, Acenaphthylene, and Benzo(g,h,i)perylene)

Henry's Law constant Pa-m

3

/mol USEPA 2005. Exceptions include USEPA 2003 (2-Methylnaphthalene) HSDB 2001a 

(7,12-Dimethylbenz(a)anthracene), HSDB 2001b (Acenaphthylene), and HSDB 2001c 

(Benzo(g,h,i)perylene)

Melting point degrees K Budavari 1996. Exceptions include USEPA 2003 (2-Methylnaphthalene), HSDB 2001a 

(7,12-Dimethylbenz(a)anthracene), HSDB 2001b (Acenaphthylene), HSDB 2001c 

(Benzo(g,h,i)perylene), and USEPA 2005 (Acenaphthene, Fluoranthene, and Fluorene)

Molecular weight g/mol Budavari 1996. Exceptions include USEPA 2003 (2-Methylnaphthalene), HSDB 2001a 

(7,12-Dimethylbenz(a)anthracene), HSDB 2001b (Acenaphthylene), HSDB 2001c 

(Benzo(g,h,i)perylene), and USEPA 2005 (Acenaphthene, Fluoranthene, and Fluorene)

Octanol-water partition 

coefficient (Kow)

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

Benzo(k)fluoranthene, and Indeno(1,2,3-cd)pyrene), and Sangster 1993 

(Benzo(b)fluoranthene)
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-22 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-15. Dioxin Chemical-Specific Parameters for 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

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

HxCDF

CAS number unitless 3268-87-9 39001-02-0 35822-46-9 67562-39-4 55673-89-7 39227-28-6 70648-26-9

Diffusion coefficient in 

pure air

m

2

/day 0.751 0.168 0.782 0.176 0.176 0.816 0.183

Diffusion coefficient in 

pure water

m

2

/day 6.91E-05 6.91E-05 6.91E-05 6.91E-05 6.91E-05 6.91E-05 6.91E-05

Henry's Law constant Pa-m

3

/mol 0.684 0.19 1.22 1.43 1.42 1.08 1.45

Melting point degrees K 603.0 259.0 538.0 236.5 222.0 546.0 499.0

Molecular weight g/mol 460 443.76 425.2 409.31 409.31 391 374.87

Octanol-water partition 

coefficient (Kow)

L[water]/L[octanol] 1.58E+08 1.00E+08 1.00E+08 2.51E+07 7.94E+06 6.31E+07 1.00E+07

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

1,2,3,7,8-

PeCDF

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

HxCDF

CAS number unitless 57653-85-7 57117-44-9 19408-74-3 72918-21-9 40321-76-4 57117-41-6 60851-34-5

Diffusion coefficient in 

pure air

m

2

/day 0.816 0.183 0.816 0.183 0.854 0.192 0.183

Diffusion coefficient in 

pure water

m

2

/day 6.91E-05 6.91E-05 6.91E-05 6.91E-05 6.91E-05 6.91E-05 6.91E-05

Henry's Law constant Pa-m

3

/mol 1.11 0.741 1.11 1.11 0.263 0.507 1.11

Melting point degrees K 558.0 506.0 517.0 509.0 513.0 499.0 512.5

Molecular weight g/mol 390.84 374.87 390.84 374.87 356.4 340.42 374.87

Octanol-water partition 

coefficient (Kow)

L[water]/L[octanol] 1.62E+08 8.24E+07 1.62E+08 3.80E+07 1.86E+07 6.17E+06 8.31E+07
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-23 6-29-2012

TRIM.FaTE Inputs

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

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

m

2

/day 0.192 0.899 0.203 US EPA 2005

Diffusion coefficient in 

pure water

m

2

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

Henry's Law constant Pa-m

3

/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 include USEPA 2000a 

(1,2,3,6,7,8-HxCDD, 1,2,3,7,8,9-HxCDF, and 1,2,3,7,8-

PeCDD), ATSDR 1998 (1,2,3,6,7,8-HxCDF, 1,2,3,7,8-

PeCDF, and 2,3,4,6,7,8-HxCDF), and NLM 2002 

(1,2,3,7,8,9-HxCDD)

Molecular weight g/mol 340.42 322 306 Mackay et al. 2000. Exceptions include ATSDR 1998 

(1,2,3,6,7,8-HxCDF, 1,2,3,7,8,9-HxCDF, 1,2,3,7,8-PeCDD, 

1,2,3,7,8-PeCDF, and 2,3,4,6,7,8-HxCDF) and NLM 2002 

(1,2,3,6,7,8-HxCDD and 1,2,3,7,8,9-HxCDD)

Octanol-water partition 

coefficient (Kow)

L[water]/L[octanol] 3.16E+06 6.31E+06 1.26E+06 Mackay et al. 1992 as cited in USEPA 2000b. Exceptions 

include Mackay et al. 2000 (1,2,3,4,7,8,9-HpCDF), USEPA 

2000a (1,2,3,6,7,8-HxCDD, 1,2,3,6,7,8-HxCDF, 1,2,3,7,8,9-

HxCDD, 1,2,3,7,8,9-HxCDF, and 2,3,4,6,7,8-HxCDF), and 

Sijm et al. 1989 as cited in USEPA 2000b (1,2,3,7,8-

PeCDD)
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-24 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-16. Cadmium Chemical-Specific Parameters for Abiotic Compartments

in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference

Air Compartment Type

Particle dry deposition velocity m/day 260 Calculated from 

Muhlbaier and Tissue 

1980.

Washout ratio m

3

[air]/m3[rain] 200,000 MacKay et al. 1986.

Surface Soil Compartment Type

Use input characteristic depth 

(Boolean)

0 = no, Else = yes 0 Professional judgment.

Root Zone Soil Compartment Type

Use input characteristic depth 

(Boolean)

0 = no, Else = yes 0 Professional judgment.

Vadose Zone Soil Compartment Type

Use input characteristic depth 

(Boolean)

0 = no, Else = yes 0 Professional judgment.

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 6-29-2012

TRIM.FaTE Inputs

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

Parameter Name Units

Value

Reference

Hg(0) Hg(2) MHg

Air Compartment Type

Particle dry deposition velocity m/day 500 500 500 CalTOX value cited in McKone et al. 2001.

Demethylation rate 1/day N/A N/A 0 Professional judgment.

Methylation rate 1/day 0 0 0 Professional judgment.

Oxidation rate 1/day 0.00385 0 0 Low end of half-life range (6 months to 2 years) in 

USEPA 1997b.

Reduction rate 1/day 0 0 0 Professional judgment.

Washout ratio m

3

[air]/m3[rain] 200,000 200,000 200,000 Professional judgment.

Surface Soil Compartment Type

Use input characteristic depth 

(Boolean)

0 = no, Else = yes 0 0 0 Professional judgment.

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 al. 1992; Hg(2) - estimate 

by USEPA using the Industrial Source Complex 

(ISC) Model - [See Vol. III, App. A of the Mercury 

Study Report (USEPA 1997b)].

Demethylation rate 1/day N/A N/A 0.06 Range reported in Porvari and Verta 1995 is 3E-2 

to 6E-2 /day; value is average maximum potential 

demethylation rate constant under anaerobic 

conditions.

Methylation rate 1/day 0 0.001 0 Range reported in Porvari and Verta 1995 is 2E-4 

to 1E-3 /day; value is average maximum potential 

methylation rate constant under anaerobic 

conditions.

Oxidation rate 1/day 0 0 0 Value assumed in USEPA 1997b.

Reduction rate 1/day 0 1.25E-05 0 Value used for untilled surface soil (2cm), 10% 

moisture content, in USEPA 1997b; general range 

is (0.0013/day)*moisture content to 

(0.0001/day)*moisture content for forested region 

(Lindberg 1996; Carpi and Lindberg 1997).
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-26 6-29-2012

TRIM.FaTE Inputs

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

Parameter Name Units

Value

Reference

Hg(0) Hg(2) MHg

Root Zone Soil Compartment Type

Use input characteristic depth (Boolean) 0 = no, Else = yes 0 0 0 Professional judgment.

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 Porvari and Verta 1995 is 3E-2 

to 6E-2 /day; value is average maximum potential 

demethylation rate constant under anaerobic 

conditions.

Methylation rate 1/day 0 0.001 0 Range reported in Porvari and Verta 1995 is 2E-4 

to 1E-3 /day; value is average maximum potential 

methylation rate constant under anaerobic 

conditions.

Oxidation rate 1/day 0 0 0 Value assumed in USEPA 1997b.

Reduction rate 1/day 0 3.25E-06 0 Value used for tilled surface soil (20cm), 10% 

moisture content, in USEPA 1997b (Lindberg 

1996; Carpi and Lindberg 1997).

Vadose Zone Soil Compartment Type

Use input characteristic depth (Boolean) 0 = no, Else = yes 0 0 0 Professional judgment.

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 Porvari and Verta 1995 is 3E-2 

to 6E-2 /day; value is average maximum potential 

demethylation rate constant under anaerobic 

conditions.

Methylation rate 1/day 0 0.001 0 Range reported in Porvari and Verta 1995 is 2E-4 

to 1E-3 /day; value is average maximum potential 

methylation rate constant under anaerobic 

conditions.

Oxidation rate 1/day 0 0 0 Value assumed in USEPA 1997b.

Reduction rate 1/day 0 3.25E-06 0 Value used for tilled surface soil (20cm), 10% 

moisture content, in USEPA 1997b (Lindberg 

1996; Carpi and Lindberg 1997).
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-27 6-29-2012

TRIM.FaTE Inputs

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

Parameter Name Units

Value

Reference

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 Porvari and Verta 1995 is 3E-2 

to 6E-2 /day; value is average maximum potential 

demethylation rate constant under anaerobic 

conditions.

Methylation rate 1/day 0 0.001 0 Range reported in Porvari and Verta 1995 is 2E-4 

to 1E-3 /day; value is average maximum potential 

methylation rate constant under anaerobic 

conditions.

Oxidation rate 1/day 1.00E-08 0 0 Small default nonzero value (0 assumed in USEPA 

1997b).

Reduction rate 1/day 0 3.25E-06 0 Value used for tilled surface soil (20cm), 10% 

moisture content, in USEPA 1997b (Lindberg 

1996; Carpi and Lindberg 1997).

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. 1995b, 

Mason et al. 1996; Hg(0) assumed same as Hg(2).

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

Solids-water partition 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, App. A).

Demethylation rate 1/day N/A N/A 0.013 Average range of 1E-3 to 2.5E-2/day from Gilmour 

and Henry 1991.

Methylation rate 1/day 0 0.001 0 Value used in EPA 1997; range is 1E-4 to 3E4/day (Gilmour and Henry 1991).

Oxidation rate 1/day 0 0 0 Professional judgment.

Reduction rate 1/day 0 0.0075 0 Value used in USEPA 1997b; reported values 

range from less than 5E-3/day for depths greater 

than 17m, up to 3.5/day (Xiao et al. 1995; Vandal 

et al. 1995; Mason et al. 1995a; Amyot et al. 

1997).
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-28 6-29-2012

TRIM.FaTE Inputs

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

Parameter Name Units

Value

Reference

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 1E-1/day from Gilmour 

and Henry 1991.

Methylation rate 1/day 0 1.00E-04 0 Value used in EPA 1997b; range is 1E-5 to 

1E-3/day, Gilmour and Henry 1991.

Oxidation rate 1/day 0 0 0 Professional judgment.

Reduction rate 1/day 0 1.00E-06 0 Inferred value based on presence of Hg(0) in 

sediment porewater (USEPA 1997b; Vandal et al. 

1995).

a

TRIM.FaTE Formula Property, which varies from 0.025 to 1.625 depending on pH and chloride concentration.

b

TRIM.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 6-29-2012

TRIM.FaTE Inputs

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

Parameter Name Units

Value

2Methyl 712DMB Acenaphthene

Acenaphthylene BaA BaP BbF BghiP BkF

Air Compartment Type

Particle dry deposition velocity m/day 500 500 500 500 500 500 500 500 500

Half-life day 0.154 0.092 0.3 0.208 0.125 0.046 0.596 0.215 0.458

Washout ratio 200000 200000 200000 200000 200000 200000 200000 200000 200000

Surface Soil Compartment Type

User input characteristic depth 

(Boolean)

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

Half-life day 18 24 56 66.5 680 530 610 415 2140

Root Zone Soil Compartment Type

User input characteristic depth 

(Boolean)

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

Half-life day 18 24 56 66.5 680 530 610 415 2140

Vadose Zone Soil Compartment Type

User input characteristic depth 

(Boolean)

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

Half-life day 36 48 112 133 1360 1060 1220 830 4280

Groundwater Compartment Type

Half-life day 36 48 112 133 1360 1060 1220 830 4280

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 3.7 325 510 317 1539 473

Half-life day 78 216 25 184 0.375 0.138 90 1670 62.4

Sediment Compartment Type

Half-life day 2290 2290 2290 2290 2290 2290 2290 2290 2290
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-30 6-29-2012

TRIM.FaTE Inputs

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

Parameter Name Units

Value

Reference

Chr DahA Fluoranthene

Fluorene

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 / upper bound measured or 

estimated value. Exceptions include ATSDR 2005 (2-

Methylnaphthalene), USEPA 1998 (7,12-

Dimethylbenz(a)anthracene, Benzo(g,h,i)perylene, 

and Fluoranthene) / average of range, HSDB 2001d 

(Acenaphthene), HSDB 2001b (Acenaphthylene), and 

Spero et al. 2000 (Fluorene).

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 Professional judgment.

Half-life day 1000 940 275 33 730 MacKay et al. 2000 / average of range. Exceptions 

include ATSDR 2005 (2-Methylnaphthalene), USEPA 

1998 (7,12-Dimethylbenz(a)anthracene, 

Benzo(g,h,i)perylene, and Fluoranthene) / average of 

range, HSDB 2001d (Acenaphthene), HSDB 2001b 

(Acenaphthylene), and HSDB 2001e (Fluorene).

Root Zone Soil Compartment Type

User input characteristic depth 

(Boolean)

0 = No, 

Else = Yes

0 0 0 0 0 Professional judgment.

Half-life day 1000 940 275 33 730 Howard et al. 1991 / upper bound measured or 

estimated value. Exceptions include ATSDR 2005 (2-

Methylnaphthalene), USEPA 1998 (7,12-

Dimethylbenz(a)anthracene, Benzo(g,h,i)perylene, 

and Fluoranthene) / average of range, HSDB 2001d 

(Acenaphthene), HSDB 2001b (Acenaphthylene), and 

HSDB 2001e (Fluorene).

Vadose Zone Soil Compartment Type

User input characteristic depth 

(Boolean)

0 = No, Else = 

Yes

0 0 0 0 0 Professional judgment.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-31 6-29-2012

TRIM.FaTE Inputs

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

Parameter Name Units

Value

Reference

Chr DahA Fluora

n-thene

Fluorene

IcdP

Vadose Zone Soil Compartment Type, continued

Half-life day 2000 1880 550 66 1460 Howard et al. 1991 / upper bound measured or 

estimated value. Exceptions include ATSDR 2005 (2-

Methylnaphthalene), USEPA 1998 (7,12-

Dimethylbenz(a)anthracene, Benzo(g,h,i)perylene, and 

Fluoranthene) / twice average of range, HSDB 2001d 

(Acenaphthene) / multiplied by 2, HSDB 2001b 

(Acenaphthylene) / multiplied by 2, and HSDB 2001e 

(Fluorene) / multiplied by 2.

Groundwater Compartment Type

Half-life day 2000 1880 550 66 1460 Howard et al. 1991 / upper bound measured or 

estimated value. Exceptions include ATSDR 2005 (2-

Methylnaphthalene), USEPA 1998 (7,12-

Dimethylbenz(a)anthracene, Benzo(g,h,i)perylene, and 

Fluoranthene) / twice average of range, HSDB 2001d 

(Acenaphthene) / multiplied by 2, HSDB 2001b 

(Acenaphthylene) / multiplied by 2, and HSDB 2001e 

(Fluorene) / multiplied by 2.

Surface Water Compartment Type

RatioOfConcInAlgaeToConcD

issolvedInWater

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

(g[chem]/L[water])

280 1388 67.4 5.8 1653 Kow from Del Vento and Dachs 2002.

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

estimated value. Exceptions include HSDB 2005 (2-

Methylnaphthalene), HSDB 2001a (7-12 

Dimethylbenz(a)anthracene), HSDB 2001d 

(Acenaphthene), HSDB 2001b (Acenaphthylene), and 

HSDB 2001c (Benzo(g,h,i)perylene), Montgomery 2000 

(Fluoranthene), and Boyle 1985 (Fluorene).

Sediment Compartment Type

Half-life day 2290 2290 2290 2290 2290 Mackay et al. 1992 / PAH values are the mean half-life 

of the log class that Mackay et al. assigned for 

sediment, except for BbF and IcdP, which were not in 

Table 2.3 of Mackay et. al.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-32 6-29-2012

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 m

3

[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 6-29-2012

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 m

3

[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 6-29-2012

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 m

3

[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 6-29-2012

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) Professional judgment.

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 Professional judgment.

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) Professional judgment.

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 6-29-2012

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 Professional judgment.

Particle on Leaf Compartment Type

Transfer factor to leaf 1/day 0.200 Professional judgment.

Root Compartment Type - Grasses and Herbsa

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

Root to Root Soil PartitionPartitioning Coefficientm

3

[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 Alphaday 28 Henning et al. 2001.

Stem Compartment Type - Grasses and Herbsa

Transpiration stream 

concentration factor (TSCF)

m

3

[soil pore 

water]/m3[xylem 

fluid]

0.45 Tsiros et al. 1999.

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 1-37 6-29-2012

TRIM.FaTE Inputs

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

Parameter Name Units

Value

Reference

Hg(0) Hg(2) MHg

Leaf Compartment Type

Transfer factor to leaf particle 1/day 0.002 0.002 0.002 Professional judgment (assumed 1% of 

transfer factor from leaf particle to leaf).

Demethylation rate 1/day N/A N/A 0.03 Calculated from Bache et al. 1973.

Methylation rate 1/day 0 0 0 Assumed from Gay 1975, Bache et al. 

1973.

Oxidation rate 1/day 1.0E+06 0 0 Professional judgment; assumed close to 

instantaneous

Reduction rate 1/day 0 0 0 Professional judgment.

Particle on Leaf Compartment Type

Transfer factor to leaf 1/day 0.2 0.2 0.2 Professional judgment.

Demethylation rate 1/day N/A N/A 0 Professional judgment.

Methylation rate 1/day 0 0 0 Professional judgment.

Oxidation rate 1/day 0 0 0 Professional judgment.

Reduction rate 1/day 0 0 0 Professional judgment.

Root Compartment Type - Grasses and Herbsa

Alpha for root-root zone bulk soil unitless 0.95 0.95 0.95 Selected value.

Root/root-zone-soil-water partition 

coefficient

m

3

[bulk root soil]/ m3[root] 0 0.18 1.2 Hg2-geometric mean Leonard et al. 1998, 

John 1972, Hogg et al. 1978; MHgassumed, based on Hogg et al. 1978.

t-alpha for root-root zone bulk soil day 21 21 21 Professional judgment.

Demethylation rate 1/day N/A N/A 0 Professional judgment.

Methylation rate 1/day 0 0 0 Professional judgment.

Oxidation rate 1/day 0 0 0 Professional judgment.

Reduction rate 1/day 0 0 0 Professional judgment.

Stem Compartment Type - Grasses and Herbsa

Transpiration stream concentration 

factor (TSCF)

m

3

[soil pore 

water]/m3[xylem fluid]

0 0.5 0.2 Calculation from Norway spruce, Scots 

pine, Bishop et al. 1998.

Demethylation rate 1/day N/A N/A 0.03 Calculated from Bache et al. 1973.

Methylation rate 1/day 0 0 0 Professional judgment.

Oxidation rate 1/day 0 0 0 Professional judgment.

Reduction rate 1/day 0 0 0 Professional judgment.

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 6-29-2012

TRIM.FaTE Inputs

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

Parameter Name Units

Value

2Methyl 712DMB Acenaphthene

Acenaphthylene BaA BaP BbF BghiP BkF

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 1.00E-04 1.00E-04

Half-life day 3.50 3.50 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 1.00E-04 1.00E-04

Half-life day 2.31 2.31 2.31 2.31 1.84 2.31 3.56 2.31 17.80

Root Compartment Type - Grasses and Herbsa

Half-life day 34.60 34.60 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 0.95 0.95

Stem Compartment Type - Grasses and Herbsa

Half-life day 3.50 3.50 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 6-29-2012

TRIM.FaTE Inputs

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

Parameter Name Units

Value

Reference

Chr DahA Fluoranthene 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 Professional judgment.

Half-life day 3.50 3.50 3.50 3.50 3.50 Edwards 1988 (calculated from metabolic rate 

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 Professional judgment.

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

half-life from Mackay et al. 1992. Exceptions 

include values that have been set equal to 

Benzo(a)pyrene (2-Methylnaphthalene; 7,12-

Dimethylbenz(a)anthracene; Acenaphthene; 

Acenaphthylene; Benzo(ghi)perylene; 

Fluoranthene; and Fluorene).

Root Compartment Type - Grasses and Herbsa

Half-life day 34.60 34.60 34.60 34.60 34.60 Edwards 1988 (calculated from metabolic rate 

constant).

Root soil water 

interaction - alpha

unitless 0.95 0.95 0.95 0.95 0.95 Professional judgment.

Stem Compartment Type - Grasses and Herbsa

Half-life day 3.50 3.50 3.50 3.50 3.50 Edwards 1988 (calculated from metabolic rate 

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 6-29-2012

TRIM.FaTE Inputs

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

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 leaf; highly uncertain.

Half-life day 70 Arjmand and Sandermann 1985, as cited in Komoba et al. 1995; 

soybean root cell culture metabolism test data for DDE.

Particle on Leaf Compartment Type

Transfer factor to leaf 1/day 0.3 Professional judgment based on USEPA 2000b (an estimate for 

mercury) and Trapp 1995; highly uncertain.

Half-life day 4.4 McCrady and Maggard 1993; photodegradation sorbed to grass 

foliage in sunlight; assumed 10% sunlight per day.

Root Compartment Type - Grasses and Herbsa

Half-life day 70 Arjmand and Sandermann 1985, as cited in Komoba et al. 1995; 

soybean root cell culture metabolism test data for DDE.

Root soil-water interaction - alpha unitless 0.95 Professional judgment.

Stem Compartment Type - Grasses and Herbsa

Half-life day 70 Arjmand and Sandermann 1985, as cited in Komoba et al. 1995; 

soybean root cell culture metabolism test data for DDE.

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

Attachment A, Addendum 1 1-41 6-29-2012

TRIM.FaTE Inputs

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

Parameter Name Units Value Reference

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 Professional judgment.

Sediment partitioning - partition 

coefficient

kg[bulk sed/kg[invertebrate wet wt] 0.27 Professional judgment.

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 Professional judgment based on Yan and Wang 

2002.

Absorption rate constant unitless 1.23 Calculated based on body weight from regression 

in Hendriks and Heikens 2001.

Elimination rate constant unitless 1.73E-02 Professional judgment.

Benthic Carnivore Compartment Type

Assimilation efficiency from food unitless 0.1 Professional judgment based on Yan and Wang 

2002.

Absorption rate constant unitless 0.66 Calculated based on body weight from regression 

in Hendriks and Heikens 2001.

Elimination rate constant unitless 1.68E-03 Professional judgment.

Water-column Herbivore Compartment Type

Assimilation efficiency from food unitless 0.1 Assumed value based on Yan and Wang 2002.

Assimilation efficiency from plants unitless 0.1 Assumed value based on Yan and Wang 2002.

Absorption rate constant unitless 2.46 Calculated based on body weight from regression 

in Hendriks and Heikens 2001.

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

Attachment A, Addendum 1 1-42 6-29-2012

TRIM.FaTE Inputs

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

Parameter Name Units Value Reference

Water-column Omnivore Compartment Type

Assimilation efficiency from food unitless 0.1 Professional judgment based on Yan and Wang 

2002.

Assimilation efficiency from plants unitless 0.1 Professional judgment based on Yan and Wang 

2002.

Absorption rate constant unitless 1.23 Calculated based on body weight from regression 

in Hendriks and Heikens 2001.

Elimination rate constant unitless 1.73E-02 Professional judgment.

Water-column Carnivore Compartment Type

Assimilation efficiency from food unitless 0.1 Professional judgment based on Yan and Wang 

2002.

Absorption rate constant unitless 0.66 Calculated based on body weight from regression 

in Hendriks and Heikens 2001.

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

Attachment A, Addendum 1 1-43 6-29-2012

TRIM.FaTE Inputs

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

Parameter Name Units

Value

Reference

Hg(0) Hg(2) MHg

Zooplankton Compartment Type

Assimilation Efficiency From Algae unitless 0.2 0.015 0.5 Environment Canada 2002.

Half-life day 1.0E+09 1.0E+09 1.0E+09 Professional judgment.

How Much Faster Hg Elimination Is Than

For MHg

unitless 3 3 1 Professional judgment.

Methylation rate 1/day 0 0 0 Professional judgment.

Oxidation rate 1/day 0 1.0E+06 0 Professional judgment.

Reduction rate 1/day 0 0 0 Professional judgment.

Benthic Invertebrate Compartment Type

Alpha of equilibrium for sediment 

partitioning

unitless 0.95 0.95 0.95 Selected value (i.e., proportion of 

equilibrium achieved by time “t”).

Benthic invertebrate-bulk sediment partition 

coefficient

kg[bulk 

sediment]/kg[invertebrate 

wet wt]

0.0824 0.0824 5.04 Hg(0) - assumed based on Hg(2) 

value; Hg(2) and MHg - Saouter 

et al. 1991.

t-alpha for equilibrium for sediment 

partitioning

day 14 14 14 Experiment duration from 

Saouter et al. 1991.

All Fish Compartments Typesa

Elimination adjustment factor unitless 3 3 1 Trudel and Rasmussen 1997.

Assimilation efficiency from food unitless 0.06 0.06 0.5 Williams et al. 2010.

Demethylation rate 1/day N/A N/A 0 Professional judgment.

Methylation rate 1/day 0 0 0 Professional judgment.

Oxidation rate 1/day 1.0E+06 0 0 Professional judgment.

Reduction rate 1/day 0 0 0 Professional judgment.

Water-column Herbivore Compartment Type

Assimilation efficiency from plankton unitless 0.06 0.06 0.5 Williams et al. 2010.

aScreening scenario includes: Benthic Omnivore, Benthic Carnivore, Water-column Herbivore, Water-column Omnivore, and Water-column Carnivore.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-44 6-29-2012

TRIM.FaTE Inputs

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

Parameter 

Name Units

Value

2Methyl 712DMB Acenaphthene

Acenaphthylene BaA BaP BbF BghiP BkF

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 42655.77 42652.5

Assimilation 

efficiency from 

algae

unitless 0.5 0.25 0.5 0.5 0.46 0.25 0.25 0.25 0.25

Elimination 

rate constant

1/day 169.68 2.03 148.07 123.44 2.073 1.3864 2.12 0.33 1.48

Half-life day 0.007788 17 0.00239 0.00239 1.284 16.5 17 17 17

Benthic Invertebrate Compartment Type

Clearance 

constant

unitless 100.6 100.6 100.6 100.6 100.6 100.6 100.6 100.6 100.6

Vd (ratio of 

concentration

in benthic 

invertebrates 

to

concentration 

in water)

ml/g 7235 7235 7235 7235 7235 7235 7235 7235 7235

Half-life day 0.722 17 0.722 0.722 1.284 16.5 17 17 17

All Fish Compartment Typesa

Gamma fish unitless 0.2 0.2 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 0.15 0.15

Half-life day 0.2 2 0.2 0.2 0.408 1.925 2 2 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 6-29-2012

TRIM.FaTE Inputs

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

Parameter Name Units

Value

Reference

Chr DahA Fluoranthene 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 Arnot et al. 2004. 

Exception is Berrojalbiz et al. 2009 

(2-Methylnaphthalene, 

Fluoranthene, and Fluorene).

Assimilation 

efficiency from algae

unitless 0.46 0.25 0.49 0.5 0.25 Kow from Arnot et al. 2004. 

Exception is maximum value from 

Wang and Wang 2006 (7,12-

Dimethylbenz(a)anthracene, 

Benzo(a)pyrene, 

Benzo(b)fluoranthene, 

Benzo(g,h,i)perylene, 

Benzo(k)fluoranthene, 

Dibenz(a,h)anthracene, and 

Indeno(1,2,3-cd)pyrene).

Elimination rate 

constant

1/day 2.3746 0.4331 8.678 81.87 0.269 Kow from Arnot et al. 2004.

Half-life day 0.495 17 0.00239 0.0002476 17 McElroy 1990. Exceptions include 

Berrojalbiz et al. 2009 (2-

Methylnaphthalene, Fluoranthene, 

and Fluorene) and Moermond et al. 

2007 (Benz(a)anthracene and 

Benzo(a)pyrene).

Benthic Invertebrate Compartment Type

Clearance constant unitless 100.6 100.6 100.6 100.6 100.6 Stehly et al. 1990.

Vd (ratio of 

concentration

in benthic 

invertebrates to

concentration in 

water)

ml/g 7235 7235 7235 7235 7235 Stehly et al. 1990.

Half-life day 0.495 17 0.722 0.722 17 Moermond et al. 2007.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-46 6-29-2012

TRIM.FaTE Inputs

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

Parameter Name Units

Value

Reference

Chr DahA Fluoranthene Fluorene IcdP

All Fish Compartment Typesa

Gamma fish unitless 0.2 0.2 0.2 0.2 0.2 Thomann 1989.

Assimilation 

efficiency from food

unitless 0.15 0.15 0.14 0.14 0.15 Lemair et al. 1992. Exceptions 

include Barber 2008 (2-

Methylnaphthalene and 

Acenaphthene) and Niimi and 

Palazzo 1986 (Acenaphthylene, 

Fluoranthene, and Fluorene).

Half-life day 0.533 2 0.165 0.2 2 Moermond et al. 2007.

aScreening scenario includes: Benthic Omnivore, Benthic Carnivore, Water-column Herbivore, Water-column Omnivore, and Water-column Carnivore.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-47 6-29-2012

TRIM.FaTE Inputs

Exhibit_Add A1-27. Dioxin Chemical-Specific Parameters for Aquatic Species 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

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

HxCDF

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

HxCDD

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

HxCDF

Zooplankton Compartment

Absorption rate constant L[water]/kg[fish 

wet wt]-day

8640 8640 8640 8640 8640 8640 8640 8640 8640

Assimilation efficiency from algae unitless 0.08 0.05 0.21 0.09 0.2 0.31 0.31 0.31 0.31

Elimination rate constant 1/day 0.0102 0.016 0.016 0.0616 0.1829 0.0252 0.1474 0.0099 0.0194

Half-life day 7E+06 7E+06 7E+06 7E+06 7E+06 7E+06 7E+06 7E+06 7E+06

Benthic Invertebrate Compartment

Clearance constant unitless 0 0 0 0 0 0 0 0 0

Sediment partitioning partition 

coefficient

kg/kg 0.0013 0.0017 0.0055 0.0012 0.042 0.033 0.0081 0.013 0.02

Sediment partitioning alpha of 

equilibrium

unitless 0.95 0.95 0.95 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 42 120 42 120 42

Vd (ratio of concentration

in benthic invertebrates to

concentration in water)

ml/g 0 0 0 0 0 0 0 0 0

Half-life day 5776.2 5776.2 5776.2 5776.2 5776.2 5776.2 5776.2 5776.2 5776.2

All Fish Compartmentsa

Assimilation efficiency from food unitless 0.08 0.05 0.21 0.09 0.2 0.37 0.31 0.31 0.31

Chemical uptake rate via gill L[water]/kg[fish 

wet wt]-day

11 6 56 25 50 102 200 300 200

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

Attachment A, Addendum 1 1-48 6-29-2012

TRIM.FaTE Inputs

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

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

2,3,7,8-TCDD

2,3,7,8-TCDF

Zooplankton Compartment

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

wt]-day

8640 8640 8640 8640 8640 8640 8640 8640

Assimilation Efficiency from Algae unitless 0.31 0.31 0.42 0.42 0.31 0.42 0.41 0.51

Elimination Rate Constant 1/day 0.0099 0.0413 0.0819 0.2316 0.0192 0.4331 0.2268 1.0375

Half-life day 7E+06 7E+06 7E+06 7E+06 7E+08 7E+08 7E+06 7E+08

Benthic Invertebrate Compartment

Clearance Constant unitless 0 0 0 0 0 0 0 0

Sediment Partitioning Partition 

Coefficient

kg/kg 0.015 0.067 0.098 0.024 0.072 0.17 0.205 0.056

Sediment Partitioning Alpha of 

Equilibrium

unitless 0.95 0.95 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 42 120 42

Vd (ratio of concentration

in benthic invertebrates to

concentration in water)

ml/g 0 0 0 0 0 0 0 0

Half-life day 5776.2 5776.2 5776.2 5776.2 5776.2 5776.2 5776.2 5776.2

All Fish Compartmentsa

Assimilation efficiency from food unitless 0.31 0.31 0.42 0.42 0.31 0.42 0.41 0.51

Chemical uptake rate via gill L[water]/kg[fish wet 

wt]-day

300 200 700 300 200 400 600 400

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

Attachment A, Addendum 1 1-49 6-29-2012

TRIM.FaTE Inputs

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

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

OCDD, 1,2,3,4,6,7,8,9-OCDF), Berntssen et al. 2007 (1,2,3,4,6,7,8-HpCDD, 

1,2,3,4,6,7,8-HpCDF), and 1,2,3,4,7,8,9-HpCDF set conservatively as approximate 

linear interpolation between values for 1,2,3,4,7,8-HxCDD and 1,2,3,4,6,7,8-HpCDD / 

1,2,3,4,6,7,8-HpCDF (i.e., 0.3 to 0.1–0.2).

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 Professional judgment.

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 Professional judgment.

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

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

Berntssen et al. 2007 ( 1,2,3,4,6,7,8-HpCDF), and 1,2,3,4,7,8,9-HpCDF set 

conservatively as approximate linear interpolation between values for 1,2,3,4,7,8-

HxCDD and 1,2,3,4,6,7,8-HpCDD / 1,2,3,4,6,7,8-HpCDF (i.e., 0.3 to 0.1–0.2).

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

wet wt]-day

Muir et al. 1985. Exception is Opperhuizen et al. 1986 (1,2,3,7,8,9-HxCDF, 1,2,3,7,8-

PeCDD, 1,2,3,7,8-PeCDF, 2,3,4,7,8-PeCDF, 2,3,7,8-TCDD, 2,3,7,8-TCDF).

Half-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 6-29-2012

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 

sediments. Organohalogen Compounds 12: 107-110.

Adriaens, P., and D. Grbic-Galic. 1992. Effect of cocontaminants and concentration on the anaerobic 

biotransformation of PCDD/F in methanogenic river sediments. Organohalogen Compounds 8: 209-

212.

ATSDR (Agency for Toxic Substances and Disease Registry). 2005. Toxicological profile for 

naphthalene,1-methylnaphthalene, and 2-methylnaphthalene. Available

at:http://www.atsdr.cdc.gov/ToxProfiles/TP.asp?id=240&tid=43.

ATSDR. 1999. Toxicological profile for cadmium. Available at

http://www.atsdr.cdc.gov/toxprofiles/tp.asp?id=48&tid=15.

ATSDR. 1998. Toxicological profile for chlorodibenzo-p-dioxins (CDDs). Available at:

http://www.atsdr.cdc.gov/toxprofiles/tp.asp?id=938&tid=194.

Ambrose, R.A., Jr., T.A. Wool, and J.L. Martin. 1995. The water quality analysis simulation program, 

WASP5, Part A: Model documentation. Athens, GA: U.S. EPA National Exposure Research 

Laboratory, Ecosystems Division.

APHA (American Public Health Association). 1995. Standard methods for the examination of water and 

waste water. Washington, DC.

Amyot, M., D. Lean, and G. Mierle. 1997. Photochemical formation of volatile mercury in high arctic 

lakes. Environmental Toxicology and Chemistry 16(10):2054-2063.

Arjmand, M., and H. Sandermann. 1985. Metabolism of DDT and related compounds in cell suspension 

cultures of soybean (Glycine max L.) and wheat (Tritucum aestivum L.) Pesticide Biochemistry and 

Physiology 23:389.

Arnot, J., and F.A. Gobas. 2004. A food web bioaccumulation model for organic chemicals in aquatic 

ecosystems. Environmental Toxicology and Chemistry 23(10):2343-2355.

Atkinson, R. 1996. Atmospheric chemistry of PCBs, PCDDs and PCDFs. Issues in Environmental

Science and Technology 6: 53-72.

Bache, C.A., W.J. Gutenmann, L.E. St. John, Jr., R.D. Sweet, H.H. Hatfield, and D.J. Lisk. 1973.

Mercury and methylmercury content of agricultural crops grown on soils treated with various

mercury compounds. Journal of Agricultural and Food Chemistry 21:607-613.

Baes, C.F., III, R.D. Sharp, A.L. Sjoreen, and R.W. Shor. 1984. A review and analysis of parameters for 

assessing transport of environmentally released radionuclides through agriculture. ORNL-5786. Oak 

Ridge National Laboratory, Oak Ridge, TN.

Barber, M.C. 2008. Dietary uptake models used for modeling the bioaccumulation of organic 

contaminants in fish. Environmental Toxicology and Chemistry 27(4):755-777.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-51 6-29-2012

TRIM.FaTE Inputs

Berntssen, M.H.G., T.A. Giskegjerde, G. Rosenlund, B.E. Torstensen, and A.K. Lundebye. 2007. 

Predicting world health organization toxic equivalency factor dioxin and dioxin-like polychlorinated 

biphenyl levels in farmed Atlantic salmon (Salmo salar) based on known levels in feed. 

Environmental Toxicology and Chemistry 26(1):13-23. doi: 10.1897/06-122r.1.

Berrojalbiz, N., S. Lacorte, A. Calbet, E. Saiz, C. Barata, and J. Dachs. 2009. Accumulation and cycling 

of polycyclic aromatic hydrocarbons in zooplankton. Environmental Science and Technology

43(7):2295-2301. doi: 10.1021/es8018226.

Bidleman, T.F. 1988. Atmospheric processes. Environmental Science and Technology 22:361-367. 

Bishop, K.H.; Lee, Y.H.; Munthe, J.; Dambrine, E. 1998. Xylem sap as a pathway for total mercury and

methylmercury transport from soils to tree canopy in the boreal forest. Biogeochemistry 40: 101-

113.

Boyle, T. 1985. Validation and predictability of laboratory methods for assessing the fate and effects of 

contaminants in aquatic ecosystems. Baltimore, MD: ASTM International.

Budavari, S. [ed.]. 1996. The Merck Index - An encyclopedia of chemicals, drugs, and biologicals. 

Whitehouse Station, NJ: Merck and Co., Inc., p. 178.

CARB (California Air Resources Board). 1994. Development of intermedia transfer factors for 

pollutants, Volume II: Metals and non-volatile organic compounds. PB95-260691. California: Air 

Resources Board. March.

Cal EPA (California Environmental Protection Agency). 1993. CalTOX, A multimedia total-exposure

model for hazardous-waste sites, Part II: The dynamic multimedia transport and transformation.

Model Prepared for: The Office of Scientific Affairs. Department of Toxic Substances Control. 

Sacramento, California. December. Draft Final.

Carpi, A., and S.E. Lindberg. 1997. Sunlight-mediated emission of elemental mercury from soil

amended with municipal sewage sludge. Environmental Science and Technology 31(7):2085-2091.

Coe, J.M., and S.E. Lindberg. 1987. The morphology and size distribution of atmospheric particles 

deposited on foliage and inert surfaces. Journal of the Air Pollution Control Association 37:237-243.

Crank, J., N.R. McFarlane, J.C. Newby, G.D. Paterson, and J.B. Pedley. 1981. Diffusion processes in 

environmental systems. In: Paterson et al. 1991. London: Macmillan Press, Ltd.

Del Vento, S., and J. Dachs. 2002. Prediction of uptake dynamics of persistent organic pollutants by 

bacteria and phytoplankton. Environmental Toxicology and Chemistry 21(10):2099-2107.

Edwards, N.T. 1988. Assimilation and metabolism of polycyclic aromatic hydrocarbons by vegetation –

an approach to this controversial issue and suggestions for future research. In: (M. Cooke and A.J. 

Dennis, eds.) Polynuclear Aromatic Hydrocarbons: A Decade of Progress. Battelle Press,

Columbus, OH; pp. 211-229.

Environment Canada. 2002. Ecosystem Health Science-Based Solutions: Canadian Tissue Residue 

Guidelines for the protection of Wildlife Consumers of Aquatic Biota: Methylmercury. Report No. 1-

4.

Gay, D.D. 1975. Biotransformation and chemical form of mercury in plants. International Conference on 

Heavy Metals in the Environment, pp. 87-95. Vol. II, Part 1. October.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-52 6-29-2012

TRIM.FaTE Inputs

Gilmour, C.C., and E.A. Henry. 1991. Mercury methylation in aquatic systems affected by acid 

deposition. Environmental Pollution 71:131-169.

Goulet, R.R., S. Krack, P.J. Doyle, L. Hare, B. Vigneault, and J.C. McGeer. 2007. Dynamic 

multipathway modeling of Cd bioaccumulation in Daphnia magna using waterbourne and dietborne 

exposures. Aquatic Toxicology. 81: 117-125. 

Hansch, C., A. Leo, and D. Hoekman. 1995. Exploring QSAR - Hydrophobic, electronic, and steric 

constants. Washington, DC: American Chemical Society 154.

Hare, L; Tessier, A; and Warren, L. 2001. Cadmium accumulation by invertebrates living at the

sediment–water interface. Environmental Toxicology and Chemistry 20: 880-889.

Harner, T., and Bidleman, T.F. 1998. Octanol-air partition coefficient for describing particle/gas

partitioning of aromatic compounds in urban air. Environmental Science and Technology 32:1494-

1502.

HSDB (Hazardous Substances Data Bank). 2005. Bethesda, MD: National Library of Medicine, U.S. 

[Last Revision Date 10/27/2005]. 2-Methylnaphthalene; Hazardous Substances Databank Number:

5274. Available at: http://toxnet.nlm.nih.gov/cgibin/sis/search/a?dbs+hsdb:@term+@DOCNO+5274.

HSDB. 2001a. Bethesda, MD: National Library of Medicine, U.S. [Last Revision Date 08/09/2001]. 7,12-

Dimethylbenz(a)anthracene; Hazardous Substances Databank Number: 2938. Available at:

http://toxnet.nlm.nih.gov/cgi-bin/sis/search/a?dbs+hsdb:@term+@DOCNO+2938.

HSDB. 2001b. Bethesda, MD: National Library of Medicine, U.S. [Last Revision Date 08/09/2001].

Acenaphthylene; Hazardous Substances Databank Number: 2661. Available at:

http://toxnet.nlm.nih.gov/cgi-bin/sis/search/a?dbs+hsdb:@term+@DOCNO+2661.

HSDB. 2001c. Bethesda, MD: National Library of Medicine, U.S. [Last Revision Date 08/09/2001].

Benzo(ghi)perylene; Hazardous Substances Databank Number: 6177. Available at:

http://toxnet.nlm.nih.gov/cgi-bin/sis/search/a?dbs+hsdb:@term+@DOCNO+6177.

HSDB. 2001d. Bethesda, MD: National Library of Medicine, U.S. [Last Revision Date 08/09/2001].

Acenaphthene; Hazardous Substances Databank Number: 2659. Available at:

http://toxnet.nlm.nih.gov/cgi-bin/sis/search/a?dbs+hsdb:@term+@DOCNO+2659.

Hendriks, AJ; and Heikens, A. 2001. The power of size. 2. Rate constants and equilibrium ratios for 

accumulation of inorganic substances related to species weight. Environmental Toxicology and 

Chemistry 20: 1421-1437.

Henning, B.J., H.G. Snyman, and T.A.S. Aveling. 2001. Plant-soil interactions of sludge-borne heavy

metals and the effect on maize (Zea mays L.) seedling growth. Water SA 27(1):71-78.

Hogg, T.J., J.R. Bettany, and J.W.B. Stewart. 1978. The uptake of 203Hg-labeled mercury compounds

by bromegrass from irrigated undisturbed soil columns. Journal of Environmental Quality 7:445-450.

Holzworth, G.C. 1972. Mixing heights, wind speeds, and potential for urban air pollution throughout the 

contiguous United States. Prepared for EPA Office of Air Programs. Research Triangle Park, NC.

Howard, P.H., R.S. Boethling, W.F. Jarvis, W.M. Meylan, and E.M. Michalenko. 1991. Handbook of 

environmental degradation rates. Chelsea, Michigan: Lewis Publishers
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-53 6-29-2012

TRIM.FaTE Inputs

Hudson, R., S.A. Gherini, C.J. Watras, and D. Porcella. 1994. Modeling the biogeochemical cycle of 

mercury in lakes: The Mercury Cycling Model (MCM) and its application to the MTL Study Lakes. In:

C.J. Watras and J.W. Huckabee, eds. Mercury pollution integration and synthesis. Lewis Publishers. 

pp. 473-523.

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.

Jackson, R.B., J. Canadell, J.R. Ehleringer, H.A. Mooney, O.E. Sala, and E.D. Schulze. 1996. A global 

analysis of root distributions for terrestrial biomes. Oecologia 108:389-411.

John, M.K. 1972. Mercury uptake from soil by various plant species. Bulletin of Environmental 

Contamination and Toxicology 8:77-80.

Kaushal, S.S., P.M. Groffman, G.E. Likens, K.T. Belt, W.P. Stack, V.R. Kelly, L.E. Band, and G.T. 

Fisher, 2005. Increased Salinization of Fresh Water in the Northeastern United States. Proceedings

of the National Academy of Sciences 102:13517-13520.

Kim, M., and P. O’Keefe. 1998. The role of natural organic compounds in photosensitized degradation 

of polychlorinated dibenzo-p-dioxins and dibenzofurans. Organohalogen Compounds 36: 377-380.

Komoba, D., C. Langebartels, and H. Sandermann. 1995. Metabolic processes for organic chemicals in 

plants. In: Plant contamination modeling and simulation of organic chemical processes. Trapp, S.,

and Mc Farlane, J.C., eds., CRC Press, Boca Raton, FL. Pages 69-103.

Leith, H. 1975. Primary productivity in the biosphere. In: H. Leith and R.W. Whitaker. Ecological 

Studies, Volume 14. Springer-Verlag. 

Lemair, P., A. Mathieu, S. Carriere, J.F. Narbonne, M. Lafaurie, and J. Giudicelli. 1992. Hepatic 

biotransformation enzymes in aquaculture European sea bass (Dicentrarchus labrax): kinetic

parameters and induction with benzo(a)pyrene. Comparative Biochemistry and Physiology 103(B):

847-853.

Leonard, T.L., G.E. Taylor, Jr., M.S. Gustin, and G.C.J. Fernandez. 1998. Mercury and plants in 

contaminated soils: 1. Uptake, partitioning, and emission to the atmosphere. Environmental 

Toxicology and Chemistry 17:2063-2071.

Lindberg, S.E., T.P. Meyers, G.E. Taylor, R.R. Turner, and W.H. Schroeder 1992. Atmosphere-Surface 

Exchange of Mercury to a Forest: Results of Modelling and Gradient Approaches. Journal of 

Geophysical Research 97(D2):2519-2528.

Lindberg, S.E. 1996. Forests and the global biogeochemical cycle of mercury: The importance of 

understanding air/vegetation exchange processes. In: W. Baeyans et al., eds. Global and regional 

mercury cycles: Sources, fluxes, and mass balances. pp. 359-380.

Mackay, D., W.Y. Shiu, and K.C. Ma. 2000. Physical-chemical properties and environmental fate 

handbook. Boca Raton, FL: CRC Press LLC.

Mackay, D., W.Y. Shiu, and K.C. Ma. 1992. Illustrated handbook of physical-chemical properties and 

environmental fate for organic chemicals: polynuclear aromatic hydrocarbons, polychlorinated

dioxins, and dibenzofurans. Chelsea, MI: Lewis Publishers.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-54 6-29-2012

TRIM.FaTE Inputs

Mackay, D., S. Patterson, and W.H. Schroeder. 1986. Model describing the rates of transfer processes

of organic chemicals between atmosphere and water. Environmental Science and Technology

20(8): 810-816.

Mason, R.P., J.R. Reinfelder, and F.M.M. Morel. 1996. Uptake, toxicity, and trophic transfer of mercury 

in a coastal diatom. Environmental Science and Technology 30(6):1835-1845.

Mason, R.P., F.M.M. Morel, and H.F. Hemond. 1995a. The role of microorganisms in elemental mercury 

formation in natural waters. Water, Air, and Soil Pollution 80:775-787.

Mason, R.P., J.R. Reinfelder, and F.M.M. Morel. 1995b. Bioaccumulation of mercury and

methylmercury. Water Air and Soil Pollution 80(1-4):915-921.

McCrady, J.K., and S.P. Maggard. 1993. Update and photodegradation of 2,3,7,8-tetrachloro-p-dioxin 

sorbed to grass foliage. Environmental Science and Technology 27: 343-350.

McElroy, A. E. 1990. Polycyclic aromatic hydrocarbon metabolism in the polychaete Nereis virens. 

Aquatic Toxicology 18(1):35-50.

McGeer, J.C., K.V. Brix, J.M. Skeaff, D.K. DeForest, S.I. Brigham, W.J. Adams, and A. Green. 2003. 

Inverse relationship between bioconcentration factor and exposure concentration for metals:

implications for hazard assessment of metals in the aquatic environment. Environmental Toxicology

and Chemistry (22)5:1017–1037.

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 the U.S. Environmental Protection Agency (Sustainable Technology Division, National

Risk Management Research Laboratory) and Environmental Defense Fund. July. LBNL-43722.

Millard, E.S., D.D. Myles, O.E. Johannsson, and K.M. Ralph. 1996. Phytoplankton photosynthesis at 

two index stations in Lake Ontario 1987-1992: assessment of the long-term response to

phosphorus control. Canadian Journal of Fisheries and Aquatic Sciences 53: 1092-1111.

Moermond, C. T. A., T.P. Traas, I. Roessink, K. Veltmam, A.J. Hendriks, and A.A. Koelmans. 2007. 

Modeling decreased food chain accumulation of PAHs due to strong sorption to carbonaceous

materials and metabolic transformation. Environmental Science and Technology 41(17):6185-6191. 

doi: 10.1021/es0702364.

Montgomery, J. 2000. Groundwater chemicals desk reference. Boca Raton, FL: CRC Press LLC, p.

1701. Morrison, H.A., D.M. Whittle, C.D. Metcalfe, and A.J. Niimi. 1999. Application of a food web 

bioaccumulation model for the prediction of polychlorinated biphenyl, dioxin, and furan congener 

concentrations in Lake Ontario aquatic biota. Canadian Journal of Fisheries and Aquatic Sciences

56(8):1389-1400.

Muhlbaier, J., and G.T. Tissue. 1980. Cadmium ion the southern basin of Lake Michigan. Water, Air 

and Soil Pollution (15):45-49.

Muir, D.C., W.K. Marshall, and G.R. Webster. 1985. Bioconcentration of PCDDs by fish: Effects of 

molecular structure and water chemistry. Chemosphere 14(6/7):829-833.

NLM (National Library of Medicine). 2002. Hazardous Substance Data Bank (HSDB). Available at: 

http://toxnet.nlm.nih.gov/cgi-bin/sis/htmlgen?HSDB.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-55 6-29-2012

TRIM.FaTE Inputs

Niimi, A., and B. Oliver. 1986. Biological half-lives of chlorinated dibenzo-p-dioxins and dibenzofurans in 

rainbow trout (Salmo gairdeneri). Environmental Toxicology 5:49-53.

Niimi, A.J,. and V. Palazzo. 1986. Biological half-lives of eight polycyclic aromatic hydrocarbons (PAHs)

in rainbow trout (Salmo gairdneri). Water Research 20(4):503-507.

Nriagu, J.O. 1980. Cadmium in the environment. Part I: Ecological cycling. New York: John Wiley &

Sons. Chapter 15: Uptake and effects of cadmium in higher plants. pp. 608-609.

Opperhuizen, A., W.J. Wagenaar, F.W.M. van der Wielen, M. van den Berg, K. Olie, and F.A.P.C. 

Gobas.1986. Uptake and elimination of PCDD/PCDF congeners by fish after aqueous exposure to a

fly-ash extract from a municipal incinerator. Chemosphere 15(9–12):2049-2053. doi: 10.1016/0045-

6535(86)90511-4.

Passivirta, J., Sinkkonen, S., Mikkelson, P., Rantio, T., Wania, F. (1999) Estimation of vapor pressures, 

solubilities and Henry’s law constants of selected persistent organic pollutants as functions of 

temperature. Chemosphere 39, 811–832.

Paterson, S., D. Mackay, and A. Gladman. 1991. A fugacity model of chemical uptake by plants from

soil and air. Chemosphere 23:539-565. 

Petersen, G., A. Iverfeldt, and J. Munthe. 1995. Atmospheric mercury species over Central and 

Northern Europe. Model calculations and comparison with observations from the Nordic Air and 

Precipitation Network for 1987 and 1988. Atmospheric Environment 29:47-68.

Porvari, P., and M. Verta. 1995. Methylmercury production in flooded soils: A laboratory study. Water, 

Air, and Soil Pollution 80:765-773.

Riederer, M. 1995. Partitioning and transport of organic chemicals between the atmospheric 

environment and leaves. In: Trapp, S. and J. C. McFarlane, eds. Plant contamination: Modeling and 

simulation of organic chemical processes. Boca Raton, FL: Lewis Publishers. pp. 153-190.

Rubinstein, N.I., R.J. Pruell, B.K. Taplin, J.A. LiVolsi, and C.B. Norwood. 1990. Bioavailability of 2,3,7,8-

TCDD, 2,3,7,8-TCDF and PCBs to marine benthos from Passaic river sediments. Chemosphere

20:1097-1102.

Sangster, J. (1993) LOGKOW, A Databank of Evaluated Octanol-Water Partition Coefficients. 1st 

Edition, Montreal, Quebec, Canada.

Saouter, E., F. Ribeyre, A. Boudou, and R. Maurybrachet. 1991. Hexagenia rigida (Ephemeroptera) as

a biological model in aquatic ecotoxicology - Experimental studies on mercury transfers from

sediment. Environmental Pollution 69:51-67.

Sijm, D.T.H.M., H. Wever, P.J. de Vries, and A. Opperhuizen. 1989. Octan-1-ol/water partition 

coefficients of polychlorinated dibenzo-p-dioxins and dibenzofurans: experimental values

determined with a stirring method. Chemosphere 19(1-6):263-266.

Simonich, S.L., and R.A. Hites. 1994. Importance of vegetation in removing polycyclic aromatic 

hydrocarbons from the atmosphere. Nature 370:49-51.

Spero, J., B. DeVito, and L. Theodore. [eds.]. 2000. Regulatory chemicals handbook. New York, NY:

CRC Press, p. 1072.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-56 6-29-2012

TRIM.FaTE Inputs

Stehly, G.R., P.F. Landrum, M.G. Henry, and C. Klemm. 1990. Toxicokinetics of PAHs in Hexagenia

Environmental Toxicology and Chemistry 9:167-174.

Thibodeaux, L.J. 1996. Environmental chemodynamics: Movement of chemicals in air, water, and soil. 

New York, NY: John Wiley and Sons, Inc.

Thomann, R.V. 1989. Bioaccumulation model of organic-chemical distribution in aquatic food-chains. 

Environmental Science and Technology 23(6):699-707.

Trapp, S. 1995. Model for uptake of xenobiotics into plants. In: Trapp, S. and J. C. McFarlane, eds.

Plant contamination: Modeling and simulation of organic chemical processes. Boca Raton, FL:

Lewis Publishers. pp. 107-151.

Trudel, M., and J.B. Rasmussen. 1997. Modeling the elimination of mercury by fish. Environmental 

Science and Technology 31:1716-1722.

Tsiros, I.X.; R.B. Ambrose, and A. Chronopoulou-Sereli. 1999. Air-vegetation partitioning of toxic 

chemicals in environmental simulation modeling. Global NEST: The International Journal 1(3):177-

184.

USEPA (U.S. Environmental Protection Agency). 2007. Draft risk assessment for the Siemens Water 

Technologies Corp. carbon reactivation facility Parker, Arizona. Appendix F. Chemical-physical 

parameters for compounds not in USEPA's HHRAP. Available at:

http://www.epa.gov/region9/waste/siemens/pdf/RiskAssessment/siemens-riskassessAppxF.pdf.

USEPA. 2005. Human Health Risk Assessment Protocol for Hazardous Waste Combustion Facilities. 

Companion Database. Available at: http://www.epa.gov/region6/6pd/rcra_c/protocol/protocol.htm.

USEPA 2003. Toxicological Review of 2-Methylnaphthalene. Available online at: 

http://www.epa.gov/iris/toxreviews/1006tr.pdf. 

USEPA. 2000a. Estimation Program Interface (EPI) suite. Office of Pollution Prevention and Toxics

(OPPT). Available at: http://www.epa.gov/oppt/exposure/docs/episuitedl.htm.

USEPA. 2000b. Draft exposure and human health reassessment of 2,3,7,8-tetrachlorodibenzo-p-dioxin 

TCDD) and related compounds, Volume 3: Properties, environmental levels, and background 

exposures, Chapter 2: Physical and chemical properties and fate and Appendix A. EPA/600/P00/001Bc. Available at: http://www.epa.gov/ncea/pdfs/dioxin/part1/volume3/chap-2.pdf.

USEPA. 1999. Screening level ecological risk assessment protocol for hazardous waste combustion 

facilities. Peer Review Draft, November. Available at:

http://www.epa.gov/epaoswer/hazwaste/combust/ecorisk.htm

USEPA. 1998. Chemical fate half-lives for Toxics Release Inventory (TRI) chemicals. July. SRC TR 98-

008.

USEPA. 1997a. Supplemental background document; Nongroundwater pathway risk assessment;

Petroleum process waste listing determination. Appendix B. Compound specific input values. 

Available at: http://www.epa.gov/osw/hazard/wastetypes/wasteid/petroref/appendb.pdf.

USEPA. 1997b. U.S. Environmental Protection Agency. Mercury study report to congress. Volume III:

Fate and transport of mercury in the environment. Office of Air Quality Planning and Standards and 

Office of Research and Development.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1-57 6-29-2012

TRIM.FaTE Inputs

Van den Berg, M., J. De Jongh, H. Poiger, and J.R. Olson. 1994. The toxicokinetics and metabolism of 

polychlorinated dibenzo-p-dioxins (PCDDs) and dibenzofurans (PCDFs) and their relevance for 

toxicity. CRC Critical Reviews in Toxicology 24(1):1-74.

Vandal, G.M., W.F. Fitzgerald, K.R. Rolfhus, and C.H.Lamborg. 1995. Modeling the elemental mercury 

cycle in Pallette Lake, Wisconsin, USA. Water, Air, and Soil Pollution 80:789-798.

Vulykh, N., and V. Shatalov. 2001. Investigation of Dioxin/Furan Composition in Emissions and in 

Environmental Media: Selection of Congeners for Modeling. Technical Note 6/2001. Meteorological 

Synthesizing Center - East.

Wang, X., and W.X. Wang. 2006. Bioaccumulation and transfer of benzo (a) pyrene in a simplified

marine food chain. Marine Ecology Progress Series 312:101-111.

Williams, J.J., J. Dutton, C.Y. Chen, and N.S. Fisher. 2010. Metal (As, Cd, Hg, and CH3Hg) 

bioaccumulation from water and food by the benthic amphipod Leptocherius plumulosus. 

Environmental Toxicology and Chemistry 29(8):1755-1761.

Wilmer, C., and M. Fricker. 1996. Stomata. Second ed. New York, NY: Chapman and Hall. p. 121.

WI DNR (Wisconsin Department of Natural Resources) 2007. A Guide to Understanding the Hydrologic 

Condition of Wisconsin’s Lake Superior Watersheds. Wisconsin Department of Natural Resources

(DNR).

Xiao, Z.F., D. Stromberg, and O. Lindqvist. 1995. Influence of humic substances on photolysis of 

divalent mercury in aqueous solution. Water, Air, and Soil Pollution 80:789-798.

Yan, X; and Wang, WX. 2002. Exposure and potential food chain transfer factor of Cd, Se and Zn in

marine fish Lutjanus argentimaculatus. Marine Ecology Progress Series 238: 173-186.

Zhang, Q., L. Yang, and W.X. Wang. 2011. Bioaccumulation and trophic transfer of dioxins in marine 

copepods and fish. Environmental Pollution 159(12):3390-3397.
[This page intentionally left blank]
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-1 6-29-2012

Description of MIRC

Addendum 2. Description of Multimedia Ingestion Risk Calculator 

(MIRC) Used for RTR Exposure and Risk Estimates
[This page intentionally left blank.]
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-3 6-29-2012

Description of MIRC

CONTENTS, ADDENDUM 2

1. Introduction................................................................................................................... 2-11

1.1. Purpose and Overview .........................................................................................2-11

1.2. Scope of MIRC .....................................................................................................2-11

1.3. Use in EPA’s Air Toxics Program..........................................................................2-12

1.4. MIRC Highlights....................................................................................................2-12

1.5. Organization of This Addendum............................................................................2-13

2. MIRC Overview............................................................................................................. 2-13

2.1. Software ...............................................................................................................2-14

2.2. Exposure Pathways ..............................................................................................2-16

2.3. Receptor Groups ..................................................................................................2-17

3. Exposure Algorithms..................................................................................................... 2-19

3.1. Farm Food Chain Algorithms ................................................................................2-19

3.1.1. Estimating Chemical Concentrations in Produce ................................... 2-20

3.1.2. Estimating Chemical Concentrations in Animal Products....................... 2-26

3.2. Chemical Intake Calculations for Adults and Non-Infant Children .........................2-29

3.2.1. Chemical Intake from Soil Ingestion....................................................... 2-31

3.2.2. Chemical Intake from Fish Ingestion...................................................... 2-31

3.2.3. Chemical Intake from Fruit Ingestion ..................................................... 2-32

3.2.4. Chemical Intake from Vegetable Ingestion............................................. 2-33

3.2.5. Chemical Intake from Animal Product Ingestion .................................... 2-34

3.2.6. Chemical Intake from Drinking Water Ingestion ..................................... 2-37

3.3. Total Chemical Intake...........................................................................................2-37

3.4. Chemical Intake Calculations for Nursing Infants..................................................2-38

3.4.1. Infant Average Daily Absorbed Dose..................................................... 2-38

3.4.2. Chemical Concentration in Breast Milk Fat ............................................ 2-39

3.4.3. Chemical Concentration in Aqueous Phase of Breast Milk .................... 2-43

3.4.4. Alternative Model for Infant Intake of Methyl Mercury ............................ 2-45

4. Dose-Response Values Used for Assessment.............................................................. 2-46

4.1. Cadmium ..............................................................................................................2-49

4.2. Dioxins (2,3,7,8-TCDD) ........................................................................................2-49

4.3. Mercury ................................................................................................................2-50

4.4. Polycyclic Organic Matter .....................................................................................2-50

5. Risk Estimation ............................................................................................................. 2-52

5.1. Cancer Risks ........................................................................................................2-52

5.2. Non-cancer Hazard Quotients ..............................................................................2-54

5.2.1. Hazard Quotients for Chemicals with a Chronic RfD.............................. 2-55
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-4 6-29-2012

Description of MIRC

5.2.2. Hazard Quotients for Chemicals with RfD Based on 

Developmental Effects .......................................................................... 2-55

5.2.3. Hazard Index for Chemicals with RfDs .................................................. 2-55

6. Model Input Options...................................................................................................... 2-56

6.1. Environmental Concentrations..............................................................................2-57

6.2. Farm-Food-Chain Parameter Values ....................................................................2-57

6.2.1. List of Farm-Food-Chain (FFC) Parameters .......................................... 2-57

6.2.2. Produce Parameter Values.................................................................... 2-59

6.2.3. Animal Product Parameter Values......................................................... 2-72

6.3. Adult and Non-Infant Exposure Parameter Values................................................2-74

6.3.1. Body Weights ........................................................................................ 2-75

6.3.2. Water Ingestion Rates ........................................................................... 2-76

6.3.3. Local Food Ingestion Rates ................................................................... 2-77

6.3.4. Local Fish Ingestion Rates .................................................................... 2-81

6.3.5. Soil Ingestion Rates............................................................................... 2-86

6.3.6. Total Food Ingestion Rates.................................................................... 2-87

6.4. Other Exposure Factor Values..............................................................................2-87

6.4.1. Exposure Frequency ............................................................................. 2-88

6.4.2. Fraction Contaminated .......................................................................... 2-88

6.4.3. Preparation and Cooking Losses........................................................... 2-88

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 ........................................................................................2-98

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.........................2-103

7.4.1. Dioxins2-103

7.4.2. Methyl Mercury.................................................................................... 2-103

8. References ................................................................................................................. 2-104

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
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-5 6-29-2012

Description of MIRC

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

6. Introduction..................................................................................................................... 2-4

6.1. Angler Behavior......................................................................................................2-4

6.2. Fish Biology............................................................................................................2-5

6.2.1. Lake Productivity ..................................................................................... 2-5

6.2.2. Proportion of Fish Biomass by Trophic Level........................................... 2-7

6.2.3. Minimum Viable Population Size ............................................................. 2-8

6.3. Summary of Assumptions for the Lake Size Analysis .............................................2-9

6.4. Equations Used to Determine Lake Fish Populations .............................................2-9

6.5. References ...........................................................................................................2-11
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-6 6-29-2012

Description of MIRC

Exhibits, Addendum 2

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 2 2-7 6-29-2012

Description of MIRC

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

Exhibit_Add A2-1. Overview of MIRC Software Application for Performing FarmFood-Chain Ingestion Exposure and Risk Calculations................................... 2-15

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

Media.............................................................................................................. 2-17

Exhibit_Add A2-3. Chemical Transfer Pathways for Produce............................................... 2-20

Exhibit_Add A2-4. Estimating Chemical Concentration in Aboveground Produce ................. 2-20

Exhibit_Add A2-5. Chemical Transfer Pathways for Animal Products ................................... 2-26

Exhibit_Add A2-6. Oral Dose-response Values Used to Calculate RTR Screening 

Threshold Emission Rates for PB-HAP Chemicalsa........................................ 2-47

Exhibit_Add A2-7. WHO 2005 Toxic Equivalency Factors (TEFs) for Dioxin 

Congeners ...................................................................................................... 2-50

Exhibit_Add A2-8. Oral Dose-response Values for Polycyclic Organic Matter 

(POM) Groupsa............................................................................................... 2-51

Exhibit_Add A2-10. Chemical-Specific Inputs for Produce Parameters for 

Chemicals Included in MIRC........................................................................... 2-59

Exhibit_Add A2-11. Chemical-Specific Inputs by Plant Type for Chemicals in 

MIRC .............................................................................................................. 2-61

Exhibit_Add A2-12. Non-Chemical-Specific Produce Inputs................................................. 2-71

Exhibit_Add A2-13. Animal Product Chemical-specific Inputs for Chemicals 

Included in MIRC ............................................................................................ 2-72

Exhibit_Add A2-14. Soil and Plant Ingestion Rates for Animals ........................................... 2-74

Exhibit_Add A2-15. Mean and Percentile Body Weight Estimates for Adults and 

Children .......................................................................................................... 2-76

Exhibit_Add A2-16. Estimated Daily Per Capita Mean and Percentile Water 

Ingestion Rates for Children and Adultsa......................................................... 2-77

Exhibit_Add A2-17. Summary of Age-Group Specific Food Ingestion Rates for 

Farm Food Items ............................................................................................ 2-78

Exhibit_Add A2-18. Daily Mean and Percentile Consumer-Only Fish Ingestion 

Rates for Children and Adults (IRCO,y)

a

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

Attachment A, Addendum 2 2-8 6-29-2012

Description of MIRC

Exhibit_Add A2-19. Fraction of Population Consuming Freshwater/Estuarine 

Fish on a Single Day (FPC,y) ............................................................................ 2-84

Exhibit_Add A2-20. Calculated Long-term Mean and Percentile per capita Fish 

Ingestion Rates for Children and Adults (IRPC,y) .............................................. 2-85

Exhibit_Add A2-21. Calculated Mean and 90th Percentile Per capita Fish 

Ingestion Rates for Populations of Recreational Fishers (IRPC,y) .................... 2-85

Exhibit_Add A2-22. Daily Mean and Percentile Soil Ingestion Rates for Children 

and Adults....................................................................................................... 2-86

Exhibit_Add A2-23. Daily Mean and Percentile Per Capita Total Food Intake for 

Children and Adults......................................................................................... 2-87

Exhibit_Add A2-24. Fraction Weight Losses from Preparation of Various Foods.................. 2-89

Exhibit_Add A2-25. Scenario- and Receptor-Specific Input Parameter Values 

Used to Estimate Infant Exposures via Breast Milk ......................................... 2-91

Exhibit_Add A2-26. Average Body Weight for Infants........................................................... 2-91

Exhibit_Add A2-27. Time-weighted Average Body Weight for Mothers ................................ 2-92

Exhibit_Add A2-28. Infant Breast Milk Intake Rates ............................................................. 2-94

Exhibit_Add A2-29. Chemical-specific Input Parameter Values for Breast Milk 

Exposure Pathway.......................................................................................... 2-95

Exhibit_Add A2-30. Farm Food Category Ingestion Rates for Health Protective 

Screening Scenario for Farming Households.................................................. 2-99

Exhibit_Add A2-31. Mean Body Weight Estimates for Adults and Childrena....................... 2-100

Exhibit_Add A2-32. Chemical-Specific Parameter Values for Input to MIRCa..................... 2-101

Exhibit_Add A2-33. Chemical and Animal-Type Specific Biotransfer Factor (Ba) 

Values for Input to MIRC............................................................................... 2-102

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 2 2-9 6-29-2012

Description of MIRC

Equations, Addendum 2

Equation 2-1. Chemical Concentration in Aboveground Produce .......................................... 2-21

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

Uptake ............................................................................................................ 2-21

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

Deposition of Particle-phase Chemical............................................................ 2-22

Equation 2-4. Chemical Concentration in Aboveground Produce Due to............................... 2-22

Equation 2-5. Conversion of Aboveground Produce Chemical Concentration from 

Dry- to Wet-Weight Basis................................................................................ 2-23

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

Organic Chemicals.......................................................................................... 2-24

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

Chemicals....................................................................................................... 2-25

Equation 2-8. Conversion of Belowground Produce Chemical Concentration from 

Dry- to Wet-Weight Basis................................................................................ 2-25

Equation 2-9. Chemical Concentration in Beef, Pork, or Total Dairy...................................... 2-26

Equation 2-10. Chemical Concentration in Poultry or Eggs ................................................... 2-27

Equation 2-11. Incidental Ingestion of Chemical in Soil by Livestock..................................... 2-27

Equation 2-12. Ingestion of Chemical in Feed by Livestock................................................... 2-28

Equation 2-13. Chemical Concentration in Livestock Feed (All Aboveground) ...................... 2-28

Equation 2-14. Chemical Concentration in Livestock Feed Due to Root Uptake................... 2-29

Equation 2-15. Average Daily Dose for Specified Age Group and Food Type ...................... 2-29

Equation 2-16. Chemical Intake from Soil Ingestion ............................................................. 2-31

Equation 2-17. Chemical Intake from Fish Ingestion ............................................................ 2-31

Equation 2-18. Consumption-weighted Chemical Concentration in Fish .............................. 2-32

Equation 2-19. Chemical Intake from Consumption of Exposed Fruits ................................. 2-33

Equation 2-20. Chemical Intake from Consumption of Protected Fruits................................ 2-33

Equation 2-21. Chemical Intake from Exposed Vegetables.................................................. 2-34

Equation 2-22. Chemical Intake from Protected Vegetables................................................. 2-34

Equation 2-23. Chemical Intake from Root Vegetables ........................................................ 2-34

Equation 2-24. Chemical Intake from Ingestion of Beef........................................................ 2-35

Equation 2-25. Chemical Intake from Dairy Ingestion........................................................... 2-35

Equation 2-26. Chemical Intake from Pork Ingestion............................................................ 2-35

Equation 2-27. Chemical Intake from Poultry Ingestion ........................................................ 2-36

Equation 2-28. Chemical Intake from Egg Ingestion............................................................. 2-36

Equation 2-29. Chemical Intake from Drinking Water Ingestion............................................ 2-37
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-10 6-29-2012

Description of MIRC

Equations 2-30 to 2-35. Total Average Daily Dose of a Chemical for Different 

Age Groups .................................................................................................... 2-37

Equation 2-31........................................................................................................................ 2-37

Equation 2-32........................................................................................................................ 2-37

Equation 2-33........................................................................................................................ 2-37

Equation 2-34........................................................................................................................ 2-37

Equation 2-35........................................................................................................................ 2-37

Equation 2-36. Lifetime Average Daily Dose (LADD) ........................................................... 2-38

Equation 2-37. Average Daily Dose of Chemical to the Nursing Infant ................................. 2-39

Equation 2-38. Chemical Concentration in Breast Milk Fat................................................... 2-40

Equation 2-39. Daily Maternal Absorbed Intake ................................................................... 2-41

Equation 2-40. Biological Elimination Rate Constant for Chemicals for Nonlactating Women............................................................................................. 2-42

Equation 2-41. Biological Elimination Constant for Lipophilic Chemicals for 

Lactating Women............................................................................................ 2-42

Equation 2-42. Chemical Concentration in Aqueous Phase of Breast Milk ........................... 2-43

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

Compartment.................................................................................................. 2-44

Equation 2-44. Biological Elimination Rate Constant for Hydrophilic Chemicals................... 2-45

Equation 2-45. Calculation of Infant Average Daily Absorbed Dose of Methyl 

Mercury........................................................................................................... 2-46

Equation 2-46. Calculation of Excess Lifetime Cancer Risk ................................................. 2-53

Equations 2-47 to 2-53. Lifetime Cancer Risk: Chemicals with a Mutagenic MOA 

for Cancer....................................................................................................... 2-54

Equation 2-48........................................................................................................................ 2-54

Equation 2-49........................................................................................................................ 2-54

Equation 2-50........................................................................................................................ 2-54

Equation 2-51........................................................................................................................ 2-54

Equation 2-52........................................................................................................................ 2-54

Equation 2-53. ELCR ................................................................................. 2-54

Equation 2-54. Hazard Quotient for Chemicals with a Chronic RfD...................................... 2-55

Equation 2-55. Hazard Index Calculation ............................................................................. 2-56

Equation 2-56. Calculation of Age-Group-Specific and Food-Specific Ingestion 

Rates .............................................................................................................. 2-80

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

Rates .............................................................................................................. 2-83





n

i

Risk i 1

( )
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-11 6-29-2012

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 Access-based and Excel-based formats; however, 

MIRC currently is not publicly available. 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-12 6-29-2012

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 2-13 6-29-2012

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 2-14 6-29-2012

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 2-15 6-29-2012

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 2-16 6-29-2012

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 2-17 6-29-2012

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

aChemical 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 2-18 6-29-2012

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 2011) 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 2-19 6-29-2012

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 2-20 6-29-2012

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 2-21 6-29-2012

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 DW(i) P AG produce DW(i) Pd(i) Pv(i) C    r    

P AG produce DW(i) Csroot-zone_produc e BrAG produce DW(i)r      
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-22 6-29-2012

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:

( ) ( )

( ( )* ( ))

( )

( )

1,000 ( ( )) (1 )

i i

k p i Tp i

i

i

Yp kp

Drdp Fw Drwp Rp e

Pd



     





a

AG i AG i

i

Ca Fv Bv VG

Pv



( ) ( )

( )

  


TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-23 6-29-2012

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-m

3

. For MIRC, the user inputs the average annual total air 

concentration of the chemical, Ca, for a specific location relative to the source in units of g/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 WW i AG produce DW i

MAF

C C
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-24 6-29-2012

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 

Kds UCF

Csroot zone produce RCF VGrootveg



 



 _ CBG-produce-WW
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-25 6-29-2012

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

root zone_ 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 2-26 6-29-2012

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

aChemical 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

where:

















   

 

n

i

Cmammal m Ba m MF SoilCh Intake m PlantCh Intake i m

1

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

Attachment A, Addendum 2 2-27 6-29-2012

Description of MIRC

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

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)   Cpoultry(m)  Ba(m)  SoilChIntake(m)  PlantChIntake(i,m)

SoilChIntake(m) QsCsslivestockBs
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-28 6-29-2012

Description of MIRC

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

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 

PlantChIntake(i,m)  F(i,m) Qp(i,m) Cfeed(i)

Cfeed(i)  Prfeed(i) Pd(i) Pv(i)
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-29 6-29-2012

Description of MIRC

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

where: 

ADD(y,i) =

Average daily dose for age group y from food type or ingestion medium i (mg 

chemical/kg body weight-day)

P feed(i) Csroot zone_ feed(i) Brfeed(i)r   



































  



365 days

(y)

(y) (y)

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

(y,i)

EF

BW AT

C IR FC ED

ADD
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-30 6-29-2012

Description of MIRC

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 2011, 

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,

– Beef products,

– Pork products, and
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-31 6-29-2012

Description of MIRC

– 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 (TL3) fish, 

equivalent to small “pan” fish such as bluegill, and trophic level 4 (TL4) fish, equivalent to game 

fish such as trout and walleye. The chemical concentration in fish in Equation 2-17 is estimated 

as the consumption-weighted chemical concentration using Equation 2-18.

Equation 2-17. Chemical Intake from Fish Ingestion









































  



365 days

EF

BW

μg

mg C IR FC 0.001 

ADD

(y)

Soil Soil(y) Soil

Soil(y)    

365 days

g

kg 0 001

1 1 1 2 

































  

    

EF

BW

C IR . FC

ADD L L

(y)

Fish Fish(y) Fish

Fish(y) Fish Fish
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-32 6-29-2012

Description of MIRC

Equation 2-18. Consumption-weighted Chemical Concentration in Fish

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 (TL3) fish on a wet-weight 

(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 from CSFII data (see Section 6.3.4 of 

this addendum) and, therefore, 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 2011) and 

included those losses in its HHRAP algorithms for chemical intake from fish (L1Fish and L2Fish in 

Equation 2-17). 

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).

    CFish  CFishT3

FT3  CFishT 4FT 4
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-33 6-29-2012

Description of MIRC

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 

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). 

    































       

g 365 days

kg 1 1 1 2 0 001 EF ADDExpFruit(y) L ExpFruit L ExpFruit CExpFruit IRExpFruit(y)

. FCExpFruit  

































     

g 365 days

kg 1 1 0 001 EF ADDProFruit(y) L ProFruit CProFruit IRProFruit(y)

. FCProFruit
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-34 6-29-2012

Description of MIRC

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)

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.

  































     

g 365 days

kg 1 1 0 001 EF ADDExpVeg(y) L ExpVeg CExpVeg IRExpVeg(y) . FCExpVeg   































     

g 365 days

kg 1 1 0 001 EF ADDProVeg(y) L ProVeg CProVeg IRProVeg(y) . FCProVeg     































       

g 365 days

kg 1 1 1 2 0 001 EF ADDRootVeg(y) L RootVeg L RootVeg CRootVeg IRRootVeg(y) . FCRootVeg
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-35 6-29-2012

Description of MIRC

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

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

   

































       

g 365 days

kg 1 1 1 2 0 001 EF ADDBeef(y) L Beef L Beef CBeef IRBeef(y) . FCBeef 































   

g 365 days

kg 0 001 EF ADDDairy(y) CDairy IRDairy(y) . FCDairy    

































       

g 365 days

kg 1 1 1 2 0 001 EF ADDPork(y) L Pork L Pork CPork IRPork(y) . FCPork
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-36 6-29-2012

Description of MIRC

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)

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)

    































       

g 365 days

kg 1 1 1 2 0 001 EF ADDPoultry(y) L Poultry L Poultry CPoultry IRPoultry(y) . FCPoultry 































   

g 365 days

kg 0 001 EF ADDEgg(y) CEgg IREgg(y) . FCEgg
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-37 6-29-2012

Description of MIRC

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.

Equation 2-34.

Equation 2-35.

































  



365 days

EF

BW

C IR FC

ADD

(y)

DW DW(y) DW

DW(y) ADD(1)  ADDbreastmilk 

  

n

i

ADD ADD i 1

(1 2) (1 2, )



  

n

i

ADD ADD i 1

(3 5) (3 5, )



  

n

i

ADD ADD i 1

(6 11) (6 11, )



  

n

i

ADD ADD i 1

(12 19) (12 19, )





n

i

ADD adult ADD adult i 1

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

Attachment A, Addendum 2 2-38 6-29-2012

Description of MIRC

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 

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 













 











 











 











 











 















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 2-39 6-29-2012

Description of MIRC

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) (U.S. EPA 1998), EPA reviewed three 

first-order kinetics models for estimating chemical concentration in breast milk fat. The model 

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     

BW AT

C f C f IR AE ED

DAI

inf

milkfat mbm aqueous mbm milk inf

inf



      



1
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-40 6-29-2012

Description of MIRC

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 

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 

 



































 



 







 elim p n  fat elac b f k t

fat elac

k t elim

fat elac fat elac bf

elim

elim fm

mat f

milkfat e

k

k

e

k k t

k

k f

DAI f

C

_ 1 1

1

_ _ _
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-41 6-29-2012

Description of MIRC

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 

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 

DAImat  ADD(adult)  AEmat
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-42 6-29-2012

Description of MIRC

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

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)

h

kelim

ln 2



fm mat

milk f mbm

fat elac elim f BW

IR f f

k k



 

_  
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-43 6-29-2012

Description of MIRC

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

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)

aq elac pm

mat pl bm

aqueous k f

DAI f Pc

C



 



_
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-44 6-29-2012

Description of MIRC

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

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

  bp RBC bp

bl bp

pl f Pc f

f f

f

 





1
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-45 6-29-2012

Description of MIRC

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 A2-28). 

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 

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 

kaq _ elac  kelim
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-46 6-29-2012

Description of MIRC

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 2-47 6-29-2012

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 foodb7440439 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 1.0E-09 ATSDR

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 2-48 6-29-2012

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 4.0E-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 2-49 6-29-2012

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

aValues as of June 2012; these values may be updated as newer ones become available.

b

There are two chronic RfDs for cadmium—one for food and one for water.

cDose-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 2-50 6-29-2012

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-dioxinb19408743 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

aSource: 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 2-51 6-29-2012

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 2-52 6-29-2012

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 2-53 6-29-2012

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

6 5

2

10 1 yr 3 1 yr

10

3 5

1 2





  



 

( - )

( - )

( 1)

ADAF

ADAF .

ADAF

   

1

2

8

3 4 yrs 1 4 yrs

3

12 19

6 11





  





(adult)

( - )

( - )

ADAF

ADAF

ADAF
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-54 6-29-2012

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 i

th age group 

ELCR = Total extra lifetime cancer risk (incremental or extra risk)

n = Number of age groups (i.e., 6)

5.2. Non-cancer Hazard Quotients

Non-cancer risks are presented as hazard quotients (HQs), that is, the ratio of the estimated 

daily intake (i.e., ADD) to the reference dose (e.g., chronic RfD). If the HQ for a chemical is 

equal to or less than 1, EPA believes that there is no appreciable risk that non-cancer health 

effects will occur. If the HQ is greater than 1 then there is at least some possibility for an 

adverse health effect. The larger the HQ value, the more likely it is that an adverse health effect 

may occur.





n

i

Risk i 1

( )
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-55 6-29-2012

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 2000a). 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 2-56 6-29-2012

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 2000a; 

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

2000a). 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 2000a). 

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 2-57 6-29-2012

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 Excel 

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 2-58 6-29-2012

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 2-59 6-29-2012

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 2-60 6-29-2012

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 2-61 6-29-2012

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.

cAs 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 U.S. 

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.

dAs 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 2-62 6-29-2012

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 2-63 6-29-2012

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 2-64 6-29-2012

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 2-65 6-29-2012

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 2-66 6-29-2012

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 2-67 6-29-2012

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 2-68 6-29-2012

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 2-69 6-29-2012

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 2-70 6-29-2012

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

aAs 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.

bAs 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).

cAs 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 2-71 6-29-2012

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. 

aBaes 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.

cHHRAP (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.

dYp 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.

eMAF 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 2-72 6-29-2012

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 2-73 6-29-2012

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.

aAs 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%.

bAs 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 2-74 6-29-2012

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 cattleb0.5

Silage 2.5

Forage 8.8

Grain 0.47

Dairy cattlec0.4

Silage 4.1

Forage 13.2

Grain 3.0

Swined0.37

Silage 1.4

Grain 3.3

Chicken (eggs)e0.022 Grain 0.2

Source: EPA 2005a HHRAP (Chapter 5).

aBeef 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.

dSwine 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. 

eChickens 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 2011) 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 2-75 6-29-2012

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 2011). 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

percentiles 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. For 

adults, BW represents the weighted average of male and female mean body weights for all 

races, ages 18-74 years, from EPA’s 1997 EFH (EPA 1997a; Tables 7-4 and 7-5). We 

considered updating the body weight for use in the RTR assessment to the revised body 

weights in the 2011 Exposure Factors Handbook (EPA 2011). However, the 2011 EFH warns 

that assessors should consider if the dose estimate (for which the body weight is used to 

calculate) will be used to estimate risk by combining it with a dose-response that was derived 

assuming a body weight of 70 kg, and that if such an inconsistency exists, an adjustment may 

be necessary. Because a 70-kg body weight commonly is used to develop IRIS dose-response 

values, using an 80 kg body weight could result in this inconsistency. Given these issues, we 

have retained the use of an adult body weight of 71.4 kg.

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.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-76 6-29-2012

Description of MIRC

Exhibit_Add A2-15. Mean and Percentile Body Weight Estimates

for Adults and Children

Lifestage

(years)

Duration 

(years)

Body Weight (kg)

Mean 5

th 10th 50th 90th 95th

Adulta (20-70) 50 71.4 52.9 56 69.3 89.7 97.6

Child < 1b1 7.83 6.03 6.38 7.76 9.24 9.66

Child 1-2

c

2 12.6 9.9 10.4 12.5 14.9 15.6

Child 3-5

d

3 18.6 13.5 14.4 17.8 23.6 26.2

Child 6-11e6 36.0 22.1 24.0 33.5 51.2 58.6

Child 12-19f8 64.2 39.5 45 64.2 83.5 89

[Child 12-19g8 64.3 41.1 44.6 60.9 88.5 98.4]

aBW represents the sample-size weighted average of male and female mean body weights (all races, 18-74 years) from EPA’s

1997 EFH (Tables 7-4 for males and 7-5 for females). Note that these weights include the weight of clothing, estimated to range 

from 0.09 to 0.28 kg. 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%). We considered updating the body weight for use in the RTR assessment to the revised body weights in the 2011 

Exposure Factors Handbook. However, the 2011 EFH warns that assessors should consider if the dose estimate (for which the 

body weight is used to calculate) will be used to estimate risk by combining it with a dose-response that was derived assuming a 

body weight of 70 kg, and that if such an inconsistency exists, an adjustment may be necessary. Because a 70 kg body weight 

commonly is used to develop IRIS dose-response values, using an 80 kg body weight could result in this inconsistency. Given 

these issues, we have retained the use of an adult body weight of 71.4 kg.

bEach 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.

cEach 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.

dBWs obtained directly from Table 8-3 of the 2008 CSEFH (age group 3 to <6 years).

eEach 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.

fMean 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. 

gEach 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. 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. Those values are provided for comparison purposes only and are not included in 

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, 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-77 6-29-2012

Description of MIRC

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 <1b504 482 969 1113 1440

Child 1-2

c

332 255 687 903 1318

Child 3-5

d

382 316 778 999 1592

Child 6-11e532 417 1149 1499 2274

Child 12-19f698 473 1641 2163 3467

Adultg1219 981 2534 3087 4567

Sources: EPA 2004b, 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. 

aSource is Kahn and Stralka 2008, also presented in the CSEFH (EPA 2008a).

bEach 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. 

cEach 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.

dEach IR represents the ingestion rate for age group 3 to <6 years from Table 3-4 of the 2008 CSEFH.

eEach 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.

fEach 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.

gAdult 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.

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

2011). 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 2011, Chapter 13): 

 

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 2-78 6-29-2012

Description of MIRC

(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

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
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-79 6-29-2012

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

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

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
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-80 6-29-2012

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

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

aPrimary source for values was the 1987–1988 NFCS survey; compiled results are presented in Chapter 13 of 2011 Exposure 

Factors Handbook (EPA, 2011). 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.

bPrimary source for values was 1987–1988 NFCS survey, compiled results presented in Chapter 13 of 2011 Exposure Factors 

Handbook (EPA, 2011). 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.

cPrimary source for children less than 3 years of age was a Khan et al.(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.

dDefault 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 2011): 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 

 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

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 2-81 6-29-2012

Description of MIRC

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.

6.3.4. Local Fish Ingestion Rates

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 2011), 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 IRs for 

children less than 6 years of age. Second, the NFCS data were collected approximately two 

decades ago. Third, the reported fish IRs are for ages 6 to 11 and 12 to 19 and are based on 

29 and 21 individuals in each age category, respectively (EPA 2011, Table 13-20). Finally, the 

IRs 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 IR data are 

very uncertain and are based on a wide variety of freshwater, estuarine, and marine fish, and 

squid (EPA 2011, Table 13-69). Therefore, a more recent survey was sought that included 

larger sample sizes, data for children younger than six years, and IRs for the parts of fish 

actually consumed. 

EPA’s (2002) analysis 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 per 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-82 6-29-2012

Description of MIRC

capita fish IR 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 IRs for many rural populations in the United States; however, it also may 

underestimate locally caught fish IRs 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 ages 3 to 17 years and for adults, MIRC includes values for the mean and the 90th, 

95th, and 99th percentile fish ingestion rates (freshwater and estuarine fish only) based on 

EPA’s analysis of 1994-96 and 1998 CSFII data (EPA 2002, 2008a). As shown in EPA’s 

2008(a) 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 IRs that are zero result from the short duration of the CSFII survey (two 

days) compared with the averaging time of interest (a year) and the relatively 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 IRs, 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 these 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): 

 

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.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-83 6-29-2012

Description of MIRC

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 A2-18 and Exhibit_Add 

A2-19. The mean and percentile per capita fish ingestion rates estimated using this 

methodology are summarized in Exhibit_Add A2-20 and are available in MIRC. The fish 

ingestion rates provided in Exhibit_Add A2-20 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 

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 angler were used. 

As noted in Section 6.4.3 of this addendum, if the user overwrites the fish IRs shown in 

Exhibit_Add A2-20 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.

PC,y CO,y FPC,y

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

Attachment A, Addendum 2 2-84 6-29-2012

Description of MIRC

Exhibit_Add A2-19. 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.

bAs 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 A2-18. 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 1-2

b

27.31 15.61 64.46 87.60 138.76*

Child 3-5

c

40.31 23.04 95.16 129.31 204.84*

Child 6-11d61.49 28.46 156.86* 247.69* 385.64*

Child 12-19e79.07 43.18 181.40* 211.15* 423.38*

Adultf81.08 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.

aPer 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.

bA 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 2-85 6-29-2012

Description of MIRC

Exhibit_Add A2-20. 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 1-2

a

1.37 0.79 3.24 4.41 6.98

Child 3-5

b

2.03 1.16 4.79 6.51 10.3

Child 6-11c2.71 1.25 6.90 10.9 17.0

Child 12-19d3.90 2.13 8.95 10.4 20.9

Adulte6.90 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.

aValues were calculated as (consumer-only IR for Child 1-2) x (fraction of population consuming fish for Child 3-5).

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

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

dValues were calculated as (consumer-only IR estimated for Child 12-19) x (fraction of population estimated to consume fish for 

Child 12-19).

eValues were calculated as (consumer-only IR for Adults) x (fraction of population consuming fish for Adults).

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 A2-21. 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, 2011). Black and female 

recreational anglers ingestion rates are presented in Burger et al. (2010). 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 

A2-21 for Hispanic, Laotian, and Vietnamese fishers were derived by EPA using information 

from Shilling et al. (2010; EPA, 2010).

Exhibit_Add A2-21. 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, 1997)

bBurger et al., 2010, weights reported are post-cooking for locally caught fish.

cShilling, F., A. White, L. Lippert, and M. Lubell. 2010. Contaminated fish consumption in California’s Central Valley Delta. 

Environmental Research 110: 334-344., weights reported are post-cooking for locally caught fish Burger et al., 2010.

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 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-86 6-29-2012

Description of MIRC

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.

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 A2-22 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 A2-22. Daily Mean and Percentile Soil Ingestion Rates 

for Children and Adults

Age Group

(years)

Soil Ingestion Rate (mg/day)

Meana50tha 90th 95th 99th

Child < 1 NA

Child 1-2 50 50 200b200b200b

Child 3-5 50 50 200b200b200b

Child 6-11 50 50 201c331d331d

Child 12-19 50 50 201c331d331d

Adult 20-70 20 20 201c331d331d

Sources: EPA 2008, EPA 2011

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.

bValues are the recommended “upper percentile” value for children from EPA’s 2011 EFH, Chapter 4, Table 4-23. The 2008 CSEFH 

and 2011 ESH included a high-end value associated with pica only, but this value has not been used.

cValues are 90th percentile adult ingestion rates calculated in Stanek et al. 1997; used to represent older children and adults.

dValues are 95th percentile adult ingestion rates calculated in Stanek et al. 1997; used to represent older children and adults.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-87 6-29-2012

Description of MIRC

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 A2-23 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.

Exhibit_Add A2-23. 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 < 1a67.0% - 99.7% h322 270 599 779 1152

Child 1-2

b

100% 1,032 996 1537 1703 2143

Child 3-5

c

100% 1,066 1,020 1,548 1,746 2,168

Child 6-11d100% 1,118 1,052 1,642 1,825 2,218

Child 12-19e100% 1,197 1,093 1,872 2,231 2,975

Adultf100% 1,100 1,034 1,738 2,002 2,736

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

Child < 1a67.0% - 99.7% h39 34 72 95 147

Child 1-2

b

100% 82 79 125 144 177

Child 3-5

c

100% 61 57 91 102 132

Child 6-11d100% 40 38 61 70 88

Child 12-19e100% 21 19 34 40 51

Adultg100% 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. 

hPercents 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), 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-88 6-29-2012

Description of MIRC

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. 

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 A2-24. All preparation 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-89 6-29-2012

Description of MIRC

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 2011).

Exhibit_Add A2-24. 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 Fruitc0.244 0.305

Exposed Vegetable 0.162d NA

Protected Fruit 0.29e NA

Protected Vegetable 0.088f NA

Root Vegetableg0.075 0.22

Beef 0.27 0.24

Pork 0.28 0.36

Poultry 0.32 0.295h

Fishi0.0 0.0 or 1.5j

Source: EPA 1997a and 2011

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 (EPA 2011, Table 13-5). For volatile or water soluble 

chemicals, a non-zero value for L2 also may be appropriate. Although EPA (2011) recommended 0.11 for L2, it varies 

substantially by chemical.

jCooking loss accounts for the difference between ingestion rates for cooked fish and calculated Hg concentrations in whole fish.

Cooking fish tends to reduce the overall weight of fish, and volatilization of mercury is unlikely to occur during cooking, increasing

the concentration of mercury by a factor of roughly 1.5 (Morgan et al. 1997). However, the default value of 0 is assumed for all 

chemicals in the screening assessment. The value of 1.5 for methyl mercury might be more appropriate, but was not used due to 

uncertainties associated with the value (also see text).

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 fruit

(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. 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-90 6-29-2012

Description of MIRC

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 from a more recent and 

larger survey, EPA’s (2002) analysis of freshwater and estuarine fish consumption from the 

USDA’s 1994-96 and 1998 CSFII. That survey reports ingestion rates of fish parts actually 

consumed, and so no loss processes for preparation are needed. The zero value for L2 

assumes that no chemical is lost by volatilization and that pan juices are consumed. The user 

may reset that value where chemical-specific data are available. For example, in the case of 

methyl mercury, the chemical is not lost by volatilization during cooking, but liquid from the fish 

is lost. This lowers the mass of fish consumed, thereby increasing the methyl mercury 

concentration. As such, an L2 value of 1.5 based on the Morgan et al. (1997) study might be 

more appropriate. (Note that due to uncertainties associated with the cooking conversion factor 

of 1.5 for methyl mercury, it was not used in the RTR multipathway screening assessment. 

However, this is an area that should be investigated further for potential use in the future.)

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. An appropriate value for L1 

would be 0.3 (EPA 2011, Table 13-69). For volatile or water soluble chemicals, a non-zero 

value for L2 also may be appropriate. Although EPA recommended 0.11 for L2 (EPA 2011), it 

can vary substantially by chemical. 

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 A2-25 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 6-29-2012

Description of MIRC

Exhibit_Add A2-25. 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 A2-26 presents 

additional values for the infant body weight parameter that the user can select instead of the 

MIRC default.

Exhibit_Add A2-26. 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 7.8a9.0 9.6

5

th 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
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-92 6-29-2012

Description of MIRC

Exhibit_Add A2-26. 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 7.8a9.0 9.6

85th percentile 7.4 9.0 10.3 11.0

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.

aMIRC 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 2004b). Exhibit_Add A2-27 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 A2-27. Time-weighted Average

Body Weight for Mothers

Statistic Weight (kg)

Mean 66.0a

5

th 47.1

10th 50.2

25th 54.3

50th 62.0

75th 72.0

90th 85.7

95th 97.0

Source: EPA 2004b

aMIRC 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 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-93 6-29-2012

Description of MIRC

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) 

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 2011, 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 2011, 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 2011). 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 2011). The resulting values are shown in 

the first two rows of Exhibit_Add A2-28. The MIRC screening-level default of 980 mL/day is an 

upper-bound estimate based on a one-year nursing period. 

Exhibit_Add A2-28 also includes the recommended values for four non-overlapping age 

categories from the CSEFH (U.S. EPA 2008a, Table 15-1). The values demonstrate that 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-94 6-29-2012

Description of MIRC

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.

Exhibit_Add A2-28. 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 2011†

0 to < 12 

months 688 0.709 980a

1.01aEPA 2011†

0 to < 1 month 510 0.525 950 0.979 EPA 2008††

1 to < 3 months 690 0.711 980 1.01 EPA 2008†

3 to < 6 months 770 0.793 1,000 1.03 EPA 2008†

6 to < 12 

months 620 0.639 1,000 1.03 EPA 2008†

aMIRC 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 

A2-29. Note that the parameters for which values are needed are different for the lipophilic 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-95 6-29-2012

Description of MIRC

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.

Exhibit_Add A2-29. 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-04b1.4E-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.

bAn 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 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-96 6-29-2012

Description of MIRC

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). 

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.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-97 6-29-2012

Description of MIRC

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. 

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.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-98 6-29-2012

Description of MIRC

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 A2-30). Fish ingestion 

rates are also available from these sources; however, as described in Section 6.3.4, these data 

were not used. 

7.1.1. Fish Ingestion Rates

The fish ingestion rates for the Tier 1 screening correspond to “as prepared” total 

freshwater/estuarine fish ingestion rates at the 99th percentile of the distribution for the general 

population, inclusive of both consumers and non-consumers of fish, as estimated in EPA’s 

Estimated Per Capita Fish Consumption in the United States (EPA 2002), Section 4. The 

original data were collected as part of the 1994–96 and 1998 USDA Continuing Survey of 

Intakes by Individuals (CSFII; USDA 2000). These data reflect per capita consumption (i.e., 

consumption rates for the general population) rather than consumption only for individuals who 

consume fish. Values reflect “as prepared” ingestion rates for which cooking and preparation 

losses (L1 and L2) did not need to be considered. “Total” fish is meant to represent 

consumption of finfish plus shellfish. Some adjustments to EPA (2002) fish ingestion rates 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 the footnotes to Exhibit_Add A2-30, these adjustments 

convert the available age-specific data on fish ingestion rates to the age-specific values needed 

for MIRC using body weight to scale the ingestion rates.

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 

“high-end” recreational angler whose diet comprises a substantial proportion of fish. This 

amount of fish is close to that consumed by a “subsistence fisher,” with total exposures from fish 

consumption similar to those observed for specific populations that consume large amounts of 

fish, such as certain ethnic groups. The scenario is not, however, intended to represent the 

maximum exposure an individual subsistence fisher might experience. As shown in 

Exhibit_Add A2-30, for adults, the rate of fish ingestion assumed in the screening scenario is 

105.1 g/day. This consumption rate is similar to the “default” value of 142.4 g/day that EPA’s 

Office of Water recommends for use in estimating exposures to subsistence fishers (EPA 

2000b).

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. 

The 99th percentile values below are believed to represent a reasonable approximation of a 

high-end consumption rate along the lines of the 90th percentile values used for homegrown 

foods and soil.

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). 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-99 6-29-2012

Description of MIRC

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. 

Exhibit_Add A2-30 also includes a sum of the 90th percentile ingestion rates for homegrown 

food categories and 99th percentile fish ingestion (preceding rows) 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 A2-30 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 A2-30. 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 milkc1.01 NA NA NA NA NA kg/day

Soil (dry) NA 200d200d201e201e201e mg/day

Fish (per individual)f NA 26.23g38.72g62.29h81.39h105.1hg/day

Fish (per kg BW)i NA 2.08 2.08 1.73 1.27 1.47 g/kg-day

Total Food Ingestion Rates for Comparison Only (not in MIRC; excludes soil and water)

Total Food: Homegrown 

onlyj NA 259 142 99 51 32 g/kg-day
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-100 6-29-2012

Description of MIRC

Exhibit_Add A2-30. 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: All Sourcesk 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 2011, EPA 2008, unless otherwise noted.

NA = not applicable

aPrimary source for values was the 1987–1988 NFCS survey; compiled results are presented in Chapter 13 of the 2011 Exposure 

Factors Handbook (EPA, 2011). 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.

bPrimary source for values was 1987–1988 NFCS survey, compiled results are presented in Chapter 13 of the 2011 Exposure Factors 

Handbook (EPA, 2011). 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.

fValues were derived based on EPA’s Estimated Per Capita Fish Consumption in the United States (2002)—Section 4.1.1.1 Table 4 (for 

adults) and Section 4.1.1.1 Table 5 (for child age categories) and are representative of 99th percentile "as prepared" values. 

g

The fish ingestion rate for children aged 3–5 years was obtained directly from Table 5 in the EPA (2002) report; 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 (12.6 kg) to the mean body weight of children 

aged 3–5-years (18.6 kg). 

h

The EPA (2002) fish ingestion estimates and Exposure Factor Handbook (EPA 2011) body weight data were used to derive time- and 

body weight-weighted average ingestion rates that adjust for the differences between the age group ranges used for the RTR screening 

and those presented in the 2002 EPA report.

i

Fish ingestion rates, original data in g/day, have been normalized to body weight in this table to allow addition into total food estimate 

using the mean body weight for each age category.

jSum 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 A2-24. The rows are then summed to get the total 

post-cooking ingestion rate.

k

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 

A2-31. 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 A2-31. Mean Body Weight Estimates for Adults and Children

a

Lifestage (years) Duration (years) Mean Body Weight (kg)

Adultb(20-70) 50 71.4

Child < 1c1 7.83

Child 1-2

c

2 12.6

Child 3-5

d

3 18.6
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-101 6-29-2012

Description of MIRC

Exhibit_Add A2-31. Mean Body Weight Estimates for Adults and Children

a

Lifestage (years) Duration (years) Mean Body Weight (kg)

Child 6-11e6 36.0

Child 12-19f8 64.2

aSources: EPA 1997, 2008a

bBW represents the sample-size weighted average of male and female mean body weights (all races, 18-74 years) from EPA’s

1997 EFH (Tables 7-4 for males and 7-5 for females). Note that these weights include the weight of clothing, estimated to range 

from 0.09 to 0.28 kg. 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%). We considered updating the body weight for use in the RTR assessment to the revised body weights in the 2011 

Exposure Factors Handbook. However, the 2011 EFH warns that assessors should consider if the dose estimate (for which the 

body weight is used to calculate) will be used to estimate risk by combining it with a dose-response that was derived assuming a 

body weight of 70 kg, and that if such an inconsistency exists, an adjustment may be necessary. Because a 70 kg body weight 

commonly is used to develop IRIS dose-response values, using an 80 kg body weight could result in this inconsistency. Given 

these issues, we have retained the use of an adult body weight of 71.4 kg.

c

These values were calculated as time-weighted average body weight (BW) from data presented in Table 8-3 of EPA’s (2008a) 

Child-Specific Exposure Factors Handbook (CSEFH).

d

These values were obtained directly from Table 8-3 of the 2008 CSEFH. 

eEach 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 A2-32 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 A2-32.

Exhibit_Add A2-32. Chemical-Specific Parameter Values for Input to MIRCa

Parameter Description Benzo(a)-

pyrene

Cadmium Mercuric 

chloride

Methyl 

mercur

y

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
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-102 6-29-2012

Description of MIRC

Exhibit_Add A2-32. Chemical-Specific Parameter Values for Input to MIRCa

Parameter Description Benzo(a)-

pyrene

Cadmium Mercuric 

chloride

Methyl 

mercur

y

2,3,7,8-

TCDD Units

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

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

aValues 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 A2-33 presents biotransfer 

factors for each of the chemicals and animal types for which screening threshold emissions 

were calculated.

Exhibit_Add A2-33. 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
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-103 6-29-2012

Description of MIRC

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 

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 A2-30); this assumption 

might overestimate total ingestion of homegrown foods by a factor of more than 2 (see 

Exhibit_Add A2-30).

 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 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-104 6-29-2012

Description of MIRC

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. 

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).

ATSDR. 1998. Toxicological profile for chlorinated dibenzo-p-dioxins. Atlanta, GA: U.S. 

Department of Health and Human Services, Public Health Service.

Bacci E., M. Cerejeira, C. Gaggi, G. Chemello, D. Calamari, and M. Vighi. 1992. Chlorinated 

dioxins: Volatilization from soils and bioconcentration in plant leaves. Bull. Environ. Contam. 

Toxicol. 48: 401-408.

Baes, C.F., R.D. Sharp, A.L. Sjoreen, and R.W. Shor. 1984. Review and analysis of parameters 

and assessing transport of environmentally released radionuclides through agriculture.

ORNL-5786. Oak Ridge National Laboratory. Oak Ridge, Tennessee. September.

Bates, M.N., D.S. Hannah, S.J. Buckland, J.A. Taucher, and T. van Mannen. 1994. Chlorinated 

organic contaminants in breast milk of New Zealand women. Environmental Health 

Perspectives 102(Supplement 1): 211-217.

Belcher, G.D., and C.C. Travis. 1989. Modeling support for the RURA and municipal waste 

combustion projects: Final report on sensitivity and uncertainty analysis for the terrestrial 

food chain model. Interagency Agreement No. 1824-A020-A1, Office of Risk Analysis, 

Health and Safety Research Division, Oak Ridge National Laboratory. Oak Ridge, 

Tennessee. October.

Boone, F.W., Y.C. Ng, and J.M. Palm. 1981. Terrestrial pathways of radionuclide particulates. 

Health Physics 41:735-747.

Briggs, G.G., R.H. Bromilow, and A.A. Evans. 1982. Relationships between lipophilicity and root 

uptake and translocation of non-ionized chemicals by barley. Pesticide Science 13: 495-504 

(As cited in EPA 2005a, Appendix A-2).

Burger J. 2010. Daily consumption of wild fish and game: Exposures of high end 

recreationalists. International Journal of Environmental Health Research 12(4): 343-354, 

July, 2010.

Butte, N.F., C. Garza, E.O. Smith, and B.L. Nichols. 1984. Human milk intake and growth in 

exclusively breast-fed infants. The Journal of Pediatrics 104(2): 187-195.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-105 6-29-2012

Description of MIRC

Byczkowski, J.Z., and J.C. Lipscomb. 2001. Physiologically based pharmacokinetic modeling of 

the lactational transfer of methylmercury. Risk Analysis 21(5): 869-882.

Chamberlain, A.C. 1970. Interception and retention of radioactive aerosols by vegetation. 

Atmospheric Environment 4: 57-78.

Clewell, H.J, J.M. Gearhart, R.P. Gentry, T.R Covington, C.B. VanLandingham, C.B. Crump, 

and A.M. Shipp. 1999. Evaluation of the uncertainty in an oral reference dose for 

methylmercury due to interindividual variability in pharmacokinetics. Risk Analysis 19: 547-

558 (As cited in Byczkowski and Lipscomb 2001).

Conley, C.L. 1974. The blood. Medical Physiology 13(2). V.B. Moutcasle, Ed. 1997. The C.V. 

Mosby Company, St. Louis, MO. 1027-1046 (As cited in EPA 1998). 

Ensminger, M.E. 1980. Poultry Science. Interstate Printers and Publishers, Inc. Danville, Illinois.

Fries, G. F. 1982. Potential polychlorinated biphenyl residues in animal products from 

application of contaminated sewage sludge to land. J. Environ. Qual. 11:14.

Fries, G.F. 1994. Personal communication between G.F. Fries, U.S. Department of Agriculture, 

and Glenn Rice and Jennifer Windholtz, U.S. Environmental Protection Agency, Office of 

Research and Development. Agricultural Research Service. March.

Fujita, M., and E. Takabatake. 1977. Mercury levels in human maternal and neonatal blood, hair 

and milk. Bull. Environ. Contam. Toxicol. 18(2): 205-209.

Gearhart, J.M., H.J. Clewell III, K.S. Crump, A.M. Shipp, and A. Silvers. 1995. Pharmacokinetic 

dose estimates of mercury in children and dose-response curves of performance tests in a 

large epidemiological study. Water, Air, Soil Pollut. 80: 49-58 (As cited in Byczkowski and 

Lipscomb 2001).

Gearhart, J., T. Covington, and H. Clewell III. 1996. Application of a physiologically based 

pharmacokinetic model for MeHg in a dose reconstruction of the Iraqi accidental exposures.

Paper presented at the Fourth International Conference on Mercury as a Global Pollutant, 

Congress Centre, Hamburg, Germany; August (As cited in Byczkowski and Lipscomb 2001).

Greenwood, M.R., T.W. Clarkson, R.A. Doherty, A.H. Gates, L. Amin-Zaki, S. Elhassani, and 

M.A. Majeed. 1978. Blood clearance half-times in lactating and nonlactating members of a 

population exposed to mercury. Environ. Res. 16: 48-54.

Harrison, K.A. 1967. Blood volume changes in severe anemia in pregnancy. Lancet 1(7480):20-

25.

Hofelt, C.S., M. Honeycutt, J.T. McCoy, and L.C. Haws. 2001. Development of a metabolism 

factor for polycyclic aromatic hydrocarbons for use in multipathway risk assessments of 

hazardous waste combustion facilities. Reg. Toxicol. Pharmacol. 33:60-65.

Hoffman, F.O., K.M. Thiessen, M.L. Frank, and B.G. Blaylock. 1992. Quantification of the 

interception and initial retention of radioactive contaminants deposited on pasture grass by 

simulated rain. Atmospheric Environ. 26a(18): 3313-3321.

Hollins, J.G., R.F. Willes, F.R. Bryce, S.M. Charbonneau, and I.C. Munro. 1975. The whole 

body retention and tissue distribution of Hg methylmercury in adult cats. Toxicol. App. 

Pharmacol. 33: 438-449. 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-106 6-29-2012

Description of MIRC

Hong, C.S., J. Xiao, A.C. Casey, B. Bush, E.F. Fitzgerald, and S.A. Hwang. 1994. Mono-ortho 

and non-ortho-substituted polychlorinated biphenyls in human milk from Mohawk and control 

women: Effects of maternal factors and previous lactation. Arch. Environ. Contam. Toxicol.

27(3): 431-437.

Jensen, A.A. 1987. Polychlorinated biphenyls (PCBs), polychlorodibenzo-p-dioxins (PCDDs) 

and polychlorodibenzofurans (PCDFs) in human milk, blood and adipose tissue. Sci. Total 

Environ. 64(3): 259-293.

Kahn, H., and K. Stralka. 2008. Estimated daily average per capita water ingestion by child and 

adult age categories based on USDA’s 1994-96 and 1998 continuing survey of food intakes 

individuals. J. Expo. Anal. Environ. Epidemiol. 1-9.

Kershaw, T.G., T.W. Clarkson , and P.H. Dhahir . 1980. The relationship between blood levels 

and dose of methylmercury in man. Arch. Environ. Health 35(1): 28-36.

Lorber, M. 1995. Development of an air-to-plant vapor phase transfer for dioxins and furans. 

Presented at the 15th International Symposium on Chlorinated Dioxins and Related 

Compounds. August 21-25, 1995 in Edmonton, Canada. Abstract in Organohalogen 

Compounds 24:179-186.

Lorber, M., and P. Pinsky. 2000. An evaluation of three empirical air-to-leaf models for 

polychlorinated dibenzo-p-dioxins and dibenzofurans. Chemosphere 41(6):931-41.

Maxwell, N.I., and D.E. Burmaster. 1993. A simulation model to estimate a distribution of lipid 

intake from breast milk during the first year of life. Journal of Exposure Analysis and 

Environmental Epidemiology 3(4): 383-406.

McLachlan, M.S. 1993. Digestive tract absorption of polychlorinated dibenzo-p-dioxins, 

dibenzofurans and biphenyls in a nursing infant. Toxicology and Applied Pharmacology

123(1): 68-72. 

Miller, C.W., and F.O. Hoffman. 1983. An examination of the environmental half-time for 

radionuclides deposited on vegetation. Health Physics 45(3): 731-744.

Morgan, J.N., M.R. Berry, and R.L. Graves. 1997. Effects of Commonly Used Cooking Practices 

on Total Mercury Concentration in Fish and Their Impact on Exposure Assessments. 

Journal of Exposure Analysis and Environmental Epidemiology 7(1):119-133.

NAS (National Academy of Sciences). 1987. Predicting Feed intake of Food-Producing Animals.

National Research Council, Committee on Animal Nutrition, Washington, D.C.

NAS. 1991. Nutrition During Lactation. National Academies Press. Washington, DC. 

NC DEHNR (North Carolina Department of Health, Environment, and Natural Resources). 1997.

North Carolina Protocol for Performing Indirect Exposure Risk Assessments for Hazardous 

Waste Combustion Units. January.

Portier K., J. Tolson, and S. Roberts. 2007. Body weight distributions for risk assessment. Risk 

Anal. 27(1): 11-26.

RTI. 2005. Methodology for predicting cattle biotransfer factors. Prepared for U.S. 

Environmental Protection Agency (EPA) Office of Solid Waste. EPA Contract No. 68-W-03-
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-107 6-29-2012

Description of MIRC

042. September. Available at: http://www.epa.gov/waste/hazard/tsd/td/combust/ 

finalmact/ssra/btfreportfull05.pdf. 

Schecter, A., P. Fürst, C. Fürst, O. Päpke, M. Ball, J. Ryan, H. Cau, L. Dai, H. Quynh, H.Q. 

Cuong, N. Phuong, P. Phiet, A. Beim, J. Constable, J. Startin, M. Samedy, and Y. Seng. 

1994. Chlorinated dioxins and dibenzofurans in human tissue from general populations: A 

selective review. Environmental Health Perspectives 102(Supplement 1): 159-171.

Sherlock, J., D. Hislop, G. Newton, G. Topping, and K. Whittle. 1984. Elevation of mercury in 

human blood from controlled ingestion of methylmercury in fish. Human Toxicology 3: 117-

131 (As cited in USFDA 2009).

Shilling, F., White, A. Lippert, L, and M. Lubell. 2010. Contaminated fish consumption in 

California's Central Valley Delta. Environmental Research 110: 334-344. 

Shor, R.W., C.F. Baes, and R.D. Sharp. 1982. Agricultural production in the United States by 

county: A compilation of information from the 1974 census of agriculture for use in terrestrial 

food-chain transport and assessment models. Oak Ridge National Laboratory Publication.

ORNL-5786

Sim, M.R. and J.J. McNeil. 1992. Monitoring chemical exposure using breast milk: A 

methodological review. American Journal of Epidemiology 136(1): 1-11.

Smith, A.H. 1987. Infant exposure assessment for breast milk dioxins and furans derived from 

waste incineration emissions. Risk Analysis 7:347-353.

Stanek, E.J., E.J. Calabrese, R. Barnes, P. Pekow. 1997. Soil ingestion in adults – results of a 

second pilot study. Toxicol. Environ. Safety 36:249-257.

Steinbeck, A.W. 1954. Plasma and blood volumes of normal Australian females. Australian 

Journal of Experimental Biology and Medical Science 32(1): 95-9.

Stephens, R.D., M. Petreas, and G.H. Hayward. 1995. Biotransfer and bioaccumulation of 

dioxins and furans from soil: Chickens as a model for foraging animals. Science Total 

Environment 175: 253-273. July 20.

Sullivan, M.J., S.R. Custance, and C.F. Miller. 1991. Infant exposure to dioxin in mother’s milk 

resulting from maternal ingestion of contaminated fish. Chemosphere 23(8-10): 1387-1396. 

Thomann, R.V. 1989. Bioaccumulation model of organic-chemical distribution in aquatic foodchains. Environ. Sci. Technol. 23(6): 699-707.

Travis, C.C., and A.D. Arms. 1988. Bioconcentration of organics in beef, milk, and vegetation.

Environ. Sci. Technol. 22:271-274.

Travis, C.C., H.A. Hattemer-Frey and A.A. Arms. 1988. Relationship between dietary intake of 

organic chemicals and their concentrations in human adipose tissue and breast milk. Arch. 

Environ. Contam. Toxicol. 17:473-478.

Ueland, K. 1976. Maternal cardiovascular dynamics. VII. Maternal cardiovascular dynamics. 

Intrapartum blood volume changes. Am. J. Obstetrics Gynecol. 126(6): 671-677.
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-108 6-29-2012

Description of MIRC

USDA (U.S. Department of Agriculture). 1992. Changes in Food Consumption and Expenditures 

in American Households during the 1980’s. USDA, Washington, D.C. Statistical Bulletin o. 

849. (As cited in EPA 1997)

USDA. 1993. Food and Nutrient Intakes by Individuals in the United States, 1 Day, 1987-88.

Nationwide Food Consumption Survey 1987-88, NFCS Report No. 87-I-1. (As cited in EPA

1997)

USDA. 1994a. Food Consumption and Dietary Levels of Households in the United States, 1987-

88. Agricultural Research Service, Report No. 87-H-1. (As cited in EPA 1997)

USDA. 1994b. Vegetables 1993 Summary. National Agricultural Statistics Service, Agricultural 

Statistics Board. Washington, D.C. Vg 1-2 (94). Jan.

USDA. 1994c. Noncitrus Fruits and Nuts 1993 Summary. National Agricultural Statistics 

Service, Agricultural Statistics Board, Washington, D.C. Fr Nt 1-3 (94).

USDA. 2000. 1994–96, 1998 Continuing Survey of Food Intakes by Individuals (CSFII). CDROM. Agricultural Research Service, Beltsville Human Nutrition Research Center, Beltsville, 

MD. Available from the National Technical Information Service, Springfield, VA, Accession 

Number PB-2000500027. (As cited in EPA 2008a, Chapter 14)

USFDA (US Food and Drug Administration). 2009. Draft Risk and Benefit Assessment Report 

and Draft Summary of Published Research. Center for Food Safety and Applied Nutrition. 

January 15. Available at: http://www.cfsan.fda.gov/seafood1.html

EPA (US. Environmental Protection Agency). 1990. Interim Final Methodology for Assessing 

Health Risks Associated with Indirect Exposure to Combustor Emissions. Environmental 

Criteria and Assessment Office. ORD.EPA-600-90-003. January.

EPA. 1992. Technical Support Document for the Land Application of Sewage Sludge: Volumes I 

and II. EPA 822/R-93-001a. Office of Water. Washington, D.C.

EPA. 1994a. Revised Draft Guidance for Performing Screening Level Risk Analysis at 

Combustion Facilities Burning Hazardous Wastes. Attachment C, Draft Exposure 

Assessment Guidance for RCRA Hazardous Waste Combustion Facilities. Office of 

Emergency and Remedial Response. Office of Solid Waste. December 14.

EPA. 1994b. Estimating Exposure to Dioxin-Like Compounds. Volume II: Properties, Sources, 

Occurrence, and Background Exposures. External Review Draft. Office of Research and 

Development. Washington, DC. EPA/600/6-88/005Cc. June.

EPA. 1994c. Estimating Exposure to Dioxin-Like Compounds. External Review Draft. Office of 

Research and Development, Washington, D.C. EPA/600/6-88/005Cb. June. Available at: 

http://oaspub.epa.gov/eims/eimscomm.getfile?p_download_id=438673.

EPA. 1995a. Review Draft Development of Human Health-Based and Ecologically-Based Exit 

Criteria for the Hazardous Waste Identification Project. Volumes I and II. Office of Solid 

Waste. March 3.

EPA. 1995b. Memorandum Regarding Further Studies for Modeling the Indirect Exposure 

Impacts from Combustor Emissions. From Mathew Lorber, Exposure Assessment Group, 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-109 6-29-2012

Description of MIRC

and Glenn Rice, Indirect Exposure Team, Environmental Criteria and Assessment Office.

Washington, D.C. January 20.

EPA. 1995c. Further Issues for Modeling the Indirect Exposure Impacts from Combustor 

Emissions. Office of Research and Development. Washington, D.C. January 20.

EPA. 1995d. Waste Technologies Industries Screening Human Health Risk Assessment 

(SHHRA): Evaluation of Potential Risk from Exposure to Routine Operating Emissions. 

Volume V. External Review Draft. U.S. EPA Region 5, Chicago, Illinois.

EPA. 1996. Soil Screening Guidance: User’s Guide. Office of Solid Waste and Emergency 

Response, Washington D.C. EPA/540/R-96/018, April 1996. 

EPA. 1997a. Exposure Factors Handbook. Volumes I, II, and III. Office of Research and 

Development, Washington, D.C. EPA-600-P-95-002Fa,b,c. August. Available at:

http://www.epa.gov/nceawww1/efh/.

EPA. 1997b. Mercury Study Report to Congress. Volume III: Fate and Transport of Mercury in 

the Environment. Office of Air Quality Planning and Standards and Office of Research and 

Development. EPA-452/R-97-005. December.

EPA. 1997c. Parameter Guidance Document. National Center for Environmental Assessment, 

NCEA-0238.

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-R-98-137. Available at:

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=55525.

EPA. 1999. 1999 National-Scale Air Toxics Assessment Results; Approach for Modeling POM. 

Available at: http://www.epa.gov/ttn/atw/nata1999/nsata99.html.

EPA. 2000a. Supplementary Guidance for Conducting Health Risk Assessment of Chemical 

Mixtures. Risk Assessment Forum, Washington D.C. EPA/630/R-00/002. August. Available 

at: http://www.epa.gov/ncea/raf/pdfs/chem_mix/chem_mix_08_2001.pdf.

EPA. 2000b. Methodology for Deriving Ambient Water Quality Criteria for the Protection of 

Human Health. EPA-822-B-00-004. October.

EPA. 2001a. Peer Review of EPA’s Hazardous Waste Identification Rule Risk Assessment 

Model: Breast milk exposure model for the HWIR 3MRA Model. Prepared by Eastern 

Research Group for EPA Office of Solid Wastes. 68-W5-0057.

EPA. 2001b. Water Quality Criterion for the Protection of Human Health: Methylmercury. Office 

of Water, Office of Science and Technology. Washington D.C. EPA-823-R-01-001. January.

Available at: http://www.epa.gov/ncea/raf/pdfs/chem_mix/chem_mix_08_2001.pdf.

EPA. 2002. Estimated Per capita Fish Consumption in the United States. Office of Water, Office 

of Science and Technology, Washington, D.C. EPA-821- C- 02-003. August. Available at: 

http://www.epa.gov/waterscience/fish/files/consumption_report.pdf. 

EPA. 2003a. Chapter 10 In: Multimedia, Multipathway, and Multireceptor Risk Assessment 

(3MRA) Modeling System, Volume II: Site-based, Regional, and National Data. SAB Review 

Draft. EP-530/D-03-001b. Office of Research and Development, Athens, GA, and Research 
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-110 6-29-2012

Description of MIRC

Triangle Park, NC, and Office of Solid Waste, Washington, D.C. July. Available at:

http://www.epa.gov/osw/hazard/wastetypes/wasteid/hwirwste/risk03.htm.

EPA. 2003b. Methodology for Deriving Ambient Water Quality Criteria for the Protection of 

Human Health (2000): Technical Support Document. Volume 2: Development of National 

Bioaccumulation Factors. Office of Water, Office of Science and Technology, Washington, 

D.C. EPA-822-R-03-030. December. Available at:

http://www.epa.gov/waterscience/criteria/humanhealth/method/tsdvol2.pdf.

EPA. 2003c. CSFII Analysis of Food Intake Distributions. Office of Research and Development, 

National Center for Environmental Assessment, Washington, D.C. EPA-600-R-03-29.

Available at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=56610.

EPA. 2004a. Superfund Chemical Data Matrix. OERR. Washington, D.C. January.

EPA. 2004b. Estimated Per capita Water Ingestion and Body Weight in the United States – An 

Update. Office of Water, Office of Science and Technology, Washington, D.C. EPA-822-R00-001. October. Available at: 

http://www.epa.gov/waterscience/criteria/drinking/percapita/2004.pdf

EPA. 2004c. Risk Assessment Guidance for Superfund. Volume I: Human Health Evaluation 

Manual (Part E, Supplemental Guidance for Dermal Risk Assessment), Final. Office of 

Superfund Remediation and Technology Innovation, Washington, D.C. EPA/540/R/99/005; 

OSWER 9285.7-02EP; NTIS PB99-963312. July.

EPA. 2005a. Human Health Risk Assessment Protocol for Hazardous Waste Combustion 

Facilities. Office of Solid Waste and Emergency Response, Washington, DC. EPA-530-R05-006. September. Available at: http://www.epa.gov/osw/hazard/tsd/td/combust/risk.htm.

EPA. 2005b. Guidance on Selecting Age Groups for Monitoring and Assessing Childhood 

Exposures to Environmental Contaminants. Risk Assessment Forum. Washington, DC.

November. EPA/630/P-03/003F. Available at: 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=146583.

EPA. 2005c. Guidelines for Carcinogen Risk Assessment. Risk Assessment Forum, 

Washington, DC. EPA/630/P-03/001F. March. Available from:

http://www.epa.gov/IRIS/cancer032505-final.pdf.

EPA. 2005d. Supplemental Guidance for Assessing Susceptibility from Early-Life Exposure to 

Carcinogens. Risk Assessment Forum: Washington, D.C. EPA-630/R-03-003F. March.

Available at: http://www.epa.gov/ttn/atw/childrens_supplement_final.pdf.

EPA. 2005e. Analysis of Total Food Intake and Composition of Individual's Diet Based on the 

U.S. Department of Agriculture's 1994-96, 1998 Continuing Survey of Food Intakes By 

Individuals (CSFII) (Final). Office of Research and Development, National Center for 

Environmental Assessment, Washington, D.C. EPA/600/R-05/062F. Available at:

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=132173.

EPA. 2007a. Prioritized Chronic Dose-Response Values for Screening Risk Assessments 

(Table 1). Office of Air Quality Planning and Standards; June 12, 2007. Available at: 

http://www.epa.gov/ttn/atw/toxsource/summary.html .
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2-111 6-29-2012

Description of MIRC

EPA. 2007b. Toxicological Review of 1,1,1-Trichloroethane (CAS No. 71-55-6) In Support of 

Summary Information on the Integrated Risk Information System (IRIS). Office of Research 

and Development, Washington, DC. EPA/635/R-03/006. August. Available at:

http://www.epa.gov/iris

EPA. 2008a. Child-Specific Exposure Factors Handbook. Office of Research and Development, 

Washington, D.C. EPA/600/R-06/096F. September. Available at: 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=199243 .

EPA. 2008b. Draft Report on EPA OAQPS Risk and Technology Review Methodologies: For 

Review by the EPA Science Advisory Board; Case Studies – MACT I Petroleum Refining 

Sources, Portland Cement Manufacturing. Office of Air Quality Planning and Standards, 

Office of Air and Radiation, Research Triangle Park, NC. July 14, 2008.

EPA. 2010. Personal communication from Zachary Pekar, Ambient Standards Group, Office of 

Air and Radiation, U.S. Environmental Protection Agency, to Heidi Hubbard, ICF 

International, [December 15, 2010]

EPA. 2011. Exposure Factors Handbook: 2011 Edition. Office of Research and Development, 

Washington, D.C. EPA/600/R-090/052F September 2011 Available at:

http://cfpub.epa.gov/ncea/risk/recordisplay.cfm?deid=236252.

Van den Berg, M., L.S. Birnbaum, M. Denison, M. De vito, W. Farlans, M. Feeley, H. Fiedler, H. 

Hakansson, A. Hanberg, L.. Haws, M. Rose, S. Safe, D. Schrenk, C. Tohyama, A. Tritscher, 

J. tuomisto, M. Tysklind, N. Walker, and R.E. Peterson. 2006. The 2005 World Health 

Organization reevaluation of human and mammalian toxic equivalency factors for dioxins 

and dioxin-like compounds. Toxicol Sci. 93(2): 223-41.
[This page intentionally left blank.]
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 3 3-1 6-29-2012

Dermal Risk Screening

Addendum 3. Dermal Risk Screening
[This page intentionally left blank.]
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 3 3-3 6-29-2012

Dermal Risk Screening

CONTENTS, ADDENDUM 3

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 6-29-2012

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 6-29-2012

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 (U.S. 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 (U.S. EPA 2004c). 

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 (U.S. EPA 2004c). 

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 6-29-2012

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

aOral dose response values are presented in Appendix 2. Only the resulting adjusted dose response values are presented in 

this table.

bAccording 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)

BW AT

DA EV ED EF SA DAD event



   


TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 3 3-7 6-29-2012

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 2004c) 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

6 t

DA C 2 FA K

 

   

event w p event DA  C K t

DAevent Cs  AF  ABS CF
TRIM-Based Tiered Screening Methodology for RTR

Attachment A, Addendum 3 3-8 6-29-2012

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 18,150g6,878h

Child <1b3,992 1,772

Child 1-2

c

5,700 2,405

Child 3-5

d

7,600 3,354

Child 6-11e10,800 4,501

Child 12-19f17,150 6,906

aSources 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.

bRepresents a time-weighted average for age groups birth to <1 month, 1 to <3 months, 3 to <6 months, and 6 

to <12 months. 

cRepresents a time-weighted average for age groups 1 to <2 years and 2 to <3 years. 

dValues for age group 3 to <6 years in the 2008 CSEFH.

eValues 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.

fRepresents 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.

gRepresents the average total surface area of adults from Table C-1 of RAGS Part E.

hRepresents 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 6-29-2012

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 6-29-2012

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 0.045a0.03 0.13 Values from RAGS Part E, 

Exhibit 3-4, unless otherwise 

noted

aValue 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 6-29-2012

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 6-29-2012

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 <1Child

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 <1Child

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 6-29-2012

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

aRepresents 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 6-29-2012

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.

U.S. EPA (Environmental Protection Agency). 2004c. 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

U.S. 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.

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

Attachment B – Tier 2 B-1 6-29-2012

Attachment B. Tier 2 Screening Methodology
[This page intentionally left blank.]
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-3 6-29-2012

CONTENTS, ATTACHMENT B

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

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

B.2 Selection of Site-Specific Characteristics to Include in the Tier 2 Analysis ......................B-7

B.3 Estimation of Adjustment Factors for Selected Site-Specific Parameters ........................B-8

B.3.1 Selection Values for Variables of Interest .............................................................. B-9

B.3.2 Estimation of Adjustment Factors ........................................................................ B-12

B.3.2.1 General Approach .................................................................................B-12

B.3.2.2 Incorporation of the Risk Equivalency Approach....................................B-14

B.4 Preparing National Databases of Lake and Meteorological Data...................................B-16

B.4.1 Processing Lake Data for Tier 2 Analysis ............................................................ B-16

B.4.2 Processing Meteorological Data for Tier 2 Analysis ............................................. B-19

B.4.2.1 Sources of Meteorological Data.............................................................B-19

B.4.2.2 Coverage of Meteorological Stations Compared with Facility 

Locations...............................................................................................B-20

B.4.2.3 Data Processing ....................................................................................B-20

B.5 Implementation of Tier 2 Analysis .................................................................................B-23

B.5.1 Facility List for Tier 2 Screen (Step 3).................................................................. B-26

B.5.2 Facility/Lake Distance Table (Step 4)................................................................... B-26

B.5.3 Finding the Closest Meteorological Station (Step 5)............................................. B-27

B.5.4 Assembling Threshold Adjustment Factors (Step 6) ............................................ B-29

B.5.5 Finalizing Threshold Adjustment Factors and Assembling Screening 

Results (Step 7)................................................................................................... B-30

B.5.6 Assembling Results (Step 8)................................................................................ B-30

B.6 References ...................................................................................................................B-33
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-4 6-29-2012

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-18

Exhibit_Att B-6. An Early Analysis of the Distance Between the Point Source 

Facilities and the Closest Meteorological Stationa,b,c.......................................B-22

Exhibit_Att B-7. Example of the Dashboard To Conduct the Tier 2 Analysis ........................B-25

Exhibit_Att B-8. Example of Global Inputs Used in the Tier 2 Analysis.................................B-25

Exhibit_Att B-9. Example of the Facility Input Data Required To Conduct the 

Tier 2 Analysis ................................................................................................B-26

Exhibit_Att B-10. Example of the Lake Distance Data Required To Conduct the 

Tier 2 Analysis ................................................................................................B-27

Exhibit_Att B-11. Example Results of the Meteorological Station Matching 

Required To Conduct the Tier 2 Analysis........................................................B-28

Exhibit_Att B-12. Example Results of the Octant Analysis Required To Conduct 

the Tier 2 Analysis ..........................................................................................B-29

Exhibit_Att B-13. Example of the TRIM.FaTE Matrix Results Required To 

Conduct the Tier 2 Analysis ............................................................................B-30

Exhibit_Att B-15. Example of Detailed Output Table Created by the Tier 2 Toola.................B-32

Exhibit_Att B-16. Example of Individual Output Table Created by the Tier 2 Tool ................B-32
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-5 6-29-2012

B.1 Overview of Approach

The Tier 1 screening scenario 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. 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, a balance was 

struck between the: 1) degree of impact on the potential risk estimate; 2) ease of 

implementation in TRIM.FaTE; and 3) ease of obtaining site-specific values on a facility-byfacility 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. 

The overall implementation of Tier 2 is shown in Exhibit_Att B-1. First (shown in green), the 

starting point is the ratio of the facility emission to the Tier 1 threshold. Next, two different sets of 

information are needed. 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 so many facilities need to be evaluated in Tier 2, the implementation focuses on 

estimating refined risk using pre-calculated databases rather than trying to gather the input 

parameters and running them through TRIM.FaTE and MIRC 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 6-29-2012

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 could be 

accessed readily 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 annual-average summary statistics on wind direction, wind speed, and precipitation for 

more than 1,500 surface stations located throughout the United States. These surface data 

cover years 2006 through 2010 and were obtained from the National Climatic Data Center’s 

Quality Controlled Local Climatological Data. The mixing heights for each surface station were 

estimated using data from the upper-air station closest to the surface station. These mixing 

height values cover years 1987 through 1990 (where available) for over 50 stations, and they 

are available from EPA’s Support Center for Regulatory Atmospheric Modeling. 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 25 acres in area 

are included.

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 

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. 
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-7 6-29-2012

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:

 

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® Excel 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 

information for the facilities being evaluated.24 To determine which scenario characteristics 

 

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 
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-8 6-29-2012

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 

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. 

 

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 6-29-2012

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 identified in Section 0 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 a 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 

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
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-10 6-29-2012

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

aBold indicates the value is equal to the value used in Tier 1.

bWind 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 

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 

 

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

27National Climatic Data Center Historical Climate Series (NCDC-HCS) (2007).
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-11 6-29-2012

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 5

th percentile (i.e., lower) of annual average mixing heights for 463 U.S. 

locations, based on data obtained from EPA’s SCRAM Web site.29 To estimate adjustment 

factors in the Tier 2 analysis, mdoel simulations were conducted with the parameter value set to 

three larger values (i.e., 865 m, 1,079 m, and 1,537 m; values also shown in Exhibit_Att B-2). 

These values correspond to North Little Rock, Arkansas, Boise, Idaho, and Tucson, Arizona and 

are intended to encompass the range of annual average mixing heights experienced in different 

parts of the United States. Based on these levels, the following bins were selected for 

categorization of mixing height: 710–865 m, 865–1,079 m, 1,079–1,537 m, and above 1,537. 

In all modeled cases, increasing the mixing heights while maintaining constant lake location, 

wind direction, wind speed, and precipitation rate led to Tier 2 estimations of high-end risk or 

hazard that were smaller than that of Tier 1. To be health-protective, a facility was assigned the 

lower end value of the bin into which it was placed. Facilities with mixing heights above 1,537 m 

and those below 710 m were assumed to have mixing heights of 1,537 m and 710 m, 

respectively. 

Lake Location: Moving the lake included in the hypothetical Tier 1 scenario to a location 

farther from the actual source in the modeled domain will reduce modeled (TRIM.FaTE) 

deposition to the lake and its watershed and consequently reduce exposures associated with 

the fish consumption pathway, which is an important pathway of exposure for several chemicals 

(for example, in the case of methyl mercury, it is by far the predominant exposure pathway). 

For the scenario modeled in Tier 1, the center of the lake was situated approximately 2 km from 

the source. To estimate lake location adjustment factors for use in Tier 2, we completed a 

series of model runs in which the lake was located 5 km, 10 km, 20 km, and 40 km from the 

source, as well as runs with no lake (values also shown in Exhibit_Att B-2). Accordingly, the 

bins used to classify lake location relative to the facility are: no lake, 2–5 km, 5–10 km, 10–20 

km, 20–40 km, and 40–50 km (lakes farther than 50 km are not considered). To be healthprotective, 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 

 

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 6-29-2012

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 6-29-2012

Exhibit_Att B-3. Layouts for Tier 2 TRIM.FaTE Simulations Using Alternate Distances 

Between the Facility and the Fishable Lakea

a

The no-lake scenario is not shown.
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-14 6-29-2012

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:

 H H H

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 6-29-2012

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: 

 Q H H H 

Then, the Tier 1 risk ratio is estimated by comparing the risk-equivalent BaP emissions to the 

BaP emission threshold:

 H 

 Q 

 

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:

 H 

 Q 

 

Using the definition of the risk-equivalent BaP emissions, this can be re-expressed for a given 

PAH species as:

 H 

 H H H 

 

This expression may be further reconfigured, after some algebraic rearrangement, in terms of 

the Tier 1 ratio as:

 H H 

 

 

 

 H 

 H 

 

 

 

 

 H 

 H 

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 6-29-2012

 H H 

 

 

 

 H 

 H 

 H 

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 ArcGIS 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 run through the Tier 2 methods, 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 

characteristics inherent to the assumed exposure scenario. To estimate the relationship 

between fish consumption at a high level, harvest rates, and lake size, the following 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, trophic level 3 fish (TL3) assumed to be consumed by anglers are 

represented by benthic carnivores (BC) and trophic level 4 fish (TL4) assumed to be consumed 

by anglers are represented by water column carnivores (WCC). Together, these two trophic 

levels are referred to as piscivorous fish.

1. Piscivorous fish, when present, comprise approximately 21 percent of the standing biomass 

of fish in an annual average. TL3 fish represent 17.5 percent of the standing fish biomass; 

 

30 Specifically, the geospatial file in the ESRI Data & Maps 2009 Data Update for ArcGIS 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 6-29-2012

TL4 fish account for 3.5 percent of this total fish biomass. Thus, TL4 fish, when present, 

represent the limiting compartment for angler fish harvesting and consumption.

2. Humans can harvest 10 percent of the TL4 biomass without threatening the population due 

to overharvesting.

3. The minimum viable effective population size for a single TL4 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 a TL4 fish is edible fillet muscle. A 0.33 edible fraction is used to 

estimate total fish biomass associated with human consumption.

5. A productivity level of 40 g wet weight/m2represents 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 shading in the exhibit provides an 

indication of where short- and longer term sustainable fishing is expected to occur (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). Thus, a lake size 

threshold of 25 acres was used. The final database included over 100,000 lakes with surface 

areas of at least 25 acres but smaller than 100,000 acres. Larger lakes 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.

The 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 for subsistence anglers (i.e., 26 g ww fillet per day in a 25-acre 

lake compared with 78.13 g ww fillet per day in the Tier 1 analysis). However, the decision was 

made to use the smallest lake size that supported an upper trophic level population without 

explicitly linking the lake size with the subsistence angler ingestion rate in order to maintain an 

overall health-protective approach. 

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 was 0.
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-18 6-29-2012

Exhibit_Att B-4. Maximum Fish Ingestion Rate (g/day) Associated with Sustainable Fishinga

Standing 

Biomass 

(g ww/m2)

b

Size of Pond or Lake (acres)

1 2 3 4 5 7.5 10 15 25 35 50 75 100 150 200 400

2 0 0 0 0 0 0 1 1 1 2 3 4 5 8 10 20

3 0 0 0 0 0 1 1 1 2 3 4 6 8 12 15 31

4 0 0 0 0 1 1 1 2 3 4 5 8 10 15 20 41

5.7 0 0 0 1 1 1 1 2 4 5 7 11 15 22 29 58

10 0 1 1 1 1 2 3 4 6 9 13 19 26 38 51 102

15 0 1 1 2 2 3 4 6 10 13 19 29 38 58 77 154

20 1 1 2 2 3 4 5 8 13 18 26 38 51 77 102 205

30 1 2 2 3 4 6 8 12 19 27 38 58 77 115 154 307

35 1 2 3 4 4 7 9 13 22 31 45 67 90 134 179 359

40 1 2 3 4 5 8 10 15 26 36 51 77 102 154 205 410

50 1 3 4 5 6 10 13 19 32 45 64 96 128 192 256 512

60 2 3 5 6 8 12 15 23 38 54 77 115 154 231 307 615

70 2 4 5 7 9 13 18 27 45 63 90 134 179 269 359 717

80 2 4 6 8 10 15 20 31 51 72 102 154 205 307 410 820

90 2 5 7 9 12 17 23 35 58 81 115 173 231 346 461 922

100 3 5 8 10 13 19 26 38 64 90 128 192 256 384 512 1025

110 3 6 8 11 14 21 28 42 70 99 141 211 282 423 563 1127

120 3 6 9 12 15 23 31 46 77 108 154 231 307 461 615 1229

130 3 7 10 13 17 25 33 50 83 117 166 250 333 499 666 1332

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 the likelihood to provide long-term

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/m2

, a lake that is 25 acres in size would sustain a maximum fish ingestion rate of 26 g/day for a 

single angler.
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-19 6-29-2012

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 2010a). 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, EPA 

obtained surface meteorological data from the Quality Controlled Local Climatological Data 

(QCLCD), a dataset available from the National Climatic Data Center (NCDC 2012). The 

processing began with over 2,000 stations located across the 50 states plus Puerto Rico and 

the U.S. Virgin Islands with at least some data in the 5-year period of 2006 through 2010. We

narrowed the stations to consider over 1,500 stations with data at both the beginning and end of 

the period. 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).

In the QCLCD data, many stations had unreliable or missing precipitation data. The numeric or 

text flag used to indicate missing data was not consistent, making automated processing of the 

whole dataset with respect to this one variable difficult. To reduce the uncertainty in the 

precipitation estimates, 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 hundred stations in the QCLCD 

dataset, we used the QCLCD precipitation data where available. If 30-year normal precipitation 

data and QCLCD precipitation data were not available for a station, as was the case for over 50 

stations in the QCLCD dataset, we flagged the station. 

As described later in Section B.3.2.3, over 50 additional stations were flagged due to missing 

wind data. Stations with precipitation or wind flags, totaling over 100, were not removed from the 

Tier 2 meteorology database. Flagged stations were kept in the database for informational 

purposes (i.e., so the user knows that the station was in the original 5-year dataset but its data 

cannot be used for Tier 2 screening methods). 

The QCLCD surface meteorological data cannot provide information on mixing heights, which 

must be estimated from upper air measurements. EPA’s Support Center for Regulatory 

Atmospheric Modeling archived estimated morning and afternoon mixing heights at more than 

50 locations across the 50 states and Puerto Rico corresponding to years in the late 1980s and 

early 1990s (EPA 2010b). More recent years are not available in that archived dataset. We 

selected a 4-year period (1987 through 1990; an arbitrary selection) of mixing height data for the 

Tier 2 analysis. Each surface meteorological station was paired with the closest mixing height 
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-20 6-29-2012

location. At this time, these pairings of surface meteorological stations with mixing height 

locations were not further scrutinized.

B.3.2.2 Coverage of Meteorological Stations Compared with Facility Locations

Early in the process of examining the meteorology data, we compared the locations of the U.S. 

point source facilities (from the 2005 NATA) to locations of surface and upper-air stations. 

These locations are shown in Exhibit_Att B-5 so that the spatial coverage of the surface 

meteorological stations can be compared to the spatial coverage of most of the point source 

facilities that might undergo an RTR multipathway analysis in the future.31. It should be noted 

that there are a couple hundred meteorology stations that were included in the final dataset 

discussed in the previous section but not shown in the exhibit. This is because additional 

scrutiny and processing of the meteorology data took place after the exhibit was created, 

leading to the inclusion of a couple hundred additional stations. These differences are not 

expected to significantly affect the analysis presented in the next paragraph. 

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. Exhibit_Att B-6 again shows 

each of the U.S. point source facilities from the 2005 NATA, and the location markers are 

shaded and sized according to how far the facility is from the closest of the surface 

meteorological stations. See again the cautionary note in the previous paragraph regarding the 

completeness of the meteorology dataset. The average and median distances are 21 km and 

15 km, respectively, between a facility and its closest surface meteorological station with 

complete data. This average distance is far smaller than the average distance between the 

petroleum refining facilities and meteorology stations used in the 2009 RTR report (72 km; EPA 

2009) and is likely much smaller than the average distance between all facilities and 

meteorological stations used in the 1996 NATA (less than 50 km; EPA 2001b). Approximately 

32 percent of facilities are less than 10 km from a surface meteorological station, while 29 

percent are between 10 and 20 km, 7 percent are farther than 50 km, and less than 1 percent

are farther than 100 km. If this analysis were performed again using the final meteorology 

dataset used in Tier 2, these facility-to-station distances would likely decrease by small amounts 

due to a greater number of stations in the final dataset.

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

 

31The 2005 NATA was the most recent, comprehensive, finalized dataset of nationwide point source emitters of 

hazardous air pollutants, and is used here only for illustrative 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-21 6-29-2012

Exhibit_Att B-5. An Early Analysis of the Locations of Meteorological Stations and Point Source Facilitiesa,b

a

The 2005 NATA was the most recent, comprehensive, finalized dataset of nationwide point source emitters of hazardous air pollutants, and it is used here only for illustrative 

purposes. The 2005 NATA used a meteorological dataset different from the one used in this report.

bNote that this analysis was performed early in the Tier 2 development process. The final meteorology dataset used in Tier 2 contains a couple hundred more surface stations than are 

shown here.
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-22 6-29-2012

Exhibit_Att B-6. An Early Analysis of the Distance Between the Point Source Facilities and the Closest Meteorological 

Stationa,b,c

a

The high spatial density of facilities in some areas (e.g., the Great Lakes area) causes some facility dots to be plotted on top of other facility dots. In these areas, most or all stations 

might appear to have distances of a certain range (e.g., less than 50 km, denoted by blue shades), when in fact some other distance values (e.g., at least 50 km but less than 100 km, 

denoted by green shades) might also be present but are not visible.

b

The 2005 NATA was the most recent, comprehensive, finalized dataset of nationwide point source emitters of hazardous air pollutants, and it is used here only for illustrative 

purposes. The 2005 NATA used a meteorological dataset different from the one used in this report.

cNote that this analysis was performed early in the Tier 2 development process. The final meteorology dataset used in Tier 2 contains a couple hundred more surface stations than are 

shown here.
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-23 6-29-2012

Using the data for each of the surface meteorological stations, EPA developed a software 

program to calculate the following statistics for each station-month combination in the 5-year 

period of surface meteorological data:

 Number of hourly observations,

 Number of hours with calm winds or missing winds,

 Number of hours the wind was blowing into each directional octant,

 Average wind speed blowing into each octant, and

 Total precipitation (irrespective of wind octant and preferring 30-year normal data if 

available).

This program then calculated the following statistics for each station’s period of record, using 

the above monthly totals and averages:

 Number of months with more than 10 percent of hours missing wind data,

 Percentage of time the wind blows into each octant (after excluding missing wind hours),

 Median wind speed when wind was blowing into each octant, and

 Average annual precipitation at the station.

The program flags a station if more than 10 percent of the months in the data period are missing 

wind data for more than 10 percent of the hours in those months. The program also flags a 

station if no precipitation data are available (as described in Section B.3.2.1). These flagged 

stations, totaling over 100, remained in the Tier 2 meteorology database for informational 

purposes, but their data were not used.

Mixing height averages were calculated outside of the software program used for surface 

meteorological data. The average annual mixing height was calculated at each mixing height 

location (averaged between the morning and afternoon mixing height values provided in the 

EPA upper-air dataset). These mixing height locations, with their average mixing height values, 

were proximity matched to surface meteorological stations and their wind and precipitation 

statistics.

B.4 Implementation of Tier 2 Analysis

The Tier 2 screening analysis is conducted using a Microsoft ® Excel tool. The tool has two 

main functions: 1) finding the closest surface meteorological station to a facility, and 2) 

calculating Tier 2 threshold values. The tool is created so that all facilities in multiple source 

categories can be screened at once, if desired. The tool is controlled by a dashboard control 

panel (see Exhibit_Att B-7), where each of 8 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 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. In Step 2, the user 

enters basic ‘global inputs’ that include the PB-HAPs32 and source categories included in the 

analysis (Exhibit_Att B-8). 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. Two 

additional input tables are created using the Tier 1 Microsoft Access screening tool and the 

ArcGIS ® lake database (see Section B.2.1), as described in Sections B.4.1 and B.4.2 below. 

 

32 At this time, the user cannot “turn on” or “turn off” processing of individual species of a larger PB-HAP “group”. For 

example, the user can select to process all PAH species, or none, but not some.
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-24 6-29-2012

Once these tables are created, the user enters the associated data in Steps 3 and 4, 

respectively. After all these input data have been supplied, Steps 5 and 6 conduct the Tier 2 

analysis and Steps 7 and 8 produce output tables that summarize the results, as described in 

Sections B.4.3 and B.4.4.

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 these 

results are provided to illustrate the overall 8-step process. These screen shots and 

accompanying descriptions represent the tool at one point in time (approximately three months 

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. 
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-25 6-29-2012

Exhibit_Att B-7. Example of the Dashboard To Conduct the Tier 2 Analysis

Exhibit_Att B-8. Example of Global Inputs Used in the Tier 2 Analysis
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-26 6-29-2012

B.4.1 Facility List for Tier 2 Screen (Step 3)

After clicking Button 3 on the dashboard (see Exhibit_Att B-7), 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 for 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), and the ratio of the REF-adjusted emission rate to the Tier 1 screening threshold for 

each evaluated chemical. This table is generated using the Tier 1 Microsoft® Access screening 

tool, and it is pasted into the “Facility Inputs” sheet of the Excel tool, as shown in Exhibit_Att 

B-9. 

Exhibit_Att B-9. Example of the Facility Input Data Required 

To Conduct the Tier 2 Analysis

B.4.2 Facility/Lake Distance Table (Step 4)

After clicking Button 4 on the dashboard (see Exhibit_Att B-7), 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 

Excel tool using the following steps. First, the location of each facility is imported into ArcGIS ® 

along with the final database of lake centroids (Section B.2.1). Then, from within the ArcGIS ® 

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 25 acres or larger than 100,000 acres). If no lake is present within 50 km in a 

given octant, the table includes dashes to indicate no lake is present within the radius selected. 

The lake names (where available) are scrutinized to subjectively remove industrial and 
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-27 6-29-2012

treatment water bodies, as discussed in Section B.2.1. This table is pasted in the 

“LakeDistance” sheet as shown in Exhibit_Att B-10. 

Exhibit_Att B-10. Example of the Lake Distance Data Required 

To Conduct the Tier 2 Analysis

B.4.3 Finding the Closest Meteorological Station (Step 5)

Now that the user has entered all required input data, clicking Button 5 initiates an analysis that 

finds the closest surface meteorological station (having complete summary statistics) to each 

facility in the Facility List using straight-line distance. If the facility and the meteorological 

station are more than 50 km apart, the tool returns the closest station but flags the match with 

the text “Review” to alert the user that further scrutiny of the facility/station pair might be 

necessary. In addition, the tool also alerts the user if the closest station was flagged for 

incomplete data, in which case the next closest station was selected instead. The results of the 

meteorological analysis are shown in the “MetAnalysis” sheet (an example is shown in 

Exhibit_Att B-11). In the example in Exhibit_Att B-11, the closest surface meteorological station 

to Facility 005 was approximately 34 km away, but the methodology rejected that station 

because it had too much missing data, choosing instead a different station approximately 37 km 

away with fewer missing data. For Facility 008, the closest station had complete data but was 

located 57 km from the facility, causing the analysis to display a warning about being beyond 

50 km away.
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-28 6-29-2012

Exhibit_Att B-11. Example Results of the Meteorological Station Matching 

Required To Conduct the Tier 2 Analysis
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-29 6-29-2012

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-7), 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-12. 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-13). 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 adjustment factors indicate the ratio of the Tier 2 threshold to the Tier 1 threshold. Thus, 

values above 1 (which nearly all are) indicate a risk reduction in Tier 2 relative to Tier 1, and 

larger and larger values (above 1) indicate larger and larger risk reductions. The Tier 1 result 

(the ratio of emissions to the Tier 1 emission screening threshold) is divided by the Tier 2 

adjustment factor, providing the Tier 2 result (the ratio of emissions to the Tier 2 emission 

screening threshold). 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-equivalent using the 

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-12. Example Results of the Octant Analysis Required 

To Conduct the Tier 2 Analysis
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-30 6-29-2012

Exhibit_Att B-13. Example of the TRIM.FaTE Matrix Results Required 

To Conduct the Tier 2 Analysis

B.4.5 Finalizing Threshold Adjustment Factors and Assembling Screening 

Results (Step 7)

This step was removed from the tool because its information was largely redundant relative to 

other sheets. 

B.4.6 Assembling Results (Step 8)

By clicking Button 8, the Excel 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-14). Cells with red highlighting call attention to cases 

where facilities exceeded the Tier 1 or Tier 2 thresholds.33 Using Source Category A as an 

example – there were 52 facilities in the category, 51 of which emitted cadmium, 46 emitted 

dioxins, 51 emitted mercury, and 20 emitted PAHs. No facilities in this category emitted 

cadmium or mercury at levels exceeding the Tier 1 threshold, so the summary table shows 

“Below Tier 1 Threshold” as the result for these two PBHAP groups. Because they did not 

exceed the Tier 1 threshold, they by definition did not exceed the Tier 2 threshold; the Tier 2 

analysis was still conducted for these PB-HAPs from these facilities, for simplicity and 

transparency in displaying Tier 1 results alongside Tier 2 results. Five of the facilities in the 

category exceeded the Tier 1 threshold for PAHs, with the maximum-emitting facility exceeding 

the threshold by a factor of 3.8 (or 4; in practice, these numbers are often interpreted by 

rounding). The Tier 2 methodology brought 4 of these 5 facilities below the Tier 2 threshold; the 

 

33 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 6-29-2012

fifth facility was very close to the Tier 2 threshold (1.1; typically interpreted by rounding to 1). All 

46 dioxin-emitting facilities in the category exceed the Tier 1 dioxin threshold. Three of the 46 

facilities did not exceed the Tier 2 threshold, leaving 43 exceeding the Tier 2 threshold but by 

much smaller amounts compared to Tier 1 (the maximum-emitting facility exceeds the Tier 2 

threshold by a factor of 958, compared to a factor of 5,570 for for Tier 1).

Exhibit_Att B-14. Example of Summary Output Table Created by the Tier 2 Toola

a

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, it would instead say “Below Tier 2 Threshold”, and its value for number of 

facilities exceeding would go from 1 to 0.

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-15). This 

detailed table is helpful because it shows which facilities exceeded the thresholds and by how 

much. As discussed in the previous paragraph, the summary table shows that 46 and 43 

facilities, respectively, exceeded the Tiers 1 and 2 thresholds for dioxins, with the maximumemitting facility exceeding Tiers 1 and 2 thresholds by respective factors of 5,570 and 958. This 

detailed summary table shows that Facility 001 is the facility emitting the most dioxins after 

adjusting the emissions for toxicity and exposure. The screen shot in Exhibit_Att B-15 shows 

exceedance factors for dioxins and PAHs for a subset of 8 other facilities in Source Category A 

(other facilities and source categories are in the table but not shown in the screen shot). 

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-16). 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. 
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-32 6-29-2012

Exhibit_Att B-15. Example of Detailed Output Table Created by the Tier 2 Toola

a

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 tenth data row would not be shaded red and it would instead say “Below Tier 2 Threshold”.

Exhibit_Att B-16. Example of Individual Output Table Created by the Tier 2 Tool

a

The tool shading schemes have been revised so that only ratios of at least 1.5 indicate an exceedance of the threshold. For 

example, the 1.3 value in the third data row would not be shaded red.
TRIM-Based Tiered Screening Methodology for RTR

Attachment B – Tier 2 B-33 6-29-2012

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

EPA. (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 (U.S. Environmental Protection Agency). (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. (2010a) 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.

EPA. (2010b) Support Center for Regulatory Atmospheric Modeling (SCRAM) Mixing Height 

Data. Available online at http://www.epa.gov/ttn/scram/mixingheightdata.htm. Accessed 

01/15/2012. Last updated 05/13/2010.

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 B1-1 6-29-2012

TRIM.FaTE Parameters

Addendum 1. Summary of TRIM.FaTE Parameters Considered for 

Inclusion in Tier 2 Analysis
[This page intentionally left blank.]
Attachment B, Addendum 1 B1-3 6-29-2012

TRIM.FaTE Parameters

Exhibits, Addendum 1

Exhibit Add B1-1. TRIM.FaTE Parameters Considered for Inclusion in Tier 2 

Analysis ............................................................................................................ 1-4
Attachment B, Addendum 1 B1-4 6-29-2012

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
Attachment B, Addendum 1 B1-5 6-29-2012

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
Attachment B, Addendum 1 B1-6 6-29-2012

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
Attachment B, Addendum 1 B1-7 6-29-2012

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 

ArcGIS (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
Attachment B, Addendum 1 B1-8 6-29-2012

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 watersoluble or particle-attached 

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
Attachment B, Addendum 1 B1-9 6-29-2012

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 particlebound 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
Attachment B, Addendum 1 B1-10 6-29-2012

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
Attachment B, Addendum 2 B2-1 6-29-2012

TRIM.FaTE Parameters

Addendum 2. Analysis of Lake Size and 

Sustainable Fish Population
[This page intentionally left blank.]
Attachment B, Addendum 2 B2-3 6-29-2012

Lake Size Analysis

CONTENTS, ADDENDUM 2

1. Introduction..................................................................................................................... 2-4

1.1. Angler Behavior......................................................................................................2-4

1.2. Fish Biology............................................................................................................2-5

1.2.1. Lake Productivity ..................................................................................... 2-5

1.2.2. Proportion of Fish Biomass by Trophic Level........................................... 2-7

1.2.3. Minimum Viable Population Size ............................................................. 2-8

1.3. Summary of Assumptions for the Lake Size Analysis .............................................2-9

1.4. Equations Used to Determine Lake Fish Populations .............................................2-9

1.5. References ...........................................................................................................2-11
Attachment B, Addendum 2 B2-4 6-29-2012

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 1.1 below). Other 

factors depend upon the general biology of fish populations in North American ecoregions (see 

Section 1.2). 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 1.3). Then, equations were developed (see Section 

1.4) that were used to create Exhibit_Att B-4, which was used to determine the threshold lake 

size of 25 acres.

1.1. Angler Behavior

Several assumptions regarding angler behavior are important for estimating a minimum lake 

size that is fishable. The first assumption is that anglers (and their family members) consume 

about 50:50 TL3 and TL4 fish by biomass. TL3 fish are benthic carnivores (BC) such as catfish 

and chub that consume benthic invertebrates and small benthic fish. TL4 fish are pelagic 

piscivores—water column carnivores (WCC) such as largemouth bass and walleye. This 

assumption of 50:50 TL3 and TL4 consumption appears reasonable without being overly 

conservative. Although anglers might prefer to catch and consume the pelagic (TL4) game fish 

species, their generally lower abundance compared with lower trophic-level fish dictates that 

anglers will more often capture the benthic (TL3) 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 associated with human consumption.

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. 

Another important consideration related to angler behavior is how much fish removal in a lake is 

possible without resulting in local fish populations declining to extirpation. 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 various models to 

predict sustainable harvests of different fisheries are numerous and complex. Species-specific 

parameters of importance 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 
Attachment B, Addendum 2 B2-5 6-29-2012

Lake Size Analysis

of predator (TL4) fish. For this lake size analysis, angler harvesting of fish is assumed to be 10 

percent of the biomass of pelagic TL4 fish.

1.2. Fish Biology 

Estimation of the minimum surface area of a pond or lake that is fishable also must be based on 

assumptions concerning fish biology, and these assumptions have been drawn from several 

published studies across the United States. 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 Tier 2: 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 (i.e., 

TL3 and TL4 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).

1.2.1. 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 for three categories of lakes:

Oligo- ( 0 μg/ ) = 1.9 g ww/m2

Meso-eutro (TP = 30 μg/ ) = 3.7 g ww/m2

Eutro- yp ( 00 μg/ ) = 8.5 g ww/m2

Nürnberg (1996) also summarized total fish biomass limits from Bachmann et al. (1996) for the 

lake trophic status categories based on a sample of 60 lakes in Florida:

Oligo-meso (TP 0 μg/ ) = 7.4 g ww/m2

Meso- ( 30 μg/ ) = 10.6 g ww/m2

Eutro- yp ( 00 μg/ ) = 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 

121° E to 122° W longitude. Their linear regression relating TP to total fish standing biomass 

(B) used the following equation and had a correlation coefficient (r

2

) of 0.84:

B = 0.792 + 0.072 (TP)
Attachment B, Addendum 2 B2-6 6-29-2012

Lake Size Analysis

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/m2for 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/m2over 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/m2in 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.

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. The general ranges of fish 

productivity across lakes of various surface areas, therefore, are considered to be independent 

of lake size over a wide range of lake sizes. Thus, lake size alone cannot be used to estimate 

fish productivity or maximal fish harvesting rates.

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 that related TP 

to total fish standing biomass (B) used the following equation and had an r2of 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 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.

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 

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.
Attachment B, Addendum 2 B2-7 6-29-2012

Lake Size Analysis

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 somewhat higher than a mean value for representative samples

of natural lakes in the United States. For the purpose of the Tier 2 analysis, we therefore 

rounded that value down to a single significant digit of 40 g ww/m2as the upper limit for total fish 

biomass in a lake.

1.2.2. Proportion of Fish Biomass by Trophic Level

As indicated in Section 1.1 of this addendum, for the Tier 2 analysis, the proportion of fish in an 

angler’s diet that consists of TL3 and TL4 fish 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 (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 TL3 and TL4 

relative to total standing fish biomass are examined assuming TL4 fish are present.

Examination of several studies of fish biomass by trophic habits indicated that top trophic level 

fish might comprise approximately 20 percent of the standing fish biomass in many locations. 

Ploskey and Jenkins (1982) estimated that piscivorous fish, both those that are generally freeswimming 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). Their model was designed to predict the 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—especially during early development, but also as adults, when they may switch between 

zooplankton and zoobenthos and from small fish to larger fish as they grow. A “distribution 

coefficient” was used 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, Håkanson 

and Boulion (2004) 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.

Based on the information above, the top trophic level (TL3 and TL4) 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 
Attachment B, Addendum 2 B2-8 6-29-2012

Lake Size Analysis

total fish biomass in a benthic carnivore (TL3) compartment and 3.5 percent of the total in a 

pelagic piscivore (TL4) compartment. Thus, the TL4 fish, when present, represent the limiting 

compartment for angler fish harvesting and consumption. 

1.2.3. Minimum Viable Population Size

The final step in estimating the minimum lake size that can support sustainable fishing of its TL4

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 general 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).

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 as 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 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” 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 TL3 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. 
Attachment B, Addendum 2 B2-9 6-29-2012

Lake Size Analysis

1.3. 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 sustainable fish ingestion and harvest rates and lake size

(see Section B.3.1 of the main report).

1. Piscivorous fish (TL3 plus TL4), when present, comprise approximately 21 percent of the 

standing biomass of fish (ignoring seasonal changes). The TL3 fish represent 17.5 percent 

of the standing fish biomass; TL4 fish account for 3.5 percent of this total fish biomass. 

Thus, TL4 fish, when present, represent the limiting compartment for angler fish harvesting 

and consumption.

2. Humans can harvest 10 percent of the TL4 biomass without threatening the population due 

to overharvesting.

3. The MVP size for a single TL4 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 a TL4 fish is edible fillet muscle.

1.4. Equations Used to Determine Lake Fish Populations

The standing biomass of TL4 fish supported in Lake X (TL4 SB) 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. 

TL4 SB = Total SB × Fraction TL4 (Equation 1)

where:

TL4 SB = Standing biomass of TL4 fish (g wet weight [ww]/m2

) in Lake X

Total SB = Total standing biomass of fish (g ww/m2

) in Lake X

Fraction TL4 = Fraction of TL4 fish in Lake X (0.035)

Using TL4 SB and the size of Lake X (Lake Size), the total number of TL4 fish supported in 

Lake X can be calculated using Equation 2 below.

(Equation 2)

where:

No. TL4 = Total number of TL4 fish in Lake X

Lake Size = Size of Lake X (acres)

TL4 SB = Standing biomass of TL4 fish (g ww/m2

; from Equation 1)

CF = 4047 m2

/acre

BWa = Body weight of adult TL4 fish (2000 g; assumed based on professional judgment)

The likely annual productivity of TL4 fish (kg/year) in Lake X can be estimated using Equation 3.

BWa

LakeSize T4SB CF

No T4

× ×

. =
Attachment B, Addendum 2 B2-10 6-29-2012

Lake Size Analysis

(Equation 3)

where:

Productivity TL4 = Likely annual productivity of TL4 fish in Lake X (kg/year)

Lake Size = Size of the Lake X (acres)

TL4 SB = Standing biomass of TL4 fish (g ww/m2

; from Equation 1)

CF1 = 4047 m2

/acre

CF2 = 1000 g/kg

The maximum fish ingestion rate (g/day) for TL3 plus TL4 fish associated with sustainable 

fishing can be predicted using Equation 4. It assumes 50:50 consumption behaviors of TL3 and 

TL4 fish, represented by the factor of 2 in Equation 4. 

(Equation 4)

where:

Max Sustain IR TL3 + 

TL4 = Predicted maximum sustainable ingestion rate for TL3 plus TL4 fish (g/day)

Productivity TL4 = Likely annual productivity of TL4 fish in Lake X (kg/year; from Equation 3)

FF = Fillet fraction; represents the assumed edible portion of fish (0.33; unitless)

HF = Annual harvest fraction (0.10; unitless)

CF1 = 1000 g/kg

CF2 = 365 days/year

CF2

LakeSize T4SB CF1

Productivity T4

× ×

=

CF2

2 ProductivityT4 FF HF CF1

MaxSustainIR T3 T4

× × × ×

( + ) =
Attachment B, Addendum 2 B2-11 6-29-2012

Lake Size Analysis

1.5. References

Akçakaya, H.R., Burgman, M.A., and Ginzburg, L.R. 1999. Applied Population Ecology: 

Principles and Computer Exercises using RAMAS EcoLab. Sunderland, MA: Sinauer 

Associates, Inc. Publishers.

Barnthouse, L.W., Munns, W.R. Jr., and Sorensen, M.T. 2008. Population-Level Ecological Risk 

Assessment. Boca Raton, FL: CRC Taylor & Francis Group; Pnesacola, FL: Society of 

Environmental Toxicology and Chemistry (SETAC).

Boyce, M.S. 1993. Population viability analysis: adaptive management for threatened and 

endangered species. In: Wildlife Management Institute, Transactions of the 58th North 

American Wildlife and Natural Resources Conferences. Proceedings WWRC-93-19, pp. 

520-527.

Brönmark, C. and S.E.B. Weisner. 1996. Decoupling of cascading trophic interactions in a 

freshwater benthic food chain. Oecologia 108: 534-541.

Demers, E., McQueen, D.J., Ramcharan, C.W., and A. Perez-Fuentetaja. 2001. Did piscivores 

regulate changes in fish community structure? Advanc. Limnol. 56: 49-80.

Ebert, E., N. Harrington, K. Boyle, J. Knight, and R. Keenan. 1993. Estimating consumption of 

freshwater fish among Maine anglers. N. Am. J. Fisheries Manage. 13: 737-745.

Ewens, W.J., Brockwell, P.J., Gani, J.M., and Resnick, S.I. 1987. Minimum viable population 

sizes in the presence of catastrophes. In: M.E. Soulé (ed.) Viable Populations for 

Conservation. United Kingdom, Cambridge: Cambridge University Press, pp. 59-68.

Frankel, O.H., and Soulé, M.E. 1981. Conservation and evolution. United Kingdom, Cambridge: 

Cambridge University Press. 

Franklin, I.R. 1980. Evolutionary change in small populations. In: M.E. Soulé and B.A. Wilcox 

(eds.) Conservation Biology: An Evolutionary-Ecological Perspective. Sunderland, MA: 

Sinauer Associates; pp. 135-150. As cited by Shaffer, 1987.

Håkanson, L. and V.V. Boulion. 2004. Modeling production and biomasses of prey and 

predatory fish in lakes. Hydrobiologia 511: 125–150.

Hanson, J.M., and W.C. Leggett. 1982. Empirical prediction of fish biomass and yield. Can. J. 

Fish. Aquat. Sci. 39: 257-263. 

Hoyer, M.V., and D.E. Canfield, Jr. 1991. A phosphorus-fish standing crop relationship for 

streams? Lake Reserv. Manage. 7: 25-32.

Lande, R., and Barrowclough, G.F. 1987. Effective population size, genetic variation, and their 

use in population management. In: M.E. Soulé (ed.) Viable Populations for Conservation. 

United Kingdom, Cambridge: Cambridge University Press, pp. 87-123.

Leidy, G.R., and Jenkins, R.M. 1977. The development of fishery compartments and population 

rate coefficients for use in reservoir ecosystem modeling. Contract Report Y-77-1 prepared 

by the National Reservoir Research Program, U.S. Fish and Wildlife Service, for the U.S. 

Army Corps of Engineers, Waterways Experiment Station, Vicksburg, Mississippi, USA.
Attachment B, Addendum 2 B2-12 6-29-2012

Lake Size Analysis

Menzie, C., Bettinger, N., Fritz, A., Kapustka, L., Regan, H., Moller, V., and Noel, H. 2008. 

Population protection goals. In: Barnthouse, L.W., Munns, W.R. Jr., and Sorensen, M.T. 

2008. Population-Level Ecological Risk Assessment. Boca Raton, FL: CRC Taylor & Francis 

Group; Pnesacola, FL: Society of Environmental Toxicology and Chemistry (SETAC), pp. 

41-68.

NRC (National Research Council). 1986. Ecological Knowledge and Environmental Problem 

Solving. Committee on the Applications of Ecological Theory to Environmental Problems. 

Washington, DC: National Academies of Science Press.

Nürnberg, G.K. 1996. Trophic state of clear and colored, soft- and hardwater lakes with special 

consideration of nutrients, anoxia, phytoplankton and fish. Journal of Lake and Reservoir 

Management 12(4): 432-447.

Ploskey, G.R., and R.M. Jenkins. 1982. Biomass model of reservoir fish and fish-food 

interactions, with implications for management. North American Journal of Fisheries 

Management 2(2): 105-121.

Rieman, B.E., and Allendorf, F.W. 2001. Effective population size and genetic conservation 

criteria for bull trout. N. A. J. Fisheries Manage. 21:756-764.

Senner, J.W. 1980. Inbreeding depression and the survival of zoo populations. In: M.E. Soulé 

and B.A. Wilcox (eds.) Conservation Biology: An Evolutionary-Ecological Perspective. 

Sunderland, MA: Sinauer Associates; pp. 209-244. As cited by Shaffer, 1987.

Schaffer, M.L. 1981. Minimum population sizes for species conservation. Bioscience 31: 131-

134.

Shaffer, M. 1987. Minimum viable populations: coping with uncertainty. In: M.E. Soulé (ed.) 

Viable Populations for Conservation. United Kingdom, Cambridge: Cambridge University 

Press, pp. 69-86.

Soulé, M.E. 1980. Thresholds for survival: maintaining fitness and evolutionary potential. In: 

M.E. Soulé and B.A. Wilcox (eds.) Conservation Biology: An Evolutionary-Ecological 

Perspective. Sunderland, MA: Sinauer Associates; pp. 151-170. As cited by Shaffer, 1987.

Soulé, M.E. (ed.) 1987. Viable Populations for Conservation. United Kingdom, Cambridge: 

Cambridge University Press.

USEPA (US Environmental Protection Agency). 1989. Assessing human health risks from 

chemically contaminated fish and shellfish: a guidance manual. EPA-503/8-89-002. 

Washington, DC. 

Yurk, J.J., and J.J. Ney. 1989. Phosphorus-fish community biomass relationships in southern 

Appalachian reservoirs: can lakes be too clean for fish? Lake and Reservoir Management 

5(2): 83-90.
[This page intentionally left blank.]