Appendix 5

Multipathway Analysis

Technical Support Document for TRIM‐Based Multipathway Screening

Scenario for RTR: Summary of Approach and Evaluation

Memorandum on: Post Screening Analysis of PB‐HAP Emissions for Ferroalloys Source Category
Technical Support Document for TRIM-Based Multipathway Screening 

Scenario for RTR: Summary of Approach and Evaluation 
Appendix 5 i

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Appendix 5 ii

TABLE OF CONTENTS

EXECUTIVE SUMMARY ........................................................................................................... vi

1 Introduction...................................................................................................................... 1

1.1 Background.....................................................................................................................1

1.2 Organization of this Document ........................................................................................2

2 Approach for Screening Evaluation of Multipathway Exposures................................. 2

2.1 Overview.........................................................................................................................2

2.2 Chemicals of Potential Concern ......................................................................................3

2.3 Use of Screening Threshold Emission Levels..................................................................5

2.4 The TRIM-Based Screening Scenario: Basis for Threshold Emission Rates ..................7

2.4.1 Exposure Routes Evaluated................................................................................ 7

2.4.2 Approach to Configuration and Parameterization................................................ 8

2.4.3 Modeling Framework .......................................................................................... 9

2.4.3.1 Fate and Transport Modeling ...............................................................10

2.4.3.2 Exposure Modeling and Risk Characterization.....................................11

2.5 Refined Analyses ..........................................................................................................12

3 Description of Modeling Scenario .................................................................................12

3.1 TRIM.FaTE Scenario Configuration and Parameterization ............................................12

3.1.1 Chemical Properties...........................................................................................13

3.1.2 Spatial Layout ....................................................................................................13

3.1.3 Watershed and Water Body Parameterization ...................................................15

3.1.3.1 Water Balance .....................................................................................15

3.1.3.2 Sediment Balance................................................................................16

3.1.4 Meteorology.......................................................................................................17

3.1.5 Aquatic Food Web .............................................................................................19

3.1.6 Using TRIM.FaTE Media Concentrations...........................................................19

3.2 Exposure and Risk Calculations....................................................................................21

3.2.1 Calculating Concentrations in Farm Food Chain Media .....................................21

3.2.2 Ingestion Exposure Assessment........................................................................22

3.2.2.1 Ingestion Exposure Pathways and Routes of Uptake...........................22

3.2.2.2 Exposure Scenarios and Corresponding Inputs ...................................22

3.2.2.3 Calculating Average Daily Doses.........................................................24

3.2.2.4 Infant Ingestion of Breast Milk..............................................................25

3.2.3 Risk Characterization.........................................................................................25

3.2.4 Dermal Risk Screening ......................................................................................26

3.2.4.1 Hazard Identification and Dose Response Assessment .......................26

3.2.4.2 Dermal Exposure Estimation................................................................27

3.2.4.3 Screening-Level Cancer Risks and Non-Cancer Hazard Quotients......31

3.2.4.4 Dermal Screening Results ...................................................................32

3.3 Summary of Scenario Assumptions...............................................................................33
Appendix 5 iii

4 Evaluation of Screening Scenario .................................................................................36

4.1 Overview.......................................................................................................................36

4.2 Overall Chemical Mass Partitioning...............................................................................38

4.3 Comparison to Measured Concentrations .....................................................................38

4.3.1 Scope of the Evaluation .....................................................................................38

4.3.2 Methods and Organization of this Section..........................................................40

4.3.3 Chemical-Specific Comparisons ........................................................................41

4.3.3.1 Cadmium .............................................................................................41

4.3.3.2 Mercury................................................................................................45

4.3.3.3 Dioxins (2,3,7,8-TCDD)........................................................................54

4.3.3.4 PAHs (Benzo[a]pyrene) .......................................................................61

4.3.4 Summary ...........................................................................................................68

5 References ......................................................................................................................70

Attachment 1 – TRIM.FaTE Inputs

Attachment 2 – RTR Access-based Exposure and Risk Calculation Tool - Multimedia Ingestion 

Risk Calculator (MIRC)

Attachment 3 – Systematic Sensitivity Analysis 
Appendix 5 iv

LIST OF EXHIBITS

Exhibit 2-1. Conceptual Decision Tree for Evaluation of Non-Inhalation Exposures of PB-HAPs ..............4

Exhibit 2-2. OAQPS PB-HAP Compounds a................................................................................................5

Exhibit 2-3. Emission Thresholds for Screening of Multipathway Exposures..............................................6

Exhibit 2-4. Overview of Ingestion Exposure and Risk Screening Evaluation Method..............................10

Exhibit 2-5. Overview of Process Carried Out in the Multimedia Ingestion Risk Calculator......................12

Exhibit 3-1. TRIM.FaTE Surface Parcel Layout.........................................................................................13

Exhibit 3-2. Summary of Key Meteorological Inputs ..................................................................................18

Exhibit 3-3. Aquatic Biota Parameters for the TRIM.FaTE Screening Scenario........................................20

Exhibit 3-4. Spatial Considerations – TRIM.FaTE Results Selected for Calculating Farm Food Chain 

Media Concentrations and Receptor Exposures ...................................................................21

Exhibit 3-5. Summary of Ingestion Exposure Pathways and Routes of Uptake........................................23

Exhibit 3-6. Overview of Exposure Factors Used for RTR Multipathway Screening a,b.............................24

Exhibit 3-7. Dose-response Values for PB-HAPs Addressed by the Screening Scenario ........................25

Exhibit 3-8. Cancer Slope Factors and Reference Doses Based on Absorbed Dose...............................27

Exhibit 3-9. Receptor-Specific Body Surface Area Assumed to be Exposed to Chemicals ......................29

Exhibit 3-10. Scenario-Specific Exposure Values for Water and Soil Contact ..........................................30

Exhibit 3-11. Chemical-Specific Dermal Exposure Values for Water and Soil Contact.............................31

Exhibit 3-12. Summary of Dermal Non-Cancer Hazards ...........................................................................32

Exhibit 3-13. Summary of Dermal Cancer Risks........................................................................................33

Exhibit 3-14. Summary of RTR Screening Scenario Assumptions and Associated Conservatism...........34

Exhibit 4-1. Distribution of Chemical Mass in Screening Scenario ............................................................39

Exhibit 4-2. Summary of Modeled and Observed Concentrations of Cadmium in Environmental Media .43

Exhibit 4-3. TRIM.FaTE Cadmium Concentrations in Fish and Calculated Bioaccumulation Factors with 

Respect to Total Water Concentration...................................................................................44

Exhibit 4-4. Fraction of Cadmium Mass Sorbed vs. Dissolved in TRIM.FaTE Compartments..................44

Exhibit 4-5. Summary of Modeled and Observed Concentrations of Cadmium in Ingestible Media .........46

Exhibit 4-6. Estimated Contribution of Modeled Food Types to Cadmium Ingestion Exposures and 

Hazard Quotient .....................................................................................................................47

Exhibit 4-7. Summary of Modeled and Observed Concentrations of Total Mercury in Environmental 

Media......................................................................................................................................49

Exhibit 4-8. TRIM.FaTE Mercury Concentrations, Speciation, and Calculated Methyl Mercury 

Bioaccumulation Factors (in parentheses) in Fish Compartments ........................................50

Exhibit 4-9. TRIM.FaTE Mercury Speciation and Partitioning in Environmental Media Compartments....51

Exhibit 4-10. Summary of Modeled and Observed Concentrations of Total Mercury in Ingestible Media 53

Exhibit 4-11. Estimated Contribution of Modeled Food Types to Divalent Mercury and Methyl Mercury 

Ingestion Exposures...............................................................................................................54

Exhibit 4-12. Estimated Contribution of Summed Modeled Food Types to Divalent Mercury and Methyl 

Mercury Hazard Quotients .....................................................................................................54
Appendix 5 v

Exhibit 4-13. Summary of Modeled 2,3,7,8-TCDD Concentrations and Observed Total Dioxin TEQ 

Concentrations in Environmental Media ................................................................................57

Exhibit 4-14. TRIM.FaTE 2,3,7,8-TCDD Concentrations in Fish and Calculated Bioaccumulation Factors 

with Respect to Total Water Concentration ...........................................................................58

Exhibit 4-15. Fraction of 2,3,7,8 - TCDD Mass Sorbed vs. Dissolved in TRIM.FaTE Compartments.......59

Exhibit 4-16. Summary of Modeled 2,3,7,8-TCDD Concentrations and Observed Total Dioxin TEQ 

Concentrations in Ingestible Media ........................................................................................60

Exhibit 4-17. Contribution of Modeled Food Types to 2,3,7,8-TCDD Ingestion Exposures (mg/kg/day) ..61

Exhibit 4-18. Summary of Modeled and Observed Concentrations of Benzo[a]pyrene in Environmental 

Media......................................................................................................................................64

Exhibit 4-19. TRIM.FaTE Benzo[a]Pyrene Concentrations in Fish and Calculated Bioaccumulation 

Factors with Respect to Total Water Concentration ..............................................................65

Exhibit 4-20. Fraction of Benzo[a]Pyrene Mass Sorbed vs. Dissolved in TRIM.FaTE Compartments .....66

Exhibit 4-21. Summary of Modeled and Observed Concentrations of Benzo[a]pyrene in Ingestible Media

................................................................................................................................................67

Exhibit 4-22. Contribution of Modeled Food Types to Benzo[a]pyrene Ingestion Exposures (mg/kg/day)68
Appendix 5 vi

EXECUTIVE SUMMARY

This report describes the methods used by EPA to evaluate multipathway exposures to 

hazardous air pollutants and the associated human health risks. In particular, the report 

explains the methods, assumptions, and input data used to develop a screening scenario that is 

used in the first tier of Risk and Technology Review II (RTR II) assessments of such emissions. 

This report describes the development of the screening scenario and its application in 

generating qualitative, screening-level estimates of human exposure to PB-HAPs and 

consequent risk. 

BACKGROUND

The Clean Air Act directs the U.S. Environmental Protection Agency (EPA) to assess the 

residual risk from hazardous air pollutants (HAPs) emitted by sources regulated by technologybased standards. To evaluate multipathway exposures and human health risks, a two-tiered 

approach was developed. In the first tier of the approach, a screening evaluation is conducted 

that uses the identity and magnitude of HAP emissions from a source to determine whether that 

source meets certain human health risk-based criteria with respect to multipathway exposures. 

The purpose of this first-tier screening is to eliminate facilities from further analysis that pose no 

unacceptable risk to human health, while identifying those facilities that warrant a second-tier, 

more refined, site-specific analysis of residual risk.

PURPOSE OF THE SCENARIO

The approach described here for evaluating human multipathway exposures and risks consists 

of an initial, screening-level tier that can be conducted quickly and efficiently to determine those 

facilities for which multipathway risks are expected to be below levels of concern. The key 

component of the first tier of this approach is a multipathway screening scenario based on 

EPA‘s Total Risk Integrated Methodology (TRIM). The TRIM-based modeling scenario provides 

a means for quickly and efficiently completing an initial non-inhalation exposure and risk 

screening analysis of a facility. The scenario is applied for use in RTR evaluations by 

calculating screening threshold (also called de minimis) emission rates for selected PB-HAPs

that correspond to a cancer risk of 1 in 1 million or a chronic non-cancer hazard quotient (HQ) of 

1. These screening threshold emission rates then can be used in the first risk screening step 

without requiring additional model runs. Sources whose emissions exceed the screening 

threshold emission rate for any PB-HAP would be subjected to refined evaluation(s) in a second 

tier analysis.

The scenario has been used to calculate numerical exposure and risk values for 4 of the 14 

HAPs that OAQPS has identified as candidates for multipathway risk assessments: cadmium, 

mercury, dioxins (i.e., chlorinated dibenzo-p-dioxins and -furans), and polycyclic organic matter. 

These compounds were selected because they are expected, based on current knowledge of 

relative emissions and toxicity, to pose a substantial share of the non-inhalation risks to humans 

from air emissions at sources subject to residual risk provisions of the Clean Air Act. The 

scenario is not intended to be used to produce quantitative estimates of actual or potential risk. 

Rather, it provides a basis for determining if residual human health risks are of potential 

concern. Such determinations can then be used to support decisions to proceed with or forego

more definitive analyses of non-inhalation exposures to HAPs and the associated risks. The 

scenario does, however, provide a technically defensible starting point for additional fate and 

transport and exposure/risk analyses of facility emissions that are not ―screened out‖ in the first 

tier evaluation. 
Appendix 5 vii

OVERVIEW OF SCREENING SCENARIO

This screening scenario is intended to reduce the possibility that EPA will fail to identify 

unacceptable risks. Thus, the scenario‘s conservative approach most likely overestimates risk, 

which is appropriate for a screening assessment. Parameter values were defined carefully, and 

properties having more uncertainty were assigned greater conservative bias to prevent 

underestimating potential risks. The screening scenario is designed to estimate the upper end 

of the range of individual, long-term, non-inhalation exposures for situations likely to be 

encountered in the United States. The result of reviewing a broad range of conditions and 

selecting values representative of higher exposures in conceptualizing and building the 

screening tool is that the scenario is unlikely to occur at any one location but has a high 

likelihood of representing the upper end of any potential exposures. 

The screening scenario addresses non-inhalation exposures, which can occur through both 

dermal and ingestion exposure pathways. Pathways examined include incidental ingestion of 

soil; ingestion of homegrown produce, beef, cows‘ milk, poultry and eggs, and pork; ingestion of 

fish; and exposure through ingestion of breast milk as an infant (for dioxins only). Dermal 

absorption of chemicals that are originally airborne is generally relatively minor, and this 

pathway was not included in the scenario used to calculate screening emission thresholds. A 

highly conservative estimate of dermal exposures and risks was calculated for comparison to 

ingestion exposures and risks. 

For this approach, chemicals were modeled separately to evaluate the potential for risks, with 

exposures for each PB-HAP summed across all ingestion exposure pathways. Exposures were 

modeled for a hypothetical farm homestead and fishable lake near an emissions source. For 

this setting, exposures were estimated for a hypothetical individual assuming subsistence 

consumption of all potentially contaminated foodstuffs from the farm or lake. The scenario was 

purposely designed to produce conservative (i.e., health-protective) results, and certain critical 

exposure/activity assumptions, such as food ingestion rates, were selected from the upper ends 

(e.g., the 90th percentile) of representative exposure parameter distributions. The 

physical/chemical environment was parameterized with a mix of typical and health-protective

values. The scenario‘s spatial/temporal aspects and the components that influence air 

concentrations were also chosen so that concentrations in environmental media would not be 

underestimated given the range of possible settings and meteorological conditions that might be 

encountered. Properties of the environmental media were parameterized with either typical or 

conservative values, with a more protective bias introduced for properties having greater 

uncertainty.

MODELING FRAMEWORK

The approach for risk evaluation of ingestion exposures and risk screening has four 

components: 

1. fate and transport modeling of PB-HAPs emitted to air that partition into soil, water, and 

other environmental media (including fish); 

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

3. estimating ingestion exposures for the selected media contact scenarios and average 

daily ingestion doses for a hypothetical human receptor; and

4. calculating lifetime cancer risk estimates or chronic non-cancer HQs for each HAP.
Appendix 5 viii

TRIM.FATE SCENARIO CONFIGURATION AND PARAMETERIZATION

To model chemical fate and transport in the environment, EPA‘s Fate, Transport, and Ecological 

Exposure (TRIM.FaTE) module of the TRIM system was used. The physical configuration of 

the scenario was designed to be generally conservative and the environmental and chemicalspecific properties were parameterized with either conservative or central-tendency values. 

Chemical/physical properties were obtained from peer-reviewed and standard reference 

sources. The spatial layout represents a farm homestead and a fishable lake near an emissions 

source. The predominant wind direction is toward the farm and lake watershed, and the 

downwind modeling area is symmetrical around a 10-kilometer east-west line and divided into 

five pairs of parcels. The aquatic food web in the scenario is meant to represent a generic 

aquatic ecosystem within a 47-hectare lake. 

Fate and transport modeling outputs include average PB-HAP concentrations and deposition 

rates for various media (air, soil, surface water, and fish) for each year and for each parcel of 

the model scenario. TRIM.FaTE can output instantaneous chemical concentrations for a userspecified time step and also can be configured to calculate temporal averages. For the 

screening scenario, the model outputs results on a daily basis, and daily concentration results 

are averaged to obtain annual average concentrations. The source is assumed to emit for 50 

years.

EXPOSURE AND RISK CALCULATIONS

The Multimedia Ingestion Risk Calculator (MIRC) was developed to carry out required farm food 

chain transfer, ingestion exposure, and risk calculations. Concentrations in farm food chain 

media are calculated using empirical biotransfer factors (e.g., soil-to-plant factors, which are the 

ratios of the concentrations in plants to concentrations in soil). Ingestion exposures based on 

exposure factors, including food-type-specific ingestion rates, are calculated for a hypothetical 

exposed individual. Lifetime cancer risks and the potential for chronic non-cancer effects are 

estimated using chemical-specific ingestion cancer slope factors and reference doses. 

Exposure pathways evaluated include incidental ingestion of soil and consumption of fish, 

produce, and farm animals and related products. Cancer risk estimates and HQs are calculated 

separately for each PB-HAP included in an analysis.

CALCULATION OF SCREENING EMISSION THRESHOLDS

After the configuration of the TRIM.FaTE and MIRC modeling scenarios was completed, 

screening-level emission rate thresholds were calculated by conducting iterative model 

simulations to determine emission rates for cadmium, mercury, dioxins, and polycyclic organic 

matter that correspond to a cancer risk of 1 in 1 million or a chronic non-cancer hazard quotient 

(HQ) of 1. Given the generally conservative nature of the scenario inputs, these thresholds are 

assumed to be appropriate for screening sources emitting these HAPs.

EVALUATION OF SCREENING SCENARIO

Model evaluations serve as an important aspect of environmental risk assessments by 

illustrating the performance of the model under different conditions and assumptions and 

facilitating the comparison of model outputs to measurement data and other modeling results. 

Evaluations thereby provide an opportunity to gain confidence in model performance and 

identify and better characterize uncertainties associated with model construct and inputs. The 

screening scenario was analyzed through comparisons to the literature and sensitivity analyses. 
Appendix 5 ix

Outputs from the screening scenario based on screening threshold emission values were 

compared to literature values for cadmium, mercury, 2,3,7,8-TCDD, and benzo[a]pyrene. In 

general, taking into account the limitations of this type of comparison, the configuration of the 

models for screening appear to be reasonable. Chemical partitioning predicted by the model is 

generally consistent with information on environmental partitioning presented in the literature for 

media of concern. Where results are not consistent with literature values, a more detailed 

investigation of underlying assumptions may help to identify means of adjusting the scenario 

configuration.

Sensitivity analyses were performed to evaluate the influence of model parameters and to 

provide information on which parameters are likely to be most influential in dictating the 

uncertainty associated with the results. The sensitivity analyses conducted on the RTR 

screening modeling scenario encompassed the fate and transport modeling carried out using 

TRIM.FaTE and the farm food chain and ingestion exposure calculations performed using 

MIRC. A systematic sensitivity analysis was conducted by varying each input independently 

and calculating the resulting effect on the risk or hazard quotient estimates in order to rank the 

variables from most to least sensitive. The analysis suggests that several TRIM.FaTE variables 

(including wind speed, mixing height, and emission rate) have a large effect on the risk and 

hazard estimates. The estimates of hazard and risk are also highly sensitive to key parameters 

in the primary exposure pathway (i.e., ingestion of food types resulting in the highest exposures) 

for each PB-HAP. Other analyses performed indicated that accounting for temporal correlations 

in ingestion rates and body weights and varying the body weight and ingestion rate percentiles 

used in the model scenario have a limited effect on the risk and hazard estimates. The use of 

site-specific meteorological variable values (as opposed to the generic screening scenario 

values) resulted in a decrease in the risk and hazard estimates of approximately one order of 

magnitude.

In addition, media concentrations estimated using the screening scenario were compared to 

analogous outputs estimated using site-specific TRIM.FaTE model applications configured for 

two secondary lead smelting sources. The same emission rates for benzo[a]pyrene, 2,3,7,8-

TCDD, elemental mercury, and divalent mercury were entered into the screening scenario and 

the site-specific model scenarios. Model results were compared for soil, water, and sediment 

compartment types. In all media, the screening scenario produced higher concentrations for all 

chemicals than the site-specific model applications, the expected result given that the screening 

scenario is conservative (and therefore tends to result in higher media concentrations). 

Some of the evaluation results included in this document were presented in August 2009 to an 

expert panel convened by EPA‘s Science Advisory Board (SAB) for formal review and 

comment. The Panel determined that the screening approach to PB-HAPs developed by EPA 

for RTR is suitable and effective for the purposes of residual risk assessment. Specifically, in 

their final report to EPA transmitting their comments, the Panel concluded that ―...the Agency‘s 

screening approach is based on an appropriate framework and should usefully screen out 

sources that do not need a detailed site-specific multi-pathway analysis.‖ Evaluations of 

TRIM.FaTE and MIRC will continue as additional residual risk assessments are conducted and 

as the state of the science for evaluating multipathway exposures continues to evolve. 

Consequently, it is possible (or even likely) that the model configurations used to estimate RTR 

screening thresholds and conduct site-specific risk assessments will be modified as additional 

knowledge regarding model performance is acquired.
Appendix 5 1

1 Introduction

1.1 Background 

Section 112(f)(2)(A) of the Clean Air Act (CAA) directs the U.S. Environmental Protection 

Agency (EPA) to assess the risk remaining (residual risk) from hazardous air pollutants (HAPs) 

that continue to be emitted from sources after application of maximum achievable control 

technology (MACT) standards under section 112(d) of the CAA. Under these requirements, 

EPA will promulgate additional emission standards for a source category if the MACT standards 

do not provide an ―ample margin of safety‖ for human health. One aspect of human health that 

EPA must consider is the potential for exposures to HAPs via non-inhalation pathways and the 

risks associated with such exposures. 

As described in EPA‘s Risk and Technology Review (RTR) Assessment Plan (EPA 2006a), 

multipathway human health risks were preliminarily evaluated in 2006. The evaluation used 

draft National Emissions Inventory (NEI) data for RTR Phase II (RTR II) source categories and a 

simplified multipathway exposure modeling approach (see Appendix 5 in EPA 2006a).1 

However, as noted in the RTR Assessment Plan, EPA‘s intention was to develop an approach 

that would supersede the preliminary methods used in 2006 and involve the use of EPA‘s Total 

Risk Integrated Methodology (TRIM), a risk assessment modeling system for air toxics 

developed by OAQPS. The TRIM system can be used to predict the local impacts of persistent 

and bioaccumulative HAPs (PB-HAPs) from an emissions source to estimate associated human 

health risk. 

EPA will implement a two-tiered approach to evaluate multipathway exposures and human 

health risks for RTR II. In the first tier, a screening evaluation is conducted that focuses on the 

identity and magnitude of HAP emissions from a given facility to determine whether a facility 

passes certain human health risk-based criteria. Sources that are ―screened out‖ are assumed 

to pose no unacceptable risks to human health and are not considered in further analyses. For 

sources that do not pass the screen, more refined, site-specific multipathway assessments are 

conducted as appropriate. These human health risk results are considered, in combination with 

estimated inhalation human health risks, potential ecological risks, and other factors, to support 

decisions about residual risk for RTR II source categories. 

This current document describes the technical basis for the first, screening-level tier of EPA‘s 

multipathway human health evaluation of RTR emission sources. Specifically, the models, 

configurations, and inputs used to derive screening threshold (also called de minimis) emission 

rates in the first tier of the approach are described in detail here.

2

 Analyses of the screening 

scenario conducted to evaluate the scenario‘s defensibility are also discussed. EPA expects 

that refined multipathway risk assessment methods (when required) will rely on the same TRIMbased modeling approach used to derive the screening-level thresholds used in the first 

screening tier. However, the details of refined assessments will vary depending on the facility 

location, source category, chemicals emitted, and other parameters, and the specific methods 

and processes involved in multipathway evaluations beyond the first tier are not explored in 

depth in this document.

 

1

The preliminary evaluation conducted in 2006 relied on a simpler modeling approach that did not utilize TRIM and 

involved a less rigorous analysis of parameter input values. 

2 De minimis is a Latin phrase that translates to ―regarding minimal things.‖ In the current context, the term de 

minimis is used in reference to human health risk that is below a level of concern (or, more specifically, a chemical 

emission rate that is not expected to result in unacceptable risks). See also the definition for ―risk de minimis‖ in the 

―Glossary for Chemists of Terms Used in Toxicology‖ (IUPAC 1993).
Appendix 5 2

1.2 Organization of this Document

This document presents the methods, assumptions, and inputs used to develop a method for 

evaluation of human multipathway exposures and health risks for RTR II risk assessments. 

Section 2 presents an overview of the methods for evaluating multipathway exposures and 

risks, a conceptual description of the TRIM-based scenario that is the basis for the screeninglevel emission thresholds, and a brief description of how refined analyses will be conducted for 

facility emissions that cannot be screened out in the first step. Section 3 presents a technical 

description of the screening-level, TRIM-based modeling scenario and the configuration of the 

models used to estimate the screening-threshold levels. Section 4 discusses evaluation 

activities conducted for this screening scenario and summarizes uncertainty. References cited 

in this report are listed in Section 5. 

2 Approach for Screening Evaluation of Multipathway Exposures

2.1 Overview

As described above, EPA‘s method for evaluating multipathway exposures for RTR risk 

assessments consists of a two-tiered process. Exhibit 2-1 diagrams the approach for evaluating 

non-inhalation, multipathway exposures to PB-HAPs. The first tier of this approach is the

screening evaluation that relies on the TRIM-based ―screening scenario‖ as the technical basis 

for decisions regarding whether a facility passes the screen. Air toxics emitted by a source 

under consideration are reviewed to determine first whether emissions of any PB-HAPs are 

reported. If such emissions are reported, the emission rates are compared to available

threshold-screening emission levels that have been derived using the TRIM-based screening 

scenario.

3

 The list of chemicals that are PB-HAPs is discussed in Section 2.2, and the use of 

screening-level emission thresholds is discussed in Section 2.3.

The TRIM-based multipathway modeling configuration, referred to in this document as the 

―screening scenario,‖ is a key component of the first tier of this approach, as this modeling 

application is the technical basis for determining the levels of PB-HAP screening-level emission 

thresholds. The term ―screening scenario‖ is used in this document to refer collectively to the 

specific TRIM.FaTE and exposure modeling configuration described here, including the set of 

assumptions and input values associated with a hypothetical watershed and the exposure and 

risk scenarios evaluated for this watershed. The screening scenario is a static configuration, 

and its primary purpose is as a modeling tool to calculate the screening-level emission rates for 

PB-HAPs of concern. Descriptions of the components of the screening scenario are presented 

in Section 2.4.

The two potential outcomes of the screening human health evaluation are: 

Non-inhalation exposures are unlikely to pose a human health problem; or 

The potential for unacceptable non-inhalation exposures cannot be ruled out and further 

assessment is required to determine the potential for unacceptable risk. 

An ideal screening approach strikes a balance between being conservative – to ensure that 

unacceptable risks are identified, and being accurate, to minimize results suggesting that 

additional assessment is required when in fact the actual risk is low. Typically, gains in 

 

3

As described later in this report, to date, TRIM-based modeling has been used to calculate screening threshold 

emission rates only for those PB-HAPs considered as those of highest concern.
Appendix 5 3

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.

Facilities whose emissions exceed the established screening threshold emission rate for any 

PB-HAP would be subjected to more refined evaluations. Because the initial screening 

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. An overview of the anticipated approach to 

refined evaluation is described in Section 2.5.

2.2 Chemicals of Potential Concern

EPA‘s assessment of multipathway human exposures for RTR focuses on PB-HAPs that 

OAQPS has identified as candidates for multipathway risk assessments. OAQPS developed a

list of 14 chemicals and chemical groups that are PB-HAPs based on a two-step process taking 

into account the following:

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

substances, and

a semi-quantitative 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 

further explained in Chapter 14 and Appendix D of Volume I of EPA‘s Air Toxics Risk 

Assessment (ATRA) Reference Library (EPA 2004a). Exhibit 2-2 presents the 14 chemicals 

and groups that are PB-HAPs.

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 be used to quantitatively 

estimate exposures and risks for four PB-HAP compounds (indicated in bold in Exhibit 2-2). 

These compounds are the focus of the current scenario because, based on current emissions 

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 CAA.4

 

4

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 needed.
Appendix 5 4

Exhibit 2-1. Conceptual Decision Tree for Evaluation of Non-Inhalation 

Exposures of PB-HAPs

Facility screens out; no concern for unacceptable multipathway risk

Unacceptable risks are not likely, but evaluate concern on a case-by-case basis

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

Check inputs and assumptions, refine to incorporate site- specific information

Are any PB-HAPs emitted?

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

Evaluate HAP emissions by facility

Are estimated risks below levels of concern? No concern for unacceptable multipathway risk Iterate as appropriate Use risk estimates to inform risk management decisions

YES: Conduct more refined assessment of facility

YES

YES

NO

NO

NO

Does the emission rate for any PB- HAP exceed de minimis levels?

? NO YES

Diagram Key

Outcome

Process

Decision Point

SCREENING EVALUATION

REFINED EVALUATION(S) ? Decision point regarding additional refinement (based on professional judgment)
Appendix 5 5

Exhibit 2-2. OAQPS PB-HAP Compounds a

PB-HAP Compound Addressed by Screening Scenario?

Cadmium compounds Yes

Chlordane No

Chlorinated dibenzodioxins and furans Yes

DDE No

Heptachlor No

Hexachlorobenzene No

Hexachlorocyclohexane (all isomers) No

Lead compounds No

Mercury compounds Yes

Methoxychlor No

Polychlorinated biphenyls No

Polycyclic organic matter (POM) Yes

Toxaphene No

Trifluralin No

a

Source of list: EPA 2004a. Compounds in bold text can be evaluated using the current version of the 

TRIM-based screening scenario.

2.3 Use of Screening Threshold Emission Levels

The multipathway screening evaluation for RTR compares reported air emission rates of PBHAPs (summed by PB-HAP for each facility) to screening threshold emission rates derived 

using the screening scenario described here. A screening threshold emission rate is the level 

that, when input to a risk model that uses emissions as a parameter, corresponds to a specified 

cancer risk or non-cancer HQ that, for the purposes of the evaluation being conducted, is 

assumed to be below a level of concern. Threshold rates were calculated for the screening 

scenario for a cancer risk of 1 in 1 million or an HQ of 1.0 (Exhibit 2-3).5 Conceptually, a 

threshold level for the RTR multipathway screening evaluation could be obtained by backcalculating 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,‖ these rates were derived 

from regression equations established following a series of TRIM.FaTE and exposure/risk 

model runs spanning a wide range of emission rates for each chemical.

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 conservative and 

highly general nature of the screening scenario configuration. This conservatism ensures that a 

facility with cancer risk greater than 1 in 1 million or a chronic HQ greater than 1.0 is very 

unlikely to be omitted from refined evaluation.

 

5

For chemicals that are 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 will be 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 of these 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.
Appendix 5 6

Exhibit 2-3. 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 toxic equivalents) 3.60E-04 Cancer

Dioxins (as 2,3,7,8-TCDD TEQ) 9.18E-10 Cancer

Divalent Mercury 1.95E-03 Non-cancer

Cadmium 3.51E-02 Non-cancer

TPY = U.S. short tons per year

Evaluation of Chemical Groups

In the screening evaluation, emissions of PB-HAPs are summed by chemical group for each 

facility. The summed emission rates for each group are then compared to the emission 

screening threshold corresponding to the appropriate chemical.

Emissions of polycyclic organic matter (POM, a HAP chemical group that includes polyaromatic 

hydrocarbons or PAHs) are often reported in NEI as unspeciated or partially speciated groups 

(such as ―total PAHs‖ or ―16-PAH‖) rather than as specific PAH compounds. In addition, 

quantitative data are lacking for some POM compounds and groups that are suspected 

carcinogens. To evaluate risks associated with exposure to emissions of the various POM 

species, EPA has grouped each POM species included in NEI into categories and then defined 

a cancer slope factor (CSF) for each group that can be used to estimate lifetime cancer risks. 

The CSFs used to evaluate risk for emissions of POM compounds and groups are listed in 

Attachment 2. 

Similarly, polychlorinated dioxins and furans are reported in NEI as individual congeners, 

congener groups, or as toxic equivalents (TEQs) that are scaled relative to the toxicity of 

2,3,7,8-TCDD. To evaluate cancer risks and non-cancer hazards from these compounds, EPA 

has developed CSFs and reference doses (RfDs) that apply to congener groups. These values 

are also presented in Attachment 2.

In the development of the RTR screening thresholds, we determined the threshold levels for one 

compound from each of the POM and dioxin groups (i.e., benzo[a]pyrene and 2,3,7,8-TCDD). 

Then, to use this screening threshold in an evaluation of facility emissions of POMs or dioxins, a 

toxicity-weighted emissions sum is calculated for each group using the toxicity of each modeled 

chemical – benzo[a]pyrene and 2,3,7,8-TCDD – to scale the total POM or dioxin group 

emissions. This approach avoids the need to develop threshold emission rates for every POM 

and dioxin congener (some of which are not included in the existing TRIM.FaTE algorithm 

library). A consequence of this approach is that benzo[a]pyrene and 2,3,7,8-TCDD serve as 

fate and transport surrogates. That is, the behavior of these two compounds is assumed to

adequately represent the behavior of all other compounds included in the POM and dioxin PBHAP groups, respectively. 

Screening 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.6 However, only speciated emissions of divalent mercury 

 

6 Note that TRIM.FaTE models the transformation of mercury within the environment; thus, emissions of only divalent 

mercury will result in multipathway exposures to both elemental and methyl mercury also. Emissions of only 

elemental mercury will result in multipathway exposures to both divalent and methyl mercury also. 
Appendix 5 7

are screened because the sum of elemental mercury emissions across all NEI facilities is less 

than the elemental mercury screening threshold level.

2.4 The TRIM-Based Screening Scenario: Basis for Threshold Emission 

Rates

The TRIM-based modeling screening scenario described in this document was used to provide 

a means to qualitatively estimate the potential for unacceptable non-inhalation risks for PBHAPs emissions from facilities in the context of residual risk assessments conducted as part of 

RTR II. The screening scenario used to derive screening threshold emission rates is not 

intended to be representative of any particular situation. Rather, it was developed for the 

purpose of RTR to portray an exposure scenario at least as conservative as any situation that 

might plausibly be encountered in the United States. The range of conditions considered when 

conceptualizing and building the screening scenario was chosen so that any given individual, 

long-term exposure condition for a given geographic region would be reasonably likely to be 

captured. These criteria were met by constructing a hypothetical scenario that would be 

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 conservatism and accuracy called for in 

the ideal screening approach.

For this approach, exposures were modeled for a hypothetical farm homestead and fishable 

lake near an emissions source. The hypothetical individual exposed to PB-HAPs in this 

scenario was assumed to derive all potentially contaminated foodstuffs from the farm or lake. 

Many of the exposure/activity assumptions were selected from the upper ends of representative 

exposure parameter distributions. The physical/chemical environment was parameterized with 

a mix of typical values (such as national averages) and health-protective values (i.e., values that 

would tend to overestimate concentrations in media). The spatial and temporal aspects of the 

scenario and the components of the scenario that influence air concentrations were chosen 

from the upper ends of their possible ranges so that concentrations in the environmental media 

would not be underestimated given the wide range of possible settings and meteorological 

conditions that might be encountered. Chemical-specific and non-chemical-specific properties 

of the environmental media were parameterized with either typical or conservative values (with 

a greater conservative bias introduced for properties having greater uncertainty). 

The development and application of the screening scenario for residual risk evaluations 

considered EPA‘s technical and policy guidelines presented in the Residual Risk Report to 

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

(EPA 2004a, 2005); and other EPA publications. The scenario described in this document is 

the culmination of analyses completed over the past 5 years; it provides the basis for an efficient 

and scientifically defensible method for screening multipathway human health risk and is a solid 

foundation for conducting more refined analyses when necessary. 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.

2.4.1 Exposure Routes Evaluated

The screening scenario is intended to address non-inhalation exposures (inhalation exposures 

are being evaluated separately for RTR II using a dispersion modeling approach to estimate 
Appendix 5 8

ambient air concentrations). The quantitative aspects of this non-inhalation screening 

evaluation for human exposures focus primarily on human exposures via the following ingestion 

pathways:

Incidental ingestion of soil,

Ingestion of homegrown produce,

Ingestion of homegrown beef,

Ingestion of milk from homegrown cows,

Ingestion of homegrown poultry and eggs,

Ingestion of homegrown pork,

Ingestion of fish, and

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

Non-inhalation exposure to PB-HAPs also can occur by way of the dermal pathway (e.g., 

through incidental contact with PB-HAP-contaminated soil). However, dermal absorption of 

chemicals that are originally airborne is generally a relatively minor pathway of exposure 

compared to other exposure pathways (EPA 2006, CalEPA 2000). The risk from dermal 

exposure in the environmental setting from airborne toxicants is expected to be a fraction of the 

risk from inhalation exposure or exposure via ingestion of contaminated crops, soil, or breast 

milk, for example (CalEPA 2000). Preliminary calculations of estimated dermal exposures and 

risk of PB-HAPs, presented in Section 3.2.4, showed that the dermal exposure route is not a 

significant risk pathway relative to ingestion exposures. Assessment of dermal exposure 

through incidental contact with soil could be conducted on facilities that require refined 

evaluation following the screening evaluation if deemed necessary. Procedures for estimating 

dermal absorption from soil would be based on EPA‘s dermal exposure assessment principles 

and applications (EPA1992b) and EPA‘s Superfund Human Health Evaluation Manual (EPA 

2004c). 

One other non-inhalation exposure route discussed in ATRA Volume I of possible concern for 

PB-HAPs is ingestion of drinking water from surface water sources. This exposure route, 

however, is 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.7 

2.4.2 Approach to Configuration and Parameterization

This screening scenario is intended to reduce the possibility that EPA would not mis-identify 

unacceptable risks. Although the ―conservative‖ approach likely overestimates risk, EPA 

determined that conservatism is appropriate for the purposes of screening assessments. As in 

the preliminary multipathway screening for RTR conducted in 2006 (EPA 2006a), 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 these 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 

 

7

An exception to this generality would be reservoirs used for drinking water supplies. This situation may 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).
Appendix 5 9

type is health-protective, conservative 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 relatively conservative. That is, they were chosen 

from the upper ends of their respective possible ranges so that the wide range of possible 

physical settings and meteorological conditions would be captured. Chemical-specific and nonchemical-specific properties of the environmental media were parameterized with either typical 

or conservative values; properties having greater uncertainty were assigned greater 

conservative bias. 

The spatial layout of the 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, a relatively large change in soil 

characteristics within the range of possible values (e.g., organic carbon content, water content) 

might result in relatively small changes in outputs. 

The mix of conservative and typical approaches and values is expected to result in a scenario 

configuration that, on average, is likely to over-predict environmental concentrations of PBHAPs in media of interest for this evaluation. Given the intended application of this scenario as 

a screening tool, this conservative bias was deliberate, because of the desire to ensure that 

unacceptable risks are not overlooked (i.e., to minimize false negatives). Although the inclusion 

of typical values where warranted is intended to minimize the number of false positives, some 

false positives are to be expected from a screening scenario. These false positives would be 

addressed in iterations of the refined evaluation for a particular source.

2.4.3 Modeling Framework

The approach for risk screening (and ingestion exposure) evaluation described here can be 

divided into four steps:

1. Fate and transport modeling of PB-HAPs emitted to air by the source that partition into 

soil, water, and other environmental media (including fish);

8

2. Modeling of transfer and uptake of PB-HAPs into farm food chain media (produce, 

livestock, dairy products) from soil and air; 

3. Estimating ingestion exposures as a result of contact with the various 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 metrics to selected risk management points of 

departure used in the RTR II.

 

8

As discussed below, concentrations in fish calculated by the TRIM.FaTE model were used in the current approach 

to estimate ingestion exposures for humans consuming fish. Modeling of fish concentrations is therefore discussed in 

this document 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.
Appendix 5 10

The relationship among these four processes is shown in Exhibit 2-4.

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.9 Currently, however, only one component 

corresponding to the first step included in Exhibit 2-4 – 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 Access-based computer 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 

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

cover.

2.4.3.1 Fate and Transport Modeling

The fate and transport modeling step depicted in the first box in Exhibit 2-4 is implemented for 

RTR using the Fate, Transport, and Ecological Exposure module of the TRIM modeling system 

(TRIM.FaTE).

10 In developing the screening scenario, Version 3.6.2 of TRIM.FaTE was used to 

 

9

Information regarding the current status of TRIM modules as well as comprehensive documentation of modules that 

have been developed thus far can be accessed on EPA‘s Technology Transfer Network (TTN) on the Fate, 

Exposure, and Risk Analysis web site (http://www.epa.gov/ttn/fera/). 

10 TRIM.FaTE is a spatially explicit, compartmental mass balance model that describes the 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, which provide 

exposure estimates for ecological receptors (i.e., plants and animals). The output concentrations from TRIM.FaTE 

are also intended to be used as inputs to a human ingestion exposure model to estimate human exposures. 

Significant features of TRIM.FaTE include: (1) a fully coupled multimedia model; (2) user flexibility in defining 

scenarios, in terms of the links among compartments, and number and types of compartments, as appropriate for the 

application spatial and temporal scale; (3) transparent, user-accessible algorithm and input library that allows the user 

to review and modify how environmental transfer and transformation processes are modeled; (4) a full accounting of 

Exhibit 2-4. Overview of Ingestion Exposure and Risk Screening Evaluation Method
Appendix 5 11

model the fate and transport of emitted PB-HAPs and to estimate HAP concentrations in 

relevant media. Additional information about TRIM.FaTE, including support documentation, 

software, and the TRIM.FaTE public reference library, is available on EPA‘s TTN at 

http://www.epa.gov/ttn/fera/.

The algorithms used to model mercury species and PAHs are described in Volume II of the 

TRIM.FaTE Technical Support Document (EPA 2002a). A comprehensive evaluation of the 

performance of TRIM.FaTE for modeling mercury was documented in Volumes I and II of the 

TRIM.FaTE Evaluation Report (EPA 2002b, 2005b). Algorithms specific to the fate and 

transport of 14 chlorinated dibenzo-dioxin and -furan congeners were added following the 

addition of those for mercury and PAHs. Documentation of the application of TRIM.FaTE for 

dioxin emissions is contained in the third volume of the TRIM.FaTE Evaluation Report (EPA 

2004b). Parameterization of the TRIM.FaTE library used for RTR analyses with regard to 

dioxins is identical to the configuration described in that third evaluation report. More recently

(largely as part of this current project), the TRIM.FaTE public reference library has been 

updated to include information on modeling for cadmium. In general, many of the algorithms 

and properties included in the public reference library that are used to model mercury (except 

for the mercury transformation algorithms) are also applicable to cadmium. Comprehensive 

technical documentation of TRIM.FaTE algorithms specific to cadmium has not yet been 

compiled; however, all chemical-specific properties used by TRIM.FaTE to model cadmium (as 

well as PAHs, mercury, and dioxins) are documented in Attachment 1 to this document. 

Parameterization of the TRIM.FaTE scenario used for RTR screening is described in more 

detail in Section 3.

2.4.3.2 Exposure Modeling and Risk Characterization

The algorithms included in MIRC that calculate chemical concentrations in farm food chain 

media and ingestion exposures for hypothetical individuals were generally obtained from EPA‘s 

Human Health Risk Assessment Protocol for Hazardous Waste Combustion Facilities, or 

HHRAP (EPA 2005a).

11

 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 2-5. 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 

this screening scenario is presented in Section 3.2 and Attachment 2, and all inputs required by 

these calculations are documented in Attachment 2.

 

all of the pollutant as it moves among environmental compartments during simulation; (5) an embedded procedure to 

characterize uncertainty and variability; and (6) the capability to provide exposure estimates for ecological receptors.

11 The farm food chain calculations and ingestion exposure equations to be included in the TRIM.Expo software are 

expected to be very similar to those included in HHRAP.
Appendix 5 12

Exhibit 2-5. Overview of Process Carried Out in the Multimedia Ingestion Risk Calculator

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

2.5 Refined Analyses

For facilities that are not screened out by comparison to the screening emission thresholds, 

additional analysis is required to evaluate the risks via multipathway exposures. EPA envisions 

that the screening scenario provides a technically defensible ―starting point‖ for additional fate 

and transport and exposure/risk analyses, by substituting site-specific values for key parameters 

used in the modeling scenario. As appropriate, the scenario can be further refined in iterative 

steps until either the risks predicted are not of concern or sufficient information has been 

gathered to inform a risk management decision. Based on the model performance evaluations 

completed on the screening scenario, settings and parameters that might be appropriate for 

revision in a more refined analysis include meteorological inputs and spatial configuration 

assumed for TRIM.FaTE modeling, relevant ingestion exposure scenarios (based on 

surrounding land-use or other characteristics), and exposure factors such as ingestion rate for 

individual food types, among other inputs. The refined evaluation could eventually involve 

developing a site-specific TRIM.FaTE application that incorporates significant site-specific data 

(e.g., a model application that includes a site-specific spatial layout taking into account local 

geographic features and environmental parameter values selected from the best available data 

for that location). 

3 Description of Modeling Scenario

3.1 TRIM.FaTE Scenario Configuration and Parameterization

As described in Section 2.4.2, the physical configuration of the RTR Screening Scenario was 

designed to be generally conservative, and the environmental and chemical-specific properties 

were parameterized with either conservative 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 
Appendix 5 13

follow. Comprehensive documentation of TRIM.FaTE property values for this scenario is 

provided in Attachment 1.

3.1.1 Chemical Properties

The chemical/physical properties that TRIM.FaTE requires, such as Henry‘s law constant, 

molecular weight, and other ―general‖ parameters, were obtained from peer-reviewed and 

standard reference sources. Numerous other chemical-specific properties are related more 

specifically to a particular abiotic or biotic compartment type; these properties are discussed

generally in the sections that follow and are documented in Attachment 1.

3.1.2 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 

3-1. 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.

Exhibit 3-1. TRIM.FaTE Surface Parcel Layout

A lateral, downwind distance of 10 km was established for the watershed included in the 

scenario. Based on the results of dispersion modeling (results not presented here), the location 

of the maximum air concentration and deposition rate would be 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 would be 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 
Appendix 5 14

negligible.12 Given these conditions, a downwind length of 10 km was determined to be 

appropriate for the screening scenario.

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 ISC3 Dispersion Model Manual (among other 

sources).13 For a relatively neutral atmosphere (stability class D), σ at 10 km 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 10 km distance. 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 

whose areas 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 further divided into two parts, one of them tilled soil (Parcel N6) to 

represent agricultural conditions. 

The depth of the surface soil compartments was set to 1 centimeter (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 sub-soil depth) were typically set 

to represent typical or national averages as summarized by McKone et al. (2001), for example. 

Initial considerations when the layout was configured included the presence of a stream that ran

along the bisecting east-west line from the southwestern corner of Parcel N3 through the 

eastern edge of the layout. In that configuration, the eastern extent of the pond was restricted 

by a parcel (S3) directly south of Parcel N3. The stream received chemical mass from Parcels 

S3, S4, and S5 and flowed directly into the pond. Preliminary modeling runs showed that the 

existence of a stream somewhat decreased the concentrations of 2,3,7,8-TCDD in the pond 

parcel and significantly decreased concentrations in Parcels S3 and S4. These results 

indicated that the pond was receiving more 2,3,7,8-TCDD mass through surface soil transfer 

than through the stream, perhaps due to a chemical sink into stream sediment. Given the goal 

of creating a conservative scenario, a stream was not included in the final layout.

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).

 

12 Mass 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 would 

be largely 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.) 

13 http://www.epa.gov/scram001/userg/regmod/isc3v2.pdf
Appendix 5 15

3.1.3 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 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 seepage, 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.

3.1.3.1 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 screening scenario, the parameters were set to satisfy two equations relating water volume. 

The first equation maintains a balance of water entering and leaving the terrestrial portion of the 

scenario:

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

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

lake via groundwater.

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

lake:

[total runoff] + [direct precipitation to the lake] = [evaporation from the lake surface] + [outflow 

from the lake]

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 

screening 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:

o 40 percent of total precipitation contributes to overland runoff.

o 25 percent of total precipitation seeps 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 millimeters per year (mm/yr) based on data reported by Morton (1986) for 

various lakes. This estimate is probably more representative of cooler locations [by 

comparison, the overall average of evaporation rates from various reservoirs is reported by 
Appendix 5 16

McKone et al. (2001) to be close to 1,200 mm/yr]. 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 

Attachment 1. 

3.1.3.2 Sediment Balance

A simplified balance of sediment transfers between the watershed and the lake was also 

maintained for the screening scenario via the parameterization of sediment-related properties. 

As with water, the model does not internally balance sediment mass; these calculations were 

performed externally for the purposes of setting parameter values. The sediment balance 

maintained is described by the following equation, where terms represent mass of sediment:

[total surface soil transfers to the lake via erosion] = 

[removal of sediment from the water column via outflow] + [sediment burial]

where the second term (removal of sediment from the water column via outflow) is accounted 

for in TRIM.FaTE by lake flushing rate and the third term (sediment burial) is the transfer of 

sediment from the unconsolidated benthic sediment compartment to the consolidated sediment 

layer. 

To maintain the sediment balance, erosion rates were calculated for each surface soil 

compartment using the universal soil loss equation (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 flush 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 screening 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 kilograms per square meter per day (kg/m2-day), based on 

calculations using the universal soil loss equation (USLE). The HHRAP documentation notes 

that using the USLE to calculate sediment load to a lake from the surrounding watershed 

sometimes leads to overestimates (EPA 2005a). For this screening scenario, however, this 

approach was considered to be appropriate in that conservatism is a goal of the screening 
Appendix 5 17

scenario.

14

 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 3-1). Erosion and 

runoff from the source parcel are linked directly to a sink and do not enter the screening 

scenario lake. The transport of sediment to the lake via overland flow (e.g., by streams) 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 

model. 

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.

3.1.4 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, as well as 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 conservative configuration was used. The meteorology of the 

screening scenario was defined to ensure that (when used in combination with the selected 

spatial layout) the maximum exposures that might be encountered for the scenarios of interest 

would be encompassed (i.e., consumption of home-grown farm foodstuffs and self-caught fish, 

with all farm foods and fish obtained from locations impacted by chemicals emitted from the 

local source). However, ensuring that the meteorological parameters were not overly 

conservative, such as always having the wind blow toward the location of interest, 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 3-2; however, an artificial 

data set was compiled (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 3-2. 

The sensitivity of modeled 2,3,7,8-TCDD concentrations 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 into 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 annual average among 239 stations; by comparison, the mean annual average 

 

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

surface soil concentrations; however, the relative impact on resulting concentrations will be proportionally greater in 

the waterbody.
Appendix 5 18

Exhibit 3-2. Summary of Key Meteorological Inputs

Meteorological 

Property Selected Value Justification

Air temperature Constant at 298 Kelvin Recommended default value listed in HHRAP (EPA 2005a). 

Value is similar to the mean daily June temperature in the 

U.S. Deep South and to the mean daily July temperature in 

the U.S. Central Plains.a

Mixing height Constant at 710 meters Value is 5th percentile of annual average mixing heights for 

463 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 one direction 

(eastward).d 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 three 

days out of seven.

Horizontal wind 

speed

Constant at 2.8 meters per 

second

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 the U.S. Deep South.c

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.c

Total 

Precipitation

1.5 meters per year 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.d Conditional precipitation rate (rainfall rate when 

precipitation is occurring) is 9.59 mm per day, which is 

similar to conditions along the U.S. East Coast and 

Midwest.c

a National Climatic Data Center Historical Climate Series (NCDC-HCS) (2007). 

http://www5.ncdc.noaa.gov/climatenormals/hcs/HCS_MAP_7100.pdf

b Support Center for Regulatory Atmospheric Modeling; http://www.epa.gov/scram001/tt24.htm. 

c National Climatic Data Center CliMaps (NCDC-CliMaps) (2007). http://cdo.ncdc.noaa.gov/cgi-bin/climaps/climaps.pl
Appendix 5 19

wind speed is 4.0 m/s in the contiguous United States). The mixing height (mean heights from 

four states) used was 710 m (the 5th percentile of annual average among all 40 states in the 

SCRAM database).

3.1.5 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 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. 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 (ponds, lakes, 

reservoirs) waterbodies. In general, lentic bodies of water (lakes and ponds) are at a higher risk 

of accumulating contaminants in both sediments and biota than are lotic systems (rivers, 

streams). Also, the previous research (ICF 2005) suggested that a lake of at least 60 hectares

(ha) likely would be sufficient to support higher trophic level predatory fish, with some fraction of 

their diet comprising smaller fish. 

The RTR screening scenario includes a generic aquatic ecosystem with a 47-ha 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 the appropriate conditions. Also, this size was 

compatible with the overall size of the defined watershed. In the lake ecosystem defined for the 

screening scenario, benthic invertebrates are an important food source for a large proportion of 

the total fish biomass. The fish types, biomass, diet fractions, and body weights recommended 

for fish compartments for the screening scenario of TRIM.FaTE are listed in Exhibit 3-3. 

Biomass is based on an assumption that the total fish biomass for the aquatic ecosystem is 5.4

grams per square meter expressed as a wet weight (gw/m2, ICF 2005).

A strict piscivore compartment was not selected for the screening scenario because such 

species are rare in lakes of small to moderate size. In general, the food web implemented in the 

screening scenario is intended to be generally applicable across the United States and is 

intended to be generally conservative (to simulate a food web that maximizes bioaccumulation). 

3.1.6 Using TRIM.FaTE Media Concentrations

The screening 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, soil 

concentrations are provided for the surface, root, and vadose zones and grass or leaf 

concentrations as appropriate for the plants. Groundwater concentrations and deposition rates 

to the soil are also provided. For each air parcel, air concentrations are provided. For the lake, 
Appendix 5 20

surface water concentrations and concentrations in the various levels of the aquatic food chain

are included. For the ingestion exposure calculations, some concentrations are used to 

calculate direct exposure, and some are used to perform the farm food chain concentration 

calculations in the various media that humans can ingest (see Exhibit 2-4). 

Exhibit 3-3. 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 and 

insects

6.36 -- 100% algaea5.7 E-8

Macrophyte hydrilla, milfoil 500 -- -- --

Water column 

(WC) 

planktivore

young-of-theyear, minnows 2.0 35.1%

100% zooplankton

0.025

Water column 

omnivore sucker, carp 0.5 8.8% 100% WC planktivore 0.25

Water column 

carnivore

largemouth bass, 

walleye 0.2 3.5% 100% WC omnivore 2.0

Benthic 

invertebrate

aquatic insects, 

crustacean, 

mollusksb

20 --

Feeds in sediment

0.000255

Benthic 

omnivore

small catfish, 

rock bass 2.0 35.1% 100% benthic invert. 0.25

Benthic 

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

Total Fish Biomass c5.7

a

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

b

Benthic invertebrates include aquatic insects (e.g., nymphs of mayflies, caddisflies, dragonflies, and other species 

that emerge from the water when they become adults), crustacea (e.g., amphipods, crayfish), and mollusks (e.g., 

snails, mussels).

c

Total fish biomass does not include algae, zooplankton, macrophytes or benthic invertebrates.

Regardless of whether the concentration and deposition values are used to calculate ingestion 

directly or are used in farm food chain calculations, selecting the parcel that is the source of the 

values used as inputs to succeeding calculations is necessary. The locations that determine 

direct and indirect exposures were selected assuming generally conservative assumptions. In 

general, decisions regarding which TRIM.FaTE outputs to use in calculating exposures for the 

hypothetical scenario assume exposure at locations near the modeled source, thereby resulting 

in higher exposures to emitted chemicals. These assumptions are summarized in Exhibit 3-4. 

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 
Appendix 5 21

screening 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 (most of the chemicals modeled for 

RTR approach steady state well before 50 years).

Exhibit 3-4. 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)

3.2 Exposure and Risk Calculations 

This section describes the approach for modeling chemical concentrations in farm food chain 

(FFC) media (Section 3.2.1); estimating human exposures associated with ingestion of FFC 

media, incidental ingestion of soil, consumption of fish, and infant consumption of breast milk 

(Section 3.2.2); and calculating human health screening risk metrics associated with these 

exposure pathways (Section 3.2.3). All of these calculations are conducted using the MIRC 

modeling software. For this multipathway screening evaluation, partitioning into FFC media is 

calculated with the same data set used to model exposure and risk, rather than as a component 

of the TRIM.FaTE modeling scenario. Consequently, processes and inputs related to 

estimating chemical levels in FFC media are summarized in this section and discussed in detail 

in Attachment 2.

3.2.1 Calculating Concentrations in Farm Food Chain Media

As was shown in Exhibit 2-5, 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 are as follows:

exposed and protected fruit,

exposed and protected vegetables,

root vegetables,

beef,

total dairy products,

pork, and

poultry and eggs.
Appendix 5 22

The algorithms used in MIRC were obtained from EPA‘s Human Health Risk Assessment 

Protocol for Hazardous Waste Combustion Facilities (HHRAP; EPA 2005a). These algorithms 

model the transfer of concentrations of PB-HAPs in FFC media using empirical biotransfer 

factors. As noted in Section 1 of this report, the algorithms involving ingestion exposure to be 

included eventually in TRIM are expected to be very similar to those presented in HHRAP. 

Environmental media concentrations (i.e., the chemical source terms in these algorithms) were 

obtained from TRIM.FaTE. As noted in Section 3.1.6, 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 groundwater (used as drinking water);

PB-HAP concentrations in fish tissue for fish in trophic levels three and four [T3 and T4]);

and 

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

In general, plant- and animal-specific parameter values, including chemical-specific transfer 

factors for FFC media, were obtained from the Hazardous Waste Companion Database 

included in HHRAP (EPA 2005a). A list of variables and PB-HAP-specific input parameters, 

along with the input values used in this screening scenario, are provided in Attachment 2.

3.2.2 Ingestion Exposure Assessment

3.2.2.1 Ingestion Exposure Pathways and Routes of Uptake

MIRC was used to estimate ingestion rates as average daily doses (ADDs) normalized to body 

weight for a range of exposure pathways. Exposure pathways included are incidental ingestion 

of soil and consumption of fish, produce, and farm animals and related products. The ingestion 

exposure pathways included in the screening evaluation and the environmental media through 

which these exposures occur are summarized in Exhibit 3-5.

3.2.2.2 Exposure Scenarios and Corresponding Inputs

Specific exposure scenarios are developed by defining the ingestion activity patterns (i.e., 

estimating how much of each medium is consumed and the fraction of the consumed medium

that is grown in or obtained from contaminated areas) and the characteristics of the hypothetical 

human exposed (e.g., age and body weight). MIRC computes exposure doses and risks for 

each ingestion pathway separately, enabling the pathway(s) of interest for each PB-HAP to be 

determined. Data related to exposure factors and receptor characteristics were obtained 

primarily from EPA‘s Exposure Factors Handbook (EPA 1997a).

For the screening scenario described here, exposure characteristics were selected that result in 

a highly conservative estimate of total exposure. The ingestion rate for each medium was set 

equal to the 90th percentile of the distribution of national data for that medium. 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 90th 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 ingestion screening scenario are summarized in Attachment 2. 
Appendix 5 23

Exhibit 3-5. Summary of Ingestion Exposure Pathways and Routes of Uptake

Ingestion 

Exposure 

Pathway

Medium Ingested

Intermediate 

Exposure 

Pathway – Farm 

Animals a

Environmental Uptake Route

Medium Process b

Incidental ingestion 

of soil Surface soil N/A Surface soil

Deposition; transfer through plants; 

transfer via erosion and runoff c

Consumption of 

fish

Fish from local water 

body N/A Fish tissue 

Direct uptake from water and 

consumption of food compartments 

modeled in TRIM.FaTE c

Consumption of 

breast milk d Breast milk N/A Breast milk

Ingested by mother and then 

partition to breast milk

Consumption of 

produce

Aboveground produce, 

exposed fruits and 

vegetables

N/A

Air 

Air

Soil

Deposition to leaves/plants 

Vapor transfer

Root uptake

Aboveground produce, 

protected fruits and 

vegetables

N/A Soil Root uptake

Belowground produce N/A Soil Root uptake

Consumption of 

farm animals and 

related food 

products 

Beef

Ingestion of forage Air 

Air 

Soil

Direct deposition to plant

Vapor transfer to plant

Root uptake Ingestion of silage

Ingestion of grain Soil Root uptake

Ingestion of soil Soil Ingestion from surface

Dairy (milk)

Ingestion of forage Air 

Air 

Soil

Direct deposition to plant

Vapor transfer to plant

Root uptake Ingestion of silage

Ingestion of grain Soil Root uptake

Ingestion of soil Soil Ingestion from surface

Pork

Ingestion of silage

Air 

Air 

Soil

Direct deposition to plant

Vapor transfer to plant

Root uptake

Ingestion of grain Soil Root uptake

Ingestion of soil Soil Ingestion from surface

Poultry Ingestion of grain Soil Root uptake

Ingestion of soil Soil Ingestion from surface

Eggs Ingestion of grain Soil Root uptake

Ingestion of soil Soil Ingestion from surface

a Calculation of intermediate exposure concentrations were required only for the farm animal/animal product 

ingestion pathways.

b

Process by which HAP enters medium ingested by humans.

c Modeled in TRIM.FaTE.

d

The consumption of breast milk exposure scenario is discussed in Section 3.2.2.4.
Appendix 5 24

Exhibit 3-6. Overview of Exposure Factors Used for RTR Multipathway Screening a,b

Exposure Factor Selection for Screening Assessment

Age group evaluated 

Infants under 1 year (breast milk only)

Children 1 to 2 years of age

Children 3 to 5 years of age

Children 6 to 11 years of age

Children 12 to 19 years of age

Adult (20 to 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 (varies by age and media consumed)

90th percentile of distribution of consumers 

who produce own food

Ingestion rate for fish

105.5 g/day (represents the 99

th percentile of 

fish consumption in the adult U.S. general 

population including consumers and nonconsumersc

; this estimate is considered 

within the range of average values for 

subsistence fishersd); lower values were 

assumed for younger age groups, also 

based on the 99th percentile of 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)e1

Cooking loss f

Assumed to be ―typical;‖ varies depending 

on food product (see Attachment 2). Cooking 

losses were not considered for fish 

consumption because intake rates 

represented ―as prepared‖ values.

a Values for exposure characteristics are presented in Attachment 2. Exposure parameter values were based on 

data obtained from the Exposure Factors Handbook (EPA 1997a). See Attachment 2 for details.

b

Exposure factor inputs are used in calculating average daily dose (ADD) estimates for each exposure pathway. 

ADD equations for each pathway evaluated in this screening assessment are provided in Attachment 2.

c

Fish consumption rates were obtained from Estimated Per Capita Fish Consumption in the United States (EPA 

2002c). Consumption rates were averaged by body-weight to represent 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 fishers was obtained from Methodology for Deriving Ambient

Water Quality Criteria for the Protection of Human Health (EPA 2000).

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. 

f Cooking loss inputs were included to simulate the amount of a food product that is not ingested due to loss during 

preparation or cooking, or after cooking.

3.2.2.3 Calculating Average Daily Doses

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

per kg of body weight per day). Equations used to calculate ADDs were adapted from the 

algorithms provided in the technical documentation of EPA‘s Multimedia, Multipathway, and 

Multireceptor Risk Assessment (3MRA) Modeling System (EPA 2003), which derived much of 

its input data from the Exposure Factors Handbook (EPA 1997a). The ingestion exposure 
Appendix 5 25

modeling approach embodied by 3MRA is conceptually similar to that presented in HHRAP. A 

discussion of exposure dose estimation is provided in Attachment 2. The equations to calculate 

ADDs for each ingestion pathway are provided in Attachment 2.

3.2.2.4 Infant Ingestion of Breast Milk

A nursing mother exposed to contaminants by any ingestion pathway described above can pass 

the contaminants on to her infant through breast milk (EPA 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. Exposures can occur for 

infants via this pathway for dioxins and possibly also for mercury.

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

account for approximately 30% of an adult‘s lifetime ADD, based on the assumptions and 

configuration of the screening scenario. Exposure via the breast milk pathway during the first 

year of life was, therefore, accounted for in developing the threshold screening level for dioxins.

For mercury, cadmium and POM, however, the breast milk pathway did not contribute a 

significant portion of an adult‘s lifetime ADD. Therefore, exposures to these chemicals via the 

breast milk pathway were not considered in developing the screening emission thresholds for 

mercury, cadmium, and POM.

3.2.3 Risk Characterization

MIRC was used to calculate excess lifetime cancer risk (ELCR) and non-cancer hazard 

(expressed as an HQ) using the calculated ADDs and ingestion dose-response values. 

Chemical dose-response data include CSFs for ingestion and non-cancer oral RfDs for chronic 

exposures. The CSFs and RfDs for the PB-HAPs included in this screening scenario are 

presented in Exhibit 3-7 and are discussed in more detail in Attachment 2. Equations used to 

estimate cancer risk and non-cancer hazard are provided in Attachment 2. 

Exhibit 3-7. 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 not available 5E-4 IRIS

Elemental Mercury NA not available

Divalent Mercury not available 3E-4 IRIS

Methyl Mercury not available 1E-4 IRIS

Organics a

Benzo[a]pyrene 10 IRIS not available

2,3,7,8-TCDD 1.5E+5 EPA ORD 1E-9 ATSDR

Source: EPA OAQPS 2007

CSF = cancer slope factor; RfD = reference dose; IRIS = Integrated Risk Information System; CalEPA 

= California Environmental Protection Agency; ASTDR = Agency for Toxic Substances and Disease 

Registry; EPA ORD = EPA‘s Office of Research and Development

NA = not applicable

a

The CSF listed in IRIS for benzo[a]pyrene was adjusted for consistency with the overall approach for 

dose-response assessment of PAHs; see Attachment 2.
Appendix 5 26

Estimated individual cancer risks for the PAHs included in the screening scenario were adjusted 

upward to account for the higher mutagenic cancer potency of these compounds during 

childhood, as specified by EPA in supplemental guidance for cancer risk assessment (EPA 

2005c). Specifically, cancer potency is assumed to be tenfold greater for the first 2 years of life 

and threefold greater for the next 14 years for PAHs. These factors were incorporated into a 

time-weighted 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 Attachment 2.

3.2.4 Dermal Risk Screening

Non-inhalation exposure to PB-HAPs can occur by way of the dermal pathway through contact 

with PB-HAP-contaminated soil and water. However, dermal absorption of chemicals that are 

originally airborne is generally a relatively minor pathway of exposure compared to other 

exposure pathways (EPA 2006, CalEPA 2000). This section demonstrates that for the 

conservative 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 assessment followed the protocol for evaluating a reasonable maximum exposure 

as described in EPA‘s Risk Assessment Guidance for Superfund (RAGS), Volume I: Human 

Health Evaluation Model, Part E, Supplemental Guidance for Dermal Risk Assessment (EPA

2004c). 

3.2.4.1 Hazard Identification and Dose Response Assessment

To assess the potential contribution of dermal exposure to non-inhalation exposure, we 

evaluated the potential for cancer and chronic non-cancer effects for the four PB-HAPs currently 

assessed in the multipathway screening evaluation for RTR: cadmium, divalent mercury, 

2,3,7,8-TCDD, and benzo(a)pyrene. EPA has not developed carcinogenic potency slope 

factors (CSFs) and non-cancer reference doses (RfDs) specifically for evaluating potential 

human health concerns associated with dermal exposure to PB-HAPs. Instead, dermal toxicity 

values can be derived from oral toxicity values via route-to-route extrapolation by adjusting for 

gastrointestinal (GI) absorption. EPA recommends making this adjustment only when GI 

absorption of the chemical is significantly less than 100% (i.e., less than 50 percent). 

Otherwise, a default value of complete (100 percent) oral absorption is assumed, and no 

adjustment is made (EPA 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: 

G I

o

ABS ABS

CSF CSF

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)
Appendix 5 27

The absorbed reference dose (RfDABS) is based on the oral reference dose (RFDO) and the 

fraction of the contaminant absorbed in the gastrointestinal track (ABSGI), as shown below.

RfDABS RfDo ABSGI

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 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 3-8, no adjustments to the available oral CSFs were required. Similarly, no adjustment 

to the oral RfD for 2,3,7,8-TCDD was 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 3-8.

Exhibit 3-8. Cancer Slope Factors and Reference Doses Based on Absorbed Dose

PB-HAP ABSGI

(unitless)

CSFABS

a

(mg/kg-day)-1

RfDABS

a

(mg/kg-day)

Cadmium Compounds 0.05 NA 2.5E-05

Divalent Mercury 0.07 NA 2.1E-05

2,3,7,8-TCDD No adjustment 

required b 1.5E+05 1.0E-09

Benzo[a]pyrene

No adjustment 

required b 1.0E+01 NA

NA = Not applicable

a Oral dose response values are presented in Exhibit 3-7. Only the resulting adjusted dose response values are 

presented in this table.

b

According to RAGS Part E, Exhibit 4-1, GI absorption is expected to be greater than 50%.

3.2.4.2 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. 
Appendix 5 28

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). DAD is calculated separately for the water and soil pathways. 

BW AT

DA EV ED EF SA

DAD event

×

× × × ×

=

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)

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:

event event

event w p

6 t

DA C 2 FA K

Water – Inorganic Chemicals:

event w p event DA C K t

Soil – All Chemicals:

DAevent Cs AF ABS CF

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)
Appendix 5 29

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 estimated of exposure (i.e., exposures are likely overestimated). Parameter 

values were primarily obtained or estimated from RAGS Part E (EPA 2004c) and the 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.

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 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 3-9. 

Exhibit 3-9. Receptor-Specific Body Surface Area Assumed to be 

Exposed to Chemicals 

Age Group a

(years)

Surface Area for 

Water Exposure (cm

2

)

Surface Area for 

Soil Exposure (cm

2

)

Adult 20-70 18,150 g6,878

h

Child <1 b3,992 1,772

Child 1-2 c5,700 2,405

Child 3-5 d7,600 3,354

Child 6-11 e10,800 4,501

Child 12-19 f17,150 6,906

a Sources for the child groups included Table 7-1 (total body surface area for SA-Water), and 

Table 7-2 (fraction of total body surface area for SA-Soil) of the 2008 CSEFH.

b Represents a time-weighted average for age groups birth to <1 month, 1 to <3 months, 3 to <6 

months, and 6 to <12 months. 

c Represents a time-weighted average for age groups 1 to <2 years and 2 to <3 years. 

d Values for age group 3 to <6 years in the 2008 CSEFH.

e Values for age group 6 to <11 years in the 2008 CSEFH. Represents a conservative (i.e., 

slightly low) estimate for ages 6 through 11 years since 11-year olds are not included in this 

CSEFH age group.

f Represents a time-weighted average for age groups 11 to <16 years and 16 to <21 years. Note 

that estimated values include 11-year-olds and individuals through age 20, which contributes to 

uncertainty in the estimates for 12 to 19 years.

g Represents the average total surface area of adults from Table C-1 of RAGS Part E.

h Represents the average surface area of adults for head, forearms, hands, lower legs, and feet 

from Table C-1 of RAGS Part E.
Appendix 5 30

Scenario-Specific Parameters

Exhibit 3-10 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. 

Exhibit 3-10. 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 & 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) x 365 

days/year is used. Same value used in ingestion exposure calculations.

For non-cancer assessment, an AT equal to the exposure duration (ED) 

x 365 days/year is used, so AT will vary by receptor group. Same value 

used in ingestion exposure calculations.

Chemical-Specific Parameters

The chemical-specific parameters required to quantitatively evaluate dermal pathway exposures 

are listed in Exhibit 3-11. 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 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.
Appendix 5 31

Exhibit 3-11. 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 

B-4 (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 

(τevent) (hr/event) NA NA 6.82 2.69

Values from RAGS Part E, 

Exhibits B-3; only used for 

organic chemicals

Dermal absorption 

fraction (ABS) from soil 

(unitless)

0.001 0.045 a0.03 0.13

Values from RAGS Part E, 

Exhibit 3-4, unless otherwise 

noted

a

Value obtained from Bioavailability in Environmental Risk Assessment (Hrudey et al. 1996).

3.2.4.3 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. 

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 x 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). 

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)
Appendix 5 32

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.

3.2.4.4 Dermal Screening Results

Exhibit 3-12 presents a summary of estimated dermal non-cancer hazards by age group. A 

summary of estimated lifetime dermal cancer risks is provided in Exhibit 3-13. All HQ values 

were 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. 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 estimated for the same screening 

threshold emission rate. 

Exhibit 3-12. 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 <1Child

1-2

Child

3-5

Child

6-11

Child

12-19

Adult

20-70 Hazard Quotient

Receptor

Divalent Mercury Water

Soil

Total
Appendix 5 33

Exhibit 3-13. Summary of Dermal Cancer Risks

PB-HAP Dermal Lifetime 

Cancer Risk Magnitude of Difference

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

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.

3.3 Summary of Scenario Assumptions

As emphasized previously, the screening scenario created for evaluating PB-HAP emissions 

from RTR facilities is intended to be generally conservative to prevent underestimating risk. The 

overall degree of conservatism of the scenario is the sum total of the multiple assumptions that 

affect the outputs of the fate and transport, exposure, and risk modeling. Exhibit 3-14

summarizes important characteristics that influence exposure and risk estimates for this 

scenario and indicates the general degree of conservatism 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.
Appendix 5 34

Exhibit 3-14. Summary of RTR Screening Scenario Assumptions and Associated 

Conservatism

Characteristic Value Conservatism Comments on Assumptions

General Spatial Attributes

Farm location 375 m from source; 

generally downwind

Conservative 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

Conservative Location dictates where impacted fish 

population is located.

Surface soil 

properties

Typical values or 

national averages

Neutral Based on existing EPA documentation and 

other references.

Size of farm parcel About 4 ha Conservative Relatively small parcel size results in higher 

chemical concentration.

Size of lake 46 ha; about 3 m 

average depth

Conservative 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 Conservative Intended to represent rainy U.S. location; 

set to highest state-wide average for the 

contiguous U.S.

Precipitation 

frequency (with 

respect to impacted 

farm/lake)

2/3 of total 

precipitation fall on 

farm/lake and 

watershed

Conservative Most of total precipitation occurs when the 

farm/lake are downwind of the source.

Wind direction Farm/lake are 

downwind 40% of the 

time

Conservative Farm/lake located in the predominantly 

downwind direction. Temporal dominance 

of wind direction based on data from 

Yakima, WA, where wind is predominantly 

from the west.

Wind speed 2.8 m/sec Conservative Low wind speed (5th percentile of long-term 

averages for contiguous U.S.); increases 

net deposition to lake/watershed.

Air temperature 298 K Neutral Typical for summer temperatures in central 

and southern U.S.

Mixing height 710 m Conservative Relatively low long-term average mixing 

height (5th percentile of long-term averages 

for contiguous U.S.); increases estimated 

air concentration.

Watershed and Water Body Characteristics

Evaporation of lake 

surface water

700 mm/yr Neutral Value is representative of cooler climates.

Surface runoff into 

lake

Equal to 40% of total 

precipitation

Conservative Based on typical water flow in wetter U.S. 

locations; higher runoff results in greater 

transfer of chemical to lake.
Appendix 5 35

Exhibit 3-14. Summary of RTR Screening Scenario Assumptions and Associated 

Conservatism

Characteristic Value Conservatism Comments on Assumptions

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. May overestimate flush 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 were 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). May 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

Multi-level; includes 

large, upper trophic 

level fish

Conservative 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)

Conservative Assumes livestock feed sources (including 

grains and silage) are all derived from most 

highly impacted locations.

Soil- and air-to-plant 

transfer factors for 

produce and related 

parameters

Typical (see 

Attachment 2 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 

Attachment 2 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)

Conservative Probably overestimates bioavailability in 

soil; many chemicals are less bioavailable 

in soil than in plants.

Ingestion Exposure Assumptions
Appendix 5 36

Exhibit 3-14. Summary of RTR Screening Scenario Assumptions and Associated 

Conservatism

Characteristic Value Conservatism Comments on 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

Conservative 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 1.48 g/kg-day for 

adults; between 1.29

and 2.08 g/kg-day for 

children aged 1-19

Conservative Rates are based on the 99

th percentile of 

general population fish consumption from 

EPA‘s analysis of freshwater and estuarine 

fish consumption derived from the USDA‘s 

Continuing Survey of Food Intake by 

Individuals (2002c). Consumption rates 

were averaged by body-weight to represent 

the age groups used in the screening 

scenario. These ingestion rates likely 

overestimate long-term fish consumption 

rates for the general population. See 

Attachment 2 for a detailed discussion.

Exposure frequency Consumption of 

contaminated food 

items occurs 365 

days/yr

Conservative All meals from local farm products.

Body weight Mean of national 

distribution

Neutral Note that this does not affect the body 

weight-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 

conservative

Values used are those determined to be 

appropriate for risk assessment by OAQPS; 

some values may be health-protective.

4 Evaluation of Screening Scenario

4.1 Overview

To evidence our understanding of the models used, their configuration, and the total uncertainty 

associated with this model application, the screening scenario developed for RTR was 

subjected to a series of evaluations. These analyses were somewhat more focused on 
Appendix 5 37

TRIM.FaTE, given the complexity and variability associated with the fate and transport modeling 

phase relative to other aspects of the screening calculations. These evaluations emphasized

the application of this approach (and especially TRIM.FaTE) in the context of ingestion 

exposure and risk screening for RTR. The analyses were not intended to be general model 

performance evaluations of TRIM.FaTE. EPA has conducted such analyses, which are 

documented in detail in Volumes I and II of the TRIM.FaTE Evaluation Report (EPA 2002b, 

2005b).

The purpose of the current evaluations, however, is similar to that of the previous model 

evaluations, in that both are intended to increase the confidence associated with model 

performance. Although the focus of the screening scenario evaluation (assessing the utility of 

the constructed modeling scenario) differs from the purpose of TRIM.FaTE model evaluations, 

the underlying objectives are comparable. In summary, the current evaluations are intended to

achieve several objectives:

enhance understanding of how the scenario operates;

better characterize the uncertainty of model results;

measure model sensitivity to changes in parameter values and scenario configuration;

and

strengthen the defensibility of the scenario‘s application as a component of air toxics 

residual risk assessment.

That these analyses do not attempt to validate or ―prove‖ the accuracy of model results is 

important to note. As specified in the TRIM.FaTE mercury test case evaluation report (EPA 

2005b):

 ̳Validation‘ of such models, in the classic sense (e.g., proving the model 

produces accurate results across a range of input conditions), is not generally 

possible, in part because there are no comprehensive data sets of measured 

chemical concentrations (and associated contributing pollutant sources) for use 

in such comprehensive studies, nor are there other fully validated multimedia 

models against which TRIM.FaTE can be benchmarked. Thus, evaluation of 

TRIM.FaTE is not a yes/no exercise but a continuing accumulation of evidence 

leading to model refinement and eventually to increasing levels of confidence in 

the model results.

The screening scenario was evaluated through a series of analyses:

a general evaluation of TRIM.FaTE fate and transport modeling outputs;

comparisons of model outputs to the literature (e.g., measured concentrations of the 

chemicals evaluated, information on the expected distribution of chemical mass in the 

environment);

sensitivity analyses of TRIM.FaTE and MIRC inputs and model configurations on 

endpoints of interest; and

an evaluation of related information. 

As noted previously, most of these evaluations focused on the TRIM.FaTE modeling scenario. 

Methods and results of this evaluation are discussed in the remainder of Section 4 and in 

Attachment 3. 
Appendix 5 38

4.2 Overall Chemical Mass Partitioning

A general evaluation of TRIM.FaTE outputs from simulations run using the screening scenario 

was conducted to inform the set-up and parameterization of the scenario and confirm that the 

model outputs were generally reasonable. One approach for evaluating the overall performance 

of the model is to review the distribution of chemical mass among the various compartment 

types and chemical sinks included in the scenario. In general, many persistent and 

bioaccumulative chemicals emitted to the air do not readily deposit within close range of a 

source but instead are transported in the air away from the source (sometimes great distances) 

before depositing on land or water surfaces or being transformed to other chemical species. 

This is true of the PB-HAPs that can be evaluated using the RTR screening scenario, and thus 

much of the chemical mass emitted by the modeled sources, regardless of chemical, was not 

expected to accumulate in the soil, water, or sediment compartments. Instead, most of the 

chemical mass emitted by the source was expected to be removed through advective transport 

in the air and to end up in the air sinks (the compartments that capture chemical mass blown 

outside of the modeling domain). This outcome also would be expected, given the relatively 

small modeling domain. For chemical mass that remains in the domain (is deposited and does 

not degrade or leave the domain by other means, such as sediment burial), the overall 

distribution would be expected to approximate environmental observations, with much of the 

mass remaining in the soil or benthic sediment.

Exhibit 4-1 illustrates the distribution of chemical mass for the screening scenario for the PBHAPs of primary concern (one dioxin, one PAH, cadmium, and mercury). Results show the 

distribution at the end of a 50-year simulation (or similar duration) for each chemical performed 

at the screening threshold emission rate (the overall distribution for each chemical is not 

expected to be substantially different for other emission rates, given a relatively long simulation 

period). As anticipated, much of the chemical mass emitted to the screening scenario is largely 

removed from the scenario via air advection processes and transported to air sinks (blown out 

of the modeled domain of the scenario). The amount remaining in the scenario varies by 

chemical, with a larger fraction of mass deposited for mercury (which deposits relatively quickly 

as a divalent species) and much lower deposition for dioxin. Of the chemical mass deposited to 

plants, land, or water and remaining in the modeled domain, most accumulates in the soil. For 

2,3,7,8-TCDD and the two metals evaluated (mercury and cadmium), the sediment 

compartment also contains a significant amount of mass. 

The mass distribution for mercury presented in Exhibit 4-1 was calculated from a single 

simulation with all emissions assumed to be of the divalent mercury species. Recall that 

TRIM.FaTE models mercury transformation among elemental, divalent, and methyl mercury 

species. Consequently, the distribution of mercury mass shown in the middle and lower parts of 

the table accounts for inter-species transformation within media as well as movement between 

media (and all methyl mercury in the scenario was derived from emitted divalent or elemental 

mercury).

4.3 Comparison to Measured Concentrations

4.3.1 Scope of the Evaluation 

This section presents environmental measurements of the PB-HAPs evaluated for RTR and 

compares them to modeled concentrations estimated by TRIM.FaTE and the Multimedia 

Ingestion Risk Calculator (MIRC). Model outputs correspond to the estimates associated with 
Appendix 5 39

Exhibit 4-1. Distribution of Chemical Mass in Screening Scenario

2,3,7,8- 

TCDD

Benzo[a] 

pyrene

Cadmium Divalent 

Mercury

Elemental 

Mercury

Methyl 

Mercury

Total 

Mercury

Emitted chemical mass 

removed from scenario and 

deposited into air sinks 99.5% 81.6% 97.3% 85.4% --

b

--

b

--

b

Emitted chemical mass 

remaining in scenario (not 

in air sinks)a0.5% 18.4% 2.7% 14.6% --

b

--

b

--

b

Air 0.30% 0.2% 0.01% 0.001% 0.002% 0.0% 0.001%

Soil 89.2% 97.6% 96.5% 86.0% 91.1% 97.9% 83.3%

Plants 6.0% 1.6% 0.1% 0.7% 0.0% 0.0002% 0.7%

Surface Water 0.02% 0.01% 0.2% 0.0% 0.5% 0.03% 0.06%

Sediment 4.4% 0.5% 3.2% 13.2% 8.5% 2.0% 16.0%

Aquatic Biota 0.03% 0.055% 0.039% 0.0004% 0.0002% 0.028% 0.0010%

Groundwater 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Surface Soil 98.8% 98.9% 30.1% 98.9% 9.3% 98.9% 97.9%

Root Zone Soil 1.2% 1.1% 69.9% 1.1% 90.7% 1.1% 2.1%

Vadose Zone Soil 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Total mass distributions

Distribution of mass remaining in scenario c

Soil percentages

a

 Fraction includes mass transferred from air to soil or other media, but subsequently removed from the modeling 

domain (e.g., accumulated in sediment burial or degradation/reaction sink).

b

 Distribution of total mercury mass including divalent, elemental and methyl mercury, from facilities emitting 

exclusively divalent or elemental mercury

c

 Fractions represent amount of mass remaining in non-sink compartments.

emission rates at the screening threshold levels for each representative PB-HAP calculated for 

RTR multipathway screening. We emphasize that this analysis is not intended to ―validate‖ the 

accuracy of the models used for RTR or the specific model configurations used to calculate 

screening threshold emission levels. In general, although the comparison of environmental data 

to model results is often a central component of model evaluation, deriving useful conclusions 

from such comparisons is complicated by a range factors, including: fundamental differences 

between the modeling scenario and the environmental system in which samples were collected; 

temporal and spatial issues (e.g., model results representative of long-term average 

concentrations vs. measured point estimates); data quality issues; and other factors. In the 

current evaluation, it is difficult to derive conclusions from model-to-measurement comparisons 

given the use of a modeling scenario that is intended to be representative but hypothetical and 

conservative (i.e., possessing characteristics that lead to higher exposures while not 

representing any real location or specific site). In addition, the RTR modeling scenario is 

intended to calculate incremental environmental concentrations and exposures without 

accounting for any ―background‖ concentrations from natural or anthropogenic sources.

In spite of these complications, this comparative analysis is an appropriate—and necessary—

aspect of the model evaluation process because environmental data provide a general frame of 

reference for the model outputs. Basic differences between the modeling scenario and the data 

sets collected preclude the verification or validation of model configuration, performance, or 
Appendix 5 40

results, and the comparisons should be viewed in light of the complicating issues listed above. 

However, we believe that this comparative analysis, considered in conjunction with other 

aspects of the evaluation, is a useful tool in gauging the effectiveness of the screening scenario 

in informing regulatory decision-making. 

4.3.2 Methods and Organization of this Section

The TRIM.FaTE and MIRC outputs presented in this section are those used to calculate the 

screening threshold emission levels in the screening scenario (see Section 2 for discussion of 

screening threshold emission rates). Model outputs from the source parcel are not used for 

comparisons; instead, concentrations from the land parcel closest to the source (N1) are used in 

comparisons for soil results, while outputs from the pond parcel are used for comparing results 

for water, sediment, and fish (See Exhibit 3-1 for Surface Parcel Layout). Chemical 

concentrations in air are not evaluated in this section for two reasons. First, the TRIM.FaTE 

model was not designed to estimate air concentrations for the assessment of inhalation 

exposure, as noted in Section 2.4.1. Second, chemical concentrations in the air compartment 

affect exposure concentrations less directly than deposition of the chemical. Therefore, the 

comparison of chemical concentrations in air to observed values is not included in this 

evaluation. 

The purpose of the 50-year duration of the scenario is to represent long-term input of source 

emissions over a hypothetical facility lifetime (many compartments also attain an approximate 

steady state by this time). For all environmental media compartments, the maximum annual 

average concentrations, taken from the final year of the simulation, are used in MIRC to 

determine the maximum average concentrations in ingestible products. These concentrations, 

along with ingestion rates for each food product, are used to calculate average daily doses 

(ADDs) for each chemical, from which the lifetime cancer risk or age-group-specific hazard 

quotient (HQ) is then calculated. The model results presented in this section are the 

environmental media concentrations used to calculate the screening threshold emission rates 

(i.e., those concentrations calculated for the 50th year of the TRIM.FaTE simulation) and the 

ingestible media concentrations calculated in MIRC using those environmental media 

concentrations (see Exhibit 3-5 for TRIM.FaTE outputs used in exposure calculations).

The evaluations described in this section are presented separately for each chemical for which 

a screening threshold has been derived. Each chemical-specific section provides a review of 

the emission profiles used in the modeling process, and a summary of the expected chemical 

behavior in the environment. This is followed by comparisons between model results and 

ranges of observed concentrations in environmental media, partitioning behavior, and observed 

concentrations in ingestible products. 

In the charts displaying the comparisons between model results and observed concentrations, 

the horizontal bars in the graph represent measured concentrations reported in the literature for 

various environmental media. The green bars show ranges of measured concentrations that 

were reported as less than a maximum concentration or detection limit, with the upper bound of 

the bar representing the limit of detection. Blue bars indicate a range of values reported by a 

particular source. The vertical red lines in the graph represent the modeled TRIM.FaTE 

concentrations obtained using the screening threshold emission rates. Observed data from 

areas exposed to varying ranges of chemical emissions were chosen for the comparison in 

order to represent a variety of scenarios. In general, the low end of the range represents

concentrations from remote areas not located near a considerable pollution source, while the 
Appendix 5 41

high end of the range represents concentrations observed near industrial sources (e.g., metal 

smelters, chlor-alkali plants, incineration/combustion facilities) and/or in urban areas. 

The observed values provided as examples in this section include data collected from a range 

of sampling techniques, locations, emissions profiles, historical contributions, and other factors 

influencing concentration. They do not represent the entire range of possible measured values, 

and therefore should not be combined to create a contiguous range of concentrations 

representative of all patterns in the United States. Model outputs were derived using a 

generally conservative set of fate and transport inputs, and they reflect the estimated 

environmental levels assuming concentrations from only one source and no ambient 

background concentrations. 

4.3.3 Chemical-Specific Comparisons

4.3.3.1 Cadmium

Behavior in the Environment

Based upon reviewed literature, some of the largest anthropogenic sources of cadmium to air 

are facilities that process, mine, or smelt cadmium-zinc or cadmium-zinc-lead ores; coal and oilfired boilers; other urban and industrial facilities; phosphate fertilizer manufacturing facilities; 

road dust; and municipal sewage sludge incinerators (ATSDR 2008). These facilities can emit 

airborne cadmium particles that are capable of traveling long distances before depositing onto

soil or water bodies via dust, rain, or snow. 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). It 

may enter surface waters through atmospheric fallout, runoff, or wastewater streams, but most 

cadmium compounds will be removed from the surface water compartment through adsorption 

to organic matter in sediment or to other suspended compounds. Concentrations in bed 

sediment are expected to be roughly an order of magnitude higher than those in overlying 

surface water (HSDB 2005a). 

Freshwater fish accumulate cadmium primarily through direct uptake of the dissolved form 

through the gills and secondarily through diet, which plays a variable role in total cadmium 

uptake (Reinfelder et al. 1998, Chen et al. 2000, Saiki et al. 1995). The degree to which 

cadmium bioaccumulates in fish is largely dependent upon water pH and humus content 

(ATSDR 2008). Reported bioaccumulation factors (BAFs), or bioconcentration factors (BCFs), 

of 3 to 4,190 (ATSDR 2008) and 907 (EPA 2005a) have been reported for freshwater organisms 

and fish. Although some biomagnification of cadmium has been reported for aquatic food 

chains involving fish 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 have generally 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). 

Emission Profile

The cadmium screening threshold emission rate derived from the RTR PB-HAP screening is 

3.51E-02 TPY (tons per year) or 87.2 g/day (based on an HQ of 1 in children aged 1-2). 

Although the annual concentration from year 50 is used for comparison, modeled 

concentrations of cadmium in most compartments generally leveled off after year 20. One 

notable exception was the soil compartment in parcel N6, where the soil in the screening 
Appendix 5 42

scenario is tilled and no plants are modeled in the TRIM.FaTE scenario. Cadmium 

concentrations in this compartment continued to rise through year 50. The steady build-up of 

cadmium in this parcel is likely due to the thickness of the surface soil layer in the N6 parcel. 

Because this parcel is tilled, the surface soil layer is thicker than in the other parcels, and the 

rate of exchange between the surface soil and the root zone soil is correspondingly lower.

Concentrations in Environmental Media

Exhibit 4-2 displays measured concentrations in environmental media with TRIM.FaTE outputs 

for the screening scenario. Modeled concentrations and BAFs in fish compartments are shown 

in Exhibit 4-3. Because relationships between fish compartments in the screening scenario are 

established within a food web structure, there is no explicit delineation to specific trophic levels. 

However, for evaluation of trophic level patterns, the water column herbivore (WCH) can be 

considered to represent the lowest trophic level among fish, while the water column carnivore 

(WCC) represents the highest trophic level.

The cadmium concentrations output by the RTR screening scenario were consistent with 

reported values in all environmental media compartments. Consistent with trends noted in the 

literature, modeled cadmium concentrations were highest in fish at the lower trophic levels (i.e.,

BC). Though modeled concentrations did not uniformly decrease with an increase in trophic 

level, the concentrations for all but the benthic carnivore (BC) compartment were markedly 

lower than those in the WCH compartment. It should be noted that BAFs for the other 

chemicals evaluated in this section are several orders of magnitude greater—and span much 

wider ranges—than the BAFs reported for cadmium in fish. Thus, in the context of comparison 

between chemicals, the difference between cadmium BAFs in fish compartments is quite small.

The BAFs calculated using model outputs from the RTR screening scenario were consistent 

with the range of values presented in the literature for freshwater systems. Overall, modeled 

concentrations and BAFs in all fish compartments were reasonably consistent with observed 

trends, with cadmium levels generally diminishing from lower to higher trophic levels, and 

consistent with the assumption that diet plays a variable role in the bioaccumulation of this 

substance in fish.

Partitioning Behavior

Because most cadmium emitted to the air will eventually deposit onto soils, cadmium has been 

observed to partition primarily to soil when released to the environment (ATSDR 2008). 

Cadmium mobility 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 binds strongly to organic matter, rendering the metal immobile; however, 

some plants are able to efficiently take up cadmium, thus providing an entry point for cadmium 

into the food chain (ATSDR 2008). 

Most cadmium in a natural water column is dissolved, but some adsorption to humic substances 

and other organic complexing agents can occur. This behavior can be especially important in 

polluted or organic-rich waters, playing a dominant role in cadmium transport (ATSDR 2008). 

Concentrations in surface water tend to be lower than those in bed sediment because cadmium 

readily adsorbs to mineral surfaces, hydrous metal oxides, and organic materials present in 

sediment. Cadmium that has adsorbed to mineral surfaces in the sediment is not easily 

bioaccumulated in the aquatic food web unless the sediment is disturbed, and the metal is 

redissolved.
Appendix 3 43

Exhibit 4-2. Summary of Modeled and Observed Concentrations of Cadmium in Environmental Media 

0.0001 0.001 0.01 0.1 1 10 100 1000

Northeastern U.S. Lakes (d)

Sacramento River System Acid-Mine Drainage (c)

U.S. Average (a)

Spring River Basin Mining District, KS, MO & OK (a)

Unpolluted U.S. Soils (a)

Superfund Site, KS (a)

Near Smelter, MT (a)

Patroon Creek Reservoir, NY, Avg. (a)

Spring River Basin Mining District, KS, MO, & OK (c)

Great Lakes (b)

Trout, Remote Stream, MD (a)

Catfish, Mining-Contaminated Water, OK (a)

Redear Sunfish, SWP, FL, Avg. (a)

Salmon Flesh (a)

Largemouth Bass, SWP, FL, Avg. (a) Surface Water (μg/L) Surface Soil (mg/kg dry wt.) Sediment (mg/kg dry wt.) Fish (mg/kg fresh wt.)

Cadmium Concentration (see units at left)

Reported value was less than limit of detection (LOD = upper bound of bar) Value or range from literature

Modeled concentration corresponding to screening threshold emission rate

WCC BC

T S US

WCC = Water Column Carnivore, BC = Benthic Carnivore, SWP = Stormwater Pond, TS = Tilled Soil, US = Untilled Soil

a

Source: ATSDR Toxicological Profile for Cadmium (ATSDR 2008)

b

Source: Hazardous Substances Databank Record for Cadmium Compounds (HSDB 2005a)

c

Source: Copper, Cadmium, and Zinc Concentrations in Aquatic Food Chains from the Upper Sacramento River and Selected Tributaries (Saiki et al. 1995)

d

Source: Accumulation of Heavy Metals in Food Web Components Across a Gradient of Lakes (Chen et al. 2000)
Appendix 5 44

Exhibit 4-3. TRIM.FaTE Cadmium Concentrations in Fish and Calculated 

Bioaccumulation Factors with Respect to Total Water Concentration

0

8,285

16,570

24,855

33,140

41,425

49,710

57,995

66,280

74,566

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1.80

Zooplankton Water Column

Herbivore

Benthic

Omnivore

Water Column

Omnivore

Benthic

Carnivore

Water Column

Carnivore Bioaccumulation Factors (BAF) Concentration in Fish (mg/kg wet weight)

For the RTR screening scenario, approximately 5 percent of cadmium in the pond compartment 

partitioned to surface water, while 95 percent partitioned to sediment. The percentages of 

cadmium in sorbed and dissolved states in surface water, sediment, and surface soil are 

presented in Exhibit 4-4. The partitioning behavior modeled in TRIM.FaTE is consistent with the 

behavior of cadmium in the natural environment. The presence of cadmium primarily in the 

dissolved state in surface water and sorbed to sediment and surface soil is consistent with 

trends noted in supporting literature. 

Exhibit 4-4. Fraction of Cadmium Mass Sorbed vs. Dissolved in 

TRIM.FaTE Compartments

Compartment Sorbed Dissolved

Surface Soil 100.0% 0.0%

Surface Water 4.6% 95.4%

Sediment 99.9% 0.1%

Concentrations in Ingestible Products

The major source of non-inhalation exposure to cadmium outside of occupational settings is 

through dietary intake. Available data indicate that cadmium accumulates in plants, aquatic 

organisms, and animals, offering multiple ingestion exposure pathways (ATSDR 2008). 

However, actual cadmium levels in ingestible products can vary based upon type of food, 
Appendix 5 45

agricultural and cultivation practices, atmospheric deposition rates, conditions in environmental 

media, and presence of other anthropogenic pollutants. General trends indicate that high levels 

of cadmium can be found in green, leafy vegetables; peanuts; soybeans; and sunflower seeds. 

Meat and fish generally contain lower amounts of cadmium, overall, but cadmium can be found 

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). 

Modeled cadmium concentrations in ingestible products are displayed in Exhibit 4-5 along with 

cadmium concentrations reported in literature. In the screening scenario we assume that 

individuals consume equal amounts of benthic carnivores and water column carnivores, so the 

concentration for fish is given as the average of these two fish compartments. 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 highest modeled concentrations. 

To determine media types most relevant to exposure and risk, the estimated media 

concentrations must be combined with ingestion exposure factors (i.e., a higher concentration

for a particular medium does not necessarily mean higher risk). The contribution of ingestion 

exposure pathways to the ADD (and thus the HQ) for the different age categories are displayed 

in Exhibit 4-6. The HQ of 1 for children aged 1-2 was used to determine the screening threshold

level for cadmium in the screening scenario. The dominant exposure pathway for this age 

group was consumption of fish, which comprises almost 96 percent of ADD.

4.3.3.2 Mercury

Behavior in the Environment

Mercury emitted by anthropogenic sources undergoes changes in form and species as it moves 

through environmental media. The three most relevant forms of mercury in the environment are 

elemental mercury, divalent inorganic mercury, and methyl mercury. Based upon reviewed 

literature, 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 (ASTDR 1999). Stack emissions can include a mixture of 

elemental and divalent forms, mostly in the gaseous phase, with some divalent forms in particlebound phases (EPA 1997b). Although elemental mercury is more prevalent in the atmosphere, 

divalent mercury can be found at higher concentrations near emissions sources. Elemental 

mercury has a very long residence time in the atmosphere and will thus be distributed relatively 

evenly on a global scale, resulting in negligible impacts from any single source on local and 

regional scales. Divalent mercury, however, is removed from the atmosphere at a faster rate 

than elemental mercury, and may be transferred to the surface near the emission source via wet 

or dry deposition, where it appears to adsorb tightly to soil particles (EPA 1997b). Some 

adsorbed divalent mercury can be reduced to elemental mercury and be revolatilized back into 

the atmosphere. Methylation to methyl mercury by microbes can also occur in the soil; 

however, divalent mercury is generally the dominant species in soil (EPA 1997b). Small 

amounts of divalent and methyl mercury in soil can be transported to surface water through

runoff and leaching. Direct deposition to water can occur through atmospheric fallout. Once in 

the water body, divalent mercury may be methylated through microbial activity, and both

divalent and methyl mercury may be further reduced to elemental mercury, which will reenter

the atmosphere. But, as in soil, divalent mercury will generally be the dominant species in both
Appendix 5 46

Exhibit 4-5. Summary of Modeled and Observed Concentrations of Cadmium in 

Ingestible Media 

0.000001 0.0001 0.01 1 100

Meat, Poultry & Fish, Urban Areas (b)

Beef (Prepared) (c)

Urban Areas (b)

Eggs (Prepared) (c)

Apples (c)

All Fruits, Urban Areas (b)

Strawberries (c)

All Fruits, Silver Mine-Contaminated Gardens, CO (c)

All Vegetables, Urban Areas (b)

Spinach (Prepared) (c)

Broccoli, Lead-Zinc Mining Area, England (a)

Trout, Remote Stream, MD (b)

Catfish, Mining-Contaminated Water, OK (a)

Redear Sunfish, SWP, FL, Avg. (b)

Largemouth Bass, SWP, FL, Avg. (b)

Salmon Flesh (b)

Meat, Poultry & Fish, Urban Areas (b)

Pork Products (Prepared) (c)

Poultry (Prepared) (c)

Meat, Poultry & Fish, Urban Areas (b)

Mean & Max, Meat & Poultry, Urban Areas, Canada (c)

Oranges (c)

All Fruits, Urban Areas (b)

Cantaloupe (c)

All Fruits, Silver Mine-Contaminated Gardens, CO (c)

Winter Squash (Prepared) (c)

All Vegetables, Urban Areas (b)

Potatoes (Prepared) (c)

Carrots, Lead-Zinc Mining Area, England (a)

Unpolluted U.S. Soils (b)

Superfund Site, KS (b)

Near Smelter, MT (b)

Dairy Products, U.S. (c)

Urban Areas, U.S. (b)

Mean & Max, Urban Areas, Canada (a)

Beef Eggs Exposed Fruit 

Exposed 

Vegetable Fish Pork Poultry Prot. Fruit

Prot. Veg. Root

Soil 

(mg/kg dry wt.) Dairy

Cadmium Concentration (mg/kg wet wt.*)

Reported value was less than limit of detection (LOD = upper bound of bar)

Value or range from literature

Modeled concentration corresponding to screening threshold emission rate

*except soil, which is expressed as mg/kg dry weight.

SWP = Stormwater Pond

a Source: Hazardous Substances Databank for Cadmium Compounds (HSDB 2005a)

b Source: ATSDR Toxicological Profile for Cadmium (ATSDR 2008)

c Source: Total Diet Study, Market Baskets 1991-3 through 2005-4 (FDA 2007)
Appendix 5 47

Exhibit 4-6. Estimated Contribution of Modeled Food Types to Cadmium Ingestion 

Exposures and Hazard Quotient

0.0E+00

2.0E-01

4.0E-01

6.0E-01

8.0E-01

1.0E+00

0

0.0001

0.0002

0.0003

0.0004

0.0005

Children 

1-2

Children 

3-5

Children 

6-11

Children 

12-19

Adults 

20-70 Hazard Quotient ADD (mg/kg/day)

Meat, Dairy, & Eggs Soil Fish Vegetables Fruits

surface water and sediment. Methyl mercury is readily bioaccumulated and efficiently 

biomagnified in aquatic organisms and is typically the species of greatest concern for mercury 

exposure via the food chain.

Emission Profile 

TRIM.FaTE assumes that only elemental mercury and divalent mercury are emitted from the 

source (see Section 2.3); but once in the environment, these species may change form, 

resulting in ingestion exposure to methyl mercury as well as divalent mercury. Relative to these 

two species, elemental mercury does not comprise a significant amount of total mercury in 

environmental media other than air. Because human health effects from exposure to methyl 

mercury and divalent mercury are not additive, two screening threshold rates for emissions of 

divalent mercury were calculated for exposure to methyl mercury and divalent mercury, 

respectively, to determine the most health-protective level. The emission rate calculated for the 

HQ for methyl mercury was lower, and thus was used to define the emission threshold. 

The screening threshold emission rate used for mercury in the RTR screening scenario is 

1.95E-03 TPY or 4.8 g/day of divalent mercury (based on an HQ of 1 for methyl mercury 

exposure for children aged 1-2). Annual concentrations from year 50, which were used to 

calculate the emission screening threshold are presented here, and these values are used for 

comparison to concentrations found in literature. Modeled concentrations of mercury in all 

compartments but untilled soil continued to rise up to the end of the simulation, with the rate of 

increase steadily diminishing over time.
Appendix 5 48

Concentrations in Environmental Media

Exhibit 4-7 displays measured ranges of total mercury concentrations in environmental media 

with TRIM.FaTE outputs for the screening scenario. The total mercury concentrations 

estimated by TRIM.FaTE were generally consistent with reported values in all environmental 

media compartments. Overall, modeled total mercury concentrations were generally within the 

range of values reported for areas near a significant pollution point source (e.g., smelter, chloralkali facility).

Methyl mercury is formed via microbial transformation of inorganic mercury in sediment, surface 

water, and soil and readily accumulates in the tissues of planktivorous and piscivorous fish. 

There seems to be a relationship between methyl mercury levels in fish and water pH, showing 

higher levels of methyl mercury in fish found in acidic lakes (EPA 1997b). Lake alkalinity and 

dissolved oxygen content may also influence the ability of fish to bioaccumulate methyl mercury

(EPA 1997b). BAFs on the order of 105to 107have been reported for methyl mercury

concentrations in fish (Alpers et al. 2008). Modeled concentrations of mercury species in fish 

compartments along with estimated methyl mercury BAFs are shown in Exhibit 4-8. The 

TRIM.FaTE outputs for the fish compartments mirror a trend noted in the literature in which 

divalent mercury is observed more prominently in lower trophic level organisms, while methyl 

mercury is the more prominent species in higher trophic level organisms (ATSDR 1999). 

Calculated BAFs are consistent with reported values, with the higher trophic levels exhibiting 

BAFs of approximately 106. The biomagnification of the methylated species in the aquatic 

ecosystem can be observed in Exhibit 4-8, with modeled methyl mercury concentrations 

increasing substantially from the lower to higher trophic levels. 

Partitioning Behavior

Soil acts as the primary reservoir for divalent mercury emitted from anthropogenic sources. In 

some cases, divalent mercury will be adsorbed onto forms of dissolved organic carbons 

(DOCs), which are susceptible to runoff. However, most divalent mercury from atmospheric 

deposition remains in the soil profile in the form of inorganic compounds bound to soil organic 

matter. The ability of mercury to form complexes with soil organic matter is highly dependent 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). Although inorganic compounds containing divalent mercury 

are relatively soluble, this complexing behavior with organic matter significantly limits mercury 

transport. Only very small amounts of mercury present in soil are partitioned to runoff. 

Elemental mercury present in soil (e.g., as a result of the reduction of divalent mercury) will 

readily volatilize, especially in acidic soils, and this species therefore comprises very little of the 

total mercury content in soil. Typically, methyl mercury comprises 1 to 3 percent of total 

mercury in soil (EPA 1997b). For the TRIM.FaTE results in the RTR screening scenario, 

divalent mercury comprised approximately 95 percent of total mercury in the surface soil

compartment; this result is consistent with environmental trends. 

Solubility of mercury in water depends on the species and form of mercury, water pH, chloride 

ion concentrations, and other factors (ATSDR 1999). Solid forms of inorganic mercury 

compounds will partition to particulates in the water column and then partition to the sediment 

bed (EPA 1997b). Low pH favors the methylation of mercury in the water column, typically 

performed by sulfur-reducing bacteria in anaerobic conditions. Methyl mercury generally 

comprises 10 percent of total mercury observed in surface water (EPA 1997b). A considerable
Appendix 5 49

Exhibit 4-7. Summary of Modeled and Observed Concentrations of Total Mercury in Environmental Media

0.0001 0.001 0.01 0.1 1 10 100 1000 10000

Lake Cresent, WA (b)

U.S. Samples (b)

Crab Orchard Lake, IL (a)

Typical U.S. (b)

U.S. Chlor-alkali Facility, Avg. (b)

Near Mine/Smelter, Spain (b)

Little Rock Lake, WI (b)

U.S. Lakes, Avg. (b)

80 MN Lakes (b)

Canadian Lakes Near Smelters (a)

Predatory Fish, ME (b)

Paper Mills Using Chlorine (c)

42 Lakes and Rivers, NJ (b)

U.S.Min/Max, 1990–1995 (a) Surface Water (ng/L) Surface Soil (mg/kg dry wt.) Sediment (ng/g dry wt.) Fish (μg/g fresh wt.)

Total Mercury Concentration (see units at left) 

Reported value was less than limit of detection (LOD = upper bound of bar) Value or range from literature

Modeled concentration corresponding to screening threshold emission rate

BC WCC

T S US

BC = Benthic Carnivore, WCC = Water Column Carnivore, TS = Tilled Soil, US = Untilled Soil

a Source: ATSDR Toxicological Profile for Mercury (ATSDR 1999)

b

Source: Mercury Study Report to Congress (EPA 1997b)

c

Source: National Study of Chemical Residues in Fish (EPA 1992a)
Appendix 5 50

Exhibit 4-8. TRIM.FaTE Mercury Concentrations, Speciation, and Calculated Methyl 

Mercury Bioaccumulation Factors (in parentheses) in Fish Compartments 

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.10

Zooplankton Water Column

Herbivore

Benthic

Omnivore

Water Column

Omnivore

Benthic

Carnivore

Water Column

Carnivore Concentration in Fish (mg/kg wet weight)

Elemental

Divalent

Methyl

(5,398.791)

(420,827)

(1,020,527)

(50,245)

(205,018) (55,535)

amount (25 to 60 percent) of both divalent mercury compounds and methyl mercury will be 

strongly particle-bound in the water column (EPA 1997b). The remaining mercury will be in a

dissolved state. Most of the elemental mercury produced as a result of reduction of divalent 

mercury by humic acid will volatilize. 

Screening scenario modeling results for mercury speciation in environmental media 

compartments are displayed in Exhibit 4-9 along with the percent of each species that was 

present in both adsorbed and dissolved states in surface soil, surface water, and sediment. 

Consistent with observed trends, 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. For the RTR screening scenario, roughly 99 percent of total mercury 

in the pond (i.e., surface water and sediment) was divalent mercury. The TRIM.FaTE outputs of 

approximately 2 percent methyl mercury in soil and 1 percent methyl mercury in surface water 

are also consistent with trends noted in the literature (EPA 1997b).

Overall, the partitioning behavior in the RTR screening scenario is consistent with the behavior 

of mercury in the natural environment. Within the pond compartment of the model, 95 percent 

or more of each mercury species partitioned to the sediment. Though all mercury in natural 

soils and sediment may not be in the sorbed state, the percentage of mercury that does not 

complex with organic matter would likely be negligible. The behavior in the TRIM.FaTE surface 

water compartment is consistent with trends observed in natural waters in which divalent and 

methyl mercury species exhibit a strong preference for adsorption to suspended matter in the 
Appendix 5 51

water column. These species may also form complexes with dissolved organic carbon and thus 

be considered as dissolved in the water column; however, this is not as prevalent as 

complexation with suspended solids. Conversely, elemental mercury does not adsorb readily to 

suspended particles, and is therefore found primarily in the dissolved form in natural waters, 

where it is highly susceptible to revolatilization into the atmosphere. The high percent dissolved 

elemental mercury estimated by TRIM.FaTE (when compared to percent dissolved divalent or 

methyl mercury) in surface water is consistent with this trend. It has been estimated that more 

than 97 percent of dissolved gaseous mercury in the water column is elemental mercury (EPA 

1997b).

Exhibit 4-9. TRIM.FaTE Mercury Speciation and Partitioning in 

Environmental Media Compartments 

0%

25%

50%

75%

100%

Surface

Soil

Surface Water Sediment

Percent of Total Mercury

Elemental Divalent Methyl

.

Chemical

Methyl Mercury Elemental Mercury Divalent Mercury

Sorbed Dissolved Sorbed Dissolved Sorbed Dissolved

Surface soil 100.0% 0.0% 100.0% 0.0% 100.0% 0.0%

Surface Water 80.7% 19.3% 4.8% 80.7% 83.0% 17.0%

Sediment 100.0% 0.0% 100.0% 0.0% 100.0% 0.0%

Concentrations in Ingestible Products

Available data indicate that mercury bioaccumulates in plants, aquatic organisms, and terrestrial 

animals, providing multiple ingestion exposure pathways (EPA 1997b, ASTDR 1999). Low 

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

than potatoes, grains, legumes, and other vegetables and fruits (ASTDR 1999, EPA 1997b). 

Cattle are capable of demethylating mercury in the rumen, and therefore store little of the small

amount of mercury that is transferred to livestock via foraging or silage/grain consumption. 

Mercury content in meat and cow‘s milk is therefore low (ASTDR 1999). Concentrations of 
Appendix 5 52

methyl mercury in fish are generally highest in larger, older specimens at the higher trophic 

levels (EPA 1997b). 

Concentrations of total mercury in ingestible products are presented in Exhibit 4-910. Though 

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 influence the mercury HQs in the model can be 

seen in Exhibit 4-11, which displays the contributions of ingestion exposure pathways to the 

methyl mercury and divalent mercury ADDs for all age groups analyzed in the screening 

scenario. As can be seen in Exhibit 4-11, the dominant exposure pathway for methyl mercury 

was ingestion of fish for all age groups. Methyl mercury concentrations in fish were very high 

relative to divalent mercury (approximately 95 percent of total mercury), and fish consumption 

contributed the overwhelming majority to the methyl mercury ADD. All ingestion products except 

for beef and dairy contributed to the ADD for divalent mercury. However, as can be seen in 

Exhibit 4-12, the screening level HQ is driven by methyl mercury ingestion, and this primarily is 

from fish ingestion.
Appendix 5 53

Exhibit 4-10. Summary of Modeled and Observed Concentrations of Total Mercury in 

Ingestible Media 

1E-08 0.000001 0.0001 0.01 1 100

Germany (b) 

Background Level, Japan (c) 

Raw Beef (c) 

Natural and Abnormal (a) 

Denmark, Germany & U.K. (a) 

Average, Germany (b) 

Exposed Fruits (e) 

Apples, Background Mean, Netherlands (c) 

All Fruits, Belgium, Germany & U.K. (a) 

Background Means, Netherlands (c) 

U.S. Chlor-alkali Plant (c) 

Alfalfa (Above-Ground Parts), Near Mine/Smelter, Spain (c) 

Predatory Fish, ME (c) 

Near Paper Mills Using Chlorine (d) 

42 Lakes and Rivers, NJ (c) 

U.S. Min/Max, 1990 – 1995 (b) 

Raw & Sausage Hg2 (c) 

Background Level, Japan (c) 

Chicken, Background Level, Japan (c) 

Average, Germany (b) 

Chicken, Raw & Lunch Meat, 33 – 80% Hg2 (c) 

Garden Fruits, U.S. (a) 

All Fruits, U.S. (a) 

All Fruits/Vegetables, Belgium, Germany & U.K. (a) 

Legume Vegetables, U.K. (a) 

Maize (c) 

All Fruits/Vegetables, Belgium, Germany & U.K. (a) 

Potatoes, U.S. (a) 

Root Vegetable, U.S. Avg.(a) 

Carrot/Potato Background Means, Netherlands (b) 

Typical U.S. Soils (c) 

U.S. Chlor-alkali Facility (c) 

Mine/Smelter, Spain (c) 

Dairy Products, U.S. (a) 

Milk, Germany (b) 

Germany and U.K. (a) 

Beef Eggs

Exposed 

Fruit 

Exposed 

Vegetable Fish Pork Poultry

Protected 

Fruit Prot. Veg. Root

Soil

(mg/kg dry 

wt.) Dairy

Mercury Concentration (mg/kg wet wt.*)

Reported value was less than limit of detection (LOD = upper bound of bar)

Value or range from literature

Modeled concentration corresponding to screening threshold emission rate

Non-Detect With No Limit of Detection Specified

*except soil, which is expressed as mg/kg dry weight

a

Source: Hazardous Substances Databank Report for Mercury Compounds (HSDB 2005b)

b

Source: ATSDR Toxicological Profile for Mercury (ATSDR 1999)

c Source: Mercury Study Report to Congress (EPA 1997b)

d

Source: National Study of Chemical Residues in Fish (EPA 1992a)

e

Source: Total Diet Study, Market Baskets 1991-3 through 2005-4 (FDA 2007)
Appendix 5 54

Exhibit 4-12. Estimated Contribution of Summed Modeled Food Types to 

Divalent Mercury and Methyl Mercury Hazard Quotients

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Children 

1-2

Children 

3-5

Children 

6-11

Children 

12-19

Adults 

20-70 Hazard Quotient

Divalent Mercury Methyl Mercury

4.3.3.3 Dioxins (2,3,7,8-TCDD)

Behavior in the Environment

Polychlorinated dibenzo-dioxins and -furans15 are reported in the National Emission Inventory 

(NEI) as individual congeners, congener groups, or toxic equivalents (TEQs). The dioxin 

congener 2,3,7,8-TCDD was used as a surrogate to evaluate risks for chlorinated dibenzo-pdioxins in the screening scenario. Of the chlorinated dioxin congeners, this compound has been 

the most widely studied, and is considered to be one of the two most toxic congeners to 

 

15 Group is commonly referred to as ―dioxins;‖ this short hand is used in this section.

Exhibit 4-11. Estimated Contribution of Modeled Food Types to Divalent Mercury 

and Methyl Mercury Ingestion Exposures

0

0.00002

0.00004

0.00006

0.00008

0.0001

0.00012

Children 1-2 Children 3-5 Children 6-11 Children 12-19Adults 20-70

Methyl Mercury ADD (mg/kg/day)

Fish Fruits & Vegetables Soil Eggs, Pork, & Poultry Beef & Dairy

0

0.000001

0.000002

0.000003

4E-06

5E-06

6E-06

Children 1-2 Children 3-5 Children 6-11 Children 12-19Adults 20-70

Divalent Mercury ADD (mg/kg/day)

Beef & Dairy Fish Eggs, Pork, & Poultry Fruits & Vegetables Soil
Appendix 5 55

vertebrates (ASTDR 1998). As a result, the World Health Organization (WHO) assigned a toxic 

equivalency factor (TEF) of 1 to this congener, meaning that the concentrations of the other 

congeners are scaled relative to the toxicity of 2,3,7,8-TCDD (See Attachment 2 for TEFs). We 

deemed this species an appropriate surrogate to represent the fate and transport behavior of 

the dioxin group.

Incineration/combustion processes are believed to be the primary emission sources for 

chlorinated dioxins (ASTDR 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. Dioxins emitted to the atmosphere 

may be transported long-range as vapors or bound to particulates, depositing in soils and water 

bodies in otherwise pristine locations far from the source. Though 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 (ASTDR 1998).

Dioxins strongly adsorb to organic matter in soil and show very little vertical movement, 

particularly in soils with a high organic carbon content (ASTDR 1998). Most surface water 

deposition occurs through dry deposition from the atmosphere and from wind-transported 

eroded soil particles contaminated with dioxins. Most dioxins entering the water column will 

partition to bed sediment, which appears to be the primary environmental sink for this chemical 

group (EPA 2004b). Although dioxins bound to aquatic sediment will primarily be buried in the 

sediment compartment, some resuspension and remobilization of congeners may occur as a 

result of disturbance of sediments by benthic organisms (ASTDR 1998). 

Bioaccumulation factors in fish are high for 2,3,7,8-TCDD as a result of the lipophilic nature of 

chlorinated dioxins. Though the processes by which freshwater fish accumulate dioxins are not 

well understood, it has been shown that fish and invertebrates both bioaccumulate congeners 

when exposed to contaminated sediments, and bioconcentrate congeners dissolved in water 

(EPA 2004b). However, because most dioxins in the aquatic environment are adsorbed to 

suspended particles, it is unlikely that direct uptake from the water is the primary route of 

exposure for most aquatic organisms at higher trophic levels (ASTDR 1998). At the lower 

trophic levels, the primary route of exposure appears to be through uptake of water in 

contaminated sediment pores, whereas the primary route of exposure in the higher trophic 

levels appears to be through food chain transfer. Following ingestion, some fish are capable of 

slowly metabolizing certain congeners, such as 2,3,7,8-TCDD, and releasing the polar 

metabolites in bile. This may ultimately limit bioaccumulation at higher trophic levels (ASTDR 

1998). Measured bioaccumulation factors for 2,3,7,8-TCDD are not widely reported due to the 

difficulty of detecting trace levels of congeners in ambient water. Reported bioconcentration 

factors, which can be measured in laboratory conditions, but do not account for exposure 

pathways other than direct uptake from water, range from 200-100,000, depending on the 

species and medium (ASTDR 1998). 

Emission Profile 

The 2,3,7,8-TCDD threshold emission rate used for the RTR screening scenario is 9.18E-10 

TPY or 2.3E-06 g/day (based on lifetime cancer risk of 1-in-a-million). Although the annual 

concentration from year 50 is used for comparison, modeled concentrations of 2,3,7,8-TCDD

either reached an approximate steady state or had begun to level off in all compartments by

year 20.
Appendix 5 56

Concentrations in Environmental Media

Measured ranges of total dioxin TEQ concentrations16 in environmental media are displayed 

with modeled TEQ concentrations of 2,3,7,8-TCDD in Exhibit 4-13. Modeled concentrations 

and BAFs in fish compartments are shown in Exhibit 4-14. 

The 2,3,7,8-TCDD concentrations output by the RTR screening scenario were consistently 

lower than values reported in the literature. Three main factors likely contributed to this trend. 

These are the high background values resulting from the ubiquitous nature of dioxins in the 

environment, limited availability of 2,3,7,8-TCDD-specific data, and the use of conservative 

exposure factors to calculate the screening threshold. Firstly, due to the quantity of dioxins that 

have been emitted in the past and their persistence in the environment, dioxins are typically 

present in environmental media at relatively high background levels when compared with

concentrations output from the screening scenario, which only considers emissions from a 

single facility and does not consider background levels. As a result, it is not unreasonable that 

some of the TRIM.FaTE outputs were several orders of magnitude smaller than those reported 

in the literature, as background levels will be higher than incremental levels. Secondly, though 

measured data for 2,3,7,8-TCDD were used when available, concentrations in most media were 

available only for total dioxin TEQ concentrations. Thus, those ranges are not representative of 

2,3,7,8-TCDD alone and may contain values that are higher and ranges that are wider than 

those for a single chemical. Thirdly, it should be noted that in order to maintain a healthprotective approach to screening emissions, a collection of conservative exposure factors were 

used, which likely resulted in low, but not implausible media concentrations. 

It should also be noted that because dioxin congeners are often present at trace levels, 

analytical instruments must be very sensitive if congeners are to be detected in environmental 

media. As a result, limits of detection in many instruments may not be sufficiently sensitive to 

produce quantifiable results for very low levels of dioxin congeners. Many of the concentrations 

output by TRIM.FaTE are low enough to be considered nondetects by most analytical 

instruments.

Though the 2,3,7,8-TCDD concentrations in environmental media estimated by TRIM.FaTE are 

generally low, they are not outside the range of plausible values when considered as a 

surrogate for the dioxin profile. In the screening scenario, the amount of chemical mass in the 

water column is small relative to the amount in the sediment. This is consistent with the trend 

that most congeners are removed from water bodies through adsorption to organic matter in soil 

and sediment. The screening scenario BAFs are also consistent with observed trends that 

indicate that 2,3,7,8-TCDD accumulates much less readily in herbivorous fish than in 

carnivorous fish that consume other contaminated organisms (ATSDR 1998). 

 

16 It was not possible to confirm that TEQ concentrations reported in the literature and summarized here were all 

estimated using the most recent TEF scheme adopted by the WHO in 1998. TEQ concentrations reported here may 

represent values determined using TEFs from EPA‘s 1989 scheme, WHO‘s 1994 scheme, or WHO‘s 1998 scheme 

(EPA 1997b). 
Appendix 5 57

Exhibit 4-13. Summary of Modeled 2,3,7,8-TCDD Concentrations and Observed Total Dioxin TEQ Concentrations in 

Environmental Media

1.E-06 1.E-05 1.E-04 1.E-03 1.E-02 1.E-01 1.E+00 1.E+01 1.E+02 1.E+03

North America (a) 

Stormwater Runoff (a) 

Rural, North America (a) 

Urban, North America (a) 

Industrial, Austria (a) 

Waste-t o-Energy Facility, OH (a) 

Polluted River, Japan (a) 

North America (a) 

Manmade Lakes, MS (a) 

Industrial Area, Venice Lagoon (a) 

Rural NY (b)

Southern MS Lakes (a) 

Non-background Sites, U.S. (a) 

Great Lakes (a) Surface Water (ppq) Surface Soil (ppt dry wt.) Sediment (ppt dry wt.) Fish (ppt fresh wt.)

2,3,7,8-TCDD Concentration (see units at left)

Reported value was less than limit of detection (LOD = upper bound of bar) Value or range from literature

Modeled concentration corresponding to screening threshold emission rate

BC WCC

T S US

BC = Benthic Carnivore, WCC = Water Column Carnivore, TS = Tilled Soil, US = Untilled Soil

a Source: Exposure and Human Health Reassessment of 2,3,7,8-Tetrachlorodibenzo-p-Dioxin (TCDD) and Related Compounds (EPA 2004b) 

b

Source: Toxicological Profile for Chlorinated Dibenzo-p-dioxins (ATSDR 1998)
Appendix 5 58

Exhibit 4-14. TRIM.FaTE 2,3,7,8-TCDD Concentrations in Fish and Calculated 

Bioaccumulation Factors with Respect to Total Water Concentration

0

388,889

777,778

1,166,667

1,555,556

1,944,444

2,333,333

0.0E+00

1.0E-09

2.0E-09

3.0E-09

4.0E-09

5.0E-09

6.0E-09

Zooplankton Water Column

Herbivore

Benthic

Omnivore

Water Column

Omnivore

Benthic

Carnivore

Water Column

Carnivore Bioaccumulation Factors (BAF) Concentration in Fish (mg/kg wet weight)

Partitioning Behavior

Chlorinated dioxins have been observed to partition mostly to soil when released to the 

environment, and sorbed congeners are unlikely to leach or volatilize out of the soil profile. In 

soils with a high organic carbon content, congeners below the top few millimeters of the soil are 

very strongly adsorbed to organic matter and exhibit very little migration. Most dioxins

deposited onto soil are expected to remain buried in the soil profile, and erosion of 

contaminated soil particles is the only significant mechanism for transport to water bodies. 

Because of the hydrophobic nature of the 2,3,7,8-TCDD congener, and its affinity for organic 

carbon, 70 to 80 percent of the congener is expected to bind to suspended organic particles in 

natural waters. The remainder in the water column is associated with dissolved organic 

substances (ASTDR 1998). Because most of the dioxins in water are in a sorbed state, the 

ultimate fate of most congener-laden particles is in the bed sediment. For the RTR screening

scenario, more than 99.5 percent of 2,3,7,8-TCDD in the pond compartment was found in the 

sediment. The percentages of 2,3,7,8-TCDD in sorbed and dissolved states in surface water, 

sediment, and soil are presented in Exhibit 4-15. Also, because 2,3,7,8-TCDD is lipophilic, it 

readily partitions to breast milk. 

The partitioning behavior exhibited 2,3,7,8-TCDD in TRIM.FaTE is consistent with observed 

trends. In the surface water compartment, approximately 75 percent of 2,3,7,8-TCDD was 

present in the sorbed state, which is within the reported 70-80 percent range reported in the 

literature. In the sediment and soil compartments, 100 percent of the congener is in the sorbed 

state, which is representative of the strong preference of dioxins for adsorption to soil and 

sediment organic matter. 
Appendix 5 59

Exhibit 4-15. Fraction of 2,3,7,8 - TCDD Mass Sorbed vs. Dissolved 

in TRIM.FaTE Compartments

Compartment Sorbed Dissolved

Surface Soil 100.0% 0.0%

Surface Water 75.1% 24.9%

Sediment 100.0% 0.0%

Concentrations in Ingestible Products

The primary source of non-inhalation exposure to dioxins outside of occupational settings is 

through dietary intake, which accounts for over 90 percent of daily dioxin intake (ATSDR 1998). 

Available data indicate that dioxins concentrate in plants, aquatic organisms, and animals, 

offering multiple ingestion exposure pathways. However, actual congener levels in ingestible 

products can vary based upon type of food, agricultural and cultivation practices, atmospheric 

deposition rates, conditions in environmental media, and presence of other anthropogenic 

pollutants. Dioxins appear to enter the terrestrial food chain primarily through vapor phase 

deposition onto plant surfaces, 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 are the dominant exposure pathways, 

comprising 90 percent of dioxin dietary intake (ATSDR 1998). Though concentrations in 

vegetables are generally exceptionally low, root vegetables normally contain slightly higher 

concentrations of dioxins than vegetables that are affected primarily by atmospheric deposition, 

such as lettuce and peas (ATSDR 1998). The 2,3,7,8-TCDD concentrations in ingestible 

product compartments are displayed in Exhibit 4-16. 

Data for concentrations of dioxin congeners in ingestible products are not abundant and 

sophisticated analytical instruments with sufficiently sensitive limits of detection were not widely 

available for older studies, which likely resulted in a greater number of nondetects in samples. 

As a result, the data available for comparison were limited. Overall, modeled concentrations of 

2,3,7,8-TCDD in ingestible products were slightly lower than the available dioxin TEQ values 

(Exhibit 4-16). Given the conservative nature of the ingestion rates used in the screening 

assessment, the modeled values are not unreasonable. Consistent with the literature, the 

modeled concentration 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. The percent contributions of ingestion exposure pathways to the 

lifetime ADD are displayed in Ingestion of breast milk during infancy and fish ingestion 

contribute to nearly all (99%) of lifetime dioxin exposure 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. However, some studies note that specific subpopulations, such as 

subsistence farmers and fishers, may 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 are appropriate within the 

context of this analysis. 

Exhibit 4-17
Appendix 5 60

Exhibit 4-16. Summary of Modeled 2,3,7,8-TCDD Concentrations and Observed Total Dioxin TEQ 

Concentrations in Ingestible Media 

1 E-1 5 1 E-1 3 1 E-1 1 1 E-0 9 1 E-0 7 1 E-0 5 0.001

U.S. Samples (a)

North America (a) 

Beef Products, U.S. (b) 

North America (a) 

Southern MS Samples (a) 

U.K. Samples (a) 

Fresh Fruit, U.K. (a)

All Fruits, Average, Germany (a)

Green Vegetables, U.K. (a) 

Non-green Vegetables, U.K. (a) 

Above-ground Vegetables, Germany (a) 

Rural NY (b)

Southern MS Lakes (a) 

Non-background Sites, U.S. (a) 

Great Lakes (a) 

Pork Sausage, MS (a) 

U.S. Samples (a) 

North America (a) 

Chicken, MS (a) 

Chicken, U.S. (a) 

North America (a) 

Fresh Fruit, U.K. (a)

All Fruits, Average, Germany (a)

Non-green Vegetables, U.K. (a) 

Above-ground Vegetables, Germany (a) 

Potatoes, UK (a)

Average, Germany (a)

Rural, North America (a) 

Urban, North America (a) 

Industrial, Austria (a) 

Waste-to-Energy Facility, OH (a) 

Dairy Products, Rural NY (b) 

Milk and Dairy, North America (a) 

Dairy Products, Southern MS (a) 

Beef Eggs

Exp. Fruit 

Exposed 

Vegetable Fish Pork Poultry

Prot. 

Fruit

Prot. 

Veg. Root

Soil

(mg/kg dry wt.) Dairy

2,3,7,8-TCDD Concentration (mg/kg wet wt.*)

Reported value was less than limit of detection (LOD = upper bound of bar)

Value or range from literature

Modeled concentration corresponding to screening threshold emission rate

*except for soil, which is expressed as mg/kg dry weight.

a Source: Exposure and Human Health Reassessment of 2,3,7,8-Tetrachlorodibenzo-p-Dioxin (TCDD) and Related Compounds (EPA 2004b)

b

Source: Toxicological Profile for Chlorinated Dibenzo-p-dioxins (ATSDR 1998)
Appendix 5 61

Ingestion of breast milk during infancy and fish ingestion contribute to nearly all (99%)

of lifetime dioxin exposure 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. However, some studies note that specific subpopulations, such as 

subsistence farmers and fishers, may 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 are appropriate within the context of this analysis. 

Exhibit 4-17. Contribution of Modeled Food Types to 2,3,7,8-TCDD Ingestion 

Exposures (mg/kg/day)

Beef, 1.8E-14, 0.3%

Fish, 4.6E-12, 69%

Dairy, 6.2E-14, 0.9%

Breast Milk, 2.0E-12, 

30%

Beef Fish Dairy Eggs, Pork, & Poultry Fruits & Vegetables Soil Breast Milk

4.3.3.4 PAHs (Benzo[a]pyrene)

Behavior in the Environment

Emissions of POMs are often reported in terms of unspeciated or partially speciated groups, 

such as total PAHs, because they are often found in the environment as mixtures of two or more 

compounds exhibiting comparable behavior and toxicity. Because there are relatively few data 

concerning the behavior and toxicity of individual PAH compounds, EPA has proposed 

assigning a relative potency factor to PAHs based on the relative toxicity of these compounds to 

the most carcinogenic PAH compound(s) (EPA 1993). Like TEFs, this allows for risks from 

exposure to certain PAH compounds that are likely carcinogens to be determined relative to the 

toxicity of other PAH compounds that have been identified as probable carcinogens. 

Benzo[a]pyrene, a high molecular weight PAH identified by EPA as a probable carcinogenic 

compound (possibly the most potent carcinogen of the PAH group), was used to represent 

PAHs in the screening scenario. A relative potency factor of 1 has been proposed by EPA for 
Appendix 5 62

this compound (EPA 1993). However, because the relative potency index has not been widely 

adopted by the scientific community, modeled concentrations of benzo[a]pyrene are mostly 

compared to reported concentrations of this species, rather than to total PAHs. Data for 

benzo[a]pyrene comprises much of the available exposure information on carcinogenic PAHs 

for the last few decades. 

PAHs can enter the atmosphere as a result of a variety of combustion processes, both natural 

and anthropogenic. Based upon reviewed literature, stationary emission sources account for 

approximately 80 percent of total annual PAH emissions. Though the primary producer 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 (ASTDR 1995). 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 (ASTDR 1995).

As a result of sustained input from anthropogenic combustion sources and other sources, PAHs 

are ubiquitous in soil. Like other high molecular weight PAHs, benzo[a]pyrene strongly adsorbs 

to organic carbon in soil, indicating that adsorption to soil particles will limit the mobility of these 

compounds following deposition to soil. Most PAHs enter the water column directly through 

atmospheric fallout (ASTDR 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 (ASTDR 1995). Aquatic organisms may 

accumulate PAHs via uptake of water, sediment, or food. Though fish and other organisms 

readily take up PAHs from contaminated food, biomagnification generally does not occur 

because many organisms are capable of rapidly metabolizing them (ASTDR 1995). As a result, 

concentrations of PAHs have generally been observed to decrease with increasing trophic 

levels (ASTDR 1995). Based upon observed data, bioaccumulation factors in fish are also not 

expected to be especially high because fish are able to readily metabolize the compound. 

BCFs in the range of 10-10,000 have been reported for fish and crustaceans, with the higher 

end of the range attributable to greater accumulation of the higher molecular weight 

compounds, such as benzo[a]pyrene (ASTDR 1995). Higher BCFs have also been observed in 

species at lower trophic levels. Additionally, sediment-dwelling organisms may experience 

increased exposure to PAHs through association (e.g., direct uptake and/or consumption) with 

contaminated sediment (ASTDR 1995). 

Emission Profile 

The benzo[a]pyrene threshold emission rate used for the RTR screening scenario is 3.60E-04

TPY or 0.89 g/day (based on lifetime cancer risk of 1-in-a-million). Although the annual 

concentration from year 50 is used for comparison, modeled concentrations benzo[a]pyrene

either reached a steady state or had begun to level off in all compartments by year 10. 
Appendix 5 63

Concentrations in Environmental Media

Measured ranges of benzo[a]pyrene (and occasionally PAH-group concentrations) in 

environmental media are presented in Exhibit 4-18 with concentrations from the screening 

scenario. Modeled concentrations and BAFs in fish compartments are shown in Exhibit 4-19. 

The benzo[a]pyrene concentrations output by the RTR screening scenario were consistently 

lower than values reported in the literature. Three main factors likely contributed to this trend. 

These are the high background values resulting from ubiquitous nature of PAHs in the 

environment, limited availability of benzo[a]pyrene-specific data, and use of conservative 

exposure factors to calculate the screening threshold. Firstly, due to the quantity of PAHs that 

are emitted from mobile sources (~20 percent), as well as stationary synthetic and natural 

sources, PAHs are typically present in the environment at relatively high background levels. 

The concentrations output from the screening scenario consider only emissions from a single 

facility. As a result, it is not unreasonable that some of the TRIM.FaTE outputs were several 

orders of magnitude smaller than those reported in the literature, as background exposure will 

be higher than incremental exposure. Secondly, though measured data for benzo[a]pyrene 

were used when available, concentrations in many media were available only for groups of 

PAHs or total PAHs. Thus, those ranges are not representative of benzo[a]pyrene alone and 

may contain values that are higher— and ranges that are wider— than those for a single 

chemical. Thirdly, it should be noted that in order to maintain a health-protective approach to 

screening emissions, a collection of moderately conservative exposure factors were used, which 

likely resulted in low, but not implausible values. For these reasons, we believe that the 

TRIM.FaTE outputs for concentrations of benzo[a]pyrene in environmental media are within the 

range of plausible values for this chemical. However, because of this discrepancy between the 

reported data and the screening scenario outputs, further investigation is necessary regarding 

site-appropriate biotransfer factors for facilities that do not pass the screen as a result of PAH 

emissions.

The screening scenario BAFs for benzo[a]pyrene in fish compartments were consistent with 

trends reported in the literature. No biomagnification of this chemical was exhibited in the fish 

compartments in the screening scenario, and the highest concentration of benzo[a]pyrene was 

in the zooplankton compartment, which represents the lowest trophic level evaluated in the 

scenario. 
Appendix 5 64

Exhibit 4-18. Summary of Modeled and Observed Concentrations of Benzo[a]pyrene in Environmental Media

1.E-06 1.E-05 1.E-04 1.E-03 1.E-02 1.E-01 1.E+00 1.E+01 1.E+02 1.E+03 1.E+04 1.E+05 1.E+06

Great Lakes

Urban Runoff

(Individual PAHs)

Industrial Effluent

(Individual PAHs)

Rural

Agricultural

Urban

Contaminated Sites

Great Lakes

Boston Harbor

(High Molecular Mass PAHs)

Various Aquatic Organisms Surface Water (ng/L) Surface Soil (μg/kg dry wt.) Sediment (μg/kg dry wt.) Fish (mg/kg w wt)

Benzo[a]pyrene Concentration* (see units at left)

Reported value was less than limit of detection (LOD = upper bound of bar) Value or range from literature

Modeled concentration corresponding to screening threshold emission rate

BC WCC

T S US

* Source of literature data: ATSDR Toxicological Profile for Polycyclic Aromatic Compounds (PAHs) (ATSDR 1995). Data are for benzo[a]pyrene (BaP)

exclusively, unless otherwise noted.
Appendix 5 65

Partitioning Behavior

Data suggest that benzo[a]pyrene partitions mainly to soil (82 percent) and sediment (~17

percent) following deposition to a 1 km2area adjacent to an emission source (ASTDR 1995). 

Once in soil, PAHs can volatilize, undergo abiotic or biotic degradation, accumulate in plants, or 

remain sorbed to soil organic matter. High molecular weight PAHs, such as benzo[a]pyrene, 

tend to adsorb more strongly to organic carbon than PAHs having lower molecular weights 

(ASTDR 1995). As a result of this affinity for organic carbon, volatilization is not an important 

loss mechanism for benzo[a]pyrene from soils (ASTDR 1995). Because of its low solubility and 

affinity for organic carbon, most benzo[a]pyrene is expected to be particle-bound in natural 

waters. Less than one-third of PAHs in aquatic systems are generally present in the dissolved 

phase (ASTDR 1995). The remainder may be associated with suspended particles in the water 

column or particles that have settled on the bed sediment. Because most of the benzo[a]pyrene 

found in natural water is in a sorbed state, the ultimate fate of most contaminant-laden particles 

is burial in the bed sediment. For the RTR screening scenario, approximately 99 percent of 

benzo[a]pyrene in the pond compartment partitioned to the benthic sediment. The percentage 

of benzo[a]pyrene in sorbed and dissolved states in soil, surface water, and sediment are 

presented in Exhibit 4-20.

Exhibit 4-19. TRIM.FaTE Benzo[a]Pyrene Concentrations in Fish and Calculated 

Bioaccumulation Factors with Respect to Total Water Concentration

0

477,918

955,837

1,433,755

1,911,674

2,389,592

2,867,511

3,345,429

3,823,348

0.0E+00

1.0E-04

2.0E-04

3.0E-04

4.0E-04

5.0E-04

6.0E-04

7.0E-04

8.0E-04

Zooplankton Water Column

Herbivore

Benthic

Omnivore

Water Column

Omnivore

Benthic

Carnivore

Water Column

Carnivore Bioaccumulation Factors (BAF) Concentration in Fish (mg/kg wet weight)
Appendix 5 66

Exhibit 4-20. Fraction of Benzo[a]Pyrene Mass Sorbed vs. 

Dissolved in TRIM.FaTE Compartments

Compartment Sorbed Dissolved

Surface Soil 100.0% 0.0%

Surface Water 30.7% 69.3%

Sediment 100.0% 0.0%

The partitioning behavior of benzo[a]pyrene in the screening scenario is generally consistent 

with trends reported in the literature. The presence of this chemical in the sorbed state in the 

soil and sediment compartments is consistent with reported trends. A different trend was 

observed in the surface water compartment where more of the chemical was estimated by 

TRIM.FaTE to be dissolved. The dissolved concentration in TRIM.FaTE is affected by 

suspended sediment concentration, organic carbon content, and suspended sediment 

deposition and resuspension rates. Additional evaluation may be required to determine the 

specific factors affecting this behavior.

Concentrations in Ingestible Products

The primary source of non-inhalation exposure to benzo[a]pyrene outside of occupational 

settings is through dietary intake. Exposure may depend equally on the origin of the food 

(higher values often recorded at contaminated sites) and the method of food preparation (higher 

values 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. However, these compounds are readily metabolized by higher trophic level organisms, 

including humans, so biomagnification is not considered to be significant (ASTDR 1995). Plants 

accumulate PAHs primarily through atmospheric deposition, but chemical concentrations tend to 

be below detection levels. In general, grains and cereals may contain slightly higher 

concentrations of benzo[a]pyrene than fruits and vegetables. PAHs in meat have also 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 may be significantly higher than those output by MIRC. Measured concentrations of 

benzo[a]pyrene in ingestible products are presented in Exhibit 4-21 along with RTR screening 

scenario concentrations.

The RTR screening scenario concentrations 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 is 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 benzo[a]pyrene in ingestible 

products. The percent contributions of ingestion exposure pathways to the lifetime ADD for

benzo[a]pyrene are displayed in Exhibit 4-22.

For the screening scenario, ingestion of fish drives human exposure to benzo[a]pyrene, but all 

media contribute a small amount to total exposure. 
Appendix 5 67

Exhibit 4-21. Summary of Modeled and Observed Concentrations of Benzo[a]pyrene in 

Ingestible Media

1E-10 1E-08 0.000001 0.0001 0.01 1 100

Cooked (Not Smoked or Grilled), U.S. (b)

Grilled/Barbequed, U.S. (b)

Beef Sausage (c)

U.S. Samples (b)

Italian Samples (a)

Various, U.S. (b)

Apples (a)

Leafy Greens, U.S. (b)

Various, Italy (a)

Spinach/Kale, Near Airport Runway (a)

Various (No Leafy Greens), U.S. (b)

Smoked Fish, France (c)

Trout and Cod (Unsmoked), Italy (a)

Smoked Salmon (c)

Smoked Pork, France (c)

Pork, Italy (a)

Pork Sausage (c)

Smoked Poultry, France (c)

Chicken, Italy (a)

Various, U.S. (b)

Citrus Fruits, Italy (a)

Corn, Italy (a)

Various, U.S. (b)

Various, U.S. (b)

Potatoes, Italy (a)

Agricultural (c)

Rural (c)

Urban (c)

Contaminated Sites (c)

Dairy Products, U.S. (b)

Dairy Products, Italy (a)

Cheese (a)

Beef Eggs

Exp.Fruit

Exposed

Vegetable Fish Pork Poultry

Prot.Fruit

Prot.Veg. Root

Soil

(mg/kg dry wt.) Dairy

Benzo[a]pyrene Concentration (mg/kg wet wt.*)

Reported value was less than limit of detection (LOD = upper bound of bar)

Value or range from literature

Modeled concentration corresponding to screening threshold emission rate

* except for soil, which is expressed as mg/kg dry weight

a

Source: Hazardous Substances Databank Record for Benzo[a]pyrene (HSDB 2005c)

b

Source: Analysis of 200 Food items for Benzo[a]pyrene and Estimation of its Intake in an Epidemiologic Study (Kazerouni et al. 2001)

c

Source: ATSDR Toxicological Profile for Polycyclic Aromatic Compounds (PAHs) (ATSDR 1995)
Appendix 5 68

Exhibit 4-22. Contribution of Modeled Food Types to Benzo[a]pyrene 

Ingestion Exposures (mg/kg/day)

Soil, 1.8E-09, 2% Fruit, 3.7E-09, 4% Vegetables, 2.7E-09, 

3%

Dairy, 5.4E-09, 5%

Beef, 1.1E-09, 1%

Eggs, Pork, & Poultry, 

5.1E-10, 0.5%

Fish, 8.5E-08, 85%

Soil Fruit Vegetables Dairy Beef Eggs, Pork, & Poultry Fish

4.3.4 Summary

This analysis compared outputs from the RTR screening scenario (using screening threshold

emission values) to observed data reported in the literature for cadmium, mercury, 2,3,7,8-

TCDD, and benzo[a]pyrene. In general, most results from TRIM.FaTE appear to be reasonable 

for a screening modeling approach based on this comparison to measured values. Briefly, the 

results of the evaluation are as follows:

Cadmium: Modeled concentrations in environmental media and ingestible products and 

behavior with respect to partitioning and bioaccumulation appear to be reasonable.

Mercury: Modeled concentrations in environmental media are comparable to levels for 

contaminated sites, and modeled concentrations in ingestible products are generally 

consistent with reported values. Speciation of mercury appears to be consistent with 

observed patterns in the environment.

2,3,7,8-TCDD: Modeled concentrations in environmental media and ingestible products 

are slightly lower than reported values, but still within a reasonable range. Both modeled 

values and measured values were primarily located in the noise around the limit of 

detection, which increases the uncertainty of the data.

Benzo[a]pyrene: Modeled concentrations in both environmental media and ingestible 

products are generally lower than those reported in the literature, in some cases by 

several orders of magnitude. Because this chemical is found in the environment at 

background levels that would far exceed concentrations resulting from single facilityemissions, a comparison to measured concentrations is not as informative for this 

chemical.
Appendix 5 69

Note that the exposure factors driving farm food chain ingestion rates are also conservative, and 

that these parameters were not considered in this evaluation, meaning that the ―actual‖ 

concentration associated with a given risk level (if such a value could be calculated) would be 

higher than the level associated with the modeled screening threshold emission rate. Also, 

even ―correct‖ model results may mask incorrect model assumptions that combine to produce 

compensating errors. Where results differ significantly from reported values, such as with 

benzo[a]pyrene, a more detailed investigation of underlying assumptions may be necessary to 

determine appropriate adjustments to the scenario configuration. As noted in the overview of 

this section, this type of evaluation cannot be used to verify model results.
Appendix 5 70

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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. Environmental Protection Agency (EPA). 2006b. Air Quality Criteria for Lead (Final). 

Office of Research and Development. EPA-600/R-05-44aF and -bF. Washington, DC. 

October.

U.S. Environmental Protection Agency (EPA). 2006c. Air Toxics Risk Assessment Reference 

Library; Volume 3 - Community Scale Assessment; Part II, Multisource Cumulative Human 

Health Assessment: Inhalation. Chapter 3, Overview of a Human Health Multisource 

Cumulative Inhalation Assessment. Office of Air Quality Planning and Standards, Research 

Triangle Park, NC. April. Available at: 

http://www.epa.gov/ttn/fera/data/risk/vol_3/Chapter_03_April_2006.pdf.

U.S. Environmental Protection Agency (EPA). 2006d. Child-Specific Exposure Factors 

Handbook, External Review Draft. EPA-600-R-06-096A. National Center for Environmental 

Assessment, Office of Research and Development; September. Available at: 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=56747

U.S. Environmental Protection Agency (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 .

U.S. Food and Drug Administration (FDA). 2007. Total Diet Study Statistics on Elemental 

Results (original samples), Revision 4.1, Market Baskets 1991-3 through 2005-4. Center for 

Food Safety and Applied Nutrition, College Park, MD. December 11, 2007.

U.S. Geological Survey (USGS). 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. 

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.
 

Technical Support Document for TRIM-Based Multipathway Screening 

Scenario for RTR: Summary of Approach and Evaluation

ATTACHMENT 1: TRIM.FaTE Inputs 
 

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Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

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TABLE OF CONTENTS

1 TRIM.FaTE Inputs .................................................................................................................. 1

1.1 Introduction ........................................................................................................................ 1

Exhibit 1. TRIM.FaTE Simulation Parameters ............................................................................ 2

Exhibit 2. Meteorological Inputs ................................................................................................. 3

Exhibit 3. Air Parameters .......................................................................................................... 4

Exhibit 4. Soil and Groundwater Parameters ............................................................................. 5

Exhibit 5. Runoff Assumptions .................................................................................................. 8

Exhibit 6. USLE Erosion Parameters ......................................................................................... 9

Exhibit 7. Terrestrial Plant Placement .......................................................................................10

Exhibit 8. Terrestrial Plant Parameters .....................................................................................11

Exhibit 9. Surface Water Parameters .......................................................................................13

Exhibit 10. Sediment Parameters .............................................................................................14

Exhibit 11. Aquatic Plant Parameters .......................................................................................15

Exhibit 12. Aquatic Animals Food Chain, Density, and Mass ....................................................16

Exhibit 13. Cadmium Chemical-Specific ...................................................................................17

Exhibit 14. Mercury Chemical-Specific Properties ....................................................................18

Exhibit 15. PAH Chemical-Specific Properties ..........................................................................19

Exhibit 16. Dioxin Chemical-Specific Properties .......................................................................20

Exhibit 17. Cadmium Chemical-Specific Properties for Abiotic Compartments .........................23

Exhibit 18. Mercury Chemical-Specific Properties for Abiotic Compartments ...........................24

Exhibit 19. PAH Chemical-Specific Properties for Abiotic Compartments .................................28

Exhibit 20. Dioxin Chemical-Specific Properties for Abiotic Compartments ...............................30

Exhibit 21. Cadmium Chemical-Specific Properties for Plant Compartments ...........................34

Exhibit 22. Mercury Chemical-Specific Properties for Plant Compartments ..............................35

Exhibit 23. PAH Chemical-Specific Properties for Plant Compartments ...................................37

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

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Exhibit 24. Dioxin Chemical-Specific Properties for Plant Compartments .................................38

Exhibit 25. Cadmium Chemical-Specific Properties for Aquatic Species ..................................39

Exhibit 26. Mercury Chemical-Specific Properties for Aquatic Species .....................................40

Exhibit 27. PAH Chemical-Specific Properties for Aquatic Species ..........................................41

Exhibit 28. Dioxin Chemical-Specific Properties for Aquatic Species ........................................42

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

1-iii 
 

 

 

 

 

 

 

 

1 TRIM.FaTE Inputs 

1.1 Introduction 

This attachment provides the tables of the detailed modeling inputs for the TRIM.FaTE 

screening scenario. Exhibit 1 presents runtime settings for TRIM.FaTE. Exhibits 2 and 3 

present air parameters entered into the model. Exhibits 3 through 8 present the terrestrial 

parameters. Exhibits 9 through 12 present the lake parameters, and 13 through 28 present the 

chemical specific parameters. 

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

1-1 
 

 

 

 

 

Exhibit 1. TRIM.FaTE Simulation Parameters 

for the TRIM.FaTE Screening Scenario 

Parameter Name Units Value Used Reference 

Start of simulation date/time 1/1/1990, midnight Consistent with met data. 

End of simulation date/time 1/1/2040, midnight Consistent with met data set; selected to 

provide a 50-year modeling period. 

Simulation time step hr 1 Selected value. 

Output time stepa hr 4 Selected value. 

a Output time step is set in TRIM.FaTE using the scenario properties "simulationStepsPerOutputStep" and 

"simulationTimeStep." 

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

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Exhibit 2. Meteorlogical Inputs

for the TRIM.FaTE Screening Scenario

Parameter Name Units Value Used Reference 

Meteorological Inputs (all TRIM.FaTE scenario properties, except mixing height) 

Air temperature degrees K 298 USEPA 2005. 

Horizontal wind speed m/sec 2.8 5th percentile annual average value for contiguous US, 

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 steady-state runs are needed). 

isDay_SteadyState_forOther unitless -­

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

 1-3 
 

 

 

 

 

 

Exhibit 3. Air Parameters

for the TRIM.FaTE Screening Scenario

Parameter Name Units Value Used Reference 

Atmospheric dust load kg[dust]/m3

[air] 6.15E-08 Bidleman 1988 

Density of air g/cm3 0.0012 U.S. EPA 1997 

Dust density kg[dust]/m3

[dust] 1,400 Bidleman 1988 

Fraction organic matter on 

particulates unitless 0.2 Harner and Bidleman 1998 

Height m 800 5th percentile for United States 

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

 1-4 
Exhibit 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 [Link 

property] unitless variesb See Erosion and Runoff Fraction table. 

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 availabe 

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 [Link 

property] unitless variesb See Erosion and Runoff Fraction table. 

Total erosion rate kg [soil]/m2

/day variesb See Total Erosion Rates table. 

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] 0.19 McKone et al 2001 (Table 15). 

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

 1-5 
Exhibit 4. Soil and Groundwater Parameters 

for the TRIM.FaTE Screening Scenario 

 Parameter Name Units Value Used Reference 

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. 

Thickness - untilleda m 0.79 McKone et al. 2001 (Tabel 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 (Table 16 and A3, U.S. Average). 

pH unitless 6.8 Professional judgment. 

Water content volume[water]/volume[compartment] 0.21 McKone et al. 2001 (Table 16). 

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 Pofessional judgment. 

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

Organic carbon fraction unitless 0.003 McKone et al. 2001 (Table 16 and A3, 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). 

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

 1-6 
 

 

Exhibit 4. Soil and Groundwater Parameters 

for the TRIM.FaTE Screening Scenario 

 Parameter Name Units Value Used Reference 

Ground Water 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). a

Set using the volume element properties file 

b

See separate tables for erosion/runoff fractions and total erosion rates. 

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

 1-7 
Exhibit 5. Runoff Assumptions

for the TRIM.FaTE Screening Scenario

Originating Compartment Destination Compartment Runoff/Erosion Fraction 

SurfSoil_Source 

SurfSoil_N1 0.0 

SurfSoil_S1 0.0 

sink 1.0 

SurfSoil_N1 

SW_Pond 1.0 

SurfSoil_Source 0.0 

SurfSoil_N6 0.0 

SurfSoil_S1 0.0 

sink 0.0 

SurfSoil_S1 

SW_Pond 1.0 

SurfSoil_Source 0.0 

SurfSoil_N1 0.0 

sink 0.0 

SurfSoil_N6 

SW_Pond 1.0 

SurfSoil_N1 0.0 

SurfSoil_N7 0.0 

sink 0.0 

SurfSoil_N7 

SW_Pond 1.0 

SurfSoil_N6 0.0 

SurfSoil_N3 0.0 

sink 0.0 

SurfSoil_N3 

SW_Pond 1.0 

SurfSoil_N7 0.0 

SurfSoil_N4 0.0 

sink 0.0 

SurfSoil_N4 

SW_Pond 1.0 

SurfSoil_N3 0.0 

SurfSoil_N5 0.0 

SurfSoil_S4 0.0 

sink 0.0 

SurfSoil_S4 

SW_Pond 1.0 

SurfSoil_N4 0.0 

SurfSoil_S5 0.0 

sink 0.0 

SurfSoil_N5 a 

SW_Pond 0.0 

SurfSoil_N4 0.5 

SurfSoil_S5 0.5 

sink 0.0 

SurfSoil_S5 a 

SW_Pond 0.0 

SurfSoil_N5 0.0 

SurfSoil_S4 1.0 

sink 0.0 

a

 Assumes that N5 is higher ground that S5, and half of the runoff flows into N4, and the other 

half in S5. Assumes all runoff from S5 flows into S4. 

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

 1-8 
Exhibit 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 

m 2 R (100 ftton/ac) 

K 

(ton/ac/(100 ftton/acre)) 

LS 

(USCS) type C 

(USCS) P A 

(ton/ac/yr) 

A 

(kg/m2

/d) SDRa 

calculated 

(adjusted) 

erosion rate 

(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 a

Calculated using SD = 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). 

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

 1-9 
Exhibit 7. Terrestial 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) 

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 

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

 1-10 
Exhibit 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]/ m2[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 dep interception 

fraction (boolean) 1=yes, 0=no 0 Professional judgment. 0 Professional judgment. 

Correction exponent, octanal 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/m3 820 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 dep 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 & 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 -

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

 1-11 
Exhibit 8. Terrestrial Plant Parameters 

for the TRIM.FaTE Screening Scenario 

 Parameter Name Units Coniferousa Grass/Herba

Value Used Reference Value Used Reference 

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/m3 820 Soybean, Paterson et al. 1991. 

Wet mass per soil area kg/m2 1.4 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/m3 1,000 Professional judgment. 

Density of xylem fluid kg/cm3 900 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/m3 830 Professional judgment. 

Wet mass per soil area kg/m2 0.24 

Calculated from leaf and root 

biomass density based on 

professional judgment. a

See separate table for assignment of plant types to surface soil compartments. 

b

Begins March 9 (set to 1), ends November 7 (set to 0). Nation-wide 80th percentile.

Begins November 7, ends December 6; rate = 0.15/day during this time (value assumes 99 percent of leaves fall in 30 days). c

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

 1-12 
Exhibit 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 

vol 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 2005 - 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 1997. 

Total suspended sediment 

concentration 

kg[sediment]/m3[water 

column] 0.05 USEPA 2005. 

Water temperature degrees K 298 USEPA 2005. 

a

Set using the volume element properties named "top" and "bottom." 

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

 1-13 
Exhibit 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 1998. 

Solid material density in 

sediment kg[sediment]/m3

[sediment] 2,600 McKone et al. 2001 (Table 3). 

a

 Set using the volume element properties named "top" and "bottom." 

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

 1-14 
Exhibit 11. Aquatic Plants

for the TRIM.FaTE Screening Scenario

Parameter Name Units Value Used Reference 

Macrophyte biomass per water area kg/m2 0.5 Professional judgment. 

Density of macrophytes kg/L 1 Professional judgment. 

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

 1-15 
Exhibit 12. Aquatic Animals Food Chain, Density, and Mass 

for the TRIM.FaTE Screening Scenario 

Aquatic Biota 

(Consuming 

Organism) 

Fraction Diet 

Biomass 

(g/m2

) 

Body 

Weight Reference 

Algae

Macrophyte

Zooplankto

n

Bethic 

Invertebrate

Water

Column

Planktivore

Benthic

Omnivore

Water

Column

Omnivore

Benthic

Carnivore

Water

Column

Carnivore 

Algae 0% 0% 0% 0% 0% 0% 0% 0% 0% 7.95 -­ Professional 

judgment 

Macrophyte 0% 0% 0% 0% 0% 0% 0% 0% 0% 500 -­ Professional 

judgment 

Zooplankton 100% 0% 0% 0% 0% 0% 0% 0% 0% 6.36 5.70E-08 Professional 

judgment 

Benthic 

Invertebrate 0% 0% 0% 0% 0% 0% 0% 0% 0% 20 2.55E-04 Professional 

judgment 

Water Column 

Planktivore 0% 0% 100% 0% 0% 0% 0% 0% 0% 2 0.025 Professional 

judgment 

Benthic Omnivore 

0% 0% 0% 100% 0% 0% 0% 0% 0% 2 0.25 Professional 

judgment 

Water Column 

Omnivore 0% 0% 0% 0% 100% 0% 0% 0% 0% 0.5 0.25 Professional 

judgment 

Benthic Carnivore 

0% 0% 0% 50% 0% 50% 0% 0% 0% 1 2 Professional 

judgment 

Water Column 

Carnivore 0% 0% 0% 0% 0% 0% 100% 0% 0% 0.2 2 Professional 

judgment 

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

 1-16 
Exhibit 13. Cadmium Chemical-Specific Properties

Documentation for the TRIM.FaTE Screening Scenario

Parameter Namea Units Value Reference 

CAS numberb unitless 7440-43-9 -

Diffusion coefficient in pure air m 2

[air]/day 0.71 USEPA 1999 (Table A-2­

35). 

Diffusion coefficient in pure 

water m 2

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

35). 

Henry's Law Constant Pa-m3

/mol 1.00E-37 USEPA 1999 (Table A-2-35; 

assumed to be zero). 

Melting point degrees K 594 ATSDR 1999. 

Molecular weight g/mol 112.41 ATSDR 1999. 

Octanol-air partition coefficient 

(Koa) m3[air]/m3[octanol] - -

Octanol-carbon partition 

coefficient (Koc) - -

Octanol-water partition 

coefficient (Kow) L[water]/kg[octanol] - -

a

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

These CAS numbers apply to elemental Cd; however, the cations of cadmium are being modeled.

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

 1-17
Exhibit 14. Mercury Chemical-Specific Properties

Documentation 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 1997. 

Diffusion coefficient in 

pure water m 2

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

Henry's Law constant Pa-m3

/mol 719 7.19E-05 0.0477 USEPA 1997. 

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

Molecular weight g/mol 201 201 216 USEPA 1997. 

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 1997, based on 

Petersen et al. 1995. 

a

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

b

On this and all following tables, Hg(0) = elemental mercury, Hg(2) = divalent mercury, and MHg = methyl mercury. 

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

 1-18 
Exhibit 15. PAH Chemical-Specific Properties

Documentation for the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference BaP BaA BbF BkF Chr DahA IcdP 

CAS number unitless 50-32-8 56-55-3 205-99-2 207-08-9 218-01-9 53-70-3 193-39-5 -

Diffusion coefficient in 

pure air m 2

/day 0.188 0.213 0.197 0.197 0.214 0.156 0.164 USEPA 1998. 

Diffusion coefficient in 

pure water m 2

/day 5.05E-05 5.37E-05 4.74E-05 4.74E-05 5.37E-05 5.19E-05 4.89E-05 USEPA 1998. 

Henry's Law constant Pa-m3

/mol 8.50E-02 3.67E-01 6.26E-01 4.20E-02 1.23E-01 1.13E-03 4.92E-04 USEPA 1998. 

Melting point degrees K 452 433 441 490 531 539 437 Budavari 1996. 

Molecular weight g/mol 252.32 228.29 252.32 252.32 228.29 278.33 276.34 Budavari 1996. 

Octanol-water partition 

coefficient (Kow) L[water]/L[octanol] 9.33E+05 6.17E+05 3.98E+06 6.92E+06 5.37E+05 3.16E+06 4.57E+07 Hansch et al. 1995. 

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

 1-19 
 

 Exhibit 16. Dioxin Chemical-Specific Properties

 Documentation 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 0.0883 0.123 0.0925 0.129 0.129 0.0958 0.135 

Diffusion coefficient in pure 

water m 2

/day 3.08E-06 3.15E-05 3.24E-05 3.33E-05 3.33E-05 3.43E-05 3.53E-05 

Henry's Law constant Pa-m3

/mol 0.68 0.19 1.28 1.43 1.43 1.08 1.45 

Melting Point degrees K 603 259 538 236.5 222 546 499.0 

Molecular weight g/mol 460.0 443.76 425.2 409.31 409.31 391.0 374.87 

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

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

 1-20
 

 Exhibit 16. Dioxin Chemical-Specific Properties

 Documentation for the TRIM.FaTE Screening Scenario

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 0.0958 0.135 0.0958 0.135 0.101 0.142 0.135 

Diffusion coefficient in pure 

water m 2

/day 3.43E-05 3.53E-05 3.43E-05 3.53E-05 3.65E-05 3.76E-05 3.53E-05 

Henry's Law constant Pa-m3

/mol 1.08 0.74 1.08 0.74 3.33 0.5 0.74 

Melting point degrees K 558.0 506.0 517.0 509.0 513.0 499.0 512.5 

Molecular weight g/mol 390.84 374.9 390.8 374.9 356.4 340.4 374.9 

Octanol-water partition 

coefficient (Kow) L[water]/L[octanol] 1.62E+08 8.24E+07 1.62E+08 3.80E+07 4.37E+06 6.17E+06 8.31E+07 

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

 1-21 
 

 

 

 

 

 

 

 

 

 

Exhibit 16. Dioxin Chemical-Specific Properties

Documentation 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 0.142 0.106 0.149 USEPA 1999. 

Diffusion coefficient in pure 

water m 2

/day 3.76E-05 5.68E-05 4.04E-05 USEPA 1999. 

Henry's Law constant Pa-m3

/mol 0.5 3.33 1.46 

Mackay et al. 1992 as cited in USEPA 2000a. Exceptions include 

1,2,3,4,6,7,8,9-OCDF; 1,2,3,4,7,8-HxCDF; and 1,2,3,6,7,8-HxCDF which 

are calculated by the VP/WS Ratio Technique (USEPA 2000a) 

Melting point degrees K 469.3 578.0 500.0 

Mackay et al. 2000, exceptions include USEPA 2000b (1,2,3,6,7,8­

HxCDD; 1,2,3,7,8,9-HxCDF; 1,2,3,7,8--PeCDD), ATSDR 1998(1,2,3,6,7,8­

HxCDF; 1,2,3,7,8-PeCDF; 2,3,4,6,7,8-HxCDF) and NLM 2002 (1,2,3,7,8,9­

HxCDD) 

Molecular weight g/mol 340.4 322.0 306.0 

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; 2,3,4,6,7,8­

HxCDF) and NLM 2002 (1,2,3,6,7,8-HxCDD; 1,2,3,7,8,9-HxCDD) 

Mackay et al. 1992a as cited in USEPA 2000a, exceptions include: 

Octanol-water partition USEPA 2000b (1,2,3,6,7,8-HxCDD; 1,2,3,6,7,8-HxCDF; 1,2,3,7,8,9­ L[water]/L[octanol] 3.16E+06 6.31E+06 1.26E+06 coefficient (Kow) HxCDD; 1,2,3,7,8,9-HxCDF; 2,3,4,6,7,8-HxCDF) and Sijm et al. 1989 

(1,2,3,7,8-PeCDD) 

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

 1-22 
Exhibit 17. Cadmium Chemical-Specific Properties for Abiotic 

Compartments Documentation for the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference 

Air Compartment Type 

Particle dry deposition velocity m/day 260 

Calculated from 

Mulbaier and Tisue 

1980. 

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

Surface Soil Compartment Type 

Use input characteristic depth 

(boolean) 0 = no, Else = yes 0 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. 

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

 1-23 
Exhibit 18. Mercury Chemical-Specific Properties for Abiotic Compartments

Documentation for 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 1997. 

Reduction rate 1/day 0 0 0 Professional judgment. 

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

Surface Soil Compartment Type 

Input characteristic depth (user supplied) m 0.08 0.08 0.08 Not used (model set to calculate value). 

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 1997. 

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, 1997)]. 

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 1997. 

Reduction rate 1/day 0 1.25E-05 0 

Value used for untilled surface soil (2cm), 10% 

moisture content, in USEPA 1997; general range is 

(0.0013/day)*moisture content to 

(0.0001/day)*moisture content for forested region 

(Lindberg 1996; Carpi and Lindberg 1997). 

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

 1-24 
Exhibit 18. Mercury Chemical-Specific Properties for Abiotic Compartments

Documentation for the TRIM.FaTE Screening Scenario

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

Root Zone Soil Compartment Type 

Input characteristic depth (user supplied) m 0.08 0.08 0.08 Not used (model set to calculate value). 

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 1997 

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 1997. 

Reduction rate 1/day 0 3.25E-06 0 

Value used for tilled surface soil (20cm), 10% 

moisture content, in USEPA 1997 (Lindberg 1996; 

Carpi and Lindberg, 1997). 

Vadose Zone Soil Compartment Type 

Input characteristic depth (user supplied) m 0.08 0.08 0.08 Not used (model set to calculate value), 

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 1997. 

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 1997. 

Reduction rate 1/day 0 3.25E-06 0 

Value used for tilled surface soil (20cm), 10% 

moisture content, in USEPA 1997 (Lindberg 1996; 

Carpi and Lindberg, 1997). 

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

 1-25 
Exhibit 18. Mercury Chemical-Specific Properties for Abiotic Compartments

Documentation for the TRIM.FaTE Screening Scenario

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

Ground Water Compartment Type 

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

wet wt] 1,000 58,000 7,000 USEPA 1997. 

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 

1997). 

Reduction rate 1/day 0 3.25E-06 0 

Value used for tilled surface soil (20cm), 10% 

moisture content, in USEPA 1997 (Lindberg 1996; 

Carpi and Lindberg 1997). 

Surface Water Compartment Type 

Algal surface area-specific uptake rate 

constant 

nmol/[μm2

-daynmol] 

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[octan 

ol] 0 -

a -b Mason et al. 1996. 

Solids-water partition coefficient L[water]/kg[solids 

wet wt] 1,000 100,000 100,000 USEPA 1997. 

Vapor dry deposition velocity m/day N/A 2500 USEPA 1997 (Vol. III, App. A). 

Demethylation rate 1/day N/A N/A 0.013 Average of 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 from 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 1997; 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). 

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

 1-26 
 

 

Exhibit 18. Mercury Chemical-Specific Properties for Abiotic Compartments

Documentation for 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 1997. 

Demethylation rate 1/day N/A N/A 0.5 

Value based on Lake Waccamaw, NC in Knightes, 

et al, 2009. 

Methylation rate 1/day 0 0.02 0 Value based on Lake Waccamaw, NC in Knightes, 

et al, 2009. 

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 1997; 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. 

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

 1-27 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Exhibit 19. PAH Chemical-Specific Properties for Abiotic Compartments

Documentation for the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference BaP BaA BbF BkF Chr DahA IcdP 

Air Compartment Type 

Particle dry deposition 

velocity m/day 500 500 500 500 500 500 500 McKone et al. 2001. 

Half-life day 0.046 0.125 0.596 0.458 0.334 0.178 0.262 Howard et al. 1991 / upper bound 

measured or estimated value. 

Washout Ratio 200,000 200,000 200,000 200,000 200,000 200,000 200,000 Mackay et al. 1986. 

Surface Soil Compartment Type 

Input characteristic depth m 0.08 0.08 0.08 0.08 0.08 0.08 0.08 Not used (model set to calculate value). 

Use input characteristic 

depth (boolean) 0 = No, Else = Yes 0 0 0 0 0 0 0 Professional judgment. 

Halflife day 530 680 610 2140 1000 940 730 MacKay et al. 2000. 

Root Zone Soil Compartment Type 

Input characteristic depth m 0.08 0.08 0.08 0.08 0.08 0.08 0.08 Not used (model set to calculate value). 

Use input characteristic 

depth 0 = No, Else = Yes 0 0 0 0 0 0 0 Professional judgment. 

Half-life day 530 680 610 2140 1000 940 730 MacKay et al. 2000. 

Vadose Zone Soil Compartment Type 

Input characteristic depth m 0.08 0.08 0.08 0.08 0.08 0.08 0.08 Not used (model set to calculate value). 

Use input characteristic 

depth (boolean) 0 = No, Else = Yes 0 0 0 0 0 0 0 Professional judgment. 

Half-life day 1060 1360 1220 4280 2000 1880 1460 

Howard et al. 1991 / upper bound 

measured or estimated value for 

groundwater. 

Groundwater Compartment Type 

Half-life day 1060 1360 1220 4280 2000 1880 1460 

Howard et al. 1991 / upper bound 

measured or estimated value for 

groundwater. 

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

 1-28 
 

 

 

 

 

 

 

Exhibit 19. PAH Chemical-Specific Properties for Abiotic Compartments

Documentation for the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference BaP BaA BbF BkF Chr DahA IcdP 

Surface Water Compartment Type 

RatioOfConcInAlgaeToConc 

DissolvedInWater 

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

(g[chem]/L[water]) 3610 3610 3610 3610 3610 3610 3610 BCF data for green algae for BaP from Lu et al. 1977 

Half-life day 0.138 0.375 90 62.4 1.626 97.8 750 Howard et al. 1991 / upper bound 

measured or estimated value. 

Sediment Compartment Type 

Half-life day 2290 2290 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 on Table 

2.3. 

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

 1-29 
Exhibit 20. Dioxin Chemical-Specific Properties for Abiotic Compartments 

Documentation 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 

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 

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]) 1.025 1.025 1.025 1.025 1.025 1.025 

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 

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

 1-30
Exhibit 20. Dioxin Chemical-Specific Properties for Abiotic Compartments 

Documentation for 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 

Halflife 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 

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 

Halflife 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 

Halflife 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]) 1.025 1.025 1.025 1.025 1.025 1.025 

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 

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

 1-31 
Exhibit 20. Dioxin Chemical-Specific Properties for Abiotic Compartments 

Documentation for 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 

Halflife 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 

Halflife 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 

Halflife 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 

Halflife 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]) 1.025 1.025 1.025 1.025 1.025 

Half-life day 0.19 0.58 0.19 2.7 0.18 

Sediment Compartment Type 

Half-life day 1095 1095 1095 1095 1095 

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

 1-32 
Exhibit 20. Dioxin Chemical-Specific Properties for Abiotic Compartments

Documentation for the TRIM.FaTE Screening Scenario

Parameter Name Reference 

Air Compartment Type 

Deposition Velocity McKone et al. 2001 

Halflife Atkinson 1996 as cited in USEPA 2000; 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 

Halflife Mackay et al. 2000; the degradation rate was cited by multiple authors, 

Root Zone Soil Compartment Type 

Input characteristic depth not used (model set to calculate value) 

Use input characteristic depth professional judgment 

Halflife Mackay et al. 2000; the degradation rate was cited by multiple authors, 

Vadose Zone Soil Compartment Type 

Input characteristic depth Not used (model set to calculate value). 

Use input characteristic depth (boolean) Professional judgment. 

Halflife Average value of the range presented in Mackay et al. 2000; based on 

Groundwater Compartment Type 

Half-life Average value of the range presented in Mackay et al. 2000; based on 

Surface Water Compartment Type 

Ratio Of Conc In Algae To Conc Dissolved 

In Water 

BCF data for green algae for 2,3,7,8-TCDD from Isense 1978, at 32 

days. 

Half-life Kim and O’Keefe. 1998, as cited in USEPA. 2000. 

Sediment Compartment Type 

Half-life Estimation based on Adriaens and Grbic-Galic 1992,1993 and 

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

 1-33 
Exhibit 21. Cadmium Chemical-Specific Properties for Plant Compartments 

Documentation for 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 Herbs a 

Root to Root Soil 

Partition- Alpha of 

Steady State 

unitless 0.95 Henning et al. 2001. 

Root to Root Soil 

Partition- Partitioning 

Coefficient 

m 3[bulk root 

soil]/m3

[root] 

0.23 

Nriagu 1980; based on average value 

calculated from various agricultural plant 

species. 

Root to Root Soil 

Partition- Time to 

Reach Alpha 

day 28 Henning et al. 2001. 

Stem Compartment Type - Grasses and Herbs a 

Transpiration stream 

concentration factor 

(TSCF) 

m 3[soil pore 

water]/m3

[xylem 

fluid] 

0.45 Tsiros et al. 1999. 

Aquatic Plants 

Macrophyte Compartment Type 

Water Column 

Dissolved PartitionAlpha of Equilibrium unitless 0.95 

Maine et al. 2001; based on assumption that 

equilibrium was nearly reached during 21 day 

experiment. 

Water Column 

Dissolved PartitionPartition Coefficient 

L[water]/kg[macrop 

hyte wet wt] 100 Maine et al. 2001; based on calculations from 

an average of four macrophyte species. 

Water Column 

Dissolved PartitionTime to Reach 

Equilibrium 

day 21 Maine et al. 2001. 

a Roots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE. 

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

 1-34 
Exhibit 22. Mercury Chemical-Specific Properties for Plant Compartments Documentation for 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 Herbs a 

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]/ 

m 3

[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. 

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

 1-35 
Exhibit 22. Mercury Chemical-Specific Properties for Plant Compartments Documentation for the TRIM.FaTE

Screening Scenario

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

Stem Compartment Type - Grasses and Herbs a 

Transpiration stream concentration 

factor (TSCF) 

m 3[soil pore water]/ 

m 3[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. 

Aquatic Plants 

Macrophyte Compartment Type 

Water Column Dissolved Partition-Alpha 

of Equilibrium unitless 0.95 0.95 0.95 Selected value. 

Water Column Dissolved PartitionPartition Coefficient 

L[water]/ kg[macrophyte 

wet wt] 0.883 0.883 4.4 Elodea densa, Ribeyre and Boudou 1994. 

Water Column Dissolved Partition-Time 

to Reach Equilibrium unitless 0.95 0.95 0.95 Selected value. 

Oxidation rate 1/day 1.00E+09 0 0 Professional judgment. 

t-alpha day 18 18 18 Experiment duration from Ribeyre and 

Boudou 1994. a Roots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE. 

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

 1-36 
Exhibit 23. PAH Chemical-Specific Properties for Plant Compartments Documentation for the TRIM.FaTE Screening 

Scenario

 Parameter Name Units Value Reference BaP BaA BbF BkF Chr DahA IcdP 

Terrestrial Plants 

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 Professional judgment. 

Half-life day 3.5 3.5 3.5 3.5 3.5 3.5 3.5 Edwards 1988 (as cited in Efroymson 1997)/ 

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 1.00E-04 1.00E-04 Professional judgment. 

Half-life day 2.31 1.84 3.56 17.8 4.12 17.8 17.8 Edwards 1988 (as cited in Efroymson 1997)/ 

calculated from metabolic rate constant 

Root Compartment Type - Grasses and Herbs a 

Half-life day 34.6 34.6 34.6 34.6 34.6 34.6 34.6 Edwards 1988 (as cited in Efroymson 1997)/ 

calculated from metabolic rate constant. 

Stem Compartment Type - Grasses and Herbs a 

Half-life day 3.5 3.5 3.5 3.5 3.5 3.5 3.5 Edwards 1988 (as cited in Efroymson 1997)/ 

calculated from metabolic rate constant. 

Aquatic Plants 

Macrophyte Compartment Type 

Half-life days 3.5 3.5 3.5 3.5 3.5 3.5 3.5 Edwards 1988 (as cited in Efroymson 1997)/ 

calculated from metabolic rate constant. a Roots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE. 

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

 1-37 
Exhibit 24. Doixin Chemical-Specific Properties for Plant Compartments Documentation for the 

TRIM.FaTE Screening Scenario

 Parameter Name Units Value Reference All Dioxins 

Terrestrial Plants 

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 2000c (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 Herbs a 

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 Herbs a 

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

1995; soybean root cell culture metabolism test data for DDE. 

Aquatic Plants 

Macrophyte Compartment Type 

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

soybean root cell culture metabolism test data for DDE. 

a Roots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE. 

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

 1-38 
Exhibit 25. Cadmium Chemical-Specific Properties for Aquatic Species

Documentation for the TRIM.FaTE Screening Scenario

 Parameter Name Units Value Reference 

Zooplankton Compartment Type 

Assimilation efficiency from food unitless 0.5 Goulet et al, 2007 

Absorption Rate Constant L/kg(ww)-day 1500 Goulet et al, 2007 

Elimination Rate Constant /day 0.03 Mean of reported range from Goulet et al, 2007 

Benthic Invertebrate Compartment Type

Sediment Partitioning - Alpha of 

Equilibrium unitless 0.95 Professional judgment 

Sediment Partitioning - Partition 

Coefficient 

kg[bulk 

sed/kg[inverteb 

rate wet wt] 

0.27 Professional judgment 

Sediment Partitioning - Time to 

Reach Alpha of Equilibrium day 21 Hare et al. 2001 

Absorption Rate Constant unitless 1.23 Calculated based on body weight from regression in 

Hendriks & Heikens 2000 

Elimination Rate Constant unitless 2.82E-03 Professional judgment 

Benthic Omnivore Compartment Type 

Assimilation efficiency from food unitless 0.1 Professional judgment based on Yan and Wang 2002. 

Absorption Rate Constant unitless 1.23E+00 Calculated based on body weight from regression in 

Hendriks & Heikens 2000 

Elimination Rate Constant unitless 2.82E-03 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 6.60E-01 Calculated based on body weight from regression in 

Hendriks & Heikens 2000 

Elimination Rate Constant unitless 1.68E-03 Professional judgment 

Water-column Herbivore 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 2.46 Calculated based on body weight from regression in 

Hendriks & Heikens 2000 

Elimination Rate Constant unitless 5.02E-03 Professional judgment 

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 1.2320207 Calculated based on body weight from regression in 

Hendriks & Heikens 2000 

Elimination Rate Constant unitless 2.82E-03 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 0.6602235 Calculated based on body weight from regression in 

Hendriks & Heikens 2000 

Elimination Rate Constant unitless 1.68E-03 Professional judgment 

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

 1-39 
 

Exhibit 26. Mercury Chemical-Specific Properties for Aquatic Species

Documentation for the TRIM.FaTE Screening Scenario

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

Benthic Invertebrate Compartment Type 

Alpha of equilibrium for sediment 

partitioning unitless 0.95 0.95 0.95 Selected value. 

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 Types a 

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

Assimilation efficiency from food unitless 0.06 0.06 b 0.5 c Professional judgment based on 

Trudel and Rasmussen 2001 & 

Muir, 1986. 

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 plants unitless 1 1 1 Phillips and Gregory 1979. 

Water-column Omnivore Compartment Type 

Assimilation efficiency from food unitless 1 1 1 Phillips and Gregory 1979. 

a Screening scenario includes Algae, Macrophyte, Zooplankton, Benthic Invertebrates, Water-column Herbivore, Benthic Omnivore, Water-column Omnivore,

Benthic Carnivore, and Water-column Carnivore b Zooplankton were assigned an assimilation efficiency of 0.2 for divalent mercury to compensate for the absence of naturally occurring direct diffusion processes

in the model. c

 Water Column Carnivores were assigned an assimilation efficiency of 0.2 for methyl mercury to compensate absence of naturally occurring death , growth,

reproduction and predation processes in the scenario/model.

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

 1-40 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Exhibit 27. PAH Chemical-Specific Properties for Aquatic Species

Documentation for the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference BaP BaA BbF BkF Chr DahA IcdP 

Zooplankton Compartment Type 

Assimilation efficiency from 

food 

unitless 0.43 0.43 0.43 0.43 0.43 0.43 0.43 Estimated from Arnot & Gobas, 2004 

Absorption Rate Constant L/kg(ww)­

day 

4.8E+05 4.8E+05 4.8E+05 4.8E+05 4.8E+05 4.8E+05 4.8E+05 Estimated from Muir, D.C.G., A.L. 

Yarechewski, A. Knoll, et al. 1986a 

Elimination Rate Constant /day 5.2E-01 5.2E-01 5.2E-01 5.2E-01 5.2E-01 5.2E-01 5.2E-01 Estimated from Kow 

Half Life day 16.5 16.5 16.5 16.5 16.5 16.5 16.5 Moermond, 2007. 

Benthic Invertebrate Compartment Type 

Clearance constant unitless 157.6 157.6 157.6 157.6 157.6 157.6 157.6 Stehly et al. 1990 / estimated for 

mayfly, 120-day-old nymphs. 

alpha of equilibrium for 

sediment partitioning unitless 0.95 0.95 0.95 0.95 0.95 0.95 0.95 Professional judgment. 

talpha for equilibrium for 

sediment partitioning days 14 14 14 14 14 14 14 Professional judgment. 

Vd (ratio of concentration 

in benthic invertebrates to 

concentration in water) 

ml/g 7235.0 7235.0 7235.0 7235.0 7235 7235 7235 Stehly et al. 1990 / estimated for 

mayfly, 120-day-old nymphs. 

Half-life day 1.5 1.5 1.5 1.5 1.5 1.5 1.5 

Stehly et al. 1990 / calculated from 

estimated elimination/depuration rate 

constant estimated for mayfly, 120-dayold nymphs. 

All Fish Compartment Typesa 

Gamma_fish unitless 0.2 0.2 0.2 0.2 0.2 0.2 0.2 Thomann 1989. 

Assimilation efficiency from 

foodb unitless 1.0 1.0 1.0 1.0 1.0 1.0 1.0 Professional judgment. 

Half-life day 2.8 2.8 2.8 2.8 2.8 2.8 2.8 

Spacie et al. 1983, as cited in MacKay 

et al. 1992 (bluegill sunfish) for 

benzo(a)pyrene. 

Benthic Omnivore Compartment Type 

Assimilation efficiency from 

plantsb unitless 1.0 1.0 1.0 1.0 1.0 1.0 1.0 Professional judgment. 

Water Column Omnivore Compartment Type 

Assimilation efficiency from 

plantsb unitless 1.0 1.0 1.0 1.0 1.0 1.0 1.0 Professional judgment. 

a Screening scenario includes: Benthic Omnivore, Benthic Carnivore, Water-column Herbivore, Water-column Omnivore, and Water-column Carnivore. 

b

All ingestion assimilation efficiencies set to 1 to be consistent with excretion rate calculations. 

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

 1-41 
 

 

 

Exhibit 28. Dioxin Chemical-Specific Properties for Aquatic Species

Documentation 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

1,2,3,6,7,8­

HxCDD

1,2,3,6,7,8­

HxCDF 

Zooplankton Compartment Type 

Assimilation efficiency from food unitless 0.41 0.41 0.41 0.41 0.41 0.41 0.41 0.41 0.41 

Absorption Rate Constant L/kg(ww)/day 380 380 380 380 380 380 380 380 380 

Elimination Rate Constant /day 5.5E-04 5.5E-04 5.5E-04 5.5E-04 5.5E-04 5.5E-04 5.5E-04 5.5E-04 5.5E-04 

Half Life day 1.0E+09 1.0E+09 1.0E+09 1.0E+09 1.0E+09 1.0E+09 1.0E+09 1.0E+09 1.0E+09 

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 5776.23 5776.23 5776.2 5776.23 5776.23 5776.23 5776.23 5776.23 5776.23 

All Fish Compartments a 

Assimilation efficiency from food unitless 0.03 0.03 0.5 0.5 0.5 0.5 0.5 0.5 0.5 

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

wt]-day 142 N/A b 14 N/A b N/A b 127 N/A b 127 N/A b 

Gamma_fish unitless N/A b 0.2 N/A b 0.2 0.2 N/A b 0.2 N/A b 0.2 

Half-life day 693.147 346.574 346.57 346.574 346.574 495.105 495.105 495.105 495.105 

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

 1-42 
 

 

Exhibit 28. Dioxin Chemical-Specific Properties for Aquatic Species

Documentation 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

1,2,3,6,7,8­

HxCDD

1,2,3,6,7,8­

HxCDF 

Water Column Herbivore Compartment 

Assimilation efficiency from plants unitless 0.03 0.03 0.5 0.5 0.5 0.5 0.5 0.5 0.5 

Water Column Omnivore Compartment 

Assimilation efficiency from plants unitless 0.03 0.03 0.5 0.5 0.5 0.5 0.5 0.5 0.5 

a Screening scenario includes: Benthic Omnivore, Benthic Carnivore, Water-column Herbivore, Water-column Omnivore, and Water-column Carnivore. 

b N/A = not applicable. This parameter is used in calculating the uptake when measured data are unavailable. 

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

 1-43 
 

 

 

Exhibit 28. Dioxin Chemical-Specific Properties for Aquatic Species

Documentation for 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 Type 

Assimilation efficiency from food unitless 0.41 0.41 0.41 0.41 0.41 0.41 0.41 0.41 

Absorption Rate Constant L/kg(ww)/day 380 380 380 380 380 380 380 380 

Elimination Rate Constant /day 5.5E-04 5.5E-04 5.5E-04 5.5E-04 5.5E-04 5.5E-04 5.5E-04 5.5E-04 

Half Life day 1.0E+09 1.0E+09 1.0E+09 1.0E+09 1.0E+09 1.0E+09 1.0E+09 1.0E+09 

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 

V_d (ratio of concentration in 

benthic invertebrates to 

concentration in water) 

ml/g 0 0 0 0 0 0 0 0 

Half-life day 5776.23 5776.23 5776.23 5776.23 5776.23 5776.23 5776.23 5776.23 

All Fish Compartments a 

Assimilation efficiency from food unitless 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 

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

wt]-day 127 N/A b 700 N/A b N/A b N/A b 380 N/A b 

Gamma_fish unitless N/A b 0.2 N/A b 0.2 0.2 0.2 N/A b 0.2 

Half-life day 495.105 495.105 420.089 420.089 495.105 420.089 5251.12 5251.12 

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

 1-44 
 

 

Exhibit 28. Dioxin Chemical-Specific Properties for Aquatic Species

Documentation 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

1,2,3,6,7,8­

HxCDD 

Water Column Herbivore Compartment 

Assimilation efficiency from plants unitless 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 

Water Column Omnivore Compartment 

Assimilation efficiency from plants unitless 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 

a Screening scenario includes: Benthic Omnivore, Benthic Carnivore, Water-column Herbivore, Water-column Omnivore, and Water-column Carnivore. b N/A 

b N/A = not applicable. This parameter is used in calculating the uptake when measured data are unavailable. 

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

 1-45
 

 

 

Exhibit 28. Dioxin Chemical-Specific Properties for Aquatic Species

Documentation for the TRIM.FaTE Screening Scenario

 Parameter Name Units Reference 

Zooplankton Compartment Type 

Assimilation efficiency from food unitless Morrison, 1999. 

Absorption Rate Constant L/kg(ww)-day Based on fish value in Muir, D.C.G., A.L. Yarechewski, A. Knoll, et al. 1986a 

Elimination Rate Constant /day Professional judgment based on water column herbivore value. 

Half Life day Professional judgment. 

Benthic Invertebrate Compartment 

Clearance constant unitless Professional judgment. 

Sediment Partitioning Partition 

Coefficient kg/kg TCDD data for sandworm in Rubenstein et al. 1990; dry weight sediment. PeCDF: multiplied TCDD partition coefficient for sandworm by congenerSediment Partitioning Alpha of 

Equilibrium unitless Professional judgment. 

Sediment Partitioning Time to 

Reach Alpha of Equilibrium days TCDD: professional judgment; PeCDF: Rubinstein et al. 1990; data for TCDF in sandworm. 

V_d (ratio of concentration in 

benthic invertebrates to 

concentration in water) 

ml/g Professional judgment. 

Half-life day Change source to f-pass 

All Fish Compartments a 

Assimilation efficiency from food unitless TCDD: calculated from data in Kleeman et al. 1986b trout data as cited in 

USEPA 1993; PeCDF: used assimilation efficiency for TCDD in trout. 

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

wt]-day Muir et al. 1986. 

Gamma_fish unitless Thomann 1989 

Half-life day Change source to f-pass

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

 1-46 
 

 

Exhibit 28. Dioxin Chemical-Specific Properties for Aquatic Species

Documentation for the TRIM.FaTE Screening Scenario

 Parameter Name Units Reference 

Water Column Herbivore Compartment

Assimilation efficiency from plants unitless TCDD: calculated from data in Kleeman et al. 1986b trout data as cited in 

USEPA 1993; PeCDF: used assimilation efficiency for TCDD in trout. 

Water Column Omnivore Compartment 

Assimilation efficiency from plants unitless TCDD: calculated from data in Kleeman et al. 1986b trout data as cited in 

USEPA 1993; PeCDF: used assimilation efficiency for TCDD in trout. a Screening scenario includes: Benthic Omnivore, Benthic Carnivore, Water-column Herbivore, Water-column Omnivore, and Water-column Carnivore. 

b N/A = not applicable. This parameter is used in calculating the uptake when measured data are unavailable. 

Attachment 1 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

 1-47 
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

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Technical Support Document for TRIM-Based Multipathway Screening

Scenario for RTR: Summary of Approach and Evaluation

ATTACHMENT 2: Description of Multimedia Ingestion Risk 

Calculator (MIRC) Used for RTR Exposure and Risk Estimates
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TABLE OF CONTENTS

1 Introduction.........................................................................................................................................1

1.1 Purpose and Overview ................................................................................................................1

1.2 Scope of MIRC ............................................................................................................................1

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

1.4 MIRC Highlights...........................................................................................................................3

1.5 Organization of This Document ...................................................................................................3

2 MIRC Overview ...................................................................................................................................5

2.1 Software.......................................................................................................................................5

2.2 Exposure Pathways .....................................................................................................................7

2.3 Receptor Groups .........................................................................................................................8

3 Exposure Algorithms .......................................................................................................................11

3.1 Farm Food Chain Algorithms.....................................................................................................11

3.1.1 Estimating Chemical Concentrations in Produce................................................................11

3.1.2 Estimating Chemical Concentrations in Animal Products ...................................................18

3.2 Chemical Intake Calculations for Adults and Non-Infant Children ............................................22

3.2.1 Chemical Intake from Soil Ingestion....................................................................................24

3.2.2 Chemical Intake from Fish Ingestion...................................................................................24

3.2.3 Chemical Intake from Fruit Ingestion ..................................................................................26

3.2.4 Chemical Intake from Vegetable Ingestion .........................................................................26

3.2.5 Chemical Intake from Animal Product Ingestion .................................................................28

3.2.6 Chemical Intake from Drinking Water Ingestion..................................................................30

3.3 Total Chemical Intake................................................................................................................30

3.4 Chemical Intake Calculations for Nursing Infants......................................................................32

3.4.1 Infant Average Daily Absorbed Dose..................................................................................32

3.4.2 Chemical Concentration in Breast Milk Fat.........................................................................33

3.4.3 Chemical Concentration in Aqueous Phase of Breast Milk ................................................37

3.4.4 Alternative Model for Infant Intake of Methyl Mercury.........................................................39

4 Dose-Response Values Used for Assessment..............................................................................41

5 Risk Estimation.................................................................................................................................46

5.1 Cancer Risks .............................................................................................................................46

5.2 Non-cancer Hazard Quotients ...................................................................................................48

5.2.1 Hazard Quotients for Chemicals with a Chronic RfD ..........................................................48

5.2.2 Hazard Quotients for Chemicals with RfD Based on Developmental Effects .....................48

5.2.3 Hazard Index for Chemicals with RfDs ...............................................................................48

6 Model Input Options.........................................................................................................................50

6.1 Environmental Concentrations...................................................................................................50

6.2 Farm-Food-Chain Parameter Values ........................................................................................51

6.2.1 List of Farm-Food-Chain (FFC) Parameters .......................................................................51

6.2.2 Produce Parameter Values .................................................................................................52
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6.2.3 Animal Product Parameter Values ......................................................................................59

6.3 Adult and Non-Infant Exposure Parameter Values ...................................................................61

6.3.1 Body Weights ......................................................................................................................61

6.3.2 Water Ingestion Rates.........................................................................................................62

6.3.3 Local Food Ingestion Rates ................................................................................................63

6.3.4 Local Fish Ingestion Rates..................................................................................................67

6.3.5 Soil Ingestion Rates ............................................................................................................72

6.3.6 Total Food Ingestion Rates .................................................................................................73

6.4 Other Exposure Factor Values ..................................................................................................74

6.4.1 Exposure Frequency ...........................................................................................................74

6.4.2 Fraction Contaminated........................................................................................................75

6.4.3 Preparation and Cooking Losses ........................................................................................75

6.5 Breast-Milk Infant Exposure Pathway Parameter Values..........................................................77

6.5.1 Receptor-specific Parameters.............................................................................................77

6.5.2 Chemical-Specific Parameter Values..................................................................................82

7 Summary of MIRC Default Exposure Parameter Settings............................................................85

7.1 Default Ingestion Rates .............................................................................................................85

7.2 Default Screening-Level Population-Specific Parameter Values ..............................................87

7.3 Default Chemical-Specific Parameter Values for Screening Analysis.......................................87

7.4 Screening-Level Parameter Values for Nursing Infant Exposure..............................................89

7.4.1 Dioxins.................................................................................................................................89

7.4.2 Methyl Mercury....................................................................................................................90

8 REFERENCES...................................................................................................................................91
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LIST OF EXHIBITS

Exhibit 2-1. Overview of Access-based MIRC Software Application for Performing Farm-Food-Chain 

Ingestion Exposure and Risk Calculations .........................................................................6

Exhibit 2-2. Transfer Pathways for Modeled Farm Food Chain (FFC) Media .............................................8

Exhibit 3-1. Chemical Transfer Pathways for Produce ..............................................................................12

Exhibit 3-2. Estimating Chemical Concentration in Aboveground Produce...............................................12

Exhibit 3-3. Chemical Transfer Pathways for Animal Products .................................................................19

Exhibit 4-1. Oral Dose-response Values for PB-HAP Chemicals Used to Calculate RTR Screening 

Threshold Emission Rates ................................................................................................41

Exhibit 4-2. WHO 2005 Toxic Equivalency Factors (TEFs) for Dioxins .....................................................43

Exhibit 4-3. Oral Dose-response Values for Polycyclic Organic Matter (POM) Groups ............................44

Exhibit 6-1. MIRC Parameters Used to Estimate Chemical Concentrations in Farm Foods.....................51

Exhibit 6-2. Chemical-Specific Inputs for Produce Parameters.................................................................53

Exhibit 6-3. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC ............................................54

Exhibit 6-4. Non-Chemical-Specific Produce Inputs ..................................................................................58

Exhibit 6-5. Animal Product Chemical-specific Inputs for Chemicals Included in MIRC ...........................59

Exhibit 6-6. Soil and Plant Ingestion Rates for Animals.............................................................................60

Exhibit 6-7. Mean and Percentile Body Weight Estimates for Adults and Children..................................62

Exhibit 6-8. Estimated Daily Per Capita Mean and Percentile Water Ingestion Rates for Children and 

Adults a .............................................................................................................................63

Exhibit 6-9. Summary of Age-Group-Specific Food Ingestion Rates for Farm Food Items.......................64

Exhibit 6-10. Daily Mean and Percentile Consumer-Only Fish Ingestion Rates........................................70

Exhibit 6-11. Fraction of Population Consuming Freshwater/Estuarine Fish on a Single Day (FPC,y) ....70

Exhibit 6-12. Calculated Long-term Mean and Percentile Per capita Fish Ingestion Rates for Children and 

Adults (IRPC,y) .................................................................................................................71

Exhibit 6-13. Calculated Mean and 90th Percentile Per capita Fish Ingestion Rates for Populations of 

Recreational Fishers (IRPC,y) .........................................................................................71

Exhibit 6-14. Daily Mean and Percentile Soil Ingestion Rates for Children and Adults .............................73

Exhibit 6-15. Daily Mean and Percentile Per Capita Total Food Intake for Children and Adults...............74

Exhibit 6-16. Fraction Weight Losses from Preparation of Various Foods ................................................76

Exhibit 6-17. Scenario- and Receptor-Specific Input Parameter Values Used to Estimate Infant 

Exposures via Breast Milk.................................................................................................78

Exhibit 6-18. Average Body Weight for Infants ..........................................................................................78

Exhibit 6-19. Time-weighted Average Body Weight for Mothers ................................................................79

Exhibit 6-20. Infant Breast Milk Intake Rates .............................................................................................81

Exhibit 6-21. Chemical-specific Input Parameter Values for Breast Milk Exposure Pathway ....................82

Exhibit 7-1. Farm Food Category Ingestion Rates for Conservative Screening Scenario for Farming 

Households .......................................................................................................................86
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Exhibit 7-2. Mean Body Weight Estimates for Adults and Children a........................................................87

Exhibit 7-3. Chemical-Specific Parameter Values for Input to MIRC a......................................................88

Exhibit 7-4. Chemical and Animal-Type Specific Biotransfer Factor (Ba) values for Input to MIRC .........89
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LIST OF EQUATIONS

Equation 3-1. Chemical Concentration in Aboveground Produce .............................................................13

Equation 3-2. Chemical Concentration in Aboveground Produce Due to Root Uptake ............................13

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

Chemical ...........................................................................................................................14

Equation 3-4. Chemical Concentration in Aboveground Produce Due to..................................................15

Equation 3-5. Conversion of Aboveground Produce Chemical Concentration from..................................16

Equation 3-6. Chemical Concentration in Belowground Produce: Nonionic Organic Chemicals .............17

Equation 3-7. Chemical Concentration in Belowground Produce: Inorganic Chemicals ..........................18

Equation 3-8. Conversion of Belowground Produce Chemical Concentration from ..................................18

Equation 3-9. Chemical Concentration in Beef, Pork, or Total Dairy.........................................................19

Equation 3-10. Chemical Concentration in Poultry or Eggs.......................................................................20

Equation 3-11. Incidental Ingestion of Chemical in Soil by Livestock........................................................20

Equation 3-12. Ingestion of Chemical in Feed by Livestock ......................................................................21

Equation 3-13. Chemical Concentration in Lifestock Feed (All Aboveground)..........................................21

Equation 3-14. Chemical Concentration in Livestock Feed Due to Root Uptake ......................................22

Equation 3-15. Average Daily Dose for Specified Age Group and Food Type..........................................22

Equation 3-16. Chemical Intake from Soil Ingestion..................................................................................24

Equation 3-17. Chemical Intake from Fish Ingestion .................................................................................25

Equation 3-18. Consumption-weighted Chemical Concentration in Fish ..................................................25

Equation 3-19. Chemical Intake from Consumption of Exposed Fruits .....................................................26

Equation 3-20. Chemical Intake from Consumption of Protected Fruits....................................................26

Equation 3-21. Chemical Intake from Exposed Vegetables ......................................................................27

Equation 3-22. Chemical Intake from Protected Vegetables .....................................................................27

Equation 3-23. Chemical Intake from Root Vegetables.............................................................................27

Equation 3-24. Chemical Intake from Ingestion of Beef ............................................................................28

Equation 3-25. Chemical Intake from Dairy Ingestion ...............................................................................28

Equation 3-26. Chemical Intake from Pork Ingestion ................................................................................29

Equation 3-27. Chemical Intake from Poultry Ingestion.............................................................................29

Equation 3-28. Chemical Intake from Egg Ingestion .................................................................................30

Equation 3-29. Chemical Intake from Drinking Water Ingestion ................................................................30

Equations 3-30 to 3-35. Total Average Daily Dose of a Chemical for Different Age Groups ....................31

Equation 3-36. Lifetime Average Daily Dose (LADD) ................................................................................31

Equation 3-37. Average Daily Dose of Chemical to the Nursing Infant .....................................................33

Equation 3-38. Chemical Concentration in Breast Milk Fat .......................................................................34

Equation 3-39. Daily Maternal Absorbed Intake ........................................................................................35
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Equation 3-40. Biological Elimination Rate Constant for Chemicals for Non-lactating Women ................36

Equation 3-41. Biological Elimination Constant for Lipophilic Chemicals for Lactating Women ...............36

Equation 3-42. Chemical Concentration in Aqueous Phase of Breast Milk...............................................37

Equation 3-43. Fraction of Total Chemical in Body in the Blood Plasma Compartment ...........................38

Equation 3-44. Biological Elimination Rate Constant for Hydrophilic Chemicals ......................................39

Equation 3-45. Calculation of Infant Average Daily Absorbed Dose of Methyl Mercury............................40

Equation 5-1. Calculation of Excess Lifetime Cancer Risk........................................................................46

Equations 5-2 to 5-8. Lifetime Cancer Risk: Chemicals with a Mutagenic MOA for Cancer .....................47

Equation 5-9. Hazard Quotient for Chemicals with a Chronic RfD ............................................................48

Equation 5-10. Hazard Index Calculation ..................................................................................................49

Equation 6-1. Calculation of Age-Group-Specific and Food-Specific Ingestion Rates..............................67

Equation 6-2. Calculation of Alternative Age-Group-Specific Fish Ingestion Rates ..................................69
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1 Introduction

1.1 Purpose and Overview

This document provides a detailed description of the Multimedia Ingestion Risk Calculator

(MIRC), an Access-based 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. 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., conservative 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 conservative 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 

included to estimate exposure and risks to nursing infants.

MIRC was developed to be a flexible, transparent application using Microsoft Access software. 

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 

grown in an area of chemical deposition. Vegetables and fruits in such areas can become 
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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).

MIRC is an Access-based tool that facilitates calculation of risks associated with the indirect 

ingestion exposure pathways for persons consuming produce and animal products grown in an 

air depositional area of concern. The tool uses algorithms described in or adapted from HHRAP

to calculate exposures from the ingestion of produce and animal products. Included with MIRC 

is a fish ingestion pathway and drinking water ingestion pathway for scenarios in which those 

pathways may be important. MIRC also includes a breast milk ingestion pathway for nursing 

infants based primarily on EPA’s Methodology for Assessing Health Risks Associated with 

Multiple Pathways of Exposure to Combustor Emissions (hereafter MPE; EPA 1998).

1.3 Use in EPA’s Air Toxics Program

MIRC was designed to help predict human health risks from PB HAPs for EPA’s RTR 

assessments. EPA evaluates the fate of HAP releases to air from source categories after 

implementation of technology-based Maximum Achievable Control Technology (MACT) 

standards. For volatile chemicals that do not partition to other environmental media and for 

non-persistent chemicals that degrade relatively quickly in the environment, evaluation of health 

risks from direct inhalation of the chemical in air can provide reasonable estimates of total risk. 

For PB-HAPs, however, 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 
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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.

1

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 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. 

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 Document

Sections 2 through 5 of this document 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 

 

1 General information about the TRIM system is available at http://www.epa.gov/ttn/fera/trim_gen.html.
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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 

conservative risk screening assessments, and Section 8 provides the references. Appendix A 

provides guidance to users on how to set up and run MIRC for their own applications.

Note that the default parameterization described in Section 7 was used to estimate screening 

threshold releases of PB-HAPs from facilities assumed to pose negligible risk to subsistence 

communities in the vicinity of a facility emitting the HAPs to air. Users of MIRC can modify the 

default values for many of the parameters to better represent a specific exposure scenario. 
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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 

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) 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). 

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 2-1 provides a flowchart displaying the types of inputs required or optional and general 

flow of calculations carried out by the tool. 
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Exhibit 2-1. Overview of Access-based 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
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An Access 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 (Section 2.2). The FFC algorithms and transfer factor values included in MIRC are based 

on those presented in Chapter 5 of EPA’s Human Health Risk Assessment Protocol for 

Hazardous Waste Combustion Facilities, hereafter referred to as 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 PAHs that are 

carcinogenic by a mutagenic mode of action).

The tool assumes that the same individuals (farming family or household that gardens and 

raises animals) are exposed via all of the pathways specified (i.e., pathways with non-zero 

ingestion rates). The tool therefore is useful in estimating risk to the maximally exposed 

individuals (MEI) in a risk assessment. To evaluate multiple populations, the user must specify 

the full exposure scenario for each population 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 2-2 summarizes the pathways by which chemicals are transferred to these food media. 

Note that for a general 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).
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Exhibit 2-2. Transfer Pathways for Modeled Farm Food Chain (FFC) Media

Farm Food Media Chemical Transfer Pathways

Exposed fruit and vegetables Direct deposition from air of particle-bound chemical

Air-to-plant transfer of vapor phase chemical

Root uptake from soil

Protected fruit and vegetables 

(including root vegetables)

Root uptake from soil

Beef and total dairy 

(including milk)

Ingestion of forage, silage, and grain a

Soil ingestion

Pork Ingestion of silage and grain a

Soil ingestion

Poultry and eggs Ingestion of grain a

Soil ingestion

a Chemical concentrations in forage, silage, and grain are estimated via 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.

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.

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. 
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

2-9

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 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 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 1997a) 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, 

1994) remains the most recent survey of ingestion rates for home-grown foods, and EPA’s 

analysis of those data, published in its 1997 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. 
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

2-10

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). 

Exposure and risks to infants under 1 year of age are estimated only for the breast-milkingestion pathway. 

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 some of the PAHs. 
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

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3 Exposure Algorithms

The exposure algorithms in MIRC are described below in four sections. Section 3.1 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, 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 unitized 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.

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 3-1 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 screening threshold emission rates for PB-HAPs 

in its RTR assessments (EPA 2008b), and as described in the “Technical Support Document for 

TRIM-Based Multipathway Screening Scenario for RTR”. Sections 3.1.1.1 and 3.1.1.2 describe the 

transfer pathways and algorithms for aboveground and belowground produce, respectively.
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

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Exhibit 3-1. 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 3-2. First, particlebound 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 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. 

The chemical concentration in the aboveground plant due to root uptake from soil (PrAG-produceDW) 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 rootzone depth in the produce-growing area (Csroot-zone_produce). 

Exhibit 3-2. Estimating Chemical 

Concentration in Aboveground Produce

Deposition 

of Particles 

(Pd)

Root Uptake 

from Soil 

(PrAG-produce)

Vapor 

Transfer 

(Pv)

Chemical Concentration in 

Aboveground Produce
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

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Equation 3-1. Chemical Concentration in Aboveground Produce 

AG produce DW (i) P AG produce DW (i ) Pd(i) Pv(i) C r

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 3-2. Chemical Concentration in Aboveground Produce Due to Root Uptake

P AG produce DW (i) Csroot-zone_produc e BrAG produce DW (i )r

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)
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

2-14

Equation 3-3. Chemical Concentration in Aboveground Produce Due to Deposition of 

Particle-phase Chemical

( ) ( )

( ( )* ( ))

( )

( )

1,000 ( ( )) (1 )

i i

k p i Tp i

i

i

Yp k p

Drdp Fw Drwp Rp e

Pd

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 3-3 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 3-3. 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.
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

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Equation 3-4. Chemical Concentration in Aboveground Produce Due to 

Air-to-Plant Transfer of Vapor-phase Chemical

a

AG i AG i

i

Ca Fv Bv VG

Pv ( ) ( )

( )

 

where:

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 3-4 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 3-5 and the moisture 

content (MAF) of the FFC food category.
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

2-16

Equation 3-5. Conversion of Aboveground Produce Chemical Concentration from

Dry- to Wet-Weight Basis

100

(100 ) ( )

( ) ( )

i

AG produce WW i AG produce DW i

MAF

C C

where:

CAG-produce-WW(i) =

Chemical concentration in edible portion of aboveground produce type i on a 

wet-weight (WW) basis (mg/kg produce WW)

CAG-produce-DW(i) =

Chemical concentration in edible portion of aboveground produce type i on a 

dry-weight (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)

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 3-6. 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. 
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

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Equation 3-6. Chemical Concentration in Belowground Produce: Nonionic Organic 

Chemicals

Kds UCF

Csroot zone _ produce RCF VGrootveg CBG-produce-W W

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 

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, one must use empirical values for the root/soil 

bioconcentration factor measured for specific chemicals. 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 3-7 is used instead of 

Equation 3-6.
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

2-18

Equation 3-7. Chemical Concentration in Belowground Produce: Inorganic Chemicals

1

root zone _ produce BG produce DW rootveg

BG-produce-DW

Cs Br VG

C

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 3-6) 

(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 3-8. 

Equation 3-8. Conversion of Belowground Produce Chemical Concentration from 

Dry- to Wet-Weight Basis

100

(100 ) BG

BG produce WW BG produce DW

MAF C C

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 3-5, but single value for below

ground produce) (percent water)

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 3-3 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 PBHAPs (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 

3-3, 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. 
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

2-19

Exhibit 3-3. Chemical Transfer Pathways for Animal Products

Farm Food Media Chemical Transfer Pathways

Animal Products

Beef and total dairy 

(including milk)

Ingestion of forage, silage, and grain a

Incidental soil ingestion

Pork Ingestion of silage and grain a

Incidental soil ingestion

Poultry and eggs Ingestion of grain a

Incidental soil ingestion

a Chemical concentrations in plant feed (i.e., forage, silage, and grain) are estimated via intermediate 

calculations (see Equations 3-13, 3-14, 3-3, and 3-4). 

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 3-9 to calculate the 

concentration of chemical in beef, pork, or total dairy and Equation 3-10 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 3-9). 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 conservative assumption is that birds do not metabolize any chemicals; 

therefore, the MF is omitted from Equation 3-10 for poultry and eggs.

Equation 3-9. Chemical Concentration in Beef, Pork, or Total Dairy

n

i

Cmammal m Ba m MF SoilCh Intake m PlantCh Intake i m

1

( ) ( ) ( ) ( , )

where:

Cmammal(m) =

Concentration of chemical in mammalian animal product m, where m = beef, 

pork, or total dairy (mg chemical/kg animal product WW)

Ba(m) =

Chemical-specific biotransfer factor for chemical in diet to chemical in animal 

food product m, where m = beef, pork, or total dairy ([mg chemical/kg animal 

product WW] / [mg chemical intake/day] or day/kg WW)

MF =

Chemical-specific mammalian metabolism factor that accounts for endogenous 

degradation of the chemical (unitless)

SoilCh-Intake(m) =

Incidental ingestion of chemical in surface soils by livestock type m during 

grazing or consumption of foods placed on the ground (mg/day); see Equation 

3-11 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 3-12 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.)
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

2-20

Equation 3-10. Chemical Concentration in Poultry or Eggs

Cpoultry(m) Ba(m) SoilCh Intake(m) Plant Ch Intake(i,m)

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 3-11

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 3-12

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 3-11. 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). 

Equation 3-11. Incidental Ingestion of Chemical in Soil by Livestock

SoilCh Intake(m) Qs Css livestock Bs

where:

SoilCh-Intake(m) =

Incidental ingestion of the chemical in surface soils by livestock type m during 

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

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

Css-livestock =

Chemical concentration in surface soil in contaminated area where livestock 

feed (mg chemical/kg soil DW)

Bs =

Soil bioavailability factor for livestock (unitless) (assumed to be the same for 

birds and mammals)

Animal ingestion of the chemical in feed is calculated for each type of livestock based on their 

assumed diets. For m = beef and dairy cattle, chemical intake is estimated for all three feed 

types: i = forage, silage, and grain. For pork, chemical intake is estimated only for silage and 

grain. The chemical intake for poultry is based on grain consumption only. The intake of 

chemical with each feed type, i, PlantCh-Intake(i,m), is calculated separately according to Equation 

3-12. Note that the animal feed ingestion rates are on a dry-weight (DW) basis; hence, no DW 

to wet weight (WW) conversion is needed.
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

2-21

Equation 3-12. Ingestion of Chemical in Feed by Livestock

Plant Ch Intake(i,m) F(i,m) Qp(i,m) Cfeed(i)

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 3-13. The equation is the same as that for aboveground 

produce in Equation 3-1, 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 3-13. Chemical Concentration in Livestock Feed (All Aboveground)

feed(i) P feed(i ) Pd(i) Pv(i) C r

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 3-14 below

Pd(i) =

Concentration of chemical in plant feed type i due to wet and dry deposition of 

particle-phase chemical (mg/kg DW), where i = forage, silage, or grain; when i

= grain, the Pd term equals zero 

Pv(i) =

Concentration of chemical in plant feed type i due to air-to-plant transfer of 

vapor-phase chemical (μg/g [or mg/kg] DW) where i = forage, silage, or grain; 

when i = grain, the Pd term equals zero 

MIRC calculates the chemical concentration in animal feed due to root uptake from the soil 

using Equation 3-14. The equation is the same as Equation 3-2, 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, 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).
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

2-22

Equation 3-14. Chemical Concentration in Livestock Feed Due to Root Uptake

P feed(i) Csroot zone _ feed(i) Brfeed(i)r

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., 

Equations 3-3 and 3-4, 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 3-15, are expressed in milligrams of chemical 

per kilogram of receptor body weight per day (mg/kg-day).

Equation 3-15. Average Daily Dose for Specified Age Group and Food Type

365 days

(y)

(y) (y)

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

(y,i)

EF

BW AT

C IR FC ED

ADD

where:

ADD(y,i) =

Average daily dose for age group y from food type or ingestion medium i (mg

chemical/kg body weight-day)

C(i) =

Concentration of chemical in food type i harvested from the contaminated area 

(mg chemical/kg food or mg food/L water)

IR(y,i) = Ingestion rate for age group y of food type i (kg/day or L/day)

FC(i) = Fraction of food type i that was harvested from contaminated area (unitless)

ED(y) = Exposure duration for age group y (years)

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

AT(y) =

Averaging time for calculation of daily dose (years) for age group y, set equal 

to ED in MIRC

EF(y) = Annual exposure frequency for age group y (days)

Equation 3-15 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 
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

2-23

of the food obtained from contaminated areas, and the consumer’s body weight (EPA 1997a, 

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. 

An exception is for the breast-milk exposure pathway, where the dose absorbed by the mother 

is relevant to calculating the dose available to and absorbed by her nursing infant, as discussed 

in Section 3.4. 

MIRC evaluates only one contaminated area (set of environmental concentrations), or 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. 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 3-15. 

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:

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:

o Milk and other dairy products from cows,

o Beef products,

o Pork products, and

o Poultry and eggs;

Ingestion of drinking water from specified source; and

Ingestion of breast milk by infants.

Note that the last exposure pathway is limited to infants. 

The algorithms for the first six exposure pathways listed above are described in Sections 3.2.1 

through 3.2.6. The algorithms for the breast-milk ingestion pathway are described in Section 

3.4.
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

2-24

3.2.1 Chemical Intake from Soil Ingestion

Equation 3-16 shows the equation used to estimate chemical intake through incidental ingestion 

of soil. 

Equation 3-16. Chemical Intake from Soil Ingestion

365 days

EF

BW

μ g

mg C IR F C 0.001 

ADD

(y)

Soil Soil(y) Soil

Soil(y)

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 3-17). Two types of fish are included in the exposure algorithm: trophic level 3 (T3) 

fish, equivalent to small “pan” fish such as bluegill, and trophic level 4 (T4) fish, equivalent to 

game fish such as trout and walleye. The chemical concentration in fish in Equation 3-17 is 

estimated as the consumption-weighted chemical concentration using Equation 3-18.
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

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Equation 3-17. Chemical Intake from Fish Ingestion

365 days

g

kg 0 001

1 1 1 2

EF

BW

C IR . F C

ADD L L

(y)

Fish Fish(y) Fish

Fish(y) Fish Fish

Equation 3-18. Consumption-weighted Chemical Concentration in Fish

CFish CFishT 3FT 3 CFishT 4FT 4

where:

ADDFish(y) =

Average daily chemical intake from ingestion of local fish for age group y 

(mg/kg-day)

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)

CFishT3 =

Chemical concentration in whole fish for trophic level 3 (T3) fish on a wetweight (WW) basis (mg/kg WW)

CFishT4 =

Chemical concentration in whole fish for trophic level 4 (T4) fish on a wetweight (WW) basis (mg/kg WW)

FT3 = Fraction of fish intake that is from T3 (unitless)

FT4 = Fraction of fish intake that is from T4 (unitless)

CFish =

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

specified by Equation 3-18) (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), the tables of fish ingestion rate options included in MIRC are from 

CSFII data (see Section 6.3.4) 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.

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 (Exposure Factors 

Handbook Table 13-5, EPA 1997a) and included those losses in its HHRAP algorithms for 

chemical intake from fish (L1Fish and L2Fish in Equation 3-17). 
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

2-26

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 (Equations 3-19 and 3-20, respectively).

Equation 3-19. Chemical Intake from Consumption of Exposed Fruits

g 365 days

kg 1 1 1 2 0 001 EF ADDExpFruit(y) L ExpFruit L ExpFruit CExpFruit IRExpFruit(y)

. F CExpFruit

Equation 3-20. Chemical Intake from Consumption of Protected Fruits

g 365 days

kg 1 1 0 001 EF ADDProFruit(y) L ProFruit CProFruit IRProFruit(y)

. F CProFruit

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 Equations 3-21, 3-22, and 3-23, respectively). 
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

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Equation 3-21. Chemical Intake from Exposed Vegetables

g 365 days

kg 1 1 0 001 EF ADDExpVeg(y) L ExpVeg CExpVeg IRExpVeg(y) . F CExpVeg

Equation 3-22. Chemical Intake from Protected Vegetables

g 365 days

kg 1 1 0 001 EF ADDProVeg(y) L ProVeg CProVeg IRProVeg(y) . F CProVeg

Equation 3-23. Chemical Intake from Root Vegetables

g 365 days

kg 1 1 1 2 0 001 EF ADDRootVeg(y) L RootVeg L RootVeg CRootVeg IRRootVeg(y) . F CRootVeg

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 weightday)
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

2-28

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 Equations 3-24 through 3-28 for homegrown beef, dairy (milk), pork, 

poultry, and eggs, respectively.

Equation 3-24. Chemical Intake from Ingestion of Beef

g 365 days

kg 1 1 1 2 0 001 EF ADDBeef(y) L Beef L Beef CBeef IRBeef(y) . F CBeef

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 3-25. Chemical Intake from Dairy Ingestion

g 365 days

kg 0 001 EF ADDDairy(y) CDairy IRDairy(y) . F CDairy

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)
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

2-29

Equation 3-26. Chemical Intake from Pork Ingestion

g 365 days

kg 1 1 1 2 0 001 EF ADDPork(y) L Pork L Pork CPork IRPork(y) . F CPork

where:

ADDPork(y) =

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

L1Pork =

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

during cooking; averaged over various cuts and preparation methods

L2Pork =

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

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

and preparation methods

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

EF =

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

specified for scenario ( 365 days)

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

FCPork = Fraction of pork obtained from contaminated area (unitless)

The reduction in the weight of pork during and after cooking may correlate with an increase or 

decrease in the concentration of the chemical in the pork as consumed depending on the 

chemical and depending on the cooking method.

Equation 3-27. Chemical Intake from Poultry Ingestion

g 365 days

kg 1 1 1 2 0 001 EF ADDPoultry(y) L Poultry L Poultry CPoultry IRPoultry(y) . F CPoultry

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)
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

2-30

Equation 3-28. Chemical Intake from Egg Ingestion

g 365 days

kg 0 001 EF ADDEgg(y) CEgg IREgg(y) . F CEgg

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)

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). 

Equation 3-29. Chemical Intake from Drinking Water Ingestion

365 days

EF

BW

C IR FC

ADD

(y)

DW DW(y) DW

DW(y)

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 3-30 through 3-35 below.
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

2-31

Equations 3-30 to 3-35. Total Average Daily Dose of a Chemical for Different Age Groups

Equation 3-30. ADD( 1) ADDbreastmilk

Equation 3-31. 

n

i

ADD ADD i 1

(1 2) (1 2, )

Equation 3-32.

n

i

ADD ADD i 1

(3 5) (3 5, )

Equation 3-33.

n

i

ADD ADD i 1

(6 11) (6 11, )

Equation 3-34. 

n

i

ADD ADD i 1

(12 19) (12 19, )

Equation 3-35.

n

i

ADD adult ADD adult i 1

( ) ( , )

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 3-36).

Equation 3-36. Lifetime Average Daily Dose (LADD)

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)

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. For risk assessments for 

chemicals with a subchronic RfD or for developmental effects in children, ADD(y) values for the 

child age groups are compared with the RfD (see Section 5).
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

2-32

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 3-37. 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 3-37 is EPA’s (EPA 1998) modification of an average 

daily dose for the infant model first published by Smith (1987) and includes variables for both 

the concentration of the chemical in the breast milk fat (Cmilkfat) and the concentration of the 

chemical in the aqueous phase of breast milk (Caqueous). The remainder of the DAIinf-associated 

equations assume that most chemicals of concern will partition either to the lipid phase or to the 

aqueous phase of breast milk, although some chemicals may partition significantly to both 

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

equal to zero and hence drops out of the equation. 

For the parameters in Equation 3-37 (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. The user also can overwrite 

those parameter values with a different value from the literature as appropriate.
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

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Equation 3-37. Average Daily Dose of Chemical to the Nursing Infant

BW AT

C f C f IR AE ED

DAI

inf

milkfat mbm aqueous mbm milk inf

inf

1

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 3-38)

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 3-42)

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 3-37 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. Different models are used to estimate 

Cmilkfat (described in Section 3.4.2) and Caqueous (described in Section 3.4.3).

3.4.2 Chemical Concentration in Breast Milk Fat

When developing MPE (EPA 1998), EPA reviewed three first-order kinetics models for 

estimating chemical concentration in breast milk fat. The model selected for use in MPE is the 

model used in MIRC. It is a changing-concentration model that EPA adapted from a model by 

Sullivan et al. (1991). The model, shown in Equation 3-38, 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 3-38 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). 
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

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Equation 3-38. Chemical Concentration in Breast Milk Fat

elim pn fat elac bf k t

fat elac

k t elim

fat elac fat elac bf

elim

elim f m

mat f

milkfat e

k

k

e

k k t

k

k f

DAI f

C

_ 1 1

1

_ _ _

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 

weight-day; calculated using Equation 3-39)

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)

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 3-40)

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 3-41)

tbf = Duration of breast feeding (days)

tpn =

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

feeding (days)

Equation 3-38 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 

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 3-38. 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 3-38, 

see Appendix B of MPE (EPA 1998).
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

2-35

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, Equation 3-35), is multiplied by an absorption efficiency 

(AEmat) or fraction of the chemical absorbed by the oral route of exposure, as shown in Equation 

3-39. 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 3-39.

Equation 3-39. Daily Maternal Absorbed Intake

DAI mat ADD(adult) AEmat

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, Equation 3-35)

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)

Equation 3-35, 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), 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 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.

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 3-40 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.
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

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Equation 3-40. Biological Elimination Rate Constant for Chemicals for Non-lactating 

Women

h

kelim

ln2

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. 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 lipidphase of milk, EPA has used Equation 3-41 to estimate the total chemical elimination rate for 

lactating women, kfat_elac (EPA 1998). 

Equation 3-41. Biological Elimination Constant for Lipophilic Chemicals for Lactating 

Women

fm mat

milk f mbm

fat elac elim f BW

IR f f

k _ k

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 3-40)

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

ff =

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

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

EPA default)

fmbm = Fraction of fat in breast milk (unitless)

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

BWmat =

Maternal body weight over the entire duration of the mother’s exposure to the 

chemical including during pregnancy and lactation (kg)

Equation 3-41 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), 
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

2-37

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 3-41, 

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 3-39. Body weight 

values for the mother are described in Section 6.5. 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 3-42 (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 3-42. Chemical Concentration in Aqueous Phase of Breast Milk

aq elac pm

mat pl bm

aqueous k f

DAI f Pc

C

_

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 

3-39)

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 3-43)

Pcbm =

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

aqueous phase (unitless); assumed to equal 1.0

kaq_elac =

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

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

Equation 3-44)

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

Equation 3-42 is a steady-state concentration model that, like the Equation 3-38 for Cmilkfat, is 

dependent on the maternal absorbed daily intake (DAImat). In Equation 3-42, 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 
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

2-38

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 3-42 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 3-43, 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 3-43. Fraction of Total Chemical in Body in the Blood Plasma Compartment

bp RBC bp

bl bp

pl f Pc f

f f

f

1

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

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 3-42 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 
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

2-39

3-40. The extent to which kelim is an underestimate of kaq_elac for a given chemical will determine 

the extent of conservative bias in kaq_elac. 

Equation 3-44. Biological Elimination Rate Constant for Hydrophilic Chemicals

kaq _ elac kelim

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 nonlactating women (per day; e.g., via urine, bile to feces, exhalation; value from 

literature or calculated from half-life using Equation 3-40)

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 6-21). This 

parameter could be estimated using Equation 3-43 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 Byckowski and Lipscomb (2001) had added a lactational transfer 

module to the Clewell et al. (1999) model. Byckowski and Lipscomb compared their model’s 

predictions to epidemiological data from mother-nursing-infant pairs obtained following an 

accidental high-dose poisoning in Iraq (Amin-Zaki et al. 1976) and from 34 mother-nursinginfant pairs examined in a low-dose, chronic exposure environment (Fujita and Takabatake 

1977). Using data from the Iraq incident, Byckowski 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, 

Byckowski 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 3-42).
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

2-40

Equation 3-45. Calculation of Infant Average Daily Absorbed Dose of Methyl Mercury

DAIinf_MeHg DAI mat_MeHg

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) 
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

2-41

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 cancer 

slope factors (CSFs) and RfDs for chemicals used to calculate PB-HAP emission thresholds are 

provided in Exhibit 4-3. Dose-response values in MIRC that are used for EPA’s Risk and 

Technology Review (RTR) evaluations are consistent with dose-response data that the 

Agency’s Office of Air Quality Planning and Standards (OAQPS) uses for risk assessments of 

hazardous air pollutants (HAPs) (EPA 2007). 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 (CalEPA). 

Exhibit 4-1. Oral Dose-response Values for PB-HAP Chemicals Used to Calculate RTR 

Screening Threshold Emission Rates

Chemical CAS No.

Cancer Slope Factor Reference Dose

1

kg day

mg

Source

kg day

mg

Source

Inorganics

Cadmium compounds in food 7440439 not available 1.0E-03 IRIS

Mercury (elemental) 7439976 NA not available

Mercuric chloride 7487947 not available 3.0E-04 IRIS

Methyl mercury (MeHg) 22967926 not available 1.0E-04 IRIS

Organics

Benzo(a)pyrene 50328 1.0E+01 EPA OAQPS anot available

2,3,7,8-TCDD 1746016 1.5E+05 EPA ORD 1.0E-09 ATSDR

ATSDR = Agency for Toxic Substances and Disease Registry IRIS = Integrated Risk Information System

EPA OAQPS = EPA’s Office of Air Quality Planning and Standards NA = not applicable

EPA ORD = EPA’s Office of Research and Development

a The method to assign oral cancer slope factors for polycyclic organic matter (POM) is the same as was used in the 

1999 National Air Toxics Assessment (EPA 1999b). A complete description of the methodology is available at: 

http://www.epa.gov/ttn/atw/nata1999/99pdfs/pomapproachjan.pdf.

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 (no drinking of water is 

assumed to occur when calculating screening thresholds). Users of MIRC who assess 

exposures via drinking water may need to use the RfD for Cd compounds in water (i.e., 5.0E-4 

mg/kg-day).

Mercury

EPA’s RfD for MeHg of 1E-04 mg/kg-day is based on a Benchmark Dose Lower Limit (BMDL) to 

dose-response data from an epidemiological study of neurobehavioral effects in children for 

which mercury concentrations had been measured in cord blood at birth. The island 

populations included in the study had been exposed for many years to MeHg in their seafood. 

The RfD applies to the pregnant mother as well as young children. EPA has not specified the 
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

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minimum exposure duration at the RfD level of exposure that is appropriate to use in 

characterizing risk; we assume ten years for women of child-bearing 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). 

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 value) 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.

For 2,3,7,8-TCDD, although exposures may start in utero, a period of special concern is the 

nursing stage, because the highly lipophilic chemical is effectively transferred to the infant in the 

lipid phase of its mother’s milk. ATSDR has established a minimal risk level (MRL) of 1E-09

mg/kg-day for exposures of the mother or infant of 365 days or longer to 2,3,7,8-TCDD. The 

MRL is based on a behavioral study of offspring of female rhesus macaques that were exposed 

prior to conception, during gestation, and while nursing (ATSDR 1998). In the critical study, all 

mothers were exposed for seven months prior to opportunities for mating; however, dates of 

conception ranged over a five month period (some females did not conceive for several cycles). 

When the offspring were born, their mothers had been exposed for an average of 16.2 months. 

Exposure continued for the 4-month lactation period, after which the offspring were weaned and 

tested for non-social and social behavioral deficits (ATSDR 1998, Bowman et al. 1989, Schantz 

and Bowman 1989, Schantz et al. 1992). It is not known whether the behavior deficits resulted

from pre- or post-natal exposures or both. In this case, it is appropriate to compare the 365-day 

MRL to the ADD for women of child-bearing age and to the ADD for a nursing infant less than 1 

year of age during risk characterization (see Section 5). 

The convention for assessing risk from mixtures of dioxins is by application of a toxic 

equivalency factor (TEF) 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 two most toxic congeners. It is therefore assigned a TEF of one, 

with the other dioxin congener TEQ concentrations scaled relative to 2,3,7,8-TCDD 

concentrations on the basis of toxicity. The World Health Organization (WHO) 2005 TEFs 

presented in Exhibit 4-2 are used for risk assessment of dioxins for RTR.
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

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Exhibit 4-2. WHO 2005 Toxic Equivalency Factors (TEFs) for Dioxins

Dioxin Congener CAS No.

WHO 2005 Toxic 

Equivalency Factor

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-Hexachlorod-benzofuran 70648269 0.1

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

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

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

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

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

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

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

1,2,3,4,6,7,8,9-Octachlorodibenzofuran 39001020 0.0003

1,2,3,4,6,7,8,9-Octachlorodibenzo-p-dioxin 3268879 0.0003

1,2,3,7,8-Pentachlorodibenzofuran 57117416 0.03

2,3,4,7,8-Pentachlorodibenzofuran 57117314 0.3

1,2,3,7,8-Pentachlorodibenzo-p-dioxin 40321764 1

2,3,7,8-Tetrachlorodibenzofuran 51207319 0.1

2,3,7,8-Tetrachlorodibenzo-p-dioxin 1746016 1

Source: van den Berg et al. 2006

Polycyclic Organic Matter

Previously, for risk assessment of inhalation exposures to polycyclic organic matter (POM) for 

EPA’s National 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 

NEI. Individual PAHs 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 group. The same approach 

was used to derive oral CSFs for POMs for use in multipathway risk assessment for RTR. POM 

groups (with their member POM species reported in NEI) and the corresponding CSFs used for 

RTR risk assessment are presented in Exhibit 4-3. As noted in the main TSD, a screening

threshold for non-inhalation risk was derived only for benzo[a]pyrene, and facility emissions 

were then screened by comparing the total POM emissions to this threshold (where a toxicityweighted sum of POM emissions is calculated for each evaluated source).
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

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Exhibit 4-3. Oral Dose-response Values for Polycyclic Organic Matter 

(POM) Groups

Individual POM or POM group CAS No.

Cancer Slope Factor1

1

kg day

mg

POM Group 71002

Benz(a)Anthracene/Chrysene (7PAH) 103

0.5

Total PAH 234

Polycyclic Organic Matter 246

16-PAH 40

16PAH-7PAH 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

D[a,h]pyrene 189640
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

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Exhibit 4-3, continued. Oral Dose-response Values for Polycyclic Organic 

Matter (POM) Groups

Individual PAH or PAH group CAS No.

Cancer Slope Factor a

1

kg day

mg

POM Group 75002

3-Methylcholanthrene 56495

10

D[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

B(j)fluoranthen 205823

Benzo(b)fluoranthene 205992

Benzo(k)fluoranthene 207089

D(a,j)acridine 224420

Benz(a)anthracene 56553

POM Group 77002

Chrysene 218019 0.1

POM Group 77002

7-PAH 75 0.5

a

The method to assign oral cancer slope factors for polycyclic organic matter (POM) is the same as 

was used in the 1999 National Air Toxics Assessment (EPA 1999b). A complete description of the 

methodology is available at: 

http://www.epa.gov/ttn/atw/nata1999/99pdfs/pomapproachjan.pdf.
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

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5 Risk Estimation

For persistent and bioaccumulative hazardous air pollutants (BP-HAPs), risks from inhalation of 

a chemical directly from air generally will be negligible compared with risks from ingestion of the 

chemical with foodstuffs grown in an area subject to air deposition of the chemical. For other 

(non-PB) HAPs, inhalation risks can be estimated separately and compared with risks 

associated with ingestion exposure to determine the focus of subsequent tiers of the risk 

assessment. Risk characterization for carcinogens with a linear mode of action at low doses is 

described in Section 5.1. 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 (SF):

Equation 5-1. 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/kg-day)

CSF = Oral carcinogenic potency slope factor for chemical (per mg/kg-day)

As described in Section 3.3, the LADD (in mg/kg-day) for a chemical is calculated to reflect agerelated 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. 
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

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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:

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

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. 

Equations 5-2 to 5-8. Lifetime Cancer Risk: Chemicals with a Mutagenic MOA for Cancer

Equation 5-2. Risk(<1) = ADD(0-<1) x 10 x CSF x (1 yr/70 yr) 

Equation 5-3. Risk(1-2) = ADD(1-2) x 6.5 x CSF x (2 yr/70 yr) 

Equation 5-4. Risk(3-5) = ADD(3-5) x 3 x CSF x (3 yr/70 yr) 

Equation 5-5. Risk(6-11) = ADD(6-11) x 3 x CSF x (6 yr/70 yr) 

Equation 5-6. Risk(12-19) = ADD(12-19) x 2 x CSF x (8 yr/70 yr) 

Equation 5-7. Risk(adult) = ADD(adult) x 1 x CSF x (50 yr/70 yr) 

Equation 5-8. ELCR 

n

i

Risk i 1

( )

In other words, Equation 5-8 indicates that the total extra lifetime cancer risk (ELCR) equals the 

sum of the age-group-specific risks estimated by Equations 5-2 through 5-7, 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)
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

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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, however, EPA cautions that adverse health 

effects are possible, although an HQ above 1 does not indicate an effect will definitely occur. 

This is because of the margin of safety inherent in the derivation of all RfD values. The larger 

the HQ value, the more likely it is that an adverse effect may occur.

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 5-9 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.

Equation 5-9. Hazard Quotient for Chemicals with a Chronic RfD

RfD

ADD HQ

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 in 

infants, children, or young animals, 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 conservative 

approach is to compare the highest ADD from among the child age categories provided in MIRC 

to the RfD. 

5.2.3 Hazard Index for Chemicals with RfDs

When conducting screening-level assessments for multiple chemicals, it can be informative to 

calculate a hazard index (HI) for toxicologically similar chemicals (EPA 2000). The HI is the 

sum of HQs across chemicals as shown in Equation 5-12. 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 is advised to 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. 
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

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Equation 5-10. Hazard Index Calculation

HI = HQ1 + HQ2 ... HQn

where:

HI = Hazard index (unitless)

HQ1 = Hazard Quotient for chemical 1 (unitless)

HQ2 = Hazard Quotient for chemical 2 (unitless)

HQn = Hazard Quotient for chemical n (unitless)

The HI approach can be appropriate for chemicals with the same MOA and same target organ; 

however, MOA often is difficult to determine. An HI usually is “developed for each exposure 

route of interest, and for a single toxic effect or for toxicity to a single target organ” (EPA 2000; p 

79). If a receptor is exposed to multiple chemicals that affect different target organs or that 

operate by different MOAs, and if more than one HQ is close to 1, the risk assessor is advised 

to perform a follow-on evaluation of assumptions and to consider whether chemical interactions 

may play a role in chemical toxicity (EPA 2000). Exposures to more than one chemical can 

result in a greater or lesser toxic response than might be predicted on the basis of one or the 

other chemical acting alone (toxicologically independent) or acting in concert (toxicologically 

similar chemicals). Users are referred to EPA’s Supplementary Guidance for Conducting Health 

Risk Assessment of Chemical Mixtures for approaches to assessing the potential for adverse 

health effects from exposure to multiple chemicals (EPA 2000). 

Note that users of MIRC are responsible for determining how to interpret HQs for multiple 

chemicals. 
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

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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. Values for farm-food-chain (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 agegroup-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, 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, and the HHRAP inputs provided for other chemicals have not been 

evaluated. 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. 

6.1 Environmental Concentrations 

As noted in Section 2, MIRC is intended to estimate exposures and risks to self-sufficient 

farming and fishing families from airborne chemicals. The tool analyzes one exposure scenario 

at a time; 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:

1. surface soil in produce growing area,

2. surface soil where livestock feed,

3. root-zone soil in produce growing area, and

4. 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). 

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. For RTR evaluations, a tool to facilitate 

this process was developed using a Microsoft Excel routine written in Visual Basic. 
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

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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. Parameter values required for these HHRAP 

algorithms, including chemical-specific media transfer factors (e.g., soil-to-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, the 

HHRAP-recommended parameter values are the default values in MIRC; however, these and 

other inputs in MIRC can be edited as needed. Exhibit 6-1 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 6-1. MIRC Parameters Used to Estimate Chemical Concentrations in Farm Foods

Parameter Description Units

Plants/Produce

BrAG-produce-DW(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 dryweight produce to the corresponding chemical 

concentration for full-weight 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
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

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Exhibit 6-1, continued. MIRC Parameters Used to Estimate Chemical Concentrations in 

Farm Foods

Parameter Description Units

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

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 6-2 and Exhibit 6-3 provide the chemical-specific input values that are the current 

defaults for produce FFC food types in MIRC. Exhibit 6-4 presents additional non-chemicalspecific 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. 
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

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Exhibit 6-2. 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

Organics

Benzo(a)anthracene 0.6 5.7E+03 6.0E+04 1.9E+04

Benzo(a)pyrene 0.6 9.7E+03 1.6E+05 1.2E+05

Benzo(b)fluoranthene 0.6 1.2E+04 1.0E+04 1.7E+03

Benzo(k)fluoranthene 0.6 1.2E+04 1.9E+05 2.1E+05

Chrysene 0.6 5.7E+03 6.0E+04 6.9E+02

Dibenz(a,h)anthracene 0.6 2.3E+04 5.8E+05 3.1E+07

Indeno(1,2,3-cd) 

pyrene

0.6

2.8E+04 5.3E+05 3.7E+05

2,3,7,8-TCDD 0.6 4.0E+04 3.9E+04 6.6E+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 gamma-emitting 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 

3-6. EPA estimated chemical-specific 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 from EPA or other sources.

c

As discussed in HHRAP (EPA 2005a), Appendix A, Kds describes the partitioning of a compound between soil 

pore-water and soil particles and strongly influences the release and movement of a compound into the 

subsurface soils and underlying aquifer. Kds values for mercuric compounds were obtained from EPA (1997b). 

For all PAHs except for benzo(b)fluoranthene, Kds values were obtained from EPA 2004a. For 

benzo(b)fluoranthene and 2,3,7,8-TCDD, Kds values were calculated using the correlation equation provided in 

EPA 1993.

d

As discussed in HHRAP (EPA 2005a), Appendix A, the value for mercuric chloride was obtained from EPA

1997b. BvAG(i) values for PAHs were calculated using the correlation equation derived for azalea leaves as cited in 

Bacci et al. (1992), then reducing this value by a factor of 100, as suggested by Lorber (1995), who concluded that 

the Bacci factor reduced by a factor of 100 was similar to his own observations in various studies. The 2,3,7,8-

TCDD value was 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.

f

Speciation 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).
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

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Exhibit 6-3. 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 

Factor- Aboveground 

Produce 

(VGAG(i)) (unitless) c

Inorganics

Cadmium compounds

Exp. Fruit 1.3E-01 NA 1.0E+00

Exp. Veg. 1.3E-01 NA 1.0E+00

Forage 3.6E-01 NA 1.0E+00

Grain 6.2E-02 NA NA

Prot. Fruit 1.3E-01 NA NA

Prot. Veg. 1.3E-01 NA NA

Root 6.4E-02 1.0E+00 NA

Silage 3.6E-01 NA 5.0E-01

Mercury (elemental)

Exp. Fruit NA NA 1.0E+00

Exp. Veg. NA NA 1.0E+00

Forage NA NA 1.0E+00

Grain NA NA NA

Prot. Fruit NA NA NA

Prot. Veg. NA NA NA

Root NA 1.0E+00 NA

Silage NA NA 5.0E-01

Mercuric chloride

Exp. Fruit 1.5E-02 NA 1.0E+00

Exp. Veg. 1.5E-02 NA 1.0E+00

Forage 0 NA 1.0E+00

Grain 9.3E-03 NA NA

Prot. Fruit 1.5E-02 NA NA

Prot. Veg. 1.5E-02 NA NA

Root 3.6E-02 1.0E+00 NA

Silage 0 NA 5.0E-01

Methyl mercury

Exp. Fruit 2.9E-02 NA 1.0E-02

Exp. Veg. 2.9E-02 NA 1.0E-02

Forage 0 NA 1.0E+00

Grain 1.9E-02 NA NA

Prot. Fruit 2.9E-02 NA NA

Prot. Veg. 2.9E-02 NA NA

Root 9.9E-02 1.0E-02 NA

Silage 0 NA 5.0E-01
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

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Exhibit 6-3, continued. 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 

Factor- Aboveground 

Produce 

(VGAG(i)) (unitless) c

Organics

Benzo(a)anthracene

Exp. Fruit 2.0E-02 NA 1.0E-02

Exp. Veg. 2.0E-02 NA 1.0E-02

Forage 2.0E-02 NA 1.0E+00

Grain 2.0E-02 NA NA

Prot. Fruit 2.0E-02 NA NA

Prot. Veg. 2.0E-02 NA NA

Root 9.5E-02 1.0E-02 NA

Silage 2.0E-02 NA 5.0E-01

Benzo(a)pyrene

Exp. Fruit 1.3E-02 NA 1.0E-02

Exp. Veg. 1.3E-02 NA 1.0E-02

Forage 1.3E-02 NA 1.0E+00

Grain 1.3E-02 NA NA

Prot. Fruit 1.3E-02 NA NA

Prot. Veg. 1.3E-02 NA NA

Root 6.1E-02 1.0E-02 NA

Silage 1.3E-02 NA 5.0E-01

Benzo(b)fluoranthene

Exp. Fruit 1.1E-02 NA 1.0E-02

Exp. Veg. 1.1E-02 NA 1.0E-02

Forage 1.1E-02 NA 1.0E+00

Grain 1.1E-02 NA NA

Prot. Fruit 1.1E-02 NA NA

Prot. Veg. 1.1E-02 NA NA

Root 1.2E+00 1.0E-02 NA

Silage 1.1E-02 NA 5.0E-01

Benzo(k)fluoranthene

Exp. Fruit 1.2E-02 NA 1.0E-02

Exp. Veg. 1.2E-02 NA 1.0E-02

Forage 1.2E-02 NA 1.0E+00

Grain 1.2E-02 NA NA

Prot. Fruit 1.2E-02 NA NA

Prot. Veg. 1.2E-02 NA NA

Root 6.1E-02 1.0E-02 NA

Silage 1.2E-02 NA 5.0E-01

Chrysene

Exp. Fruit 2.0E-02 NA 1.0E-02

Exp. Veg. 2.0E-02 NA 1.0E-02

Forage 2.0E-02 NA 1.0E+00

Grain 2.0E-02 NA NA

Prot. Fruit 2.0E-02 NA NA

Prot. Veg. 2.0E-02 NA NA

Root 9.5E-02 1.0E-02 NA

Silage 2.0E-02 NA 5.0E-01
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

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Exhibit 6-3, continued. 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 

Factor- Aboveground 

Produce 

(VGAG(i)) (unitless) c

Dibenz(a,h)anthracene

Exp. Fruit 6.8E-03 NA 1.0E-02

Exp. Veg. 6.8E-03 NA 1.0E-02

Forage 6.8E-03 NA 1.0E+00

Grain 6.8E-03 NA NA

Prot. Fruit 6.8E-03 NA NA

Prot. Veg. 6.8E-03 NA NA

Root 4.1E-02 1.0E-02 NA

Silage 6.8E-03 NA 5.0E-01

Indeno(1,2,3-cd)pyrene

Exp. Fruit 5.9E-03 NA 1.0E-02

Exp. Veg. 5.9E-03 NA 1.0E-02

Forage 5.9E-03 NA 1.0E+00

Grain 5.9E-03 NA NA

Prot. Fruit 5.9E-03 NA NA

Prot. Veg. 5.9E-03 NA NA

Root 5.3E-02 1.0E-02 NA

Silage 5.9E-03 NA 5.0E-01

2,3,7,8-TCDD

Exp. Fruit 4.6E-03 NA 1.0E-02

Exp. Veg. 4.6E-03 NA 1.0E-02

Forage 4.6E-03 NA 1.0E+00

Grain 4.6E-03 NA NA

Prot. Fruit 4.6E-03 NA NA

Prot. Veg. 4.6E-03 NA NA

Root 1.0E+00 1.0E-02 NA

Silage 4.6E-03 NA 5.0E-01

Source: EPA 2005a. NA = not applicable. 

a

As discussed in HHRAP (EPA 2005a), the BrAG-produce-DW(i) for aboveground produce and forage accounts for the 

uptake from soil and the subsequent transport of contaminants through the roots to the aboveground plant parts. 

For organics, correlation equations to calculate values for Br on a dry weight basis were obtained from Travis and 

Arms (1988). For cadmium, Br values were derived from uptake slope factors provided in EPA 1992. Uptake 

slope is the ratio of contaminant concentration in dry weight plant tissue to the mass of contaminant applied per 

hectare soil. Br aboveground values for mercuric chloride and methyl mercury were calculated using methodology 

and data from Baes et al. (1984). Br forage values for mercuric chloride and methyl mercury (on a dry weight 

basis) were obtained from EPA 1997b. The HHRAP methodology assumes that elemental mercury doesn’t 

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

b

As discussed in HHRAP (EPA 2005a), Appendix B, VGrootveg represents an empirical correction factor that 

reduces produce concentration. Because of the protective outer skin, size, and shape of bulky produce, transfer of 

lipophilic chemicals (i.e., log Kow greater than 4) to the center of the produce is not likely. In addition, typical 

preparation techniques, such as washing, peeling, and cooking, further reduce the concentration of the chemical in 

the vegetable as consumed by removing the high concentration of chemical on and in the outer skin, leaving the 

flesh with a lower concentration than would be the case if the entire vegetable were pureed without washing. For 

belowground produce, HHRAP (EPA 2005a) recommends using a VGrootveg value of 0.01 for PB-HAP with a log 

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

EPA 1994b. In developing these values, EPA (1994b) assumed that the density of the skin and the whole 

vegetable are equal (potentially overestimating the concentration of PB-HAP in belowground produce due to root 

uptake).
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

2-57

Exhibit 6-3, continued. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC

c

As discussed in HHRAP (EPA 2005a), Appendix B, VGag represents an empirical correction factor that reduces 

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

versus bulkier aboveground produce (e.g., apples). Because of the protective outer skin, size, and shape of bulky 

produce, transfer of lipophilic PB-HAP (log Kow greater than 4) to the center of the produce is not likely. In 

addition, typical preparation techniques, such as washing, peeling, and cooking, further reduces residues. For 

aboveground produce, HHRAP (EPA 2005a) recommends using a VGag value of 0.01 for PB-HAP with a log Kow 

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

1994b. In developing these values, EPA (1994b) assumed the following: (1) translocation of compounds 

deposited on the surface of aboveground vegetation to inner parts of aboveground produce would be insignificant 

(potentially underestimating the concentration of PB-HAP in aboveground produce due to air-to-plant transfer); (2) 

the density of the skin and the whole vegetable are equal (potentially overestimating the concentration of PB-HAP 

in aboveground produce due to air-to-plant transfer); and (3) the thickness of vegetable skin and broadleaf tree 

skin are equal (effects on the concentration of PB-HAP in aboveground produce due to air-to-plant 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 site-specific silage, this value may under- or overestimate the actual value.
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

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Exhibit 6-4. Non-Chemical-Specific Produce Inputs

Plant Part

Interception 

Fraction 

(Rp(i))

(unitless) a

Plant 

Surface 

Loss 

Coefficient 

(kp(i))

(1/year) b

Length of 

Plant 

Exposure to 

Deposition 

(Tp(i))

(year) c

Yield or 

Standing 

Crop 

Biomass 

(Yp(i))

(kg/m2) d

Plant TissueSpecific 

Moisture 

Adjustment 

Factor (MAF(i))

(percent) e

Exposed Vegetable 0.982 18 0.16 5.66 92

Protected Fruit NA NA NA NA 90

Protected 

Vegetable NA NA NA NA 80

Forage (animal 

feed) 0.5 18 0.12 0.24 92

Exposed Fruit 0.053 18 0.16 0.25 85

Root Vegetables NA NA NA NA 87

Silage (animal feed) 0.46 18 0.16 0.8 92

Grain (animal feed) NA NA NA NA 90

Source: EPA 2005a. NA = not applicable. 

a

Baes et al. (1984) used an empirical relationship developed by Chamberlain (1970) to identify a correlation 

between initial Rp values and pasture grass productivity (standing crop biomass [Yp]) to calculate Rp values for 

exposed vegetables, exposed fruits, forage, and silage. Two key uncertainties are associated with using these 

values for Rp: (1) Chamberlain’s(1970) empirical relationship developed for pasture grass may not accurately 

represent aboveground produce. (2) The empirical constants developed by Baes et al. (1984) for use in the 

empirical relationship developed by Chamberlain (1970) may not accurately represent the site-specific mixes of 

aboveground produce consumed by humans or the site-specific mixes of forage or silage consumed by livestock.

b

The term kp is a measure of the amount of chemical that is lost to natural physical processes (e.g., wind, water) 

over time. The HHRAP-recommended value of 18 yr-1(also recommended by EPA 1994a and 1998) represents 

the midpoint of a range of values reported by Miller and Hoffman (1983). There are two key uncertainties 

associated with using these values for kp: (1) The recommended equation for calculating kp includes a conservative 

bias in that it does not consider chemical degradation processes. (2) Given the reported range of kp values from 

7.44 to 90.36 yr-1, plant concentrations could range from about 1.8 times higher to about 5 times lower than the 

plant concentrations estimated in FFC media using the midpoint kp value of 18.

c HHRAP (EPA 2005a) recommends using a Tp value of 0.16 years for aboveground produce and cattle silage. 

This is consistent with earlier reports by EPA (1994a, 1998) and NC DEHNR (1997), which recommended treating 

Tp as a constant based on the average period between successive hay harvests. Belcher and Travis (1989) 

estimated this period at 60 days. Tp is calculated as 60 days ÷ 365 days/year = 0.16 years. For forage, the 

average of the average period between successive hay harvests (60 days) and the average period between 

successive grazing (30 days) is used (that is, 45 days), and Tp is calculated as (60 days + 30 days)/ 2 ÷ 365 

days/yr = 0.12 yr. Two key uncertainties are associated with use of these values for Tp: (1) The average period 

between successive hay harvests (60 days) may not reflect the length of the growing season or the length between 

successive harvests for site-specific aboveground produce crops. The concentration of chemical in aboveground 

produce due to direct (wet and dry) deposition (Pd) will be underestimated if the site-specific value of Tp is less than 

60 days, or overestimated if the site-specific value of Tp is more than 60 days.

d

Yp values for aboveground produce and forage were calculated using an equation presented in Baes et al. (1984) 

and Shor et al. (1982): Yp = Yhi / Ahi, where Yhi = Harvest yield of i

th crop (kg DW) and Ahi = Area planted to ith crop 

(m2), and using values for Yh and Ah from USDA (1994b and 1994c). A production-weighted U.S. average Yp of 

0.8 kg DW/m2 for silage was obtained from Shor et al. 1982.

e MAF represents the plant tissue-specific moisture adjustment factor to convert dry-weight concentrations into wetweight 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.
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

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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 6-5 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 6-6.

Exhibit 6-5. 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

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

Benzo(a)anthracene 1 4.0E-02 8.4E-03 4.8E-02 0.01 1.7E-02 2.9E-02 NA

Benzo(a)pyrene 1 3.8E-02 7.9E-03 4.5E-02 0.01 1.6E-02 2.8E-02 NA

Benzo(b)fluoranthene 1 3.6E-02 7.6E-03 4.4E-02 0.01 1.5E-02 2.7E-02 NA

Benzo(k)fluoranthene 1 3.6E-02 7.7E-03 4.4E-02 0.01 1.5E-02 2.7E-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)anthracene 1 3.1E-02 6.5E-03 3.7E-02 0.01 1.3E-02 2.3E-02 NA

Indeno(1,2,3-cd) 

pyrene

1 2.9E-02 6.2E-03 3.6E-02 0.01 1.2E-02 2.2E-02 NA

2,3,7,8-TCDD 1 2.6E-02 5.5E-03 3.2E-02 1 1.1E-02 1.9E-02 NA

Source: EPA 2005a, unless otherwise indicated. NA = not applicable.

a

As discussed in HHRAP (EPA 2005a), Appendix A, biotransfer factors for mercury compounds were obtained 

from EPA 1997b. Considering speciation, fate, and transport of mercury from emission sources, elemental 

mercury is assumed to be vapor-phase and hence is assumed not to deposit to soil or transfer into aboveground 

plant parts. As a consequence, there is no transfer of elemental mercury into animal tissues. Biotransfer factors 

for cadmium compounds were obtained from EPA 1995b. Biotransfer factors for 2,3,7,8-TCDD and PAHs were 

calculated from chemical octanol-water partitioning coefficients (Kow values) using the correlation equation from 

RTI (2005) and assuming the following fat contents: milk - 4%; beef - 19%; pork - 23%; poultry -14%; and eggs -

8%.

b

As discussed in HHRAP (EPA 2005a), EPA (1995c) recommends using a metabolism factor (MF) to account for 

metabolism of PAHs by mammals to offset the amount of bioaccumulation suggested by biotransfer factors. EPA 

has recommended an MF of 0.01 for bis(2-ethylhexyl)phthalate (BEHP) and 1.0 for all other chemicals (EPA

1995d). For MIRC, an MF of 0.01 is also used to calculate concentrations of PAHs in food products from 

mammalian species based on the work of Hofelt et al. (2001). This factor takes into account the P450-mediated 

metabolism of PAHs in mammals; applying this factor in our approach reduced the concentrations of chemicals in 

beef, pork, and dairy by two orders of magnitude.
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

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Exhibit 6-6. Soil and Plant Ingestion Rates for Animals

Animal Soil Ingestion Rate –

Qs(m) (kg/day) a

Plant Part Consumed 

by Animal 

Plant Ingestion Rate –

Qp(I,m) (kg/day)

Beef cattle b0.5

Silage 2.5

Forage 8.8

Grain 0.47

Dairy cattle c0.4

Silage 4.1

Forage 13.2

Grain 3.0

Swine d0.37

Silage 1.4

Grain 3.3

Chicken (eggs) e0.022 Grain 0.2

Source: EPA 2005a HHRAP (Chapter 5).

a

Beef cattle: NC DEHNR (1997) and EPA (1994b) recommended a soil ingestion rate for subsistence beef cattle 

of 0.5 kg/day based on Fries (1994) and NAS (1987). As discussed in HHRAP, Fries (1994) reported soil 

ingestion to be 4 percent of the total dry matter intake. NAS (1987) cited an average beef cattle weight of 590 kg, 

and a daily dry matter intake rate (non-lactating cows) of 2 percent of body weight. This results in a daily dry 

matter intake rate of 11.8 kg DW/day and a daily soil ingestion rate of about 0.5 kg/day. 

Dairy cattle: NC DEHNR (1997) and EPA (1994b) recommended a soil ingestion rate for dairy cattle of 0.4 kg/day 

based on Fries (1994) and NAS (1987). As discussed in HHRAP, Fries (1994) reported soil ingestion to be 2

percent of the total dry matter intake. NAS (1987) cited an average beef cattle weight of 630 kg and a daily dry 

matter intake rate (non-lactating cows) of 3.2 percent of body weight. This resulted in a daily dry matter intake 

rate of 20 kg/day DW, and a daily soil ingestion rate of approximately 0.4 kg/day. Uncertainties associated with 

Qs include the lack of current empirical data to support soil ingestion rates for dairy cattle and the assumption of

uniform contamination of soil ingested by cattle.

Swine: NC DEHNR (1997) recommended a soil ingestion rate for swine of 0.37, estimated by assuming a soil 

intake that is 8% of the plant ingestion rate of 4.3 kg DW/day. Uncertainties include the lack of current empirical 

data to support soil ingestion rates and the assumption of uniform contamination of the soil ingested by swine.

Chicken: HHRAP (EPA 2005a) assumes that chickens consume 10 percent of their total diet (which is 

approximately 0.2 kg/day grain) as soil, a percentage that is consistent with the study from Stephens et al. (1995). 

Uncertainties include the lack of current empirical data to support soil ingestion rates for chicken and the 

assumption of uniform contamination of soil ingested by chicken.

b

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

DW/day (based on NAS [1987] reporting a daily dry matter intake that is 2 percent of an average beef cattle body 

weight of 590 kg) and are supported by NC DEHNR (1997), EPA (1994b and 1990), and Boone et al. (1981). The 

principal uncertainty associated with these Qp values is the variability between forage, silage, and grain ingestion 

rates for cattle. 

c

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

DW/day (NAS 1987; EPA 1992) as recommended by NC DEHNR (1997). Uncertainties include the proportion of 

each food type in the diet, which varies from location to location. Assuming uniform contamination of plant

materials consumed by cattle also introduces uncertainty.

d

Swine are not grazing animals and are assumed not to eat forage (EPA 1998). EPA (1994b and 1998) and NC 

DEHNR (1997) recommended including only silage and grains in the diet of swine. EPA (1995c) recommended 

an ingestion rate of 4.7 kg DW/day for a swine, referencing NAS (1987). Assuming a diet of 70 percent grain and 

30 percent silage (EPA 1990), HHRAP estimated ingestion rates of 3.3 kg DW/day (grain) and 1.4 kg DW/day 

(silage). Uncertainties associated with Qp include variability of the proportion of grain and silage in the diet, which 

varies from location to location. 

e Chickens consume grain provided by the farmer. The daily quantity of grain feed consumed by chicken is 

assumed to be 0.2 kg/day (Ensminger (1980), Fries (1982), and NAS (1987). Uncertainties associated with this 

variable include the variability of actual grain ingestion rates from site to site. In addition, assuming uniform 

contamination of plant materials consumed by chicken introduces some uncertainty.
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

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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 

conservative screening scenario. Parameter value options were primarily obtained or estimated 

from EPA’s Exposure Factors Handbook (EFH; EPA 1997a) 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), time-weighted average 

values were estimated for the MIRC age groups from the available data. 

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), as presented in the original data analysis (EPA 1997a). The body weight 

parameter values presented in Exhibit 6-7, 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 6-7, 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. 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 6-7. 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). 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 6-7, 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 differed by 

approximately 10 percent or less.
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

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Exhibit 6-7. Mean and Percentile Body Weight Estimates 

for Adults and Children

Lifestage

(years)

Duration 

(years)

Body Weight (kg)

Mean 5

th 10th 50th 90th 95th

Adult (20-70) a50 71.4 52.9 56.0 69.3 89.7 97.6

Child < 1 b1 7.83 6.03 6.38 7.76 9.24 9.66

Child 1-2 c2 12.6 9.9 10.4 12.5 14.9 15.6

Child 3-5 d3 18.6 13.5 14.4 17.8 23.6 26.2

Child 6-11 e6 31.8 19.7 21.3 29.3 45.6 52.5

Child 12-19 f8 64.2 39.5 45.0 64.2 83.5 89.0

[Child 12-19 g8 64.3 41.1 44.6 60.9 88.5 98.4]

a

BW 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%).

b

Each BW represents a time-weighted average of body weights for age groups birth to <1 month, 1 to <3 months, 

3 to <6 months, and 6 to <12 months from Table 8-3 of the 2008 CSEFH. Original sample sizes for each of these 

age groups can also be found in Table 8-3.

c

Each BW represents a time-weighted average of body weights for age groups 1 to <2 years and 2 to <3 years 

from Table 8-3 of the 2008 CSEFH. Original sample sizes for each of these age groups can also be found in 

Table 8-3.

d BWs obtained directly from Table 8-3 of the 2008 CSEFH (age group 3 to <6 years).

e

BWs obtained directly from Table 8-3 of the 2008 CSEFH (age group 6 to <11 years). This value represents a 

conservative (i.e., slightly low) estimate of BW for ages 6 through 11 years since 11-year olds are not included in 

this CSEFH age group.

f Mean BW estimated using Table 8-22 of the 2008 CSEFH, which is based on NHANES IV data as presented in 

Portier et al. (2007). This estimate was calculated as the average of the 8 single-year age groups from 12 to 13 

years through 19 to 20 years. Values for the other percentiles were estimated using Portier et al., 2007. 

g

Each BW represents a time-weighted average of body weights for age groups 11 to <16 years and 16 to <21 

years from Table 8-3 of the 2008 CSEFH. 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 6-8 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 
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

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data collected by the USDA’s 1994–1996 and 1998 CSFII (USDA 2000). Adult ingestion rates, 

presented in Exhibit 6-8, represent community water ingestion, both direct and indirect as 

defined above, for males and females combined, ages 20 years and older. 

Exhibit 6-8. Estimated Daily Per Capita Mean and Percentile Water Ingestion Rates for 

Children and Adults a

Lifestage (years)

Ingestion Rates, Community Water (mL/day)

Mean 50th 90th95th 99th

Child <1 b324 146 866 * 1,011 * 1,377 *

Child 1-2 c294 217 654 857 1,290 *

Child 3-5 d380 291 834 1,078 1,654

Child 6-11 e447 350 980 1,235 1,870 *

Child 12-19 f697 516 1,537 2,022 * 3,195 *

Adult g1,098 920 2,224 2,801 4,488 

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. 

a

Source is Kahn and Stralka 2008, also presented in the CSEFH (EPA 2008a).

b

Each IR represents a time-weighted average of ingestion rates for age groups birth to <1 month, 1 to <3 months, 

3 to <6 months, and 6 to <12 months from Table 3-4 of the 2008 CSEFH. 

c

Each IR represents a time-weighted average of ingestion rates for age groups 1 to <2 years and 2 to <3 years 

from Table 3-4 of the 2008 CSEFH.

d

Each IR represents the ingestion rate for age group 3 to <6 years from Table 3-4 of the 2008 CSEFH.

e

Each IR represents the ingestion rate for age group 6 to <11 years from Table 3-4 of the 2008 CSEFH. This 

value represents a conservative (i.e., slightly low) estimate of IR for ages 6 through 11 years since 11-year olds 

are not included in this CSEFH age group.

f

Each IR represents a time-weighted average of ingestion rates for age groups 11 to <16 years, 16 to <18, and 18 

to <21 years from Table 3-4 of the 2008 CSEFH. Note that estimated values include 11-year-olds and individuals 

through age 20, which contributes to uncertainty in the estimates for 12 to 19 years.

g

Adult drinking water ingestion rates were obtained from EPA (2004b), Appendix E, Part I, Table A1 for community 

water, both sexes (ages 20+), direct plus indirect water ingestion.

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

1997a). 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.

2

 

For the adult age group in MIRC, we compiled data on food-specific IRs separately for two types 

of households as indicated in the “Response to Questionnaire” (EPA 1997a, Chapter 13): (1) 

households that farm (F) and (2) households that garden or raise animals (HG for homegrown). 

 

2 Note 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.
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

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This division reflects EPA’s data analysis. EPA tabulated IRs for fruits and vegetables for 

households that that farm and for households that garden. 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 home grown/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 6-9. The user can specify use of the generally less conservative, non-farming 

household (HG) ingestion rates if they are more appropriate for the user’s exposure scenario 

(second column of IR values under Adults in Exhibit 6-9).

Exhibit 6-9. Summary of Age-Group-Specific Food Ingestion Rates for Farm Food 

Items

Product

Ingestion Rate by Age Group (g/kg-day)

Children (Farm and Homegrown) e Adults

Child 

<1

Child 

1-2

Child 

3-5

Child 

6-11

Child 

12-19 Farm f Homegrown g

Mean

Beef a NA 1.49 2.21 3.77 1.72 2.63 2.66

Dairy d NA 67 37 24.79 10.90 17.1 15.9

Eggs d NA 2.5 1.4 0.86 0.61 0.90 0.75

Exposed Fruit b NA 1.8 2.6 2.52 1.33 2.32 1.55

Exposed Vegetable NA 3.5 1.7 1.39 1.07 2.17 1.57

Pork d NA 2.2 2.1 1.49 1.17 1.30 1.34

Poultry d NA 3.6 3.4 2.13 1.59 1.54 1.58

Protected Fruit d NA 19 13 8.13 5.44 5.19 c5.9

Protected Vegetable NA 2.5 1.3 1.1 0.776 1.3 1.01

Root Vegetable NA 2.5 1.3 1.32 0.937 1.39 1.15
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

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Exhibit 6-9, continued. Summary of Age-Group-Specific Food Ingestion Rates for Farm 

Food Items

Product

Ingestion Rate by Age Group (g/kg-day)

Children (Farm and Homegrown) e Adults

Child 

<1

Child 

1-2

Child 

3-5

Child 

6-11

Child 

12-19 Farm f Homegrown g

50th Percentile

Beef a NA 0.84 1.23 2.11 1.51 1.64 1.83

Dairy d NA 102 60 39 14 12.1 10.8

Eggs d NA 1.5 0.79 0.56 0.49 0.67 0.48

Exposed Fruit b NA 1.2 1.82 1.11 0.609 1.3 0.88

Exposed Vegetable NA 1.9 1.2 0.643 0.656 1.38 0.89

Pork d NA 1.8 1.4 1.02 1.02 0.924 0.97

Poultry d NA 2.9 2.7 1.4 1.4 1.06 1.37

Protected Fruit d NA 10.2 7.6 4.2 2.3 2.08 c2.42

Protected Vegetable NA 1.94 1.04 0.791 0.583 0.599 0.64

Root Vegetable NA 0.92 0.46 0.523 0.565 0.88 0.67

90th Percentile

Beef a NA 4.5 6.7 11.4 3.53 5.39 5.39

Dairy d NA 148 82 54.67 26.98 34.9 34.9

Eggs d NA 5.1 2.8 1.8 1.34 1.65 1.36

Exposed Fruit b NA 3.7 5.4 6.98 3.41 5 3.41

Exposed Vegetable NA 10.7 3.47 3.22 2.35 6.01 3.63

Pork d NA 4.5 4.4 3.04 2.65 3.08 2.9

Poultry d NA 7.4 6.8 4.58 3.28 3.47 2.93

Protected Fruit d NA 53 36 24.14 16.19 15.14 c16

Protected Vegetable NA 3.9 2.5 2.14 1.85 3.55 2.32

Root Vegetable NA 7.3 4.3 3.83 2.26 3.11 2.81

95th Percentile

Beef 

a NA 5.0 7.3 12.5 3.57 7.51 7.51

Dairy d NA 139 75 52 27 44 44

Eggs d NA 5.5 3.7 2.4 1.5 1.85 1.85

Exposed Fruit b NA 4.1 6.1 12 4.8 6.12 5.0

Exposed Vegetable NA 11.9 6.29 5.5 3.8 6.83 5.45

Pork d NA 6.2 6.0 4.7 3.3 3.7 3.4

Poultry d NA 8.2 7.2 5.3 3.7 4.8 3.3

Protected Fruit d NA 59 42 28 20 19.16 c19.1

Protected Vegetable NA 9.4 5.1 3.12 2.2 5.4 3.05

Root Vegetable NA 10.4 4.73 5.6 3.3 4.6 3.64
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

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Exhibit 6-9, continued. Summary of Age-Group-Specific Food Ingestion Rates for Farm 

Food Items

Product

Ingestion Rate by Age Group (g/kg-day)

Children (Farm and Homegrown) e Adults

Child 

<1

Child 

1-2

Child 

3-5

Child 

6-11

Child 

12-19 Farm f Homegrown g

99th Percentile

Beef 

a NA 5.3 7.8 13.3 4.3 11 12.5

Dairy d NA 113 56 37 24 80 80

Eggs d NA 16 12 8.6 5.0 6.6 6.6

Exposed Fruit b NA 22 32.5 16 5.9 16 12.9

Exposed Vegetable NA 12 7.4 13 5.7 10 10

Pork d NA 9.1 9.9 6.3 4.2 4.9 4.3

Poultry d NA 10 10 6.4 4.8 6.2 5.3

Protected Fruit d NA 113 81 57 45 34.42 c47.3

Protected Vegetable NA 9.4 5.3 5.4 2.69 9.2 6.49

Root Vegetable NA 10.4 4.7 7.5 5.1 7.5 7.5

Source: EPA 1997a (Chapter 13), unless otherwise noted.

NA = not applicable

a No data are available for Child 1-2 or Child 3-5. The value for Child 6-11 was used, scaled down by the ratio of 

the mean body weight for Child 1-2 or Child 3-5, as appropriate, to the mean body weight of Child 6-11. 

b No data are available for Child 1-2. The value for this age group is the IR for Child 3-5, scaled down by the ratio 

of the mean body weight for Child 1-2 to the mean body weight for Child 3-5. 

c

These values represent a time-weighted average IR for two age groups, using exposure duration (ED) for the 20-

39 (ED=20 years) and 40-69 year age groups (ED=30 years).

d

In many cases, intake rates for children were not available in EPA’s 1997 EFH. Intakes for these receptor 

groups were calculated using the methodology recommended in HHRAP (EPA 2005a), Section 6.2.2.2. Sources 

to develop these values included EPA 1997a and EPA 2003b.

e

In Chapter 13 of the 1997 EPA EFH, age group-specific IRs are provided for home produced items as a whole; 

separate IRs are not presented for children from households that raise animals and households that farm.

f

These values represent the IRs for “households who farm.”

g

These values represent the IRs for “households who raise animals.”

For children, EPA estimated food-specific IRs for four age categories (EPA 1997a): 1 to 2 

years, 3 to 5 years, 6 to 11 years, and 12 to 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

6-9). 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. Foodspecific intake rates (IRs) for those child age groups and food items not included in Chapter 13 

of the 1997 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.
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

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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 6-2 was 

used to calculate the “missing” age-specific consumer-only IRs:

Equation 6-1. 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

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 1997a), includes family-caught fish ingestion rates 

by age category. There are several disadvantages, however, to using that data source to 
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

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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 1997a, Table 13-23). 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 1997a, Table 13-5). 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 

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.3

 

3 Most of these data also are provided in Table 10-9 of the CSEFH; the median values, however, are not presented in 

the CSEFH, and values for the mean and all other percentiles are slightly different due to rounding.
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

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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 6-2 was used to calculate the alternative, per capita fish ingestion rates by age group 

(IRPC,y): 

Equation 6-2. Calculation of Alternative Age-Group-Specific Fish Ingestion Rates

PC,y CO,y FPC,y

IR IR

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 6-10 and Exhibit 6-11. The 

mean and percentile per capita fish ingestion rates estimated using this methodology are 

summarized in Exhibit 6-12 and are available in MIRC. (Note that as detailed in Appendix 5, 

Exhibit 3-6, in developing the screening threshold emission rates, a conservative fish ingestion 

rate of 105.5 g/day was used.) 

The fish ingestion rates provided in Exhibit 6-12 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.

As noted in Section 6.4.3, if the user overwrites the fish IRs shown in Exhibit 6-12 with freshweight 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 3-15. 

Suggested values are presented in Section 6.4.3.
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

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Exhibit 6-11. Fraction of Population Consuming Freshwater/Estuarine Fish on a 

Single Day (FPC,y)

Lifestage (years) Fraction Consuming Fish

Child 3-5 0.0503 a

Child 6-11 0.0440 b

Child 12-19 0.0493 c

Adult 0.08509d

Sources: EPA 2002, 2008a

a

This value was calculated using the ages 3 to 5 sample size for consumers only divided by the sample 

size for the U.S. population divided by 2 to represent the proportion consuming fish on a single day (the 

consumers-only group includes individuals who consumed fish on at least one of two survey days) to 

match the one-day ingestion rate.

b

As in footnote a, the value was calculated using the ages 6 to 10 sample size for consumers only divided 

by the sample size for U.S. population divided by 2.

c

The value was calculated by summing the ages 11 to 15 and 16 to 17 sample sizes for consumers only

and dividing by both by the sum of the sample sizes for U.S. population and by a factor of 2.

d

The value was calculated using the ages 18 and older sample size for consumers only divided by the 

sample size for U.S. population from Section 5.1.1.1 Table 4. The result was divided by 2 to represent a 

one-day sampling period in order to match the one-day ingestion rate.

Exhibit 6-10. 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 b27.31 15.61 64.46 87.60 138.76 *

Child 3-5 c40.31 23.04 95.16 129.31 204.84 *

Child 6-11 d61.49 28.46 156.86 * 247.69 * 385.64 *

Child 12-19 e79.07 43.18 181.40 * 211.15 * 423.38 *

Adult f81.08 47.39 199.62 * 278.91 505.65 *

Sources: EPA 2002, 2008a

NA = not applicable; it is assumed that children < 1 year of age do not consume fish.

* Indicates that the sample size does not meet minimum reporting requirements as described in EPA 2002. Owing 

to the small sample sizes, these upper percentiles values are highly uncertain.

a

Per capita fish ingestion (FI) rates for children by age group are available from Chapter 10 of the CSEFH (EPA

2008a); however, all 50th and some 90th percentile ingestion rates are zero. Per capita FI rates were therefore 

estimated as described in Equation 6-2 to provide reasonable, non-zero values for all age groups and percentiles.

b

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

ages 3 to 5 from EPA (2002) (Section 5.2.1.1 Table 5 [freshwater/estuarine habitat]), scaled down by the ratio of 

the mean Child 1-2 body weight to the mean Child 3-5 body weight. 

c

These values represent the consumer-only fish ingestion rate for ages 3 to 5 from EPA (2002), Section 5.2.1.1 

Table 5 (freshwater/estuarine habitat). Sample size = 442.

d

These values represent the consumer-only fish ingestion rate for ages 6 to 10 from EPA (2002), Section 5.2.1.1 

Table 5 (freshwater/estuarine habitat). Sample size = 147.

e

These values represent the time-weighted average per capita fish ingestion rate for ages 11 to 15 and 16 to 17 

years from EPA (2002), Section 5.1.1.1 Table 5 (freshwater/estuarine habitat); the value may underestimate 

ingestion rate for ages 12 to 19 years. Sample size = 135.

f These values represent the consumer-only fish ingestion rate for individuals 18 years and older from EPA (2002), 

Section 5.2.1.1 Table 4 (freshwater/estuarine habitat). Sample size = 1,633.
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

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Exhibit 6-12. 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 a1.37 0.79 3.24 4.41 6.98

Child 3-5 b2.03 1.16 4.79 6.51 10.3

Child 6-11c2.71 1.25 6.90 10.9 17.0

Child 12-19 d3.90 2.13 8.95 10.4 20.9

Adult e6.90 4.03 16.99 23.73 43.02

Sources: EPA 2002, 2008a

NA = not applicable; it is assumed that children < 1 year of age do not consume fish.

a

Values were calculated as (consumer-only IR for Child 1-2) x (fraction of population consuming fish for Child 3-5).

b

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

c

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

11).

d

Values were calculated as (consumer-only IR estimated for Child 12-19) x (fraction of population estimated to 

consume fish for Child 12-19).

e

Values were calculated as (consumer-only IR for Adults) x (fraction of population consuming fish for Adults).

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 6-13 These populations are culturally or 

economically disposed to higher rates of fish ingestion than the general population. 

Recreational angler values are the EFH (EPA, 1997.) 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 (n of 30 to 45), 95th percentile values were believed to be unrealistically high. The 

90th percentile ingestion rate estimates presented in Exhibit 6-13 were derived by EPA using 

information from Shilling et al. (2010; EPA, 2010).

Exhibit 6-13. Calculated Mean and 90th Percentile Per capita Fish Ingestion Rates for Populations of 

Recreational Fishers (IRPC,y)

Subpopulation

Percentile Units Recreational 

Fisher a

Female 

Recreational 

Fisher b

Black 

Recreational 

Fisher b

Hispanic 

Recreational 

Fisher c

Laotian 

Recreational 

Fisher c

Vietnamese 

Recreational 

Fisher c

Ingestion of Fish

Mean g/day 8 39.1 171 25.8 47.2 27.1

90th % g/day 11 123 446 98 144.8 99.1

a

1997 Exposure Factors Handbook (USEPA, 1997)

b

Burger et al., 2010, weights reported are post-cooking for locally caught fish

c

Shilling, F., A. White, L. Lippert, and M. Lubell. 2010. Contaminated fish consumption in California’s Central Valley Delta. 

Environmental Research 110: 334-344., weights reported are post-cooking for locally caught fish Burger et al., 2010
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Applications to date of MIRC have used whole fish concentrations estimated by TRIM.FaTE. 

The proportion lipid in TL3 and TL4 fish in TRIM.FaTE is assumed to be 5.7 percent (by weight) 

for the whole fish, based on information provided by Thomann (1989). The lipid content of the 

part(s) of the fish normally consumed is likely to be less than 5.7 percent. For example, EPA 

estimated a consumption-weighted mean lipid value for fillets of fish from TL3 to be 2.6 percent 

and from TL4 to be 3.0 percent (Table 6-9 in EPA 2003b). If a user of MIRC wishes to account 

for reduced chemical concentration in fillet compared with whole fish for lipophilic chemicals, the 

user can specify a “preparation” loss of chemical (see Section 6.4).

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 that the chemical concentration in the fillet is the 

same as in the whole fish may result in a conservative 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 is protective.

6.3.5 Soil Ingestion Rates

Adult gardeners 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 6-14 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.
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Exhibit 6-14. Daily Mean and Percentile Soil Ingestion Rates for Children and Adults

Age Group

(years)

Soil Ingestion Rate (mg/day)

Mean a50th a90th 95th 99th

Child < 1 NA

Child 1-2 50 50 400 b400 b400 b

Child 3-5 50 50 400 b400 b400 b

Child 6-11 50 50 201 c331 d331 d

Child 12-19 50 50 201 c331 d331 d

Adult 20-70 50 50 201 c331 d331 d

Sources: EPA 1997a, 2008a

a

For the 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 

1997 EFH, Chapter 4, Table 4-23.

b

These values are the recommended “upper percentile” value for children from EPA’s 1997 EFH, Chapter 4,

Table 4-23. The 2008 CSEFH included a high-end value associated with pica only.

c

These values are 90th percentile adult ingestion rates calculated in Stanek et al. 1997, and they are used to 

represent older children and adults.

d

These values are 95th percentile adult ingestion rates calculated in Stanek et al. 1997, and they are used to 

represent older children and adults. 

6.3.6 Total Food Ingestion Rates

Although not included in MIRC for deterministic screening-level exposure and risk assessments, 

total food ingestion rates would be included in any probabilistic module developed for MIRC. 

The total food ingestion rates presented in Exhibit 6-15 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.
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Exhibit 6-15. 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 < 1 a67.0% - 99.7% h322 270 599 779 1152

Child 1-2 b100% 1,032 996 1537 1703 2143

Child 3-5 c100% 1,066 1,020 1,548 1,746 2,168

Child 6-11 d100% 1,118 1,052 1,642 1,825 2,218

Child 12-19 e100% 1,197 1,093 1,872 2,231 2,975

Adult f

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

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

Child < 1 a67.0% - 99.7% h39 34 72 95 147

Child 1-2 b100% 82 79 125 144 177

Child 3-5 c100% 61 57 91 102 132

Child 6-11 d100% 40 38 61 70 88

Child 12-19 e100% 21 19 34 40 51

Adult g

100% 14.8 13.9 23.7 27.6 35.5

Sources: EPA 2005e, 2008a

a

These values represent a time-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. 

b

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 conservative (i.e., slightly low) estimate for ages 6 through 11 years since 11-year olds are not 

included in this CSEFH age group.

e

These values represent a time-weighted average for age groups 11 to <16 years (N=975) and 16 to <21 (N=743) 

years from Table 14-3 of the 2008 CSEFH. Note that estimated values include 11-year-olds and individuals 

through age 20, which contributes to uncertainty in the estimates.

f

These values represent a time-weighted average for age groups 20 to 39 years (N=2,950) and 40 to 69 years 

(N=4,818) from Table 5B of the 2005 EPA analysis of CSFII. 

g

These values represent a time-weighted average for age groups 20 to 39 years (N=2,950) and 40 to 69 years 

(N=4,818) from Table 5A of the 2005 EPA analysis of CSFII. 

h

Percents consuming foods from Table 14-3 of the 2008 CSEFH include: 67.0% (birth to <1 month); 74.7% (1 to 

<3 months); 93.7% (3 to <6 months); and 99.7% (6 to <12 months). Infants under the age of 1 that consume 

breast milk are classified as “non-consumers” of food.

6.4 Other Exposure Factor Values

The other exposure parameters included in the MIRC algorithms are exposure frequency 

(Section 6.4.1), fraction of the food type obtained from the contaminated area (Section 6.4.2), 

and reduction in the weight of the food types during preparation and cooking (Section 6.4.3). 

For the breast milk ingestion pathway, additional exposure parameters are included in the FFC 

algorithms (Section 6.5). 

6.4.1 Exposure Frequency 

The exposure frequency (EF) represents the number of days per year that an individual 

consumes home-produced food items that are contaminated with the chemical being evaluated. 

In MIRC, the default value for EF is 365 days/year for all exposure sources and all potential 
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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 conservative 

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 6-16. All preparation and 

cooking loss parameter values were estimated as specified in the Exhibit’s endnotes from data 

presented in Chapter 13 of the EPA’s 1997 EFH.
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Exhibit 6-16. Fraction Weight Losses from Preparation of Various Foods

Product

Mean Cooking, Paring, or 

Preparation Loss 

(Cooking Loss Type 1 [L1])

(unitless) a

Mean Net Post Cooking

(Cooking Loss Type 2 [L2])

(unitless) b

Exposed Fruit c0.244 0.305

Exposed Vegetable 0.162 d NA

Protected Fruit 0.29 e NA

Protected Vegetable 0.088 f NA

Root Vegetable g0.075 0.22

Beef 0.27 0.24

Pork 0.28 0.36

Poultry 0.32 0.295 h

Fish i0.0 0.0 or 1.5j

Source: EPA 1997a (Chapter 13; Tables 13-5 [meats], 13-6 [fruits], and 13-7 [vegetables])

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 0.3 (EPA 1997a, Table 13-5). 

For volatile or water soluble chemicals, a non-zero value for L2 also may be appropriate. Although EPA (1997a) 

recommended 0.11 for L2, it varies substantially by chemical.

J Cooking 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, Berry, 

and Graves, 1997)4

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, 

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 

 

4 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.
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gains) due to hydration of dried vegetables (e.g., peas and lima beans), which increases the 

range of L1 values across different vegetables. 

Note that the default L1 and L2 values for fish are set to zero with the exception of methyl 

mercury. That is because the data source for the fish ingestion rates is not the USDA’s 1987 to 

1988 NFCS (USDA 1993, 1994) 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. 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, L2 is set at 1.5 as detailed by Burger et 

al (2010).

5

(Note that due to uncertainties associated with the cooking conversion factor of 1.5 

for methyl mercury, it was not used in the 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 1997a, Table 13-5). For volatile or water soluble chemicals, a non-zero 

value for L2 also may be appropriate. Although EPA recommended 0.11 for L2 (EPA 1997a, 

Table 13-5), it varies substantially by chemical. 

6.5 Breast-Milk Infant Exposure Pathway Parameter Values

Values used for parameters in the breast-milk exposure pathway algorithms (Section 3.4) 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 scenario-specific 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 chemical-specific 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 6-17 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.

 

5

Burger J. 2010. Daily consumption of wild fish and game: Exposures of high end recreationalists. Environmental 

Health Research 12(4): 343-354.
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Exhibit 6-17. 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 3-36

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 its first birthday from EPA’s (2008) Child Specific 

Exposure Factors Handbook (CSEFH; EPA 2008a). Exhibit 6-18 presents additional values for 

the infant body weight parameter that the user can select instead of the MIRC default.

Exhibit 6-18. Average Body Weight for Infants 

Statistic 0 to < 6 months 

(kg)

0 to < 12 months 

(kg)

0 to < 18 months 

(kg)

0 to < 24 months 

(kg)

Mean 6.5 7.8a

9.0 9.6

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
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Exhibit 6-18. Average Body Weight for Infants 

Statistic 0 to < 6 months 

(kg)

0 to < 12 months 

(kg)

0 to < 18 months 

(kg)

0 to < 24 months 

(kg)

Mean 6.5 7.8a

9.0 9.6

75th percentile 7.1 8.6 9.9 10.5

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.

a MIRC default

Maternal body weight (BWmat). This parameter represents the body weight of the mother 

averaged over the entire duration of the mother’s exposure to the chemical of concern. The 

maternal body weight is needed to calculate the biological elimination constant for the lipophilic 

chemical in lactating women (kfat_elac). MIRC assumes that the mother will be pregnant for 9 

months (i.e., 0.75 year) and will be lactating for 1 year. The MIRC default maternal body weight 

also assumes that the mother has been exposed for 10 years total. For 8.25 years, she is not 

pregnant or lactating, for 0.75 year she is pregnant, and for 1 year she is lactating. The MIRC 

default BWmat of 66 kg is based on CSFII data compiled by EPA for non-lactating and nonpregnant women between the ages of 15 and 44 (i.e., women of child-bearing age), lactating 

women, and pregnant women (EPA 2004b). Exhibit 6-19 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 6-19. 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

a MIRC default value

Exposure duration (ED). See discussion of AT and ED above.

Fraction of mother’s whole blood that is plasma (fbp). Steinbeck (1954) reported that plasma 

volume accounts for approximately 60 percent of the total blood volume in non-lactating human 
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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 3-38, 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 conservative value (EPA 2001a). To establish a 

conservative screening scenario, the MIRC default value for ffm is 0.30.

Fraction of fat in mother’s breast milk (fmbm). The Cmilkfat model (Equation 3-38) 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 1997a, 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 1997a, Chapter 14, Table 14-15). 

To estimate an “average” value, EPA first estimated study-sample-size weighted average 

values for 1, 3, 6, 9, and 12 months of age and then developed time-weighted average milk 

ingestion rates from those (EPA 1997a). 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 1997a). The resulting 

values are shown in the first two rows of Exhibit 6-20. The MIRC screening-level default of 980 

mL/day is an upper-bound estimate based on a one-year nursing period. 
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Exhibit 6-20 also includes the recommended values for four non-overlapping age categories 

from the CSEFH (EPA 2008, Table 15-1). The values demonstrate that although infants grow 

substantially from birth to one year of age, the “upper bound” estimates of their milk ingestion 

rates are very close to 1 liter per day at all stages of development in the first year.

Exhibit 6-20. 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 1997a†

0 to < 12 

months 688 0.709 980a

1.01aEPA 1997a†

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†

a MIRC default; †

Based on review of multiple studies; †† Based on a single study

Duration of breast feeding (tbf). This parameter is equal to the infant’s exposure duration (ED) 

and the infant’s averaging time (AT). In its MPE Methodology, EPA asserts a conservative 

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 conservative, given than 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 conservative 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 3-38 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 3-38 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 3-38 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 of assessment for dioxins, we assume an exposure duration 

equal to the half-life of the chemical, or 10 years. Only 3285 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 conservative bias for this parameter value. 
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6.5.2 Chemical-Specific Parameter Values

The chemical-specific parameters in the breast-milk pathway in MIRC are listed in Exhibit 6-21. 

Note that the parameters for which values are needed are different for the lipophilic chemicals 

(i.e., dioxins), for which lactational transfer is assumed to occur via milk fat, and inorganic 

chemicals, for which the transfer is assumed to occur via the aqueous phase of breast milk (i.e., 

mercury). 

Absorption efficiency of the chemical by the oral route of exposure for the infant (AEinf). The 

models included in MIRC assume that the AEinf from the lipid phase of breast milk is equal to the 

AEinf from the aqueous phase of the milk. Reviewers of the model stated that this assumption 

may not be valid and that ideally, the equation DAIinf would include variables for the AEinf from 

the breast milk fat and the AEinf from the aqueous phase of breast milk (EPA 2001a). However, 

since the MIRC assumption is that chemicals will partition to either the lipid or aqueous phase of 

milk, it is not necessary at this time to have multiple AEinf values for a given chemical. If data on 

the AE from the mother or an adult but not for the infant are available, data for the adult may be 

used for AEinf. Reviewers also recommended that chemical-specific values come from studies 

that account for absorption of the chemical from milk, because absorption from other matrices 

(e.g., solid foods) may not be relevant (EPA 2001a). If chemical-specific data are not available 

for adults or infants, a conservative 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).

Exhibit 6-21. 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 identified b

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 half1.9E-04b

1.4E-02 c
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life)

kfat_elac

Rate constant for total elimination of lipophilic 

chemicals by lactating women (per day)

Est. using 

Equation 3-41 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 proteinbound chemical between red blood cells (RBC) 

and plasma in maternal blood (mL whole blood/mL 

RBC)

NA 40 (Hollins et al. 

1975)

NA = not applicable. ND = not yet determined from literature. 

a

This value is based on a single-dose study and may not be appropriate for a chronic exposure model.

b

An empirical value for this variable is currently missing for application of model.

d

This value was calculated from biological half-life (h) using Equation 3-40. 

Note that AE values for some inorganic compounds are substantially less than 1.0. For 

cadmium, for example, AEs for adults of 0.025 to 0.05 have been reported (EPA 2004c, Exhibit 

B-4). 

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 3-43. 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 conservative 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 
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MeHg via bread prepared from wheat treated with a fungicide that contained MeHg. The data 

indicated a mean half-life for MeHg of approximately 42 days. Sherlock et al. (1984) reported 

an average measured half-life for MeHg of 50 days with a range of 42-70 days. The MIRC 

default for MeHg is set to the longer average half life of 50 days.

Chemical elimination rate constant for lactating women – aqueous (kaq_elac). The parameter 

kaq_elac is equal to kelim plus the loss rate for the chemical in the aqueous phase of breast-milk 

during lactation. EPA has yet to propose a term for the additional elimination of a chemical in 

the aqueous phase of milk from breast feeding. In the absence of empirical values, a 

reasonable assumption for water soluble chemicals is that kaq_elac is equal to kelim as discussed 

for Equation 3-43. The extent to which kelim is an underestimate of kaq_elac for a given chemical 

will determine the extent of conservative 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 3-40. 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 3-41. 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 3-42, 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). 
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

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7 Summary of MIRC Default Exposure Parameter Settings

The default settings included in MIRC are intended to be characteristic of a conservative (but 

plausible) exposure scenario that results in a negligible or extremely low chance of 

underestimating risk to farming households in an area with chemical concentrations and air 

deposition rates as specified by the user. 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. 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, presented in Section 

7.1, and population-specific characteristic assumptions, presented in Section 7.2, that are

generally conservative 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.

7.1 Default Ingestion Rates

The screening-level (or default) values for ingestion rates for soil, fish, breast milk, and for each 

farm food type are equal to the 90th percentile of the distribution of national data for that 

ingestion medium (Exhibit 7-1). The default settings also assume that all food types are 

obtained from the area of chemical deposition specified by the user (i.e., fraction of food from 

contaminated area = 1.0). 

For estimates of screening threshold emission rates for PB-HAPS, environmental 

concentrations and air deposition rates were estimated using TRIM.FaTE for the area of 

maximal deposition in the vicinity of a hypothetical facility, and thus represent risks estimated for 

a maximally exposed individual/farm/family. 

Exhibit 7-1 also includes a sum of the 90th percentile ingestion rates for homegrown food 

categories and fish ingestion (preceding rows) to show the implied total food ingestion rate 

associated with setting multiple food-type-specific ingestion rates at a 90th percentile. Because 

the 90th percentiles for each farm food category are likely to reflect different individuals, it is 

likely that addition of multiple 90th percentile intake values will exceed the total food ingestion 

rates likely for the general population. 

The final row in Exhibit 7-1 lists the likely magnitude of the overestimates by age category. The 

preceding row includes 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 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 (which 

are based on consumption of prepared foods). 
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Exhibit 7-1. Farm Food Category Ingestion Rates for Conservative Screening

Scenario for Farming Households

Product

90th Percentile Consumer Ingestion Rate

Units Infants a

< 1 yr

Child 

1-2 yrs

Child 

3-5

yrs

Child 

6-11 

yrs

Child 

12-19 

yrs

Adult 

Farm Food Item

Beef NA 4.5 6.7 11.4 3.53 5.39 g/kg-day

Dairy c NA 148 82 54.7 27.0 34.9 g/kg-day

Eggs c NA 5.1 2.8 1.80 1.34 1.65 g/kg-day

Exposed Fruit NA 3.7 5.4 6.98 3.41 5 g/kg-day

Exposed Vegetable NA 10.7 3.5 3.22 2.35 6.01 g/kg-day

Pork c NA 4.5 4.4 3.04 2.65 3.08 g/kg-day

Poultry c NA 7.4 6.8 4.58 3.28 3.47 g/kg-day

Protected Fruit c NA 53 36 24.1 16.2 15.1 bg/kg-day

Protected Vegetable NA 3.9 2.5 2.14 1.85 3.55 g/kg-day

Root Vegetable NA 7.3 4.3 3.83 2.26 3.11 g/kg-day

Other

Breast milk d1.01 NA NA NA NA NA kg/day

Soil (dry) NA 400e400e201 f201 f201 f mg/day

Water NA 654 834 980 1537 2224 mL/day

Fish (per individual) g NA 3.24 4.79 6.9 8.95 17 g/day

Fish (per kg BW) h NA 0.26 0.26 0.22 0.14 0.24 g/kg-day

Total Food Ingestion Rates for Comparison Only (not in MIRC; excludes soil and water)

Total Food: 

Homegrown only i NA 219 131 95 52 67 g/kg-day

Total Food: All 

Sources j NA 125 91 61 34 23.7 g/kg-day

Overestimate (ratio of 

Homegrown/Total) k NA 1.8 1.4 1.6 1.5 2.8 (unitless)

Sources: EPA 1997a (Chapter 13), unless otherwise noted.

NA = not applicable

a

As indicated by the units, the ingestion rates for produce and animal products are already normalized to 

consumer body weight. Ingestion rates for soil (mg/day) and water (mL/day) are not normalized to body 

weight. Soil is reported as dry weight, water as volume, and the remaining values on a wet-weight basis.

b

This value represents a weighted average for the 20-39 and 40-69 age groups.

c

For several farm food categories, ingestion rates were not available in EPA’s 1997 EFH or 2008 CSEFH 

(EPA 1997a, 2008a). Ingestion rates for these child age categories were calculated using the methodology 

recommended in HHRAP (EPA 2005a), Section 6.2.2.2, as described in Section 6.3.3 of this document. 

Sources to develop these values included EPA 1997a and EPA 2003c. 

d

Infants are assumed to consume only breast milk for one year. 

e

This value represents an estimated “upper percentile” for children (EPA 1997a).

f

These values represent soil ingestion rates for individuals who consume homegrown food products from 

Stanek et al. 1997. 

g

90th percentile adult fish ingestion rates are based on data from 1995-1996 and 1998 CSFII as 

summarized in EPA 2002; child fish ingestion rates are based on the same survey data, but estimated by 

multiplying average two-day consumption rate for children who consumed fish on one or both days of the 

survey by the frequency of fish consumption (i.e., proportion of children that reported consuming fish out of 

all children sampled).

h 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.

i Sum of 90th percentile 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 6-16. The rows are then summed to get the total post-cooking ingestion rate.

j

90th percentile total food intake rates from EPA 2008a and 2005e based on CSFII data 1994-96 and 1998; 

see Exhibit 6-15 of this document.
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The final row of Exhibit 7-1 is the ratio of the two preceding rows. The magnitude of the 

overestimate that results from adding 90th percentile values for 10 different categories of 

homegrown foods for children is a factor of 1.8. The overestimate of the 90th percentile total 

food ingestion rate for adults is larger, a factor of 2.8. This bias may be considered when 

evaluating the cancer risks and noncancer hazard quotients estimated by MIRC.

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 7-2. 

As stated in Section 6, EPA recommends using the mean BW for each age group when using 

upper (90th) percentile values for medium ingestion rates. Use of the mean body weights 

introduces no bias toward over- or underestimating risk.

Exhibit 7-2. Mean Body Weight Estimates for Adults and 

Children a

Lifestage (years) Duration (years) Mean Body Weight (kg)

Adult b (20-70) 50 71.4

Child < 1 c1 7.83

Child 1-2 c2 12.6

Child 3-5 d3 18.6

Child 6-11 e6 31.8

Child 12-19 f8 64.2

a

Sources: EPA 1997, 2008a

b

These values were calculated from data presented in EPA’s (1997a) Exposure 

Factors Handbook.

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. 

e

These values were obtained directly from Table 8-3 of the 2008 CSEFH for 

age group 6 to <11 years. The values represents a slight underestimate of BW 

for ages 6 through 11 years, since 11-year olds are not included in this CSEFH 

age group.

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 7-3 presents chemical-specific parameter values for input to MIRC for the screeninglevel analysis. Values for reference dose (RfD), cancer slope factor (CSF), 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 soil-water partition coefficient (Kds) are presented in Exhibit 

7-3. 
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Exhibit 7-3. Chemical-Specific Parameter Values for Input to MIRC a

Parameter Description Benzo(a)-

pyrene

Cadmium Mercuric 

chloride

Methyl 

mercury

2,3,7,8-

TCDD Units

RfD Reference Dose for Chemical 0 0.0005 0.0003 0.0001 1E-09 mg/kg-day

CSF Oral carcinogenic potency slope factor 

for chemical

10 0 0 0 150,000 mg/kg-day

Bs Soil bioavailability factor for livestock 1 1 1 1 1

unitless

MF Mammalian metabolism factor 0.01 1 1 1 1

unitless

VGrootveg

Empirical correction factor for 

belowground produce, i.e., tuber or 

root vegetable, to account for possible 

overestimate of the transfer of 

chemicals from the outside to the 

inside of bulky tubers or roots (based 

on carrots and potatoes)

0.01 1 1 0.01 0.01 unitless

Fw

Fraction of wet deposition that adheres 

to plant surfaces; 0.2 for anions, 0.6 for 

cations and most organics

0.6 0.6 0.6 0.6 0.6 unitless

BvAG

Air-to-plant biotransfer factor for 

aboveground produce for vapor-phase 

chemical in air

124,742 0 1,800 0 65,500

[mg/g produce DW]

/ [mg/g air]

RCF Chemical-specific root concentration 

factor for tubers and root produce

9,684 0 0 0 39,999

L soil pore water/kg 

root WW

Kds Chemical-specific soil/water partition 

coefficient

160,000 75 58,000 7,000 38,904.51

L soil pore water/kg 

soil DW

a

Values presented in this Exhibit are previously presented in Exhibit 4-1, Exhibit 6-2, Exhibit 6-3, and Exhibit 6-5. However, exact values used in the 

analysis are presented here, rather than values restricted by significant figures.
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

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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 6-3, 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 7-4 presents biotransfer factors 

for each of the chemicals and animal types assessed in the screening level assessment.

Exhibit 7-4. 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 Poultry Eggs

Benzo(a)pyrene 0.03756242 0.00790788 0.0454703 0.02767758 0.01581576

Cadmium 0.00012 0.0000065 0.00019149 0.10625 0.0025

Mercuric chloride 0.000105 0.00000143 0.00003393 0.023925 0.023925

Methyl mercury 0.00124 0.0000169 0.00000507 0.003575 0.003575

2,3,7,8-TCDD 0.02612123 0.00549921 0.03162044 0.01924722 0.01099841

Note: Exact values used in the analysis are presented here, rather than values restricted by significant figures.

Sensitivity analyses were conducted to identify which, if any, of these parameter values 

significantly influence risk estimates from MIRC; results are presented elsewhere. Further 

evaluation of the possible range and distribution of values can be conducted for any parameter 

that appears important to the model outputs.

7.4 Screening-Level Parameter Values for Nursing Infant Exposure

EPA also included an assessment of 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. 

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 1998a). 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 some 

overestimate 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.

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 90th percentile ingestion rates for the 

different homegrown foods (see discussion for Exhibit 7-1); this assumption might 

overestimate total ingestion of homegrown foods by a factor of more than 3.
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

2-90

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 Byckowski and Lipscomb (2001) to support a very 

simple predictive model. Both the human data and the PBPK model indicated that for relatively 

low MeHg exposures, the concentration of MeHg in the nursing infant’s blood is similar to its 

concentration in the mother’s blood. The PBPK model suggested in addition that the average 

daily dose of MeHg absorbed from milk by the nursing infant (DAIinf) is indistinguishable from the 

dose of MeHg absorbed by its mother from her food (DAImat). The data are limited, and the 

model includes various assumptions; however, there is no known directional bias in the 

estimates. 
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

2-91

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U.S. Environmental Protection Agency (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.

U.S. Environmental Protection Agency (EPA). 2009. Guidance for Implementing the January 

2001 Methylmercury Water Quality Criterion. Office of Water, Office of Science and 

Technology, Washington, D.C. EPA 823-R-09-002. January. Available at: 

http://www.epa.gov/waterscience/criteria/methylmercury/pdf/guidance-final.pdf. 

U.S. Food and Drug Administration (FDA). 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

U.S. Environmental Protection Agency (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]

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.

Whitehead, R.G., and A.A. Paul. 1981. Infant growth and human milk requirements: A fresh 

approach. Lancet 2(8239): 161-163. 
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Technical Support Document for TRIM-Based Multipathway Screening

Scenario for RTR: Summary of Approach and Evaluation

ATTACHMENT 3: Systematic Sensitivity Analysis 
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TABLE OF CONTENTS 

1 Systematic Sensitivity Analysis Variables and Results ................................... 1

1.1 Sensitivity Analyses .........................................................................................................................1

1.1.1 Systematic Sensitivity Analysis .............................................................................................2

1.1.2 Evaluation of Ingestion Rate Assumptions ...........................................................................6

1.1.3 Evaluation of Body Weight Assumptions ............................................................................14

1.1.4 Sensitivity When Accounting for Temporally Correlated Body Weight and Ingestion Rates

15

1.1.5 Comparison of Scenarios Using Site-Specific and Screening Threshold Meteorological 

Data.....................................................................................................................................16

Exhibit 1. The 26 Variables with the Highest Elasticities for Benzo[a]Pyrene Lifetime Risk (-5% 

Perturbation of Variable) ...............................................................................................................................8

Exhibit 2. The 25 Variables with the Highest Elasticities for 2,3,7,8-TCDD Lifetime Risk (-5% Perturbation 

of Variable)....................................................................................................................................................9

Exhibit 3. The 28 Variables with the Highest Elasticities for Cadmium Hazard Quotient for Child 1-2 (-5% 

Perturbation of Variable) .............................................................................................................................10

Exhibit 4. The 25 Variables with the Highest Elasticities for Divalent Mercury Hazard Quotient for Child 1-

2 (-5% Perturbation of Variable) .................................................................................................................11

Exhibit 5. The 26 Variables with the Highest Elasticities for Methyl Mercury Hazard Quotient for Child 1-2 

(-5% Perturbation of Variable) ....................................................................................................................12

Exhibit 6. Ratio of the Modeled Total Ingestion Rates and the USEPA Total Ingestion Rates .................13

Exhibit 7. Comparison of the Risks and Hazard Quotients in the Screening Threshold and Alternate 

Ingestion Cases ..........................................................................................................................................14

Exhibit 8. The Risk or Hazard Quotient Estimates Using Alternate Body Weight Percentiles ...................15

Exhibit 9. Comparison in the Elasticities In Lifetime Risk in the Correlated and Uncorrelated Analyses 

Assuming a 5% Decrease in the Input Variables........................................................................................16

Exhibit 10. Summary of Site-specific Meteorological Data Parameters .....................................................17

Exhibit 11. Percent Change in Risk or Hazard Quotient Using Site-specific Meteorological Data.............17

Exhibit 12. The Wind Speed and the Direction Toward Which the Wind is Blowing for All Conditions for 

Site 1 ...........................................................................................................................................................18

Exhibit 13. Variables Included in the Systematic Sensitivity Analysis ........................................................19

Exhibit 14. Elasticities and Rankings for the Variables with the Highest Elasticities for Benzo[a]Pyrene..25

Exhibit 15. Elasticities and Rankings for the Variables with the Highest Elasticities for 2,3,7,8 - TCDD ...27

Exhibit 16. Elasticities and Rankings for the Variables with the Highest Elasticities for Cadmium ............29

Exhibit 17. Elasticities and Rankings for the Variables with the Highest Elasticities for Divalent Mercury 31

Exhibit 18. Elasticities and Rankings for the Variables with the Highest Elasticities for Methyl Mercury ...33
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1 Systematic Sensitivity Analysis 

The sensitivity analysis presented in this attachment was originally performed on a previous 

configuration of the screening scenario. Since that time, the model configuration of the 

screening scenario has been refined to improve its sophistication and accuracy. The screening 

thresholds and model parameters presented earlier in this document are based on the updated 

model configuration. Time constraints precluded the performance of a revised sensitivity 

analysis on the updated model configuration, and therefore, the sensitivity analysis presented 

below has not been updated to reflect the latest model configuration on which the current 

screening thresholds are based. Readers may note that while some of the specific numeric 

estimates of the relative sensitivity of model parameters are likely to be different in the updated 

model configuration from the estimates presented below, the general trends are expected to be 

similar. One of the significant changes since the updating of the model configuration has been 

the dominance of the fish ingestion pathway for all the modeled PB-HAPs. Parameters that 

influence exposure via the fish ingestion route are therefore expected to be more sensitive in 

the current model configuration than estimated in the analysis presented below.

This attachment provides the tables of the variables included in the systematic sensitivity 

analysis in Exhibit 13. The variables are organized into three categories: TRIM.FaTE variables, 

MIRC farm food chain variables, and MIRC ingestion and bodyweight variables. 

This attachment also provides detailed elasticities and rankings for the variables with the 

highest elasticities for Benzo[a]Pyrene (Exhibit 14), 2,3,7,8–TCDD (Exhibit 15), cadmium 

(Exhibit 16), divalent mercury (Exhibit 17), and methyl mercury (Exhibit 18). In each case, the 

elasticities and rankings are provided for the local systematic sensitivity analysis (variables 

perturbed up and down by 5%) and the range systematic sensitivity analysis (variables 

perturbed up and down by 50%). 

1.1 Sensitivity Analyses

The hypothetical subsistence farmer/fisher exposure scenario used to screen emissions of PBHAPs described in this document was parameterized using generally conservative inputs. The 

goal was to construct a modeling scenario that is sufficiently health-protective (i.e., 

conservative) such that it can be used with confidence to screen out emissions that do not pose 

unacceptable multipathway risks, while also avoiding overly conservative characteristics that 

diminish the functionality of the scenario (i.e., by allowing too many “false positives,” or facilities 

that fail the screen for which the risks are actually acceptable). The level of conservatism of the 

scenario is dictated largely by the values selected by the user for model inputs. A sensitivity 

analysis is a useful method for evaluating the influence of model parameters and user 

selections for parameter values. By providing quantitative information on the importance of 

model parameters to a selected output, a sensitivity analysis thus also provides information on 

which parameters may be most influential in dictating the uncertainty associated with the 

results.

The sensitivity analyses conducted on the RTR screening modeling scenario encompassed the 

fate and transport modeling carried out using TRIM.FaTE and the farm food chain (FFC) and 

ingestion exposure modeling performed using MIRC.1 A systematic sensitivity analysis was 

conducted on these parameters to obtain information regarding the relative importance of user 

 

1

PB-HAP dose-response values used for risk calculation (i.e., oral cancer slope factors and oral 

reference doses, plus the mutagenicity correction factor applied to certain age groups) were not included 

in the sensitivity analysis. 
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inputs (Section 4.4.1). In addition, several other analyses were performed to evaluate model 

performance with respect to parameterization of ingestion rates (Section 4.4.2), body weight 

(Section 4.4.3), assumed relationships between ingestion and body weight over time (Section 

4.4.4), and meteorological conditions (Section 4.4.5). Each section presents the methods 

employed to conduct the sensitivity analyses, evaluation results, and notes regarding 

interpretation of the results. Results of these sensitivity analyses are useful in informing the 

level of uncertainty in screening results, highlighting parameters and aspects of the modeling 

scenario worthy of additional research if refinement is appropriate (e.g., for evaluating sources 

of PB-HAPs that do not pass the screen), and suggesting variables that are likely to be worthy

of more detailed examination if a more quantitative uncertainty analysis is desired (e.g., using 

probabilistic methods).

1.1.1 Systematic Sensitivity Analysis

1.1.1.1 Methods

As with all predictive models, the user-specified inputs for TRIM.FaTE and MIRC influence 

model outputs and thus estimation of emission screening thresholds. The degree to which 

model outputs diverge from a nominally “correct” answer (e.g., the actual incremental risks for 

an individual exposed to PB-HAP emissions) encompasses both uncertainty (because values 

are not known with perfect accuracy) and variability (because each value selected represents a 

discrete choice out of a range of possible parameter values within a distribution, such as 

variability in body weights among individuals in an exposed population). In this analysis, we did 

not attempt to separately evaluate uncertainty and variability, and variation in model outputs is 

assumed to encompass both uncertainty and variability. Representation of each parameter with 

a single fixed value without representing uncertainty or variability in model calculations is a 

limitation of deterministic model application. 

As a first step to quantifying the combined uncertainty and variability in model outputs and the 

screening thresholds, an analysis was performed to identify the variables to which the risk or 

hazard quotient calculations are most sensitive. Model sensitivity was evaluated separately for 

each of the PB-HAPs. The model output to which sensitivity was measured varied by chemical 

depending on the health impacts of greatest concern. Parameter influence on cancer risk was 

measured for benzo[a]pyrene and 2,3,7,8-TCDD, and influence on non-cancer hazard quotient 

was measured for cadmium, divalent mercury, and methyl mercury. This part of the analysis 

was conducted systematically by “perturbing,” or changing, the value of each selected 

parameter independently (i.e., one at a time, holding all other inputs at their base value) by a 

certain percentage and calculating the corresponding percent change in the risk or hazard 

quotient value. This metric is referred to as elasticity (i.e., ratio of the percent change of the 

model output to percent change in the input variable), with higher elasticities corresponding to 

greater influence. 

This type of systematic sensitivity analysis has the advantage of focusing on a wide range of 

inputs at once so that the variables can be ranked in order of importance. However, by 

perturbing the variables by fixed percentages, the analysis does not necessarily focus on the 

most physically relevant values of each parameter. An alternate analysis could examine each 

variable independently (taking into account the plausible range of input values) and look at the 

effects of using different plausible variable values on the risk or hazard quotient estimates. We 

focus on a systematic sensitivity analysis here with the goal of prioritizing variables; additional 

examinations may be helpful in the future to better define the uncertainty.
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In the systematic sensitivity analysis, we estimated both local sensitivity, quantified as the 

elasticity when a parameter value is perturbed by a small percentage of its base (or default) 

value, and range sensitivity, for which parameters were varied by a larger percentage of the 

base value. These two elasticities can be different if the relationship between input and output 

is nonlinear (e.g., if the perturbed parameter is in the denominator of an equation in the 

calculation). For the evaluation of risks associated with exposures to PB-HAPs, a large 

difference between local and range sensitivity could indicate that variables that are less 

important in the base scenario used to calculate screening thresholds scenario assumptions 

may be more important in a less conservative site-specific evaluation. In this case, following 

recommendations in the Risk Assessment Guidance for Superfund (EPA 2001; see Volume III, 

Part A, Appendix A), we perturbed parameter values by +/-5% of the base case default value in 

the local sensitivity analysis and +/-50% in the range sensitivity analysis. Thus, with some 

exceptions noted below, this resulted in four model simulations and corresponding elasticities 

for each parameter included in the analysis for each of the PB-HAPs.

Typically, a range sensitivity analysis (also referred to as “nominal sensitivity analysis”) 

measures the effect on model outputs across the entire expected or plausible range of values 

for a given input. This provides more comprehensive information on the behavior of the model 

with respect to the input being varied by demonstrating the maximum potential influence of that 

input on model outputs. In the case of the RTR modeling scenario, information on the expected 

or most likely input ranges is available on only a few of the parameters defined by the user, and 

there is a relatively large number of parameters to be evaluated. The selection of both 5 and 50 

percent as perturbation increments was intended to be an efficient compromise between 

conducting an authentic range sensitivity analysis and evaluating only more localized sensitivity.

To obtain a comprehensive estimate of the relative sensitivity of risk results across the range of 

numerical user inputs, as many TRIM.FaTE and MIRC inputs as possible were included in the 

analysis. To that end, all the MIRC ingestion and exposure variables were included. For 

TRIM.FaTE, properties assumed a priori to have greater influence on model outputs were 

included. The large number of user-specified numerical inputs to TRIM.FaTE and 

computational limitations made inclusion of all TRIM.FaTE inputs in this sensitivity analysis 

impractical. TRIM.FaTE inputs for this sensitivity evaluation were selected based on results 

obtained from previous TRIM.FaTE evaluations (including the TRIM.FaTE mercury test case; 

EPA 2005b) and professional judgment/intuition drawing on experience with the model.

The full set of inputs included in the systematic analysis is shown in Exhibit 13. Parameters are 

grouped by the model in which they are used, with the MIRC variables further divided into farm 

food chain and ingestion/body weight categories. In some cases, inputs included in the analysis 

could not be increased by either 5% or 50% or both, since the variable has a physical upper 

bound which cannot be exceeded (e.g., the number of days of exposure per year was already 

set to 365, so it cannot be increased by 5% or 50%). The footnotes in Exhibit 13 indicate the 

variables that could not be increased by 5% and/or 50%. For some inputs, the ability to perturb 

the input depends on the PB-HAP being modeled, as indicated. In addition, some inputs were 

assigned a baseline value of zero for estimating a screening threshold (e.g., the empirical 

correction factor for protected vegetables and the exposure variables related to the water 

pathway). These variables are not included in the systematic sensitivity analysis. In all, 

approximately 240 variables were examined.

Two types of inputs included in the systematic analysis, pH and the moisture adjustment factor 

(MAF), received special treatment. 
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pH, which is a user input for the surface soil, root zone soil, surface water, and sediment 

compartments, is measured on a logarithmic scale, and this input is (in some cases) used 

in the exponent of TRIM.FaTE algorithms. To obtain sensitivity metrics for pH inputs that 

can be compared to results for other inputs with more linear relationships with risk, the 

values for pH were changed to the equivalent hydrogen ion concentration (that is, 10pH) 

and that value was varied by 5 or 50%. Then, the log base ten of the varied hydrogen ion 

concentration was calculated to serve as the pH to use in TRIM.FaTE for the sensitivity 

case. 

The moisture adjustment factor indicates the percent of a produce item that is water; this 

factor is used to convert from wet weight to dry weight concentration in MIRC. This 

calculation is made by finding the percent of the produce that is not water (i.e., 100 minus 

MAF) and multiplying by the wet-weight basis concentration. Because the physical 

variable of interest is really the fraction of produce that is NOT water, this value (100 

minus MAF) was used in the sensitivity analysis as the base value (rather than MAF 

itself). 

In addition, in two cases, known or assumed correlations between inputs were taken into 

account in the systematic sensitivity analysis. 

The fractions of T3 and T4 fish that are consumed must add to one in MIRC; thus, when 

one was changed by a given percentage, the other was also changed to ensure the sum 

was still one. 

In parameterizing several sediment and water inputs used in TRIM.FaTE, soil/sediment 

and runoff/surface water balance is assumed. These relationships are not explicitly 

accounted for by TRIM.FaTE but rather were calculated off-line prior to setting the input 

value in the model. TRIM.FaTE variables subject to soil or sediment balances include 

erosion rate, precipitation rate, runoff rate, sediment deposition velocity, suspended 

sediment concentration, sediment porosity, water retention time in the pond, and the 

suspended sediment concentration. For example, increasing the sediment deposition 

velocity results in an increase in the sediment resuspension velocity to ensure the 

sediment mass balance is preserved (the specific relationships assumed between inputs 

are described elsewhere). These correlations were respected in the systematic sensitivity 

analysis such that when an independent input was perturbed, any dependent input 

variables were altered correspondingly to preserve the water and sediment balances.

Other TRIM.FaTE and MIRC inputs are also related. Some of the relationships involving 

ingestion rates and body weight were evaluated in the context of the sensitivity analysis, as 

described in the sections that follow. In general, however, the current analysis did not endeavor 

to determine additional correlations or account for them in the sensitivity calculations conducted. 

1.1.1.2 Results

Exhibit 1 through Exhibit 5 display the variables with the highest elasticities for each PB-HAP. 

The inputs were sorted according to the elasticities estimated for a decrease in input by 5%. 

This case (as opposed to the case where variables were increased by 5%) was chosen 

because all inputs, including those for which the base case value was set at the maximum, 

could be part of the sorting procedure. Parameters were sorted by absolute value of elasticity, 

and the bars in the charts are color coded to distinguish whether the input is used in 

TRIM.FaTE, MIRC farm food chain calculations, or MIRC ingestion exposure calculations. The 
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top 25 inputs are shown for all PB-HAPs; for some chemicals, additional inputs are included if 

more than one input corresponded to the 25th highest elasticity. 

In general, mixing height, emission rate, and horizontal wind speed always appear near the top 

of the ranking for all PB-HAPs analyzed. The emission rate elasticity is always 1.00, indicating 

that a 5% reduction in the emission rate gives a 5% reduction in the risk or hazard quotient. 

The mixing height and horizontal wind speed elasticities are always negative and are either 

above or below 1.00. When the mixing height is decreased, the PB-HAP emissions spread over 

a smaller volume and more of the PB-HAP mass remains near the surface for deposition. 

When the horizontal velocity decreases, the PB-HAP remains within the model domain for a 

longer time and the concentrations in the model compartments increase accordingly. 

A fourth input, sediment deposition/resuspension rate, has an elasticity greater than one in 

magnitude for methyl mercury. This sensitivity case represents a decrease in the user-specified 

input for sediment deposition velocity by 5% and a concurrent 7% decrease in the resuspension 

velocity (calculated to maintain the sediment balance2). This value is particularly important for 

methyl mercury HQ because methylation of mercury occurs to a large degree in the sediment, 

and a decrease in deposition velocity results in a larger, proportionate decrease in the 

resuspension velocity, indicating a longer residence time in the sediment and a higher degree of 

methylation. 

Only the above four inputs (mixing height, emission rate, horizontal wind speed, and sediment 

deposition) have absolute elasticities greater than or equal to one. Beyond these, the inputs 

with the highest elasticities are those that influence the primary exposure pathways for each PBHAP. Primary ingestion exposure pathways (in this case defined as the food categories that 

account for 75% to 85% of the total risk or hazard) are indicated at the top of each of the charts. 

For benzo[a]pyrene (Exhibit 1), the primary pathways are ingestion of fruits, vegetables, 

and dairy products, and most of the inputs with the highest elasticities are used in farm

food chain and ingestion exposure calculations. Because wet deposition is of particular 

importance, the exposed fruits and vegetables represent the more sensitive pathway 

compared with the protected fruits and vegetables. In addition, the analysis reveals that 

within the food chain diet of the dairy cows, risk is most sensitive to the inputs used to 

estimate chemical transfer via the cow’s forage pathway (as opposed to the silage or grain 

pathways). 

Sensitivity results obtained for 2,3,7,8-TCDD (Exhibit 2) are consistent with the 

observation that the primary pathways are fish, beef, and dairy ingestion. Results also 

reinforce the conclusion that cancer risk is more sensitive to the forage pathway for dairy 

and beef than the grain or silage pathways. 

Sensitivity results for cadmium (Exhibit 3) reflect the primary pathways of fruits, 

vegetables, and fish, but in this case the hazard quotient is more sensitive to the protected 

fruits and vegetables (and thus also the soil variables) than the exposed fruits and 

vegetables. The HQ is also more sensitive to variables affecting the T3 fish than the T4 

 

2

The sediment balance maintains a zero net flux of sediment mass into the sediment at all times (i.e., a steady state) 

by balancing the deposition, resuspension, and burial fluxes. The burial flux is calculated by subtracting the amount 

of sediment flushed from the pond from the amount introduced into the pond via erosion at every time step. The 

deposition rate is specified. Then, the resuspension rate is calculated by adding the burial and deposition fluxes to 

ensure no net flux into or out of the sediment.
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fish, consistent with higher cadmium concentrations estimated by TRIM.FaTE for that fish 

type. 

Elasticities for divalent mercury (Exhibit 4) reflect the primary pathways of soil, fruits, and 

vegetables through the importance of the rain and erosion variables and the protected fruit 

and vegetable variables. Finally, results for methyl mercury (Exhibit 5) are consistent with 

fish consumption as the primary pathway, with inputs specific to T4 fish more important 

than those specific to T3 fish due to the higher bioaccumulation potential (and therefore 

exposures) associated with higher trophic level fish. 

Looking across the different PB-HAPs, several of the TRIM.FaTE variables with high elasticities 

are highly dependent on assumptions made in configuring the TRIM.FaTE modeling scenario, 

including mixing height, horizontal wind speed, rain rate, dry deposition velocity, surface water 

retention time (correlated directly with pond depth and other inputs given the water balance that 

is assumed), and surface water temperature. These inputs, which would likely be set differently 

for a site-specific analysis, represent influential parameters worthy of additional evaluation to 

quantify the conservative nature of the scenario. The area of the parcels and the temporal 

pattern of the precipitation may also be influential; however, these were not evaluated 

quantitatively in the systematic sensitivity analysis given the complexity involved with adjusting 

these inputs.

Differences between the results of the local and range sensitivity analyses are presented in 

Attachment 3, Exhibits 2 through 6. These tables show elasticities and elasticity rankings by 

PB-HAP for each input that appears in the top 25 elasticities for all four analyses (i.e., 

perturbation by -50%, -5%, 5%, and 50% of the base case value). The rankings for some inputs 

are somewhat different across the four cases, but no drastic differences are noted when 

comparing the range and local sensitivity analyses for any of the PB-HAPs. This suggests the 

local sensitivity analysis may be sufficient for drawing some conclusions about the relative 

influence of user-specified inputs on the risk and hazard quotient estimates.

The results of this sensitivity analysis indicate the 25-30 variables to which the risk and hazard 

quotient estimates are most sensitive. In proceeding with a probabilistic analysis which would 

quantify the uncertainty in the model estimates, these variables would be of primary importance 

and should be the focus when developing input variable distributions. Further research could 

also confirm that these variables are set in an appropriately conservative fashion for the 

purposes of developing a screening scenario.

1.1.2 Evaluation of Ingestion Rate Assumptions

To calculate the screening threshold levels, the conservative assumption was made that a 

person ingests food at the 90th percentile level for each food type included in the analysis. 

These ingestion rates can then be added to get the assumed total ingestion rate.3 USEPA 2008

and 2005d provide estimates of the 90th percentile total ingestion rate for different age groups 

(that is, percentiles for total amount of food ingested, rather than a sum of individual percentiles) 

based on CSFII data 1994-96 and 1998 (see Exhibit 6-12 of Attachment 2). When comparing 

the two, the total ingestion rates used to determine the screening thresholds tend to be a factor 

of two to three times higher than the 90th percentile total ingestion rates (see Exhibit 7-1 of 

Attachment 2). An alternative approach that still captures the high-end would be to use the 90th

 

3

This calculated total ingestion rate will not include any other food consumed by the homestead family 

outside of the MIRC food categories. In making the comparison with the total ingestion rates, then, the 

assumption is made that the entire diet of the family is captured by the MIRC food categories.
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percentile ingestion rates for food types associated with highest risks for a given PB-HAP (to 

account for the individual who may happen to eat higher-than-typical amounts for each of those 

food categories) while setting ingestion rates for other food types to a lower percentile in order 

to bring the total ingestion rate to a more realistic level (e.g., to something closer to the 90th

percentile total ingestion rate).

In order to determine the effect of lowering the ingestion rates for the non-primary ingestion 

pathways (and provide information on whether this refinement would be worth the additional 

complexity in the model set-up), ingestion rates were kept at the 90th percentile for the primary 

(risk-driving) food types for each PB-HAP but were reduced to median ingestion rates for the 

same population (subsistence famers) for the other types of food. This scenario, referred to 

here as the “alternate ingestion case,” was evaluated for each PB-HAP. Specifically, the total 

food ingestion rates was calculated for the alternate ingestion cases for each PB-HAP, and 

these values were compared with the USEPA 2008 and 2005d estimates of 90th percentile total 
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Exhibit 1. The 26 Variables with the Highest Elasticities for Benzo[a]Pyrene Lifetime Risk (-5% Perturbation of Variable)

Primary Pathways: Fruits, Vegetables, and Dairy

Mxng Ht, -1.05

Emission Rate, 1.00

Horiz Wind Spd, -0.97

Annual Rain, 0.63

Avg Wet Dep Particle, 0.45

Frac Wet Dep, 0.45

Mammal Metab Fctr, 0.41

Yield (Frge), -0.36

Intercept Frac (Frge), 0.34

Biotrans Fctr (Dairy), 0.34

Exp Fctr (Dairy), 0.34

Frac Contam (Dairy), 0.34

Avg Dry Dep Particle, 0.30

Qty Frge Eaten (Dairy), 0.29

Frac Frge Contm (Dairy), 0.29

Plnt Srf Loss (Frge), -0.25

Yield (Exp Frt), -0.23

Exp Fctr (Exp Frt), 0.22

Frac Contam (Exp Frt), 0.22

Intercept Frac (Exp Frt), 0.21

Plnt Srf Loss (Exp Frt), -0.19

Yield (Exp Veg), -0.17

100-Mst Adj Fctr (Exp Veg), 0.17

Exp Fctr (Exp Veg), 0.17

Frac Contam (Exp Veg), 0.17

100-Mst Adj Fctr (Exp Frt), 0.22

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2

Farm Food Chain

TRIM

Ingestion/Body Weight
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Exhibit 2. The 25 Variables with the Highest Elasticities for 2,3,7,8-TCDD Lifetime Risk (-5% Perturbation of Variable)

Primary Pathways: Fish, Beef, and Dairy

Mxng Ht, -1.05

Emission Rate, 1.00

Horiz Wind Spd, -0.96

Frac Contam (Fish), 0.56

Exp Fctr (Fish), 0.56

Body Weight, -0.45

Chem Conc T4 Fish, 0.43

Ing Rate (Fish), 0.43

Mammal Metab Fctr, 0.43

Water Temp, 0.39

Water Org Carb Frac, -0.38

Sed Dep and Resusp, 0.36

Biotrans Fctr (Dairy), 0.32

Exp Fctr (Dairy), 0.32

Frac Contam (Dairy), 0.32

Frac Fish T3, -0.30

Frac Fish T4, 0.30

Yield (Frge), -0.30

Intercept Frac (Frge), 0.28

Avg Dry Dep Particle, 0.28

Suspended Sed Conc, -0.25

Qty Frge Eaten (Dairy), 0.23

Frac Frge Contm (Dairy), 0.23

Plnt Srf Loss (Frge), -0.21

Ing Rate (Dairy), 0.20

Annual Rain, 0.20

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2

Farm Food Chain

TRIM

Ingestion/Body Weight
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Exhibit 3. The 28 Variables with the Highest Elasticities for Cadmium Hazard Quotient for Child 1-2 (-5% Perturbation of 

Variable)

Primary Pathways: Fruits, Vegetables, and Fish

Mxng Ht, -1.03

Horiz Wind Spd, -1.03

Emission Rate, 1.00

Annual Rain, 0.75

Soil Conc RootZn Produce, 0.63

Bioconc Fctr (Pro Frt), 0.39

Ing Rate (Pro Frt), 0.39

Exp Fctr (Pro Fruit), 0.39

Frac Contam (Pro Frt), 0.39

100-Mst Adj Fctr (Pro Frt), 0.39

Body Weight, -0.28

Erosion Rate, -0.27

Water Temp, 0.27

Ing Rate (Fish), 0.19

Exp Fctr (Fish), 0.19

Frac Contam (Fish), 0.19

Surf Soil Frac Air, 0.17

Loss 1 (Pro Frt), -0.16

Chem Conc T3 Fish, 0.15

Water Retention Time, 0.15

Sed Dep and Resusp, 0.13

100-Mst Adj Fctr (Exp Veg), 0.11

Ing Rate (Exp Veg), 0.11

Exp Fctr (Exp Veg), 0.11

Frac Contam (Exp Veg), 0.11

Frac Fish T3, 0.11

Frac Fish T4, -0.11

Surf Soil Org Carb Frac, 0.10

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2

Farm Food Chain

TRIM

Ingestion/Body Weight
Attachment 3 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

3-11

Exhibit 4. The 25 Variables with the Highest Elasticities for Divalent Mercury Hazard Quotient for Child 1-2 (-5% 

Perturbation of Variable)

Primary Pathways: Soil, Fruits and Vegetables

Emission Rate, 1.00

Mxng Ht, -0.97

Horiz Wind Spd, -0.97

Erosion Rate, -0.77

Annual Rain, 0.74

Body Weight, -0.61

Soil Conc Human Ing, 0.56

Frac Contam (Soil), 0.56

Ing Rate (Soil), 0.56

Soil Conc RootZn Produce, 0.33

Bioconc Fctr (Pro Frt), 0.15

Ing Rate (Pro Frt), 0.15

100-Mst Adj Fctr (Pro Frt), 0.15

Exp Fctr (Pro Fruit), 0.15

Frac Contam (Pro Frt), 0.15

Surf Soil Frac Air, 0.13

Bioconc Fctr (Root Veg), 0.11

Ing Rate (Root Veg), 0.11

100-Mst Adj Fctr (Root Veg), 

0.11

Exp Fctr (Root Veg), 0.11

Frac Contam (Root Veg), 0.11

Correct Fctr (Root Veg), 0.11

Soil Conc Animal Ing, 0.08

Soil Bioav Fctr (Livestock), 0.08

Exp Fctr (Soil), 0.56

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2

Farm Food Chain

TRIM

Ingestion/Body Weight
Attachment 3 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

3-12

Exhibit 5. The 26 Variables with the Highest Elasticities for Methyl Mercury Hazard Quotient for Child 1-2 (-5% Perturbation 

of Variable)

Primary Pathway: Fish

Body Weight, -1.00

Emission Rate, 1.00

Ing Rate (Fish), 0.90

Frac Contam (Fish), 0.90

Exp Fctr (Fish), 0.90

Mxng Ht, -0.70

Horiz Wind Spd, -0.70

Annual Rain, 0.64

Chem Conc T4 Fish, 0.55

Erosion Rate, -0.45

Sed Porosity, -0.40

Chem Conc T3 Fish, 0.36

Water Temp, -0.22

Frac Fish T3, -0.19

Frac Fish T4, 0.19

Suspended Sed Conc, -0.18

Surf Soil Frac Air, 0.14

Water Retention Time, 0.14

Fish Mass All Species, 0.07

Total Runoff, 0.07

Soil Conc Human Ing, 0.05

Ing Rate (Soil), 0.05

Exp Fctr (Soil), 0.05

Frac Contam (Soil), 0.05

Soil Conc RootZn Prod, 0.04

Sed Dep and

 Resusp, 1.56

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2

Farm Food Chain

TRIM

Ingestion/Body Weight
Attachment 3 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

3-13

ingestion (referred to as the “EPA total ingestion rate”) via a ratio (Exhibit 6). Because the EPA 

total ingestion rate represents the total amount of cooked food consumed, preparation losses 

were applied to each of the food category-specific ingestion rates input into MIRC before the 

ratio was calculated. Soil consumption was not included in these calculations. In addition, the 

estimated screening level cancer risk or HQ for each PB-HAP was compared to the recalculated 

risk/HQ for the alternate ingestion cases (Exhibit 7).

As expected, the ratios presented in Exhibit 6 demonstrate that using the alternate ingestion 

rate combination tends to result in lower estimated total ingestion rates, approaching the (more 

realistic) USEPA total ingestion rates for the child age groups. The degree to which the total 

ingestion rate is decreased depends on the number of pathways labeled “primary pathways” for 

risk (as well as the mass of food represented by a given pathway). Thus, it is reasonable that 

lower total ingestion rates (and lower ratios with respect to EPA total ingestion rate) are 

calculated for methyl mercury, for which only one pathway (fish) drives risk, and higher rates 

and ratios are calculated for benzo[a]pyrene, for which several food types are important. 

Exhibit 7 compares the lifetime risk or child age 1-2 HQ for the alternate ingestion rate case to 

the corresponding risk/HQ for the screening threshold case. Overall, the changes in risk and 

HQ were relatively modest, ranging between 7% and 20%. This result indicates that relaxing 

the conservative assumption of high ingestion rates for all the pathways does not significantly 

change the risk and hazard calculations but does drive the total ingestion rates closer to the 

USEPA 90th percentile total ingestion rates. However, implementing this multi-percentile 

technique requires prior knowledge of the dominant pathways for each chemical, which is 

determined primarily from the model estimates themselves. Assuming 90th percentile ingestion 

rates for all food categories has the advantage of simplicity (using the same set of ingestion

rates for all chemicals) and does not dramatically impact the risk and hazard quotient estimates.

Exhibit 6. Ratio of the Modeled Total Ingestion Rates and the USEPA Total 

Ingestion Rates

Ratio of Ingestion Rates

Child

(1-2)

Child

(3-5)

Child

(6-11)

Child

(12-19)

Adult

(20-70)

Benzo[a]Pyrene Alternate Ingestion 1.7 1.3 1.4 1.4 2.6

2,3,7,8 - TCDD Alternate Ingestion 1.3 1.0 1.0 1.0 1.8

Cadmium Alternate Ingestion 1.3 1.1 1.2 1.0 1.6

Divalent Mercury Alternate Ingestion 1.3 1.1 1.1 1.0 1.6

Methyl Mercury Alternate Ingestion 0.9 0.8 0.8 0.6 0.8

Screening Threshold Ingestion 1.8 1.4 1.6 1.5 2.8
Attachment 3 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

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Exhibit 7. Comparison of the Risks and Hazard Quotients in the Screening 

Threshold and Alternate Ingestion Cases

PB-HAP

Categories with 

90th Percentile 

Ingestion in 

Alternate Case a

Categories with 

Median Ingestion 

Rates in Alternate 

Case a

Case Value

Benzo[a]pyrene

Pro. fruit, pro. 

veg., exp. fruit, 

exp. veg., root 

veg., and dairy

Soil, fish, beef, pork 

poultry, and eggs

Screening threshold

lifetime risk 1.00E-06

Alternate ingestion case 7.98E-07

Percent Change in Risk -20.2%

2,3,7,8 - TCDD Fish and dairy

Soil, pro. fruit, pro. 

veg., exp. fruit, exp. 

veg., root veg., 

beef, pork, poultry,

and eggs

Screening threshold

lifetime risk 1.00E-06

Alternate ingestion case 9.14E-07

Percent Change in Risk -8.6%

Cadmium

Fish, pro. fruit, 

pro. veg., exp. 

fruit, exp. veg., 

and root veg.

Soil, beef, dairy, 

pork, poultry, and 

eggs

Screening threshold child 

(1-2) hazard quotient 1.00E+00

Alternate ingestion case 9.05E-01

Percent Change in Risk -9.5%

Divalent 

Mercury

Soil, pro. fruit, 

pro. veg., exp. 

fruit, exp. veg., 

and root veg.

Fish, beef, dairy, 

pork, poultry, and 

eggs

Screening threshold child 

(1-2) hazard quotient 1.00E+00

Alternate ingestion case 9.26E-01

Percent Change in Risk -7.4%

Methyl Mercury Fish

Soil, pro. fruit, pro. 

veg., exp. fruit, exp. 

veg., root veg., 

beef, dairy, pork, 

poultry, and eggs

Screening threshold child 

(1-2) hazard quotient 5.62E-01

Alternate ingestion case 5.19E-01

Percent Change in Risk -7.8%

a Pro. fruit is protected fruits, pro. veg. is protected vegetables, exp. fruit is exposed fruits, exp. veg. is 

exposed vegetables, and root veg. is root vegetables.

1.1.3 Evaluation of Body Weight Assumptions

As stated in Attachment 2, Section 6.1.3, the screening threshold rates were calculated using 

mean body weight following EPA’s default screening recommendation. This assumption does 

not represent the most conservative assumption, however, since lower percentile body weights 

will give higher risk and hazard quotient estimates. In contrast, the ingestion rates were set to 

the more conservative 90th percentile level. To investigate the sensitivity of using alternate body 

weight percentiles, an alternate MIRC run was performed using the 5th, 50th, 90th, and 95th

percentiles for body weight while keeping all other variable values as specified in the screening 

threshold calculations. These alternate risk or hazard quotient model estimates and the percent 

change relative to the screening threshold calculations are shown in Exhibit 8. Changing from 

the mean to the median body weights produces only very modest changes to the risks/hazard 

quotients. Changing to the 5th percentile body weights gives the most conservative model 

estimates, with percent changes of up to 22% for the PB-HAPs used to set the screening 

threshold rates and a 26% change in methyl mercury. Using upper percentiles for body weights 

produces modest decreases in risk or hazard quotient of 3% to 16% for the PB-HAPs used to 

set the screening threshold rates. Thus, changing the body weight to either a more 
Attachment 3 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

3-15

conservative or less conservative percentile produces changes in the risk or hazard quotient 

which are appreciable but not dramatic (i.e., typically less than 25%), suggesting the mean body 

weight assumption may be an appropriate choice for the screening scenario.

Exhibit 8. The Risk or Hazard Quotient Estimates Using Alternate Body Weight Percentiles

Screening 

Threshold

5th Percentile Median 90th Percentile 95th Percentile

Risk or 

HQ a

% 

Ch b

Risk or 

HQ a

% 

Ch b

Risk or 

HQ a

% 

Ch b

Risk or 

HQ a

% 

Ch b

Benzo[a]pyrene c1.0E-06 1.1E-06 6% 1.0E-06 0% 9.7E-07 -3% 9.6E-07 -4%

2,3,7,8-TCDD c1.0E-06 1.2E-06 22% 1.0E-06 2% 8.8E-07 -12% 8.4E-07 -16%

Cadmium d1.00 1.07 7% 1.00 0% 0.96 -4% 0.95 -5%

Divalent Mercury d 1.00 1.16 16% 1.00 0% 0.91 -9% 0.89 -11%

Methyl Mercury d0.56 0.71 26% 0.57 1% 0.48 -15% 0.46 -18%

a HQ is the hazard quotient for a child aged 1-2.

b % Ch Is the percent change in the risk or hazard quotient relative to the screening threshold calculations.

c Percent change in lifetime risk.

d Percent change in the hazard quotient for a child age 1 to 2.

1.1.4 Sensitivity When Accounting for Temporally Correlated Body Weight and 

Ingestion Rates

In calculating lifetime cancer risks, age-specific ingestion rates and body weights are used to 

estimate the lifetime average daily dose. In the systematic sensitivity analysis, the body weights 

and ingestion rates for each of the five age categories were changed independently to assess 

the sensitivity of lifetime risk to each input separately (for example, body weight was first 

perturbed only for a child 1-2 yrs old, leaving body weight unchanged for other age groups). 

However, in actuality, ingestion rates (and body weights) for an individual during different age 

groups will likely be correlated across that individual’s lifetime. For example, a person who eats 

higher-than-average amounts of poultry when they are 11 can be reasonably expected to eat 

higher-than-average amounts when they are 50 due to lifetime dietary preferences. To estimate 

the effect this correlation has on sensitivity of cancer risk to ingestion rate, a perturbation of -5% 

for a given food type was applied to ingestion rate for all age categories and the resulting 

elasticity was compared to the previous result (obtained ignoring temporal/age-group 

correlations). This assumption was applied separately to each ingestion rate category, and 

separately to body weight, to estimate the effect of age group correlations for these inputs 

(Exhibit 9). Because only benzo[a]pyrene and 2,3,7,8-TCDD were evaluated for cancer risks, 

this analysis was only performed for these two PB-HAPs (the other PB-HAPs use HQs 

calculated separately for each age group, and the correlation analysis is not applicable).

The systematic sensitivity analysis suggested that cancer risks associated with benzo[a]pyrene 

are relatively insensitive to body weight and any individual food ingestion rate (where both 

inputs were varied for a single age group at a time). When body weight or ingestion rate for a 

given food type is decreased by 5% for all age groups in concert, the corresponding elasticity 

increases by a factor of two to three. This increase is significant enough to cause the dairy 

ingestion, exposed fruit ingestion, body weight, and exposed vegetable ingestion variables to be 

ranked among the most influential variables on the basis of absolute elasticity.

For 2,3,7,8-TCDD, the systematic sensitivity analysis illustrated that cancer risk is sensitive to 

body weight, fish ingestion rate, and dairy ingestion rate. Elasticities for these inputs increase

by a factor of 1.3 to 1.6 when the inputs are kept constant across age groups. This difference 

results in rise in the systematic sensitivity ranking of these variables. 
Attachment 3 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

3-16

Exhibit 9. Comparison in the Elasticities In Lifetime Risk in the Correlated and 

Uncorrelated Analyses Assuming a 5% Decrease in the Input Variables

Elasticity in the 

Correlated 

Analysis

Maximum 

Elasticity in 

Uncorrelated 

Analysis

Age Group of 

Maximum 

Elasticity in 

Uncorrelated 

Analysis

Benzo[a]pyrene

Dairy Ingestion 0.34 0.11 Child 1-2

Exp. Fruit Ingestion 0.22 0.10 Adult

Body Weight -0.17 -0.06 Child 1-2

Exp. Veg. Ingestion 0.17 0.09 Adult

Soil Ingestion 0.12 0.05 Child 1-2

Beef Ingestion 0.07 0.03 Adult

Fish Ingestion 0.04 0.02 Adult

Poultry Ingestion 0.02 0.01 Adult

Pro. Fruit Ingestion 0.01 < 0.01 Adult

Egg Ingestion 0.01 < 0.01 Adult

Pork Ingestion < 0.01 < 0.01 Adult

Prot. Veg. Ingestion < 0.01 < 0.01 Adult

Root Veg. Ingestion < 0.01 < 0.01 Adult

2,3,7,8 - TCDD

Body Weight -0.59 -0.45 Adult

Fish Ingestion 0.56 0.43 Adult

Dairy Ingestion 0.32 0.20 Child 1-2

Beef Ingestion 0.09 0.06 Adult

Pork Ingestion 0.01 0.01 Adult

Soil Ingestion < 0.01 < 0.01 Adult

Exp. Fruit Ingestion < 0.01 < 0.01 Adult

Exp. Veg. Ingestion < 0.01 < 0.01 Adult

Root Veg. Ingestion < 0.01 < 0.01 Adult

Poultry Ingestion < 0.01 < 0.01 Adult

Egg Ingestion < 0.01 < 0.01 Adult

Pro. Fruit Ingestion < 0.01 < 0.01 Adult

Prot. Veg. Ingestion < 0.01 < 0.01 Adult

Overall, however, these differences represent a modest percent change in the lifetime risk 

values and indicate that accounting for the assumed correlation between the body weights and 

ingestion rates throughout an individual's lifetime does not significantly influence the model's 

predicted risks. For the other ingestion rates, the lifetime risks are not very sensitive to the 

parameters when the inputs are decreased by 5%, and accounting for the correlations between 

the variables does not affect this conclusion.

1.1.5 Comparison of Scenarios Using Site-Specific and Screening Threshold

Meteorological Data

For RTR, the screening threshold emission levels were calculated using a conservative and 

hypothetical exposure scenario for a farm homestead. If the modeling scenario has been 

configured as intended, the substitution of site-specific data will nearly always result in a lower 

(or equal) exposure and risk estimate. To inform the degree of conservatism associated with 
Attachment 3 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

3-17

the baseline meteorological inputs, three additional analyses were conducted in which observed 

(site-specific) meteorology data were used in place of the meteorological values set for the 

screening threshold scenario. The three locations were selected to take advantage of readilyavailable TRIM.FaTE meteorological data. Exhibit 10 compares some of the summary statistics 

for the three meteorological data sets with those used for the screening threshold scenario.4 

Exhibit 10. Summary of Site-specific Meteorological Data Parameters

Parameter (units a)

Screening 

Scenario b Site 1 c

Site 2 cSite 3 c

Average air temperature (K) 298 291.9 284.7 283.5

Average horizontal wind speed (m/s) 2.8 4.4 4 3

Annual precipitation (m/yr) 1.5 1.1 1.1 1.1

Average urban mixing height (m) 710 1,087 1,225 861

a

K = Kelvin, m/s = meters/second, m/yr = meters per year, m = meters

b

Values listed for the baseline scenario indicate the fixed value used in the baseline screening scenario.

c

Site-specific values are arithmetic averages of single or multiyear data sets; Site 1 = 1989–1993; Site 2 = 1994; 

Site 3 = 1990–1995.

The results of using these three site-specific meteorological data sets on the risk or hazard 

quotient for each PB-HAP are summarized in Exhibit 11. The results indicate that the wind 

direction – and specifically how often the wind blows from the source toward the hypothetical 

watershed, or toward due east – is an important influence on the estimated media 

concentrations and ingestion exposures. For the three data sets used in this analysis, the 

largest decrease in risk or hazard quotient was observed for Site 1. This finding is consistent 

with the underlying patterns in wind direction and their relationship to the locations where 

exposure is assumed to occur. Exhibit 12 shows that wind direction towards due east occurs 

less than two percent of the time for this location, reinforcing the conclusion that a limited wind 

flow directly from the source to the watershed will decrease contamination. These results 

suggest that using meteorological data more representative of a specific site will decrease the 

estimated risk or hazard quotient by as much as an order of magnitude for the hypothetical 

receptor represented. 

Exhibit 11. Percent Change in Risk or Hazard Quotient Using Site-specific Meteorological 

Data

Screening 

Threshold

Site 1 Site 2 Site 3

Risk or HQa % Chb Risk or 

HQa % Chb Risk or HQa % Chb

Benzo[a]pyrene c1.00E-06 1.05E-07 -89% 2.43E-07 -76% 2.19E-07 -78%

2,3,7,8-TCDD c1.00E-06 8.39E-08 -92% 3.76E-07 -62% 2.56E-07 -74%

Cadmium d1.00 0.07 -93% 0.14 -86% 0.15 -85%

Divalent Mercury d 1.00 0.08 -92% 0.19 -81% 0.18 -82%

Methyl Mercury d0.56 0.02 -97% 0.06 -90% 0.05 -91%

a HQ is the hazard quotient for a child aged 1-2.

b % Ch Is the percent change in the risk or hazard quotient relative to the screening threshold calculations.

c Percent change in lifetime risk.

d Percent change in the hazard quotient for a child age 1 to 2.

 

4

Exhibit does not include a measure of the frequency and average of wind directions, which can be 

illustrated with a wind rose but cannot be effectively characterized with a single value.
Attachment 3 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

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Exhibit 12. The Wind Speed and the Direction Toward Which the Wind is Blowing for All 

Conditions for Site 1

Wind Speed and Direction Blowing From- All Conditions- Site 1

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%

0 - 22.5 22.5 -

45

45 -

67.5

67.5 -

90

90 -

112.5

112.5 -

135

135 -

157.5

157.5 -

180

180 -

202.5

202.5 -

225

225 -

247.5

247.5 -

270

270 -

292.5

292.5 -

315

315 -

337.5

337.5 -

360

Direction (degrees) Frequency

21+

17-21

11-16

7-10

4-6

1-3

0-1
Attachment 3 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

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Exhibit 13. Variables Included in the Systematic Sensitivity Analysis

Variable Name Variable Description

TRIM.FaTE Variables

AirTemp Ambient Air Temperature

AlgaeCarbonCont Carbon Content of Algae in Pond

AlgaeDensity Density of Algae in Pond

AlgaeGrowthRate Growth Rate of Algae in Pond

AlgaeRadius Radius of Single Algae in Pond

Biomass Biomass in Pond for all Aquatic Species

ChlorideConc Water- Chloride Concentration

ChlorophyllConc Water- Chlorophyll Concentration

EmissionRate Emission Rate of all PBHAPs

EroRate Erosion Rate for all Parcels

FishMass Fish Body Weight for all Aquatic Species

HorizWindSpeed Horizontal Wind Speed

MixHeight Mixing Height

Rain Annual Rainfall

RootSoilAir Root Soil- Fraction Air

RootSoilOCC Root Soil- Organic Carbon Fraction

RootSoilpH Root Soil- pH

RootSoilSand Root Soil- Fraction Sand

RootSoilVertVel Root Soil- Average vertical velocity of water (percolation)

RootSoilWat Root Soil- Fraction Water

RunoffRate Total Water Runoff Rate

SedDepVel, 

SedRusVel Sediment Deposition Velocity and Resuspension Velocity

SedOCC Sediment- Organic Carbon Fraction

SedpH Sediment- pH

SedPorosity Sediment Porosity

SedSand Sediment- Fraction Sand

StackHeight Stack Height

SurfSoilAir Surface Soil- Fraction Air

SurfSoilOCC Surface Soil- Organic Carbon Fraction

SurfSoilpH Surface Soil- pH

SurfSoilSand Surface Soil- Fraction Sand

SurfSoilVertVel Surface Soil- Average vertical velocity of water (percolation)

SurfSoilWater Surface Soil- Fraction Water

SurfWatTemp Water Temperature

WatFractSand Water- Fraction Sand

WatOCC Water- Organic Carbon Fraction

WatpH Water- pH

WatRetTime Rentention Time in Pond

WatSuspSed Water- Suspended Sediment Concentration
Attachment 3 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

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Exhibit 13. Variables Included in the Systematic Sensitivity Analysis

Variable Name Variable Description

MIRC Farm Food Chain Variables

100-MAF_ef 100 - Moisture adjustment factor, Exposed Fruit

100-MAF_ev 100 - Moisture adjustment factor, Exposed Vegetable

100-MAF_pf 100 - Moisture adjustment factor, Protected Fruit

100-MAF_pv 100 - Moisture adjustment factor, Protected Vegetable

100-MAF_rv 100 - Moisture adjustment factor, Root Vegetable

Ba_beef PBHAP-specific biotransfer factor, Beef

Ba_dairy PBHAP-specific biotransfer factor, Dairy

Ba_egg PBHAP-specific biotransfer factor, Eggs

Ba_pork PBHAP-specific biotransfer factor, Pork

Ba_poultry PBHAP-specific biotransfer factor, Poultry

Br_AG_produce_ef Plant-soil PBHAP bioconcentration factor, Exposed Fruit

Br_AG_produce_ev Plant-soil PBHAP bioconcentration factor, Exposed Vegetables

Br_AG_produce_fo Plant-soil PBHAP bioconcentration factor, Forage Feed

Br_AG_produce_gr Plant-soil PBHAP bioconcentration factor, Grain Feed

Br_AG_produce_pf Plant-soil PBHAP bioconcentration factor, Protected Fruit

Br_AG_produce_pv Plant-soil PBHAP bioconcentration factor, Protected Vegetable

Br_AG_produce_si Plant-soil PBHAP bioconcentration factor, Silage Feed

Br_AG_rootveg Plant-soil PBHAP bioconcentration factor, Root Vegetables

Bs a,b Soil bioavailability factor for livestock

Bv_AG Air-to-plant biotransfer factor for aboveground produce for vapor-phase 

PBHAP in air

C_FishT3 Concentration of PBHAP in whole fish for T3 fish

C_FishT4 Concentration of PBHAP in whole fish for T4 fish

C_Soil Concentration of PBHAP in soil from contaminated area

Ca Average annual total PBHAP concentration in air

Cs_root_zone_feed Average soil concentration in contaminated area used to grow animal feed

Cs_root_zone_ 

produce

Average PBHAP concentration in soil at root-zone depth in produce-growing 

area 

Cs_S_animal_ 

ingest PBHAP concentration in surface soil in contaminated area where livestock feed

Drdp Average annual dry deposition of particle-phase PBHAP

Drwp Average annual wet deposition of particle-phase PBHAP

Fv b,c Fraction of airborne PBHAP in vapor phase

Fw Fraction of wet deposition that adheres to plant surfaces

Kds PBHAP-specific soil/water partition coefficient

Kp_ef Plant surface loss coefficient, Exposed Fruit

Kp_ev Plant surface loss coefficient, Exposed Vegetable

Kp_fo Plant surface loss coefficient, Forage

Kp_si Plant surface loss coefficient, Silage

L1_beef Loss type 1, Beef

L1_ExpFruit Loss type 1, Exposed Fruit

L1_ExpVeg Loss type 1, Exposed Vegetable

L1_Fish Loss type 1, Fish

L1_pork Loss type 1, Pork
Attachment 3 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

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Exhibit 13. Variables Included in the Systematic Sensitivity Analysis

Variable Name Variable Description

L1_poultry Loss type 1, Poultry

L1_ProFruit Loss type 1, Protected Fruit

L1_ProVeg Loss type 1, Protected Vegetable

L1_RootVeg Loss type 1, Root Vegetable

L2_beef Loss type 2, Beef

L2_ExpFruit Loss type 2, Exposed Fruit

L2_Fish Loss type 2, Fish

L2_pork Loss type 2, Pork

L2_poultry Loss type 2, Poultry

L2_RootVeg Loss type 2, Root Vegetable

MF d,e Mammalian metabolism factor

_a Density of air

Qp_fo_beef Quantity of forage plant type eaten per animal per day, Beef

Qp_fo_dairy Quantity of forage plant type eaten per animal per day, Dairy

Qp_gr_beef Quantity of grain plant type eaten per animal per day, Beef

Qp_gr_dairy Quantity of grain plant type eaten per animal per day, Dairy

Qp_gr_egg Quantity of grain plant type eaten per animal per day, Eggs

Qp_gr_pork Quantity of grain plant type eaten per animal per day, Pork

Qp_gr_poultry Quantity of grain plant type eaten per animal per day, Poultry

Qp_si_beef Quantity of silage plant type eaten per animal per day, Beef

Qp_si_dairy Quantity of silage plant type eaten per animal per day, Dairy

Qp_si_pork Quantity of silage plant type eaten per animal per day, Pork

Qs_beef Quantity of soil eaten by the animal each day, Beef

Qs_dairy Quantity of soil eaten by the animal each day, Dairy

Qs_egg Quantity of soil eaten by the animal each day, Eggs

Qs_pork Quantity of soil eaten by the animal each day, Pork

Qs_poultry Quantity of soil eaten by the animal each day, Poultry

RCF PBHAP-specific root concentration factor for tubers and root produce

Rp_ef Interception fraction of the edible portion of plant, Exposed Fruit

Rp_ev Interception fraction of the edible portion of plant, Exposed Vegetable

Rp_fo Interception fraction of the edible portion of plant, Forage

Rp_si Interception fraction of the edible portion of plant, Silage

Tp_ef Length of plant exposure to deposition per harvest, Exposed Fruit

Tp_ev Length of plant exposure to deposition per harvest, Exposed Vegetable

Tp_fo Length of plant exposure to deposition per harvest, Forage

Tp_si Length of plant exposure to deposition per harvest, Silage

VG_AG_ef f,g Empirical correction factor, Exposed Fruit

VG_AG_ev f,g Empirical correction factor, Exposed Vegetable

VG_AG_fo a,b Empirical correction factor, Forage

VG_AG_si Empirical correction factor, Silage

VG_rootveg f,g Empirical correction factor, Root Vegetable

Yp_ef Yield or standing crop biomass, Exposed Fruit

Yp_ev Yield or standing crop biomass, Exposed Vegetable

Yp_fo Yield or standing crop biomass, Forage

Yp_si Yield or standing crop biomass, Silage
Attachment 3 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

3-22

Exhibit 13. Variables Included in the Systematic Sensitivity Analysis

Variable Name Variable Description

MIRC Ingestion and Body Weight Variables

BW_adlt Body weight, Adult 20-70

BW_ch1 Body weight, Child 1-2

BW_ch2 Body weight, Child 3-5

BW_ch3 Body Weight, Child 6-11

BW_ch4 Body Weight, Child 12-19

EF_beef a,b Exposure factor, Beef

EF_dairy a,b Exposure factor, Dairy

EF_egg a,b Exposure factor, Eggs

EF_ExpFruit a,b Exposure factor, Exposed Fruit

EF_ExpVeg a,b Exposure factor, Exposed Vegetables

EF_Fish a,b Exposure factor, Fish

EF_pork a,b Exposure factor, Pork

EF_poultry a,b Exposure factor, Poultry

EF_ProFruit a,b Exposure factor, Protected Fruit

EF_ProVeg a,b Exposure factor, Protected Vegetables

EF_RootVeg a,b Exposure factor, Root Vegetables

EF_Soil a,b Exposure factor, Soil

EF_water a,b Exposure factor, Water

F_fo_beef a,b Fraction of forage plant type from contaminated area used to grow feed, Beef

F_fo_dairy a,b Fraction of forage plant type from contaminated area used to grow feed, Dairy

F_gr_beef a,b Fraction of grain plant type from contaminated area used to grow feed, Beef

F_gr_dairy a,b Fraction of grain plant type from contaminated area used to grow feed, Dairy

F_gr_egg a,b Fraction of grain plant type from contaminated area used to grow feed, Eggs

F_gr_pork a,b Fraction of grain plant type from contaminated area used to grow feed, Pork

F_gr_poultry a,b Fraction of grain plant type from contaminated area used to grow feed, Poultry

F_si_beef a,b Fraction of silage plant type from contaminated area used to grow feed, Beef

F_si_dairy a,b Fraction of silage plant type from contaminated area used to grow feed, Dairy

F_si_pork a,b Fraction of silage plant type from contaminated area used to grow feed, Pork

F_T3 Fraction of fish intake that is from T3

F_T4 Fraction of fish intake that is from T4

FC_beef a,b Fraction contaminated, Beef

FC_dairy a,b Fraction contaminated, Dairy

FC_egg a,b Fraction contaminated, Eggs

FC_ExpFruit a,b Fraction contaminated, Exposed Fruit

FC_ExpVeg a,b Fraction contaminated, Exposed Vegetable

FC_Fish a,b Fraction contaminated, Fish

FC_pork a,b Fraction contaminated, Pork

FC_poultry a,b Fraction contaminated, Poultry

FC_ProFruit a,b Fraction contaminated, Protected Fruit

FC_ProVeg a,b Fraction contaminated, Protected Vegetable

FC_RootVeg a,b Fraction contaminated, Root Vegetable

FC_Soil a,b Fraction contaminated, Soil

IR_beef_adlt Ingestion rate, Beef, Adult 20-70

IR_beef_ch1 Ingestion rate, Beef, Child 1-2
Attachment 3 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

3-23

Exhibit 13. Variables Included in the Systematic Sensitivity Analysis

Variable Name Variable Description

IR_beef_ch2 Ingestion rate, Beef, Child 3-5

IR_beef_ch3 Ingestion rate, Beef, Child 6-11

IR_beef_ch4 Ingestion rate, Beef, Child 12-19

IR_dairy_adlt Ingestion rate, Dairy, Adult 20-70

IR_dairy_ch1 Ingestion rate, Dairy, Child 1-2

IR_dairy_ch2 Ingestion rate, Dairy, Child 3-5

IR_dairy_ch3 Ingestion rate, Dairy, Child 6-11

IR_dairy_ch4 Ingestion rate, Dairy, Child 12-19

IR_egg_adlt Ingestion rate, Eggs, Adult 20-70

IR_egg_ch1 Ingestion rate, Eggs, Child 1-2

IR_egg_ch2 Ingestion rate, Eggs, Child 3-5

IR_egg_ch3 Ingestion rate, Eggs, Child 6-11

IR_egg_ch4 Ingestion rate, Eggs, Child 12-19

IR_ExpFruit_adlt Ingestion rate, Exposed Fruit, Adult 20-70

IR_ExpFruit_ch1 Ingestion rate, Exposed Fruit, Child 1-2

IR_ExpFruit_ch2 Ingestion rate, Exposed Fruit, Child 3-5

IR_ExpFruit_ch3 Ingestion rate, Exposed Fruit, Child 6-11

IR_ExpFruit_ch4 Ingestion rate, Exposed Fruit, Child 12-19

IR_ExpVeg_adlt Ingestion rate, Exposed Vegetable, Adult 20-70

IR_ExpVeg_ch1 Ingestion rate, Exposed Vegetable, Child 1-2

IR_ExpVeg_ch2 Ingestion rate, Exposed Vegetable, Child 3-5

IR_ExpVeg_ch3 Ingestion rate, Exposed Vegetable, Child 6-11

IR_ExpVeg_ch4 Ingestion rate, Exposed Vegetable, Child 12-19

IR_Fish_adlt Ingestion rate, Fish, Adult 20-70

IR_Fish_ch1 Ingestion rate, Fish, Child 1-2

IR_Fish_ch2 Ingestion rate, Fish, Child 3-5

IR_Fish_ch3 Ingestion rate, Fish, Child 6-11

IR_Fish_ch4 Ingestion rate, Fish, Child 12-19

IR_pork_adlt Ingestion rate, Pork, Adult 20-70

IR_pork_ch1 Ingestion rate, Pork, Child 1-2

IR_pork_ch2 Ingestion rate, Pork, Child 3-5

IR_pork_ch3 Ingestion rate, Pork, Child 6-11

IR_pork_ch4 Ingestion rate, Pork, Child 12-19

IR_poultry_adlt Ingestion rate, Poultry, Adult 20-70

IR_poultry_ch1 Ingestion rate, Poultry, Child 1-2

IR_poultry_ch2 Ingestion rate, Poultry, Child 3-5

IR_poultry_ch3 Ingestion rate, Poultry, Child 6-11

IR_poultry_ch4 Ingestion rate, Poultry, Child 12-19

IR_ProFruit_adlt Ingestion rate, Protected Fruit, Adult 20-70

IR_ProFruit_ch1 Ingestion rate, Protected Fruit, Child 1-2

IR_ProFruit_ch2 Ingestion rate, Protected Fruit, Child 3-5

IR_ProFruit_ch3 Ingestion rate, Protected Fruit, Child 6-11

IR_ProFruit_ch4 Ingestion rate, Protected Fruit, Child 12-19

IR_ProVeg_adlt Ingestion rate, Protected Vegetable, Adult 20-70

IR_ProVeg_ch1 Ingestion rate, Protected Vegetable, Child 1-2
Attachment 3 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

3-24

Exhibit 13. Variables Included in the Systematic Sensitivity Analysis

Variable Name Variable Description

IR_ProVeg_ch2 Ingestion rate, Protected Vegetable, Child 3-5

IR_ProVeg_ch3 Ingestion rate, Protected Vegetable, Child 6-11

IR_ProVeg_ch4 Ingestion rate, Protected Vegetable, Child 12-19

IR_RootVeg_adlt Ingestion rate, Root Vegetable, Adult 20-70

IR_RootVeg_ch1 Ingestion rate, Root Vegetable, Child 1-2

IR_RootVeg_ch2 Ingestion rate, Root Vegetable, Child 3-5

IR_RootVeg_ch3 Ingestion rate, Root Vegetable, Child 6-11

IR_RootVeg_ch4 Ingestion rate, Root Vegetable, Child 12-19

IR_Soil_adlt Ingestion rate, Soil, Adult 20-70

IR_Soil_ch1 Ingestion rate, Soil, Child 1-2

IR_Soil_ch2 Ingestion rate, Soil, Child 3-5

IR_Soil_ch3 Ingestion rate, Soil, Child 6-11

IR_Soil_ch4 Ingestion rate, Soil, Child 12-19

IR_water_adlt Ingestion rate, Water, Adult 20-70

IR_water_ch1 Ingestion rate, Water, Child 1-2

IR_water_ch2 Ingestion rate, Water, Child 3-5

IR_water_ch3 Ingestion rate, Water, Child 6-11

IR_water_ch4 Ingestion rate, Water, Child 12-19

a Values cannot be increased by 5% because the variable has an upper bound.

b Values cannot be increased by 50% because the variable has an upper bound.

c Values can only be increased by 5% for 2,3,7,8 – TCDD and Benzo[a]Pyrene because the variable has an upper bound.

d Values can only be increased by 5% for Benzo[a]Pyrene because the variable has an upper bound.

e Values can only be increased by 50% for Benzo[a]Pyrene because the variable has an upper bound.

f Values can only be increased by 5% for Benzo[a]Pyrene, 2,3,7,8-TCDD, and methyl mercury because the variable has an upper bound.

g Values can only be increased by 5% for Benzo[a]Pyrene, 2,3,7,8-TCDD, and methyl mercury because the variable has an upper bound.
Attachment 3 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

3-25

Exhibit 14. Elasticities and Rankings for the Variables with the Highest Elasticities for Benzo[a]Pyrene

Name of Variable 

Changed Variable Definition Variable Category a

Elasticity of Risk for Input 

Variable Perturbation

Ranking of Elasticity for 

Input Variable 

Perturbation

-50% -5% 5% 50% -50% -5% 5% 50%

MixHeight Mixing Height TRIM -1.97 -1.05 -0.95 -0.66 1 1 2 2

EmissionRate Emission Rate of all Chemicals TRIM 1.00 1.00 1.00 1.00 3 2 1 1

HorizWindSpeed Horizontal Wind Speed TRIM -1.74 -0.97 -0.88 -0.63 2 3 3 3

Rain Annual Rainfall TRIM 0.63 0.63 0.63 0.63 5 4 4 4

Drwp Average annual wet deposition of 

particle-phase chemical FFC 0.45 0.45 0.45 0.45 6 5 5 5

Fw Fraction of wet deposition that 

adheres to plant surfaces FFC 0.45 0.45 0.45 0.45 7 6 6 6

MF Mammalian metabolism factor FFC 0.41 0.41 0.41 0.41 9 7 7 7

Yp_fo Yield or standing crop biomass, 

Forage FFC -0.69 -0.36 -0.33 -0.23 4 8 10 12

Rp_fo Interception fraction of the edible 

portion of plant, Forage FFC 0.34 0.34 0.34 0.34 10 9 8 8

Ba_dairy Chemical-specific biotransfer 

factor, Dairy FFC 0.34 0.34 0.34 0.34 12 10 9 9

EF_dairy Exposure factor, Dairy Ing./BW 0.34 0.34 N/A N/A 13 11

FC_dairy Fraction contaminated, Dairy Ing./BW 0.34 0.34 N/A N/A 14 12

Drdp Average annual dry deposition of 

particle-phase chemical FFC 0.30 0.30 0.30 0.30 16 13 11 10

Qp_fo_dairy Quantity of forage plant type 

eaten per animal per day, Dairy FFC 0.29 0.29 0.29 0.29 18 14 12 11

F_fo_dairy

Fraction of forage plant type 

obtained from contaminated area 

used to grow feed, Dairy

Ing./BW 0.29 0.29 N/A N/A 17 15

Kp_fo Plant surface loss coefficient, 

Forage FFC -0.34 -0.25 -0.24 -0.19 11 16 13 15

Yp_ef Yield or standing crop biomass, 

Exposed Fruit FFC -0.43 -0.23 -0.20 -0.14 8 17 16 17

100-MAF_ef 100 - Moisture adjustment factor, 

Exposed Fruit FFC 0.22 0.22 0.22 0.22 22 18 14 13

EF_ExpFruit Exposure factor, Exposed Fruit Ing./BW 0.22 0.22 N/A N/A 20 19
Attachment 3 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

3-26

Exhibit 14. Elasticities and Rankings for the Variables with the Highest Elasticities for Benzo[a]Pyrene

Name of Variable 

Changed Variable Definition Variable Category a

Elasticity of Risk for Input 

Variable Perturbation

Ranking of Elasticity for 

Input Variable 

Perturbation

-50% -5% 5% 50% -50% -5% 5% 50%

FC_ExpFruit Fraction contaminated, Exposed 

Fruit Ing./BW 0.22 0.22 N/A N/A 21 20

Rp_ef Interception fraction of the edible 

portion of plant, Exposed Fruit FFC 0.21 0.21 0.21 0.21 23 21 15 14

Kp_ef Plant surface loss coefficient, 

Exposed Fruit FFC -0.27 -0.19 -0.17 -0.13 19 22 17 18

Yp_ev Yield or standing crop biomass, 

Exposed Vegetable FFC -0.33 -0.17 -0.16 -0.11 15 23 19 21

100-MAF_ev 100 - Moisture adjustment factor, 

Exposed Vegetable FFC 0.17 0.17 0.17 0.17 26 24 18 16

EF_ExpVeg Exposure factor, Exposed 

Vegetables Ing./BW 0.17 0.17 N/A N/A 27 25

FC_ExpVeg Fraction contaminated, Exposed 

Vegetable Ing./BW 0.17 0.17 N/A N/A 28 26

Rp_ev

Interception fraction of the edible 

portion of plant, Exposed 

Vegetable

FFC 0.16 0.16 N/A N/A 29 27

Kp_ev Plant surface loss coefficient, 

Exposed Vegetable FFC -0.21 -0.14 -0.13 -0.10 24 28 20 25

C_Soil Concentration of chemical in soil 

from contaminated area FFC 0.12 0.12 0.12 0.12 31 29 21 19

EF_Soil Exposure factor, Soil Ing./BW 0.12 0.12 N/A N/A 32 30

FC_Soil Fraction contaminated, Soil Ing./BW 0.12 0.12 N/A N/A 33 31

IR_dairy_ch1 Ingestion rate, Dairy, Child 1-2 Ing./BW 0.11 0.11 0.11 0.11 34 32 22 20

IR_ExpFruit_adlt Ingestion rate, Exposed Fruit, 

Adult 20-70 Ing./BW 0.10 0.10 0.10 0.10 35 33 23 23

Tp_fo Length of plant exposure to 

deposition per harvest, Forage FFC 0.17 0.10 0.09 0.06 25 34 26 31

IR_dairy_adlt Ingestion rate, Dairy, Adult 20-70 Ing./BW 0.10 0.10 0.10 0.10 36 35 24 24

L2_ExpFruit Loss type 2, Exposed Fruit FFC -0.10 -0.10 -0.10 -0.10 37 36 25 26

a

FFC refers to farm food chain variables and Ing./BW refers to ingestion and body weight variables.
Attachment 3 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

3-27

Exhibit 15. Elasticities and Rankings for the Variables with the Highest Elasticities for 2,3,7,8 - TCDD

Name of Variable 

Changed Variable Definition Variable Category a

Elasticity of Risk for Input 

Variable Perturbation

Ranking of Elasticity for 

Input Variable 

Perturbation

-50% -5% 5% 50% -50% -5% 5% 50%

MixHeight Mixing Height TRIM -1.99 -1.05 -0.95 -0.67 1 1 2 2

EmissionRate Emission Rate of all Chemicals TRIM 1.00 1.00 1.00 1.00 3 2 1 1

HorizWindSpeed Horizontal Wind Speed TRIM -1.80 -0.96 -0.87 -0.61 2 3 3 3

FC_Fish Fraction contaminated, Fish Ing./BW 0.56 0.56 N/A N/A 8 4

EF_Fish Exposure factor, Fish Ing./BW 0.56 0.56 N/A N/A 7 5

BW_adlt Body weight, Adult 20-70 Ing./BW -0.86 -0.45 -0.41 -0.29 4 6 6 11

C_FishT4 Concentration of chemical in 

whole fish for T4 fish FFC 0.43 0.43 0.43 0.43 9 7 4 5

IR_Fish_adlt Ingestion rate, Fish, Adult 20-70 Ing./BW 0.43 0.43 0.43 0.43 10 8 5 6

MF Mammalian metabolism factor FFC 0.43 0.43 N/A N/A 11 9

SurfWatTemp Water Temperature TRIM 0.32 0.39 0.41 0.53 16 10 7 4

WatOCC Water- Organic Carbon Fraction TRIM -0.62 -0.38 -0.35 -0.25 5 11 9 14

SedDepVel Sediment Deposition Velocity and 

Resuspension Velocity TRIM 0.28 0.36 0.39 0.30 20 12 8 8

Ba_dairy Chemical-specific biotransfer 

factor, Dairy FFC 0.32 0.32 0.32 0.32 13 13 10 7

EF_dairy Exposure factor, Dairy Ing./BW 0.32 0.32 N/A N/A 14 14

FC_dairy Fraction contaminated, Dairy Ing./BW 0.32 0.32 N/A N/A 15 15

F_T3 Fraction of fish intake that is from 

T3 Ing./BW -0.30 -0.30 -0.30 -0.30 17 16 12 10

F_T4 Fraction of fish intake that is from 

T4 Ing./BW 0.30 0.30 0.30 0.30 18 17 11 9

Yp_fo Yield or standing crop biomass, 

Forage FFC -0.56 -0.30 -0.27 -0.19 6 18 15 19

Rp_fo Interception fraction of the edible

portion of plant, Forage FFC 0.28 0.28 0.28 0.28 19 19 13 12

Drdp Average annual dry deposition of 

particle-phase chemical FFC 0.28 0.28 0.28 0.28 22 20 14 13

WatSuspSed Water- Suspended Sediment 

Concentration TRIM -0.37 -0.25 -0.24 -0.19 12 21 16 18
Attachment 3 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

3-28

Exhibit 15. Elasticities and Rankings for the Variables with the Highest Elasticities for 2,3,7,8 - TCDD

Name of Variable 

Changed Variable Definition Variable Category a

Elasticity of Risk for Input 

Variable Perturbation

Ranking of Elasticity for 

Input Variable 

Perturbation

-50% -5% 5% 50% -50% -5% 5% 50%

Qp_fo_dairy Quantity of forage plant type 

eaten per animal per day, Dairy FFC 0.23 0.23 0.23 0.23 24 22 17 15

F_fo_dairy

Fraction of forage plant type 

obtained from contaminated area 

used to grow feed, Dairy

Ing./BW 0.23 0.23 N/A N/A 23 23

Kp_fo Plant surface loss coefficient, 

Forage FFC -0.28 -0.21 -0.20 -0.16 21 24 19 21

IR_dairy_adlt Ingestion rate, Dairy, Adult 20-70 Ing./BW 0.20 0.20 0.20 0.20 25 25 18 16

Rain Annual Rainfall TRIM 0.20 0.20 0.20 0.19 26 26 20 17

SurfSoilAir Surface Soil- Fraction Air TRIM 0.13 0.14 0.15 0.17 29 27 21 20

C_FishT3 Concentration of chemical in 

whole fish for T3 fish FFC 0.13 0.13 0.13 0.13 28 28 22 22

Cs_S_animal_ing

est

Chemical concentration in 

surface soil in contaminated area 

where livestock feed

FFC 0.12 0.12 0.12 0.12 31 29 23 23

Bs Soil bioavailability factor for 

livestock FFC 0.12 0.12 N/A N/A 30 30

Ba_beef Chemical-specific biotransfer 

factor, Beef FFC 0.09 0.09 0.09 0.09 33 31 24 24

EF_beef Exposure factor, Beef Ing./BW 0.09 0.09 N/A N/A 34 32

FC_beef Fraction contaminated, Beef Ing./BW 0.09 0.09 N/A N/A 35 33

Tp_fo Length of plant exposure to 

deposition per harvest, Forage FFC 0.14 0.08 0.08 0.05 27 34 25 29

Qs_dairy Quantity of soil eaten by the 

animal each day, Dairy FFC 0.07 0.07 0.07 0.07 38 35 26 25

a

FFC refers to farm food chain variables and Ing./BW refers to ingestion and body weight variables.
Attachment 3 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

3-29

Exhibit 16. Elasticities and Rankings for the Variables with the Highest Elasticities for Cadmium

Name of Variable 

Changed Variable Definition Variable Category a

Elasticity of Risk for Input 

Variable Perturbation

Ranking of Elasticity for 

Input Variable 

Perturbation

-50% -5% 5% 50% -50% -5% 5% 50%

MixHeight Mixing Height TRIM -1.92 -1.03 -0.93 -0.66 1 1 2 3

HorizWindSpeed Horizontal Wind Speed TRIM -1.92 -1.03 -0.93 -0.66 2 2 3 4

EmissionRate Emission Rate of all Chemicals TRIM 1.00 1.00 1.00 1.00 3 3 1 1

Rain Annual Rainfall TRIM 0.78 0.75 0.74 0.71 4 4 4 2

Cs_root_zone_pr

oduce

Average chemical concentration 

in soil at root-zone depth in 

produce-growing area

FFC 0.63 0.63 0.63 0.63 5 5 5 5

Br_AG_produce_

pf

Plant-soil chemical 

bioconcentration factor, Protected 

Fruit

FFC 0.39 0.39 0.39 0.39 7 6 6 8

IR_ProFruit_ch1 Ingestion rate, Protected Fruit, 

Child 1-2

Ing./BW 0.39 0.39 0.39 0.39 10 7 7 9

EF_ProFruit Exposure factor, Protected Fruit Ing./BW 0.39 0.39 N/A N/A 8 8

FC_ProFruit Fraction contaminated, Protected 

Fruit Ing./BW 0.39 0.39 N/A N/A 9 9

100-MAF_pf 100 - Moisture adjustment factor, 

Protected Fruit FFC 0.39 0.39 0.39 0.39 11 10 8 7

BW_ch1 Body weight, Child 1-2 Ing./BW -0.54 -0.28 -0.26 -0.18 6 11 11 13

EroRate Erosion Rate for all Parcels TRIM -0.31 -0.27 -0.26 -0.24 12 12 10 10

SurfWatTemp Water Temperature TRIM 0.17 0.27 0.30 0.54 16 13 9 6

IR_Fish_ch1 Ingestion rate, Fish, Child 1-2 Ing./BW 0.19 0.19 0.19 0.19 15 14 12 12

EF_Fish Exposure factor, Fish Ing./BW 0.19 0.19 N/A N/A 13 15

FC_Fish Fraction contaminated, Fish Ing./BW 0.19 0.19 N/A N/A 14 16

SurfSoilAir Surface Soil- Fraction Air TRIM 0.15 0.17 0.17 0.19 19 17 13 11

L1_ProFruit Loss type 1, Protected Fruit FFC -0.16 -0.16 -0.16 -0.16 18 18 14 14

C_FishT3 Concentration of chemical in 

whole fish for T3 fish FFC 0.15 0.15 0.15 0.15 21 19 15 15

WatRetTime Rentention Time in Pond TRIM 0.16 0.15 0.14 0.13 17 20 17 16

SedDepVel Sediment Deposition Velocity and 

Resuspension Velocity TRIM 0.10 0.13 0.14 0.07 30 21 16 32
Attachment 3 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

3-30

Exhibit 16. Elasticities and Rankings for the Variables with the Highest Elasticities for Cadmium

Name of Variable 

Changed Variable Definition Variable Category a

Elasticity of Risk for Input 

Variable Perturbation

Ranking of Elasticity for 

Input Variable 

Perturbation

-50% -5% 5% 50% -50% -5% 5% 50%

100-MAF_ev 100 - Moisture adjustment factor, 

Exposed Vegetable FFC 0.11 0.11 0.11 0.11 23 22 18 18

IR_ExpVeg_ch1 Ingestion rate, Exposed 

Vegetable, Child 1-2

Ing./BW 0.11 0.11 0.11 0.11 26 23 19 19

EF_ExpVeg Exposure factor, Exposed 

Vegetables Ing./BW 0.11 0.11 N/A N/A 24 24

FC_ExpVeg Fraction contaminated, Exposed 

Vegetable Ing./BW 0.11 0.11 N/A N/A 25 25

F_T3 Fraction of fish intake that is from 

T3 Ing./BW 0.11 0.11 0.11 0.11 28 26 20 20

F_T4 Fraction of fish intake that is from 

T4 Ing./BW -0.11 -0.11 -0.11 -0.11 29 27 21 21

SurfSoilOCC Surface Soil- Organic Carbon 

Fraction TRIM 0.15 0.10 0.10 0.08 20 28 22 25

SurfSoilVertVel Surface Soil- Average vertical 

velocity of water (percolation) TRIM -0.11 -0.10 -0.10 -0.09 27 29 23 22

SurfSoilpH Surface Soil- pH TRIM 0.13 0.09 0.09 0.07 22 30 25 31

a

FFC refers to farm food chain variables and Ing./BW refers to ingestion and body weight variables.
Attachment 3 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

3-31

Exhibit 17. Elasticities and Rankings for the Variables with the Highest Elasticities for Divalent Mercury

Name of Variable 

Changed Variable Definition Variable Category a

Elasticity of Risk for Input 

Variable Perturbation

Ranking of Elasticity for 

Input Variable 

Perturbation

-50% -5% 5% 50% -50% -5% 5% 50%

EmissionRate Emission Rate of all Chemicals TRIM 1.00 1.00 1.00 1.00 5 1 1 1

MixHeight Mixing Height TRIM -1.73 -0.97 -0.89 -0.63 1 2 2 3

HorizWindSpeed Horizontal Wind Speed TRIM -1.73 -0.97 -0.89 -0.63 2 3 3 4

EroRate Erosion Rate for all Parcels TRIM -1.27 -0.77 -0.71 -0.52 3 4 5 7

Rain Annual Rainfall TRIM 0.76 0.74 0.74 0.71 6 5 4 2

BW_ch1 Body weight, Child 1-2 Ing./BW -1.17 -0.61 -0.56 -0.39 4 6 8 8

C_Soil Concentration of chemical in soil 

from contaminated area FFC 0.56 0.56 0.56 0.56 7 7 6 5

EF_Soil Exposure factor, Soil Ing./BW 0.56 0.56 N/A N/A 8 8

FC_Soil Fraction contaminated, Soil Ing./BW 0.56 0.56 N/A N/A 9 9

IR_Soil_ch1 Ingestion rate, Soil, Child 1-2 Ing./BW 0.56 0.56 0.56 0.56 10 10 7 6

Cs_root_zone_pr

oduce

Average chemical concentration 

in soil at root-zone depth in 

produce-growing area

FFC 0.33 0.33 0.33 0.33 11 11 9 9

Br_AG_produce_

pf

Plant-soil chemical 

bioconcentration factor, Protected 

Fruit

FFC 0.15 0.15 0.15 0.15 12 12 10 10

IR_ProFruit_ch1 Ingestion rate, Protected Fruit, 

Child 1-2

Ing./BW 0.15 0.15 0.15 0.15 15 13 11 11

100-MAF_pf 100 - Moisture adjustment factor, 

Protected Fruit FFC 0.15 0.15 0.15 0.15 16 14 12 12

EF_ProFruit Exposure factor, Protected Fruit Ing./BW 0.15 0.15 N/A N/A 13 15

FC_ProFruit Fraction contaminated, Protected 

Fruit Ing./BW 0.15 0.15 N/A N/A 14 16

SurfSoilAir Surface Soil- Fraction Air TRIM 0.11 0.13 0.13 0.15 17 17 13 13

Br_AG_rootveg

Plant-soil chemical 

bioconcentration factor, Root 

Vegetables

FFC 0.11 0.11 0.11 0.11 18 18 14 15

IR_RootVeg_ch1 Ingestion rate, Root Vegetable, 

Child 1-2

Ing./BW 0.11 0.11 0.11 0.11 21 19 15 16
Attachment 3 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

3-32

Exhibit 17. Elasticities and Rankings for the Variables with the Highest Elasticities for Divalent Mercury

Name of Variable 

Changed Variable Definition Variable Category a

Elasticity of Risk for Input 

Variable Perturbation

Ranking of Elasticity for 

Input Variable 

Perturbation

-50% -5% 5% 50% -50% -5% 5% 50%

100-MAF_rv 100 - Moisture adjustment factor, 

Root Vegetable FFC 0.11 0.11 0.11 0.11 23 20 16 17

EF_RootVeg Exposure factor, Root Vegetables Ing./BW 0.11 0.11 N/A N/A 19 21

FC_RootVeg Fraction contaminated, Root 

Vegetable Ing./BW 0.11 0.11 N/A N/A 20 22

VG_rootveg Empirical correction factor, Root 

Vegetable FFC 0.11 0.11 N/A N/A 22 23

Cs_S_animal_ing

est

Chemical concentration in 

surface soil in contaminated area 

where livestock feed

FFC 0.08 0.08 0.08 0.08 25 24 17 18

Bs Soil bioavailability factor for 

livestock FFC 0.08 0.08 N/A N/A 24 25

L1_ProFruit Loss type 1, Protected Fruit FFC -0.06 -0.06 -0.06 -0.06 26 26 19 19

SedDepVel Sediment Deposition Velocity and 

Resuspension Velocity TRIM 0.02 0.05 0.06 0.11 48 27 18 14

Ba_egg Chemical-specific biotransfer 

factor, Eggs FFC 0.05 0.05 0.05 0.05 27 28 20 20

IR_egg_ch1 Ingestion rate, Eggs, Child 1-2 Ing./BW 0.05 0.05 0.05 0.05 30 29 21 21

EF_egg Exposure factor, Eggs Ing./BW 0.05 0.05 N/A N/A 28 30

FC_egg Fraction contaminated, Eggs Ing./BW 0.05 0.05 N/A N/A 29 31

Qs_egg

Quantity of soil eaten by the 

animal each day, Eggs FFC 0.05 0.05 0.05 0.05 31 32 22 22

Ba_poultry Chemical-specific biotransfer 

factor, Poultry FFC 0.03 0.03 0.03 0.03 32 33 23 23

IR_poultry_ch1 Ingestion rate, Poultry, Child 1-2 Ing./BW 0.03 0.03 0.03 0.03 35 34 24 24

EF_poultry Exposure factor, Poultry Ing./BW 0.03 0.03 N/A N/A 33 35

FC_poultry Fraction contaminated, Poultry Ing./BW 0.03 0.03 N/A N/A 34 36

Qs_poultry Quantity of soil eaten by the 

animal each day, Poultry FFC 0.03 0.03 0.03 0.03 36 37 25 25

a

FFC refers to farm food chain variables and Ing./BW refers to ingestion and body weight variables.
Attachment 3 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

3-33

Exhibit 18. Elasticities and Rankings for the Variables with the Highest Elasticities for Methyl Mercury

Name of Variable 

Changed Variable Definition Variable Category a

Elasticity of Risk for Input 

Variable Perturbation

Ranking of Elasticity for 

Input Variable 

Perturbation

-50% -5% 5% 50% -50% -5% 5% 50%

SedDepVel Sediment Deposition Velocity and 

Resuspension Velocity TRIM 0.78 1.56 2.01 3.67 9 1 1 1

BW_ch1 Body weight, Child 1-2 Ing./BW -1.91 -1.00 -0.91 -0.64 1 2 3 4

EmissionRate Emission Rate of all Chemicals TRIM 1.00 1.00 1.00 1.00 5 3 2 2

IR_Fish_ch1 Ingestion rate, Fish, Child 1-2 Ing./BW 0.90 0.90 0.90 0.90 8 4 4 3

FC_Fish Fraction contaminated, Fish Ing./BW 0.90 0.90 N/A N/A 7 5

EF_Fish Exposure factor, Fish Ing./BW 0.90 0.90 N/A N/A 6 6

MixHeight Mixing Height TRIM -1.02 -0.70 -0.66 -0.51 3 7 5 8

HorizWindSpeed Horizontal Wind Speed TRIM -1.01 -0.70 -0.65 -0.51 4 8 6 9

Rain Annual Rainfall TRIM 0.70 0.64 0.63 0.60 10 9 7 6

C_FishT4 Concentration of chemical in 

whole fish for T4 fish FFC 0.55 0.55 0.55 0.55 12 10 8 7

EroRate Erosion Rate for all Parcels TRIM -0.57 -0.45 -0.43 -0.35 11 11 10 11

SedPorosity Sediment Porosity TRIM -0.30 -0.40 -0.43 -0.60 15 12 9 5

C_FishT3 Concentration of chemical in 

whole fish for T3 fish FFC 0.36 0.36 0.36 0.36 13 13 11 10

SurfWatTemp Water Temperature TRIM -0.22 -0.22 -0.22 -0.21 18 14 12 12

F_T3 Fraction of fish intake that is from 

T3 Ing./BW -0.19 -0.19 -0.19 -0.19 19 15 13 13

F_T4 Fraction of fish intake that is from 

T4 Ing./BW 0.19 0.19 0.19 0.19 20 16 14 14

WatSuspSed Water- Suspended Sediment 

Concentration TRIM -0.32 -0.18 -0.16 -0.12 14 17 15 16

SurfSoilAir Surface Soil- Fraction Air TRIM 0.14 0.14 0.15 0.15 21 18 16 15

WatRetTime Rentention Time in Pond TRIM 0.23 0.14 0.13 0.09 17 19 17 17

FishMass Fish Body Weight for all Aquatic 

Species TRIM 0.10 0.07 0.07 0.06 22 20 19 19

Runoff Total Water Runoff Rate TRIM 0.07 0.07 0.07 0.07 23 21 18 18

C_Soil Concentration of chemical in soil 

from contaminated area FFC 0.05 0.05 0.05 0.05 24 22 20 20
Attachment 3 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR

3-34

Exhibit 18. Elasticities and Rankings for the Variables with the Highest Elasticities for Methyl Mercury

Name of Variable 

Changed Variable Definition Variable Category a

Elasticity of Risk for Input 

Variable Perturbation

Ranking of Elasticity for 

Input Variable 

Perturbation

-50% -5% 5% 50% -50% -5% 5% 50%

IR_Soil_ch1 Ingestion rate, Soil, Child 1-2 Ing./BW 0.05 0.05 0.05 0.05 27 23 21 21

EF_Soil Exposure factor, Soil Ing./BW 0.05 0.05 N/A N/A 25 24

FC_Soil Fraction contaminated, Soil Ing./BW 0.05 0.05 N/A N/A 26 25

Cs_root_zone_pr

oduce

Average chemical concentration 

in soil at root-zone depth in 

produce-growing area

FFC 0.04 0.04 0.04 0.04 28 26 22 22

100-MAF_pf 100 - Moisture adjustment factor, 

Protected Fruit FFC 0.03 0.03 0.03 0.03 33 27 25 23

Br_AG_produce_

pf

Plant-soil chemical 

bioconcentration factor, Protected 

Fruit

FFC 0.03 0.03 0.03 0.03 29 28 23 24

IR_ProFruit_ch1 Ingestion rate, Protected Fruit, 

Child 1-2

Ing./BW 0.03 0.03 0.03 0.03 32 29 24 25

a

FFC refers to farm food chain variables and Ing./BW refers to ingestion and body weight variables.
   

   

MEMORANDUM

October 7, 2011

To: Dave Guinnup, Group Leader, Air Toxics Assessment Group, EPA/OAQPS/HEID

From: Dave Burch, Greg Carter, Chris Holder, and Arun Varghese, ICF

Re: Post Screening Analysis of PB‐HAP Emissions for Ferroalloys Source Category

1. INTRODUCTION

As part of its Risk and Technology Review (RTR) program in support of the CAA, EPA’s Office of Air

Quality Planning and Standards (OAQPS) assesses risk from hazardous air pollutant (HAP) emissions after

the application of maximum achievable control technology (MACT) standards. To support the RTR

approach for evaluating risks from non‐inhalation exposures to HAPs, ICF developed emissions

thresholds for four persistent and bioaccumulative HAPs (PB‐HAPs):  cadmium, divalent mercury, dioxins

(as 2,3,7,8‐TCDD), and polycyclic organic matter (POM, evaluated as benzo[a]pyrene).  These thresholds

represent a level of emissions below which it is reasonably certain that no adverse human health

impacts will result. These screening‐level emissions thresholds were developed using a hypothetical

environmental and exposure modeling scenario with a generally health protective approach (i.e., the

scenario is intended to encompass the upper bound of potential exposures to PB‐HAPs emitted by

sources evaluated by EPA for residual risk). The parameterization of the modeling scenario used to the

thresholds (referred to as the “screening scenario” for assessment of RTR multipathway exposures) is

described in detail in an appendix to OAPQS’ residual risk assessment reports developed for source

category assessments (this appendix has been labeled Appendix 5). For facilities that exceed the

emissions thresholds, additional analysis incorporating site‐specific information is warranted to better

determine the actual potential for unacceptable risk.

This memorandum documents a post‐screening analysis (PSA) that was performed for the ferroalloys

source category. Specifically, we assessed how facilities with emission rates in exceedance of the

screening thresholds differ from the screening scenario in some of their most risk‐sensitive parameters

and thereafter incorporated these site‐specific differences into revised modeling scenarios to estimate

post‐screening  thresholds specific to those facilities . The remainder of this memorandum provides an

overview of the ferroalloys source category and notes facilities reporting emissions in exceedance of the

screening thresholds (Section 2), describes the assumptions and set‐up of the PSA (Section 3), and

summarizes the results with a discussion of uncertainties and conclusions (Section 4).  
Ferroalloys PSA Memorandum 2 10/7/2011

2. FERROALLOYS SCREENING ANALYSIS  

Ferroalloy is an alloy of iron with some element other than carbon (often chromium, silicon,

molybdenum, manganese, and/or titanium) that is introduced into molten metal during production to

give steel and cast iron distinctive qualities. Most ferroalloy production occurs in submerged electric arc

furnaces, and the specific pollutants emitted and their quantities depend on the raw materials used.

Particulates are generated during several stages of ferroalloy production, and organic materials are

mostly generated during smelting.

Emissions of mercury and POM exceeded multipathway risk‐based emission thresholds for two facilities,

Eramet in Marietta, Ohio (NEI11660), and New Haven in New Haven, West Virginia (NEIWV053FELMAN).

As illustrated in Table 1, the Eramet facility exceeded the screening level for divalent mercury and for

POM by 28‐fold and 562‐fold, respectively, and the New Haven facility exceeded the screening levels for

divalent mercury and for POM as BAP by 7‐fold and 54‐fold, respectively.

Table 1. Mercury and POM Emissions Summary for Ferroalloy Facilities Exceeding Screening

Thresholds

NEI ID

Facility Name and

Location PB‐HAP

Emissions

(TPY)

Screening

Level

(TPY)

Ratio of Emissions

to Screening Level

NEI11660 Eramet, Marietta, OH

Divalent Mercury 5.37E‐02 1.95E‐03 28

POM as BaP 2.02E‐01 3.60E‐04 562

NEIWV053FELMAN New Haven Plant, New

Haven, WV

Divalent Mercury 1.41E‐02 1.95E‐03 7

POM as BaP 1.93E‐02 3.60E‐04 54

3. POST‐SCREENING ANALYSIS:  METHODS AND IMPLEMENTATION

3.1. Selection of Model Parameters for Site‐Specific Calibration  

The objective of the post‐screening analysis is to determine whether facilities with emission rates in

excess of the screening threshold can be ruled out as posing unacceptable multipathway health risk by

incorporating site‐specific values for fate and transport model parameters.  Because the screening

scenario was designed to be health‐protective, it is possible that relaxing of some of the conservative

assumptions used in the screening scenario (through the use of site‐specific values for some model

parameters) may allow EPA to conclude that emissions from those facilities need not be considered

potentially hazardous.  

The first step in the development of facility‐specific post‐screening scenario is the identification of a

small sub‐set of parameters for further evaluation, based on a combination of (i) their influence on

estimated health risk (i.e., sensitivity), (ii) high potential for inter‐site parameter variability, and (iii)

level‐of‐effort requirements for data collection and implementation.  Two types of model parameters

were selected for refinement for the PSA of the ferroalloys source category:  layout of the modeling

domain (specifically, location of lakes and farms), and meteorological characteristics.
Ferroalloys PSA Memorandum 3 10/7/2011

Lake Location.   Theoretical considerations suggest that the surface deposition of emitted chemicals

decreases significantly with increasing distance from the emission stack. Previous simulations have

shown that the majority of risk associated with each of the four PB‐HAPs is a result of exposures via the

fish consumption pathway (assuming a level of fish consumption consistent with subsistence

fishers/recreational anglers as described in Appendix 5). Consequently, the physical location of the

water body in the TRIM.FaTE modeling domain has a significant influence on risk for all four PB‐HAPs,

and this characteristic of the modeling scenario was evaluated in the PSA.   

Farm Location. PB‐HAP concentrations in farm produce are also likely to decrease significantly with

increasing distance of farmland from the emission stack. The farm produce food chain can be a

substantial source of risk for chemicals like POM. Therefore, the PSA also assessed land‐use around the

two facilities to determine if the location of farms could justifiably be set back from the emission stack

compared to the screening scenario.

Meteorological Parameters.  Meteorological parameters in TRIM.FaTE also have significant influence on

estimated risk. Specifically, the frequency with which the prevailing wind blows in the direction of the

lake affects the amount of chemical depositing to the lake and, consequently, accumulation of chemical

mass in the aquatic food chain. Wind speed also is a influential parameter because higher wind speeds

tend to blow the emitted PB‐HAP rapidly outside of the modeling domain. Similarly, greater mixing

heights have been demonstrated to result in lower risks. Higher rainfall also results in greater deposition

and risk within the modeling domain, particularly for water soluble chemicals. For these reasons, site

representative values for these parameters were used in the post‐screening analysis for ferroalloys.

The same meteorological parameters were assumed to apply to both facilities because they are

physically proximate. The distance to a lake capable of supporting a substantial fish population is also

similar at the two sites. An evaluation of the land use did not produce positive evidence for setting back

the assumed farm location at either of the two sites. Based on these similarities, the same scenario was

used to model both the ferroalloy facilities with emissions exceeding the screening threshold and the

same post‐screening threshold levels were applicable to both facilities. . More site‐specific details about

land use, topography and meteorology are provided below.

3.2. Geospatial Configuration of Modeling Scenario

Location of Farms.  Based on an evaluation of satellite imagery and spatial land use data, the

surrounding land use was investigated for the Eramet and New Haven facilities. At the Eramet facility,

most of the nearby agricultural land was determined to be in pasture/hay; however, we were unable to

determine if livestock was raised in the vicinity of the facility. The nearby areas of cultivated crops

appeared to be relatively few in number, small in size, and mostly farther than 10 km away from the

facility. We were unable to determine what types of crops were grown in the vicinity of the facility. At

the New Haven facility, many fairly large areas of pasture/hay are in the vicinity of the facility, although

we found no information on whether livestock was raised in the area. The crops grown nearby likely are

corn and soy, with an area of approximately 4 km2 of possible corn and soy production approximately 3

km away from the facility. We located four water bodies within 10 km of the facility.
Ferroalloys PSA Memorandum 4 10/7/2011

Location of Water Bodies.  In identifying water bodies that potentially contribute to risk, we excluded

lakes of under five hectares (50,000 m2

) in area from our assessment because based on previous

analyses, it has been determined that they are unlikely to support stable fish communities and therefore

are generally not evaluated for bioaccumulative chemicals. Three water bodies were found that were

within roughly 10 km of the Eramet facility, but they were determined to be either too small to support

sustenance fishing or were determined to be industrial ponds/surface impoundments. The closest water

body that has the potential to support sustenance fishing is Veto Lake, approximately 12 km from the

facility. The location of Veto Lake with respect to the Eramet facility was used in designing the secondary

screening scenario layouts and meteorology data. Around the New Haven facility, the largest water body

is an unnamed lock and dam on the Ohio River in a wilderness area that might be fishable; although we

found no direct evidence to support that it is fished. Another lake – Turkey Run Lake – is roughly 13 km

from the facility, is fishable, and was determined to be large enough to support sustenance fishing. The

locations of Turkey Run Lake and the lock and dam with respect to the New Haven facility were used in

designing the secondary screening scenario layouts and meteorology data.  

Revised Spatial Layout for Both Facilities.  As discussed above, the spatial similarities around the two

facilities justify the use of the same revised layout for both facilities. Based on the spatial assessment

detailed above, we situated the lake in the TRIM.FaTE scenario at a distance of 10 km from the stack

(compared to approximately 2 km in the screening scenario). We maintained the same watershed‐area

to lake‐area ratio that was used in the screening scenario for both runoff and erosion. All other

topographical parameters were set consistent with the screening scenario. In the absence of definitive

information about the location of subsistence farms around the facilities, the position of the farm was

maintained identical to the screening scenario. The spatial layout that was used in the refined

TRIM.FaTE modeling scenario is presented in Figure 1.  
Ferroalloys PSA Memorandum 5 10/7/2011

Figure 1. Spatial Layout for Ferroalloys Post‐Screening Analysis (Both Facilities)

3.3. Meteorological Data

As described in Appendix 5, for the screening scenario, the rainfall rate, wind speed, and mixing height

were held constant at 0.4 mm/hr, 2.8 m/s, and 710 m, respectively. The winds blow in the direction of

the model domain three days of the week (43 percent of the time). Rain falls continuously on three days

of the week (43 percent of the time), two of which are days when winds blow into the model domain.

This leads to winds blowing towards the domain and rain falling 29 percent of the time.

Our goals were to modify these variables in order to better reflect the conditions at the two ferroalloys

facilities, while still reflecting the simplicity of the original screening scenario meteorology data.

Hourly surface meteorology data were needed that are representative of the two ferroalloys facilities.

The closest currently operating hourly Automated Surface Observing System (ASOS) to both facilities is

in Parkersburg, WV (Parkersburg Wood County Airport; Call Sign KPKB; Weather Bureau Army Navy ID

03804; approximately 8 km east‐southeast of the Eramet facility and 61 km northeast of the New Haven

facility). Using 2001–2003 hourly data from Parkersburg1

, we calculated the frequency that winds blew

towards each of the three lakes noted above. We also calculated the precipitation frequency and

average wind speeds for time periods when the wind blew toward and away from the lake. We then

                                                           

1 NCDC: ISH Data, http://www.ncdc.noaa.gov/oa/mpp/digitalfiles.html#DIG
Ferroalloys PSA Memorandum 6 10/7/2011

averaged these numbers across the three lake scenarios to arrive at the values shown in Table 2 below.

On average at the two ferroalloy sites and at the three lakes, winds blew towards the lake eight percent

of the time, which we represent as two hours per day. Precipitation also fell eight percent of the time,

half of which was during times when winds blew towards the lake. Wind speeds tended to be slightly

stronger during times when winds blew towards the lake (3.2–3.5 m/s versus 2.4–2.9 m/s) and during

times when precipitation was falling. Given the 2001–2003 annual average precipitation of 941 mm, and

given the precipitation frequency of two hours per day, we arrived at a rainfall rate of 1.29 mm/hr.  

These wind direction and rainfall frequencies are much smaller than in the original screening scenario,

which reduces the amount of time that the emitted pollutants deposit via both wet and dry deposition

to surfaces within the modeling domain. This rainfall rate is much greater than in the original screening

scenario to account for the reduced rainfall frequency.  

Table 2. Summary of Wind Direction Frequency, Precipitation Frequency, and Average Wind Speeds at

the Parkersburg Station, 2001–2003.

Number of Hours per Day

(Overall Average) Winds Blowing Towards Lake? Precipitating? Average Wind Speed (m/s)

1 Yes Yes 3.5

1 Yes No 3.2

1 No Yes 2.9

21 No No 2.4

We also calculated a new, constant mixing height that better reflects the Parkersburg area. The mixing

height in the original screening scenario (710 m) was designed to be conservative at the national‐level.

The closest station to the two ferroalloy facilities was Huntington, WV (Huntington Tri‐State Airport; Call

Sign KHTS; Weather Bureau Army Navy ID 03860; approximately 142 km southwest of the Eramet facility

and 58 km southwest of the New Haven facility). At the Huntington, WV, station, the 1991 annual

average of morning and afternoon mixing heights was 996 m. We used this mixing height to better

reflect mixing heights near the facilities. These new wind direction frequencies, rainfall frequencies and

rates, wind speeds, and mixing height data were incorporated into a site‐specific meteorological data

file. As noted above, because of the physical proximity of the two sites, the same meteorological inputs

were used in our evaluation of both facilities.

4. CONCLUSIONS AND UNCERTAINTIES

The site‐specific spatial layout and meteorological data described above were run through the

TRIM.FaTE model using the screening emission rates. Risks were computed from the TRIM.FaTE results

using the MIRC model (see Appendix 5, Attachment 2). Post‐screening emission relaxation factors were

computed as the ratio of the risk from the screening scenario to the risk from the PSA (Table 3). Because

risks scale linearly with emissions in the model, this ratio is independent of the emission rate and can be

used to scale the screening threshold emission rates upwards, resulting in a post‐screening threshold for
Ferroalloys PSA Memorandum 7 10/7/2011

each facility2

. In other words, as can be seen in Table 3, a new facility‐specific post‐screening emission

threshold can be calculated by multiplying the screening emission rate by a factor of 12.5 for POM and

by a factor of 8.8 for divalent mercury, resulting in PSA emissions thresholds of 4.50E‐03 tons/year for

POM as benzo[a]pyrene, and 1.72E‐02 tons/year for divalent mercury for each of the two facilities.

Table 3. Risks and Emission Relaxation Factors based on Post‐screening Analysis

Chemical

Screening

Threshold

Emission Rate

(TPY)

Screening

Risk/HQ

Post‐screening

Analysis Risk/HQ

Post‐screening

Emission

Relaxation Factor  

Facility‐Specific

Revised Emissions

Threshold

(TPY)

PAHs (in BaP

equivalents) 3.60E‐04 1E‐6 8.00E‐08 12.5 4.50E‐03

Divalent Hg 1.95E‐03 1 0.114 8.8 1.72E‐02

The percentage contribution of risk from the fish consumption pathway for POM dropped from 85

percent in the screening scenario (see Exhibit 4‐22 in Appendix 5) to approximately 53 percent in the

post‐screening analysis. For divalent mercury, the risk continued to be driven almost exclusively by

methyl mercury in fish.  

Table 4. Comparison of Facility Emissions PSA Emissions Thresholds

Facility Name PB‐HAP

Emissions

(TPY)

Facility‐specific

PSA Emissions

Threshold

Exceed

PSA

Threshold?

Exceedance Ratio

(Actual Emissions:

PSA Threshold)

Eramet Divalent Hg 5.37E‐02 1.72E‐02 Yes 3.1

Eramet POM as BaP 2.02E‐01 4.50E‐03 Yes 44.9

New Haven Plant Divalent Hg 1.41E‐02 1.72E‐02 No 0.8

New Haven Plant POM as BaP 1.93E‐02 4.50E‐03 Yes 4.3

Table 4 compares the PSA emission thresholds with the estimated emissions from each facility. For the

sites and PB‐HAPs considered, only divalent mercury emissions at the New Haven, WV were less than

the facility‐specific PSA thresholds. In other words, for the Eramet facility, emissions of both divalent

mercury and POM exceeded the facility‐specific PSA emissions thresholds, and for the New Haven

facility, emissions of POM exceeded the facility‐specific PSA threshold.  

Although the post‐screening analysis is expected to provide a more refined facility‐specific emission

threshold for the two facilities of interest, it is not a intended to involve a full‐fledged site‐specific risk

assessment. Physical, chemical, and biological parameters were retained at the values used in the

screening scenario analysis. Except for the position of the lake, topographical parameters such as the

watershed‐area to lake‐area ratio, lake flush rate, and land cover were identical to the screening

scenario. The position of the farm was also maintained at the same distance from stack used in the

                                                           

2 As noted earlier, the similarities in the spatial layouts and meteorology of the two facilities justified a combined analysis that

generated the same post‐screening facility‐specific thresholds for both facilities.
Ferroalloys PSA Memorandum 8 10/7/2011

screening scenario. (If there was positive evidence to justify setting back the position of the farm in the

modeled domain, or to ignore the farm‐product consumption pathway, the risks from POM would be

lower and the PSA threshold for POM would be higher.) Also, the post‐screening analysis used the same

exposure factors as the screening scenario without detailed consideration of whether populations at the

two sites would be accurately represented by the same exposure factors as envisaged in the screening

scenario. Potential next steps in furthering this analysis include incorporating site‐specific exposure

factors, evaluation of the mercury and PAH emission profiles for the two facilities of interest, more

detailed spatial assessment of the location of farms, and the use of more representative physical,

chemical and biological fate and transport parameters.