Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 1

Residual Risk Assessment for the 

Aerospace Manufacturing and Rework Facilities Source Category

in Support of the January, 2015 Risk and Technology Review 

Proposal

EPA’s Office of Air Quality Planning and Standards

Office of Air and Radiation

January 2015
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 2

Table of Contents

1 Introduction ....................................................................................................................5

2 Methods..........................................................................................................................5

2.1 Emissions and source data ........................................................................................5

2.2 Dispersion modeling for inhalation exposure assessment ..........................................6

2.3 Estimating chronic human inhalation exposure .........................................................9

2.4 Acute Risk Screening and Refined Assessments.....................................................10

2.5 Multipathway human health and environmental risk analysis..................................11

2.6 Dose-response assessment ......................................................................................14

2.6.1 Sources of chronic dose-response information .................................................14

2.6.2 Sources of acute dose-response information ....................................................25

2.7 Risk characterization ..............................................................................................30

2.7.1 General............................................................................................................30

2.7.2 Mixtures..........................................................................................................31

2.7.3 MACT-Allowable Emissions and Risks ..........................................................32

3 Risk results for the Aerospace Manufacturing and Rework facilities................................32

3.1 Source Category Emissions ....................................................................................32

3.2 Risk characterization ..............................................................................................38

3.2.1 Baseline Actual Emission Risks ...........................................................................38

MACT-level Inhalation Assessment Results (Actual)................................................39

MACT-level Multipathway Assessment Results (Actual)..........................................42

MACT-level Environmental Assessment Results (Actual) ........................................43

Facility-wide Inhalation Assessment Results (Actual) ...............................................43

3.2.2 Baseline Allowable Emission Risks .....................................................................44

MACT-level Inhalation Assessment Results (Allowable)..........................................44

4 General discussion of uncertainties and how they have been addressed .........................45

4.1 Exposure modeling uncertainties............................................................................45

4.2 Uncertainties in the dose-response relationships .....................................................45

5 References....................................................................................................................55

Index of Tables

Table 2-1 AERMOD version 14134 model options for RTR modeling ..................................7

Table 2.6-1 Dose-Response Values for Chronic Inhalation Exposure to Carcinogens...........18

Table 2.6-2 (A) Dose-Response Values for Chronic Inhalation Exposure to Noncarcinogens

.............................................................................................................................................21

Table 2.6-2 (B) Dose-Response Values for Chronic Oral Exposure to Noncarcinogens........25

Table 2.6-3 Dose-Response Values for Acute Exposures.....................................................27

Table 3.1-1 Baseline Actual Emissions from Aerospace and Availability of Dose-Response 

Values..................................................................................................................................33

Table 3.2-1. Risk Summary for the Aerospace Manufacturing .........................................38

Table 3.2-2. MACT Level Actual Emissions Chronic Inhalation Risks for...........................39

Aerospace Manufacturing and Rework Source Category ......................................................39

Table 3.2-3. MACT Level Actual Emissions Acute Inhalation Risks for...............................41

Aerospace Manufacturing and Rework Source Category ......................................................41
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 3

Table 3.2-4. MACT Level Actual Emissions – Summary of Multipathway Screening for 42

Aerospace Manufacturing and Rework Source Category ......................................................42

Table 3.2-5 MACT Level Actual Emissions - Summary of Environmental Risk Screen 

Results for the Aerospace Manufacturing and Rework Facilities Source Category ................43

Table 3.2-6 Source Category Contribution to Facility-Wide.................................................44

Chronic Cancer Risks (Actual Emissions).............................................................................44

Appendices

Appendix 1 Aerospace Manufacturing and Rework Facilities RTR Modeling File 

Preparation

Appendix 2 Technical Support Document for HEM3 Modeling

Appendix 3 Meteorological Data for HEM3 Modeling

Appendix 4 Dispersion Model Receptor Revisions and Additions for the Aerospace Source 

Category

Appendix 5 Technical Support Document for TRIM-Based Multipathway Tiered Screening 

Methodology for RTR 

Appendix 6 Environmental Risk Screen

Appendix 7 Detailed Risk Modeling Results

Appendix 8 Acute Impacts Refined Analysis

Index of Acronyms

AERMOD American Meteorological Society/EPA Regulatory Model

AEGL Acute exposure guideline level

ASTDR US Agency for Toxic Substances and Disease Registry

CalEPA California Environmental Agency

CTE Central Tendency Estimate

ERPG Emergency Response Planning Guideline

HAP Hazardous Air Pollutant

HEM Human Exposure Model

HI Hazard index

HQ Hazard quotient

IRIS Integrated Risk Information System

MACT Maximum Achievable Control Technology

MIR Maximum Individual Risk

MOA Mode of action

NAC National Advisory Committee

NAAQS National Ambient Air Quality Standards

NATA National Air Toxics Assessment

NEI National Emissions Inventory

NPRM Notice of Proposed Rulemaking

PB-HAP Persistent and Bioaccumulative - HAP
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 4

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
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 5

1 Introduction

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

initial technology-based standard.

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

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

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

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

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

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

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

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

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

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

Key Recommendations 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 MACT standard) performed for the Aerospace Manufacturing and 

Rework Facilities 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 Aerospace Manufacturing and Rework facilities source category includes all facilities 

that manufacture aerospace vehicles and/or vehicle components and all facilities that rework 

or repair these items. An aerospace vehicle or component is any fabricated, processed, or 
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 6

assembled set of parts or complete unit of any aircraft including, but not limited to, airplanes, 

helicopters, missiles, rockets, and space vehicles. 

Organic and inorganic HAP emissions in aerospace facilities originate from cleaning, primer 

application, topcoat application, paint stripping, and chemical milling maskant application 

among other secondary emission points. Organic HAP emissions from primer, topcoat, and 

chemical milling maskant application operations are the result of the evaporation of solvent 

contained in the coatings. These applications can take place in large open areas, such as 

hangars, or in partially or fully enclosed spaces, such as within spray booths. Primer and 

topcoat applications can also produce by-product inorganic HAP emissions. Cleaning and 

paint stripping operations can result in the release of organic HAP emissions due to the 

evaporation of the volatile portion of the cleaning solvents and chemical paint strippers, 

respectively. Cleaning emissions are nearly always fugitive in nature and occur at essentially 

every processing step. A complete description of the aerospace manufacturing and rework 

facilities source category can be found in the text of the Notice of Proposed Rulemaking 

(NPRM). 

Based on 2011 survey data from an information collection request (ICR) as well as emission 

data updated in 2013 and 2014, we currently estimate that there are 144 Aerospace 

Manufacturing and Rework facilities operating in the U.S. and 87 of these facilities are 

identified as major Based on 2011 survey data from an information collection request (ICR) 

as well as emission data updated in 2013 and 2014, we currently estimate that there are 144 

Aerospace Manufacturing and Rework facilities operating in the U.S. and 87 of these facilities 

are identified as major sources. 

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

found in Appendix 1. Section 3 below provides a summary of the emissions. 

2.2 Dispersion modeling for inhalation exposure assessment

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

each facility in the source category were estimated using the Human Exposure Model in 

combination with the American Meteorological Society/EPA Regulatory Model dispersion 

modeling system (HEM-AERMOD, or HEM3). The approach used in applying this modeling 

system is outlined below, and further details are provided in Appendix 2 to this document

(Technical Support Document for HEM3 Modeling). 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 14134 (the latest version of 

AERMOD that was available at the time of the risk analysis), is a state-of-the-science 

Gaussian plume dispersion model that is preferred by EPA for modeling point, area, and 

volume sources of continuous air emissions from facility applications [5]. Further details on 

AERMOD can be found in the AERMOD Users Guide [6]. The model is used to develop 
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 7

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.

Table 2-1 AERMOD version 14134 model options for RTR modeling

Modeling Option Selected Parameter for chronic exposure

Type of calculations Hourly Ambient Concentration

Source type Point, area represented as pseudo point source

Receptor orientation Polar (13 rings and 16 radials)

Discrete (census block centroids) and user-supplied receptors

Terrain characterization Actual from USGS 1-degree DEM data

Building downwash Not Included

Plume deposition/depletion Not Included

Urban source option No

Meteorology 1 year representative NWS from nearest site ( 824 stations)

In HEM3, meteorological data are ordinarily selected from a list of over 800 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. When considering off-site 

meteorological data most site specific dispersion, EPA modeling guidelines suggest that 

modeling efforts employ up to five years of data to capture variability in weather patterns 

from year to year. However, because dispersion model runtimes using five years of 

meteorological data would be too long for RTR source categories with many sources, we

modeled only a single year, 2011. While the selection of a single year may result in underprediction of long-term ambient levels at some locations, likewise it may result in overprediction 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 18

miles. Appendix 3 to this document (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 AERMOD meteorological data processing 

program. The document “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” (US EPA, 2009) 

provides more details on these modeling approaches.
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 8

Beginning with version 12345, AERMET allows for a minimum wind speed of 0.5 m/s to be 

utilized when processing monitoring stations that are equipped with sonic anemometers and 

for an adjustment to the surface friction velocity (u*). The public version of AERMET 

available at the time we conducted the AERMET processing did not include the surface 

friction velocity adjustment. Also at the time, an updated version of AERMET (and 

AERMOD) was under development that incorporated further surface friction velocity

adjustments to AERMET version 12345. In anticipation of the update, EPA processed 

meteorological data using a pre-public release version of AERMET and incorporated the 

updated surface friction velocity adjustment in the output. It was EPA’s judgement that the 

pre-public release version of AERMET would generate AERMOD ready meteorological data 

very close to that generated with the final public release version. 

EPA has posted the AERMET meteorological data used in the analysis on the EPA’s FERA 

(Fate, Exposure, and Risk Analysis) website under the HEM model page. AERMET and 

AERMOD (version 13350) were released to the public in late December 2013 on the EPA’s 

Support Center for Regulatory Atmospheric Modeling (SCRAM) website.

The HEM3 system estimates ambient concentrations at the geographic centroids of census 

blocks (using the 2010 Census) and at other receptor locations that can be specified by the 

user. See Appendix 4 of this document (Dispersion Model Receptor Revisions and Additions

for the Petroleum Refining Sector) for a discussion of user receptors and centroid location 

changes specific to this source category. The model accounts for the effects of multiple 

facilities when estimating concentration impacts at each block centroid. When evaluating the 

risks associated with a particular source category we combined the impacts of all facilities 

within the same source category, and assessed chronic exposure and risk for all census blocks 

with at least one resident (i.e., locations where people may reasonably be assumed to reside 

rather than receptor points at the fenceline of a facility). Chronic ambient concentrations were 

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

block centroid. Possible future residential use of currently uninhabited areas was not 

considered. Census blocks, the finest resolution available in the census data, are typically 

comprised of approximately 40 people or about ten households. 

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). 

Short-term emission rates were needed to screen for the potential for hazard via acute 

exposures. The average hourly emissions rate used for chronic risk modeling is defined as the 

total emissions for a year divided by the total number of operating hours in the year. If a 

source operated 8,760 hours per year this emissions rate would be appropriate for predicting 

potential acute impacts. However, many sources in this source sector emit on a more 
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 9

intermittent basis and thus specific hourly emissions may be higher than those utilized for 

annual or chronic risks modeling. While hourly emissions data for each source are not 

available, in this assessment, we utilized source category specific engineering judgment to 

determine what the peak hourly emissions for any given process could be. For the aerospace 

category this factor was estimated to be 1.2 times the annual emissions level. Further 

information on this factor is provided in the emissions memo presented in Appendix 1 of this 

report. 

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

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

resolution of about 90 meters. Elevations of polar grid points used in estimating short- and 

long-term ambient concentrations were assumed to be equal to the highest elevation of any 

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

does not contain any blocks, the model defaults the elevation to that of the nearest block. If 

an elevation is not provided for the emission source, the model uses the average elevation of 

all sectors within the innermost model ring. 

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

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

on how hill heights were estimated and used in the AERMOD modeling see Appendix 2 of 

this document. 

2.3 Estimating chronic 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, including indoor air concentrations, 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 percent 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. 

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 where 

people are likely to live; 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 (assumes a person stays in one location for 70 years) nor does 
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 10

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. This was generally the facility 

fenceline.

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 

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 began with a screening assessment, which relies on readily available data and 

uses conservative assumptions that in combination approximate a worst-case exposure. The 

result of this screening process is that either the facility being assessed poses no potential 

acute health risks (i.e., it “screens out”), or that it requires further, more refined assessment. 

A more refined acute assessment could use industry- or site-specific data on the temporal 

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

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

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

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

necessary. 

Acute health risk screening was performed for each facility as the first step. We used 

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

the following worst-case assumptions in our screening approach:

 Peak 1-hour emissions were obtained from the ICR and based on the operating 

characteristics and engineering judgement of aerospace facility emission as described 

in Appendix 1. 

 We assumed that the peak emissions 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 
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 11

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 modeled 

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 may entail determining the actual physical layout and boundaries of a 

facility to more accurately gauge where people might reasonably be exposed for an hour. 

For the aerospace source category, the refinement included a review of the layout of the 

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 run. Refer to 

Appendices 5 and 6 for the screening and refined acute results, respectively. 

2.5 Multipathway human health and environmental risk analysis

The EPA conducted a screening analysis examining the potential for significant human health 

risks due to exposures via routes other than inhalation (i.e., ingestion). We first determined 

whether any sources in the source categories emitted any hazardous air pollutants known to be 

persistent and bio-accumulative 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 [7]. With respect to PB-HAP emissions other than lead, emissions were 

evaluated for potential non-inhalation risks impacts using a Tiered screening approach. We 

first determined whether the facility-specific emission rates of each of the emitted PB–HAP 

were large enough to create the potential for significant non-inhalation human health risks 

under reasonable worst-case conditions. To facilitate this step, we developed emissions rate 

thresholds for each PB–HAP using a hypothetical upper-end screening exposure scenario 

developed for use in conjunction with the EPA’s Total Risk Integrated Methodology. Fate, 

Transport, and Ecological Exposure (TRIM.FaTE[8]) model. We conducted a sensitivity 

analysis on the screening scenario to ensure that its key design parameters would represent the 

upper end of the range of possible values, such that it would represent a conservative but not 

impossible scenario. The facility-specific emissions rates of each of the PB–HAP were 

compared to the emission rate threshold values for each of the PB–HAP identified to assess 

 

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). 
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 12

the potential for significant human health risks via non-inhalation pathways. We call this 

application of the TRIM.FaTE model the Tier 1 TRIM- Screen.

For the purpose of developing emissions rates for our Tier 1 TRIM-Screen, we derived 

emission levels for each PB-HAP (other than lead) at which the maximum excess lifetime 

cancer risk would be 1-in-1 million or, for HAP that cause non-cancer health effects, the 

maximum HQ would be 1. If the emissions rate of any PB-HAP exceeds the Tier 1 screening 

emissions rate for any facility, we conduct a Tier 2 multi-pathway screen. In the Tier 2 screen, 

the location of each facility that exceeded the Tier 1 emission rate is used to refine the 

assumptions associated with the environmental scenario while maintaining the exposure 

scenario assumptions. We then adjust the risk-based Tier 1 screening level for each PB-HAP 

for each facility based on an understanding of how exposure concentrations estimated for the 

screening scenario change with meteorology and environmental assumptions. PB-HAP 

emissions that do not exceed these new Tier 2 screening levels are considered to pose no 

unacceptable risks. When facilities exceed the Tier 2 screening levels, it does not mean that 

multi-pathway impacts are significant, only that we cannot rule out that possibility based on 

the results of the screen. These facilities, if needed, may be further evaluated for multipathway risks using the TRIM.FaTE model and more site specific information. Appendix 5

of this document (Technical Support Document for TRIM-Based Multipathway Tiered 

Screening Methodology for RTR) contains a complete discussion of the multipathway Tier 1 

and 2 screening approach. 

In evaluating the potential multipathway risks from emissions of lead compounds, rather than 

developing a screening emission rate for them, we compared maximum estimated 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 

multipathway risks of any significance.

The NAAQS value, a public health policy judgment, incorporated the Agency’s most recent 

health evaluation of air effects of lead exposure for the purposes of setting a national ambient 

air quality standard. In setting this value, the Administrator promulgated a standard that was 

requisite to protect public health with an adequate margin of safety. We consider values 

below the level of the primary NAAQS to protect against multipathway risks because as 

mentioned above, the primary NAAQS is set as to protect public health with an adequate 

margin of safety. However, ambient air lead concentrations above the NAAQS are 

considered to pose the potential for increased risk to public health. We consider this NAAQS 

assessment to be a refined analysis given: 1) the numerous health studies, detailed risk and 

exposure analyses, and level of external peer and public review that went into the 

development of the primary NAAQS for lead, combined with: 2) the site-specific dispersion 

modeling used in this assessment to estimated ambient lead concentrations due to the source 

category emissions. It should be noted, however, that this comparison does not account for 

possible population exposures to lead from sources other than the one being modeled; for 

example, via consumption of water from untreated local sources or ingestion of locally grown 

food. Nevertheless, the Administrator judged that such a standard would protect, with an 

adequate margin of safety, the health of children and other at-risk populations against an array 

of adverse health effects, most notably including neurological effects, particularly 

neurobehavioral and neurocognitive effects, in children (73 FR 67007). The Administrator, in 
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 13

setting the standard, also recognized that no evidence-or risk based bright line indicated a 

single appropriate level. Instead a collection of scientific evidence and other information was 

used to select the standard from a range of reasonable values (73 FR 67006).

We further note that comparing ambient lead concentrations to the NAAQS for lead, 

considering the level, averaging time, form and indicator, also informs whether there is the 

potential for adverse environmental effects. This is because the secondary lead NAAQS, set 

to protect against adverse welfare effects (including adverse environmental effects), has the 

same averaging time, form, and level as the primary standard. Thus, ambient lead 

concentrations above the NAAQS for lead also indicate the potential for adverse 

environmental effects.

Adverse environmental effects

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 conditions 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 II.

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 
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 14

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 

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 National Ambient Air Quality 

Standard (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.”2

Ecological benchmarks included in the environmental risk screen are presented in Appendix 

6.

2.6 Dose-response assessment

2.6.1 Sources of chronic dose-response information 

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

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

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

(OAQPS) existing recommendations for HAP [9], which were 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 

 

2 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.”
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 15

received. The prioritization process was aimed at incorporating into our assessments the best 

available science with respect to dose-response information. The recommendations are based 

on the following sources, in order of priority: 

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

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

assessments typically provide a qualitative statement regarding the strength of scientific 

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

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

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

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

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

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

during a lifetime.” The 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/m3in air.” The 

SF is “an upper bound, approximating a 95 percent confidence limit, on the increased 

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

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

Integrated Risk Information System (IRIS) [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” website3for detailed information on status 

and scheduling of current individual IRIS assessments and updates. 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 

 

3http://cfpub.epa.gov/ncea/iristrac/ 
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 16

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

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

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

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

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

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

Environmental Health Hazard Assessment has developed dose-response assessments for 

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

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

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

CalEPA Technical Support Document for developing their chronic assessments4: “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 HAP based 

on professional judgment, as follows: 

1) In the case of manganese (Mn), consistent with Agency policy supported by the SAB (as 

mentioned above), the EPA considers the ATSDR MRL for manganese the most 

appropriate value to be used in RTR assessments. There is an existing IRIS RfC for 

manganese (Mn) published in 1993.5 Also, ATSDR published an assessment of Mn 

toxicity (2012) which includes a chronic inhalation reference value (i.e., an ATSDR 

Minimal Risk Level or MRL).6 Both the 1993 IRIS RfC and the 2012 ATSDR MRL were 

based on the same study (Roels et al., 1993), however, ATSDR used updated doseresponse modeling methodology (benchmark dose approach) and considered recent 

pharmacokinetic findings to support their MRL derivation. Because of the updated 

methods, EPA has determined that the ATSDR MRL is the appropriate health value to use 

in RTR risk assessments.

2) In the case of nickel compounds, to provide a conservative estimate of the potential cancer 

risks, the EPA considers the IRIS URE value for nickel subsulfide (which is considered 

the most potent carcinogen among all nickel compounds) to be the most appropriate value 

to be used in RTR assessments. Based on consistent views of major scientific bodies (i.e., 

 

4 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 )

5 USEPA Integrated Risk Information System Review of Manganese (1993) available at 

http://www.epa.gov/iris/subst/0373.htm

6 Agency for Toxic Substances & Disease Registry Toxicological Profile for Manganese (2012) available at 

http://www.atsdr.cdc.gov/toxprofiles/tp.asp?id=102&tid=23
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 17

National Toxicology Program (NTP) in their 12th Report of the Carcinogens (RoC),7

International Agency for Research on Cancer (IARC),8and other international agencies9

that consider all nickel compounds to be carcinogenic, we currently consider all nickel 

compounds to have the potential of being carcinogenic to humans. The 12th RoC 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 carcinogenic effects in humans is not always clear, studies 

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

in industrial emissions of nickel mixtures cause cancer in humans (these studies are 

summarized in a review by Grimsrud et al., 2010).10 The major scientific bodies 

mentioned above have also recognized that there may be differences in the toxicity and/or 

carcinogenic potential across the different nickel compounds. For this reason, and given 

that there are two additional URE values11 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 considers it reasonable, in some instances (e.g., when high quality data is 

available on the composition of nickel emissions from a specific source category), 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.

For this specific assessment, based on the lack of information on the composition of nickel 

mixtures emitted by the Aerospace Manufacturing and Rework source category and to 

provide a conservative estimate of potential cancer risks, the EPA considers the IRIS URE 

for nickel (which is considered the most potent carcinogen among all nickel compounds) 

to be the most appropriate value to be used to estimate the risks associated with nickel 

emissions from this source category.

3) In the case of 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. For POM emissions that are not speciated into individual 

compounds, we apply the same simplifying assumptions to assessments that are used in 

 

7

 National Toxicology Program (NTP), 2011. Report on Carcinogens (RoC). 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.occup-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 ).
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 18

NATA.12 The NATA approach partitions POM into eight different non-overlapping 

“groups” that are modeled as separate pollutants. Each POM group includes POM species 

of similar carcinogenic potency, for which we can apply the same URE.

4) In the case of formaldehyde, the EPA determined in 2004 that the Chemical Industry 

Institute of Toxicology (CIIT) cancer dose-response value (5.5 x 10-9per μg/m3) was 

based on better science than the IRIS cancer dose-response value (1.3 x 10-5per μg/m3), 

and the EPA decided to switch 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 the 

1991 IRIS value. The EPA is currently revising the formaldehyde IRIS assessment and in 

the interim, we will present findings using the 1991 IRIS value as a primary estimate. 

EPA may also consider other information as the science evolves.

The emissions inventory for the aerospace source category includes emissions of HAP with 

available chronic quantitative inhalation dose-response values. Of these, 42 are classified as 

known, probably, or possible carcinogens, with quantitative cancer dose-response values 

available. These 42 HAP, their quantitative inhalation chronic cancer dose-response values, 

and the source of each value are listed in Table 2.6-1. One hundred and eleven HAP have 

quantitative inhalation chronic noncancer threshold values available; two of these one 

hundred and eleven HAP (cadmium and mercury), for which a screening multipathway 

assessment was performed, also have quantitative oral chronic noncancer threshold values 

available. These HAP, their threshold values, and the source of the value are listed in Table 

2.6-2(A) and Table 2.6-2(B).

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

URE (unit risk estimate for cancer)13 = cancer risk per μg/m3of 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.

Pollutant CAS Number14 URE

(1/μg/m3)

Source

1,3-Butadiene 106990 0.00003 IRIS

1,4-Dioxane 123911 0.000005 IRIS

2,4-Toluene diisocyanate 584849 0.000011 CAL

2-Nitropropane 79469 0.0000056 EPA OAQPS

3,3'-Dichlorobenzidine 91941 0.00034 CAL

4,4'-Methylene bis(2-chloroaniline) 101144 0.00043 CAL

 

12 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

13 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/m3in air. URE’s are considered upper bound estimates meaning they represent 

a plausible upper limit to the true value. 

14 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.
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 19

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

URE (unit risk estimate for cancer)13 = cancer risk per μg/m3of 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.

Pollutant CAS Number14 URE

(1/μg/m3)

Source

4,4'-Methylenedianiline 101779 0.00046 CAL

p-Dichlorobenzene 106467 0.000011 CAL

Acetaldehyde 75070 0.0000022 IRIS

Acrylamide 79061 0.00016 IRIS

Acrylonitrile 107131 0.000068 IRIS

Aniline 62533 0.0000016 CAL

Arsenic compounds 7440382 0.0043 IRIS

Benzene15 71432 0.0000078 IRIS

Beryllium compounds 7440417 0.0024 IRIS

Bis(2-ethylhexyl)phthalate 117817 0.0000024 CAL

Cadmium compounds 7440439 0.0018 IRIS

Carbon tetrachloride 56235 0.000006 IRIS

Chromium & Chromium compounds

 Barium chromate 10294403 0.012 IRIS

 Calcium chromate 13765190 0.012 IRIS

 Chromic acid (VI) 7738945 0.012 IRIS

 Chromium (VI) as lead chromate 7758976 0.012 IRIS

 Chromium (VI) compounds 18540299 0.012 IRIS

 Chromium (VI) trioxide, chromic acid 

mist 11115745 0.012 IRIS

 Sodium chromate 7775113 0.012 IRIS

 Sodium dichromate 10588019 0.012 IRIS

 Strontium chromate 7789062 0.012 IRIS

 Zinc chromate 13530659 0.012 IRIS

 Zinc potassium chromate 11103869 0.012 IRIS

Epichlorohydrin (1-Chloro-2,3-

epoxypropane) 106898 0.0000012 IRIS

Ethyl benzene 100414 0.0000025 CAL

Ethylene oxide 75218 0.000088 CAL

Formaldehyde16 50000 0.000013 IRIS

Hexachlorobenzene 118741 0.00046 IRIS

Methylene chloride 75092 0.000000016 IRIS

Naphthalene 91203 0.000034 CAL

 

15 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.

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

, to characterize formaldehyde cancer risk in 

some instances.
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 20

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

URE (unit risk estimate for cancer)13 = cancer risk per μg/m3of 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.

Pollutant CAS Number14 URE

(1/μg/m3)

Source

Nickel & Nickel Compounds17

 Nickel compounds 7440020 0.00048 IRIS

 Nickel oxide 1313991 0.00048 IRIS

Propylene oxide 75569 0.0000037 IRIS

Tetrachloroethene 127184 0.00000026 IRIS

Trichloroethylene 79016 0.0000048 IRIS

Vinyl chloride 75014 0.0000088 IRIS

 

17 The EPA IRIS assessments for nickel compounds provide a range of plausible UREs. This assessment used 

the highest value in that range which is equal to the URE for nickel subsulfide, 4.8E-04 per ug/m3. The low end 

of the range is equal to 50% of the URE for nickel subsulfide, 2.4E-04 per ug/m3.
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 21

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 Number18 RfC 

(mg/m3)

Source19

1,1,1-Trichloroethane (methyl chloroform) 71556 5 IRIS - M

1,2-Epoxybutane 106887 0.02 IRIS - M

1,3-Butadiene 106990 0.002 IRIS - M

1,4-Dioxane 123911 0.03 IRIS - M

2,4-Toluene diisocyanate 584849 0.00007 IRIS - M

2-Nitropropane 79469 0.02 IRIS - L

4,4'-Methylenedianiline 101779 0.02 CAL

n-Hexane 110543 0.7 IRIS - M

p-Dichlorobenzene 106467 0.8 IRIS - M

Acetaldehyde 75070 0.009 IRIS - L

Acrylamide 79061 0.006 IRIS - M

Acrylic acid 79107 0.001 IRIS - M

Acrylonitrile 107131 0.002 IRIS - M

Aniline 62533 0.001 IRIS - L

Antimony & Antimony Compounds

 Antimony compounds 7440360 0.0002 IRIS - L

 Antimony oxide 1327339 0.0002 IRIS - L

 Antimony pentoxide 1314609 0.0002 IRIS - L

 Antimony trioxide 1309644 0.0002 IRIS - L

Arsenic compounds 7440382 0.000015 CAL

Benzene 71432 0.03 IRIS - M

Beryllium compounds 7440417 0.00002 IRIS - M

Bis(2-ethylhexyl)phthalate 117817 0.01 CAL

Cadmium compounds 7440439 0.00001 ATSDR

Carbon tetrachloride 56235 0.1 IRIS - M

Chlorobenzene 108907 1 CAL

Chloroform 67663 0.098 ATSDR

Chromium & Chromium Compounds

 

18 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.

19 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.
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 22

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 Number18 RfC 

(mg/m3)

Source19

 Barium chromate 10294403 0.0001 IRIS - M

 Calcium chromate 13765190 0.0001 IRIS - M

 Chromic acid (VI) 7738945 0.0001 IRIS - M

 Chromium (VI) as lead chromate 7758976 0.0001 IRIS - M

 Chromium (VI) compounds 18540299 0.0001 IRIS - M

 Chromium (VI) trioxide, chromic 

 acid mist 11115745 0.000008 IRIS - L

 Sodium chromate 7775113 0.0001 IRIS - M

 Sodium dichromate 10588019 0.0001 IRIS - M

 Strontium chromate 7789062 0.0001 IRIS - M

 Zinc chromate 13530659 0.0001 IRIS - M

 Zinc potassium chromate 11103869 0.0001 IRIS - M

Cobalt & Cobalt Compounds

 Cobalt aluminate 1345160 0.0001 ATSDR

 Cobalt compounds 7440484 0.0001 ATSDR

 Cobalt naphtha 61789513 0.0001 ATSDR

 Cobalt oxide 1307966 0.0001 ATSDR

 Hexanoic acid, 2-ethyl-, cobalt(2+) 

 salt 136527 0.0001 ATSDR

Cresols (mixed) 1319773 0.6 CAL

 m-Cresol 108394 0.6 CAL

 o-Cresol 95487 0.6 CAL

 p-Cresol 106445 0.6 CAL

Cumene 98828 0.4 IRIS - M

Cyanide compounds20 57125 0.0008 IRIS – L/M

Diethanolamine 111422 0.003 CAL

Dimethyl formamide 68122 0.03 IRIS - M

Epichlorohydrin (1-Chloro-2,3 

epoxypropane) 106898 0.001 IRIS - M

Ethyl benzene 100414 1 IRIS - L

Ethylene glycol 107211 0.4 CAL

Ethylene oxide 75218 0.03 CAL

Formaldehyde 50000 0.0098 ATSDR

Glycol Ethers21

 

20 The value for hydrogen cyanide was used as a surrogate for all cyanide compounds without an RfC.

21 The RfC value for ethylene glycol methyl ether was used as a surrogate for all glycol ethers without an RfC.
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 23

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 Number18 RfC 

(mg/m3)

Source19

 1,2-Dimethoxyethane 110714 0.02 IRIS - M

 2-(Hexyloxy)ethanol 112254 0.02 IRIS - M

 2-Butoxyethyl acetate 112072 0.02 IRIS - M

 Butyl Carbitol acetate 124174 0.02 IRIS - M

 Carbitol acetate 112152 0.02 IRIS - M

 Diethylene glycol dimethyl ether 111966 0.02 IRIS - M

 Diethylene glycol monobutyl ether 112345 0.02 HEAST

 Diethylene glycol monoethyl ether 111900 0.02 IRIS - M

 Diethylene glycol monomethyl ether 111773 0.02 IRIS - M

 Ethylene glycol ethyl ether 110805 0.2 IRIS - M

 Ethylene glycol ethyl ether acetate 111159 0.3 CAL

 Ethylene glycol methyl ether 109864 0.02 IRIS - M

 Ethylene glycol methyl ether acetate 110496 0.09 CAL

 Glycol ethers 181 0.02 IRIS - M

 Propyl cellosolve 2807309 0.02 IRIS - M

Hexachlorobenzene 118741 0.003 CAL

Hexamethylene-1,6-diisocyanate 822060 0.00001 IRIS - L

Hydrochloric acid (hydrogen chloride) 7647010 0.02 IRIS - L

Hydrofluoric acid (hydrogen fluoride) 7664393 0.014 CAL

Isophorone 78591 2 CAL

Lead and Lead Compounds

 Lead (II) oxide 1317368 0.00015 EPA OAQPS

 Lead as lead chromate 7758976 0.00015 EPA OAQPS

 Lead compounds 7439921 0.00015 EPA OAQPS

 Lead dioxide 1309600 0.00015 EPA OAQPS

Maleic anhydride 108316 0.0007 CAL

Manganese and Manganese Compounds

 Manganese compounds 7439965 0.0003 ATSDR

 Manganese dioxide 1313139 0.0003 ATSDR

 Manganese tetroxide 1317357 0.0003 ATSDR

 Manganese trioxide 1317346 0.0003 ATSDR

Mercury and Mercury Compounds

 Gaseous divalent mercury 201 0.0003 IRIS - M

 Mercury (elemental) 7439976 0.0003 IRIS - M

 Particulate divalent mercury 184 0.0003 IRIS - M

Methanol 67561 20 IRIS – M/H
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 24

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 Number18 RfC 

(mg/m3)

Source19

Methyl isobutyl ketone 108101 3 IRIS - L/M

Methyl methacrylate 80626 0.7 IRIS - M/H

Methylene chloride 75092 0.6 IRIS – M/H

Methylene diphenyl diisocyanate 101688 0.0006 IRIS - M

Naphthalene 91203 0.003 IRIS - M

Nickel and Nickel Compounds

 Nickel compounds 7440020 0.00009 ATSDR

 Nickel oxide 1313991 0.00002 CAL

Phenol 108952 0.2 CAL

Phthalic anhydride 85449 0.02 CAL

Propylene oxide 75569 0.03 IRIS - M

Selenium and Selenium Compounds

 Selenious acid 7783008 0.02 CAL

 Selenium compounds 7782492 0.02 CAL

Styrene 100425 1 IRIS - M

Tetrachloroethene 127184 0.04 IRIS -M

Toluene 108883 5 IRIS - H

Trichloroethylene 79016 0.002 IRIS - H

Triethylamine 121448 0.007 IRIS - L

Vinyl acetate 108054 0.2 IRIS - H

Vinyl chloride 75014 0.1 IRIS - M

Xylenes (mixed)22 1330207 0.1 IRIS - M

 m-Xylene 108383 0.1 IRIS - M

 o-Xylene 95476 0.1 IRIS - M

 p-Xylene 106423 0.1 IRIS - M

 

22 The RfC for mixed xylene was used as a surrogate for each of the isomers.
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 25

Table 2.6-2 (B) Dose-Response Values for Chronic Oral Exposure to Noncarcinogens

RfD (reference dose) = an estimate (with uncertainty spanning perhaps an order of magnitude) of a 

continuous oral 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, ATSDR = US Agency for Toxic 

Substances and Disease Registry.

Pollutant CAS Number23 RFD

(mg/kg/d))

Source24

Cadmium compounds 7440439 0.0005 IRIS-H

Mercuric chloride25 7439976 0.0003 IRIS-H

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 

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

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

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

Exposure Guideline Levels (AEGLs) and Emergency Response Planning Guidelines 

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

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

level and any limitations expressed therein. Comparisons among different available 

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

found in an EPA document of graphical arrays [18].

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. [19]. 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 

 

23 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.

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

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

25 The multipathway exposure assessment for mercury included fate and transport analysis, that included 

separate oral exposure estimates for divalent mercury and methylmercury.
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 26

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 

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 
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 27

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) [20] 

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 https://www.aiha.org/getinvolved/aihaguidelinefoundation/emergencyresponseplanningguidelines/Pages/default.aspx 

are described in their supporting documentation as follows: “ERPGs are air concentration 

guidelines for single exposures to agents and are intended for use as tools to assess the 

adequacy of accident prevention and emergency response plans, including transportation 

emergency planning, community emergency response plans, and incident prevention and 

mitigation.” 

ERPG-1 and ERPG-2 values are defined by AIHA as follows:

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

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

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

odor.”

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

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

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

ability to take protective action.”

The emissions inventory for the aerospace source category includes emissions of 59 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 Exposures

Pollutant

CAS 

Number26

AEGL-1

(1-hr)

(mg/m3)

AEGL-2

(1-hr)

(mg/m3)

ERPG-1

(mg/m3)

ERPG-2

(mg/m3) REL 

1,1,1-Trichloroethane 

(methyl chloroform) 71556 1300 3300 1900 3800 68

1,2-Epoxybutane 106887 210 410

 

26 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.
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 28

Table 2.6-3 Dose-Response Values for Acute Exposures

Pollutant

CAS 

Number26

AEGL-1

(1-hr)

(mg/m3)

AEGL-2

(1-hr)

(mg/m3)

ERPG-1

(mg/m3)

ERPG-2

(mg/m3) REL 

1,3-Butadiene 106990 1500 12000 22 440

1,4-Dioxane 123911 61 1200 3

2,4-Toluene 

diisocyanate 584849 0.14 0.59 0.071 1.1

n-Hexane 110543 12000

Acetaldehyde 75070 81 490 18 360 0.47

Acrylic acid 79107 4.4 140 2.9 150 6

Acrylonitrile 107131 10 120 22 77

Aniline 62533 30 46

Arsenic compounds 7440382 0.0002

Benzene 71432 170 2600 160 480 1.3

Beryllium compounds 7440417 0.025

Carbon tetrachloride 56235 280 1200 130 630 1.9

Chlorobenzene 108907 46 690

Chloroform 67663 310 240 0.15

Cumene 98828 250 1500

Dimethyl formamide 68122 270 6 300

Epichlorohydrin (1-

Chloro-2,3-

epoxypropane) 106898 22 91 19 76 1.3

Ethyl acrylate 140885 34 150 0.041 120

Ethyl benzene 100414 140 4800

Ethylene oxide 75218 81 90

Formaldehyde 50000 1.1 17 1.2 12 0.055

Glycol Ethers27

 1,2-Dimethoxyethane 110714 0.093

 2-(Hexyloxy)ethanol 112254 0.093

 2-Butoxyethyl 

acetate 112072 0.093

 Butyl carbitol acetate 124174 0.093

 Carbitol acetate 112152 0.093

 Diethylene glycol 

dimethyl ether 111966 0.093

 Diethylene glycol 

 monobutyl ether 112345 0.093

 Diethylene glycol 

 monoethyl ether 111900 0.093

 Diethylene glycol 111773 0.093

 

27 The acute REL for ethylene glycol methyl ether was used as a surrogate for glycol ether compounds without 

an acute REL.
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 29

Table 2.6-3 Dose-Response Values for Acute Exposures

Pollutant

CAS 

Number26

AEGL-1

(1-hr)

(mg/m3)

AEGL-2

(1-hr)

(mg/m3)

ERPG-1

(mg/m3)

ERPG-2

(mg/m3) REL 

 monomethyl ether

 Ethylene glycol ethyl 

ether 110805 0.37

 Ethylene glycol ethyl 

ether 

 acetate 111159 0.14

 Ethylene glycol 

methyl 

 ether 109864 0.093

 Ethylene glycol 

methyl 

 ether acetate 110496 0.093

 Glycol ethers 181 0.093

 Propyl cellosolve 2807309 0.093

Hydrochloric acid 7647010 2.7 33 4.5 30 2.1

Hydrofluoric acid 7664393 0.82 20 1.6 16 0.24

Maleic anhydride 108316 0.8 8

Mercury (elemental) 7439976 1.7 2 0.0006

Methanol 67561 690 2700 260 1300 28

Methyl methacrylate 80626 70 490

Methylene chloride 75092 690 1900 1000 2600 14

Methylene diphenyl 

diisocyanate 101688 5

Phenol 108952 58 89 38 190 5.8

Propylene oxide 75569 170 690 120 590 3.1

Styrene 100425 85 550 210 1100 21

Tetrachloroethene 127184 240 1600 680 1400 20

Toluene 108883 750 4500 190 1100 37

Trichloroethylene 79016 700 2400 540 2700

Triethylamine 121448 2.8

Vinyl acetate 108054 24 630 18 260

Vinyl chloride 75014 640 3100 1300 13000 180

Xylenes (mixed)28 1330207 560 4000 22

 m-Xylene 108383 22

 o-Xylene 95476 22

 p-Xylene 106423 22

 

28 The REL for mixed xylenes was used as a surrogate for each isomer.
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 30

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 

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

1995 Policy for Risk Characterization [21] 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 [22], in 2002 by the Agency’s information quality guidelines [23], and in the 

OMB/OSTP September 2007 Memorandum on Updated Principles for Risk Analysis29, and 

are incorporated in these assessments.

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

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

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

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

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

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

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

limitations.

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

individual and population cancer risks were calculated by multiplying the corresponding 

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

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

assumed human lifespan) at that exposure. Because UREs for most HAP 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 HAP that act by a mutagenic mode-of-action [24], we applied 

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

Carcinogens [25]. 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 

 

29Memorandum 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/sites/default/files/omb/memoranda/fy2007/m07-24.pdf
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 31

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 [26] 

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 

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

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

estimate of the number of cancer cases per year.

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

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

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

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

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

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

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

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

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

this on possible health interpretations. 

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

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

RfCs incorporate generally conservative uncertainty factors in the face of uncertain 

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

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

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

Screening for potentially significant acute inhalation exposures also followed the HQ 

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

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

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

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

information is used to determine 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.
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 32

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

[27,28] default assumption of additivity of effects, and combine risks by summing them using 

the independence formula in the mixtures guidelines.

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

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

to aggregate HQs. In consideration of these mode-of-action differences, the mixtures 

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

To conform to these guidelines, we aggregated non-cancer HQs of HAP 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 HAP 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 MACT-Allowable Emissions and Risks 

The emissions data in the dataset for the aerospace 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. For the aerospace source category, estimates of MACT-allowable 

emissions were also estimated, and the risk results based on those emissions are presented 

below as well. For more detail, please refer to the emissions memo in Appendix 1 to this

document.

3 Risk results for the Aerospace Manufacturing and Rework 

facilities

3.1 Source Category Emissions 
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 33

Based on 2011 survey data from an information collection request (ICR) as well as emission 

data updated in 2013 and 2014, we currently estimate that there are 144 Aerospace 

Manufacturing and Rework facilities operating in the U.S. and 87 of these facilities are 

identified as major sources. 

The baseline actual emissions for the Aerospace Manufacturing and Rework facilities source 

category NPRM data set (of 144 facilities) are summarized in Table 3.1-1. The total HAP 

emissions for the source category are approximately 1,130 tons per year. Based on these data, 

the HAP emitted in the largest quantities across these 144 facilities are: toluene, 

tetrachloroethene, methyl isobutyl ketone, xylenes (mixed), methylene chloride, phenol, Nhexane, ethylene glycol ethyl ether, ethyl benzene, methanol, diethylene glycol monobutyl 

ether, diethanolamine, propyl cellosolve, 2-butoxyethyl acetate, 1,1,1-trichloroethane, styrene, 

manganese compounds, ethylene glycol methyl ether, glycol ethers, ethylene glycol, 

methylene diphenyl diisocyanate, butyl carbitol acetate and dimethyl formamide. These 23 

HAP account for 99 percent of the mass of the total emissions by mass. Persistent and 

bioaccumulative HAP (PB HAP) 30 reported as emissions from these facilities include 

cadmium, mercury, lead compounds and hexachlorobenzene. The following environmental 

HAP are emitted from the Aerospace Manufacturing and Rework Facilities source category 

and are included in the environmental risk screen: lead, mercury (mercuric chloride and 

methyl mercury), cadmium, hydrogen chloride and hydrogen fluoride.

 

Table 3.1-1 Baseline Actual Emissions from Aerospace and Availability of Dose-Response Values

HAPa Emissions 

(tpy)

Number of 

Facilities 

Reporting 

HAP (144

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?

Toluene 348 133  

Tetrachloroethene 139 17   

Methyl isobutyl ketone 136 129 

Xylenes (mixed) 115 131  

Methylene chloride 111 56   

Phenol 100 62  

n-Hexane 36 50  

Ethylene glycol ethyl ether 27 20  

Ethyl benzene 16 129   

Methanol 14 90  

 

30 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. 
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 34

Table 3.1-1 Baseline Actual Emissions from Aerospace and Availability of Dose-Response Values

HAPa Emissions 

(tpy)

Number of 

Facilities 

Reporting 

HAP (144

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?

Diethylene glycol monobutyl 

ether 14 28  

Diethanolamine 12 12 

Propyl cellosolve 11 20  

2-Butoxyethyl acetate 8 41  

1,1,1-Trichloroethane 6 8  

Styrene 5 27  

Manganese compounds 3 36 

Ethylene glycol methyl ether 3 20  

Glycol ethers 3 19  

Ethylene Glycol 3 34 

Methylene diphenyl 

diisocyanate 3 42  

Butyl carbitol acetate 2 23  

Dimethyl formamide 1 4  

Ethylene glycol ethyl ether

acetate 1 22  

Cumene 1 66  

Trichloroethylene 0.7 9   

Naphthalene 0.7 23  

Strontium chromate 0.6 94  

Manganese dioxide 0.6 13 

Hexamethylene-1,6-diisocyanate 0.6 83 

Bis(2-ethylhexyl)phthalate 0.6 17  

1,2-Epoxybutane 0.4 6  

Cyanide compounds 0.4 6 

Dibutylphthalate 0.4 31

Methyl methacrylate 0.4 21  

Acrylic acid 0.3 12  

Nickel compounds 0.3 43  

Triethylamine 0.3 16  

4,4'-Methylenedianiline 0.2 3  

Formaldehyde 0.2 61   

Diethylene glycol monoethyl 

ether 0.2 7  

o-Xylene 0.2 4  

Benzene 0.1 33   

Diethylene glycol dimethyl ether 0.1 5  

2,4-Toluene diisocyanate 0.1 25   
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 35

Table 3.1-1 Baseline Actual Emissions from Aerospace and Availability of Dose-Response Values

HAPa Emissions 

(tpy)

Number of 

Facilities 

Reporting 

HAP (144

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?

Chlorobenzene 0.1 7  

Cobalt compounds 0.1 38 

Lead compoundsc0.1 41  

Chromium (iii) compounds 0.07 49

Diethylene glycol monomethyl 

ether 0.07 6  

Isophorone 0.06 5 

Cadmium compounds 0.05 31   

Barium chromate 0.05 36  

Chromium (vi) compounds 0.05 60  

2-Nitropropane 0.05 17  

Cresols (mixed) 0.05 11 

Hydroquinone 0.04 17

Ethyl acrylate 0.04 13 

Antimony compounds 0.03 41 

p-Xylene 0.03 10  

Antimony trioxide 0.03 26 

Epichlorohydrin 0.02 11   

Zinc chromate 0.02 38  

2,4-Dinitrophenol 0.02 1

4-Nitrophenol 0.02 2

N,N-Dimethylaniline 0.01 9

Selenium compounds 0.009 14 

Hydrochloric acid 0.007 1  

p-Cresol (4-Methyl phenol) 0.006 2 

1,3-Butadiene 0.006 4   

o-Cresol 0.005 5 

Lead (II) oxidec0.004 2  

Ethylene oxide 0.004 7   

Acrylonitrile 0.003 5   

m-Cresol (3-Methyl phenol) 0.003 3 

Calcium chromate 0.003 20  

Carbitol acetate 0.003 6  

m-Xylene 0.002 5  

Chromic oxide 0.002 17

Ethylene glycol methyl ether 

acetate 0.002 1  

Carbon tetrachloride 0.002 9   
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 36

Table 3.1-1 Baseline Actual Emissions from Aerospace and Availability of Dose-Response Values

HAPa Emissions 

(tpy)

Number of 

Facilities 

Reporting 

HAP (144

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?

Sodium chromate 0.002 1  

Arsenic compounds 0.002 27   

Chromic acid (vi) 0.001 20  

Chromium (vi) trioxide, 

Chromic acid mist 0.001 13  

p-Dichlorobenzene 0.001 2  

Acetophenone 0.001 3

2-(Hexyloxy)ethanol 0.001 2  

Acetaldehyde 0.001 9   

1,4-Dioxane 0.001 6   

Aniline 0.0008 5   

Hydrofluoric acid 0.0008 11  

Vinyl acetate 0.0007 1  

Propylene oxide 0.0007 8   

2,4,5-Trichlorophenol 0.0005 1

Catechol 0.0004 2

1,2-Dimethoxyethane 0.0004 1  

Dimethyl phthalate 0.0003 1

Zinc potassium chromate 0.0002 10  

Maleic anhydride 0.0002 1  

Mercury (elemental) 0.0002 6   

Cobalt naphtha 0.0001 2 

p-Phenylenediamine 0.0001 1

Gaseous divalent mercury 0.0001 6  

4,4'-Methylene bis(2-

chloroaniline) 0.0001 1 

Particulate divalent mercury 0.00008 6  

2,2,4-Trimethylpentane 0.00005 1

Sodium dichromate 0.00003 5  

Antimony oxide 0.00003 1 

Phthalic anhydride 0.00003 1 

Antimony pentoxide 0.00003 4 

Cobalt aluminate 0.00002 5 

Manganese trioxide 0.00002 1 

Cobalt oxide 0.00001 1 

Chromium (vi) As lead 

chromate 0.000006 6  

Lead As lead chromatec0.000006 6  

Nickel oxide 0.000003 5  
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 37

Table 3.1-1 Baseline Actual Emissions from Aerospace and Availability of Dose-Response Values

HAPa Emissions 

(tpy)

Number of 

Facilities 

Reporting 

HAP (144

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?

Chloroform 0.000002 1  

Zinc chromite 0.000002 2

Selenious acid 0.000001 1 

Chromium zinc oxide 0.000001 1

Vinyl chloride 0.000001 1   

Acrylamide 0.0000005 1  

Hexanoic acid, 2-ethyl-, 

Cobalt(2+) salt 0.0000003 2 

Beryllium compounds 0.0000002 11   

3,3'-Dichlorobenzidine 0.0000001 1 

Lead dioxidec0.00000007 1  

Hexachlorobenzene 0.00000005 1   

Manganese tetroxide 0.00000002 1 

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 

“chromium (III) compounds” and “chromium (VI) compounds” according to the individual emitting process 

speciation profile 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

 For emissions reported generically as “mercury” or “mercury & compounds,” emissions are speciated for this 

category as “mercury (elemental)” and “mercuric chloride.” Mercury 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. 

 For emissions of any chemicals or chemical groups classified as POM, emissions were grouped into POM 

subgroups as found on EPA’s Technology Transfer Network website for the 2005 National-Scale Air Toxics 

Assessment at http://www.epa.gov/ttn/atw/nata2005/methods.html#pom (Approach to Modeling POM).

 For emissions reported generically as “Glycol Ethers” or specific glycol ethers not found on EPA’s Technology 

Transfer Network for air toxics (see footnote b), emissions were treated as ethylene glycol methyl ether.

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

toxics at 

http://www2.epa.gov/fera/dose-response-assessment-assessing-health-risks-associated-exposure-hazardous-air-pollutants

c There is no reference concentration for lead. In considering noncancer hazards for lead in this assessment, we compared 

rolling three-month average exposure estimates to the National Ambient Air Quality Standard (NAAQS) for lead (0.15 

μg/m3). These NAAQS for lead were recently 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.
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 38

3.2 Risk characterization

This section presents the results of the risk assessment for Aerospace Manufacturing and 

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

Tables 3.2-1 presents the summary of the risks from for the Aerospace Manufacturing and 

Rework Source Category. The maximum individual lifetime cancer risk (MIR) for both 

baseline actual emissions and baseline allowable emissions is 10 in a million. When 

considering whole facility baseline actual emissions the predicted MIR is 20 in a million. 

The maximum chronic hazard index is predicted to be below 1 considering all emissions 

scenarios. The acute inhalation impact screening assessment predicted the maximum hazard 

quotients is 2. Both multipathway and ecological screening risks are below their respective 

screening thresholds. More details on each component of the risks assessment are presented 

in the sections and tables below.

Table 3.2-1. Risk Summary for the Aerospace Manufacturing

3.2.1 Baseline Actual Emission Risks 

All baseline risk results are developed using the best estimates of actual HAP emissions 

summarized in the previous section. 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 

assessment 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 at the highest exposure location, Definitions of these and other 

Inhalation Cancer Risk Population Cancer Risk Max Chronic Individual NonCancer Risk

Max Acute 

Non-Cancer 

Risk

Multipathway 

Analysis

MIR

(in 1 

million)

Risk Driver

Cancer 

Incidence

(cases per

year)

≥ 1 in 10 

in 1 

million

≥ 1 in 1 

million

HI

(TOSHI) Risk Driver HQ Risk Driver

Max PB-HAP 

Emissions / 

Screening Level

Baseline Actual Emissions

Source 

Category 10 Strontium chromate 0.02 1,500 180,000 0.5 Cadmium compounds 2

Ethylene 

glycol 

ethyl 

ether 

acetate 

(REL)

Cadmium 

Compounds: 1 

(Tier 1)

Divalent 

Mercury: 1 (Tier 

1)

Whole 

Facility 20

Arsenic, 

Chromium 

compounds

0.04 3,200 270,000 0.5 Hexamethylene-1,6-

diisocyanate --- --- ---

Baseline Allowable Emissions

Source 

Category 10 Strontium chromate 0.02 2,000 180,000 0.5 Cadmium Compounds --- --- ---
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 39

relevant risk-related terms can be found on the 2005 National-Scale Air Toxics Assessment 

Glossary Web page. A detailed summary of the facility-specific inhalation and multipathway 

risk assessment results is available in Appendix 7 of this document (Detailed Risk Modeling 

Results).

MACT-level Inhalation Assessment Results (Actual)

Tables 3.2-2 and 3.2-3 summarize the chronic and acute inhalation risk results for this source 

category based upon baseline actual emissions. The results of the chronic inhalation cancer 

risk assessment are that the maximum lifetime individual cancer risk posed by the 144

facilities could be 10 in a million considering both the maximum facility, and the entire 

source sector. Emissions of strontium chromate, from coating operations are the major 

contributors to this risk. The total estimated cancer incidence from this source category is 

0.02 excess cancer cases per year, or one excess case in every 50 years. Approximately 1,500

people were estimated to have cancer risks above 10 in a million and approximately 180,000

people were estimated to have cancer risks above 1 in a million considering the 144 facilities 

in this source category. The maximum chronic noncancer TOSHI value for the source 

category could be up to 0.5 (kidney) driven by emissions of cadmium compounds from blast 

depainting. 

Worst-case acute hazard quotients (HQs) were calculated for every HAP shown in 

Table 3.1-1 that has an acute benchmark, and the highest refined screening acute HQ value of 

2 (based on the acute REL for ethylene glycol ethyl ether acetate) is shown in Table 3.2-2. 

This value includes a refinement of determining the highest HQ value that is outside facility 

boundaries. It’s also important to note that the highest HQ assumes that the primary source of 

the ethylene glycol ethyl ether acetate emissions from coating operations, was modeled with 

an hourly emissions multiplier of 1.2 times the annual emissions rate. Further, this 

exceedance was only predicted to occur in a remote, non-inhabited area just adjacent to the 

facility fence line for 2 hours a year. No facilities are estimated to have an AEGL or an EPRG 

greater than 1. Acute estimates for each plant and pollutant are provided in Appendix 7 of 

this document, while refined estimates are provided in Appendix 8 of this document (Acute 

Impacts Refined Analysis). 

Table 3.2-2. MACT Level Actual Emissions Chronic Inhalation Risks for

Aerospace Manufacturing and Rework Source Category

Result HAP “Drivers”

Facilities in Source Category

Number of Facilities Estimated to be in 

Source Category 144 n/a

Number of Facilities Identified by ICR and 

Modeled in Risk Assessment 144 n/a

Cancer Risks
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 40

Table 3.2-2. MACT Level Actual Emissions Chronic Inhalation Risks for

Aerospace Manufacturing and Rework Source Category

Result HAP “Drivers”

Maximum Individual Lifetime Cancer Risk 

(in 1 million) 10

strontium chromate, tetracholoroethene, 

chromium (VI) compounds, zinc 

chromate, barium chromate

Number of Facilities with Maximum Individual Lifetime Cancer Risk:

Greater than or equal to 100 in 1 million 0 n/a

Greater than or equal to 10 in 1 million 2

strontium chromate, tetracholoroethene, 

chromium (VI) compounds, zinc 

chromate, barium chromate

Greater than or equal to 1 in 1 million 27

strontium chromate, tetracholoroethene, 

chromium (VI) compounds, zinc 

chromate, barium chromate, cadmium 

compounds, ethyl benzene, sodium 

chromate, 4,4'-methylenedianiline, nickel 

compounds, chromium (VI) trioxide 

(chromic acid mist), methylene chloride, 

zinc potassium chromate, 

trichloroethylene, calcium chromate, 

arsenic compounds, naphthalene, chromic 

acid (VI), bis(2-ethylhexyl)phthalate, 2,4-

toluene diisocyanate, formaldehyde, pdichlorobenzene

Chronic Noncancer Risks

Maximum Kidney Hazard Index 0.5 cadmium compounds

Number of Facilities with Maximum Respiratory Hazard Index:

Greater than 1 0 n/a

Acute Noncancer Screening Results

Maximum Acute Hazard Quotient31

2

0.03

0.02

0.01

0.005

Ethylene glycol ethyl ether acetate (REL)

Phenol (ERPG-1)

Phenol (AEGL-1)

Phenol (AEGL-2)

Phenol (ERPG-2)

Number of Facilities With Potential for 

Acute Effects 1 Ethylene glycol ethyl ether acetate

Population Exposure

Number of People Living Within 50 

Kilometers of Facilities Modeled 130,000,000 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 1,500 n/a

 

31 As shown in Tables 2.6.3 and 3.2-2 there are no AEGL or ERPG values for nickel or arsenic compounds. 

Additionally, there are no short-term occupational values for these metals to compare to the REL values.
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 41

Table 3.2-2. MACT Level Actual Emissions Chronic Inhalation Risks for

Aerospace Manufacturing and Rework Source Category

Result HAP “Drivers”

Greater than or equal to 1 in 1 

million 180,000 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) 0.02 n/a

Contribution of HAP to Cancer Incidence

strontium chromate 66% n/a

chromium (VI) compounds 15% n/a

4,4'-methylenedianiline 3% n/a

nickel compounds 3% n/a

cadmium compounds 3% n/a

Contribution of Process to Cancer Incidence

Coatings (CT) 46% n/a

Specialty Coatings (SC) 44% n/a

Blast Depainting (BD) 8% n/a

Solvent Cleaning (CL) 2% n/a

 

Table 3.2-3. MACT Level Actual Emissions Acute Inhalation Risks for

Aerospace Manufacturing and Rework Source Category 

Refined Results Maximum Acute Hazard Quotients Acute Dose-Response Values

HAP

Max. 1-

hr. Air 

Conc. 

(mg/m3)

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)

Ethylene 

glycol ethyl 

ether acetate 0.3 2 0.14

Notes on Refined Process:

1) The initial 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.
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 42

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.

MACT-level Multipathway Assessment Results (Actual)

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

performed a screening analysis (Tier 1 and 2) that compared emissions of PB-HAP emitted 

from the petroleum refining source sector (based on actual emissions) to screening emission 

rates (see section 2.5). The PB-HAP emitted by facilities in the subject source category 

include cadmium compounds (31 facilities) and mercury compounds (6 facilities). 

Following the above approach, no facility emitted cadmium or mercury compounds above the 

Tier 1 screening level. Table 3.2-4 summarizes the results of this screening (Tier 1) 

comparison. Due to the theoretical construct of the screening model, these factors are not 

directly translatable into estimates of risk or hazard quotients for these facilities. 

Table 3.2-4. MACT Level Actual Emissions – Summary of Multipathway Screening for

Aerospace Manufacturing and Rework Source Category

 

Tier 1 Tier 2

PB-HAP

Num 

Facilities 

Emitting PBHAP

(144 in source 

category)

Max 

PB-HAP

Emissions 

(TPY)

Max PB-HAP

Emissions /

Screening 

Level

Num 

Facilities 

Exceeding

PB-HAP

Screening 

Level

Max

PB-HAP

Emissions /

Screening 

Level

Num 

Facilities 

Exceeding 

PB-HAP

Screening 

Level

Cadmium Compounds 31 0.015 1 0 na na

Divalent Mercury 6 0.00019 1 0 na na

In evaluating the potential for multipathway effects from emissions of lead, modeled 

maximum annual lead concentrations were compared to the NAAQS for lead (0.15 μg/m3). 

Lead emissions were reported from 103 facilities. Results of this analysis estimate that with 

the exception of one facility, the NAAQS for lead would not be exceeded by any facility. At 

the one facility, the lead NAAQS is exceeded 1.5 times at an on-site location. Off-site at this 

facility, the concentrations are below the NAAQS. 
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 43

MACT-level Environmental Assessment Results (Actual)

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

associated with emissions of the following environmental HAP from the Aerospace 

Manufacturing and Rework Facilities source category: lead, mercury, cadmium, hydrogen 

chloride and hydrogen fluoride. The results of the environmental screening analysis are 

summarized in Table 3.2-5. 

In the Tier 1 screening analysis for PB-HAP (other than lead, which was evaluated 

differently) the individual modeled Tier 1 concentrations for mercury and cadmium did not 

exceed any ecological benchmark for any facility in the source category. For lead, we did not 

estimate any exceedances of the secondary lead NAAQS.

For hydrogen fluoride and hydrogen chloride, the average modeled concentration around each 

facility (i.e., the average concentration of all off-site data points in the modeling domain) did 

not exceed the ecological benchmarks. In addition, each individual modeled concentration of 

hydrogen chloride and hydrogen fluoride (i.e., each off-site data point in the modeling

domain) was below the ecological benchmarks for all facilities. 

Table 3.2-5 MACT Level Actual Emissions - Summary of Environmental Risk Screen Results 

for the Aerospace Manufacturing and Rework Facilities 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 NA NA 0% 0%

Hg NA None NA NA 0% 0%

Cd None None NA NA 0% 0%

Acid 

Gases

HF3 NA None - - NA 0%

HCL4 NA None - - NA 0%

NA – Not Applicable. 

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

is a one tier 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. 

3 – For HF, we evaluated two benchmarks, one from Canada and the other from the State of Washington. 

Although, they are both considered to be LOELs - the level between a NOAEL and a LOAEL, we have listed the 

results under the LOAEL column.

4 – For HCL, we evaluated one benchmark at the LOAEL level.

 

Facility-wide Inhalation Assessment Results (Actual)
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 44

The facility-wide chronic MIR and TOSHI, available in Appendix 7, are based on emissions 

from all sources at the identified facilities (both MACT and non MACT sources). The results 

of the facility-wide assessment for cancer risks are summarized in Table 3.2-6. The results 

indicate that 44 facilities with aerospace manufacturing and rework processes have a facilitywide cancer MIR greater than or equal to 1 in a million. The maximum facility-wide cancer 

MIR is 20 in a million, mainly driven by arsenic, and chromium (VI) compounds, from 

internal combustion engines. The maximum facility-wide TOSHI for the source category is 

estimated to be less than 1, mainly driven by emissions of hexamethylene-1,6-diisocyanate 

from non MACT specialty coatings operations.

Table 3.2-6 Source Category Contribution to Facility-Wide 

Chronic Cancer Risks (Actual Emissions)

Petroleum Refining Source 

Sector

Number of Facilities Binned by Facility-Wide 

MIR (in 1 million)

Source Sector MIR Contribution 

to Facility-Wide MIR

<1 1≤ MIR<10 10≤ MIR<100 > 100 Total

> 90% 49 22 1 0 72

50-90% 16 1 2 0 19

10-50% 14 2 1 0 17

< 10% 21 15 0 0 36

Total 100 40 4 0 144

3.2.2 Baseline Allowable Emission Risks 

Baseline risk results were also developed using the best estimates of allowable HAP 

emissions under the current MACT standards. The basic chronic inhalation risk estimates 

presented here are the maximum individual lifetime cancer risk, the maximum chronic hazard 

index, and the cancer incidence. Acute, multipathway or ecological risks are not determined 

for allowable emissions.

MACT-level Inhalation Assessment Results (Allowable)

Analysis of potential differences between actual emissions levels and the maximum emissions 

allowable under the MACT standards (i.e., MACT-allowable emissions) were also calculated 

for the source category. Risk results from the inhalation risk assessment using the MACTallowable emissions indicate that the maximum lifetime individual cancer risk could as high 

as 10 in a million, and that the maximum chronic noncancer Hazard Index value is about 0.5. 

The total estimated cancer incidence from this source category considering allowable 

emissions is expected to be about 0.02 excess cancer cases per year or one excess case in 

every 50 years. Based on allowable emission rates approximately 2,000 people were 

estimated to have cancer risks above 10 in a million and approximately 180,000 people were 

estimated to have cancer risks above 1.
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 45

4 General discussion of uncertainties and how they have been 

addressed

4.1 Exposure modeling uncertainties

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

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

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

analysis. 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 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 percent below the predicted value to as much as 84 percent 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 percent lower to 84

percent 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 refined our assessment by developing a better understanding of the geography of the 

facility relative to potential exposure locations. 

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 ( -
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 46

term (>24 hours up to 30 days), and subchronic (>30 days up to 10 percent of lifetime) 

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

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

associated with the emissions included in an assessment, we rely on both chronic (cancer and 

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

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

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 [29] (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 
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 47

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).32 In some circumstances, the true risk 

could be as low as zero; however, in other circumstances the risk could also be greater.33 

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.34 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 [30]. Some of the major sources of uncertainty and 

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

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

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

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

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

tumors would occur in humans.35 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 

 

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

33 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.

34 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.

35 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.”
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 48

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 

ncertainties 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 percent 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 
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 49

slope of this relationship may not be the same as the slope estimated by the straight line 

approach. 

EPA most frequently utilizes a linear low-dose extrapolation approach as a baseline sciencepolicy choice (a “default”) when available data do not allow a compound-specific 

determination. This approach is designed to not underestimate risk in the face of uncertainty 

and variability. EPA believes that linear dose-response models, when appropriately applied 

as part of EPA’s cancer risk assessment process, provide an upper bound estimate of risk and 

generally provide a health protective approach. Note that another source of uncertainty is the 

characterization of low-dose nonlinear, non-threshold relationships. The National Academy 

of Sciences has encouraged the exploration of sigmoidal type functions (e.g., log-probit 

models) in representing dose response relationships due to the variability in response within 

human populations. Another National Research Council (NRC) report [31] 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) [32]. 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. 
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 50

However, one approach used commonly in EPA assessments that may help address variability 

in response is to extrapolate human response from results observed in the most sensitive 

species and sex tested, resulting typically in the highest URE which can be supported by 

reliable data, thus supporting estimates that are designed not to underestimate risk in the face 

of uncertainty and variability.

Chronic noncancer assessment

Chronic noncancer reference values represent chronic exposure levels that are intended to be 

health-protective. That is, EPA and other organizations which develop noncancer reference 

values (e.g., the Agency for Toxic Substances and Disease Registry – ATSDR) utilize an 

approach that is intended not to underestimate risk in the face of uncertainty and variability. 

When there are gaps in the available information, uncertainty factors (UFs) are applied to 

derive reference values that are intended to be protective against appreciable risk of 

deleterious effects. Uncertainty factors are commonly default values36 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.37 To derive values that are intended to be “without 

appreciable risk,” EPA’s methodology relies upon an uncertainty factor (UF) approach [33], 

[34] 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 

 

36

 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. 

37 See IRIS glossary
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 51

using statistical modeling of the available data, and then determines the appropriate point of 

departure (POD) for derivation of the reference value. A POD is determined by (in order of 

preference): (1) a statistical estimation using the benchmark dose (BMD) approach; (2) use of 

the dose or concentration at which the toxic response was not significantly elevated (no 

observed adverse effect level— NOAEL); or (3) use of the lowest observed adverse effect 

level (LOAEL).

A series of downward adjustments using default UFs is then applied to the POD to estimate 

the reference value [35]. 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 
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 52

humans are more sensitive than rodents when compared on a mg/kg/day basis. The default 

choice of 10 for the interspecies UF is consistent with these differences. For a specific 

chemical, differences in species responses may be greater or less than this value.

Pharmacokinetic models are useful to examine species differences in pharmacokinetic 

processing and associated uncertainties; however, such dosimetric adjustments are not always 

possible. Information may not be available to quantitatively assess toxicokinetic or 

toxicodynamic differences between animals and humans, and in many cases a 10-fold UF 

(with separate factors of 3 for toxicokinetic and toxicodynamic components) is used to

account for expected species differences and associated uncertainty in extrapolating from 

laboratory animals to humans in the derivation of a reference value. If information on one or 

the other of these components is available and accounted for in the cross-species 

extrapolation, a UF of 3 may be used for the remaining component.

3) In the case of reference values for chronic exposures where only data from shorter 

durations are available (e.g., 90-day subchronic studies in rodents) or when such data are

judged more appropriate for development of an RfC, an additional UF of 3 or 10-fold is 

typically applied unless the available scientific information supports use of a different value.

4) Toxicity data are typically limited as to the dose or exposure levels that have been 

tested in individual studies; in an animal study, for example, treatment groups may differ in 

exposure by up to an order of magnitude. The preferred approach to arrive at a POD is to use 

BMD analysis; however, this approach requires adequate quantitative results for a meaningful 

analysis, which is not always possible. Use of a NOAEL is the next preferred approach after 

BMD analysis in determining a POD for deriving a health effect reference value. However, 

many studies lack a dose or exposure level at which an adverse effect is not observed (i.e., a 

NOAEL is not identified). When using data limited to a LOAEL, a UF of 10 or 3-fold is 

often applied. 

5) The database UF is intended to account for the potential for deriving an 

underprotective RfD/RfC due to a data gap preventing complete characterization of the 

chemical’s toxicity. In the absence of studies for a known or suspected endpoint of concern, a 

UF of 10 or 3-fold is typically applied.

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 this facility, 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 
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 53

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 

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.
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 54

Acute noncancer assessment

Many of the UFs used to account for variability and uncertainty in the development of acute 

reference values are quite similar to those developed for chronic durations, but more often 

using individual UF values that may be less than 10. UFs are applied based on chemicalspecific or health effect-specific information (e.g., simple irritation effects do not vary 

appreciably between human individuals, hence a value of 3 is typically used), or based on the 

purpose for the reference value (see the following paragraph). The UFs applied in acute 

reference value derivation include: 1) heterogeneity among humans; 2) uncertainty in 

extrapolating from animals to humans; 3) uncertainty in LOAEL to NOAEL adjustments; and 

4) uncertainty in accounting for an incomplete database on toxic effects of potential concern. 

Additional adjustments are often applied to account for uncertainty in extrapolation from 

observations at one exposure duration (e.g., 4 hours) to arrive at a POD for derivation of an 

acute reference value at another exposure duration (e.g., 1 hour). 

Not all acute reference values are developed for the same purpose and care must be taken 

when interpreting the results of an acute assessment of human health effects relative to the 

reference value or values being exceeded. Where relevant to the estimated exposures, the 

lack of threshold values at different levels of severity should be factored into the risk 

characterization as potential uncertainties. 
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 55

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34. 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.

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

http://www.epa.gov/ncea/iris/RFC_FINAL1.pdf
1

Appendix 1

Aerospace Manufacturing and Rework Facilities RTR Modeling File Preparation
2

MEMORANDUM

TO: Anne Pope and Kim Teal, U.S. EPA

FROM: Stacie Enoch, Jason Huckaby, Ashley Penrod, and Darcy Wilson, ERG

DATE: December 17, 2014

SUBJECT: Aerospace Manufacturing and Rework Facilities RTR Modeling File Preparation

I. Introduction

The purpose of this memorandum is to provide information on the U.S. Environmental Protection 

Agency (EPA) Risk and Technology Review (RTR) database used in estimating residual risk for the 

Aerospace Manufacturing and Rework Facilities National Emission Standards For Hazardous Air 

Pollutants (NESHAP) source category. This NESHAP covers only major sources.

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

emissions of hazardous air pollutants (HAPs) from stationary sources. In the first stage, section 

112(d) requires the EPA to develop technology-based standards for categories of industrial sources. 

In the second stage, known as the residual risk stage, section 112(f)(2) requires EPA to assess the 

health and environmental risks that remain after sources come into compliance with standards 

based on the Maximum Achievable Control Technology (MACT). If additional risk reductions are 

necessary to protect public health with an ample margin of safety or to prevent adverse 

environmental effects, EPA must develop standards to address these remaining risks. As part of this 

second stage, data were gathered to assess the residual risks from the Aerospace Manufacturing

NESHAP source category.

The 2002 National Emissions Inventory (NEI) data served as the starting point for the initial phase of 

this assessment. Using the process regulatory code,1a subset of the NEI was developed that 

contained facility, process, and emissions data for each facility potentially subject to the Aerospace 

Manufacturing and Rework Facilities NESHAP (40 CFR 63, subpart GG) and in the Aerospace 

Manufacturing source category (regulatory code 63GG). The 2002 NEI data for Aerospace 

Manufacturing facilities were released through an Advanced Notice of Proposed Rulemaking (ANPR) 

in March of 2007, where NEI revisions for facilities in the MACT source category were solicited from 

State and local agencies, industry representatives, and EPA. The revisions that were provided to EPA 

were incorporated into the 2005 NEI.

 

1 Assigning data with regulatory codes allows EPA to determine reductions attributable to the MACT program. The 

NEI associates regulatory codes corresponding to MACT source categories with stationary major and area source 

data.

www.erg.com
3

The 2005 NEI data then served as the starting point for the next phase of the RTR assessment. 

On February 4, 2011, EPA sent out an information collection request (ICR) requesting data on 

aerospace-related processes and general information about each facility. Information was requested 

on operations subject to 40 CFR 63, subpart GG (coatings, blast depainting operations, solvent 

depainting operations, and solvent cleaning operations) as well as specialty coatings, chemical 

milling and metal finishing operations, composite processing, storage tanks, and wastewater 

treatment. Information was also requested on booth characteristics and control devices and 

locational coordinates. 

On October 2, 2012, EPA sent out an ICR requesting stack emissions testing data for select coating 

operations and spray booths and blast depainting, composite processing and metal finishing 

operations.

On May 23, 2013, EPA provided the estimated facility-wide emissions rates for major sources and 

synthetic minor sources based on the results of the ICR data collection effort for industry review.

On September 18, 2013, EPA issued a request for supplemental coatings analyses for select facilities.

On May 15, 2014, EPA provided industry the opportunity for a final review of the estimated 

emissions (and emission estimation methods) for processes subject to 40 CFR 63, subpart GG and

other processes included in the February 4, 2011 ICR request, as well as other facility operations 

included in the 2011 NEI. Review comments were also solicited on stack parameters (including 

fugitive area dimensions) and locational coordinates.

The following sections detail how EPA calculated emissions and conducted quality assurance/quality 

control (QA/QC) of the data. 

II. Category and Specialty Coatings Actual Emissions Development

a. Regulated processes included coatings, blast depainting, solvent depainting, and 

solvent cleaning. Non-regulated processes included specialty coatings, chemical 

milling and metal finishing, composite processing, storage tanks, and wastewater 

treatment.

b. February 4, 2011 ICR Data Compilation

The ICR requested data on coating usage and physical property data, emissions, booth 

characteristics, and locational coordinates. Forms A-1 and A-2 requested facility 

address and contact information. Form B-1 requested paint spray-booth-specific 

information. Form C-1 (and C-2 in metric units) requested coating usage and physical 

property data that EPA then used in calculating HAP emission estimates. Forms D-1, 

E-1 through E-3, G-1, and H-1 requested HAP emission estimates from tanks used for 

chemical milling or metals finishing operations, blast depainting/cleaning operations, 

solvent depainting operations, solvent cleaning operations, storage tanks, and 

wastewater treatment. Form F-1 requested resins usage and physical property data 

that EPA then used in calculating HAP emission estimates from composites processing 
4

operations. Forms J-1 through J-10 requested information specific to each control 

device used.

c. Development of Actual Emissions Estimates

The following equations and assumptions were used to develop actual emissions 

estimates from the ICR data.

Coating Operations (C1)

Emissions were calculated for each HAP within a coating and for each booth in which 

the coating was used. The following equations were used to calculate coating 

emissions in tons per year.

 Organic HAP stack emissions from coating usage inside a booth:

Volume ∗ Density ∗ HAP Mass Fraction ∗

(Coating Usage Pct inside booth/100) ∗ (Capture Efficiency/100) ∗

(1 − (Control Efficiency/100)) ∗ (1/2000)

 Organic HAP fugitive emissions from coating usage inside a booth:

Volume ∗ Density ∗ HAP Mass Fraction ∗

(Coating Usage Pct inside booth/100) ∗ (1 − Capture Efficiency/100) ∗

(1/2000)

 Organic HAP fugitive emissions from coating usage outside of a booth:

Volume ∗ Density ∗ HAP Mass Fraction

∗ (Coating Usage Pct outside booth/100) ∗ (1/2000)

 Inorganic HAP stack emissions:

Volume ∗ Density ∗ HAP Mass Fraction ∗

(Coating Usage Pct inside booth⁄100) ∗ Adjustment Factor ∗

(Capture Efficiency/100) ∗ (1 − (Filter Control Efficiency/100)) ∗

(1/2000)

 Inorganic HAP fugitive emissions:

Volume ∗ Density ∗ HAP Mass Fraction ∗

(Coating Usage Pct inside booth⁄100) ∗ Adjustment Factor ∗ (1 −

Capture Efficiency/100) ∗ (1/2000)

 Inorganic HAP fugitive emissions (out of booth usage):

Volume ∗ Density ∗ HAP Mass Fraction ∗

(Coating Usage Pct outside booth⁄100) ∗ Adjustment Factor ∗(1/2000)
5

Where:

Volume = total coating volume used (gal/yr);

Density = density of the coating (lb coating/gal coating);

HAP Mass Fraction = the fraction of a specific HAP in the coating (lb HAP/lb 

coating);

Coating Usage Pct = the portion of the annual coating usage at the specified 

location (i.e., in the identified booth, or outside of booth usage);

Adjustment Factor = factor to account for the transfer of coating solids to the 

part being coated (assumed 65%) and fallout for coatings applied within a 

booth (assumed 50%);

Capture Efficiency = the percent of emissions captured by the booth;

Control Efficiency = the percent reduction achieved by the control device 

(organic);

Filter control efficiency = the percent reduction achieved by the booth filter 

(inorganic HAP).

Assumptions

 Due to the overall lack of control efficiency data, all control efficiency values 

were assumed based on the type of control device (95% emission control for 

carbon adsorbers and scrubbers and 98% control for thermal incinerators). If no 

control device was listed, the control efficiency was assumed to be zero. 

 All materials applied inside a booth were assumed to be spray applied.

 If no filter control efficiency was provided, a value of 98% was assumed.

 If no capture efficiency was provided, the capture efficiency was assumed based 

on the booth type and pollutant type. 

o For organic pollutants, the capture efficiencies were assumed to be as 

follows: (open/open face/other: 90%, permanent total enclosure (PTE):

100%; enclosed but not a PTE: 98%); if the facility did not provide a 

capture efficiency or booth type, a value of 90% was assumed. 

o For inorganic pollutants, the capture efficiencies were assumed to be as 

follows: (open/open face/other: 99%, permanent total enclosure (PTE):

100%; enclosed but not a PTE: 99.5%); if the facility did not provide a 

capture efficiency or booth type, a value of 99% was assumed.

 It was assumed that inorganic fugitive emissions from application of coatings 

outside of the booth are zero if the coatings were not spray applied. If the 

coatings were spray applied, the transfer efficiency was assumed to be 65%. If 

the application method for coatings applied outside of the booth was not 

specified, it was assumed to be spray applied.

Chemical Milling and Metal Finishing Operations (D1) 

Facilities conducting these types of operations were required to report emissions 

directly instead of providing the usage/composition data required to calculate 

emissions (such as in C1). Therefore, the only calculation required was to convert 

emissions from pounds per year to tons per year.
6

Blast Depainting/Cleaning Operations (E1), Solvent Depainting Operations (E2), and 

Storage Tanks (G1)

Facilities conducting these types of operations were required to report emissions 

directly instead of providing the usage/composition data required to calculate 

emissions (such as in C1). However; it was assumed that the emission values reported 

(Emislbyr in equations below) were stack emissions after capture and control, and it 

was therefore necessary to back-calculate the fugitive emissions from the stack 

emissions. The following equations were used to calculate emissions for E1, E2, and 

G1 operations in tons per year.

 Stack emissions:

Emislbyr ∗ (1/2000)

 Fugitive emissions for units not equipped with a control device:

(Emislbyr/(Capture Efficiency/100)) ∗ (1

− (Capture Efficiency/100)) ∗ (1/2000)

 Fugitive emissions for units equipped with a control device:

(Emislbyr/((Capture Efficiency/100) ∗ (1 − (Control Efficiency/

100)))) ∗ (1 − (Capture Efficiency/100)) ∗ (1/2000)

Assumptions

 As stated above, it was assumed that the emission values reported in the 

survey are stack emissions after capture and control.

 If no capture efficiency was provided, a value of 70% was used for E1 and E2 

operations, and zero was used for G1 operations. 

 All control efficiency values were assumed based on the type of control device, 

as outlined for C1 operations. 

 If a facility indicated that total mass balance emissions were reported instead 

of stack emissions after control, then the emissions were adjusted to take into 

account the capture and control efficiencies as requested.

Solvent Cleaning Operations – Point and Fugitive Sources (E3)

Facilities conducting solvent cleaning operations were required to report emissions 

directly as either stack or fugitive emissions. Therefore, the only calculation required 

was to convert emissions from pounds per year to tons per year.

Composite Processing Operations (F1 Resins)

Emissions were calculated for each HAP within the resin and for each booth in which 

the resin was used. The following equations were used to calculate emissions from 

composite processing operations in tons per year.
7

 Stack emissions:

Volume ∗ Density ∗ HAP Mass Fraction ∗ (Pct HAP emitted/100) ∗

(Resin Usage Pct inside booth/100) ∗ (Capture Efficiency/100) ∗ (1 −

(Control Efficiency/100)) ∗ (1/2000)

 Fugitive emissions:

Volume ∗ Density ∗ HAP Mass Fraction ∗ (Pct HAP emitted/100) ∗

(Resin Usage Pct inside booth/100) ∗ (1 − (Capture Efficiency/100)) ∗

(1/2000)

 Fugitive emissions (out of booth usage):

Volume ∗ Density ∗ HAP Mass Fraction ∗ (Pct HAP emitted/100) ∗

(Resin Usage Pct outside booth/100) ∗ (1/2000)

Assumptions

 All control efficiency values were assumed based on the type of control device, 

as outlined for C1 operations.

 All materials applied inside a booth were assumed to be spray applied.

 If no capture efficiency was provided, the capture efficiency was assumed 

based on the booth type and pollutant type. 

o For organic pollutants, the capture efficiencies are assumed to be as 

follows: (open/open face/other: 90%, permanent total enclosure (PTE):

100%; enclosed but not a PTE: 98%); if the facility did not provide a 

capture efficiency or booth type, a value of 90% was assumed. 

o For inorganic pollutants, the capture efficiencies are assumed to be as 

follows: (open/open face/other: 99%, permanent total enclosure (PTE):

100%; enclosed but not a PTE: 99.5%); if the facility did not provide a 

capture efficiency or booth type, a value of 99% was assumed. 

 For outside the booth application, if the percent HAP emitted was not 

provided, a value of 100 percent was assumed for organic pollutants. For 

inorganic pollutants from resins that are applied outside of a booth that are 

not spray applied, it was assumed that 0% was emitted. If the out of booth 

resins are spray applied, the transfer efficiency was assumed to be 65% (or 35% 

emitted). If the application method for resins applied outside of the booth was 

not specified, it was assumed to be spray applied.

 For inorganic pollutants from resins applied inside a booth, the percent HAP 

emitted was adjusted to account for an assumed transfer efficiency of 65% and 

50% fallout within the booth (see adjustment factor used in equations for C1).

Wastewater Treatment Operations (H1)

Facilities conducting wastewater treatment operations were required to report 

emissions directly as either stack or fugitive emissions. Therefore, the only calculation 

required was to convert emissions from pounds per year to tons per year. 
8

Assumptions

It was assumed that the reported stack emission values are after capture and control. 

Therefore, no capture or control efficiencies were applied. If a facility did not report 

whether an emission value was fugitive or stack, it was assumed to be stack. 

d. Regulatory Code and Emission Process Group Assignments

i. The regulatory code “63GG” was assigned for processes that are currently 

subject to 40 CFR 63, Subpart GG: coatings, blast depainting, solvent 

depainting, and solvent cleaning.

ii. Emission process group and source classification code (SCC) were assigned as 

shown below based on information provided in the ICR response:

Table 1. Aerospace Manufacturing and Rework Emission Process Groups and SCCs

EMISSION_PROCESS_GROUP SCC SCC Description

Blast Depainting 30900201

Industrial Processes; Fabricated Metal Products; Abrasive 

Blasting of Metal Parts; General

Blast Depainting 68241001

MACT Source Categories; Miscellaneous Processes; Paint 

Stripper Users - Non-chemical Strippers; Media Blasting

Coatings 40202401

Petroleum and Solvent Evaporation; Surface Coating 

Operations; Large Aircraft; Prime Coating Operation

Coatings 40202406

Petroleum and Solvent Evaporation; Surface Coating 

Operations; Large Aircraft; Topcoat Operation

Coatings 40202499

Petroleum and Solvent Evaporation; Surface Coating 

Operations; Large Aircraft; Other Not Classified

Solvent Cleaning 40202402

Petroleum and Solvent Evaporation; Surface Coating 

Operations; Large Aircraft; Cleaning/Pretreatment

Solvent Depainting 68240030

MACT Source Categories; Miscellaneous Processes; Paint 

Stripper Users - Chemical Strippers; Application, 

Degradation, and Coating Removal Steps

Specialty Coatings 40200801

Petroleum and Solvent Evaporation; Surface Coating 

Operations; Coating Oven - General; General

Specialty Coatings 40202401

Petroleum and Solvent Evaporation; Surface Coating 

Operations; Large Aircraft; Prime Coating Operation

Specialty Coatings 40202406

Petroleum and Solvent Evaporation; Surface Coating 

Operations; Large Aircraft; Topcoat Operation

Specialty Coatings 40202499

Petroleum and Solvent Evaporation; Surface Coating 

Operations; Large Aircraft; Other Not Classified

Chemical Milling and Metal 

Finishing 30901018

Industrial Processes; Fabricated Metal Products; 

Electroplating Operations; Hard Chromium - Electroplating 

Tank
9

Table 1. Aerospace Manufacturing and Rework Emission Process Groups and SCCs

EMISSION_PROCESS_GROUP SCC SCC Description

Chemical Milling and Metal 

Finishing 30901038

Industrial Processes; Fabricated Metal Products; 

Electroplating Operations; Chromic Acid Anodizing -

Anodizing Tank

Chemical Milling and Metal 

Finishing 30901042

Industrial Processes; Fabricated Metal Products; 

Electroplating Operations; Copper (cyanide, including strike) 

- Electroplating Tank

Chemical Milling and Metal 

Finishing 30901048

Industrial Processes; Fabricated Metal Products; 

Electroplating Operations; Copper (general) - Electroplating 

Tank 1000 amp-hr current applied

Chemical Milling and Metal 

Finishing 30901052

Industrial Processes; Fabricated Metal Products; 

Electroplating Operations; Cadmium (cyanide) -

Electroplating Tank 1000 amp-hr current applied

Chemical Milling and Metal 

Finishing 30901058

Industrial Processes; Fabricated Metal Products; 

Electroplating Operations; Cadmium (general) -

Electroplating Tank 1000 amp-hr current applied

Chemical Milling and Metal 

Finishing 30901065

Industrial Processes; Fabricated Metal Products; 

Electroplating Operations; Nickel (sulfamate or watts) -

Electroplating Tank 1000 amp-hr current

Chemical Milling and Metal 

Finishing 30901068

Industrial Processes; Fabricated Metal Products; 

Electroplating Operations; Nickel (general) - Electroplating 

Tank

Chemical Milling and Metal 

Finishing 30901098

Industrial Processes; Fabricated Metal Products; 

Electroplating Operations; Other Not Classified

Chemical Milling and Metal 

Finishing 30901101

Industrial Processes; Fabricated Metal Products; Conversion 

Coating of Metal Products; Alkaline Cleaning Bath

Chemical Milling and Metal 

Finishing 30901102

Industrial Processes; Fabricated Metal Products; Conversion 

Coating of Metal Products; Acid Cleaning Bath (Pickling)

Chemical Milling and Metal 

Finishing 30901103

Industrial Processes; Fabricated Metal Products; Conversion 

Coating of Metal Products; Anodizing Kettle

Chemical Milling and Metal 

Finishing 30901199

Industrial Processes; Fabricated Metal Products; Conversion 

Coating of Metal Products; Other Not Classified

Chemical Milling and Metal 

Finishing 30901501

Industrial Processes; Fabricated Metal Products; Chemical 

Milling of Metal Products; Milling Tank

Chemical Milling and Metal 

Finishing 30904001

Industrial Processes; Fabricated Metal Products; Metal 

Deposition Processes; Metallizing: Wire Atomization and 

Spraying

Chemical Milling and Metal 

Finishing 30904010

Industrial Processes; Fabricated Metal Products; Metal 

Deposition Processes; Thermal Spraying of Powdered Metal

Chemical Milling and Metal 

Finishing 30999999

Industrial Processes; Fabricated Metal Products; Other Not 

Classified; Other Not Classified
10

Table 1. Aerospace Manufacturing and Rework Emission Process Groups and SCCs

EMISSION_PROCESS_GROUP SCC SCC Description

Composite Processing 30800720

Industrial Processes; Rubber and Miscellaneous Plastics 

Products; Fiberglass Resin Products; General

Storage Tanks 40100550

Petroleum and Solvent Evaporation; Organic Solvent 

Evaporation; Solvent Storage; General Processes: Drum 

Storage - Pure Organic Chemicals

Wastewater Treatment 40282001

Petroleum and Solvent Evaporation; Surface Coating 

Operations; Wastewater, Aggregate; Process Area Drains

III. Testing ICR

a. The test data were reviewed to confirm there were no additional source ID or 

pollutant matches that were missed when incorporating test data into the modeling 

file.

b. The methodology that was used to include (or not include) emissions test data in 

the modeling file was reviewed. Findings were as follows:

 Test data were compiled for 15 facilities.

 The compiled test data for one facility were not included for modeling file 

updates, because the units of measure could not be converted to a pound per 

hour or other comparable emissions rate.

 One test point for one facility was excluded from the modeling file updates 

because it overlapped with a different test point that was included in the 

modeling file updates.

 With the exception of the test point noted above, test data for the remaining 

14 facilities were added to the modeling file if the data met three criteria:

 There was at least one test run where emissions were not below 

detection limits. For the purpose of risk analysis for the aerospace 

source category, test points where all three runs were below detection 

limits were assumed to not emit that HAP from that process type and 

these emissions were not included in the risk modeling file. This 

assumption was made for this source category because of the variability 

in processes and input materials used at aerospace facilities (for 

example, some coatings do not contain certain HAP and, therefore, are 

not expected to be sources of those HAP).

 The data that could be mapped to source IDs were already included in 

the modeling file.

 The test pollutants were not reported in the original ICR data. If the test 

pollutants were already reported in the original ICR data for the 
11

corresponding source ID, then the stack test data were not added to the 

modeling file.

 If the test data met the three criteria, then the average emissions from the test 

runs were added to the modeling file. If at least one run was above the 

detection limit, one-half of the detection limit was used for any runs that were 

below the detection limit. If all three runs were below the detection limit, then 

emissions of that HAP were assumed to not be present for that process in the

aerospace source category. Stack test data were added to the modeling file for 

11 out of 14 facilities. 

c. Industry-supplied materials and prior EC/R analysis of emissions test data and 

capture efficiencies were reviewed to determine whether any calculations or 

assumptions currently used should be revised.

d. Emissions test data amendments and results of coatings test data were reviewed to: 

1) identify which pollutants in the emissions test data were not being reported 

under material balance-based coatings records already (i.e., should be added to the 

coatings category records); 2) which pollutants were measured in the emission 

tests, but were not found in coatings tests, and therefore should not be allocated to 

the coatings process group; 3) confirm that those pollutants in the emissions test 

data that were not flagged for appending were reflected in material balance-based 

coatings records; and 4) provide analytical test data instead of MSDS-supplied 

metals content values where the tested coating could be matched up to coatings 

reportedly being used.

IV. Speciation

a. Chromium (Cr) emissions from coatings and specialty coatings were speciated into 

its two most prevalent oxidation states: trivalent chromium (Cr III) and hexavalent 

chromium (Cr VI). Chromium from ICR processes were speciated using a profile of 

20.6% Cr VI and 79.4% Cr III. The coatings test results showed that strontium 

chromate is the predominant compound. Therefore, as a first step, a 25.5% weight 

fraction was applied to estimate how much of the compound is chromium. The 

25.5% weight fraction was then multiplied by 0.807 to adjust this factor downward, 

because the test results indicated that not all of the Cr in the coatings is Cr VI. The 

average ratio of Cr VI to Cr in the coatings test data is 0.807, as can be seen in the Cr 

VI to Cr ratio calculation from coatings test data found in Appendix A. Records that 

were previously speciated using a 25% Cr VI/75% Cr III were re-speciated using the 

profile described above for ICR processes. 

b. Mercury (Hg) emissions were speciated into three forms (elemental gaseous, 

particulate divalent, and gaseous divalent) using SCC-based NEI defaults. If a SCC 

from the modeling file did not match the NEI defaults, the generic 20% particulate 

divalent, 30% gaseous divalent, and 50% elemental gaseous profile was used.

V. Industry Reviews
12

a. Draft modeling file records were provided to all parent companies (in the EPA 

modeling file format) on May 15, 2014, for review. The file included the coating 

emission calculations in addition to modeling data. EPA provided review files to 

171 facilities. Responses were received for 84 facilities. Nine facilities were not 

included in the draft modeling file as of May 2014 and were added after the industry 

review period.

b. Revisions to the modeling file records included changes to emission values, control 

device types, stack parameters, locational coordinates, fugitive area dimensions, 

pollutant information, and removal of units or processes that are shutdown or 

otherwise not part of the facility. Revisions to the coating calculations included 

adding missing coatings and pollutants, removing pollutants no longer emitted, and 

revising the coating calculation inputs (volume, density, HAP mass fraction, coating 

usage percent, booth type and capture percent, control efficiency, filter control 

efficiency, and the adjustment factor).

VI. Development of Allowable Emissions for Category

Facilities were asked to provide a multiplier in the ICR survey to scale up average hourly 

emissions to maximum hourly emissions for air dispersion modeling. As there are typically a 

large number of emission points at a facility, determining the maximum hourly emissions 

from each and every emission point can be difficult. Therefore, EPA chose to use a single 

multiplier of 1.02 to scale average annual emissions to allowable annual emissions. The 

allowable emissions multiplier is based on the difference between 2008 production 

utilization rate of 83.1 and the 20-year historical maximum production utilization rate of 

85.0 (i.e., 85 ÷ 83.1 = 1.02).

VII. Development of Acute Emissions for Category

EPA determined that an acute factor of 1.2 was appropriate for the aerospace 

manufacturing industry. This multiplier is based on the ratio of the 2008 production 

utilization rate (83.1) and the maximum production rate of 100 (i.e., 100 ÷ 83.1 = 1.2).

VIII. Non-Category Aerospace Manufacturing Facility Emissions 

a. Non-category emissions for aerospace manufacturing facilities were obtained from 

the 2011 NEI. Non-category emissions were not available in the 2011 NEI or 2008 NEI 

for three facilities; emissions were pulled from the 2005 National-Scale Air Toxics 

Assessment (NATA) inventory for these three facilities.

b. Appendix B shows a list of SCCs that were included in the non-category records. 

Appendix C shows a list of additional SCCs that were included in the non-category 

records only if the record had the specific unit description shown. Individual 

chromium records were excluded if they were found to overlap with a regulated 

process.

c. Speciation
13

i. Chromium

SCC-based NEI Cr speciation defaults were used for non-ICR processes. If a SCC 

from the modeling file did not match the NEI default, the Cr profile for ICR 

processes was used. 

ii. Mercury

Mercury (Hg) emissions were speciated into three forms (elemental gaseous, 

particulate divalent, and gaseous divalent) using SCC-based NEI defaults. If a SCC 

from the modeling file did not match the NEI defaults, the generic 20% 

particulate divalent, 30% gaseous divalent, and 50% elemental gaseous profile 

was used.

d. Industry Review 

The 2014 industry review responses described in Section V above included 

changes for non-category records from NEI.

IX. Quality Assurance/Quality Control (QA/QC)

All of the QA/QC procedures discussed in the revised Quality Assurance Project Plan (QAPP) 

dated June 6, 2014 were implemented for the aerospace manufacturing and rework 

emissions estimates, locational coordinates, stack parameters, fugitive parameters, and 

non-category emissions records. Manual and computerized verification by a team member 

who did not perform the original data entry or calculations was conducted to ensure that all 

ICR data were correctly incorporated, and that all automated emission calculations were 

implemented correctly. Additional computerized QA/QC checks were then implemented, 

thereby conducting checks on close to 100% of the records.

a. Aerospace Manufacturing and Rework Facilities Emission Estimates

The Aerospace Manufacturing and Rework Facilities emission records included in the 

modeling file were reviewed to verify that no emissions were double counted, and 

that the emitted pollutants were consistent with the reported SCCs/unit or process 

descriptions. HAPs not typically emitted from the processes were removed. For 

example, EPA removed homopolymers from coating emissions where facilities 

reported both the homopolymer and hexamethylene diisocyanate (HMDI) 

components of a coating material and adjusted HMDI estimates that were 

overestimated using an average ratio from the study “Determination of 1,6-

Hexamethylene Diisocyanate (HDI) Emissions from Spray Booth Operations2.” Outliers 

(e.g., HAPs emitted in very large quantities relative to similar processes at other 

facilities) were flagged for investigation. For example, one facility reported specialty 

coating emissions in pounds per year instead of reporting coating inputs; EPA adjusted 

these emissions to tons per year. EPA contacted four facilities and made revisions for 

two facilities as needed based on responses. For example, specialty coating emissions 

were reassigned to non-category processes and duplicate emission values were 

 

2 Ontario Ministry of the Environment, April 2006.
14

removed for one facility, and fugitive chromium emissions from surface coating were 

removed for the other facility.

b. Locational Coordinates

Location coordinates (X_COORDINATE and Y_COORDINATE) were reviewed using 

ArcGIS®. Coordinates that placed stack emission release points over facility buildings, 

stacks, hangers, etc., were considered suitable. If coordinates placed an emission 

release point over parking lots, vegetation, residential housing, or other non-facility 

building, new coordinates were assigned. Fugitive emission release points are 

represented in modeling files as rectangles or squares. Coordinates of these release 

points should be located in the southwest corner of the fugitive area to be modeled. 

Fugitive coordinates were also QA’ed based on the fugitive length (y), width (x), and 

angle associated with the fugitive area or building. These parameters are discussed 

below and an example is displayed in Figure 1. If a fugitive area was shown to be over 

vegetation, residential housing, etc., the coordinates were reassigned to the 

southwest corner of the facility building based on the fugitive length, width, and 

angle.

Figure 1. Depiction of Fugitive Area Source Parameters3

c. Stack Parameters

Stack parameters included in the ICR submittals and the modeling file include stack 

diameter, stack height, exhaust stream temperature, and stream flow rate. Stream 

velocity is provided in the modeling file and was calculated based on stack diameter 

and flow rate. If one or more of the ICR stack parameters was not provided, aerospace 

stack parameter defaults were assigned using the average of stack parameters 

 

3

Figure created by ICF International.
15

reported in the ICR for other aerospace release points. The average aerospace stack 

parameters assigned as defaults are:

Stack height: 48.43091684 ft

Stack temperature: 76.27270788°F

Stack diameter: 4.846738822 ft

Stack velocity: 50.7294305266868 ft/sec

Stack flow rate: 978.086679026503 ft3/sec

The QA ranges for stack parameters for the aerospace modeling file are listed below. 

One exception to the QA ranges is for engine testing and internal combustion engines 

where the temperature can exceed the normal QA maximum. If either diameter or 

velocity was out of range for category records, diameter, velocity and flow rate were 

all set to the aerospace stack parameter defaults.

Table 2. Aerospace Stack Parameter QA Ranges

Parameter

QA Range for Category 

Records

QA Range for NonCategory Records Units

Min Max Min Max

Stack Height 0.1 1,200 0.1 1,200 ft

Exhaust Gas 

Temperature 50 1,800 50 1,800 °F

Stack Diameter 0.1 40 0.1 50 ft

Exhaust Gas Velocity 0.1 200 0.1 200 ft/sec

Exhaust Gas Flow Rate ft3/sec

d. Fugitive Parameters

Fugitive parameters in the modeling file include the same stack parameters as well as 

fugitive length, width, and angle. The fugitive area is comprised of the southwest 

corner of the representative building, or latitude and longitude, the length and width, 

or x and y depicted in Figure 1, and the angle at which the area is rotated in relation to 

the southwest corner. The fugitive angle must be between 0 and 90 degrees. The 

national defaults for fugitive parameters are listed below.

Table 3. Fugitive Stack Parameter Defaults Applied

National Defaults Units

Stack Height 10 ft

Exhaust Gas 

Temperature 72 °F

Stack Diameter 0.003 ft

Exhaust Gas Velocity 0.0003 ft/sec
16

Exhaust Gas Flow Rate 0 ft3/sec

e. Addition of new HAPs to RTR file

There were several new HAPs provided in the facility ICRs that were not yet included 

in the RTR (or NEI) pollutant look-up tables. The HAPs added to the RTR look-up table 

are provided in the following table.

Table 4. Additional Aerospace HAPs

Pollutant Pollutant Code

Cadmium Selenide Sulfide 12626367

Cobalt Neodecanoate 27253312

Cobalt(II) Nitrate 10141056

Cobalt(II) Phosphate Octahydrate 10294505

Coronene 191071

Dibasic Lead Phosphate 15845520

Iron Chromite Brown Spinel (C.I. Pigment Brown 35) 68187097

Lead Chromate Molybdate Sulfate (C.I. Pigment Red 104) 12656858

Manganese Ferrite Black Spinel (C.I. Pigment Black 26) 68186947

Magnesium Dichromate 14104859

Phosphoric Acid, Reaction Products with Aluminum Hydroxide 

and Chromium Oxide (CrO3)

92203026

Zinc Potassium Chromate 37224570

f. Non-category Emission Estimates

The NEI non-aerospace emission records included in the modeling file were 

reviewed to verify that no emissions were double counted, and that the emitted 

pollutants were consistent with the reported SCCs/unit or process descriptions. 

HAPs not typically emitted from the processes were removed. Outliers (e.g., HAPs 

emitted in very large quantities relative to similar processes at other facilities) were 

flagged for investigation. For example, the NEI emissions from a wastewater 

treatment process at one facility were adjusted. EPA contacted three facilities and 

made revisions as needed based on responses. For example, combustion emissions

were corrected at two facilities, and tetrachloroethylene emissions were removed 

from the third facility. 
A-1

Appendix A. Coatings Test Data where Chromium and Chromium (VI) Data are Available

Facility MSDSCoatingID AvgCrVIContent_mgPerkg AvgChromiumContent_mgPerkg FractionChromeVI Adjusted Fraction ChromeVI

1

Black Super Koropon base 

#528x310 31.1 48.6 0.639917695 0.639917695

1

Eco Prime Yellow-Green # 

EWDEO72 A Base 19590 21005 0.932635087 0.932635087

2 515X349 BASE COMPONENT 25033.33333 30015.33333 0.834018168 0.834018168

2

BR 127 Corrosion Inhibiting 

Primer 2080.666667 2699.333333 0.770807607 0.770807607

5

10P20-44_High Solids Epoxy 

Primer 36912 49047.96411 0.752569463 0.752569463

6

10P8-10NF; Fluid Resistant 

Epoxy Primer 14814.66667 21178.15975 0.699525683 0.699525683

6

515X346 Room Temperature 

Bonding Primer 15944.5 37971.39558 0.41990819 0.41990819

8

463-6-4; Aluminized Epoxy 

Primer Base 639.5 2801 0.228311317 0.228311317

8

BMS10-11AA-TYPE I-CLASS A 

GRADE E BASE 64922.5 65226 0.995346948 0.995346948

9 10P8-11LC Primer 18874.5 22901 0.824177983 0.824177983

10

MIL-PRF-85582D, TYPE I, CLASS 

C2 BASE 34477 40365 0.854131054 0.854131054

10

MIL-PRF-85582D-TYPE I-CLASS 

C2-CATALYST 2.53 1.92 1.317708333a 1

11

MIL-PRF-23377K TYPE 1 CLASS 

C2 PART A BASE 35750 36691 0.974353384 0.974353384

11

MIL-PRF-23377K TYPE 1 CLASS 

C2 PART B CATALYST 3.07 3.03 1.01320132a 1

11

MIL-PRF-85582D, TYPE I, CLASS 

C2, 44GN072 BASE 66158 84002 0.787576486 0.787576486

13 10P8-10NMF Base 19021.5 21092.5 0.901813441 0.901813441

13 BMS10-11A Type I Class A Cat 7.54 3.8 1.984210526a 1
A-2

Appendix A. Coatings Test Data where Chromium and Chromium (VI) Data are Available

Facility MSDSCoatingID AvgCrVIContent_mgPerkg AvgChromiumContent_mgPerkg FractionChromeVI Adjusted Fraction ChromeVI

13

BMS10-11A Type I Class A 

Primer 64112.5 67715.5 0.946792093 0.946792093

14

AQUAPRIME PRIMER, MIL-PRF85582D, TYPE I, CLASS C2, 

PART A 30680 39440 0.777890467 0.777890467

Average: 0.807

a Measured Cr VI was greater than Cr; all reported Cr is assumed to be Cr VI.
B-1

Appendix B. Non-category SCCs

SCC SCC Description

10100224 External Combustion Boilers; Electric Generation; Subbituminous Coal; Boiler, Spreader Stoker

10100501 External Combustion Boilers; Electric Generation; Distillate Oil - Grades 1 and 2; Boiler, Normal firing

10100602 External Combustion Boilers; Electric Generation; Natural Gas; Boiler < 100 Million BTU, except tangential

10102101 External Combustion Boilers; Electric Generation; Other Oil; All

10200204 External Combustion Boilers; Industrial; Bituminous Coal; Spreader Stoker

10200206 External Combustion Boilers; Industrial; Bituminous Coal; Underfeed Stoker

10200212 External Combustion Boilers; Industrial; Bituminous Coal; Pulverized Coal: Dry Bottom (Tangential)

10200401 External Combustion Boilers; Industrial; Residual Oil - Grade 6; Boiler

10200404 External Combustion Boilers; Industrial; Residual Oil; Grade 5 Oil

10200501 External Combustion Boilers; Industrial; Distillate Oil - Grades 1 and 2; Boiler

10200502 External Combustion Boilers; Industrial; Distillate Oil; 10-100 Million BTU/hr **

10200503 External Combustion Boilers; Industrial; Distillate Oil; < 10 Million BTU/hr **

10200601 External Combustion Boilers; Industrial; Natural Gas; > 100 Million BTU/hr

10200602 External Combustion Boilers; Industrial; Natural Gas; 10-100 Million BTU/hr

10200603 External Combustion Boilers; Industrial; Natural Gas; < 10 Million BTU/hr

10200799 External Combustion Boilers; Industrial; Process Gas; Other: Specify in Comments

10300203 External Combustion Boilers; Commercial/Institutional; Bituminous Coal; Cyclone Furnace

10300209 External Combustion Boilers; Commercial/Institutional; Bituminous Coal; Spreader Stoker

10300402 External Combustion Boilers; Commercial/Institutional; Residual Oil; 10-100 Million BTU/hr **

10300403 External Combustion Boilers; Commercial/Institutional; Residual Oil; < 10 Million BTU/hr **

10300404 External Combustion Boilers; Commercial/Institutional; Residual Oil; Grade 5 Oil

10300501 External Combustion Boilers; Commercial/Institutional; Distillate Oil - Grades 1 and 2; Boiler

10300502 External Combustion Boilers; Commercial/Institutional; Distillate Oil; 10-100 Million BTU/hr **

10300503 External Combustion Boilers; Commercial/Institutional; Distillate Oil; < 10 Million BTU/hr **

10300504 External Combustion Boilers; Commercial/Institutional; Distillate Oil; Grade 4 Oil

10300601 External Combustion Boilers; Commercial/Institutional; Natural Gas; > 100 Million BTU/hr

10300602 External Combustion Boilers; Commercial/Institutional; Natural Gas; 10-100 Million BTU/hr

10300603 External Combustion Boilers; Commercial/Institutional; Natural Gas; < 10 Million BTU/hr
B-2

Appendix B. Non-category SCCs

SCC SCC Description

10301002 External Combustion Boilers; Commercial/Institutional; Liquified Petroleum Gas (LPG); Propane

10500105 External Combustion Boilers; Space Heaters; Industrial; Distillate Oil

10500106 External Combustion Boilers; Space Heaters; Industrial; Natural Gas

10500206 External Combustion Boilers; Space Heaters; Commercial/Institutional; Natural Gas

20100102 Internal Combustion Engines; Electric Generation; Distillate Oil (Diesel); Reciprocating

20100107 Internal Combustion Engines; Electric Generation; Distillate Oil (Diesel); Reciprocating: Exhaust

20100201 Internal Combustion Engines; Electric Generation; Natural Gas; Turbine

20100202 Internal Combustion Engines; Electric Generation; Natural Gas; Reciprocating

20100901 Internal Combustion Engines; Electric Generation; Kerosene/Naphtha (Jet Fuel); Turbine

20200102 Internal Combustion Engines; Industrial; Distillate Oil (Diesel); Reciprocating

20200103 Internal Combustion Engines; Industrial; Distillate Oil (Diesel); Turbine: Cogeneration

20200104 Internal Combustion Engines; Industrial; Distillate Oil (Diesel); Reciprocating: Cogeneration

20200201 Internal Combustion Engines; Industrial; Natural Gas; Turbine

20200202 Internal Combustion Engines; Industrial; Natural Gas; Reciprocating

20200203 Internal Combustion Engines; Industrial; Natural Gas; Turbine: Cogeneration

20200252 Internal Combustion Engines; Industrial; Natural Gas; 2-cycle Lean Burn

20200253 Internal Combustion Engines; Industrial; Natural Gas; 4-cycle Rich Burn

20200254 Internal Combustion Engines; Industrial; Natural Gas; 4-cycle Lean Burn

20200401 Internal Combustion Engines; Industrial; Large Bore Engine; Diesel

20200402 Internal Combustion Engines; Industrial; Large Bore Engine; Dual Fuel (Oil/Gas)

20200901 Internal Combustion Engines; Industrial; Kerosene/Naphtha (Jet Fuel); Turbine

20201001 Internal Combustion Engines; Industrial; Liquified Petroleum Gas (LPG); Propane: Reciprocating

20201012 Internal Combustion Engines; Industrial; Liquified Petroleum Gas (LPG); Reciprocating Engine

20300101 Internal Combustion Engines; Commercial/Institutional; Distillate Oil (Diesel); Reciprocating

20300102 Internal Combustion Engines; Commercial/Institutional; Distillate Oil (Diesel); Turbine

20300201 Internal Combustion Engines; Commercial/Institutional; Natural Gas; Reciprocating

20300203 Internal Combustion Engines; Commercial/Institutional; Natural Gas; Turbine: Cogeneration

20300301 Internal Combustion Engines; Commercial/Institutional; Gasoline; Reciprocating
B-3

Appendix B. Non-category SCCs

SCC SCC Description

20301001 Internal Combustion Engines; Commercial/Institutional; Liquified Petroleum Gas (LPG); Propane: Reciprocating

20400101 Internal Combustion Engines; Engine Testing; Aircraft Engine Testing; Turbojet

20400102 Internal Combustion Engines; Engine Testing; Aircraft Engine Testing; Turboshaft

20400110 Internal Combustion Engines; Engine Testing; Aircraft Engine Testing; Jet A Fuel

20400199 Internal Combustion Engines; Engine Testing; Aircraft Engine Testing; Other Not Classified

20400201 Internal Combustion Engines; Engine Testing; Rocket Engine Testing; Rocket Motor: Solid Propellant

20400302 Internal Combustion Engines; Engine Testing; Turbine; Diesel/Kerosene

20400305 Internal Combustion Engines; Engine Testing; Turbine; Kerosene/Naphtha

20400402 Internal Combustion Engines; Engine Testing; Reciprocating Engine; Diesel/Kerosene

20400406 Internal Combustion Engines; Engine Testing; Reciprocating Engine; Kerosene/Naphtha (Jet Fuel)

30101602 Industrial Processes; Chemical Manufacturing; Phosphoric Acid: Wet Process; Gypsum Pond

30188801 Industrial Processes; Chemical Manufacturing; Fugitive Emissions; Specify in Comments Field

30400360 Industrial Processes; Secondary Metal Production; Grey Iron Foundries; Castings Finishing

30400401 Industrial Processes; Secondary Metal Production; Lead; Pot Furnace

30400409 Industrial Processes; Secondary Metal Production; Lead; Casting

30400867 Industrial Processes; Secondary Metal Production; Zinc; Kettle (Pot) Melting Furnace

30400873 Industrial Processes; Secondary Metal Production; Zinc; Casting

30402201 Industrial Processes; Secondary Metal Production; Metal Heat Treating; Furnace: General

30499999 Industrial Processes; Secondary Metal Production; Other Not Classified; Specify in Comments Field

30501050 Industrial Processes; Mineral Products; Coal Mining, Cleaning, and Material Handling; Vehicle Traffic: Light/Medium Vehicles

30600101 Industrial Processes; Petroleum Industry; Process Heaters; Oil-fired **

30601301 Industrial Processes; Petroleum Industry; Coke Handling System; Storage and Transfer

30622003 Industrial Processes; Petroleum Industry; Underground Storage Remediation & Other Remediation; Soil: Natural Gas

30700752

Industrial Processes; Pulp and Paper and Wood Products; Plywood Operations; Softwood Plywood, Veneer Dryer, Direct 

Natural Gas-Fired, Heated Zones

30900500 Industrial Processes; Fabricated Metal Products; Welding; General

30901003 Industrial Processes; Fabricated Metal Products; Electroplating Operations; Entire Process: Nickel

30901006 Industrial Processes; Fabricated Metal Products; Electroplating Operations; Entire Process: Chrome
B-4

Appendix B. Non-category SCCs

SCC SCC Description

30901018 Industrial Processes; Fabricated Metal Products; Electroplating Operations; Hard Chromium - Electroplating Tank

30901028 Industrial Processes; Fabricated Metal Products; Electroplating Operations; Decorative Chromium - Electroplating Tank

30901098 Industrial Processes; Fabricated Metal Products; Electroplating Operations; Other Not Classified

30901102 Industrial Processes; Fabricated Metal Products; Conversion Coating of Metal Products; Acid Cleaning Bath (Pickling)

30901103 Industrial Processes; Fabricated Metal Products; Conversion Coating of Metal Products; Anodizing Kettle

30901104 Industrial Processes; Fabricated Metal Products; Conversion Coating of Metal Products; Rinsing/Finishing

30901199 Industrial Processes; Fabricated Metal Products; Conversion Coating of Metal Products; Other Not Classified

30905100 Industrial Processes; Fabricated Metal Products; Shielded Metal Arc Welding (SMAW); General

30905200 Industrial Processes; Fabricated Metal Products; Gas Metal Arc Welding (GMAW); General

30990003 Industrial Processes; Fabricated Metal Products; Fuel Fired Equipment; Natural Gas: Process Heaters

30999999 Industrial Processes; Fabricated Metal Products; Other Not Classified; Other Not Classified

31299999 Industrial Processes; Machinery, Miscellaneous; Miscellaneous Machinery; Other Not Classified

31399999 Industrial Processes; Electrical Equipment; Other Not Classified; Other Not Classified

31499999 Industrial Processes; Transportation Equipment; Other Not Classified; Other Not Classified

39000699 Industrial Processes; In-process Fuel Use; Natural Gas; General

39001299 Industrial Processes; In-process Fuel Use; Solid Waste; General

39090001 Industrial Processes; In-process Fuel Use; Fuel Storage - Fixed Roof Tanks; Residual Oil: Breathing Loss

39090003 Industrial Processes; In-process Fuel Use; Fuel Storage - Fixed Roof Tanks; Distillate Oil (No. 2): Breathing Loss

39090004 Industrial Processes; In-process Fuel Use; Fuel Storage - Fixed Roof Tanks; Distillate Oil (No. 2): Working Loss

39090011 Industrial Processes; In-process Fuel Use; Fuel Storage - Fixed Roof Tanks; Dual Fuel (Gas/Oil): Breathing Loss

39091004 Industrial Processes; In-process Fuel Use; Fuel Storage - Floating Roof Tanks; Distillate Oil (No. 2): Withdrawal Loss

39091011 Industrial Processes; In-process Fuel Use; Fuel Storage - Floating Roof Tanks; Dual Fuel (Gas/Oil): Standing Loss

39900601 Industrial Processes; Miscellaneous Manufacturing Industries; Process Heater/Furnace; Natural Gas

39990003

Industrial Processes; Miscellaneous Manufacturing Industries; Miscellaneous Manufacturing Industries; Natural Gas: Process 

Heaters

39999992 Industrial Processes; Miscellaneous Manufacturing Industries; Miscellaneous Industrial Processes; Other Not Classified

39999993 Industrial Processes; Miscellaneous Manufacturing Industries; Miscellaneous Industrial Processes; Other Not Classified

39999999 Industrial Processes; Miscellaneous Manufacturing Industries; Miscellaneous Industrial Processes; Other Not Classified
B-5

Appendix B. Non-category SCCs

SCC SCC Description

40100198 Petroleum and Solvent Evaporation; Organic Solvent Evaporation; Dry Cleaning; Other Not Classified

40200801 Petroleum and Solvent Evaporation; Surface Coating Operations; Coating Oven - General; General

40301001

Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes); Gasoline RVP 

13: Breathing Loss (67000 Bbl. Tank Size)

40301005

Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes); Gasoline RVP 

10: Breathing Loss (250000 Bbl. Tank Size)

40301007

Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes); Gasoline RVP 

13: Working Loss (Tank Diameter Independent)

40301016

Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes); Jet Kerosene: 

Breathing Loss (67000 Bbl. Tank Size)

40301017

Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes); Jet Kerosene: 

Breathing Loss (250000 Bbl. Tank Size)

40301018

Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes); Jet Kerosene: 

Working Loss (Tank Diameter Independent)

40301019

Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes); Distillate Fuel

#2: Breathing Loss (67000 Bbl. Tank Size)

40301020

Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes); Distillate Fuel

#2: Breathing Loss (250000 Bbl. Tank Size)

40301021

Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes); Distillate Fuel 

#2: Working Loss (Tank Diameter Independent)

40301098

Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes); Specify Liquid: 

Breathing Loss (250000 Bbl. Tank Size)

40301099

Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes); Specify Liquid:

Working Loss (Tank Diameter Independent)

40301114

Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Floating Roof Tanks (Varying Sizes); Jet Kerosene: 

Standing Loss (250000 Bbl. Tank Size)

40301119

Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Floating Roof Tanks (Varying Sizes); Jet Kerosene: 

Withdrawal Loss

40400102

Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 10: Breathing Loss 

(67000 Bbl Capacity) - Fixed Roof Tank

40400108 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 10: Working Loss 
B-6

Appendix B. Non-category SCCs

SCC SCC Description

(Diameter Independent) - Fixed Roof Tank

40400131

Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 13: Standing Loss -

Ext. Floating Roof w/ Primary Seal

40400250 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Loading Racks

40400254 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Tank Truck Vapor Losses

40400270

Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Specify Liquid: Standing Loss - Int. 

Floating Roof w/ Secondary Seal

40400316

Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Oil and Gas Field Storage and Working Tanks; 

Fixed Roof Tank, Diesel, working+breathing+flashing losses

40400402

Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products - Underground Tanks; 

Gasoline RVP 13: Working Loss

40400411

Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products - Underground Tanks; Jet 

Kerosene: Breathing Loss

40400412

Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products - Underground Tanks; Jet 

Kerosene: Working Loss

40400497

Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products - Underground Tanks; 

Specify Liquid: Breathing Loss

40400498

Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products - Underground Tanks; 

Specify Liquid: Working Loss

40600131

Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank Cars and Trucks; Gasoline: 

Submerged Loading (Normal Service)

40600133

Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank Cars and Trucks; Jet Naphtha: 

Submerged Loading (Normal Service)

40600136

Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank Cars and Trucks; Gasoline: 

Splash Loading (Normal Service)

40600301

Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline Retail Operations - Stage I; 

Splash Filling

40600302

Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline Retail Operations - Stage I; 

Submerged Filling w/o Controls

40600306

Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline Retail Operations - Stage I; 

Balanced Submerged Filling
B-7

Appendix B. Non-category SCCs

SCC SCC Description

40600307

Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline Retail Operations - Stage I; 

Underground Tank Breathing and Emptying

40600401

Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Filling Vehicle Gas Tanks - Stage II; 

Vapor Loss w/o Controls

40600402

Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Filling Vehicle Gas Tanks - Stage II; 

Liquid Spill Loss w/o Controls

40600503

Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Pipeline Petroleum Transport -

General - All Products; Pump Station

40600707

Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Consumer (Corporate) Fleet 

Refueling - Stage I; Underground Tank Breathing and Emptying

40799997 Petroleum and Solvent Evaporation; Organic Chemical Storage; Miscellaneous; Specify in Comments

41082001 Petroleum and Solvent Evaporation; Dry Cleaning; Petroleum Solvent - Wastewater, Aggregate; Process Area Drains

42500101 Petroleum and Solvent Evaporation; Fixed Roof Tanks (210 Bbl Size) Breathing Loss

42500102 Petroleum and Solvent Evaporation; Fixed Roof Tanks (210 Bbl Size) Working Loss

50100402 Waste Disposal; Solid Waste Disposal - Government; Landfill Dump; Fugitive Emissions

50100404 Waste Disposal; Solid Waste Disposal - Government; Landfill Dump; Trench Method

50100406 Waste Disposal; Solid Waste Disposal - Government; Landfill Dump; Gas Collection System: Other

50100601 Waste Disposal; Solid Waste Disposal - Government; Fire Fighting; Structure: Jet Fuel

50200103 Waste Disposal; Solid Waste Disposal - Commercial/Institutional; Incineration; Controlled Air

50200602

Waste Disposal; Solid Waste Disposal - Commercial/Institutional; Landfill Dump; Municipal: Fugitive Emissions ** (Use 5-01-

004-02)

50300205 Waste Disposal; Solid Waste Disposal - Industrial; Open Burning; Rocket Propellant

50300702 Waste Disposal; Solid Waste Disposal - Industrial; Liquid Waste; Waste Treatment: General
C-1

Appendix C. Additional Non-Category SCCs with Unit Descriptions

Emission Unit Description SCC SCC Description

Building 1665 - open top 

chemical tank 39999995

Industrial Processes; Miscellaneous Manufacturing Industries; Miscellaneous Industrial Processes; 

Other Not Classified

Explosives Manufacturing 49099998

Petroleum and Solvent Evaporation; Organic Solvent Evaporation; Miscellaneous Volatile Organic 

Compound Evaporation; Identify the Process and Solvent in Comments

FIRING RANGE TRAINING 

OPERATIONS 40200101

Petroleum and Solvent Evaporation; Surface Coating Operations; Surface Coating Application -

General; Paint: Solvent-base

Solids Handling 39999994

Industrial Processes; Miscellaneous Manufacturing Industries; Miscellaneous Industrial Processes; 

Other Not Classified

Autoclave (from facility ID 

5555) 40201001 Petroleum and Solvent Evaporation; Surface Coating Operations; Coating Oven Heater; Natural Gas

Autoclave (NTR) 40201001 Petroleum and Solvent Evaporation; Surface Coating Operations; Coating Oven Heater; Natural Gas

Boilers and Humidifiers (I 

and S) 40201001 Petroleum and Solvent Evaporation; Surface Coating Operations; Coating Oven Heater; Natural Gas

Clean Line - Heating Units 

(I and S) 40201001 Petroleum and Solvent Evaporation; Surface Coating Operations; Coating Oven Heater; Natural Gas

Coating Ovens (NTR) 40201001 Petroleum and Solvent Evaporation; Surface Coating Operations; Coating Oven Heater; Natural Gas

GAS OVENS-PAINT 

CURING 40201001 Petroleum and Solvent Evaporation; Surface Coating Operations; Coating Oven Heater; Natural Gas

HEATERS (I and S and 

NTR) 40201001 Petroleum and Solvent Evaporation; Surface Coating Operations; Coating Oven Heater; Natural Gas

Ovens 1-10 (from facility 

ID 5555)(I and S) 40201001 Petroleum and Solvent Evaporation; Surface Coating Operations; Coating Oven Heater; Natural Gas

Fire training pit 39999994

Industrial Processes; Miscellaneous Manufacturing Industries; Miscellaneous Industrial Processes; 

Other Not Classified

Building No. 4035 - hard 

chrome plating tank 

[MACT, Subpart N] 39999994

Industrial Processes; Miscellaneous Manufacturing Industries; Miscellaneous Industrial Processes; 

Other Not Classified

CADMIUM 

ELECTROPLATING LINE 39999995

Industrial Processes; Miscellaneous Manufacturing Industries; Miscellaneous Industrial Processes; 

Other Not Classified

Wastewater Treatment 39999995

Industrial Processes; Miscellaneous Manufacturing Industries; Miscellaneous Industrial Processes; 

Other Not Classified
C-2

Appendix C. Additional Non-Category SCCs with Unit Descriptions

Emission Unit Description SCC SCC Description

Wastewater treatment 

facility 40188898

Petroleum and Solvent Evaporation; Organic Solvent Evaporation; Fugitive Emissions; Specify in 

Comments Field

Welding Sources 39999995

Industrial Processes; Miscellaneous Manufacturing Industries; Miscellaneous Industrial Processes; 

Other Not Classified
Appendix 2

Technical Support Document for HEM-AERMOD Modeling

Modeling for the Residual Risk and Technology Review 

Using the Human Exposure Model 3 – AERMOD Version

Updated 01/08/2014

Technical Support Document

Prepared for: 

U.S. Environmental Protection Agency

Office of Air and Radiation 

Office of Air Quality Planning and Standards

Health and Environmental Impacts Division 

Air Toxics Assessment Group

Research Triangle Park, NC 27711

Prepared by:

EC/R Incorporated 

501 Eastowne Drive, Suite 250

Chapel Hill, North Carolina 27514
iii

Disclaimer

The research described in this document has been funded by the United States 

Environmental Protection Agency contracts 68-D-01-076, EP-D-06-119, and 

currently EP-W-12-011 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.
iv

Contents

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

2. Overview of the HEM-3 – 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 HEM-3..........................................................6

2.2.2 Use of Dilution Factors................................................................................................7

2.3 Postprocessing of AERMOD Results in HEM-3 ................................................................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...........................................................................................................11

2.4 Data Libraries Used in HEM-3.........................................................................................12

2.4.1 Chemical Health Effects Information.........................................................................12

2.4.2 Census Block Locations and Elevation Data ..............................................................13

2.4.3 Meteorological Data ..................................................................................................14

2.4.4 Gaseous Deposition Parameters.................................................................................14

3. Modeling for the Residual Risk Technology Review .............................................................15

3.1 Emission Source Inputs....................................................................................................15

3.2 Pollutant Cross-Referencing ............................................................................................15

3.3 Meteorological Data ........................................................................................................16

3.4 Model Options Selected ...................................................................................................19

3.4.1 Urban or Rural Dispersion Characteristics.................................................................19
v

3.4.2 Deposition and Plume Depletion ...............................................................................19

3.4.3 Cutoff Distance for Modeling of Individual Blocks ...................................................20

3.4.4 Facility Boundary Assumptions.................................................................................20

3.5 Modeling of Multiple Facilities........................................................................................22

4. Quality Assurance .................................................................................................................23

4.1 Engineering Review.........................................................................................................23

4.2 Geographic Pre-Modeling Checks....................................................................................23

4.3 Geographic Post-Modeling Checks..................................................................................24

5. Uncertainties.........................................................................................................................26

6. References.............................................................................................................................32

Figures

Figure 3-1. AERMOD Meteorological Stations.........................................................................18

Tables

Table 2-1. Parameters Used to Delineate the Modeling Domain in HEM-3..................................4

Table 3-1. HEM-3 Domain and Set-Up Options As Used in the Residual Risk and Technology 

Review Assessments.................................................................................................................21

Table 5-1. Summary of General Uncertainties Associated with Risk and Technology Review 

Risk Assessments......................................................................................................................28
1

1. Introduction

This document describes the general modeling approach used to estimate the risks to 

human populations in support of the Residual Risk and Technology Review (RTR) currently 

being carried out by the U.S. Environmental Protection Agency (EPA). It is important to note 

that risk characterizations of individual source categories under the RTR program may not 

follow every item/approach noted in this document. The reader is referred to the main body of 

the risk assessment document for more details on source category specific approaches that may 

have been included in the analysis. 

The model used in these risk assessments is the Human Exposure Model, Version 3 

(HEM-3). HEM-3 incorporates 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 HEM-3-AERMOD 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 HEM-3 – AERMOD System

HEM-3 performs three main operations: dispersion modeling, estimation of population 

exposure, and estimation of human health risks. The state-of-the-art American Meteorological 

Society (AMS) EPA Regulatory Model (AERMOD)1,2 is used for dispersion modeling.

AERMOD can handle a wide range of different source types which may be associated with an 

industrial source complex, including stack (point) sources, area and polygon sources, and volume 

sources. 

To prepare dispersion modeling inputs and carry out risk calculations, HEM-3 draws on 

four data libraries, which are provided with the model. The first is a library of meteorological 

data for over 824 stations, which are used for dispersion calculations. A second library of Census 

block (“centroid”) internal point locations and populations provides the basis of human exposure 

calculations. The Census library also includes the elevations of every Census block, which are 

used in the dispersion calculations for the RTR assessments. 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. The fourth data library, which 

provides deposition parameters for gaseous pollutants, is used only when the user opts to 

compute deposition and plume depletion (not computed for the RTR assessments to date). 

HEM-3 has been implemented in two versions: a single facility version, and a multiple 

facility version (“Multi HEM-3”). Multi HEM-3 is used in the RTR risk assessment modeling. 

Both versions operate under the same general principles. In essence, Multi HEM-3 provides a 

platform for running the single facility 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. Single HEM-3 includes a graphical user interface (GUI) for 

the selection of various dispersion modeling options. In Multi HEM-3, 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 (or “centroids”), at concentric rings 

surrounding the facility center, 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, HEM-3 estimates the total incremental cancer risks for people living within 

different distances of the modeled emission sources. 

Potential noncancer health effects due to chronic exposures are quantified using hazard 

quotients and hazard indices for various target organs. The “hazard quotient” (HQ) for a given 

chemical and receptor site is the ratio of the ambient concentration of the chemical to the 

reference concentration. The “hazard index” (HI) for a given organ is the sum of hazard 

quotients for substances that affect that organ. HEM-3 computes target-organ-specific hazard

indices (TOSHI) for HAPs and other toxic air pollutants, and estimates the numbers of people 
3

exposed to different hazard index levels. In addition, maximum short term (“acute”) 

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 HEM-3–AERMOD system. 

Section 2.1 describes the preparation of dispersion modeling inputs, Section 2.2 describes the 

running of AERMOD, Section 2.3 describes calculations performed by HEM-3 to calculate risks 

and exposures, and Section 2.4 details the sources and methods used to produce HEM-3’s data 

libraries. The HEM-3 User’s Manuals – for single HEM-3 and Multi HEM-3 – provide 

additional details on the input data and algorithms used in the model.3 Specific model options

used in the RTR assessments are discussed in Chapter 3.

2.1 Preparation of Dispersion Modeling Inputs 

HEM-3 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. HEM-3 can analyze point sources, 

area and polygon sources, and volume sources - configurations that are described in AERMOD's 

documentation.1, 2 For stack (point) 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 provide the 

terrain elevation at the base of each source. (The controlling hill height is also used in 

AERMOD’s flow calculations. Calculation of the controlling hill height by HEM-3 is discussed 

in Section 2.4.2.) If the terrain elevations are not provided by the user, HEM-3 will calculate 

elevations and controlling hill heights based on elevations and hill heights provided by the 

Census database for the Census blocks nearest to the facility. 

If particulate deposition and plume depletion are to be considered, then HEM-3 requires a 

third input file to specify the particle size distribution. This input file must include the average 

particle diameter, the mass fraction percentage, and the average particle density for each size 

range emitted. Another optional file can be used to specify building dimensions if building wake 

effects are to be modeled.
4

2.1.2 Defining the Modeling Domain and Receptors 

HEM-3 defines a modeling domain for each facility that is analyzed based on parameters 

specified by the model user or calculated by the model. 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). HEM-3 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 

generally set to 3 km for the RTR assessments, although it can be set differently 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 “user receptors” may 

include facility boundary locations, monitoring sites, individual residences, schools, or other 

locations of interest. 

For Census blocks in the outer group, 

beyond this modeling cutoff distance, 

emission 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 facility 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 within AERMOD. The cutoff distance for modeling of 

individual Census blocks may be chosen by the user, but is typically 3000 meters for the RTR 

assessments. 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.

HEM-3 checks Census blocks that are very close to the facility in order to assess whether 

they overlap any point, area or volume emission sources. In addition, the user can specify an 

overlap distance, within which receptors will be considered to be on facility property. The 

default value for the overlap distance is 30 meters, or approximately equal to the width of a 

narrow buffer and a roadway. HEM-3 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 

Table 2-1. Parameters Used to Delineate the 

Modeling Domain in HEM-3

Parameter

Typical 

value

Modeling domain size – maximum 

radial distance to be modeled from 

facility center

50 km

Cutoff distance for modeling of 

individual blocksa 3,000 m

Overlap distance – where receptors 

are considered on facility propertya 30 m

Polar receptor network:

Distance to the innermost ringb ≥100 m

Number of concentric rings 13

Number of radial directions 16

a Measured from each stack at the facility, and from the 

 edges of each area or volume source

b Generally model-calculated to encompass all emission 

 sources but not less than 100 meters from the facility 

 center
5

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 user-specified receptor locations.

Polar receptor network

The polar receptor network used in HEM-3 serves three functions. First, it is used to 

estimate default impacts if one or more Census locations are inside the overlap cutoff distance 

used to represent the facility boundary. Second, it is used to evaluate potential acute effects that 

may occur due to short-term exposures in locations outside the facility 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. 

Generally, the model calculates the inner radius (or first ring distance) for the polar 

receptor network to be just outside the emission source locations, but not less than 100 meters 

from the facility center. However, the user can override the default distance calculated by the 

model to fit the size and shape of the facility properties to be modeled. Likewise, the model will 

also use default values for the number of concentric rings to be analyzed (13 rings by default), 

and the number of radial directions (16 radials by default), although these default values can also 

be changed by the user to meet the needs of a specific modeling study. The inner radius of the 

polar network should be the minimum distance from the facility center that is generally outside 

of facility property. (For complex facility shapes, it is sometimes useful to specify an inner ring 

that encroaches on facility property in some directions.) HEM-3 will distribute the radial 

directions evenly around the facility. For the concentric rings, the model will generate a 

logarithmic progression of distances starting at the inner ring radium and ending at the outer 

radium of the modeling domain. 

Elevations and hill heights for model receptors 

HEM-3 includes terrain elevations by default for the RTR assessments, but the user can 

choose to exclude terrain effects when running AERMOD. If the default terrain option is used, 

HEM-3 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. 

Elevations and controlling hill heights for the polar grid receptors are also estimated 

based on values from the Census library. HEM-3 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.
6

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 HEM-3 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, HEM-3 will select the 

closest station to the center of the modeling domain, as is generally done for the RTR 

assessments.

2.2 Running of AERMOD

Based on the user input data and other data described in the previous section, HEM-3 

produces an input file suitable for AERMOD. HEM-3 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 HEM-3.

2.2.1 AERMOD Dispersion Options Used by HEM-3

AERMOD provides a wide array of options for controlling dispersion modeling 

calculations. In general, HEM-3 uses the regulatory default options when running AERMOD.1

These options include the following: 

 Use stack-tip downwash (except for Schulman-Scire downwash);

 Use buoyancy-induced dispersion (except for Schulman-Scire downwash);

 Do not use gradual plume rise (except for building downwash);

 Use the “calms processing” routines;

 Use upper-bound concentration estimates for sources influenced by building downwash

from super-squat buildings;

 Use default wind profile exponents;

 Use low wind speed threshold;

 Use default vertical potential temperature gradients;

 Use of missing-data processing routines; and

 Consider terrain effects.

The following additional AERMOD options are available to the HEM-3 user: 

 Calculation of wet and dry deposition rates for vapor and particulate matter;

 Consideration of plume depletion (due to deposition) when calculating air concentrations;

 Consideration of building wake effects;

 Calculation of short term (acute) impacts; and 

 Use of the FASTALL option, which conserves model runtime by simplifying the 

AERMOD algorithms used to represent meander of the pollutant plume. 
7

As noted in Section 2.1, the calculation of deposition or depletion and the consideration of 

building wake effects require additional user inputs. 

The user can opt to analyze short term impacts on a number of different time scales (i.e., 

1 hour, 6 hours, 8 hours, or 24 hours) 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. For RTR assessments, acute impacts are generally included in the modeling and the 

default multiplier of 10 is used, unless more source-specific information is available upon which 

to base the acute factor for the source category being modeled.

2.2.2 Use of Dilution Factors 

To save computer run time when analyzing the impacts of multiple pollutants, HEM-3 

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 and plume depletion, the dilution factor does 

not vary from pollutant to pollutant. If deposition and depletion is chosen as a model option, 

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 HEM-3, 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 HEM-3 

HEM-3 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 (MIR) 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.

For acute impacts, HEM-3 calculates the maximum short term concentrations for all 

pollutants emitted by the facility. These maximum short term concentrations are compared with 

various threshold levels for acute health effects (e.g., the California EPA reference exposure 

level [REL] for no adverse effects). 
8

At the option of the model user, HEM-3 will also compute the long term and short term 

predicted ambient concentrations of all pollutants emitted by the facility 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, HEM-3 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. HEM-3 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 [(μg/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 type j (particulate or gas)

 Ei,k = emissions of pollutant k from source i and in pollutant type j

 UREk = cancer unit risk factor for pollutant k

 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 = non-cancer health effect reference concentration for pollutant k

 
9

The above equations are equivalent to the following simpler equations: 

CRT = ∑i,k ACi,k × UREk

HIT = ∑i,k ACi,k / RCk

where: 

ACi,k = ambient concentration (μg/m3) for pollutant k at the given receptor. This is the same 

 as [Ei,k × DFi.j × CF]

However, use of these simpler equations would require modeling all pollutants individually in 

AERMOD, and performing separate risk calculations for each pollutant. 

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. 

HEM-3 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 = Ei,k x DFi,j × CF

Short term concentrations: 

ACT = ∑i ACi,k

ACi,k = Ei,k x DFi,j × CF × M

where: 

ACT,k = total estimated ambient concentration for pollutant k at a given receptor

 ∑i = the sum over all sources i (μg/m3)

ACi,k = estimated ambient concentration of pollutant k at the given receptor as a result of 

emissions from source i (μg/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 cutoff distance 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. If terrain elevation is part of the modeling, then an 

elevation is estimated for each polar receptor. HEM-3 estimates elevations and controlling hill 

heights for the polar grid receptors based on values from the census library. HEM-3 divides the 
10

modeling domain into sectors based on the polar grid receptor network, with each census block 

assigned to the sector corresponding to the closest polar grid receptor. 

HEM-3 then assigns each polar grid receptor 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. 

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 = IA1,r + (IA2,r – IA1,r) × (a – A1) / (A2 – A1)

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 receptor

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, HEM-3 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
11

Furthermore, HEM-3 estimates the contributions of different chemicals and emission sources to 

total annual cancer incidence for the overall modeling domain using the following equations:

TCRi,j = ∑m [(∑k Ei,k × UREk) × DFi,j,m × CF  LT]

TCRi,k = TCRi j × Ei,k × UREk  (∑k Ei,k × UREk)

where: 

TCRi,j = the estimated total annual cancer incidence (cancers/year) to the population in the 

modeling domain due to emissions from pollutant type j (1 = particulate, 2 = gas) and 

emission source i

 ∑m = the sum over all Census blocks m within distance the modeling domain

 ∑k = the sum over all pollutant k, within pollutant type j

 Ei,k = emissions of pollutant k from source i (tons/year)

UREk = unit risk factor for pollutant k

DFi,j,m = dilution factor at receptor m, for emissions of pollutant type j (which includes 

pollutant k), from source i

 CF = conversion factor, 0.02877 [(g/sec) / (ton/year)]

TCRi,k = the estimated annual cancer incidence (cancers/year) of the population in the

modeling domain due to emissions of pollutant k (in pollutant type j) from emission 

source i

2.3.4 Model Outputs

The following is a summary of the outputs produced by HEM-3. These are written to a 

collection of files in ExcelTM and dBaseTM format (dbf).

 Maximum long term impacts at populated locations 

o maximum lifetime individual cancer risk (MIR) 

o maximum TOSHI for the following health effects 

- respiratory system effects 

- liver effects 

- 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 

- whole body effects 

o locations of the maximum cancer risk and TOSHI 

o Census block identification codes for the maximum cancer risk and TOSHI, and 

number of people in the Census block 
12

o contributions of different chemicals and emission sources to the maximum risk and 

TOSHI 

 Maximum acute impacts

o maximum short term ambient concentration for each chemical

o threshold levels for acute health effects of each chemical (compared with the 

maximum short term concentrations)

o locations of the maximum impacts for different chemicals (often polar receptors)

o Census block identification codes at the locations of maximum concentration, and 

number of people in the block

o contribution of each emission source at the facility to the maximum short term 

concentration of each chemical

 Outputs for all receptors

o maximum individual cancer risk and TOSHI (all target organs) for each Census 

block and each user-specified discrete receptor (monitoring sites, etc.)

o maximum individual cancer risk and TOSHI (all target organs) for each polar grid 

receptor

o estimated deposition (optional)

o predicted ambient concentration resulting from each emission source at each 

Census block and polar grid receptor (optional)

 Population exposures and total cancer risk, or incidence

o estimated numbers of people exposed to different levels of lifetime individual 

cancer risk (1 in a million, 1 in 100,000, etc.)

o estimated numbers of people exposed to different levels of TOSHI (1, 2, 10, etc.)

o total cancer risk, or incidence, in estimated cancer deaths per year, over the entire 

modeling domain, and for each pollutant and source combination

2.4 Data Libraries Used in HEM-3 

2.4.1 Chemical Health Effects Information

HEM-3 includes a library of available health effects data for HAPs. For each pollutant, 

the library includes the following parameters, where available:

 unit risk estimate (URE) for cancer

 reference concentration (RfC) for chronic noncancer health effects

 reference concentrations for acute health effects 

 target organs affected by the chemical for chronic noncancer health effects

Unit risk estimates and reference concentrations included in the HEM-3 chemical library have 

been taken from EPA’s database of recommended dose-response factors for HAPs, which is 

updated periodically, consistent with continued research on these parameters.4 The URE

represents the upper-bound excess lifetime cancer risk estimated to result from continuous 

exposure to an agent at a concentration of 1 microgram per cubic meter (μg/m3) in air (e.g., if the
13

URE = 1.5 x 10-6per μg/m3, 1.5 excess tumors are expected to develop per 1 million people if all

1 million people were exposed daily for a lifetime to 1 microgram of the chemical in 1 cubic 

meter of air).5

The RfC is a concentration estimate of a continuous inhalation exposure to the human 

population that is likely to be without an appreciable “risk” of deleterious non-cancer health 

effects during a lifetime. No adverse effects are expected as a result of exposure if the ratio of the 

potential exposure concentration to the RfC, defined as the hazard quotient (HQ), is less than 1.5

The reference benchmark concentration for acute health effects, similar to the chronic 

RfC, is the concentration below which no adverse health effects are anticipated when an 

individual is exposed to the benchmark concentration for 1 hour (or 8 hours, depending on the 

specific acute benchmark used and the formulation of that benchmark). Target organs are those 

organs (e.g., kidney) or organ systems (e.g., respiratory) which may be impacted with chronic 

non-cancer health effects by exposure to the chemical in question. A more in-depth discussion of 

the development and use of these parameters for estimating cancer risk and non-cancer hazard 

may be found in the EPA’s Air Toxics Risk Assessment Library.6

The model user can add pollutants and associated health effects to HEM-3’s chemical 

health effects (dose-response) library, as needed.

2.4.2 Census Block Locations and Elevation Data 

The HEM-3 Census library includes Census block identification codes, locations, 

populations, elevations, and controlling hill heights for all of the over 6 million Census blocks 

identified in the 2010 Census and the over 5 million Census blocks identified in the 2000 

Census. The model user may choose to use either Census database according to their modeling 

needs. The location coordinates reflect the internal “centroid” 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 For the 

2000 Census7and from the U.S. Census Bureau website for the 2010 Census.8 Locations and

populations for blocks in the Virgin Islands were obtained from the U.S. Census Bureau website. 

U.S. Geological Survey data was used to estimate the elevation of each census block in 

the continental U.S. and Hawaii. The data used for the 2000 Census elevations have a resolution 

of 3 arc seconds, or about 90 meters.9 The data used for the 2010 Census elevations have a

resolution of 1/3 of an arc second, or about 10 meters.10 Using analysis tools (ArcGIS® 9.1

software application for the 2000 Census, and ArcGIS® 10 for the 2010 Census), elevation was 

estimated for each census block in Alaska and the U.S. Virgin Islands. The point locations of the 

census blocks in Alaska and the U.S. Virgin Islands were overlaid with a raster layer of North 

American Digital Elevation Model (DEM) elevations (in meters).9 An elevation value was

assigned to each census block point based on the closest point in the ArcGIS elevation raster file. 

An algorithm used in AERMAP, the AERMOD terrain processor, is used to determine 

controlling hill heights.11,12 These values are used for flow calculations within AERMOD. 
14

To save run time and resources, the HEM-3 census block elevation database is 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 is projected away from the block centroid location, representing a 10% 

elevation grade. The controlling hill height is selected based on the highest elevation above that 

10% grade (in accordance with the AERMAP methodology). The distance cutoff for this 

calculation is 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

HEM-3 includes an extensive library of meteorological data to support the AERMOD 

dispersion model. Currently 824 meteorological stations have been preprocessed for AERMOD 

as part of the RTR effort. Section 3.3 includes a depiction of these meteorological stations and 

Appendix 2 discusses the preparation of meteorological data for the RTR in more detail.

2.4.4 Gaseous Deposition Parameters

HEM-3 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 

the deposition and depletion option 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).13 HEM-3

includes a library of these parameters for most gaseous HAPs. This library is based on a 

compendium of gaseous deposition parameters developed by Argonne National Laboratories.14 It 

should be noted, however, that the deposition and depletion option of HEM-3 and AERMOD 

have not been used to date for the RTR assessments.
15

3. Modeling for the Residual Risk Technology Review

This section discusses the general approach used to implement the HEM-3 AERMOD 

system for the RTR modeling analyses. Separate reports have been prepared for each of the 

emission source categories analyzed to date. These reports provide information on the emissions 

inputs and results for specific emission categories.

3.1 Emission Source Inputs 

HEM-3 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 and other area sources);

 stack diameter (or configuration of fugitive and other area sources);

 emission velocity; and

 emission temperature.

Emissions data for the RTR assessments are compiled form a variety of data sources (i.e., 

the 2005 National Emissions Inventory (NEI)15 ,information data requests). Each source 

category under the RTR program, in most cases, utilizes the latest best available data. These 

data include HAP emission rates, emission source coordinates, stack heights, stack diameters, 

flow rates, and exit temperatures. EPA performs 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 is 

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 in the docket prepared for the 

specific RTR source category proposed or final rule. 

As noted in the previous section, industrial emission sources can be characterized in 

AERMOD as point, area, polygon, or volume sources. 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.

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). HEM-3 requires an exact match in 
16

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. Therefore, all 

of the HAP names used in the NEI were linked to the appropriate chemical names in the HEM-3 

reference file. 

Pollutant-specific dose response values are used in the HEM-3 modeling whenever 

available, including when modeling POM pollutants. Pollutant groupings, such as POM 

groupings, are used for POMs without a chemical-specific unit URE’s. These POMs are assigned 

a URE associated with various POM compounds having similar characteristics. “An Overview of 

Methods for EPA’s National-Scale Air Toxics Assessment” 2011 document16 provides more

details regarding POM modeling, including: 

[S]ome emissions of POM were reported in [the] NEI as “7-PAH” or “16-PAH,” 

representing subsets of certain POM, or simply as “total PAH” or “polycyclic organic 

matter.” In other cases, individual POM compounds are reported for which no 

quantitative cancer dose-response value has been published in the sources used for 

NATA. As a result, simplifying assumptions that characterize emissions reported as POM 

are applied so that cancer risk can be quantitatively evaluated for these chemicals without 

substantially under- or overestimating risk (which can occur if all reported emissions of 

POM are assigned the same URE). To accomplish this, POM emissions as reported in 

NEI are grouped into categories. EPA assigns dose-response values based on the known 

or estimated toxicity for POM within each group and on information for the POM 

speciation of emission sources, such as wood fires and industrial processes involving 

combustion. 

Emissions of metal compounds are 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 EMSHAP.17,18

3.3 Meteorological Data

Nationwide meteorological data files are accessed by HEM-3 and used for the RTR 

modeling. The current HEM-3 AERMOD Meteorological Library includes over 800 nationwide 

locations, depicted in Figure 3-1. This library contains surface and upper air meteorological data 

from National Weather Service (NWS) observation stations, which are named beginning with the 

state abbreviation for the state in which the station is located. AERMOD requires surface and 

upper air meteorological data that meet specific format requirements.19,20 Appendix 2 discusses 
17

the preprocessing performed on the meteorological data used by AERMOD and includes a 

detailed listing of the 824 meteorological station pairs.
18

Figure 3-1. AERMOD Meteorological Stations
19

3.4 Model Options Selected 

HEM-3 presents a number of options for characterizing the modeling domain and data 

sources. As many sources are generally modeled in RTR assessments, established defaults and 

common practices are relied on to make these choices. The choices available to a HEM-3 user 

and the selections that are made in most RTR assessments are presented in Table 3-1. Some of 

the key selections are discussed in more detail in the paragraphs below. 

It should be noted that 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,21 but no facility-specific analyses of these data were performed to characterize shortterm emissions from these emission sources, and upset emissions are generally not modeled for 

the RTR risk assessments. 

3.4.1 Urban or Rural Dispersion Characteristics 

Current RTR source category assessments which use the 2010 Census are based on either 

urban or rural dispersion characteristics, depending on the land characteristics surrounding each 

modeled facility. The EPA provides guidance on whether to select urban or rural dispersion 

coefficients in its Guideline on Air Quality Models.22 In general, the urban option is used if (1) 

the land use is classified as urban for more than 50% of the land within a 3-kilometer radius of 

the emission source, or (2) the population density within a 3-kilometer radius is greater than 750 

people per square kilometer. Of these two criteria, the land use criterion is more definitive. 

Using the 2010 Census, the HEM-3 model determines, by default, whether to use rural or 

urban dispersion characteristics. HEM-3 will find the nearest census block to the facility center 

and determine whether that census block is in an urban area, as designated by the 2010 Census.23

The population of the designated urban area will be used to specify the population input for 

AERMOD's urban mode. (Alternatively, a user may select the rural or urban option to override 

determination by the model. If a user selects an urban dispersion environment, then the user must 

provide the urban population as well.) 

For the 2008 and prior screening-level RTR assessments of 51 source categories, the rural 

option was chosen to be most conservative (i.e., more likely to overestimate risk results). The 

rural option is also chosen by default by the HEM-3 model whenever the 2000 Census is selected 

by the user. 

3.4.2 Deposition and Plume Depletion 

The RTR modeling analysis to date has not taken into account the depletion of pollutant 

concentrations in the plume due to wet or dry deposition, although HEM-3 can model deposition

and depletion using AERMOD. In addition, reactivity and decay have not been considered. 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 
20

exposed (the maximum individual risk, or MIR). 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 may 

consider deposition and depletion. 

3.4.3 Cutoff Distance for Modeling of Individual Blocks 

The cutoff distance for modeling individual Census blocks is initially set to 3 km by 

default. This distance generally ensures that the maximum individual cancer risk and the 

maximum TOSHI are modeled explicitly and not interpolated. Following a modeling run, the 

results for each facility are checked to determine whether the maximum impacts are located 

inside the modeling cutoff distance. If the maximum impacts are outside the cutoff distance, and 

if any of the impacts are significant, then HEM-3 is rerun for the facility with a cutoff distance 

greater than 3 km. In general, this is done if the cancer risk exceeds 1 in 1 million or any TOSHI 

exceeds 1. However, the risks for such facilities are generally very low, since the maximum 

impacts are in most cases only interpolated when the nearest Census block is more than 3 km 

from the facility (i.e., in sparsely populated areas). 

3.4.4 Facility Boundary Assumptions 

The main input mechanisms for incorporating facility boundary information in HEM-3 

are the overlap distance, the distance to the innermost polar receptor ring, and user-specified 

receptor locations. The NEI does not provide information on facility boundaries. However, 

satellite/aerial images are used to locate residential populations that are closer to a facility than 

the Census block centroid. User-specified receptor locations are used in such assessments to 

avoid underestimating risk. Conservative default assumptions are used for the overlap distance 

and the innermost polar receptor ring. However, these are adjusted for some categories where 

facility sites are known to be large. In addition, satellite imagery is used to check the facility 

boundary assumptions for facilities with large projected impacts. These checks are discussed 

further in the section on Quality Assurance (Section 4).
21

Table 3-1. HEM-3 Domain and Set-Up Options As Used in the Residual Risk and 

Technology Review Assessments

Option Selection

Dispersion model AERMOD

Census database: 2010 or 2000 Based on date of 

analysis

Type of analysis: chronic, acute, or both Both

Averaging time for short term impacts 1-hour

Multiplier for short term emissions 10 generally, although 

multiple source typespecific factors are 

also used if available 

Dispersion characteristics: urban or rural, as determined by model, 

based on closest 2010 Census block to each facility (when using 

2010 Census). Rural by default, when using the 2000 Census.

Urban or Rural based 

on facility location; 

(Rural for 2000

Census)

Include terrain impacts Yes

Include building wake effects No

Calculate deposition (wet, dry, or both) & include impacts of plume 

depletion

No

User-specified receptor locations (for residential population 

locations, facility boundary sites, or other sites of interest)

Yes, for some 

facilities

Modeling domain size – maximum distance to be modeled 50 km

Cutoff distance for modeling of individual blocks 3 kma

Overlap distance where receptors are considered to be on facility 

property – measured from each source measured from each source

30 mb

Polar receptor network specifications:

Distance from the facility center to the innermost ring ≥ 100 mc

Number of rings 13

Number of directions 16

Meteorology data Closest site

 

a The individual block modeling cutoff is increased for categories and for some facilities to ensure that the 

 maximum individual risk values are not interpolated.

 

b The overlap distance is adjusted for some facilities to avoid modeling locations that are on facility property 

 (see section 4.2).

 

c HEM-3 sets the innermost ring distance to be just outside the emission sources but not < 100 m.
22

3.5 Modeling of Multiple Facilities 

HEM-3 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 are 

summed at each Census block for all facilities affecting the Census block. 

As described earlier (Section 2.3.4), HEM-3 produces detailed output tables containing 

the risk and population for every Census block in the modeling domain. These detailed tables are 

combined for all facilities in a source category and the risk for each Census block is summed, 

using the RTR Summary Program add-on module to the Multi HEM-3 model.3 Thus, the effect 

of multiple facilities in the same source category on the same receptor are estimated. The 

resulting “combined facility” or “cluster-effect” Census block risks are used to calculate 

population exposure to different cancer risk levels, noncancer hazard indices, and source 

category incidence.
23

4. Quality Assurance

The National Emissions Inventory (NEI) 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.24 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 performs an 

engineering review of NEI data for the emission source categories included in the RTR analysis. 

This engineering review includes two main components. The first component addresses the list 

of facilities included in each source category. EPA engineers review independent sources of 

information to identify all sources in the category that are included in the NEI. In addition, EPA 

reviews 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 focuses on the appropriateness of 

facility emissions. EPA reviews the list of HAPs reportedly emitted by each facility to make sure 

that the pollutants are appropriate to the source category. In addition, EPA engineers review the 

magnitude of those HAP emissions. In cases where new or better data are known to exist for a 

particular source category, that information is 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 Pre-Modeling 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.25 In addition, coordinates for all emission sources at a given facility 

are checked to ensure that they are within 3 km of one another. These QA checks happen prior to 

HEM-3 modeling and the results of such checks are reflected in the HEM-3 input files. 

Another pre-modeling geographic QA check regards the location of the census block 

receptors. As noted above, to estimate ambient concentrations for evaluating long-term 

exposures, the HEM-3 model uses the census block centroids as dispersion model receptors. The 

census block centroids are often 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 facility 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 is within 300 meters of 
24

any emission source, aerial images of the facility are reviewed to determine whether the block 

centroid is 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. Block 

centroids confirmed to be located on facility property are moved 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, block centroids are relocated in some cases, or additional user-specified receptors are 

added to a block. Aerial images of all large census blocks within one kilometer of any emission 

source are examined. 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 does not 

represent the residential locations, it is relocated in the HEM-3 input files to better represent 

them. If residential locations cannot be represented by a single receptor (that is, the residences 

are spread out over the block), additional user-specified receptors are included in the HEM-3 

input files to represent residences nearer to the facility than the centroid. 

4.3 Geographic Post-Modeling Checks 

As part of the RTR modeling analysis, additional geographical QA checks are made for 

some facilities, after initial HEM-3 modeling results are reviewed. Facilities subjected to these 

additional checks include: 

 cases where the initial estimates of maximum risks are particularly high 

o maximum individual cancer risk of over 1 in 10,000 

o any maximum TOSHI above 10 

 cases where no Census blocks are identified by the model within 3 km of the facility 

HEM-3 produces a detailed Google EarthTM map of the modeled point and area emission 

sources and surrounding receptors (including Census block centroids, polar receptors and userspecified receptors) overlaying Google EarthTM’s satellite imagery. This map allows a QA check 

of the specific source locations, as well as an approximate check of the facility boundaries. The 

emission source coordinates are reviewed for each of these facilities and compared with the 

address reported for the facility. If the address and the coordinates represent the same location, 

then the coordinates are taken to be correct. For more recent modeling of source categories, the 

emission coordinates initially modeled by HEM-3 tend to be correct, as they undergo premodeling scrutiny and QA checks (as discussed in Section 4.2).

More rarely, the modeled emission coordinates will be determined post initial modeling 

not to be located on facility property. If the facility and emission coordinate locations are 
25

different, then the satellite imagery for the address and the coordinate location are reviewed to 

determine whether either photograph includes an industrial facility. Generally for the 2008 and 

prior screening-level RTR assessments of 51 source categories, where the two locations were 

different, the facility address was found to be correct (and the emission source coordinates 

required correction). 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 are found to be incorrect, 

HEM-3 is 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 are also reviewed. This review draws on detailed Census block boundary maps and 

satellite imagery. 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 centroid 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 

facility property. 

In general, block coordinates are considered to be on facility property if they are located 

between the different emission source locations listed for the facility. In these situations, HEM-3 

is rerun with an expanded overlap distance, in order to exclude the Census block coordinates that 

appear to be located on facility property. The distance to the innermost polar receptor ring is also 

adjusted to ensure that this ring is not on facility property, but as close to the apparent facility 

boundaries as possible.
26

5. Uncertainties

The RTR risk assessments using HEM-3 and AERMOD are subject to a number of 

uncertainties. For instance, model verification studies for AERMOD show 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 1.0.25

In addition, a number of simplifying assumptions are made in these modeling analyses. 

First, the coordinates reported by the Department of Census for Census block internal points 

(“centroids”) 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 HEM-3 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 longterm 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 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 analyses, only direct inhalation is 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 

hazardous air pollutants (PB HAP). Screening level evaluations of the potential human health 

risks associated with emissions of PB HAP from the modeled facilities are used to determine if 

additional analyses are needed, but these analyses are outside the scope of this document. 

Because the HEM-3 AERMOD analyses are restricted to the inhalation pathway, the impacts of 

plume depletion due to deposition are not taken into account. Thus, inhalation impacts may be 

overestimated for some pollutants, but exposures through other pathways would be 

underestimated.

A number of other simplifications are made in the dispersion modeling analyses, as noted 

in Table 3-1. For instance, building wake effects are not considered. In addition, meteorological 
27

observations are based on the closest station in the HEM-3 meteorological library (see Figure 

3-1). Alternative meteorological stations may be more appropriate for some facilities. Ideally, 

facility-specific meteorological observations would be used. A single year of meteorological data 

(2011) is currently used for AERMOD’s dispersion modeling. (The 2008 and prior screeninglevel RTR assessments of 51 source categories used meteorological data based on the year 

1991.) 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 analyses and the extent of the 

dispersion modeling analysis (national scale), it is not practical to model five years of data. Other 

national studies such as NATA also consider 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 station.26

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 

analyses 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 analyses. 

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 analyses: 

 emissions inventory; 

 fate and transport modeling; 

 exposure assessment; and 

 toxicity assessment. 

It must also be noted that individual source categories may be subject to additional uncertainties. 

These are discussed in separate reports which are prepared for each emission source category 

included in the RTR assessments.
28

Table 5-1. Summary of General Uncertainties Associated with Risk and Technology Review Risk Assessments

Parameter Assumption Uncertainty/Variability Discussion

Potential Direction of Bias on 

Risk Estimates

Emissions Inventory

Individual 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 current emissions inventory is based on the 2005 

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. For the 2008 screening level 

assessments, nearly half of the sources in a given 

source category submitted a review of their 

emissions and facility characteristics data. Some 

detailed data, such as property boundary information 

is not available for most facilities. This is an 

important consideration in determining acute 

impacts.

Unbiased overall, magnitude 

variable

Multiplier for short-term 

emission rates

Generally, 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 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.

Potential overestimate due to 

the fact that worst-case 

emissions are assumed to 

occasionally coincide with 

worst-case meteorology.

Overestimate due to lack of 

actual information on shortterm emission rates.

Fate and Transport Modeling

Atmospheric dispersion 

model choice

AERMOD is EPA's recommended 

dispersion model for assessing 

pollutant concentrations from 

industrial facilities

Field testing of dispersion models, including 

AERMOD, have shown results to generally be within 

a factor of 2 of measured concentrations.

Unbiased overall

Building downwash Not included in assessments

Use of this algorithm in AERMOD could improve 

the dispersion calculations at individual facilities. 

However, data are not readily available to utilize this 

option.

Potential underestimate of 

maximum risks near facility. 

No effect on risks further out.

Plume deposition and 

depletion Not included in assessments

Ignoring these impacts for pollutants that deposit 

minimally, and whose risks derive predominantly 

from inhalation, should have minimal effect on risk 

estimates.

Unbiased or minimal 

overestimate.
29

Table 5-1. Summary of General Uncertainties Associated with Risk and Technology Review Risk Assessments

(continued)

Parameter Assumption Uncertainty/Variability Discussion

Potential Direction of Bias on 

Risk Estimates

Meteorology

One year of meteorological data from 

the nearest weather station (selected 

from 824 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.

Minimal underestimate or 

overestimate.

Reactivity Not included in the assessments

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 impact on maximum risk 

estimates. Minimal impact on 

population risks and incidence.

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 effect on maximum 

individual risks. Minimal 

underestimation of incidence.

Exposure Assessment

Locations and short-term 

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.

Unbiased across population for 

most pollutants and individuals, 

likely overestimate for most 

exposed and underestimate for 

least exposed persons.
30

Table 5-1. Summary of General Uncertainties Associated with Risk and Technology Review Risk Assessments

(continued)

Parameter Assumption Uncertainty/Variability Discussion

Potential Direction of Bias on 

Risk Estimates

Long-term movements of 

individuals

MIR individual is exposed 

continuously to the highest exposure 

concentration for a 70-year lifetime.

Maximum individual risk (MIR) is defined in this 

way to be a maximum theoretical risk at a point 

where a person can actually reside.

Unbiased for most individuals, 

likely overestimate for the 

actual individual most exposed 

and likely underestimate for the 

least exposed. Incidence 

remains unbiased unless 

population around facilities 

increases or decreases over 70 

years.

Toxicity Assessment

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 organ-specific hazard 

indices.

In general, EPA derives RfCs 

using procedures whose goal is 

to avoid underestimating risks 

in light of uncertainty and 

variability. The greater the 

uncertainties, the greater the 

potential for overestimating 

risks.

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 shortduration, high-dose animal or work-related exposures 

to longer duration, lower-dose environmental 

impacts.

Overestimate of risks for 

nonlinear carcinogens and for 

linear carcinogens with sparse 

health effects data. In general, 

EPA derives URE values using 

procedures aimed at 

overestimating risks in light of 

uncertainty and variability.

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.

Unbiased overall. Some 

mixtures may have 

underestimated risks, some 

overestimated, and some 

correctly estimated.
31

Table 5-1. Summary of General Uncertainties Associated with Risk and Technology Review Risk Assessments

(continued)

Parameter Assumption Uncertainty/Variability Discussion

Potential Direction of Bias on 

Risk Estimates

Surrogate dose- response 

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 dose-response values in the 

group. For others, such as unspeciated 

metals, we have applied speciation 

profiles appropriate to the source 

category to develop a composite doseresponse 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.

Overestimate where most 

conservative values used. 

Unbiased where categoryspecific profiles applied.

There is the potential to 

underestimate risks for 

pollutants which are not 

included in the assessment.
32

6. References

1. EPA. 2004. AERMOD: Description of Model Formulation. EPA-454/R-03-004, U.S. 

Environmental Protection Agency, Research Triangle Park, NC. 

http://www.epa.gov/ttn/scram/7thconf/aermod/aermod_mfd.pdf

2. EPA. 2004. User's Guide for the AMS/EPA Regulatory Model - AERMOD. EPA-454/B03-001, U.S. Environmental Protection Agency, Research Triangle Park, NC. 

www.epa.gov/scram001/dispersion_prefrec.htm#aermod

3. EPA. 2013. The Human Exposure Model (HEM): Single-facility HEM-3 User’s Guide; 

and Multi-facility HEM-3 and RTR Summary Programs User’s Guide. Prepared for the 

U.S. Environmental Protection Agency, Research Triangle Park, NC, by EC/R Inc., 

Chapel Hill, NC. February 2013. http://www.epa.gov/ttn/fera/human_hem.html

4. EPA. 2012. Dose-Response Assessment for Assessing Health Risks Associated With 

Exposure to Hazardous Air Pollutants. U.S. Environmental Protection Agency, Research 

Triangle Park, NC. Available at: http://www.epa.gov/ttn/atw/toxsource/summary.html

5. EPA. 2011. Glossary of Key Terms. Technology Transfer Network Air Toxics, 2005 

National-Scale Air Toxics Assessment. U.S. Environmental Protection Agency. 

http://www.epa.gov/ttn/atw/natamain/gloss1.html. 

6. EPA. 2004. Air Toxics Risk Assessment Reference Library, EPA-453-K-04-001A, U.S. 

Environmental Protection Agency, Research Triangle Park, NC. April 2004. 

www.epa.gov/ttn/fera/risk_atra_vol1.html

7. Census. 2000. LandView® 5 on DVD. U.S. Census Bureau, Washington, DC.

8. Census. 2010. Census Summary File 1 – United States: 

http://www2.census.gov/census_2010/04-Summary_File_1/ prepared by the U.S. Census 

Bureau, Washington, DC, 2011. See also Technical Documentation for the 2010 Census 

Summary File 1.

9. USGS. 2006. US GeoData Digital Elevation Models – Fact Sheet 040-00 (April 2000). 

U.S. Department of the Interior - U.S. Geological Survey, Washington, DC. 

http://data.geocomm.com/sdts/fs04000.pdf

10. USGS. 2010. USGS Seamless Data Warehouse. U.S. Department of the Interior - U.S. 

Geological Survey, Washington, DC. http://nationalmap.gov/viewer.html
33

11. 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

12. EPA. 2011. Addendum to the User’s Guide for the AERMOD Terrain Preprocessor 

(AERMAP) (EPA-454/B-03-003, October 2004), U.S. Environmental Protection Agency, 

Research Triangle Park, NC. March 2011.

http://www.epa.gov/scram001/dispersion_related.htm#aermap

13. EPA. 2012. Addendum: User's Guide for the AMS/EPA Regulatory Model – AERMOD 

(EPA-454/B-03-001, September 2004) (Section 2.2 related to deposition). U.S. 

Environmental Protection Agency, Research Triangle Park, NC. December 2012. 

http://www.epa.gov/scram001/dispersion_prefrec.htm#aermod. See also the AERMOD 

Deposition Algorithms – Science Document (Revised Draft)” at

http://www.epa.gov/ttn/scram/7thconf/aermod/aer_scid.pdf

14. Wesely, M.L., P.V. Doskey, and J.D. Shannon. 2002. Deposition Parameterizations for 

the Industrial Source Complex (ISC3) Model. ANL/ER/TR–01/003, Argonne National 

Laboratory, Argonne, Illinois 60439. See “AERMOD Deposition Parameterizations 

Document” pdf link under “Model Supporting Documents” section of TTN’s 

Preferred/Recommended Models webpage at 

http://www.epa.gov/scram001/dispersion_prefrec.htm#aermod

15. EPA. 2012. 2005 National Emissions Inventory Data & Documentation. U.S. 

Environmental Protection Agency, Research Triangle Park, NC. 

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

16. EPA. 2011. An Overview of Methods for EPA’s National-Scale Air Toxics Assessment. 

Prepared for EPA’s Office of Air Quality, Planning, and Standards, Research Triangle

Park, NC, by ICF International, Durham, NC. January 2011, p 55. 

http://www.epa.gov/ttn/atw/nata2005/05pdf/nata_tmd.pdf. 

17. EPA. 2004. EMS-HAP Modeling System. Environmental Protection Agency, Research 

Triangle Park, NC. http://www.epa.gov/scram001/dispersion_related.htm#ems-hap

18. Strum, Madeleine, Richard Mason, and James Thurman. 2004. User's Guide for the 

Emissions Modeling System for Hazardous Air Pollutants (EMS-HAP) Version 3.0. 

EPA-454/B-03-006, Environmental Protection Agency, Research Triangle Park, NC. 

August 2004. http://www.epa.gov/ttn/scram/userg/other/emshapv3ug.pdf
34

19. EPA. 2004. User’s Guide for the AERMOD Meteorological Preprocessor (AERMET), 

EPA-454/B-03-002, U.S. Environmental Protection Agency, Research Triangle Park, 

NC. http://www.epa.gov/scram001/7thconf/aermod/aermetugb.pdf

20. EPA. 2012. Addendum to the User’s Guide for the AERMOD Meteorological 

Preprocessor (AERMET) (EPA-454/B-03-002, November 2004), U.S. Environmental 

Protection Agency, Research Triangle Park, NC. December 2012. 

http://www.epa.gov/ttn/scram/metobsdata_procaccprogs.htm#aermet

21. Texas Commission on Environmental Quality. 2006. Reports on Air Emission Events. 

http://www.tceq.texas.gov/field/eventreporting

22. 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. 

Appendix W of 40 CFR Part 51. 

http://www.epa.gov/ttn/scram/guidance/guide/appw_05.pdf

23. Federal Register, 2012. Qualifying Urban Areas for the 2010 Census. FR 77:59 (27 

March 2012). p.18652. http://www.gpo.gov/fdsys/pkg/FR-2012-03-27/pdf/2012-6903.pdf

24. EPA. 2006. NEI Quality Assurance and Data Augmentation for Point Sources. U.S. 

Environmental Protection Agency, Emission Inventory Group, RTP, NC. 

http://www.epa.gov/ttn/atw/nata2005/05pdf/2002nei_qa_augmentation_report0206.pdf 

25. 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

26. EPA, 2001, National-Scale Air Toxics Assessment for 1996; EPA-453/R-01-003; EPA 

454/B-03-003, U.S. Environmental Protection Agency, Research Triangle Park, NC. 

http://www.epa.gov/ttn/atw/nata/peer.html
DRAFT

METEOROLOGICAL DATA PROCESSING 

USING AERMET

FOR HEM3

US EPA 

Air Toxic Assessment Group

RTP NC 27711

September, 2014
Meteorological Data Processing using AERMET December 2013

For HEM3

1

BACKGROUND

The AERMOD meteorological processor, AERMET, and its supporting modeling system 

(AERSURFACE and AERMINUTE) were used to process one year of meteorological 

data for over 800 observation stations across the continental United States, Alaska, 

Hawaii, and Puerto Rico.

METEOROLOGICAL DATA

To estimate the boundary layer parameters required by AERMOD, AERMET requires 

hourly surface weather observations (which may include hourly values calculated from 1-

minute data) 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. 

USEPA meteorologists obtained calendar year 2011 Integrated Surface Hourly Data 

(ISHD) for over 800 Automated Surface Observation System (ASOS) stations spanning 

the entire US, as well as Puerto Rico and the US Virgin Islands, from the National 

Climatic Data Center (NCDC). To support AERMET, ASOS 1-minute data for each 

surface station were also obtained from NCDC in a DSI 6405 format.

Further, upper air sounding data for the same time period for over 80 observation sites 

were obtained from the “NOAA/ESRL” online Radiosonde Database. These datasets 

were produced by ESRL in Forecast Systems Laboratory (FSL) format. Appendix 1 lists 

the surface stations, as well as the location, ground elevation, and anemometer height for 

each station. Figure 1 is a map that shows the locations of all the surface stations. 

Appendix 2 lists the upper air stations and their locations.

AERMET PROCESSING

Utilizing the AERMET meteorological data preprocessor, and the ASOS surface and FSL 

upper air stations, surface and profile files for input into AERMOD were generated 

nationwide. The surface stations were paired with representative upper air stations by 

taking the upper air station closest to each surface station. The AERSURFACE tool was 

used to estimate the surface characteristics for input into AERMET utilizing land cover 

data surrounding the surface station. In addition, the AERMINUTE preprocessor was 

used to process 1-minute ASOS wind data for input into AERMET. Table 1 outlines the 

approach/inputs each of the data preprocessors and tools used to generate the AERMOD

meteorological data.
Meteorological Data Processing using AERMET December 2013

For HEM3

2

Figure 1. Surface Stations
Meteorological Data Processing using AERMET December 2013

For HEM3

3

Table 1. AERMET Processing Options

AERMET Options Version 13350 1

ASOS Site Yes

Surface Data Format NCDC TD-3505 (ISHD)

Upper Air Data Format FSL, all levels, tenths m/s

Wind Speed Threshold 0.5 m/s

Beta Option (U*) Yes

AERMINUTE Options Version 11325

Include 1 minute ASOS 

Data

Yes, where available TD-6405 

format

AERSURFACE Options Version 13016

Landcover data USGS NLCD92 GeoTIFF format 

(except Alaska and Hawaii which 

used 2001 landcover data)

Radius for Surface 

Roughness Calculations

1 km

Site Characteristics Airport Site (where applicable)

Arid Regions for all sites in AZ, 

NM, UT only

Temporal resolution Monthly, 12 sectors

Site Surface Moisture Average

Snow Cover Late Winter/Winter without 

continuous snow cover – all states 

(except Alaska where we used 

continuous snow)

1 : generated with the pre-public release version of AERMET 13350.

RESULTS

Beginning with version 12345, AERMET allows for a minimum wind speed of 0.5 m/s to 

be utilized when processing monitoring stations that are equipped with sonic 

anemometers and for an adjustment to the surface friction velocity (u*). The public 

version of AERMET available at the time we conducted the processing of this data did 

not include the surface friction velocity adjustment. Also at the time, an updated version 

of AERMET (and AERMOD) was under development that incorporated further surface 

friction velocity adjustments to AERMET version 12345. In anticipation of the update, 

EPA processed meteorological data using a pre-public release version of AERMET and 

incorporated the updated surface friction velocity adjustment in the output. It was EPA’s 

judgement that the pre-public release version of AERMET would generate AERMOD 

ready meteorological data very close to that generated with the 13350 version of 

AERMET. The AERMET meteorological data posted on the EPA’s FERA (Fate, 

Exposure, and Risk Analysis) website under the HEM model page was generated with the 

pre-public release version of AERMET 13350. 
Meteorological Data Processing using AERMET December 2013

For HEM3

4

ly, USEPA meteorologists ran AERMOD using a model plant. Further, the surface files 

were examined for completeness. If more than 10 percent of the data were missing, the 

station was not considered suitable for the HEM-3 meteorological database. In all, 824 

met station pairs ran successfully and will be included in the HEM-3 meteorological 

library. A summary of these station pairs are presented in Appendix 3 of this document.
Appendix 4

Dispersion Model Receptor Revisions and Additions

for the Aerospace Source Category
Dispersion Model Receptor Revisions and Additions 

To estimate ambient concentrations for evaluating long-term exposures, the HEM-3 

model uses the geographic centroids of census blocks (currently utilizing the 2010 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 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 these source categories, 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.
Block ID NEI ID New Latitude New 

Longitude

Comment

010730003003000 33.551805 -86.759025 On plant property

020900018002007 64.674932 -147.069107 Centroid doesn't represent population

051139501003052 34.549923 -94.20052 Centroid doesn't represent population

060375742022013 33.797825 -118.148638 On plant property

090076802002008 41.559581 -72.606323 Centroid doesn't represent population

090076802002030 41.530871 -72.568172 Centroid doesn't represent population

090035106001020 41.743352 -72.631209 On plant property

120090671001045 28.218591 -80.604812 On plant property

120310138002003 30.389219 -81.398452 Centroid doesn't represent population

130099705003027 33.083926 -83.306343 Centroid doesn't represent population

150030087012030 21.392368 -157.979934 Centroid doesn't represent population

191751903001185 41.050938 -94.330568 Centroid doesn't represent population

191751903001184 41.057247 -94.336577 Centroid doesn't represent population

200354933001017 37.183071 -97.024316 Centroid doesn't represent population

201259507002092 37.164787 -95.764402 Centroid doesn't represent population

201730073022000 37.683842 -97.235703 Centroid doesn't represent population

201730092002069 37.670818 -97.435695 Centroid doesn't represent population

201730096031008 37.628536 -97.425269 Centroid doesn't represent population

201730058001012 37.616576 -97.29574 Centroid doesn't represent population

240054515003002 39.326719 -76.438174 Centroid doesn't represent population

240010020003030 39.565437 -78.855722 Centroid doesn't represent population

240010020001059 39.567312 -78.853521 Centroid doesn't represent population

240150305034001 39.608694 -75.862987 Centroid doesn't represent population

250092544023011 42.641297 -71.193259 On plant property

270530251003004 44.848346 -93.232734 Centroid doesn't represent population

280890302041010 32.513103 -90.115196 Centroid doesn't represent population

291833102022024 Remove - no apparent population

291833104002011 38.798652 -90.472628 Centroid doesn't represent population

330110026003011 42.947922 -71.445989 Centroid doesn't represent population

330110026002029 42.941096 -71.420333 Centroid doesn't represent population

361119505001033 42.023843 -74.103 Centroid doesn't represent population

371330010001078 Remove - no apparent population

390210104003056 40.113107 -83.756062 Centroid doesn't represent population

390210106003038 40.095165 -83.758549 Centroid doesn't represent population

390210115045026 40.090555 -83.760095 Centroid doesn't represent population

390610232011004 39.231316 -84.441067 Centroid doesn't represent population

391093653021008 40.022496 -84.222607 On plant property

391336009012020 41.163415 -81.25601 Centroid doesn't represent population

401091074032002 35.417778 -97.364281 Centroid doesn't represent population

401430059001112 36.173684 -95.850039 Centroid doesn't represent population

401430059001102 36.169252 -95.852922 Centroid doesn't represent population

420034511052004 40.507363 -80.22897 Centroid doesn't represent population

420550104002034 Remove - no apparent population

470370157001012 36.112564 -86.694519 Centroid doesn't represent population

481210203083090 32.997107 -97.326708 Centroid doesn't represent population

482013413012014 29.56424 -95.106548 Centroid doesn't represent population

482319611003051 33.072415 -96.086581 Centroid doesn't represent population

482319611003064 33.057274 -96.077758 Centroid doesn't represent population

490111252002238 41.114114 -112.003497 On plant property

490111255022016 Remove - no apparent population

490351139071081 Remove - no apparent population

Dispersion Modeling Receptor Revisions and Addiitons

page 1 of 3
Block ID NEI ID New Latitude New 

Longitude

Comment

Dispersion Modeling Receptor Revisions and Addiitons

530330253021004 47.502702 -122.203415 On plant property

530330309011001 47.281439 -122.246982 On plant property

530530714063015 47.075226 -122.345873 Centroid doesn't represent population

530530731082001 47.085115 -122.335884 Centroid doesn't represent population

530610419032005 47.910351 -122.259637 Centroid doesn't represent population

530610419011004 47.888713 -122.280334 On plant property

530610413042008 47.92782 -122.286347 Centroid doesn't represent population

530610419031003 47.922014 -122.258728 Centroid doesn't represent population

550870126013002 44.246606 -88.500529 On plant property

050272001 33.279137 -93.24732 Additional receptor

01015223 33.636798 -85.917779 Additional receptor

01015223 33.64429 -85.922 Additional receptor

010515017 32.483183 -85.891947 Additional receptor

05113377 34.553318 -94.201272 Additional receptor

05143331 36.163941 -94.129957 Additional receptor

05143331 36.164968 -94.134175 Additional receptor

05143331 36.162945 -94.133167 Additional receptor

06037429 33.799406 -118.155953 Additional receptor

06037490 33.921879 -118.378028 Additional receptor

06037490 33.921247 -118.378083 Additional receptor

06065520 33.946768 -117.464655 Additional receptor

06065520 33.943259 -117.468022 Additional receptor

06065520 33.942524 -117.462057 Additional receptor

06065520 33.943152 -117.460244 Additional receptor

06065520 33.946694 -117.45886 Additional receptor

09001688 41.230118 -73.188762 Additional receptor

09001688 41.229658 -73.181106 Additional receptor

09001693 41.250318 -73.102572 Additional receptor

09001693 41.254451 -73.100727 Additional receptor

09003626 41.859277 -72.699027 Additional receptor

09003626 41.860102 -72.696426 Additional receptor

090032025 41.702315 -72.861 Additional receptor

090075021 41.583854 -72.709395 Additional receptor

090075021 41.584044 -72.712449 Additional receptor

13067506 33.93187 -84.521904 Additional receptor

13067506 33.928956 -84.541948 Additional receptor

13153431 32.63233 -83.606056 Additional receptor

132332017 34.000155 -85.037232 Additional receptor

19163356 41.545325 -90.610764 Additional receptor

20035486 37.174104 -97.022622 Additional receptor

20173193 37.665652 -97.376544 Additional receptor

24015282 39.611523 -75.873373 Additional receptor

24015282 39.60952 -75.870575 Additional receptor

25009450 42.641822 -71.190422 Additional receptor

25009450 42.639385 -71.195099 Additional receptor

25009450 42.634538 -71.187894 Additional receptor

25009450 42.635331 -71.179925 Additional receptor

25009450 42.636161 -71.19115 Additional receptor

25009450 42.631334 -71.184271 Additional receptor

25009450 42.631605 -71.178394 Additional receptor

25009477 42.456095 -70.970509 Additional receptor

page 2 of 3
Block ID NEI ID New Latitude New 

Longitude

Comment

Dispersion Modeling Receptor Revisions and Addiitons

29183329 38.802311 -90.477323 Additional receptor

29183329 38.80376 -90.479095 Additional receptor

33011652 42.945817 -71.445529 Additional receptor

36103521 40.70906 -73.39568 Additional receptor

36103521 40.710546 -73.396134 Additional receptor

36111630 42.025358 -74.104536 Additional receptor

39017343 39.300248 -84.445037 Additional receptor

391335027 41.162827 -81.25431 Additional receptor

40143460 36.170512 -95.849741 Additional receptor

401432003 36.204015 -95.90583 Additional receptor

401432013 36.2485 -95.92349 Additional receptor

42003603 40.509554 -80.23638 Additional receptor

4703758 36.121575 -86.697288 Additional receptor

4703758 36.123731 -86.697024 Additional receptor

482015020 29.564051 -95.07879 Additional receptor

48231376 33.063178 -96.079797 Additional receptor

483672020 32.774411 -97.80383 Additional receptor

484392009 32.807578 -97.154854 Additional receptor

53033532 47.271577 -122.237263 Additional receptor

54067418 38.226333 -80.582851 Additional receptor

54067418 38.229495 -80.584709 Additional receptor

550876001 44.258407 -88.506004 Additional receptor

2017365 37.68934 -97.207254 Additional receptor

2017365 37.687577 -97.207454 Additional receptor

15003346 21.334582 -157.95132 Additional receptor

page 3 of 3
APPENDIX 5: Technical Support Document for the TRIM-Based

Multipathway Tiered Screening Methodology for RTR
Technical Support Document

for the TRIM-Based Multipathway

Tiered Screening Methodology for RTR

October 2014 

Prepared For:

U.S. Environmental Protection Agency

Office of Air Quality Planning and Standards

Research Triangle Park, NC 27711

Prepared By:

ICF International

2635 Meridian Parkway

Suite 200

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

CONTENTS

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

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

1.1.1 Chemicals of Concern ................................................................................ 2

1.1.2 Development of Emission Thresholds......................................................... 4

1.2 Tier 2 ......................................................................................................................... 7

1.3 Tier 3 .......................................................................................................................10

1.4 Site-Specific Assessment ........................................................................................11

2. Tier 1 Methodology ...........................................................................................................12

2.1 Introduction..............................................................................................................12

2.2 Summary of Approach .............................................................................................12

2.2.1 Overview ...................................................................................................12

2.2.2 Chemicals of Potential Concern.................................................................15

2.2.3 Conceptual Exposure Scenario .................................................................16

2.2.4 Approach to Development of the Tier 1 Scenario.......................................18

2.2.5 Fate and Transport Modeling (TRIM.FaTE) ...............................................20

2.2.6 Exposure Modeling and Risk Characterization (MIRC) ..............................21

2.2.7 Implementation of Risk-based Emission Scaling Factors for 

POM and Dioxin Emissions.......................................................................22

2.3 Description of Environmental Modeling Scenario.....................................................26

2.3.1 Chemical Properties ..................................................................................26

2.3.2 Spatial Layout............................................................................................27

2.3.3 Watershed and Water Body Parameterization ...........................................28

2.3.4 Meteorology...............................................................................................30

2.3.5 Aquatic Food Web.....................................................................................32

2.3.6 Using TRIM.FaTE Media Concentrations ..................................................33

2.4 Description of Exposure and Risk Modeling Scenario..............................................34

2.4.1 Calculating Concentrations in Farm Food Chain Media .............................35

2.4.2 Ingestion Exposure....................................................................................35

2.4.3 Calculating Risk.........................................................................................38

2.4.4 Summary of Tier 1 Assumptions................................................................39

2.5 Evaluation of Screening Scenario............................................................................42

2.5.1 Introduction................................................................................................42

2.5.2 Cadmium Compounds...............................................................................42

2.5.3 Mercury Compounds .................................................................................44

2.5.4 Dioxins ......................................................................................................46

2.5.5 Polycyclic Aromatic Hydrocarbons.............................................................48

2.5.6 Summary...................................................................................................50

3. Tier 2 Methodology ...........................................................................................................52

3.1 Overview of Approach .............................................................................................52

3.2 Selection of Site-Specific Characteristics for the Tier 2 Assessment........................55

3.3 Estimation of Adjustment Factors for Selected Site-Specific 

Parameters..............................................................................................................55
TRIM-Based Tiered Screening Methodology for RTR

3.3.1 Selection Values for Variables of Interest ..................................................56

3.3.2 Estimation of Adjustment Factors ..............................................................59

3.4 Preparing National Databases of Lake and Meteorological Data .............................64

3.4.1 Processing Lake Data for Tier 2 Assessment ............................................64

3.4.2 Processing Meteorological Data for Tier 2 Assessment.............................68

3.5 Implementation of Tier 2 Assessment......................................................................71

4. Tier 3 Methodology ...........................................................................................................74

4.1 Overview of Approach .............................................................................................74

4.2 Lake Assessment ....................................................................................................74

4.3 Plume-rise Assessment ...........................................................................................76

4.4 Assessment of Time-series Meteorology and Effective Release 

Heights....................................................................................................................76

5. References .......................................................................................................................78

Description of Multimedia Ingestion Risk Calculator (MIRC) 

Used for RTR Exposure and Risk Estimates................................................. B-1

Dermal Risk Screening ................................................................................. C-1

Summary of TRIM.FaTE Parameters Considered for Inclusion in 

Tier 2 Assessment ........................................................................................ D-1

Analysis of Lake and Sustainable Fish Population ............................... E-1
TRIM-Based Tiered Screening Methodology for RTR

1. Introduction and Background

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

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

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

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

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

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

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

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

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

pathways, namely ingestion and dermal exposure. EPA’s assessment for RTR focuses on 

specific PB-HAPs that the Office of Air Quality Planning and Standards (OAQPS) has identified 

as candidates for multipathway risk assessments (selection of the PB-HAPs is discussed in

Section 2.2.2). These non-inhalation human health risks are considered in combination with

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

support decisions about residual risk for RTR source categories. For PB-HAPs, exposures via 

ingestion are anticipated to be significantly higher than any dermal exposures that might occur 

as a result of the same emissions (see Attachment C). Consequently, a methodology has been 

developed to evaluate ingestion exposure and risk for PB-HAPs efficiently in the context of 

EPA’s RTR program.

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

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

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

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

concern within the category. To estimate exposure and risk, two models are used: the Fate, 

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

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

Multimedia Ingestion Risk Calculator (MIRC) to estimate transfer and uptake into the food chain 

and exposure to receptors consuming contaminated food products and soil. This approach is 

divided into three tiers of increasing refinement, as follows.

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

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

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

hypothetical environmental and exposure scenario, assuming ingestion of locally caught 

fish, locally grown produce and livestock, and local soil. This hypothetical “screening 

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

to be exceeded at any actual facility evaluated through the RTR program. The 

thresholds for Tier 1 are derived by estimating the emission rate that corresponds to a 

lifetime cancer risk of 1 in 1 million or a chronic non-cancer hazard quotient (HQ) of 1 for 

an individual exposed according to the characteristics associated with the screening 

scenario. For a facility, if the emission rate of each PB-HAP is less than the Tier 1 

threshold emission rate, risks are assumed to be low and no additional multipathway 

screening is done. If, however, the emission rate of any PB-HAP exceeds the Tier 1 

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

• In Tier 2, the actual location of the facility is used to refine some assumptions associated 

with the environmental scenario while maintaining the Tier 1 ingestion exposure scenario 

assumptions. The environmental scenario assumptions are refined by incorporating 

binned site-specific meteorological data and and locations of fishable lakes near the 

facility (see below). The risk-based threshold for each PB-HAP is then adjusted for that 

Introduction and Background 1 October 2014
TRIM-Based Tiered Screening Methodology for RTR

facility based on an understanding of how exposure concentrations estimated for the 

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 no additional multipathway assessment is required. Facilities having emissions that 

exceed the adjusted thresholds for Tier 2 may require additional analysis. 

• A Tier 3 screening assessment can be conducted on facilities that do not screen out with 

the Tier 2 assessment, at the discretion of the risk assessor. The Tier 3 screening 

approach consists of three individual assessments that further refine the Tier 2 

screening scenario based on additional site-specific data and evaluations. One of the 

Tier 3 assessments (i.e., the lake assessment) results in the rescreening of the facility’s 

emissions using the Tier 2 methods and using a revised lake database. The other two 

assessments (i.e., the plume-rise and time-series assessments) each result in an 

adjustment factor to be applied to the screening result reflecting the Tier 3 lake 

assessment. Facilities having emissions that exceed the adjusted thresholds for Tier 3 

may require additional analysis. 

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

of concern based on the screening assessments, a refined, site-specific, multipathway 

risk assessment can be conducted. Such an assessment would incorporate location- or 

facility-specific characteristics regarding the environment to which PB-HAPs are emitted, 

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

parameters. A range of exposure scenarios could be evaluated as part of a site-specific

assessment, resulting in a range of risk estimates.

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

the remainder of this document, each of the tiers in the multipathway assessment approach is 

described in additional detail. The attachments provide a comprehensive record of the 

characteristics and methods associated with Tier 1 and Tier 2 assessments. If a site-specific

assessment is conducted, a separate report detailing that assessment will be prepared.

1.1 Tier 1

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

multiple facilities in a source category quickly and efficiently and to remove from consideration 

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

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

scenario used to estimate Tier 1 thresholds is designed to be health-protective in estimating 

exposures and risks; specifically, it is intended to avoid underestimating exposures to PB-HAPS 

that might be encountered for any location throughout the United States. The scenario also is 

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

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

1.1.1 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).

Introduction and Background 2 October 2014
TRIM-Based Tiered Screening Methodology for RTR

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

for PB-HAPs

Introduction and Background 3 October 2014
TRIM-Based Tiered Screening Methodology for RTR

Based on current emissions, bioaccumulation potential, and toxicity considerations, emissions 

of these four PB-HAPs are expected to pose the majority of the non-inhalation risks to humans 

from air emissions at sources subject to residual risk provisions of the CAA1 Thus, although 

EPA has identified nine other PB-HAPs that should be evaluated as part of residual risk 

assessments, the methods for multipathway assessment described here encompass only these 

four at this time. 

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

for that PB-HAP is compared to the threshold emissions rate derived for that chemical using the 

hypothetical TRIM-based screening scenario. This threshold is the emission rate that, when 

input to the models used in evaluating multipathway risk for RTR, results in a specified cancer 

risk or non-cancer HQ threshold level of concern. For the screening scenario, threshold

emission rates were calculated for a cancer risk of 1 in 1 million or an HQ of 1.0, depending on 

the more sensitive health effect of the PB-HAP. However, due to the hypothetical nature of the 

screening scenario, exceeding the threshold emissions rate by 60 times, for dioxins for 

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

threshold emissions rate by 60 times for dioxin implies that it is highly unlikely that the actual 

risk would exceed 60 in 1 million. 

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

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

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

in Section 2. 

1.1.2 Development of Emission Thresholds

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

four components (Exhibit 2): 

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

other environmental media (including fish uptake); 

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

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

food items by a hypothetical exposed human; and 

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

and corresponding screening threshold emission rates. 

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

calculations for the other three components.2 To derive the emission thresholds used in Tier 1, 

these models are used to estimate the emission rate corresponding to a cancer risk of 1 in 

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

Ambient Air Quality Standard for lead, which takes into account multipathway risks. 

2 EPA’s TRIM methodology was conceived as a comprehensive modeling framework for evaluating risks from air 

toxics. It was designed to address each of the four steps in screening ingestion risk; however, only the fate and 

transport module currently is available for use. For the RTR screening scenario, the Multimedia Ingestion Risk 

Calculator (MIRC) was constructed to complete the calculations required for estimating PB-HAP concentrations in 

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

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

the current status of TRIM modules and documentation of modules developed thus far can be accessed on EPA’s 

Technology Transfer Network (TTN) on the Fate, Exposure, and Risk Analysis website (http://www.epa.gov/ttn/fera/).

Introduction and Background 4 October 2014

 
TRIM-Based Tiered Screening Methodology for RTR

1 million or a noncancer HQ of 1 (depending on the PB-HAP) for each PB-HAP within the 

hypothetical environmental scenario, as described in more detail in the following sections. 

Exhibit 2. Overview of Ingestion Exposure and Risk Screening

Evaluation Method

1.1.2.1 Modeling Fate and Transport 

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

Tier 1, the TRIM.FaTE module of the TRIM system was used.3 The two main components of 

the fate and transport modeling are (1) the modeled domain, including the meteorological data 

and (2) the environmental and chemical-specific properties associated with fate and transport 

through the environment. The hypothetical modeled domain includes a farm homestead and a 

fishable lake near (i.e., 2 km) an emissions source, which are assumed to be the primary food 

sources for exposed individuals. The spatial layout and other physical aspects of the modeled 

domain configuration are designed to be health-protective, which results in an ingestion 

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

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

PB-HAPs are parameterized with either conservative (i.e., health protective) values or centraltendency values. The mix of health protective and central-tendency assumptions and 

parameterization is expected to result in a scenario configuration that, on average, is likely to 

overpredict environmental concentrations of PB-HAPs in media of interest for this evaluation. 

The inclusion of central-tendency values where warranted is intended to minimize the number of 

false positives. 

Based on sensitivity analyses and model testing it is generally recognized that the spatial layout 

of the modeled domain (e.g, distance to a fishable lake) and the meteorological data used (or a 

combination of these two factors) are more influential than physical/chemical parameters in 

dictating the resulting chemical concentrations in air, soil, water, sediment, and fish within 

TRIM.FaTE. The Tier 1 assumptions about these two components of fate and transport 

modeling are refined with relatively more site-specific data in subsequent tiers. The spatial 

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

methodology are presented in Section 2. 

3 http://www.epa.gov/ttn/fera/trim_fate.html

Chemical Emissions to Air

Cancer Risk

Hazard Quotient

Chemical fate 

and transport:

Physical environment 

and aquatic ecosystem

Uptake and 

transfer: 

Produce and livestock

Human 

Ingestion 

Exposure

Risk and 

Hazard 

Estimation

Introduction and Background 5 October 2014

 
TRIM-Based Tiered Screening Methodology for RTR

1.1.2.2 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. 

1.1.2.3 Estimating Ingestion Exposure

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

body weight for the following exposure pathways: 

• Incidental ingestion of soil

• Ingestion of homegrown produce

• Ingestion of homegrown beef

• Ingestion of milk from homegrown 

cows

• Ingestion of homegrown poultry/eggs

• Ingestion of homegrown pork 

• Ingestion of fish

• Ingestion of breast milk (children <1 year

old; dioxins only)4

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

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

scenario used in Tiers 1 and 2, exposure characteristics were selected that result in a highly 

health protective estimate of total exposure. The ingestion rate for each exposure pathway 

listed above was set (as feasible) equal to an upper percentile value (99th percentile for fish and 

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

2011a) or other sources as appropriate. All media were assumed to be obtained from a location 

impacted by the modeled source. This approach results in an overestimate of total chemical 

exposure for a hypothetical exposure scenario. For example, the resulting total food ingestion 

rate is extremely high for a hypothetical consumer, with ingestion rates at the 99th percentile for 

fish and the 90th percentile for every other farm food type. These health protective exposure 

assumptions can be replaced in a site-specific assessment as appropriate (e.g., with 

distributions of the data for key exposure factors).

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 Section 2 and Attachment C). 

4 Breast milk ingestion is an important exposure pathway for lipophilic compounds like dioxins and has been shown 

not to contribute meaningfully for exposures to mercury, cadmium, and POM. See Section 2.4.2.3 and Section 3.4 of 

Attachment B for full discussions of infant exposures via breast milk ingestion. 

Introduction and Background 6 October 2014

 
TRIM-Based Tiered Screening Methodology for RTR

1.1.2.4 Calculating Lifetime Cancer Risk and Non-Cancer HQs

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

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

estimates are calculated separately for each PB-HAP. As provided in Section 5.1 of Attachment 

B, age-group specific cancer risk estimates are calculated and the estimated lifetime cancer risk 

equals the sum of these age-group specific risks. Similarly, HQs are calculated separately for 

each PB-HAP and for each age group. However, as detailed in Section 5.2 of Attachment B, the 

HQ for the most sensitive age group is used to determine the screening threshold emission rate. 

1.1.2.5 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.

1.2 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 potential. Once the Tier 1 screen is complete, however, 

facilities whose emissions exceed the emission screening threshold for any PB-HAP can be 

scrutinized further. Based on screening assessments conducted for RTR to date, many

facilities might not screen out of the Tier 1 assessment for some source categories. However, 

conducting a full site-specific assessment 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 some site-specific characteristics instead of the 

generic characteristics used in Tier 1 can justify adjusting the threshold emission rate for a given 

PB-HAP at that facility, potentially screening out the facility while maintaining a high degree of 

confidence that risks above levels of concern have not been overlooked. In addition, using a 

scenario in which a fisher fishes from multiple nearby lakes, catching an amount of fish from 

each lake that is limited by the lake’s theoretical fish productivity, creates a more site-specific 

approach while also maintaining health protectiveness. 

In selecting the scenario characteristics to modify in Tier 2, a balance was struck between the 

degree of impact on the risk estimate, the ease of implementation, and the ease of obtaining 

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

program. As discussed in the remainder of this section, some of these characteristics affect the 

PB-HAP concentrations in environmental media estimated by TRIM.FaTE. Other characteristics 

affect the sources of ingested fish and, potentially, the fish ingestion rates. 

Introduction and Background 7 October 2014
TRIM-Based Tiered Screening Methodology for RTR

1.2.1.1 Site Specific Considerations

For Tier 2, location-specific data on two types of variables are taken into account: 

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

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

and 

• Locations of fishable lake(s) relative to the facility5 (including the absence of a fishable 

lake). 

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

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

the screening scenario for four of the five selected variables (lake location, wind speed, 

precipitation rate, and mixing height). Wind direction affects only whether the chemical mass 

advects toward the farms and lakes, so the effect of site-specific wind directions can be 

evaluated outside of the TRIM.FaTE simulations. The values of each of the four variables were 

changed, independent of any other changes. The values (four to six for each variable, including 

the original, Tier 1 scenario values) were selected using statistics on U.S. meteorological data 

or professional judgment to capture the expected range in the data. Four to six values were 

selected to result in a total number of runs that was reasonable. 

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 Tier 1 

screening scenario and the risk metric obtained from the Tier 2 run. For a given facility, for each 

PB-HAP it emits and each nearby lake evaluated, an adjusted Tier 2 emission threshold can be 

estimated by dividing the Tier 1 emission threshold by the adjustment factor that best 

corresponds to the meteorological conditions present at the site and the location of the lake

(hypothetical farms are assessed for every facility, at a constant distance from the facility). The 

contribution of each exposure media toward each scenario’s Tier 2 risk metric (i.e., the

individual contributions of fish ingestion, soil ingestion, beef ingestion, etc. toward the total Tier 

2 risk metric) was included in the matrix because the Tier 2 assessment separates chemical 

exposure from fish ingestion from exposure from farm food chain ingestion.

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

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

lake data were created that could be accessed readily during a Tier 2 evaluation. These 

databases are described in more detail in Section 3.4. The meteorological database is based on 

the same hourly meteorology data used for RTR inhalation assessments. The meteorology 

database includes annual summary statistics on wind direction, wind speed, precipitation, and 

mixing heights for more than 800 surface stations, paired with their closest upper-air station with 

available data, located throughout the United States (data available from the National Oceanic 

and Atmospheric Administration). The lake database, based on U.S. Geological Survey 

(USGS) data and including location and size information, consists of hundreds of thousands of 

water bodies classified as “Lake/Pond” or “Reservoir” but not designated for disposal, 

evaporation, or treatment. To focus on lakes that can support fishing of upper trophic level fish, 

a minimum lake surface area of 25 acres is recommended. Very large lakes and bays (i.e., 

those larger than 100,000 acres) are not included because their watersheds are too large and 

5The lake size also was changed for each lake distance allowing for a constant ratio between watershed and erosion 

area compared with lake area within the TRIM modeling structure.

Introduction and Background 8 October 2014

 
TRIM-Based Tiered Screening Methodology for RTR

their lake dynamics are too complex to realistically model in the TRIM.FaTE system. Lakes and 

bays larger than 100,000 acres include the Great Lakes, the Great Salt Lake, Lake 

Okeechobee, Lake Pontchartrain, Lake Champlain, Green Bay, and Galveston Bay. 

When the Tier 2 screening is conducted, three additional processing steps are completed for 

each facility and PB-HAP that will be analyzed in Tier 2 before looking up the appropriate 

adjustment factors. First, a proximity analysis for each lake is conducted, whereby each 

relevant lake within 50 km of the facility is identified and matched to its respective directional 

“octant” relative to the facility. For the purposes of Tier 2, a “relevant” lake meets the size and 

designation criteria discussed in the previous paragraph. Second, the lakes are manually 

evaluated to remove lakes whose names suggest uses related to disposal, evaporation, or 

treatment (sometimes the name indicates one of these uses while the USGS designations do 

not; for example, the Gavin Fly Ash Impooundment would not be included in the screening 

process). Third, the lakes around the facility that remain after the first two processing steps are 

ranked in order of highest to lowest PB-HAP concentrations in fish. These rankings are then 

used to refine the Tier 2 risk metric for the fisher, as discussed in the next section. 

To perform Tier 2 assessments, a Microsoft® AccessTM tool was created that merges Tier 1 

screening results with the Tier 2 adjustment factors and the lake and meteorology information 

relevant to a specific facility. In the tool, each facility is matched with the same meteorology 

station used in RTR inhalation assessments, and the values for the four relevant meteorological 

parameters at that station are recorded. The distances from the facility to relevant lakes are 

computed in the tool as well. These five parameter values become the set of facility-specific 

inputs in Tier 2. Then, the Tier 2 adjustment factors are determined for each PB-HAP for the 

combination of these five variables. As described above, the Tier 1 screening emission 

threshold is then divided by the appropriate adjustment factor to obtain a revised, Tier 2 

emission threshold for that PB-HAP. More information about Tier 2 assessment methods can be 

found in Section 3. 

1.2.1.2 Refined Tier 2 Fisher Assessment

The refined fisher scenario is based on the idea that an adult fisher might travel to multiple lakes 

if the first lake (i.e., the lake with the highest concentrations in fish for a given PB-HAP) is 

unable to provide him an adequate catch to satisfy the assumed ingestion rate (i.e., 373 g/d for 

adults). This refined fisher assessment uses the assumption that the biological productivity 

limitation of each lake is 1 gram of fish (wet weight) per acre of lake; meaning that in order to 

fulfill the adult ingestion rate, the fisher will need to fish from 373 total acres of lakes. 

In the refined fisher scenario, a fisher travels to each relevant lake in order of highest to lowest 

chemical concentration in fish (of a given PB-HAP) and catches fish up to the lake’s assumed 

biological productivity limitation. A maximum travel radius of 50 km relative to the facility is used 

to maintain a realistic scenario. The final Tier 2 screening result for the fisher can be expressed 

as the sum of the screening result from each lake that is fished (which is based on the amount 

of fish ingested for each lake multiplied by the PB-HAP concentration in fish). If the highestconcentration lake is at least 373 acres, the ingestion rate is not altered (i.e., remains 373 g/d 

for adults). If the cumulative size of multiple visited lakes exceeds 373 acres, the fisher catches 

from the final lake only the amount of fish necessary to satisfy the ingestion rate (i.e., to reach 

the 373 g/d). If there are not 373 total acres of lakes, the risk reflects a reduced ingestion rate 

based on the cumulative lake acreage. 

Introduction and Background 9 October 2014
TRIM-Based Tiered Screening Methodology for RTR

1.3 Tier 3

A Tier 3 screening assessment can be conducted on facilities that do not screen out with the

Tier 2 assessment, at the discretion of the risk assessor. The Tier 3 screening approach 

consists of three individual assessments that further refine the screening scenario (beyond the 

refinements in Tier 2) based on additional site-specific data and evaluations. Because the Tier 3 

assessment introduces additional site-specificity to the screening scenario, it requires a 

potentially higher level of effort than the Tier 2 assessment, but still a much lower level of effort 

than the full site-specific assessment (discussed in Section 1.4). One of the Tier 3 assessments 

(i.e., the lake assessment) potentially results in the rescreening of the facility’s emissions using 

the Tier 2 methods described in Section 1.2 and using a revised lake dataset. The other two 

assessments (i.e., the plume-rise and time-series assessments) each result in an adjustment 

factor to be applied to the screening result reflecting the Tier 3 lake assessment. The 

adjustment factors of the plume-rise and time-series assessments cannot be compounded (i.e., 

the time-series assessment, if conducted, supplants the plume-rise assessment already 

conducted). 

One component of the Tier 3 assessment is evaluating the fishability of the lakes used in Tier 2. 

The USGS dataset occasionally includes lakes that appear to be misclassified, no longer exist, 

or are estuarian by nature. These lakes are removed from the Tier 3 assessment after 

evaluating their validity using aerial imagery and other available data. These additional data 

sources are not used in the Tier 2 screening. Examples of lakes removed from Tier 3 source 

category assessments are provided in Section 4.2. If one or more lakes are removed from a 

facility’s assessment, the facility’s emissions are rescreened using the revised lake database

and the Tier 2 methods described in Section 1.2. If removing a lake(s) causes the originallyfished lakes to sum to less than 373 total acres, then in the rescreening, the fisher will catch and 

consume fish from an additional lake(s) if available. In this situation, the Tier 3 lake assessment 

is conducted on the newly added lake(s), and another rescreening is conducted, and so on until 

no further lakes are removed or added to the assessment. The Tier 3 lake assessment is more 

thoroughly described in Section 4.2. 

If, after the lake assessment, the Tier 3 screening result is still above a level of concern, the risk 

assessor may choose to conduct a plume-rise assessment. Atmospheric conditions coupled 

with the physical parameters of the chemical release point can cause the chemical plume to rise 

substantially beyond the physical release height. This process is not explicitly modeled by 

TRIM.FaTE but can substantially reduce ground-level chemical exposure if the plume frequently 

rises above the mixing height. This assessment uses a scenario in which the chemical release

height varies over time due to hourly meteorological conditions and the parameters associated 

with the chemical release point (i.e., physical release height and diameter, exit velocity, and gas 

temperature). If the resulting “effective release height” is above the mixing height for a given 

hour, then in the TRIM.FaTE modeling system there is no chemical deposition or exposure for 

that hour. If this occurs across many hours, it will substantially reduce total PB-HAP exposure 

and reduce the screening result. The plume-rise adjustment factor—the number of hours when 

the effective release height remains below the mixing height, divided by the number of total 

modeled hours—is multiplied by the Tier 2 screening result, thus lowering the screening result. 

A more thorough description is described in Section 4.3. 

If the Tier 3 screening result after the lake and plume-rise assessments still is above a level of 

concern, the risk assessor may choose to conduct a time-series assessment. This assessment

utilizes hourly effective release heights (computed in the plume-rise assessment above) along 

with the hourly meteorology data associated with the facility (i.e., the meteorology data that was 

summarized and used in Tier 2). Using hourly meteorology data adds additional site-specificity 

compared to the summarized, binned meteorology statistics used in Tier 2. Using these data in 

Introduction and Background 10 October 2014
TRIM-Based Tiered Screening Methodology for RTR

combination with hourly effective release heights is a more complete evaluation of hourly 

chemical losses due to plume rise compared with the Tier 3 plume-rise assessment described 

above. These time-varying release height and meteorology files are used in a run of TRIM.FaTE 

that also uses the facility’s PB-HAP emissions and the Tier 2 spatial scenario associated with 

the lake being assessed. The TRIM.FaTE modeling, and subsequent exposure and risk 

characterization conducted using MIRC, leads directly to a screening-level cancer risk or HQ

(i.e., a revised screening result). For simplicity in the software implementation of the Tiers 2 and 

3 screening assessments, the result of this Tier 3 time-series assessment is converted to a 

time-series adjustment factor—the revised screening result divided by the screening result after 

the Tier 3 lake assessment. This ratio can then be multiplied by the screening result after the 

Tier 3 lake assessment, yielding the revised screening result accounting for the time-series 

assessment. 

1.4 Site-Specific Assessment

If, based on results of the screening assessments, a risk assessor concludes that there is a 

reasonable probability that individuals could be adversely affected by the facility emissions, a 

refined site-specific multipathway assessment might be performed. Examples of recent refined 

multipathway assessments include residual risk assessments of a ferroalloys production facility

(EPA 2014a), petroleum refinery facility (EPA 2014b), two secondary lead smelting facilities

(EPA 2011b), assessments of two coal-fired electric utility units conducted in support of EPA’s 

utility rule (EPA 2011c), and a case study evaluation of a Portland cement facility included with 

other RTR materials presented to the Science Advisory Board for review (Appendix I of EPA 

2009). 

Whereas Tier 2 and Tier 3 assessments incorporate some site-specific and regional information 

on meteorology and water bodies, a refined multipathway assessment uses detailed sitespecific data to parameterize more accurately (to the extent possible) each important parameter 

that affects pollutant fate and transport. These site-specific properties are incorporated into 

model scenarios configured in TRIM.FaTE and MIRC. Important site-specific data 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 properties, erosion and runoff rates, 

surface water and sediment properties, water transfer rates, and aquatic ecosystem information. 

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

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

risk (i.e., risk and/or HQ values). Additional analyses of the media concentrations, exposure 

estimates, and risk estimates for the various ingested media using a range of ingestion rates for 

each modeled PB-HAP allows the risk assessor to understand, based on TRIM.FaTE and 

MIRC, the sources and pathways of possible human health risk from emissions of PB-HAPs. 

Introduction and Background 11 October 2014
TRIM-Based Tiered Screening Methodology for RTR

2. Tier 1 Methodology

2.1 Introduction

As discussed in Section 1, the U.S. Environmental Protection Agency (EPA) implements a 

tiered approach to evaluate multipathway exposures and human health risks for the Risk and 

Technology Review (RTR) program. EPA’s assessment for RTR focuses on persistent and 

bioaccumulative hazardous air pollutants (PB-HAPs) that the Office of Air Quality Planning and 

Standards (OAQPS) has identified as candidates for multipathway risk assessments (selection 

of the PB-HAPs is discussed in Section 2.2.2). In the first tier, a screen is conducted that 

focuses on the identity and magnitude of emissions of PB-HAPs from a given facility to 

determine whether a facility passes certain human health risk-based criteria. Sources that are 

“screened out” in the Tier 1 assessment are assumed to pose no risks to human health above 

levels of concern and and are not considered in further assessments. For sources that do not 

pass the Tier 1 screen, more refined assessments, up to and including site-specific 

multipathway assessments, can be conducted as appropriate. 

This section describes the technical basis for the first screening-level tier of EPA’s multipathway 

human health evaluation of PB-HAP emissions from RTR sources. Specifically, the scenarios, 

models, configurations, and inputs used to derive screening threshold emission rates in the first 

tier of the approach are described in detail in the following sections.

• Section 2.2 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 2.3 and 2.4 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 2.5 provides a brief discussion of the screening threshold emissions for each of 

the chemicals assessed. 

The Tier 2 and Tier 3 screening methods are discussed in Sections 3 and 4, respectively, and 

references cited are provided in Section 5. 

2.2 Summary of Approach 

2.2.1 Overview

The Tier 1 approach for evaluating non-inhalation, multipathway exposures to PB-HAPs for RTR 

is diagrammed in Exhibit 3. 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 Tier 1 

threshold-screening emission rates that have been derived using the TRIM-based Tier 1 

scenario described in this document (see Exhibit 4 for threshold screening emission rates). 

Tier 1 Methodology 12 October 2014
TRIM-Based Tiered Screening Methodology for RTR

Exhibit 3. Conceptual Decision Tree for Tier 1 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

Tier 1 Methodology 13 October 2014
TRIM-Based Tiered Screening Methodology for RTR

The TRIM-based multipathway modeling configuration, referred to in this document as the Tier 1 

scenario, forms the technical basis for determining the Tier 1 emission thresholds. The term, 

Tier 1 scenario, is used to refer collectively to the specific TRIM.FaTE and exposure modeling 

configuration described herein, including the set of assumptions and input values associated 

with a hypothetical watershed and the exposure and risk scenarios evaluated for this watershed. 

The Tier 1 scenario is a static configuration, and its primary purpose is as a modeling tool to 

calculate the Tier 1 emission rate thresholds for PB-HAPs of concern. 

The two potential outcomes of the Tier 1 evaluation are: 

• Non-inhalation exposures are unlikely to pose a human health problem (i.e., the 

emissions evaluated “pass” the screen); or 

• Risks above the levels of concern from non-inhalation exposures cannot be ruled out. 

An ideal screening approach strikes a balance between being health-protective—to ensure that 

risks above levels of concern are identified, and being accurate—to minimize results suggesting 

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

accuracy in environmental modeling are accompanied by additional resource requirements. 

Stated another way, a suitable approach minimizes both false negatives and false positives. 

False negatives (i.e., results that suggest that the risk is acceptable when in fact the actual risk 

is high) can lead to inappropriate and non-protective health or environmental policy decisions. 

False positives (i.e., results that suggest more assessment is required when in fact the actual 

risk is low) can result in wasted resources by leading to additional, unnecessary analysis. For 

the evaluation of multipathway human health exposures to PB-HAPs, the methods for screening 

described in this document are intended to achieve this balance.

Because the Tier 1 evaluation enables EPA to confidently eliminate from consideration those 

facilities where risks from non-inhalation exposures are projected to be minimal, resources can 

be targeted toward those facilities that do not pass the screening test. For facilities that do not 

pass the Tier 1 screening, in additional tiers of analysis, some of the Tier 1 parameters are 

reassessed, and if appropriate, are changed to more accurately reflect site-specific 

characteristics. With each successive tier of the assessment, additional Tier 1 assumptions are 

evaluated and refined to better reflect site-specific characteristics of the facility being modeled. 

The Tier 1 screening evaluation for RTR compares reported air emission rates of PB-HAPs 

(summed by PB-HAP for each facility) to screening threshold emission rates derived using the 

Tier 1 scenario. A threshold emission rate is the level that, when input to a risk model using

emissions as a parameter, corresponds to a specified cancer risk or non-cancer hazard quotient 

(HQ) that, for the purposes of the evaluation being conducted, is assumed to be below a level of 

concern. Tier 1 threshold emission rates were calculated for a cancer risk of 1 in 1 million or an 

HQ of 1.0 and are presented in Exhibit 4.6 Conceptually, a threshold level for the RTR 

multipathway screening evaluation could be obtained by back-calculating the emission rate that 

results in the specified cancer risk or HQ level, taking into account the exposure and fate and 

transport calculations included in the model. Because the models used in this assessment are 

not designed to run “backwards,” the rates instead were derived from regression equations 

established following a series of TRIM.FaTE and exposure/risk model runs spanning a wide 

6For 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.

Tier 1 Methodology 14 October 2014

 
TRIM-Based Tiered Screening Methodology for RTR

range of emission rates for each chemical. The estimated screening-level emission rates are 

verified by performing model runs using the estimated threshold emission rate to confirm that 

the emission rates result in a cancer risk of 1 in 1 million or an HQ of 1.0.

Exhibit 4. Emission Thresholds for Screening of Multipathway Exposures

Chemical

Screening 

Threshold 

Emission Rate

(TPY)

Basis of Threshold 

(Type of Health Endpoint)

POM (as benzo(a)pyrene equivalents)a 2.58E-03 Cancer

Dioxins (as 2,3,7,8-TCDD equivalents)a 2.81E-09 Cancer

Cadmium 1.18E-02 Non-cancer

Mercury (as divalent mercury emissions) 3.16E-04 Non-cancer

TPY = U.S. short tons per year a

See Section 2.2.7 for a discussion on the derivation of equivalent emissions.

The more probable risk for each emission rate would be lower than the level corresponding to 

the screening threshold risk quantities in nearly all circumstances, given the health protective

and hypothetical nature of the Tier 1 screening configuration. It is considered very unlikely that 

the estimated risk at a real site would be greater than the estimated risk for the simulated Tier 1 

scenario at equivalent emission rates. This is because the Tier 1 scenario assumes, for many of 

the most risk influential parameters in the model, parameter values that result in high-end risk 

estimates. In the real world, the probability of such risk-maximizing conditions prevailing across 

multiple parameters is very low. For example, the Tier 1 scenario assumes a fishable lake 

approximately 2 km from any given facility, when in reality, a lake may be more than 50 km 

away. Additional conservative assumptions used in the Tier 1 screen are described in Section

2.4.4. 

Tier 1 emission thresholds were developed individually for elemental and divalent mercury. 

Both were based on the lower of the thresholds associated with multipathway exposures to 

divalent mercury and methyl mercury.7 Only speciated emissions of divalent mercury are 

screened because the sum of elemental mercury emissions across all National Emission 

Inventory (NEI) facilities is less than the elemental mercury screening threshold level. See 

Section 2.5.3 for a detailed discussion of mercury. 

2.2.2 Chemicals of Potential Concern

EPA’s assessment of multipathway human exposures for RTR focuses on PB-HAPs 8 that the 

Office of Air Quality Planning and Standards (OAQPS) has identified as candidates for 

multipathway risk assessments. OAQPS developed a list of 14 chemicals and chemical groups 

that are PB-HAPs based on a two-step process taking into account the following (EPA 2004a): 

7Note that TRIM.FaTE models the transformation of mercury within the environment; thus, emissions of both divalent 

and elemental mercury will result in multipathway exposures to elemental mercury, methyl mercury, and divalent 

mercury. 

8Although POM (polycyclic organic matter) is the name of the HAP listed in the Clean Air Act, the term “polycyclic 

aromatic hydrocarbons” or PAHs is used in many cases. Much of the literature regarding toxicity and fate and 

transport of this chemical group refers to PAHs rather than POM. In addition, the individual POM species that are of 

concern with respect to health risk for RTR evaluations are all PAHs (i.e., there are no POM species explicitly 

evaluated for RTR that do not include an aromatic ring). The terms are used interchangeably throughout this text.

Tier 1 Methodology 15 October 2014

 
TRIM-Based Tiered Screening Methodology for RTR

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

substances, and

• a semiquantitative ranking of toxicity and bioaccumulation potential of the entire list of 

HAPs. 

The list’s development and utility in hazard identification for multipathway risk assessment are 

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

Assessment (ATRA) Reference Library (EPA 2004a). Exhibit 5 presents the 14 chemicals and 

groups that are PB-HAPs. 

Exhibit 5. OAQPS PB-HAP Compounds

PB-HAP Compounda Addressed by Screening Scenario?

Cadmium compounds Yes

Chlordane No

Chlorinated dibenzodioxins and furans Yes

DDE (1,1-dichloro-2,2-bis(p-chlorophenyl) ethylene) No

Heptachlor No

Hexachlorobenzene No

Hexachlorocyclohexane (all isomers) No

Lead compounds No

Mercury compounds Yes

Methoxychlor No

Polychlorinated biphenyls No

Polycyclic organic matter (POM) Yes

Toxaphene No

Trifluralin No

a

Source of list: EPA (2004a). 

The screening scenario described in this document is not configured for evaluating the risk 

potential for all 14 PB-HAPs on the list. Currently, the scenario can only be used to estimate 

exposures and risks quantitatively for 4 of the 14 PB-HAPs (as indicated in Exhibit 5). These 

four PB-HAPs are the focus of the current scenario because, based on current emissions, 

bioaccumulation potential, and toxicity considerations, they are expected to pose the vast 

majority of the non-inhalation risks to humans from air emissions at sources subject to residual 

risk provisions of the Clean Air Act.9

2.2.3 Conceptual Exposure Scenario

A conceptual exposure scenario was developed that encompasses the specific exposure routes 

and pathways of interest for the four PB-HAPs that are assessed in the Tier 1 assessment. 

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);

9Potential impacts on human health from non-inhalation exposures to lead are evaluated for RTR using the National 

Ambient Air Quality Standard for lead, which takes into account multipathway risks. Non-inhalation exposures to the 

other nine PB-HAPs not addressed by the modeling scenario discussed in this report will be evaluated on an 

individual facility or source category basis as appropriate. 

Tier 1 Methodology 16 October 2014

 
TRIM-Based Tiered Screening Methodology for RTR

• A retention medium, or a transport mechanism and subsequent retention medium in 

cases involving media transfer of chemicals; 

• A point of potential human contact with the contaminated medium; and 

• An exposure route. 

An exposure route is the particular means of entry into the body. Receptors are exposed to 

chemicals emitted to the atmosphere via two primary routes: either directly via inhalation, or 

indirectly via ingestion or dermal contact with various media that have been contaminated with 

the emitted PB-HAPs. (Inhalation pathways are assessed separately and are not considered in 

the Tier 1 assessment presented here.)

PB-HAPs can persist in the environment for long periods of time and also build up in soil and in 

the food chain, including fish, fruits and vegetables, and animal products (e.g., meat, dairy, 

eggs). For this reason, ingestion of foods grown within an area impacted by RTR sources can 

be an important source of exposure to PB-HAPs. 

To assess risks from hazardous waste combustion facilities, EPA identified several hypothetical

receptor scenarios, noting that these scenarios are considered appropriate for a broad range of 

situations, rather than to represent any actual scenario. These scenarios are described in 

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

HHRAP (EPA 2005a). In this document, EPA recommends assessment of the following 

hypothetical receptors: a Farmer, Farmer Child, Resident, Resident Child, Fisher, Fisher Child, 

Acute Receptor, and Nursing Infant. These receptors are distinguished by their pathways of 

exposures. EPA further notes in HHRAP that some exposure settings might warrant including 

additional exposure pathways; such as including exposure through fish ingestion for the farmer 

receptor. For the RTR screening scenario, risks are assessed for a single hypothetical receptor. 

Based on the guidance provided in HHRAP, a health protective exposure scenario was 

developed whereby the hypothetical receptor receives ingestion exposure via both the farm food 

chain and the fish ingestion pathways. The exposure scenario for the RTR Tier 1 assessment

includes the following ingestion pathways:

• Incidental ingestion of soil,

• Ingestion of homegrown fruits and vegetables,

• Ingestion of homegrown beef,

• Ingestion of dairy products from homegrown cows,

• Ingestion of homegrown poultry and eggs,

• Ingestion of homegrown pork, 

• Ingestion of locally caught fish, and

• Ingestion of breast milk (for children less than 1 year old and for dioxins only).10

As discussed in detail in Section 2.4.2, exposure via these pathways is assessed for adults, 

various age categories for children, and nursing infants (for dioxins only).

10 Breast milk ingestion is an important exposure pathway for lipophilic compounds like dioxins and has been shown 

not to contribute meaningfully for exposures to mercury, cadmium, and POM. See Section 2.4.2.3 below and Section 

3.4 of Attachment B for full discussions of infant exposures via breast milk ingestion.

Tier 1 Methodology 17 October 2014

 
TRIM-Based Tiered Screening Methodology for RTR

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 assessment

because the likelihood that humans would use a lake as a drinking water source was assumed 

to be low.11 Dermal absorption of chemicals that are originally airborne has been shown to a 

relatively minor pathway of exposure compared to other exposure pathways (EPA 2006, 

Cal/EPA 2000). Preliminary calculations of estimated dermal exposure and risk of PB-HAPs, 

presented in Attachment C, showed that the dermal exposure route is not a significant risk 

pathway relative to ingestion exposures.

2.2.4 Approach to Development of the Tier 1 Scenario

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

qualitatively estimate the potential for non-inhalation risks above the levels of concern for PBHAPs emissions from facilities in the context of residual risk assessments conducted as part of 

RTR. The Tier 1 scenario used to derive the threshold emission rates is not intended to be 

representative of any particular situation. Rather, it was developed for the purpose of RTR to 

portray a hypothetical exposure scenario that will generate emissions screening levels that are 

health protective for any potential exposure situation that might plausibly be encountered in the 

United States. A range of conditions was assessed when conceptualizing and developing the 

screening scenario. The final configuration was chosen so that for a given individual, any

potential long-term exposure levels for a given geographic region would be reasonably unlikely 

to exceed those of the Tier 1 configuration. These criteria were met by constructing a 

hypothetical scenario that would be health-protective in key aspects, including spatial 

orientation, meteorology, types of exposures, and ingestion rates. The overall result is a 

scenario that is unlikely to occur at any one location but has a high likelihood of representing the 

upper end of all potential exposures. This latter aspect accomplishes the goal of striking a 

balance between health protectiveness and the level of accuracy called for in the ideal 

screening approach previously discussed. 

The development and application of the Tier 1 scenario for residual risk evaluations considered 

EPA’s technical and policy guidelines presented in the Residual Risk Report to Congress (EPA

1999); Volumes I and II of the Air Toxics Risk Assessment Reference Library (EPA 2004a,

2005a); and other EPA publications. The scenario described herin is the culmination of 

assessments 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 assessments, as described in Sections 3 and 4, 

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.

2.2.4.1 Modeling Framework

The approach for multipathway risk screening and evaluation for RTR can be divided into four 

steps: 

11An exception to this generality would be reservoirs used for drinking water supplies. This situation might be worthy 

of additional analysis, if warranted by the characteristics of a given assessment (e.g., to estimate PB-HAP 

concentrations in treated drinking water derived from reservoirs).

Tier 1 Methodology 18 October 2014

 
TRIM-Based Tiered Screening Methodology for RTR

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

water, and other environmental media (including fish12); 

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

livestock, dairy products) from soil and air, and calculation of FFC media concentrations; 

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 6. 

As shown in Exhibit 6, two models are used to evaluate the four steps outlined above. EPA’s 

TRIM methodology was conceived as a comprehensive modeling framework for evaluating risks 

from air toxics, and the TRIM system was designed to address each of the four steps involved in 

screening ingestion risk.13 Currently, however, only one component corresponding to the first 

step included in Exhibit 6—the fate and transport module—is available for application in an 

ingestion risk assessment. EPA has completed some development activities for 

TRIM.Expo-Ingestion and TRIM.Risk-Human Health, two additional modules that cover the 

other three steps. Modeling software, however, is not currently available for these modules. 

For the RTR screening scenario, the Multimedia Ingestion Risk Calculator (MIRC), a Microsoft®

ExcelTM-based computing framework, was constructed to complete the calculations required for 

estimating PB-HAP concentrations in farm food chain media, average daily ingestion doses, and 

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

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

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

second step of the modeling framework.

13Information about the current status of TRIM modules and comprehensive documentation of modules developed 

thus far can be accessed on EPA’s Technology Transfer Network (TTN) on the Fate, Exposure, and Risk Analysis 

website (http://www.epa.gov/ttn/fera/). 

Exhibit 6. 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

Tier 1 Methodology 19 October 2014

 
TRIM-Based Tiered Screening Methodology for RTR

cancer risks and chronic non-cancer HQs. This framework is conceptually identical to the 

ingestion exposure and risk assessments that TRIM is intended to cover. 

2.2.4.2 Model Configuration and Parameterization

The Tier 1 scenario is intended to reduce the possibility that EPA would underestimate potential 

multipathway human health risks. Although the health protective approach likely overestimates 

risk, EPA determined that this approach is appropriate for the purposes of an initial 

multipathway screening assessment. As was done with the preliminary multipathway screening 

for RTR conducted in 2006 (EPA 2006), exposures were modeled for a hypothetical farm 

homestead and fishable lake located adjacent to an emissions source. The hypothetical 

individual for which exposures were calculated was assumed to derive all potentially 

contaminated foodstuffs from adjacent locations, and many of the exposure/activity assumptions 

(e.g., amount of food consumed per day) were selected from the upper ends of representative 

exposure parameter distributions. 

The physical/chemical environment represented in the screening scenario was parameterized 

with two types of values. One type is typical values, such as national averages. The second 

type is health-protective values, or values that would tend to overestimate concentrations in 

media driving ingestion exposures for humans, based on knowledge of exposure patterns. In 

general, the spatial and temporal aspects of the scenario and the components of the scenario 

that influence air concentrations and deposition rates (which in turn affect all other exposures) 

were defined to be health protective. Chemical-specific and non-chemical-specific properties of 

the environmental media were parameterized with either typical or health protective values; 

properties having greater uncertainty were assigned a greater level of health protective bias. 

The spatial layout of the Tier 1 scenario and the meteorological data (or a combination of these 

two factors) are generally more influential than physical/chemical parameters in dictating the 

screening model outcomes, taking into account the potential range of variation in possible 

values. For example, where and how the layout is spatially oriented relative to the dominant 

wind direction can dramatically affect the concentrations in air, thereby driving estimated 

concentrations of PB-HAPs in soil, water, and biota. In contrast, relatively large changes in soil 

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

typically result in relatively small changes in media concentrations. 

The mix of health protective and central-tendency assumptions and parameterization is 

expected to result in a scenario configuration that, on average, is likely to overpredict 

environmental concentrations of PB-HAPs in media of interest for this evaluation. Given the 

intended application of this scenario as a screening tool, this health protective bias was 

deliberate, because of the desire to ensure that risks above levels of potential concern are not 

overlooked (i.e., to minimize false negatives). Although the inclusion of central-tendency values 

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

to be expected from a screening scenario. False positives are addressed in subsequent tiers of 

the screening evaluation for a particular source. 

2.2.5 Fate and Transport Modeling (TRIM.FaTE)

The fate and transport modeling step depicted in the first box in Exhibit 6 is implemented for 

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

(TRIM.FaTE).14 In developing the Tier 1 scenario, Version 3.6.2 of TRIM.FaTE was used to 

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

of pollutants over time, through a user-defined, bounded system that includes both biotic and abiotic compartments. 

Outputs include pollutant concentrations in multiple environmental media and biota. 

Tier 1 Methodology 20 October 2014

 
TRIM-Based Tiered Screening Methodology for RTR

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 (EPA 2002a). A 

comprehensive evaluation of the performance of TRIM.FaTE for modeling mercury was 

documented in Volumes I and II of the TRIM.FaTE Evaluation Report (EPA 2002b, 2005b). 

Algorithms specific to the fate and transport of chlorinated dibenzo-dioxin and -furan congeners 

were added following the addition of those for mercury and PAHs. Documentation of the 

application of TRIM.FaTE for dioxin emissions is contained in the third volume of the 

TRIM.FaTE Evaluation Report (EPA 2004). More recently, the TRIM.FaTE public reference 

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

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

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

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

yet been compiled; however, all chemical-specific properties used by TRIM.FaTE to model 

cadmium (as well as PAHs, mercury, and dioxins) are documented in Attachment A. 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 2.3. 

2.2.6 Exposure Modeling and Risk Characterization (MIRC)

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

media and ingestion exposures for hypothetical individuals were obtained from EPA’s Human 

Health Risk Assessment Protocol for Hazardous Waste Combustion Facilities, or HHRAP (EPA

2005a).15 These algorithms, and the required exposure factors and other parameter values, 

were compiled into a database. An overview of the computational processes this tool carries 

out and the types of input data it requires is presented in Exhibit 7. 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 2.4 and Attachment B, and all inputs required by 

these calculations are documented in Attachment B. 

15The farm food chain calculations and ingestion exposure equations to be included in the TRIM.Expo software are 

expected to be very similar to those included in HHRAP.

Tier 1 Methodology 21 October 2014

 
TRIM-Based Tiered Screening Methodology for RTR

Exhibit 7. Overview of Process Carried Out in the 

Multimedia Ingestion Risk Calculator

2.2.7 Implementation of Risk-based Emission Scaling Factors for POM and Dioxin 

Emissions

Two of the four PB-HAPs for which screening emission thresholds have been developed for 

RTR—POM and dioxins—are chemical groups comprising numerous individual entities. The 

members of these categories reported in NEI include both specific chemicals and groups 

containing multiple chemicals. For example, for POM, emissions reported in NEI include 

various species, such as benz[a]anthracene, 2-methylnaphthalene, and chrysene, as well as 

non-specific entries, such as “PAH, total.” The constituents included in the POM and dioxin 

PB-HAP categories are grouped together not only because they are types of the “same” HAP, 

but also because members of these groups are assumed to have similar characteristics with 

respect to toxicity and behavior in the environment.

To facilitate a practical application of the multipathway screening methods for RTR, reported 

emissions of POM and dioxins are normalized or scaled to a single reference chemical for each 

group. The reference chemicals used in RTR for POM and dioxins are benzo[a]pyrene and 

2,3,7,8-TCDD, respectively. These compounds were selected because they are relatively wellstudied among the members of the two groups and are also among the most toxic species 

within each group. 

Derivation of appropriate scaling factors begins with an evaluation of the basic relationship used 

to characterize health risk: 

Risk ∝ (Exposure Concentration) × (Toxicity)

For a given air pollutant, the incremental exposure concentration is directly proportional to the 

emissions of that substance. That is, as the emissions increase, so too does the exposure to 

Farm Food Chain 

Concentration 

Calculator

Ingestion 

Exposure 

Calculator

Risk and Hazard 

Calculator

TRIM.FaTE 

outputs

Human activity/

exposure factors

Plant- and 

animal-specific 

parameters 

Chemicalspecific uptake/

transfer factors

Ingestion 

dose-response 

values

FFC media 

concentrations

Average daily 

doses

Cancer risks and 

hazard quotients

Access db 

process

Tool 

output Input Key to symbols: data

Tier 1 Methodology 22 October 2014
TRIM-Based Tiered Screening Methodology for RTR

that substance. Furthermore, toxicity is assumed to increase linearly with concentration. 

Consequently, emissions of one substance (e.g., chrysene) can be scaled proportional to a 

reference compound (e.g., benzo(a)pyrene or BaP) by applying weighting factors corresponding 

to the relative differences in exposure behavior and toxicity. Using the POM group as an 

example and BaP as the reference compound, this scaling can be expressed through an 

equation as follows:

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

where: 

EmissPAHi:BaP = Risk-weighted emissions of PAHi (weighted according to cancer risk

relative to BaP for oral exposures)

EmissPAHi = Emission rate of PAHi

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

relative oral exposure between PAHi and BaP

TEFPAHi:BaP = 

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

oral route between PAHi and BaP

In combination, the product of the EEF and TEF for a given substance is considered to be a 

“risk equivalency factor” for the purposes of RTR evaluations that enables scaling of emissions 

of a given substance for a given exposure scenario.

The TEF for each PAH and dioxin species can be calculated on the basis of relative toxicities. 

Toxicities were not evaluated separately for RTR but are based on analyses conducted by EPA 

elsewhere. For PAHs, oral toxicity values for individual species have been derived following the 

same approach used to develop inhalation toxicity values. For dioxins, TEFs are based on the 

relative toxicities developed by EPA recently and are ultimately based on the values developed 

by the World Health Organization (van den Berg et al. 2006). Refer to Section 4 of 

Attachment B for more information on these values. 

The EEFs can be calculated directly for each individual chemical that can be modeled in 

TRIM.FaTE and MIRC. TRIM.FaTE is configured for 14 POM congeners and 17 dioxin/furan 

congeners. For these substances, EEFs were calculated directly using the modeling approach 

and parameterization scheme for the Tier 1 scenario described in this document. Several other 

POM and dioxin emissions, however, are reported in the NEI. For these, exposure surrogates 

must be assigned after evaluating the correlation between chemical properties of the POM or 

dioxin congener and exposure quantified as lifetime average daily dose. The specific 

calculations for EEFs and exposure surrogates for each chemical group are discussed in the 

sections that follow.

2.2.7.1 Calculation of Scaling Factors for POM Congeners

The calculated EEFs, TEFs, and total REFs for the 14 POM congeners that are configured in 

TRIM.FaTE, plus 15 others not configured in TRIM.FaTE, are shown in Exhibit 8. To determine 

appropriate exposure surrogates for chemicals not parameterized in TRIM.FaTE, EPA

evaluated the relationships between chemical-specific properties (e.g., Kow and Henry’s law 

constant, kh) and intermediate modeled values (e.g., deposition, soil concentration) and 

exposure in terms of lifetime average daily dose (LADDs) where the average daily doses 

(ADDs) for the youngest two age groups were adjusted by the age-dependent adjustment 

factors (ADAFs) to account for the mutagenic mode of action of PAHs. The correlation between 

Kow and exposure is stronger than for any other chemical-specific property. Exposure 

Tier 1 Methodology 23 October 2014
TRIM-Based Tiered Screening Methodology for RTR

surrogates were thus identified for each congener by calculating Total Lifetime Average Daily 

Dose (Age Adjusted) for each based on the congener’s Kow and the power regression of the 

modeled PAHs. Exhibit 9 shows that as Kow increases, so too does exposure.

Exhibit 8. Exposure, Toxicity, and Risk Equivalency Factors Relative to BaP

for POM Congeners Currently Evaluated in Tiers 1 and 2 Assessments

Chemical

Fully 

Parameterized 

in 

TRIM.FaTE?

Exposure 

Equivalency 

Factor (EEF)

Toxicity 

Equivalency 

Factor 

(TEF)a

Risk 

Equivalency 

Factor 

(REF)

Dibenzo(a,i)pyrene n 27.5 16.4 452

7,12-Dimethylbenz(a)anthracene Y 5.4 34.2 186

3-Methylcholanthrene n 4.3 3.0 12.9

Dibenz(a,h)anthracene Y 8.0 0.6 4.5

Benzo(k)fluoranthene Y 18.0 0.2 3.0

Benzo(a)pyrene Y 1.0 1.00 1.00

Benzo(b)flouranthene Y 11.4 0.2 1.9

Indeno(1,2,3-cd)pyrene Y 4.5 0.2 0.7

PAH, total n 5.1 0.07 0.3

Polycyclic Organic Matter n 5.1 0.07 0.3

Benzo(g,h,i)perylene Y 4.3 0.07 0.3

Benzo(e)pyrene n 4.5 0.07 0.3

Retene n 3.7 0.07 0.3

Dibenzo(a,j)acridine n 0.78 0.2 0.1

Perylene n 1.2 0.07 0.08

Benzo(a)anthracene Y 0.09 0.16 0.01

Chrysene Y 0.25 0.02 0.004

2-Acetylaminofluorene n 0.005 1.4 0.01

Fluoranthene Y 0.04 0.07 0.003

Acenaphthylene Y 0.04 0.07 0.003

2-Chloronaphthalene n 0.03 0.07 0.002

Fluorene Y 0.03 0.07 0.002

Acenaphthene Y 0.02 0.07 0.002

1-Methylnaphthalene n 0.02 0.07 0.001

2-Methylnaphthalene Y 0.02 0.07 0.001

Carbazole n 0.01 0.003 0.00003

Anthracene n 0.06 0b 0

Phenanthrene n 0.06 0b 0

Pyrene n 0.15 0b 0

aTEFs are calculated as the ratio of the cancer slope factor (CSF) for each specific POM congener 

to the CSF for benzo(a)pyrene. Dose response values, including CSFs, that are used in the 

screening assessment are discussed in Section 4 of Attachment B. bWeight of evidence evaluations indicated that the available data were adequate to determine that 

three PAHs (anthracene, phenanthrene, and pyrene) were not carcinogenic (EPA 2010a),

For POMs reported as unspeciated groups (i.e., “PAH, total” and “Polycyclic Organic Matter”) 

EPA assigned surrogates with Kow values near the upper end of the range of all of the Kow

values, corresponding to an exposure near the upper end of the range (log Kow = 6.5). This 

assignment is assumed to be health protective and likely will not under predict exposure. 

Tier 1 Methodology 24 October 2014
TRIM-Based Tiered Screening Methodology for RTR

Exhibit 9. Relationship between Exposure and Kow

for POM Congeners

It is worthy of noting that naphthalene is not included in the POM category for the RTR 

multipathway (i.e., non-inhalation) analyses. Naphthalene is listed individually as a HAP under 

Section 112(b) of the Clean Air Act. POM also is listed as a HAP under Section 112(b) and is 

defined as organic compounds with more than one benzene ring and a boiling point greater than 

or equal to 100° C (see http://www.epa.gov/ttn/atw/orig189.html). Under this definition, 

naphthalene could be considered as part of the POM listing. However, naphthalene is shortlived in environmental media due to its tendency to volatilize and biodegrade and, consequently, 

will not build up in environmental media over time (ATSDR 2005). Additionally, based on a log 

Kow of 3.29, it has a moderate affinity for lipids and will undergo short-term bioaccumulation in 

tissues; however, biochemical processes lead to its biodegradation and elimination. Because it 

is neither persistent nor bioaccumulative, it is not considered a PB-HAP, and EPA believes that 

its inclusion in the POM category would result in artificially inflated media concentrations and 

multipathway risk estimates for POM. 

2.2.7.2 Calculation of Scaling Factors for Dioxin Congeners

The calculated EEFs, TEFs, and REFs for the 17 dioxin congeners that are configured in 

TRIM.FaTE are presented in Exhibit 10. Although there are many dioxins reported in the NEI 

other than the 17 configured for TRIM.FaTE, to date, none of them have been included in 

emissions datasets that have been screened. Therefore, no surrogate EEF’s have been 

developed for dioxins. In future screening assessments, if surrogate EEFs are needed, an 

approach similar to that used for POM will be used to develop surrogate EEFs for dioxins.

Many facilities report dioxins as “Dioxins, Total, without Individual Isomers Reported,” “Dioxins,” 

or as “2,3,7,8-TCDD TEQ,” and in these cases, we do not adjust or scale the emissions. That is, 

we assume that they behave like and possess the toxic characteristics of TCDD. This approach 

could be improved by obtaining information on the speciation of dioxin emissions for each 

facility or an average speciation profile that could be assumed to apply to all facilities in a source 

category.

Tier 1 Methodology 25 October 2014
TRIM-Based Tiered Screening Methodology for RTR

Exhibit 10. Exposure and Toxicity Equivalency Factors Relative to TCDD for Modeled 

Dioxin Congeners

Chemical

Exposure 

Equivalency 

Factor (EEF)

Toxicity 

Equivalency 

Factor (TEF)a

Risk 

Equivalency 

Factor (REF)

PentaCDD, 1,2,3,7,8- 3.8 1 3.8

TetraCDD, 2,3,7,8- 1 1 1

Dioxins, Total, w/o Indiv. Isomers Rptd. 1 1 1

Dioxins 1 1 1

2,3,7,8-TCDD TEQ 1 1 1

HexaCDD, 1,2,3,4,7,8- 1.6 0.1 0.2

PentaCDF, 2,3,4,7,8- 0.4 0.3 0.1

HexaCDD, 1,2,3,6,7,8- 1.0 0.1 0.1

HexaCDF, 1,2,3,7,8,9- 0.5 0.1 0.05

HexaCDF, 1,2,3,6,7,8- 0.5 0.1 0.05

HexaCDD, 1,2,3,7,8,9 - 1.0 0.04 0.04

HexaCDF, 1,2,3,4,7,8- 0.3 0.1 0.03

HexaCDF, 2,3,4,6,7,8- 0.2 0.1 0.02

PentaCDF, 1,2,3,7,8- 0.4 0.03 0.01

TetraCDF, 2,3,7,8- 0.1 0.1 0.01

HeptaCDD, 1,2,3,4,6,7,8- 1.0 0.01 0.01

HeptaCDF, 1,2,3,4,6,7,8- 0.2 0.01 0.002

HeptaCDF, 1,2,3,4,7,8,9- 0.2 0.01 0.002

OctaCDD, 1,2,3,4,6,7,8,9- 1.1 0.0003 0.0003

OctaCDF, 1,2,3,4,6,7,8,9- 0.2 0.0003 0.0001

a Values from Van den Berg et al. (2006), except for 1,2,3,7,8,9-hexaCDD, which is calculated based on the ratio of the IRISbased CSF for 1,2,3,7,8,9-hexaCDD to the IRIS-based CSF for 2,3,7,8-TCDD. Dose response values, including CSFs, that are 

used in the screening assessment are discussed in Section 4 of Attachment B. 

2.3 Description of Environmental Modeling Scenario

As described in Section 2.2.4.2, the physical configuration of the RTR Screening Scenario was 

designed to encompass the upper end of potential long-term PB-HAP exposures, and the 

environmental and chemical-specific properties were parameterized with either health protective

or central-tendency values. Information regarding the scenario configuration and important 

aspects of the parameterization process, justifications for selecting particular property values, 

and model uncertainties is presented in the sections that follow. Comprehensive documentation 

of TRIM.FaTE property values for this scenario is provided in Attachment A. 

2.3.1 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 

Tier 1 Methodology 26 October 2014
TRIM-Based Tiered Screening Methodology for RTR

a particular abiotic or biotic compartment type; these properties are discussed generally in the 

sections that follow and are documented in Attachment A. 

2.3.2 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 11. The source parcel is parameterized as a square with sides of 250 m, which is 

assumed to be a fair estimation for the size of a relatively small-to-medium facility at the fence 

line. With a predominant wind direction toward the east, the modeled layout is generally 

symmetric about an east-west line and is wedge-shaped to reflect Gaussian dispersion of the 

emission plume.

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

scenario. Based on the results of dispersion modeling, the location of the maximum air 

concentration and deposition rate is expected to occur relatively close to the facility (probably 

within a few hundred meters, with the exact location varying with stack height and other 

parameters) and well within a 10-km radius. Additionally, deposition rates for the PB-HAPs for 

which this screening scenario is applicable is expected to decrease by about two orders of 

magnitude relative to the predicted maximum rate within a 10-km radius. Extending the 

modeling layout beyond a 10-km downwind distance would increase the amount of deposition 

“captured” by the modeled watershed, but the incremental chemical mass expected to 

accumulate in the watershed diminishes rapidly with distance. In addition, the impact of this 

additional deposited mass on ingestion exposures is expected to be negligible.16 Given these 

conditions, a downwind length of 10 km was determined to be appropriate for the screening 

scenario.

Exhibit 11. TRIM.FaTE Surface Parcel Layout

16Mass deposited at the outer edge of the watershed is expected to result in only a very small increase in estimated 

exposure via fish consumption by increasing the chemical mass transported to the lake through erosion and runoff. 

The distance from these more distant locations to the lake would attenuate transport of chemical mass by erosion 

and runoff, dampening the effect of including additional deposition beyond 10 km. (Other exposure pathways largely 

would be unaffected; the soil concentrations used to calculate exposures for the farm food scenario are derived from 

soil parcels located close to the source and unaffected by deposition to the far reaches of the watershed.) 

Tier 1 Methodology 27 October 2014

 
TRIM-Based Tiered Screening Methodology for RTR

The north-south width of the wedge-shaped watershed was set based on the observed behavior 

of chemicals emitted to the ambient air. If meteorological stability is known or can be assumed, 

the lateral spread of the plume (σy, measured from the centerline) at a certain distance from the 

source can be estimated using the Pasquill-Gifford curves. Turner (1970) derived the equations 

for these curves, which can be found in the Industrial Source Complex 3 Dispersion Model 

Manual (among other sources).17 For a relatively neutral atmosphere (stability class D), σ at 

10km is about 550 m using this estimation. In a Gaussian distribution, about 99.6 percent of the

plume spread area is contained within 3σ of the median line. Therefore, the plume σ was set at 

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

width for these conditions is expected to be about twice this distance, or 3.5 km. These 

dimensions were used to define the dimensions of the overall air and surface parcel layouts for 

the screening scenario.

The surface (land and surface water) modeling area was initially divided into five pairs of parcels 

the areas of which increase with distance from the source, which approximately corresponds to 

the spatial gradient that is expected in the downwind direction from the source. The second 

north parcel from the source was divided further into two parts; one of them tilled soil (Parcel 

N6) to represent agricultural conditions and the other (Parcel N7) untilled to represent pasture. 

The depth of the surface soil compartments was set to 1 cm, except for Parcel N6, for which the 

depth was set to 20 cm to simulate the effect of tillage. Characteristics of the soil layers (e.g., 

organic carbon content, air and water content, and subsoil depth) generally were set to 

represent typical or national averages as summarized by McKone et al. (2001). 

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).

2.3.3 Watershed and Water Body Parameterization

Properties associated with the watershed soil and lake determine how pollutants in the system 

are transported through and accumulate in various media compartments. These properties 

describe the physical characteristics of the environmental media included in the modeled region, 

as well as the assumed connections and relationships between media types and modeled 

spatial components that in turn affect chemical transport via water runoff, ground infiltration, 

deposition of suspended sediments in the water column, and other processes. This section 

presents the justification for setting the key properties of the soil, water, and sediment 

compartments. Also discussed are some of the chemical properties related to watershed and 

waterbody processes (chemical-specific compartment properties in TRIM.FaTE) and the 

configuration of terrestrial plants included in the scenario.

2.3.3.1 Water Balance

Water-related properties of the lake and related watershed characteristics (e.g., runoff rates 

from each surface soil compartment) were set so that a simplified water balance is achieved. 

Although TRIM.FaTE maintains a chemical mass balance, the model does not calculate or 

maintain media mass balances (e.g., for water) except where specified in certain formulas. For 

the Tier 1 scenario, the parameters were set to satisfy two equations relating water volume. 

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

scenario:

17http://www.epa.gov/scram001/userg/regmod/isc3v2.pdf

Tier 1 Methodology 28 October 2014

 
TRIM-Based Tiered Screening Methodology for RTR

[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.

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

the lake.

• 40 percent of total precipitation contributes to overland runoff. 

For these calculations, the source parcel was considered to be outside the watershed and 

therefore was not included in the water balance. The evaporation rate from the lake was 

assumed to be 700 mm/yr based on data reported by Morton (1986) for various lakes. The 

runoff rate was defined to be spatially constant and temporally constant (i.e., it is not linked to 

precipitation events) throughout the modeled domain. Based on these assumptions, the outflow 

of water from the lake is about 18 million m3/yr, which translates to a volumetric turnover rate of 

about 12.2 lake volumes per year. 

Other quantitative water body and watershed characteristics TRIM.FaTE uses are listed in 

Attachment A. 

2.3.3.2 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 

Tier 1 Methodology 29 October 2014
TRIM-Based Tiered Screening Methodology for RTR

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 (EPA 2005a). For the Tier 1

scenario, however, this approach was considered to be appropriate in that health protective 

assumptions are a goal of the screening scenario.18 Surface soil compartments adjacent to the 

lake are linked directly to the lake for the purposes of estimating erosion and runoff transfers 

(see layout in Exhibit 11). 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.

2.3.4 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 

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

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

the waterbody.

Tier 1 Methodology 30 October 2014

 
TRIM-Based Tiered Screening Methodology for RTR

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 12 but an artificial data set 

was compiled for this assessment (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 12. 

The sensitivity of modeled PB-HAPs to changes in these meteorological variables was tested. 

Lower wind speeds and mixing heights affected concentrations the most. This sensitivity is not 

unexpected because lower wind speeds should increase pollutant deposition onto the soil and 

lower mixing heights should reduce the volume through which pollutants disperse. The wind 

speed used for the screening scenario was 2.8 m/s, the 5th percentile of the annual averages 

among 239 stations; by comparison, the mean annual average wind speed is approximately 4 

m/s in the contiguous United States). The mixing height (mean heights from 4 states) used was 

710 m (the 5th percentile of annual averages among all 40 states in the SCRAM database).

Exhibit 12. Summary of Key Meteorological Inputs

Meteorological 

Property Selected Value Justification

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

Value is similar to the mean daily June temperature in much of the 

U.S. Southern Plains and Southeast.a

Mixing height Constant at 710 m Value is 5th percentile of annual average mixing heights for 75 

U.S. locations, using data obtained from EPA’s SCRAM Web 

site.b Value is the approximate U.S. median for periods without 

precipitation, based on data compiled by Holzworth (1972). Value 

is conservative compared to the 1- to 2-km typical mid-latitude 

daytime value (Stull 1988).

Wind direction Blows from source 

parcel into scenario 

domain (west to east) 3 

days per week; during 

other times does not 

blow into domain 

A wind direction that favors the location of interest (for example, a 

watershed downwind of a source of concern) will tend to result in 

more emitted mass accumulating in the location of interest. For 

much of the U.S. mid-Atlantic and western regions, the wind tends 

to favor the eastward direction.a Among the NCDC 1981–2010 

normalized wind vector data, the average wind direction had a 

strong eastward component at over one-third of the stations.c For 

the hypothetical RTR scenario, a more extreme example of this 

pattern is represented by conditions in Yakima, Washington, 

where the wind blows eastward approximately 40 percent of the 

time based on a review of wind direction data compiled by the 

National Weather Service (NCDC 1995). This pattern was 

approximated in the RTR scenario with a configuration in which 

the modeled domain is downwind of the source 3 out of 7 days.

Horizontal wind 

speed

Constant at 2.8 m/sec Set to 5th percentile of annual average speed for 239 stations 

across the contiguous United States (about 50 years of data per 

station). Value is similar to the annual average wind speeds of 

Tier 1 Methodology 31 October 2014
TRIM-Based Tiered Screening Methodology for RTR

Meteorological 

Property Selected Value Justification

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 

2.3.5 Aquatic Food Web

The aquatic food web is an important part of the screening scenario because the chemical 

concentrations modeled in fish are used to calculate human ingestion exposure and risks 

associated with eating contaminated local fish. A biokinetic approach to modeling 

bioaccumulation in fish is used in the RTR screening scenario. The primary producers (first 

trophic level) in the TRIM.FaTE aquatic ecosystems are algae and macrophytes in the water 

column and detritus in the sediments (the latter simulated as sediment particles). Zooplankton 

feed on algae in the water column, while benthic invertebrates, represented as a single

compartment, consume detritus that settles to the sediment compartment. In the water column, 

small young-of-the-year fish and minnows feed on zooplankton and phytopolankton. The small 

fish are in turn consumed by larger or “pan” fish (e.g., bluegills, white perch), which are in turn 

consumed by the top consumers (e.g., gar, pickerel). The invertebrates in the sediments of the 

benthic environment support small bottom-feeding fish (young-of-the-year fish for many 

species), which in turn are consumed by larger bottom-feeding fish (e.g., catfish). For 

TRIM.FaTE to provide reasonable predictions of the distribution of a chemical across biotic and 

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

represent all biota in the system and the distribution of biomass among the trophic levels and 

groups must be as realistic as possible. 

To support the development of a relatively generic freshwater aquatic ecosystem in which to 

model bioaccumulation in fish, a literature search, review, and analysis was conducted

(ICF 2005). This research demonstrated that the diversity of species and food webs across 

U.S. aquatic ecosystems is substantial, reflecting the wide range of sizes, locations, and

physical/chemical attributes of both flowing (rivers, streams) and low-flow water bodies (ponds, 

lakes, reservoirs). In general, lentic bodies of water (lakes and ponds) can accumulate higher 

levels of contaminants in both sediments and biota than lotic systems (rivers, streams). Also,

that initial research (ICF 2005) suggests that a lake of at least 60 hectares (ha) or 150 acres 

could support higher trophic level predatory fish, with some fraction of their diet comprising

smaller fish. 

The RTR Tier 1 scenario includes a generic aquatic ecosystem with a 47-hectare (116-acre) 

lake. Although slightly smaller than the size suggested by the previous review (ICF 2005), a 47-

ha lake is large enough to support higher trophic level fish given appropriate conditions (e.g., 

high productivity given a sufficient nutrient base and average temperature and growing season). 

Tier 1 Methodology 32 October 2014
TRIM-Based Tiered Screening Methodology for RTR

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 13. Biomass is based on an 

assumption that the total fish biomass (wet-weight) for the aquatic ecosystem is 5.7 grams per 

square meter (gw/m2, ICF 2005). That assumption yields health protective (i.e., higher) 

estimates of chemical concentrations in fish than would the assumption of higher standing 

biomass and fish productivity. 

In general, the food web implemented in the Tier 1 scenario is consistent with aquatic food webs 

that support trophic level 4 fish (to maximize bioaccumulation), and is intended to be generally 

health protective. 

Exhibit 13. Aquatic Biota Parameters for the TRIM.FaTE Screening Scenario

TRIM.FaTE 

Compartment

Type

Organisms 

Represented by 

Compartment

Biomass

Diet

Average 

Body Weight

(kg)

Areal density 

(gw/m2) 

Fraction of 

Total Fish 

Biomass

Algae

green algae, 

diatoms, bluegreen algae7.95 – Autotrophic – 

Zooplankton

crustaceans, 

rotifers, 

protozoans

6.36 – 100% algaea 5.7 E−8

Macrophyte hydrilla, milfoil 500 – – –

Water column 

planktivore

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

Water column 

omnivore

bluegill, white 

perch 0.5 8.8% 100% water column planktivore 0.25

Water column 

carnivore

largemouth bass, 

walleye 0.2 3.5% 100% water column omnivore 2.0

Benthic 

invertebrate

aquatic insect 

larvae, 

crustaceans, 

mollusksb

20 – detritus in sediments 0.000255

Benthic 

omnivore

small catfish, 

rock bass 2.0 35.1% 100% benthic invert. 0.25

Benthic 

carnivore

large catfish, 

sculpins 1.0 17.5% 50% benthic invert.50% benthic omniv. 2.0

Total Fish Biomassc 5.7

a

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

b

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

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

c

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

2.3.6 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 

Tier 1 Methodology 33 October 2014
TRIM-Based Tiered Screening Methodology for RTR

calculations, some concentrations are used to calculate direct exposure (e.g., soil ingestion), 

and some are used to perform the farm food chain concentration calculations in the various 

media that humans can ingest. 

The locations that determine exposures were selected to be health protective. Decisions 

regarding which TRIM.FaTE outputs to use in calculating exposures for the Tier 1 scenario 

assume exposure at locations very close to the modeled source. These locations are predicted 

to have amongst the highest media concentrations consistent with the specified spatial layout, 

thereby resulting in higher exposures to the emitted chemicals. These assumptions are 

summarized in Exhibit 14. 

Exhibit 14. 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 fisher Water column carnivore compartment in lake (50% 

of fish consumed) and benthic carnivore in lake 

(50% of fish consumed)

TRIM.FaTE can output instantaneous chemical concentrations for a user-specified time step 

and also can be configured to calculate temporal averages (e.g., annual averages). For the 

Tier 1 scenario, the model is set up to output results on a daily basis, largely because daily is 

the smallest time step over which input data change (i.e., wind direction and precipitation rate). 

Daily concentration results were averaged to obtain annual average concentrations. The default 

assumption is annual average concentrations for media during the fiftieth year of emissions. 

For the chemicals modeled in this scenario, long-term concentrations in environmental media 

will be relatively constant at 50 years. Aside from mercury, chemicals modeled for RTR 

approach steady state before 50 years. And, although mercury concentrations do not achieve 

steady state after 50 years in the modeled screening scenario configuration, the rate of change 

in mercury concentrations shows a decreasing trend. 

2.4 Description of Exposure and Risk Modeling Scenario 

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

(FFC) media (Section 2.4.1); estimating human exposures associated with ingestion of FFC 

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

(Section 2.4.2); and calculating human health screening risk metrics associated with these 

exposure pathways (Section 2.4.3). All of these calculations are conducted using MIRC. For 

this multipathway screening evaluation, partitioning of PB-HAPs into FFC media is modeled with

MIRC, not as a part of the TRIM.FaTE modeling. Consequently, processes and inputs related 

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

detail in Attachment B. 

Tier 1 Methodology 34 October 2014
TRIM-Based Tiered Screening Methodology for RTR

2.4.1 Calculating Concentrations in Farm Food Chain Media

As was shown in Exhibit 7, 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; 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 1.1.2.2, the TRIM.FaTE outputs included as 

inputs to MIRC are the following:

• PB-HAP concentrations in air;

• air-to-surface deposition rates for PB-HAPs in both particle and vapor phases;

• PB-HAP concentrations in fish tissue for water column carnivores and benthic 

carnivores; and 

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

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

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

included in HHRAP (EPA 2005a). Attachment B provides parameter values used in MIRC for 

the Tier 1 assessment.

2.4.2 Ingestion Exposure 

MIRC was used to estimate ingestion rates as ADDs, normalized to body weight for a range of 

exposure pathways. Exposure pathways included are incidental ingestion of soil, consumption 

of fish, produce, farm animals and related products, and ingestion of breast milk by infants. The 

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

through which these exposures occur are summarized in Exhibit 15. 

2.4.2.1 Exposure Scenarios and Corresponding Inputs

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

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

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

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

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

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

primarily from EPA’s Exposure Factors Handbook (EPA 2011).

Tier 1 Methodology 35 October 2014
TRIM-Based Tiered Screening Methodology for RTR

Exhibit 15. Summary of Ingestion Exposure Pathways and Routes of Uptake

Ingestion 

Exposure 

Pathway

Medium Ingested

Intermediate 

Exposure 

Pathway – Farm 

Animalsa

Environmental Uptake Route

Medium Processb

Incidental 

ingestion of soil Untilled surface soil N/A Surface soil Deposition; transfer via erosion and runoff c

Consumption of 

fish

Fish from local water 

body N/A Fish tissue 

Direct uptake from water and 

consumption of food 

compartments modeled in 

TRIM.FaTEc

Consumption of 

breast milkd Breast milk N/A Breast milk

Contaminant ingested by 

mother partitions to breast 

milk

Consumption of 

produce

Aboveground 

produce, exposed 

fruits and vegetables

N/A

Air 

Air

Soil

Deposition to leaves/plants 

Vapor transfer

Root uptake

Aboveground 

produce, protected 

fruits and vegetables

N/A Soil Root uptake

Belowground 

produce

N/A Soil Root uptake

Consumption of 

farm animals 

and related 

food products 

Beef

Ingestion of forage Air 

Air 

Soil

Direct deposition to plant

Vapor transfer to plant

Root uptake Ingestion of silage

Ingestion of grain Soil Root uptake

Ingestion of soil Soil Ingestion from surface

Dairy (milk)

Ingestion of forage Air 

Air 

Soil

Direct deposition to plant

Vapor transfer to plant

Root uptake Ingestion of silage

Ingestion of grain Soil Root uptake

Ingestion of soil Soil Ingestion from surface

Pork

Ingestion of silage

Air 

Air 

Soil

Direct deposition to plant

Vapor transfer to plant

Root uptake

Ingestion of grain Soil Root uptake

Ingestion of soil Soil Ingestion from surface

Poultry Ingestion of grain Soil Root uptake

Ingestion of soil Soil Ingestion from surface

Eggs Ingestion of grain Soil Root uptake

Ingestion of soil Soil Ingestion from surface

a

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

Process by which HAP enters medium ingested by humans.

c

Modeled in TRIM.FaTE.

d

The consumption of breast milk exposure scenario is discussed in Section 2.4.2.3.

For the Tier 1 scenario described here, exposure characteristics that would result in a highly 

health protective estimate of total exposure were selected. The ingestion rate for each medium 

was set at high-end values (equal to the 90th percentile values for all food types except for fish, 

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

impacted by the modeled source. Although this approach could result in an overestimate of 

Tier 1 Methodology 36 October 2014
TRIM-Based Tiered Screening Methodology for RTR

total chemical exposure for a hypothetical exposure scenario (for example, note that the total 

food ingestion rate that results is extremely high for a hypothetical consumer with ingestion 

rates in the upper percentile for every food type), it was selected to avoid underestimating

exposure for any single farm food type. The exposure characteristics selected for the Tier 1

scenario are summarized in Exhibit 16. 

Exhibit 16. Overview of Exposure Factors Used for RTR Multipathway Screeninga,b

Exposure Factor Selection for Screening Assessment

Age group evaluated Infants under 1 year (breast milk only)

Children 1–2 years of age

Children 3–5 years of age

Children 6–11 years of age

Children 12–19 years of age

Adult (20–70 years)

Body weight (BW; varies by age) Weighted mean of national distribution or 

recommended value

Ingestion rate (IR) for farm produce and animal 

products other than fish (varies by age and medium)

90th percentile of distribution of consumers who 

produce own food

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

rate representative of subsistence fisher woman. 

For children, based on 99th percentile, asprepared, consumer-only, national ingestion rates

– adjusted.

Exposure frequency (EF) 365 days/year

Exposure duration Lifetime, for estimating cancer risk; varies by 

chemical for chronic non-cancer evaluation

Fraction contaminated (FC) (varies by media 

consumed)c

1

Cooking lossd Assumed to be “typical”; varies depending on food 

product (see Attachment B). Cooking losses were 

not considered for fish consumption because 

intake rates represent “as prepared” values.

Food preparation/cooking adjustment factor for fishe Mercury = 1.5

Cadmium = 1.5

Dioxin = 0.7

PAH = 1.0

a

Data for exposure characteristics are presented in Attachment B. Exposure parameter values were based on data obtained

primarily from the Exposure Factors Handbook (EPA 2011). See Attachment B 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 Attachment B. c

Fraction contaminated represents the fraction of food product that is derived from the environment included in the screening 

scenario (e.g., produce grown on soil impacted by PB-HAPs). This parameter is defined separately for each FFC medium; 

however, for the Tier 1 scenario, all ingested media are assumed to be impacted. 

d

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

cooking, or after cooking.

e

Because “as consumed,” fish consumption rates are used with whole-fish concentrations, adjustments might be appropriate to 

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

discussion. 

2.4.2.2 Calculating Average Daily Doses

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

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

provided in the technical documentation of EPA’s Multimedia, Multipathway, and Multireceptor 

Risk Assessment (3MRA) Modeling System (EPA 2003), which derived much of its input data 

Tier 1 Methodology 37 October 2014
TRIM-Based Tiered Screening Methodology for RTR

from the Exposure Factors Handbook (EPA 2011). The ingestion exposure modeling approach 

embodied by 3MRA is conceptually similar to that presented in HHRAP. A discussion of 

exposure dose estimation and the equations to calculate ADDs for each ingestion pathway are 

provided in Attachment B. 

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. 

Reports of bioaccumulation of lipophilic compounds such as polychlorinated biphenyls (PCBs), 

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

Due to their high lipophilicity, these compounds partition almost exclusively to the milk fat of 

breast milk rather than the aqueous phase (EPA 1998). PCBs, PCDFs, and PCDDs are the 

most documented groups of contaminants found in breast milk. Other compounds with lower 

octanol-water partition coefficients, such as phenol, benzene, halobenzenes, and PAHs, are 

found in both the milk fat and the aqueous phase of breast milk. Heavy metals such as arsenic, 

lead, cadmium, and mercury have been found in the aqueous phase of the breast milk. 

However, given their chemical and physical characteristics (and the impact such qualities have 

on partitioning within the body and pharmacokinetics), substances that do not partition as 

strongly to the lipophilic phase of breast milk tend to be of lower concern with regard to 

exposures to nursing infants. Because of the greater concern with regard to dioxins for this 

exposure pathway, it is the only PB-HAP included in the breast milk exposure pathway for RTR 

at this time. This approach is consistent with the risk assessment procedures included in EPA’s 

Human Health Risk Assessment Protocol (EPA 2005a).

Exposure via the breast milk consumption pathway is estimated in MIRC for dioxins only. This 

pathway is included in computing total exposure for developing the screening threshold for 

dioxins. In the absence of congener-specific data, dioxin congeners were assumed to manifest 

the same tendency to accumulate in breast milk as 2,3,7,8-TCDD. 

2.4.3 Calculating Risk 

MIRC was used to calculate excess lifetime cancer risk and non-cancer hazard (expressed as 

the hazard quotient or HQ) using the calculated ADDs and ingestion dose-response values. 

Chemical dose-response data include cancer slope factors (CSFs) for ingestion and non-cancer 

oral RfDs. The CSFs and RfDs for the PB-HAPs included in the Tier 1 scenario are presented 

in Exhibit 17 and are discussed in more detail in Attachment B. Equations used to estimate 

cancer risk and non-cancer hazard also are provided in Attachment B. 

Tier 1 Methodology 38 October 2014
TRIM-Based Tiered Screening Methodology for RTR

Exhibit 17. Dose-response Values for PB-HAPs Addressed 

by the Screening Scenario

PB-HAP CSF

([mg/kg-day]-1) Source RfD(mg/kg-day) Source

Inorganics

Cadmium compounds (as Cd) not available 1E−3 IRIS

Elemental mercury not available not available

Divalent mercury not available 3E−4 IRIS

Methyl mercury not available 1E−4 IRIS

Organics

Benzo[a]pyrenea 7.3 IRIS not available

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

Source: EPA (2007).

CSF = cancer slope factor; RfD = reference dose; IRIS = EPA’s Integrated Risk Information System; Cal/EPA = California 

Environmental Protection Agency; EPA ORD = EPA’s Office of Research and Development a

For consistency with the overall approach for dose-response assessment of PAHs, the CSF listed in IRIS for benzo[a]pyrene ([7.3 

mg/kg-day]-1) was adjusted due to its mutagenic mode of action as discussed below (see also Attachment B).

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

upward to account for the mutagenic cancer potency of these compounds during childhood, as 

specified by EPA in supplemental guidance for cancer risk assessment (EPA 2005c). 

Specifically, cancer potency for PAHs is assumed to be tenfold greater for the first 2 years of life 

and threefold greater for the next 14 years. These factors were incorporated into a timeweighted total increase in potency over a lifetime of 70 years. The cancer potency adjustment 

for chemicals with a mutagenic mode of action is discussed in Attachment B. 

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 

scenario also is intended to avoid grossly overestimating risk to the point where no emissions 

screen. The overall degree to which the scenario is health protective is the sum of the multiple 

assumptions that affect the outputs of the fate and transport, exposure, and risk modeling. 

Exhibit 18 summarizes important characteristics that influence exposure and risk estimates for 

this scenario and indicates the general degree of health protectiveness associated with the 

values for each assumption. Although this summary does not provide a quantitative estimate of 

the output uncertainty or the degree to which exposures and risks estimated using the scenario 

would be overestimated, it does demonstrate qualitatively that the scenario generally 

overestimates exposure and thus favors a health-protective risk output. 

Exhibit 18. Summary of RTR Tier 1 Screening Scenario Assumptions

Characteristic Value

Neutral or 

Health 

Protective?

Comments on Assumptions

General Spatial Attributes

Farm location 375 m from 

source; generally 

downwind

Health 

Protective

Location dictates soil and air concentrations 

and deposition rates used to calculate 

chemical levels in farm produce.

Lake location 375 m from 

source; generally 

downwind

Health 

Protective

Location dictates where impacted fish 

population is located.

Tier 1 Methodology 39 October 2014
TRIM-Based Tiered Screening Methodology for RTR

Characteristic Value

Neutral or 

Health 

Protective?

Comments on Assumptions

Surface soil 

properties

Typical values or 

national averages

Neutral Based on existing EPA documentation and 

other references.

Size of farm parcel About 4 ha Health 

Protective

Relatively small parcel size results in higher 

chemical concentration.

Size of lake 47 ha; about 3 m 

average depth

Health 

Protective

Lake is large enough to support an aquatic 

ecosystem with higher trophic-level fish, but 

is relatively small and shallow (thus 

increasing surface area-to-volume ratio).

Meteorological Inputs

Total precipitation 1.5 m/yr Health 

Protective

Intended to represent rainy U.S. location; 

set to highest state-wide average for the 

contiguous United States.

Precipitation 

frequency (with 

respect to impacted 

farm/lake)

2/3 of total 

precipitation fall on 

farm/lake and 

watershed

Health 

Protective

Most of total precipitation occurs when the 

farm/lake are downwind of the source.

Wind direction Farm/lake are 

downwind 40% of 

the time

Health 

Protective

Farm/lake located in the predominantly 

downwind direction. Temporal dominance 

of wind direction based on data from 

Yakima, Washington, where wind is 

predominantly from the west.

Wind speed 2.8 m/sec Health 

Protective

Low wind speed (5th percentile of long-term 

averages for contiguous United States); 

increases net deposition to lake/watershed.

Air temperature 298 K Neutral Typical for summer temperatures in central 

and southern United States.

Mixing height 710 m Health 

Protective

Relatively low long-term average mixing 

height (5th percentile of long-term averages 

for contiguous United States); increases 

estimated air concentration.

Watershed and Water Body Characteristics

Evaporation of lake 

surface water

700 mm/yr Neutral Based on sensitivity analyses, value is not 

expected to under- or overestimate 

concentration in surface water. 

Surface runoff into 

lake

Equal to 40% of 

total precipitation

Health 

Protective

Based on typical water flow in wetter U.S. 

locations; higher runoff results in greater 

transfer of chemical to lake.

Surface water 

turnover rate in lake

About 12 turnovers 

per year

Neutral Consistent with calculated water balance; 

reasonable in light of published values for 

small lakes. Might overestimate flushing

rate if water inputs are also overestimated. 

Note that after evapotranspiration, 

remaining water volume added via 

precipitation is assumed to flow into or 

through lake.

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Characteristic Value

Neutral or 

Health 

Protective?

Comments on Assumptions

Soil erosion from 

surface soil into lake

Varies by parcel; 

ranges from 0.002 

to 0.01 kg/m2-day

Neutral Erosion rates calculated using the universal 

soil loss equation (USLE); inputs to USLE 

were selected to be generally conservative 

with regard to concentration in the pond 

(i.e., higher erosion rates were favored). 

Might underestimate erosion for locations 

susceptible to high erosion rates. Note that 

higher erosion increases concentration in 

lake (and fish) but decreases levels in 

surface soil (and farm products). 

Aquatic food web 

structure and 

components

Multilevel; includes 

large, upper 

trophic-level fish

Health 

Protective

Inclusion of upper trophic-level fish and 

absence of large-bodied 

herbivore/detritivore fish favor higher 

concentrations of bioaccumulative 

chemicals and result in higher 

concentrations in consumed fish. Linear 

food-chain maximizes concentration of 

bioaccumulative chemicals in higher

trophic-level fish.

Parameters for Estimating Concentrations in Farm Food Chain Media

Fraction of plants 

and soil ingested by 

farm animals that is 

contaminated

1.0 (all food and 

soil from 

contaminated 

areas)

Health 

Protective

Assumes livestock feed sources (including 

grains and silage) are derived from most 

highly impacted locations.

Soil- and air-to-plant 

transfer factors for 

produce and related 

parameters

Typical (see 

Attachment B for 

details)

Neutral Obtained from peer-reviewed and standard 

EPA reference sources.

Biotransfer factors 

for efficiency of 

uptake by animal of 

chemical in food/soil

Typical (see 

Attachment B 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.

Ingestion Exposure Assumptions

Ingestion rates for all 

farm 

produce/livestock 

types

Person obtains all 

food sources from 

local farm; 

ingestion rate is 

90th percentile of 

rates for homeproduced food 

items

Health Protective All food derived from impacted farm; total 

food ingestion rate would exceed expected 

body weight-normalized ingestion rates 

(prevents underestimating any individual 

food type).

Fish ingestion rate Adult: 373 g/day 

Child age groups: 

1 to 2: 108 g/day

3 to 5: 159 g/day

6 to 11: 268 g/day

12 to 19: 331 g/day

Health Protective The adult rate is the 99th percentile value 

for adult females from Burger (2002) and is

considered representative of subsistence 

fishers. 

Rates for children are based on the 99th

percentile, consumer-only fish ingestion

rates from EPA 2002. Rates were adjusted 

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Characteristic Value

Neutral or 

Health 

Protective?

Comments on Assumptions

to be representative of the age groups used 

in the screening scenario. See Attachment 

B 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.

2.5 Evaluation of Screening Scenario

2.5.1 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. As described previously 

the major PB-HAP categories of concern for this assessment are cadmium compounds (Section 

2.5.2), mercury compounds (Section 2.5.3), dioxins (Section 2.5.4), and POM (Section 2.5.5). 

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.

2.5.2 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). 

2.5.2.1 Behavior in the Environment

Once emitted into the environment, airborne cadmium particles can be transported over long 

distances before being deposited. Cadmium has been observed to partition primarily to soil 

when released to the environment (ATSDR 2008). The mobility of cadmium in soil depends 

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strongly on soil pH, clay content, and availability of organic matter—factors that determine 

whether the cadmium is dissolved or sorbed in surface soil. In general, cadmium adsorbs to soil 

particles in the surface layers of the soil profile, but to a lesser degree than many other heavy 

metals (HSDB 2005). Cadmium also binds strongly to organic matter, rendering the metal 

relatively immobile in some soils. Nonetheless, some plants still can take up cadmium

efficiently, thus providing an entry point for cadmium into the food chain (ATSDR 2008). 

Cadmium also enters surface waters, which can occur via atmospheric deposition, runoff and 

erosion, or wastewater streams. Most cadmium compounds entering the water column are 

quickly removed through adsorption to organic matter in sediment or to other suspended 

compounds. Cadmium that remains in the water column is expected to exist primarily in the 

dissolved state where it is available for uptake by aquatic organisms.

Freshwater fish accumulate cadmium primarily through direct uptake of the dissolved form 

through the gills and secondarily through the diet, which plays a variable role in total cadmium

uptake (Reinfelder et al. 1998; Chen et al. 2000; Saiki et al. 1995). Although some 

biomagnification of cadmium has been reported for aquatic food chains in saltwater systems, 

biomagnification in freshwater systems appears to be present only at lower trophic levels (Chen 

et al. 2000) and in narrowly defined niches (e.g., plankton/macroinvertebrate food chains; 

Croteau et al. 2005). Biomagnification factors (BMFs) of less than 1 generally have been 

reported for fish at higher trophic levels, indicating that cadmium concentrations generally 

biodiminish from lower to higher trophic levels (Chen et al. 2000; Mason et al. 2000). 

For the RTR screening scenario, the partitioning behavior modeled in TRIM.FaTE was

consistent with the behavior of cadmium expected in the natural environment.

2.5.2.2 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. 

2.5.2.3 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 19, the contributions of 

ingestion exposure pathways to the average daily dose (ADD) (and thus the HQ) for the 

different age categories are presented. As shown in the exhibit, fish ingestion is the dominant 

exposure pathway across all age categories, accounting for nearly 100 percent of the ADD for 

all groups. The combined contribution from all other exposure pathways accounts for less than 

0.7 percent of the total ADD for all age groups. Most of the additional exposure was from 

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ingestion of fruits and vegetables. The highest ADD corresponds to children aged 1–2 years; 

thus, the exposure corresponding to this group was used to determine the emission threshold 

for cadmium. In other words, the threshold emissions rate for cadmium is set at the level where 

the HQ for this age category is equal to 1.0. 

Exhibit 19. Estimated Contributions of Modeled Food Types to 

Cadmium Ingestion Exposures and Hazard Quotients

2.5.3 Mercury Compounds

Some of the largest anthropogenic sources of mercury to air are facilities that process, mine, or 

smelt mercury ores; industrial/commercial boilers; fossil fuel combustion activities (primarily 

coal); cement production facilities; other urban and industrial facilities; and medical and 

municipal waste incinerators (ATSDR 1999). These facilities can emit a mixture of elemental 

and divalent mercury, mostly in the gaseous phase, with some divalent forms in particle-bound 

phases (EPA 1997). 

2.5.3.1 Behavior in the Environment

Once emitted into the environment, mercury undergoes changes in form and species as it 

moves through environmental media. Elemental mercury is the most prevalent species of 

mercury in the atmosphere. Due to the long residence time of elemental mercury in the 

atmosphere, this compound is relatively well distributed, even on a global scale.

Divalent mercury is removed from the atmosphere at a faster rate than elemental mercury, and 

it can be transferred to the surface near the emission source via wet or dry deposition where it 

appears to adsorb tightly to soil particles (EPA 1997) or dissolved organic carbon. Divalent 

mercury in soil also can be methylated by microbes or reduced to elemental mercury and 

revolatilized back into the atmosphere. Most divalent mercury from atmospheric deposition will 

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remain in the soil profile, however, in the form of inorganic compounds bound to soil organic 

matter. Although this complexing behavior with organic matter significantly limits mercury

transport, the ability of mercury to form these complexes greatly depends on soil conditions 

such as pH, temperature, and soil humic content. For example, mercury strongly adsorbs to 

humic materials and sesquioxides in soil at pH > 4 and in soils with high iron and aluminum 

content (ATSDR 1999). Small amounts of mercury in soil can be transported to surface water 

via runoff or leaching. 

Mercury could also enter the water column through atmospheric fallout. Once in the water 

body, divalent mercury can be methylated through microbial activity. In addition, divalent and 

methyl mercury can be further reduced to elemental mercury, which can volatilize and reenter

the atmosphere. Solid forms of inorganic mercury compounds could adsorb to particulates in 

the water column or partition to the sediment bed (EPA 1997). 

The solubility of mercury in water depends on the species and form of mercury present as well 

as properties of the water such as water pH and chloride ion concentration (ATSDR 1999). Low 

pH favors the methylation of mercury in the water column, typically performed by sulfur-reducing 

bacteria in anaerobic conditions. Methyl mercury is typically of greatest concern because it 

readily bioaccumulates and efficiently biomagnifies in aquatic organisms. A considerable

amount (25–60 percent) of both divalent mercury compounds and methyl mercury is strongly 

bound to particulates in the water column (EPA 1997). The remaining mercury is dissolved. 

Most of the elemental mercury produced as a result of reduction of divalent mercury volatilizes 

back into the atmosphere. 

For the screening scenario, the partitioning behavior modeled in TRIM.FaTE generally was 

consistent with trends noted in the literature. Divalent mercury was the most prevalent species 

in modeled surface soil, surface water, and sediment compartments, while methyl mercury was 

the dominant species in fish. 

2.5.3.2 Concentrations in Ingestible Products

Available data indicate that mercury bioaccumulates in plants, aquatic organisms, and terrestrial 

animals, providing multiple ingestion exposure pathways (EPA 1997; ATSDR 1999). Low levels 

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

potatoes, grains, legumes, and other vegetables and fruits (ATSDR 1999; EPA 1997). Cattle 

demethylate mercury in the rumen and, therefore, store very little of the mercury they ingest by 

foraging or consuming silage or grain. Thus, mercury content in meat and cow’s milk is low 

(ATSDR 1999). Concentrations of methyl mercury in fish are generally highest in larger, older 

specimens at the higher trophic levels (EPA 1997). 

Although data on mercury in foods other than fish are not abundant in the literature, total 

mercury concentrations output by MIRC were generally consistent with the reported values that 

were available. The exposure pathways that most influenced the mercury HQs in the model are 

presented in Exhibit 20. 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). 

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Exhibit 20. Estimated Contributions of Modeled Food Types 

to Methyl Mercury Ingestion Exposures

2.5.3.3 Average Daily Dose

In Exhibit 20, 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.

2.5.4 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. 

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2.5.4.1 Behavior in the Environment

Dioxins emitted to the atmosphere can be transported long distances in vapor form or bound to 

particulates, depositing in soils and water bodies in otherwise pristine locations far from the 

source. Although airborne dioxins are susceptible to wet and dry deposition, most dioxins 

emitted to the atmosphere through incineration/combustion processes are not deposited close 

to the source (ATSDR 1998).

In soil, dioxins strongly adsorb to organic matter and show very little vertical movement, 

particularly in soils with a high organic carbon content (ATSDR 1998). Most dioxins deposited 

in soil are expected to remain buried in the soil profile, with erosion of contaminated soil 

particles the only significant mechanism for transport to water bodies.

The dry deposition of dioxins from the atmosphere to water bodies is another important 

transport process. Because of the hydrophobic nature of dioxins, most dioxins entering the 

water column are expected to adsorb to suspended organic particles or partition to bed 

sediment, which appears to be the primary environmental sink for this chemical group (EPA

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

compartment, some resuspension and remobilization of congeners can occur if sediments are 

disturbed by benthic organisms (ATSDR 1998). 

Bioaccumulation factors (BAFs) in fish are high as a result of the lipophilic nature of chlorinated 

dioxins. Although the processes by which freshwater fish accumulate dioxins are not well 

understood, both fish and invertebrates bioaccumulate congeners that have partitioned to 

sediment or have become suspended in water (EPA 2004c). Because most dioxins in the 

aquatic environment are adsorbed to suspended particles, however, direct uptake from the 

water is unlikely to be the primary route of exposure for most aquatic organisms at higher 

trophic levels (ATSDR 1998). At lower trophic levels, the primary route of exposure appears to 

be through uptake of water in contaminated sediment pores, and the primary route of exposure 

in the higher trophic levels appears to be through food chain transfer. Following ingestion, 

some fish can slowly metabolize certain congeners, such as 2,3,7,8-TCDD, and release the 

polar metabolites in bile. This process ultimately might limit bioaccumulation at higher trophic 

levels (ATSDR 1998). 

For the RTR screening scenario, the partitioning behavior modeled in TRIM.FaTE was 

consistent with the behavior of 2,3,7,8-TCDD expected in the natural environment. Also of note 

is that dioxins readily partition into breast milk due to the lipophilic nature of these compounds.

2.5.4.2 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 

Tier 1 Methodology 47 October 2014
TRIM-Based Tiered Screening Methodology for RTR

scenario was at least one order of magnitude greater than concentrations in the other 

compartments. Among the compartments with the lowest concentrations were fruits and 

vegetables, which do not readily accumulate 2,3,7,8-TCDD. 

Ingestion of breast milk during infancy and fish ingestion contribute to over 97 percent of lifetime 

dioxin exposure for 2,3,7,8-TCDD in the screening scenario. Daily intakes of 2,3,7,8-TCDD 

from cow’s milk, produce, and fish have been reported in the literature to comprise 27 percent, 

11 percent, and 10 percent, respectively, of the total daily intake in the general population. 

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

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 

assessment. 

2.5.4.3 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 21. 

Based on the modeling methodology and assumptions used, exposures via the breast milk 

pathway consistently account for approximately 30 percent of the lifetime exposure for all 

congeners, while exposure via fish, soil, and the various farm food chain pathways is highly 

variable across congeners. This variability can be explained in part by differences in the 

physiochemical properties that drive the environmental transport processes of these congeners 

(e.g., Kow, molecular weight). The differences are also likely attributed to differences in the 

congener-specific half-life in abiotic media and the degree to which the congener is metabolized 

in biotic media. 

2.5.5 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). 

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Exhibit 21. Estimated Contributions of Modeled Food

Types to Dioxin Ingestion Exposures

2.5.5.1 Behavior in the Environment

PAHs emitted to the atmosphere can travel long distances in vapor form or attached to particles, 

or they can deposit relatively close to an emission source via wet or dry deposition onto water,

soil, and vegetation. In the atmosphere, PAHs are found primarily in the particle-bound phase, 

and atmospheric residence time and transport distances are highly influenced by climatic 

conditions and the size of the particles to which they are bound (ATSDR 1995).

As a result of sustained input from anthropogenic sources, PAHs are ubiquitous in soil. High 

molecular weight PAHs, such as benzo(a)pyrene, strongly adsorb to organic carbon in soil, 

indicating that adsorption to soil particles will limit the mobility of these compounds following 

deposition to soil (ATSDR 1995). 

Most PAHs enter the water column directly through atmospheric deposition (ATSDR 1995). 

Following deposition onto surface waters, approximately two-thirds of PAHs adsorb strongly to 

sediment and suspended particles, while only small amounts revolatilize back to the 

atmosphere (ATSDR 1995). Aquatic organisms can accumulate PAHs via uptake from water, 

sediment, or food. Although fish and other organisms readily take up PAHs from contaminated 

food (e.g., aquatic insects, other benthic invertegrates, smaller fish), biomagnification generally 

does not occur because many organisms can rapidly metabolize PAHs (ATSDR 1995). As a 

result, concentrations of PAHs have generally been observed to decrease with increasing 

trophic levels (ATSDR 1995). Due to this ability to metabolize PAHs, BAFs in fish are not 

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

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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. 

2.5.5.2 Concentrations in Ingestible Products

The primary source of non-inhalation exposure to benzo(a)pyrene outside of occupational 

settings is through dietary intake. Exposure can depend on the origin of the food (higher values 

are often recorded at contaminated sites) and the method of food preparation (higher values 

have been reported for food that is smoked or grilled). PAHs have been observed to

bioaccumulate in aquatic organisms and terrestrial animals through uptake of contaminated 

water, soil, and food. These compounds are readily metabolized by higher trophic level 

organisms, including humans, however, so biomagnification is not considered to be significant 

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

chemical concentrations tend to be below detection levels. PAHs in meat have been observed 

at concentrations below detection levels up to higher concentrations when the meat is smoked. 

Similar concentrations have been reported for fish, with smoked fish concentrations sometimes 

quadruple those found in terrestrial animals. Because PAH concentrations are highest in 

products that are smoked or grilled, most of the available data for benzo(a)pyrene in food is for 

products that have been prepared using these processes. As a result, reported values might be 

significantly higher than those output by MIRC. 

For the RTR screening scenario, concentrations output by MIRC were generally lower than the 

reported ranges for benzo(a)pyrene in ingestible products. This trend is likely the result of 

background exposure in reported measurements and available data that are skewed toward 

concentrations in highly contaminated products. Considering these mitigating factors, the RTR 

screening scenario output concentrations are within the range of plausible values for PAHs in 

ingestible products.

2.5.5.3 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 22. As shown, the variability in the driving exposure 

pathways across PAHs is significant, with fish, beef, dairy, fruits, and vegetables comprising 

between 90 and 99 percent of exposure for different PAHs.

This variability can be accounted for in part by differences in the physiochemical properties that

drive the environmental fate and transport processes of these PAHs (e.g., Kow, molecular 

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

degree to which the PAHs are metabolized in biotic media. The variability in exposure 

pathways is consistent with information provided in the literature.

2.5.6 Summary

This assessment provides a summary of the fate and transport processes and the major routes 

of exposure for the PB-HAP categories of interest to EPA’s RTR Program, as modeled in 

TRIM.FaTE. In general, the modeled behavior of the compounds is consistent with data found 

in the literature. 

This assessment 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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Exhibit 22. Estimated Contributions of Modeled Food 

Types to PAH Ingestion Exposures 

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3. Tier 2 Methodology

3.1 Overview of Approach

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

allow for quick application to a large number of facilities in a source category with a minimal 

chance of returning false negatives for risk. Based on screening assessments conducted for 

RTR to date, many facilities might not screen out of the Tier 1 assessment for some source 

categories. Because the Tier 1 screen uses numerous health-protective assumptions, the 

assessment must be refined to determine whether the facility is actually expected to pose a risk

above levels of concern. 

One way to refine the risk estimates is to conduct a site-specific assessment where the Tier 1 

model values and layout are replaced with site-specific values and layouts wherever possible. 

However, this approach would not be feasible given the resource intensive nature of a sitespecific assessment and the number of facilities that might not screen out of the Tier 1 

assessment in some source categories. 

As an intermediate approach, we instead refine our Tier 1 screening estimates to Tier 2 

screening estimates. This is done by replacing some of the more health-protective assumptions 

in a Tier 1 screen with more site-appropriate values. Specifically, for Tier 2, the following 

parameter values are varied from their Tier 1 values:

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

direction of each farm and lake (using wind direction), the wind speed, the precipitation 

rate, and the mixing height; and 

• Locations of fishable lake(s) relative to the facility (including the absence of a fishable 

lake). 

In addition, a refined fisher scenario is used to model risks associated with nearby lakes. The 

refined fisher scenario is based on the idea that an adult fisher might fish from multiple lakes if 

the first (i.e., highest-concentration) lake is unable to provide him an adequate catch to satisfy 

the assumed ingestion rate (i.e., 373 g/d for adults). This assessment uses the assumption that 

the biological productivity limitation of each lake is 1 gram of fish per acre of water: meaning that 

in order to fulfill the adult ingestion rate, the fisher will need to fish from 373 total acres of lakes.

In selecting the fate and transport variables listed above to include in Tier 2 adjustments, a 

balance was struck between: 1) the degree of impact on the potential risk estimate; 2) the ease 

of implementation in TRIM.FaTE; and 3) the ease of obtaining site-specific values on a facilityby-facility basis. Because of the expected variability in exposure parameters amongst any 

population living around a given facility, the exposure parameters pertaining to farm ingestion 

pathways remain fixed at their health-protective Tier 1 levels. However, the fish ingestion rates 

might vary on a site-specific level using the refined fisher scenario based on the availability of 

lakes to fish. 

Tier 2 screening assessments are performed for those facilities that do not screen out during the 

Tier 1 assessment. The overall implementation of Tier 2 is shown in Exhibit 23. The starting 

point (shown in green) is the ratio of the facility emission rate for the PB-HAP of concern to the 

Tier 1 threshold for that PB-HAP. Next, the facility-specific estimates of the Tier 2 

meteorological and lake-location parameters listed above must be gathered for each facility 

(shown in red). Then, the associated TRIM.FaTE and MIRC risk estimates must be calculated 

(shown in orange). Because of the volume of facilities that need to be evaluated in Tier 2, the 

implementation focuses on estimating refined risk using pre-calculated databases (discussed 

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TRIM-Based Tiered Screening Methodology for RTR

below) rather than gathering the input data and performing TRIM.FaTE and MIRC modeling 

speartely for each facility.

Exhibit 23. Basic Process for Implementing Tier 2

First, databases of the relevant U.S. meteorological and lake data were created that can be 

accessed during a Tier 2 evaluation (shown in red in the figure). These meteorological and lake 

data are discussed in more detail in Section 3.5. The meteorological database includes annualaverage summary statistics on wind direction, wind speed, and precipitation for more than 800

surface stations paired with their closest upper-air stations located throughout the United States. 

These surface data cover year 2011 and are the same AERMOD-ready data used by EPA 

OAQPS for RTR inhalation modeling. As discussed below, in most cases the 2011 precipitation 

data were not used, and instead, the 30-year average annual precipitation data for each station 

were used. The database of lakes, available from ESRI® and based on U.S. Geological Survey 

data, includes information on the location, size, use or type designation, and name (if available) 

of all lakes in the United States. To focus on lakes that can support angling of upper trophic 

level fish, lakes used for disposal, evaporation, or treatment were excluded, and only lakes 

greater than 25 acres in area are included. Lakes larger than 100,000 acres in area are not 

included because the sizes of their watersheds and the complexity of their lake dynamics are 

not feasible to model with the TRIM.FaTE modeling system.

In parallel to the meterological and lake data collection, a series of TRIM.FaTE simulations was 

performed that systematically varied the values for four of the five selected fate and transport 

variables (shown in orange in the figure, consisting of lake location, wind speed, precipitation 

rate, and mixing height). Wind direction affects only whether the chemical mass advects toward 

the farm and lake, so the effect of site-specific wind directions can be evaluated outside 

TRIM.FaTE simulations. These simulations do not simulate specific facilities; instead, four or 

five alternative values for each of the four variables were selected using statistics on U.S. 

meteorological data or professional judgment to capture the expected range in the facility data. 

TRIM.FaTE simulations were performed for every possible combination of these variable values 

to enable the estimation of appropriate site-specific threshold adjustment factors for scenarios 

with the corresponding characteristics. Based on the TRIM.FaTE results of these simulations 

Identify Meteorology Statistics and Lake 

Distance Values

(Reflecting the expected range of meteorology 

conditions and lake locations near most U.S. facilities)

Run TRIM.FaTE/MIRC Test Cases to Create 

Matrix of Tier 2 Adjustment Factors

(Reflecting all combinations of meteorology statistics 

and lake distance values)

Facility’s Tier 1 

Screening Results

Identify Facility’s Closest 

Meteorology Station 

and All Qualifying Lakes

One Time

For Each Facility 

Assessment

Identify and Apply Tier 2 

Adjustment Factors

Tier 2 Methodology 53 October 2014
TRIM-Based Tiered Screening Methodology for RTR

(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. The 

contribution of each exposure media toward each scenario’s Tier 2 risk metric (i.e., the 

individual contributions of fish ingestion, soil ingestion, beef ingestion, etc. toward the total 

Tier 2 risk metric) was included in the matrix because the Tier 2 assessment separates 

chemical exposure from fish ingestion from exposure from farm food chain ingestion.

These TRIM.FaTE and MIRC simulations are used to estimate part of the Tier 2 threshold 

adjustment factors. Wind direction is the other part of the Tier 2 threshold adjustment factors.

The wind direction adjustment for a given facility is the ratio of the frequency that winds blow 

toward the Tier 1 farm and lake (43 percent of the time) and the frequency that winds blow 

toward the facility-specific farm and lake in Tier 2. These Tier 2 threshold adjustment factors are 

multiplied by each other and represent the ratio between the risk metric (i.e., cancer risk or HQ) 

obtained using the Tier 1 screening scenario and the risk metric obtained from the Tier 2 

TRIM.FaTE runs. For a given facility, an adjusted Tier 2 ratio (emissions compared to the 

emission threshold) can be estimated by dividing the Tier 1 emission ratio (the output of the Tier 

1 screen) by the adjustment factor that best corresponds to the meteorological conditions 

present at the site and the presence and location of lakes at the site:

Tier 2 Ratio = Tier 1 Ratio ÷ Tier 2 Adjustment Factor

Matrices of threshold adjustment factors from the TRIM.FaTE and MIRC simulations were 

separately developed for the four PB-HAPs that currently have screening emission thresholds in 

the Tier 1 process: benzo(a)pyrene (BaP, representative of PAHs), cadmium, divalent mercury 

and 2,3,7,8-Tetrachlorodibenzo-p-Dioxin (TCDD, representative of dioxins). The wind direction 

threshold adjustment factors were irrespective of chemical (specific only to the facility). In 

implementing the Tier 2 process, a risk equivalency approach was used to convert speciated 

emissions of PAHs and dioxins into BaP and TCDD equivalents, respectively, similar to the Tier 

1 screening approach.

Finally, to implement the Tier 2 screen, a Microsoft® AccessTM tool was created that includes 

the database of meteorological data and lake data as well as the Tier 2 adjustment factors for 

the combinations of variables simulated in the Tier 2 TRIM.FaTE simulations. In the tool, each 

facility is matched with the same surface meteorological station used in the RTR inhalation risk 

assessment, and the values for the four relevant parameters at that station are recorded (wind 

speed, wind direction, precipitation rate, and mixing height). The tool also facilitates the 

identification of all qualifying lakes in the area surrounding the facility and their distances and 

directions relative to the facility. These five values become the set of facility-specific 

parameters. The threshold adjustment factor corresponding to this set of site-specific data is 

then estimated using the matrix of adjustment factors and the wind direction adjustment. Wind 

direction values are used as-is with no rounding or binning. If one of the four facility matrix 

variables (wind speed, precipitation rate, mixing height, or lake location) is between two of the 

computed levels available for that variable in the simulation matrix, the more health-protective of 

the two levels is selected (i.e., the level that resulted in the smaller adjustment to the Tier 1 

exposure). The Tier 1 screening emission threshold for a PB-HAP is then divided by the 

appropriate adjustment factors to obtain an updated Tier 2 emission threshold for that PB-HAP 

at that facility. A facility then screens out in Tier 2 if the emissions are below the Tier 2 threshold

for the fisher scenario and for the farmer scenario. 

Tier 2 Methodology 54 October 2014
TRIM-Based Tiered Screening Methodology for RTR

3.2 Selection of Site-Specific Characteristics for the Tier 2 Assessment

The screening scenario used to derive Tier 1 thresholds incorporates assumptions regarding 

meteorological conditions, the spatial configuration of the hypothetical exposure setting, 

physical parameters of the environment, and chemical-specific parameters that result in 

generally health-protective results. In Tier 2, selected assumptions used in the fate and 

transport modeling conducted using TRIM.FaTE are modified to reflect more site-specific 

information for the facilities being evaluated.19 To determine which scenario characteristics 

should be incorporated into the Tier 2 assessment, the following issues were considered for 

TRIM.FaTE model parameters:

• How sensitive are the modeled risks to a specific user-input model parameter (e.g., wind 

direction, wind speed)? 

• Do the plausible values for a given parameter span a large range when comparing 

different RTR facilty locations? 

• Which site-specific characteristics can be found easily and reliably for facilities with 

emissions exceeding Tier 1 thresholds? 

• In general, is the uncertainty associated with the parameter high or low? 

• How complicated or time-consuming is the incorporation of a given parameter (e.g., wind 

speed) into the screening scenario set-up? 

Attachment D provides an exhibit showing all the TRIM.FaTE variables considered for the Tier 2 

assessment. 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 assessment: 

• Wind direction (the percent of time the wind blows toward the lake and farm),

• Wind speed,

• Precipitation,

• Mixing height, and

• Location of fishable lakes relative to the facility. 

These parameters were judged to represent a balance between range of potential variability, 

ease of implementation within the modeling scheme used for RTR, and ease of obtaining sitespecific values with a relatively high level of confidence.

3.3 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 estimate of risk associated with facility emissions. This purpose is achieved within the 

assessment by generating revised emission thresholds of potential concern specific to a given 

PB-HAP on a facility-by-facility basis. However, instead of performing full-fledged, site-specific 

model runs for each facility that does not “screen out” in Tier 1, a set of generally applicable 

threshold adjustment factors for each PB-HAP was developed based on a set of model runs. 

19Only 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.

Tier 2 Methodology 55 October 2014

 
TRIM-Based Tiered Screening Methodology for RTR

For each PB-HAP, these runs corresponded to unique combinations of values for wind speed, 

precipitation rate, mixing height, and lake location (wind direction is assessed separately). 

These adjustment factors were based on a set of runs in which the values for these parameters 

were varied systematically. The wind direction adjustment factor is the ratio of the frequency 

that winds blow toward the lake and farm in the Tier 1 screening scenario (43 percent) and the 

frequency that winds blow toward the facility’s farm and lake in Tier 2, with a separate wind 

direction adjustment factor for each directional octant (i.e., separate factors for the farm and 

lakes in the northern octant, the farm and lakes in the northeastern octant, etc.). The 

appropriate adjustment factors are then applied to the Tier 1 threshold for that PB-HAP when 

evaluating a facility in Tier 2. 

The analyses conducted to select the parameters to derive the threshold adjustment factors are 

described in this section. Section 3.3.2 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 

facilities evaluated in RTR.

3.3.1 Selection Values for Variables of Interest 

For each site-specific parameter that is assessed in the Tier 2 assessment, other than wind 

direction, adjustment factors were estimated that correspond to a set of four to six particular 

values for the parameter. The adjustment factor for wind direction directly relates Tier 1 and 

site-specific Tier 2 wind direction frequencies, with respect to the directions of the lake and farm. 

These individual adjustment factors can the be multiplied for a particular variable combination to 

get an overall adjustment factor. To facilitate this, bins were created for each parameter of 

interest and a relevant range of values, with the exception of wind direction (as described below, 

representative bins were not necessary for this parameter). The rationale for selecting the 

range for each bin for each parameter of interest is described below. 

Wind Direction: Within the context of the hypothetical exposure scenario used in Tier 1 (and 

under actual conditions), when the frequency with which the wind blows towards the modeled 

domain (i.e., where the hypothetical farm and lake are located) increases, greater pollutant

deposition will occur over and around the farm and lake. The percentage of time the wind blows 

toward the farm and lake is therefore positively correlated with ingestion exposure and risk. In 

the screening scenario used to estimate Tier 1 thresholds, the wind is assumed to blow toward 

the modeled domain 3 days a week, or 43 percent of the time. This assumption is intended to 

approximate an unusually consistent long-term wind pattern and is representative of wind 

direction patterns in Yakima, Washington (where the wind blows eastward approximately 40

percent of the time, based on a review of wind direction data compiled by the National Weather 

Service). 

In the model runs to develop the Tier 2 bins, this factor was changed to 1, 2, and 4 days per

week, corresponding to 14 percent, 29 percent, and 57 percent of the time. This range of 

values was chosen to obtain a good understanding of the impact of wind direction on risk for the 

range of wind direction patterns likely to be present at real facilities. Review of these results 

indicated that, within this modeling scenario, estimated ingestion exposure varies directly with 

percentage of time the wind blows toward the farm and lake. Given the exactly linear nature of 

the relationship observed in model results obtained from these runs, the adjustment of the 

threshold for wind direction in Tier 2 is a direct, linear adjustment using the actual site value

rather than an incremental, bin-based approach. In other words, the Tier 1 threshold is adjusted 

for wind direction in direct proportion to the difference between conditions for the actual facility 

location and the wind direction parameters included in the Tier 1 screening scenario (i.e., 

blowing toward the lake/farm 43 percent of the time on average).

Tier 2 Methodology 56 October 2014
TRIM-Based Tiered Screening Methodology for RTR

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 assessment assumed a wind 

speed of 2.8 m/s, corresponding to the 5th percentile (i.e., slower) of annual average speed for 

the contiguous United States (distribution was based on data from a climate publication from the 

National Oceanic and Atmospheric Administration [NOAA], which used data from over 200

stations nationwide).20 This value is similar to the annual average wind speeds of the U.S. 

Deep South.21 In the model runs to develop the Tier 2 threshold adjustment factors, we 

calculated the change to exposure resulting from increasing this value to 3.5 m/s, 4 m/s, and 5 

m/s (values also shown in Exhibit 24; 5 m/s is the 88th percentile in the NOAA data). Based on 

these values, the bins used to classify wind speed are: 2.8–3.5 m/s, 3.5–4 m/s, 4–5 m/s and 

above 5 m/s. In all modeled cases, increasing the wind speed while maintaining constant lake 

location, wind direction, mixing height, and precipitation led to Tier 2 estimations of high-end risk 

or hazard that were smaller than that of Tier 1. To ensure that the approach is health-protective, 

a facility was assigned the lower end value of the bin into which it was placed. Facilities with 

wind speeds less than 2.8 m/s were assumed to have a wind speed of 2.8 m/s.

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 assessment assumed an annual precipitation 

rate of 1,500 mm/year. This level of annual precipitation was estimated to represent rainy 

conditions in the U.S., such as parts of the U.S. Deep South and parts of the U.S. Northwest 

Coast22. Though the rate was an estimate, it does correspond to approximately the 95th

percentile (i.e., higher rate) precipitation in the National Climatic Data Center’s (NCDC) 30-year 

(1981–2010) data from U.S. stations.23 To estimate adjustment factors in the Tier 2 

assessment, model simulations were conducted with the parameter value set to three lower 

values (i.e., 1,187 mm/year, 924 mm/year and 512 mm/year; values also shown in Exhibit 24), 

corresponding to the 75th, 50th and 25th percentiles, respectively, of the NCDC data. Locations 

with lower precipitation levels were assumed to have a minimum precipitation of 512 mm/year. 

Based on these levels, the bins used to classify precipitation were: 0-512 mm/year, 512-924 

mm/year, 924-1,187 mm/year, 1,187-1,500 mm/year and above 1,500 mm/year. In nearly all 

modeled cases, decreasing the precipitation rate while maintaining constant lake location, wind 

speed, wind direction, and mixing height led to Tier 2 estimations of high-end risk or hazard that 

were smaller than that of Tier 1. To be health-protective, a facility was assigned the upper end 

value of the bin in which it was placed. Facilities with precipitation levels above 1,500 mm/year 

were assumed to experience precipitation of 1,500 mm/year.

20http://ols.nndc.noaa.gov/plolstore/plsql/olstore.prodspecific?prodnum=C00095-PUB-A0001#TABLES – this website 

is updated yearly, so its current data may not match the data used to develop wind speeds for screening analyses.

21National Climatic Data Center CliMaps (NCDC-CliMaps) (2007). 

22National Climatic Data Center Historical Climate Series (NCDC-HCS) (2007).

23http://www.ncdc.noaa.gov/oa/climate/normals/usnormals.html

Tier 2 Methodology 57 October 2014

 
TRIM-Based Tiered Screening Methodology for RTR

Exhibit 24. Lake Distance and Meteorological Parameter Values for which Adjustment 

Factors were Developed in Tier 2a,b

Parameter Value

Wind Speed (m/s)

2.8

3.5

4

5

Precipitation (mm/yr)

512

924

1,187

1,500

Mixing Height (m)

710

865

1,097

1,537

Lake Distance (km)

No lake

2

5

10

20

40 a

Bold indicates the value is equal to the value used in Tier 1. b

Wind direction is not shown here because its effect on modeled exposure and risk in TRIM.FaTE is linear.

Mixing Height: Greater mixing heights can dilute the concentration of pollutants in air, 

resulting in lower deposition and other transfer rates from air to surfaces and consequently also 

lower ingestion exposures. The Tier 1 screening assessment assumed a mixing height of 710 

meters. This value is the 5th percentile (i.e., lower) of annual average mixing heights for 463 

U.S. locations, based on data obtained from EPA’s SCRAM Web site.24 To estimate adjustment 

factors in the Tier 2 assessment, mdoel simulations were conducted with the parameter value 

set to three larger values (i.e., 865 m, 1,079 m, and 1,537 m; values also shown in Exhibit 24). 

These values correspond to North Little Rock, Arkansas; Boise, Idaho; and Tucson, Arizona; 

and they are intended to encompass the range of annual average mixing heights experienced in 

different parts of the United States. Based on these levels, the following bins were selected for 

categorization of mixing height: 710–865 m, 865–1,079 m, 1,079–1,537 m, and above 1,537. 

In all modeled cases, increasing the mixing heights while maintaining constant lake location, 

wind direction, wind speed, and precipitation rate led to Tier 2 estimations of high-end risk or 

hazard that were smaller than that of Tier 1. To be health-protective, a facility was assigned the 

lower end value of the bin into which it was placed. Facilities with mixing heights above 1,537 m 

and those below 710 m were assumed to have mixing heights of 1,537 m and 710 m, 

respectively. 

Lake Location: Moving the lake included in the hypothetical Tier 1 scenario to a location 

farther from the actual source in the modeled domain will reduce modeled (TRIM.FaTE) 

deposition to the lake and its watershed and consequently reduce exposures associated with 

24Support Center for Regulatory Atmospheric Modeling; http://www.epa.gov/scram001/tt24.htm

Tier 2 Methodology 58 October 2014

 
TRIM-Based Tiered Screening Methodology for RTR

the fish consumption pathway. For the scenario modeled in Tier 1, the center of the lake was 

situated approximately 2 km from the source. To estimate lake location adjustment factors for 

use in Tier 2, we completed a series of model runs in which the lake was located 5 km, 10 km, 

20 km, and 40 km from the source, as well as runs with no lake (values also shown in Exhibit 

24). Accordingly, the bins used to classify lake location relative to the facility are: no lake, 2–5 

km, 5–10 km, 10–20 km, 20–40 km, and 40–50 km (lakes farther than 50 km are not 

considered). To be health-protective, a facility was assigned the lower end value of the bin into 

which it was placed. A facility with a lake closer than 2 km was assumed to have a lake at 2 km. 

In resituating the lake at these alternative locations, we maintained ratios consistent with those 

included in the Tier 1 screening scenario for (1) lake area to total land area in the modeled 

domain, (2) runoff watershed area to lake area, and (3) erosion watershed area to lake area. 

Exhibit 25 provides a diagram of the TRIM.FaTE layout in each of the alternate lake location 

simulations except no-lake. We used a “thin” lake shape (i.e., downwind width much smaller 

than the cross-wind length) that minimized the potential effect of declining deposition with 

distance from stack that might affect lakes that are long in the downwind direction. By 

controlling for these potentially confounding effects, we could isolate the effect of lake location 

on risk appropriately. Moving the lakes to increasing distances from the stack required 

expansion of the modeled domain. Maintaining the same overall ratio of land area to lake area 

in each domain resulted in scenarios with increasingly large lakes, with surface area increasing 

with distance from the source. This approach also was taken for reasons of modeling

convenience (i.e., taking into account resource requirements associated with reconfiguring the 

TRIM.FaTE spatial layout). The changes in lake size between these four runs are not expected 

to have a substantial independent effect on exposure and risk because the effect of increased 

lake size is offset by greater total deposition and runoff. Furthermore, the lake depth was not 

changed, which might be as important a parameter as lake surface area in determining the 

chemical concentrations in the water column and sediment. As noted above, we set up the 

configurations to ensure that the lakes in the different scenarios received runoff and erosion 

from equivalent watersheds on a per surface area basis. 

3.3.2 Estimation of Adjustment Factors 

Adjustment factors were estimated for each variable described above and applied as divisors to 

the Tier 1 emissions thresholds. The resulting Tier 2 emissions thresholds are used to assess 

whether facilities with corresponding configurations carry some potential for significant 

multipathway health risks by comparing their actual emissions to the Tier 2 thresholds. 

3.3.2.1 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 HQ of 1, depending on the toxic effect of the chemical in question). 

Tier 2 Methodology 59 October 2014
TRIM-Based Tiered Screening Methodology for RTR

Exhibit 25. Layouts for Tier 2 TRIM.FaTE Simulations Using Alternate Distances 

Between the Facility and the Fishable Lakea

a

The no-lake scenario is not shown.

Tier 2 Methodology 60 October 2014
TRIM-Based Tiered Screening Methodology for RTR

To account for potential interactions between the chosen Tier 2 variables, matrix adjustment 

factors were estimated by performing TRIM.FaTE runs for each unique combination of the 

specified variable values (that is, all permutations of the selected values for the wind speed, 

precipitation, mixing height, and lake location). Adjustment factors for each configuration were 

then estimated as the ratio of risks in the Tier 1 assessment to the estimated risk for the 

particular TRIM.FaTE configuration. This approach results in a large matrix of adjustment 

factors and has the advantage of accounting for all possible interaction effects between the 

variables. The contribution of each exposure media toward each scenario’s Tier 2 risk metric 

(i.e., the individual contributions of fish ingestion, soil ingestion, beef ingestion, etc. toward the 

total Tier 2 risk metric) was included in the matrix because the Tier 2 assessment separates 

chemical exposure from fish ingestion from exposure from farm food chain ingestion. 

The adjustment factor for wind direction is handled separately. Based on TRIM.FaTE test runs, 

the fraction of the time the wind blows in the direction of the farm and lake was observed to 

have a direct, linear effect on the resulting risk estimates. For this reason, it was not necessary 

to include this variable in the TRIM runs conducted to create the matrix of adjustment factors, as 

described above. Instead, the adjustment factor is calculated with a linear factor that divides the 

Tier 1 value (0.43, or 43% of the time in the direction of the farm and lake) by the site-specific 

facility value, as follows:

Adjustment Factor Wind Direction = 0.43 / (fraction of time wind blows towards domain) 

The adjustment factor for wind direction is then multiplied by the matrix adjustment factor 

discussed above to obtain a consolidated threshold adjustment factor that accounts for all the 

five variables considered in Tier 2.

3.3.2.2 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. The REFs for PAHs and 

dioxins represent the ratio of the risk posed by a particular species to the risk posed by BaP and 

TCDD, respectively, at equivalent emissions rates in a given scenario. 

The REFs can be represented as the product of exposure equivalency factors (EEFs) and 

toxicity equivalency factors (TEFs). For the PAHs, this can be expressed as:

REFPAH = EEFPAH × TEFPAH

The EEFs for PAHs represent the ratio of the exposure to a particular species to the exposure 

to BaP (and similarly for dioxins and TCDD) at equivalent emission rates. These ratios are thus 

specific to the TRIM.FaTE layout and input assumptions. The TEFs for PAHs and dioxins 

represent the ratio of the oral cancer slope factor (CSF) for a particular species to the CSF for 

BaP and TCDD, respectively, and are the same in Tier 1 and in Tier 2.

The EEFs depend on the TRIM.FaTE configuration, including layout and meteorlogical input 

values. For example, the exposure profile (i.e., how the different ingestion pathways contribute 

to total exposure and risk) is different for each PAH. For a PAH where fish is a dominant driver 

of risk, moving the lake will have a large effect on the overall risk; however, if produce is the 

dominant driver of risk, moving the lake will have a much smaller effect on overall risk. Thus, for 

Tier 2, EEFs were recomputed for each of the representative scenarios modeled in Tier 2 

separately for the PAH species currently evaluated (some based on direct TRIM.FaTE 

modeling, others 15 based on Kow-based regression estimates) and the dioxin species currently 

Tier 2 Methodology 61 October 2014
TRIM-Based Tiered Screening Methodology for RTR

evaluated (most based on direct TRIM.FaTE modeling, and a small number assumed to behave 

like TCDD). 

The following mathematical formulas demonstrate how the Tier 2 adjustment factors are 

estimated for the PAH and dioxin species. The formulas presented below are for a 

representative PAH species, but they are also applied to dioxin species.

For a given PAH congener emitted at a rate EPAH at a facility, the risk-equivalent BaP emission 

level can be expressed as: 

EBAP EQIV = EPAH SPECIES × EEFPAH SPECIES × TEFPAH SPECIES

Then, the Tier 1 screening result is estimated by comparing the risk-equivalent BaP emissions 

to the BaP emission threshold:

RatioTIER 1_PAH SPECIES = 

ETIER 1_BAP EQUIV

ThresholdTIER 1_BAP

If the ratio is less than 1, the facility “screens out” of the Tier 1 assessment. Similarly, for Tier 2, 

the ratio of risk-equivalent BaP emissions to the Tier 2 BaP threshold may be expressed as:

RatioTIER 2_PAH SPECIES = 

ETIER 2_BAP EQUIV

ThresholdTIER 2_BAP

Using the definition of the risk-equivalent BaP emissions, this can be re-expressed for a given 

PAH species as:

RatioTIER 2_PAH SPECIES = 

EPAH SPECIES × EEFTIER 2_PAH SPECIES × TEFPAH SPECIES

ThresholdTIER 2_BAP

This expression may be further reconfigured, after some algebraic rearrangement, in terms of 

the Tier 1 ratio as:

RatioTIER 2_PAH SPECIES = RatioTIER 1_PAH SPECIES × 

ThresholdTIER 1_BAP

ThresholdTIER 2_BAP

× 

EEFTIER 2_PAH SPECIES

EEFTIER 1_PAH SPECIES

TTTTTTTT 2 AAAAAAAAAAAAAA AAAA FFFFFF FF PPPPPP SS SSSSSSSSSS = 

ThresholdTIER 1_BAP

ThresholdTIER 2_BAP

× 

EEFTIER 2_PAH SPECIES

EEFTIER 1_PAH SPECIES

These equations simply state that to adjust the Tier 1 threshold to a Tier 2 threshold for a 

particular PAH species, the adjustment factor must include the ratio of the BaP Tier 1 and 2 

thresholds (as is true for cadmium, mercury, and TCDD, as discussed above) and the ratio of 

the EEFs for the particular PAH species in Tier 2 and Tier 1. This additional EEF factor is 

needed to account for the fact that the EEFs are different for each Tier 2 TRIM.FaTE 

configuration.

Tier 2 Methodology 62 October 2014
TRIM-Based Tiered Screening Methodology for RTR

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: 

RatioTIER 2_ALL PAHs = � RatioTIER 1_PAH SPECIES × 

ThresholdTIER 1_BAP

ThresholdTIER 2_BAP

× 

EEFTIER 2_PAH SPECIES

EEFTIER 1_PAH SPECIES

ALL PAHs

If the ratio of total BaP equivalents contributed by all PAH species to the BaP Tier 2 threshold is 

greater than 1, the facility would not have screened out of the Tier 2 assessment for the PAH 

group.

3.3.2.3 Refined Fisher Scenario

The refined fisher scenario is based on the idea that an adult fisher might fish from multiple 

lakes if the first (i.e., highest-concentration) lake is unable to provide an adequate catch to 

satisfy the assumed ingestion rate (i.e., 373 g/d for adults). This assessment uses the 

assumption that the biological productivity limitation of each lake is 1 gram of fish per acre of 

water: meaning that in order to fulfill the adult ingestion rate, the fisher will need to fish from 373 

total acres of lakes (see Attachment E for further discussion on lake productivity). 

Under the refined fisher scenario, a fisher fishes from each surrounding lake in order of highest 

chemical concentration in fish to lowest concentration and catches fish up to the biological 

productivity limit. A maximum travel radius is used to maintain a realistic scenario. The total 

screening result to the fisher can be expressed as the sum of all screening results for each lake 

(which is based on the ingestion rate and fish concentration for each respective lake). 

A 50 km radius of lakes is used to maintain a realistic scenario. Depending on the model 

configuration, very large modeling configurations put a strain on computing resources, and 

TRIM.FaTE can crash if the domain is very large or if the domain is divided into too many

compartments. In most scenarios, the vast majority of chemical deposition occurs across 

distances smaller than 50 km, so the added computing resources needed to model a domain 

larger than 50 km will usually not be offset by additional, substantial chemical deposition and 

exposure. Indeed, wind speeds must exceed 13 m/s (approximately 29 mph) sustained across 

an hour in order for the chemical plume to travel farther than 50 km; those wind speeds are 

unlikely to occur across the many days or weeks needed to substantially affect chronic 

exposure. In addition, a 50-km limit also puts a reasonable constraint on the domain of lakes for 

the fisher scenario. 

In order for the Tier 2 screening to remain health protective, it is assumed that the fisher visits 

lakes in order of highest to lowest with respect to risk. If the first lake is 373 acres or greater, the 

fisher will only travel to that lake. If the acreage is smaller than 373 acres, then the fisher will 

travel to the next highest risk lake, and continues to travel to subsequent lakes until a total 

acreage of 373 acres is achieved, or until all qualifying lakes have been visited. 

The associated risk for each lake is then scaled by the total adult ingestion rate (equations 1 

and 2). 

(1) SS SS TT2 FF h rr = TT2 FF h rr × �

LL SS ( )

373 �

Equation 1: Scaled fish risk for each lake 1 to n-1 

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(2) SS SS TT2 rr = TT2 FF h rr × �

373 − ∑(SS oo PP PP PP VV LL ( ))

373 acs �

Equation 2: Scaled fish risk for nth lake

The risk associated with each lake visited is then summed together for a final risk (equation 3). 

(3) TT SS SS TT2 FF h rr = (Eq. 2) +�(Eq. 1)

Equation 3: Total fish risk for scenario

If the total acreage of fishable lakes surrounding the facility fails to exceed 373 acres, then the 

risk for each lake is found using equation 1, and the total risk is the sum of all scaled risks 

(equation 4). 

(4) TT SS SS TT2 FF h rr = �(Eq. 1)

Equation 4: Total fish risk if total acreage of lakes does not reach 373 acres

3.4 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 wind direction, wind speed, 

precipitation, and estimated mixing height). The development and content of these two 

databases are discussed in the following sections.

3.4.1 Processing Lake Data for Tier 2 Assessment

The lake database was built using a geospatial file (U.S. Water Bodies) provided by ESRI® for 

their ArcGISTM products.25 Because this geospatial file excluded water bodies in Alaska, Puerto 

Rico, and the U.S. Virgin Islands, it was augmented with water body information (directly from 

the USGS National Hydrography Dataset) for those other locations. The data generally have an 

estimated horizontal accuracy of 50 m. For the Tier 2 assessment, we focused on the hundreds 

of thousands of water bodies classified as “Lake/Pond” or “Reservoir” but not designated for 

disposal, evaporation, or treatment. We refer to these water bodies simply as “lakes” in the 

remainder of this document. The approximately 100,000 other water bodies (classified as 

canal/ditch, ice mass, inundation area, playa, stream/river, swamp marsh, or unclassified) were 

not included. In a more subjective step during the processing of the lake database for a specific 

source category (when Tier 2 is run “operationally”), the lake names (when provided) are 

scrutinized manually, and lakes are removed from the assessment when their names suggest 

industrial or treatment use (e.g., wastewater treatment ponds, sludge ponds, fly ash ponds, 

paper mill ponds, sewage pools, etc.). In this respect, the lake dataset is never truly final; lake 

names are scrutinized each time a new set of facilities is assessed using the Tier 2 methods

and tools, which can lead to the permanent removal of some lakes from the dataset. 

Early in the process of compiling this database, we encountered the question: “What size water 

body qualifies as a ‘lake’ for the purposes of this assessment?” The Tier 2 assessment must 

focus on lakes large enough to support relatively intensive angling pressure to be compatible 

25 Specifically, the geospatial file in the ESRI® Data & Maps 2009 Data Update for ArcGISTM version 9.3.1. It was

derived by the United States Geological Survey (USGS), EPA, and ESRI® from the USGS National Hydrography 

Dataset (USGS 2012).

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TRIM-Based Tiered Screening Methodology for RTR

with the assumed exposure scenario. To estimate the relationship between high fish 

consumption rates, harvest rates, and lake size, the following five key assumptions were made. 

Information and citations to peer-reviewed literature that support these assumptions are 

provided in Attachment E. Note that in the TRIM.FaTE model screening scenario, water-column 

carnivores (WCCs) are modeled as trophic level 4 (TL4) fish (e.g., pickerel, largemouth bass), 

with all of their diet consisting of smaller “prey” or “pan” fish in the water column that are 

simulated as trophic level 3 (TL3). The benthic carnivores (BCs) in TRIM.FaTE are modeled to 

represent an intermediate trophic level between 3 and 4, i.e., TL3.5. The BCs (e.g., catfish) 

obtain half of their diet from TL2 (benthic invertebrates that feed on detritus at the sediment 

surface) and half from TL3 fish in the benthic environment. For the screening scenario, we 

assume that fishers consume fish biomass in a ratio of 50:50 from the BC and WCC 

compartments, respectively. Together, these two fish compartments are referred to as 

piscivorous fish.

1. Piscivorous fish, when present, comprise approximately 21 percent of the standing biomass 

of fish. BC fish represent 17.5 percent of the standing fish biomass in natural lakes; WCC 

fish account for 3.5 percent of this total fish biomass. Thus, WCC (or TL4) fish represent the 

limiting compartment for fish harvesting and consumption.

2. Humans can harvest 10 percent of any single fish compartment without threatening the 

population due to overharvesting.

3. The minimum viable effective population size for a single fish species is at least 50 adult fish 

for a local population to survive over the short term (e.g., more than a decade). 

4. Only 33 percent of the fish caught for consumption is edible fillet muscle. A 0.33 edible 

fraction is used to estimate total fish biomass associated with human consumption.

5. A total fish standing biomass of 40 g wet weight/m2 represents an approximate upper bound 

for natural ponds and lakes in the United States.

Using the above assumptions and a series of equations (see Attachment E), the maximum fish 

ingestion rates as a function of standing biomass and lake size were estimated. Exhibit 26

presents these findings, where the grey shading indicating when WCC fish would probably not 

be present, the white (unshaded) cells represent combinations of lake size and productivity that 

could sustain the listed fish ingestion rates over some time, and the yellow cells represent likely 

long-term sustainability associated with more than 500 adult WCC fish in the lake (see Exhibit

notes). 

The Tier 2 assessment is intended to retain most of the health-protective attributes of the 

screening scenario used in Tier 1 so that no facilities of potential concern erroneously “pass” the 

screen. For a given facility, the smaller the lake size threshold, the greater the number of lakes 

and the greater the probability that a lake is closer to the facility. Lakes closer to a facility will 

result in higher chemical concentrations in fish compared with lakes farther from the facility. 

Thus, Exhibit 26 was used to determine the smallest lake size that would support a TL4 

population. At the assumed upper-limit standing fish biomass of 40 g ww/m2, this corresponds 

to 25 acres (the first “white” box when moving from smaller to larger lakes). 

The fish ingestion rate supported by a 25-acre lake is not as high as the adult ingestion rate 

used in the Tier 1 and Tier 2 assessments (i.e., 373 g ww fillet per day). Exhibit 26 indicates 

that at a total fish biomass productivity of 40 g ww/m2, the maximum sustainable fish harvest 

from the WCC compartment would correspond to an ingestion rate of only 26 g/day for those 

fish, with an additional 26 g/day from the benthic carnivores (which are not the limiting 

population), for a total of 52 g per day. However, to be health protective and to ensure that small 

lakes that might be more highly contaminated than estimated by the TRIM.FaTE screens were 

Tier 2 Methodology 65 October 2014
TRIM-Based Tiered Screening Methodology for RTR

not eliminated, we selected 25 acres as the “cutoff” for the minimum size for an actual lake near 

a facility to be included in the Tier 2 assessment. In addition, larger lakes (larger than 100,000 

acres) were not considered since they cannot be readily modeled in TRIM.FaTE. For the 

purposes of proximity matching lakes to emitting facilities, the location of each lake is identified 

as the geographic centroid inside the lake.

It should be noted that an individual lake as small as 25 acres could be stocked annually at a 

rate adequate to support the assumed fish ingestion rate. For stocked fish, however, we would 

have to assume that when introduced to the lake, the fish were uncontaminated by the 

chemicals of interest. Moreover, the period over which accumulation of chemical from the lake 

could occur would be roughly three to six months (fishing season) for the majority of the fish 

stocked as adults (i.e., at approximately 2 kg), instead of several years for fish hatched in the 

lake. 

To place an upper bound on the radial distance from the source up to which lake-derived risks 

need to be assessed, we took into account the limitations of TRIM.FaTE. We limited the lake 

analysis to an outward radial bound of 50 km from the stack. For facilities with no lakes within 

50 km, lake-derived risk is assumed 0. 

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Exhibit 26. Maximum Fish Ingestion Rate (g/day) Associated with Sustainable Fishinga

Standing 

Biomass 

(g ww/m2)

b Size of Pond or Lake (acres)

1 2 3 4 5 7.5 10 15 25 35 50 75 100 150 200 400

2 0 0 0 0 0 0 1 1 1 2 3 4 5 8 10 20

3 0 0 0 0 0 1 1 1 2 3 4 6 8 12 15 31

4 0 0 0 0 1 1 1 2 3 4 5 8 10 15 20 41

5.7 0 0 0 1 1 1 1 2 4 5 7 11 15 22 29 58

10 0 1 1 1 1 2 3 4 6 9 13 19 26 38 51 102

15 0 1 1 2 2 3 4 6 10 13 19 29 38 58 77 154

20 1 1 2 2 3 4 5 8 13 18 26 38 51 77 102 205

30 1 2 2 3 4 6 8 12 19 27 38 58 77 115 154 307

35 1 2 3 4 4 7 9 13 22 31 45 67 90 134 179 359

40 1 2 3 4 5 8 10 15 26 36 51 77 102 154 205 410

50 1 3 4 5 6 10 13 19 32 45 64 96 128 192 256 512

60 2 3 5 6 8 12 15 23 38 54 77 115 154 231 307 615

70 2 4 5 7 9 13 18 27 45 63 90 134 179 269 359 717

80 2 4 6 8 10 15 20 31 51 72 102 154 205 307 410 820

90 2 5 7 9 12 17 23 35 58 81 115 173 231 346 461 922

100 3 5 8 10 13 19 26 38 64 90 128 192 256 384 512 1025

110 3 6 8 11 14 21 28 42 70 99 141 211 282 423 563 1127

120 3 6 9 12 15 23 31 46 77 108 154 231 307 461 615 1229

130 3 7 10 13 17 25 33 50 83 117 166 250 333 499 666 1332

a

Dark gray shading indicates insufficient population size for TL4 (WCC) fish (<50 adults) to be sustainable for more than a decade; yellow-shaded cells indicate the likelihood to provide long-term

sustainable fish populations with at least 500 TL4 adult fish present; white area indicates medium-term sustainability. b

Represents the standing biomass of TL4 fish. At the upper-limit standing biomass of 40 g ww/ m2 estimated for natural lakes, 25 acres could support a water-column TL4 fish population, but 

would provide for no more than 26 grams of fillet per day for a single fisher over a full year.

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3.4.2 Processing Meteorological Data for Tier 2 Assessment

In addition to the lake database, a database of U.S. surface stations with complete data was 

created, so that each source category facility can be paired with the closest meteorological 

station data. This process of pairing dozens or hundreds of facilities with meteorological data is 

not unprecedented. In their report to the Science Advisory Board (SAB) on the 1996 NATA, 

EPA described pairing each facility with the closest meteorological station in an inventory of 

over 350 meteorological stations nationwide, creating an average facility-to-station distance of 

less than 50 km in the 1996 NATA (EPA 2001b). In a separate 2009 report to the SAB on the 

RTR program, EPA described using 158 meteorological stations to choose from nationwide, 

with a standard practice of selecting the station nearest to each facility unless the facility 

provides onsite meteorological data (EPA 2009). Using 156 petroleum refineries as a sample 

data set, the average facility-to-station distance was 72 km. In both instances, the SAB 

accepted this matching as standard practice when modeling large numbers of sources, although 

they recommended providing high-level siting maps (e.g., meteorological stations overlaid with 

terrain gradients or regional climate regimes) to qualify some of the uncertainties related to 

meteorological data in air dispersion modeling (EPA 2001a; EPA 2010b). The current effort 

builds on this practice but increases the number of available meteorological stations as 

described below.

3.4.2.1 Sources of Meteorological Data

To construct a database of meteorological statistics for all available U.S. surface stations for use 

in multipathway screening assessments, EPA started with the same U.S. meteorology dataset 

used in RTR inhalation assessments. RTR inhalation assessments use data from 824 ASOS 

(Automated Surface Observing System) stations that record hourly and sub-hourly 

measurements. These data represent the year 2011, and the surface stations are paired with 

their closest, regularly-reporting upper-air stations. This number of stations is far greater than 

the 350 and 158 stations, respectively, used in the 1996 NATA report (EPA 2001b) and the 

2009 RTR report (EPA 2009). 

The 2011 precipitation measurements reflect 2011 weather conditions, and, like any other year, 

some areas of the country experienced rainfall that was significantly less than normal, and some 

areas received much more rainfall than normal. To reduce this bias in precipitation data, we

used average annual precipitation data from the 1981−2010 National Climatic Data Center 30-

year normal dataset wherever possible. If 30-year normal precipitation data were not available 

for a station, as was the case for a few stations in the dataset, we used the ASOS precipitation 

data as-is. 

3.4.2.2 Coverage of Meteorological Stations Compared with Facility Locations

Exhibit 27 shows the proximity of the evaluated meteorology sites to the locations of U.S. point 

source facilities from the 2005 NATA. Generally, the spatial density of the surface 

meteorological stations in this dataset was similar to the spatial density of the 2005 NATA 

facilities. That is, the density tends to be greatest in the Great Lakes region, along the East and 

West Coasts, and in the Southern Plains, and tends to be lowest in the Rockies (except 

Colorado) and Northern Plains. 

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3.4.2.3 Data Processing

To facilitate application of the meteorological data to the Tier 2 assessment, EPA gathered wind 

information in directional octants that could be linked to the direction of the relevant lakes (see 

Introduction and Section 3.5). 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

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Exhibit 27. The Locations of Meteorological Stations and Point Source Facilitiesa

a

The 2005 NATA was the most recent, comprehensive, finalized dataset of nationwide point source emitters of hazardous air pollutants, and it is used here only for illustrative 

purposes. The 2005 NATA used a meteorological dataset different from the one used in this report.

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A software program was developed to calculate the following statistics for each of these 

stations:

• Number of hourly observations,

• Number of hours with calm winds or missing winds,

• Percentage of time the wind blows into each octant (after excluding missing wind hours),

• Median wind speed blowing into each octant, 

• Median mixing height used (if heat flux > 0, convective mixing height was used, 

otherwise mechanical mixing height was used), and

• Average annual precipitation (irrespective of wind octant and preferring 30-year normal 

data if available).

Total annual precipitation data in the 2011 meteorological data included anomalies (relative to 

normal conditions) in areas that experienced extreme drought conditions or large rainfall 

surplusses. To address this, the 30-year (1981-2010) average annual precipitation was used 

instead.26 

The choice of using median values for wind speed and mixing height was based on a 

comparison of median and mean values. For nearly all stations, the median value was smaller 

than the mean value; because smaller values are more health protective, we selected the 

median statistic for all stations.

3.5 Implementation of Tier 2 Assessment

The Tier 2 screening assessment is conducted using a Microsoft® AccessTM tool that was 

developed specifically for this purpose. Similar to the Tier 1 screening assessment tool, the Tier 

2 tool was created so that facilities that do not screen out during the Tier 1 assessment can be 

screened concurrently, if desired. 

The Tier 2 tool requires three inputs to run: the results of the Tier 1 screening assessment, a list 

of facilities to model, associated meteorology stations that can be matched to using a proximity 

analysis, and the matrix of adjustments factors that are used to adjust the Tier 1 screening 

thresholds to the Tier 2 screening levels. The matrix is used to standardize the scenario based 

on the chemical and on the lake distance and meteorology characteristics of the assessed 

facility (refer to Section 3.3 for a discussion of these adjustment factors).

The tool contains procedures that match each facility to all lakes within a user-defined radius 

(currently 50 km) that are within user-defined size limitations (currently 25 acres to 100,000 

acres), and based on water body type (e.g., they are not waste disposal or treatment facilities, 

retention ponds, etc). In addition, each facility is matched to the same meteorology station used 

in RTR inhalation assessments, which is usually the nearest meteorology station; when the 

meteorology station match is not known from the RTR inhalation assessment, the tool conducts

a proximity analysis. The tool allows the user to review matching lakes and exclude ones that 

are not considered suitable for modeling (e.g., based on names indicating industrial, waste, or 

treatment purposes, as discussed in Section 3.4.1.) Any excluded lakes are recorded by the tool 

so that they are omitted from subsequent screening assessments.

26 30-year average annual precipitation was obtained from the National Climatic Data Center (NCDC). 

http://www.ncdc.noaa.gov/oa/climate/normals/usnormals.html. 

Tier 2 Methodology 71 October 2014

 
TRIM-Based Tiered Screening Methodology for RTR

Per facility and PB-HAP, the tool evaluates the screening result at each lake and hypothetical 

farm. For each lake, the initial calculations produce screening results consistent with the full 

screening scenario ingestion rate (i.e., 373 g/day for adults, regardless of assumed lake fish 

productivity), and then additional calculations adjust the screening results for the lakes based on 

the initial screening results and areas of the lakes (see Section 3.3.2.3). Each modeling 

scenario consists of a farm and a lake (or, in some cases, no lake). Although the distance from 

the facility to the modeled farm remains fixed for each octant, the location of the lake affects 

chemical concentrations in some farm food chain media due to modeled runoff and erosion 

patterns that are directed mostly toward the lake. Therefore, there can be multiple different farm 

screening results in the same octant for the same facility, each corresponding to a different lakedistance scenario. The tool identifies the largest farm screening result from all scenarios 

evaluated for a facility. The tool presents the final Tier 2 screening results for the fisher and 

farmer separately, including results specific to individual farm food chain media, for each 

chemical, and then summed together for each PB-HAP group.

The tool also can incorporate the results of Tier 3 assessments. Since the tool allows the user to 

manually exclude lakes, if any lakes are removed from the assessment in Tier 3, those lakes 

can be removed in the tool and the Tier 2 screening can be repeated with the revised lake 

database. If Tier 3 plume-rise or time-series assessments are conducted, the resulting Tier 3 

adjustment factors can be entered into the tool and it will update the Tier 2 screening results to 

produce Tier 3 screening results. 

The tool outputs several results tables, including an intermediate table that provides information 

on each lake and farm, including the fish ingestion and screening-level risk associated with each 

lake. The final screening results for each facility and PB-HAP group are also output. A summary 

table identifies the facilities exceeding the Tier 2 emission screening threshold of each PB-HAP 

group (separately for the fisher and farmer) and the largest screening results. All intermediate 

and final results tables present the Tier 1 and Tier 2 screening results side-by-side for 

comparison. Exhibit 28 through Exhibit 30 provide screen shots of tool output tables. 

Exhibit 28. Example of the Summary Output Table from Tier 2 Toola

a The screen shot examples shown in this section are for actual facilities in actual source categories; however their source 

categories, NEI IDs, and coordinates have been altered or masked so the data are not linked to specific facilities. “SV” means 

“screening value”, which is the ratio of emissions to emission screening threshold.

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Exhibit 29. Example of the Facility Output Table from Tier 2 Toola, b

a The screen shot examples shown in this section are for actual facilities in actual source categories; however their source 

categories, NEI IDs, and coordinates have been altered or masked so the data are not linked to specific facilities. “SV” means 

“screening value”, which is the ratio of emissions to emission screening threshold.

b Only a portion of the table is shown. Individual farm ingestion media are in the table but only “soil” is displayed for space reasons. 

Exhibit 30. Example of the Lake Assessment Output Table from Tier 2 Toola,b

a The screen shot examples shown in this section are for actual facilities; however their source categories, NEI IDs, and coordinates 

have been altered or masked so the data are not linked to specific facilities. Only a portion of the table might be shown. The headers 

“Stationary Fisher” and “Traveling Fisher” are shorthand references to the initial fisher scenario (i.e., catches and consumes 373 g/d 

per lake, regardless of lake area) and the refined fisher scenario (i.e., assumes a 1 g/acre/d lake productivity), respectively. “SV” 

means “screening value”, which is the ratio of emissions to emission screening threshold.

b The table displayed reflects a Tier 3 Screening Assesment having been conducted. In a Tier 2 Screening Assesssment, the Add’l 

Lake Assessment, Plume Rise Adj Factors, and Time Series Adj Factors would not be shown.

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4. Tier 3 Methodology

4.1 Overview of Approach

The Tier 3 screening assessment is an additional assessment that can be conducted, at the 

discretion of the risk assessor, on facilities that do not screen out with the Tier 2 assessment.

The Tier 3 screening approach consists of three individual assessments that further refine the 

screening scenario (beyond the refinements in Tier 2) based on additional site-specific data and 

evaluations. The assessments introduce additional site-specificity to the screening scenario, 

requiring a potentially higher level of effort than the Tier 2 assessment, but still a much lower 

level of effort than the full site-specific assessment. One of the Tier 3 assessments (i.e., the lake 

assessment) results in the rescreening of the facility’s emissions using the Tier 2 methods and 

using a revised lake database. The other two assessments (i.e., the plume-rise and time-series 

assessments) each result in an adjustment factor to be applied to the screening result reflecting 

the Tier 3 lake assessment. The adjustment factors of the plume-rise and time-series 

assessments cannot be compounded (i.e., the time-series assessment, if conducted, supplants 

the plume-rise assessment already conducted).

The remainder of this section discusses each of these assessments. 

4.2 Lake Assessment

Other than verifying a facility’s emission rates, one of the simplest assessments that can be 

conducted (beyond the Tier 2 methods) is to assess the existence, the potential purpose, the 

accessibility and fishability, and the suitability of lakes for the models and methods used in 

Tier 2. The full USGS dataset of lakes and reservoirs nationwide that is used in Tier 2 does not 

contain information on lake accessibility or fishability. In addition, it occasionally identifies a lake 

that no longer exists (e.g., has evaporated or been drained) or it uses a classification that might 

not accurately reflect the lake’s purpose or type. Aerial and street-view imagery and internet 

searches can be used to quickly ascertain if an assessed lake actually exists, if it is likely not 

fished (e.g., appears swampy or covered in algae), if it is likely used for industrial or waste 

disposal/treatment purposes, and/or if it is adjacent to or connected to a river or saltwater body 

(estuaries, rivers, and saltwater areas are not ideal for the assessment models and methods).

For example, the blue outline in Exhibit 31 identifies a lake from the USGS dataset that 

originally qualified for Tier 2 based on the information provided in that dataset, but aerial 

imagery shows it is likely evaporated or drained. Similarly, the outline in Exhibit 32 identifies a 

lake that originally qualified, but aerial imagery shows that it is directly adjacent to an industrial 

facility and likely used only for on-site industrial purposes. 

For facilities undergoing Tier 3 screening, all lakes are assessed from which the fisher catches 

and consumes fish according to the refined Tier 2 fisher methods discussed in Section 3. All 

lakes meeting the criteria discussed in the previous paragraphs are permantly removed from the 

screening assessment so that they are no longer used in any screening assessment. If a lake is 

removed from the facility’s assessment, it might be necessary for the hypothetical fisher to catch 

and consume fish from additional lake(s) in order to fish a total of 373 acres. In this case, 

additional lakes that were not fished previously are evaluated, using the methods discussed in 

Section 3, in descending order of initial Tier 2 fisher screening result and lake area.

If any lakes are removed from the screening, the screening assessment is conducted again 

(using the Tier 2 methods) with the revised lake dataset. Although this additional lake 

assessment is conducted based on the Tier 2 screening results of a particular set of facilities, 

lakes removed during that assessment could affect the screening results of other facilities in the 

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TRIM-Based Tiered Screening Methodology for RTR

source category beyond that original set. This can happen if an assessed facility is within 50 km 

of another assessed facility, whereby removing a lake could affect the screening results of both 

facilities. It should be noted, however, that screening results across different facilities are not 

summed. 

Exhibit 31. Example of Lake Removed from Screening—Likely Evaporated or Drained

Note: Aerial imagery from ESRI World Imagery (2014).

 

Exhibit 32. Example of Lake Removed from Screening—Likely an Industrial Lake

Note: Aerial imagery from ESRI World Imagery (2014). This map was created during the 

assessment of a specific source category, thus the reference to “the assessment in this report” in 

the text box.

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4.3 Plume-rise Assessment

In Tiers 1 and 2, all chemicals are emitted inside the mixing layer and are available for groundlevel exposure. In reality, the physical height of an emission source, in combination with the 

temperature and velocity of the chemical plume as it leaves the source, can cause some of the 

chemical plume to reach areas above the mixing layer. In TRIM.FaTE modeling, the chemical 

mass deposited above the mixing layer (i.e., the model’s upper-air layer) is unavailable for 

ground-level exposure (i.e., the upper-air layer functions as a chemical sink). Many emission 

sources in RTR are physically taller than the mixing height during some hours, and hot exit gas 

temperatures (i.e., buoyancy) and/or high exit gas velocities (i.e., momentum) can further 

elevate the chemical plume.

The Tier 3 methodology utilizes methods summarized by Seinfeld and Pandis (1998) to 

estimate how often a facility’s emissions reach the upper-air sink, which decreases availability 

for ground-level exposure. The methods to estimate the amount of chemical lost to the upper-air 

sink require use of the facility’s corresponding meteorology data (e.g., air temperature and 

vertical temperature gradient, wind speed, and atmospheric stability), the mass of the PB-HAP 

emitted from each source, the physical characteristics of the sources (i.e., release height, inside 

diameter at the release point, and exit gas temperature and velocity), and an estimate of the 

size of the facility. 

For each relevant emission source, estimates of the hourly effective release height (i.e., sum of 

actual release height and plume rise) are compared to the hourly mixing height to determine the 

mass of chemical remaining in the mixing layer. The mass of chemical remaining in the mixing 

layer, summed across the sources, is compared to the total emitted mass of the chemical. This 

results in an adjustment factor expressing the fraction of emissions remaining in the mixing layer 

after accounting for release height and plume rise. This final plume-rise adjustment factor is 

then applied to the Tier 2 screening results. 

4.4 Assessment of Time-series Meteorology and Effective Release Heights

As discussed in Section 3, the Tier 2 screening results are based on the average meteorological 

conditions prevailing at the facility being assessed. The use of time-series meteorology data, 

which captures hour-by-hour changes in each of the assessed meteorological parameters 

instead of using constant average values, increases the accuracy of the estimates of potential 

risk by accounting for potential statistical interactions between the meteorology parameters and 

by improving the method of accounting for mass advected into the modeling domain. 

Tier 2 screening results also are based on a constant emission release height that is always 

within the mixing layer. Including hourly effective release heights along with hourly meteorology 

data further increases the accuracy of the screening results. 

For a facility undergoing a time-series assessment, a time series of hourly effective release 

heights is developed and the Tier 2 spatial scenario that best matches each lake from which the 

hypothetical fisher fishes is identified. Using these data along with the facility’s matching hourly 

meteorology data, the facility’s emission rates, and the fish ingestion rates, chemical fate and 

transport is modeled in TRIM.FaTE and corresponding screening-level risks are calculated 

using MIRC. This results in a screening-level risk value for each lake. If the farmer scenario is 

specifically being assessed, the modeling will also result in a screening-level risk value for each 

farm. The screening-level risk associated with each lake is multiplied by the percent of daily 

ingested fish caught from the lake; summing those products across all modeled lakes results in 

the screening-level fisher risk (i.e., fisher screening result) associated with the evaluated PBTier 3 Methodology 76 October 2014
TRIM-Based Tiered Screening Methodology for RTR

HAP emitted by the facility. The time-series-adjusted Tier 3 screening results are compared to 

the Tier 2 screening results calculated previously. 

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5. References

ATSDR (Agency for Toxic Substances and Disease Registry). 1995. Toxicological profile for 

polycyclic aromatic hydrocarbons. Atlanta, GA: U.S. Department of Health and Human 

Services, Public Health Service.

ATSDR. 1998. Toxicological profile for chlorinated dibenzo-p-dioxins. Atlanta, GA: U.S. 

Department of Health and Human Services, Public Health Service.

ATSDR. 1999. Toxicological profile for Mercury. Atlanta, GA: U.S. Department of Health and 

Human Services, Public Health Service.

ATSDR. 2008. Toxicological profile for Cadmium. Atlanta, GA: U.S. Department of Health and 

Human Services, Public Health Service.

Burger, J. 2002. Daily consumption of wild fish and game: Exposures of high end recreationists. 

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Tier 3 Methodology 79 October 2014
TRIM-Based Tiered Screening Methodology for RTR

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TRIM-Based Tiered Screening Methodology for RTR

TRIM.FaTE Inputs

Attachment A i October 2014

TRIM.FaTE Inputs
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TRIM-Based Tiered Screening Methodology for RTR

Exhibits, Attachment A

Exhibit A-1. TRIM.FaTE Simulation Parameters for the TRIM.FaTE Screening 

Scenario ........................................................................................................... A-1

Exhibit A-2. Meteorological Inputs for the TRIM.FaTE Screening Scenario............................ A-2

Exhibit A-3. Air Parameters for the TRIM.FaTE Screening Scenario ...................................... A-3

Exhibit A-4. Soil and Groundwater Parameters for the TRIM.FaTE Screening 

Scenario ........................................................................................................... A-4

Exhibit A-5. Runoff Assumptions for the TRIM.FaTE Screening Scenario.............................. A-6

Exhibit A-6. USLE Erosion Parameters for the TRIM.FaTE Screening Scenario .................... A-7

Exhibit A-7. Terrestrial Plant Placement for the TRIM.FaTE Screening Scenario.................... A-8

Exhibit A-8. Terrestrial Plant Parameters for the TRIM.FaTE Screening Scenario ................. A-9

Exhibit A-9. Surface Water Parameters for the TRIM.FaTE Screening Scenario.................. A-11

Exhibit A-10. Sediment Parameters for the TRIM.FaTE Screening Scenario ....................... A-12

Exhibit A-11. Aquatic Animals Food Chain, Density, and Mass for the 

TRIM.FaTE Screening Scenario ..................................................................... A-13

Exhibit A-12. Cadmium Chemical-Specific Parameters for the TRIM.FaTE 

Screening Scenario......................................................................................... A-14

Exhibit A-13. Mercury Chemical-Specific Parameters for the TRIM.FaTE 

Screening Scenario......................................................................................... A-15

Exhibit A-14. PAH Chemical-Specific Parameters for the TRIM.FaTE Screening 

Scenario ......................................................................................................... A-16

Exhibit A-15. Dioxin Chemical-Specific Parameters for the TRIM.FaTE 

Screening Scenario......................................................................................... A-18

Exhibit A--16. Cadmium Chemical-Specific Parameters for Abiotic 

Compartments in the TRIM.FaTE Screening Scenario................................... A-20

Exhibit A-17. Mercury Chemical-Specific Parameters for Abiotic Compartments 

in the TRIM.FaTE Screening Scenario............................................................ A-21

Exhibit A-18. PAH Chemical-Specific Parameters for Abiotic Compartments in 

the TRIM.FaTE Screening Scenario ............................................................... A-25

Exhibit A-19. Dioxin Chemical-Specific Parameters for Abiotic Compartments in 

the TRIM.FaTE Screening Scenario ............................................................... A-28

Exhibit A-20. Cadmium Chemical-Specific Parameters for Plant Compartments 

in the TRIM.FaTE Screening Scenario............................................................ A-32

Exhibit A-21. Mercury Chemical-Specific Parameters for Plant Compartments in 

the TRIM.FaTE Screening Scenario ............................................................... A-33

Exhibit A-22. PAH Chemical-Specific Parameters for Plant Compartments in the 

TRIM.FaTE Screening Scenario ..................................................................... A-34

Exhibit A-23. PAH Chemical-Specific Parameters for Plant Compartments in the 

TRIM.FaTE Screening Scenario ..................................................................... A-35

Attachment A iii October 2014

TRIM.FaTE Inputs
TRIM-Based Tiered Screening Methodology for RTR

Exhibit A-24. Cadmium Chemical-Specific Parameters for Aquatic Species in the 

TRIM.FaTE Screening Scenario ..................................................................... A-37

Exhibit A-25. Mercury Chemical-Specific Parameters for Aquatic Species in the 

TRIM.FaTE Screening Scenario ..................................................................... A-39

Exhibit A-26. PAH Chemical-Specific Parameters for Aquatic Species in the 

TRIM.FaTE Screening Scenario ..................................................................... A-40

Exhibit A-27. Dioxin Chemical-Specific Parameters for Aquatic Species in the 

TRIM.FaTE Screening Scenario ..................................................................... A-43

Attachment A iv October 2014

TRIM.FaTE Inputs
TRIM-Based Tiered Screening Methodology for RTR

This attachment provides tables of the modeling inputs for the TRIM.FaTE screening scenario. 

Exhibit A-1 presents runtime settings for TRIM.FaTE. Exhibit A-2 and Exhibit A-3 present 

meteorological and air parameters, respectively, entered into the model. Exhibit A-4. , 

Exhibit A-5, and Exhibit A-6 present the parameters for soil and groundwater, runoff 

assumptions, and the USLE (universal soil loss equation) erosion parameters, respectively, for 

the screening scenario. Exhibit A-7 and Exhibit A-8 present terrestrial parameters. Exhibit A-9

through Exhibit A-11 present lake parameters, and Exhibit A-12 through Exhibit A-27 present 

parameters specific to the chemicals modeled in the scenario.

Exhibit A-1. TRIM.FaTE Simulation Parameters 

for the TRIM.FaTE Screening Scenario

Parameter Name Units Value Used Reference

Start of simulation date/time 1/1/1990, midnight Consistent with met data.

End of simulation date/time 1/1/2040, midnight Consistent with met data set; selected to 

provide a 50-year modeling period.

Simulation time step hr 1 Selected value.

Output time stepa hr 4 Selected value.

a

Output time step is set in TRIM.FaTE using the scenario properties "simulationStepsPerOutputStep" and "simulationTimeStep."

.

Attachment A A-1 October 2014

TRIM.FaTE Inputs
TRIM-Based Tiered Screening Methodology for RTR

Exhibit A-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 Assumption; 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 --

Attachment A A-2 October 2014

TRIM.FaTE Inputs
TRIM-Based Tiered Screening Methodology for RTR

Exhibit A-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

Attachment A A-3 October 2014

TRIM.FaTE Inputs
TRIM-Based Tiered Screening Methodology for RTR

Exhibit A-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 Assumption ; area assumed rural.

Fraction of area available for runoff m2[area available]/m2[total] 1 Assumption ; area assumed rural.

Fraction of area available for vertical 

diffusion

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

Fraction sand unitless 0.25 Assumption.

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

and A-3).

pH unitless 6.8 Assumption.

Runoff fraction unitless variesb See Exhibit A-5.

Total erosion rate kg [soil]/m2/day variesb See Exhibit A-6.

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 Assumption.

Attachment A A-4 October 2014

TRIM.FaTE Inputs
TRIM-Based Tiered Screening Methodology for RTR

Exhibit A-4. Soil and Groundwater Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units Value Used Reference

Root Zone Soil Compartment Type, continued

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

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

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

average).

pH unitless 6.8 Assumption.

Water content volume[water]/volume[compartment] 0.15 McKone et al. 2001

Vadose Zone Soil Compartment Type

Air content volume[air]/volume[compartment] 0.22 McKone et al. 2001 (Table 17).

Average vertical velocity of water 

(percolation)

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

for New England in McKone et al. 2001.

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

Fraction sand unitless 0.35 Assumption.

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 Assumption.

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 Assumption.

Organic carbon fraction unitless 0.004 Assumption.

pH unitless 6.8 Assumption.

Porosity volume[total pore 

space]/volume[compartment]

0.2 Default in McKone et 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 (Exhibit A-5 and Exhibit A-6) for erosion/runoff fractions and total erosion rates.

Attachment A A-5 October 2014

TRIM.FaTE Inputs
TRIM-Based Tiered Screening Methodology for RTR

Exhibit A-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. 

Attachment A A-6 October 2014

TRIM.FaTE Inputs
TRIM-Based Tiered Screening Methodology for RTR

Exhibit A-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).

Attachment A A-7 October 2014

TRIM.FaTE Inputs
TRIM-Based Tiered Screening Methodology for RTR

Exhibit A-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

Attachment A A-8 October 2014

TRIM.FaTE Inputs
TRIM-Based Tiered Screening Methodology for RTR

Exhibit A-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 Assumption. 0 Assumption.

Correction exponent, octanol to 

lipid

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

Degree stomatal opening unitless 1 Assumed value of 1 for daytime 

(stomatal diffusion is turned off at 

night using a different property, 

IsDay).

1 Assumed value of 1 for daytime 

(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 Assumption. 0.05 Assumption.

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

Litter fall rate 1/day 0.0021 value assumes 1st-order 

relationship and that 99% of leaves 

fall over 6 years

Seasonalc -

Stomatal area normalized 

effective diffusion path length

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

Vegetation attenuation factor 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 -

Attachment A A-9 October 2014

TRIM.FaTE Inputs
TRIM-Based Tiered Screening Methodology for RTR

Exhibit A-8. Terrestrial Plant Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units

Coniferousa Grass/Herba

Value 

Used Reference Value Used Reference

Particle on Leaf Compartment Type, continued

Volume particle per area leaf 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.

Root Compartment Type – Nonwoody Only

Allow exchange 1=yes, 0=no Seasonalb -

Correction exponent, octanol to 

lipid

unitless 0.76 Trapp 1995.

Lipid content of root kg/kg wet weight 0.011 Calculated.

Water content of root kg/kg wet weight 0.8 Assumption.

Wet density of root kg/m3 820 Soybean, Paterson et al. 1991.

Wet mass per soil area kg/m2 1.4 Temperate grassland, Jackson et al. 

1996.

Stem Compartment Type – Nonwoody Only

Allow exchange 1=yes, 0=no Seasonalb -

Correction exponent, octanol to 

lipid

unitless 0.76 Trapp 1995.

Density of phloem fluid kg/m3 1,000 Assumption.

Density of xylem fluid kg/cm3 900 Assumption.

Flow rate of transpired water per 

leaf area

m3[water]/m2[leaf

]

0.0048 Crank et al. 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 Assumption.

Wet mass per soil area kg/m2 0.24 Calculated from leaf and root biomass 

density. a

See Exhibit A-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).

Attachment A A-10 October 2014

TRIM.FaTE Inputs
TRIM-Based Tiered Screening Methodology for RTR

Exhibit A-9. Surface Water Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units Value Used Reference

Algae carbon content 

(fraction)

unitless 0.465 APHA 1995.

Algae density in water 

column

g[algae]/L[water] 0.0025 Millard et al. 1996 as cited in ICF 

2005.

Algae growth rate 1/day 0.7 Hudson et al. 1994 as cited in 

Mason et al. 1995b.

Algae radius um 2.5 Mason et al. 1995b.

Algae water content (fraction) unitless 0.9 APHA 1995.

Average algae cell density 

(per volume cell, not water)

g[algae]/m3[algae] 1,000,000 Mason et al. 1995b, Mason et al. 

1996.

Boundary layer thickness 

above sediment

m 0.02 Cal EPA 1993.

Chloride concentration mg/L 8.0 Kaushal et al. 2005.

Chlorophyll concentration mg/L 0.0029 ICF 2005.

Deptha m 3.18 WI DNR 2007 - calculation based 

on relationship between drainage 

basin and lake area size.

Dimensionless viscous 

sublayer thickness

unitless 4 Ambrose et al. 1995.

Drag coefficient for water 

body

unitless 0.0011 Ambrose et al. 1995.

Flush rate 1/year 12.17 Calculated based on pond 

dimensions and flow calculations.

Fraction Sand unitless 0.25 Assumption.

Organic carbon fraction in 

suspended sediments

unitless 0.02 Assumption.

pH unitless 7.3 Assumption.

Suspended sediment 

deposition velocity 

m/day 2 USEPA 1997b.

Total suspended sediment 

concentration

kg[sediment]/m3[water 

column]

0.05 USEPA 2005.

Water temperature degrees K 298 USEPA 2005.

a

Set using the volume element properties named "top" and "bottom."

Attachment A A-11 October 2014

TRIM.FaTE Inputs
TRIM-Based Tiered Screening Methodology for RTR

Exhibit A-10. Sediment Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units Value Used Reference

Deptha m 0.05 McKone et al. 2001 (Table 

3).

Fraction sand unitless 0.25 Assumption.

Organic carbon fraction unitless 0.02 McKone et al. 2001 (Table 

3).

Porosity of the sediment 

zone

volume[total pore 

space]/volume[sediment 

compartment]

0.6 USEPA 2005.

Solid material density in 

sediment

kg[sediment]/m3[sediment] 2,600 McKone et al. 2001 (Table 

3).

pH unitless 7.3 Assumption.

Sediment resuspension 

velocity

m/day 6.69E-05 Calculated from water 

balance model.

a

Set using the volume element properties named "top" and "bottom."

Attachment A A-12 October 2014

TRIM.FaTE Inputs
TRIM-Based Tiered Screening Methodology for RTR

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

Aquatic Biota 

(Consuming 

Organism)

Fraction Diet

Biomass

(kg/m2) 

Body 

Weight 

(kg)

Reference

Algae

Zooplankto

n 

Benthic 

Invertebrate

Water 

Column 

Herbivore

Benthic 

Omnivore

Water 

Column 

Omnivore

Benthic 

Carnivore

Water 

Column 

Carnivore

Benthic 

Invertebrate

0% 0% 0% 0% 0% 0% 0% 0% 0.020 2.55E-04 Assumption.

Water Column 

Herbivore

0% 100% 0% 0% 0% 0% 0% 0% 0.002 0.025 Assumption.

Benthic Omnivore 0% 0% 100% 0% 0% 0% 0% 0% 0.002 2.50E-01 Assumption.

Water Column 

Omnivore

0% 0% 0% 100% 0% 0% 0% 0% 0.001 0.25 Assumption.

Benthic Carnivore 0% 0% 50% 0% 50% 0% 0% 0% 0.001 2.0 Assumption.

Water Column 

Carnivore

0% 0% 0% 0% 0% 100% 0% 0% 0.0002 2.0 Assumption.

Zooplankton 100% 0% 0% 0% 0% 0% 0% 0% 0.0064 5.70E-08 Assumption.

Attachment A A-13 October 2014

TRIM.FaTE Inputs
TRIM-Based Tiered Screening Methodology for RTR

Exhibit A-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.

Attachment A A-14 October 2014

TRIM.FaTE Inputs
TRIM-Based Tiered Screening Methodology for RTR

Exhibit A-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.

Attachment A A-15 October 2014

TRIM.FaTE Inputs
TRIM-Based Tiered Screening Methodology for RTR

Exhibit A-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

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 0.0076 1.96 9.81 0.03

Melting point degrees K 490 531 539 383.15 383.15 437

Molecular weight g/mol 252.32 228.29 278.33 202.26 166.20 276.34

Octanol-water 

partition coefficient 

(Kow)

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

6 

Attachment A A-16 October 2014

TRIM.FaTE Inputs
TRIM-Based Tiered Screening Methodology for RTR

Exhibit A-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)

Attachment A A-17 October 2014

TRIM.FaTE Inputs
TRIM-Based Tiered Screening Methodology for RTR

Exhibit A-15. Dioxin Chemical-Specific Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units

Value

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

OCDD

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

OCDF

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

HpCDD

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

HpCDF

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

HpCDF

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

HxCDD

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

HxCDF

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

Diffusion coefficient in 

pure air

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

Parameter Name Units

Value

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

HxCDD

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

HxCDF

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

HxCDD

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

HxCDF

1,2,3,7,8- 

PeCDD

1,2,3,7,8- 

PeCDF

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

HxCDF

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

Diffusion coefficient in 

pure air

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

Attachment A A-18 October 2014

TRIM.FaTE Inputs
TRIM-Based Tiered Screening Methodology for RTR

Exhibit A-15. Dioxin Chemical-Specific Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units

Value

Reference

2,3,4,7,8- 

PeCDF

2,3,7,8- 

TCDD

2,3,7,8- 

TCDF

CAS number unitless 57117-31-4 1746-01-6 51207-31-9 -

Diffusion coefficient in 

pure air

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

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

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

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

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

PeCDD)

Attachment A A-19 October 2014

TRIM.FaTE Inputs
TRIM-Based Tiered Screening Methodology for RTR

Exhibit A-16. Cadmium Chemical-Specific Parameters for Abiotic Compartments

in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference

Air Compartment Type

Particle dry deposition velocity m/day 260 Calculated from 

Muhlbaier and Tissue 

1980.

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

Surface Soil Compartment Type

Use input characteristic depth 

(Boolean)

0 = no, Else = yes 0 Assumption.

Root Zone Soil Compartment Type

Use input characteristic depth 

(Boolean)

0 = no, Else = yes 0 Assumption.

Vadose Zone Soil Compartment Type

Use input characteristic depth 

(Boolean)

0 = no, Else = yes 0 Assumption.

Surface Water Compartment Type

Ratio of concentration in water to 

concentration in algae to 

concentration dissolved in water

L[water]/g[algae 

wet wt]

1.87 McGeer et al. 2003.

Attachment A A-20 October 2014

TRIM.FaTE Inputs
TRIM-Based Tiered Screening Methodology for RTR

Exhibit A-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 Assumption.

Methylation rate 1/day 0 0 0 Assumption.

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 Assumption.

Washout ratio m3[air]/m3[rain] 200,000 200,000 200,000 Assumption.

Surface Soil Compartment Type

Use input characteristic depth 

(Boolean)

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

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

wet wt]

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

Vapor dry deposition velocity m/day 50 2500 0 Hg(0) - from Lindberg et al. 1992; Hg(2) - estimate 

by USEPA using the Industrial Source Complex 

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

Study Report (USEPA 1997b)].

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

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

demethylation rate constant under anaerobic 

conditions.

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

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

methylation rate constant under anaerobic 

conditions.

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

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

moisture content, in USEPA 1997b; general range 

is (0.0013/day)*moisture content to 

(0.0001/day)*moisture content for forested region 

(Lindberg 1996; Carpi and Lindberg 1997).

Attachment A A-21 October 2014

TRIM.FaTE Inputs
TRIM-Based Tiered Screening Methodology for RTR

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

Parameter Name Units

Value

Reference Hg(0) Hg(2) MHg

Root Zone Soil Compartment Type

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

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

wet wt]

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

Demethylation rate 1/day N/A N/A 0.06 Range reported in 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 Assumption.

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

wet wt]

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

Demethylation rate 1/day N/A N/A 0.06 Range reported in 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).

Attachment A A-22 October 2014

TRIM.FaTE Inputs
TRIM-Based Tiered Screening Methodology for RTR

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

Parameter Name Units

Value

Reference Hg(0) Hg(2) MHg

Groundwater Compartment Type

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

wet wt]

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

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

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

demethylation rate constant under anaerobic 

conditions.

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

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

methylation rate constant under anaerobic 

conditions.

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

1997b).

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

moisture content, in USEPA 1997b (Lindberg 

1996; Carpi and Lindberg 1997).

Surface Water Compartment Type

Algal surface area-specific uptake rate 

constant

nmol/[μm2-day-nmol] 0 2.04E-10 3.60E-10 Assumes radius = 2.5mm, Mason et al. 1995b, 

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

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

Solids-water partition coefficient L[water]/kg[solids 

wet wt]

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

Vapor dry deposition velocity m/day N/A 2500 USEPA 1997b (Vol. III, App. A).

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

and Henry 1991.

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

Oxidation rate 1/day 0 0 0 Assumption.

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).

Attachment A A-23 October 2014

TRIM.FaTE Inputs
TRIM-Based Tiered Screening Methodology for RTR

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

Parameter Name Units

Value

Reference Hg(0) Hg(2) MHg

Sediment Compartment Type

Solids-water partition coefficient L[water]/kg[solids 

wet wt]

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

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

and Henry 1991.

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

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

Oxidation rate 1/day 0 0 0 Assumption.

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.

Attachment A A-24 October 2014

TRIM.FaTE Inputs
TRIM-Based Tiered Screening Methodology for RTR

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

Parameter Name Units

Value

2Methyl 712DMB Acenaphthene

Acenaphthylene BaA BaP BbF BghiP BkF

Air Compartment Type

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

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

Washout ratio 200000 200000 200000 200000 200000 200000 200000 200000 200000

Surface Soil Compartment Type

User input characteristic depth 

(Boolean)

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

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

Root Zone Soil Compartment Type

User input characteristic depth 

(Boolean)

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

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

Vadose Zone Soil Compartment Type

User input characteristic depth 

(Boolean)

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

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

Groundwater Compartment Type

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

Surface Water Compartment Type

Ratio Of Conc In Algae To

Conc Dissolved In Water

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

(g[chem]/L[water])

2.6 333.4 3 3.7 325 510 317 1539 473

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

Sediment Compartment Type

Half-life day 2290 2290 2290 2290 2290 2290 2290 2290 2290

Attachment A A-25 October 2014

TRIM.FaTE Inputs
TRIM-Based Tiered Screening Methodology for RTR

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

Parameter Name Units

Value

Reference

Chr DahA Fluoranthene

Fluorene

IcdP

Air Compartment Type

Particle dry deposition velocity m/day 500 500 500 500 500 McKone et al. 2001.

Half-life day 0.334 0.178 0.46 0.46 0.262 Howard et al. 1991 / upper bound measured or 

estimated value. Exceptions include ATSDR 2005 (2-

Methylnaphthalene), USEPA 1998 (7,12-

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

and Fluoranthene) / average of range, HSDB 2001d 

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

Spero et al. 2000 (Fluorene).

Washout ratio 200000 200000 200000 200000 200000 Mackay et al. 1986.

Surface Soil Compartment Type

User input characteristic depth 

(Boolean)

0 = No, 

Else = Yes

0 0 0 0 0 Assumption.

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

include ATSDR 2005 (2-Methylnaphthalene), USEPA 

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

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

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

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

Root Zone Soil Compartment Type

User input characteristic depth 

(Boolean)

0 = No, 

Else = Yes

0 0 0 0 0 Assumption.

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 Assumption.

Attachment A A-26 October 2014

TRIM.FaTE Inputs
TRIM-Based Tiered Screening Methodology for RTR

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

Parameter Name Units

Value

Reference Chr DahA Fluora

n-thene

Fluorene

IcdP

Vadose Zone Soil Compartment Type, continued

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

estimated value. Exceptions include ATSDR 2005 (2-

Methylnaphthalene), USEPA 1998 (7,12-

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

Fluoranthene) / twice average of range, HSDB 2001d 

(Acenaphthene) / multiplied by 2, HSDB 2001b 

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

(Fluorene) / multiplied by 2.

Groundwater Compartment Type

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

estimated value. Exceptions include ATSDR 2005 (2-

Methylnaphthalene), USEPA 1998 (7,12-

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

Fluoranthene) / twice average of range, HSDB 2001d 

(Acenaphthene) / multiplied by 2, HSDB 2001b 

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

(Fluorene) / multiplied by 2.

Surface Water Compartment Type

RatioOfConcInAlgaeToConcD

issolvedInWater

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

(g[chem]/L[water])

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

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

estimated value. Exceptions include HSDB 2005 (2-

Methylnaphthalene), HSDB 2001a (7-12 

Dimethylbenz(a)anthracene), HSDB 2001d 

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

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

(Fluoranthene), and Boyle 1985 (Fluorene).

Sediment Compartment Type

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

of the log class that Mackay et al. assigned for 

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

Table 2.3 of Mackay et. al.

Attachment A A-27 October 2014

TRIM.FaTE Inputs
TRIM-Based Tiered Screening Methodology for RTR

Exhibit A-19. Dioxin Chemical-Specific Parameters for Abiotic

Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units

Value

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

-OCDD

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

-OCDF

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

HpCDD

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

HpCDF

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

HpCDF

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

HxCDD

Air Compartment Type

Deposition Velocity m/day 500 500 500 500 500 500

Half-life day 162 321 64 137 122 42

Washout Ratio m3[air]/m3[rain] 91000 22000 64000 32000 32000 9000

Surface Soil Compartment Type

Input characteristic depth m 0.08 0.08 0.08 0.08 0.08 0.08

Use input characteristic 

depth (Boolean)

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

Halflife day 3650 3650 3650 3650 3650 3650

Root Zone Soil Compartment Type

Input characteristic depth m 0.08 0.08 0.08 0.08 0.08 0.08

Use input characteristic 

depth

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

Half-life day 3650 3650 3650 3650 3650 3650

Vadose Zone Soil Compartment Type

Input characteristic depth m 0.08 0.08 0.08 0.08 0.08 0.08

Use input characteristic 

depth (Boolean)

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

Half-life day 1008 1008 1008 1008 1008 1008

Groundwater Compartment Type

Half-life day 1008 1008 1008 1008 1008 1008

Surface Water Compartment Type

Ratio Of Conc In Algae To 

Conc Dissolved In Water

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

(g[chem]/L[water])

5.31 4.54 4.54 2.83 1.9 3.88

Half-life day 0.67 0.58 47 0.58 0.58 6.3

Sediment Compartment Type

Half-life day 1095 1095 1095 1095 1095 1095

Attachment A A-28 October 2014

TRIM.FaTE Inputs
TRIM-Based Tiered Screening Methodology for RTR

Exhibit A-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

Attachment A A-29 October 2014

TRIM.FaTE Inputs
TRIM-Based Tiered Screening Methodology for RTR

Exhibit A-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

Attachment A A-30 October 2014

TRIM.FaTE Inputs
TRIM-Based Tiered Screening Methodology for RTR

Exhibit A-19. Dioxin Chemical-Specific Parameters for Abiotic

Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Reference

Air Compartment Type

Deposition velocity McKone et al. 2001.

Half-life Atkinson 1996 as cited in USEPA 2000b; vapor phase 

reaction with hydroxyl radical.

Washout ratio Vulykh et al. 2001.

Surface Soil Compartment Type

Input characteristic depth Not used (model set to calculate value).

Use input characteristic depth (Boolean) Assumption.

Half-life Mackay et al. 2000; the degradation rate was cited by 

multiple authors, value is for 2,3,7,8-TCDD.

Root Zone Soil Compartment Type

Input characteristic depth Not used (model set to calculate value).

Use input characteristic depth Assumption.

Half-life Mackay et al. 2000; the degradation rate was cited by 

multiple authors, value is for 2,3,7,8-TCDD.

Vadose Zone Soil Compartment Type

Input characteristic depth Not used (model set to calculate value).

Use input characteristic depth (Boolean) Assumption.

Half-life Average value of the range presented in Mackay et al. 2000; 

based on estimated unacclimated aerobic biodegradation 

half-life, value is for 2,3,7,8-TCDD.

Groundwater Compartment Type

Half-life Average value of the range presented in Mackay et al. 2000; 

based on estimated unacclimated aerobic biodegradation 

half-life, value is for 2,3,7,8-TCDD.

Surface Water Compartment Type

Ratio Of Conc In Algae To Conc Dissolved 

In Water

Estimated from Kow value using model from DelVento and 

Dachs 2002

Half-life Kim and O’Keefe 1998, as cited in USEPA 2000b. 

Sediment Compartment Type

Half-life Estimation based on Adriaens and Grbic-Galic 1992,1993 

and Adriaens et al. 1995 as cited in USEPA 2000b.

Attachment A A-31 October 2014

TRIM.FaTE Inputs
TRIM-Based Tiered Screening Methodology for RTR

Exhibit A-20. Cadmium Chemical-Specific Parameters for

Plant Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference

Leaf Compartment Type

Transfer factor to leaf particle 1/day 0.002 Assumption.

Particle on Leaf Compartment Type

Transfer factor to leaf 1/day 0.200 Assumption.

Root Compartment Type - Grasses and Herbsa

Root to Root Soil PartitionAlpha of Steady Stateunitless 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 Alphaday 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.

Attachment A A-32 October 2014

TRIM.FaTE Inputs
TRIM-Based Tiered Screening Methodology for RTR

Exhibit A-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 Assumed based on 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 Assumed to be nearly instantaneous

Reduction rate 1/day 0 0 0 Assumption.

Particle on Leaf Compartment Type

Transfer factor to leaf 1/day 0.2 0.2 0.2 Assumption.

Demethylation rate 1/day N/A N/A 0 Assumption.

Methylation rate 1/day 0 0 0 Assumption.

Oxidation rate 1/day 0 0 0 Assumption.

Reduction rate 1/day 0 0 0 Assumption.

Root Compartment Type - Grasses and 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; MHgassumed, based on Hogg et al. 1978.

t-alpha for root-root zone bulk soil day 21 21 21 Assumption.

Demethylation rate 1/day N/A N/A 0 Assumption.

Methylation rate 1/day 0 0 0 Assumption.

Oxidation rate 1/day 0 0 0 Assumption.

Reduction rate 1/day 0 0 0 Assumption.

Stem Compartment Type - Grasses and 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 Assumption.

Oxidation rate 1/day 0 0 0 Assumption.

Reduction rate 1/day 0 0 0 Assumption. a

Roots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE.

Attachment A A-33 October 2014

TRIM.FaTE Inputs
TRIM-Based Tiered Screening Methodology for RTR

Exhibit A-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.

Attachment A A-34 October 2014

TRIM.FaTE Inputs
TRIM-Based Tiered Screening Methodology for RTR

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

Parameter Name Units

Value

Reference

Chr DahA Fluoranthene Fluorene IcdP

Leaf Compartment Type

Transfer factor to 

leaf particle

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

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 Assumption.

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 Assumption.

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.

Attachment A A-35 October 2014

TRIM.FaTE Inputs
TRIM-Based Tiered Screening Methodology for RTR

Exhibit A-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 Assumption 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 Assumption.

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.

Attachment A A-36 October 2014

TRIM.FaTE Inputs
TRIM-Based Tiered Screening Methodology for RTR

Exhibit A-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 Assumption.

Sediment partitioning - partition 

coefficient

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

Sediment partitioning - time to reach 

alpha of equilibrium

day 21 Hare et al. 2001.

Benthic Omnivore Compartment Type

Assimilation efficiency from food unitless 0.1 Assumption based on Yan 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 Assumption.

Benthic Carnivore Compartment Type

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

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

in Hendriks and Heikens 2001.

Elimination rate constant unitless 1.68E-03 Assumption.

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 Assumption.

Attachment A A-37 October 2014

TRIM.FaTE Inputs
TRIM-Based Tiered Screening Methodology for RTR

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

Parameter Name Units Value Reference

Water-column Omnivore Compartment Type

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

Assimilation efficiency from plants unitless 0.1 Assumption 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 Assumption.

Water-column Carnivore Compartment Type

Assimilation efficiency from food unitless 0.1 Assumption 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 Assumption

Attachment A A-38 October 2014

TRIM.FaTE Inputs
TRIM-Based Tiered Screening Methodology for RTR

Exhibit A-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 Assumption.

How Much Faster Hg Elimination Is Than

For MHg

unitless 3 3 1 Assumption.

Methylation rate 1/day 0 0 0 Assumption.

Oxidation rate 1/day 0 1.0E+06 0 Assumption.

Reduction rate 1/day 0 0 0 Assumption.

Benthic Invertebrate Compartment Type

Alpha of equilibrium for sediment 

partitioning

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

equilibrium achieved by time “t”).

Benthic invertebrate-bulk sediment partition 

coefficient

kg[bulk 

sediment]/kg[invertebrate 

wet wt]

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

value; Hg(2) and MHg - Saouter 

et al. 1991.

t-alpha for equilibrium for sediment 

partitioning

day 14 14 14 Experiment duration from 

Saouter et al. 1991.

All Fish Compartments Typesa

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

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

Demethylation rate 1/day N/A N/A 0 Assumption.

Methylation rate 1/day 0 0 0 Assumption.

Oxidation rate 1/day 1.0E+06 0 0 Assumption.

Reduction rate 1/day 0 0 0 Assumption.

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.

Attachment A A-39 October 2014

TRIM.FaTE Inputs
TRIM-Based Tiered Screening Methodology for RTR

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

Parameter 

Name Units

Value

2Methyl 712DMB Acenaphthene

Acenaphthylene BaA BaP BbF BghiP BkF

Zooplankton Compartment Type

Absorption rate 

constant

L[water]/kg[fish 

wet wt]-day

790 42650.94 42231 42302.18 42650.81 42652.78 42650.68 42655.77 42652.5

Assimilation 

efficiency from 

algae

unitless 0.5 0.25 0.5 0.5 0.46 0.25 0.25 0.25 0.25

Elimination 

rate constant

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

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

Benthic Invertebrate Compartment Type

Clearance 

constant

unitless 100.6 100.6 100.6 100.6 100.6 100.6 100.6 100.6 100.6

Vd (ratio of 

concentration

in benthic 

invertebrates 

to

concentration 

in water)

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

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

All Fish Compartment Typesa

Gamma fish unitless 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2

Assimilation 

efficiency from 

food

unitless 0.5 0.15 0.5 0.32 0.15 0.15 0.15 0.15 0.15

Half-life day 0.2 2 0.2 0.2 0.408 1.925 2 2 2

a

Screening scenario includes: Benthic Omnivore, Benthic Carnivore, Water-column Herbivore, Water-column Omnivore, and Water-column Carnivore.

Attachment A A-40 October 2014

TRIM.FaTE Inputs
TRIM-Based Tiered Screening Methodology for RTR

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

Parameter Name Units

Value

Reference

Chr DahA Fluoranthene Fluorene IcdP

Zooplankton Compartment Type

Absorption rate 

constant

L[water]/kg[fish 

wet wt]-day

42649.95 42655.48 142000 15000 42655.93 Kow from Arnot et al. 2004. 

Exception is Berrojalbiz et al. 2009 

(2-Methylnaphthalene, 

Fluoranthene, and Fluorene).

Assimilation 

efficiency from algae

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

Exception is maximum value from 

Wang and Wang 2006 (7,12-

Dimethylbenz(a)anthracene, 

Benzo(a)pyrene, 

Benzo(b)fluoranthene, 

Benzo(g,h,i)perylene, 

Benzo(k)fluoranthene, 

Dibenz(a,h)anthracene, and 

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

Elimination rate 

constant

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

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

Berrojalbiz et al. 2009 (2-

Methylnaphthalene, Fluoranthene, 

and Fluorene) and Moermond et al. 

2007 (Benz(a)anthracene and 

Benzo(a)pyrene).

Benthic Invertebrate Compartment Type

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

Vd (ratio of 

concentration

in benthic 

invertebrates to

concentration in 

water)

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

Half-life day 0.495 17 0.722 0.722 17 Moermond et al. 2007.

Attachment A A-41 October 2014

TRIM.FaTE Inputs
TRIM-Based Tiered Screening Methodology for RTR

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

Parameter Name Units

Value

Reference Chr DahA Fluoranthene Fluorene IcdP

All Fish Compartment Typesa

Gamma fish unitless 0.2 0.2 0.2 0.2 0.2 Thomann 1989.

Assimilation 

efficiency from food

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

include Barber 2008 (2-

Methylnaphthalene and 

Acenaphthene) and Niimi and 

Palazzo 1986 (Acenaphthylene, 

Fluoranthene, and Fluorene).

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

a

Screening scenario includes: Benthic Omnivore, Benthic Carnivore, Water-column Herbivore, Water-column Omnivore, and Water-column Carnivore.

Attachment A A-42 October 2014

TRIM.FaTE Inputs
TRIM-Based Tiered Screening Methodology for RTR

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

Parameter Name Units

Value

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

OCDD

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

OCDF

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

HpCDD

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

HpCDF

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

HpCDF

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

HxCDD

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

HxCDF

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

HxCDD

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

HxCDF

Zooplankton Compartment

Absorption rate constant L[water]/kg[fish 

wet wt]-day

8640 8640 8640 8640 8640 8640 8640 8640 8640

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

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

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

Benthic Invertebrate Compartment

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

Sediment partitioning partition 

coefficient

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

Sediment partitioning alpha of 

equilibrium

unitless 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95

Sediment partitioning time to

reach alpha of equilibrium

days 120 42 120 42 42 120 42 120 42

Vd (ratio of concentration

in benthic invertebrates to

concentration in water)

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

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

All Fish Compartmentsa

Assimilation efficiency from food unitless 0.08 0.05 0.21b 0.09 0.2 0.31c 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

Attachment A A-43 October 2014

TRIM.FaTE Inputs
TRIM-Based Tiered Screening Methodology for RTR

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

Parameter Name Units

Value

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

HxCDD

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

HxCDF 

1,2,3,7,8-

PeCDD

1,2,3,7,8-

PeCDF

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

HxCDF

2,3,4,7,8-

PeCDF

2,3,7,8-TCDD

2,3,7,8-TCDF

Zooplankton Compartment

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

wt]-day

8640 8640 8640 8640 8640 8640 8640 8640

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

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

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

Benthic Invertebrate Compartment

Clearance Constant unitless 0 0 0 0 0 0 0 0

Sediment Partitioning Partition 

Coefficient

kg/kg 0.015 0.067 0.098 0.024 0.072 0.17 0.205 0.056

Sediment Partitioning Alpha of 

Equilibrium

unitless 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95

Sediment Partitioning Time to

Reach Alpha of Equilibrium

days 120 42 120 42 42 42 120 42

Vd (ratio of concentration

in benthic invertebrates to

concentration in water)

ml/g 0 0 0 0 0 0 0 0

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

All Fish Compartmentsa

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

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

wt]-day

300 200 700 300 200 400 600 400

Half-life day 70 70 70 70 70 70 70 70

Attachment A A-44 October 2014

TRIM.FaTE Inputs
TRIM-Based Tiered Screening Methodology for RTR

Exhibit A-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 Assumption.

Sediment partitioning partition coefficient kg/kg Rubinstein et al. 1990; used TCDD data for sandworm.

Sediment partitioning alpha of equilibrium unitless Rubinstein et al. 1990.

Sediment Partitioning Time to Reach 

Alpha of Equilibrium

days Rubinstein et al. 1990.

Vd (ratio of concentration in benthic 

invertebrates to concentration in water)

ml/g Assumption.

Half-life day Rubinstein et al. 1990; used TCDD data for sandworm.

All Fish Compartmentsa

Assimilation Efficiency from Food unitless Morrison et al. 1999. Exceptions include Niimi and Oliver 1996 (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. b

0.21 used for benthic omnivore, water column omnivore, and water column herbivore; 0.13 used for benthic carnivore and water column carnivore.

c

0.31 used for benthic omnivore, water column omnivore, benthic carnivore, and water column carnivore; 0.37 used for water column herbivore.

Attachment A A-45 October 2014

TRIM.FaTE Inputs
TRIM-Based Tiered Screening Methodology for RTR

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TRIM.FaTE Inputs
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Attachment A A-53 October 2014

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TRIM-Based Tiered Screening Methodology for RTR

Description of Multimedia Ingestion Risk Calculator 

(MIRC) Used for RTR Exposure and Risk Estimates

Attachment B i October 2014

Description of MIRC
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TRIM-Based Tiered Screening Methodology for RTR

CONTENTS, ATTACHMENT B 

1. Introduction ..................................................................................................................... B-1

1.1. Purpose and Overview .......................................................................................... B-1

1.2. Scope of MIRC ...................................................................................................... B-1

1.3. Use in EPA’s Air Toxics Program........................................................................... B-2

1.4. MIRC Highlights..................................................................................................... B-2

1.5. Organization of This Attachment............................................................................ B-3

2. MIRC Overview............................................................................................................... B-3

2.1. Software ................................................................................................................ B-4

2.2. Exposure Pathways ............................................................................................... B-6

2.3. Receptor Groups ................................................................................................... B-7

3. Exposure Algorithms....................................................................................................... B-9

3.1. Farm Food Chain Algorithms ................................................................................. B-9

3.1.1. Estimating Chemical Concentrations in Produce ................................... B-10

3.1.2. Estimating Chemical Concentrations in Animal Products....................... B-16

3.2. Chemical Intake Calculations for Adults and Non-Infant Children ........................ B-19

3.2.1. Chemical Intake from Soil Ingestion....................................................... B-21

3.2.2. Chemical Intake from Fish Ingestion...................................................... B-21

3.2.3. Chemical Intake from Fruit Ingestion ..................................................... B-23

3.2.4. Chemical Intake from Vegetable Ingestion............................................. B-23

3.2.5. Chemical Intake from Animal Product Ingestion .................................... B-25

3.2.6. Chemical Intake from Drinking Water Ingestion ..................................... B-27

3.3. Total Chemical Intake .......................................................................................... B-27

3.4. Chemical Intake Calculations for Nursing Infants................................................. B-28

3.4.1. Infant Average Daily Absorbed Dose..................................................... B-29

3.4.2. Chemical Concentration in Breast Milk Fat ............................................ B-30

3.4.3. Chemical Concentration in Aqueous Phase of Breast Milk .................... B-33

3.4.4. Alternative Model for Infant Intake of Methyl Mercury ............................ B-35

4. Dose-Response Values Used for Assessment.............................................................. B-36

4.1. Cadmium ............................................................................................................. B-39

4.2. Dioxins (2,3,7,8-TCDD) ....................................................................................... B-39

4.3. Mercury ............................................................................................................... B-40

4.4. Polycyclic Organic Matter .................................................................................... B-40

5. Risk Estimation ............................................................................................................. B-42

5.1. Cancer Risks ....................................................................................................... B-42

5.2. Non-cancer Hazard Quotients ............................................................................. B-44

5.2.1. Hazard Quotients for Chemicals with a Chronic RfD.............................. B-45

Attachment B iii October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

5.2.2. Hazard Quotients for Chemicals with RfD Based on 

Developmental Effects .......................................................................... B-45

5.2.3. Hazard Index for Chemicals with RfDs .................................................. B-45

6. Model Input Options...................................................................................................... B-46

6.1. Environmental Concentrations ............................................................................. B-47

6.2. Farm-Food-Chain Parameter Values ................................................................... B-47

6.2.1. List of Farm-Food-Chain (FFC) Parameters .......................................... B-47

6.2.2. Produce Parameter Values.................................................................... B-49

6.2.3. Animal Product Parameter Values......................................................... B-62

6.3. Adult and Non-Infant Exposure Parameter Values............................................... B-64

6.3.1. Body Weights ........................................................................................ B-65

6.3.2. Water Ingestion Rates ........................................................................... B-66

6.3.3. Local Food Ingestion Rates ................................................................... B-66

6.3.4. Local Fish Ingestion Rates .................................................................... B-70

6.3.5. Soil Ingestion Rates............................................................................... B-77

6.3.6. Total Food Ingestion Rates.................................................................... B-77

6.4. Other Exposure Factor Values............................................................................. B-78

6.4.1. Exposure Frequency ............................................................................. B-78

6.4.2. Fraction Contaminated .......................................................................... B-79

6.4.3. Preparation and Cooking Losses........................................................... B-79

6.4.4. Food Preparation/Cooking Adjustment Factor (FPCAF) for 

Fish B-81

6.5. Breast-Milk Infant Exposure Pathway Parameter Values ..................................... B-82

6.5.1. Receptor-specific Parameters................................................................ B-82

6.5.2. Chemical-Specific Parameter Values..................................................... B-86

7. Summary of MIRC Default Exposure Parameter Settings ............................................. B-89

7.1. Default Ingestion Rates ....................................................................................... B-89

7.1.1. Fish Ingestion Rates.............................................................................. B-90

7.1.2. Farm Food Chain Ingestion ................................................................... B-90

7.2. Default Screening-Level Population-Specific Parameter Values .......................... B-92

7.3. Default Chemical-Specific Parameter Values for Screening Analysis .................. B-93

7.4. Screening-Level Parameter Values for Nursing Infant Exposure.......................... B-94

7.4.1. Dioxins B-94

7.4.2. Methyl Mercury...................................................................................... B-95

8. References ................................................................................................................... B-96

Attachment B iv October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

Exhibits, Attachment B

Exhibit B-1. Overview of MIRC Software Application for Performing Farm-FoodChain Ingestion Exposure and Risk Calculations .............................................. B-5

Exhibit B-2. Transfer Pathways for Modeled Farm Food Chain (FFC) Media ......................... B-7

Exhibit B-3. Chemical Transfer Pathways for Produce ......................................................... B-10

Exhibit B-4. Estimating Chemical Concentration in Aboveground Produce............................ B-10

Exhibit B-5. Chemical Transfer Pathways for Animal Products.............................................. B-16

Exhibit B-6. Oral Dose-response Values Used to Calculate RTR Screening 

Threshold Emission Rates for PB-HAP Chemicalsa ........................................ B-37

Exhibit B-7. WHO 2005 Toxic Equivalency Factors (TEFs) for Dioxin Congeners ................ B-40

Exhibit B-8. Oral Dose-response Values for Polycyclic Organic Matter (POM) 

Groupsa .......................................................................................................... B-41

Exhibit B-9. MIRC Parameters Used to Estimate Chemical Concentrations in 

Farm Foods .................................................................................................... B-48

Exhibit B-10. Chemical-Specific Inputs for Produce Parameters for Chemicals 

Included in MIRC ............................................................................................ B-49

Exhibit B-11. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC...................... B-51

Exhibit B-12. Non-Chemical-Specific Produce Inputs........................................................... B-61

Exhibit B-13. Animal Product Chemical-specific Inputs for Chemicals Included in 

MIRC .............................................................................................................. B-62

Exhibit B-14. Soil and Plant Ingestion Rates for Animals...................................................... B-64

Exhibit B-15. Mean and Percentile Body Weight Estimates for Adults and 

Children .......................................................................................................... B-65

Exhibit B-16. Estimated Daily Per Capita Mean and Percentile Water Ingestion 

Rates for Children and Adultsa ........................................................................ B-66

Exhibit B-17. Summary of Age-Group Specific Food Ingestion Rates for Farm 

Food Items...................................................................................................... B-67

Exhibit B-18. Fish Ingestion Rates Used in Screening Assessment ..................................... B-72

Exhibit B-19. Daily Mean and Percentile Consumer-Only Fish Ingestion Rates 

for Children and Adults (IRCO,y)a ...................................................................... B-74

Exhibit B-20. Fraction of Population Consuming Freshwater/Estuarine Fish on a 

Single Day (FPC,y)............................................................................................ B-75

Exhibit B-21. Calculated Long-term Mean and Percentile per capita Fish 

Ingestion Rates for Children and Adults (IRPC,y) .............................................. B-75

Exhibit B-22. Calculated Mean and 90th Percentile Per capita Fish Ingestion 

Rates for Populations of Recreational Fishers (IRPC,y) .................................... B-76

Exhibit B-23. Daily Mean and Percentile Soil Ingestion Rates for Children and 

Adults ............................................................................................................. B-77

Exhibit B-24. Daily Mean and Percentile Per Capita Total Food Intake for 

Children and Adults......................................................................................... B-78

Attachment B v October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

Exhibit B-25. Fraction Weight Losses from Preparation of Various Foods............................ B-80

Exhibit B-26. Scenario- and Receptor-Specific Input Parameter Values Used to 

Estimate Infant Exposures via Breast Milk ...................................................... B-83

Exhibit B-27. Average Body Weight for Infants..................................................................... B-83

Exhibit B-28. Time-weighted Average Body Weight for Mothers .......................................... B-84

Exhibit B-29. Infant Breast Milk Intake Rates ....................................................................... B-86

Exhibit B-30. Chemical-specific Input Parameter Values for Breast Milk 

Exposure Pathway.......................................................................................... B-87

Exhibit B-31. Farm Food Category Ingestion Rates for Health Protective 

Screening Scenario for Farming Households .................................................. B-91

Exhibit B-32. Mean Body Weight Estimates for Adults and Childrena ................................... B-92

Exhibit B-33. Chemical-Specific Parameter Values for Input to MIRCa ................................. B-93

Exhibit B-34. Chemical and Animal-Type Specific Biotransfer Factor (Ba) Values 

for Input to MIRC ............................................................................................ B-94

Attachment B vi October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

Equations, Attachment B

Equation B-1. Chemical Concentration in Aboveground Produce.......................................... B-11

Equation B-2. Chemical Concentration in Aboveground Produce Due to Root 

Uptake ............................................................................................................ B-11

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

Deposition of Particle-phase Chemical............................................................ B-12

Equation B-4. Chemical Concentration in Aboveground Produce Due to Air-toPlant Transfer of Vapor-phase Chemical ........................................................ B-12

Equation B-5. Conversion of Aboveground Produce Chemical Concentration from 

Dry- to Wet-Weight Basis................................................................................ B-13

Equation B-6. Chemical Concentration in Belowground Produce: Nonionic 

Organic Chemicals.......................................................................................... B-14

Equation B-7. Chemical Concentration in Belowground Produce: Inorganic 

Chemicals....................................................................................................... B-15

Equation B-8. Conversion of Belowground Produce Chemical Concentration from 

Dry- to Wet-Weight Basis................................................................................ B-15

Equation B-9. Chemical Concentration in Beef, Pork, or Total Dairy ..................................... B-16

Equation B-10. Chemical Concentration in Poultry or Eggs................................................... B-17

Equation B-11. Incidental Ingestion of Chemical in Soil by Livestock .................................... B-17

Equation B-12. Ingestion of Chemical in Feed by Livestock .................................................. B-18

Equation B-13. Chemical Concentration in Livestock Feed (All Aboveground)...................... B-18

Equation B-14. Chemical Concentration in Livestock Feed Due to Root Uptake .................. B-19

Equation B-15. Average Daily Dose for Specified Age Group and Food Type...................... B-19

Equation B-16. Chemical Intake from Soil Ingestion............................................................. B-21

Equation B-17. Chemical Intake from Fish Ingestion............................................................ B-22

Equation B-18. Consumption-weighted Chemical Concentration in Fish .............................. B-22

Equation B-19. Chemical Intake from Consumption of Exposed Fruits................................. B-23

Equation B-20. Chemical Intake from Consumption of Protected Fruits ............................... B-23

Equation B-21. Chemical Intake from Exposed Vegetables.................................................. B-24

Equation B-22. Chemical Intake from Protected Vegetables ................................................ B-24

Equation B-23. Chemical Intake from Root Vegetables........................................................ B-24

Equation B-24. Chemical Intake from Ingestion of Beef ....................................................... B-25

Equation B-25. Chemical Intake from Dairy Ingestion .......................................................... B-25

Equation B-26. Chemical Intake from Pork Ingestion ........................................................... B-25

Equation B-27. Chemical Intake from Poultry Ingestion........................................................ B-26

Equation B-28. Chemical Intake from Egg Ingestion ............................................................ B-26

Equation B-29. Chemical Intake from Drinking Water Ingestion ........................................... B-27

Attachment B vii October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

Equation B-30. ...................................................................................................................... B-27

Equation B-31. ...................................................................................................................... B-27

Equation B-32. ...................................................................................................................... B-27

Equation B-33. ...................................................................................................................... B-28

Equation B-34. ...................................................................................................................... B-28

Equation B-35. ...................................................................................................................... B-28

Equation B-36. Lifetime Average Daily Dose (LADD)........................................................... B-28

Equation B-37. Average Daily Dose of Chemical to the Nursing Infant................................. B-29

Equation B-38. Chemical Concentration in Breast Milk Fat .................................................. B-30

Equation B-39. Daily Maternal Absorbed Intake................................................................... B-31

Equation B-40. Biological Elimination Rate Constant for Chemicals for Nonlactating Women ............................................................................................. B-32

Equation B-41. Biological Elimination Constant for Lipophilic Chemicals for 

Lactating Women............................................................................................ B-32

Equation B-42. Chemical Concentration in Aqueous Phase of Breast Milk........................... B-33

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

Compartment.................................................................................................. B-34

Equation B-44. Biological Elimination Rate Constant for Hydrophilic Chemicals .................. B-35

Equation B-45. Calculation of Infant Average Daily Absorbed Dose of Methyl 

Mercury........................................................................................................... B-36

Equation B-46. Calculation of Excess Lifetime Cancer Risk ................................................. B-43

Equation B-47. ...................................................................................................................... B-44

Equation B-48. ...................................................................................................................... B-44

Equation B-49. ...................................................................................................................... B-44

Equation B-50. ...................................................................................................................... B-44

Equation B-51. ...................................................................................................................... B-44

Equation B-52. ...................................................................................................................... B-44

Equation B-53. ...................................................................................................................... B-44

Equation B-54. Hazard Quotient for Chemicals with a Chronic RfD...................................... B-45

Equation B-55. Hazard Index Calculation............................................................................. B-46

Equation B-56. Calculation of Age-Group-Specific and Food-Specific Ingestion 

Rates .............................................................................................................. B-70

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

Rates .............................................................................................................. B-73

Attachment B viii October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

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.27 MIRC 

was designed to estimate risks to humans from ingestion of produce or animal products, fish, 

and water in the vicinity of a source of chemical emissions to air. The user can evaluate either 

generalized (e.g., health protective default) or more site-specific scenarios using the same tool. 

MIRC includes a database of exposure parameter values, offering the user the option of 

selecting mean, median, and upper percentile values for many parameters, data permitting. 

Generally health protective default values were assigned to each parameter in the tool and the 

default configuration is used for initial risk screening efforts by EPA’s Office of Air Quality 

Planning and Standards’ (OAQPS) for Risk and Technology Review (RTR) multimedia risk 

assessments. MIRC also allows the user to define the farm food chain (FFC) parameter values 

and receptor characteristics to better represent a site-specific scenario.

With user-input concentrations for one or more chemicals in air and soil and air-to-surface 

deposition rates, MIRC calculates the chemical’s concentrations in home- or farm-grown 

produce and animal food products using FFC algorithms adapted from EPA’s Human Health 

Risk Assessment Protocol for Hazardous Waste Combustion Facilities (hereafter referred to as 

HHRAP; EPA 2005a). MIRC uses these calculated concentrations, along with user-input 

chemical concentrations for fish and drinking water, to estimate chemical intake rates, as 

average daily doses (ADDs), for adults, children, and nursing infants. Users can obtain 

chemical input concentrations and deposition rates from measurements at an actual site or from 

a transport and fate model, such as TRIM.FaTE, as is done for RTR risk assessment. 

For a specified set of chemical concentrations and MIRC parameter options, MIRC calculates 

ADDs separately for adults, four age groups of children, and infants to reflect differences in food 

ingestion rates and diet at different lifestages. MIRC estimates age-specific hazard quotients 

(HQs) as the ratio of age-specific ADDs to the reference dose (RfD) for a chemical. The most 

appropriate HQ for a chemical depends on its toxic mode of action and the duration of exposure 

required to produce an effect. MIRC also estimates average lifetime ADDs and compares those 

to cancer slope factors (CSFs) to estimate cancer risks. A breast milk ingestion pathway is 

available to estimate exposure and risks to nursing infants.

MIRC was developed to be a flexible, transparent application. The tool includes chemical 

transfer and ingestion exposure algorithms and a database of parameter values, many with 

several options, used by these equations. The MIRC database includes values for the relevant 

physiochemical properties and toxicity reference values for more than 500 chemicals, including 

approximately 60 inorganics taken primarily from a database developed for HHRAP (EPA 

2005a). Although designed for OAQPS’ RTR assessments for sources of hazardous air 

pollutants (HAPs), the tool is flexible in its design and can be used to assess risks in many other 

contexts where soil and air concentrations are predicted or measured. 

1.2. Scope of MIRC

For persistent and bioaccumulative (PB) chemicals, risks from direct inhalation of the chemical 

can be much less than risks from ingestion of the chemical in water, fish, and food products 

27Fully functional versions of MIRC have been developed in both AccessTM-based and ExcelTM-based formats; 

however, MIRC currently is not publicly available. 

Attachment B B-1 October 2014

Description of MIRC

 
TRIM-Based Tiered Screening Methodology for RTR

grown in an area of chemical deposition. Vegetables and fruits in such areas can become 

contaminated directly by deposition of the airborne chemical to foliage, fruits, and vegetables or 

indirectly by root uptake of the chemical deposited to soils. Livestock can be exposed to the PB 

chemicals via ingestion of contaminated forage and incidental ingestion of contaminated soils. 

For PB chemicals, evaluation of the inhalation pathway for air pollutants may reveal only a 

portion of the risk to individuals in such populations. Households that consume high quantities 

of self-caught fish or locally grown produce and animal products may be particularly susceptible 

to ingestion of chemicals transferred from air in the vicinity of an air emissions source. For PB 

chemicals in particular, therefore, EPA developed methods of estimating risk from indirect 

exposure pathways associated with the deposition of airborne chemicals to gardens and farms, 

as described in HHRAP (EPA 2005a).

1.3. Use in EPA’s Air Toxics Program 

For PB-HAPs, indirect exposure pathways, such as ingestion, might contribute more to total risk 

than the inhalation pathway. EPA therefore developed several computer software tools to assist 

in evaluating exposure and risk from non-inhalation pathways. EPA developed the Total Risk 

Integrated Methodology (TRIM) Environmental Fate, Transport, and Ecological Exposure 

(TRIM.FaTE) computer program to simulate the release, transport, and fate of HAPs from a 

specific source throughout the area in which local (non-source) chemical deposition is likely to 

be a concern. TRIM.FaTE models the transport of individual chemicals from the source through 

air by advection (wind) of particle- and vapor-phase chemical and deposition of the chemical 

from air to terrestrial and aquatic ecosystems by wet and dry deposition. Movement of the 

chemical through a watershed via erosion and runoff, uptake by plants, and other abiotic and 

biotic transfer processes also are simulated. For the chemical that reaches surface waters, 

TRIM.FaTE models uptake and bioaccumulation to trophic level (TL) 3 and 4 fish (i.e., pan fish 

and game fish, respectively). 

MIRC was developed to process TRIM.FaTE results, in particular, air deposition rates and the 

concentrations of a chemical, after a specified duration of emissions, in several spatially explicit 

environmental compartments, including air, surface and root-zone soils, surface and ground 

waters, and fish. MIRC uses those results to calculate exposure to the chemical through 

ingestion of locally grown foods, including various types of fruits and vegetables, poultry, swine, 

and dairy (and beef) cattle. MIRC also calculates the associated risks for individuals who 

consume those foods. MIRC was designed to use specific TRIM.FaTE results to estimate FFC 

concentrations, ingestion exposures, and human health risks for OAQPS’ RTR assessments. It 

uses the same approach that OAQPS intends to implement directly in its TRIM system via three 

modules beyond TRIM.FaTE: TRIM Farm Food Chain, TRIM.ExpoIngestion, and TRIM.Risk.28

1.4. MIRC Highlights

Although designed to assist EPA OAQPS in its RTR assessments, MIRC is a stand-alone 

software application that can be used in other contexts. A user can supply either measured or 

estimated chemical concentrations for soil, air, water, and fish, and also can provide air 

deposition rates likely for the location(s) of interest based on local meteorology. The user can 

accept the default values for many exposure parameters and screen for small possibilities of 

risk, or the user can select other options or overwrite parameter values to tailor the estimates to 

a specific scenario or location. 

28 General information about the TRIM system is available at http://www.epa.gov/ttn/fera/trim_gen.html.

Attachment B B-2 October 2014

Description of MIRC

 
TRIM-Based Tiered Screening Methodology for RTR

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 Attachment

Sections 2 through 5 of this attachment describe the exposure and risk models implemented in 

MIRC. Section 2 provides an overview of the FFC exposure scenario and indicates options 

available to a user to tailor the scenario to specific applications. Section 3 describes the 

exposure algorithms used in MIRC, including how ADDs are calculated. Section 4 presents the 

toxicity reference values included in MIRC to calculate risks. Section 5 describes the risk 

characterization algorithms in MIRC. Section 6 of this document describes data input options 

for the model. Section 7 describes the default parameterization of MIRC for application to 

health protective risk screening assessments, and Section 8 provides the references. 

Note that the default parameterization described in Section 7 was used to estimate Tier 1 

screening threshold emission rates of PB-HAPs from RTR facilities. These emissions levels are 

assumed to pose negligible risk to subsistence communities in the vicinity of a facility emitting 

the PB-HAPs to air. Users of MIRC can modify the default values for many of the parameters to 

better represent a specific exposure scenario. 

2. MIRC Overview

The Multimedia Ingestion Risk Calculator (MIRC) software package is designed to allow rapid 

calculation of screening-level exposures and risks associated with subsistence and recreational 

farmer/fisher populations in the vicinity of a source of chemical emissions to air. The tool allows 

a user to assess human exposures via ingestion pathways, including drinking water 

Attachment B B-3 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

consumption, incidental soil ingestion, fish ingestion, and ingestion of ten types of farm food 

chain (FFC) products: exposed fruits, protected fruits, exposed vegetables, protected 

vegetables, root vegetables, beef, total dairy, pork, poultry, and eggs. The tool also includes a 

breast milk ingestion and risk module for nursing infants. For fruits and vegetables, the terms 

“exposed” and “protected” refer to whether the edible portion of the plant is exposed to the 

atmosphere. 

The remainder of this overview consists of three sections. The first (Section 2.1 of this 

attachment) provides an overview of the MIRC software package. The second and third 

sections summarize the ingestion exposure pathways included in the tool and the “built-in” 

receptor age categories, respectively (Sections 2.2 and 2.3 of this attachment). 

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 B-1 provides a flowchart displaying the types of required and optional inputs and the 

general flow of calculations carried out by the tool. 

Attachment B B-4 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

Exhibit B-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

Attachment B B-5 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

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 assessment of various exposure scenarios. To 

begin an assessment, 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 upper trophic-level fish.

Users can input measured values or values estimated by TRIM.FaTE or other models for these 

parameters. 

The MIRC application uses the input data and a variety of empirical transfer factor values 

(included in its database) to estimate chemical concentrations in nine categories of FFC food 

types (see Section 2.2 of this attachment). The FFC algorithms and transfer factor values 

included in MIRC are based on those presented in Chapter 5 of EPA’s HHRAP (EPA 2005a). 

For outputs, MIRC is designed to calculate individual cancer risk and non-cancer hazard

quotients for one chemical at a time. It is up to the risk assessor to determine if cancer risks or 

hazard quotients may be additive across two or more chemicals (i.e., if they cause toxic effects 

in the same target organ by the same mode of action, such as multiple polycyclic aromatic 

hydrocarbons (PAHs) that are carcinogenic by a mutagenic mode of action).

The tool assumes that an individual is exposed via all of the pathways specified (e.g, fruits and 

vegetables, animal products, soil, etc.). The tool therefore is useful in estimating risk to the 

maximally exposed individuals in a risk assessment. To evaluate other receptor populations, 

the user must specify the each exposure scenario separately.

2.2. Exposure Pathways

MIRC estimates the concentrations of chemicals in FFC food categories grown in an area of 

airborne chemical deposition using algorithms and parameter values provided in HHRAP (EPA 

2005a). FFC foods are evaluated in ten categories: exposed fruit, protected fruit, exposed 

vegetables, protected vegetables, root vegetables, beef, total dairy, pork, poultry, and eggs. 

Exhibit B-2 summarizes the pathways by which chemicals are transferred to these food media. 

Note that for a general Tier 1 screening-level assessment, all of the pathways can be modeled, 

as is the case for EPA’s Risk and Technology Review (RTR) calculation of screening threshold

emission rates for persistent and bioaccumulative hazardous air pollutants (PB-HAPs) (EPA

2008b).

Attachment B B-6 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

Exhibit B-2. Transfer Pathways for Modeled 

Farm Food Chain (FFC) Media

Farm Food Media Chemical Transfer Pathways

Exposed fruit and vegetables • Direct deposition from air of particle-bound chemical

• Air-to-plant transfer of vapor phase chemical

• Root uptake from soil

Protected fruit and vegetables 

(including root vegetables)

• Root uptake from soil

Beef and total dairy 

(including milk)

• Ingestion of forage, silage, and graina

• Soil ingestion

Pork • Ingestion of silage and graina

• Soil ingestion

Poultry and eggs • Ingestion of graina

• Soil ingestion

a

Chemical concentrations in forage, silage, and grain are estimated via intermediate calculations analogous to 

those used for aboveground produce.

Produce types included in the FFC can accumulate a chemical directly from air and/or soil. For 

exposed produce, chemical mass is assumed to be transferred to plants from the air in two 

ways. First, particle-bound chemical can deposit directly on the plant surface. Second, the 

uptake of vapor-phase chemicals by plants through their foliage can occur. For both exposed 

and protected produce, the concentration in the plant derived from exposure to the chemical in 

soil is estimated using an empirical bioconcentration factor (BCF) that relates the concentration 

in the plant to the concentration present in the soil. For belowground root vegetables, a root 

concentration factor is applied. The algorithms used to estimate produce concentrations are 

presented in Section 3.1.1 of this attachment. 

Chemical concentrations in animal products are estimated based on the amount of chemical 

consumed through the diet, including incidental ingestion of soil while grazing. The diet options 

for farm animals in MIRC include forage (plants grown on-site for animal grazing, such as 

grass), silage (wet forage grasses, fresh-cut hay, or other fresh plant material that has been 

stored and fermented), and feed grain products grown on the farm (e.g., corn, soybeans). All 

three animal feed products are assumed to accumulate chemical via root uptake from the soil. 

Forage and silage also can accumulate chemical via direct deposition of particle-bound 

chemical and vapor transfer. 

The algorithms in MIRC are based on the assumptions that beef and dairy cattle consume all 

three feed products, while pigs consume only silage and grain and chickens consume only 

grain. The incidental ingestion of the chemical in soils during grazing or consumption of foods 

placed on the ground is estimated using empirical soil ingestion values. For secondary animal 

products (dairy products and eggs), chemical concentrations are estimated by applying a 

biotransfer factor to the estimated concentration in the “source” animal (cows and chickens, 

respectively). The algorithms used to estimate animal product concentrations are described in 

Section 3.1.2 of this attachment. 

2.3. Receptor Groups

As noted in EPA risk assessment guidelines (EPA 2005b,c,d, 2008a), exposures of children are 

expected to differ from exposures of adults due to differences in body weights, ingestion rates, 

dietary preferences, and other factors. It is important, therefore, to evaluate the contribution of 

exposures during childhood to total lifetime risk using appropriate exposure factor values. 

Attachment B B-7 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

EPA’s HHRAP (Chapter 4, EPA 2005a) recommends assessing exposures for children and 

adults separately, but considers all non-infant children in one category. Specifically, HHRAP 

recommends eight categories of receptor: farmer, child farmer, resident, child resident, fisher, 

child fisher, acute receptor, and nursing infant. Over time, different EPA programs have used 

different child age groupings to evaluate body weights, ingestion rates, and other parameter 

values needed to estimate chemical exposures and risks to children. 

To improve the match between age groups used to estimate values across exposure 

parameters, in 2005, EPA recommended a standard set of child age categories for exposure 

and risk assessments (EPA 2005b). EPA recommended four age groups for infants: birth to < 1 

month; 1 to < 3 months; 3 to < 6 months; and 6 to < 12 months. For young children, EPA 

recommended an additional four age groups: 1 to < 2 years; 2 to < 3 years; 3 to < 6 years; and 

6 to < 11 years. Two age groupings were recommended for teenagers and young adults: 11 to 

< 16 years; and 16 to < 21 years. These age groupings correspond to different developmental 

stages and reflect different food ingestion rates per unit body weight, with the highest ingestion 

rates occurring for the youngest, most rapidly growing, age groups.

For purposes of RTR assessment using MIRC, the selection of age categories is limited by the 

categories for which most of the FFC food ingestion rates have been calculated. In Chapter 13 

of both its Exposure Factors Handbook (EFH; EPA 2011a) and its Child-Specific Exposure 

Factors Handbook (CSEFH; EPA 2008a), EPA summarized home-grown/raised food ingestion 

rates for four children’s age groups: 1 to < 3 years; 3 to < 6 years; 6 to < 12 years; and 12 to < 

20 years. Intake rates were not calculated for children younger than 1 year because infants are 

unlikely to consume those foods. They are more likely to be nursing or to be fed formula and 

other commercial baby-food products. 

Although the age groupings used to estimate FFC ingestion rates do not match precisely the 

groupings that EPA recommended in 2005 for Agency exposure assessments (EPA 2005b), 

they are the only age-groupings for which such data are available. The U.S. Department of 

Agriculture’s (USDA’s) 1987-1988 Nationwide Food Consumption Survey (USDA 1992, 1993, 

1994a) remains the most recent survey of ingestion rates for home-grown foods, and EPA’s 

analysis of those data, published in its 2011 EFH, remains the most recently published major

analysis of those data. Because ingestion of home-grown produce and animal products are the 

primary exposure pathways for which MIRC was developed, those are the age groupings used 

for all child parameter values used to estimate exposure and risk in MIRC. 

Thus, in MIRC, values for each exposure parameter were estimated for adults (20 to 70 years) 

and five children’s age groups: 

• infants under 1 year (i.e., 0 to < 1 year); 

• children ages 1 through 2 years (i.e., 1 to < 3 years); 

• children ages 3 through 5 years (i.e., 3 to < 6 years); 

• children ages 6 through 11 years (i.e., 6 to < 12 years); and 

• children ages 12 through 19 years (i.e., 12 to < 20 years). 

See Sections 5.1 and 5.2 for descriptions of the risk characterization algorithms used to 

calculate cancer and non-cancer effects, respectively, for the above age groupings. Exposure 

and risks to infants under 1 year of age are estimated only for the breast-milk-ingestion 

pathway. 

Attachment B B-8 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

For assessment of cancer risks from early-life exposure, EPA recognizes that infants and 

children may be more sensitive to a carcinogenic chemical than adults, with cancers appearing 

earlier in life or with lower doses experienced during childhood (EPA 2005c, d). Thus, the 

“potency” of a carcinogen might be higher for infants and children than for adults. To date, 

however, data by which to evaluate the relative sensitivity of children and adults to the same 

daily dose of a carcinogen remain limited. Based on analyses of radioactive and other 

carcinogenic chemicals, EPA recommends evaluating two lifestages for children separately from 

adults for chemicals that cause cancer by a mutagenic mode of action (MOA): from birth to < 2 

years and from 2 to < 16 years (EPA 2005c,d). EPA also suggests that, as data become 

available regarding carcinogens with a mutagenic MOA, further refinements of these age 

groupings may be considered. 

For assessing risks from exposures to carcinogenic chemicals that act via a mutagenic MOA, 

the two early lifestages recommended by EPA (EPA 2005c,d) also are included in MIRC: 

• children under the age of 2 years (i.e., 0 to < 2 years); and

• children from 2 through 15 years (i.e., 2 to < 16 years). 

Different age groupings are needed for the assessment of risks from carcinogenic chemicals 

with a mutagenic MOA and other carcinogens with other or unknown MOAs. Currently in MIRC, 

the only PB-HAPs with a mutagenic mode of carcinogenesis are the carcinogenic PAHs. See 

Section 5.1 for a description of the age-dependent adjustment factors (ADAFs) that are used to 

calculate cancer risks for chemicals with a mutagenic MOA. 

3. Exposure Algorithms

The exposure algorithms in MIRC are described below in four sections. Section 3.1 of this 

attachment presents the algorithms used to estimate chemical concentrations in FFC foods from 

chemical concentrations in soil and air. Pathway-specific algorithms used to estimate chemical 

intakes by adults and non-infant children are described in Section 3.2, and total chemical intake 

calculations are described in Section 3.3. Finally, the sets of algorithms used to estimate 

chemical intake via consumption of breast milk by nursing infants are described in Section 3.4. 

As noted previously, the exposure algorithms used in MIRC are based on those presented in 

HHRAP (EPA 2005b). Any differences between MIRC and HHRAP are explained in this 

section.

3.1. Farm Food Chain Algorithms

The algorithms and parameters used to estimate chemical concentrations in produce and 

animal products are described in Sections 3.1.1 and 3.1.2 of this attachment, respectively. 

Discussions of the parameter value options and the values selected as defaults in MIRC for 

RTR risk assessment are provided in Section 6.2. The use of TRIM.FaTE to model chemical 

fate and transport in the environment prior to FFC calculations drives the most significant 

difference between the FFC algorithms included in HHRAP and the equations used for RTR. 

The approach in HHRAP uses estimated ambient air concentrations and deposition rates from 

dispersion model simulations that use unit emission rates. Chemical-specific emission rates 

(adjusted for vapor and particle-bound fractions) are then incorporated into some of the HHRAP 

FFC algorithms to calculate concentrations in FFC media. Soil concentrations are calculated 

using a similar approach in HHRAP. For assessment of multipathway exposures for RTR, 

TRIM.FaTE is used to estimate air concentrations, air-to-surface deposition rates, and soil 

concentrations, and these outputs are used in the FFC algorithms.

Attachment B B-9 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

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 B-3 lists the pathways by which chemicals are transferred to the FFC produce 

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

modeled, as was done for EPA’s calculation of Tier 1 screening threshold emission rates for PBHAPs in its RTR assessments (EPA 2008b), and as described in the Technical Support 

Document. Sections 3.1.1.1 and 3.1.1.2 below describe the transfer pathways and algorithms 

for aboveground and belowground produce, respectively.

Exhibit B-3. Chemical Transfer Pathways for Produce

Farm Food Media Chemical Transfer Pathways

Aboveground 

Produce

Exposed fruits and 

vegetables

Direct deposition from air of 

particle-bound chemical

Air-to-plant transfer of vapor 

phase chemical

Root uptake from soil

Protected fruits and 

vegetables 

Root uptake from soil

Belowground 

Produce

Root vegetables Root uptake from soil

3.1.1.1. Aboveground Produce

For aboveground exposed produce, 

chemical mass is assumed to be 

transferred to plants from the air in three 

ways, as illustrated in Exhibit B-4. First, 

particle-bound chemical can deposit 

directly on the plant surface via deposition 

(Pd). The amount of chemical 

accumulated is estimated based on the 

areal fraction of chemical deposition 

intercepted by the plant surface, minus a 

loss factor that is intended to account for 

removal of deposited chemical by wind and 

rain and changes in concentration due to 

growth dilution. Second, for chemical 

present in air in the vapor phase, the 

concentration of chemical accumulated by 

the plant’s foliage is estimated using an 

empirical air-to-plant biotransfer factor (Pv). 

Third, the chemical concentration in the plant due to root uptake from the soil (PrAG-produce) is 

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

Exhibit B-4. Estimating Chemical 

Concentration in Aboveground Produce

Deposition 

of Particles 

(Pd)

Root Uptake 

from Soil 

(PrAG-produce)

Vapor 

Transfer 

(Pv)

Chemical Concentration in 

Aboveground Produce

Attachment B B-10 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

concentration in the plant to the average chemical concentration in the soil at the root-zone 

depth in the produce-growing area (Csroot-zone_produce). 

The edible portions of aboveground protected produce are not subject to contamination via 

particle deposition (Pd) or vapor transfer (Pv). Therefore, root uptake of chemicals is the 

primary mechanism through which aboveground protected produce becomes contaminated. As 

shown below, the chemical concentration in the aboveground plant due to root uptake from soil

(PrAG-produce- DW) is estimated using an empirical bioconcentration factor (BrAG-produce-DW) that 

relates the chemical concentration in the plant to the average chemical concentration in the soil 

at the root-zone depth in the produce-growing area (Csroot-zone_produce). These equations all 

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

Equation B-1. Chemical Concentration in Aboveground Produce 

where: 

CAG-produce-DW(i) = Concentration of chemical in edible portion of aboveground produce type i, 

exposed or protected, on a dry-weight (DW) basis (mg/kg produce DW)

Pd(i) = 

Chemical concentration in edible portion of aboveground produce type i due to 

deposition of particles (mg/kg produce DW); for protected aboveground 

produce, Pd equals zero

PrAG-produce-DW(i) = 

Chemical concentration in edible portion of aboveground produce type i, 

exposed or protected, due to root uptake from soil at the root-zone depth of the 

produce growing area (mg/kg produce DW)

Pv(i) = 

Chemical concentration in edible portion of aboveground produce type i due to 

air-to-plant transfer (μg/g [or mg/kg] produce DW); for protected aboveground 

produce, Pv equals zero

Equation B-2. Chemical Concentration in Aboveground Produce Due to Root Uptake 

where: 

PrAG-produce-DW(i) = 

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

exposed or protected, due to root uptake from soil at root-zone depth in the 

produce-growing area, on a dry-weight (DW) basis (mg/kg produce DW)

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

area (mg/kg soil DW)

BrAG-produce-DW(i) = 

Chemical-specific plant/soil chemical bioconcentration factor for edible portion 

of aboveground produce type i, exposed or protected (g soil DW / g produce 

DW)

AG produce DW i )( P AG produce DW PvPd iii )()()( C −− = r −− ++

P AG produce DW i )( Csroot-zone_produce BrAG produce DW i )( r −− = × −−

Attachment B B-11 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

Equation B-3. Chemical Concentration in Aboveground Produce Due to Deposition of 

Particle-phase Chemical

where:

Pd(i) = 

Chemical concentration in aboveground produce type i on a dry-weight (DW) 

basis due to particle deposition (mg/kg produce DW); set equal to zero for 

protected aboveground produce

Drdp = Average annual dry deposition of particle-phase chemical (g/m2-yr)

Fw = Fraction of wet deposition that adheres to plant surfaces; 0.2 for anions, 0.6 

for cations and most organics (unitless)

Drwp = Average annual wet deposition of particle-phase chemical (g/m2-yr)

Rp(i) = Interception fraction of the edible portion of plant type i (unitless)

kp(i) = Plant surface loss coefficient for plant type i (yr -1) 

Tp(i) = Length of exposure to deposition in the field per harvest of the edible portion of 

plant type i (yr)

Yp(i) = 

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

DW/m2) 

Note that Equation B-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 B-3. In addition, in MIRC, Drdp and Drwp, the average annual dryand wet-particle-phase deposition rates, respectively, are in units of g/m2-yr. Users of 

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

are in units of g/m2-day; therefore, users must adjust the TRIM.FaTE output values to units of

g/m2-yr (i.e., multiply by 365 days/yr) before inputting values for Drdp and Drwp into MIRC.

Equation B-4. Chemical Concentration in Aboveground Produce Due to 

Air-to-Plant Transfer of Vapor-phase Chemical

 

)()(

(*)(( ))

)(

)(

,1000 (( )) 1( )

ii

ikp Tp i

i

i Yp kp

DrwpFwDrdp Rp e

Pd

×

−×××+× =

−

a

AG i AG i

i

FvCa Bv VG

Pv

ρ

)( )(

)(

××× =

Attachment B B-12 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

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 B-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 B-5 and the moisture 

content (MAF) of the FFC food category.

Equation B-5. Conversion of Aboveground Produce Chemical Concentration from

Dry- to Wet-Weight Basis

where:

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

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

MAF(i) = 

Moisture adjustment factor for aboveground produce type i to convert the chemical 

concentration estimated for dry-weight produce to the corresponding chemical 

concentration for full-weight fresh produce (percent water) 















 −

−− = −− ×

100

(100 ))(

)( )(

i

AG produce WW i AG produce DW i

MAF

C C

Attachment B B-13 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

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 B-6. The RCF 

relates the chemical concentration in the plant on a wet-weight basis to the average chemical 

concentration in the root-zone soil (Csroot-zone_produce) on a dry-weight basis. Belowground 

produce (i.e., tubers or root vegetables) are protected from the deposition and vapor transfer by 

being covered by soil. Therefore, root uptake of chemicals is the primary mechanism through 

which belowground produce becomes contaminated. 

Equation B-6. Chemical Concentration in Belowground Produce: Nonionic Organic 

Chemicals

where:

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

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

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

area, on a dry-weight (DW) basis (mg chemical/kg soil DW)

RCF = 

Chemical-specific root concentration factor for tubers and root produce (L soil 

pore water/kg root WW)*

VGrootveg = 

Empirical correction factor for belowground produce (i.e., tuber or root 

vegetable) to account for possible overestimate of the diffusive transfer of 

chemicals from the outside to the inside of bulky tubers or roots (based on 

carrots and potatoes) (unitless) *

Kds = Chemical-specific soil/water partition coefficient (L soil pore water/kg soil DW)

UCF = Units conversion factor of 1 kg/L

*Note that there is only one type of BG produce; hence there are no plant-type-specific subscripts. 

The RCF, as developed by Briggs et al. (1982), is the ratio of the chemical concentration in the 

edible root on a wet-weight basis to its concentration in the soil pore water. RCFs are based on 

experiments with growth solutions (hydroponic) instead of soils; therefore, it is necessary to 

divide the soil concentration by the chemical-specific soil/water partition coefficient (Kds). There 

is no conversion of chemical concentrations in belowground produce from DW to WW because 

the values are already on a WW basis. 

For nonionic organic chemicals, it is possible to predict RCF values and Kds values (for a 

specified soil organic carbon content) from an estimate of the chemical’s Kow from empirically 

Kds UCF

Cs zoneroot produce RCF VGrootveg

×

×× = − _ CBG-produce-WW

Attachment B B-14 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

derived regression models. Those models are shown in HHRAP Appendix A-2, Equations 

A-2-14 and A-2-15 (RCF) and in Equations A-29 and A-2-10 (Kds). The RCF and Kds values 

so calculated for many of the chemicals in HHRAP are included in the MIRC database 

(including the values for PAHs and dioxins). 

3.1.1.2.2. Inorganic and Ionic Organic Chemicals 

For inorganic chemicals and ionized organic chemicals, it is not possible to predict RCF or Kds

values from Kow . For inorganic chemicals, chemical specific empirical values for the root/soil 

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

BrBG-produce-DW, must be obtained from the literature for each inorganic chemical on a DW basis. 

For inorganic chemicals, therefore, Equation B-7 is used instead of Equation B-6. 

Equation B-7. Chemical Concentration in Belowground Produce: Inorganic Chemicals

where: 

CBG-produce-DW = 

Concentration of chemical in edible portion of aboveground produce, due to 

root uptake from soil at root-zone depth in the produce-growing area, on a dryweight (DW) basis (mg/kg produce DW)

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

area (mg/kg soil DW)

BrBG-produce-DW = 

Chemical-specific root/soil chemical bioconcentration factor for edible portion 

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

VGrootveg = Empirical correction factor for belowground produce (as in Equation B-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 B-8. 

Equation B-8. Conversion of Belowground Produce Chemical Concentration from 

Dry- to Wet-Weight Basis

where:

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

CBG-produce-DW = 

Concentration of chemical in edible portion of aboveground produce, due to root 

uptake from soil at root-zone depth in the produce-growing area, on a dryweight (DW) basis (mg/kg produce DW)

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

ground produce) (percent water)

1

zoneroot _ produce BG produce DW rootveg

BG-produce-DW

Cs Br VG

C

× × = − −−





 



 − −− = −− ×

100

(100 ) BG

BG produce WW BG produce DW

MAF C C

Attachment B B-15 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

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 B-5 summarizes 

the pathways by which chemicals are transferred to these home- or farm-raised animal food 

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

modeled, as is done for EPA’s RTR calculation of screening threshold emission rates for 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 B-5, the algorithms in MIRC for chemical intake with plant feeds (PlantCh-Intake(i,m)) are 

based on the assumptions that beef and dairy cattle consume all three plant feed products, 

while pigs consume only silage and grain, and chickens consume only grain. 

Exhibit B-5. Chemical Transfer Pathways for Animal Products

Farm Food Media Chemical Transfer Pathways

Animal Products Beef and total dairy 

(including milk)

• Ingestion of forage, silage, and graina

• Incidental soil ingestion

Pork • Ingestion of silage and graina

• Incidental soil ingestion

Poultry and eggs • Ingestion of graina

• Incidental soil ingestion

a

Chemical concentrations in plant feed (i.e., forage, silage, and grain) are estimated via intermediate calculations (see 

Equation B-13, Equation B-14, Equation B-3, and Equation B-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 B-9 to calculate the 

concentration of chemical in beef, pork, or total dairy and Equation B-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 B-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 B-10 for poultry and eggs.

Equation B-9. Chemical Concentration in Beef, Pork, or Total Dairy

 

















××= +∑=

− −

n

i

Cmammal m Ba m MF SoilCh Intake m PlantCh Intake mi

1

)()( )( ),(

Attachment B B-16 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

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 

B-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 B-12 below 

(If m = beef or total dairy, then n = 3 and i = forage, silage, and grain; m = pork, 

then n = 2 and i = silage and grain; m = poultry, then n = 1 and I = grain.)

Equation B-10. Chemical Concentration in Poultry or Eggs

where:

Cpoultry(m) = Concentration of chemical in food product m, where m = poultry or eggs (mg 

chemical/kg animal product WW)

Ba(m) = 

Chemical-specific biotransfer factor for food product m, where m = poultry or 

eggs (day/kg animal product WW) 

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

ground (mg chemical/day) where m = poultry; see Equation B-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 B-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 B-11. At this time, the default value for Bs in MIRC for all chemicals is 1.0 (i.e., the 

chemical in soil is assumed to be 100 percent bioavailable to the animal). This assumption may 

be reasonably accurate for the soil surface to which airborne chemical is deposited. MIRC 

allows the user to enter a surface soil concentration for areas where livestock forage, CsS-livestock, 

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

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

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

( ) Cpoultry m Ba m)()( ×= SoilCh−Intake m)( + PlantCh−Intake mi ),(

SoilCh−Intake m)( Qs ×= Css−livestock ×Bs

Attachment B B-17 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

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 B-12. Note that the animal feed ingestion rates are on a dry-weight (DW) basis; 

hence, no DW to wet weight (WW) conversion is needed.

Equation B-12. Ingestion of Chemical in Feed by Livestock

where:

Plant Ch-Intake(i,m) = Ingestion of chemical in plant feed type i (mg chemical/day), where i = forage, 

silage, or grain, for livestock type m

F(i,m) = Fraction of plant feed type i obtained from contaminated area used to grow 

animal feed, where I = forage, silage, or grain (unitless) for livestock type m

Qp(i,m) = Quantity of plant feed type i consumed per animal per day (kg plant feed 

DW/day), where i = forage, silage, or grain, for livestock type m

Cfeed(i) = Concentration of chemical in ingested plant feed type i (mg chemical/kg plant 

feed DW), where i = forage, silage, or grain

The concentrations of chemical in the three different types of plant feeds for livestock are 

calculated according to Equation B-13. The equation is the same as that for aboveground 

produce in Equation B-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 B-13. Chemical Concentration in Livestock Feed (All Aboveground)

 

where:

Cfeed(i) = Concentration of chemical in plant feed type i on a dry-weight (DW) basis (mg 

chemical/kg plant feed DW), where i = forage, silage, or grain

PlantCh−Intake F mimi Qp mi ),(),(),( ××= Cfeed i )(

Cfeed(i) feed(i) PdPr (i) ++= Pv(i)

Attachment B B-18 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

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 B-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 B-14. The equation is the same as Equation B-2, except that a Br value 

appropriate to grasses is used and MIRC allows for different soil concentrations in the area 

used to grow animal feed than in the area used to grow produce for human consumption (see 

Section 6.1 of this attachment, user inputs). Note that for feed type i = grains, the Pd and Pv

terms do not apply (are set to zero), because the feed products (i.e., corn kernels, soy beans) 

are protected from the air (i.e., by husks, pods).

Equation B-14. Chemical Concentration in Livestock Feed Due to Root Uptake

where:

Prfeed(i) = 

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

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

or grain 

Csroot-zone_feed(i) = Average chemical concentration in soil at root-zone depth in area used to grow 

plant feed type i (mg chemical/kg soil DW), where i = forage, silage, or grain

Brfeed(i) = 

Chemical-specific plant-soil bioconcentration factor for plant feed type i (kg soil 

DW/kg plant feed DW), where i = forage, silage, or grain

The algorithms used to calculate Pd(i) and Pv(i) when plant feed type i = forage and silage are 

identical to those used to calculate Pd(i) and Pv(i) for aboveground exposed produce (i.e., 

Equation B-3 and Equation B-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 B-15, are expressed in milligrams of 

chemical per kilogram of receptor body weight per day (mg/kg-day).

Equation B-15. Average Daily Dose for Specified Age Group and Food Type

P feed i )( Cs zoneroot feed i )(_ Brfeed i )( r = − ×

































×

××× = 365 days (y)

(y) (y)

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

(y,i)

EF

ATBW

C IR FC ED

ADD

Attachment B B-19 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

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 B-15 takes into account the chemical concentration in each food type i (or in water), 

the quantity of food brought into the home for consumption, the loss of some of the mass of the 

foods due to preparation and cooking, how much of the food is consumed per year, the amount 

of the food obtained from contaminated areas, and the consumer’s body weight (EPA 2011a, 

2003a). In MIRC, ADDs are calculated separately for each chemical, home-grown food type, 

and consumer age group. 

ADD values, expressed as intakes, not absorbed doses, are appropriate for comparison with 

RfDs and for use with cancer slope factors (CSFs) to estimate risk, as discussed in Section 5 of 

this attachment. An exception is for the breast-milk exposure pathway, where calculating the 

dose available to and absorbed by the nursing infant is related to the dose absorbed by the 

mother as discussed in Section 3.4 of this attachment. 

MIRC evaluates only one exposure scenario at a time. For screening-level assessments, all 

components of this equation are assumed to remain constant for consumers in a given age 

group over time (e.g., seasonal and annual variations in diet are not explicitly taken into 

account). To calculate an ADD(y,i) from the contaminated area for food group i over an entire 

lifetime of exposure, age-group-specific ingestion rates and body weights are used for the age 

groups described in Section 2.3 of this attachment. 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 B-15. 

For each chemical included in a screening scenario, total average daily exposure for age 

group y (ADD(y)) is estimated as the sum of chemical intake from all ingestion pathways 

combined: Note that the last exposure pathway is limited to infants. 

• Incidental soil ingestion;

• Ingestion of fish;

• Ingestion of homegrown fruits (exposed and protected);

• Ingestion of homegrown vegetables (exposed, protected, and root);

• Ingestion of animal products from home-raised animals:

– Milk and other dairy products from cows,

Attachment B B-20 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

– Beef products,

– Pork products, and

– Poultry and eggs;

• Ingestion of drinking water from specified source; and

• Ingestion of breast milk by infants.

The algorithms for the first six exposure pathways listed above are described in Sections 3.2.1

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

described in Section 3.4. 

3.2.1. Chemical Intake from Soil Ingestion

Equation B-16 shows the equation used to estimate chemical intake through incidental ingestion 

of soil. 

Equation B-16. Chemical Intake from Soil Ingestion

where: 

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

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

CSoil = 

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

(μg/g soil DW)

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

FCSoil = Fraction of soil ingested that is from contaminated area (unitless)

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

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

for scenario (≤ 365 days)

Note: MIRC saves soil ingestion rates in units of mg/day (not g/day); therefore, there is an additional 0.001 g/mg conversion unit in 

the actual MIRC algorithm than shown here. 

3.2.2. Chemical Intake from Fish Ingestion

Ingestion of locally caught fish is included as a possible exposure pathway in MIRC (Equation 

B-17). Two types of fish are included in the exposure algorithm: trophic level 3.5 (abbreviated 

as TL3) fish, equivalent to benthic carnivores such as catfish and trophic level 4 (TL4) fish in the 

water column, equivalent to game fish such as lake trout and walleye. The chemical 

concentration in fish in Equation B-17 is estimated as the consumption-weighted chemical 

concentration using Equation B-18. 









































×××

= 365 days

EF

BW

μg

mg C IR FC 0.001 

ADD 

(y)

Soil Soil(y) Soil

Soil(y)

Attachment B B-21 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

Equation B-17. Chemical Intake from Fish Ingestion

Equation B-18. Consumption-weighted Chemical Concentration in Fish

CFish = (CFishTL3 × FTL3) + (CFishTL4 × FTL4)

where:

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

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

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

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

(unitless)

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

CFishTL4 = 

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

(WW) basis (mg/kg WW)

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

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

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

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

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

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

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

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

specified for scenario (≤ 365 days)

*Parameter values must be internally consistent. In contrast to the tables included in MIRC for ingestion rate options for 

homegrown food products, which are based on the products as brought into the home from the field (see Section 6.3.3

of this attachment), the tables of fish ingestion rate options included in MIRC are on an “as consumed” basis (i.e., after 

preparation and cooking losses), and L1 and L2 therefore are set equal to zero. If the user wishes to enter local fish 

ingestion rates on an “as harvested” basis, the user also should enter L1 and L2 values as specified in Section 6.4.3 of 

this attachment.

When whole fish are prepared for cooking, it is usual for the viscera, head, and fins to be 

removed, particularly for larger fish. Many persons also remove (or do not eat) the skin, bones, 

and belly fat. EPA has, therefore, estimated the proportion of the weight of whole fish that tends 

to be lost during preparation and cooking across a variety of fish species (EFH; EPA 2011a) and 

included those losses in its HHRAP algorithms for chemical intake from fish (L1Fish and L2Fish in 

Equation B-17). 

( ) ( ) days365

g

kg 0 001

2111 















×

















×× ×

×−×−=

EF

BW

C IR . FC

ADD L L

(y)

Fish Fish(y) Fish

Fish(y) Fish Fish

Attachment B B-22 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

3.2.3. Chemical Intake from Fruit Ingestion

Average daily doses of a chemical from homegrown exposed fruits are calculated separately for 

exposed and protected fruits (Equation B-19 and Equation B-20, respectively).

Equation B-19. Chemical Intake from Consumption of Exposed Fruits

Equation B-20. Chemical Intake from Consumption of Protected Fruits

where:

ADDExpFruit(y) 

ADDProFruit(y)

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

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

L1ExpFruit = 

Mean reduction in fruit weight resulting from removal of skin or peel, core or pit, 

stems or caps, seeds and defects, and from draining liquids from canned or 

frozen forms (unitless)

L1ProFruit = 

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

techniques for protected fruits (unitless)

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

forms of the fruit (unitless)

CExpFruit 

CProFruit

= 

Chemical concentration in whole exposed fruits or whole protected fruits 

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

fruit WW)

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

specified for scenario (≤ 365 days)

FCExpFruit 

FCProFruit

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

from contaminated area (unitless)

IRExpFruit(y) 

IRProFruit(y)

= Ingestion rate of home-grown exposed fruits or protected fruits (depending on 

subscript) for age y (g WW/kg body weight-day) 

Fruit ingestion rates in the survey were based on weights of unprepared fruits (e.g., one apple; 

one pear) or the weight of a can of fruit (e.g., 8 oz can). The weight of the fruit ingested is less 

than the initial weight owing to common preparation actions (L1ExpFruit and L1ProFruit; e.g., coring 

apples and pears; peeling apples; pitting cherries). Cooking of exposed fruit (e.g., berries, 

apples, peaches) often results in further weight loss that results from liquids lost during cooking 

and drained from the cooking vessel (L2ExpFruit). EPA has assumed that cooking of protected 

fruit results in no loss of weight for the fruit.

3.2.4. Chemical Intake from Vegetable Ingestion

MIRC includes three separate algorithms for homegrown vegetables adapted from EPA’s 

HHRAP Modeling System (EPA 2005a): one for exposed vegetables such as asparagus, 

broccoli, lettuce, and tomatoes (although they are actually a fruit); one for protected vegetables 

( ) ( ) 















×













 −= −× × × × ×

g 365 days

kg 2111 0 001 EF ADDExpFruit(y) L ExpFruit L ExpFruit CExpFruit IRExpFruit(y) . FCExpFruit

( ) 















×













 −= × × × ×

g 365 days

kg 11 0 001 EF ADDProFruit(y) L ProFruit CProFruit IRProFruit(y) . FCProFruit

Attachment B B-23 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

such as corn, cabbage, soybeans, and peas; and one for root vegetables such as carrots, 

beets, and potatoes (see Equation B-21, Equation B-22, and Equation B-23, respectively). 

Equation B-21. Chemical Intake from Exposed Vegetables

Equation B-22. Chemical Intake from Protected Vegetables

Equation B-23. Chemical Intake from Root Vegetables

where:

ADDExpVeg(y)

ADDProVeg(y) 

ADDRootVeg(y)

= 

Average chemical intake from ingestion of exposed vegetables, protected 

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

chemical/kg body weight-day)

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

includes removing stalks, paring skins, discarding damaged leaves

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

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

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

removal of tops and paring skins

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

liquids and removal of skin after cooking (unitless)

CExpVeg 

CProVeg 

CRootVeg

= 

Chemical concentration in exposed vegetables, protected vegetables, or root 

vegetables (depending on subscript) on a wet-weight (WW) basis (mg 

chemical/kg vegetable WW)

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

specified for scenario (≤365 days)

FCExpVeg 

FCProVeg 

FCRootVeg

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

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

IRExpVeg(y)

IRProVeg(y) 

IRRootVeg(y)

= Ingestion rate of exposed vegetables, protected vegetables, or root vegetables 

(depending on subscript) for age group y (g vegetable WW/kg body weight-day)

( ) 















×













 −= ×× × ×

g 365 days

kg 11 0 001 EF ADDExpVeg(y) L ExpVeg CExpVeg IRExpVeg(y) . FCExpVeg

( ) 















×















××−= × ×

g 365 days

kg 11 0 001 EF ADDProVeg(y) L ProVeg CProVeg IRProVeg(y) . FCProVeg

( ) ( ) 















×













 −= −× × × × ×

g 365 days

kg 2111 0 001 EF ADDRootVeg(y) L RootVeg L RootVeg CRootVeg IRRootVeg(y) . FCRootVeg

Attachment B B-24 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

3.2.5. Chemical Intake from Animal Product Ingestion

Calculations of chemical intake from the consumption of farm animals and related food products 

are provided below in Equation B-24 through Equation 2-28 for homegrown beef, dairy (milk), 

pork, poultry, and eggs, respectively.

Equation B-24. Chemical Intake from Ingestion of Beef

where:

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

L1Beef = Mean net cooking loss for beef (unitless)

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

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

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

specified for scenario (≤ 365 days)

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

FCBeef = 

Fraction of beef consumed raised on contaminated area or fed contaminated 

silage and grains (unitless)

Equation B-25. Chemical Intake from Dairy Ingestion

where: 

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

(mg/kg-day)

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

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

specified for scenario (≤ 365 days)

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

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

Equation B-26. Chemical Intake from Pork Ingestion

( ) ( ) 















×















×××−×−= ×

g 365 days

kg 2111 0 001 EF ADDBeef(y) L Beef Beef CL Beef IRBeef(y) . FCBeef

















×















××= ×

g 365 days

kg 0 001 EF ADDDairy(y) CDairy IRDairy(y) . FCDairy

( ) ( ) 















×















×××−×−= ×

g 365 days

kg 2111 0 001 EF ADDPork(y) L Pork Pork CL Pork IRPork(y) . FCPork

Attachment B B-25 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

where:

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

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

during cooking; averaged over various cuts and preparation methods

L2Pork = 

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

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

and preparation methods

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

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

specified for scenario (≤365 days)

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

FCPork = Fraction of pork obtained from contaminated area (unitless)

Equation B-27. Chemical Intake from Poultry Ingestion

where:

ADDPoultry(y) = Average daily dose (chemical intake) from ingestion of poultry (mg/kg-day)

L1Poultry = Mean net cooking loss for poultry (unitless)

L2Poultry = Mean net post cooking loss for poultry (unitless) 

CPoultry = Concentration of chemical in poultry (mg/kg WW)

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

specified for scenario (≤365 days)

IRPoultry(y) = Ingestion rate of poultry for age group y (g WW/kg-day)

FCPoultry = 

Fraction of poultry from contaminated area or fed contaminated grains 

(unitless)

The reduction in the weight of beef, pork, and poultry during and after cooking may correlate 

with an increase or decrease in the concentration of the chemical in the food as consumed 

depending on the chemical and depending on the cooking method.

Equation B-28. Chemical Intake from Egg Ingestion

where:

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

( ) ( ) 















×















−×−= ×× × ×

g 365 days

kg 2111 0 001 EF ADDPoultry(y) L Poultry L Poultry CPoultry IRPoultry(y) . FCPoultry

















×















××= ×

g 365 days

kg 0 001 EF ADDEgg(y) CEgg IREgg(y) . FCEgg

Attachment B B-26 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

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

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

specified for scenario (≤ 365 days)

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

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

3.2.6. Chemical Intake from Drinking Water Ingestion

If the user chooses to evaluate chemical ingestion via drinking water, the user specifies a

chemical concentration in g/L (equivalent to mg/mL) based on their particular scenario. The 

chemical concentration could represent water from groundwater wells, community water, nearby 

surface waters, or other source. For this exposure pathway, ingestion rates are in units of 

milliliters of water per day (mL/day) (see Equation B-29). 

Equation B-29. Chemical Intake from Drinking Water Ingestion

where:

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

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

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

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

FCDW = Fraction of drinking water obtained from contaminated area (unitless)

BW(y) = Body weight of age group y (kg)

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

specified for scenario (≤365 days)

3.3. Total Chemical Intake 

To estimate the total ADD, or intake of a chemical from all of the exposure media that a single 

individual in each age group is expected to contact (e.g., soil, local fish, five types of homegrown produce, and five types of home-raised animals or animal products), the media-specific 

chemical intakes are summed for each age group. Total average daily exposure for a particular 

age group y (ADD(y)) is estimated as the sum of chemical intake from all ingestion pathways 

combined, as illustrated in Equation B-30 through Equation B-35 below.

Equations B-30 to B-35. Total Average Daily Dose of a Chemical for Different Age 

Groups

Equation B-30.

Equation B-31. 

Equation B-32. 

















×













 ×× = 365 days

EF

BW

C IR FC

ADD

(y)

DW DW(y) DW

DW(y)

ADD < )1( = ADDbreastmilk

∑ = − = −

n

i

ADD ADD i 1 )21( ),21(

∑ = − = −

n

i

ADD ADD i 1 )53( ),53(

Attachment B B-27 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

Equation B-33. 

Equation B-34. 

Equation B-35. 

where i represents the i

th food type or ingestion medium and n equals the total number of food 

types or ingestion media, and ADD parameters are defined below:

ADD(<1) = 

Total average daily dose of chemical for infants less than one year from 

ingestion of breast milk (mg/kg-day) 

ADD(1–2) = Total average daily dose of chemical from all ingestion sources for children 

ages 1 through 2 years (mg/kg-day)

ADD(3–5) = Total average daily dose for children ages 3 through 5 years (mg/kg-day)

ADD(6–11) = Total average daily dose for children ages 6 through 11 years (mg/kg-day)

ADD(12–19) = Total average daily dose for children ages 12 through 19 years (mg/kg-day)

ADD(adult) = Total average daily dose for adult age 20 up to 70 years (mg/kg-day)

The lifetime average daily dose (LADD) is calculated as the time-weight average of the ADD

values for each age group (Equation B-36).

Equation B-36. Lifetime Average Daily Dose (LADD)

The time-weighting factors simply equal the duration of exposure for the specified age category 

in years divided by the total lifespan, assumed to be 70 years. 

3.4. Chemical Intake Calculations for Nursing Infants

The scientific literature indicates that infants can be exposed to some chemicals via their 

mothers’ breast milk. The magnitude of the exposure can be estimated from information on the 

mother’s exposure, data on the partitioning of the chemical into various compartments of the 

mother’s body and into breast milk, and information on the infant’s consumption of milk and 

absorption of the chemical. To add this exposure pathway to the MIRC application, we adapted 

exposure algorithms and default assumptions from EPA’s Methodology for Assessing Health 

Risks Associated with Multiple Pathways of Exposure to Combustor Emissions (EPA 1998), 

hereafter referred to as MPE, as explained below. 

Note that this pathway generally is of most concern for lipophilic bioaccumulative chemicals 

(e.g., dioxins) that can cause developmental effects. The period of concern for the more

hydrophilic chemicals that cause developmental effects generally is earlier, that is, from 

conception to birth. Hydrophilic chemicals generally exchange well between the maternal and 

fetal blood supplies at the placenta.

∑ = − = −

n

i

ADD ADD i 1 6( 11) 6( 11 ),

∑ = − = −

n

i

ADD ADD i 1 ( 1912 ) ( 1912 ),

∑ = = n

i

ADD adult ADD adult i 1 )( ),(





 



  +



 



  +



 



  +



 



  +



 



  +



 



 = < 70

50

70

8

70

6

70

3

70

2

70

1 LADD ADD( 1) ADD(1-2) ADD(3-5) ADD(6-11) ADD(12-19) ADD(adult)

Attachment B B-28 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

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 B-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 B-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 B-37 (and the equations that follow) that are not calculated from 

another equation, an EPA default value and options for other values available in MIRC for the 

infant breast-milk-exposure pathway are described in Section 6.4 of this attachment. The user 

also can overwrite those parameter values with a different value from the literature as 

appropriate.

Equation B-37. Average Daily Dose of Chemical to the Nursing Infant

where:

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

weight-day)

Cmilkfat = 

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

lipid; calculated using Equation B-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 B-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 B-37 includes terms for the chemical in both the lipid- and nonlipid phases of milk. The remaining equations, however, assume that a chemical of concern will 

partition to the lipid or aqueous phase of breast milk, but not to both. Different models are used 

to estimate Cmilkfat (described in Section 3.4.2 of this attachment) and Caqueous (described in 

Section 3.4.3 of this attachment).

[( ) ( ( ))]

ATBW

CfC f IR EDAE

DAI

inf

milkfat mbm aqueous mbm milk inf

inf

×

+× ×××−× = 1

Attachment B B-29 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

3.4.2. Chemical Concentration in Breast Milk Fat

When developing the Methodology for Assessing Health Risks Associated with Multiple 

Pathways of Exposure to Combustor Emissions (MPE) (EPA 1998), EPA reviewed three firstorder kinetics models for estimating chemical concentration in breast milk fat. The model 

selected for use in MIRC is the model selected in MPE. The other two models were not 

considered in MPE because one used a biotransfer factor (BTF) approach considered more of a 

screening model than a predictive tool (Travis et al. 1988) and the other assumed that the 

contaminant concentration in the maternal fat compartment is at steady state and that the 

concentration in breast milk fat is the same as in maternal body fat (Smith 1987). The model in 

MIRC is a changing-concentration model that EPA adapted from a model by Sullivan et al. 

(1991). The model, shown in Equation B-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 B-38 accounts for the changing 

concentration in breast milk fat, but estimates one average value to represent the concentration 

over the entire duration of breast feeding. The model is dependent on the maternal body 

burden of the chemical and assumes that the chemical concentration in breast milk fat is the 

same as the concentration in general maternal body fat. According to reviewers of the model, 

this assumption warrants further investigation because milk fat appears to be synthesized in the 

mammary glands and may have lower chemical concentrations than general body fat stores 

(EPA 2001a). 

Equation B-38. Chemical Concentration in Breast Milk Fat

where:

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

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

ff = 

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

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

or EPA default - see Section 6.5 of this attachment) 

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 B-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 B-41) 

tbf = Duration of breast feeding (days) 

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

(days) 

( )















 − 













 −− ×

× +

×

× = − elim pn − fat bfelactk

fat elac

tk elim

fat elac fat bfelac

elim

elim fm

mat f

milkfat e

k

k

e

tkk

k

fk

DAI f C _ 1 1 1

_ _ _

Attachment B B-30 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

Equation B-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 B-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 B-38, 

see Appendix B of MPE (EPA 1998).

To estimate an average daily dose absorbed by an infant’s mother, or DAImat, the average daily 

dose (ADD) (in mg/kg-day) for the chemical from all sources that MIRC calculates for adults 

(ADD(adult), described in Section 3.3 of this attachment, Equation B-35), is multiplied by an 

absorption efficiency (AEmat) or fraction of the chemical absorbed by the oral route of exposure, 

as shown in Equation B-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 B-39. 

Equation B-39. Daily Maternal Absorbed Intake

where:

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

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

Section 3.3 of this attachment, Equation B-35) 

AEmat = 

Absorption efficiency of the chemical by the oral exposure route (i.e., chemicalspecific fraction of ingested chemical that is absorbed) by the mother (unitless) 

(value from literature or EPA default – see Section 6.4 of this attachment) 

DAImat = ADD(adult) × AEmat

Attachment B B-31 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

Equation B-35, used to calculate ADD(adult), is based on many medium-specific ingestion rates 

that are normalized to body weight. The adult body weights to which the homegrown food 

ingestion rates are normalized are the body weights of the consumers in the original USDA 

survey (see Section 6.3.3 of this attachment), 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 of this attachment. 

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 B-40 is used to convert a measured half-life for elimination of a chemical for 

adults or non-lactating women to an elimination rate constant (EPA 1998). The equation can be 

used to estimate any kind of chemical loss rate constant from a measured chemical half-life.

Equation B-40. Biological Elimination Rate Constant for Chemicals 

for Non-lactating Women

where:

kelim = 

Chemical-specific elimination rate constant for elimination of the chemical for 

non-lactating women (per day; e.g., via urine, bile to feces, exhalation)

ln2 = Natural log of 2 (unitless constant)

h = Chemical-specific biological half-life of chemical for non-lactating women (days) 

For chemicals transferred from the body of lactating women to breast milk, the rate of chemical 

elimination is augmented by the rate of chemical loss via the milk through breast feeding. The 

total elimination rate for lactating women sometimes is measured directly and reported in the 

literature. Where direct measurements are not available, and for chemicals that partition 

predominantly to the lipid-phase of milk, EPA has used Equation B-41 to estimate the total

chemical elimination rate for lactating women, kfat_elac (EPA 1998). 

Equation B-41. Biological Elimination Constant for Lipophilic Chemicals 

for Lactating Women

where:

h

kelim

ln2 =

fm mat

milk f mbm

fat elac elim f BW

IR ff kk

×

×× _ +=

Attachment B B-32 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

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 B-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 B-41 is based on a model from Smith (1987) and accounts for the additional 

elimination pathway for lipophilic chemicals via the breast milk fat. The term Kfat_elac is 

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

to kelim, which is the chemical-specific total elimination rate constant for non-lactating women. 

The breast feeding losses are estimated from the infant’s intake rate of breast milk (IRmilk), the 

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

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

weight (BWmat), and the fraction of the mother’s weight that is body fat (ffm). In Equation B-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 B-39. Body weight 

values for the mother are described in Section 6.5 of this attachment. 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 B-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 B-42. Chemical Concentration in Aqueous Phase of Breast Milk

elacaq pm

plmat bm

aqueous fk

DAI f Pc

C

×

×× =

_

Attachment B B-33 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

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 B-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 

B-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 B-44) 

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

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

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

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

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

milk (Pcbm). For highly water-soluble chemicals that are not transported via special carrier 

molecules, the chemical is assumed to diffuse passively from the mother’s blood serum to the 

aqueous phase of her milk, in which case Pcbm would equal 1.0. The denominator includes the 

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

women (kaq_elac) and the fraction of the mother’s weight that is plasma (fpl). Because the model 

assumes steady-state, it does not account for chemical species with long half-lives in the body 

or for body burden losses due to lactation. These factors are important for highly lipophilic 

chemicals and for non-lipophilic chemicals such as methyl mercury, lead, and cadmium that 

partition into body compartments such as red blood cells and bone. While these latter 

chemicals or forms of these chemicals are water-soluble when free, they have relatively long 

half-lives because they are in equilibrium with the chemical bound to macromolecules in some 

tissue compartments. Lead is of particular concern because it can be released from the bone 

into the blood during lactation, and thus into the breast milk (EPA 2001a). Due to this limitation, 

the model may over- or underestimate exposure to the infant. 

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

where:

fpl = Fraction of chemical in body (based on absorbed intake) that is in the blood 

plasma compartment (unitless); chemical-specific

( ) bp RBC bp

bpbl

pl f Pc f

ff

f

−+

× = 1

Attachment B B-34 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

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 B-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 

B-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 B-44. Biological Elimination Rate Constant for Hydrophilic Chemicals

where: 

kaq_elac = 

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

women for hydrophilic chemicals (per day)

kelim = 

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

women (per day; e.g., via urine, bile to feces, exhalation; value from literature or 

calculated from half-life using Equation B-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 B-29). This 

parameter could be estimated using Equation B-43 if a suitable chemical-specific fraction of 

chemical in the body that is in the whole blood (fbl) could be found. However, the value found for 

fbl is based on a single-dose study and is not considered reliable for use in chronic exposure 

calculations. 

We therefore conducted a literature search to identify existing physiologically based 

toxicokinetic (PBTK) models of lactational transfer of methylmercury (MeHg) in humans. Most 

PBTK models that we identified focused on gestational transfer of mercury between mother and 

_ elacaq = kk elim

Attachment B B-35 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

fetus, including a PBTK dynamic compartmental model for gestational transfer of MeHg in 

humans developed by Gearhart et al. (1995, 1996), and reparameterized by Clewell et al. 

(1999). 

We did find, however, that Byczkowski and Lipscomb (2001) had added a lactational transfer 

module to the Clewell et al. (1999) model. Byczkowski and Lipscomb compared their model’s 

predictions to epidemiological data from mother-nursing-infant pairs obtained following an 

accidental high-dose poisoning in Iraq (Amin-Zaki et al. 1976) and from 34 mother-nursinginfant pairs examined in a low-dose, chronic exposure environment (Fujita and Takabatake 

1977). Using data from the Iraq incident, Byczkowski and Lipscomb (2001) found good 

agreement between their model’s predictions and the clinical data relating MeHg concentrations 

in breast milk to MeHg concentrations in infant’s blood with time following the poisoning. To 

compare their model’s predictions to data from chronic exposure to low doses of MeHg, 

Byczkowski and Lipscomb (2001) simulated MeHg intake for 500 days prior to conception, 

continued through gestation, and 6.5 months (200 days) of lactation. Their model’s predictions 

were consistent with Fujita and Takabatake’s (1977) study, although use of hair/blood partition 

coefficients based on the results of the 1977 study precluded use of this comparison as model 

validation. Both the model predictions and the mean values from the 1977 data indicated that 

the concentration of MeHg in the blood of nursing infants was close to the MeHg concentration 

in their mothers’ blood (approximately 0.025 to 0.027 mg/L, Figure 4 of report). At those blood 

concentrations, the PBTK model estimated the average maternal intake of MeHg to be 0.68 ± 

0.33 (SD) μg/kg-day and the average infant intake of MeHg to be 0.80 ± 0.38 μg/kg-day. 

Therefore, for purposes of MIRC, the DAIinf of MeHg is estimated to be the same as the 

maternal intake per unit body weight (Equation B-42).

Equation B-45. Calculation of Infant Average Daily Absorbed Dose of Methyl Mercury

where: 

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

DAImat_MeHg = Average daily dose of methyl mercury absorbed by the mother, predominantly 

from fish (mg/kg-day) 

4. Dose-Response Values Used for Assessment

Chemical dose-response values included in MIRC include carcinogenic potency slope factors

for ingestion and non-cancer oral reference doses (RfDs) for chronic exposures. The doseresponse values currently used in MIRC for RTR assessments are shown in Exhibit B-6 For 

some chemicals, OAQPS has identified dose-response values for use in RTR (EPA 2007a), and 

these dose-response values are used in MIRC for RTR assessments. In general, OAQPS chose 

these values based on the following hierarchy of sources: EPA’s Integrated Risk Information 

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

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

Environmental Health Hazard Assessment (OEHHA) Toxicity Criteria Database. For PB-HAP 

chemicals that are currently evaluated in MIRC but do not currently have dose-response values 

identified by OAQPS for RTR, alternative methods for deriving values were used (see Sections 

4.2 and 4.4 of this attachment).

DAIinf_MeHg = DAImat_MeHg

Attachment B B-36 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

Exhibit B-6. Oral Dose-response Values Used to Calculate RTR Screening Threshold 

Emission Rates for PB-HAP Chemicalsa

Chemical CAS No.

Cancer Slope Factor Reference Dose

Value 

(mg/kg-day)

-1 Source

Value 

(mg/kgday)Source

Inorganics

Cadmium compounds in foodb 7440439 not available 1.0E-03 IRIS

Mercury (elemental) 7439976 not available not available

Mercuric chloride 7487947 not available 3.0E-04 IRIS

Methyl mercury (MeHg) 22967926 not available 1.0E-04 IRIS

Dioxins

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

Heptachlorodibenzo-p-dioxin 35822469 1.50E+03 Derived from WHO 2005 TEFsc not available

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

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

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

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

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

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

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

Hexachlorodibenzofuran

70648269 1.50E+04 Derived from WHO 

2005 TEFsc not available

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

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

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

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

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

Hexachlorodibenzo-p-dioxin 19408743 6.20E+03 IRIS not available

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

Hexachlorodibenzofuran 72918219 1.50E+04 Derived from WHO 2005 TEFsc not available

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

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

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

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

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

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

1,2,3,7,8-

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

1,2,3,7,8-

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

2,3,4,7,8-

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

2,3,7,8-

Tetrachlorodibenzo-p-dioxin 1746016 1.50E+05 EPA ORD 7E-10 IRIS

2,3,7,8-

Tetrachlorodibenzofuran 51207319 1.50E+04 Derived from WHO 2005 TEFsc not available

Attachment B B-37 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

Exhibit B-6. Oral Dose-response Values Used to Calculate RTR Screening Threshold 

Emission Rates for PB-HAP Chemicalsa

Chemical CAS No.

Cancer Slope Factor Reference Dose

Value 

(mg/kg-day)

-1 Source

Value 

(mg/kgday)Source

Polycyclic Organic Matter

1-Methylnaphthalene 90120 5.0E-01 EPA 1999, POM 

Group 72002d 7.0E-02 ATSDR

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

Group 75002d not available

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

Group 72002d 8.0E-02 IRIS

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

Group 72002d 5.0E-02 ATSDR

3-Methylcholanthrene 56495 2.2E+1 CalEPA not available

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

Acenaphthene 83329 5.0E-01 EPA 1999, POM 

Group 72002d 6.0E-02 IRIS

Acenaphthylene 208968 5.0E-01 EPA 1999, POM 

Group 72002d not available

Anthracene 120127 0e IRIS 3.0E-01 IRIS

Benz(a)anthracene 56553 1.2E+00 CalEPA not available

Benzo(a)pyrene 50328 7.3E+00 IRIS not available

Benzo(b)fluoranthene 205992 1.2E+00 CalEPA not available

Benzo(e)pyrene 192972 5.0E-01 EPA 1999, POM 

Group 72002d not available

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

Group 72002d not available

Benzo(k)fluoranthene 207089 1.2E+00 CalEPA not available

Carbazole 86748 2.0E-02 HEAST not available

Chrysene 218019 1.2E-01 CalEPA not available

Dibenzo(a,h)anthracene 53703 4.1E+00 CalEPA not available

Dibenzo(a,i)pyrene 189559 1.2E+02 CalEPA not available

Dibenzo(a,j)acridine 224420 1.2E+00 CalEPA not available

Fluoranthene 206440 5.0E-01 EPA 1999, POM 

Group 72002d 4.0E-02 IRIS

Fluorene 86737 5.0E-01 EPA 1999, POM 

Group 72002d 4.0E-02 IRIS

Indeno(1,2,3-c,d)pyrene 193395 1.2E+00 CalEPA not available

PAH, total 234 5.0E-01 EPA 1999, POM 

Group 71002d not available

Perylene 198550 5.0E-01 EPA 1999, POM 

Group 72002d not available

Phenanthrene 85018 0e IRIS not available

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

Group 71002d not available

Attachment B B-38 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

Exhibit B-6. Oral Dose-response Values Used to Calculate RTR Screening Threshold 

Emission Rates for PB-HAP Chemicalsa

Chemical CAS No.

Cancer Slope Factor Reference Dose

Value 

(mg/kg-day)

-1 Source

Value 

(mg/kgday)Source

Pyrene 129000 0e IRIS 3.0E-02 IRIS

Retene 483658 5.0E-01 EPA 1999, POM 

Group 72002d not available

ATSDR = Agency for Toxic Substances and Disease Registry, IRIS = Integrated Risk Information System, EPA OAQPS = EPA’s 

Office of Air Quality Planning and Standards, CalEPA = California EPA, EPA ORD = EPA’s Office of Research and Development, 

HEAST = EPA Health Effects Assessment Tables, TEF = toxic equivalency factor

a

Values as of June 2012; these values may be updated as newer ones become available.

b

There are RfDs for both water ingestion and food ingestion for cadmium – the RfD for food is used. c

Dose-response values for these dioxin congeners are not available from EPA sources. CSFs for these congeners were derived as 

discussed in Section 4.2 of this attachment. d

The method to assign oral cancer slope factors to polycyclic organic matter (POM) without CSFs available from other EPA sources is 

the same as that used in the 1999 National Air Toxics Assessment (EPA 1999). A complete description of the methodology is 

available at: http://www.epa.gov/ttn/atw/nata1999/99pdfs/pomapproachjan.pdf and is summarized in Section 4.4 of this attachment. e

Weight of evidence evaluations indicated that the available data were adequate to determine that this chemical was not carcinogenic 

(EPA 2010a), 

4.1. Cadmium

EPA has developed two chronic RfDs for cadmium (Cd), one for food and one for water, based 

on data in IRIS indicating a lower absorption efficiency of cadmium from food than from water. 

The default RfD set in MIRC is the higher RfD for Cd compounds in food (as described in 

Section A.2.3, the drinking water exposure pathway is not modeled in the screening scenario 

because the likelihood that humans would use a lake as a drinking water source is assumed to 

be low). Users of MIRC who assess exposures via drinking water would need to use the RfD 

for Cd compounds in water (i.e., 5.0E-04 mg/kg-day). 

4.2. Dioxins (2,3,7,8-TCDD)

For chemicals for which the critical health effect is developmental, either in utero and/or during 

the first months or years of life, the exposure duration and timing of exposure for comparison 

with the RfD (or comparable values) require special consideration. The most sensitive health 

endpoints for both mercury and 2,3,7,8-TCDD are neurological effects during development that 

have long-lasting effects on learning and social behaviors. To ensure a protective risk 

characterization for these chemicals, it is important to use the shortest exposure duration 

appropriate, at the appropriate life stage, for comparison with the toxicity reference values. This 

approach avoids “dilution” of an estimated average ADD that would result from averaging the 

lower daily chemical intake rates normalized to body weight for older children and adults with 

the potentially higher daily intake rates of infants over a longer exposure averaging period.

The convention for assessing risk from mixtures of dioxins is by application of toxic equivalency 

factors (TEFs) to dioxin concentrations, which are then expressed as toxic equivalents (TEQs). 

Of the dioxin congeners, 2,3,7,8-TCDD is the most widely studied and considered to be one of 

the most toxic congeners. It is therefore assigned a TEF of 1, with the other dioxin congener 

TEQ concentrations scaled relative to 2,3,7,8-TCDD concentrations on the basis of toxicity. For 

risk assessment of dioxins for RTR (with one exception), the World Health Organization (WHO) 

2005 TEFs presented in Exhibit B-7 were used to derive the CSFs (shown in Exhibit B-6) for 

dioxin congeners without available EPA dose response values. The one exception is 

1,2,3,7,8,9-Hexachlorodibenzo-p-dioxin, whose TEF was based on data from IRIS.

Attachment B B-39 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

Exhibit B-7. WHO 2005 Toxic Equivalency Factors (TEFs) for Dioxin 

Congeners

Dioxin Congener CAS No. WHO 2005 Toxic 

Equivalency Factora

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

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

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

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

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

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

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

1,2,3,7,8,9-Hexachlorodibenzo-p-dioxinb 19408743 0.04

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

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

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

1,2,3,4,6,7,8,9-Octachlorodibenzofuran 39001020 3E-04

1,2,3,7,8-Pentachlorodibenzo-p-dioxin 40321764 1

1,2,3,7,8-Pentachlorodibenzofuran 57117416 0.03

2,3,4,7,8-Pentachlorodibenzofuran 57117314 0.3

2,3,7,8-Tetrachlorodibenzo-p-dioxin 1746016 1

2,3,7,8-Tetrachlorodibenzofuran 51207319 0.1

a

Source: van den Berg et al. 2006, with the one exception in the next footnote. b

For 1,2,3,7,8,9-HexCDD, OAQPS identified an oral cancer slope factor from IRIS. For the purposes of 

these multipathway assessments, EPA uses the TEF derived from this IRIS oral CSF (6200 1/mg/kd/d, 

equaling a TEF of 0.041) rather than the WHO 2005 TEF of 0.1.

4.3. Mercury

The RfD applies to the pregnant mother as well as young children. EPA has not specified the 

minimum exposure duration at the RfD level of exposure that is appropriate to use in 

characterizing risk; we assume 10 years for women of childbearing age and 1 year for infants. 

We note that human exposures to MeHg are primarily through the consumption of fish and 

shellfish (EPA 2001b). EPA found that, on average, approximately 76 percent of the exposure 

to MeHg for women of childbearing age could be attributed to ingestion of mercury in freshwater 

and estuarine fish and shellfish, with the remaining 24 percent derived from marine fish and 

shellfish. Other sources accounted for less than 0.06 percent of total exposures (EPA 2001b). 

4.4. Polycyclic Organic Matter

Dose-response values for some of the POM species that are included in the screening 

assessment were not identified by OAQPS; for these POM species, an alternative methodology 

for identifying CSFs was needed. Previously, for risk assessment of inhalation exposures to 

polycyclic organic matter (POM) for EPA’s National-Scale Air Toxics Assessments (NATA) and 

for RTR, OAQPS developed an approach for characterizing risks associated with the individual 

POM species and POM groups reported in the National Emissions Inventory (NEI). Individual 

POMs were assigned to one of eight POM groups according to cancer potencies derived by 

EPA for IRIS and by CalEPA, and based on assumptions regarding relative carcinogenicity. 

OAQPS then estimated an inhalation CSF for each POM group. The same approach was used 

to derive oral CSFs for POMs without available CSFs. Each POM group (with all its member 

Attachment B B-40 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

POM species reported in NEI, not just the species currently evaluated in this assessment) and 

the corresponding CSFs using this methodology are presented in Exhibit B-8. These group 

CSFs are used only when OAQPS has not, for the purposes of RTR, identified a CSF specific to 

the individual chemical. 

Exhibit B-8. Oral Dose-response Values for

Polycyclic Organic Matter (POM) Groupsa

Individual POM or POM Group CAS No. Cancer Slope Factorb

(mg/kg-day)-1

POM Group 71002

Benz(a)anthracene/chrysene (7-PAH) 103

0.5

Total PAH 234

Polycyclic organic matter 246

16-PAH 40

16-PAH–7-PAH 75040

POM Group 72002

Anthracene 120127

0.5

Pyrene 129000

Benzo(g,h,i) perylene 191242

Benzo(e)pyrene 192972

Benzo(c)phenanthrene 195197

Perylene 198550

Benzo(g,h,i)fluoranthene 203123

Benzo(a)fluoranthene 203338

Fluoranthene 206440

Acenaphthylene 208968

1-Methylpyrene 2381217

12-Methylbenz(a)anthracene 2422794

Methylbenzopyrenes 247

Methylchrysene 248

Methylanthracene 26914181

Benzofluoranthenes 56832736

9-Methylbenz(a)anthracene 779022

1-Methylphenanthrene 832699

Acenaphthene 83329

Phenanthrene 85018

Fluorene 86737

2-Methylnaphthalene 91576

2-Chloronaphthalene 91587

POM Group 73002

7,12-Dimethylbenz(a)anthracene 57976 1000

POM Group 74002

Dibenzo(a,i)pyrene 189559

100 Dibenzo(a,h)pyrene 189640

Attachment B B-41 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

Exhibit B-8. Oral Dose-response Values for

Polycyclic Organic Matter (POM) Groupsa

Individual POM or POM Group CAS No. Cancer Slope Factorb

(mg/kg-day)-1

POM Group 75002

3-Methylcholanthrene 56495

10

Dibenzo(a,e)pyrene 192654

5-Methylchrysene 3697243

Benzo(a)pyrene 50328

Dibenzo(a,h)anthracene 53703

POM Group 76002

Benzo(b+k)fluoranthene 102

1 

Indeno(1,2,3-c,d)pyrene 193395

Benzo(j)fluoranthene 205823

Benzo(b)fluoranthene 205992

Benzo(k)fluoranthene 207089

Dibenzo(a,j)acridine 224420

Benz(a)anthracene 56553

POM Group 77002

Chrysene 218019 0.1

POM Group 78002

7-PAH 75 0.5

a

These group CSFs are used only when OAQPS has not, for the purposes of RTR, identified a CSF specific to 

the individual chemical.

b

The method to assign oral cancer slope factors to POM groups was the same as that used in the 1999 National 

Air Toxics Assessment (EPA 1999). A complete description of the methodology is available at: 

http://www.epa.gov/ttn/atw/nata1999/99pdfs/pomapproachjan.pdf. 

5. Risk Estimation 

For persistent and bioaccumulative hazardous air pollutants (PB-HAPs), risks from inhalation of 

a chemical directly from air generally will be negligible compared with risks from ingestion of the 

chemical from foodstuffs grown in an area subject to air deposition of the chemical. Risk 

characterization for carcinogens with a linear mode of action at low doses is described in 

Section 5.1 of this attachment. 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): 

Attachment B B-42 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

Equation B-46. Calculation of Excess Lifetime Cancer Risk

ELCR = LADD × CSF

where: 

ELCR = 

Estimated excess lifetime cancer risk from a chemical summed across all 

exposure pathways and media (unitless)

LADD = Lifetime average total daily dose from all exposure pathways and media (mg/kgday)

CSF = Oral carcinogenic potency slope factor for chemical (per mg/kg-day)

As described in Section 3.3 of this attachment, the LADD (in mg/kg-day) for a chemical is 

calculated to reflect age-related differences in exposure rates that are experienced by a 

hypothetical individual throughout his or her lifetime of exposure. The total chemical intake is 

normalized to a lifetime, which for the purposes of this assessment is assumed to be 70 years. 

EPA considers the possibility that children might be more sensitive than adults to toxic 

chemicals, including chemical carcinogens (EPA 2005b,c). Where data allow, EPA 

recommends development of lifestage-specific cancer potency CSFs. To date, EPA has 

developed a separate slope factor for early lifestage exposure for only one chemical (i.e., 1,1,1-

trichloroethane; EPA 2007b), and current data availability for most chemicals preclude this 

approach. EPA has, therefore, examined options for default adjustments of the CSF to protect 

children. To date, the only mode of action (MOA) for carcinogenesis for which EPA has 

adequate data to develop a reasonable quantitative default approach is mutagenesis (EPA

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

carcinogenic potency of a chemical may be approximately tenfold greater for the first 2 years of 

life (i.e., birth up to second birthday) and threefold greater for the next 14 years of life (i.e., ages 

2 through 15) than for adults (EPA 2005c). These conclusions are represented by agedependent adjustment factors (ADAFs) of 10, 3, and 1 for the first two lifestages and for adults, 

respectively. 

These three lifestages do not match the age categories for the home-grown food ingestion 

rates, the age categories in MIRC. As a consequence, ADAFs for the age groups in MIRC are 

adapted as time-weighted average values as follows:

To estimate total lifetime risk from a lifetime of exposure to such a chemical, EPA recommends 

estimating the cancer risk for each of the three lifestages separately and then adding the risks

for i = 1 to 6 age groups. 

( ) ( )

3

56

2

10 1 yr yr13

10

53

21

=

= ×+× =

< =

)-(

)-(

( 1)

ADAF

ADAF .

ADAF

( ) ( )

1

2

8

43 yrs 41 yrs

3

1912

6 11

=

= ×+× =

=

(adult)

)-(

)-(

ADAF

ADAF

ADAF

Attachment B B-43 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

Equations B-47 to B-53. Lifetime Cancer Risk: Chemicals with a

Mutagenic MOA for Cancer

Equation B-47. Risk(<1) = ADD(0–<1) × 10 × CSF × (1 yr/70 yr)

Equation B-48. Risk(1–2) = ADD(1–2) × 6.5 × CSF × (2 yr/70 yr)

Equation B-49. Risk(3–5) = ADD(3–5) × 3 × CSF × (3 yr/70 yr)

Equation B-50. Risk(6–11) = ADD(6–11) × 3 × CSF × (6 yr/70 yr)

Equation B-51. Risk(12–19) = ADD(12–19) × 2 × CSF × (8 yr/70 yr)

Equation B-52. Risk(adult) = ADD(adult) × 1 × CSF × (50 yr/70 yr)

Equation B-53. ELCR 

 

In other words, Equation B-53 indicates that the total excess lifetime cancer risk (ELCR) equals 

the sum of the age-group-specific risks estimated by Equation B-47 through Equation B-52, 

where:

Risk(<1) = Risk from chemical ingestion in first year of life

Risk(1-2) = Risk from chemical ingestion from first birthday through age 2 years

Risk(3-5) = Risk from chemical ingestion from age 3 through 5 years of age 

Risk(6-11) = Risk from chemical ingestion from age 6 through 11 years of age

Risk(12-19) = Risk from chemical ingestion from age 12 through 19 years of age

Risk(adult) = Risk from chemical ingestion from age 20 to 70 years age

ADD(<1) = Average daily dose for infants under one year of age (mg/kg-day)

ADD(1-2) = Average daily dose from first birthday through age 2 years of age (mg/kg-day)

ADD(3-5) = Average daily dose from age 3 through 5 years of age (mg/kg-day)

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

ADD(12-19) = Average daily dose from age 12 through 19 years of age (mg/kg-day)

ADD(adult) = Average daily dose for adults age 20 to 70 years of age (mg/kg-day)

CSF = Oral carcinogenic potency slope factor for chemical (per mg/kg-day)

Risk(i) = Risk from chemical ingestion for the ith age group 

ELCR = Total extra lifetime cancer risk (incremental or extra risk)

n = Number of age groups (i.e., 6)

5.2. Non-cancer Hazard Quotients

Non-cancer risks are presented as hazard quotients (HQs), that is, the ratio of the estimated 

daily intake (i.e., ADD) to the reference dose (e.g., chronic RfD). If the HQ for a chemical is 

equal to or less than 1, EPA believes that there is no appreciable risk that non-cancer health 

effects will occur. If the HQ is greater than 1 then there is at least some possibility for an 

adverse health effect. The larger the HQ value, the more likely it is that an adverse health effect 

may occur.

∑ = = n

i

Risk i 1 )(

Attachment B B-44 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

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 B-54 to indicate the potential for adverse health effects associated with chronic 

exposure via ingestion pathways. The HQ is the ratio of a long-term, daily average exposure 

normalized to the receptor's body weight (i.e., ADD) to the RfD for that chemical. HQs are 

threshold effects and are not additive across age groups. 

Equation B-54. Hazard Quotient for Chemicals with a Chronic RfD

where:

HQ = Hazard quotient for chemical (unitless)

ADD = Average daily ingested dose of chemical (mg/kg-day) from all food types and 

ingested media for the age group 

RfD = Chronic oral reference dose for chemical (mg/kg-day)

5.2.2. Hazard Quotients for Chemicals with RfD Based on Developmental 

Effects

For chemicals for which the toxicity reference value is an RfD based on developmental effects, 

a shorter exposure duration (ED) and averaging time (AT) may be required. For this type of 

chemical (e.g., methylmercury, 2,3,7,8-TCDD), the appropriate ED/AT and sensitive lifestage for 

exposure may need to be estimated from the information provided in the critical developmental 

study(ies) from which the RfD was derived (e.g., in consultation with the RfD documentation in 

EPA’s IRIS or in a toxicological profile developed for the chemical). For screening-level risk 

assessments, however, a health protective approach is to compare the highest ADD from 

among the child age categories provided in MIRC to the RfD, as is done for all PB-HAPs. This 

approach ensures that the highest exposure from among the various age groups evaluated is 

taken into consideration, regardless of which age group might be most relevant to the health 

effect of interest (i.e., the age group on which the RfD is based).

5.2.3. Hazard Index for Chemicals with RfDs 

When conducting screening-level assessments for multiple chemicals, it can be informative to 

calculate a hazard index (HI) for toxicologically similar chemicals (EPA 2000). The HI is the 

sum of HQs across chemicals (not age groups) as shown in Equation B-55. As with the HQ, if 

the HI value is less than 1, adverse health effects are not expected for that suite of chemicals. If 

the screening level HI exceeds 1, however, the risk assessor may in some instances, evaluate 

the assumptions of the screening-level assessment to determine if more realistic local values 

are available for parameters that drive risk. In addition, the risk assessor may need to examine 

the mode of action (MOA) and target organ(s) for the chemicals with the highest HQs to develop 

an appropriate approach to assessing their potential joint action. 

RfD

ADD HQ =

Attachment B B-45 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

Equation B-55. Hazard Index Calculation

HI = HQ1 + HQ2 ... HQn

where:

HI = Hazard index (unitless)

HQ1 = Hazard Quotient for chemical 1 (unitless)

HQ2 = Hazard Quotient for chemical 2 (unitless)

HQn = Hazard Quotient for chemical n (unitless)

The HI approach can be appropriate for chemicals with the same MOA and same target organ; 

however, MOA often is difficult to determine. An HI usually is “developed for each exposure 

route of interest, and for a single toxic effect or for toxicity to a single target organ” (EPA 2000; p 

79). If a receptor is exposed to multiple chemicals that affect different target organs or that 

operate by different MOAs, and if more than one HQ is close to 1, the risk assessor in some 

circumstances, may consider whether chemical interactions play a role in chemical toxicity (EPA

2000). Exposures to more than one chemical can result in a greater or lesser toxic response

than might be predicted on the basis of one or the other chemical acting alone (toxicologically 

independent) or acting in concert (toxicologically similar chemicals). Users are referred to 

EPA’s Supplementary Guidance for Conducting Health Risk Assessment of Chemical Mixtures 

for approaches to assessing the potential for adverse health effects from exposure to multiple 

chemicals (EPA 2000). 

Note that users of MIRC are responsible for determining how to interpret HQs for multiple 

chemicals. 

6. Model Input Options

This section describes the input options currently included in MIRC. Required user inputs for 

environmental media concentrations and air deposition rates, such as those predicted by 

(output of) TRIM.FaTE, are described in Section 6.1 of this attachment. Values for farm-foodchain (FFC) parameters for specific types of produce and animal products are discussed in 

Section 6.2. Options for parameterizing receptor characteristics are described in Section 6.3, 

including age-group-specific values for body weight, water ingestion, and food ingestion by food 

type. Options for other exposure parameter values in MIRC, such as exposure frequency and

loss of chemical during food preparation and cooking, are provided in Section 6.4. 

Where values for chemical-specific parameters are presented, values are presented only for 

PB-HAP chemicals currently evaluated using the TRIM-based RTR screening scenario. The 

database included with MIRC contains chemical-specific parameter values for a large number of 

chemicals (more than 500), because all of the chemical-specific input data compiled by EPA for 

use in HHRAP were uploaded into MIRC. However, only chemicals that are PB-HAPs 

evaluated for RTR are discussed in this document (see Sections 1.1.1 and 2.2.2), and the 

HHRAP inputs provided for other chemicals have not been reviewed or verified. The data 

presented in this chapter were reviewed and used to develop the set of modeling defaults used 

to calculate screening threshold emission rates for RTR. Note that the default values used to 

estimate RTR screening thresholds, and the justification for selecting a specific value from the 

data sets described in this chapter, are discussed in Chapter 7. 

Attachment B B-46 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

6.1. Environmental Concentrations 

As noted in Section 2 of this attachment, MIRC is intended to estimate exposures and risks to 

self-sufficient farming and fishing families from ingestion of FFC media in an area of airborne 

chemical deposition. The tool analyzes one exposure scenario at a time (e.g., adult farmer 

exposed to dioxin from ingestion of beef); therefore, it is best used to evaluate a maximally 

exposed individual (MEI) or family when MIRC is used to screen for possible risks.

The following values specific to the air pollutant of concern are required inputs to MIRC:

• a single air concentration (in g/m3);

• the fraction of chemical in the air that is in the vapor phase;

• air-to-surface deposition rates for both vapor- and particle-phase chemical in the air (in 

g/m2-yr);

• two fish tissue concentrations, one each for forage and game fish (i.e., fish in TL 3 and 

TL 4) (in mg/kg wet weight); 

• concentrations in drinking water (in g/L); and 

• four chemical concentrations in soil (in μg/g dry weight), one each for:

– surface soil in produce growing area,

– surface soil where livestock feed,

– root-zone soil in produce growing area, and

– root-zone soil in livestock feed growing area.

The MIRC software is configured to estimate ingestion exposures via drinking water for a 

specified chemical concentration in the drinking water source (e.g., groundwater well). 

However, no exposure via drinking water is assumed to occur when calculating the Tier 1 

screening thresholds. As discussed in Section 2.2.3, the drinking water exposure pathway is 

not modeled for the scenario developed for the Tier 1 assessment because the likelihood that 

humans would use a lake as a drinking water source is assumed to be low.

The user must provide the inputs listed above; no default values are included for these 

parameters in MIRC. Media concentrations output by TRIM.FaTE can be entered into the tool 

manually from model output files or can be imported directly from the TRIM.FaTE output files. 

For RTR evaluations, a tool to facilitate this process was developed using a Microsoft® ExcelTM 

routine written in Visual Basic. 

6.2. Farm-Food-Chain Parameter Values 

Using the chemical information specified in Section 6.1 above as inputs, MIRC calculates 

chemical concentrations in foods that are commonly grown or raised on family farms: exposed 

and protected fruits; exposed and protected vegetables; root vegetables; beef; total dairy 

products; pork; and poultry and eggs. 

6.2.1. List of Farm-Food-Chain (FFC) Parameters

MIRC estimates chemical concentrations in the produce identified above using algorithms from 

HHRAP (EPA 2005a) as described in Section 3.2 of this attachment. Parameter values

required for these HHRAP algorithms, including chemical-specific media transfer factors (e.g., 

Attachment B B-47 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

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 of this attachment, the HHRAP-recommended parameter values are the 

default values in MIRC; however, these and other inputs in MIRC can be revised as needed. 

Exhibit B-9 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 B-9. MIRC Parameters Used to Estimate Chemical Concentrations 

in Farm Foods

Parameter Description Units

Plants/Produce

BrAG-produceDW(i)Chemical-specific plant/soil chemical bioconcentration factor for 

edible portion of aboveground produce type i, exposed or 

protected

Unitless (g soil DW / g 

produce DW) 

BvAG(i) Chemical-specific air-to-plant biotransfer factor for aboveground 

produce type i for vapor-phase chemical in air

Unitless ([mg chemical /

g DW plant] / [mg 

chemical / g air])

Fw Fraction of wet deposition that adheres to plant surfaces; 0.2 for 

anions, 0.6 for cations and most organics

Unitless

Kds Chemical-specific soil/water partition coefficient L soil pore water / kg soil 

DW

kp(i) Plant-specific surface loss coefficient for aboveground exposed 

produce and animal forage and silage

yr-1

MAF(i) Moisture adjustment factor for aboveground produce type i to 

convert the chemical concentration estimated for dry-weight 

produce to the corresponding chemical concentration for fullweight fresh produce

Percent water

RCF Chemical-specific root concentration factor for tubers and root 

produce on a wet-weight (WW) basis

L soil pore water/ kg root 

WW

Rp(i) Plant-specific interception fraction for the edible portion of 

aboveground exposed produce or animal forage and silage

Unitless

Tp(i) Length of plant exposure to deposition per harvest of the edible 

portion of aboveground exposed produce or animal forage and 

silage

Year

VGAG(i) Empirical correction factor for aboveground exposed produce 

type i to address possible overestimate of the diffusive transfer of 

chemical from the outside to the inside of bulky produce, such as 

fruit 

Unitless

VGrootveg Empirical correction factor for belowground produce (i.e., tuber or 

root vegetable) to account for possible overestimate of the 

diffusive transfer of chemicals from the outside to the inside of 

bulky tubers or roots (based on carrots and potatoes)

Unitless

Yp(i) Plant-specific yield or standing crop biomass of the edible portion 

of produce or animal feed

kg produce DW/m2

Animal Products

Bs Soil bioavailability factor for livestock Unitless

MF Chemical-specific mammalian metabolism factor that accounts 

for endogenous degradation of the chemical

Unitless

Attachment B B-48 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

Exhibit B-9. MIRC Parameters Used to Estimate Chemical Concentrations 

in Farm Foods

Parameter Description Units

Ba(beef) Chemical-specific biotransfer factor for chemical in diet of cow to 

chemical in beef on a fresh-wet (FW; equivalent to WW) basis

mg chemical/kg FW 

tissue/mg chemical/day 

or day/kg FW tissue

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

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

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

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

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

Qp(i,m) Quantity of plant feed type i consumed per animal type m each 

day 

kg/day

Source: EPA Source: EPA 2005a 

DW = dry weight; FW = fresh weight; WW = wet weight

6.2.2. Produce Parameter Values

Exhibit B-10 and Exhibit B-11 provide the chemical-specific input values that are the current 

defaults for produce FFC food types in MIRC. Exhibit B-12 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. 

Exhibit B-10. Chemical-Specific Inputs for Produce Parameters

for Chemicals Included in MIRC

Chemical

Fraction of 

Wet 

Deposition 

(Fw) 

(unitless)a

Root 

Concentration 

Factor (RCF) 

(belowground)

(L/kg)b

Soil-Water 

Partition 

Coefficient 

(Kds) 

(L/kg)c

Chemical Air-toPlant 

Biotransfer 

Factor (BvAG(i)) 

(unitless)d

Inorganics

Cadmium compounds 0.6 NA 7.5E+01 NAe

Mercury (elemental) 0.6 NA 1.0E+03 0f

Mercuric chloride 0.6 NA 5.8E+04 1.8E+03

Methyl mercury 0.6 NA 7.0E+03 0f

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

Attachment B B-49 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

Exhibit B-10. Chemical-Specific Inputs for Produce Parameters

for Chemicals Included in MIRC

Chemical

Fraction of 

Wet 

Deposition 

(Fw) 

(unitless)a

Root 

Concentration 

Factor (RCF) 

(belowground)

(L/kg)b

Soil-Water 

Partition 

Coefficient 

(Kds) 

(L/kg)c

Chemical Air-toPlant 

Biotransfer 

Factor (BvAG(i)) 

(unitless)d

Benzo(b)fluoranthene 0.6 6.6E+03 3.8E+03 1.7E+05

Benzo(g,h,i)perylene 0.6 3.0E+04 2.6E+04 2.3E+06

Benzo(k)fluoranthene 0.6 8.7E+03 5.5E+03 2.8E+05

Chrysene 0.6 6.0E+03 3.4E+03 1.4E+04

Dibenz(a,h)anthracene 0.6 2.3E+04 1.4E+04 6.2E+06

Fluoranthene 0.6 2.2E+03 3.9E+02 9.0E+02

Fluorene 0.6 3.8E+02 6.2E+01 1.6E+01

Indeno(1,2,3-cd)pyrene 0.6 3.5E+04 3.2E+04 2.8E+06

Dioxins

OctaCDD, 

1,2,3,4,6,7,8,9- 0.6 4.8E+05 7.8E+05 2.4E+06

OctaCDF, 1,2,3,4,6,7,8,9- 0.6 3.4E+05 4.9E+05 2.3E+06

HeptaCDD, 1,2,3,4,6,7,8- 0.6 3.4E+05 4.9E+05 9.1E+05

HeptaCDF, 1,2,3,4,6,7,8- 0.6 1.2E+05 1.2E+05 8.3E+05

HeptaCDF, 1,2,3,4,7,8,9- 0.6 4.8E+04 3.9E+04 8.3E+05

HexaCDD, 1,2,3,4,7,8- 0.6 2.4E+05 3.1E+05 5.2E+05

HexaCDF, 1,2,3,4,7,8- 0.6 5.7E+04 4.9E+04 1.6E+05

HexaCDD, 1,2,3,6,7,8- 0.6 4.9E+05 8.0E+05 5.2E+05

HexaCDF, 1,2,3,6,7,8- 0.6 2.9E+05 4.1E+05 1.6E+05

HexaCDD, 1,2,3,7,8,9 - 0.6 4.9E+05 8.0E+05 5.2E+05

HexaCDF, 1,2,3,7,8,9- 0.6 1.6E+05 1.9E+05 1.6E+05

HexaCDF, 2,3,4,6,7,8- 0.6 2.9E+05 4.1E+05 1.6E+05

PentaCDD, 1,2,3,7,8- 0.6 9.2E+04 9.2E+04 2.4E+05

PentaCDF, 1,2,3,7,8- 0.6 3.9E+04 3.0E+04 9.8E+04

PentaCDF, 2,3,4,7,8- 0.6 2.3E+04 1.6E+04 9.8E+04

Attachment B B-50 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

Exhibit B-10. Chemical-Specific Inputs for Produce Parameters

for Chemicals Included in MIRC

Chemical

Fraction of 

Wet 

Deposition 

(Fw) 

(unitless)a

Root 

Concentration 

Factor (RCF) 

(belowground)

(L/kg)b

Soil-Water 

Partition 

Coefficient 

(Kds) 

(L/kg)c

Chemical Air-toPlant 

Biotransfer 

Factor (BvAG(i)) 

(unitless)d

TetraCDD, 2,3,7,8- 0.6 4.0E+04 3.1E+04 6.6E+04

TetraCDF, 2,3,7,8- 0.6 1.2E+04 6.2E+03 4.6E+04

Source: EPA 2005a. NA = not applicable. 

a

6E-01 is the value for cations and most organic chemicals. As described in HHRAP (EPA 2005a), Appendix B (available at 

http://www.epa.gov/osw/hazard/tsd/td/combust/finalmact/ssra/05hhrapapb.pdf), EPA estimated this value (EPA 1994a, 1995a) 

from a study by Hoffman et al. (1992) in which soluble gamma-emitting radionuclides and insoluble particles tagged with gammaemitting radionuclides were deposited onto pasture grass via simulated rain. Note that the values developed experimentally for 

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

behavior of insoluble particles tagged with radionuclides may not accurately represent the behavior of organic compounds under 

site-specific conditions. b

For nonionic organic chemicals, as described in HHRAP (EPA 2005a), Appendix A (available at 

http://www.epa.gov/osw/hazard/tsd/td/combust/finalmact/ssra/05hhrapapa.pdf ), RCF is used to calculate the below-ground 

transfer of contaminants from soil to a root vegetable on a wet-weight basis as shown in Equation B-6. EPA estimated chemicalspecific values for RCF from empirical regression equations developed by Briggs et al. (1982) based on their experiments 

measuring uptake of compounds into barley roots from growth solution. Briggs’ regression equations allow calculation of RCF 

values from log Kow. For metals and mercuric compounds, empirical values for soil to root vegetable transfer on a dry-weight 

basis are available in the literature, thus the RCF was not needed.

c

As discussed in HHRAP (EPA 2005a), Appendix A, Kds describes the partitioning of a compound between soil pore-water and 

soil particles and strongly influences the release and movement of a compound into the subsurface soils and underlying aquifer. 

Kds values for mercuric compounds were obtained from EPA (1997b). Kds for cadmium compounds were obtained from EPA

1996. For all PAHs and dioxins, Kds was calculated by multiplying Koc times the screening scenario’s fraction organic carbon

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

dibenz(a,h)anthracene, fluoranthene, and fluorene in USEAP 1996. For all other organic compounds, the Koc was calculated 

using the correlation equations presented in USEAP 2005a.

d

As discussed in HHRAP (EPA 2005a), Appendix A, the value for mercuric chloride was obtained from EPA 1997b. BvAG(i) 

values for PAHs were calculated using the correlation equation derived for azalea leaves as cited in Bacci et al. (1992), then 

reducing this value by a factor of 100, as suggested by Lorber (1995), who concluded that the Bacci factor reduced by a factor of 

100 was similar to his own observations in various studies. The values for dioxins were obtained from Lorber and Pinsky (2000). e

It is assumed that metals, with the exception of vapor-phase elemental mercury, do not transfer significantly from air into leaves.

fSpeciation and fate and transport of mercury from emissions suggest that BvAG(i) values for elemental and methyl mercury are 

likely to be zero (EPA 2005a).

Exhibit B-11. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part

Plant-Soil BioConcentration 

Factor 

(BrAG-produce-DW(i))

(unitless)

a

Empirical Correction 

Factor- Belowground 

Produce 

(VGrootveg) (unitless)b

Empirical 

Correction FactorAboveground 

Produce 

(VGAG(i)) (unitless)c

Inorganics

Cadmium compounds

Exp. Fruit 1.3E-01 - 1.0E+00

Exp. Veg. 1.3E-01 - 1.0E+00

Forage 3.6E-01 - 1.0E+00

Grain 6.2E-02 - -

Prot. Fruit 1.3E-01 - -

Prot. Veg. 1.3E-01 - -

Root 6.4E-02 1.0E+00 -

Silage 3.6E-01 - 5.0E-01

Attachment B B-51 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

Exhibit B-11. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part

Plant-Soil BioConcentration 

Factor 

(BrAG-produce-DW(i))

(unitless)

a

Empirical Correction 

Factor- Belowground 

Produce 

(VGrootveg) (unitless)b

Empirical 

Correction FactorAboveground 

Produce 

(VGAG(i)) (unitless)c

Mercury (elemental)

Exp. Fruit - - 1.0E+00

Exp. Veg. - - 1.0E+00

Forage - - 1.0E+00

Grain - - -

Prot. Fruit - - -

Prot. Veg. - - -

Root - 1.0E+00 -

Silage - - 5.0E-01

Mercuric chloride

Exp. Fruit 1.5E-02 - 1.0E+00

Exp. Veg. 1.5E-02 - 1.0E+00

Forage 0.0E+00 - 1.0E+00

Grain 9.3E-03 - -

Prot. Fruit 1.5E-02 - -

Prot. Veg. 1.5E-02 - -

Root 3.6E-02 1.0E+00 -

Silage 0.0E+00 - 5.0E-01

Methyl mercury

Exp. Fruit 2.9E-02 - 1.0E-02

Exp. Veg. 2.9E-02 - 1.0E-02

Forage 0.0E+00 - 1.0E+00

Grain 1.9E-02 - -

Prot. Fruit 2.9E-02 - -

Prot. Veg. 2.9E-02 - -

Root 9.9E-02 1.0E-02 -

Silage 0.0E+00 - 5.0E-01

PAHs

2-Methylnaphthalene

Exp. Fruit 2.3E-01 - 1.0E+00

Exp. Veg. 2.3E-01 - 1.0E+00

Forage 2.3E-01 - 1.0E+00

Grain 2.3E-01 - -

Prot. Fruit 2.3E-01 - -

Prot. Veg. 2.3E-01 - -

Root 4.4E+00 1.0E+00 -

Silage 2.3E-01 - 5.0E-01

7,12-

Dimethylbenz(a)anthra

cene

Exp. Fruit 1.7E-02 - 1.0E-02

Exp. Veg. 1.7E-02 - 1.0E-02

Forage 1.7E-02 - 1.0E+00

Grain 1.7E-02 - -

Prot. Fruit 1.7E-02 - -

Prot. Veg. 1.7E-02 - -

Root 1.7E+00 1.0E-02 -

Silage 1.7E-02 - 5.0E-01

Attachment B B-52 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

Exhibit B-11. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part

Plant-Soil BioConcentration 

Factor 

(BrAG-produce-DW(i))

(unitless)

a

Empirical Correction 

Factor- Belowground 

Produce 

(VGrootveg) (unitless)b

Empirical 

Correction FactorAboveground 

Produce 

(VGAG(i)) (unitless)c

Acenaphthene

Exp. Fruit 2.1E-01 - 1.0E+00

Exp. Veg. 2.1E-01 - 1.0E+00

Forage 2.1E-01 - 1.0E+00

Grain 2.1E-01 - -

Prot. Fruit 2.1E-01 - -

Prot. Veg. 2.1E-01 - -

Root 6.2E+00 1.0E+00 -

Silage 2.1E-01 - 5.0E-01

Acenaphthylene

Exp. Fruit 1.9E-01 - 1.0E-02

Exp. Veg. 1.9E-01 - 1.0E-02

Forage 1.9E-01 - 1.0E+00

Grain 1.9E-01 - -

Prot. Fruit 1.9E-01 - -

Prot. Veg. 1.9E-01 - -

Root 4.1E+00 1.0E-02 -

Silage 1.9E-01 - 5.0E-01

Benz(a)anthracene

Exp. Fruit 1.7E-02 - 1.0E-02

Exp. Veg. 1.7E-02 - 1.0E-02

Forage 1.7E-02 - 1.0E+00

Grain 1.7E-02 - -

Prot. Fruit 1.7E-02 - -

Prot. Veg. 1.7E-02 - -

Root 2.3E+00 1.0E-02 -

Silage 1.7E-02 - 5.0E-01

Benzo(a)pyrene

Exp. Fruit 1.4E-02 - 1.0E-02

Exp. Veg. 1.4E-02 - 1.0E-02

Forage 1.4E-02 - 1.0E+00

Grain 1.4E-02 - -

Prot. Fruit 1.4E-02 - -

Prot. Veg. 1.4E-02 - -

Root 1.2E+00 1.0E-02 -

Silage 1.4E-02 - 5.0E-01

Attachment B B-53 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

Exhibit B-11. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part

Plant-Soil BioConcentration 

Factor 

(BrAG-produce-DW(i))

(unitless)

a

Empirical Correction 

Factor- Belowground 

Produce 

(VGrootveg) (unitless)b

Empirical 

Correction FactorAboveground 

Produce 

(VGAG(i)) (unitless)c

Benzo(b)fluoranthene

Exp. Fruit 1.8E-02 - 1.0E-02

Exp. Veg. 1.8E-02 - 1.0E-02

Forage 1.8E-02 - 1.0E+00

Grain 1.8E-02 - -

Prot. Fruit 1.8E-02 - -

Prot. Veg. 1.8E-02 - -

Root 1.7E+00 1.0E-02 -

Silage 1.8E-02 - 5.0E-01

Benzo(g,h,i)perylene

Exp. Fruit 5.7E-03 - 1.0E-02

Exp. Veg. 5.7E-03 - 1.0E-02

Forage 5.7E-03 - 1.0E+00

Grain 5.7E-03 - -

Prot. Fruit 5.7E-03 - -

Prot. Veg. 5.7E-03 - -

Root 1.1E+00 1.0E-02 -

Silage 5.7E-03 - 5.0E-01

Benzo(k)fluoranthene

Exp. Fruit 1.4E-02 - 1.0E-02

Exp. Veg. 1.4E-02 - 1.0E-02

Forage 1.4E-02 - 1.0E+00

Grain 1.4E-02 - -

Prot. Fruit 1.4E-02 - -

Prot. Veg. 1.4E-02 - -

Root 1.6E+00 1.0E-02 -

Silage 1.4E-02 - 5.0E-01

Chrysene

Exp. Fruit 1.9E-02 - 1.0E-02

Exp. Veg. 1.9E-02 - 1.0E-02

Forage 1.9E-02 - 1.0E+00

Grain 1.9E-02 - -

Prot. Fruit 1.9E-02 - -

Prot. Veg. 1.9E-02 - -

Root 1.7E+00 1.0E-02 -

Silage 1.9E-02 - 5.0E-01

Attachment B B-54 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

Exhibit B-11. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part

Plant-Soil BioConcentration 

Factor 

(BrAG-produce-DW(i))

(unitless)

a

Empirical Correction 

Factor- Belowground 

Produce 

(VGrootveg) (unitless)b

Empirical 

Correction FactorAboveground 

Produce 

(VGAG(i)) (unitless)c

Dibenz(a,h)anthracene

Exp. Fruit 6.8E-03 - 1.0E-02

Exp. Veg. 6.8E-03 - 1.0E-02

Forage 6.8E-03 - 1.0E+00

Grain 6.8E-03 - -

Prot. Fruit 6.8E-03 - -

Prot. Veg. 6.8E-03 - -

Root 1.6E+00 1.0E-02 -

Silage 6.8E-03 - 5.0E-01

Fluoranthene

Exp. Fruit 4.0E-02 - 1.0E-02

Exp. Veg. 4.0E-02 - 1.0E-02

Forage 4.0E-02 - 1.0E+00

Grain 4.0E-02 - -

Prot. Fruit 4.0E-02 - -

Prot. Veg. 4.0E-02 - -

Root 5.6E+00 1.0E-02 -

Silage 4.0E-02 - 5.0E-01

Fluorene

Exp. Fruit 1.5E-01 - 1.0E-02

Exp. Veg. 1.5E-01 - 1.0E-02

Forage 1.5E-01 - 1.0E+00

Grain 1.5E-01 - -

Prot. Fruit 1.5E-01 - -

Prot. Veg. 1.5E-01 - -

Root 6.2E+00 1.0E-02 -

Silage 1.5E-01 - 5.0E-01

Indeno(1,2,3-

cd)pyrene

Exp. Fruit 5.1E-03 - 1.0E-02

Exp. Veg. 5.1E-03 - 1.0E-02

Forage 5.1E-03 - 1.0E+00

Grain 5.1E-03 - -

Prot. Fruit 5.1E-03 - -

Prot. Veg. 5.1E-03 - -

Root 1.1E+00 1.0E-02 -

Silage 5.1E-03 - 5.0E-01

Attachment B B-55 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

Exhibit B-11. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part

Plant-Soil BioConcentration 

Factor 

(BrAG-produce-DW(i))

(unitless)

a

Empirical Correction 

Factor- Belowground 

Produce 

(VGrootveg) (unitless)b

Empirical 

Correction FactorAboveground 

Produce 

(VGAG(i)) (unitless)c

Dioxins

OctaCDD, 

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

Exp. Fruit 7.1E-04 - 1.0E-02

Exp. Veg. 7.1E-04 - 1.0E-02

Forage 7.1E-04 - 1.0E+00

Grain 7.1E-04 - -

Prot. Fruit 7.1E-04 - -

Prot. Veg. 7.1E-04 - -

Root 6.1E-01 1.0E-02 -

Silage 7.1E-04 - 5.0E-01

OctaCDF, 

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

Exp. Fruit 9.2E-04 - 1.0E-02

Exp. Veg. 9.2E-04 - 1.0E-02

Forage 9.2E-04 - 1.0E+00

Grain 9.2E-04 - -

Prot. Fruit 9.2E-04 - -

Prot. Veg. 9.2E-04 - -

Root 6.8E-01 1.0E-02 -

Silage 9.2E-04 - 5.0E-01

HeptaCDD, 

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

Exp. Fruit 9.2E-04 - 1.0E-02

Exp. Veg. 9.2E-04 - 1.0E-02

Forage 9.2E-04 - 1.0E+00

Grain 9.2E-04 - -

Prot. Fruit 9.2E-04 - -

Prot. Veg. 9.2E-04 - -

Root 6.8E-01 1.0E-02 -

Silage 9.2E-04 - 5.0E-01

HeptaCDF, 

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

Exp. Fruit 2.0E-03 - 1.0E-02

Exp. Veg. 2.0E-03 - 1.0E-02

Forage 2.0E-03 - 1.0E+00

Grain 2.0E-03 - -

Prot. Fruit 2.0E-03 - -

Prot. Veg. 2.0E-03 - -

Root 9.4E-01 1.0E-02 -

Silage 2.0E-03 - 5.0E-01

Attachment B B-56 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

Exhibit B-11. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part

Plant-Soil BioConcentration 

Factor 

(BrAG-produce-DW(i))

(unitless)

a

Empirical Correction 

Factor- Belowground 

Produce 

(VGrootveg) (unitless)b

Empirical 

Correction FactorAboveground 

Produce 

(VGAG(i)) (unitless)c

HeptaCDF, 

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

Exp. Fruit 4.0E-03 - 1.0E-02

Exp. Veg. 4.0E-03 - 1.0E-02

Forage 4.0E-03 - 1.0E+00

Grain 4.0E-03 - -

Prot. Fruit 4.0E-03 - -

Prot. Veg. 4.0E-03 - -

Root 1.2E+00 1.0E-02 -

Silage 4.0E-03 - 5.0E-01

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

Exp. Fruit 1.2E-03 - 1.0E-02

Exp. Veg. 1.2E-03 - 1.0E-02

Forage 1.2E-03 - 1.0E+00

Grain 1.2E-03 - -

Prot. Fruit 1.2E-03 - -

Prot. Veg. 1.2E-03 - -

Root 7.6E-01 1.0E-02 -

Silage 1.2E-03 - 5.0E-01

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

Exp. Fruit 3.5E-03 - 1.0E-02

Exp. Veg. 3.5E-03 - 1.0E-02

Forage 3.5E-03 - 1.0E+00

Grain 3.5E-03 - -

Prot. Fruit 3.5E-03 - -

Prot. Veg. 3.5E-03 - -

Root 1.2E+00 1.0E-02 -

Silage 3.5E-03 - 5.0E-01

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

Exp. Fruit 7.0E-04 - 1.0E-02

Exp. Veg. 7.0E-04 - 1.0E-02

Forage 7.0E-04 - 1.0E+00

Grain 7.0E-04 - -

Prot. Fruit 7.0E-04 - -

Prot. Veg. 7.0E-04 - -

Root 6.1E-01 1.0E-02 -

Silage 7.0E-04 - 5.0E-01

Attachment B B-57 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

Exhibit B-11. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part

Plant-Soil BioConcentration 

Factor 

(BrAG-produce-DW(i))

(unitless)

a

Empirical Correction 

Factor- Belowground 

Produce 

(VGrootveg) (unitless)b

Empirical 

Correction FactorAboveground 

Produce 

(VGAG(i)) (unitless)c

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

Exp. Fruit 1.0E-03 - 1.0E-02

Exp. Veg. 1.0E-03 - 1.0E-02

Forage 1.0E-03 - 1.0E+00

Grain 1.0E-03 - -

Prot. Fruit 1.0E-03 - -

Prot. Veg. 1.0E-03 - -

Root 7.1E-01 1.0E-02 -

Silage 1.0E-03 - 5.0E-01

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

Exp. Fruit 7.0E-04 - 1.0E-02

Exp. Veg. 7.0E-04 - 1.0E-02

Forage 7.0E-04 - 1.0E+00

Grain 7.0E-04 - -

Prot. Fruit 7.0E-04 - -

Prot. Veg. 7.0E-04 - -

Root 6.1E-01 1.0E-02 -

Silage 7.0E-04 - 5.0E-01

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

Exp. Fruit 1.6E-03 - 1.0E-02

Exp. Veg. 1.6E-03 - 1.0E-02

Forage 1.6E-03 - 1.0E+00

Grain 1.6E-03 - -

Prot. Fruit 1.6E-03 - -

Prot. Veg. 1.6E-03 - -

Root 8.5E-01 1.0E-02 -

Silage 1.6E-03 - 5.0E-01

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

Exp. Fruit 1.0E-03 - 1.0E-02

Exp. Veg. 1.0E-03 - 1.0E-02

Forage 1.0E-03 - 1.0E+00

Grain 1.0E-03 - -

Prot. Fruit 1.0E-03 - -

Prot. Veg. 1.0E-03 - -

Root 7.1E-01 1.0E-02 -

Silage 1.0E-03 - 5.0E-01

Attachment B B-58 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

Exhibit B-11. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part

Plant-Soil BioConcentration 

Factor 

(BrAG-produce-DW(i))

(unitless)

a

Empirical Correction 

Factor- Belowground 

Produce 

(VGrootveg) (unitless)b

Empirical 

Correction FactorAboveground 

Produce 

(VGAG(i)) (unitless)c

PentaCDD, 1,2,3,7,8- 

Exp. Fruit 2.4E-03 - 1.0E-02

Exp. Veg. 2.4E-03 - 1.0E-02

Forage 2.4E-03 - 1.0E+00

Grain 2.4E-03 - -

Prot. Fruit 2.4E-03 - -

Prot. Veg. 2.4E-03 - -

Root 1.0E+00 1.0E-02 -

Silage 2.4E-03 - 5.0E-01

PentaCDF, 1,2,3,7,8- 

Exp. Fruit 4.6E-03 - 1.0E-02

Exp. Veg. 4.6E-03 - 1.0E-02

Forage 4.6E-03 - 1.0E+00

Grain 4.6E-03 - -

Prot. Fruit 4.6E-03 - -

Prot. Veg. 4.6E-03 - -

Root 1.3E+00 1.0E-02 -

Silage 4.6E-03 - 5.0E-01

PentaCDF, 2,3,4,7,8- 

Exp. Fruit 6.8E-03 - 1.0E-02

Exp. Veg. 6.8E-03 - 1.0E-02

Forage 6.8E-03 - 1.0E+00

Grain 6.8E-03 - -

Prot. Fruit 6.8E-03 - -

Prot. Veg. 6.8E-03 - -

Root 1.5E+00 1.0E-02 -

Silage 6.8E-03 - 5.0E-01

TetraCDD, 2,3,7,8- 

Exp. Fruit 4.5E-03 - 1.0E-02

Exp. Veg. 4.5E-03 - 1.0E-02

Forage 4.5E-03 - 1.0E+00

Grain 4.5E-03 - -

Prot. Fruit 4.5E-03 - -

Prot. Veg. 4.5E-03 - -

Root 1.3E+00 1.0E-02 -

Silage 4.5E-03 - 5.0E-01

Attachment B B-59 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

Exhibit B-11. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part

Plant-Soil BioConcentration 

Factor 

(BrAG-produce-DW(i))

(unitless)

a

Empirical Correction 

Factor- Belowground 

Produce 

(VGrootveg) (unitless)b

Empirical 

Correction FactorAboveground 

Produce 

(VGAG(i)) (unitless)c

TetraCDF, 2,3,7,8- 

Exp. Fruit 1.2E-02 - 1.0E-02

Exp. Veg. 1.2E-02 - 1.0E-02

Forage 1.2E-02 - 1.0E+00

Grain 1.2E-02 - -

Prot. Fruit 1.2E-02 - -

Prot. Veg. 1.2E-02 - -

Root 1.9E+00 1.0E-02 -

Silage 1.2E-02 - 5.0E-01

a

As discussed in HHRAP (EPA 2005a), the BrAG-produce-DW(i) for aboveground produce and forage accounts for the uptake from soil 

and the subsequent transport of contaminants through the roots to the aboveground plant parts. For organics, correlation 

equations to calculate values for Br on a dry weight basis were obtained from Travis and Arms (1988). For cadmium, Br values 

were derived from uptake slope factors provided in EPA 1992. Uptake slope is the ratio of contaminant concentration in dry weight 

plant tissue to the mass of contaminant applied per hectare soil. Br aboveground values for mercuric chloride and methyl mercury 

were calculated using methodology and data from Baes, et al. (1984). Br forage values for mercuric chloride and methyl mercury 

(on a dry weight basis) were obtained from EPA 1997b. The HHRAP methodology assumes that elemental mercury doesn’t 

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

As discussed in HHRAP (EPA 2005a), Appendix B, VGrootveg represents an empirical correction factor that reduces produce 

concentration. Because of the protective outer skin, size, and shape of bulky produce, transfer of lipophilic chemicals (i.e., log Kow

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

cooking, further reduce the concentration of the chemical in the vegetable as consumed by removing the high concentration of 

chemical on and in the outer skin, leaving the flesh with a lower concentration than would be the case if the entire vegetable were 

pureed without washing. For belowground produce, HHRAP (EPA 2005a) recommends using a VGrootveg value of 0.01 for PB-HAP 

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

1994b. In developing these values, EPA (1994b) assumed that the density of the skin and the whole vegetable are equal

(potentially overestimating the concentration of PB-HAP in belowground produce due to root uptake). c

As discussed in HHRAP (EPA 2005a), Appendix B, VGag represents an empirical correction factor that reduces aboveground 

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

produce (e.g., apples). Because of the protective outer skin, size, and shape of bulky produce, transfer of lipophilic PB-HAP (log 

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

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

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

EPA 1994b. In developing these values, EPA (1994b) assumed the following: (1) translocation of compounds deposited on the 

surface of aboveground vegetation to inner parts of aboveground produce would be insignificant (potentially underestimating the 

concentration of PB-HAP in aboveground produce due to air-to-plant transfer); (2) the density of the skin and the whole vegetable 

are equal (potentially overestimating the concentration of PB-HAP in aboveground produce due to air-to-plant transfer); and (3) the 

thickness of vegetable skin and broadleaf tree skin are equal (effects on the concentration of PB-HAP in aboveground produce 

due to air-toplant transfer unknown). 

For forage, HHRAP recommends a VGag value of 1.0, also based on information provided in EPA 1994b. 

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

however, NC DEHNR does not present a specific rationale for this recommendation. Depending on the composition of the sitespecific silage, this value may under- or overestimate the actual value. 

Attachment B B-60 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

Exhibit B-12. Non-Chemical-Specific Produce Inputs

Plant Part

Interception 

Fraction 

(Rp(i)) 

(unitless)a

Plant 

Surface 

Loss 

Coefficient 

(kp(i)) 

(1/year)b

Length of 

Plant 

Exposure to 

Deposition 

(Tp(i)) 

(year)c

Yield or 

Standing 

Crop 

Biomass 

(Yp(i)) 

(kg/m2)

d

Plant TissueSpecific 

Moisture 

Adjustment 

Factor (MAF(i)) 

(percent)

e

Exposed Vegetable 0.982 18 0.16 5.66 92

Protected Fruit NA NA NA NA 90

Protected 

Vegetable NA NA NA NA 80

Forage (animal 

feed) 0.5 18 0.12 0.24 92

Exposed Fruit 0.053 18 0.16 0.25 85

Root Vegetables NA NA NA NA 87

Silage (animal feed) 0.46 18 0.16 0.8 92

Grain (animal feed) NA NA NA NA 90

Source: EPA 2005a. NA = not applicable. 

a

Baes et al. (1984) used an empirical relationship developed by Chamberlain (1970) to identify a correlation between initial Rp 

values and pasture grass productivity (standing crop biomass [Yp]) to calculate Rp values for exposed vegetables, exposed fruits, 

forage, and silage. Two key uncertainties are associated with using these values for Rp: (1) Chamberlain’s (1970) empirical 

relationship developed for pasture grass may not accurately represent aboveground produce. (2) The empirical constants 

developed by Baes et al. (1984) for use in the empirical relationship developed by Chamberlain (1970) may not accurately 

represent the site-specific mixes of aboveground produce consumed by humans or the site-specific mixes of forage or silage 

consumed by livestock.

b

The term kp is a measure of the amount of chemical that is lost to natural physical processes (e.g., wind, water) over time. The 

HHRAP-recommended value of 18 yr-1 (also recommended by EPA 1994a and 1998) represents the midpoint of a range of values 

reported by Miller and Hoffman (1983). There are two key uncertainties associated with using these values for kp: (1) The 

recommended equation for calculating kp includes a health protective bias in that it does not consider chemical degradation 

processes. (2) Given the reported range of kp values from 7.44 to 90.36 yr-1, plant concentrations could range from about 1.8 

times higher to about 5 times lower than the plant concentrations estimated in FFC media using the midpoint kp value of 18. c

HHRAP (EPA 2005a) recommends using a Tp value of 0.16 years for aboveground produce and cattle silage. This is consistent 

with earlier reports by EPA (1994a, 1998) and NC DEHNR (1997), which recommended treating Tp as a constant based on the 

average period between successive hay harvests. Belcher and Travis (1989) estimated this period at 60 days. Tp is calculated 

as 60 days ÷ 365 days/year = 0.16 years. For forage, the average of the average period between successive hay harvests (60 

days) and the average period between successive grazing (30 days) is used (that is, 45 days), and Tp is calculated as (60 days + 

30 days)/ 2 ÷ 365 days/yr = 0.12 yr. Two key uncertainties are associated with use of these values for Tp: (1) The average period 

between successive hay harvests (60 days) may not reflect the length of the growing season or the length between successive 

harvests for site-specific aboveground produce crops. The concentration of chemical in aboveground produce due to direct (wet 

and dry) deposition (Pd) will be underestimated if the site-specific value of Tp is less than 60 days, or overestimated if the sitespecific value of Tp is more than 60 days. d

Yp values for aboveground produce and forage were calculated using an equation presented in Baes et al. (1984) and Shor et al. 

(1982): Yp = Yhi / Ahi, where Yhi = Harvest yield of i

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

and Ah from USDA (1994b and 1994c). A production-weighted U.S. average Yp of 0.8 kg DW/m2 for silage was obtained from 

Shor et al. 1982. e

MAF represents the plant tissue-specific moisture adjustment factor to convert dry-weight concentrations into wet-weight 

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

Chapter 10 of EPA's 2003 SAB Review materials for 3MRA Modeling System, Volume II, “Farm Food Chain and Terrestrial Food 

Web Data” (EPA 2003a), which references EPA 1997c. Note that the value for grain used as animal feed is based on corn and 

soybeans, not seed grains such as barley, oats, or wheat.

Attachment B B-61 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

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 B-13 for the chemicals included in MIRC to date. The 

HHRAP algorithms require additional inputs for the animal products calculations that are not 

specific to PB-HAPs, but are specific to the animal and animal product type. The soil and plant 

ingestion rates recommended in HHRAP for beef cattle, dairy cattle, swine, and chicken are 

provided in Exhibit B-14. 

Exhibit B-13. Animal Product Chemical-specific Inputs 

for Chemicals Included in MIRC 

Compound Name

Soil BioAvailability 

Factor (Bs) 

(unitless)

Biotransfer Factors (Bam) (day/kg FW tissue)a

and Metabolism Factors (MF) (unitless)b 

Mammal Non-mammal

Beef 

(Babeef)

Dairy 

(Badairy)

Pork 

(Bapork) MF Eggs (Baeggs)

Poultry 

(Bapoultry) MF

Inorganics

Cadmium compounds 1 1.2E-04 6.5E-06 1.9E-04 1 2.5E-03 1.1E-01 NA

Mercury (elemental) 1 0 0 0 1 0 0 NA

Mercuric chloride 1 1.1E-04 1.4E-06 3.4E-05 1 2.4E-02 2.4E-02 NA

Methyl mercury 1 1.2E-03 1.7E-05 5.1E-06 1 3.6E-03 3.6E-03 NA

PAHs

2-Methylnaphthalene 1 2.4E-02 5.0E-03 2.9E-02 0.01 1.0E-02 1.7E-02 NA

7,12-

Dimethylbenz(a)anthra

cene

1 3.9E-02 8.3E-03 4.8E-02 0.01 1.7E-02 2.9E-02 NA

Acenaphthene 1 2.5E-02 5.2E-03 3.0E-02 0.01 1.0E-02 1.8E-02 NA

Acenaphthylene 1 2.6E-02 5.5E-03 3.1E-02 0.01 1.1E-02 1.9E-02 NA

Benz(a)anthracene 1 3.9E-02 8.3E-03 4.8E-02 0.01 1.7E-02 2.9E-02 NA

Benzo(a)pyrene 1 3.8E-02 8.0E-03 4.6E-02 0.01 1.6E-02 2.8E-02 NA

Benzo(b)fluoranthene 1 3.9E-02 8.3E-03 4.8E-02 0.01 1.7E-02 2.9E-02 NA

Benzo(g,h,i)perylene 1 2.9E-02 6.1E-03 3.5E-02 0.01 1.2E-02 2.1E-02 NA

Benzo(k)fluoranthene 1 3.8E-02 8.0E-03 4.6E-02 0.01 1.6E-02 2.8E-02 NA

Chrysene 1 4.0E-02 8.4E-03 4.8E-02 0.01 1.7E-02 2.9E-02 NA

Dibenz(a,h)anthracen

e

1 3.1E-02 6.5E-03 3.8E-02 0.01 1.3E-02 2.3E-02 NA

Fluoranthene 1 4.0E-02 8.5E-03 4.9E-02 0.01 1.7E-02 3.0E-02 NA

Fluorene 1 2.9E-02 6.1E-03 3.5E-02 0.01 1.2E-02 2.1E-02 NA

Indeno(1,2,3-

cd)pyrene 1 2.7E-02 5.8E-03 3.3E-02 0.01 1.2E-02 2.0E-02 NA

Dioxins

OctaCDD, 

1,2,3,4,6,7,8,9- 1 6.9E-03 1.4E-03 8.3E-03 1 2.9E-03 5.1E-03 NA

OctaCDF, 

1,2,3,4,6,7,8,9- 1 8.8E-03 1.8E-03 1.1E-02 1 3.7E-03 6.5E-03 NA

HeptaCDD, 

1,2,3,4,6,7,8- 1 8.8E-03 1.8E-03 1.1E-02 1 3.7E-03 6.5E-03 NA

Attachment B B-62 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

Exhibit B-13. Animal Product Chemical-specific Inputs 

for Chemicals Included in MIRC 

Compound Name

Soil BioAvailability 

Factor (Bs) 

(unitless)

Biotransfer Factors (Bam) (day/kg FW tissue)a

and Metabolism Factors (MF) (unitless)b 

Mammal Non-mammal

Beef 

(Babeef)

Dairy 

(Badairy)

Pork 

(Bapork) MF Eggs (Baeggs)

Poultry 

(Bapoultry) MF

HeptaCDF, 

1,2,3,4,6,7,8- 1 1.6E-02 3.5E-03 2.0E-02 1 6.9E-03 1.2E-02 NA

HeptaCDF, 

1,2,3,4,7,8,9- 1 2.4E-02 5.1E-03 3.0E-02 1 1.0E-02 1.8E-02 NA

HexaCDD, 1,2,3,4,7,8- 1 1.1E-02 2.3E-03 1.3E-02 1 4.6E-03 8.1E-03 NA

HexaCDF, 1,2,3,4,7,8- 1 2.3E-02 4.8E-03 2.8E-02 1 9.6E-03 1.7E-02 NA

HexaCDD, 1,2,3,6,7,8- 1 6.8E-03 1.4E-03 8.2E-03 1 2.9E-03 5.0E-03 NA

HexaCDF, 1,2,3,6,7,8- 1 9.7E-03 2.0E-03 1.2E-02 1 4.1E-03 7.1E-03 NA

HexaCDD, 1,2,3,7,8,9 

- 1 6.8E-03 1.4E-03 8.2E-03 1 2.9E-03 5.0E-03 NA

HexaCDF, 1,2,3,7,8,9- 1 1.4E-02 2.9E-03 1.7E-02 1 5.8E-03 1.0E-02 NA

HexaCDF, 2,3,4,6,7,8- 1 9.6E-03 2.0E-03 1.2E-02 1 4.1E-03 7.1E-03 NA

PentaCDD, 1,2,3,7,8- 1 1.8E-02 3.9E-03 2.2E-02 1 7.8E-03 1.4E-02 NA

PentaCDF, 1,2,3,7,8- 1 2.6E-02 5.5E-03 3.2E-02 1 1.1E-02 1.9E-02 NA

PentaCDF, 2,3,4,7,8- 1 3.1E-02 6.5E-03 3.8E-02 1 1.3E-02 2.3E-02 NA

TetraCDD, 2,3,7,8- 1 2.6E-02 5.5E-03 3.2E-02 1 1.1E-02 1.9E-02 NA

TetraCDF, 2,3,7,8- 1 3.6E-02 7.7E-03 4.4E-02 1 1.5E-02 2.7E-02 NA

Source: EPA 2005a, unless otherwise indicated. NA = not applicable.

a

As discussed in HHRAP (EPA 2005a), Appendix A, biotransfer factors for mercury compounds were obtained from EPA 1997b. Considering 

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

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

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

chemical octanol-water partitioning coefficients (Kow values) using the correlation equation from RTI (2005) and assuming the following fat contents: 

milk - 4%; beef - 19%; pork - 23%; poultry -14%; and eggs - 8%. b

As discussed in HHRAP (EPA 2005a), EPA (1995c) recommends using a metabolism factor (MF) to account for metabolism of PAHs by mammals 

to offset the amount of bioaccumulation suggested by biotransfer factors. EPA has recommended an MF of 0.01 for bis(2-ethylhexyl)phthalate 

(BEHP) and 1.0 for all other chemicals (EPA 1995d). For MIRC, an MF of 0.01 is also used to calculate concentrations of PAHs in food products 

from mammalian species based on the work of Hofelt et al. (2001). This factor takes into account the P450-mediated metabolism of PAHs in 

mammals; applying this factor in our approach reduced the concentrations of chemicals in beef, pork, and dairy by two orders of magnitude.

Attachment B B-63 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

Exhibit B-14. Soil and Plant Ingestion Rates for Animals

Animal Soil Ingestion Rate –

Qs(m) (kg/day)a

Plant Part Consumed 

by Animal 

Plant Ingestion Rate –

Qp(I,m) (kg/day)

Beef cattleb 0.5

Silage 2.5

Forage 8.8

Grain 0.47

Dairy cattlec 0.4

Silage 4.1

Forage 13.2

Grain 3.0

Swined 0.37 Silage 1.4

Grain 3.3

Chicken (eggs)e 0.022 Grain 0.2

Source: EPA 2005a HHRAP (Chapter 5). a

Beef cattle: NC DEHNR (1997) and EPA (1994b) recommended a soil ingestion rate for subsistence beef cattle of 0.5 kg/day 

based on Fries (1994) and NAS (1987). As discussed in HHRAP, Fries (1994) reported soil ingestion to be 4 percent of the total 

dry matter intake. NAS (1987) cited an average beef cattle weight of 590 kg, and a daily dry matter intake rate (non-lactating 

cows) of 2 percent of body weight. This results in a daily dry matter intake rate of 11.8 kg DW/day and a daily soil ingestion rate of 

about 0.5 kg/day. 

Dairy cattle: NC DEHNR (1997) and EPA (1994b) recommended a soil ingestion rate for dairy cattle of 0.4 kg/day based on Fries 

(1994) and NAS (1987). As discussed in HHRAP, Fries (1994) reported soil ingestion to be 2 percent of the total dry matter 

intake. NAS (1987) cited an average beef cattle weight of 630 kg and a daily dry matter intake rate (non-lactating cows) of 3.2 

percent of body weight. This resulted in a daily dry matter intake rate of 20 kg/day DW, and a daily soil ingestion rate of 

approximately 0.4 kg/day. Uncertainties associated with Qs include the lack of current empirical data to support soil ingestion 

rates for dairy cattle and the assumption of uniform contamination of soil ingested by cattle. 

Swine: NC DEHNR (1997) recommended a soil ingestion rate for swine of 0.37, estimated by assuming a soil intake that is 8% of 

the plant ingestion rate of 4.3 kg DW/day. Uncertainties include the lack of current empirical data to support soil ingestion rates

and the assumption of uniform contamination of the soil ingested by swine.

Chicken: HHRAP (EPA 2005a) assumes that chickens consume 10 percent of their total diet (which is approximately 0.2 kg/day 

grain) as soil, a percentage that is consistent with the study from Stephens et al. (1995). Uncertainties include the lack of current 

empirical data to support soil ingestion rates for chicken and the assumption of uniform contamination of soil ingested by chicken. b

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

on NAS [1987] reporting a daily dry matter intake that is 2 percent of an average beef cattle body weight of 590 kg) and are 

supported by NC DEHNR (1997), EPA (1994b and 1990), and Boone et al. (1981). The principal uncertainty associated with 

these Qp values is the variability between forage, silage, and grain ingestion rates for cattle. c

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

1987; EPA 1992) as recommended by NC DEHNR (1997). Uncertainties include the proportion of each food type in the diet, 

which varies from location to location. Assuming uniform contamination of plant

materials consumed by cattle also introduces uncertainty. d

Swine are not grazing animals and are assumed not to eat forage (EPA 1998). EPA (1994b and 1998) and NC DEHNR (1997) 

recommended including only silage and grains in the diet of swine. EPA (1995c) recommended an ingestion rate of 4.7 kg 

DW/day for a swine, referencing NAS (1987). Assuming a diet of 70 percent grain and 30 percent silage (EPA 1990), HHRAP 

estimated ingestion rates of 3.3 kg DW/day (grain) and 1.4 kg DW/day (silage). Uncertainties associated with Qp include

variability of the proportion of grain and silage in the diet, which varies from location to location. e

Chickens consume grain provided by the farmer. The daily quantity of grain feed consumed by chicken is assumed to be 0.2 

kg/day (Ensminger (1980), Fries (1982), and NAS (1987). Uncertainties associated with this variable include the variability of 

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

introduces some uncertainty.

6.3. Adult and Non-Infant Exposure Parameter Values

The exposure parameters included in MIRC and their default and other value options are 

summarized in the following subsections. The default values were selected to result in a highly 

health protective screening scenario. Parameter value options were primarily obtained or 

estimated from EPA’s Exposure Factors Handbook (EFH; EPA 2011a) and Child-Specific 

Exposure Factors Handbook (CSEFH; EPA 2008a). Where values were reported for age 

groupings other than those used in MIRC (see Section 2.3 above for MIRC age groups), timeweighted average values were estimated for the MIRC age groups from the available data. 

Attachment B B-64 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

In MIRC, ingestion rates for home-produced farm food items are included for exposed fruit, 

protected fruit, exposed vegetables, protected vegetables, root vegetables, beef, total dairy, 

pork, poultry, and eggs. Those ingestion rates are already normalized to body weight (i.e., gwet 

weight/kg-day) (EPA 2011a). The body weight parameter values presented in Exhibit B-15, 

therefore, are not applied in the chemical intake (ADD) equations for these food types. 

In MIRC, ingestion rates also are included for drinking water (mL/day), soil (mg/day), and fish 

(g/day). These ingestion rates, however, are on a per person basis (i.e., not normalized for 

body weight). The body weight parameter values presented in Exhibit B-15, therefore, are 

applied in the chemical intake (ADD) equations for these media.

6.3.1. Body Weights

Body weight (BW) options included in MIRC include mean, 5th, 10th, 50th, 90th, and 95th

percentile values for adults and the five children’s age groups: <1 year; 1–2 years; 3–5 years; 

6–11 years; and 12–19 years. For its default screening assessment, EPA uses the mean BW 

for each age group. The BWs currently in the MIRC database are listed in Exhibit B-15. 

In general, BW values for the five children’s age groups were calculated from the summary data

provided in Table 8-3 of EPA’s 2008 CSEFH. For purposes of comparison, alternative BW 

values for children ages 12 through 19 years also were estimated using data from Portier et al. 

(2007). These values are listed in the last row of Exhibit B-15, but are not included in MIRC. 

The means calculated using the two methods for children ages 12 through 19 years were 

essentially identical at 64 kg. The other percentile values for this age group using the two 

methods differed by approximately 10 percent or less.

Exhibit B-15. Mean and Percentile Body Weight Estimates

for Adults and Children

Lifestage

(years)

Duration 

(years)

Body Weight (kg)

Mean 5th 10th 50th 90th 95th

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

Child < 1b 1 7.83 6.03 6.38 7.76 9.24 9.66

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

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

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

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

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

a

BW represents the recommended body weight from EPAs 2011 EFH. Although the 18 to 74 year age category in EPA’s EFH 

does not match exactly the age 20 to 70 year categorization of adults in MIRC, the magnitude of error in the mean and percentile 

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

Each BW represents a time-weighted average of body weights for age groups birth to <1 month, 1 to <3 months, 3 to <6 

months, and 6 to <12 months from Table 8-3 of the 2008 CSEFH. Original sample sizes for each of these age groups can also 

be found in Table 8-3. c

Each BW represents a time-weighted average of body weights for age groups 1 to <2 years and 2 to <3 years from Table 8-3 of 

the 2008 CSEFH. Original sample sizes for each of these age groups can also be found in Table 8-3. d

BWs obtained directly from Table 8-3 of the 2008 CSEFH (age group 3 to <6 years). e

Each BW represents a time-weighted average of body weights for age groups 6 to <11 years and 11 to <16 years from Table 8-

3 of the 2008 CSEFH. Original sample sizes for each of these age groups can also be found in Table 8-3. f

Mean BW estimated using Table 8-22 of the 2008 CSEFH, which is based on NHANES IV data as presented in Portier et al. 

(2007). This estimate was calculated as the average of the 8 single-year age groups from 12 to 13 years through 19 to 20 years. 

Values for the other percentiles were estimated using Portier et al., 2007. g

Each BW represents a time-weighted average of body weights for age groups 11 to <16 years and 16 to <21 years from Table 

8-3 of the 2008 CSEFH. Estimated values include 11-year-olds and individuals through age 20, which contributes to uncertainty 

in the estimates for 12 to 19 years. Those values are provided for comparison purposes only and are not included in MIRC.

Attachment B B-65 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

6.3.2. Water Ingestion Rates

MIRC also includes the option of calculating chemical ingestion via drinking water obtained from 

surface-water sources or from wells (i.e., from groundwater) in the contaminated area. Users 

have the option in MIRC to set drinking water ingestion rates to zero or to revise the drinking 

water ingestion rates in MIRC to better reflect site-specific water uses. The 2008 CSEFH 

recommends values for drinking water ingestion rates for children based on a study reported by 

Kahn and Stralka (2008). Table 3-4 of the CSEFH provides per capita estimates of community 

water ingestion rates by age categories. Community water ingestion includes both direct and 

indirect ingestion of water from the tap. Direct ingestion is defined as direct consumption of 

water as a beverage, while indirect ingestion includes water added during food or beverage 

preparation. The source of these data is the 1994-1996 and 1998 U.S. Department of 

Agriculture’s (USDA’s) Continuing Survey of Food Intakes by Individuals (CSFII) (USDA 2000). 

Exhibit B-16 includes the drinking water ingestion rates for children that are included in MIRC. 

Mean and percentile adult drinking water ingestion rates were obtained from EPA (2004b), 

which presents estimated per capita water ingestion rates for various age categories based on 

data collected by the USDA’s 1994–1996 and 1998 CSFII (USDA 2000). Adult ingestion rates, 

presented in Exhibit B-16, represent community water ingestion, both direct and indirect as 

defined above, for males and females combined, ages 20 years and older. 

Exhibit B-16. Estimated Daily Per Capita Mean and Percentile Water Ingestion Rates for 

Children and Adultsa

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

Mean 50th 90th 95th 99th

Child <1b 504 482 969 1113 1440

Child 1-2c 332 255 687 903 1318

Child 3-5d 382 316 778 999 1592

Child 6-11e 532 417 1149 1499 2274

Child 12-19f 698 473 1641 2163 3467

Adultg 1219 981 2534 3087 4567

Sources: EPA 2004, 2008a

*The sample size does not meet minimum reporting requirements as described in EPA 2008a. For some of these MIRC age 

groupings, the values are based on the time-weighted average value for 2 or more age ranges from CSEFH Table 3-4. One or 

more age ranges within the group may not meet the minimum reporting requirements, but not necessarily all of them fall within this 

category. 

a

Source is Kahn and Stralka 2008, also presented in the CSEFH (EPA 2008a). b

Each IR represents a time-weighted average of ingestion rates for age groups birth to <1 month, 1 to <3 months, 3 to <6 months, 

and 6 to <12 months from Table 3-4 of the 2008 CSEFH. c

Each IR represents a time-weighted average of ingestion rates for age groups 1 to <2 years and 2 to <3 years from Table 3-4 of 

the 2008 CSEFH.

d

Each IR represents the ingestion rate for age group 3 to <6 years from Table 3-4 of the 2008 CSEFH. e

Each IR represents the ingestion rate for age group 6 to <11 years from Table 3-4 of the 2008 CSEFH. This value represents a 

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

age group.

f

Each IR represents a time-weighted average of ingestion rates for age groups 11 to <16 years, 16 to <18, and 18 to <21 years 

from Table 3-4 of the 2008 CSEFH. Note that estimated values include 11-year-olds and individuals through age 20, which 

contributes to uncertainty in the estimates for 12 to 19 years.

g

Adult drinking water ingestion rates were obtained from EPA (2004b), Appendix E, Part I, Table A1 for community water, both 

sexes (ages 20+), direct plus indirect water ingestion.

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 

Attachment B B-66 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

percentile values are from EPA’s analysis of data from the USDA’s 1987 to 1988 Nationwide

Food Consumption Survey (NFCS) (USDA 1993), as presented in Chapter 13 of the Agency’s

Exposure Factors Handbook (i.e., Intake Rates for Various Home Produced Food Items) (EPA

2011a). Consumers-only means that individuals who did not report eating a specified type of 

food during the three-day period covered by the food ingestion part of the survey were not 

included in the analysis of ingestion rates for that food type. The questionnaire included the 

options for a household to self-identify in one or more of five categories: as a household that 

gardens, raises animals, hunts, fishes, or farms. As of September 2008, that survey was the 

most recent NFCS available (EPA 2008a, CSEFH), and we are not aware of any that might be 

more recent.29 

For the adult age group in MIRC, data were compiled on food-specific IRs separately for two 

types of households as indicated in the “Response to Questionnaire” (EPA 2011a, Chapter 13): 

(1) households that farm (F) and (2) households that garden or raise animals (HG for 

homegrown). This division reflects EPA’s data analysis. EPA tabulated IRs for fruits and 

vegetables separately for households that farm and households that garden. Similarly, EPA 

tabulated IRs for animals and animal products for households that farm and for households that 

raise animals. Thus, the first type of household, F, represents farmers who may both grow 

crops and raise animals and who are likely to consume more homegrown/raised foods than the 

second type of household. The second type of household, HG, represents the non-farming 

households that may consume lower amounts of home-grown or raised foods (i.e., HG 

encompasses both households that garden and households that raise animals). 

The food-specific ingestion rates are based on the amount of each food type that households 

that farm (F) or households that garden and raise animals (HG) produced and brought into their 

homes for consumption and the number of persons consuming the food. EPA averaged the 

actual consumption rate for home-grown foods over the 1-week survey period. 

The default food-specific ingestion rates in MIRC for adults are those for farming households (F) 

in Exhibit B-17. The user can specify use of the non-farming household (HG) ingestion rates if 

they are more appropriate for the user’s exposure scenario. 

Exhibit B-17. Summary of Age-Group Specific Food Ingestion Rates for

Farm Food Items

Product

Child (age in yr) Adult 

(20–70 

yrs) <1 1–2 3–5 6–11 12–19

Mean ingestion rates (g/kg-day)

Beefa N/A 4.14 4.00 3.77 1.72 1.93

Dairyb N/A 91.6 50.9 27.4 13.6 2.96

Eggsa N/A 2.46 1.42 0.86 0.588 0.606

Exposed Fruita N/A 6.14 2.60 2.52 1.33 1.19

Exposed Vegetablea N/A 3.48 1.74 1.39 1.07 1.38

Porka N/A 2.23 2.15 1.50 1.28 1.10

Poultrya N/A 3.57 3.35 2.14 1.50 1.37

29Note that EPA’s 2008 CSEFH does not distinguish between exposed and protected fruits and vegetables when 

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

for its EFH therefore remains the most appropriate data source for use in MIRC.

Attachment B B-67 October 2014

Description of MIRC

 
TRIM-Based Tiered Screening Methodology for RTR

Exhibit B-17. Summary of Age-Group Specific Food Ingestion Rates for

Farm Food Items

Product

Child (age in yr) Adult 

(20–70 

yrs) <1 1–2 3–5 6–11 12–19

Protected Fruita N/A 16.6 12.4 8.50 2.96 5.19

Protected Vegetablea N/A 2.46 1.30 1.10 0.78 0.862

Root Vegetablea N/A 2.52 1.28 1.32 0.94 1.03

Water (mL/day)c N/A 332 382 532 698 1218

Median ingestion rates (g/kg-day)

Beefa N/A 2.51 2.49 2.11 1.51 1.55

Dairyb N/A 125 66.0 34.4 15.5 2.58

Eggsa N/A 1.51 0.83 0.561 0.435 0.474

Exposed Fruita N/A 1.82 1.11 0.61 0.62 0.593

Exposed Vegetablea N/A 1.89 1.16 0.64 0.66 0.812

Porka N/A 1.80 1.49 1.04 0.89 0.802

Poultrya N/A 3.01 2.90 1.48 1.30 0.922

Protected Fruita N/A 7.59 5.94 3.63 1.23 2.08

Protected Vegetablea N/A 1.94 1.04 0.79 0.58 0.564

Root Vegetablea N/A 0.46 0.52 0.57 0.56 0.59

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

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

Beefa N/A 9.49 8.83 11.4 3.53 4.41

Dairyb N/A 185 92.5 57.4 30.9 6.16

Eggsa N/A 4.90 3.06 1.90 1.30 1.31

Exposed Fruita N/A 12.7 5.41 6.98 3.41 2.37

Exposed Vegetablea N/A 10.7 3.47 3.22 2.35 3.09

Porka N/A 4.90 4.83 3.72 3.69 2.23

Poultrya N/A 7.17 6.52 4.51 3.13 2.69

Protected Fruita N/A 44.8 32.0 23.3 7.44 15.1

Protected Vegetablea N/A 3.88 2.51 2.14 1.85 1.81

Root Vegetablea N/A 7.25 4.26 3.83 2.26 2.49

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

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

Beefa N/A 12.9 12.5 12.5 3.57 5.83

Dairyb N/A 167 89.9 56.0 32.3 7.80

Eggsa N/A 5.38 3.62 2.37 1.43 1.59

Exposed Fruita N/A 14.6 6.07 11.7 4.78 3.38

Exposed Vegetablea N/A 11.9 6.29 5.47 3.78 4.46

Porka N/A 6.52 6.12 4.73 6.39 2.60

Poultrya N/A 8.10 7.06 5.07 3.51 3.93

Protected Fruita N/A 48.3 35.1 26.9 11.4 19.2

Protected Vegetablea N/A 9.42 5.10 3.12 2.20 2.83

Root Vegetablea N/A 10.4 4.73 5.59 3.32 3.37

Attachment B B-68 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

Exhibit B-17. Summary of Age-Group Specific Food Ingestion Rates for

Farm Food Items

Product

Child (age in yr) Adult 

(20–70 

yrs) <1 1–2 3–5 6–11 12–19

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

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

Beefa N/A 20.9 19.8 13.3 4.28 6.84

Dairyb N/A 180 87.2 54.8 34.7 9.20

Eggsa N/A 16.2 11.2 8.19 4.77 1.83

Exposed Fruita N/A 25.2 32.5 15.7 5.9 13.0

Exposed Vegetablea N/A 12.1 7.36 13.3 5.67 8.42

Porka N/A 8.71 9.74 6.61 4.29 3.87

Poultrya N/A 9.63 10.24 6.12 4.60 4.93

Protected Fruita N/A 109 71.2 58.2 19.1 34.4

Protected Vegetablea N/A 9.42 5.31 5.40 2.69 5.56

Root Vegetablea N/A 10.4 4.73 7.47 5.13 7.57

Water (mL/day)c N/A 1318 1592 2274 3467 4567

a

Primary source for values was the 1987–1988 NFCS survey; compiled results are presented in Chapter 13 of 2011 Exposure 

Factors Handbook (EPA, 2011a). When data were unavailable for a particular age group, intake rate for all age groups was used 

multiplied by the age-specific ratio of intake based on national population intake rates from CSFII. b

Primary source for values was 1987–1988 NFCS survey, compiled results presented in Chapter 13 of 2011 Exposure Factors 

Handbook (EPA, 2011a). When data were unavailable for a particular age group, intake rate for all age groups was used multiplied 

by the age-specific ratio of intake based on national population intake rates from an NHANES 2003–2006 analysis in Chapter 11 of 

the Exposure Factors Handbook.

c

Primary source for children less than 3 years of age was a Kahn and Stralka (2008) analysis of CSFII data, and from EPA’s 

analysis of NHANES 2003–2006 data for children and adults greater than three. All data tables that were used and justifications for 

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

d

Default ingestion rate percentile used in MIRC for Tier 1 assessments and chemical threshold calculations.

For children, EPA estimated food-specific IRs for four age categories (EPA 2011a): 1–2 years, 

3–5 years, 6–11 years, and 12–19 years. Sample sizes were insufficient to distinguish IRs for 

children in different types of households; hence, for children, a single IR value represents both F 

and HG households for a given food type and age category (Exhibit B-17). For some food types 

and age categories, there were insufficient data for EPA to provide consumer-only intake rates

(i.e., data set for the subpopulation consisted of fewer than 20 observations). The HHRAP 

methodology, Section 6.2.2.2, recommends a method by which to calculate the “missing” 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.

Attachment B B-69 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

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 B-56

was used to calculate the “missing” age-specific consumer-only IRs: 

Equation B-56. Calculation of Age-Group-Specific and Food-Specific Ingestion Rates

where:

IRCO, age_group_x = Mean or percentile-specific consumer-only intake of the food type from all 

sources, as consumed, for the specific child age group X

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

brought into the home, for the total NFCS survey population 

IRPC, age_group_x = Mean or percentile-specific per capita intake of the food type from all sources, 

as consumed, for the specific child age group X from the CSFII 

IRPC_total = Mean or percentile-specific per capita intake of the farm food item for the total

CSFII survey population

In this discussion, per capita (as opposed to consumer-only) indicates the intake rates are 

based on the entire population rather than the subset of the population that ingests the 

particular food category (i.e., consumers). Here, the use of per capita ingestion rates are 

recommended by the HHRAP methodology because no consumer-only percentile-specific 

intakes are provided for the different age groups. 

The above calculation implicitly assumes that the distribution of the consumption rate for a food 

type for a specific age group (consumers only) has the same shape as the distribution of the 

consumption rate for a food type for a specific age group in the general population (per capita). 

Otherwise, the separate calculation of each percentile might yield intake estimates that 

decrease as the percentile increases. This calculation artifact could occur if the shapes of the 

two distributions differ in the upper percentiles (or “tails”) of the distributions. 

In the instances where the above calculations were used to fill data gaps in the above exhibit, 

only the dairy child-specific age group intake estimates are not strictly increasing with increasing 

percentile. The distributions likely track better (and thus the above assumption of equal ratios is 

more reasonable) for lower percentiles, with deviations occurring due to outlier ingestion rates 

based on only a few respondents in the tails of the distributions. The MIRC defaults use the 90th

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

estimates in this particular calculation.

6.3.4. Local Fish Ingestion Rates

6.3.4.1. Screening Scenario

The USDA’s 1987 to 1988 Nationwide Food Consumption Survey (NFCS) (USDA 1993, 1994a), 

as presented in Chapter 13 of the Agency’s Exposure Factors Handbook (i.e., Intake Rates for 

IR

IR IR

IR 

PC_total

CO_total PC, age_group_x

CO, age_group_x

× =

Attachment B B-70 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

Various Home Produced Food Items) (EPA 2011a), includes family-caught fish ingestion rates 

by age category. There are several disadvantages, however, to using that data source to 

estimate fish ingestion rates. First, due to inadequate sample sizes, EPA did not report fish 

ingestion rates for children less than 6 years of age. Second, the NFCS data were collected 

more than two decades ago. Third, the reported fish ingestion rates are for ages 6 to 11 and 12 

to 19 and are based on 29 and 21 individuals in each age category, respectively (EPA 2011a, 

Table 13-20). Finally, the ingestion rates from NFCS data are based on total weight of fish as 

brought into the home, and do not include losses from preparation of the fish (i.e., removal of 

inedible parts and, possibly, the skin). Estimates of preparation losses for fish intended to apply 

to the NFCS fish ingestion rate data are very uncertain and are based on a wide variety of 

freshwater, estuarine, and marine fish, and squid (EPA 2011a, Table 13-69). Additionally, when 

considering the multipathway screening methodology, it is important that potential health effects 

to those individuals who are most likely to have the greatest PB-HAP exposure are not 

underestimated and, therefore, ingestion rates that are reflective of subsistence fisher ingestion 

rates are desired. Therefore, a more recent survey was sought that included larger sample 

sizes, data for children younger than six years, ingestion rates for the parts of fish actually 

consumed, and ingestion rates reflective of subsistence fisher ingestion rates.

Taking all of these issues into consideration, the default adult fish ingestion rate selected for use 

in MIRC is 373 g/day, which is the the estimated 99th percentile of fish ingestion rates for 

woman fishers as reported by Burger (2002). This rate is based upon ingestion of “wild-caught” 

fish, which includes freshwater, estuarine, and marine species, while our screening scenarios 

focus only on freshwater fish from lakes. This is notable because a number of studies indicate 

that fish ingestion rates are limited by species and habitat (i.e., lake, river, estuary, and ocean) 

and that the majority of the fish consumed in the United States are from river, marine and 

estuarine habitats versus lakes. Thus, although the fish ingestion rate for this group of 

subsistence fishers is not the highest fish ingestion rate available for use by EPA, we do believe 

it strikes the appropriate balance between being health protective and having screening 

scenarios so conservative that they are of limited use in the decision making process. This 

high-end fish ingestion rate is appropriate in the context of the conservative screening scenario 

used in the RTR process. This methodology is particularly applicable for national rulemakings 

given that it is very likely that subsistence woman fishers of child bearing age are located 

throughout the United States. Finally, we note that using a high-end subsistence fish ingestion 

rate is consistent with section 112 of the CAA, which focuses on risks associated with maximally 

exposed individuals.

Because Burger (2002) did not estimate fish ingestion rates for children, another data source 

was needed to develop ingestion rates for the child age categories that are used in MIRC. The 

child ingestion rates need to be consistent with the Burger adult ingestion rate, reflective of 

subsistence fisher ingestion rates, and based on adequate sample sizes. To satisfy these 

requirements, data on child ingestion rates from EPA’s Estimated Per Capita Fish Consumption 

in the United States (EPA 2002) were selected for use. Specifically, the estimated 99th

percentile of as-prepared, consumer-only ingestion rates for finfish and shellfish were selected 

(see Section 4.2.1.1 Table 5 of EPA 2002).The original data were collected as part of the 1994–

96 and 1998 USDA Continuing Survey of Intakes by Individuals (CSFII; USDA 2000). Values 

reflect “as prepared” ingestion rates for which cooking and preparation losses (L1 and L2) did 

not need to be considered. “Total” fish as reported in this source represents consumption of 

finfish plus shellfish. 

Because the child age categories used in MIRC differ from the CSFII age categories presented 

in EPA 2002, the CSFII data were adjusted for use in MIRC. The CSFII data did not provide 

ingestion rates for the 1–2 year age category. To estimate ingestion rates for this age group, 

Attachment B B-71 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

EPA used the ingestion rate for the 3–5 year age category scaled downward by the ratio of the 

mean body weight of the 1–2 year age group to the mean body weight of the 3–5 year age 

group. Because MIRC uses a 3–5 year age category, no adjustment was needed for CSFII data 

from that age category. For the 6–11 and 12–19 age categories, time-weighted average 

ingestion rates were calculated based on the CSFII ingestion rates. Exhibit B-18 provides the 

fish ingestion rates used in the screening assessment. 

Exhibit B-18. Fish Ingestion Rates Used in Screening Assessment

Ingestion Rates (g/day)

Infants 

<1 yr

Child

1–2 yrs

Child 

3–5 yrs

Child 

6–11 yrs

Child 

12–19 yrs

Adult

20–70 yrs 

NA 107.7a 159.0b 268.2c 331.0c 373d

a

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

ages 3 to 5 from EPA (2002) (Section 4.2.1.1 Table 5 [freshwater/estuarine habitat]), scaled down by the ratio of the mean 

Child 1-2 body weight to the mean Child 3-5 body weight. b

This value represents the consumer-only fish ingestion rate for ages 3 to 5 from EPA (2002), Section 4.2.1.1 Table 5 

(freshwater/estuarine habitat) rounded to the nearest full number. c

These values represent time-weighted average consumer-only fish ingestion rates based on ingestion rates from EPA

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

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

6.3.4.2. Other Values Available in MIRC

EPA’s (2002) analysis of freshwater and estuarine fish consumption from the USDA’s 

Continuing Survey of Food Intake by Individuals (CSFII) for 1994-96 and 1998 was chosen to 

provide fish ingestion rate options by age category in MIRC. Although the fish consumption 

rates reported in the CSFII include all sources, commercial and self-caught, for purposes of 

screening level risk assessments, it was assumed that all freshwater and estuarine fish 

consumed are self-caught. The inclusion of commercially obtained and estuarine fish will 

overestimate locally caught freshwater fish ingestion rates for most populations in the United 

States; however, it also might underestimate locally caught fish ingestion rates for some 

populations (e.g., Native Americans, Asian and Pacific Island communities, rural African 

American communities). Because consumption of locally caught fish varies substantially from 

region to region in the United States and from one population or ethnic group to the next, users 

of MIRC are encouraged to use more locally relevant data whenever available. 

For children, MIRC also includes values for the mean and the 90th, 95th, and 99th percentile 

fish per-capita ingestion rates (freshwater and estuarine fish only) based on EPA’s analysis of 

1994-96 and 1998 CSFII data (EPA 2002, 2008a). Those rates include individuals who eat fish 

and those who do not eat fish. As shown in EPA’s 2008 CSEFH, Table 10-7, the 90th percentile 

per capita ingestion rates estimated from the two-day CSFII recall period are zero for some child 

age groups. Although not presented in CSEFH Table 10-7, median ingestion rates for all child 

age groups would be zero (considering the “consumer only” sample sizes [CSEFH Table 10-9] 

relative to the “per-capita” sample sizes in Table 10-7). 

The high percentile fish ingestion rates that are zero result from the short duration of the CSFII 

recall period (two days) compared with the averaging time of interest (a year) and the relatively 

infrequent consumption of fish (e.g., on the order of once a week to once a month or less) 

compared with the near daily ingestion of other types of food products (e.g., dairy, produce, 

meat). Use of zero for fish ingestion rates, however, is not useful in MIRC. As a result, an 

alternative method was used to estimate fish ingestion rates for children and adults that could 

provide reasonable, non-zero values for all age groups and percentiles. 

Attachment B B-72 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

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.30 

• Fraction of the population consuming freshwater/estuarine fish, FPC,y, calculated as 

consumer-only sample size / U.S. population sample for age group y. The data to 

calculate those fractions are available in the 2008 CSEFH and EPA 2002.

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

(IRPC,y): 

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

where:

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

IRCO,y = 

Consumer-only fish ingestion rates for age group y (g/day) (EPA 2002, Section 

5.2.1.1, Table 5, for freshwater/estuarine habitat)

FPC,y = 

Fraction of the population consuming freshwater/estuarine fish, calculated as 

consumer-only sample size / total U.S. population sample size for age group y 

(unitless) (2008 CSEFH, EPA 2002) 

In the above, per capita (as opposed to consumer-only) indicates the intake rates are based on 

the entire population rather than the subset of the population that ingests the particular food 

category. Here, per capita ingestions are recommended by the HHRAP methodology because 

no consumer-only percentile-specific intakes are provided for the different age groups. 

The mean and percentile consumer-only fish ingestion rates for children and adults and the 

fraction of the population consuming freshwater/estuarine fish used in calculating long-term per 

capita fish ingestion rates by age group are presented in Exhibit B-19 and Exhibit B-20. The 

mean and percentile per capita fish ingestion rates estimated using this methodology are 

summarized in Exhibit B-21 and are available in MIRC. The fish ingestion rates provided in 

Exhibit B-21 and included in MIRC are intended to represent the harvest and consumption of 

fish in surface waters in a hypothetical depositional area. For site-specific application of this 

tool, users should consider using more localized survey data to estimate more appropriate fish 

ingestion rates. The fishing season varies substantially across the United States by latitude, 

and fish consumption patterns also vary by type of water body (e.g., ponds, lakes, rivers, 

streams, estuaries, coastal marine), cultural heritage, and general geographic area. Therefore, 

use of more localized information is encouraged. Note that as indicated in Exhibit 16 and 

Exhibit 18, in developing the screening threshold emission rates, health protective fish ingestion 

rates for child and adult fish consumers that more closely represent exposures of a high-end 

recreational fisher were used. 

30Most 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.

PC,y CO,y FPC,y IR IR ×=

Attachment B B-73 October 2014

Description of MIRC

 
TRIM-Based Tiered Screening Methodology for RTR

As noted in Section 6.4.3 of this attachment, if the user overwrites the fish IRs shown in Exhibit 

B-21 with fresh-weight as caught values (e.g., values obtained from a local creel survey), the 

user is advised to set non-zero values for the preparation and cooking loss factors L1 and L2 in 

Equation B-15. Suggested values are presented in Section 6.4.3. 

Exhibit B-19. Daily Mean and Percentile Consumer-Only Fish Ingestion Rates

for Children and Adults (IRCO,y)

a 

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

Mean 50th 90th 95th 99th

Child <1 NA NA NA NA NA

Child 1-2b 27.31 15.61 64.46 87.60 138.76*

Child 3-5c 40.31 23.04 95.16 129.31 204.84*

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

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

Adultf 81.08 47.39 199.62* 278.91 505.65*

Sources: EPA 2002, 2008a

NA = not applicable; it is assumed that children < 1 year of age do not consume fish.

*Indicates that the sample size does not meet minimum reporting requirements as described in EPA 2002. Owing to the small 

sample sizes, these upper percentiles values are highly uncertain.

a

Per capita fish ingestion (FI) rates for children by age group are available from Chapter 10 of the CSEFH (EPA 2008a); however, 

all 50th and some 90th percentile ingestion rates are zero. Per capita FI rates were therefore estimated as described in Equation 

B-57 to provide reasonable, non-zero values for all age groups and percentiles. b

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

EPA (2002) (Section 5.2.1.1 Table 5 [freshwater/estuarine habitat]), scaled down by the ratio of the mean Child 1-2 body weight to 

the mean Child 3-5 body weight. c

These values represent the consumer-only fish ingestion rate for ages 3 to 5 from EPA (2002), Section 5.2.1.1 Table 5 

(freshwater/estuarine habitat). Sample size = 442. d

These values represent the consumer-only fish ingestion rate for ages 6 to 10 from EPA (2002), Section 5.2.1.1 Table 5 

(freshwater/estuarine habitat). Sample size = 147. e

These values represent the time-weighted average per capita fish ingestion rate for ages 11 to 15 and 16 to 17 years from EPA

(2002), Section 5.1.1.1 Table 5 (freshwater/estuarine habitat); the value may underestimate ingestion rate for ages 12 to 19 years. 

Sample size = 135.

f

These values represent the consumer-only fish ingestion rate for individuals 18 years and older from EPA (2002), Section 5.2.1.1 

Table 4 (freshwater/estuarine habitat). Sample size = 1,633.

Attachment B B-74 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

Exhibit B-20. Fraction of Population Consuming Freshwater/Estuarine Fish on a Single 

Day (FPC,y) 

Lifestage (years) Fraction Consuming Fish

Child 3-5 0.0503a

Child 6-11 0.0440b

Child 12-19 0.0493c

Adult 0.08509d

Sources: EPA 2002, 2008a a

This value was calculated using the ages 3 to 5 sample size for consumers only divided by the sample size for the U.S. 

population divided by 2 to represent the proportion consuming fish on a single day (the consumers-only group includes individuals 

who consumed fish on at least one of two survey days) to match the one-day ingestion rate. b

As in footnote a, the value was calculated using the ages 6 to 10 sample size for consumers only divided by the sample size for 

U.S. population divided by 2. c

The value was calculated by summing the ages 11 to 15 and 16 to 17 sample sizes for consumers only and dividing by both by 

the sum of the sample sizes for U.S. population and by a factor of 2.

d

The value was calculated using the ages 18 and older sample size for consumers only divided by the sample size for U.S. 

population from Section 5.1.1.1 Table 4. The result was divided by 2 to represent a one-day sampling period in order to match the 

one-day ingestion rate.

Exhibit B-21. Calculated Long-term Mean and Percentile per capita Fish Ingestion Rates

for Children and Adults (IRPC,y) 

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

Mean 50th 90th 95th 99th

Child <1 NA NA NA NA NA

Child 1-2a 1.37 0.79 3.24 4.41 6.98

Child 3-5b 2.03 1.16 4.79 6.51 10.3

Child 6-11c 2.71 1.25 6.90 10.9 17.0

Child 12-19d 3.90 2.13 8.95 10.4 20.9

Adulte 6.90 4.03 16.99 23.73 43.02

Sources: EPA 2002, 2008a

NA = not applicable; it is assumed that children < 1 year of age do not consume fish. a

Values were calculated as (consumer-only IR for Child 1-2) x (fraction of population consuming fish for Child 3-5). b

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

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

Values were calculated as (consumer-only IR estimated for Child 12-19) x (fraction of population estimated to consume fish for 

Child 12-19). e

Values were calculated as (consumer-only IR for Adults) x (fraction of population consuming fish for Adults).

Attachment B B-75 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

MIRC also includes values for the mean and the 90th percentile fish ingestion rates for 

recreational fishers, black and female recreational fishers, and fishers of Hispanic, Laotian, and 

Vietnamese descent which are shown in Exhibit B-22. These latter three populations are 

culturally or economically disposed to higher rates of fish ingestion than the general population. 

Recreational fisher values are from the EFH (EPA, 2011a). Black and female recreational 

fishers ingestion rates are presented in Burger (2002). The fish ingestion rates for Hispanic, 

Laotian, and Vietnamese populations were derived from a study by Shilling et al. (2010) of 

contaminated fish consumption in California’s Central Valley Delta. Shilling et al. (2010) 

reported mean and 95th percentile ingestion rates for each subpopulation. In part due to the low 

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

unrealistically high. The 90th percentile ingestion rate estimates presented in Exhibit B-22 for 

Hispanic, Laotian, and Vietnamese fishers were derived by EPA using information from Shilling 

et al. (2010; EPA, 2010).

Exhibit B-22. Calculated Mean and 90th Percentile Per capita Fish Ingestion Rates for 

Populations of Recreational Fishers (IRPC,y) 

Subpopulation

Percentile Units

Recreational 

Fishera

Female 

Recreational 

Fisherb

Black 

Recreational 

Fisherb

Hispanic 

Recreational 

Fisherc

Laotian 

Recreational 

Fisherc

Vietnamese 

Recreational 

Fisherc

Ingestion of Fish

Mean g/day 8 39.1 171 25.8 47.2 27.1

90th g/day 11 123 446 98 144.8 99.1

a

1997 Exposure Factors Handbook (USEPA, 1997a) b

Burger (2002) weights are “as consumed” for locally caught fish. c

Shilling, F., A. White, L. Lippert, and M. Lubell. 2010. Contaminated fish consumption in California’s Central Valley Delta. 

Environmental Research 110:334-344.

Applications to date of MIRC have used whole fish concentrations estimated by TRIM.FaTE. 

The proportion of lipid in TL3 and TL4 fish in TRIM.FaTE is assumed to be 5.7 percent (by 

weight) for the whole fish, based on information provided by Thomann (1989). The lipid content 

of the part(s) of the fish normally consumed is likely to be less than 5.7 percent. For example, 

EPA estimated a consumption-weighted mean lipid value for fillets of fish from TL3 to be 2.6 

percent and from TL4 to be 3.0 percent (Table 6-9 in EPA 2003b). If a user of MIRC wishes to 

account for reduced chemical concentration in fillet compared with whole fish for lipophilic 

chemicals, the user can specify a “preparation” loss of chemical (see Section 6.4 of this 

attachment).

For lipophilic chemicals (e.g., log Kow greater than 4), which partition primarily into the fatty 

tissues of fish, much of the higher concentration tissues might be stripped from the fish during 

preparation (e.g., belly fat, viscera which includes fat in liver, etc., fat under skin). The degree to 

which the concentration of chemical in a fillet is less than the average total concentration in the 

whole fish is chemical specific. Assuming the chemical concentration in the fillet is the same as 

in the whole fish may result in a health protective bias for highly lipophilic chemicals. For 

persons who prefer to consume fillets with the skin on and do not discard belly fat, assuming the 

same concentration of chemical in the fish consumed as in the whole fish also is protective.

Attachment B B-76 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

6.3.5. Soil Ingestion Rates

Adult gardeners and farmers may incidentally ingest soils from gardening activities, and 

gardening and farming families might ingest soil particles that adhere to exposed fruits and 

exposed and belowground vegetables. Soils that are re-suspended in the air by wind can 

resettle on exposed fruits and vegetables. Children may incidentally ingest soils in those ways, 

but in addition, children playing outdoors may ingest soils directly or by hand-to-mouth activities 

during play. MIRC includes soil ingestion rate options by age group for these types of 

exposures. MIRC does not include options for children who may exhibit pica, or the recurrent 

ingestion of unusually high amounts of soil (i.e., on the order of 1,000 - 5,000 mg/day or more) 

(EPA 2008a). 

Data on soil ingestion rates are sparse; the soil ingestion rates listed in Exhibit B-23 and 

included in MIRC are based on very limited data, as is evident from the values listed. The 

studies evaluated by EPA for children generally focused on children between the ages of 1 and 

3 to 6 years and were not specific to families that garden or farm. The default ingestion rates in 

MIRC are the 90th percentile values, as for other ingestion rate parameters.

Exhibit B-23. Daily Mean and Percentile Soil Ingestion Rates 

for Children and Adults

Age Group

(years)

Soil Ingestion Rate (mg/day)

Meana 50th a 90th 95th 99th

Child < 1 NA

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

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

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

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

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

Sources: EPA 2008a, EPA 2011a a

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

CSEFH, Table 5-1. For adults, value is the recommended mean value for adults from EPA’s 2011 EFH, Chapter 5, Table 5-1. b

Values are the recommended “upper percentile” value for children from EPA’s 2011 EFH, Chapter 4, Table 4-23. The 2008 CSEFH 

and 2011 EFH included a high-end value associated with pica only, but this value has not been used. c

Values are 90th percentile adult ingestion rates calculated in Stanek et al. 1997; used to represent older children and adults. d

Values are 95th percentile adult ingestion rates calculated in Stanek et al. 1997; used to represent older children and adults.

6.3.6. Total Food Ingestion Rates

Although not included in MIRC for deterministic screening-level exposure and risk assessments, 

total food ingestion rates would be included in any probabilistic module developed for MIRC. 

The total food ingestion rates presented in Exhibit B-24 will be used to normalize or to truncate 

the sum of food-specific ingestion rates to reasonable values. This procedure is particularly 

important when chemical intake from multiple upper-percentile food ingestion rates for different 

types of food are added together. Individuals representing the upper percentile ingestion rate 

for one food category might not be the same individuals who reported high percentile ingestion 

rates for one or any of the other food categories.

Attachment B B-77 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

Exhibit B-24. Daily Mean and Percentile Per Capita Total Food Intake for 

Children and Adults 

Lifestage (years) Percent of Group 

Consuming Food Mean 50th 90th 95th 99th

Total Food Intake (g/day, as consumed)

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

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

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

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

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

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

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

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

Child 1-2b 100% 82 79 125 144 177

Child 3-5c 100% 61 57 91 102 132

Child 6-11d 100% 40 38 61 70 88

Child 12-19e 100% 21 19 34 40 51

Adultg 100% 14.8 13.9 23.7 27.6 35.5

Sources: EPA 2005e, 2008a a

These values represent a time-weighted average for age groups birth to <1 month (N=88), 1 to <3 months (N=245), 3 to <6 

months (N=411), and 6 to <12 months (N=678) from Table 14-3 of the 2008 CSEFH. 

These values represent a time-weighted average for age groups 1 to <2 years (N=1,002) and 2 to <3 years (N=994) from Table 

14-3 of the 2008 CSEFH. c

These values were obtained from Table 14-3 of the 2008 CSEFH (age group 3 to <6 years, N=4,112). d

These values were obtained from Table 14-3 of the 2008 CSEFH (age group 6 to <11 years, N=1,553). These values represents 

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

group.

e

These values represent a time-weighted average for age groups 11 to <16 years (N=975) and 16 to <21 (N=743) years from 

Table 14-3 of the 2008 CSEFH. Note that estimated values include 11-year-olds and individuals through age 20, which 

contributes to uncertainty in the estimates.

f

These values represent a time-weighted average for age groups 20 to 39 years (N=2,950) and 40 to 69 years (N=4,818) from 

Table 5B of the 2005 EPA analysis of CSFII. g

These values represent a time-weighted average for age groups 20 to 39 years (N=2,950) and 40 to 69 years (N=4,818) from 

Table 5A of the 2005 EPA analysis of CSFII. h

Percents consuming foods from Table 14-3 of the 2008 CSEFH include: 67.0% (birth to <1 month); 74.7% (1 to <3 months); 

93.7% (3 to <6 months); and 99.7% (6 to <12 months). Infants under the age of 1 that consume breast milk are classified as “nonconsumers” of food.

6.4. Other Exposure Factor Values

The other exposure parameters included in the MIRC algorithms are exposure frequency 

(Section 6.4.1), fraction of the food type obtained from the contaminated area (Section 6.4.2), 

and reduction in the weight of the food types during preparation and cooking (Section 6.4.3). 

For the breast milk ingestion pathway, additional exposure parameters are included in the FFC 

algorithms (Section 6.5). 

6.4.1. Exposure Frequency 

The exposure frequency (EF) represents the number of days per year that an individual 

consumes home-produced food items that are contaminated with the chemical being evaluated. 

In MIRC, the default value for EF is 365 days/year for all exposure sources and all potential 

receptors. This assumption is consistent with the food ingestion rates used in MIRC (i.e., daily 

intake rates equivalent to annual totals divided by 365 days) and does not imply that residents 

necessarily consume home-produced food products every day of the year. 

Attachment B B-78 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

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 B-25. All preparation and 

cooking loss parameter values were estimated as specified in the exhibit’s endnotes from data 

presented in Chapter 13 of the EPA’s 1997 and 2011 EFH (EPA 1997a and 2011a).

Attachment B B-79 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

Exhibit B-25. Fraction Weight Losses from Preparation of Various Foods

Product

Mean Cooking, Paring, or 

Preparation Loss

(Cooking Loss Type 1 [L1])

(unitless)a

Mean Net Post Cooking

(Cooking Loss Type 2 [L2])

(unitless)b

Exposed Fruitc 0.244 0.305

Exposed Vegetable 0.162d NA

Protected Fruit 0.29e NA

Protected Vegetable 0.088f NA

Root Vegetableg 0.075 0.22

Beef 0.27 0.24

Pork 0.28 0.36

Poultry 0.32 0.295h

Fishi 0.0 0.0

Source: EPA 1997a and 2011a

NA = Not Available

a

For fruits, includes losses from draining cooked forms. For vegetables, includes losses due to paring, trimming, flowering the 

stalk, thawing, draining, scraping, shelling, slicing, husking, chopping, and dicing and gains from the addition of water, fat, or other 

ingredients. For meats, includes dripping and volatile losses during cooking. b

For fruits, includes losses from removal of skin or peel, core or pit, stems or caps, seeds and defects; may also include losses 

from removal of drained liquids from canned or frozen forms. For vegetables, includes losses from draining or removal of skin. 

For meats, includes losses from cutting, shrinkage, excess fat, bones, scraps, and juices. c

These values represent averages of means for all fruits with available data (except oranges) (Table 13-6). d

This value represents an average of means for all exposed vegetables with available data (Table 13-7). Exposed vegetables 

include asparagus, broccoli, cabbage, cucumber, lettuce, okra, peppers, snap beans, and tomatoes.

e

This value was set equal to the value for oranges (Table 13-6). f

This value represents an average of means for all protected vegetables with available data (Table 13-7). Protected vegetables 

include pumpkin, corn, peas, and lima beans.

g

These values represent averages of means for all root vegetables with available data (Table 13-7). Root vegetables include 

beets, carrots, onions, and potatoes.

h

This value represents an average of means for chicken and turkey (Table 13-5). i

If the user changes fish ingestion rates to match a survey of the whole weight of fish brought into the home from the field (divided 

by the consumers of the fish), an appropriate value for L1 would be 0.31 and an appropriate L2 would be 0.11 (EPA 2011a).

There are substantial uncertainties associated with the L1 and L2 parameters, including the 

wide variation in values across produce types that were averaged together to recommend a 

central tendency value for each. For example, the L2 factor does not distinguish between 

weight loss during cooking by water evaporation, which might leave the chemical in the food 

(chemical not lost), and pouring the cooking liquid down the drain (chemical lost) or using the 

liquid to create a sauce (chemical not lost). In addition, the concentration of chemical might be 

highest in the skin, which often is discarded, and lower in the consumed portion of many bulky 

fruits and vegetables. Finally, the data EPA used to evaluate L1 included negative losses (i.e., 

weight gains) due to hydration of dried vegetables (e.g., peas and lima beans), which increases 

the range of L1 values across different vegetables. 

Note that the default L1 and L2 values for fish are set to zero. That is because the data source 

for the fish ingestion rates is not the USDA’s 1987 to 1988 NFCS (USDA 1993, 1994a) as 

reported in EPA’s EFH, which reported food as brought into the home, as is the case for the 

other food categories. Instead, the fish IR data included in MIRC are based on parts actually 

consumed, and so no loss processes for preparation are needed. 

If the user manually changes fish ingestion rates to match a local survey of the whole weight of 

fish brought into the home from the field (divided by number of persons consuming the fish), the 

user should also set the L1 and L2 parameter to non-zero values. 

Attachment B B-80 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

6.4.4. Food Preparation/Cooking Adjustment Factor (FPCAF) for Fish

In addition to estimating the weight of the food that is lost to preparation and cooking, there also 

can be changes in the chemical concentrations due to cooking. Because the fish consumption 

rates are “as consumed” and the fish concentration is based on uncooked fish, adjustments 

should be made to reflect the chemical concentrations in fish after cooking. In order to account 

for this phenomenon, an FPCAF can be applied to the uncooked fish concentration to estimate 

a concentration in cooked fish. The following sections discuss FPCAFs for each of the 4 PBHAPs. 

6.4.4.1. Mercury

In the U.S. EPA Revised Technical Support Document: National-Scale Assessment of Mercury 

Risk to Populations with High Consumption of Self-caught Freshwater Fish (EPA 2011b), a food 

preparation/cooking adjustment factor (FPCAF) of 1.5 was used to adjust methyl-mercury 

(MeHg) concentrations in consumed fish (i.e., a 50% increase in MeHg concentration due to 

cooking). Cooking fish typically increases MeHg levels per unit fish (as consumed) because 

MeHg concentrates in the muscle, while preparation involves removal primarily of non-muscle 

elements of the fish. The value is based on a study by Morgan et al. (1997).

6.4.4.2. Cadmium

Similar to mercury, cadmium will bind to muscle and will be retained during the cooking process. 

As such, the same FPCAF of 1.5 that is used for mercury is assumed for cadmium.

6.4.4.3. Dioxin

Dioxins are lipophilic and have been demonstrated to be lost during cooking. Based on a 

literature review, an FPCAF of 0.7 to is applied to account for these losses during the cooking 

process. A brief summary of supporting literature follows.

• Schecter et al. (1998) found that the mass of PCDD and PCDF in fresh catfish fillet (skin 

on) decreased by about 50 percent per serving portion during cooking. Given the 

simultaneous losses of moisture/fats during broiling of the catfish, the PCDDs and 

PCDFs concentrations decreased by 33 percent (i.e., multiply uncooked concentration in 

fresh fish by a factor of 0.66 = 0.70 to one significant digit).

• Reinert et al. (1972) reported higher losses of another highly lipophilic chemical, DDT, 

from cooking fish fillets of bloaters, yellow perch, lake trout, and coho salmon. 

Concentrations of DDT in fish fillet portions for lake trout and coho salmon, top 

predators, were reduced by 64 to 72 percent by frying or broiling, primarily through 

preferential loss of fat (and lipophilic DDT) during cooking. The investigators did not 

report skin on or off; however, they used steak cuts instead of flat fillets, which provide a 

smaller ratio of skin to muscle than is the case for fillets that constitute one side of the 

fish.

• Zabik and Zabik (1995) quantified the reduction in TCDD concentration of cooked, with 

the skin off, fillets compared with uncooked fillet with skin for fish harvested from the 

Great Lakes. Concentrations in the cooked fish with the skin off were reduced relative to 

the raw fillet with the skin on by approximately 44 percent for walleye, 80 percent for 

white bass, and 61 percent for lake trout. Comparing losses of TCDD for fillets cooked 

with the skin on versus fillets that were both skinned and cooked, Zabik and Zabik 

(1995) found reductions in TCDD concentrations of approximately 43 percent for 

Chinook Salmon cooked with the skin on and 57 percent for chinook salmon cooked with 

Attachment B B-81 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

the skin off. They found a 37 percent reduction of TCDD concentration for carp fillets 

cooked with the skin on and 54 percent reduction if the skin was removed.

The three studies listed above indicate that the 0.7 factor is not likely to overestimate loss of 

PCDD/PCDFs from fish during cooking (pan frying, broiling, grilling). Reductions in TCDD 

concentrations could be much higher with skin removal and trimming of fat.

6.4.4.4. PAHs

While it is reasonable to assume that there might be losses of lipophilic PAHs during the 

cooking process, there is insufficient information to distinguish what the net loss (or gain) during 

cooking might be because cooking can create PAHs from proteins in the tissue. The literature 

acknowledges these competing forces, but does not provide information to disentangle the gain 

and loss mechanisms. As such, a neutral approach was taken, which is to assume an 

adjustment factor of 1.0 (i.e., no adjustment) for PAHs. 

6.5. Breast-Milk Infant Exposure Pathway Parameter Values

Values used for parameters in the breast-milk exposure pathway algorithms (see Section 3.4 of 

this attachment) can be scenario-specific, receptor-specific, and/or chemical-specific and might 

be empirically derived or estimated by an appropriate model. For parameters that are scenariospecific or for which empirical values are required, the default values provided in MIRC are 

listed. For parameters for which MIRC calculates values, the appropriate equation is listed. 

Scenario- and receptor-specific parameters are discussed in Section 6.5.1 and chemicalspecific parameters are discussed in Section 6.5.2. 

6.5.1. Receptor-specific Parameters

Receptor-specific values are needed for parameters that describe the characteristics or 

activities of the exposed individual. In this context, there are two relevant receptors: the mother 

and the infant. Exhibit B-26 lists the parameters and their default values. The text that follows 

describes the input value or value options for each exposure parameter required by MIRC to 

calculate the infant absorbed chemical intake rate, or DAIinf. For parameter values that can be 

estimated when empirical values are not available, see the equation description in Section 3.4

of this attachment. 

Attachment B B-82 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

Exhibit B-26. Scenario- and Receptor-Specific Input Parameter Values Used to 

Estimate Infant Exposures via Breast Milk 

Parameter Description Default Value 

AT Averaging time for infant’s exposure via breast milk, i.e., duration of 

nursing (days) =ED

BWinf Body weight of infant (kg) averaged over duration of nursing exposure 7.8

BWmat Body weight of mother (kg) averaged over duration of mother’s 

exposure

66

DAImat Daily absorbed intake of chemical by mother (mg/kg-day) Equation B-36

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

AT/ED Averaging time divided by exposure duration 1.0

fbp Fraction of mother’s whole blood that is plasma (unitless) 0.65

ffm Fraction of mother’s body weight that is fat (unitless) 0.30

fmbm Fraction of fat in mother’s breast milk (unitless) 0.04

fpm Fraction of mother’s body weight that is plasma (unitless) 0.046

IRmilk Mean infant milk ingestion rate over duration of nursing (kg/day) 0.709

tbf Duration of breast feeding (days) 365

tpn Duration of maternal chemical exposure prior to nursing (days) 3285 

Averaging time (AT) and exposure duration (ED). AT refers to the time over which the infant’s 

exposure to the chemical of concern is averaged. ED refers to the duration of the infant’s 

exposure. For the exposure scenario considered for this age group, both AT and ED equal the 

duration of the nursing period, and they therefore cancel each other out in the infant average 

daily dose equation. 

Infant body weight (BWinf). The user selects a value for BWinf , the time-weighted average body 

weight of the infant over the entire duration of breast feeding, based on the age at which the 

infant stops breast feeding. For example, if the infant breast feeds for one year, the user should 

select the body weight for an infant that is averaged from birth to the first birthday. Similarly, if 

an infant breast feeds for 6 months, the user should select the body weight for an infant that is 

averaged from birth to six months. Because the default breast feeding duration (tbf) is one year 

(i.e., 365 days), the default infant body weight is 7.8 kg, which is the time-weighted average for 

the mean infant body weight between birth and the first birthday from EPA’s 2008 Child Specific 

Exposure Factors Handbook (CSEFH; EPA 2008a). Exhibit B-27 presents additional values for 

the infant body weight parameter that the user can select instead of the MIRC default.

Exhibit B-27. Average Body Weight for Infants 

Statistic 0 to < 6 months 

(kg)

0 to < 12 months 

(kg)

0 to < 18 months 

(kg)

0 to < 24 months 

(kg)

Mean 6.5 7.8a 9.0 9.6

5th percentile 5.0 6.0 7.0 7.5

10th percentile 5.3 6.4 7.4 7.8

15th percentile 5.5 6.7 7.7 8.2

25th percentile 5.8 7.0 8.1 8.7

50th percentile 6.4 7.8 8.9 9.5

75th percentile 7.1 8.6 9.9 10.5

85th percentile 7.4 9.0 10.3 11.0

Attachment B B-83 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

Exhibit B-27. Average Body Weight for Infants 

Statistic 0 to < 6 months 

(kg)

0 to < 12 months 

(kg)

0 to < 18 months 

(kg)

0 to < 24 months 

(kg)

Mean 6.5 7.8a 9.0 9.6

90th percentile 7.7 9.2 10.6 11.3

95th percentile 8.0 9.7 11.1 11.8

Source: EPA (2008a); each value is the time-weighted average from the data summaries presented in the CSEFH, Table 8-3. a

MIRC default

Maternal body weight (BWmat). This parameter represents the body weight of the mother 

averaged over the entire duration of the mother’s exposure to the chemical of concern. The 

maternal body weight is needed to calculate the biological elimination constant for the lipophilic 

chemical in lactating women (kfat_elac). MIRC assumes that the mother will be pregnant for 9 

months (i.e., 0.75 year) and will be lactating for 1 year. The MIRC default maternal body weight 

also assumes that the mother has been exposed for 10 years total. For 8.25 years, she is not 

pregnant or lactating, for 0.75 year she is pregnant, and for 1 year she is lactating. The MIRC 

default BWmat of 66 kg is based on CSFII data compiled by EPA for non-lactating and nonpregnant women between the ages of 15 and 44 (i.e., women of child-bearing age), lactating 

women, and pregnant women (EPA 2004). Exhibit B-28 presents additional values for the 

maternal body weight parameter which the user may choose to use instead of the MIRC default. 

The BWmat value is not the value that MIRC uses to estimate the mother’s absorbed daily intake 

(DAImat). The daily ingestion rates for home-grown/raised food products in MIRC are for men 

and women combined, with the rates normalized to body weight. The ingestion rates for soil, 

water, and fish included in MIRC are not normalized to body weight but are based on both men 

and women. For those ingestion rates, MIRC uses an average body weight value for males and 

females to estimate the average daily dose (intake) of the chemical in mg/kg-day. These values 

are subject to the assumption that the body-weight normalized ingestion rates and resulting 

ADD values are applicable to nursing mothers.

Exhibit B-28. Time-weighted Average

Body Weight for Mothers

Statistic Weight (kg)

Mean 66.0a

5th 47.1

10th 50.2

25th 54.3

50th 62.0

75th 72.0

90th 85.7

95th 97.0

Source: EPA 2004

a

MIRC default value

Exposure duration (ED). See discussion of AT and ED above.

Fraction of mother’s whole blood that is plasma (fbp). Steinbeck (1954) reported that plasma 

volume accounts for approximately 60 percent of the total blood volume in non-lactating human 

females (EPA 1998). Harrison (1967) and Ueland (1976) reported plasma volumes between 63 

to 70 percent in postpartum women (EPA 1998). The default value in MIRC of 65 percent (0.65) 

Attachment B B-84 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

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 B-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 B-38) assumes that a 

constant fraction of breast milk is fat, even though there is evidence that indicates variation in 

the fat content of breast milk throughout lactation (Sim and McNeil 1992). Different studies 

suggest a fat content of breast milk in humans of between 1 and 5 percent (Jensen 1987, 

Schecter et al. 1994, Hong et al. 1994, McLachlan 1993, Bates et al. 1994, NAS 1991, Butte et 

al. 1984, Maxwell and Burmaster 1993, EPA 2011a, Smith 1987, Sullivan et al. 1991). The 

MIRC default value for fmbm of 0.04 (i.e., 4 percent) is the value EPA recommended for MPE

(EPA 1998). 

Fraction of maternal weight that is plasma (fpm). Altmann and Dittmer (1964) estimated that 

plasma volume for adult women ranged from 37 to 60 mL/kg of body weight and averaged 

about 45 mL/kg. Ueland (1976) observed that the average plasma volume of women 6 weeks 

postpartum was 45 mL/kg of body weight. Using a value of 1.026 for the specific gravity of 

plasma from Conley (1974), EPA estimated a value of 0.046 for the fraction of maternal weight 

that is plasma (EPA 1998). The MIRC default for fpm therefore is 0.046. 

Infant breast milk ingestion rate (IRmilk). Milk ingestion rates vary with several factors, including 

the age and size of the infant and use of other foods such as formula. Based on its review of a 

several studies, EPA recommended time-weighted average and upper percentile milk ingestion 

rates for infants that nurse for six and for twelve months (EPA 2011a, Table 15-3). To estimate 

an “average” value, EPA first estimated study-sample-size weighted average values for 1 

through 12 months of age and then developed time-weighted average milk ingestion rates from 

those (EPA 2011a). EPA estimated an upper percentile (upper bound) value as the mean plus 

two standard deviations. MIRC converts the ingestion rates measured volumetrically (mL/day) 

to mass-based estimates (kg/day) assuming the density of human milk to be 1.03 g/mL

(reported by NAS 1991 and recommended by EPA 2011a). The resulting values are shown in 

the first two rows of Exhibit B-29. The MIRC screening-level default of 980 mL/day is an upperbound estimate based on a one-year nursing period. 

Exhibit B-29 also includes the recommended values for four non-overlapping age categories 

from the CSEFH (EPA 2008a, Table 15-1). The values demonstrate that although infants grow 

substantially from birth to one year of age, the “upper bound” estimates of their milk ingestion 

rates are very close to 1 liter per day at all stages of development in the first year.

Attachment B B-85 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

Exhibit B-29. Infant Breast Milk Intake Rates

Age Category Average 

(mL/d)

Average 

(kg/d)

“Upper Bound” 

(mL/d)

“Upper Bound”

(kg/d) Reference

1 to 6 months 742 0.764 1,033 1.064 EPA 2011a†

0 to < 12 

months 688 0.709 980a 1.01a EPA 2011a†

0 to < 1 month 510 0.525 950 0.979 EPA 2008a††

1 to < 3 months 690 0.711 980 1.01 EPA 2008a†

3 to < 6 months 770 0.793 1,000 1.03 EPA 2008a†

6 to < 12 

months 620 0.639 1,000 1.03 EPA 2008a†

a

MIRC default; † Based on review of multiple studies; †† Based on a single study

Duration of breast feeding (tbf). This parameter is equal to the infant’s exposure duration (ED) 

and the infant’s averaging time (AT). In its MPE Methodology, EPA asserts a health protective 

value for the duration of breast feeding of 1 year (i.e., 365 days) and a central tendency 

estimate of 6 months (180 days) (EPA 1998). Reviewers of MPE noted that 365 days may be 

overly health protective, given that only 20 percent of infants are breast fed for 6 months, at 

which point alternative foods are introduced, at least in addition to breast milk (EPA 2001a). 

Nonetheless, to establish a health protective screening scenario, the MIRC default for tbf is 365 

days.

Duration of the mother’s exposure to the chemical of concern prior to nursing (tpn). The model 

shown as Equation B-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 B-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 B-38 with the tpn term (EPA 1998, Table 9-6). 

However, at longer pre-natal exposures of the mother, the magnitude of the over-estimate is 

reduced: for a 10-year exposure, the magnitude of the overestimate without the tpn term is 2.28, 

and for a 30-year exposure, the overestimate is reduced to 1.39. 

For purposes of the screening-level assessment, we assume an exposure duration equal to the 

MIRC default half-life for dioxins, or 10 years. Only 3,285 days of that period are pre-natal (i.e., 

3650 minus 365 days, assuming 1 year lactation period). Although longer exposure periods are 

possible for the screening scenario, there is sufficient uncertainty in the model to merit 

accepting a health protective bias for this parameter value. 

6.5.2. Chemical-Specific Parameter Values

The chemical-specific parameters in the breast-milk pathway in MIRC are listed in Exhibit B-30. 

Note that the parameters for which values are needed are different for the lipophilic chemicals 

(i.e., dioxins), for which lactational transfer is assumed to occur via milk fat, and inorganic 

chemicals, for which the transfer is assumed to occur via the aqueous phase of breast milk (i.e., 

mercury). All dioxin congeners were assumed to manifest identical values as TCDD in regard to 

breast milk-related parameters.

Attachment B B-86 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

Exhibit B-30. Chemical-specific Input Parameter Values for 

Breast Milk Exposure Pathway

Parameter and Description 2,3,7,8-

TCDD MeHg

AEinf Infant absorption efficiency of the chemical by the 

oral route of exposure (i.e., fraction of ingested 

chemical that is absorbed by the infant; unitless)

1.0 (default) 1.0 (default)

AEmat Maternal absorption efficiency of the chemical by 

the oral route of exposure (i.e., fraction of ingested 

chemical that is absorbed by the mother; unitless)

1.0 (default) 1.0 (default)

fbl Fraction of steady-state total body burden of 

hydrophilic chemical in mother that is in the 

mother’s whole blood compartment (unitless)

NA 0.059 (Kershaw et 

al. 1980)a

ff Fraction of steady-state lipophilic chemical body 

burden in mother that is stored in body fat (unitless)

≥0.90 (ATSDR 

1992)

NA

fpl Fraction of steady-state total hydrophilic chemical 

body burden in mother that is in the blood plasma 

compartment (unitless)

NA Not yet identifiedb

h Biological half-life for chemical in non-lactating 

women (days)

3650 (EPA 

1994c)

50 (Sherlock et al. 

1984)

kaq_elac Rate constant for total elimination of hydrophilic 

chemicals by lactating women (per day)

NA = kelim

kelim Rate constant for elimination of chemical for nonlactating women (per day; related to chemical halflife)1.9E-04b 1.4E-02 c

kfat_elac Rate constant for total elimination of lipophilic 

chemicals by lactating women (per day)

Est. using 

Equation B-41

NA

Pcbm Partition coefficient for hydrophilic chemical 

between maternal blood plasma and aqueous 

phase of breast milk (g milk/g plasma; model 

assumption)

NA 1.0 (model 

assumption)

PcRBC Partition coefficient for hydrophilic or protein-bound 

chemical between red blood cells (RBC) and 

plasma in maternal blood (mL whole blood/mL 

RBC)

NA 40 (Hollins et al. 

1975) 

NA = not applicable. ND = not yet determined from literature. a

This value is based on a single-dose study and may not be appropriate for a chronic exposure model. b

An empirical value for this variable is currently missing for application of model. 

c

This value was calculated from biological half-life (h) using Equation B-40. 

Absorption efficiency of the chemical by the oral route of exposure for the infant (AEinf). The 

models included in MIRC assume that the AEinf from the lipid phase of breast milk is equal to the 

AEinf from the aqueous phase of the milk. Reviewers of the model stated that this assumption 

may not be valid and that ideally, the equation DAIinf would include variables for the AEinf from 

the breast milk fat and the AEinf from the aqueous phase of breast milk (EPA 2001a). However, 

since the MIRC assumption is that chemicals will partition to either the lipid or aqueous phase of 

milk, it is not necessary at this time to have multiple AEinf values for a given chemical. If data on 

the AE from the mother or an adult but not for the infant are available, data for the adult may be 

used for AEinf. Reviewers also recommended that chemical-specific values come from studies 

that account for absorption of the chemical from milk, because absorption from other matrices 

(e.g., solid foods) may not be relevant (EPA 2001a). If chemical-specific data are not available 

Attachment B B-87 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

for adults or infants, a health protective default value for AEinf for a screening level assessment 

is 1.0, which assumes 100 percent absorption (EPA 1998). 

The default value for AEinf in MIRC for both MeHg and dioxin is 1.0. For ingested lipophilic 

chemicals, it is reasonable to assume that absorption will be high (EPA 2004c). ATSDR (1998) 

reported that dioxins are well absorbed by the oral route of exposure, with one human 

experiment indicating more than 86 percent absorption. It is EPA policy to assume 100 percent 

absorption for chemicals with reported AEs of 50 percent or higher (EPA 2004c). MeHg also is 

well absorbed, with measured values as high as 95 percent, and so a value of 100 percent is 

used in MIRC (EPA 2001b).

Absorption efficiency of the chemical by the oral route of exposure for the mother (AEmat). The 

default value for both dioxins and MeHg is 1.0, as described in the previous paragraph. 

Fraction of total maternal chemical body burden that is in the whole blood (fbl). The default 

value for MeHg in MIRC, 0.059, is from Kershaw et al. (1980), which reported kinetics of blood 

deposition and clearance of MeHg in humans. Individuals consumed one meal of fish that 

contained between 18 and 22 μg Hg/kg body weight. The fraction of the dose deposited in the 

blood volume after mercury was fully distributed in tissues was 5.9 percent or 0.059. This study 

used a single-dose and thus may not be appropriate for a chronic exposure analysis.

Fraction of total maternal chemical body burden that is in body fat (ff). Based on ATSDR’s 

Toxicological Profile for Selected PCBs (ATSDR 1992) and Sullivan et al. (1991), EPA 

concluded that the “fraction of ingested contaminant stored in fat may be >90%” for lipophilic 

chemicals such as PCBs and dioxins (EPA 1998). This statement was interpreted to mean that

90 percent of the maternal body burden of chemical at “steady state” is located in body fat for 

dioxins at steady state. 

Fraction of total maternal chemical body burden that is in blood plasma (fpl). For hydrophilic 

chemicals, this parameter represents the steady-state fraction of the total chemical in the body 

that is circulating in the blood plasma. Values for fpl may be available for some chemicals in the 

scientific literature. No value for this parameter for methyl mercury has been identified from the 

literature at this time. A value can be calculated using Equation B-43. However, this equation 

requires a reliable value for fbl, and the value found for mercury may not be appropriate for a 

chronic exposure analysis (see above).

Chemical half-life in non-lactating women (h). In general, highly lipophilic chemicals tend to 

have relatively long biological half-lives. EPA estimates that the half-life for dioxins is between 7 

and 10 years (EPA 1994a). ATSDR estimates that the half-life for 2,3,7,8-TCDD in particular 

may be as long as 12 years (ATSDR 1998). To establish a health protective screening 

scenario, the MIRC default half-life for dioxins is set to 10 years or 3650 days. 

The half-life for methylmercury is on the order of weeks, not years. Greenwood et al. (1978) 

measured blood clearance rates for MeHg in lactating Iraqi women exposed accidentally to 

MeHg via bread prepared from wheat treated with a fungicide that contained MeHg. The data 

indicated a mean half-life for MeHg of approximately 42 days. Sherlock et al. (1984) reported 

an average measured half-life for MeHg of 50 days with a range of 42-70 days. The MIRC 

default for MeHg is set to the longer average half-life of 50 days.

Chemical elimination rate constant for lactating women – aqueous (kaq_elac). The parameter 

kaq_elac is equal to kelim plus the loss rate for the chemical in the aqueous phase of breast-milk 

during lactation. EPA has yet to propose a term for the additional elimination of a chemical in 

the aqueous phase of milk from breast feeding. In the absence of empirical values, a 

Attachment B B-88 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

reasonable assumption for water soluble chemicals is that kaq_elac is equal to kelim as discussed 

for Equation B-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 B-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 B-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 B-42, assumes that the concentrations 

in the plasma and aqueous phase of breast milk are directly proportional (EPA 1998). 

Therefore, the default value for this parameter for MeHg in MIRC is 1.0. 

Partition coefficient for chemical between red blood cells and plasma in maternal blood (PcRBC). 

Chemical-specific values for this parameter should be located in the scientific literature. If 

chemical-specific values are unavailable and it is assumed that there is equal distribution of the 

chemical in the plasma and red blood cells, EPA suggests a default value of 1.0 (EPA 1998). 

For MeHg, MIRC includes a value of 40 based on Hollins et al. (1975) study of cats exposed to 

MeHg, which reported a ratio of radio-labeled mercury in red blood cells to plasma of 97.7 to 2.3 

(i.e., ratio of 42.5). 

7. Summary of MIRC Default Exposure Parameter Settings

The default settings included in MIRC are intended to be characteristic of a health protective 

(but plausible) exposure scenario that results in a negligible or extremely low chance of 

underestimating risk. These default parameter values were used to derive the screening 

threshold emission rates used for screening emissions of PB-HAPs from sources included in 

RTR risk assessments. These values are the default, or initial setting, for parameter values in 

MIRC as described in Section 6 of this attachment. This section summarizes the default 

parameter values used to calculate screening thresholds.

This chapter is organized to present the chemical- and scenario-specific inputs to MIRC by data 

type. The screening-level analysis uses 90th percentile ingestion rates for soil, breast milk, and 

farm food items and 99th percentile ingestion rates for fish (presented in Section 7.1) and 

population-specific characteristic assumptions (presented in Section 7.2), that are generally 

health protective in nature. Screening thresholds were derived for five RTR chemical species: 

benzo(a)pyrene, cadmium, mercuric chloride, methyl mercury, and 2,3,7,8-TCDD; Section 7.3

presents chemical-specific parameter inputs for these five chemicals. Finally, Section 7.4

presents default parameter values for the nursing infant exposure scenario, which applied only 

to dioxin and methyl mercury as discussed in Section 3.4. 

7.1. Default Ingestion Rates

The screening-level (or default) values for ingestion rates for soil, breast milk, and for each farm 

food item are equal to the 90th percentile of the distribution of national data for that ingestion 

Attachment B B-89 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

medium. In general, these values were obtained from the 2011 Exposure Factors Handbook or 

the 2008 Child-Specific Exposure Factors Handbook (see Exhibit B-31). Fish ingestion rates 

are also available from these sources; however, as described in Section 6.3.4, these sources 

were not used to obtain fish ingestion rates. 

7.1.1. Fish Ingestion Rates

The adult fish ingestion rate was obtained from Burger (2002), a study that examined daily 

consumption of wild-caught fish for high-end recreationalists (white, black and female) in South 

Carolina. For female high-end consumers of wild-caught fish, Burger identified average and 

higher-percentile consumption rates as follows: 39.1 g/day (mean), 123 g/day (90th percentile), 

172 g/day (95th percentile), and 373 g/day (99th percentile). As shown in Exhibit B-31 and 

discussed in Section 6.3.4, for adults, the rate of fish ingestion assumed in the screening 

scenario is 373 g/day, which corresponds to the 99th percentile value estimated by Burger for 

adult females. This value was selected to be representative of subsistence fishers. 

For the child age groups, as discussed in Section 6.3.4, the baseline fish ingestion rates for the 

screening scenario are based on “as prepared” total freshwater/estuarine fish ingestion rates at 

the 99th percentile of the distribution for the consumer-only population (i.e., inclusive only of 

people who consume fish, rather than per-captita rates, which include both consumers and nonconsumers), as estimated in EPA (2002), Section 4.2.1.1. Some adjustments were necessary 

because the age groups evaluated for RTR (which correspond to the age groups for which farm 

food ingestion rates are available) do not all directly correspond to the age groups in the EPA 

(2002) report. As described in Section 6.3.4, these adjustments convert the available agespecific data on fish ingestion rates to the age-specific values needed for MIRC. 

For the screening-level fish ingestion exposure scenario, the consumer evaluated is an 

individual who regularly consumes a large amount of fish that he or she has caught locally over 

the course of a 70-year lifetime. Modeled exposures are intended to encompass those of a 

subsistance fisher whose diet comprises a substantial proportion of fish. The scenario is not, 

however, intended to represent the maximum possible exposure an individual subsistence fisher 

might experience. 

Although the fish ingestion rates presented here are representative of the 99th percentile of the 

evaluated data set, the use of these inputs (compared with 90th percentile values used for other 

food types) is not considered to be inconsistent. This is due to the idiosyncrasies of the survey 

data on fish consumption, the fact that the data sets for homegrown foods and fish are not 

parallel, and the consideration of rates appropriate for subsistence fishers, as described above. 

As discussed above, EPA believes that use of these fish ingestion rates strikes the appropriate 

balance between being health protective and having screening scenarios so conservative that 

they are of limited use in the decision making process. This high-end fish ingestion rate is 

appropriate in the context of the conservative screening scenario used in the RTR process and 

is applicable for national rulemakings given that it is very likely that subsistence woman fishers 

of child bearing age are located throughout the United States. Using a high-end subsistence fish 

ingestion rate also is consistent with section 112 of the CAA, which focuses on risks associated 

with maximally exposed individuals.

7.1.2. Farm Food Chain Ingestion

The default settings assume that all food types are obtained from the area of chemical 

deposition specified by the user (i.e., fraction of food from contaminated area = 1.0). 

Attachment B B-90 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

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 B-31 also includes a sum of the 90th percentile ingestion rates for homegrown food 

categories and 99th percentile fish ingestion to show the implied total food ingestion rate 

associated with setting multiple food-type-specific ingestion rates at upper percentiles. Because 

these upper percentile values for each farm food category are likely to reflect different 

individuals, it is likely that addition of multiple upper percentile intake values will exceed the total 

food ingestion rates expected for the general population. This sum is shown on the third row 

from the bottom (Total Food: Homegrown Only). 

The second row from the bottom presents the 90th percentile of the distribution of individual total 

food ingestion rates from the USDA’s 1994-96 and 1998 Continuing Survey of Food Intakes by 

Individuals (CSFII) (USDA 2000) data sets, as analyzed by EPA (EPA 2005e). The total 

ingestion rate for the farming households (third row from bottom) takes into account the cooking 

losses typical of each food category to provide a better comparison with the 90th percentile 

individual total food ingestion rates from CSFII (which are based on consumption of prepared 

foods). The final row of Exhibit B-31 shows the likely magnitude of the overestimates by age 

category by presenting the ratio of the two preceding rows. The values in this row demonstrate 

the potential for overestimating intake by using upper percentile values for all food groups. This 

bias may be considered when evaluating the results estimated by MIRC.

Exhibit B-31. Farm Food Category Ingestion Rates for Health Protective Screening Scenario

for Farming Households

Product

Screening-Level Consumer Ingestion Rate

Units Infants 

<1 yr

Child

1–2 yrs

Child 

3–5 yrs

Child 

6–11 yrs

Child 

12–19 yrs

Adult

20–70 yrs

Farm Food Item

Beefa NA 9.49 8.83 11.4 3.53 4.41 g/kg-day

Dairyb NA 185 92.5 57.4 30.9 6.16 g/kg-day

Eggsa NA 4.90 3.06 1.90 1.30 1.31 g/kg-day

Exposed Fruita NA 12.7 5.41 6.98 3.41 2.37 g/kg-day

Exposed Vegetablea NA 10.7 3.47 3.22 2.35 3.09 g/kg-day

Porka NA 4.90 4.83 3.72 3.69 2.23 g/kg-day

Poultrya NA 7.17 6.52 4.51 3.13 2.69 g/kg-day

Protected Fruita NA 44.8 32.0 23.3 7.44 15.1 g/kg-day

Protected Vegetablea NA 3.88 2.51 2.14 1.85 1.81 g/kg-day

Root Vegetablea NA 7.25 4.26 3.83 2.26 2.49 g/kg-day

Other

Breast milkc 1.01 NA NA NA NA NA kg/day

Soil (dry) NA 200d 200d 201e 201e 201e mg/day

Fish (per individual)f NA 107.7g 159.0g 268.2h 331.0h 373 g/day

Attachment B B-91 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

Exhibit B-31. Farm Food Category Ingestion Rates for Health Protective Screening Scenario

for Farming Households

Product

Screening-Level Consumer Ingestion Rate

Units Infants 

<1 yr

Child

1–2 yrs

Child 

3–5 yrs

Child 

6–11 yrs

Child 

12–19 yrs

Adult

20–70 yrs

Total Food Ingestion Rates for Comparison Only (not in MIRC; excludes soil and water)

Total Food: Homegrown 

onlyi NA 259 142 99 51 35.5 g/kg-day

Total Food: All Sourcesj NA 125 91 61 34 23.7 g/kg-day

Overestimate (ratio of 

Homegrown/Total) NA 2.1 1.6 1.6 1.5 1.3 (unitless)

Sources: EPA 2011a, EPA 2008a, unless otherwise noted.

NA = not applicable

a

Primary source for values was the 1987–1988 NFCS survey; compiled results are presented in Chapter 13 of the 2011 Exposure 

Factors Handbook (EPA, 2011a). When data were unavailable for a particular age group, the intake rate for all age groups was 

multiplied by the age-specific ratio of intake based on national population intake rates from CSFII. b

Primary source for values was 1987–1988 NFCS survey, compiled results are presented in Chapter 13 of the 2011 Exposure Factors 

Handbook (EPA, 2011a). When data were unavailable for a particular age group, the intake rate for all age groups was multiplied by the 

age-specific ratio of intake based on national population intake rates from an NHANES 2003–2006 analysis in Chapter 11 of the 

Exposure Factors Handbook.

c

Infants are assumed to consume only breast milk for one year. 

d

These values are the recommended “upper percentile” value for children from EPA’s 2011 EFH, Chapter 4, Table 4-23. The 2008 

CSEFH and 2011 EFH included a high-end value associated with pica only, but this value has not been used. e

These values are 90th percentile adult ingestion rates calculated in Stanek et al. 1997, and they are used to represent older children 

and adults.

f

The ingestion rate for adults was obtained from Burger (2002) and is the 99th percentile value for adult females considered high-end 

recreationists; this value is believed to be representative of subsistence fishers. The 99th percentile values for children were derived 

based on EPA’s Estimated Per Capita Fish Consumption in the United States (2002)—Section 4.2.1.1 Table 5 (for child age categories)

adjusted and scaled. Values reflect “as prepared” ingestion rates. 

g

The fish ingestion rate for children aged 3–5 years was obtained directly from Section 4.2.1.1, Table 5 in the EPA (2002) report (value 

presented is rounded); for these children, the RTR age-group range matches the EPA (2002) age category. Fish ingestion rates for 

children less than 3 years old, however, were not provided. Therefore, for children aged 1–2 years, the fish ingestion rate was calculated 

using the ingestion rate for children aged 3–5 years scaled downward by the ratio of the mean body weight of children aged 1–2 years 

to the mean body weight of children aged 3–5-years. h

Time-weighted average ingestion rates were calculated using the EPA (2002) fish ingestion estimates in order to adjust for the 

differences between the age group ranges used for the RTR screening and those presented in the 2002 EPA report. i

Sum of post-cooking food ingestion rates. This estimate is calculated by multiplying the food ingestion rates on previous rows (excluding

soil and water) by (1-L1)x(1-L2), where L1 and L2 are the loss rates from Exhibit B-25. The rows are then summed to get the total postcooking ingestion rate.

j

90th percentile total food intake rates from EPA 2008a and 2005e based on CSFII data 1994-96 and 1998; see Section 6.3.6 of this 

document.

7.2. Default Screening-Level Population-Specific Parameter Values 

The screening-level values for body weights (BWs) for the RTR screening threshold analysis, 

which serve as the default values in MIRC, are mean values and are presented in Exhibit B-32. 

As stated in Section 6 of this attachment, EPA recommends using the mean BW for each age 

group when using upper percentile values for medium ingestion rates. Use of the mean body 

weights introduces no bias toward over- or underestimating risk.

Exhibit B-32. Mean Body Weight Estimates for Adults and Childrena

Lifestage (years) Duration (years) Mean Body Weight (kg)

Adultb (20-70) 50 80.0

Child < 1c 1 7.83

Child 1-2c 2 12.6

Child 3-5d 3 18.6

Attachment B B-92 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

Exhibit B-32. Mean Body Weight Estimates for Adults and Childrena

Lifestage (years) Duration (years) Mean Body Weight (kg)

Child 6-11e 6 36.0

Child 12-19f 8 64.2

a

Sources: EPA 1997, 2008a

b EPA-recommended value (EPA 2011a). d

These values were obtained directly from Table 8-3 of the 2008 CSEFH. e

Each BW represents a time-weighted average of BWs for age groups 6 to <11 years and 11 to <16 years from Table 8-3 of the 

2008 CSEFH. Original sample sizes for each of these age groups can also be found in Table 8-3. f

These values were calculated as time-weighted average BW for age groups 11 to <16 years and 16 to <21 years from Table 8-3 

of the 2008 CSEFH. The direction of the possible bias is unknown. The values match the estimate based on Table 8-22 of the 

NHANES IV data as presented by Portier et al. (2007).

7.3. Default Chemical-Specific Parameter Values for Screening Analysis 

Exhibit B-33 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 B-33.

Exhibit B-33. Chemical-Specific Parameter Values for Input to MIRCa

Parameter Description Benzo(a)-

pyrene

Cadmium Mercuric 

chloride

Methyl 

mercury

2,3,7,8-

TCDD Units

Bs Soil bioavailability 

factor for livestock

1 1 1 1 1 unitless

MF Mammalian 

metabolism factor

0.01 1 1 1 1 unitless

VGrootveg Empirical correction 

factor for 

belowground 

produce, i.e., tuber 

or root vegetable, to 

account for possible 

overestimate of the 

transfer of chemicals 

from the outside to 

the inside of bulky 

tubers or roots 

(based on carrots 

and potatoes)

0.01 1 1 0.01 0.01 unitless

Fw Fraction of wet 

deposition that 

adheres to plant 

surfaces; 0.2 for 

anions, 0.6 for 

cations and most 

organics

0.6 0.6 0.6 0.6 0.6 unitless

Attachment B B-93 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

Exhibit B-33. Chemical-Specific Parameter Values for Input to MIRCa

Parameter Description Benzo(a)-

pyrene

Cadmium Mercuric 

chloride

Methyl 

mercury

2,3,7,8-

TCDD Units

BvAG Air-to-plant 

biotransfer factor for 

aboveground 

produce for vaporphase chemical in 

air

174,523 0 1,800 0 65,500 [mg/g 

produce 

DW]/[mg/g 

air]

RCF Chemical-specific 

root concentration 

factor for tubers and 

root produce

9,180 0 0 0 40,002 L soil pore 

water/kg root 

WW

Kds Chemical-specific 

soil/water partition 

coefficient

7,750 75 58,000 7,000 31,126 L soil pore 

water/kg soil 

DW

a

Values presented in this exhibit are also presented in previous exhibits; however exact values used in the assessment are 

presented here, rather than values restricted by significant figures. In addition, only values for those chemicals that are specifically 

used in the screening assessment are provided here.

Only single estimates were developed for each of these parameters for HHRAP (EPA 2005a), 

and the potential direction and magnitude of bias toward over- or underestimating risks were not 

investigated in this assessment. The inputs that are both chemical-specific and plant-typespecific, as presented in Exhibit B-11, are not repeated here. Again, only single estimates were 

developed for these parameters and the potential direction and magnitude of bias toward overor underestimating risks were not investigated. Finally, Exhibit B-34 presents biotransfer factors 

for each of the chemicals and animal types for which screening threshold emissions were 

calculated.

Exhibit B-34. Chemical and Animal-Type Specific Biotransfer Factor (Ba) Values for 

Input to MIRC

([mg chemical/kg WW tissue or dairy] / [mg chemical intake/day] = day/kg WW tissue or dairy)

Chemical Beef Dairy Pork Eggs Poultry

Benzo(a)pyrene 3.8E-02 8.0E-03 4.6E-02 1.6E-02 2.8E-02

Cadmium 1.2E-04 6.5E-06 1.9E-04 2.5E-03 1.1E-01

Mercuric chloride 1.1E-04 1.4E-06 3.4E-05 2.4E-02 2.4E-02

Methyl mercury 1.2E-03 1.7E-05 5.1E-06 3.6E-03 3.6E-03

2,3,7,8-TCDD 3.6E-02 7.7E-03 4.4E-02 1.5E-02 2.7E-02

7.4. Screening-Level Parameter Values for Nursing Infant Exposure 

MIRC is configured to evaluate risk to nursing infants exposed to dioxins and to methylmercury 

(MeHg) in their mother’s milk for a family farming and catching fish in the area of maximal air 

deposition of chemical. Input values were summarized in Section 6.5 of this attachment. 

7.4.1. Dioxins

For dioxins, chemical intake via breast milk by nursing infants was estimated using the model 

presented in EPA’s Methodology for Assessing Health Risks Associated with Multiple Pathways 

of Exposure to Combustor Emissions (MPE, EPA 1998). The assumption that lactational 

transfer of dioxins to the infant occurs via the lipid-phase of milk appears reasonable. The 

Attachment B B-94 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

following screening-level assumptions used in that model should bias the results toward healthprotective estimates of risks.

• Duration of nursing is a full year and no other foods or liquids are consumed by the 

infant; a more “typical” value would be six months.

• Absorption efficiency of dioxin in food or milk by mother and infant are 100 percent; this 

assumption might overestimate absorption but probably by no more than 15 percent 

(see Section 6.5.2).

• The fat content of human milk is assumed to be 4 percent, a value toward the high end 

of the reported range of values (1 to 5 percent).

• The maternal chemical intake is estimated using upper percentile ingestion rates for the 

different homegrown foods (see discussion for Exhibit B-31); this assumption might 

overestimate total ingestion of homegrown foods by a factor of more than 2 (see Exhibit 

B-31). 

• If the fraction of the maternal body burden of dioxin that is in the body fat compartment is 

greater than 90 percent, as suggested by ATSDR (1998), then actual exposures of the 

infant may be less than estimated.

There also are parameter values and model assumptions for the lipid-phase breast-milk 

pathway for which possible bias is unknown.

• The accuracy of the model is unknown; it has not been verified or validated with 

empirical data.

• Using a half-life of 10 years for dioxins may over- or under-estimate risks.

Finally, there is one assumption that might possibly introduce some bias toward underestimating 

risks. The model results are sensitive to the biological half-life of the chemical in the mother 

relative to the length of her exposure prior to the lactation period. Using an exposure duration 

for the mother equal to the assumed half-life for dioxins, 10 years, may underestimate the 

duration of exposure of the mother.

7.4.2. Methyl Mercury

For MeHg, empirical data from a single human study (Fujita and Takabatake 1977) was used in 

conjunction with a physiologically based pharmacokinetic (PBPK) model of lactational transfer of 

MeHg developed and partially validated by Byczkowski and Lipscomb (2001) to support a very 

simple predictive model. Both the human data and the PBPK model indicated that for relatively 

low MeHg exposures, the concentration of MeHg in the nursing infant’s blood is similar to its 

concentration in the mother’s blood. The PBPK model suggested in addition that the average 

daily dose of MeHg absorbed from milk by the nursing infant (DAIinf) is indistinguishable from the 

dose of MeHg absorbed by its mother from her food (DAImat). The data are limited, and the 

model includes various assumptions; however, there is no known directional bias in the 

estimates. 

Attachment B B-95 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

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Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

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Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

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Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

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Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

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Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

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Update. Office of Water, Office of Science and Technology, Washington, D.C. EPA-822-RAttachment B B-101 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

00-001. October. Available at: 

http://www.epa.gov/waterscience/criteria/drinking/percapita/2004.pdf

EPA. 2004c. Risk Assessment Guidance for Superfund. Volume I: Human Health Evaluation 

Manual (Part E, Supplemental Guidance for Dermal Risk Assessment), Final. Office of 

Superfund Remediation and Technology Innovation, Washington, D.C. EPA/540/R/99/005; 

OSWER 9285.7-02EP; NTIS PB99-963312. July.

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

Facilities. Office of Solid Waste and Emergency Response, Washington, DC. EPA-530-R05-006. September. Available at: http://www.epa.gov/osw/hazard/tsd/td/combust/risk.htm. 

EPA. 2005b. Guidance on Selecting Age Groups for Monitoring and Assessing Childhood 

Exposures to Environmental Contaminants. Risk Assessment Forum. Washington, DC.

November. EPA/630/P-03/003F. Available at: 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=146583. 

EPA. 2005c. Guidelines for Carcinogen Risk Assessment. Risk Assessment Forum, 

Washington, DC. EPA/630/P-03/001F. March. Available from:

http://www.epa.gov/IRIS/cancer032505-final.pdf. 

EPA. 2005d. Supplemental Guidance for Assessing Susceptibility from Early-Life Exposure to 

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

Available at: http://www.epa.gov/ttn/atw/childrens_supplement_final.pdf. 

EPA. 2005e. Analysis of Total Food Intake and Composition of Individual's Diet Based on the 

U.S. Department of Agriculture's 1994-96, 1998 Continuing Survey of Food Intakes By 

Individuals (CSFII) (Final). Office of Research and Development, National Center for 

Environmental Assessment, Washington, D.C. EPA/600/R-05/062F. Available at:

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=132173. 

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

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

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

EPA. 2007b. Toxicological Review of 1,1,1-Trichloroethane (CAS No. 71-55-6) In Support of 

Summary Information on the Integrated Risk Information System (IRIS). Office of Research 

and Development, Washington, DC. EPA/635/R-03/006. August. Available at:

http://www.epa.gov/iris

EPA. 2008a. Child-Specific Exposure Factors Handbook. Office of Research and Development, 

Washington, D.C. EPA/600/R-06/096F. September. Available at: 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=199243 . 

EPA. 2008b. Draft Report on EPA OAQPS Risk and Technology Review Methodologies: For 

Review by the EPA Science Advisory Board; Case Studies – MACT I Petroleum Refining 

Sources, Portland Cement Manufacturing. Office of Air Quality Planning and Standards, 

Office of Air and Radiation, Research Triangle Park, NC. July 14, 2008.

EPA. 2010. U.S. EPA. Development of a Relative Potency Factor (RPF) Approach for Polycyclic 

Aromatic Hydrocarbon (PAH) Mixtures (External Review Draft). Washington, DC, 

EPA/635/R-08/012A February. Available at:

http://cfpub.epa.gov/ncea/iris_drafts/recordisplay.cfm?deid=194584 

Attachment B B-102 October 2014

Description of MIRC
TRIM-Based Tiered Screening Methodology for RTR

EPA. 2011a. Exposure Factors Handbook: 2011 Edition. Office of Research and Development, 

Washington, D.C. EPA/600/R-090/052F. September. Available at:

http://cfpub.epa.gov/ncea/risk/recordisplay.cfm?deid=236252. 

EPA. 2011b. Revised Technical Support Document: National-Scale Assessment of Mercury 

Risk to Populations with High Consumption of Self-caught Freshwater Fish; In Support of 

the Appropriate and Necessary Finding for Coal- and Oil-Fired Electric Generating Units. 

Office of Air Quality Planning and Standards, Research Triangle Park, NC. EPA-452/R-11-

009. December. 

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. 

Zabik, ME; Zabik, MJ. 1995. Tetra-chlorodibenzo-p-dioxin residue reduction by 

cooking/processing of fish fillets harvested from the Great Lakes. Bulletin of Environmental 

Contamination and Toxicology. 55:264-269.

Attachment B B-103 October 2014

Description of MIRC
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TRIM-Based Tiered Screening Methodology for RTR

Dermal Risk Screening

Attachment C i October 2014

Dermal Risk Screening
[This page intentionally left blank.] 
TRIM-Based Tiered Screening Methodology for RTR

CONTENTS, ATTACHMENT C

1. Hazard Identification and Dose Response Assessment .................................................. C-1

2. Dermal Exposure Estimation........................................................................................... C-2

2.1. Equations for Estimating Dermal Exposure............................................................ C-2

2.2. Exposure Factors and Assumptions ...................................................................... C-3

2.3. Receptor-Specific Parameters ............................................................................... C-4

2.4. Scenario-Specific Parameters ............................................................................... C-4

2.5. Chemical-Specific Parameters............................................................................... C-5

3. Screening-Level Cancer Risks and Non-Cancer Hazard Quotients................................. C-6

3.1. Dermal Cancer Risk............................................................................................... C-6

3.2. Dermal Hazard Quotient ........................................................................................ C-7

4. Dermal Screening Results .............................................................................................. C-7

5. References ................................................................................................................... C-10

Attachment C iii October 2014

Dermal Risk Screening
TRIM-Based Tiered Screening Methodology for RTR

Exhibits, Addendum 3

Exhibit C-1. Cancer Slope Factors and Reference Doses Based on Absorbed 

Dose ................................................................................................................. C-2

Exhibit C-2. Receptor-Specific Body Surface Area Assumed to be Exposed to 

Chemicals......................................................................................................... C-4

Exhibit C-3. Scenario-Specific Exposure Values for Water and Soil Contact.......................... C-5

Exhibit C-4. Chemical-Specific Dermal Exposure Values for Water and Soil 

Contact ............................................................................................................. C-6

Exhibit C-5. Summary of Dermal Non-Cancer Hazards.......................................................... C-8

Exhibit C-6. Summary of Dermal Cancer Risks ...................................................................... C-9

Attachment C iv October 2014

Dermal Risk Screening
TRIM-Based Tiered Screening Methodology for RTR

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

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

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

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

conservative tiered screening scenario developed for RTR multipathway evaluation, the dermal 

exposure route is not a significant risk pathway when compared to the ingestion pathway. In 

general, the RTR multipathway assessment followed the protocol for evaluating a reasonable 

maximum exposure as described in EPA’s Risk Assessment Guidance for Superfund (RAGS), 

Volume I: Human Health Evaluation Model, Part E, Supplemental Guidance for Dermal Risk 

Assessment (EPA 2004). 

1. Hazard Identification and Dose Response Assessment

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

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

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

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

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

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

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

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

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

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

adjustment is made (EPA 2004). 

The absorbed cancer slope factor (CSFABS) is based on the oral cancer slope factor (CSFO) and 

the fraction of the contaminant absorbed in the gastrointestinal track (ABSGI), as follows: 

where:

CSFABS = Absorbed slope factor (mg/kg-day)-1

CSFo = Oral slope factor (mg/kg-day)-1

ABSGI = Fraction of chemical absorbed in gastrointestinal tract (unitless)

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

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

where:

RfDABS = Absorbed reference dose (mg/kg-day)

RfDo = Oral reference dose (mg/kg-day)

ABSGI = Fraction of chemical absorbed in gastrointestinal tract (unitless)

GI

o

ABS ABS

CSF CSF =

RfDABS RfDo ×= ABSGI

Attachment C C-1 October 2014

Dermal Risk Screening
TRIM-Based Tiered Screening Methodology for RTR

The GI absorptions for 2,3,7,8-TCDD and all polycyclic aromatic hydrocarbons (PAHs) (which 

includes benzo[a]pyrene) were estimated to be greater than 50 percent. Therefore, as shown in 

Exhibit C-1, no adjustments to the available oral CSFs were required. For cadmium and 

divalent mercury, adjustments were made based on absorption data provided in RAGS Part E, 

Exhibit 4-1. The RfDs for dermal exposure to cadmium and divalent mercury are also shown in 

Exhibit C-1. 

Exhibit C-1. Cancer Slope Factors and Reference Doses Based on Absorbed Dose

PB-HAP

Fraction of Contaminant 

Absorbed in GI Tract (ABSGI) 

(unitless)

Absorbed Cancer 

Slope Factor (CSFABS)

a 

(mg/kg-day)-1

Absorbed Reference 

Dose (RfDABS)

a 

(mg/kg-day)

Cadmium 

Compounds

0.05 NA 2.5E-05

Divalent 

Mercury

0.07 NA 2.1E-05

2,3,7,8-TCDD No adjustment 

required b

1.5E+05 1.0E-09

Benzo[a]pyrene No adjustment 

required b

1.0E+01 NA

NA = Not applicable

a

Oral dose response values are presented in Appendix 2. Only the resulting adjusted dose response values are presented in 

this table.

b

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

2. Dermal Exposure Estimation

Dermal exposures and risks resulting from absorption of the chemical through the skin from 

contact with contaminated water and soil were evaluated for the RTR screening scenario. 

Individuals were assumed to be exposed on a fraction of their bodies (i.e., their head, forearms, 

hands, lower legs, and feet) to contaminated soil from the TRIM.FaTE surface soil parcel with 

the highest concentration (N1) on a daily basis. For the water evaluation, individuals were 

assumed to be exposed to contaminated surface water with the same PB-HAP concentration as 

the TRIM.FaTE screening scenario lake over their entire bodies on a daily basis. 

2.1. Equations for Estimating Dermal Exposure

The general equation used to estimate dermal absorbed dose (DAD) for water or soil is shown 

below, and is expressed in milligrams of PB-HAP per kilogram of receptor body weight per day

(mg/kg-day). DADs are calculated separately for the water and soil pathways and then added 

together for each age group. 

where:

DAevent = Absorbed dose per event; chemical-specific; equation for DAevent also differs 

depending on water or soil contact (mg/cm2-event)

EV = Event frequency (events/day)

ED = Exposure duration (years)

ATBW

DA SAEFEDEV DAD event

×

×××× =

Attachment C C-2 October 2014

Dermal Risk Screening
TRIM-Based Tiered Screening Methodology for RTR

EF = Exposure frequency (days/year)

SA = Skin surface area available for contact (cm2) 

BW = Body weight (kg)

AT = Averaging time; for non-cancer effects, equals ED x 365 days/year; for cancer 

effects, equals 70 years x 365 days/year (days)

DAevent is estimated to be the total dose absorbed through the skin at the end of exposure and 

the equation for calculation is different for organic and inorganic chemicals in water and for soil. 

The equations for calculating these chemical-specific DAevent values for water contact are 

provided in RAGS Part E, Chapter 3 (see Equations 3.2 – 3.4). For soil, the equation for 

calculating these chemical-specific DAevent values is provided in RAGS Part E, Chapter 3 (see 

Equation 3.12).

Water – Organic Chemicals:

Water – Inorganic Chemicals:

Soil – All Chemicals:

where:

DAevent = Absorbed dose per event (mg/cm2-event)

Cw 

Cs

= Chemical concentration in water (mg/cm3) or soil (mg/kg)

Kp = Chemical-specific dermal permeability coefficient of compound in water (cm/hr) 

FA = 

Chemical-specific fraction absorbed; accounts for loss due to the regular 

shedding of skin cells of some chemical originally dissolved into skin (unitless)

τevent = Chemical-specific lag time per event (hr/event)

tevent = Receptor-specific event duration (hr/event)

AF = Receptor- and activity-specific adherence factor of soil to skin (mg/cm2-event)

ABS = Chemical-specific dermal absorption fraction (unitless)

CF = Conversion factor (10-6 kg/mg)

2.2. Exposure Factors and Assumptions 

The exposure parameters included in this assessment and their default and other value options 

are summarized in this subsection. Default values were selected to result in a highly

conservative estimate of exposure (i.e., exposures are likely overestimated). Parameter values 

were primarily obtained or estimated from RAGS Part E (EPA 2004) and the Child-Specific 

Exposure Factors Handbook (CSEFH, EPA 2008). Receptor-and scenario-specific exposure 

assumptions are discussed first, and a discussion of chemical-specific parameters values 

follows. Estimated water and soil exposure concentrations are presented at the end of this 

subsection.

π

τ event event

event w p

t6 DA 2C FA K ××

×××=

event pw event DA ××= tKC

DAevent Cs ABSAF ×××= CF

Attachment C C-3 October 2014

Dermal Risk Screening
TRIM-Based Tiered Screening Methodology for RTR

2.3. Receptor-Specific Parameters

Dermal exposures and risks were estimated for the same age groups used in the ingestion 

exposure assessment: adults (ages 20 to 70 years) and five child age groups: <1 year; 1 to 2 

years; 3 to 5 years; 6 to 11 years; and 12 to 19 years. The body weight values used in the 

ingestion exposure assessment were used in the dermal exposure assessment. 

Body surface areas (SAs) for water and soil exposures for adults were calculated using

Appendix C, Exhibit C-1, of RAGS Part E. For children, SAs for water and soil exposures for

the five children’s age groups were estimated using Tables 7-1 and 7-2 of the CSEFH, 

respectively. For SA (water), individuals were assumed to shower or bathe in the water with 

100 percent of their body exposed. For SA (soil), it was assumed that individuals were exposed 

on a fraction of their total body, specifically their head, forearms, hands, lower legs, and feet. 

Based on information provided in RAGS Part E, the SA for forearms was calculated using the 

SA for arms and assuming a forearm-to-arm ratio of 0.45, and the SA for lower legs was 

estimated using the SA for legs and assuming a lower leg-to-leg ratio of 0.4.

Values for body SA by age group are summarized in Exhibit C-2. 

Exhibit C-2. Receptor-Specific Body Surface Area Assumed to be

Exposed to Chemicals 

Age Groupa

(years)

Surface Area for

Water Exposure (cm2)

Surface Area for

Soil Exposure (cm2)

Adult 20-70 18,150g 6,878h

Child <1b 3,992 1,772

Child 1-2c 5,700 2,405

Child 3-5d 7,600 3,354

Child 6-11e 10,800 4,501

Child 12-19f 17,150 6,906

a

Sources for the child groups included Table 7-1 (total body surface area for SA-Water), and Table 7-2 

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

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

to <12 months. 

c

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

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

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

estimate for ages 6 through 11 years since 11-year olds are not included in this CSEFH age group. f

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

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

estimates for 12 to 19 years.

g

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

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

C-1 of RAGS Part E.

2.4. Scenario-Specific Parameters

Exhibit C-3 summarizes the exposure values related to frequency and duration of contact. In 

general, these are the recommended defaults for calculating a reasonable maximum exposure 

(RME) for a residential scenario as proposed by EPA in RAGS Part E, Chapter 3. 

Attachment C C-4 October 2014

Dermal Risk Screening
TRIM-Based Tiered Screening Methodology for RTR

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

Exposure Parameter Receptor Value Source

Water Contact

Event Duration (tevent) 

(hr/event)

Child 1 Reasonable maximum exposure 

scenario for showering/bathing Adult 0.58 from RAGS Part E, Exhibit 3-2 

Soil Contact

Soil Adherence Factor (AF) 

(mg/cm2) 

Child 0.2 For children, value is geometric 

mean value for children playing 

(wet soil) and for adults, value is 

geometric mean value for an 

adult farmer from RAGS Part E, 

Exhibit 3-3 

Adult 0.1

Both Media

Event Frequency (EV) 

(events/day)

All 1 Reasonable maximum exposure 

scenario from RAGS Part E, 

Exhibits 3-2 and 3-5. Exposure Frequency (EF) 

(days/year)

All 350

Exposure Duration (ED) 

(years)

Child <1 1 Represents the number of years 

included in the age group; also 

used in ingestion exposure 

calculations.

Child 1-2 2

Child 3-5 3

Child 6-11 6

Child 12-19 8

Adult 20-70 50

Averaging Time (AT) (days) For cancer assessment, an AT equal to a lifetime (70 years) × 365 

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

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

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

used in ingestion exposure calculations.

2.5. Chemical-Specific Parameters

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

are listed in Exhibit C-4. For the water concentration in the dermal analysis, the modeled 

TRIM.FaTE chemical concentration in the screening scenario pond at the screening threshold

emission rate was used. For the soil concentration, the modeled TRIM.FaTE chemical 

concentration in surface soil in parcel N1 (untilled soil, closest to facility) of the screening 

scenario at threshold emission rate was used. This same soil concentration was also used in 

ingestion exposure calculations for soil ingestion.

Dermal absorption of chemicals in water is based on the use of a dermal permeability coefficient 

(Kp), which measures the rate that a chemical penetrates the skin. Dermal absorption of soilbound chemicals is based on the use of a dermal absorption fraction (ABS), which is a measure 

of how much of a chemical the skin absorbs through contact with soil.

Attachment C C-5 October 2014

Dermal Risk Screening
TRIM-Based Tiered Screening Methodology for RTR

Exhibit C-4. Chemical-Specific Dermal Exposure Values for 

Water and Soil Contact

PB-HAP Cadmium Divalent 

Mercury

2,3,7,8-

TCDD

Benzo[a]

pyrene

Source

Chemical concentration in 

Water (Cw) (mg/cm3) 

2.4E-08 1.9E-09 2.6E-18 2.1E-13 TRIM.FaTE modeled 

concentration in screening 

scenario pond

Chemical concentration in 

Soil (Cs) (mg/kg)

6.9E-02 6.3E-02 2.2E-10 1.4E-04 TRIM.FaTE modeled 

concentration in surface soil 

in parcel N1 in screening 

scenario

Permeability coefficient in 

water (Kp) (cm/hour)

0.001 0.001 0.81 0.7 Values from RAGS Part E, 

Exhibits B-3 (organics) and B4 (inorganics)

Fraction absorbed water 

(FA) (unitless)

NA NA 0.5 1.00 Values from RAGS Part E, 

Exhibits B-3; only used for 

organic chemicals

Lag time per event (event) 

(hr/event)

NA NA 6.82 2.69 Values from RAGS Part E, 

Exhibits B-3; only used for 

organic chemicals

Dermal absorption fraction 

(ABS) from soil (unitless)

0.001 0.045a 0.03 0.13 Values from RAGS Part E, 

Exhibit 3-4, unless otherwise 

noted

a

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

3. Screening-Level Cancer Risks and Non-Cancer Hazard Quotients

Toxicity values were used in conjunction with exposure information to evaluate the potential for 

cancer risks and non-cancer health hazards. Risk estimation methods are presented below. 

3.1. Dermal Cancer Risk

Cancer risk for the dermal route was calculated as the product of the age-specific DADs and the 

absorbed CSF for each chemical, as follows:

Dermal Cancer Risk = DAD × CSFABS

where:

DAD = Dermal Absorbed Dose (mg/kg-day)

CSFABS = Absorbed cancer slope factor (mg/kg-day)-1

Lifetime dermal cancer risks were calculated for 2,3,7,8-TCDD and benzo[a]pyrene. The total 

risk accounts for dermal exposures that an individual might receive from these PB-HAPs in 

water plus soil over his or her lifetime (70 years). 

Attachment C C-6 October 2014

Dermal Risk Screening
TRIM-Based Tiered Screening Methodology for RTR

3.2. Dermal Hazard Quotient 

Dermal hazard quotient (HQ) was estimated as the ratio of age-specific DADs to the absorbed 

RfD for each chemical, as shown below:

Dermal HQ = DAD / RfDABS

where:

DAD = Dermal Absorbed Dose (mg/kg-day)

RfDABS = Absorbed reference dose (mg/kg-day)

The aggregate HQ accounts for exposures that an individual in a receptor group may receive 

from the PB-HAP in water and soil over the exposure duration. Non-cancer hazard is not 

additive across the age groups evaluated here. 

4. Dermal Screening Results

Exhibit C-5 presents a summary of estimated dermal non-cancer hazards by age group. A 

summary of estimated lifetime dermal cancer risks is provided in Exhibit C-6. The highest HQ 

value was 0.006 (representing divalent mercury exposure for children less than 1 year of age) or 

less. This is approximately 170 times less than the potential ingestion hazard quotients 

associated with the screening scenario (i.e. emissions of divalent mercury in the screening 

scenario resulted in an ingestion hazard quotient of 1). The highest estimated individual lifetime 

cancer risk associated with potential dermal exposures was 4.1E-09 for benzo[a]pyrene; this 

value is approximately 240 times smaller than the ingestion risk (i.e., 1E-06) estimated for the 

same screening threshold emission rate. 

Attachment C C-7 October 2014

Dermal Risk Screening
TRIM-Based Tiered Screening Methodology for RTR

Exhibit C-5. Summary of Dermal Non-Cancer Hazards

0

0.0000005

0.000001

0.0000015

0.000002

0.0000025

0.000003

0.0000035

0.000004

0.0000045

Child

<1

Child

1-2

Child

3-5

Child

6-11

Child

12-19

Adult

20-70

Hazard Quotient

Receptor

2,3,7,8-TCDD Water

Soil

Total

0

0.0001

0.0002

0.0003

0.0004

0.0005

0.0006

0.0007

Child

<1

Child

1-2

Child

3-5

Child

6-11

Child

12-19

Adult

20-70

Hazard Quotient

Receptor

Cadmium Water

Soil

Total

0

0.001

0.002

0.003

0.004

0.005

0.006

0.007

Child

<1

Child

1-2

Child

3-5

Child

6-11

Child

12-19

Adult

20-70

Hazard Quotient

Receptor

Divalent Mercury Water

Soil

Total

Attachment C C-8 October 2014

Dermal Risk Screening
TRIM-Based Tiered Screening Methodology for RTR

Exhibit C-6. Summary of Dermal Cancer Risks

PB-HAP Dermal Lifetime 

Cancer Risk Magnitude of Differencea

2,3,7,8-TCDD 

Water 2.64E-10 >3,700

Soil 1.49E-11 >67,300

Total 2.79E-10 >3,500

Benzo[a]pyrene

Water 1.50E-09 >600

Soil 2.63E-09 >300

Total 4.12E-09 >200

a

Represents the magnitude of difference between the estimated dermal risk and

the potential ingestion risk associated with the screening scenario.

Based on these results and taking into consideration the extremely conservative nature of the 

dermal exposure calculations, it was assumed that it is not necessary to incorporate dermal 

exposures in calculating multipathway screening threshold levels. Specifically, the daily 

exposure durations of 0.58 hour for adults and 1 hour for children used to calculate dermal 

exposure from water are highly conservative and assume that the individual is bathing in 

surface water taken directly from a contaminated lake or is swimming in the lake for 350 days of 

the year. The exposure frequency of 350 days and corresponding skin surface area available 

for contact with contaminated soils (i.e., head, hands, arms, legs, and feet) likely also grossly 

overestimates dermal exposure to soil.

Attachment C C-9 October 2014

Dermal Risk Screening
TRIM-Based Tiered Screening Methodology for RTR

5. References

Cal/EPA (California Environmental Protection Agency) Office of Environmental Health Hazard 

Assessment (OEHHA). 2000. Air Toxics Hot Spots Program Risk Assessment Guidelines; 

Part IV, Exposure Assessment and Stochastic Analysis Technical Support Document. 

Section 6, Dermal Exposure Assessment. September. Available at: 

http://www.oehha.ca.gov/air/hot_spots/pdf/chap6.pdf. 

Hrudey, S.E., W. Chen, and C.G. Roussex, 1996. Bioavailability in environmental risk 

assessment. CRC Press, Inc, Lewis publishers.

EPA (Environmental Protection Agency). 2004. Risk Assessment Guidance for Superfund 

Volume 1: Human Health Evaluation Manual (Part E, Supplemental Guidance for Dermal 

Risk Assessment). EPA/540/R99/005. Available at: 

http://www.epa.gov/oswer/riskassessment/ragse/index.htm

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

November 20. Available at: 

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

EPA. 2008. Child-Specific Exposure Factors Handbook. Office of Research and Development, 

Washington, D.C. EPA/600/R-06/096F. September. Available at: 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=199243 . 

Attachment C 10 October 2014

Dermal Risk Screening
TRIM-Based Tiered Screening Methodology for RTR

 Summary of TRIM.FaTE Parameters Considered for 

Inclusion in Tier 2 Assessment

Attachment D i October 2014RIM.FaTE Parameters
[This page intentionally left blank.]
TRIM-Based Tiered Screening Methodology for RTR

Exhibits, Addendum 1

Exhibit Add B1-1. TRIM.FaTE Parameters Considered for Inclusion in Tier 2 

Analysis ............................................................................................................ D-1

Attachment B, Addendum 1 iii October 2014

Tier 2 TRIM.FaTE Parameters
[This page intentionally left blank.]
TRIM-Based Tiered Screening Methodology for RTR

Exhibit D-1. TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Assessment

Parameter Mechanism of Potential 

Influence in TRIM

Assessment of Parameter 

Significance and Ease in 

Implementation

Uncertainty in 

Site-Specific 

Data for 

Facilities

Priority 

for 

Inclusion

Meteorological Parameters

Wind direction 

(% of time wind 

blows toward 

the lake and 

farm)

In previous runs, direct 

deposition accounted for the 

bulk of chemical input onto 

farms and into lakes. 

Because wind direction is 

strongly correlated to direct 

deposition in a given 

location, media 

concentrations are 

potentially highly sensitive to 

this parameter. Also, 

because the percentage of 

time the prevailing wind 

blows in the direction of 

lakes and farms can vary 

considerably across 

locations, differences in this 

parameter might also result 

in significant changes in 

important environmental 

concentrations. 

Highly Significant: Previous 

sensitivity analyses have 

confirmed this to be a very 

sensitive parameter in the Tier 

1 Screening modeling set-up. 

Changing the fraction of time 

the wind blows toward the lake 

and farm by a factor of two 

corresponds to a change in 

the risk by a factor of two.

Low Effort to Implement:

This variable is relatively 

straightforward to vary in the 

Tier 2 screening scenarios. 

Low to 

Moderate: The 

average fraction 

of time the wind 

blows in a given 

direction can be 

estimated for any 

surface 

meteorological 

station. Then, 

facilities can be 

linked to the 

closest surface 

meteorological 

station.

High

Wind speed Wind speed can affect the 

location of the “peak” 

concentration and deposition 

patterns in a given model 

configuration, as well as the 

risk-distance profile.

Highly Significant: Previous 

sensitivity analyses have 

confirmed this to be a very 

sensitive parameter. However, 

wind speed does not vary 

widely across U.S. locations

which could reduce its 

potential influence.

Low Effort to Implement: This 

variable is relatively 

straightforward to vary in 

the Tier 2 screening 

scenarios.

Low to Moderate: 

The annuallyaveraged wind 

speed can be 

estimated for 

any surface 

meteorological 

station. Then, 

facilities can be 

linked to the 

closest surface 

meteorological 

station.

High

Attachment D D-1 October 2014

Tier 2 TRIM.FaTE Parameters
TRIM-Based Tiered Screening Methodology for RTR

Exhibit D-1. TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Assessment

Parameter Mechanism of Potential 

Influence in TRIM

Assessment of Parameter 

Significance and Ease in 

Implementation

Uncertainty in 

Site-Specific 

Data for 

Facilities

Priority 

for 

Inclusion

Precipitation Chemicals for which wet 

vapor or wet particle 

deposition processes are 

important are likely to be 

sensitive to the assumed 

level of precipitation. 

Highly Significant: Previous 

sensitivity analyses have 

indicated a relatively high 

sensitivity of risk to 

precipitation for most PBHAPs (PAHs, cadmium, and 

mercury). 

Moderate Effort to 

Implement: In implementing 

changes in precipitation in 

TRIM, care must be taken to 

also preserve the overall water 

balance in the model. 

Low to 

Moderate: The 

annuallyaveraged 

precipitation rate 

can be estimated 

for the subset of 

surface 

meteorological 

stations that 

capture rainfall 

data. Then, 

facilities can be 

linked to the 

closest surface 

meteorological 

station with 

available data.

High

Attachment D D-2 October 2014

Tier 2 TRIM.FaTE Parameters
TRIM-Based Tiered Screening Methodology for RTR

Exhibit D-1. TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Assessment

Parameter Mechanism of Potential 

Influence in TRIM

Assessment of Parameter 

Significance and Ease in 

Implementation

Uncertainty in 

Site-Specific 

Data for 

Facilities

Priority 

for 

Inclusion

Mixing height Greater mixing heights 

increase the dispersion of 

pollutants in the atmosphere 

and consequently reduce 

deposition to the ground in 

the areas around the stack. 

This is likely to be a highly 

sensitive parameter if there 

is a sizeable variation in 

mixing heights between 

facilities.

Highly Significant: Previous 

sensitivity analyses have 

shown risk to be very sensitive 

to mixing height.

Low Effort to Implement: 

This variable is relatively 

straightforward to vary in the 

Tier 2 screening scenarios. 

Moderate to 

High: Mixing 

height estimates 

are available for 

upper air 

meteorological 

stations, and this 

set of stations is 

more limited than 

the set of surface 

meteorological 

stations. Each 

surface station 

can be linked to 

the closest upper 

air station to 

estimate the 

average mixing 

height. Then, 

facilities can be 

linked to the 

closest surface 

meteorological 

station. The 

relative 

uncertainty in 

mixing height for 

a given facility is 

high, given 

diurnal variations 

in mixing height 

and the smaller 

number of upper 

air stations.

High

Attachment D D-3 October 2014

Tier 2 TRIM.FaTE Parameters
TRIM-Based Tiered Screening Methodology for RTR

Exhibit D-1. TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Assessment

Parameter Mechanism of Potential 

Influence in TRIM

Assessment of Parameter 

Significance and Ease in 

Implementation

Uncertainty in 

Site-Specific 

Data for 

Facilities

Priority 

for 

Inclusion

Configurational Parameters

Distance of 

lake from 

stack

Deposition is known to 

decrease with distance from 

stack, although this 

relationship also depends on 

meteorological parameters 

such as wind speed and 

wind direction. 

Significance Difficult to 

Determine: Limited results 

from previous TRIM model 

runs show an inconclusive 

relationship between risk and 

distance from stack, possibly 

as a result of limited statistical 

power. Some studies in the 

literature show a definite 

decreasing risk gradient with 

distance but others report too 

many confounding factors to 

isolate the precise 

relationship. 

Moderate Effort to 

Implement: This variable 

requires updates to the layout 

coordinates and requires more 

effort to vary in the Tier 2 

screening scenarios than the 

meteorological parameters.

Low: The lakes 

within a given 

radius of each 

facility can be 

found using 

ArcGISTM (see 

section 4).

High

Distance of 

farm from 

stack

Deposition is known to 

decrease with distance from 

stack, although this 

relationship also depends on 

meteorological parameters 

such as wind speed and 

wind direction. 

Significance Difficult to 

Determine: Limited results 

from previous TRIM model 

runs show an inconclusive 

relationship between risk and 

distance from stack, possibly 

as a result of limited statistical 

power. Some studies in the 

literature show a definite 

decreasing risk gradient with 

distance but others report too 

many confounding factors to 

isolate the precise 

relationship.

Moderate Effort to 

Implement: This variable 

requires updates to the layout 

coordinates and requires more 

effort to vary in the Tier 2 

screening scenarios than the 

meteorological parameters.

High: Although 

the distance to 

the farm will likely 

affect risk, it is 

difficult to 

determine the 

precise land 

parcels near 

each facility that 

are actually used 

for farming now 

or in the future. 

Medium

Attachment D D-4 October 2014

Tier 2 TRIM.FaTE Parameters
TRIM-Based Tiered Screening Methodology for RTR

Exhibit D-1. TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Assessment

Parameter Mechanism of Potential 

Influence in TRIM

Assessment of Parameter 

Significance and Ease in 

Implementation

Uncertainty in 

Site-Specific 

Data for 

Facilities

Priority 

for 

Inclusion

Watershed:

lake area ratio

A higher watershed:lake 

area ratio potentially 

increases the chemical input 

of water-soluble or particleattached chemicals into the 

lake. But the associated 

higher flush rate will likely 

reduce this effect.

Significance Difficult to 

Determine: Changes in the 

watershed to lake ratio affect 

risk, but the interaction 

depends on other variables 

involved in the water balance.

Moderate Effort to 

Implement: In implementing 

changes in the watershed:lake 

ratios in TRIM, care must be 

taken to also preserve the 

overall water balance in the 

model. 

High: The 

portion of land 

serving as a 

watershed to a 

particular lake is 

difficult to 

determine.

Medium

Area and 

depth of lake 

A higher lake area would 

capture more deposition but 

this effect might be 

counterbalanced by the 

ensuing larger volume of 

water, which reduces 

chemical concentration. 

Similarly, a deeper lake 

would also reduce 

concentrations, but this 

effect might be 

counterbalanced by the 

ensuing lower flush rates at 

a constant level of 

precipitation/runoff.

Significance Difficult to 

Determine: The impact of 

these parameters is 

inconclusive based on current 

studies using the TRIM model. 

Moderate Effort to 

Implement: The lake area 

variable requires updates to 

the layout coordinates and 

requires more effort to vary in 

the Tier 2 screening scenarios 

than the meteorological 

parameters. In implementing 

changes in these variables in 

TRIM, care must be taken to 

also preserve the overall water 

balance in the model. 

High: While the 

area of lakes 

near a facility can 

be determined 

using GIS, the 

depth cannot. 

Medium

Attachment D D-5 October 2014

Tier 2 TRIM.FaTE Parameters
TRIM-Based Tiered Screening Methodology for RTR

Exhibit D-1. TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Assessment

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

Attachment D D-6 October 2014

Tier 2 TRIM.FaTE Parameters
TRIM-Based Tiered Screening Methodology for RTR

Exhibit D-1. TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Assessment

Parameter Mechanism of Potential 

Influence in TRIM

Assessment of Parameter 

Significance and Ease in 

Implementation

Uncertainty in 

Site-Specific 

Data for 

Facilities

Priority 

for 

Inclusion

Chemical Parameters

Methylation/ 

demethylation 

rates (Hg)

For Hg, methylation and 

demethylation rates in lake 

sediment and surface water 

are potentially sensitive 

parameters affecting risk. A 

literature survey has 

indicated a relatively high 

range for rate constants 

describing these processes.

Highly Significant: Previous 

analyses run in TRIM have 

confirmed the high sensitivity 

of these parameters for Hg. 

Low Effort to Implement: 

This variable is relatively 

straightforward to vary in the 

Tier 2 screening scenarios.

High: The 

specific 

methylation / 

demethylation 

rates for mercury 

in the vicinity of a 

specific facility 

cannot be readily 

determined. 

Low

Total 

phosphorus 

levels in the 

lake 

The total phosphorus 

content of a lake is used as 

part of the TRIM.FaTE 

parameterization process to 

estimate the biomass 

content of different trophic 

levels. These biomass levels 

affect the biomagnification of 

chemicals up the food chain 

and potentially risk to human 

consumers of fish.

Not Significant: Previous 

analyses have shown limited 

sensitivity to total phosphorus 

levels. This is likely because 

the empirical equations 

predicting biomass in each 

trophic level depend in similar 

ways on the level of total 

phosphorus. So changes in 

total phosphorus do not 

significantly affect the ratio of 

biomass between the different 

trophic levels.

Low Effort to Implement: 

This variable is relatively 

straightforward to vary in the 

Tier 2 screening scenarios.

High: The total 

phosphorus 

levels in lakes 

near a specific 

facility cannot be 

readily 

determined.

Low

Attachment D D-7 October 2014

Tier 2 TRIM.FaTE Parameters
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TRIM-Based Tiered Screening Methodology for RTR

Analysis of Lake Size and 

Sustainable Fish Population

Attachment E i October 2014

Lake Size Analysis
[This page intentionally left blank.]
TRIM-Based Tiered Screening Methodology for RTR

CONTENTS, ADDENDUM 2

1. Introduction..................................................................................................................... E-1

2. Angler Behavior .............................................................................................................. E-1

3. Fish Biology .................................................................................................................... E-2

3.1. Lake Productivity ................................................................................................... E-2

3.2. Proportion of Fish Biomass by Trophic Level......................................................... E-4

3.3. Minimum Viable Population Size............................................................................ E-5

4. Summary of Assumptions for the Lake Size Analysis...................................................... E-6

5. Equations Used to Determine Lake Fish Populations...................................................... E-6

6. References ..................................................................................................................... E-8

Attachment E iii October 2014

Lake Size Analysis
TRIM-Based Tiered Screening Methodology for RTR

[This page intentionally left blank.]

Attachment E iv October 2014

Lake Size Analysis
TRIM-Based Tiered Screening Methodology for RTR

1. Introduction

Identifying the smallest size of a lake that might maintain self-sustaining populations of fish from 

trophic levels (TL) 3 and 4 and is sufficient to support at least one fisher at a specified fish 

ingestion rate requires consideration of many factors. Some factors depend on assumptions 

about the behavior of fishers who consume fish from a lake (see Section 2 below). Other 

factors depend upon the general biology of fish populations in North American ecoregions (see 

Section 3). Based on evaluation of these factors, a set of assumptions was developed to 

support the estimation of minimum lake sizes that are needed to sustain a particular total human 

ingestion rate in grams/day (see Section 4). Then, equations were developed (see Section 5) 

that were used to create Exhibit 26 (in Section 3.4 of the main body of the report), which was 

used to determine the threshold lake size of 25 acres.

2. Fisher Behavior

Several assumptions regarding fisher behavior are important for estimating a minimum lake size 

that is fishable. The first is a conservative assumption that fishers (and their family members) 

consume about 50:50 top carnivore fish from both the benthos and from the water column. 

Benthic carnivores (BC), such as catfish and chub that consume benthic invertebrates and small 

benthic fish, can grow to large sizes (e.g., 2 kg). Assuming a diet of 50 percent benthic 

invertebrates and 50 percent small benthic fish (e.g., young of the year that feed on algae or 

detritus), the BC fish category would represent trophic level (TL) 3.5. Pelagic piscivores—water 

column carnivores (WCC)—include species such as largemouth bass, lake trout, pickerel, and 

walleye. The WCC are modeled as TL 4, with 100 percent of their diet comprised of water 

column omnivores (WCO, e.g., pan fish such as bluegill and white perch), and the diet of the 

WCO is assumed to be 100 percent minnow-sized fish that feed on zooplankton and algae . The 

assumption of 50:50 WCC and BC consumption is conservative, because smaller, pan fish, are 

not included. Although fishers might prefer to catch and consume the pelagic (TL4) game fish 

species, their generally lower abundance compared with fish that also consume benthic 

invertebrates dictates that fishers will more often capture the benthic (TL3.5) carnivores. Some 

TL2 (herbivorous) fish such as carp attain “catchable” size, but they generally are not popular 

fish for consumption and are not considered here. . 

A second assumption is that fishers 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 assessment, a 0.33 

edible fraction for TL4 fish was assumed. That factor is roughly one-third, which we considered 

preferable to 0.30 to account for some proportion of salmon likely in the diet. This factor is used 

in the lake size analysis to estimate total fish biomass required to support specified human fish 

consumption rates.

A third assumption relates to ingestion rates of the fisher or fisher family. Fish ingestion rates 

used for the purpose of the Tier 2 assessment are the same as those in Tier 1 and are 

consistent with subsistence fisher ingestion rates (see Exhibit 18. Summary of RTR Tier 1 

Screening Scenario Assumptions). 

The final assumption is that the lake must support a sustainable fishery despite fish harvesting 

by humans; in other words, the lake shouldn’t be “fished out” by the harvest rate required to 

meet the subsistence fisher fish ingestion rate. The productivity of any particular fishery (local 

population of a species of fish) and the proportion of adult fish that can be harvested for human 

consumption are difficult values to estimate, and models to predict sustainable harvests of 

different fisheries are numerous and complex. Species-specific parameters of key to such 

Attachment E E-1 October 2014

Lake Size Analysis
TRIM-Based Tiered Screening Methodology for RTR

models include fecundity with age and size; survivorship of eggs, fry, and juveniles to sexual 

maturity (recruitment); natural predation pressures; and temporal variation in food availability. 

For the purpose of this analysis, simplifying assumptions are required. In the analysis by 

Håkanson and Boulion (2004), which included a survey of 122 lakes, the authors noted that a 

typical loss from fishing by birds, mammals, and humans would be approximately 10 percent of 

the fish biomass in the prey fish compartment (TL3) and 10 percent of the biomass in the 

predator fish compartment (TL4). The authors also found that as overall lake productivity 

increased, the biomass of prey (TL3) fish increased more rapidly than the biomass of predator 

(TL4) fish. For our lake size analysis, we assumed that fishers can harvest 10 percent of the 

biomass of pelagic WCC fish without diminishing the fish population size. 

3. Fish Biology 

Fish life histories also are key to estimating the minimum surface area of a pond or lake that 

could support a sustainable fish population of WCC. The productivity and trophic structure of 

fish communities in ponds and lakes across the United States are varied. Thus, any set of 

assumptions is unlikely to all hold true at any given location. Nonetheless, three factors are 

important to any estimate of a minimum lake surface area for sustainable pelagic TL4 fishing: 

the general productivity of a lake (expressed as grams of fish wet weight per meter squared, g 

ww/m2); the maximum likely proportion of the total fish biomass in a lake that is comprised of the 

top trophic level fish; and the minimum viable population (MVP) size required for the fish 

species to be self-sustaining in the short term (for at least a few decades).

3.1. Lake Productivity 

The general productivity of a lake depends on many factors, including latitude, seasonal 

temperatures, and nutrients supporting the base of the food web. For lakes at approximately 

the same latitude in the same climate, nutrients play a key role in the total fish biomass that a 

lake might support. In a regression analysis of data on total phosphorus (TP) and fish biomass 

for 31 lakes across North America, Europe, and Russia, Nürnberg (1996) summarized the 

“limits” among three TP-defined lake trophic status categories with respect to total fish wet 

weight biomass per unit area:

Oligo-meso (TP = 10 μg/L) = 1.9 g ww/m2

Meso-eutro (TP = 30 μg/L) = 3.7 g ww/m2

Eutro-hypereutro (TP = 100 μg/L) = 8.5 g ww/m2

Nürnberg (1996) also summarized total fish biomass limits from Bachmann et al. (1996) for the 

same lake trophic status categories based on a sample of 60 lakes in Florida:

Oligo-meso (TP = 10 μg/L) = 7.4 g ww/m2

Meso-eutro (TP = 30 μg/L) = 10.6 g ww/m2

Eutro-hypereutro (TP = 100 μg/L) = 15.6 g ww/m2

As might be expected, for the same TP concentrations, standing fish biomass per unit area in 

the Florida lakes is two to three times higher than standing fish biomass for more northerly lakes 

with shorter growing seasons.

Hanson and Legget (1982) estimated the relationship between TP and standing stock of fish 

using a regression model based on samples from 21 lakes ranging in surface area from 

0.1–25,000 hectares (~0.25–62,000 acres) and located between 0° and 56° N latitude and 

Attachment E E-2 October 2014

Lake Size Analysis
TRIM-Based Tiered Screening Methodology for RTR

121° E to 122° W longitude. Their linear regression relating TP to total fish standing biomass 

(B) had a correlation coefficient (r2) of 0.84: 

B = 0.792 + 0.072 (TP)

where:

B = total fish biomass (kg/hectare) 

TP = total phosphorous (μg/L) 

The regression model of Hanson and Legget (1982) predicted total fish biomass densities in 

lakes of 3.0–9.5 g ww/m2 for TP concentrations ranging from 10–50 μg/L for oligo-mesotrophic 

to mid-range eutrophic lakes. Another regression model from Hoyer and Canfield (1991) 

predicted fish biomass densities in streams of 2.6–6.6 g ww/m2 over the same range of TP 

concentrations.

In general, for very small lakes, relatively low fish productivity is likely. For example, Demers et 

al. (2001) found fish standing biomass values of 2.73 and 3.81 g ww/m2 in two lakes of 27 and 

22 acres (11 and 9 hectares), respectively, in south-central Ontario. Brönmark and Weisner 

(1996) reported on aquatic communities from a sample of 44 small ponds in southern Sweden 

(most were less than 5 hectares ≈ 12 acres). They found no fish in 5 of the smaller ponds 

(mean surface area of 0.20 ± 0.097 acres)—which also exhibited lower TP concentrations than 

the larger ponds—and no piscivorous fish in another 11 of the 44 ponds (mean surface area of 

0.46 ± 0.27 acres). For the 28 ponds with piscivorous (TL4) fish present, the mean pond 

surface area was 1.4 (±1.3 SD) acres.

Scientists have also examined the relationship between TP and total fish biomass in reservoirs. 

Yurk and Ney (1989) examined the relationship between TP and standing stock of fish in 22 

reservoirs in southern Appalachia sampled in 1973. Their logarithmic regression relating TP to 

total fish standing biomass (B) used the following equation and had an r2 of 0.75:

Log10 (B) = 1.07 + 1.14 × Log10 (TP)

Use of the equations from Hanson and Legget (1982) and Yurk and Ney (1989) yielded similar 

predications of total fish biomass at low to intermediate TP concentrations. At low TP (e.g., 10 

μg/L), predictions of total fish biomass were 3.0 g ww/m2 (Yurk and Ney, 1989) and 1.6 g ww/m2 

(Hanson and Legget, 1982); at high TP (e.g., 100 μg/L), fish biomass predicted by the two 

models were 15.5 and 22.4 g ww/m2, respectively.

Ideally, one would have data indicating TP levels in lakes in the vicinity of facilities for a Tier 2 

assessment. Such data, however, are rarely readily available. For purposes of the screening 

assessment, therefore, we assume that fish productivity per unit area is independent of lake 

size over a wide range of lake sizes and that TP levels are unknown. 

Leidy and Jenkins (1977) reported analyses of several large data sets to support modeling of 

fish productivity and carrying capacity in reservoirs across the United States for the National 

Reservoir Research Program. The analyses included studies of fish standing biomass by 

species in 61 reservoirs across the midwestern and eastern United States sampled at different 

times between 1952 and 1975. Only reservoirs of at least 500 acres (202 hectares) in size were 

included, with some exceeding 65,000 acres (in the Missouri drainage basin). Considering all 

61 reservoirs, the mean biomass density of fish was 41.3 (± 30.4 SD) g ww/m2. The minimum 

Attachment E E-3 October 2014

Lake Size Analysis
TRIM-Based Tiered Screening Methodology for RTR

and maximum total fish biomass densities were 3.2 and 133.2 g ww/m2, respectively, and the 

median value was 30.9 g ww/m2. Reservoirs typically have large drainage basins, which in 

some areas can receive excess TP from large expanses of agricultural areas.

In summary, the fish productivity in lakes and reservoirs can vary by more than three orders of

magnitude. The reservoirs surveyed by Leidy and Jenkins (1977) in general were much larger 

(and were often more shallow and nutrient rich) than the natural lakes surveyed by others 

discussed above. The mean standing fish biomass of approximately 41.3 g ww/m2 from the 

reservoir survey is likely to be higher than a mean value for representative samples of natural 

lakes in the United States. For the purpose of estimating the minimum lake size that would 

support a sustainable WCC fishery, we rounded that value down to a single significant digit of 

40 g ww/m2 as the upper limit for total fish biomass in a lake. That standing biomass is higher 

than predicted by the regression models of Hanson and Legget (1982), Yurk and Ney (1989),

and Nürnberg (1996) at a high total phosphorus of 100 μg/L (where phosphorous is the limiting 

nutrient). Less productive lakes would support fewer fish per unit area, and, therefore, would 

have to be larger to support a specified fish ingestion rate.

3.2. Proportion of Fish Biomass by Trophic Level

As indicated previously, for the Tier 2 assessment, the proportion of fish in a fisher’s diet that 

consists of WCC (TL4) and BC (TL3.5) is assumed to be 50:50 by biomass (not numbers) for 

lakes that support the four trophic levels. In smaller lakes, TL4 fish are likely to be missing or 

rare, with TL3 fish in the water column (e.g., sunfish) being the highest trophic level supported 

by the primary productivity (algal/plant production) in some lakes. As a “rule of thumb” in 

ecology, 10 percent or less of the energy produced at one trophic level usually can be converted 

to biomass in the next trophic level (i.e., approximately 90 percent loss of energy) per trophic 

step. However, with different species having different energy assimilation efficiencies and with 

smaller species generally having higher turnover rates than larger species, the 10 percent 

energy rule does not necessarily translate into a standing biomass pyramid of similar 

proportions. In this section, the proportion of fish (based on biomass) that might be expected in 

the WCC and the BC fish compartments relative to total standing fish biomass are examined 

assuming that the lake is large enough to support WCC (pelagic TL4 fish).

Examination of several studies of fish biomass by trophic level indicated that top trophic level 

fish might comprise up to 20 percent of the standing fish biomass in many locations. Ploskey 

and Jenkins (1982) estimated that piscivorous fish, both those that are generally free-swimming 

or pelagic (e.g., pike, gar, walleye) and those that rest and forage primarily in the benthos (e.g., 

various species of catfish, suckers) comprise 22 percent of the total fish biomass in DeGray 

Lake, Arkansas (averaged across several years). Leidy and Jenkins (1977) estimated that 18 

percent of the fish biomass across the 61 reservoirs they examined was piscivorous (minimum 

of 14 percent and maximum 24 percent). Demers et al. (2001) categorized 2 percent and 15 

percent of the total fish biomass in two small lakes of 27 and 22 acres in size, respectively, as 

piscivorous/benthivorous fish (e.g., largemouth bass, creek chub); primary benthivores (e.g., 

catfish, suckers) dominated at >70 percent in both lakes. 

One of the more recent food web models for freshwater lakes is that of Håkanson and Boulion 

(2004). They designed their model to predict productivity and standing crop of prey and 

predatory fishes in lakes of northern Europe. The authors acknowledged that fish feeding 

patterns are complicated by the fact that fish change their feeding preferences as they 

age. Some fish species consume zooplankton or benthic invertebrates in their first year, and 

switch to small fish and then to larger fish as they mature and grow in size. 

Attachment E E-4 October 2014

Lake Size Analysis
TRIM-Based Tiered Screening Methodology for RTR

Håkanson and Boulion (2004) created a “distribution coefficient” to indicate what proportion of 

the total fish biomass in a lake is prey versus predatory fish. Based on data from 122 lakes in 

Europe and North America, they concluded that 27 percent by biomass is a “normal” portion of 

predatory fish in a balanced system. They noted further, however, that for eutrophic lakes with 

TP levels >100 μg/L, the proportion of fish represented by piscivores declines to less than 20

percent. The piscivores included both benthic and pelagic species. We note that most benthic 

piscivores also consume benthic macroinvertebrates. 

Based on the information above, the top trophic level fish are assumed to comprise 21 percent 

of the total fish biomass. With the bulk of productivity in lakes originating from detritus in the 

benthos, the total biomass of strictly pelagic game fish is expected to be less than that of

benthic fish. Therefore, for purpose of this lake size analysis, the piscivorous/benthivorous fish 

were separated into two compartments, with 17.5 percent of the total fish biomass in a benthic 

carnivore (TL3.5 or BC) compartment and 3.5 percent of the total in a pelagic piscivore (WCC) 

compartment. Thus, the TL4 fish, when present, represent the limiting compartment for fish 

harvesting and consumption. 

3.3. Minimum Viable Population Size

The final step in estimating the minimum lake size that can support sustainable fishing of its 

WCC fish species is to invoke the concept of minimum viable population (MVP) size. MVP is a 

concept used frequently in conservation biology for animals and is defined as the smallest 

population that will persist for a specified duration (e.g., 100 years) with a given probability (e.g., 

95 percent). MVP for any given species and location depends on many attributes of the species 

biology (e.g., body size, reproductive rate, home range size, corridors between populations, 

variability in environmental characteristics that impact fecundity and survival). At lower 

numbers, the likelihood of population extinction increases due to environmental and 

demographic stochasticity (Menzie et al., 2008). As for fisheries biology, entire text books have 

been dedicated to applied population ecology with population simulations incorporating 

demographic and life-history characteristics, spatial separation of habitat patches and 

metapopulations, the probability of local catastrophes, genetic variation (e.g., drift), and other 

factors with predictions of time-to-extinction or probability of extinction within specified time 

periods (e.g., Soulé, 1987; Akçakaya et al., 1999). Consideration of such models in populationlevel ecological risk assessment has begun, but faces many challenges (Barnthouse et al., 

2008). Moreover, that level of effort is beyond the resources available for screening-level 

assessments. 

Much of the initial work on MVP investigated the genetic minima required for short-term survival, 

continuing adaptation to environmental change, and ultimately evolution. Inbreeding has been 

considered the primary threat to short-term survival and genetic drift the principal threat to losing 

the genetic variation required for adaptation (Shaffer, 1987). Several analyses (Senner, 1980; 

Franklin, 1980; Soulé, 1980; Frankel and Soulé, 1981; Lande and Barrowclough, 1987) have led 

to the conclusion that minimum effective population sizes on the order of 50 are required for 

short-term survival (e.g., several generations, decades), while effective population sizes on the 

order of 500 are necessary to provide adequate genetic variation for continuing adaptation over 

the long term (e.g., tens of generations, centuries for some animals) (Shaffer, 1981; 1987). 

Effective population size, Ne, is a measure of the rate of genetic drift (loss of genetic diversity or 

inbreeding), and its definition generally depends on the population in question (Rieman and 

Allendorft, 2001). Ne can be estimated mathematically based on stochastic behavior of gene 

frequencies in a diploid population. Simple models assume a fixed population size, constant 

fecundity, specified sex ratio, and no overlap between generations (see studies cited in NRC, 

1986). For animals with 50:50 sex ratios, the effective population size is essentially the same 

as the actual breeding adult population size (Ewens et al., 1987). One of the most extensive 

Attachment E E-5 October 2014

Lake Size Analysis
TRIM-Based Tiered Screening Methodology for RTR

population viability analyses in the United States has been conducted on the spotted owl 

(Boyce, 1993). Given the number and complexity of factors that influence MVP, however,

including the definitions of time horizon (e.g., 100 years) and probability of survival (e.g., 95

percent), population biologists caution against using a “rule of thumb” for MVP across 

circumstances (Ewens et al., 1987).

Note that the MVP is appropriate for a single species of fish, not for generic categories of fish 

such as WCC or TL4. For this Tier 2 assessment, the MVP of 50 associated with short-term 

population survival was assumed for a TL4 fish species isolated in a lake. In reality, short-term 

extirpations from a lake can be countered by purposeful introductions from other lakes or during 

flooding events. Thus, an MVP of 500 was not considered necessary for game fish in lakes. 

4. Summary of Assumptions for the Lake Size Analysis

The following assumptions were used in processing lake data for the Tier 2 assessment and in 

estimating the relationship between fish ingestion and sustainable harvest rates and lake size

(see Section 3.4.1 of Attachment B).

1. Piscivorous fish (WCC and BC), when present, comprise approximately 21 percent of the 

standing biomass of fish (ignoring seasonal changes). The BC fish represent 17.5 percent 

of the standing fish biomass; WCCs account for 3.5 percent of the total fish biomass. Thus, 

WCC fish, when present, represent the limiting compartment for fish harvesting and 

consumption.

2. Humans can harvest 10 percent of the biomass of a fish compartment without threatening 

the population due to overharvesting.

3. The MVP size for a single WCC species is at least 50 adult fish for a local population to 

survive over the short term (more than a decade). 

4. Only 33 percent of the fish is edible fillet muscle. 

5. Equations Used to Determine Lake Fish Populations

The standing biomass of WCC (TL4) fish supported in Lake X can be calculated as the total 

standing biomass of fish (Total SB) multiplied by 0.035, based on the assumption that TL4 fish 

represent approximately 3.5 percent of the standing biomass in Lake X. 

WWWWWW SSSS = TT SSSS xx FF FFFF FF WWWWWW (Equation 1)

where:

WCC SB = Standing biomass of WCC fish (g wet weight [ww]/m2) in Lake X

Total SB = Total standing biomass of fish (g ww/m2) in Lake X

Fraction WCC = Fraction of WCC fish in Lake X (i.e., 0.035)

Using WCC SB and the size of Lake X (Lake Size), the total number of WCC fish supported in 

Lake X can be calculated using Equation 2 below.

NN . WWWWWW = LLLLLLLL SSSSSS xx WW SSSS xx CC

WWaa (Equation 2)

where:

Attachment E E-6 October 2014

Lake Size Analysis
TRIM-Based Tiered Screening Methodology for RTR

No. WCC = Total number of WCC fish in Lake X

Lake Size = Size of Lake X (acres) 

WCC SB = Standing biomass of WCC fish (g ww/m2; from Equation 1) 

CF = 4047 (unit conversion factor m2/acre) 

BWa = Body weight of adult TL4 fish (2000 g; assumed) 

The likely annual productivity of WCC fish (kg/year) in Lake X can be estimated using

Equation 3. 

PP PP PP WWWWWW = LLLLLLLL SSSSSS xx WW SSSS xx CC1

CC2 (Equation 3)

where:

Productivity WCC= Likely annual productivity of WCC fish in Lake X (kg/year)

Lake Size = Size of the Lake X (acres) 

WCC SB = Standing biomass of WCC fish (g ww/m2; from Equation 1) 

CF1 = 4047 (unit conversion factor 1, m2/acre) 

CF2 = 1000 (unit conversion factor 2, g/kg) 

The maximum fish ingestion rate (g/day) for WCCs plus BCs associated with sustainable fishing 

can be predicted using Equation 4. It assumes that the fishers consume 50 percent WCC and 

50 percent BC, represented by the factor of 2 in Equation 4. 

MM SS IIII ( + WWWWWW) = 2 xx PPPPPPPPPPPPPP PP PPPP WW xx xx xx CC1

CC2 (Equation 4)

where:

Max Sustain IR (BC + WCC) = Predicted maximum sustainable ingestion rate for BC and WCCfish (g/day)

Productivity WCC = Likely annual productivity of WCC fish in Lake X (kg/year; from Equation 3)

FF = Fillet fraction; represents the assumed edible portion of fish (0.33; unitless) 

HF = Annual harvest fraction (0.10; unitless) 

CF1 = 1000 (unit conversion factor 1, g/kg) 

CF2 = 365 (unit conversion factor 2, days/year) 

Attachment E E-7 October 2014

Lake Size Analysis
TRIM-Based Tiered Screening Methodology for RTR

6. 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.

Bachmann, RW; Jones, BL; Fox, DD; Hoyer, M; Bull, LA; Canfield, DE. 1996. Relations 

between trophic state indicators and fish in Florida (USA) lakes. Canadian Journal of 

Fisheries and Aquatic Sciences. 53:842-855.

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 

Attachment E E-8 October 2014

Lake Size Analysis
TRIM-Based Tiered Screening Methodology for RTR

by the National Reservoir Research Program, U.S. Fish and Wildlife Service, for the U.S. 

Army Corps of Engineers, Waterways Experiment Station, Vicksburg, Mississippi, USA.

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.

Scaffer, 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.

Attachment E E-9 October 2014

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Appendix 6 – Environmental Risk Screen
Table 1 – PB-HAP Benchmarks Included in the Environmental Risk Screen

EcoHAP Assessment Endpoint Benchmark Effects Level Benchmark Value

Benchmark 

Source

Tier 1 Eco 

Screening 

Threshold 

(TPY)

Cadmium Fish - Avian Piscivores NOAEL (merganser) 0.7 (mg/kg BW/day) d 2.03E-01

Cadmium Fish - Avian Piscivores LOAEL (merganser) 1 (mg/kg BW/day) d 2.90E-01

Cadmium Fish - Mammalian Piscivores NOAEL (mink) 0.742 (mg/kg BW/day) e 4.06E-01

Cadmium Fish - Mammalian Piscivores LOAEL (mink) 7.42 (mg/kg BW/day) e 4.06E+00

Cadmium Sediment Community No-effect Level 0.6 (mg/kg dry wt) b 1.73E+00

Cadmium Sediment Community Threshold Level 1.2 (mg/kg dry wt) a 3.45E+00

Cadmium Sediment Community Probable-effect Level 3.5 (mg/kg dry wt) b 1.01E+01

Cadmium Surface Soil - Dist. 1 - 312 m Threshold - Mammalian Insectivores (shrew) 0.36 (mg/kg dry wt) a 3.64E-01

Cadmium Surface Soil - Dist. 1 - 312 m Threshold - Avian Ground Insectivores (woodcock) 0.77 (mg/kg dry wt) a 7.79E-01

Cadmium Surface Soil - Dist. 1 - 312 m Threshold Level - Plant Community 32 (mg/kg dry wt) a 3.24E+01

Cadmium Surface Soil - Dist. 1 - 312 m Threshold Level - Invertebrate Community 140 (mg/kg dry wt) a 1.42E+02

Cadmium Surface Soil - Dist. 2 - 850 m Threshold - Mammalian Insectivores (shrew) 0.36 (mg/kg dry wt) a 5.21E-01

Cadmium Surface Soil - Dist. 2 - 850 m Threshold - Avian Ground Insectivores (woodcock) 0.77 (mg/kg dry wt) a 1.11E+00

Cadmium Surface Soil - Dist. 2 - 850 m Threshold Level - Plant Community 32 (mg/kg dry wt) a 4.63E+01

Cadmium Surface Soil - Dist. 2 - 850 m Threshold Level - Invertebrate Community 140 (mg/kg dry wt) a 2.03E+02

Cadmium Surface Soil - Dist. 3 - 1,500 m Threshold - Mammalian Insectivores (shrew) 0.36 (mg/kg dry wt) a 7.53E-01

Cadmium Surface Soil - Dist. 3 - 1,500 m Threshold - Avian Ground Insectivores (woodcock) 0.77 (mg/kg dry wt) a 1.61E+00

Cadmium Surface Soil - Dist. 3 - 1,500 m Threshold Level - Plant Community 32 (mg/kg dry wt) a 6.69E+01

Cadmium Surface Soil - Dist. 3 - 1,500 m Threshold Level - Invertebrate Community 140 (mg/kg dry wt) a 2.93E+02

Cadmium Surface Soil - Dist. 4 - 3,500 m Threshold - Mammalian Insectivores (shrew) 0.36 (mg/kg dry wt) a 1.05E+00

Cadmium Surface Soil - Dist. 4 - 3,500 m Threshold - Avian Ground Insectivores (woodcock) 0.77 (mg/kg dry wt) a 2.24E+00

Cadmium Surface Soil - Dist. 4 - 3,500 m Threshold Level - Plant Community 32 (mg/kg dry wt) a 9.32E+01

Cadmium Surface Soil - Dist. 4 - 3,500 m Threshold Level - Invertebrate Community 140 (mg/kg dry wt) a 4.08E+02

Cadmium Surface Soil - Dist. 5 - 7,500 m Threshold - Mammalian Insectivores (shrew) 0.36 (mg/kg dry wt) a 2.99E+00

Cadmium Surface Soil - Dist. 5 - 7,500 m Threshold - Avian Ground Insectivores (woodcock) 0.77 (mg/kg dry wt) a 6.40E+00

Cadmium Surface Soil - Dist. 5 - 7,500 m Threshold Level - Plant Community 32 (mg/kg dry wt) a 2.66E+02

Cadmium Surface Soil - Dist. 5 - 7,500 m Threshold Level - Invertebrate Community 140 (mg/kg dry wt) a 1.16E+03

Cadmium Water-column Community Threshold Level 0.25 (ug/L) c 7.66E-01

Cadmium Water-column Community Frank-effect Level 2 (ug/L) c 6.12E+00
Table 1 – PB-HAP Benchmarks Included in the Environmental Risk Screen

Dioxin Fish - Avian Piscivores NOAEL (merganser) 0.0000014 (mg/kg BW/day) f 3.17E-05

Dioxin Fish - Avian Piscivores LOAEL (merganser) 0.000014 (mg/kg BW/day) f 3.17E-04

Dioxin Fish - Mammalian Piscivores NOAEL (mink) 0.000000771 (mg/kg BW/day) f 4.12E-05

Dioxin Fish - Mammalian Piscivores LOAEL (mink) 0.00000771 (mg/kg BW/day) f 4.12E-04

Dioxin Sediment Community Threshold Level 0.00000116 (mg/kg dry wt) g 3.10E-05

Dioxin Surface Soil - Dist. 1 - 312 m Threshold - Mammalian Insectivores (shrew) 0.0000002 (mg/kg dry wt) h 6.69E-07

Dioxin Surface Soil - Dist. 2 - 850 m Threshold - Mammalian Insectivores (shrew) 0.0000002 (mg/kg dry wt) h 9.48E-07

Dioxin Surface Soil - Dist. 3 - 1,500 m Threshold - Mammalian Insectivores (shrew) 0.0000002 (mg/kg dry wt) h 1.28E-06

Dioxin Surface Soil - Dist. 4 - 3,500 m Threshold - Mammalian Insectivores (shrew) 0.0000002 (mg/kg dry wt) h 2.08E-06

Dioxin Surface Soil - Dist. 5 - 7,500 m Threshold - Mammalian Insectivores (shrew) 0.0000002 (mg/kg dry wt) h 4.39E-06

Dioxin Water-column Community Threshold Level 0.000012 (ug/L) i 3.84E-03

Dioxin Water-column Community Frank-effect Level 0.1 (ug/L) i 3.20E+01

Mercuric Chloride Sediment Community Threshold Level 0.15 (mg/kg dry wt) a 9.11E-03

Mercuric Chloride Sediment Community Probable-effect Level 0.7 (mg/kg dry wt) j 4.25E-02

Mercuric Chloride Surface Soil - Dist. 1 - 312 m Threshold Level - Plant Community 0.3 (mg/kg dry wt) k 9.25E-03

Mercuric Chloride Surface Soil - Dist. 1 - 312 m Threshold Level - Invertebrate Community 0.1 (mg/kg dry wt) k 3.08E-03

Mercuric Chloride Surface Soil - Dist. 2 - 850 m Threshold Level - Plant Community 0.3 (mg/kg dry wt) k 1.35E-02

Mercuric Chloride Surface Soil - Dist. 2 - 850 m Threshold Level - Invertebrate Community 0.1 (mg/kg dry wt) k 4.50E-03

Mercuric Chloride Surface Soil - Dist. 3 - 1,500 m Threshold Level - Plant Community 0.3 (mg/kg dry wt) k 1.78E-02

Mercuric Chloride Surface Soil - Dist. 3 - 1,500 m Threshold Level - Invertebrate Community 0.1 (mg/kg dry wt) k 5.93E-03

Mercuric Chloride Surface Soil - Dist. 4 - 3,500 m Threshold Level - Plant Community 0.3 (mg/kg dry wt) k 2.70E-02

Mercuric Chloride Surface Soil - Dist. 4 - 3,500 m Threshold Level - Invertebrate Community 0.1 (mg/kg dry wt) k 8.99E-03

Mercuric Chloride Surface Soil - Dist. 5 - 7,500 m Threshold Level - Plant Community 0.3 (mg/kg dry wt) k 7.23E-02

Mercuric Chloride Surface Soil - Dist. 5 - 7,500 m Threshold Level - Invertebrate Community 0.1 (mg/kg dry wt) k 2.41E-02

Mercuric Chloride Water-column Community Threshold Level 0.77 (ug/L) c 7.78E-01

Mercuric Chloride Water-column Community Frank-effect Level 1.4 (ug/L) c 1.42E+00

Mercury (methyl) Fish - Avian Piscivores NOAEL (merganser) 0.013 (mg/kg BW/day) f 7.55E-03

Mercury (methyl) Fish - Avian Piscivores LOAEL (merganser) 0.078 (mg/kg BW/day) f 4.53E-02

Mercury (methyl) Fish - Mammalian Piscivores NOAEL (mink) 0.0247 (mg/kg BW/day) e 4.01E-02

Mercury (methyl) Fish - Mammalian Piscivores LOAEL (mink) 0.123 (mg/kg BW/day) e 2.00E-01

Mercury (methyl) Sediment Community Threshold Level 0.2 (mg/kg dry wt) l 5.43E+00

Mercury (methyl) Sediment Community Probable-effect Level 1 (mg/kg dry wt) l 2.72E+01

Mercury (methyl) Surface Soil - Dist. 1 - 312 m Threshold Level - Invertebrate Community 0.67 (mg/kg dry wt) I 1.25E+00
Table 1 – PB-HAP Benchmarks Included in the Environmental Risk Screen

References:

a. U.S. EPA OSWER

b. Environment Canada

c. U.S. EPA OW

d. CA DTSC HERD (2009)

e. Sample, et. al, 1996

f. U.S. EPA 1995

g. U.S. EPA Region 3, 4, and 5.

h. U.S. EPA Region

i. U.S. EPA Region 4

j. Florida DEP, NOAA

k. ORNL and EPA R4, R6

l. MacDonald, et. al. 2000

m. U.S. EPA Region 6

n. NAAQS for Lead, Secondary 

Standard

Mercury (methyl) Surface Soil - Dist. 2 - 850 m Threshold Level - Invertebrate Community 0.67 (mg/kg dry wt) i 1.82E+00

Mercury (methyl) Surface Soil - Dist. 3 - 1,500 m Threshold Level - Invertebrate Community 0.67 (mg/kg dry wt) i 2.40E+00

Mercury (methyl) Surface Soil - Dist. 4 - 3,500 m Threshold Level - Invertebrate Community 0.67 (mg/kg dry wt) i 3.62E+00

Mercury (methyl) Surface Soil - Dist. 5 - 7,500 m Threshold Level - Invertebrate Community 0.67 (mg/kg dry wt) i 9.71E+00

Mercury (methyl) Water-column Community Threshold Level 0.0028 (ug/L) c 3.30E-01

Mercury (methyl) Water-column Community Frank-effect Level 0.099 (ug/L) c 1.17E+01

PAH Fish - Mammalian Piscivores NOAEL (mink) 0.417 (mg/kg BW/day) e 4.72E+02

PAH Fish - Mammalian Piscivores LOAEL (mink) 4.17 (mg/kg BW/day) e 4.72E+03

PAH Sediment Community No-effect Level 0.032 (mg/kg dry wt) b 4.73E+00

PAH Sediment Community Threshold Level 0.15 (mg/kg dry wt) l, h 2.22E+01

PAH Sediment Community Probable-effect Level 1.45 (mg/kg dry wt) l 2.14E+02

PAH Surface Soil - Dist. 1 - 312 m Threshold - Mammalian Insectivores (shrew) 1.52 (mg/kg dry wt) h 3.45E+00

PAH Surface Soil - Dist. 2 - 850 m Threshold - Mammalian Insectivores (shrew) 1.52 (mg/kg dry wt) h 5.09E+00

PAH Surface Soil - Dist. 3 - 1,500 m Threshold - Mammalian Insectivores (shrew) 1.52 (mg/kg dry wt) h 8.22E+00

PAH Surface Soil - Dist. 4 - 3,500 m Threshold - Mammalian Insectivores (shrew) 1.52 (mg/kg dry wt) h 2.02E+01

PAH Surface Soil - Dist. 5 - 7,500 m Threshold - Mammalian Insectivores (shrew) 1.52 (mg/kg dry wt) h 5.06E+01

PAH Water-column Community Threshold Level 0.014 (ug/L) a, h, m 1.84E+01

PAH Water-column Community Frank-effect Level 0.24 (ug/L) a 3.16E+02

Lead Ambient Air NAAQS Secondary Standard 0.15 ug/m3 n NA
Table 2 – Acid Gas Benchmarks Included in the Environmental Risk Screen

a- Note that the human health RfC is 20 μg/m3, which is lower than the ecological benchmark.

Acid Gas Chronic 90-day Benchmark in μg/m3

Hydrochloric acid - LOEL 50a

Hydrofluoric acid – Plant Community LOEL 0.5

Hydrofluoric acid – Plant Community LOEL 0.4
Appendix 7 

Detailed Risk Modeling Results 
Facility NEI ID Facility Name Address City State

01015223 Anniston Army Depot, Anniston Alabama 7 Frankford Avenue Anniston AL

01045482

U.S. Army Aviation Center of Excellence and Fort 

Rucker Dilly Branch Road, Bldg. 1120, Attn: ATZQ-DPW Fort Rucker AL

010515017 GKN WESTLAND AEROSPACE, INC. 3951 ALABAMA HWY 229 TALLASSEE AL

01073446

Alabama Aircraft Industries Incorporated - 

Birmingham 1943 50th Street North Birmingham AL

01083198 Pratt & Whitney Automation 15091 AL HWY 20 Madison AL

010892023 George C Marshall Space Flight Center Marshall Space Flight Center Huntsville AL

02090348 Eielson Air Force Base 2310 Central Avenue, Suite 100 Eielson AFB AK

02090529

United States Army Garrison Fort Wainwright, 

Alaska 1060 Gaffney Road #6000 Fairbanks North Star Borough AK

02150683 U.S. Coast Guard Base Support Unit Kodiak Bldg. N38 Rezanof Hwy and Cape Sarichef Road Kodiak AK

04013510 Ohlinger Industries Inc. 2111 West Melinda Lane Phoenix AZ

04013602

US Airways Maintenance and Technical 

Operations Center 4000 East Sky Harbor Boulevard Phoenix AZ

04019300 Learjet Inc. 1255 East Aero Park Boulevard Tucson AZ

04019433 Davis-Monthan Air Force Base, Tucson, AZ 3791 S. Third Street Tucson AZ

050272001 American Fuel Cells and Coated Fabrics 601 Firestone Drive Magnolia AR

05051409 Triumph Fabrications Hot Springs 1923 Central Ave Hot Springs AR

05113377 Crider Aircraft Painting, Inc. 104 Airport Lane Mena AR

05143331 Pratt & Whitney PSD Inc. 275 E. Robinson Ave. Springdale AR

060292024 Naval Air Weapons Station 1 Administration Circle China Lake CA

06029545 Edwards Air Force Base 5 Popson Ave., Building 2650A Edwards Air Force Base CA

060372022 Palmdale 3520 East Avenue M Palmdale CA

06037429 The Boeing Company - C-17 Facility 2401 E Wardlow Rd Long Beach CA

06037490 El Segundo 500 N Douglas Ave El Segundo CA

060376004 Gulfstream Aerospace Corporation 4150 Donald Douglas Drive Long Beach CA

06059538 DUCOMMUN AEROSTRUCTURES INC 1885 N BATAVIA ST ORANGE CA

06065520 Rohr, Inc. 8200 Arlington Avenue Riverside CA

06071540 Ducommun AeroStructures El Mirage Facility 4001 El Mirage Road El Mirage CA

06073306 Naval Air Station North Island NAS North Island San Diego CA

060812015

United Airlines Technical Operations 

Maintenance Center 800 S. Airport Blvd San Francisco CA

09001684 Sikorsky Aircraft - Bridgeport 1 Facility 1210 South Avenue Bridgeport CT

09001688 Helicopter Support Inc. 116/124 Quarry Road Trumbull CT

09001693 Sikorsky Aircraft Corporation - Stratford Facility 6900 Main Street Stratford CT

090032025 EDAC Technologies 1798 New Britain Avenue Farmington CT

09003626

Kaman Aerospace Corporation - Helicopters 

Division 50 Old Windsor Road Bloomfield CT

Table 1 - Facility Identification Information

1 of 5
Facility NEI ID Facility Name Address City State

Table 1 - Facility Identification Information

090075021

Kaman Aerospace Corporation, Precision 

Products Division 217 Smith Street Middletown CT

10003349

DASSAULT FALCON JET - WILMINGTON 

CORP 191 North Dupont Hwy New Castle DE

12005313 Tyndall AFB 325 FW / Tyndall AFB Panama City FL

12009358 Patrick Air Force Base 1201 Edward H. White II Street Patrick Air Force BaseFL

12023284

TIMCO Aviation Services, Inc. - Lake City Base 

Maintenance Operations 102 SE Academic Avenue Lake City FL

12031634 U.S. Naval Station Mayport Old Mayport Road Jacksonville FL

12061380 Piper Aircraft, Inc. 2926 Piper Drive Vero Beach FL

12083270

LOCKHEED MARTIN MISSILES AND FIRE 

CONTROL 498 OAK ROAD OCALA FL

120862016 AAR LANDING GEAR SERVICES 9371 NW 100 STREET MIAMI FL

12099695

Sikorsky Aircraft Corporation - Jupiter, Florida 

Facility 17900 Beeline Highway (SR-710) JUPITER FL

13009224

Triumph Aerostructures - Vought Integrated 

Programs Division 90 Hwy 22 W Milledgeville GA

130516005 Gulfstream Aerospace Corporation P.O. Box 2206 Savannah GA

13063388 Delta Air Lines, Inc. Technical Operations Center 1775 Aviation Blvd. Atlanta GA

13067506

U.S. Air Force Plant 6 Lockheed Martin 

Aeronautics Company 86 South Cobb Drive Marietta GA

131276002 Gulfstream Aerospace Corporation 550 Connole Street Brunswick GA

13153431 Robins Air Force Base Highway 247 and Watson BoulevardRobins Air Force BaseGA

132332017

Meggitt (Rockmart), Inc. (formerly Engineered 

Fabrics Corp) 669 Goodyear Street Rockmart GA

15003346 Joint Base Pearl Harbor Hickam Commander Navy Region Hawaii 850 Ticonderoga Street, Suite 110 Pearl Harbor HI

18097487 Rolls-Royce Corporation 2001/2355 S. Tibbs Avenue Indianapolis IN

181035004 Grissom Air Reserve Base 7207 South Grissom Avenue Grissom Air Reserve Base IN

181412011 Honeywell International Inc; South Bend 3520 Westmoor Street South Bend IN

19163356 Carleton Life Support Systems Inc. 2734 Hickory Grove Road Davenport IA

20035486 GE Engine Services, LLC 7577 4th and A St. Arkansas City KS

201252005 Cessna Aircraft Company One Cessna Boulevard Independence KS

20173193 Globe Engineering Company, Inc. 1539 South St. Paul Wichita KS

201732006 Cessna Aircraft Company - Mid-Continent Facility One Cessna Boulevard Wichita KS

201732007 Cessna Aircraft Company 5800 E. Pawnee Street Wichita KS

201732027 Spirit Aerosystems, Inc. 3801 South Oliver Wichita KS

20173453 Learjet, Inc One Learjet Way Wichita KS

2017365 Hawker Beechcraft Corporation 9709 East Central Wichita KS

24005458 Middle River Aircraft Systems 103 Chesapeake Park Plaza Baltimore MD

2 of 5
Facility NEI ID Facility Name Address City State

Table 1 - Facility Identification Information

24015282 Alliant Techsystems Inc. 55 Thiokol Road Elkton MD

24017525

NAVFAC Washington, PWD South Potomac, 

Naval Support Facility Indian Head Environmental Division, 3972 Ward Rd, Ste. 101 Indian Head MD

25009450 Raytheon Integrated Air Defense Center 350, 358 and 362 Lowell St Andover MA

25009477 General Electric Aviation 1000 Western Avenue Lynn MA

26065644 Pratt & Whitney AutoAir, Inc. 5640 Enterprise Drive Lansing MI

27053389 Delta Air Lines, Inc. MSP TechOps 7500 Airline Drive Minneapolis MN

280475024 1108 TASMG Hangar 1, Hewes Ave. Gulfport MS

28089367 L-3 Vertex Aerospace 555 Industrial Drive South Madison MS

29183329 The Boeing Company-St. Charles 2600 North 3rd Street St. Charles MO

29189332 The Boeing Company - St. Louis Airport and McDonnell Blvd. St. Louis MO

29189513 GKN Aerospace - St. Louis 142 James S. McDonnell BoulevardHazelwood MO

33011652 PGM of New England LLC 5 Perimeter Road Manchester NH

36103518

EDO Corporation Antenna Products & 

Technologies 585 Johnson Ave Bohemia NY

36103521 EDO Marine and Aircraft Systems 1250-1500 New Horizon Blvd North Amityville NY

36111630 Ametek Rotron, Inc. 55 Hasbrouck Lane Woodstock NY

370492008 Dept. of the Navy, Fleet Readiness Center East A Street - Marine Corps Air StationCherry Point NC

37049483 Marine Corps Air Station - Cherry Point NC Highway 101 at US 70 West Cherry Point NC

37051340

Headquarters XVIII Airborne Corps and Fort 

Bragg 2175 Reilly Road, Stop A Fort Bragg NC

37119600 US Airways, Inc. - CLT (Charlotte, NC) 5020 Hangar Road Charlotte NC

37133455 US Marine Corps Base Camp Lejeune 1 Camp Lejeune Camp Lejeune NC

39017343 CTL Aerospace, Inc. 5616 Spellmire Drive Cincinnati OH

39021672 Honeywell Aerospace 550 State Route 55 Urbana OH

39057430 Wright-Patterson AFB 1450 Littrell Road Dayton OH

39061524 GE Aviation, Evendale One Neumann Way Cincinnati OH

39109419 Goodrich 101 Waco St Troy OH

391335027

SAINT-GOBAIN PERFORMANCE PLASTICS 

CORPORATION 335 N. DIAMOND STREET RAVENNA OH

39151479 PCC Airfoils, LLC 3860 Union Ave. S.E. Minerva OH

401092026 Tinker Air Force Base, OK 72 ABW CEAN, 7535 5th Street, BLDG 400 Tinker AFB OK

401432003 Bizjet International 1925 N. Sheridan Tulsa OK

401432013 Nordam I&S and NTRSD Divisions 6910 and 6911 N. Whirlpool Dr. Tulsa OK

401432014 Quality Plating Co. of Tulsa 2665 N. Darlington Tulsa OK

401432021

American Airlines Maintenance and Engineering 

Center 3800 N MINGO RD TULSA OK

40143460 NORDAM Repair Division 11200 East Pine Tulsa OK

40143494 Spirit AeroSystems 3330 North Mingo Road Tulsa OK

40143523 Southwest United Industries, Inc. 422 S. St. Louis Ave. Tulsa OK

3 of 5
Facility NEI ID Facility Name Address City State

Table 1 - Facility Identification Information

42003603 US Airways Pittsburgh International Airport 500 Tower Road Coraopolis PA

42029689 Sikorsky Global Helicopters 110 E. Stewart Huston Drive Coatesville PA

42045473

The Boeing Company, Defense, Space & 

Security, Rotorcraft Programs, Philadelphia Stewart Avenue and Route 291 Ridley Township PA

42055233 Letterkenny Army Depot 1 Overcash Avenue Chambersburg PA

42081660 Avco Corporation d/b/a Lycoming Engines 652 Oliver Street Williamsport PA

45045417 Lockheed Martin Aeronautics Company 244 Terminal Road Greenville SC

45063428 Eagle Aviation Inc 2700 Aviation Way West Columbia SC

4703758 Triumph Aerostructures, LLC 1431 Vultee Blvd Nashville TN

48027213

Department of the Army, U.S. Army Garrison-Fort 

Hood 4622 Engineer Dr. Fort Hood TX

48029468 The Boeing Company - San Antonnio 375 Airlift Drive 2nd Floor/C43 San Antonio TX

480295025 Randolph Air Force Base 1 Washington Circle Randolph AFB TX

481136003 Dallas Love Field P.O. Box 7415 Dallas TX

481215032 American Airlines - AFW 2000 Eagle Parkway Fort Worth TX

482015019 NASA Ellington Field 12400 Brantly Ave. Houston TX

482015020 NASA Lyndon B. Johnson Space Center 2101 NASA Pkwy Houston TX

48231376 L-3 Communications Intergrated Systems MID 10001 Jack Finney Blvd Greenville TX

483553000 US Army Corpus Christi Army Depot 308 Crecy St. Corpus Christi TX

483672020 WEATHERFORD AEROSPACE INC. 610 W. THIRD ST. WEATHERFORD TX

484392009 Bell Helicopter Textron Plant 1 600 East Hurst BLVD Ft. Worth TX

484392010 Bell Helicopter Textron Plant 5 1700 N Highway 360 Grand Prairie TX

48439416

Lockheed Martin Corporation d.b.a. Lockheed 

Martin Aeronautics Company 1 Lockheed Boulevard Fort Worth TX

48439508 Goodrich Interiors Fort Worth 1201 Forum Way South Fort Worth TX

49003696 ATK Launch Systems, Promontory 9160 North Highway 83 Promontory UT

490115010 Alliant Techsystems Aerospace Structures Building C-14 Clearfield Freeport Center Cleafield UT

490115012 ATK Launch Systems, Promontory Buildings H-7, H-5, G-12, G-13 Freeport Center Clearfield UT

49011663 Hill Air Force Base 75 CEG/CEV Hill Air Force Base UT

490355013 ATK Launch Systems, Bacchus 5000 South 8400 West West Valley City UT

516505023

LANGLEY AIR FORCE BASE JOINT BASE 

LANGLEY-EUSTIS 37 SWEENEY BOULEVARD LANGLEY AFB VA

51700334 Joint Base Langley-Eustis 1407 Washington Blvd. Fort Eustis VA

51710293 Naval Station Norfolk, Norfolk Virginina Naval Station Norfolk Norfolk VA

51810292 Naval Air Station Oceana, Virginia Beach, VA 1750 Tomcat Blvd Virginia Beach VA

53029438 Naval Air Station Whidbey Island (Ault Field) 1115 W. Lexington Street (building 103) Oak Harbor WA

53033383 Boeing Commercial Airplane Group - Renton 800 Logan Ave. N Renton WA

53033384

Boeing Commercial Airplane Group-North Boeing 

Field 7700 E. Marginal Way Seattle WA

53033406 Hexcel Corporation 19819 84th Avenue South Kent WA

53033532 Boeing Auburn 700 15th St. SW Auburn WA

4 of 5
Facility NEI ID Facility Name Address City State

Table 1 - Facility Identification Information

53053447

Boeing Commercial Airplanes Fabrication 

Division 18001 Canyon Rd Puyallup WA

53061398 Boeing Everett 3003 West Casino Road Everett WA

530615014 AVIATION TECHNICAL SERVICES, INC. 3121 109TH ST SW EVERETT WA

54033246 Pratt and Whitney Engine Services 1525 Midway Park Drive Bridgeport WV

540572002 Allegany Ballistics Laboratory 210 State Route 956 Rocket Center WV

54067418 BE Aerospace / SMR Technologies 93 Nettie Fenwick Road Fenwick WV

55075288 Goodrich Cabin Seating Systems 701 Maple Street Peshtigo WI

55079687 Derco Repair Services 8065 West Fairlane Avenue Milwaukee WI

550876001 Gulfstream Aerospace Corporation W6365 Discovery Drive Appleton WI

5 of 5
Cancer 

MIR

Cancer

 Incidence

Noncancer 

Max HI

Target 

Organ

Cancer 

MIR

Noncancer 

Max HI

Target 

Organ

01015223 2.10E-12 3.92E-10 2.46E-07 respiratory 3.50E-06 4.00E-01 neurological 0%

01045482 5.55E-10 3.39E-08 1.49E-02 neurological 1.18E-06 4.32E-02 kidney 0%

01073446 4.00E-10 1.05E-07 5.62E-05 neurological 7.01E-09 1.51E-04 kidney 6%

01083198 1.51E-09 1.42E-07 6.61E-05 respiratory 1.51E-09 6.61E-05 respiratory 100%

2017365 4.25E-07 3.87E-05 7.60E-03 reproductive 5.70E-07 7.60E-03 reproductive 75%

02090348 7.08E-11 2.61E-09 1.95E-04 respiratory 7.45E-08 9.35E-02 developmental 0%

02090529 5.17E-07 1.87E-05 4.47E-04 respiratory 5.17E-07 4.48E-04 respiratory 100%

02150683 1.17E-07 1.51E-07 9.70E-05 respiratory 1.17E-07 4.86E-03 neurological 100%

04013510 0.00E+00 0.00E+00 5.82E-04 neurological 8.70E-06 2.49E-02 respiratory 0%

04013602 5.41E-09 3.64E-06 1.54E-05 neurological 5.41E-09 1.54E-05 neurological 100%

04019300 1.49E-07 4.30E-05 3.47E-04 neurological 1.49E-07 3.47E-04 neurological 100%

04019433 2.10E-07 9.98E-05 2.62E-02 respiratory 2.04E-07 2.62E-02 respiratory 100%

4703758 2.09E-06 5.43E-04 1.10E-02 respiratory 2.09E-06 1.11E-02 respiratory 100%

05051409 1.07E-05 4.29E-04 4.47E-02 respiratory 1.23E-05 4.59E-02 respiratory 87%

05113377 1.69E-07 8.05E-07 1.49E-01 respiratory 1.78E-07 1.56E-01 respiratory 95%

05143331 3.11E-10 1.62E-08 1.69E-05 neurological 3.24E-10 1.76E-05 neurological 96%

06029545 6.30E-09 6.78E-07 6.63E-06 respiratory 1.09E-08 9.07E-05 kidney 58%

06037429 2.51E-08 4.69E-05 3.17E-04 respiratory 3.31E-08 9.51E-04 respiratory 76%

06037490 1.97E-08 7.08E-06 1.79E-04 neurological 5.94E-08 3.02E-04 respiratory 33%

06059538 2.87E-08 1.49E-05 2.76E-03 neurological 1.29E-07 6.31E-03 neurological 22%

06065520 2.77E-07 4.19E-05 8.04E-03 respiratory 2.87E-07 1.24E-02 respiratory 96%

06071540 3.05E-07 3.79E-06 2.98E-02 neurological 3.05E-07 2.98E-02 neurological 100%

06073306 1.86E-09 1.16E-07 2.95E-03 respiratory 1.14E-06 2.40E-02 respiratory 0%

09001684 0.00E+00 0.00E+00 5.81E-07 developmental 0.00E+00 5.37E-07 developmental -

09001688 0.00E+00 0.00E+00 7.30E-07 neurological 0.00E+00 8.55E-07 neurological -

09001693 1.84E-07 7.03E-05 9.18E-03 respiratory 6.18E-06 4.00E-02 respiratory 3%

09003626 3.65E-08 2.38E-06 3.74E-03 respiratory 3.28E-07 3.78E-03 respiratory 11%

10003349 5.56E-08 1.12E-05 8.92E-05 neurological 3.13E-07 3.24E-04 respiratory 18%

010515017 6.48E-09 3.50E-07 7.65E-03 respiratory 6.48E-09 7.65E-03 respiratory 100%

010892023 9.35E-08 3.98E-05 3.59E-03 liver 1.53E-06 6.88E-03 kidney 6%

12005313 5.22E-09 9.91E-07 3.70E-03 respiratory 5.23E-09 3.70E-03 respiratory 100%

12009358 1.55E-09 9.77E-08 3.00E-05 respiratory 7.65E-09 3.00E-05 respiratory 20%

12023284 3.15E-07 4.12E-06 5.12E-03 respiratory 3.10E-07 5.12E-03 respiratory 100%

12031634 2.25E-07 3.00E-05 1.60E-03 respiratory 2.26E-07 1.60E-03 respiratory 100%

12061380 7.06E-07 4.67E-05 4.70E-03 respiratory 7.06E-07 4.70E-03 respiratory 100%

12083270 2.36E-10 1.41E-08 8.20E-07 respiratory 2.75E-10 9.50E-04 neurological 86%

12099695 0.00E+00 0.00E+00 4.42E-10 developmental 4.24E-10 9.44E-06 kidney 0%

13009224 1.12E-07 1.49E-05 2.44E-02 respiratory 1.15E-07 2.54E-02 respiratory 97%

13063388 5.32E-07 3.56E-04 1.28E-02 respiratory 8.61E-07 1.68E-02 kidney 62%

13067506 1.07E-06 9.57E-04 5.22E-03 respiratory 1.09E-06 5.27E-03 respiratory 99%

13153431 3.47E-06 3.32E-04 2.06E-01 respiratory 3.71E-06 2.12E-01 respiratory 93%

15003346 1.31E-08 1.62E-06 2.65E-04 respiratory 1.31E-08 2.65E-04 respiratory 100%

18097487 2.21E-08 5.06E-06 2.85E-04 liver 1.16E-07 2.05E-03 respiratory 19%

19163356 1.30E-07 1.01E-05 1.20E-03 respiratory 1.30E-07 1.20E-03 respiratory 100%

20035486 3.79E-08 1.34E-06 8.02E-02 respiratory 9.78E-07 8.50E-02 respiratory 4%

20173193 1.34E-08 4.50E-07 1.07E-05 respiratory 1.34E-08 3.42E-05 skeletal 100%

20173453 1.03E-07 7.74E-06 2.57E-03 respiratory 6.29E-07 6.37E-03 respiratory 16%

24005458 5.14E-06 1.64E-03 1.43E-02 reproductive 5.31E-06 1.48E-02 reproductive 97%

24015282 1.97E-07 1.32E-05 1.44E-03 neurological 2.00E-07 1.77E-02 respiratory 99%

24017525 2.83E-10 4.05E-07 4.03E-06 neurological 2.22E-08 7.45E-03 respiratory 1%

25009450 1.50E-10 3.02E-08 1.89E-05 neurological 3.53E-09 5.25E-05 kidney 4%

25009477 1.73E-11 1.84E-09 2.51E-04 reproductive 3.82E-08 1.01E-03 kidney 0%

26065644 2.37E-08 5.80E-06 3.93E-03 respiratory 2.37E-08 3.93E-03 respiratory 100%

27053389 1.69E-08 1.79E-05 2.39E-03 respiratory 4.12E-08 2.42E-03 respiratory 41%

28089367 7.71E-08 1.90E-06 1.41E-04 reproductive 7.71E-08 1.41E-04 reproductive 100%

29183329 1.12E-07 5.68E-06 1.75E-03 respiratory 1.26E-07 1.85E-03 respiratory 89%

29189332 1.65E-07 4.77E-05 3.37E-02 respiratory 2.23E-06 3.44E-02 respiratory 7%

29189513 3.91E-07 1.78E-04 1.23E-02 respiratory 7.07E-07 1.24E-02 respiratory 55%

33011652 2.94E-09 9.45E-07 1.99E-04 respiratory 2.94E-09 1.99E-04 respiratory 100%

36103518 0.00E+00 0.00E+00 4.15E-03 neurological 0.00E+00 4.15E-03 neurological -

36103521 3.45E-09 6.61E-07 1.08E-04 neurological 3.57E-09 1.19E-04 neurological 97%

36111630 6.00E-08 5.39E-07 5.63E-03 neurological 6.00E-08 5.63E-03 neurological 100%

37049483 6.67E-10 4.79E-08 1.37E-04 respiratory 5.77E-06 9.22E-02 developmental 0%

37051340 5.58E-09 6.43E-07 1.01E-03 respiratory 1.24E-06 9.74E-03 respiratory 0%

Table 2a – 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 3
Cancer 

MIR

Cancer

 Incidence

Noncancer 

Max HI

Target 

Organ

Cancer 

MIR

Noncancer 

Max HI

Target 

Organ

Table 2a – 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

37119600 9.99E-09 5.83E-06 2.79E-04 respiratory 1.02E-08 2.81E-04 respiratory 98%

37133455 5.44E-09 3.52E-07 2.63E-04 respiratory 1.12E-06 1.28E-01 respiratory 0%

39017343 1.53E-07 3.35E-05 1.30E-04 respiratory 1.53E-07 1.30E-04 respiratory 100%

39021672 4.71E-07 3.04E-05 1.02E-01 respiratory 4.78E-07 1.03E-01 respiratory 98%

39057430 2.08E-10 2.28E-08 5.09E-05 respiratory 8.55E-08 4.49E-03 developmental 0%

39061524 3.10E-06 8.06E-04 4.14E-02 respiratory 3.11E-06 4.14E-02 respiratory 100%

39109419 9.17E-08 1.02E-06 6.00E-02 respiratory 8.83E-07 6.03E-02 respiratory 10%

39151479 5.44E-06 2.18E-04 1.07E-01 respiratory 5.44E-06 1.07E-01 respiratory 100%

40143460 2.21E-06 2.30E-04 8.11E-03 respiratory 2.21E-06 8.11E-03 respiratory 100%

40143494 9.09E-08 4.20E-05 6.55E-03 respiratory 9.24E-08 6.56E-03 respiratory 98%

40143523 9.49E-06 3.18E-04 4.58E-01 kidney 1.16E-05 4.58E-01 respiratory 82%

42003603 1.05E-07 1.14E-05 2.77E-03 respiratory 1.07E-07 2.78E-03 respiratory 98%

42029689 0.00E+00 0.00E+00 2.32E-06 developmental 0.00E+00 2.59E-06 developmental -

42045473 6.69E-08 4.23E-05 5.26E-03 respiratory 2.96E-07 6.11E-03 respiratory 23%

42055233 3.25E-11 1.15E-08 9.49E-07 neurological 4.12E-08 3.50E-04 kidney 0%

42081660 2.05E-06 1.51E-04 7.99E-02 kidney 2.36E-06 3.95E-01 neurological 87%

45045417 2.26E-08 1.02E-05 4.70E-04 respiratory 2.91E-08 5.41E-04 respiratory 78%

45063428 1.07E-07 3.96E-06 2.38E-03 liver 1.32E-08 2.38E-03 liver 100%

48027213 1.64E-10 4.12E-08 3.42E-04 respiratory 2.98E-07 3.77E-03 kidney 0%

48029468 3.67E-06 3.45E-04 1.07E-01 respiratory 3.56E-06 1.04E-01 respiratory 100%

48231376 9.07E-08 1.18E-05 5.51E-04 neurological 9.26E-08 6.27E-04 neurological 98%

48439416 2.90E-07 1.84E-05 1.55E-01 respiratory 3.07E-06 1.57E-01 respiratory 9%

48439508 2.16E-11 2.07E-08 1.97E-06 reproductive 2.16E-11 1.97E-06 reproductive 100%

49003696 7.05E-08 8.21E-07 8.24E-03 respiratory 8.51E-08 8.24E-03 respiratory 83%

49011663 6.56E-06 7.58E-04 9.20E-03 respiratory 6.68E-06 1.63E-02 respiratory 98%

050272001 2.35E-07 1.53E-06 3.75E-03 neurological 2.84E-07 3.81E-03 neurological 83%

51700334 7.29E-11 4.41E-08 6.07E-08 respiratory 1.67E-07 3.35E-03 kidney 0%

51710293 8.47E-06 2.54E-03 2.86E-02 respiratory 8.48E-06 2.86E-02 respiratory 100%

51810292 1.87E-06 4.41E-04 4.89E-02 respiratory 1.87E-06 4.89E-02 respiratory 100%

53029438 9.78E-07 4.63E-05 9.62E-04 respiratory 1.05E-06 1.46E-03 respiratory 93%

53033383 2.37E-06 7.21E-04 1.30E-02 reproductive 2.38E-06 1.30E-02 reproductive 100%

53033384 2.67E-06 3.76E-04 2.60E-02 respiratory 2.65E-06 2.59E-02 respiratory 100%

53033406 8.75E-08 1.17E-05 2.17E-04 neurological 8.85E-08 7.42E-03 respiratory 99%

53033532 4.11E-07 1.87E-04 1.30E-02 respiratory 8.07E-07 1.36E-02 respiratory 51%

53053447 5.30E-07 1.83E-04 1.63E-03 respiratory 5.40E-07 1.71E-03 respiratory 98%

53061398 6.03E-07 1.05E-03 1.01E-02 respiratory 6.16E-07 1.03E-02 respiratory 98%

54033246 2.27E-10 3.96E-08 1.89E-07 respiratory 2.30E-10 4.96E-06 neurological 99%

54067418 1.21E-08 1.45E-08 4.34E-04 neurological 3.49E-08 5.52E-04 kidney 35%

55075288 4.95E-09 1.09E-07 4.20E-05 neurological 3.26E-08 2.97E-04 kidney 15%

55079687 0.00E+00 0.00E+00 5.55E-09 neurological 0.00E+00 5.46E-09 neurological -

060292024 4.27E-11 6.75E-09 1.96E-07 reproductive 3.28E-06 1.32E-02 kidney 0%

060372022 8.36E-08 9.93E-06 2.40E-03 respiratory 2.96E-07 3.60E-02 respiratory 28%

060376004 6.59E-08 1.15E-04 5.52E-04 neurological 7.51E-08 5.40E-04 neurological 88%

060812015 2.65E-08 1.34E-05 1.08E-02 respiratory 4.02E-07 1.54E-02 respiratory 7%

090032025 7.42E-11 3.75E-09 2.21E-06 neurological 7.42E-11 2.21E-06 neurological 100%

090075021 1.36E-07 1.83E-06 1.02E-02 neurological 1.67E-07 1.02E-02 neurological 81%

120862016 1.02E-07 9.40E-05 2.38E-04 neurological 2.89E-07 2.38E-04 respiratory 35%

130516005 9.79E-07 7.27E-05 1.22E-02 respiratory 9.80E-07 1.22E-02 respiratory 100%

131276002 2.47E-08 8.77E-07 1.23E-04 reproductive 2.47E-08 1.23E-04 reproductive 100%

132332017 2.32E-06 5.00E-05 5.97E-02 neurological 2.32E-06 5.97E-02 neurological 100%

181035004 1.88E-08 1.60E-06 9.61E-03 respiratory 1.62E-06 9.30E-03 respiratory 1%

181412011 1.90E-07 2.18E-05 1.58E-04 respiratory 2.68E-07 1.20E-03 kidney 71%

201252005 1.70E-08 5.06E-07 5.28E-04 respiratory 1.71E-08 5.31E-04 respiratory 99%

201732006 3.12E-08 1.03E-05 3.86E-02 neurological 5.17E-08 3.91E-02 neurological 60%

201732007 2.54E-06 3.93E-04 4.90E-02 respiratory 2.56E-06 4.90E-02 respiratory 99%

201732027 1.42E-05 5.25E-03 1.09E-01 neurological 1.49E-05 1.13E-01 neurological 96%

280475024 5.60E-10 1.30E-07 5.17E-05 reproductive 9.36E-06 7.80E-03 respiratory 0%

370492008 7.20E-06 4.92E-04 3.09E-02 respiratory 1.82E-05 1.28E-01 developmental 40%

391335027 1.46E-08 6.18E-07 1.36E-03 neurological 1.46E-08 1.36E-03 neurological 100%

401092026 1.54E-06 4.96E-04 8.44E-02 respiratory 1.54E-06 8.44E-02 respiratory 100%

401432003 5.82E-09 2.16E-06 5.40E-04 respiratory 5.82E-09 5.40E-04 respiratory 100%

401432013 1.19E-06 6.15E-05 8.73E-02 respiratory 1.19E-06 8.73E-02 respiratory 100%

401432014 5.42E-06 1.87E-04 3.35E-01 respiratory 5.44E-06 3.36E-01 respiratory 100%

401432021 2.80E-06 5.36E-04 2.27E-01 respiratory 3.00E-06 2.27E-01 respiratory 93%

2 of 3
Cancer 

MIR

Cancer

 Incidence

Noncancer 

Max HI

Target 

Organ

Cancer 

MIR

Noncancer 

Max HI

Target 

Organ

Table 2a – 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

480295025 1.02E-08 7.74E-07 1.69E-01 respiratory 1.05E-07 3.31E-01 respiratory 10%

481136003 8.30E-08 5.78E-05 5.01E-04 respiratory 1.11E-07 5.40E-04 respiratory 75%

481215032 2.17E-08 1.94E-05 1.76E-02 respiratory 2.17E-08 1.76E-02 respiratory 100%

482015019 5.68E-09 2.53E-06 8.60E-04 respiratory 5.95E-09 8.71E-04 respiratory 95%

482015020 1.23E-09 8.96E-07 3.31E-06 respiratory 2.03E-08 2.05E-04 kidney 6%

483553000 3.28E-07 4.33E-05 2.47E-02 respiratory 1.99E-06 4.39E-01 respiratory 16%

483672020 3.09E-07 1.19E-05 2.97E-02 neurological 3.09E-07 2.97E-02 neurological 100%

484392009 1.13E-06 1.01E-04 1.45E-01 respiratory 1.16E-06 1.42E-01 respiratory 97%

484392010 1.79E-10 4.98E-08 1.78E-05 reproductive 1.80E-09 1.62E-04 neurological 10%

490115010 3.71E-07 6.36E-05 3.07E-04 respiratory 3.41E-07 2.89E-04 respiratory 100%

490115012 2.07E-08 4.44E-06 1.66E-03 respiratory 7.42E-07 6.80E-03 respiratory 3%

490355013 1.66E-07 6.15E-05 2.99E-03 respiratory 1.66E-07 2.99E-03 respiratory 100%

516505023 9.43E-09 1.87E-06 4.81E-04 respiratory 3.72E-07 7.74E-03 kidney 3%

530615014 5.83E-11 2.98E-08 2.12E-03 reproductive 7.17E-12 2.12E-03 reproductive 100%

540572002 4.00E-07 9.54E-06 3.78E-03 respiratory 2.10E-06 5.44E-02 respiratory 19%

550876001 7.05E-07 8.68E-05 3.67E-03 liver 7.17E-07 3.64E-03 liver 98%

1 BOLD indicates a cancer risk great than 1 in a million or a noncancer risk greater than 1

3 of 3
Cancer 

MIR

Cancer

 Incidence

Noncancer 

Max HI

Target 

Organ

01015223 2.11E-12 3.94E-10 2.46E-07 respiratory

01045482 5.57E-10 3.40E-08 1.49E-02 neurological

01073446 4.08E-10 1.07E-07 5.73E-05 neurological

01083198 1.54E-09 1.45E-07 6.74E-05 respiratory

2017365 4.33E-07 3.94E-05 7.75E-03 reproductive

02090348 7.22E-11 2.66E-09 1.97E-04 respiratory

02090529 5.20E-07 1.88E-05 4.50E-04 respiratory

02150683 1.20E-07 1.54E-07 9.89E-05 respiratory

04013510 0.00E+00 0.00E+00 5.94E-04 neurological

04013602 5.51E-09 3.71E-06 1.57E-05 neurological

04019300 1.52E-07 4.39E-05 3.54E-04 neurological

04019433 2.08E-07 9.91E-05 2.67E-02 respiratory

4703758 2.09E-06 5.43E-04 1.10E-02 respiratory

05051409 1.09E-05 4.37E-04 4.51E-02 respiratory

05113377 1.51E-07 6.15E-07 1.52E-01 respiratory

05143331 3.11E-10 1.62E-08 1.70E-05 neurological

06029545 6.43E-09 6.92E-07 6.74E-06 respiratory

06037429 2.55E-08 4.77E-05 3.22E-04 respiratory

06037490 1.97E-08 7.08E-06 1.80E-04 neurological

06059538 2.93E-08 1.52E-05 2.82E-03 neurological

06065520 2.80E-07 4.23E-05 8.20E-03 respiratory

06071540 3.11E-07 3.86E-06 3.04E-02 neurological

06073306 1.75E-09 1.03E-07 2.98E-03 respiratory

09001684 0.00E+00 0.00E+00 5.92E-07 developmental

09001688 0.00E+00 0.00E+00 7.45E-07 neurological

09001693 1.84E-07 7.04E-05 9.29E-03 respiratory

09003626 3.67E-08 2.37E-06 3.76E-03 respiratory

10003349 5.67E-08 1.14E-05 9.10E-05 neurological

010515017 6.48E-09 3.46E-07 7.65E-03 respiratory

010892023 4.94E-08 2.08E-05 3.66E-03 liver

12005313 5.32E-09 1.01E-06 3.77E-03 respiratory

12009358 1.57E-09 9.89E-08 3.04E-05 respiratory

12023284 3.16E-07 4.04E-06 5.22E-03 respiratory

12031634 2.29E-07 3.06E-05 1.63E-03 respiratory

12061380 7.20E-07 4.76E-05 4.80E-03 respiratory

12083270 2.39E-10 1.43E-08 8.25E-07 respiratory

12099695 0.00E+00 0.00E+00 4.51E-10 developmental

13009224 1.13E-07 1.51E-05 2.49E-02 respiratory

13063388 5.42E-07 3.63E-04 1.30E-02 respiratory

13067506 1.09E-06 9.74E-04 5.25E-03 respiratory

13153431 3.53E-06 3.36E-04 2.10E-01 respiratory

15003346 1.34E-08 1.65E-06 2.70E-04 respiratory

18097487 1.45E-08 2.77E-06 2.91E-04 liver

19163356 1.33E-07 1.03E-05 1.22E-03 respiratory

20035486 3.79E-08 1.34E-06 8.02E-02 respiratory

20173193 1.37E-08 4.57E-07 1.09E-05 respiratory

20173453 1.05E-07 7.89E-06 2.62E-03 respiratory

24005458 5.14E-06 1.64E-03 1.43E-02 reproductive

24015282 1.98E-07 1.33E-05 1.47E-03 neurological

24017525 2.89E-10 4.13E-07 4.11E-06 neurological

25009450 1.38E-10 2.50E-08 1.89E-05 neurological

25009477 1.73E-11 1.84E-09 2.54E-04 reproductive

26065644 2.37E-08 5.80E-06 3.93E-03 respiratory

27053389 1.72E-08 1.82E-05 2.43E-03 respiratory

28089367 7.86E-08 1.94E-06 1.44E-04 reproductive

29183329 1.14E-07 5.79E-06 1.76E-03 respiratory

29189332 1.68E-07 4.83E-05 3.38E-02 respiratory

29189513 3.98E-07 1.81E-04 1.23E-02 respiratory

33011652 3.00E-09 9.62E-07 2.03E-04 respiratory

36103518 0.00E+00 0.00E+00 4.24E-03 neurological

36103521 3.52E-09 6.74E-07 1.11E-04 neurological

36111630 6.00E-08 5.39E-07 5.63E-03 neurological

37049483 6.80E-10 4.88E-08 1.40E-04 respiratory

37051340 5.69E-09 6.55E-07 1.03E-03 respiratory

Facility NEI ID

 Category Chronic Risk 1

Table 2b – Maximum Predicted HEM-3 Chronic Risks

Allowable Emissions

1 of 3
Cancer 

MIR

Cancer

 Incidence

Noncancer 

Max HI

Target 

Facility NEI ID Organ

 Category Chronic Risk 1

Table 2b – Maximum Predicted HEM-3 Chronic Risks

Allowable Emissions

37119600 1.02E-08 5.94E-06 2.79E-04 respiratory

37133455 5.55E-09 3.59E-07 2.68E-04 respiratory

39017343 1.56E-07 3.42E-05 1.32E-04 respiratory

39021672 4.79E-07 3.09E-05 1.04E-01 respiratory

39057430 1.99E-10 2.19E-08 5.19E-05 respiratory

39061524 3.11E-06 8.09E-04 4.14E-02 respiratory

39109419 9.36E-08 1.04E-06 6.12E-02 respiratory

39151479 5.55E-06 2.22E-04 1.09E-01 respiratory

40143460 2.24E-06 2.34E-04 8.23E-03 respiratory

40143494 9.12E-08 4.21E-05 6.65E-03 respiratory

40143523 9.66E-06 3.24E-04 4.67E-01 kidney

42003603 1.06E-07 1.15E-05 2.79E-03 respiratory

42029689 0.00E+00 0.00E+00 2.36E-06 developmental

42045473 6.49E-08 4.12E-05 5.26E-03 respiratory

42055233 3.31E-11 1.18E-08 9.66E-07 neurological

42081660 2.09E-06 1.54E-04 8.15E-02 kidney

45045417 2.31E-08 1.04E-05 4.70E-04 respiratory

45063428 1.34E-08 5.22E-07 2.43E-03 liver

48027213 1.67E-10 4.16E-08 3.42E-04 respiratory

48029468 3.67E-06 3.45E-04 1.07E-01 respiratory

48231376 9.25E-08 1.20E-05 5.52E-04 neurological

48439416 2.92E-07 1.85E-05 1.57E-01 respiratory

48439508 2.17E-11 2.08E-08 1.97E-06 reproductive

49003696 7.16E-08 8.30E-07 8.24E-03 respiratory

49011663 6.56E-06 7.57E-04 9.24E-03 respiratory

050272001 2.40E-07 1.56E-06 3.83E-03 neurological

51700334 7.43E-11 4.39E-08 6.19E-08 respiratory

51710293 8.47E-06 2.54E-03 2.88E-02 respiratory

51810292 1.86E-06 4.40E-04 4.99E-02 respiratory

53029438 9.66E-07 4.56E-05 9.66E-04 respiratory

53033383 2.39E-06 7.27E-04 1.32E-02 reproductive

53033384 2.72E-06 3.83E-04 2.65E-02 respiratory

53033406 8.91E-08 1.20E-05 2.19E-04 neurological

53033532 4.18E-07 1.90E-04 1.33E-02 respiratory

53053447 5.30E-07 1.83E-04 1.63E-03 respiratory

53061398 6.06E-07 1.07E-03 1.03E-02 respiratory

54033246 2.32E-10 4.04E-08 1.93E-07 respiratory

54067418 1.21E-08 1.45E-08 4.38E-04 neurological

55075288 5.05E-09 1.11E-07 4.29E-05 neurological

55079687 0.00E+00 0.00E+00 5.66E-09 neurological

060292024 4.32E-11 6.83E-09 2.00E-07 reproductive

060372022 8.37E-08 9.94E-06 2.40E-03 respiratory

060376004 6.72E-08 1.17E-04 5.60E-04 neurological

060812015 2.66E-08 1.35E-05 1.09E-02 respiratory

090032025 7.42E-11 3.75E-09 2.24E-06 neurological

090075021 1.39E-07 1.87E-06 1.04E-02 neurological

120862016 1.04E-07 9.59E-05 2.42E-04 neurological

130516005 9.98E-07 7.41E-05 1.22E-02 respiratory

131276002 2.52E-08 8.92E-07 1.23E-04 reproductive

132332017 2.32E-06 5.00E-05 5.98E-02 neurological

181035004 1.91E-08 1.64E-06 9.80E-03 respiratory

181412011 1.94E-07 2.22E-05 1.61E-04 respiratory

201252005 1.70E-08 5.06E-07 5.28E-04 respiratory

201732006 3.10E-08 1.02E-05 3.86E-02 neurological

201732007 2.53E-06 3.92E-04 4.90E-02 respiratory

201732027 1.45E-05 5.35E-03 1.12E-01 neurological

280475024 5.72E-10 1.33E-07 5.28E-05 reproductive

370492008 7.35E-06 5.01E-04 3.11E-02 respiratory

391335027 1.47E-08 6.19E-07 1.36E-03 neurological

401092026 1.56E-06 5.02E-04 8.60E-02 respiratory

401432003 5.82E-09 2.16E-06 5.40E-04 respiratory

401432013 1.19E-06 6.18E-05 8.75E-02 respiratory

401432014 5.46E-06 1.88E-04 3.35E-01 respiratory

401432021 2.82E-06 5.40E-04 2.30E-01 respiratory

2 of 3
Cancer 

MIR

Cancer

 Incidence

Noncancer 

Max HI

Target 

Facility NEI ID Organ

 Category Chronic Risk 1

Table 2b – Maximum Predicted HEM-3 Chronic Risks

Allowable Emissions

480295025 1.00E-08 7.42E-07 1.73E-01 respiratory

481136003 7.67E-08 5.31E-05 5.11E-04 respiratory

481215032 2.20E-08 1.97E-05 1.77E-02 respiratory

482015019 5.14E-09 2.32E-06 8.62E-04 respiratory

482015020 1.11E-09 8.41E-07 3.37E-06 respiratory

483553000 3.27E-07 4.31E-05 2.47E-02 respiratory

483672020 3.15E-07 1.21E-05 3.03E-02 neurological

484392009 1.13E-06 1.02E-04 1.45E-01 respiratory

484392010 1.18E-10 3.76E-08 1.78E-05 reproductive

490115010 3.78E-07 6.48E-05 3.13E-04 respiratory

490115012 2.07E-08 4.44E-06 1.66E-03 respiratory

490355013 1.66E-07 6.15E-05 2.99E-03 respiratory

516505023 9.44E-09 1.87E-06 4.81E-04 respiratory

530615014 7.31E-12 3.74E-09 2.16E-03 reproductive

540572002 4.00E-07 9.54E-06 3.78E-03 respiratory

550876001 6.55E-07 8.19E-05 3.74E-03 liver

1 BOLD indicates a cancer risk great than 1 in a million or a noncancer risk greater than 1

3 of 3
REL AEGL1 AEGL2 ERPG1 ERPG2

01015223 Formaldehyde 2.65E-05 1.32E-06 8.57E-08 1.21E-06 1.21E-07

01015223 Xylenes (mixed) 3.04E-06 1.20E-07 1.67E-08 0.00E+00 0.00E+00

01015223 Toluene 8.15E-07 4.02E-08 6.70E-09 1.59E-07 2.74E-08

01015223 Ethyl benzene 0.00E+00 5.61E-08 1.64E-09 0.00E+00 0.00E+00

01045482 Xylenes (mixed) 7.63E-06 3.00E-07 4.20E-08 0.00E+00 0.00E+00

01045482 Toluene 2.22E-06 1.09E-07 1.82E-08 4.32E-07 7.46E-08

01045482 Ethyl benzene 0.00E+00 3.48E-07 1.02E-08 0.00E+00 0.00E+00

01045482 Cumene 0.00E+00 9.67E-09 1.61E-09 0.00E+00 0.00E+00

01073446 Toluene 2.63E-04 1.30E-05 2.16E-06 5.12E-05 8.84E-06

01073446 Xylenes (mixed) 3.03E-05 1.19E-06 1.67E-07 0.00E+00 0.00E+00

01073446 Ethyl benzene 0.00E+00 2.13E-07 6.22E-09 0.00E+00 0.00E+00

01083198 2,4-Toluene diisocyanate 0.00E+00 3.34E-05 7.93E-06 6.59E-05 4.25E-06

01083198 Xylenes (mixed) 4.63E-06 1.82E-07 2.55E-08 0.00E+00 0.00E+00

01083198 Triethylamine 2.89E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00

01083198 Ethyl benzene 0.00E+00 7.12E-07 2.08E-08 0.00E+00 0.00E+00

01083198 Toluene 4.60E-07 2.27E-08 3.78E-09 8.95E-08 1.55E-08

01083198 Cumene 0.00E+00 2.15E-10 3.58E-11 0.00E+00 0.00E+00

2017365 Propyl cellosolve 7.14E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00

2017365 2-Butoxyethyl acetate 1.05E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

2017365 Formaldehyde 2.43E-04 1.21E-05 7.85E-07 1.11E-05 1.11E-06

2017365 Xylenes (mixed) 7.25E-05 2.85E-06 3.99E-07 0.00E+00 0.00E+00

2017365 Arsenic compounds 3.82E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

2017365 Toluene 2.18E-05 1.07E-06 1.79E-07 4.24E-06 7.32E-07

2017365 Mercury (elemental) 3.80E-06 0.00E+00 1.34E-09 0.00E+00 1.14E-09

2017365 Ethyl benzene 0.00E+00 1.50E-07 4.37E-09 0.00E+00 0.00E+00

2017365 Methyl methacrylate 0.00E+00 1.23E-07 1.76E-08 0.00E+00 0.00E+00

2017365 Triethylamine 4.48E-08 0.00E+00 0.00E+00 0.00E+00 0.00E+00

2017365 Methanol 7.92E-09 3.21E-10 8.21E-11 8.53E-10 1.71E-10

2017365 Cumene 0.00E+00 7.26E-11 1.21E-11 0.00E+00 0.00E+00

02090348 Diethylene glycol monobutyl ether 4.96E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

02090348 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 3.03E-05

02090348 Triethylamine 5.01E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00

02090348 Xylenes (mixed) 1.63E-06 6.41E-08 8.97E-09 0.00E+00 0.00E+00

02090348 Toluene 4.35E-07 2.14E-08 3.57E-09 8.47E-08 1.46E-08

02090348 Formaldehyde 1.40E-07 6.98E-09 4.52E-10 6.40E-09 6.40E-10

02090348 Ethyl benzene 0.00E+00 2.88E-08 8.39E-10 0.00E+00 0.00E+00

02090348 Cumene 0.00E+00 4.47E-09 7.46E-10 0.00E+00 0.00E+00

02090529 Diethylene glycol monobutyl ether 1.42E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

02090529 2-Butoxyethyl acetate 1.32E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

02090529 Xylenes (mixed) 1.92E-05 7.54E-07 1.06E-07 0.00E+00 0.00E+00

02090529 Toluene 1.07E-05 5.26E-07 8.76E-08 2.08E-06 3.58E-07

02090529 Methylene chloride 3.78E-06 7.66E-08 2.78E-08 5.29E-08 2.03E-08

02090529 Chlorobenzene 0.00E+00 3.50E-06 2.33E-07 0.00E+00 0.00E+00

02090529 Ethylene glycol methyl ether 3.10E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00

02090529 Methanol 1.09E-06 4.44E-08 1.13E-08 1.18E-07 2.36E-08

02090529 Ethyl benzene 0.00E+00 5.67E-07 1.65E-08 0.00E+00 0.00E+00

02090529 n-Hexane 0.00E+00 0.00E+00 3.80E-08 0.00E+00 0.00E+00

02090529 Cumene 0.00E+00 2.78E-10 4.63E-11 0.00E+00 0.00E+00

02150683 Benzene 7.31E-06 5.59E-08 3.65E-09 5.94E-08 1.98E-08

02150683 Methanol 2.71E-06 1.10E-07 2.81E-08 2.92E-07 5.84E-08

02150683 Phenol 1.34E-06 1.34E-07 8.74E-08 2.05E-07 4.10E-08

02150683 Toluene 1.11E-06 5.47E-08 9.12E-09 2.16E-07 3.73E-08

Facility NEI ID Pollutant

Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient1

 1 of 27
Facility NEI ID Pollutant REL AEGL1 AEGL2 ERPG1 ERPG2

Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient1

02150683 Methylene chloride 1.25E-06 2.54E-08 9.22E-09 1.75E-08 6.73E-09

02150683 Xylenes (mixed) 9.45E-07 3.71E-08 5.20E-09 0.00E+00 0.00E+00

02150683 Ethyl benzene 0.00E+00 2.43E-08 7.08E-10 0.00E+00 0.00E+00

02150683 Cumene 0.00E+00 8.73E-10 1.45E-10 0.00E+00 0.00E+00

04013510 Xylenes (mixed) 2.15E-03 8.44E-05 1.18E-05 0.00E+00 0.00E+00

04013510 Toluene 3.31E-05 1.64E-06 2.73E-07 6.45E-06 1.11E-06

04013602 Xylenes (mixed) 9.93E-05 3.90E-06 5.46E-07 0.00E+00 0.00E+00

04013602 Toluene 6.09E-05 3.01E-06 5.01E-07 1.19E-05 2.05E-06

04013602 Hydrofluoric acid 6.86E-06 2.01E-06 8.24E-08 1.03E-06 1.03E-07

04013602 Ethyl benzene 0.00E+00 5.98E-06 1.74E-07 0.00E+00 0.00E+00

04013602 Methanol 3.80E-06 1.54E-07 3.94E-08 4.10E-07 8.19E-08

04013602 Benzene 9.00E-07 6.89E-09 4.50E-10 7.32E-09 2.44E-09

04013602 n-Hexane 0.00E+00 0.00E+00 1.17E-08 0.00E+00 0.00E+00

04019300 Xylenes (mixed) 4.45E-04 1.75E-05 2.45E-06 0.00E+00 0.00E+00

04019300 Toluene 2.18E-04 1.07E-05 1.79E-06 4.24E-05 7.32E-06

04019300 Methanol 6.03E-05 2.45E-06 6.25E-07 6.49E-06 1.30E-06

04019300 Ethyl benzene 0.00E+00 3.29E-05 9.61E-07 0.00E+00 0.00E+00

04019433 Phenol 3.77E-04 3.77E-05 2.46E-05 5.75E-05 1.15E-05

04019433 Methylene chloride 3.13E-04 6.34E-06 2.30E-06 4.38E-06 1.68E-06

04019433 Arsenic compounds 8.36E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

04019433 Butyl carbitol acetate 7.23E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

04019433 Xylenes (mixed) 3.37E-06 1.32E-07 1.85E-08 0.00E+00 0.00E+00

04019433 Toluene 1.39E-06 6.85E-08 1.14E-08 2.70E-07 4.67E-08

04019433 Ethyl benzene 0.00E+00 4.68E-07 1.36E-08 0.00E+00 0.00E+00

04019433 Cumene 0.00E+00 1.43E-08 2.38E-09 0.00E+00 0.00E+00

4703758 Ethylene glycol ethyl ether 6.02E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

4703758 Diethylene glycol monoethyl ether 3.75E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

4703758 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 2.03E-04

4703758 Toluene 1.46E-04 7.19E-06 1.20E-06 2.84E-05 4.90E-06

4703758 Xylenes (mixed) 1.55E-04 6.11E-06 8.55E-07 0.00E+00 0.00E+00

4703758 Methanol 1.32E-04 5.37E-06 1.37E-06 1.43E-05 2.85E-06

4703758 Propyl cellosolve 3.67E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

4703758 Ethyl benzene 0.00E+00 8.53E-06 2.49E-07 0.00E+00 0.00E+00

4703758 Phenol 3.03E-07 3.03E-08 1.98E-08 4.63E-08 9.26E-09

4703758 Cumene 0.00E+00 9.46E-09 1.58E-09 0.00E+00 0.00E+00

05051409 Ethylene glycol ethyl ether 7.94E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

05051409 Toluene 1.30E-03 6.40E-05 1.07E-05 2.53E-04 4.37E-05

05051409 Propyl cellosolve 1.12E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

05051409 Formaldehyde 7.63E-04 3.82E-05 2.47E-06 3.50E-05 3.50E-06

05051409 Butyl carbitol acetate 8.10E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

05051409 Xylenes (mixed) 7.15E-04 2.81E-05 3.93E-06 0.00E+00 0.00E+00

05051409 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 2.71E-04

05051409 2-Butoxyethyl acetate 1.25E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

05051409 Phenol 1.23E-05 1.23E-06 8.02E-07 1.88E-06 3.76E-07

05051409 Arsenic compounds 1.42E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

05051409 Methanol 5.25E-06 2.13E-07 5.45E-08 5.66E-07 1.13E-07

05051409 Ethyl benzene 0.00E+00 4.31E-07 1.26E-08 0.00E+00 0.00E+00

05051409 Beryllium compounds 0.00E+00 0.00E+00 0.00E+00 0.00E+00 3.73E-09

05051409 Cumene 0.00E+00 9.41E-11 1.57E-11 0.00E+00 0.00E+00

05113377 Ethylene glycol ethyl ether acetate1.88E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00

05113377 Propyl cellosolve 1.54E-01 0.00E+00 0.00E+00 0.00E+00 0.00E+00

05113377 2-Butoxyethyl acetate 1.12E-01 0.00E+00 0.00E+00 0.00E+00 0.00E+00

 2 of 27
Facility NEI ID Pollutant REL AEGL1 AEGL2 ERPG1 ERPG2

Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient1

05113377 Phenol 9.31E-03 9.31E-04 6.07E-04 1.42E-03 2.84E-04

05113377 Methylene chloride 8.67E-03 1.76E-04 6.39E-05 1.21E-04 4.67E-05

05113377 Toluene 1.92E-03 9.46E-05 1.58E-05 3.74E-04 6.45E-05

05113377 Xylenes (mixed) 1.91E-03 7.51E-05 1.05E-05 0.00E+00 0.00E+00

05113377 Ethyl benzene 0.00E+00 6.75E-05 1.97E-06 0.00E+00 0.00E+00

05113377 Methanol 2.51E-05 1.02E-06 2.60E-07 2.70E-06 5.41E-07

05143331 Formaldehyde 2.10E-05 1.05E-06 6.79E-08 9.62E-07 9.62E-08

05143331 Toluene 5.83E-06 2.88E-07 4.80E-08 1.14E-06 1.96E-07

05143331 Xylenes (mixed) 4.71E-06 1.85E-07 2.59E-08 0.00E+00 0.00E+00

05143331 Methanol 9.74E-07 3.95E-08 1.01E-08 1.05E-07 2.10E-08

05143331 Phenol 1.99E-07 1.99E-08 1.30E-08 3.04E-08 6.07E-09

05143331 Ethyl benzene 0.00E+00 7.17E-09 2.09E-10 0.00E+00 0.00E+00

06029545 Diethylene glycol monobutyl ether 2.19E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

06029545 Triethylamine 8.72E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00

06029545 Xylenes (mixed) 6.53E-06 2.57E-07 3.59E-08 0.00E+00 0.00E+00

06029545 Toluene 3.65E-07 1.80E-08 3.00E-09 7.10E-08 1.23E-08

06029545 Ethyl benzene 0.00E+00 1.42E-07 4.13E-09 0.00E+00 0.00E+00

06029545 Cumene 0.00E+00 3.57E-11 5.95E-12 0.00E+00 0.00E+00

06037429 Xylenes (mixed) 1.48E-04 5.83E-06 8.16E-07 0.00E+00 0.00E+00

06037429 Toluene 8.85E-05 4.36E-06 7.27E-07 1.72E-05 2.98E-06

06037429 Ethyl benzene 0.00E+00 1.17E-05 3.41E-07 0.00E+00 0.00E+00

06037429 Methyl methacrylate 0.00E+00 2.32E-06 3.31E-07 0.00E+00 0.00E+00

06037429 Methylene chloride 1.20E-06 2.43E-08 8.84E-09 1.68E-08 6.46E-09

06037429 Phenol 1.78E-07 1.78E-08 1.16E-08 2.71E-08 5.42E-09

06037429 Methanol 4.56E-08 1.85E-09 4.73E-10 4.91E-09 9.82E-10

06037490 Toluene 2.57E-04 1.27E-05 2.11E-06 5.00E-05 8.63E-06

06037490 Xylenes (mixed) 1.35E-05 5.32E-07 7.44E-08 0.00E+00 0.00E+00

06037490 Triethylamine 1.38E-07 0.00E+00 0.00E+00 0.00E+00 0.00E+00

06037490 Ethyl benzene 0.00E+00 9.20E-08 2.68E-09 0.00E+00 0.00E+00

06037490 Cumene 0.00E+00 1.55E-08 2.58E-09 0.00E+00 0.00E+00

06059538 Tetrachloroethene 2.50E-04 2.09E-05 3.13E-06 7.37E-06 3.58E-06

06065520 Xylenes (mixed) 2.88E-05 1.13E-06 1.58E-07 0.00E+00 0.00E+00

06065520 Toluene 1.47E-05 7.26E-07 1.21E-07 2.87E-06 4.95E-07

06065520 Formaldehyde 7.09E-06 3.55E-07 2.29E-08 3.25E-07 3.25E-08

06065520 Ethyl benzene 0.00E+00 3.13E-06 9.13E-08 0.00E+00 0.00E+00

06065520 Vinyl acetate 0.00E+00 1.10E-07 4.17E-09 1.46E-07 1.01E-08

06065520 Methanol 1.56E-08 6.33E-10 1.62E-10 1.68E-09 3.36E-10

06065520 Phenol 3.56E-09 3.56E-10 2.32E-10 5.43E-10 1.09E-10

06065520 1,4-Dioxane 5.71E-10 2.81E-11 1.43E-12 0.00E+00 0.00E+00

06065520 Cumene 0.00E+00 6.09E-12 1.02E-12 0.00E+00 0.00E+00

06071540 Tetrachloroethene 4.27E-02 3.56E-03 5.34E-04 1.26E-03 6.10E-04

06071540 Xylenes (mixed) 1.39E-03 5.46E-05 7.65E-06 0.00E+00 0.00E+00

06071540 Toluene 1.91E-04 9.42E-06 1.57E-06 3.72E-05 6.42E-06

06071540 Ethyl benzene 0.00E+00 1.43E-09 4.17E-11 0.00E+00 0.00E+00

06073306 2,4-Toluene diisocyanate 0.00E+00 1.76E-05 4.17E-06 3.46E-05 2.24E-06

06073306 Toluene 1.54E-05 7.59E-07 1.27E-07 3.00E-06 5.18E-07

06073306 Dimethyl formamide 0.00E+00 0.00E+00 2.76E-07 1.24E-05 2.48E-07

06073306 Methylene chloride 4.35E-06 8.82E-08 3.20E-08 6.08E-08 2.34E-08

06073306 Xylenes (mixed) 4.07E-06 1.60E-07 2.24E-08 0.00E+00 0.00E+00

06073306 Methanol 2.15E-06 8.73E-08 2.23E-08 2.32E-07 4.64E-08

06073306 Hydrofluoric acid 6.77E-07 1.98E-07 8.13E-09 1.02E-07 1.02E-08

06073306 Methyl methacrylate 0.00E+00 1.12E-07 1.60E-08 0.00E+00 0.00E+00

 3 of 27
Facility NEI ID Pollutant REL AEGL1 AEGL2 ERPG1 ERPG2

Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient1

06073306 Ethyl benzene 0.00E+00 3.94E-08 1.15E-09 0.00E+00 0.00E+00

06073306 Styrene 2.11E-08 5.21E-09 8.06E-10 2.11E-09 4.03E-10

06073306 Phenol 3.39E-09 3.39E-10 2.21E-10 5.17E-10 1.03E-10

06073306 n-Hexane 0.00E+00 0.00E+00 3.39E-09 0.00E+00 0.00E+00

09001684 Methanol 1.84E-05 7.46E-07 1.91E-07 1.98E-06 3.96E-07

09001688 Toluene 1.30E-05 6.43E-07 1.07E-07 2.54E-06 4.39E-07

09001688 Methanol 2.80E-06 1.13E-07 2.90E-08 3.01E-07 6.02E-08

09001693 2-Butoxyethyl acetate 1.77E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00

09001693 Butyl carbitol acetate 1.19E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00

09001693 Phenol 2.62E-04 2.62E-05 1.71E-05 4.00E-05 8.00E-06

09001693 Methylene chloride 3.37E-04 6.84E-06 2.48E-06 4.72E-06 1.82E-06

09001693 Formaldehyde 2.87E-04 1.43E-05 9.27E-07 1.31E-05 1.31E-06

09001693 Xylenes (mixed) 2.16E-04 8.48E-06 1.19E-06 0.00E+00 0.00E+00

09001693 Diethylene glycol monobutyl ether 2.07E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

09001693 Toluene 1.40E-04 6.89E-06 1.15E-06 2.72E-05 4.70E-06

09001693 Ethylene glycol ethyl ether acetate 1.38E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

09001693 Methanol 6.99E-06 2.84E-07 7.25E-08 7.53E-07 1.51E-07

09001693 Ethylene glycol ethyl ether 1.10E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00

09001693 Ethyl benzene 0.00E+00 6.03E-07 1.76E-08 0.00E+00 0.00E+00

09001693 Cumene 0.00E+00 2.42E-08 4.04E-09 0.00E+00 0.00E+00

09001693 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 1.79E-08

09001693 n-Hexane 0.00E+00 0.00E+00 5.25E-11 0.00E+00 0.00E+00

09003626 2-Butoxyethyl acetate 2.79E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

09003626 Ethylene glycol ethyl ether 2.56E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

09003626 Butyl carbitol acetate 3.03E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

09003626 Ethylene glycol methyl ether 2.53E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

09003626 Methylene chloride 2.17E-05 4.39E-07 1.60E-07 3.03E-07 1.17E-07

09003626 Formaldehyde 1.43E-05 7.15E-07 4.63E-08 6.55E-07 6.55E-08

09003626 Phenol 1.05E-05 1.05E-06 6.83E-07 1.60E-06 3.20E-07

09003626 Xylenes (mixed) 1.07E-05 4.19E-07 5.86E-08 0.00E+00 0.00E+00

09003626 Toluene 6.52E-06 3.22E-07 5.36E-08 1.27E-06 2.19E-07

09003626 Methanol 1.68E-06 6.83E-08 1.74E-08 1.81E-07 3.62E-08

09003626 Ethyl benzene 0.00E+00 1.67E-07 4.86E-09 0.00E+00 0.00E+00

09003626 Tetrachloroethene 1.08E-07 9.00E-09 1.35E-09 3.18E-09 1.54E-09

09003626 Arsenic compounds 3.89E-08 0.00E+00 0.00E+00 0.00E+00 0.00E+00

09003626 Cumene 0.00E+00 2.34E-08 3.90E-09 0.00E+00 0.00E+00

09003626 Beryllium compounds 0.00E+00 0.00E+00 0.00E+00 0.00E+00 1.00E-11

10003349 Xylenes (mixed) 2.62E-05 1.03E-06 1.44E-07 0.00E+00 0.00E+00

10003349 Styrene 1.77E-06 4.38E-07 6.78E-08 1.77E-07 3.39E-08

10003349 Methanol 6.30E-07 2.55E-08 6.53E-09 6.78E-08 1.36E-08

10003349 Ethyl benzene 0.00E+00 6.64E-07 1.94E-08 0.00E+00 0.00E+00

10003349 Arsenic compounds 2.16E-07 0.00E+00 0.00E+00 0.00E+00 0.00E+00

10003349 Toluene 3.34E-08 1.65E-09 2.75E-10 6.51E-09 1.12E-09

010515017 2-Butoxyethyl acetate 3.73E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

010515017 Methanol 4.26E-04 1.73E-05 4.41E-06 4.58E-05 9.17E-06

010515017 Xylenes (mixed) 1.80E-04 7.06E-06 9.89E-07 0.00E+00 0.00E+00

010515017 Ethylene glycol ethyl ether acetate 8.47E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

010515017 Toluene 5.44E-05 2.69E-06 4.48E-07 1.06E-05 1.83E-06

010515017 Methylene chloride 5.04E-06 1.02E-07 3.72E-08 7.06E-08 2.71E-08

010515017 Phenol 2.43E-06 2.43E-07 1.59E-07 3.72E-07 7.43E-08

010515017 Acetaldehyde 1.22E-06 7.08E-09 1.17E-09 3.19E-08 1.59E-09

010515017 Ethyl benzene 0.00E+00 5.49E-07 1.60E-08 0.00E+00 0.00E+00

 4 of 27
Facility NEI ID Pollutant REL AEGL1 AEGL2 ERPG1 ERPG2

Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient1

010515017 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 3.63E-07

010515017 Ethyl acrylate 0.00E+00 4.04E-10 9.15E-11 3.35E-07 1.14E-10

010515017 1,2-Epoxybutane 0.00E+00 8.99E-08 4.61E-08 0.00E+00 0.00E+00

010515017 Cumene 0.00E+00 2.30E-09 3.84E-10 0.00E+00 0.00E+00

010892023 Phenol 1.73E-01 1.73E-02 1.13E-02 2.65E-02 5.29E-03

010892023 Methylene chloride 4.95E-02 1.00E-03 3.65E-04 6.93E-04 2.66E-04

010892023 Xylenes (mixed) 2.72E-04 1.07E-05 1.50E-06 0.00E+00 0.00E+00

010892023 Ethyl benzene 0.00E+00 5.54E-06 1.62E-07 0.00E+00 0.00E+00

12005313 Butyl carbitol acetate 3.74E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

12005313 Diethylene glycol dimethyl ether 8.60E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

12005313 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 2.48E-04

12005313 Xylenes (mixed) 9.54E-05 3.75E-06 5.25E-07 0.00E+00 0.00E+00

12005313 Toluene 1.18E-05 5.85E-07 9.74E-08 2.31E-06 3.99E-07

12005313 Ethyl benzene 0.00E+00 1.18E-05 3.44E-07 0.00E+00 0.00E+00

12005313 Formaldehyde 1.03E-05 5.17E-07 3.34E-08 4.74E-07 4.74E-08

12005313 Triethylamine 3.11E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00

12005313 Cumene 0.00E+00 1.13E-08 1.88E-09 0.00E+00 0.00E+00

12009358 Phenol 2.71E-06 2.71E-07 1.76E-07 4.13E-07 8.26E-08

12009358 Methylene chloride 2.17E-06 4.41E-08 1.60E-08 3.04E-08 1.17E-08

12009358 Xylenes (mixed) 9.99E-07 3.92E-08 5.49E-09 0.00E+00 0.00E+00

12009358 Toluene 3.51E-07 1.73E-08 2.88E-09 6.83E-08 1.18E-08

12009358 Methanol 2.67E-07 1.08E-08 2.77E-09 2.87E-08 5.75E-09

12009358 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 2.33E-07

12009358 Ethyl benzene 0.00E+00 5.71E-08 1.67E-09 0.00E+00 0.00E+00

12009358 n-Hexane 0.00E+00 0.00E+00 4.79E-11 0.00E+00 0.00E+00

12023284 Glycol Ethers 7.97E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00

12023284 Toluene 3.45E-03 1.70E-04 2.84E-05 6.72E-04 1.16E-04

12023284 Methylene chloride 1.32E-03 2.68E-05 9.75E-06 1.85E-05 7.13E-06

12023284 Phenol 7.10E-04 7.10E-05 4.63E-05 1.08E-04 2.17E-05

12023284 Xylenes (mixed) 3.00E-04 1.18E-05 1.65E-06 0.00E+00 0.00E+00

12023284 Styrene 4.87E-05 1.20E-05 1.86E-06 4.87E-06 9.31E-07

12023284 Methanol 9.11E-06 3.70E-07 9.45E-08 9.81E-07 1.96E-07

12023284 Ethyl benzene 0.00E+00 2.64E-06 7.69E-08 0.00E+00 0.00E+00

12023284 n-Hexane 0.00E+00 0.00E+00 1.01E-07 0.00E+00 0.00E+00

12031634 Butyl carbitol acetate 2.13E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

12031634 Toluene 1.11E-04 5.50E-06 9.16E-07 2.17E-05 3.75E-06

12031634 Formaldehyde 2.70E-05 1.35E-06 8.73E-08 1.24E-06 1.24E-07

12031634 Xylenes (mixed) 2.01E-05 7.88E-07 1.10E-07 0.00E+00 0.00E+00

12031634 Phenol 1.07E-05 1.07E-06 6.98E-07 1.63E-06 3.27E-07

12031634 Methanol 2.56E-06 1.04E-07 2.66E-08 2.76E-07 5.52E-08

12031634 Ethyl benzene 0.00E+00 6.26E-07 1.82E-08 0.00E+00 0.00E+00

12031634 Methylene chloride 3.86E-07 7.84E-09 2.85E-09 5.41E-09 2.08E-09

12031634 1,1,1-Trichloroethane 2.51E-07 1.31E-08 5.17E-09 8.98E-09 4.49E-09

12031634 Styrene 4.33E-08 1.07E-08 1.65E-09 4.33E-09 8.26E-10

12031634 n-Hexane 0.00E+00 0.00E+00 3.35E-08 0.00E+00 0.00E+00

12031634 Cumene 0.00E+00 2.19E-08 3.65E-09 0.00E+00 0.00E+00

12031634 Benzene 1.34E-08 1.02E-10 6.69E-12 1.09E-10 3.62E-11

12061380 Ethylene glycol ethyl ether 4.24E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00

12061380 2-Butoxyethyl acetate 2.39E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00

12061380 Toluene 9.15E-04 4.51E-05 7.52E-06 1.78E-04 3.08E-05

12061380 Formaldehyde 1.58E-04 7.90E-06 5.11E-07 7.24E-06 7.24E-07

12061380 Methanol 1.86E-05 7.56E-07 1.93E-07 2.01E-06 4.01E-07

 5 of 27
Facility NEI ID Pollutant REL AEGL1 AEGL2 ERPG1 ERPG2

Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient1

12061380 Ethyl benzene 0.00E+00 3.66E-06 1.07E-07 0.00E+00 0.00E+00

12061380 Arsenic compounds 1.24E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00

12061380 Cumene 0.00E+00 5.97E-09 9.96E-10 0.00E+00 0.00E+00

12083270 Formaldehyde 2.06E-05 1.03E-06 6.65E-08 9.42E-07 9.42E-08

12083270 Ethyl acrylate 0.00E+00 6.05E-09 1.37E-09 5.01E-06 1.71E-09

12083270 Xylenes (mixed) 3.11E-07 1.22E-08 1.71E-09 0.00E+00 0.00E+00

12083270 Toluene 2.44E-07 1.20E-08 2.01E-09 4.75E-08 8.21E-09

12083270 Ethyl benzene 0.00E+00 1.19E-08 3.46E-10 0.00E+00 0.00E+00

12083270 Cumene 0.00E+00 2.21E-10 3.69E-11 0.00E+00 0.00E+00

12099695 Methanol 2.46E-06 9.99E-08 2.55E-08 2.65E-07 5.30E-08

13009224 Formaldehyde 5.87E-03 2.94E-04 1.90E-05 2.69E-04 2.69E-05

13009224 Toluene 1.98E-03 9.75E-05 1.62E-05 3.85E-04 6.65E-05

13009224 Xylenes (mixed) 1.72E-03 6.75E-05 9.46E-06 0.00E+00 0.00E+00

13009224 Epichlorohydrin 2.45E-04 1.45E-05 3.51E-06 1.68E-05 4.20E-06

13009224 Ethyl benzene 0.00E+00 3.94E-05 1.15E-06 0.00E+00 0.00E+00

13009224 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 3.79E-05

13009224 Methanol 1.52E-05 6.17E-07 1.58E-07 1.64E-06 3.27E-07

13009224 Phenol 2.65E-06 2.65E-07 1.73E-07 4.04E-07 8.08E-08

13009224 Styrene 1.39E-06 3.44E-07 5.31E-08 1.39E-07 2.66E-08

13009224 Arsenic compounds 1.72E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00

13009224 Beryllium compounds 0.00E+00 0.00E+00 0.00E+00 0.00E+00 1.98E-10

13063388 Butyl carbitol acetate 2.39E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

13063388 Diethylene glycol monobutyl ether 2.38E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

13063388 Ethylene glycol ethyl ether 5.34E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

13063388 Ethylene glycol methyl ether 5.20E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

13063388 Formaldehyde 1.46E-04 7.32E-06 4.73E-07 6.71E-06 6.71E-07

13063388 Arsenic compounds 1.19E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

13063388 Toluene 7.40E-05 3.65E-06 6.09E-07 1.44E-05 2.49E-06

13063388 Xylenes (mixed) 8.60E-05 3.38E-06 4.73E-07 0.00E+00 0.00E+00

13063388 Ethylene glycol ethyl ether acetate 3.19E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

13063388 Phenol 1.88E-05 1.88E-06 1.23E-06 2.88E-06 5.75E-07

13063388 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 1.39E-05

13063388 Methylene chloride 9.67E-06 1.96E-07 7.13E-08 1.35E-07 5.21E-08

13063388 1,2-Dimethoxyethane 7.12E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00

13063388 Styrene 4.79E-06 1.18E-06 1.83E-07 4.79E-07 9.14E-08

13063388 2,4-Toluene diisocyanate 0.00E+00 1.92E-06 4.55E-07 3.78E-06 2.44E-07

13063388 Methanol 4.93E-06 2.00E-07 5.12E-08 5.31E-07 1.06E-07

13063388 Carbitol acetate 5.33E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00

13063388 2-Butoxyethyl acetate 4.13E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00

13063388 2-(Hexyloxy)ethanol 3.24E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00

13063388 Ethyl benzene 0.00E+00 2.66E-06 7.76E-08 0.00E+00 0.00E+00

13063388 Acrylic acid 1.97E-07 2.69E-07 8.45E-09 3.94E-07 7.88E-09

13063388 Benzene 7.54E-07 5.77E-09 3.77E-10 6.13E-09 2.04E-09

13063388 Epichlorohydrin 4.17E-07 2.47E-08 5.96E-09 2.85E-08 7.14E-09

13063388 Methyl methacrylate 0.00E+00 8.84E-08 1.26E-08 0.00E+00 0.00E+00

13063388 m-Xylene 8.74E-08 0.00E+00 0.00E+00 0.00E+00 0.00E+00

13063388 Cumene 0.00E+00 4.55E-08 7.58E-09 0.00E+00 0.00E+00

13063388 o-Xylene 3.49E-08 0.00E+00 0.00E+00 0.00E+00 0.00E+00

13063388 Chloroform 2.79E-08 0.00E+00 1.35E-11 0.00E+00 1.75E-11

13063388 n-Hexane 0.00E+00 0.00E+00 5.22E-09 0.00E+00 0.00E+00

13063388 Trichloroethylene 0.00E+00 1.68E-09 4.91E-10 2.18E-09 4.37E-10

13063388 Carbon tetrachloride 3.19E-09 2.16E-11 5.05E-12 4.66E-11 9.61E-12

 6 of 27
Facility NEI ID Pollutant REL AEGL1 AEGL2 ERPG1 ERPG2

Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient1

13063388 Beryllium compounds 0.00E+00 0.00E+00 0.00E+00 0.00E+00 1.37E-09

13063388 Triethylamine 4.83E-10 0.00E+00 0.00E+00 0.00E+00 0.00E+00

13063388 Aniline 0.00E+00 7.60E-12 4.95E-12 0.00E+00 0.00E+00

13063388 1,4-Dioxane 4.06E-12 1.99E-13 1.00E-14 0.00E+00 0.00E+00

13063388 Propylene oxide 3.93E-12 7.20E-14 1.80E-14 1.01E-13 2.10E-14

13063388 Ethylene oxide 0.00E+00 0.00E+00 1.50E-13 0.00E+00 1.35E-13

13067506 Propyl cellosolve 5.76E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

13067506 2-Butoxyethyl acetate 2.60E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

13067506 Diethylene glycol monobutyl ether 1.47E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

13067506 Xylenes (mixed) 6.13E-04 2.41E-05 3.37E-06 0.00E+00 0.00E+00

13067506 Diethylene glycol monomethyl ether3.93E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

13067506 Butyl carbitol acetate 2.42E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

13067506 Toluene 1.00E-04 4.95E-06 8.26E-07 1.96E-05 3.38E-06

13067506 Triethylamine 1.13E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

13067506 Ethylene glycol methyl ether 3.34E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

13067506 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 2.94E-05

13067506 Arsenic compounds 1.55E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

13067506 Phenol 8.53E-06 8.53E-07 5.56E-07 1.30E-06 2.60E-07

13067506 Methanol 3.64E-06 1.48E-07 3.77E-08 3.92E-07 7.84E-08

13067506 Formaldehyde 2.33E-06 1.16E-07 7.54E-09 1.07E-07 1.07E-08

13067506 Ethyl benzene 0.00E+00 1.39E-06 4.05E-08 0.00E+00 0.00E+00

13067506 Carbitol acetate 1.37E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00

13067506 Methyl methacrylate 0.00E+00 2.05E-07 2.94E-08 0.00E+00 0.00E+00

13067506 Propylene oxide 5.23E-08 9.53E-10 2.35E-10 1.35E-09 2.75E-10

13067506 Carbon tetrachloride 2.92E-08 1.98E-10 4.63E-11 4.27E-10 8.82E-11

13067506 n-Hexane 0.00E+00 0.00E+00 2.35E-08 0.00E+00 0.00E+00

13067506 Beryllium compounds 0.00E+00 0.00E+00 0.00E+00 0.00E+00 8.29E-09

13067506 Cumene 0.00E+00 6.40E-09 1.07E-09 0.00E+00 0.00E+00

13067506 p-Xylene 1.97E-09 0.00E+00 0.00E+00 0.00E+00 0.00E+00

13153431 Butyl carbitol acetate 5.01E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

13153431 2,4-Toluene diisocyanate 0.00E+00 2.60E-04 6.17E-05 5.13E-04 3.31E-05

13153431 Phenol 3.65E-04 3.65E-05 2.38E-05 5.57E-05 1.11E-05

13153431 Methylene chloride 4.54E-04 9.21E-06 3.34E-06 6.35E-06 2.44E-06

13153431 2-Butoxyethyl acetate 3.89E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

13153431 Xylenes (mixed) 2.29E-04 9.01E-06 1.26E-06 0.00E+00 0.00E+00

13153431 Toluene 8.99E-05 4.44E-06 7.40E-07 1.75E-05 3.03E-06

13153431 Methanol 3.91E-05 1.59E-06 4.05E-07 4.21E-06 8.42E-07

13153431 Ethyl acrylate 0.00E+00 4.79E-08 1.09E-08 3.97E-05 1.36E-08

13153431 Formaldehyde 2.96E-05 1.48E-06 9.58E-08 1.36E-06 1.36E-07

13153431 Ethyl benzene 0.00E+00 2.28E-05 6.66E-07 0.00E+00 0.00E+00

13153431 Benzene 1.29E-06 9.85E-09 6.44E-10 1.05E-08 3.49E-09

13153431 Cumene 0.00E+00 1.63E-07 2.72E-08 0.00E+00 0.00E+00

13153431 Ethylene oxide 0.00E+00 0.00E+00 2.01E-08 0.00E+00 1.81E-08

15003346 Toluene 2.38E-06 1.18E-07 1.96E-08 4.64E-07 8.02E-08

15003346 Xylenes (mixed) 6.14E-07 2.41E-08 3.38E-09 0.00E+00 0.00E+00

15003346 Ethyl benzene 0.00E+00 4.82E-08 1.41E-09 0.00E+00 0.00E+00

18097487 Phenol 7.56E-04 7.56E-05 4.93E-05 1.15E-04 2.31E-05

18097487 Methylene chloride 6.14E-04 1.24E-05 4.52E-06 8.59E-06 3.30E-06

18097487 Toluene 4.37E-05 2.16E-06 3.59E-07 8.51E-06 1.47E-06

18097487 Formaldehyde 6.48E-06 3.24E-07 2.10E-08 2.97E-07 2.97E-08

18097487 1,4-Dioxane 2.06E-06 1.01E-07 5.16E-09 0.00E+00 0.00E+00

18097487 Xylenes (mixed) 4.31E-07 1.69E-08 2.37E-09 0.00E+00 0.00E+00

 7 of 27
Facility NEI ID Pollutant REL AEGL1 AEGL2 ERPG1 ERPG2

Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient1

18097487 Methanol 2.85E-07 1.15E-08 2.95E-09 3.06E-08 6.13E-09

18097487 Ethyl benzene 0.00E+00 2.94E-09 8.58E-11 0.00E+00 0.00E+00

19163356 Toluene 1.41E-04 6.98E-06 1.16E-06 2.75E-05 4.76E-06

19163356 Xylenes (mixed) 5.30E-06 2.08E-07 2.92E-08 0.00E+00 0.00E+00

19163356 Methanol 3.51E-06 1.42E-07 3.64E-08 3.78E-07 7.56E-08

19163356 Formaldehyde 3.02E-06 1.51E-07 9.77E-09 1.38E-07 1.38E-08

19163356 n-Hexane 0.00E+00 0.00E+00 3.10E-07 0.00E+00 0.00E+00

19163356 Ethyl benzene 0.00E+00 5.56E-08 1.62E-09 0.00E+00 0.00E+00

19163356 Cumene 0.00E+00 8.07E-09 1.34E-09 0.00E+00 0.00E+00

20035486 Diethylene glycol monobutyl ether 9.73E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00

20035486 2-Butoxyethyl acetate 1.85E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00

20035486 2,4-Toluene diisocyanate 0.00E+00 2.63E-03 6.24E-04 5.19E-03 3.35E-04

20035486 Formaldehyde 3.15E-03 1.58E-04 1.02E-05 1.44E-04 1.44E-05

20035486 Xylenes (mixed) 3.10E-03 1.22E-04 1.71E-05 0.00E+00 0.00E+00

20035486 Acrylic acid 6.17E-04 8.41E-04 2.64E-05 1.23E-03 2.47E-05

20035486 Toluene 1.03E-03 5.06E-05 8.43E-06 2.00E-04 3.45E-05

20035486 Ethylene glycol methyl ether 4.38E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

20035486 Phenol 1.66E-04 1.66E-05 1.08E-05 2.53E-05 5.06E-06

20035486 Methanol 1.34E-04 5.42E-06 1.38E-06 1.44E-05 2.88E-06

20035486 Ethyl benzene 0.00E+00 9.24E-05 2.69E-06 0.00E+00 0.00E+00

20035486 Acrylonitrile 0.00E+00 1.59E-08 1.22E-09 7.21E-09 2.06E-09

20035486 n-Hexane 0.00E+00 0.00E+00 6.35E-11 0.00E+00 0.00E+00

20173193 Ethyl benzene 0.00E+00 1.32E-07 3.85E-09 0.00E+00 0.00E+00

20173193 Xylenes (mixed) 1.30E-07 5.10E-09 7.14E-10 0.00E+00 0.00E+00

20173193 Cumene 0.00E+00 1.62E-08 2.70E-09 0.00E+00 0.00E+00

20173453 2-Butoxyethyl acetate 1.72E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

20173453 Xylenes (mixed) 2.63E-04 1.03E-05 1.45E-06 0.00E+00 0.00E+00

20173453 Toluene 1.48E-04 7.31E-06 1.22E-06 2.89E-05 4.99E-06

20173453 Ethyl benzene 0.00E+00 2.55E-06 7.44E-08 0.00E+00 0.00E+00

20173453 Methyl methacrylate 0.00E+00 1.75E-06 2.50E-07 0.00E+00 0.00E+00

20173453 Methanol 1.01E-07 4.10E-09 1.05E-09 1.09E-08 2.18E-09

24005458 Ethylene glycol methyl ether 2.31E-01 0.00E+00 0.00E+00 0.00E+00 0.00E+00

24005458 Propyl cellosolve 9.23E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

24005458 Dimethyl formamide 0.00E+00 0.00E+00 2.66E-05 1.20E-03 2.39E-05

24005458 Glycol ethers 7.73E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

24005458 Butyl carbitol acetate 7.29E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

24005458 Toluene 1.37E-04 6.78E-06 1.13E-06 2.68E-05 4.62E-06

24005458 Xylenes (mixed) 5.03E-05 1.98E-06 2.77E-07 0.00E+00 0.00E+00

24005458 Mercury (elemental) 6.40E-06 0.00E+00 2.26E-09 0.00E+00 1.92E-09

24005458 Methanol 1.63E-06 6.62E-08 1.69E-08 1.76E-07 3.51E-08

24005458 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 5.11E-07

24005458 Ethyl benzene 0.00E+00 2.74E-09 8.00E-11 0.00E+00 0.00E+00

24015282 Tetrachloroethene 2.99E-04 2.49E-05 3.74E-06 8.80E-06 4.27E-06

24015282 Xylenes (mixed) 8.63E-06 3.39E-07 4.74E-08 0.00E+00 0.00E+00

24015282 Toluene 4.15E-06 2.05E-07 3.41E-08 8.08E-07 1.39E-07

24015282 Ethyl benzene 0.00E+00 7.31E-08 2.13E-09 0.00E+00 0.00E+00

24015282 Cumene 0.00E+00 2.98E-10 4.97E-11 0.00E+00 0.00E+00

24017525 Xylenes (mixed) 5.66E-05 2.22E-06 3.11E-07 0.00E+00 0.00E+00

24017525 Ethyl benzene 0.00E+00 2.53E-06 7.37E-08 0.00E+00 0.00E+00

24017525 Toluene 1.40E-06 6.89E-08 1.15E-08 2.72E-07 4.70E-08

24017525 Cumene 0.00E+00 2.83E-07 4.72E-08 0.00E+00 0.00E+00

25009450 Toluene 6.80E-06 3.36E-07 5.59E-08 1.33E-06 2.29E-07

 8 of 27
Facility NEI ID Pollutant REL AEGL1 AEGL2 ERPG1 ERPG2

Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient1

25009450 Ethylene glycol methyl ether 5.05E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00

25009450 Diethylene glycol monobutyl ether 4.26E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00

25009450 Xylenes (mixed) 1.06E-06 4.16E-08 5.82E-09 0.00E+00 0.00E+00

25009450 Methylene chloride 4.77E-07 9.68E-09 3.51E-09 6.68E-09 2.57E-09

25009450 Methanol 1.31E-08 5.32E-10 1.36E-10 1.41E-09 2.82E-10

25009450 Ethyl benzene 0.00E+00 9.91E-09 2.89E-10 0.00E+00 0.00E+00

25009450 n-Hexane 0.00E+00 0.00E+00 1.08E-09 0.00E+00 0.00E+00

25009477 Diethylene glycol monobutyl ether 2.95E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

25009477 Glycol ethers 7.70E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

25009477 Toluene 1.03E-05 5.07E-07 8.45E-08 2.00E-06 3.46E-07

25009477 Methanol 1.01E-05 4.09E-07 1.05E-07 1.09E-06 2.17E-07

25009477 Formaldehyde 4.76E-07 2.38E-08 1.54E-09 2.18E-08 2.18E-09

25009477 Xylenes (mixed) 2.34E-08 9.19E-10 1.29E-10 0.00E+00 0.00E+00

25009477 Ethyl benzene 0.00E+00 7.49E-10 2.19E-11 0.00E+00 0.00E+00

26065644 2,4-Toluene diisocyanate 0.00E+00 2.88E-04 6.84E-05 5.68E-04 3.67E-05

26065644 Methanol 1.32E-04 5.36E-06 1.37E-06 1.42E-05 2.84E-06

26065644 Xylenes (mixed) 6.18E-05 2.43E-06 3.40E-07 0.00E+00 0.00E+00

26065644 Toluene 2.04E-05 1.01E-06 1.68E-07 3.97E-06 6.85E-07

26065644 Ethyl benzene 0.00E+00 1.10E-05 3.21E-07 0.00E+00 0.00E+00

26065644 Formaldehyde 1.40E-06 7.00E-08 4.53E-09 6.42E-08 6.42E-09

27053389 Diethylene glycol monobutyl ether 2.77E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

27053389 Diethylene glycol monomethyl ether2.17E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

27053389 Ethylene glycol ethyl ether acetate 1.87E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

27053389 2-Butoxyethyl acetate 1.52E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

27053389 2,4-Toluene diisocyanate 0.00E+00 4.34E-06 1.03E-06 8.56E-06 5.52E-07

27053389 Xylenes (mixed) 1.04E-05 4.08E-07 5.71E-08 0.00E+00 0.00E+00

27053389 Toluene 4.66E-06 2.30E-07 3.83E-08 9.07E-07 1.57E-07

27053389 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 1.15E-06

27053389 Methanol 2.06E-07 8.35E-09 2.13E-09 2.22E-08 4.43E-09

27053389 Phenol 1.62E-07 1.62E-08 1.06E-08 2.48E-08 4.96E-09

27053389 Ethyl benzene 0.00E+00 1.73E-07 5.05E-09 0.00E+00 0.00E+00

27053389 Styrene 8.74E-08 2.16E-08 3.34E-09 8.74E-09 1.67E-09

27053389 Propylene oxide 9.66E-08 1.76E-09 4.34E-10 2.49E-09 5.07E-10

27053389 Formaldehyde 5.49E-08 2.75E-09 1.78E-10 2.52E-09 2.52E-10

27053389 Methyl methacrylate 0.00E+00 3.70E-08 5.29E-09 0.00E+00 0.00E+00

27053389 Acrylic acid 2.46E-09 3.36E-09 1.05E-10 4.92E-09 9.84E-11

27053389 n-Hexane 0.00E+00 0.00E+00 3.00E-09 0.00E+00 0.00E+00

27053389 Cumene 0.00E+00 9.17E-12 1.53E-12 0.00E+00 0.00E+00

28089367 Glycol Ethers 1.06E-01 0.00E+00 0.00E+00 0.00E+00 0.00E+00

28089367 Xylenes (mixed) 1.68E-03 6.61E-05 9.25E-06 0.00E+00 0.00E+00

28089367 Toluene 9.24E-04 4.56E-05 7.59E-06 1.80E-04 3.11E-05

28089367 Ethyl benzene 0.00E+00 4.79E-05 1.40E-06 0.00E+00 0.00E+00

28089367 Methanol 8.92E-06 3.62E-07 9.25E-08 9.60E-07 1.92E-07

29183329 Butyl carbitol acetate 9.22E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

29183329 Xylenes (mixed) 3.57E-04 1.40E-05 1.97E-06 0.00E+00 0.00E+00

29183329 Glycol Ethers 7.81E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

29183329 Methanol 2.02E-05 8.20E-07 2.09E-07 2.17E-06 4.35E-07

29183329 Toluene 1.29E-05 6.36E-07 1.06E-07 2.51E-06 4.34E-07

29183329 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 1.58E-05

29183329 Carbitol acetate 5.34E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00

29183329 Phenol 3.61E-06 3.61E-07 2.35E-07 5.51E-07 1.10E-07

29183329 Cumene 0.00E+00 3.06E-06 5.10E-07 0.00E+00 0.00E+00

 9 of 27
Facility NEI ID Pollutant REL AEGL1 AEGL2 ERPG1 ERPG2

Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient1

29183329 Ethylene glycol methyl ether 3.52E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00

29183329 Propyl cellosolve 2.96E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00

29183329 p-Xylene 2.82E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00

29183329 Formaldehyde 1.51E-06 7.56E-08 4.89E-09 6.93E-08 6.93E-09

29183329 Ethylene glycol ethyl ether acetate 9.18E-07 0.00E+00 0.00E+00 0.00E+00 0.00E+00

29183329 Chlorobenzene 0.00E+00 3.23E-07 2.15E-08 0.00E+00 0.00E+00

29183329 Ethyl benzene 0.00E+00 2.00E-07 5.83E-09 0.00E+00 0.00E+00

29183329 Acrylic acid 1.85E-08 2.52E-08 7.92E-10 3.70E-08 7.39E-10

29183329 Ethylene glycol ethyl ether 5.95E-08 0.00E+00 0.00E+00 0.00E+00 0.00E+00

29183329 Ethyl acrylate 0.00E+00 3.78E-12 8.56E-13 3.13E-09 1.07E-12

29183329 Epichlorohydrin 2.48E-09 1.46E-10 3.54E-11 1.69E-10 4.23E-11

29183329 Benzene 9.06E-10 6.93E-12 4.53E-13 7.36E-12 2.45E-12

29183329 Arsenic compounds 5.60E-10 0.00E+00 0.00E+00 0.00E+00 0.00E+00

29183329 n-Hexane 0.00E+00 0.00E+00 3.60E-10 0.00E+00 0.00E+00

29183329 1,3-Butadiene 0.00E+00 1.70E-14 2.00E-15 1.18E-12 5.90E-14

29189332 2,4-Toluene diisocyanate 0.00E+00 6.06E-04 1.44E-04 1.19E-03 7.71E-05

29189332 Propyl cellosolve 7.59E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

29189332 Butyl carbitol acetate 6.15E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

29189332 Xylenes (mixed) 3.73E-04 1.46E-05 2.05E-06 0.00E+00 0.00E+00

29189332 Toluene 2.76E-04 1.36E-05 2.27E-06 5.38E-05 9.30E-06

29189332 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 2.07E-04

29189332 Methanol 1.39E-04 5.65E-06 1.44E-06 1.50E-05 3.00E-06

29189332 Diethylene glycol monobutyl ether 1.64E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

29189332 Formaldehyde 8.21E-05 4.11E-06 2.66E-07 3.77E-06 3.77E-07

29189332 Phenol 6.59E-05 6.59E-06 4.29E-06 1.01E-05 2.01E-06

29189332 Ethyl acrylate 0.00E+00 8.30E-08 1.88E-08 6.89E-05 2.35E-08

29189332 Methylene chloride 3.66E-05 7.43E-07 2.70E-07 5.12E-07 1.97E-07

29189332 Ethylene glycol methyl ether 2.16E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

29189332 Glycol Ethers 1.83E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

29189332 Ethylene glycol ethyl ether acetate 1.26E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

29189332 Diethylene glycol monoethyl ether 1.04E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

29189332 Carbitol acetate 7.99E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00

29189332 Methyl methacrylate 0.00E+00 4.38E-06 6.26E-07 0.00E+00 0.00E+00

29189332 Cumene 0.00E+00 2.83E-06 4.72E-07 0.00E+00 0.00E+00

29189332 Acrylic acid 3.58E-07 4.88E-07 1.53E-08 7.16E-07 1.43E-08

29189332 Styrene 1.04E-06 2.56E-07 3.96E-08 1.04E-07 1.98E-08

29189332 Chlorobenzene 0.00E+00 1.34E-06 8.92E-08 0.00E+00 0.00E+00

29189332 Ethyl benzene 0.00E+00 1.36E-06 3.98E-08 0.00E+00 0.00E+00

29189332 Triethylamine 1.28E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00

29189332 Ethylene glycol ethyl ether 8.15E-07 0.00E+00 0.00E+00 0.00E+00 0.00E+00

29189332 p-Xylene 4.42E-07 0.00E+00 0.00E+00 0.00E+00 0.00E+00

29189332 1,2-Epoxybutane 0.00E+00 2.53E-07 1.29E-07 0.00E+00 0.00E+00

29189332 Ethylene oxide 0.00E+00 0.00E+00 1.21E-08 0.00E+00 1.09E-08

29189332 n-Hexane 0.00E+00 0.00E+00 4.32E-09 0.00E+00 0.00E+00

29189332 Benzene 1.87E-09 1.43E-11 9.36E-13 1.52E-11 5.07E-12

29189332 Epichlorohydrin 3.30E-10 1.95E-11 4.72E-12 2.26E-11 5.65E-12

29189332 Acrylonitrile 0.00E+00 3.70E-14 3.00E-15 1.70E-14 5.00E-15

29189513 Toluene 8.01E-04 3.95E-05 6.59E-06 1.56E-04 2.69E-05

29189513 Diethylene glycol monobutyl ether 5.27E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

29189513 Glycol Ethers 1.33E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

29189513 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 1.07E-04

29189513 Methanol 2.51E-05 1.02E-06 2.60E-07 2.70E-06 5.40E-07

 10 of 27
Facility NEI ID Pollutant REL AEGL1 AEGL2 ERPG1 ERPG2

Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient1

29189513 Xylenes (mixed) 2.67E-05 1.05E-06 1.47E-07 0.00E+00 0.00E+00

29189513 Phenol 1.25E-05 1.25E-06 8.18E-07 1.92E-06 3.83E-07

29189513 Tetrachloroethene 1.15E-05 9.62E-07 1.44E-07 3.40E-07 1.65E-07

29189513 Methylene chloride 1.11E-05 2.25E-07 8.16E-08 1.55E-07 5.96E-08

29189513 Benzene 1.41E-06 1.08E-08 7.06E-10 1.15E-08 3.83E-09

29189513 Ethyl benzene 0.00E+00 5.39E-07 1.57E-08 0.00E+00 0.00E+00

29189513 n-Hexane 0.00E+00 0.00E+00 1.48E-07 0.00E+00 0.00E+00

33011652 Toluene 6.33E-05 3.12E-06 5.21E-07 1.23E-05 2.13E-06

33011652 Xylenes (mixed) 2.11E-05 8.30E-07 1.16E-07 0.00E+00 0.00E+00

33011652 Phenol 1.78E-06 1.78E-07 1.16E-07 2.72E-07 5.43E-08

33011652 Methylene chloride 1.84E-06 3.74E-08 1.36E-08 2.58E-08 9.93E-09

33011652 Ethyl benzene 0.00E+00 1.29E-06 3.75E-08 0.00E+00 0.00E+00

33011652 Formaldehyde 7.25E-07 3.62E-08 2.34E-09 3.32E-08 3.32E-09

33011652 Methanol 1.09E-08 4.41E-10 1.13E-10 1.17E-09 2.34E-10

33011652 Cumene 0.00E+00 6.77E-09 1.13E-09 0.00E+00 0.00E+00

33011652 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 3.63E-09

36103518 Xylenes (mixed) 1.76E-03 6.91E-05 9.67E-06 0.00E+00 0.00E+00

36103518 Toluene 7.90E-04 3.90E-05 6.49E-06 1.54E-04 2.66E-05

36103521 Xylenes (mixed) 9.26E-05 3.64E-06 5.09E-07 0.00E+00 0.00E+00

36103521 Toluene 2.94E-05 1.45E-06 2.42E-07 5.73E-06 9.90E-07

36103521 Ethyl benzene 0.00E+00 1.59E-06 4.65E-08 0.00E+00 0.00E+00

36111630 Glycol Ethers 6.54E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00

36111630 Xylenes (mixed) 1.66E-03 6.52E-05 9.13E-06 0.00E+00 0.00E+00

36111630 Toluene 1.05E-03 5.19E-05 8.65E-06 2.05E-04 3.54E-05

36111630 Formaldehyde 2.60E-04 1.30E-05 8.42E-07 1.19E-05 1.19E-06

36111630 Ethyl benzene 0.00E+00 8.74E-06 2.55E-07 0.00E+00 0.00E+00

37049483 Methanol 3.44E-05 1.40E-06 3.57E-07 3.71E-06 7.41E-07

37049483 Xylenes (mixed) 2.33E-05 9.15E-07 1.28E-07 0.00E+00 0.00E+00

37049483 Phenol 1.35E-05 1.35E-06 8.79E-07 2.06E-06 4.12E-07

37049483 Toluene 1.40E-05 6.92E-07 1.15E-07 2.73E-06 4.72E-07

37049483 Methylene chloride 1.27E-05 2.58E-07 9.35E-08 1.78E-07 6.84E-08

37049483 Methyl methacrylate 0.00E+00 6.22E-07 8.89E-08 0.00E+00 0.00E+00

37049483 Ethyl benzene 0.00E+00 3.86E-07 1.13E-08 0.00E+00 0.00E+00

37049483 Formaldehyde 2.94E-07 1.47E-08 9.50E-10 1.35E-08 1.35E-09

37049483 Benzene 7.51E-08 5.75E-10 3.76E-11 6.10E-10 2.03E-10

37049483 Cumene 0.00E+00 1.62E-08 2.69E-09 0.00E+00 0.00E+00

37051340 Xylenes (mixed) 1.42E-05 5.56E-07 7.79E-08 0.00E+00 0.00E+00

37051340 Toluene 2.89E-06 1.43E-07 2.38E-08 5.63E-07 9.73E-08

37051340 Ethyl benzene 0.00E+00 7.44E-07 2.17E-08 0.00E+00 0.00E+00

37051340 Cumene 0.00E+00 4.54E-10 7.57E-11 0.00E+00 0.00E+00

37119600 Xylenes (mixed) 3.48E-04 1.37E-05 1.91E-06 0.00E+00 0.00E+00

37119600 Toluene 2.77E-04 1.36E-05 2.27E-06 5.39E-05 9.30E-06

37119600 Formaldehyde 1.28E-04 6.39E-06 4.13E-07 5.86E-06 5.86E-07

37119600 Phenol 4.22E-05 4.22E-06 2.75E-06 6.44E-06 1.29E-06

37119600 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 2.85E-05

37119600 Ethyl benzene 0.00E+00 1.31E-05 3.83E-07 0.00E+00 0.00E+00

37119600 Hydrofluoric acid 3.47E-06 1.02E-06 4.17E-08 5.21E-07 5.21E-08

37119600 Styrene 2.96E-06 7.31E-07 1.13E-07 2.96E-07 5.65E-08

37119600 Methylene chloride 7.33E-07 1.49E-08 5.40E-09 1.03E-08 3.95E-09

37119600 Tetrachloroethene 5.13E-07 4.28E-08 6.41E-09 1.51E-08 7.33E-09

37119600 1,1,1-Trichloroethane 1.51E-07 7.89E-09 3.11E-09 5.40E-09 2.70E-09

37119600 Benzene 9.83E-08 7.52E-10 4.91E-11 7.99E-10 2.66E-10

 11 of 27
Facility NEI ID Pollutant REL AEGL1 AEGL2 ERPG1 ERPG2

Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient1

37119600 n-Hexane 0.00E+00 0.00E+00 1.62E-08 0.00E+00 0.00E+00

37119600 Arsenic compounds 6.05E-09 0.00E+00 0.00E+00 0.00E+00 0.00E+00

37119600 Methanol 2.55E-09 1.04E-10 2.65E-11 2.75E-10 5.49E-11

37133455 Toluene 4.25E-05 2.10E-06 3.50E-07 8.29E-06 1.43E-06

37133455 2,4-Toluene diisocyanate 0.00E+00 8.65E-06 2.05E-06 1.71E-05 1.10E-06

37133455 Xylenes (mixed) 1.54E-05 6.06E-07 8.49E-08 0.00E+00 0.00E+00

37133455 Ethyl benzene 0.00E+00 9.06E-07 2.64E-08 0.00E+00 0.00E+00

37133455 Cumene 0.00E+00 7.84E-07 1.31E-07 0.00E+00 0.00E+00

37133455 Arsenic compounds 1.42E-07 0.00E+00 0.00E+00 0.00E+00 0.00E+00

37133455 Methanol 7.96E-09 3.23E-10 8.26E-11 8.57E-10 1.71E-10

37133455 Mercury (elemental) 4.39E-10 0.00E+00 1.55E-13 0.00E+00 1.32E-13

39017343 Ethylene glycol ethyl ether 2.88E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

39017343 Butyl carbitol acetate 1.19E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

39017343 2-Butoxyethyl acetate 8.02E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

39017343 Propyl cellosolve 6.13E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

39017343 Toluene 2.18E-06 1.07E-07 1.79E-08 4.24E-07 7.33E-08

39017343 Xylenes (mixed) 1.91E-06 7.51E-08 1.05E-08 0.00E+00 0.00E+00

39017343 2,4-Toluene diisocyanate 0.00E+00 5.07E-07 1.20E-07 9.99E-07 6.45E-08

39017343 Methanol 1.27E-06 5.16E-08 1.32E-08 1.37E-07 2.74E-08

39017343 Ethyl benzene 0.00E+00 5.83E-08 1.70E-09 0.00E+00 0.00E+00

39017343 Arsenic compounds 2.42E-08 0.00E+00 0.00E+00 0.00E+00 0.00E+00

39021672 2-Butoxyethyl acetate 9.02E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

39021672 Xylenes (mixed) 5.50E-04 2.16E-05 3.02E-06 0.00E+00 0.00E+00

39021672 2,4-Toluene diisocyanate 0.00E+00 1.11E-04 2.64E-05 2.19E-04 1.42E-05

39021672 Formaldehyde 2.22E-04 1.11E-05 7.18E-07 1.02E-05 1.02E-06

39021672 Toluene 1.71E-04 8.41E-06 1.40E-06 3.32E-05 5.74E-06

39021672 Diethylene glycol monoethyl ether 1.86E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

39021672 Ethylene glycol methyl ether 8.73E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

39021672 Phenol 1.83E-05 1.83E-06 1.19E-06 2.79E-06 5.58E-07

39021672 Ethyl benzene 0.00E+00 2.00E-05 5.82E-07 0.00E+00 0.00E+00

39021672 Methylene chloride 1.52E-05 3.08E-07 1.12E-07 2.12E-07 8.16E-08

39021672 Epichlorohydrin 1.36E-06 8.02E-08 1.94E-08 9.29E-08 2.32E-08

39021672 Acrylic acid 1.25E-07 1.70E-07 5.34E-09 2.49E-07 4.99E-09

39021672 Cumene 0.00E+00 1.64E-07 2.74E-08 0.00E+00 0.00E+00

39021672 Benzene 1.81E-07 1.38E-09 9.04E-11 1.47E-09 4.90E-10

39021672 Arsenic compounds 5.80E-08 0.00E+00 0.00E+00 0.00E+00 0.00E+00

39021672 n-Hexane 0.00E+00 0.00E+00 5.51E-09 0.00E+00 0.00E+00

39021672 Beryllium compounds 0.00E+00 0.00E+00 0.00E+00 0.00E+00 1.88E-11

39057430 Glycol Ethers 1.10E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

39057430 Toluene 2.87E-06 1.42E-07 2.36E-08 5.59E-07 9.65E-08

39057430 Xylenes (mixed) 2.00E-06 7.85E-08 1.10E-08 0.00E+00 0.00E+00

39057430 Methylene chloride 3.85E-07 7.82E-09 2.84E-09 5.40E-09 2.08E-09

39057430 Ethyl benzene 0.00E+00 2.99E-08 8.74E-10 0.00E+00 0.00E+00

39057430 Methanol 1.60E-08 6.48E-10 1.66E-10 1.72E-09 3.44E-10

39057430 Propylene oxide 9.68E-09 1.77E-10 4.35E-11 2.50E-10 5.09E-11

39057430 Benzene 8.27E-09 6.33E-11 4.14E-12 6.72E-11 2.24E-11

39057430 Cumene 0.00E+00 7.16E-10 1.19E-10 0.00E+00 0.00E+00

39057430 Acetaldehyde 2.02E-10 1.17E-12 1.94E-13 5.28E-12 2.64E-13

39061524 Diethylene glycol dimethyl ether 7.16E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

39061524 Toluene 1.96E-05 9.65E-07 1.61E-07 3.81E-06 6.58E-07

39109419 Xylenes (mixed) 1.84E-06 7.25E-08 1.01E-08 0.00E+00 0.00E+00

39109419 Ethyl benzene 0.00E+00 2.90E-08 8.45E-10 0.00E+00 0.00E+00

 12 of 27
Facility NEI ID Pollutant REL AEGL1 AEGL2 ERPG1 ERPG2

Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient1

40143460 2-Butoxyethyl acetate 5.48E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

40143460 Butyl carbitol acetate 5.01E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

40143460 Ethylene glycol ethyl ether acetate 4.41E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

40143460 Ethylene glycol ethyl ether 3.75E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

40143460 Ethylene glycol methyl ether 1.57E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

40143460 Toluene 1.11E-03 5.49E-05 9.14E-06 2.17E-04 3.74E-05

40143460 Xylenes (mixed) 1.14E-03 4.50E-05 6.29E-06 0.00E+00 0.00E+00

40143460 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 7.63E-05

40143460 Ethyl benzene 0.00E+00 4.58E-05 1.34E-06 0.00E+00 0.00E+00

40143460 Formaldehyde 1.68E-05 8.41E-07 5.44E-08 7.71E-07 7.71E-08

40143460 Methanol 9.91E-07 4.02E-08 1.03E-08 1.07E-07 2.13E-08

40143494 Formaldehyde 4.26E-03 2.13E-04 1.38E-05 1.95E-04 1.95E-05

40143494 Phenol 3.63E-04 3.63E-05 2.37E-05 5.55E-05 1.11E-05

40143494 2-Butoxyethyl acetate 2.01E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

40143494 Toluene 1.19E-04 5.85E-06 9.75E-07 2.31E-05 3.99E-06

40143494 Xylenes (mixed) 2.69E-05 1.06E-06 1.48E-07 0.00E+00 0.00E+00

40143494 Ethyl benzene 0.00E+00 7.88E-08 2.30E-09 0.00E+00 0.00E+00

40143494 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 6.97E-08

40143494 Cumene 0.00E+00 4.87E-09 8.12E-10 0.00E+00 0.00E+00

40143523 Ethylene glycol methyl ether acetate7.32E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

40143523 Xylenes (mixed) 6.69E-04 2.63E-05 3.68E-06 0.00E+00 0.00E+00

40143523 Toluene 3.17E-04 1.56E-05 2.60E-06 6.17E-05 1.07E-05

40143523 Phenol 3.93E-05 3.93E-06 2.56E-06 6.00E-06 1.20E-06

40143523 Ethyl benzene 0.00E+00 3.20E-05 9.35E-07 0.00E+00 0.00E+00

40143523 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 1.69E-05

40143523 Methanol 8.14E-06 3.30E-07 8.44E-08 8.76E-07 1.75E-07

40143523 Cumene 0.00E+00 1.84E-08 3.07E-09 0.00E+00 0.00E+00

42003603 Xylenes (mixed) 5.17E-05 2.03E-06 2.84E-07 0.00E+00 0.00E+00

42003603 Toluene 2.79E-05 1.37E-06 2.29E-07 5.43E-06 9.37E-07

42003603 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 3.25E-05

42003603 Phenol 1.22E-05 1.22E-06 7.93E-07 1.86E-06 3.72E-07

42003603 Formaldehyde 1.38E-05 6.92E-07 4.48E-08 6.34E-07 6.34E-08

42003603 Styrene 1.82E-06 4.50E-07 6.96E-08 1.82E-07 3.48E-08

42003603 Ethyl benzene 0.00E+00 1.21E-06 3.54E-08 0.00E+00 0.00E+00

42003603 Arsenic compounds 8.85E-07 0.00E+00 0.00E+00 0.00E+00 0.00E+00

42003603 Methanol 5.74E-07 2.33E-08 5.95E-09 6.18E-08 1.24E-08

42003603 n-Hexane 0.00E+00 0.00E+00 3.23E-09 0.00E+00 0.00E+00

42003603 Cumene 0.00E+00 8.64E-10 1.44E-10 0.00E+00 0.00E+00

42045473 Propyl cellosolve 6.62E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00

42045473 2-Butoxyethyl acetate 8.57E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

42045473 Phenol 1.06E-03 1.06E-04 6.93E-05 1.62E-04 3.24E-05

42045473 Methylene chloride 1.20E-03 2.43E-05 8.82E-06 1.68E-05 6.45E-06

42045473 Toluene 4.05E-04 2.00E-05 3.33E-06 7.88E-05 1.36E-05

42045473 Xylenes (mixed) 3.78E-04 1.49E-05 2.08E-06 0.00E+00 0.00E+00

42045473 Ethyl acrylate 0.00E+00 2.53E-07 5.73E-08 2.10E-04 7.16E-08

42045473 Diethylene glycol monobutyl ether 1.61E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

42045473 Butyl carbitol acetate 1.37E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

42045473 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 5.33E-05

42045473 Ethylene glycol methyl ether 4.27E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

42045473 Ethylene glycol ethyl ether acetate 2.25E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

42045473 Ethyl benzene 0.00E+00 1.42E-05 4.15E-07 0.00E+00 0.00E+00

42045473 Formaldehyde 1.11E-05 5.54E-07 3.59E-08 5.08E-07 5.08E-08

 13 of 27
Facility NEI ID Pollutant REL AEGL1 AEGL2 ERPG1 ERPG2

Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient1

42045473 Methyl methacrylate 0.00E+00 5.91E-06 8.44E-07 0.00E+00 0.00E+00

42045473 Methanol 5.32E-06 2.16E-07 5.52E-08 5.73E-07 1.15E-07

42045473 Epichlorohydrin 4.16E-06 2.46E-07 5.94E-08 2.84E-07 7.11E-08

42045473 Acrylic acid 1.19E-07 1.62E-07 5.10E-09 2.38E-07 4.76E-09

42045473 Benzene 5.86E-08 4.48E-10 2.93E-11 4.76E-10 1.59E-10

42045473 Styrene 1.74E-08 4.29E-09 6.63E-10 1.74E-09 3.32E-10

42045473 n-Hexane 0.00E+00 0.00E+00 2.08E-08 0.00E+00 0.00E+00

42045473 Acetaldehyde 6.06E-09 3.52E-11 5.82E-12 1.58E-10 7.92E-12

42055233 Xylenes (mixed) 5.30E-05 2.08E-06 2.91E-07 0.00E+00 0.00E+00

42055233 Toluene 1.04E-05 5.12E-07 8.54E-08 2.02E-06 3.49E-07

42055233 Ethyl benzene 0.00E+00 1.15E-06 3.36E-08 0.00E+00 0.00E+00

42055233 n-Hexane 0.00E+00 0.00E+00 2.69E-09 0.00E+00 0.00E+00

42081660 Diethylene glycol monobutyl ether 9.25E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

42081660 Xylenes (mixed) 3.22E-04 1.27E-05 1.77E-06 0.00E+00 0.00E+00

42081660 Toluene 1.98E-04 9.75E-06 1.63E-06 3.85E-05 6.65E-06

42081660 Ethyl benzene 0.00E+00 2.53E-05 7.37E-07 0.00E+00 0.00E+00

42081660 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 9.44E-06

45045417 Propyl cellosolve 5.77E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

45045417 Butyl carbitol acetate 2.77E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

45045417 2-Butoxyethyl acetate 1.74E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

45045417 Ethylene glycol ethyl ether acetate 1.64E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

45045417 Toluene 5.67E-05 2.80E-06 4.67E-07 1.10E-05 1.91E-06

45045417 Xylenes (mixed) 1.50E-05 5.91E-07 8.27E-08 0.00E+00 0.00E+00

45045417 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 1.07E-05

45045417 Ethylene glycol methyl ether 8.20E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00

45045417 Diethylene glycol monoethyl ether 6.28E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00

45045417 Diethylene glycol monobutyl ether 5.66E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00

45045417 Methanol 2.55E-06 1.04E-07 2.65E-08 2.75E-07 5.49E-08

45045417 Phenol 4.01E-07 4.01E-08 2.61E-08 6.12E-08 1.22E-08

45045417 Formaldehyde 4.82E-07 2.41E-08 1.56E-09 2.21E-08 2.21E-09

45045417 Ethyl benzene 0.00E+00 4.44E-07 1.30E-08 0.00E+00 0.00E+00

45045417 Benzene 4.74E-08 3.62E-10 2.37E-11 3.85E-10 1.28E-10

45045417 Methylene chloride 4.46E-08 9.05E-10 3.29E-10 6.25E-10 2.40E-10

45045417 Methyl methacrylate 0.00E+00 1.68E-08 2.40E-09 0.00E+00 0.00E+00

45045417 n-Hexane 0.00E+00 0.00E+00 1.11E-08 0.00E+00 0.00E+00

45045417 Cumene 0.00E+00 2.15E-09 3.58E-10 0.00E+00 0.00E+00

45063428 Methylene chloride 4.93E-03 1.00E-04 3.63E-05 6.90E-05 2.65E-05

45063428 Phenol 2.93E-03 2.93E-04 1.91E-04 4.47E-04 8.94E-05

45063428 Xylenes (mixed) 4.63E-04 1.82E-05 2.55E-06 0.00E+00 0.00E+00

45063428 Toluene 2.73E-04 1.35E-05 2.25E-06 5.32E-05 9.19E-06

48027213 Methanol 5.00E-06 2.03E-07 5.19E-08 5.39E-07 1.08E-07

48027213 Toluene 1.01E-06 4.98E-08 8.29E-09 1.96E-07 3.39E-08

48027213 Xylenes (mixed) 1.06E-06 4.15E-08 5.82E-09 0.00E+00 0.00E+00

48027213 Methylene chloride 1.02E-07 2.07E-09 7.52E-10 1.43E-09 5.49E-10

48027213 Ethyl benzene 0.00E+00 3.20E-08 9.35E-10 0.00E+00 0.00E+00

48029468 Ethyl acrylate 0.00E+00 1.93E-06 4.38E-07 1.60E-03 5.48E-07

48029468 Formaldehyde 1.42E-03 7.12E-05 4.60E-06 6.52E-05 6.52E-06

48029468 Xylenes (mixed) 2.04E-04 8.00E-06 1.12E-06 0.00E+00 0.00E+00

48029468 Toluene 1.30E-04 6.39E-06 1.06E-06 2.52E-05 4.36E-06

48029468 2-Butoxyethyl acetate 8.39E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

48029468 Benzene 3.93E-05 3.01E-07 1.97E-08 3.20E-07 1.07E-07

48029468 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 2.49E-05

 14 of 27
Facility NEI ID Pollutant REL AEGL1 AEGL2 ERPG1 ERPG2

Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient1

48029468 Methanol 9.04E-06 3.67E-07 9.38E-08 9.74E-07 1.95E-07

48029468 Ethyl benzene 0.00E+00 8.48E-06 2.47E-07 0.00E+00 0.00E+00

48029468 Ethylene glycol methyl ether 6.75E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00

48029468 Phenol 3.06E-06 3.06E-07 1.99E-07 4.66E-07 9.33E-08

48029468 Styrene 1.41E-06 3.48E-07 5.37E-08 1.41E-07 2.69E-08

48029468 Methyl methacrylate 0.00E+00 1.41E-06 2.01E-07 0.00E+00 0.00E+00

48029468 Cumene 0.00E+00 2.73E-07 4.54E-08 0.00E+00 0.00E+00

48029468 p-Xylene 1.98E-07 0.00E+00 0.00E+00 0.00E+00 0.00E+00

48029468 Acrylonitrile 0.00E+00 1.41E-09 1.08E-10 6.40E-10 1.83E-10

48029468 n-Hexane 0.00E+00 0.00E+00 6.74E-10 0.00E+00 0.00E+00

48231376 Toluene 2.41E-04 1.19E-05 1.98E-06 4.69E-05 8.11E-06

48231376 Xylenes (mixed) 1.98E-04 7.79E-06 1.09E-06 0.00E+00 0.00E+00

48231376 Styrene 5.95E-05 1.47E-05 2.27E-06 5.95E-06 1.14E-06

48231376 Ethylene glycol methyl ether 2.16E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

48231376 2,4-Toluene diisocyanate 0.00E+00 3.98E-06 9.44E-07 7.84E-06 5.06E-07

48231376 Methanol 1.56E-06 6.34E-08 1.62E-08 1.68E-07 3.36E-08

48231376 Ethyl benzene 0.00E+00 1.88E-07 5.47E-09 0.00E+00 0.00E+00

48231376 n-Hexane 0.00E+00 0.00E+00 7.90E-08 0.00E+00 0.00E+00

48439416 Butyl carbitol acetate 2.73E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

48439416 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 3.14E-04

48439416 Xylenes (mixed) 7.73E-05 3.03E-06 4.25E-07 0.00E+00 0.00E+00

48439416 Diethylene glycol dimethyl ether 4.71E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

48439416 Ethylene glycol methyl ether 2.75E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

48439416 Toluene 1.97E-05 9.73E-07 1.62E-07 3.84E-06 6.63E-07

48439416 Dimethyl formamide 0.00E+00 0.00E+00 4.10E-07 1.85E-05 3.69E-07

48439416 Propyl cellosolve 1.02E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

48439416 Formaldehyde 6.34E-06 3.17E-07 2.05E-08 2.91E-07 2.91E-08

48439416 2-Butoxyethyl acetate 1.79E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00

48439416 Arsenic compounds 1.41E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00

48439416 p-Xylene 1.42E-07 0.00E+00 0.00E+00 0.00E+00 0.00E+00

48439416 Ethyl benzene 0.00E+00 1.30E-07 3.80E-09 0.00E+00 0.00E+00

48439416 Cumene 0.00E+00 3.43E-08 5.71E-09 0.00E+00 0.00E+00

48439416 Methyl methacrylate 0.00E+00 1.58E-08 2.25E-09 0.00E+00 0.00E+00

48439416 Carbon tetrachloride 9.68E-09 6.57E-11 1.53E-11 1.41E-10 2.92E-11

48439416 n-Hexane 0.00E+00 0.00E+00 2.68E-09 0.00E+00 0.00E+00

48439416 Aniline 0.00E+00 6.13E-10 4.00E-10 0.00E+00 0.00E+00

48439416 Beryllium compounds 0.00E+00 0.00E+00 0.00E+00 0.00E+00 2.02E-10

48439508 2-Butoxyethyl acetate 5.09E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

48439508 Tetrachloroethene 1.90E-06 1.58E-07 2.37E-08 5.58E-08 2.71E-08

48439508 Toluene 1.05E-06 5.16E-08 8.60E-09 2.04E-07 3.52E-08

48439508 Xylenes (mixed) 8.80E-07 3.46E-08 4.84E-09 0.00E+00 0.00E+00

48439508 Phenol 5.49E-08 5.49E-09 3.58E-09 8.37E-09 1.67E-09

48439508 Ethyl benzene 0.00E+00 4.59E-08 1.34E-09 0.00E+00 0.00E+00

48439508 Methanol 1.14E-08 4.61E-10 1.18E-10 1.22E-09 2.45E-10

49003696 1,1,1-Trichloroethane 1.88E-03 9.81E-05 3.87E-05 6.71E-05 3.36E-05

49003696 Xylenes (mixed) 1.55E-03 6.08E-05 8.52E-06 0.00E+00 0.00E+00

49003696 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 2.66E-04

49003696 Toluene 9.95E-05 4.91E-06 8.18E-07 1.94E-05 3.35E-06

49003696 Ethylene glycol ethyl ether 7.64E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

49003696 Ethyl benzene 0.00E+00 3.62E-05 1.06E-06 0.00E+00 0.00E+00

49003696 Trichloroethylene 0.00E+00 4.76E-06 1.39E-06 6.17E-06 1.23E-06

49003696 Tetrachloroethene 1.06E-05 8.84E-07 1.33E-07 3.12E-07 1.52E-07

 15 of 27
Facility NEI ID Pollutant REL AEGL1 AEGL2 ERPG1 ERPG2

Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient1

49003696 1,2-Epoxybutane 0.00E+00 4.54E-07 2.32E-07 0.00E+00 0.00E+00

49003696 Formaldehyde 5.14E-07 2.57E-08 1.66E-09 2.36E-08 2.36E-09

49003696 Cumene 0.00E+00 3.16E-07 5.27E-08 0.00E+00 0.00E+00

49003696 Methanol 5.05E-08 2.05E-09 5.24E-10 5.44E-09 1.09E-09

49003696 Hydrofluoric acid 2.06E-08 6.02E-09 2.47E-10 3.09E-09 3.09E-10

49003696 Arsenic compounds 3.75E-09 0.00E+00 0.00E+00 0.00E+00 0.00E+00

49011663 Propyl cellosolve 1.46E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00

49011663 Diethylene glycol dimethyl ether 9.01E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

49011663 Butyl carbitol acetate 3.07E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

49011663 Formaldehyde 8.18E-04 4.09E-05 2.65E-06 3.75E-05 3.75E-06

49011663 Phenol 4.92E-04 4.92E-05 3.21E-05 7.51E-05 1.50E-05

49011663 Methylene chloride 4.54E-04 9.22E-06 3.35E-06 6.36E-06 2.45E-06

49011663 Diethylene glycol monobutyl ether 3.99E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

49011663 Xylenes (mixed) 3.44E-04 1.35E-05 1.89E-06 0.00E+00 0.00E+00

49011663 Toluene 2.61E-04 1.29E-05 2.15E-06 5.09E-05 8.79E-06

49011663 Methanol 1.41E-04 5.74E-06 1.47E-06 1.52E-05 3.05E-06

49011663 Ethylene glycol ethyl ether 1.28E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

49011663 Ethylene glycol methyl ether 8.41E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

49011663 Dimethyl formamide 0.00E+00 0.00E+00 1.59E-06 7.15E-05 1.43E-06

49011663 Glycol Ethers 5.61E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

49011663 2-(Hexyloxy)ethanol 4.78E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

49011663 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 3.00E-05

49011663 Diethylene glycol monomethyl ether2.21E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

49011663 2-Butoxyethyl acetate 1.28E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

49011663 Acetaldehyde 6.61E-06 3.84E-08 6.34E-09 1.73E-07 8.63E-09

49011663 1,2-Epoxybutane 0.00E+00 4.26E-06 2.18E-06 0.00E+00 0.00E+00

49011663 Ethylene glycol ethyl ether acetate 3.72E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00

49011663 Acrylonitrile 0.00E+00 2.16E-06 1.66E-07 9.84E-07 2.81E-07

49011663 Ethyl benzene 0.00E+00 3.34E-06 9.74E-08 0.00E+00 0.00E+00

49011663 Carbon tetrachloride 2.63E-06 1.78E-08 4.16E-09 3.84E-08 7.93E-09

49011663 Benzene 2.40E-06 1.83E-08 1.20E-09 1.95E-08 6.49E-09

49011663 Tetrachloroethene 1.27E-06 1.06E-07 1.59E-08 3.74E-08 1.82E-08

49011663 Cumene 0.00E+00 7.92E-07 1.32E-07 0.00E+00 0.00E+00

49011663 Triethylamine 7.82E-07 0.00E+00 0.00E+00 0.00E+00 0.00E+00

49011663 Methyl methacrylate 0.00E+00 5.07E-07 7.25E-08 0.00E+00 0.00E+00

49011663 1,1,1-Trichloroethane 3.36E-07 1.76E-08 6.93E-09 1.20E-08 6.02E-09

49011663 Chlorobenzene 0.00E+00 3.10E-07 2.06E-08 0.00E+00 0.00E+00

49011663 Styrene 1.99E-07 4.92E-08 7.60E-09 1.99E-08 3.80E-09

49011663 Acrylic acid 5.21E-08 7.10E-08 2.23E-09 1.04E-07 2.08E-09

49011663 Aniline 0.00E+00 9.24E-08 6.03E-08 0.00E+00 0.00E+00

49011663 Trichloroethylene 0.00E+00 3.60E-08 1.05E-08 4.67E-08 9.34E-09

49011663 Ethyl acrylate 0.00E+00 9.00E-11 2.04E-11 7.46E-08 2.55E-11

49011663 n-Hexane 0.00E+00 0.00E+00 2.38E-08 0.00E+00 0.00E+00

49011663 m-Xylene 1.07E-08 0.00E+00 0.00E+00 0.00E+00 0.00E+00

49011663 Epichlorohydrin 8.77E-09 5.18E-10 1.25E-10 6.00E-10 1.50E-10

49011663 1,4-Dioxane 2.45E-10 1.20E-11 6.12E-13 0.00E+00 0.00E+00

49011663 Ethylene oxide 0.00E+00 0.00E+00 6.04E-13 0.00E+00 5.44E-13

050272001 Toluene 3.91E-03 1.93E-04 3.22E-05 7.62E-04 1.32E-04

050272001 Xylenes (mixed) 1.21E-03 4.76E-05 6.67E-06 0.00E+00 0.00E+00

050272001 Methanol 1.62E-04 6.56E-06 1.68E-06 1.74E-05 3.48E-06

050272001 Ethyl benzene 0.00E+00 5.44E-05 1.59E-06 0.00E+00 0.00E+00

050272001 n-Hexane 0.00E+00 0.00E+00 1.44E-06 0.00E+00 0.00E+00

 16 of 27
Facility NEI ID Pollutant REL AEGL1 AEGL2 ERPG1 ERPG2

Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient1

51700334 Ethyl benzene 0.00E+00 2.24E-10 6.54E-12 0.00E+00 0.00E+00

51710293 2,4-Toluene diisocyanate 0.00E+00 1.01E-03 2.39E-04 1.99E-03 1.28E-04

51710293 Toluene 1.60E-03 7.87E-05 1.31E-05 3.11E-04 5.37E-05

51710293 Xylenes (mixed) 1.56E-03 6.13E-05 8.58E-06 0.00E+00 0.00E+00

51710293 Methylene chloride 1.46E-04 2.96E-06 1.07E-06 2.04E-06 7.85E-07

51710293 Formaldehyde 8.61E-05 4.30E-06 2.79E-07 3.95E-06 3.95E-07

51710293 Benzene 1.21E-05 9.28E-08 6.07E-09 9.86E-08 3.29E-08

51710293 Ethyl benzene 0.00E+00 5.92E-06 1.73E-07 0.00E+00 0.00E+00

51710293 Hydrofluoric acid 8.45E-07 2.47E-07 1.01E-08 1.27E-07 1.27E-08

51710293 Cumene 0.00E+00 1.93E-07 3.21E-08 0.00E+00 0.00E+00

51810292 Methylene chloride 8.10E-04 1.64E-05 5.97E-06 1.13E-05 4.36E-06

51810292 Phenol 5.70E-04 5.70E-05 3.72E-05 8.70E-05 1.74E-05

51810292 Xylenes (mixed) 1.59E-04 6.24E-06 8.74E-07 0.00E+00 0.00E+00

51810292 Toluene 7.26E-05 3.58E-06 5.97E-07 1.41E-05 2.44E-06

51810292 Methanol 3.71E-05 1.51E-06 3.85E-07 4.00E-06 8.00E-07

51810292 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 2.95E-06

51810292 Ethyl benzene 0.00E+00 5.78E-07 1.69E-08 0.00E+00 0.00E+00

51810292 Cumene 0.00E+00 1.23E-08 2.05E-09 0.00E+00 0.00E+00

51810292 n-Hexane 0.00E+00 0.00E+00 3.85E-09 0.00E+00 0.00E+00

53029438 Phenol 6.69E-04 6.69E-05 4.36E-05 1.02E-04 2.04E-05

53029438 Methylene chloride 6.43E-04 1.31E-05 4.74E-06 9.00E-06 3.46E-06

53029438 Toluene 8.65E-05 4.27E-06 7.11E-07 1.68E-05 2.91E-06

53029438 Benzene 1.10E-05 8.43E-08 5.52E-09 8.96E-08 2.99E-08

53029438 Ethyl benzene 0.00E+00 6.18E-06 1.80E-07 0.00E+00 0.00E+00

53029438 Xylenes (mixed) 8.13E-07 3.20E-08 4.47E-09 0.00E+00 0.00E+00

53029438 Cumene 0.00E+00 2.63E-07 4.39E-08 0.00E+00 0.00E+00

53029438 n-Hexane 0.00E+00 0.00E+00 4.57E-09 0.00E+00 0.00E+00

53033383 Diethylene glycol monobutyl ether 2.39E-01 0.00E+00 0.00E+00 0.00E+00 0.00E+00

53033383 Toluene 1.25E-03 6.15E-05 1.03E-05 2.43E-04 4.20E-05

53033383 2-Butoxyethyl acetate 1.43E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

53033383 Xylenes (mixed) 7.65E-05 3.01E-06 4.21E-07 0.00E+00 0.00E+00

53033383 Methanol 2.74E-05 1.11E-06 2.84E-07 2.95E-06 5.90E-07

53033383 Tetrachloroethene 4.08E-06 3.40E-07 5.10E-08 1.20E-07 5.83E-08

53033383 Methylene chloride 3.89E-06 7.89E-08 2.87E-08 5.44E-08 2.09E-08

53033383 Ethyl benzene 0.00E+00 2.67E-06 7.80E-08 0.00E+00 0.00E+00

53033383 Arsenic compounds 2.71E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00

53033383 Cumene 0.00E+00 1.69E-08 2.81E-09 0.00E+00 0.00E+00

53033383 Beryllium compounds 0.00E+00 0.00E+00 0.00E+00 0.00E+00 8.78E-10

53033384 Diethylene glycol monobutyl ether 9.69E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

53033384 Xylenes (mixed) 1.49E-04 5.84E-06 8.18E-07 0.00E+00 0.00E+00

53033384 2,4-Toluene diisocyanate 0.00E+00 4.53E-05 1.07E-05 8.93E-05 5.76E-06

53033384 2-Butoxyethyl acetate 9.86E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

53033384 Toluene 5.00E-05 2.47E-06 4.11E-07 9.74E-06 1.68E-06

53033384 Phenol 1.08E-05 1.08E-06 7.06E-07 1.65E-06 3.31E-07

53033384 Methylene chloride 1.01E-05 2.05E-07 7.44E-08 1.41E-07 5.44E-08

53033384 Arsenic compounds 8.35E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00

53033384 Ethyl benzene 0.00E+00 5.77E-06 1.68E-07 0.00E+00 0.00E+00

53033384 Beryllium compounds 0.00E+00 0.00E+00 0.00E+00 0.00E+00 2.71E-09

53033384 Cumene 0.00E+00 2.01E-10 3.36E-11 0.00E+00 0.00E+00

53033406 Xylenes (mixed) 1.45E-04 5.70E-06 7.99E-07 0.00E+00 0.00E+00

53033406 Toluene 1.58E-05 7.80E-07 1.30E-07 3.08E-06 5.32E-07

53033406 Ethyl benzene 0.00E+00 4.84E-06 1.41E-07 0.00E+00 0.00E+00

 17 of 27
Facility NEI ID Pollutant REL AEGL1 AEGL2 ERPG1 ERPG2

Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient1

53033406 Methanol 3.56E-10 1.44E-11 3.69E-12 3.83E-11 7.66E-12

53033532 2-Butoxyethyl acetate 1.82E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00

53033532 Toluene 2.91E-04 1.43E-05 2.39E-06 5.66E-05 9.78E-06

53033532 Xylenes (mixed) 1.23E-04 4.84E-06 6.77E-07 0.00E+00 0.00E+00

53033532 Methanol 6.02E-05 2.44E-06 6.24E-07 6.48E-06 1.30E-06

53033532 Mercury (elemental) 1.59E-05 0.00E+00 5.63E-09 0.00E+00 4.78E-09

53033532 Methylene chloride 1.14E-05 2.32E-07 8.42E-08 1.60E-07 6.16E-08

53033532 Phenol 6.83E-06 6.83E-07 4.45E-07 1.04E-06 2.08E-07

53033532 Arsenic compounds 7.44E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00

53033532 Ethyl benzene 0.00E+00 4.70E-06 1.37E-07 0.00E+00 0.00E+00

53033532 2,4-Toluene diisocyanate 0.00E+00 8.74E-07 2.07E-07 1.72E-06 1.11E-07

53033532 Formaldehyde 1.41E-06 7.05E-08 4.56E-09 6.46E-08 6.46E-09

53033532 Benzene 9.55E-07 7.31E-09 4.78E-10 7.76E-09 2.59E-09

53033532 Glycol Ethers 6.89E-07 0.00E+00 0.00E+00 0.00E+00 0.00E+00

53033532 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 4.71E-07

53033532 Maleic anhydride 0.00E+00 0.00E+00 0.00E+00 2.08E-07 2.08E-08

53033532 Triethylamine 6.94E-08 0.00E+00 0.00E+00 0.00E+00 0.00E+00

53033532 Cumene 0.00E+00 5.62E-09 9.37E-10 0.00E+00 0.00E+00

53033532 Ethylene oxide 0.00E+00 0.00E+00 3.06E-10 0.00E+00 2.75E-10

53033532 Hydrofluoric acid 1.37E-11 4.00E-12 1.64E-13 2.05E-12 2.05E-13

53033532 n-Hexane 0.00E+00 0.00E+00 2.14E-13 0.00E+00 0.00E+00

53053447 Glycol ethers 9.30E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

53053447 Toluene 2.23E-04 1.10E-05 1.84E-06 4.35E-05 7.51E-06

53053447 Xylenes (mixed) 4.84E-05 1.90E-06 2.66E-07 0.00E+00 0.00E+00

53053447 2-Butoxyethyl acetate 2.52E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

53053447 Methanol 9.51E-06 3.86E-07 9.86E-08 1.02E-06 2.05E-07

53053447 Ethyl benzene 0.00E+00 2.43E-06 7.07E-08 0.00E+00 0.00E+00

53053447 Phenol 5.84E-08 5.84E-09 3.81E-09 8.92E-09 1.78E-09

53053447 Arsenic compounds 4.49E-08 0.00E+00 0.00E+00 0.00E+00 0.00E+00

53053447 Benzene 1.96E-08 1.50E-10 9.80E-12 1.59E-10 5.31E-11

53053447 Beryllium compounds 0.00E+00 0.00E+00 0.00E+00 0.00E+00 1.31E-11

53061398 Diethylene glycol monobutyl ether 4.26E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00

53061398 Propyl cellosolve 4.03E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00

53061398 Arsenic compounds 1.08E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

53061398 Toluene 5.76E-04 2.84E-05 4.74E-06 1.12E-04 1.94E-05

53061398 Styrene 4.23E-04 1.04E-04 1.61E-05 4.23E-05 8.07E-06

53061398 2,4-Toluene diisocyanate 0.00E+00 1.20E-04 2.85E-05 2.37E-04 1.53E-05

53061398 Mercury (elemental) 1.06E-04 0.00E+00 3.75E-08 0.00E+00 3.19E-08

53061398 Xylenes (mixed) 5.87E-05 2.31E-06 3.23E-07 0.00E+00 0.00E+00

53061398 Ethylene glycol ethyl ether acetate 4.53E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

53061398 Formaldehyde 3.98E-05 1.99E-06 1.29E-07 1.82E-06 1.82E-07

53061398 Methylene chloride 3.93E-05 7.98E-07 2.90E-07 5.51E-07 2.12E-07

53061398 Phenol 2.93E-05 2.93E-06 1.91E-06 4.48E-06 8.96E-07

53061398 Methanol 2.95E-05 1.20E-06 3.06E-07 3.18E-06 6.36E-07

53061398 Epichlorohydrin 1.81E-05 1.07E-06 2.59E-07 1.24E-06 3.10E-07

53061398 Hydrofluoric acid 5.90E-06 1.73E-06 7.08E-08 8.84E-07 8.84E-08

53061398 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 5.23E-06

53061398 Trichloroethylene 0.00E+00 1.21E-06 3.53E-07 1.57E-06 3.14E-07

53061398 Ethyl benzene 0.00E+00 1.73E-06 5.04E-08 0.00E+00 0.00E+00

53061398 Benzene 1.44E-06 1.10E-08 7.22E-10 1.17E-08 3.91E-09

53061398 Tetrachloroethene 1.25E-06 1.04E-07 1.56E-08 3.68E-08 1.79E-08

53061398 Glycol Ethers 1.13E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00

 18 of 27
Facility NEI ID Pollutant REL AEGL1 AEGL2 ERPG1 ERPG2

Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient1

53061398 Cumene 0.00E+00 3.01E-07 5.01E-08 0.00E+00 0.00E+00

53061398 Chlorobenzene 0.00E+00 2.42E-07 1.61E-08 0.00E+00 0.00E+00

53061398 Acrylic acid 5.04E-08 6.87E-08 2.16E-09 1.01E-07 2.01E-09

53061398 Acetaldehyde 1.78E-07 1.03E-09 1.70E-10 4.64E-09 2.32E-10

53061398 Propylene oxide 1.71E-07 3.11E-09 7.67E-10 4.41E-09 8.97E-10

53061398 Ethylene glycol ethyl ether 7.10E-08 0.00E+00 0.00E+00 0.00E+00 0.00E+00

53061398 Methyl methacrylate 0.00E+00 3.07E-08 4.39E-09 0.00E+00 0.00E+00

53061398 n-Hexane 0.00E+00 0.00E+00 2.05E-08 0.00E+00 0.00E+00

53061398 Ethyl acrylate 0.00E+00 1.95E-12 4.42E-13 1.62E-09 5.52E-13

53061398 Carbon tetrachloride 5.13E-10 3.48E-12 8.12E-13 7.50E-12 1.55E-12

53061398 1,4-Dioxane 1.31E-11 6.43E-13 3.30E-14 0.00E+00 0.00E+00

53061398 p-Xylene 2.12E-12 0.00E+00 0.00E+00 0.00E+00 0.00E+00

53061398 Acrylonitrile 0.00E+00 4.27E-13 3.30E-14 1.94E-13 5.60E-14

54067418 Toluene 3.35E-03 1.65E-04 2.75E-05 6.52E-04 1.13E-04

54067418 Formaldehyde 1.10E-03 5.48E-05 3.55E-06 5.02E-05 5.02E-06

54067418 Xylenes (mixed) 2.03E-04 7.97E-06 1.12E-06 0.00E+00 0.00E+00

54067418 Tetrachloroethene 7.09E-05 5.91E-06 8.86E-07 2.09E-06 1.01E-06

54067418 Ethyl benzene 0.00E+00 8.85E-06 2.58E-07 0.00E+00 0.00E+00

55075288 Toluene 3.77E-05 1.86E-06 3.10E-07 7.34E-06 1.27E-06

55075288 Xylenes (mixed) 4.27E-05 1.68E-06 2.35E-07 0.00E+00 0.00E+00

55075288 Methanol 1.39E-05 5.64E-07 1.44E-07 1.50E-06 3.00E-07

55075288 Ethyl benzene 0.00E+00 1.11E-06 3.25E-08 0.00E+00 0.00E+00

55075288 Arsenic compounds 1.17E-07 0.00E+00 0.00E+00 0.00E+00 0.00E+00

55075288 Cumene 0.00E+00 8.41E-09 1.40E-09 0.00E+00 0.00E+00

55079687 Toluene 1.26E-07 6.20E-09 1.03E-09 2.45E-08 4.23E-09

060292024 Propyl cellosolve 1.84E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

060292024 Methylene chloride 7.96E-07 1.62E-08 5.87E-09 1.11E-08 4.29E-09

060292024 Xylenes (mixed) 1.27E-07 5.01E-09 7.01E-10 0.00E+00 0.00E+00

060292024 Toluene 1.01E-07 4.99E-09 8.32E-10 1.97E-08 3.40E-09

060292024 Methanol 3.18E-08 1.29E-09 3.30E-10 3.43E-09 6.86E-10

060292024 Hydrofluoric acid 1.01E-08 2.96E-09 1.22E-10 1.52E-09 1.52E-10

060292024 Ethyl benzene 0.00E+00 2.50E-09 7.30E-11 0.00E+00 0.00E+00

060372022 Xylenes (mixed) 3.23E-04 1.27E-05 1.78E-06 0.00E+00 0.00E+00

060372022 2,4-Toluene diisocyanate 0.00E+00 6.34E-05 1.50E-05 1.25E-04 8.07E-06

060372022 Carbitol acetate 2.07E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

060372022 Toluene 1.29E-04 6.38E-06 1.06E-06 2.52E-05 4.35E-06

060372022 Formaldehyde 5.04E-05 2.52E-06 1.63E-07 2.31E-06 2.31E-07

060372022 Ethylene glycol methyl ether 3.14E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

060372022 Ethylene glycol ethyl ether acetate 1.50E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

060372022 2-Butoxyethyl acetate 1.10E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

060372022 Benzene 1.45E-06 1.11E-08 7.27E-10 1.18E-08 3.94E-09

060372022 Styrene 1.03E-06 2.54E-07 3.92E-08 1.03E-07 1.96E-08

060372022 Methyl methacrylate 0.00E+00 6.40E-07 9.14E-08 0.00E+00 0.00E+00

060372022 1,3-Butadiene 0.00E+00 9.52E-09 1.19E-09 6.49E-07 3.25E-08

060372022 Methanol 4.69E-07 1.90E-08 4.86E-09 5.05E-08 1.01E-08

060372022 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 3.48E-07

060372022 Ethyl benzene 0.00E+00 2.65E-07 7.72E-09 0.00E+00 0.00E+00

060372022 Triethylamine 2.44E-07 0.00E+00 0.00E+00 0.00E+00 0.00E+00

060372022 Chlorobenzene 0.00E+00 1.65E-07 1.10E-08 0.00E+00 0.00E+00

060372022 Acrylic acid 1.76E-08 2.40E-08 7.54E-10 3.52E-08 7.04E-10

060372022 Cumene 0.00E+00 2.11E-08 3.51E-09 0.00E+00 0.00E+00

060372022 n-Hexane 0.00E+00 0.00E+00 4.59E-09 0.00E+00 0.00E+00

 19 of 27
Facility NEI ID Pollutant REL AEGL1 AEGL2 ERPG1 ERPG2

Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient1

060376004 Xylenes (mixed) 2.22E-04 8.74E-06 1.22E-06 0.00E+00 0.00E+00

060376004 Toluene 1.40E-04 6.91E-06 1.15E-06 2.73E-05 4.71E-06

060376004 Styrene 8.38E-05 2.07E-05 3.20E-06 8.38E-06 1.60E-06

060376004 2-Butoxyethyl acetate 4.24E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

060376004 Diethylene glycol monobutyl ether 3.63E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

060376004 Methylene chloride 8.09E-06 1.64E-07 5.96E-08 1.13E-07 4.36E-08

060376004 Methanol 2.45E-06 9.95E-08 2.54E-08 2.64E-07 5.28E-08

060376004 Formaldehyde 1.47E-06 7.33E-08 4.74E-09 6.72E-08 6.72E-09

060376004 Trichloroethylene 0.00E+00 1.38E-07 4.02E-08 1.79E-07 3.58E-08

060376004 Phenol 1.93E-07 1.93E-08 1.26E-08 2.95E-08 5.89E-09

060376004 n-Hexane 0.00E+00 0.00E+00 2.45E-07 0.00E+00 0.00E+00

060376004 Carbon tetrachloride 2.10E-07 1.42E-09 3.32E-10 3.06E-09 6.32E-10

060376004 Ethylene glycol ethyl ether acetate 2.04E-07 0.00E+00 0.00E+00 0.00E+00 0.00E+00

060376004 Ethyl benzene 0.00E+00 1.13E-07 3.29E-09 0.00E+00 0.00E+00

060376004 Acetaldehyde 1.08E-07 6.27E-10 1.04E-10 2.82E-09 1.41E-10

060812015 Xylenes (mixed) 9.21E-06 3.62E-07 5.07E-08 0.00E+00 0.00E+00

060812015 Toluene 1.82E-06 8.99E-08 1.50E-08 3.55E-07 6.13E-08

090032025 Toluene 1.52E-05 7.51E-07 1.25E-07 2.96E-06 5.12E-07

090032025 Methanol 2.81E-06 1.14E-07 2.92E-08 3.03E-07 6.06E-08

090032025 Xylenes (mixed) 4.06E-07 1.59E-08 2.23E-09 0.00E+00 0.00E+00

090032025 Ethyl benzene 0.00E+00 1.93E-08 5.64E-10 0.00E+00 0.00E+00

090075021 Xylenes (mixed) 7.18E-06 2.82E-07 3.95E-08 0.00E+00 0.00E+00

090075021 Trichloroethylene 0.00E+00 6.15E-07 1.79E-07 7.97E-07 1.59E-07

090075021 Toluene 7.51E-07 3.71E-08 6.18E-09 1.46E-07 2.53E-08

090075021 Ethyl benzene 0.00E+00 7.13E-08 2.08E-09 0.00E+00 0.00E+00

120862016 Toluene 8.66E-03 4.27E-04 7.12E-05 1.69E-03 2.91E-04

120862016 Xylenes (mixed) 6.55E-04 2.57E-05 3.60E-06 0.00E+00 0.00E+00

120862016 Ethyl benzene 0.00E+00 2.45E-06 7.14E-08 0.00E+00 0.00E+00

130516005 2-Butoxyethyl acetate 1.42E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

130516005 Toluene 4.04E-04 2.00E-05 3.33E-06 7.88E-05 1.36E-05

130516005 Diethylene glycol monoethyl ether 2.25E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

130516005 Propyl cellosolve 1.19E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

130516005 Ethylene glycol ethyl ether acetate 1.00E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

130516005 Styrene 6.33E-05 1.56E-05 2.42E-06 6.33E-06 1.21E-06

130516005 Xylenes (mixed) 7.76E-05 3.05E-06 4.27E-07 0.00E+00 0.00E+00

130516005 Methanol 1.53E-05 6.20E-07 1.58E-07 1.65E-06 3.29E-07

130516005 Ethylene glycol ethyl ether 8.96E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00

130516005 Phenol 5.63E-06 5.63E-07 3.67E-07 8.59E-07 1.72E-07

130516005 Methylene chloride 3.60E-06 7.31E-08 2.66E-08 5.05E-08 1.94E-08

130516005 Ethyl benzene 0.00E+00 2.06E-06 6.00E-08 0.00E+00 0.00E+00

130516005 Diethylene glycol monobutyl ether 4.03E-07 0.00E+00 0.00E+00 0.00E+00 0.00E+00

130516005 Formaldehyde 3.43E-07 1.72E-08 1.11E-09 1.57E-08 1.57E-09

130516005 n-Hexane 0.00E+00 0.00E+00 2.69E-07 0.00E+00 0.00E+00

130516005 Carbon tetrachloride 1.69E-07 1.15E-09 2.68E-10 2.47E-09 5.11E-10

130516005 Cumene 0.00E+00 9.59E-08 1.60E-08 0.00E+00 0.00E+00

130516005 Benzene 3.35E-08 2.56E-10 1.67E-11 2.72E-10 9.06E-11

130516005 o-Xylene 2.65E-08 0.00E+00 0.00E+00 0.00E+00 0.00E+00

130516005 m-Xylene 2.65E-08 0.00E+00 0.00E+00 0.00E+00 0.00E+00

130516005 p-Xylene 2.65E-08 0.00E+00 0.00E+00 0.00E+00 0.00E+00

130516005 Acetaldehyde 7.97E-09 4.62E-11 7.64E-12 2.08E-10 1.04E-11

130516005 Methyl methacrylate 0.00E+00 6.03E-09 8.61E-10 0.00E+00 0.00E+00

130516005 Propylene oxide 4.30E-10 7.84E-12 1.93E-12 1.11E-11 2.26E-12

 20 of 27
Facility NEI ID Pollutant REL AEGL1 AEGL2 ERPG1 ERPG2

Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient1

130516005 Aniline 0.00E+00 9.32E-11 6.08E-11 0.00E+00 0.00E+00

131276002 2-Butoxyethyl acetate 1.27E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00

131276002 Toluene 1.92E-04 9.48E-06 1.58E-06 3.74E-05 6.47E-06

131276002 Xylenes (mixed) 6.01E-05 2.36E-06 3.30E-07 0.00E+00 0.00E+00

131276002 Styrene 4.14E-05 1.02E-05 1.58E-06 4.14E-06 7.91E-07

131276002 Ethylene glycol ethyl ether acetate 2.17E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

131276002 Ethyl benzene 0.00E+00 3.70E-06 1.08E-07 0.00E+00 0.00E+00

131276002 n-Hexane 0.00E+00 0.00E+00 1.60E-07 0.00E+00 0.00E+00

131276002 Cumene 0.00E+00 4.99E-10 8.32E-11 0.00E+00 0.00E+00

132332017 Toluene 1.79E-02 8.83E-04 1.47E-04 3.48E-03 6.02E-04

132332017 Xylenes (mixed) 4.71E-03 1.85E-04 2.59E-05 0.00E+00 0.00E+00

132332017 Methanol 7.13E-04 2.89E-05 7.39E-06 7.68E-05 1.54E-05

132332017 Phenol 3.12E-04 3.12E-05 2.03E-05 4.76E-05 9.52E-06

132332017 Ethyl benzene 0.00E+00 1.71E-04 5.00E-06 0.00E+00 0.00E+00

132332017 2,4-Toluene diisocyanate 0.00E+00 4.85E-05 1.15E-05 9.56E-05 6.17E-06

132332017 Formaldehyde 1.34E-04 6.71E-06 4.34E-07 6.15E-06 6.15E-07

132332017 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 5.58E-05

132332017 n-Hexane 0.00E+00 0.00E+00 3.43E-05 0.00E+00 0.00E+00

132332017 Tetrachloroethene 5.57E-06 4.64E-07 6.96E-08 1.64E-07 7.95E-08

132332017 Chlorobenzene 0.00E+00 5.90E-06 3.93E-07 0.00E+00 0.00E+00

132332017 Ethyl acrylate 0.00E+00 6.08E-09 1.38E-09 5.05E-06 1.72E-09

132332017 Benzene 7.59E-07 5.80E-09 3.79E-10 6.16E-09 2.05E-09

181035004 Butyl carbitol acetate 1.52E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

181035004 Toluene 1.91E-06 9.40E-08 1.57E-08 3.71E-07 6.41E-08

181035004 Xylenes (mixed) 5.15E-07 2.02E-08 2.83E-09 0.00E+00 0.00E+00

181035004 Ethyl benzene 0.00E+00 3.91E-08 1.14E-09 0.00E+00 0.00E+00

181412011 Xylenes (mixed) 2.12E-04 8.33E-06 1.17E-06 0.00E+00 0.00E+00

181412011 Toluene 2.83E-05 1.40E-06 2.33E-07 5.51E-06 9.51E-07

181412011 Phenol 5.65E-06 5.65E-07 3.68E-07 8.62E-07 1.72E-07

181412011 Ethyl benzene 0.00E+00 9.15E-07 2.67E-08 0.00E+00 0.00E+00

201252005 Glycol Ethers 6.04E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00

201252005 Toluene 1.54E-04 7.58E-06 1.26E-06 2.99E-05 5.17E-06

201252005 Xylenes (mixed) 1.33E-04 5.21E-06 7.30E-07 0.00E+00 0.00E+00

201252005 Ethyl benzene 0.00E+00 3.55E-06 1.03E-07 0.00E+00 0.00E+00

201252005 n-Hexane 0.00E+00 0.00E+00 1.48E-07 0.00E+00 0.00E+00

201732006 Ethylene glycol ethyl ether 1.63E-01 0.00E+00 0.00E+00 0.00E+00 0.00E+00

201732006 Glycol Ethers 9.05E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00

201732006 Formaldehyde 1.12E-03 5.58E-05 3.61E-06 5.11E-05 5.11E-06

201732006 Styrene 4.94E-04 1.22E-04 1.89E-05 4.94E-05 9.43E-06

201732006 Toluene 2.38E-04 1.18E-05 1.96E-06 4.64E-05 8.02E-06

201732006 Methanol 1.15E-04 4.68E-06 1.20E-06 1.24E-05 2.48E-06

201732006 Xylenes (mixed) 1.03E-04 4.05E-06 5.66E-07 0.00E+00 0.00E+00

201732006 Methylene chloride 7.06E-05 1.43E-06 5.20E-07 9.88E-07 3.80E-07

201732006 2,4-Toluene diisocyanate 0.00E+00 1.68E-05 3.99E-06 3.31E-05 2.14E-06

201732006 Ethyl benzene 0.00E+00 4.38E-06 1.28E-07 0.00E+00 0.00E+00

201732006 Triethylamine 1.29E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00

201732006 n-Hexane 0.00E+00 0.00E+00 2.13E-07 0.00E+00 0.00E+00

201732006 Phenol 3.82E-09 3.82E-10 2.49E-10 5.83E-10 1.17E-10

201732007 Glycol Ethers 3.40E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00

201732007 Toluene 9.07E-04 4.48E-05 7.46E-06 1.77E-04 3.05E-05

201732007 Methylene chloride 6.09E-04 1.24E-05 4.49E-06 8.53E-06 3.28E-06

201732007 Xylenes (mixed) 4.08E-04 1.60E-05 2.24E-06 0.00E+00 0.00E+00

 21 of 27
Facility NEI ID Pollutant REL AEGL1 AEGL2 ERPG1 ERPG2

Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient1

201732007 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 3.17E-05

201732007 Styrene 1.59E-05 3.92E-06 6.06E-07 1.59E-06 3.03E-07

201732007 Ethyl benzene 0.00E+00 9.56E-06 2.79E-07 0.00E+00 0.00E+00

201732007 Methanol 7.62E-07 3.09E-08 7.90E-09 8.20E-08 1.64E-08

201732007 n-Hexane 0.00E+00 0.00E+00 3.72E-08 0.00E+00 0.00E+00

201732027 2-Butoxyethyl acetate 4.21E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00

201732027 Tetrachloroethene 1.04E-02 8.68E-04 1.30E-04 3.06E-04 1.49E-04

201732027 Arsenic compounds 8.76E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

201732027 Toluene 5.15E-03 2.54E-04 4.24E-05 1.00E-03 1.73E-04

201732027 Xylenes (mixed) 1.75E-03 6.89E-05 9.65E-06 0.00E+00 0.00E+00

201732027 Formaldehyde 1.60E-03 7.98E-05 5.16E-06 7.31E-05 7.31E-06

201732027 Benzene 4.85E-04 3.71E-06 2.42E-07 3.94E-06 1.31E-06

201732027 Glycol Ethers 1.79E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

201732027 Diethylene glycol monobutyl ether 1.31E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

201732027 Ethyl benzene 0.00E+00 5.24E-05 1.53E-06 0.00E+00 0.00E+00

201732027 Methanol 2.25E-05 9.15E-07 2.34E-07 2.43E-06 4.86E-07

201732027 Cumene 0.00E+00 7.53E-08 1.25E-08 0.00E+00 0.00E+00

201732027 2,4-Toluene diisocyanate 0.00E+00 2.51E-08 5.96E-09 4.95E-08 3.20E-09

201732027 n-Hexane 0.00E+00 0.00E+00 2.92E-10 0.00E+00 0.00E+00

280475024 2-Butoxyethyl acetate 1.26E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00

280475024 Xylenes (mixed) 5.17E-05 2.03E-06 2.85E-07 0.00E+00 0.00E+00

280475024 Toluene 6.20E-06 3.06E-07 5.10E-08 1.21E-06 2.08E-07

280475024 Ethyl benzene 0.00E+00 1.81E-06 5.27E-08 0.00E+00 0.00E+00

280475024 Cumene 0.00E+00 1.25E-07 2.08E-08 0.00E+00 0.00E+00

370492008 Butyl carbitol acetate 1.64E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00

370492008 Diethylene glycol monoethyl ether 8.02E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

370492008 Propyl cellosolve 6.97E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

370492008 2-Butoxyethyl acetate 4.82E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

370492008 Methylene chloride 2.44E-03 4.96E-05 1.80E-05 3.42E-05 1.32E-05

370492008 Ethylene glycol ethyl ether acetate 1.03E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

370492008 Toluene 1.50E-04 7.39E-06 1.23E-06 2.92E-05 5.04E-06

370492008 Methanol 1.57E-04 6.38E-06 1.63E-06 1.69E-05 3.39E-06

370492008 Arsenic compounds 1.32E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

370492008 Xylenes (mixed) 1.24E-04 4.87E-06 6.81E-07 0.00E+00 0.00E+00

370492008 Diethylene glycol dimethyl ether 9.75E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

370492008 2,4-Toluene diisocyanate 0.00E+00 2.92E-05 6.92E-06 5.75E-05 3.71E-06

370492008 Formaldehyde 8.34E-05 4.17E-06 2.70E-07 3.82E-06 3.82E-07

370492008 Ethylene glycol ethyl ether 4.51E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

370492008 Ethylene glycol methyl ether 2.04E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

370492008 Phenol 6.02E-06 6.02E-07 3.92E-07 9.19E-07 1.84E-07

370492008 Carbitol acetate 3.80E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00

370492008 Styrene 1.91E-06 4.73E-07 7.30E-08 1.91E-07 3.65E-08

370492008 Ethyl benzene 0.00E+00 2.30E-06 6.71E-08 0.00E+00 0.00E+00

370492008 Cumene 0.00E+00 1.43E-06 2.39E-07 0.00E+00 0.00E+00

370492008 Tetrachloroethene 1.42E-06 1.18E-07 1.77E-08 4.17E-08 2.03E-08

370492008 Methyl methacrylate 0.00E+00 4.90E-07 7.01E-08 0.00E+00 0.00E+00

370492008 Propylene oxide 5.27E-07 9.61E-09 2.37E-09 1.36E-08 2.77E-09

370492008 Acetaldehyde 1.44E-07 8.35E-10 1.38E-10 3.76E-09 1.88E-10

370492008 Acrylic acid 2.39E-08 3.26E-08 1.03E-09 4.95E-08 9.58E-10

370492008 Mercury (elemental) 5.94E-08 0.00E+00 2.10E-11 0.00E+00 1.78E-11

370492008 Hydrofluoric acid 2.77E-08 8.10E-09 3.32E-10 4.15E-09 4.15E-10

370492008 n-Hexane 0.00E+00 0.00E+00 3.21E-08 0.00E+00 0.00E+00

 22 of 27
Facility NEI ID Pollutant REL AEGL1 AEGL2 ERPG1 ERPG2

Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient1

370492008 Benzene 2.31E-08 1.77E-10 1.16E-11 1.88E-10 6.27E-11

370492008 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 1.31E-08

370492008 Trichloroethylene 0.00E+00 3.67E-09 1.07E-09 4.76E-09 9.51E-10

370492008 Epichlorohydrin 7.15E-09 4.22E-10 1.02E-10 4.89E-10 1.22E-10

370492008 1,3-Butadiene 0.00E+00 3.20E-11 3.99E-12 2.18E-09 1.09E-10

370492008 1,4-Dioxane 6.74E-10 3.32E-11 1.69E-12 0.00E+00 0.00E+00

370492008 Ethylene oxide 0.00E+00 0.00E+00 7.96E-11 0.00E+00 7.17E-11

370492008 Vinyl chloride 3.06E-11 8.62E-12 1.78E-12 4.24E-12 4.24E-13

391335027 Ethylene glycol ethyl ether acetate 2.08E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

391335027 Xylenes (mixed) 6.02E-04 2.37E-05 3.31E-06 0.00E+00 0.00E+00

391335027 Toluene 1.49E-04 7.36E-06 1.23E-06 2.91E-05 5.02E-06

391335027 Ethyl benzene 0.00E+00 2.36E-06 6.88E-08 0.00E+00 0.00E+00

401092026 Butyl carbitol acetate 1.31E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

401092026 Methylene chloride 8.79E-04 1.78E-05 6.48E-06 1.23E-05 4.73E-06

401092026 Phenol 6.39E-04 6.39E-05 4.17E-05 9.76E-05 1.95E-05

401092026 Toluene 2.92E-04 1.44E-05 2.40E-06 5.69E-05 9.83E-06

401092026 Formaldehyde 1.94E-04 9.68E-06 6.26E-07 8.87E-06 8.87E-07

401092026 Ethylene glycol ethyl ether acetate 1.10E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

401092026 Xylenes (mixed) 8.13E-05 3.19E-06 4.47E-07 0.00E+00 0.00E+00

401092026 Ethyl acrylate 0.00E+00 6.92E-08 1.57E-08 5.74E-05 1.96E-08

401092026 Hydrochloric acid 3.60E-06 2.80E-06 2.29E-07 1.68E-06 2.52E-07

401092026 Epichlorohydrin 5.83E-06 3.45E-07 8.34E-08 3.99E-07 9.98E-08

401092026 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 5.12E-06

401092026 Methanol 1.11E-06 4.52E-08 1.15E-08 1.20E-07 2.40E-08

401092026 Benzene 9.46E-07 7.24E-09 4.73E-10 7.69E-09 2.56E-09

401092026 Ethyl benzene 0.00E+00 5.19E-07 1.51E-08 0.00E+00 0.00E+00

401092026 Cumene 0.00E+00 1.37E-07 2.28E-08 0.00E+00 0.00E+00

401092026 p-Xylene 5.59E-08 0.00E+00 0.00E+00 0.00E+00 0.00E+00

401092026 Ethylene oxide 0.00E+00 0.00E+00 1.47E-08 0.00E+00 1.33E-08

401432003 Toluene 7.65E-05 3.77E-06 6.29E-07 1.49E-05 2.57E-06

401432003 Methanol 3.20E-05 1.30E-06 3.32E-07 3.44E-06 6.89E-07

401432003 Xylenes (mixed) 1.94E-05 7.64E-07 1.07E-07 0.00E+00 0.00E+00

401432003 Ethyl benzene 0.00E+00 5.12E-07 1.49E-08 0.00E+00 0.00E+00

401432013 Ethylene glycol ethyl ether 3.85E-01 0.00E+00 0.00E+00 0.00E+00 0.00E+00

401432013 2-Butoxyethyl acetate 2.43E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

401432013 Formaldehyde 1.73E-03 8.63E-05 5.58E-06 7.91E-05 7.91E-06

401432013 Xylenes (mixed) 9.12E-04 3.58E-05 5.01E-06 0.00E+00 0.00E+00

401432013 Toluene 1.58E-04 7.77E-06 1.30E-06 3.07E-05 5.30E-06

401432013 Methanol 1.69E-04 6.88E-06 1.76E-06 1.82E-05 3.65E-06

401432013 2,4-Toluene diisocyanate 0.00E+00 3.07E-05 7.29E-06 6.06E-05 3.91E-06

401432013 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 5.11E-05

401432013 Ethyl benzene 0.00E+00 4.31E-05 1.26E-06 0.00E+00 0.00E+00

401432014 Xylenes (mixed) 9.99E-04 3.92E-05 5.49E-06 0.00E+00 0.00E+00

401432014 Toluene 8.59E-05 4.24E-06 7.06E-07 1.67E-05 2.89E-06

401432014 Ethyl benzene 0.00E+00 4.74E-05 1.38E-06 0.00E+00 0.00E+00

401432014 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 4.60E-05

401432014 Phenol 4.57E-06 4.57E-07 2.98E-07 6.97E-07 1.39E-07

401432014 Arsenic compounds 5.10E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00

401432014 Methanol 9.46E-07 3.84E-08 9.82E-09 1.02E-07 2.04E-08

401432014 Cumene 0.00E+00 4.77E-08 7.94E-09 0.00E+00 0.00E+00

401432014 Beryllium compounds 0.00E+00 0.00E+00 0.00E+00 0.00E+00 1.65E-09

401432021 Diethylene glycol monobutyl ether 8.65E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

 23 of 27
Facility NEI ID Pollutant REL AEGL1 AEGL2 ERPG1 ERPG2

Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient1

401432021 2-Butoxyethyl acetate 5.50E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

401432021 Diethylene glycol monomethyl ether1.25E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

401432021 Toluene 3.57E-04 1.76E-05 2.94E-06 6.95E-05 1.20E-05

401432021 2,4-Toluene diisocyanate 0.00E+00 5.05E-05 1.20E-05 9.96E-05 6.43E-06

401432021 Xylenes (mixed) 5.52E-05 2.17E-06 3.04E-07 0.00E+00 0.00E+00

401432021 Styrene 2.03E-05 5.01E-06 7.75E-07 2.03E-06 3.87E-07

401432021 Methylene chloride 1.85E-05 3.75E-07 1.36E-07 2.59E-07 9.95E-08

401432021 Methanol 5.93E-06 2.41E-07 6.15E-08 6.38E-07 1.28E-07

401432021 1,1,1-Trichloroethane 4.65E-06 2.43E-07 9.58E-08 1.66E-07 8.32E-08

401432021 Methyl methacrylate 0.00E+00 3.91E-06 5.58E-07 0.00E+00 0.00E+00

401432021 Ethylene glycol ethyl ether acetate 3.99E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00

401432021 Phenol 1.66E-06 1.66E-07 1.08E-07 2.53E-07 5.07E-08

401432021 Ethyl benzene 0.00E+00 2.16E-06 6.30E-08 0.00E+00 0.00E+00

401432021 Formaldehyde 1.46E-06 7.28E-08 4.71E-09 6.68E-08 6.68E-09

401432021 Carbon tetrachloride 3.16E-07 2.15E-09 5.01E-10 4.62E-09 9.54E-10

401432021 Benzene 1.14E-07 8.74E-10 5.72E-11 9.29E-10 3.10E-10

401432021 n-Hexane 0.00E+00 0.00E+00 1.09E-07 0.00E+00 0.00E+00

401432021 Tetrachloroethene 3.16E-08 2.63E-09 3.95E-10 9.30E-10 4.52E-10

401432021 Cumene 0.00E+00 3.08E-08 5.14E-09 0.00E+00 0.00E+00

401432021 1,2-Epoxybutane 0.00E+00 8.82E-09 4.52E-09 0.00E+00 0.00E+00

401432021 Acrylic acid 2.49E-09 3.40E-09 1.07E-10 5.16E-09 9.97E-11

401432021 Epichlorohydrin 3.51E-10 2.08E-11 5.02E-12 2.40E-11 6.01E-12

480295025 Formaldehyde 1.37E-04 6.85E-06 4.44E-07 6.28E-06 6.28E-07

480295025 Xylenes (mixed) 1.98E-05 7.77E-07 1.09E-07 0.00E+00 0.00E+00

480295025 Methanol 1.13E-05 4.59E-07 1.17E-07 1.22E-06 2.44E-07

480295025 Toluene 1.01E-05 5.00E-07 8.33E-08 1.97E-06 3.41E-07

480295025 Methylene chloride 9.54E-06 1.94E-07 7.03E-08 1.34E-07 5.14E-08

480295025 Styrene 4.47E-06 1.10E-06 1.71E-07 4.47E-07 8.53E-08

480295025 Cumene 0.00E+00 1.11E-06 1.85E-07 0.00E+00 0.00E+00

480295025 Ethyl benzene 0.00E+00 8.74E-07 2.55E-08 0.00E+00 0.00E+00

480295025 Phenol 3.47E-07 3.47E-08 2.26E-08 5.29E-08 1.06E-08

480295025 n-Hexane 0.00E+00 0.00E+00 4.82E-09 0.00E+00 0.00E+00

481136003 2-Butoxyethyl acetate 6.60E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

481136003 Phenol 1.60E-03 1.60E-04 1.04E-04 2.44E-04 4.87E-05

481136003 Methylene chloride 1.50E-03 3.05E-05 1.11E-05 2.11E-05 8.10E-06

481136003 Toluene 1.88E-04 9.26E-06 1.54E-06 3.65E-05 6.31E-06

481136003 Xylenes (mixed) 6.79E-05 2.67E-06 3.74E-07 0.00E+00 0.00E+00

481136003 Ethylene glycol ethyl ether acetate 5.95E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00

481136003 Ethyl benzene 0.00E+00 3.29E-06 9.60E-08 0.00E+00 0.00E+00

481136003 Trichloroethylene 0.00E+00 2.69E-07 7.86E-08 3.49E-07 6.98E-08

481136003 n-Hexane 0.00E+00 0.00E+00 5.33E-08 0.00E+00 0.00E+00

481136003 Styrene 1.46E-08 3.60E-09 5.56E-10 1.46E-09 2.78E-10

481215032 Diethylene glycol monobutyl ether 1.43E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00

481215032 Diethylene glycol monomethyl ether6.66E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

481215032 Xylenes (mixed) 6.70E-05 2.63E-06 3.69E-07 0.00E+00 0.00E+00

481215032 Toluene 1.79E-05 8.83E-07 1.47E-07 3.49E-06 6.02E-07

481215032 Methylene chloride 2.02E-05 4.10E-07 1.49E-07 2.83E-07 1.09E-07

481215032 Ethyl benzene 0.00E+00 3.17E-06 9.24E-08 0.00E+00 0.00E+00

481215032 Methanol 2.39E-06 9.70E-08 2.48E-08 2.58E-07 5.15E-08

481215032 n-Hexane 0.00E+00 0.00E+00 6.08E-09 0.00E+00 0.00E+00

482015019 Methylene chloride 7.46E-05 1.51E-06 5.50E-07 1.05E-06 4.02E-07

482015019 Phenol 4.93E-05 4.93E-06 3.21E-06 7.52E-06 1.50E-06

 24 of 27
Facility NEI ID Pollutant REL AEGL1 AEGL2 ERPG1 ERPG2

Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient1

482015019 2-Butoxyethyl acetate 3.18E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

482015019 Formaldehyde 1.76E-05 8.80E-07 5.69E-08 8.06E-07 8.06E-08

482015019 Toluene 8.55E-06 4.22E-07 7.03E-08 1.66E-06 2.88E-07

482015019 Xylenes (mixed) 2.42E-06 9.51E-08 1.33E-08 0.00E+00 0.00E+00

482015019 1,1,1-Trichloroethane 6.14E-07 3.21E-08 1.26E-08 2.20E-08 1.10E-08

482015019 Arsenic compounds 8.89E-08 0.00E+00 0.00E+00 0.00E+00 0.00E+00

482015019 Ethyl benzene 0.00E+00 5.51E-08 1.61E-09 0.00E+00 0.00E+00

482015019 Methanol 9.67E-09 3.92E-10 1.00E-10 1.04E-09 2.08E-10

482015019 Hydrofluoric acid 5.70E-10 1.67E-10 6.84E-12 8.55E-11 8.55E-12

482015020 Methylene chloride 4.36E-05 8.85E-07 3.21E-07 6.11E-07 2.35E-07

482015020 Xylenes (mixed) 3.06E-06 1.20E-07 1.68E-08 0.00E+00 0.00E+00

482015020 Toluene 6.78E-07 3.35E-08 5.58E-09 1.32E-07 2.28E-08

482015020 Methanol 3.38E-07 1.37E-08 3.51E-09 3.64E-08 7.29E-09

482015020 Ethyl benzene 0.00E+00 8.83E-08 2.57E-09 0.00E+00 0.00E+00

482015020 Formaldehyde 6.03E-09 3.02E-10 1.95E-11 2.76E-10 2.76E-11

482015020 m-Xylene 3.04E-09 0.00E+00 0.00E+00 0.00E+00 0.00E+00

482015020 n-Hexane 0.00E+00 0.00E+00 1.02E-10 0.00E+00 0.00E+00

483553000 2-Butoxyethyl acetate 1.06E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00

483553000 Toluene 2.90E-04 1.43E-05 2.39E-06 5.65E-05 9.77E-06

483553000 Methylene chloride 3.15E-04 6.38E-06 2.32E-06 4.40E-06 1.69E-06

483553000 Xylenes (mixed) 1.54E-04 6.04E-06 8.46E-07 0.00E+00 0.00E+00

483553000 Formaldehyde 1.91E-05 9.53E-07 6.17E-08 8.74E-07 8.74E-08

483553000 Phenol 3.66E-06 3.66E-07 2.39E-07 5.59E-07 1.12E-07

483553000 Ethyl benzene 0.00E+00 3.64E-06 1.06E-07 0.00E+00 0.00E+00

483553000 Methanol 8.82E-07 3.58E-08 9.15E-09 9.50E-08 1.90E-08

483553000 Cumene 0.00E+00 2.27E-07 3.79E-08 0.00E+00 0.00E+00

483553000 1,2-Epoxybutane 0.00E+00 7.35E-08 3.76E-08 0.00E+00 0.00E+00

483553000 Hydrofluoric acid 4.10E-08 1.20E-08 4.92E-10 6.15E-09 6.15E-10

483553000 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 3.99E-08

483553000 n-Hexane 0.00E+00 0.00E+00 1.12E-08 0.00E+00 0.00E+00

483553000 Trichloroethylene 0.00E+00 2.99E-09 8.72E-10 3.88E-09 7.75E-10

483553000 Methyl methacrylate 0.00E+00 1.44E-11 2.06E-12 0.00E+00 0.00E+00

483553000 Benzene 8.30E-14 1.00E-15 0.00E+00 1.00E-15 0.00E+00

483672020 Tetrachloroethene 5.70E-03 4.75E-04 7.13E-05 1.68E-04 8.15E-05

484392009 Propyl cellosolve 4.49E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00

484392009 Butyl carbitol acetate 1.44E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

484392009 Methylene chloride 2.67E-04 5.41E-06 1.96E-06 3.73E-06 1.44E-06

484392009 2,4-Toluene diisocyanate 0.00E+00 4.91E-05 1.16E-05 9.68E-05 6.25E-06

484392009 Toluene 5.32E-05 2.63E-06 4.38E-07 1.04E-05 1.79E-06

484392009 Glycol ethers 4.95E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

484392009 Ethylene glycol ethyl ether 4.25E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

484392009 o-Xylene 2.92E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

484392009 Xylenes (mixed) 2.20E-05 8.64E-07 1.21E-07 0.00E+00 0.00E+00

484392009 Ethyl acrylate 0.00E+00 2.44E-08 5.54E-09 2.03E-05 6.93E-09

484392009 Formaldehyde 1.54E-05 7.68E-07 4.97E-08 7.04E-07 7.04E-08

484392009 Triethylamine 7.08E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00

484392009 Diethylene glycol monobutyl ether 6.28E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00

484392009 Ethyl benzene 0.00E+00 2.58E-06 7.53E-08 0.00E+00 0.00E+00

484392009 Benzene 1.27E-06 9.72E-09 6.36E-10 1.03E-08 3.44E-09

484392009 Methanol 1.72E-07 6.98E-09 1.78E-09 1.85E-08 3.70E-09

484392009 Cumene 0.00E+00 6.18E-08 1.03E-08 0.00E+00 0.00E+00

484392009 p-Xylene 1.87E-08 0.00E+00 0.00E+00 0.00E+00 0.00E+00

 25 of 27
Facility NEI ID Pollutant REL AEGL1 AEGL2 ERPG1 ERPG2

Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient1

484392010 Propyl cellosolve 1.81E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

484392010 Toluene 6.19E-05 3.06E-06 5.09E-07 1.21E-05 2.08E-06

484392010 Methylene chloride 5.98E-06 1.21E-07 4.40E-08 8.37E-08 3.22E-08

484392010 Xylenes (mixed) 1.57E-07 6.19E-09 8.66E-10 0.00E+00 0.00E+00

484392010 Diethylene glycol monobutyl ether 1.24E-08 0.00E+00 0.00E+00 0.00E+00 0.00E+00

484392010 Ethyl benzene 0.00E+00 4.04E-09 1.18E-10 0.00E+00 0.00E+00

484392010 Cumene 0.00E+00 7.52E-10 1.25E-10 0.00E+00 0.00E+00

484392010 Ethyl acrylate 0.00E+00 9.55E-13 2.17E-13 7.92E-10 2.71E-13

484392010 Formaldehyde 5.90E-10 2.95E-11 1.91E-12 2.71E-11 2.71E-12

484392010 Benzene 4.05E-11 3.10E-13 2.00E-14 3.29E-13 1.10E-13

484392010 Methanol 5.01E-12 2.03E-13 5.20E-14 5.40E-13 1.08E-13

484392010 Triethylamine 6.90E-14 0.00E+00 0.00E+00 0.00E+00 0.00E+00

490115010 Xylenes (mixed) 2.49E-05 9.78E-07 1.37E-07 0.00E+00 0.00E+00

490115010 Toluene 1.20E-06 5.91E-08 9.86E-09 2.33E-07 4.03E-08

490115010 Ethyl benzene 0.00E+00 7.01E-07 2.05E-08 0.00E+00 0.00E+00

490115012 Xylenes (mixed) 4.13E-05 1.62E-06 2.27E-07 0.00E+00 0.00E+00

490115012 Toluene 1.02E-05 5.05E-07 8.41E-08 1.99E-06 3.44E-07

490115012 1,1,1-Trichloroethane 6.34E-06 3.32E-07 1.31E-07 2.27E-07 1.13E-07

490115012 Ethyl benzene 0.00E+00 2.07E-06 6.05E-08 0.00E+00 0.00E+00

490355013 2,4-Toluene diisocyanate 0.00E+00 7.37E-05 1.75E-05 1.45E-04 9.38E-06

490355013 Xylenes (mixed) 1.70E-04 6.69E-06 9.37E-07 0.00E+00 0.00E+00

490355013 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 1.63E-04

490355013 Toluene 1.06E-04 5.21E-06 8.68E-07 2.06E-05 3.55E-06

490355013 Methylene chloride 2.91E-05 5.91E-07 2.14E-07 4.07E-07 1.57E-07

490355013 1,1,1-Trichloroethane 2.02E-05 1.06E-06 4.17E-07 7.24E-07 3.62E-07

490355013 Ethyl benzene 0.00E+00 6.11E-06 1.78E-07 0.00E+00 0.00E+00

490355013 Formaldehyde 4.83E-06 2.41E-07 1.56E-08 2.21E-07 2.21E-08

490355013 Methanol 1.49E-06 6.04E-08 1.54E-08 1.60E-07 3.21E-08

490355013 Benzene 5.14E-07 3.93E-09 2.57E-10 4.18E-09 1.39E-09

490355013 Cumene 0.00E+00 2.14E-07 3.56E-08 0.00E+00 0.00E+00

490355013 1,3-Butadiene 0.00E+00 3.26E-09 4.07E-10 2.22E-07 1.11E-08

490355013 n-Hexane 0.00E+00 0.00E+00 9.65E-09 0.00E+00 0.00E+00

516505023 Xylenes (mixed) 2.76E-05 1.08E-06 1.52E-07 0.00E+00 0.00E+00

516505023 Toluene 1.10E-05 5.45E-07 9.09E-08 2.15E-06 3.72E-07

516505023 Phenol 5.60E-07 5.60E-08 3.65E-08 8.54E-08 1.71E-08

516505023 Methylene chloride 5.22E-07 1.06E-08 3.84E-09 7.30E-09 2.81E-09

516505023 Ethyl benzene 0.00E+00 9.92E-08 2.89E-09 0.00E+00 0.00E+00

516505023 Triethylamine 3.26E-08 0.00E+00 0.00E+00 0.00E+00 0.00E+00

530615014 Butyl carbitol acetate 1.36E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00

530615014 Diethylene glycol monobutyl ether 1.36E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00

530615014 Toluene 2.98E-04 1.47E-05 2.45E-06 5.79E-05 1.00E-05

530615014 Methylene chloride 1.91E-06 3.87E-08 1.41E-08 2.67E-08 1.03E-08

530615014 Phenol 3.29E-07 3.29E-08 2.14E-08 5.02E-08 1.00E-08

540572002 Xylenes (mixed) 3.03E-04 1.19E-05 1.67E-06 0.00E+00 0.00E+00

540572002 Toluene 1.84E-04 9.10E-06 1.52E-06 3.59E-05 6.20E-06

540572002 Formaldehyde 1.03E-04 5.17E-06 3.35E-07 4.74E-06 4.74E-07

540572002 Diethylene glycol monomethyl ether1.36E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

540572002 Phenol 4.22E-06 4.22E-07 2.75E-07 6.45E-07 1.29E-07

540572002 Ethyl benzene 0.00E+00 4.51E-06 1.31E-07 0.00E+00 0.00E+00

540572002 Methanol 3.56E-06 1.44E-07 3.69E-08 3.83E-07 7.66E-08

540572002 Ethylene glycol ethyl ether 9.42E-07 0.00E+00 0.00E+00 0.00E+00 0.00E+00

540572002 Styrene 1.67E-08 4.13E-09 6.38E-10 1.67E-09 3.19E-10

 26 of 27
Facility NEI ID Pollutant REL AEGL1 AEGL2 ERPG1 ERPG2

Table 3 – Maximum Predicted Acute Risks

Actual Emissions

Maximum Hazard Quotient1

540572002 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 1.78E-08

540572002 n-Hexane 0.00E+00 0.00E+00 3.50E-10 0.00E+00 0.00E+00

550876001 Phenol 1.67E-02 1.67E-03 1.09E-03 2.55E-03 5.09E-04

550876001 2-Butoxyethyl acetate 7.30E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

550876001 Methylene chloride 6.94E-03 1.41E-04 5.11E-05 9.71E-05 3.74E-05

550876001 Ethylene glycol ethyl ether acetate 2.70E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

550876001 Toluene 5.26E-04 2.60E-05 4.33E-06 1.02E-04 1.77E-05

550876001 Xylenes (mixed) 5.34E-04 2.10E-05 2.94E-06 0.00E+00 0.00E+00

550876001 Diethylene glycol monoethyl ether 3.23E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00

550876001 Methanol 2.45E-04 9.94E-06 2.54E-06 2.64E-05 5.28E-06

550876001 Styrene 3.44E-05 8.51E-06 1.31E-06 3.44E-06 6.57E-07

550876001 Diethylene glycol monobutyl ether 3.65E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00

550876001 Ethyl benzene 0.00E+00 9.14E-06 2.67E-07 0.00E+00 0.00E+00

550876001 Ethylene glycol methyl ether 6.54E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00

550876001 Ethylene glycol ethyl ether 5.63E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00

550876001 Formaldehyde 4.33E-06 2.16E-07 1.40E-08 1.98E-07 1.98E-08

550876001 Glycol Ethers 4.50E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00

550876001 Propyl cellosolve 2.33E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00

550876001 Methyl methacrylate 0.00E+00 1.40E-06 2.00E-07 0.00E+00 0.00E+00

550876001 Acetaldehyde 8.31E-07 4.82E-09 7.97E-10 2.17E-08 1.09E-09

550876001 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 6.85E-07

550876001 n-Hexane 0.00E+00 0.00E+00 4.73E-07 0.00E+00 0.00E+00

550876001 Carbon tetrachloride 1.49E-07 1.01E-09 2.36E-10 2.18E-09 4.50E-10

550876001 m-Xylene 6.48E-08 0.00E+00 0.00E+00 0.00E+00 0.00E+00

550876001 Propylene oxide 2.84E-08 5.18E-10 1.28E-10 7.34E-10 1.49E-10

550876001 Benzene 1.20E-08 9.21E-11 6.02E-12 9.78E-11 3.26E-11

550876001 Cumene 0.00E+00 1.09E-09 1.81E-10 0.00E+00 0.00E+00

550876001 o-Xylene 1.22E-09 0.00E+00 0.00E+00 0.00E+00 0.00E+00

550876001 p-Xylene 1.22E-09 0.00E+00 0.00E+00 0.00E+00 0.00E+00

550876001 Aniline 0.00E+00 4.11E-11 2.68E-11 0.00E+00 0.00E+00

201732006a Glycol Ethers 1.02E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00

201732006a Toluene 6.41E-04 3.16E-05 5.27E-06 1.25E-04 2.16E-05

201732006a Methanol 3.86E-04 1.56E-05 4.00E-06 4.15E-05 8.31E-06

201732006a n-Hexane 0.00E+00 0.00E+00 1.77E-06 0.00E+00 0.00E+00

201732006a Phenol 3.17E-08 3.17E-09 2.06E-09 4.83E-09 9.66E-10

39021672a Toluene 7.22E-08 3.56E-09 5.93E-10 1.41E-08 2.43E-09

39021672a Benzene 1.13E-09 8.64E-12 5.65E-13 9.18E-12 3.06E-12

 Note: BOLD indicates acute risks greater than 1

1 Some maximum acute impacts may be at onsite locations.

 27 of 27
Facility NEI ID Pollutant Tier 11

Tier 21

10892023 Cadmium 2.66E-01 Below Tier 1 Threshold

4019433 Cadmium 1.00E-01 Below Tier 1 Threshold

5051409 Cadmium 1.33E-06 Below Tier 1 Threshold

60372022 Cadmium 3.43E-05 Below Tier 1 Threshold

9001693 Cadmium 5.53E-07 Below Tier 1 Threshold

9003626 Cadmium 2.00E-09 Below Tier 1 Threshold

12005313 Cadmium 1.04E-05 Below Tier 1 Threshold

12023284 Cadmium 2.02E-05 Below Tier 1 Threshold

12031634 Cadmium 7.59E-04 Below Tier 1 Threshold

120862016 Cadmium 6.39E-03 Below Tier 1 Threshold

13009224 Cadmium 4.32E-08 Below Tier 1 Threshold

13063388 Cadmium 6.54E-05 Below Tier 1 Threshold

13067506 Cadmium 8.75E-03 Below Tier 1 Threshold

201732006 Cadmium 1.98E-03 Below Tier 1 Threshold

201732027 Cadmium 2.65E-02 Below Tier 1 Threshold

2017365 Mercury (methyl) 1.86E-03 Below Tier 1 Threshold

24005458 Mercury (methyl) 5.68E-03 Below Tier 1 Threshold

370492008 Cadmium 1.33E-01 Below Tier 1 Threshold

370492008 Mercury (methyl) 5.34E-06 Below Tier 1 Threshold

37119600 Cadmium 3.64E-06 Below Tier 1 Threshold

37133455 Mercury (methyl) 1.14E-07 Below Tier 1 Threshold

39021672 Cadmium 4.12E-04 Below Tier 1 Threshold

401432014 Cadmium 1.38E-06 Below Tier 1 Threshold

401432021 Cadmium 1.29E+00 Below Tier 1 Threshold

40143523 Cadmium 1.26E+00 Below Tier 1 Threshold

42003603 Cadmium 1.25E-04 Below Tier 1 Threshold

42081660 Cadmium 1.11E+00 Below Tier 1 Threshold

484392009 Cadmium 1.12E-02 Below Tier 1 Threshold

484392010 Cadmium 1.55E-08 Below Tier 1 Threshold

49011663 Cadmium 4.15E-02 Below Tier 1 Threshold

53033383 Cadmium 3.51E-06 Below Tier 1 Threshold

53033384 Cadmium 5.78E-06 Below Tier 1 Threshold

53033532 Cadmium 5.64E-03 Below Tier 1 Threshold

53033532 Mercury (methyl) 1.54E-02 Below Tier 1 Threshold

53053447 Cadmium 3.50E-08 Below Tier 1 Threshold

Table 4 – Multipathway Screening Analysis Results

Actual Emissions

 1 of 2
Facility NEI ID Pollutant Tier 11

Tier 21

Table 4 – Multipathway Screening Analysis Results

Actual Emissions

53061398 Cadmium 5.54E-03 Below Tier 1 Threshold

53061398 Mercury (methyl) 6.02E-01 Below Tier 1 Threshold

1 Numeric value indicates number of times facility level emissions above screening threshold.

 2 of 2
Appendix 8 Acute Impacts Refined Analysis 
Refined Acute Modeling Approach 

Initial acute screening risk calculations were performed with the HEM-3 

model. HEM-3 estimates acute (1-hour) impacts at both polar and census block 

receptors. It is assumed for this short period of time that an exposed individual 

could be located at any off-site location. The lack of readily available detailed 

property boundary information for many of the facilities evaluated made it difficult 

to determine whether receptors were on- or off-site. In the absence of such 

information, the first ring of polar receptors was placed 100 meters from the plant 

center for many facilities. However, these polar rings often transected on-site 

locations, restricting public access to exposures at these levels and thereby 

overestimating exposures. 

 The screening approach used by HEM-3 to estimate maximum 1-hour 

exposures also likely overestimates. To estimate maximum 1-hour 

concentrations at each receptor, HEM-3 sums the maximum concentrations 

attributed to each source, regardless of whether those maximum concentrations 

occurred during the same hour. In other words, HEM-3 assumes that the 

maximum impact from each source at each receptor occurs at the same time. In 

actuality, maximum impacts from different sources may occur at different times. 

 This appendix includes the results of refined acute assessments 

performed for facilities that exceeded short-term health benchmarks using the 

HEM-3 approach. The refinements can address both areas of conservatism 

described above. The simplest refinement is to plot the HEM-3 polar grid results 

on aerial photographs of the facilities; This allows the assessment of off-site 

locations that may be accessible to the public (e.g., roadways and public 

buildings.). The attached figure 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. 
HEM-3

(Screening)

Refined

Results 

5113377

Ethylene glycol 

ethyl ether acetate REL 2 2

Max off-site HQ at centroid of census tract 

(populated)

Petroleum Refining Source Sector

1

 Indicates modeling technique used to refined estimates; see figures depicting off-site impacts

Table 1 – Maximum Predicted Acute Risks Greater than 1 (Refined Approach)

Facility NEI ID Pollutant Criteria Refined Modeling Approach 1

Baseline - Actuals
Figure 1 – 05113377 

Acute Ethylene Glycol Ethyl Ether Acetate HQ (REL)