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Deliberative Draft Document for EPA Internal Review

Draft 

Residual Risk Assessment for the Amino/Phenolic Resins 

Production Source Category

by

EPA’s Office of Air Quality Planning and Standards

Office of Air and Radiation

December 2013
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Table of Contents

1 Introduction ....................................................................................................................4

2 Methods..........................................................................................................................4

2.1 Emissions and Source Data.......................................................................................4

2.2 Dispersion Modeling for Inhalation Exposure Assessment........................................5

2.3 Estimating Human Inhalation Exposure....................................................................8

2.4 Acute Risk Screening and Refined Assessments.......................................................8

2.5 Multipathway and Environmental Risk Screening...................................................10

2.6 Dose-Response Assessment....................................................................................12

2.6.1 Sources of chronic dose-response information.................................................12

2.6.2 Sources of acute dose-response information ....................................................18

2.7 Risk Characterization .............................................................................................20

2.7.1 General............................................................................................................20

2.7.2 Mixtures..........................................................................................................22

2.7.3 Facility-wide Risks..........................................................................................23

2.7.4 MACT-allowable Risk ....................................................................................23

3 Risk Results for the Amino/Phenolic Resins Production Source Category.....................24

3.1 Source Category Description and Results ...............................................................24

3.2 Risk Characterization .............................................................................................26

4 General Discussion of Uncertainties and How They Have Been Addressed...................29

4.1 Exposure Modeling Uncertainties...........................................................................29

4.2 Uncertainties in the Dose-Response Relationships..................................................30

5 References....................................................................................................................39

Index of Tables

Table 2.2-1 AERMOD version 11353 model options for RTR modeling ...............................6

Table 2.6-1(a) Dose-Response Values for Chronic Inhalation Exposure to Carcinogens ......16

Table 2.6-2(a) Dose-Response Values for Chronic Inhalation Exposure to Noncarcinogens.17

Table 2.6-3 Dose-Response Values for Acute Exposure.......................................................20

Table 3.1-1 Summary of Emissions from the Amino/Phenolic Resins Production Source 

Category and Availability of Dose-Response Values............................................................25

Table 3.2-1 Summary of Source Category Level Inhalation Risks for Amino/Phenolic Resins 

Production............................................................................................................................27

Table 3.2-2 Summary of Refined Acute Results for Amino/Phenolic Resins Facilities.........28

Table 3.2-3 Source Category Contribution to Facility-Wide Cancer Risks...........................29

Table 3.2-4 Summary of Environmental Risk Screen Results for the Amino/Phenolic Resins 

Production Source Category .................................................................................................29

Appendices

Appendix 1 Technical Support Document for HEM3 Modeling

Appendix 2 Meteorological Data for HEM3 Modeling

Appendix 3 Analysis of data on short-term emission rates relative to long-term emission 

rates
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Appendix 4 Technical Support Document for TRIM-Based Multipathway Screening 

Scenario for RTR: Summary of Approach and Evaluation

Appendix 5 Detailed Risk Modeling Results

Appendix 6 Acute Impacts Refined Analysis

Appendix 7 Dispersion Model Receptor Revisions and Additions for the Amino/Phenolic 

Resins Production Source Category

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 Protection 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 Standard

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

This document contains the methods and the results of baseline risk assessments (i.e., after the 

implementation of the respective MACT standards) performed for the amino/phenolic resins 

(APR) production source category. The methods discussion includes descriptions of the 

methods used to develop refined estimates of chronic inhalation exposures and human health 

risks for cancer and noncancer endpoints, as well as descriptions of the methods used to 

screen for acute health risks, chronic non-inhalation health risks, and adverse environmental 

effects.

2 Methods

2.1 Emissions and Source Data

The 2005 National-Scale Air Toxics Assessment (NATA) National Emissions Inventory 

(NEI) served as the starting point for this assessment. The 2005 NEI purportedly contains 

information on actual emissions during the entire 2005 base year. Using the process MACT 

codes, we developed a subset of this inventory that contains emissions and facility data for the 

source category. Next, we performed an engineering review of the information using EPA 

engineers who were directly involved in the development of the MACT standards for the 
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source category, and/or who have extensive knowledge of the characteristics of this industry. 

NEI data were updated with available industry supplied data. The goal of the engineering 

review was to identify limitations and issues with the emissions data (particularly those that 

would greatly influence risk estimates) and to make changes to the data where possible to 

address these issues and decrease the uncertainties associated with the assessment.

Details on the development of the emissions and source data for this source category are 

discussed in Section 3. The emissions data and modifications made to the NEI data are 

discussed in a memorandum to the rulemaking docket, entitled Emissions Data and Acute 

Risk Factor Used in Residual Risk Modeling: Acrylic and Modacrylic Fibers, Amino/Phenolic 

Resins, and Polycarbonate Production.

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 (HEM3), which incorporates the American Meteorological Society/EPA Regulatory 

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

modeling system is outlined below, and further details are provided in Appendix 1. The 

HEM3 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 HEM3 system, AERMOD version 11353, is a state-of-thescience 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 HEM3 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 11353 model options for RTR modeling

Modeling Option Selected Parameter for chronic exposure

Type of calculations Hourly Ambient Concentration

Source type Point, area 

Receptor orientation Polar (13 rings and 16 radials)

Discrete (census block centroids) and user-supplied receptors

Terrain characterization Actual from USGS 1/3 Arc Second NED data

Building downwash Not Included

Plume deposition/depletion Not Included

Urban source option No

Meteorology 1 year representative NWS from nearest site (over 200

stations)

In HEM3, meteorological data are ordinarily selected from a list of over 200 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, because 

we had an insufficient number of appropriately formatted model input files derived from 

available meteorological data, 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 HEM3) 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 HEM3 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 effects 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 
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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. For each facility in this 

source category census block locations were carefully evaluated for proximity to each 

facilities property line (see Appendix 7). 

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 ICR contains only annual emission totals, we generally apply the assumption to all 

source categories that the maximum one-hour emission rate from any source is ten times the 

average annual hourly emission rate for that source.

The average hourly emissions rate is defined as the total emissions for a year divided by the 

total number of operating hours in the year. The choice of a factor of ten for acute screening 

was originally based on engineering judgment. To develop a more robust peak-to-mean 

emissions factor, and in response to one of the key messages from the SAB consultation on 

our RTR Assessment Plan, we performed an analysis using a short-term emissions dataset 

from a number of sources located in Texas (originally reported on by Allen et al. 2004)[7]. In 

that report, the Texas Environmental Research Consortium Project compared hourly and 

annual emissions data for volatile organic compounds for all facilities in a heavilyindustrialized 4-county area (Harris, Galveston, Chambers, and Brazoria Counties, TX) over 

an eleven-month time period in 2001. We obtained the dataset and performed our own 

analysis, focusing that analysis on sources which reported emitting high quantities of HAP 

over short periods of time (see Appendix 3, Analysis of data on short-term emission rates 

relative to long-term emission rates). Most peak emission events were less than twice the 

annual average, the highest was a factor of 74 times the annual average, and the 99th

percentile ratio of peak hourly emission rate to the annual hourly emission rate was 9. Based 

on these results, we typically chose a factor of ten for the initial screening. While there have 

been some documented emission excursions above this level, our analysis of the data from the 

Texas Environmental Research Consortium suggests that this factor should cover more than 

99% of the short-term peak gaseous or volatile HAP emissions from typical industrial 

sources. For equipment leak emissions from the APR production source category, we estimate 

that a factor of two is more appropriate. These factors are intended to cover all possible 

hourly peaks associated with routinely-variable emissions. We have no data relating 

specifically to peak short-term emissions of particulate HAP. In the absence of source 

category-specific data, we use this same default approach for particulate emissions as well. 

 

Census block elevations for HEM3 modeling were determined nationally from the US 

Geological Service 1/3 Arc Second National Elevation Dataset, which has a spatial resolution 

of about 10 meters. Elevations of polar grid points used in estimating short- and long-term 

ambient concentrations were assumed to be equal to the highest elevation of any census block 
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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. 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 Human Inhalation Exposure

We used the estimated 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 

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 HAP; it will also reduce risk estimates for gaseous HAP, but typically by much 

less. Second, basing exposure estimates on average ambient 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; 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 theoretical 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 increasing the estimated 

number of people at specific risk levels. 

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 Acute Risk Screening and Refined Assessments

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 under the residual risk program. That is, we 

developed a tiered, iterative approach. This approach to risk assessment was endorsed by the 
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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. 

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 begin 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. 

A refined 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 would be needed 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. 

Acute health risk screening was performed 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 10 times the average 1-hour emission 

rates.

• 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 of local meteorology) was assumed to occur 

at the same time the peak emission rates occur. The recommended EPA local-scale 

dispersion model, AERMOD, is used for simulating atmospheric dispersion.

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

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

meters.

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

HAP being assessed to determine if 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 includes the use of a refined emissions multiplier to estimate the peak hourly 

emission rates from the average rates (rather than the default factor of 10). In other cases, this 

entails determining the actual physical layout and boundaries of a facility to more accurately 

gauge where people might reasonable be exposed for an hour. For the APR production source 

category, the default emissions multiplier of 10 was used for all emission sources except for 
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equipment leaks, for which a factor of two was used. The memorandum entitled, Emissions 

Data and Acute Risk Factor Used in Residual Risk Modeling: Acrylic and Modacrylic Fibers, 

Amino/Phenolic Resins, and Polycarbonate Production, includes a detailed description of 

how the maximum hourly emissions were developed for this source category and can be 

found in the docket for this rule making. We also conducted a review of the layout of 

emission points at the facilities with the facility boundaries to determine the maximum off-site 

acute impact for the facilities that did not screen out during the initial model run. Refer to 

Appendices 5 and 6 for the detailed results for these sites.

2.5 Multipathway and Environmental Risk Screening

The potential for significant human health risks 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)1. The PB-HAP compounds or compound classes are identified 

for the screening from the EPA’s Air Toxics Risk Assessment Library [8]. Examples of PBHAP 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. Emissions of cadmium, lead, and POM were identified in the 

emissions inventories for the APR production source category. 

With respect to PB-HAP emissions other than lead, emissions were evaluated for potential 

non-inhalation risks and adverse environmental impacts using our screening scenario which 

was developed for use with the TRIM.FaTE2 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.3 In 

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 emission rates developed 

for screening potentially significant multi-pathway impacts. For the purpose of developing

emission rates for our multi-pathway screening, we derived emission levels for each PB-HAP 

(other than lead) 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. 

 1 Although the two-letter chemical symbol for lead is Pb, in this assessment PB-HAP refers to the many air 

pollutants known to be persistent and bioaccumulative in the environment. In instances where the report is 

specifically referring to lead, it is spelled out (i.e., the two-letter chemical symbol for lead is not used in this 

document). 2 EPA’s Total Risk Integrated Methodology (General Information) http://epa.gov/ttn/fera/trim_gen.html

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

developing a screening emission rate for them, we compared maximum modeled chronic 

atmospheric concentrations with the current National Ambient Air Quality Standard 

(NAAQS) for lead. Values below the NAAQS were considered to have a low potential for 

multi-pathway risks.

The EPA has developed a screening approach to examine the potential for adverse 

environmental effects as required under section 112(f)(2)(A) of the CAA. The environmental 

screen focuses on the following seven environmental HAP: 

• Five persistent bioaccumulative HAP (PB-HAP) – cadmium, dioxins/furans, 

polycyclic organic matter (POM), mercury (both inorganic mercury and 

methyl mercury) and lead.

• Two acid gases - hydrogen chloride (HCl), and hydrogen fluoride (HF). 

HAP that persist and bioaccumulate are of particular environmental concern because they 

accumulate in the soil, sediment and water. The acid gases, HCl and HF, were included due to 

their well-documented potential to cause direct damage to terrestrial plants.

For the environmental risk screening analysis, EPA first determined whether any facilities in 

the source category emitted any of the seven environmental HAP. If one or more of the seven 

environmental HAP evaluated are emitted by at least one facility in the source category we 

proceed to the second step of the evaluation.

For cadmium, mercury, POM and dioxins/furans, the environmental screening analysis 

consists of two tiers. In the first tier, TRIM.FaTE modeling was conducted under worst-cast 

environmental conditionals to determine whether the facility-specific off-site emission rates 

of each of the emitted environmental HAP exceeded ecological benchmarks represented as 

emissions threshold values. If off-site emissions from a facility do not exceed the Tier 1 

thresholds the facility “passes” the screen, and therefore, is not evaluated further under the 

screening approach. If off-site emissions from a facility exceed the Tier 1 thresholds, we 

evaluate the facility further in Tier 2.

In Tier 2 of the environmental screening analysis, the screening emission thresholds are 

adjusted to account for local meteorology and the actual location of lakes in the vicinity of 

facilities that did not pass the Tier 1 screen. If off-site emissions from a facility do not exceed 

the Tier 2 thresholds, the facility passes the screen, and is typically not evaluated further. If 

off-site emissions from a facility exceed the Tier 2 thresholds, the facility does not pass the 

screen and, therefore, may have the potential to cause adverse environmental effects. Such 

facilities are evaluated further to investigate factors such as the magnitude and characteristics 

of the area of exceedance. 

For acid gases, the environmental screening analysis evaluates the potential phytotoxicity and 

reduced productivity of plants due to chronic exposure to acid gases. The environmental risk 

screening methodology for acid gases is a single-tier screen that compares the average off-site 

ambient air concentration over the modeling domain to ecological benchmarks for each of the 
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acid gases. For purposes of ecological risk screening, EPA identifies a potential for adverse 

environmental effects to plant communities from exposure to acid gases when the average 

concentration of the HAP around a facility exceeds the LOAEL ecological benchmark. In 

such cases, we further investigate factors such as the magnitude and characteristics of the area 

of exceedance (e.g., land use of exceedance area, size of exceedance area) to determine if 

there is an adverse environmental effect. 

For lead compounds, we currently do not have the ability to calculate media concentrations 

using the TRIM.FaTE model. However, air concentrations of lead are already calculated as 

part of the human health exposure and risk analysis using the HEM-3 AERMOD air 

dispersion model. To evaluate the potential for adverse environmental effects from lead, we 

compare the average modeled air emission concentrations of lead from each facility in the 

source category emissions to the level of the secondary NAAQS for lead. The secondary lead 

NAAQS is a reasonable means of evaluating environmental risk because it is set to provide 

substantial protection against adverse welfare effects which can include “effects on soils, 

water, crops, vegetation, man-made materials, animals, wildlife, weather, visibility and 

climate, damage to and deterioration of property, and hazards to transportation, as well as 

effects on economic values and on personal comfort and well-being.”4

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 HAPs reported in the emissions inventory for the APR 

production source category were based on the EPA Office of Air Quality Planning and 

Standards’ existing recommendations for HAPs [9], also used for NATA [10]. 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 

 4 A secondary standard, as defined in Section 109(b)(2), must “specify a level of air quality the attainment and 

maintenance of which, in the judgment of the Administrator, based on criteria, is requisite to protect the public 

welfare from any known or anticipated adverse effects associated with the presence of [the] pollutant in the 

ambient air.” Welfare effects as defined in section 302(h) (42 U.S.C. 7602(h)) include, but are not limited to, 

“effects on soils, water, crops, vegetation, man-made materials, animals, wildlife, weather, visibility and climate, 

damage to and deterioration of property, and hazards to transportation, as well as effects on economic values and 

on personal comfort and well-being.”
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sensitive subgroups) that is likely to be without an appreciable risk of deleterious effects 

during a lifetime.” The RfD is “an estimate (with uncertainty spanning perhaps an order 

of magnitude) of a daily oral 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/m3 in 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 dose-response 

assessment information in several forms, based on the level of review. The Integrated 

Risk Information System (IRIS) [11] 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 

Risk Levels (MRLs) [12] 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 
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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 [13] and NAS [14].” The non-cancer information 

includes available inhalation health risk guidance values expressed as chronic inhalation 

reference exposure levels (RELs) [15]. 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 [16], 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 metal 

compounds and POM, we apply category-specific chemical speciation profiles appropriate 

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

3) In 2004, the EPA determined that the Chemical Industry Institute of Toxicology (CIIT)

cancer dose-response value for formaldehyde (5.5 x 10-9 per μg/m3

) was based on better 

science than the IRIS cancer dose-response value (1.3 x 10-5 per μg/m3

), and we switched 

from using the IRIS value to the CIIT value in risk assessments supporting regulatory 

actions. Subsequent research published by EPA suggested that the CIIT model was not 

appropriate and in 2010 EPA returned to using 1991 IRIS value. EPA has been working 

on revising the formaldehyde IRIS assessment and the National Academy of Sciences 

(NAS) completed its review of the EPA’s draft assessment in April of 2011.

6 EPA will 

follow the NAS Report recommendations and will present results obtained by 

implementing the biologically-based dose-response (BBDR) model for formaldehyde. 

EPA will compare these estimates with those currently presented in the External Review 

draft of the assessment and will discuss their strengths and weaknesses. As recommended 

by the NAS committee, appropriated sensitivity and uncertainty analyses will be an 

integral component of implementing the BBDR model. In the interim, we will present 

findings using the 1991 IRIS value as a primary estimate, and EPA may also consider 

other information as the science evolves. 

 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) 6 http://www.nap.edu/catalog.php?record_id=13142
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4) In the case of nickel compounds, to provide a health-protective estimate of potential 

cancer risks, we used the IRIS URE value for nickel subsulfide in this assessment. Based 

on past scientific and technical considerations, the determination of the percent of nickel 

subsulfide was considered a major factor for estimating the extent and magnitude of the 

risks of cancer due to nickel-containing emissions. Nickel speciation information for 

some of the largest nickel-emitting sources (including oil combustion, coal combustion, 

and others) suggested that at least 35 percent of total nickel emissions may be soluble 

compounds and that the URE for the mixture of inhaled nickel compounds (based on 

nickel subsulfide, and representative of pure insoluble crystalline nickel) could be derived 

to reflect the assumption that 65 percent of the total mass of nickel may be carcinogenic. 

Based on consistent views of major scientific bodies (i.e., National Toxicology Program 

(NTP) in their 12th Report of the Carcinogens (ROC)

7

, International Agency for Research 

on Cancer (IARC)

8

, and other international agencies)9 that consider all nickel compounds 

to be carcinogenic, we currently consider all nickel compounds to have the potential of 

being as carcinogenic to humans. The 12th Report of the Carcinogens states that the 

“combined results of epidemiological studies, mechanistic studies, and carcinogenic 

studies in rodents support the concept that nickel compounds generate nickel ions in target 

cells at sites critical for carcinogenesis, thus allowing consideration and evaluation of 

these compounds as a single group.” Although the precise nickel compound (or 

compounds) responsible for the carcinogenic effects in humans is not always clear, studies 

indicate that nickel sulfate and the combinations of nickel sulfides and oxides encountered 

in the nickel refining industries cause cancer in humans (these studies are summarized in a 

review by Grimsrud et al., 201010). The major scientific bodies mentioned above have 

also recognized that there are differences in toxicity and/or carcinogenic potential across 

the different nickel compounds. In this inhalation risk assessment, to take a conservative 

approach, we have considered all nickel compounds to be as carcinogenic as nickel 

subsulfide and have applied the IRIS URE for nickel subsulfide without a factor to reflect 

the assumption that 100 percent of the total mass of nickel may be as carcinogenic as pure 

nickel subsulfide. In addition, given that there are two additional URE11 values derived 

for exposure to mixtures of nickel compounds, as a group, that are 2-3 fold lower than the 

IRIS URE for nickel subsulfide, the EPA also considers it reasonable to use a value that is 

50 percent of the IRIS URE for nickel subsulfide for providing an estimate of the lower 

end of the plausible range of cancer potency values for different mixtures of nickel 

compounds. 

 7 National Toxicology Program (NTP), 2011. Report on carcinogens. 12th ed. Research Triangle Park, NC: US 

Department of Health and Human Services (DHHS), Public Health Service. Available online at 

http://ntp.niehs.nih.gov/ntp/roc/twelfth/roc12.pdf

8 International Agency for Research on Cancer (IARC), 1990. IARC monographs on the evaluation of 

carcinogenic risks to humans. Chromium, nickel, and welding. Vol. 49. Lyons, France: International Agency 

for Research on Cancer, World Health Organization Vol. 49:256.

9 World Health Organization (WHO, 1991) and the European Union’s Scientific Committee on Health and 

Environmental Risks (SCHER, 2006). 10 Grimsrud TK and Andersen A. Evidence of carcinogenicity in humans of water-soluble nickel salts. J Occup 

Med Toxicol 2010, 5:1-7. Available online at http://www.ossup-med.com/content/5/1/7 . 11 Two UREs (other than the current IRIS values) have been derived for nickel compounds as a group: one 

developed by the California Department of Health Services 

(http://www.arb.ca.gov/toxics/id/summary/nickel_tech_b.pdf) and the other by the Texas Commission on 

Environmental Quality (http://www.epa.gov/ttn/atw/nata1999/99pdfs/healtheffectsinfo.pdf).
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5) 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 are used in NATA [17]. 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. 

The emissions inventory for the APR production source category includes emissions of 24 

HAP with available chronic quantitative inhalation dose-response values. Of these, ten are 

classified as known or probable carcinogens, with quantitative cancer dose-response values 

available. These HAP, their quantitative inhalation chronic cancer dose-response values, and 

the source of each value are listed in Table 2.6-1(a). Twenty-four HAP have quantitative 

inhalation chronic noncancer threshold values available. These HAP, their threshold values, 

and the source of the value are listed in Tables 2.6-2(a).

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

URE (unit risk estimate for cancer)12 = cancer risk per μg/m3 of average lifetime exposure. 

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

Research & Development, EPA OAQPS = EPA Office of Air Quality Planning & 

Standards, CAL = California EPA Office of Environmental Health Hazard Assessment. 

HEAST = EPA Health Effects Assessment Tables, Conv. Oral = Oral unit risk converted to 

inhalation.

Pollutant CAS 

Number13

URE11

(1/μg/m3)

Source

Acetaldehyde 75070 0.0000022 IRIS

Benzene14 71432 0.0000078 IRIS

Cadmium compounds 7440439 0.0018 IRIS

Chromium compounds 7440473 0.012 IRIS

Chromium (VI) compounds 18540299 0.012 IRIS

Ethyl benzene 100414 0.0000025 CAL

Formaldehyde15 50000 0.000013 IRIS

Nickel compounds 7440020 0.00048 ATSDR

PAH, Total 187 0.000088 CAL

Polycyclic Organic Matter16 187 0.000088 CAL

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

agent at a concentration of 1 μg/m3 in air. URE’s are considered upper bound estimates meaning they represent 

a plausible upper limit to the true value. 

13 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. 14 The EPA IRIS assessment for benzene provides a range of plausible UREs. This assessment used the highest 

value in that range, 7.8E-06 per ug/m3

. The low end of the range is 2.2E-06 per ug/m3. 15 The EPA has used the CIIT URE value, 5.5X10-9 per mg/m3

, to characterize formaldehyde cancer risk in 

some instances.
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Table 2.6-2(a) 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, HEAST = EPA Health Effects Assessment Tables, EPA OAQPS = EPA Office of Air 

Quality Planning & Standards, EPA ORD = EPA Office of Research and Development

Pollutant CAS Number12 RfC 

(mg/m3)

Source17

Acetaldehyde 75070 0.009 IRIS - L

Benzene 71432 0.03 IRIS - M

Cadmium compounds 7440439 0.00001 ATSDR

Chromium compounds 7440473 0.0001 IRIS - M

Chromium (VI) compounds 18540299 0.0001 IRIS - M

Cresols (mixed) 1319773 0.6 CAL

Ethyl benzene 100414 1 IRIS - L

Ethylene Glycol 107211 0.4 CAL

Ethylene Glycol Ethyl Ether Acetate 111159 0.3 CAL

Formaldehyde18 50000 0.0098 ATSDR

Lead Compounds 7439921 0.00015 EPA OAQPS

Maleic Anhydride 108316 0.0007 CAL

M-cresol (3-methylphenol) 108394 0.6 CAL

Methanol 67561 4 CAL

Methyl Isobutyl Ketone 108101 3 IRIS - L/M

Nickel compounds 7440020 0.00009 ATSDR

N-hexane 110543 0.7 IRIS - M

O-cresol 95487 0.6 CAL

P-cresol (4-methy Phenol) 106445 0.6 CAL

Phenol 108952 0.2 CAL

Styrene 100425 1 IRIS - M

Toluene 108883 5 IRIS - H

Triethylamine 121448 0.007 IRIS - L

Xylenes (mixed) 1330207 0.1 IRIS - M

 16 POM without a chemical-specific URE are assigned a URE associated with a mixture of POM compounds 

having similar characteristics. Details of this method, also used in the 2002 National Air Toxics Assessment, are 

available at http://www.epa.gov/ttn/atw/nata2002/02pdfs/pom_approach.pdf. 17 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 IRIS.

18 The EPA has used the CIIT URE value, 5.5X10-9 per mg/m3

, to characterize formaldehyde cancer risk in 

some instances.
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2.6.2 Sources of acute dose-response information 

Hazard identification and dose-response assessment information for preliminary acute 

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

HAPs [18]. 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 a newly released EPA document [19]. 

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. [20]. 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 

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 that “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 
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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.”

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

Association (AIHA) has developed Emergency Response Planning Guidelines (ERPGs) [21] 

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/getinvolved/AIHAGuidelineFoundation/EmergencyResponsePlanningGuidelines/Pages/default.a

spx) 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:
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“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 APR production source category includes emissions of 14

HAP with relevant and available quantitative acute dose-response threshold values. These 

HAP, their acute threshold values, and the source of the value are listed below in Table 2.6-3.

Table 2.6-3 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 

Acetaldehyde 75070 81 490 18 360 0.47

Benzene 71432 170 2600 160 480 1.3

Ethyl benzene 100414 140 4800

Ethylene glycol ethyl ether 

acetate 111159 0.14

Formaldehyde 50000 1.1 17 1.2 12 0.055

Maleic anhydride 108316 0.8 8

Methanol 67561 690 2700 260 1300 28

n-Hexane 110543 12000

Nickel compounds 7440020 0.0002

Phenol 108952 58 89 38 190 5.8

Styrene 100425 85 550 210 1100 21

Toluene 108883 750 4500 190 1100 37

Triethylamine 121448 2.8

Xylenes (mixed) 1330207 560 4000 22

2.7 Risk Characterization

2.7.1 General

The final product of the risk assessment is the risk characterization, in which the information 

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

1995 Policy for Risk Characterization [22] 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 [23], in 2002 by the Agency’s information quality guidelines [24], and in the 

OMB/OSTP September 2007 Memorandum on Updated Principles for Risk Analysis19, 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 the 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 [25], we applied 

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

Carcinogens [26]. 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 [27] 

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

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

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 

 19Memorandum 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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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.

In the case of benzene, the high end of the reported cancer URE range was used in our 

assessments to provide a conservative estimate of potential cancer risks. Use of the high end 

of the range provides risk estimates that are approximately 3.5 times higher than use of the 

equally plausible low end value. If the estimated benzene–associated risks exceed 1 in a 

million, we also evaluated the impact of using the low end of the URE range on our risk 

results.

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 if 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’ 

[28,29] default assumption of additivity of effects, and combine risks by summing them using 

the independence formula in the mixtures guidelines.
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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 

guidelines support aggregating effects of different substances in specific and limited ways. 

To conform to 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 examined “facility-wide” risks using 

ICR data and modeling as described in Section 2.2. For the facilities in the APR production

source category, we estimated the maximum inhalation cancer and chronic noncancer risks 

associated with all HAP emissions sources at the facility, including emissions sources that are 

not part of the source categories but are located within a contiguous area and are under 

common control. We analyzed risks due to the inhalation of HAP for the populations residing 

within 50 kilometers of each facility. The results of the facility-wide assessment are 

summarized below in the Risk characterization section of this document. The complete 

results of the facility-wide assessment are provided in Appendix 5.

2.7.4 MACT-allowable Risk

The emissions data in the data set for the APR production source category are estimates of 

actual emissions on an annual basis. The risk results presented in the following sections are 

based on these actual emissions. To estimate emissions at the MACT-allowable level, a ratio 

of MACT-allowable to actual emissions for each source type was developed. This ratio was 

based on the level of control required by the MACT standard compared to the level of 

reported actual emissions and available information from the ICR on the level of control 

achieved by the emissions controls in use. The memorandum entitled, MACT Allowable 

Emissions and Risks for the Acrylic and Modacrylic Fibers, Amino/Phenolic Resins, and 
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Polycarbonate Production Source Categories, includes a detailed discussion on the 

development of the MACT-allowable emissions (available in the docket for this rule making).

3 Risk Results for the Amino/Phenolic Resins Production 

Source Category

3.1 Source Category Description and Results

The APR production source category consists of facilities that manufacture amino or phenolic 

resins. These two products can be broadly classified as formaldehyde-based thermosetting 

resins. Amino/phenolic resins are used in the manufacture of plywood, particle board, 

adhesives, wood furniture, and plastic parts. The hazardous air pollutant (HAP) emission 

sources at amino/phenolic resins production facilities include storage vessels, continuous 

process vents, batch process vents, equipment leaks, and heat exchange systems. In the 

production of amino/phenolic resins, HAP are primarily used as reactants or extraction 

solvents. 

We currently estimate that there are 18 APR production facilities operating in the U.S. The 

data set contains 18 facilities identified with a Polymer & Resins III (amino/phenolic resins)

MACT code 1347 in the 2005 National-scale Air Toxics Assessment (NATA) National 

Emissions Inventory (NEI). The 2005 NATA NEI was updated for this category with data 

from industry and enhanced quality assurance of facility and emission locations. All 18 of 

these facilities are identified as major sources in the NEI. 

The emissions for the APR production source category data set (of 18 facilities) are 

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

approximately 380 tons per year. Based on these data, the HAP emitted in the largest 

quantities across these 18 facilities are methanol, formaldehyde, phenol, xylenes (mixed), 

triethylamine, and maleic anhydride. These 6 chemicals account for 99 percent of the total 

emissions by mass. Three PB-HAP20 are reported to be emitted from this source category: 

cadmium compounds, polycyclic organic matter (analyzed as benzo(a)pyrene), and lead 

compounds. These HAP are also considered to be environmental HAP.

 20 Persistent and bioaccumulative HAP are defined in the EPA’s Air Toxics Risk Assessment Library, Volume 1, 

EPA-453K-04-001A, as referenced in the NPRM and provided on the EPA’s Technology Transfer Network 

website for Fate, Exposure, and Risk Assessment at http://www.epa.gov/ttn/fera/risk_atra_vol1.html. 
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Table 3.1-1 Summary of Emissions from the Amino/Phenolic Resins Production Source Category and 

Availability of Dose-Response Values

HAPa Emissions 

(tpy)

Number of 

Facilities 

Reporting 

HAP (12

facilities in 

data set)

Prioritized Inhalation Dose-Response 

Value Identified by OAQPSb

PBHAP?

Unit 

Risk 

Estimate 

for 

Cancer?

Reference 

Concentration 

for 

Noncancer?

Health 

Benchmark 

Values for 

Acute 

Noncancer?

Methanol 261 16    

Formaldehyde 56 15    

Phenol 50 12   

Xylenes (mixed) 7 1   

Triethylamine 3 5   

Maleic Anhydride 3 1    

Ethyl Benzene 1 1    

Acetaldehyde 0.2 1    

O-cresol 0.2 1  

N-hexane 0.2 2   

Toluene 0.1 3   

Ethylene Glycol Ethyl Ether Acetate 0.04 1    

Ethylene Glycol 0.03 1   

Benzene 0.02 3    

M-cresol (3-methylphenol) 0.007 1   

Styrene 0.005 1   

Polycyclic Organic Matter 

 Polycyclic organic matter 0.004 1   

 PAH, Total 0.00007 1   

P-cresol (4-methy Phenol) 0.003 1  

Cresols (mixed) 0.002 1  

Methyl Isobutyl Ketone 0.0005 1   

Nickel Compounds 0.0001 2    

Cadmium Compounds 0.0001 2    

Chromium compounds

 Chromium (III) Compounds 0.00008 1

 Chromium (VI) Compounds 0.00003 1   

Lead Compounds 0.00001 1 c  a Notes for how HAP were speciated for risk assessment:

• For most metals, emissions reported as the elemental metal are combined with metal compound emissions (e.g., 

“cadmium” emissions modeled as “cadmium & compounds”). In the absence of speciation information, we assume 

the reported mass is 100 percent metal.

• For emissions reported generically as “chromium” or “chromium & compound,” emissions are speciated 75% 

“chromium (III) compounds” and 25% “chromium (VI) compounds” for this source category. Chromium 

speciation profiles can be found on the EPA’s Technology Transfer Network website for emissions inventories at 

http://www.epa.gov/ttn/chief/net/2005inventory.html.

Notes for Summary of Emissions from the Polymers & Resins III Source Category table: 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 and are also available in Appendix 3 of Methodology for 
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Developing Preliminary Risk Estimates for Source Categories Previously Subjected to Technology-based Standards (U.S. 

EPA Office of Air Quality Planning and Standards. EPA-HQ-OAR-2006-0859-0102. April 2007). c There is no reference concentration for lead. In considering noncancer hazards for lead in this assessment, we compared 

maximum annual ambient exposure estimates to the National Ambient Air Quality Standards (NAAQS) for lead (0.15 

μg/m3

). These NAAQS for lead were reviewed with revisions adopted in October 2008 

(http://www.epa.gov/air/lead/actions.html). The primary (health-based) standard is a maximum or not-to-be-exceeded, rolling 

three-month average, measured as total suspended particles (TSP). The secondary (welfare-based) standard is identical to the 

primary standard.

3.2 Risk Characterization

This section presents the results of the risk assessment for the APR production source 

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

individual lifetime cancer risk, the maximum chronic hazard index, and the cancer incidence. 

We also present results from our acute inhalation impact screening in the form of maximum 

hazard quotients, as well as the results of our preliminary 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 hazard index 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 indicate that maximum lifetime individual cancer risks could 

be up to 9 in a million, driven by maleic anhydride emissions from equipment leaks. 

Approximately 6,300 people were estimated to have cancer risks above 1 in a million. The 

total estimated cancer incidence from this source category is 0.001 excess cancer cases per 

year, or one excess case in every 1,000 years. The estimated maximum chronic noncancer 

target organ specific hazard index (TOSHI) value for the source category is 0.2, indicating 

low potential for chronic noncancer impacts.

 

To identify potential multipathway health risks from PB-HAP other than lead, we first 

performed a screening analysis (Tier 1) that compared emissions of PB-HAP emitted from 

the source category (based on actual emissions) to screening emission rates (see section 2.5).

The PB-HAP emitted by facilities in this source category include cadmium compounds (2 

facilities), POM (analyzed as benzo(a)pyrene TEQ) (2 facilities), and lead compounds (1 

facility). No facilities emit cadmium compounds or POM above the respective threshold 

emissions levels. Therefore, there is low potential for multipathway health risks from 

emissions of these HAP. Lead is a PB-HAP, but the National Ambient Air Quality Standards 

(NAAQS) value (which was used for the chronic noncancer risk assessment) takes into 

account multipathway exposures, so a separate multipathway screening value was not 

developed. Since we did not estimate any exceedances of the NAAQS in our chronic 

noncancer risk assessment, the potential is low for multipathway exposure and risk due to lead 

emissions from these facilities.
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Table 3.2-1 Summary of Source Category Level Inhalation Risks for Amino/Phenolic Resins 

Production

Result HAP “Drivers”

Facilities in Source Category

Number of Facilities Estimated to be in Source 

Category 18 n/a

Number of Facilities Identified in the NEI and 

Modeled in Preliminary Risk Assessment 18 n/a

Cancer Risks

Maximum Individual Lifetime Cancer Risk (in 1 

million) 9 formaldehyde

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 0 n/a

Greater than or equal to 1 in 1 million 7 formaldehyde

Chronic Noncancer Risks

Maximum Respiratory Hazard Index 0.2 maleic anhydride

Number of Facilities with Maximum Respiratory Hazard Index:

Greater than 1 0 n/a

Acute Noncancer Refined Results

Maximum Acute Hazard Quotient

50

3

5

0.2

0.5

formaldehyde (REL)

formaldehyde (AEGL-1)

maleic anhydride (ERPG-1)

formaldehyde (AEGL-2)

maleic anhydride (ERPG-2)

Number of Facilities With Potential for Acute 

Effects 9 formaldehyde, maleic anhydride

Population Exposure

Number of People Living Within 50 Kilometers 

of Facilities Modeled 16,000,000 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 0 n/a

Greater than or equal to 1 in 1 million 6,300 n/a

Number of People Exposed to Noncancer Respiratory Hazard Index:

Greater than 1 0 n/a

Estimated Cancer Incidence (excess cancer cases 

per year) 0.001 n/a

Contribution of HAP to Cancer Incidence:

 formaldehyde 99% n/a
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Table 3.2-2 Summary of Refined Acute Results for Amino/Phenolic Resins Facilities 

Refined Results

MAXIMUM ACUTE HAZARD 

QUOTIENTS

ACUTE DOSE-RESPONSE VALUES

Based on 

REL

Based on 

AEGL-1/

ERPG-1

Based on 

AEGL-2/

ERPG-2

REL

(mg/m3)

AEGL-1 

(1-hr) 

(mg/m3

)

ERPG-1 

(mg/m3)

AEGL-2

(1-hr)

(mg/m3

)

ERPG-2 

(mg/m3)

HAP Max. 1-

hr. Air 

Conc. 

(mg/m3

)

formaldehyde 0.6 10 0.5 / 0.5 0.04 / 0.05 0.055 1.1 1.2 17 12

maleic anhydride 0.6 na na / 0.8 na / 0.08 0.8 8

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.

The facility-wide MIR and TOSHI, available in Appendix 5, are based on emissions from all 

emissions sources at the identified facilities. The results of the facility-wide assessment are 

summarized in Table 3.2-3. The results indicate that there are ten facilities with APR 

production processes that have facility-wide cancer MIR greater than or equal to 1 in a 

million. The maximum facility-wide MIR is 30 in a million, with APR production

contributing approximately 2 percent of the risk. The remaining risk is driven by emissions of 

formaldehyde from processes other than APR production. The maximum facility-wide 

TOSHI is 0.6, driven by emissions of manganese compounds from unspecified processes. 

The source category contributes approximately 2 percent to the facility-wide TOSHI.
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Table 3.2-3 Source Category Contribution to Facility-Wide Cancer Risks 

Amino/Phenolic Resins

Number of Facilities Binned by Facility-Wide 

MIR (in 1 million)

Source Category MIR Contribution 

to Facility-Wide MIR

<1 1≤ MIR<10 10≤ MIR<100 > 100 Total

> 90% 2 3 0 0 5

50-90% 2 4 0 0 6

10-50% 0 0 0 0 0

< 10% 4 1 2 0 7

Total 8 8 2 0 18

We conducted a screening-level evaluation of the potential adverse environmental risks 

associated with emissions of the following environmental HAP from the APR production 

source category: lead, cadmium, and POM. The results of the environmental screening 

analysis are summarized in Table 3.2-4. In our Tier 1 analysis, emissions of POM and 

cadmium did not exceed the threshold emission rates for any of the ecological benchmarks 

(either the LOAEL or NOAEL) for any facility in the source category. Therefore, we did not 

conduct a Tier 2 environmental screening analysis. For lead, modeled air concentrations did 

not exceed the secondary lead NAAQS for any individually modeled point for any facility. 

Table 3.2-4 Summary of Environmental Risk Screen Results for the Amino/Phenolic 

Resins Production Source Category

Environmental 

HAP

Number of Facilities In Category 

Exceeding

Percent of Modeled 

Area in Category 

Exceeding2

Tier 1 Screen Tier 2 Screen1

NOAEL LOAEL NOAEL LOAEL NOAEL LOAEL

PBHAP

Pb None None None None 0% 0%

Cd None None None None 0% 0%

POM None None None None 0% 0%

NA – Not Applicable. 

1- Tier 2 screen is performed for PB-HAP when there are exceedances of the Tier 1 screen.

2 - A value of 0% indicates that none of the modeled data points exceeded the benchmark. For PB-HAP the 

percent area is based on the Tier 2 results, if a Tier 2 analysis is performed. Otherwise, the percent area is based 

on the Tier 1 results.

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

estimates. The chronic ambient modeling uncertainties are considered relatively small in 

comparison, since we are using EPA’s refined local dispersion model with site-specific 

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. Furthermore, 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, 

we have refined our assessment by developing a better understanding of the geography of the 

facility relative to potential exposure locations and the true variability of short-term emission 

rates. 

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.
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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 [30] (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 

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).21 In some circumstances, the true risk 

 21 IRIS glossary (www.epa.gov/NCEA/iris/help_gloss.htm).
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could be as low as zero; however, in other circumstances the risk could also be greater.22 

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.23 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 [31]. 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 is 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 

tumors would occur in humans.24 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 

 22 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. 23 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. 24 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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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 

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. 
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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 [32] 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) [33]. 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 

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 
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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 values25 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. 

These values provide an estimate (with uncertainty spanning perhaps an order of magnitude) 

of daily oral exposure (RfD) or of 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.26 To derive values that are intended to be “without 

appreciable risk,” EPA’s methodology relies upon an uncertainty factor (UF) approach 

[34],[35] 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 

 25 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.

26 See IRIS glossary
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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 [36]. 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., inter-individual 

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.

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.
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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.

There is no RfD or other comparable chronic health benchmark value for lead compounds. 

Thus, to address multipathway human health and environmental risks associated with 

emissions of lead from these facilities, ambient lead concentrations were compared to the 

NAAQS for lead. In developing the NAAQS for lead, EPA considered human health 

evidence reporting adverse health effects associated with lead exposure, as well as an EPA 

conducted multipathway risk assessment that applied models to estimate human exposures to 

air-related lead and the associated risk (73FR at 66979). EPA also explicitly considered the 

uncertainties associated with both the human health evidence and the exposure and risk 

analyses when developing the NAAQS for lead. For example, EPA considered uncertainties 

in the relationship between ambient air lead and blood lead levels (73FR at 66974), as well as 

uncertainties between blood lead levels and loss of IQ points in children (73FR at 66981). In 

considering the evidence and risk analyses and their associated uncertainties, the EPA 

Administrator noted his view that there is no evidence- or risk-based bright line that indicates 

a single appropriate level. Instead, he noted, there is a collection of scientific evidence and 

judgments and other information, including information about the uncertainties inherent in 
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many relevant factors, which needs to be considered together in making this public health 

policy judgment and in selecting a standard level from a range of reasonable values (73FR at 

66998). In so doing, the Administrator decided that, a level for the primary lead standard of

0.15 μg/m3, in combination with the specified choice of indicator, averaging time, and form, 

is requisite to protect public health, including the health of sensitive groups, with an adequate 

margin of safety (73FR at 67006). A thorough discussion of the health evidence, risk and 

exposure analyses, and their associated uncertainties can be found in EPA’s final rule revising 

the lead NAAQS (73 FR 66970-66981, November 12, 2008). 

We also note the uncertainties associated with the health-based (i.e., primary) NAAQS are 

likely less than the uncertainties associated with dose-response values developed for many of 

the other HAP, particularly those HAP for which no human health data exist. In 1988, EPA’s 

IRIS program reviewed the health effects data regarding lead and its inorganic compounds 

and determined that it would be inappropriate to develop an RfD for these compounds, 

saying, “A great deal of information on the health effects of lead has been obtained through 

decades of medical observation and scientific research. This information has been assessed in 

the development of air and water quality criteria by the Agency's Office of Health and 

Environmental Assessment (OHEA) in support of regulatory decision-making by the Office 

of Air Quality Planning and Standards (OAQPS) and by the Office of Drinking Water 

(ODW). By comparison to most other environmental toxicants, the degree of uncertainty 

about the health effects of lead is quite low. It appears that some of these effects, particularly 

changes in the levels of certain blood enzymes and in aspects of children's neurobehavioral 

development, may occur at blood lead levels so low as to be essentially without a threshold. 

The Agency's RfD Work Group discussed inorganic lead (and lead compounds) at two 

meetings (07/08/1985 and 07/22/1985) and considered it inappropriate to develop an RfD for 

inorganic lead.” EPA’s IRIS assessment for Lead and compounds (inorganic) (CASRN 7439-

92-1), http://www.epa.gov/iris/subst/0277.htm. 

We note further that because of the multi-pathway, multi-media impacts of lead, the risk 

assessment supporting the NAAQS considered direct inhalation exposures and indirect airrelated multi-pathway exposures from industrial sources like primary and secondary lead 

smelting operations. It also considered background lead exposures from other sources (like 

contaminated drinking water and exposure to lead-based paints). In revising the NAAQS for 

lead, we note that the Administrator placed more weight on the evidence-based framework 

and less weight on the results from the risk assessment, although he did find the risk estimates 

to be roughly consistent with and generally supportive of the evidence-based framework 

applied in the NAAQS determination (73FR at 67004). Thus, when revising the NAAQS for 

lead to protect public health with an adequate margin of safety, EPA considered both the 

evidence-based framework and the risk assessment, albeit to different extents.

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 
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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. 

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. Allen, D., C. Murphy, Y. Kimura, W. Vizuete, T. Edgar, H. Jeffries, B.-U. Kim, M. Webster, 

and M. Symons, 2004. Variable industrial VOC emissions and their impact on ozone formation 

in the Houston Galveston Area. Final Report: Texas Environmental Research Consortium 

Project H-13. http://files.harc.edu/Projects/AirQuality/Projects/H013.2003/H13FinalReport.pdf

8. 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.
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9. 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

10. US EPA, 2005. 1999 National Air Toxics Risk Assessment. 

http://www.epa.gov/ttn/atw/nata1999

11. US EPA, 2006. Integrated Risk Information System. http://www.epa.gov/iris/index.html.

12. US Agency for Toxic Substances and Disease Registry. 2006. Minimum Risk Levels (MRLs) 

for Hazardous Substances. http://www.atsdr.cdc.gov/mrls/index.html.

13. 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.

14. NRC, 1994. National Research Council. Science and Judgment in Risk Assessment. 

Washington, DC: National Academy Press.

15. 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. 

16. 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.

17. 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.

18. 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

19. U.S. EPA, 2009. Graphical Arrays of Chemical-Specific Health Effect Reference Values for 

Inhalation Exposures [Final Report]. EPA/600/R-09/061, 2009. 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=211003

20. 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

21. American Industrial Hygiene Association , 2009. Current AIHA ERPG Values. 

http://www.aiha.org/insideaiha/volunteergroups/Documents/ERP-erpglevels.pdf.

22. US EPA, 1995. Guidance for Risk Characterization. Science Policy Council. 

http://www.epa.gov/OSA/spc/pdfs/rcguide.pdf.

23. US EPA, 2000. Risk Characterization Handbook. EPA 100-B-00-002.
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24. 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

25. 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

26. 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.

27. 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

28. 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

29. 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.

30. US EPA, 2005. Guidelines for Carcinogen Risk Assessment (2005). U.S. Environmental 

Protection Agency, Washington, DC, EPA/630/P-03/001F, 2005. 

http://cfpub.epa.gov/ncea/raf/recordisplay.cfm?deid=116283

31. US EPA, 2000. Risk Characterization Handbook. EPA 100-B-00-002.

32. NRC (National Research Council) 2006. Assessing the Human Health Risks of 

Trichloroethylene. National Academies Press, Washington DC.

33. 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.

34. US EPA. 1993. Reference Dose (RfD): Description and Use in Health Risk Assessments. 

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

35. US EPA. 1994. Methods for Derivation of Inhalation Reference Concentrations and 

Application of Inhalation Dosimetry. 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=71993.

36. US EPA. 2002. A Review of the Reference Dose and Reference Concentration Processes. 

http://www.epa.gov/ncea/iris/RFD_FINAL1.pdf
Appendix 1 

Technical Support Document for HEM-3 Modeling 
Modeling for the Residual Risk and Technology Review 

Using the Human Exposure Model 3 – AERMOD Version

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 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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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-3 Modeling 
 

2

DRAFT 

METEOROLOGICAL DATA PROCESSING 

USING AERMET 

FOR 

HEM-3 

Submitted by: 

MACTEC Federal Programs, Inc. 

5001 S. Miami Blvd., Suite 300 

Durham, NC 27703 

919-941-0333 

July 28, 2006 

Revisions May 14, 2012 
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 

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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 
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SURFACE WBAN LAT LON UPPER AIR WBAN LAT LON 

Kansas City, MO 03947 39.30 -94.73 Topeka, KS 13996 39.07 -95.63 

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 
Meteorological Data Processing using AERMET July 28, 2006 

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SURFACE WBAN LAT LON UPPER AIR WBAN LAT LON 

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 

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 
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SURFACE WBAN LAT LON UPPER AIR WBAN LAT LON 

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 

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 

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Meteorological Data Processing using AERMET July 28, 2006 

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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 

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7 

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 

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8 

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. 

ADDENDUM – May 2012 

Since development of the original meteorological data set (122 stations) discussed above, 

additional sites have been added for Risk and Technology Review (RTR) analyses. 

These include meteorological data provided by state and local agencies, as well as 

additional sites processed by EPA risk staff to better represent dispersion conditions at 

Albedo 0.15 

Bowen Ratio 1.0 

Surface Roughness (m) 0.25 
Meteorological Data Processing using AERMET July 28, 2006 

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9 

specific facility locations. In each case, data were processed using the latest available 

version of AERMET and processing approach outlined on the USEPA’s SCRAM 

website. A listing of these additional sites is provided in Table 5. 
Meteorological Data Processing using AERMET July 28, 2006 

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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%
Meteorological Data Processing using AERMET July 28, 2006 

Rev. 05/12 

For HEM-AERMOD 

11 

WBAN Station Calms % Calm Missing % Missing Total % Total

13963 Little Rock, AR 1090 12.4% 0 0.00% 1090 12.4%

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%
Meteorological Data Processing using AERMET July 28, 2006 

Rev. 05/12 

For HEM-AERMOD 

12 

WBAN Station Calms % Calm Missing % Missing Total % Total

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%

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%

 
Meteorological Data Processing using AERMET July 28, 2006 

Rev. 05/12 

For HEM-AERMOD 

13 

TABLE 5. 

ADDITIONAL METEOROLOGICAL STATIONS 

ADDED FOR RTR ANALYSES 

City State Year

Fairbanks AK 2001

Anaheim CA 2007

Azusa CA 2007

Banning Airport CA 2007

Burbank CA 2007

Central LA CA 2007

Costa Mesa CA 2007

Crestline CA 2007

Fontana CA 2007

Indio CA 2007

La Habra CA 2007

Lake Elsinore CA 2007

LAX CA 2007

Long Beach CA 2007

Lynnwood CA 2007

Mission Viejo CA 2007

Palm Springs CA 2007

Perris CA 2007

Pico Rivera CA 2007

Pomona CA 2007

Redlands CA 2007

Reseda CA 2007

Riverside CA 2007

San Bernardino CA 2007

Santa Clarita CA 2007

Santa Monica CA 2009

Upland CA 2007

Van Nuys Airport CA 2009

West LA CA 2007

Apalachicola FL 2005

Ft. Myers FL 2005

Gainesville FL 2005

Tallahassee FL 2005

Burlington IA 2004

Cedar Rapids IA 2004

Des Moines IA 2004

Dubuque IA 2004

Mason City IA 2004

Sioux City IA 2004
Meteorological Data Processing using AERMET July 28, 2006 

Rev. 05/12 

For HEM-AERMOD 

14 

City State Year

Waterloo IA 2004

Moline IL 2004

Iron Mountain MI 1999

Duluth MN 1999

Red Wing MN 1999

Herculaneum MO 1998

Omaha NE 2004

Sioux Falls SD 2004

Clintonville WI 1999

Eagle River WI 1999

Eau Claire WI 1999

Green Bay WI 1999

Hayward WI 1999

Juneau WI 1999

La Crosse WI 1999

Madison WI 1999

Manitowoc WI 1999

Marshfield WI 1999

Milwaukee WI 1999

Mineral Point WI 1999

Mosinee WI 1999

Oshkosh WI 1999

Rhinelander WI 1999

Sturgeon Bay WI 1999

Wausau WI 1999

Wisconsin Dells WI 1999

Wisconsin Rapids WI 1999

Parkersburg WV 2011
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 Tiered Screening Methodology for RTR
[This page intentionally left blank.]
Technical Support Document

for the TRIM-Based Multipathway

Tiered Screening Methodology for RTR

June 29, 2012

Prepared For:

U.S. Environmental Protection Agency

Office of Air Quality Planning and Standards

Research Triangle Park, NC 27711

Prepared By:

ICF International

2222 East NC-54

Suite 480

Durham, NC 27713
[This page intentionally left blank.]
iii

Table of Contents

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

2. Tier 1 ................................................................................................................................. 2

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

2.2 Development of Emission Thresholds ............................................................................. 4

2.2.1 Modeling Fate and Transport ........................................................................... 4

2.2.2 Modeling Transfer and Uptake......................................................................... 5

2.2.3 Estimating Ingestion Exposure......................................................................... 6

2.2.4 Calculating Lifetime Cancer Risk and Non-Cancer HQs................................... 6

2.2.5 Determining Threshold Emission Rates ........................................................... 6

3. Tier 2 ................................................................................................................................. 7

4. Tier 3 ................................................................................................................................. 8

5. Tier 4 ................................................................................................................................. 9

6. References .......................................................................................................................10
[This page intentionally left blank.]
TRIM-Based Tiered Screening Methodology for RTR 6-29-2012

1

1. Introduction and Background

Section 112 of the Clean Air Act (CAA) directs the U.S. Environmental Protection Agency (EPA) 

to assess the risk remaining (i.e., residual risk) from emissions of hazardous air pollutants 

(HAPs) following the implementation of maximum achievable control technology (MACT) 

standards for emission sources. This risk assessment is a major component of EPA’s Risk and 

Technology Review (RTR) program. As part of this program, EPA must consider additional 

emission standards for a source category if the current emission standards—with MACT

regulations in place—do not provide an “ample margin of safety” for human health. One aspect 

of human health that EPA must consider under RTR is the potential for health effects resulting 

from exposures to persistent and bioaccumulative HAPs (PB-HAPs) via non-inhalation 

pathways, namely ingestion and dermal exposure. These non-inhalation human health risks are 

considered in combination with estimated inhalation human health risks, potential ecological 

impacts, and other factors to support decisions about residual risk for RTR source categories. 

For PB-HAPs, exposures via ingestion are anticipated to be significantly higher than any dermal 

exposures that might occur as a result of the same emissions. Consequently, a methodology 

has been developed to evaluate ingestion exposure and risk for PB-HAPs efficiently in the 

context of EPA’s RTR program.

To evaluate ingestion exposures and human health risks for RTR on a source category basis, 

an iterative approach was developed that enables EPA to confidently screen out PB-HAP 

emissions unlikely to pose health risks above levels of concern (i.e., a cancer risk of 1 in 

1 million or a noncancer hazard of 1.0) and to focus additional resources on sources of greater 

concern within the category. To estimate exposure and risk, the methodology uses two models: 

the Fate, Transport, and Ecological Exposure module of EPA’s Total Risk Integrated 

Methodology (TRIM.FaTE) to model the fate and transport of pollutants released to the 

environment and the Multimedia Ingestion Risk Calculator (MIRC) to estimate transfer and 

uptake into the food chain and exposure to receptors consuming contaminated food products 

and soil. This approach is divided into four tiers of increasing refinement, as follows.

• Tier 1 of the approach begins by identifying the facility-level emissions of PB-HAPs 

within a source category and comparing them to risk-based emission thresholds. The 

risk-based thresholds are derived using the aforementioned models applied for a 

hypothetical environmental and exposure scenario, assuming ingestion of locally caught 

fish, locally grown produce and livestock, and local soil. This “screening scenario” is 

intended to represent a situation in which the ingestion exposure is unlikely to be 

exceeded at any facility evaluated through the RTR program. The thresholds for Tier 1 

are derived by estimating the emission rate that corresponds to a lifetime cancer risk of 1 

in 1 million or a chronic non-cancer hazard quotient (HQ) of 1 for an individual exposed 

according to the characteristics associated with the screening scenario. For a facility, if 

the emission rate of each PB-HAP is less than the Tier 1 threshold emission rate, risks 

are assumed to be below levels of concern and no additional multipathway assessment 

for RTR is required. If, however, the emission rate of any PB-HAP exceeds the Tier 1 

threshold emission rate, the facility must be evaluated further in Tier 2. 

• In Tier 2, the location of the facility emitting PB-HAPs is used to refine the assumptions 

associated with the environmental scenario while maintaining the Tier 1 ingestion 

exposure scenario assumptions. The assumptions are refined by incorporating sitespecific meteorological data and evaluating the presence and location of fishable lakes 

near the facility. The risk-based threshold for each PB-HAP is then adjusted for that 

facility based on an understanding of how exposure concentrations estimated for the 
TRIM-Based Tiered Screening Methodology for RTR 6-29-2012

2

screening scenario change with meteorology and lake location. PB-HAP emissions that 

do not exceed the adjusted threshold are assumed to pose risks below levels of 

concern, and facilities having emissions that exceed the thresholds for Tier 2 are 

evaluated further in the next tier. 

• In Tier 3, the health-protective assumptions for ingestion rates associated with the 

exposure scenario are relaxed. Estimated exposures are considered for a distribution of 

exposure assumptions that take into account the likelihood of individual foodstuffs being 

consumed, rather than the single set of health-protective assumptions that underlie the 

screening scenario used in Tiers 1 and 2. The results of this analysis are used to derive 

a distribution of individual risk. These analyses are intended to provide a more 

informative representation of the range of potential risks associated with a facility. (Note 

that at present, the full methodology for Tier 3 has not been developed. Nevertheless, an 

overview of the general approach and the topics that will be explored in the development 

of this tier are provided in this overview document.) 

• For facilities emitting PB-HAPs at levels that cannot be ruled out as being above levels 

of concern following a Tier 3 analysis, a Tier 4 analysis can be conducted by completing 

a site-specific assessment. Such an assessment would incorporate location- or facilityspecific characteristics regarding the environment to which PB-HAPs are emitted, 

relevant exposure pathways, ingestion rates or other exposure factors, and other 

parameters. A range of exposure scenarios could be evaluated as part of a Tier 4 

assessment, resulting in a range of risk estimates.

The key processes and decisions that make up this approach are summarized in Exhibit 2-1. In 

the remainder of this overview, each of the four tiers in the multipathway assessment method is 

described in additional detail. Attachments to this appendix provide a comprehensive record of 

the characteristics and methods associated with Tier 1 (Attachment A), Tier 2 (Attachment B), 

and Tier 3 (Attachment C) of this approach. 

2. Tier 1

The methods used in Tier 1 are intended to enable EPA to evaluate PB-HAP emissions from 

multiple sources in a particular category quickly and efficiently and to remove from consideration 

those that are unlikely to pose risks above the level of concern, while also minimizing the 

possibility of EPA’s failing to identify risks that exceed levels of concern. The scenario used to 

estimate Tier 1 thresholds is designed to be health-protective in estimating exposures and risks; 

specifically, it is intended to avoid underestimating exposures to PB-HAPS that might be 

encountered for any location throughout the United States. The scenario also is intended to 

avoid grossly overestimating risk to the point where no emissions screen out (i.e.,

overprotective, resulting in too many “false positives”). 

2.1 Chemicals of Concern

The assessment of risk from multipathway exposures begins with a review of data for sources in 

a particular category to determine if emissions of any of the following PB-HAPs are reported:

• Cadmium compounds, 

• Chlorinated dibenzodioxins and furans (dioxins), 

• Mercury compounds, and

• Polycyclic organic matter (POM).
TRIM-Based Tiered Screening Methodology for RTR 6-29-2012

3

Exhibit 2-1. Conceptual Decision Tree for Evaluating Non-Inhalation Exposures for PB-HAPs

Diagram Key

Decision 

point

Outcome

Process

Tools / Data

Tier 1 Threshold Emission Rate Tier 2 Facility-specific Adjusted Threshold Emission Rate Percentile Distribution of Individual Risk

TIER 1 TIER 2 TIER 3

Facility screens out - no 

NO further analysis warranted

Facility proceeds to 

Tier 2

NO

Facility proceeds to 

Tier 3

Prioritize facilities for sitespecific analysis

Do the emission rates 

for any PB-HAP exceed 

Tier 1 threshold?

Do the emission rates 

exceed Tier 2 adjusted 

threshold?

Conservative 

Meteorological Data

Emissions Inventory 

Data

Conservative 

Exposure 

Assumptions

Local Meteorological 

Data 

Tier 2 Adjustments

Revised Exposure 

Factors

Site-specific Lake 

Distance

Facility screens out - no 

further analysis warranted

Estimates of Site Specific Risk

TIER 4

Full Set of Sitespecific Fate & 

Transport 

Parameters

Site-specific 

Exposure 

Assumptions

Risk Characterization

Evaluate HAP emissions by facility

YES

YES

Is site-specific analysis 

warranted? 

YES

No further analysis is 

warranted NO
TRIM-Based Tiered Screening Methodology for RTR 6-29-2012

4

Based on current emissions and toxicity considerations, emissions of these four PB-HAPs 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.1

 Thus, although EPA has identified nine 

other PB-HAPs that should be evaluated as part of residual risk assessments, the methods for 

multipathway assessment described here encompass only these four. If a facility under 

evaluation in RTR emits other PB-HAPs, these PB-HAPs must be evaluated using other 

methods and exposure scenarios appropriate to them.

If emissions of any of the four PB-HAPs are reported for a facility, the emission rate for each 

PB-HAP is compared to the threshold emissions rate derived for that chemical using the TRIMbased screening scenario. This threshold is the emission rate that, when input to the models 

used in evaluating multipathway risk for RTR, results in a specified cancer risk or non-cancer 

HQ threshold level of concern. For the screening scenario, thresholds were calculated for a 

cancer risk of 1 in 1 million or an HQ of 1.0, depending on the more sensitive health effect of the 

PB-HAP.

Important to note for dioxins and POM is that the screening methodology assesses individual 

congeners, taking into account differences in both the fate and transport and the toxicity among

the various congeners. The details of the methods for assessing dioxins and POM are provided 

in Attachment A—Technical Support Document for TRIM-Based Multipathway Tier 1 Scenario 

for RTR—to this appendix. 

2.2 Development of Emission Thresholds

Generally, the approach used to assess ingestion exposures and resulting risks for RTR has 

four components: 

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

3. Estimating ingestion exposures in terms of average daily dose for consumption of 

farm food items by a hypothetical exposed human; and 

4. Calculating lifetime cancer risk estimates or chronic non-cancer HQs for each HAP

and corresponding screening threshold emission rates. 

The TRIM.FaTE model is used in the first component, and the MIRC model is used to conduct 

calculations for the other three components. To derive the emission thresholds used in Tier 1, 

these models are used to estimate the emission rate corresponding to health risk levels of 

concern for each PB-HAP within the hypothetical environmental scenario, as described in more 

detail in the following sections. 

2.2.1 Modeling Fate and Transport 

To model chemical fate and transport in the environment when deriving emission thresholds for 

Tier 1, the TRIM.FaTE module of the TRIM system was used. The modeled domain includes a 

 

1 Potential impacts on human health from non-inhalation exposures to lead are evaluated for RTR using the National 

Ambient Air Quality Standard for lead, which takes into account multipathway risks. 
TRIM-Based Tiered Screening Methodology for RTR 6-29-2012

5

farm homestead and a fishable lake near an emissions source, which are assumed to be the 

primary food sources for exposed individuals. The spatial layout and other physical aspects of 

the scenario configuration are designed to be generally health-protective, which results in an 

ingestion exposure situation that is unlikely to be exceeded at any facility evaluated under the 

RTR program. The environmental and chemical-specific properties governing fate and transport 

of PB-HAPs are parameterized with either conservative (i.e., health protective) values or 

central-tendency values. For this modeling approach, the spatial layout of the modeled domain 

and the meteorological data used (or a combination of these two factors) are generally more 

influential than physical/chemical parameters in dictating the resulting chemical concentrations 

in air, soil, water, sediment, and fish. The Tier 1 assumptions about these two components of 

fate and transport modeling are refined with site-specific data in subsequent tiers. The spatial 

layout used to develop the threshold emission levels in Tier 1 and other details of the Tier 1 

methodology are presented in Attachment A to this appendix.

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

Chemical Emissions to Air

Cancer Risk

Hazard Quotient

Chemical fate 

and transport:

Physical environment 

and aquatic ecosystem

Uptake and 

transfer: 

Produce and livestock

Human 

Ingestion 

Exposure

Risk and 

Hazard 

Estimation

2.2.2 Modeling Transfer and Uptake 

MIRC was developed to conduct the required calculations involving farm food chain transfer, 

ingestion exposure, and risk. TRIM.FaTE outputs that are used as inputs to MIRC include: 

• PB-HAP concentrations in air, 

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

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

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

From these inputs, MIRC calculates the transfer and uptake of PB-HAPs through the farm food 

chain using algorithms based on those included in EPA’s Human Health Risk Assessment 

Protocol for Hazardous Waste Combustion Facilities (EPA 2005) and biotransfer factors (e.g., 

soil-to-plant factors, which are the ratios of the concentrations in plants to concentrations in 

soil). The outputs of MIRC are PB-HAP concentrations in contaminated food items. 
TRIM-Based Tiered Screening Methodology for RTR 6-29-2012

6

2.2.3 Estimating Ingestion Exposure

MIRC is used to estimate exposure in terms of average daily doses (ADDs), normalized to body 

weight for the following exposure pathways: 

• Incidental ingestion of soil

• Ingestion of homegrown produce

• Ingestion of homegrown beef

• Ingestion of milk from homegrown 

cows

• Ingestion of homegrown poultry/eggs

• Ingestion of homegrown pork 

• Ingestion of fish

• Ingestion of breast milk (children <1 year

old; dioxins only)

Chemicals are modeled separately to evaluate the potential for risks, with exposures (in terms 

of ADD) for each PB-HAP summed across all ingestion exposure pathways. For the screening 

scenario used in Tiers 1 and 2 of this analysis, exposure characteristics were selected that 

result in a highly health protective estimate of total exposure. The ingestion rate for each 

exposure pathway listed above was set (as feasible) equal to the 90th percentile of the 

distribution of national data of consumers of that food type recommended by EPA in the 

Exposure Factors Handbook (U.S. EPA 2011a) or in other sources as appropriate. All media 

were assumed to be obtained from a location impacted by the modeled source. This approach 

results in an overestimate of total chemical exposure for a hypothetical exposure scenario. For 

example, the resulting total food ingestion rate is extremely high for a hypothetical consumer, 

with ingestion rates in the 90th percentile for every farm food type. This scenario, however,

intentionally was designed to avoid underestimating exposure for any single farm food type. 

These health protective exposure assumptions are replaced in subsequent tiers of the 

assessment as appropriate (e.g., with distributions of the data for key exposure factors).

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

pathway was not included in the scenario used to calculate Tier 1 emission thresholds (this topic 

is discussed further in Attachment A). 

2.2.4 Calculating Lifetime Cancer Risk and Non-Cancer HQs

Lifetime cancer risks and the potential for chronic non-cancer effects are estimated using 

chemical-specific oral cancer slope factors and oral reference doses. Lifetime cancer risk 

estimates are calculated separately for each PB-HAP. Similarly, HQs are calculated separately 

for each PB-HAP and for each age group, and the HQ for the most sensitive age group is used 

to determine the screening threshold emission rate.

2.2.5 Determining Threshold Emission Rates

Tier 1 emission thresholds were calculated by conducting iterative model simulations in 

TRIM.FaTE and MIRC using the screening scenario described above to determine emission 

rates for cadmium, mercury, dioxins, and POM that correspond to a cancer risk of 1 in 1 million 

or a chronic non-cancer HQ of 1. Given the generally health protective nature of the scenario 

inputs, these thresholds are assumed to be appropriate for screening facilities emitting these 

PB-HAPs.
TRIM-Based Tiered Screening Methodology for RTR 6-29-2012

7

3. Tier 2 

The Tier 1 screening approach is, by design, generic and health protective. It was constructed 

for quick application to a large number of facilities in a source category with the least chance of 

returning false negatives for risk. Once the initial screen is complete, however, facilities that fail 

for any PB-HAPs must be scrutinized further. Based on screening analyses conducted for RTR 

to date, many facilities could “fail” the Tier 1 screen for some source categories. A full sitespecific analysis of all facilities that cannot be screened out in Tier 1 would not be practical. 

Site-specific values for some influential variables, however, can be determined without intensive 

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

Tier 1 can be used to justify adjusting the screening threshold for a given PB-HAP at that facility, 

potentially eliminating the facility from concern while maintaining a high degree of confidence 

that risks above levels of concern have not been overlooked. Specifically, for Tier 2, locationspecific data on two types of variables are taken into account: 

• Meteorological characteristics, including the fraction of time the wind blows toward the 

farm and lake (“wind direction”), wind speed, precipitation rate, and mixing height; and 

• Distance from the facility to the nearest fishable lake(s).2 

These variables affect the PB-HAP concentrations in environmental media estimated by 

TRIM.FaTE (and thus can be used to justify upward scaling of the emission threshold 

associated with risks above the level of concern), but they are not related to specific exposure 

assumptions. The exposure assumptions, such as ingestion rate and fraction of diet derived 

from the lake and farm remain at fixed, health-protective values in Tier 2. In selecting the fate 

and transport variables to include in Tier 2, a balance was struck between the degree of impact 

on the risk estimate, the ease of implementation in TRIM.FaTE, and the ease of obtaining 

relatively certain site-specific values for all facilities that might be evaluated under the RTR 

program. 

For efficient Tier 2 evaluation of the impacts these parameters could have on specific facilities, a 

series of TRIM.FaTE simulations was performed that systematically varied the values used in 

the screening scenario for the five selected variables (four meteorological variables and lake 

location). The values of each of the five variables were changed, independent of any other 

changes. The alternative values (three or four for each variable) were selected using statistics 

on U.S. meteorological data or professional judgment to capture the expected range in the 

facility data. Three or four values were selected to result in a total number of runs that was 

reasonable. This set of values was used to develop “bins” for each variable. 

Based on the TRIM.FaTE results of these simulations (and the subsequent exposure and risk 

characterization, conducted using MIRC), threshold adjustment factors were calculated for each 

unique combination of the five parameters, for each PB-HAP. These adjustment factors 

represent the ratio between the risk metric (i.e., cancer risk or HQ) obtained using the baseline 

Tier 1 screening scenario and the risk metric obtained from the adjusted run. For a given 

facility, an adjusted Tier 2 emission threshold can be estimated by multiplying the Tier 1 

emission threshold by the adjustment factor that best corresponds to the meteorological 

conditions present at the site and the presence and location of lakes at the site. 

 

2

The lake size also was changed for each new facility lake distance, which allowed for the simulations to maintain a 

constant ratio between watershed and erosion area compared with lake area.
TRIM-Based Tiered Screening Methodology for RTR 6-29-2012

8

To facilitate the implementation of this approach without requiring facility-specific data searches 

for each new source category evaluated, databases of the relevant U.S. meteorological and 

lake data were created that could be accessed on the fly during a Tier 2 evaluation. The 

meteorological database includes annual-average summary statistics on wind direction, wind 

speed, and precipitation for 1,305 surface stations located throughout the United States. The 

mixing heights for these stations were estimated using the closest upper air data. The database 

of fishable lakes includes information on the location and size of lakes in the United States. To 

focus on lakes that can support angling of upper trophic level fish, only lakes greater than 25 

acres are included. 

When the Tier 2 screening is conducted, one additional processing step is completed before

looking up the appropriate adjustment factors. For each facility that will be analyzed in Tier 2, 

the distance to the closest relevant lake near the facility in each of eight directional “octants” is 

recorded using GIS software. For the purposes of Tier 2, a “relevant” lake is considered to be 

one located within a 50-km radius of the facility and above the size threshold of 25 acres. To 

access these databases, a Microsoft® Excel tool was created that merges the TRIM.FaTE 

Tier 2 adjustment factors with the lake and meteorology information relevant to a specific facility

from the databases. In the tool, each facility is matched with the closest meteorological station, 

and the values for the four relevant parameters at that station are recorded. The distances from 

the facility to the nearest lakes estimated using GIS are also imported. These five values 

become the set of facility-specific parameters. Then, the adjustment factors for each chemical 

for the combination of these five variables are determined. The Tier 1 screening emission 

threshold is then multiplied by the appropriate adjustment factor to obtain an updated, Tier 2 

emission threshold for that PB-HAP. Any emissions below the adjusted Tier 2 threshold are 

assumed to pose a risk below the level of concern. 

4. Tier 3

If emissions of any PB-HAP at a facility are above the Tier 2 threshold, the facility is considered 

further in Tier 3. In Tier 3, the site-specific meteorological and lake location parameters 

estimated in Tier 2 for that facility continue to be used. In Tier 3, however, exposure 

assumptions are varied from their health-protective values in Tiers 1 and 2 to a distribution of all 

possible values in the U.S. population. Unlike for the first two tiers, Tier 3 does not result in a 

simple “pass” or “fail” conclusion. Instead, this tier enables the risk assessor to determine how 

much the exposure assumptions would have to be adjusted to obtain a risk in an acceptable 

range. Tier 3 does not facilitate the absolute elimination of facilities from concern (as is possible 

in the first two tiers), but it does provide additional information regarding estimated risks. This 

information could be used to inform next steps, such as the prioritization of facilities that might 

require labor-intensive, comprehensive site-specific analyses in Tier 4, or policy determinations 

for a source category. 

The exposure assumptions examined in Tier 3 include the pathway-dependent ingestion rates 

and the fraction of the diet derived from the nearby lake and farm. In the Tier 1 and 2 analyses, 

the ingestion rates are set to relatively high values representative of subsistence anglers and 

subsistence farmers. For Tier 3, a broader distribution of individual ingestion rates (and not just 

those associated with subsistence lifestyles) is evaluated for each ingestion pathway. In 

addition, the assumption that individual dietary consumption of farm products and fish all derive

from the area farm and lake is relaxed in Tier 3 to develop distributions of dietary fractions.

To estimate the risk distributions for each PB-HAP at each facility, Monte Carlo techniques will

be used to create a large set of pathway ingestion rates and fractions of diet coming from the 
TRIM-Based Tiered Screening Methodology for RTR 6-29-2012

9

lake and farm. For each simulated hypothetical individual (i.e., each “Monte Carlo realization”), 

each pathway ingestion rate distribution and dietary fraction distribution will be independently 

sampled. This sampling involves selecting a random number between 0 and 1 and using the 

percentiles as bins to select a variable value. For example, if the random number for poultry 

ingestion rate is 0.87, the value corresponding to the closest percentile (0.9) would be used for 

that individual. Whether each variable should be considered independently or some exposure 

assumptions should be correlated also will be decided. In addition, the total ingestion rate 

percentiles for all pathways together will be used to ensure no person consumes far too little or 

far too much food in total, compared with the actual distribution. 

By sampling all ingestion rates and dietary fractions independently, a set of thousands of Monte 

Carlo realizations can be created. Tier 3 risks associated with each facility will be computed for 

each realization using Tier 2 concentrations combined with the Tier 3 exposure estimates for 

that realization. These calculations can be made using only MIRC simulations, without having 

to run TRIM.FaTE, which will require significantly fewer resources.

The estimates of individual Tier 3 risks associated with each facility can be presented as a 

percentile plot along with indications of policy-relevant risk levels. A risk assessor then can 

judge at which individual risk percentile the policy-relevant risk levels fall. The lower the 

percentile, the greater the chance that individuals will be adversely affected by the facility 

emissions and the higher the priority for running a full Tier 4 analysis. (As noted above, the 

Tier 3 methodology, which has not been fully developed, is presented here only to provide 

information on the methods that are being considered.)

5. Tier 4

In examining the Tier 3 risk distributions, if a risk assessor concludes that there is a reasonable 

probability that individuals could be adversely affected by the facility emissions, a Tier 4 sitespecific analysis might be performed. Examples of recent Tier 4-type analyses include 

assessments of two coal-fired electric utility units conducted in support of EPA’s utility rule (U.S. 

EPA 2011c), residual risk assessments of two secondary lead smelting facilities (U.S. EPA 

2011b), and a case study evaluation of a portland cement facility included with other RTR 

materials presented to the Science Advisory Board for review (Appendix I of U.S. EPA 2009). 

Whereas a Tier 2 analysis incorporates some site-specific but low-resolution information on 

meteorology and water bodies, and a Tier 3 analysis calculates ranges of possible health risk 

from possible ingestion behaviors, a Tier 4 analysis uses site-specific data to parameterize

more accurately (to the extent possible) each important factor that affects pollutant fate and 

transport. These site-specific properties are incorporated into model scenarios configured in 

TRIM.FaTE and MIRC. Important site-specific properties likely would include emission release 

height and plume buoyancy, hourly meteorology (e.g., wind flow, temperature, mixing height, 

and precipitation), surface compartments based on watershed and terrain data, local farms and 

water bodies, land use, soil, erosion, runoff, surface water and sediment, water transfer, and 

aquatic ecosystems. 

The outputs from the site-specific run of TRIM.FaTE (i.e., chemical concentrations in 

environmental media and fish) are used in MIRC to produce estimates of exposure and health 

risk. These media concentrations, exposure estimates, and risk estimates can be subset by 

various compartments (e.g., surface compartments, food web compartments) and ingestion 

rates for each modeled PB-HAP. These subsets enable the risk assessor to understand, based 
TRIM-Based Tiered Screening Methodology for RTR 6-29-2012

10

on TRIM.FaTE and MIRC, the sources and pathways of possible human health risk from 

emissions of PB-HAPs. 

6. References

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

Protocol for Hazardous Waste Combustion Facilities. EPA Office of Solid Waste. EPA-530/R05-006. 09/2005. 

U.S. EPA. (2009) Risk and Technology Review (RTR) Risk Assessment Methodologies: For 

Review by the EPA’s Science Advisory Board with Case Studies – MACT I Petroleum Refining 

Sources and Portland Cement Manufacturing. EPA Office of Air Quality Planning and 

Standards. EPA-452/R-09-006. 06/2009.

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

Environmental Assessment, Washington, DC; EPA/600/R-09/052F. Available from the National 

Technical Information Service, Springfield, VA, and online at http://www.epa.gov/ncea/efh.

U.S. EPA. (2011b) Human Health Multipathway Residual Risk Assessment for the Secondary 

Lead Smelting Source Category. Draft Report. Prepared by ICF International for EPA Risk and 

Exposure Assessment Group. 02/15/2011.

U.S. EPA. (2011c) Technical Support Document: Case Study Analyses of Potential Local-scale 

Human Health Risks Associated with Mercury Emissions from Electric Utility Steam-generating 

Units. Draft. Prepared by ICF International for EPA Office of Air Quality Planning and Standards. 

02/17/2011.
Technical Support Document for the TRIM-Based Multipathway Tiered Screening 

Methodology for RTR 

Attachment A. Tier 1 Screening Methodology 
 

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Attachment A – 6/29/12 i 

TABLE OF CONTENTS 

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

2 Summary of Approach ........................................................................................................................... 1

2.1 Overview .................................................................................................................................... 1

2.2 Chemicals of Potential Concern ................................................................................................ 4

2.3 Conceptual Exposure Scenario ................................................................................................. 5

2.4 Approach to Development of the Tier 1 Scenario ..................................................................... 7

2.4.1 Modeling Framework ................................................................................................................. 8

2.4.2 Model Configuration and Parameterization ............................................................................... 9

2.5 Fate and Transport Modeling (TRIM.FaTE) ............................................................................ 10

2.6 Exposure Modeling and Risk Characterization (MIRC) ........................................................... 10

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

Emissions ................................................................................................................................ 11

2.7.1 Calculation of Scaling Factors for POM Congeners ............................................................... 13

2.7.2 Calculation of Scaling Factors for Dioxin Congeners.............................................................. 15

3 Description of Environmental Modeling Scenario ................................................................................ 16

3.1 Chemical Properties ................................................................................................................ 16

3.2 Spatial Layout .......................................................................................................................... 16

3.3 Watershed and Water Body Parameterization ........................................................................ 19

3.3.1 Water Balance ......................................................................................................................... 19

3.3.2 Sediment Balance ................................................................................................................... 20

3.4 Meteorology ............................................................................................................................. 21

3.5 Aquatic Food Web ................................................................................................................... 23

3.6 Using TRIM.FaTE Media Concentrations ................................................................................ 24

4 Description of Exposure and Risk Modeling Scenario ......................................................................... 25

4.1 Calculating Concentrations in Farm Food Chain Media .......................................................... 26

4.2 Ingestion Exposure .................................................................................................................. 26

4.2.1 Exposure Scenarios and Corresponding Inputs ..................................................................... 28

4.2.2 Calculating Average Daily Doses ............................................................................................ 28

4.2.3 Infant Ingestion of Breast Milk ................................................................................................. 28

4.3 Calculating Risk ....................................................................................................................... 30

4.4 Summary of Tier 1 Assumptions ............................................................................................. 30

5 Evaluation of Screening Scenario ........................................................................................................ 34

5.1 Introduction .............................................................................................................................. 34

5.2 Cadmium Compounds ............................................................................................................. 34

5.2.1 Behavior in the Environment ................................................................................................... 34

5.2.2 Concentrations in Ingestible Products .................................................................................... 35

5.2.3 Average Daily Dose (ADD) ..................................................................................................... 35

5.3 Mercury Compounds ............................................................................................................... 35
Attachment A – 6/29/12 ii 

5.3.1 Behavior in the Environment ................................................................................................... 35

5.3.2 Concentrations in Ingestible Products .................................................................................... 37

5.3.3 Average Daily Dose ................................................................................................................ 37

5.4 Dioxins ..................................................................................................................................... 37

5.4.1 Behavior in the Environment ................................................................................................... 38

5.4.2 Concentrations in Ingestible Products .................................................................................... 39

5.4.3 Lifetime Average Daily Dose (LADD) ...................................................................................... 39

5.5 Polycyclic Aromatic Hydrocarbons .......................................................................................... 40

5.5.1 Behavior in the Environment ................................................................................................... 41

5.5.2 Concentrations in Ingestible Products .................................................................................... 41

5.5.3 Lifetime Average Daily Dose ................................................................................................... 42

5.6 Summary ................................................................................................................................. 42

6 References ........................................................................................................................................... 43

Addendum 1 – TRIM.FaTE Inputs 

Addendum 2 – RTR Excel-based Exposure and Risk Calculation Tool – Multimedia Ingestion Risk 

Calculator (MIRC) 

Addendum 3 – Dermal Risk Screening 
Attachment A- 6/29/12 iii

LIST OF EXHIBITS 

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

Exhibit 2. Emission Thresholds for Screening of Multipathway Exposures ................................................. 4

Exhibit 3. OAQPS PB-HAP Compounds ...................................................................................................... 6

Exhibit 4. Overview of Ingestion Exposure and Risk Screening Evaluation Method ................................... 9

Exhibit 5. Overview of Process Carried Out in the Multimedia Ingestion Risk Calculator ......................... 11

Exhibit 6. Exposure, Toxicity, and Risk Equivalency Factors Relative to BaP for Modeled POM 

Congeners .............................................................................................................................. 13

Exhibit 7. Relationship between Exposure and Kow for POM Congeners ................................................ 14

Exhibit 8.. Exposure and Toxicity Equivalency Factors Relative to TCDD for Modeled Dioxin 

Congeners .............................................................................................................................. 14

Exhibit 9. TRIM.FaTE Surface Parcel Layout ............................................................................................ 18

Exhibit 10. Summary of Key Meteorological Inputs ................................................................................... 22

Exhibit 11.. Aquatic Biota Parameters for the TRIM.FaTE Screening Scenario ........................................ 24

Exhibit 12. Spatial Considerations – TRIM.FaTE Results Selected for Calculating Farm Food 

Chain Media Concentrations and Receptor Exposures ......................................................... 25

Exhibit 13. Summary of Ingestion Exposure Pathways and Routes of Uptake ......................................... 27

Exhibit 14. Overview of Exposure Factors Used for RTR Multipathway Screeninga,b ............................... 29

Exhibit 15. Dose-response Values for PB-HAPs Addressed by the Screening Scenario ......................... 30

Exhibit 16. Summary of RTR Screening Scenario Assumptions ............................................................... 31

Exhibit 17. Estimated Contributions of Modeled Food Types to Cadmium Ingestion Exposures 

and Hazard Quotients ............................................................................................................ 36

Exhibit 18. Estimated Contributions of Modeled Food Types to Methyl Mercury Ingestion 

Exposures .............................................................................................................................. 38

Exhibit 19. Estimated Contributions of Modeled Food Types to Dioxin Ingestion Exposures ................... 40

Exhibit 20. Estimated Contributions of Modeled Food Types to PAH Ingestion Exposures ...................... 42
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Attachment A- 6/29/12 1 

1 Introduction 

As discussed in the Overview Document, the U.S. Environmental Protection Agency (EPA) will 

implement a four-tiered approach to evaluate multipathway exposures and human health risks 

for the Risk and Technology Review (RTR) program. In the first tier, a screening evaluation is 

conducted that focuses on the identity and magnitude of emissions of hazardous air pollutant 

(HAP), as defined in Section 112 of the Clean Air Act, from a given facility to determine whether 

a facility passes certain human health risk-based criteria. Sources that are “screened out” in the 

Tier 1 analysis are assumed to pose no risks to human health above the levels of concern (e.g., 

a cancer risk of 1 in 1,000,000 or a noncancer hazard index of 1.0 or greater) and are not 

considered in further analyses. For sources that do not pass the Tier 1 screen, more refined 

assessments, up to and including site-specific multipathway assessments, are conducted as 

appropriate. The results of these analyses are considered, in combination with estimated 

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

about residual risk for RTR source categories. 

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

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

models, configurations, and inputs used to derive screening threshold emission rates in the first 

tier of the approach are described in detail in the following sections. 

 Section 2 presents an overview of how screening is conducted in Tier 1, the chemicals and 

exposure scenario evaluated in Tier 1, and the models and methods used to conduct the 

screen. 

 Sections 3 and 4 present technical descriptions of the hypothetical environmental setting 

and the exposure modeling scenario used in Tier 1 as well as the models used in the 

screen. 

 Section 5 provides a brief discussion of the screening threshold emissions for each of the 

chemicals assessed. References cited in this report are listed in Section 6. 

More refined multipathway risk assessment methods, when required for facilities that do not 

screen out of the Tier 1 analysis, use as their starting point the same TRIM-based modeling 

approach described herein, making adjustments to modeling inputs based on some site-specific 

characteristics of the facility being assessed. Tier 2 and Tier 3 screening methods and tools are 

discussed separately in Attachment B and Attachment C, respectively. Tier 4 consists of a full 

site-specific multipathway risk assessment. Although a Tier 4 analysis uses the same models 

and methods to estimate risks, the modeling and exposure scenarios are developed 

independently based on site-specific characteristics and are not based on the scenarios 

presented herein. 

2 Summary of Approach 

2.1 Overview 

The Tier 1 approach for evaluating non-inhalation, multipathway exposures to PB-HAPs for RTR 

is diagrammed in Exhibit 1. Air toxics emitted by a source under consideration are reviewed to 

determine, first, whether emissions are reported for any of the four PB-HAPs of concern for noninhalation pathways. If such emissions are reported, the emission rates are compared to 
Attachment A- 6/29/12 2 

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

Exposures of PB-HAPs 

Facility screens out

Non-inhalation risks not likely, 

but evaluate on a case-by-case 

basis

Facility screens out; no concern for 

multipathway risk

(check emissions of PB-HAPs other than 

Cd, dioxins, Hg, POM, Pb on a case-bycase basis)

Are any PB-HAPs 

emitted?

Are PB-HAPs

of primary concern 

for RTR emitted? 

(Cd, dioxins, Hg, 

POM)?

Evaluate HAP emissions by 

facility

YES

YES

NO

NO

NO

Does the emission 

rate for any PBHAP exceed Tier 

1 threshold?

Diagram Key

Outcome

Process

Decision 

Point

TIER 1 EVALUATION

Proceed to Tier 2

YES
Attachment A- 6/29/12 3 

Tier 1 threshold-screening emission rates that have been derived using the TRIM-based Tier 1 

scenario described in this document. 

The TRIM-based multipathway modeling configuration, referred to in this document as the Tier 1 

scenario, is a key component of the first tier of this approach, and is the technical basis for 

determining the Tier 1 emission thresholds. The term, Tier 1 scenario, is used to refer 

collectively to the specific TRIM.FaTE and exposure modeling configuration described herein, 

including the set of assumptions and input values associated with a hypothetical watershed and 

the exposure and risk scenarios evaluated for this watershed. The Tier 1 scenario is a static 

configuration, and its primary purpose is as a modeling tool to calculate the Tier 1 emission rate 

thresholds for PB-HAPs of concern. 

The two potential outcomes of the Tier 1 evaluation are: 

 Non-inhalation exposures are unlikely to pose a human health problem (i.e., the emissions 

evaluated “pass” the screen); or 

 The potential for non-inhalation exposures leading to risks above the levels of concern 

cannot be ruled out and further assessment is required to determine the potential for 

exceeding the levels of concern. 

An ideal screening approach strikes a balance between being health-protective—to ensure that 

risks above levels of concern are identified, and being accurate—to minimize results suggesting 

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

accuracy in environmental modeling are accompanied by additional resource requirements. 

Stated another way, a suitable approach minimizes both false negatives and false positives. 

False negatives (i.e., results that suggest that the risk is acceptable when in fact the actual risk 

is high) can lead to inappropriate and non-protective health or environmental policy decisions. 

False positives (i.e., results that suggest more assessment is required when in fact the actual 

risk is low) can result in wasted resources by leading to additional, unnecessary analysis. For 

the evaluation of multipathway human health exposures to PB-HAPs, the methods for screening 

described in this document are intended to achieve this balance. 

Because the Tier 1 evaluation enables EPA to confidently eliminate from consideration those 

facilities where risks from non-inhalation exposures are projected to be minimal, resources can 

be targeted toward those facilities that do not pass the screening test. For facilities that do not 

pass the Tier 1 screening, in additional tiers of analysis, some of the Tier 1 parameters are 

reassessed, and if appropriate, are changed to more accurately reflect site-specific 

characteristics. With each successive tier of the assessment, additional Tier 1 assumptions are 

evaluated and refined to better reflect site-specific characteristics of the facility being modeled. 

The Tier 1 screening evaluation for RTR compares reported air emission rates of PB-HAPs 

(summed by PB-HAP for each facility) to screening threshold emission rates derived using the 

Tier 1 scenario. A threshold emission rate is the level that, when input to a risk model using 

emissions as a parameter, corresponds to a specified cancer risk or non-cancer hazard quotient 

(HQ) that, for the purposes of the evaluation being conducted, is assumed to be below a level of 

concern. Tier 1 threshold rates were calculated for a cancer risk of 1 in 1 million or an HQ of 1.0 

and are presented in 
Attachment A- 6/29/12 4 

Exhibit 2.1 Conceptually, a threshold level for the RTR multipathway 

Exhibit 2. Emission Thresholds for Screening of Multipathway Exposures 

Chemical Screening Threshold 

Emission Rate (TPY)

Basis of Threshold 

(Type of Health Endpoint) 

POM (as benzo(a)pyrene equivalents)a 3.09E−03 Cancer 

Dioxins (as 2,3,7,8-TCDD equivalents)a 6.38E-09 Cancer 

Cadmium 7.00E−02 Non-cancer 

Divalent mercury 1.94E−03 Non-cancer 

TPY = U.S. short tons per year 

a

See Section 2.7 for a discussion on the derivation of equivalent emissions. 

screening evaluation could be obtained by back-calculating the emission rate that results in the 

specified cancer risk or HQ level, taking into account the exposure and fate and transport 

calculations included in the model. Because the models used in this assessment are not 

designed to run “backwards,” the rates instead were derived from regression equations 

established following a series of TRIM.FaTE and exposure/risk model runs spanning a wide 

range of emission rates for each chemical. The estimated screening-level emission rates are 

verified by performing model runs using the estimated threshold emission rate to confirm that 

the emission rates result in a cancer risk of 1 in 1 million or an HQ of 1.0. 

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 

(health protective) and highly general nature of the Tier 1 scenario configuration. 

Tier 1 emission thresholds were developed individually for elemental and divalent mercury. 

Both were based on the lower of the thresholds associated with multipathway exposures to 

divalent mercury and methyl mercury.2

 Only speciated emissions of divalent mercury are 

screened because the sum of elemental mercury emissions across all National Emission 

Inventory (NEI) facilities is less than the elemental mercury screening threshold level. 

2.2 Chemicals of Potential Concern 

EPA’s assessment of multipathway human exposures for RTR focuses on persistent and 

bioaccumulative hazardous air pollutants (PB-HAPs) 3

 that the Office of Air Quality Planning and 

 

1

For chemicals known to cause both cancer and chronic non-cancer impacts, and for which acceptable 

quantitative dose-response values are available for both cancer and non-cancer endpoints, the endpoint 

that results in the lower threshold emission level is used for screening (i.e., the threshold will be based on 

the effect that occurs at the lower exposure level). For the set of PB-HAPs for which screening threshold 

levels have been derived, only chlorinated dibenzo-dioxins and -furans meet both criteria. Because the 

cancer dose-response value is lower than that for non-cancer effects, the screening threshold value is 

based on the cancer endpoint. 

2

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. 

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

mercury. 

3

Although POM (polycyclic organic matter) is the name of the HAP listed in the Clean Air Act, the term 

“polycyclic aromatic hydrocarbons” or PAHs is used in many cases. Much of the literature regarding 
Attachment A- 6/29/12 5 

Standards (OAQPS) has identified as candidates for multipathway risk assessments. OAQPS 

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

process taking into account the following (U.S. EPA 2004a): 

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

substances, and 

 a semiquantitative ranking of toxicity and bioaccumulation potential of the entire list of 

HAPs. 

The list’s development and utility in hazard identification for multipathway risk assessment are 

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

Assessment (ATRA) Reference Library (U.S. EPA 2004a). Exhibit 3 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 estimate 

exposures and risks quantitatively for four PB-HAPs (indicated in bold in Exhibit 3). These PBHAPs 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 Clean Air Act.4

2.3 Conceptual Exposure Scenario 

A conceptual exposure scenario was developed that encompasses the specific exposure routes 

and pathways of interest for the four PB-HAPs that are assessed quantitatively in the Tier 1 

analysis. Exposure routes and pathways describe the movement of air toxics from the point of 

release to the point where exposure occurs and generally consist of the following elements: 

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

 A retention medium, or a transport mechanism and subsequent retention medium in cases 

involving media transfer of chemicals; 

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

 An exposure route. 

An exposure route is the particular means of entry into the body. Receptors are exposed to 

chemicals emitted to the atmosphere via two primary routes: either directly via inhalation, or 

indirectly via ingestion or dermal contact with various media that have been contaminated with 

the emitted PB-HAPs. (Inhalation pathways are assessed separately and are not considered in 

the Tier 1 assessment presented here.) 

PB-HAPs can persist in the environment for long periods of time and also build up in soil and in 

the food chain, including fish, fruits and vegetables, and animal products (e.g., meat, dairy, 

 

toxicity and fate and transport of this chemical group refers to PAHs rather than POM. In addition, the 

individual POM species that are of concern with respect to health risk for RTR evaluations are all PAHs 

(i.e., there are no POM species explicitly evaluated for RTR that do not include an aromatic ring). The 

terms are used interchangeably throughout this text. 

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. Noninhalation exposures to the other nine PB-HAPs not addressed by the modeling scenario discussed in 

this report will be evaluated on an individual facility or source category basis as appropriate. 
Attachment A- 6/29/12 6 

eggs). For this reason, ingestion of foods grown within an area impacted by RTR sources can 

be an important source of exposure to PB-HAPs. 

Exhibit 3. OAQPS PB-HAP Compounds 

PB-HAP Compounda Addressed by Screening Scenario? 

Cadmium compounds Yes 

Chlordane No 

Chlorinated dibenzodioxins and furans Yes 

DDE (1,1-dichloro-2,2-bis(p-chlorophenyl) ethylene) No 

Heptachlor No 

Hexachlorobenzene No 

Hexachlorocyclohexane (all isomers) No 

Lead compounds No 

Mercury compounds Yes 

Methoxychlor No 

Polychlorinated biphenyls No 

Polycyclic organic matter (POM) Yes 

Toxaphene No 

Trifluralin No 

a

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

the TRIM-based screening scenario. 

To assess risks from hazardous waste combustion facilities, EPA identified several hypothetical 

receptor scenarios, noting that these scenarios are considered appropriate for a broad range of 

situations, rather than to represent any actual scenario. These scenarios are described in 

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

HHRAP (U.S. EPA 2005a). In this document, EPA recommends assessment of the following 

hypothetical receptors: a Farmer, Farmer Child, Resident, Resident Child, Fisher, Fisher Child, 

Acute Receptor, and Nursing Infant. These receptors are distinguished by their pathways of 

exposures. EPA further notes in HHRAP that some exposure settings might warrant including 

additional exposure pathways; such as including exposure through fish ingestion for the farmer 

receptor. For the RTR screening scenario, risks are assessed for a single hypothetical receptor. 

Based on the guidance provided in HHRAP, a health protective exposure scenario was 

developed whereby the hypothetical receptor receives ingestion exposure via both the farm food 

chain and the fish ingestion pathways. The exposure scenario for the RTR Tier 1 analysis 

includes the following ingestion pathways: 

 Incidental ingestion of soil, 

 Ingestion of homegrown fruits and vegetables, 

 Ingestion of homegrown beef, 
Attachment A- 6/29/12 7 

 Ingestion of dairy products from homegrown cows, 

 Ingestion of homegrown poultry and eggs, 

 Ingestion of homegrown pork, 

 Ingestion of locally caught fish, and 

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

As discussed in detail in Section 4.2, exposure via these pathways is assessed for adults, 

various age categories for children, and nursing infants (for dioxins only). 

Other non-inhalation exposure routes of possible concern for PB-HAPs discussed in HHRAP 

include the use of surface waters as a drinking water source and dermal exposure. These 

exposure routes, however, are not evaluated in the current assessment. The drinking water 

exposure pathway is not likely for the modeling scenario developed for this analysis because 

the likelihood that humans would use a lake as a drinking water source was assumed to be 

low.5

 Dermal absorption of chemicals that are originally airborne has been shown to a relatively 

minor pathway of exposure compared to other exposure pathways (U.S. EPA 2006, Cal/EPA 

2000). Preliminary calculations of estimated dermal exposure and risk of PB-HAPs, presented 

in Addendum 3, 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 if deemed necessary. 

2.4 Approach to Development of the Tier 1 Scenario 

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

qualitatively estimate the potential for non-inhalation risks above the levels of concern for PBHAPs emissions from facilities in the context of residual risk assessments conducted as part of 

RTR. The Tier 1 scenario used to derive the threshold emission rates is not intended to be 

representative of any particular situation. Rather, it was developed for the purpose of RTR to 

portray an exposure scenario that is inclusive of any potential exposure situation that might 

plausibly be encountered in the United States. A range of conditions was assessed when 

conceptualizing and developing the screening scenario. The final configuration was chosen so 

that for a given individual, any potential long-term exposure conditions 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 

health protectiveness and the level of accuracy called for in the ideal screening approach 

previously discussed. 

The development and application of the Tier 1 scenario for residual risk evaluations considered 

EPA’s technical and policy guidelines presented in the Residual Risk Report to Congress

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

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

 

5

An exception to this generality would be reservoirs used for drinking water supplies. This situation might 

be worthy of additional analysis, if warranted by the characteristics of a given assessment (e.g., to 

estimate PB-HAP concentrations in treated drinking water derived from reservoirs). 
Attachment A- 6/29/12 8 

culmination of analyses completed since 2005; it provides the basis for an efficient and 

scientifically defensible method for screening multipathway human health risk and provides a 

solid baseline from which to perform Tier 2 and Tier 3 analyses, as described in Attachment B 

and Attachment C, respectively. Nevertheless, this scenario should not be considered “final” 

but rather a product that can continue to evolve based on feedback from the scientific 

community and Agency reviewers, lessons learned as the scenario is further applied for RTR, 

variations in EPA’s needs and requirements, and other factors. 

2.4.1 Modeling Framework 

The approach for multipathway risk screening and evaluation for RTR 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 fish6

); 

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

livestock, dairy products) from soil and air; 

3. Estimating exposures from ingestion of selected media and estimating average daily 

ingestion doses for a hypothetical human receptor; and 

4. Calculating lifetime cancer risk estimates or chronic non-cancer HQs, as appropriate, for 

each PB-HAP and comparing these to selected evaluation criteria. 

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

As shown in Exhibit 4, two models are used to evaluate the four steps outlined above. EPA’s 

TRIM methodology was conceived as a comprehensive modeling framework for evaluating risks 

from air toxics, and the TRIM system was designed to address each of the four steps involved in 

screening ingestion risk.7

 Currently, however, only one component corresponding to the first 

step included in Exhibit 4—the fate and transport module—is available for application in an 

ingestion risk assessment. EPA has completed some development activities for 

TRIM.Expo-Ingestion and TRIM.Risk-Human Health, two additional modules that cover the 

other three steps. Modeling software, however, is not currently available for these modules. 

For the RTR screening scenario, the Multimedia Ingestion Risk Calculator (MIRC), a Microsoft 

Excel-based 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. 

 

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

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

herein as part of the fate and transport modeling. Uptake of PB-HAPs into all other biotic media assumed 

to be ingested is modeled in the second step of the modeling framework. 

7

 Information about the current status of TRIM modules and comprehensive documentation of modules 

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

Exposure, and Risk Analysis website (http://www.epa.gov/ttn/fera/). 
Attachment A- 6/29/12 9 

2.4.1 Model Configuration and Parameterization 

The Tier 1 scenario is intended to reduce the possibility that EPA would not mis-identify risks 

above levels of concern. Although the health protective approach likely overestimates risk, EPA 

determined that this approach is appropriate for the purposes of screening assessments. As 

was done with the preliminary multipathway screening for RTR conducted in 2006 (U.S. EPA 

2006), exposures were modeled for a hypothetical farm homestead and fishable lake located 

adjacent to an emissions source. The hypothetical individual for which exposures were 

calculated was assumed to derive all potentially contaminated foodstuffs from adjacent 

locations, and many of the exposure/activity assumptions (e.g., amount of food consumed per 

day) were selected from the upper ends of representative exposure parameter distributions. 

The physical/chemical environment represented in the screening scenario was parameterized 

with two types of values. One type is typical values, such as national averages. The second 

type is health-protective values, or values that would tend to overestimate concentrations in 

media driving ingestion exposures for humans, based on knowledge of exposure patterns. In 

general, the spatial and temporal aspects of the scenario and the components of the scenario 

that influence air concentrations and deposition rates (which in turn affect all other exposures) 

were defined to be health protective. Chemical-specific and non-chemical-specific properties of 

the environmental media were parameterized with either typical or health protective values; 

properties having greater uncertainty were assigned a greater level of health protective bias. 

The spatial layout of the Tier 1 scenario and the meteorological data (or a combination of these 

two factors) are generally more influential than physical/chemical parameters in dictating the 

screening model outcomes, taking into account the potential range of variation in possible 

values. For example, where and how the layout is spatially oriented relative to the dominant 

wind direction can dramatically affect the concentrations in air, thereby driving estimated 

concentrations of PB-HAPs in soil, water, and biota. In contrast, 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 media concentrations. 

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

Chemical fate and 

transport: 

Physical environment 

and aquatic ecosystem

TRIM.FaTE

Uptake & transfer 

into produce and 

livestock

Multimedia Ingestion Risk Calculator (MIRC)

Human 

ingestion 

exposure

Risk & hazard 

estimation

Chemical Emissions to Air

Cancer Risk

Hazard Quotient
Attachment A- 6/29/12 10 

The mix of health protective and central-tendency assumptions and parameterization is 

expected to result in a scenario configuration that, on average, is likely to overpredict 

environmental concentrations of PB-HAPs in media of interest for this evaluation. Given the 

intended application of this scenario as a screening tool, this health protective bias was 

deliberate, because of the desire to ensure that risks above levels of concern are not 

overlooked (i.e., to minimize false negatives). Although the inclusion of central-tendency values 

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

to be expected from a screening scenario. False positives are addressed in subsequent tiers of 

the screening evaluation for a particular source. 

2.5 Fate and Transport Modeling (TRIM.FaTE) 

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

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

(TRIM.FaTE).8

 In developing the Tier 1 scenario, Version 3.6.2 of TRIM.FaTE was used to 

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

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

and the TRIM.FaTE public reference library, is available at http://www.epa.gov/ttn/fera/. 

The algorithms used to model mercury species and polyaromatic hydrocarbons (PAHs) are 

described in Volume II of the TRIM.FaTE Technical Support Document (U.S. EPA 2002a). A 

comprehensive evaluation of the performance of TRIM.FaTE for modeling mercury was 

documented in Volumes I and II of the TRIM.FaTE Evaluation Report (U.S. EPA 2002b, 2005b). 

Algorithms specific to the fate and transport of 14 chlorinated dibenzo-dioxin and -furan 

congeners were added following the addition of those for mercury and PAHs. Documentation of 

the application of TRIM.FaTE for dioxin emissions is contained in the third volume of the 

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

project), the TRIM.FaTE public reference library was updated to include information on 

modeling for cadmium. In general, many of the algorithms and properties included in the public 

reference library that are used to model mercury (except for the mercury transformation 

algorithms) are also applicable to cadmium. Comprehensive technical documentation of 

TRIM.FaTE algorithms specific to cadmium has not yet been compiled; however, all chemicalspecific properties used by TRIM.FaTE to model cadmium (as well as PAHs, mercury, and 

dioxins) are documented in Addendum 1 to this document. Based on a thorough 2011 

evaluation of TRIM.FaTE performance in modeling mercury’s fate, transport, and transformation 

in the aquatic food web, a zooplankton compartment was added to TRIM.FaTE’s aquatic 

compartment to increase the resolution and accuracy of the aquatic food web modeling. 

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

detail in Section 3. 

2.6 Exposure Modeling and Risk Characterization (MIRC) 

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

media and ingestion exposures for hypothetical individuals were obtained from EPA’s Human 

Health Risk Assessment Protocol for Hazardous Waste Combustion Facilities, or HHRAP (U.S. 

 

8

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

transformation of pollutants over time, through a user-defined, bounded system that includes both biotic 

and abiotic compartments. Outputs include pollutant concentrations in multiple environmental media and 

biota. 
Attachment A- 6/29/12 11 

EPA 2005a).9 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 5. This exhibit 

demonstrates the general relationships between the relevant TRIM.FaTE outputs (i.e., chemical 

concentrations in environmental media and fish) and the ingestion exposure and risk 

calculations carried out using MIRC. Additional discussion of exposure and risk calculations for 

the Tier 1 scenario is presented in Section 4 and Addendum 2, and all inputs required by these 

calculations are documented in Addendum 2. 

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

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

Dioxin Emissions 

Two of the four PB-HAPs for which screening emission thresholds have been developed for 

RTR—POM and dioxins—are chemical groups comprising numerous individual entities. The 

members of these categories reported in NEI include both specific chemicals and groups 

containing multiple chemicals. For example, for POM, emissions reported in NEI include 

various species, such as benz [a]anthracene, 2-methylnaphthalene, and chrysene, as well as 

non-specific entries, such as “PAH, total.” The constituents included in the POM and dioxin 

PB-HAP categories are grouped together not only because they are types of the “same” HAP, 

but also because members of these groups are assumed to have similar characteristics with 

respect to toxicity and behavior in the environment. 

To facilitate a practical application of the multipathway screening methods for RTR, reported 

emissions of POM and dioxins are normalized or scaled to a single reference chemical for each 

group. The reference chemicals used in RTR for POM and dioxins are benzo[a]pyrene and 

 

9

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. 
Attachment A- 6/29/12 12 

2,3,7,8-TCDD, respectively. These compounds were selected because they are relatively wellstudied among the members of the two groups and are also among the most toxic species 

within each group. 

Derivation of appropriate scaling factors begins with an evaluation of the basic relationship used 

to characterize health risk: 

Risk ∝ (Exposure Concentration) × (Toxicity) 

For a given air pollutant, the incremental exposure concentration is directly proportional to the 

emissions of that substance. That is, as the emissions increase, so too does the exposure to 

that substance. Furthermore, toxicity is assumed to increase linearly with concentration. 

Consequently, emissions of one substance (e.g., chrysene) can be scaled proportional to a 

reference compound (e.g., benzo(a)pyrene or BaP) by applying weighting factors corresponding 

to the relative differences in exposure behavior and toxicity. Using the POM group as an 

example and BaP as the reference compound, this scaling can be expressed through an 

equation as follows: 

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

where 

 EmissPAHi:BaP = Risk-weighted emissions of PAHi (weighted according to cancer risk 

relative to BaP for oral exposures) 

 EmissPAHi = Emission rate of PAHi

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

relative oral exposure between PAHi and BaP 

 = Toxicity equivalency (weighting) factor accounting for difference in relative 

toxicity via oral route between PAHi and BaP 

In combination, the product of the EEF and TEF for a given substance is considered to be a 

“risk equivalency factor” for the purposes of RTR evaluations that enables scaling of emissions 

of a given substance for a given exposure scenario. 

The TEF for each PAH and dioxin species can be calculated on the basis of relative toxicities. 

Toxicities were not evaluated separately for RTR but are based on analyses conducted by EPA 

elsewhere. For PAHs, oral toxicity values for individual species have been derived following the 

same approach used to develop inhalation toxicity values. For dioxins, TEFs are based on the 

relative toxicities developed by EPA recently and are ultimately based on the values developed 

by the World Health Organization (van der Berg et al. 2006). Refer to Addendum B for more 

information on these values. 

The EEFs can be calculated directly for each individual chemical that can be modeled in 

TRIM.FaTE and MIRC. TRIM.FaTE is configured for 14 POM congeners and 17 dioxin/furan 

congeners. For these substances, EEFs were calculated directly using the modeling approach 

and parameterization scheme for the Tier 1 scenario described in this document. Several other 

POM and dioxin emissions, however, are reported in the NEI. For these, exposure surrogates 
Attachment A- 6/29/12 13 

must be assigned after evaluating the correlation between chemical properties of the POM or 

dioxin congener and exposure quantified as lifetime average daily dose. The specific 

calculations for EEFs and exposure surrogates for each chemical group are discussed in the 

sections that follow. 

2.7.1 Calculation of Scaling Factors for POM Congeners 

The calculated EEFs, TEFs, and total REFs for the 14 POM congeners that are configured in 

TRIM.FaTE are shown in Exhibit 6. To determine appropriate exposure surrogates for 

chemicals not parameterized in TRIM.FaTE, EPA evaluated the relationships between 

chemical-specific properties (e.g., Kow and Henry’s law constant, kh) and intermediate modeled 

Exhibit 6. Exposure, Toxicity, and Risk Equivalency Factors Relative to BaP 

for Modeled POM Congeners 

Chemical 

Exposure 

Equivalency 

Factor (EEF) 

Toxicity 

Equivalency 

Factor (TEF)a

Risk 

Equivalency 

Factor (REF) 

7,12-Dimethylbenz(a)anthracene 2.07 34.2 70.83 

Dibenz[a,h]anthracene 4.67 0.56 2.62 

Benzo(a)pyrene 1.0 1.0 1.0 

Benzo(k)fluoranthene 4.20 0.16 0.69 

Benzo(b)fluoranthene 3.25 0.16 0.53 

Indeno(1,2,3-cd)pyrene 2.51 0.16 0.41 

Benzo(g,h,i)perylene 3.21 0.07 0.22 

Benzo(a)anthracene 0.07 0.16 0.012 

Chrysene 0.18 0.014 0.0024 

Fluoranthene 0.018 0.068 0.0012 

Acenaphthylene 0.012 0.068 0.00082 

Fluorene 0.010 0.068 0.00066 

Acenaphthene 0.0073 0.068 0.00050 

2-Methylnaphthalene 0.0051 0.068 0.00035 

a

TEFs are calculated as the ratio of the CSF for each specific POM congener to the CSF for 

benzo(a)pyrene. Dose response values, including CSFs, that are used in the screening assessment are 

discussed in Section 4 of Addendum 2. 

values (e.g., deposition) and exposure in terms of lifetime average daily dose (LADDs) where the average 

daily doses (ADDs) for the youngest two age groups were adjusted by the age-dependent adjustment 

factors (ADAFs) to account for the mutagenic mode of action of PAHs. The correlation between Kow and 

exposure is stronger than for any other chemical-specific property. Exposure surrogates were thus 

identified for each congener by calculating Total Lifetime Average Daily Dose (Age Adjusted) for each 
Attachment A- 6/29/12 14 

based on the congener’s Kow and the power regression of the modeled PAHs. Exhibit 7 shows that as 

Kow increases, so too does exposure. 

For POMs reported as unspeciated groups (i.e., “PAH, total” and “Polycyclic Organic Matter”) 

EPA assigned surrogates with Kow values near the upper end of the range of all of the Kow 

Exhibit 7. Relationship between Exposure and Kow for POM 

Congeners 

values, corresponding to an exposure near the upper end of the range (log Kow = 6.5). This assignment is 

assumed to be health protective and likely will not under predict exposure. 

The calculated EEFs, TEFs, and REFs for the 17 dioxin congeners that are configured in TRIM.FaTE are 

presented in Exhibit 8. Although there are many dioxins reported in the NEI other than the 17 configured 

for TRIM.FaTE, to date, none of them have been included in emissions datasets that have been 

screened. Therefore, no surrogate EEF’s have been developed for dioxins. In future screening 

assessments, if surrogate EEFs are needed, an approach similar to that used for POM will be used to 

develop surrogate EEFs for dioxins. 

Exhibit 8. Exposure and Toxicity Equivalency Factors Relative to TCDD for Modeled 

Dioxin Congeners 

Chemical 

Exposure 

Equivalency 

Factor (EEF) 

Toxicity 

Equivalency 

Factor (TEF)a

Risk 

Equivalency 

Factor (REF) 

PentaCDD, 1,2,3,7,8- 3.96 1.0 3.96 

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

Dioxins, Total, w/o Indiv. Isomers Rptd. 1.0 1.0 1.0 
Attachment A- 6/29/12 15 

Exhibit 8. Exposure and Toxicity Equivalency Factors Relative to TCDD for Modeled 

Dioxin Congeners 

Chemical 

Exposure 

Equivalency 

Factor (EEF) 

Toxicity 

Equivalency 

Factor (TEF)a

Risk 

Equivalency 

Factor (REF) 

Dioxins 1.0 1.0 1.0 

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

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

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

HexaCDD, 1,2,3,6,7,8- 1.11 0.1 0.11 

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

HexaCDF, 1,2,3,6,7,8- 0.63 0.10 0.063 

HexaCDD, 1,2,3,7,8,9 - 1.11 0.041 0.046 

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

HexaCDF, 2,3,4,6,7,8- 0.36 0.1 0.036 

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

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

HeptaCDD, 1,2,3,4,6,7,8- 1.17 0.01 0.012 

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

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

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

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

a Values from Van den Berg et al. (2006), except for 1,2,3,7,8,9-hexaCDD, which is calculated based on the ratio 

of the IRIS-based CSF for 1,2,3,7,8,9-hexaCDD to the IRIS-based CSF for 2,3,7,8-TCDD. Dose response 

values, including CSFs, that are used in the screening assessment are discussed in Section 4 of Addendum 2. 

2.7.1 Calculation of Scaling Factors for Dioxin Congeners 

Many facilities report dioxins as “Dioxins, Total, without Individual Isomers Reported,” “Dioxins,” 

or as “2,3,7,8-TCDD TEQ,” and in three cases, we do not adjust or scale the emissions. That is, 

we assume that they behave like and possess the toxic characteristics of TCDD. This approach 

could be improved by obtaining information on the speciation of dioxin emissions for each 

facility or an average speciation profile that could be assumed to apply to all facilities in a source 

category. 
Attachment A- 6/29/12 16 

3 Description of Environmental Modeling Scenario 

As described in Section 2.4.1, the physical configuration of the RTR Screening Scenario was 

designed to encompass the upper end of potential long-term PB-HAP exposures, and the 

environmental and chemical-specific properties were parameterized with either health protective 

or central-tendency values. Information regarding the scenario configuration and important 

aspects of the parameterization process, justifications for selecting particular property values, 

and model uncertainties is presented in the sections that follow. Comprehensive documentation 

of TRIM.FaTE property values for this scenario is provided in Addendum 1. 

3.1 Chemical Properties 

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

constant, molecular weight, and melting point, were obtained from peer-reviewed and standard 

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

a particular abiotic or biotic compartment type; these properties are discussed generally in the 

sections that follow and are documented in Addendum 1. 

3.2 Spatial Layout 

For the purpose of estimating media concentrations, the TRIM.FaTE scenario is intended to 

represent a farm homestead and a fishable lake (and its surrounding watershed) located near 

the emissions source of interest. A diagram of the surface parcel layout is presented in Exhibit 

9. The source parcel is parameterized as a square with sides of 250 m, which is assumed to be 

a fair estimation for the size of a relatively small-to-medium facility at the fence line. With a 

predominant wind direction toward the east, the modeled layout is generally symmetric about an 

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

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

scenario. Based on the results of dispersion modeling (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 

negligible.10 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, 

 

10Mass deposited at the outer edge of the watershed is expected to result in only a very small increase in 

estimated exposure via fish consumption by increasing the chemical mass transported to the lake through 

erosion and runoff. The distance from these more distant locations to the lake would attenuate transport 

of chemical mass by erosion and runoff, dampening the effect of including additional deposition beyond 

10 km. (Other exposure pathways largely would be unaffected; the soil concentrations used to calculate 

exposures for the farm food scenario are derived from soil parcels located close to the source and 

unaffected by deposition to the far reaches of the watershed.) 
Attachment A- 6/29/12 17 

the lateral spread of the plume (σy, measured from the centerline) at a certain distance from the 

source can be estimated using the Pasquill-Gifford curves. Turner (1970) derived the equations 

for these curves, which can be found in the Industrial Source Complex 3 Dispersion Model 

Manual (among other sources).11 For a relatively neutral atmosphere (stability class D), σ at 

10km is about 550 m using this estimation. In a Gaussian distribution, about 99.6 percent of the

 

11http://www.epa.gov/scram001/userg/regmod/isc3v2.pdf 
Attachment A- 6/29/12 18 

Exhibit 9. TRIM.FaTE Surface Parcel Layout 

plume spread area is contained within 3σ of the median line. Therefore, the plume σ was set at 

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

width for these conditions is expected to be about twice this distance, or 3.5 km. These 

dimensions were used to define the dimensions of the overall air and surface parcel layouts for 

the screening scenario. 

The surface (land and surface water) modeling area was initially divided into five pairs of parcels 

the areas of which increase with distance from the source, which approximately corresponds to 

the spatial gradient that is expected in the downwind direction from the source. The second 

north parcel from the source was divided further into two parts; one of them tilled soil (Parcel 

N6) to represent agricultural conditions and the other untilled to represent pasture. 

The depth of the surface soil compartments was set to 1 cm, except for Parcel N6, for which the 

depth was set to 20 cm to simulate the effect of tillage. Characteristics of the soil layers (e.g., 

organic carbon content, air and water content, and subsoil depth) generally were set to 

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

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 scenario that is protective of health, 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). 
Attachment A- 6/29/12 19 

3.3 Watershed and Water Body Parameterization 

Properties associated with the watershed soil and lake determine how pollutants in the system 

are transported through and accumulate in various compartments. These properties describe 

the physical characteristics of the environmental media included in the modeled region, as well 

as the assumed connections and relationships between media types and modeled spatial 

components that in turn affect chemical transport via water runoff, ground infiltration, deposition 

of suspended sediments in the water column, and other processes. This section presents the 

justification for setting the key properties of the soil, water, and sediment compartments. Also 

discussed are some of the chemical properties related to watershed and waterbody processes 

(chemical-specific compartment properties in TRIM.FaTE) and the configuration of terrestrial 

plants included in the scenario. 

3.3.1 Water Balance 

Water-related properties of the lake and related watershed characteristics (e.g., runoff rates 

from each surface soil compartment) were set so that a simplified water balance is achieved. 

Although TRIM.FaTE maintains a chemical mass balance, the model does not calculate or 

maintain media mass balances (e.g., for water) except where specified in certain formulas. For 

the Tier 1 scenario, the parameters were set to satisfy two equations relating water volume. 

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

scenario: 

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

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

lake via groundwater. 

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

lake: 

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

[outflow from the lake]

Note that TRIM.FaTE actually uses only some of these properties (e.g., precipitation rate and 

surface runoff, but not evapotranspiration). The water characteristics assumed for the Tier 1 

scenario are meant to represent a relatively wet and moderately warm location in the United 

States (USGS 1987). Following are the assumptions for this scenario: 

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

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

runoff. 

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

 40 percent of total precipitation contributes to overland runoff. 

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

lake. 

For these calculations, the source parcel was considered to be outside the watershed and 

therefore was not included in the water balance. The evaporation rate from the lake was 
Attachment A- 6/29/12 20 

assumed to be 700 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 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 

Addendum 1. 

3.3.2 Sediment Balance 

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

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

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

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

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

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

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

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

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

sediment from the unconsolidated benthic sediment compartment to the consolidated sediment 

layer. 

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

compartment using the universal soil loss equation (USLE, Wischmeier and Smith 1978), 

assuming a relatively high rate of erosion. The total suspended sediment concentration is 

assumed to remain constant in TRIM.FaTE, and the flushing rate of the lake (calculated via the 

water balance approach described above) was then used to estimate the removal of sediment 

from the modeling domain via lake water outflow. The difference between these sediment 

fluxes was taken to be the sediment burial rate. The sediment burial rate is the rate at which 

sediment particles in the unconsolidated benthic sediment layer are transported to the 

consolidated sediment, where the particles can no longer freely interact with the water column. 

In TRIM.FaTE, the consolidated sediment layer is represented with a sediment sink; as with all 

sinks in TRIM.FaTE, chemical mass sorbed to buried sediment that is transported to the sink 

cannot be returned to the modeling domain. The burial rate is a formula property calculated by 

the model according to the difference between user-specified values for sediment deposition 

velocity (from the water column to the benthic sediment) and sediment resuspension velocity 

(back into the water column from the benthic sediments). These formula properties assume a 

constant volume of particles in the sediment layer (because the densities for benthic and 

suspended sediment particles were defined to the same value, the mass of particles in the 

sediment is also constant). 

For the Tier 1 scenario described here, the average sediment delivery rate (i.e., transfer of 

sediment mass from watershed surface soil to the lake due to erosion) for the entire watershed 

was estimated to be about 0.0036 kg/m2

-day, based on calculations using the USLE. The 

HHRAP documentation notes that using the USLE to calculate sediment load to a lake from the 

surrounding watershed sometimes leads to overestimates (U.S. EPA 2005a). For the Tier 1 
Attachment A- 6/29/12 21 

scenario, however, this approach was considered to be appropriate in that health protective 

assumptions are a goal of the screening scenario.12 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 9). Erosion and runoff from the source parcel are linked directly to a sink 

and do not enter the Tier 1 scenario lake. The transport of sediment to the lake via overland is 

thus assumed to be efficient. Note that erosion from parcels not directly adjacent to the lake is 

assumed to be somewhat attenuated, effected by using a lower sediment delivery ratio in the 

USLE. 

Using the calculated surface soil erosion rates for the scenario, the total average daily sediment 

load to the lake from the watershed is about 16,600 kg/day. About 15 percent of this load is 

removed from the lake via outflow of suspended sediments (based on a calculated flush rate of 

12.2 volume turnovers per year) with the remainder of the sediment input to the lake transferred 

to the sediment burial sink. 

3.4 Meteorology 

Meteorological properties used in TRIM.FaTE algorithms include air temperature, mixing height, 

wind speed and direction, and precipitation rate. These properties, which can vary significantly 

among geographic locations, and seasonally and hourly for a single location, greatly influence 

the chemical concentrations predicted in media of interest. Because the screening scenario is 

intended to be generally applicable for any U.S. location, and to minimize the frequency of false 

negatives, a health protective configuration was used. The meteorology of the screening 

scenario was defined to ensure that (when used in combination with the selected spatial layout) 

the maximum exposures that might be encountered for the scenarios of interest would be 

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

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

source). Ensuring that the meteorological parameters were not overly protective of health, such 

as always having the wind blow toward the location of interest, however, was also important to 

avoid too many false positives. 

The meteorological data for the screening scenario are intended to be representative of a 

location with a low wind speed, a wind direction that strongly favors the watershed, and a 

relatively high amount of total precipitation falling on the watershed. The values used were 

based on actual data trends for U.S. locations as specified in Exhibit 10 but an artificial data set 

was compiled for this analysis (for example, temporally variable meteorological parameters 

were made to vary only on a daily basis). This simplified approach allowed for greater control 

(relative to selecting a data set for an actual location) so that desired trends or outcomes could 

be specified. Also, using a meteorological data set with values varying on a daily basis rather 

than a shorter period (such as hourly, which is the typical temporal interval for meteorological 

measurements) reduced required model run time. Meteorological inputs are summarized in 

Exhibit 10. 

 

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

decrease surface soil concentrations; the relative impact on resulting concentrations, however, will be 

proportionally greater in the waterbody. 
Attachment A- 6/29/12 22 

Exhibit 10. Summary of Key Meteorological Inputs 

Meteorological 

Property Selected Value Justification 

Air temperature Constant at 298 Kelvin Recommended default value listed in HHRAP (U.S. EPA 

2005a). Value is similar to the mean daily June temperature in 

much of the U.S. Southern Plains and Southeast.a

Mixing height Constant at 710 m Value is 5th percentile of annual average mixing heights for 75 

U.S. locations, using data obtained from EPA’s SCRAM Web 

site.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 midlatitude daytime value (Stull 1988). 

Wind direction Blows from source parcel 

into scenario domain (west 

to east) 3 days per week; 

during other times does 

not blow into domain 

A wind direction that favors the location of interest (for 

example, a watershed downwind of a source of concern) will 

tend to result in more emitted mass accumulating in the 

location of interest. For much of the U.S. mid-Atlantic and 

western regions, the wind tends to favor the eastward 

direction.a

 Among the NCDC 1981–2010 normalized wind 

vector data, the average wind direction had a strong eastward 

component at over one-third of the stations.c

 For the 

hypothetical RTR scenario, a more extreme example of this 

pattern is represented by conditions in Yakima, Washington, 

where the wind blows eastward approximately 40 percent of 

the time based on a review of wind direction data compiled by 

the National Weather Service (NCDC 1995). This pattern was 

approximated in the RTR scenario with a configuration in which 

the modeled domain is downwind of the source 3 out of 7 days.

Horizontal wind 

speed 

Constant at 2.8 m/sec Set to 5th percentile of annual average speed for 239 stations 

across the contiguous United States (about 50 years of data 

per station). Value is similar to the annual average wind 

speeds of many areas of the U.S. east coast and west coast.a

Precipitation 

frequency 

Precipitation occurs 3 

days per week; wind 

direction blows into 

domain 2 of these days 

This value was selected so that two-thirds of the total 

precipitation occurs when the domain is downwind of the 

modeled source. This pattern approximates that for rainy U.S. 

locations, where precipitation occurs 35–40% of the time 

(Holzworth 1972). These locations include parts of the U.S. 

Northeast and Northwest.a

Total 

Precipitation 

1.5 m/yr Assumed to represent rainy conditions for the United States. 

This annual precipitation amount is experienced in parts of the 

U.S. Deep South and parts of the U.S. northwest coast.a

 

Conditional precipitation rate (rainfall rate when precipitation is 

occurring) is 9.59 mm/d, which is similar to conditions in many 

areas along the U.S. east coast and in the Midwest and 

Plains.a

a

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

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

National Climatic Data Center 1981-2010 Climate Normals; 

http://www.ncdc.noaa.gov/oa/climate/normals/usnormals.html 
Attachment A- 6/29/12 23 

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 onto the soil and lower mixing heights should reduce the volume through which 

pollutants disperse. The wind speed used for the screening scenario was 2.8 m/s, the 

5thpercentile of the annual averages among 239 stations; by comparison, the mean annual 

average wind speed is approximately 4 m/s in the contiguous United States). The mixing height 

(mean heights from 4 states) used was 710 m (the 5th percentile of annual averages among all 

40 states in the SCRAM database). 

3.5 Aquatic Food Web 

The aquatic food web is an important part of the screening scenario because the chemical 

concentrations modeled in fish are used to calculate human ingestion exposure and risks 

associated with eating contaminated local fish. A biokinetic approach to modeling 

bioaccumulation in fish is used in the RTR screening scenario. The primary producers (first 

trophic level) in the TRIM.FaTE aquatic ecosystems are algae and macrophytes in the water 

column and detritus in the sediments (the latter simulated as sediment particles). The scenario 

includes a benthic invertebrate compartment to represent the primary invertebrate consumers 

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

higher tropic levels in the aquatic system. For TRIM.FaTE to provide reasonable predictions of 

the distribution of a chemical across biotic and abiotic compartments in aquatic systems, the 

biomass of the aquatic biotic compartments must represent all biota in the system and the 

distribution of biomass among the trophic levels and groups must be as realistic as possible. 

To support the development of a relatively generic freshwater aquatic ecosystem in which to 

model bioaccumulation in fish, a literature search, review, and analysis was conducted in 

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

2005). This research demonstrated that the diversity of species and food webs across U.S. 

aquatic ecosystems is substantial, reflecting the wide range of sizes, locations, and 

physical/chemical attributes of both flowing (rivers, streams) and low-flow water bodies (ponds, 

lakes, reservoirs). In general, lentic bodies of water (lakes and ponds) can accumulate higher 

levels of contaminants in both sediments and biota than lotic systems (rivers, streams). Also, 

the previous research (ICF 2005) suggests that a lake of at least 60 hectares (ha) or 150 acres 

could support higher trophic level predatory fish, with some fraction of their diet comprising 

smaller fish. 

The RTR Tier 1 scenario includes a generic aquatic ecosystem with a 47-ha (116-acre) lake. 

Although slightly smaller than the size suggested by the previous review (ICF 2005), a 47-ha 

lake is large enough to support higher trophic level fish given appropriate conditions (e.g., high 

productivity given a sufficient nutrient base and temperature). Also, this size was compatible 

with the overall size of the defined watershed in the screening scenario. The fish types, 

biomass, diet fractions, and body weights recommended for fish compartments for the Tier 1 

scenario are listed in Exhibit 11. Biomass is based on an assumption that the total fish 

biomass (wet-weight) for the aquatic ecosystem is 5.7 grams per square meter (gw/m2

, ICF 

2005). 

In general, the food web implemented in the Tier 1 scenario is intended to be generally 

applicable across the United States and is intended to be generally health protective (to 

simulate a food web that maximizes bioaccumulation). 
Attachment A- 6/29/12 24 

Exhibit 11. Aquatic Biota Parameters for the TRIM.FaTE Screening Scenario 

TRIM.FaTE 

Compartment 

Type 

Organisms 

Represented by 

Compartment 

Biomass 

Diet 

Average 

Body Weight

(kg) Areal density 

(gw/m2) 

Fraction of 

Total Fish 

Biomass 

Algae 

green algae, 

diatoms, bluegreen algae 7.95 – Autotrophic – 

Zooplankton 

crustaceans, 

rotifers, 

protozoans 

6.36 – 100% algaea 5.7 E−8 

Macrophyte hydrilla, milfoil 500 – – – 

Water column 

planktivore 

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

Water column 

omnivore sucker, carp 0.5 8.8% 100% water column planktivore 0.25 

Water column 

carnivore 

largemouth bass, 

walleye 0.2 3.5% 100% water column omnivore 2.0 

Benthic 

invertebrate 

aquatic insect 

larvae, 

crustaceans, 

mollusksb

20 – detritus in sediments 0.000255 

Benthic 

omnivore 

small catfish, 

rock bass 2.0 35.1% 100% benthic invert. 0.25 

Benthic 

carnivore 

large catfish, 

sculpins 1.0 17.5% 50% benthic invert. 50% benthic omniv. 2.0 

Total Fish 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), crustaceans (e.g., amphipods, crayfish), 

and mollusks (e.g., snails, mussels). 

c

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

3.6 Using TRIM.FaTE Media Concentrations 

The Tier 1 scenario outputs include average PB-HAP concentrations and deposition rates for 

each year and for each parcel of the model scenario. In each surface parcel, deposition rates to 

the soil are provided as are soil concentrations for the surface, root, and vadose zones and 

grass or leaf concentrations as appropriate for the plants. For each air parcel, air 

concentrations are provided. For the lake, surface water concentrations and concentrations in 

the various levels of the aquatic food chain are provided. For the ingestion exposure 

calculations, some concentrations are used to calculate direct exposure, and some are used to 

perform the farm food chain concentration calculations in the various media that humans can 

ingest (see Exhibit 4). 
Attachment A- 6/29/12 25 

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 health protective assumptions. 

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

the Tier 1 scenario assume exposure at locations near the modeled source, thereby resulting in 

higher exposures to emitted chemicals. These assumptions are summarized in Exhibit 12. 

TRIM.FaTE can output instantaneous chemical concentrations for a user-specified time step 

and also can be configured to calculate temporal averages (e.g., annual averages). For the 

Tier 1 scenario, the model is set up to output results on a daily basis, largely because daily is 

the smallest time step over which input data change (i.e., wind direction and precipitation rate). 

Daily concentration results were averaged to obtain annual average concentrations. The default 

assumption is annual average concentrations for media during the fiftieth year of emissions. 

For the chemicals modeled in this scenario, long-term concentrations in environmental media 

will be relatively constant at 50 years (aside from mercury, chemicals modeled for RTR 

approach steady state before 50 years). 

Exhibit 12. Spatial Considerations – TRIM.FaTE Results Selected for Calculating Farm 

Food Chain Media Concentrations and Receptor Exposures 

TRIM.FaTE Output Used in Exposure 

Calculations Representative Compartment 

Concentration in air, for uptake by plants via vapor 

transfer Air compartment in air Parcel N2 (air over tilled soil) 

Deposition rates, for uptake by farm produce Deposition to surface soil compartment in surface 

Parcel N6 (tilled soil) 

Concentration in surface soil, for incidental 

ingestion by humans and farm animals 

Surface soil compartment in surface Parcel N1 

(untilled soil, closest to facility) 

Concentration in soil, for uptake by farm produce 

and animal feed 

Surface soil compartment in surface Parcel N6 

(tilled soil) 

Concentration in fish consumed by angler 

Water column carnivore compartment in lake (50% 

of fish consumed) and benthic carnivore in lake 

(50% of fish consumed) 

4 Description of Exposure and Risk Modeling Scenario 

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

(FFC) media (Section 4.1); estimating human exposures associated with ingestion of FFC 

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

(Section 4.2); and calculating human health screening risk metrics associated with these 

exposure pathways (Section 4.3). All of these calculations are conducted using MIRC. For this 

multipathway screening evaluation, partitioning into FFC media is accomplished with the same 

model used to model exposure and risk, not as a part 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 Addendum 2. 
Attachment A- 6/29/12 26 

4.1 Calculating Concentrations in Farm Food Chain Media 

As was shown in Exhibit 5., MIRC was compiled to calculate concentrations of PB-HAPs in 

foodstuffs that are part of the farm food chain. The FFC media included in this screening 

scenario include: 

 exposed and protected fruit, 

 exposed and protected vegetables, 

 root vegetables, 

 beef, 

 dairy products, 

 pork, and 

 poultry and eggs. 

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

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

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

factors. Environmental media concentrations (i.e., the chemical source terms in these 

algorithms) are obtained from TRIM.FaTE. As noted in Section 0, the TRIM.FaTE outputs 

included as inputs to MIRC are the following: 

 PB-HAP concentrations in air; 

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

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

and 

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

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

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

included in HHRAP (U.S. EPA 2005a). Addendum 2 provides parameter values used in MIRC 

for the Tier 1 assessment. 

4.2 Ingestion Exposure 

MIRC was used to estimate ingestion rates as ADDs, normalized to body weight for a range of 

exposure pathways. Exposure pathways included are incidental ingestion of soil 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 13. 
Attachment A- 6/29/12 27 

Exhibit 13. Summary of Ingestion Exposure Pathways and Routes of Uptake

Ingestion 

Exposure 

Pathway 

Medium Ingested 

Intermediate 

Exposure 

Pathway – Farm 

Animalsa

Environmental Uptake Route 

Medium Processb

Incidental ingestion 

of soil Untilled surface soil N/A Surface 

soil 

Deposition; transfer via erosion and 

runoff c

Consumption of 

fish 

Fish from local water 

body N/A Fish tissue 

Direct uptake from water and 

consumption of food compartments 

modeled in TRIM.FaTEc

Consumption of 

breast milkd Breast milk N/A Breast 

milk 

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 0.
Attachment A- 6/29/12 28 

4.2.1 Exposure Scenarios and Corresponding Inputs 

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

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

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

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

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

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

primarily from EPA’s Exposure Factors Handbook (U.S. EPA 2011). 

For the Tier 1 scenario described here, exposure characteristics that would result in a highly 

health protective estimate of total exposure were selected. The ingestion rate for each medium 

was set at high-end values (typically 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 Tier 1 scenario are summarized in 

Exhibit 14. 

4.2.2 Calculating Average Daily Doses 

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

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

provided in the technical documentation of EPA’s Multimedia, Multipathway, and Multireceptor 

Risk Assessment (3MRA) Modeling System (U.S. EPA 2003), which derived much of its input 

data from the Exposure Factors Handbook (U.S. EPA 2011). The ingestion exposure modeling 

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

of exposure dose estimation and the equations to calculate ADDs for each ingestion pathway 

are provided in Addendum 2. 

4.2.3 Infant Ingestion of Breast Milk 

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

the contaminants to her infant through breast milk (ATSDR 1998). The nursing infant’s 

exposure can be estimated from the levels of chemical concentrations in the breast milk, which 

in turn can be estimated based on the mother’s chemical intake. Exposures can occur for 

infants via this pathway for dioxins. 

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

account for approximately 30 percent of an adult’s lifetime ADD. This ratio was determined to be 

independent of the scenario being modeled and is based on the assumption that the mother’s 

exposure is approximately equal to the adult lifetime ADD exclusive of breast milk exposure. 

The 30 percent ratio and the validity of the supporting assumption were established by 

performing multiple MIRC model iterations using different scenarios. In the absence of 

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

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

breast milk consumption during the first year of life was assumed to account for approximately 

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

pathway during the first year of life was, in this way, accounted for in developing the threshold 
Attachment A- 6/29/12 29 

Exhibit 14. Overview of Exposure Factors Used for RTR Multipathway Screeninga,b

Exposure Factor Selection for Screening Assessment 

Age group evaluated 

Infants under 1 year (breast milk only) 

Children 1–2 years of age 

Children 3–5 years of age 

Children 6–11 years of age 

Children 12–19 years of age 

Adult (20–70 years) 

Body weight (BW; varies by age) Weighted mean of national distribution 

Intake rate and ingestion rate (IR) for farm produce and animal 

products (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 anglersd); 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 lossf 

Assumed to be “typical”; varies depending 

on food product (see Addendum 2). 

Cooking losses were not considered for fish 

consumption because intake rates represent 

“as prepared” values. 

a

Data for exposure characteristics are presented in Addendum 2. Exposure parameter values were based on data 

obtained from the Exposure Factors Handbook (U.S. EPA 2011). See Addendum 2 for details. b

Exposure factor inputs are used in calculating ADD estimates for each exposure pathway. ADD equations for each 

pathway evaluated in this screening assessment are provided in Addendum 2. c

Fish consumption rates were obtained from Estimated Per Capita Fish Consumption in the United States (U.S. 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 anglers was obtained from Methodology for Deriving Ambient 

Water Quality Criteria for the Protection of Human Health (U.S. 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; however, for the Tier 1 scenario, all ingested media are assumed to be impacted. 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.

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. 
Attachment A- 6/29/12 30 

4.3 Calculating Risk 

MIRC was used to calculate excess lifetime cancer risk and non-cancer hazard (expressed as 

the hazard quotient or HQ) using the calculated ADDs and ingestion dose-response values. 

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

and RfDs for the PB-HAPs included in the Tier 1 scenario are presented in Exhibit 15 and are 

discussed in more detail in Addendum 2. Equations used to estimate cancer risk and noncancer hazard also are provided in Addendum 2. 

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

upward to account for the mutagenic cancer potency of these compounds during childhood, as 

specified by EPA in supplemental guidance for cancer risk assessment (U.S. EPA 2005c). 

Specifically, cancer potency for PAHs is assumed to be tenfold greater for the first 2 years of life 

and threefold greater for the next 14 years. These factors were incorporated into a timeweighted total increase in potency over a lifetime of 70 years. The cancer potency adjustment 

for chemicals with a mutagenic mode of action is discussed in Addendum 2. 

Exhibit 15. Dose-response Values for PB-HAPs Addressed by the Screening Scenario 

PB-HAP CSF 

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

Inorganics 

Cadmium compounds (as Cd) not available 1E−3 IRIS 

Elemental mercury not available not available 

Divalent mercury not available 3E−4 IRIS 

Methyl mercury not available 1E−4 IRIS 

Organics 

Benzo[a]pyrenea 10 IRIS not available 

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

Source: U.S. EPA (2007). 

CSF = cancer slope factor; RfD = reference dose; IRIS = Integrated Risk Information System; Cal/EPA = California 

Environmental Protection Agency; EPA ORD = EPA’s Office of Research and Development a

For consistency with the overall approach for dose-response assessment of PAHs, the CSF listed in IRIS for 

benzo[a]pyrene ([7.3 mg/kg-day]-1) was adjusted due to its mutagenic mode of action as discussed below (see also 

Addendum 2).

4.4 Summary of Tier 1 Assumptions 

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

from RTR facilities is intended to be health protective to prevent underestimating risk. The 

overall degree to which the scenario is health protective is the sum of the multiple assumptions 

that affect the outputs of the fate and transport, exposure, and risk modeling. Exhibit 16 

summarizes important characteristics that influence exposure and risk estimates for this 

scenario and indicates the general degree of health protectiveness associated with the values 

for each assumption. Although this summary does not provide a quantitative estimate of the 

output uncertainty or the degree to which exposures and risks estimated using the scenario 

would be overestimated, it does demonstrate qualitatively that the scenario generally 

overestimates exposure and thus favors a health-protective risk output. 
Attachment A- 6/29/12 31 

Exhibit 16. Summary of RTR Screening Scenario Assumptions 

Characteristic Value Neutral or 

Health 

Protective? 

Comments on Assumptions 

General Spatial Attributes 

Farm location 375 m from source; 

generally 

downwind 

Health Protective Location dictates soil and air concentrations 

and deposition rates used to calculate 

chemical levels in farm produce. 

Lake location 375 m from source; 

generally 

downwind 

Health Protective Location dictates where impacted fish 

population is located. 

Surface soil 

properties 

Typical values or 

national averages 

Neutral Based on existing EPA documentation and 

other references. 

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

chemical concentration. 

Size of lake 46 ha; about 3 m 

average depth 

Health Protective Lake is large enough to support an aquatic 

ecosystem with higher trophic-level fish, but 

is relatively small and shallow (thus 

increasing surface area-to-volume ratio). 

Meteorological Inputs 

Total precipitation 1.5 m/yr Health Protective Intended to represent rainy U.S. location; 

set to highest state-wide average for the 

contiguous United States. 

Precipitation 

frequency (with 

respect to impacted 

farm/lake) 

2/3 of total 

precipitation fall on 

farm/lake and 

watershed 

Health Protective Most of total precipitation occurs when the 

farm/lake are downwind of the source. 

Wind direction Farm/lake are 

downwind 40% of 

the time 

Health Protective Farm/lake located in the predominantly 

downwind direction. Temporal dominance 

of wind direction based on data from 

Yakima, Washington, where wind is 

predominantly from the west. 

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

averages for contiguous United States); 

increases net deposition to lake/watershed. 

Air temperature 298 K Neutral Typical for summer temperatures in central 

and southern United States. 

Mixing height 710 m Health Protective Relatively low long-term average mixing 

height (5th percentile of long-term averages 

for contiguous United States); increases 

estimated air concentration. 

Watershed and Water Body Characteristics 

Evaporation of lake 

surface water 

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

Surface runoff into 

lake 

Equal to 40% of 

total precipitation 

Health Protective Based on typical water flow in wetter U.S. 

locations; higher runoff results in greater 

transfer of chemical to lake. 
Attachment A- 6/29/12 32 

Exhibit 16. Summary of RTR Screening Scenario Assumptions 

Characteristic Value Neutral or 

Health 

Protective? 

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. Might overestimate flushing 

rate if water inputs are also overestimated. 

Note that after evapotranspiration, 

remaining water volume added via 

precipitation is assumed to flow into or 

through lake. 

Soil erosion from 

surface soil into lake 

Varies by parcel; 

ranges from 0.002 

to 0.01 kg/m2

-day 

Neutral Erosion rates 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). Might underestimate erosion for 

locations susceptible to high erosion rates. 

Note that higher erosion increases 

concentration in lake (and fish) but 

decreases levels in surface soil (and farm 

products). 

Aquatic food web 

structure and 

components 

Multilevel; includes 

large, upper 

trophic-level fish 

Health Protective Inclusion of upper trophic-level fish and 

absence of large-bodied 

herbivore/detritivore fish favor higher 

concentrations of bioaccumulative 

chemicals and result in higher 

concentrations in consumed fish. Linear 

food-chain maximizes concentration of 

bioaccumulative chemicals in higher 

trophic-level fish. 

Parameters for Estimating Concentrations in Farm Food Chain Media 

Fraction of plants 

and soil ingested by 

farm animals that is 

contaminated 

1.0 (all food and 

soil from 

contaminated 

areas) 

Health Protective Assumes livestock feed sources (including 

grains and silage) are derived from most 

highly impacted locations. 

Soil- and air-to-plant 

transfer factors for 

produce and related 

parameters 

Typical (see 

Addendum 2 for 

details) 

Neutral Obtained from peer-reviewed and standard 

EPA reference sources. 

Biotransfer factors 

for efficiency of 

uptake by animal of 

chemical in food/soil 

Typical (see 

Addendum 2 for 

details) 

Neutral Obtained from peer-reviewed and standard 

EPA reference sources. 

Bioavailability of 

chemicals in soil (for 

soil ingested by 

animals) 

1.0 (relative to 

bioavailability of 

chemical in plant 

matter) 

Health Protective Probably overestimates bioavailability in 

soil; many chemicals are less bioavailable 

in soil than in plants. 
Attachment A- 6/29/12 33 

Exhibit 16. Summary of RTR Screening Scenario Assumptions 

Characteristic Value Neutral or 

Health 

Protective? 

Comments on Assumptions 

Ingestion Exposure Assumptions

Ingestion rates for all 

farm 

produce/livestock 

types 

Person obtains all 

food sources from 

local farm; 

ingestion rate is 

90th percentile of 

rates for homeproduced food 

items 

Health Protective All food derived from impacted farm; total 

food ingestion rate would exceed expected 

body weight-normalized ingestion rates 

(prevents underestimating any individual 

food type). 

Fish ingestion rate 1.48 g/kg-day for 

adults; between 

1.29 and 2.08 

g/kg-day for 

children ages 1–19 

years 

Health Protective Rates are based on the 99th percentile of 

general population fish consumption from 

EPA’s analysis of freshwater and estuarine 

fish consumption derived from the USDA 

Continuing Survey of Food Intake by 

Individuals (2002). 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 

Addendum 2 for a detailed discussion. 

Exposure frequency Consumption of 

contaminated food 

items occurs 365 

days/yr 

Health Protective All meals from local farm products. 

Body weight Mean of national 

distribution 

Neutral Note that this does not affect the bodyweight-normalized rates for produce and 

animal products. 

Other Chemical-Specific Characteristics

General chemical 

properties used in 

fate and transport 

modeling (Henry’s 

law, Kow, etc.) 

Varies Neutral Obtained from peer-reviewed sources; 

intended to be representative of typical 

behavior and characteristics. 

"General" physical 

properties (plant 

matter density, 

aquatic life biomass, 

algal growth rate, 

etc.) 

Varies Neutral Obtained from peer-reviewed sources; 

intended to be representative of typical 

behavior and characteristics. 

Dose-response 

values 

Varies Neutral to Health 

Protective 

Values used are those determined to be 

appropriate for risk assessment by OAQPS; 

values are developed to be health 

protective. 
Attachment A- 6/29/12 34 

5 Evaluation of Screening Scenario 

5.1 Introduction 

The screening scenario developed for assessing multipathway human health risk for EPA’s Risk 

and Technology Review has been subjected to a series of evaluations. The major PB-HAP 

categories of concern for this analysis are cadmium compounds (Section 5.2), mercury 

compounds (Section 5.3), dioxins (Section 5.4), and POM (Section 5.5). The scenario 

evaluations were focused primarily on assessing the behavior of these HAP categories in the 

environment, the accumulation of these chemicals in ingestible food products, and the 

predominant pathways of human exposure. 

5.2 Cadmium Compounds 

Some of the largest anthropogenic sources of cadmium to air are facilities that process, mine, or 

smelt cadmium-zinc ores or cadmium-zinc-lead ores, coal- and oil-fired boilers, other urban and 

industrial facilities, phosphate fertilizer manufacturing facilities, road dust, and municipal sewage 

sludge incinerators (ATSDR 2008). 

5.2.1 Behavior in the Environment 

Once emitted into the environment, airborne cadmium particles can be transported over long 

distances before being they are deposited. Cadmium has been observed to partition primarily 

to soil when released to the environment (ATSDR 2008). The mobility of cadmium in soil 

depends strongly on soil pH, clay content, and availability of organic matter—factors that 

determine whether the cadmium is dissolved or sorbed in surface soil. In general, cadmium 

adsorbs to soil particles in the surface layers of the soil profile, but to a lesser degree than many 

other heavy metals (HSDB 2005a). Cadmium also binds strongly to organic matter, rendering 

the metal relatively immobile in some soils. Nonetheless, some plants still can take up cadmium 

efficiently, thus providing an entry point for cadmium into the food chain (ATSDR 2008). 

Cadmium also enters surface waters, which can occur via atmospheric deposition, runoff and 

erosion, or wastewater streams. Most cadmium compounds entering the water column are 

quickly removed through adsorption to organic matter in sediment or to other suspended 

compounds. Cadmium that remains in the water column is expected to exist primarily in the 

dissolved state where it is available for uptake by aquatic organisms. 

Freshwater fish accumulate cadmium primarily through direct uptake of the dissolved form 

through the gills and secondarily through the diet, which plays a variable role in total cadmium 

uptake (Reinfelder et al. 1998; Chen et al. 2000; Saiki et al. 1995). Although some 

biomagnification of cadmium has been reported for aquatic food chains in saltwater systems, 

biomagnification in freshwater systems appears to be present only at lower trophic levels (Chen 

et al. 2000) and in narrowly defined niches (e.g., plankton/macroinvertebrate food chains; 

Croteau et al. 2005). Biomagnification factors (BMFs) of less than 1 generally have been 

reported for fish at higher trophic levels, indicating that cadmium concentrations generally 

biodiminish from lower to higher trophic levels (Chen et al. 2000; Mason et al. 2000). 

For the RTR screening scenario, the partitioning behavior modeled in TRIM.FaTE was 

consistent with the behavior of cadmium expected in the natural environment. 
Attachment A- 6/29/12 35 

5.2.2 Concentrations in Ingestible Products 

Most non-inhalation exposure to cadmium outside of occupational settings is through dietary 

intake. Available data indicate that cadmium accumulates in plants, aquatic organisms, and 

terrestrial animals, offering multiple ingestion exposure pathways (ATSDR 2008). Actual 

cadmium levels in ingestible products, however, varies based on type of food, agricultural and 

cultivation practices, atmospheric deposition rates, characteristics of environmental media, and 

presence of other anthropogenic pollutants. Meat and fish generally contain lower amounts of 

cadmium overall, but cadmium can be highly concentrated in certain organ meats, such as 

kidney and liver (ATSDR 2008). In a study of cadmium concentrations in 14 food groups 

(including prepared foods), meat, cheese, and fruits generally contained low levels of cadmium 

(ATSDR 2008). 

For the RTR screening scenario, the cadmium concentrations output by MIRC were consistent 

with reported values in all ingestible media products. The products with higher reported 

cadmium levels in the literature, including soil, plants, and fish, also contained the higher 

modeled concentrations. 

5.2.3 Average Daily Dose (ADD) 

To determine the media most relevant to exposure and risk, the ingestion exposure factors must 

be considered in addition to the estimated media concentrations (i.e., a higher concentration for 

a particular medium does not necessarily mean higher risk). In Exhibit 17, the contributions of 

ingestion exposure pathways to the average daily dose (ADD) (and thus the HQ) for the 

different age categories are presented. As shown in the exhibit, fish ingestion is the dominant 

exposure pathway across all age categories, accounting for as much as 98 percent of the ADD 

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

less than 5 percent of the total ADD for all age groups. Most of the additional exposure was 

from ingestion of fruits and vegetables. The highest ADD corresponds to children aged 1–2 

years; thus, the exposure corresponding to this group was used to determine the emission 

threshold for cadmium. 

5.3 Mercury Compounds 

Some of the largest anthropogenic sources of mercury to air are facilities that process, mine, or 

smelt mercury ores; industrial/commercial boilers; fossil fuel combustion activities (primarily 

coal); cement production facilities; other urban and industrial facilities; and medical and 

municipal waste incinerators (ATSDR 1999). These facilities can emit a mixture of elemental 

and divalent mercury, mostly in the gaseous phase, with some divalent forms in particle-bound 

phases (U.S. EPA 1997). 

5.3.1 Behavior in the Environment 

Once emitted into the environment, mercury undergoes changes in form and species as it 

moves through environmental media. Elemental mercury is the most prevalent species of 

mercury in the atmosphere. Due to the long residence time of elemental mercury in the 

atmosphere, this compound is relatively well distributed, even on a global scale. 
Attachment A- 6/29/12 36 

Exhibit 17. Estimated Contributions of Modeled Food Types to 

Cadmium Ingestion Exposures and Hazard Quotients 

0

0.2

0.4

0.6

0.8

1

0

0.0001

0.0002

0.0003

0.0004

0.0005

Child 1-2 Child 3-5 Child 6-11 Child 12-19 Adult

Hazard Quotient

ADD

(mg/kg/day)

Fish Meat, Dairy, & Eggs

Fruits & Vegetables Soil

Divalent mercury is removed from the atmosphere at a faster rate than elemental mercury, and 

it can be transferred to the surface near the emission source via wet or dry deposition where it 

appears to adsorb tightly to soil particles (U.S. EPA 1997) or dissolved organic carbon. Divalent 

mercury in soil also can be methylated by microbes or reduced to elemental mercury and 

revolatilized back into the atmosphere. Most divalent mercury from atmospheric deposition will 

remain in the soil profile, however, in the form of inorganic compounds bound to soil organic 

matter. Although this complexing behavior with organic matter significantly limits mercury 

transport, the ability of mercury to form these complexes greatly depends on soil conditions 

such as pH, temperature, and soil humic content. For example, mercury strongly adsorbs to 

humic materials and sesquioxides in soil at pH > 4 and in soils with high iron and aluminum 

content (ATSDR 1999). Small amounts of mercury in soil can be transported to surface water 

via runoff or leaching. 

Mercury could also enter the water column through atmospheric fallout. Once in the water 

body, divalent mercury can be methylated through microbial activity. In addition, divalent and 

methyl mercury can be further reduced to elemental mercury, which can volatilize and reenter 

the atmosphere. Solid forms of inorganic mercury compounds could adsorb to particulates in 

the water column or partition to the sediment bed (U.S. EPA 1997). 

The solubility of mercury in water depends on the species and form of mercury present as well 

as properties of the water such as water pH and chloride ion concentration (ATSDR 1999). Low 
Attachment A- 6/29/12 37 

pH favors the methylation of mercury in the water column, typically performed by sulfur-reducing 

bacteria in anaerobic conditions. Methyl mercury is typically of greatest concern because it 

readily bioaccumulates and efficiently biomagnifies in aquatic organisms. A considerable 

amount (25–60 percent) of both divalent mercury compounds and methyl mercury is strongly 

bound to particulates in the water column (U.S. EPA 1997). The remaining mercury is 

dissolved. Most of the elemental mercury produced as a result of reduction of divalent mercury 

volatilizes back into the atmosphere. 

For the screening scenario, the partitioning behavior modeled in TRIM.FaTE generally was 

consistent with trends noted in the literature. Divalent mercury was the most prevalent species 

in modeled surface soil, surface water, and sediment compartments, while methyl mercury was 

the dominant species in fish. 

5.3.2 Concentrations in Ingestible Products 

Available data indicate that mercury bioaccumulates in plants, aquatic organisms, and terrestrial 

animals, providing multiple ingestion exposure pathways (U.S. EPA 1997; ATSDR 1999). Low 

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

than potatoes, grains, legumes, and other vegetables and fruits (ATSDR 1999; EPA 1997). 

Cattle demethylate mercury in the rumen and, therefore, store very little of the mercury they 

ingest by foraging or consuming silage or grain. Thus, mercury content in meat and cow’s milk 

is low (ATSDR 1999). Concentrations of methyl mercury in fish are generally highest in larger, 

older specimens at the higher trophic levels (U.S. EPA 1997). 

Although data on mercury in foods other than fish are not abundant in the literature, total 

mercury concentrations output by MIRC were generally consistent with the reported values that 

were available. The exposure pathways that most influenced the mercury HQs in the model are 

presented in Exhibit 18. As shown, the dominant exposure pathway for all age groups is 

ingestion of fish. Relative to divalent mercury, methyl mercury concentrations in fish were very 

high (approximately 95 percent of total mercury). 

5.3.3 Average Daily Dose 

In Exhibit 18, the contributions of ingestion exposure pathways to the ADD (and thus the HQ) for 

methyl mercury across the different age categories are presented. As shown, fish is the 

dominant exposure pathway across all age categories, accounting for nearly 100 percent of the 

ADD for each group. The combined contribution of all other exposure pathways accounts for 

less than 1 percent of the total ADD for all age groups. The high degree of exposure to methyl 

mercury through fish ingestion is attributed to the ease with which this compound 

bioaccumulates and biomagnifies in fish and to the health protective ingestion assumptions 

used in the screening scenario. The highest ADD corresponds to children aged 1–2 years; 

thus, the exposure corresponding to this group was used to determine the emission threshold 

for mercury. 

5.4 Dioxins 

Incineration and combustion processes are believed to be the primary emission sources for 

chlorinated dioxins (ATSDR 1998). The five stationary source categories that generate the vast 

majority of 2,3,7,8-TCDD emissions in the United States are municipal waste incineration, 

medical waste incineration, hazardous waste kilns from Portland cement manufacturing, 

secondary aluminum smelting, and biological incineration. 
Attachment A- 6/29/12 38 

Exhibit 18. Estimated Contributions of Modeled Food Types 

to Methyl Mercury Ingestion Exposures 

0

0.2

0.4

0.6

0.8

1

0

0.00002

0.00004

0.00006

0.00008

0.0001

Child 1-2 Child 3-5 Child 6-11 Child 12-19 Adult

Hazard Quotient

ADD

(mg/kg/day)

Fish Meat, Dairy, & Eggs

Fruits & Vegetables Soil

5.4.1 Behavior in the Environment 

Dioxins emitted to the atmosphere can be transported long distances in vapor form or bound to 

particulates, depositing in soils and water bodies in otherwise pristine locations far from the 

source. Although airborne dioxins are susceptible to wet and dry deposition, most dioxins 

emitted to the atmosphere through incineration/combustion processes are not deposited close 

to the source (ATSDR 1998). 

In soil, dioxins strongly adsorb to organic matter and show very little vertical movement, 

particularly in soils with a high organic carbon content (ATSDR 1998). Most dioxins deposited 

in soil are expected to remain buried in the soil profile, with erosion of contaminated soil 

particles the only significant mechanism for transport to water bodies. 

The dry deposition of dioxins from the atmosphere to water bodies is another important 

transport process. Because of the hydrophobic nature of dioxins, most dioxins entering the 

water column are expected to adsorb to suspended organic particles or partition to bed 

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

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

sediment compartment, some resuspension and remobilization of congeners can occur if 

sediments are disturbed by benthic organisms (ATSDR 1998). 
Attachment A- 6/29/12 39 

Bioaccumulation factors (BAFs) in fish are high as a result of the lipophilic nature of chlorinated 

dioxins. Although the processes by which freshwater fish accumulate dioxins are not well 

understood, both fish and invertebrates bioaccumulate congeners that have partitioned to 

sediment or have become suspended in water (U.S. EPA 2004c). Because most dioxins in the 

aquatic environment are adsorbed to suspended particles, however, direct uptake from the 

water is unlikely to be the primary route of exposure for most aquatic organisms at higher 

trophic levels (ATSDR 1998). At lower trophic levels, the primary route of exposure appears to 

be through uptake of water in contaminated sediment pores, and the primary route of exposure 

in the higher trophic levels appears to be through food chain transfer. Following ingestion, 

some fish can slowly metabolize certain congeners, such as 2,3,7,8-TCDD, and release the 

polar metabolites in bile. This process ultimately might limit bioaccumulation at higher trophic 

levels (ATSDR 1998). 

For the RTR screening scenario, the partitioning behavior modeled in TRIM.FaTE was 

consistent with the behavior of 2,3,7,8-TCDD expected in the natural environment. Also of note 

is that dioxins readily partition into breast milk due to the lipophilic nature of these compounds. 

5.4.2 Concentrations in Ingestible Products 

The primary source of non-inhalation exposure to dioxins outside of occupational settings is 

through dietary intake, which accounts for more than 90 percent of daily dioxin exposure 

(ATSDR 1998). Available data indicate that dioxins concentrate in plants, aquatic organisms, 

and animals, offering multiple ingestion exposure pathways. Actual congener levels in 

ingestible products, however, can vary based on type of food, agricultural and cultivation 

practices, atmospheric deposition rates, characteristics of environmental media, and presence 

of other anthropogenic pollutants. Dioxins appear to enter the terrestrial food chain primarily 

through vapor-phase deposition onto surfaces of plants, which are then consumed by larger 

animals. Another major source of exposure to dioxins is through ingestion of contaminated soil 

by animals. 

Observed trends indicate that meat, dairy, and fish consumption are the dominant exposure 

pathways, comprising 90 percent of dioxin dietary intake (ATSDR 1998). Consistent with the 

literature, the modeled concentration of 2,3,7,8-TCDD in the fish compartment for the screening 

scenario was at least one order of magnitude greater than concentrations in the other 

compartments. Among the compartments with the lowest concentrations were fruits and 

vegetables, which do not readily accumulate 2,3,7,8-TCDD. 

Ingestion of breast milk during infancy and fish ingestion contribute to nearly 94 percent of 

lifetime dioxin exposure for 2,3,7,8-TCDD in the screening scenario. Daily intakes of 

2,3,7,8-TCDD from cow’s milk, produce, and fish have been reported in the literature to 

comprise 27 percent, 11 percent, and 10 percent, respectively, of the total daily intake in the 

general population. Some studies note that specific subpopulations, such as subsistence 

farmers and anglers, however, might have very different exposure profiles in which fish, meat, 

and dairy drive congener exposure (ATSDR 1998). Given the subsistence diet modeled in the 

RTR screening scenario, the high exposure from consumption of fish is appropriate within the 

context of this analysis. 

5.4.3 Lifetime Average Daily Dose (LADD) 

The contributions of ingestion exposure pathways to the lifetime average daily dose (LADD) 

(and thus lifetime cancer risk) for the modeled dioxin congeners are presented in Exhibit 19. 

Based on the modeling methodology and assumptions used, exposures via the breast milk 
Attachment A- 6/29/12 40 

Exhibit 19. Estimated Contributions of Modeled Food 

Types to Dioxin Ingestion Exposures 

pathway consistently account for approximately 30 percent of the lifetime exposure for all 

congeners, while exposure via fish, soil, and the various farm food chain pathways is highly 

variable across congeners. This variability can be explained in part by differences in the 

physiochemical properties that drive the environmental transport processes of these congeners 

(e.g., Kow, molecular weight). The differences are also likely attributed to differences in the 

congener-specific half-life in abiotic media and the degree to which the congener is metabolized 

in biotic media. 

5.5 Polycyclic Aromatic Hydrocarbons 

PAHs can enter the atmosphere as a result of a variety of combustion processes, both natural 

and anthropogenic. Stationary emission sources account for approximately 80 percent of total 

annual PAH emissions. Although the primary source of stationary source PAH emissions is 

thought to be residential wood burning, other processes such as power generation; incineration; 

coal tar, coke, and asphalt production; and petroleum catalytic cracking are also major 

contributors (ATSDR 1995). 
Attachment A- 6/29/12 41 

5.5.1 Behavior in the Environment 

PAHs emitted to the atmosphere can travel long distances in vapor form or attached to particles, 

or they can deposit relatively close to an emission source via wet or dry deposition onto water, 

soil, and vegetation. In the atmosphere, PAHs are found primarily in the particle-bound phase, 

and atmospheric residence time and transport distances are highly influenced by climatic 

conditions and the size of the particles to which they are bound (ATSDR 1995). 

As a result of sustained input from anthropogenic sources, PAHs are ubiquitous in soil. High 

molecular weight PAHs, such as benzo(a)pyrene, strongly adsorb to organic carbon in soil, 

indicating that adsorption to soil particles will limit the mobility of these compounds following 

deposition to soil (ATSDR 1995). 

Most PAHs enter the water column directly through atmospheric deposition (ATSDR 1995). 

Following deposition onto surface waters, approximately two-thirds of PAHs adsorb strongly to 

sediment and suspended particles, while only small amounts revolatilize back to the 

atmosphere (ATSDR 1995). Aquatic organisms can accumulate PAHs via uptake from water, 

sediment, or food. Although fish and other organisms readily take up PAHs from contaminated 

food, biomagnification generally does not occur because many organisms can rapidly 

metabolize PAHs (ATSDR 1995). As a result, concentrations of PAHs have generally been 

observed to decrease with increasing trophic levels (ATSDR 1995). Due to this ability to 

metabolize PAHs, BAFs in fish are not expected to be especially high. Sediment-dwelling 

organisms can experience increased exposure to PAHs through association (e.g., direct uptake, 

consumption) with contaminated sediment (ATSDR 1995). 

For the screening scenario, the partitioning behavior of benzo(a)pyrene is generally consistent 

with trends reported in the literature. 

5.5.2 Concentrations in Ingestible Products 

The primary source of non-inhalation exposure to benzo(a)pyrene outside of occupational 

settings is through dietary intake. Exposure can depend on the origin of the food (higher values 

are often recorded at contaminated sites) and the method of food preparation (higher values 

have been reported for food that is smoked or grilled). PAHs have been observed to 

bioaccumulate in aquatic organisms and terrestrial animals through uptake of contaminated 

water, soil, and food. These compounds are readily metabolized by higher trophic level 

organisms, including humans, however, so biomagnification is not considered to be significant 

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

chemical concentrations tend to be below detection levels. PAHs in meat have been observed 

at concentrations below detection levels up to higher concentrations when the meat is smoked. 

Similar concentrations have been reported for fish, with smoked fish concentrations sometimes 

quadruple those found in terrestrial animals. Because PAH concentrations are highest in 

products that are smoked or grilled, most of the available data for benzo(a)pyrene in food is for 

products that have been prepared using these processes. As a result, reported values might be 

significantly higher than those output by MIRC. 

For the RTR screening scenario, concentrations output by MIRC were generally lower than the 

reported ranges for benzo(a)pyrene in ingestible products. This trend is likely the result of 

background exposure in reported measurements and available data that are skewed toward 

concentrations in highly contaminated products. Considering these mitigating factors, the RTR 
Attachment A- 6/29/12 42 

screening scenario output concentrations are within the range of plausible values for PAHs in 

ingestible products. 

5.5.3 Lifetime Average Daily Dose 

The contributions of ingestion exposure pathways to the LADD (and thus lifetime cancer risk) for 

various PAHs are presented in Exhibit 20. As shown, the variability in the driving exposure 

pathways across PAHs is significant, with fish, beef, dairy, fruits, and vegetables comprising 

between 86 and 99 percent of exposure for different PAHs. 

This variability can be accounted for in part by differences in the physiochemical properties that 

drive the environmental fate and transport processes of these PAHs (e.g., Kow, molecular 

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

degree to which the PAHs are metabolized in biotic media. The variability in exposure 

pathways is consistent with information provided in the literature. 

Exhibit 20. Estimated Contributions of Modeled Food 

Types to PAH Ingestion Exposures 

5.6 Summary 

This analysis provides a summary of the fate and transport processes and the major routes of 

exposure for the PB-HAP categories of interest to EPA’s Risk and Technology Review Program, 

as modeled in TRIM.FaTE. In general, the modeled behavior of the compounds is consistent 

with data found in the literature. 
Attachment A- 6/29/12 43 

This analysis reveals that fish ingestion is a major route of exposure for cadmium, mercury, 

dioxins, and PAHs. For organics (i.e., dioxins and PAHs), the farm-food-chain also is a major 

route of exposure, with beef and dairy contributing significantly to the LADD. 

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Attachment A- 6/29/12 44 

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Science of the Total Environment 219: 117-135. 

Saiki, M.K., D.T. Castleberry, T.W. May, B.A. Martin, and F.N. Bullard. 1995. Copper, cadmium, 

and zinc concentrations in aquatic food chains from the upper Sacramento River 

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U.S. EPA. 1997. Mercury Study Report to Congress. Volume III: Fate and Transport of Mercury 

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U.S. EPA. 2002a. Total Risk Integrated Methodology: TRIM.FaTE Technical Support Document. 

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NC. September. 
Attachment A- 6/29/12 45 

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

NC. September. 

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

U.S. EP. 2003. Multimedia, Multipathway, and Multireceptor Risk Assessment (3MRA) Modeling 

System, Volume II: Site-based, Regional, and National Data. SAB Review Draft. EP530/D-03-001b. Office of Research and Development, Athens, GA, and Research 

Triangle Park, NC, and Office of Solid Waste, Washington, DC. July. Available at: 

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U.S. EPA. 2004a. Air Toxics Risk Assessment Reference Library; Volume 1 – Technical 

Resource Document, Part III, Human Health Risk Assessment: Multipathway Chapter 

14, Overview and Getting Started: Planning and Scoping the Multipathway Risk 

Assessment. Office of Air Quality Planning and Standards, Research Triangle Park, NC. 

April. Available at: http://www.epa.gov/ttn/fera/data/risk/vol_1/chapter_14.pdf. 

U.S. EPA. 2004b. Evaluation of TRIM.FaTE Volume III: Model Comparison Focusing on Dioxin 

Test Case. EPA-453/R-04-002. Available at: 

http://www.epa.gov/ttn/fera/data/trim/eval_rept_vol3_2005.pdf 

U.S. EPA. 2004c. Exposure and Human Health Reassessment of 2,3,7,8-Tetrachlorodibenzo-pDioxin (TCDD) and Related Compounds. Volume 2: Properties, Environmental Levels, 

and Background Exposures. Dioxin Reassessment, NAS Review Draft. U.S. 

Environmental Protection Agency, Washington, D.C., EPA/600/P-00/001Cb. Available 

at: http://www.epa.gov/ncea/pdfs/dioxin/nas-review/ 

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Research Triangle Park, NC. July. 

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

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

of Hexachlorodibenzo-p-dioxin (HxCDD), mixture of 1,2,3,6,7,8-HxCDD and 1,2,3,7,8,9-
Attachment A- 6/29/12 46 

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

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

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World Health Organization reevaluation of human and mammalian toxic equivalency 

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

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

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

pp. 
Technical Support Document for the TRIM-Based Multipathway 

Tiered Screening Methodology for RTR

ADDENDUM 1: TRIM.FaTE Inputs
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Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

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Exhibits

Exhibit 1. TRIM.FaTE Simulation Parameters for the TRIM.FaTE Screening Scenario .............................. 1

Exhibit 2. Meteorological Inputs for the TRIM.FaTE Screening Scenario .................................................... 2

Exhibit 3. Air Parameters for the TRIM.FaTE Screening Scenario............................................................... 3

Exhibit 4. Soil and Groundwater Parameters for the TRIM.FaTE Screening Scenario ................................ 4

Exhibit 5. Runoff Assumptions for the TRIM.FaTE Screening Scenario ...................................................... 7

Exhibit 6. USLE Erosion Parameters for the TRIM.FaTE Screening Scenario ............................................ 8

Exhibit 7. Terrestrial Plant Placement for the TRIM.FaTE Screening Scenario ........................................... 9

Exhibit 8. Terrestrial Plant Parameters for the TRIM.FaTE Screening Scenario........................................10

Exhibit 9. Surface Water Parameters for the TRIM.FaTE Screening Scenario ..........................................14

Exhibit 10. Sediment Parameters for the TRIM.FaTE Screening Scenario................................................ 15

Exhibit 11. Aquatic Animals Food Chain, Density, and Mass for the TRIM.FaTE 

Screening Scenario...................................................................................................................16

Exhibit 12. Cadmium Chemical-Specific Parameters for the TRIM.FaTE Screening 

Scenario ....................................................................................................................................17

Exhibit 13. Mercury Chemical-Specific Parameters for the TRIM.FaTE Screening 

Scenario ....................................................................................................................................18

Exhibit 14. PAH Chemical-Specific Parameters for the TRIM.FaTE Screening Scenario..........................19

Exhibit 15. Dioxin Chemical-Specific Parameters for the TRIM.FaTE Screening Scenario .......................22

Exhibit 16. Cadmium Chemical-Specific Parameters for Abiotic Compartments in the 

TRIM.FaTE Screening Scenario ...............................................................................................25

Exhibit 17. Mercury Chemical-Specific Parameters for Abiotic Compartments in the 

TRIM.FaTE Screening Scenario ...............................................................................................26

Exhibit 18. PAH Chemical-Specific Parameters for Abiotic Compartments in the 

TRIM.FaTE Screening Scenario ...............................................................................................31

Exhibit 19. Dioxin Chemical-Specific Parameters for Abiotic Compartments in the 

TRIM.FaTE Screening Scenario ...............................................................................................37

Exhibit 20. Cadmium Chemical-Specific Parameters for Plant Compartments in the 

TRIM.FaTE Screening Scenario ...............................................................................................41

Exhibit 21. Mercury Chemical-Specific Parameters for Plant Compartments in the 

TRIM.FaTE Screening Scenario ...............................................................................................42

Exhibit 22. PAH Chemical-Specific Parameters for Plant Compartments in the 

TRIM.FaTE Screening Scenario ...............................................................................................44

Exhibit 23. Dioxin Chemical-Specific Parameters for Plant Compartments in the 

TRIM.FaTE Screening Scenario ...............................................................................................46

Exhibit 24. Cadmium Chemical-Specific Parameters for Aquatic Species in the 

TRIM.FaTE Screening Scenario ...............................................................................................47

Exhibit 25. Mercury Chemical-Specific Parameters for Aquatic Species in the TRIM.FaTE 

Screening Scenario...................................................................................................................49
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

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Exhibit 26. PAH Chemical-Specific Parameters for Aquatic Species in the TRIM.FaTE 

Screening Scenario...................................................................................................................51

Exhibit 27. Dioxin Chemical-Specific Parameters for Aquatic Species in the TRIM.FaTE 

Screening Scenario...................................................................................................................55

References..................................................................................................................................................61
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

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This attachment provides tables of the modeling inputs for the TRIM.FaTE screening scenario. 

Exhibit 1 presents runtime settings for TRIM.FaTE. Exhibits 2 and 3 present meteorological and 

air parameters, respectively, entered into the model. Exhibits 4, 5, and 6 present the parameters 

for soil and groundwater, runoff assumptions, and the USLE (universal soil loss equation) 

erosion parameters, respectively, for the screening scenario. Exhibits 7 and 8 present terrestrial 

parameters. Exhibits 9 through 11 present lake parameters, and Exhibits12 through 27 present 

parameters specific to the chemicals modeled in the scenario.

.

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."
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

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Exhibit 2. Meteorological Inputs for the TRIM.FaTE Screening Scenario

Parameter Name Units Value Used Reference

Meteorological Inputs

Air temperature degrees K 298 USEPA 2005.

Horizontal wind speed m/sec 2.8 5th percentile annual average value for contiguous United States, calculated from 

30 yrs of annual normal temperature values.

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

the version of the TRIM.FaTE library used for screening.

Wind direction

degrees 

clockwise from 

N (blowing 

from)

3-days-on

4-days-off

On is defined as time during which wind is blowing into the model domain. A 

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

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

conservative estimate would be approximately 42% of the time.

Rainfall rate

m3

[rain]/m2 

[surface area]-

day

varies daily

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

contiguous United States, excluding Rhode Island because of extreme weather 

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

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

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

1972.

Mixing height (used to set air 

VE property named “top”) m 710 5th percentile annual average mixing heights (calculated from daily morning and afternoon values), for all stations on SCRM (40 state, 70 stations).

isDay_SteadyState_forAir unitless -- Value not used in current dynamic runs (would need to be reevaluated if steadystate runs are needed). isDay_SteadyState_forOther unitless --
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

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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 USEPA 1997b 

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

Fraction organic matter 

on particulates unitless 0.2 Harner and Bidleman 1998
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

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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 unitless variesb See Exhibit 5. 

Fraction of area available for 

erosion m2

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

rural.

Fraction of area available for runoff m2[area available]/m2[total] 1 Professional judgment; area assumed 

rural.

Fraction of area available for 

vertical diffusion m2

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

rural.

Fraction sand unitless 0.25 Professional judgment.

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

16 and A-3).

pH unitless 6.8 Professional judgment.

Runoff fraction unitless variesb See Exhibit 5. 

Total erosion rate kg [soil]/m2/day variesb See Exhibit 6. 
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

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Exhibit 4. Soil and Groundwater Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units Value Used Reference

Total runoff rate m3[water]/m2/day 1.64E-03

Calculated using scenario-specific 

precipitation rate and assumptions 

associated with water balance.

Water content volume[water]/volume[compartment] 0.15 McKone et al. 2001

Root Zone Soil Compartment Type

Air content volume[air]/volume[compartment] 0.25 McKone et al 2001 (Table 16).

Average vertical velocity of water 

(percolation) m/day 8.22E-04

Assumed as 0.2 times average 

precipitation for New England in McKone 

et al. 2001.

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

Fraction sand unitless 0.25 Professional judgment.

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

average).

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

16).

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

U.S. average).

pH unitless 6.8 Professional judgment.

Water content volume[water]/volume[compartment] 0.15 McKone et al. 2001

Vadose Zone Soil Compartment Type

Air content volume[air]/volume[compartment] 0.22 McKone et al. 2001 (Table 17).

Average vertical velocity of water 

(percolation) m/day 8.22E-04

Assumed as 0.2 times average 

precipitation for New England in McKone 

et al. 2001.
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

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Exhibit 4. Soil and Groundwater Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units Value Used Reference

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

Fraction sand unitless 0.35 Professional judgment.

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

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

U.S. average).

pH unitless 6.8 Professional judgment.

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

average).

Groundwater Compartment Type

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

Fraction sand unitless 0.4 Professional judgment.

Organic carbon fraction unitless 0.004 Professional judgment.

pH unitless 6.8 Professional judgment.

Porosity volume[total pore 

space]/volume[compartment] 0.2 Default in McKone et al. 2001 (Table 3).

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

a

Set using the volume element properties file. b

See separate tables (Exhibits 5 & 6) for erosion/runoff fractions and total erosion rates.
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

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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_N5a

SW_Pond 0.0

SurfSoil_N4 0.5

SurfSoil_S5 0.5

sink 0.0

SurfSoil_S5a

SW_Pond 0.0

SurfSoil_N5 0.0

SurfSoil_S4 1.0

sink 0.0

a

Assumes that N5 is higher ground than S5, and half of the runoff flows into N4, and the other half into S5. Assumes 

all runoff from S5 flows into S4. 
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

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

Code m2 R (100 ft-ton/ac)

K 

(ton/ac/(100 

ft-ton/acre))

LS 

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

/d) SDRa kg/m2/d

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

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

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

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

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

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

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

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

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

a

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

deposition (m2

), and b is the empirical slope coefficient (always 0.125).
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

9 

Exhibit 7. Terrestrial Plant Placement

for the TRIM.FaTE Screening Scenario

Surface Soil 

Volume Element

Surface Soil 

Depth (m)

Coniferous 

Forest

Grasses/

Herbs None

Source 0.01 x 

N1 0.01 x 

N6 0.20 (tilled) x 

N7 0.01 x 

N3 0.01 x 

N4 0.01 x 

N5 0.01 x 

S1 0.01 x 

S4 0.01 x 

S5 0.01 x 
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

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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 m2[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 deposition

interception fraction 

(Boolean)

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

Correction exponent, 

octanol to lipid unitless 0.76 From roots, Trapp 1995. 0.76 From roots, Trapp 1995.

Degree stomatal opening unitless 1 

Set to 1 for daytime based on 

professional judgment (stomatal 

diffusion is turned off at night 

using a different property, IsDay).

1 

Set to 1 for daytime based on 

professional judgment (stomatal 

diffusion is turned off at night 

using a different property, IsDay).

Density of wet leaf kg/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.
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

11

Exhibit 8. Terrestrial Plant Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units

Coniferousa Grass/Herba

Value 

Used Reference Value Used Reference

Vegetation attenuation 

factor m2

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

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

Wet deposition interception 

fraction unitless 0.2 Calculated based on 5 years of local met data, 1987-1991. 0.2 Calculated based on 5 years of local met data, 1987-1991.

Wet mass of leaf per soil 

area

kg[fresh 

leaf]/m2

[area] 2.0

Calculated from leaf area index, 

leaf thickness (Simonich and

Hites, 1994), density of wet 

foliage.

0.6 Calculated from leaf area index 

and Leith 1975.

Particle on Leaf Compartment Type

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

Volume particle per area 

leaf

m3[leaf 

particles]/m2[leaf] 1.00E-09

Based on particle density and 

size distribution for atmospheric 

particles measured on an 

adhesive surface, Coe and 

Lindberg 1987.

1.00E-09

Based on particle density and 

size distribution for atmospheric 

particles measured on an 

adhesive surface, Coe and 

Lindberg 1987.
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

12

Exhibit 8. Terrestrial Plant Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units

Coniferousa Grass/Herba

Value 

Used Reference Value Used Reference

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.
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

13

Exhibit 8. Terrestrial Plant Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units

Coniferousa Grass/Herba

Value 

Used Reference Value Used Reference

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 m3

[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 Exhibit 7 for assignment of plant types to surface soil compartments.

b

Begins March 9 (set to 1), ends November 7 (set to 0). Nationwide 80th percentile.

c

Begins November 7, ends December 6; rate = 0.15/day during this time (value assumes 99 percent of leaves fall in 30 days).
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

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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 volume cell, not water) g[algae]/m3

[algae] 1,000,000 Mason et al. 1995b, Mason et al. 

1996.

Boundary layer thickness 

above sediment m 0.02 Cal EPA 1993.

Chloride concentration mg/L 8.0 Kaushal et al. 2005.

Chlorophyll concentration mg/L 0.0029 ICF 2005.

Deptha m 3.18

WI DNR 2007 - calculation 

based on relationship between 

drainage basin and lake area 

size.

Dimensionless viscous 

sublayer thickness unitless 4 Ambrose et al. 1995.

Drag coefficient for water 

body unitless 0.0011 Ambrose et al. 1995.

Flush rate 1/year 12.17

Calculated based on pond 

dimensions and flow 

calculations.

Fraction Sand unitless 0.25 Professional judgment.

Organic carbon fraction in 

suspended sediments unitless 0.02 Professional judgment.

pH unitless 7.3 Professional judgment.

Suspended sediment 

deposition velocity m/day 2 USEPA 1997b. 

Total suspended sediment 

concentration

kg[sediment]/m3[water 

column] 0.05 USEPA 2005.

Water temperature degrees K 298 USEPA 2005.

a

Set using the volume element properties named "top" and "bottom."
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

15

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 2005. 

Solid material density 

in sediment kg[sediment]/m3

[sediment] 2,600 McKone et al. 2001 (Table 

3).

pH unitless 7.3 Professional judgment.

Sediment 

resuspension velocity m/day 6.69E-05 Calculated from water balance model.

a

Set using the volume element properties named "top" and "bottom."
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

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

Aquatic Biota 

(Consuming 

Organism)

Fraction Diet

Biomass

(kg/m2) 

Body 

Weight 

(kg)

Reference

AlgaeZooplanktonBenthicInvertebrateWater Column HerbivoreBenthic OmnivoreWater Column OmnivoreBenthic CarnivoreWater Column Carnivore

Benthic 

Invertebrate 0% 0% 0% 0% 0% 0% 0% 0% 0.020 2.55E-04 Professional judgment.

Water Column 

Herbivore 0% 100% 0% 0% 0% 0% 0% 0% 0.002 0.025 Professional judgment.

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

judgment.

Water Column 

Omnivore 0% 0% 0% 100% 0% 0% 0% 0% 0.001 0.25 Professional judgment.

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

judgment.

Water Column 

Carnivore 0% 0% 0% 0% 0% 100% 0% 0% 0.0002 2.0 Professional judgment.

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

judgment.
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

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Exhibit 12. Cadmium Chemical-Specific Parameters

for the TRIM.FaTE Screening Scenario

Parameter Namea Units Value Reference

CAS numberb unitless 7440-43-9 - 

Diffusion coefficient in pure 

air m2

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

35).

Diffusion coefficient in pure 

water m2

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

35).

Henry's Law constant Pa-m3/mol 1.00E-37 USEPA 1999 (Table A-2-

35; assumed to be zero).

Melting point degrees K 594 ATSDR 1999.

Molecular weight g/mol 112.41 ATSDR 1999.

Octanol-air partition 

coefficient (Koa) m3

[air]/m3[octanol] - - 

Octanol-carbon partition 

coefficient (Koc) - - 

Octanol-water partition 

coefficient (Kow) L[water]/kg[octanol] - - 

a

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

b

This CAS numbers applies to elemental Cd; however, the cations of cadmium are being modeled.
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

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Exhibit 13. Mercury Chemical-Specific Parameters

for the TRIM.FaTE Screening Scenario

Parameter Name Units

Value

Reference Hg(0)b Hg(2)b MHgb

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

Diffusion coefficient 

in pure air m2

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

Diffusion coefficient 

in pure water m2

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

Henry's Law 

constant Pa-m3

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

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

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

Octanol-water 

partition coefficient 

(Kow) 

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

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

USEPA 1997b, 

based on Petersen 

et al. 1995.

a

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

On this and all following tables, Hg(0) = elemental mercury, Hg(2) = divalent mercury, and MHg = methyl mercury.
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

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

Parameter Name Units Value

2Methyl 712DMB Acenaphthene Acenaphthylene BaA BaP BbF BghiP

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

Diffusion 

coefficient in pure 

air

m2

/day 0.451 0.691 0.009 0.388 0.441 0.372 0.009 0.190

Diffusion 

coefficient in pure 

water

m2

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

Henry's Law 

constant Pa-m3

/mol 50.56 0.20 18.50 12.70 1.22 0.07 0.05 0.03

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

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

Octanol-water 

partition 

coefficient (Kow) 

L[water]/L[octanol] 7.24E+03 6.31E+05 8.32E+03 1.00E+04 6.17E+05 9.33E+05 6.03E+05 4.27E+06
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

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

Parameter Name Units Value

BkF Chr DahA Fluoranthene Fluorene IcdP

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

Diffusion coefficient in 

pure air m2

/day 0.009 0.009 0.009 0.009 0.009 0.009

Diffusion coefficient in 

pure water m2

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

Henry's Law constant Pa-m3/mol 0.04 0.53 0.01 1.96 9.81 0.03

Melting point degrees K 490 531 539 383.15 383.15 437

Molecular weight g/mol 252.32 228.29 278.33 202.26 166.20 276.34

Octanol-water partition 

coefficient (Kow) L[water]/L[octanol] 8.71E+05 5.37E+05 3.16E+06 1.45E+05 1.51E+04 5.25E+06
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

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

Parameter Name Units Reference

CAS number unitless - 

Diffusion coefficient in 

pure air m2

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

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

Diffusion coefficient in 

pure water m2

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

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

Henry's Law constant Pa-m3/mol

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

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

(Benzo(g,h,i)perylene)

Melting point degrees K

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

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

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

Molecular weight g/mol

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

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

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

Octanol-water partition 

coefficient (Kow) L[water]/L[octanol]

Hansch et al. 1995. Exceptions include Passivirta et al. 1999 (Acenaphthylene, 

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

(Benzo(b)fluoranthene)
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

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

Parameter Name Units

Value

1,2,3,4,6,7,8,9-OCDD1,2,3,4,6,7,8,9-OCDF1,2,3,4,6,7,8- HpCDD1,2,3,4,6,7,8- HpCDF1,2,3,4,7,8,9- HpCDF1,2,3,4,7,8- HxCDD1,2,3,4,7,8- 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 m2

/day 0.751 0.168 0.782 0.176 0.176 0.816 0.183

Diffusion coefficient in 

pure water m2

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

Henry's Law constant Pa-m3/mol 0.684 0.19 1.22 1.43 1.42 1.08 1.45

Melting point degrees K 603.0 259.0 538.0 236.5 222.0 546.0 499.0

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

Octanol-water partition 

coefficient (Kow) L[water]/L[octanol] 1.58E+08 1.00E+08 1.00E+08 2.51E+07 7.94E+06 6.31E+07 1.00E+07
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

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

Parameter Name Units

Value

1,2,3,6,7,8- HxCDD1,2,3,6,7,8- HxCDF1,2,3,7,8,9- HxCDD1,2,3,7,8,9- HxCDF1,2,3,7,8- PeCDD1,2,3,7,8- PeCDF2,3,4,6,7,8- 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 m2

/day 0.816 0.183 0.816 0.183 0.854 0.192 0.183

Diffusion coefficient in pure 

water m2

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

Henry's Law constant Pa-m3/mol 1.11 0.741 1.11 1.11 0.263 0.507 1.11

Melting point degrees K 558.0 506.0 517.0 509.0 513.0 499.0 512.5

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

Octanol-water partition 

coefficient (Kow) L[water]/L[octanol] 1.62E+08 8.24E+07 1.62E+08 3.80E+07 1.86E+07 6.17E+06 8.31E+07
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

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

Parameter Name Units

Value

Reference

2,3,4,7,8- PeCDF2,3,7,8- TCDD2,3,7,8- TCDF

CAS number unitless 57117-31-4 1746-01-6 51207-31-9 - 

Diffusion coefficient in 

pure air m2

/day 0.192 0.899 0.203 US EPA 2005

Diffusion coefficient in 

pure water m2

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

Henry's Law constant Pa-m3/mol 0.505 3.33 1.46 US EPA 2005

Melting point degrees K 469.3 578.0 500.0

Mackay et al. 2000. Exceptions include USEPA 2000a 

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

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

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

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

Molecular weight g/mol 340.42 322 306

Mackay et al. 2000. Exceptions include ATSDR 1998 

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

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

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

Octanol-water partition 

coefficient (Kow) 

L[water]/L[octan

ol] 3.16E+06 6.31E+06 1.26E+06

Mackay et al. 1992 as cited in USEPA 2000b. Exceptions 

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

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

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

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

PeCDD)
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

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Exhibit 16. Cadmium Chemical-Specific Parameters for Abiotic Compartments in 

the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference

Air Compartment Type

Particle dry deposition velocity m/day 260

Calculated from 

Muhlbaier and Tissue 

1980.

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

Surface Soil Compartment Type

Use input characteristic depth 

(Boolean) 0 = no, Else = yes 0 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.
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

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

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

Air Compartment Type

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

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

Methylation rate 1/day 0 0 0 Professional judgment.

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

USEPA 1997b.

Reduction rate 1/day 0 0 0 Professional judgment.

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

Surface Soil Compartment Type

Use input characteristic depth 

(Boolean) 0 = no, Else = yes 0 0 0 Professional judgment.

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

wet wt] 1,000 58,000 7,000 USEPA 1997b. 

Vapor dry deposition velocity m/day 50 2500 0 

Hg(0) - from Lindberg et al. 1992; Hg(2) - estimate 

by USEPA using the Industrial Source Complex 

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

Study Report (USEPA 1997b)].

Demethylation rate 1/day N/A N/A 0.06

Range reported in Porvari and Verta 1995 is 3E-2 

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

demethylation rate constant under anaerobic 

conditions.

Methylation rate 1/day 0 0.001 0 

Range reported in Porvari and Verta 1995 is 2E-4 

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

methylation rate constant under anaerobic 

conditions.
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

27

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

Parameter Name Units Value Reference

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

Reduction rate 1/day 0 1.25E-05 0 

Value used for untilled surface soil (2cm), 10% 

moisture content, in USEPA 1997b; general range 

is (0.0013/day)*moisture content to 

(0.0001/day)*moisture content for forested region 

(Lindberg 1996; Carpi and Lindberg 1997).

Root Zone Soil Compartment Type

Use input characteristic depth 

(Boolean) 0 = no, Else = yes 0 0 0 Professional judgment.

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

wet wt] 1,000 58,000 7,000 USEPA 1997b. 

Demethylation rate 1/day N/A N/A 0.06

Range reported in Porvari and Verta 1995 is 3E-2 

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

demethylation rate constant under anaerobic 

conditions.

Methylation rate 1/day 0 0.001 0 

Range reported in Porvari and Verta 1995 is 2E-4 

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

methylation rate constant under anaerobic 

conditions.

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

Reduction rate 1/day 0 3.25E-06 0 

Value used for tilled surface soil (20cm), 10% 

moisture content, in USEPA 1997b (Lindberg 

1996; Carpi and Lindberg 1997).

Vadose Zone Soil Compartment Type

Use input characteristic depth 

(Boolean) 0 = no, Else = yes 0 0 0 Professional judgment.

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

wet wt] 1,000 58,000 7,000 USEPA 1997b. 
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

28

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

Parameter Name Units Value Reference

Demethylation rate 1/day N/A N/A 0.06

Range reported in Porvari and Verta 1995 is 3E-2 

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

demethylation rate constant under anaerobic 

conditions.

Methylation rate 1/day 0 0.001 0 

Range reported in Porvari and Verta 1995 is 2E-4 

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

methylation rate constant under anaerobic 

conditions.

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

Reduction rate 1/day 0 3.25E-06 0 

Value used for tilled surface soil (20cm), 10% 

moisture content, in USEPA 1997b (Lindberg 

1996; Carpi and Lindberg 1997).

Groundwater Compartment Type

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

wet wt] 1,000 58,000 7,000 USEPA 1997b. 

Demethylation rate 1/day N/A N/A 0.06

Range reported in Porvari and Verta 1995 is 3E-2 

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

demethylation rate constant under anaerobic 

conditions.

Methylation rate 1/day 0 0.001 0 

Range reported in Porvari and Verta 1995 is 2E-4 

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

methylation rate constant under anaerobic 

conditions.

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

1997b).

Reduction rate 1/day 0 3.25E-06 0 

Value used for tilled surface soil (20cm), 10% 

moisture content, in USEPA 1997b (Lindberg 

1996; Carpi and Lindberg 1997).
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

29

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

Parameter Name Units Value Reference

Surface Water Compartment Type

Algal surface area-specific uptake 

rate constant nmol/[μm2

-day-nmol] 0 2.04E-10 3.60E-10 Assumes radius = 2.5mm, Mason et al. 1995b, 

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

Dow ("overall Kow") L[water]/kg[octanol] 0 -

a -b Mason et al. 1996.

Solids-water partition coefficient L[water]/kg[solids 

wet wt] 1,000 100,000 100,000 USEPA 1997b. 

Vapor dry deposition velocity m/day N/A 2500 USEPA 1997b (Vol. III, App. A).

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

and Henry 1991.

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

Oxidation rate 1/day 0 0 0 Professional judgment.

Reduction rate 1/day 0 0.0075 0 

Value used in USEPA 1997b; reported values 

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

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

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

1997).

Sediment Compartment Type

Solids-water partition coefficient L[water]/kg[solids 

wet wt] 3,000 50,000 3,000 USEPA 1997b. 

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

and Henry 1991.

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

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

Oxidation rate 1/day 0 0 0 Professional judgment.
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

30

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

Parameter Name Units Value Reference

Reduction rate 1/day 0 1.00E-06 0 

Inferred value based on presence of Hg(0) in 

sediment porewater (USEPA 1997b; Vandal et al. 

1995).

a

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

TRIM.FaTE Formula Property, which varies from 0.075 to 1.7 depending on pH and chloride concentration.
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

31

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

Parameter Name Units

Values

2Methyl 712DMB Acenaphthene

Acenaphthylene BaA BaP BbF BghiP BkF

Air Compartment Type

Particle dry deposition 

velocity m/day 500 500 500 500 500 500 500 500 500

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

Washout ratio 200000 200000 200000 200000 200000 200000 200000 200000 200000

Surface Soil Compartment Type

User input characteristic 

depth (Boolean) 0 = No, Else = Yes 0 0 0 0 0 0 0 0 0 

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

Root Zone Soil Compartment Type

User input characteristic 

depth (Boolean) 0 = No, Else = Yes 0 0 0 0 0 0 0 0 0 

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

Vadose Zone Soil Compartment Type

User input characteristic 

depth (Boolean) 0 = No, Else = Yes 0 0 0 0 0 0 0 0 0 

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

Groundwater Compartment Type

Half-life day 36 48 112 133 1360 1060 1220 830 4280
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

32

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

Parameter Name Units

Values

2Methyl 712DMB Acenaphthene

Acenaphthylene BaA BaP BbF BghiP BkF

Surface Water Compartment Type

Ratio Of Conc In Algae To

Conc Dissolved In Water

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

(g[chem]/L[water]) 2.6 333.4 3 3.7 325 510 317 1539 473

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

Sediment Compartment Type

Half-life day 2290 2290 2290 2290 2290 2290 2290 2290 2290
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

33

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

Parameter Name Units

Value

Chr DahA Fluoranthene

Fluorene

IcdP

Reference

Air Compartment Type

Particle dry deposition velocity m/day 500 500 500 500 500 McKone et al. 2001. 

Half-life day 0.334 0.178 0.46 0.46 0.262

Howard et al. 1991 / upper bound 

measured or estimated value. 

Exceptions include ATSDR 2005 (2-

Methylnaphthalene), USEPA 1998 

(7,12-Dimethylbenz(a)anthracene, 

Benzo(g,h,i)perylene, and 

Fluoranthene) / average of range, 

HSDB 2001d (Acenaphthene), HSDB 

2001b (Acenaphthylene), and Spero 

et al. 2000 (Fluorene). 

Washout ratio 200000 200000 200000 200000 200000 Mackay et al. 1986.

Surface Soil Compartment Type

User input characteristic depth 

(Boolean)

0 = No, 

Else = Yes 0 0 0 0 0 Professional judgment. 

Half-life day 1000 940 275 33 730

MacKay et al. 2000 / average of 

range. Exceptions include ATSDR 

2005 (2-Methylnaphthalene), USEPA 

1998 (7,12-

Dimethylbenz(a)anthracene, 

Benzo(g,h,i)perylene, and 

Fluoranthene) / average of range, 

HSDB 2001d (Acenaphthene), HSDB 

2001b (Acenaphthylene), and HSDB 

2001e (Fluorene).
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

34

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

Parameter Name Units

Value

Chr DahA Fluoranthene

Fluorene

IcdP

Reference

Root Zone Soil Compartment Type

User input characteristic depth 

(Boolean)

0 = No, 

Else = Yes 0 0 0 0 0 Professional judgment. 

Half-life day 1000 940 275 33 730

Howard et al. 1991 / upper bound 

measured or estimated value. 

Exceptions include ATSDR 2005 (2-

Methylnaphthalene), USEPA 1998 

(7,12-Dimethylbenz(a)anthracene, 

Benzo(g,h,i)perylene, and 

Fluoranthene) / average of range, 

HSDB 2001d (Acenaphthene), HSDB 

2001b (Acenaphthylene), and HSDB 

2001e (Fluorene). 

Vadose Zone Soil Compartment Type

User input characteristic depth 

(Boolean)

0 = No, 

Else = Yes 0 0 0 0 0 Professional judgment. 

Half-life day 2000 1880 550 66 1460

Howard et al. 1991 / upper bound 

measured or estimated value. 

Exceptions include ATSDR 2005 (2-

Methylnaphthalene), USEPA 1998 

(7,12-Dimethylbenz(a)anthracene, 

Benzo(g,h,i)perylene, and 

Fluoranthene) / twice average of 

range, HSDB 2001d (Acenaphthene) / 

multiplied by 2, HSDB 2001b 

(Acenaphthylene) / multiplied by 2, 

and HSDB 2001e (Fluorene) / 

multiplied by 2.
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

35

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

Parameter Name Units

Value

Chr DahA Fluoranthene

Fluorene

IcdP

Reference

Groundwater Compartment Type

Half-life day 2000 1880 550 66 1460

Howard et al. 1991 / upper bound 

measured or estimated value. 

Exceptions include ATSDR 2005 (2-

Methylnaphthalene), USEPA 1998 

(7,12-Dimethylbenz(a)anthracene, 

Benzo(g,h,i)perylene, and 

Fluoranthene) / twice average of 

range, HSDB 2001d (Acenaphthene) / 

multiplied by 2, HSDB 2001b 

(Acenaphthylene) / multiplied by 2, 

and HSDB 2001e (Fluorene) / 

multiplied by 2.

Surface Water Compartment Type

RatioOfConcInAlgaeToConcDiss

olvedInWater

(g[chem]/k

g[algae]) / 

(g[chem]/L[

water])

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

Half-life day 1.626 97.8 160 8.5 750

Howard et al. 1991 / upper bound 

measured or estimated value. 

Exceptions include HSDB 2005 (2-

Methylnaphthalene), HSDB 2001a (7-

12 Dimethylbenz(a)anthracene), 

HSDB 2001d (Acenaphthene), HSDB 

2001b (Acenaphthylene), and HSDB 

2001c (Benzo(g,h,i)perylene), 

Montgomery 2000 (Fluoranthene), 

and Boyle 1985 (Fluorene). 
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

36

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

Parameter Name Units

Value

Chr DahA Fluoranthene

Fluorene

IcdP

Reference

Sediment Compartment Type

Half-life day 2290 2290 2290 2290 2290

Mackay et al. 1992 / PAH values are 

the mean half-life of the log class that 

Mackay et al. assigned for sediment, 

except for BbF and IcdP, which were 

not in Table 2.3 of Mackay et. al.
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

37

Exhibit 19. Dioxin Chemical-Specific Parameters for Abiotic 

Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units

Value

1,2,3,4,6,7,8,9-OCDD1,2,3,4,6,7,8,9-OCDF1,2,3,4,6,7,8-HpCDD1,2,3,4,6,7,8-HpCDF1,2,3,4,7,8,9-HpCDF1,2,3,4,7,8-HxCDD

Air Compartment Type

Deposition Velocity m/day 500 500 500 500 500 500

Halflife day 162 321 64 137 122 42

Washout Ratio m3[air]/m3[rain] 91000 22000 64000 32000 32000 9000

Surface Soil Compartment Type

Input characteristic depth m 0.08 0.08 0.08 0.08 0.08 0.08

Use input characteristic 

depth (Boolean) 0 = No, Else = Yes 0 0 0 0 0 0 

Halflife day 3650 3650 3650 3650 3650 3650

Root Zone Soil Compartment Type

Input characteristic depth m 0.08 0.08 0.08 0.08 0.08 0.08

Use input characteristic 

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

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]) 5.31 4.54 4.54 2.83 1.9 3.88

Half-life day 0.67 0.58 47 0.58 0.58 6.3

Sediment Compartment Type

Half-life day 1095 1095 1095 1095 1095 1095
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

38

Exhibit 19. Dioxin Chemical-Specific Parameters for Abiotic 

Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units

Value

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

HxCDF

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

HxCDD

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

HxCDF

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

HxCDD

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

HxCDF

1,2,3,7,8-

PeCDD

Air Compartment Type

Deposition velocity m/day 500 500 500 500 500 500

Half-life day 78 28 55 28 51 18

Washout ratio m3[air]/m3[rain] 10000 9000 10000 9000 10000 18000

Surface Soil Compartment Type

Input characteristic depth m 0.08 0.08 0.08 0.08 0.08 0.08

Use input characteristic 

depth (Boolean) 0 = No, Else = Yes 0 0 0 0 0 0 

Half-life day 3650 3650 3650 3650 3650 3650

Root Zone Soil Compartment Type

Input characteristic depth m 0.08 0.08 0.08 0.08 0.08 0.08

Use input characteristic 

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

Half-life day 3650 3650 3650 3650 3650 3650

Vadose Zone Soil Compartment Type

Input characteristic depth m 0.08 0.08 0.08 0.08 0.08 0.08

Use input characteristic 

depth (Boolean) 0 = No, Else = Yes 0 0 0 0 0 0 

Half-life day 1008 1008 1008 1008 1008 1008

Groundwater Compartment Type

Half-life day 1008 1008 1008 1008 1008 1008

Surface Water Compartment Type

Ratio Of Conc In Algae To 

Conc Dissolved In Water

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

(g[chem]/L[water]) 2.06 5.36 4.25 5.36 3.26 1.55

Half-life day 0.58 6.3 0.58 6.3 0.58 2.7

Sediment Compartment Type

Half-life day 1095 1095 1095 1095 1095 1095
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

39

Exhibit 19. Dioxin Chemical-Specific Parameters for Abiotic 

Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units

Value

1,2,3,7,8-

PeCDF

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

HxCDF

2,3,4,7,8-

PeCDF

2,3,7,8-TCDD

2,3,7,8-TCDF

Air Compartment Type

Deposition velocity m/day 500 500 500 500 500

Half-life day 31 59 33 12 19

Washout ratio m3[air]/m3[rain] 13000 10000 14000 18000 19000

Surface Soil Compartment Type

Input characteristic depth m 0.08 0.08 0.08 0.08 0.08

Use input characteristic depth 

(Boolean) 0 = No, Else = Yes 0 0 0 0 0 

Half-life day 3650 3650 3650 3650 3650

Root Zone Soil Compartment Type

Input characteristic depth m 0.08 0.08 0.08 0.08 0.08

Use input characteristic depth 0 = No, Else = Yes 0 0 0 0 0 

Half-life day 3650 3650 3650 3650 3650

Vadose Zone Soil Compartment Type

Input characteristic depth m 0.08 0.08 0.08 0.08 0.08

Use input characteristic depth 

(Boolean) 0 = No, Else = Yes 0 0 0 0 0 

Half-life day 1008 1008 1008 1008 1008

Groundwater Compartment Type

Half-life day 1008 1008 1008 1008 1008

Surface Water Compartment Type

Ratio Of Conc In Algae To 

Conc Dissolved In Water

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

(g[chem]/L[water]) 1.75 4.26 1.39 1.76 0.71

Half-life day 0.19 0.58 0.19 2.7 0.18

Sediment Compartment Type

Half-life day 1095 1095 1095 1095 1095
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

40

Exhibit 19. Dioxin Chemical-Specific Parameters for Abiotic 

Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Reference

Air Compartment Type

Deposition velocity McKone et al. 2001. 

Half-life Atkinson 1996 as cited in USEPA 2000b; vapor phase 

reaction with hydroxyl radical.

Washout ratio Vulykh et al. 2001. 

Surface Soil Compartment Type

Input characteristic depth Not used (model set to calculate value). 

Use input characteristic depth (Boolean) Professional judgment. 

Half-life Mackay et al. 2000; the degradation rate was cited by 

multiple authors, value is for 2,3,7,8-TCDD.

Root Zone Soil Compartment Type

Input characteristic depth Not used (model set to calculate value).

Use input characteristic depth Professional judgment. 

Half-life Mackay et al. 2000; the degradation rate was cited by 

multiple authors, value is for 2,3,7,8-TCDD.

Vadose Zone Soil Compartment Type

Input characteristic depth Not used (model set to calculate value).

Use input characteristic depth (Boolean) Professional judgment.

Half-life

Average value of the range presented in Mackay et al. 2000; 

based on estimated unacclimated aerobic biodegradation 

half-life, value is for 2,3,7,8-TCDD.

Groundwater Compartment Type

Half-life

Average value of the range presented in Mackay et al. 2000; 

based on estimated unacclimated aerobic biodegradation 

half-life, value is for 2,3,7,8-TCDD.

Surface Water Compartment Type

Ratio Of Conc In Algae To Conc 

Dissolved In Water

Estimated from Kow value using model from DelVento and 

Dachs 2002

Half-life Kim and O’Keefe 1998, as cited in USEPA 2000b. 

Sediment Compartment Type

Half-life Estimation based on Adriaens and Grbic-Galic 1992,1993 

and Adriaens et al. 1995 as cited in USEPA 2000b.
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

41

Exhibit 20. Cadmium Chemical-Specific Parameters for 

Plant Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference

Leaf Compartment Type

Transfer factor to leaf particle 1/day 0.002 Professional judgment.

Particle on Leaf Compartment Type

Transfer factor to leaf 1/day 0.200 Professional judgment.

Root Compartment Type - Grasses and Herbsa

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

Root to Root Soil PartitionPartitioning Coefficientm3[bulk root 

soil]/m3[root] 0.23

Nriagu 1980; based on average 

value calculated from various 

agricultural plant species.

Root to Root Soil PartitionTime to Reach Alpha day 28 Henning et al. 2001.

Stem Compartment Type - Grasses and Herbsa

Transpiration stream 

concentration factor (TSCF)

m3

[soil pore 

water]/m3

[xylem 

fluid]

0.45 Tsiros et al. 1999.

a

Roots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE.
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

42

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

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

Leaf Compartment Type

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

transfer factor from leaf particle to leaf).

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

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

1973.

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

instantaneous

Reduction rate 1/day 0 0 0 Professional judgment.

Particle on Leaf Compartment Type

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

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

Methylation rate 1/day 0 0 0 Professional judgment.

Oxidation rate 1/day 0 0 0 Professional judgment.

Reduction rate 1/day 0 0 0 Professional judgment.

Root Compartment Type - Grasses and Herbsa

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

Root/root-zone-soil-water partition 

coefficient

m3[bulk root soil]/ 

m3[root] 0 0.18 1.2

Hg2-geometric mean Leonard et al. 

1998, John 1972, Hogg et al. 1978; 

MHg- assumed, 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.
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

43

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

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

Methylation rate 1/day 0 0 0 Professional judgment.

Oxidation rate 1/day 0 0 0 Professional judgment.

Reduction rate 1/day 0 0 0 Professional judgment.

Stem Compartment Type - Grasses and Herbsa

Transpiration stream concentration 

factor (TSCF)

m3[soil pore water]/ 

m3

[xylem fluid] 0 0.5 0.2 Calculation from Norway spruce, Scots pine, Bishop et al. 1998.

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

Methylation rate 1/day 0 0 0 Professional judgment.

Oxidation rate 1/day 0 0 0 Professional judgment.

Reduction rate 1/day 0 0 0 Professional judgment.

a

Roots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE.
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

44

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

Parameter 

Name Units

Value

2Methyl 712DMB Acenaphthene

Acenaphthylene BaA BaP BbF BghiP BkF

Leaf Compartment Type

Transfer factor to 

leaf particle 1/day 1.00E-04 1.00E-04 1.00E-04 1.00E-04 1.00E-04 1.00E-04 1.00E-04 1.00E-04 1.00E-04

Half-life day 3.50 3.50 3.50 3.50 3.50 3.50 3.50 3.50 3.50

Particle on Leaf Compartment Type

Transfer factor to 

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

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

Root Compartment Type - Grasses and Herbsa

Half-life day 34.60 34.60 34.60 34.60 34.60 34.60 34.60 34.60 34.60

Root soil-water 

interaction - 

alpha

unitless 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95

Stem Compartment Type - Grasses and Herbsa

Half-life day 3.50 3.50 3.50 3.50 3.50 3.50 3.50 3.50 3.50

a Roots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE.
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

45

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

Parameter 

Name Units

Value

Reference Chr DahA Fluoranthene Fluorene IcdP

Leaf Compartment Type

Transfer factor to 

leaf particle 1/day 1.00E-04 1.00E-04 1.00E-04 1.00E-04 1.00E-04 Professional judgment.

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

constant).

Particle on Leaf Compartment Type

Transfer factor to 

leaf 1/day 1.00E-04 1.00E-04 1.00E-04 1.00E-04 1.00E-04 Professional judgment.

Half-life day 4.12 17.80 2.31 2.31 17.80

Calculated as 2 times the measured photolysis 

half-life from Mackay et al. 1992. Exceptions 

include values that have been set equal to 

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

Dimethylbenz(a)anthracene; Acenaphthene; 

Acenaphthylene; Benzo(ghi)perylene; 

Fluoranthene; and Fluorene). 

Root Compartment Type - Grasses and Herbsa

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

constant).

Root soil water 

interaction - 

alpha

unitless 0.95 0.95 0.95 0.95 0.95 Professional judgment.

Stem Compartment Type - Grasses and Herbsa

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

constant).

a

Roots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE.
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

46

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

Parameter Name Units Value Reference All Dioxins

Leaf Compartment Type

Transfer factor to leaf particle 1/day 0.003 Calculated as 1 percent of transfer factor to leaf; highly uncertain.

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

soybean root cell culture metabolism test data for DDE.

Particle on Leaf Compartment Type

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

mercury) and Trapp 1995; highly uncertain.

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

foliage in sunlight; assumed 10% sunlight per day.

Root Compartment Type - Grasses and Herbsa

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

soybean root cell culture metabolism test data for DDE.

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

Stem Compartment Type - Grasses and Herbsa

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

soybean root cell culture metabolism test data for DDE.

a

Roots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE.
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

47

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

Parameter Name Units Value Reference

Zooplankton Compartment Type

Absorption rate constant L[water]/kg[fish wet wt]-day 1500 Goulet 2007. 

Assimilation efficiency from algae unitless 0.5 Goulet 2007. 

Elimination rate constant 1/day 0.03 Goulet 2007. 

Benthic Invertebrate Compartment Type

Sediment partitioning - alpha of 

equilibrium unitless 0.95 Professional judgment. 

Sediment oartitioning - partition 

coefficient

kg[bulk sed/kg[invertebrate wet 

wt] 0.27 Professional judgment. 

Sediment partitioning - time to reach 

alpha of equilibrium day 21 Hare et al. 2001. 

Benthic Omnivore Compartment Type

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

2002.

Absorption rate constant unitless 1.23 Calculated based on body weight from 

regression in Hendriks and Heikens 2001. 

Elimination rate constant unitless 1.73E-02 Professional judgment. 

Benthic Carnivore Compartment Type

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

2002.

Absorption rate constant unitless 0.66 Calculated based on body weight from 

regression in Hendriks and Heikens 2001.
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

48

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

Parameter Name Units Value Reference

Elimination rate constant unitless 1.68E-03 Professional judgment. 

Water-column Herbivore Compartment Type

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

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

Absorption rate constant unitless 2.46 Calculated based on body weight from 

regression in Hendriks and Heikens 2001.

Elimination rate constant unitless 1.73E-02 Professional judgment. 

Water-column Omnivore Compartment Type

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

2002.

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

2002.

Absorption rate constant unitless 1.23 Calculated based on body weight from 

regression in Hendriks and Heikens 2001.

Elimination rate constant unitless 1.73E-02 Professional judgment. 

Water-column Carnivore Compartment Type

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

2002.

Absorption rate constant unitless 0.66 Calculated based on body weight from 

regression in Hendriks and Heikens 2001.

Elimination rate constant unitless 1.73E-02 Professional judgment. 
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

49

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

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

Zooplankton Compartment Type

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

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

How Much Faster Hg Elimination Is Than

For MHg unitless 3 3 1 Professional judgment.

Methylation rate 1/day 0 0 0 Professional judgment.

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

Reduction rate 1/day 0 0 0 Professional judgment.

Benthic Invertebrate Compartment Type

Alpha of equilibrium for sediment 

partitioning unitless 0.95 0.95 0.95

Selected value (i.e., proportion 

of equilibrium achieved by time 

“t”).

Benthic invertebrate-bulk sediment 

partition coefficient

kg[bulk 

sediment]/kg[invertebrate 

wet wt]

0.0824 0.0824 5.04

Hg(0) - assumed based on 

Hg(2) value; Hg(2) and MHg - 

Saouter et al. 1991.

t-alpha for equilibrium for sediment 

partitioning day 14 14 14 Experiment duration from Saouter et al. 1991.
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

50

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

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

All Fish Compartments Typesa

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

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

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

Methylation rate 1/day 0 0 0 Professional judgment.

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

Reduction rate 1/day 0 0 0 Professional judgment.

Water-column Herbivore Compartment Type

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

a

Screening scenario includes: Benthic Omnivore, Benthic Carnivore, Water-column Herbivore, Water-column Omnivore, and Water-column Carnivore.
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

51

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

Parameter 

Name Units

Value

2Methyl 712DMB Acenaphthene

Acenaphthylene BaA BaP BbF BghiP BkF

Zooplankton Compartment Type

Absorpotion 

rate constant

L[water]/kg[fish 

wet wt]-day 790 42650.94 42231 42302.18 42650.81 42652.78 42650.68 42655.77 42652.5

Assimilation 

efficiency 

from algae

unitless 0.5 0.25 0.5 0.5 0.46 0.25 0.25 0.25 0.25

Elimination 

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

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

Benthic Invertebrate Compartment Type

Clearance 

constant unitless 100.6 100.6 100.6 100.6 100.6 100.6 100.6 100.6 100.6
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

52

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

Parameter 

Name Units

Value

2Methyl 712DMB Acenaphthene

Acenaphthylene BaA BaP BbF BghiP BkF

Vd (ratio of 

concentration

in benthic 

invertebrates 

to

concentration 

in water)

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

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

All Fish Compartment Typesa

Gamma fish unitless 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2

Assimilation 

efficiency 

from food

unitless 0.5 0.15 0.5 0.32 0.15 0.15 0.15 0.15 0.15

Half-life day 0.2 2 0.2 0.2 0.408 1.925 2 2 2 a

Screening scenario includes: Benthic Omnivore, Benthic Carnivore, Water-column Herbivore, Water-column Omnivore, and Water-column Carnivore.
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

53

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

Parameter Name Units

Value

Reference Chr DahA Fluoranthene Fluorene IcdP

Zooplankton Compartment Type

Absorpotion rate 

constant

L[water]/kg[fish 

wet wt]-day 42649.95 42655.48 142000 15000 42655.93

Kow from Arnot et al. 2004. 

Exception is Berrojalbiz et al. 

2009 (2-Methylnaphthalene, 

Fluoranthene, and Fluorene). 

Assimilation 

efficiency from 

algae

unitless 0.46 0.25 0.49 0.5 0.25

Kow from Arnot et al. 2004. 

Exception is maximum value 

from Wang and Wang 2006 

(7,12-

Dimethylbenz(a)anthracene, 

Benzo(a)pyrene, 

Benzo(b)fluoranthene, 

Benzo(g,h,i)perylene, 

Benzo(k)fluoranthene, 

Dibenz(a,h)anthracene, and 

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

Elimination rate 

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

Half-life day 0.495 17 0.00239 0.0002476 17

McElroy 1990. Exceptions 

include Berrojalbiz et al. 2009 

(2-Methylnaphthalene, 

Fluoranthene, and Fluorene) 

and Moermond et al. 2007

(Benz(a)anthracene and 

Benzo(a)pyrene).

Benthic Invertebrate Compartment Type

Clearance 

constant unitless 100.6 100.6 100.6 100.6 100.6 Stehly et al. 1990. 
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

54

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

Parameter Name Units

Value

Reference Chr DahA Fluoranthene Fluorene IcdP

Vd (ratio of 

concentration

in benthic 

invertebrates to

concentration in 

water)

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

Half-life day 0.495 17 0.722 0.722 17 Moermond et al. 2007.

All Fish Compartment Typesa

Gamma fish unitless 0.2 0.2 0.2 0.2 0.2 Thomann 1989.

Assimilation 

efficiency from 

food

unitless 0.15 0.15 0.14 0.14 0.15

Lemair et al. 1992. Exceptions 

include Barber 2008 (2-

Methylnaphthalene and 

Acenaphthene) and Niimi and 

Palazzo 1986 

(Acenaphthylene, 

Fluoranthene, and Fluorene).

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

Screening scenario includes: Benthic Omnivore, Benthic Carnivore, Water-column Herbivore, Water-column Omnivore, and Water-column 

Carnivore.
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

55

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

Parameter Name Units

Value

1,2,3,4,6,7,8,9-OCDD1,2,3,4,6,7,8,9-OCDF1,2,3,4,6,7,8-HpCDD1,2,3,4,6,7,8-HpCDF1,2,3,4,7,8,9-HpCDF1,2,3,4,7,8-HxCDD1,2,3,4,7,8-HxCDF1,2,3,6,7,8-HxCDD1,2,3,6,7,8-HxCDF

Zooplankton Compartment

Absorption rate constant L[water]/kg[fish wet 

wt]-day 8640 8640 8640 8640 8640 8640 8640 8640 8640

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

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

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

Benthic Invertebrate Compartment

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

Sediment partitioning partition 

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

Sediment partitioning alpha of 

equilibrium unitless 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95

Sediment partitioning time to

reach alpha of equilibrium days 120 42 120 42 42 120 42 120 42

Vd (ratio of concentration

in benthic invertebrates to

concentration in water)

ml/g 0 0 0 0 0 0 0 0 0 
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

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

Parameter Name Units

Value

1,2,3,4,6,7,8,9-OCDD1,2,3,4,6,7,8,9-OCDF1,2,3,4,6,7,8-HpCDD1,2,3,4,6,7,8-HpCDF1,2,3,4,7,8,9-HpCDF1,2,3,4,7,8-HxCDD1,2,3,4,7,8-HxCDF1,2,3,6,7,8-HxCDD1,2,3,6,7,8-HxCDF

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

All Fish Compartmentsa

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

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

wt]-day 11 6 56 25 50 102 200 300 200

Half-life day 70 70 70 70 70 70 70 70 70

a

Screening scenario includes: Benthic Omnivore, Benthic Carnivore, Water-column Herbivore, Water-column Omnivore, and Water-column Carnivore.
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

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

Parameter Name Units

Value

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

HxCDD

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

HxCDF

1,2,3,7,8-

PeCDD

1,2,3,7,8-

PeCDF

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

HxCDF

2,3,4,7,8-

PeCDF

2,3,7,8-TCDD

2,3,7,8-TCDF

Zooplankton Compartment

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

wt]-day 8640 8640 8640 8640 8640 8640 8640 8640

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

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

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

Benthic Invertebrate Compartment

Clearance Constant unitless 0 0 0 0 0 0 0 0 

Sediment Partitioning Partition 

Coefficient kg/kg 0.015 0.067 0.098 0.024 0.072 0.17 0.205 0.056

Sediment Partitioning Alpha of 

Equilibrium unitless 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95

Sediment Partitioning Time to

Reach Alpha of Equilibrium days 120 42 120 42 42 42 120 42

Vd (ratio of concentration

in benthic invertebrates to

concentration in water)

ml/g 0 0 0 0 0 0 0 0 

Half-life day 5776.2 5776.2 5776.2 5776.2 5776.2 5776.2 5776.2 5776.2
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

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

Parameter Name Units

Value

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

HxCDD

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

HxCDF

1,2,3,7,8-

PeCDD

1,2,3,7,8-

PeCDF

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

HxCDF

2,3,4,7,8-

PeCDF

2,3,7,8-TCDD

2,3,7,8-TCDF

All Fish Compartmentsa

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

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

wt]-day 300 200 700 300 200 400 600 400

Half-life day 70 70 70 70 70 70 70 70

a Screening scenario includes: Benthic Omnivore, Benthic Carnivore, Water-column Herbivore, Water-column Omnivore, and Water-column Carnivore.
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

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

Parameter Name Units Reference

Zooplankton Compartment

Absorption rate constant L[water]/kg[fish wet 

wt]-day Zhang et al. 2011; used copepod ku value. 

Assimilation efficiency from algae unitless

Morrison et al. 1999. Exceptions include Niimi and Oliver 1986 (1,2,3,4,6,7,8,9-

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

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

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

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

Elimination rate constant 1/day Arnot and Gobas 2004; used Kow value. 

Half-life day Morrison et al. 1999; used metabolic rates for invertebrates. 

Benthic Invertebrate Compartment

Clearance constant unitless Professional judgment. 

Sediment partitioning partition 

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

Sediment partitioning alpha of 

equilibrium unitless Rubinstein et al. 1990. 

Sediment Partitioning Time to 

Reach Alpha of Equilibrium days Rubinstein et al. 1990. 

Vd (ratio of concentration in benthic 

invertebrates to concentration in 

water)

ml/g Professional judgment. 

Half-life day Rubinstein et al. 1990; used TCDD data for sandworm. 
Addendum 1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

60

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

Parameter Name Units Reference

All Fish Compartmentsa

Assimilation Efficiency from Food unitless

Morrison et al. 1999. Exceptions include Niimi and Oliver 1996 (1,2,3,4,6,7,8,9-

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

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

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

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

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

wt]-day

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

1,2,3,7,8-PeCDD, 1,2,3,7,8-PeCDF, 2,3,4,7,8-PeCDF, 2,3,7,8-TCDD, 2,3,7,8-

TCDF). 

Half-life day Berntssen et al. 2007. 

a

Screening scenario includes: Benthic Omnivore, Benthic Carnivore, Water-column Herbivore, Water-column Omnivore, and Water-column Carnivore.
Addendum1 to Technical Support Document for TRIM-Based Multipathway Screening Methodology June 2012

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Tiered Screening Methodology for RTR 

Addendum 2: Description of Multimedia Ingestion Risk 

Calculator (MIRC) Used for RTR Exposure and Risk Estimates
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TABLE OF CONTENTS 

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

1.1 Purpose and Overview .................................................................................................... 1

1.2 Scope of MIRC ............................................................................................................... 2

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

4.1 Cadmium ....................................................................................................................... 42

4.2 Mercury ......................................................................................................................... 42

4.3 Dioxins (2,3,7,8-TCDD) ............................................................................................... 42

4.4 Polycyclic Organic Matter ............................................................................................ 43

5 Risk Estimation ................................................................................................................. 46

5.1 Cancer Risks ................................................................................................................. 46
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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

6.2.3 Animal Product Parameter Values .......................................................................... 64

6.3 Adult and Non-Infant Exposure Parameter Values ...................................................... 67

6.3.1 Body Weights .......................................................................................................... 67

6.3.2 Water Ingestion Rates ............................................................................................. 68

6.3.3 Local Food Ingestion Rates .................................................................................... 69

6.3.4 Local Fish Ingestion Rates ...................................................................................... 74

6.3.5 Soil Ingestion Rates ................................................................................................ 78

6.3.6 Total Food Ingestion Rates ..................................................................................... 79

6.4 Other Exposure Factor Values ...................................................................................... 80

6.4.1 Exposure Frequency ................................................................................................ 80

6.4.2 Fraction Contaminated ............................................................................................ 81

6.4.3 Preparation and Cooking Losses ............................................................................. 81

6.5 Breast-Milk Infant Exposure Pathway Parameter Values ............................................ 83

6.5.1 Receptor-specific Parameters .................................................................................. 83

6.5.2 Chemical-Specific Parameter Values ..................................................................... 88

7 Summary of MIRC Default Exposure Parameter Settings ............................................. 91

7.1 Default Ingestion Rates ................................................................................................. 91

7.2 Default Screening-Level Population-Specific Parameter Values ................................. 93

7.3 Default Chemical-Specific Parameter Values for Screening Analysis ......................... 94

7.4 Screening-Level Parameter Values for Nursing Infant Exposure ................................. 96

7.4.1 Dioxins .................................................................................................................... 96

7.4.2 Methyl Mercury ...................................................................................................... 97

8 REFERENCES .................................................................................................................... 98
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LIST OF EXHIBITS 

Exhibit 2-1. Overview of 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 Dioxin Congeners and 

Summary of Derived Cancer Slope Factors for RTR ................................................................... 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 .......................... 55

Exhibit 6-4. Non-Chemical-Specific Produce Inputs .................................................................. 63

Exhibit 6-5. Animal Product Chemical-specific Inputs for Chemicals Included in MIRC ......... 64

Exhibit 6-6. Soil and Plant Ingestion Rates for Animals ............................................................. 66

Exhibit 6-7. Mean and Percentile Body Weight Estimates for Adults and Children ................. 68

Exhibit 6-8. Estimated Daily Per Capita Mean and Percentile Water Ingestion Rates for Children 

and Adults a ................................................................................................................................... 69

Exhibit 6-9. Summary of Age-Group Specific Food Ingestion Rates for Farm Food Items ....... 70

Exhibit 6-10. Daily Mean and Percentile Consumer-Only Fish Ingestion Rates ........................ 76

Exhibit 6-11. Fraction of Population Consuming Freshwater/Estuarine Fish on a Single Day

(FPC,y) ............................................................................................................................................ 77

Exhibit 6-12. Calculated Long-term Mean and Percentile Per capita Fish Ingestion Rates for 

Children and Adults (IRPC,y) ......................................................................................................... 77

Exhibit 6-13. Calculated Mean and 90th Percentile Per capita Fish Ingestion Rates for 

Populations of Recreational Fishers (IRPC,y) ................................................................................ 78

Exhibit 6-14. Daily Mean and Percentile Soil Ingestion Rates for Children and Adults ............ 79

Exhibit 6-15. Daily Mean and Percentile Per Capita Total Food Intake for Children and Adults

....................................................................................................................................................... 80

Exhibit 6-16. Fraction Weight Losses from Preparation of Various Foods ................................ 82

Exhibit 6-17. Scenario- and Receptor-Specific Input Parameter Values Used to Estimate Infant 

Exposures via Breast Milk ............................................................................................................ 84

Exhibit 6-18. Average Body Weight for Infants .......................................................................... 84

Exhibit 6-19. Time-weighted Average Body Weight for Mothers ............................................... 85

Exhibit 6-20. Infant Breast Milk Intake Rates ............................................................................. 87

Exhibit 6-21. Chemical-specific Input Parameter Values for Breast Milk Exposure Pathway .... 88

Exhibit 7-1. Farm Food Category Ingestion Rates for Health protective Screening Scenario for 

Farming Households ..................................................................................................................... 92

Exhibit 7-2. Mean Body Weight Estimates for Adults and Children a ........................................ 93

Exhibit 7-3. Chemical-Specific Parameter Values for Input to MIRC a ...................................... 95

Exhibit 7-4. Chemical and Animal-Type Specific Biotransfer Factor (Ba) values for Input to 

MIRC ............................................................................................................................................ 96
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Screening Methodology for RTR 

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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 Particlephase 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 Livestock 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

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
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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 .............. 73

Equation 6-2. Calculation of Alternative Age-Group-Specific Fish Ingestion Rates .................. 75

 
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1 Introduction

1.1 Purpose and Overview 

This document provides a detailed description of the Multimedia Ingestion Risk Calculator 

(MIRC), a modeling tool and database designed to assist in estimating risks via multiple 

ingestion pathways, particularly for food products grown or raised at home or on a farm.1

 MIRC 

was designed to estimate risks to humans from ingestion of produce or animal products, fish, 

and water in the vicinity of a source of chemical emissions to air. The user can evaluate either 

generalized (e.g., health protective default) or more site-specific scenarios using the same tool. 

MIRC includes a database of exposure parameter values, offering the user the option of 

selecting mean, median, and upper percentile values for many parameters, data permitting. 

Generally health protective default values were assigned to each parameter in the tool and the 

default configuration is used for initial risk screening efforts by EPA’s Office of Air Quality 

Planning and Standards’ (OAQPS) for Risk and Technology Review (RTR) multimedia risk 

assessments. MIRC also allows the user to define the farm food chain (FFC) parameter values 

and receptor characteristics to better represent a site-specific scenario. 

With user-input concentrations for one or more chemicals in air and soil and air-to-surface 

deposition rates, MIRC calculates the chemical’s concentrations in home- or farm-grown 

produce and animal food products using FFC algorithms adapted from EPA’s Human Health 

Risk Assessment Protocol for Hazardous Waste Combustion Facilities (hereafter referred to as 

HHRAP; EPA 2005a). MIRC uses these calculated concentrations, along with user-input 

chemical concentrations for fish and drinking water, to estimate chemical intake rates, as 

average daily doses (ADDs), for adults, children, and nursing infants. Users can obtain 

chemical input concentrations and deposition rates from measurements at an actual site or from 

a transport and fate model, such as TRIM.FaTE, as is done for RTR risk assessment. 

For a specified set of chemical concentrations and MIRC parameter options, MIRC calculates 

ADDs separately for adults, four age groups of children, and infants to reflect differences in food 

ingestion rates and diet at different lifestages. MIRC estimates age-specific hazard quotients 

(HQs) as the ratio of age-specific ADDs to the reference dose (RfD) for a chemical. The most 

appropriate HQ for a chemical depends on its toxic mode of action and the duration of exposure 

required to produce an effect. MIRC also estimates average lifetime ADDs and compares those 

to cancer slope factors (CSFs) to estimate cancer risks. A breast milk ingestion pathway is 

available to estimate exposure and risks to nursing infants. 

MIRC was developed to be a flexible, transparent application. The tool includes chemical 

transfer and ingestion exposure algorithms and a database of parameter values, many with 

several options, used by these equations. The MIRC database includes values for the relevant 

physiochemical properties and toxicity reference values for more than 500 chemicals, including 

approximately 60 inorganics taken primarily from a database developed for HHRAP (EPA 

2005a). Although designed for OAQPS’ RTR assessments for sources of hazardous air 

pollutants (HAPs), the tool is flexible in its design and can be used to assess risks in many other 

contexts where soil and air concentrations are predicted or measured. 

 

1

 Fully functional versions of MIRC are available in both Access-based and Excel-based formats. 
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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 

contaminated directly by deposition of the airborne chemical to foliage, fruits, and vegetables or 

indirectly by root uptake of the chemical deposited to soils. Livestock can be exposed to the PB 

chemicals via ingestion of contaminated forage and incidental ingestion of contaminated soils. 

For PB chemicals, evaluation of the inhalation pathway for air pollutants may reveal only a 

portion of the risk to individuals in such populations. Households that consume high quantities 

of self-caught fish or locally grown produce and animal products may be particularly susceptible 

to ingestion of chemicals transferred from air in the vicinity of an air emissions source. For PB 

chemicals in particular, therefore, EPA developed methods of estimating risk from indirect 

exposure pathways associated with the deposition of airborne chemicals to gardens and farms, 

as described in HHRAP (EPA 2005a). 

1.3 Use in EPA’s Air Toxics Program 

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 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology for RTR 

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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.

2

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 

 

2

 General information about the TRIM system is available at http://www.epa.gov/ttn/fera/trim_gen.html.
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology 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 

health protective risk screening assessments, and Section 8 provides the references. 

Note that the default parameterization described in Section 7 was used to estimate screening 

threshold emission rates of PB-HAPs from RTR facilities. These emissions levels are assumed 

to pose negligible risk to subsistence communities in the vicinity of a facility emitting the PBHAPs to air. Users of MIRC can modify the default values for many of the parameters to better 

represent a specific exposure scenario. 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology for RTR 

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2 MIRC Overview 

The Multimedia Ingestion Risk Calculator (MIRC) software package is designed to allow rapid 

calculation of screening-level exposures and risks associated with subsistence and recreational 

farmer/fisher populations in the vicinity of a source of chemical emissions to air. The tool allows 

a user to assess human exposures via ingestion pathways, including drinking water 

consumption, incidental soil ingestion, fish ingestion, and ingestion of ten types of farm food 

chain (FFC) products: exposed fruits, protected fruits, exposed vegetables, protected 

vegetables, root vegetables, beef, total dairy, pork, poultry, and eggs. The tool also includes a 

breast milk ingestion and risk module for nursing infants. For fruits and vegetables, the terms 

“exposed” and “protected” refer to whether the edible portion of the plant is exposed to the 

atmosphere. 

The remainder of this overview consists of three sections. The first (Section 2.1) provides an 

overview of the MIRC software package. The second and third sections summarize the 

ingestion exposure pathways included in the tool and the “built-in” receptor age categories, 

respectively (Sections 2.2 and 2.3). 

2.1 Software 

The MIRC application includes the following components: 

 A graphical user interface through which the user locates and accesses various input 

and output tables. 

 Input tables in which the user can enter environmental concentrations of a chemical 

estimated for air, soil, drinking water, and fish tissue. 

 Internal chemical transfer and exposure algorithms and database of options for FFC

algorithm parameter value, chemical-specific inputs, and exposure factors. 

 Tabulated outputs of calculated chemical concentrations in the various farm food 

products (e.g., fruits, vegetables, beef, eggs) and ADDs for those foods and for water 

and fish ingestion for each receptor category. 

 Output tables with estimated cancer risks and non-cancer hazard estimates associated 

with total ingestion exposure to each chemical for each receptor category. 

Exhibit 2-1 provides a flowchart displaying the types of inputs required or optional and general 

flow of calculations carried out by the tool. 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology for RTR 

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Exhibit 2-1. Overview of MIRC Software Application for 

Performing Farm-Food-Chain Ingestion Exposure and Risk Calculations 

User Selects Receptor Characteristics

From Options or Over-write Body Weight

User Specifies Environmental Concentrations for Chemical of Concern

Air Root-zone and Surface Soils Fish Drinking Water

User Option to Add Breast Milk Pathway yes

no

User Selects BMP 

Parameter Values

Uptake by foliage / Uptake by roots: 

Vegetables, Fruits Grains Hay, Grass Animal Products

Home Grown 

Food Product 

Ingestion Rates

Fish and Water 

Ingestion Rates

Duration Breast Feeding; maternal 

and infant characteristics

Average Daily Dose (ADD) for Age Group y; y =1 to 5

Chemical Intake with Food/ 

Medium Type i; i = 1 to 10

Lifetime Average 

Daily Dose

Risk Characterization Module

Farm Food Chain Biotransfer Calculations

Breast Milk Exposure 

Module

ADD Maternal 

[C] in milk

Infant Dose

Adult ADD x 

absorption efficiency

Exposure Module

Chemical 

Toxicity 

Reference

Values:

SF and RfDs

Age-specific & Lifetime Exposure Doses

Lifetime Cancer Risks

Age-Specific Hazard Quotients

User Selects Chemical(s) of Concern: For Each, MIRC Calculates Risks
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology for RTR 

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A form within the graphical user interface enables the user to construct specific scenarios by 

choosing ingestion sources, receptor ages, and other input choices (e.g., diet composition, body 

weight percentiles). This feature facilitates the analysis of various exposure scenarios. To 

begin an analysis, the user must supply values for the following chemical-specific parameters 

for the scenario being evaluated: 

 Air concentration of total chemical, 

 Fraction chemical in air in vapor-phase, 

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

 Drinking water concentration, 

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

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

 Chemical concentrations in pan fish and in game fish. 

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

parameters. 

The MIRC application uses the input data and a variety of empirical transfer factor values 

(included in its database) to estimate chemical concentrations in nine categories of FFC food 

types (Section 2.2). The FFC algorithms and transfer factor values included in MIRC are based 

on those presented in Chapter 5 of EPA’s HHRAP (EPA 2005a). 

For outputs, MIRC is designed to calculate individual cancer risk and non-cancer hazard 

quotients for one chemical at a time. It is up to the risk assessor to determine if cancer risks or 

hazard quotients may be additive across two or more chemicals (i.e., if they cause toxic effects 

in the same target organ by the same mode of action, such as multiple polycyclic aromatic 

hydrocarbons (PAHs) that are carcinogenic by a mutagenic mode of action). 

The tool assumes that 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). 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology for RTR 

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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. 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology for RTR 

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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 2011) and its Child-Specific Exposure 

Factors Handbook (CSEFH; EPA 2008a), EPA summarized home-grown/raised food ingestion 

rates for four children’s age groups: 1 to < 3 years; 3 to < 6 years; 6 to < 12 years; and 12 to < 

20 years. Intake rates were not calculated for children younger than 1 year because infants are 

unlikely to consume those foods. They are more likely to be nursing or to be fed formula and 

other commercial baby-food products. 

Although the age groupings used to estimate FFC ingestion rates do not match precisely the 

groupings that EPA recommended in 2005 for Agency exposure assessments (EPA 2005b), 

they are the only age-groupings for which such data are available. The U.S. Department of 

Agriculture’s (USDA’s) 1987-1988 Nationwide Food Consumption Survey (USDA 1992, 1993, 

1994) remains the most recent survey of ingestion rates for home-grown foods, and EPA’s 

analysis of those data, published in its 2011 EFH, remains the most recently published major 

analysis of those data. Because ingestion of home-grown produce and animal products are the 

primary exposure pathways for which MIRC was developed, those are the age groupings used 

for all child parameter values used to estimate exposure and risk in MIRC. 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology for RTR 

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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. 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology for RTR 

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3 Exposure Algorithms 

The exposure algorithms in MIRC are described below in four sections. Section 3.1 presents 

the algorithms used to estimate chemical concentrations in FFC foods from chemical 

concentrations in soil and air. Pathway-specific algorithms used to estimate chemical intakes by 

adults and non-infant children are described in Section 3.2, and total chemical intake 

calculations are described in Section 3.3. Finally, the sets of algorithms used to estimate 

chemical intake via consumption of breast milk by nursing infants are described in Section 3.4. 

As noted previously, the exposure algorithms used in MIRC are based on those presented in 

HHRAP (EPA 2005b). Any differences between MIRC and HHRAP are explained in this 

section. 

3.1 Farm Food Chain Algorithms 

The algorithms and parameters used to estimate chemical concentrations in produce and 

animal products are described in Sections 3.1.1 and 3.1.2, respectively. Discussions of the 

parameter value options and the values selected as defaults in MIRC for RTR risk assessment 

are provided in Section 6.2. The use of TRIM.FaTE to model chemical fate and transport in the 

environment prior to FFC calculations drives the most significant difference between the FFC 

algorithms included in HHRAP and the equations used for RTR. The approach in HHRAP uses 

estimated ambient air concentrations and deposition rates from dispersion model simulations 

that use unitized emission rates. Chemical-specific emission rates (adjusted for vapor and 

particle-bound fractions) are then incorporated into some of the HHRAP FFC algorithms to 

calculate concentrations in FFC media. Soil concentrations are calculated using a similar 

approach in HHRAP. For assessment of multipathway exposures for RTR, TRIM.FaTE is used 

to estimate air concentrations, air-to-surface deposition rates, and soil concentrations, and 

these outputs are used in the FFC algorithms. 

3.1.1 Estimating Chemical Concentrations in Produce 

Produce (vegetables and fruits) can become contaminated directly by deposition of airborne 

chemicals to foliage and fruits or indirectly by uptake of chemicals deposited to the soil. Given 

these two contamination processes, produce is divided into two main groups: aboveground and 

belowground produce. Aboveground produce is divided into fruits and vegetables. These 

groups are further subdivided into “exposed” and “protected” depending on whether the edible 

portion of the plant is exposed to the atmosphere or is protected by a husk, hull, or other outer 

covering. 

Exhibit 3-1 lists the pathways by which chemicals are transferred to the FFC produce 

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

modeled, as was done for EPA’s calculation of screening threshold emission rates for PB-HAPs 

in its RTR assessments (EPA 2008b), and as described in the Technical Support Document. 

Sections 3.1.1.1 and 3.1.1.2 describe the transfer pathways and algorithms for aboveground 

and belowground produce, respectively. 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology 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 Aboveground Produce 

For aboveground exposed produce, 

chemical mass is assumed to be transferred 

to plants from the air in three ways, as 

illustrated in Exhibit 3-2. First, particlebound chemical can deposit directly on the 

plant surface via deposition (Pd). The 

amount of chemical accumulated is 

estimated based on the areal fraction of 

chemical deposition intercepted by the plant 

surface, minus a loss factor that is intended 

to account for removal of deposited 

chemical by wind and rain and changes in 

concentration due to growth dilution. 

Second, for chemical present in air in the 

vapor phase, the concentration of chemical 

accumulated by the plant’s foliage is 

estimated using an empirical air-to-plant 

biotransfer factor (Pv). Third, the chemical 

concentration in the plant due to root uptake from the soil (PrAG-produce) is estimated using an 

empirical bioconcentration factor (BrAG-produce ) that relates the chemical concentration in the plant 

to the average chemical concentration in the soil at the root-zone depth in the produce-growing 

area (Csroot-zone_produce). 

The edible portions of aboveground protected produce are not subject to contamination via 

particle deposition (Pd) or vapor transfer (Pv). Therefore, root uptake of chemicals is the 

primary mechanism through which aboveground protected produce becomes contaminated. 

The chemical concentration in the aboveground plant due to root uptake from soil (PrAG-produceDW) is estimated using an empirical bioconcentration factor (BrAG-produce-DW) that relates the 

chemical concentration in the plant to the average chemical concentration in the soil at the rootzone depth in the produce-growing area (Csroot-zone_produce). 

Exhibit 3-2. Estimating Chemical 

Concentration in Aboveground Produce 

Deposition 

of Particles 

(Pd)

Root Uptake 

from Soil 

(PrAG-produce)

Vapor 

Transfer 

(Pv) 

Chemical Concentration in 

Aboveground Produce
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology 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) 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology 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. 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology 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. 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology 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 Belowground Produce 

The equations by which chemical concentrations are estimated in belowground produce are 

different for nonionic organic chemicals than for inorganic chemicals and ionic organic 

chemicals. 

3.1.1.2.1 Nonionic Organic Chemicals 

For belowground produce, the nonionic organic chemical concentration in the tuber or root 

vegetable is derived from exposure to the chemical in soil and is estimated using an empirical 

root concentration factor (RCF) and the average chemical concentration in the soil at the rootzone depth in the produce-growing area (Csroot-zone_produce), as shown in Equation 3-6. The RCF 

relates the chemical concentration in the plant on a wet-weight basis to the average chemical 

concentration in the root-zone soil (Csroot-zone_produce) on a dry-weight basis. Belowground 

produce (i.e., tubers or root vegetables) are protected from the deposition and vapor transfer by 

being covered by soil. Therefore, root uptake of chemicals is the primary mechanism through 

which belowground produce becomes contaminated. 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology 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. 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology 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. 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology 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 health protective 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.) 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology 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. 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology 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). 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology for RTR 

2-22 

Equation 3-14. Chemical Concentration in Livestock Feed Due to Root Uptake 

P ifeed )( Cs zoneroot ifeed )(_ Br ifeed )( r   

where: 

Prfeed(i) = 

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

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

silage, or grain 

Csroot-zone_feed(i) = Average chemical concentration in soil at root-zone depth in area used to grow 

plant feed type i (mg chemical/kg soil DW), where i = forage, silage, or grain 

Brfeed(i) = Chemical-specific plant-soil bioconcentration factor for plant feed type i (kg soil 

DW/kg plant feed DW), where i = forage, silage, or grain 

The algorithms used to calculate Pd(i) and Pv(i) when plant feed type i = forage and silage are 

identical to those used to calculate Pd(i) and Pv(i) for aboveground exposed produce (i.e., 

Equations 3-3 and 3-4, respectively). 

There are no conversions of DW feed to WW feed, because all feed ingestion rates for livestock 

are based on DW feed. 

3.2 Chemical Intake Calculations for Adults and Non-Infant Children 

MIRC calculates human chemical intake rates from the ingestion of home-grown foods as 

average daily doses (ADDs) normalized to body weight for each age group, chemical, and food 

type separately. ADDs, calculated using Equation 3-15, are expressed in milligrams of chemical 

per kilogram of receptor body weight per day (mg/kg-day). 

Equation 3-15. Average Daily Dose for Specified Age Group and Food Type 



































  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 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology for RTR 

2-23 

of the food obtained from contaminated areas, and the consumer’s body weight (EPA 2011, 

2003a). In MIRC, ADDs are calculated separately for each chemical, home-grown food type, 

and consumer age group. 

ADD values, expressed as intakes, not absorbed doses, are appropriate for comparison with 

RfDs and for use with cancer slope factors (CSFs) to estimate risk, as discussed in Section 5. 

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. 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology 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. 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology 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 (EFH, EPA 2011) and 

included those losses in its HHRAP algorithms for chemical intake from fish (L1Fish and L2Fish in 

Equation 3-17). 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology 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). 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology for RTR 

2-27 

Equation 3-21. Chemical Intake from Exposed Vegetables 

  





























    

g days 365

kg 11 0010 EF ADDExpVeg(y) L ExpVeg ExpVeg IRC ExpVeg(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 2111 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) 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology 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 Beef Beef Beef(y) .IRCL 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) 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology 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) 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology 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. 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology 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). 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology 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. 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology 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 the Methodology for Assessing Health Risks Associated with Multiple 

Pathways of Exposure to Combustor Emissions (MPE) (U.S. EPA 1998), EPA reviewed three 

first-order kinetics models for estimating chemical concentration in breast milk fat. The model 

selected for use in 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). 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology 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). 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology 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. 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology 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), 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology 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 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology 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 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology 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 health protective 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 6-20). 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). 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology 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) 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology for RTR 

2-41 

4 Dose-Response Values Used for Assessment 

Chemical dose-response values included in MIRC include carcinogenic potency slope factors 

for ingestion and non-cancer oral reference doses (RfDs) for chronic exposures. The cancer 

slope factors (CSFs) and RfDs for chemicals used to calculate persistent and bioaccumulative 

hazardous air pollutant (PB-HAP) emission thresholds are provided in Exhibit 4-1. Doseresponse 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 2007a). In general, OAQPS chose these values based on the following hierarchy of 

sources: EPA’s Integrated Risk Information System (IRIS); the Centers for Disease Control’s 

Agency for Toxic Substances and Disease Registry (ATSDR); and the California Environmental 

Protection Agency’s (CalEPA’s) Office of Environmental Health Hazard Assessment (OEHHA) 

Toxicity Criteria Database. For PB-HAPs without dose-response values from these sources, 

alternative methods for deriving values were used (see Sections 4.3 and 4.4). 

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 

Value 

(mg/kg-day)-1 Source Value (mg/kg-day) 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 

Dioxins a

2,3,7,8-TCDD 1746016 1.5E+05 EPA ORD 7.0E-10 IRIS 

1,2,3,7,8,9-HexaCDD 19408743 6.2E+03 IRIS not available

Polycyclic Organic Matter 

2-Methylnaphthalene 91576 5.0E-01 EPA OAQPS b 5.0E-02 ATSDR 

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

Acenaphthene 83329 5.0E-01 EPA OAQPS b 6.0E-02 IRIS

Acenaphthylene 208968 5.0E-01 EPA OAQPS b not available 

Benz(a)anthracene 56553 1.2E+00 CalEPA not available 

Benzo(a)pyrene 50328 7.3E+00 IRIS not available

Benzo(b)fluoranthene 205992 1.2E+00 CalEPA not available

Benzo(g,h,i)fluoranthene 203123 5.0E-01 EPA OAQPS b not available 

Benzo(k)fluoranthene 207089 1.2E+00 CalEPA not available

Chrysene 218019 1.2E-01 CalEPA not available

Dibenzo(a,h)anthracene 53703 4.1E+00 CalEPA not available

Fluoranthene 206440 5.0E-01 EPA OAQPS b 4.0E-02 IRIS
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology for RTR 

2-42 

Exhibit 4 1, continued. Oral Dose-response Values for PB-HAP Chemicals Used to 

Calculate RTR Screening Threshold Emission Rates 

Chemical CAS No. 

Cancer Slope Factor Reference Dose 

Value 

(mg/kg-day)-1 Source Value (mg/kg-day) Source 

Fluorene 86737 5.0E-01 EPA OAQPS b 4.0E-02 IRIS

Indeno(1,2,3-c,d)pyrene 193395 1.2E+00 CalEPA not available

ATSDR = Agency for Toxic Substances and Disease Registry IRIS = Integrated Risk Information System 

EPA OAQPS = EPA’s Office of Air Quality Planning and Standards CalEPA = California EPA 

EPA ORD = EPA’s Office of Research and Development NA = not applicable 

a Dose-response values for other dioxin congeners are not available from EPA sources. CSFs for these congeners 

were derived as discussed in Section 4.3, and the derived CSFs are presented in Exhibit 4-2. b The method to assign oral cancer slope factors to polycyclic organic matter (POM) without CSFs available from 

other EPA sources is the same as that used in the 1999 National Air Toxics Assessment (EPA 1999b). A complete 

description of the methodology is available at: http://www.epa.gov/ttn/atw/nata1999/99pdfs/pomapproachjan.pdf and 

is summarized in Section 4.4. 

4.1 Cadmium 

EPA has developed two chronic RfDs for cadmium (Cd), one for food and one for water, based 

on data in IRIS indicating a lower absorption efficiency of cadmium from food than from water. 

The default RfD set in MIRC is the higher RfD for Cd compounds in food (no drinking of water is 

assumed to occur when calculating screening thresholds). Users of MIRC who assess 

exposures via drinking water would need to use the RfD for Cd compounds in water (i.e., 

5.0E-04 mg/kg-day). 

4.2 Mercury 

EPA’s RfD for MeHg of 1.0E-04 mg/kg-day is based on a benchmark dose lower confidence 

limit (BMDL) on 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 seafood. The 

RfD applies to the pregnant mother as well as young children. EPA has not specified the 

minimum exposure duration at the RfD level of exposure that is appropriate to use in 

characterizing risk; we assume 10 years for women of childbearing age and 1 year for infants. 

We note that human exposures to MeHg are primarily through the consumption of fish and 

shellfish (EPA 2001b). EPA found that, on average, approximately 76 percent of the exposure 

to MeHg for women of childbearing age could be attributed to ingestion of mercury in freshwater 

and estuarine fish and shellfish, with the remaining 24 percent derived from marine fish and 

shellfish. Other sources accounted for less than 0.06 percent of total exposures (EPA 2001b). 

4.3 Dioxins (2,3,7,8-TCDD) 

For chemicals for which the critical health effect is developmental, either in utero and/or during 

the first months or years of life, the exposure duration and timing of exposure for comparison 

with the RfD (or comparable values) require special consideration. The most sensitive health 

endpoints for both mercury and 2,3,7,8-TCDD are neurological effects during development that 

have long-lasting effects on learning and social behaviors. To ensure a protective risk 

characterization for these chemicals, it is important to use the shortest exposure duration 

appropriate, at the appropriate life stage, for comparison with the toxicity reference values. This 

approach avoids “dilution” of an estimated average ADD that would result from averaging the 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology for RTR 

2-43 

lower daily chemical intake rates normalized to body weight for older children and adults with 

the potentially higher daily intake rates of infants over a longer exposure averaging period. 

The convention for assessing risk from mixtures of dioxins is by application of toxic equivalency 

factors (TEFs) to dioxin concentrations, which are then expressed as toxic equivalents (TEQs). 

Of the dioxin congeners, 2,3,7,8-TCDD is the most widely studied and considered to be one of 

the most toxic congeners. It is therefore assigned a TEF of 1, with the other dioxin congener 

TEQ concentrations scaled relative to 2,3,7,8-TCDD concentrations on the basis of toxicity. For 

risk assessment of dioxins for RTR, the World Health Organization (WHO) 2005 TEFs 

presented in Exhibit 4-2 were used to derive the CSFs for dioxin congeners without available 

EPA dose response values. 

Exhibit 4-2. WHO 2005 Toxic Equivalency Factors (TEFs) for Dioxin Congeners 

and Summary of Derived Cancer Slope Factors for RTR 

Dioxin Congener CAS No. WHO 2005 Toxic 

Equivalency Factor a

Cancer Slope Factor 

for RTR 

(mg/kg-day)-1

1,2,3,4,6,7,8-Heptachlorodibenzofuran 67562394 0.01 1.5E+03 

1,2,3,4,7,8,9-Heptachlorodibenzofuran 55673897 0.01 1.5E+03 

1,2,3,4,7,8-Hexachlorodibenzofuran 70648269 0.1 1.5E+04 

1,2,3,6,7,8-Hexachlorodibenzofuran 57117449 0.1 1.5E+04 

1,2,3,7,8,9-Hexachlorodibenzofuran 72918219 0.1 1.5E+04 

2,3,4,6,7,8-Hexachlorodibenzofuran 60851345 0.1 1.5E+04 

1,2,3,4,7,8-Hexachlorodibenzo-p-dioxin 39227286 0.1 1.5E+04 

1,2,3,6,7,8-Hexachlorodibenzo-p-dioxin 57653857 0.1 1.5E+04 

1,2,3,7,8,9-Hexachlorodibenzo-p-dioxin 19408743 0.1 6.2E+03 

(see Exhibit 4-1) 

1,2,3,4,6,7,8-Heptachlorodibenzo-p-dioxin 35822469 0.01 1.5E+03 

1,2,3,4,6,7,8,9-Octachlorodibenzofuran 39001020 0.0003 4.5E+01 

1,2,3,4,6,7,8,9-Octachlorodibenzo-p-dioxin 3268879 0.0003 4.5E+01 

1,2,3,7,8-Pentachlorodibenzofuran 57117416 0.03 4.5E+03 

2,3,4,7,8-Pentachlorodibenzofuran 57117314 0.3 4.5E+04 

1,2,3,7,8-Pentachlorodibenzo-p-dioxin 40321764 1 1.5E+05 

2,3,7,8-Tetrachlorodibenzofuran 51207319 0.1 1.5E+04 

2,3,7,8-Tetrachlorodibenzo-p-dioxin 1746016 1 1.5E+05 

(see Exhibit 4-1) a

 Source: van den Berg et al. 2006 

4.4 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 

the National Emissions Inventory (NEI). Individual POMs were assigned to one of eight POM 

groups according to cancer potencies derived by EPA for IRIS and by CalEPA, and based on 

assumptions regarding relative carcinogenicity. OAQPS then estimated an inhalation CSF for 

each POM group. The same approach was used to derive oral CSFs for POMs for use in 

multipathway risk assessment for RTR. CSF values for the 14 POM congeners that are 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology for RTR 

2-44 

included in the screening analysis are provided in Exhibit 4-1. 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 shown in Exhibit 4-1, dose-response values for six of the POM 

species (all in POM group 72002) that are included in the screening analysis were not available 

from IRIS, ATDSR, or CalEPA; for these, the CSF of 5.0E-01 mg/kg-day-1 as derived by OAQPS 

is provided. 

Exhibit 4-3. Oral Dose-response Values for 

Polycyclic Organic Matter (POM) Groups

Individual POM or POM Group CAS No. Cancer Slope Factor a

(mg/kg-day)-1

POM Group 71002 

Benz(a)anthracene/chrysene (7-PAH) 103

0.5

Total PAH 234

Polycyclic organic matter 246

16-PAH 40

16-PAH–7-PAH 75040

POM Group 72002 

Anthracene 120127

0.5

Pyrene 129000

Benzo(g,h,i) perylene 191242

Benzo(e)pyrene 192972

Benzo(c)phenanthrene 195197

Perylene 198550

Benzo(g,h,i)fluoranthene 203123

Benzo(a)fluoranthene 203338

Fluoranthene 206440

Acenaphthylene 208968

1-Methylpyrene 2381217

12-Methylbenz(a)anthracene 2422794

Methylbenzopyrenes 247

Methylchrysene 248

Methylanthracene 26914181

Benzofluoranthenes 56832736

9-Methylbenz(a)anthracene 779022

1-Methylphenanthrene 832699

Acenaphthene 83329

Phenanthrene 85018

Fluorene 86737

2-Methylnaphthalene 91576

2-Chloronaphthalene 91587
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology for RTR 

2-45 

Exhibit 4-3, continued. Oral Dose-response Values for 

Polycyclic Organic Matter (POM) Groups

Individual POM or POM Group CAS No. Cancer Slope Factor a

(mg/kg-day)-1

POM Group 73002 

7,12-Dimethylbenz(a)anthracene 57976 1000

POM Group 74002 

Dibenzo(a,i)pyrene 189559 100 Dibenzo(a,h)pyrene 189640

POM Group 75002 

3-Methylcholanthrene 56495

10

Dibenzo(a,e)pyrene 192654

5-Methylchrysene 3697243

Benzo(a)pyrene 50328

Dibenzo(a,h)anthracene 53703

POM Group 76002 

Benzo(b+k)fluoranthene 102

1

Indeno(1,2,3-c,d)pyrene 193395

Benzo(j)fluoranthene 205823

Benzo(b)fluoranthene 205992

Benzo(k)fluoranthene 207089

Dibenzo(a,j)acridine 224420

Benz(a)anthracene 56553

POM Group 77002 

Chrysene 218019 0.1

POM Group 78002 

7-PAH 75 0.5 a

 The method to assign oral cancer slope factors to POM groups was the same as that used in the 

1999 National Air Toxics Assessment (EPA 1999b). A complete description of the methodology is 

available at: http://www.epa.gov/ttn/atw/nata1999/99pdfs/pomapproachjan.pdf. 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology 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 (CSF): 

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. 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology 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) 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology 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 health 

protective 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. 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology 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. 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology for RTR 

2-50 

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. 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology for RTR 

2-51 

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 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology for RTR 

2-52 

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 6-4 and Exhibit 6-6 provide the chemical-specific input values that are the current 

defaults for produce FFC food types in MIRC. Exhibit 6-10 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. 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology for RTR 

2-53 

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

PAHs

2-Methylnaphthalene 0.6 2.2E+02 5.0E+01 1.4E+00 

7,12-

Dimethylbenz(a)anthrace

ne 

0.6 6.8E+03 4.0E+03 4.2E+04 

Acenaphthene 0.6 2.4E+02 3.9E+01 4.6E+00 

Acenaphthylene 0.6 2.8E+02 6.8E+01 8.1E+00 

Benz(a)anthracene 0.6 6.7E+03 2.9E+03 6.8E+03 

Benzo(a)pyrene 0.6 9.2E+03 7.8E+03 1.7E+05 

Benzo(b)fluoranthene 0.6 6.6E+03 3.8E+03 1.7E+05 

Benzo(g,h,i)perylene 0.6 3.0E+04 2.6E+04 2.3E+06 

Benzo(k)fluoranthene 0.6 8.7E+03 5.5E+03 2.8E+05 

Chrysene 0.6 6.0E+03 3.4E+03 1.4E+04 

Dibenz(a,h)anthracene 0.6 2.3E+04 1.4E+04 6.2E+06 

Fluoranthene 0.6 2.2E+03 3.9E+02 9.0E+02 

Fluorene 0.6 3.8E+02 6.2E+01 1.6E+01 

Indeno(1,2,3-cd)pyrene 0.6 3.5E+04 3.2E+04 2.8E+06 

Dioxins 

OctaCDD, 

1,2,3,4,6,7,8,9- 0.6 4.8E+05 7.8E+05 2.4E+06 

OctaCDF, 1,2,3,4,6,7,8,9- 0.6 3.4E+05 4.9E+05 2.3E+06 

HeptaCDD, 1,2,3,4,6,7,8- 0.6 3.4E+05 4.9E+05 9.1E+05 

HeptaCDF, 1,2,3,4,6,7,8- 0.6 1.2E+05 1.2E+05 8.3E+05 

HeptaCDF, 1,2,3,4,7,8,9- 0.6 4.8E+04 3.9E+04 8.3E+05 

HexaCDD, 1,2,3,4,7,8- 0.6 2.4E+05 3.1E+05 5.2E+05 

HexaCDF, 1,2,3,4,7,8- 0.6 5.7E+04 4.9E+04 1.6E+05 

HexaCDD, 1,2,3,6,7,8- 0.6 4.9E+05 8.0E+05 5.2E+05 

HexaCDF, 1,2,3,6,7,8- 0.6 2.9E+05 4.1E+05 1.6E+05 

HexaCDD, 1,2,3,7,8,9 - 0.6 4.9E+05 8.0E+05 5.2E+05 

HexaCDF, 1,2,3,7,8,9- 0.6 1.6E+05 1.9E+05 1.6E+05 

HexaCDF, 2,3,4,6,7,8- 0.6 2.9E+05 4.1E+05 1.6E+05 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology for RTR 

2-54 

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

PentaCDD, 1,2,3,7,8- 0.6 9.2E+04 9.2E+04 2.4E+05 

PentaCDF, 1,2,3,7,8- 0.6 3.9E+04 3.0E+04 9.8E+04 

PentaCDF, 2,3,4,7,8- 0.6 2.3E+04 1.6E+04 9.8E+04 

TetraCDD, 2,3,7,8- 0.6 4.0E+04 3.1E+04 6.6E+04 

TetraCDF, 2,3,7,8- 0.6 1.2E+04 6.2E+03 4.6E+04 

Source: EPA 2005a. NA = not applicable. 

a

 6E-01 is the value for cations and most organic chemicals. As described in HHRAP (EPA 2005a), Appendix B 

(available at http://www.epa.gov/osw/hazard/tsd/td/combust/finalmact/ssra/05hhrapapb.pdf), EPA estimated this 

value (EPA 1994a, 1995a) from a study by Hoffman et al. (1992) in which soluble gamma-emitting radionuclides 

and insoluble particles tagged with 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 in the literature, thus the 

RCF was not needed. 

c

 As discussed in HHRAP (EPA 2005a), Appendix A, Kds describes the partitioning of a compound between soil 

pore-water and soil particles and strongly influences the release and movement of a compound into the 

subsurface soils and underlying aquifer. Kds values for mercuric compounds were obtained from EPA (1997b). 

Kds for cadmium compounds were obtained from U.S. EPA 1996. For all PAHs and dioxins, Kds was calculated 

by multiplying Koc times the screening scenario’s fraction organic carbon content (0.008). Empirical information 

for Koc was available for acenaphthene, benz(a)anthracene, benzo(a)pyrene, dibenz(a,h)anthracene, 

fluoranthene, and fluorene in USEAP 1996. For all other organic compounds, the Koc was calculated using the 

correlation equations presented in USEAP 2005a. d

 As discussed in HHRAP (EPA 2005a), Appendix A, the value for mercuric chloride was obtained from EPA 

1997b. BvAG(i) values for PAHs were calculated using the correlation equation derived for azalea leaves as cited in 

Bacci et al. (1992), then reducing this value by a factor of 100, as suggested by Lorber (1995), who concluded that 

the Bacci factor reduced by a factor of 100 was similar to his own observations in various studies. The values for 

dioxins were obtained from Lorber and Pinsky (2000). 

e

 It is assumed that metals, with the exception of vapor-phase elemental mercury, do not transfer significantly from 

air into leaves. 

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). 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology for RTR 

2-55 

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 - 1.0E+00 

Exp. Veg. 1.3E-01 - 1.0E+00 

Forage 3.6E-01 - 1.0E+00 

Grain 6.2E-02 - - 

Prot. Fruit 1.3E-01 - - 

Prot. Veg. 1.3E-01 - - 

Root 6.4E-02 1.0E+00 - 

Silage 3.6E-01 - 5.0E-01 

Mercury (elemental) 

Exp. Fruit - - 1.0E+00 

Exp. Veg. - - 1.0E+00 

Forage - - 1.0E+00 

Grain - - - 

Prot. Fruit - - - 

Prot. Veg. - - - 

Root - 1.0E+00 - 

Silage - - 5.0E-01 

Mercuric chloride 

Exp. Fruit 1.5E-02 - 1.0E+00 

Exp. Veg. 1.5E-02 - 1.0E+00 

Forage 0.0E+00 - 1.0E+00 

Grain 9.3E-03 - - 

Prot. Fruit 1.5E-02 - - 

Prot. Veg. 1.5E-02 - - 

Root 3.6E-02 1.0E+00 - 

Silage 0.0E+00 - 5.0E-01 

Methyl mercury 

Exp. Fruit 2.9E-02 - 1.0E-02 

Exp. Veg. 2.9E-02 - 1.0E-02 

Forage 0.0E+00 - 1.0E+00 

Grain 1.9E-02 - - 

Prot. Fruit 2.9E-02 - - 

Prot. Veg. 2.9E-02 - - 

Root 9.9E-02 1.0E-02 - 

Silage 0.0E+00 - 5.0E-01 

PAHs 

2-Methylnaphthalene 

Exp. Fruit 2.3E-01 - 1.0E+00 

Exp. Veg. 2.3E-01 - 1.0E+00 

Forage 2.3E-01 - 1.0E+00 

Grain 2.3E-01 - - 

Prot. Fruit 2.3E-01 - - 

Prot. Veg. 2.3E-01 - - 

Root 4.4E+00 1.0E+00 - 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology for RTR 

2-56 

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

Silage 2.3E-01 - 5.0E-01 

7,12-

Dimethylbenz(a)anthracen

e 

Exp. Fruit 1.7E-02 - 1.0E-02 

Exp. Veg. 1.7E-02 - 1.0E-02 

Forage 1.7E-02 - 1.0E+00 

Grain 1.7E-02 - - 

Prot. Fruit 1.7E-02 - - 

Prot. Veg. 1.7E-02 - - 

Root 1.7E+00 1.0E-02 - 

Silage 1.7E-02 - 5.0E-01 

Acenaphthene 

Exp. Fruit 2.1E-01 - 1.0E+00 

Exp. Veg. 2.1E-01 - 1.0E+00 

Forage 2.1E-01 - 1.0E+00 

Grain 2.1E-01 - - 

Prot. Fruit 2.1E-01 - - 

Prot. Veg. 2.1E-01 - - 

Root 6.2E+00 1.0E+00 - 

Silage 2.1E-01 - 5.0E-01 

Acenaphthylene 

Exp. Fruit 1.9E-01 - 1.0E-02 

Exp. Veg. 1.9E-01 - 1.0E-02 

Forage 1.9E-01 - 1.0E+00 

Grain 1.9E-01 - - 

Prot. Fruit 1.9E-01 - - 

Prot. Veg. 1.9E-01 - - 

Root 4.1E+00 1.0E-02 - 

Silage 1.9E-01 - 5.0E-01 

Benz(a)anthracene 

Exp. Fruit 1.7E-02 - 1.0E-02 

Exp. Veg. 1.7E-02 - 1.0E-02 

Forage 1.7E-02 - 1.0E+00 

Grain 1.7E-02 - - 

Prot. Fruit 1.7E-02 - - 

Prot. Veg. 1.7E-02 - - 

Root 2.3E+00 1.0E-02 - 

Silage 1.7E-02 - 5.0E-01 

Benzo(a)pyrene 

Exp. Fruit 1.4E-02 - 1.0E-02 

Exp. Veg. 1.4E-02 - 1.0E-02 

Forage 1.4E-02 - 1.0E+00 

Grain 1.4E-02 - - 

Prot. Fruit 1.4E-02 - - 

Prot. Veg. 1.4E-02 - - 

Root 1.2E+00 1.0E-02 - 

Silage 1.4E-02 - 5.0E-01 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology for RTR 

2-57 

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

Benzo(b)fluoranthene 

Exp. Fruit 1.8E-02 - 1.0E-02 

Exp. Veg. 1.8E-02 - 1.0E-02 

Forage 1.8E-02 - 1.0E+00 

Grain 1.8E-02 - - 

Prot. Fruit 1.8E-02 - - 

Prot. Veg. 1.8E-02 - - 

Root 1.7E+00 1.0E-02 - 

Silage 1.8E-02 - 5.0E-01 

Benzo(g,h,i)perylene 

Exp. Fruit 5.7E-03 - 1.0E-02 

Exp. Veg. 5.7E-03 - 1.0E-02 

Forage 5.7E-03 - 1.0E+00 

Grain 5.7E-03 - - 

Prot. Fruit 5.7E-03 - - 

Prot. Veg. 5.7E-03 - - 

Root 1.1E+00 1.0E-02 - 

Silage 5.7E-03 - 5.0E-01 

Benzo(k)fluoranthene 

Exp. Fruit 1.4E-02 - 1.0E-02 

Exp. Veg. 1.4E-02 - 1.0E-02 

Forage 1.4E-02 - 1.0E+00 

Grain 1.4E-02 - - 

Prot. Fruit 1.4E-02 - - 

Prot. Veg. 1.4E-02 - - 

Root 1.6E+00 1.0E-02 - 

Silage 1.4E-02 - 5.0E-01 

Chrysene 

Exp. Fruit 1.9E-02 - 1.0E-02 

Exp. Veg. 1.9E-02 - 1.0E-02 

Forage 1.9E-02 - 1.0E+00 

Grain 1.9E-02 - - 

Prot. Fruit 1.9E-02 - - 

Prot. Veg. 1.9E-02 - - 

Root 1.7E+00 1.0E-02 - 

Silage 1.9E-02 - 5.0E-01 

Dibenz(a,h)anthracene 

Exp. Fruit 6.8E-03 - 1.0E-02 

Exp. Veg. 6.8E-03 - 1.0E-02 

Forage 6.8E-03 - 1.0E+00 

Grain 6.8E-03 - - 

Prot. Fruit 6.8E-03 - - 

Prot. Veg. 6.8E-03 - - 

Root 1.6E+00 1.0E-02 - 

Silage 6.8E-03 - 5.0E-01 

Fluoranthene Exp. Fruit 4.0E-02 - 1.0E-02 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology for RTR 

2-58 

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

Exp. Veg. 4.0E-02 - 1.0E-02 

Forage 4.0E-02 - 1.0E+00 

Grain 4.0E-02 - - 

Prot. Fruit 4.0E-02 - - 

Prot. Veg. 4.0E-02 - - 

Root 5.6E+00 1.0E-02 - 

Silage 4.0E-02 - 5.0E-01 

Fluorene 

Exp. Fruit 1.5E-01 - 1.0E-02 

Exp. Veg. 1.5E-01 - 1.0E-02 

Forage 1.5E-01 - 1.0E+00 

Grain 1.5E-01 - - 

Prot. Fruit 1.5E-01 - - 

Prot. Veg. 1.5E-01 - - 

Root 6.2E+00 1.0E-02 - 

Silage 1.5E-01 - 5.0E-01 

Indeno(1,2,3-cd)pyrene 

Exp. Fruit 5.1E-03 - 1.0E-02 

Exp. Veg. 5.1E-03 - 1.0E-02 

Forage 5.1E-03 - 1.0E+00 

Grain 5.1E-03 - - 

Prot. Fruit 5.1E-03 - - 

Prot. Veg. 5.1E-03 - - 

Root 1.1E+00 1.0E-02 - 

Silage 5.1E-03 - 5.0E-01 

Dioxins 

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

Exp. Fruit 7.1E-04 - 1.0E-02 

Exp. Veg. 7.1E-04 - 1.0E-02 

Forage 7.1E-04 - 1.0E+00 

Grain 7.1E-04 - - 

Prot. Fruit 7.1E-04 - - 

Prot. Veg. 7.1E-04 - - 

Root 6.1E-01 1.0E-02 - 

Silage 7.1E-04 - 5.0E-01 

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

Exp. Fruit 9.2E-04 - 1.0E-02 

Exp. Veg. 9.2E-04 - 1.0E-02 

Forage 9.2E-04 - 1.0E+00 

Grain 9.2E-04 - - 

Prot. Fruit 9.2E-04 - - 

Prot. Veg. 9.2E-04 - - 

Root 6.8E-01 1.0E-02 - 

Silage 9.2E-04 - 5.0E-01 

HeptaCDD, 1,2,3,4,6,7,8- Exp. Fruit 9.2E-04 - 1.0E-02 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology for RTR 

2-59 

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

Exp. Veg. 9.2E-04 - 1.0E-02 

Forage 9.2E-04 - 1.0E+00 

Grain 9.2E-04 - - 

Prot. Fruit 9.2E-04 - - 

Prot. Veg. 9.2E-04 - - 

Root 6.8E-01 1.0E-02 - 

Silage 9.2E-04 - 5.0E-01 

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

Exp. Fruit 2.0E-03 - 1.0E-02 

Exp. Veg. 2.0E-03 - 1.0E-02 

Forage 2.0E-03 - 1.0E+00 

Grain 2.0E-03 - - 

Prot. Fruit 2.0E-03 - - 

Prot. Veg. 2.0E-03 - - 

Root 9.4E-01 1.0E-02 - 

Silage 2.0E-03 - 5.0E-01 

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

Exp. Fruit 4.0E-03 - 1.0E-02 

Exp. Veg. 4.0E-03 - 1.0E-02 

Forage 4.0E-03 - 1.0E+00 

Grain 4.0E-03 - - 

Prot. Fruit 4.0E-03 - - 

Prot. Veg. 4.0E-03 - - 

Root 1.2E+00 1.0E-02 - 

Silage 4.0E-03 - 5.0E-01 

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

Exp. Fruit 1.2E-03 - 1.0E-02 

Exp. Veg. 1.2E-03 - 1.0E-02 

Forage 1.2E-03 - 1.0E+00 

Grain 1.2E-03 - - 

Prot. Fruit 1.2E-03 - - 

Prot. Veg. 1.2E-03 - - 

Root 7.6E-01 1.0E-02 - 

Silage 1.2E-03 - 5.0E-01 

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

Exp. Fruit 3.5E-03 - 1.0E-02 

Exp. Veg. 3.5E-03 - 1.0E-02 

Forage 3.5E-03 - 1.0E+00 

Grain 3.5E-03 - - 

Prot. Fruit 3.5E-03 - - 

Prot. Veg. 3.5E-03 - - 

Root 1.2E+00 1.0E-02 - 

Silage 3.5E-03 - 5.0E-01 

HexaCDD, 1,2,3,6,7,8- Exp. Fruit 7.0E-04 - 1.0E-02 

Exp. Veg. 7.0E-04 - 1.0E-02 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology for RTR 

2-60 

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

Forage 7.0E-04 - 1.0E+00 

Grain 7.0E-04 - - 

Prot. Fruit 7.0E-04 - - 

Prot. Veg. 7.0E-04 - - 

Root 6.1E-01 1.0E-02 - 

Silage 7.0E-04 - 5.0E-01 

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

Exp. Fruit 1.0E-03 - 1.0E-02 

Exp. Veg. 1.0E-03 - 1.0E-02 

Forage 1.0E-03 - 1.0E+00 

Grain 1.0E-03 - - 

Prot. Fruit 1.0E-03 - - 

Prot. Veg. 1.0E-03 - - 

Root 7.1E-01 1.0E-02 - 

Silage 1.0E-03 - 5.0E-01 

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

Exp. Fruit 7.0E-04 - 1.0E-02 

Exp. Veg. 7.0E-04 - 1.0E-02 

Forage 7.0E-04 - 1.0E+00 

Grain 7.0E-04 - - 

Prot. Fruit 7.0E-04 - - 

Prot. Veg. 7.0E-04 - - 

Root 6.1E-01 1.0E-02 - 

Silage 7.0E-04 - 5.0E-01 

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

Exp. Fruit 1.6E-03 - 1.0E-02 

Exp. Veg. 1.6E-03 - 1.0E-02 

Forage 1.6E-03 - 1.0E+00 

Grain 1.6E-03 - - 

Prot. Fruit 1.6E-03 - - 

Prot. Veg. 1.6E-03 - - 

Root 8.5E-01 1.0E-02 - 

Silage 1.6E-03 - 5.0E-01 

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

Exp. Fruit 1.0E-03 - 1.0E-02 

Exp. Veg. 1.0E-03 - 1.0E-02 

Forage 1.0E-03 - 1.0E+00 

Grain 1.0E-03 - - 

Prot. Fruit 1.0E-03 - - 

Prot. Veg. 1.0E-03 - - 

Root 7.1E-01 1.0E-02 - 

Silage 1.0E-03 - 5.0E-01 

PentaCDD, 1,2,3,7,8- 

Exp. Fruit 2.4E-03 - 1.0E-02 

Exp. Veg. 2.4E-03 - 1.0E-02 

Forage 2.4E-03 - 1.0E+00 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology for RTR 

2-61 

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

Grain 2.4E-03 - - 

Prot. Fruit 2.4E-03 - - 

Prot. Veg. 2.4E-03 - - 

Root 1.0E+00 1.0E-02 - 

Silage 2.4E-03 - 5.0E-01 

PentaCDF, 1,2,3,7,8- 

Exp. Fruit 4.6E-03 - 1.0E-02 

Exp. Veg. 4.6E-03 - 1.0E-02 

Forage 4.6E-03 - 1.0E+00 

Grain 4.6E-03 - - 

Prot. Fruit 4.6E-03 - - 

Prot. Veg. 4.6E-03 - - 

Root 1.3E+00 1.0E-02 - 

Silage 4.6E-03 - 5.0E-01 

PentaCDF, 2,3,4,7,8- 

Exp. Fruit 6.8E-03 - 1.0E-02 

Exp. Veg. 6.8E-03 - 1.0E-02 

Forage 6.8E-03 - 1.0E+00 

Grain 6.8E-03 - - 

Prot. Fruit 6.8E-03 - - 

Prot. Veg. 6.8E-03 - - 

Root 1.5E+00 1.0E-02 - 

Silage 6.8E-03 - 5.0E-01 

TetraCDD, 2,3,7,8- 

Exp. Fruit 4.5E-03 - 1.0E-02 

Exp. Veg. 4.5E-03 - 1.0E-02 

Forage 4.5E-03 - 1.0E+00 

Grain 4.5E-03 - - 

Prot. Fruit 4.5E-03 - - 

Prot. Veg. 4.5E-03 - - 

Root 1.3E+00 1.0E-02 - 

Silage 4.5E-03 - 5.0E-01 

TetraCDF, 2,3,7,8- 

Exp. Fruit 1.2E-02 - 1.0E-02 

Exp. Veg. 1.2E-02 - 1.0E-02 

Forage 1.2E-02 - 1.0E+00 

Grain 1.2E-02 - - 

Prot. Fruit 1.2E-02 - - 

Prot. Veg. 1.2E-02 - - 

Root 1.9E+00 1.0E-02 - 

Silage 1.2E-02 - 5.0E-01 

a

 As discussed in HHRAP (EPA 2005a), the BrAG-produce-DW(i) for aboveground produce and forage accounts for the 

uptake from soil and the subsequent transport of contaminants through the roots to the aboveground plant parts. For 

organics, correlation equations to calculate values for Br on a dry weight basis were obtained from Travis and Arms 

(1988). For cadmium, Br values were derived from uptake slope factors provided in EPA 1992. Uptake slope is the 

ratio of contaminant concentration in dry weight plant tissue to the mass of contaminant applied per hectare soil. Br
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology for RTR 

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Exhibit 6-3. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part 

Plant-Soil BioConcentration 

Factor 

(BrAG-produce-DW(i)) 

(unitless) a

Empirical Correction 

Factor- Belowground 

Produce 

(VGrootveg) (unitless) b

Empirical Correction 

Factor- Aboveground 

Produce 

(VGAG(i)) (unitless) c

aboveground values for mercuric chloride and methyl mercury were calculated using methodology and data from 

Baes, et al. (1984). Br forage values for mercuric chloride and methyl mercury (on a dry weight basis) were obtained 

from EPA 1997b. The HHRAP methodology assumes that elemental mercury doesn’t deposit onto soils. Therefore, 

it’s assumed that there is no plant uptake through the soil. 

b

 As discussed in HHRAP (EPA 2005a), Appendix B, VGrootveg represents an empirical correction factor that reduces 

produce concentration. Because of the protective outer skin, size, and shape of bulky produce, transfer of lipophilic 

chemicals (i.e., log Kow greater than 4) to the center of the produce is not likely. In addition, typical preparation 

techniques, such as washing, peeling, and cooking, further reduce the concentration of the chemical in the vegetable 

as consumed by removing the high concentration of chemical on and in the outer skin, leaving the flesh with a lower 

concentration than would be the case if the entire vegetable were pureed without washing. For belowground 

produce, HHRAP (EPA 2005a) recommends using a VGrootveg value of 0.01 for PB-HAP with a log Kow greater than 

4 and a value of 1.0 for PB-HAP with a log Kow less than 4 based on information provided in EPA 1994b. In 

developing these values, EPA (1994b) assumed that the density of the skin and the whole vegetable are equal 

(potentially overestimating the concentration of PB-HAP in belowground produce due to root uptake).

c

 As discussed in HHRAP (EPA 2005a), Appendix B, VGag represents an empirical correction factor that reduces 

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

versus bulkier aboveground produce (e.g., apples). Because of the protective outer skin, size, and shape of bulky 

produce, transfer of lipophilic PB-HAP (log Kow greater than 4) to the center of the produce is not likely. In addition, 

typical preparation techniques, such as washing, peeling, and cooking, further reduces residues. For aboveground 

produce, HHRAP (EPA 2005a) recommends using a VGag value of 0.01 for PB-HAP with a log Kow greater than 4 

and a value of 1.0 for PB-HAP with a log Kow less than 4 based on information provided in EPA 1994b. In 

developing these values, EPA (1994b) assumed the following: (1) translocation of compounds deposited on the 

surface of aboveground vegetation to inner parts of aboveground produce would be insignificant (potentially 

underestimating the concentration of PB-HAP in aboveground produce due to air-to-plant transfer); (2) the density of 

the skin and the whole vegetable are equal (potentially overestimating the concentration of PB-HAP in aboveground 

produce due to air-to-plant transfer); and (3) the thickness of vegetable skin and broadleaf tree skin are equal 

(effects on the concentration of PB-HAP in aboveground produce due to air-toplant transfer unknown). 

For forage, HHRAP recommends a VGag value of 1.0, also based on information provided in EPA 1994b. 

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

DEHNR (1997); however, NC DEHNR does not present a specific rationale for this recommendation. Depending on 

the composition of the site-specific silage, this value may under- or overestimate the actual value.
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Exhibit 6-4. Non-Chemical-Specific Produce Inputs 

Plant Part 

Interception 

Fraction 

(Rp(i)) 

(unitless) a

Plant 

Surface 

Loss 

Coefficient 

(kp(i)) 

(1/year) b

Length of 

Plant 

Exposure to 

Deposition 

(Tp(i)) 

(year) c

Yield or 

Standing 

Crop 

Biomass 

(Yp(i)) 

(kg/m2) d

Plant TissueSpecific 

Moisture 

Adjustment 

Factor (MAF(i)) 

(percent) e

Exposed Vegetable 0.982 18 0.16 5.66 92 

Protected Fruit NA NA NA NA 90 

Protected 

Vegetable NA NA NA NA 80 

Forage (animal 

feed) 0.5 18 0.12 0.24 92 

Exposed Fruit 0.053 18 0.16 0.25 85 

Root Vegetables NA NA NA NA 87 

Silage (animal feed) 0.46 18 0.16 0.8 92 

Grain (animal feed) NA NA NA NA 90 

Source: EPA 2005a. NA = not applicable. 

a

 Baes et al. (1984) used an empirical relationship developed by Chamberlain (1970) to identify a correlation 

between initial Rp values and pasture grass productivity (standing crop biomass [Yp]) to calculate Rp values for 

exposed vegetables, exposed fruits, forage, and silage. Two key uncertainties are associated with using these 

values for Rp: (1) Chamberlain’s (1970) empirical relationship developed for pasture grass may not accurately 

represent aboveground produce. (2) The empirical constants developed by Baes et al. (1984) for use in the 

empirical relationship developed by Chamberlain (1970) may not accurately represent the site-specific mixes of 

aboveground produce consumed by humans or the site-specific mixes of forage or silage consumed by livestock. b

 The term kp is a measure of the amount of chemical that is lost to natural physical processes (e.g., wind, water) 

over time. The HHRAP-recommended value of 18 yr-1 (also recommended by EPA 1994a and 1998) represents 

the midpoint of a range of values reported by Miller and Hoffman (1983). There are two key uncertainties 

associated with using these values for kp: (1) The recommended equation for calculating kp includes a health 

protective bias in that it does not consider chemical degradation processes. (2) Given the reported range of kp

values from 7.44 to 90.36 yr-1, plant concentrations could range from about 1.8 times higher to about 5 times lower 

than the plant concentrations estimated in FFC media using the midpoint kp value of 18. c

 HHRAP (EPA 2005a) recommends using a Tp value of 0.16 years for aboveground produce and cattle silage. 

This is consistent with earlier reports by EPA (1994a, 1998) and NC DEHNR (1997), which recommended treating 

Tp as a constant based on the average period between successive hay harvests. Belcher and Travis (1989) 

estimated this period at 60 days. Tp is calculated as 60 days ÷ 365 days/year = 0.16 years. For forage, the 

average of the average period between successive hay harvests (60 days) and the average period between 

successive grazing (30 days) is used (that is, 45 days), and Tp is calculated as (60 days + 30 days)/ 2 ÷ 365 

days/yr = 0.12 yr. Two key uncertainties are associated with use of these values for Tp: (1) The average period 

between successive hay harvests (60 days) may not reflect the length of the growing season or the length between 

successive harvests for site-specific aboveground produce crops. The concentration of chemical in aboveground 

produce due to direct (wet and dry) deposition (Pd) will be underestimated if the site-specific value of Tp is less than 

60 days, or overestimated if the 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.
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6.2.3 Animal Product Parameter Values 

MIRC also requires chemical-specific inputs for many of the animal product algorithms. The 

relevant values are shown in Exhibit 6-5 for the chemicals included in MIRC to date. The 

HHRAP algorithms require additional inputs for the animal products calculations that are not 

specific to PB-HAPs, but are specific to the animal and animal product type. The soil and plant 

ingestion rates recommended in HHRAP for beef cattle, dairy cattle, swine, and chicken are 

provided in Exhibit 6-6. 

Exhibit 6-5. Animal Product Chemical-specific Inputs for Chemicals Included in MIRC 

Compound Name 

Soil BioAvailability 

Factor (Bs) 

(unitless) 

Biotransfer Factors (Bam) (day/kg FW tissue) a 

and Metabolism Factors (MF) (unitless) b 

Mammal Non-mammal 

Beef 

(Babeef)

Dairy 

(Badairy)

Pork 

(Bapork) MF Eggs (Baeggs) 

Poultry 

(Bapoultry) MF

Inorgaincs 

Cadmium compounds 1 1.2E-04 6.5E-06 1.9E-04 1 2.5E-03 1.1E-01 NA 

Mercury (elemental) 1 0 0 0 1 0 0 NA 

Mercuric chloride 1 1.1E-04 1.4E-06 3.4E-05 1 2.4E-02 2.4E-02 NA 

Methyl mercury 1 1.2E-03 1.7E-05 5.1E-06 1 3.6E-03 3.6E-03 NA 

PAHs 

2-Methylnaphthalene 1 2.4E-02 5.0E-03 2.9E-02 0.01 1.0E-02 1.7E-02 NA 

7,12-

Dimethylbenz(a)anthracene 1 3.9E-02 8.3E-03 4.8E-02 0.01 1.7E-02 2.9E-02 NA 

Acenaphthene 1 2.5E-02 5.2E-03 3.0E-02 0.01 1.0E-02 1.8E-02 NA 

Acenaphthylene 1 2.6E-02 5.5E-03 3.1E-02 0.01 1.1E-02 1.9E-02 NA 

Benz(a)anthracene 1 3.9E-02 8.3E-03 4.8E-02 0.01 1.7E-02 2.9E-02 NA 

Benzo(a)pyrene 1 3.8E-02 8.0E-03 4.6E-02 0.01 1.6E-02 2.8E-02 NA 

Benzo(b)fluoranthene 1 3.9E-02 8.3E-03 4.8E-02 0.01 1.7E-02 2.9E-02 NA 

Benzo(g,h,i)perylene 1 2.9E-02 6.1E-03 3.5E-02 0.01 1.2E-02 2.1E-02 NA 

Benzo(k)fluoranthene 1 3.8E-02 8.0E-03 4.6E-02 0.01 1.6E-02 2.8E-02 NA 

Chrysene 1 4.0E-02 8.4E-03 4.8E-02 0.01 1.7E-02 2.9E-02 NA 

Dibenz(a,h)anthracene 1 3.1E-02 6.5E-03 3.8E-02 0.01 1.3E-02 2.3E-02 NA 

Fluoranthene 1 4.0E-02 8.5E-03 4.9E-02 0.01 1.7E-02 3.0E-02 NA 

Fluorene 1 2.9E-02 6.1E-03 3.5E-02 0.01 1.2E-02 2.1E-02 NA 

Indeno(1,2,3-cd)pyrene 1 2.7E-02 5.8E-03 3.3E-02 0.01 1.2E-02 2.0E-02 NA 

Dioxins 

OctaCDD, 1,2,3,4,6,7,8,9- 1 6.9E-03 1.4E-03 8.3E-03 1 2.9E-03 5.1E-03 NA 

OctaCDF, 1,2,3,4,6,7,8,9- 1 8.8E-03 1.8E-03 1.1E-02 1 3.7E-03 6.5E-03 NA 

HeptaCDD, 1,2,3,4,6,7,8- 1 8.8E-03 1.8E-03 1.1E-02 1 3.7E-03 6.5E-03 NA 

HeptaCDF, 1,2,3,4,6,7,8- 1 1.6E-02 3.5E-03 2.0E-02 1 6.9E-03 1.2E-02 NA 

HeptaCDF, 1,2,3,4,7,8,9- 1 2.4E-02 5.1E-03 3.0E-02 1 1.0E-02 1.8E-02 NA 

HexaCDD, 1,2,3,4,7,8- 1 1.1E-02 2.3E-03 1.3E-02 1 4.6E-03 8.1E-03 NA 

HexaCDF, 1,2,3,4,7,8- 1 2.3E-02 4.8E-03 2.8E-02 1 9.6E-03 1.7E-02 NA 

HexaCDD, 1,2,3,6,7,8- 1 6.8E-03 1.4E-03 8.2E-03 1 2.9E-03 5.0E-03 NA 

HexaCDF, 1,2,3,6,7,8- 1 9.7E-03 2.0E-03 1.2E-02 1 4.1E-03 7.1E-03 NA 

HexaCDD, 1,2,3,7,8,9 - 1 6.8E-03 1.4E-03 8.2E-03 1 2.9E-03 5.0E-03 NA 
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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

HexaCDF, 1,2,3,7,8,9- 1 1.4E-02 2.9E-03 1.7E-02 1 5.8E-03 1.0E-02 NA 

HexaCDF, 2,3,4,6,7,8- 1 9.6E-03 2.0E-03 1.2E-02 1 4.1E-03 7.1E-03 NA 

PentaCDD, 1,2,3,7,8- 1 1.8E-02 3.9E-03 2.2E-02 1 7.8E-03 1.4E-02 NA 

PentaCDF, 1,2,3,7,8- 1 2.6E-02 5.5E-03 3.2E-02 1 1.1E-02 1.9E-02 NA 

PentaCDF, 2,3,4,7,8- 1 3.1E-02 6.5E-03 3.8E-02 1 1.3E-02 2.3E-02 NA 

TetraCDD, 2,3,7,8- 1 2.6E-02 5.5E-03 3.2E-02 1 1.1E-02 1.9E-02 NA 

TetraCDF, 2,3,7,8- 1 3.6E-02 7.7E-03 4.4E-02 1 1.5E-02 2.7E-02 NA 

Source: EPA 2005a, unless otherwise indicated. NA = not applicable.

a

 As discussed in HHRAP (EPA 2005a), Appendix A, biotransfer factors for mercury compounds were obtained 

from EPA 1997b. Considering speciation, fate, and transport of mercury from emission sources, elemental mercury 

is assumed to be vapor-phase and hence is assumed not to deposit to soil or transfer into aboveground plant parts. 

As a consequence, there is no transfer of elemental mercury into animal tissues. Biotransfer factors for cadmium 

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

chemical octanol-water partitioning coefficients (Kow values) using the correlation equation from RTI (2005) and 

assuming the following fat contents: milk - 4%; beef - 19%; pork - 23%; poultry -14%; and eggs - 8%. b

 As discussed in HHRAP (EPA 2005a), EPA (1995c) recommends using a metabolism factor (MF) to account for 

metabolism of PAHs by mammals to offset the amount of bioaccumulation suggested by biotransfer factors. EPA 

has recommended an MF of 0.01 for bis(2-ethylhexyl)phthalate (BEHP) and 1.0 for all other chemicals (EPA 

1995d). For MIRC, an MF of 0.01 is also used to calculate concentrations of PAHs in food products from 

mammalian species based on the work of Hofelt et al. (2001). This factor takes into account the P450-mediated 

metabolism of PAHs in mammals; applying this factor in our approach reduced the concentrations of chemicals in 

beef, pork, and dairy by two orders of magnitude.
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology for RTR 

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Exhibit 6-6. Soil and Plant Ingestion Rates for Animals 

Animal Soil Ingestion Rate – 

Qs(m) (kg/day) a

Plant Part Consumed 

by Animal 

Plant Ingestion Rate –

Qp(I,m) (kg/day) 

Beef cattle 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.
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6.3 Adult and Non-Infant Exposure Parameter Values 

The exposure parameters included in MIRC and their default and other value options are 

summarized in the following subsections. The default values were selected to result in a highly 

health protective screening scenario. Parameter value options were primarily obtained or 

estimated from EPA’s Exposure Factors Handbook (EFH; EPA 2011) and Child-Specific 

Exposure Factors Handbook (CSEFH; EPA 2008a). Where values were reported for age 

groupings other than those used in MIRC (see Section 2.3 above for MIRC age groups), timeweighted average values were estimated for the MIRC age groups from the available data. 

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 2011). 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). We considered updating the body weight for use in the RTR assessment to the 

revised body weights in the 2011 Exposure Factors Handbook (EPA 2011). However, the 2011 

EFH warns that assessors should consider if the dose estimate (for which the body weight is 

used to calculate) will be used to estimate risk by combining it with a dose-response that was 

derived assuming a body weight of 70 kg, and that if such an inconsistency exists, an 

adjustment may be necessary. Because a 70 kg body weight commonly is used to develop IRIS 

dose-response values, using an 80 kg body weight could result in this inconsistency. Given 

these issues, we have retained the use of an adult body weight of 71.4 kg. 

In general, BW values for the five children’s age groups were calculated from the summary data 

provided in Table 8-3 of EPA’s 2008 CSEFH. For purposes of comparison, alternative BW 

values for children ages 12 through 19 years also were estimated using data from Portier et al. 

(2007). These values are listed in the last row of Exhibit 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. 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology 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 a (20-70) 50 71.4 52.9 56 69.3 89.7 97.6 

Child < 1 b 1 7.83 6.03 6.38 7.76 9.24 9.66 

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

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

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

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

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

 BW represents the sample-size weighted average of male and female mean body weights (all races, 18-74 

years) from EPA’s 1997 EFH (Tables 7-4 for males and 7-5 for females). Note that these weights include the 

weight of clothing, estimated to range from 0.09 to 0.28 kg. Although the 18 to 74 year age category in EPA’s EFH 

does not match exactly the age 20 to 70 year categorization of adults in MIRC, the magnitude of error in the mean 

and percentile body weights is likely to be very small (i.e., less than 1%). We considered updating the body weight 

for use in the RTR assessment to the revised body weights in the 2011 Exposure Factors Handbook. However, 

the 2011 EFH warns that assessors should consider if the dose estimate (for which the body weight is used to 

calculate) will be used to estimate risk by combining it with a dose-response that was derived assuming a body 

weight of 70 kg, and that if such an inconsistency exists, an adjustment may be necessary. Because a 70 kg body 

weight commonly is used to develop IRIS dose-response values, using an 80 kg body weight could result in this 

inconsistency. Given these issues, we have retained the use of an adult body weight of 71.4 kg. b

 Each BW represents a time-weighted average of body weights for age groups birth to <1 month, 1 to <3 months, 

3 to <6 months, and 6 to <12 months from Table 8-3 of the 2008 CSEFH. Original sample sizes for each of these 

age groups can also be found in Table 8-3.

c

 Each BW represents a time-weighted average of body weights for age groups 1 to <2 years and 2 to <3 years 

from Table 8-3 of the 2008 CSEFH. Original sample sizes for each of these age groups can also be found in 

Table 8-3.

d BWs obtained directly from Table 8-3 of the 2008 CSEFH (age group 3 to <6 years). e

 Each BW represents a time-weighted average of body weights for age groups 6 to <11 years and 11 to <16 

years from Table 8-3 of the 2008 CSEFH. Original sample sizes for each of these age groups can also be found 

in Table 8-3.

f

Mean BW estimated using Table 8-22 of the 2008 CSEFH, which is based on NHANES IV data as presented in 

Portier et al. (2007). This estimate was calculated as the average of the 8 single-year age groups from 12 to 13 

years through 19 to 20 years. Values for the other percentiles were estimated using Portier et al., 2007. 

g

 Each BW represents a time-weighted average of body weights for age groups 11 to <16 years and 16 to <21 

years from Table 8-3 of the 2008 CSEFH. 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 
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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, 

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 504 482 969 1113 1440 

Child 1-2 c 332 255 687 903 1318 

Child 3-5 d 382 316 778 999 1592 

Child 6-11 e 532 417 1149 1499 2274 

Child 12-19 f 698 473 1641 2163 3467 

Adult g 1219 981 2534 3087 4567 

Sources: EPA 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 health protective (i.e., slightly low) estimate of IR for ages 6 through 11 years since 11-year 

olds are not included in this CSEFH age group. 

f

 Each IR represents a time-weighted average of ingestion rates for age groups 11 to <16 years, 16 to <18, and 18 

to <21 years from Table 3-4 of the 2008 CSEFH. Note that estimated values include 11-year-olds and individuals 

through age 20, which contributes to uncertainty in the estimates for 12 to 19 years. 

g

 Adult drinking water ingestion rates were obtained from EPA (2004b), Appendix E, Part I, Table A1 for community 

water, both sexes (ages 20+), direct plus indirect water ingestion.

6.3.3 Local Food Ingestion Rates 

MIRC includes mean, median, 90th, 95th, and 99th percentile food-specific ingestion rates (IRs) 

for consumers-only of farm food chain (FFC) media for adults and children. The mean and 

percentile values are from EPA’s analysis of data from the USDA’s 1987 to 1988 Nationwide 

Food Consumption Survey (NFCS) (USDA 1993), as presented in Chapter 13 of the Agency’s 

Exposure Factors Handbook (i.e., Intake Rates for Various Home Produced Food Items) (EPA 

2011). Consumers-only means that individuals who did not report eating a specified type of 

food during the three-day period covered by the food ingestion part of the survey were not 

included in the analysis of ingestion rates for that food type. The questionnaire included the 
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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.3

 

For the adult age group in MIRC, data were compiled on food-specific IRs separately for two 

types of households as indicated in the “Response to Questionnaire” (EPA 2000, 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 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 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

Child (age in yr) Adult 

(20–70 <1 1–2 3–5 6–11 12–19 yrs)

Mean ingestion rates 

Beefa N/A 4.14 4.00 3.77 1.72 1.93 

Dairyb N/A 91.6 50.9 27.4 13.6 2.96 

Eggsa N/A 2.46 1.42 0.86 0.588 0.606 

Exposed Fruita N/A 6.14 2.60 2.52 1.33 1.19 

Exposed Vegetablea N/A 3.48 1.74 1.39 1.07 1.38 

Porka N/A 2.23 2.15 1.50 1.28 1.10 

Poultrya N/A 3.57 3.35 2.14 1.50 1.37 

Protected Fruita N/A 16.6 12.4 8.50 2.96 5.19 

Protected Vegetablea N/A 2.46 1.30 1.10 0.78 0.862 

Root Vegetablea N/A 2.52 1.28 1.32 0.94 1.03 

 

3

 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. 
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Exhibit 6-9. Summary of Age-Group Specific Food Ingestion Rates for Farm Food Items 

Product

Child (age in yr) Adult 

(20–70 <1 1–2 3–5 6–11 12–19 yrs)

Water (mL/day)c N/A 332 382 532 698 1218 

Median ingestion rates 

Beefa N/A 2.51 2.49 2.11 1.51 1.55 

Dairyb N/A 125 66.0 34.4 15.5 2.58 

Eggsa N/A 1.51 0.83 0.561 0.435 0.474 

Exposed Fruita N/A 1.82 1.11 0.61 0.62 0.593 

Exposed Vegetablea N/A 1.89 1.16 0.64 0.66 0.812 

Porka N/A 1.80 1.49 1.04 0.89 0.802 

Poultrya N/A 3.01 2.90 1.48 1.30 0.922 

Protected Fruita N/A 7.59 5.94 3.63 1.23 2.08 

Protected Vegetablea N/A 1.94 1.04 0.79 0.58 0.564 

Root Vegetablea N/A 0.46 0.52 0.57 0.56 0.59 

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

90th percentile ingestion rates d

Beefa N/A 9.49 8.83 11.4 3.53 4.41 

Dairyb N/A 185 92.5 57.4 30.9 6.16 

Eggsa N/A 4.90 3.06 1.90 1.30 1.31 

Exposed Fruita N/A 12.7 5.41 6.98 3.41 2.37 

Exposed Vegetablea N/A 10.7 3.47 3.22 2.35 3.09 

Porka N/A 4.90 4.83 3.72 3.69 2.23 

Poultrya N/A 7.17 6.52 4.51 3.13 2.69 

Protected Fruita N/A 44.8 32.0 23.3 7.44 15.1 

Protected Vegetablea N/A 3.88 2.51 2.14 1.85 1.81 

Root Vegetablea N/A 7.25 4.26 3.83 2.26 2.49 

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

95th percentile ingestion rates 

Beefa N/A 12.9 12.5 12.5 3.57 5.83 

Dairyb N/A 167 89.9 56.0 32.3 7.80 

Eggsa N/A 5.38 3.62 2.37 1.43 1.59 

Exposed Fruita N/A 14.6 6.07 11.7 4.78 3.38 

Exposed Vegetablea N/A 11.9 6.29 5.47 3.78 4.46 

Porka N/A 6.52 6.12 4.73 6.39 2.60 

Poultrya N/A 8.10 7.06 5.07 3.51 3.93 

Protected Fruita N/A 48.3 35.1 26.9 11.4 19.2 

Protected Vegetablea N/A 9.42 5.10 3.12 2.20 2.83 
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Exhibit 6-9. Summary of Age-Group Specific Food Ingestion Rates for Farm Food Items 

Product

Child (age in yr) Adult 

(20–70 <1 1–2 3–5 6–11 12–19 yrs)

Root Vegetablea N/A 10.4 4.73 5.59 3.32 3.37 

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

99th percentile ingestion rates 

Beefa N/A 20.9 19.8 13.3 4.28 6.84 

Dairyb N/A 180 87.2 54.8 34.7 9.20 

Eggsa N/A 16.2 11.2 8.19 4.77 1.83 

Exposed Fruita N/A 25.2 32.5 15.7 5.9 13.0 

Exposed Vegetablea N/A 12.1 7.36 13.3 5.67 8.42 

Porka N/A 8.71 9.74 6.61 4.29 3.87 

Poultrya N/A 9.63 10.24 6.12 4.60 4.93 

Protected Fruita N/A 109 71.2 58.2 19.1 34.4 

Protected Vegetablea N/A 9.42 5.31 5.40 2.69 5.56 

Root Vegetablea N/A 10.4 4.73 7.47 5.13 7.57 

Water (mL/day)c N/A 1318 1592 2274 3467 4567 

a

 Primary source for values was the 1987–1988 NFCS survey; compiled results are presented in Chapter 13 of 2011 

Exposure Factors Handbook (EPA, 2011). When data were unavailable for a particular age group, intake rate for all 

age groups was used multiplied by the age-specific ratio of intake based on national population intake rates from 

CSFII. 

b

 Primary source for values was 1987–1988 NFCS survey, compiled results presented in Chapter 13 of 2011 

Exposure Factors Handbook (EPA, 2011). When data were unavailable for a particular age group, intake rate for all 

age groups was used multiplied by the age-specific ratio of intake based on national population intake rates from an 

NHANES 2003–2006 analysis in Chapter 11 of the Exposure Factors Handbook. 

c

 Primary source for children less than 3 years of age was a Khan et al.(2008) analysis of CSFII data, and from EPA’s 

analysis of NHANES 2003–2006 data for children and adults greater than three. All data tables that were used and 

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

 Default ingestion rate percentile used in MIRC for Tier 1 assessments and chemical threshold calculations 
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For children, EPA estimated food-specific IRs for four age categories (EPA 2011): 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” agespecific consumer-only ingestion rates, as explained below. Food-specific intake rates (IRs) for 

those child age groups and food items not included in Chapter 13 of the 2011 EFH, that is 

IRage_group_x, were derived using the following information: 

 Mean or percentile-specific consumer-only intake of the farm food item, as brought into 

the home, for the total NFCS survey population (from EFH Chapter 13) – IRCO_total;

 Mean or percentile-specific per capita intake of the food type from all sources, as 

consumed, for the specific child age group, from Chapter 3 of the CSFII Analysis of Food 

Intake Distributions (EPA 2003c) – IRPC, age_group_x; and 

 Mean or percentile-specific per capita intake of the farm food item for the total CSFII 

survey population (from Chapter 3 of EPA 2003c) – IRPC_total.

The ratio of IRPC, age_group_x to IRPC_total from the CSFII data shows the consumption rate of a 

particular food type by a specific age group relative to the consumption rate for that food type for 

the population as a whole. The ratio of IRCO, age_group_x to IRCO_total, that is the consumption rate 

of a particular food type by a specific age group (consumers only) relative to the consumption 

rate for that food type for the NFCS survey population as a whole (consumers only), should be 

approximately the same. Given the assumption that the two ratios are equal, Equation 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. 
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The above calculation implicitly assumes that the distribution of the consumption rate for a food 

type for a specific age group (consumers only) has the same shape as the distribution of the 

consumption rate for a food type for a specific age group in the general population (per capita). 

Otherwise, the separate calculation of each percentile might yield intake estimates that 

decrease as the percentile increases. This calculation artifact could occur if the shapes of the 

two distributions differ in the upper percentiles (or “tails”) of the distributions. 

In the instances where the above calculations were used to fill data gaps in the above exhibit, 

only the dairy child-specific age group intake estimates are not strictly increasing with increasing 

percentile. The distributions likely track better (and thus the above assumption of equal ratios is 

more reasonable) for lower percentiles, with deviations occurring due to outlier ingestion rates 

based on only a few respondents in the tails of the distributions. The MIRC defaults use the 90th

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

estimates in this particular calculation. 

6.3.4 Local Fish Ingestion Rates 

The USDA’s 1987 to 1988 Nationwide Food Consumption Survey (NFCS) (USDA 1993, 1994a), 

as presented in Chapter 13 of the Agency’s Exposure Factors Handbook (i.e., Intake Rates for 

Various Home Produced Food Items) (EPA 2011), includes family-caught fish ingestion rates by 

age category. There are several disadvantages, however, to using that data source to estimate 

fish ingestion rates. First, due to inadequate sample sizes, EPA did not report fish IRs for 

children less than 6 years of age. Second, the NFCS data were collected approximately two 

decades ago. Third, the reported fish IRs are for ages 6 to 11 and 12 to 19 and are based on 

29 and 21 individuals in each age category, respectively (EPA 2011, Table 13-20). Finally, the 

IRs from NFCS data are based on total weight of fish as brought into the home, and do not 

include losses from preparation of the fish (i.e., removal of inedible parts and, possibly, the 

skin). Estimates of preparation losses for fish intended to apply to the NFCS fish IR data are 

very uncertain and are based on a wide variety of freshwater, estuarine, and marine fish, and 

squid (EPA 2011, Table 13-69). Therefore, a more recent survey was sought that included 

larger sample sizes, data for children younger than six years, and IRs for the parts of fish 

actually consumed. 

EPA’s (2002) analysis freshwater and estuarine fish consumption from the USDA’s Continuing 

Survey of Food Intake by Individuals (CSFII) for 1994-96 and 1998 was chosen to provide per 

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 
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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.4

 

 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 6-10and Exhibit 6-11. The 

mean and percentile per capita fish ingestion rates estimated using this methodology are 

summarized in Exhibit 6-12 and are available in MIRC. (Note that as detailed in Appendix 5, 

 

4

 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.
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Exhibit 3-6, in developing the screening threshold emission rates, a health protective 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. 

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 6-11. 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 6-12. 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, 2011.) Black and female recreational anglers 

ingestion rates are presented in Burger et al. (2010.) The fish ingestion rates for Hispanic, 

Laotian, and Vietnamese populations were derived from a study by Shilling et al. (2010) of 

contaminated fish consumption in California’s Central Valley Delta. Shilling et al. (2010) 

reported mean and 95th percentile ingestion rates for each subpopulation. In part due to the low 
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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

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 health protective bias for highly lipophilic chemicals. 

For persons who prefer to consume fillets with the skin on and do not discard belly fat, 

assuming the same concentration of chemical in the fish consumed as in the whole fish 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 
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3 to 6 years and were not specific to families that garden or farm. The default ingestion rates in 

MIRC are the 90th percentile values, as for other ingestion rate parameters. 

Exhibit 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 50 200 b 200 b 200 b

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

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

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

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

 Sources: EPA 2008, EPA 2011 

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 

2011 EFH, Chapter 5, Table 5-1. 

b These values are the recommended “upper percentile” value for children from EPA’s 2011 EFH, Chapter 4, Table 

4-23. The 2008 CSEFH and 2011 ESH included a high-end value associated with pica only, but this value has not 

been used. 

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 health protective (i.e., slightly low) estimate for ages 6 through 11 years since 11-year olds are 

not included in this CSEFH age group. 

e

 These values represent a time-weighted average for age groups 11 to <16 years (N=975) and 16 to <21 (N=743) 

years from Table 14-3 of the 2008 CSEFH. Note that estimated values include 11-year-olds and individuals 

through age 20, which contributes to uncertainty in the estimates. 

f

 These values represent a time-weighted average for age groups 20 to 39 years (N=2,950) and 40 to 69 years 

(N=4,818) from Table 5B of the 2005 EPA analysis of CSFII. 

g

 These values represent a time-weighted average for age groups 20 to 39 years (N=2,950) and 40 to 69 years 

(N=4,818) from Table 5A of the 2005 EPA analysis of CSFII. h

 Percents consuming foods from Table 14-3 of the 2008 CSEFH include: 67.0% (birth to <1 month); 74.7% (1 to 

<3 months); 93.7% (3 to <6 months); and 99.7% (6 to <12 months). Infants under the age of 1 that consume 

breast milk are classified as “non-consumers” of food. 

6.4 Other Exposure Factor Values 

The other exposure parameters included in the MIRC algorithms are exposure frequency 

(Section 6.4.1), fraction of the food type obtained from the contaminated area (Section 6.4.2), 

and reduction in the weight of the food types during preparation and cooking (Section 6.4.3). 

For the breast milk ingestion pathway, additional exposure parameters are included in the FFC 

algorithms (Section 6.5). 

6.4.1 Exposure Frequency 

The exposure frequency (EF) represents the number of days per year that an individual 

consumes home-produced food items that are contaminated with the chemical being evaluated. 

In MIRC, the default value for EF is 365 days/year for all exposure sources and all potential 
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receptors. This assumption is consistent with the food ingestion rates used in MIRC (i.e., daily 

intake rates equivalent to annual totals divided by 365 days) and does not imply that residents 

necessarily consume home-produced food products every day of the year. 

If the user wishes to evaluate daily intake rates based on shorter averaging times, the user can 

overwrite both the food-specific ingestion rates and the EF for each home-grown food product. 

Users of MIRC might want to specify a lower EF values for various food types where residents 

obtain some of their diet from commercial sources and where consumption of home grown 

produce is seasonal. 

6.4.2 Fraction Contaminated 

The fraction contaminated (FC) represents the fraction of each food product consumed that is 

contaminated by the chemical at a level consistent with environmental concentrations in the 

area of concern (e.g., area with maximum deposition rates). Obviously, the most health 

protective assumption is that all food products consumed (i.e., 100 percent) are from the 

location represented by the chemical concentrations input into MIRC. 

For non-infant children and the adult age cohorts, MIRC includes the default FC of 1, assuming 

that 100 percent of the food product consumed is produced by households that farm, garden, or 

raise animals. The user can vary this default FC value for individual food products to tailor the 

assessment to a particular exposure scenario. 

6.4.3 Preparation and Cooking Losses 

Food preparation and cooking losses are included in the FFC exposure calculations to account 

for the amount of a food product as brought into the home that is not ingested due to loss during 

preparation, cooking, or post-cooking. These losses need to be accounted for in the ADD 

equations because the food ingestion rates calculated from the USDA 1987 to 1988 NFCS are 

based on the weight of home grown produce and animal products brought from the field into the 

house prior to any type of preparation. Not all of the produce or products were eventually 

ingested. In general, some parts of the produce and products are discarded during preparation 

while other parts might not be consumed even after cooking (e.g., bones). Thus, the actual food 

ingested is generally less than the amount brought into the home. 

Three distinct types of preparation and cooking losses are included in the ingestion exposure 

algorithms in MIRC: (1) loss of parts of the food type from paring (i.e., removing the skin from 

vegetables and fruits) or other types of preparation (e.g., removing pits, coring, deboning), (2) 

additional loss of weight for the food type during cooking (e.g., evaporation of water), and (3) 

post-cooking losses (e.g., non-consumption of bones, draining cooking liquid [e.g., spinach]). 

MIRC includes mean values for these three types of preparation and cooking losses for 

exposed fruit, protected fruit, exposed vegetables, protected vegetables, root vegetables, beef, 

pork, poultry, and fish. Different types of losses apply to different types of foods. Therefore, the 

losses can be represented by only two parameters, L1 and L2, the definitions of which vary 

according to the food type as explained in the endnotes in Exhibit 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 and 2011 EFH (EPA 1997a and 2011). 
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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 and 2011 

NA = Not Available 

a

 For fruits, includes losses from draining cooked forms. For vegetables, includes losses due to paring, trimming, 

flowering the stalk, thawing, draining, scraping, shelling, slicing, husking, chopping, and dicing and gains from the 

addition of water, fat, or other ingredients. For meats, includes dripping and volatile losses during cooking. b

 For fruits, includes losses from removal of skin or peel, core or pit, stems or caps, seeds and defects; may also 

include losses from removal of drained liquids from canned or frozen forms. For vegetables, includes losses from 

draining or removal of skin. For meats, includes losses from cutting, shrinkage, excess fat, bones, scraps, and 

juices. 

c

 These values represent averages of means for all fruits with available data (except oranges) (Table 13-6). d

 This value represents an average of means for all exposed vegetables with available data (Table 13-7). Exposed 

vegetables include asparagus, broccoli, cabbage, cucumber, lettuce, okra, peppers, snap beans, and tomatoes. e

 This value was set equal to the value for oranges (Table 13-6). 

f

 This value represents an average of means for all protected vegetables with available data (Table 13-7). 

Protected vegetables include pumpkin, corn, peas, and lima beans. 

g

 These values represent averages of means for all root vegetables with available data (Table 13-7). Root 

vegetables include beets, carrots, onions, and potatoes. h

 This value represents an average of means for chicken and turkey (Table 13-5). 

i If the user changes fish ingestion rates to match a survey of the whole weight of fish brought into the home from 

the field (divided by the consumers of the fish), an appropriate value for L1 would be 0.31 (EPA 2011, Table 13-5). 

For volatile or water soluble chemicals, a non-zero value for L2 also may be appropriate. Although EPA (2011) 

recommended 0.11 for L2, it varies substantially by chemical. 

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).5

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 

 

5 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. 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology 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).

6

 (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 2011, Table 13-69). For volatile or water soluble chemicals, a non-zero 

value for L2 also may be appropriate. Although EPA recommended 0.11 for L2, 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. 

 

6

 Burger J. 2010. Daily consumption of wild fish and game: Exposures of high end recreationalists. Environmental 

Health Research 12(4): 343-354. 
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Exhibit 6-17. Scenario- and Receptor-Specific Input Parameter Values Used to 

Estimate Infant Exposures via Breast Milk 

Parameter Description Default Value 

AT Averaging time for infant’s exposure via breast milk, i.e., duration of 

nursing (days) =ED 

BWinf Body weight of infant (kg) averaged over duration of nursing exposure 7.8 

BWmat

Body weight of mother (kg) averaged over duration of mother’s 

exposure 

66 

DAImat Daily absorbed intake of chemical by mother (mg/kg-day) Equation 3-36 

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

AT/ED Averaging time divided by exposure duration 1.0 

fbp Fraction of mother’s whole blood that is plasma (unitless) 0.65 

ffm Fraction of mother’s body weight that is fat (unitless) 0.30 

fmbm Fraction of fat in mother’s breast milk (unitless) 0.04 

fpm Fraction of mother’s body weight that is plasma (unitless) 0.046 

IRmilk Mean infant milk ingestion rate over duration of nursing (kg/day) 0.709 

tbf Duration of breast feeding (days) 365 

tpn Duration of maternal chemical exposure prior to nursing (days) 3285 

Averaging time (AT) and exposure duration (ED). AT refers to the time over which the infant’s 

exposure to the chemical of concern is averaged. ED refers to the duration of the infant’s 

exposure. For the exposure scenario considered for this age group, both AT and ED equal the 

duration of the nursing period, and they therefore cancel each other out in the infant average 

daily dose equation. 

Infant body weight (BWinf). The user selects a value for BWinf , the time-weighted average body 

weight of the infant over the entire duration of breast feeding, based on the age at which the 

infant stops breast feeding. For example, if the infant breast feeds for one year, the user should 

select the body weight for an infant that is averaged from birth to the first birthday. Similarly, if 

an infant breast feeds for 6 months, the user should select the body weight for an infant that is 

averaged from birth to six months. Because the default breast feeding duration (tbf) is one year 

(i.e., 365 days), the default infant body weight is 7.8 kg, which is the time-weighted average for 

the mean infant body weight between birth and its first birthday from EPA’s (2008) Child Specific 

Exposure Factors Handbook (CSEFH; EPA 2008a). Exhibit 6-18 presents additional values for 

the infant body weight parameter that the user can select instead of the MIRC default. 

Exhibit 6-18. Average Body Weight for Infants 

Statistic 0 to < 6 months 

(kg) 

0 to < 12 months 

(kg) 

0 to < 18 months 

(kg) 

0 to < 24 months 

(kg) 

Mean 6.5 7.8a

 9.0 9.6 

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 
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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 health protective value (EPA 2001a). To establish 

a health protective screening scenario, the MIRC default value for ffm is 0.30.

Fraction of fat in mother’s breast milk (fmbm). The Cmilkfat model (Equation 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 2011, Smith 1987, Sullivan et al. 1991). The MIRC 

default value for fmbm of 0.04 (i.e., 4 percent) is the value EPA recommended for MPE (EPA 

1998). 

Fraction of maternal weight that is plasma (fpm). Altmann and Dittmer (1964) estimated that 

plasma volume for adult women ranged from 37 to 60 mL/kg of body weight and averaged 

about 45 mL/kg. Ueland (1976) observed that the average plasma volume of women 6 weeks 

postpartum was 45 mL/kg of body weight. Using a value of 1.026 for the specific gravity of 

plasma from Conley (1974), EPA estimated a value of 0.046 for the fraction of maternal weight 

that is plasma (EPA 1998). The MIRC default for fpm therefore is 0.046. 

Infant breast milk ingestion rate (IRmilk). Milk ingestion rates vary with several factors, including 

the age and size of the infant and use of other foods such as formula. Based on its review of a 

several studies, EPA recommended time-weighted average and upper percentile milk ingestion 

rates for infants that nurse for six and for twelve months (EPA 2011, Table 15-3). To estimate 

an “average” value, EPA first estimated study-sample-size weighted average values for 1 

through 12 months of age and then developed time-weighted average milk ingestion rates from 

those (EPA 2011). EPA estimated an upper percentile (upper bound) value as the mean plus 

two standard deviations. MIRC converts the ingestion rates measured volumetrically (mL/day) 

to mass-based estimates (kg/day) assuming the density of human milk to be 1.03 g/mL 

(reported by NAS 1991 and recommended by EPA 2011). The resulting values are shown in 

the first two rows of Exhibit 6-20. The MIRC screening-level default of 980 mL/day is an upperbound 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 (U.S. EPA 2008a, Table 15-1). The values demonstrate that although infants 

grow substantially from birth to one year of age, the “upper bound” estimates of their milk 

ingestion rates are very close to 1 liter per day at all stages of development in the first year. 

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 2011†

0 to < 12 

months 688 0.709 980a

 1.01a EPA 2011†

0 to < 1 month 510 0.525 950 0.979 EPA 2008††

1 to < 3 months 690 0.711 980 1.01 EPA 2008†

3 to < 6 months 770 0.793 1,000 1.03 EPA 2008†

6 to < 12 

months 620 0.639 1,000 1.03 EPA 2008†

a MIRC default; †

 Based on review of multiple studies; †† Based on a single study

Duration of breast feeding (tbf). This parameter is equal to the infant’s exposure duration (ED) 

and the infant’s averaging time (AT). In its MPE Methodology, EPA asserts a health protective 

value for the duration of breast feeding of 1 year (i.e., 365 days) and a central tendency 

estimate of 6 months (180 days) (EPA 1998). Reviewers of MPE noted that 365 days may be 

overly health protective, given that only 20 percent of infants are breast fed for 6 months, at 

which point alternative foods are introduced, at least in addition to breast milk (EPA 2001a). 

Nonetheless, to establish a health protective screening scenario, the MIRC default for tbf is 365 

days. 

Duration of the mother’s exposure to the chemical of concern prior to nursing (tpn). The model 

shown as Equation 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 health protective 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). All dioxin congeners were assumed to manifest identical values as TCDD in regard to 

breast milk-related parameters. 

Absorption efficiency of the chemical by the oral route of exposure for the infant (AEinf). The 

models included in MIRC assume that the AEinf from the lipid phase of breast milk is equal to the 

AEinf from the aqueous phase of the milk. Reviewers of the model stated that this assumption 

may not be valid and that ideally, the equation DAIinf would include variables for the AEinf from 

the breast milk fat and the AEinf from the aqueous phase of breast milk (EPA 2001a). However, 

since the MIRC assumption is that chemicals will partition to either the lipid or aqueous phase of 

milk, it is not necessary at this time to have multiple AEinf values for a given chemical. If data on 

the AE from the mother or an adult but not for the infant are available, data for the adult may be 

used for AEinf. Reviewers also recommended that chemical-specific values come from studies 

that account for absorption of the chemical from milk, because absorption from other matrices 

(e.g., solid foods) may not be relevant (EPA 2001a). If chemical-specific data are not available 

for adults or infants, a health protective default value for AEinf for a screening level assessment 

is 1.0, which assumes 100 percent absorption (EPA 1998). 

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
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kelim

Rate constant for elimination of chemical for nonlactating women (per day; related to chemical halflife) 

1.9E-04b

 1.4E-02 c

kfat_elac

Rate constant for total elimination of lipophilic 

chemicals by lactating women (per day) 

Est. using 

Equation 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. c

 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 health protective screening 

scenario, the MIRC default half-life for dioxins is set to 10 years or 3650 days. 
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The half-life for methylmercury is on the order of weeks, not years. Greenwood et al. (1978) 

measured blood clearance rates for MeHg in lactating Iraqi women exposed accidentally to 

MeHg via bread prepared from wheat treated with a fungicide that contained MeHg. The data 

indicated a mean half-life for MeHg of approximately 42 days. Sherlock et al. (1984) reported 

an average measured half-life for MeHg of 50 days with a range of 42-70 days. The MIRC 

default for MeHg is set to the longer average half life of 50 days. 

Chemical elimination rate constant for lactating women – aqueous (kaq_elac). The parameter 

kaq_elac is equal to kelim plus the loss rate for the chemical in the aqueous phase of breast-milk 

during lactation. EPA has yet to propose a term for the additional elimination of a chemical in 

the aqueous phase of milk from breast feeding. In the absence of empirical values, a 

reasonable assumption for water soluble chemicals is that kaq_elac is equal to kelim as discussed 

for Equation 3-43. The extent to which kelim is an underestimate of kaq_elac for a given chemical 

will determine the extent of health protective bias in kaq_elac. 

Chemical elimination rate constant for non-lactating women (kelim). Although values for this 

parameter often are reported directly in the literature, MIRC estimates kelim from chemical halflife assuming first-order kinetics as shown in Equation 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). 
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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 health protective 

(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 health protective in nature. Screening thresholds were derived for five RTR chemical 

species: benzo(a)pyrene, cadmium, mercuric chloride, methyl mercury, and 2,3,7,8-TCDD; 

Section 7.3 presents chemical-specific parameter inputs for these five chemicals. Finally, 

Section 7.4 presents default parameter values for the nursing infant exposure scenario, which 

applied only to dioxin and methyl mercury. 

7.1 Default Ingestion Rates 

The screening-level (or default) values for ingestion rates for soil, fish, breast milk, and for each 

farm food type are equal to the 90th percentile of the distribution of national data for that 

ingestion medium (Exhibit 7-1). The default settings also assume that all food types are 

obtained from the area of chemical deposition specified by the user (i.e., fraction of food from 

contaminated area = 1.0). 

For estimates of screening threshold emission rates for PB-HAPS, environmental 

concentrations and air deposition rates were estimated using TRIM.FaTE for the area of 

maximal deposition in the vicinity of a hypothetical facility, and thus represent risks estimated for 

a maximally exposed individual/farm/family. 

Exhibit 7-1 also includes a sum of the 90th percentile ingestion rates for homegrown food 

categories and fish ingestion (preceding rows) to show the implied total food ingestion rate 

associated with setting multiple food-type-specific ingestion rates at a 90th percentile. Because 

the 90th percentiles for each farm food category are likely to reflect different individuals, it is 

likely that addition of multiple 90th percentile intake values will exceed the total food ingestion 

rates likely for the general population. 

The final row in Exhibit 7-1 lists the likely magnitude of the overestimates by age category. The 

preceding row includes the 90th percentile of the distribution of individual total food ingestion

rates from the USDA’s 1994-96 and 1998 Continuing Survey of Food Intakes by Individuals

(CSFII) (USDA 2000) data sets, as analyzed by EPA (EPA 2005e). The total ingestion rate for 

the farming households takes into account the cooking losses typical of each food category to 

provide a better comparison with the 90th percentile individual total food ingestion rates (which 

are based on consumption of prepared foods). 
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Exhibit 7-1. Farm Food Category Ingestion Rates for Health protective Screening 

Scenario for Farming Households

Product 

90th Percentile Consumer Ingestion Rate 

Units Infants 

< 1 yr 

Child 

1-2 yrs 

Child 

3-5 

yrs 

Child 

6-11 

yrs 

Child 

12-19 

yrs 

Adult 

Farm Food Item 

Beefa N/A 9.49 8.83 11.4 3.53 4.41 g/kg-day 

Dairyb N/A 185 92.5 57.4 30.9 6.16 g/kg-day 

Eggsa N/A 4.90 3.06 1.90 1.30 1.31 g/kg-day 

Exposed Fruita N/A 12.7 5.41 6.98 3.41 2.37 g/kg-day 

Exposed Vegetablea N/A 10.7 3.47 3.22 2.35 3.09 g/kg-day 

Porka N/A 4.90 4.83 3.72 3.69 2.23 g/kg-day 

Poultrya N/A 7.17 6.52 4.51 3.13 2.69 g/kg-day 

Protected Fruita N/A 44.8 32.0 23.3 7.44 15.1 g/kg-day 

Protected Vegetablea N/A 3.88 2.51 2.14 1.85 1.81 g/kg-day 

Root Vegetablea N/A 7.25 4.26 3.83 2.26 2.49 g/kg-day 

Other 

Breast milkc 1.01 NA NA NA NA NA kg/day 

Soil (dry) NA 200d 200d 201 e 201 e 201 e mg/day 

Fish (per individual) f NA 26.23 38.72 57.09 82.66 105.47 g/day 

Fish (per kg BW) g NA 2.08 2.08 1.59 1.29 1.48 g/kg-day 

Total Food Ingestion Rates for Comparison Only (not in MIRC; excludes soil and water)

Total Food: 

Homegrown only h NA 260 143 100 52 32 g/kg-day 

Total Food: All 

Sources i NA 125 91 61 34 23.7 g/kg-day 

Overestimate (ratio of 

Homegrown/Total) NA 2.1 1.6 1.6 1.5 1.3 (unitless) 

Sources: EPA 2011, EPA 2008, unless otherwise noted. 

NA = not applicable 

a

 Primary source for values was the 1987–1988 NFCS survey; compiled results are presented in Chapter 13 

of the 2011 Exposure Factors Handbook (EPA, 2011). When data were unavailable for a particular age 

group, the intake rate for all age groups was multiplied by the age-specific ratio of intake based on national 

population intake rates from CSFII. 

b

 Primary source for values was 1987–1988 NFCS survey, compiled results are presented in Chapter 13 of 

the 2011 Exposure Factors Handbook (EPA, 2011). When data were unavailable for a particular age group, 

the intake rate for all age groups was multiplied by the age-specific ratio of intake based on national 

population intake rates from an NHANES 2003–2006 analysis in Chapter 11 of the Exposure Factors 

Handbook. 

c Infants are assumed to consume only breast milk for one year. d

 These values are the recommended “upper percentile” value for children from EPA’s 2011 EFH, Chapter 

4, Table 4-23. The 2008 CSEFH and 2011 EFH included a high-end value associated with pica only, but 

this value has not been used. 

e

 These values are 90th percentile adult ingestion rates calculated in Stanek et al. 1997, and they are used 

to represent older children and adults. 

f Based on data from U.S. EPA 2002, adjusted to be "as prepared" 

g 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. 

h 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. i

 90th percentile total food intake rates from EPA 2008a and 2005e based on CSFII data 1994-96 and 1998; 

see Exhibit 6-15 of this document. 
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The final row of Exhibit 7-1 is the ratio of the two preceding rows. The values in this row 

demonstrates the potential for overestimating intake by using upper percentile values for all food 

groups. 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 36.0 

Child 12-19 f 8 64.2 a

 Sources: EPA 1997, 2008a

b

 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%). We 

considered updating the body weight for use in the RTR assessment to the 

revised body weights in the 2011 Exposure Factors Handbook. However, the 

2011 EFH warns that assessors should consider if the dose estimate (for which 

the body weight is used to calculate) will be used to estimate risk by combining 

it with a dose-response that was derived assuming a body weight of 70 kg, and 

that if such an inconsistency exists, an adjustment may be necessary. Because 

a 70 kg body weight commonly is used to develop IRIS dose-response values, 

using an 80 kg body weight could result in this inconsistency. Given these 

issues, we have retained the use of an adult body weight of 71.4 kg. c

 These values were calculated as time-weighted average body weight (BW) 

from data presented in Table 8-3 of EPA’s (2008a) Child-Specific Exposure 

Factors Handbook (CSEFH). d

 These values were obtained directly from Table 8-3 of the 2008 CSEFH. e

 Each BW represents a time-weighted average of BWs for age groups 6 to <11 

years and 11 to <16 years from Table 8-3 of the 2008 CSEFH. Original sample 

sizes for each of these age groups can also be found in Table 8-3. 

f

 These values were calculated as time-weighted average BW for age groups 11 

to <16 years and 16 to <21 years from Table 8-3 of the 2008 CSEFH. The 

direction of the possible bias is unknown. The values match the estimate based 

on Table 8-22 of the NHANES IV data as presented by Portier et al. (2007). 
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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 bioavailability when ingested in soil (Bs), mammalian metabolism 

factors (MF), correction factors for belowground produce (VGrootveg), wet deposition fractions 

(Fw), air to plant transfer factors (BvAG), root concentration factors (RCF), and soil-water 

partition coefficient (Kds) are presented in Exhibit 7-3. 
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Exhibit 7-3. Chemical-Specific Parameter Values for Input to MIRCa 

Parameter Description Benzo(a)- pyrene Cadmium Mercuric chloride Methyl mercury 2,3,7,8- TCDD 

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 also presented in previous exhibits; however exact values used in the analysis are presented here, 

restricted by significant figures. In addition, only values for those chemicals that are specifically used in the screening analysis are provid
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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 for which screening threshold emissions were 

calculated. 

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 Eggs Poultry 

Benzo(a)pyrene 3.8E-02 8.0E-03 4.6E-02 1.6E-02 2.8E-02

Cadmium 1.2E-04 6.5E-06 1.9E-04 2.5E-03 1.1E-01

Mercuric chloride 1.1E-04 1.4E-06 3.4E-05 2.4E-02 2.4E-02

Methyl mercury 1.2E-03 1.7E-05 5.1E-06 3.6E-03 3.6E-03

2,3,7,8-TCDD 3.6E-02 7.7E-03 4.4E-02 1.5E-02 2.7E-02

7.4 Screening-Level Parameter Values for Nursing Infant Exposure 

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. 

 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. 
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 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. 
Addendum 2 to Technical Support Document for TRIM-Based Multipathway Screening Scenario for RTR 

2-98 

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Edition. Office of Research and Development, Washington, D.C. EPA/600/R-090/052F 

September 2011 Available at: http://cfpub.epa.gov/ncea/risk/recordisplay.cfm?deid=236252. 

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

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

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

Organization reevaluation of human and mammalian toxic equivalency factors for dioxins and 

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

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 

Tiered Screening Methodology for RTR

ADDENDUM 3: Dermal Risk Screening 
Addendum 3 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methdology June 2012

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Addendum 3 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methdology June 2012

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TABLE OF CONTENTS

1 Dermal Risk Screening................................................................................................. 1-1

1.1 Hazard Identification and Dose Response Assessment ...............................................1-1

1.2 Dermal Exposure Estimation........................................................................................2-2

1.2.1 Equations for Estimating Dermal Exposure...................................................... 2-2

1.2.2 Exposure Factors and Assumptions................................................................. 2-3

1.2.3 Receptor-Specific Parameters ......................................................................... 2-3

1.2.4 Scenario-Specific Parameters.......................................................................... 2-4

1.2.5 Chemical-Specific Parameters......................................................................... 2-5

1.3 Screening-Level Cancer Risks and Non-Cancer Hazard Quotients..............................3-6

1.4 Dermal Screening Results............................................................................................4-6

References ............................................................................................................................ 4-9
Addendum 3 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methdology June 2012

iii

LIST OF EXHIBITS 

Exhibit 1-1. Cancer Slope Factors and Reference Doses Based on Absorbed Dose.............................. 1-2

Exhibit 1-2. Receptor-Specific Body Surface Area Assumed to be Exposed to Chemicals ..................... 2-4

Exhibit 1-3. Scenario-Specific Exposure Values for Water and Soil Contact ........................................... 2-4

Exhibit 1-4. Chemical-Specific Dermal Exposure Values for Water and Soil Contact.............................. 2-5

Exhibit 1-5. Summary of Dermal Non-Cancer Hazards ............................................................................ 4-7

Exhibit 1-6. Summary of Dermal Cancer Risks......................................................................................... 4-7
Addendum 3 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methdology June 2012

1-1 

Dermal Risk Screening

Non-inhalation exposure to PB-HAPs can occur by way of the dermal pathway through contact with PBHAP-contaminated soil and water. However, dermal absorption of chemicals that are originally airborne 

is generally a relatively minor pathway of exposure compared to other exposure pathways (U.S. EPA 

2006, Cal/EPA 2000). This section demonstrates that for the conservative 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 (U.S. EPA 2004c). 

1 Hazard Identification and Dose Response Assessment

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

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

multipathway screening evaluation for RTR: cadmium, divalent mercury, 2,3,7,8-TCDD, and 

benzo(a)pyrene. EPA has not developed carcinogenic potency slope factors (CSFs) and non-cancer 

reference doses (RfDs) specifically for evaluating potential human health concerns associated with 

dermal exposure to PB-HAPs. Instead, dermal toxicity values can be derived from oral toxicity values via 

route-to-route extrapolation by adjusting for gastrointestinal (GI) absorption. EPA recommends making 

this adjustment only when GI absorption of the chemical is significantly less than 100% (i.e., less than 50 

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

adjustment is made (U.S. EPA 2004c). 

The absorbed cancer slope factor (CSFABS) is based on the oral cancer slope factor (CSFO) and the 

fraction of the contaminant absorbed in the gastrointestinal track (ABSGI), as follows: 

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)

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 1-1, no 

adjustments to the available oral CSFs were required. Similarly, no adjustment to the oral RfD for 2,3,7,8-
Addendum 3 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methdology June 2012

2-2 

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 1-1. 

Exhibit 1-1. 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 Appendix 2. Only the resulting adjusted dose response values 

are presented in this table.

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

2 Dermal Exposure Estimation

Dermal exposures and risks resulting from absorption of the chemical through the skin from contact with 

contaminated water and soil were evaluated for the RTR screening scenario. Individuals were assumed 

to be exposed on a fraction of their bodies (i.e., their head, forearms, hands, lower legs, and feet) to 

contaminated soil from the TRIM.FaTE surface soil parcel with the highest concentration (N1) on a daily 

basis. For the water evaluation, individuals were assumed to be exposed to contaminated surface water 

with the same PB-HAP concentration as the TRIM.FaTE screening scenario lake over their entire bodies 

on a daily basis. 

2.1 Equations for Estimating Dermal Exposure

The general equation used to estimate dermal absorbed dose (DAD) for water or soil is shown below, and 

is expressed in milligrams of PB-HAP per kilogram of receptor body weight per day (mg/kg-day). 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)
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2-3 

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:

π

τ event event

event w p

t6 DA KFA2C ××

×××=

Water – Inorganic Chemicals: event pw event DA ××= tKC

Soil – All Chemicals: DAevent Cs ABSAF ×××= CF

where:

DAevent = Absorbed dose per event (mg/cm2

-event)

Cw 

Cs

= Chemical concentration in water (mg/cm3) or soil (mg/kg)

Kp = Chemical-specific dermal permeability coefficient of compound in water (cm/hr)

FA = Chemical-specific fraction absorbed; accounts for loss due to the regular 

shedding of skin cells of some chemical originally dissolved into skin (unitless)

τevent = Chemical-specific lag time per event (hr/event)

tevent = Receptor-specific event duration (hr/event)

AF = Receptor- and activity-specific adherence factor of soil to skin (mg/cm2

-event)

ABS = Chemical-specific dermal absorption fraction (unitless)

CF = Conversion factor (10-6 kg/mg)

2.2 Exposure Factors and Assumptions 

The exposure parameters included in this assessment and their default and other value options are 

summarized in this subsection. Default values were selected to result in a highly conservative 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.

2.3 Receptor-Specific Parameters

Dermal exposures and risks were estimated for the same age groups used in the ingestion exposure 

assessment: adults (ages 20 to 70 years) and five child age groups: <1 year; 1 to 2 years; 3 to 5 years; 

6 to 11 years; and 12 to 19 years. The body weight values used in the ingestion exposure assessment 

were used in the dermal exposure assessment. 

Body surface areas for water and soil exposures for adults were calculated using Appendix C, Exhibit C1, 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.
Addendum 3 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methdology June 2012

2-4 

Values for body SA by age group are summarized in Exhibit 2-1. 

Exhibit 2-1. 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.

2.4 Scenario-Specific Parameters

Exhibit 2-2 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 2-2. 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 from RAGS Part E, Exhibit 3-2 0.58

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 

Child 1-2 2

Child 3-5 3
Addendum 3 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methdology June 2012

2-5 

Exhibit 2-2. Scenario-Specific Exposure Values for Water and Soil Contact

Exposure Parameter Receptor Value Source

Child 6-11 6 calculations.

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.

2.5 Chemical-Specific Parameters

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

in Exhibit 2-3. 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 soil-bound 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.

Exhibit 2-3. 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).
Addendum 3 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methdology June 2012

4-6 

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)

The aggregate HQ accounts for exposures that an individual in a receptor group may receive from the

PB-HAP in water and soil over the exposure duration. Non-cancer hazard is not additive across the age 

groups evaluated here. 

4 Dermal Screening Results

Exhibit 4-1 presents a summary of estimated dermal non-cancer hazards by age group. A summary of 

estimated lifetime dermal cancer risks is provided in Exhibit 4-2. 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. 
Addendum 3 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methdology June 2012

4-7 

Exhibit 4-1. 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

Exhibit 4-2. 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 
Addendum 3 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methdology June 2012

4-8 

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.
Addendum 3 to Technical Support Document for TRIM-Based Multipathway Tiered Screening Methdology June 2012

4-9 

References

Cal/EPA (California Environmental Protection Agency) Office of Environmental Health Hazard 

Assessment (OEHHA). 2000. Air Toxics Hot Spots Program Risk Assessment Guidelines; Part IV, 

Exposure Assessment and Stochastic Analysis Technical Support Document. Section 6, Dermal 

Exposure Assessment. September. Available at: http://www.oehha.ca.gov/air/hot_spots/pdf/chap6.pdf. 

Hrudey, S.E., W. Chen and C.G. Roussex, 1996. Bioavailability in environmental risk assessment. 

CRC Press, Inc, Lewis publishers.

U.S. EPA. 2004c. Risk Assessment Guidance for Superfund Volume 1: Human Health Evaluation 

Manual (Part E, Supplemental Guidance for Dermal Risk Assessment). EPA/540/R99/005. Available at: 

http://www.epa.gov/oswer/riskassessment/ragse/index.htm

U.S. Environmental Protection Agency (EPA). 2006. Risk and Technology Review (RTR) Assessment 

Plan. Office of Air and Radiation. November 20, 2006. Available at: 

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

U.S. Environmental Protection Agency (EPA). 2008. Child-Specific Exposure Factors Handbook. Office 

of Research and Development, Washington, D.C. EPA/600/R-06/096F. September. Available at: 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=199243 .
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Technical Support Document for the TRIM-Based Multipathway Tiered Screening 

Methodology for RTR 

Attachment B. Tier 2 Screening Methodology 
 

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Attachment B – 6/29/12 i 

TABLE OF CONTENTS 

1 Overview of Approach ............................................................................................................................ 1

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

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

3.1 Selection Values for Variables of Interest ..................................................................................... 4

3.2 Estimation of Adjustment Factors ................................................................................................. 6

3.2.1 General Approach ..................................................................................................................... 6

3.2.2 Incorporation of the Risk Equivalency Approach ...................................................................... 8

4 Preparing National Databases of Lake and Meteorological Data ........................................................ 10

4.1 Processing Lake Data for Tier 2 Analysis ................................................................................... 10

4.2 Processing Meteorological Data for Tier 2 Analysis ................................................................... 15

4.2.1 Sources of Meteorological Data .............................................................................................. 15

4.2.2 Coverage of Meteorological Stations Compared with Facility Locations ................................ 16

4.2.3 Data Processing ...................................................................................................................... 16

5 Implementation of Tier 2 Analysis ........................................................................................................ 19

5.1 Facility List for Tier 2 Screen (Step 3) ......................................................................................... 21

5.2 Facility/Lake Distance Table (Step 4) ......................................................................................... 21

5.3 Finding the Closest Meteorological Station (Step 5) ................................................................... 22

5.4 Assembling Threshold Adjustment Factors (Step 6) ................................................................... 23

5.5 Finalizing Threshold Adjustment Factors and Assembling Screening Results (Step 7) ............. 25

5.6 Assembling Results (Step 8) ....................................................................................................... 25

6 References ........................................................................................................................................... 28

Supplement A. Summary of TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Analysis ............ 29

Supplement B. Analysis of Lake Size and Sustainable Fish Population .................................................... 35

B.1. Angler Behavior ........................................................................................................................... 35

B.2. Fish Biology ................................................................................................................................. 36

B.2.1. Lake Productivity ................................................................................................................. 36

B.2.2. Proportion of Fish Biomass by Trophic Level ............................................................................ 38

B.2.3. Minimum Viable Population Size ............................................................................................... 39

B.3. Summary of Assumptions for the Lake Size Analysis ................................................................. 40

B.4. Equations Used to Determine Lake Fish Populations................................................................. 40

B.5. References .................................................................................................................................. 42
Attachment B – 6/29/12 ii 

List of Exhibits 

Exhibit 1. Layouts for Tier 2 TRIM.FaTE Simulations Using Alternate Distances Between 

the Facility and the Fishable Lake ................................................................................................ 7

Exhibit 2. Maximum Fish Ingestion Rate (g/day) Associated with Sustainable Fishinga ........................... 12

Exhibit 3. Lake Distance – Hazard Relationship for Mercury ..................................................................... 13

Exhibit 4. Lake Distance – Hazard Relationship for Cadmium .................................................................. 13

Exhibit 5. Lake Distance – Risk Relationship for 2,3,7,8 - TCDD .............................................................. 14

Exhibit 6. Lake Distance – Risk Relationship for Benzo(a)pyrene............................................................. 14

Exhibit 7. Locations of Meteorological Stations and Point Source Facilitiesa ............................................ 17

Exhibit 8. Distance Between the Point Source Facilities and the Closest Meteorological Stationa,b ......... 18

Exhibit 9. Example of the Dashboard To Conduct the Tier 2 Analysis ...................................................... 20

Exhibit 10. Example of Global Inputs Used in the Tier 2 Analysis ............................................................. 20

Exhibit 11. Example of the Facility Input Data Required To Conduct the Tier 2 Analysis ......................... 21

Exhibit 12. Example of the Lake Distance Data Required To Conduct the Tier 2 Analysis ...................... 22

Exhibit 13. Example Results of the Meteorological Station Matching Required To Conduct 

the Tier 2 Analysis ................................................................................................................... 23

Exhibit 14. Example Results of the Octant Analysis Required To Conduct the Tier 2 Analysis ............... 24

Exhibit 15. Example of the TRIM.FaTE Matrix Results Required To Conduct the Tier 2 Analysis .......... 24

Exhibit 16. Example Summarized Results of the Octant Analysis Conducted in the Tier 2 Tool. ............ 25

Exhibit 17. Example of Summary Output Table Created by the Tier 2 Tool .............................................. 26

Exhibit 18. Example of Detailed Output Table Created by the Tier 2 Tool ................................................ 27

Exhibit 19. Example of Individual Output Table Created by the Tier 2 Tool .............................................. 27
Attachment B – 6/29/12 1 

1 Overview of Approach 

The Tier 1 screening scenario is, by design, generic and health-protective. It was constructed 

for quick application to a large number of facilities in a source category with the least chance of 

returning false negatives for risk. Once the initial screen is complete, however, the remaining 

facilities that fail for any PB-HAPs must be scrutinized further to reduce the number of potential 

false positives for risk. 

Based on screening analyses conducted for RTR to date, many facilities could “fail” the Tier 1 

screen for some source categories (e.g., 50+ facilities). A full site-specific analysis of all 

facilities that cannot be screened out in Tier 1 would not be practical. Site-specific values for 

some influential variables, however, can be determined without intensive effort during the 

assessment. 

The use of these site-specific values instead of the values used in Tier 1 can be used to justify 

adjusting the screening threshold for a given PB-HAP at that facility, potentially eliminating the 

facility from concern while maintaining a high degree of confidence that risks of concern have 

not been overlooked. Specifically, for Tier 2, data on two types of variables are utilized: 

 Meteorological characteristics, including the fraction of time the wind blows in the 

direction of the farm and lake (“wind direction”), the wind speed, the precipitation rate, 

and the mixing height; and 

 Distance from the facility to the nearest fishable lake(s).1 

These variables affect the PB-HAP concentrations in environmental media estimated by 

TRIM.FaTE (and thus can be used to justify scaling upwards the emission threshold associated 

with the risk/HQ level of concern), but they are not related to specific exposure assumptions. 

The exposure assumptions, such as ingestion rate and fraction of diet derived from the lake and 

farm, are expected to be variable for the population around any given facility and remain at 

fixed, health-protective values in Tier 2. In selecting the fate and transport variables to include in 

Tier 2, a balance was struck between the degree of impact on the risk estimate, the ease of 

implementation in TRIM.FaTE, and the ease of obtaining relatively certain site-specific values 

for all facilities that might be evaluated under the RTR program. 

To evaluate the impacts these parameters have on exposure and risk, a series of TRIM.FaTE 

simulations was performed that systematically varied the values used in the screening scenario 

for the five selected variables (i.e., four meteorological variables and lake location). Four or five 

alternative values for each variable were selected using statistics on U.S. meteorological data or 

professional judgment to capture the expected range in the facility data. TRIM.FaTE 

simulations were performed for every possible combination of these variable values to enable 

the estimation of appropriate site-specific threshold adjustment factors for scenarios with the 

corresponding characteristics. Based on the TRIM.FaTE results of these simulations (and the 

subsequent exposure and risk characterization, conducted using MIRC), a matrix of Tier 2 

threshold adjustment factors was calculated, with each element of the matrix corresponding to a 

unique combination of values from each of the selected variables. 

 

1

The lake size was also changed for each new facility lake distance. This change allowed the simulations 

to maintain a constant ratio between watershed and erosion area compared with the lake area. 
Attachment B – 6/29/12 2 

The Tier 2 threshold adjustment factors represent the ratio between the risk metric (i.e., cancer 

risk or HQ) obtained using the baseline Tier 1 screening scenario and the risk metric obtained 

from the Tier 2 TRIM.FaTE runs. For a given facility, an adjusted Tier 2 emission threshold can 

be estimated by multiplying the Tier 1 emission threshold by the adjustment factor that best 

corresponds to the meteorological conditions present at the site and the presence and location 

of lakes at the site: 

ݎ݋ݐܽܿܨ ݐ݉݁݊ݐݏݑ݆݀ܣ 2 ݎܶ݅݁ ൈ ݈݀݋݄ݏ݁ݎ݄ܶ 1 ݎܶ݅݁ ൌ ݈݀݋݄ݏ݁ݎ݄ܶ 2 ݎܶ݅݁

Matrices of threshold adjustment factors were separately developed for the four PB-HAPs that 

currently have screening emission thresholds in the Tier 1 process: benzo(a)pyrene (BaP, 

representative of polycyclic aromatic hydrocarbons), cadmium, divalent mercury and 2,3,7,8-

tetrachlorodibenzodioxin (TCDD, representative of dioxins). In implementing the Tier 2 process, 

a risk equivalency approach was used to convert speciated emissions of PAHs and dioxins into 

BaP and TCDD equivalents, respectively, similar to the Tier 1 screening approach. 

To facilitate the implementation of this approach without requiring facility-specific data searches 

for each new source category evaluated, databases of the relevant U.S. meteorological and 

lake data were created that could be accessed on the fly during a Tier 2 evaluation. The 

meteorological database includes annual-average summary statistics on wind direction, wind 

speed, and precipitation for 1,305 surface stations located throughout the United States. The 

mixing heights for these stations were estimated using the closest upper air data. The database 

of fishable lakes includes information on the location and size of all lakes in the United States. 

To focus on lakes that can support angling of upper trophic level fish, only lakes greater than 25 

acres in area are included. 

Once the databases are created, one further processing step is necessary before implementing 

the Tier 2 screening tool. For each facility that will be analyzed in Tier 2, the distance to the 

closest relevant lake near the facility in each of eight directional “octants” is recorded using GIS 

software. For the purposes of Tier 2, a “relevant” lake is considered to be one located within a 

radius of 50 km from the facility and above the size threshold of 25 acres. To access these 

databases, a Microsoft® Excel tool was created that merges the TRIM.FaTE Tier 2 adjustment 

factors with the stored lake and meteorology information corresponding by location to an 

individual facility. In the tool, each facility is matched with the closest meteorological station, 

and the values for the four relevant parameters at that station are recorded. The distance from 

the facility to the nearest lakes estimated using GIS are also imported. These five values 

become the set of facility-specific parameters. The threshold adjustment factor corresponding to 

this set of site-specific data is then looked up in the matrix of adjustment factors. If a facility 

variable assumed a value in between two of the computed levels available in the matrix, the 

more health-protective of the two levels of that variable was selected (i.e., the level that resulted 

in the smaller adjustment to the baseline Tier 1 exposure). The Tier 1 screening emission 

threshold is then multiplied by the appropriate adjustment factor to obtain an updated Tier 2 

emission threshold for that PB-HAP. Any emissions below the adjusted Tier 2 threshold are 

assumed to pose no health risk above levels of concern. 

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

Analysis 

The screening scenario used to derive Tier 1 thresholds incorporates assumptions regarding 

meteorological conditions, the spatial configuration of the hypothetical exposure setting, 

physical parameters of the environment, and chemical-specific parameters that result in 
Attachment B – 6/29/12 3 

generally health-protective results. In Tier 2, selected assumptions used in the fate and 

transport modeling conducted using TRIM.FaTE are relaxed to reflect site-specific information 

for the facilities being evaluated.2

 To determine which scenario characteristics should be 

incorporated into the Tier 2 analysis, the following issues were considered for TRIM.FaTE 

model parameters: 

 How sensitive are the modeled risks to a user-input model parameter? 

 Do the plausible values for a variable span a wide spectrum of values at different sites? 

 Which site-specific characteristics can be found easily and reliably for facilities with 

emissions exceeding Tier 1 thresholds? Is the uncertainty in the value matched to each 

facility high or low? 

 How complicated or time-consuming is the incorporation of changes in parameter values 

into the Tier 1 screening scenario set-up? 

Supplement A provides an exhibit showing all the TRIM.FaTE variables considered for the Tier 

2 analysis. These variables were evaluated qualitatively using the criteria above to determine 

whether the variable was of high, medium, or low priority. The following five “high priority” 

variables were selected for implementation in the current Tier 2 analysis: 

 Wind direction (the percent of time the wind blows toward the lake and farm), 

 Wind speed, 

 Precipitation, 

 Mixing height, and 

 Distance to the nearest fishable lake. 

These parameters were judged to represent a balance between high risk sensitivity, range of 

potential variability, ease of implementation within the modeling scheme used for RTR, and 

ease of obtaining site-specific values with a relatively high level of confidence. 

3 Estimation of Adjustment Factors for Selected Site-Specific 

Parameters 

The purpose of including site-specific detail for a facility evaluated in Tier 2 is to develop a more 

realistic (and presumably lower) estimate of risk associated with facility emissions. This is 

implemented within the analysis by generating revised threshold emissions specific to that 

PB-HAP at the selected facility. However, instead of performing full-fledged model runs for 

each facility that does not “screen out” in Tier 1, a set of generally applicable threshold 

adjustment thresholds was developed based on a set of model runs. Specifically, a total of 320 

adjustment factors were developed for each PB-HAP corresponding to unique combinations of 

variable levels for each variable discussed above (with one exception for wind direction, which 

was adjusted on a linear basis, as explained below). These adjustment factors were based on a 

set of runs in which the values for the selected variables were varied systematically. The 

 

2

Only TRIM.FaTE parameters were considered for inclusion in Tier 2 adjustments because of the difficulty in 

identifying substantial location-related differences in values for exposure factors (and other inputs to MIRC). The 

exposure characteristics used in MIRC are considered to be generally consistent across different locations and 

facilities. 
Attachment B – 6/29/12 4 

appropriate adjustment factor is then applied when evaluating a source in Tier 2 based on the 

facility’s location. 

The analyses conducted to select the parameters to adjust and derive the threshold adjustment 

factors are described in this section. Section 3.2 also describes the development of appropriate 

“bins” for the selected parameters. These bins correspond to the subset of parameter values for 

which adjustment factors were calculated, based on the anticipated range of plausible values for 

that parameter for facilities evaluated in RTR. 

3.1 Selection Values for Variables of Interest 

For each site-specific parameter identified in Section 2 other than wind direction, adjustment 

factors were estimated that correspond to a set of four or five particular values for the 

parameter. To facilitate this, ICF created bins (i.e., relevant ranges of values) for each 

parameter of interest, with the exception of wind direction (as described below, representative 

bins were not necessary for this parameter). The rationale for selecting the range for each bin 

for each parameter of interest is described below. 

Wind Direction: Within the context of the hypothetical exposure scenario used in Tier 1 (and 

presumably under actual conditions), when the frequency with which the wind blows towards the 

modeled domain increases, greater pollutant deposition is likely to occur over and around the 

farm and lake. The percentage of time the wind blows toward the farm and lake is therefore 

expected to be positively correlated with ingestion exposure and risk. In the screening scenario 

used to estimate Tier 1 thresholds, the wind is assumed to blow toward the modeled domain 

(i.e., where the hypothetical farm and lake are located) 3 days a week, or 43 percent of the time. 

This assumption is intended to approximate an unusually consistent long-term wind pattern and 

is representative of wind direction patterns in Yakima, Washington (where the wind blows 

eastward approximately 40 percent of the time, based on a review of wind direction data 

compiled by the National Weather Service). 

In the Tier 2 analysis, this factor was changed to 1, 2, and 4 days a week, corresponding to 14 

percent, 29 percent, and 57 percent of the time. This spectrum of values was chosen to obtain 

a good understanding of the impact of wind direction on risk for the range of meteorological 

conditions likely to be present at real facilities. Review of these results indicated that, within this 

modeling scenario, estimated ingestion exposure varies directly with percentage of time the 

wind blows toward the farm and lake. Given the exactly linear nature of the relationship 

observed in model results obtained from ICF’s test runs, the adjustment of the threshold for 

wind direction in Tier 2 is a direct, linear adjustment using the actual site value rather than an 

incremental, bin-based approach. In other words, the Tier 2 threshold is adjusted for wind 

direction in direct proportion to the difference between conditions for the facility location and the 

wind direction parameters included in the screening scenario (i.e., blowing toward the lake/farm 

43 percent of the time on average). 

Wind Speed: Although the impact of wind speed on non-inhalation risks also is likely to depend 

on configurational parameters such as the location of farms and lakes, in general it is 

reasonable to assume that higher wind speeds lead to more rapid chemical transfer out of the 

modeled domain, allowing less time for chemical deposition and, therefore, less total near-field 

deposition and a lower exposure and risk. The Tier 1 screening analysis assumed a wind 

speed of 2.8 m/s, corresponding to the 5th percentile of annual average speed for the 

contiguous United States (distribution was based on data for 239 stations collected by the 

National Oceanic and Atmospheric Administration [NOAA], with approximately 50 years of data 
Attachment B – 6/29/12 5 

per station).3 This value is similar to the annual average wind speeds of the U.S. Deep South.4

In the Tier 2 analysis, ICF calculated the adjustment to exposure resulting from increasing this 

factor to 3.5 m/s, 4 m/s, and 5 m/s (the 88th percentile in the NOAA data). Based on these 

levels, the bins used to classify wind speed are: 2.8–3.5 m/s, 3.5–4 m/s, 4–5 m/s and above 5 

m/s. To ensure that the approach is health-protective, a facility was assigned the lower end 

value of the bin into which it was placed. Facilities with wind speeds less than 2.8 m/s were 

assumed to have a wind speed of 2.8 m/s. 

Precipitation: Higher levels of precipitation over the modeled domain are expected to increase 

non-inhalation risks by increasing particulate and gaseous wet deposition near-field to the 

source. The screening scenario used in Tier 1 assumed an annual precipitation of 1,500 

mm/year. This level of annual precipitation is experienced in parts of the U.S. Deep South and 

parts of the U.S. Northwest Coast5

 and corresponds to approximately the 95th percentile 

precipitation in the National Climatic Data Center’s (NCDC) 30-year (1981–2010) data for over 

9,000 U.S. stations.6

 To estimate adjustment factors in the Tier 2 analysis, model simulations 

were conducted with the parameter value set to three lower values (i.e., 1,187 mm/year, 924 

mm/year and 512 mm/year), corresponding to the 75th, 50th and 25th percentiles, respectively, of 

the NCDC data. Locations with lower precipitation levels were assumed to have a minimum 

precipitation of 512 mm/year. Based on these levels, the bins used to classify precipitation 

were: 0-512 mm/year, 512-924 mm/year, 924-1,187 mm/year, 1,187-1,500 mm/year and above 

1,500 mm/year. To be health-protective, a facility was assigned the upper end value of the bin in 

which it was placed. Facilities with precipitation levels above 1,500 mm/year were assumed to 

experience precipitation of 1,500 mm/year. In setting up the wind direction runs described 

earlier, the Tier 2 analysis maintained the same wind direction to frequency of rain 

characteristics as in the Tier 1 analysis to control for effects of potential interactions between 

these factors. That is, the Tier 2 analysis maintained the same ratio of the percentage of time 

precipitation was experienced when the wind was blowing toward the lake and farm as in the 

Tier 1 analysis. 

Mixing Height: Greater mixing heights can dilute the concentration of pollutants in air, resulting 

in lower deposition and other transfers from air to surfaces and consequently also lower 

ingestion exposures. The Tier 1 screening analysis assumed a mixing height of 710 meters. 

This value is the 5th percentile of annual average mixing heights for 463 U.S. locations, based 

on data obtained from EPA’s SCRAM Web site.7

 In the Tier 2 analysis, ICF evaluated the effect 

on exposure of increasing the value for mixing height to 865 m, 1,079 m, and 1,537 m. These 

values correspond to North Little Rock, Arkansas, Boise, Idaho, and Tucson, Arizona and are 

intended to encompass the range of annual average mixing heights experienced in different 

parts of the United States. Based on these levels, the following bins were selected for 

categorization of mixing height: 710–865 m, 865–1,079 m, 1,079–1,537 m, and above 1,537. 

To be health-protective, a facility was assigned the lower end value of the bin into which it was 

placed. Facilities with mixing heights above 1,537 m and those below 710 m were assumed to 

have mixing heights of 1,537 m and 710 m, respectively. 

Lake Distance: Moving the lake included in the hypothetical scenario to a location farther from 

the source in the modeled domain will reduce deposition to the lake and its watershed and 

consequently reduce exposures associated with the fish consumption pathway, which is an 

 

3

http://ols.nndc.noaa.gov/plolstore/plsql/olstore.prodspecific?prodnum=C00095-PUB-A0001#TABLES 4

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

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

6

http://www.ncdc.noaa.gov/oa/climate/normals/usnormals.html

7

Support Center for Regulatory Atmospheric Modeling; http://www.epa.gov/scram001/tt24.htm
Attachment B – 6/29/12 6 

important pathway of exposure for several chemicals (for example, in the case of methyl 

mercury, it is by far the predominant exposure pathway). For the scenario modeled in Tier 1, 

the center of the lake was situated approximately 2 km from the source. To estimate “lake 

distance” adjustment factors for use in Tier 2, ICF completed a series of model runs in which the 

lake was located 10 km, 20 km, 30 km, and 40 km from the source. Accordingly, the bins used 

to classify lake distance are: 2–10 km, 10–20 km, 30–40 km, and >40 km. Facilities with lakes 

situated closer than 2 km or farther than 40 km from the source were assumed to have lakes at 

2 km and 40 km, respectively. 

In resituating the lake in these alternative configurations, ICF maintained ratios consistent with 

those included in the screening scenario for (1) lake area to total land area in the modeled 

domain, (2) runoff watershed area to lake area, and (3) erosion watershed area to lake area. 

Exhibit 1 provides a diagram of the TRIM.FaTE layout in each of the alternate lake distance 

simulations. ICF used a “thin” lake shape (i.e., downwind width much smaller than the crosswind length) that minimized the potential effect of declining deposition with distance from stack 

that might affect lakes that are long in the downwind direction. By controlling for these 

potentially confounding effects, ICF could isolate the effect of lake distance on risk 

appropriately. Moving the lakes to increasing distances from the stack required expansion of 

the modeled domain. Maintaining the same overall ratio of land area to lake area in each 

domain resulted in scenarios with increasingly large lakes, with surface area increasing with 

distance from the source. This approach also was taken for reasons of configurational 

convenience (i.e., taking into account resource requirements associated with reconfiguring the 

TRIM.FaTE spatial layout). The changes in lake size between these four runs are not expected 

to have a substantial independent effect on exposure and risk because the effect of increased 

lake size is offset by greater total deposition and runoff. Furthermore, the lake depth was not 

changed, which is a much more important parameter than lake surface area in determining the 

chemical concentrations in the water column and sediment. As noted above, ICF set up the 

configurations to ensure that the lakes in the different scenarios received runoff and erosion 

from equivalent watersheds on a per surface area basis. 

3.2 Estimation of Adjustment Factors 

Adjustment factors were estimated and applied as multipliers to the Tier 1 emissions thresholds. 

The resulting Tier 2 emissions thresholds are used to assess whether facilities with 

corresponding configurations pose greater than acceptable risks. Facilities with emissions 

exceeding the resulting adjusted emission (Tier 2) thresholds would be deemed to have “failed” 

the Tier 2 screen (i.e., the potential for health risk of concern cannot be unequivocally ruled out). 

3.2.1 General Approach 

The core principle in the estimation of adjustment factors is the assumption of direct 

proportionality of risk and emissions in the modeling approach used for RTR involving 

TRIM.FaTE and MIRC. Although not strictly present across all variations due to feedback 

mechanisms and other processes encompassed by the TRIM.FaTE model, a generally linear 

relationship between risk and emissions has been observed across model simulations 

conducted for RTR. This suggests that the ratio of total estimated exposures (and consequently 

risks) obtained for the screening scenario and any alternative configuration could be used as an 

adjustment factor to scale emissions for that specific alternative configuration. The risk in the 

alternative configuration following such a scaling of emissions would be equal to the risk in the 

screening scenario (which in Tier 1 was set at a risk level of 1E−6 incremental lifetime cancer 

risk or an incremental hazard quotient of 1, depending on the toxic effect of the chemical in 

question)
Attachment B – 6/29/12 7 

Exhibit 1. Layouts for Tier 2 TRIM.FaTE Simulations Using Alternate Distance

Between the Facility and the Fishable Lake 
Attachment B – 6/29/12 8 

To account for potential interactions between the chosen Tier 2 variables, matrix adjustment 

factors were estimated by performing TRIM.FaTE runs for each unique combination of the 

specified variable values (that is, all permutations of the selected values for the wind speed, 

precipitation, mixing height, and lake distance). Wind direction was handled separately, as 

explained below. Implementing the approach required 320 runs per chemical. Adjustment 

factors for each configuration were then estimated as the ratio of risks in the Tier 1 analysis to 

the estimated risk in the particular TRIM.FaTE configuration. This approach results in a large 

matrix of adjustment factors and has the advantage of accounting for all possible interaction 

effects between the variables. 

The adjustment factor for wind direction is handled separately. The fraction of the time the wind 

blows in the direction of the farm and lake was observed to have a directly linear effect on the 

resulting risk estimates. For this reason, it was not necessary to perform the actual TRIM.FaTE 

simulations for a set of values. Instead, it is calculated a linear factor that divides the Tier 1 

value (0.43, or 43% of the time in the direction of the farm and lake) by the facility value, as 

follows: 

Adjustment Factor Wind Direction = 0.43 / (fraction of time wind blows towards domain). 

The adjustment factor for wind direction is multiplied by the matrix adjustment factor discussed 

above to obtain a consolidated threshold adjustment factor that accounts for all the five 

variables considered in Tier 2. 

3.2.2 Incorporation of the Risk Equivalency Approach 

The adoption of a risk-equivalency approach to convert speciated emissions of PAHs and 

dioxins to BaP and TCDD equivalents, respectively, in Tier 1 required the development of risk 

equivalency factors (REFs) for each reported species in these groups. REFs for PAHs and 

dioxins represent the ratio of the risk posed by a particular species to the risk posed by BaP and 

TCDD, respectively, at equivalent emissions rates in a given scenario. 

REFs can be represented as the product of exposure equivalency factors (EEFs) and toxicity 

equivalency factors (TEFs). For the PAHs, this can be expressed as: 

ܴܨܧ஺௉ு ൌ ܨܧܧ஺௉ு ൈ ܶܨܧ஺௉ு

EEFs for PAHs and dioxins represent the ratio of the exposure to a particular species to the 

exposure to BaP and TCDD, respectively, at equivalent emission rates in a given scenario. 

TEFs for PAHs and dioxins represent the ratio of the cancer slope factor (CSF) for a particular 

species to the CSF for BaP and TCDD, respectively. 

EEFs are scenario-specific and depend on the TRIM.FaTE configuration. For example, the 

exposure profile (i.e., how ingestion pathways contribute to total exposure and risk) varies 

differently for each PAH when the lake is moved, owing to a larger or smaller contribution of fish 

ingestion to risk relative to the contribution of other ingestion pathways, such as consumption of 

produce and animal products. Thus, EEFs were recomputed for each of the 320 representative 

scenarios modeled in Tier 2 separately for 29 PAH species (14 based on direct TRIM.FaTE 

modeling and 15 based on Kow-based regression estimates) and 19 dioxin species (17 based on 

direct TRIM.FaTE modeling and two assumed to behave like TCDD). 
Attachment B – 6/29/12 9 

The following mathematical formulas demonstrate how the Tier 2 adjustment factors are 

estimated for the PAH and dioxin species. The formulas presented below are for a 

representative PAH species, but they can also be applied to dioxin species. 

For a given PAH emitted at a rate EPAH at a facility, the risk-equivalent BaP emission level can 

be expressed as: 

ܧ ௉஺஻ாொூ௏ ൌ ܧ஺௉ு ൈ ܨܧܧ஺௉ு ൈ ܶܨܧ஺௉ு

Then, the Tier 1 risk ratio is estimated by comparing the risk-equivalent BaP emissions to the 

BaP emission threshold: 

 

ܴܽݐ݅݋்ூாோ ଵ_௉஺ு ൌ ܧ ௉஺஻ாொ௎ூ௏_ ்ூாோ ଵ

ଵ ூாோ்஻஺௉ ݈݀݋݄ݏ݁ݎ݄ܶ

If the ratio is less than 1, the facility “screens out” of the Tier 1 analysis. Similarly, for Tier 2, the 

ratio of risk-equivalent BaP emissions to the Tier 2 BaP threshold may be expressed as: 

ܴܽݐ݅݋்ூாோ ଶ_௉஺ு ൌ ܧ ௉஺஻ாொ௎ூ௏_ ்ூாோ ଶ

ଶ ூாோ்஻஺௉ ݈݀݋݄ݏ݁ݎ݄ܶ

Using the definition of the risk-equivalent BaP emissions, this can be re-expressed for a given 

PAH species as: 

ܴܽݐ݅݋் ௥௘௜ଶ_௉஺ு ൌ ܧ஺௉ு ൈ ܨܧܧ஺௉ு_்ூாோ ଶ ൈ ܶܨܧ஺௉ு

ଶ ூாோ்஻஺௉ ݈݀݋݄ݏ݁ݎ݄ܶ

This expression may be further reconfigured, after some algebraic rearrangement, in terms of 

the Tier 1 ratio as: 

ൈ ଵುಲಹ௜௘௥ ்݋݅ݐܴܽ ൌ ଶುಲಹ௜௘௥ ்݋݅ݐܴܽ

ଵ ூாோ்஻஺௉ ݈݀݋݄ݏ݁ݎ݄ܶ

ଶ ூாோ்஻஺௉ ݈݀݋݄ݏ݁ݎ݄ܶ

ൈ

ܨܧܧ஺௉ு ்ூாோ ଶ

ܨܧܧ஺௉ு ்ூாோ ଵ

ൌ ݎ݋ݐܽܿܨ ݐ݉݁݊ݐݏݑ݆݀ܣ 2 ݎܶ݅݁

ଵ ூாோ்஻஺௉ ݈݀݋݄ݏ݁ݎ݄ܶ

ଶ ூாோ்஻஺௉ ݈݀݋݄ݏ݁ݎ݄ܶ

ൈ

ܨܧܧ஺௉ு ்ூாோ ଶ

ܨܧܧ஺௉ு ்ூாோ ଵ

These equations simply state that to adjust the Tier 1 threshold to a Tier 2 threshold for a PAH 

species, the adjustment factor must include the ratio of the BaP Tier 1 and 2 thresholds (as for 

all chemicals, as discussed above) and the ratio of the EEFs for the particular PAH in Tier 2 and 

Tier 1. This additional EEF factor is needed to account for the fact that the EEFs are different 

for each Tier 2 TRIM.FaTE configuration. 

Finally, the ratio of total BaP equivalents contributed by all PAH species at a given facility to the 

BaP Tier 2 threshold may be expressed, by summing the above expression, as: 
Attachment B – 6/29/12 10 

ܴܽݐ݅݋் ௥௘௜ଶ_௉஺ு_஺௟௟ ൌ ෍ ܴܽݐ݅݋் ௥௘௜ଵ_௉஺ு ൈ

ଵ ூாோ்஻஺௉ ݈݀݋݄ݏ݁ݎ݄ܶ

ଶ ூாோ்஻஺௉ ݈݀݋݄ݏ݁ݎ݄ܶ

ൈ

ܨܧܧ஺௉ு ்ூாோ ଶ

ܨܧܧ஺௉ு ்ூாோ ଵ

஺௟௟ ௉஺ு௦

If the ratio of total BaP equivalents contributed by all PAH species to the BaP Tier 2 threshold is 

greater than 1, the facility would be deemed to have failed the Tier 2 screen for the PAH group. 

4 Preparing National Databases of Lake and Meteorological Data 

To facilitate the effective application of the Tier 2 screening procedures, databases were 

prepared containing national-scale information about lakes (locations and sizes) and 

meteorological data at available surface stations (including average wind direction, wind speed, 

precipitation, and estimated mixing height). The development and content of these two 

databases are discussed in the following sections. 

4.1 Processing Lake Data for Tier 2 Analysis 

The lake database was built using a geospatial file provided by ESRI for their ArcGIS products, 

the “U.S. Water Bodies” geospatial file in the ESRI Data & Maps 2009 Data Update for ArcGIS 

version 9.3.1. The water body geospatial data were derived by the United States Geological 

Survey (USGS), EPA, and ESRI from the USGS National Hydrography Dataset (USGS 2012). 

Because this geospatial file excluded water bodies in Alaska, Puerto Rico, and the U.S. Virgin 

Islands, it was augmented with water body information (also from the USGS National 

Hydrography Dataset) for those other locations. The data represent 2004 and generally have an 

estimated horizontal accuracy of 50 m. For the Tier 2 analysis, ICF focused on the 432,950 

water bodies classified as “Lake/Pond” or “Reservoir” but not designated for disposal, 

evaporation, or treatment. We refer to these water bodies simply as “lakes” in the remainder of 

this document. The approximately 99,192 other water bodies (classified as canal/ditch, ice 

mass, inundation area, playa, stream/river, swamp marsh, or unclassified) were not included. In 

a more subjective step during the processing of the lake database for a specific source 

category, the lake names (when provided) are scrutinized manually, and lakes are removed 

from the analysis when their names suggest industrial or treatment use (e.g., wastewater 

treatment ponds, sludge ponds, fly ash ponds, paper mill ponds, sewage pools, etc.). 

Early in the process of compiling this database, ICF encountered the question: “What size 

water body qualifies as a ‘lake’ for the purposes of this assessment?” The Tier 2 analysis must 

focus on lakes large enough to support relatively intensive angling pressure to be compatible 

with the characteristics inherent to the assumed exposure scenario. To estimate the 

relationship between fish consumption at a high level, harvest rates, and lake size, the following 

key assumptions were made. Information and citations to peer-reviewed literature that support 

these assumptions are provided in Supplement B to this document. Note that in the TRIM.FaTE 

model screening scenario, trophic level 3 fish (T3) assumed to be consumed by anglers are 

represented by benthic carnivores (BC) and trophic level 4 fish (T4) assumed to be consumed 

by anglers are represented by water column carnivores (WCC). Together, these two trophic 

levels are referred to as piscivorous fish. 

1. Piscivorous fish, when present, comprise approximately 21 percent of the standing 

biomass of fish in an annual average. T3 fish represent 17.5 percent of the standing fish 

biomass; T4 fish account for 3.5 percent of this total fish biomass. Thus, T4 fish, when 

present, represent the limiting compartment for angler fish harvesting and consumption. 
Attachment B – 6/29/12 11 

2. Humans can harvest 10 percent of the T4 biomass without threatening the population 

due to overharvesting. 

3. The minimum viable effective population size for a single T4 species is at least 50 adult 

fish for a local population to survive over the short term (more than a decade). 

4. Only 33 percent of a T4 fish is edible fillet muscle. A 0.33 edible fraction is used to 

estimate total fish biomass associated with human consumption. 

5. A productivity level of 40 g wet weight/m2 represents an approximate upper bound for 

natural ponds and lakes in the United States. 

Using the above assumptions and a series of equations (see Supplement B), the maximum fish 

ingestion rates as a function of standing biomass and lake size were estimated. Exhibit 2 

presents these findings, where the shading in the exhibit provides an indication of where shortand longer term sustainable fishing is expected to occur (see exhibit notes). 

The Tier 2 analysis is intended to retain most of the health-protective attributes of the screening 

scenario used in Tier 1 so that no facilities of potential concern erroneously pass the screen. 

For a given facility, the smaller the lake size threshold, the greater the number of lakes and the 

greater the probability that a lake is closer to the facility. Lakes closer to a facility will result in 

higher fish concentrations compared with lakes farther from the facility. Thus, Exhibit 2 was 

used to determine the smallest lake size that would support a T4 population. At the assumed 

upper-limit standing fish biomass of 40 g ww/m2

, this corresponds to 25 acres (the first “white” 

box when moving from smaller to larger lakes). Thus, a lake size threshold of 25 acres was 

used. The final database included 117,842 lakes with surface areas of at least 25 acres but 

smaller than 100,000 acres. 

The ingestion rate supported by a 25-acre lake is not as high as the adult ingestion rate used in 

the Tier 1 and Tier 2 analyses for subsistence anglers (i.e., 26 g ww fillet per day in a 25-acre 

lake compared with 78.13 g ww fillet per day in the Tier 1 analysis). However, because the lake 

correction will also be used in Tier 3 where a wider range of ingestion rates will be considered, 

the decision was made to use the smallest lake size that supported an upper trophic level 

population without explicitly linking the lake size with the subsistence angler ingestion rate. 

To place an upper bound on the radial distance from the source up to which lake-derived risks 

need to be assessed, ICF performed a series of analyses that examined how lake-derived risks 

declined with lake distance at alternative emission rates. Exhibits 3 through 6 present the 

results of these analyses. To put these graphs in perspective, ICF superimposed the 

percentiles corresponding to each emission rate based on actual emissions data from all 

available source categories. For mercury and cadmium, these analyses suggested that a 

negligible number of facilities reported emissions at high enough levels that would continue to 

pose a risk beyond 50 km. For BaP and TCDD, results indicate that lake risks beyond 50 km 

would continue to be significant for a sizeable number of facilities. However, performing a sitespecific Tier 4 analysis beyond 50 km would not be feasible, given the limitations to the 

accuracy of TRIM.FaTE results beyond this distance. Based on these considerations, ICF 

limited the lake analysis to an outward radial bound of 50 km from the stack. For facilities with 

no lakes within 50 km, we used the health-protective approach of assuming a lake at 50 km. 
Attachment B – 6/29/12 12 

Exhibit 2. Maximum Fish Ingestion Rate (g/day) Associated with Sustainable Fishing

Standing 

Biomass 

(g ww/m2)b Size of Pond or Lake (acres) 

1 2 3 4 5 7.5 10 15 25 35 50 75 100 150 

2 0 0 0 0 0 0 1 1 1 2 3 4 5 8 3 0 0 0 0 0 1 1 1 2 3 4 6 8 12 4 0 0 0 0 1 1 1 2 3 4 5 8 10 15 

5.7 0 0 0 1 1 1 1 2 4 5 7 11 15 22 

10 0 1 1 1 1 2 3 4 6 9 13 19 26 38 15 0 1 1 2 2 3 4 6 10 13 19 29 38 58 20 1 1 2 2 3 4 5 8 13 18 26 38 51 77 30 1 2 2 3 4 6 8 12 19 27 38 58 77 115 35 1 2 3 4 4 7 9 13 22 31 45 67 90 134 40 1 2 3 4 5 8 10 15 26 36 51 77 102 154 50 1 3 4 5 6 10 13 19 32 45 64 96 128 192 60 2 3 5 6 8 12 15 23 38 54 77 115 154 231 70 2 4 5 7 9 13 18 27 45 63 90 134 179 269 80 2 4 6 8 10 15 20 31 51 72 102 154 205 307 90 2 5 7 9 12 17 23 35 58 81 115 173 231 346 

100 3 5 8 10 13 19 26 38 64 90 128 192 256 384 110 3 6 8 11 14 21 28 42 70 99 141 211 282 423 120 3 6 9 12 15 23 31 46 77 108 154 231 307 461 130 3 7 10 13 17 25 33 50 83 117 166 250 333 499 

aDark gray shading indicates insufficient population size for T4 (WCC) fish (<50 adults) to be sustainable for more than a decade; yellow-shade

provide long-term sustainable fish populations with at least 500 T4 adult fish present; white area indicates medium-term sustainability. bRepresents the standing biomass of T4 fish. At the upper-limit standing biomass of 40 g ww/m2, a lake that is 25 acres in size would sustain a 

26 g/day for a single angler.
Attachment B – 6/29/12 13 

Exhibits 3 through 6 present ICF’s lake distance analyses for mercury, cadmium, 2,3,7,8- TCDD 

and BaP. 

Exhibit 3. Lake Distance – Hazard Relationship for Mercury 

Exhibit 4. Lake Distance – Hazard Relationship for Cadmium 

0.001

0.01

0.1

1

10

100

1000

10000

0 10 20 30 40 50 60 70 80 90 100

HazardQuotient

Lake Distance (km)

T (p83)

5T (p97)

10T (p99)

25T (p99)

50T (p100)

100T (p100)

500T (p100)

1000T (p100)

5000T (p100)

HQ Threshold

xT represents

multiples of the

Tier 1 emission

threshold.

Percentiles of the

actual emissions

data from all

source categories

are reported in

parantheses.

0.01

0.1

1

10

100

1000

10000

0 10 20 30 40 50 60 70 80 90 100

HazardQuotient

Lake Distance (km)

T (p98)

5T (p99)

10T (p99)

25T (p99)

50T (p99)

100T (p100)

500T (p100)

1000T (p100)

5000T (p100)

HQ Threshold

xT represents

multiples of the

Tier 1 emission

threshold.

Percentiles of the

actual emissions

data from all

source categories

are reported in

parantheses.
Attachment B – 6/29/12 14 

Exhibit 5. Lake Distance – Risk Relationship for 2,3,7,8 - TCDD 

Exhibit 6. Lake Distance – Risk Relationship for Benzo(a)pyrene 

1.00E‐08

1.00E‐07

1.00E‐06

1.00E‐05

1.00E‐04

1.00E‐03

1.00E‐02

1.00E‐01

1.00E+00

0 10 20 30 40 50 60 70 80 90 100

Risk

Lake Distance (km)

T (p14)

5T (p34)

10T (p40)

25T (p58)

50T (p66)

100T (p75)

500T (p90)

1000T (p94)

5000T (p100)

Risk Threshold

xT represents

multiples of

the Tier 1

emission

threshold.

Percentiles of

the actual

emissions data

from all

source

categories are

reportedin

parantheses.

1.00E‐09

1.00E‐08

1.00E‐07

1.00E‐06

1.00E‐05

1.00E‐04

1.00E‐03

1.00E‐02

1.00E‐01

1.00E+00

0 10 20 30 40 50 60 70 80 90 100

Risk

Lake Distance (km)

1T (p43)

5T (p56)

10T (p59)

25T (p62)

50T (p65)

100T (p68)

500T (p93)

1000T (p97)

5000T (p99)

Risk Threshold

xT represents

multiples of the

Tier 1 emission

threshold.

Percentiles of

the actual

emissions data

from all source

categories are

reportedin

parantheses.
Attachment B – 6/29/12 15 

4.2 Processing Meteorological Data for Tier 2 Analysis 

In addition to the lake database, a database of average meteorological data at U.S. surface 

stations with complete data was created, so that each source category facility can be paired 

with the closest meteorological station data. This process of pairing dozens or hundreds of 

facilities with meteorological data is not unprecedented. In their report to the Science Advisory 

Board (SAB) on the 1996 NATA, EPA described pairing each facility with the closest 

meteorological station in an inventory of over 350 meteorological stations nationwide, creating 

an average facility-to-station distance of less than 50 km in the 1996 NATA (EPA 2001b). In a 

separate 2009 report to the SAB on the RTR program, EPA described using 158 meteorological 

stations to choose from nationwide, with a standard practice of selecting the station nearest to 

each facility unless the facility provides onsite meteorological data (EPA 2009). Using 156 

petroleum refineries as a sample data set, the average facility-to-station distance was 72 km. In 

both instances, the SAB accepted this matching as standard practice when modeling large 

numbers of sources, although they recommended providing high-level siting maps (e.g., 

meteorological stations overlaid with terrain gradients or regional climate regimes) to qualify 

some of the uncertainties related to meteorological data in air dispersion modeling (EPA 2001a; 

EPA 2010a). The current effort builds on this practice but increases the number of available 

meteorological stations as described below. 

4.2.1 Sources of Meteorological Data 

To construct a database of average meteorological data for all available surface stations in the 

United States, EPA obtained surface meteorological data from the Quality Controlled Local 

Climatological Data (QCLCD), available from the National Climatic Data Center (NCDC 2012). 

The processing began with the approximately 2,090 stations located in any of the 50 states plus 

Puerto Rico and the U.S. Virgin Islands with at least some data in the 5-year period of 2006 

through 2010. ICF narrowed the stations to consider the 1,709 stations with data at both the 

beginning and end of the period. In addition, owing to significant amounts of missing data at the 

end of 2010, ICF discarded this year of data from the analysis. During the processing of these 

1,709 stations (described later in this section), ICF removed 329 (19 percent) additional stations 

because of large amounts of missing wind data during the 4-year period. The criteria for 

removing these 329 stations are described later in this section. 

In the QCLCD data, many stations had unreliable or largely missing precipitation data. The 

numeric or text flag used to indicate missing data was not consistent, making automated 

processing of the whole dataset with respect to this one variable difficult. To reduce the 

uncertainty in the precipitation estimates, ICF used average annual precipitation data from the 

1981−2010 National Climatic Data Center 30-year normal dataset wherever possible. ICF 

matched the 30-year normal precipitation data to 992 of the remaining QCLCD stations. Among 

the remaining 385 stations without clear matches to the 30-year normal dataset, ICF removed 

72 (5 percent of total stations) from processing because they had no precipitation data from 

QCLCD. The remaining 316 stations used the values estimated from the QCLCD data. The 

final dataset contained 1,308 surface metrological stations for 2005 through 2009, which is far 

greater than the 350 and 158 stations, respectively, used in the 1996 NATA report (EPA 2001b) 

and the 2009 RTR report (EPA 2009). 

The QCLCD surface meteorological data alone cannot provide information on mixing heights, 

which must be estimated from upper air measurements. EPA’s Support Center for Regulatory 

Atmospheric Modeling archived estimated morning and afternoon mixing heights at 76 locations 

across the 50 states and Puerto Rico corresponding to years 1984 through 1991 where possible 
Attachment B – 6/29/12 16 

(EPA 2010b). The data in the 4-year period of 1987 through 1990 was averaged into one 

annual average morning mixing height and one annual average afternoon mixing height for the 

Tier 2 analysis. Each surface meteorological station was paired with its closest mixing height 

location, leading to 68 mixing height locations being matched with surface stations (the 

remaining 8 were not the closest locations to any surface station). At this time, these pairings of 

surface meteorological stations with mixing height locations were not further scrutinized. 

4.2.2 Coverage of Meteorological Stations Compared with Facility Locations 

The locations of the 1,308 surface meteorological stations and 68 mixing height locations used 

in this methodology are shown in Exhibit 7. For illustrative purposes only, the locations of the 

65,292 U.S. point source facilities in the 2005 NATA are also shown in Exhibit 7 so that the 

spatial coverage of the surface meteorological stations used in this methodology can be 

compared to the spatial coverage of most of the point source facilities that might undergo an 

RTR multipathway analysis in the future.8

 Generally, the spatial density of the surface 

meteorological stations in this methodology was similar to the spatial density of the 2005 NATA 

facilities. That is, the density tends to be greatest in the Great Lakes region, along the East and 

West Coasts, and in the Southern Plains, and tends to be lowest in the Rockies (except 

Colorado) and Northern Plains. 

Exhibit 8 again shows each of the 65,292 U.S. point source facilities from the 2005 NATA, and 

the location markers are shaded and sized according to how far the facility is from the closest of 

the surface meteorological stations used in this methodology. The average and median 

distances are 21 km and 15 km, respectively, between a facility and its closest surface 

meteorological station with complete data. This average distance is far smaller than the 

average distance between the petroleum refining facilities and meteorology stations used in the 

2009 RTR report (72 km; EPA 2009) and is likely much smaller than the average distance 

between all facilities and meteorological stations used in the 1996 NATA (less than 50 km; EPA 

2001b). Approximately 32 percent of facilities are less than 10 km from a surface 

meteorological station, while 29 percent are between 10 and 20 km, 7 percent are farther than 

50 km, and less than 1 percent are farther than 100 km. 

4.2.3 Data Processing 

To facilitate application of the meteorological data to the Tier 2 analysis, EPA gathered wind 

information in directional octants that could be linked to the direction of the closest lake (see 

Introduction and Section 5). EPA divided the periphery around a meteorological station into 

eight octants representing the direction toward which the wind was blowing: 

N: >337.5 to 22.5 degrees 

NE: >22.5 to 67.5 degrees 

E: >67.5 to 112.5 degrees 

SE: >112.5 to 157.5 degrees 

S: >157.5 to 202.5 degrees 

SW: >202.5 to 247.5 degrees 

W: >247.5 to 292.5 degrees 

NW: >292.5 to 337.5 degrees

 

8

The 2005 NATA was the most recent, comprehensive, finalized dataset of nationwide point source emitters of 

hazardous air pollutants, and is used here only for illustrative purposes. The 2005 NATA used a meteorological 

dataset different from the one used in this report. 
Attachment B – 6/29/12 17 

Exhibit 7. Locations of Meteorological Stations and Point Source Facilitiesa

aThe 2005 NATA was the most recent, comprehensive, finalized dataset of nationwide point source emitters of hazardous a

used here only for illustrative purposes. The 2005 NATA used a meteorological dataset different from the one used in this 
Attachment B – 6/29/12 18 

Exhibit 8. Distance Between the Point Source Facilities and the Closest Meteorological St

aThe high spatial density of facilities in some areas (e.g., the Great Lakes area) causes some facility dots to be plotted on t

In these areas, most or all stations might appear to have distances of a certain range (e.g., less than 50 km, denoted by blu

some other distance values (e.g., at least 50 km but less than 100 km, denoted by green shades) might also be present bu

bThe 2005 NATA was the most recent, comprehensive, finalized dataset of nationwide point source emitters of hazardous a

used here only for illustrative purposes. The 2005 NATA used a meteorological dataset different from the one used in this 
Attachment B – 6/29/12 19 

Using the data for each of the 1,308 surface meteorological stations (with proximity-matched 

mixing height data), EPA developed a software program to calculate the following statistics for 

each station-month combination in the 4-year period of surface meteorological data: 

 Number of hourly observations, 

 Number of hours with calm winds or missing winds, 

 Number of hours the wind was blowing into each directional octant, 

 Average wind speed blowing into each octant, and 

 Total precipitation (irrespective of wind octant and preferring 30-year normal data if 

available). 

This program then calculated the following statistics for each station’s period of record, using 

the above monthly totals and averages: 

 Number of months with more than 10 percent of hours missing wind data, 

 Percentage of time the wind blows into each octant (after excluding missing wind hours), 

 Median wind speed when wind was blowing into each octant, 

 Average annual precipitation at the station, and 

 Average mixing height at the matched upper air station (averaged between morning and 

afternoon mixing height values). 

The program flags a station if its total number of months missing 10 percent or more of QLCLD 

data is greater than 4 (i.e., greater than 10 percent of the 4-year period of record) or if no annual 

precipitation data are reported over the 4-year period; that station is not used in Tier 2 analysis. 

As stated earlier in this section, 401 of the original 1,709 stations were removed because of 

missing wind or precipitation data. 

5 Implementation of Tier 2 Analysis 

The Tier 2 screening analysis is implemented using a Microsoft ® Excel tool. The tool has two 

main functions: 1) finding the closest surface meteorological station to a facility, and 2) 

calculating Tier 2 threshold values. The tool is created so that all facilities in multiple source 

categories can be screened at once, if desired. The tool is controlled by a dashboard control 

panel (see Exhibit 9), where each of 8 sequential steps are controlled by a button on the panel. 

From the dashboard, the user is prompted to enter data on four worksheets. Steps 1 and 2 are 

user input steps to prepare the tool for the analysis. In Step 1, the user verifies that the Tier 2 

matrix results developed using the TRIM.FaTE model are current. In Step 2, the user enters 

basic ‘global inputs’ that include the PB-HAP groups and source categories included in the 

analysis (Exhibit 10). Steps 1 and 2 on the dashboard should be performed prior to the start of 

any analysis to ensure that the user is using the most current information. Two additional input 

tables are created using the Tier 1 Microsoft Access screening tool and the ArcGIS ® lake 

database (see Section 3.1), as described in Sections 5.1 and 5.2 below. Once these tables are 

created, the user enters the associated data in steps 3 and 4, respectively. After all these input 

data have been supplied, steps 5 and 6 conduct the Tier 2 analysis and steps 7 and 8 produce 

output tables that summarize the results, as described in Sections 5.3 and 5.4. 
Attachment B – 6/29/12 20 

Exhibit 9. Example of the Dashboard To Conduct the Tier 2 Analysis 

Exhibit 10. Example of Global Inputs Used in the Tier 2 Analysis 
Attachment B – 6/29/12 21 

To facilitate explanation of the Tier 2 analysis implementation, example source categories with 

hypothetical PB-HAP emissions were run through the tool and screen shots including these 

results are provided to illustrate the overall 8-step process. 

5.1 Facility List for Tier 2 Screen (Step 3) 

After clicking Button 3 on the dashboard, the user is brought to the facility input sheet to enter a 

list of all facilities in a source category (or multiple source categories) with emissions above the 

Tier 1 thresholds for any PB-HAP group. Included in this list are the average latitude and 

longitude of the emission sources at the facility, the list of chemicals emitted (and their 

respective PB-HAP groups), and the ratio of the REF-adjusted emission rate to the Tier 1 

screening threshold for each chemical within a PB-HAP group. This table is generated using 

the Tier 1 Microsoft® Access screening tool, and it is pasted into the “3) Facility Inputs” sheet of 

the Excel tool, as shown in Exhibit 11. 

Exhibit 11. Example of the Facility Input Data Required To Conduct the Tier 2 Analysis 

5.2 Facility/Lake Distance Table (Step 4) 

After clicking Button 4 on the dashboard, the user is brought to the lake distance sheet to enter 

details on the closest lake to each facility in each of the directional octants (N, NE, E, SE, S, 

SW, W, and NW). These lake data are assembled outside of this Excel tool using the following 
Attachment B – 6/29/12 22 

steps. First, the location of each facility with emissions above a Tier 1 threshold emission is 

imported into ArcGIS ®. The lake database (Section 3.1) is used to calculate the inside 

geographic centroid of each of the 117,842 lakes greater than 25 acres but less than 100,000 

acres in the U.S. plus Puerto Rico and the U.S. Virgin Islands. Then, from within the ArcGIS ® 

software, all lakes with centroids within 50 km of each facility are identified. A table is created 

that summarizes how far away the nearest lake is and how large that lake is in each of the 

directional octants within 50 km. If no lake is present within 50 km in a given octant, the table 

includes dashes to indicate no lake is present within the radius selected. The lake names 

(where available) are scrutinized to subjectively remove industrial and treatment water bodies, 

as discussed in Section 3.1. This table is pasting in the “4) lake distance” sheet as shown in 

Exhibit 12. 

Exhibit 12. Example of the Lake Distance Data Required To Conduct the Tier 2 Analysis 

5.3 Finding the Closest Meteorological Station (Step 5) 

Now that the user has entered all required input data, clicking Button 5 initiates an analysis that 

finds the closest surface meteorological station (having complete summary statistics) to each 

facility in the Facility List using straight-line distance. If the facility and the meteorological 

station are more than 50 km apart, the tool returns the closest station but flags the match with 

the text “Review” to alert the user that further scrutiny of the facility/station pair might be 

necessary. In addition, the tool also alerts the user if the closest station did not have complete 

summary data and the next closest station was selected instead. The results of the 

meteorological analysis are shown in the “5) Met Analysis” sheet (an example is shown in 

Exhibit 13). In the example in Exhibit 13, the closest surface meteorological station to Facility 

005 was approximately 34 km away, but the methodology rejected that station because it had 

too much missing data, choosing instead a different station approximately 37 km away with 

fewer missing data. For Facility 008, the closest station had acceptably complete data but was 
Attachment B – 6/29/12 23 

located 57 km from the facility, causing the analysis to display a warning about being beyond 50 

km away. 

Exhibit 13. Example Results of the Meteorological Station Matching 

Required To Conduct the Tier 2 Analysis 

5.4 Assembling Threshold Adjustment Factors (Step 6) 

Next, the tool calculates the facility-specific, octant-specific threshold adjustment factor for each 

chemical within a PB-HAP group. When Button 6 is clicked, the tool uses the meteorological 

parameters and lake distances to find the correct adjustment factor bin for each of the Tier 2 

characteristics, as discussed in Section 3. See example screen shot in Exhibit 14. The 

completed analysis is shown in the “6) Octant Analysis” sheet. The analysis uses the distance 

to the closest lake in each octant, the estimated median wind speed in each octant, the average 

percentage of the time winds blow into each octant, and the estimated average annual 

precipitation and annual average daily mixing height for the facility, and the analysis matches 

these meteorological parameters to the closest TRIM.FaTE run with similar parameters. The 

TRIM.FaTE runs supply individual adjustment factors for each of the meteorological parameters, 

as shown in the “1) Tier2Matrix” sheet (see screen shot example in Exhibit 15). The matching 

to TRIM.FaTE results is done in a health-protective manner, taking note of the direction of the 

correlation of the variable with risk. For example, larger mixing heights are associated with 

lower risks. Thus, mixing heights are assigned to bins by selecting the closest TRIM.FaTE 

value that is higher than the estimated facility mixing height. 

The adjustment factors indicate the ratio of the Tier 2 threshold to the Tier 1 threshold. Thus, 

larger values indicate a risk reduction in Tier 2 relative to Tier 1. The ratio between the REFadjusted emissions and the Tier 1 thresholds are divided by the overall Tier 2 threshold 

adjustment factor for each chemical in each octant of a facility. Within a PB-HAP group, the 

chemical-specific adjustment factors at a facility are summed for each individual octant, 

resulting in octant-specific PB-HAP adjustment factors for the facility. To maintain a healthprotective focus in the analysis, the octant analysis then identifies the octant with the smallest 
Attachment B – 6/29/12 24 

Exhibit 14. Example Results of the Octant Analysis Required 

To Conduct the Tier 2 Analysis 

Exhibit 15. Example of the TRIM.FaTE Matrix Results Required 

To Conduct the Tier 2 Analysis 

adjustment factor for each PB-HAP at each facility. A smaller adjustment factor leads to a 

smaller Tier 2 threshold and, therefore, a larger ratio of facility REF-adjusted emissions to the 

Tier 2 threshold (discussed in Section 5.5). 
Attachment B – 6/29/12 25 

5.5 Finalizing Threshold Adjustment Factors and Assembling Screening 

Results (Step 7) 

Clicking Button 7 collects the final Tiers 1 and 2 screening results for each PB-HAP at each 

facility, using the data from the larger octant analysis table (discussed in Section 5.4). These 

collected data are displayed in the “7) Results” sheet. Any ratio above 1 can be shaded red (as 

shown in the example screen shot in Exhibit 16) to easily identify facilities that exceed the Tier 1 

threshold or the Tier 2 threshold. If the ratio is less than 1, the value displayed is “Below Tier 1 

Threshold” or “Below Tier 2 Threshold”, indicating that the REF-adjusted emissions are lower 

than the threshold and the facility is deemed not to be of concern. In the example shown in 

Exhibit 16, many facilities exceeded the Tier 1 threshold and many of those facilities also 

exceed the Tier 2 threshold. 

Exhibit 16. Example Summarized Results of the Octant Analysis 

Conducted in the Tier 2 Tool. 

5.6 Assembling Results (Step 8) 

By clicking Button 8, the Excel tool creates three separate results tables, shown in the sheets 

“8) SummaryOutput,” “8) DetailedOutput,” and “8) IndividualOutput.” 

The summary table shows the Tiers 1 and 2 human health multipathway screening results at the 

level of source category and PBHAP Group (see example screen shot in Exhibit 17). Cells with 

red highlighting call attention to cases where facilities exceeded the Tier 1 or Tier 2 thresholds. 

Using Source Category A as an example – there were 52 facilities in the category, 51 of which 

emitted cadmium, 46 emitted dioxins, 51 emitted mercury, and 20 emitted PAHs. No facilities in 

this category emitted cadmium or mercury at levels exceeding the Tier 1 threshold, so the 

summary table shows “Below Tier 1 Threshold” as the result for these two PBHAP groups. 

Because they did not exceed the Tier 1 threshold, they by definition did not exceed the Tier 2 

threshold. Five of the facilities in the category exceeded the Tier 1 threshold for PAHs, with the 

maximum-emitting facility exceeding the threshold by a factor of 3.8. The Tier 2 methodology 

brought 4 of these 5 facilities below the Tier 2 threshold, leaving only 1 exceeding the Tier 2 
Attachment B – 6/29/12 26 

Exhibit 17. Example of Summary Output Table Created by the Tier 2 Tool 

threshold (by a factor of 1.1). All 46 dioxin-emitting facilities in the category exceed the Tier 1 

dioxin threshold. Three of the 46 facilities did not exceed the Tier 2 threshold, leaving 43 

exceeding the Tier 2 threshold but by much smaller amounts compared to Tier 1 (the maximumemitting facility exceeds the Tier 2 threshold by a factor of 958.1, compared to a factor of 

5,570.9 for Tier 1). 

The detailed summary table shows much of the same information as the summary table, but the 

screening results of each facility are shown (see example screen shot in Exhibit 18). This 

detailed table is helpful because it shows which facilities exceeded the thresholds and by how 

much. As discussed in the previous paragraph, the summary table shows that 46 and 43 

facilities respectively exceeded the Tiers 1 and 2 thresholds for dioxins, with the maximumemitting facility exceeding Tiers 1 and 2 thresholds by respective factors of 5,570.9 and 958.1. 

This detailed summary table shows that Facility 001 is that facility emitting the most dioxins after 

adjusting the emissions for toxicity and exposure. The screen shot in Exhibit 18 shows 

exceedance factors for dioxins and PAHs for a subset of 8 other facilities in Source Category A 

(other facilities and source categories are in the table but not shown in the screen shot). The 

individual summary table is similar to the detailed summary table, but it adds the details of the 

screening results for each chemical (not just PB-HAP group) and the Tier 2 adjustment factors 

(see example screen shot in Exhibit 19). The Tier 2 adjustment factors are specific to each 

chemical and each facility. The ratios to screening level by chemical are summed at the facility 

level to produce the ratios to screening level by PB-HAP group. 
Attachment B – 6/29/12 27 

Exhibit 18. Example of Detailed Output Table Created by the Tier 2 Tool 

Exhibit 19. Example of Individual Output Table Created by the Tier 2 Tool 
Attachment B – 6/29/12 28 

6 References 

National Climatic Data Center (NCDC). (2012) Quality Controlled Local Climatological Data. 

Available online at http://cdo.ncdc.noaa.gov/qclcd/QCLCD?prior=N

EPA. (2001a) NATA – Evaluating the National-scale Air Toxics Assessment, 1996 DATA – An 

SAB Advisory. U.S EPA Science Advisory Board. EPA-SAB-EC-ADV-02-001. 12/2001. 

EPA (U.S. Environmental Protection Agency). (2001b) National-scale Air Toxics Assessment for 

1996, Draft for EPA Science Advisory Board Review. EPA Office of Air Quality Planning 

and Standards. EPA-453/R-01-003. 01/18/2001. 

EPA. (2009) Risk and Technology Review (RTR) Risk Assessment Methodologies: For Review 

by the EPA’s Science Advisory Board with Case Studies – MACT I Petroleum Refining 

Sources and Portland Cement Manufacturing. EPA Office of Air Quality Planning and 

Standards. EPA-452/R-09-006. 06/2009. 

EPA. (2010a) Review of EPA’s draft entitled, “Risk and Technology Review (RTR) Risk 

Assessment Methodologies: For Review by the EPA’s Science Advisory Board with 

Case Studies – MACT I Petroleum Refining Sources and Portland Cement 

Manufacturing.” EPA Science Advisory Board. EPA-SAB-10-007. 05/07/2010. 

EPA. (2010b) Support Center for Regulatory Atmospheric Modeling (SCRAM) Mixing Height 

Data. Available online at http://www.epa.gov/ttn/scram/mixingheightdata.htm. Accessed 

01/15/2012. Last updated 05/13/2010. 

USGS (U.S. Geological Survey). (2012) National Hydrography Dataset. Available online at 

http://nhd.usgs.gov/. 
Attachment B – 6/29/12 29 

Supplement A. Summary of TRIM.FaTE Parameters Considered 

for Inclusion in Tier 2 Analysis 

Parameter Mechanism of Potential 

Influence in TRIM 

Assessment of Parameter 

Significance and Ease in 

Implementation 

Uncertainty in 

Site-Specific Data 

for Facilities 

Priority 

for 

Inclusion

Meteorological Parameters 

Wind direction 

(% of time wind 

blows toward the 

lake and farm) 

In previous runs, direct deposition 

accounted for the bulk of chemical 

input onto farms and into lakes. 

Because wind direction is strongly 

correlated to direct deposition in a 

given location, media 

concentrations are potentially 

highly sensitive to this parameter. 

Also, because the percentage of 

time the prevailing wind blows in 

the direction of lakes and farms 

can vary considerably across 

locations, differences in this 

parameter might also result in 

significant changes in important 

environmental concentrations. 

Highly Significant: Previous 

sensitivity analyses have 

confirmed this to be a very 

sensitive parameter in the Tier 1 

Screening modeling set-up. 

Changing the fraction of time the 

wind blows toward the lake and 

farm by a factor of two 

corresponds to a change in the 

risk by a factor of two. 

Low Effort to Implement: This 

variable is relatively 

straightforward to vary in the 

Tier 1 screening scenario. 

Low to Moderate:

The average 

fraction of time the 

wind blows in a 

given direction can 

be estimated for 

any surface 

meteorological 

station. Then, 

facilities can be 

linked to the closest 

surface 

meteorological 

station. 

High

Wind speed Wind speed can affect the location 

of the “peak” concentration and 

deposition patterns in a given 

model configuration, as well as the 

risk-distance profile. 

Highly Significant: Previous 

sensitivity analyses have 

confirmed this to be a very 

sensitive parameter. However, 

wind speed does not vary widely 

across scenarios, which could 

reduce its potential influence. 

Low Effort to Implement: This 

variable is relatively 

straightforward to vary in the 

Tier 1 screening scenario. 

Low to Moderate: 

The annuallyaveraged wind 

speed can be 

estimated for any 

surface 

meteorological 

station. Then, 

facilities can be 

linked to the closest 

surface 

meteorological 

station. 

High

Precipitation Chemicals for which wet vapor or 

wet particle deposition processes 

are important are likely to be 

sensitive to the assumed level of 

precipitation. 

Highly Significant: Previous 

sensitivity analyses have 

indicated a relatively high 

sensitivity of risk to precipitation 

for most PB-HAPs (PAHs, 

cadmium, and mercury) 

Moderate Effort to Implement:

In implementing changes in 

precipitation in TRIM, care must 

be taken to also preserve the 

overall water balance in the 

model. 

Low to Moderate: 

The annuallyaveraged 

precipitation rate 

can be estimated 

for the subset of 

surface 

meteorological 

stations that 

capture rainfall 

data. Then, 

facilities can be 

linked to the closest 

surface 

meteorological 

station with 

available data. 

High
Attachment B – 6/29/12 30 

Parameter Mechanism of Potential 

Influence in TRIM 

Assessment of Parameter 

Significance and Ease in 

Implementation 

Uncertainty in 

Site-Specific Data 

for Facilities 

Priority 

for 

Inclusion

Mixing height Greater mixing heights increase 

the dispersion of pollutants in the 

atmosphere and consequently 

reduce deposition to the ground in 

the areas around the stack. This is 

likely to be a highly sensitive 

parameter if there is a sizeable 

variation in mixing heights 

between facilities. 

Highly Significant: Previous 

sensitivity analyses have shown 

risk to be very sensitive to 

mixing height. 

Low Effort to Implement: This 

variable is relatively 

straightforward to vary in the 

Tier 1 screening scenario. 

Moderate to High:

Mixing height 

estimates are 

available for upper 

air meteorological 

stations, and this 

set of stations is 

more limited than 

the set of surface 

meteorological 

stations. Each 

surface station can 

be linked to the 

closest upper air 

station to estimate 

the average mixing 

height. Then, 

facilities can be 

linked to the closest 

surface 

meteorological 

station. The relative 

uncertainty in 

mixing height for a 

given facility is 

high, given diurnal 

variations in mixing 

height and the 

smaller number of 

upper air stations. 

High

Configurational Parameters

Distance of lake 

from stack 

Deposition is known to decrease 

with distance from stack, although 

this relationship also depends on 

meteorological parameters such 

as wind speed and wind direction. 

Significance Difficult to 

Determine: Limited results from 

previous TRIM model runs show 

an inconclusive relationship 

between risk and distance from 

stack, possibly as a result of 

limited statistical power. Some 

studies in the literature show a 

definite decreasing risk gradient 

with distance but others report 

too many confounding factors to 

isolate the precise relationship. 

Moderate Effort to Implement: 

This variable requires updates 

to the layout coordinates and 

requires more effort to vary in 

the Tier 1 screening scenario 

than the meteorological 

parameters. 

Low: The lakes 

within a given 

radius of each 

facility can be found 

using ArcGIS (see 

section 4). 

High
Attachment B – 6/29/12 31 

Parameter Mechanism of Potential 

Influence in TRIM 

Assessment of Parameter 

Significance and Ease in 

Implementation 

Uncertainty in 

Site-Specific Data 

for Facilities 

Priority 

for 

Inclusion

Distance of farm 

from stack 

Deposition is known to decrease 

with distance from stack, although 

this relationship also depends on 

meteorological parameters such 

as wind speed and wind direction. 

Significance Difficult to 

Determine: Limited results from 

previous TRIM model runs show 

an inconclusive relationship 

between risk and distance from 

stack, possibly as a result of 

limited statistical power. Some 

studies in the literature show a 

definite decreasing risk gradient 

with distance but others report 

too many confounding factors to 

isolate the precise relationship. 

Moderate Effort to Implement: 

This variable requires updates 

to the layout coordinates and 

requires more effort to vary in 

the Tier 1 screening scenario 

than the meteorological 

parameters. 

High: Although the 

distance to the farm 

will likely affect risk, 

it is difficult to 

determine the 

precise land 

parcels near each 

facility that are 

actually used for 

farming now or in 

the future. 

Medium

Watershed: 

lake area ratio 

A higher watershed:lake area ratio 

potentially increases the chemical 

input of water-soluble or particleattached chemicals into the lake. 

But the associated higher flush 

rate will likely reduce this effect. 

Significance Difficult to 

Determine: Changes in the 

watershed to lake ratio affect 

risk, but the interaction depends 

on other variables involved in 

the water balance. 

Moderate Effort to Implement: 

In implementing changes in the 

watershed:lake ratios in TRIM, 

care must be taken to also 

preserve the overall water 

balance in the model. 

High: The portion 

of land serving as a 

watershed to a 

particular lake is 

difficult to 

determine. 

Medium

Area and depth 

of lake 

A higher lake area would capture 

more deposition but this effect 

might be counterbalanced by the 

ensuing larger volume of water, 

which reduces chemical 

concentration. Similarly, a deeper 

lake would also reduce 

concentrations, but this effect 

might be counterbalanced by the 

ensuing lower flush rates at a 

constant level of 

precipitation/runoff. 

Significance Difficult to 

Determine: The impact of these 

parameters is inconclusive 

based on current studies using 

the TRIM model. 

Moderate Effort to Implement: 

The lake area variable requires 

updates to the layout 

coordinates and requires more 

effort to vary in the Tier 1 

screening scenario than the 

meteorological parameters. In 

implementing changes in these 

variables in TRIM, care must be 

taken to also preserve the 

overall water balance in the 

model. 

High: While the 

area of lakes near a 

facility can be 

determined using 

GIS, the depth 

cannot. 

Medium
Attachment B – 6/29/12 32 

Parameter Mechanism of Potential 

Influence in TRIM 

Assessment of Parameter 

Significance and Ease in 

Implementation 

Uncertainty in 

Site-Specific Data 

for Facilities 

Priority 

for 

Inclusion

Physical Parameters 

Flush rate A higher flush rate out of the lake 

would result in a higher rate of 

chemical output from the lake, 

assuming constant inflow and 

volume. 

Significance Difficult to 

Determine: The impact of this 

parameter is inconclusive based 

on current studies using the 

TRIM model. 

Moderate Effort to Implement: 

In implementing changes in the 

flush rate in TRIM, care must be 

taken to also preserve the 

overall water balance in the 

model. 

High: The flush 

rate of a lake 

cannot be 

determined easily 

for any lake found 

near a facility. In 

addition, erosion 

rates, watershed 

information, and 

lake depth needed 

to estimate the 

flushing rate are not 

readily available. 

Medium

Runoff rate and 

fraction 

A higher runoff rate (or fraction) 

would likely result in greater 

chemical input into the lake for 

some chemicals but also 

potentially a higher flush rate out 

of the lake. 

Significance Difficult to 

Determine: The impact of this 

parameter is inconclusive based 

on current studies using the 

TRIM model. 

Moderate Effort to Implement: 

In implementing changes in the 

runoff rate and fraction in TRIM, 

care must be taken to also 

preserve the overall water 

balance in the model. 

High: As with the 

flush rate, the 

runoff rate and 

fraction for any lake 

near a facility 

cannot be readily 

determined. 

Medium

Erosion rate and 

fraction 

A higher erosion rate would likely 

result in greater chemical input 

into the lake for particle-bound 

chemicals. It would also result in 

greater chemical transport onto 

farmlands, but this might be 

counterbalanced by equally 

greater erosion off farmland. 

Highly Significant: Previous 

analyses have shown risk to be 

sensitive to this parameter for 

some chemicals. 

Moderate Effort to Implement: 

In implementing changes in the 

erosion rate and fraction in 

TRIM, care must be taken to 

also preserve the overall water 

balance in the model. 

High: As with the 

flush rate, the 

erosion rate and 

fraction for any lake 

near a facility 

cannot be readily 

determined. 

Medium

Chemical Parameters 

Methylation/ 

demethylation 

rates (Hg)

For Hg, methylation and 

demethylation rates in lake 

sediment and surface water are 

potentially sensitive parameters 

affecting risk. A literature survey 

has indicated a relatively high 

range for rate constants describing 

these processes. 

Highly Significant: Previous 

analyses run in TRIM have 

confirmed the high sensitivity of 

these parameters for Hg. 

Low Effort to Implement: This 

variable is relatively 

straightforward to vary in the 

Tier 1 screening scenario 

High: The specific 

methylation / 

demethylation rates 

for mercury in the 

vicinity of a specific 

facility cannot be 

readily determined. 

Low
Attachment B – 6/29/12 33 

Parameter Mechanism of Potential 

Influence in TRIM 

Assessment of Parameter 

Significance and Ease in 

Implementation 

Uncertainty in 

Site-Specific Data 

for Facilities 

Priority 

for 

Inclusion

Total 

phosphorus 

levels in the lake 

The total phosphorus content of a 

lake is used as part of the 

TRIM.FaTE parameterization 

process to estimate the biomass 

content of different trophic levels. 

These biomass levels affect the 

biomagnification of chemicals up 

the food chain and potentially risk 

to human consumers of fish. 

Not Significant: Previous 

analyses have shown limited 

sensitivity to total phosphorus 

levels. This is likely because the 

empirical equations predicting 

biomass in each trophic level 

depend in similar ways on the 

level of total phosphorus. So 

changes in total phosphorus do 

not significantly affect the ratio 

of biomass between the different 

trophic levels. 

Low Effort to Implement: This 

variable is relatively 

straightforward to vary in the 

Tier 1 screening scenario. 

High: The total 

phosphorus levels 

in lakes near a 

specific facility 

cannot be readily 

determined. 

Low
Attachment B – 6/29/12 34 
Attachment B – 6/29/12 35 

Supplement B. Analysis of Lake Size and Sustainable Fish Population 

Identifying the smallest size of a lake that might maintain self-sustaining populations of fish from 

trophic levels (TL) 3 and 4 and is sufficient to support at least one angler at a specified fish 

ingestion rate requires consideration of many factors. Some factors depend on assumptions 

about the behavior of anglers who consume fish from a lake (Section B.1). Other factors 

depend upon the general biology of fish populations in North American ecoregions (see Section 

B.2). Based on evaluation of these factors, a set of assumptions was developed to support the 

estimation of minimum lake sizes that are needed to sustain a particular total human ingestion 

rate in grams/day (see Section B.3). Then, equations were developed (see Section B.4) that 

were used to create Exhibit 2, which was used to determine the threshold lake size of 25 acres. 

B.1. Angler Behavior 

Several assumptions regarding angler behavior are important for estimating a minimum lake 

size that is fishable. The first assumption is that anglers (and their family members) consume 

about 50:50 TL3 and TL4 fish by biomass. TL3 fish are benthic carnivores (BC) such as catfish 

and chub that consume benthic invertebrates and small benthic fish. TL4 fish are pelagic 

piscivores—water column carnivores (WCC) such as largemouth bass and walleye. This 

assumption of 50:50 TL3 and TL4 consumption appears reasonable without being overly 

conservative. Although anglers might prefer to catch and consume the pelagic (TL4) game fish 

species, their generally lower abundance compared with lower trophic-level fish dictates that 

anglers will more often capture the benthic (TL3) carnivores. Some T2 (herbivorous) fish such 

as carp attain “catchable” size, but they generally are not popular fish for consumption and are 

not considered here. 

A second assumption is that anglers and their family members consume only the fillet portion of 

a fish. According to Ebert et al. (1993), the edible fraction of fish as a proportion of total fresh 

body weight is 0.4 for salmon, 0.78 for smelt, and 0.3 for all other species. EPA recommends 

use of 0.30 for the consumable fraction of fish (USEPA, 1989). For this analysis, a 0.33 edible 

fraction for T4 fish was assumed. This factor is used in the lake size analysis to estimate total 

fish biomass associated with human consumption. 

A third assumption relates to ingestion rates of the angler or angler family. Fish ingestion rates 

used for the purpose of the Tier 2 analysis are the same as those in Tier 1 and are consistent 

with subsistence angler ingestion rates. 

Another important consideration related to angler behavior is how much fish removal in a lake is 

possible without resulting in local fish populations declining to extirpation. The productivity of 

any particular fishery (local population of a species of fish) and the proportion of adult fish that 

can be harvested for human consumption are difficult values to estimate, and various models to 

predict sustainable harvests of different fisheries are numerous and complex. Species-specific 

parameters of importance include fecundity with age and size; survivorship of eggs, fry, and 

juveniles to sexual maturity (recruitment); natural predation pressures; and temporal variation in 

food availability. For the purpose of this analysis, simplifying assumptions are required. In the 

analysis by Håkanson and Boulion (2004), which included a survey of 122 lakes, the authors 

noted that a typical loss from fishing by birds, mammals, and humans would be approximately 

10 percent of the fish biomass in the prey fish compartment (T3) and 10 percent of the biomass 

in the predator fish compartment (T4). The authors also found that as overall lake productivity 

increased, the biomass of prey (T3) fish increased more rapidly than the biomass of predator 
Attachment B – 6/29/12 36 

(T4) fish. For this lake size analysis, angler harvesting of fish is assumed to be 10 percent of 

the biomass of pelagic T4 fish. 

B.2. Fish Biology 

Estimation of the minimum surface area of a pond or lake that is fishable also must be based on 

assumptions concerning fish biology, and assumptions have been drawn from several published 

studies across the United States. The productivity and trophic structure of fish communities in 

ponds and lakes across the United States are varied. Thus, any set of assumptions is unlikely 

to all hold true at any given location. Nonetheless, three factors are important to any estimate of 

a minimum lake surface area for Tier 2: the general productivity of a lake (expressed as grams 

of fish wet weight per meter squared, g ww/m2

); the maximum likely proportion of the total fish 

biomass in a lake that is comprised of the top trophic level fish (i.e., T3 and T4 fish); and the 

minimum viable population (MVP) size required for the fish species to be self-sustaining in the 

short term (for at least a few decades). 

B.2.1. Lake Productivity 

The general productivity of a lake depends on many factors, including latitude, seasonal 

temperatures, and nutrients supporting the base of the food web. For lakes at approximately 

the same latitude in the same climate, nutrients play a key role in the total fish biomass that a 

lake might support. In a regression analysis of data on total phosphorus (TP) and fish biomass 

for 31 lakes across North America, Europe, and Russia, Nürnberg (1996) summarized the 

“limits” among three TP-defined lake trophic status categories with respect to total fish wet 

weight biomass per unit area for three categories of lakes: 

Oligo-meso (TP = 10 μg/L) = 1.9 g ww/m2

Meso-eutro (TP = 30 μg/L) = 3.7 g ww/m2

Eutro-hypereutro (TP = 100 μg/L) = 8.5 g ww/m2

Nürnberg (1996) also summarized total fish biomass limits from Bachmann et al. (1996) for the 

lake trophic status categories based on a sample of 60 lakes in Florida: 

Oligo-meso (TP = 10 μg/L) = 7.4 g ww/m2

Meso-eutro (TP = 30 μg/L) = 10.6 g ww/m2

Eutro-hypereutro (TP = 100 μg/L) = 15.6 g ww/m2

As might be expected, for the same TP concentrations, standing fish biomass per unit area in 

the Florida lakes is two to three times higher than standing fish biomass for more northerly lakes 

with shorter growing seasons. 

Hanson and Legget (1982) estimated the relationship between TP and standing stock of fish 

using a regression model based on samples from 21 lakes ranging in surface area from 

0.1–25,000 hectares (~0.25–62,000 acres) and located between 0° and 56° N latitude and 

121° E to 122° W longitude. Their linear regression relating TP to total fish standing biomass 

(B) used the following equation and had a correlation coefficient (r2

) of 0.84: 

B = 0.792 + 0.072 (TP) 
Attachment B – 6/29/12 37 

where: 

B = total fish biomass (kg/hectare) 

TP = total phosphorous (μg/L) 

The regression model of Hanson and Legget (1982) predicted total fish biomass densities in 

lakes of 3.0–9.5 g ww/m2

 for TP concentrations ranging from 10–50 μg/L for oligo-mesotrophic 

to mid-range eutrophic lakes. Another regression model from Hoyer and Canfield (1991) 

predicted fish biomass densities in streams of 2.6–6.6 g ww/m2

 over the same range of TP 

concentrations. 

In general, for very small lakes, relatively low fish productivity is likely. For example, Demers et 

al. (2001) found fish standing biomass values of 2.73 and 3.81 g ww/m2

 in two lakes of 27 and 

22 acres (11 and 9 hectares), respectively, in south-central Ontario. Brönmark and Weisner 

(1996) reported on aquatic communities from a sample of 44 small ponds in southern Sweden 

(most were less than 5 hectares ≈ 12 acres). They found no fish in 5 of the smaller ponds 

(mean surface area of 0.20 ± 0.097 acres)—which also exhibited lower TP concentrations than 

the larger ponds—and no piscivorous fish in another 11 of the 44 ponds (mean surface area of 

0.46 ± 0.27 acres). For the 28 ponds with piscivorous (TL4) fish present, the mean pond 

surface area was 1.4 (±1.3 SD) acres. 

Ideally, one would have data indicating TP levels in lakes in the vicinity of facilities for a Tier 2 

analysis. Such data, however, are rarely readily available. The general ranges of fish 

productivity across lakes of various surface areas, therefore, are considered to be independent 

of lake size over a wide range of lake sizes. Thus, lake size alone cannot be used to estimate 

fish productivity or maximal fish harvesting rates. 

Scientists have also examined the relationship between TP and total fish biomass in reservoirs. 

Yurk and Ney (1989) examined the relationship between TP and standing stock of fish in 22 

reservoirs in southern Appalachia sampled in 1973. Their logarithmic regression that related TP 

to total fish standing biomass (B) used the following equation and had an r2

 of 0.75: 

Log10 (B) = 1.07 + 1.14 × Log10 (TP) 

Use of the equations from Hanson and Legget (1982) and Yurk and Ney (1989) yielded similar 

predications of total fish biomass at intermediate TP concentrations. At low TP (e.g., 10 μg/L), 

predictions of total fish biomass were 3.0 g ww/m2 (Yurk and Ney, 1989) and 1.6 g ww/m2 

(Hanson and Legget, 1982); at high TP (e.g., 100 μg/L), fish biomass predicted by the two 

models were 15.5 and 22.4 g ww/m2

, respectively.

Leidy and Jenkins (1977) reported analyses of several large data sets to support modeling of 

fish productivity and carrying capacity in reservoirs across the United States for the National 

Reservoir Research Program. The analyses included studies of fish standing biomass by 

species in 61 reservoirs across the midwestern and eastern United States sampled at different 

times between 1952 and 1975. Only reservoirs of at least 500 acres (202 hectares) in size were 

included, with some exceeding 65,000 acres (in the Missouri drainage basin). Considering all 

61 reservoirs, the mean biomass density of fish was 41.3 (± 30.4 SD) g ww/m2

. The minimum 

and maximum total fish biomass densities were 3.2 and 133.2 g ww/m2

, respectively, and the 

median value was 30.9 g ww/m2

. Reservoirs typically have large drainage basins, which in 

some areas can receive excess TP from large expanses of agricultural areas. 
Attachment B – 6/29/12 38 

In summary, the fish productivity in lakes and reservoirs can vary by more than three orders of 

magnitude. The reservoirs surveyed by Leidy and Jenkins (1977) in general were much larger 

(and were often more shallow and nutrient rich) than the natural lakes surveyed by others 

discussed above. The mean standing fish biomass of approximately 41.3 g ww/m2 from the 

reservoir survey is likely to be higher than a mean value for any representative sample of 

natural lakes in the United States. For the purpose of the Tier 2 analysis, 40 g ww/m2

 was 

identified as the upper limit for total fish biomass in a lake. 

B.2.2. Proportion of Fish Biomass by Trophic Level 

As indicated in Section B.1, for the Tier 2 analysis, the proportion of fish in an angler’s diet that 

consists of TL3 and TL4 fish is assumed to be 50:50 by biomass (not numbers) for lakes that 

support the four trophic levels. In smaller lakes, TL4 fish are likely to be missing or rare, with 

TL3 fish (e.g., sunfish) being the highest trophic level supported by the primary productivity 

(algal/plant production) in some lakes. As a “rule of thumb” in ecology, 10 percent or less of the 

energy produced at one trophic level usually can be converted to biomass in the next trophic 

level (i.e., approximately 90 percent loss of energy) per trophic step. However, with different 

species having different energy assimilation efficiencies and with smaller species generally 

having higher turnover rates than larger species, the 10 percent energy rule does not 

necessarily translate into a standing biomass pyramid of similar proportions. In this section, the 

proportion of fish (based on biomass) that might be expected in TL3 and TL4 relative to total 

standing fish biomass are examined assuming TL4 fish are present. 

Examination of several studies of fish biomass by trophic habits indicated that top trophic level 

fish might comprise approximately 20 percent of the standing fish biomass in many locations. 

Ploskey and Jenkins (1982) estimated that piscivorous fish, both those that are generally freeswimming or pelagic (e.g., pike, gar, walleye) and those that rest and forage primarily in the 

benthos (e.g., various species of catfish, suckers) comprise 22 percent of the total fish biomass 

in DeGray Lake, Arkansas (averaged across several years). Leidy and Jenkins (1977) 

estimated that 18 percent of the fish biomass across the 61 reservoirs they examined was 

piscivorous (minimum of 14 percent and maximum 24 percent). Demers et al. (2001) 

categorized 2 percent and 15 percent of the total fish biomass in two small lakes of 27 and 22 

acres in size, respectively, as piscivorous/benthivorous fish (e.g., largemouth bass, creek chub); 

primary benthivores (e.g., catfish, suckers) dominated at >70 percent in both lakes. 

One of the more recent food web models for freshwater lakes is that of Håkanson and Boulion 

(2004). Their model was designed to predict the productivity and standing crop of prey and 

predatory fishes in lakes of northern Europe. The authors acknowledged that fish feeding 

patterns are complicated by the fact that fish change their feeding preferences as they 

age—especially during early development, but also as adults, when they may switch between 

zooplankton and zoobenthos and from small fish to larger fish as they grow. A “distribution 

coefficient” was used to indicate what proportion of the total fish biomass in a lake is prey 

versus predatory fish. Based on data from 122 lakes in Europe and North America, Håkanson 

and Boulion (2004) concluded that 27 percent by biomass is a “normal” portion of predatory fish 

in a balanced system. They noted further, however, that for eutrophic lakes with TP levels 

>100 μg/L, the proportion of fish represented by piscivores declines to less than 20 percent. 

Based on the information above, the top trophic level (T3 and T4) fish are assumed to comprise 

21 percent of the total fish biomass. With the bulk of productivity in lakes originating from 

detritus in the benthos, the total biomass of strictly pelagic game fish is expected to be less than 

that of benthic fish. Therefore, for purpose of this lake size analysis, the 
Attachment B – 6/29/12 39 

piscivorous/benthivorous fish were separated into two compartments, with 17.5 percent of the 

total fish biomass in a benthic carnivore (T3) compartment and 3.5 percent of the total in a 

pelagic piscivore (T4) compartment. Thus, the T4 fish, when present, represent the limiting 

compartment for angler fish harvesting and consumption. 

B.2.3. Minimum Viable Population Size 

The final step in estimating the minimum lake size that can support sustainable fishing of its T4 

fish species is to invoke the concept of minimum viable population (MVP) size. MVP is a 

concept used frequently in conservation biology for animals and is defined as the smallest 

population that will persist for a specified duration (e.g., 100 years) with a given probability (e.g., 

95 percent). MVP for any given species and general location depends on many attributes of the 

species biology (e.g., body size, reproductive rate, home range size, corridors between 

populations, variability in environmental characteristics that impact fecundity and survival). At 

lower numbers, the likelihood of population extinction increases due to environmental and 

demographic stochasticity (Menzie et al., 2008). As for fisheries biology, entire text books have 

been dedicated to applied population ecology with population simulations incorporating 

demographic and life-history characteristics, spatial separation of habitat patches and 

metapopulations, the probability of local catastrophes, genetic variation (e.g., drift), and other 

factors with predictions of time-to-extinction or probability of extinction within specified time 

periods (e.g., Soulé, 1987; Akçakaya et al., 1999). Consideration of such models in populationlevel ecological risk assessment has begun, but faces many challenges (Barnthouse et al., 

2008). 

Much of the initial work on MVP investigated the genetic minima required for short-term survival, 

continuing adaptation to environmental change, and ultimately evolution. Inbreeding has been 

considered the primary threat to short-term survival and genetic drift as the principal threat to 

losing the genetic variation required for adaptation (Shaffer, 1987). Several analyses (Senner, 

1980; Franklin, 1980; Soulé, 1980; Frankel and Soulé, 1981; Lande and Barrowclough, 1987) 

have led to the conclusion that minimum effective population sizes on the order of 50 are 

required for short-term survival (e.g., several generations, decades), while effective population 

sizes on the order of 500 are necessary to provide adequate genetic variation for continuing 

adaptation over the long term (e.g., tens of generations, centuries for some animals) (Shaffer, 

1981; 1987). Effective population size, Ne, is a measure of the rate of genetic drift (loss of 

genetic diversity or inbreeding), and its definition generally depends on the population in 

question (Rieman and Allendorft, 2001). Ne can be estimated mathematically based on 

stochastic behavior of gene frequencies in a diploid population. Simple models assume a fixed 

population size, constant fecundity, specified sex ratio, and no overlap between generations 

(see studies cited in NRC, 1986). For animals with 50:50 sex ratios, the effective population 

size is essentially the same as the actual breeding adult population size (Ewens et al., 1987). 

One of the most extensive population viability analyses in the United States has been 

conducted on the spotted owl (Boyce, 1993). Given the number and complexity of factors that 

influence MVP, however, including the definitions of time horizon (e.g., 100 years) and 

probability of survival (e.g., 95 percent), population biologists caution against using a “rule” for 

MVP across circumstances (Ewens et al., 1987). 

Note that the MVP is appropriate for a single species of fish, not for generic categories of fish 

such as TL3 or TL4. For this Tier 2 analysis, the MVP of 50 associated with short-term 

population survival was assumed for a T4 fish species isolated in a lake. In reality, short-term 

extirpations from a lake can be countered by purposeful introductions from other lakes or during 

flooding events. Thus, an MVP of 500 was not considered necessary for game fish in lakes. 
Attachment B – 6/29/12 40 

B.3. Summary of Assumptions for the Lake Size Analysis 

The following assumptions were used in processing lake data for the Tier 2 analysis and in 

estimating the relationship between sustainable fish ingestion and harvest rates and lake size 

(see Section 4.1 of the main report). 

1. Piscivorous fish (T3 plus T4), when present, comprise approximately 21 percent of the 

standing biomass of fish (ignoring seasonal changes). The T3 fish represent 17.5 

percent of the standing fish biomass; T4 fish account for 3.5 percent of this total fish 

biomass. Thus, T4 fish, when present, represent the limiting compartment for angler fish 

harvesting and consumption. 

2. Humans can harvest 10 percent of the T4 biomass without threatening the population 

due to overharvesting. 

3. The MVP size for a single T4 species is at least 50 adult fish for a local population to 

survive over the short term (more than a decade). 

4. Only 33 percent of a T4 fish is edible fillet muscle. 

B.4. Equations Used to Determine Lake Fish Populations 

The standing biomass of T4 fish supported in Lake X (T4 SB) can be calculated as the total 

standing biomass of fish (Total SB) multiplied by 0.035, based on the assumption that T4 fish 

represent approximately 3.5 percent of the standing biomass in Lake X. 

T4 SB = Total SB × Fraction T4 (Equation 1) 

where: 

T4 SB = Standing biomass of T4 fish (g wet weight [ww]/m2

) in Lake X

Total SB = Total standing biomass of fish (g ww/m2

) in Lake X 

Fraction T4 = Fraction of T4 fish in Lake X (0.035) 

Using T4 SB and the size of Lake X (Lake Size), the total number of T4 fish supported in Lake X 

can be calculated using the equation below. 

BWa

CFSBT4SizeLake T4No   .  (Equation 2)

where: 

No. T4 = Total number of T4 fish in Lake X

Lake Size = Size of Lake X (acres) 
Attachment B – 6/29/12 41 

T4 SB = Standing biomass of T4 fish (g ww/m2

; from Equation 1) 

CF = 4047 m2

/acre 

BWa

= Body weight of adult T4 fish (2000 g; assumed based on professional 

judgment) 

The likely annual productivity of T4 fish (kg/year) in Lake X can be estimated using Equation 3. 

CF2

CF1SBT4SizeLake Productivi T4ty    (Equation 3)

where: 

Productivity T4 = Likely annual productivity of T4 fish in Lake X (kg/year)

Lake Size = Size of the Lake X (acres) 

T4 SB = Standing biomass of T4 fish (g ww/m2

; from Equation 1) 

CF1 = 4047 m2

/acre 

CF2 = 1000 g/kg 

The maximum fish ingestion rate (g/day) for T3 plus T4 fish associated with sustainable fishing 

can be predicted using Equation 4. It assumes 50:50 consumption behaviors of T3 and T4 fish, 

represented by the factor of 2 in Equation 4. 

CF2

2 Productivi CF1HFFFT4ty Max T4T3IRSustain     )( 

 (Equation 4)

where: 

Max Sustain IR T3 

+ T4 = Predicted maximum sustainable ingestion rate for T3 plus T4 fish 

(g/day) 

Productivity T4 = Likely annual productivity of T4 fish in Lake X (kg/year; from Equation 3)

FF = Fillet fraction; represents the assumed edible portion of fish (0.33; 

unitless) 

HF = Annual harvest fraction (0.10; unitless) 

CF1 = 1000 g/kg 

CF2 = 365 days/year 
Attachment B – 6/29/12 42 

B.5. References 

Akçakaya, H.R., Burgman, M.A., and Ginzburg, L.R. 1999. Applied Population Ecology: 

Principles and Computer Exercises using RAMAS EcoLab. Sunderland, MA: Sinauer 

Associates, Inc. Publishers. 

Barnthouse, L.W., Munns, W.R. Jr., and Sorensen, M.T. 2008. Population-Level Ecological Risk 

Assessment. Boca Raton, FL: CRC Taylor & Francis Group; Pnesacola, FL: Society of 

Environmental Toxicology and Chemistry (SETAC). 

Boyce, M.S. 1993. Population viability analysis: adaptive management for threatened and 

endangered species. In: Wildlife Management Institute, Transactions of the 58th North 

American Wildlife and Natural Resources Conferences. Proceedings WWRC-93-19, pp. 

520-527. 

Brönmark, C. and S.E.B. Weisner. 1996. Decoupling of cascading trophic interactions in a 

freshwater benthic food chain. Oecologia 108: 534-541. 

Demers, E., McQueen, D.J., Ramcharan, C.W., and A. Perez-Fuentetaja. 2001. Did piscivores 

regulate changes in fish community structure? Advanc. Limnol. 56: 49-80. 

Ebert, E., N. Harrington, K. Boyle, J. Knight, and R. Keenan. 1993. Estimating consumption of 

freshwater fish among Maine anglers. N. Am. J. Fisheries Manage. 13: 737-745. 

Ewens, W.J., Brockwell, P.J., Gani, J.M., and Resnick, S.I. 1987. Minimum viable population 

sizes in the presence of catastrophes. In: M.E. Soulé (ed.) Viable Populations for 

Conservation. United Kingdom, Cambridge: Cambridge University Press, pp. 59-68. 

Frankel, O.H., and Soulé, M.E. 1981. Conservation and evolution. United Kingdom, Cambridge: 

Cambridge University Press. 

Franklin, I.R. 1980. Evolutionary change in small populations. In: M.E. Soulé and B.A. Wilcox 

(eds.) Conservation Biology: An Evolutionary-Ecological Perspective. Sunderland, MA: 

Sinauer Associates; pp. 135-150. As cited by Shaffer, 1987. 

Håkanson, L. and V.V. Boulion. 2004. Modeling production and biomasses of prey and 

predatory fish in lakes. Hydrobiologia 511: 125–150. 

Hanson, J.M., and W.C. Leggett. 1982. Empirical prediction of fish biomass and yield. Can. J. 

Fish. Aquat. Sci. 39: 257-263. 

Hoyer, M.V., and D.E. Canfield, Jr. 1991. A phosphorus-fish standing crop relationship for 

streams? Lake Reserv. Manage. 7: 25-32. 

Lande, R., and Barrowclough, G.F. 1987. Effective population size, genetic variation, and their 

use in population management. In: M.E. Soulé (ed.) Viable Populations for 

Conservation. United Kingdom, Cambridge: Cambridge University Press, pp. 87-123. 

Leidy, G.R., and Jenkins, R.M. 1977. The development of fishery compartments and population 

rate coefficients for use in reservoir ecosystem modeling. Contract Report Y-77-1 

prepared by the National Reservoir Research Program, U.S. Fish and Wildlife Service, 
Attachment B – 6/29/12 43 

for the U.S. Army Corps of Engineers, Waterways Experiment Station, Vicksburg, 

Mississippi, USA. 

Menzie, C., Bettinger, N., Fritz, A., Kapustka, L., Regan, H., Moller, V., and Noel, H. 2008. 

Population protection goals. In: Barnthouse, L.W., Munns, W.R. Jr., and Sorensen, M.T. 

2008. Population-Level Ecological Risk Assessment. Boca Raton, FL: CRC Taylor & 

Francis Group; Pnesacola, FL: Society of Environmental Toxicology and Chemistry 

(SETAC), pp. 41-68. 

NRC (National Research Council). 1986. Ecological Knowledge and Environmental Problem 

Solving. Committee on the Applications of Ecological Theory to Environmental 

Problems. Washington, DC: National Academies of Science Press. 

Nürnberg, G.K. 1996. Trophic state of clear and colored, soft- and hardwater lakes with special 

consideration of nutrients, anoxia, phytoplankton and fish. Journal of Lake and Reservoir 

Management 12(4): 432-447. 

Ploskey, G.R., and R.M. Jenkins. 1982. Biomass model of reservoir fish and fish-food 

interactions, with implications for management. North American Journal of Fisheries 

Management 2(2): 105-121. 

Rieman, B.E., and Allendorf, F.W. 2001. Effective population size and genetic conservation 

criteria for bull trout. N. A. J. Fisheries Manage. 21:756-764. 

Senner, J.W. 1980. Inbreeding depression and the survival of zoo populations. In: M.E. Soulé 

and B.A. Wilcox (eds.) Conservation Biology: An Evolutionary-Ecological Perspective. 

Sunderland, MA: Sinauer Associates; pp. 209-244. As cited by Shaffer, 1987. 

Schaffer, M.L. 1981. Minimum population sizes for species conservation. Bioscience 31: 131-

134. 

Shaffer, M. 1987. Minimum viable populations: coping with uncertainty. In: M.E. Soulé (ed.) 

Viable Populations for Conservation. United Kingdom, Cambridge: Cambridge University 

Press, pp. 69-86. 

Soulé, M.E. 1980. Thresholds for survival: maintaining fitness and evolutionary potential. In: 

M.E. Soulé and B.A. Wilcox (eds.) Conservation Biology: An Evolutionary-Ecological 

Perspective. Sunderland, MA: Sinauer Associates; pp. 151-170. As cited by Shaffer, 

1987. 

Soulé, M.E. (ed.) 1987. Viable Populations for Conservation. United Kingdom, Cambridge: 

Cambridge University Press. 

USEPA (US Environmental Protection Agency). 1989. Assessing human health risks from 

chemically contaminated fish and shellfish: a guidance manual. EPA-503/8-89-002. 

Washington, DC. 

Yurk, J.J., and J.J. Ney. 1989. Phosphorus-fish community biomass relationships in southern 

Appalachian reservoirs: can lakes be too clean for fish? Lake and Reservoir 

Management 5(2): 83-90.
[This page intentionally left blank.] 
Technical Support Document for the TRIM-Based Multipathway Tiered Screening 

Methodology for RTR

Attachment C. Tier 3 Methodology

[Reserved]
[This page intentionally left blank.]
Appendix 5 

Detailed Risk Modeling Results 
Facility NEI ID Facility Name Address City C

NEI11136

GEORGIA-PACIFIC TAYLORSVILLE OPERATIONS HWY. 28 W. TAYLORSVILLE S

NEI11443 TEMBEC BTLSR INC. 2112 SYLVAN AVE TOLEDO L

NEI12297 3M COTTAGE GROVE CENTER 10746 INNOVATION RD COTTAGE GROVE WNEI12810 LUFKIN RESIN PLANT 1429 E LUFKIN AVE LUFKIN ANEI13335 Dynea Overlays, Inc. 2301 N COLUMBIA BLVD PORTLAND MNEI2OHT$6395 SOLUTIA PORT PLASTICS 356 THREE RIVERS PKY. ADDYSTON HNEI32872 NUPLEX RESINS 4730 CRITTENDEN DR LOUISVILLE JNEI43353 SI GROUP INC /ROTT JCT FAC 1000 MAIN ST|ST RTE 5S ROTTERDAM SNEI45175 GEORGIA PACIFIC RESINS 2665 HIGHWAY 99 NORTH EUGENE LNEI46350 GEORGIA-PACIFIC CONWAY OPERATIONS 200 Ampac Road CONWAY NNEI46838 GEORGIA-PACIFIC VIENNA OPERATIONS 7TH S. ST. VIENNA DNEI56034 3M Cordova 22614 Route 84 N Cordova RNEI56485 Hexion Specialty Chemicals, Inc. 1411 Industrial Drive FAYETTEVILLE CNEI6258 PIONEER PLASTICS CORP 1 PIONITE RD AUBURN A

NEI7964 NEVAMAR CO LLC ONE NEVAMAR PLACE HAMPTON HNEIART$10736 GEORGIA-PACIFIC RESINS, INC HWY 82 & PAPER MILL ROAD CROSSETT ANEIMA01301151 INEOS MELAMINES 730B WORCESTER ST SPRINGFIELD HNEINC0595 Emerald Carolina Chemical, LLC 8309 WILKINSON BV Charlotte M

Table 1 - Facility Identification Information

1 of 1
Cancer 

MIR

Cancer

 Incidence

Noncancer 

Max HI

Target 

Organ

Cancer 

MIR

Noncancer 

Max HI

Target 

Organ

NEI11136 6.73E-07 5.85E-05 6.74E-03 liver 2.96E-05 5.54E-01 neurological 2%

NEI11443 8.68E-06 5.98E-04 6.81E-02 respiratory 8.68E-06 6.81E-02 respiratory 100%

NEI12297 0.00E+00 0.00E+00 2.32E-05 liver 4.27E-07 5.28E-03 respiratory 0%

NEI12810 9.84E-07 4.97E-05 7.72E-03 respiratory 1.18E-06 9.27E-03 respiratory 83%

NEI13335 0.00E+00 0.00E+00 9.41E-03 respiratory 3.04E-08 2.24E-02 respiratory 0%

NEI2OHT$6395 5.52E-07 2.31E-05 4.34E-03 respiratory 5.52E-07 4.34E-03 respiratory 100%

NEI32872 4.64E-07 1.00E-04 3.64E-03 respiratory 5.30E-07 3.70E-03 respiratory 88%

NEI43353 4.80E-07 9.95E-06 6.73E-03 liver 1.29E-05 7.31E-02 respiratory 4%

NEI45175 1.89E-07 1.59E-05 1.49E-03 respiratory 2.65E-07 1.43E-01 respiratory 71%

NEI46350 1.29E-06 1.35E-05 1.01E-02 respiratory 2.15E-06 1.69E-02 respiratory 60%

NEI46838 3.98E-06 1.59E-05 3.24E-02 respiratory 3.98E-06 3.24E-02 respiratory 100%

NEI56034 0.00E+00 0.00E+00 5.71E-04 respiratory 5.08E-07 2.66E-02 respiratory 0%

NEI56485 2.95E-06 1.24E-04 2.50E-02 respiratory 3.44E-06 2.98E-02 respiratory 86%

NEI6258 1.46E-07 9.59E-06 7.04E-04 respiratory 5.90E-06 1.23E-01 respiratory 2%

NEI7964 3.08E-06 2.97E-05 2.42E-02 respiratory 4.04E-06 6.77E-02 respiratory 76%

NEIART$10736 3.01E-06 1.05E-04 2.11E-01 respiratory 3.04E-06 2.14E-01 respiratory 99%

NEIMA0130$$1 2.55E-07 3.94E-05 2.00E-03 respiratory 2.55E-07 2.00E-03 respiratory 100%

NEINC0595 5.34E-09 6.33E-07 4.31E-05 developmental 8.64E-07 3.01E-01 respiratory 1%

1 BOLD indicates a cancer risk great than 1 in a million or a noncancer risk greater than 1

Table 2 – Maximum Predicted HEM-3 Chronic Risks

Actual Emissions

Facility NEI ID

 Category Chronic Risk 1 Facility Chronic Risk 1

SC % of 

Facility-wide 

Cancer Risk

1 of 1
REL AEGL1 AEGL2 ERPG1 ERPG2

NEI11136 Benzene 2.56E-05 1.96E-07 1.28E-08 2.08E-07 6.94E-08

NEI11136 Formaldehyde 1.52E+00 7.62E-02 4.93E-03 6.98E-02 6.98E-03

NEI11136 Methanol 5.04E-01 2.05E-02 5.23E-03 5.43E-02 1.09E-02

NEI11136 n-Hexane 0.00E+00 0.00E+00 2.38E-06 0.00E+00 0.00E+00

NEI11136 Nickel compounds 5.55E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI11136 Phenol 3.78E-01 3.78E-02 2.46E-02 5.76E-02 1.15E-02

NEI11136 Toluene 1.80E-06 8.89E-08 1.48E-08 3.51E-07 6.06E-08

NEI11443 Formaldehyde 2.65E+00 1.33E-01 8.58E-03 1.21E-01 1.21E-02

NEI12297 Methanol 7.98E-02 3.24E-03 8.27E-04 8.59E-03 1.72E-03

NEI12297 Phenol 4.41E-02 4.41E-03 2.87E-03 6.73E-03 1.35E-03

NEI12297 Triethylamine 6.00E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI12810 Formaldehyde 5.89E-01 2.94E-02 1.90E-03 2.70E-02 2.70E-03

NEI12810 Methanol 8.82E-01 3.58E-02 9.14E-03 9.49E-02 1.90E-02

NEI12810 Phenol 5.34E-04 5.34E-05 3.48E-05 8.14E-05 1.63E-05

NEI13335 Methanol 6.30E-02 2.56E-03 6.53E-04 6.78E-03 1.36E-03

NEI13335 Phenol 8.24E-03 8.24E-04 5.37E-04 1.26E-03 2.51E-04

NEI13335 Triethylamine 1.37E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI2OHT$6395 Formaldehyde 3.87E+00 1.94E-01 1.25E-02 1.78E-01 1.78E-02

NEI2OHT$6395 Methanol 5.09E-02 2.07E-03 5.28E-04 5.48E-03 1.10E-03

NEI32872 Formaldehyde 7.98E-01 3.99E-02 2.58E-03 3.66E-02 3.66E-03

NEI32872 Methanol 1.02E-03 4.12E-05 1.05E-05 1.09E-04 2.19E-05

NEI43353 Benzene 1.00E-03 7.68E-06 5.02E-07 8.16E-06 2.72E-06

NEI43353 Ethyl benzene 0.00E+00 3.89E-04 1.14E-05 0.00E+00 0.00E+00

NEI43353 Ethylene glycol ethyl ether acetat 4.86E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI43353 Formaldehyde 2.05E-01 1.03E-02 6.65E-04 9.42E-03 9.42E-04

NEI43353 Methanol 1.47E-04 5.98E-06 1.53E-06 1.59E-05 3.18E-06

NEI43353 Phenol 2.29E-01 2.29E-02 1.49E-02 3.49E-02 6.99E-03

NEI43353 Styrene 1.44E-04 3.56E-05 5.51E-06 1.44E-05 2.75E-06

NEI43353 Toluene 1.42E-04 7.02E-06 1.17E-06 2.77E-05 4.79E-06

NEI43353 Triethylamine 6.60E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI43353 Xylenes (mixed) 1.27E-02 5.00E-04 7.00E-05 0.00E+00 0.00E+00

NEI45175 Formaldehyde 3.15E-01 1.58E-02 1.02E-03 1.44E-02 1.44E-03

NEI45175 Methanol 3.62E-03 1.47E-04 3.75E-05 3.90E-04 7.80E-05

NEI45175 Phenol 6.05E-03 6.05E-04 3.94E-04 9.23E-04 1.85E-04

NEI46350 Benzene 4.44E-06 3.40E-08 2.22E-09 3.61E-08 1.20E-08

NEI46350 Formaldehyde 4.25E+00 2.13E-01 1.38E-02 1.95E-01 1.95E-02

NEI46350 Methanol 1.40E-01 5.69E-03 1.45E-03 1.51E-02 3.02E-03

NEI46350 n-Hexane 0.00E+00 0.00E+00 4.13E-07 0.00E+00 0.00E+00

NEI46350 Phenol 2.10E-01 2.10E-02 1.37E-02 3.20E-02 6.41E-03

NEI46350 Toluene 2.53E-07 1.25E-08 2.08E-09 4.92E-08 8.51E-09

NEI46838 Acetaldehyde 9.29E-03 5.39E-05 8.91E-06 2.43E-04 1.21E-05

NEI46838 Formaldehyde 1.76E+01 8.80E-01 5.69E-02 8.06E-01 8.06E-02

NEI46838 Methanol 8.26E-02 3.35E-03 8.57E-04 8.90E-03 1.78E-03

NEI46838 Phenol 4.97E-02 4.97E-03 3.24E-03 7.59E-03 1.52E-03

NEI56034 Methanol 1.81E-02 7.36E-04 1.88E-04 1.95E-03 3.91E-04

NEI56034 Triethylamine 1.19E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI56485 Formaldehyde 5.42E+01 2.71E+00 1.75E-01 2.48E+00 2.48E-01

NEI56485 Methanol 1.23E-01 5.01E-03 1.28E-03 1.33E-02 2.66E-03

NEI56485 Nickel compounds 2.94E-07 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI56485 Phenol 1.53E-01 1.53E-02 9.96E-03 2.33E-02 4.66E-03

NEI56485 Triethylamine 1.08E-01 0.00E+00 0.00E+00 0.00E+00 0.00E+00

NEI6258 Formaldehyde 1.09E-01 5.47E-03 3.54E-04 5.01E-03 5.01E-04

Facility NEI ID Pollutant

Table 3 – Maximum Predicted Acute Risks (HEM-3)

Actual Emissions

Maximum Hazard Quotient1

 1 of 2
Facility NEI ID Pollutant REL AEGL1 AEGL2 ERPG1 ERPG2

Table 3 – Maximum Predicted Acute Risks (HEM-3)

Actual Emissions

Maximum Hazard Quotient1

NEI6258 Methanol 9.32E-02 3.78E-03 9.66E-04 1.00E-02 2.01E-03

NEI6258 Phenol 1.44E-04 1.44E-05 9.39E-06 2.20E-05 4.40E-06

NEI7964 Formaldehyde 1.93E+01 9.63E-01 6.23E-02 8.83E-01 8.83E-02

NEI7964 Phenol 7.65E-03 7.65E-04 4.99E-04 1.17E-03 2.34E-04

NEIART$10736 Formaldehyde 3.94E+01 1.97E+00 1.27E-01 1.80E+00 1.80E-01

NEIART$10736 Maleic anhydride 0.00E+00 0.00E+00 0.00E+00 4.83E+00 4.83E-01

NEIART$10736 Methanol 9.72E-03 3.94E-04 1.01E-04 1.05E-03 2.09E-04

NEIART$10736 Phenol 2.32E-01 2.32E-02 1.51E-02 3.54E-02 7.08E-03

NEIMA0130$$1 Formaldehyde 2.12E+00 1.06E-01 6.87E-03 9.73E-02 9.73E-03

NEIMA0130$$1 Methanol 9.02E-02 3.66E-03 9.36E-04 9.72E-03 1.94E-03

NEINC0595 Formaldehyde 5.40E-03 2.70E-04 1.75E-05 2.47E-04 2.47E-05

NEINC0595 Methanol 6.31E-02 2.56E-03 6.54E-04 6.79E-03 1.36E-03

 Note: BOLD indicates acute risks greater than 1

1 Some maximum acute impacts may be at onsite locations.

 2 of 2
Appendix 6 Acute Impacts Refined Analysis 
1 

Refined Acute Modeling Approach 

 Initial acute screening risk calculations were performed with the HEM3 model 

using the maximum hourly emissions estimates described in the memorandum, Emissions 

Data and Acute Risk Factor Used in Residual Risk Modeling: Acrylic and Modacrylic 

Fibers, Amino/Phenolic Resins, and Polycarbonate Production, which can be found in 

the rule docket for this source category. The HEM3 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 is established based on the emission point 

locations, with all the points being within the first polar ring. This first ring (and other 

rings) may still transect 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 addresses refinements to determine the maximum off-site values. 

The refinements include the use of a revised emissions multiplier for some emission 

points, and the plotting of the HEM-3 polar grid results on aerial photographs of the 

facilities to determine off-site locations that may be accessible to the 

public (e.g., roadways and public buildings.). The attached figures present the estimated 

hazard quotient values (the modeled 1-hour concentration of a pollutant divided by its 

short-term health benchmark) at the polar receptors near the facilities. Table 1 provides 

the resulting change in acute risks taking into account these off-site locations. 

Table 1. Refined Acute Risks Due to Off-site Receptors 

Facility NEI ID Pollutant Criteria 

Original 

Acute 

Hazard 

Quotient 

Off-site 

Hazard 

Quotient 

05003NEIART$10736 Formaldehyde REL 40 7 

05003NEIART$10736 Formaldehyde AEGL-1 2 <1 

05003NEIART$10736 Formaldehyde ERPG-1 2 <1 

05003NEIART$10736 Maleic anhydride ERPG-1 5 <1 

13093NEI46838 Formaldehyde REL 20 2 

25013NEIMA0130$$1 Formaldehyde REL 2 <1 
2 

28129NEI11136 Formaldehyde REL 2 <1 

37051NEI56485 Formaldehyde REL 50 10 

37051NEI56485 Formaldehyde AEGL-1 3 <1 

37051NEI56485 Formaldehyde ERPG-1 3 <1 

37131NEI46350 Formaldehyde REL 4 <1 

39061NEI2OHT$6395 Formaldehyde REL 4 <1 

39095NEI11443 Formaldehyde REL 3 <1 

45049NEI7964 Formaldehyde REL 20 1 
Amino/Phenolic Resin Amino/Phenolic Resin

Production Facilities
Figure 1 – NEIART10736 Acute Formaldehyde HQ 

(REL)
Figure 2 – NEIART10736 Acute Formaldehyde HQ 

(AEGL-1)
Figure 3 – NEIART10736 Acute Formaldehyde HQ 

(ERPG-1)
Figure 4 – NEIART10736 Acute Maleic Anhydride HQ 

(ERPG-1)
Figure 5– NEI46838 Acute Formaldehyde HQ (REL)
Figure 6 – NEIMA0130$$1 Acute Formaldehyde HQ 

(REL)
Figure 7 – NEI11136 Acute Formaldehyde HQ (REL)
Figure 8 – NEI56485 Acute Formaldehyde HQ (REL)
Figure 9 – NEI56485 Acute Formaldehyde HQ (AEGL-1)
Figure 10 – NEI56485 Acute Formaldehyde HQ

(ERPG-1)
Figure 11 – NEI46350 Acute Formaldehyde HQ (REL)
Figure 12 – NEI2OHT$6395 Acute Formaldehyde HQ 

(REL)
Figure 13 – NEI11443 Acute Formaldehyde HQ (REL)
Figure 14 – NEI7964 Acute Formaldehyde HQ (REL)
Appendix 7 

 Dispersion Model Receptor Revisions and Additions 
Dispersion Model Receptor Revisions and Additions for the Amino/Phenolic Resins 

Production 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 

Amino/Phenolic Resins Source Category 

Centroid Revisions

Block ID NEI ID New Latitude New Longitude Comment

480050006004020 NEI12810 31.33858  ‐94.709032 On plant property

281299503005013 NEI11136 31.834075  ‐89.458191 On plant property

130939703001135 NEI46838 32.080046  ‐83.79081 On plant property

450499202001065 NEI7964 32.875087  ‐81.109077 On plant property

Additional Receptors

480050006004020 NEI12810 31.339673  ‐94.711822   

480050006004020 NEI12810 31.338576  ‐94.710821   

281299503005031 NEI11136 31.835186  ‐89.472162   

281299503004000 NEI11136 31.841912  ‐89.464958   

390950025001003 NEI11443 41.662997  ‐83.573447   

390950025001005 NEI11443 41.662268  ‐83.573457   

390950025001004 NEI11443 41.661384  ‐83.573457   

390950025001008 NEI11443 41.660695  ‐83.573429   

210150704012005 NEI43304 39.126916  ‐84.725847   

371319201006005 NEI46350 36.428193  ‐77.22946