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Deliberative Draft Document for EPA Internal Review

Draft 

Residual Risk Assessment for the Primary Aluminum Production 

Source Category

by

EPA’s Office of Air Quality Planning and Standards

Office of Air and Radiation

November 2011
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Table of Contents

1 Introduction.......................................................................................................................44

2 Methods ............................................................................................................................44

2.1 Emissions and source data .........................................................................................44

2.2 Dispersion modeling for inhalation exposure assessment .........................................55

2.3 Estimating chronic human inhalation exposure .........................................................77

2.4 Screening for potential acute impacts of concern ......................................................88

2.5 Multipathway human health and environmental risk screening ................................99

2.6 Dose-response assessment .....................................................................................1111

2.6.1 Sources of chronic dose-response information...............................................1111

2.6.2 Sources of acute dose-response information ..................................................1515

2.7 Risk characterization..............................................................................................1717

2.7.1 General............................................................................................................1717

2.7.2 Mixtures..........................................................................................................1919

2.7.3 Facility-wide risks ..........................................................................................2020

3 Risk results for the primary aluminum production source category.........................2020

3.1 Source category description and results.................................................................2020

3.2 Baseline risk characterization ................................................................................2121

3.3 Post-control risk characterization...........................................................................2525

4 General discussion of uncertainties and how they have been addressed......................2626

4.1 Exposure modeling uncertainties...........................................................................2626

4.2 Uncertainties in the dose-response relationships...................................................2727

5 References.....................................................................................................................3535

Index of Tables

Table 2.2-1 AERMOD version 09292 model options for RTR modeling ..............................66

Table 2.6-1 (a) Dose-Response Values for Chronic Inhalation Exposure to Carcinogens.1313

Table 2.6-1 (b) Dose-Response Values for Chronic Oral Exposure to Carcinogens .........1414

Table 2.6-1 (c) Dose-Response Values for Chronic Inhalation Exposure to Noncarcinogens

..............................................................................................................................................1515

Table 2.6-2 Dose-Response Values for Acute Exposure.....................................................1717

Table 3.1-1 Summary of Emissions from the Primary Aluminum Production Source Category

and Availability of Dose-Response Values..........................................................................2121

Table 3.2-1 Summary of Source Category Level Inhalation Risks for Primary Aluminum 

Production.............................................................................................................................2323

Table 3.2-2 Summary of Refined Acute Results for Secondary Aluminum Production 

Facilities................................................................................................................................2424

Appendices

Appendix 1 Technical Support Document for HEM-AERMOD Modeling

Appendix 2 Meteorological Data for HEM-AERMOD Modeling
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Appendix 3 Analysis of data on short-term emission rates relative to long-term emission 

rates

Appendix 4 Technical Support Document for TRIM-Based Multipathway Screening 

Scenario for RTR: Summary of Approach and Evaluation

Appendix 5 Draft Detailed Risk Modeling Results

Appendix 6 Acute Impacts Refined Analysis Figures

Appendix 7 Development of the Chronic Screening Level for Carbonyl Sulfide

Index of Acronyms

AERMOD American Meteorological Society/EPA Regulatory Model

AEGL Acute exposure guideline level

ASTDR US Agency for Toxic Substances and Disease Registry

CalEPA California Environmental Agency

CTE Central Tendency Estimate

ERPG Emergency Response Planning Guideline

HAP Hazardous Air Pollutant

HEM Human Exposure Model

HI Hazard index

HQ Hazard quotient

IRIS Integrated Risk Information System

MACT Maximum Achievable Control Technology

MIR Maximum Individual Risk

MOA Mode of action

NAC National Advisory Committee

NAAQS National Ambient Air Quality Standards

NATA National Air Toxics Assessment

NEI National Emissions Inventory

NPRM Notice of Proposed Rulemaking

PB-HAP Persistent and Bioaccumulative - HAP

POM Polycyclic organic matter

REL Reference exposure level

RfC Reference concentration

RfD Reference dose

RME Reasonable Maximum Exposure

RTR Risk and Technology

TOSHI Target-organ-specific hazard index

URE Unit risk estimate
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1 Introduction

Section 112 of the Clean Air Act (CAA) establishes a two-stage regulatory process for 

addressing emissions of hazardous air pollutants (HAPs) from stationary sources. In the first 

stage, section 112(d) requires the Environmental Protection Agency (EPA, or the Agency) to 

develop technology-based standards for categories of sources (e.g., petroleum refineries, pulp 

and paper mills, etc.) [1]. EPA has largely completed the initial Maximum Achievable 

Control Technology (MACT) standards as required under this provision. Under section 

112(d)(6), EPA must review each of these technology-based standards at least every eight 

years and revise a standard, as necessary, “taking into account developments in practices, 

processes and control technologies.” In the second stage, EPA is required under section 

112(f)(2) to assess the health and environmental risks that remain after implementation of the 

MACT standards. If additional risk reductions are necessary to protect public health with an 

ample margin of safety or to prevent an adverse environmental effect, EPA must develop 

standards to address these remaining risks. This second stage of the regulatory process is 

known as the residual risk stage. For each source category for which EPA issued MACT 

standards, the residual risk stage must be completed within eight years of promulgation of the 

initial technology-based standard.

In December of 2006 we consulted with a panel from the EPA's Science Advisory Board 

(SAB) on the “Risk and Technology Review (RTR) Assessment Plan,” and in June of 2007, 

we received a letter with the results of that consultation. Subsequent to the consultation, in 

June of 2009, a meeting was held with an SAB panel for a formal peer review of the “Risk 

and Technology Review (RTR) Assessment Methodologies” [2]. We received the final SAB 

report on this review in May of 2010 [3]. Where appropriate, we have responded to the key 

messages from this review in developing our current risk assessments and we will be 

continuing our efforts to improve our assessments by incorporating updates based on the SAB 

recommendations as they are developed and become available. Our responses to the key

recommendations of the SAB are outlined in a memo entitled, “EPA’s Actions in Response to 

Key Recommendations of the SAB Review of RTR Risk Assessment Methodologies” [4].

This document contains the methods and the results of baseline risk assessments performed 

for the Primary Aluminum Production source category. The methods discussion includes 

descriptions of the methods used to screen for acute health risks, chronic non-inhalation 

health risks, and adverse environmental effects, as well as descriptions of the methods used to 

develop refined estimates of chronic inhalation exposures and human health risks for cancer 

and noncancer endpoints.

2 Methods

2.1 Emissions and source data

The preliminary dataset was based on information in the 2005 National Air Toxics 

Assessment (NATA) Inventory. This information was then supplemented with test data and 

other information through an Information Collection Request (ICR), received in April 2011, 

from 11 of the 15 facilities in the source category. These ICR data were then used along with 

the 2005 NATA inventory data to develop the emissions dataset for this source category. We 
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then performed an engineering review of the information using EPA engineers to identify

readily apparent limitations and issues with the emissions data. Notably, one facility in this 

source category, Columbia Falls, has not been fully operational since 1999 and has been 

completely closed for 2 years and therefore we did not receive recent emissions data to 

calculate risks based on actual emissions. However, we did estimate risks based on estimates 

of MACT allowable emissions for this one facility to provide an indication of what risks it 

might pose if it were to re-open. Details on the development of the emissions and source data 

for this source category, including the estimation of allowable emissions from the Columbia 

Falls facility, are discussed in: Draft Summary Primary Aluminum RTR Modeling File 

Development, available in the docket for this rulemaking. Section 3 below provides a 

summary of the emissions. 

2.2 Dispersion modeling for inhalation exposure assessment

Both long- and short-term inhalation exposure concentrations and associated health risk from 

each facility in the source category of interest were estimated using the Human Exposure 

Model in combination with the American Meteorological Society/EPA Regulatory Model 

dispersion modeling system (HEM-AERMOD). The approach used in applying this modeling 

system is outlined below, and further details are provided in Appendix 1. HEM-AERMOD 

performs three main operations: atmospheric dispersion modeling, estimation of individual 

human exposures and health risks, and estimation of population risks. This section focuses on 

the dispersion modeling component. The exposure and risk characterization components are 

discussed in other subsections of Sections 2 and 3.

The dispersion model in the HEM-AERMOD system, AERMOD version 09292, is a state-ofthe-science Gaussian plume dispersion model that is preferred by EPA for modeling point, 

area, and volume sources of continuous air emissions from facility applications [5]. Further 

details on AERMOD can be found in the AERMOD Users Guide [6]. The model is used to 

develop annual average ambient concentrations through the simulation of hour-by-hour 

dispersion from the emission sources into the surrounding atmosphere. Hourly emission rates 

used for this simulation are generated by evenly dividing the total annual emission rate from 

the inventory into the 8,760 hours of the year.

The first step in the application of the HEM-AERMOD modeling system is to predict ambient 

concentrations at locations of interest. The AERMOD model options employed are 

summarized in Table 2.2-1 and are discussed further below.
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Table 2.2-1 AERMOD version 09292 model options for RTR modeling

Modeling Option Selected Parameter for chronic exposure

Type of calculations Hourly Ambient Concentration

Source type Point, area represented as pseudo point source

Receptor orientation Polar (13 rings and 16 radials)

Discrete (census block centroids)

Terrain characterization Actual from USGS 1-degree DEM data

Building downwash Not Included

Plume deposition/depletion Not Included

Urban source option No

Meteorology 1 year representative NWS from nearest site (158 stations)

In HEM-AERMOD, meteorological data are ordinarily selected from a list of 158 National 

Weather Service (NWS) surface observation stations across the continental United States, 

Alaska, Hawaii, and Puerto Rico. In most cases the nearest station is selected as 

representative of the conditions at the subject facility. Ideally, when considering off-site 

meteorological data most site specific dispersion modeling efforts will employ up to five 

years of data to capture variability in weather patterns from year to year. However, to 

minimize data processing time and resources for this assessment, we modeled only a single 

year, typically 1991. While the selection of a single year may result in under-prediction of 

long-term ambient levels at some locations, likewise it may result in over-prediction at others. 

For each facility identified by its characteristic latitude and longitude coordinates, the closest 

meteorological station was used in the dispersion modeling. The average distance between a 

modeled facility and the applicable meteorological station was 40 miles (72 km). Appendix 2

(Meteorological Data Processing Using AERMET for HEM-AERMOD) provides a complete 

listing of stations and assumptions along with further details used in processing the data 

through AERMET. The sensitivity of model results to the selection of the nearest weather 

station and the use of one year of meteorological data is discussed in “Risk and Technology

Review (RTR) Risk Assessment Methodologies” [2].

The HEM-AERMOD system estimates ambient concentrations at the geographic centroids of 

census blocks (using the 2000 Census) and at other receptor locations that can be specified by 

the user. The model accounts for the impacts of multiple facilities when estimating 

concentration impacts at each block centroid. Typically we combined only the impacts of 

facilities within the same source category, and assessed chronic exposure and risk only for 

census blocks with at least one resident (i.e., locations where people may reasonably be 

assumed to reside rather than receptor points at the fenceline of a facility). Chronic ambient 

concentrations were calculated as the annual average of all estimated short-term (one-hour) 

concentrations at each block centroid. Possible future residential use of currently uninhabited 

areas was not considered. Census blocks, the finest resolution available in the census data, 

are typically comprised of approximately 40 people or about ten households. 
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In contrast to the development of ambient concentrations for evaluating long-term exposures, 

which was performed only for occupied census blocks, worst-case short-term (one-hour) 

concentrations were estimated both at the census block centroids and at points nearer the 

facility that represent locations where people may be present for short periods, but generally 

no nearer than 100 meters from the center of the facility (note that for large facilities, this 

100-meter ring could still contain locations inside the facility property). Since short-term 

emission rates were needed to screen for the potential for hazard via acute exposures, and 

since the NEI contains only annual emission totals, we typically applied the general 

assumption to all source categories that the maximum one-hour emission rate from any source 

was ten times the average annual hourly emission rate for that source (see Appendix 3)1.

However, for this source category, an engineering analysis demonstrated that peak 1-hour 

emissions are approximately 5 times average hourly emissions, and thus a factor of 5, rather 

than the default factor of 10 was used in the acute risk assessment.

2

Census block elevations for HEM-AERMOD modeling were determined nationally from the 

US Geological Service 1-degree digital elevation model (DEM) data files, which have a 

spatial resolution of about 90 meters. Elevations of polar grid points used in estimating shortand long-term ambient concentrations were assumed to be equal to the highest elevation of 

any census block falling within the polar grid sector corresponding to the grid point. If a 

sector does not contain any blocks, the model defaults the elevation to that of the nearest 

block. If an elevation is not provided for the emission source, the model uses the average 

elevation of all sectors within the innermost model ring.

In addition to using receptor elevation to determine plume height, AERMOD adjusts the 

plume’s flow if nearby elevated hills are expected to influence the wind patterns. For details 

on how hill heights were estimated and used in the AERMOD modeling see Appendix 1. 

2.3 Estimating chronic human inhalation exposure

We used the annual average ambient air concentration of each HAP at each census block 

centroid as a surrogate for the lifetime inhalation exposure concentration of all the people who 

reside in the census block. That is, the risk analysis did not consider either the short-term or 

long-term behavior (mobility) of the exposed populations and its potential influence on their 

exposure.

We did not address short-term human activity for two reasons. First, our experience with the 

1996, 1999, 2002, and 2005 NATA assessments (which modeled daily activity using EPA’s 

HAPEM model) suggests that, given our current understanding of microenvironment 

concentrations and daily activities, modeling short-term activity would, on average, reduce 

risk estimates about 25% for particulate HAPs; it will also reduce risk estimates for gaseous 

HAPs, but typically by much less. Second, basing exposure estimates on average ambient 

1

In situations where we have information on hourly emissions, we will take this into account when evaluating 

acute risks.

2

Justification for applying an acute factor of 5 rather than the default value of 10 can be found in the memo 

“Acute Risk Modeling of Primary Aluminum Potroom Emissions” which is located in the docket supporting the 

proposed rulemaking.
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concentrations at census block centroids may underestimate or overestimate actual exposure 

concentrations at some residences. Further reducing exposure estimates for the most highly 

exposed residents by modeling their short-term behavior could add a systematic low bias to 

these results.

We did not address long-term migration nor population growth or decrease over 70 years, 

instead basing the assessment on the assumption that each person’s predicted exposure is 

constant over the course of their lifetime which is assumed to be 70 years. In assessing cancer 

risk, we generally estimated three metrics: the maximum individual risk (MIR), which is 

defined as the risk associated with a lifetime of exposure at the highest concentration (note 

that the MIR can be estimated for each source as well as for the entire source category); the 

population risk distribution; and the cancer incidence. The assumption of not considering 

short or long-term population mobility does not bias the estimate of the MIR nor does it affect 

the estimate of cancer incidence since the total population number remains the same. It does, 

however, affect the shape of the distribution of individual risks across the affected population, 

shifting it toward higher estimated individual risks at the upper end and reducing the number 

of people estimated to be at lower risks, thereby biasing the risk estimates high. 

When screening for potentially significant acute exposures, we used an estimate of the highest 

hourly ambient concentration at any off-site location as the surrogate for the maximum 

potential acute exposure concentration for any individual.

2.4 Screening for potential acute impacts of concern

In establishing a scientifically defensible approach for the assessment of potential health risks 

due to acute exposures to HAP, we followed the same general approach that has been used for 

developing chronic health risk assessments for other rules under the residual risk program. 

That is, we developed a tiered, iterative approach. This approach to risk assessment was

endorsed by the National Academy of Sciences in its 1993 publication “Science and Judgment 

in Risk Assessment” and subsequently was adopted in the EPA’s “Residual Risk Report to 

Congress” in 1999. 

With respect to screening for potential acute health risks, the assessment methodology is 

designed to eliminate from further consideration those facilities for which we have confidence 

that no acute adverse health effects of concern will occur. To do so, we use what is called a 

tiered, iterative approach to the assessment. This means that we began with a screening 

assessment, which relies on readily available data and uses conservative assumptions that in 

combination approximate a worst-case exposure. The result of this screening process is that 

either the facility being assessed poses no potential acute health risks (i.e., it “screens out”), or 

that it requires further, more refined assessment with respect to potential acute exposures from

emitted HAP. A more refined acute assessment could use industry- or site-specific data on 

the temporal pattern of emissions, the layout of emission points at the facility, the boundaries 

of the facility, and/or the local meteorology. In some cases, all of these site-specific data are 

used to refine the assessment; in others, lesser amounts of site-specific data can be used to 

determine that acute exposures are not a concern, and significant additional data collection is

not necessary.
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Acute health risk screening was performed for each facility as the first step. We used 

conservative assumptions for emission rates, meteorology, and exposure location. We used 

the following worst-case assumptions in our screening approach:

• Peak 1-hour emissions were assumed to equal 5 times the average 1-hour emission 

rates except in the case of fugitive dust (see section 2.2)

• For facilities with multiple emission points, peak 1-hour emissions were assumed to 

occur at all emission points at the same time.

• For facilities with multiple emission points, 1-hour concentrations at each receptor 

were assumed to be the sum of the maximum concentrations due to each emission 

point, regardless of whether those maximum concentrations occurred during the same 

hour. 

• Worst-case meteorology (from one year at the nearest NWS site) was assumed to 

occur at the same time the peak emission rates occur. The recommended EPA localscale dispersion model, AERMOD, was used for simulating atmospheric dispersion

during this worst-case hour.

• A person was assumed to be located downwind at the point of maximum modeled 

impact during this same worst-case 1-hour period, but no nearer to the source than 100 

meters. As a result, the maximum modeled 1-hour off-site concentration equals the

worst-case acute exposure concentration.

• The maximum impact was compared to multiple short-term health benchmarks for the 

HAP being assessed to determine whether a possible acute health risk might exist. 

These benchmarks are described in section 2.6 of this report.

As mentioned above, when we identify acute impacts which exceed their relevant 

benchmarks, we pursue refining our acute screening estimates to the extent possible. In some 

cases, this may include the use of a facility-specific emissions multiplier to estimate the peak 

hourly emission rates from the average rates (rather than the default factor of 10, as is the case 

for this source category, see section 3.2). In other cases, this may entail determining the 

actual physical layout and boundaries of a facility to more accurately gauge where people 

might reasonably be exposed for an hour.

2.5 Multipathway human health and environmental risk screening

The potential for significant human health risks and adverse environmental impacts due to 

exposures via routes other than inhalation (i.e., multipathway exposures) was screened by first 

determining whether any sources emitted any hazardous air pollutants known to be persistent 

and bioaccumulative in the environment (PB-HAP). The PB-HAP compounds or compound 

classes are identified for the screening from the EPA’s Air Toxics Risk Assessment Library

[7], and they are cadmium compounds, chlordane, chlorinated dibenzodioxins and furans, 

DDE, heptachlor, hexachlorobenzene, hexachlorocyclohexane, lead compounds, mercury

compounds, methoxychlor, polychlorinated biphenyls, polycyclic organic matter (POM), 

toxaphene, and trifluralin. 

With respect to PB-HAP other than lead, emissions were evaluated for potential noninhalation risks and adverse environmental impacts using our recently-developed screening 
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scenario which was developed for use with the TRIM.FaTE3 model. This screening scenario 

uses environmental media outputs from the peer-reviewed TRIM.FaTE to estimate the 

maximum potential ingestion risks for any specified emission scenario by using a generic 

farming/fishing exposure scenario that simulates a subsistence environment. The screening 

scenario retains many of the ingestion and scenario inputs developed for EPA’s Human 

Health Risk Assessment Protocols (HHRAP) for hazardous waste combustion facilities.4In

the development of the screening scenario a sensitivity analysis was conducted to ensure that 

its key design parameters were established such that environmental media concentrations 

were not underestimated, and to also minimize the occurrence of false positives for human 

health endpoints. See Appendix 4 for a complete discussion of the development and testing 

of the screening scenario, as well as for the values of facility-level screening emission rates 

developed for screening potentially significant multi-pathway impacts. In general, for the 

purpose of developing emission rates for our multi-pathway screening, we derived emission 

levels for each PB HAP at which the maximum human health risk would be 1 in a million for 

lifetime cancer risk or a hazard quotient of 1.0 for noncancer impacts. 

In evaluating the potential multi-pathway risks from emissions of lead compounds, rather than 

developing a screening emission rate, we generally compare maximum estimated chronic (3-

month average) atmospheric concentrations with the current primary National Ambient Air 

Quality Standard (NAAQS) for lead. Values below the NAAQS are considered to have a low 

potential for multi-pathway risks. Notably, for this source category, POM was the only PBHAP identified in the emissions inventory.

We also evaluated the potential for significant direct environmental exposures to HAP by

evaluating the potential for exceedances of chronic human health inhalation reference values 

in the ambient air near these facilities. Human health dose-response reference values are 

generally derived from studies conducted on laboratory animals (such as rodents) and 

developed with the inclusions of uncertainty factors that could be as high as 3000. As a 

result, these human reference values are often significantly lower than the level expected to 

cause an adverse effect in an exposed rodent. It should be noted that there is a scarcity of data 

on the direct atmospheric impact of these HAP on other receptors, such as plants, birds, and 

wildlife. Thus, if the maximum inhalation hazard in an ecosystem is below the level of 

concern for humans (i.e., is below the human health reference value which is itself a lower 

reference value than would be expected for an ecologic effect), we have generally concluded 

that mammalian receptors should be at no risk of adverse effects due to inhalation exposures 

from non-PB-HAP, and have assurance that other ecological receptors are also not at any 

significant risk from direct atmospheric impact. In some isolated cases where we have data 

indicating potential adverse impacts on plants, birds, or other wildlife due to the direct 

atmospheric impacts of specific HAP, we note that as an uncertainty and, where possible, 

refine our analysis by comparing our modeled impacts to available reference values from the 

scientific literature.

3 EPA’s Total Risk Integrated Methodology (General Information) http://epa.gov/ttn/fera/trim_gen.html

4 EPA’s Human Health Risk Assessment Protocol (HHRAP) for Hazardous Waste Combustion Facilities;

http://www.epa.gov/epaoswer/hazwaste/combust/riskvol.htm#volume1
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2.6 Dose-response assessment

2.6.1 Sources of chronic dose-response information 

Dose-response assessment (carcinogenic and non-carcinogenic) for chronic exposure (either 

by inhalation or ingestion) for the HAP reported in the emissions inventory for the Primary

Aluminum Production sources were based on the EPA Office of Air Quality Planning and 

Standards’ (OAQPS) existing recommendations for HAP [8], also used for NATA 1999, 

2002, and 2005 [9]. This information has been obtained from various sources and prioritized 

according to (1) conceptual consistency with EPA risk assessment guidelines and (2) level of 

peer review received. The prioritization process was aimed at incorporating into our 

assessments the best available science with respect to dose-response information. The 

recommendations are based on the following sources, in order of priority: 

1) US Environmental Protection Agency (EPA). EPA has developed dose-response 

assessments for chronic exposure for many of the pollutants in this study. These 

assessments typically provide a qualitative statement regarding the strength of scientific 

data and specify a reference concentration (RfC, for inhalation) or reference dose (RfD, 

for ingestion) to protect against effects other than cancer and/or a unit risk estimate (URE, 

for inhalation) or slope factor (SF, for ingestion) to estimate the probability of developing 

cancer. The RfC is defined as an “estimate (with uncertainty spanning perhaps an order of 

magnitude) of a continuous inhalation exposure to the human population (including 

sensitive subgroups) that is likely to be without an appreciable risk of deleterious effects 

during a lifetime.” The URE is defined as “the upper-bound excess cancer risk estimated 

to result from continuous lifetime exposure to an agent at a concentration of 1 μg/m3in 

air.” The SF is “an upper bound, approximating a 95% confidence limit, on the increased 

cancer risk from a lifetime exposure to an agent. This estimate, [is] usually expressed in 

units of proportion (of a population) affected per mg/kg-day...” EPA disseminates doseresponse assessment information in several forms, based on the level of review. The 

Integrated Risk Information System (IRIS) [10] is an EPA database that contains scientific 

health assessment information, including dose-response information. All IRIS assessments 

since 1996 have also undergone independent external peer review. The current IRIS 

process includes review by EPA scientists, interagency reviewers from other federal 

agencies, and the public, and peer review by independent scientists external to EPA. New 

IRIS values are developed and old IRIS values are updated as new health effects data 

become available. Refer to the “IRIS Track” website for detailed information on status 

and scheduling of current individual IRIS assessments and updates 

(http://cfpub.epa.gov/ncea/iristrac/index.cfm). EPA’s science policy approach, under the 

current carcinogen guidelines, is to use linear low-dose extrapolation as a default option 

for carcinogens for which the mode of action (MOA) has not been identified. We expect 

future EPA dose-response assessments to identify nonlinear MOAs where appropriate, 

and we will use those analyses (once they are peer reviewed) in our risk assessments. At 

this time, however, there are no available carcinogen dose-response assessments for 

inhalation exposure that are based on a nonlinear MOA.

2) US Agency for Toxic Substances and Disease Registry (ATSDR). ATSDR, which is part 

of the US Department of Health and Human Services, develops and publishes Minimum 
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Risk Levels (MRLs) [11] for inhalation and oral exposure to many toxic substances. As 

stated on the ATSDR web site: “Following discussions with scientists within the 

Department of Health and Human Services (HHS) and the EPA, ATSDR chose to adopt a 

practice similar to that of the EPA's Reference Dose (RfD) and Reference Concentration 

(RfC) for deriving substance specific health guidance levels for non neoplastic endpoints.” 

The MRL is defined as “an estimate of daily human exposure to a substance that is likely

to be without an appreciable risk of adverse effects (other than cancer) over a specified 

duration of exposure.” ATSDR describes MRLs as substance-specific estimates to be 

used by health assessors to select environmental contaminants for further evaluation. 

Exposures above an MRL do not necessarily represent a threat, and MRLs are therefore 

not intended for use as predictors of adverse health effects or for setting cleanup levels.

3) California Environmental Protection Agency (CalEPA). The CalEPA Office of 

Environmental Health Hazard Assessment has developed dose-response assessments for 

many substances, based both on carcinogenicity and health effects other than cancer. The 

process for developing these assessments is similar to that used by EPA to develop IRIS 

values and incorporates significant external scientific peer review. As cited in the 

CalEPA Technical Support Document for developing their chronic assessments5: “The 

guidelines for developing chronic inhalation exposure levels incorporate many

recommendations of the U.S. EPA [12] and NAS [13].” The non-cancer information 

includes available inhalation health risk guidance values expressed as chronic inhalation 

reference exposure levels (RELs) [14]. CalEPA defines the REL as “the concentration 

level at or below which no health effects are anticipated in the general human population.” 

CalEPA's quantitative dose-response information on carcinogenicity by inhalation 

exposure is expressed in terms of the URE [15], defined similarly to EPA's URE. 

In developing chronic risk estimates, we adjusted dose-response values for some HAPs based 

on professional judgment, as follows: 

1) In the case of HAP categories such as glycol ethers and cyanide compounds, the most 

conservative dose-response value of the chemical category is used as a surrogate for other 

compounds in the group for which dose-response values are not available. This is done in 

order to examine, under conservative assumptions, whether these HAPs that lack doseresponse values may pose an unacceptable risk and require further examination, or screen 

out from further assessment. 

2) Where possible for emissions of unspecified mixtures of HAP categories such as POM

(see Tables 2.6-1 (a) to 2.6-1 (c), we apply category-specific chemical speciation profiles 

appropriate to the source category to develop a composite dose-response value for the 

category. 

5 Air Toxics Hot Spots Program, Risk Assessment Guidelines, Part III - Technical Support Document

for the Determination of Non-cancer Chronic Reference Exposure Levels. Air Toxicology and Epidemiology 

Section, Office of Environmental Health Hazard Assessment, California Environmental Protection Agency. 

February 2000 (http://www.oehha.ca.gov/air/chronic_rels/pdf/relsP32k.pdf)
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3) Emissions from Primary Aluminum Production facilities include POM. A substantial 

proportion of POM reported to EPA’s national emission inventory (NEI) are not speciated 

into individual compounds. As a result, it is necessary to apply the same simplifying 

assumptions to assessments that were used for the 1999 NATA study [16]. The NATA 

approach partitions POM into eight different non-overlapping “groups” that are modeled 

as separate pollutants. Each POM group comprises POM species of similar carcinogenic 

potency, for which we can apply the same URE. 

4) A chronic screening level of 163 ug/m3was developed for carbonyl sulfide (COS) from a 

No Observed Adverse Effects Level (NOAEL) of 200 ppm based on brain lesions and 

neurophysiological alterations in rodents. A more detailed discussion of the studies used 

to develop the COS chronic screening level is provided in Appendix 7. The screening 

level includes a total uncertainty factor (UF) of 3,000: 10x for extrapolation for 

interspecies differences, 10x for consideration of intraspecies variability, 10x for 

extrapolation from subchronic to chronic duration, and 3x for database insufficiencies. 

See section 5 of this document for a detailed discussion of exposure modeling 

uncertainties. 

The chronic screening level for COS is used only as a screening level assessment to 

identify areas with significant inhalation risk potential. A high COS chronic risk based on 

the screening level does not necessarily indicate that further action is required. 

The emissions inventory for the Primary Aluminum Production source category includes 

emissions of HAP with available chronic quantitative inhalation dose-response values. These 

HAP, their dose-response values, and the source of the values are listed in Tables 2.6-1 (a) 

through (c).

Table 2.6-1 (a) Dose-Response Values for Chronic Inhalation Exposure to 

Carcinogens

URE (unit risk estimate for cancer)6= cancer risk per •g/m3of average lifetime exposure. 

Sources: IRIS = EPA Integrated Risk Information System, CAL = California EPA Office of 

Environmental Health Hazard Assessment, EPA/OAQPS = interim value recommended by the 

EPA Office of Air Quality Planning and Standards. EPA ORD = EPA Office of Research and 

Development

Pollutant CAS Number7 URE5

(1/•g/m3)

Source

Naphthalene 91203 0.000034 CAL

Polycyclic Organic Matter8(POM)

(as Benzo [a] pyrene toxic 

equivalent) 50328 0.0016 CAL

6 The URE is the upper-bound excess cancer risk estimated to result from continuous lifetime exposure to an 

agent at a concentration of 1 •g/m3in air. URE’s are considered upper bound estimates meaning they represent 

a plausible upper limit to the true value. 

7 Chemical Abstract Services identification number. For groups of compounds that lack a CAS number we have 

used a surrogate 3-digit identifier corresponding to the group’s position on the CAA list of HAPs.
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Table 2.6-1 (b) Dose-Response Values for Chronic Oral Exposure to Carcinogens

SF (oral slope factor for cancer) = cancer risk per mg/kg/d of average lifetime exposure. Sources: IRIS 

= EPA Integrated Risk Information System, CAL = California EPA Office of Environmental Health 

Hazard Assessment, EPA/OAQPS = interim value recommended by the EPA Office of Air Quality 

Planning and Standards.

Pollutant CAS Number10

SF

(1/mg/kg/d) Source

POM (as Benzo [a] pyrene toxic 

equivalent) 50328 10 CAL

8 Assigned the URE associated with a mixture of POM compounds having a similar potency. Details of this 

method, also used in the 1999 National Air Toxics Assessment, are available at 

http://www.epa.gov/ttn/atw/nata1999/99pdfs/pomapproachjan.pdf
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Table 2.6-1 (c) Dose-Response Values for Chronic Inhalation Exposure to Noncarcinogens

RfC (reference inhalation concentration) = an estimate (with uncertainty spanning perhaps an order of 

magnitude) of a continuous inhalation exposure to the human population (including sensitive subgroups) 

that is likely to be without an appreciable risk of deleterious effects during a lifetime. Sources: IRIS = EPA 

Integrated Risk Information System, CAL = California EPA Office of Environmental Human Health 

Assessment, ATSDR = US Agency for Toxic Substances Disease Registry. 

Pollutant CAS Number6 RfC 

(mg/m3)

Source9

Carbonyl sulfide 463581 0.163 ORD Screening Value

Hydrofluoric acid 7664393 0.014 CAL

Naphthalene 91203 0.003 IRIS - M

2.6.2 Sources of acute dose-response information 

Hazard identification and dose-response assessment information for preliminary acute 

inhalation exposure assessments is based on the existing recommendations of OAQPS for 

HAPs [17]. Depending on availability, the results from screening acute assessments are 

compared to both “no effects” reference levels for the general public, such as the California 

Reference Exposure Levels (RELs), as well as emergency response levels, such as Acute 

Exposure Guideline Levels (AEGLs) and Emergency Response Planning Guidelines 

(ERPGs), with the recognition that the ultimate interpretation of any potential risks associated 

with an estimated exceedance of a particular reference level depends on the definition of that 

level and any limitations expressed therein. Comparisons among different available 

inhalation health effect reference values (both acute and chronic) for selected HAPs can be 

found in an EPA document.

10

California Acute Reference Exposure Levels (RELs). The California Environmental 

Protection Agency (CalEPA) has developed acute dose-response reference values for many 

substances, expressing the results as Acute inhalation Reference Exposure Levels (RELs). 

The acute REL (http://www.oehha.ca.gov/air/pdf/acuterel.pdf) is defined by CalEPA as 

“the concentration level at or below which no adverse health effects are anticipated for a 

specified exposure duration. [18]. RELs are based on the most sensitive, relevant, 

adverse health effect reported in the medical and toxicological literature. RELs are 

designed to protect the most sensitive individuals in the population by the inclusion of

margins of safety. Since margins of safety are incorporated to address data gaps and

uncertainties, exceeding the REL does not automatically indicate an adverse health 

impact.” Acute RELs are developed for 1-hour (and 8-hour) exposures. The values 

incorporate uncertainty factors similar to those used in deriving EPA’s Inhalation 

9 The descriptors L (low), M (medium), and H (high) have been added for IRIS RfC values to indicate the 

overall level of confidence in the RfC value, as reported in the IRIS file.

10 See: U.S. EPA. Graphical Arrays of Chemical-Specific Health Effect Reference Values for Inhalation 

Exposures [Final Report]. (U.S. Environmental Protection Agency, Washington, D.C., EPA/600/R-09/061, 

2009. http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=211003.)
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Reference Concentrations (RfCs) for chronic exposures (and, in fact, California also has

developed chronic RELs).

Acute Exposure Guideline Levels (AEGLs). AEGLs are developed by the National Advisory

Committee (NAC) on Acute Exposure Guideline Levels (NAC/AEGL) for Hazardous 

Substances, and then reviewed and published by the National Research Council. As described 

in the Committee’s “Standing Operating Procedures (SOP)”

(http://www.epa.gov/opptintr/aegl/pubs/sop.pdf), AEGLs “represent threshold exposure limits 

for the general public and are applicable to emergency exposures ranging from 10 min to 8 h.”

Their intended application is “for conducting risk assessments to aid in the development of 

emergency preparedness and prevention plans, as well as real time emergency response 

actions, for accidental chemical releases at fixed facilities and from transport carriers.” The 

document states: “the primary purpose of the AEGL program and the NAC/AEGL Committee 

is to develop guideline levels for once-in-a-lifetime, short-term exposures to airborne 

concentrations of acutely toxic, high-priority chemicals.” In detailing the intended application 

of AEGL values, the document states that, “It is anticipated that the AEGL values will be 

used for regulatory and nonregulatory purposes by U.S. Federal and State agencies, and 

possibly the international community in conjunction with chemical emergency response, 

planning, and prevention programs. More specifically, the AEGL values will be used for 

conducting various risk assessments to aid in the development of emergency preparedness and 

prevention plans, as well as real-time emergency response actions, for accidental chemical 

releases at fixed facilities and from transport carriers.” The NAC/AEGL defines AEGL-1 and 

AEGL-2 as:

“AEGL-1 is the airborne concentration (expressed as ppm or mg/m3) of a substance above 

which it is predicted that the general population, including susceptible individuals, could 

experience notable discomfort, irritation, or certain asymptomatic nonsensory effects. 

However, the effects are not disabling and are transient and reversible upon cessation of 

exposure.”

“AEGL-2 is the airborne concentration (expressed as ppm or mg/m3) of a substance above 

which it is predicted that the general population, including susceptible individuals, could 

experience irreversible or other serious, long-lasting adverse health effects or an impaired 

ability to escape.”

“Airborne concentrations below AEGL-1 represent exposure levels that can produce mild 

and progressively increasing but transient and nondisabling odor, taste, and sensory

irritation or certain asymptomatic, nonsensory effects. With increasing airborne 

concentrations above each AEGL, there is a progressive increase in the likelihood of 

occurrence and the severity of effects described for each corresponding AEGL. Although 

the AEGL values represent threshold levels for the general public, including susceptible 

subpopulations, such as infants, children, the elderly, persons with asthma, and those with 

other illnesses, it is recognized that individuals, subject to unique or idiosyncratic 

responses, could experience the effects described at concentrations below the 

corresponding AEGL.”
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Emergency Response Planning Guidelines (ERPGs). The American Industrial Hygiene 

Association (AIHA) has developed Emergency Response Planning Guidelines (ERPGs) [19] 

for acute exposures at three different levels of severity. These guidelines represent 

concentrations for exposure of the general population (but not particularly sensitive persons) 

for up to 1 hour associated with effects expected to be mild or transient (ERPG-1), 

irreversible or serious (ERPG-2), and potentially life-threatening (ERPG-3). 

ERPG values (http://www.aiha.org/1documents/Committees/ERP-erpglevels.pdf) are

described in their supporting documentation as follows: “Emergency Response Planning 

Guidelines (ERPGs) were developed for emergency planning and are intended as health based 

guideline concentrations for single exposures to chemicals. These guidelines (i.e., the ERPG 

Documents and ERPG values) are intended for use as planning tools for assessing the 

adequacy of accident prevention and emergency response plans, including transportation 

emergency planning and for developing community emergency response plans. The emphasis 

is on ERPGs as planning values: When an actual chemical emergency occurs there is seldom 

time to measure airborne concentrations and then to take action.” ERPG-1 and ERPG-2

values are defined by AIHA as follows:

“ERPG-1 is the maximum airborne concentration below which it is believed that nearly all 

individuals could be exposed for up to 1 hour without experiencing other than mild 

transient adverse health effects or without perceiving a clearly defined, objectionable 

odor.”

“ERPG-2 is the maximum airborne concentration below which it is believed that nearly all 

individuals could be exposed for up to 1 hour without experiencing or developing 

irreversible or other serious health effects or symptoms which could impair an individual's 

ability to take protective action.”

The emissions inventory for the Primary Aluminum Production source category includes 

emissions of two HAP with relevant and available quantitative acute dose-response threshold 

values. These HAPs, their acute threshold values, and the source of the value are listed below 

in Table 2.6-2.

Table 2.6-2 Dose-Response Values for Acute Exposure

Pollutant CAS Number

AEGL-1

(1-hr)

(mg/m3)

AEGL-2

(1-hr)

(mg/m3)

ERPG-1

(mg/m3)

ERPG-2

(mg/m3) REL 

Carbonyl sulfide 463581 140

Hydrofluoric acid 7664393 0.82 20 1.6 20 0.24

2.7 Risk characterization

2.7.1 General

The final product of the risk assessment is the risk characterization, in which the information 
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from the previous steps is integrated and an overall conclusion about risk is synthesized that is 

complete, informative, and useful for decision makers. In general, the nature of this risk 

characterization depends on the information available, the application of the risk information 

and the resources available. In all cases, major issues associated with determining the nature 

and extent of the risk are identified and discussed. Further, the EPA Administrator’s March 

1995 Policy for Risk Characterization [20] specifies that a risk characterization “be prepared 

in a manner that is clear, transparent, reasonable, and consistent with other risk 

characterizations of similar scope prepared across programs in the Agency.” These principles 

of transparency and consistency have been reinforced by the Agency’s Risk Characterization 

Handbook [21], in 2002 by the Agency’s information quality guidelines [22], and in the 

OMB/OSTP September 2007 Memorandum on Updated Principles for Risk Analysis11, and 

are incorporated in these assessments.

Estimates of health risk are presented in the context of uncertainties and limitations in the data 

and methodology. Through our tiered, iterative analytical approach, we have attempted to 

reduce both uncertainty and bias to the greatest degree possible in these assessments, within 

the limitations of available time and resources. We provide summaries of risk metrics 

(including maximum individual cancer risks and noncancer hazards, as well as cancer 

incidence estimates) along with a discussion of the major uncertainties associated with their 

derivation to provide decision makers with the fullest picture of the assessment and its 

limitations.

For each carcinogenic HAP included in an assessment that has a potency estimate available, 

individual and population cancer risks were calculated by multiplying the corresponding 

lifetime average exposure estimate by the appropriate URE. This calculated cancer risk is 

defined as the upper-bound probability of developing cancer over a 70-yr period (i.e., the 

assumed human lifespan) at that exposure. Because UREs for most HAPs are upper-bound 

estimates, actual risks at a given exposure level may be lower than predicted, and could be 

zero.

For EPA’s list of carcinogenic HAPs that act by a mutagenic mode-of-action [23], we applied 

EPA’s Supplemental Guidance for Assessing Susceptibility from Early-Life Exposure to 

Carcinogens [24]. This guidance has the effect of adjusting the URE by factors of 10 (for 

children aged 0-1), 3 (for children aged 2-15), or 1.6 (for 70 years of exposure beginning at 

birth), as needed in risk assessments. In this case, this has the effect of increasing the 

estimated life time risks for these pollutants by a factor of 1.6. In addition, although only a 

small fraction of the total POM emissions may be reported as individual compounds, EPA 

expresses carcinogenic potency for compounds in this group in terms of benzo[a]pyrene 

equivalence, based on evidence that carcinogenic POM have the same mutagenic mechanism 

of action as does benzo[a]pyrene. For this reason, EPA implementation policy [25] 

recommends applying the Supplemental Guidance to all carcinogenic PAHs for which risk 

estimates are based on relative potency. Accordingly, we applied the Supplemental Guidance 

11Memorandum for the Heads of Executive Departments and Agencies - Updated Principles for Risk Analysis

(September 19, 2007), From Susan E. Dudley, Administrator, Office of Information and Regulatory Affairs, 

Office of Management and Budget; and Sharon L. Hays, Associate Director and Deputy Director for Science, 

Office of Science and Technology Policy (http://www.whitehouse.gov/omb/memoranda/fy2007/m07-24.pdf)
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to all unspeciated POM mixtures.

Increased cancer incidence for the entire receptor population within the area of analysis was 

estimated by multiplying the estimated lifetime cancer risk for each census block by the 

number of people residing in that block, then summing the results for the entire modeled 

domain. This lifetime population incidence estimate was divided by 70 years to obtain an 

estimate of the number of cancer cases per year.

Unlike linear dose-response assessments for cancer, noncancer health hazards generally are 

not expressed as a probability of an adverse occurrence. Instead, “risk” for noncancer effects 

is expressed by comparing an exposure to a reference level as a ratio. The “hazard quotient” 

(HQ) is the estimated exposure divided by a reference level (e.g., the RfC). For a given HAP, 

exposures at or below the reference level (HQ•1) are not likely to cause adverse health 

effects. As exposures increase above the reference level (HQs increasingly greater than 1), 

the potential for adverse effects increases. For exposures predicted to be above the RfC, the 

risk characterization includes the degree of confidence ascribed to the RfC values for the 

compound(s) of concern (i.e., high, medium, or low confidence) and discusses the impact of 

this on possible health interpretations. 

The risk characterization for chronic effects other than cancer is expressed in terms of the HQ 

for inhalation, calculated for each HAP at each census block centroid. As discussed above, 

RfCs incorporate generally conservative uncertainty factors in the face of uncertain 

extrapolations, such that an HQ greater than one does not necessarily suggest the onset of 

adverse effects. The HQ cannot be translated to a probability that adverse effects will occur, 

and is unlikely to be proportional to adverse health effect outcomes in a population.

Screening for potentially significant acute inhalation exposures also followed the HQ 

approach. We divided the maximum estimated acute exposure by each available short-term 

threshold value to develop an array of HQ values relative to the various acute endpoints and 

thresholds. In general, when none of these HQ values are greater than one, there is no 

potential for acute risk. In those cases where HQ values above one are seen, additional 

information is used to determine whether there is a potential for significant acute risks.

2.7.2 Mixtures

Since most or all receptors in these assessments receive exposures to multiple pollutants 

rather than a single pollutant, we estimated the aggregate health risks associated with all the 

exposures from a particular source category combined.

To combine risks across multiple carcinogens, our assessments use the mixtures guidelines’ 

[26,27] default assumption of additivity of effects, and combine risks by summing them using 

the independence formula in the mixtures guidelines.

In assessing noncancer hazard from chronic exposures, in cases where different pollutants 

cause adverse health effects via completely different modes of action, it may be inappropriate 

to aggregate HQs. In consideration of these mode-of-action differences, the mixtures 
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guidelines support aggregating effects of different substances in specific and limited ways. 

Consistent with these guidelines, we aggregated non-cancer HQs of HAPs that act by similar 

toxic modes of action, or (where this information is absent) that affect the same target organ. 

This process creates, for each target organ, a target-organ-specific hazard index (TOSHI), 

defined as the sum of hazard quotients for individual HAPs that affect the same organ or 

organ system. All TOSHI calculations presented here were based exclusively on effects 

occurring at the “critical dose” (i.e., the lowest dose that produces adverse health effects). 

Although HQs associated with some pollutants have been aggregated into more than one 

TOSHI, this has been done only in cases where the critical dose affects more than one target 

organ. Because impacts on organs or systems that occur above the critical dose have not been 

included in the TOSHI calculations, some TOSHIs may have been underestimated. As with 

the HQ, the TOSHI should not be interpreted as a probability of adverse effects, or as strict 

delineation of “safe” and “unsafe” levels. Rather, the TOSHI is another measure of the 

potential for adverse health outcomes associated with pollutant exposure, and needs to be 

interpreted carefully by health scientists and risk managers.

Because of the conservative nature of the acute inhalation screening and the variable nature of 

emissions and potential exposures, acute impacts were screened on an individual pollutant 

basis, not using the TOSHI approach.

2.7.3 Facility-wide risks

To help place the source category risks in context, we typically examine “facility-wide” risks 

using facility-wide and source category data files developed by EPA through emission data 

updates from industry and refined EPA profiles for the source category. For this source 

category we did not receive facility-wide emissions, and therefore, a facility-wide assessment 

was not performed. However, most all primary aluminum production facilities are co-located 

with secondary aluminum production facilities and a facility-wide analysis will be part of the 

risk assessment that is currently being done for the secondary aluminum source category12. 

3 Risk results for the primary aluminum production source category

3.1 Source category description and results

The Primary Aluminum Production source category consists of facilities that produce 

aluminum from refined ore (alumina) using a reduction process whereby the alumina is 

dissolved in an electrolytic bath in reduction cells which are connected electrically in series to 

form “potlines” in the presence of fluoride. The main raw materials in this process are 

alumina (Al2O3), coke, pitch, and fluoride. The process of aluminum reduction includes 

passing a high electrical direct current from a carbon anode to a carbon cathode which results 

in purified molten aluminum sinking to the bottom of the reduction cell, beneath the 

electrolytic bath. 

12 The results of this analysis will be available in the docket for the secondary aluminum source category.
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There are currently 14 secondary aluminum smelters operating in the U.S. The NPRM data 

set for the Primary Aluminum Production category contains 14 facilities identified with a 

Primary Aluminum Reduction MACT code in the 2005 National-Scale Air Toxics 

Assessment (NATA) National Emissions Inventory (NEI), September 2010 version. All 14 of 

these facilities are identified as major sources in the NEI. 

The emissions in the Primary Aluminum Production source category NPRM data set (of 15

facilities) are summarized in Table 3.1-1. The total HAP emissions for the source category

are approximately 6,000 tons per year. Air emissions from Primary Aluminum Production

facilities include 4 MACT regulated toxic air pollutants: polycyclic organic matter (POM), 

carbonyl sulfide, hydrofluoric acid, and naphthalene.

Table 3.1-1 Summary of Emissions from the Primary Aluminum Production Source Category and 

Availability of Dose-Response Values

HAPa

Emission

Estimates

(tpy)

Number of 

Facilities 

where HAP 

Reported or 

Estimated 

(14 facilities 

in NPRM 

data set)

Prioritized Inhalation Dose-Response Value 

Identified by OAQPSb

PBHAP? Unit Risk 

Estimate for 

Cancer?

Reference 

Concentration 

for Chronic 

Noncancer?

Health 

Benchmark 

Values for Acute 

Noncancer?

Carbonyl sulfide 4,564 13 Υ

c Υ

Hydrofluoric acid 1,351 13 Υ Υ

POM 91 14 Υ Υ

Naphthalene 0.72 14 Υ Υ

a Notes for how HAP were speciated for risk assessment:

• There were 15 total facilities, but actual emission estimates were not provided for the Columbia Falls facility. 

• For emissions of any chemicals or chemical groups classified as polycyclic organic matter (POM), emissions were 

grouped into POM subgroups as found on EPA’s Technology Transfer Network website for the 2002 National-Scale Air 

Toxics Assessment at http://www.epa.gov/nata2002/methods.html#pom. (Approach to Modeling POM). For this source 

category, all POMs are PAHs.

b

Specific dose-response values for each chemical are identified on EPA’s Technology Transfer Network website for air 

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

C The chronic, noncancer health effects benchmark value for carbonyl sulfide is an ORD screening value.

3.2 Baseline risk characterization

This section presents the results of the risk assessment for the Primary Aluminum Production

source category based on the modeling methods described in the previous sections. All 

baseline risk results are developed using the best estimates of actual and MACT-allowable 

HAP emissions. The basic chronic inhalation risk estimates presented here are the maximum 

individual lifetime cancer risk, the maximum target organ specific chronic hazard index, and 

the cancer incidence. We also present results from our acute inhalation impact screening 

assessments in the form of maximum hazard quotients, as well as the results of our screen for 

potential non-inhalation risks from PB HAP. Also presented are the HAP “drivers,” which 

are the HAP that collectively contribute 90 percent of the maximum cancer risk or maximum 
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hazard at the highest exposure location. A detailed summary of the facility-specific risk 

assessment results is available in Appendix 5.

Tables 3.2-1 and 3.2-2 summarize the preliminary chronic and acute inhalation risk results for 

this source category. The results of the chronic inhalation cancer risk assessment indicate that 

the maximum lifetime individual cancer risk, considering actual emissions, could be up to 30

in a million, driven by POM emissions primarily from pot line roof vents and anode bake 

furnaces. The maximum cancer risks for this source category exceeded a cancer risk of 1 in a 

million at 12 of 14 facilities. The total estimated cancer incidence from this source category

based on actual emission levels is 0.005 excess cancer cases per year, or one excess case in 

every 200 years. Approximately 900 people were estimated to have cancer risks above 10 in 

a million and approximately 41,000 people were estimated to have cancer risks above 1 in a 

million considering all facilities in this source category. Based on engineering judgment, 

MACT-allowable emissions for this source category could be up to 1.5 times actual 

emissions; thus considering MACT allowable emissions, the MIR could be up to 50-in a 

million for the facilities presented in Table 3.2-1. 

In addition to the facilities presented in Table 3.2-1, we also estimated cancer risks for the 

Columbia Falls facility which has not been fully operational since 1999 and has been 

completely closed for 2 years, and thus, did not report actual emissions for the year 2010. For 

this facility, we calculated an estimate of allowable emissions to approximate the cancer risk 

should this facility resume its primary aluminum production operations.13 The results of this 

assessment indicated that should this plant resume its operations, cancer risk could be as high 

as 100-in a million, resulting predominantly from pot line roof vent emissions of POM.

With respect to chronic inhalation noncancer risk, we estimate a maximum TOSHI value of 

0.4 for the Primary Aluminum source category, with pot line vent emissions of HF

dominating those impacts. Considering MACT-allowable emissions, this maximum TOSHI

value is estimated to be 0.6. With respect to Columbia Falls, should this facility resume its 

primary aluminum production operations, we estimate a maximum TOSHI value of 0.2

primarily from emissions of HF from pot line roof vents.

13 See the emissions chapter of the TSD document for a discussion of how emissions were calculated for the 

Columbia Falls facility (available in the docket for the proposed rulemaking).
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Table 3.2-1 Summary of Source Category Level Inhalation Risks for Primary 

Aluminum Production

Result HAP “Drivers”

Facilities in Source Category

Number of Facilities Estimated to be in Source 

Category 15 n/a

Number of Facilities Identified in NEI and 

Modeled in Preliminary Risk Assessment 1414 n/a

Cancer Risks

Maximum Individual Lifetime Cancer Risk (in 

1 million) 30 POM

Number of Facilities with Maximum Individual Lifetime Cancer Risk:

Greater than or equal to 100 in 1 

million 0 n/a

Greater than or equal to 10 in 1 

million 2 POM 

Greater than or equal to 1 in 1 million 12 POM 

Chronic Noncancer Risks

Maximum Developmental Hazard Index 0.4 hydrofluoric acid

Number of Facilities with Maximum Developmental Hazard Index:

Greater than 1 0 n/a

Acute Noncancer Results

Maximum Acute Hazard Quotient

10

4

2

hydrofluoric acid (REL)

hydrofluoric acid (AEGL-1)

hydrofluoric acid (ERPG-1)

Number of Facilities With Potential for Acute 

Effects 8 hydrofluoric acid

Population Exposure

Number of People Living Within 50 

Kilometers of Facilities Modeled 3,300,000 n/a

Number of People Exposed to Cancer Risk:

Greater than or equal to 100 in 1 

million 0 n/a

Greater than or equal to 10 in 1 

million 900 n/a

Greater than or equal to 1 in 1 million 41,200 n/a

Number of People Exposed to Noncancer Developmental Hazard Index:

Greater than 1 0 n/a

Estimated Cancer Incidence (excess cancer 

cases per year) 0.005 n/a

Contribution of HAP to Cancer Incidence

POM 99% n/a

14 The Columbia Falls facility has not been fully operational since 1999 and has been completely closed for 2

years and therefore, EPA did not generate risk estimates based on actual emissions for this facility. However, as 

seen below, EPA did estimate risk based on MACT allowable emissions for this facility. 
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Table 3.2-2 Summary of Refined Acute Results for Secondary Aluminum Production 

Facilities

Refined Results

MAXIMUM ACUTE HAZARD 

QUOTIENTS

ACUTE DOSE-RESPONSE 

VALUES

Based on 

REL

Based on 

AEGL-1

Based on

ERPG-1

REL

(mg/m3)

AEGL-1 

(1-hr) 

(mg/m3)

ERPG-1 

(mg/m3)

HAP Max. 1-hr. Air 

Conc. (mg/m3)

hydrofluoric acid 3.0 10 4 2 0.24 0.82 1.6

Notes on Refined Process:

1) The screening was performed for all emitted HAP with available acute dose-response values. Only those pollutants whose 

screening HQs were greater than 1 for at least one acute threshold value are shown in the table.

2) HAP with available acute dose-response values which are not in the table do not carry any potential for posing acute health 

risks, based on an analysis of currently available emissions data.

Notes on Acute Dose-Response Values:

REL – California EPA reference exposure level for no adverse effects. Most, but not all, RELs are for 1-hour 

exposures.

AEGL – Acute exposure guideline levels represent emergency exposure (1-hour) limits for the general public.

AEGL-1 is the exposure level above which it is predicted that the general population, including susceptible individuals, could 

experience effects that are notable discomfort, but which are transient and reversible upon cessation of exposure.

AEGL-2 is the exposure level above which it is predicted that the general population, including susceptible individuals, could 

experience irreversible or other serious, long-lasting adverse health effects of an impaired ability to escape.

EPRG – Emergency Removal Program guidelines represent emergency exposure (1-hour) limits for the general 

public.

ERPG-1 is the maximum level below which it is believed that nearly all individuals could be exposed for up to 1 hour 

without experiencing other than mild, transient adverse health effects.

ERPG-2 is the maximum exposure below which it is believed that nearly all individuals could be exposed for up to 1 hour 

without experiencing or developing irreversible or other serious health effects or symptoms which could impair an 

individual’s ability to take protective action.

For this source category, there were two pollutants that had relevant acute health effect 

screening values: carbonyl sulfide and hydrofluoric acid. With respect to carbonyl sulfide, we 

did not find any potential for acute health concerns for the 14 operational facilities based on 

their actual emissions. However, hydrofluoric acid emissions did not screen out. Our 

screening analysis showed that worst-case screening-level acute HQ values for hydrofluoric 

acid could exceed a value of 1 (based on the REL, AEGL-1, or ERPG-1) at 9 of the 14 

facilities. We refined our acute screening values somewhat by taking into account whether 

acute screening value locations identified in the initial screen were within a facility’s 

boundary (they must be off-site to represent a potential exposure to the general public, see 

Appendix 5 and Appendix 6). Considering this refinement, the highest HQ for hydrofluoric 

acid based on a REL is 10, based on an AEGL-1 is 4, and based on an ERPG-1 is 2 (see Table 

3.2-2). Moreover, after refinement, 8 of the 14 operational facilities show the potential for 

worst-case acute HQ values greater than 1 based on the REL, 4 of 14 facilities show the 

potential for worst-case acute HQ values greater than 1 based on the AEGL-1 and 4 of 14 

facilities show the potential for worst-case acute HQ values greater than or equal to 1 based 

on the ERPG-1.15 Notably in humans, concentrations above the AEGL-1 or at or above the

15 Individual facility acute HQ values for all facilities can be found in Appendix 5, Table 4, of this document. 

Acute HQ values exceeding a value of 1 based on the REL were as follows: 10, 10, 9, 9, 5, 3, 2 and 2. Acute HQ 
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ERPG-1 for hydrofluoric acid are associated with reversible minor irritation of the skin and 

eyes, as well an increase in pulmonary inflammation. That being said, we note again that this 

is still a conservative, worst-case screen with built-in assumptions that bias the results high. 

As a result, there is considerable uncertainty as to whether concentrations of hydrofluoric acid 

above the AEGL-1 or at or above the ERPG-1 benchmark value could actually occur near 

some of these facilities. Thus, these results only indicate that there may be some potential for 

acute health effects from emissions of hydrofluoric acid from facilities in this source category; 

they do not indicate that effects are actually occurring.

In addition to the analyses presented above, to screen for potential multipathway effects from 

emissions of PAHs, we compared actual PAH emission rates from 14 facilities in this source 

category to the screening value for PAHs described in section 2.5 (also see Appendix 4 for a 

more detailed discussion of screening emission rates). Results of this worst- case screen 

estimate that actual PAH emissions from all facilities exceed the PAH screening emission rate 

by as much as approximately 7,000 times. With respect to these exceedences of the worstcase multipathway screening level for PAHs, we note that this only indicates the potential for

multipathway-related cancer risks of concern from PAHs. That is, while in general, emission 

rates below the worst-case multipathway screening level indicate no significant potential for 

multipathway related health effects, emission levels above this worst-case screening level 

only indicate the potential for multipathway-related health risks of concern based on a worstcase scenario. Due to data limitations, we were not able to refine our multi-pathway analysis 

beyond the worst-case screening assessment. Thus, we note that these results are biased high 

for purposes of screening and are therefore subject to significant uncertainties. As a result, we 

cannot say whether multi-pathway risks may be a concern.

With respect to the potential for significant and widespread environmental effects from non 

PB-HAP, and in particular hydrofluoric acid which can potentially have adverse effects on 

vegetation, we note that all chronic non-cancer HQ values for all pollutants considering actual 

emissions are well below 1 using human health reference values. Thus, we believe that it is 

unlikely that adverse environmental effects would occur at the actual HAP concentrations 

estimated in our human health risk assessment. 

3.3 Post-control risk characterization

The proposed changes to the current NESHAP (see discussion in the proposed rule) will have 

negligible effect on all the estimated risks associated with all the currently operating facilities 

in the Primary Aluminum category. For the one facility which is currently not operational 

(the Columbia Falls facility), however, the cancer MIR under MACT-allowable emissions 

would be reduced from 100 in a million to approximately 70 in a million, effectively capping 

the potential cancer risks from this facility should it resume operations16. 

values greater than a value of 1 based on the AEGL-1 were as follows: 4, 4, 3 and 3. Acute HQ values greater 

than or equal to a value of 1 based on the ERPG-1 were as follows: 2, 2, 1 and 1.

16 EPA is also proposing to lower the POM limit for existing pitch storage tanks, but we do not anticipate that 

this will have an appreciable effect on the risk estimates presented above since pitch storage tanks are not a 

significant driver of risk. 
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4 General discussion of uncertainties and how they have been 

addressed

4.1 Exposure modeling uncertainties

Although every effort has been made to identify all the relevant facilities and emission points, 

as well as to develop accurate estimates of the annual emission rates for all relevant HAP, the 

uncertainties in our emission inventory likely dominate the uncertainties in our exposure 

modeling estimates. The chronic exposure modeling uncertainties are considered relatively 

small in comparison, since we are using EPA’s refined local dispersion model with sitespecific parameters and reasonably representative meteorology. If anything, the population 

exposure estimates are biased high by not accounting for short- or long-term population 

mobility, and by neglecting processes like deposition, plume depletion, and atmospheric 

degradation. Additionally, estimates of the maximum individual risk (MIR) contain 

uncertainty, because they are derived at census block centroid locations rather than actual 

residences. This uncertainty is known to create potential underestimates and overestimates of 

the actual MIR values for individual facilities, but, overall, it is not thought to have a 

significant impact on the estimated MIR for a source category. Finally, we did not factor in 

the possibility of a source closure occurring during the 70-year chronic exposure period, 

leading to a potential upward bias in both the MIR and population risk estimates; nor did we 

factor in the possibility of population growth during the 70-year chronic exposure period, 

leading to a potential downward bias in both the MIR and population risk estimates.

A sensitivity analysis performed for the 1999 NATA found that the selection of the 

meteorology dataset location could result in a range of chronic ambient concentrations which 

varied from as much as 17% below the predicted value to as much as 84% higher than the 

predicted value. This variability translates directly to the predicted exposures and risks in our 

assessment, indicating that the actual risks could vary from 17% lower to 84% higher than the 

predicted values.

We have purposely biased the acute screening results high, considering that they depend upon 

the joint occurrence of independent factors, such as hourly emissions rates, meteorology and 

human activity patterns. However, we note that there is an unquantified level of uncertainty

regarding the emission estimates and the nature of the worst-case conditions for estimating the 

worst-case impacts of acute emissions of fugitive dusts. The current set of assumptions used 

in deriving the estimate assumes the average hourly emission level (annual emissions divided 

by 8760 hours per year) to occur at the default worst-case meteorological conditions (low 

winds with a stable atmosphere). The actual risks may be higher or lower than the estimates 

based upon the current set of assumptions. In cases where multiple acute threshold values are 

considered scientifically acceptable we have chosen the most conservative of these threshold 

values, erring on the side of overestimating potential health risks from acute exposures. In the 

cases where these results indicated the potential for exceeding short-term health thresholds

post-controls, we refined our assessment by developing a better understanding of the 

geography of the facility relative to potential exposure locations. 
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4.2 Uncertainties in the dose-response relationships

In the sections that follow, separate discussions are provided on uncertainty associated with 

cancer potency factors and for noncancer reference values. Cancer potency values are derived 

for chronic (lifetime) exposures. Noncancer reference values are generally derived for 

chronic exposures (up to a lifetime), but may also be derived for acute (•<24 hours), short-term 

(>24 hours up to 30 days), and subchronic (>30 days up to 10% of lifetime) exposure 

durations, all of which are derived based on an assumption of continuous exposure throughout 

the duration specified. For the purposes of assessing all potential health risks associated with 

the emissions included in an assessment, we rely on both chronic (cancer and noncancer) and 

acute (noncancer) benchmarks, which are described in more detail below.

Although every effort is made to identify peer-reviewed dose-response values for all HAPs

emitted by the source category included in an assessment, some HAP have no peer-reviewed 

cancer potency values or reference values for chronic non-cancer or acute effects (inhalation 

or ingestion). Since exposures to these pollutants cannot be included in a quantitative risk 

estimate, an understatement of risk for these pollutants at environmental exposure levels is 

possible.

Additionally, chronic dose-response values for certain compounds included in the assessment 

may be under EPA IRIS review and revised assessments may determine that these pollutants 

are more or less potent than currently thought. We will re-evaluate risks if, as a result of these 

reviews, a dose-response metric changes enough to indicate that the risk assessment may

significantly mischaracterize human health risk

Cancer assessment

The discussion of dose-response uncertainties in the estimation of cancer risk below focuses 

on the uncertainties associated with the specific approach currently used by the EPA to 

develop cancer potency factors. In general, these same uncertainties attend the development 

of cancer potency factors by CalEPA, the source of peer-reviewed cancer potency factors 

used where EPA-developed values are not yet available. To place this discussion in context, 

we provide a quote from the EPA’s Guidelines for Carcinogen Risk Assessment [28] (herein 

referred to as Cancer Guidelines). “The primary goal of EPA actions is protection of human 

health; accordingly, as an Agency policy, risk assessment procedures, including default 

options that are used in the absence of scientific data to the contrary, should be health 

protective.” The approach adopted in this document is consistent with this approach as 

described in the Cancer Guidelines.

For cancer endpoints EPA usually derives an oral slope factor for ingestion and a unit risk 

value for inhalation exposures. These values allow estimation of a lifetime probability of 

developing cancer given long-term exposures to the pollutant. Depending on the pollutant 

being evaluated, EPA relies on both animal bioassay and epidemiological studies to 

characterize cancer risk. As a science policy approach, consistent with the Cancer 
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Guidelines, EPA uses animal cancer bioassays as indicators of potential human health risk 

when other human cancer risk data are unavailable. 

Extrapolation of study data to estimate potential risks to human populations is based upon 

EPA’s assessment of the scientific database for a pollutant using EPA’s guidance documents 

and other peer-reviewed methodologies. The EPA Cancer Guidelines describes the Agency’s 

recommendations for methodologies for cancer risk assessment. EPA believes that cancer 

risk estimates developed following the procedures described in the Cancer Guidelines and 

outlined below generally provide an upper bound estimate of risk. That is, EPA’s upper 

bound estimates represent a “plausible upper limit to the true value of a quantity” (although 

this is usually not a true statistical confidence limit).17 In some circumstances, the true risk 

could be as low as zero; however, in other circumstances the risk could also be greater.18

When developing an upper bound estimate of risk and to provide risk values that do not 

underestimate risk, EPA generally relies on conservative default approaches.19 EPA also uses 

the upper bound (rather than lower bound or central) estimates in its assessments, although it 

is noted that this approach can have limitations for some uses (e.g. priority setting, expected 

benefits analysis).

Such health risk assessments have associated uncertainties, some which may be considered 

quantitatively, and others which generally are expressed qualitatively. Uncertainties may vary 

substantially among cancer risk assessments associated with exposures to different pollutants, 

since the assessments employ different databases with different strengths and limitations and 

the procedures employed may differ in how well they represent actual biological processes for 

the assessed substance. EPA’s Risk Characterization Handbook also recommends that risk 

characterizations present estimates demonstrating the impact on the assessment of alternative 

choices, data, models and assumptions [29]. Some of the major sources of uncertainty and 

variability in deriving cancer risk values are described more fully below. 

(1) The qualitative similarities or differences between tumor responses observed in 

experimental animal bioassays and those which would occur in humans are a source of 

uncertainty in cancer risk assessment. In general, EPA does not assume that tumor sites 

observed in an experimental animal bioassay are necessarily predictive of the sites at which 

17 IRIS glossary (www.epa.gov/NCEA/iris/help_gloss.htm).

18 The exception to this is the URE for benzene, which is considered to cover a range of values, each end of 

which is considered to be equally plausible, and which is based on maximum likelihood estimates.

19 According to the NRC report Science and Judgment in Risk Assessment (NRC, 1994) “[Default] options are 

generic approaches, based on general scientific knowledge and policy judgment, that are applied to various 

elements of the risk-assessment process when the correct scientific model is unknown or uncertain.” The 1983 

NRC report Risk Assessment in the Federal Government: Managing the Process defined default option as “the 

option chosen on the basis of risk assessment policy that appears to be the best choice in the absence of data to 

the contrary” (NRC, 1983a, p. 63). Therefore, default options are not rules that bind the agency; rather, the 

agency may depart from them in evaluating the risks posed by a specific substance when it believes this to be 

appropriate. In keeping with EPA’s goal of protecting public health and the environment, default assumptions 

are used to ensure that risk to chemicals is not underestimated (although defaults are not intended to overtly 

overestimate risk). See EPA 2004 An Examination of EPA Risk Assessment Principles and Practices, 

EPA/100/B-04/001 available at: http://www.epa.gov/osa/pdfs/ratf-final.pdf.
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tumors would occur in humans.20 However, unless scientific support is available to show 

otherwise, EPA assumes that tumors in animals are relevant in humans, regardless of target 

organ concordance. For a specific pollutant, qualitative differences in species responses can 

lead to either under-estimation or over-estimation of human cancer risks. 

(2) Uncertainties regarding the most appropriate dose metric for an assessment can also lead 

to differences in risk predictions. For example, the measure of dose is commonly expressed 

in units of mg/kg/d ingested or the inhaled concentration of the pollutant. However, data may

support development of a pharmacokinetic model for the absorption, distribution, metabolism 

and excretion of an agent, which may result in improved dose metrics (e.g., average blood 

concentration of the pollutant or the quantity of agent metabolized in the body). Quantitative 

uncertainties result when the appropriate choice of a dose metric is uncertain or when dose 

metric estimates are themselves uncertain (e.g., as can occur when alternative 

pharmacokinetic models are available for a compound). Uncertainty in dose estimates may

lead to either over or underestimation of risk.

(3) For the quantitative extrapolation of cancer risk estimates from experimental animals to 

humans, EPA uses scaling methodologies (relating expected response to differences in 

physical size of the species), which introduce another source of uncertainty. These 

methodologies are based on both biological data on differences in rates of process according 

to species size and empirical comparisons of toxicity between experimental animals and 

humans. For a particular pollutant, the quantitative difference in cancer potency between 

experimental animals and humans may be either greater than or less than that estimated by

baseline scientific scaling predictions due to uncertainties associated with limitations in the 

test data and the correctness of scaled estimates. 

(4) EPA cancer risk estimates, whether based on epidemiological or experimental animal data, 

are generally developed using a benchmark dose (BMD) analysis to estimate a dose at which 

there is a specified excess risk of cancer, which is used as the point of departure (or POD) for 

the remainder of the calculation. Statistical uncertainty in developing a POD using a 

benchmark dose (BMD) approach is generally addressed though use of the 95% lower 

confidence limit on the dose at which the specified excess risk occurs (the BMDL), 

decreasing the likelihood of understating risk. EPA has generally utilized the multistage 

model for estimation of the BMDL using cancer bioassay data (see further discussion below).

(5) Extrapolation from high to low doses is an important, and potentially large, source of 

uncertainty in cancer risk assessment. EPA uses different approaches to low dose risk 

assessment (i.e., developing estimates of risk for exposures to environmental doses of an 

agent from observations in experimental or epidemiological studies at higher dose) depending 

on the available data and understanding of a pollutant’s mode of action (i.e., the manner in 

which a pollutant causes cancer). EPA’s Cancer Guidelines express a preference for the use 

of reliable, compound-specific, biologically-based risk models when feasible; however, such 

models are rarely available. The mode of action for a pollutant (i.e., the manner in which a 

20 Per the EPA Cancer Guidelines: “The default option is that positive effects in animal cancer studies indicate 

that the agent under study can have carcinogenic potential in humans.” and “Target organ concordance is not a 

prerequisite for evaluating the implications of animal study results for humans.”
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pollutant causes cancer) is a key consideration in determining how risks should be estimated 

for low-dose exposure. A reference value is calculated when the available mode of action 

data show the response to be nonlinear (e.g., as in a threshold response). A linear low-dose 

(straight line from POD) approach is used when available mode of action data support a linear 

(e.g., nonthreshold response) or as the most common default approach when a compound’s 

mode of action is unknown. Linear extrapolation can be supported by both pollutant-specific 

data and broader scientific considerations. For example, EPA’s Cancer Guidelines generally

consider a linear dose-response to be appropriate for pollutants that interact with DNA and 

induce mutations. Pollutants whose effects are additive to background biological processes in 

cancer development can also be predicted to have low-dose linear responses, although the 

slope of this relationship may not be the same as the slope estimated by the straight line 

approach. 

EPA most frequently utilizes a linear low-dose extrapolation approach as a baseline sciencepolicy choice (a “default”) when available data do not allow a compound-specific 

determination. This approach is designed to not underestimate risk in the face of uncertainty

and variability. EPA believes that linear dose-response models, when appropriately applied 

as part of EPA’s cancer risk assessment process, provide an upper bound estimate of risk and 

generally provide a health protective approach. Note that another source of uncertainty is the 

characterization of low-dose nonlinear, non-threshold relationships. The National Academy

of Sciences has encouraged the exploration of sigmoidal type functions (e.g., log-probit 

models) in representing dose response relationships due to the variability in response within 

human populations. Another National Research Council (NRC) report [30] suggests that 

models based on distributions of individual thresholds are likely to lead to sigmoidal-shaped 

dose-response functions for a population. This report notes sources of variability in the 

human population: “One might expect these individual tolerances to vary extensively in 

humans depending on genetics, coincident exposures, nutritional status, and various other 

susceptibility factors...” Thus, if a distribution of thresholds approach is considered for a 

carcinogen risk assessment, application would depend on ability of modeling to reflect the 

degree of variability in response in human populations (as opposed to responses in bioassays 

with genetically more uniform rodents). Note also that low dose linearity in risk can arise for 

reasons separate from population variability: due to the nature of a mode of action and 

additivity of a chemical’s effect on top of background chemical exposures and biological 

processes.

As noted above, EPA’s current approach to cancer risk assessment typically utilizes a straight 

line approach from the BMDL. This is equivalent to using an upper confidence limit on the 

slope of the straight line extrapolation. The impact of the choice of the BMDL on bottom line 

risk estimates can be quantified by comparing risk estimates using the BMDL value to central 

estimate BMD values, although these differences are generally not a large contributor to 

uncertainty in risk assessment (Subramaniam et. al., 2006) [31]. It is important to note that 

earlier EPA assessments, including the majority of those for which risk values exist today, 

were generally developed using the multistage model to extrapolate down to environmental 

dose levels and did not involve the use of a POD. Subramaniam et.,al. (2006) also provide 

comparisons indicating that slopes based on straight line extrapolation from a POD do not 
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show large differences from those based on the upper confidence limit of the multistage 

model.

(6) Cancer risk estimates do not generally make specific adjustments to reflect the variability

in response within the human population — resulting in another source of uncertainty in 

assessments. In the diverse human population, some individuals are likely to be more 

sensitive to the action of a carcinogen than the typical individual, although compound-specific 

data to evaluate this variability are generally not available. There may also be important life 

stage differences in the quantitative potency of carcinogens and, with the exception of the 

recommendations in EPA’s Supplemental Cancer Guidance for carcinogens with a mutagenic 

mode of action, risk assessments do not generally quantitatively address life stage differences. 

However, one approach used commonly in EPA assessments that may help address variability

in response is to extrapolate human response from results observed in the most sensitive 

species and sex tested, resulting typically in the highest URE which can be supported by

reliable data, thus supporting estimates that are designed not to underestimate risk in the face 

of uncertainty and variability.

Chronic noncancer assessment

Chronic noncancer reference values represent chronic exposure levels that are intended to be 

health-protective. That is, EPA and other organizations which develop noncancer reference 

values (e.g., the Agency for Toxic Substances and Disease Registry – ATSDR) utilize an 

approach that is intended not to underestimate risk in the face of uncertainty and variability. 

When there are gaps in the available information, uncertainty factors (UFs) are applied to 

derive reference values that are intended to be protective against appreciable risk of 

deleterious effects. Uncertainty factors are commonly default values21 e.g., factors of 10 or 3, 

used in the absence of compound-specific data; where data are available, uncertainty factors 

may also be developed using compound-specific information. When data are limited, more 

assumptions are needed and more default factors are used. Thus there may be a greater 

tendency to overestimate risk—in the sense that further study might support development of 

reference values that are higher (i.e., less potent) because fewer default assumptions are 

needed. However, for some pollutants it is possible that risks may be underestimated.

For non-cancer endpoints related to chronic exposures, EPA derives a Reference Dose (RfD) 

for exposures via ingestion, and a Reference Concentration (RfC) for inhalation exposures. 

21 According to the NRC report Science and Judgment in Risk Assessment (NRC, 1994) “[Default] options are 

generic approaches, based on general scientific knowledge and policy judgment, that are applied to various 

elements of the risk-assessment process when the correct scientific model is unknown or uncertain.” The 1983 

NRC report Risk Assessment in the Federal Government: Managing the Process defined default option as “the 

option chosen on the basis of risk assessment policy that appears to be the best choice in the absence of data to 

the contrary” (NRC, 1983a, p. 63). Therefore, default options are not rules that bind the agency; rather, the 

agency may depart from them in evaluating the risks posed by a specific substance when it believes this to be 

appropriate. In keeping with EPA’s goal of protecting public health and the environment, default assumptions 

are used to ensure that risk to chemicals is not underestimated (although defaults are not intended to overtly 

overestimate risk). See EPA 2004 An examination of EPA Risk Assessment Principles and Practices,

EPA/100/B-04/001 available at: http://www.epa.gov/osa/pdfs/ratf-final.pdf. 
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These values provide an estimate (with uncertainty spanning perhaps an order of magnitude) 

of daily oral exposure (RfD) or a continuous inhalation exposure (RfC) to the human 

population (including sensitive subgroups) that is likely to be without an appreciable risk of 

deleterious effects during a lifetime.22 To derive values that are intended to be “without 

appreciable risk,” EPA’s methodology relies upon an uncertainty factor (UF) approach (U.S. 

EPA, 1993, 1994) which includes consideration of both uncertainty and variability.

EPA begins by evaluating all of the available peer-reviewed literature to determine noncancer endpoints of concern, evaluating the quality, strengths and limitations of the available 

studies. EPA typically chooses the relevant endpoint that occurs at the lowest dose, often 

using statistical modeling of the available data, and then determines the appropriate point of 

departure (POD) for derivation of the reference value. A POD is determined by (in order of 

preference): (1) a statistical estimation using the benchmark dose (BMD) approach; (2) use of 

the dose or concentration at which the toxic response was not significantly elevated (no 

observed adverse effect level— NOAEL); or (3) use of the lowest observed adverse effect 

level (LOAEL).

A series of downward adjustments using default UFs is then applied to the POD to estimate 

the reference value (U.S. EPA 1994, 2002). While collectively termed “UFs”, these factors 

account for a number of different quantitative considerations when utilizing observed animal 

(usually rodent) or human toxicity data in a risk assessment. The UFs are intended to account 

for: (1) variation in susceptibility among the members of the human population (i.e., interindividual variability); (2) uncertainty in extrapolating from experimental animal data to 

humans (i.e., interspecies differences); (3) uncertainty in extrapolating from data obtained in a 

study with less-than-lifetime exposure (i.e., extrapolating from subchronic to chronic 

exposure); (4) uncertainty in extrapolating from a LOAEL in the absence of a NOAEL; and 

(5) uncertainty when the database is incomplete or there are problems with applicability of 

available studies. When scientifically sound, peer-reviewed assessment-specific data are not 

available, default adjustment values are selected for the individual UFs. For each type of 

uncertainty (when relevant to the assessment), EPA typically applies an UF value of 10 or 3 

with the cumulative UF value leading to a downward adjustment of 10-3000 fold from the 

selected POD. An UF of 3 is used when the data do not support the use of a 10-fold factor. If 

an extrapolation step or adjustment is not relevant to an assessment (e.g., if applying human 

toxicity data and an interspecies extrapolation is not required) the associated UF is not used. 

The major adjustment steps are described more fully below.

1) Heterogeneity among humans is a key source of variability as well as uncertainty. 

Uncertainty related to human variation is considered in extrapolating doses from a subset or 

smaller-sized population, often of one sex or of a narrow range of life stages (typical of 

occupational epidemiologic studies), to a larger, more diverse population. In the absence of 

pollutant-specific data on human variation, a 10-fold UF is used to account for uncertainty

associated with human variation. Human variation may be larger or smaller; however, data to 

examine the potential magnitude of human variability are often unavailable. In some 

situations, a smaller UF of 3 may be applied to reflect a known lack of significant variability

among humans.

22 See IRIS glossary
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2) Extrapolation from results of studies in experimental animals to humans is a 

necessary step for the majority of chemical risk assessments. When interpreting animal data, 

the concentration at the POD (e.g. NOAEL, BMDL) in an animal model (e.g. rodents) is 

extrapolated to estimate the human response. While there is long-standing scientific support 

for the use of animal studies as indicators of potential toxicity to humans, there are 

uncertainties in such extrapolations. In the absence of data to the contrary, the typical 

approach is to use the most relevant endpoint from the most sensitive species and the most 

sensitive sex in assessing risks to the average human. Typically, compound specific data to 

evaluate relative sensitivity in humans versus rodents are lacking, thus leading to uncertainty 

in this extrapolation. Size-related differences (allometric relationships) indicate that typically 

humans are more sensitive than rodents when compared on a mg/kg/day basis. The default 

choice of 10 for the interspecies UF is consistent with these differences. For a specific 

chemical, differences in species responses may be greater or less than this value.

Pharmacokinetic models are useful to examine species differences in pharmacokinetic 

processing and associated uncertainties; however, such dosimetric adjustments are not always 

possible. Information may not be available to quantitatively assess toxicokinetic or 

toxicodynamic differences between animals and humans, and in many cases a 10-fold UF 

(with separate factors of 3 for toxicokinetic and toxicodynamic components) is used to 

account for expected species differences and associated uncertainty in extrapolating from 

laboratory animals to humans in the derivation of a reference value. If information on one or 

the other of these components is available and accounted for in the cross-species 

extrapolation, a UF of 3 may be used for the remaining component.

3) In the case of reference values for chronic exposures where only data from shorter 

durations are available (e.g., 90-day subchronic studies in rodents) or when such data are 

judged more appropriate for development of an RfC, an additional UF of 3 or 10-fold is 

typically applied unless the available scientific information supports use of a different value.

4) Toxicity data are typically limited as to the dose or exposure levels that have been 

tested in individual studies; in an animal study, for example, treatment groups may differ in 

exposure by up to an order of magnitude. The preferred approach to arrive at a POD is to use 

BMD analysis; however, this approach requires adequate quantitative results for a meaningful 

analysis, which is not always possible. Use of a NOAEL is the next preferred approach after 

BMD analysis in determining a POD for deriving a health effect reference value. However, 

many studies lack a dose or exposure level at which an adverse effect is not observed (i.e., a 

NOAEL is not identified). When using data limited to a LOAEL, a UF of 10 or 3-fold is 

often applied. 

5) The database UF is intended to account for the potential for deriving an 

underprotective RfD/RfC due to a data gap preventing complete characterization of the 

chemical’s toxicity. In the absence of studies for a known or suspected endpoint of concern, a 

UF of 10 or 3-fold is typically applied.
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Acute noncancer assessment

Many of the UFs used to account for variability and uncertainty in the development of acute 

reference values are quite similar to those developed for chronic durations, but more often 

using individual UF values that may be less than 10. UFs are applied based on chemicalspecific or health effect-specific information (e.g., simple irritation effects do not vary

appreciably between human individuals, hence a value of 3 is typically used), or based on the 

purpose for the reference value (see the following paragraph). The UFs applied in acute 

reference value derivation include: 1) heterogeneity among humans; 2) uncertainty in 

extrapolating from animals to humans; 3) uncertainty in LOAEL to NOAEL adjustments; and 

4) uncertainty in accounting for an incomplete database on toxic effects of potential concern. 

Additional adjustments are often applied to account for uncertainty in extrapolation from 

observations at one exposure duration (e.g., 4 hours) to arrive at a POD for derivation of an 

acute reference value at another exposure duration (e.g., 1 hour). 

Not all acute reference values are developed for the same purpose and care must be taken 

when interpreting the results of an acute assessment of human health effects relative to the 

reference value or values being exceeded. Where relevant to the estimated exposures, the 

lack of threshold values at different levels of severity should be factored into the risk 

characterization as potential uncertainties. 
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5 References

1. US EPA. National Emission Standards for Hazardous Air Pollutants. 

http://www.epa.gov/ttn/atw/mactfnlalph.html. 

2 US EPA, 2009. Risk and Technology Review (RTR) Risk Assessment Methodologies: For 

Review by the EPA’s Science Advisory Board with Case Studies – MACT I Petroleum Refining 

Sources and Portland Cement Manufacturing. EPA-452/R-09-006. 

http://www.epa.gov/ttn/atw/rrisk/rtrpg.html.

3. US EPA, 2010. SAB’s Response to EPA’s RTR Risk Assessment Methodologies. 

http://yosemite.epa.gov/sab/sabproduct.nsf/4AB3966E263D943A8525771F00668381/$File/EP

A-SAB-10-007-unsigned.pdf

4. US EPA, 2010. Memorandum from Dave Guinnup to RTR Wood Furniture Docket, entitled, 

“EPA’s Actions in Response to Key Recommendations of the SAB Review of RTR Risk 

Assessment Methodologies.

5. US EPA, 2005. Revision to the Guideline on Air Quality Models: Adoption of a Preferred

General Purpose (Flat and Complex Terrain) Dispersion Model and Other Revisions; Final Rule. 

40 CFR Part 51. http://www.epa.gov/EPA-AIR/2005/November/Day-09/a21627.htm

6. US EPA, 2004. Users’ guide for the AMS/EPA regulatory model – AERMOD. EPA-454/B03-001. http://www.epa.gov/scram001/7thconf/aermod/aermodugb.pdf.

7. US EPA, 2004. Air Toxics Risk Assessment Reference Library, Volume 1. EPA-453-K-04-

001A. http://www.epa.gov/ttn/fera/risk_atra_vol1.html. 

8. US EPA, 2005. Table 1. Prioritized Chronic Dose-Response Values (2/28/05). Office of 

Air Quality Planning and Standards. http://www.epa.gov/ttn/atw/toxsource/table1.pdf

9. US EPA, 2005. 1999 National Air Toxics Risk Assessment. 

http://www.epa.gov/ttn/atw/nata1999

10. US EPA, 2006. Integrated Risk Information System. http://www.epa.gov/iris/index.html.

11. US Agency for Toxic Substances and Disease Registry. 2006. Minimum Risk Levels (MRLs) 

for Hazardous Substances. http://www.atsdr.cdc.gov/mrls/index.html.

12. US EPA, 1994. US Environmental Protection Agency. Methods for Derivation of 

Inhalation Reference Concentrations and Application of Inhalation Dosimetry. 

EPA/600/8-90/066F. Office of Research and Development. Washington, DC: U.S.EPA.

13. NRC, 1994. National Research Council. Science and Judgment in Risk Assessment. 

Washington, DC: National Academy Press.

14. CA Office of Environmental Health Hazard Assessment, 2005. Chronic Reference 

Exposure Levels Adopted by OEHHA as of December 2008. 

http://www.oehha.ca.gov/air/chronic_rels/AllChrels.html. 
Draft Risk Assessment for the Primary Aluminum Production Source Category --

FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

36

15. CA Office of Environmental Health Hazard Assessment, 2005. Technical Support Document 

for Describing Available Cancer Potency Factors, May 2005. 

http://www.oehha.ca.gov/air/hot_spots/may2005tsd.html.

16. US EPA, 2006. Approach for modeling POM. Technical support information for the 1999 

National Air Toxics Assessment. 

http://www.epa.gov/ttn/atw/nata1999/99pdfs/pomapproachjan.pdf.

17. US EPA, 2005. Table 2. Acute Dose-Response Values for Screening Risk Assessments 

(6/02/2005). Office of Air Quality Planning and Standards. 

http://www.epa.gov/ttn/atw/toxsource/table2.pdf

18. CA Office of Environmental Health Hazard Assessment, 2000. All Acute Reference 

Exposure Levels developed by OEHHA as of May 2000. 

http://www.oehha.ca.gov/air/acute_rels/allAcRELs.html

19. American Industrial Hygiene Association , 2009. Current AIHA ERPG Values. 

http://www.aiha.org/insideaiha/volunteergroups/Documents/ERP-erpglevels.pdf.

20. US EPA, 1995. Guidance for Risk Characterization. Science Policy Council. 

http://www.epa.gov/OSA/spc/pdfs/rcguide.pdf.

21. US EPA, 2000. Risk Characterization Handbook. EPA 100-B-00-002.

22. US EPA, 2002. EPA’s Guidelines for Ensuring and Maximizing the Quality, Objectivity, 

Utility, and Integrity of Information Disseminated by the Environmental Protection Agency. 

EPA Office of Environmental Information. EPA/260R-02-008. 

http://www.epa.gov/quality/informationguidelines/documents/EPA_InfoQualityGuidelines.pdf

23. US EPA, 2006. Performing risk assessments that include carcinogens described in the 

Supplemental Guidance as having a mutagenic mode of action. Science Policy Council Cancer 

Guidelines Implementation Workgroup Communication II: Memo from W.H. Farland dated 

14 June 2006. http://www.epa.gov/osa/spc/pdfs/CGIWGCommunication_II.pdf

24. US EPA, 2005. Supplemental guidance for assessing early-life exposure to carcinogens. 

EPA/630/R-03003F. http://www.epa.gov/ttn/atw/childrens_supplement_final.pdf.

25. US EPA, 2005. Science Policy Council Cancer Guidelines Implementation Workgroup 

Communication I: Memo from W.H. Farland dated 4 October 2005 to Science Policy

Council. http://www.epa.gov/osa/spc/pdfs/canguid1.pdf

26. US EPA, 1986. Guidelines for the Health Risk Assessment of Chemical Mixtures. EPA-630-

R-98-002. http://www.epa.gov/NCEA/raf/pdfs/chem_mix/chemmix_1986.pdf

27. US EPA, 2000. Supplementary Guidance for Conducting Health Risk Assessment of 

Chemical Mixtures. EPA-630/R-00-002. 

http://www.epa.gov/ncea/raf/pdfs/chem_mix/chem_mix_08_2001.pdf.

28. US EPA, 2005. Guidelines for Carcinogen Risk Assessment (2005). U.S. Environmental 
Draft Risk Assessment for the Primary Aluminum Production Source Category --

FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

37

Protection Agency, Washington, DC, EPA/630/P-03/001F, 2005.

http://cfpub.epa.gov/ncea/raf/recordisplay.cfm?deid=116283

29. US EPA, 2000. Risk Characterization Handbook. EPA 100-B-00-002.

30. NRC (National Research Council) 2006. Assessing the Human Health Risks of 

Trichloroethylene. National Academies Press, Washington DC.

31. R.P. Subramaniam, P. White and V.J. Cogliano. 2006. Comparison of cancer slope factors 

using different statistical approaches, Risk Anal. Vol 26, p. 825–830.
Appendix 1 

Technical Support Document for HEM-3 Modeling 
Modeling for the Residual Risk and Technology Review 

Using the Human Exposure Model 3 (HEM-3)

Updated 8/7/2008 

Technical Support Document 

Prepared for: 

U. S. Environmental Protection Agency 

Office of Air and Radiation 

Office of Air Quality Planning and Standards 

Emissions Standards Division, Risk and Exposure Group 

Research Triangle Park, NC 27711 

Prepared by: 

EC/R Incorporated 

501 Eastowne Drive, Suite 250 

Chapel Hill, North Carolina 27514 
ii 

Disclaimer

The research described in this document has been funded by the 

United States Environmental Protection Agency contracts 68-D01-076 and 68-D-06-119 to EC/R Incorporated. Although it has 

been subject to the Agency's review, it does not necessarily reflect 

the views of the Agency, and no official endorsement should be 

inferred. 
iii 

Contents

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

2. Overview of the HEM3– AERMOD System..............................................................................2

2.1 Preparation of Dispersion Modeling Inputs ........................................................................3

2.1.1 Compiling Emission Source Data ..............................................................................3

2.1.2 Defining the Modeling Domain and Receptors .........................................................4

Treatment of Nearby Census Blocks and Screening for Overlapping Blocks ...............4

Polar receptor network ...................................................................................................5

Elevations and hill heights for model receptors .............................................................5

2.1.3 Selection of Meteorological Data ..............................................................................6

2.2 Running of AERMOD ........................................................................................................6

2.2.1 AERMOD Dispersion Options Used by HEM3 ........................................................6

2.2.2 Use of Dilution Factors ..............................................................................................7

2.3 Postprocessing of AERMOD Results in HEM3 .................................................................7

2.3.1 Calculation of Impacts at Modeled Receptors ...........................................................8

2.3.2 Interpolation of Impacts at Outer Census Blocks ......................................................9

2.3.3 Calculation of Population Exposures and Incidence ...............................................10

2.3.4 Model Outputs .........................................................................................................10

2.4 Data Libraries Used in HEM3 ..........................................................................................11

2.4.1 Chemical Health Effects Information ......................................................................11

2.4.2 Census Block Locations and Elevation Data ...........................................................12

2.4.3 Meteorological Data .................................................................................................12

2.4.4 Gaseous Deposition Parameters ...............................................................................13

3. Modeling for the Residual Risk Technology Review ..............................................................14

3.1 Emission Source Inputs .....................................................................................................14

3.2 Pollutant Cross-Referencing .............................................................................................15

3.3 Meteorological Data..........................................................................................................15

3.4 Model Options Selected ....................................................................................................18

3.4.1 Urban or Rural Dispersion Characteristics ..............................................................19

3.4.2 Deposition and Plume Depletion .............................................................................19

3.4.3 Cutoff Distance for Modeling of Individual Blocks ................................................20

3.4.4 Plant Boundary Assumptions ...................................................................................20

3.5 Modeling of Multiple Facilities ........................................................................................20

4. Quality Assurance .....................................................................................................................21

4.1 Engineering Review ..........................................................................................................21

4.2 Geographic Checks ...........................................................................................................21

5. Uncertainties .............................................................................................................................24
iv 

6. References .................................................................................................................................31

 

Figures

Figure 3-1. Meteorological data available in the HEM3 library for AERMOD. ..........................18

Figure 4-1. Illustration of a quality assurance test for Census block coordinates located on plant 

property. .............................................................................................................................23

Tables

Table 2-1. Parameters Used to Delineate the Modeling Domain in HEM3 ...................................4

Table 3-1. Groupings Used for Polycyclic Organic Matter in the RTR Phase II Modeling Effort 

. . ........................................................................................................................................16

Table 3-2. Modeling Domain and Model Set-Up Options in the HEM-3 Model As Used in the 

Residual Risk and Technology Review Assessments ........................................................19

Table 5-1. Summary of General Uncertainties Associated with Risk and Technology Review 

Risk Assessment ................................................................................................................26
1 

1. Introduction

 This document describes the modeling approach used to estimate the risks to human 

populations in support of the Residual Risk and Technology Review currently being carried out 

by the U.S. Environmental Protection Agency (EPA). The model used in these risk assessments 

was the Human Exposure Model, Version 3 (HEM3). HEM3 incorporates the AERMOD, a state 

of the art air dispersion model developed under the direction of the American Meteorological 

Society / Environmental Protection Agency Regulatory Model Improvement Committee 

(AERMIC). 

 Section 2 of this report provides an overview of the HEM3 system; and Section 3 

describes inputs and choices made in implementing the model for the RTR program. Quality 

assurance efforts undertaken in the modeling effort are discussed in Section 4, and uncertainties 

associated with the modeling effort are discussed in Section 5. 
2 

2. Overview of the HEM3– 

AERMOD System

 HEM3 performs three main operations: dispersion modeling, estimation of population 

exposure, and estimation of human health risks. Two options are provided for dispersion 

modeling. The first is the American Meteorological Society (AMS) EPA Regulatory Model 

(AERMOD).1,2 The second option is the Industrial Source Complex - Short Term, Version 3 

(ISCST3) model. Both of these dispersion models handle a wide range of different source types 

which may be associated with an industrial source complex, including stack sources, area 

sources, and volume sources. AERMOD was used for the RTR modeling effort. 

 To prepare dispersion modeling inputs and carry out risk calculations, HEM3 draws on 

four data libraries, which are provided with the model. The first is a library of meteorological 

data for over 120 stations, which are used for dispersion calculations. A second library of 

Census block internal point locations and populations provides the basis of human exposure 

calculations (Census, 2000). This Census library also includes the elevation of each Census 

block, which can also be used in dispersion calculations, at the option of the model user. A third 

library of pollutant unit risk estimates and reference concentrations is used to calculate 

population risks. These unit risk estimates and reference concentrations are based on the latest 

values recommended by EPA for hazardous air pollutants (HAP) and other toxic air pollutants 

(EPA, 2007). The fourth data library is used only with AERMOD and only when the user opts to 

compute deposition or plume depletion. This database provides deposition parameters for 

gaseous pollutants. 

 HEM3 has been implemented in two versions: a single-plant version, and a community 

and sector version. The latter was used in this modeling effort. Both versions operate under the 

same general principles. In essence, the community and sector version provides a platform for 

running the single plant version multiple times. In both versions, source location and emissions 

data are input through a set of ExcelTM spreadsheets. The main difference is in the user interface 

for other model inputs. The single plant includes a graphical user interface (GUI) for the 

selection of various dispersion modeling options. In the community and sector version of 

HEM3, a control file replaces many of these GUI inputs. 

 The model estimates cancer risks and noncancer adverse health effects due to inhalation 

exposure at Census block internal point locations, and at other receptor locations that can be 

specified by the user. Cancer risks are computed using EPA’s recommended unit risk estimates 

for Hazardous Air Pollutants (HAP) and other toxic air pollutants. The resulting estimates 

reflect the excess cancer risk for an individual breathing the ambient air at a given receptor site 

24-hours per day over a 70-year lifetime. The model estimates the numbers of people exposed to 

various cancer risk levels. In addition, HEM3 estimates the total incremental cancer risks for 

people living within different distances of the modeled emission sources. 
3 

 Potential noncancer health effects due to chronic exposures are quantified using hazard 

quotients and hazard indices for various target organs. The “hazard quotient” for a given 

chemical and receptor site is the ratio of the ambient concentration of the chemical to the 

reference concentration. The “hazard index” for a given organ is the sum of hazard quotients for 

substances that affect that organ. HEM3 computes target-organ-specific hazard indices (TOSHI) 

for HAPs and other toxic air pollutants, and estimates the numbers of people exposed to different 

hazard index levels. In addition, maximum short term concentrations are computed for all 

pollutants, and concentrations are compared with threshold levels for acute health effects. 

 The following sections outline the methodologies used in the HEM3–AERMOD system. 

Section 2.1 describes the preparation of dispersion modeling inputs, Section 2.2 describes the 

running of AERMOD, Section 2.3 describes calculations performed by HEM3 to calculate risks 

and exposures, and Section 2.4 details the sources and methods used to produce HEM3’s data 

libraries. The HEM3 User’s Manual provides additional details on the input data and algorithms 

used in the model.3

 Specific model options used in the RTR are discussed in Chapter 3. 

2.1 Preparation of Dispersion Modeling Inputs

 HEM3 compiles data that will be needed for dispersion modeling, and prepares an input 

file suitable for running AERMOD. The dispersion modeling inputs can be divided into three 

main components: emission source data, information on the modeling domain and receptors for 

which impacts will be computed, and meteorological data. 

2.1.1 Compiling Emission Source Data

 A series of ExcelTM spreadsheet files are used to specify the emissions and configuration 

of the facility to be modeled. At a minimum, two files are needed: a pollutant emission file, and 

an emission location file. The emission file includes an emission source identification code for 

each emission source at the facility, the names of pollutants emitted by each source, and the 

emission rate for each pollutant. In addition, if the model run is to incorporate deposition or 

plume depletion, the emission file must also specify the percentage of each pollutant that is in the 

form of particulate matter. The balance is assumed to be in vapor form. 

 The emission location file includes the coordinates of each source, as well as information 

on the configuration and other characteristics of the source. HEM3 can analyze stack sources, 

area sources, or volume sources.a

 For stack sources, the location file must provide the stack 

height, stack diameter, emission velocity, and emission temperature. The file must also provide 

dimensions for each area or volume source, as well as the height of the source above the ground. 

For area sources, the angle of rotation from north can also be specified. The user can also 

 

I

For definitions of stack, area, and volume sources, please see the AERMOD documentation. AERMOD 

provides the capability to analyze some other emission source configurations, such as storage piles, however these 

configurations are not supported by HEM3.
4 

provide the terrain elevation at the base of each source, and the controlling hill height for each 

source. (The controlling hill height is used in AERMOD’s flow calculations. Calculation of the 

hill height is discussed in Section 2.4.2.) If these are not provided, HEM3 will calculate terrain 

elevations and controlling hill heights based on elevations and hill heights for the Census blocks 

nearest to the facility. 

 If particulate deposition or plume depletion are to be considered, then HEM-3 requires a 

third input file to specify the particle size distribution and scavenging parameters for various 

particle size ranges. Another optional file can be used to specify building dimensions if building 

wake effects are to be modeled. 

2.1.2 Defining the Modeling Domain and Receptors

 HEM3 defines a modeling domain for each facility that is analyzed based on parameters 

specified by the model user. These parameters are summarized in Table 2-1. The modeling 

domain is circular, and is centered on the facility, with a radius specified by the user. For the 

RTR analysis, the radius of the modeling domain is 50 kilometers (km). HEM3 identifies all of 

the Census block locations in the modeling domain from its Census database, and divides the 

blocks into two groups based on their distance from the facility. For the inner group of Census 

blocks (closest to the facility), each block location is modeled as a separate receptor in 

AERMOD. The cutoff distance for modeling individual Census blocks is determined by the 

model user. The model user can also provide an ExcelTM spreadsheet specifying additional 

locations to be included as model receptors in AERMOD. These additional discrete receptors 

may include plant boundary locations, monitoring sites, individual residences, schools, or other 

locations of interest. 

 For Census blocks in the outer group, 

beyond this modeling cutoff distance, 

emissions impacts are interpolated based on 

modeling results for a polar receptor network. 

The user also specifies an “overlap” distance, 

within which Census block coordinates will 

be considered to be on plant property. The 

following paragraphs provide more details on 

the treatment of blocks near the facility, on 

the polar receptor network, and on the 

determination of receptor elevations and 

controlling hill heights to be used in 

AERMOD. 

Treatment of Nearby Census Blocks and 

Screening for Overlapping Blocks

 Census block locations near the 

facility are modeled as separate receptors 

Table 2-1. Parameters Used to Delineate 

the Modeling Domain in HEM3

Parameter 

Typical 

value 

Modeling domain size – maximum 

distance to be modeled a 50 km 

Cutoff distance for modeling of 

individual blocks b

2,000 – 

3,000 m

Overlap distance – where receptors will 

be considered to be on plant property b 30 m 

Polar receptor network 

Distance to the innermost ring a 100 m 

Number of concentric rings 13 

Number of radial directions 16 

 aMeasured from the center of the facility. 

 b

Measured from each stack at the facility, and 

from the edges of each area or volume source. 
5 

within AERMOD. The cutoff distance for modeling of individual Census blocks is chosen by 

the user, and is tpically between 2000 and 3000 meters. This distance is not measured from the 

center of the facility, but is the minimum distance from any source at the facility. Therefore, any 

Census block location that is within the cutoff distance from any emission source is treated as a 

discrete AERMOD receptor. 

 HEM3 checks Census blocks that are very close to the facility in order to assess whether 

they overlap any area or volume emission sources. In addition, the user can specify an overlap 

distance, within which receptors will be considered to be on plant property. The default value 

for the overlap distance is 30 meters, or approximately equal to the width of a narrow buffer and 

a roadway. HEM3 tests each nearby receptor to determine whether it is within this distance from 

any stack or from the perimeter of any area or volume source. If a receptor falls within this 

distance, HEM-3 will not calculate risks based on the location of that receptor, but will instead 

assume that the risks associated with the receptor are the same as the highest predicted value for 

any receptor that is not overlapping. The location for calculating the default impact may be 

either another Census block, one of the polar grid receptors, or one of the additional discrete 

receptor locations provided by the model user. 

Polar receptor network

 The polar receptor network used in HEM3 serves three functions. First, it is used to 

estimate default impacts if one or more Census locations are inside the overlap cutoff distance is 

used to represent the plant boundary. Second, it is used to evaluate potential acute effects that 

may occur due to short-term exposures in unpopulated locations outside the plant boundary. 

Third, the polar receptor network is used to interpolate long-term and short-term impacts at 

Census block locations that are outside the cutoff distance for modeling of individual blocks. 

 The user defines the inner radius for the polar receptor network, the number of concentric 

rings to be analyzed, and the number of radial directions. The inner radius of the polar network 

should the minimum distance from the facility center that is generally outside of plant property. 

(For complex plant shapes, it is sometimes useful to specify an inner ring that encroaches on 

facility property in some directions.) The default, and also the minimum inner radius for the 

polar network is 100 meters (m). A typical run includes 13 concentric rings and 16 radial 

directions. HEM3 will distribute the radial directions evenly around the plant. For the 

concentric rings, the model will generate logarithmic progression of distances starting at the 

inner ring radium and ending at the outer radius of the modeling domain. In the single plant 

version of HEM3, the user can change these ring distances to meet the needs of a specific study. 

Elevations and hill heights for model receptors

 The user can specify whether HEM3 will include the effects of terrain when running 

AERMOD. If the terrain option is selected, HEM3 obtains elevations and controlling hill heights 

for Census block receptors from its internal Census location library. Section 2.4.2 describes the 

derivation of these elevations and hill heights. 
6 

 Elevations and controlling hill heights for the polar grid receptors are also estimated 

based on values from the Census library. HEM3 divides the modeling domain into sectors based 

on the polar receptor network, with each Census block assigned to the sector corresponding to 

the closest polar grid receptor. Each polar grid receptor is then assigned an elevation based on 

the highest elevation for any Census block in its sector. The controlling hill height is also set to 

the maximum hill height within the sector. If a sector does not contain any blocks, the model 

defaults to the elevation and controlling hill height of the nearest block outside the sector. 

2.1.3 Selection of Meteorological Data

 In addition to source and receptor information, AERMOD requires surface and upper air 

meteorological observations in a prescribed format. The model user can select a meteorological 

station from the HEM3 meteorological data library, or add new files to the library if site-specific 

data are available. If the user does not specify a meteorological station, HEM3 will select the 

closest station to the center of the modeling domain. 

2.2 Running of AERMOD

 Based on the user input data and other data described in the previous section, HEM3 

produces an input file suitable for AERMOD. HEM3 then runs AERMOD as a compiled 

executable program. No changes have been made from the version of AERMOD released to the 

public by EPA. The following sections give additional information on how AERMOD is used 

within HEM3. 

2.2.1 AERMOD Dispersion Options Used by HEM3

 AERMOD provides a wide array of options for controlling dispersion modeling 

calculations. In general, HEM3 uses the regulatory default options when running AERMOD.1 

These options include the following: 

! consideration of stack-tip downwash (except for Schulman-Scire downwash) 

! use of calm processing routines 

! use of missing-data processing routines 

! consideration of terrain effects 

However, the model user can opt to exclude the effects of terrain when running HEM3. The 

following additional options are available to the HEM3 user: 

! calculation of wet and dry deposition rates for vapor and particulate mater 

! consideration of plume depletion (due to deposition) when calculating air concentrations 

! consideration of building wake effects 

! calculation of short term impacts 

As noted in Section 2.1, the calculation of deposition or depletion and the consideration of 

building wake effects require additional user inputs. 
7 

 The user can opt to analyze short term impacts on a number of different time scales (e.g. 

1 hour, 2 hours, etc.), however only one short term time scale can be selected per run. If the user 

chooses to analyze short term impacts, a multiplier must be specified to reflect the ratio between 

the maximum short term emission rate and the long term average emission rate. The default 

multiplier for short term emissions is a factor of 10. This means that in the default case the 

maximum short term emission rate is assumed to be 10 times the long term average emission 

rate. The multiplier can be set to 1.0 if emissions from the facility are known to be constant. 

 HEM3 uses the AERMOD TOXICS run option, which optimizes the area source 

integration routine to reduce model runtimes. The TOXICS Sampled Chronological Input Model 

(SCIM) is not used, since the HEM3 AERMOD meteorological library only includes one year of 

observations for each station. (The SCIM option can be used to skip a portion of the hourly 

observations when meteorological data are available for multiple years.) 

2.2.2 Use of Dilution Factors

 To save computer run time when analyzing the impacts of multiple pollutants, HEM3 

does not model each pollutant separately. Instead, AERMOD is used to compute a series of 

dilution factors, specific to each emission source and receptor. The dilution factor for a 

particular emission source and receptor is defined as the predicted ambient impact from the given 

source and at the given receptor, divided by the emission rate from the given source. 

 If the user chooses not to analyze deposition or plume depletion, the dilution factor does 

not vary from pollutant to pollutant. If either deposition or depletion is chosen with the 

AERMOD dispersion model option of HEM3, separate dilution coefficients must be computed 

for each gaseous pollutant. In addition, separate dilution factors must be computed for different 

components of particulate matter if the components do not have the same particle size 

distribution. In the current version of HEM3, this can be done by creating a separate emission 

record for each pollutant emitted by from each source. (Common location data and source 

configurations can be used for different pollutant records representing the same emission 

source.) 

2.3 Postprocessing of AERMOD Results in HEM3

 HEM3 estimates total excess cancer risks and potential chronic noncancer health effects 

for all Census block locations in the modeling domain, all user-defined receptors, and all points 

in the polar receptor network. Potential chronic noncancer health effects are expressed in terms 

of TOSHI. Based on the results for Census blocks and other receptors, HEM-3 estimates the 

maximum individual risk and maximum TOSHI for populated receptors, and determines the 

locations of these maximum impacts. The model also determines the concentrations of different 

pollutants at the sites of the maximum risks, and the contributions of different emission sources 

to these maximum estimated risks. It should be noted that the locations may differ for the 

maximum individual cancer risk and for the hazard indices for different target organs. 
8 

 For acute impacts, HEM3 calculates the maximum short term concentrations for all 

pollutants emitted by the facility. These maximum short term concentrations are compared with 

various thresholds for acute health effects. 

 At the option of the model user, HEM3 will also compute the long term and short term 

predicted ambient concentrations of all pollutants emitted by the plant at all of the receptors in 

the modeling domain. In addition, pollutant contributions from each emission source at the 

facility are computed under this option. 

 Section 2.3.1. describes methods used to calculate cancer risks and hazard indices for 

receptors that are explicitly modeled using AERMOD. Section 2.3.2 describes the interpolation 

approach used to estimate cancer risks and hazard indices at Census blocks that are not explicitly 

modeled. 

2.3.1 Calculation of Impacts at Modeled Receptors

 As noted in Section 2.2.2, HEM3 does not model each pollutant separately unless 

deposition or depletion is being analyzed. Instead, AERMOD is used to compute a series of 

dilution factors, specific to each emission source and receptor. HEM3 also conserves computer 

memory by computing cancer risks and hazard indices directly, without recording the 

concentration of each pollutant at each receptor. The following algorithms are used to compute 

cancer risks and TOSHI for chronic noncancer health effects. 

For cancer risk: 

CRT = i, j CRi, j

CRi, j = DFi, j  CF  k [Ei, k  UREk]

For TOSHI: 

TOSHIT = i, j TOSHIi, j

TOSHIi, j = DFi, j  CF  k [Ei, k  RfCk ]

where: 

 CRT = total cancer risk at a given receptor (probability for one person) 

i, j = the sum over all sources i and pollutant types j (particulate or gas) 

 CRi, j = cancer risk at the given receptor for source i and pollutant type j 

 DFi, j = dilution factor [(g/m3

) / (g/sec)] at the given receptor for source i and pollutant 

type j 

 CF = conversion factor, 0.02877 [(g/sec) / (ton/year)] 

k = sum over all pollutants k within pollutant group j (particulate or gas) 

 Ei, k = emissions of pollutant k from source i and in pollutant type j 

 UREk = cancer unit risk estimate for pollutant k 
9 

 TOSHIT = total target-organ-specific hazard index at a given receptor 

 TOSHIi, j = target-organ-specific hazard index at the given receptor for source i and 

pollutant type j 

 RfCk = noncancer health effect reference concentration for pollutant k 

 If the cancer unit risk estimate is not available for a given chemical, then that chemical is 

not included in the calculation of cancer risk. Likewise, if the noncancer reference concentration 

is not available for a given chemical, that chemical is not included in the calculation of hazard 

indices. Note also that separate reference concentrations are used for acute and chronic hazard 

indices. 

 HEM3 computes short term concentrations and records the highest short term 

concentration for each pollutant. In addition, the user can opt to compute and record the short 

term and long concentrations at each receptor. Concentrations are computed as follows. 

Long term concentrations: 

ACT, k = i ACi, k

ACi, k = DFi, j  CF  Ei, k

Short term concentrations: 

ACT = i ACi, k

ACi, k = DFi, j  CF  Ei, k  M 

where: 

 ACT, k = total estimated ambient concentration for pollutant k at a given receptor 

i = the sum over all sources i (g/m3) 

 ACi, k = estimated ambient concentration of pollutant k at the given receptor as a 

result of emissions from source i (g/m3

) 

 M = ratio between the estimated maximum short term emission rate and the long term 

average emission rate (dimensionless) 

2.3.2 Interpolation of Impacts at Outer Census Blocks

 For Census blocks outside of the distance cutoff for individual block modeling, HEM-3 

estimates cancer risks and hazard indices by interpolation from the polar receptor network. 

Impacts at the polar grid receptors are estimated using AERMOD modeling results and the 

algorithms described in Section 2.3.1. The impacts at each outer Census block are interpolated 

from the four nearest polar grid receptors. The interpolation is linear in the angular direction, 

and logarithmic in the radial direction, as summarized in the following equations: 

Ia, r = I1, r + (I2, r – I1, r)  (a – A1)  (A2 – A1) 
10 

IA1, r = exp{ln(IA1,R1) + [ln(IA1,R2) – ln(IA1,R1)]  [ln r) – ln(R1)]  [ln(R2) – ln(R1)]} 

IA2, r = exp{ln(IA2,R1) + [ln(IA2,R2) – ln(IA2,R1)]  [ln r) – ln(R1)]  [ln(R2) – ln(R1)]} 

where: 

 Ia, r = the impact (cancer risk, hazard index, or concentration) at an angle, a, from north, 

and radius, r, from the center of the modeling domain 

 a = the angle of the target receptor, from north 

 r = the radius of the target receptor, from the center of the modeling domain 

 A1 = the angle of the polar network receptors immediately counterclockwise from the 

target receptors 

 A2 = the angle of the polar network receptors immediately clockwise from the target 

receptor 

 R1 = the radius of the polar network receptors immediately inside the target receptor 

 R2 = the radius of the polar network receptors immediately outside the target receptor 

2.3.3 Calculation of Population Exposures and Incidence

 Using the predicted impacts for Census blocks, HEM3 estimates the numbers of people 

exposed to various cancer risk levels and TOSHI levels. This is done by adding up the 

populations for receptors that have predicted cancer risks or TOSHI above the given threshold. 

 The model also estimates the total annual excess cancer risk (incidence) for the entire 

modeling region. The following equation is used: 

TCR = m [CRm  Pm ]  LT 

where: 

 TCR = the estimated total annual cancer risk, or incidence, (cancers/year) to the 

population living within the modeling domain 

m = the sum over all Census blocks m within distance the modeling domain 

 CRm = the total lifetime cancer risk (from all modeled pollutants and emission sources) at 

Census block m 

 Pm = the population at Census block m 

 LT = the average lifetime used to develop the cancer unit risk factor, 70 years 

2.3.4 Model Outputs

 The following is a summary of the outputs produced by HEM3. These are written to a 

collection of files in ExcelTM and dBaseTM format. 

! Maximum long term impacts at populated locations 

" maximum lifetime individual cancer risk 

" maximum TOSHI for the following health effects 

- respiratory system effects 

- liver effects 
11 

- neurological system effects 

- developmental effects 

- reproductive system effects 

- kidney effects 

- ocular system effects 

- endocrine system effects 

- hematological system effects 

- immunological system effects 

- skeletal system effects 

- spleen effects 

- thyroid effects 

" locations of the maximum cancer risk and TOSHI 

" Census block identification codes for the maximum cancer risk and TOSHI, and 

number of people in the Census block 

" contributions of different chemicals and emission sources to the maximum risk 

and TOSHI 

! Maximum acute impacts 

" maximum short term ambient concentration for each chemical 

" threshold levels for acute health effects of each chemical (compared with the 

maximum short term concentrations) 

" locations of the maximum impacts for different chemicals 

" Census block identification codes at the locations of maximum concentration, and 

number of people in the block 

" contribution of each emission source at the facility to the maximum short term 

concentration of each chemical 

! Outputs for all receptors 

" maximum individual cancer risk and TOSHI (all target organs) for each Census 

block and each user-specified discrete receptor (monitoring sites, etc.) 

" maximum individual cancer risk and TOSHI (all target organs) for each polar grid 

receptor 

" estimated deposition (optional) 

" predicted ambient concentration resulting from each emission source at each 

Census block (optional) 

! Population exposures and total cancer risk, or incidence 

" estimated numbers of people exposed to different levels of lifetime individual 

cancer risk (1 in a million, 1 in 100,000, etc.) 

" estimated numbers of people exposed to different levels of TOSHI (1, 2, 10, etc.) 

" total cancer risk, or incidence, in estimated cancer deaths per year, over the entire 

modeling domain 

2.4 Data Libraries Used in HEM3

2.4.1 Chemical Health Effects Information
12 

 HEM3 includes a library of available health effects data for HAPs. For each pollutant, 

the library includes the following parameters, where available: 

! unit risk estimate for cancer 

! reference concentration for chronic noncancer health effects 

! reference concentrations for acute health effects 

! target organs affected by the chemical for chronic health effects 

Unit risk estimates and reference concentrations included in the HEM3 chemical library have 

been taken from EPA’s database of recommended dose-response factors for HAPs.4

2.4.2 Census Block Locations and Elevation Data

 The HEM3 Census library includes Census block identification codes, locations, 

populations, elevations, and controlling hill heights for all of the over 5 million Census blocks 

identified in the 2000 Census. The location coordinates reflect the internal point of the block, 

which is a point selected by the Census to be roughly in the center of the block. For complex 

shapes, the internal point may not be in the geographic center of the block. Locations and 

population data for Census blocks in the 50 states and Puerto Rico were extracted from the 

LandView® database.5

 Locations and populations for blocks in the Virgin Islands were obtained 

from the Census web site. 

 The elevation of each Census block in the continental U.S. and Hawaii was estimated by 

interpolating the elevation at the block’s internal point from the U.S. Geological Service’s 

1:250,000 Digital Elevation Model (DEM) data.6 These data have a resolution of 3 arc seconds, 

or about 90 meters. The estimation of the elevation of each Census block in Alaska and the U.S. 

Virgin Islands was performed with analysis tools in the ArcGis 9.1 software application. The 

point locations of the Census block centroids in Alaska and the Virgin Islands were overlaid with 

a raster layer of North American Digital Elevation Model elevations (in meters). Each Census 

block point was assigned an elevation value based on the closest point in the ArcGis elevation 

raster file. 

 Hill heights were determined with base on the algorithm used in AERMAP, the 

AERMOD terrain processor.7

 In order to save run time and resources, the HEM3 Census block 

elevation database was substituted for the DEM data generally used in AERMAP. As noted 

above, the Census block elevations were originally derived from the DEM database. To 

determine the controlling hill height for each Census block, a cone was projected away from the 

block location, representing a 10% elevation grade. The controlling hill height was selected 

based on the highest elevation above that 10% grade (in accordance with the AERMAP 

methodology). The distance cutoff for this calculation was 100 km. (This corresponds to an 

elevation difference, at a 10% grade, of 10,000 m, which considerably exceeds the maximum 

elevation difference in North America.) 

2.4.3 Meteorological Data
13 

 HEM3 includes an extensive library of meteorological data to support both the 

AERMOD and ISCST3 dispersion models. 150 meteorological stations have been preprocessed 

for AERMOD as part of the RTR effort. In addition, 87 stations have previously been processed 

for ISCST3. Section 3.3 discusses the preparation of meteorological data for the RTR. 

2.4.4 Gaseous Deposition Parameters

 HEM3 provides options to compute the deposition of air pollutants, and to take into 

account the impacts of plume depletion due to deposition of gaseous and particulate pollutants. 

If either of these options is selected by the model user for gaseous pollutants, a number of 

pollutant properties are required by AERMOD. These include the diffusivity of the pollutant in 

air, the diffusivity of the pollutant in water, the Henry’s Law constant, and a parameter reflecting 

the cuticular resistance to uptake of the pollutant by leaves rCL).8

 HEM3 includes a library of 

these parameters for most gaseous HAPs. This library is based on an compendium of gaseous 

deposition parameters developed by Argonne National Laboratories.9

 It should be noted, 

however, that the deposition and depletion options of HEM3 and AERMOD were not used in the 

this component of the RTR analysis. 
14 

3. Modeling for the Residual Risk 

Technology Review 

 This section discusses the general approach used to implement the AERMOD version of 

HEM3 for the RTR modeling analysis. Separate reports have been prepared for all of the emission 

source categories included in the RTR analysis. These reports provide information on the 

emissions inputs and results for specific emission categories. 

3.1 Emission Source Inputs

 HEM3 and AERMOD require detailed data on emissions from each emission source 

included in the modeling analysis. These data include: 

! pollutants emitted 

! emission rate for each pollutant 

! emission source coordinates 

! stack height (or emission height for fugitive sources) 

! stack diameter (or configuration of fugitive sources) 

! emission velocity 

! emission temperature 

 Emissions data for the modeling effort were taken from the 2002 National Emissions 

Inventory (NEI) Final Version 1.10 These data include HAP emission rates, emission source 

coordinates, stack heights, stack diameters, flow rates, and exit temperatures. EPA performed an 

engineering review of the NEI data. In cases where new or better data were known to exist for a 

particular source category, that information was integrated into the data used in modeling for that 

category. For each source category, the emissions are summarized in the source category specific 

report. Detailed computer files containing all emission and release characteristics are available on 

the RTR website. 

 As noted in the previous section, industrial emission sources can be characterized in 

AERMOD and other dispersion models as point, area, or volume sources. The NEI includes a 

framework for characterizing area and volume sources; however, these fields are not filled in the 

2002 Final Version 1 of the NEI. Therefore, fugitive emissions are generally characterized as low 

point sources with minimal exit velocities. For some categories, additional information was 

available on the configuration of fugitive emission sources. This information was incorporated 

into the emissions database as part of the engineering review. Thus, fugitive emission sources 

were characterized as area or volume sources when sufficient configuration information was 

available. 
15 

3.2 Pollutant Cross-Referencing

 Because the NEI is developed from a number of different data sources, a single chemical 

may be listed in the inventory under different names (i.e. a “common name” and one or more 

structure-based names). In addition, pollutant groupings such as polycyclic organic matter 

(POM), can be listed in the NEI under the names of individual member compounds, and under 

different synonyms (e.g. polynuclear aromatic hydrocarbons). HEM3 requires an exact match in 

the chemical name in order to link emissions to the appropriate dose-response factors. The model 

will not process any pollutant that is not specifically listed in the chemical library (see Table 2-1). 

Therefore, all of the HAP names used in the NEI were linked to the appropriate chemical names in 

the HEM3 reference file. 

 The individual POM chemicals listed in the NEI were grouped into seven subclasses. 

Table 3-1 shows these groupings, and gives the cancer unit risk estimate used for each subclass. 

Emissions of metal compounds were also adjusted using algorithms developed for the Emissions 

Modeling System for Hazardous Air Pollutants (EMS-HAP) under the National-scale Air Toxics 

Assessment (NATA). A mass adjustment factor was applied to the emissions of metal compounds 

to account for a particular portion (e.g., the lead portion of lead sulfate) or to partition them among 

multiple pollutant categories (e.g. chromium arsenate into chromium VI compounds and arsenic 

compounds). In addition, where no specific compound information was available, metals were 

speciated into appropriate oxidation states (e.g. chromium compounds into chrome VI and chrome 

III) based on factors that have been developed for specific source categories and average factors 

that have been developed for the inventory as a whole. The adjustment factors and speciation 

factors were taken from the HAP Table module of EMS-HAP.11 ,12

3.3 Meteorological Data

 AERMOD meteorological data for the RTR analysis were derived from three sources: (1) 

AERMET processing of 122 National Weather Service (NWS) surface observation stations, (2) 

State agencies, and (3) industry (on-site). The 122 NWS sites were selected to provide thorough 

coverage of the U.S. For the RTR analysis, the average distance between a modeled facility and 

the applicable meteorological station was 72 km. One year of hourly data for these sites were 

processed through the AERMET program, and this is documented in “Meteorological Data 

Processing Using AERMET.”13 Four states provided AERMOD-ready meteorology files. These 

states were: Alabama (5 stations), Florida (4 stations), North Carolina (6 stations), and Wisconsin 

(22 stations). Eleven of the state supplied meteorology sites were co-located with sites in the list 

of 122. The data at the state supplied sites were more recent, so the state data for the 11 co-located 

sites were used. In addition, for the RTR petroleum refinery modeling, two refineries (Fairbanks, 

Alaska and the Virgin Islands) provided on-site AERMOD-ready meteorology files. These on-site 

meteorological data files were used with their respective refinery facilities for the HEM3 refinery 

modeling. Figure 3-1 shows the location of the 150 surface meteorological stations used in the 

RTR analysis. 

 As noted above, 122 NWS stations were processed through the AERMET program. To 

estimate the parameters needed by AERMOD, AERMET requires hourly surface weather 
16 

observations and the full twice-daily upper air soundings (i.e., meteorological variables reported at 

all levels). The surface and upper air stations are paired to produce the required input data for 

AERMOD. To simplify processing and minimize the amount of quality assurance needed, the 

analysis was restricted to meteorological data collected prior to the installation of the Automated 

Surface Observation System (ASOS). The ASOS has previously been found to omit the ceiling 

height for a large percentage of the observations at a number of meteorological stations. 

Installation and operation of ASOS equipment began in 1992; therefore, data for 1991 were 

processed with AERMET (note; the most recent version of AERMET has corrected this issue and 

is now compatible with ASOS data, processing of meteorological data in the future will utilize this 

revised version of AERMET and more recent years of meteorology). Data were retrieved from 

products available from the National 

Table 3-1. Groupings Used for Polycyclic Organic Matter in the RTR Phase II 

Modeling Effort

POM subgroup Names used in the NEI 

Cancer URE 

used for the 

subgroup 

(m3/g) 

POM 71002 16-PAH 0.000055 

 PAH, total 0.000055 

Polycyclic Organic Matter 0.000055 

POM72002 2-Chloronaphthalene 0.000055 

 2-Methylnaphthalene 0.000055 

 Acenaphthene 0.000055 

 Acenaphthylene 0.000055 

 Anthracene 0.000055 

 Benzo(c)phenanthrene 0.000055 

 Benzo[e]Pyrene 0.000055 

 Benzo[g,h,i,]Perylene 0.000055 

 Fluoranthene 0.000055 

 Fluorene 0.000055 

 Perylene 0.000055 

 Phenanthrene 0.000055 

 Pyrene 0.000055 

POM 73002 7,12-Dimethylbenz[a]Anthracene 0.1 

POM 74002 3-Methylcholanthrene 0.01 

POM 75002 5-Methylchrysene 0.001 

 Benzo[a]Pyrene 0.001 

 Dibenzo[a,h]Anthracene 0.001 

POM 76002 B[j]Fluoranthen 0.0001 

 Benz[a]Anthracene 0.0001 

 Benzo[b]Fluoranthene 0.0001 

 Benzo[k]Fluoranthene 0.0001 

 Indeno[1,2,3-c,d]Pyrene 0.0001 
17 

POM 77002 Chrysene 0.00001 

Climatic Data Center (NCDC). The surface data for 1991 were retrieved from the Hourly United 

States Weather Observation (HUSWO) CD. Upper air soundings were obtained from the 

Radiosonde Data of North America CDs produced by NCDC and the Forecast Systems Laboratory 

(FSL). 

 The input to and output from AERMET were examined for indications of missing input 

data. The 1200 Greenwich Mean Time (GMT) sounding is required for AERMET to calculate the 

convective boundary layer height and several associated parameters. There were many isolated 

days in which the 1200 GMT sounding was missing for many of the stations. However there also 

were several stations for which there were two or more consecutive days of missing 1200 GMT 

soundings. To minimize the impact on the output from AERMET, upper air data from a 

representative upper air station were substituted for those periods for which the 1200 GMT 

sounding was missing for two or more consecutive days. 

 One of the important requirements of applying the AERMOD model is the specification of 

surface characteristics for use in processing the meteorological data using AERMET. Application 

of AERMET requires specification of the surface roughness length, the Bowen ratio (an indicator 

of surface moisture), and the albedo (an indicator of surface reflectivity). These surface 

characteristics are used by AERMET to calculate the level of shear-induced mechanical turbulence 

generated by flow over the surface and for the energy balance calculations used in the 

determination of the Monin-Obukhov stability parameter and the convective velocity scale. For 

this application, the following surface characteristics were used: 

! Surface roughness length = 0.25 m. At the airport meteorological site, the surface 

roughness Includes runways, terminal buildings and other airport structures. In addition, 

off-airport structures often are within 3 kilometers of the measurement site. his 

combination of land covers suggests a value of 0.2 – 0.3 meters is appropriate. 

! Bowen ratio = 1.0. Representing an equal partition of the heat fluxes 

! Albedo = 0.15. Representing conditions for all seasons, including winter without 

continuous snow cover. 

The file STNS.TXT located on the HUSWO CD was used for the anemometer heights required by 

AERMET. These heights are to the nearest meter and were deemed appropriate for use in this 

application. 

 Two problems in the AERMET computer program were fixed before completing this 

application. The first pertained to hourly surface observations. Incorrect missing indicators in the 

code for wind speed and direction were output when the data were extracted from the raw input 

file in Stage 1, which caused the program to stop running in Stage 3. The AERMET program was 

changed to use correct missing indicators. The second problem was associated with the upper air 

soundings. AERMET would not process an entire station’s data if the surface pressure for the first 

sounding was less than 850 millibars (mb). Surface pressures less than 850 mb are not uncommon 

for stations at high elevations. The AERMET program was modified to expand the allowable 

surface pressures. 
18 

Figure 3.1. Locations of RTR AERMOD Meteorological Stations 

 In early 2007, AERMOD was modified to version 07026 which checks the header record 

of the surface meteorological input file to verify that its version number is 06341. As a result of 

this modification, the 122 NWS stations were re-processed through a newer version of AERMET 

(version 06431) to create meteorological data files compatible with AERMOD version 07026. 

The meteorology data received from the States were already at version 06341. 

3.4 Model Options Selected

 HEM3 presents a number of options for characterizing the modeling domain and data 

sources. As many sources were modeled in this assessment, established defaults and common 

practices were relied on to make these choices. The choices available to a HEM3 user and the 

selections that were made in this assessment are presented in Table 3-2. Some of the key 

selections are discussed in more detail in the paragraphs below. 

 Although routine emissions are not expected to vary significantly with time, nonroutine 

(upset) emissions can be significant relative to routine emissions. Upset emissions occur during 

periods of startup, shutdown, and malfunction. Upset emissions are not likely for equipment or 

storage tanks, but do result from malfunctioning control devices and leaks in cooling tower heat 

exchangers. There is some limited data on upset emissions available,14 but no facility-specific 
19 

analyses of these data were performed to characterize short-term emissions from these emission 

sources, and upset emissions were not modeled for the RTR risk assessments. 

Table 3-2. Modeling Domain and Model Set-Up Options in the HEM-3 Model As Used in 

the Residual Risk and Technology Review Assessments

Option Selection

Dispersion model: AERMOD or ISCST3 AERMOD 

Type of analysis: chronic, acute, or both Both 

Averaging time for short term impacts 1-hour 

Multiplier for short term emissions 10 a

Urban or rural dispersion characteristics Rural 

Include terrain impacts Yes 

Include building wake effects No 

Calculate deposition (wet, dry, or both) No 

Include impacts of plume depletion due to deposition No 

User-specified receptor locations (for plant boundary sites or other sites of interest) No 

Modeling domain size – maximum distance to be modeled 50 km 

Cutoff distance for modeling of individual blocks 3 km b

Overlap distance where receptors are considered to be on plant property – measured 

from each source 

30 m c

Polar receptor network specifications 

Distance from the plant center to the innermost ring 100 m c

Number of rings 13 

Number of directions 16 

Meteorology data Closest site 

 a For the secondary aluminum production category, the ratio of maximum short-term emissions to 

long-term average emissions was set to 100, based on the engineering review. 

 b The individual block modeling cutoff was increased for categories and for some facilities to ensure 

that the maximum individual risk values were not interpolated. 

 c

 The overlap distance and inner ring distance were adjusted for some facilities to avoid modeling 

locations that are on plant property (see section 4.2).

3.4.1 Urban or Rural Dispersion Characteristics

 Since 51 source categories were considered in these screening-level RTR assessments, the 

rural option was chosen to be most conservative (i.e., more likely to overestimate risk results). 

3.4.2 Deposition and Plume Depletion

 This modeling analysis did not take into account the depletion of pollutant concentrations 

in the plume due to wet or dry deposition. In addition, reactivity and decay were not considered. 
20 

It is possible that this approach, may overestimate air concentrations and therefore risk. However, 

one of the main metrics used by EPA in the residual risk program is the risk to the individual most 

exposed (maximum risk). Because the maximum risk usually occurs at a receptor very close to 

the emission source, it is unlikely to be influenced by altered plume dispersion characteristics of 

this type. For more refined, multipathway assessments, EPA will consider deposition and 

depletion. 

3.4.3 Cutoff Distance for Modeling of Individual Blocks

 The cutoff distance for modeling individual Census blocks was initially set to 3 km. This 

distance generally ensures that the maximum individual cancer risk and the maximum TOSHI are 

modeled explicitly and not interpolated. Following the modeling run, the results for each facility 

were checked to determine whether the maximum impacts were located inside the modeling cutoff 

distance. If the maximum impacts were outside the cutoff distance any of the impacts were 

significant, then HEM3 was rerun for the facility with an expanded modeling distance cutoff. In 

general, this was done if the cancer risk exceeded 1 in 10 million or any TOSHI exceeded 0.1. 

However, the risks for these facilities were generally very low, since the maximum impacts were 

generally only interpolated when the nearest Census block was more than 3 km from the facility. 

3.4.4 Plant Boundary Assumptions

 The main input mechanisms for incorporating plant boundary information in HEM3 are the 

overlap distance, the distance to the innermost polar receptor ring, and user-specified receptor 

locations. The NEI does not provide information on plant boundaries. Therefore, the userspecified receptor file was not used in this analysis. Conservative default assumptions were used 

for the overlap distance and the innermost polar receptor ring. However, these were adjusted for 

some categories where plant sites are known to be large. In addition, satellite imagery was used to 

check the plant boundary assumptions for facilities with large projected impacts. These checks are 

discussed further in the section on Quality Assurance (Section 4). 

3.5 Modeling of Multiple Facilities

 HEM3 models one facility at a time. However, clusters of nearby facilities may impact the 

same people, resulting in higher risk to those people. To account for this situation, risks were 

summed at each Census block for all facilities affecting the Census block. 

 As described earlier (Section 2.3.4), HEM3 produces detailed output tables containing the 

risk and population for every Census block in the modeling domain. These detailed tables were 

combined for all facilities in a source category and the risk for each Census block was summed. 

Thus, the effect of multiple facilities in the same source category on the same receptor could be 

estimated. The resulting “combined facility” or “cluster-effect” Census block risks were used to 

calculate population exposure to different cancer risk levels and source category incidence. 
21 

4. Quality Assurance

 The National Emissions Inventory is subject to an extensive program of quality assurance 

(QA) and quality control (QC). The QA/QC program for the point source component of the NEI 

is documented in a separate report, available from the NEI website.15 This section describes QA 

activities carried out under the RTR modeling analysis. 

4.1 Engineering Review

 In addition to the standardized QA steps taken for the entire NEI, EPA performed an 

engineering review of NEI data for the emission source categories included in the RTR analysis. 

This engineering review included two main components. The first component addressed the list of 

facilities included in each source category. EPA engineers reviewed independent sources of 

information to identify all sources in the category that are included in the NEI. In addition, EPA 

reviewed the list of sources represented as part of each category in the NEI to make sure that the 

facilities actually manufacture products characteristic of the source category. 

 The second component of the engineering review focused on the appropriateness of facility 

emissions. EPA reviewed the list of HAPs emitted by each facility to make sure that the 

pollutants were appropriate to the source category. In addition, EPA engineers review the 

magnitude of those HAP emissions. In cases where new or better data were known to exist for a 

particular source category, that information was integrated into the data used in modeling for that 

category. In these cases, the source category specific documents provide additional details on the 

emissions inputs used. 

4.2 Geographic Checks

 The NEI QA process includes some basic checks on location data for point sources. The 

coordinates for each source are checked to ensure that they are in the county that has been 

specified for the source. If this is not the case, or if no geographic coordinates are available for the 

emission source, then the coordinates are set to a default location based on the nature of the 

emission source category.15 In addition, coordinates for all emission sources at a given facility are 

checked to ensure that they are withing 3 km of one another. 

 As part of the RTR modeling analysis, additional geographical QA checks were made for 

some facilities. Facilities subjected to these additional checks include: 

! cases where the initial estimates of maximum risks were particularly high 

" maximum individual cancer risk of over 1 in 10,000 

" any maximum TOSHI above 10 

! cases where no Census blocks were identified within 3 km of the facility 

 The emission source coordinates were reviewed for each of these facilities, and compared 

with the address reported for the facility. Both the coordinates and the facility address were 
22 

entered into Internet mapping sites which provide the option of viewing aerial or satellite 

photography. These sites include Google Earth® (earth.google.com/), Google Maps® 

(local.google.com/), MapQuest® (www.mapquest.com) and Microsoft Terraserver® 

(www.terraserver.com/). If the address and the coordinates represented the same location, then the 

coordinates were taken to be correct. If the locations were different, then the aerial photographs 

for the address and the coordinate location were reviewed to determine whether either photograph 

included an industrial facility. Generally, where the two locations were different, the facility 

address was found to be correct. In some cases, this comparison could not be made because the 

reported address was a Post Office box or a headquarters address. Where this occurred, the aerial 

photograph for the coordinate location was reviewed to determine whether an industrial facility 

was located at or near the location. If emission source coordinates were found to be incorrect, 

HEM3 was rerun using corrected coordinates. These changes are described in the source category 

documents. 

 For the high-risk facilities, the coordinates used to represent the most impacted Census 

blocks were also reviewed. This review drew on detailed Census block boundary maps and aerial 

photographs. Large industrial facilities will frequently occupy one or more entire Census blocks. 

However, these blocks may also include one or more residences on the periphery of the industrial 

land. Generally, the coordinates listed for a Census block are near the center of the block. In 

these cases of mixed industrial and residential blocks, the coordinates may be on plant property. 

 The potential for overlap between an industrial facility and a Census block coordinate is 

illustrated in Figure 4-1. The figure shows Census blocks divisions around a large industrial 

facility, with the coordinates of emission sources at the facility illustrated by red squares. Census 

block boundaries are shown, and the coordinates given in the Census data base for populated 

blocks are indicated by blue circles. As the figure shows, the facility is spread over five Census 

blocks. A number of populated blocks can be seen to the Southwest of the facility, and most of 

the blocks covering the facility are not populated. However, the block containing most of the 

facility’s emission points also includes at least one residence (the total population given for the 

block is 5). The location of the residence is at the periphery of the block, near the one of the 

adjacent populated blocks. However, the coordinates given for the block are near its center, 

illustrated by the blue star, clearly between various emission sources at the facility. 

 In general, block coordinates were considered to be on plant property if they were located 

between the different emission source locations listed for the facility. In these situations, HEM3 

was rerun with an expanded overlap distance, in order to exclude the Census block coordinates 

that appeared to be located on plant property. The distance to the innermost polar receptor ring 

was also adjusted to ensure that this ring was not on plant property. 
23 

Figure 4-1. Illustration of a quality assurance test for Census block coordinates located on 

plant property. 

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S#

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S#

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S#

S#

S#

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S# S#

S#

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S#

S#

S#

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S#

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S#

S#

S#

S#

S#

%U Source locations

Coordinates of populated blocks

ÊÚ Coordinates located on plant property

S# Other populated blocks

Census block boundaries

1 0 1 Kilometers
24 

5. Uncertainties

 This analysis is subject to a number of uncertainties. For instance, model verification 

studies for AERMOD showed predicted maximum annual concentrations ranging from 0.3 to 1.6 

times measured values, with an average of 0.9. Predicted maximum short term (1 to 24 hours) 

concentrations were 0.25 to 2.5 times measured values, with an average of 1.0.16

 In addition, a number of simplifying assumptions have been made in this modeling 

analysis. First, the coordinates reported by the Department of Census for Census block “internal 

points” have been used as a surrogate for long-term population exposures. Locations of actual 

residences have not been modeled. In addition, the current version of HEM3 does not take into 

account the movement of people from one Census block to another during the course of their lives, 

or commuting patterns during a given day. Nor does the model take into account the attenuation 

of pollutant from outside emission sources in indoor air. Ideally, risks to individuals would be 

modeled as they move through their communities and undertake different activities. However, 

such modeling is time- and resource-intensive and can only capture a portion of the uncertainty 

associated with the full range of human activities. In general, it is expected that long-term 

exposures will be overstated for high-end estimates (as most individuals will not spend all their 

time at their highly affected residences), but may understate the total population exposed (as some 

individuals living outside the modeled area may regularly commute into the area for work or 

school). 

 When considering long-term or lifetime exposures, it should be noted that relatively few 

people in the United States reside in one place for their entire lives. For the purposes of this 

assessment, cancer risk estimates are based on a lifetime exposure at the 2000 Census-identified 

place of residence. While it is impossible to know how this assumption affects the risk 

experiences by a particular individual (as people can move into higher- or lower-risk areas), it is 

likely that this assumption will overstate the exposure to those most exposed (i.e., people already 

living in high exposure areas are unlikely to move to yet higher exposure areas). However, this 

assumption will also tend to underestimate the total number of people exposed and population risk 

(i.e., incidence) because population levels are generally increasing. 

 In the current analysis, only direct inhalation has been modeled. Other pathways such as 

the deposition of pollutants to drinking water, and to bioaccumulation of deposited pollutants in 

the food supply may be a significant source of exposure for persistent and bioaccumulative 

pollutants. These exposures will be modeled in subsequent analyses. Because this analysis is 

restricted to the inhalation pathway, the impacts of plume depletion due to deposition have not 

been into account. Thus, inhalation impacts may be overestimated for some pollutants, but 

exposures through other pathways would be underestimated. 

 A number of other simplifications have been made in the dispersion modeling 

analysis, as has been noted in Table 3-1. For instance, building wake effects have not been 

considered. Rural dispersion conditions have also been used for all facilities. This is viewed as a 

conservative assumption, since rural conditions result in less mixing close to the emission source. 
25 

In addition, meteorological observations are based on the closest station in the HEM3 

meteorological library (see Figure 2-1). Alternative meteorological stations may be more 

appropriate for some facilities. Ideally, facility-specific meteorological observations would be 

used. A single year of meteorological data (1991) was selected for dispersion modeling. When 

considering off-site meteorological data most site specific dispersion modeling efforts will employ 

up to five years of data to capture variability in weather patterns from year to year. However, 

because of the large number of facilities in the analysis and the extent of the dispersion modeling 

analysis (national scale), it was not practical to model five years of data. Other national studies 

such as NATA have also considered only a single year of meteorological data. A sensitivity 

analyses performed by the NATA assessment found that variability attributable to the selection of 

the meteorology location/time (both temporal and spatial) resulted in a 17-84% variation in 

predicted concentrations at a given station17. 

 Finally, risk and exposure factors are also subject to uncertainty. Not all individuals 

experience the same degree of exposure or internal dose of a given pollutant due to individualspecific parameters such as weight, age, and gender. While the health benchmarks used in the 

analysis crudely account for sensitive populations, a prototypical human (e.g., body weight, 

ventilation rate) is used to define the benchmark. Because of the variability of these parameters in 

the population, this factor will result in a degree of uncertainty in the resulting risk estimate. 

 Table 5-1 summarizes the general sources of uncertainty for the RTR modeling analysis. 

The table also gives a qualitative indication of the potential direction of bias on risk estimates. 

The sources of uncertainty in Table 5-1 are divided into four categories, based on the major 

components of the analysis: 

! emissions inventory 

! fate and transport modeling 

! exposure assessment 

! toxicity assessment 

It must also be noted that individual source categories may be subject to additional uncertainties. 

These are discussed in separate reports which have been prepared for each emission source 

category included in the RTR. 
26 

Table 5-1. Summary of General Uncertainties Associated with Risk and Technology Review Risk 

Parameter Assumption Uncertainty/Variability Discussion 

Poten

o

Emissions InventoryIndividual HAP emissions rates and facility characteristics (stack parameters, property boundaries) 

Emissions and facility characteristics from the NEI provide an accurate characterization of actual source emissions. 

Our emissions inventory is based on the 2002 NEI, our internal review, and public comments received. The degree to which the data in our inventory represents actual emissions is likely to vary across sources. Nearly half of the sources in the source category submitted a review of their emissions and facility characteristics data. Some detailed data, such as property boundary information was not available for most facility. This is an important consideration in determining acute impacts. 

Unbiavariab

Multiplier for short-term emission rates 

Maximum short term emission rates are estimated by applying a simple multiplier (a factor of 10) to average annual emissions. 

The ratio between short-term and long-term average emission rates may vary from among the different emission sources at a facility. In addition, the use of a simple multiplier means that impacts of maximum short term emissions are modeled for all meteorological conditions, including the worst-case conditions for population exposure. 

Potentthe facemissioccasiworst-Overeactual term e

Fate and Transport ModelingAtmospheric dispersion model choice 

AERMOD is one of EPA's recommended models for assessing pollutant concentrations from industrial facilities 

Field testing of these models have shown results to generally be within a factor of 2 of measured concentrations. 

Overe
Table 5-1. Summary of General Uncertainties Associated with Risk and Technology Review Risk 

 (continued) 

27 

Parameter Assumption Uncertainty/Variability Discussion 

Poten

o

Building downwash Not included in assessment 

Use of this algorithm in AERMOD could improve the dispersion calculations at individual facilities. However, data were not readily available to utilize this option. 

PotentmaximNo effout. 

Plume depletion and deposition Not included in assessment 

Ignoring these impacts for pollutants that deposit minimally and whose risks derive predominantly from inhalation should have minimal effect on risk estimates. 

Unbiaoveres

Meteorology 

One year of meteorological data from the nearest weather station (selected from 122 nationwide) is representative of long-term weather conditions at the facility. 

The use of one year of data rather than the five or more adds uncertainty based on whether that year is representative of each location’s climatology. Use of weather station data rather than on-site data can add to uncertainty. Additionally, the use of default surface parameters in the generation of the meteorological datasets imparts uncertainty to the results from any individual facility. 

Minimoveres

Reactivity Not included in the assessment. 

Chemical reactions and transformations of individual HAP into other compounds due to solar radiation and reactions with other chemicals happens in the atmosphere. However, in general, the HAP in this assessment do not react quickly enough for these transformations to be important near the sources, where the highest individual risks are estimated. Further, most of the HAP do not react quickly enough for these transformations to be important to risk estimates in the entire modeled domain (i.e., within 50 km of the source). 

No imestimaon popincide
Table 5-1. Summary of General Uncertainties Associated with Risk and Technology Review Risk 

 (continued) 

28 

Parameter Assumption Uncertainty/Variability Discussion 

Poten

o

Maximum modeling distance 

50 kilometers from center of facility 

This distance is considered to be the maximum downwind distance for a Gaussian plume model such as AERMOD. This is because, in general, winds cannot be considered to follow straight line trajectories beyond this distance. 

No effindiviundereincide

Exposure AssessmentLocations and shortterm movements of individuals 

Ambient concentration at centroid of each off-site census block is equal to the exposure concentration for all people living in that census block. Effect of human activity patterns on exposures is not included in the assessment. 

People live at different areas within block that may have higher or lower exposures than at the centroid. Individuals also move from outdoors to indoors and from home to school/work to recreation, etc., and this can affect their total exposure from these sources. 

Unbiafor moindivioveresexposfor lea

Long-term movements of individuals 

MIR individual is exposed continuously to the highest exposure concentration for a 70-year lifetime. Population moves into and out of exposure area consistent with national frequency distribution of residency. 

MIR (maximum individual risk) is defined in this way to be a maximum theoretical risk at a point where a person can actually reside. Individuals may move into or out of areas impacted by the facilities of interest in ways that don’t correspond exactly with national norms or local demographic patterns, and this could lead to localized biases. In general, source categories with larger numbers of facilities should carry less potential for bias. 

Unbiaindivioveresindivilikely least eremainpopulaincrea70 yea

Toxicity Assessment
Table 5-1. Summary of General Uncertainties Associated with Risk and Technology Review Risk 

 (continued) 

29 

Parameter Assumption Uncertainty/Variability Discussion 

Poten

o

Reference concentrations (RfC) 

Consistent with EPA guidance, RfCs are developed including uncertainty factors to be protective of sensitive subpopulations. Additionally, RfCs are developed based on the level producing an effect in the most sensitive target organ or system. 

While other organ systems may be impacted at concentrations above the RfC, these are not included in the calculation of target organspecific hazard indices. 

In genRfCs uwhose undereof uncvariabuncertpotentrisks. 

Unit Risk Estimate (URE) 

Use of unit risk estimates developed from dose-response models such as linear low-dose extrapolation. 

Uncertainty in extrapolating the impacts from short-duration, high-dose animal or work-related exposures to longer duration, lower-dose environmental impacts. 

Overenonlinfor linsparse generavalues aimed risks iand va

Toxicity of mixtures 

Cancer risks and noncancer hazard quotients were calculated for each HAP individually and then summed into a total risk or hazard index (assumption of additivity). 

Concurrent exposures to multiple chemicals may result in either increased or decreased toxicity due to chemical interactions but the data needed to quantify these effects are generally not available. 

Unbiamixtuundereoverescorrec
Table 5-1. Summary of General Uncertainties Associated with Risk and Technology Review Risk 

 (continued) 

30 

Parameter Assumption Uncertainty/Variability Discussion 

Poten

o

Surrogate doseresponse values for HAPs without values 

In the case of groups of HAPs such as glycol ethers, the most conservative dose-response value of the chemical group was used as a surrogate for missing doseresponse values in the group. For others, such as unspeciated metals, we have applied speciation profiles appropriate to the source category to develop a composite dose-response value for the group. For HAP which are not in a group and for which no URE’s or RfC’s are available from credible sources, no assessment of risk is made. 

Rather than neglecting the assessment of risks from some HAPs lacking dose response values, conservative assumptions allow the examination of whether these HAPs may pose an unacceptable risk and require further examination, or whether the conservative level examination with surrogates screens out the HAPs from further assessment. 

OvereconserUnbiaspecifThere underepollutinclud
31 

6. References

 

1. EPA. 2004. AERMOD: Description of Model Formulation. EPA-454/R-03-004, U.S. 

Environmental Protection Agency, Research Triangle Park, NC. 

www.epa.gov/scram001/dispersion_prefrec.htm#aermod

2. EPA. 2004. User's Guide for the AMS/EPA Regulatory Model - AERMOD. EPA454/B-03-001, U.S. Environmental Protection Agency, Research Triangle Park, NC. 

www.epa.gov/scram001/dispersion_prefrec.htm#aermod

3. EC/R Incorporated. 2006. The HEM-3 User’s Guide. Prepared for the U.S. 

Environmental Protection Agency, Sector-Based Assessment Group, Research Triangle 

Park, NC. 

4. EPA. 2007. OAQPS Prioritized Chronic Dose-Response Values (6/12/2007). U.S. 

Environmental Protection Agency, Research Triangle Park, NC. Available at: 

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

5. Census. 2000. LandView® 5 on DVD. U.S. Census Bureau, Washington, DC. 

6. USGS. 2003. 1:250,000 Scale Digital Elevation Models (DEM). U.S. Geologic Survey, 

Washington, DC. http://edcsgs9.cr.usgs.gov/glis/hyper/guide/1_dgr_demfig/states.html

7. EPA. 2004. User's Guide for the AERMOD Terrain Preprocessor (AERMAP). EPA454/B-03-003, U.S. Environmental Protection Agency, Research Triangle Park, NC. 

http://www.epa.gov/scram001/dispersion_related.htm#aermap

8. EPA. 2004. Addendum: User's Guide for the AMS/EPA Regulatory Model - AERMOD

(related to deposition). U.S. Environmental Protection Agency, Research Triangle Park, 

NC. http://www.epa.gov/scram001/dispersion_prefrec.htm#aermod

9. Wesely, M.L., P.V. Doskey, and J.D. Shannon. 2002. Deposition Parameterizations for 

the Industrial Source Complex (ISC3) Model. Argonne National Laboratory, Argonne, 

Illinois. http://www.epa.gov/scram001/dispersion_prefrec.htm#aermod

10. EPA. 2006. 2002 National Emissions Inventory Data & Documentation. U.S. 

Environmental Protection Agency, Research Triangle Park, NC. 

http://www.epa.gov/ttn/chief/net/2002inventory.html

11. EPA. 2002. EMS-HAP Modeling System. Environmental Protection Agency, Research 

Triangle Park, NC. http://www.epa.gov/scram001/dispersion_related.htm#ems-hap
32 

 

12. Strum, Madeleine, Richard Mason, and Diane Linderman. 2002. User's Guide for the 

Emissions Modeling System for Hazardous Air Pollutants (EMS-HAP) Version 2.0. 

EPA-454/B-02-001, Environmental Protection Agency, Research Triangle Park, NC. 

http://www.epa.gov/scram001/dispersion_related.htm#ems-hap

13. MACTEC. 2006. Meteorological Data Processing Using Aermet for HEM-AERMOD. 

Prepared by MACTEC Federal Programs, Inc., Durham, NC for EC/R Incorporated, 

Chapel Hill, NC. 

14. Texas Commission on Environmental Quality. 2006. Reports on Air Emission Events. 

http://www.tceq.state.tx.us/compliance/field_ops/eer/

15. EPA. 2006. NEI Quality Assurance and Data Augmentation for Point Sources. U.S. 

Environmental Protection Agency, Emission Inventory Group, Research Triangle Park, 

NC. 

16. EPA. 2003. AERMOD: Latest Features and Evaluation Results. EPA-454/R-03-003, 

U.S. Environmental Protection Agency, Research Triangle Park, NC. 

http://www.epa.gov/scram001/7thconf/aermod/eval.pdf

17 EPA, 2001, National-Scale Air Toxics Assessment for 1996; EPA-453/R-01-003; EPA454/B-03-003, U.S. Environmental Protection Agency, Research Triangle Park, NC. 

http://www.epa.gov/ttn/atw/nata/peer.html 
Appendix 2

Meteorological Data for HEM-AERMOD Modeling
 

2

DRAFT 

METEOROLOGICAL DATA PROCESSING 

USING AERMET 

FOR 

HEM-AERMOD 

Submitted by: 

MACTEC Federal Programs, Inc. 

5001 S. Miami Blvd., Suite 300 

Durham, NC 27703 

919-941-0333 

July 28, 2006 
Meteorological Data Processing using AERMET July 28, 2006 

For HEM-AERMOD 

i 

TABLE OF CONTENTS 

PROJECT BACKGROUND ...............................................................................................1 

METEOROLOGICAL DATA PROCESSING ...................................................................1 

MISSING DATA.................................................................................................................5 

ANEMOMETER HEIGHTS ...............................................................................................6 

SURFACE CHARACTERISTICS ......................................................................................6 

MODIFICATIONS TO AERMET CODE ..........................................................................7 

RESULTS ............................................................................................................................7 

LIST OF TABLES 

Table

1 SURFACE STATIONS PAIRED WITH UPPER AIR STATION.........................1 

2 UPPER AIR STATIONS WITH MISSING 1200 GMT SOUNDINGS 

 AND STATION USED FOR DATA SUBSTITUTION.........................................5 

3 SURFACE CHARACTERISTICS ..........................................................................7 

4 PERCENT CALM AND MISSING BY STATION ...............................................8 

LIST OF FIGURES 

Figure

1 SURFACE AND UPPER AIR STATIONS ............................................................4 
Meteorological Data Processing using AERMET July 28, 2006 

For HEM-AERMOD 

1 

PROJECT DESCRIPTION AND BACKGROUND 

The AERMOD meteorological processor, AERMET, was used to process one year of 

hourly data from 122 National Weather Service (NWS) surface observation stations 

across the continental United States, Alaska, Hawaii, and Puerto Rico. 

METEOROLOGICAL DATA PROCESSING 

To estimate the boundary layer parameters required by AERMOD, AERMET requires 

hourly surface weather observations and the full (i.e., meteorological variables reported at 

all levels) twice-daily upper air soundings. The surface and upper air stations are paired 

to produce the required input data for AERMOD. 

One of the requirements of the project was to use surface observations prior to the 

introduction of the Automated Surface Observation System (ASOS). Installation and 

operation of ASOS equipment began in 1992; therefore, data for 1991 were processed 

with AERMET. 

A list of 86 previously paired surface and upper air stations was provided to MACTEC. 

On review of these stations, it was determined that data for 1991 for three of the stations 

– Galveston, TX, Houston-Hobby, TX and Boulder, CO – was not available. The 

remaining 83 stations did not provide adequate coverage for the MACT sources to be 

modeled with AERMOD. As a result, an additional 39 surface stations were identified to 

include for processing. These additional surface stations were paired with representative 

upper air stations. A list of the surface and paired upper air stations are in Table 1. 

Data were retrieved from products available from the National Climatic Data Center 

(NCDC). The surface data for 1991 were retrieved from the Hourly United States 

Weather Observation (HUSWO) CD. Upper air soundings were obtained from the 

Radiosonde Data of North America CDs produced by NCDC and the Forecast Systems 

Laboratory (FSL). 

TABLE 1 

SURFACE STATIONS WITH PAIRED UPPER AIR STATION 

SURFACE WBAN LAT LON UPPER AIR WBAN LAT LON 

Paducah, KY 03816 37.07 -88.77 Paducah, KY 03816 37.07 -88.77 

Huntsville, AL 03856 34.65 -86.78 Nashville, TN 13897 36.13 -86.68 

Huntington, WV 03860 38.37 -82.55 Huntington, WV 03860 38.37 -82.55 

Greenville, SC 03870 34.90 -82.22 Athens, GA 13873 33.95 -83.32 

Fort Worth, TX 03927 32.90 -97.02 Stephenville, TX 13901 32.22 -98.18 

Wichita, KS 03928 37.65 -97.45 Topeka, KS 13996 39.07 -95.63 

Lake Charles, LA 03937 30.12 -93.23 Lake Charles, LA 03937 30.12 -93.23 

Jackson, MS 03940 32.32 -90.08 Jackson, MS 03940 32.32 -90.08 

Kansas City, MO 03947 39.30 -94.73 Topeka, KS 13996 39.07 -95.63 
Meteorological Data Processing using AERMET July 28, 2006 

For HEM-AERMOD 

2 

SURFACE WBAN LAT LON UPPER AIR WBAN LAT LON 

San Juan, PR 11641 18.43 -66.02 San Juan, PR 11641 18.43 -66.00 

Orlando, FL 12815 28.45 -81.32 Tampa, FL 12842 27.70 -82.40 

Gainesville, FL 12816 29.68 -82.27 Waycross, GA 13861 31.25 -82.40 

Tampa, FL 12842 27.97 -82.53 Tampa Bay, FL 12842 27.97 -82.53 

W. Palm Beach, FL 12844 26.68 -80.10 W. Palm Beach, FL 12844 26.68 -80.12 

Victoria, TX 12912 28.85 -96.92 Corpus Christi, TX 12924 29.77 -97.50 

New Orleans, LA 12916 30.00 -90.25 Slidell, LA 53813 30.33 -89.82 

Port Arthur, TX 12917 29.95 -94.02 Lake Charles, LA 03937 30.12 -93.23 

Brownsville, TX 12919 25.90 -97.43 Brownsville, TX 12919 25.90 -97.43 

San Antonio, TX 12921 29.53 -98.47 Del Rio, TX 22010 29.37 -100.92 

Corpus Christi, TX 12924 27.77 -97.52 Corpus Christi, TX 12924 27.77 -97.52 

Houston, TX 12960 29.98 -95.37 Lake Charles, LA 03937 30.12 -93.23 

Raleigh, NC 13722 35.87 -78.78 Greensboro, NC 13723 36.10 -79.95 

Greensboro, NC 13723 36.08 -79.95 Greensboro, NC 13723 36.08 -79.95 

Norfolk, VA 13737 36.90 -76.20 Wallops Island, VA 93739 37.93 -75.47 

Philadelphia, PA 13739 39.87 -75.23 Atlantic City, NJ 93755 39.75 -74.67 

Richmond, VA 13740 37.50 -77.32 Sterling, VA 93734 38.98 -77.47 

Roanoke, VA 13741 37.32 -79.97 Greensboro, NC 13723 36.10 -79.95 

Wilmington, NC 13748 34.27 -77.90 Charleston, SC 13880 32.90 -80.03 

Wilmington, DE 13781 39.67 -75.60 Atlantic City, NJ 93755 39.75 -74.67 

Meridian, MS 13865 32.33 -88.75 Jackson, MS 03940 32.32 -90.08 

Charleston, WV 13866 38.38 -81.58 Huntington, WV 03860 38.37 -82.55 

Atlanta, GA 13874 33.63 -84.45 Athens, GA 13873 33.95 -83.33 

Birmingham, AL 13876 33.57 -86.75 Centerville, AL 03881 32.90 -87.25 

Bristol, TN 13877 36.47 -82.40 Huntington, WV 03860 38.37 -82.55 

Charleston, SC 13880 32.90 -80.03 Charleston, SC 13880 32.90 -80.03 

Charlotte, NC 13881 35.22 -80.93 Greensboro, NC 13723 36.08 -79.95 

Columbia, SC 13883 33.95 -81.12 Athens, GA 13873 33.95 -83.33 

Jacksonville, FL 13889 30.50 -81.70 Waycross, GA 13861 31.25 -82.40 

Memphis, TN 13893 35.05 -89.98 N. Little Rock, AR 03952 34.83 -92.27 

Mobile, AL 13894 30.68 -88.25 Slidell, LA 53813 30.33 -89.82 

Montgomery, AL 13895 32.30 -86.40 Centerville, AL 03881 32.90 -87.25 

Nashville, TN 13897 36.13 -86.68 Nashville, TN 13897 36.13 -86.68 

Shreveport, LA 13957 32.45 -93.82 Longview, TX 03951 32.35 -94.65 

Little Rock, AR 13963 34.73 -92.23 N. Little Rock, AR 03952 34.83 -92.27 

Wichita Falls, TX 13966 33.98 -98.50 Stephenville, TX 13901 32.22 -98.18 

Oklahoma City, OK 13967 35.40 -97.60 Norman, OK 03948 35.23 -97.47 

Baton Rouge, LA 13970 30.53 -91.15 Slidell, LA 53813 30.33 -89.82 

Dodge City, KS 13985 37.77 -99.97 Dodge City, KS 13985 37.77 -99.97 

St. Louis, MO 13994 38.75 -90.37 Peoria, IL 14842 40.67 -89.68 

Springfield, MO 13995 38.82 -92.27 Monet, MO 03946 36.87 -93.97 

Buffalo, NY 14733 42.93 -78.73 Buffalo, NY 14733 42.93 -78.73 

Newark, NJ 14734 41.72 -75.17 Atlantic City, NJ 93755 39.75 -74.67 

Albany, NY 14735 42.75 -73.82 Albany, NY 14735 42.75 -73.82 

Allentown, PA 14737 40.65 -75.45 Albany, NY 14735 42.75 -73.82 

Boston, MA 14739 42.37 -71.02 Chatham, MA 14684 41.67 -69.97 

Hartford, CN 14740 41.93 -72.68 Albany, NY 14735 42.75 -73.80 

Burlington, VT 14742 44.47 -73.15 Albany, NY 14735 42.75 -73.82 
Meteorological Data Processing using AERMET July 28, 2006 

For HEM-AERMOD 

3 

SURFACE WBAN LAT LON UPPER AIR WBAN LAT LON 

Portland, ME 14764 43.65 -70.30 Portland, ME 14764 43.65 -70.30 

Providence, RI 14765 41.73 -71.43 Chatham, MA 14684 41.67 -69.97 

Syracuse, NY 14771 43.12 -76.12 Buffalo, NY 14733 42.93 -78.73 

Williamsport, PA 14778 41.27 -77.05 Pittsburgh, PA 94823 40.53 -80.23 

Cleveland, OH 14820 41.40 -81.85 Pittsburgh, PA 94823 40.53 -80.23 

Columbus, OH 14821 39.98 -82.88 Dayton, OH 13840 39.83 -84.05 

Fort Wayne, IN 14827 41.00 -85.20 Dayton, OH 13840 39.83 -84.05 

Lansing, MI 14836 42.78 -84.58 Flint, MI 14826 42.97 -83.75 

Madison, WI 14837 43.13 -89.33 Green Bay, WI 14898 44.48 -88.13 

Erie, PA 14860 42.08 -80.18 Buffalo, NY 14733 42.93 -78.73 

Akron, OH 14895 40.92 -81.43 Pittsburgh, PA 94823 40.50 -80.23 

Green Bay, WI 14898 44.50 -88.12 Green Bay, WI 14898 44.50 -88.12 

Duluth, MN 14913 46.83 -92.22 Int'l Falls, MN 14918 48.57 -93.40 

Minneapolis, MN 14922 44.88 -93.23 St. Cloud, MN 14926 45.55 -94.05 

Moline, IL 14923 41.45 -90.52 Peoria, IL 14842 40.67 -89.68 

Rochester, MN 14925 43.92 -92.50 St. Cloud, MN 14926 45.55 -94.08 

Sioux Falls, SD 14944 43.57 -96.73 Huron, SD 14936 44.38 -98.22 

Eau Claire, WI 14991 44.87 -91.48 St. Cloud, MN 14926 45.55 -94.08 

Lihue, HI 22536 21.98 -159.33 Lihue, HI 22536 21.98 -159.33 

Midland, TX 23023 31.95 -102.18 Midland, TX 23023 31.95 -102.18 

El Paso, TX 23044 31.82 -106.38 El Paso, TX 23044 31.82 -106.38 

Amarillo, TX 23047 35.22 -101.70 Amarillo, TX 23047 35.22 -101.70 

Albuquerque, NM 23050 35.03 -106.62 Albuquerque, NM 23050 35.03 -106.62 

Denver, CO 23062 39.77 -104.87 Denver, CO 23062 39.77 -104.88 

Goodland, KS 23065 39.37 -101.70 Dodge City, KS 13985 37.77 -99.97 

Grand Junction, CO 23066 39.12 -108.53 Grand Junction, CO 23066 39.12 -108.53 

Tonopah, NV 23153 38.05 -117.08 Ely, NV 23154 39.30 -114.85 

Tucson, AZ 23160 32.12 -110.93 Tucson, AZ 23160 32.12 -110.93 

Daggett, CA 23161 34.87 -116.78 Desert Rock, NV 03160 36.62 -116.02 

Los Angeles, CA 23174 33.93 -118.38 Miramar, CA 03190 32.87 -117.15 

Prescott, AZ 23184 34.65 -112.43 Tucson, AZ 23160 32.12 -110.93 

San Diego, CA 23188 32.73 -117.17 Miramar, CA 03190 32.87 -117.15 

Sacramento, CA 23232 38.52 -121.50 Oakland, CA 23230 37.75 -122.22 

San Francisco, CA 23234 37.62 -122.40 Oakland, CA 23230 37.72 -122.22 

Bismarck, ND 24011 46.78 -100.75 Bismarck, ND 24011 46.78 -100.75 

Cheyenne, WY 24018 41.15 -104.77 Denver, CO 23062 39.77 -104.88 

Billings, MT 24033 45.80 -108.55 Great Falls, MT 24143 47.47 -111.38 

Casper, WY 24089 42.90 -106.47 Lander, WY 24021 42.82 -108.73 

Rapid City, SD 24090 44.05 -103.07 Rapid City, SD 24090 44.05 -103.07 

Salt Lake City, UT 24127 40.78 -111.97 Salt Lake City, UT 24127 40.78 -111.97 

Great Falls, MT 24143 47.47 -111.38 Great Falls, MT 24143 47.47 -111.38 

Spokane, WA 24157 47.62 -117.53 Spokane, WA 24157 47.62 -117.53 

Medford, OR 24225 42.37 -122.87 Medford, OR 24225 42.37 -122.87 

Portland, OR 24229 45.60 -122.60 Salem, OR 24232 44.90 -123.00 

Salem, OR 24232 44.90 -123.00 Salem, OR 24232 44.90 -123.00 

Seattle, WA 24233 47.45 -122.32 Quillayute, WA 94240 47.93 -124.55 

Juneau, AK 25309 58.37 -134.58 Annette Is., AK 25308 55.03 -131.57 

Cold Bay, AK 25624 55.20 -162.72 Cold Bay, AK 25624 55.20 -162.72 
Meteorological Data Processing using AERMET July 28, 2006 

For HEM-AERMOD 

4 

SURFACE WBAN LAT LON UPPER AIR WBAN LAT LON 

Fairbanks, AK 26411 64.80 -147.88 Fairbanks, AK 26411 64.80 -147.88 

Valdez, AK 26442 61.13 -146.35 Anchorage, AK 26409 61.22 -149.85 

Anchorage, AK 26451 61.20 -150.00 Anchorage, AK 26409 61.22 -149.85 

Nome, AK 26617 64.50 -165.43 Nome, AK 26617 64.50 -165.43 

Colo. Springs 93037 38.82 -104.72 Denver, CO 23062 39.77 -104.88 

Fresno, CA 93193 36.78 -119.72 Oakland, CA 23230 37.72 -122.22 

Baltimore, MD 93721 39.18 -76.67 Sterling, VA 93734 38.98 -77.47 

Sterling, VA 93738 38.95 -77.45 Sterling, VA 93734 38.98 -77.47 

Evansville, IN 93817 38.05 -87.52 Paducah, KY 03816 37.05 -88.77 

Indianapolis, IN 93819 39.73 -86.28 Dayton, OH 13840 39.83 -84.05 

Lexington, KY 93820 38.03 -84.60 Dayton, OH 13840 39.83 -84.05 

Louisville, KY 93821 38.18 -85.73 Dayton, OH 13840 39.83 -84.05 

Springfield, IL 93822 39.85 -89.68 Peoria, IL 14842 40.67 -89.68 

Pittsburgh, PA 94823 40.53 -80.23 Pittsburgh, PA 94823 40.50 -80.23 

Toledo, OH 94830 41.58 -83.80 Flint, MI 14826 42.97 -83.73 

Chicago, IL 94846 42.00 -87.93 Peoria, IL 14842 40.67 -89.68 

Detroit, MI 94847 42.23 -83.33 Flint, MI 14826 42.97 -83.75 

Figure 1 shows the surface stations (green circles) and upper air stations (red pentagons) 

used in this study. 

FIGURE 1. SURFACE AND UPPER AIR STATIONS 
Meteorological Data Processing using AERMET July 28, 2006 

For HEM-AERMOD 

5 

MISSING DATA 

While AERMET will run if input observations are missing, the output will be incomplete, 

with missing data indicators in the output file. The input to and output from AERMET 

were examined for indications of missing input data. 

The 1200 Greenwich Mean Time (GMT) sounding is required for AERMET to calculate 

the convective boundary layer height and several associated parameters. There were 

many isolated days in which the 1200 GMT sounding was missing for many of the 

stations. However there also were several stations for which there were two or more 

consecutive days of missing 1200 GMT soundings. The longest period of consecutive 

missing 1200 GMT soundings was at Dayton, Ohio with seven days of missing 

soundings. To minimize the impact on the output from AERMET and not consume too 

much time and effort, MACTEC substituted upper air data from a representative upper air 

station for those periods for which the 1200 GMT sounding was missing for two or more 

consecutive days. Table 2 shows the station substitutions. 

TABLE 2 

UPPER AIR STATIONS WITH MISSING 1200 GMT SOUNDINGS AND STATION 

USED FOR DATA SUBSTITUTION 

Upper Air Station 

With Missing Data 

WBAN Station Used for Data 

Substitution 

WBAN 

Amarillo, TX 23047 Norman, OK 03948 

Athens, GA 13873 Greensboro, NC 13723 

Brownsville, TX 12929 Corpus Christi, TX 12924 

Centerville, AL 03881 Jackson, MS 03940 

Chatham, MA 14684 Portland, ME 14764 

Dayton, OH 13840 Paducah, KY 03826 

Ely, NV 23154 Winnemucca, NV 24128 

Grand Junction, CO 23066 Salt Lake City, UT 24127 

Lake Charles, LA 03937 Slidell, LA 53813 

Paducah, KY 03826 Nashville, TN 13897 

Peoria, IL 14842 Omaha, NE 94918 

Pittsburgh, PA 94823 Huntington, WV 03860 

Salt Lake City, UT 24127 Ely, NV 23154 

St. Cloud, MN 14926 Huron, SD 14936 
Meteorological Data Processing using AERMET July 28, 2006 

For HEM-AERMOD 

6 

ANEMOMETER HEIGHTS 

The file STNS.TXT located on the HUSWO CD was used for the anemometer heights 

required by AERMET. These heights are to the nearest meter and were deemed 

appropriate for use in this application. 

SURFACE CHARACTERISTICS 

One of the important requirements of applying the AERMOD model is the specification 

of surface characteristics for use in processing the meteorological data using AERMET. 

Application of AERMET requires specification of the surface roughness length, the 

Bowen ratio (an indicator of surface moisture), and the albedo (an indicator of surface 

reflectivity). These surface characteristics are used by AERMET to calculate the level of 

shear-induced mechanical turbulence generated by flow over the surface and for the 

energy balance calculations used in the determination of the Monin-Obukhov stability 

parameter and the convective velocity scale. 

The surface characteristics can be varied by wind direction sector (upwind) and by month 

or season. Current guidance specifies defining the characteristics for the meteorological 

measurement site and extending outward from the measurement location for three 

kilometers. In an application of this magnitude, it is impractical to evaluate and vary the 

surface characteristics for each of the 122 stations, therefore, MACTEC used annual 

values for one sector, as shown in Table 3. 

The albedo used here is representative of conditions for all seasons which includes winter 

without continuous snow cover. Most stations fall into this category and continuous 

snow coverage for several months even at northern stations in the continental United 

States is unlikely. 

Without a detailed and individual analysis for each of the 122 station locations and for 

this large an area for an entire year, a value representing an equal partition of the fluxes, 

or a Bowen ratio of 1.0, is the best representation to use in this type of analysis. 

The surface roughness not only includes runways and areas between runways, but usually 

terminal buildings and other airport structures. In addition, in urban environments, offairport structures often are within 3 kilometers of the measurement site. This 

combination of land covers suggests a value of 0.2 – 0.3 meters is appropriate. 

MACTEC used 0.25 for the roughness length. 
Meteorological Data Processing using AERMET July 28, 2006 

For HEM-AERMOD 

7 

TABLE 3 

SURFACE CHARACTERISTICS 

MODIFICATIONS TO AERMET CODE 

Recent use of AERMET identified two ‘bugs’ in the code that required fixing before 

running AERMET. The first had to do with the hourly surface observations. Incorrect 

missing indicators in the code for wind speed and direction were output when the data 

were extracted from the raw input file in Stage 1. When AERMET encountered these 

flags in Stage 3, AERMET simply stopped processing data, resulting in files with less 

data than requested. MACTEC modified the code to use correct missing indicators. 

MACTEC also checked other meteorological variables to be sure the correct missing 

indicators were used. No other missing indicators were in error. 

The second ‘bug’ was associated with the upper air soundings. AERMET would not 

process an entire station’s data if the surface pressure for the first sounding was less than 

850 millibars (mb). Surface pressures less than 850 mb is not uncommon for stations at 

high elevations such as Denver, CO and Albuquerque, NM. MACTEC modified the code 

to expand the allowable surface pressures. 

Both ‘bugs’ were fixed prior to running AERMET with the result that AERMET 

successfully ran to completion for all files. 

RESULTS 

AERMET successfully ran for all 122 stations. While the reports and output from 

AERMET provide an indication of the success of the runs, MACTEC ran AERMOD with 

a single source and single receptor to obtain information on the number of hours with 

calm winds or missing data. Table 4 shows these values by surface station. 

Albedo 0.15 

Bowen Ratio 1.0 

Surface Roughness (m) 0.25 
Meteorological Data Processing using AERMET July 28, 2006 

For HEM-AERMOD 

8 

TABLE 4 

PERCENT CALM AND MISSING BY STATION 

WBAN Station Calms % Calm Missing % Missing Total % Total

03816 Paducah, KY 820 9.4% 66 0.75% 886 10.1%

03856 Huntsville, AL 719 8.2% 0 0.00% 719 8.2%

03860 Huntington, WV 340 3.9% 28 0.32% 368 4.2%

03870 Greenville, SC 810 9.2% 20 0.23% 830 9.5%

03927 Fort Worth, TX 508 5.8% 24 0.27% 532 6.1%

03928 Wichita, KS 215 2.5% 0 0.00% 215 2.5%

03937 Lake Charles, LA 616 7.0% 15 0.17% 631 7.2%

03940 Jackson, MS 1228 14.0% 58 0.66% 1286 14.7%

03947 Kansas City, MO 231 2.6% 0 0.00% 231 2.6%

11641 San Juan, PR 670 7.6% 11 0.13% 681 7.8%

12815 Orlando, FL 144 1.6% 0 0.00% 144 1.6%

12816 Gainesville, FL 952 10.9% 11 0.13% 963 11.0%

12842 Tampa, FL 572 6.5% 0 0.00% 572 6.5%

12844 W. Palm Beach, FL 566 6.5% 8 0.09% 574 6.6%

12912 Victoria, TX 320 3.7% 0 0.00% 320 3.7%

12916 New Orleans, LA 721 8.2% 8 0.09% 729 8.3%

12917 Port Arthur, TX 736 8.4% 10 0.11% 746 8.5%

12919 Brownsville, TX 418 4.8% 36 0.41% 454 5.2%

12921 San Antonio, TX 913 10.4% 11 0.13% 924 10.5%

12924 Corpus Christi, TX 371 4.2% 0 0.00% 371 4.2%

12960 Houston, TX 1097 12.5% 13 0.15% 1110 12.7%

13722 Raleigh, NC 810 9.2% 0 0.00% 810 9.2%

13723 Greensboro, NC 542 6.2% 24 0.27% 566 6.5%

13737 Norfolk, VA 598 6.8% 39 0.45% 637 7.3%

13739 Philadelphia, PA 154 1.8% 2 0.02% 156 1.8%

13740 Richmond, VA 605 6.9% 0 0.00% 605 6.9%

13741 Roanoke, VA 1287 14.7% 1 0.01% 1288 14.7%

13748 Wilmington, NC 976 11.1% 0 0.00% 976 11.1%

13781 Wilmington, DE 837 9.6% 0 0.00% 837 9.6%

13865 Meridian, MS 1567 17.9% 56 0.64% 1623 18.5%

13866 Charleston, WV 1257 14.3% 29 0.33% 1286 14.7%

13874 Atlanta, GA 309 3.5% 21 0.24% 330 3.8%

13876 Birmingham, AL 1228 14.0% 15 0.17% 1243 14.2%

13877 Bristol, TN 2541 29.0% 28 0.32% 2569 29.3%

13880 Charleston, SC 487 5.6% 0 0.00% 487 5.6%

13881 Charlotte, NC 985 11.2% 0 0.00% 985 11.2%

13883 Columbia, SC 1340 15.3% 20 0.23% 1360 15.5%

13889 Jacksonville, FL 1134 12.9% 12 0.14% 1146 13.1%

13893 Memphis, TN 809 9.2% 0 0.00% 809 9.2%

13894 Mobile, AL 527 6.0% 8 0.09% 535 6.1%

13895 Montgomery, AL 1027 11.7% 19 0.22% 1046 11.9%

13897 Nashville, TN 87 1.0% 0 0.00% 87 1.0%

13957 Shreveport, LA 1088 12.4% 11 0.13% 1099 12.5%

13963 Little Rock, AR 1090 12.4% 0 0.00% 1090 12.4%
Meteorological Data Processing using AERMET July 28, 2006 

For HEM-AERMOD 

9 

WBAN Station Calms % Calm Missing % Missing Total % Total

13966 Wichita Falls, TX 622 7.1% 23 0.26% 645 7.4%

13967 Oklahoma City, OK 158 1.8% 0 0.00% 158 1.8%

13970 Baton Rouge, LA 787 9.0% 9 0.10% 796 9.1%

13985 Dodge City, KS 30 0.3% 32 0.37% 62 0.7%

13994 St. Louis, MO 416 4.7% 26 0.30% 442 5.0%

13995 Springfield, MO 212 2.4% 39 0.45% 251 2.9%

14733 Buffalo, NY 181 2.1% 0 0.00% 181 2.1%

14734 Newark, NJ 197 2.2% 0 0.00% 197 2.2%

14735 Albany, NY 1217 13.9% 8 0.09% 1225 14.0%

14737 Allentown, PA 465 5.3% 9 0.10% 474 5.4%

14739 Boston, MA 36 0.4% 38 0.43% 74 0.8%

14740 Hartford, CN 204 2.3% 8 0.09% 212 2.4%

14742 Burlington, VT 155 1.8% 9 0.10% 164 1.9%

14764 Portland, ME 381 4.3% 25 0.29% 406 4.6%

14765 Providence, RI 215 2.5% 39 0.45% 254 2.9%

14771 Syracuse, NY 481 5.5% 0 0.00% 481 5.5%

14778 Williamsport, PA 1403 16.0% 24 0.27% 1427 16.3%

14820 Cleveland, OH 271 3.1% 27 0.31% 298 3.4%

14821 Columbus, OH 734 8.4% 0 0.00% 734 8.4%

14827 Fort Wayne, IN 440 5.0% 0 0.00% 440 5.0%

14836 Lansing, MI 632 7.2% 10 0.11% 642 7.3%

14837 Madison, WI 685 7.8% 30 0.34% 715 8.2%

14860 Erie, PA 286 3.3% 4 0.05% 290 3.3%

14895 Akron, OH 232 2.6% 28 0.32% 260 3.0%

14898 Green Bay, WI 414 4.7% 28 0.32% 442 5.0%

14913 Duluth, MN 336 3.8% 0 0.00% 336 3.8%

14922 Minneapolis, MN 311 3.6% 3 0.03% 314 3.6%

14923 Moline, IL 812 9.3% 24 0.27% 836 9.5%

14925 Rochester, MN 10 0.1% 0 0.00% 10 0.1%

14944 Sioux Falls, SD 492 5.6% 0 0.00% 492 5.6%

14991 Eau Claire, WI 1193 13.6% 0 0.00% 1193 13.6%

22536 Lihue, HI 6 0.1% 0 0.00% 6 0.1%

23023 Midland, TX 207 2.4% 19 0.22% 226 2.6%

23044 El Paso, TX 262 3.0% 0 0.00% 262 3.0%

23047 Amarillo, TX 113 1.3% 6 0.07% 119 1.4%

23050 Albuquerque, NM 606 6.9% 0 0.00% 606 6.9%

23062 Denver, CO 530 6.1% 0 0.00% 530 6.1%

23065 Goodland, KS 176 2.0% 41 0.47% 217 2.5%

23066 Grand Junction, CO 295 3.4% 15 0.17% 310 3.5%

23153 Tonopah, NV 341 3.9% 11 0.13% 352 4.0%

23160 Tucson, AZ 154 1.8% 24 0.27% 178 2.0%

23161 Daggett, CA 616 7.0% 11 0.13% 627 7.2%

23174 Los Angeles, CA 944 10.8% 21 0.24% 965 11.0%

23184 Prescott, AZ 376 4.3% 23 0.26% 399 4.6%

23188 San Diego, CA 473 5.4% 21 0.24% 494 5.6%

23232 Sacramento, CA 1324 15.1% 77 0.88% 1401 16.0%

23234 San Francisco, CA 376 4.3% 77 0.88% 453 5.2%

24011 Bismarck, ND 676 7.7% 22 0.25% 698 8.0%
Meteorological Data Processing using AERMET July 28, 2006 

For HEM-AERMOD 

10 

WBAN Station Calms % Calm Missing % Missing Total % Total

24018 Cheyenne, WY 110 1.3% 0 0.00% 110 1.3%

24033 Billings, MT 273 3.1% 25 0.29% 298 3.4%

24089 Casper, WY 138 1.6% 37 0.42% 175 2.0%

24090 Rapid City, SD 736 8.4% 33 0.38% 769 8.8%

24127 Salt Lake City, UT 581 6.6% 22 0.25% 603 6.9%

24143 Great Falls, MT 111 1.3% 26 0.30% 137 1.6%

24157 Spokane, WA 537 6.1% 36 0.41% 573 6.5%

24225 Medford, OR 1937 22.1% 0 0.00% 1937 22.1%

24229 Portland, OR 1180 13.5% 0 0.00% 1180 13.5%

24232 Salem, OR 1799 20.5% 0 0.00% 1799 20.5%

24233 Seattle, WA 66 0.8% 14 0.16% 80 0.9%

25309 Juneau, AK 1781 20.3% 6 0.07% 1787 20.4%

25624 Cold Bay, AK 119 1.4% 41 0.47% 160 1.8%

26411 Fairbanks, AK 1393 15.9% 0 0.00% 1393 15.9%

26442 Valdez, AK 661 7.5% 2 0.02% 663 7.6%

26451 Anchorage, AK 724 8.3% 0 0.00% 724 8.3%

26617 Nome, AK 358 4.1% 24 0.27% 382 4.4%

93037 Colo. Springs 195 2.2% 0 0.00% 195 2.2%

93193 Fresno, CA 1578 18.0% 76 0.87% 1654 18.9%

93721 Baltimore, MD 361 4.1% 3 0.03% 364 4.2%

93738 Sterling, VA 995 11.4% 0 0.00% 995 11.4%

93817 Evansville, IN 1434 16.4% 66 0.75% 1500 17.1%

93819 Indianapolis, IN 190 2.2% 0 0.00% 190 2.2%

93820 Lexington, KY 234 2.7% 0 0.00% 234 2.7%

93821 Louisville, KY 385 4.4% 0 0.00% 385 4.4%

93822 Springfield, IL 287 3.3% 25 0.29% 312 3.6%

94823 Pittsburgh, PA 493 5.6% 26 0.30% 519 5.9%

94830 Toledo, OH 898 10.3% 13 0.15% 911 10.4%

94846 Chicago, IL 343 3.9% 38 0.43% 381 4.3%

94847 Detroit, MI 468 5.3% 11 0.13% 479 5.5%
1

Appendix 3: 

Analysis of data on short-term emission rates 

relative to long-term emission rates 
2

Analysis of data on short-term emission rates 

relative to long-term emission rates 

Ted Palma 

Roy Smith 

EPA/OAQPS/SBAG 

1. Introduction 

1.1. The problem 

The process of listing hazardous air pollutants (HAPs) provided by the Clean Air Act (CAA, 

section 112(b)(2)) explicitly includes acute toxicity as a listing criterion. For this reason, in 

addition to chronic exposures, EPA considers acute exposures in risk-based decision-making for 

the HAP regulatory program. Estimating acute exposures via dispersion modeling requires input 

data on hourly meteorological conditions (available for most areas of the US) and short-term 

emission rates of individual facilities (almost universally absent from the National Emissions 

Inventory (NEI), the Toxic Release Inventory (TRI), and state emission databases). 

Lacking short-term emission rates, we must estimate peak short-term rates based on annual 

average rates, which are available. For Risk and Technology Review (RTR) rulemakings, we 

have assumed that the 1-hour emission rate for each facility could exceed the annual average 

hourly emission rate by as much as tenfold, and further assumed that this tenfold emission spike 

could coincide with worst-case meteorological conditions and the presence of a human receptor 

at the facility boundary, as a means of screening for potentially significant acute exposures. 

In a consultation on the “RTR Assessment Plan”, a panel of the EPA’s Science Advisory Board 

(SAB), several reviewers questioned the appropriateness of the factor of ten; some even 

suggested that this tenfold assumption may underestimate actual maximum short-term emissions 

for some facilities, and thereby also underestimate maximum acute risks. The SAB 

recommended an analysis of available short-term emissions data for HAP to test this assumption. 

This analysis responds to that SAB recommendation and attempts to test the protectiveness of the 

tenfold assumption using a database of “event emissions” collected from facilities in the 

Houston-Galveston area, to compare events representative of HAP releases to long-term release 

rates. We welcome comments from the public on the methods used and the conclusions reached 

by this analysis. 

2. Methods 

2.1. Texas Commission on Environmental Quality event emissions database 

The Texas Commission on Environmental Quality (TCEQ) collects emissions data using online 

reporting required of any facility releasing 100 pounds or more of a listed chemical (primarily 

ozone-forming VOCs) during a non-routine event. The TCEQ data are intended to improve the 

state’s knowledge of how short-term releases affect tropospheric ozone levels in that area. The 

database we utilized in our analysis was a subset of the TCEQ data covering emission events that 
3

occurred in an eight-county area in eastern Texas during a 756-day period between January 31, 

2003 and February 25, 2005. 

The complete emissions event data were obtained in April 2007 from Cynthia Folsom Murphy, a 

research scientists with the University of Texas at Austin (UTA) Center for Energy and 

Environmental Resources. The data were provided in four Excel spreadsheets generated from an 

original MS Access file. We used these Excel files to reconstruct a MS Access database in order 

to facilitate selection of a representative subset of records for this analysis. 

Although some of the released substances were HAPs, this was incidental to the database’s 

primary purpose of enhancing the TCEQ’s knowledge of photochemical activity. Thus, more 

than 80% of the released mass was ethene and propene, neither of which are HAPs. The 

database included release events caused by accidents, equipment failures, maintenance, startup, 

and shutdown. It also contained facility names, information on amounts of individual 

compounds released. To provide a basis for comparing the event releases with “typical” 

emissions, the UTA staff included total VOC emissions data for each facility for calendar year 

2004, obtained from the EPA Toxic Release Inventory (TRI). The database did not contain any 

records for facilities that did not experience any reportable events during this period. 

2.2. Data filtering 

Because the event release data were intended for modeling short-term releases of ozoneproducing VOCs, the database includes releases from accidents (which are regulated under 

section 112(r) of the CAA and are therefore not considered in residual risk assessments) and 

releases of light hydrocarbon compounds that are not HAPs and are much more volatile than 

most HAPs. This intent of this analysis, on the other hand, was to evaluate short-term releases of 

HAPs due to normal process variability or scheduled startups, shutdowns, and maintenance, 

relative to long-term release rates. Because the full emission events database was not 

representative of likely HAP emissions normally considered under the residual risk program, we 

filtered the release data as follows in an attempt to improve its representativeness: 

1. Hydrocarbons of C5 or less were dropped, except that all HAPs (including non-VOCs) 

were retained regardless of molecular structure; 

2. Accidental releases were dropped, but all others (including startup, shutdown, and 

maintenance) were retained; 

3. Only facilities whose long-term VOC releases exceeded 0.068 tons per day (25 tons per 

year) were retained, to approximate the population of facilities likely to be subject to 

residual risk standards (i.e., major facilities); 

4. A few release records had to be dropped because their facility numbers did not link to any 

facility in the database; 

5. A few facilities had to be dropped because the database did not include their 2004 TRI 

VOC release information. 

2.3. Analysis 
4

Annual VOC emissions and emission event release data were both converted to lb/hr. In order to 

conform to our atmospheric dispersion models, which estimate ambient concentrations for 

periods of 1 hour or more, amounts released during events shorter than 1 hour were assigned to 

the whole hour. For example, a release of 100 lb in ten minutes was converted to 100 lb/hr. 

Events longer than 1 hour were converted normally, e.g., a release of 100 lb in 120 minutes was 

converted to 50 lb/hr. The event release rates for individual compounds were summed, yielding 

a total release rate for each event. This total release rate for each event was divided by the 

annual VOC release rate for the facility to derive the ratio of peak-to-mean emission rate for the 

event. 

3. Results and Discussion 

3.1. Database filtering 

The original database contained 505 individual contaminants, including multiple redundancies. 

These redundancies did not affect this analysis, so we did not resolve them. After filtering out 

light, non-HAP, VOCs, 317 contaminants remained (Table 1). 

The database contained release records for 150 unique facilities. Of these, 48 facilities (Table 2) 

were major VOC emitters that reported releases of at least one of the contaminants in Table 1. 

The database contained 3641 individual release events reported by the original 150 facilities. Of 

these, 319 events involved a Table 1 contaminant released by a Table 2 facility during startup, 

shutdown, or maintenance. For evaluating short-term releases for residual risk assessments, 

these 319 events comprise the most representative subset of the full database. 

3.2. Descriptive statistics 

For this subset of emission events, ratios of event release rate to long-term release rate varied 

from 0.00000004 to 74. Distribution statistics appear in Tables 3 and 4. The 99th percentile ratio 

was 9 (i.e., an event release rate nine times the long-term average). Only 3 ratios exceeded our 

default assumption of 10, and of these only one exceeded 11. The full cumulative probability 

density of the ratios is shown in Figure 1. 

Figure 2 shows the relationship between ratio and event duration. As expected, the ratio 

declined as duration increased. Only 18 events lasted less than 2 hours, but these events 

produced the three highest ratios. Figure 3 is a similar ratio vs. duration plot, but with duration 

as a percentage of total time. Only 35 events exceeded 1% of the total period covered by the 

database. Figure 4 shows the relationship between ratio and total amount released, and suggests 

that the highest ratios were produced by facilities whose routine VOC emissions were relatively 

small. Thus, the events themselves also tended to be relatively small in absolute terms. 

3.3. Discussion 

These results suggest that the tenfold ratio assumption for short-term releases is protective, and 

that the facilities for which it may underestimate event releases may tend to be smaller emitters. 

However, this analysis is limited in the following ways by the nature of the database and the 

filtering that we applied: 
5

1. The only long-term release data available from the database were total VOC emissions 

for 2004. Ideally, we would have preferred to have routine release rates for each 

individual contaminant. However, retrieving these data from other sources and linking 

them to this database was not feasible. 

2. Removing VOCs that are not representative of HAPs, and comparing the releases against 

all VOCs, would tend to underestimate the true ratios. This effect could be quantitatively 

large. 

3. Retaining HAPs that are not VOCs (such as toxic metals), and including them in the total 

to be compared against all VOCs, would tend to overestimate the true ratios. The size of 

this effect is not known, but seems likely to be less than for (2) above. 

4. The database contains only facilities that had at least one release event during the 

reporting period. The number of facilities in the statistical population that did not 

experience an event is not known. The lack of data for these facilities (whose ratios in 

this analysis would have been zero) would cause the descriptive statistics to be skewed 

toward an overestimate. The size of this effect is unknown. 

Table 1. Event emissions in the Houston-Galveston area. 

Representative contaminants included in the analysis, selected because 

they are either HAPs or VOCs with more than 5 carbon atoms. (These 

data were retrieved directly from the original database, which included 

multiple redundancies that did not affect the analysis and were left 

intact.) 

Contaminant HAP CAS SAROAD 

2-Methyloctane No 3221-61-2 90008 

2-Methylpentane No 107-83-5 43229 

2-methylhexane No 591-76-4 43263 

2-Methylpentane No 107-83-5 43229 

2,2,3-Trimethylpentane No 564-02-3

2,2,4-Trimethylpentane Yes 540-84-1 43250 

dimethyl butane No 75-83-2 43291 

2,3-Dimethylbutane No 79-29-8 43276 

2,3,4-Trimethylpentane No 565-75-3 43252 

2,3-Dimethylbutane No 79-29-8 43276 

2,4-Dimethylpentane No 108-08-7 43247 

2-methylheptane No 592-27-8 43296 

2-methylhexane No 591-76-4 43263 

2-Methylpentane No 107-83-5 43229 

3-Methylhexane No 589-34-4 43295 

3-Methylpentane No 96-14-0 43230 

3-Methylhexane No 589-34-4 43295 

3-Methylpentane No 96-14-0 43230 

3-Methylheptane No 589-81-1 43253 

3-Methylhexane No 589-34-4 43295 

3-Methylpentane No 96-14-0 43230 
6

Table 1. Event emissions in the Houston-Galveston area. 

Representative contaminants included in the analysis, selected because 

they are either HAPs or VOCs with more than 5 carbon atoms. (These 

data were retrieved directly from the original database, which included 

multiple redundancies that did not affect the analysis and were left 

intact.) 

Contaminant HAP CAS SAROAD 

Acetaldehyde Yes 75-07-0 43503 

Acetic Acid No 64-19-7 43404 

Acetonitrile Yes 75-05-8 70016 

Acetophenone Yes 98-86-2

Acrolein Yes 107-02-8 43505 

Acrylic acid Yes 79-10-7 43407 

Acrylonitrile Yes 107-13-1 43704 

alkylphenol No none

Benzene Yes 71-43-2 45201 

Benzo[a]anthracene Yes 56-55-3 46716 

Benzo[a]pyrene Yes 50-32-8 46719 

Benzo[b]fluoranthene Yes 205-99-2 46717 

Biphenyl Yes 92-52-4 45226 

Butanol No 35296-72-1

Butyl Acrylate No 141-32-2 43440 

t-Butyl Alcohol No 75-65-0 43309 

butylcyclohexane No 1678-93-9 90101 

Butyraldehyde No 123-72-8 43510 

C9 Aromatics No none

Naphthalene Yes 91-20-3 46701 

Nonane No 111-84-2 43235 

C9+ No none

Carbon tetrachloride Yes 56-23-5 43804 

Carbonyl Sulfide Yes 463-58-1 43933 

Chloral No 75-87-6

Trichloromethane Yes 67-66-3 43803 

Chlorothalonil No 1897-45-6

Petroleum No 8002-05-9

Petroleum No 8002-05-9

Cumene Yes 98-82-8 45210 

Cyclohexane No 110-82-7 43248 

Cyclohexanol No 108-93-0 43317 

Cyclohexanone No 108-94-1 43561 

Cyclohexanone No 108-94-1 43561 

Decane No 124-18-5 43238 

Decane No 124-18-5 43238 

1,2-Dichloroethane No 107-06-2 43815 

Diethylbenzene (mixture) No 25340-17-4 45106 

Methyl Ether No 115-10-6 43350 

Dimethylcyclohexane No 27195-67-1 98059 

Dimethylcyclopentane No 28729-52-4 90064 
7

Table 1. Event emissions in the Houston-Galveston area. 

Representative contaminants included in the analysis, selected because 

they are either HAPs or VOCs with more than 5 carbon atoms. (These 

data were retrieved directly from the original database, which included 

multiple redundancies that did not affect the analysis and were left 

intact.) 

Contaminant HAP CAS SAROAD 

Dimethylcyclopentane No 28729-52-4 90064 

Dimethyl formamide Yes 68-12-2 43450 

Dimethylhexane No 28777-67-5 90067 

Dimethyl pentane No 38815-29-1 90063 

Epichlorohydrin Yes 106-89-8 43863 

Ethyl Alcohol No 64-17-5 43302 

Ethyl Acrylate Yes 140-88-5 43438 

Ethyl Alcohol No 64-17-5 43302 

Ethyl Benzene Yes 100-41-4 45203 

Ethyl Chloride Yes 75-00-3 43812 

Ethylcyclohexane No 1678-91-7 43288 

ethylacetylene No 107-00-6 43281 

Ethyl Benzene Yes 100-41-4 45203 

Ethylene Oxide Yes 75-21-8 43601 

ethylmethylbenzene No 25550-14-5 45104 

formaldehyde Yes 50-00-0 43502 

Furfural No 98-01-1 45503 

straight-run middle distillate No 64741-44-2

Gasoline No 86290-81-5

Gasoline No 86290-81-5

Heavy Olefins No none

n-Heptane No 142-82-5 43232 

n-Heptane No 142-82-5 43232 

Heptylene No 25339-56-4

hexane Yes 110-54-3 43231 

hexane Yes 110-54-3 43231 

2-Methylpentane No 107-83-5 43229 

hexane Yes 110-54-3 43231 

Hexene No 25264-93-1 43289 

Indeno[1,2,3-cd]pyrene Yes 193-39-5 46720 

Isobutyraldehyde No 78-84-2 43511 

2-Methyl-1-propanol No 78-83-1 43306 

2-Methyl-1-propanol No 78-83-1 43306 

Isobutyraldehyde No 78-84-2 43511 

Isoheptanes (mixture) No 31394-54-4 43106 

2-Methylpentane No 107-83-5 43229 

2,2,4-Trimethylpentane No 540-84-1 43250 

2,2,4-Trimethylpentane No 540-84-1 43250 

Isopar E No 

Isoprene No 78-79-5 43243 

2-Propanol No 67-63-0 43304 
8

Table 1. Event emissions in the Houston-Galveston area. 

Representative contaminants included in the analysis, selected because 

they are either HAPs or VOCs with more than 5 carbon atoms. (These 

data were retrieved directly from the original database, which included 

multiple redundancies that did not affect the analysis and were left 

intact.) 

Contaminant HAP CAS SAROAD 

2-Propanol No 67-63-0 43304 

Cumene Yes 98-82-8 45210 

Isopropylcyclohexane No 696-29-7 90128 

Diisopropyl ether No 108-20-3 85005 

Kerosene No 64742-81-0

Methyl ethyl ketone No 78-93-3 43552 

Methyl isobutenyl ketone Yes 141-79-7

Methanol Yes 67-56-1 43301 

Methyl Acetylene No 74-99-7 43209 

Cresol Yes 1319-77-3 45605 

Methyl Chloride Yes 74-87-3 43801 

methyl cyclohexane No 108-87-2 43261 

Methyl ethyl ketone No 78-93-3 43552 

Iodomethane No 74-88-4 86025 

Methyl Mercaptan No 74-93-1 43901 

methyl cyclohexane No 108-87-2 43261 

Methylcyclopentane No 96-37-7 43262 

2-Methyldecane No 6975-98-0 98155 

Methylheptane No 50985-84-7 90045 

2-methylheptane No 592-27-8 43296 

2-Methyl nonane No 871-83-0 90047 

Tert-butyl methyl ether No 1634-04-4 43376 

meta-xylene No 108-38-3 45205 

Nonane No 111-84-2 43235 

Naphtha No 8030-30-6 45101 

Naphthalene Yes 91-20-3 46701 

Naphtha No 8030-30-6 45101 

Naphthalene No 91-20-3 46701 

Butyl acetate No 123-86-4 43435 

Butyraldehyde No 123-72-8 43510 

Nonane No 111-84-2 43235 

Nonane No 111-84-2 43235 

Octadecene No 27070-58-2

n-Octane No 111-65-9 43233 

Octene (mixed isomers) No 25377-83-7

ortho-xylene No 95-47-6 45204 

Parathion Yes 56-38-2

4-Aminohippuric Acid No 61-78-9

Phenol Yes 108-95-2 45300 

Silicone No 63148-62-9

Naphtha No 8030-30-6 45101 
9

Table 1. Event emissions in the Houston-Galveston area. 

Representative contaminants included in the analysis, selected because 

they are either HAPs or VOCs with more than 5 carbon atoms. (These 

data were retrieved directly from the original database, which included 

multiple redundancies that did not affect the analysis and were left 

intact.) 

Contaminant HAP CAS SAROAD 

Naphtha No 8030-30-6 45101 

Polyethylene No 9002-88-4

Poly(Isobutylene) No 9003-27-4

Chloromethyl pivalate No 18997-19-8

Process fuel gas No none

Propionic Acid No 79-09-4 43405 

Propylene oxide No 75-56-9 43602 

para-xylene No 106-42-3 45206 

Styrene Yes 100-42-5 45220 

Sulfolane No 126-33-0

t-Butyl Alcohol No 75-65-0 43309 

t-Butyl Alcohol No 75-65-0 43309 

tert-butyl hydroperoxide No 75-91-2

Toluene Yes 108-88-3 45202 

Aqualyte(TM), LSC cocktail No 25551-13-7 45107 

1,3,4-Trimethylbenzene No 95-63-6 45208 

trimethylcyclopentane No 30498-64-7 98058 

trimethylpentane No 29222-48-8 90092 

Undecane No 1120-21-4 43241 

Vinyl acetate Yes 108-05-4 43453 

Vinyl acetate Yes 108-05-4 43453 

Vinyl chloride Yes 75-01-4 43860 

vinyl resin No none

Vinylcyclohexane No 695-12-5

xylenes Yes 1330-20-7 45102 

xylenes Yes 1330-20-7 45102 

meta-xylene Yes 108-38-3 45205 

ortho-xylene Yes 95-47-6 45204 

para-xylene Yes 106-42-3 45206 

Mineral spirits No 64475-85-0 43118 

Propylene glycol No 57-55-6 43369 

Vinyl chloride Yes 75-01-4 43860 

1-Decene No 872-05-9 90014 

2-Ethyl-1-hexanol No 104-76-7 43318 

2-Pyrrolidone No 616-45-5

Aromatic No none

Decene No 25339-53-1 90014 

2-N,N-Dibutylaminoethanol No 102-81-8 86007 

Diisopropanolamine No 110-97-4 86004 

N,N-Dimethylethanolamine No 108-01-0 84004 

trifluoroethane No 27987-06-0
10

Table 1. Event emissions in the Houston-Galveston area. 

Representative contaminants included in the analysis, selected because 

they are either HAPs or VOCs with more than 5 carbon atoms. (These 

data were retrieved directly from the original database, which included 

multiple redundancies that did not affect the analysis and were left 

intact.) 

Contaminant HAP CAS SAROAD 

2,2'-Oxybisethanol No 111-46-6 43367 

Hydrocarbons No none

Methyl Formate No 107-31-3 43430 

Isopropylamine No 75-31-0 86014 

n-Butanol No 71-36-3 43305 

Polypropylene glycol ether No 

N-Vinyl-2-Pyrrolidinone No 88-12-0

1,1-Di(t-Amylperoxy) 

Cyclohexane No 15667-10-4

1,2,3-Trimethyl-4-ethylbenzene No none

2-Methyldecane No 6975-98-0 98155 

2-methylheptane No 592-27-8 43296 

2-Methyl nonane No 871-83-0 90047 

2,5-Dimethylhexane-2,5-

dihydroperoxide No 3025-88-5

Butyl ether No 142-96-1 43372 

1,2-Dichloroethane Yes 107-06-2 43815 

Hydrindene No 496-11-7 98044 

Methylheptane No 50985-84-7 90045 

methyl methacrylate No 80-62-6 43441 

Naphtha No 8030-30-6 45101 

hexane Yes 110-54-3 43231 

tert-amyl hydroperoxide No 3425-61-4

1,3,4-Trimethylbenzene No 95-63-6 45208 

n-Butanol No 71-36-3 43305 

2-Butoxy ethanol Yes 111-76-2 43308 

hexane Yes 110-54-3 43231 

cycloheptane No 291-64-5 43115 

n-Heptane No 142-82-5 43232 

n-Octane No 111-65-9 43233 

Hexyl Carbitol No 112-59-4

Nonene No 27215-95-8

Silane, ethenyltrimethoxy No 2768-02-7

tetrahydrofuran No 109-99-9 70014 

Vinyl chloride Yes 75-01-4 43860 

Methyl Formate No 107-31-3 43430 

Phenyl ether No 101-84-8

phosgene Yes 75-44-5

1,2-Dichloroethane No 107-06-2 43815 

2-Butoxy ethanol Yes 111-76-2 43308 

Gasoline No 86290-81-5

1-Tridecanol No 112-70-9
11

Table 1. Event emissions in the Houston-Galveston area. 

Representative contaminants included in the analysis, selected because 

they are either HAPs or VOCs with more than 5 carbon atoms. (These 

data were retrieved directly from the original database, which included 

multiple redundancies that did not affect the analysis and were left 

intact.) 

Contaminant HAP CAS SAROAD 

1,2,4-Trichlorobenzene Yes 120-82-1 45208 

2-(2-Butoxyethoxy)ethanol Yes 112-34-5 43312 

2,3,4-trihydroxybenzophenone 

Ester No 1143-72-2

Methyl n-amyl ketone No 110-43-0 43562 

4,4-Cyclohexylidenebis[phenol] No 843-55-0

Anisole No 100-66-3

2-Butoxy ethanol Yes 111-76-2 43308 

Cresol-Formaldehyde novolac 

Resin No proprietary

Decane No 124-18-5 43238 

gamma-Butyrolactone No 96-48-0

Dimethyl pentane No 38815-29-1 90063 

Dodecyl Benzenesulfonic Acid No 27176-87-0

Ethanol Amine No 141-43-5 43777 

ethyl lactate No 687-47-8

Hexamethyldisilazane No 999-97-3

Methyl ethyl ketone No 78-93-3 43552 

Cresol Yes 1319-77-3 45605 

Naphthalene Sulfonic Acid Resin No 

Naphthalene Sulfonic Acid Resin No 

n-Butanol No 71-36-3 43305 

Decane No 124-18-5 43238 

1-Methyl-2-pyrrolidinone No 872-50-4 70008 

Pentyl Ester Acetic Acid No 

Phenol Formaldehyde Resin, 

Novolac No 

Phenol Formaldehyde Resin, 

Novolac No 

Propylene Glycol Monomethyl 

Ether No 107-98-2 70011 

Pyrocatechol No 120-80-9

Carbon Disulfide Yes 75-15-0 43934 

Hexene No 592-41-6 43245 

VOC No none

Methacrylic acid No 79-41-4 84009 

Methyl 3-hydroxybutyrate No 1487-49-6

t-Butyl Alcohol No 75-65-0 43309 

methyl valeraldehyde No 123-15-9

Butyl Methacrylate No 97-88-1 85008 

dipropyl ether No 111-43-3

n-Propanol No 71-23-8 43303 

Propyl propionate No 106-36-5 86052 
12

Table 1. Event emissions in the Houston-Galveston area. 

Representative contaminants included in the analysis, selected because 

they are either HAPs or VOCs with more than 5 carbon atoms. (These 

data were retrieved directly from the original database, which included 

multiple redundancies that did not affect the analysis and were left 

intact.) 

Contaminant HAP CAS SAROAD 

1,2-Epoxybutane Yes 106-88-7

Methylamine No 74-89-5

1,1-Dimethylcyclohexane No 590-66-9

1,1-Dimethylcyclopentane No 1638-26-2

2-Methylpentane No 107-83-5 43229 

dimethyl butane No 75-83-2 43291 

2,3,3-Trimethylpentane No 560-21-4

2,3-Dimethylhexane No 584-94-1

2,3-Dimethylpentane No 565-59-3

2,4-Dimethylhexane No 589-43-5

2,5-Dimethyl-hexane No 592-13-2

2-Butoxy ethanol Yes 111-76-2 43308 

2-mercaptoethanol No 60-24-2

Bisphenol A No 80-05-7

straight-run middle distillate No 64741-44-2

4-Vinylcyclohexene No 100-40-3

straight-run middle distillate No 64741-44-2

Allyl alcohol No 107-18-6

xylenes Yes 1330-20-7 45102 

Naphthalene Yes 91-20-3 46701 

3-Methylethylcyclohexane No 

VOC No none

Gasoline No 86290-81-5

Butyl ether No 142-96-1

dimethyl butane No 75-83-2

Dodecene No 25378-22-7

Styrene Yes 100-42-5 45220 

tetrahydrofuran No 109-99-9 70014 

hexane Yes 110-54-3 43231 

2-Propanol No 67-63-0 43304 

liquified petroleum gas No 68476-85-7

Methyl acetylene propadiene No 

methyl isobutyl ketone Yes 108-10-1

Methyl n-amyl ketone No 110-43-0 43562 

Methylpentane No 43133-95-5

Tert-butyl methyl ether Yes 1634-04-4 43376 

Toluene Yes 108-88-3 45202 

Mineral oil No 8012-95-1

Gasoline No 86290-81-5

2,2-Dimethylpropane No 463-82-1 43222 

n-propylbenzene No 103-65-1
13

Table 1. Event emissions in the Houston-Galveston area. 

Representative contaminants included in the analysis, selected because 

they are either HAPs or VOCs with more than 5 carbon atoms. (These 

data were retrieved directly from the original database, which included 

multiple redundancies that did not affect the analysis and were left 

intact.) 

Contaminant HAP CAS SAROAD 

propylcyclohexane No 1678-92-8

n-Octane No 111-65-9 43233 

ortho-xylene No 95-47-6 45204 

Gasoline No 86290-81-5

propylenimine No 75-55-8

Gasoline No 86290-81-5

Technical White Oil No 

Total Alkylate - non-speciated No 

Trichloroethylene Yes 79-01-6

Di(2-ethylhexyl) 

peroxydicarbonate No 16111-62-9

trimethylcyclopentane No 30498-64-7 98058 

Ultraformate No 

4-Vinylcyclohexene No 100-40-3
14

Table 2. Event emissions in the Houston-Galveston area. Major emitters 

reporting at least one release event of a representative substance. 

Company Name 

2004 VOC Emission 

Rate (lb/h) 

ATOFINA PETROCHEMICALS LA PORTE PLANT 47.88 

BALL METAL BEVERAGE CONTAINER CONROE 

FACILITY 

24.18 

BASF FREEPORT SITE 46.47 

BELVIEU ENVIRONMENTAL FUELS 112.3 

BOC GROUP CLEAR LAKE BOC GASES PLANT 9.52 

BP AMOCO CHEMICAL CHOCOLATE BAYOU PLANT 130.4 

BP AMOCO CHEMICAL PASADENA PLANT 36.92 

BP AMOCO POLYMERS 57.18 

BP PRODUCTS NORTH AMERICA TEXAS CITY 737.4 

BP TEXAS CITY CHEMICAL PLANT B 112.2 

CELANESE BAY CITY PLANT 17.12 

CELANESE CLEAR LAKE PLANT 53.11 

CELANESE PASADENA PLANT 5.934 

CHEVRON PHILLIPS CEDAR BAYOU PLANT 105.3 

CHEVRON PHILLIPS CHEMICAL SWEENY COMPLEX 106.7 

CHEVRON PHILLIPS HOUSTON CHEMICAL COMPLEX 215.7 

CROWN BEVERAGE PACKAGING 18.05 

CROWN CENTRAL PETROLEUM PASADENA PLANT 114.3 

CROWN CORK & SEAL 18.10 

DEER PARK LIQUID STORAGE TERMINAL 124.8 

DOW CHEMICAL LA PORTE SITE 5.902 

DOW TEXAS OPERATIONS FREEPORT 203.2 

E I DUPONT DE NEMOURS AND COMPANY - LA 

PORTE PLANT 

51.30 

EQUISTAR CHEMICALS CHANNELVIEW COMPLEX 275.4 

EQUISTAR CHEMICALS CHOCOLATE BAYOU 

COMPLEX 

84.87 

EQUISTAR CHEMICALS LA PORTE COMPLEX 90.97 

EXXON MOBIL CHEMICAL BAYTOWN OLEFINS PLANT 84.73 

EXXONMOBIL CHEMICAL BAYTOWN CHEMICAL 

PLANT 

313.7 

EXXONMOBIL CHEMICAL MONT BELVIEU PLASTICS 

PLANT 

40.64 

GOODYEAR HOUSTON CHEMICAL PLANT 85.68 

ISP TECHNOLOGIES TEXAS CITY PLANT 22.12 

KANEKA TEXAS CORPORATION 20.55 

KINDER MORGAN LIQUID TERMINALS PASADENA 913.9 

KINDER MORGAN LIQUIDS TERMINALS 132.7 

LBC HOUSTON BAYPORT TERMINAL 12.83 

LYONDELL CHEMICAL BAYPORT PLANT 30.04 

LYONDELL CHEMICAL CHANNELVIEW 74.15 

MARATHON ASHLAND PETROLEUM TEXAS CITY 

REFINERY 

111.8 

MOBIL CHEMICAL HOUSTON OLEFINS PLANT 26.29 

MORGANS POINT PLANT 31.03 

PASADENA PLANT 13.40 
15

Table 2. Event emissions in the Houston-Galveston area. Major emitters 

reporting at least one release event of a representative substance. 

Company Name 

2004 VOC Emission 

Rate (lb/h) 

SHELL OIL DEER PARK 405.2 

SOLUTIA CHOCOLATE BAYOU PLANT 53.09 

STOLTHAVEN HOUSTON TERMINAL 7.347 

SWEENY COMPLEX 157.1 

UNION CARBIDE TEXAS CITY OPERATIONS 174.4 

VALERO REFINING TEXAS CITY 260.1 

WHARTON GAS PLANT 7.552 

Table 3. Frequency distribution for ratio of event 

emission rate to long-term emission rate 

Bin Frequency 

Cumulative 

Frequency 

1.00E-08 0 0 

3.16E-08 0 0 

1.00E-07 2 2 

3.16E-07 1 3 

1.00E-06 0 3 

3.16E-06 2 5 

1.00E-05 1 6 

3.16E-05 2 8 

1.00E-04 5 13 

3.16E-04 9 22 

1.00E-03 15 37 

3.16E-03 28 65 

1.00E-02 33 98 

3.16E-02 41 139 

1.00E-01 59 198 

3.16E-01 38 236 

1.00E+00 33 269 

3.16E+00 31 300 

1.00E+01 16 316 

3.16E+01 2 318 

1.00E+02 1 319 

3.16E+02 0 319 
16

Table 4. Statistics for ratio of event emission 

rate to long-term emission rate 

Statistic for Ratio Value 

Median 0.043923 

75th %ile 0.342655 

90th %ile 2.204754 

95th %ile 3.344422 

96th %ile 3.400832 

97th %ile 3.8126 

98th %ile 4.790098 

99th %ile 8.973897 

Max 74.37138 

Average 0.815352 

Figure 1. Cumulative probability density for ratio of event to routine emission rates. 

Cumulative probability of event ratios

0

50

100

150

200

250

300

350

1.E-07 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

Ratio of event emission rate to long-term emission rate

n
17

Figure 2. Relationship between ratio of event to duration emission rate and emission 

duration.

Event ratio vs. duration

1.E-08

1.E-07

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 10 100 1,000 10,000 100,000

Event duration (min)

Ratio -- event emission rate to long-term emission rate

Figure 3. Relationship between ratio of event to duration emission rate and emission duration, as 

percentage of total time. 

Event ratio vs. duration

1.E-08

1.E-07

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

0% 1% 2% 3% 4% 5% 6% 7% 8%

Event duration (as % of total time)

Ratio -- event emission rate to long-term emission rate
18

Figure 4. Relationship between ratio of event to duration emission rate and total amount emitted 

during the event. 

Event ratio vs. 2004 VOC releases -- by event

1.E-08

1.E-07

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 10 100 1,000

Long-term VOC releases (lb/hr)

Ratio -- event emission rate to long-term emission rate
APPENDIX 4: Technical Support Document for TRIM-Based 

Multipathway Screening Scenario for RTR: Summary of Approach and 

Evaluation 
 i 

[This page intentionally left blank.] 
 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
 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 
 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
 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
 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. 
 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. 
 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. 
 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.
1 

1 Introduct ion 

1.1 Backgrou nd 

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). 
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. 
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. 
4 

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

Exposures of PB-HAPs
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. 
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. 
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 
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). 
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. 
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 
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. 
12 

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

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 
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 
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 
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 
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 
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. 
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.b

 Value is the approximate U.S. median 

for periods without precipitation, based on data compiled by 

Holzworth (1972). Value is conservative compared to the 1- 

to 2-km typical mid-latitude daytime value (Stull 1988). 

Wind direction Blows from source parcel into 

scenario domain (west to 

east) 3 days per week; during 

other times does not blow into 

domain 

A wind direction that favors the location of interest (for 

example, a watershed downwind of a source of concern) will 

tend to result in more emitted mass accumulating in the 

location of interest. For much of the U.S. mid-Atlantic and 

western regions, the wind tends to favor 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 
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, 
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 algae 7.95 -- Autotrophic -- 

Zooplankton 

Crustaceans, 

rotifers, 

protozoans and 

insects 

6.36 -- 100% algaea 5.7 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 c 5.7 a

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

b

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

that 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 
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. 
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. 
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.
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 99th 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)e 1 

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 

modeling approach embodied by 3MRA is conceptually similar to that presented in HHRAP. A 
25 

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.
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: 

GI

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) 
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. 
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. 

ATBW

DA SAEFEDEV

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

t6 KFA2CDA 



Water – Inorganic Chemicals: event pw event    tKCDA

Soil – All Chemicals: event  s   CFABSAFCDA

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) 
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 (cm2) 

Surface Area for 

Soil Exposure (cm2) 

Adult 20-70 18,150 g 6,878 h

Child <1 b 3,992 1,772 

Child 1-2 c 5,700 2,405 

Child 3-5 d 7,600 3,354 

Child 6-11 e 10,800 4,501 

Child 12-19 f 17,150 6,906 a Sources for the child groups included Table 7-1 (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.
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. 
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 a

 0.03 0.13 

Values from RAGS Part E, 

Exhibit 3-4, unless otherwise 

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

3.2.4.3 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) 
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

<1

Child

1-2

Child

3-5

Child

6-11

Child

12-19

Adult

20-70

Hazard Quotient

Receptor

2,3,7,8-TCDD Water

Soil

Total

0

0.0001

0.0002

0.0003

0.0004

0.0005

0.0006

0.0007

Child

<1

Child

1-2

Child

3-5

Child

6-11

Child

12-19

Adult

20-70

Hazard Quotient

Receptor

Cadmium Water

Soil

Total

0

0.001

0.002

0.003

0.004

0.005

0.006

0.007

Child

<1

Child

1-2

Child

3-5

Child

6-11

Child

12-19

Adult

20-70

Hazard Quotient

Receptor

Divalent Mercury Water

Soil

Total
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. 
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. 
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 
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 99th 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 
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. 
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 
39 

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

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 

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)a 0.5% 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.
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 
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 
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.
43 

Exhibit 4-2. Summary of Modeled and Observed Concentrations of Cadmium in Environmen

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 Tributaried Source: Accumulation of Heavy Metals in Food Web Components Across a Gradient of Lakes (Chen et al. 2000) 

0.0001 0.001 0.01 0.1 1 10 100 

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 literatureModeled concentration corresponding to screening threshold emission rate

WCC BC

TS US
44 

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

Bioaccumulation Factors with Respect to Total Water Concentration 

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, 

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)
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
46 

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

Ingestible Media 

*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)

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
47 

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

Exposures and Hazard Quotient 

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. 

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
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 105

 to 107 have 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 
49 

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

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) 

0.0001 0.001 0.01 0.1 1 10 100 1

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 literatureModeled concentration corresponding to screening threshold emission rate

BC WCC

TS US
50 

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

Mercury Bioaccumulation Factors (in parentheses) in Fish Compartments 

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 

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 i n Fish 

(mg/kg wet weight)

Elemental

Divalent

Methyl

(5,398.791)

(420,827)

(1,020,527)

(50,245)

(205,018) (55,535)
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 

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 

methyl mercury in fish are generally highest in larger, older specimens at the higher trophic 

levels (EPA 1997b). 

0%

25%

50%

75%

100%

Surface

Soil

Surface Water Sediment

Percent of Total Mercury

Elemental Divalent Methyl

.
52 

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. 
53 

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

Ingestible Media 

*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) 

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
54 

Exhibit 4-12. Estimated Contribution of Summed Modeled Food Types to 

Divalent Mercury and Methyl Mercury Hazard Quotients 

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. 

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

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 

12-19

Adults 

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 

12-19

Adults 

20-70

Divalent Mercury ADD (mg/kg/day)

Beef & Dairy Fish Eggs, Pork, & Poultry Fruits & Vegetables Soil
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. 
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). 
57 

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

Environmental Media 

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 20b Source: Toxicological Profile for Chlorinated Dibenzo-p-dioxins (ATSDR 1998) 

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.

North America (a) 

Stormwater Runoff (a) 

Rural, North America (a) 

Urban, North America (a) 

Industrial, Austria (a) 

Waste-to-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 literatureModeled concentration corresponding to screening threshold emission rate

BC WCC

TS US
58 

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

Bioaccumulation Factors with Respect to Total Water Concentration 

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. 

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 i n Fish 

(mg/kg wet weight)
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
60 

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

Concentrations in Ingestible Media 

*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)

1E-15 1E-13 1E-11 1E-09 1E-07 1E-05 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
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) 

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 

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
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. 
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. 
64 

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

* Source of literature data: ATSDR Toxicological Profile for Polycyclic Aromatic Compounds (PAHs) (ATSDR 1995). Data are for benzo

exclusively, unless otherwise noted. 

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+0

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 literatureModeled concentration corresponding to screening threshold emission rate

BC WCC

TS US
65 

 

Partitioning Behavior 

Data suggest that benzo[a]pyrene partitions mainly to soil (82 percent) and sediment (~17 

percent) following deposition to a 1 km2

 area 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 i n Fish 

(mg/kg wet weight)
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. 
67 

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

Ingestible Media 

* 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)

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
68 

Exhibit 4-22. Contribution of Modeled Food Types to Benzo[a]pyrene 

Ingestion Exposures (mg/kg/day) 

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. 

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
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. 
 70 

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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 

1-ii 
 

 

 

 

 

 

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 

 1-2 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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 

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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 

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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 

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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 
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
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

2-i 

[This page intentionally left blank.] 
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

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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 
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 ...................................................................................... 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
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

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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 
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

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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.
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

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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 Softw are 

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. 
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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. 
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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 

2-11 

3 Expos ure 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 

2-12 

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 Abovegrou nd 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 

2-13 

Equation 3-1. Chemical Concentration in Aboveground Produce 

AG produce iDW )( P AG produce PvPd iiiDW )()()( 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 iDW )( Cs -root zone_produce BrAG produce iDW )( 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 

)()(

))(*)((

)(

)(

((000,1 1()) )

ii

iTpikp

i

i kpYp

DrwpFwDrdp 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 

2-15 

Equation 3-4. Chemical Concentration in Aboveground Produce Due to 

Air-to-Plant Transfer of Vapor-phase Chemical 

a

iAG iAG

i

BvFvCa 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-m3

. For MIRC, the user inputs the average annual total air 

concentration of the chemical, Ca, for a specific location relative to the source in units of g/m3

; 

MIRC includes a chemical-specific default value for Fv for chemicals included in its database. 

The air concentration might be a value measured near that location or a value estimated by a 

fate and transport model such as TRIM.FaTE. Users of TRIM.FaTE should note that the 

average annual concentration of the total chemical in air (i.e., total of both vapor and particulate 

phases), Ca, output from TRIM.FaTE is in units μg/m3

; therefore, the user must adjust the value 

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

The calculations of chemical concentration in aboveground produce, (CAG-produce-DW), shown 

above, are on a dry-weight (DW) basis. The family FFC food ingestion rates, on the other hand, 

are on a fresh- or wet-weight (WW) basis. MIRC therefore calculates the concentration in 

aboveground produce on a wet-weight basis, CAG-produce-WW, using Equation 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 iWW AG produce iDW

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 Below ground 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 

2-17 

Equation 3-6. Chemical Concentration in Belowground Produce: Nonionic Organic 

Chemicals 

UCFKds

Cs zoneroot produce VGRCF rootveg



    _ C WW-produce-BG

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

zoneroot _ 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 m SoilMFBa mIntakeCh Plant miIntakeCh

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 

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

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 

Soil CsQs Bs  mIntakeCh )(   slivestock 

where: 

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

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

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

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) 

ifeed )(  P ifeed  (i))(  Pv Pd (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 ifeed )(  Csr  ifeedzoneroot )(_ Br ifeed )(

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 



































  days 365 (y)

(y) (y)

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

(y,i)

EF

ATBW

EDFCIRC

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

μg

mg IRC FC 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 

2-25 

Equation 3-17. Chemical Intake from Fish Ingestion 

   days365

g

kg 0010

2111 

































 



EF

BW

.IRC FC

L ADD L

(y)

Fish Fish(y) Fish

Fish(y) Fish Fish

Equation 3-18. Consumption-weighted Chemical Concentration in Fish 

    Fish  FishT 3  T 3  FishT 4 FCFCC T 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 days 365

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

Equation 3-20. Chemical Intake from Consumption of Protected Fruits 

  





























     

g days 365

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

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 

2-27 

Equation 3-21. Chemical Intake from Exposed Vegetables 

  





























     

g days 365

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

Equation 3-22. Chemical Intake from Protected Vegetables 

  































  

g days 365

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

Equation 3-23. Chemical Intake from Root Vegetables 

   





























      

g days 365

kg 11 21 0010 EF ADDRootVeg(y) L RootVeg L RootVeg CRootVeg IRRootVeg(y) . FCRootVeg

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 days 365

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

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 days 365

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

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 days 365

kg 2111 0010 EF ADDPork(y) L Pork Pork Pork Pork(y) .IRCL FCPork

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 days 365

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

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 days 365

kg 0010 EF ADDEgg(y) Egg Egg(y) .IRC FCEgg

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 































   days 365

EF

BW

IRC 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 )21( ),21(

 Equation 3-32.    

n

i

ADD ADD i 1 )53( ),53(

 Equation 3-33.    

n

i

ADD ADD i 1 )116( ),116(

 Equation 3-34.    

n

i

ADD ADD i 1 )1912( ),1912(

 Equation 3-35.   n

i

ADD adult ADD iadult 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 

2-33 

Equation 3-37. Average Daily Dose of Chemical to the Nursing Infant 

    

ATBW

CfC EDAEIRf

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 

2-34 

Equation 3-38. Chemical Concentration in Breast Milk Fat 

 













  













  

 



   pnelim  bfelacfat tk

elacfat

tk elim

elacfat bfelacfat

elim

fmelim

fmat

milkfat e

k

k

e

tkk

k

fk

fDAI 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 

DAImat  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 

2-36 

Equation 3-40. Biological Elimination Rate Constant for Chemicals for Non-lactating 

Women 

h

kelim

2ln 

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

mbmfmilk

elacfat elim BWf

ffIR kk



  _ 

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

elacaq pm

plmat bm

aqueous fk

PcfDAI

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

bpbl

pl fPcf

ff

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 

_ elacaq  kk elim

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  DAImat_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-Re sponse 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

daykg

mg 

















 Source 















 daykg

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 a not 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 

2-42 

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

daykg

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 

2-45 

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

daykg

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 

2-46 

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 

2-47 

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

56

2

yr13 yr110

10

53

21



  

 

)-(

)-(

1)(

ADAF

ADAF .

ADAF

  

1

2

8

yrs41yrs43

3

1912

116



  



(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 ith 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 

2-48 

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 

2-49 

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 

2-54 

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 

2-55 

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 

2-56 

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 

2-58 

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 

2-59 

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 

2-60 

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 b 0.5 

Silage 2.5 

Forage 8.8 

Grain 0.47 

Dairy cattle c 0.4 

Silage 4.1 

Forage 13.2 

Grain 3.0 

Swine d 0.37 Silage 1.4 

Grain 3.3 

Chicken (eggs) e 0.022 Grain 0.2 

Source: EPA 2005a HHRAP (Chapter 5). 

a

 Beef cattle: NC DEHNR (1997) and EPA (1994b) recommended a soil ingestion rate for subsistence beef cattle 

of 0.5 kg/day based on Fries (1994) and NAS (1987). As discussed in HHRAP, 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 

2-61 

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 5th 10th 50th 90th 95th

Adult (20-70) a 50 71.4 52.9 56.0 69.3 89.7 97.6 

Child < 1 b 1 7.83 6.03 6.38 7.76 9.24 9.66 

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

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

Child 6-11 e 6 31.8 19.7 21.3 29.3 45.6 52.5 

Child 12-19 f 8 64.2 39.5 45.0 64.2 83.5 89.0 

[Child 12-19 g 8 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 

data collected by the USDA’s 1994–1996 and 1998 CSFII (USDA 2000). Adult ingestion rates, 
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

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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 90th 95th 99th

Child <1 b 324 146 866 * 1,011 * 1,377 * 

Child 1-2 c 294 217 654 857 1,290 * 

Child 3-5 d 380 291 834 1,078 1,654 

Child 6-11 e 447 350 980 1,235 1,870 *

Child 12-19 f 697 516 1,537 2,022 * 3,195 * 

Adult g 1,098 920 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). 

This division reflects EPA’s data analysis. EPA tabulated IRs for fruits and vegetables for 

 

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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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 c 5.9 

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 c 2.42 

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 c 16 

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 c 19.1 

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 c 47.3 

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 

2-68 

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 

yPC,  FIR IR yPC,yCO,

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 this Appendix, 

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 b 27.31 15.61 64.46 87.60 138.76 * 

Child 3-5 c 40.31 23.04 95.16 129.31 204.84 * 

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

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

Adult f 81.08 47.39 199.62 * 278.91 505.65 * 

Sources: EPA 2002, 2008a 

NA = not applicable; it is assumed that children < 1 year of age do not consume fish. 

* Indicates that the sample size does not meet minimum reporting requirements as described in EPA 2002. Owing 

to the small sample sizes, these upper percentiles values are highly uncertain. a Per capita fish ingestion (FI) rates for children by age group are 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.
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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 a 1.37 0.79 3.24 4.41 6.98 

Child 3-5 b 2.03 1.16 4.79 6.51 10.3 

Child 6-11c 2.71 1.25 6.90 10.9 17.0 

Child 12-19 d 3.90 2.13 8.95 10.4 20.9 

Adult e 6.90 4.03 16.99 23.73 43.02 

Sources: EPA 2002, 2008a 

NA = not applicable; it is assumed that children < 1 year of age do not consume fish. a

 Values were calculated as (consumer-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
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

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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 a 50th a 90th 95th 99th

Child < 1 NA 

Child 1-2 50 50 400 b 400 b 400 b

Child 3-5 50 50 400 b 400 b 400 b

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

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

Adult 20-70 50 50 201 c 331 d 331 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 a 67.0% - 99.7% h 322 270 599 779 1152 

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

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

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

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

Adult f

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

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

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

Child 1-2 b 100% 82 79 125 144 177 

Child 3-5 c 100% 61 57 91 102 132 

Child 6-11 d 100% 40 38 61 70 88 

Child 12-19 e 100% 21 19 34 40 51 

Adult g

100% 14.8 13.9 23.7 27.6 35.5 

Sources: EPA 2005e, 2008a 

a

 These values represent a time-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 
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

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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. 
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

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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 c 0.244 0.305 

Exposed Vegetable 0.162 d NA 

Protected Fruit 0.29 e NA 

Protected Vegetable 0.088 f NA 

Root Vegetable g 0.075 0.22 

Beef 0.27 0.24 

Pork 0.28 0.36 

Poultry 0.32 0.295 h

Fish i 0.0 0.0 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. 
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

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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. 
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

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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 

5th percentile 5.0 6.0 7.0 7.5 

10th percentile 5.3 6.4 7.4 7.8 

15th percentile 5.5 6.7 7.7 8.2 

25th percentile 5.8 7.0 8.1 8.7 

50th percentile 6.4 7.8 8.9 9.5 
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

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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

5th 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

 1.01a EPA 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 half- 1.9E-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 
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

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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). 
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

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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 b g/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 d 1.01 NA NA NA NA NA kg/day 

Soil (dry) NA 400e 400e 201 f 201 f 201 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. 
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

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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 c 1 7.83 

Child 1-2 c 2 12.6 

Child 3-5 d 3 18.6 

Child 6-11 e 6 31.8 

Child 12-19 f 8 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. 
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

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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 

RfD Reference Dose for Chemical 0 0.0005 0.0003 0.0001 1E-09 CSF Oral carcinogenic potency slope factor for chemical 10 0 0 0 150,000 Bs Soil bioavailability factor for livestock 1 1 1 1 1 MF Mammalian metabolism factor 0.01 1 1 1 1 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 

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 

BvAG

Air-to-plant biotransfer factor for aboveground produce for vapor-phase chemical in air 

124,742 0 1,800 0 65,500 [m

RCF Chemical-specific root concentration factor for tubers and root produce 9,684 0 0 0 39,999 L sKds Chemical-specific soil/water partition coefficient 160,000 75 58,000 7,000 38,904.51 L sa Values presented in this Exhibit are previously presented in Exhibit 4-1, Exhibit 6-2, Exhibit 6-3, and Exhibit 6-5. However, exact value

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 

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

Protocol for Hazardous Waste Combustion Facilities. Office of Solid Waste and Emergency 

Response, Washington, DC. EPA-530-R-05-006. September. Available at: 

http://www.epa.gov/osw/hazard/tsd/td/combust/risk.htm. 

U.S. Environmental Protection Agency (EPA). 2005b. Guidance on Selecting Age Groups for 

Monitoring and Assessing Childhood Exposures to Environmental Contaminants. Risk 

Assessment Forum. Washington, DC. November. EPA/630/P-03/003F. Available at: 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=146583. 

U.S. Environmental Protection Agency (EPA). 2005c. Guidelines for Carcinogen Risk 

Assessment. Risk Assessment Forum, Washington, DC. EPA/630/P-03/001F. March. 

Available from: http://www.epa.gov/IRIS/cancer032505-final.pdf. 

U.S. Environmental Protection Agency (EPA). 2005d. Supplemental Guidance for Assessing 

Susceptibility from Early-Life Exposure to Carcinogens. Risk Assessment Forum: Washington, 

D.C. EPA-630/R-03-003F. March. Available at: 

http://www.epa.gov/ttn/atw/childrens_supplement_final.pdf. 

U.S. Environmental Protection Agency (EPA). 2005e. Analysis of Total Food Intake and 

Composition of Individual's Diet Based on the U.S. Department of Agriculture's 1994-96, 1998 

Continuing Survey of Food Intakes By Individuals (CSFII) (Final). Office of Research and 

Development, National Center for Environmental Assessment, Washington, D.C. EPA/600/R05/062F. Available at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=132173. 

U.S. Environmental Protection Agency (EPA). 2007a. Prioritized Chronic Dose-Response 

Values for Screening Risk Assessments (Table 1). Office of Air Quality Planning and 

Standards; June 12, 2007. Available at: http://www.epa.gov/ttn/atw/toxsource/summary.html . 

U.S. Environmental Protection Agency (EPA). 2007b. Toxicological Review of 1,1,1-

Trichloroethane (CAS No. 71-55-6) In Support of Summary Information on the Integrated Risk 

Information System (IRIS). Office of Research and Development, Washington, DC. 

EPA/635/R-03/006. August. Available at: http://www.epa.gov/iris 

U.S. Environmental Protection Agency (EPA). 2008a. Child-Specific Exposure Factors 

Handbook. Office of Research and Development, Washington, D.C. EPA/600/R-06/096F. 

September. Available at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=199243 . 
Attachment 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

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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. 
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 Sensitiv ity 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. 
Attachment 3 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

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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. 
Attachment 3 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

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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 
Attachment 3 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

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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. 
Attachment 3 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

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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. 
Attachment 3 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

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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% Perturb

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, 0.45Frac Wet Dep, 0.45

Mammal Metab Fctr, 0.41

Yield (Frge), -0.36

Intercept Frac (Frge), 0.34

Biotrans Fctr (Dairy), 0.34Exp Fctr (Dairy), 0.34Frac Contam (Dairy), 0.34

Avg Dry Dep Particle, 0.30

Qty Frge Eaten (Dairy), 0.29Frac Frge Contm (Dairy), 0.29

Plnt Srf Loss (Frge), -0.25

Yield (Exp Frt), -0.23

Exp Fctr (Exp Frt), 0.22Frac 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), 0.17Exp Fctr (Exp Veg), 0.17Frac 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 

Farm Food ChainTRIMIngestion/Body Weight
Attachment 3 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

3-9

Exhibit 2. The 25 Variables with the Highest Elasticities for 2,3,7,8-TCDD Lifetime Risk (-5% Perturba

Primary Pathways: Fish, Beef, and Dairy 

Mxng Ht, -1.05

Emission Rate, 1.00

Horiz Wind Spd, -0.96

Frac Contam (Fish), 0.56Exp 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), 0.32Exp Fctr (Dairy), 0.32Frac 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), 0.23Frac 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 

Farm Food ChainTRIMIngestion/Body Weight
Attachment 3 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

3-10

Exhibit 3. The 28 Variables with the Highest Elasticities for Cadmium Hazard Quotient for Child 1-2 (-5

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), 0.39Ing Rate (Pro Frt), 0.39Exp Fctr (Pro Fruit), 0.39Frac Contam (Pro Frt), 0.39100-Mst Adj Fctr (Pro Frt), 0.39

Body Weight, -0.28

Erosion Rate, -0.27

Water Temp, 0.27

Ing Rate (Fish), 0.19Exp Fctr (Fish), 0.19Frac 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), 0.11Ing Rate (Exp Veg), 0.11Exp Fctr (Exp Veg), 0.11Frac 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 

Farm Food ChainTRIMIngestion/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 

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, 0.56Frac Contam (Soil), 0.56Ing Rate (Soil), 0.56

Soil Conc RootZn Produce, 0.33

Bioconc Fctr (Pro Frt), 0.15Ing Rate (Pro Frt), 0.15100-Mst Adj Fctr (Pro Frt), 0.15Exp Fctr (Pro Fruit), 0.15Frac Contam (Pro Frt), 0.15

Surf Soil Frac Air, 0.13

Bioconc Fctr (Root Veg), 0.11Ing Rate (Root Veg), 0.11100-Mst Adj Fctr (Root Veg), 

0.11

Exp Fctr (Root Veg), 0.11Frac Contam (Root Veg), 0.11Correct Fctr (Root Veg), 0.11

Soil Conc Animal Ing, 0.08Soil Bioav Fctr (Livestock), 0.08

Exp Fctr (Soil), 0.56

-2 -1.5 -1 -0.5 0 0.5 1 1.5 

Farm Food ChainTRIMIngestion/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

of Variable) 

Primary Pathway: Fish 

Body Weight, -1.00

Emission Rate, 1.00

Ing Rate (Fish), 0.90Frac Contam (Fish), 0.90Exp 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, 0.05Ing Rate (Soil), 0.05Exp Fctr (Soil), 0.05Frac Contam (Soil), 0.05

Soil Conc RootZn Prod, 0.04

Sed De

 Resusp

-2 -1.5 -1 -0.5 0 0.5 1 1.5 

Farm Food ChainTRIMIngestion/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 

3-14 

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 c 1.0E-06 1.1E-06 6% 1.0E-06 0% 9.7E-07 -3% 9.6E-07 -4% 

2,3,7,8-TCDD c 1.0E-06 1.2E-06 22% 1.0E-06 2% 8.8E-07 -12% 8.4E-07 -16% 

Cadmium d 1.00 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 d 0.56 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 

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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 c Site 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 c 1.00E-06 1.05E-07 -89% 2.43E-07 -76% 2.19E-07 -78% 

2,3,7,8-TCDD c 1.00E-06 8.39E-08 -92% 3.76E-07 -62% 2.56E-07 -74% 

Cadmium d 1.00 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 d 0.56 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 

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

Name of Variable 

Changed Variable Definition Variable Category a

Elasticity of Risk for Input 

Variable Perturbation 

Rankin

In

P

-50% -5% 5% 50% -50% -5

MixHeight Mixing Height TRIM -1.97 -1.05 -0.95 -0.66 1 

EmissionRate Emission Rate of all Chemicals TRIM 1.00 1.00 1.00 1.00 3 

HorizWindSpeed Horizontal Wind Speed TRIM -1.74 -0.97 -0.88 -0.63 2 

Rain Annual Rainfall TRIM 0.63 0.63 0.63 0.63 5 

Drwp Average annual wet deposition of particle-phase chemical FFC 0.45 0.45 0.45 0.45 6 

Fw Fraction of wet deposition that adheres to plant surfaces FFC 0.45 0.45 0.45 0.45 7 

MF Mammalian metabolism factor FFC 0.41 0.41 0.41 0.41 9 

Yp_fo Yield or standing crop biomass, Forage FFC -0.69 -0.36 -0.33 -0.23 4 

Rp_fo Interception fraction of the edible portion of plant, Forage FFC 0.34 0.34 0.34 0.34 10 

Ba_dairy Chemical-specific biotransfer factor, Dairy FFC 0.34 0.34 0.34 0.34 12 

EF_dairy Exposure factor, Dairy Ing./BW 0.34 0.34 N/A N/A 13 

FC_dairy Fraction contaminated, Dairy Ing./BW 0.34 0.34 N/A N/A 14 

Drdp Average annual dry deposition of particle-phase chemical FFC 0.30 0.30 0.30 0.30 16 

Qp_fo_dairy Quantity of forage plant type eaten per animal per day, Dairy FFC 0.29 0.29 0.29 0.29 18 

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 

Kp_fo Plant surface loss coefficient, Forage FFC -0.34 -0.25 -0.24 -0.19 11 Yp_ef Yield or standing crop biomass, Exposed Fruit FFC -0.43 -0.23 -0.20 -0.14 8 

100-MAF_ef 100 - Moisture adjustment factor, Exposed Fruit FFC 0.22 0.22 0.22 0.22 22 

EF_ExpFruit Exposure factor, Exposed Fruit Ing./BW 0.22 0.22 N/A N/A 20 
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

Name of Variable 

Changed Variable Definition Variable Category a

Elasticity of Risk for Input 

Variable Perturbation 

Rankin

In

P

-50% -5% 5% 50% -50% -5

FC_ExpFruit Fraction contaminated, Exposed Fruit Ing./BW 0.22 0.22 N/A N/A 21 

Rp_ef Interception fraction of the edible portion of plant, Exposed Fruit FFC 0.21 0.21 0.21 0.21 23 

Kp_ef Plant surface loss coefficient, Exposed Fruit FFC -0.27 -0.19 -0.17 -0.13 19 

Yp_ev Yield or standing crop biomass, Exposed Vegetable FFC -0.33 -0.17 -0.16 -0.11 15 

100-MAF_ev 100 - Moisture adjustment factor, Exposed Vegetable FFC 0.17 0.17 0.17 0.17 26 

EF_ExpVeg Exposure factor, Exposed Vegetables Ing./BW 0.17 0.17 N/A N/A 27 

FC_ExpVeg Fraction contaminated, Exposed Vegetable Ing./BW 0.17 0.17 N/A N/A 28 

Rp_ev 

Interception fraction of the edible 

portion of plant, Exposed 

Vegetable 

FFC 0.16 0.16 N/A N/A 29 

Kp_ev Plant surface loss coefficient, Exposed Vegetable FFC -0.21 -0.14 -0.13 -0.10 24 

C_Soil Concentration of chemical in soil from contaminated area FFC 0.12 0.12 0.12 0.12 31 

EF_Soil Exposure factor, Soil Ing./BW 0.12 0.12 N/A N/A 32 

FC_Soil Fraction contaminated, Soil Ing./BW 0.12 0.12 N/A N/A 33 

IR_dairy_ch1 Ingestion rate, Dairy, Child 1-2 Ing./BW 0.11 0.11 0.11 0.11 34 

IR_ExpFruit_adlt Ingestion rate, Exposed Fruit, Adult 20-70 Ing./BW 0.10 0.10 0.10 0.10 35 

Tp_fo Length of plant exposure to deposition per harvest, Forage FFC 0.17 0.10 0.09 0.06 25 

IR_dairy_adlt Ingestion rate, Dairy, Adult 20-70 Ing./BW 0.10 0.10 0.10 0.10 36 

L2_ExpFruit Loss type 2, Exposed Fruit FFC -0.10 -0.10 -0.10 -0.10 37 

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,

Name of Variable 

Changed Variable Definition Variable Category a

Elasticity of Risk for Input 

Variable Perturbation 

Rankin

In

P

-50% -5% 5% 50% -50% -5

MixHeight Mixing Height TRIM -1.99 -1.05 -0.95 -0.67 1 

EmissionRate Emission Rate of all Chemicals TRIM 1.00 1.00 1.00 1.00 3 

HorizWindSpeed Horizontal Wind Speed TRIM -1.80 -0.96 -0.87 -0.61 2 

FC_Fish Fraction contaminated, Fish Ing./BW 0.56 0.56 N/A N/A 8 

EF_Fish Exposure factor, Fish Ing./BW 0.56 0.56 N/A N/A 7 

BW_adlt Body weight, Adult 20-70 Ing./BW -0.86 -0.45 -0.41 -0.29 4 

C_FishT4 Concentration of chemical in whole fish for T4 fish FFC 0.43 0.43 0.43 0.43 9 

IR_Fish_adlt Ingestion rate, Fish, Adult 20-70 Ing./BW 0.43 0.43 0.43 0.43 10 

MF Mammalian metabolism factor FFC 0.43 0.43 N/A N/A 11 

SurfWatTemp Water Temperature TRIM 0.32 0.39 0.41 0.53 16 

WatOCC Water- Organic Carbon Fraction TRIM -0.62 -0.38 -0.35 -0.25 5 

SedDepVel Sediment Deposition Velocity and Resuspension Velocity TRIM 0.28 0.36 0.39 0.30 20 

Ba_dairy Chemical-specific biotransfer factor, Dairy FFC 0.32 0.32 0.32 0.32 13 

EF_dairy Exposure factor, Dairy Ing./BW 0.32 0.32 N/A N/A 14 

FC_dairy Fraction contaminated, Dairy Ing./BW 0.32 0.32 N/A N/A 15 

F_T3 Fraction of fish intake that is from T3 Ing./BW -0.30 -0.30 -0.30 -0.30 17 F_T4 Fraction of fish intake that is from T4 Ing./BW 0.30 0.30 0.30 0.30 18 

Yp_fo Yield or standing crop biomass, Forage FFC -0.56 -0.30 -0.27 -0.19 6 

Rp_fo Interception fraction of the edible portion of plant, Forage FFC 0.28 0.28 0.28 0.28 19 

Drdp Average annual dry deposition of particle-phase chemical FFC 0.28 0.28 0.28 0.28 22 

WatSuspSed Water- Suspended Sediment Concentration TRIM -0.37 -0.25 -0.24 -0.19 12 
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,

Name of Variable 

Changed Variable Definition Variable Category a

Elasticity of Risk for Input 

Variable Perturbation 

Rankin

In

P

-50% -5% 5% 50% -50% -5

Qp_fo_dairy Quantity of forage plant type eaten per animal per day, Dairy FFC 0.23 0.23 0.23 0.23 24 

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 

Kp_fo Plant surface loss coefficient, Forage FFC -0.28 -0.21 -0.20 -0.16 21 

IR_dairy_adlt Ingestion rate, Dairy, Adult 20-70 Ing./BW 0.20 0.20 0.20 0.20 25 

Rain Annual Rainfall TRIM 0.20 0.20 0.20 0.19 26 

SurfSoilAir Surface Soil- Fraction Air TRIM 0.13 0.14 0.15 0.17 29 

C_FishT3 Concentration of chemical in whole fish for T3 fish FFC 0.13 0.13 0.13 0.13 28 

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 

Bs Soil bioavailability factor for livestock FFC 0.12 0.12 N/A N/A 30 

Ba_beef Chemical-specific biotransfer factor, Beef FFC 0.09 0.09 0.09 0.09 33 

EF_beef Exposure factor, Beef Ing./BW 0.09 0.09 N/A N/A 34 

FC_beef Fraction contaminated, Beef Ing./BW 0.09 0.09 N/A N/A 35 

Tp_fo Length of plant exposure to deposition per harvest, Forage FFC 0.14 0.08 0.08 0.05 27 

Qs_dairy Quantity of soil eaten by the animal each day, Dairy FFC 0.07 0.07 0.07 0.07 38 

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 Cad

Name of Variable 

Changed Variable Definition Variable Category a

Elasticity of Risk for Input 

Variable Perturbation 

Rankin

In

P

-50% -5% 5% 50% -50% -5

MixHeight Mixing Height TRIM -1.92 -1.03 -0.93 -0.66 1 

HorizWindSpeed Horizontal Wind Speed TRIM -1.92 -1.03 -0.93 -0.66 2 

EmissionRate Emission Rate of all Chemicals TRIM 1.00 1.00 1.00 1.00 3 

Rain Annual Rainfall TRIM 0.78 0.75 0.74 0.71 4 

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 

Br_AG_produce_

pf 

Plant-soil chemical 

bioconcentration factor, Protected 

Fruit 

FFC 0.39 0.39 0.39 0.39 7 

IR_ProFruit_ch1 Ingestion rate, Protected Fruit, Child 1-2 Ing./BW 0.39 0.39 0.39 0.39 10 

EF_ProFruit Exposure factor, Protected Fruit Ing./BW 0.39 0.39 N/A N/A 8 

FC_ProFruit Fraction contaminated, Protected Fruit Ing./BW 0.39 0.39 N/A N/A 9 

100-MAF_pf 100 - Moisture adjustment factor, Protected Fruit FFC 0.39 0.39 0.39 0.39 11 

BW_ch1 Body weight, Child 1-2 Ing./BW -0.54 -0.28 -0.26 -0.18 6 

EroRate Erosion Rate for all Parcels TRIM -0.31 -0.27 -0.26 -0.24 12 

SurfWatTemp Water Temperature TRIM 0.17 0.27 0.30 0.54 16 

IR_Fish_ch1 Ingestion rate, Fish, Child 1-2 Ing./BW 0.19 0.19 0.19 0.19 15 

EF_Fish Exposure factor, Fish Ing./BW 0.19 0.19 N/A N/A 13 

FC_Fish Fraction contaminated, Fish Ing./BW 0.19 0.19 N/A N/A 14 

SurfSoilAir Surface Soil- Fraction Air TRIM 0.15 0.17 0.17 0.19 19 

L1_ProFruit Loss type 1, Protected Fruit FFC -0.16 -0.16 -0.16 -0.16 18 

C_FishT3 Concentration of chemical in whole fish for T3 fish FFC 0.15 0.15 0.15 0.15 21 

WatRetTime Rentention Time in Pond TRIM 0.16 0.15 0.14 0.13 17 

SedDepVel Sediment Deposition Velocity and Resuspension Velocity TRIM 0.10 0.13 0.14 0.07 30 
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 Cad

Name of Variable 

Changed Variable Definition Variable Category a

Elasticity of Risk for Input 

Variable Perturbation 

Rankin

In

P

-50% -5% 5% 50% -50% -5

100-MAF_ev 100 - Moisture adjustment factor, Exposed Vegetable FFC 0.11 0.11 0.11 0.11 23 

IR_ExpVeg_ch1 Ingestion rate, Exposed Vegetable, Child 1-2 Ing./BW 0.11 0.11 0.11 0.11 26 

EF_ExpVeg Exposure factor, Exposed Vegetables Ing./BW 0.11 0.11 N/A N/A 24 

FC_ExpVeg Fraction contaminated, Exposed Vegetable Ing./BW 0.11 0.11 N/A N/A 25 

F_T3 Fraction of fish intake that is from T3 Ing./BW 0.11 0.11 0.11 0.11 28 F_T4 Fraction of fish intake that is from T4 Ing./BW -0.11 -0.11 -0.11 -0.11 29 

SurfSoilOCC Surface Soil- Organic Carbon Fraction TRIM 0.15 0.10 0.10 0.08 20 

SurfSoilVertVel Surface Soil- Average vertical velocity of water (percolation) TRIM -0.11 -0.10 -0.10 -0.09 27 

SurfSoilpH Surface Soil- pH TRIM 0.13 0.09 0.09 0.07 22 

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 Divalen

Name of Variable 

Changed Variable Definition Variable Category a

Elasticity of Risk for Input 

Variable Perturbation 

Rankin

In

P

-50% -5% 5% 50% -50% -5

EmissionRate Emission Rate of all Chemicals TRIM 1.00 1.00 1.00 1.00 5 

MixHeight Mixing Height TRIM -1.73 -0.97 -0.89 -0.63 1 

HorizWindSpeed Horizontal Wind Speed TRIM -1.73 -0.97 -0.89 -0.63 2 

EroRate Erosion Rate for all Parcels TRIM -1.27 -0.77 -0.71 -0.52 3 

Rain Annual Rainfall TRIM 0.76 0.74 0.74 0.71 6 

BW_ch1 Body weight, Child 1-2 Ing./BW -1.17 -0.61 -0.56 -0.39 4 

C_Soil Concentration of chemical in soil from contaminated area FFC 0.56 0.56 0.56 0.56 7 

EF_Soil Exposure factor, Soil Ing./BW 0.56 0.56 N/A N/A 8 

FC_Soil Fraction contaminated, Soil Ing./BW 0.56 0.56 N/A N/A 9 

IR_Soil_ch1 Ingestion rate, Soil, Child 1-2 Ing./BW 0.56 0.56 0.56 0.56 10 

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 

Br_AG_produce_

pf 

Plant-soil chemical 

bioconcentration factor, Protected 

Fruit 

FFC 0.15 0.15 0.15 0.15 12 

IR_ProFruit_ch1 Ingestion rate, Protected Fruit, Child 1-2 Ing./BW 0.15 0.15 0.15 0.15 15 

100-MAF_pf 100 - Moisture adjustment factor, Protected Fruit FFC 0.15 0.15 0.15 0.15 16 

EF_ProFruit Exposure factor, Protected Fruit Ing./BW 0.15 0.15 N/A N/A 13 

FC_ProFruit Fraction contaminated, Protected Fruit Ing./BW 0.15 0.15 N/A N/A 14 

SurfSoilAir Surface Soil- Fraction Air TRIM 0.11 0.13 0.13 0.15 17 

Br_AG_rootveg 

Plant-soil chemical 

bioconcentration factor, Root 

Vegetables 

FFC 0.11 0.11 0.11 0.11 18 

IR_RootVeg_ch1 Ingestion rate, Root Vegetable, Child 1-2 Ing./BW 0.11 0.11 0.11 0.11 21 
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 Divalen

Name of Variable 

Changed Variable Definition Variable Category a

Elasticity of Risk for Input 

Variable Perturbation 

Rankin

In

P

-50% -5% 5% 50% -50% -5

100-MAF_rv 100 - Moisture adjustment factor, Root Vegetable FFC 0.11 0.11 0.11 0.11 23 

EF_RootVeg Exposure factor, Root Vegetables Ing./BW 0.11 0.11 N/A N/A 19 

FC_RootVeg Fraction contaminated, Root Vegetable Ing./BW 0.11 0.11 N/A N/A 20 

VG_rootveg Empirical correction factor, Root Vegetable FFC 0.11 0.11 N/A N/A 22 

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 

Bs Soil bioavailability factor for livestock FFC 0.08 0.08 N/A N/A 24 

L1_ProFruit Loss type 1, Protected Fruit FFC -0.06 -0.06 -0.06 -0.06 26 

SedDepVel Sediment Deposition Velocity and Resuspension Velocity TRIM 0.02 0.05 0.06 0.11 48 

Ba_egg Chemical-specific biotransfer factor, Eggs FFC 0.05 0.05 0.05 0.05 27 

IR_egg_ch1 Ingestion rate, Eggs, Child 1-2 Ing./BW 0.05 0.05 0.05 0.05 30 

EF_egg Exposure factor, Eggs Ing./BW 0.05 0.05 N/A N/A 28 

FC_egg Fraction contaminated, Eggs Ing./BW 0.05 0.05 N/A N/A 29 

Qs_egg Quantity of soil eaten by the animal each day, Eggs FFC 0.05 0.05 0.05 0.05 31 

Ba_poultry Chemical-specific biotransfer factor, Poultry FFC 0.03 0.03 0.03 0.03 32 

IR_poultry_ch1 Ingestion rate, Poultry, Child 1-2 Ing./BW 0.03 0.03 0.03 0.03 35 

EF_poultry Exposure factor, Poultry Ing./BW 0.03 0.03 N/A N/A 33 

FC_poultry Fraction contaminated, Poultry Ing./BW 0.03 0.03 N/A N/A 34 

Qs_poultry Quantity of soil eaten by the animal each day, Poultry FFC 0.03 0.03 0.03 0.03 36 

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 Methy

Name of Variable 

Changed Variable Definition Variable Category a

Elasticity of Risk for Input 

Variable Perturbation 

Rankin

In

P

-50% -5% 5% 50% -50% -5

SedDepVel Sediment Deposition Velocity and Resuspension Velocity TRIM 0.78 1.56 2.01 3.67 9 

BW_ch1 Body weight, Child 1-2 Ing./BW -1.91 -1.00 -0.91 -0.64 1 

EmissionRate Emission Rate of all Chemicals TRIM 1.00 1.00 1.00 1.00 5 

IR_Fish_ch1 Ingestion rate, Fish, Child 1-2 Ing./BW 0.90 0.90 0.90 0.90 8 

FC_Fish Fraction contaminated, Fish Ing./BW 0.90 0.90 N/A N/A 7 

EF_Fish Exposure factor, Fish Ing./BW 0.90 0.90 N/A N/A 6 

MixHeight Mixing Height TRIM -1.02 -0.70 -0.66 -0.51 3 

HorizWindSpeed Horizontal Wind Speed TRIM -1.01 -0.70 -0.65 -0.51 4 

Rain Annual Rainfall TRIM 0.70 0.64 0.63 0.60 10 

C_FishT4 Concentration of chemical in whole fish for T4 fish FFC 0.55 0.55 0.55 0.55 12 

EroRate Erosion Rate for all Parcels TRIM -0.57 -0.45 -0.43 -0.35 11 

SedPorosity Sediment Porosity TRIM -0.30 -0.40 -0.43 -0.60 15 

C_FishT3 Concentration of chemical in whole fish for T3 fish FFC 0.36 0.36 0.36 0.36 13 

SurfWatTemp Water Temperature TRIM -0.22 -0.22 -0.22 -0.21 18 

F_T3 Fraction of fish intake that is from T3 Ing./BW -0.19 -0.19 -0.19 -0.19 19 F_T4 Fraction of fish intake that is from T4 Ing./BW 0.19 0.19 0.19 0.19 20 

WatSuspSed Water- Suspended Sediment Concentration TRIM -0.32 -0.18 -0.16 -0.12 14 

SurfSoilAir Surface Soil- Fraction Air TRIM 0.14 0.14 0.15 0.15 21 

WatRetTime Rentention Time in Pond TRIM 0.23 0.14 0.13 0.09 17 

FishMass Fish Body Weight for all Aquatic Species TRIM 0.10 0.07 0.07 0.06 22 

Runoff Total Water Runoff Rate TRIM 0.07 0.07 0.07 0.07 23 C_Soil Concentration of chemical in soil from contaminated area FFC 0.05 0.05 0.05 0.05 24 
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 Methy

Name of Variable 

Changed Variable Definition Variable Category a

Elasticity of Risk for Input 

Variable Perturbation 

Rankin

In

P

-50% -5% 5% 50% -50% -5

IR_Soil_ch1 Ingestion rate, Soil, Child 1-2 Ing./BW 0.05 0.05 0.05 0.05 27 

EF_Soil Exposure factor, Soil Ing./BW 0.05 0.05 N/A N/A 25 

FC_Soil Fraction contaminated, Soil Ing./BW 0.05 0.05 N/A N/A 26 

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 

100-MAF_pf 100 - Moisture adjustment factor, Protected Fruit FFC 0.03 0.03 0.03 0.03 33 

Br_AG_produce_

pf 

Plant-soil chemical 

bioconcentration factor, Protected 

Fruit 

FFC 0.03 0.03 0.03 0.03 29 

IR_ProFruit_ch1 Ingestion rate, Protected Fruit, Child 1-2 Ing./BW 0.03 0.03 0.03 0.03 32 

a FFC refers to farm food chain variables and Ing./BW refers to ingestion and body weight variables. 
Appendix 5: Draft Detailed Risk Modeling Results

   
   

Table 1 - Facility Identification Information 

Source 

Category Facility NEI ID Facility Name Address City State 

Primary 

Aluminum NEI10933 

ALUMINUM COMPANY OF 

AMERICA, INC. - SOUTH 

PLANT 

300 NORTH HALL 

ROAD ALCOA TN 

Primary 

Aluminum NEI11365 

ALCAN INGOT & 

RECYCLING 9404 HWY 2096 ROBARDS KY 

Primary 

Aluminum NEI32661 

ALCOA INC. - WARRICK 

OPERATIONS 

State Road 66 & 

State Road 61 NEWBURGH IN 

Primary 

Aluminum NEI34464 

NORANDA ALUMINUM INC, 

NEW MADRID 

ST. JUDE 

INDUSTRIAL 

PARK 

(MARSTON) NEW MADRID MO 

Primary 

Aluminum NEI39458 

ALCOA MASSENA 

OPERATIONS (WEST PLANT) PARK AVE EAST MASSENA NY 

Primary 

Aluminum NEI41217 

ALUMAX OF SOUTH 

CAROLINA 3575 HWY 52 N GOOSE CREEK SC 

Primary 

Aluminum NEI42328 

Aluminum Co of America 

Wenatchee Works 

6200 Malaga Alcoa 

Hwy Malaga WA 

Primary 

Aluminum NEI46970 

REYNOLDS METALS ST 

LAWRENCE REDUCTION PL 

194 COUNTY RT 

45 MASSENA NY 

Primary 

Aluminum NEI5988 

REYNOLDS METAL 

COMPANY/LAKE CHARLES 

CARBON COMPANY 

3943 GRANGER 

RD LAKE CHARLES LA 

Primary 

Aluminum NEI6464 Alcoa Inc. P.O. Box 472 Rockdale TX 

Primary 

Aluminum NEI700 

CENTURY ALUMINUM OF 

WEST VIRGINIA INC CENTURY RD RAVENSWOOD WV 

Primary 

Aluminum NEIKY0912$$1 

CENTURY ALUMINUM OF 

KENTUCKY LLC 

1627 State Route 

271 N HAWESVILLE KY 

Primary 

Aluminum NEIOH1110001 

ORMET PRIMARY 

ALUMINUM CORP. 

43840 STATE 

ROUTE 7 HANNIBAL OH 

Primary 

Aluminum NEIWA19906 

Alcoa Primary Metals Intalco 

Works 

4050 Mountain 

View Rd Ferndale WA 
Table 2 – Maximum Predicted HEM-3 Chronic Risks 

Facility NEI ID 

 Source Category Chronic Risk 1

Cancer 

MIR 

Cancer 

 Incidence 

Noncancer 

Max HI 

Target 

Organ 

NEI10933 1.82E-05 1.05E-03 4.05E-01 skeletal 

NEI11365 2.85E-06 1.44E-04 5.52E-02 skeletal 

NEI32661 1.49E-06 3.73E-04 1.04E-02 skeletal 

NEI34464 1.76E-06 2.25E-04 3.59E-02 skeletal 

NEI39458 3.02E-06 1.45E-04 1.01E-02 skeletal 

NEI41217 1.36E-06 7.21E-04 1.94E-02 skeletal 

NEI42328 4.35E-06 8.23E-05 1.12E-01 skeletal 

NEI46970 3.23E-05 1.18E-03 4.57E-02 skeletal 

NEI5988 5.97E-07 9.43E-05 6.64E-05 respiratory 

NEI6464 8.75E-07 1.62E-05 8.24E-02 skeletal 

NEI700 5.52E-06 3.21E-04 1.33E-02 skeletal 

NEIKY0912$$1 3.57E-06 2.13E-04 5.47E-02 skeletal 

NEIOH1110001 2.03E-06 6.02E-05 1.82E-01 skeletal 

NEIWA19906 3.48E-06 8.13E-05 3.02E-02 neurological 

1. BOLD indicates a cancer risk great than 1 in a million or a noncancer risk greater than 1 

   
Table 3 – Maximum Predicted Acute Risks (HEM-AERMOD) 

Facility NEI ID Pollutant 

Maximum Hazard Quotient1

REL AEGL1 AEGL2 ERPG1 ERPG2 

NEI10933 Hydrofluoric acid 6.22E+00 1.82E+00 7.46E-02 9.32E-01 9.32E-02

NEI10933 Carbonyl sulfide 0.00E+00 0.00E+00 1.68E-03 0.00E+00 0.00E+00

NEI10933 Naphthalene 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI10933 POM 72002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI10933 POM 75002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI10933 POM 76002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI10933 POM 77002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI11365 Hydrofluoric acid 6.17E+00 1.81E+00 7.41E-02 9.26E-01 9.26E-02

NEI11365 Carbonyl sulfide 0.00E+00 0.00E+00 2.57E-02 0.00E+00 0.00E+00

NEI11365 Naphthalene 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI11365 POM 72002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI11365 POM 75002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI11365 POM 76002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI11365 POM 77002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI32661 Hydrofluoric acid 1.27E+00 3.73E-01 1.53E-02 1.91E-01 1.91E-02

NEI32661 Carbonyl sulfide 0.00E+00 0.00E+00 2.33E-03 0.00E+00 0.00E+00

NEI32661 Naphthalene 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI32661 POM 72002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI32661 POM 75002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI32661 POM 76002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI32661 POM 77002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI34464 Hydrofluoric acid 8.86E+00 2.59E+00 1.06E-01 1.33E+00 1.33E-01

NEI34464 Carbonyl sulfide 0.00E+00 0.00E+00 7.76E-02 0.00E+00 0.00E+00

NEI34464 Naphthalene 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI34464 POM 72002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI34464 POM 75002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI34464 POM 76002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI34464 POM 77002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI39458 Hydrofluoric acid 2.69E-01 7.88E-02 3.23E-03 4.04E-02 4.04E-03

NEI39458 Carbonyl sulfide 0.00E+00 0.00E+00 1.52E-03 0.00E+00 0.00E+00

NEI39458 Naphthalene 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI39458 POM 72002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI39458 POM 75002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI39458 POM 76002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI39458 POM 77002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI41217 Hydrofluoric acid 2.21E+00 6.48E-01 2.66E-02 3.32E-01 3.32E-02

NEI41217 Carbonyl sulfide 0.00E+00 0.00E+00 9.63E-04 0.00E+00 0.00E+00

NEI41217 Naphthalene 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI41217 POM 72002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI41217 POM 75002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI41217 POM 76002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI41217 POM 77002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI42328 Hydrofluoric acid 1.23E+01 3.60E+00 1.48E-01 1.84E+00 1.84E-01

NEI42328 Carbonyl sulfide 0.00E+00 0.00E+00 3.14E-03 0.00E+00 0.00E+00
NEI42328 Naphthalene 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI42328 POM 72002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI42328 POM 75002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI42328 POM 76002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI42328 POM 77002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI46970 Hydrofluoric acid 8.94E+00 2.62E+00 1.07E-01 1.34E+00 1.34E-01

NEI46970 Carbonyl sulfide 0.00E+00 0.00E+00 1.09E-03 0.00E+00 0.00E+00

NEI46970 Naphthalene 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI46970 POM 72002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI46970 POM 75002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI46970 POM 76002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI46970 POM 77002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI5988 Naphthalene 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI5988 POM 72002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI5988 POM 75002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI5988 POM 76002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI5988 POM 77002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI6464 Hydrofluoric acid 1.47E+01 4.31E+00 1.77E-01 2.21E+00 2.21E-01

NEI6464 Carbonyl sulfide 0.00E+00 0.00E+00 1.35E-02 0.00E+00 0.00E+00

NEI6464 Naphthalene 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI6464 POM 72002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI6464 POM 75002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI6464 POM 76002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI6464 POM 77002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI700 Hydrofluoric acid 6.91E-01 2.02E-01 8.29E-03 1.04E-01 1.04E-02

NEI700 Carbonyl sulfide 0.00E+00 0.00E+00 3.65E-04 0.00E+00 0.00E+00

NEI700 Naphthalene 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI700 POM 72002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI700 POM 75002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI700 POM 76002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI700 POM 77002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEIKY0912$$1 Hydrofluoric acid 2.75E+00 8.06E-01 3.30E-02 4.13E-01 4.13E-02

NEIKY0912$$1 Carbonyl sulfide 0.00E+00 0.00E+00 4.10E-04 0.00E+00 0.00E+00

NEIKY0912$$1 Naphthalene 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEIKY0912$$1 POM 72002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEIKY0912$$1 POM 75002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEIKY0912$$1 POM 76002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEIKY0912$$1 POM 77002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEIOH1110001 Hydrofluoric acid 1.80E+01 5.28E+00 2.16E-01 2.71E+00 2.71E-01

NEIOH1110001 Carbonyl sulfide 0.00E+00 0.00E+00 6.44E-03 0.00E+00 0.00E+00

NEIOH1110001 Naphthalene 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEIOH1110001 POM 72002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEIOH1110001 POM 75002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEIOH1110001 POM 76002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEIOH1110001 POM 77002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEIWA19906 Hydrofluoric acid 6.65E-01 1.95E-01 7.98E-03 9.97E-02 9.97E-03

NEIWA19906 Carbonyl sulfide 0.00E+00 0.00E+00 1.44E-02 0.00E+00 0.00E+00

NEIWA19906 Naphthalene 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00
NEIWA19906 POM 72002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEIWA19906 POM 75002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEIWA19906 POM 76002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEIWA19906 POM 77002 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

1 Some maximum acute impacts may be at onsite locations. 

Note: BOLD indicates a REL or AEGL‐1 value greater than 1, or an ERPG‐1 greater than or equal to 1
Table 4 – Maximum Predicted Acute Risks Greater than 1 (Refined Approach) 

Facility NEI ID Pollutant Criteria 

HEM-3 

(Screening)

Refined 

Results 1

 Refinements to Screening Approach 2

Primary Aluminum 

NEIOH1110001 Hydrofluoric acid REL 1.80E+01 1.00E+01

Examined results for maximum off-site impact 

NEIOH1110001 Hydrofluoric acid AEGL1 5.28E+00 4.00E+00

NEIOH1110001 Hydrofluoric acid ERPG1 2.71E+00 2.00E+00

NEIKY0912$$1 Hydrofluoric acid REL 2.75E+00 2.00E+00

NEI6464 Hydrofluoric acid REL 1.47E+01 1.00E+01

NEI6464 Hydrofluoric acid AEGL1 4.31E+00 4.00E+00

NEI6464 Hydrofluoric acid ERPG1 2.21E+00 2.00E+00

NEI46970 Hydrofluoric acid REL 8.94E+00 3.00E+00

NEI46970 Hydrofluoric acid AEGL1 2.62E+00 8.00E-01

NEI46970 Hydrofluoric acid ERPG1 1.34E+00 4.00E-01

NEI42328 Hydrofluoric acid REL 1.23E+01 9.00E+00

NEI42328 Hydrofluoric acid AEGL1 3.60E+00 3.00E+00

NEI42328 Hydrofluoric acid ERPG1 1.84E+00 1.00E+00

NEI41217 Hydrofluoric acid REL 2.21E+00 1.00E+00

NEI34464 Hydrofluoric acid REL 8.86E+00 9.00E+00

NEI34464 Hydrofluoric acid AEGL1 2.59E+00 3.00E+00

NEI34464 Hydrofluoric acid ERPG1 1.33E+00 1.00E+00

NEI11365 Hydrofluoric acid REL 6.17E+00 2.00E+00

NEI11365 Hydrofluoric acid AEGL1 1.81E+00 7.00E-01

NEI10933 Hydrofluoric acid REL 6.22E+00 5.00E+00

NEI10933 Hydrofluoric acid AEGL1 1.82E+00 1.00E+00

1

 Results presented to 1 significant figure 

2

 Indicates modeling technique used to refine estimates; see Appendix 6 for figures depicting off-site impacts 
Appendix 6 

Acute Impacts Refined Analysis Figures 
Refined Acute Modeling Approach 

 Initial acute screening risk calculations were performed with the HEM-3 model. 

HEM-3 estimates acute (1-hour) impacts at both polar and census block receptors. It is 

assumed for this short period of time that an exposed individual could be located at any 

off-site location. The lack of readily available detailed property boundary information 

for many of the facilities evaluated made it difficult to determine whether receptors were 

on- or off-site. In the absence of such information, the first ring of polar receptors was 

placed 100 meters from the plant center for many facilities. However, these polar rings 

often transected on-site locations, restricting public access to exposures at these levels 

and thereby overestimating exposures. 

 The screening approach used by HEM-3 to estimate maximum 1-hour exposures 

also likely overestimates. To estimate maximum 1-hour concentrations at each receptor, 

HEM-3 sums the maximum concentrations attributed to each source, regardless of 

whether those maximum concentrations occurred during the same hour. In other words, 

HEM-3 assumes that the maximum impact from each source at each receptor occurs at 

the same time. In actuality, maximum impacts from different sources may occur at 

different times. 

 This appendix includes the results of a more refined acute assessment performed 

for facilities that exceeded short-term health benchmarks using the HEM-3 approach. The 

refinements presented in this appendix can address the first conservatism described 

above. That is, to determine if the highest acute concentration reported by HEM-3 was 

within a facility’s boundary, and hence not accessible to the public, we plotted the HEM3 polar grid results on aerial photographs of the facilities. This allows the assessment of 

off-site locations that may be accessible to the public (e.g., roadways and public 

buildings.), and hence a potential location where an acute exposure may occur. The 

attached figures present the estimated hazard quotient values based on the REL (the 

modeled 1-hour concentration of a pollutant divided by its REL), at polar receptors near 

the facilities. If a facility also had AEGl-1 and/or ERPG-1 values of potential concern 

(see Table 4 in Appendix 5), the same location as the highest REL value was chosen as 

being off-site and results were scaled accordingly. Finally, we note that this refinement 

does not address the second conservatism described above (i.e., the assumption in HEM3 that the maximum impact from each source at each receptor occurs at the same time); 

thus, even these somewhat more refined estimates of acute risk are still likely to be 

overestimates. 
Primary Aluminum Producti

• All figures represent HQ values based on the RE

All figures represent HQ values based on the RE

• If a facility also had AEGl If a facility also had AEGl-1 and/or ERPG 1 and/or ERPG-1 valu

1 valu

potential concern (see Table 4 in Appendix 5), thlocation as the highest REL was chosen as bein

and the results the results were scaled accordingly were scaled accordingly
Figure 1 – NEI110001 Hydrofluoric acid 
Figure 2 – NEI0912$$1 Hydrofluoric acid 
Figure 3 – NEI6464 Hydrofluoric acid 
Figure 4 – NEI46970 Hydrofluoric acid 
Figure 5 – NEI42328 Hydrofluoric acid 
Figure 6 – NEI41217 Hydrofluoric acid 
Figure 7 – NEI34464 Hydrofluoric acid 
Figure 8 – NEI11365 Hydrofluoric acid 
Figure 9 – NEI10933 Hydrofluoric acid 
Appendix 7 - Development of the Chronic Screening Level for Carbonyl Sulfide 

Although the health effects data for carbonyl sulfide (COS) are very limited, a series of studies 

(ref 1,2,3) conducted by the National Toxicology Program have shown that the major concern 

regarding exposure to COS is its potential for neurotoxicity. These studies have shown 

consistently and at the same range of COS concentrations that the brain is a target organ for COS 

toxicity. A summary of the effects and levels at which they are observed is presented below. 

Since health appropriate effects benchmarks have not been derived by our preferred sources of 

dose-response data including IRIS, ATSDR and Cal EPA, the EPA has used the data from the 

above mentioned studies to derive a chronic screening benchmark level for COS. The rationale 

for this screening analysis is presented below. 

A chronic screening level of 163 ug/m3was developed for carbonyl sulfide (COS) from a No 

Observed Adverse Effects Level (NOAEL) of 200 ppm based on brain lesions and 

neurophysiological alterations in rodents as cited in the references below. 

The NOAEL value of 200 ppm was converted to a value of 490,000 ug/m3

 as follows: 

1 ppm of COS = 2.45 mg/m3 (Molecular Weight = 60.08) 

200 ppm x 2.45 mg/m3 = 490 mg/m3 = 490,000 ug/m3

A total uncertainty factor (UF) of 3,000 was applied to this effect level: 10x for extrapolation for 

interspecies differences, 10x for consideration of intraspecies variability, 10x for extrapolation 

from subchronic to chronic duration, and 3x for database insufficiencies. 

10 x 10 x 10 x 3 = 3,000 

(490,000 ug/m3) / 3,000 = 163 ug/m3

Summary of Findings from NTP Studies on Carbonyl Sulfide (COS)

Experimental Design: 

Several studies were conducted using F344 rats exposed to carbonyl sulfide (COS) by inhalation 

at various exposure concentrations and durations. Details are available in the references cited 

below. 

Results Synopsis: 

Pathology: 

 Brain lesions at 400 and 500 ppm after exposure for 2 weeks. NOAEL = 300 ppm 

[Reference 1] 

 Brain lesions at 400 ppm after exposure for 12 weeks. NOAEL = 300 ppm [Reference 1, 

Reference 3] 
Biochemistry [Reference 1]: 

 Inhibited cytochrome oxidase activity in posterior colliculus and parietal cortex at 200, 

300, and 400 ppm COS after exposure for 3, 6, and 12 weeks. 

Neurophysiology: 

 Changes in Brainstem Auditory Evoked Responses (BAERs) at 400 ppm after 2 weeks 

exposure. [Reference 1] 

 Changes in somatosensory evoked potentials (SEPs) at 400 ppm after 2 or 12 weeks. 

[Reference 2] 

 Changes in BAERs at 300 and 400 ppm COS for 12 weeks. NOAEL = 200 ppm. 

[Reference 2] 

Behavior: 

 Changes in Functional Observational Battery (FOB) at 400 and 500 ppm with 2 week 

exposure [Reference 1], and motor activity and FOB altered by 400 ppm COS with 2 

week exposure. [Reference 2] 

 Some changes in FOB at 200, 300, and 400 ppm after 6 weeks exposure, with recovery 

indicated by 12 weeks. [Reference 1] 

Summary

 NOAEL of 200-300 ppm selected based on brain pathological lesions and 

neurophysiological alterations. 

 Behavioral effects were observed at 400 ppm, and observed in some studies but not 

others at 200-300 ppm. 

 Biochemistry changes were observed in brains of rats at all dose levels including 200 

ppm, the lowest dose evaluated. A NOAEL was not determined for biochemical changes. 

References: 

1. Morgan, D.L., Little, P.B., Herr, D.W., Moser, V.C., Collins, B., Herbert, R., Johnson, G.A., 

Maronpot, R.R., Harry, G.J., and Sills, R.C.: Neurotoxicity of carbonyl sulfide in F344 rats 

following inhalation exposure for up to 12 weeks. Toxicol. Appl. Pharmacol. 200(2):131-

145, 2004. 

2. Herr, D.W., Graff, J.E., Moser, V.C., Crofton, K.M., Little, P.B., Morgan, D.L., and Sills, 

R.C.: Inhalational exposure to carbonyl sulfide produced altered brainstem auditory and 

somatosensory-evoked potentials in Fischer 344N rats. Toxicol. Sci. 95(1):118-135, 2007. 

3. Sills, R.C., Morgan, D.L., Herr, D.W., Little, P.B., George, N.M., Ton, T.V., Love, N.E., 

Maronpot, R.R., and Johnson, G.A.: Contribution of magnetic resonance microscopy in the 

12-week neurotoxicity evaluation of carbonyl sulfide in Fischer 344 rats. Toxicol. Pathol. 

32:501-510, 2004. 

Other Studies: 

1. Sills, R.C., Harry, G.J., Valentine, W.M., and Morgan, D.L.: Interdisciplinary 

neurotoxicity inhalation studies: Carbon disulfide and carbonyl sulfide research in F344 

rats. Toxicol. Appl. Pharmacol. 207(Suppl 2):S245-S250, 2005. 
Appendix 8 

Dispersion Model Receptor Revisions and Additions 
Dispersion Model Receptor Revisions and Additions for the Primary Aluminum 

Source Category

To estimate ambient concentrations for evaluating long-term exposures, the HEM-3 

model uses the geographic centroids of census blocks (currently utilizing the 2000 Census) as 

dispersion model receptors. The census block centroids are generally good surrogates for where 

people live within a census block. A census block generally encompasses about 40 people or 10-

15 households. However, in cases where a block centroid is located on industrial property, or 

where a census block is large and the centroid less likely to be representative of the block’s 

residential locations, the block centroid may not be an appropriate surrogate. 

Census block centroids that are on facility property can sometimes be identified by their 

proximity to emission sources. In cases where a census block centroid was within 300 meters of 

any emission source, we viewed aerial images of the facility to determine whether the block 

centroid was likely located on facility property. The selection of the 300-meter distance reflects a 

compromise between too few and too many blocks identified as being potentially on facility 

property. Distances smaller than 300 meters would identify only block centroids very near the 

emission sources and could exclude some block centroids that are still within facility boundaries, 

particularly for large facilities. Distances significantly larger than 300 meters would identify 

many block centroids that are outside facility boundaries, particularly for small facilities. Where 

we confirmed a block centroid on facility property, we moved the block centroid to a location 

that best represents the residential locations in the block. 

In addition, census block centroids for blocks with large areas may not be representative 

of residential locations. Risk estimates based on such centroids can be understated if there are 

residences nearer to a facility than the centroid, and overstated if the residences are farther from 

the facility than the centroid. To avoid understating the maximum individual risk associated 

with a facility, in some cases we relocated block centroids, or added dispersion model receptors 

other than the block centroid. We examined aerial images of all large census blocks within one 

kilometer of any emission source. Experience from previous risks characterizations show that in 

most cases the MIR is generally located within 1 km of the facility boundary. If the block 

centroid did not represent the residential locations, we relocated it to better represent them. If 

residential locations could not be represented by a single receptor (that is, the residences were 

spread out over the block), we added additional receptors for residences nearer to the facility 

than the centroid. 

For this source category, the table below contains each census block for which we 

changed the centroid location because it was on facility property or was otherwise not 

representative of the residential locations in the block. The table also contains the locations of 

additional receptors that were included to represent residential locations nearer to the facility 

than the block centroid. 
Revised Census Block Centroid Locations and Additional Receptors for the Primary 

Aluminum Source Category 

Centroid Revisions

Block ID NEI ID New Latitude New Longitude Comment

530079612001002 NA 47.363889 ‐120.141685 On plant property

211010207014001 NA 37.887141 ‐87.326495

Not representative of

population

210919901002003 NA 37.924376 ‐86.771039

Not representative of

population

210919902003000 NA 37.984427 ‐86.80841

Not representative of

population

220190018002017 NA 30.129079 ‐93.31509

Not representative of

population

360899903006010 NA 44.968245 ‐74.784211

Not representative of

population

541030306003000 NA 39.704355 ‐80.822976

Not representative of

population

391119666004005 NEIOH1110001 39.699881 ‐80.858675 User receptor added

530079612001005 NEI42328 47.357443 ‐120.156278 User receptor added
Risk and Technology Review -

Analysis of Socio-Economic Factors for Populations 

Living Near Primary Aluminum Facilities

Prepared by:

EC/R Incorporated

501 Eastowne Drive, Suite 250

Chapel Hill, NC 27514

EPA Contract No. EP-D-06-119

Work Assignment No. 4-01

Prepared for:

Regina Chappell, Work Assignment Manager

Community and Tribal Programs Group

Office of Air Quality Planning and Standards

U.S. Environmental Protection Agency

Research Triangle Park, North Carolina 27711

September 26, 2011
ii

Disclaimer

Although the analysis described in this document has been funded 

wholly or in part by the United States Environmental Protection 

Agency contract EP-D-06-119 to EC/R Incorporated, it has not 

been subject to the Agency's review and therefore does not 

necessarily reflect the views of the Agency, and no official 

endorsement should be inferred.
1

Contents

1. Introduction 2

2. Census Data 33

3. Calculation Methods 55

3.1 Racial, Ethnic and Age Categories and the Total Population 55

3.2 Level of Education 66

3.3 Household Income 77

3.4 Poverty Level 88

3.5 Disability 88

3.6 Linguistic Isolation 99

4. Results 99

5. Uncertainty Discussion 1111

Appendix A A-11
2

1. Introduction

This document describes the approach used to evaluate the potential cancer risks 

associated with inhalation and air-related exposures to hazardous air pollutants (HAP) in 

different social, demographic, and economic groups within the population living near primary

aluminum manufacturing facilities in the United States. This work was carried out in support of 

the U.S. Environmental Protection Agency’s Residual Risk and Technology Review (RTR) for 

primary aluminum manufacturing emissions subject to Maximum Available Control Technology

(MACT) requirements under 40 CFR 63 Subpart LL.

In the RTR analysis, the Human Exposure Model, Version 3 (HEM-3)

1,2 was used to 

estimate cancer risks due to the inhalation of HAP for the populations residing within 50 

kilometers of each primary aluminum facility in the country. HEM-3 estimates cancer risks at 

the level of census blocks using the AERMOD state-of-the-art air dispersion model developed 

under the direction of the American Meteorological Society (AMS) / EPA Regulatory Model 

Improvement Committee (AERMIC). Each census block typically includes about 50 people. 

Additional information on the risk analysis is available in the docket for the proposed National 

Emissions Standards for Hazardous Air Pollutants: Primary Aluminum Reduction Plants

rulemaking where a report is provided, covering the inputs and specific assumptions, and 

addressing uncertainties.

In the current analysis, cancer risk estimates from the primary aluminum HEM-3

modeling effort were linked to detailed census data in order to evaluate the distribution of risks 

for different demographic groups (including racial, ethnic, age, economic, educational, disabled 

and linguistically isolated population categories). The following population categories were 

included in this analysis:

• Total population

• White

• Minority

• African American (or Black)

• Native Americans

• Other races and multiracial

• Hispanic or Latino

• Children 18 years of age and under

• Adults 19 to 64 years of age

• Adults 65 years of age and over

1. EC/R. 2006. Modeling for the Residual Risk and Technology Review Using the Human Exposure Model 

3 – AERMOD Version. Prepared by EC/R Incorporated for the U.S. Environmental Protection Agency, 

Research Triangle Park, NC. 

2. EC/R. 2008. HEM-3 User’s Guide. Prepared by EC/R Incorporated for the U.S. Environmental 

Protection Agency, Research Triangle Park, NC. http://www.epa.gov/ttn/fera/human_hem.html#guide
3

• Adults without a high school diploma

• Households earning under the national median income

• People living below the poverty line

• Working aged people (16-64) with a disability

• Linguistically isolated people

The HEM-3 results for a particular census block reflect the estimated level of cancer risk that 

would be experienced by an individual residing in the block for 70 years. In this analysis, the 

demographic composition of the population estimated to experience a risk greater than 1 in 1 

million as a result of primary aluminum manufacturing emissions is compared to the

demographic composition of the overall nationwide population.

The census data used in this analysis is described in Section 2. The algorithms used to 

compute the distributions of risk and exposure are presented in Section 3. The results of this 

analysis are presented in Section 4. 

2. Census Data

Table 1 summarizes the census data used in this analysis, showing the source of each 

dataset and the level of geographic resolution. All of the data are from the 2000 Decennial 

census. Race, ethnicity and age data were obtained at the census block level. Distributions 

regarding educational status, poverty status, household income, disabilities and linguistic 

isolation were obtained at the block group level. A census block contains about 50 people on 

average; and a block group contains about 26 blocks on average, or about 1,350 people. (For 

comparison, a census tract is larger than a block group, with each tract containing an average of 

3 block groups, or about 4,300 people.)

Table 1. Summary of Census Data used to Analyze Risks for Different Socioeconomic Groups

Type of population category Source of data

Level of 

geographic 

resolution

Racial and ethnic categories Landview® Census block

Age groups SF1 Table P12 Census block

Level of education - adults without a high 

school diploma

SF3 Table 37 Block group

Households earning below the national 

median income

SF3 Table 52 Block group

People living below the poverty line SF3 Table P87 Block group

Working age people (16-64) with a

disability

SF3 Table P42 Block group

Linguistically isolated people SF3 Table P20 Block group
4

Data on race and ethnicity were obtained primarily from the Landview® database 

compiled by the Census Department.3Landview® gives a breakdown for the population of each 

census block among different racial classifications, including: White, African American or 

Black, American Indian or Native Alaskan, Asian, Native Hawaiian or other South Pacific 

Islander, other race, and two or more races. In the current analysis, the Asian, Native Hawaiian 

or other South Pacific Islander, and other race categories were combined into a single category. 

The Landview® database also indicates the number of people in each tract that are of Hispanic 

or Latino ethnicity. Landview® covers the 50 states, the District of Columbia, and Puerto Rico, 

but does not cover the Virgin Islands. Race and ethnicity data on the Virgin Islands were 

obtained from the Virgin Islands Summary File.4

Data on age distributions in the U.S. and Puerto Rico for each census block were 

obtained from the 2000 Census of Population and Housing Summary File 1 (SF1) Short Form, 

Table P12. Data on poverty status, household income, education level, disabilities and linguistic 

isolation for each block group in the U.S. and Puerto Rico were obtained from the 2000 Census 

of Population and Housing Summary File 3 (SF3) Long Form. For the U.S. this file was 

accessed on a DVD version prepared by GeoLytics.5SF3 data for Puerto Rico were obtained 

from the Census Department website,6and data for the Virgin Islands were retrieved from 

similar tables in the Virgin Islands Summary File.4

The SF3 data set consists of over 800 separate tables, each providing information on a 

different subject. For the current analysis, data were obtained from Tables P20, P37, P42, P52, 

and P87. Table P37 analyzes the level of education attained by men and women over 25 years of 

age (e.g. some high school but no high school diploma, high school graduate, some college, etc.)

in each block group. Table P52 gives information on household income in 1999. Table P87 

estimates the number of people living below the poverty line in each block group. Table P42 

estimates the number of people in different age categories with disabilities in each block group. 

And Table P20 estimates the fraction of households in each block group that are considered 

linguistically isolated.

3. Census. 2002. LandView 5 on DVD [electronic resource] : a viewer for EPA, Census and USGS data and 

maps. U.S. Census Bureau, Washington, D.C.

4. Census. 2008. Virgin Islands Summary File. U.S. Census Bureau, Washington, D.C.

www.factfinder.census.gov

5. Census. 2004. Census DVD 2000 Long Form SF3, Release 2.2. Geolytics, Inc., East Brunswick, NJ. 

www.geolytics.com

6. Census. 2008. SF3 Data for Puerto Rico. U.S. Census Bureau, Washington, D.C. 

www.factfinder.census.gov
5

3. Calculation Methods

The HEM-3 models the cancer and noncancer risk at a point near the geographic center 

of each census block.7For the current analysis, this risk estimate was assumed to apply to all 

individuals residing in the block. We used block identification codes to link the HEM-3

modeling results for each block to the appropriate census statistics. This allowed us to estimate 

the numbers of people falling into different population categories within each block. We then 

analyzed the distribution of estimated inhalation risks within each population category, given the 

numbers of people within the category that are exposed to different risk levels. Each distribution 

involved a tabulation of all the census blocks modeled for the primary aluminum source

category. We also computed the average risk for all individuals in each population category.

Distributions of risk and average risks were computed for the raw HEM-3 model results 

for primary aluminum operations. For comparison, the nationwide demographic composition 

(i.e., population percentage in each demographic group for the country as a whole, based on the 

2000 Census) is also provided in the results table.

Section 3.1 describes the calculation method used for categories where block-level data 

were available – racial, ethnic and age categories and the total population. Sections 3.2 

through 3.6 describe calculation methods for education status, household income, poverty status, 

disability and linguistic isolation, respectively. 

3.1 Racial, Ethnic and Age Categories and the Total Population

Since race, ethnicity and age data are available at the census block level, the calculation 

of risk distributions for these categories involved a simple block-by-block accumulation of the 

people in each category. We began by identifying a set of bins reflecting the level of risk. The 

population of each block was then assigned to the appropriate risk bin based on the modeled risk 

level in the block. The numbers of people in each risk bin were then added together for all of the 

blocks modeled for the primary aluminum source category:

H(Rab,s) = •i

(Ra•Ri<Rb) [N(s,i)] (1)

where:

H(Rab,s) = the population count for risk bin Rab, which is between Ra and Rb for population 

subgroup “s”

Ri = the modeled risk level in block “í” (estimated lifetime cases of cancer per 

million population)

•i

(Ra•Ri<Rb) refers to the summation over all blocks i where Ri falls in bin Rab, between Ra

and Rb

N(s,i) = the number of people within population subcategory s, in block i

7. HEM-3 generally uses the coordinates given by the census for the internal point, or “centroid” of each 

block. However, when the footprint of an industrial facility includes the block centroid, the model is 

designed to identify the highest-risk point outside of the facility’s footprint.
6

The same approach was used for the total population. The average risk for a given population 

category or for the total population was then calculated using the following equation:

A(S) = •i [N(s,i) × Ri] • •i [N(s,i)] (2)

where:

A(s) = the average risk for population subgroup “s” (estimated lifetime cases of cancer 

per million population)

•i refers to the summation over all blocks “í” modeled for the emission source 

category

N(s,i) and Ri were defined above

3.2 Level of Education

Table P37 of the SF3 dataset specifies the education status for men and women age 25 

and older for each census block group, based on the last grade completed. To obtain the total 

number of adults without a high school degree, we added together the numbers who had 

completed grades below a high school senior. Thus, the number of people without a high school 

degree equals the sum of the number of males with no schooling, the number of females with no 

schooling, the numbers of males and females who have completed nursery school through 4th

grade, up to the numbers of males and females who have completed some high school but not 

received a high school degree.

The number of adults without a high school degree as a fraction of the total population 

was assumed to be the same for each block in the block group. Thus, the number of adults 

without a high school degree in each block was computed as follows:

N(nhs,b/bg) = N(t,b/bg) × N(nhs,bg) • N(t,bg) (3)

where:

N(nhs,b/bg) = number of adults without a high school diploma, in block “b” of block group 

“bg”

N(t,b/bg) = total number of people in block “b” of block group “bg”

N(nhs,bg) = number of adults without a high school diploma in block group “bg”

N(t,bg) = total number of people in block group “bg”

Equation (1) was then used to generate risk distributions based on the block-level results, and 

Equation (2) was used to compute the average risk for adults without a high school diploma. 
7

3.3 Household Income

Table P52 of the SF3 dataset estimates the numbers of households in each block group 

with income for the year 1999 in various ranges, generally divided into $5,000 increments (e.g. 

$10,000 to $14,999, $15,000 to $19,999, etc.). The median national income for 1999 was about 

$42,000 per year. Therefore, in order to determine the number of households with incomes 

under the median income, we added the estimates for the ranges below that level. We assumed 

that the household incomes in the $40,000 to $44,999 increment were evenly distributed over 

this range. Therefore, 40% of the households in the $40,000 to $44,999 income range were 

assumed to be below the national median income of about $42,000. The following equation was 

used to estimate the fraction of households below the national median income within each census

block group:

F(nm,bg) = [C<10 + C10-15 + .... + C35-40 + (0.4×C40-45)] • CT (4)

where:

F(nm,bg) = fraction of households in block group “bg” with incomes below the median 

national income

C<10 = number of households with incomes under $10,000

C10-15 = number of households with incomes from $10,000 to $14,999

C35-40 = number of households with incomes from $35,000 to $39,999

C40-45 = number of households with incomes from $40,000 to $44,999

CT = total number of households in block group “bg”

The fraction of people living in households below the median income for each block 

within the block group was assumed to be the same as the fraction of households below the 

median income for the block group.

N(nm,b/bg) = F(sm,bg) × N(t,b/bg) (5)

where:

N(nm,b/bg) = number of people in block “b” of block group “bg” living in households below 

the national median income

F(nm,bg) = fraction of households in block group “bg” below the national median income

N(t,b/bg) = total number of people in block “b” of block group “bg”

Equation (1) was then used to generate risk distributions based on the block-level results, and 

Equation (2) was used to compute the average risk for people living in households below the 

national median income. It must be noted that this approach neglects any potential relationship 

between household size and income level within a particular block group. However, it is 

expected to provide a reasonable indication of the risk level of people living below the national 

median income, relative to the population as a whole. 
8

3.4 Poverty Level

Table P87 of the SF3 dataset estimates the total number people in each block group living 

below the poverty level, as well as the numbers of people below the poverty level in different age 

groups. The current study did not include an analysis of poverty status by age group, only of the 

total population below the poverty line. The fraction of people below the poverty line was 

assumed to be the same for each block in the block group. Thus, the population below the 

poverty line in each block was computed as follows:

N(p,b/bg) = N(T,b/bg) × N(p,bg) • N(T,bg) (6)

where:

N(p,b/bg) = number of people below the poverty line in block “b” of block group “bg”

N(T,b/bg) = total number of people in block “b” of block group “bg”

N(p,bg) = number of people below the poverty line in block group “bg”

N(T,bg) = total number of people in block group “bg”

Equation (1) was then used to generate risk distributions based on the block-level results, and 

Equation (2) was used to compute the average risk for people living below the poverty level. 

3.5 Disability

Table P42 of the SF3 dataset estimates the total number of people in each block group 

with disabilities, as well as the numbers of people with disabilities in different age groups. This 

analysis includes the total number of working aged people (16 through 64 years of age) with a 

disability. The fraction of working aged people with a disability was assumed to be the same for 

each block in the block group. Thus, the population of working aged people with a disability in 

each block was computed as follows:

N(d,b/bg) = N(T,b/bg) × N(d,bg) • N(T,bg) (7)

where:

N(d,b/bg) = number of working aged people with a disability in block “b” of block group 

“bg”

N(T,b/bg) = total number of people in block “b” of block group “bg”

N(d,bg) = number of working aged people with a disability in block group “bg”

N(T,bg) = total number of people in block group “bg”

Equation (1) was then used to generate risk distributions based on the block-level results, and 

Equation (2) was used to compute the average risk for working aged people with a disability.
9

3.6 Linguistic Isolation

Table P20 of the SF3 dataset estimates the fraction of households in linguistic isolation in 

each block group. For this analysis, the fraction of people living in linguistic isolation for each 

block within the block group was assumed to be the same as the fraction of households in 

linguistic isolation for the block group. Thus, the population of linguistically isolated people in 

each block was computed as follows:

N(li,b/bg) = F(li,bg) × N(t,b/bg) (8)

where:

N(li,b/bg) = number of people in block “b” of block group “bg” living in linguistically 

isolated households

F(li,bg) = fraction of households in block group “bg” in linguistic isolation

N(t,b/bg) = total number of people in block “b” of block group “bg”

Equation (1) was then used to generate risk distributions based on the block-level results, and 

Equation (2) was used to compute the average risk for people living in linguistic isolation.

4. Results

The distribution of estimated lifetime inhalation cancer risks greater than or equal to1 in a 

million for different demographic groups among the population living near primary aluminum

facilities is shown in Table 28. For comparison purposes, Table 2 provides the nationwide 

percentages of the various demographic groups. Detailed demographics data and analyses used 

to create Table 2 can be found in Appendix A of this document.

With respect to those individuals residing near these facilities and exposed to a cancer 

risk greater than 1-in-1 million, Table 2 indicates that while the overall percentage of Minorities 

is substantially less than the corresponding national percentage (12% compared to a national 

percentage of 25%), the percentage of Native Americans with a cancer risk greater than or equal 

to 1-in-1 million is higher than the corresponding national percentage (4.8% compared to a 

national percentage of 0.9%). Furthermore, those Ages 65 and Up, Below the Poverty Level, 

Over 25 Without a High School Diploma, and Working Age with Disability are somewhat higher 

than their respective national percentages (by 4, 2, 2 and 1 percentage points, respectively).

Finally, we note that the percentages for all other demographic groups are below or similar to the 

corresponding national percentages.

8

In addition to examining the distribution of lifetime inhalation cancer risks greater than or equal to1 in a million for 

different demographic groups, we typically perform a similar analysis for individuals with chronic non-cancer target 

organ specific hazard index (TOSHI) values greater than 1. However, for this source category no TOSHI values 

were greater than 1, and therefore this additional analysis was not performed.
10

Table 2. Summary of Demographic Assessment of Risk Results for the Primary Aluminum Source Category

Emissions 

Basis

Demographic Group

Total Minority

African 

American

Other and

Multiracial

Hispanic

or 

Latino

Native 

American

Ages 

0 

to 18

Ages 

19 

to 64

Ages 65 

and up

Below 

the 

Poverty 

Level

Over 25 

Without 

a HS 

Diploma

Working 

Age with

Disability

Linguistic 

Isolation

Nationwide 

Demographic 

Breakdown

n/a 285,339,128 25% 12% 12% 14% 0.9% 27% 60% 12% 13% 13% 12% 5.4%

Maximum 

Risk 

(in 1 

million)

Population With Cancer Risk Greater Than or Equal to 1 in 1 million

Source 

Category 30 41,192 12% 4.6% 3% 2% 4.8% 24% 59% 16% 15% 15% 13% 0.9%

Notes:

Source Category emissions information is available in the document: Draft Summary Primary Aluminum RTR Modeling File Development, 

available in the docket for the Primary Aluminum Source Category rulemaking.

Minority population is the total population minus the white population.

Population figures are for the population residing within 50 km from the center of these facilities whose cancer risks are estimated to be greater 

than or equal to 1 in a million.
11

5. Uncertainty Discussion

Our analysis of the distribution of risks across various demographic groups is subject to 

the typical uncertainties associated with census data (e.g., errors in filling out and transcribing 

census forms), which are generally thought to be small, as well as the additional uncertainties 

associated with the extrapolation of census-block group data (e.g., income level and education 

level) down to the census block level.

The uncertainties in these risk estimates include the same uncertainties in emissions data 

sets, in air dispersion modeling, in inhalation exposure and in dose response relationships that are 

associated with our source category risk estimates.

The methodology for our demographic analyses is still evolving. While this is our best 

attempt to provide useful information now, our thinking is continuously advancing. EPA is in 

the process of developing technical guidance for environmental justice analyses. We present 

these analyses, with their associated uncertainties, to EPA decision makers and the public as 

additional analyses to inform RTR decisions.
A-1

Appendix A
A-2

Total 

population White

African

American

Native 

American

Other and 

multiracial

Hispanic or

Latino

c

0 to 1 3,230,262 2,764,867 336,495 16,533 112,367 96,377

1 to 5 36,156 31,934 1,679 1,524 1,019 802

5 to 10 4,121 3,588 179 289 65 56

10 to 20 528 470 37 17 4 8

20 to 30 221 146 9 59 7 1

30 to 40 166 87 0 75 4 7

40 to 50 0 0 0 0 0 0

Total number 3,271,454 2,801,092 338,399 18,497 113,466 97,251

0.0985 0.0992 0.0745 0.5411 0.081 0.0653

Notes:

c

The Hispanic or Latino population is double-counted in this analysis, since different individuals within the 

category may classify themselves as White, African American, Native American, or other.

Table A-1. Distribution of Inhalation Cancer Risk for Racial and Ethnic Groups

aModeled risks are for a 70-year lifetime, based on the predicted outdoor concentration and not adjusted for 

exposure factors.

Range of lifetime 

individual cancer risk

(chance in one million)a

Average risk (chances 

in one million)

b

Distributions by race are based on demographic information at the census block level. Risks from primary

aluminum emissions were modeled at the census block level.

Modeled risk from the primary aluminum 

source category

Numbers of people in different ranges for lifetime cancer riskb
A-3

Total 

population

Ages 0 thru 

18

Ages 19 thru 

64

Ages 65 and 

up

0 to 1 3,230,262 791,417 2,014,558 424,287

1 to 5 36,156 8,841 21,516 5,799

5 to 10 4,121 982 2,401 738

10 to 20 528 121 338 69

20 to 30 221 71 131 19

30 to 40 166 44 92 30

40 to 50 0 0 0 0

Total number 3,271,454 801,476 2,039,036 430,942

0.0985 0.0995 0.097 0.1042

Notes:

Table A-2. Distribution of Risk for Different Age Groups

Modeled risk from the primary aluminum source 

category

b

Distributions by age are based on modeling data and age data at the census block level.

Range of lifetime individual 

cancer risk (chance in one

million)a

Numbers of people in different ranges for lifetime cancer

riskb

Average risk (chances in one

million)

aModeled risks are for a 70-year lifetime, based on the predicted outdoor concentration and 

not adjusted for exposure factors.
A-4

Total 

population

Total number 

25 and older

Number 25 and older 

without a high school

diploma

0 to 1 3,230,262 2,090,294 434,891

1 to 5 36,156 23,801 5,424

5 to 10 4,121 2,786 671

10 to 20 528 371 100

20 to 30 221 140 32

30 to 40 166 121 21

40 to 50 0 0 0

Total number 3,271,454 2,117,513 441,140

0.0985 0.0995 0.0985

Notes:

Table A-3. Distribution of Risk for Adults with and without a High School Diploma

Modeled risk from the primary aluminum source 

category

b

Distributions by education level are based on modeling data at the census block level, and 

education data at the block group level. All census blocks in a block group are assumed to 

have the same education level distribution.

Range of lifetime individual cancer

risk (chance in one million)a

Numbers of people in different ranges for lifetime 

cancer riskb

Average risk (chances in one

million)

aModeled risks are for a 70-year lifetime, based on the predicted outdoor concentration and 

not adjusted for exposure factors.
A-5

Total 

population

People living in 

households below 

the national median 

income

c

People living 

below the

poverty line

0 to 1 3,230,262 1,835,838 449,177

1 to 5 36,156 21,879 5,373

5 to 10 4,121 2,724 747

10 to 20 528 343 100

20 to 30 221 136 37

30 to 40 166 97 31

40 to 50 0 0 0

Total number 3,271,454 1,861,017 455,466

0.0985 0.1009 0.1027

Notes:

c

The median income is the national median household income in 1999, about $42,000.

Modeled risk from the primary aluminum source 

category

Average risk (chances in one

million)

aModeled risks are for a 70-year lifetime, based on the predicted outdoor concentration 

and not adjusted for exposure factors.

b

Distributions by income are based on modeling data at the census block level, and income 

data at the block group level. All census blocks in a block group are assumed to have the 

same income distribution.

Table A-4. Distribution of Risk for People Living in Households below the

National Median Income and Below the Poverty Line

Range of lifetime individual 

cancer risk (chance in one

million)a

Numbers of people in different ranges for lifetime 

cancer riskb
A-6

Total 

population

Working aged people 

with a disability

People living in 

linguistic isolation

0 to 1 3,230,262 405,568 34,920

1 to 5 36,156 4,750 359

5 to 10 4,121 589 18

10 to 20 528 85 1

20 to 30 221 31 1

30 to 40 166 22 3

40 to 50 0 0 0

Total number 3,271,454 411,045 35,302

0.0985 0.102 0.0786

Notes:

aModeled risks are for a 70-year lifetime, based on the predicted outdoor concentration 

and not adjusted for exposure factors.

b

Distributions of disability and linguistic isolation are based on modeling data at the census 

block level, and demographic population data at the block group level. All census blocks 

in a block group are assumed to have the same disability and linguistic isolation population 

distributions.

Table A-5. Distribution of Risk for People Living with Disabilities and in 

Linguistic Isolation

Range of lifetime individual 

cancer risk (chance in one

million)a

Numbers of people in different ranges for lifetime 

cancer riskb

Modeled risk from the primary aluminum source 

category

Average risk (chances in one

million)