Residual Risk Assessment for the Synthetic Organic Chemical Manufacturing Industry
(SOCMI) Source Category in Support of the 2023 Risk and Technology Review Proposed Rule
Note: The official version of this document will be available in the docket for the proposed rulemaking at
www.regulations.gov when the proposal publishes in the Federal Register. While you may use this
unofficial version to learn more about the work underlying the proposed rule, please refer only to the
official version of this document and its corresponding document number in providing comments or
corresponding with EPA on the proposed rule.
Residual Risk Assessment for the
Synthetic Organic Chemical Manufacturing Industry (SOCMI)
Source Category in Support of the
2023 Risk and Technology Review Proposed Rule
EPA's Office of Air Quality Planning and Standards
Office of Air and Radiation
March 2023
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Residual Risk Assessment for the Synthetic Organic Chemical Manufacturing Industry (SOCMI)
Source Category in Support of the 2023 Risk and Technology Review Proposed Rule
Table of Contents
Executive Summary 5
1 Introduction 11
2 Methods 12
2.1 Emissions and source data 12
2.2 Dispersion modeling for inhalation exposure assessment 13
2.3 Estimating chronic human inhalation exposure 16
2.4 Acute risk screening and refined assessments 17
2.5 Multipathway human health risk assessment 18
2.6 Environmental risk assessment 24
2.7 Community-based risk assessment 27
2.8 Dose-response assessment 27
2.8.1 Sources of chronic dose-response information 27
2.8.2 Sources of acute dose-response information 32
2.9 Risk characterization 35
2.9.1 General 35
2.9.2 Mixtures 36
3 Risk results for the Hazardous Organic NESHAP source category 37
3.1 Source category description and emissions 37
3.2 Baseline risk characterization 47
3.2.1 Risk assessment results based on actual emissions 47
3.2.2 Risk assessment results based on allowable emissions 57
3.3 Post-control risk characterization 57
4 General discussion of uncertainties in the risk assessment 60
4.1 Emissions inventory uncertainties 60
4.2 Exposure modeling uncertainties 61
4.2.1 Inhalation exposure modeling 61
4.2.2 Multipathway exposure modeling 63
4.2.3 Environmental risk screening assessment 64
4.3 Uncertainties in the dose-response relationships 65
5 References 74
Index of Tables
Table 2.2-1. AERMOD version 21112 Model Options for RTR Modeling 14
Table 2.5-1. Multipathway Scenarios and Ingestion Pathways 19
Table 3.1-1 Summary of Emissions from the SOCMI Source Category and Dose-Response
Values 39
Used in the Residual Risk Assessment 39
Table 3.2-1. Source Category Level Inhalation Risks for the SOCMI Based on Actual
Emissions 48
Table 3.2-2 Source Category Contribution to Facility-Wide Cancer Risks Based on Actual
Emissions 50
Table 3.2-3 Community Level Inhalation Risks Based on SOCMI Actual Emissions 51
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Residual Risk Assessment for the Synthetic Organic Chemical Manufacturing Industry (SOCMI)
Source Category in Support of the 2023 Risk and Technology Review Proposed Rule
Table 3.2-4. Source Category Level Multipathway Screening Assessment Risk Results for
the SOCMI Source Category 52
Table 3.2-5. Source Category Level Environmental Risk Screening Assessment PB-HAP
Results for the SOCMI Source Category 55
Table 3.3-1. Source Category Level Inhalation Risks for the SOCMI Based on Post-Control
Emissions 58
Appendices
Appendix 1 Emissions Inventory Support Documents
Appendix 2 The HEM4 User's Guide
Appendix 3 Meteorological Data for HEM Modeling
Appendix 4 Dispersion Model Receptor Revisions and Additions
Appendix 5 Technical Support Document for Acute Risk Screening Assessment
Appendix 6 Technical Support Document for TRIM-Based Multipathway Tiered Screening
Methodology for RTR
Appendix 7 Protocol for Site-Specific Multipathway Risk Assessment
Appendix 8 Dose-Response Values Used in the RTR Risk Assessments
Appendix 9 Technical Support Document for Environmental Risk Screening Assessment
Appendix 10 Detailed Risk Modeling Results
Appendix 11 Site-Specific Human Health Multipathway Residual Risk Assessment Report
Index of Acronyms
AirToxScreen Air Toxics Screening Assessment
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(s)
HEM Human Exposure Model
HI Hazard index
HQ Hazard quotient
IRIS Integrated Risk Information System
MACT Maximum Achievable Control Technology
MIR Maximum Individual Risk
MO A 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
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Residual Risk Assessment for the Synthetic Organic Chemical Manufacturing Industry (SOCMI)
Source Category in Support of the 2023 Risk and Technology Review Proposed Rule
PB-HAP Persistent and Bioaccumulative - HAP
PAH Polycyclic aromatic hydrocarbon
POM Polycyclic organic matter
REL Reference exposure level
RfC Reference concentration
RfD Reference dose
RTR Risk and Technology Review
TOSHI Target-organ-specific hazard index
TRIM Total Risk Integrated Methodology
TRIM.FaTE TRIM Environmental Fate, Transport, and Ecological Exposure
URE Unit risk estimate
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Residual Risk Assessment for the Synthetic Organic Chemical Manufacturing Industry (SOCMI)
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Executive Summary
This document describes the risk assessment that the U.S. Environmental Protection Agency
(EPA) conducted to assess the human health and environmental risks posed by hazardous air
pollutant (HAP) emissions from the synthetic organic chemical manufacturing industry. This
rule is commonly known as the Synthetic Organic Chemical Manufacturing Industry
(SOCMI). Section 112 of the Clean Air Act (CAA) establishes a two-stage regulatory process
for addressing emissions of HAP from stationary sources. In the first stage, EPA must
promulgate technology-based national emission standards for hazardous air pollutants
(NESHAP) for categories of sources. EPA has completed this stage. For NESHAP that
require maximum achievable control technology (MACT) standards, EPA is required to
complete a second stage of the regulatory process - the residual risk review. In this second
stage, EPA is required to assess the health and environmental risks that remain after
implementation of the standards. EPA must also review each of the technology-based
standards at least every eight years and revise them, as necessary, taking into account
developments in practices, processes and control technologies. If appropriate based on the
results of the risk and technology reviews, the Agency will revise the rule. For efficiency, the
Agency includes the analyses in the same regulatory package and calls the rulemakings the
Risk and Technology Review (RTR).
The specific source category results contained in this document are from the SOCMI risk and
technology review in support of EPA's 2023 proposed rule, New Source Performance
Standards for the Synthetic Organic Chemical Manufacturing Industry and National Emission
Standards for Hazardous Air Pollutants for the Synthetic Organic Chemical Manufacturing
Industry and Group I & II Polymers and Resins Industry. The EPA is proposing amendments
to the NESHAP for this source category, under 40 CFR part 63, subparts F, G, H, and I, to
address the results of the RTR review of the MACT standards, required under Section 112.
The Synthetic Organic Chemical Manufacturing Industry (SOCMI) source category, also
referred to the Hazardous Organic NESHAP (HON), includes synthetic organic chemical
manufacturing industry facilities, regulated under subparts F, G, and H. The SOCMI is a
segment of the chemical manufacturing industry that includes the production of many high-
volume organic chemicals, derived from petrochemical feedstocks. Of the hundreds of
organic chemicals that are produced by the SOCMI, some are final products, and some are the
feedstocks for production of other non-SOCMI chemicals or synthetic products such as
plastics, fibers, surfactants, pharmaceuticals, synthetic rubber, dyes, and pesticides. The
SOCMI source category also applies to equipment leaks from certain non-SOCMI processes
located at chemical plants, regulated under subpart I. Emission points include pressure relief
devices, equipment leaks, process vents, flares, wastewater, heat exchange systems, storage
tanks, and transfer racks. We estimate that there are 195 HON-subject facilities in the SOCMI
source category operating in the U.S. These 195 HON facilities correspond to 222 Emission
Information System (EIS) facility IDs used in the risk assessment. The total reported
emissions of HAP for the source category are approximately 8,200 tons per year. The reported
HAP emitted in the largest quantity are methanol, n-hexane, toluene, xylenes (mixed),
benzene, styrene, hydrochloric acid, ethylene glycol, acetonitrile, ethylene dichloride, methyl
chloride, vinyl acetate, vinyl chloride, 1,3-butadiene, ethyl benzene, chlorine, acetaldehyde,
methyl methacrylate, phenol, chlorobenzene, maleic anhydride, cumene, phthalic anhydride,
acrylonitrile, methylene chloride, chloroform, ethyl chloride, formaldehyde, naphthalene,
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Residual Risk Assessment for the Synthetic Organic Chemical Manufacturing Industry (SOCMI)
Source Category in Support of the 2023 Risk and Technology Review Proposed Rule
methyl isobutyl ketone, ethylene oxide, methyl bromide, propylene oxide, carbonyl sulfide, p-
xylene, tetrachloroethene, carbon tetrachloride, hydrogen cyanide, 2,2,4-trimethylpentane,
acrylic acid, carbon disulfide, methyl tert-butyl ether, diethanolamine, biphenyl, aniline,
1,1,1-trichloroethane, glycol ethers, trichloroethylene, epichlorohydrin, propionaldehyde, 2-
nitropropane, acetophenone, polycyclic aromatic hydrocarbons (PAH), bromoform,
phenanthrene, 1,1,2-trichloroethane, o-xylene, triethylamine, hydrofluoric acid, and 1,4-
dioxane. Emissions of these 60 pollutants make up over 99 percent of the total HAP emissions
by mass. Emissions of persistent and bioaccumulative HAP (PB-HAP) include polycyclic
organic matter (POM), lead compounds, arsenic compounds, cadmium compounds, mercury
compounds, and dioxins. Emissions of environmental HAP include the above PB-HAP plus
hydrochloric acid (HC1) and hydrofluoric acid (HF).
The below table summarizes the results of the risk assessment for the SOCMI Source
Category. The results of the chronic inhalation cancer risk assessment are estimated using
modeling, which has been EPA's standard approach for residual risk analyses under CAA
section 112 (f)(2) and applies to all risk results (both risk estimates and numbers of people
exposed to such risks) presented here and in subsequent sections. Based on actual emissions
from the source category, the modeled estimates indicate that the maximum lifetime
individual cancer risk posed by the 222 facilities could be as high as 2,000-in-l million, with
ethylene oxide emissions from pressure relief devices and equipment leaks as the major
contributors to the risk. The total estimated cancer incidence from this source category is one
excess cancer case every 8 months. Approximately 50,000,000 people live within 50
kilometers of the 222 modeled HON facilities, and 7,200,000 people are estimated to have a
cancer risk at or above 1-in-l million from HAP emitted from the facilities in this source
category, with 87,000 people estimated to have a cancer risk above 100-in-l million.
Risk Summary for the SOCMI Source Category
Inhalation Cancer Risk
Population Cancer Risk
Max Chronic Individual
Noncancer Risk
Max Acute
Noncancer Risk
Multipathway
Assessment0
Maximum
Individual
Risk*
(in 1 million)
Risk Driver
Cancer
Incidence
(cases per
year)
>100 in 1
million
> 1 in 1
million
Hazard
Index
(TOSHI)
Risk
Driver
Hazard
Quotient1*
Risk
Driver
Risk Driver
and
Health Endpoints
Baseline Actual Emissions
Source
Category
2,000
ethylene
oxide
2
87,000
7,200,000
2
2
maleic
anhydride
chlorine
3
3
chlorine
acrolein
Non-cancer Tier 3
= 60 (methyl
mercury)
Cancer Tier 3 = 20
(POM)
Whole
Facility
2,000
ethylene
oxide
2
95,000
8,900,000
4
chlorine
...
...
...
Baseline Allowable Emissions (same as Baseline Actual Emissions)
Source
Category
2,000
ethylene
oxide
2
87,000
7,200,000
2
2
maleic
anhydride
chlorine
3
3
chlorine
acrolein
Same as Baseline
Actuals
Post-Control Emissions
Source
Category
100
acrylonitrile
0.4
0
5,700,000
2
2
maleic
anhydride
chlorine
3
3
chlorine
acrolein
Same as Baseline
Actuals
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Residual Risk Assessment for the Synthetic Organic Chemical Manufacturing Industry (SOCMI)
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a. The MIR facility for this source category and the facility with the highest risk for all modeled health matrix
is the Indorama Port Neches Plant, located in Port Neches, TX. The excess cancer risk of 2000-in-l million
is driven by fugitive ethylene oxide emissions from equipment leaks and pressure relief valves.
b. The max acute off-site HQ = 3, based upon the 1 -hr REL for chlorine and acrolein, no other acute health
benchmarks for these pollutants were exceeded. The max off-site acute value of 3 for chlorine is located
alongside a public highway, while the max off-site acute value of 3 for acrolein is located in a
public/residential area.
c. The maximum non-cancer screening value (SV) for cadmium and mercury are based upon upper-bound
ingestion rates for the fisher scenario, with mercury having a non-cancer Tier 3 SV = 60 and a Tier 3 SV = 2
for cadmium. For the gardener scenario, the maximum Tier 3 cancer SV = 20 for POM emissions. Based
upon site-specific assessments conducted for each of the above PB-H APs from other source categories, we
would expect decreases of the Tier 3 SV to values that are protective of public health for both cancer and
non-cancer health effects.
Regarding the noncancer risk assessment, the maximum chronic noncancer hazard index
value for the source category could be up to 2 (for the respiratory hazard index) driven by
emissions of maleic anhydride from a process vent operation at one facility and chlorine from
three control devices at another facility. Of the 50,000,000 people living within 50 kilometers
of these facilities, approximately 80 people are exposed to a noncancer hazard index above 1,
based on actual source category emissions. The maximum acute offsite hazard quotient from
actual emissions is 3. The estimated worst-case off-site acute exposures to emissions from the
SOCMI source category result in a maximum modeled acute noncancer HQ of 3 based on the
REL for chlorine at one facility and acrolein at another facility. HON process emissions from
two other facilities result in acute noncancer HQs of 2 based on the RELs for formaldehyde
and chloroform.
Whole facility (or "facility-wide") emissions include those regulated under this source
category plus all other emissions generated at each facility. The results of the chronic
inhalation cancer risk assessment based on whole facility emissions are more uncertain and
rely on the quality of the emissions data collected for source categories outside this regulatory
review. These emissions sources may not undergo the same level of data quality review as
those being assessed in this regulatory assessment. The maximum lifetime individual cancer
risk posed by the 222 facilities, based on whole facility emissions, is 2,000-in-l million with
ethylene oxide from pressure relief devices and equipment leaks driving the risk. The total
estimated cancer incidence based on whole facility emissions is 2 excess cancer cases per
year, or one excess case every 6 months. Approximately 8,900,000 people are estimated to
have cancer risks above 1 -in-1 million from HAP emitted from all sources at the facilities in
this source category, with 95,000 people estimated to have a cancer risk above 100-in-l
million. For the noncancer risk assessment, the maximum chronic noncancer hazard index
posed by whole facility emissions is estimated to be 4 (for the respiratory hazard index)
driven by emissions of chlorine, acrylic acid, and acrylonitrile from whole facility sources
(heat exchange systems and equipment leaks) coming mostly from 2 facilities. Emissions
from one facility contribute to 83 percent of the TOSHI, with approximately 60 percent of the
total TOSHI from non-source category emissions of chlorine and another 15 percent from
source category emissions of chlorine. Emissions from the second facility contribute to 15
percent of the TOSHI, with approximately 11 percent of the total TOSHI from source
category emissions of acrylic acid and 2 percent from source category emissions of
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Residual Risk Assessment for the Synthetic Organic Chemical Manufacturing Industry (SOCMI)
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acrylonitrile. Approximately 1,100 people are exposed to noncancer hazard index levels
greater than 1, based on whole facility emissions from the 222 facilities in this source
category.
We conducted a community-based risk assessment estimating cancer risks from all stationary
point sources in communities near HON facilities, including the source category and whole
facility emissions. The community-based MIR is 2000-in-l million and cancer incidence is 2
excess cancer cases per year, which are the same as the source category and whole facility
assessments. Approximately 100,000 people are estimated to have cancer risks greater than
100-in-l million.
Potential multipathway health risks under a fisher, farmer, and gardener scenario were
identified using a 3-Tier screening assessment of the PB-HAP emitted by facilities in this
source category and if necessary a site-specific assessment utilizing TRIM.FaTE. Of the 222
facilities in the source category, 34 facilities reported emissions of carcinogenic PB-HAP
(arsenic, POM, and dioxins) and 11 facilities reported emissions of non-carcinogenic PB-HAP
(cadmium and mercury) that exceed the Tier 1 screening value of 1. For facilities that
exceeded the Tier 1 multipathway screening values for one or more PB-HAP, we used
additional facility site-specific information to perform a Tier 2 assessment and determine the
maximum chronic cancer and non-cancer impacts for the source category.
In the Tier 2 cancer screening assessment, arsenic and dioxin screening values were below the
Tier 2 screening threshold and therefore no additional assessment was deemed necessary. For
mercury and cadmium, a Tier 3 non-cancer screening assessment was conducted for the fisher
scenario while a Tier 3 screening assessment was conducted for POM for the gardener
scenario. In the Tier 3 non-cancer screening for the fisher scenario, the screening values for
mercury and cadmium were 60 and 2, respectively. The Tier 3 gardener screening assessment
indicated the maximum Tier 3 cancer screening value for POM was 20.
The EPA determined that it is not necessary to go beyond the Tier 3 analysis or conduct a
site-specific assessment for cadmium, mercury, or POM. The EPA compared the Tier 2
screening results to site-specific risk estimates for five previously assessed source categories.
These assessments indicated that cancer and noncancer site-specific risk values were at least
50 times lower than the respective Tier 2 screening values for the assessed facilities, with the
exception of noncancer risks for cadmium for the gardener scenario, where the reduction was
at least 10 times.
Based on our review of these analyses, if the Agency was to perform a site-specific
assessment for the SOCMI Source Category, the Agency would expect similar magnitudes of
decreases from the Tier 2 SVs (which were the same as the Tier 2 values for mercury and
cadmium). As such, given the conservative nature of the screens and the level of additional
refinements that would go into a site-specific multipathway assessment, were one to be
conducted, we are confident that the HQ for ingestion exposure, specifically cadmium and
mercury through fish ingestion, is at or below 1. For POM, the maximum cancer risk under
the rural gardener scenario would likely decrease to below 1-in-l million. For this reason and
considering the conservative nature of the multipathway exposure screening scenario, further
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Residual Risk Assessment for the Synthetic Organic Chemical Manufacturing Industry (SOCMI)
Source Category in Support of the 2023 Risk and Technology Review Proposed Rule
analyses were not performed, refer to Section 3.2.1 and App. 10 - 11 for more detail results
on the screening analysis and the site-specific Tier 3 screen.
In evaluating the potential multipathway risk from emissions of lead compounds, rather than
developing a screening threshold emission rate, we compare maximum estimated chronic
inhalation exposure concentrations to the level of the current National Ambient Air Quality
Standard (NAAQS) for lead of 0.15 |ig/m3. 1 Values above the level of the primary (health-
based) lead NAAQS are considered to have a potential for multipathway risk. Based upon a
Pb screening approach for this source category the estimated max off-site 3-month
concentration was below the Pb NAAQS. Based on the results of the risk screening analysis,
we do not expect an adverse health and/or environmental effect because of lead emissions
from this source category if facilities are complying with the NESHAP.
A screening-level evaluation of the potential adverse environmental risk associated with
emissions of arsenic, cadmium, dioxin, methyl mercury, divalent mercury, POM, hydrofluoric
acid, and hydrochloric acid indicated that no ecological benchmarks were exceeded for
arsenic, hydrofluoric acid, or hydrochloric acid. For the other pollutants, the maximum Tier 1
screening value was 200 for methyl mercury emissions for the surface soil No Observed
Adverse Effects Level (NOAEL) avian ground insectivores benchmark. For all pollutants that
had Tier 1 screening values above various benchmarks (cadmium, dioxins, POMs, divalent
mercury, methyl mercury) a Tier 2 screening assessment was performed.
In the Tier 2 screen, cadmium, dioxins, and POM emissions did not exceed any ecological
benchmark. The following Tier 2 screening values were exceeded for methyl mercury
emissions: a screening value of 5 for the fish-eating birds NOAEL benchmark (specifically
for the small duck called the merganser), a screening value of 2 for the maximum allowable
toxicant level for the merganser, and a screening value of 3 for avian ground insectivores
(woodcock). The following Tier 2 screening values were exceeded for divalent mercury
emissions: a screening value of 4 for a sediment threshold level and a screening value of 2 for
an invertebrate threshold level.
Since there were Tier 2 exceedances, we conducted a Tier 3 environmental risk screen. In the
Tier 3 environmental risk screen, we looked at aerial photos of the lake being impacted by
mercury emissions from the three HON-subject facilities. The aerial photos show that the
"lake" is located in an industrialized area, has been channelized, and largely filled/drained.
Therefore, it was determined that this "lake" would not support a fish population.
1 In doing so, the EPA notes that the legal standard for a primary NAAQS - that a standard is
requisite to protect public health and provide an adequate margin of safety (CAA section 109(b))
- differs from the CAA section 112(f) standard (requiring, among other things, that the standard
provide an "ample margin of safety to protect public health"). However, the primary lead NAAQS
is a reasonable measure of determining risk acceptability (i.e., the first step of the Benzene
NESHAP analysis) since it is designed to protect the most susceptible group in the human
population - children, including children living near major lead emitting sources. 73 FR 67002/3;
73 FR 67000/3; 73 FR 67005/1. In addition, applying the level of the primary lead NAAQS at the
risk acceptability step is conservative, since that primary lead NAAQS reflects an adequate
margin of safety.
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Residual Risk Assessment for the Synthetic Organic Chemical Manufacturing Industry (SOCMI)
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Potential differences between actual emission levels and the maximum emissions allowable
under EPA's standards (i.e., "allowable emissions") were also determined for the HON
facilities. For this source category, allowable emissions are equal to baseline actual emissions.
Therefore, the cancer and noncancer risk assessment results based on allowable emissions are
the same as the risk assessment results based on actual emissions, summarized above.
In addition to the baseline source category and whole facility analyses, an analysis of post-
control emissions was performed, a scenario which modeled proposed ethylene oxide
controls. The results of the chronic inhalation cancer risk assessment based on these post-
control emissions from the SOCMI source category indicate that the maximum lifetime
individual cancer risk posed by the 222 facilities could be as high as 100-in-l million
(compared to 2,000-in-l million at baseline), with acrylonitrile emissions from equipment
leaks and waste operations as the major contributors to the risk. The total estimated cancer
incidence based on post-control emissions is one excess cancer case every 2.4 years.
Approximately 5,700,000 people are estimated to have a cancer risk at or above 1-in-l
million from HAP emitted from the facilities in this source category under the post-control
scenario, with no one estimated to have a cancer risk above 100-in-l million. The
community-based cancer MIR would be reduced from 2,000-in-l million (pre-control) to
1,000-in-l million (post-control), with 98 percent of the post-control MIR attributable to
ethylene oxide emissions from non-HON processes at a HON facility. Within 10km of a
SOCMI facility, there is an estimated reduction in community-level cancer incidence to 0.7
excess cancer cases per year (post-control), from 2 excess cancer cases per year (pre-
control). The number of people estimated to have a cancer risk greater than 100-in-l million
would be reduced from 100,000 (pre-control) to 4,000 (post-control). Regarding the
noncancer risk assessment, the maximum chronic noncancer hazard index posed by post-
control emissions is estimated to be 2 (for the respiratory hazard index) driven by emissions
of maleic anhydride from process vent analyzer operations. Approximately 80 people are
exposed to noncancer hazard index levels above 1, based on post-control emissions from the
222 facilities in the SOCMI source category. The maximum acute offsite hazard quotient
from post-control emissions is 3, the same as based on baseline actual emissions.
This document summarizes the methods used to conduct the risk assessment of this source
category as well as the results. Section 1 discusses the relevant regulatory framework
including background on the Clean Air Act sections which require the EPA to conduct these
source category risk assessments. Methods described in Section 2 include those used by EPA
to develop refined estimates of chronic inhalation exposures and human health risks for
cancer and noncancer endpoints, as well as those used to screen for acute health risks, chronic
non-inhalation (i.e., multipathway) health risks, and adverse environmental effects. The
source category-specific results for the risks are presented in Section 3. Section 4 contains a
discussion of the uncertainties of the risk assessment, including uncertainties in the exposure
assessment and in the dose-response values. The appendices to this risk report contain
detailed descriptions of the methods used and the results.
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Residual Risk Assessment for the Synthetic Organic Chemical Manufacturing Industry (SOCMI)
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1 Introduction
Section 112 of the Clean Air Act (CAA) establishes a two-stage regulatory process for
addressing emissions of hazardous air pollutants (HAP) from stationary sources. In the first
stage, section 112(d) requires the Environmental Protection Agency (EPA, or the Agency) to
develop technology-based National Emission Standards for Hazardous Air Pollutants
(NESHAP) for categories of sources (e.g., petroleum refineries, pulp and paper mills, etc.).
EPA has completed this stage. For NESHAP that require maximum achievable control
technology (MACT) standards, EPA is required to complete a second stage of the regulatory
process - the residual risk review. In this 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. 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.
Also, under section 112(d)(6), EPA must review each of the technology-based standards at
least every eight years and revise it, as necessary, taking into account developments in
practices, processes and control technologies. If appropriate based on the results of the risk
and technology reviews, the Agency will revise the rule. For efficiency, the Agency includes
the 112(f) and 112(d) analyses in the same regulatory package and calls the rulemakings the
Risk and Technology Review (RTR).
In December 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 2007 we received
a letter with the results of that consultation. Subsequent to the consultation, in June 2009,
EPA met with an SAB panel for a formal peer review of the "Risk and Technology Review
(RTR) Assessment Methodologies" (USEPA, 2009a). We received the final SAB report on
this review in May 2010 (USEPA, 2010a). Where appropriate, we responded to the SAB's
key recommendations in developing our current risk assessments and continue our efforts to
improve our assessments by incorporating updates that address the SAB's 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" (USEPA,
2010b). EPA has updated several aspects of the risk assessment methodologies contained in
the 2009 document. In 2017, we submitted these updated methodologies to SAB for review.
The updated methodologies are described in, Screening Methodologies to Support Risk and
Technology Reviews (RTR): A Case Study Analysis. The SAB's findings for this review,
Review of EPA 's draft technical report entitled Screening Methodologies to Support Risk and
Technology Reviews (RTR): A Case Study Analysis was submitted to EPA in September 2018.
This document contains the methods we use to conduct the risk assessment, the results of the
residual risk assessment performed for the Synthetic Organic Chemical Manufacturing
Industry (SOCMI) source category, and a description of associated uncertainties.
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Residual Risk Assessment for the Synthetic Organic Chemical Manufacturing Industry (SOCMI)
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2 Methods
A risk assessment consists of four steps: 1) hazard identification, 2) dose-response
assessment, 3) exposure assessment, and 4) risk characterization. The first step, hazard
identification, determines whether the pollutants of concern can be linked to the health effects
in question (cancer and/or noncancer). Section 112 of the CAA identifies the HAP to be
considered in the risk assessment for this source category. The second step is the dose-
response assessment, which quantifies the relationship between the dose of a pollutant and the
resultant health effects. Dose-response assessments are performed by EPA through the
Integrated Risk Information System (IRIS) process as well as by other agencies, such as the
Agency for Toxic Substances and Disease Registry (ATSDR). See Section 2.7 of this
document for more information on dose-response assessments. The third and fourth steps, the
exposure assessment and the risk characterization, respectively, are specific to the source
category and are described throughout this report. The exposure assessment includes
characterization of HAP emissions, environmental fate and transport, and population exposure
for both inhalation and non-inhalation pathways. The fourth and final step, risk
characterization, integrates all the information from the previous steps and describes the
outcome of the assessment. This four-step approach to risk assessment was endorsed by the
National Academy of Sciences in its publication "Science and Judgment in Risk Assessment"
(NAS, 1994) and subsequently was adopted in the EPA's "Residual Risk Report to Congress"
(USEPA, 1999).
The EPA conducts a risk assessment that provides estimates of the maximum individual risk
(MIR) posed by the HAP emissions from each source in the source category, the hazard index
(HI) for chronic exposures to HAP with potential to cause chronic (or long-term) noncancer
health effects and the hazard quotient (HQ) for acute exposures to HAP with the potential to
cause noncancer health effects. The MIR is defined as the cancer risk associated with a
lifetime of exposure at the highest concentration of HAP where people are likely to live. The
HQ is the ratio of the potential exposure to the HAP to the level at or below which no adverse
effects are expected; the HI is the sum of HQs for HAP that affect the same target organ or
organ system. The risk assessment also provides estimates of the distribution of cancer risks
within the exposed populations, cancer incidence and an evaluation of the potential for
adverse environmental effects. The following sections describe how we estimate HAP
emissions and conduct steps three and four of the risk assessment. The methods used to assess
risks are consistent with those peer reviewed by a panel of the EPA's Science Advisory Board
(SAB) in 2009 and described in their peer review report issued in 2010 (USEPA, 2010a).
2.1 Emissions and source data
To conduct the exposure assessment, EPA gathers the best available data on emissions,
emissions release parameters, and other relevant source category-specific parameters. EPA
determines the HAP emissions levels from emission points in the source category and
identifies the emissions release characteristics of these emission points (e.g., stack height).
EPA often begins with the National Emissions Inventory (NEI) database as the starting point
for emissions and emissions release characteristics for the source category. The NEI database
contains information about sources that emit HAP and it contains annual air pollutant
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emissions estimates. EPA's industry experts review the source category data for consistency
and completeness. This includes an evaluation of facilities contained in the source category,
the emissions units expected to be included for the processes in the source category, and the
HAP compounds and emissions levels typically seen. If necessary, EPA will conduct a formal
information collection request (CAA, Section 114) for emissions data and other data from the
industry associated with the source category under review. Following the creation of the
initial data set, the EPA performs the technology review and the residual risk assessment. If
appropriate, based on the results of these reviews, the EPA proposes regulatory action for the
source category in a Notice of Proposed Rulemaking (NPRM) published in a Federal Register
notice. The NPRM data sets are available for public review in the rulemaking docket.
Industry, state and local agencies, as well as the public have an opportunity to provide
comments on the data, analyses, and results used to support the proposed action. EPA
incorporates the comments, as appropriate, conducts any re-assessment, and summarizes and
responds to comments before finalizing the action. Through source category-specific
engineering reviews, information collection efforts, and public comment, EPA ensures that
the data used to conduct risk assessments in support of the RTR rulemakings are of high
quality.
In order to put the source category risks in context, we also examine the risks from the entire
"facility," where the facility includes all HAP-emitting operations within a contiguous area
and under common control. In other words, we examine the HAP emissions not only from the
source category emission points of interest, but also from all other emission sources at the
facility for which we have data. Using the most current available NEI data at the time of the
assessment, the EPA develops "facility-wide" emissions estimates. It is important to note that
the NEI facility-wide inventory may not always reflect the level of detail or be representative
of the same temporal period that is found in the source category-specific inventory. Further
information on the NEI, which is developed from federal/state/local/tribal submitted data, can
be found on the EPA's web site at: https://www.epa.gov/air-emissions-inventories/national-
emissions-inventory.
Details on the development of the source data, emissions, and associated uncertainties in the
data for the SOCMI source category can be found in Appendix 1 (.Emissions Inventory Support
Documents). Section 3 provides a summary of the processes and emissions associated with this
source category.
2.2 Dispersion modeling for inhalation exposure assessment
For the residual risk analyses, we estimate both long- and short-term inhalation exposure
concentrations and associated health risks from each facility in the source category. To do
this, we use the Human Exposure Model 4 (HEM4 or HEM-AERMOD) modeling system -
which combines the Human Exposure Model (HEM) with the American Meteorological
Society/EPA Regulatory Model (AERMOD) dispersion modeling system. HEM4 performs
three main operations: atmospheric dispersion modeling, estimation of individual human
exposures and health risks, and estimation of population risks. The approach used in applying
this modeling system is outlined below. Further details are provided in Appendix 2 to this
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document (The HEM4 User's Guide). This section focuses on the dispersion modeling
component.
The dispersion model in the HEM4 modeling system, AERMOD version 21112 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 (USEPA,
2005a). Further details on AERMOD can be found in the AERMOD User's Guide (USEPA,
2021a) and the AERMOD Implementation Guide (USEPA, 2021b).2 The model is used to
develop annual average ambient concentrations through the simulation of hour-by-hour
dispersion from the emission sources into the surrounding atmosphere. Unless data are
available on the hours of operation for a source category, default 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 HEM4 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 2.2-1. AERMOD version 21112 Model Options for RTR Modeling
Modeling Option
Selected Parameter for chronic exposure
Type of calculations
Hourly Ambient Concentration
Source types
Point Volume
Area Polygon
Line Buoyant Line
Receptor orientation
Polar (13 rings and 16 radials)
Discrete (census block centroids) and user-supplied receptors
Terrain characterization
Actual from USGS 1/3-arc-second DEM data
Building downwash
Not Included
Plume deposition/depletion
Not Included
Urban source option
Site Specific (See Appendix 2)
Meteorology
1-year representative NWS from nearest site (838 stations);
791 stations contain 2019 met data, 47 stations contain 2016
through 2018 met data
In HEM4, meteorological data are ordinarily selected from a list of more than 800 National
Weather Service (NWS) surface observation stations across the continental United States,
Alaska, Hawaii, and Puerto Rico, and HEM4 defaults to the station closest to each modeled
facility. We use data from other stations in special circumstances if we have reason to believe
2 An explanation of the updates from the previous version of AERMOD can be found at
https://www.epa.gOv/scram/air-aualitv-dispersion-modeling-preferred-and-recommended-models#aermod and
corresponding updates to HEM can be found https://www.epa.gov/fera/human-exposure-model-users-guides.
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that other data are more representative for certain facilities. In this analysis, the average
distance between a modeled facility and the respective meteorological station was 14 miles
(22 km). The meteorological data in HEM4's library are for a single year, and 2019 is the
most recent full year of available data. EPA's Revisions to the Guideline on Air Quality
Models addresses the regulatory application of air quality models for assessing criteria
pollutants and requires five years of data to capture variability in weather patterns from year
to year. We follow the guideline for air toxics modeling also; however, because dispersion
model runtimes using five years of meteorological data would be too long for RTR source
categories with many sources, we model only a single year. While the selection of a single
year may result in under-prediction of long-term ambient levels at some locations, it may
result in over-prediction at others. The sensitivity of model results to the selection of the
nearest weather station and the use of one year of meteorological data is discussed in "Risk
and Technology Review (RTR) Risk Assessment Methodologies" (USEPA, 2009a).
We use the AERMET meteorological data preprocessor and the Automated Surface
Observing System (ASOS) surface data and Forecast Systems Laboratory (FSL) upper air
data to generate nationwide surface and profile files for input into AERMOD. In 2021, the
Agency released to the public on the EPA's Support Center for Regulatory Atmospheric
Modeling (SCRAM) website both AERMET and AERMOD (version 21112). Appendix 3 to
this document (Meteorological Data for HEM Modeling) provides a complete listing of
meteorological stations and assumptions, along with further details used in processing the data
through AERMET. EPA has posted the AERMET meteorological data (2019) used in this
analysis on the EPA's Fate, Exposure, and Risk Analysis (FERA) website under the Human
Exposure Model (HEM) page.
The HEM4 modeling 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.3 See Appendix 4 of this document {Dispersion Model Receptor Revisions and
Additions) for a discussion of user receptors and centroid location changes specific to this
source category. HEM4 accounts for the effects of multiple facilities when estimating
concentration impacts at each block centroid. We typically combine the impacts of all
facilities within the same source category and assess chronic exposure and risk for all census
blocks4 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). We then calculate ambient
concentrations as the annual average of all estimated short-term (one-hour) concentrations at
each block centroid. We do not consider possible future residential use of currently
uninhabited areas.
To assess the potential impacts from short-term exposures, we estimate reasonable worst-case
one-hour concentrations (i.e., 99th percentile) at the census block centroids and at points closer
3 We also estimate ambient concentrations for a grid of polar receptors that is specific to each facility, and these
receptors are used to interpolate concentrations for census blocks in the outer part of the modeling domain, and
for finding the maximum offsite concentrations.
4 Census blocks, the finest resolution available in the census data, are typically comprised of approximately 50
people or about 20 households.
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to the facility (using either the polar receptors or user-specified receptors) that represent
locations where people may be present for short periods5. Note that this is in contrast to the
development of ambient concentrations for evaluating long-term exposures, which we
perform only for occupied census blocks. Since short-term emission rates are needed to screen
for the potential for hazard via acute exposures, and since the emission data typically contain
only annual emission totals, we generally apply the assumption to all source categories that
the maximum one-hour emission rate from any source is ten times the average annual hourly
emission rate for that source. However, sources may emit on a more intermittent basis and
source category-specific data may support the use of engineering judgement to determine
peak hourly emissions for any given process. Further information on the factor used to
estimate short-term emissions for this source category is provided in Appendix 1, and further
discussion of the acute risk assessment can be found in Section 2.4.
We determine census block elevations for HEM4 nationally from the US Geological Survey
1/3 Arc Second National Elevation Dataset, which has a spatial resolution of about 10 meters.
Each polar receptor is assigned the highest elevation of any census block in its neighborhood
(all blocks closer to that polar receptor than any other polar receptor). If an elevation is not
provided for an emission source, the model uses the average elevation of all polar receptors
on the innermost polar 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 are estimated and used in the AERMOD modeling,
see Appendix 2 of this document.
2.3 Estimating chronic human inhalation exposure
We use the estimated annual average ambient air concentration of each HAP at each census
block centroid or user-defined receptor as a surrogate for the lifetime inhalation exposure
concentration of all the people who reside in the census block. The risk assessment does not
consider either the short-term or long-term behavior (mobility) of the exposed populations
and its potential influence on their exposure.
We do not address short-term human activity, including indoor air concentrations. Our
experience with our national Air Toxics Screening Assessment (AirToxScreen), the successor
to the National Air Toxics Assessment (NATA), which models daily human activity using
EPA's HAPEM. suggests that given our current understanding of the ratio of exposure
concentrations to ambient values, including short-term human activity in RTR analyses
would, on average, reduce risk estimates by up to about 25 percent for particulate HAP and
typically by much less for gaseous HAPs. To ensure the risk characterization is health
protective, EPA risk assessors do not include this small potential reduction in exposure
concentrations when calculating risks.
We do not address long-term migration or population growth or decrease over the 70-year
modeling period. Instead, we assume that each person's predicted exposure is constant over
5 Generally, we estimate these concentrations at locations 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).
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the course of their lifetime, which is assumed to be 70 years. 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 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
higher risk levels.
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 follow a similar approach to that for chronic health risk
assessments under the residual risk program, in that we begin with a screening assessment and
then, if appropriate, perform a refined assessment.
The approach for the acute health risk screening assessment is designed to eliminate from
further consideration those facilities for which we have confidence that no acute adverse
health effects of concern will occur. For this screening assessment, we use readily available
data and conservative assumptions for emission rates, meteorology, and exposure location
that, in combination, approximate a reasonable worst-case exposure.
The following are the steps we take and assumptions we make in the acute screening
assessment:
• When available, we use peak 1-hour emission data obtained from data collection
efforts or estimated based on the operating characteristics and engineering judgement
of facility emission sources; otherwise, we use a default emission adjustment factor of
10 based on an analysis using a short-term emissions data set from a number of
sources located in Texas (originally reported on by Allen et al. 2004) (see Appendix 5
of this document, Technical Support Document for Acute Risk Screening Assessment).
• We assume 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 are
assumed to be the sum of the maximum concentrations due to each emission point,
regardless of whether those maximum concentrations occurred during the same hour.
• Reasonable worst-case air dispersion6 (from one year of local meteorology) is
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 is assumed to be at the location of the reasonable worst-case modeled
impact, but no nearer to the source than 100 meters.
6 An explanation of reasonable worst-case air dispersion is provided in Appendix 5 of the report: Technical
Support Document for A cute Risk Screening Assessment.
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As a result of this screening assessment, the 99th percentile HAP concentration is compared to
multiple acute dose-response values for the HAP being assessed to determine whether a
possible acute health risk might exist. The acute dose-response values are described in section
2.7.2 of this report.
A facility will either be found to pose no potential acute health risks (i.e., it will "screen out")
or will need to undergo a more refined assessment. When we identify levels of a HAP that
exceed its acute health benchmarks, we perform a more refined assessment, if possible.
Where we have used engineering judgement to estimate emissions, a refinement may be to
obtain facility-specific data on HAP emissions. Other refinements may include the temporal
pattern of emissions (number of working hours, batch vs continuous operation), the location
of emission points, 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 may be used to determine that acute exposures are not a concern, and
significant additional data collection is not necessary. See Section 3 of this document for the
approach used for this source category.
2.5 Multipathway human health risk assessment
Due to the potential for significant human health risks due to exposure via routes other than
inhalation (e.g., ingestion), we determine whether any sources emit HAP known to be
persistent and bioaccumulative in the environment (PB-HAP).7 The set of PB-HAP
compounds or compound classes initially identified for potential screening assessment (from
EPA's Air Toxics Risk Assessment (ATRA) Library) included the following: cadmium
compounds, chlordane, chlorinated dibenzodioxins and furans (dioxins), l,l-dichloro-2,2-
bis(p-chlorophenyl) ethylene (DDE), heptachlor, hexachlorobenzene, hexachlorocyclohexane,
lead compounds, mercury compounds, methoxychlor, polychlorinated biphenyls (PCB),
polycyclic organic matter (POM), toxaphene, and trifluralin. Of these, EPA identified
cadmium compounds, dioxins, mercury compounds, lead, POM, as well as arsenic, as PB-
HAP of primary concern, based on assessment of national emission totals, toxicity
considerations, and bioaccumulation potential. We assess these six PB-HAP for human health
risks due to non-inhalation exposure.
We use a tiered approach to evaluate emissions of these PB-HAP for potential non-inhalation
risks. This approach is designed to eliminate from further consideration those facilities for
which we have confidence that human health risks will not occur due to non-inhalation
exposure to their PB-HAP emissions. The approach was developed for use with EPA's peer-
reviewed Total Risk Integrated Methodology: Fate. Transport, and Ecological Exposure
(TRIM.FaTE) model.
For each carcinogenic PB-HAP, we have derived a screening threshold emission rate at which
the maximum excess lifetime cancer risk would be 1 -in-1 million. For each PB-HAP that
7 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. When this report is specifically
referring to lead, the term is spelled out (i.e., the two-letter chemical symbol for lead is not used in this
document).
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causes noncancer health effects, we have derived a screening threshold emission rate for
which the maximum HQ would be 1. The ratio of facility emissions to the screening threshold
emission rate is termed a "screening value;" facility emissions that exceed the screening
threshold emission rate have a screening value greater than 1. A screening value greater than 1
in any of the tiered screening methods represents a high-end estimate of what the risk or
hazard may be; it cannot be equated with a risk value or a HQ (or HI). For example, for a
carcinogen, a screening value of 30 (i.e., facility emissions are 30 times above the screening
threshold emission rate) means that we are confident that the cancer risk is lower than 30-in-l
million. Similarly, for a non-carcinogen, a screening value of 2 (i.e., facility emissions are 2
times above the screening threshold emission rate) can be interpreted to mean that we are
confident that the noncancer HQ would be lower than 2.
For Tier 1, 2, and 3 assessments, we use hypothetical exposure scenarios to assess whether
non-inhalation exposures pose a potential human health risk. Exposure scenarios were
developed to simulate generic gardening and subsistence farming and subsistence fishing
lifestyles. Each screening exposure scenario is designed to represent the upper end of the
range of possible exposure levels, such that it is a conservative but not impossible scenario.
The exposure scenarios were developed for use in conjunction with the TRIM.FaTE model.
These hypothetical exposure scenarios and associated ingestion exposure pathways are shown
in Table 2.5-1.
Table 2.5-1. Multipathway Scenarios and Ingestion Pathways
Hypothetical
Exposure
Scenario
Fish
Breast
Milk3
Beef/Pork
/Chicken
Dairy
Milk
Eggs
Soil
Fruits and
Vegetables b
Combined
Fisher and
Farmer
(Tier 1)
X
X
X
X
X
X
X
Fisher
(Tier 2)
X
X
Farmerc
(Tier 2)
X
X
X
X
X
X
Gardener
(urban or
rural)
(Tier 2)
X
X
X
X
Pollutants of
Concern d
Hg,
Cd,
As,
dioxin,
POM
dioxin
As,
dioxin,
POM
As,
dioxin,
POM
As,
dioxin,
POM
As,
dioxin,
POM
As, dioxin,
POM
a Health risks from the breast milk pathway are only associated with exposure to dioxins.
b Both protected and unprotected fruits and vegetables are included.
0 This scenario may be included in a Tier 2 assessment in cases where the Tier 2 farmer scenario exceeds a level
of concern and further screening is required to reflect alternative ingestion rates, that may be more common for
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the area (i.e., either in an urban or rural environment).
d The health endpoint for exposure to Hg (as methylmercury) and Cd is noncancer and the health endpoint for
exposure to As (as inorganic arsenic), dioxin, and POM is cancer.
For the Tier 1 screening assessment, we determine whether the facility-specific emission rates
for each emitted PB-HAP are high enough to create the potential for significant non-
inhalation human health risks under reasonable worst-case conditions. We do this by
comparing the facility-specific emission rates to the screening threshold emission rates for
each PB-HAP for a hypothetical upper-end screening exposure scenario - the combined fisher
and farmer scenario. The subsistence fisher scenario assumes a high-end fish consumption
rate of 373 g/day for adults, a 99th percentile ingestion rate (Burger, 2002); fish consumption
rates for other age groups are presented in Appendix 6. The farmer scenario involves an
individual that lives for a 70-year lifetime on a farm near the source and consumes produce
grown, and meat and animal products raised, on the farm. The ingestion rates used for these
food groups, and for incidental soil ingestion, are set at the 90th percentile of EPA's Exposure
Factors Handbook: 2011 Edition (USEPA, 2011) and are considered upper-bound levels. The
fisher and farmer exposure scenarios are combined for the Tier 1 TRIM.FaTE model
application. See Appendix 6 (Technical Support Document for TRIM-Based Multipathway
Tiered Screening Methodology for RTR) for a complete discussion of the development and
testing of the screening scenario and the screening threshold emission rates.
For those facilities with PB-HAP emissions that exceed the Tier 1 screening threshold
emission rate, we conduct a Tier 2 multipathway screening assessment. For the Tier 2
screening assessment, we refine the assessment by using the facility locations and considering
two separate exposure scenarios - the fisher scenario and the farmer scenario, with the home
gardener scenario as appropriate (rural or urban classification) when the Tier 2 farmer
scenario exceeds a level of concern. In some cases, if supported by site-specific information,
the subsistence farmer scenario is retained throughout the screening and potentially
throughout the site-specific multipathway assessment, if needed. For each facility, we use the
Tier 1 PB-HAP screening threshold emission rate, but with adjustments based on the ingested
media and based on an understanding of how exposure concentrations estimated for the
screening scenario change with use of the local meteorology and environmental assumptions.
For Tier 2, separate farmer and fisher scenarios replace the Tier 1 combined fisher and farmer
scenario as more likely exposure scenarios. The farmer and gardener scenarios are primarily
evaluated for exposure to carcinogenic PB-HAP (i.e., arsenic, dioxin, and POM) because the
evaluated non-carcinogens (i.e., mercury and cadmium) do not readily accumulate in soil and
the farm food chain, when compared to the amounts observed in fish tissue.
For the gardener scenario, the Tier 1 PB-HAP screening threshold emission rates are adjusted
for the farmer to reflect exposure only through soil and farm produce (fruits, eggs, and
vegetables), based on the rural/urban classification of the facility site (with urban gardeners
growing and ingesting less home-grown produce than rural gardeners). The gardener
scenarios (rural and urban) involve an individual that maintains a garden and consumes
produce from this garden for 70 years at his/her residence. The evaluated locations of the
gardener correspond to the maximum impacted residential receptor according to the RTR
inhalation cancer assessment for each of the 8 wind octants (N, NE, E, SE, ...) for all
carcinogenic HAPs combined. The screening threshold emission rate can be different at each
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of these gardener locations, based on distance from the facility and based on local
meteorology conditions. The ingestion rates used for the food groups are set at the 90th
percentile and mean values for rural and urban, respectively, based on data from EPA's
Exposure Factors Handbook: 2011 Edition (USEPA, 2011); both gardeners have incidental
soil ingestion rates equal to those of the farmer. The largest of the gardener screening values
is identified for each PB-HAP.
The fisher scenario is conducted for all of the currently evaluated PB-HAP, whose Tier 1 PB-
HAP screening threshold emission rates are adjusted to reflect exposure only through fish
ingestion. For the Tier 2 assessment, to fulfill the adult ingestion rate for the fisher scenario, if
needed, more than one lake may be included in the modeling in order to reach a cumulative
total of 373 acres and achieve the 373-g/day fish ingestion rate. A complete discussion of the
bioassay studies used to support the assumption that the biological productivity limitation of
each lake is 1 gram of fish caught and consumed per acre of water per day is provided in
Appendix 6 of this document. The screening threshold emission rate can be different at each
lake location, based on distance from the facility and based on local meteorology conditions.
If we need to include more than one lake in the Tier 2 screening assessment to achieve the
373 g/day ingestion rate, we begin with the lake with the highest modeled chemical
concentration of a given PB-HAP group and "fish" up to the lake's biological productivity.
We then systematically proceed to other lakes based on concentration, until the 373 g/day
target is met. A maximum travel radius of 50 km relative to the facility is used to maintain a
realistic scenario for the fisher. 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 from each lake multiplied by the chemical concentration in fish).
If the highest-concentration lake is at least 373 acres in size, the adult fisher catches and
consumes 373 g/day of fish from that lake. If the cumulative size of multiple visited lakes
exceeds 373 acres, the model includes from the final lake only the amount of fish necessary to
satisfy the ingestion rate (i.e., to reach 373 g/day). If the total acreage of lakes within 50 km is
less than 373 acres, the screening result reflects a reduced ingestion rate based on the smaller
lake acreage. The order of fished lakes for a facility follows the order of PB-HAP
concentration in fish from highest to lowest based on the facility's emissions. However, the
resulting screening value calculations described above also potentially consider chemical
inputs from emissions from multiple facilities. If a fished lake for one facility ("Facility A") is
also within 50 km of another facility ("Facility B") in the source category, then the lake
receives chemical input from emissions from two facilities. The order of fished lakes for
Facility A considers only Facility A's chemical inputs to the lake, but the final fisher
screening values for Facility A include the summed chemical inputs of Facility A and Facility
B. If that lake was also fished for the Facility B scenario, then the same process would be
applied to Facility B.
The Tier 2 assessment yields a facility-specific screening value for each PB-HAP for the
fisher scenario, farmer scenario, and the gardener scenario if warranted. If information is
available to identify subsistence farming operations, the Tier 2 assessment will also include a
screening value for the farmer site-specific location. Tier 2 screening values are evaluated for
the source category to determine whether further refined screening is necessary for those
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facilities that may pose a significant risk. A finding that a facility's emissions exceed the Tier
2 screening threshold emission rate does not necessarily mean that multipathway impacts are
significant, only that we cannot rule out that possibility based on the results of the screening
assessment. See Appendix 6 of this document for a complete discussion of the Tier 2
screening assessment.
For facilities for which the Tier 2 screening value(s) indicate a potential health risk to the
public, we can conduct a Tier 3 multipathway screening assessment. The Tier 3 screening
assessment has three individual stages; we progress through these stages until the facility's
screening values indicate that the emissions are unlikely to pose health risks to the public, or
until all three stages are complete.
The first stage of a Tier 3 screening assessment, the lake-assessment stage, is a refinement of
the fisher scenario. We examine the fished lakes from Tier 2 and evaluate the existence, the
potential purpose, the accessibility and fishability, and the suitability of the lakes for the
models and methods used in the screening assessments. We do not reasonably expect a
subsistence fisher to catch and consume fish from lakes or ponds that are for industrial or
wastewater disposal; are covered in thick plant growth (e.g., swamps or marshes); are clearly
closed to public use; or no longer exist (i.e., filled or drained). TRIM.FaTE is not configured
to model chemical processes and environmental fate and transport mechanisms in saltwater or
brackish waters, nor is it configured to model the very large watersheds and water dynamics
of rivers, bays or very large lakes (e.g., larger than 100,000 acres)8. We use aerial imagery
and web inquires to evaluate whether any Tier 2 fished lakes meet these disqualifying criteria
and, if so, remove those lakes from all future screening assessments. If we remove a lake from
a facility's assessment, and the total acres of fished lakes drops below the target of 373 acres,
we evaluate the previously unfished lake with the highest chemical concentration, and so on,
until the sizes of the qualifying lakes collectively comprise at least 373 acres or all lakes have
been evaluated. We then rerun the fisher screening scenario with the revised lake data set. If
the PB-HAP emissions for a facility exceed the fisher screening threshold emission rate based
on the revised lake data set, we can conduct the next stage of the Tier 3 screening assessment
(i.e., the plume-rise screen); otherwise, the emissions are considered unlikely to pose
significant health risks in the fisher scenario.
The second stage of a Tier 3 screening assessment, the plume-rise stage, is a refinement of the
previously assessed scenarios (i.e., Tier 2 site-specific farmer [if known], Tier 2 gardener,
Tier 3 lake-assessment fisher) where emissions exceeded screening threshold emission rates
and may pose health risks. We use site-specific hourly meteorology and facility-specific
emission-point characteristics to estimate the fraction of annual emissions that stay within
TRIM.FaTE's mixing layer where exposure occurs (i.e., that do not exit the mixing layer). In
Tiers 1 and 2, all chemicals are emitted inside the mixing layer and are available for ground-
level exposure. In reality, meteorological conditions and emission-point characteristics can
cause emissions occasionally to reach higher than the mixing layer. In TRIM.FaTE, any
8 Very large lakes and bays (i.e., those larger than 100,000 acres) are not included because their watersheds are
too large and 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.
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emissions exiting the mixing layer do not reenter the mixing layer, resulting in no ground-
level exposure for those emissions. In this Tier 3 stage, we use thermodynamic equations with
local hourly meteorology and facility stack parameters to calculate hourly plume-rise heights.
The fraction of annual hours during which the plume-rise height is less than the mixing-layer
height equals the fraction of annual emissions available for human exposure in the screening
assessment. We calculate these fractions for the location of each fished lake and for each
relevant farm/garden because lakes and farms/gardens can be in different directions from the
facility; thus, these calculations are conditional on wind direction. The results of this stage of
Tier 3 are revised fisher and/or farmer/gardener screening values for each relevant PB-HAP
and facility, accounting for emissions deposited above the mixing layer. If the revised
screening value still indicates potential health risks to the public, we can proceed to the final
stage of the Tier 3 screening assessment (i.e., the time-series screen); otherwise, the PB-HAP
emissions are considered unlikely to pose significant risks.
In the third and final stage of a Tier 3 screening assessment, the time-series assessment, we
can conduct new runs of TRIM.FaTE for each relevant lake and/or garden location for a
facility for every PB-HAP that represents a risk concern based upon the Tier 3 plume-rise
assessment. For these model runs, we start with the screening configuration corresponding to
the lake and/or garden location, and we use site-specific hourly meteorology and the hourly
plume-rise values calculated in the Tier 3 plume-rise assessment. Allowing TRIM.FaTE- to
model chemical fate and transport with hour-by-hour changes in meteorology and plume rise
produces a more accurate estimate of chemical concentrations in media of interest, as
compared to the static values used in Tier 2 and the post-processing adjustments made in the
Tier 3 plume-rise assessment. If a facility's model-estimated PB-HAP screening-level cancer
risk is below 1-in-l million (or screening-level HQ is below 1 for non-carcinogens), the
emissions are considered unlikely to pose significant risks.
If a facility's PB-HAP Tier 3 screening results still indicate a potential health risk to the
public and data are available, we may elect to conduct a more refined multipathway
assessment. A refined assessment replaces some of the assumptions made in the screening
with site-specific data. The refined assessment also uses the TRIM.FaTE model and facility-
specific emission rates for each PB-HAP. Many variables are available to consider in a
refined multipathway assessment, and we have developed a protocol to maintain consistency
across source categories. This protocol can be found in Appendix 7 of this document
(Protocol for Site-Specific Multipathway Risk Assessment) and details of the site-specific
multipathway assessment can be found in Appendix 11 of this document {Site-Specific
Human Health Multipathway Residual Risk Assessment Report).
Lead
We take a different approach for assessing lead compounds than we do for other HAP. In
evaluating the potential multipathway risks from emissions of lead compounds, rather than
developing a screening emission rate for them, we multiply the maximum annual estimated
atmospheric concentration by 4, to represent a "worst case" 3-month concentration, and
compare it to the national ambient air quality standard (NAAQS) for lead (0.15 ug/m3, 3-
month rolling average). Values below the NAAQS are considered to have a low potential
for multipathway risks. Where values exceed the NAAQS, and where data are available to
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support doing so, further assessment is performed. We calculate 3-month rolling averages
based on modeling and/or monitoring information. Any 3-month rolling average
concentration that is above 0.15 ug/m3 indicates a potential public health concern.
The primary NAAQS for lead, a public health policy standard, incorporates 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 noted above, the primary NAAQS is set 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 the lead
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 estimate ambient lead concentrations due to the source
category emissions.
The Administrator judged that the lead NAAQS 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 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).
It should be noted that the comparison to the Lead NAAQS described above does not
account for possible population exposures to lead from sources other than the one being
modeled, such as exposure via consumption of water from untreated local sources or
ingestion of locally grown food.
We further note that comparing ambient lead concentrations to the secondary NAAQS for
lead, 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.
See Appendix 11, Attachment A of this document (Application of the Lead NAAQS for RTR
Risk Assessments) for more detailed information on the lead screening assessment.
2.6 Environmental risk assessment
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
screening assessment focuses on the following eight environmental HAP:
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• Six persistent bioaccumulative HAP (PB-HAP) - cadmium, dioxins, POM,
mercury (both inorganic mercury and methylmercury), arsenic, and lead;
• Two acid gases - hydrochloric acid (HC1) and hydrofluoric acid (HF).
HAP that persist and bioaccumulate are of particular environmental concern because they
accumulate in the soil, sediment, and water. The acid gases - HC1 and HF - were included
due to their well-documented potential to cause direct damage to terrestrial plants. See
Appendix 9 of this document (Environmental Risk Screening Assessment) for a more
detailed discussion of the environmental risk screening assessment.
For the environmental risk screening assessment, EPA first determines whether any facilities
in the source category emit any of the eight environmental HAP. If one or more of the
environmental HAP are emitted by at least one facility in the source category, we proceed to
the second step of the environmental risk screening assessment.
For cadmium, mercury, POM, arsenic, and dioxins, the environmental screening assessment
consists of the same three tiers used in the multipathway human health risk assessment (see
Section 2.5). In the first tier, the same TRIM.FaTE modeling used in human health risk
assessment is conducted, using reasonable worst-case environmental conditions to identify
screening threshold emission rates corresponding to ecological benchmarks for soil, fish,
surface water, and sediment. For each facility and PB-HAP, facility emissions are compared
to these screening threshold emission rates to determine the potential for significant impacts
on off-site ecological receptors. The ratio of facility emissions to the screening threshold
emission rate is termed a "screening value." Facility emissions that exceed the screening
threshold emission rate have a screening value greater than 1, and risks above levels of
concern for ecological receptors are possible. Screening values below 1 indicate that risks to
ecological receptors are likely below levels of concern.
For those facilities with PB-HAP emissions that exceed a Tier 1 screening threshold
emission rate, we conduct a Tier 2 screening assessment. In Tier 2, the Tier 1 screening
threshold emission rates are adjusted to account for local meteorology and environmental
assumptions. For lake-related ecological receptors, actual locations of lakes within 50 km of
the facility are identified, and the screening threshold emission rate can be different at each
lake location based on distance from the facility and based on local meteorology conditions.
After the screening value (i.e., ratio of facility emissions to screening threshold emission
rate) is calculated at each lake, the largest screening value is identified. Screening threshold
emission rates for soil receptors are evaluated at many locations surrounding the facility and
are also impacted by distance from facility and local meteorology. For soil receptors in Tier
2, we are interested in the overall average screening value across all soil receptors (for a
given facility and PB-HAP), and we are also interested in the total area in the vicinity of the
facility where screening values are above 1 (for a given facility and PB-HAP). If a lake-
related screening value is above 1, or the soil screening value is above 1 at any location, or the
overall average soil screening value is above 1, it does not necessarily mean that the
ecological effects are significant, but only that we cannot rule out that possibility. For
facilities with Tier 2 screening values above 1, we can evaluate their emissions further in
Tier 3.
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Like in the multipathway human health risk assessment, in Tier 3 of the environmental
screening assessment, we examine the suitability of the lakes around the facilities to support
life and remove those that are not (e.g., lakes that have been filled in or are industrial
ponds), adjust emissions for plume-rise, and conduct hour-by-hour time-series assessments.
For the lake assessment, we remove from the screening any lakes that appear to be
industrial, for wastewater disposal, or no longer exist. TRIM.FaTE is not configured to
model chemical processes and environmental fate and transport mechanisms in saltwater or
brackish waters, nor is it configured to model the very large watersheds and water dynamics
of rivers or very large lakes (e.g., larger than 100,000 acres); these types of water bodies are
also removed from the screening assessment. Unlike the multipathway human health risk
assessment, we assume that if lakes that are swampy or are not publicly accessible, they still
can support ecological life and some animals will still eat from them. After lakes are
removed that meet these disqualifying criteria, lake-related receptors are rescreened. For the
plume-rise assessment, as in the human health assessment, we adjust the facility's
previously calculated screening value based on the fraction of facility emissions that remain
in the mixing layer where exposure occurs, after accounting for plume rise (which is based
on site-specific meteorology and facility-specific emission-point characteristics). If these
Tier 3 adjustments still indicate that ecological risks could be above levels of concern (i.e.,
screening values are above 1), as in the human health assessment, we can conduct new
TRIM.FaTE modeling using the screening configuration corresponding to the relevant lake
and/or soil locations, site-specific hourly meteorology, and hourly plume-rise values. If such
modeling results in screening-level media concentrations or doses above benchmark levels,
we may elect to conduct a more refined assessment using more site-specific information. If,
after additional refinement, the media concentrations or doses are above benchmark levels,
the facility may have the potential to cause adverse environmental effects.
For acid gases, the environmental screening assessment 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 screening assessment 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 an ecological risk
screening assessment, EPA identifies a potential for adverse environmental effects to plant
communities from exposure to acid gases when the average off-site ambient air
concentration over the modeling domain for a facility exceeds the ecological benchmark for
that acid gas. In such cases, we further investigate factors such as the magnitude of the
exceedance and the characteristics of the area of exceedance (e.g., land use of exceedance
area, size of exceedance area) to determine whether the facility's emissions have the
potential to cause adverse environmental effects.
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 assessment using HEM4. To evaluate the
potential for adverse environmental effects from lead, we compare the average annual
modeled air concentrations of lead around each facility in the source category to the level of
the secondary NAAQS for lead. The secondary lead NAAQS is a reasonable means of
evaluating environmental risk because it is set to provide substantial protection against
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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." 9 We investigate any modeled exceedances of the
lead NAAQS in a manner similar to that noted above for acid gases.
2.7 Community-based risk assessment
To estimate inhalation cancer exposure more comprehensively, the EPA has developed a
community-based risk assessment. This assessment estimates the total inhalation cancer risk
from all nearby point sources of air toxics. Specifically, this analysis combines the modeled
inhalation cancer risk from the residual risk analyses with modeled impacts from all other
local stationary point sources of HAP for which we have emissions data ("other sources").
The source category inhalation cancer risk is modeled as described in sections 2.2 and 2.3.
The cancer risks for other sources are estimated using a similar approach. Other sources are
modeled using AERMOD at populated census block receptors (based on the 2010 census)
within 10km of any emission point at a HON facility. Emissions are primarily based on the
2018 NEI. Meteorological data is derived for 12 km gridded data from version 3.8 of the
Weather Research and Forecasting (WRF)10 model for calendar year 2018 and processed
through the Mesoscale Model Interface Program (MMIF)11.
Using the modeled results from the residual risk analysis for the source category and with the
other nearby stationary sources we can combine the impacts to estimate total cancer risks due
to the inhalation of all HAP emitted by all point sources in the area. To be consistent with the
model domain from the other point source modeling, we specifically focus our assessment on
the census blocks within 10km of a facility in the source category.
2.8 Dose-response assessment
2.8.1 Sources of chronic dose-response information
Dose-response assessments (carcinogenic and non-carcinogenic) for chronic exposure (either
by inhalation or ingestion) for the HAP reported in the emissions inventory for this source
category are based on the EPA Office of Air Quality Planning and Standards' (OAQPS)
existing recommendations for HAP (USEPA, 2021c). This information has been obtained
from various sources and prioritized according to (1) conceptual consistency with EPA risk
assessment guidelines and (2) level of peer review received. The prioritization process was
aimed at incorporating into our assessments the best available science with respect to dose-
9 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."
10 https://www.mmm.ucar.edu/models/wrf
11 https://www.epa.gOv/scram/air-quality-dispersion-modeling-related-model-support-programs#mmif
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response information. The recommendations are based on the following sources, in order of
priority:
1) U.S. Environmental Protection Agency (EPA). EPA has developed dose-response
assessments for chronic exposure for many HAP. 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 |ig/m3 in 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 dose-response assessment information in several forms, based on
the level of review. The Integrated Risk Information System (IRIS) 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, as well as
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 Agenda for 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 MO As 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) U.S. Agency for Toxic Substances and Disease Registry (ATSDR). ATSDR, which
is part of the US Department of Health and Human Services, develops and publishes
Minimal Risk Levels (MRLs) 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
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for non-neoplastic endpoints." The MRL is defined as "an estimate of daily human
exposure to a substance that is likely to be without an appreciable risk of adverse
effects (other than cancer) over a specified duration of exposure." ATSDR describes
MRLs as substance-specific estimates to be used by health assessors to select
environmental contaminants for further evaluation.
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. The
noncancer information includes available inhalation health risk guidance values
expressed as chronic inhalation reference exposure levels (RELs). CalEPA defines the
REL as a concentration level at (or below) which no health effects are anticipated, a
concept that is substantially similar to EPA's noncancer dose-response assessment
perspective. CalEPA's dose response assessments for carcinogens and noncarcinogens
are available on-line.
For certain HAP, the dose-response information, based on this prioritization, is limited. To
address data gaps, increase accuracy, and avoid underestimating risk, we made additional
changes to some of the chronic inhalation exposure values. These important changes, outlined
below and reflected in Appendix 8 (Dose-Response Values Used in the RTR Risk
Assessments) to this document, are as follows:
1) Acrolein. The EPA derived an IRIS RfC for acrolein in 2003 (USEPA, 2003), which
was based on a 1978 subchronic rodent study that identified a lowest-observed-
adverse-effect level (LOAEL) for nasal lesions (Feron et al., 1978). In 2008, the
California EPA derived a chronic reference exposure level for acrolein that was based
on a more recent subchronic rodent study, which identified a no-observed-adverse-
effect level (NOAEL) for nasal lesions (CalEPA, 2008; Dorman et al., 2008). Because
both studies identified nasal lesions as the critical effect and because the Dorman et al.
(2008) study identified a NOAEL, we have decided to use the CalEPA REL for
acrolein in this RTR risk assessment. The EPA is in the process of updating the IRIS
RfC for acrolein. If the RfC is updated prior to signature of the final rule, we will use
it in the risk assessment for the final rule.
2) Manganese. The EPA considers the ATSDR MRL for manganese (Mn) the most
appropriate chronic inhalation reference value to be used in RTR assessments. There is
an existing IRIS RfC for Mn (USEPA, 1993a), and ATSDR published an assessment
of Mn toxicity which includes a chronic inhalation reference value (i.e., an ATSDR
Minimal Risk Level, MRL). (ATSDR, 2012). Both the 1993 IRIS RfC and the 2012
ATSDR MRL were based on the same study (Roels et al., 1992); however, ATSDR
used updated dose-response 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 reference value to use in RTR risk assessments.
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3) Polycyclic Organic Matter. EPA has identified appropriate UREs for many
individual compounds of POM, published in the sources used for RTR risk
assessments. When an individual POM compound is reported in the emission
inventory for the source category, we use the appropriate URE for that compound.
However, if in the emission inventory for the source category a POM compound is
reported for which EPA has not identified a URE, or when POM are not speciated into
individual compounds, then EPA applies simplifying assumptions so that cancer risk
can be quantitatively evaluated without substantially under- or over-estimating risk
(which can occur if all reported POM emissions were assigned the same URE). To
accomplish this, EPA places each POM compound into one of eight POM groups,
generally defined by toxicity and the estimated emission profile of POM compounds.
POM Groups 1 and 2 include unspeciated POM (emissions reported as "polycyclic
organic matter") and individual POM compounds with no URE assigned from the
sources used in RTR risk assessments. With two exceptions, both Groups 1 and 2 are
assigned a URE equal to 5 percent of that for pure benzo[a]pyrene; the two exceptions
are benzo[a]fluoranthene and generic "benzofluoranthenes", which received the URE
of benzo[b]fluoranthene. POM Groups 3 through 7 comprise POM compounds for
which UREs are available from the sources used for RTR risk assessments, except for
benzo[b+k]fluoranthene and benzo[g,h,i]fluoranthene which receive the URE of
benzo[b]fluoranthene. If reported emissions are for a specific compound in these
groups, then EPA evaluates the cancer risk of the compound using its unique URE if
one has been derived or its group URE if one has not been specifically derived. If the
reported emissions are for a specific POM group rather than a compound within the
group, then EPA evaluates the cancer risk of the POM group using a URE value that is
close to the average of the UREs of the individual compounds within the group. POM
Group 8 is composed of unspeciated polycyclic aromatic hydrocarbons (PAH)
reported as 7-PAH and are assigned a URE equal to approximately 18 percent of that
for pure benzo[a]pyrene. In addition, we have concluded that three PAHs—
anthracene, phenanthrene and pyrene—are not carcinogenic and therefore no URE is
assigned. Details of the analysis that led to this conclusion can be found in the
document titled Development of a Relative Potency Factor (RPF) Approach for
Polycyclic Aromatic Hydrocarbon (PAH) Mixtures: In Support of Summary
Information of the Integrated Risk Information System (IRIS).
4) Glycol Ethers. Often in an emission inventory, the glycol ethers are reported only as
the total mass for the entire group without distinguishing among individual glycol
ether compounds. In other cases, emissions of individual glycol ether compounds that
had not been assigned dose-response values were reported. To avoid underestimating
the health hazard associated with glycol ethers, we protectively apply the RfC for
ethylene glycol methyl ether (the most toxic glycol ether for which an assessment
exists) to glycol ether emissions of unspecified composition.
5) Lead. We consider the primary NAAQS for lead, which incorporates an adequate
margin of safety, to be protective of all potential health effects for the most susceptible
populations. The NAAQS was developed using the EPA Integrated Exposure, Uptake,
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Biokinetic Model, using the best available toxicity and dose-response information on
the noncancer adverse impacts of lead. The NAAQS for lead was set to protect the
health of the most susceptible children and other potentially at-risk populations against
an array of adverse health effects, most notably including neurological effects,
particularly neurobehavioral and neurocognitive effects (which are the effects to
which children are most sensitive). The lead NAAQS rolling 3-month average level of
lead in total suspended particles is used in the RTR risk assessment as a screening
value for chronic noncancer hazard.
6) 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,
such as the National Toxicology Program (NTP) in their 14th Report of the
Carcinogens (RoC) (NTP, 2016), the International Agency for Research on Cancer
(IARC, 1990), and other international agencies (WHO, 1991) that consider all nickel
compounds to be carcinogenic, we currently consider all nickel compounds to have the
potential of being carcinogenic to humans. The 14th 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). 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 values12 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 are 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.
7) Carbonyl Sulfide. Although the health effects data for carbonyl sulfide (COS) are
very limited, a series of studies (Morgan et. al., 2004; Herr et. al., 2007; Sills et. al.,
2004) conducted by the National Toxicology Program have shown that the major
concern regarding exposure to COS is its potential for neurotoxicity. These studies
have shown consistently and at the same range of COS concentrations that the brain is
12 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/summarv/nickel tech b.pdf) and the other by the Texas Commission on
Environmental Quality
(http://www.tcea.texas.gov/assets/public/implementation/tox/dsd/facts/nickel & compounds.pdf).
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a target organ for COS toxicity. Since appropriate health effects benchmarks have not
been derived by our preferred sources of dose-response data including IRIS, ATSDR,
and Cal EPA, the EPA has used the data from the above referenced studies to derive a
chronic screening benchmark level for COS. A chronic screening level of 163 |ig/m3
was developed for COS from a No Observed Adverse Effects Level (NOAEL) of 200
ppm based on brain lesions and neurophysiological alterations in rodents. Additional
details on the derivation of the chronic screening level for COS can be found in
Appendix 8.
8) Pollutant Groups. In the case of HAP groups such as cyanide compounds, mercury
compounds, antimony compounds and others, the most conservative dose-response
value in the chemical group is used as a surrogate for other compounds in the group
for which dose-response values are not available. This is done to examine, under
conservative assumptions, whether those HAP that lack dose-response values may
pose an unacceptable risk and require further examination.
9) Mutagenic Mode of Action. For carcinogenic chemicals acting via a mutagenic
mode of action (i.e., chemicals that cause cancer by damaging genes), we estimate
risks to reflect the increased carcinogenicity of such chemicals during childhood. This
approach is explained in detail in the Supplemental Guidance for Assessing
Susceptibility from Early-Life Exposure to Carcinogens. Where available data do not
support a chemical-specific evaluation of differences between adults and children, the
Supplemental Guidance recommends using the following default adjustment factors
for early-life exposures: increase the carcinogenic potency by 10-fold for children up
to 2 years old and by 3-fold for children 2 to 15 years old. These adjustments have the
aggregate effects of increasing by about 60 percent the estimated risk (a 1.6-fold
increase) for a lifetime of constant inhalation exposure. EPA uses these default
adjustments only for carcinogens known to be mutagenic for which data to evaluate
adult and juvenile differences in toxicity are not available. The UREs for several HAP
(see Appendix 8) were adjusted upward, by multiplying by a factor of 1.6, to account
for the increased risk during childhood exposures. Although trichloroethylene is
carcinogenic by a mutagenic mode of action, the age-dependent adjustment factor for
the URE only applies to the portion of the slope factor reflecting risk of kidney cancer.
For full lifetime exposure to a constant level of trichloroethylene exposure, the URE is
adjusted upward by a factor of 1.12 (rather than 1.6 as discussed above). For more
information on applying age-dependent adjustment factors in cases where exposure
varies over the lifetime, see Toxicological Review of Trichloroethylene. The URE for
vinyl chloride includes exposure from birth, although the IRIS assessment contains
UREs for both exposure from birth and exposure during adulthood. This value already
accounts for childhood exposure; thus, no additional factor is applied.
2.8.2 Sources of acute dose-response information
Hazard identification and dose-response assessment information for preliminary acute
inhalation exposure assessments is based on the existing recommendations of OAQPS for
HAP (USEPA, 202Id). When the benchmarks are available, the results from acute screening
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assessments are compared to both "no effects" reference levels for the general public, such as
the California Reference Exposure Levels (RELs), and to 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 (USEPA, 2009b).
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 RELs.
The acute REL is defined by CalEPA as "the concentration level at or below which no
adverse health effects are anticipated for a specified exposure duration (OEHHA, 2019).
RELs are based on the most sensitive, relevant, adverse health effect reported in the
medical and toxicological literature. RELs are designed to protect the most sensitive
individuals in the population by the inclusion of margins of safety. Since margins of
safety are incorporated to address data gaps and uncertainties, exceeding the REL does
not automatically indicate an adverse health impact." Acute RELs are developed for 1-
hour (and 8-hour) exposures. The values incorporate uncertainty factors similar to those
used in deriving EPA's inhalation RfCs for chronic exposures.
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. 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, "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."
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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 above AEGL-1 represent exposure levels that can produce mild
and progressively increasing but transient and nondisabling odor, taste, and sensory
irritation or certain asymptomatic, nonsensory effects. With increasing airborne
concentrations above each AEGL, there is a progressive increase in the likelihood of
occurrence and the severity of effects described for each corresponding AEGL. Although
the AEGL values represent threshold levels for the general public, including susceptible
subpopulations, such as infants, children, the elderly, persons with asthma, and those with
other illnesses, it is recognized that individuals, subject to unique or idiosyncratic
responses, could experience the effects described at concentrations below the
corresponding AEGL."
Emergency Response Planning Guidelines (ERPGs). The American Industrial Hygiene
Association (AIHA) has developed ERPGs 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 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's as follows:
ERPG-1 is the maximum airborne concentration below which nearly all individuals could
be exposed for up to 1 hour without experiencing more than mild, transient health effects
or without perceiving a clearly defined objectionable odor.
ERPG-2 is the maximum airborne concentration below which nearly all individuals could
be exposed for up to 1 hour without experiencing or developing irreversible or other
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serious adverse health effects or symptoms that could impair an individual's ability to take
protective action.
2.9 Risk characterization
2.9.1 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, it is EPA's policy 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 (USEPA, 2000a), in the Agency's information quality guidelines
(USEPA, 2002a), and in the Office of Management and Budget (OMB) Memorandum on
Updated Principles for Risk Analysis (OMB, 2007), and they 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 for which a potency estimate is
available, individual and population cancer risks are 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-year
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.
Increased cancer incidence for the entire population within the area of analysis is 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 is 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, the estimated human health
risk for noncancer effects is expressed by comparing an exposure to a reference level as a
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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 developed using 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 1 does not necessarily suggest the onset of adverse effects. The
Hazard Index (HI) is the sum of hazard quotients for substances that affect the same target
organ or organ system and is an approximation of the aggregate effect on a specific target
organ (e.g., the lungs). The HQ and HI cannot be translated to a probability that adverse
effects will occur, and it is unlikely to be proportional to adverse health effect outcomes in a
population.
Screening for potentially significant acute inhalation exposures also follows the HQ approach.
We divide the 99th percentile estimated acute exposure concentration by each available acute
dose-response value to develop an array of HQs. In general, when none of these HQs is
greater than one, there is no potential for acute risk. When one or more HQ is above 1, we
evaluate additional information (e.g., proximity of the facility to potential exposure locations)
to determine whether there is a potential for significant acute risks.
2.9.2 Mixtures
Since most or all receptors in these assessments receive exposures to multiple pollutants
rather than a single pollutant, we estimate the aggregate health risks associated with exposure
to all of the HAP from a particular source category.
To combine risks across multiple carcinogens, our assessments use the mixtures guidelines'
default assumption of additivity of effects and combine risks by summing them using the
independence formula in the mixtures guidelines (USEPA, 1986; USEPA, 2000b).
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 aggregate noncancer 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 HQs for individual HAP that affect the same organ or organ system. For the
RTRs, TOSHI calculations are 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
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Residual Risk Assessment for the Synthetic Organic Chemical Manufacturing Industry (SOCMI)
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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 TO SHI 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 assessment and the
variable nature of emissions and potential exposures, acute impacts are screened on an
individual pollutant basis, not using the TOSHI approach.
3 Risk results for the Hazardous Organic NESHAP source category
3.1 Source category description and emissions
The Synthetic Organic Chemical Manufacturing Industry (SOCMI) source category includes
synthetic organic chemical manufacturing industry (SOCMI) facilities. The SOCMI is a
segment of the chemical manufacturing industry that includes the production of many high-
volume organic chemicals, derived from petrochemical feedstocks. Of the hundreds of
organic chemicals that are produced by the SOCMI, some are final products, and some are the
feedstocks for production of other non-SOCMI chemicals or synthetic products such as
plastics, fibers, surfactants, pharmaceuticals, synthetic rubber, dyes, and pesticides. The
SOCMI source category also applies to equipment leaks from certain non-SOCMI processes
located at chemical plants. Emission points include pressure relief devices, equipment leaks,
process vents, flares, wastewater, heat exchange systems, storage tanks, and transfer racks.
The MACT standards for the SOCMI source category are contained in 40 CFR part 63,
subparts F, G, and H (for the SOCMI processes), and subpart I (for the non-SOCMI
equipment leaks). A complete description of the source category can be found in the text of
the NPRM.
The emission estimates for this source category were obtained from a 2022 information
collection request (ICR) survey, updated with more recent data from industry stakeholders, and
reviewed to ensure quality control of facility and emission locations. We estimate that there are
222 HON facilities operating in the U.S. Emissions from the SOCMI source category are
summarized in Table 3.1-1. The total HAP emissions from the source category are
approximately 8,200 tons per year. The HAP emitted in the largest quantities are methanol, n-
hexane, toluene, xylenes (mixed), benzene, styrene, hydrochloric acid, ethylene glycol,
acetonitrile, ethylene dichloride, methyl chloride, vinyl acetate, vinyl chloride, 1,3-butadiene,
ethyl benzene, chlorine, acetaldehyde, methyl methacrylate, phenol, chlorobenzene, maleic
anhydride, cumene, phthalic anhydride, acrylonitrile, methylene chloride, chloroform, ethyl
chloride, formaldehyde, naphthalene, methyl isobutyl ketone, ethylene oxide, methyl bromide,
propylene oxide, carbonyl sulfide, p-xylene, tetrachloroethene, carbon tetrachloride, hydrogen
cyanide, 2,2,4-trimethylpentane, acrylic acid, carbon disulfide, methyl tert-butyl ether,
diethanolamine, biphenyl, aniline, 1,1,1-trichloroethane, glycol ethers, trichloroethylene,
epichlorohydrin, propionaldehyde, 2-nitropropane, acetophenone, polycyclic aromatic
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hydrocarbons (PAH), bromoform, phenanthrene, 1,1,2-trichloroethane, o-xylene,
triethylamine, hydrofluoric acid, and 1,4-dioxane. Emissions of these 60 HAP make up over 99
percent of the total emissions by mass. The PB-HAP reported as emissions from these
facilities include polycyclic organic matter (POM), lead compounds, arsenic compounds,
cadmium compounds, mercury compounds, and dioxins. The following environmental HAP
are emitted from the SOCMI sources and are included in the environmental risk screening
assessment: hydrochloric acid, POM, hydrofluoric acid, lead compounds, arsenic compounds,
cadmium compounds, mercury compounds, and dioxins.
The emissions for this 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.
Facility-wide emissions were also estimated and the risk results based on those emissions are
presented below as well. Details on the development of the actual and facility-wide emission
estimates and the source of the data for this source category can be found in Appendix 1.
For the chronic inhalation risk assessment, the emissions inventory for the SOCMI source
category includes emissions of 213 HAP and 171 of these have available chronic inhalation
dose-response values. Of these, 103 are classified as known, probable, or possible
carcinogens, with quantitative cancer dose-response values available and 123 HAP have
quantitative noncancer dose-response values available. These HAP, their emissions and dose-
response values are listed in Table 3.1-1 and the source of each dose-response value is listed
in Appendix 8.
For the acute inhalation risk assessment, for the SOCMI source category, maximum
hourly emissions estimates were available, so . we did not use a default acute emissions
multiplier of 10 (as described in Section 2.4), but rather, we used process level-specific
acute emissions multipliers, generally ranging from a factor of 2 to 10 as was done in past
chemical and petrochemical residual risk reviews such as for the 2015 the Petroleum
Refinery Sector rule, 2020 MON rule, 2020 EMACT rule, and 2020 OLD rule, where
similar emission sources and standards exist. See Appendix 1 to this document for a
detailed description of how the maximum hourly emissions were developed for this source
category.
The emissions inventory for the SOCMI source category includes emissions of 83 HAP with
relevant and available quantitative acute dose-response values. These HAP, their emissions
and acute and chronic dose-response values are listed in Table 3.1-1 and the source of each
dose-response value is listed in Appendix 8.
As mentioned previously, when we identify acute impacts which exceed their relevant dose-
response values, we refine our acute screening estimates to the extent possible. For the
SOCMI source category, the acute screening results were refined to ensure all locations
were off facility property. The acute results for the source category are summarized in the
following section and detailed information is contained in Appendix 10 to this document
(Detailed Risk Modeling Results).
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Residual Risk Assessment for the Synthetic Organic Chemical Manufacturing Industry (SOCMI)
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For the multipathway risk assessment, PB-HAP identified in the emissions inventory for
the SOCMI source category include POM (of which PAH is a subset), lead compounds,
arsenic compounds, cadmium compounds, mercury compounds, and dioxins. Of these, all but
lead have quantitative chronic oral cancer or noncancer dose-response values available, which
are presented in Table 3.1-1, and were screened for non-inhalation risks using a tiered
screening approach described in Section 2.5. In evaluating the potential multipathway risks
from emission of lead compounds, we compared maximum estimated chronic atmospheric
concentrations with the current NAAQS for lead, also as described in Section 2.5. The results
of the multipathway assessment for the source category are summarized in the following
section and detailed information is contained in Appendix 10 to this document for the
multipathway screening assessment and Appendix 11 of this document for the site-specific
multipathway assessment.
For the environmental risk assessment, the PB-HAP identified above as well as two acid
gases (hydrochloric acid and hydrofluoric acid) were screened for potential adverse
environmental effects as described in Section 2.5. The benchmark values and a detailed
discussion of the approach for this assessment can be found in Appendix 9. The results of the
environmental assessment for the source category are summarized in the following section
and detailed information is contained in Appendix 10 to this document.
Table 3.1-1 Summary of Emissions from the SOCMI Source Category and Dose-Response Values
Used in the Residual Risk Assessment
Number of
Prioritized Inhalation Dose-Response Value
Identified by OAQPS
PB-HAP Oral
Benchmark
Facilities
Health
Values for
HAP
Emissions
Reporting
Unit Risk
Reference
Benchmark
Cancer
(tpy)
HAP (222
Estimate for
Concentration
Values for
(l/(mg/kg/d))
facilities in
Cancer
for Noncancer
Acute
and/or
data set)
(1/Oig/m3))
(mg/m3)
Noncancer
(mg/m3)
Noncancer
(mg/kg/d)a
Methanol
1819
148
20
28 (REL)
10000
n-Hexane
860
120
0.7
(AEGL-2
(1-hr))
190
Toluene
570
131
5
(ERPG-1)
Xylenes (mixed)
493
107
0.1
22 (REL)
160
Benzene
386
136
0.0000078b
0.03
(ERPG-1)°
Styrene
319
85
1
21 (REL)
Hydrochloric Acid
228
79
0.02
2.1 (REL)
Ethylene Glycol
206
66
0.4
Acetonitrile
182
34
0.06
22 (AEGL-1
(1-hr))
200
Ethylene Dichloride
166
34
0.000026
2.4
(ERPG-1)
Methyl Chloride
143
34
0.09
310
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Residual Risk Assessment for the Synthetic Organic Chemical Manufacturing Industry (SOCMI)
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Table 3.1-1 Summary of Emissions from the SOCMI Source Category and Dose-Response Values
Used in the Residual Risk Assessment
Number of
Prioritized Inhalation Dose-Response Value
Identified by OAQPS
PB-HAP Oral
Benchmark
Facilities
Health
Values for
HAP
Emissions
Reporting
Unit Risk
Reference
Benchmark
Cancer
(tpy)
HAP (222
Estimate for
Concentration
Values for
(l/(mg/kg/d))
facilities in
Cancer
for Noncancer
Acute
and/or
data set)
(1/Oig/m3))
(mg/m3)
Noncancer
(mg/m3)
Noncancer
(mg/kg/d)a
(ERPG-1)
Vinyl Acetate
135
24
0.2
18 (ERPG-1)
Vinyl Chloride
134
28
0.0000088
0.1
180 (REL)
1,3-Butadiene
134
79
0.00003
0.002
22 (ERPG-1 )d
Ethyl Benzene
130
103
0.0000025
0.3
140 (AEGL-1
(1-hr))
Chlorine
123
72
0.00015
0.21 (REL)
Acetaldehyde
120
70
0.0000022
0.009
0.47 (REL)
Methyl Methacrylate
118
18
0.7
70 (AEGL-1
(1-hr))
Phenol
115
75
0.2
5.8 (REL)
Chlorobenzene
109
26
1
46 (AEGL-1
(1-hr))
Maleic Anhydride
107
22
0.0007
0.8 (ERPG-1)
Cumene
98
70
0.4
250 (AEGL-1
(1-hr))
Phthalic Anhydride
91
15
0.02
Acrylonitrile
89
25
0.000068
0.002
3.7 (AEGL-2
(1-hr))
Methylene Chloride
84
43
0.000000016
0.6
14 (REL)
Chloroform
77
43
0.098
0.15 (REL)
Ethyl Chloride
72
39
10
Formaldehyde
70
85
0.000013
0.0098
0.055 (REL)
Naphthalene
69
83
0.000034
0.003
Methyl Isobutyl Ketone
68
34
3
Ethylene Oxide
66
24
0.005
0.03
81 (AEGL-2
(1-hr))
Methyl Bromide
58
12
0.005
3.9 (REL)
Propylene Oxide
55
21
0.0000037
0.03
3.1 (REL)
Carbonyl Sulfide
55
20
0.163e
140 (AEGL-2
(1-hr))
p-Xylene
50
5
0.1
22 (REL)
T etrachloroethene
48
47
0.00000026
0.04
20 (REL)
Carbon Tetrachloride
46
32
0.000006
0.1
1.9 (REL)
Hydrogen Cyanide
43
26
0.0008
0.34 (REL)
2,2,4-Trimethylpentane
41
36
Acrylic Acid
39
21
0.001
2.9 (ERPG-1)
Carbon Disulfide
33
32
0.7
3.1 (ERPG-1)
Methyl Tert-butyl Ether
29
26
0.00000026
3
180 (AEGL-1
(1-hr))
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Residual Risk Assessment for the Synthetic Organic Chemical Manufacturing Industry (SOCMI)
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Table 3.1-1 Summary of Emissions from the SOCMI Source Category and Dose-Response Values
Used in the Residual Risk Assessment
Number of
Prioritized Inhalation Dose-Response Value
Identified by OAQPS
PB-HAP Oral
Benchmark
Facilities
Health
Values for
HAP
Emissions
Reporting
Unit Risk
Reference
Benchmark
Cancer
(tpy)
HAP (222
Estimate for
Concentration
Values for
(l/(mg/kg/d))
facilities in
Cancer
for Noncancer
Acute
and/or
data set)
(1/Oig/m3))
(mg/m3)
Noncancer
(mg/m3)
Noncancer
(mg/kg/d)a
Diethanolamine
28
29
0.003
Biphenyl
22
54
61 (AEGL-2
(1-hr))
Aniline
21
13
0.0000016
0.001
30 (AEGL-1
(1-hr))
1,1,1 -Trichloroethane
19
21
5
68 (REL)
Glycol Ethers:
Glycol Ethers
16
23
0.02
0.093 (REL)
Ethylene Glycol Methyl
Ether
2
10
0.02
0.093 (REL)
Ethylene Glycol Ethyl
Ether
0.05
1
0.2
0.37 (REL)
Diethylene Glycol
Monobutyl Ether
0.0001
2
0.02
0.093 (REL)
540
T richloroethy lene
14
24
0.0000048
0.002
(ERPG-1)
Epichlorohydrin
14
10
0.0000012
0.001
1.3 (REL)
Propionaldehyde
13
21
0.008
110 (AEGL-1
(1-hr))
2-Nitropropane
13
3
0.0000056
0.02
Acetophenone
12
14
Poly cyclic Organic Matter (POM):
PAH, Total
12
32
0.000048
0.05 (cancer)
Phenanthrene
9
24
f
Polycyclic Organic Matter
0.3
19
0.000048
0.05 (cancer)
Anthracene
0.2
13
f
Fluoranthene
0.1
6
0.000048
0.05 (cancer)
Acenaphthene
0.04
4
0.000048
0.05 (cancer)
Benzo(ghi)perylene
0.02
15
0.000048
0.05 (cancer)
Fluorene
0.02
7
0.000048
0.05 (cancer)
Acenaphthylene
0.008
4
0.000048
0.05 (cancer)
2-Acetylaminofluorene
0.007
1
0.00208
1 (cancer)
Pyrene
0.004
7
f
Bcnzo|a|pvrcnc
0.0008
6
0.00096
0.000002
1 (cancer)
Benz[a]anthracene
0.0007
5
0.000096
0.1 (cancer)
Benzo [b] fluoranthene
0.0006
4
0.000096
0.1 (cancer)
Chrysene
0.0005
5
0.00000096
0.001 (cancer)
Indeno 11.2.3 -c,dlpyrene
0.0002
4
0.000096
0.1 (cancer)
Dibenzo [a, h] anthracene
0.0002
4
0.00096
1 (cancer)
41
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Residual Risk Assessment for the Synthetic Organic Chemical Manufacturing Industry (SOCMI)
Source Category in Support of the 2023 Risk and Technology Review Proposed Rule
Table 3.1-1 Summary of Emissions from the SOCMI Source Category and Dose-Response Values
Used in the Residual Risk Assessment
Number of
Prioritized Inhalation Dose-Response Value
Identified by OAQPS
PB-HAP Oral
Benchmark
Facilities
Health
Values for
HAP
Emissions
Reporting
Unit Risk
Reference
Benchmark
Cancer
(tpy)
HAP (222
Estimate for
Concentration
Values for
(l/(mg/kg/d))
facilities in
Cancer
for Noncancer
Acute
and/or
data set)
(1/Oig/m3))
(mg/m3)
Noncancer
(mg/m3)
Noncancer
(mg/kg/d)a
Benzo rilfluoranthene
0.0002
1
0.000096
0.1 (cancer)
Benzo |k|fluoranthcnc
0.0001
4
0.0000096
0.01 (cancer)
Dibenzo [a,h]pyrene
0.0001
1
0.0096
2-Methylnaphthalene
0.00001
5
0.000048
0.05 (cancer)
3 -Methy lcholanthrene
0.0000002
2
0.01008
22 (cancer)
Bromoform
11
10
0.0000011
1,1,2-Trichloroethane
9
22
0.000016
o-Xylene
8
5
0.1
22 (REL)
Triethylamine
7
15
0.007
2.8 (REL)
Hydrofluoric Acid
7
22
0.014
0.24 (REL)
1,4-Dioxane
7
16
0.000005
0.03
3 (REL)
Nitrobenzene
7
8
0.00004
0.009
Methoxytriglycol
6
3
0.02
0.093 (REL)
Chloroprene
6
11
0.00048
0.02
2000
Vinylidene Chloride
4
15
0.2
(ERPG-2)
Ethylidene Dichloride
4
17
0.0000016
0.5
Acrylamide
4
7
0.00016
0.006
m-Xylene
4
2
0.1
22 (REL)
Hydroquinone
3
15
Dimethyl Formamide
3
10
0.03
6 (ERPG-1)
Allyl Chloride
3
11
0.000006
0.001
8.8 (AEGL-1
(1-hr))
0.041
Ethyl Aery late
3
9
(ERPG-1)
Acrolein
2
33
0.00035
0.0025 (REL)
Propylene Dichloride
2
15
0.004
1,1,2,2-Tetrachloroethane
2
22
Cresols (mixed)
2
36
0.6
Cobalt Compounds
2
29
0.0001
Lead Compounds
2
43
0.00015®
Nickel Compounds
1
43
0.00048
0.00009
h
Hexachlorobutadiene
1
9
0.000022
11 (ERPG-1)
Methyl Iodide
1
5
130 (AEGL-1
(1-hr))
Methylene Diphenyl
Diisocyanate
1
5
0.0006
0.012 (REL)
1,2-Epoxybutane
1
10
0.02
210 (AEGL-1
(1-hr))
42
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Residual Risk Assessment for the Synthetic Organic Chemical Manufacturing Industry (SOCMI)
Source Category in Support of the 2023 Risk and Technology Review Proposed Rule
Table 3.1-1 Summary of Emissions from the SOCMI Source Category and Dose-Response Values
Used in the Residual Risk Assessment
Number of
Prioritized Inhalation Dose-Response Value
Identified by OAQPS
PB-HAP Oral
Benchmark
Facilities
Health
Values for
HAP
Emissions
Reporting
Unit Risk
Reference
Benchmark
Cancer
(tpy)
HAP (222
Estimate for
Concentration
Values for
(l/(mg/kg/d))
facilities in
Cancer
for Noncancer
Acute
and/or
data set)
(1/Oig/m3))
(mg/m3)
Noncancer
(mg/m3)
Noncancer
(mg/kg/d)a
Phosphorus
1
4
3.7 (AEGL-1
(1-hr))
Phosgene
1
7
0.0003
0.004 (REL)
Ethylene Dibromide
0.9
6
0.0006
0.009
130 (AEGL-1
(1-hr))
Manganese Compounds
0.7
33
0.00031
p-Phenylenediamine
0.7
4
4,4'-Methylenedianiline
0.6
5
0.00046
0.02
Chloroacetic Acid
0.6
4
26 (AEGL-2
(1-hr))
Bis(2-ethylhexyl)phthalate
0.6
8
0.0000024
Dibutylphthalate
0.5
8
Hexachloroethane
0.5
12
0.03
Isophorone
0.4
2
2
o-Toluidine
0.4
8
0.000051
2,4-Dinitrotoluene
0.4
4
0.000089
Catechol
0.3
5
2,4-Dinitrophenol
0.3
4
1,3 -Dichloropropene
0.3
7
0.000004
0.02
Hydrazine
0.3
5
0.0049
0.0002
0.13 (AEGL-
1 (1-hr))
Dibenzofuran
0.3
3
Antimony Compounds
0.3
22
0.0002
Dimethyl Phthalate
0.2
4
Chromium Compounds:
Chromium (III) Compounds
0.2
35
Chromium (VI) Compounds
0.08
36
0.012
0.0001
Selenium Compounds
0.2
19
0.02
Arsenic Compounds
0.2
26
0.0043
0.000015
0.0002 (REL)
1.5 (cancer)
2,4,6-Trichlorophenol
0.2
4
0.0000031
Cadmium Compounds
0.2
28
0.0018
0.00001
0.1 (AEGL-1
(1-hr))
0.001
(noncancer)
Coal Tar
0.1
2
0.00099
Polychlorinated Biphenyls
0.1
7
0.0001
0.025
Beryllium Compounds
0.1
19
0.0024
0.00002
(ERPG-2)
Anisidine
0.1
2
Benzyl Chloride
0.1
5
0.000049
0.24 (REL)
Titanium Tetrachloride
0.08
4
0.0001
5 (ERPG-1)
43
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Residual Risk Assessment for the Synthetic Organic Chemical Manufacturing Industry (SOCMI)
Source Category in Support of the 2023 Risk and Technology Review Proposed Rule
Table 3.1-1 Summary of Emissions from the SOCMI Source Category and Dose-Response Values
Used in the Residual Risk Assessment
Number of
Prioritized Inhalation Dose-Response Value
Identified by OAQPS
PB-HAP Oral
Benchmark
Facilities
Health
Values for
HAP
Emissions
Reporting
Unit Risk
Reference
Benchmark
Cancer
(tpy)
HAP (222
Estimate for
Concentration
Values for
(l/(mg/kg/d))
facilities in
Cancer
for Noncancer
Acute
and/or
data set)
(1/Oig/m3))
(mg/m3)
Noncancer
(mg/m3)
Noncancer
(mg/kg/d)a
p-Dichlorobenzene
0.07
15
0.000011
0.06
4-Aminobiphenyl
0.07
2
Hexamethylene-1,6-
diisocyanate
0.07
3
0.00001
Hexachlorobenzene
0.07
11
0.00046
Quinoline
0.06
4
Mercury Compounds:
0.0001
Gaseous Divalent Mercury
0.04
36
0.0003
(noncancer)
Mercury (elemental)
0.03
36
0.0003
0.0006 (REL)
-i
Particulate Divalent
0.0001
Mercury
0.01
36
0.0003
(noncancer)
Dimethyl Sulfate
0.04
5
0.12 (AEGL-
1 (1-hr))
Vinyl Bromide
0.03
2
0.000032
0.003
2,4-d, Salts and Esters
0.03
2
Ethyl Carbamate
0.03
1
0.000464
Lindane (gamma-HCH)
0.02
2
0.00031
1,2 -Propy leneimine
0.02
2
28 (AEGL-2
(1-hr))
1,2 -Dipheny lhy drazine
0.02
1
0.00022
3,3 '-Dichlorobenzidine
0.02
1
0.00034
3,3 '-Dimethoxybenzidine
0.02
1
3,3 '-Dimethy lbenzidine
0.02
1
Benzotrichloride
0.02
1
Chlorobenzilate
0.02
1
0.000078
Chloromethyl Methyl Ether
0.02
1
1.6 (AEGL-2
(1-hr))
Dimethylcarbamoyl Chloride
0.02
1
Ethylene Thiourea
0.02
1
0.000013
p-Dimethylaminoazobenzene
0.02
1
0.0013
1,1 -Dimethy lhy drazine
0.02
1
7.4 (AEGL-2
(1-hr))
N-Nitroso-n-methylurea
0.02
1
Bis(chloromethyl)ether
0.02
1
0.062
0.21 (AEGL-
2 (1-hr))
Ethylene Imine (aziridine)
0.02
1
8.1 (AEGL-2
(1-hr))
Methyl Hydrazine
0.02
1
1.7 (AEGL-2
(1-hr))
44
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Residual Risk Assessment for the Synthetic Organic Chemical Manufacturing Industry (SOCMI)
Source Category in Support of the 2023 Risk and Technology Review Proposed Rule
Table 3.1-1 Summary of Emissions from the SOCMI Source Category and Dose-Response Values
Used in the Residual Risk Assessment
Number of
Prioritized Inhalation Dose-Response Value
Identified by OAQPS
PB-HAP Oral
Benchmark
Facilities
Health
Values for
HAP
Emissions
Reporting
Unit Risk
Reference
Benchmark
Cancer
(tpy)
HAP (222
Estimate for
Concentration
Values for
(l/(mg/kg/d))
facilities in
Cancer
for Noncancer
Acute
and/or
data set)
(1/Oig/m3))
(mg/m3)
Noncancer
(mg/m3)
Noncancer
(mg/kg/d)a
2,4-Toluene Diamine
0.02
2
0.0011
Pentachlorophenol
0.02
4
0.0000051
o-Cresol
0.02
5
0.6
1,2,4-Trichlorobenzene
0.01
5
0.2
Dichloroethyl Ether
0.009
6
0.00033
4,6-Dinitro-o-cresol
0.008
1
2,4-Toluene Diisocyanate
0.006
6
0.000011
0.00007
0.002 (REL)
Trifluralin
0.005
2
2,4,5-Trichlorophenol
0.004
1
Asbestos
0.003
4
p-Cresol (4-methy phenol)
0.003
4
0.6
m-Cresol (3-methylphenol)
0.003
3
0.6
Acetamide
0.002
3
0.00002
Chlordane
0.002
1
0.0001
0.0007
Phosphine
0.002
1
0.0003
0.7 (ERPG-2)
Methoxychlor
0.002
4,4'-Methylene bis(2-
chloroaniline)
0.001
1
0.00043
Heptachlor
0.0006
1
0.0013
4-nitrophenol
0.0005
0.058
Methyl Isocyanate
0.0005
1
0.001
(ERPG-1)
Captan
0.0004
1
Toxaphene
0.0003
1
0.00032
Ouinone
0.0002
1,2 -Dibromo-3 -chloropropane
0.0002
1
0.002
0.0002
Propyl Cellosolve
0.0002
1
0.02
0.093 (REL)
Diethyl Sulfate
0.0002
Hexachlorocyclopentadiene
0.0002
0.0002
Benzidine
0.0001
1
0.1072
Hexamethylphosphoramide
0.0001
1
Dioxins and Furans:
1,2,3,4,6,7,8,9-
octachlorodibenzofuran
0.00003
9
0.0099
0.00013
45 (cancer)
1,2,3,4,6,7,8-
heptachlorodibenzofuran
0.00001
9
0.33
0.000004
1500 (cancer)
1,2,3,4,7,8,9-
heptachlorodibenzofuran
0.000002
8
0.33
0.000004
1500 (cancer)
45
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Residual Risk Assessment for the Synthetic Organic Chemical Manufacturing Industry (SOCMI)
Source Category in Support of the 2023 Risk and Technology Review Proposed Rule
Table 3.1-1 Summary of Emissions from the SOCMI Source Category and Dose-Response Values
Used in the Residual Risk Assessment
HAP
Emissions
(tpy)
Number of
Facilities
Reporting
HAP (222
facilities in
data set)
Prioritized Inhalation Dose-Response Value
Identified by OAQPS
PB-HAP Oral
Benchmark
Values for
Cancer
(l/(mg/kg/d))
and/or
Noncancer
(mg/kg/d)a
Unit Risk
Estimate for
Cancer
(1/Oig/m3))
Reference
Concentration
for Noncancer
(mg/m3)
Health
Benchmark
Values for
Acute
Noncancer
(mg/m3)
1,2,3,4,6,7,8,9-
octachlorodibenzo-p-dioxin
0.000002
9
0.0099
0.00013
45 (cancer)
7,12-
dimethy lbenz|a] anthracene
0.000002
3
0.1136
250 (cancer)
1,2,3,4,7,8-
hexachlorodibenzofuran
0.000002
9
3.3
0.0000004
15000 (cancer)
1,2,3,6,7,8-
hexachlorodibenzofuran
0.0000008
9
3.3
0.0000004
15000 (cancer)
2,3,7,8-
tetrachlorodibenzofuran
0.0000007
8
3.3
0.0000004
15000 (cancer)
1,2,3,4,6,7,8-
heptachlorodibenzo-p-dioxin
0.0000005
9
0.33
0.000004
1500 (cancer)
2,3,4,6,7,8-
hexachlorodibenzofuran
0.0000003
8
3.3
0.0000004
15000 (cancer)
1,2,3,7,8-
pentachlorodibenzofuran
0.0000002
9
0.99
0.0000013
4500 (cancer)
2,3,4,7,8-
pentachlorodibenzofuran
0.0000002
9
9.9
0.00000013
45000 (cancer)
1,2,3,7,8,9-
hexachlorodibenzofuran
0.00000006
6
3.3
0.0000004
15000 (cancer)
1,2,3,6,7,8-
hexachlorodibenzo -p-dioxin
0.00000005
8
3.3
0.0000004
6200 (cancer)
1,2,3,7,8,9-
hexachlorodibenzo -p-dioxin
0.00000004
8
3.3
0.0000004
6200 (cancer)
1,2,3,4,7,8-
hexachlorodibenzo -p-dioxin
0.00000003
7
3.3
0.0000004
15000 (cancer)
1,2,3,7,8-
pentachlorodibenzo-p-dioxin
0.00000002
8
33
0.00000004
150000
(cancer)
2,3,7,8-tetrachlorodibenzo-
p-dioxin
0.000000005
6
33
0.00000004
150000
(cancer)
Notes:
a Benchmark values are provided only for PB-HAPs for which multipathway risk is assessed (via TRIM). There
may be other PB-HAPs in this table, even though no benchmark is presented.
b The EPA IRIS assessment for benzene provides a range of plausible UREs. This assessment used the highest
value in that range, 7.8E-06 |ig/m3. The low end of the range is 2.2E-06 |ig/m\
0 Based on examination of California EPA's acute (1-hour) REL for benzene, and considering aspects of the
methodology used in the derivation of the value and how this assessment stands in comparison to the ATSDR
toxicological assessment, we have decided not to use this value to support EPA's risk and technology review
rules.
d Based on examination of California EPA's acute (1-hour) REL for this pollutant and considering aspects of the
methodology used in the derivation of the value, we have decided not to use this value to support EPA's risk and
technology review rules.
46
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Residual Risk Assessment for the Synthetic Organic Chemical Manufacturing Industry (SOCMI)
Source Category in Support of the 2023 Risk and Technology Review Proposed Rule
e A chronic screening level of 0.163 mg/m3 was developed for carbonyl sulfide by EPA ORD from a No
Observed Adverse Effects Level of 200 ppm based on brain lesions and neurophysiological alterations in
rodents.
f IRIS has determined this POM to be not carcinogenic.
g There is no reference concentration for lead. 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. In considering noncancer hazards for lead in this assessment,
as a first screening step, we multiply the maximum annual estimated atmospheric concentration by 4, to
represent a "worst case" 3-month concentration, and compare it to the national ambient air quality standard
(NAAQS) for lead (0.15 |ig/m3. 3-month rolling average). Where values exceed the NAAQS and where data are
available to support doing so, further assessment is performed. We calculate 3-month rolling averages based on
modeling and/or monitoring information. Any 3-month rolling average concentration that is above 0.15 ug/m3
indicates a potential public health concern.
h Based on an in-depth examination of the available acute value for nickel [California EPA's acute (1-hour)
REL], we have concluded that this value is not appropriate to use to support EPA's risk and technology review
rules. This conclusion considers: the effect on which the acute REL is based; aspects of the methodology used in
its derivation; and how this assessment stands in comparison to the ATSDR toxicological assessment, which
considered the broader nickel health effects database. (79 FR 60247-8; October 6, 2014)
1 EPA uses the ATSDR MRL for manganese (Mn) instead of the IRIS RfC in RTR assessments. Both the 1993
IRIS RfC and the 2012 ATSDR MRL were based on the same study (Roels et al., 1992); however, ATSDR used
updated dose-response 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 reference value to use in RTR risk assessments.
J The predominant form of mercury assessed in our multipathway risk screening assessment is methyl mercury,
which is a transformation product of divalent mercury and accumulates in fish. While elemental mercury
emissions can convert to divalent mercury in the atmosphere, such transformations generally occur beyond the
50 km modeling domain around the emissions sources in our assessment. *Emissions reported as "mercury
compounds" is speciated into elemental, particulate divalent, and gaseous divalent and modeled accordingly in
the multipathway screening assessment.
3.2 Baseline risk characterization
This section presents the results of the risk assessment for the SOCMI source category based
on the modeling methods described in the previous sections. 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 screening assessment in the form of
maximum acute hazard quotients for the reasonable worst-case exposure scenario, as well as
the results of our preliminary screening assessment for potential non-inhalation risks and
environmental risk 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. A detailed summary of the facility-specific inhalation and
multipathway risk assessment results is available in Appendix 10 of this document.
3.2.1 Risk assessment results based on actual emissions
Source Category Inhalation
Table 3.2-1 summarizes 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 222 facilities
47
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Residual Risk Assessment for the Synthetic Organic Chemical Manufacturing Industry (SOCMI)
Source Category in Support of the 2023 Risk and Technology Review Proposed Rule
could be as high as 2,000-in-l million, with ethylene oxide emissions from pressure relief
devices and equipment leaks as the major contributors to the risk. The total estimated cancer
incidence from this source category is one excess cancer case every 8 months. Approximately
50,000,000 people live within 50 kilometers of the 222 modeled HON facilities, and
7,200,000 people are estimated to have a cancer risk at or above 1-in-l million from HAP
emitted from the facilities in this source category, with 2,300,000 of those people estimated to
have a cancer risk at or above 10-in-l million, 150,000 people estimated to have a cancer risk
at or above 100-in-l million, 87,000 people estimated to have a cancer risk above 100-in-l
million, and 3,000 people estimated to have a cancer risk at or above 1,000-in-l million. The
maximum chronic noncancer hazard index value for the source category could be up to 2
(respiratory) driven by emissions of maleic anhydride from a process vent operation.
Approximately 80 people are exposed to a noncancer hazard index above 1, based on actual
source category emissions.
Maximum acute HQs were calculated for every HAP that has an acute dose-response value, as
shown in Table 3.1-1. For cases where the screening HQ was greater than 1, we further
refined the estimates by determining the highest HQ that might occur outside facility
boundaries. Based on actual baseline emissions, the highest refined screening acute HQ of 3
(based on the acute RELs for chlorine and acrolein) is shown in Table 3.2-2. This value
includes a refinement of determining the highest HQ that is outside facility boundaries. It is
also important to note that the highest HQ assumes that the primary sources of the chlorine
emissions and acrolein emissions driving the HQ values were modeled with an hourly
emissions multiplier of 10 times the annual emissions rate. Further, this exceedance for
acrolein was predicted to occur in a residential land area just adjacent to the facility fence line
for 1 hour a year. The exceedance for chlorine was only predicted to occur in a remote, non-
inhabited area just adjacent to the facility fence line for 1 hour a year. No facilities are
estimated to have an HQ based on AEGL or EPRG greater than 1. Acute HQ estimates for
each plant and pollutant are provided in Appendix 10 of this document.
Table 3.2-1. Source Category Level Inhalation Risks for the SOCMI Based on Actual
Emissions
Result
HAP "Drivers"
Facilities in Source Category
Number of Facilities Estimated to be in
222
n/a
Source Category
Number of Facilities Modeled in Risk
222
n/a
Assessment
Cancer Risks
Maximum Individual Lifetime Cancer Risk
(in 1 million)
2,000
ethylene oxide
Number of Facilities with Maximum Individual Lifetime Cancer Risk:
Greater than or equal to 1,000-in-l million
1
ethylene oxide
Greater than 100-in-l million
8
ethylene oxide
Greater than or equal to 100-in-l million
14
ethylene oxide, acrylonitrile, ethylene
dichloride, naphthalene, vinyl chloride
Greater than or equal to 10-in-l million
46
Top 10: ethylene oxide, acrylonitrile,
ethylene dichloride, naphthalene, vinyl
48
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Residual Risk Assessment for the Synthetic Organic Chemical Manufacturing Industry (SOCMI)
Source Category in Support of the 2023 Risk and Technology Review Proposed Rule
Table 3.2-1. Source Category Level Inhalation Risks for the SOCMI Based on Actual
Emissions
Result
HAP "Drivers"
chloride, chloroprene, benzene, hydrazine,
acrylamide, nickel compounds
Greater than or equal to 1-in-l million
112
Top 10: ethylene oxide, acrylonitrile,
ethylene dichloride, naphthalene, vinyl
chloride, chloroprene, benzene, hydrazine,
acrylamide, nickel compounds
Chronic Noncancer Risks
Maximum Respiratory Hazard Index
2
maleic anhydride, chlorine
Number of Facilities with Maximum Respiratory Hazard Index:
Greater than 1
2
maleic anhydride, chlorine, nickel
compounds, hydrochloric acid
Acute Noncancer Screening Results
Maximum Acute Hazard Quotient
3
chlorine (REL), acrolein (REL)
Number of Facilities with Potential for
Acute Effects
3
chlorine, acrolein, formaldehyde,
chloroform
Population Exposure
Number of People Living Within 50
Kilometers of HON Facilities Modeled
50,000,000
n/a
Number of People Exposed to Cancer Risk within 50 km of HON facilities:
Greater than or equal to 1,000-in-l million
2,900
n/a
Greater than 100-in-l million
87,000
n/a
Greater than or equal to 100-in-l million
150,000
n/a
Greater than or equal to 10-in-l million
2,300,000
n/a
Greater than or equal to 1-in-lmillion
7,200,000
n/a
Number of People Exposed to Noncancer Respiratory Hazard Index:
Greater than 1
80
n/a
Estimated Cancer Incidence (excess cancer
cases per year)
2
n/a
Contribution of HAP to Cancer Incidence
ethylene oxide
89%
n/a
naphthalene
2%
n/a
1,3-butadiene
2%
n/a
acrylonitrile
1%
n/a
benzene
1%
n/a
ethylene dichloride
1%
n/a
chloroprene
1%
n/a
arsenic compounds
0.5%
n/a
95 other HAP
each < 0.5%
n/a
Facility-wide Inhalation
The facility-wide chronic MIR and TOSHI, available in Appendix 10, 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, as compared to the SOCMI source category
assessment, are summarized in Table 3.2-2. The results indicate that 141 facilities have a
facility-wide cancer MIR at or above 1-in-l million, 60 of those facilities have a facility-wide
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Residual Risk Assessment for the Synthetic Organic Chemical Manufacturing Industry (SOCMI)
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cancer MIR at or above 10-in-l -million, 20 facilities have a facility-wide cancer MIR at or
above 100-in-l million, 14 facilities have a facility-wide cancer MIR above 100-in-l million,
and one facility has a facility-wide cancer MIR at or above 1,000-in-l million. The maximum
facility-wide cancer MIR is 2,000-in-l million, mainly driven by ethylene oxide emissions
from pressure relief devices and equipment leaks. The total estimated cancer incidence from
the whole facility is 2 excess cancer cases per year, or one excess case every 6 months.
Approximately 8,900,000 people are estimated to have cancer risks at or above 1-in-l million
from exposure to HAP emitted from both MACT and non-MACT sources at the 222 facilities
in this source category, with 3,500,000 of those people estimated to have cancer risks at or
above 10-in-l million, 180,000 people estimated to have cancer risks at or above 100-in-l
million, 95,000 people estimated to have cancer risks above 100-in-l million, and 2,900
people estimated to have cancer risks at or above 1,000-in-l million. The maximum facility
wide TOSHI for the source category is estimated to be 3, mainly driven by emissions of
chlorine from non-MACT sources, heat exchange systems, and equipment leaks.
Approximately 1,100 people are exposed to noncancer (respiratory) hazard index levels above
1, based on facility-wide emissions from the 222 facilities in this source category.
Table 3.2-2 Source Category Contribution to Facility-Wide Cancer Risks Based on
Actual Emissions
SOCMI
Number of Facilitie
s Binned by Facility-Wide MIR
in 1 million)
Source Category MIR
Contribution to Facility-
Wide MIR
<1
1< MIR<10
10< MIR<100
>100
Total
> 90%
39
41
20
10
110
50-90%
12
15
5
4
36
10-50%
3
12
8
4
27
< 10%
27
13
7
2
49
Total
81
81
40
20
222
Community-based Inhalation
The results of the community-based assessment for cancer risks, as compared to the SOCMI
source category and facility-wide assessments, are summarized in Table 3.2-3. The maximum
community-based cancer MIR is the same as the source-category and facility-wide
assessments, at 2,000-in-l million. More than 99.9 percent of the MIR is attributable to
ethylene oxide emissions from one facility in the SOCMI source category. Within 10km of a
SOCMI facility, the estimated community-based total cancer incidence is 2 excess cancer
cases per year, or one excess case every 6 months. Emissions from SOCMI source category
processes account for 69 percent of the cancer incidence, 16 percent is from emissions of non-
category processes at SOCMI facilities (that is, a total of 85 percent from facility-wide
emissions) and 15 percent is from emissions from other nearby stationary sources that are not
SOCMI facilities. On a pollutant basis, the incidence is largely driven by emissions of
ethylene oxide at 72%, predominately from SOCMI facilities (>95%). The next highest
pollutant is chromium VI at 4%.
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Residual Risk Assessment for the Synthetic Organic Chemical Manufacturing Industry (SOCMI)
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Approximately 5,800,000 people are estimated to have cancer risks at or above 1-in-l million
from exposure to HAP emitted from all nearby point sources within 10km of the 222 facilities
in this source category, with 2,400,000 of those people estimated to have cancer risks at or
above 10-in-l million, 200,000 people estimated to have cancer risks at or above 100-in-l
million, 100,000 people estimated to have cancer risks above 100-in-l million, and 2,900
people estimated to have cancer risks at or above 1,000-in-l million.
Table 3.2-3 Community Level Inhalation Risks Based on SOCMI Actual
Emissions
Result
Source Category
Facility-Wide
Community
Number of Facilities Modeled
in Risk Assessment
222
222
3,169a
Maximum Individual Lifetime
Cancer Risk (in 1 million)
2,000
2,000
2,000
Number of People Living
Within 10 Kilometers of
Facilities Modeled
9,300,000
Number of People Exposed to Cancer Risk within 10km of HON facilities
Greater than or equal to 1,000-
in-l million
2,900
2,900
2,900
Greater than 100-in-l million
87,000
95,000
100,000
Greater than or equal to 100-
in-l million
150,000
180,000
200,000
Greater than or equal to 10-in-
1 million
1,500,000
2,100,000
2,400,000
Greater than or equal to 1-in-
lmillion
2,800,000
3,200,000
5,800,000
a 2,947 nearby non-SOCMI facilities in addition to the 222 facilities in the SOCMI source category
Multipathwav
Table 3.2-3 summarizes the multipathway risk results for this source category based on
baseline actual emissions. The PB-HAP emitted by facilities in this source category include
POM (of which PAH is a subset), lead compounds, arsenic compounds, cadmium compounds,
mercury compounds, and dioxins. To identify potential multipathway health risks from PB-
HAP other than lead, we first performed a tiered screening assessment (Tiers 1, 2, and 3)
based on emissions of PB-HAP emitted from each facility in the source category (see section
2.5).
Of the 222 facilities in the source category, 34 facilities reported emissions of carcinogenic
PB-HAP (arsenic, POM, and dioxins) and 11 facilities reported emissions of non-carcinogenic
PB-HAP (cadmium and mercury) that exceed a Tier 1 screening value of 1. For carcinogenic
PB-HAP, the maximum emission rates of arsenic, POM, and dioxins exceeded the Tier 1
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screening values by a factor of 700, 2,000, and 1,000, respectively. For the non-carcinogens,
mercury and cadmium were emission exceeded the screening value with the maximum
exceedance by a factor of 300 for mercury and 30 for cadmium. Due to the theoretical
construct of the screening model, these factors are not directly translatable into estimates of
risk or hazard quotients; rather they indicate that the initial multipathway screening
assessment does not rule out the potential for multipathway impacts of concern. Table 3.2-3
summarizes the results of the Tier 1 screening assessment.
For the PB-HAP and facilities that exceeded the Tier 1 multipathway screening value, we
used facility site-specific information to refine some of the assumptions associated with the
local area around the facilities. While maintaining the exposure assumptions, we refine the
scenario to examine a subsistence fisher and a gardener separately to develop a Tier 2
screening value. (See Section 2.5 and Appendix 6 of this document for more information on
the Tier 2 screening assessment.) The additional site-specific information included the land
use around the facilities, the location of fishable lakes, and local wind direction and speed.
The result of this assessment was the development of site-specific emission screening values
for each of the PB-HAP. Based on this Tier 2 screening assessment, the arsenic and dioxin
emission rates for all facilities were below levels of concern. The maximum Tier 2 screening
value for POM was 6 for the fisher scenario and 100 for the gardener scenario. For mercury,
the maximum Tier 2 screening value was 60 for the fisher scenario. Finally, for cadmium, the
maximum Tier 2 screening value was 2 for the fisher scenario. Table 3.2-3 summarizes the
results of the Tier 2 screening assessment.
For the PB-HAP and facilities that exceeded the Tier 2 multipathway screening values, we
conducted a Tier 3 multipathway lake screening assessment for the fisher scenario and a
direction and proximity for the gardener scenario. (See Section 2.5 and Appendix 6 of this
document for more information on Tier 3.) Based on this Tier 3 screening assessment, the
maximum screening values for POM was reduced to 20 for the gardener scenario while the
maximum screening value for mercury and cadmium remained at 60 and 2, respectively for
the fisher scenario. Table 3.2-2 summarizes the results of the Tier 3 screening assessment.
An exceedance of a screening value in any of the tiers cannot be equated with a risk value or a
hazard quotient (or hazard index). Rather, it represents a high-end estimate of what the risk or
hazard may be. For example, facility emissions exceeding the screening value by a factor of 2
for a non-carcinogen can be interpreted to mean that we are confident that the HQ would be
lower than 2. Similarly, facility emissions exceeding the screening value by a factor of 30 for
a carcinogen means that we are confident that the risk is lower than 30-in-l million. Our
confidence comes from the conservative, or health-protective, assumptions encompassed in
the screening tiers: we choose inputs from the upper end of the range of possible values for
the influential parameters used in the screening tiers; and we assume that the exposed
individual exhibits ingestion behavior that would lead to a high total exposure.
Table 3.2-4. Source Category Level Multipathway Screening Assessment Risk
Results for the SOCMI Source Category
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Residual Risk Assessment for the Synthetic Organic Chemical Manufacturing Industry (SOCMI)
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Tiered Multipathway Maximum Screening
Values (SV)
SV (# facilities SV greater than l)a
Tier 1
Tier 2
Tier 3
Facilities
Fisher and
Fisher
Gardener
Fisher
Emitting
Farmer
and/or
PB-HAP
PB-HAP
Gardener
Carcinogensb
Arsenic
24
700 (9)
5(1)
70 (4)
NA
Dioxins as 2,3,7,8-TCDD
9
2000 (9)
20 (2)
2(1)
NA
Polycyclic Organic Matter
as Benzo(a)pyrene TEQ
59
1000 (20)
6(2)
100 (3)
20 (3)
Arsenic + Dioxins + POM
69
2000 (35)
20 (5)
100 (7)
20 (3)
Non-carcinogens
Cadmium Compounds
26
30(3)
2(2)
0.6 (0)
2(2)
Mercury Compounds
27
300 (9)
60 (2)
0.04 (0)
60 (2)
Notes:
aThe maximum Tier 2 and Tier 3 screening values consider aggregate impacts from nearby facilities
while the number of facilities with a screening value above 1 only considers impacts from individual
facilities
bPOM and dioxin emissions were normalized to BaP and 2,3,7,8-TCDD, respectively, for oral toxicity
and modeled for environmental fate and transport.
When tiered screening values for any facility indicate a potential health risk to the
public, we can conduct a more refined multipathway assessment for a specific
facility. A refined assessment replaces some of the assumptions made in the tiered
screening with facility-specific information. We determined that it is not necessary to
go beyond the Tier 3 lake analysis or conduct a site-specific assessment for cadmium,
mercury, or POM. We compared the Tier 2 screening results to site-specific risk
estimates for five previously assessed source categories. These are the five source
categories, assessed over the past 4 years, which had characteristics that make them
most useful for interpreting the HON screening results. For these source categories,
the EPA assessed fisher and/or gardener risks for arsenic, cadmium, and/or mercury
by conducting site-specific assessments. The EPA used AERMOD for modeling air
dispersion and Tier 2 screens that used multi-facility aggregation of chemical loading
to lakes where appropriate. These assessments indicated that cancer and noncancer
site-specific risk values were at least 50 times lower than the respective Tier 2
screening values for the assessed facilities, with the exception of noncancer risks for
cadmium for the gardener scenario, where the reduction was at least 10 times (refer
to EPA Docket ID: EPA-HQ-OAR-2017-0015 and EPA-HQ-OAR-2019-0373 for a
copy of these reports).13
13 EPA Docket records (EPA-HQ-OAR-2017-0015): Appendix 11 of the Residual Risk
Assessment for the Taconite Manufacturing Source Category in Support of the Risk and
Technology Review 2019 Proposed Rule; Appendix 11 of the Residual Risk Assessment for the
Integrated Iron and Steel Source Category in Support of the Risk and Technology Review
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Residual Risk Assessment for the Synthetic Organic Chemical Manufacturing Industry (SOCMI)
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Based on our review of these analyses, if we were to perform a site-specific
assessment for the SOCMI Source Category, we would expect similar magnitudes of
decreases from the Tier 2 screening values. As such, given the conservative nature of
the screens and the level of additional refinements that would go into a site-specific
multipathway assessment, were one to be conducted, we are confident that the HQ
for ingestion exposure, specifically cadmium and mercury through fish ingestion, is
at or below 1. For POM, the maximum cancer risk under the rural gardener scenario
would likely decrease to below 1-in-l million.
In evaluating the potential for multipathway effects from emissions of lead, modeled
maximum annual ambient lead concentrations, multiplied by 4, were compared to the
NAAQS for lead (0.15 |ig/m3). Lead emissions were reported from 103 facilities. The
maximum ambient lead concentration was 0.004 |ig/m3. That value, multiplied by 4, is well
below the NAAQS for lead , and therefore the NAAQS for lead is not expected to be
exceeded by any facility. See Appendix 11 of this document {Site-Specific Human Health
Multipathway Residual Risk Assessment Report) for more detailed information on the lead
screening assessment.
Environmental
We conducted a screening-level evaluation of the potential adverse environmental risks
associated with emissions of the following environmental HAP for the SOCMI source
category: hydrochloric acid, POM, hydrofluoric acid, lead compounds, arsenic compounds,
cadmium compounds, mercury compounds, and dioxins.
An environmental screening assessment was conducted for PB-HAP. Table 3.2-5
summarizes the source category level environmental risk screening assessment PB-HAP
results. For the Tier 1 environmental screening assessment:
• Arsenic emissions had no Tier 1 exceedances for any ecological benchmark.
• Methyl mercury emissions had Tier 1 exceedances, with a maximum screening value
of 200 for a surface soil NOAEL avian ground insectivores benchmark.
• Divalent mercury emissions had Tier 1 exceedances, with a maximum screening
value of 100 for a surface soil threshold invertebrate benchmark.
• POM emissions had Tier 1 exceedances, with a maximum screening value of 30 for a
sediment community no-effect level benchmark.
2019 Proposed Rule; Appendix 11 of the Residual Risk Assessment for the Portland Cement
Manufacturing Source Category in Support of the 2018 Risk and Technology Review Final
Rule; Appendix 11 of the Residual Risk Assessment for the Coal and Oil-Fired EGU Source
Category in Support of the 2018 Risk and Technology Review Proposed Rule; and EPA
Docket: (EPA-HQ-OAR-2019-0373): Appendix 11 of the Residual Risk Assessment for Iron
and Steel Foundries Source Category in Support of the 2019 Risk and Technology Review
Proposed Rule.
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Residual Risk Assessment for the Synthetic Organic Chemical Manufacturing Industry (SOCMI)
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• Dioxin emissions had Tier 1 exceedances, with a maximum screening value of 10 for
a surface soil NOAEL mammalian insectivores benchmark.
• Cadmium emissions had Tier 1 exceedances, with a maximum screening value of 9
for a surface soil NOAEL mammalian insectivores benchmark.
A Tier 2 screening assessment was performed for methyl mercury, divalent mercury,
cadmium, dioxins, and POM emissions.
• Cadmium, dioxin, and POM emissions had no Tier 2 exceedances for any
ecological benchmark.
• Methyl mercury emissions exceeded the Tier 2 screen with a maximum screening
value of 5 for the fish-eating birds NOAEL benchmark.
• Divalent mercury emissions exceeded the Tier 2 screen with a maximum screening
value of 4 for a sediment threshold level benchmark.
Since there were Tier 2 exceedances, we conducted a Tier 3 environmental risk screen. In
the Tier 3 environmental risk screen, we looked at aerial photos of the lake being impacted
by mercury emissions from the three HON-subject facilities (all of the Tier 2 exceedances
are the result of emissions from 3 facilities acting on the same lake). The aerial photos
show that the "lake" is located in an industrialized area, has been channelized, and largely
filled/drained. Therefore, it was determined that this "lake" would not support a fish
population. We also looked at aerial photos of the facility that was driving the invertebrate
and insectivore Tier 2 soil exceedances due to mercury emissions. The aerial photos show
that the facility is located in a heavily industrialized area with the nearest "natural areas"
being located more than 1500 meters from the facility. We re-calculated the soil screening
values with the industrial areas removed and calculated a maximum Tier 3 soil screen
value for mercury of 1.
Table 3.2-5. Source Category Level Environmental Risk Screening Assessment PB-
HAP Results for the SOCMI Source Category
TIER 1
TIER 2
Facilities
Max SV
Max SV
% Soil Area
Emitting
Ecological
Benchmark
(#of
(#of
with SV >1
PB-HAP
PB-HAP
Endpoint
Effect Level
facilities
with SV
>1)
facilities
with SV >1)
for Highest
Facility
Cadmium
26
Surface Soil
NOAEL-
Mammalian
Insectivores
(shrew)
9(3)
<1(0)
0.1%
NOAEL-
4(3)
<1(0)
0%
Avian Ground
Insectivores
(woodcock)
Fish - Avian
NOAEL
3(2)
<1(0)
NA
Piscivores
(merganser)
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Residual Risk Assessment for the Synthetic Organic Chemical Manufacturing Industry (SOCMI)
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GMATL
3(2)
<1(0)
NA
(merganser)
LOAEL
2(1)
<1(0)
NA
(merganser)
Fish
NOAEL
2(1)
<1(0)
NA
Mammalian
(mink)
Piscivores
Dioxin
9
Surface Soil
NOAEL-
Mammalian
Insectivores
(shrew)
10(4)
<1(0)
2.2%
Divalent
27
Surface Soil
Threshold
100 (6)
2(1)
21.8%
Mercury
Level -
Invertebrate
Community
Threshold
50 (3)
<1(0)
10.6%
Level - Plant
Community
Sediment
Threshold
10(1)
4(1)
NA
Community
Level
Probable-
2(1)
<1(0)
NA
effect Level
Methyl
90
Surface Soil
NOAEL-
200 (9)
3(1)
42.8%
Mercury
Avian Ground
Insectivores
(woodcock)
NOAEL-
30(3)
<1(0)
4.5%
Mammalian
Insectivores
(shrew)
Threshold
2(1)
<1(0)
0%
Level -
Invertebrate
Community
Fish - Avian
NOAEL
10(1)
5(1)
NA
Piscivores
(merganser)
GMATL
5(1)
2(1)
NA
(merganser)
LOAEL
2(1)
<1(0)
NA
(merganser)
Fish-
NOAEL
2(1)
<1(0)
NA
Mammalian
(mink)
Piscivores
POM
59
Sediment
Community
No-effect
Level
30(5)
1(0)
NA
Threshold
7(1)
<1(0)
NA
Level
Water-
Threshold
4(1)
<1(0)
NA
column
Level
Community
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Residual Risk Assessment for the Synthetic Organic Chemical Manufacturing Industry (SOCMI)
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Surface Soil
NOAEL-
4(1)
< 1
0.2%
Mammalian
Insectivores
(shrew)
NA - Not Applicable
NP - Not performed
SV - Screening Value
For lead, we did not estimate any exceedances of the secondary lead NAAQS.
For HC1 and HF, each individual concentration (i.e., each off-site data point in the modeling
domain) was below the ecological benchmarks for all facilities.
3.2.2 Risk assessment results based on allowable emissions
Inhalation
Potential differences between actual emissions levels and the maximum emissions allowable
under the MACT standards (i.e., MACT-allowable emissions) were also determined for the
HON facilities. For this category, allowable emissions are equal to baseline actual emissions,
and therefore the cancer and noncancer risk assessment results based on allowable emissions
are the same as the risk assessment results based on SOCMI baseline actual emissions,
summarized above in Section 3.2.1.
3.3 Post-control risk characterization
Ethylene oxide emissions are primarily driving the baseline risks. Given this, using the same
risk assessment methods described above, we estimated what the risks would be if ethylene
oxide emissions were controlled from equipment leaks, flares, heat exchange systems,
maintenance vents, process vents, storage vessels, and wastewater at HON processes. The
results of the chronic inhalation cancer risk assessment based on these post-control
emissions from the source category are summarized in Table 3.3-1. Based on this scenario,
we estimate that the cancer MIR would be reduced from 2,000-in-l million (i.e., pre-control)
to approximately 100-in-l million (i.e., post-control), with acrylonitrile from equipment
leaks and waste operations driving the post-control risk. There is an estimated reduction in
cancer incidence to 0.4 excess cancer cases per year (post-control), from 2 excess cancer
cases per year (pre-control). In addition, the number of people estimated to have a cancer
risk greater than or equal to 1-in-l million would be reduced from 7,200,000 (pre-control) to
5,700,000 (post-control). The number of people estimated to have a cancer risk greater than
or equal to 10-in-l million would be reduced from 2,300,00 (pre-control) to 570,000 (post-
control). The number of people estimated to have a cancer risk greater than 100-in-l million
would be reduced from 87,000 (pre-control) to 0 (post-control); and the number of people
estimated to have a cancer risk greater than or equal to 1,000-in-l million would be reduced
from 2,900 people (pre-control) to 0 (post-control).
The community-based cancer MIR would be reduced from 2,000-in-l million (pre-control)
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Residual Risk Assessment for the Synthetic Organic Chemical Manufacturing Industry (SOCMI)
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to 1,000-in-l million (post-control), with 98 percent of the post-control MIR attributable to
ethylene oxide emissions from non-HON processes at a HON facility.14 Within 10km of a
SOCMI facility, there is an estimated reduction in community-based cancer incidence to 0.7
excess cancer cases per year (post-control), from 2 excess cancer cases per year (pre-
control). The number of people estimated to have a cancer risk greater than or equal to 1-in-l
million within 10km would remain approximately the same between pre- and post-control at
5.8 million people. The number of people estimated to have a cancer risk greater than or
equal to 10-in-l million would be reduced from 2,400,000 (pre-control) to 1,600,000 (post-
control). The number of people estimated to have a cancer risk greater than 100-in-l million
would be reduced from 100,000 (pre-control) to 4,000 (post-control); and the number of
people estimated to have a cancer risk greater than or equal to 1,000-in-l million would be
reduced from 2,900 people (pre-control) to 0 (post-control).
Regarding noncancer risk, the control of ethylene oxide emissions is not expected to
significantly change the baseline chronic and acute noncancer risk assessment. Accordingly,
the maximum chronic noncancer hazard index posed by post-control emissions is the same
as the baseline actual emissions, which is estimated to be 2 (for the respiratory hazard index)
driven by emissions of maleic anhydride from process vent analyzer operations at one
facility and by emissions of chlorine from three control devices at another facility.
Approximately 80 people are exposed to noncancer hazard index levels above 1, based on
post-control emissions from the 222 facilities in the SOCMI source category. The maximum
acute offsite hazard quotient from post-control emissions is 3 driven by emissions of
chlorine at one facility and by emissions of acrolein at another facility, the same as based on
baseline actual emissions.
Likewise, the multipathway assessment based on post-control emissions is not expected to
differ significantly from the multipathway assessment based on pre-control (baseline actual)
emissions, discussed above in Section 3.2.1.
Table 3.3-1. Source Category Level Inhalation Risks for the SOCMI Based on Post-
Control Emissions
Result
HAP "Drivers"
Cancer Risks
Maximum Individual Lifetime Cancer Risk
(in 1 million)
100
acrylonitrile
Number of Facilities with Maximum Individual Lifetime Cancer Risk:
Greater than or equal to 1,000-in-l million
0
n/a
Greater than 100-in-l million
0
n/a
Greater than or equal to 100-in-l million
4
acrylonitrile, ethylene oxide, ethylene
dichloride, naphthalene, vinyl chloride
Greater than or equal to 10-in-l million
43
Top 10: acrylonitrile, ethylene oxide,
ethylene dichloride, naphthalene, vinyl
14 Community-based post-control risks include controls proposed both for the SOCMI source category and
Neoprene Production source category
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Residual Risk Assessment for the Synthetic Organic Chemical Manufacturing Industry (SOCMI)
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Table 3.3-1. Source Category Level Inhalation Risks for the SOCMI Based on Post-
Control Emissions
Result
HAP "Drivers"
chloride, chloroprene, benzene, hydrazine,
propylene oxide, ethyl benzene
Greater than or equal to 1-in-l million
112
Top 10: acrylonitrile, ethylene oxide,
ethylene dichloride, naphthalene, vinyl
chloride, chloroprene, benzene, hydrazine,
propylene oxide, ethyl benzene
Chronic Noncancer Risks
Maximum Respiratory Hazard Index
2
maleic anhydride, chlorine
Number of Facilities with Maximum Respiratory Hazard Index:
Greater than 1
2
maleic anhydride, chlorine, nickel
compounds, hydrochloric acid
Acute Noncancer Screening Results
Maximum Acute Hazard Quotient
3
chlorine (REL), acrolein (REL)
Number of Facilities with Potential for
Acute Effects
3
chlorine, acrolein, formaldehyde,
chloroform
Population Exposure
Number of People Living Within 50
Kilometers of Facilities Modeled
50,000,000
n/a
Number of People Exposed to Cancer Risk:
Greater than or equal to 1,000-in-l million
0
n/a
Greater than 100-in-l million
0
n/a
Greater than or equal to 100-in-l million
4,700
n/a
Greater than or equal to 10-in-l million
570,000
n/a
Greater than or equal to 1-in-lmillion
5,700,000
n/a
Number of People Exposed to Noncancer Respiratory Hazard Index:
Greater than 1
80
n/a
Estimated Cancer Incidence (excess cancer
cases per year)
0.4
n/a
Contribution of HAP to Cancer Incidence
ethylene oxide
61%
n/a
naphthalene
7%
n/a
1,3-butadiene
6%
n/a
acrylonitrile
5%
n/a
benzene
4%
n/a
ethylene dichloride
4%
n/a
chloroprene
3%
n/a
arsenic compounds
2%
n/a
chromium (VI) compounds
2%
n/a
nickel compounds
1%
n/a
coal tar
1%
n/a
vinyl chloride
1%
n/a
formaldehyde
1%
n/a
bis(chloromethyl)ether
1%
n/a
PAH
1%
n/a
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Table 3.3-1. Source Category Level Inhalation Risks for the SOCMI Based on Post-
Control Emissions
Result
HAP "Drivers"
54 other HAP
each < 0.5%
n/a
4 General discussion of uncertainties in the risk assessment
The uncertainties in virtually all of the RTR risk assessments can be divided into three areas:
1) uncertainties in the emission data sets, 2) exposure modeling uncertainties, and 3)
uncertainties in the dose-response relationships. Uncertainties in the emission estimates and in
the air quality models lead to uncertainty in air concentrations. Uncertainty in exposure
modeling can arise due to uncertain activity patterns, the locations of individuals within a
census tract, and the microenvironmental concentrations as reflected in the exposure model.
Finally, uncertainty in the shape of the relationship between exposure and effects, the URE
and the RfC, also contributes to uncertainties in the risk assessment. These three areas of
uncertainty are discussed below.
4.1 Emissions inventory uncertainties
Although the development of the RTR emissions data set involves an extensive quality
assurance/quality control process, the accuracy of emission values will vary depending on
certain factors, for example, the source of the data, the degree to which data are incomplete or
missing, the degree to which assumptions made to complete the data sets are accurate, and the
extent to which there are errors in these emission estimates. The emission estimates used in
the risk assessment generally are annual totals for certain years, and they do not reflect short-
term fluctuations during the course of a year or variations from year to year.
For the acute screening assessment, therefore, in the absence of available specific estimates or
measurements, we use estimates of peak hourly emission rates. These estimates typically are
calculated by first estimating the average annual hourly emissions rates by evenly dividing the
total annual emission rate from the inventory into the 8,760 hours of the year. An emission
adjustment factor that is intended to account for emission fluctuations during normal facility
operations is then applied to these average annual hourly emission rates. The adjustment
factor can be based on actual fluctuations seen in the available emission data for sources in a
category or on engineering judgment; in the absence of such information, a default factor is
applied.
To prepare the emissions data set, EPA gathers the best available data on emissions, emission
release parameters, and other relevant source category-specific parameters. EPA often begins
with its National Emissions Inventory (NEI) database as the starting point for emission rates,
emissions release characteristics, and locations of the emission release points for each facility
in the source category. The NEI is a composite of emission measurements and estimates
produced by state and local regulatory agencies, industry, and EPA. EPA's industry experts
then review the data for consistency and completeness and conduct extensive quality
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assurance/quality control checks. Available information, which may include compliance data,
information from project files, permits, and other sources regarding facilities and emission
sources, are also incorporated into the data set. This additional information may be
incorporated in addition to the NEI data or in place of the NEI data, depending on EPA's
evaluation of the quality of the various sources of data. In order to fill data gaps, EPA may
conduct a formal information collection request (ICR) under the authority of section 114 of
the Clean Air Act to obtain current, complete emissions data and other data from the facility
owners and operators associated with the source category under review.
Uncertainty in the emissions data set stems from data gaps, default assumptions, and the
emission models used to develop emissions inventory estimates. A variety of methods, such
as emission factors, material balances, engineering judgement, air permit information and
source testing, are used to develop emission estimates. Other parameters that are part of the
emissions data set, including facility location and emission point parameters, may also be a
source of uncertainty. Some release point locations use an average facility location instead of
the location of each specific unit within the facility. In some instances, default release point
parameters may be in the inventory. Where fugitive release parameters are not available,
default values are included. Another potential source of emission estimate uncertainty may be
low or poor quality data (e.g., out-of-date parameter values). For more information on the
uncertainties in the emission estimates for this source category see Appendix 1 (Emissions
Inventory Support Documents) of this document.
4.2 Exposure modeling uncertainties
4.2.1 Inhalation exposure modeling
Although every effort is made to identify all of 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
assessment. The ambient air 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 not addressing processes like deposition, plume depletion, and atmospheric degradation.
Additionally, estimates of maximum individual risk (MIR) contain uncertainty because they
are derived at census block centroid locations rather than actual residences. This uncertainty is
known to create potential underestimates and overestimates of the actual MIR values for
individual facilities; however, overall, it is not thought to have a significant impact on the
estimated MIR for a source category. We also do 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 do we factor in the possibility of
population growth during the 70-year chronic exposure period, which could lead to a potential
downward bias in both the MIR and population risk estimates. Finally, we do not factor in
time an individual spends indoors.
We did not include the effects of human mobility on exposures in the assessment.
Specifically, short-term mobility and long-term mobility between census blocks in the
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modeling domain were not considered. (Short-term mobility is movement from one micro-
environment to another over the course of hours or days. Long-term mobility is movement
from one residence to another over the course of a lifetime.) The approach of not considering
short or long-term population mobility does not bias the estimate of the theoretical MIR (by
definition), nor does it affect the estimate of cancer incidence because 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 high risk levels (e.g., l-in-10 thousand
or 1-in-l million).
In addition, the assessment predicted the chronic exposures at the centroid of each populated
census block as surrogates for the exposure concentrations for all people living in that block.
Using the census block centroid to predict chronic exposures tends to over-predict exposures
for people in the census block who live farther from the facility and under-predict exposures
for people in the census block who live closer to the facility. Thus, using the census block
centroid to predict chronic exposures may lead to a potential understatement or overstatement
of the true maximum impact, but is an unbiased estimate of average risk and incidence. We
reduce this uncertainty by analyzing large census blocks near facilities using aerial imagery
and adjusting the location of the block centroid to better represent the population in the block,
as well as adding additional receptor locations where the block population is not well
represented by a single location.
The assessment evaluates the cancer inhalation risks associated with pollutant exposures over
a 70-year period, which is the assumed lifetime of an individual. In reality, both the length of
time that modeled emission sources at facilities actually operate (i.e., more or less than 70
years) and the domestic growth or decline of the modeled industry (i.e., the increase or
decrease in the number or size of domestic facilities) will influence the future risks posed by a
given source or source category. Depending on the characteristics of the industry, these
factors will, in most cases, result in an overestimate both in individual risk levels and in the
total estimated number of cancer cases. However, in the unlikely scenario where a facility
maintains, or even increases, its emissions levels over a period of more than 70 years,
residents live beyond 70 years at the same location, and the residents spend more of their days
at that location, then the cancer inhalation risks could potentially be underestimated.
However, annual cancer incidence estimates from exposures to emissions from these sources
would not be affected by the length of time an emissions source operates.
The exposure estimates used in these analyses assume chronic exposures to ambient (outdoor)
levels of pollutants. Because most people spend the majority of their time indoors, actual
exposures may not be as high, depending on the characteristics of the pollutants modeled. For
many of the HAP, indoor levels are roughly equivalent to ambient levels, but for very reactive
pollutants or larger particles, indoor levels are typically lower. This factor has the potential to
result in an overestimate of 25 to 30 percent of exposures (USEPA, 2001).
A sensitivity analysis, discussed in "Risk and Technology Review (RTR) Risk Assessment
Methodologies" (USEPA, 2009a), found that the selection of the meteorology data set
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location could have an impact on the risk estimates. The analysis found that cancer MIR
derived using different meteorological stations varied by as much as 63 percent below to 51
percent above the value derived using the nearest meteorological station. Cancer incidence
estimated using different meteorological stations varied by as much as 68 percent below to
120 percent above the value estimated using the nearest meteorological station. Similarly, air
concentrations estimated using different meteorological stations varied by as much as 49
percent below to 21 percent above the value estimated using the nearest meteorological
station. Since this analysis was performed EPA has increased the number of meteorological
stations used in our risk assessments; thus, we expect variability to be reduced.
For the acute screening assessment, the results are intentionally biased high, and thus health-
protective, by assuming the co-occurrence of independent factors, such as hourly emission
rates, meteorology and human activity patterns. Furthermore, in cases where multiple acute
dose-response values for a pollutant are considered scientifically acceptable, we choose the
most conservative of these dose-response values, erring on the side of overestimating
potential health risks from acute exposures. In cases where these results indicate the potential
for exceeding acute HQs, we refine our assessment by developing a better understanding of
the geography of the facility relative to potential exposure locations.
4.2.2 Multipathway exposure modeling
In modeling the fate and transport of pollutants through the environment and the non-
inhalation exposure (i.e., ingestion) to these pollutants, TRIM.FaTE uses simplified
representations of many complex real-world processes. This simplified representation
introduces uncertainty. Uncertainties arise from model assumptions and structure, as reflected
in the algorithms that describe the environmental movement of pollutants, and in the input
values for numerous environmental parameters.
Uncertainty in the algorithms is inherent to any model attempting to represent complex
processes in the real world. How persistent, bioaccumulative chemicals such as mercury,
cadmium, arsenic, PAHs, and dioxins behave in the environment is highly complex, and many
natural processes are represented in a simplified manner by TRIM.FaTE, including, for
example:
- gaseous and particulate deposition from air;
- biogeochemical cycling in the aquatic environment, particularly mercury
transformations through methylation and demethylation at the sediment-surface
interface;
- mixing processes in air, water, and sediment;
- suspended and benthic sediment dynamics in lakes; and
- biotic processes such as growth, reproduction, and predation.
Even though some processes, such as diffusion, are known to follow second-order dynamics,
the TRIM.FaTE model represents all fate and transport processes in terms of first-order
differential equations. TRIM.FaTE also does not explicitly deal with lateral or vertical
dispersion in the air compartments. Some algorithms, such as those addressing methylation
and sediment transport, for example, do not consider all of the factors known to affect the
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process. Biotic processes including chemical absorption, chemical elimination, growth,
reproduction, predation, and death have been represented relatively simplistically in the
model. Although the model's algorithms have been validated and are based on professional
judgment, some level of uncertainty results from such simplifications.
The input values for parameters are also associated with uncertainty. Algorithms that describe
the environmental movement of pollutants depend on numerous environmental parameters for
which the values might be naturally variable and for which available data are often limited.
Examples of parameters for which input values are variable and uncertain include aquatic
food web structure (e.g., diet of each fish species), biokinetic parameters that influence
bioaccumulation (e.g., assimilation efficiencies and elimination rates), topographic
characteristics (e.g., lake depth, runoff rates, and erosion rates), meteorological parameters
(e.g., evaporation and precipitation rates), chemical transformation rates (e.g., methylation
and demethylation rates, in the case of mercury), and human exposure parameters (especially
fish consumption rates).
For TRIM.FaTE modeling, we use central tendency values and combinations of values that
would lead to estimates of reasonable maximum exposures to bound risk estimates. We have
conducted analyses of the sensitivity of risk estimates to parameter input values. For those
parameters to which the model is particularly sensitive, we have continued to collect
additional data to better quantify the variability and distribution of input values.
A more comprehensive explanation of the uncertainties related to fate, transport, and exposure
modeling using TRIM.FaTE is provided in Appendix 6 (Technical Support Document for
TRIM-Based Multipathway Tiered Screening Methodology for RTR) of this report for the
tiered assessments and Appendix 11 {Site-Specific Human Health Multipathway Residual Risk
Assessment Report) of this report for a site-specific assessment if one was conducted.
4.2.3 Environmental risk screening assessment
For each source category, we generally rely on site-specific levels of environmental HAP
emissions to perform an environmental screening assessment. The environmental screening
assessment is based on the outputs from models that estimate environmental HAP
concentrations. The same models, specifically the TRIM.FaTE multipathway model and the
AERMOD air dispersion model, are used to estimate environmental HAP concentrations for
both the human multipathway screening analysis and for the environmental screening
analysis. Therefore, both screening assessments have similar modeling uncertainties. Two
important types of uncertainty associated with the use of these models in RTR environmental
screening assessments (and inherent to any assessment that relies on environmental modeling)
are model uncertainty and input uncertainty.
Model uncertainty concerns whether the selected models are appropriate for the assessment
being conducted and whether they adequately represent the movement and accumulation of
environmental HAP emissions in the environment. For example, does the model adequately
describe the movement of the pollutant through the soil? This type of uncertainty is difficult
to quantify. However, based on feedback received from previous EPA SAB reviews and other
reviews, we are confident that the models used in the screening assessments are appropriate
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and state-of-the-art for the environmental risk assessments conducted in support of our RTR
analyses.
Input uncertainty is concerned with how accurately the models have been configured and
parameterized for the assessment at hand. For Tier 1 of the environmental screening
assessment for PB-HAP, we configured the models to avoid underestimating exposure and
risk to reduce the likelihood that the results indicate the risks are lower than they actually are.
This was accomplished by selecting upper-end values from nationally-representative datasets
for the more influential parameters in the environmental model, including selection and
spatial configuration of the area of interest, the location and size of any bodies of water,
meteorology, surface water and soil characteristics, and structure of the aquatic food web. In
Tier 1, we use the maximum facility-specific emissions for the PB-HAP (other than lead
compounds, which were evaluated by comparison to the Secondary Lead NAAQS) that are
included in the environmental screening assessment and each of the media when comparing to
ecological benchmarks. This is consistent with the conservative design of the Tier 1 screening
assessment. In Tier 2 of the environmental screening assessment for PB-HAP, we refine the
model inputs to account for meteorological patterns in the vicinity of the facility versus using
upper-end national values, and we identify the locations of water bodies near the facility
location. By refining the screening approach in Tier 2 to account for local geographical and
meteorological data, we decrease the likelihood that concentrations in environmental media
are overestimated, thereby increasing the usefulness of the screening assessment. To better
represent widespread impacts, the modeled soil concentrations are averaged in Tier 2 to
obtain one average soil concentration value for each facility and for each PB-HAP. For PB-
HAP concentrations in water, sediment, and fish tissue, the highest value for each facility for
each pollutant is used.
For the environmental screening assessment for acid gases, we employ a single-tiered
approach. We use the modeled air concentrations and compare those with ecological
benchmarks.
For both Tiers 1 and 2 of the environmental screening assessment, our approach to addressing
model input uncertainty is generally cautious. We choose model inputs from the upper end of
the range of possible values for the influential parameters used in the models, and we assume
that the exposed individual exhibits ingestion behavior that would lead to a high total
exposure. This approach reduces the likelihood of not identifying potential risks for adverse
environmental impacts.
4.3 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 dose-response values are generally derived for
chronic exposures (up to a lifetime) but may also be derived for acute (less than 24 hours),
short-term (from 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
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risks associated with the emissions included in an assessment, we rely on both chronic (cancer
and noncancer) and acute (noncancer) dose-response values, 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
values. Since exposures to these pollutants cannot be included in a quantitative risk estimate,
an understatement of risk for these pollutants at estimated exposure levels is possible. To help
alleviate this potential underestimate, where we conclude similarity with a HAP for which a
dose-response assessment value is available, we use that value as a surrogate for the
assessment of the HAP for which no value is available. To the extent use of surrogates
indicates appreciable risk, we may identify a need to increase priority for a new IRIS
assessment of that substance. We additionally note that, generally speaking, HAP of greatest
concern due to environmental exposures and hazards are those for which dose-response
assessments have been performed, reducing the likelihood of understating risk. Further, HAP
not included in the quantitative assessment are assessed qualitatively and considered in the
risk characterization that informs the risk management decisions, including with regard to
consideration of HAP reductions achieved by various control options.
Additionally, chronic dose-response values for certain compounds included in the assessment
may be under EPA IRIS review. In those cases, revised assessments may determine in the
future that these pollutants are more or less potent than currently thought.
For a group of compounds that are unspeciated (e.g., glycol ethers), we conservatively use the
most protective reference value of an individual compound in that group to estimate risk.
Similarly, for an individual compound in a group (e.g., ethylene glycol diethyl ether) that does
not have a specified reference value, we apply the most protective reference value from the
other compounds in the group to estimate 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 (herein
referred to as Cancer Guidelines). (USEPA, 2005d) "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
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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 describe the Agency's
recommendations for methodologies for cancer risk assessment. EPA believes that cancer risk
estimates developed following the procedures described in the Cancer Guidelines and
outlined below generally provide an upper bound estimate of risk. That is, EPA's upper bound
estimates represent a plausible upper limit to the true value of a quantity (although this is
usually not a true statistical confidence limit). In some circumstances, the true risk could be as
low as zero; however, in other circumstances the risk could also be greater.15 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.16 EPA also
uses the upper bound (rather than lower bound or central tendency) 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. 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 assessments. 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.17 However, unless scientific support is available to show
15 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.
16 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.
17 Per the EPA Cancer Guidelines: "The default option is that positive effects in animal cancer studies indicate
that the agent under study can have carcinogenic potential in humans." and "Target organ concordance is not a
prerequisite for evaluating the implications of animal study results for humans."
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otherwise, EPA assumes that tumors in animals are relevant in humans, regardless of target
organ concordance. For a specific pollutant, qualitative differences in species responses can
lead to either under-estimation or over-estimation of human cancer risks.
(2) Uncertainties regarding the most appropriate dose metric for an assessment can also lead
to differences in risk predictions. For example, the measure of dose is commonly expressed in
units of mg/kg/d ingested or the inhaled concentration of the pollutant. However, data may
support development of a pharmacokinetic model for the absorption, distribution, metabolism
and excretion of an agent, which may result in improved dose metrics (e.g., average blood
concentration of the pollutant or the quantity of agent metabolized in the body). Quantitative
uncertainties result when the appropriate choice of a dose metric is uncertain or when dose
metric estimates are themselves uncertain (e.g., as can occur when alternative
pharmacokinetic models are available for a compound). Uncertainty in dose estimates may
lead to either over or underestimation of risk.
(3) For the quantitative extrapolation of cancer risk estimates from experimental animals to
humans, EPA uses scaling methodologies (relating expected response to differences in
physical size of the species), which introduce another source of uncertainty. These
methodologies are based on both biological data on differences in rates of process according
to species size and empirical comparisons of toxicity between experimental animals and
humans. For a particular pollutant, the quantitative difference in cancer potency between
experimental animals and humans may be either greater than or less than that estimated by
baseline scientific scaling predictions due to uncertainties associated with limitations in the
test data and the correctness of scaled estimates.
(4) EPA cancer risk estimates, whether based on epidemiological or experimental animal data,
are generally developed using a benchmark dose (BMD) analysis to estimate a dose at which
there is a specified excess risk of cancer, which is used as the point of departure (or POD) for
the remainder of the calculation. Statistical uncertainty in developing a POD using a
benchmark dose (BMD) approach is generally addressed though use of the 95 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 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.,
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nonthreshold) response or as the most common default approach when a compound's mode of
action is unknown. Linear extrapolation can be supported by both pollutant-specific data and
broader scientific considerations. For example, EPA's Cancer Guidelines generally consider a
linear dose-response to be appropriate for pollutants that interact with DNA and induce
mutations. Pollutants whose effects are additive to background biological processes in cancer
development can also be predicted to have low-dose linear responses, although the slope of
this relationship may not be the same as the slope estimated by the straight line approach.
EPA most frequently utilizes a linear low-dose extrapolation approach as a baseline science-
policy 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 (NAS, 1994) 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 report (NRC, 2006) 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). 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
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sensitive to the action of a carcinogen than the typical individual, although compound-specific
data to evaluate this variability are generally not available. There may also be important life
stage differences in the quantitative potency of carcinogens and, with the exception of the
recommendations in EPA's Supplemental Cancer Guidance for carcinogens with a mutagenic
mode of action, risk assessments do not generally quantitatively address life stage differences.
However, one approach used commonly in EPA assessments that may help address variability
in response is to extrapolate human response from results observed in the most sensitive
species and sex tested, resulting typically in the highest URE which can be supported by
reliable data, thus supporting estimates that are designed not to underestimate risk in the face
of uncertainty and variability.
Chronic noncancer assessment
Chronic noncancer reference values represent chronic exposure levels that are intended to be
health-protective. That is, EPA and other organizations, such as the Agency for Toxic
substances and disease Registry (ATSDR), which develop noncancer dose-response values
use 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 values18 (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 noncancer endpoints related to chronic exposures, EPA derives a reference dose (RfD) for
exposures via ingestion, and a reference concentration (RfC) for inhalation exposures. As
stated in the IRIS Glossary, 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. To derive values that are
intended to be "without appreciable risk," EPA's methodology relies upon an uncertainty
factor (UF) approach (USEPA, 1993b; USEPA, 1994) which includes consideration of both
uncertainty and variability.
1 R
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.
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EPA begins by evaluating all of the available peer-reviewed literature to determine noncancer
endpoints of concern, evaluating the quality, strengths and limitations of the available studies.
EPA typically chooses the relevant endpoint that occurs at the lowest dose, often using
statistical modeling of the available data, and then determines the appropriate POD for
derivation of the reference value. A POD is determined by (in order of preference): (1) a
statistical estimation using the 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 (USEPA, 2002b). 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
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sensitive sex in assessing risks to the average human. Typically, compound specific data to
evaluate relative sensitivity in humans versus rodents are lacking, thus leading to uncertainty
in this extrapolation. Size-related differences (allometric relationships) indicate that typically
humans are more sensitive than rodents when compared on a mg/kg/day basis. The default
choice of 10 for the interspecies UF is consistent with these differences. For a specific
chemical, differences in species responses may be greater or less than this value.
Pharmacokinetic models are useful to examine species differences in pharmacokinetic
processing and associated uncertainties; however, such dosimetric adjustments are not always
possible. Information may not be available to quantitatively assess toxicokinetic or
toxicodynamic differences between animals and humans, and in many cases a 10-fold UF
(with separate factors of 3 for toxicokinetic and toxicodynamic components) is used to
account for expected species differences and associated uncertainty in extrapolating from
laboratory animals to humans in the derivation of a reference value. If information on one or
the other of these components is available and accounted for in the cross-species
extrapolation, a UF of 3 may be used for the remaining component.
3) In the case of reference values for chronic exposures where only data from shorter
durations are available (e.g., 90-day subchronic studies in rodents) or when such data are
judged more appropriate for development of an RfC, an additional UF of 3 or 10-fold is
typically applied unless the available scientific information supports use of a different value.
4) Toxicity data are typically limited as to the dose or exposure levels that have been
tested in individual studies; in an animal study, for example, treatment groups may differ in
exposure by up to an order of magnitude. The preferred approach to arrive at a POD is to use
BMD analysis; however, this approach requires adequate quantitative results for a meaningful
analysis, which is not always possible. Use of a NOAEL is the next preferred approach after
BMD analysis in determining a POD for deriving a health effect reference value. However,
many studies lack a dose or exposure level at which an adverse effect is not observed (i.e., a
NOAEL is not identified). When using data limited to a LOAEL, a UF of 10 or 3-fold is often
applied.
5) The database UF is intended to account for the potential for deriving an
underprotective RfD/RfC due to a data gap preventing complete characterization of the
chemical's toxicity. In the absence of studies for a known or suspected endpoint of concern, a
UF of 10 or 3-fold is typically applied.
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. For acute
reference values, though, individual UF values may be less than 10. UFs are applied based on
chemical- or health effect-specific information or based on the purpose of the reference value.
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
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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 dose-response 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 dose-response values at different levels of severity should be factored into the risk
characterization as potential uncertainties.
Environmental Risk Screening Assessment
Uncertainty also exists in the ecological benchmarks for the environmental risk screening
assessment. We established a hierarchy of preferred benchmark sources to allow selection of
benchmarks for each environmental HAP at each ecological assessment endpoint. In general,
EPA benchmarks used at a programmatic level (e.g., Office of Water, Superfund Program)
were used if available. If not, we used EPA benchmarks used in regional programs (e.g.,
Superfund Program). If benchmarks were not available at a programmatic or regional level,
we used benchmarks developed by other agencies (e.g., NOAA) or by state agencies.
In all cases (except for lead compounds, which were evaluated through a comparison to the
NAAQS), we searched for benchmarks at the following three effect levels, as described in
Section 2.6 of this report and in Appendix 9 (Environmental Risk Screening Assessment) of
this report: a no-effect level (i.e., NOAEL), threshold-effect level (i.e., LOAEL), and
probable-effect level (i.e., PEL).
For some ecological assessment endpoint/environmental HAP combinations, we could
identify benchmarks for all three effect levels, but for most we could not. In one case, where
different agencies derived significantly different numbers to represent a threshold for effect,
we included both. In several cases, only a single benchmark was available. In cases where
multiple effect levels were available for a particular PB-HAP and assessment endpoint, we
used all of the available effect levels to help us determine whether risk exists if risks could be
considered significant and widespread.
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USEPA, 1994. US Environmental Protection Agency. Methods for Derivation of Inhalation
Reference Concentrations and Application of Inhalation Dosimetry. EPA/600/8-90/066F.
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derivation-inhalation-reference-concentrations-and-application-inhalation-dosimetry
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10/documents/osp risk characterization handbook 2000.pdf
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Chemical Mixtures. EPA-630/R-00-002.
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Utility, and Integrity of Information Disseminated by the Environmental Protection Agency.
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and-integritv-information
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General Purpose (Flat and Complex Terrain) Dispersion Model and Other Revisions; Final
Rule. 40 CFR Part 51. https://www.federalregister.gov/documents/2005/ll/09/05-
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purpose-flat-and
USEPA, 2005b. Supplemental guidance for assessing early-life exposure to carcinogens.
EPA/630/R-03003F. https://www3.epa.gov/ttn/atw/childrens supplement final.pdf
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01/documents/cgiwgcommuniation i.pdf
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Supplemental Guidance as having a mutagenic mode of action. Science Policy Council
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dated 14 June 2006. https://www.epa.gov/sites/production/files/2015-01/documents/cgiwg-
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USEPA, 2009a. Risk and Technology Review (RTR) Risk Assessment Methodologies: For
Review by the EPA's Science Advisory Board with Case Studies - MACT I Petroleum
Refining Sources and Portland Cement Manufacturing. EPA-452/R-09-006.
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Inhalation Exposures [Final Report], EPA/600/R-09/061, 2009.
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Risk Assessment Methodologies".
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USEPA, 2021c. Table 1. Prioritized Chronic Dose-Response Values (9/29/2021). Office of
Air Quality Planning and Standards, https://www.epa.gov/fera/dose-response-assessment-
assessing-health-risks-associated-exposure-hazardous-air-pollutants
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(8/31/2021). Office of Air Quality Planning and Standards, https://www.epa.gov/fera/dose-
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Health and Environmental Risks (SCHER, 2006).
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Appendix 1
Emissions Inventory Support Documents
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*ERG
www.erg.com
MEMORANDUM
TO: Andrew Bouchard, U.S. EPA/OAQPS/SPPD - EPA Office of Air Quality
Planning and Standards
FROM: Eastern Research Group, Inc.
DATE: March 2023
SUBJECT: Emissions Data Used in Technology Review Modeling Files for Facilities
Located in the SOCMI and Neoprene Production Source Categories that are
Associated with Processes Subject to HON and P&R I
1.0 INTRODUCTION
The U.S. Environmental Protection Agency (EPA) is proposing amendments to the
National Emission Standards for Hazardous Air Pollutants (NESHAP) for three subparts in 40
CFR 63 (subparts F, G, and H) that apply to the Synthetic Organic Chemical Manufacturing
Industry (SOCMI) and for one subpart in 40 CFR 63 (subpart I) that applies to equipment leaks
from certain non-SOCMI processes located at chemical plants. These four NESHAP are more
commonly referred together as the Hazardous Organic NESHAP (HON). The emissions sources
affected by the current HON includes heat exchange systems and maintenance wastewater
regulated under NESHAP subpart F; process vents, storage vessels, transfer racks, and
wastewater streams regulated under NESHAP subpart G; equipment leaks associated with
SOCMI processes regulated under NESHAP subpart H; and equipment leaks from certain non-
SOCMI processes at chemical plants regulated under NESHAP subpart I.
The Group I Polymers and Resins NESHAP (P&R I, codified at 40 CFR 63, subpart U)
regulates the following elastomer product source categories:
• Butyl rubber
• Epichlorohydrin elastomer
• Ethylene propylene rubber
• Halobutyl rubber
• Hypalon™
• Neoprene
• Nitrile butadiene latex
• Nitrile butadiene rubber
• Polybutadiene rubber/styrene butadiene rubber by solution
• Polysulfide rubber
• Styrene butadiene latex
• Styrene butadiene rubber by emulsion
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The EPA conducted a residual risk and technology review for the HON in 2006 and
Neoprene Production source category in P&R I in 2008, concluding that there was no need to
revise the standards under either CAA section 112(f) or 112(d)(6). As part of the residual risk
review, the EPA conducted a risk assessment, and based on the results of the risk assessment,
determined that the current level of control called for by the existing MACT standards both
reduced HAP emissions to levels that presented an acceptable level of risk and protected public
health with an ample margin of safety (see 71 FR 76603, December 21, 2006 and 73 FR 76220,
December 16, 2008, for additional details). This action constitutes another 112(d)(6) technology
review for the SOCMI (HON) and Neoprene Production source categories. We note that
although there is no statutory CAA obligation under CAA section 112(f) for the EPA to conduct
a second residual risk review of the SOCMI and Neoprene Production source categories, the
EPA retains discretion to revisit its residual risk reviews where the Agency deems that is
warranted. For the SOCMI source category, the EPA is concerned about the risks posed from
ethylene oxide and chloroprene, due to the fact that revisions to the EPA's Integrated Risk
Information System (IRIS) inhalation unit risk estimate (URE) for ethylene oxide were finalized
in 2016 showing it to be more toxic than previously known as well as because of the
development of the EPA's IRIS inhalation URE for chloroprene in 2010. Similarly, for the
Neoprene Production source category, the EPA is concerned about the risks posed from
chloroprene due to the development of the EPA's IRIS inhalation URE for chloroprene in 2010.
Thus, since the EPA was unable to consider these factors in its residual risk review for the
SOCMI source category in 2006 and Neoprene source category in 2008, it is conducting a risk
assessment in this action so that the results of the risk assessment can be considered to ensure
that the MACT standards continue to provide an ample margin of safety to protect public health.
This memorandum describes the methodology used to develop the risk modeling file used for
this additional review.
2.0 INITIAL FACILITIES LIST DEVELOPMENT
The list of existing facilities potentially subject to the HON and Neoprene Production
standards was initially developed using several sources. First, the EPA compiled a list of
facilities representing the chemical manufacturing sector from the 2017 National Emissions
Inventory (NEI) and in the Toxics Release Inventory (TRI) with a primary facility North
American Industry Classification System (NAICS) code beginning with 325. Second, this list
was supplemented with information from the Office of Enforcement and Compliance
Assurance's (OECA) Enforcement and Compliance History Online (ECHO) tool1 as well as
other internal chemical sector facility lists from the EPA's recent petrochemical sector RTR
rulemakings (e.g., Miscellaneous Organic Chemical Manufacturing NESHAP (40 CFR part 63,
subpart FFFF), Organic Liquids Distribution NESHAP (40 CFR part 63, subpart EEEE),
Ethylene Production NESHAP (40 CFR part 63, subparts XX and YY), Petroleum Refineries
NESHAP (40 CFR part 63, subparts CC and UUU)).2 Third, the list was overlaid with the
1 See https://echo.epa.gov/facilities/facility-search?srch=adv.
2 See 85 FR 49084, August 12, 2020, 85 FR 40740, July 7, 2020, 85 FR 40386, July 6, 2020, and 80 FR
75178, December 1, 2015, respectively.
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facility list the EPA used for the latest review of the HON back in 2006 and Neoprene
Production back in 2008.
To determine which facilities on the comprehensive chemical manufacturing sector
facility list were subject to the HON and P&R I standards for Neoprene Production, the EPA
obtained title V air permits from publicly available online State databases (where available). In
cases where an online database was incomplete or did not exist, the EPA contacted the Region
and/or State for help in obtaining the air permits or determining whether a facility was subject to
the HON. The EPA also conducted internet searches to determine the status of the facility (e.g.,
whether the facility was still open, permanently closed, and/or sold). In some cases where a
permit could not be obtained, the EPA assumed that the facility was subject to the HON.
Lastly, the EPA shared a draft of the compiled facility list with the American Chemistry
Council (ACC) in October 2021. Based on feedback provided by ACC, a facility list consisting
of 207 hazardous organic chemical manufacturing facilities subject to the HON standards, herein
referred to as "HON facilities," was finalized and used to assess impacts for this rulemaking. The
list of facilities located in the United States that are major sources of HAP and part of the
SOCMI source category with processes subject to HON is available in the memorandum titled:
"Lists of Facilities Subject to the HON, Group I and Group II Polymers and Resins NESHAPs,
and NSPS subparts VV, VVa, III, NNN, and RRR" (ERG, 2023a). For the 207 HON facilities,
only 195 had reported HAP emissions in the 2017 NEI, and we note that two facilities included
in the 207 are new/under construction and were not operating in 2017. We also note that one
facility was identified as a Neoprene Production facility (which is also subject to the HON).
3.0 PROCEDURES USED TO OBTAIN BASELINE EMISSIONS
For each HON and Neoprene Production facility (see Section 2.0 of this memorandum),
we gathered emissions data from the January 2021 version of the 2017 NEI. The 2017 NEI was
the most vetted and recent publicly available data set at the time of this analysis. However, in a
few instances where facility-specific data was not available in the 2017 NEI, we attempted to
obtain data from a more recent data set (i.e., from 2018 NEI or 2019 or 2020 state submittals to
the Emissions Inventory System (EIS) for NEI). The more recent data are not part of a larger,
publicly available, triennial NEI; and therefore, have not undergone the same level of review as
the 2017 NEI data set.3 Ultimately, the EPA deemed this data set as the baseline emissions for
the HON source category (and improvements to this baseline emissions data set are discussed in
Section 4 of this memorandum).
We then reviewed description data fields for each NEI record in the baseline emissions
data set associated with any ethylene oxide emitting HON facility.4 For each of these specific
NEI records, we allocated the record to one of the emission process groups identified in Table 1
using information provided in the description data fields for each emission unit, process, release
3 Refer to the 2017 NEI Technical Support Document for detailed discussion on the types of review and
augmentation performed for 2017 NEI (https://www.epa.gov/sites/default/files/2021-
02/documents/nei2017_tsdJiillJan2021.pdf).
4 Although EPA conducts whole facility risk assessments of all HON facilities, it was anticipated that HON
facilities emitting ethylene oxide would likely require a more elaborate review of specific emission process
groups.
4
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point, and standard classification code (SCC). We used automated queries (see Appendix A) for
much of this task; however, assignments were also made manually.
Table 1. Emission Process Groups Related to Ethylene Oxide Emitting HON Facilities
Kinission Process(iroup Description1
Bottoms Receiver
Equipment Leak
Heat Exchange System
Hotwell
Nitrogen Inert System
Process Vent2
Storage Tank
Surge Control Vessel
Transfer Rack
Wastewater
Control Device (UnknownEPG)3
Flare4
Non-CMATTR Source Category Process Group5
Unknown6
1 If discernible, we differentiated between maintenance and non-maintenance activities for each emission process
group.
2 If discernible, we identified analyzer vents separate from process vents.
3 Although a specific control device (e.g., carbon adsorber, incinerator, or thermal oxidizer) could often be
determined using the various description data fields associated with the NEI record, we could not determine the
emission process group associated with the control device, including whether the record involves co-mingled
emissions from more than one emission process group due to a shared control device.
4 If discernible, we differentiated between emergency and non-emergency flaring activities, as well as the
emission process group associated with the flare, and whether the flare is operating in a Texas county subject to
specific flare control requirements for highly reactive volatile organic compounds.
5 These are instances where we determined the NEI record is either: (1) entirely outside the HON source category
(e.g., abrasive blasting operations, degreasers, emergency generators, marine loading operations, painting
operations, etc), or (2) already considered in a previous EPA residual risk review for the Organic Liquid
Distribution (OLD) NESHAP, Ethylene Production (EMACT) NESHAP, or Miscellaneous Organic NESHAP
(MON).
6 These are instances when the description data fields of the NEI record are not descriptive enough to assign an
emission process group.
4.0 PROCEDURES USED TO IMPROVE DATA
4.1 Responses to Section 114 Request
A CAA section 114 information collection request (ICR) was developed and sent to nine
entities (comprising of 18 facilities5 which we identified through initial review of the source
category) (ERG, 2023b). Many of these entities were chosen because they have some facilities
that produce, use, and emit ethylene oxide or chloroprene, which are pollutants with considerable
concern for cancer risk for the HON source category.
The first CAA section 114 ICR, sent on June 15, 2021, went to Denka Performance
Elastomers, LLC to gather information about emissions from their chemical plant and the various
5 The ICR originally encompassed 22 facilities; however, the EPA reduced this number to 18 facilities based on a
March 3, 2022 petition that the EPA received from industry.
5
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NESHAP they are subject to, including the HON (and others such as the Group I Polymers and
Resins NESHAP (40 CFR part 63, subpart U)). In addition, on January 19, 2022, eight other
entities (BASF Corporation, The Dow Chemical Company, Eastman Chemical Company,
Formosa Plastics Corporation, Huntsman Petrochemical, Indorama Ventures Oxides and
Derivatives, Sasol Chemicals, and Union Carbide Corporation) received CAA section 114 ICRs
to ask for additional information about their HON processes, processes subject to other chemical
sector NESHAP, and SOCMINew Source Performance Standards (NSPS) that apply to emission
sources at their chemical manufacturing facilities. These CAA section 114 ICRs sought to gather
specific information about various emission sources, emission inventories (using the 2017 NEI
as a baseline), and chemical manufacturing production processes via a questionnaire (Component
1) as well as emissions data via requests for historical data, stack testing, and fugitive emissions
testing with fenceline monitoring (Component 2). For more information regarding the CAA
section 114 ICRs, please refer to the memorandum entitled "Data Received from Information
Collection Request for Chemical Manufacturers." (ERG, 2023b).
The EPA requested facilities (those that were part of the January 19, 2022 CAA section
114 ICR) review their NEI records for completeness and accuracy, given that these records
formed the underlying basis of our emissions modeling input files for the residual risk review.
The NEI records were sent to entities in separate Microsoft Excel worksheet(s) via email
requesting review (and revise, if necessary) emission values, emission release point parameters,
coordinates, emission unit descriptions, periods of operation, and emission process group
assignments. We used all this information to reevaluate our emission process group assignments
(see Table 1) for each NEI record in the modeling file (i.e., records associated with any ethylene
oxide emitting HON facility). We also used this information to update emission release point
parameter data. In other words, we used the CAA section 114 response data wherever possible
(in lieu of the 2017 NEI), unless it failed our QA checks (see Section 5.0 of this memorandum).
For example, if a CAA section 114 response indicates the emission release point is associated
with a process vent, but the modeling file says a storage vessel, we updated the modeling file to
reflect a process vent. Also, as another example, if a CAA section 114 response indicates a stack
height of 10 feet, but the modeling file says the stack height is 7 feet, we updated the modeling
file to reflect the stack height of 10 feet.
Once each of the steps discussed above were complete, we performed an overall review
of the RTR emissions modeling file to determine if the data for each facility were both complete
and representative.
4.1.1 Stack Test Data for Dioxins and Furans & Chlorine
We reviewed stack test data from nine HON facilities that tested for, among other things,
dioxins and furans (D/F) in 2010, 2011, and 2014 and that formed the basis of our proposed
emission standard for these pollutants. These stack test reports are available in the rulemaking
docket (EPA-HQ-OAR-2022-0730). Upon review of the records in 2017 NEI for these nine
facilities, we found that emission records for these pollutants were missing. Accordingly, we
added records consistent with this stack test data for each incinerator/thermal oxidizer that
controls emissions from a vinyl chloride monomer (VCM)/ethylene dichloride (EDC) chemical
manufacturing process unit that was stack tested for D/F emissions at these nine HON facilities.
6
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A list of these facilities and the number of incinerator/thermal oxidizer at each facility for which
emissions data for D/F emissions were added can be found in the table 2 below.
Table 2. D/F Emitting HOTS
Facilities
Facility Name in 2017 NEI
# of Incinerators or
thermal oxidizers at
Facility
Additional Notes
Formosa Plastics Corp
Louisiana
2
DEER PARK VCM PLANT
2
This is the Oxyvinyls plant.
Shintech Louisiana LLC -
Shintech Plaquemine Plant
2
Axiall LLC - Westlake Lake
Charles North
2
Formerly Georgia Gulf-Lake
Charles as it relates to stack test
data.
Westlake Vinyls Co LP
1
Axiall LLC - Plaquemine
Facility
2
Westlake acquired Axiall.
Eagle US 2 LLC - Lake
Charles Complex
4
Formerly PPG Lake Charles as it
relates to stack test data.
BLUE CUBE OPERATIONS
FREEPORT
2
Formerly Dow Oyster Creek as it
relates to stack test data.
FORMOSA POINT
COMFORT PLANT
3
For chlorine, Formosa Plastics Corp Louisiana had reported higher than expected
emissions from their VCM production Incinerators A & B of 16.0 tons/yr and 21.3 tons/yr,
respectively. Following a brief conference call with the company on October 5, 2022, the
company conveyed that these reported values to the 2017 NEI were based on emissions stack
testing that occurred in 1992, well before the HON was finalized in 1994. More recent stack
testing for Incinerator B was conducted in 2014 (and was also tested for D/F emissions and is in
the docket for this action). This post-HON compliance test is much more recent and represents
post-HON controls and much more current operations. It shows that the annual average chlorine
emissions for this incinerator are actually 0.56 tons/yr. Thus, the emissions for chlorine for
Incinerators A & B were revised to this annual emissions value.
7
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4.1.2 CAA Section 114 and Other Ethylene Oxide Specific Revisions
After EPA reviewed CAA section 114 ICR data, we reviewed ethylene oxide records to
determine whether the emissions were associated with HON processes or non-HON processes
and updated the regulatory code in the risk modeling input files to account for this review. We
also reviewed the 2021 EPA Region 6 emissions modeling6 and reviewed reported upset
emissions data, and made minor revisions to ethylene oxide emissions records. Amendments
were made to the ethylene oxide emissions records for select emission sources at the following
facilities:
Huntsman Petrochemical - Conroe Plant
Eastman Chemical Company - Texas Operations
Union Carbide Corporation - Seadrift Operations
Indorama Ventures - Port Neches Operations
As part of the CAA section 114 ICR data submission, Huntsman Petrochemical
suggested an amendment to the reported ethylene oxide emissions associated with the Pump P-
G-125 seal flush. The reported ethylene oxide emissions in the 2017 NEI, assumed a continuous
annual operation of 8,760 hours per year. At the request of Huntsman Petrochemical, we
amended the ethylene oxide emissions to reflect eight hours of operation. The emissions from
this operation are associated maintenance activities on the pump, rather than a continuous
operation.
As part of EPA's review of reported emissions upset data, ethylene oxide emissions were
amended for the model at the Eastman Chemical Company, Texas Operations and Union Carbide
Corporation, Seadrift Operations facilities. At the Eastman facility, we added upset emissions
associated with a control valve as reported to the Texas Commission on Environmental Quality
(TCEQ) in Incident Report 254349, to the NEI emissions record for Cooling Tower 56U-501.
Similarly, at the Seadrift Operations we added ethylene oxide upset emissions associated with a
leak in the condenser (heat exchange) system, reported to the TCEQ in Incident Report 293911.
The emissions in the model reflect, estimated releases under a 45 day window of repair
consistent with the HON. EPA estimated the release using an average of the attached emissions
models, and added a new record to the model associated with the release. This is discussed
further in our memorandum, entitled "Analysis of Control Options for Heat Exchange Systems to
Reduce Residual Risk of Ethylene Oxide in the SOCMI Source Category for Processes Subject
to HON" (ERG, 2023b).
Table 3 below includes the emission unit specific amendments made at the Huntsman
Conroe, Eastman Texas Operations, and Union Carbide Corporation Seadrift facilities:
Table 3. Adjusted Ethylene Oxide Emissions (Relative To 2017 NEI) For Certain Facilities
6 https://www.epa.gov/system/files/documents/2021-07/region-6-risk-assessment-of-ethylene-oxide-
emitting-facilities-in-texas-and-louisian-jul-8-2021.pdf
8
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Company Name
Conroe
Facility
Emission Unit
Ethylene oxide
Emissions (tpy)
Data Source
2017 NEI
Adjusted
Huntsman
Petrochemical
Conroe
Facility
Pump Seal
P-G-125
0.5618
0.0039
CAA Section
114 ICR Data
Submission
Eastman Chemical
Company
Texas
Operations
Cooling Tower
56U-501
0.57
0.8849
TCEQ Incident
Report 254349
Union Carbide
Corporation
Seadrift
Oxide Glycol
Heat Exchange
System
NA
6.52
TCEQ Incident
Report 293911
Additionally, in an attempt to better include upset releases at the Port Neches facility, we
utilized model values reflective of 2018 emissions data collected by EPA Region 6 and compiled
in the 2018 NEI. This data was used in lieu of the 2017 NEI records for Port Neches. In
correspondence with the facility regarding these upsets, we also received updated stack test
characteristics for the Port Neches regenerator and reabsorber vents (see Appendix C); therefore,
we used this information in lieu of the stack test characteristics in the 2017 NEI records.
Finally, although other emissions revisions were suggested by facilities as part of the
CAA section 114 ICR responses, we did not use this data. Instead, we continued to use emissions
reported in the 2017 NEI because there was insufficient information provided to support the
suggested changes from industry.
4.1.3 CAA Section 114 and Chloroprene Specific Revisions
EPA reviewed CAA section 114 ICR data from Denka Performance Elastomers, LLC. In
particular, EPA requested emission inventories from the past 5 years (i.e., 2016-2020) from the
facility's operations as part of this request. As 2017 NEI data did not represent current controls
being employed at Denka Performance Elastomers, LLC, EPA chose to use the most current data
it had available and that is reflective of current operations and emissions. Given concerns about
decreased production and emissions in 2020 from the COVID-19 pandemic, EPA elected to use
Denka's 2019 emissions inventory submitted as part of the CAA section 114 request in its risk
assessment for the HON and Neoprene Production source categories in lieu of the 2017 NEI
data. EPA also reviewed chloroprene emission records to determine whether the emissions were
associated with HON processes, neoprene processes, or other non-HON and non-neoprene
processes and updated the regulatory code in the risk modeling input files to account for this
review.
9
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5.0 EMISSION RELEASE POINT QA STEPS
The emission release point parameters in the modeling file are stack height, exit gas
temperature, stack diameter, exit gas velocity, and exit gas flow rate. As described in Section 3.0
above, priority was given to emission release point parameters provided in the CAA section 114
responses. If emission release point parameters from the CAA section 114 responses were
missing or out of range, then the original NEI parameters were retained. If the emission release
point parameters from the NEI data were missing or outside of typical QA range checks, then the
missing or out of range parameters were calculated where possible. An example of this
calculation is using reported diameter and velocity to calculate a missing exit gas flow rate. If it
was not possible to calculate a missing value, then a surrogate value was assigned based on the
SCC.7 All diameters, velocities, and flow rates for fugitive releases were set to default values of
0.003 feet (ft), 0.0003 feet per second (ft/sec), and 0 actual cubic feet per second (aefs),
respectively. If height and/or temperature were not available for fugitive sources, default values
of 10 ft for stack height and 72 degrees Fahrenheit for temperature were assigned.
6.0 WHOLE FACILITY EMISSIONS ESTIMATES
Our analyses and data quality review efforts were primarily focused on emissions of
ethylene oxide and chloroprene, given that this is of central relevance to the residual risk review.
A simpler cursory review of the whole facility emissions was also done to ensure that any
emissions of major risk driving pollutants was reflective of best available emissions data.
7.0 ACUTE EMISSIONS MULTIPLIER & MACT-ALLOWABLE EMISSIONS
To develop estimates of acute exposures, the Agency generally assumes the 1-hr
emissions rate for any emission point could be 10 times higher than its average hourly emissions
(calculated by dividing the actual annual emissions by 8,760 hours per year) in situations where
the EPA lacks sufficient information on hourly emissions for given emissions sources. The basis
for this assumption was derived from an analysis of short-term release information collected
from a Texas study of facilities in a four-county area (Harris, Galveston, Chambers, and Brazoria
Counties, Texas) which was then compared against routine emissions rates for an entire facility.
The conclusions for this analysis were that the ratio of hourly emissions from any single release
event to the average annual volatile organic compound (VOC) release rate for an entire facility
was seldom greater than a factor of 10. We used additional knowledge of the emission point
release characteristics to refine the default factor for the SOCMI and Neoprene Production
source categories. The acute multipliers we used are in Table 5 which are based on the acute
multipliers that we used for the MON source category (EPA, 2020). These values were also used
in other more recent risk reviews previously discussed in this memorandum such as for
Petroleum Refineries and Ethylene Production sources.
Table 5. Acute Multipliers
Kmissioiis Source
Acute Multiplier
Bottoms Receiver
6
Equipment Leak
2
7 In certain instances where we added a record to the modeling file due to information received from the Section
114, the SCC may not have been included. For these records, we assigned a default SCC based on the emission
process group assignment.
10
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Kmissioiis Source
Acute Multiplier
Heat Exchange System
2
Hotwell
6
Nitrogen Inert System
6
Process Vent
6
Storage Tank
4
Surge Control Vessel
6
Transfer Rack
10
Wastewater
4
Control Device (UnknownEPG)
10
Flare
10
Unknown
10
8.0 Quality Assurance (QA) Procedures
In addition to the procedures used to improve the modeling file data described in Section
4.0 above, Appendix B to this memo describes the general procedures used to review and correct
RTR modeling files that were also conducted in the QA of our modeling file.
9.0 REFERENCES
EPA, 2020. Residual Risk Assessment for the Miscellaneous Organic Chemical Manufacturing
Source Category in Support of the 2020 Risk and Technology Review Final Rule. EPA
Docket No. EPA-HQ-OAR-2018-0746-0189.
ERG, 2023a. Lists of Facilities Subject to the HON, Group I and Group II Polymers and Resins
NESHAPs, and NSPS subparts VV, VVa, III, NNN, and RRR. March 2023. EPA Docket No.
EPA-HQ-OAR-2022-0730.
ERG, 2023b. Analysis of Control Options for Heat Exchange Systems to Reduce Residual Risk
of Ethylene Oxide in the SOCMI Source Category for Processes Subject to HON. EPA
Docket No. EPA-HQ-OAR-2022-0730.
ERG. 2023b. Data Received from Information Collection Request for Chemical Manufacturers.
March 2023. EPA Docket No. EPA-HQ-OAR-2022-0730.
11
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Appendix A
The following automated queries were used to assign an emission process group.
(These queries were run in the order presented below. If no query is provided below for a specific
emission process group, then the assignment was made manually.)
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If the record was assigned to EMACT, MON, or OLD, we left it alone, and labeled it as a "Non-CMATTR
Source Category Process Group" emission process group.
To be assigned to the "Process Vents" emission process group, we searched emission unit description, process
description, and see description:
o Like "*oxidation*" Or Like "*distillation*" Or Like "*reactor*"
o Like "*vent*" And Not Like "*solvent*"
To be assigned to the "Equipment Leak" emission process group, we searched emission unit description,
process description, release point description, and see description:
o Like "*fUg*"
To be assigned to the "Heat Exchange System" emission process group, we searched emission unit description,
process description, release point description, and see description:
o Like "*cool*"
To be assigned to the "Storage Tank" emission process group, we searched see description:
o Like "*storage*" And Not Like "*wastewater*"
To be assigned to the "Transfer Rack" emission process group, we searched emission unit description, process
description, and see description:
o Like "*transfer*" (for emission unit description)
o Like "*trans*" (for process description)
o Like "*load*" (for see description)
To be assigned to the "Wastewater" emission process group, we searched emission unit description and see
description:
o Like "*wastewater*"
To differentiate between maintenance and non-maintenance activities for each emission process group, we
searched emission unit description, process description, release point description, and see description:
o Like "*maintenance*"
To be assigned to the "Non-CMATTR Source Category Process Group" emission process group, we searched
emission unit description, process description, release point description, and see description:
o Like "*boiler*"
o Like "*coating*"
o Like "*abrasive*"
o Like "*dust*"(excluded from see description search)
o Like "*silo*"
o Like "*hopper*"
o Like "*degreaser*"
o Like "*R&D*"
o Like "*pilot plant*"
o Like "*baghouse*"
o Like "*bag filter*"
o Like "*fabric filter*
o Like "*bagfilter*"
o Like "*fabricfilter*"
o Like "*HEPA*"
o Like "*cracking*"
o Like "*marine*"
o Like "*barge*"
o Like "*paint*"
o Like "*gasoline*"
o Like "generator*"
o Like "*diesel*"
o Like "*heater*"
o Like "*compressor*"
o Like "*combustion*"
o Like "*engine*"
o Like "*groundwater*"
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Appendix B
RTR QA Documentation
INTRODUCTION
This document provides an overview of the QA checks and corrections implemented in Risk and
Technology Review (RTR) modeling files.
The QA checks conducted by the EPA are intended to identify clearly incorrect data and missing
data, and in any instance where a value was replaced or a default value was applied, those data
are in the record. Note that use of defaults or replacement of incorrect data are functions that
occur throughout various data systems (e.g., the NEI), and any changes made through the QA
process serve to improve the accuracy of the data.
GENERAL QA OF MODELING FILE FIELDS
The following modeling file fields should not be null after a file is developed. EPA checks for
null entries in these fields and populates them where possible using existing EPA data sets,
facility-specific information, and/or valid codes from lookup tables:
• FRS ID - cannot always be populated
• SPPD Facility ID
• Region
• State Abbreviation
• County Name
• State County FIPS
• Tribal Code
• Facility Name
• Location Address
• City
• Zip Code
• NAICS Code (NAICS Primary)
• Facility Category Code
• Emission Unit ID
• Process ID
• SCC
• Regulatory Code
• Emission Process Group
• Emission Release Point ID
• Emission Release Point Type Code
• Stack Height (ft)
• Stack Default Flag
• Pollutant Code
14
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• Actual Emissions (tpy)
• Start Date
• End Date
• Data Source Code
• Emission Calc Method Code
Similarly, the following fields are primary keys and must be populated. If identifier fields
are not populated, EPA assigns IDs as needed:
• SPPD Facility ID
• Emission Unit ID
• Process ID
• Emission Release Point ID
• Pollutant Code
Additional Checks for Invalid and Null Values
EPA checks to see if the fields listed below are populated with invalid information or are null.
EPA uses code lookup tables to QA and augment reported values for data fields that use codes.
• Control Measure Code
• Control Status Code
• Emission Calc Method Code
• Emission Release Point Type Code
• Facility Category Code
• Location Default Flag
• NAICS Code (NAICS Primary)
• North American Datum
• Pollutant Code
• Regulatory Code
• see
• Stack Default Flag - use Stack Default Code to populate
• Start/End Dates - must be in YYYYMMDD format
• State County FIPS
• Tribal Code
EMISSION RELEASE POINT AND FUGITIVE RELEASE QA
The first step for stack and fugitive parameter review is to QA the Emission Release Point Type
Code. RTR modelers use the Emission Release Point Type Code to determine how to model the
release. If the Emission Release Point Type Code is incorrect, it can greatly affect risk results. In
15
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RTR modeling files, the Emission Release Point Type Code identifies the type of release.
Emission Release Point Type Codes in RTR modeling files include the following:
Emission Release Point Type Codes
1-Fugitive General
2-Vertical Stack
3-Horizontal Stack
4-Goose Neck Stack
5-Vertical with rain cap Stack
6-Downward-facing vent Stack
7-Fugitive Area (Reserved for historical data)
8-Low Flow Vent
9-Fugitive Two-dimensional
10-Fugitive Three-dimensional
Low Flow Vent source (<10sqft) is an emission release from a single point. Examples include a
single roof or wall vent for building fugitives.
Required parameters are:
• release height (ft),
• exit gas temperature >50F,
• stack diameter (default is 0.1 (ft),
• exit gas velocity (ft/sec) (default is 0.1 ft/sec),
• exit gas flow rate (cu ft/sec) (default is 0.0008 cu ft/sec), and
• lat/lon of release
Fugitive two-dimensional source (>10sqft) is an emission release on one plane. For example,
an elongated roof vent or a wastewater holding pond.
Instructions for populating the required parameters of a two-dimensional release:
Pick the midpoints of two opposing sides of the source, and enter the lat/lon of these midpoints.
A width is also required, which is the distance between the remaining two sides of the source
(that is, the width is perpendicular to the line between the two midpoints). For irregularly shaped
sources, first create a rectangle that best approximates the shape of the actual source, then
determine the parameters described above. Also, estimate the height where the release occurs.
See the examples of fugitive two-dimensional sources in Figures 1 and 2.
16
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Note: Height
of release
Figure 1. Example 1 of Fugitive Two-dimensional Source
17
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Fugitive three-dimensional source has multiple release vents, a few examples would be a
building with many wall and roof vents or an outdoor material storage pile.
Required parameters are:
• side length (ft) [length and width are equal with three-dimensional sources]
• lat/lon is the center of the footprint of the square and
• height of the three-dimensional source
Figure 3. Depiction of Fugitive Three-dimensional Source Parameters
Fugitive area source (>10 sqft) is an alternative way of representing a fugitive two-dimensional
source. It is an emission release on one plane. For example, an elongated roof vent or a
wastewater holding pond.
Required parameters description:
• Enter the coordinates of the southwest corner of the release. The figure below shows
examples of how fugitive area source rectangles are created. The red dashed lines
represent the coordinate plane with north towards the top. The purple SW points to the
southwest corner to show correct location of fugitive coordinates.
• The X and Y represent fugitive length and width.
• The rotation of each angle is also shown. You may wish to review your coordinates and
fugitive areas in a GIS program or Google Earth to verify the accuracy.
18
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NORTH
Angle = 0°
X
Angle = 45° \
Y
Uswj
\ sw |! Y
.Angle = 45°
i
Angle = 0°
X
( sw 1 !
Y
\ \
Figure 4. Depiction of Fugitive Area Source Parameters
Quality assurance (QA) range checks implemented by EPA include range checks for release
parameters (stack height, exit gas temperature, stack diameter, and exit gas velocity). The
acceptable QA ranges are shown below. If values are outside of these ranges, then the record is
examined to see if it is in fact correct for the facility or if it appears to be incorrect.
• Height: 1 - 1300 ft
• Temperature: 30 - 1800 °F (temperatures should be >250 °F for combustion sources)
• Diameter: 0.1 - 100 ft
• Velocity: 0.1 - 200 ft/sec
• Stack height > diameter
When stack parameters are missing or incorrect, the missing or incorrect value is replaced with a
calculated value where possible. For example, valid diameter and velocity can be used to
calculate a missing or invalid exit gas flow rate. If it is not possible to calculate a replacement
stack parameter value, average stack parameters for similar emission units at the same facility
are used as default parameters. If there are no similar emission units at the same facility, then
average stack parameters for the source category are used as default parameters. The reported
flow rate is compared to the calculated flow rate using the reported diameter and velocity. If the
reported flow rate is not within ten percent of the calculated flow rate, then all three related
parameters are examined to determine which values are correct.
For fugitive releases including low flow vents that have missing or out of range height and/or
temperature, the default values of 10 ft for stack height and 72 degrees Fahrenheit for
19
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temperature are assigned. For a low flow vent, the default diameter and velocity are set to the
minimum values of the QA range checks (i.e., 0.1 ft for diameter and 0.1 ft/sec for velocity.
The stack default flag description field in the emissions modeling file indicates which stack
parameters are original or are revised for each modeling file record. If stack parameters were
reviewed and accepted or revised by industry, then those are considered "original" values.
Table 1 below summarizes the required parameters and QA range check values for each release
type.
Finally, coordinates and fugitive dimensions are plotted and reviewed using ArcGIS Online
maps to verify accuracy.
20
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Uelcsise Psirsimelcr
Point (Slsick) Source
l.ow Mow \ cut
Source
l-'ughivc Tliree-
Dimcnsionnl
Source
l-'ughive Two-
Dimcnsioiiiil
Source
l'"u«itive Aresi
(Reserved for
llisloric:il l):il:i)
Fugitive Length (ft)
NA
NA
NA - only a single
side required
NA
Required (Between 1
and 10,000)
Fugitive Width (ft)
NA
NA
Required
(Between 1 and
10,000)
Required
(Between 1 and
10,000)
Required (Between 1
and 10,000)
Fugitive Angle
NA
NA
NA
NA
Required (Between 0 -
90)
Stack Diameter (ft)
Required (Between 0.1 - 100)
0.1 (Default)
NA
NA
NA
Exit Gas Velocity (ft/sec)
Required (Between 0.1 - 200)
0.1 (Default)
NA
NA
NA
Exit Gas Flow Rate (cu
ft/sec)
Calculated based on velocity
and stack diameter (assuming
round stack)
0.0008 (Calculated
Default = (tz R2 )V
NA
NA
NA
Release Height (ft)
Required (Between 1 - 1300)
Required (Between 1
- 1300)
Use 1 for ground-
level releases
Required >0
(Top of Three-
Dimensional
Source)
Release height
required >0
Required (Between 1 -
1300)
Use 1 for ground-level
releases
Exit Gas Temperature (F)
Required (Between 30 - 1800)
Required (Between
30- 1800)
NA
NA
NA
Latitude (decimal degrees),
Longitude (decimal degrees)
Required
Required
Required, center
of source footprint
Two sets of
1 at/long for the
midpoints of
opposing sides of
source
Required Southwest
corner of source
Examples
APCD stack, powered building
vent
Single roof
vent/opening/window
for building fugitives
Entire building
with multiple
release point on
walls and/or roof,
outdoor storage
pile
Wastewater
holding pond,
building with
elongated roof
vent, haul road
Wastewater holding
pond, building with
elongated roof vent,
haul road
21
-------
22
-------
Appendix C
Stack Test Characteristics
(Provided to the EPA on 12/8/2022 by Port Neches Facility for regenerator and reabsorber vents.)
23
-------
Emission Source
Stack Parameters
Exit Gas Conditions
Coordinates
Height (ft)
Diameter (ft)
Velocity (ft/sj
Temp (F)
Lat
Long
F4 Regenerator Vent
183
1.3
242.6
204.7
29.96257
-93.93254
F6 Regenerator Vent
186
1.67
202.4
193
29.96537
- 3 3 3 31——
F8 Regenerator Vent
150
1.67
202 7
23.3626S
-•33 3-3 3 g
F4 ReabsorberVent
43.6
0.33
Varies1"*""
100
29.96562
-93.93186
F6 Reabsorber Vent
64.3
0,67
Varies1"2
100
29.96354
-S3.33332
FB Reabsorber Vent
97.8
0.5
Varies1"1
100
25 3623?
-33.93303
Notes:
1. This emission point is a safety release and does not have a continuous flow to
determine normal velocity. Max and average numbers have been provided below based
on actual emission events.
En ss en Source
F4 ReabsorberVent
F6 Reabsorber Vent
F8 Reabsrober Vent
E>: t Gas Ve cc'ty *
Ms*
77
31
2. This emission point is a safety release and does not have a continuous flow. The
release hours provided below were determined by the ancunt of t'ne the reabsorber
vent opened to "n 2021 during an enss.cn event.
c"i ss cn Source
Pe ease
Ho u'5
F4 ReabsorberVent
5.13
F6 Reabsorber Vent
4.82
FS Reabsrober Vent
49.4
24
-------
Appendix 2
Technical Support Document for HEM4 Modeling
1
-------
The HEM4 User's Guide
Instructions for using the Human Exposure Model 4
for Single and Multiple Facility Exposure and Risk Modeling
Open-Source Version 1.0
October 2020
Prepared by:
SC&A Incorporated
-I'SC&A
1414 Raleigh Road, Suite 450
Chapel Hill, NC 27517
Prepared for:
Air Toxics Assessment Group
Health and Environmental Impacts Division
Office of Air Quality Planning & Standards
U. S. Environmental Protection Agency
Research Triangle Park, NC 27711
EPA Contract EP-W-12-011
-------
Disclaimer
The development of HEM4 and this User's Guide has been
funded by the United States Environmental Protection Agency
under contracts EP-D-06-119 and EP-W-12-011 to SC&A Inc.
However, the information presented in this User's Guide does
not necessarily reflect the views of the Agency. No official
endorsement should be inferred for products mentioned in this
report.
HEM4 User's Guide
Page ii
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Contents
Disclaimer ii
Figures vi
Tables viii
1. Introduction 1
1.1 Organization of the HEM4 User's Guide 1
1.2 Main Features of HEM4 2
1.3 Differences between HEM4 and 2019 Version of HEM-3 4
1.4 Strengths and Limitations of HEM4 6
1.5 Requirements for Running HEM4 7
2. Installing HEM4 8
2.1 Downloading the HEM4 Program 8
2.2 Downloading Chemical Health Effects Data 9
2.2.1 Description of Chemical Health Effects Library 9
2.3 Downloading Census Data 10
2.3.1 Description of Census Library 11
2.4 Downloading Meteorological Data 11
2.4.1 Description of Meteorological Library 12
3. Preparing HEM4 Input Files 15
3.1 Overview and General Rules 15
3.2 Facility List Options File 17
3.2.1 Fields in the Facility List Options File 18
3.2.2 Meteorological Station and Period Options 22
3.2.3 Rural and Urban Dispersion Options 23
3.2.4 Modeling Domain Options 24
3.2.5 Acute Options 26
3.2.6 Deposition and Depletion Options 27
3.2.7 Elevation Option 31
3.2.8 User Receptors Option 31
3.2.9 Building Downwash Option 31
3.2.10 FASTALL Option 32
3.2.11 Emissions Variation Option 32
3.3 HAP Emissions File 33
3.3.1 Pollutant Emissions per Source 34
3.3.2 Percent Particulate for Deposition and Depletion 35
3.4 Emissions Location File 35
3.4.1 Source Types and Parameter Requirements 40
3.4.2 Particle Deposition Method 45
3.5 Additional Input Files 45
3.5.1 Polygon Vertex Input File for Modeling Polygon Emission Sources 46
3.5.2 Buoyant Line Parameter Input File for Modeling Buoyant Line Sources 48
3.5.3 Particle Data Input File for Modeling Particulate Deposition and Depletion 49
3.5.4 Input Files Required for Modeling Vapor Deposition and Depletion 51
3.5.5 Building Dimensions Input File for Modeling Building Downwash 55
3.5.6 User-Defined Receptors File 56
3.5.7 Emissions Variation Input Files 59
3.5.8 Alternate Receptors file 62
3.5.9 Census Update file 64
3.5.10 Updating the Chemical Unit Risk Estimates and Health Benchmarks Input Files .66
4. Step-by-Step Instructions for Running HEM4 67
HEM4 User's Guide Page iii
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4.1 Provide Standard Input Files and Indicate Receptors 69
4.2 Provide Additional Input Files 71
4.3 Provide Deposition and Depletion Input Files 72
4.4 Check HEM4 Log 74
4.5 Summarize Risks 76
4.6 Analyze Outputs 77
4.7 Revise Census Data Option 81
4.8 Error Messages and Failed Runs 82
5. HEM4 Modeling Calculations for each Facility 85
5.1 Dispersion Modeling 85
5.1.1 Regulatory Default, ALPHA and BETA Options 85
5.1.2 Dilution Factors 86
5.2 Estimating Risks and Hazard Indices 86
5.2.1 Explicit Modeling of Inner Receptors, User Receptors and Polar Receptors 86
5.2.2 Interpolated Modeling of Outer Receptors using the Polar Receptor Network 88
5.2.3 Maximum Individual Risks, Hazard Indices, and Hazard Quotients 89
5.2.4 Maximum Offsite Impacts 89
5.2.5 Contributions of Different Pollutants and Emission Sources 89
5.3 Population Exposures and Incidence 90
5.4 Summarizing Human Health Impacts 92
6. HEM4 Output Files 93
6.1 Facility-Specific Outputs 93
6.1.1 Maximum Individual Risk 93
6.1.2 Maximum Offsite Impacts 95
6.1.3 Risk Breakdown 95
6.1.4 Block Summary Chronic 96
6.1.5 Ring Summary Chronic 97
6.1.6 Source Risk KMZ Image 98
6.1.7 Incidence 99
6.1.8 Cancer Risk Exposure 100
6.1.9 Noncancer Risk Exposure 100
6.1.10 All Inner Receptors 101
6.1.11 All Outer Receptors 101
6.1.12 All Polar Receptors 102
6.1.13 AERMOD Outputs 103
6.1.14 Input Selection Options 105
6.1.15 Acute Maximum Concentrations (Optional) 106
6.1.16 Acute Populated Concentrations (Optional) 106
6.1.17 Acute Breakdown (Optional) 108
6.2 Run Group Outputs 109
6.2.1 Facility Max Risk and HI 109
6.2.2 Facility Cancer Risk Exposure 110
6.2.3 Facility TOSHI Exposure 110
6.2.4 Additional Run Group Outputs 110
7. Risk Summary Reports 112
7.1 Max Risk Summary 112
7.2 Cancer Drivers Summary 113
7.3 Hazard Index Drivers Summary 113
7.4 Risk Histogram Summary 114
7.5 Hazard Index Histogram Summary 115
7.6 Incidence Drivers Summary 116
HEM4 User's Guide
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7.7 Acute Impacts Summary 116
7.8 Multipathway Summary 117
7.9 Source Type Risk Histogram Summary 119
8. Understanding the Risk Results 120
9. Quality Assurance Remodeling 122
10. References 126
11. Appendix A: Sample HEM4 Output Files 129
HEM4 User's Guide Page v
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Figures
Figure 1. Summary of Key Improvements for HEM4 versus 2019 HEM-3 6
Figure 2. HEM4 Meteorological Stations 14
Figure 3. Example Orientations of Area Emission Sources for the HEM4 Model 44
Figure 4. HEM4 Title Screen 69
Figure 5. Run HEM4 with U.S. Census Receptors 70
Figure 6. Run HEM4 with Alternate Receptors 70
Figure 7. Confirm HEM4 Run Pop-Up Start Box 71
Figure 8. Provide Additional Input Files 72
Figure 9. Provide Deposition and Depletion Input Files 73
Figure 10. Log Screen 75
Figure 11. Run the Risk Summary Programs 76
Figure 12. View and Analyze Outputs 77
Figure 13. Hazard Index Drivers File Opened via Spreadsheet App 78
Figure 14. Select Data to Plot Widget 78
Figure 15. Chronic Risk Map shown in Google Earth™ 79
Figure 16. Acute Map View of HTML File 80
Figure 17. Example Graphical Visualization of Incidence by Pollutant and Source Type 81
Figure 18. Revise Census Data Screen 82
Figure 19. Sample Google Earth™ Map of Results 99
Figure 20. Sample Max Risk Summary Output 112
Figure 21. Sample Cancer Drivers Summary Output 113
Figure 22. Sample Hazard Index Drivers Summary Output 114
Figure 23. Sample Risk Histogram Summary Output 115
Figure 24. Sample Hazard Index Histogram Summary Output (Partial) 115
Figure 25. Sample Incidence Drivers Summary Output 116
Figure 26. Sample Acute Impacts Summary Output (abbreviated) 118
Figure 27. Sample Multipathway Summary Output 118
Figure 28. Sample Sourcetype_Histogram_Sorted RTR Summary Output 119
Figure 29. Sample Source_risk.kmz HEM4 Output 123
Figure 30. Sample Maximum Individual Risk HEM4 Output (facility-specific) 129
Figure 31. Sample Maximum Offsite Risk HEM4 Output (facility-specific) 129
Figure 32. Sample Risk Breakdown HEM4 Output (facility-specific, abbreviated) 130
Figure 33. Sample Block Summary Chronic HEM4 Output (facility-specific, abbreviated) 131
Figure 34. Sample Ring Summary Chronic HEM4 Output (facility-specific, abbreviated) 132
Figure 35. Sample Source Risk KMZ Google Earth™ Image (facility-specific) 133
Figure 36. Sample Incidence HEM4 Output (facility-specific, abbreviated) 134
Figure 37. Sample Cancer Risk Exposure HEM4 Output (facility-specific) 135
Figure 38. Sample Noncancer Risk Exposure HEM4 Output (facility-specific) 135
Figure 39. Sample All Inner Receptors HEM4 Output (facility-specific, abbreviated) 136
Figure 40. Sample All Outer Receptors HEM4 Output file (facility-specific, abbreviated) 137
Figure 41. Sample All Polar Receptors HEM4 Output file (facility-specific, abbreviated) 138
Figure 42. Sample AERMOD.inp file (facility-specific, abbreviated) 139
Figure 43. Sample AERMOD.out file (facility-specific, abbreviated) 140
Figure 44. Sample plotfile.plt output file (facility-specific, abbreviated) 141
Figure 45. Sample maxhour.plt output file (optional facility-specific, abbreviated) 142
Figure 46. Sample Input Selection Options HEM4 Output file (facility-specific, abbreviated) ..143
HEM4 User's Guide Page vi
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Figure 47. Sample Acute Maximum Concentrations HEM4 Output file (optional facility specific,
abbreviated) 143
Figure 48. Sample Acute Populated Concentrations HEM4 Output file (optional facility-specific,
abbreviated) 144
Figure 49. Sample Acute Breakdown HEM4 Output file (optional facility-specific) 144
Figure 50. Sample Facility Max Risk and HI HEM4 Output file (for run group, abbreviated) ...145
Figure 51. Sample Facility Cancer Risk Exposure HEM4 Output file (for run group) 145
Figure 52. Sample Facility TOSHI Exposure HEM4 Output file (for run group) 145
Figure 53. Sample All Facility Source Locations Google Earth™ Image (for run group) 146
Figure 54. Sample HEM4 Log Output file (for run group, abbreviated) 147
HEM4 User's Guide
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Tables
Table 1. Fields in the Facility List Options Input File (Required) 18
Table 2. Sample Deposition and Depletion Options and Model Results 29
Table 3. Format Guidelines for the HAP Emissions Input File (Required) 33
Table 4. Sample HAP Emissions Input File 34
Table 5. Fields in the Emissions Location Input File (Required) 35
Table 6. Sample Emissions Location Input File 39
Table 7. Format Guidelines for the Polygon Vertex File 46
Table 8. Sample Polygon Vertex File 47
Table 9. Format Guidelines for the Buoyant Line Parameter Input File 49
Table 10. Sample Buoyant Line Parameter Input File 49
Table 11. Format Guidelines for the Particle Data Input File 50
Table 12. Sample Particle Data Input File 50
Table 13. Format Guidelines for Land Use Input File 53
Table 14. Sample Input File for Land Use 53
Table 15. Format Guidelines for Month-to-Seasons Input File 54
Table 16. Sample Month-to-Seasons Input File 54
Table 17. Format Guidelines for the Building Dimensions File 55
Table 18. Sample Building Dimensions Input File 56
Table 19. Format Guidelines for the User-Defined Receptors File 58
Table 20. Sample Input File for User-Defined Receptors 58
Table 21. Format Guidelines for the Emissions Variation Input Files 60
Table 22. Sample Emissions Variation File based on Seasons (4 factors) 60
Table 23. Sample Emissions Variation File based on Hour of Day (24 factors) 61
Table 24. Sample Emissions Variation File based on Month (12 factors) 61
Table 25. Sample Emissions Variation File based on Season and Hour of Day (96 factors) ....61
Table 26. Sample Emissions Variation File based on Wind Speed (6 factors) 61
Table 27. Format Guidelines for Alternate Receptors File (CSV) 63
Table 28. Sample Input File for Alternate Receptor Input File 63
Table 29. Format Guidelines for the Census Update File 65
Table 30. Sample Census Update File 65
Table 31. Summary of HEM4 Template Input Files 67
Table 32. Sample List of Error Messages and Causes in HEM4 83
Table 33. Fields Included in the Maximum Individual Risk & Maximum Offsite Impacts Files...94
Table 34. Fields Included in the Risk Breakdown File 96
Table 35. Fields Included in the Block Summary and Ring Summary Chronic Files 98
Table 36. Fields Included in the Incidence File 100
Table 37. Fields Included in the All Inner and All Outer Receptor Files 102
Table 38. Fields included in the All Polar Receptors File 103
Table 39. Fields included in the Acute Chem Max and Acute Chem Pop Files 107
HEM4 User's Guide Page viii
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1. Introduction
The Human Exposure Model 4 (HEM4) Open Source Version 1.0 is a streamlined, but rigorous
tool you can use for estimating ambient concentrations, human exposures and health risks that
may result from air pollution emissions from complex industrial facilities. HEM4 can be used to
model impacts from a single facility or from multiple facilities located across the entire United
States (U.S.) and its territories, as well as anywhere in the world. HEM4 is designed for use by
the U.S. Environmental Protection Agency (EPA), states, local agencies, industry and other
stakeholders, and is currently used in the Risk & Technology Review (RTR) assessments by
EPA of entire source categories. In RTR assessments, HEM4 - like its predecessor, HEM-3 - is
used to model emissions and the resulting ambient concentrations from hundreds of facilities,
located both near as well as thousands of miles away from each other. The model then predicts
the potential exposures and inhalation health risks posed by these emissions, including in zones
with combined impacts from multiple nearby facilities. Compared to HEM-3, HEM4 incorporates
additional front-end and back-end features and capabilities in the model platform, including
additional modeling options, risk summary reports that summarize the cancer risk and
noncancer health impacts for your modeled group of facilities, and multiple output viewing and
analysis tools. Unlike HEM-3, HEM4 also enables the user to model concentrations, risk and
health impacts for their own receptors inside or outside the U.S. HEM4 is available for download
at http://www.epa.gov/fera/download-human-exposure-model-hem.
1.1 Organization of the HEM4 User's Guide
This User's Guide is organized into 10 sections plus an appendix:
Section 1 Provides a brief introduction to HEM4, including the main features and
requirements of the model and a comparison to HEM-3
Section 2 Provides instructions for installing HEM4, including descriptions of the
data libraries provided during installation
Section 3 Provides instructions for preparing the input data files needed by HEM4
Section 4 Provides step-by-step instructions for running HEM4
Section 5 Describes the calculations performed by HEM4 for each modeled facility
Section 6 Describes the facility-specific outputs produced by HEM4
Section 7 Describes the risk summary reports produced for each run group
Section 8 Explains how to understand the basic risk results
Section 9 Discusses quality assurance remodeling
Section 10 References
Section 11 Appendix A: Sample HEM4 Output Files
HEM4 User's Guide
Page 1
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1.2 Main Features of HEM4
HEM4 performs three main operations: dispersion modeling, estimation of population exposure,
and estimation of human health risks. For dispersion modeling, the American Meteorological
Society - U.S. EPA Regulatory Model (AERMOD) is run by HEM4 as a compiled executable
program. AERMOD is a state-of-the-science Gaussian plume dispersion model that EPA prefers
for most industrial source modeling applications for air toxics applications (EPA 2005).
AERMOD was developed under the auspices of the American Meteorological Society -
Environmental Protection Agency Regulatory Model Improvement Committee (AERMIC) as
summarized on EPA's AERMOD website. (See https://www.epa.gov/scram/air-qualitv-
dispersion-modelinq-preferred-and-recommended-models#aermod for all AERMOD model
documentation as well as links to AERMOD's preprocessors, AERMET, AERMAP,
AERSCREEN, AERSURFACE and BPIPPRIM and post-processor, LEADPOST.)
This version 1.0 of HEM4 incorporates AERMOD version 19191 which was originally made
available to the public in August 2019 (EPA 2019a, EPA 2019b). AERMOD can handle a wide
range of different source types that may be associated with an industrial source complex,
including stack sources, area sources, and volume sources. Additionally, AERMOD is capable
of modeling polygon, line and buoyant line source types. AERMOD can also optionally model
emissions that vary in time or with wind speed, deposition with or without plume depletion, and
other complex plume processes such as building downwash.
HEM4 supplies AERMOD with meteorological data pre-processed by AERMET and required for
AERMOD's dispersion calculations. HEM4's Meteorology Library contains meteorological
("met") data from over 800 observation stations across the continental U.S., Alaska, Hawaii, and
Puerto Rico. Section 2.4 provides information on how to download the met data used by HEM4,
discusses how the met files were processed and the data contained in each, and includes a
national map of the locations for all 2019 met stations.
HEM4 runs AERMOD as many times as is necessary to address the gaseous pollutants and
particulate matter emitted from each modeled facility. AERMOD outputs annual average
ambient concentrations at discretely modeled receptor locations, through the simulation of hour-
by-hour dispersions from the emission sources into the surrounding atmosphere.
For U.S. emission sources, after running AERMOD for dispersion modeling, HEM4 estimates
population exposure and human health risks by drawing on additional data libraries that are
provided with the model, including a U.S. Census Library and a Chemical (Pollutant) Health
Effects Library. The Census Library of census block internal point ("centroid") locations and
populations provides the basis of human exposure calculations. The model includes location
and population data from the 2010 U.S. Census. HEM4 draws upon the Census Library to
identify all census block locations within the study domain as defined by the default modeling
radius around each facility or a radius that you specify. The Census Library includes locations
and populations, elevations, and controlling hill heights for all of the approximately 6.3 million
populated blocks tabulated in the 2010 U.S. Census (Census 2010). Section 2.3 provides
information on how to download the census data and discusses the data contained in HEM4's
Census Library.
Alternatively, HEM4 can model without the U.S. Census Library by using Alternate Receptors
that the user can provide within the U.S. or anywhere in the world.
HEM4 User's Guide
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HEM4 uses the Chemical Health Effects Library of pollutant unit risk estimates (URE) and
reference concentrations (RfCs) to calculate population cancer risks and noncancer health
hazards. These risk factors and RfCs are based on the latest values recommended by the EPA
for hazardous air pollutants (HAP) and other toxic air pollutants. More information on how EPA
uses these dose-response values in risk assessments, including the source for these values, is
provided in EPA's Dose-Response Assessment webpage (EPA 2018a) and in Section 2.2.
Using the air concentration results from AERMOD in combination with the data supplied by
HEM4's Census and Chemical Health Effects Libraries, HEM4 estimates cancer risks and
noncancer "risks" (health hazard indices) due to inhalation exposure at U.S. Census block
locations and at other receptor locations that you may specify. As noted above, HEM4 (unlike
the previous HEM-3 version of the model) can also be used outside the U.S., without U.S.
Census block receptors, to predict concentrations and risk anywhere in the world at receptors
specified by the user surrounding emission sources. The predicted risk estimates are generally
conservative with respect to the modeled emissions because they are not adjusted for
attenuating exposure factors (such as indoor/outdoor concentration ratios, daily hours spent
away from the residential receptor site, and years of lifetime spent living elsewhere than the
current residential receptor site).
HEM4 computes cancer risks using the EPA's UREs for HAP and other toxic air pollutants. The
resulting estimates reflect the risk of developing cancer for an individual breathing the ambient
air at a given receptor site 24 hours per day over a 70-year lifetime. HEM4 estimates noncancer
"risk" (or health hazards) using hazard quotients (HQs) and hazard indices for 14 "target" organs
or systems. The HQ for a given pollutant and receptor site is the ratio of the ambient
concentration of the pollutant to the RfC at which (and below which) no adverse effects are
expected. The chronic hazard index (HI) for a given target organ is the sum of HQs for
substances that affect that organ. HEM4 computes target organ-specific hazard indices
(TOSHIs) for the following 14 organ systems: the respiratory system; the liver; the neurological
system; developmental effects; the reproductive system; the kidneys; the ocular system; the
endocrine system; the hematological system; the immunological system; the skeletal system;
the spleen; the thyroid; and whole body effects. Like the cancer risk estimates, noncancer
hazard indices are not adjusted for attenuating exposure factors and are therefore considered
conservative estimates.
Optionally, HEM4 can estimate acute (short-term, such as hourly) concentrations for each
pollutant and receptor site, including the location of the maximum acute concentration for each
pollutant emitted from the facility. In addition, the model outputs a listing of the associated acute
benchmarks for each pollutant (at or below which certain acute adverse effects are not
expected). From these acute concentrations and benchmarks, the ratio of the maximum acute
concentration to the associated benchmark is computed to determine the maximum acute HQ
for each pollutant of concern. Acute noncancer HQs, like chronic noncancer TOSHIs and cancer
risk are conservative estimates in HEM4. Section 2.2.1 discusses the terms URE, RfC, HQ, HI
and TOSHI in more detail.
HEM4 estimates the predicted lifetime cancer risk, chronic noncancer TOSHIs, annual
concentrations, and (optionally) acute concentrations at every receptor location, and also
identifies receptor locations where the impact is highest. For these locations, the model gives
the concentrations of the modeled pollutants (HAP) emitted from each emission source driving
the overall cancer risks, chronic TOSHIs, and acute impacts. The model also estimates the
number of people exposed to various cancer risk levels and TOSHI levels.
HEM4 User's Guide
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HEM4 provides these results for each individual modeled facility and also consolidates facility-
specific results into output files that provide results for all modeled facilities. HEM4's post-
processors, the risk summary programs, produce additional outputs of combined and
summarized results that are useful in capturing the risk and health hazards, as well as the
pollutant and emission source drivers of these impacts, for a group of modeled facilities as a
whole (e.g., an entire source category of facilities modeled under the EPA's RTR program).
HEM4 provides a browser-based option of viewing all the summarized results in graphical form,
including an interactive map of the facilities modeled, pie and bar charts of overall cancer
incidence, population risks, and pollutant and source risk drivers, and an interactive table of the
main results for each facility.
1.3 Differences between HEM4 and 2019 Version of HEM-3
HEM was originally developed as a screening tool for exposure assessment in the 1980s (EPA
1986). The original model was upgraded to run in a Windows™ environment, eventually called
HEM-3, and regularly improved and re-released by EPA in several HEM-3 versions over the
years, including most recently in 2007, 2014, 2017 and 2019. HEM4 is written in the open-
source software language Python™, while HEM-3 is written in the FoxPro® language, last
published by Microsoft® in 2007 and now unsupported. In addition, HEM4 includes improved
and streamlined user interfaces as well as enhanced graphical output capabilities compared to
HEM-3, as listed below, and summarized in Figure 1.
• HEM4 bases model selection options primarily on the data in your input files, rather than
on responses to user interface questions, which is less prone to user error.
• HEM4 can model impacts anywhere in the world with user-provided "alternate
receptors", in addition to U.S. Census block receptors.
• HEM4 includes an integrated processor to change the U.S. Census database you use to
model by zeroing out block populations, moving blocks, and/or deleting blocks.
• HEM4 will default to using the full year of selected met data, but you may instead model
with a specified period of met data by indicating a start and end date and even hour.
• HEM4 allows you to specify the exact location of the facility center or use the center
location calculated by the model.
• HEM4 allows you to specify polar ring distances or use the polar ring locations
calculated by the model.
• HEM4 allows you to choose Method 1 or Method 2 for particle deposition. Method 2
requires less knowledge of the particle size distribution of your emissions compared to
Method 1, which requires a detailed particle size input file.
• HEM4 allows you to choose a different acute high value for each facility (e.g., maximum,
99th percentile, 98th percentile), rather than modeling each facility with the same
maximum acute value.
HEM4 User's Guide
Page 4
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• HEM4 includes the Risk Summary Report programs (previously called the RTR
Summary Programs) integrated into the model itself, rather than as an add-on suite of
programs.
• HEM4's Risk Summary Reports are enhanced. The HI Histogram output accounts for all
14 TOSHIs (not just three). The Incidence Drivers output is now sorted in descending
order of pollutant-specific incidence and includes the pollutant's percentage contribution
to total incidence. The Source Type Risk Histogram output includes the maximum
overall risk histogram and incidence for all modeled facilities in your run group, in
addition to the histogram and incidence specific to each source type.
• HEM4 performs consistency checks on your input files and includes more specific and
instructive error messages, to aid you in rectifying any errors or inconsistencies in your
input files before the model run begins.
• In addition to spreadsheet output files, HEM4 includes enhanced capabilities for
visualization and analysis of outputs, including browser-based interactive tables, graphs,
and mapping options.
• Note: In addition to the enhancements listed above, HEM4 has maintained all the
capabilities of the 2019 HEM-3 version, which included numerous enhancements
compared to the previous versions.
Model Feature
HEM4
2019 HEM-3
Software language
Written in open-source
Python™ language
Written in Microsoft FoxPro®
language, now unsupported
Minimal user interface
Model options based primarily
on data in input files; less
prone to user error
Model options based on input
files as well as responses to
user interface questions;
more prone to user error
Receptor enhancement and
flexibility
Modeling can occur anywhere
in the world because users
can specify alternate
populated receptors in lieu of
U.S. Census blocks
Only U.S. modeling was
possible because U.S.
Census receptor data was
required for any model run
Census database revisions
Census blocks may be
revised or removed using an
integrated processor
Census database could not
be edited by user
Meteorological Period Options
Period start and end fields
allow you to specify exactly
what met period HEM4
should instruct AERMOD to
use for your modeling run,
down to the year, month, day
and even hour
HEM-3 always used the
default annual period of met
data
Facility center
User may specify the location
of the facility center
The facility center was always
calculated by model based on
source locations
HEM4 User's Guide
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Model Feature
HEM4
2019 HEM-3
Polar ring distances
User may specify polar ring
distances or use defaults
Polar ring distances were set
by default only
Particle deposition
User can choose AERMOD's
Method 1 or 2 to model
particle deposition. Method 2
requires less particle data.
Particle deposition was
always modeled via
AERMOD Method 1, which
requires detailed particle size
distribution data
Acute high value
User can specify a different
percentile to use as the acute
high value for each facility
The same maximum value
had to be used for every
facility in the modeling run
Risk Summary Programs
Risk Summary Programs are
integrated into HEM4
RTR Summary Programs
were a separate executable
Risk Summary Report
Enhancements
The HI Histogram output
accounts for all 14 TOSHIs.
The Incidence Drivers output
is sorted in descending order
of pollutant-specific incidence
and includes the pollutant's
percentage contribution to
total incidence. The Source
Type Risk Histogram output
includes the maximum overall
histogram for the run group.
HEM-3 accounted for only 3
TOSHIs in the HI Histogram
output. HEM-3's Incidence
Drivers output was unsorted
and did not include the
percentage that each
pollutant contributes to the
total incidence. HEM-3's
Source Type Risk Histogram
did not include the maximum
overall column for the run.
Error messages
Input file inconsistency
checks are automatically
made prior to model run with
more specific and instructive
error messages to aid user in
correcting errors pre-run
Error messages were not
specific enough and did not
capture many input file
inconsistencies prior to runs
Graphical outputs
Browser-based interactive
tables, graphs, and mapping
options for visualization and
analysis of outputs, in
addition to spreadsheet-
based output files
Graphical output options were
not available in HEM-3
Figure 1. Summary of Key Improvements for HEM4 versus 2019 HEM-3
1.4 Strengths and Limitations of HEM4
HEM4 is designed to perform detailed and rigorous analyses of chronic and acute air pollution
risks for populations located near industrial emission sources. The model was previously
updated with the goal of simplifying the running of AERMOD without sacrificing any of
AERMOD's strengths. In keeping with this goal, you can specify complex emission source
configurations, including point sources for stacks, area and volume sources for fugitive
emissions, obliquely oriented area sources for roadways, line sources for airport runways,
buoyant line sources for roof vents, and polygon sources for a variety of area source shapes
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including entire census blocks and tracts. The model identifies all receptors located near each
facility, including census blocks (if in the U.S.) and alternate receptors. You can also specify the
locations of individual houses, schools, facility boundaries, monitors, or other user-defined
receptors to model. HEM4 can account for impacts of terrain, building downwash effects,
pollutant deposition and plume depletion, and temporally-varying emissions. HEM4 also
analyzes multiple pollutants concurrently, with the capability of including particulate and
gaseous pollutants in the same model run.
However, HEM4's framework has some limitations. First, AERMOD, like all air pollutant
dispersion models, is subject to uncertainties. Likewise, pollutant UREs for cancer, RfCs for
noncancer HI, and benchmarks for acute health effects are subject to uncertainties. Another
limitation of HEM4 is that, when modeling with census block receptors in the U.S., the model
estimates pollutant concentrations and risks for the block centroid, as defined by the U.S.
Census Bureau. Values calculated for this internal point are not representative of the range of
values over the entire block, and may not represent where most people reside within a block.
Further, these values do not account for the movement of people from their home census blocks
to other census blocks, due to commuting or other daily activities. In addition, as previously
noted, HEM4 calculates outdoor concentrations of air pollutants. These concentrations do not
account for indoor sources of pollution, or the reduction of outdoor pollution in indoor air.
HEM4 performs several tests on user input data—including ensuring consistency of input files
and some parameters—before using AERMOD to calculate air pollution impacts. However,
there are some potential problems users may introduce to their input files that HEM4 may not
detect in these initial tests. To avoid this, carefully review the model input guidelines to make
sure that the contents and format of your input files meet these guidelines before launching
HEM4.
1.5 Requirements for Running HEM4
You can use HEM4 on any Windows™-based personal computer running Windows 98™ or
later. Disk space requirements will depend on the number of census and meteorological files
that you use. To model an individual facility, the model requires, at minimum, 10 megabytes
(MB) of disk space for a small facility and 1 to 2 gigabytes (GB) for a large, complex facility.
Furthermore, disk space requirements can be 10 to 20 times larger (than 2 GB) for complex
facilities located in densely populated urban areas (i.e., with many receptors), depending on the
modeling options you choose. The full census and meteorological libraries that you can
download in addition to the model require about 3.3 GB of space. The HEM4 model also will
need a minimum of 8 GB of random-access memory (RAM). Once installed, you can use HEM4
to model risks and exposures for any location in the U.S. or around the world, and for a wide
range of emission source configurations.
For each model analysis, you should provide emission rates for all HAP and emission source
locations in the form of Excel™ spreadsheet files. HEM4 requires separate estimates of
emission rates of each pollutant, from each emission source, for each facility to be modeled.
The model also requires detailed information on each emission source, including location,
release height, emission velocity and temperature for point (stack) sources, and the
configuration of non-point emission sources (e.g., area sources which emit with negligible
velocity at ambient temperature). You will be able to design the model receptor network around
each facility to be modeled via an input spreadsheet file. You can also use an optional
spreadsheet file to provide the dimensions of buildings near emission sources, for use in
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computing building downwash effects. When modeling particulate emissions, you can use an
optional spreadsheet file to provide particle size information and deposition parameters. If you
opt to model deposition of gaseous emissions, you will need to provide additional spreadsheet
input files describing the land use and vegetation surrounding the facility. You will be prompted
to indicate the location of your input spreadsheet files through user input screens, which are
discussed in more detail in Section 4, Step-by-Step Instructions for Running HEM4.
This user's guide is designed to provide all the information you will need to run HEM4. However,
some of the options for running HEM4 draw on advanced features of AERMOD. If unfamiliar
with the AERMOD dispersion model, you may need to refer to the AERMOD documentation
(available at https://www.epa.gov/scram/air-gualitv-dispersion-modeling-preferred-and-
recommended-models#aermod.) in order to develop some of the inputs needed for HEM4 (EPA
2019a, EPA 2019b). This is particularly true for some of the more complex modeling options,
such as plume deposition and depletion, building downwash, temporal and wind speed emission
variations, and complex source configurations.
2. Installing HEM4
This section provides instructions for downloading and installing the HEM4 model and required
data libraries from the EPA's HEM Download Page.
2.1 Downloading the HEM4 Program
The HEM4 model is available from EPA's HEM Download webpage at
http://www.epa.qov/fera/download-human-exposure-model-hem. This site includes general
installation instructions, including hardware and software requirements, as well as links to
download and install HEM4. Download the HEM4 zip install package under "Software available
for download." HEM4 can be installed anywhere on your PC and the root folder is not required
to be named HEM4. However, for the purposes of this User's Guide, it is assumed the root
folder will be named "HEM4". HEM4 is started by running the executable file ending in ".exe".
Note: The HEM4 source code is available on github.com/USEPA/HEM4.
In addition to user-supplied inputs describing the nature and location of the emissions
(discussed in Section 3.1), HEM4 relies upon several data libraries that supply other required
inputs for a modeling run. To complete the installation of HEM4, download the following data
libraries:
• the Chemical Health Effects Library containing the pollutant (hazardous air pollutant,
HAP)-specific dose response values and benchmark values for affected organs, a.k.a.
"Toxicity Value Files" (Note: upon installation, HEM4's resources folder will include a
Dose Response Library and Target Organ Endpoints table);
• the Census Library containing nationwide files that provide the population numbers and
terrain elevation data surrounding a facility location (based on the 2010 Census); Note:
upon installation, HEM4's census folder will include the census files needed to run the
template/sample files only; and
• the Meteorological Library containing met station files (a surface and profile file for each
station) with data for over 800 stations nationwide; Note: upon installation, HEM4's
AERMOD MetData folder will include the meteorological files needed to run the
template/sample files only.
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You will find links to these data libraries on the HEM Download Page. The following sections
provide instructions for downloading these files, along with a brief description of each of these
data libraries.
2.2 Downloading Chemical Health Effects Data
HEM4 uses a chemical health effects library of pollutant unit risk estimates (UREs) and
reference concentrations (RfCs) to calculate risks. To download these values, click on the
"Toxicity Value Files" link on EPA's HEM Download Page (http://www.epa.gov/fera/download-
human-exposure-model-hem). Before initiating a modeling run, always check for updated
versions of these files on the HEM Download Page. When updated files become available, copy
these into the "resources" folder under the HEM4 directory that you selected during installation.
Be sure to unzip the files and verify they are located in the specified folder when finished. The
folder for chemical health effects data is "HEM4\resources."
2.2.1 Description of Chemical Health Effects Library
For each pollutant or HAP, the Chemical Health Effects Library includes the following
parameters, where available:
• URE for cancer;
• RfC for chronic noncancer health effects;
• reference benchmark concentration for acute health effects; and
• target organs affected by the pollutant (for chronic noncancer effects).
These parameters are based on the EPA's database of recommended dose response values for
HAP (EPA 2018a), which is updated periodically, consistent with continued research on these
parameters. The URE represents the upper-bound excess lifetime cancer risk estimated to
result from continuous exposure to an agent (HAP) at a concentration of 1 microgram per cubic
meter (|jg/m3) in air. For example, if the URE is 1.5 x 10"6 per |jg/m3, then 1.5 excess cancer
cases are expected per 1 million people, if all 1 million people were exposed daily for a lifetime
to 1 microgram of the pollutant in 1 cubic meter of air. UREs are considered plausible upper
limits to the true value; the true risk is likely to be less but could be greater (EPA 2018b).
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 noncancer health
effects during a lifetime (including to sensitive subgroups such as children, asthmatics and the
elderly). No adverse effects are expected to result from exposure if the ratio of the potential
exposure concentration to the RfC, defined as the hazard quotient (HQ), is less than one (1).
Note that the uncertainty of the RfC estimates can span an order of magnitude. (EPA 2018b).
Target organs are those organs (e.g., kidney) or organ systems (e.g., respiratory) which may be
impacted with chronic noncancer health effects by exposure to the pollutant in question. The
hazard index (HI) is the sum of hazard quotients for substances that affect the same target
organ or organ system, also known as the target organ specific hazard index (TOSHI).
The reference concentrations for acute health effects include both "no effects" reference levels
for the general public, such as the California Reference Exposure Levels (RELs), and
emergency response levels, such as Acute Exposure Guideline Levels (AEGLs) and
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Emergency Response Planning Guidelines (ERPGs). A more in-depth discussion of the
development and use of the health reference values may be found in the EPA's Air Toxics Risk
Assessment Library (EPA 2017), available for download at http://www.epa.gov/fera/risk-
assessment-and-modelinq-air-toxics-risk-assessment-reference-librarv.
You can add pollutants and associated health effect values, as needed, to the two Excel™
spreadsheets comprising HEM4's Chemical Health Effects Library: the Dose Response
Library file and the Target Organ Endpoints file. These files are located in HEM4's resources
folder:
• HEM4\resources\Dose_Response_Library.xlsx; and
• HEM4\resources\Target_Organ_Endpoints.xlsx.
The Dose Response Library file includes a listing of HAP and other toxic pollutants and the
various URE values, RfC values, and acute benchmark values associated with these pollutants.
The Target Organ Endpoints file includes a listing of HAP and other toxic pollutants and the
organs or organ systems that may be impacted with chronic noncancer health effects, by
exposure to these pollutants above the RfC level.
Note that each pollutant you list in your facility-specific input files (discussed in Section
3.1) needs to match exactly (the spelling of) a pollutant name in HEM4's Dose Response
Library file, and there can be no extra pollutants listed in your facility-specific input files
that are not also listed in the Dose Response Library file. The Target Organ Endpoints file
need not contain every pollutant listed in your inputs. You should ensure, however, that every
pollutant in your input files that has chronic noncancer health effects associated with it - and
that you wish to model as such - has an RfC value in the Dose Response Library file and is also
listed in the Target Organ Endpoints file, with the impacted organs and organ systems checked.
Note: Only pollutants with RfC values need to be listed in the Target Organ Endpoints file.
2.3 Downloading Census Data
You will need census files for the region or regions you wish to model. You can obtain
nationwide files from the 2010 Census on the HEM Download Page
(http://www.epa.qov/fera/download-human-exposure-model-hem) of EPA's FERA website.
Nationwide files are provided on a state-by-state basis in JavaScript Object Notation format
(.json). HEM4 will access census files to cover the area within 50 kilometers of each facility you
are modeling. Multiple states may be needed to model a particular facility if the facility is located
within 50 kilometers of a state boundary.
Download, unzip and copy the nationwide census files into the census folder under the HEM4
folder you selected during installation. Once unzipped, check to be sure that these files are now
located in the specified folders when finished. The census folder is "HEM4\census".
Do not delete the Census_key.json file (HEM4\census\Census_key.json). This file is required for
HEM4 modeling runs. Note that the Illinois and North Carolina files for the 2010 Census are also
included with the installation package to allow running of the template input files (discussed in
Section 3) with or without downloading of all nationwide census files.
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2.3.1 Description of Census Library
The HEM4 Census Library includes census block identification codes, locations, populations,
elevations, and controlling hill heights for the over 6 million populated census blocks identified in
the 2010 Census. The location coordinates reflect an internal point selected by the Census
Bureau 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, but they are still referred to as "centroids" in this guide.
Locations and population data for census blocks in the 50 states, Puerto Rico, and the Virgin
Islands are extracted from the U.S. Census Bureau website for Census 2010 (Census 2010).
HEM4's census database includes elevation and controlling hill height data, in addition to the
population and location data supplied by the Census Bureau. U.S. Geological Survey data were
used to estimate the elevation of each census block in the continental U.S. and Hawaii. The
elevation data contained within the 2010 Census files were derived from North American Digital
Elevation Model (DEM) data at a resolution of 1/3 of an arc second, or about 10 meters (USGS
2015). Using the ArcGIS® 10 analysis tool, 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 DEM elevations (in meters) (USGS
2000). An elevation value was assigned to each census block point based on the closest point
in the ArcGIS elevation raster file. HEM4 uses these block elevations to estimate the elevation
of each nearby polar grid receptor and the elevation of each source, if the user does not provide
source elevations, as discussed later in this guide.
An algorithm used in AERMAP, the AERMOD terrain processor (EPA 2018c), is used to
determine controlling hill heights. These values are used for flow calculations within AERMOD.
To save run time and resources, the HEM4 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 kilometers. (This corresponds to an elevation difference at a 10% grade of
10,000 meters, which considerably exceeds the maximum elevation difference in North
America.)
In addition to census block location, population, elevation and controlling hill height data, the
HEM4 Census Library also includes the locations for over 125,000 schools and 1,000 monitors.
School location data is for public and private schools, spanning pre-kindergarten through high
school, and are from the NCES 2009 data (NCES 2009a, NCES 2009b). You can obtain
monitoring locations from the Air Toxics Data section of the EPA's Technology Transfer Network
Ambient Monitoring Technology Information Center (EPA 2018d). Note that the precision of the
latitude/longitude location of these monitors varies and, in some cases, is precise to only two
decimal places (roughly ± 600 meters), making comparison with HEM4 modeling results
inexact.
2.4 Downloading Meteorological Data
You can obtain nationwide meteorological data files from the HEM Download Page
(http://www.epa.gov/fera/download-human-exposure-model-hem). Each set of meteorological
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files contains surface data and upper air data and is named beginning with the state
abbreviation for the state in which the station is located. Generally, the closest set of stations
will be most representative of the meteorology in the modeling domain. However, there are
several situations where a different combination of meteorological stations will be more
representative. For instance, if the modeling domain is located on the Gulf of Mexico, a surface
station near the Gulf may be more representative than an inland station, even if there is a closer
inland station.
Download the nationwide meteorological files into the "MetData" folder in the "aermod" folder
under the HEM4 folder you selected during installation. Unzip the meteorological files. After
unzipping, verify they are located in the specified folder. The meteorological folder is
"HEM4\aermod\MetData." AERMOD uses two files for each meteorological station and these
files have extensions of SFC (surface data) and PFL (profile data).
Note that when you download the HEM4 model (as described in Section 2.1), the installation
package will place an Excel™ spreadsheet named "metlib_AERMOD.xlsx" in your
"HEM4\resources" folder. This spreadsheet lists all the SFC and PFL met stations that are
provided in the nationwide meteorological data files (those available on the HEM Download
Page on the date you download the model). You may edit this spreadsheet to include additional
met station files, but you must provide the new met station data as both SFC and PFL files in
your "HEM4\aermod\MetData" folder. Be careful that the SFC and PFL file names match the
new rows you have added to the metlib_AERMOD.xlsx spreadsheet in your resources folder.
You may also edit rows in this spreadsheet or delete met station entries entirely. (A Python error
message will be displayed if HEM4 cannot locate the metlib_AERMOD.xlsx spreadsheet in your
resources folder.)
2.4.1 Description of Meteorological Library
AERMOD requires surface and upper air meteorological data that meet specific format
requirements. HEM4 includes a library of meteorological data from National Weather Service
(NWS) observation stations. The current HEM4 AERMOD Meteorological Library includes over
800 nationwide locations, depicted in Figure 2.
USEPA meteorologists obtained calendar year 2019 Integrated Surface Hourly Data (ISHD) for
over 800 Automated Surface Observation System (ASOS) (http://www.nws.noaa.gov/asos/)
stations spanning the entire US, as well as Puerto Rico and the US Virgin Islands, from the
National Centers for Environmental Information (NCEI) (formerly, the National Climatic Data
Center (NCDC)). The AERMOD meteorological processor, AERMET (EPA 2019c) and its
supporting modeling system (AERSURFACE and AERMINUTE) were used to process the
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.
To support AERMET, ASOS 1-minute data for each surface station were obtained from NCEI in
a DSI 6405 format. Further, upper air sounding data for the same time period for over 80
observation sites were obtained from the National Oceanic & Atmospheric Administration
(NOAA) Earth System Research Laboratory's (ESRL) online Radiosonde Database (see
http://www.esrl.noaa.gov/raobs/General lnformation.html). These datasets were produced by
ESRL in Forecast Systems Laboratory (FSL) format.
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AERMET Processing
Utilizing the AERMET meteorological data pre-processor, 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 pre-processor was used to process 1-minute ASOS wind data for
input into AERMET. The following provides more detail regarding the pre-processors, AERMET
and AERMINUTE, used to generate the AERMOD meteorological data.
• AERMET Options: Version 19191 used to process ASOS site data; surface data in NCEI
TD-3505 (ISHD) format; upper air data in FSL (all levels, tenths m/s) format; used the
ADJ_U* non-Default BETA option to adjust the friction velocity (u* or ustar) for low wind
speed stable conditions.
• AERMINUTE Options: Version 15272 used for 1-minute ASOS data in TD-6405 format
where available.
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 HEM4 meteorological database. In all,
838 met station pairs were found suitable and are included in the HEM4 meteorological library,
as depicted in Figure 2. Of these 838 met stations, 791 stations contain 2019 met data, while
the rest are 2016 through 2018.
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Figure 2. HEM4 Meteorological Stations
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3. Preparing HEM4 Input Files
This section explains how to prepare the required and optional user-supplied input files for
HEM4. In addition to the instructions provided in this section regarding how to set up your input
files, especially for more advanced modeling options, it is important to review the AERMOD
documentation for further guidance (EPA 2019a, EPA 2019b).
3.1 Overview and General Rules
HEM4 requires a series of Excel™ spreadsheet files to specify the emissions and configuration
of the facilities (or facility) you are modeling. HEM4 accepts all recent Microsoft Excel™
versions using the xlsx spreadsheet format (e.g., Excel 2007 and later). It should be noted that
Excel 2007/2010, 2013, 2016, and 2019 versions have a 1,048,576-row capacity (and 16,384-
column capacity).
To use HEM4 to calculate ambient pollutant concentrations (using AERMOD), you will need the
following three files at minimum:
• a facility list options file, which is the primary driver of the model run listing the facilities
to be modeled and specifying the model run parameters and options;
• an emissions location file, which provides emission source locations and configurations
for the facilities being modeled; and
• a HAP emissions file, which provides the names and amounts of the pollutants emitted
from each emission source at the modeled facilities.
You may also need the following additional input files, depending on the options you choose to
use in your modeling run.
• a polygon vertex file - this file is required if one or more of your sources is configured as
a polygon; it specifies the location of the polygon(s) by providing coordinates of the
vertices. (Note: this file is not needed for area sources.)
• a buoyant line parameter file - this file is required if one or more of your sources is a
buoyant line; it defines the values for a single buoyant line source (or the average values
for a group of parallel buoyant lines) including building length, building height, building
width, line source width, building separation (between the individual lines when multiple
lines are averaged) and buoyancy parameter.
• a building dimensions file - this file is required to model building downwash effects; it
describes building dimensions or other obstructions near emission sources that would
produce wake effects.
• An emission variations file - this file provides emission rate factors for individual sources
for one or more of the facilities you specify and is required to model temporally-varying
emissions (e.g., emissions reflecting diurnal, weekly, monthly, and seasonal variations)
or emissions impacted by wind speed variations.
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• a particle data file - this file is required to model particulate deposition; it specifies the
particle size distribution for various size ranges.
• the gas parameter file (included in HEM4's resources folder) - this file is required to
model gaseous deposition; it specifies the parameters needed for modeling dry and/or
wet deposition of gaseous (vapor) pollutants including diffusion coefficients, cuticular
resistance and Henry's Law coefficients. (Note: defaults are provided by the model
automatically, but you should provide pollutant-specific parameters if available by editing
the Gas_param.xlsx file as discussed in Section 3.5.4.)
• a land use and month-to-seasons files - these two files are required to model dry
deposition of gaseous pollutants; they describe the land use and vegetative land cover
surrounding emission source(s) for facilities listed in the files.
• a user-defined receptors file - this file specifies the locations of additional discrete
receptors and is required if you want HEM4 to compute pollutant concentrations and
risks at locations you specify (e.g., houses, schools, or other sites near a facility), in
addition to U.S. census block receptors. (Note: your facility list options file must indicate
the facilities to be modeled with user receptors.)
• an alternate receptor file - this file is required if you wish to use receptors other than
U.S. Census block centroids in your modeling run and instead provide your own list of
receptors for modeling within the U.S. or anywhere in the world; the file specifies the ID,
location, elevation, hill height and population of the alternate receptors to be modeled.
These files are described in more detail below in Sections 3.2 through 3.5. In addition to the
above list of input files, you can also optionally revise the census database (as described below
in Section 3.5.9) and also revise the chemical health effect input files - the dose response
values and target organ assumptions - used in the model (as described below in Section
3.5.10).
HEM4 will prompt you to provide the input files required for your model run by opening up
Browse lines that allow you to search your computer for the location of each required input file.
Directly inputting data from spreadsheets avoids having to retype the emission rates and other
calculated parameters. However, this method of input has its drawbacks. Notably, HEM4 will not
run successfully unless you have formatted the input files exactly as specified in the format
guidelines. This section describes general rules you should follow to avoid common mistakes.
To make formatting easier, specific formatting requirements are exemplified in template input
files, which are provided in the default "HEM4\lnputs" folder. Note: If this is your first time
running HEM4, it is highly recommended that you first run the model with the template
input files provided, as practice, and to confirm that HEM4 installed properly on your
computer.
General Rules for Input Files
• Use a separate Excel™ workbook for each input file. Ensure your Microsoft Office™
Trust Center settings allow Excel™ version 5 and higher to be fully opened and
operational (i.e., not in protected view only).
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• Use only one input file worksheet per workbook.
• Match columns with the format specified for the input file. You can use the template input
files and substitute actual data for template data. Delete any extra lines of template data.
• Do not insert columns between data columns. HEM4 will read these, including any extra
hidden columns, as data.
• Use the number of header rows indicated in the template input files (included with the
HEM4 download) at the top of each spreadsheet file for all required and optional input
files.
• Do not include text in numerical data fields (for instance "<0.001"). HEM4 may read
these fields as Os (zeroes) or may accept only a portion of the number.
• For location coordinates, HEM4 will accept latitudes and longitudes in decimal degrees
as well as Universal Transverse Mercator (UTM) coordinates. The maximum precision
HEM4 uses for latitude and longitude decimal degrees is 5 places after the
decimal. (HEM4 will convert latitudes/longitudes to UTMs for use in AERMOD.) You
must enter coordinates in the World Geodetic System of 1984 (WGS84) format.1 The
1983 North American Datum (NAD83) and the WGS84 are identical for most
applications, so no conversion is needed if using coordinates based on NAD83.
However, if coordinates are based on the 1927 North American Datum (NAD27)
geographic system format, they would need to be converted to WGS84 before being
used in HEM4.
• Match the units used for parameters, such as emission rates and stack parameters,
with the units given in the file's format guidelines provided in the following sections
(for example: meters/second, meters, tons/year, etc.). The required units are also
indicated in parentheses in the header rows of the template input files which are
included with the model.
• Note that the length and decimal places indicated in the format guidelines for each field
in the various input files is, in most cases, the suggested length based on HEM4's
internal rounding conventions. For the Source ID field, however, it should be noted that
AERMOD does not accept Source IDs longer than 8 characters.
3.2 Facility List Options File
The Facility List Options Excel™ file is the primary driver specifying the parameters and options
of the modeling run and is required for any HEM4 run. This file is an enhanced version of the
Facility List Options file used in Multi HEM-3, with several columns added allowing for additional
features and several columns re-arranged for more intuitive grouping of fields. The Facility List
Options file contains one row for every facility that will be run with the various modeling options
1 WGS84, NAD83 and NAD27 are different world reference frames (a.k.a. geographic systems) that are
used as the basis for projected coordinate systems like UTMs. HEM4 uses WGS84. For more information
see https://www.nqa.mil/ProductsServices/GeodesvandGeophvsics/PaqesAA/orldGeodeticSvstem.aspx
and https://qisqeoqraphv.com/wqs84-world-qeodetic-svstem/.
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listed as columns for each facility row. If you use all default modeling options, the only field
requiring input is the Facility ID. All other fields have defaults which are employed when the
field in the Facility List Options file is left blank.
3.2.1 Fields in the Facility List Options File
Table 1 shows the fields included in the Facility List Options file. These fields are columns in the
actual Facility_List_Options.xlsx input file that you must provide to HEM4, and each row is for a
different facility as identified by the Facility ID. The rows in Table 1 are shown in the same
column order required by HEM4 in the input file. (For a template, see HEM4_
_Facility_List_Options.xlsx in your HEM4 inputs folder.) The options listed in Table 1 are
described in more detail following the table.
Table 1. Fields in the Facility List Options Input File (Required)
Field
Default Setting
(if field left blank)
Description of Facility List Options Field
Facility ID
(Facility ID)
Met Station
(met_station)
Rural/Urban
(rural_urban)
Urban Population
(urban_pop)
Max distance
(max_dist)
Modeling distance
(model_dist)
Radials
(radials)
Met station selected
by model as closest
to the facility
HEM4 determines
when using U.S.
Census block
receptors: HEM4
defaults to rural for
alternate receptors
Defaults to 50,000
people if left blank,
but only used and
needed if "U"
specified in
Rural/Urban field
50.000 meters
3,000 meters
16
You must enter an alphanumeric string identifying the facility
being modeled. This field is mandatory: all other fields have
default values when blank.
The name of the meteorological surface station (e.g.,
NAME02.SFC) to be used by AERMOD when modeling
each facility. The met station closest to facility is chosen
unless you specify a name.
Used to set the type of dispersion environment for
AERMOD. "R" indicates rural land use surrounding the
facility: "U" indicates urban land use. If left blank when
modeling using U.S. Census block receptors. HEM4 will
determine whether the closest census block to the facility is
located in an urbanized area, based on the 2010 Census.
When using alternate receptors instead of U.S. Census
block receptors, a blank in this column will cause HEM4 to
default to a rural dispersion environment.
If you indicate "U" for urban land use (in Rural/Urban field
above), then you should provide the model with the urban
population size, otherwise leave blank. Note: If you specify
"U" in the Rural/Urban field but provide no urban population
value in this field, HEM4 will use a default urban population
of 50,000 people.
The outside max radius of the modeling domain in meters
(must be > the modeling distance and < 50.000 meters).
The cutoff distance (in meters) for individual modeling of
ambient impacts at census blocks; beyond this distance
ambient impacts are interpolated rather than explicitly
modeled. Note: For polygon source types, set the modeling
distance > the largest distance across the polygon.
The number of radials in the polar receptor network
emanating from the facility center (must be > 4).
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Field
Default Setting
(if field left blank)
Description of Facility List Options Field
Circles
(circles)
Overlap distance
(overlap_dist)
First ring distance
(ringl)
Facility Center
Ring Distances
Acute
(acute)
Hours
(hours)
Acute Multiplier
(multiplier)
High Value
(high_value)
13
30 meters
If left blank,
calculated by HEM4
to be just outside the
source locations, but
not less than 100 m
from facility center
If left blank,
calculated by HEM4
based on the source
locations in the
emissions locations
input file
HEM4 will
automatically place
13 polar rings
(circles) by default
N
1-hour
10
Maximum acute
value is used as the
high value when this
field is left blank
The number of concentric circles in the polar receptor
network, centered on the facility center (must be > 3).
The distance (in meters) between an emissions source and
a census block or alternate receptor, within which you do not
want the receptor to be considered as a point of maximum
exposure/risk because it might be on facility property.
Must be an integer value > 1 meter and < 500 meters.
The distance to the first ring (circle) of the polar network as
measured from the facility center. You can override the
default distance calculated by HEM4 to fit the size and
shape of the facility properties to be modeled.
You can enter the facility center location in this field to
override HEM4's (default) location. Enter as a comma
separated list that should start with either "U" (if using UTM
coordinates) or "L" (if using lat/lon coordinates). The list
should contain two values if L for latitude followed by
longitude (L. 35.91.-78.89) or three values if U for northing,
easting and UTM zone number with hemisphere (U.
3975044. 690891. 17N). Hemisphere is S or N and defaults
to N if omitted.
You can override HEM4's placement of polar rings (circles)
by specifying a list of distances in this field. Enter a comma
separated list that contains at least 3 values representing the
distance in meters for each polar ring from the facility center.
The distances entered must be > 0 and <= 50,000 meters,
and the values must be increasing (e.g.,
100,500,1000,5000,10000,50000).
Entering "Y" directs HEM4 to calculate short-term (acute)
concentrations for that facility. If left blank or "N" is entered,
acute impacts are not estimated in the model run.
The short-term (acute) averaging period that AERMOD will
use for ambient concentrations, for that facility. The
averaging period options are: 1, 2, 3, 4, 6, 8, 12 and 24-
hours. The default is 1-hour.
The acute multiplier applied to the average annual emission
rate and used to approximate the short-term emission rate
(e.g.. 10 times the rate entered in the HAP Emissions file).
Note: HEM4 also assumes that this short-term rate can
occur at the same time as the worst-case meteorological
conditions. Two-decimal precision is accommodated:
minimum value is 1.00
This field indicates which acute concentration to report as
the high acute value in the outputs, for each facility. If you
wish to use a value other than the maximum (e.g., the 98th or
99th percentile), then enter the value in this field. The number
you enter must be an integer and is calculated based on the
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Field
Default Setting
(if field left blank)
Description of Facility List Options Field
Deposition
(dep)
N
Depletion
(depl)
N
Particle
Deposition
(pdep)
NO
Particle Depletion
(pdep I)
NO
Vapor(gaseous)
Deposition
(vdep)
NO
number of hourly values in the modeled run. For example, if
you want the 98th percentile acute value used from a data
set of 8,760 hourly values (in one year), then enter 175 in
this field, which is the truncated product of 0.02 x 8760.
Similarly, if you want to use the 99th percentile acute value,
then enter 87 in the text box, which is the truncated product
of 0.01 x8760. The default acute high value (if this field is
left blank) is the maximum modeled acute concentration.
Deposition is not modeled by default: entering "Y" directs the
model to calculate deposition in the model run (particle,
vapor, or both as designated below) and provide the
deposition flux in the output files. You may model deposition
with or without plume depletion (below). Note that you
cannot model deposition/depletion for any facility that
contains a buoyant line.
Depletion is not modeled by default; entering "Y" directs the
model to deplete the plume by the calculated deposition flux.
Note: You may enter "Y" here even if you chose "N" for
deposition; in that case the model will internally calculate
deposition flux to deplete the plume but will not provide the
deposition flux values in the output files. (This option saves
space if you do not need the deposition flux.) Note that you
cannot model deposition/depletion for any facility that
contains a buoyant line.
The value "WD" directs the model to incorporate both wet
and dry deposition for particles. Use "WO" for wet only
particle deposition: use "DO" for dry only particle deposition:
use "NO" (or leave blank) if not modeling deposition of
particles. If you enter WD. WO or DO in this field for a given
facility (or facilities), then HEM4 will prompt you to provide a
particle size input file for that facility (or facilities), if you are
using Method 1 for deposition. Note that you cannot model
deposition/depletion for any facility that contains a buoyant
line.
The value "WD" directs the model to incorporate both wet
and dry depletion of particles from the plume. Use "WO" for
wet only particle depletion; use "DO" for dry only particle
depletion; use "NO" (or leave blank) if not modeling depletion
of particles from the plume. If you enter WD, WO or DO in
this field for a given facility (or facilities), then HEM4 will
prompt you to provide a particle size input file for that facility
(or facilities), if you are using Method 1 for deposition. Note
that you cannot model deposition/depletion for any facility
that contains a buoyant line.
The value "WD" directs the model to incorporate both wet
and dry vapor deposition of pollutants: use "WO" for wet only
vapor deposition: use "DO" for dry only vapor deposition: use
"NO" (or leave blank) if not modeling deposition of vapor
pollutants. If you entered WD or DO in this field. HEM4 will
prompt you to provide a land use input file and a month-to-
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Field
Default Setting
(if field left blank)
Description of Facility List Options Field
Vapor (gaseous)
Depletion
(vdepl)
NO
Elevations
(elev)
User receptors
(user_recpt)
Building
Downwash
(bldg_dw)
Y
N
N
FASTALL
(fastall)
Emissions
Variation
(emiss_var)
N
N
Annual
(annual)
Y
seasons input file, which are needed for dry deposition/
depletion modeling. Note that you cannot model
deposition/depletion for any facility that contains a buoyant
line.
The value "WD" directs the model to incorporate both wet
and dry depletion of vapor pollutants from the plume. Use
"WO" for wet only vapor depletion; use "DO" for dry only
vapor depletion; use "NO" (or leave blank) if not considering
depletion of vapor pollutants from the plume. If you entered
WD or DO in this field, HEM4 will prompt you to provide a
land use input file and a month-to-seasons input file, which
are needed for dry deposition/depletion modeling. Note that
you cannot model deposition/depletion for any facility that
contains a buoyant line.
Elevations of receptors are accounted for by default:
entering an "N" excludes elevations from the model run.
Enter "Y" to include user receptors in the modeling run, for
each facility. User receptors are not included by default.
Note: if you are modeling using user receptors, HEM4 will
prompt you for a separate user receptor input file.
Enter "Y" in this field for each facility containing point
sources for which you wish to model downwash over a
nearby building. Building downwash is not included by
default. If you are modeling building downwash. HEM4 will
prompt you for a separate input file that must contain
building dimension information, for (applicable point sources
in) each facility marked with a "Y" in this column. Note that
building downwash may only be modeled with vertical point
(P). capped point (C). and horizontal point (H) source types.
Entering "Y" directs HEM4 to use AERMOD's control option
FASTALL for modeling that facility, which conserves model
run time by simplifying AERMOD's dispersion algorithms.
FASTALL is not used by default. Note that you cannot use
FASTALL for any facility that contains a buoyant line.
Entering "Y" indicates that you want to vary the emissions of
one or more sources at this facility. This field allows the
application of variations to the emission inputs from specific
sources by different user-supplied time scales (e.g.. by
season, month, hour of day. day of week), or by different
wind speeds (6 ranges). If you enter a "Y" for a given facility,
then HEM4 will prompt you for a separate emissions
variation input file for that facility, and that file must contain
variation factors for at least one source at each facility
marked with a "Y".
Entering an "N" in the annual field indicates that you want
the modeling run to be based on meteorological data from a
period other than an annual period. If you enter an "N" in this
annual field, then you must enter values in the "period_start"
and "period_end" fields (below). Leaving this field blank or
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Field
Default Setting
(if field left blank)
Description of Facility List Options Field
Period Start
(period_start)
Period End
(period_end)
[Entry required if an
"N" is entered in
Annual field above]
[Entry required if an
"N" is entered in
Annual field above]
entering a "Y" will cause HEM4/AERMOD to calculate
annual concentration averages using the entire met data file,
which is the default.
The period_start field indicates the start of the
meteorological period during which AERMOD will run. You
should enter a comma separated list of 3 or optionally 4
values here indicating the year, month, day and (optionally)
hour of when the modeling period should begin. For
example, if you enter 2016.02.11.12 then the model will use
2016 met data starting on February 11th at the 12th hour
(noon) and end on the date and time indicated in the
period_end field. Note that if you do not enter an hour here,
then the model will use hour 1 as the default.
The period_end field indicates the end of the meteorological
period during which AERMOD will run. You should enter a
comma separated list of 3 or optionally 4 values here
indicating the year, month, day and (optionally) hour of when
the modeling period should end. For example, if you enter
2016,06,30,17 then the model will use the met data starting
on the date and time indicated in the previous period_start
field and ending in 2016 on June 30th at the 17th hour (5
pm). Note that if you do not enter an hour here, then the
model will use hour 24 as the default.
Note: Take care when filling out the Facility List Options File, as this file drives and
controls the modeling run. To avoid error, this file must be consistent with your other
input files. For example, if you indicate 100% particles in the Percent Particulate column of
your HAP Emissions input file and you wish to model deposition and/or depletion, then you
cannot choose to model vapor deposition and/or depletion (by entering a "Y" in either the vdep
or vdepl columns of your Facility List Options file). In addition, the modeling options you indicate
in the Facility List Options file may require additional input files for modeling. For example, if you
indicate in the Facility List Options file that you would like building downwash modeled for
certain facilities (by entering a "Y" in this field), then one or more point sources at those facilities
must be included in the separate building dimensions input file that HEM4 will prompt you for.
You will also need to provide consistent input files if you marked a "Y" for any facilities in the
user receptor or emissions variations fields. The various modeling options driven by the Facility
List Options file are discussed more in the next sections.
3.2.2 Meteorological Station and Period Options
HEM4's library of meteorological (met) station data is described in Section 2.4.1. By default,
HEM4 chooses the met station closest to the facility to be modeled (i.e., if this field is left blank).
If you do not want HEM4 to choose the closest met station's data to use for your modeling run,
in the meteorological station (met_station) column/field of the Facility List Options file, enter the
name of the met surface station you want AERMOD to use when modeling each facility (e.g.,
NC13722.SFC). The names of all stations in the met library can be found in the
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metlib_aermod.xlsx file in "HEM4\resources" folder, and the stations' met data can be found in
the "HEM4\aermod\MetData folder". You can also add your own met station to the
metlib_aermod.xlsx file in the HEM4's resources subfolder and provide the new met station data
as both SFC and PFL files in your "HEM4\aermod\MetData" folder, as explained in more detail
in Section 2.4.
The other fields related to met data are at the end of the Facility List Options file, on the far-right
side of the spreadsheet, and include "annual", "period_start", and "period_end". These columns,
as noted above in Table 1, allow you to choose to model with a period other than the default
annual period of met data. And the period start and period end fields allow you to specify exactly
what met period HEM4 should instruct AERMOD to use for your modeling run, down to the year,
month, day and even hour. The period start and end dates you specify must be included in
the meteorological files being used. If the set of meteorological files you specify, or that
HEM4 chooses, does not cover the dates you specify, AERMOD will generate an error
and that facility will not be modeled. These period options are useful if modeling, for
example, facilities that come on and offline during different parts of a year. The options may also
be helpful in performing analyses to determine what time periods in the year produce the
highest local concentrations and impacts.
It should be noted that the selection of the met station and met period for your modeling run can
have a significant effect on the air concentrations and therefore risk and HI estimates that HEM4
produces. See Table 1 for HEM4's default settings used in the Facility List Options for the met
station and period options.
3.2.3 Rural and Urban Dispersion Options
The Rural or Urban column/field is used by HEM4 to set the type of dispersion environment for
AERMOD, for each facility. If you are modeling using U.S. Census blocks as receptors, then by
default HEM4 will find the nearest U.S. Census block to the facility center and determine
whether that census block is located in an urbanized area, as designated by the 2010 Census
(FR 77:59). If the block is in an urbanized area, then the population of the designated urbanized
area will be used to specify the population input for AERMOD's urban mode for that facility. If
the block is not in an urbanized area, then AERMOD will use a rural dispersion environment for
that facility.
If you are modeling using alternate receptors instead of census blocks (e.g., outside the U.S.),
ideally you should determine which dispersion environment to use for each facility. If instead
you leave the rural/urban field blank when using alternate receptors, then AERMOD will default
to a rural dispersion environment, resulting typically in more conservative (higher) concentration
predictions.
The EPA provides guidance on whether to select urban or rural dispersion in its Guideline on Air
Quality Models (Appendix W). In general, use the urban option 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. If you choose the urban
dispersion environment for the model run, you should specify the population of the urban area
surrounding the facility, if known, by entering it in the urban population column/field (urban_pop)
of the Facility List Options file. This is true whether you are modeling with U.S. Census block
receptors or with alternate receptors. If you choose to model using an urban dispersion
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environment and do not provide a population, HEM4 will set your urban population column/field
(urban_pop) to 50,000 people. As noted above, AERMOD uses the urban population value in its
dispersion algorithms for urban areas.
3.2.4 Modeling Domain Options
You will provide HEM4 the parameters that define each facility's modeling domain in columns E
through L of the Facility List Options file. The modeling domain is circular and centered on each
facility, with a user-specified radius. HEM4 identifies all of the receptor locations in the modeling
domain - census blocks for U.S. runs based on the census database, or alternate receptors for
non-census modeling runs. The model then divides the blocks into two groups - inner and outer
receptors - based on their distance from the facility. For the inner group of receptors (closest to
the facility), each census block or alternate receptor location is modeled as a separate receptor
in AERMOD.
Maximum Distance: In column E of the Facility List Options file, enter the maximum radius (in
meters) to be modeled; this is the radius around each facility of the entire modeling domain. The
maximum distance must be greater than or equal to the "modeling distance" (discussed next),
but not greater than 50,000 meters because, as a Gaussian dispersion model, AERMOD is not
recommended beyond 50 kilometers. If you leave this field blank, HEM4 will use a default
maximum distance of 50,000 meters. The maximum distance is the radius of the circular study
area for which HEM4 will model ambient impacts (at census block centroid receptors or
alternate receptors, polar grid receptors, and user receptors, as explained below in this section).
The center of this modeling domain is by default the geographical center of each facility (based
on source locations for each facility) you are modeling, but you can change this center using the
"facility center" column K, as discussed below.
Modeling Distance: In column F of the Facility List Options file, enter the distance (in meters)
within which census blocks will be modeled individually. This is the cutoff distance around each
facility for explicitly including census block or alternate receptors in the AERMOD run. Within
this radial distance measured from the facility center, AERMOD will model each census block
centroid or alternate receptor explicitly as a receptor. Outside of this radius, AERMOD will not
model the census blocks or alternate receptors directly; ambient impacts at receptors beyond
the modeling distance will be interpolated using dispersion modeling results for the polar
receptor network, described below. If you leave this field blank, HEM4 will by default use a
modeling distance of 3,000 meters. It should be noted that the Modeling Distance may not be
greater than the Maximum Distance (above),
It should be noted that larger values for this cutoff modeling distance will require more time to
model, because the number of receptors requiring explicit AERMOD modeling will be higher.
However, you should set this cutoff value at a large enough distance so that the maximum risk
receptor (discussed in Section 6.1.1) will be modeled individually. This distance will vary
depending on the configuration of the sources but is generally between 1,500 and 2,000 meters.
A typical modeling cutoff distance for larger facilities is 3,000 meters (or 3 km). When modeling
large sources configured as polygons (e.g., U.S. Census tracts), set this modeling cutoff
distance to be greater than the largest distance across the polygon, to ensure discrete modeling
of all census blocks within the polygon.
Radials: In column G of the Facility List Options file, enter the number of radials in the area to
be modeled. The polar grid receptors of the polar network are located at the intersection of a
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radial and a polar ring (or "circle", described next). A typical run would include 13 concentric
rings and 12 or 16 radial directions. HEM4 will distribute the radial directions evenly around the
facility. For instance, if you select 16 directions, receptors will be modeled at compass bearings
of 0, 22.5, 45, 67.5, 90, 112.5, 135, 157.5, 180, 202.5, 225, 247.5, 270, 292.5, 315, and 337.5
degrees. If you leave this field blank, by default HEM4 will use 16 radial directions. If you
choose to enter a different number of radials, you must specify at least 4 radials in this field.
Circles: In column H of the Facility List Options file, enter the number of concentric circles
(rings) in the polar receptor network around each facility, centered on the facility center. You
must enter at least 3 rings. If you leave this field blank, by default HEM4 will use 13 rings. Also,
by default, HEM4 will calculate the inner radius of the polar network, unless you choose to
specify a distance to the first ring (or "RingT', described below). This model-calculated first ring
distance is based on the location of the emission sources and the facility center. HEM4 selects
the distance that places the first modeling ring just beyond all emission sources, but not less
than 100 meters from the facility center. HEM4 will place the concentric rings at a logarithmic
progression of distances starting at the inner ring distance and ending at the outer radius of the
modeling domain. However, you have the option to specify different ring distances (than
HEM4's calculated distances) in the "ring_dists" column, described below. Although the polar
grid receptors are used primarily for interpolating risks at census blocks outside of the modeling
cutoff distance, it is important to include some rings close to the facility.
Overlap Distance: In column I of the Facility List Options file, enter the distance (in meters)
where source and receptor are considered to be overlapping. This distance must be greater
than or equal to 1 meter and less than or equal to 500 meters. If you leave this field blank,
HEM4 by default will use an overlap distance of 30 meters, which is approximately equal to the
width of a narrow buffer and a roadway. Within this distance, sources and receptors will be
considered to be overlapping, as measured from each source at the facility (e.g., stack, edges
of area and volume sources). This feature is provided to address situations, for example,
wherein U.S. Census blocks are very close to a facility and have complex shapes. In such
cases, the centroid of a census block may be much closer to the facility than the nearest actual
dwelling. (In fact, if a census block surrounds a portion of the facility, the centroid of the block
may be on facility property.) If a receptor falls within this distance, HEM4 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 does not overlap
facility property (including polar receptors). An exception to this occurs when modeling polygon
sources. Unlike other sources, when modeling polygons, overlapping of source and receptor is
permitted. This allows the impacts, for example, of a U.S. Census tract modeled as a polygon
source (e.g. mobile source emissions modeled uniformly across a census tract) to be calculated
within the census tract being modeled.
Rinql or First Ring: In column J of the Facility List Options file, enter the distance (in meters) to
the first ring (circle) of the polar network for each facility, as measured from the facility center.
As noted above (under "Circles"), if you leave this field blank then HEM4 will calculate the
default value to the first ring to be just outside the source locations, but not less than 100 meters
from the facility center. You can override the default distance calculated by the model to fit the
size and shape of the facility properties to be modeled. For example, you should set the first
receptor ring to less than 100 meters (or conversely greater than what HEM4 calculates), if
appropriate to the size and shape of the facility property. Place the nearest polar receptor ring
as close as possible to the facility boundary— this inner radius of the polar network should be
the minimum distance from the facility center that is generally outside of facility property. For
complex or irregularly shaped facilities however, you may find it useful to specify an inner ring
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that encroaches on facility property in some directions. Furthermore, you may want to specify a
set of boundary receptors by employing the user-defined receptors file (as described in Section
3.2.8). Note that the first ring distance must be less than the modeling cutoff distance (for
explicit modeling of receptors).
Facility Center: In column K of the Facility List Options file, you may specify the facility center
location to override HEM4's determination of where the facility center is located. If you leave this
field blank, HEM4 will by default choose the facility center by determining the geographic center
of the locations of all emission sources for that facility in your Emissions Location file (discussed
in Section 3.4). If you wish to specify a different facility center location, then enter its location in
this field as a comma separated list that should start with either "U" (if using UTM coordinates)
or "L" (if using latitude/longitude coordinates). The list should contain two values if L for latitude
followed by longitude (L, 35.91,-78.89) or three values if U for northing, easting and UTM zone
number with hemisphere (U, 3975044, 690891, 17N). Hemisphere is S or N and defaults to N if
omitted.
Ring distances: In column L of the Facility List Options file, you may override HEM4's placement
of polar rings (circles) by specifying a list of distances in this field. To do so, enter a comma
separated list that contains at least 3 values representing the distance in meters for each polar
ring from the facility center. The distances entered must be greater than 0 and less than or
equal to 50,000 meters, and the values must be increasing (e.g., 100,500,1000,5000,10000,
50000). If you leave this field blank, HEM4 will by default place 13 polar rings (circles), as noted
above under "Circles".
A note about the Polar Network: Columns G and H of the Facility List Options file, and optionally
columns J, K and L, define HEM4's polar network. In addition to ambient impacts at receptors
(census block centroids or alternate receptors) within the modeling cutoff distance, HEM4 (using
AERMOD) also explicitly models ambient impacts at polar grid receptors within the polar
network. This polar network extends beyond the modeling cutoff distance to the maximum
(outside) radius. The polar receptor network in HEM4 serves three functions:
(1) it is used to estimate default impacts if one or more U.S. Census block receptor or
alternate receptor locations are inside the overlap cutoff distance;
(2) it is used to evaluate potential acute effects that may occur due to short-term
exposures in unpopulated locations outside the facility boundary; and
(3) it is used to interpolate long- and short-term impacts at receptors (U.S. Census block
locations or alternate receptors) that are outside the cutoff distance for modeling of
individual receptors
Note that, if modeling with terrain effects, the elevation of each polar grid receptor is based on
the elevation of nearby individually (explicitly) modeled or "discrete" receptors (including census
blocks, alternate receptors and user receptors). The maximum elevation of nearby discrete
receptors is assigned to each polar receptor, to ensure terrain effects on receptor
concentrations are conservatively estimated. The importance of the polar network is
discussed further in Section 5.
3.2.5 Acute Options
As introduced in Section 1.2, you can use HEM4 to estimate chronic health risks and, optionally,
acute (short-term) health risks as well. Chronic health risks are estimated based on long-term
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average concentrations, as predicted by AERMOD. The time frame of this average is
determined by the number of years covered by the meteorological data file selected for the
model run: the default is generally one year when running AERMOD, although periods other
than one year can be chosen as discussed in Section 3.2.2 above regarding met station and
period options. Acute health risks are based on short-term average exposures such as 1, 2, 3,
4, 6, 8, 12 and 24 hours.
You can choose to model acute health risks using columns M, N, O and P of the Facility List
Options file. HEM4 uses what you input in these fields for each facility to direct AERMOD to
model acute concentrations, and then HEM4 uses these acute concentration predictions by
AERMOD to estimate acute health risks. Enter a Y (for "yes") in column M "acute" to indicate
you want HEM4/AERMOD to model short-term (acute) concentrations for that facility. (If you
leave this field blank then by default HEM4 will not model acute impacts, regardless of what you
put in columns N, O and P.) Next, in column N "hours", enter the short-term (acute) averaging
period that AERMOD will use for ambient concentrations, for each facility. The averaging period
options are: 1, 2, 3, 4, 6, 8, 12 and 24 hours. (If you entered Y in column M and leave column N
blank, then HEM4 will by default use an averaging period of 1 hour.)
In column O "multiplier", enter the acute multiplier for each facility. This multiplier is applied to
the average annual emission rate (in tons/year from your HAP Emissions input file, which the
model converts to grams/second) and used to approximate the short-term emission rate. If you
entered a Y in column M, but leave this field blank, then by default HEM4 will use a multiplier of
10 for that facility (e.g., the default of 10 times the average annual emission rate entered in the
HAP Emissions file might be used to approximate short-term emission spikes). Regarding short-
term spikes, it is important to note that AERMOD applies this short-term rate over the course of
the entire met period chosen (in Section 3.2.2) and the peak acute value will occur at the
same time as the worst-case meteorological conditions. Therefore, the acute results
produced with an appropriate multiplier can be viewed as conservative estimates. Two-decimal
precision is accommodated in the multiplier column O, but the multiplier entered must be greater
than or equal to 1.00.
The peak acute value reported by HEM4 is also impacted by what you enter in column P "high
value". This field indicates which acute concentration to report as the high acute value in the
outputs, for each facility. If you wish to use a value other than the maximum (e.g., the 98th or
99th percentile), then enter the associated value in this field. The number you enter must be an
integer and is dependent on the number of hourly values in the modeled run. For example, if
you want the 98th percentile acute value used from a dataset of 8,760 hourly values (in one
year), then enter 175 in this text box, which is the truncated product of 0.02 x 8,760. Similarly, if
you want to use the 99th percentile acute value, then enter 87 in the text box, which is the
truncated product of 0.01 x 8,760. If instead you leave column P blank, then HEM4 will by
default use the maximum modeled acute concentration as the "high value".
3.2.6 Deposition and Depletion Options
Deposition and Depletion: Deposition and depletion are not modeled by default by HEM4.
However, depending on the deposition and depletion options you choose in the Facility List
Options file in columns Q through V, HEM4 will (1) calculate and output a deposition flux and (2)
deplete the plume (or not) based on the calculated deposition. Generally speaking, deposition
modeled with plume depletion will reduce the ambient impacts from the emission sources by
removing pollutants from the plume. Air concentrations will be depleted as pollutants are
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deposited to the ground. Alternatively, you may choose to calculate the deposition flux, but not
deplete the plume (to allow for non-depleted air concentrations that a standard run would
produce). Deposition without plume depletion will not affect the air concentrations but will
provide a deposition flux in the outputs. Whether you choose to deplete the plume or not, the
modeled deposition flux may be then used as an input to a separate multipathway model such
as the Total Risk Integrated Methodology (TRIM) (EPA 2018e).
Enter a Y (for "yes") in column Q of your Facility List Options file if you would like AERMOD to
model deposition and HEM4 to output a deposition flux column (in g/m2/y)2 for all polar
receptors and for the inner discretely modeled receptors. Enter a Y in column R if you would like
AERMOD to model depletion (i.e., deplete the plume based on a calculated deposition flux). If
you enter a Y in both columns Q and R, then HEM4 will output a deposition flux column AND
deplete the plume. If you enter a Y in only column R (and leave column Q blank or enter an "N"),
then no deposition flux will be provided, but the plume will be depleted (based on an internally
calculated deposition flux). If you do not need the deposition flux output by the model, this
option saves space.
HEM4 uses AERMOD to calculate deposition and depletion effects for particulate matter, vapor
(gaseous) pollutants, or both. The make-up of your emissions - that is, the percentage
particulate and gas - is dictated to HEM4 by your HAP Emissions input file. Specifically, column
E in the HAP Emission input file ("Fraction emitted as particulate matter (%)") indicates to HEM4
whether your emissions are 100% particle (if column E is populated with 100 for all pollutants),
100% gas (if column E is left blank or populated with 0 for all pollutants), or a mixture of
particles and gas. However, for each facility, you can choose to model deposition and/or
depletion for merely the particulate portion of your emissions (if you have a particulate portion),
the vapor portion of your emissions (if you have a gas portion), or both (if you have both particle
and gas, as indicated in column E of your HAP Emissions input file).
Particle and Vapor Deposition and Depletion Types (Wet and Dry; Wet Only; Dry Only; None): If
you entered "Y" in column Q and/or R regarding modeling deposition and/or depletion, you must
also indicate what type of deposition and/or depletion you wish HEM4 to direct AERMOD to
model: wet and dry (WD), dry only (DO), wet only (WO), or none (No or leave blank). Use
columns S, T, U and V of your Facility List Options file to indicate what kinds of deposition
and/or depletion you want modeled for particulates and vapor (gas). In column S "pdep" you
should indicate the type of deposition of particles you want modeled, if any. In column T "pdepl",
you should indicate the type of depletion of particles you want modeled, if any. Do likewise in
columns U "vdep" and V "vdepl" for the types of deposition and depletion of your vapor
pollutants, respectively. See the AERMOD User's Guide (EPA 2019a) and AERMOD
Implementation Guide (EPA 2019b) for a more detailed discussion of these processes.
You can mix and match the type of deposition and depletion you tell HEM4 to model. For
example, you can direct HEM4 to model wet and dry (WD) deposition, and then deplete the
plume based on those wet and dry (WD) deposition processes. Alternatively, you can choose
wet and dry deposition (WD), but then only deplete the plume based on the wet deposition
process (WO). In addition, the "none" option (No or blank) allows you to model deposition for
particles only, for example, even if your HAP Emissions file shows a mixture of particles and
gas. To do this, you can indicate in column S "pdep" what type of deposition to model for your
particle emissions (WD, WO or DO) and then leave column U "vdep" blank or enter "No". You
2 If you specify a PERIOD average instead of an ANNUAL average, deposition results will be given in
g/m2.
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may use these same options for depletion-only modeling. Table 2 below provides a partial list of
some deposition/ depletion combinations and their modeling results.
Table 2. Sample Deposition and Depletion Options and Model Results
Entries in Columns Q - V of the Facility List Options File*
Model Results*
Q: dep
R: depl
S: pdep
T: pdepl
U:vdep
V: vdepl
Y
Y
WD
WD
WD
WD
Deposition flux will be provided
and the plume will be depleted,
using wet and dry processes for
both particles and vapor, for both
deposition and depletion
Y
WO
DO
Deposition flux will be provided
with no depletion of the plume,
using wet-only processes for
particles and dry-only processes
for vapor
Y
WD
WD
No deposition flux will be
provided but the plume will be
depleted using both wet and dry
processes for particle and vapor
Y
Y
DO
WO
Deposition flux will be provided
and the plume will be depleted,
using dry only processes for
particle-only deposition and wet-
only processes for particle-only
depletion
Y
WO
No deposition flux will be
provided but the plume will be
depleted using wet-only
processes for vapor only
Y
WD
Deposition flux will be provided
with no depletion of the plume,
using wet and dry processes for
particle-only deposition
Y
Y
WD
WO
WD
DO
Deposition flux will be provided
and the plume will be depleted,
using wet and dry processes for
particle and vapor deposition, but
wet-only processes for particle
depletion and dry-only processes
for vapor depletion
[The above is merely a partial list of some of the possible deposition/depletion combinations, for
illustration purposes. Many more variations may be chosen that are not illustrated here.]
*Note: These Model Results will happen if your column entries are consistent with your emissions (e.g.,
you cannot model deposition and/or depletion of particulates if your emissions have no particulates in
column E of your HAP Emissions file).
Concentration Outputs Broken Out into Particle and Vapor: Also, if your pollutants are a mixture
of both particles and vapor and you would like the concentration outputs broken down by
particle and vapor (instead of combined, as is the default in a standard run), you can also use
the deposition/depletion fields in the Facility List Options file to do this. In other words, you can
direct HEM4 merely to produce more detailed concentration outputs, showing the breakdown of
particle and vapor concentration at each receptor location, without modeling either deposition or
depletion. To do so, enter "Y" in column Q "dep" but leave all other deposition/depletion fields
blank (indicating No or None). Neither deposition nor depletion will be modeled in this case.
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However, the outputs will show distinct rows for particles ("P") and vapor ("V") at each location,
rather than the standard combined ("C") row. Again, this is helpful only if your HAP Emissions
file shows a mixture of particles and gas.
Additional Deposition/Depletion Input Files: Depending on the type of deposition and/or
depletion you indicate in columns Q through V for each facility, and depending also on the
method of particle deposition you indicate for each source at these facilities in your Emissions
Location file (explained further in Section 3.4.2), HEM4 will prompt you to provide additional
files. These files are introduced below and described in detail in Sections 3.5.3 and 3.5.4.
If you want to model deposition and/or depletion of particles in your emissions using Method 1
(described further in Section 3.4.2), HEM4 requires a particle data file. This additional input file
will need to contain particle size (diameter) information, mass fraction percentages for each
size, and particle density for each size, for emissions from each source (for which you wish to
model particle deposition and/or depletion using Method 1). The particle data file is described
further in Section 3.5.3.
If you want to model dry deposition and/or depletion of gaseous/vapor pollutants, HEM4
requires a land use input file and a month-to-seasons input file. These additional input files are
needed to describe the land use and vegetation surrounding each facility at which you wish to
model dry only (DO) or wet and dry (WD) deposition and/or depletion of gaseous pollutants, as
discussed in Section 3.5.4. If you wish to model wet only (WO) deposition and/or depletion of
gaseous pollutants, these additional input files are not needed by HEM4. (These files are also
not needed for 100% particulate emissions.)
Finally, you should check to ensure that the gaseous pollutants in your HAP Emissions file are
included in the Gas Parameter (Gas Param) reference file, described further in Section 3.5.4. If
these pollutants are not included - or if you wish to include different parameter values than the
Gas Parameter file currently uses - you should edit the Gas Parameter file, as discussed in
Section 3.5.4. Otherwise, generic default gas parameter values will be used.
It should be noted that HEM4 requires additional modeling time compared to a standard run
(with no deposition and/or depletion modeling). Furthermore, HEM4 requires significantly more
time to run if you opt to model deposition and/or depletion and you are also modeling acute
impacts. The exact run time will depend on the particular source configuration and modeling
domain, but the combination of acute calculations and deposition/depletion will generally
increase run times from a few minutes to over an hour, or more, per facility.
Deposition and plume depletion have more of an effect on ambient concentrations farther from
the facility than these processes do closer to the facility, where the maximum impact generally
occurs. Therefore, if you select the deposition and/or depletion options for a model run, you may
save time by performing two separate runs. For example, you can use the first HEM4 run to
calculate chronic effects and include deposition and plume depletion. You can then use the
second run to calculate acute effects without deposition and depletion.
It should also be noted that HEM4 does not model deposition and/or depletion at census block
and alternate receptors beyond the modeling distance, except at the polar receptors. This
means that deposition and/or depletion is modeled at only the "inner receptors" (discussed in
Section 6.1.10) and the polar receptors. If you need deposition and/or depletion modeled for the
entire modeling domain at all census block or alternate receptors, you should set the modeling
distance equal to the maximum distance. HEM4 will require additional modeling time in this
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scenario, compared to using a smaller modeling distance. As noted above, you may save
modeling time by performing two separate runs, especially if you are also modeling acute
impacts.
3.2.7 Elevation Option
HEM4 includes terrain elevations by default in your modeling run if you leave column W "elev"
blank or enter a "Y" in this field in your Facility List Options file. To exclude terrain elevations in
your modeling run (i.e., to model as flat terrain), enter an "N" in this field for a given facility.
Elevated terrain around the facility can cause local impacts to increase, though impacts will
differ for each set of sources and elevations. It is especially important to include terrain
elevations if the height of receptors around the facility may exceed the height of any stacks at
the facility. Consult the EPA's Guideline on Air Quality Models (also published as Appendix W of
40 CFR Part 51) (EPA 2005) for more explicit directions on when the use of terrain elevations is
recommended. If you choose to include elevations in the model run, you can specify elevations
for each source in the Emissions Location file. If you do not provide elevations in the Emissions
Location file, HEM4 will calculate source elevations from neighboring census block elevations.
Note: You should provide elevations for every source or for no sources at each facility, as noted
in Section 3.4 regarding the Emissions Location file.
3.2.8 User Receptors Option
If you would like to include additional "user receptors" in your model run for one or more facilities
- in addition to the census block or alternate receptors, enter a "Y" in column X "user_rcpt" of
your Facility List Options file. HEM4 does not include user receptors by default, so if this column
is blank then user receptors will not be included for that facility. If you are modeling impacts at
user receptor locations, HEM4 will prompt you for a separate input file containing the user
receptor information, for each facility marked with a "Y". The user receptor input file is described
in Section 3.5.6.
3.2.9 Building Downwash Option
If you would like to model building downwash over a building, which is under or near a point
source, then enter "Y" in column Y "bldg_dw" of your Facility List Options file. HEM4 does not
model building downwash by default and you should simply leave this field blank if you do not
wish to model it as part of the plume dispersion. If you are modeling building downwash, HEM4
will prompt you for a separate input file that must contain building dimension information, for
applicable point sources in each facility marked with a "Y" in this column. Note that building
downwash may only be modeled with vertical point (P), capped point (C), and horizontal point
(H) source types. The building dimension input file is described in more detail in Section 3.5.5.
Under AERMOD's regulatory option, the effects of building downwash should be taken into
account when a building is close enough to impact dispersion from an emission source. Building
downwash will affect dispersion predictions when:
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• the stack height is less than either 2.5 times the building height or the sum of the
building height and 1.5 times the building width; and
• the distance between the stack and the nearest part of the building is less than or equal
to five times the lesser of the height or the projected width of the building (EPA 1995, pg.
1-22 and 1-23).
AERMOD incorporates the Plume Rise Model Enhancements (PRIME) algorithms (Schulman
2000) for estimating enhanced plume growth and restricted plume rise for plumes affected by
building wakes (EPA 2019d). A building may impact emissions from multiple sources. To model
the impact of building downwash, HEM4 requires information on the configuration of the building
when viewed from different wind directions, and this information is contained in the building
dimensions input file, described further in Section 3.5.5.
3.2.10 FASTALL Option
To conserve model run time by simplifying the dispersion algorithms used to model a given
facility's emissions, enter a "Y" in column Z "fastall" of your Facility List Options file. HEM4 does
not employ FASTALL by default, so if you leave this field blank AERMOD will use the more
rigorous (non-simplified) dispersion algorithms.
The FASTALL option conserves model runtime by simplifying the AERMOD algorithms used to
represent meander of the pollutant plume. This simplification is achieved by eliminating the
upwind component of dispersion for point and volume sources, and by reducing the requirement
for uniformity of emissions over the extent of area sources (EPA 2019a). For faster runs, you
may want to select the FASTALL option which includes these plume and source simplifications.
(More information on AERMOD's FASTALL option is available for download at
https://www.epa.qov/scram/air-qualitv-dispersion-modelinq-preferred-and-recommended-
models#aermod.)
Note that if a facility listed in your Facility List Options file includes buoyant line sources in your
accompanying Emissions Location file, you cannot use the FASTALL option for that facility. You
may, however, use FASTALL for the other facilities in your Facility List Options file.
3.2.11 Emissions Variation Option
Enter a "Y" in column AA "emiss_var" of your Facility List Options to apply variations to the
emissions from one or more sources at a given facility. You may vary emissions by different
user-supplied time scales (e.g., by season, month, day of week, hour of day), or by different
wind speeds (6 ranges). Note: HEM4 will prompt you for an emissions variation file if you
entered "Y" for one or more facilities, and that file must contain variation factors for at least one
source at each facility marked with a "Y". The emission variation input files are described in
more detail in Section 3.5.7.
Finally, it should be noted that these emission variation factors will compound the effects of the
acute multiplier (specified in column O "multiplier") on the short-term/acute emission rates used
by AERMOD. For example, whatever factors you supply in an emission variations input file
(described in Section 3.5.7) will be multiplied by an acute multiplier of 10 (if the default multiplier
is used) to derive the short-term emission rate. Therefore, if applying hour-of-day emission
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variation factors, you may want to set the acute multiplier to 1, unless it is reasonable to assume
that the short-term rate may still exceed the hour-of-day factors by an additional multiple.
3.3 HAP Emissions File
The HAP Emissions Excel™ file, like the Facility List Options file, is required for any HEM4
modeling run. This file includes emissions in tons per year (tpy) for each HAP emitted from
modeled sources, for all facilities listed in the Facility List Options file. Tables 3 and 4 give the
format guidelines for the HAP Emissions file and a sample HAP emissions input file,
respectively. A template input file is provided in the HEM4 Inputs folder named
HEM4_HAP_Emiss.xlsx. The pollutants emitted per source at each facility are required in every
HAP Emissions file and are discussed in Section 3.3.1. The percent particulate emitted from
each source is generally only required if you are modeling deposition or depletion (see Section
3.2.6) and is discussed in Section 3.3.2.
Table 3. Format Guidelines for the HAP Emissions Input File (Required)
Field
Type
Description
Facility ID
Source ID
Pollutant
Emission
Amount
Percent
Particulate
Character An alphanumeric string identifying the facility being
modeled
Character An alphanumeric character string up to 8 characters long.
It must contain at least one alphabetic character and all
Source IDs must match a Source ID used in the Emissions
Location file. Note: AERMOD allows a maximum of 8
characters for the Source ID; and all Source IDs will be
converted to upper case by AERMOD.
Character The pollutant name must correspond to one of the chemical
names listed in the dose response library, (see
Dose_Response_Library.xlsx in the resources folder)
Numeric The emitted amount of the pollutant in tons per year (tpy).
Numeric The percent of pollutant emitted as particulate. Required if
deposition and/or depletion will be modeled, or if a
breakdown by particulate and vapor is desired in the
concentration outputs. If left blank, defaults to 0%
particulate when deposition is modeled. If deposition is not
modeled, this field is ignored by HEM4.
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Table 4. Sample HAP Emissions Input File
Facility ID
Source ID
Pollutant
Emissions
(tons/year)
Fraction
Emitted as
Particulate
Matter (%)
Fac2-IL
CT0001
Antimony compounds
1.2E-01
100.0
Fac2-IL
CT0001
Chromium (VI) compounds
3.2E-04
100.0
Fac2-IL
CT0001
Mercury (elemental)
4.2E-02
50.0
Fac2-IL
CV0001
Dibenzofuran
1.1E-01
90.0
Fac2-IL
CV0001
Xylenes (mixed)
1.3E+00
0.0
Fac1-NC
SR0001
Benz(a)anthracene
7.3E-06
11.9
Fac1-NC
SR0001
Benzo(a)pyrene
2.5E-08
23.9
Fac1-NC
SR0001
Benzo(b)fluoranthene
2.8E-06
17.8
Fac1-NC
MS0001
Chrysene
3.2E-05
52.3
Fac1-NC
MS0001
Dibenz(a,h)anthracene
3.6E-08
99.3
Fac1-NC
MS0001
lndeno(1,2,3-cd)pyrene
1.1E-07
98.9
Fac1-NC
RW0001
Chromium (VI) compounds
3.8E-05
100.0
Fac1-NC
RW0001
Mercury (elemental)
3.6E-04
50.0
Fac1-NC
RV0001
Nickel compounds
4.8E-03
100.0
Fac1-NC
RV0001
Selenium compounds
2.1E-04
100.0
3.3.1 Pollutant Emissions per Source
You should include one record (row) for each combination of facility (Facility ID), emission
source (Source ID) and chemical (Pollutant) in your HAP Emissions file. The Source ID is a key
parameter in the HAP Emissions file, because HEM4 uses the Source ID to link the emitted
HAP at that source to other input files, such as the Emissions Location input file (discussed in
Section 3.4) and other optional input files (discussed in Section 3.5). The Source ID should
provide each source a distinct name, and different sources should have unique Source IDs even
if they will be modeled at the same location. AERMOD requires that the Source ID be
restricted to eight (8) characters (or fewer) and it must consist of all alphanumeric
characters. Do not use spaces at the beginning or in the middle of the Source ID. In addition,
AERMOD converts all letters in the Source ID string to upper case. Therefore, upper and
lowercase characters cannot be discriminated between; so "ABC" and "abc" would be
treated as the same Source ID. While each source should have a unique Source ID, it is
advantageous to group certain types of sources within part of the Source ID. For example, "ST"
could be used in the Source ID to indicate a storage tank and each distinct storage tank could
be given a number (e.g., ST01, ST02). Such grouping is important for certain summary
programs, as discussed in Section 4.5.
Each chemical you name in the HAP Emissions file (under "Pollutant" in the sample shown in
Table 4) must match one of the chemical names listed in the dose response table located in the
HEM4 resources folder. The dose response values are part of HEM4's Chemical Health Effects
Library, described in Section 2.2. If necessary, you can add pollutants to the two Excel™
spreadsheets comprising HEM4's Chemical Health Effects Library: the dose response table and
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the target organ endpoints table. Section 3.5.10 explains how to make changes to the Chemical
Health Effects Library. Finally, emission amounts for each HAP emitted from each Source
ID must be expressed in tons/year. Be sure your input files use the correct units.
3.3.2 Percent Particulate for Deposition and Depletion
If you are modeling deposition or depletion, or if you want separate records for particle phase
and vapor phase at each receptor location in the concentration outputs, then you must provide
HEM4 with the breakdown between vapor and particulate matter in the emission inputs. Provide
this breakdown in column E of the HAP Emissions file, expressed as the fraction emitted as
particulate for each emission record (each combination of source and pollutant). For a given
facility, if you are not modeling deposition or depletion, then HEM4 will ignore the field. If you
are modeling deposition or depletion and have left this field blank, then HEM4 assigns the blank
a default value of 0% particulate. Note that if you are modeling deposition or depletion, you will
need additional input files depending on the type of deposition to be modeled, as described in
Section 3.2.6 and Sections 3.5.3 and 3.5.4. (Note: You do not need any additional input files if
you merely want a breakdown of particle and vapor in your outputs.)
3.4 Emissions Location File
The Emissions Location Excel™ file, like the HAP Emissions file and the Facility List Options
file, is required for any HEM4 run. The file includes emission source locations and types (e.g.,
the latitude and longitude of a stack) for all Source IDs listed in the HAP Emissions file, for all
facilities listed in the Facility List Options file. Tables 5 and 6 display the format guidelines for
the fields in the Emissions Location file and a sample file, respectively. A template input file is
provided in the HEM4 Inputs folder named HEM4_Emiss_Loc.xlsx. For each Source ID at every
facility, the Emissions Location file includes the location, source type and required parameters,
as discussed in Section 3.4.1. Additionally, the Emissions Location file includes the particle
deposition method you will identify, for any sources for which you wish to model particle
deposition or depletion, as discussed in Section 3.4.2.
Table 5. Fields in the Emissions Location Input File (Required)
Field
Type
Source
type(s)*
Description
Facility ID
Character
all
An alphanumeric string identifying the facility being
modeled
Source ID**
Character
all
Source ID is a unique alphanumeric character
string up to 8 characters long, with no spaces. It
must match exactly the Source ID in other input
files (e.g., the HAP Emissions file). Note: AERMOD
allows a maximum of 8 characters for the Source
ID; and all Source IDs will be converted to upper
case by AERMOD.
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Field
Type
Source
type(s)*
Description
Coordinate
system
Character
all
Type of coordinates: L = latitude, longitude; U =
UTM. Base all coordinates on the WGS84
geographic system. Note: NAD83 and WGS84 are
identical for most applications, but coordinates
based on NAD27 need to be converted to WGS84
before being used in HEM4.
X-coordinate
Numeric
all
UTM east coordinate, in meters (if coordinate
system = U) or decimal longitude (if system = L) of
the center of point or volume sources, the
southwest corner of area sources, the first vertex of
polygon sources, or the starting point of line and
buoyant line sources.*** For longitudes, 5 decimal
place accuracy is recommended, corresponding to
1-meter accuracy.
Y-coordinate
Numeric
all
UTM north coordinate, in meters (if coordinate
system = U) or decimal latitude (if system = L) of
the center of point or volume sources, the
southwest corner of area sources, the first vertex of
polygon sources, or the starting point of line and
buoyant line sources. *** For latitudes, 5 decimal
place accuracy is recommended, corresponding to
1-meter accuracy.
UTM zone
Character
all
UTM zone where the source is located if the
coordinate system = U; leave this field blank if the
coordinate system = L. If using the UTM
coordinate system, enter the UTM Zone from 1 to
60 followed by the hemisphere (S or N). For
example, 17N. If you do not include a hemisphere,
HEM4/AERMOD will default to N.
Source type
Character
all
Type of source*: P = vertical point, C = capped
point, H = horizontal point, A = area, V = volume,
I = polygon, N = line, B = buoyant line
Length - x
Numeric
A, N
Length in meters in x-dimension direction for area
and line sources. For area source types, the x
direction refers to the direction before the source is
rotated (if it is rotated). For line source types, enter
the width (m), which must be >= 1 meter.
Length -y
Numeric
A
Length in meters in y-dimension direction for area
sources. This is the length in the y direction before
the source is rotated (if it is rotated).
Angle
Numeric
A
Angle of rotation: blank except for area sources.
For area source tvoes, enter the anale of rotation
(from North) between 0 and 90 degrees. (HEM4
defaults to 0 if left blank).
Lateral
Numeric
V
Initial lateral/horizontal dimension (in meters) for
volume sources.
Vertical
Numeric
V, A, 1,
N
Initial vertical dimension (in meters) for volume
sources. Optional for area, polygon & line sources.
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Field
Type
Source
type(s)*
Description
Release height
Numeric
V, A, I,
N, B
Height of release (in meters) for area, volume,
polygon, line and buoyant line sources. Use the
height (top) of the source for area and polygon
sources and the vertical center for volume sources.
Note: that for buoyant line sources, AERMOD
requires a minimum release height of 2 meters.
Stack height Numeric P, C, H Release height above ground (in meters) for all
point source types.
Diameter Numeric P, C, H Diameter of stack (in meters) for all point source
types.
Velocity Numeric P, C, H Velocity at which emissions are released from the
stack (in meters/second) for all point source types.
Temperature Numeric P, C, H Temperature (in Kelvin) at which emissions exit the
stack for all point source types.
Elevation Numeric all Elevation above sea level in meters at the source
location. Use when modeling terrain effects and
user-specified elevations are desired. This field is
optional; HEM4 will calculate if all source
elevations are left blank. Note: if an elevation value
is provided by the user for one or more sources,
any blanks (i.e., non-entries for other source
elevations) will be interpreted by the model as an
elevation of 0 meters; therefore, either enter
elevations for every source or leave all blank.
X-coordinate2 Numeric N, B
Second X (end) coordinate for line and buoyant
line source types. UTM east coordinate, in meters
(if coordinate system = U) or decimal longitude (if
system = L) of the ending point of line and buoyant
line sources.*** For longitudes, 5 decimal place
accuracy is recommended, corresponding to 1-
meter accuracy.
Y-coordinate2 Numeric N, B Second Y (end) coordinate for line and buoyant
line source types. UTM north coordinate, in meters
(if coordinate system = U) or decimal latitude (if
system = L) of the ending point of line and buoyant
line sources.*** For latitudes, 5 decimal place
accuracy is recommended, corresponding to 1-
meter accuracy.
Method Numeric Any but The Method field indicates the type of particle
B deposition AERMOD should use. Enter 1 or leave
blank for Method 1 (which is the default); enter 2
for Method 2. Use Method 1 when greater than 10
percent of the total particulate mass has a diameter
of 10 jjm or larger, or when the particle size
distribution is known. For Method 1, these source-
specific particle size distributions must be provided
in a separate particle data file (described in Section
3.5.3). Method 2 may be used when the particle
size distribution is not well-known and when a
small fraction (less than 10 percent of the mass) is
in particles with a diameter of 10 |jm or larger. The
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Field
Type
Source
type(s)*
Description
particle data required for Method 2 is less specific
than Method 1 but requires that you enter the mass
fraction of fine particles and the mass-mean
particle diameter for the given source in the next
two fields.
Mass Fraction
Numeric
All,
except
B
The Mass Fraction field refers to the fraction of the
particle mass emitted from this source in the fine
particle category (less than 2.5 microns). Leave
this field blank if you are using Method 1. For
Method 2, you should enter a number between 0
and 1 that is the fraction of particles emitted in the
fine category (a blank will be interpreted as a 1, the
default, meaning that all are emitted as fine
particles). For example, if one-half of the emissions
from this source are fine particles (< 2.5 microns),
enter a mass fraction in this field of 0.50.
Particle
Diameter
Numeric
All,
except
B
The Particle Diameter field is the representative
mass-mean aerodynamic particle diameter in
microns emitted from this source when using
Method 2 for particle deposition (a blank is
interpreted as 1 micron, the default). Leave this
field blank for Method 1. For Method 2, enter the
mass-mean particle diameter in microns.
Table Notes:
* Source types for which the parameter is used: all = needed for every source type, A = area, P =
vertical point, C = capped point, H = horizontal point, V = volume, I (capital "i") = polygon, N = line, B =
Buoyant line. Note that currently AERMOD cannot model deposition/depletion for buoyant lines (B), nor
can the FASTALL option be used with buoyant lines. For additional information on these source types,
including what additional fields are needed, see the AERMOD User's Guide at
https://www3.epa.aov/ttn/scram/models/aermod/aermod userauide.pdf
** If you are modeling deposition or depletion and pollutant properties are known to vary, use a separate
record for each pollutant and source. Thus, if you are modeling vapor deposition/depletion, use a unique
Source ID for each pollutant emitted from a given source (e.g., SAMPLE3A for benzene, SAMPLE3B for
1,3-butadiene). The same is true for particulate deposition/depletion if the particulate properties (size
and density distributions) are known and vary by pollutant, not just source. If you are not modeling vapor
deposition/depletion and the same properties are assumed for all particulates emitted from a source,
one Source ID per emission source is sufficient (e.g., SAMPLE3 for all modeled pollutants from the
same source).
*** Start/end coordinates for buoyant line sources generally should be entered in order from West to
East, and from South to North. However, in the case where the buoyant lines are parallel to the Y axis,
the order that the lines should be entered is dependent on which endpoint is entered first, the southern
or northern endpoint of the lines. If the southern endpoint is entered first, the lines should be entered in
the order of the eastern most line to the western most line. If the northern endpoint is entered first, lines
should be ordered west to east. Incorrect ordering of these parameters will result in an AERMOD error
stating "Input buoyant line sources not in correct order"
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Table 6. Sample Emissions Location Input File
Source Locations & Types
Dimensions & Release Height (non-point sources)
Facility ID
Source ID
Coordinate
system
(U = UTM,
L= latitude/
longitude)
(All source
types)
X-coordinate
Longitude
(decimal)
or UTM East
(m)
(All source
types)
Y-coordinate
Latitude
(decimal) or
UTM North
(m)
(All source
types)
UTM
zone
Source type
(P, C, H =
point,
A = area
V= volume
I = polygon
N = line
B = buoyant
line)
Length in x-
direction
(m)
A & N
sources
(width for N
sources)
Length in y-
direction
(m)
A sources
Angle
(degrees)
A sources
Lateral
Dim.
(m)
V sources
Vertical Dim.
(m)
V sources or
optionally
A, I and N
sources
Release
height
(m)
A, V, I, N
and B
sources
continued
Fac2-IL
CT0001
L
-88.257293
41.480164
P [or C or HI
Fac2-IL
CV0001
L
-88.256715
41.481944
A
130
120
45
2
Fac1-NC
SR0001
L
-78.883686
35.900628
V
20
3
10
Fac1-NC
MS0001
L
-78.888792
35.905920
I
5
Fac1-NC
RW0001
L
-78.888430
35.901810
N
20
50
Fac1-NC
RV0001
U
690891
3975044
17
B
40
Point Source Parameters
Buoyant & Line Endpoints
Particle Deposition Method
...continued
from
above
(Source type
indicated for
reference)
Stack height
(m)
P, C, orH
sources
Stack Diameter
(m)
P, C, orH
sources
Exit Velocity
(m/s)
P, C, orH
sources
Exit
Temperature
(K)
P, C, orH
sources
Elevation
(m)
HEM4 will
calculate if
blank for
every
source
X-coord.2
Longitude
(decimal) or
UTM East
(m)
B & N
sources
Y-coord.2
Latitude
(decimal) or
UTM North
(m)
B & N
sources
Method
(1 or 2;
defaults to 1)
All sources,
except B
Mass
Fraction
(decimal > 0
and < 1 for
Method 2
only)
All sources,
except B
Particle
Diameter
(microns, for
Method 2
only)
All sources,
except B
...(P, C or H)
50
2.8
21.83
322
2
0.04
0.0006
...(A)
¦ ¦¦(V)
...(I)
...(N)
-78.886303
35.902183
...(B)
691291
3975044
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3.4.1 Source Types and Parameter Requirements
Generally, the Emissions Location file should include one record for each individual source
(e.g., stack/point source, area source, line source, buoyant line source) to be modeled, at each
facility. For certain modeling situations, more than one record per source is recommended.3
This record provides information on the location, size, height, and configuration for each source.
You must enter every Facility ID to be modeled in column A of the Emissions Location file. Enter
each Source ID in column B, taking care to match each named Source ID with a corresponding
Source ID in the HAP Emissions file, described in Section 3.3.
Source Locations: In column C "Coordinate system", you can enter source locations as UTM
coordinates, or as latitude and longitude (which HEM4 will convert to UTM coordinates for use
in AERMOD). Complete the coordinate system field for each source record and specify which
coordinates you are entering. Enter "U" for UTM or "L" for latitude and longitude. If using UTM
coordinates, specify the UTM zone (in each emission source record). Enter the location
coordinates for each source in column D "X coordinate, Longitude (decimal) or UTM East (m)"
and in column E "Y coordinate, Latitude (decimal) or UTM North (m)". (The endpoints for line
and buoyant line source types, discussed further below, will be entered is columns S and T.) If
you are using longitudes and latitudes, 5-decimal places are recommended which corresponds
to an accuracy of roughly 1 meter. See Table 5 above for further specifications for these fields.
You must base all coordinates on the WGS84 geographic system. As noted in Section 3.1,
NAD83 and WGS84 are identical for most applications, so no conversion is needed if using
coordinates based on NAD83. However, if coordinates are based on NAD27, they would need
to be converted to WGS84 before being used in HEM4. There are various commercial computer
programs available that can perform this conversion.
Source Types: Use the source type field in column G to indicate whether the emission source is
a vertical non-capped point source (P), a capped point source (C), a horizontal point source (H),
an area source (A), a volume source (V), a polygon source (I, for upper case "i"), a line source
(N), or a buoyant line source (B)4. For additional information on these source types, including
assumptions used by AERMOD to model their emissions as well as the additional parameters
needed for each, you should consult the AERMOD User's Guide at
https://www3.epa.gov/ttn/scram/models/aermod/aermod userquide.pdf.
Point Sources - Vertical stack, Horizontal stack, and Capped stack: Point source types include
vertical stacks (P), horizontal stacks (H) and capped stacks (C) source types. These point
sources require you to specify the stack height (in meters in column N), the stack diameter (in
meters in column O), the exit velocity (in meters/second in column P), and the exit/release
temperature (in Kelvin in column Q) for the pollutant plume. Although capped and horizontal
3 If modeling deposition or depletion (described in Section 3.2.6) at a facility, and pollutant properties are
known to vary, we recommend you include a separate Source ID record for each pollutant and source—
that is, a unique Source ID—for each pollutant being emitted from the same source. This is generally
recommended for modeling of vapor deposition/depletion and for modeling of particulate deposition/
depletion if the size or density distributions are known for each pollutant (HAPJ and vary for each
pollutant. If you are not modeling deposition/depletion of vapor phase pollutants, and the same particulate
properties are assumed for all pollutants being emitted from a given source, one record per source in the
emissions location input file is sufficient.
4 Note that the current AERMOD version 19191 cannot model deposition or depletion for buoyant lines
(B), nor can the FASTALL option in the Facility List Options file be used with buoyant lines.
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stacks (C and H, respectively) require the same user-specified parameters as vertical stacks
(P), AERMOD models these point sources differently than vertical stacks (EPA 2019a, EPA
2019b).
Non-Point Sources: Columns H through N in the Emissions Location file pertain to area (A)
sources, volume (V) sources, polygon (I for capital "i) sources, line (N) sources, and buoyant
line (B) sources. Table 5 above provides guidance on what you should provide in each of these
fields. Fugitive emissions are often modeled as rectangular area (A) sources. A conveyor belt, in
which release temperature is assumed to be ambient and release velocity zero or negligible,
may be simulated as volume (V) sources. A polygon (I) can be used to represent a complex
(non-rectangular) area source with many vertices. A polygon (I) may also be used to represent
an entire U.S. Census tract from which a source is modeled as a uniform emission (e.g., for
mobile sources). Polygon source types require a Polygon Vertex file as an additional input, as
discussed in Section 3.5.1. Line source (N) types can be used to represent roadways and
airport runways and may be used instead of similarly shaped area sources.
Unlike point source types (P, C, or H), area (A), volume (V), polygon (I) and line (N) source
types in AERMOD all assume ambient pollutant release temperatures and zero or negligible
pollutant release/exit velocities. Buoyant line sources (B), on the other hand, are useful in
simulating continuous vents along a roofline where the emissions, similar to point sources (P, C
or H), are released at elevated (non-ambient) temperature and with a non-zero release velocity.
However, unlike tall stack sources where the plume can move in all directions without
impediment, buoyant line source types simulate pollutants emitted close to a building's roof
where vertical wind shear and building downwash effects become important. Buoyant line (B)
source types require a Buoyant Line Parameters file as an additional input, as discussed in
Section 3.5.2.These non-point source types are discussed in more detail below.
Area Sources: An area source (A) type represents a rectangular area from which emissions are
released at ambient temperature and with zero or negligible velocity (e.g., fugitive emissions
from a building or tank farm). In AERMOD, area sources can be at ground level, or at a height
above ground level. Specifying a release height (in column M) is optional and defaults to 0. The
default orientation for area sources is with one axis in the north-south direction, but you can
rotate these sources using the "angle" parameter (in column J), which specifies the rotation of
the source from north (in the clockwise direction), to better fit the orientation of the source you
are modeling. The X and Y coordinates you choose (in columns D and E) should reflect the
southwest corner of the area source. The length in the X direction you enter (in column H)
should reflect the length of the area source in the easterly direction, or in the southeasterly
direction if the source is rotated. The length in the Y direction you enter (in column I) should
reflect the length of the area source in the northerly direction, or the northeasterly direction if the
source is rotated. Unlike AERMOD, where 360-degree rotation is allowed, the angle parameter
for HEM4 area sources must be between 0 and 90 degrees. You can use this angle to represent
any possible orientation by switching the X and Y lengths (shown in Figure 3). You can also
optionally enter an initial vertical dimension of the area source (in column L).
Volume Sources: Volume source (V) types - such as multiple vents and conveyor belts - are
specified by a lateral /horizontal dimension (you enter in column K), a vertical dimension (you
enter in column L), and a release height (you enter in column M). Emissions from a volume
source are assumed to be released at ambient temperature and with zero or negligible velocity.
Both the release height (in column M) and the source location coordinates (in columns D and E)
should reflect the center of the source.
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Polygon Sources: You can create a polygon source (I, for capital "i") type to represent a polygon
with 3 sides or many more (up to 20 sides). This source type provides considerable flexibility in
specifying the shape of an area source. You can use a polygon source type to reflect U.S.
Census tract boundaries, for example, when modeling mobile source emissions provided at the
tract level. An associated polygon vertex input file is required when modeling polygon source
types. Section 3.5.1 discusses this in more detail. The shape of the polygon source, as defined
in the Polygon Vertex Input file, is determined by a list of X and Y coordinates representing the
vertices of the polygon. You can order these X and Y coordinates in either a clockwise or
counterclockwise direction. However, the first coordinates entered in the Polygon Vertex Input
file must match the coordinates entered in the emissions location file (in columns D and E) as
the location of the first vertex of the polygon. You can also optionally enter an initial vertical
dimension of the polygon (in column L). Emissions from polygon source types are assumed to
be released at ambient temperature and zero or negligible velocity.
Line Sources: The line source (N) type allows you to specify long, narrow sources, such as
roadways or airport runways. You must enter a start-point (in columns D and E) and end-point
of the line (in columns S and T), as well as the width of the line (a value equal to or greater than
1 meter that you enter in column H). Optionally, you can also specify an initial vertical dimension
(in column L). In this way, the line source can be used as an alternative to a rectangular area
source (A). [Note: According to the AERMOD User's Guide (EPA 2019a, p.3-100) the line
source type utilizes the same routines as the area source type and will give identical results,
given the same inputs.] Like area, volume and polygon source types, emissions from line source
types are assumed to be released at ambient temperature and zero or negligible velocity.
Buoyant Line Sources: Like the line source, for the buoyant line source (B), you must enter the
starting coordinates (in columns D and E) and the end coordinates (in columns S and T).5 The
buoyant line source (B) type was first developed to simulate the transport and diffusion of
emissions from aluminum reduction plants in which some emissions from the reduction process
escape through continuous (rooftop) ridge ventilators (ERT 1980). In general, the buoyant line
source can be used to characterize emissions from a continuous roof vent that spans a portion
or the entire building. Emissions from such buoyant line sources result in enhanced plume rise
(especially from multiple rows of closely spaced emission lines) and the plume is subject to
vertical wind shear and building downwash effects. This source type incorporates an average
buoyancy parameter (in meters4/seconds3) as well as the average building dimensions (in
meters) of the building(s) on which the buoyant line source is located. You must provide HEM4
with these inputs for your buoyant line source type in a Buoyant Line Parameters Input file, as
discussed in Section 3.5.2. It should be noted that AERMOD 19191 requires a minimum release
height (in your Emissions Location file) of 2 meters and a minimum wind speed (determined
from your met station data) of 1 meter-per-second for buoyant line sources. (If you enter a
release height less than 2 meters, AERMOD will change it to 2 meters.) Also, as noted
previously, AERMOD 19191 cannot model deposition or depletion for buoyant lines, nor can the
FASTALL option in the Facility List Options file be used with buoyant lines. For more detailed
information regarding the necessary inputs for the buoyant line source type, see the AERMOD
5 You may wish to use a series of buoyant lines to represent multiple roof vent lines. AERMOD requires a
strict ordering of these lines in order to run properly. The start/end coordinates for buoyant line sources
generally should be entered in order from West to East, and from South to North. However, in the case
where the buoyant lines are parallel to the Y axis, the order that the lines should be entered is dependent
on which endpoint is entered first, the southern or northern endpoint of the lines. If the southern endpoint
is entered first, the lines should be entered in the order of the eastern most line to the western most line.
If the northern endpoint is entered first, lines should be ordered west to east.
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User's Guide (EPA 2019a), as well as documentation for the buoyant line and point source
(BLP) dispersion model (ERT 1980).
Elevation: If you wish to consider terrain impacts in your modeling, you can specify the elevation
above sea level in meters for each emission source. Enter elevations (in column R) for every
source or for no sources; do not enter a partial list, because in that case blanks/non-entries will
be interpreted by the model as a zero (0) elevation if a value is entered for one or more other
sources. If you leave the elevation field blank for all sources, and if you chose to model
elevations in the Facility List Options file, then HEM4 will estimate an elevation for the emission
sources based on the elevations of nearby U.S. Census blocks or alternate receptors. Note that
if you chose to not model elevations in your Facility List Options file, then no elevations will be
considered in the model run including for sources in the Emissions Location file.
It should be noted that HEM4 will model area, volume, polygon, line, and buoyant line sources
as flat surfaces, which can result in strangely located (underground) impacts if the source is
located, for example, on a hillside with varying elevations. To avoid this, either opt to model with
no elevations in the Facility List Options file, or break-up the source into smaller pieces with
uniform elevations.
It should also be noted that "release height" (in column M) is different than elevation and
indicates the height above the ground elevation where emissions are released (in which the
ground is set to an elevation above sea level, or not, as reported in the preceding paragraphs
discussing the elevation field). For point sources, fill in the "stack height" field (in column N) to
designate the release height (for vertical stack, horizontal stack and capped stack source
types). For all other source types (area, volume, polygon, line and buoyant line), you should fill
in the "release height" (in column M) with the source's height above the ground (in meters). If
you leave this field blank, HEM4 will assume the release height is zero (0), meaning at ground
level.
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X-dim= L
Y-dim = S
Angle = 45°
Figure 3. Example Orientations of Area Emission Sources for the HEM4 Model
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3.4.2 Particle Deposition Method
Columns U (Method), V (Massfrac), and W (Partdiam) of the Emissions Location file should only
be filled in if you wish to model particle deposition or depletion using Method 2. If you do not
wish to model particle deposition/depletion or if you wish to use AERMOD's Method 1 to model
particle deposition/ depletion, then leave these fields blank for those sources.
Particle Deposition/Depletion Method: The Method field (in column U) indicates to HEM4 the
type of particle deposition AERMOD should use. As noted above, you should enter 1 or leave
this field blank for Method 1 (which is the default). Method 1 should be used when a significant
fraction (greater than about 10 percent) of the total particulate mass has a diameter of 10 |jm or
larger, or when the particle size distribution is known. The particle size distribution must be
known reasonably well in order to use Method 1 and these source-specific particle size
distributions must be provided in a separate Particle Data file, as discussed in Section 3.5.3.
You should also leave this field (column U) blank if you are not modeling particle deposition/
depletion at all. Enter 2 in this field if you wish to model particle deposition or depletion for the
given source using AERMOD's Method 2. Method 2 may be used when the particle size
distribution is not well known and when a small fraction (less than 10 percent of the mass) is in
particles with a diameter of 10 |jm or larger. The particle data required for Method 2 is less
detailed than Method 1 but does require that you enter the mass fraction of fine particles and
the mass-mean particle diameter for the given source in the next two fields.
Mass Fraction for Method 2: The Mass Fraction field (in column V) refers to the fraction of the
particle mass emitted from this source in the fine particle category (less than 2.5 microns).
Leave this field blank if you are using Method 1, or if you are not modeling particle deposition/
depletion at all. For Method 2, you should enter a number between 0 and 1 that is the fraction of
particles emitted in the fine category (a blank will be interpreted by the model as a 1, the default,
meaning that all are emitted as fine particles). For example, if one-half of the emissions from
this source are fine particles (< 2.5 microns), enter a mass fraction in this field of 0.50.
Particle Diameter for Method 2: The Particle Diameter field (in column W) is the representative
mass-mean aerodynamic particle diameter in microns emitted from this source when using
Method 2 for particle deposition (a blank is interpreted by the model as 1 micron, the default).
Leave this field blank for Method 1, or if you are not modeling particle deposition/depletion at all.
For Method 2, enter the mass-mean particle diameter in microns.
3.5 Additional Input Files
In addition to the three required input files (Facility List Option, HAP Emissions, and Emissions
Location) discussed in Sections 3.2, 3.3 and 3.4, other files may be required for your modeling
run depending on (a) what modeling options you chose in the Facility List Options file, (b) what
source types you are modeling in your Emissions Location file, (c) what kinds of receptors you
are modeling with, and/or (d) what changes you may wish to make to HEM4's underlying
databases and resource files. These additional input files are discussed in the next sections.
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3.5.1 Polygon Vertex Input File for Modeling Polygon Emission Sources
If your Emissions Location input file contains one or more polygons (source type "I"), then HEM4
will prompt you for a Polygon Vertex file. This file provides HEM4 with the locations of the
polygon vertices. Polygons are useful for complex source configurations at a facility, and for
modeling U.S. Census tracts as sources (e.g., for mobile source emissions modeled uniformly
across a tract).
Include a separate record for each vertex of the polygon in the Polygon Vertex file. A polygon
may have any number of vertices (> 3 and < 20). Each record must include information for one
vertex of the polygon. As noted in Section 3.4.1, you can order the X and Y vertex coordinates
in either a clockwise or counterclockwise direction. The first and last vertex must have identical
coordinates, and these coordinates must match the coordinates listed as the location of the first
vertex of the polygon source in your Emissions Location file. The first record for each polygon
source must also include the number of vertices for the polygon and the total area of the
polygon, in meters squared. You can enter coordinates as UTM coordinates, or as longitudes
and latitudes. If using UTM coordinates, you must specify the UTM zone. Base all coordinates
on the WGS84 reference system.
Optionally, you can assign an ID (name) to the polygon. This may be useful, for example, if you
are using the polygon to model a U.S. Census tract. In this case, you may wish to use the U.S.
Census tract ID as the polygon ID and enter it in the last column of the Polygon Vertex file.
Tables 7 and 8 give the format guidelines for the Polygon Vertex file, and a sample Polygon
Vertex file, respectively. A template input file is provided in the HEM4 Inputs folder named
HEM4_polygon_ vertex, xlsx.
Table 7. Format Guidelines for the Polygon Vertex File
Field
Type
Description
Facility ID
Source ID
Coordinate
system
X-coordinate
Y-coordinate
Character An alphanumeric character identifying the facility being
modeled
Character An alphanumeric character string up to 8 characters long,
with no spaces. The Source ID must be listed as polygon
(Type = I) source types in the Emissions Location file. Note:
AERMOD allows a maximum of 8 characters for the Source
ID; and all Source IDs will be converted to upper case by
AERMOD.
Character Type coordinates: L = longitude, latitude; U = UTM
[WGS84],
Numeric UTM east coordinate, in meters (if Coordinate System = U)
or decimal longitude (if System = L). For longitudes, 5
decimal place accuracy is recommended, corresponding to
1-meter accuracy.
Numeric UTM north coordinate, in meters (if Coordinate System = U)
or decimal latitude (if System = L). For latitudes, 5 decimal
place accuracy is recommended, corresponding to 1-meter
accuracy.
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Field Type Description
UTM zone
Numeric
If using the UTM coordinate system (U), enter the UTM
Zone from 1 to 60 followed by the hemisphere (S or N). For
example, 17N (default hemisphere is N if not specified). If
using longitudes/latitudes, leave this cell blank.
Num of Vertices
Numeric
Number of vertices in the polygon. This number must be 3
or greater. The upper limit is 20.
Area
Numeric
Size of area within polygon, in meters squared.
Polygon ID
Character
Optional ID to indicate the name of the polygon (e.g., a
U.S. Census tract is sometimes modeled as a polygon and
the polygon ID may be the U.S. Census tract ID).
Table 8. Sample Polygon Vertex File
Facility ID
Source ID
Coordinate
system
(U = UTM, L
= latitude,
longitude)
Longitude
(decimal) or
UTM East
(m)
Latitude
(decimal) or
UTM North
(m)
UTM
zone
Num of
Vertices
(> 3 and
<20)
Area
(m2)
Polygon
ID
(optional)
Fac1-TX
SAMPLE4
L
-95.3586
29.7674
9
402939.4
Fac1-TX
SAMPLE4
L
-95.3524
29.7685
0
Fac1-TX
SAMPLE4
L
-95.3515
29.7663
0
Fac1-TX
SAMPLE4
L
-95.3533
29.7654
0
Fac1-TX
SAMPLE4
L
-95.3533
29.7622
0
Fac1-TX
SAMPLE4
L
-95.3574
29.7634
0
Fac1-TX
SAMPLE4
L
-95.3582
29.7651
0
Fac1-TX
SAMPLE4
L
-95.3575
29.7661
0
Fac1-TX
SAMPLE4
L
-95.3586
29.7674
0
Fac1-TX
SAMPLE5
L
-95.3512
29.7688
11
710176.8
Fac1-TX
SAMPLE5
L
-95.3524
29.7685
0
Fac1-TX
SAMPLE5
L
-95.3515
29.7663
0
Fac1-TX
SAMPLE5
L
-95.3509
29.7653
0
Fac1-TX
SAMPLE5
L
-95.3533
29.7654
0
Fac1-TX
SAMPLE5
L
-95.3533
29.7622
0
Fac1-TX
SAMPLE5
L
-95.3574
29.7634
0
Fac1-TX
SAMPLE5
L
-95.3582
29.7651
0
Fac1-TX
SAMPLE5
L
-95.3575
29.7661
0
Fac1-TX
SAMPLE5
L
-95.3586
29.7674
0
Fac1-TX
SAMPLE5
L
-95.3512
29.7688
0
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3.5.2 Buoyant Line Parameter Input File for Modeling Buoyant Line Sources
If your Emissions Location input file contains one or more buoyant line sources (source type
"B"), then HEM4 will prompt you for a Buoyant Line Parameter file. Buoyant line source types
are useful in simulating continuous rooftop vents in which emissions are released at non-
ambient (elevated) temperature and non-negligible velocity, as discussed in Section 3.4.1.
Because building downwash effects are especially important with buoyant line source types, the
Buoyant Line Parameter file must provide HEM4 with the length, width, and height of the
building(s) on which the buoyant line source type (e.g., rooftop vent) sits. In addition, the file
must contain the width of the buoyant line source(s), the distance between the buildings (zero
for a solitary buoyant line), and the buoyancy parameter for the buoyant line source(s).
The buoyancy parameter of a line source is calculated from an equation based on the line
source length (m) and width (m), the exit/release velocity (m/s), the exit/release temperature (K),
the ambient temperature (K) and the acceleration due to gravity (9.81 m/s2), as presented in
Equation 2-47 on page 2-37 of the Buoyant Line and Point Source Dispersion Model User's
Guide (ERT 1980).6 These parameters should be average values for the array of buoyant
line sources, if multiple parallel buoyant line sources are present (EPA 2019a). You must
provide the following parameters in the Buoyant Line Parameter File:
Average Building Length (in meters);
Average Building Height (in meters);
Average Building Width (in meters);
Average Line Source Width, of the individual lines (in meters);
Average Building Separation, between the individual lines (in meters); and
Average Buoyancy Parameter (in meters4/seconds3)
Note: The current AERMOD version 19191 allows modeling only a single buoyant line source
(comprised of one or multiple lines) per modeling run, so HEM4 allows a single buoyant line
source per facility. Multiple model runs are recommended to adequately model the emissions
from multiple non-parallel buoyant line sources at a given facility. (See the AERMOD User's
Guide page 3-85 for further information; EPA 2019a.)
Tables 9 and 10 provide the format guidelines for the Buoyant Line Parameter input file and a
sample input file, respectively. A template input file is provided in the HEM4 Inputs folder named
HEM4_buoyant_line_param.xlsx. See also the resources shown in footnote 6 below for helpful
guidance in setting up a buoyant line source.
6 In addition, diagrams detailing buoyant line equation parameters and sample calculations are available
in: Source Characterizations: Buoyant Line Sources, Missouri Department of Natural Resources Air
Pollution Control Program, http://dnr.mo.qov/env/apcp/docs/buovantlinesources10-24-12.pdf on website
http://dnr.mo.gov/env/apcp/permitmodelinq/sourcecharacterizations.htm. November 12, 2013.
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Table 9. Format Guidelines for the Buoyant Line Parameter Input File
Field
Type
Description
Facility ID
Character
An alphanumeric character string identifying the facility being
modeled
Average Building
Length
Numeric
The average length of the building or buildings on which the
parallel buoyant line source types are located (in meters)
Average Building
Height
Numeric
The average height of the building or buildings on which the
parallel buoyant line source types are located (in meters)
Average Building
Width
Numeric
The average width of the building or buildings on which the parallel
buoyant line source types are located (in meters)
Average Line Source
Width
Numeric
The average width of the individual buoyant line source types (in
meters)
Average Building
Separation Distance
Numeric
The average building separation distance between the (parallel)
individual buoyant lines (in meters)
Average Buoyancy
Parameter
Numeric
The average buoyancy parameter for the buoyant line emission
plumes (in meters4/seconds3); See BLP Dispersion Model
documentation (ERT 1980).
Table 10. Sample Buoyant Line Parameter Input File
Facility ID
Avg Building
Length(m)
Avg Building
Height (m)
Avg Building
Width (m)
Avg Line
Source Width
(m)
Avg Building
Separation (m)
Avg
Buoyancy
(m4/s3)
Fac1-NC 454.3 16.76 40 5.73 40.95 3335.49
3.5.3 Particle Data Input File for Modeling Particulate Deposition and Depletion
AERMOD can implement dry and wet deposition and plume depletion of both particulate and
vapor emissions (EPA 2019a). This section describes the input file needed for modeling
particulate deposition and/or particulate depletion.
If you indicated in your Facility List Options file that your run will model deposition or depletion of
particulate emissions AND you chose (in your Emissions Location file) to use Method 1 for
particle deposition for one or more sources, then you must provide HEM4 with a separate
Particle Data input file describing the particle size distribution. In this file, include a separate
record for each particle size range emitted by each emission source, for which HEM4/AERMOD
will model particle deposition/depletion using Method 1. Each record must include an average
particle diameter for the size range, the percentage that the size range represents in terms of
the total mass of particulate matter from the given emission source, and the average density of
particles in the size range. The mass percentages must total to 100 for each emission source
(for which you are modeling particle deposition/depletion using Method 1). Tables 11 and 12
provide format guidelines for the Particle Data input file and a sample input file, respectively. A
template input file is provided in the HEM4 Inputs folder named HEM4_particle_data.xlsx.
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Table 11. Format Guidelines for the Particle Data Input File
Field
Type
Description
Facility ID
Character
An alphanumeric character string identifying
the facility being modeled
Source ID
Character
The Source ID is a unique alphanumeric
character string up to 8 characters long with
no spaces. It must match a Source ID in the
HAP Emissions and Emissions Location file.
Note: AERMOD allows a maximum of 8
characters for the Source ID; and all Source
IDs will be converted to upper case by
AERMOD.
Particle diameter
Numeric
The average diameter (in |am) for the particle
size range covered by this record.
Mass fraction
Numeric
The percentage (by mass) of particulate
matter in this size range. Must add up to
100% for each Source ID.
Particle density
Numeric
The average density of the particles in this
size range (in g/cm3).
Table 12. Sample Particle Data Input File
Particle diameter
Mass fraction
Particle density
Facility ID
Source ID
(|j.m)
(%)
(g/cm3)
Fac1-TX
SAMPLE1
0.50
72.0
1.00
Fac1-TX
SAMPLE1
1.50
8.0
0.75
Fac1-TX
SAMPLE1
2.50
4.0
0.50
Fac1-TX
SAMPLE1
4.00
4.0
1.00
Fac1-TX
SAMPLE1
10.00
12.0
0.35
Fac1-TX
SAMPLE2
0.50
60.0
1.00
Fac1-TX
SAMPLE2
1.50
8.0
0.80
Fac1-TX
SAMPLE2
2.50
4.0
0.15
Fac1-TX
SAMPLE2
4.00
4.0
0.90
Fac1-TX
SAMPLE2
10.00
24.0
1.00
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3.5.4 Input Files Required for Modeling Vapor Deposition and Depletion
As described in Section 3.2.6, AERMOD can model dry and wet deposition of both particulate
and vapor (gaseous) emissions and the resulting plume depletion (EPA 2019a). This section
describes the inputs required for modeling vapor deposition and vapor depletion.
Gas Parameter File for Modeling Deposition/Depletion of Vapor Pollutants: To model wet and/or
dry deposition or depletion of vapor pollutants, you must provide HEM4 with the necessary
information to evaluate the scavenging of these pollutants in precipitation and deposition on
vegetation and other surfaces. When modeling any type of vapor deposition or depletion (wet,
dry, or both wet and dry), HEM4 accesses a gas parameter file containing pollutant properties
related to gaseous deposition. Note: The Gas Parameter file is included in HEM4's resources
folder, which is included in the model's installation files; therefore, HEM4 will NOT prompt you
for this file. (The default file pathway is "HEM4\resources\Gas_Param.xlsx".) This file includes
the following four parameters for each pollutant:
• diffusivity in air (Da, in cm2/sec);
• diffusivity in water (Dw, in cm2/sec);
• cuticular resistance to uptake by lipids for individual leaves (rci, in sec/cm); and
• Henry's Law coefficient (H, in Pascal-m3/mol).
Values for these parameters are provided in the Gas Parameter file for 129 pollutants, based on
a study by Argonne National Laboratories (Wesely 2002) and a more recent paper which
compiles Henry's Law coefficients from numerous other sources (Sander 2015). When modeling
a vapor/gaseous pollutant that is not listed in the Gas_Param file, HEM4 uses the following
default parameters:
Da = 0.07 cm2/sec, Dw = 0.7 cm2/sec, rci = 2,000 sec/cm, H = 5.0 Pascal-m3/mol.
These defaults are based on the logarithmic average of parameters for the 129 pollutant
species currently contained in the Gas Parameter file, using one significant figure accuracy. It
should be emphasized that these defaults are averages taken over ranges sometimes in excess
of ten orders of magnitude and may not be appropriate for the pollutants of interest to you.
You can calculate parameters for additional pollutants and add these to the Gas_Param.xlsx file
or revise the values in the Gas_Param file, as appropriate. For example, you may wish to
estimate parameters for pollutants of interest to you by calculating averages based on the
values in the Gas Parameter file for smaller groups of pollutants in the same chemical family
and of similar molecular weight to your pollutant of interest (e.g., polycyclic aromatic
hydrocarbons, PAHs).
Parameter values for additional pollutant species are available in the literature cited here
(Wesely 2002 and Sander 2015), as well as in EPA's Human Health Risk Assessment Protocol
for Hazardous Waste Combustion Facilities Final Report (dated September 2005 and available
at https://epa-prqs.ornl.gov/radionuclides/2005 HHRAP.pdf). Wesely 2002 also describes a
methodology for estimating cuticular resistance, which is less commonly cited in the literature.
It should be noted that the Gas Parameter Input File is needed only when modeling deposition
(wet, dry, or both wet and dry) of vapor/gaseous pollutants. It is not required to model deposition
(of any type) of particulate emissions.
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Land Use and Month-to-Seasons Input Files for Modeling Dry Deposition of Vapor Pollutants
If you chose to model dry (or wet and dry) vapor deposition or dry (or wet and dry) vapor
depletion in your Facility List Options file, then HEM4 will prompt you to provide two additional
input files described in this section. To quantify dry deposition of vapor (gaseous) pollutants to
vegetation, AERMOD requires information on the land use and vegetation surrounding the
emission source. You must provide this information in Excel™ spreadsheets called the land use
and month-to-seasons input files.
Land Use Input File: In the land use input file, you must enter a code characterizing the average
land use for 36 directions from the emission sources (which emit vapor pollutants at a facility
you chose to model dry deposition or dry depletion at), at increments of 10 degrees compass
bearing. Table 13 gives the format guidelines for the land use input file, and Table 14 shows a
sample land use input file. A template input file is provided in the HEM4 Inputs folder named
HEM4_landuse.xlsx.
Month-to-Seasons Input File: You must also provide HEM4 the month-to-seasons input file
containing further information on the typical stage of vegetation in the modeled region during
each month of the year. As the format guidelines in Table 15 show, this file associates each
month with a season code, describing the stage of vegetation ranging from lush midsummer
vegetation to winter snow coverage. Table 16 shows a sample input table for the month-to-
seasons input file. A template input file is provided in the HEM4 Inputs folder named
HEM4_month-to-seasons.xlsx.
Again, it should be noted that the Land Use and Month-to-Seasons input files are required
only if you choose to model dry (or wet and dry) vapor deposition or dry (or wet and dry)
vapor depletion in your Facility List Options file. These files are not required for modeling wet
deposition or depletion of vapor emissions, nor are they required for modeling any kind of (wet
or dry) deposition/depletion of particulate emissions.
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Table 13. Format Guidelines for Land Use Input File
Field Type Description
Facility ID
Character
An alphanumeric character string identifying the facility
being modeled
Direction Sector 1
Numeric
Land use code (value = 1-9) for the modeling domain at a
compass bearing of 10 degrees from the emission release
point:
1
Urban land, no vegetation
2
Agricultural land
3
Rangeland
4
Forest
5
Suburban areas, grassy
6
Suburban areas, forested
7
Bodies of water
8
Barren land, mostly desert
9
Non-forested wetlands
Direction Sector n
Numeric
Land use code at a bearing of n * 10
(n = 2 thru 35)
Direction Sector 36
Numeric
Land use code at a bearing of 360 degrees
Table 14. Sample Input File for Land Use
D01
D02
D03
D04
D05
D36
Facility ID
(10°)
(20°)
(30°)
(40°)
(50°)
(360°)
Fac1-NC
1
9
5
5
6
1
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Field
Table 15. Format Guidelines for Month-to-Seasons Input File
Type
Description
Facility ID
January
November
December
Character
An alphanumeric character string identifying the facility being
modeled
Numeric Seasonal category (value = 1-5) for month 1 (January):
1 Midsummer with lush vegetation
2 Autumn with unharvested crop land
3 Late autumn after frost and harvest, or with no snow
4 Winter with snow on ground
5 Transitional spring with partial green coverage or
short annuals
Numeric Seasonal category (value = 1-5) for month 11
Numeric Seasonal category (value = 1-5) for month 12
Table 16. Sample Month-to-Seasons Input File
Facility ID
M01
M02
M03
M04
M05
M12
Fac1-NC
4
4
5
5
1
4
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3.5.5 Building Dimensions Input File for Modeling Building Downwash
If you chose to model building downwash in your Facilities List Options file for one or more
facilities, then HEM4 will prompt you for a Building Dimensions input file, which is required by
AERMOD to model building downwash effects. The following parameters are required in the
building dimensions input file:
• building height (keyword=BUILDHGT);
• projected building width perpendicular to the direction of flow (keyword=BUILDWID);
• building length in the direction of flow (keyword=BUILDLEN);
• distance from the stack to the center of the upwind face of the building parallel to the
direction of flow (keyword=XBADJ); and
• distance from the stack to the center of the upwind face of the building perpendicular to
the direction of flow (keyword=YBADJ).
You must provide these parameters for 36 wind directions, at increments of 10 degrees
(compass bearing). Calculate these parameters using the EPA's Building Profile Input Program
for PRIME (BPIPPRM). You can download the BPIPPRM model code and documentation from
the EPA's Support Center for Regulatory Atmospheric Modeling (SCRAM) website at
https://www.epa.gov/scram/air-quality-dispersion-modeling-related-model-support-
proqrams#bpipprm.
Table 17 gives the format guidelines for the Excel™ Building Dimensions input file, and Table
18 shows a sample Excel™ Building Dimensions file. A template input file is provided in the
HEM4 Inputs folder named HEM4_bldg_dimensions.xlsx.
Table 17. Format Guidelines for the Building Dimensions File
Field (notes) Type Description
An alphanumeric character string identifying the facility
being modeled
"SO" should always be entered in this field because it
represents a source pathway record, which corresponds
to the code used in the AERMOD input file.
Specifies which values are given in this record (row), as
follows:
BUILDHGT = building height
BUILDWID = projected building width perpendicular
to the direction of flow
BUILDLEN = building length in the direction of flow
XBADJ = along-flow distance from the stack to the
upwind face of the building
YBADJ = across-flow distance from the stack to the
upwind face of the building
Facility ID - Character
Pathway ~ Character
Keyword ~ Character
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Field (notes) Type
Description
Source ID
Value 1
Value 2
Value n
Value 36
(n = 1)
(n = 2)
(n = 3
to 35)
(n = 36)
Character The Source ID is a unique alphanumeric character
string up to 8 characters long with no spaces. It must
match a Source ID in the HAP Emissions and
Emissions Location file. Note: AERMOD allows a
maximum of 8 characters for the Source ID; and all
Source IDs will be converted to upper case by
AERMOD.
Numeric Dimension or distance (depending on the Keyword
parameter) viewed from a compass bearing of 10
degrees from north (clockwise direction) of the emission
release point.
Numeric Dimension or distance of the building at a bearing of 20
degrees.
Numeric Dimension or distance of the building at a bearing of [n
x 10] degrees.
Numeric Dimension or distance of the building at a bearing of
360 degrees.
Table 18. Sample Building Dimensions Input File
Facility
ID
Pathway
Keyword
Source ID
Value 1
(10°)
Value 2
(20°)
Value 3
(30°)
Value 36
(360°)
Fac1-NC
SO
BUILDHGT
SAMPLE1
26.00
26.00
26.00
26.00
Fac1-NC
SO
BUILDWID
SAMPLE1
111.07
107.16
100.00
111.60
Fac1-NC
so
BUILDLEN
SAMPLE1
128.17
115.85
100.00
136.60
Fac1-NC
so
XBADJ
SAMPLE1
-93.97
-98.48
-100.00
-86.60
Fac1-NC
so
YBADJ
SAMPLE1
55.54
53.58
50.00
55.80
3.5.6 User-Defined Receptors File
If you opted to include user receptors in your Facility List Options file for one or more facilities,
then HEM4 will prompt you for a User Receptors file. HEM4 will automatically calculate ambient
concentrations and resultant cancer risks and noncancer hazard indices for all U.S. Census
blocks or for all alternate receptors within the defined modeling domain. You can also specify
additional receptor sites to model, such as schools, ambient monitors, residential areas other
than the census block's centroid, or facility boundaries.
Specify the locations of these sites in the User Receptors input file, using a separate record to
indicate the location of each user receptor. You must enter locations of each user receptor using
UTM coordinates, or in longitude and latitude. If using UTM coordinates, you must specify the
UTM zone. Base all coordinates on the WGS84 reference system.
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If you chose in your Facility List Options file to include elevations in your model run, you can
enter the elevation above sea level for each user receptor. If you leave this field blank in the
User Receptors input file (but did choose to include elevations in your model run in your Facility
List Options file), then HEM4 will assume an elevation for each user receptor based on the
surrounding U.S. Census block elevations or alternate receptor elevations. Specifically, if you
leave the elevation field empty in the User Receptor file for every receptor, then HEM4 will use
the elevation of the closest U.S. Census block or alternate receptor (if not using U.S. Census
blocks in your modeling run). Note: You should enter an elevation for every user receptor, or
leave the elevation field blank for all, to allow HEM4 to provide the elevations. (Otherwise, if you
enter an elevation for some but not all user receptors, HEM4 will assign a 0 value to the
receptors you left blank.)
In addition, you may provide hill heights in the User Receptor file, or you may leave the hill
height field blank for HEM4 to calculate these values. AERMOD uses the controlling hill height
for flow calculations. Controlling hill height is defined as the highest elevation that is above a
10% grade from the receptor. [For more information on the use and calculation of controlling hill
heights using an algorithm in AERMAP, the AERMOD terrain processor (EPA 2018c), see
Section 2.3.1.1 If you leave the hill height field blank in the User Receptors file (but did choose to
include elevations in your model run in your Facility List Options file), then HEM4 will assign the
hill height of that user receptor to be the maximum of: 1) the hill height of the closest U.S.
Census block or alternate receptor (if not using U.S. Census blocks in your modeling run), 2) the
elevation of the closest U.S. Census block or alternate receptor, or 3) the user receptor
elevation that you provide. Note: As cautioned above for user receptor elevation, you should
enter a hill height for every user receptor, or leave the hill height blank for all, to allow HEM4 to
provide the hill heights. (Otherwise, if you enter a hill height for some but not all user receptors,
HEM4 will assign a 0 value to the receptors you left blank.)
In the User Receptor file, you should specify a "receptor type code" indicating the type of
receptor. A code of "P" represents populated sites like houses/residences, "B" represents facility
boundary sites, and "M" represents ambient monitors. You may name your user receptors
with up to 9 characters and HEM4 will display these names in the output files for ease of
reference. Each user receptor name must be unique.
Tables 19 and 20 give format guidelines for the User Receptors file and a sample input file,
respectively. In addition, a template input file is provided in the HEM4 Inputs folder named
HEM4_user_receptors.xlsx.
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Table 19. Format Guidelines for the User-Defined Receptors File
Field Type Description
An alphanumeric character string identifying the facility
being modeled
Type of coordinates: L = longitude, latitude; U = UTM
[WGS84]
UTM east coordinate, in meters (if Coordinate System = U)
or decimal longitude (if System = L). For longitudes, 5
decimal place accuracy is recommended, corresponding to
1-meter accuracy.
UTM north coordinate, in meters (if Coordinate System = U)
or decimal latitude (if System = L). For latitudes, 5 decimal
place accuracy is recommended, corresponding to 1-meter
accuracy.
If using the UTM coordinate system (U), enter the UTM
Zone from 1 to 60 followed by the hemisphere (S or N). For
example, 17N (default hemisphere is N if not specified). If
using longitudes/latitudes, leave this cell blank.
Elevation of the receptor above sea level, in meters.
Optional: HEM4 will calculate if left blank and you are
modeling terrain effects*
Type of receptor: P = populated site (e.g., house or school);
B = facility boundary; M = monitor.
Receptor ID Alpha-numeric Name of receptor provided by user, containing letters and
numbers, no symbols or spaces. The name you provide
must be 9 characters or less. This name will be displayed
in the outputs.
Hill Height Numeric Hill height scale, in meters. Optional: HEM4 will calculate if
left blank and you are modeling terrain effects* (You may
leave all hill heights blank, even if you enter elevations for
your user receptors in the elevation field.)
*Note: Fill-in for every receptor or for none. If you enter one or more values, then HEM4 will assign a zero
(0) to any blank values.
Table 20. Sample Input File for User-Defined Receptors
Facility
ID
Location
type
(U - UTM,
L =
latitude/
longitude)
X-
coordinate
(decimal)
or UTM
East
(m)
Y-
coordinate
(decimal)
or UTM
North
(m)
UTM
zone
Elevation
(m)
Receptor type
(P = populated
site, B = facility
boundary, M =
monitor)
Receptor ID
Hill Height
(m)
Fac1
L
-78.88875
35.90016
100
P
UHouse12
Fac2
U
560005
441000
16
244
M
UMonitor3
Facility ID Character
Coordinate Character
system
X-coordinate Numeric
Y-coordinate Numeric
UTM zone Numeric
Elevation Numeric
Receptor type Character
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3.5.7 Emissions Variation Input Files
If you chose to model emissions variations for one or more facilities in your Facility List Options
file, then HEM4 will prompt you for a separate Emissions Variation input file. AERMOD
computes hourly concentration data based on user-supplied emission inputs. AERMOD also
gives you the option of specifying variable emission rate factors for individual sources. You can
base these source-specific factors on different temporal scales—such as season, month, day of
the week, and hour of day—or on wind speed.
For HEM4 to calculate temporal or wind speed emissions variations, AERMOD requires
information on the type of variation and the factors to use for each variation. These variation
types and factors will be applied to one or more sources at each of the facilities you indicated in
your Facility List Options file. You must supply this information in an Emissions Variation input
file in the form of an Excel™ spreadsheet. The types of variations AERMOD can apply include
the following (with the HEM4 template file provided in parentheses, as well as the "n" number of
factors):
• SEASON (HEM4_emisvar_season.xlsx): emission rates vary seasonally (n=4);
MONTH (HEM4_emisvar_month.xlsx)\ emission rates vary monthly (n=12);
HROFDY (HEM4_emisvar_hrofdy.xlsx)\ emission rates vary by hour-of-day (n=24);
HRDOW (HEM4_emisvar_hrdow.xlsx)\ emission rates vary by hour-of-day, and day-of-
week [M-F, Sat, Sun] (n=72);
SEASHR (HEM4_emisvar_seashr.xlsx): emission rates vary by season and hour-of-day
(n=96);
HRDOW7 (HEM4_emisvar_hrdow7.xlsx): emission rates vary by hour-of-day, and the
seven days of the week [M, Tu, W, Th, F, Sat, Sun] (n=168);
SHRDOW (HEM4_emisvar_shrdow.xlsx)\ emission rates vary by season, hour-of-day,
and day-of-week [M-F, Sat, Sun] (n=288);
SHRDOW7 (HEM4_emisvar_shrdow7.xlsx)\ emission rates vary by season, hour-of-day,
and the seven days of the week [M, Tu, W, Th, F, Sat, Sun] (n=672);
MHRDOW (HEM4_emisvar_mhrdow.xlsx)\ emission rates vary by month, hour-of-day,
and day-of-week [M-F, Sat, Sun] (n=864);
MHRDOW7 (HEM4_emisvar_mhrdow7.xlsx)\ - emission rates vary by month, hour-of-
day, and the seven days of the week [M, Tu, W, Th, F, Sat, Sun] (n=2,016); and
WSPEED (HEM4_emisvar_wspeed.xlsx)\ emission rates vary by wind speed (n=6)
(Note: the 6 factors are applied to the wind speed categories used by AERMOD that
have the following default upper bound speeds in m/s of 1.54, 3.09, 5.14, 8.23, 10.8 and
no upper bound).
HEM4 User's Guide Page 59
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Table 21 provides the format guidelines for the Emissions Variation input files. Tables 22, 23,
24, and 25 provide sample Emissions Variation input files for a sample of the variations
AERMOD can accommodate including: seasonal emission variations (4 factors), hour of day
emission variations (24 factors), monthly emission variations (12 factors), and both season and
hour of day emission variations (96 factors), respectively. Table 26 provides a sample input file
for varying source-specific emissions by wind speed. It should be noted that HEM4 expects a
maximum of 12 factor columns across these Emissions Variation input spreadsheets (for a total
of 15 columns, including the Facility ID, Source ID and Variation keyword).
It should also be noted that although the types of emission variations described above and the
samples provided below are for a single type of emissions variation, you can also choose to use
different variation types for different sources and/or facilities, within the same input file. The only
limitation is that each source can only have a single type of variation applied in a model run. A
template input file containing multiple emissions variations in one file is also provided in the
HEM4 Inputs folder and is named HEM4_emisvar_multiple_variations.xlsx. See the AERMOD
User's Guide (EPA 2019a) for more detailed information regarding the temporal and wind speed
factors available for varying source-specific emissions.
Table 21. Format Guidelines for the Emissions Variation Input Files
Field
Type
Description
Facility ID
Character
An alphanumeric character string identifying the facility being modeled
Source ID
Character
The Source ID is a unique alphanumeric character string up to 8 characters
long with no spaces. It must match a Source ID in the HAP Emissions and
Emissions Location file. Note: AERMOD allows a maximum of 8 characters
for the Source ID; and all Source IDs will be converted to upper case by
AERMOD.
Variation
Character
Type of variable emission rates being used (SEASON, MONTH, HROFDY,
HRDOW, SEASHR, HRDOW7, SHRDOW, SHRDOW7, MHRDOW,
MHRDOW7 or WSPEED)*
Factor 1
Character
First factor to be applied to emission rate.
Factor 2
Character
Second factor to be applied to emission rate.
Factor 3
Character
Third factor to be applied to emission rate.
Factor n
Character
nth factor to be applied to emission rate.
*Each emission variation type has a set number of "n" factors. The number of factors are as follows: SEASON=4,
MONTH=12, HROFDY=24, HRDOW=72, SEASHR=96, HRDOW7=168, SHRDOW=288, SHRDOW7=672,
MHRDOW=864, MHRDOW7=2,016, WSPEED=6. See HEM4's template input files for examples and consult the
AERMOD User's Guide for additional information.
Table 22. Sample Emissions Variation File based on Seasons (4 factors)
Facility ID
Source ID
Variation
Winter
Spring
Summer
Fall
Fac1
SAMPLE1
SEASON
0.50
0.75
1.00
1.00
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Table 23. Sample Emissions Variation File based on Hour of Day (24 factors)
Facility
ID
Source
ID
Variation
Hour
factor
(1)
Hour
factor
(2)
Hour
factor
(3)
Hour
factor
(4)
Hour
factor
(5)
Hour
factor
(6)
Hour
factor
(12)
Fac1
SAMPLE1
HROFDY
0.2138
0.1433
1.2928
0.098
0.1342
0.3301
1.4356
(13)
(14)
(15)
(16)
(17)
(18)
(24)
Fac1
SAMPLE1
HROFDY
1.3959
1.2728
0.1079
1.5255
1.5255
1.5519
1.799
Table 24. Sample Emissions Variation File based on Month (12 factors)
Facility ID
Source ID
Variation
JAN
FEB
MAR
APR
MAY
JUN
DEC
Fac1
SAMPLE1
MONTH
0.2138
0.1433
1.2928
0.098
0.1342
0.3301
1.4356
Table 25. Sample Emissions Variation File based on Season and Hour of Day (96 factors)
Season-
Season-
Season-
Season-
Season-
Season-
Season
Season-
Facility
hour
hour
hour
hour
hour
hour
-hour
hour
ID
Source ID
Variation
Factor
Factor
Factor
Factor
Factor
Factor
Factor
Factor
Winter
Winter
Winter
Winter
Winter
Winter
Winter
1
2
3
4
5
6
12
Fac1
SAMPLE1
SEASHR
0.2138
0.1433
1.2928
0.098
0.1342
0.3301
1.4356
Winter
Winter
Winter
Winter
Winter
Winter
Winter
13
14
15
16
17
18
24
Fac1
SAMPLE1
SEASHR
1.3959
1.2728
0.1079
1.5255
1.5255
1.5519
1.799
Spring
Spring
Spring
Spring
Spring
Spring
Spring
1
2
3
4
5
6
12
Fac1
SAMPLE1
SEASHR
1.9045
1.9475
1.4684
1.0435
0.8305
0.6952
0.3979
Spring
Spring
Spring
Spring
Spring
Spring
Spring
13
14
15
16
17
18
24
Fac1
SAMPLE1
SEASHR
0.2138
0.1433
1.2928
0.098
0.1342
0.3301
1.4356
Summer
Summer
Summer
Summer
Summer
Summer
Summer
1
2
3
4
5
6
12
Fac1
SAMPLE1
SEASHR
1.3959
1.2728
0.1079
1.5255
1.5255
1.5519
1.799
Summer
Summer
Summer
Summer
Summer
Summer
Summer
13
14
15
16
17
18
24
Fac1
SAMPLE1
SEASHR
1.9045
1.9475
1.4684
1.0435
0.8305
0.6952
0.3979
Fall
Fall
Fall
Fall
Fall
Fall
Fall
1
2
3
4
5
6
12
Fac1
SAMPLE1
SEASHR
0.2138
0.1433
1.2928
0.098
0.1342
0.3301
1.4356
Fall
Fall
Fall
Fall
Fall
Fall
Fall
13
14
15
16
17
18
24
Fac1
SAMPLE1
SEASHR
0.2138
0.1433
1.2928
0.098
0.1342
0.3301
1.4356
Table 26. Sample Emissions Variation File based on Wind Speed (6 factors)
Facility ID Source ID Variation Cat. 1 Cat. 2 Cat. 3 Cat. 4 Cat. 5 Cat. 6
Fac1 SAMPLE1 WSPEED 0.2138 0.1433 1.2928 0.098 0.1342 0.3301
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3.5.8 Alternate Receptors file
As noted previously, HEM4 can model based on U.S. Census blocks or based on alternate
receptors you provide. If you check "Use alternate receptors" on the required inputs user
interface (discussed below in Section 4.1), then HEM4 will prompt you for an Alternate Receptor
file, in lieu of using U.S. Census blocks for the model run. This allows you to model with HEM4
anywhere in the world, both within the U.S and outside the U.S.
The Alternate Receptor file must be a CSV file and provide HEM4 with a list of receptor
locations, the type of each receptor (populated "P" or various types of non-populated receptors,
such as boundary "B" and monitor "M" receptors), and the populations represented by each
receptor. It is important to note that only populated "P" receptors are chosen by HEM4 to be the
sites of maximum risk or hazard index; and only "P" receptors are used by HEM4 in cancer
incidence calculations. This is discussed further below in Sections 5 and 6. Note: For HEM4 to
run using alternate receptors, you must provide population values for every Alternate
Receptor of type "P". The population you provide may be any integer value, 0 or greater.
Even if only one populated Alternate Receptor is missing a value in its population field, HEM4
will not commence the modeling run.
In addition, if you chose in your Facility List Options file to include elevations in your model run,
then you must also provide HEM4 the elevation above sea level for each alternate receptor, as
well as the hill height of each receptor. To model terrain effects, the alternate receptor file must
be filled-in completely for every elevation and hill height. Any blanks in the elevation fields or
hill height fields of the Alternate Receptors file will cause AERMOD to be run in the FLAT
mode with no terrain effects.
AERMOD uses the controlling hill height for flow calculations. Controlling hill height is defined as
the highest elevation that is above a 10% grade from the receptor. For more information on the
use and suggested calculation of controlling hill heights using an algorithm in AERMAP, the
AERMOD terrain processor (EPA 2018c), see Section 2.3.1. It is important to again note that if
you leave any hill height field blank in the Alternate Receptors file, then AERMOD will be run in
the FLAT mode with no terrain effects (even if you opt to include elevations in your model run in
your Facility List Options file and also provide elevations for your alternate receptors).
Alternatively, you can choose to model with the elevation option turned off in your Facility List
Options file. In such a modeling run, you do not need to provide any elevations or hill heights in
the Alternate Receptor file, as HEM4 will model everything on a flat plane.
Tables 27 and 28 give format guidelines for the Alternate Receptors file and a sample input file,
respectively. In addition, a template input file is provided in the HEM4 Inputs folder named
HEM4_alternate_receptors. csv.
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Table 27. Format Guidelines for Alternate Receptors File (CSV)
Field
Type
Sample
Value
Description
Receptor ID
Type of
receptor
Coordinate
system
X-coordinate
Y-coordinate
UTM zone with
hemisphere
Elevation
Hill Height
Population
Numeric 1 A unique number identifying the Receptor
Character P Type of receptor: P = populated (e.g., house),
B = boundary, M = monitor
Character L Type of coordinates: L = longitude, latitude; U = UTM
[WGS84]
Numeric -52.74629 UTM east coordinate, in meters (if Coordinate System =
U) or decimal longitude (if System = L). 5 decimal place
precision is recommended for longitude, corresponding to
1 meter
Numeric 47.53796 UTM north coordinate, in meters (if Coordinate System =
U) or decimal latitude (if System = L). 5 decimal place
precision is recommended for latitude, corresponding to 1
meter
Character 17N UTM zone where the receptor is located if Coordinate
System = U
Numeric 219.7 Elevation of the receptor above sea level, in meters.
Required if you are modeling terrain effects (i.e. choose to
model elevations in the Facility List Options file)
Numeric 219.7 Hill height scale, in meters. Required if you are modeling
terrain effects (i.e. choose to model elevations in the
Facility List Options file)
Numeric 45 Population represented by the alternate receptor; required
by HEM4 for every "P" type alternate receptor for
incidence calculations.
Table 28. Sample Input File for Alternate Receptor Input File
Receptor
ID
Type of
Receptor
(P, B, M)
Coordinate
System
(U = UTM
L =
latitude,
longitude)
X-
coordinate:
Longitude
(decimal) or
UTM East
(m)
Y-
coordinate:
Latitude
(decimal) or
UTM North
(m)
UTM zone
with
hemisphere
Elevation
(m)
Hill Height
(m)
Population
1
B
L
-52.746286
47.53880
219.7
219.7
0
2
P
L
-52.74685
47.54225
219.3
219.3
5
3
P
L
-52.74817
47.53796
220.6
220.6
25
4
P
L
-52.74760
47.53683
262.7
262.7
7
5
M
L
-52.75023
47.53795
263.4
263.4
0
6
P
L
-52.74708
47.53599
292.1
292.1
45
n
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3.5.9 Census Update file
HEM4 provides you the option to change the census file, as discussed below in Section 4.7.
Before you use this option, it should be noted that these changes are permanent to your census
files. For this reason, it is recommended that you save your original census files to a separate
location before using this file to change the official census database provided on EPA's HEM4
webpage.
With the Census Update file, you can:
(1) Zero-out the population of a specific U.S. Census block;
(2) Move a block to a new latitude and longitude location; and/or
(3) Delete a U.S. Census block.
You may wish to Zero-out the population of the block if it is clear no residences are present in
the block. This change will keep the block in the dataset, so concentrations and risks are
modeled, but this receptor will not be considered for maximum risk purposes.
You may wish to Move a block to different coordinates that better represent the population.
You may wish to Delete or remove a block from the dataset; for example, because there are no
people living in the block. However, it should be noted that once removed, the block cannot be
added back.
Tables 29 and 30 give format guidelines for the Census Update file and a sample update file,
respectively. In addition, a template input file is provided in the HEM4 Inputs folder named
HEM4_Census_block_update_template.xlsx.
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Table 29. Format Guidelines for the Census Update File
(used to permanently change your U.S. Census files)
Field
Type
Sample Value
Description
Facility ID
Run Group
Block ID
Latitude
Character
Character
Character
(not numeric)
Numeric
Fac2
Landfills
170010001001003
39.96789
Longitude
Numeric
-91.37989
Change
Character
Move
The Facility ID field in the Census Update file
is optional and may be left blank. You may
wish to use it outside of HEM4 to track the
source of changes.
The Run Group field in the Census Update
file is optional and may be left blank. You
may wish to use it outside of HEM4 to track
the source of changes.
In this field, enter the 15-digit U.S. Census
block ID. Enter the block ID as text
characters rather than numerals, because
some block IDs have leading zeroes.
If the Change is a "Move", enter the Latitude
(decimal) of where the block should be
moved. 5 decimal places are recommended,
corresponding to 1-meter accuracy. You may
leave this field blank for "Zero" and "Delete"
changes.
If the Change is a "Move", enter the
Longitude (decimal) of where the block
should be moved. 5 decimal places are
recommended, corresponding to 1-meter
accuracy. You may leave this field blank for
"Zero" and "Delete" changes.
The potential changes include: Zero, Move,
and Delete
Table 30. Sample Census Update File
Run
Latitude
Longitude
Change
Facility ID
Group
Block ID
(decimal)
(decimal)
Fac1 -TX
Landfills
170010001001003
Zero
Fac1 -TX
Landfills
170010001001009
39.96789
-91.37989
Move
Fac1 -TX
Landfills
170010001001010
Delete
Fac1 -TX
Landfills
370010201001001
Zero
Fac1 -TX
Landfills
370010201001002
36.34567
-79.45678
Move
Fac1 -TX
Landfills
370010201001003
Delete
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3.5.10 Updating the Chemical Unit Risk Estimates and Health Benchmarks Input Files
As discussed in Section 2.2.1, the Chemical Health Effects Library contains chemical health
effects data, including dose response toxicity values. You can make changes to the Chemical
Health Effects Library by editing the Excel™ spreadsheet files that comprise the library—entitled
Dose_Response_Library.xlsx and Target_Organ_Endpoints.xlsx. These files are located in
HEM4's resources folder. You can add new pollutants to these files or edit the values for the
chemicals already in the files. If you want to keep your files consistent with the data EPA uses in
their HAP risk assessments, check for updated toxicity values on EPA's Dose Response
Assessment webpage (EPA 2018a).
When adding new chemical names to the Dose Response Library file, use the same
spelling as used in the HAP emissions input file. The Chemical Abstracts Service (CAS)
number field in the Chemical Health Effects Library is optional. If you do not specify a cancer
URE for a new pollutant, then the URE will be assumed to be 0 (zero) and cancer risks will not
be evaluated for that pollutant. Similarly, if you do not specify a noncancer chronic RfC or acute
benchmark for a new pollutant, HEM4 will not calculate adverse noncancer chronic or acute
health effects, respectively. If a noncancer chronic RfC is indicated in the Dose Response
Library file for a pollutant you add, you must also enter the pollutant in the Target Organ
Endpoints file and indicate what organs or organ systems may be impacted.
For future model runs, to ensure you have the most recent file versions, you should again check
EPA's HEM download webpage (https://www.epa.gov/fera/download-human-exposure-model-
hem) for the date listed next to the "Toxicity Value Files" link. EPA regularly updates these files.
If EPA's update is more recent than the dates shown for the files in HEM4's resources folder,
then download the newer files from EPA's HEM download webpage (from link above) and
replace your outdated Dose Response Library and/or Target Organ Endpoints files. You may
also manually modify the files in your HEM4's resources folder based on updated values from
EPA's HEM download page, or from EPA's Dose Response Assessment webpage (EPA
2018a).
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4. Step-by-Step Instructions for Running HEM4
Before you initiate a HEM4 modeling run7, you should ensure you have the necessary input files
prepared for your specific modeling needs. Section 3 provides detailed descriptions of all HEM4
input files, and template input files for each are provided in the HEM4 Inputs folder. Table 31
provides a summary of the template files provided in your HEM4 Inputs folder and for what kind
of run each file is needed. In addition to the files listed in Table 31, a HEM4 run requires the
U.S. Census (if not using alternate receptors) and meteorological databases, and the files
located in HEM4's resources folder. These include the Dose_Response_Library.xlsx file, the
Target_ Organ_Endpoints.xlsx file, and, for vapor deposition/depletion, the Gas_Param.xlsx file.
Table 31. Summary of HEM4 Template Input Files
Template Input File Name
Description
When Needed
HEM4 Fac List Options.xlsx
Facility List Options file
Every run
HEM4 HAP Emiss.xlsx
HAP [Pollutant] Emissions file
Every run
HEM4 Emiss Loc.xlsx
Emissions Location file
Every run
H E M4_alte rn ate_rece pto rs. csv
Alternate Receptor file
Required if modeling with alternate
receptors (whether outside or inside the
U.S.) instead of census block receptors
HEM4_user_receptors.xlsx
User Receptor file
Required if the user receptor column in
the Faclist has a "Y" for one or more
facilities
HEM4_buoyant_line_param.xlsx
Buoyant Line Source Parameter file
Required if a source in the Emissions
Location file is a buoyant line
H E M 4_p o ly g o n_ve rtex. x I sx
Polygon Vertex file
Required if a source in the Emissions
Location file is a polygon
HEM4_bldg_dimensions.xlsx
Building Dimensions file
Required if the building downwash
column in the FacList has a "Y" for one
or more facilities
HEM4_particle_data.xlsx
File containing particle size
distribution of emissions per source
Required if the deposition OR depletion
column AND Pdep OR Pdepl column in
FacList has a "Y", AND if Method 1 (the
default) is indicated in EmissLoc. (HAP
Emiss must also contain particulates)
HEM4_landuse.xlsx
File describing land use surrounding
emissions source
Required if the deposition OR depletion
column AND Vdep OR Vdepl column in
FacList has a "Y". (HAP Emiss must
also contain gases/vapor)
HEM4_month-to-seasons.xlsx
File describing monthly stage of
vegetation surrounding emissions
source
Required if the deposition OR depletion
column AND Vdep OR Vdepl column in
FacList has a "Y". (HAP Emiss must
also contain gases/vapor)
HEM4_emisvar_season.xlsx
Emissions Variation file
Required if the Emissions Variation
column in Faclist has a "Y" and
seasonal variations are desired (4
factors)
HEM4_emisvar_month.xlsx
Emissions Variation file
Required if the Emissions Variation
column in Faclist has a "Y" and monthly
variations are desired (12 factors)
HEM4_emisvar_hrofdy.xlsx
Emissions Variation file
Required if the Emissions Variation
column in Faclist has a "Y" and hour-of-
day variations are desired (24 factors)
7 Note: It is advisable to close and re-start HEM4 between modeling runs, which clears memory for each
new run and avoids potential issues by ensuring a full reset.
HEM4 User's Guide
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Template Input File Name
Description
When Needed
H E M 4_e m i s va r_h rd o w. x 1 sx
Emissions Variation file
Required if the Emissions Variation
column in Faclist has a "Y" and hour-of-
day + type-of-day (M-F, Sat, Sun)
variations are desired (72 factors)
HEM4_emisvar_seashr.xlsx
Emissions Variation file
Required if the Emissions Variation
column in Faclist has a "Y" and season
+ hour-of-day variations are desired (96
factors)
HEM4_emisvar_hrdow7.xlsx
Emissions Variation file
Required if the Emissions Variation
column in Faclist has a "Y" and hour-of-
day + day-of-week (7) variations are
desired (n=168);
H E M 4_e m i s va r_s h rd o w. xl sx
Emissions Variation file
Required if the Emissions Variation
column in Faclist has a "Y" and season
+ hour of day + type-of-day (weekday,
Sat, Sun) variations are desired (288
factors)
HEM4_emisvar_shrdow7.xlsx
Emissions Variation file
Required if the Emissions Variation
column in Faclist has a "Y" and season
+ hour-of-day + day-of-week (7)
variations are desired (672 factors)
H E M 4_e m i s va r_m h rd o w. x 1 sx
Emissions Variation file
Required if the Emissions Variation
column in Faclist has a "Y" and month +
hour-of-day + type-of-day (weekday,
Sat, Sun) variations are desired (864
factors)
HEM4_emisvar_mhrdow7.xlsx
Emissions Variation file
Required if the Emissions Variation
column in Faclist has a "Y" and month +
hour-of-day + day-of-week (7) variations
are desired (2,016 factors)
HEM4_emisvar_wspeed.xlsx
Emissions Variation file
Required if the Emissions Variation
column in Faclist has a "Y" and wind
speed (m/s) variations are desired (6
factors)
Finally, to ensure you have the most recent model version, as well as the most recent chemical
health effect (toxicity) values, U.S. Census data, and meteorological data, you should check
EPA's HEM download webpage for updates (https://www.epa.gov/fera/download-human-
exposure-model-hem). EPA updates these files periodically. If EPA's update is more recent than
the version of HEM4 on your computer, then download the newer model version from EPA's
HEM download webpage (from link above) and start the newer model. If the chemical health
effect files (e.g., Dose Response Library file, Target Organ Endpoints file) on EPA's website are
more recent than the ones currently in HEM4's resources folder, then replace the files in your
subfolder with the ones you download from EPA's website. Likewise, check the timestamp and
update your U.S. Census data (in HEM4's "census" subfolder) and the meteorological data (in
HEM4's "aermod" subfolder), as necessary.
After you have ensured the HEM4 model and integrated databases are up-to-date and after you
have prepared the input files for the modeling application, start HEM4 by using Windows File
Explorer™ to navigate to the folder where HEM4 was unzipped and double click on the HEM4
executable file. The HEM4 title screen will be displayed, as shown below in Figure 4. Note that
the buttons near the bottom of the menu bar on the left - the HEM4 USER GUIDE and the
AERMOD USER GUIDE buttons - link to this HEM4 guide (at https://www.epa.gov/fera/risk-
assessment-and-modelinq-human-exposure-model-hem) and to AERMOD's user guide (at
https://www3.epa.gov/ttn/scram/models/aermod/aermod userguide.pdf), respectively, and you
HEM4 User's Guide
Page 68
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should access them whenever you need further instruction and understanding regarding the
inputs or outputs of HEM4, or when troubleshooting a modeling run issue.
HEM4
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Figure 4. HEM4 Title Screen
The RUN HEM4 button at the top of the menu bar on the left will take you to the next screen,
from which you can initiate a model run. To view this HEM4 User's Guide or the AERMOD
User's Guide, on this screen or any subsequent screen, click on the buttons on the bottom of
the menu bar.
4.1 Provide Standard Input Files and Indicate Receptors
On the initial input screen (RUN HEM4) shown below in Figure 5, you must first indicate
whether you will use U.S. Census receptors or alternate receptors for your model run. Within the
U.S., you can use either U.S. Census receptors or alternate receptors that you provide. For
modeling runs outside the U.S., you must use alternate receptors. Figure 5 shows the input
selection buttons for the three required input files: the Facility List Options file, the HAP
Emissions file, and the Emissions Location file. Clicking on each of these buttons will allow you
to browse your computer to select the appropriate file. The Facility List Options file, HAP
Emissions file, and Emissions Location file are described in detail in Sections 3.2, 3.3 and 3.4,
respectively.
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- ~ X
(q RUN HEM4
|J| REVISE CENSUS DATA
SUMMARIZE RISKS
\" ANALYZE OUTPUTS
0 LOG
p Use U.S. Census receptors r Use alternate receptors
Name Run Group (optional): Q
3 1. Please select a Facilities List Options file:
-fl 2. Please select a HAP Emissions file:
-Q 3. Please select an Emissions Location file:
D HEM4 USER GUIDE
D AERMOD USER GUIDE
Q EXIT
Figure 5. Run HEM4 with U.S. Census Receptors
If you choose to use alternate receptors, then an additional input selection button will appear
near the bottom middle of the screen, as shown in Figure 6, that requires you to browse for and
select an alternate receptor CSV file. (Note: It may take several minutes for your Alternate
Receptor file to upload for modeling. Do not click Next until it has uploaded ) The
Alternate Receptors file is described in Section 3.5.8. As with all modeling runs, for a run using
alternate receptors, you must also browse for and select the Facility List Options, HAP
Emissions, and Emissions Location input files.
(q RUN HEM4
0 REVISE CENSUS DATA
SUMMARIZE RISKS
ANALYZE OUTPUTS
0 LOG
c Use U.S. Census receptors <* Use alternate receptors
Name Run Group (optional):
-Q 1. Please select a Facilities List Options file:
-Q 2. Please select a HAP Emissions file:
-Q 3. Please select an Emissions Location file:
HEM4 USER GUIDE
AERMOD USER GUIDE
Q EXIT
-Q Please select an alternate receptor CSV file:
Figure 6. Run HEM4 with Alternate Receptors
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For either type of run, you can (optionally) enter a run group name in the Name Run Group box
provided. This is recommended because the name will be used to identify the subfolder
containing the results of your run, located within the "output" folder, and will be helpful in
identifying which folder the post-modeling tools for summarizing, viewing and analysis should be
pointed towards. The name you enter in the "Name Run Group" box will also be prepended to
the output files containing the results for the run as a whole.
After you have indicated what type of receptors should be used for the modeling run and
entered the three required input files on this initial screen, click Next at the bottom right corner
of the screen to continue. If no additional input files are needed beyond the Facility List Options,
Emissions Location and HAP Emissions files already entered, then a pop-up box will appear
asking you to confirm the start of the HEM4 run, as shown below in Figure 7.
MW Confirm HEM4 Run X
©Clicking OK will start HEM4. Check the log tab for updates
on your modeling run.
OK Cancel
Figure 7. Confirm HEM4 Run Pop-Up Start Box
Clicking 'OK' in this box will initiate the modeling, and a log of the modeling progress will appear
as shown and described in Section 4.4. Click Cancel if you need to change any input files
already entered. If additional input files are required, one or two additional screens will appear
after you click Next, which are discussed in Sections 4.2 and 4.3
4.2 Provide Additional Input Files
If additional inputs are required, one of two screens will appear next, depending on the nature of
your sources in the Emissions Location file and the modeling options you indicated in your
Facility List Options file. One screen that may appear is shown below in Figure 8. The other
input screen which may appear is shown and discussed in Section 4.3.
This screen will prompt you for one or more of the following additional input files: a user
receptors file; an emissions variation file; a buoyant line parameters file; a polygon vertex file;
and/or a building dimensions file. For example, if you indicated in your Facility List Options file
that you'd like to include emissions variations for one or more facilities to be modeled, then a
button will appear on this screen asking for the location of your Emissions Variation file (as
shown in Figure 8). Likewise, if one of the sources in your Emissions Location file is a buoyant
line source, then a button will appear prompting you to browse your computer and select a
buoyant line parameter file. If other input files are needed based on your Facility List Options file
and Emissions Location file, additional buttons will appear and request that you browse for and
select the required file. When you hover over each of these input file buttons, instructions will be
displayed on the top of the screen describing each file type.
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(Q RUN HEM4
|Jj REVISE CENSUS DATA
SUMMARIZE RISKS
ANALYZE OUTPUTS
0 LOG
-Q Please select a User Receptors file:
-Q Please select an Emissions Variation file:
-Q Please select associated Buoyant Line Parameters file
-J Please select associated Polygon Vertex file
-Q Please select associated Building Dimensions file
m HEM4 USER GUIDE
AERMOD USER GUIDE
Q EXIT
Back
Figure 8. Provide Additional Input Files
Next
After you have entered these additional input files, click A/exf at the bottom right corner of the
screen to continue. If no other inputs are needed, HEM4 will display the pop-up box, shown
above in Figure 7, stating "Clicking 'OK' will start HEM4. Check the log tab for updates on your
modeling run." Click Cancel if you need to change any input files. If you are ready for HEM4 to
start your modeling run, click OK, and a log of the modeling progress will appear as shown and
described in Section 4.4. If additional inputs are needed for deposition and depletion modeling,
another input screen will open next, as shown and discussed in Section 4.3.
4.3 Provide Deposition and Depletion Input Files
When modeling deposition/depletion, HEM4 can direct AERMOD to (1) calculate a deposition
flux and (2) deplete the plume based on the calculated deposition. You can direct HEM4 to
provide the deposition flux in the outputs, or not (to save space). Generally speaking, deposition
modeled with plume depletion will reduce the ambient impacts from the emission source by
removing pollutants from the plume. Air concentrations will be depleted as pollutants are
deposited to the ground. Deposition and plume depletion have more of an effect on ambient
concentrations farther from the facility than it does closer to the facility where the maximum
impact generally occurs. Alternatively, you may choose to calculate the deposition flux, but not
deplete the plume (to allow for higher, more conservative air concentrations). Either way, the
modeled deposition flux may be used as an input to a separate multipathway model such as the
Total Risk Integrated Methodology (TRIM) (EPA 2018e).
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In most cases, if you chose to model deposition and/or depletion in the Facility List Option file,
HEM4 will require additional input files8. HEM4 uses AERMOD to calculate deposition and
depletion effects for particulate matter, vapor (gaseous) pollutants, or both. The make-up of your
emissions - that is, the percentage particulate and gas - is dictated to HEM4 by your HAP
Emissions input file. Specifically, the fifth column in the HAP Emission input file ("Fraction
emitted as particulate matter") indicates to HEM4 whether your emissions are 100% particle (if
this column is populated with 100 for all pollutants), 100% vapor (if this column is left blank or
populated with 0 for all pollutants), or a mixture of particles and gas. You will need to browse
your computer and select the additional files needed for modeling of deposition and/or depletion
on the screen depicted in Figure 9. You will be prompted to provide between 1 and 3 deposition/
depletion related input files, depending on your modeling options and the nature of the
emissions to-be-modeled.
-ox
[p] RUN HEM4
0 REVISE CENSUS DATA
gj. SUMMARIZE RISKS
\^_ analyze outputs -J Please select Particle Size File
-Q Please select Land Use file
-Q Please select Month-to-Season Vegetation file
0 LOG
HEM4 USER GUIDE
AERMOD USER GUIDE
Q EXIT
Figure 9. Provide Deposition and Depletion Input Files
If your Facility List Options file indicates that you chose to model particle deposition and/or
particle depletion using AERMOD's Method 1 (as discussed in Section 3.4.2) AND your HAP
Emissions file indicates that some of the emissions are in particle form, then a particle data file
is required by HEM4/AERMOD. Upload the particle data input file containing the particle size
information, mass fraction and particle size density for each pollutant (HAP) by browsing your
computer for it at the first Browse button on this screen, as shown in Figure 9.
8 Note: The one deposition and/or depletion modeling case, which requires no additional inputs and
therefore no deposition/depletion input screen, is if you are modeling only particle deposition and/or
depletion AND chose in your Emissions Location input file to use Method 2 for the Deposition Method. It
should also be noted that AERMOD does not model deposition or depletion of emissions from buoyant
line sources. Therefore, if you indicate in your Facility List Options file that deposition or depletion should
be modeled for a facility with buoyant line sources in your Emissions Location file, AERMOD will not run
successfully. In this case, remove the buoyant line source IDs from your input files and model that source
separately, without deposition or depletion.
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If your Facility List Options file indicates that you chose to model vapor (gaseous) deposition
and/or vapor depletion AND your HAP Emissions file indicates that some of the emissions are in
vapor form, then HEM4 will instruct AERMOD to model vapor deposition and/or depletion.
Depending on the type of vapor deposition/depletion you indicated in your Facilities List Option
file, two additional inputs may be required by HEM4/AERMOD: a land use input file and a
month-to-seasons input file. These additional input files are needed only to quantify dry (or "wet
and dry") deposition and/or depletion of vapor emissions, as discussed in Section 3.5.4. If you
wish to model "wet only" deposition and/or depletion of gaseous pollutants, these additional
input files are not needed by HEM4. (These files are also not needed to model particle-only
deposition and/or depletion.) Upload these files by browsing your computer for them at the
second and third buttons on this screen shown in Figure 9.
As noted in Section 3.5.4, you should also check to ensure that the vapor (gaseous) pollutants
in your HAP Emissions file are included in the Gas Parameter reference file. If these pollutants
are not included - or if you wish to include different parameter values than the Gas Parameter
file currently uses - you should edit the Gas Parameter file located in HEM4's resources folder,
as discussed in Section 3.5.4. Otherwise, generic default gas parameter values will be used.
(The default file pathway is "HEM4\resources\Gas_Param.xlsx".)
It should be noted that HEM4 requires significantly more time to run if you opt to model
deposition and/or depletion. The exact run time will depend on the particular source
configuration and modeling domain but can be over an hour or more per facility. You can utilize
the FASTALL option in the Facility List Options file to expedite the run. As noted in Section
3.2.10, FASTALL conserves model runtime by simplifying the AERMOD algorithms used to
represent the meander of the pollutant plume (EPA 2019a).
After you enter the required files on the deposition/depletion input screen, click Next on the
bottom right and HEM4 will display the pop-up box (shown above in Figure 7) stating "Clicking
'OK' will start HEM4. Check the log tab for updates on your modeling run." Click Cancel if you
need to change any file locations on this screen, and the Back button to change any input files
on the previous screen. If you are ready for HEM4 to start your modeling run, click OK and a log
of the modeling progress will appear as shown and described in Section 4.4.
4.4 Check HEM4 Log
After HEM4 starts modeling your facilities (or facility), the LOG screen will appear to show you
HEM4's progress in real-time including any errors in processing, if there are any. The Log
screen is shown below in Figure 10. (Note: The cursor is visually disabled on the log screen, but
it is recommended that you not place your cursor on the log tab screen itself, because doing so
may reset where the log displays the next line of progress and result in seemingly non-
sequential progress messages; rather use the scroll bar on the right to show more of the log
screen, as needed.) Once the modeling run is complete, HEM4 also produces a log text file as a
permanent record of the modeling.
The Log screen and text file will provide you with the following modeling run information:
• the meteorological period used, whether annual (the default) or a different period you
selected;
• the full list of input files uploaded for the modeling run;
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• any mismatch between input files prior to you correcting the mismatched files (e.g.,
mismatched Source IDs between the HAP Emissions and Emissions Location files);
• the default values used for any parameters with out-of-range (unacceptable) values
specified in your input files;
• the run group name;
• the Facility IDs modeled and the location of each facility's center;
• the start and end time for the AERMOD portion of the modeling run;
• the full list of outputs produced; and
• the number of minutes required for HEM4 to model each facility and produce the facility-
specific outputs.
ww - ~ x
Facility Fac1-NC: Using period start = 2019 02 11 12
Facility Fac1-NC: Using period end = 2019 06 30 1
Facility Fac2-IL: Using annual met option.
Uploaded facilities options list file for 2 facilities.
Uploaded HAP emissions file for 101 source-HAP combinations.
Uploaded emissions location file for 13 facility-source combinations.
Uploaded user receptors for [Fac1-NC]
Uploaded buoyant line parameters for [Fac1-NC]
Uploaded polyvertex sources for [Fac1-NC]
Uploaded building downwash parameters for [Fac1-NC]
Uploaded particle data for [Fac2-ILJ
Uploaded land use data for [Fac1-NC,Fac2-IL]
Uploaded seasonal variation data for [Fac1-NC,Fac2-IL]
HEM4 is starting...
Figure 10. Log Screen
After the modeling is complete, the log text file, named HEM4.log, will be located in the run
group folder you name (as discussed in Section 4.1) and will contain information about the
facilities modeled in your run. The log file will also indicate what default values HEM4 used
(listed in Sections 3.2, 3.3, and 3.4) for the three required input files, in lieu of erroneous out-of-
range values you may have included in your input files, as discussed further in Section 4.8.
Finally, the log file will also indicate what facilities failed to run successfully, including what
errors caused the failure, which is also discussed further in Section 4.8.
The Appendix includes a sample HEM4 log file produced for a two-facility modeling run. The log
file will also list any risk summary program outputs you opted to produce. The next section
discusses how to run the risk summary programs.
•
RUN HEM4
0
REVISE CENSUS DATA
R
SUMMARIZE RISKS
ANALYZE OUTPUTS
0
LOG
ABORT HEM RUN
m
HEM4 USER GUIDE
m
AERMOD USER GUIDE
a
EXIT
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4.5 Summarize Risks
The SUMMARIZE RISKS button on the menu bar on the left allows you to summarize HEM4
results using one or more summary programs to produce the following risk summary reports,
which are based on all facilities modeled in the run group:
Max Risk Report;
Cancer Drivers;
Hazard Index Drivers;
Risk Histogram;
Hazard Index Histogram;
Incidence Drivers;
Acute Impacts;
Multipathway; and
Source Type Risk Histogram.
The Summarize Risks screen is shown in Figure 11. Note: Before you choose to summarize
your risk results via these reports, you may wish to perform certain QA checks on the modeled
facility-specific results, as described in Section 9.
[q RUN HEM4
0 REVISE CENSUS DATA
SUMMARIZE RISKS
ANALYZE OUTPUTS
0 LOG
HEM4 USER GUIDE
AERMOD USER GUIDE
Q EXIT
¦
Select output folder
0
Max Risk Report
~
Cancer Drivers
0
Hazard Index Drivers
~
Risk Histogram
0
Hazard Index Histogram
0
Incidence Drivers
0
Acute Impacts
\y\ Muttipathway
0 Source Type Risk Histogram
Enter the position in the source ID where the
source ID type begins.The default is 1.
E Run Reports
Figure 11. Run the Risk Summary Programs
First, click on the Select output folder button to browse for the folder where the HEM4 outputs
you want summarized are located. Next, select which summaries you would like to run by
checking the box before each, and then click on the "Run Reports" button to initiate the selected
summaries. The outputs produced by these risk summary programs are report summaries of all
facilities modeled in your run as group, rather than facility-specific outputs, and are described in
Section 8.
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The Source Type Risk Histogram summary requires you to indicate where in your Source IDs
the source type begins and ends. As discussed in Section 3.3.1, it's helpful to create your
Source IDs so that the type of source is identified always in the same location in the Source ID
string. For example, if you are modeling a series of storage tanks and wastewater vessels, you
could identify them with IDs such as ST01, ST02, ST03, WW01, WW02, and so on. In this
example, the source type starts in location 1 of the Source ID string and is 2 characters long
(i.e., ST and WW). Therefore, in this case, after you check the Source Type Risk Histogram box
(shown above in Figure 11), you would enter a 1 next to "Enter the position in the Source ID
where the source type begins." You would then enter a 2 next to "Enter the number of
characters in the source type."
After you have selected the summaries you want run, check the Log screen for progress. The
HEM4.log text file will also report any errors. The Risk Summary Reports you choose to run will
be placed in the same output folder where you indicated the HEM4 results are located (which
were summarized using these programs).
4.6 Analyze Outputs
The ANALYZE OUTPUTS button on the menu bar on the left allows you to view and analyze
the HEM4 facility-specific modeling results as well as the run group-wide Risk Summary
outputs. The View and Analyze Outputs screen is show below in Figure 12.
** -ox
Open a facility or summary output table
Open a chronic or acute risk map
View summary graphical outputs in web browser
y AERMOD USER GUIOE
~ EXIT
Figure 12. View and Analyze Outputs
This screen consists of three buttons that allow you to (1) open a facility or summary output
table via a spreadsheet app for further analysis and graphing; (2) open a chronic or acute risk
map; and (3) view summary graphical outputs in web browser. After you click on these buttons,
HEM4 will prompt you to identify the location of the output files you wish to view and analyze
further.
[p] RUN HEM4
£} REVISE CENSUS DATA
SUMMARIZE RISKS
*
ANALYZE OUTPUTS
0 LOG
Pa
H HEM4 USER GUIDE
HEM4 User's Guide
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If you choose to open a facility or summary Excel or CSV output table using the first button
(shown in Figure 12), HEM4 will open the file within a spreadsheet app with numerous widgets
available for further analysis and graphing. This widget is provided by a pandastable library as
an interactive way to review and analyze HEM4's tabular output data (see
https://pandastabie.readthedocs.io/en/latest/description.html.) An example of a Hazard Index
Drivers output (spreadsheet) opened via this first button is shown in Figure 13. The spreadsheet
and graphing widgets along the right-hand side include: Load table; Save; import CSV; Load
Excel file; Copy table to clipboard; Paste table; Select data to plot; Transpose; Aggregate; Pivot;
Melt; Merge, concatenate or join; Prepare a sub-table; Filter table; Calculate; Model fitting; Clear
table; Contract columns; Expand columns; Zoom out; and Zoom in.
x
ji
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(f C;/Git_HEM4/HEM4/output/test4-20/test4-20_hazard_index_drivers.xlsx — ~
_
HI_Type
Pollutant
Hazard_lndex| Percentage
Fac1-NC
Developmental HI
9.479141
SR000001
arsenic compounds
9.431920
199.500000 I
2
Fac1-NC
Kidney HI
1.570466
SR000001
cadmium compounds
1.506065
95.900000
3
Fac1-NC
Respiratory HI
0.47091
RW000001
acrolein
0.29061
61.710000
4
Fac1 -NC
Respiratory HI
0.47091
FU000001
bi s(2-ethylhexyl)phthalate
0.132177
28.070000
5
Fac1-NC
Respiratory HI
0.47091
RW000001
acrolein
0.0321697
6.830000
6 l|
Fac1-NC
Liver HI
0.190013
FU000001
bis(2-ethylhexyl)phthalate
0.144408
76.000000
7
Fac1-NC
Liver HI
0.190013
RW000001
trichloroethylene
0.0312142
j 16.430000
8
Fac1-NC
Reproductive HI
0.090131
RV000001
1,3-butadiene
0.0887254
98.440000
9
Fac1-NC
Neurological HI
0.065151
RW000001
trichloroethylene
0.0348731
53.530000
10
Fac1-NC
Neurological HI
0.065151
FU000001
mercury (elemental)
0.0229932
35.290000
11
Fac1 NC
Neurological HI
0.065151
RW000001
trichloroethylene
0.00386036
5.930000
12
Fac1 -NC
Immunological HI
0.039509
RW000001
trichloroethylene
0.0348731
88.260000
13
Fac1-NC
Immunological HI
0.039509
RW000001
trichloroethylene
0.00386036
9.770000
14
Fac2-lL
Liver HI
0.024612
FU000001
bis(2-ethylhexyl)phthalate
0.0225351
91.560000
15
Fac2-IL
Respiratory HI
0.024087
FU000001
bi s(2-ethylhexyl)p hthalate
0.0225351
93.550000
16
Fac2-IL
Neurological HI
0.016217
FU000001
mercury (elemental)
0.0141467
87.230000
17
Fac2-IL
Neurological HI
0.016217
FU000001
mercury (elemental)
0.00155341
9.580000
18
Fac1-NC
Hematological HI
0.000931
FU000001
selenium compounds
0.00090521
97.180000
19
Fac2-lL
Hematological HI
0.000522
FU000001
selenium compounds
0.000517802
99.180000
20
Fac1-NC
Skeletal HI
0.000461
RW000001
hydrofluoric acid
0.000415156
90.030000
21
Fac1-NC
Endocrine HI
7.09803e
RV000001
cumene
5.67842e-06
80.000000
22
Fac1-NC
Endocrine HI
7.09803e
RV000001
cumene
1.41961e-06
20.000000
23
Fac24L
Reproductive HI
1.28789e
FU000001
benzo[a]pyrene
9.69533e-07
75.280000
24
Fac2-IL
Developmental HI
1.28789e
FU000001
benzo[a]pyrene
9.69533e-07
75.280000
25
Fac2-IL
Reproductive HI
1 28789e
FU000001
benzo[a]pyrene
3.18352e-07
24.720000
26
Fac2-1L
Developmental HI
1.28789e
FU000001
benzo[a]pyrene
3.18352e-07
24 720000
26 rows
x 7 columns
Intel»|o |
Figure 13. Hazard Index Drivers File Opened via Spreadsheet App
As a further example of this tool, if you click on the "Select-data-to-plot" widget on the right-hand
side of the spreadsheet, a data plot automatically pops-up with numerous formatting options for
graphing. A depiction of one plot is shown in Figure 14.
$ Plot Viewer — ~ X
r*4 Plot I Apply Options I yjj^ I X! j iTlj B | dpi 180 | f grid layout 3D plot
Base Options Annotation Grid Layout Other Options 3D Options Animate
global labels text to add
textbox textbox format
add objects
title
boxstyle
font size
12
add object
square [~
textbox
xlabel
facecolor
_u
coord system
I HI Type
white
font
data ~
ylabel
linecolor
monospace |»
Create
black j*
fontweight
HI Total value
rotate
0
normal ~
Clear
ticklabel angle
align
P
I _U
center
Figure 14. Select Data to Plot Widget
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If you choose to open a chronic or acute risk map with the second button (shown in Figure 12),
you wiii be asked to select a chronic kmz file from your modeled outputs, which HEM4 will
launch in Google Earth™. Or you can select an acute map html file to view on a satellite street
map. An example of a chronic kmz file is shown below in Figure 15 displayed via Google
Earth™, with the cancer and noncancer chronic results overlaid on the map. These results are
discussed further in Section 6. Note: The first time you run HEM4, your computer may take
several minutes to open Google Earth™; but the application will open quickly after subsequent
runs.
ft Weather
& Galtey
Figure 15. Chronic Risk Map shown in Google Earth™
To open an acute map, you must first run the Acute Impacts summary from the Summarize
Risks ("Create Risk Summary Reports") screen, shown in Figure 11. After you run the Acute
Impacts summary program, HEM4 will produce an output subfolder called "Acute Maps", which
will be located in the same place where the other facility-specific and summary outputs from
your run are located. Click on the "Open a chronic or acute map" button on the View and
Analyze Outputs screen (shown in Figure 12) and then HEM4 will ask you to select the html file
you wish to view. Choose an html file from any of the html files located in the "Acute Maps"
subfolder and HEM4 will display your map in your default browser window. An example html
acute map is shown in Figure 16, for one of the acute benchmarks (REL) based on modeled
acrolein results. The acute output files underlying these mapped results are explained in
Sections 6 and 7.
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Acrolein (AEGL-1 1-hr) Acrolein (REL) Arsenic Compounds (REL) Mercury (Elemental) (REL)
Fac1-NC Acrolein Acute HQ (REL)
Figure 16. Acute Map View of HTML File
Finally, you can choose to view summary graphical outputs in your default web browser by
clicking on the third button (shown in Figure 12). To use these statistical and graphical
visualization tools, you must choose a folder containing Risk Summary reports run from
the Summarize Risk screen (shown in Figure 11). Note that all risk summary reports must be
present in your selected folder to use these statistical and graphing tools, except the Max Risk
report, Multipathway report and Acute Impact report: these three reports may be present in your
selected folder but are not required. After you select your desired output folder, the graphical
visualizations of your results that appear in your default web browser are constructed via the
Dash app, which is a Python framework for building interactive web applications. The graphical
displays of your results offered by this application include:
• a map of your modeled facilities;
• pie charts based on the cancer incidence percentages by pollutant and source type;
• bar charts showing the number of people at increasing levels of cancer risk (e.g., less
than 1 -in-1 million risk, greater than or equal to 1 -in-1 million risk, greater than or equal
to 10-in-1 million risk, greater than or equal to 100-in-1 million risk);
• bar charts showing the number of people at increasing noncancer hazard index levels
for each of the 14 modeled target organ specific hazard indices (e.g., less than or equal
to 1, greater than 1, greater than 10, greater than 100, greater than 1000);
• bar charts showing the source and pollutant risk drivers of your modeling run for both
cancer and noncancer;
• bar charts showing the acute screening hazard quotients by benchmark and pollutant for
each facility with modeled acute impacts; and
• an interactive and exportable spreadsheet displaying the maximum cancer risk and
noncancer hazard index values for each modeled facility.
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An example of one of the several graphical visualizations of your results offered by this
application is shown in Figure 17, which displays pie charts based on the cancer incidence
percentages by pollutant and source type, for a modeling run based on 5 different pollutants and
8 different source types.
Cancer Incidence by Pollutant and Source Type
(Total Incidence is 4.80E-02)
Cancer Incidence by Pollutant for test_8-l-2020
(for pollutants that contribute at least 1%)
Cancer Incidence by Source Type for test_8-l-2020
Arsenic Compounds
1,3-Butadiene
Cadmium Compounds
Naphthalene
Benzene
Figure 17. Example Graphical Visualization of Incidence by Pollutant and Source Type
The output files underlying these results are explained in Sections 6 and 7.
4.7 Revise Census Data Option
The REVISE CENSUS DATA button on the menu bar on the left allows you to change your U.S.
Census file using the census update file described in Section 3.5.9. On this screen, shown in
Figure 18, click on the "Please select a census update file" button to select an update file from
your computer. Once your census update file is selected, click on the "Revise" button on this
screen, which will change the census files that HEM4 uses to model any facilities after the
change. (Note: this revision is permanent to your census files unless you change your census
files back to their original. For this reason, it is recommended that you save your original census
files to a separate location before clicking on "Revise" using this screen.)
You can use the census update file described in Section 3.5.9 to (1) zero-out the population of a
specific U.S. Census block, (2) move a block to a new latitude and longitude location, and/or (3)
delete or remove a census block. The reasons for making such revisions to your census dataset
are also discussed in Section 3.5.9.
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.»
X
~
e
RUN HEM4
Please select a census update file:
0
REVISE CENSUS DATA
(J
Revise
b,
SUMMARIZE RISKS
0
ANALYZE OUTPUTS
LOG
m
HEM4 USER GUIDE
m
AERMOD USER GUIDE
a
EXIT
Figure 18. Revise Census Data Screen
4.8 Error Messages and Failed Runs
When initiating a model run, HEM4 will perform a series of checks on your inputs to identify
obvious errors that would cause the model (including AERMOD) to fail. Identifying these input
errors prior to HEM4 attempting to model the erroneous values avoids most unsuccessful model
runs and provides you with instructions to rectify the problem. Reviewing the AERMOD
documentation is also important and helpful if you receive an error from HEM4 or from
AERMOD (in the aermod.out file, described in Section 6.1.13) when running your inputs and the
resolution of the error is not clear (EPA 2019a, EPA 2019b).
For example, on the user interfaces that instruct you to select input files (discussed above in
Section 4), if you attempt to upload an input file with the wrong number of columns (a.k.a.
fields), then an error message will pop-up indicating that the file you uploaded had "x" columns,
but should have "y" columns. HEM4 will also compare the Source IDs in your input files to
ensure they match. If the Source IDs in your Emissions Location file do not match the Source
IDs in your HAP Emissions file, then an error message will pop-up indicating that "Your
Emissions Location and HAP Emissions files have mismatched Source IDs. Please correct one
or both files with matching sources and upload again." A sample of the kinds of pop-up error
messages and their meanings are listed in Table 32.
Additionally, if you entered a value for an input parameter that is out-of-range of the acceptable
values for that parameter, then HEM4 will replace your problematic value with the default value,
and indicate the replacement in the log file, as noted above in Section 4.4. The values HEM4
defaults to are listed for applicable parameters within each standard input file starting in Section
3.2.
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Table 32. Sample List of Error Messages and Causes in HEM4
Pop-Up Error Message
Meaning / Cause
"One or more facility IDs are missing in the List."
The uploaded file contains records without a
valid Facility ID.
"One or more met stations referenced in the Facility
List are invalid."
The uploaded Facility List Options file
contains facilities with met station references
that are not present in the master list of met
stations.
"One or more source IDs are missing in the List."
The uploaded file contains records without a
valid Source ID.
"One or more pollutants are missing in the List."
The uploaded file contains records without a
valid pollutant (HAP).
"One or more locations are missing a coordinate
system in the List."
The uploaded file contains records without
valid coordinate system values.
"One or more source types are missing a valid value in
the Emissions Locations List."
The uploaded Emissions Location file
contains records without a valid source type
value for one or more fields.
"The following pollutants were not found in HEM4's
Dose Response Library: [list of HAP names not found].
Would you like to amend your HAP Emissions file?
(They will be removed otherwise.)"
One or more HAP listed in the HAP
Emissions file is not included in the Dose
Response Library. Note: If you do not revise
your HAP Emissions file to include only HAP
listed in your Dose Response library, then
HEM4 will drop those HAP for the current
run. Alternatively, you may exit the run and
amend the Dose Response Library before
starting a new run.
"Facility : [lat/lon] value out of range in the
Emissions Locations List."
The uploaded Emissions Location file
contains an out-of-range latitude or longitude
value for one or more sources.
"Facility : UTM zone value malformed or invalid in
the Emissions Locations List."
The uploaded Emissions Location file
contains an invalid UTM zone value.
"Error: Some non-numeric values were found in
numeric columns in this data set."
The uploaded file contains non-numeric
values in a field that should have only
numbers.
"Length Mismatch: Input file has x columns but should
have y columns."
The uploaded file contains the wrong number
of columns.
" parameters are specified in the Facilities List
Options file. Please upload a File."
The Facility List Options file specifies
modeling options requiring additional input
files that have not been uploaded.
"AERMOD models building downwash from point
sources only (i.e., vertical P, horizontal H, or capped C
point sources). Your building dimensions file includes
non-point sources. Please edit your building
dimensions file to remove all non-point sources."
AERMOD models building downwash of
emissions from vertical point (P), capped
point (C), and horizontal point (H) source
types only. The uploaded Facility List
Options file indicates building downwash for
one or more facilities and the Source IDs for
those facilities in the uploaded building
dimensions input file include sources other
than P, C, or H types.
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Pop-Up Error Message
Meaning / Cause
"AERMOD cannot currently model deposition or
depletion of emissions from buoyant line sources, and
the Emissions Location file includes a buoyant line
source for one or more facilities. Please disable
deposition and depletion for each of these facilities or
remove the buoyant line source(s)."
AERMOD version 19191 can model
deposition and/or depletion from all source
types except buoyant lines. The uploaded
Facility List Options file indicates deposition
and/or depletion for one or more facilities
and one or more Source IDs for those
facilities in the uploaded Emissions Location
file are buoyant lines.
"AERMOD's FASTALL option cannot be used with
buoyant line sources, and the Emission Location file
includes a buoyant line source for one or more
facilities. Please disable FASTALL for each of these
facilities or remove the buoyant line source(s)."
AERMOD version 19191 does not allow the
FASTALL option with buoyant line sources.
The uploaded Facility List Options file
indicates FASTALL for one or more facilities
and one or more Source IDs for those
facilities in the uploaded Emissions Location
file are buoyant lines.
"AERMOD ran unsuccessfully. Please check the error
Section of the aermod.out file in the output
folder."
AERMOD didn't run successfully, for a
reason specified in the aermod.out file.
"Cannot generate summaries because there is no
Facility_Max_Risk_and_HI Excel file in the folder you
selected."
The Risk Summary reports could not be run
because the Facility_Max_Risk_and_HI
output file is needed, but is missing.
If HEM4 is unable to model a facility or facilities due to errors in the inputs, HEM4 will not only
note the errors in the log file but will also produce an Excel file entitled "Skipped Facilities" in the
run group's output subfolder. You can use the list of skipped facilities in column A of this output
file to create a new Facility List Options file, after you fix the errors, to model these facilities.
This is discussed further in Section 9.
Finally, in the event of a failed modeling run, you should close down HEM4 and then re-
start before your next modeling run. A full shutdown and re-start of HEM4 ensures the
memory has been cleared, which will reset values in the underlying model code and avoid a
variety of potential issues in the next run.
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5. HEM4 Modeling Calculations for each Facility
Section 3 describes the HEM4 input files and Section 4 describes the step-by-step instructions
for the user to initiate a HEM4 modeling run. This section describes the internal modeling
algorithms and simplifying assumptions employed by HEM4, once initiated, during a modeling
run. We list the AERMOD options used to model emission dispersion from each facility and
describe the method HEM4 implements to transform AERMOD's single pollutant concentration
modeling into multiple pollutant concentration estimations. This section also discusses HEM4's
post-dispersion computation of health impacts at modeled receptors, including cancer risk and
noncancer health hazards, as well as HEM4's calculations to estimate the contributions of
individual pollutants and emission sources to the estimated concentrations and health impacts
at the modeled receptors.
5.1 Dispersion Modeling
As noted previously in this guide, HEM4 carries out dispersion modeling by running the
AERMOD dispersion model. Section 3 describes a number of input options you can specify for
running AERMOD—for example, incorporating deposition and depletion, emissions variations,
and using urban or rural dispersion parameters. This section discusses the options that HEM4
implements by default. In addition, this section describes the dilution factor methodology used in
HEM4 for modeling multiple pollutants based on AERMOD's single pollutant modeling.
5.1.1 Regulatory Default, ALPHA and BETA Options
HEM4 uses primarily the regulatory default options when running AERMOD. These options
include the following:
• Uses stack-tip downwash (except for Schulman-Scire downwash);
• Uses buoyancy-induced dispersion (except for Schulman-Scire downwash);
• Does not use gradual plume rise (except for building downwash);
• Uses the "calms processing" routines;
• Uses upper-bound concentration estimates for sources influenced by building
downwash; from super-squat buildings;
• Uses default wind profile exponents;
• Uses low wind speed threshold;
• Uses default vertical potential temperature gradients; and
• Uses missing-data processing routines.
However, it should also be noted that AERMOD (version 19191) includes model option
keywords ALPHA and BETA for certain modeling options. The ALPHA keyword indicates one or
more options are being used that are scientific/formulation updates considered to be in the
research phase and have not been fully evaluated and peer reviewed by the scientific
community; and/or non-scientific model options in development that still need rigorous testing
and for which EPA is seeking feedback from the user community. The BETA keyword indicates
one or more options are being used that have been fully vetted through the scientific community
with appropriate evaluation and peer review. BETA options are planned for future promulgation
as regulatory options in AERMOD. See the AERMOD users guide for more information (EPA
2019a).
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For the current version of HEM4, the only ALPHA options available are Method 2 particle
deposition and gaseous (vapor) deposition. The only current BETA option in AERMOD, RLINE
(a source type intended mainly for roadway modeling), is not currently an option in HEM4. To
keep HEM4 general, the ALPHA and BETA keywords will always be included in the AERMOD
runstream file prepared by HEM4, even when no ALPHA or BETA options are being used.
5.1.2 Dilution Factors
HEM4 uses AERMOD to compute a series of dilution factors, specific to each emission source
and receptor. This approach more quickly analyzes the impacts of multiple pollutants than if
separately modeling each pollutant. 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 you choose not to analyze deposition or depletion, then the dilution factor does not vary from
pollutant to pollutant. If you do select deposition or depletion, HEM4 will compute separate
dilution factors for gaseous and particulate pollutants. In addition, you can specify different
particle sizes and densities for each particulate matter emission source. To use pollutant-
specific parameters for particulates and/or gases, requires a separate Source ID for each
pollutant at a given source. As noted in Section 3.4, you can create multiple Source IDs using
the same locations and source parameters to accommodate different pollutants when modeling
deposition or depletion.
5.2 Estimating Risks and Hazard Indices
HEM4 estimates the total cancer risk, noncancer hazard index (HI) and optionally acute hazard
quotient (HQ) for all U.S. Census block locations or alternate receptor locations in the modeling
domain, all user receptors, and all receptors in the polar network. Receptors in the HEM4
domain fall into two categories: those with impacts explicitly modeled by HEM4/AERMOD, and
those with impacts estimated via interpolation rather than explicit modeling. Section 5.2.1
describes methods used to calculate cancer risks and noncancer health hazards for receptors
that HEM4/AERMOD explicitly models. Section 5.2.2 describes the interpolation approach used
to estimate cancer risks and noncancer health hazards at receptors not explicitly modeled.
Based on the results for U.S. Census blocks or alternate receptors, and other receptors, HEM4
estimates the maximum individual risk (MIR), maximum HI, and optionally high acute value for
populated receptors (Section 5.2.3); as well as the maximum impacts for all offsite receptors,
including unpopulated locations (Section 5.2.4). For these locations, the model calculates the
contributions of individual pollutants and emission sources to cancer risks, chronic HI, and
optionally acute HQ (Section 5.2.5).
5.2.1 Explicit Modeling of Inner Receptors, User Receptors and Polar Receptors
HEM4 calculates cancer risks, target-organ-specific HI, and optionally acute HQ for three types
of discrete receptors that are explicitly modeled by AERMOD. These are (1) U.S. Census blocks
or alternate receptors within the user-defined modeling "cutoff" distance for explicit modeling of
individual receptors, (2) all user receptors, and (3) the user-defined polar receptor network.
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As noted above in Section 5.1.2, Dilution Factors, HEM4 combines pollutants into two
categories — particulates and gases (vapor) — for the purposes of dispersion modeling. The
model retains these categories to calculate cancer risks, noncancer HI and optionally acute HQ.
HEM4 uses the following algorithms:
For cancer risk:
CRt = Eij CRj, j
CRi.j = DFU xCFxXk [Ei,k x UREk]
For noncancer hazard indices:
HIt = Si.j HQi.j
HQi, j = DFi, j x CF x Sk [Ei, k / (RfC k x 1000 |a,g/mg)]
where:
CRt = total cancer risk at a given receptor (probability for one person)
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 [(|a,g/m3) / (g/sec)] at the given receptor for source i and
pollutant type j
CF = conversion factor, 0.02877 [(g/sec) / (tons/year)]
£k = sum over all pollutants k within pollutant group j (particulate or gas)
Ei, k = emissions (tons/year) of pollutant k from source i
UREk = cancer unit risk estimate [1/(|a,g/m3)] for pollutant k
(cancer risk for an individual exposed to 1 |a,g/m3 over a lifetime)
HIt = TOSHI at a given receptor and for a given organ
HQi, j = organ-specific hazard quotient at the given receptor for source i and
pollutant type j
RfC k = noncancer health effect reference concentration (mg/m3) for pollutant k
(concentration at and below which no adverse health effect is expected)
The above equations are equivalent to the following simpler equations:
CRt = Si, k ACi, k x UREk
HIt= Si, k ACi, k / (RfC k x 1000 |J.g/mg)
where:
ACi, k = ambient concentration (|j.g/m3) for pollutant k at the given receptor. This is the
same as [Ei, k x DFij x 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 (URE) is not available for a given pollutant, then that pollutant is
not included in the calculation of cancer risk. Likewise, if the noncancer reference concentration
(RfC) is not available for a given pollutant, that pollutant is not included in the calculation of HI.
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Note that separate reference concentrations are used for acute HQ and chronic HQ. As
discussed in Section 2.2.1, for acute impacts, instead of the chronic RfC, the short term
concentration is compared with various threshold or benchmark levels for acute health effects
(e.g., the California EPA reference exposure level [REL] for no adverse effects).
5.2.2 Interpolated Modeling of Outer Receptors using the Polar Receptor Network
For U.S. Census blocks and alternate receptors outside of the user-defined modeling "cutoff"
distance for individual block modeling, HEM4 estimates cancer risks, noncancer HI and
optionally acute HQ by interpolation from the polar receptor network. HEM4 estimates impacts
at the polar grid receptors using AERMOD modeling results and the algorithms described in
Section 5.2.1. If you choose to model terrain effects with the elevation option in your Facility List
Options file, then HEM4 estimates an elevation for each polar receptor. HEM4 estimates
elevations and controlling hill heights for the polar grid receptors based on values from the U.S.
Census library for modeling runs using the U.S. Census, or from the alternate receptor file for
runs not based on the U.S. Census. HEM4 divides the 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.
HEM4 then assigns each polar grid receptor an elevation based on the highest elevation for
any U.S. Census block receptor, user receptor, or alternate receptor 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 census blocks or alternate receptors, the model defaults to the elevation and controlling hill
height of the nearest block or nearest alternate receptor outside the sector, or defaults to the
elevation of the nearest source (if the polar grid receptor is closer to a source than to a block or
alternate receptor outside its sector).
HEM4 interpolates the impacts at each outer U.S. Census block receptor or alternate receptor
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:
la, r = IA1, r + (IA2, r — IA1, r) x (a — A1) / (A2 — A1)
Iai, r = exp{ln(lAi,Ri) + [ln(lAi,R2) - ln(lAi,Ri)] x [(In r) - ln(R1)] / [ln(R2) - ln(R1)]}
IA2, r = exp{ln(lA2,Ri) + [ln(lA2,R2) - ln(lA2,Ri)] X [(In r) - ln(R1)] / [ln(R2) - ln(R1)]}
the impact (cancer risk, chronic HI or acute HQ) at an angle, a, from north, and
radius, r, from the center of the modeling domain
the angle of the target receptor, from north
the radius of the target receptor, from the center of the modeling domain
the angle of the polar network receptors immediately counterclockwise from the
target receptor
the angle of the polar network receptors immediately clockwise from the target
receptor
the radius of the polar network receptors immediately inside the target receptor
the radius of the polar network receptors immediately outside the target receptor
where:
a =
r =
A1 =
A2 =
R1 =
R2 =
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5.2.3 Maximum Individual Risks, Hazard Indices, and Hazard Quotients
HEM4 evaluates the predicted chronic impacts for all populated receptors to identify the
locations of the MIR and the highest HI for various target organs (maximum TOSHIs). For these
calculations, populated receptors include all U.S. Census block locations or alternate receptors
and any user receptors you included in the run designated as type P (for populated). In general,
type P receptors should include houses near the facility boundary, as well as other residences
not represented well by the location of the U.S. Census blocks or alternate receptors.
The maximum cancer risk may occur at a location other than the maximum HI for a given organ.
Likewise, the location of the maximum HI for one organ will not necessarily be the same as the
location for a different organ. HEM4 performs a separate evaluation of the maximum impact
location for each health impact.
The model also tests for instances where U.S. Census blocks, alternate receptors or type P
user receptors appear to be located on plant property. To do so, HEM4 calculates the distance
between each receptor and each emission source. These distances are compared with the
overlap distance that you specified in the Facility List Options file. If a populated-type receptor is
located within the overlap distance, then HEM4 does not use these calculated results for this
receptor to estimate the maximum individual cancer risk or maximum HI for populated areas.
Instead, the model assumes the impacts at the overlapping receptor to be equal to the
maximum impacts for any receptors that do not overlap plant property. This could include both
populated and unpopulated receptors (e.g. polar receptors), as long as they do not overlap plant
property.
If you chose to model acute (short-term) impacts in the Facility List Options file, HEM4 will also
evaluate predicted acute impacts for all receptors to identify the locations of the highest acute
HQs. For the acute calculations, all receptors are evaluated - both populated and unpopulated
receptors - including U.S. Census blocks or alternate receptors, all user receptors you may
have specified and all polar receptors. As described in the preceding paragraph, HEM4 also
checks to ensure that the maximum populated acute receptor is not overlapped. In the case of
an overlapped populated receptor, then the next highest non-overlapped populated receptor is
chosen.
5.2.4 Maximum Offsite Impacts
In addition to evaluating the maximum cancer risks, chronic HI, and acute HQ (if modeled) for
populated receptors, HEM4 evaluates maximum offsite impacts for all receptors. All U.S.
Census blocks or alternate receptors, all user receptors (populated and unpopulated), and all
points (receptors) on the polar receptor network are included in the evaluation of maximum
offsite impacts, except for those receptors that are found to be overlapping emission sources.
5.2.5 Contributions of Different Pollutants and Emission Sources
HEM4 calculates the contributions of different pollutants and emission sources to cancer risks,
chronic HI, and acute HQ (if modeled) at the receptors where impacts are highest, both for
populated receptors and for all offsite receptors. As noted in Section 5.2.1, HEM4 groups
pollutants together when calculating total risks, HI and HQ (if modeled) for the large number of
receptors that are typically included in an overall modeling domain. Thus, the model does not
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compute the contributions of individual pollutants and emission sources for all receptors.
However, HEM4 retains the information needed to determine the contributions of individual
pollutants and emission sources at the receptors where impacts are highest. HEM4 calculates
these contributions using the following equations:
ACi, k, m = Ei, k x DFi.j, m x CF
CRi, k, m = ACi, k, m x UREk
HQi, k, m = ACi, k, m / (RfC k x 1000 |j,g/mg)
where:
ACi, k, m = the predicted ambient concentration (|j.g/m3) for pollutant k, from source i, at
receptor m
Ei, k = emissions (tons/year) of pollutant k from source i
DFi, j, m = the dilution factor [(|a,g/m3) / (g/sec)] for source i, receptor m, and pollutant group j,
which includes pollutant k
CF = conversion factor, 0.02877 [(g/sec) / (ton/year)]
CRi, k, m = the estimated cancer risk from source i, and pollutant k, at receptor m
UREk = cancer unit risk estimate [1/(|a,g/m3)] for pollutant k
(cancer risk for an individual exposed to 1 |a,g/m3 over a lifetime)
HQi, k, m = the organ-specific hazard quotient as a result of emissions of pollutant k, from
source i, at receptor m
RfC k = noncancer health effect reference concentration (mg/m3) for pollutant k
(concentration at and below which no adverse health effect is expected)
Note that the methodology outlined above for cancer and chronic noncancer impacts is similar
for acute impacts, although acute emissions are used (including any acute factor/multiplier you
may have indicated in your Facility List Options files) as well as acute benchmarks discussed in
Section 2.2.1.
5.3 Population Exposures and Incidence
Using the predicted impacts for U.S. Census blocks or alternate receptors, HEM4 estimates the
populations exposed to various cancer risk levels and noncancer HI levels. To do so, the model
adds up the populations for receptors that have predicted cancer risks or noncancer HI above a
given threshold. For cancer risk, around each facility HEM4 predicts the number of people
exposed to a risk greater than or equal to the following thresholds:
• 1 in 1,000 (or 1,000-in-1 million) risk;
• 1 in 10,000 (or 100-in-1 million) risk;
• 1 in 20,000 risk;
• 1 in 100,000 (or 10-in-1 million) risk;
• 1 in 1,000,000 (or 1-in-1 million) risk; and
• 1 in 10,000,000 (or0.1-in-1 million) risk.
For noncancer HI, around each facility HEM predicts the number of people exposed to each of
the 14 TOSHIs above the following thresholds:
• Greater than 100;
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• Greater than 50;
• Greater than 10;
• Greater than 1.0;
• Greater than 0.5; and
• Greater than 0.2.
If you opt to model acute impacts, HEM4 will provide the acute concentration for every pollutant
at every receptor, including every populated receptor, and will also include the population of
those receptors (whether U.S. Census blocks or alternate receptors). Because of the transitory
nature of acute exposures, acute health impacts are modeled not only where people reside but
at all receptors in the modeling domain. Therefore, the highest acute health impacts often occur
at unpopulated polar receptor locations. It is important to note that the maximum acute impacts
will occur at different times for different spatial locations (receptors) and are therefore not
additive. For these reasons, population exposures are not tallied by HEM4 for acute health
impacts, only for cancer and chronic noncancer TOSHI.
HEM4 also estimates the contributions of different pollutants and emission sources to total
annual cancer incidence for the overall modeling domain using the following equations:
CIj, k, m = CRi, k, m x Pm / LT
where:
Clm - Si, k [cii, k, m]
TCI = Xm [Clm]
Cli, k, m = the estimated annual cancer incidence (excess cancer cases/year) for populated
receptor m due to emissions from pollutant k and emission source i
CRi, k, m = the estimated cancer risk from source i, and pollutant k, at populated receptor m
Pm = the population of populated receptor m
LT = the average lifetime used to develop the cancer unit risk estimate, 70 years
Si, k = the sum over all modeled pollutants k and emission sources i
Clm = the estimated total cancer incidence for populated receptor m due to emissions
from all modeled pollutants and emission sources
Sm= the sum over all populated receptors m in the modeling domain
TCI = the estimated total annual cancer incidence (excess cancer cases/year) for the
population living within the modeling domain from all modeled pollutants and
emission sources
It should be noted that the above incidence calculations are made for the pollutant types "j"
being modeled (whether particulate, gas, or combined).
For each facility, HEM4 provides the estimated total annual cancer incidence (excess cancer
cases/year) predicted to be caused by all modeled pollutants emitted from all modeled sources
Increasing in specificity, HEM4 also provides the annual cancer incidence predicted to be
caused by each emission source at a facility for all pollutants emitted from that source, as well
as by each pollutant from all sources emitting that pollutant at a facility. At the greatest level of
specificity, HEM4 provides the estimated cancer incidence broken down by both pollutant and
emission source - that is, for every pollutant individually from each source separately.
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5.4 Summarizing Human Health Impacts
Section 5.1 above discusses how HEM4 uses AERMOD for dispersion modeling of your inputs
to produce multi-pollutant concentration predictions at the receptors in your modeling domain,
around a given facility. Sections 5.2 and 5.3 above discuss the methodology and algorithms
used by HEM4 to transform predicted concentrations into human health impacts around each
modeled facility. The following sections describe the outputs produced by HEM4 for each facility
and for your run group as a whole, which allow you to summarize the risk and health impacts
per facility and across all facilities you choose to group together in a modeling run.
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6. HEM4 Output Files
After running the AERMOD dispersion model to determine receptor-specific concentrations,
HEM4 completes the post-AERMOD risk and exposure calculations (explained in Section 5) and
then produces a variety of facility-specific concentration, cancer risk, noncancer hazard
quotients (HQ) and hazard indices (HI), incidence and population exposure output files. These
facility-specific outputs are discussed in Section 6.1. HEM4 also produces three summary
output files, based on the results for the entire RUN group (e.g., source category/sector) of
modeled facilities. These multi-facility outputs are updated after the output files for the individual
facilities have been created and essentially concatenate the individual facility results into group-
wide summary files. These run group summary files are discussed in Section 6.2. The Risk
Summary Reports are discussed in Section 7.
6.1 Facility-Specific Outputs
A standard HEM4 run produces the following facility-specific output files:
• 6 risk and HI files (maximum individual risk [MIR], maximum offsite impacts, risk
breakdown, block summary chronic, ring summary chronic, and source risk KMZ);
• 3 incidence and population exposure files (incidence, cancer risk exposure, noncancer
risk exposure);
• 3 concentration files (all inner receptors, all outer receptors, all polar receptors);
• dispersion model output file(s) from AERMOD (the number depends on the type run);
• 1 file cataloging modeling options used (input selection options); and
• 1 quality assurance (QA) file showing receptors discarded (overlapping source
receptors).
In addition, depending on the modeling options chosen, a HEM4 run may produce 3 other non-
standard/optional files, including the following 3 acute files:
• acute breakdown,
• acute chem populated, and
• acute chem max.
These facility-specific standard and optional files are described below in this section.
6.1.1 Maximum Individual Risk
The Maximum Individual Risk output file provides the MIR value for cancer and the max TOSHI
value for noncancer chronic health effects predicted for any populated receptor that does not
overlap facility property, such as census blocks, alternate receptors, and user-defined receptors
that are designated as "populated". (Note: user-defined receptors are considered populated
receptors but are assigned a population of zero.) This file also indicates the population and
exact location of the receptors where these maxima occur. Note that the MIR and max TOSHIs
may or may not occur at the same receptors/locations, depending on what pollutants are being
emitted from one source versus another source (indicated in the HAP Emissions input file) and
the locations and parameters of the sources (indicated in the Emissions Location input file).
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Table 33 below describes the fields of information provided in the Maximum Individual Risk file.
A sample Maximum Individual Risk output file is provided in Appendix A.
Table 33. Fields Included in the Maximum Individual Risk & Maximum Offsite Impacts
Files
Field
Description
Parameter
Value of MIR
orTOSHI
Population
Distance
Angle
Elevation
Hill Height
FIPS code
Block ID
UTM east
coordinate
UTM north
coordinate
Latitude
Longitude
Receptor type
Notes
Maximum individual cancer risk (MIR) or maximum TOSHI including maximum respiratory
HI, maximum liver HI, maximum neurological HI, etc. for 14 TOSHIs
MIR value or maximum TOSHI value, including a rounded value and a value in scientific
notation
Population at the location of the MIR or maximum HI, if it is a census block or alternate
receptor
Distance from the center of the modeling domain, in meters
Angle from north
Elevation in meters above sea level
Controlling hill height of receptor, in meters above sea level, as described in Section 2.3.1.
Five-digit Federal Information Processing Standard (FIPS) code which uniquely identifies
the county of the receptor, if the receptor is a census block. (Note: For alternate receptor
run, there is a field called "Receptor ID")
10-digit census block ID for linking to census demographic data, if the receptor is a
census block. (Note: For alternate receptor run, there is a field called "Receptor ID")
In meters
In meters
Decimal
Decimal
Census block receptor, polar grid receptor, alternate receptor, user-defined receptor,
boundary receptor, monitor location
This field indicates whether the receptor was modeled discretely or interpolated and also
indicates if the original maximum receptor was overlapped (and therefore not used). In the
case of interpolation or an overlap, you may wish to re-model the facility.
Relevant to the Maximum Individual Risk file, it should be noted that if any populated receptor is
located within the minimum overlap distance, then it is assumed that either the source location
or the receptor location is inappropriate. (A block centroid may be inappropriate as a receptor
location if the block partially encompasses an emission source, such as at a corner of the
facility.) When an overlap condition occurs, this is indicated in the Notes field/column and the
calculated results for the overlapping receptor are not used. Instead, the maximum cancer risk
and TOSHIs are assumed equal to the maximum (next highest) impacts for any receptor that
does not overlap facility property. This could include both populated (census, alternate,
populated user-defined) receptors and unpopulated (polar, unpopulated user-defined such as
boundary and monitor) receptors, as long as they do not overlap facility property. In this
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situation, check the source coordinates in the emissions location input file, and define a set of
facility boundary receptors in the user-defined receptors file.
6.1.2 Maximum Offsite Impacts
The Maximum Offsite Impacts output file provides similar information to the Maximum Individual
Risk output file, but the receptors of maximum impact in this file include any receptors, not only
populated receptors. This file lists the highest cancer risks and TOSH I predicted at any receptor
that does not overlap with the emission sources, whether the receptor is populated or
unpopulated. The receptors included in this calculation include all discretely modeled census
blocks (aka "inner receptors"), all user-defined receptors (including populated user receptors,
boundary sites and ambient monitor sites), and all points in the polar receptor network, except
for those receptors overlapping emission sources. Table 33 above describes the fields of
information provided in the Maximum Offsite Impacts file. A sample Maximum Offsite Impacts
output file is provided in Appendix A.
6.1.3 Risk Breakdown
The Risk Breakdown output file provides the breakdown of risk and TOSH I by pollutant and
source, including a listing of pollutant concentrations and unit risk estimates (URE) and
reference concentration (RfC) values. This file includes information about the MIR and HI (for
populated census block, user, and alternate receptors), as well as the maximum offsite impacts
(for any receptor, including non-populated receptors such as polar grid receptors, boundary
receptors, and monitors), as discussed in Section 5.2.
This file also shows the contributions of gaseous and particulate emissions for any pollutants
that are emitted in both forms, if you opted to model deposition/depletion or if you merely
elected to show the particulate/gaseous breakdown, as explained in Section 3.2.6. Table 34
below describes the fields of information provided in the Risk Breakdown file. A sample Risk
Breakdown output file is provided in Appendix A.
As previously noted, HEM4 computes cancer risks using the EPA's recommended UREs for
HAP and other toxic air pollutants. The resulting estimates reflect the risk of developing cancer
for an individual breathing the ambient air at a given receptor site over a 70-year lifetime.
Noncancer health effects are quantified using HQ and HI for various target organs. The HQ for a
given pollutant and receptor site is the ratio of the ambient concentration of the pollutant to the
RfC level at which no adverse effects are expected. The HI for a given organ is the sum of HQs
for substances that affect that organ.
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Table 34. Fields Included in the Risk Breakdown File
Field
Description
Site type
MIR (for max populated receptor) or maximum offsite impact (for max of
any receptor, populated or not)
Parameter
Cancer risk, all 14 TOSHIs (e.g., respiratory HI, liver HI, neurological HI)
Source ID
Individual source identification code, "Total by pollutant all sources", or
"Total" for all pollutants and all sources combined
Pollutant
Pollutant name, "all modeled pollutants" for all pollutants combined for
each source, or "all pollutants all sources" for all pollutants and all
sources combined
Emission
P = particulate, V = vapor (gas), C = combined, NA = not applicable
(Pollutant) type
Value
Cancer risk or noncancer HQ
Value_rnd
Cancer risk or noncancer HQ rounded to one significant figure
Conc_ugm3
Pollutant concentration (|j.g/m3)
Conc_rnd
Pollutant concentration (|j.g/m3) rounded to two significant figures
Emissions_tpy
Modeled tons per year (tpy) emitted of pollutant
URE
Unit risk estimate used to compute cancer risks for the pollutant
[1 / (^g/m3)]
RfC
Reference concentration used to compute HQs for the pollutant (mg/m3);
Note that HEM4 converts this to |ag/m3 to compute TOSHIs
6.1.4 Block Summary Chronic
The Block Summary Chronic file provides the total cancer risk and all 14 TOSHIs for every
populated census block receptor, populated alternate receptor, and all user receptors, and also
indicates whether the receptor is an overlap location. As noted above, if any populated receptor
is located within the minimum overlap distance, then it is assumed that either the source
location or the receptor location is inappropriate. (For example, a block centroid may be
inappropriate as a receptor location if the block partially encompasses an emission source, such
as at a corner of the facility.) When an overlap condition occurs, the calculated results for the
overlapping receptor are not used. Instead, the maximum cancer risk and HI are assumed equal
to the maximum impacts for any receptor that does not overlap facility property. This could
include both populated (census block, populated user-defined, or alternate) receptors and
unpopulated (polar, boundary, or monitor) receptors, as long as they do not overlap facility
property. In the case of an overlap, you may wish to check the coordinates in your Emissions
Location input file, and define a set of facility boundary receptors in the user-defined receptors
file.
To facilitate detailed geographic information system (GIS) analyses of HEM4 results, the file
gives the latitude and longitude, and the UTM coordinates of each receptor, in addition to
cancer risk estimates and HI. This output file also gives the county FIPS code and block
identification number for U.S. Census-based runs or alternate Receptor ID for non-census runs,
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as well as the population of each receptor. This information is intended to facilitate studies
linking HEM4 results with census information, such as demographic or economic data. Table 35
below describes the fields of information provided in the Block Summary Chronic file. A sample
Block Summary Chronic output file is provided in Appendix A.
6.1.5 Ring Summary Chronic
The Ring Summary Chronic file provides the same information provided by the Block Summary
Chronic File, but for points in the polar receptor network. However, because these are polar
receptors, the FIPS, Block, and population fields are not included in the Ring Summary Chronic
File, while three additional fields are provided: distance from center of polar network, angle from
north, and sector number. Table 35 describes the fields of information in the Ring Summary
Chronic file, and a sample file is provided in Appendix A.
Note: For both the Block Summary Chronic and Ring Summary Chronic files, in the case of an
overlapped receptor, the risk and TOSHI values for that receptor displayed in these files will not
be the originally modeled values. Instead, the maximum cancer risk and TOSHIs are assumed
equal to the maximum (next highest) impacts for any receptor that does not overlap facility
property. This could include both populated (census, alternate, populated user-defined)
receptors and unpopulated (polar, unpopulated user-defined such as boundary and monitor)
receptors, as long as they do not overlap facility property. The originally modeled values that
occurred in the location of the overlap are available in the All Inner Receptor, All Outer
Receptor, and/or All Polar Receptor files described in Sections 6.1.10, 6.1.11, and 6.1.12,
respectively.
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Table 35. Fields Included in the Block Summary and Ring Summary Chronic Files
Field Description
Latitude Decimal
Longitude Decimal
Overlap N for No, Y for Yes. If Yes, the values shown for the receptor in that row
are the next highest receptor (whether populated or non-populated), not
overlapped. See also the Overlapping Source Receptors file.
Elevation Elevation in meters above sea level
FIPS code Five-digit Federal Information Processing Standard (FIPS) code which
uniquely identifies the county of the receptor, if the receptor is a census
block. (Not part of Ring Summary Chronic File) Note: For alternate
receptor run, there is a field called "Receptor ID".
Block ID 10-digit census block ID for linking to census demographic data, if the
receptor is a census block. (Not part of Ring Summary Chronic File)
Note: For an alternate receptor run, there is a field called "Receptor ID".
X UTM Easting Coordinate
Y UTM Northing Coordinate
Hill Height Controlling hill height of receptor, in meters above sea level, as
described in Section 2.3.1
Population Population at the location of the MIR or maximum HI, if it is a census
block, or has user-provided population in the case of an alternate
receptor. (Not part of Ring Summary Chronic File)
Parameter Cancer risk, all 14 TOSHIs (e.g., respiratory HI, liver HI, neurological HI)
Discrete/ D for Discretely modeled receptor (within the modeling distance, aka
Interpolated "inner receptors"), I for Interpolated receptor (outside the modeling
distance, aka "outer receptors") (Not part of Ring Summary Chronic File)
Distance Distance in meters from the center of the polar network of the polar
receptor's location on polar ring (Not part of Block Summary Chronic
File)
Angle Angle from north of the polar radial on which the polar receptor is
(from north) located (0 to 360 degrees) (Not part of Block Summary Chronic File)
Sector Sector number within the polar network (the number depends on number
of radials indicated in your Facility List Options file; default is 1-16) (Not
part of Block Summary Chronic File)
6.1.6 Source Risk KMZ Image
The Source Risk KMZ file is a Google Earth™ map centered on the facility, as shown in Figure
19. The map displays the emission sources in the center as red circles for point/stack sources,
red rectangles for area sources, red polygons for polygon-shaped sources, and red lines for line
and buoyant line sources. The map also displays all receptors within the modeled area,
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including both census block centroid receptors or alternate receptors (displayed as squares)
and polar grid receptors (displayed as circles). The MIR receptor is marked with a red "X".
Figure 19. Sample Google Earth™ Map of Results
Click on the square census block receptors to see the total cancer risk and maximum TOSHI for
that receptor, the FIPS and block ID of the receptor (for census blocks), as well as a listing of
the top pollutants contributing to that block's total cancer risk and maximum TOSHI, Click on the
circular polar receptors to view similar information for each polar receptor. The cancer risk at the
census block and polar receptors are color coded on the Google Earth™ map. Red indicates a
receptor with a modeled total cancer risk greater than 100 in a million. Yellow indicates a risk
level between 20 and 100 in a million. Green indicates a risk less than 20 in 1 million.
Figure 19 shows an example in which only two non-populated polar grid receptors have a risk
greater than 100 in a million (shown as dark red circles). All populated census block receptors
have modeled risks between 20 and 100 in a million (shown as yellow squares) or less than 20
in a million (shown as green squares).
6.1.7 Incidence
The facility-specific Incidence file provides the overall total incidence for all modeled pollutants
from all sources in the given facility, the pollutant-specific total incidence for all sources
combined, and the individual incidence per source for each pollutant. As explained in Section
5.3, the incidence is calculated as the cancer risk of each populated receptor (e.g., census block
or alternate receptor) times the receptor population, divided by a 70-year average lifespan. This
individual populated receptor incidence is then summed over all populated receptors in the
modeling domain of the facility. Table 36 below describes the fields of information provided in
the facility-specific Incidence file. A sample Incidence output file is provided in Appendix A.
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Table 36. Fields Included in the Incidence File
Field
Description
Source ID
Pollutant
Emission (Pollutant) type
Incidence
Incidence, rounded
Individual source identification code, or "Total" for all sources
combined
Pollutant name, or "All modeled pollutants" for all pollutants
combined for each source and for the Total
P = particulate, V = vapor (gas), C = combined
Cancer risk or noncancer HQ
Cancer risk or noncancer HQ rounded to one significant
figure
6.1.8 Cancer Risk Exposure
The Cancer Risk Exposure file is a simple two column (two field) file that provides the
population numbers exposed to various cancer risk levels in the modeling domain surrounding
the facility. Population numbers are provided for the following cancer risk levels:
Greater than or equal to 1 in 1,000 (>1,000-in-a-million risk);
Greater than or equal to 1 in 10,000 (>100-in-a-million risk);
Greater than or equal to 1 in 20,000 (>50-in-a-million risk);
Greater than or equal to 1 in 100,000 (>10-in-a-million risk);
Greater than or equal to 1 in 1,000,000 (>1-in-a-million risk); and
Greater than or equal to 1 in 10,000,000 (>0.1-in-a-million risk).
A sample Cancer Risk Exposure output file is provided in Appendix A.
6.1.9 Noncancer Risk Exposure
The Noncancer Risk Exposure file, like the Cancer Risk Exposure file described above, is a
simple file that provides the population numbers exposed to various HI levels for all 14 TOSHIs,
in the modeling domain surrounding the facility. Population numbers are provided for the
following noncancer HI levels:
• Greater than 100;
• Greater than 50;
• Greater than 10;
• Greater than 1.0;
• Greater than 0.5; and
• Greater than 0.2.
Population numbers at each of the above noncancer HI levels are provided for the following
TOSHIs:
• Respiratory HI;
• Liver HI;
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• Neurological HI;
• Developmental HI;
• Reproductive HI;
• Kidney HI;
• Ocular HI;
• Endocrine HI;
• Hematological HI;
• Immunological HI;
• Skeletal HI;
• Spleen HI;
• Thyroid HI; and
• Whole Body HI.
A sample Noncancer Risk Exposure output file is provided in Appendix A.
6.1.10 All Inner Receptors
The All Inner Receptors file provides the chronic concentration (in jjg/m3) and (if optionally
modeled) the acute concentration of every populated (census block or alternate) receptor inside
the modeling distance, as well as every user-defined receptor. Note: All concentrations in this
file are discretely (explicitly) modeled, not interpolated. This file will also contain the deposition
flux (in g/m2/y) if you opted to calculate deposition with or without depletion. Columns for both
dry and wet deposition flux results are provided and will be populated with non-zero results
depending on the type of deposition modeling (wet, dry or both) you selected in the Facility List
Option fields. Table 37 below describes the fields of information provided in the All Inner
Receptors file. A sample All Inner Receptors file output file is provided in Appendix A.
6.1.11 All Outer Receptors
The All Outer Receptors file includes nearly the same information provided in the All Inner
Receptor file (described above) for every receptor located between the modeling distance (often
specified as 3 km) and the outer edge of the modeling domain (the "maximum distance" often
specified as 50 km). The dry and wet deposition fluxes provided in the All Inner Receptors file,
however, are not provided in this file, for the outer receptors. Note: All concentrations in this file
are interpolated using the polar grid receptors, not discretely (explicitly) modeled. Table 37
below describes the fields of information provided in the All Outer Receptors file. A sample All
Outer Receptors file output file is provided in Appendix A.
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Table 37.
Fields Included in the All Inner and All Outer Receptor Files
Field
Description
FIPS code
Five-digit Federal Information Processing Standard (FIPS) code which
uniquely identifies the county of the receptor if the receptor is a census
block. (Note: For alternate receptor run, there is a field called "Receptor
ID")
Block ID
10-digit census block ID for linking to census demographic data, if the
receptor is a census block. (Note: For alternate receptor run, there is a
field called "Receptor ID")
Latitude
Decimal
Longitude
Decimal
Source ID
Individual source identification code affiliated with given concentrations
Emission
P = particulate, V = vapor (gas), C = combined
(Pollutant) type
Pollutant
Pollutant name affiliated with given concentrations
Cone
Chronic air concentration in |jg/m3
Acute Cone
Acute (short-term) air concentration in |jg/m3, if modeled
Elevation
Elevation in meters above sea level
Dry deposition
Dry deposition flux in g/m2/year, if modeled (not included in All Outer
Receptor file)
Wet deposition
Wet deposition flux in g/m2/year, if modeled (not included in All Outer
Receptor file)
Population
Population of receptor
Overlap
N for No, Y for Yes. Note: the value shown is the originally modeled
value, even if overlapped (and therefore not used in other files such as
the Maximum Individual Risk, Risk Breakdown, and Block Summary
Chronic files)
6.1.12 All Polar Receptors
The All Polar Receptors file provides similar information to the All Inner Receptors and All Outer
Receptors for the nodes of the polar receptor grid, including the chronic concentration (in jjg/m3)
and (if optionally modeled) the acute concentration of every polar receptor. Note: Like the All
Inner Receptors file, all concentrations in the All Polar Receptors file are discretely (explicitly)
modeled, not interpolated. Likewise, this file will also contain the deposition flux (in g/m2/y) if you
opted to calculate deposition with or without depletion. Columns for both dry and wet deposition
flux results are provided and will be populated with non-zero results depending on the type of
deposition modeling (wet, dry or both) you selected in the Facility List Option fields. In addition,
this file will contain the distance from the center of the polar network, the angle, sector, and ring
number that describes the location of each polar receptor. Table 38 below describes the fields
of information provided in the All Polar Receptors file. A sample All Polar Receptors file output
file is provided in Appendix A.
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Table 38. Fields included in the All Polar Receptors File
Field
Description
Source ID
Individual source identification code
Emission
P = particulate, V = vapor (gas), C = combined
(Pollutant) type
Pollutant
Pollutant name affiliated with given concentrations
Cone
Chronic air concentration in |jg/m3
Acute Cone
Acute air concentration in |jg/m3
Distance
Distance in meters from the center of the polar network of the polar
receptor's location on polar rina
Angle
Anale from north of the polar radial on which the polar receptor is
(from north)
located (0 to 360 degrees)
Sector
Sector number within the polar network ("the number depends on number
of radials indicated in vour Facility List Options file: default is 1-16)
Ring number
The number of the rina ("circle") in the polar network on which the
receptor is located, beginning with number 1 closest to facility center
Elevation
Elevation in meters above sea level
Latitude
Decimal
Longitude
Decimal
Overlap
N for No, Y for Yes. Note: the value shown is the originally modeled
value, even if overlapped (and therefore not used in other files such as
the Maximum Individual Risk, Risk Breakdown, and Ring Summary
Chronic files).
Wet deposition
Wet deposition flux in g/m2/year, if modeled (not included in All Outer
Receptor files)
Dry deposition
Dry deposition flux in g/m2/year, if modeled (not included in All Outer
Receptor files)
6.1.13 AERMOD Outputs
With each run, HEM4 automatically provides a set of AERMOD text files that track the inputs
and keywords (modeling commands) passed to AERMOD, including the receptor network and
meteorological files, as well as the AERMOD outputs. The outputs produced by AERMOD are
then passed back to HEM4 and used to produce the other outputs described in this guide. You
should review these AERMOD text files (especially the aermod.out file described below) to
confirm that AERMOD completed its modeling without error. These text files are described
below:
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• aermod.inp - a text file for combined particle and vapor phase emissions listing the
inputs passed to AERMOD for modeling, including modeling control options (see
AERMOD User's Guide), rural or urban dispersion environment, averaging time, specific
input file parameters (e.g., from the Emissions Location file), the network of discrete
receptor coordinates (block or alternate receptors in UTM), elevations and hill heights,
meteorological data, and designated text formatted output files. Note: If particle and
vapor phase emissions are modeled separately, then the above information will be
provided for particle phase emissions in an aermod_P.inp file and for vapor phase
emissions in an aermod_V.inp file.
• aermod.out - a text file for combined particle and vapor phase emissions listing the
inputs received by AERMOD in the aermod.inp file (noted above), any fatal error
messages, warning messages, informational messages, indication of successful
AERMOD set-up or not, AERMOD version number used for modeling, type of deposition
and depletion modeled if any, modeling options employed, whether short-term (acute)
concentrations were modeled along with their period, number and type of sources,
number of receptors, vintage of meteorological data used, emission rates modeled for
each source (in grams per second), elevations and hill heights of every discrete (census
block or alternate) receptor and every polar grid receptor, UTM coordinates and unit
HAP chronic concentration at every receptor for each source, UTM coordinates and unit
HAP short-term/acute concentration (if modeled) based on the acute high value
selected, the number of hours processed, the number of calm (very low wind) hours
identified, the number of missing hours in the meteorological data used for modeling,
and an indication whether AERMOD finished the modeling run successfully or not. Note:
If particle and vapor phase emissions are modeled separately, then the above
information will be provided based on particle phase emissions in an aermod_P.out file
and for vapor phase emissions in an aermod_V.out file. Deposition fluxes (wet/dry) will
be provided with depletion applied to concentrations, if modeled.
• plotfile.plt - a text file for combined particle and vapor phase emissions listing the
average modeled chronic concentration at every UTM receptor location and each
modeled source. Note: If particle and vapor phase emissions are modeled separately,
then these concentrations will be provided based on particle phase emissions in a
plotfile_p.plt file and in a plotfile_v.plt file for vapor phase emissions. Deposition fluxes
(wet/dry) will be provided with depletion applied to concentrations, if modeled.
• maxhour.plt - a text file for combined particle and vapor phase emissions listing the
modeled short-term/acute concentration (based on the acute high value indicated in your
Facility List Options file) at every UTM receptor location and each modeled source. Note:
If particle and vapor phase emissions are modeled separately, then these acute
concentrations will be provided based on particle phase emissions in a maxhour_p.plt
file and for vapor phase emissions in an maxhour_v.plt file.
Note: Concentration results provided by AERMOD in the above files should not be interpreted
as predicted concentrations of any pollutant listed in the HEM4 input files. Rather, these
AERMOD results reflect concentrations attributable to a unit-emission rate (1 kg/s), which HEM4
converts to specific modeled pollutant emissions, as explained in Section 5 above. To fully
understand the AERMOD processing and output files, refer to the AERMOD documentation for
further guidance (EPA 2019a, EPA 2019b).
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6.1.14 Input Selection Options
The Input Selection Options output file is a useful QA file to refer to because it provides a record
of the modeling options you chose for the run, as well as the names and location of the input
files you indicated. The following information is provided in this file:
• Facility ID;
• AERMOD control options used;
• Phase of emissions;
• Dispersion environment (rural or urban or blank for default);
• Whether deposition was modeled;
• Whether depletion was modeled;
• Type of deposition modeled for particle and vapor;
• Type of depletion modeled for particle and vapor;
• Whether elevations were modeled (or flat terrain used);
• Acute averaging period (e.g., 1 hour);
• Acute multiplier (factor applied to annual average emissions, if any);
• Whether building downwash was modeled;
• Whether user receptors were modeled;
• Maximum domain distance used (in meters);
• Modeling distance used (in meters);
• Overlap distance used (in meters);
• Number of polar rings used;
• Number of polar radials used;
• Whether acute was modeled;
• Distance to first ring (meter);
• Whether FASTALL was used;
• Run group name;
• Facility List Options file - name/location;
• Emissions Location file - name/location;
• HAP Emissions file - name/location;
• User Receptor file - name/location (if used);
• Particle Size file - name/location (if used);
• Building downwash file - name/location (if used);
• Buoyant line file - name/location (if used);
• Landuse file - name/location (if used);
• Month-to-Seasons file - name/location (if used);
• Polygon vertex file - name/location (if used);
• Whether Alternate Receptors were used; and
• Whether any of the Alternate Receptors were missing population values. Note: To
compute incidence, population values are needed at every populated alternate receptor.
Even if only one Alternate Receptor is missing a value in its population field, incidence is
not computed by HEM4.
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6.1.15 Acute Maximum Concentrations (Optional)
If you optionally chose to model acute impacts for a given facility, HEM4 will produce an Acute
Chem Max output file. The Acute Chem Max output provides the maximum acute (short-term)
pollutant concentration at any receptor for all sources combined. The "maximum" reported in
this file refers to the acute high value you identified (e.g., the absolute maximum, the 99th
percentile, the 98th percentile) and is based on the acute multiplier you provided (e.g., 10 times
the average annual emission rate), as well as the acute averaging period (generally 1-hour) you
indicated in the respective acute fields of your Facility List Options file. The maxima provided in
the Acute Chem Max output may occur at any receptor—populated or unpopulated—including
census blocks, alternate receptors, polar grid receptors, and user-defined receptors. This file
also provides the specific location of the receptor with highest modeled concentration for each
pollutant - including UTM and latitude/longitude coordinates, FIPS, Block, distance from facility
center, and angle from north - as well as the elevation and hill height of the receptor. It should
be noted that each pollutant may cause a different receptor to be the maximum (based on
emissions of that specific pollutant). Finally, this output file also lists the acute reference
concentrations for 11 different acute benchmarks, above which adverse short-term health
impacts can be expected. For example, the file provides:
• the California Acute Reference Exposure Level (REL) benchmark;
• the Acute Exposure Guideline Level (AEGL1) for transient, reversible effects and AEGL2
for long-lasting, irreversible effects, based on one and eight hours of exposure;
• the Emergency Response Planning Guideline (ERPG-1) for mild or transient effects and
the ERPG-2 for irreversible or serious effects, based on one hour of exposure; and
• several other acute benchmark concentrations, as described in Table 41.
The EPA's Air Toxics Risk Assessment Library (EPA 2017) provides a more detailed description
of these acute benchmarks (available for download at http://www.epa.gov/fera/air-toxics-risk-
assessment-reference-library-volumes-1-3). Table 39 below describes the fields of information
provided in the Acute Chem Max file, and a sample file output file is provided in Appendix A.
Note: the concentrations reported in Table 39 are in |a,g/m3, while the acute benchmark values
(reference concentrations) are in mg/m3, and should therefore be multiplied by 1,000 for
comparison to the modeled concentrations.
6.1.16 Acute Populated Concentrations (Optional)
If you optionally chose to model acute impacts for a given facility, HEM4 will also produce an
Acute Chem Pop output file. The Acute Chem Pop file provides the same information described
above in the Acute Chem Max file, but for only populated receptors (census blocks, alternate
receptors and user-defined receptors), not unpopulated receptors. Therefore, the concentrations
shown in this file may or may not be the acute maxima/high values for all receptors; but they are
the acute high values for the populated receptors. See discussion in Section 6.1.16. Table 39
below describes the fields of information provided in the Acute Chem Max file, and a sample file
output file is provided in Appendix A. Note: the concentrations reported in Table 39 are in
|a,g/m3, while the acute benchmark values (reference concentrations) are in mg/m3, and should
therefore be multiplied by 1,000 for comparison to the modeled concentrations.
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Table 39. Fields included in the Acute Chem Max and Acute Chem Pop Files
Field Description
Pollutant
Cone
Cone sci
AEGL-1, 1-hour
AEGL-1, 8-hour
AEGL-2, 1-hour
AEGL-2, 8-hour
ERPG-1
ERPG-2
IDLH/10
MRL
REL
TEEL_0
TEEL_1
Population
Distance
Angle
Elevation
Hill
County FIPS
Census block ID
UTM east coordinate
UTM north coordinate
Latitude
Longitude
Receptor type
Notes
Pollutant name
High value maximum Acute Concentration in |ig/m3
High value maximum Acute Concentration, scientific notation, in |ig/m3
Acute Exposure Guideline Level 1 (AEGL-1) for a 1-hour exposure: the concentration
above which it is predicted that the general population, including susceptible individuals,
could experience notable discomfort, irritation, or certain asymptomatic, non-sensory
effects (mg/m3)
See AEGL-1 above, but for an 8-hour exposure
Concentration 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 for a 1-hour exposure (mg/m3)
See AEGL-2 above, but for an 8-hour exposure
Emergency Response Planning Guideline 1 (ERPG-1): concentration below which it is
believed nearly all individuals could be exposed for up to 1 hour without experiencing
other than mild transient adverse health effects or perceiving a clearly defined
objectionable odor (mg/m3)
Concentration below which it is believed nearly all individuals could be exposed for up to
1 hour without experiencing or developing irreversible or other serious health effects or
symptoms that could impair an individual's ability to take protective action (mg/m3)
Immediately Dangerous to Life or Health: concentration believed likely to cause death or
immediate or delayed permanent adverse health effects or prevent escape from such an
environment, divided by a factor of 10 (mg/m3)
Acute Minimal Risk Level: daily human exposure that is likely to be without appreciable
risk of adverse noncancer health effects over a specified duration of exposure (mg/m3)
Reference Exposure Level: concentration below which no adverse health effects are
anticipated, based on the most sensitive adverse health effect reported (mg/m3)
Temporary Emergency Exposure Limit 0 (TEEL) defined by the U.S. Department of
Energy: the threshold concentration below which most people will experience no adverse
health effects
Maximum airborne concentration below which it is believed nearly all individuals could
be exposed for up to 1 hour without experiencing more than mild, transient adverse
health effects or perceiving a clearly defined objectionable odor
If the receptor is a census block or alternate receptor
From the center of the modeling domain (in meters)
From north
In meters above sea level
Controlling hill height in meters above sea level, as described in Section 2.3.1
If the receptor is a census block. (Note: For alternate receptor run, there is a field called
"Receptor ID")
For linking to demographic data (if the receptor is a census block). (Note: For an
alternate receptor run, there is a field called "Receptor ID")
In meters
In meters
Decimal
Decimal
C = census block or alternate receptor, P = populated receptor user-defined receptor,
PG = polar grid receptor, B = boundary receptor, M = monitor
Indicates whether the receptor was discretely (explicitly) modeled or interpolated
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6.1.17 Acute Breakdown (Optional)
If you chose to optionally model acute impacts for a given facility, HEM4 also produces a third
acute output file entitled Acute Bkdn, which provides the contribution ("breakdown") of each
emission source to the receptor of maximum acute impact for each pollutant (i.e., the acute
concentration of pollutant at the maximum receptor for that pollutant, caused by each source).
This information is provided for both the maximum/high value receptor (whether populated or
nonpopulated) and for the highest populated receptor.
The acute breakdown file includes the following fields:
• pollutant;
• Source ID;
• emission type (P for particle, V for vapor, C for combined);
• the maximum pollutant concentration (|jg/m3) at a populated receptor;
• the maximum pollutant concentration (jjg/m3) at all receptors (both populated and
unpopulated); and
• columns indicating whether the pollutant's concentration at each receptor was
interpolated or not.
Note: Concentration values are interpolated outside the modeling distance (e.g., between 3 km
and 50 km).
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6.2 Run Group Outputs
In addition to the facility-specific outputs listed in Section 6.1, HEM4 produces three summary
output files, based on the results for the entire run group of modeled facilities. These multi-
facility outputs are updated after the output files for the individual facilities have been created
and essentially concatenate the individual facility results into group-wide summary files. In each
of these three xlsx files, HEM4 writes one row of information for each facility upon completion of
that facility's individual modeling run. The three group-wide output files created by HEM4 in the
following sections and sample files are provided in Appendix A. Note: These files will be
produced even if you are modeling only one facility.
6.2.1 Facility Max Risk and HI
The Facility Max Risk and HI output file provides the maximum modeled risk and hazard index
results for every facility as well as additional facility-specific modeling results, including:
• a listing of all Facility IDs modeled;
• the cancer risk at the receptor that experiences the highest risk in the modeled
radius around each facility (i.e., facility-specific MIR);
• whether or not the MIR (max cancer risk) is interpolated from nearby receptors9;
• the type of receptor where the MIR (max cancer risk) occurs (e.g., census block,
alternate receptor, polar grid, user-defined receptor);
• the latitude and longitude of the MIR (cancer) receptor;
• the census block ID, alternate receptor ID or user receptor ID of the MIR receptor;
• the 14 TOSHIs at the receptors that experience the maximum TOSHI for each facility
including: whether or not the TOSHI value is interpolated, the receptor type(s) where
the max TOSHIs occur, the latitude and longitude for certain max TOSHI receptors
(e.g., respiratory, neurological), and the census block ID, alternate receptor ID or
user receptor ID of each max TOSHI receptor;
• the population, if any, excluded from the modeling run because of any census block
centroid(s) located within the overlap distance around each emission source (and
therefore considered on facility property)10;
• the cancer incidence (predicted excess cancers per year due to modeled emissions)
at each facility;
• the file name of the meteorological station used in the modeling of each facility;
• the distance (in kilometers) from the facility center to the meteorological station used
in the modeling run;
• the latitude and longitude location of the facility center; and
• the dispersion environment used by HEM4 for modeling each facility - rural or urban.
The TOSHIs modeled by HEM4 can impact the following organs and organ systems:
respiratory; liver; neurological; developmental; reproductive; kidney; ocular; endocrine;
hematological; immunological; skeletal; spleen; thyroid; and whole body. In the sample
abbreviated Facility Max Risk and HI provided in Appendix A, only respiratory HI is shown,
9 An interpolated MIR generally suggests that the modeling distance should be increased and the facility
remodeled.
10 A value in the population overlap field generally indicates that the facility should be remodeled (e.g.,
with a smaller overlap distance specified) to ensure that the population associated with the census block
centroid(s) is accounted for.
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which is commonly the highest TOSHI level based on the dispersion and inhalation modeling
performed by AERMOD and HEM4.
6.2.2 Facility Cancer Risk Exposure
The Facility Cancer Risk Exposure output file lists the facilities by ID, their corresponding
latitudes and longitudes (of the calculated facility centers), and the population exposed to
different cancer risk levels surrounding each facility, including:
• the number of people from each facility exposed to a cancer risk level greater than
or equal to 1 in 1,000 (or 1,000 in a million);
• the number of people from each facility exposed to a cancer risk level greater than
or equal to 1 in 10,000 (or 100 in a million);
• the number of people from each facility exposed to a cancer risk level greater than
or equal to 1 in 100,000 (or 10 in a million);
• the number of people from each facility exposed to a cancer risk level greater than
or equal to 1 in 1,000,000 (or 1 in a million); and
• the number of people from each facility exposed to a cancer risk level greater than
or equal to 1 in 10,000,000 (or 0.1 in a million).
Note that each row of this output file is facility-specific and does not reflect the impacts of
multiple facilities with overlapping modeling domains (which may impact the same receptor and
increase population numbers at various risk levels beyond what each single facility causes). A
sample Cancer Risk Exposure file is provided in Appendix A.
6.2.3 Facility TOSHI Exposure
The Facility TOSHI Exposure output file lists the facilities by ID and the number of people with a
TOSHI greater than 1 for each facility and for each of the 14 TOSHIs currently modeled by
HEM4. Note: Because the convention of one significant figure is employed, an HI greater than 1
equates mathematically to an HI greater than or equal to 1.5. A Facility TOSHI Exposure file is
provided in Appendix A.
6.2.4 Additional Run Group Outputs
HEM4 will also produce several other group output files with each run, including:
• An Inputs folder containing every input file used by HEM4 (that you provided) for
your modeling run - a useful QA feature to ensure the inputs you intended to be
modeled were indeed the ones modeled
• A Google Earth™ map showing the source locations at every facility in your
modeling run - named AIIFacility_source_locations.kmz
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• A hem4.log text file, as described in Section 4.4, which provides a permanent
record of your model run - includes the files uploaded, the output files produced,
whether the run was successful and/or any errors that occurred
• If HEM4 could not model all facilities listed in your inputs, a Skipped Facilities file
(Skipped_Facilities.xlsx) will be produced which simply lists the IDs of those skipped
facilities. You may use this to remodel those facilities, after correcting or amending
the issues that caused the facilities to be skipped. This is discussed further in
Section 9.
Note: Do not change the names of the facility-level or HEM4 output files (discussed above),
as several of these files are referenced by their specific names in the code of the Risk Summary
Report programs, described next in Section 7.
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7. Risk Summary Reports
You may choose to run nine different Risk Summary Reports, as described in the step-by-step
HEM4 instructions in Section 4.5. These reports, like the Run Group outputs described in
Section 6.2, are based on risk results from all facilities modeled in your run group. However, the
Risk Summary Reports have the added benefit of taking into account multiple impacts on the
same receptor from nearby facilities. The nine Risk Summary Reports are described in this
section.
7.1 Max Risk Summary
The Max Risk Summary output (max_risk.xlsx) provides the maximum cancer risk and
maximum noncancer risk for all 14 TOSHI's at any populated receptor in the run group,
accounting for multiple impacts on receptors from neighboring facilities. This summary also
provides the FIPS and block ID for census blocks, the alternate receptor ID, or the user receptor
ID of each of the maxima, as well as the receptor's population. The Max Risk Summary also
lists the Facility ID(s) of the facility or facilities that impact these max receptors (i.e., contribute
to the max risk and max TOSHIs at these receptors). Note: The maxima reported in this
summary will equal the highest facility-specific risk and HI listed in the Facility Max Risk and HI
output (discussed in Section 6.2.1), except when more than one facility's impacts on the same
receptor cause the max risk and HI to be greater than the highest facility-specific risk and HI. A
sample Max Risk Summary file is shown in Figure 20.
RiSKTYPE
FIPS
BLOCK
POPULATION
RISK
2
mir
"35045
0613001004
126
4.8218E-07
3
respiratory
r36Q45
"0613001004
126
0.003784775
4
liver
"36045
0613001004
126
0.00016653
5
neurological
*37165
0104001092
16
0.115473556
8 _
developmental
*36045
0613001004
126
2.37305E-05
reproductive
0
0
kidney
0
0
ocular
0
G
endocrine
G
0
11
hematological
0
0
12
immunological
0
0
13
skeletal
0
c
}±
spleen
O
0
15
thyroid
0
0
16
whole body
0
0
Facilities
Facilities
Facilities
Facilities
Facilities
Facilities
Facilities
Facilities
Facilities
Facilities
Facilities
Impacting
Facilities
impacting
Impacting
Impacting
Facilities Facilities
Impacting
Impacting
Impacting
Impacting
impacting
Impacting
Impacting mir
respiratory
Impacting
neurological
developmental reproductive
Impacting Impacting
endocrine
hematological
immunological
skeletal
spleen
thyroid
1?
Block
Block
liver Block
Block
Block
Block
kidney Block ocular Block
Block
Block
Block
Block
Biock
Block
IS
'9
3e04511259
"3604511259
'"3604511259
360^511259
"3604511259
5604511259 '3604511259
"360-1511259
"3604511259
3604511259
"3604511259
3604511259
"'3604511259
whole body
Block
Figure 20. Sample Max Risk Summary Output
HEM4 User's Guide Page 112
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7.2 Cancer Drivers Summary
The Cancer Drivers output (cancer_drivers.xlsx) provides the pollutants and sources that are
driving the maximum risk at each modeled facility (i.e., those pollutant-source combinations
driving the risk at the receptor with the highest risk, for each facility). This file lists the facilities
by ID; the MIR modeled at each facility from all pollutants and emission sources acting on the
receptor; the predominant pollutant(s) and emission source(s) contributing to at least 90% of
that facility's MIR; the cancer risk associated with each of those pollutant-source combinations;
and the percentage risk contribution to the MIR for each. Figure 21 shows a sample output.
Note: The Risk Contribution column for each facility will not sum to 100%, because only the
pollutant-source combinations that add to at least 90% are displayed.
AS C D E F
Facility 10
MIR
Pollutant
Cancer Risk
Risk Contribution
Source ID
2
"27C536222U1
1.13E-06
Arsenic compounds
8.90885E-07
78.84
CEPM0005
¦%
j
270536222111
1.13E-D5
N;c
-------
facility). Figure 22 shows a sample output. Note: The Percentage column for each facility will
not sum to 100%, because only the source-pollutant combinations that add to at least 90% are
displayed.
A
8
C P
E
F
6
Facility ID
HI Type
HI Total Source ID
Pollutant
Hazard Index
Percentage
2
Faci-NC
Developmental Hi
6,9969312 SR0CC001
arsenic compounds
8.964020JO 1
99.63
3
Faci-NC
Kidney HI
1.4796741 SRG0C0DI
cadmium compounds
1.445809726
97.71
PacI-NC
Respirator> Hi
0.677C494 RW00C001
acrolein
C.557S8J699
S2 4
5
Facl-NC
Respirator,- Hi
0.677CU94 CUOCOOCI
b!5(2-etnyliexyi}phfiaiate
0.115999602
16.84
6
facl-\C
Liver HI
0.1816665 FUOOOOOi
bis{2-ethylne.xy!;pnthalate
0.113999602
62.75
7
Faci-NC
Liver Hi
0.1S1666S RWC00001
tricnloroethyler,e
0.066946044
36.85
s
Facl-NC
MeufQioc.iMi Hi
0 0S82436 RW000001
tricnloroettylene
0.066946044
75,86
9
Faci-MC
Neurological HI
C.0882496 -UOOOOG1
mercur> (elemental;
0.020602538
23.35
Faci-MC
Reproductive HI
0.074839? RVvQCOOOl
tficnloroethylene
0.066946044
S3.62
f-aci-NC
Reproductive HI
0.0746997 RVCOCOOl
I, j-Bjtadie^e
0 007751977
10.3S
12
catlAC
immunological H!
0.0671S72 RW000001
tricnloroethylene
0.066946044
99,64
•J
FacML
Liver HI
C.0405107 "UOOCGOI
bis|2-etT/llekyl;prithalate
0,037081163
91.53
•4
FacML
Respiratory HI
0.039644 =U'00C001
bis|2-ethyliiexyl;phtnalate
0.037CS1163
93.54
'5
Fac2.lL
\ etiological Hi
0.0266972 "UOOOOOi
•nercaiy (elemental!
0.023278107
87.19
'c
Fac2-!L
Neurological Hi
0.0266972 fUOOCOCi
mercury (elemental;
0.00256432
9.61
17
Faci-MC
Hematological H'
0.0CC917S fLiOOOOOl
selenium compounds
0 000910023
9?. IS
18
FatMi.
Hematological HI
C.0CC8619 -LiOOOOCI
selenium compounds
0.000354773
39.18
'9
Fad- NC
Skeletal Hi
0.C00737 RW0CC0O1
hyorofSuoricacid
0.000796977
100
;c
Facl-NC
Endocrine HI
2.296E-06 RV0000C1
c jmene
2.23579E-06
100
-i i
Fac2-il
Reproductive Hi
2 124E-G8 -JTOCOOl
bemc^alpyre-ie
1.60C48E-06
75.34
22
Fac2-IL
Developmental HI
2.124E-06 -JOOOQCJ,
benzo^ajpyreie
1.6004SE-06
75.34
23
Fac2-IL
Reproductive HI
2.124E-06 FL'000001
benzojalpyrene
5.23S44E-07
24,66
iFac2-!l
Developmental H»
2.124E-06 =U0D000i
bemcTajpywie
5.23844E-07
24,66]
Figure 22. Sample Hazard Index Drivers Summary Output
7.4 Risk Histogram Summary
The Risk Histogram output (histogram_risk.xlsx) provides the population and facility counts at
various risk levels. This file lists the number of people and facilities in the modeled run group in
the following risk bins:
• less than 1 in 1 million risk (displayed as "<1e-6");
• greater than or equal to 1 in 1 million risk (displayed as ">= 1e-6");
• greater than or equal to 10 in 1 million risk (displayed at ">=1e-5");
• greater than or equal to 100 in 1 million risk (displayed as ">=1e-4"); and
• greater than or equal to 1,000 in 1 million risk (displayed as ">=1e-3").
Note: This program assigns populations and facilities to cancer risk bins based on their risk
level after rounding to one significant figure, per EPA convention. Also, note that the Risk
Histogram Summary takes into account multiple impacts on the same receptor (from facilities
located close to one another). This may cause the population numbers from this file to differ
from the population numbers provided by the Facility Cancer Risk Exposure file. Figure 23
shows a sample output. Finally, it should also be noted that the total population modeled in the
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run group can be determined by summing cells B2 and B3; and the total number of facilities
modeled can be determined by summing cells C2 and C3.
A
B
C
1 ]
Risk level
Population
Facility count
2
=le-6
835305
2
4
>=le-5
45866
1
5
>=le-4
435
1
6
>=le-3
0
0
Figure 23. Sample Risk Histogram Summary Output
7.5 Hazard Index Histogram Summary
The Hazard Index Histogram output (hi_histogram.xlsx) provides the population and facility
counts at various noncancer HI levels, for all 14 TOSHIs. This file lists the number of people and
facilities in the modeled run group in the following noncancer HI bins:
• > 1,000;
• > 100;
• > 10;
• >1; and
• <= 1.
Note: This program assigns populations and facilities to noncancer HI bins based on their HI
level after rounding to one significant figure, per EPA convention. Also, note that the Hazard
Index Histogram Summary takes into account multiple impacts on the same receptor (from
facilities located close to one another). This may cause the population numbers from this file to
differ from the population numbers provided by the Facility TOSH I Exposure file. Figure 24
shows an abbreviated sample output for 3 TOSHIs; the actual file shows results for 14 TOSHIs.
A
8
C
D
E
F
G
1 |
HI Level
Respiratory
Pop
Respiratory
Facilities
Liver Pop
Liver
Facilities
Neurological
Pop
Neurological
Facilities
2
>1000
0
0
0
0
0
0
3
>100
0
0
0
0
0
0
4
>10
0
0
0
0
0
0
5
>1
167
1
0
0
22
1
6
<=1
3924289
1
3924289
2
3924434
1
Figure 24. Sample Hazard Index Histogram Summary Output (Partial)
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7.6 Incidence Drivers Summary
The Incidence Drivers output (incidence_drivers.xlsx) provides the pollutants driving the
incidence across the entire run group of modeled facilities. (As noted in previous sections, the
incidence is equal to the cancer risk of each block times the population of that block, divided by
a 70-year lifetime, and summed over all blocks in the modeling domain.) In this file, the total
incidence and individual incidence attributable to each pollutant are provided, as well as the
percentage that pollutant-specific incidence is of total incidence. The pollutants are listed in
descending order of contribution to the total incidence. Figure 25 shows a sample output.
A ;
1
2
3 ™
4
5
6 '
7
a
9
Pollutant
arsenic compounds
1,3-butadiene
cadmium compounds
naphthalene
benzene
bis(2-ethylhexyi)phthalate
chromium (vij compounds
trichloroethylene
Total incidence
i
Incidence
0.039060199'81.83%
0.003666767*7.68%
0.D0B205572 *6.72%
0.00105277*^.21%
0.000444901^.93%
0.000219206 *0,4616
5,§§44!-£IS *0.12%
1.01883E-05 15.02%
0.04773414 *100%
% of Total Incidence
Figure 25. Sample Incidence Drivers Summary Output
7.7 Acute Impacts Summary
The Acute Impacts output (acutejmpacts.xlsx) provides the maximum acute concentration for
every modeled pollutant, six acute benchmark values (REL, AEGL1, AEGL2, ERPG1, ERPG2
and IDLH, as defined above in Table 41), and the hazard quotient (HQ) based on the ratio of the
pollutant's max acute concentration to the dose-response values for those six benchmarks. It
should be noted that the max acute concentration is based on the acute high value you chose in
your Facility List Options file. The file also provides the receptor ID at which this max acute
concentration occurs, including the FIPS and block ID for a census block receptor, the alternate
receptor ID, the user receptor ID, or the distance and angle for a polar receptor.
The Acute Impacts Summary is available only if you entered Y in the acute column of the
Facility List Options input file prior to modeling, for one or more facilities in your run group. Note:
The pollutant concentration is provided in mg/m3 in this output (not |jg/m3 as provided by HEM4
at receptor locations in other output files) because the benchmark values are based on mg not
|jg). Figure 26 shows an abbreviated sample screenshot of the Acute Impacts Summary file.
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7.8 Multipathway Summary
The Multipathway Summary output (multi_pathway.xlsx) provides arsenic, polycyclic aromatic
hydrocarbon (PAH) and dioxin/furan (D/F) concentrations and risk at MIR receptors and within
directional octants around each facility, which are useful for a post-HEM4 multipathway
analysis.
This file lists the following information:
• the Run Group's label;
• the Facility ID;
• whether the facility was modeled using an urban or rural dispersion environment;
• whether the receptor in a given output row is an MIR or the closest receptor to the
facility center in a specific octant direction (E, N, NE, NW, S, SE, SW, W);
• the pollutants the MIR is attributable to (All HAP, As for Arsenic, PAH, or D/F for
Dioxins/Furans);
• whether the closest octant receptor is at a census block centroid, alternate receptor,
or a discrete user receptor;
• the FIPS plus Block ID of the census receptor, or the ID of alternate and user
receptor;
• the latitude and longitude location of the receptor;
• the population of the receptor;
• the total inhalation risk of that receptor (for all HAP);
• the total inhalation risk of that receptor attributable to Arsenic compounds;
• the total inhalation risk of that receptor attributable to PAHs; and
• the total inhalation risk of that receptor attributable to Dioxins/Furans.
Figure 27 shows a screenshot of a sample Multipathway Summary file. Note that blank cells
indicate that emissions from this sample facility do not include arsenic, PAH, or D/F.
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14 other
benchmark
Facility ID
Pollutant
CONCMG/MJ
REi
AFGl 1 1H columns]
HQREL
HQ AFGl 1
Facl-NC
acetaidehyde
0.014333118
0.47
81
0,03050876
0,00017703
Facl-NC
acrolein
0.100373315
0.0025
0.063
40.143526
1.45469297
Facl-NC
arsenic compounds
0.069242032
0.0002
0
346,21016!
0
Facl-NC
benz(a]anthracene
1.61754E-06
0
0
0
0
Facl-NC
benzene
0.029947323
0
170
0
0.00017616
14 other HQ
columns based
on 4 other
benchmarks!
14 columns
indicating Receptor
ID or distance and
angle for polar
receptor]
Figure 26. Sample Acute Impacts Summary Output (abbreviated)
Chem,
Facility
Rural/
Octant or
Centrold,
Total Inhalation
Total Inhalation
Total Inhalation
Total inhalation
Run Group
ID
Urban
MIR
or Discrete
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7.9 Source Type Risk Histogram Summary
The Source Type Risk Histogram Summary (source_type_risk.xlsx) output provides a table
showing the maximum cancer risk overall for the run group, as well as individually by emission
source type. For the maximum overall risk and for the source type-specific risk, the file also
provides the number of people estimated at three risk levels: >= 1 in 1 million, >= 10 in 1 million,
and >= 100 in 1 million. The overall incidence and the incidence attributable to each emission
source type is also provided. Figure 28 shows a screenshot of a sample Source Type Risk
Histogram Summary file.
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Figure 28. Sample Sourcetype_Histogram_Sorted RTR Summary Output
Note: The Maximum Overall column lists the population at various risk levels attributable to all
source types/emission process groups combined, while the other columns list the population at
various risk levels attributable to each individual source type in isolation. The sum of the
population tallies across the individual source types may not necessarily equal the
corresponding value in the maximum overall column, at a given risk level, because: (a) two or
more source types' impact in combination may be required to cause a census block population
to exceed a given risk level; or conversely (b) an individual source type's impact in isolation may
be enough to cause a census block population to exceed a given risk level, while other source
types may similarly impact the same census block population and also (in isolation) cause that
population to exceed the given risk level.
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8. Understanding the Risk Results
This section contains an overview on using some of the HEM4 outputs and Risk Summary
Reports to ascertain the cancer risks, noncancer hazards and acute impacts posed by a group
of modeled facilities.
Step 1: Open the Max_Risk.xlsx summary report output to obtain the highest cancer
risk and noncancer TOSHIs for all the modeled facilities in your run group, as well as the
max receptor IDs and population at each max receptor. You can also view the number of
facilities impacting each maximum receptor (in the case of nearby facilities impacting the
same receptor).
Step 2: Open the Facility_max_risk_and_Hl.xlsx output to obtain the facility-specific
MIR in column B (mx_can_rsk), as well as the facility-specific maximum TOSHI values in
each of their respective columns. Note: the highest facility-specific maximum is not
necessarily the run group maximum based on concurrent emissions from the entire
group of modeled facilities. Multi-facility impacts on the same receptor (from facilities
located close to one another) are not accounted for in the Facility_max_risk_and_
Hl.xlsx output file, because each row of this output file is specific to each individual
facility. Therefore, the run group maximum reported in the Max_Risk.xlsx summary
report (which, as mentioned in Step 1, accounts for multiple impacts on the same
receptor from more than one facility) will either be equal to or greater than the highest
facility-specific MIR in the Facility max risk and_HI.xlsx output.
Step 3: Open the Cancer_drivers.xlsx output to obtain the pollutant and emission
source type driving the modeled risk. To report the top cancer drivers for a run group,
use the Pollutant from column C and the Source ID from column F for all rows
associated with the facility showing the highest risk. The MIR value from this highest
facility will equal that listed in the Facilty_max_risk_and_Hl.xlsxf\\e from Step 2. Note:
This output does not account for 100% of the modeled risk, but rather provides those
pollutant-emission source combinations that contribute at least 90% to the facility's MIR
(from one or more pollutant-emission source combinations, depending on how many
combinations are needed to describe 90% of the modeled risk at each facility).
Step 4: Open the Histogram_risk.xlsx output to obtain the number of people and
facilities at various risk levels. The total population within the modeling domain (by
default a 50-kilometer radius around each facility or your user-specified radius) equals
the sum of cells B2 + B3. This histogram output counts facilities based on modeled risk
at populated census blocks, alternate receptors, and user receptors. Consequently, this
file's facility count numbers will be in accord with the manual counting of facilities at each
risk level from the Facility_max_risk_and_Hl.xlsx file. Note: What risk bin a facility falls
into in this output is based on the one significant figure rounding convention adopted by
the EPA.
Step 5: Open the Hazard_lndex_Drivers.xlsx output to obtain the pollutant and
emission source driving all (non-zero) TOSHIs at each modeled facility. To report the top
HI drivers for a run group, use the Pollutant from column E and the Source ID from
column D for all rows associated with the facility showing the highest total TOSHI in
column C ("HI Total"). The TOSHI value from this highest facility should equal the TOSHI
value listed in the Facilty_max_risk_and_Hl.xlsx file from Step 2. Note: This output
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does not account for 100% of the modeled TOSHI, but rather provides those pollutant-
emission source combinations that contribute at least 90% to the facility's total TOSHI.
Step 6: Open the Hi_histogram.xlsx output to obtain the number of people and
facilities at various HI levels for each of the 14 TOSHIs. These numbers are based on
the one significant figure rounding convention (e.g., an HI of 1.4 rounds to 1 and so is
considered <= 1).
Step 7: Open the lncidence_drivers.xlsx output to obtain the run group-wide incidence
attributable to each pollutant. This file is sorted in descending order of incidence and
column C provides the percentage each pollutant drives the total incidence for all of
your modeled facilities.
Step 8: Open the Source_type_risk.xlsx output to obtain the number of people at
various risk levels caused by each emission source type, and the incidence attributable
to each source type. This output also shows the run group MIR and the number of
people at various risk levels attributable to all source types combined ("Maximum
Overall" which accounts for impacts on the same receptor by different source types), as
well as the overall incidence.
Step 9: Open the Acutejmpacts.xlsx output, if you modeled acute impacts, to obtain
the hazard quotients (HQs) based on various benchmarks for each pollutant of interest,
as well as the highest acute concentration for each HAP. You can perform a manual
count using this output file to determine the number of facilities with an HQ >= 1.5 for
any benchmark. (Note: An HQ >=1.5 is the mathematical definition of "greater than 1"
when using EPA's one significant figure rounding convention.) This output file also
provides (in the far-right columns) the Receptor ID experiencing the maximum acute
concentration for each pollutant at every modeled facility.
Step 10: Open the AIIFacility_source_locations.kmz output to see all modeled
sources at each facility in your run group on a Google Earth™ map. This map provides a
ready view of the distance between your modeled facilities, and it allows you to perform
QA to determine whether the modeled locations of your sources are reasonable.
For additional details regarding the modeling results for each of the facilities in the run group,
open the individual facility subfolders in the output folder. Section 6 discusses these facility-
specific output files. Each facility folder also contains a source_risk.kmz output file which
displays the detailed modeled risk results for that facility on a Google Earth™ map.
Finally, HEM4 provides numerous graphical ways to review and understand your outputs, as
discussed further in Section 4.6 regarding the Analyze Outputs buttons on the HEM4 interface.
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9. Quality Assurance Remodeling
There are several quality assurance (QA) checks that you should perform after HEM4 has
completed modeling each of your facilities. These QA checks should be made before you run
the Risk Summary Reports (described in Section 4.5), to determine if any of the facilities need
to be revised and remodeled.
Ensuring the Maximum Individual Risk (MIR) and Max Target Organ-Specific Hazard Index
(TOSHIs) are Located at Populated Receptors
First, open and review the Facility max risk and Hl.xIsx fiIe to ensure that:
• the number of facilities modeled in column A equals the number of facilities in the input
files (e.g., Facility_List_Options.xlsx),
• the maximum cancer risk values in column B occur at census blocks, alternate
receptors, or populated user-defined receptors rather than at unpopulated polar grid (or
boundary or monitor) receptors, as noted in column D; and
• the TOSH I values in the various HI columns occur at census blocks, alternate receptors,
or populated user-defined receptors rather than at unpopulated polar grid (or boundary
or monitor) receptors.
The cancer risk and noncancer TOSHI QA checks described above are especially important for
facilities of interest, such as those facilities with relatively high cancer risk or TOSHI values in
the modeled set. Remodel those facilities (as described below) that failed one or more of the QA
checks before running the Risk Summary Reports. Rerunning HEM4 for such facilities will
ensure that all facilities in the run group are modeled and that the modeled maximum risk and
TOSHI values occur at populated receptors.
Follow these steps to rerun a facility when the MIR or the maximum TOSHIs occur at an
unpopulated receptor (such as a polar grid receptor)11. First, review the Source_risk.kmz file
located in the individual facility subfolder. Opening this file will start Google Earth™ if it is
installed on your computer. Figure 29 shows a sample Google Earth™ kmz output file.
Zoom in on the facility center and turn on the polar grid (by checking the box next to "Polar
receptor cancer risk" in the Places key) to make visible the polar grid receptor at which the MIR
or TOSHI value occurs. Next, find the census block centroid closest to the MIR polar receptor.
Use the 'ruler' tool to measure the distance (in meters) from the census block centroid to the
approximate facility center. Increase this distance enough to ensure that a census block
centroid near the current polar MIR receptor will be closer to the facility center than this revised
first polar ring when the facility is rerun, as explained further below. Follow these steps for all
facilities of interest requiring remodeling due to an overlapped populated receptor.
11 The MIR or maximum TOSHIs can occur at a polar grid receptor if there is a census block or alternate
receptor located within the overlap distance of the facility boundary. In this case, HEM4 will select the
closest receptor to the facility boundary (i.e., census block, alternate, user-defined, or polar) to estimate
the MIR at a location nearest to the population inside the overlap distance that has been excluded.
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Figure 29. Sample Source_risk.kmz HEM4 Output
To rerun the facility or facilities, create a copy of the input file Facility_List_Options.xlsx. Be
careful to name the new file so that it is obvious it is not the original Facility_List_Options.xlsx
file (e.g., QA 1_Facility_list_options.xlsx to indicate it is the first QA run). Delete the rows for
the facilities that do NOT have to be rerun.
Next, under the column heading ring 1', enter the value determined from the above instructions
(i.e., the distance in meters between the approximate facility center and the census block
centroid closest to the MIR polar receptor, rounded up). Save these changes and close the file.
Re-start HEM4 using the new QA1_Facility_list_ options.xlsx file as an input. (Note: There
may be extra facilities in the HAP Emissions and Emissions Location files from your original run,
because HEM4 will only use data for the facilities listed in the Facility List Options file you
specify for the QA run.) HEM4 will then remodel the facilities with revised first ring distances.
This "bumping out" of the first polar ring will allow HEM4 to choose a populated census block or
alternate receptor as the MIR or TOSH I receptor, because the first polar ring of polar receptors
will be more distant from the facility center than the closest populated receptor. When re-running
HEM4, it is advisable to name the 'output' folder using "QA1" in case additional QA runs are
necessary.
Once you have rerun the facility or facilities, check the outputs to determine if the relevant MIR
or TOSHI is now at a populated receptor by opening the QA1_Facility_max_risk_and_Hl.xlsx
file. If the MIR or TOSHI is still at a polar grid receptor, repeat the above steps (starting with
opening the Source_risk.kmz file) using the identifier QA2 for the naming convention. Make the
first ring of the polar grid more distant from the facility center than in the first adjustment.
After you have successfully adjusted the distance so that the MIR and maximum TOSHIs occur
at populated receptors, copy the most recent facility rows from all QA_Facility_max_risk_
andJHI.xIsx files (e.g., QA1, QA2, QA3) into the original Facility_max_risk_and_Hl.xlsx file.
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Perform this row replacement for each remodeled facility, using the most recent QA run
applicable to that facility. In addition, replace the original subfolder for each remodeled facility by
copying the most recent facility output subfolder (including all its revised contents) from the QA
run to the location of the original facility output. Move or delete the original subfolder.
Ensuring Maxima are Discretely Modeled, not Interpolated:
A facility may require remodeling (using the steps described above) if the MIR and/or max
TOSHI values of that facility are interpolated, rather than explicitly modeled. The Facility_
max_risk_and_Hl.xlsx output indicates interpolated maximum risk values in column C and
maximum TOSHI values in the columns to the right of each TOSHI value (e.g., column I for the
respiratory HI). If these fields are blank, then the values are not interpolated. Generally, a value
is interpolated if the maximum receptor is located outside the modeling distance within which
receptors are explicitly modeled (e.g., at a default value of 3,000 m or 3 km). This can occur if a
modeled facility is located in a sparsely populated area, where there are no census block
centroids or alternate receptors within the modeling distance (e.g., 3 km) of the facility center.
Open the Source_risk.kmz file located in the individual facility subfolder to determine if a facility
with an interpolated MIR and/or TOSHI should be remodeled with an increased modeling
distance. This Google Earth™ kmz file will show where the closest populated receptors are
located. The modeling distance should be increased to include the populated receptor(s). Use
Google Earth's™ ruler tool to determine the new modeling distance. Remember to increase this
distance slightly before remodeling the facility in a QA run, as discussed above. Note: If the risk
and all TOSHIs are considered low—and if the reason for the low values is that the facility is
located in a sparsely populated area—you may decide that revising the modeling distance and
remodeling is not necessary.
An interpolated MIR or TOSHI value may also occur if one or more of the emission sources is
mislocated - for example, with an incorrect latitude or longitude that places a source too far
from the actual facility location and other modeled sources. This interpolated situation requires
remodeling to correct the location inaccuracy. If one or more source is mislocated (as
determined by reviewing the facility specific Source_risk.kmz or the run group-wide
AIIFacility_source_locations.kmz file), perform a QA rerun for that facility using a corrected
Emissions Location file (and a corrected polygon vertex file and/or buoyant line parameters file,
if the misplaced source is configured as a polygon or buoyant line source).
In general, the image of each facility's emission sources and receptors on a Google Earth™
satellite map (i.e., the Source_risk.kmz file) is a powerful tool for QA checks of the inputs and
modeling parameters that HEM4 uses (see Figure 29 above as an example). It is best practice
to review each Source_risk.kmz file, even if all MIR and TOSHI values listed in the
Facility_max_risk_and_Hl.xlsx output occur at populated receptors and no values are
interpolated. Reviewing these map images allows you to determine if sources are mislocated
and require remodeling and if the surrounding populations are represented well enough by the
populated receptors. (If surrounding populations are not represented sufficiently by the
receptors, you can remodel with the addition of user-defined receptors placed near residences.)
This QA check of each Source_risk.kmz image is highly recommended. Even a QA check of a
kmz image that shows nothing amiss may prove useful. For example, if nothing looks amiss in
the Source_risk.kmz image, but the MIR and/or TOSHI values seem too high to be reasonable,
this may indicate an error in the emission amounts or pollutant names provided in the HAP
Emissions input file.
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Once you have performed all QA checks and remodeled any facilities, you are ready to run the
Risk Summary Reports, as described in Section 4.5. The Risk Summary Report programs need
as inputs the final Facility_max_risk_and_Hi.xisx and several facility-specific outputs (depending
on the HEM4 options you selected and which Risk Summary Reports you run). Therefore, do
not rename the HEM4 output files.
Modeling Skipped Facilities:
As noted in Section 4.8, if HEM4 is unable to model a facility or facilities due to errors in the
inputs, HEM4 will produce an Excel file entitled "Skipped Facilities" (SkippedJaciiities.xslx) in
the run group's output subfolder. After you fix the errors in the inputs, you can use the list of
skipped facilities in column A of this output file to create a new Facility List Options file. Use the
new Facility List Options file to model the facilities as a group. Then copy the resulting skipped
facility output folders back into the directory/folder containing the original group's modeled
outputs. Next, append the resulting Facility Mask Risk and HI rows into the original Facility Max
Risk and HI file (described in Section 6.2.1). Do the same appending for the Facility Cancer
Risk Exposure file (described in Section 6.2.2) and the Facility TOSH I Exposure file (described
in Section 6.2.3). Finally, run the Risk Summary Reports on the full set of HEM4 outputs (as
described in Section 4.5 and Section 7).
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10. References
Census, 2010. Census Summary File 1 - United States:
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EPA, 1986. User's Manual for the Human Exposure Model (HEM). EPA-450/5-86-001, U.S.
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EPA, 1995. User's Guide for the Industrial Source Complex (ISC3) Dispersion Models (revised)
Volume II - Description of Model Algorithms. EPA-454/B-95-003b, U.S. Environmental
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http://www.epa.qov/scram001/userq/reqmod/isc3v2.pdf (Website last accessed May 2017.)
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
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EPA, 2017. Risk Assessment and Modeling - Air Toxics Risk Assessment Reference Library,
U.S. Environmental Protection Agency, Research Triangle Park, NC.
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EPA, 2018a. Dose-Response Assessment for Assessing Health Risks Associated With
Exposure to Hazardous Air Pollutants. U.S. Environmental Protection Agency, Research
Triangle Park, NC. http://www.epa.gov/fera/dose-response-assessment-assessing-health-risks-
associated-exposure-hazardous-air-pollutants. See also "Toxicity Value Files" available for
download on the HEM Download webpage at https://www.epa.gov/fera/download-human-
exposure-model-hem. (Website last accessed May 2020.)
EPA, 2018b. NATA Glossary of Terms. 2014 National-Scale Air Toxics Assessment. U.S.
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glossarv-terms. Website updated August 17, 2018. (Website last accessed May 2020.)
EPA, 2018c. User's Guide for the AERMOD Terrain Preprocessor (AERMAP). EPA-454/B-18-
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EPA, 2018d. Air Toxics Data - Ambient Monitoring Archive, U.S. Environmental Protection
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EPA, 2018e. Total Risk Integrated Methodology (TRIM) - General. EPA's FERA (Fate,
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http://www.epa.gov/fera/total-risk-inteqrated-methodoloqv-trim-qeneral. Website updated
January 31, 2018. (Website last accessed May 2020.)
EPA, 2019a User's Guide for the AMS/EPA Regulatory Model (AERMOD). EPA-454/B-19-027,
U.S. Environmental Protection Agency, Research Triangle Park, NC. August 2019.
https://www3.epa.gov/ttn/scram/models/aermod/aermod userquide.pdf. See also Model
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EPA, 2019b. AERMOD Implementation Guide. EPA-454/B-19-035, U.S. Environmental
Protection Agency, Research Triangle Park, NC. August 2019.
https://www3.epa.gov/ttn/scram/models/aermod/aermod implementation guide.pdf. (Website
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EPA, 2019c. User's Guide for the AERMOD Meteorological Preprocessor (AERMET). EPA-
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EPA, 2019d. AERMOD Model Formulation and Evaluation. EPA-454/R-19-014, U.S.
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ERT, 1980. Buoyant line and point source (BLP) dispersion model user's guide. Prepared by
Environmental Research & Technology (ERT) for The Aluminum Association, Inc. Document P-
7304B, July 1980.
Federal Register, 2012. Qualifying Urban Areas for the 2010 Census. FR 77:59 (27 March
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ICF International, 2015. The HAPEM User's Guide, Hazardous Air Pollutant Exposure Model,
Version 7. Prepared for the Office of Air Quality Planning and Standards, U.S. Environmental
Protection Agency, Research Triangle Park, NC. July 2015.
http://www.epa.gov/sites/production/files/2015-12/documents/hapem7usersguide.pdf.
Additional HAPEM documentation available at http://www.epa.gov/fera/human-exposure-
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Jindal, M. and D. Heinold, 1991. Development of particulate scavenging coefficients to model
wet deposition from industrial combustion sources. Paper 91-59.7, 84th Annual Meeting -
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NCES, 2009a. CCD Public School data 2009-2010 school year, Institute of Education Sciences,
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NCES, 2009b. PSS Private School Universe Survey data for the 2009-2010 school year,
Institute of Education Sciences, National Center for Education Statistics (NCES) of the U.S.
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Department of Education, Alexandria, VA. http://nces.ed.gov/survevs/pss/privateschoolsearch/
based on February 2012 access.
Sander, R., 2015: Compilation of Henry's law constants (version 4.0) for water as solvent,
Atmos. Chem. Phys., 15, 4399-4981, doi:10.5194/acp-15-4399-2015, 2015. http://www.atmos-
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Schulman, L.L., D.G. Strimaitis, and J.S. Scire, 2000. Development and Evaluation of the
PRIME Plume Rise and Building Downwash Model. Journal of the Air & Waste
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Scire, J.S., D.G. Strimaitis and R.J. Yamartino, 1990. Model formulation and user's guide for the
CALPUFF dispersion model. Sigma Research Corp., Concord, MA.
USGS, 2000. US GeoData Digital Elevation Models - Fact Sheet 040-00 (April 2000). U.S.
Department of the Interior - U.S. Geological Survey, Washington, DC.
https://pubs.usgs.gov/fs/2000/004Q/report.pdf (Website last accessed May 2017.)
USGS, 2015. USGS Seamless Data Warehouse. U.S. Department of the Interior - U.S.
Geological Survey, Washington, DC. http://nationalmap.gov/viewer.html (Website last accessed
May 2017.)
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,
June 2002. Work sponsored by the U.S. Department of Energy, Office of Science, Office of
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U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards.
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11. Appendix A: Sample HEM4 Output Files
This appendix contains examples (some abbreviated to fit) of the facility-specific and run group HEM4 output files. Sample Risk Summary Reports
; A
: 8
C D
6 =.
H
I j
K
_JL
.. M =
_.N
0 ; P
Q
Value
Angle
Hill
Value scientific
Distance (from Elevation height
UTM
UTM
Parameter
Value
rounded notation
1
o
3
noith) (m)
(m) HP*
Block
easting
northing Latitude
Longitude Receptor type
Notes
Ca-xer t>$k
C.CfOol
0.0006 6.1e-C4
3
491,5557
:i7-
S9.7
89.7 37063
*501001074
^C6D5
597481*
35 P 90C1 ©
¦7ti S-b'875? User receotor
Discrete
Kidney hi
1.4?$57
1~1 5t'C0
3
491 8557
717 7
S9.7
S3.7 37Cr3
9801001C74
690603
3974S16
35,899 OS
-78.883 Census osuCk
Discrete
ocuiar hi
0
0 0
0
C
G
0
0
0
0
0
0
tnOOCfi'-e H!
2.3E-C6
7E-G6 2 3e-3*
2H
1124 139
191 4
S5,S
S5,s"H7063
G020272C57
690*84
3974103
35.89265
7S.SS73 Census bsO£K
Discrete
Henc.ji H,
C.0CC9:
C C0C9 9.2e-C4
491.S5S7
21"77
89,7
SQ.7 =7003
9801001074
6S0&0S
3974£id
35 S990S
¦7S.SS8 Le;v-u.- bioc^
Di?ireie
tnir unclog>c«.it Hi
0.08729
C 07 o-7e-C2
0
459.4366
2B 9
?0,5
9C.5 U00C0
OOOOv'^C^Tl
C3C535
39^4934
35 90016
-7S.S8S75 lAer receotor
Discrete
5*.e!etdi Hi
o tees
C 0GCF S.Ce-04
0
459.4366
733 m
sn 5
9C 5 LCCOO
OOCO'JRC-Tl
690535
39749:4
35 SCO16
-7S-SS875 ^ieneceoiox
Discrete
Soieen ht
0
0 0
0
e
0
0
0
0
0
0
0
Tnvrosu H,
0
0 0
0
0
C
0
0
0
0
o
0
wnole hodv H!
13
0 0
0
0
0
0
Ci
rt
c
n
0
Figure 30. Sample Maximum Individual Risk HEM4 Output (facility-specific)
A
8
C D
E
F
G
H
1 . j
K
t.
M
N
0
p
Angle
Elevation
Value Value^
Distance
{from
(In
Hill Height
Parameter
Value
md sci
Population
(in meters) north)
meters)
(in meters) Fips
Block
Utm_east
Utm_north
latitude
Longitude
Rec_type
Cancer risk
0.CCQ783
C,000S>7.8e-C4
0
565
67.5
32
92
6S142S
3975421
35.90438
•78.878745
^oiar grid
Respiratory Hi
0.877049
0.7 S.Se-Oi
C
459.436612
233.85
90.5
90.5 U00Q0
ococuRcm
690535
3974934
35.9GQ16
•7S.S8S75
User receptor
Liver HI
0.277505
0.3'2.se-01
0
565
90
92
92
691471
3975205
35.90242
-7S.S78321
Polar gnd
Neurologies! Hi
0.08325
0.09 8,Se-C2
0
453.436612
233.85
90.5
30.5 UO'OOO
0000URCPT1
690535
39 749 34
35.90016
-7S.8SS75
User receotor
Developmental Hi
11.47101
lo'l.le+Cl
0
565
67.5
92
32
691428
3975421
35.90438
-7S.S73745
Polar grid
Reproductive HI
0.0747
0.07 7.5e-02
G
459.436612
233,35
90.5
90.5 UOOOO
OOOCURCPTI
690535
3974934
35.90016
-7S.S8S75
User receptor
Kidney Hi
1.974576
2r2.0em
0
565
67.5
92
92
69142S
3975421
35.90433
-78.87S745
Polar grid
Ocuiar HI
0
0 0
0
0
G
0
O
0
0
0
0
Endorcnne HI
4.2E-G6
4E-06 4.2e-06
c
565
30
92
92
691471
3975205
35,90242
-7S.S7S321
Poiar grid
Hematological Hi
0.001641
0.002 l,6e-G3
c
565
90
92
92
691471
3975205
35.90242
•78,878321
Polar grid
immunological Hi
0.C671S7
G.07 6.7e-02
0
459.436612
233.85
90.5
90.5 UOOOO
000CURCPT1
690535
3974934
35.90016
•7S.SSS75
User receptor
Skeletal H!
0,000797
0.0008 "8.06-04
0
459.436612
233.85
90.5
90.5 U'0000
OOOCURCPTI
690535
3974934
35.90016
-7S.S8S75
User receptor
jsoieen Hi
0
0 0
0
O
0
0
0
0
0
0
0
Thyroid HI
0
0 0
0
0
0
0
0
0
0
0
0
Whole body Hi
0
0
Figure 31. Sample Maximum Offsite Risk HEM4 Output (facility-specific)
HEM4 User's Guide
Page 129
-------
Site type
Max indiv risk
Max indiv risk
Max indiv risk
Parameter
Cancer risk
Cancer risk
Cancer risk
Source ID
CTGG'OOOl
CTOOQOOl
CTOOOGOl
Pollutant
All modeled pollutants
2,3,4,7,8-pentachlorodibenzofuran
l,2r3,7,8-pentachlorod
-------
ABCDEF GHIJ K L M N O
Latitude Longitude
Overlap Elevation
FIPs
Block
X
Y
Hill
Population
MIR
Respiratory Hi
Liver HI
Neurological HI
36,09433 -79.38606
N
181.3
37001
203004018
645293
3995623
181.3
66
3.86E-07
0.000246666
3.85E-05
1.43E-05
36.09624 -79.37SS3
N
173.5
37001
203005002
645941
3995840
173.5
3
3.89E-07
O.O'O0248244
3.S7E-05
1.43E-05
36.09475 -79.38259
N
178.3
37001
203005005
645605
3995669
178.3
3
3.S8E-07
0.000247772
3.86E-05
1.43E-05
36.09726 -79.38345
IS)
173.7
37001
203005007
645523
3995946
173.7
55
3.85E-07
0.0-00245794
3.S3E-05
1.42E-05
36.09843 -79.3833
\
171.2
37001
203005008
645534
3996075
171.2
35
3.S4E-07
0.00024509
3.82E-05
1.42E-05
36.08863 -79.3319
!Si
174.5
37001
203005010
645678
3994991
174.5
57
3.94E-07
0.0002521
3.93E-05
1.46E-05
36.092 -79.38435
N
185.3
37001
203005011
645452
3995361
185.3
2
3.89E-07
0.000248895
3.88E-05
1.44E-05
36,09427 -79.38459
\
179.3
37001
203005012
645426
3995613
179.3
13
3.S7E-07
0.000247309
3.86E-05
1.43E-05
36.09397 -79.3S536
N
183.4
37001
203005013
645357
3995577
183.4
2
3.87E-07
0.000247214
3.86E-05
1.43E-05
36.09193 -79.38555
tV
135.4
37001
203005014
645343
3995351
185.4
13
3.E8E-07
0.000248466
3.88E-05
1.44E-05
36.09238 -79.38517
N
184.4
37001
203005015
645377
3995402
184.4
10
3.88E-07
0.000248321
3.87E-05
1.44E-05
36.09002 -79.38582
\
181
37001
203005017
645323
3995140
181
14
3.90E-07
0.000249604
3.90E-05
1.44E-05
36.08975 -79.38621
N
178
37001
203005018
645288
3995109
178
11
3.90E-07
0.000249626
3.90E-05
1.44E-05
36.08996 -79.3878
N
183.5
37001
203005019
645144
3995129
183.5
19
3.89E-07
0.000248859
3.89E-05
1.44E-05
36.0873 -79.38689
(Si
183.1
37001
203005039
645232
3994836
183.1
36
3.91E-07
0.000250958
3.92E-05
1.45E-05
36.08769 -79.3852
N
181.4
37001
203005040
645383
3994881
181.4
37
3.92E-07
0.00025139
3.93E-05
1.46E-05
36.08671 -79.3333
N
174.7
37001
203005042
645556
3994776
174.7
24
3.94E-07
0.000252807
3.95E-05
1.46E-05
36.08729 -79.38956
N
189.6
37001
203005046
644991
3994831
189.6
82
3.90E-07
0.000249883
3.90E-05
1.45E-05
36.08615 -79.38586
N
181.1
37001
203005048
645326
3994709
181.1
26
3.93E-07
0.000252133
3.94E-05
1.46E-05
36.08476 -79.38295
N
171.2
37001
203005050
645591
3994560
171.2
12
3.96E-07
0.0-00254249
3.97E-05
1.47E-05
36.08756 -79.37782
N
169.4
37001
203005052
646047
3994878
169.4
24
3.97E-07
0.000254489
3.97E-05
1.47E-05
36.0909 -79.37605
N
166.8
37001
203005053
646200
3995251
166.S
99
3.95E-07
0.0-00252943
3.94E-05
1.46E-05
36.0907 -79.37304
(S!
156.3
37001
203005054
646472
3995234
156.3
19
3.98E-07
0.000254301
3.96E-05
1.47E-05
36,09164 -79.37145
N
157.3
37001
203005056
646614
3995340
157.3
8
3.98E-07
0.000254305
3.96E-05
1.47E-05
36.09327 -79.37612
N
166
37001
203005057
646190
3995514
166
24
3.93E-07
0.0-00251308
3.92E-05
1.45E-05
36.0889 -79.37362
N
154.5
37001
203005061
646424
3995033
154.5
46
3.99E-07
0,00025531
3.98E-05
1.48E-05
36.08887 -79.371
N
157
37001
203005062
646659
3995034
157
16
4.01E-07
0.000256407
4.00E-05
1.48E-05
36.08803 -79.37061
N
159.1
37001
203005063
646696
3994941
159.1
17
4.02E-07
0.000257159
4.01E-05
1.49E-05
Figure 33. Sample Block Summary Chronic HEM4 Output (facility-specific, abbreviated)
Note: The Block Summary Chronic file is large because it includes the cancer risk ("MIR") and all 14 TOSHIs for every modeled block or alternate
receptor. The above sample file includes ellipses (...) because it shows only a partial list of rows and only 3 of the 14 TOSHI's (Respiratory HI, Liver
HI and Neurological HI). In addition, the actual file includes a final column indicating whether the concentration (and therefore risk and TOSHIs) at
each receptor were discretely modeled or interpolated.
HEM4 User's Guide
Page 131
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Angle
Elevation Respiratory Neurological Developmental Distance (from
Latitude
Longitude
Overlap (m)
X
Y
Hill
MIR
HI
Liver HI
HI HI
...
(ml
north) Sector
35.90762
-73,88444
M
92
690906
3975770
92
0.000276
0.16267133
0.06719334
0.022959781
4.061680358 ...
565
0
35,31028
-78.88437
N
85
690906
3976065
85
0.000163
0.08834714
0.03991907
0.012732447
2.405209191 ...
860
0
35.91433
-78,88426
\
86
690906
3976515
86
9.86E-05
0.05006334
0.02351642
0.007284751
1.449323028 ...
1310
0
35,92046
-78.8841
\
86
690906
3977195
86
5.67E-05
0.02748919
0.01321489
0.004022705
0,830353561 ...
1990
0
35,92956
-73.88386
N
98
690906
3978205
98
3.29E-05
0.01497071
0.00728229
0.002192643
0.476986257 ...
3000
0
35,94127
-7S.8S355
N
108
690906
3979505
108
1.98E-05
0.00851103
0.00414235
0.001235919
0.283698446 .„
4300
0
35,95749
-78.88311
\
128
690906
3981305
128
1.19E-05
0.00487219
0.00221982
0.000669144
0.164677271 .„
6100
0
35.93092
-78.88249
N
128
690906
3983905
128
7.18E-06
0,00297086
0.00127924
0.000389865
0.098200187 ...
8700
0
36.01425
-78.88159
\
126
690906
3987605
126
4.30E-06
0,00182397
0.00072968
0.000225519
0.057813435 ...
12400
0
36,0611
-78.88034
\
134
690906
3992805
134
2.61E-06
0,0011715
0.00040331
0.000128775
0.03383493 ...
17600
0
36.12777
-78.87S55
M
174
690906
4000205
174
1.60E-06
0.000871S3
0.00020231
7.08E-05
0.01S804954
25000
0
36.22237
-78,87599
\
234
690906
4010705
234
9.40 E-07
0,00061899
9.59E-05
3.64E-05
0.00985544 ...
35500
0
36,35301
-78,87245
X
207
690906
4025205
207
6.14E-07
0.00039616
6.43E-05
2.42E-05
0.00651898 ...
50000
0
35.90719
-78.88206
N
92
691122
3975727
92
0.000317
0.20423035
0.08411586
0.028758088
4.672317905 ...
565
22.5
2
35,90963
-78,88074
\
92
691235
3976000
92
0.000192
0.10610333
0.04725769
0.015216162
2,82876806 ...
860
22.5
2
35.91334
-78,87874
N
86
691407
3976415
86
0.000106
0.05416447
0.02491204
0.007832226
1.5593033 ...
1310
22,5
2
35.91895
-78.8757
\
86
691668
3977044
86
5.91E-05
0.02814812
0.01306979
0.004078483
0.86713832 ...
1990
22,5
2
35.92728
-78.S712
\
98
692054
3977977
98
3.20E-05
0.01471825
0.00679289
0.002115014
0.466186678 ...
3000
22.5
2
35.938
-78.86539
\
115
692552
3979178
115
1.81E-05
0,00825512
0.00360303
0.001123976
0.256202648 ...
4300
22.5
2
35,95285
-78.85736
\
126
693240
3980841
126
1.10E-05
0.00516917
0.00198365
0.000625377
0,149211565 ...
6100
22.5
2
35.9743
-78.84575
¦\
126
694235
3983243
126
6.79E-06
0.00327005
0.00114822
0.000363808
0.089533991 ...
8700
22.5
2
36 00481
-73.82921
\
122
695651
3986661
122
4.11E-06
0.00201365
0.00066383
0.000211097
0.053338866 ...
12400
22.5
2
36.04769
-78.80594
\
119
697641
3991465
119
2.4SE-06
0.00123679
0.00037985
0.000121813
0.031601403 ...
17600
22.5
2
36.1087
-78.77279
\!
128
700473
3998302
128
1.51E-06
0,00079302
0.00020903
6.91E-05
0,018536631
25000
22.5
2
36,19526
-78,72566
N
185
704491
4008003
185
9.14E-07
0.00055697
0.00010008
3.60E-05
0,010134297
35500
22.5
2
36.31474
-78,66039
N
161
710040
4021399
161
5.74E-07
0.0003369
6.18E-05
2.22E-05
0.006426578 .„
50000
22.5
2
35,90606
-78,88005
\
92
691306
3975605
92
0.000421
0.26615504
0.10383434
0.036941868
6.177728359 ...
565
45
3
35.90789
-78.8777
\
92
691514
3975813
92
0.000272
0.16583271
0.06683816
0.023219576
3,998420365 ...
860
45
3
Figure 34. Sample Ring Summary Chronic HEM4 Output (facility-specific, abbreviated)
Note: The Ring Summary Chronic file includes the cancer risk ("MIR") and all 14 TOSHIs for every modeled polar receptor. The above sample file
includes ellipses (...) because it shows only a partial list of rows and only 4 of the 14 TOSHI's (Respiratory HI, Liver HI, Neurological HI, and
Developmental HI). The final 3 columns shown (above) cycle through polar receptor ring distances over each angle from north (or sector) for a total
of 16 angles/sectors by default, unless you indicate a different number of radials in your Facility List Options file.
HEM4 User's Guide
Page 132
-------
^ Google Earth Pro
File Edit View Jools Add Help
~ Search
Get Directions
T Places
~ Lavers
^ Primary Database
Q Announcements
* ' V Borders and Labels
; ® Q Places
* 9 Photos
' IS Roads
~ B 3D Buildings
~ 0 Weather
~ # Gallery
* D More
' "terrain
~ ¦ My Places
* ^ "Si Temporary Places
~ ' -vi srcmao
~ * Q Emission sources
~ ' ED Domain center
~ ' ED MIR
~ ' ED User receptor cancer risk
~ ' ED User receptor TOSHI
~ ' CD Census block cancer risk
~ ' ED Census block TOSHI
~ J O Polar receptor cancer risk
~ ' ED Polar TOSHI
Figure 35. Sample Source Risk KMZ Google Earth™ Image (facility-specific)
HEM4 User's Guide
Page 133
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Emission
Incidence
Source ID
Pollutant
type
Incidence
rounded
Total
All modeled pollutants
C
0.047682
0.048
CT000001
1,2,3,4.6,7,8-heptachiorodibenzo-p-dioxin
C
1.513E-09
1.5E-09
CT000001
1,2,3,4,6,7,8-heptachSorodibenzofuran
c
6.008E-09
6E-09
CT000001
1,2,3,4,7,8-hexachlorodibenzo-p-dioxin
c
1.238E-0S
1.2E-08
CT000001
1,2,3,4,7,8-hexachtorodibenzofuran
c
1.44E-07
1.4E-07
CT000001
1,2,3,6,7,8-hexachlorodibenzo-p-dioxin
c
1.317E-0S
1.3E-08
CT000001
1,2,3,6,7,8-hexachlorodibenzofuran
c
1.296E-07
1.3E-07
CT000001
1,2,3,7,8,9-hexachlorodibenzofuran
c
2.316E-0S
2.3E-08
CT000001
1,2,3,7,8-pentachlorodibenzo-p-dioxin
c
1.744E-07
1.7E-07
Total
1,2,3,4,6,7,8,9-octachIorodibenzo-p-dioxin
c
5.041E-10
5E-10
Total
1,2,3,4,6,7,8,9-octachlorodibenzofuran
c
5.209E-11
5.2E-11
Total
1,2,3,4,6.,7,8-heptachlorodibenzo-p-dioxin
c
8.953E-09
9E-09
Total
1,2,3.4.6.7,8-heptachlorodibenzofuran
c
3.556E-03
3.6E-08
CT000001 All modeled pollutants
CVGOODOl Ail modeled pollutants
PU000001 Ail modeled pollutants
HV000001 All modeled pollutants
MSOOOOOl All modeled pollutants
RV000001 Alt modeled pollutants
RV000002 Ali modeled pollutants
RV000003 Ali modeled pollutants
RV000004 All modeled pollutants
RW000001 Ai! modeled pollutants
SROOOOOl Ail modeled pollutants
1.462E-06
3.309E-06
0.0001703
3.88E-06
5.9S3E-QS
0.0051644
3.079E-06
1.772E-C6
C.00690S2
1.116E-05
0.0354084
0.0000015
0.0000033
0.00017
0.0000039
0,000006
0.0052
0.0000031
0.0000018
0.0069
0.000011
0.035
Figure 36. Sample Incidence HEM4 Output (facility-specific, abbreviated)
Note: The sample Incidence file above includes ellipses (...) for some rows because the file is too long to depict fully. The above rows indicate the
kinds of information provided in this file.
HEM4 User's Guide
Page 134
-------
A i i
1
Level
Population
z
Greater than or equal to i in 1,000
0
3
Greater than or equal to 1 in 10,000
435
4
Greater than or equal to 1 in 20,000
2119
5
Greater than or equal to 1 In 100,000
48998
6
Greater than or equal to 1 in 1,000,000
800221
?
Greater than or equal to 1 in 10,000,000
1545731
Figure 37. Sample Cancer Risk Exposure HEM4 Output (facility-specific)
A
1 £
D ;
E i F
G ] 1-
! 1
J
K
I i N
1 , N
i °
Level
Respiratory Liver
HI HI
Neurological Developmental Reproductive Kidney Ocular Endocrine
HI HI HI HI HI HI
Hematological Immunological
HI HI
Skeletal Spleen Thyroid Whole
HI HI HI body HI
Greater than 100
0
0
0
0
0
0
0
0
0
0
0
0
0 0
Greater than 50
0
0
0
0
0
0
0
0
0
0
0
0
0 0
Greater than 10
0
0
0
0
0
0
0
0
0
0
0
0
0 0
Greater than 1.0
0
0
0
435
0
0
0
0
0
0
0
0
0 0
Greater than 0,5
0
0
0
3065
0
12
0
0
0
0
0
0
0 0
Greater than 0,2
12
0
0
19289
0
432
0
0
0
0
0
0
0 0
Figure 38. Sample Noncancer Risk Exposure HEM4 Output (facility-specific)
HEM4 User's Guide
Page 135
-------
. ft ;
B ,
C j
D | E
i F
i 6
1 H f
1 ,
J j
K j
L
, M
Dry
Wet
Emission
Cone
Elevation deposition
deposition
ff?s
Block
Latitude
Longitude Source ID
type
Pollutant
(ug/m3)
-------
A
B :
C
D
E
F
Emission
: G
H
Cone
1
Elevation
j
K
FIPs
Block
Latitude
Longitude
Source ID
type
Pollutant
fre/m3)
(ml
Population
Overlap
17093
8907002213
41.5097585
-88.271948
CT000001
p
2,3,4,7,8-pentachlorodibenzofuran
8.50E-12
177.7
5
N
17093
8907002213
41.5097585
-S8.271948
CT000001
p
1,2,3,6,7,8-hexachlorodibenzo-p-dioxin
5.51E-13
177.7
5
\
17093
8907002213
41.5097585
-88.271948
CTOO 0-0-01
P
1,2,3,7,8-pentachlorodibenzo-p-dioxin
5.87E-13
177.7
5
N
17093
8907002213
41.5097585
-88.271948
CT000001
p
1,2,3,6,7,8-hexachlorodibenzofuran
5.29E-12
177,7
5
N
17093
8907002213
41.5097585
-88.271948
CT000001
p
1,2,3,4,7,8-hexachlorodibenzofuran
5.90E-12
177.7
5
N
17093
8907002213
41.5097585
-88.271948
CTOO0001
p
1,2,3,7,8,9-hexachlorodibenzofuran
9.40E-13
177.7
5
M
17093
8907002213
41.5097585
-88.271948
CT000001
p
2,3,4,6(7t8-hexachlorodibenzofuran
3.86E-12
177.7
5
N
17093
8907002213
41.5097585
-83.271948
CT000001
P
1,2,3,4,6,7,8-heptachlorodibenzofuran
2.50E-12
177.7
5
\
17093
8907002213
41.5097585
-88.271948
CT000001
p
1,2,3,4,7,8-hexachlorodibenzo-p-dioxin
5.01E-13
177.7
5
N
17093
8907002213
41.5007585
-88.271948
CT000001
D
1,2,3,7,8-pentachlorodibenzofuran
5.39E-12
177.7
5
M
17093
8907002213
41.5097585
-88.271948
CT000001
P
indeno[l,2,3-c,d]pytene
3.44E-11
177.7
5
N
17093
8907002213
41.5097585
-88.271948
CT000001
P
1,2,3,4,6,7,8-heptachSorodibenzo-p-dioxin
6.41E-13
177.7
5
N
17093
8907002186
41.4973643
-88.294909
CTOOOOOl
P
2,3,4,7,8-pentachforodibenzofuran
5.56E-12
173.3
11
N
17093
8907002186
41.4973643
-SS.294909
CTOO'OOOl
D
1,2,3,6,7,8-hexachlorodibenzo-p-dtoxin
3.61E-13
173.3
11
N
17093
8907002186
41.4973643
-88.294909
CTOOOOOl
P
1,2,3,7,8-pentachlorodibenzo-p-dioxin
3.84E-13
173.3
11
N
17093
S9070021S6
41.4973643
-83.294909
CTOOOOOl
P
1,2,3,6,7,8-hexachlorodibenzofuran
3.46E-12
173.3
11
N
17093
8907002186
41.4973643
-88.294909
CTOOOOOl
P
1,2,3,4,7,8-hexachlorodibenzofuran
3.S6E-12
173.3
11
M
17093
8907002186
41.4973643
-88.294903
CTOOOOOl
P
1,2,3,7,8,9-hexachlorodibenzofu ran
6.15E-13
173.3
11
N
17093
8907002186
41.4973643
-88.294909
CTOOOOOl
P
2,3,4,6,7,8-hexachiorodibenzofuran
2.53E-12
173.3
11
17093
8907002186
41.4973643
-88.294909
CTOOOOOl
P
1,2,3,4,6,7,8-heptachiorodibenzofuran
1.63E-12
173.3
11
N
17093
8907002186
41.4973643
-88.294909
CTOOOOOl
P
1,2,3,4,7,8-hexachlorodibenzo-p-dioxin
3.28E-13
173.3
11
N
17093
8907002186
41.4973643
-88.294909
CTOOOOOl
P
1,2,3,7,8-pervtachlorodibenzofuran
3.53E-12
173.3
11
N
17093
8907002186
41.4973643
-88.294909
CTOOOOOl
P
indeno[l,2,3-c,d]pyrerie
2.25E-11
173,3
11
N
Figure 40. Sample All Outer Receptors HEM4 Output file (facility-specific, abbreviated)
Note: The All Outer Receptor file tends to be a very large file, especially if you chose to model with the default maximum distance for your modeling
domain of 50 kilometers and a default (discrete / inner) modeling distance of 3 kilometers. Deposition flux is not calculated for the outer modeling
domain represented by the All Outer Receptor file, so these columns will not appear in this file even if you chose to model deposition.
HEM4 User's Guide
Page 137
-------
A
i B
i C
j D
E
F
i s
: H
1
J
K , L
j M
N
Angle
Wet
Dry
Emission
Cone
Distance
=P
o
3
Ring
Elevation
deposition
deposition
Source ID'
type
Pollutant
(ug/ml)
(m)
north) Secto
number
(m)
Latitude
Longitude Overlap
(g/m2/yr)
(g/m2/yr)
CT000001
p
2,3,4,7,8-pentachlorodibenzafuran
1.61E-U
100
0
1
196
41.49089612
-88.27001629 \
2.21E-12
3.69E-12
CTQ00001
P
1,2,3,6,7,8-hexachlorodibenzo-p-dioxsn
1.04E-12
100
0
1
196
41.49089612
-88.27001629 \
1.43E-13
2.39E-13
CTOOOOOl
P
1,2,3,7,8-pentacMorodibenzo-p-dioxin
1.11E-12
100
0
1
196
41.49089612
-88.27001629 \
1.52E-13
2.55E-13
CT000001
P
1,2,3,6,7,8-hexachiorodibenzofuran
1.00E-11
100
0
1
196
41.49089612
-88.27001629 N
1.37E-12
2.30E-12
CTOOOOOl
p
1,2,3,4,7,8-hexachlorodiberizofuran
1.12E-11
100
0
196
41.49089612
-88,27001629 \
1.53E-12
2.56E-12
CT000001
D
1,2,3,7,8,9-hexachiorodibenzofuran
1.78E-12
100
0
1
196
41.49089612
-88.27001629 \
2.44E-13
4.0SE-13
CTOOOOOl
D
2,3,4,6,7,8-hexachlorodibenzofuran
7.31E-12
100
0
1
196
41.49089612
-88.27001629 M
1.00E-12
1.6SE-12
CT000001
P
l,2.3A6,7,8-heptachlorodibenzofuran
4.73E-12
100
0
1
196
41.49089612
-88.27001629 M
6.48E-13
1.08E-12
CT000001
P
1,2,3,4,7,8-hexachIorodibenzo-p-dioxin
3.49E-13
100
0
1
196
41.49089612
-88.27001629 \
1.30E-13
2.18E-13
CT000001
P
1,2,3,7,8-pervtachlorodibenzofuran
1.02E-11
100
0
1
196
41.49089612
-88.27001629 \
1.4CE-12
2.34E-12
CTOOOOOl
P
indenoil,2,3-c,d]pyrene
6.51E-11
100
0
1
196
41.49089612
-88.27001629 M
8.91E-12
1.49E-11
CTOOOOOl
P
1,2,3,4,6,7,8-heptachlorodibenzo-p-dioxin
1.21E-12
100
0
1
196
41.49089612
-88,27001629 NJ
1.66E-13
2.79E-13
CTOOOOOl
P
2,3,4,7,8-perrtachiorodibenzofuran
1.41E-11
500
0
2
196
41.49449822
-88.27008666 M
4.31E-12
3.0SE-12
CTOOOOOl
P
1,2,3,6,7,8-hexachlorodibenzo-p-dioxin
9.17E-13
500
0
2
196
41.49449822
-88.27008666 \
2.79E-13
2.00E-13
CTOOOOOl
B
l,2,3,7,8-perrtach!orodibenzo-p-dioxin
9.76E-13
500
0
2
196
41.49449822
-88.27008666 M
2.97E-13
2.13E-13
CTOOOOOl
P
1,2,3,6,7,8-hexachlorodibenzofuran
8.80E-12
500
0
2
196
41.49449822
-88.27008666 \
2.68E-12
1.92E-12
CTOOOOOl
P
1,2,3,4,7,8-hexachiorodibenzofuran
9.81E-12
500
0
2
196
41.49449822
-88.27008666 N
2,99E-12
2.14E-12
CTOOOOOl
P
1,2,3,7,8,9-hexachSorodibenzofuran
1.56E-12
500
0
2
196
41.49449822
-88.27008666 \
4.76E-13
3.41E-13
CTOOOOOl
P
2,3,4,6,7,8-hexachlorodibenzofuran
6.42E-12
500
0
2
196
41.49449822
-88.27008666 Ni
1.96E-12
1.40E-12
CTOOOOOl
P
1,2,3,4,6,7,8-heptachlorodibenzofuran
4.15 E-12
500
0
2
196
41.49449822
-88.27008666 \
1.27E-12
9.06E-13
CTOOOOOl
p
1,2,3,4,7,8-hexachlorodibenzo-p-diox in
S.34E-13
500
0
2
196
41.49449822
-88.27008666 M
2.54E-13
1.82E-13
CTOOOOOl
P
1,2,3,7,8-pentachlorodibenzofuran
S.97E-12
500
0
2
196
41.49449822
-88.27003666 N
2.73E-12
1.96E-12
CTOOOOOl
P
indeno;i,2,3-c,d]pyrerie
5.72E-11
500
0
2
196
41.49449822
-88.27008666 N
1.74E-11
1.25E-11
CTOOOOOl
P
l,2,3,4,6,7,8-heptach(orodibenzo-p-dioxin
1.07E-12
500
0
2
196
41.49449822
-88.27008666 \
3.25E-13
2.33E-13
Figure 41. Sample All Polar Receptors HEM4 Output file (facility-specific, abbreviated)
Note: The Dry deposition and Wet deposition flux columns will be blank if you did not choose to model deposition in your Facility List Options file.
HEM4 User's Guide
Page 138
-------
aeimcd.inp - Notepad
! File Edit Format Vieiv Help
CO STARTING
CO TITLE0NE =acl-NC
CO TITLETWO Combined particle and vapor-phase emissions
|CO MODELOPT CONC ALPHA BETA ELEV
I CO URBAFJOPT 347602.0
|CO AVERTIKE 1 PERIOD
I CO POLLUTID UNITHAP
I CO RUNORNOT RUM
|CO FINISHED
I SO STARTING
I SO ELEVUNIT METERS
I SO LOCATION CT008001 POINT 690956 3974986 92
I SO SRCPARAM CT000081 1000 50.292 322.04 21.06275 2.819
|SO URBANSRC CT000001
I SO BUILDHGT CT000001 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 ;
I SO BUILDhID CT000001 111.07 107.16 100.0 115.85 128.17 136.6 140.88 140.88 136.6 128.17 115.85 100.0 107.16 111.07 111.6 108.74 108
|SO BU1LDLEN CT000001 128.17 115.85 100.0 107.16 111.07 111.6 108.74 108.74 111.6 111.07 107.16 100.0 115.85 128.17 136.6 140.88 140
|SO XBAD] CT000001 -93.97 -98.48 -100.0 -107.16 -111.07 -111.6 -108.74 -108.74 -111.6 -111.07 -107.16 -100.0 -98.48 -93.97 -86.6
I SO YBAD] CT000001 55.54 53.58 50.0 40.56 29.88 18.3 6.16 -6.16 -18.3 -29.88 -40.56 -50.8 -53,58 -55.54 -55.8 -54.37 -54.37 -55.8
|SO LOCATION CV000901 PQINTCAP 690817 3975122 92
|SO SRCPARAM CV000001 1000 60.0 350.0 0.005 1.8
|SO URBANSRC CV800001
I SO LOCATION HV000001 POINTHOR 690561 3975207 92
| SO SRC PARA*", HV000001 1000 45.0 300.0 0.006 3.0
|SO URBANSRC HV800001
I SO LOCATION FU000001 AREA 698957 3974943 92
|SO SRCPARAM FU000001 0.1 2.0 100.0 100.0 45.0 0.0
|SO URBANSRC FU000001
I SO LOCATION SR000001 VOLUME 690991 3974996 92
ISO SRCPARAM SR000001 1000 10.0 10.0 18.0
iSO URBANSRC SR000001
I SO LOCATION RW800001 LINE 690560 3975117 690751 3975163 92
ISO SRCPARAM RK000001 0.0678675172 3.0 75.0 3.0
ISO URBANSRC RW000001
Ln 1. Col 1 100% Windows fCRLFl tiTf-8
Figure 42. Sample AERMOD.inp file (facility-specific, abbreviated)
~ x
Note: If particle and vapor phase emissions are modeled separately (e.g., when modeling deposition/depletion), then two aermod.inp files will be
provided in the facility folder: an aermod_P.inp file for particle phase emissions and an aermod_V.inp file for vapor phase emissions.
HEM4 User's Guide
Page 139
-------
aermod.out - Notepad
~
X
; File Edit Format View Help
i CO STARTING *
i CO TITLEOME -acl-HC
:CO TITLETWO Combined particle and vapor-phase emissions
: CO "'.ODELOPT COMC ALPHA BETA ELEV
:CO URBANOPT 347602.0
CO AVERTIV,E 1 PERIOD
'CO POLLUTID UNITHAP
CO RUNORNOT RUN
CO FINISHED
I SO STARTING
'SO ELEVUIIIT METERS
I SO LOCATION CT000001 POINT 690956 3974986 92
i SO SRC PAR AK, CT000001 1000 50.292 322.04 21.06275 2.819
i SO URBANSRC CT000001
:SO BUILPHGT CT000001 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.8 26.0 26.0 26.0 26.0 26.0 26.0 :
:SO BUILDKID CT000001 111.07 107.16 100.0 115.55 128.17 136.6 140.88 140.88 136.6 128.17 115.85 100.0 107.16 111.87 111.6 108.74 108
|SO BUILDLEM CT000001 128.17 115.85 100.0 107.16 111.07 111.6 108.74 108.74 111.6 111.07 107.16 100.0 115.85 128.17 136.6 140.88 140
: SO XBAD1 CT000001 -93.97 -98.48 -100.0 -10"?.16 -111.07 -111.6 -108.74 -108.74 -111.6 -111.87 -107.16 -100.0 -98.48 -93.97 -86.6
ISO VBAD] CT000001 55.54 53.58 50.0 40.56 29.88 18.3 6.16 -6.16 -18.3 -29.88 -40.56 -58.0 -53.58 -55.54 -55.8 -54.37 -54.37 -55.8
i SO LOCATION CV080001 POINTCAP 690817 3975122 92
:SO SRCPARAK CV000001 1000 68.0 350.0 0.005 1.8
:SO URBANSRC CV000001
:SO LOCATION HV000001 POINTHOR 690561 3975207 92
|SO SRCPARAM HV000001 1000 45.0 300.0 0.006 3.0
I SO URBANSRC HV000001
i SO LOCATION FU000001 AREA 690957 3974943 92
i SO SRC PARA1* FU000001 0.1 2.0 100.0 100.0 45.0 0.®
:SO URBANSRC FU800001
:SO LOCATION SR000001 VOLUME 690991 3^74996 92
j SO SRCPAFW-'i SR000001 1000 10.0 10.0 10.0
:SO URBANSRC SR000001
ISO LOCATION RW000001 LINE 690560 3975117 690751 3975163 92
i SO SR. CP ARAM Rr.000001 0.0678675172 3.0 75.0 3.0
I SO URBANSRC RM00#0®1 v
i Ln 30, Coi 3? 100% Windows (CRLF) UTF-S
Figure 43. Sample AERMOD.out file (facility-specific, abbreviated)
Note: If particle and vapor phase emissions are modeled separately (e.g., when modeling deposition/depletion), then two aermod.out files will be
provided in the facility folder: an aermod_P.out file for particle phase emissions and an aermod_V.out file for vapor phase emissions. Deposition
fluxes (Dry Depo and Wet Depo) will be provided with depletion applied to concentrations, if modeled.
HEM4 User's Guide
Page 140
-------
J plctfile.plt - Notepad
File Edit Format View Help
|- AERMOD ( 19191): ~acl-NC
- AERKET ( 19191):
* MODELING OPTIONS USED: NonDFAULT COMC
* PLOT FILE OF PERIOD VALUES AVEKAG
* FOS A TOTAL OF 329 RECEPTORS.
* FORMAT: (2{1X,F13.5),1X,E13.6,3(IX,rS.2},2X,A6j2XjAS,
ELEV ALPHA URBAN
ED ACROSS 0 YEARS
AVERAGE CONC
ZELEV
ZHILL
2X,I8.8,2X,A8)
ZFLAG AVE
GRP
08/25/20
12:00:45
AD3JU* BUOYLINE
FOR SOURCE GROUP: CT000001
NUH HRS NET ID
688085.00000
3975161.00800
0.253092E+02
108.00
180
00
0.00
PERIOD
CT000001
00003326
688431.00000
3974590.00000
0.2975S4E-r02
89.00
89
00
0. 00
PERIOD
CT000001
00003326
688074.00808
3974564.00000
0.275790E-r02
96.00
96
00
0.00
PERIOD
CT000001
00003326
688329.00000
3973976.00000
0.3O1464E-02
87.08
87
00
0.00
PERIOD
CT000001
80003326
688603.00000
3974075.00000
0.311166E+O2
81.00
81
00
0.00
PERIOD
CT000001
00003326
689208.00000
3973740.00000
0.356151E+02
84.00
84
00
6.00
PERIOD
CT000001
00003326
688986.00000
3973544.00000
0.3313S9E-02
86.00
86
00
0.00
PERIOD
CT000001
00083326
688843.00000
3975073.00000
0.302065E+02
87.00
87
00
0.00
PERIOD
CT000001
00003326
688627.00000
3975147.00000
0.292191E-02
94.00
94
00
0.00
PERIOD
CT000001
00003326
688703.00000
3974777.00000
0.307262 E-*-02
87.00
87
00
0.00
PERIOD
CT008001
00003326
688794.00000
3974637.00000
0.319091E-rO2
86.00
86
00
0.00
PERIOD
CT000001
00003326
688857.00000
3974368.08080
0.336242Et02
88.00
88
00
0.00
PERIOD
CT000001
00003326
688897.00000
3974590.00000
0.336846E+02
89.00
89
00
0.0#
PERIOD
CT000001
00003326
688987.00000
3974348.00000
0.358162E+02
91.00
91
00
0.08
PERIOD
CT000001
00003326
688771.00000
3973458.00000
0.318658E-r02
87.00
87
00
0.00
PERIOD
CT000001
00083326
688844.00000
3973490.00000
0.327004E+02
89.00
89
00
0.00
PERIOD
CT000001
00003326
688649.00000
3973298.00000
0.299506E-T02
85.00
85
00
0.00
PERIOD
CT000001
00003326
688548.00000
3973225.00000
0.288798E-02
83.00
83
00
0.00
PERIOD
CT000001
00003326
688950.00000
3972883.00000
0.298544E+82
79.00
79
00
0.00
PERIOD
CT000001
00003326
689303.00000
3973138.00000
0.338950E-r02
81.00
81
00
0.80
PERIOD
CT000001
00003326
689577.00000
3972790.00000
0.3:3756E+02
74.00
74
00
0.0#
PERIOD
CT000001
00003326
689172.00000
3972686.00000
0.33OO14E+02
84.00
84
00
0.00
PERIOD
CT000001
00003326
689054.00000
3972778.00000
0.316748E-02
84.00
84
00
0.00
PERIOD
CT000001
00803326
689351.00000
3972699.00000
0.3?975SE-*02
77.00
77
00
0.00
PERIOD
CT000001
00003326
688985.00000
3973958.00000
0.36744OE»02
92.00
92
00
0.00
PERIOD
CT000001
00003326
690232.00000
3977482.00000
0.245790E+O2
82.00
82
00
0.00
PERIOD
CT000001
00003326
689973.00000
3977269.08000
0.233639Et02
88.00
88
00
0.00
PERIOD
CT000001
00003326
In 1 Coll 103% Window
Figure 44. Sample plotfile.plt output file (facility-specific, abbreviated)
Note: If particle and vapor phase emissions are modeled separately (e.g., when modeling deposition/depletion), then these concentrations will be
provided based on particle phase emissions in a plotfile_p.plt file and in a plotfile_v.plt file for vapor phase emissions. Deposition fluxes (Dry Depo
and Wet Depo) will be provided with depletion applied to concentrations, if modeled.
HEM4 User's Guide
Page 141
-------
n maxhour.plt - Notepad
File Edit Format View Help
b AERWD ( 19191): Facl-NC
AERMET ( 19191): Combined particle and vapor-phase emissions
MODELING OPTIONS USED: NonDFAULT CONC ELEV ALPHA URBAN AD]_U;" BUOYLINE
PLOT FILE OF HIGH 87TH HIGH 1-HR VALUES FOR SOURCE GROUP: CT888881
FOR A TOTAL OF 329 RECEPTORS.
FORMAT: (2<1X,F13.5),1X,E13.6,3(1X,F8.2),3X,A5,2X,A8,2X,A5,5X,A8,2X,18)
AVERAGE CONC
ZELEV
ZHILL
ZFLAG
AVE
GRP
08/25/20
12:00:45
RANK
NET ID DATE(CONC)
~
X
688085.00000
3975161,00000
0.284562E+03
100,00
100.00
0
00
1-HR
CT008001
87TH
19032801
688431,00000
3974590.00000
0.316115E+03
89,00
89.00
0
00
1-HR
CT000001
87TH
19O30220
688074.00008
3974564.00000
0.301197E+03
96.00
96.00
0
00
1-HR
CT000001
87TH
19021915
688329.00008
3973976.00000
0.329636E+03
87.00
87.00
0
00
1-HR
CT000801
87TH
19022215
688603.00000
3974075.00000
0.329503E+03
81.00
81.00
0
00
1-HR
CT000001
87TH
19060919
689200.00000
3973740.00000
0.406146E+03
84.00
84.00
0
00
1-HR
CT000001
87TH
19030908
688986.00000
3973544.00000
0.375320E+03
86.00
86.00
8
80
1-HR
CT000001
S7TH
19060911
688843.00000
3975073.98000
0.336322E+03
87.00
87.00
0
00
1-HR
CT000001
87TH
19052105
688627.00000
3975147.00000
0.325032E+03
94.00
94.00
0
00
1-HR
CT000001
87TH
19060814
688703.00000
3974777.00000
0.314162E+03
87.00
87.00
0
00
1-HR
CT000001
87TH
19040119
688794.80000
3974637.00000
0.335495E+03
86.08
86.00
0
00
1-HR
CT000001
87TH
19821214
688857.00000
3974368.00000
0.370621E+03
88.00
88.00
0
00
1-HR
CT000001
87TH
19841811
688897.00000
3974590.00000
0.364171E+03
89.00
89.00
0
00
1-HR
CT000001
87TH
19021710
688987.80008
3974348.00000
0.385640E+03
91.00
91.00
0
00
1-HR
CT000001
87TH
19052724
688771.00000
3973458.00000
0.361640E+03
87.00
87.00
0
08
1-HR
CT080001
87TH
19022311
688844.00000
3973490.00000
0.364316E+03
89.00
89.00
0
00
1-HR
CT000001
87TH
19051506
688649.00000
3973298.00000
0.326036E+03
85.00
85.00
0
08
1-HR
CT000001
87TH
19042910
688548.00000
3973225.00000
0.315116E+03
83.00
83.08
e
88
1-HR
CT000001
87TH
19021121
688958,00000
3972883.00000
0.301058E+03
79.00
79.80
0
00
1-HR
CT000001
87TH
19052120
689303.00000
3973138.00000
0.344125E+03
81.00
81.00
0
00
1-HR
CT000001
87TH
19021622
689577,00000
3972790.00000
0.379355E+03
74.00
74.00
0
00
1-HR
CT000001
87TH
19021609
689172.00000
3972686.00000
0.338885E+83
84.00
84.00
0
00
1-HR
CT080001
87TH
19032014
689054.00000
3972778.00000
0.307257E+03
84.00
84.00
0
00
1-HR
CT000001
87TH
19051805
689351.00000
3972699.00000
8.353838E+03
77.00
77.00
0
00
1-HR
CT000001
87TH
19021619
688985.00000
3973950.00000
0.415687E+03
92.00
92.00
0
00
1-HR
CT000001
87TH
19032002
690232.00000
3977482.00000
8.241090E+03
82.00
82.00
0
00
1-HR
CT000001
87TH
19062313
689973.00000
3977269.00000
0.229380E+03
88.00
88.00
0
00
1-HR
CT000001
87TH
19040417
Ln 1, Col 1 100% Windows (CRLR UTF-)
Figure 45. Sample maxhour.plt output file (optional facility-specific, abbreviated)
Note: The Maxhour plot file will be produced if you opted to model acute concentrations in your Facility List Options file. If particle and vapor phase
emissions are modeled separately (e.g., when modeling deposition/depletion), then these acute concentrations will be provided based on particle
phase emissions in a maxhour_p.plt file and for vapor phase emissions in an maxhour_v.plt file.
HEM4 User's Guide
Page 142
-------
Deposition Depletion
Type Type Building User Max Discrete Number Number
Facility Emissions Rural/ Deposition Depletion (particle/ {particle/ Elevation Acute Acute Downwash Receptors Modeling Modeling Overlap of Polar of Polar Acute First Ring
ID Aermod Title2 Phase Urban (YN) (YN) vapor) vapor) {YN) Hours Multiplier (YM| (YN) Distance Distance Distance Rings Radials (YN) Distance ...
CO TITLETWO
Combined
particle and vapor-
Facl-NC phase emissions N N NO/NO NO/NO Y 1 50 Y Y 50000 3000 30 13 16 Y 565 ...
Figure 46. Sample Input Selection Options HEM4 Output file (facility-specific, abbreviated)
Note: The above Input Selection Options files does not show all information provided; the actual file contains 34 fields / columns providing chosen
modeling run options.
i b ; c
i D
E
1 F i
G
i H
J
K
t • M
l H
O
P
_ _Q
R ; S
T
Aegl_l
Distance
Angle
FISH
Elevation Height
Cone sci
lhr
Rel
(in
(from
(in
(in
Utm
Utm
Longitud Receptor
Pollutant
Cone (ug/m3) (ug/m3)
(mg/m3)
...
[mgfml)
...
Population meters)
north)
meters)
meters) Fips
Block
easting
northing
Latitude
e type
Notes
1,3-buiadiene
64.17283562 6.4e-Ci
I5C0
...
0
...
0
565
90
92
92 na
ns
691471
3975205
35,90242
-78.87832 PG
Poiar
aceta!deriyde
14,33911i>U i.4e+01
SI
...
0.47
0
459
233
90
90 UOOOC
0000URC3T1
35
3974934
35-90016
-78.8S875 P
Discrete
acroie;n
100.3738151 1.0e+02
0,069
...
0.CG25
0
459
233
90
90 U0000
C0O0URC°Ti
35'
39 74934
35,90016
-78,88875 &
Discrete
arsenic compounds
69.24203227 6.9eK!l
0
...
0,0002
...
0
565
180
92
92 na
na
690906
3974640
35,89744
-78.88471 PG
^olar
benzene
29.94732329 3.0e+Cl
170
0
...
0
565
90
92
92 na
na
691471
3975205
35.90242
-78.87832 PG
3oiar
b's(2-ethyihexyl;phtha5ate
1839,115705 "l.Se+03
0
...
0
...
0
565
180
92
92 na
na
690906
3974640
35.89744
-78,88471 PG
Doiar
cadmium compounds
7.452S29SS 7.5e+0G
0.1
...
0
...
0
565
180
92
92 na
na
690906
3974640
35 89744
-78.88471 PG
^oiar
chloroform
0.4D9275616 4.1e-01
0
...
0.15
...
0
565
67
92
92 na
na
691428
3975421
35.90438
-78.87874 PG
Polar
chromum (;.<) compounds
33.58179966 4.0e+Gl
0
...
0
...
0
565
ISO
92
92 na
na
690906
3974640
35.89744
-78.88471 PG
Polar
chromium (vO compounds
0.0395818 r4.0e-C2
0
...
0
...
0
565
ISO
92
92 na
na
690906
3974640
35-89744
-78.8S471 ^G
3olar
rumens
1.02676537 l.Ce+OD
250
0
0
565
90
92
92 na
na
691471
3975205
35.90242
-78.87832 ^G
^oiar
Figure 47. Sample Acute Maximum Concentrations HEM4 Output file (optional facility specific, abbreviated)
Note: The Acute Maximum Concentrations (acute_chem_max) file will be produced if you opted to model acute concentrations in your Facility List
Options file. The above sample file is abbreviated; the actual file contains 11 acute benchmark columns, not only the AegMhr and Rel columns
shown.
HEM4 User's Guide
Page 143
-------
Pollutant
1,3-butadiene
acetaldehyde
acrolein
arsenic compounds
benzene
bis(2-ethyfhexyl}phthaiate
cadmium compounds
chloroform
chromium {iti} compounds
chromium (vi) compounds
Cone
fug/na)
10.22*51ie'l.O(rKSl
9,936*1.06+01
69,372 *7.00+01
60.7940859 *S.le+QI
4,77143877 *4.Se«B
942,376132*9.40+02
6.5463SS92 *6.5e+«)
0.058419621.Se-02
35.3686185"l.Se+Oi
0.03586862 ®3.6e-02
0.16359219'i.Se-Oi
Aegl_l
Cone sci lhr
Jug/m3) (mg/m3)
1500
81
0,069
0
170
0
0.1
0
0
0
Rel
(mg/m3)
250
0.47
0.0025
0.0002
0
0
0
0.15
0
0
0
Population
219
7
7
2
219
2
2
2
2
2
219
Distance Angle Elevation HIM
fin (from (in Height (in
meters) north) meters) meters) Ftps
1124
383
383
492
1124
492
492
4329
492
492
1124
191
301
301
2.20
191
220
220
48
220
220
191
85
90
90
117
90
90
85 37063
97*37063
97 97 37063
90 90*37063
85 37063
90 "*37063
V
90 37063
117 *37063
90 *37063
90*37063
r
B5 37063
Block
0020272057
*0020272047
'0020272047
"0020272056
*0020272057
*0020272056
*0020272056
*0018091060
'0020272056
*0020272056
*0020272057
Utm
easting
690604
690578
690578
690588
690684
590588
69058S
694160
690588
690588
Utm
northing
3974103
3975403
3975403
3974829
3974103
3974829
3974829
3978061
3974829
3974829
3974103
Latitude
35.89265
35.90438
35.90438
35.89921
35.89265
35.89921
35.89921
35.92762
35.89921
35.89921
35.89265
Receptor
Longitude type
-78.8873 C
-78.88816 C
-78.88816 C
-78.88819 C
-78.8873 C
-75.88819 C
-78.88819 C
-78.84785 C
-78.88819 C
-78.88819 C
-78,8873 C
Notes
Discrete
Discrete
Discrete
Discrete
Discrete
Discrete
Discrete
Interpolated
Discrete
Discrete
Discrete
Figure 48. Sample Acute Populated Concentrations HEM4 Output file (optional facility-specific, abbreviated)
Note: The Acute Populated Concentrations (acute_chem_pop) file will be produced if you opted to model acute concentrations in your Facility List
Options file. The above sample file is abbreviated; the actual file contains 11 acute benchmark columns, not only the AegMhr and Rel columns
shown.
Is max
Max cone at populated Max cone at Is max cone at
populated receptor any any receptor
receptor interpolated? receptor interpolated?
; Pollutant
Source ID
type
(ug/m3) (Y/N)
(ug/m3) (Y/N)
1,2,3,4 6.7,S.9-octach)orodioenzo-p-diOMn
PJOCCCOi
c
Q.S1669E-07 N
1.3295E-06 \
1,2 3,4,6 7,S.9-octachlarodibenzofuran
FU0C0GC1
c
7.04392E-08 N
1.3738E-C7 N
l,2,3,4,6-7,S-heotach'orodiben;o-p-d>ox n
CTC0000I
c
3.11291E-09 N
2.0383E-09 N
1.2,3,4,6 7,8-heptach!crodiben;o-p-d-o)» n
CV0Q0001
c
1.S7004E-08 \
1.1S75E-C8 N
1,2,3.4 6,7r8-neotachlorodrbenEO-p-dio)tin
HV000001
c
8 13071E-09 N
2 569E-0S \
1,2,3,4,6,7, S-heptachiorodrbenzofuran
CTG0G001
1.2S623E-08 N
S.O945E-09 \
l,2,3,4,6,7.8-neptach!orodtbenzofuran
CV000001
c
7.426i9E-0S N
4.6364E-C8 M
1,2 JAgJ.S-^eptach'orodibe^zofuran
HVC0C0C1
c
3.22B94E-08 N
1.02C2E-07 \
1,2,3 4 7,s 9-neptach'orodibenzofuran
FUO'OOCCl
c
9.4297C.E-08 N
1.8391E-G7 X
I 2,3.4 7,8-hexachlorodiberzo-p-d ox n
CTD00001
c
2.54693E-09 N
I.6677E-C9 M
1,2.3.4,7 B-he^ach'orodnbenzo-D-a-OMri
CVOOCOOl
c
1.53004E-08 N
9.552E-09 \
l,2.3.4,7,S-hexachiorodtbenzo-p-0io*;n
HVGG0001
c
6.6524E-09 N
2.1019E-0S \
1,2 3,4 7,S-henachiorodfbenzofuran
CTC00001
c
2.96397E-08 N
1.9407E-0S %
1,2 3,4 7.8-hexachlorodtbenzofuran
CVOOOOOl
c
1.78057E-07 N
1.1116E-C7 \
1,2.3,4,7,8-hexachlorodibenzofuran
HVC00001
c
7.74168E-08 N
2.446E-0? N
1,2,3,4 7,S-he\achloroa?benzofufan
SVOOOOC3
c
3.945S3E-09 N
1.2858E-09 \
1.2,3r6,7,8-he*ach!orod!beri;:a-o-d.Q*;-n
CTOOOOOl
c
2.71077E-09 N
L7749E-C9 \
1,2 3,6 7,S-he^achlorodibenza-p-d!ox;n
CVOOOOOl
c
1.62S46E-0S
I.0166E-OS %
i.2,3,6,7,8-hexachiorod!ben;o-p-d-ov.-n
Hvoeoooi
c
7.0S033E-09 N
2 2371E-0S \
1,2 3.6,7 8-he.%achrorodrben:o-p-d axm
RV000002
c
5.67075E-10 N
5.9953E-11 \
1,2,3,6,7,8-hexach^orodfbenzofuran
CTOOOOOl
c
2.6S60SE-0S N
1.7457E-08 \
1.2 3.6 7,S-hexachiorod(ben;ofuran
CVOOOOOl
c
1.60162E-O7 N
9.9989E-08 \
1,2 3,6,7-8-hexachforodsbenzofuran
HV000001
c
6.96362E-08 N
2.2002E-07 \
1,2,3,6 7,S-heAach?orodibenzofuran
RV000003
c
3.54927E-09 N
I.1565E-C9 \
Figure 49. Sample Acute Breakdown HEM4 Output file (optional facility-specific)
Note: The Acute Breakdown file will be produced if you opted to model acute concentrations in your Facility List Options file.
HEM4 User's Guide
Page 144
-------
A
Facilid
E
mxcanrsk
C
can_rsk_
interpltd
D
E
F
G
H
1
J
K
L
M
N
O
P
canrcpttype
canlatitude
canlongitude
canblk
respiratory
hi
[59 TOSHI
columns]
popoverlp
incidence
metname
km_to_
metstation
fac_center_
latitude
fac_center_
longitude
rural_
urban
Facl-NC
0.000610761
N
Census block
35,8990843
-78.8880045
*9801001074
0.6770494
0
0.047682
NC13722 2019.SFC
9.2712
35.9025311
-78.884577
U
Fac2-IL
9.00146E-07
N
Census block
41.4797356
-88.2618629
'8907002218
0.03653
0
4.581E-06
iL04808_2019.SFC
35.6838
41.49
-88.27
R
Figure 50. Sample Facility Max Risk arid HI HEM4 Output file (for run group, abbreviated)
Note: The Facility Max Risk and HI file covers the entire run group with one row of output per facility. The above sample file is abbreviated; there are
59 additional columns not shown pertaining to all 14 TOSH! values and locations.
A
B
C
D
E
F
G
H
Number people
Number people
Number people
Number people
Number people
exposed to >= 1 in
exposed to >= 1 in
exposed to >= 1 in
exposed to >= 1 in
exposed to >= 1 in
Facilid
latitude
longitude
1,000 risk
10,000 risk
100,000 risk
1,000,000 risk
10,000,000 risk
Facl-NC
35.90253
-78.884577
0
435
48998
800221
1545731
Fac2-ll
41.49
-88.27
0
0
0
0
296
Figure 51. Sample Facility Cancer Risk Exposure HEM4 Output file (for run group)
A
B
c
D
E
F
G
H
1
J
K
L
M
N
O
Facility
ID
Number people
exposed to > 1
Respiratory HI
Number people
exposed to > 1
Liver HI
Number people
exposed to > 1
Neurological HI
Number people
exposed to > l
Developmental HI
Number people
exposed to > 1
Reproductive HI
Number people
exposed to > 1
Kidney HI
Number people
exposed to > 1
Ocular HI
Number people
exposed to > 1
Endocrinological HI
Number people
exposed to > 1
Hematological HI
Number people
exposed to > 1
Immunological HI
Number people
exposed to > 1
Skeletal HI
Number people
exposed to > 1
Spleen HI
Number people
exposed to > 1
Thyroid HI
Number people
exposed to > 1
Whole Body HI
Facl-NC
0
0
0
435
0
0
0
0
0
0
0
0
0
0
Fac2-IL
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Figure 52. Sample Facility TOSHI Exposure HEM4 Output file (for run group)
HEM4 User's Guide
Page 145
-------
•v Google Earth Pro
File Edit View Tools Add Help
~ Search
Get Directions History
* > My Places
' €3 Temporary Places
~ * ^ srcmap
' Q Facility Fac1-NC Emission sources
' t—I Facility Fac2-IL Emission sources
~ Layers
w' ° $ Primary Database
0 Announcements
~ ' f Borders and Labels
< ~ Places
~ J 9 Photos
: ' S Roads
* B [QS 3D Buildings
~ 0 Weather
~ # Gallery
~ D More
' Terrain
Figure 53. Sample All Facility Source Locations Google Earth™ Image (for run group)
Note: The All Facility Source Locations Google Earth™ image depicts the two sample facilities modeled in this run group - located in Illinois and
North Carolina - on a map. On the actual map image, you can zoom in to see the individual sources at each facility in more detail.
HEM4 User's Guide
Page 146
-------
"hem4.log - Notepad
File Edit Format View Help
2020-08-25 11:58:10.713299:
~
X
2020-08-25
11
:s
53.831761
I02G-W8-25
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58
53.832758
2020-08-25
11
55
53.S40773
202U-OS-25
11
58
53.0467;?
2020-08-25
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59:05.256229
2820-08-25
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59
10.760734
2820-08-25
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59
26.611239
2820-08-25
11
59
37.574244:
2020-08-25
11:59:49.214533.
2020-08-25
11:59:54.848230
2020-08-25
12:00:02.0479®9
2020-08-25
12:00:08.154159.
2020-08-25
12
00
15.346313
2020-08-25
12:00
17.496536-
HE»',4 is starting
2020-03-25
12
00
17.531435
2020-08-25
12
00
18.103843
2020-03-25
12
00
18.104840
2020-03-25
12
00
18.104840
2020-08-25
12
00
18.175088
2020-08-25
12
00
18.177082
2020-08-25
12
00
18.717272
2020-08-25
12
00
44.282425
2028-08-25
12
01
33.061657
2020-08-25
12
01
33.162382
202O-08-25
12
01
33.219247
2020-08-25
12
01
44.844850
2020-08-25
12
01
47.219294
2020-08-25
12
82
52.736947
HEM4 Logging Initialized. See output subfolder for the log of your HEM4 run.
Facility Facl-NC
Facility ^acl-NC
facility Fac2-JL
Using period start = 2019 @2 11 12
Using period end - 2019 86 38 1
Using annual met option.
Uploaded facilities options list file for 2 facilities.
Uploaded HAP emissions file for 101 source-HAP combinations.
Uploaded emissions location file for 13 facility-source combinations.
Uploaded user receptors for [Facl-NC]
Uploaded buoyant line parameters for [Facl-NC]
Uploaded polyvertex sources for {Facl-NC, MS000001]
Uploaded building downwash parameters for [Facl-NC]
Uploaded particle data for [Fac2-IL]
Uploaded land use data for [Fac2-IL,Facl-NC]
Uploaded seasonal variation data for [Fac2-IL,Facl-NC]
RUri bROUP: test2_8-25-2020
KMi2 for all sources completed
Preparing Inputs for 2 facilities
The facility ids being modeled: Facl-NC, Fac2-IL
Running facility 1 of 2
Building runstreait for cacl-IJC
Using facility center 7x, y, lat, Ion] = [690906, 3975205, 35.90253110232091, -78.88457746645928]
Running ftermod for Facl-tlC. Started at time 12:80:44
Ae^nsod fan successfully. Ended at time 12:01:33
Processing Outputs fc Facl-HC
Completed InputSelectionOptions output
Completed AllPola"Receptors output
Completed AllInnerReceptors output
Completed AllOute^Receptors output
Figure 54. Sample HEM4 Log Output file (for run group, abbreviated)
HEM4 User's Guide
Page 147
-------
End
This page intentionally left blank
-------
Appendix 3
Meteorological Data for HEM Modeling
-------
METEOROLOGICAL DATA PROCESSING
USING AERMET
FOR HEM
US EPA
Air Toxic Assessment Group
RTP, NC 27711
February, 2021
1
-------
Meteorological Data Processing using AERMET
For HEM
February 2021
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 years 2016-2019 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 DSI6405 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. Attachment 1
lists the surface and upper air stations, as well as the coordinates, ground elevation, and
anemometer height for each station.
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 and
Attachment 1 outline the approach and site specific inputs each of the data preprocessors
and tools used to generate the AERMOD meteorological data.
1
-------
Meteorological Data Processing using AERMET
For HEM
February 2021
Table 1. AERMET Processing Options
AERMET Options
Version
19191
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
15272
Include 1 minute ASOS
Yes, where available TD-6405
Data
format
AERSURFACE
Version
20060
Options
Landcover data
USGS NLC for 2011
Radius for Surface
1 km
Roughness Calculations
Seasons
Winter - Dec, Jan, Feb
Spring - Mar, Apr, May
Summer - June, July, Aug
Fall - Sept, Oct, Nov
Temporal resolution
Monthly, 12 sectors
Site Surface Moisture
See Attachment 1
Snow Cover
See Attachment 1
RESULTS
To assure that the data would support an AERMOD run, USEPA meteorologists ran
AERMOD using a model plant with each AERMET SFC and PLF pairs. Further, the
surface files were examined for completeness. If more than 10 percent of specific data
including the Monin-Obukhov length, wind speed, or cloud cover were missing, the
station was not considered suitable for the meteorological database and therefore
excluded.
In all, 838 met station pairs ran successfully in AERMOD and passed completeness
criteria. Figure 1 is a map that shows the locations of the 838 surface stations. The
processed meteorological data generated by the above approach is posted on the EPA's
FERA (Fate, Exposure, and Risk Analysis) website under the HEM model page.
3
-------
Meteorological Data Processing using AERAIET
For HEM
February 2021
Figure 1. Location of meteorological stations used in HEM.
4
-------
Surface
Surface
Ice Free
Ice Free
Surface
Surface
Surface
Anenom.
WBAN
Call
Call
Date
Name
Latitude
Longitude
Elevation
UTC
Height
Year
25308
PANT
ANN
52606
ANNETTE WSO AP
55.0389
-131.579
33
-9
10
2016
25309
PAJN
JNU
82206
JUNEAU INTL AP
58.3566
-134.564
5
-9
10
2019
25323
PAHN
HNS
102705
HAINES AP
59.2433
-135.509
5
-9
10
2019
25325
PAKT
KTN
11003
KETCHIKAN INTL AP
55.35667
-131.712
23
-9
8
2019
25331
PAAQ
PAQ
91906
PALMER MUNI AP
61.5961
-149.092
70
-9
10
2019
25333
PASI
SIT
12403
SITKA AP
57.0481
-135.365
4
-9
8
2019
25335
PAGY
SGY
110705
SKAGWAY AP
59.4556
-135.324
6
-9
8
2019
25501
PADQ
ADQ
41007
KODIAK AP
57.75111
-152.486
24
-9
10
2019
25503
PAKN
AKN
32107
KING SALMON AP
58.6829
-156.656
20
-9
10
2019
25506
PAIL
ILI
11407
ILIAMNA AP
59.7494
-154.909
44
-9
10
2019
25507
PAHO
HOM
92306
HOMER AP
59.642
-151.491
20
-9
8
2019
25516
PASO
SOV
103005
SELDOVIA AP
59.44333
-151.702
19
-9
10
2019
25624
PACD
CDB
91906
COLD BAY AP
55.22083
-162.733
24
-9
10
2019
25628
PAPB
PBV
70906
ST GEORGE ISLAND AP
56.6
-169.565
27
-9
10
2019
25713
PASN
SNP
100106
ST PAUL ISLAND AP
57.15528
-170.222
11
-9
10
2019
26409
PAMR
MRI
41707
ANCHORAGE MERRILL FLD
61.21694
-149.855
42
-9
5
2019
26410
PACV
CDV
110705
CORDOVA M K SMITH AP
60.4888
-145.451
9
-9
8
2019
26411
PAFA
FAI
82306
FAIRBANKS INTL AP
64.8039
-147.876
132
-9
10
2019
26412
PAOR
ORT
82406
NORTHWAYAP
62.9617
-141.938
522
-9
10
2018
26415
PABI
BIG
100705
BIG DELTA AP
63.9944
-145.721
389
-9
10
2019
26425
PAGK
GKN
111705
GULKANA AP
62.1591
-145.459
476
-9
10
2019
26435
PANN
ENN
102705
NENANA MUNI AP
64.55
-149.072
110
-9
10
2019
26438
PAWD
SWD
102705
SEWARD AP
60.12833
-149.417
15
-9
8
2019
26451
PANC
ANC
91806
ANCHORAGE INTLAP
61.169
-150.028
37
-9
8
2019
26492
PATO
POR
41707
PORTAGE GLACIER VC
60.785
-148.839
31
-9
10
2019
26510
PAMC
MCG
60106
MCG RATH AP
62.9574
-155.61
101
-9
10
2016
26523
PAEN
ENA
92106
KENAI MUNI AP
60.5797
-151.239
28
-9
8
2019
26528
PATK
TKA
70203
TALKEETNA AP
62.32
-150.095
107
-9
8
2019
26529
PATA
TAL
111705
TANANA CALHOUN MEM AP
65.175
-152.107
68
-9
10
2018
3
-------
Surface
Surface
Ice Free
Ice Free
Surface
Surface
Surface
Anenom.
WBAN
Call
Call
Date
Name
Latitude
Longitude
Elevation
UTC
Height
Year
26533
PABT
BTT
51806
BETTLES AP
66.9167
-151.519
196
-9
10
2019
26615
PABE
BET
91306
BETHELAP
60.785
-161.829
31
-9
8
2019
26616
PAOT
OTZ
83006
KOTZEBUE RALPH WEIN AP
66.86667
-162.633
9
-9
8
2019
26617
PAOM
OME
90206
NOME MUNI AP
64.5111
-165.44
4
-9
8
2019
27406
PASC
see
82406
DEADHORSE AP
70.1917
-148.477
19
-9
8
2019
27502
PABR
BRW
61703
BARROW POST ROGERS AP
71.2834
-156.782
9
-9
8
2019
27503
PAWI
AWI
81606
WAINWRIGHT AP
70.63917
-159.995
8
-9
8
2019
27515
PAQ.T
AQT
82206
NUIQSUT AP
70.21167
-151.002
18
-9
8
2019
3856
KHSV
HSV
50807
HUNTSVILLE INTLAP
34.64389
-86.7861
190
-6
10
2019
3878
KTOI
TOI
12109
TROY MUNI AP
31.86056
-86.0122
120
-6
10
2019
13838
KBFM
BFM
41307
MOBILE DWTN AP
30.62639
-88.0681
6
-6
10
2019
13839
KDHN
DHN
53007
DOTHAN RGNL AP
31.3167
-85.45
114
-6
10
2019
13871
KANB
ANB
11409
ANNISTON METRO AP
33.5872
-85.8556
181
-6
8
2019
13876
KBHM
BHM
30509
BIRMINGHAM AP
33.56556
-86.745
187
-6
10
2019
13894
KMOB
MOB
30507
MOBILE RGNL AP
30.68833
-88.2456
66
-6
10
2019
13895
KMGM
MGM
22009
MONTGOMERY AP
32.2997
-86.4075
62
-6
10
2019
13896
KMSL
MSL
52407
MUSCLE SHOALS RGNL AP
34.7441
-87.5997
165
-6
10
2019
53820
KGZH
GZH
32007
EVERGREEN MIDDLETON FLD
31.41556
-87.0442
77
-6
10
2019
53852
KDCU
DCU
51007
DECATUR PRYOR FLD
34.6525
-86.9453
179
-6
10
2019
63872
KEUF
NA
WEEDON FLD AP
31.95139
-85.1289
87
-6
10
2016
63874
KPRN
NA
GREENVILLE CRENSHAW AP
31.84556
-86.6108
132
-6
10
2018
93806
KTCL
TCL
11209
TUSCALOOSA MUNI AP
33.2119
-87.6161
46
-6
10
2019
3953
KJBR
JBR
100108
JONESBORO MUNI AP
35.83111
-90.6464
78
-6
10
2019
3962
KHOT
HOT
72407
HOT SPRINGS ASOS
34.479
-93.096
163
-6
8
2019
13963
KLIT
LIT
52109
LITTLE ROCK AP ADAMS FLD
34.7273
-92.2389
79
-6
10
2019
13964
KFSM
FSM
21909
FT SMITH RGNL AP
35.333
-94.3625
137
-6
10
2019
13971
KHRO
HRO
52709
HARRISON BOONE CO AP
36.2668
-93.1566
419
-6
8
2019
13977
KTXK
TXK
81607
TEXARKANA WEBB FLD
33.4536
-94.0074
110
-6
8
2019
53869
KHKA
HKA
92508
BLYTHEVILLE MUNI AP
35.94028
-89.8308
77
-6
10
2019
4
-------
Surface
WBAN
Surface
Call
Ice Free
Call
Ice Free
Date
Name
Surface
Latitude
Surface
Longitude
Surface
Elevation
UTC
Anenom.
Height
Year
53918
KBPK
BPK
71107
MOUNTAIN HOME BAXTER AP
36.36889
-92.4703
281
-6
8
2019
53919
KLLQ
LLQ
71707
MONTICELLO MUNI AP
33.6361
-91.7556
88
-6
10
2016
53920
KRUE
RUE
120606
RUSSELLVILLE MUNI AP
35.25778
-93.0947
116
-6
10
2019
53921
KMWT
MWT
60607
MOUNT IDA ASOS
34.5467
-93.5781
214
-6
10
2016
53922
KXNA
XNA
103008
FAYETTEVILLE NW AR AP
36.28333
-94.3
392
-6
10
2019
53925
KDEQ
DEQ
42607
DE QUEEN SEVIER CO AP
34.05
-94.4008
108
-6
8
2019
93988
KPBF
PBF
61907
PINE BLUFF GRIDER FLD
34.175
-91.9347
61
-6
8
2019
93992
KELD
ELD
71107
EL DORADO SAR RGNLAP
33.22083
-92.8142
77
-6
10
2019
93993
KFYV
FYV
42409
FAYETTEVILLE DRAKE FLD
36.0097
-94.1694
381
-6
10
2019
3029
KRQE
RQE
13006
WINDOW ROCK AP
35.6575
-109.061
2054
-7
8
2019
3103
KFLG
FLG
20807
FLAGSTAFF PULLIAM AP
35.1441
-111.666
2135
-7
10
2019
3124
KFHU
NA
FORT HUACHUCA
31.58833
-110.344
1415
-7
10
2016
3162
KPGA
PGA
21507
PAGE MUNI AP
36.92611
-111.448
1314
-7
8
2019
3184
KDVT
DVT
22107
PHOENIX DEER VALLEY MUNI
AP
33.68833
-112.082
443
-7
10
2019
3192
KSDL
SDL
32007
SCOTTSDALE MUNI AP
33.62278
-111.911
449
-7
10
2019
3195
KGCN
GCN
21207
GRAND CANYON NP AP
35.94611
-112.155
2014
-7
10
2019
3196
KOLS
OLS
92606
NOGALES INTL AP
31.42083
-110.846
1194
-7
10
2019
23160
KTUS
TUS
41007
TUCSON INTLAP
32.1313
-110.955
777
-7
10
2019
23183
KPHX
PHX
40307
PHOENIX SKY HARBOR INTL AP
33.4277
-112.004
337
-7
10
2019
23184
KPRC
PRC
20907
PRESCOTT LOVE FLD
34.65167
-112.421
1524
-7
10
2019
23194
KINW
INW
13006
WINSLOW MUNI AP
35.0281
-110.721
1489
-7
10
2019
93026
KDUG
DUG
22706
DOUGLAS BISBEE INL AP
31.4583
-109.606
1251
-7
10
2019
93027
KSJN
SJN
13006
ST JOHNS INDUSTRIAL AP
34.51833
-109.379
1746
-7
10
2019
93084
KSAD
SAD
30706
SAFFORD MUNI AP
32.85472
-109.635
968
-7
10
2019
93167
KIGM
IGM
22007
KINGMAN MOHAVE CO AP
35.2577
-113.933
1042
-7
10
2019
3102
KONT
ONT
92407
ONTARIO INTL AP
34.05611
-117.6
289
-8
8
2019
3104
KTRM
TRM
92806
DESERT RESORTS RGNLAP
33.62667
-116.159
-39
-8
10
2019
3131
KMYF
MYF
30707
SAN DIEGO MONTGOMERY FLD
32.81583
-117.139
127
-8
8
2019
5
-------
Surface
WBAN
Surface
Call
Ice Free
Call
Ice Free
Date
Name
Surface
Latitude
Surface
Longitude
Surface
Elevation
UTC
Anenom.
Height
Year
3144
KIPL
IPL
101706
IMPERIAL CO AP
32.83417
-115.579
-18
-8
10
2019
3159
KWJF
WJF
40507
LANCASTER WM J FOX FLD
34.7411
-118.212
713
-8
10
2019
3166
KFUL
FUL
40307
FULLERTON MUNI AP
33.87194
-117.979
29
-8
10
2019
3167
KHHR
HHR
12507
HAWTHORNE MUNI AP
33.92278
-118.334
19
-8
8
2019
3171
KRAL
RAL
71008
RIVERSIDE MUNI AP
33.95194
-117.439
245
-8
10
2019
3177
KCRQ
CRQ
41107
CARLSBAD PALOMAR AP
33.12806
-117.279
93
-8
8
2019
3178
KSDM
SDM
83007
SAN DIEGO BROWN FLD
32.57222
-116.979
157
-8
10
2019
3179
KCNO
CNO
32807
CHINO AP
33.97528
-117.636
192
-8
8
2019
23129
KLGB
LGB
40407
LONG BEACH DAUGHERTY FLD
33.8116
-118.146
9
-8
8
2019
23130
KVNY
VNY
60707
VAN NUYS AP
34.20972
-118.489
235
-8
8
2019
23136
KCMA
CMA
12507
CAMARILLO AP
34.21667
-119.083
25
-8
10
2019
23152
KBUR
BUR
20707
BURBANKGLENDALE
PASADENA AP
34.20056
-118.358
222
-8
8
2019
23155
KBFL
BFL
31407
BAKERSFIELD AP
35.4344
-119.054
149
-8
10
2019
23157
KBIH
BIH
102705
BISHOP AP
37.3711
-118.358
1250
-8
10
2019
23158
KBLH
BLH
102705
BLYTHE AP
33.6186
-114.714
120
-8
10
2019
23161
KDAG
DAG
13006
BARSTOW DAGGETT AP
34.8536
-116.786
584
-8
10
2019
23174
KLAX
LAX
102706
LOS ANGELES INTLAP
33.938
-118.389
30
-8
10
2019
23179
KEED
EED
20507
NEEDLES AP
34.7675
-114.619
271
-8
10
2019
23182
KPMD
PMD
20807
PALMDALE AP
34.62944
-118.084
769
-8
10
2019
23187
KSDB
SDB
21306
SANDBERG
34.7436
-118.724
1375
-8
10
2018
23188
KSAN
SAN
82307
SAN DIEGO LINDBERGH FLD
32.7336
-117.183
5
-8
10
2019
23190
KSBA
SBA
62207
SANTA BARBARA MUNI AP
34.4258
-119.843
3
-8
8
2019
23191
KAVX
AVX
90507
AVALON CATALINA AP
33.405
-118.416
472
-8
10
2019
23199
KNJK
NA
ELCENTRO NAF
32.81667
-115.683
-14
-8
10
2019
23213
KSTS
STS
31407
SANTA ROSA SONOMA CO AP
38.5038
-122.81
35
-8
10
2019
23225
KBLU
BLU
103102
BLUE CANYON AP
39.2774
-120.71
1608
-8
8
2019
23230
KOAK
OAK
21507
OAKLAND METRO INTL AP
37.72139
-122.221
2
-8
10
2019
23232
KSAC
SAC
81507
SACRAMENTO EXECUTIVE AP
38.5069
-121.495
5
-8
10
2019
6
-------
Surface
WBAN
Surface
Call
Ice Free
Call
Ice Free
Date
Name
Surface
Latitude
Surface
Longitude
Surface
Elevation
UTC
Anenom.
Height
Year
23233
KSNS
SNS
20607
SALINAS MUNICIPAL AP
36.6636
-121.608
23
-8
10
2019
23234
KSFO
SFO
73103
SAN FRANCISCO INTL AP
37.6197
-122.365
2
-8
10
2019
23237
KSCK
SCK
81607
STOCKTON METRO AP
37.8891
-121.226
8
-8
10
2019
23244
KNUQ
NA
MOFFETT FEDERAL AIRFIELD
37.40583
-122.048
12
-8
10
2016
23254
KCCR
CCR
82605
CONCORD BUCHANAN FLD
37.9917
-122.055
5
-8
10
2019
23257
KMCE
MCE
22706
MERCED MUNI AP
37.28472
-120.513
46
-8
8
2019
23258
KMOD
MOD
40207
MODESTO CITY CO AP
37.6241
-120.951
22
-8
8
2019
23259
KMRY
MRY
20607
MONTEREY PENINSULAP
36.58806
-121.845
50
-8
8
2019
23273
KSMX
SMX
60607
SANTA MARIA PUBLIC AP
34.8994
-120.449
74
-8
10
2019
23275
KUKI
UKI
101206
UKIAH MUNI AP
39.12583
-123.201
183
-8
6
2019
23277
KWVI
WVI
20607
WATSONVILLE MUNI AP
36.93583
-121.789
47
-8
10
2019
23285
KLVK
LVK
32307
LIVERMORE MUNI AP
37.6927
-121.814
120
-8
8
2019
23293
KSJC
SJC
30807
SAN JOSE
37.3591
-121.924
16
-8
8
2019
24215
KMHS
MHS
22706
MT SHASTA
41.3325
-122.333
1077
-8
10
2018
24216
KRBL
RBL
21306
RED BLUFF MUNI AP
40.1519
-122.254
108
-8
10
2019
24257
KRDD
RDD
80907
REDDING MUNI AP
40.5175
-122.299
151
-8
10
2019
24259
KSIY
SIY
102705
MONTAGUE SISKIYOU AP
41.78139
-122.468
807
-8
10
2019
24283
KACV
ACV
13107
ARCATA EUREKA AP
40.97806
-124.109
61
-8
10
2019
24286
KCEC
CEC
90706
CRESCENT CITY MCNAMARA AP
41.78028
-124.237
18
-8
8
2019
53119
KHJO
HJO
110705
HANFORD MUNI AP
36.31889
-119.629
77
-8
10
2019
53120
KRNM
RNM
21306
RAMONA AP
33.0375
-116.916
423
-8
8
2019
53121
KOKB
OKB
20306
OCEANSIDE MUNI AP
33.21944
-117.349
10
-8
10
2019
93110
KOXR
OXR
11007
OXNARD VENTURA CO AP
34.20083
-119.207
11
-8
8
2019
93115
KNRS
NA
IMPERIAL BEACH REAM FLD
NAS
32.56667
-117.117
7
-8
10
2018
93134
KCQ.T
CQT
20306
LOS ANGELES
DOWNTOWN USC
34.0236
-118.291
55
-8
6
2018
93138
KPSP
PSP
92607
PALM SPRINGS RGNL AP
33.8222
-116.504
125
-8
10
2019
93184
KSNA
SNA
81507
SANTA ANA JOHN WAYNE AP
33.68
-117.866
13
-8
6
2019
7
-------
Surface
Surface
Ice Free
Ice Free
Surface
Surface
Surface
Anenom.
WBAN
Call
Call
Date
Name
Latitude
Longitude
Elevation
UTC
Height
Year
93193
KFAT
FAT
40307
FRESNO YOSEMITE INTL AP
36.78
-119.719
101
-8
8
2019
93197
KSMO
SMO
41807
SANTA MONICA MUNI AP
34.01583
-118.451
53
-8
10
2019
93205
KMYV
MYV
21306
MARYSVILLE YUBA CO AP
39.1019
-121.568
19
-8
10
2019
93206
KSBP
SBP
41707
SAN LUIS OBISPO AP
35.23722
-120.641
61
-8
10
2019
93209
KPRB
PRB
22706
PASO ROBLESMUNI AP
35.6697
-120.628
247
-8
10
2019
93210
KOVE
OVE
21306
OROVILLE MUNI AP
39.49
-121.618
57
-8
10
2019
93227
KAPC
APC
31808
NAPA CO AP
38.2102
-122.285
4
-8
10
2019
93228
KHWD
HWD
20807
HAYWARD AIR TERMINAL
37.6542
-122.115
13
-8
10
2019
93230
KTVL
TVL
51707
SOUTH LAKETAHOE AP
38.8983
-119.995
1925
-8
10
2019
93241
KVCB
VCB
80707
VACAVILLE NUT TREE AP
38.3775
-121.958
33
-8
10
2019
93242
KMAE
MAE
21306
MADERA MUNI AP
36.98778
-120.111
77
-8
10
2019
94299
KAAT
AAT
42406
ALTURAS MUNI AP
41.49139
-120.564
1334
-8
10
2019
3013
KLAA
LAA
82608
LAMAR MUNI AP
38.07
-102.688
1124
-7
10
2019
3017
KDEN
DEN
91205
DENVER INTLAP
39.8328
-104.658
1650
-7
10
2019
3026
KITR
ITR
110705
BURLINGTON CARSON AP
39.24472
-102.284
1278
-7
10
2019
23061
KALS
ALS
52407
ALAMOSA SAN LUIS AP
37.4389
-105.861
2296
-7
10
2019
23066
KGJT
GJT
31307
GRAND JUNCTION WALKER FLD
39.1342
-108.54
1481
-7
10
2019
23067
KLHX
LHX
121605
LA JUNTA MUNI AP
38.0494
-103.512
1278
-7
10
2019
23070
KTAD
TAD
102808
TRINIDAD PERRY STOKES AP
37.26222
-104.338
1750
-7
10
2019
24015
KAKO
AKO
12507
AKRON WASHINGTON CO AP
40.16667
-103.217
1421
-7
10
2019
24046
KCAG
CAG
32607
CRAIG MOFFAT COUNTY AP
40.49278
-107.524
1887
-7
10
2019
93005
KDRO
DRO
40307
DURANGO LA PLATA CO AP
37.14306
-107.76
2033
-7
10
2019
93009
KLXV
LXV
82808
LEADVILLE LAKE CO AP
39.22917
-106.317
3011
-7
10
2019
93010
KLIC
LIC
100405
LIMON WSMO
39.18944
-103.716
1635
-7
10
2019
93013
KMTJ
MTJ
102705
MONTROSE REGIONAL AP
38.50583
-107.899
1743
-7
10
2019
93037
KCOS
COS
92308
COLORADO SPRINGS MUNI AP
38.81
-104.688
1884
-7
10
2019
93058
KPUB
PUB
40407
PUEBLO MEM AP
38.2901
-104.498
1439
-7
10
2019
93067
KAPA
APA
20607
DENVER CENTENNIAL AP
39.57028
-104.849
1793
-7
10
2019
8
-------
Surface
WBAN
Surface
Call
Ice Free
Call
Ice Free
Date
Name
Surface
Latitude
Surface
Longitude
Surface
Elevation
UTC
Anenom.
Height
Year
93069
KCEZ
CEZ
40407
CORTEZ MONTEZUMA COUNTY
AP
37.30694
-108.626
1801
-7
10
2019
93073
KASE
ASE
31507
ASPEN PITKIN CO AP
39.23
-106.871
2353
-7
8
2019
94050
KEEO
EEO
31307
MEEKER AIRPORT
40.04417
-107.889
1940
-7
10
2019
14707
KGON
GON
111708
GROTON NEW LONDON AP
41.3275
-72.0494
3
-5
10
2019
14740
KBDL
BDL
40907
HARTFORD BRADLEY INTL AP
41.9381
-72.6825
58
-5
10
2019
14752
KHFD
HFD
102308
HARTFORD BRAINARD FLD
41.73611
-72.6506
6
-5
10
2019
14758
KHVN
HVN
33109
NEW HAVEN TWEED AP
41.26389
-72.8872
1
-5
8
2019
54734
KDXR
DXR
71509
DANBURY MUNI AP
41.37139
-73.4828
138
-5
8
2019
54767
KIJD
IJD
102705
WILLIMANTIC WINDHAM AP
41.74194
-72.1836
75
-5
1
2019
54788
KMMK
MMK
100708
MERIDEN MARKHAM MUNI AP
41.50972
-72.8278
30
-5
10
2019
94702
KBDR
BDR
61709
BRIDGEPORT SIKORSKY MEM
AP
41.15833
-73.1289
2
-5
8
2019
13764
KGED
GED
82608
GEORGETOWN SUSSEX CO AP
38.68917
-75.3592
15
-5
10
2018
13781
KILG
ILG
91808
WILMINGTON NEW CASTLE CO
AP
39.6728
-75.6008
24
-5
10
2019
3818
KMAI
MAI
52507
MARIANNA MUNI AP
30.83556
-85.1839
33
-6
10
2019
12812
KPGD
PGD
20409
PUNTA GORDA CHARLOTTE CO
AP
26.91722
-81.9914
7
-5
10
2019
12815
KMCO
MCO
53107
ORLANDO INTL AP
28.4339
-81.325
27
-5
8
2019
12816
KGNV
GNV
30907
GAINESVILLE RGNL AP
29.6919
-82.2755
37
-5
10
2019
12818
KBKV
BKV
52407
BROOKSVILLE HERNANDO CO
AP
28.47361
-82.4544
20
-5
10
2019
12819
KLEE
LEE
60607
LEESBURG MUNI AP
28.82083
-81.8097
20
-5
10
2019
12832
KAAF
AAF
52207
APALACHICOLA AP
29.73333
-85.0333
6
-5
10
2019
12834
KDAB
DAB
13107
DAYTON A BEACH INTL AP
29.1828
-81.0483
9
-5
10
2019
12835
KFMY
FMY
20609
FT MYERS PAGE FLD AP
26.585
-81.8614
5
-5
8
2019
12836
KEYW
EYW
102204
KEY WEST INTLAP
24.555
-81.7522
1
-5
10
2019
12838
KMLB
MLB
91506
MELBOURNE INTL AP
28.1011
-80.6439
8
-5
10
2019
9
-------
Surface
WBAN
Surface
Call
Ice Free
Call
Ice Free
Date
Name
Surface
Latitude
Surface
Longitude
Surface
Elevation
UTC
Anenom.
Height
Year
12839
KMIA
MIA
71409
MIAMI INTL AP
25.7905
-80.3163
9
-5
10
2019
12841
KORL
ORL
51007
ORLANDO EXECUTIVE AP
28.54528
-81.3331
33
-5
10
2019
12842
KTPA
TPA
12709
TAMPA INTLAP
27.96194
-82.5403
6
-5
8
2019
12843
KVRB
VRB
30707
VERO BEACH INTLAP
27.651
-80.4199
9
-5
10
2019
12844
KPBI
PBI
72109
WEST PALM BEACH INTL AP
26.6847
-80.0994
6
-5
10
2019
12849
KFLL
FLL
80109
FT LAUDERDALE HOLLYWOOD
AP
26.0719
-80.1536
3
-5
10
2019
12854
KSFB
SFB
50307
ORLANDO SANFORD AP
28.77972
-81.2436
15
-5
10
2019
12871
KSRQ
SRQ
11409
SARASOTA BRADENTON AP
27.40139
-82.5586
7
-5
10
2019
12873
KPIE
PIE
41607
ST PETERSBURG INTLAP
27.91056
-82.6875
2
-5
10
2019
12876
KGIF
GIF
10509
WINTER HAVEN GILBERT AP
28.06222
-81.7542
44
-5
10
2019
12882
KOPF
OPF
70809
MIAMI OPA LOCKA AP
25.90694
-80.2803
3
-5
10
2019
12885
KFXE
FXE
73009
FT LAUDERDALE EXECUTIVE AP
26.19694
-80.1708
4
-5
10
2019
12888
KTMB
TMB
81309
MIAMI KENDALLTAMIAMI EXEC
AP
25.6475
-80.4331
2
-5
10
2019
12894
KRSW
RSW
20909
FT MYERS SWFLRGNLAP
26.53611
-81.755
9
-5
10
2019
12895
KFPR
FPR
41607
FT PIERCE ST LUCIE CO INTL AP
27.49806
-80.3767
7
-5
8
2019
12896
KMTH
MTH
22707
MARATHON AP
24.72583
-81.0517
2
-5
8
2019
12897
KAPF
APF
72809
NAPLES MUNI AP
26.1522
-81.7752
3
-5
10
2019
13884
KCEW
CEW
32907
CRESTVIEW BOB SIKES AP
30.77972
-86.5225
58
-6
10
2019
13889
KJAX
J AX
22707
JACKSONVILLE INTL AP
30.495
-81.6936
8
-5
10
2019
13899
KPNS
PNS
32707
PENSACOLA RGNL AP
30.47806
-87.1869
34
-6
10
2019
53847
KNDZ
NA
WHITING FIELD NAS SOUTH
30.70444
-87.0231
54
-6
10
2016
53853
KDTS
DTS
31407
DESTIN FT WALTON AP
30.4
-86.4717
4
-6
8
2019
53860
KCRG
CRG
30507
JACKSONVILLE CRAIG MUNI AP
30.3361
-81.5147
12
-5
8
2019
73805
KECP
NA
NW FLORIDA BEACHES INTL AP
30.349
-85.788
17
-6
10
2018
92805
KPMP
PMP
71709
POMPANO BEACH AIRPARK
26.25
-80.1083
5
-5
10
2019
92806
KSPG
SPG
10809
ST PETERSBURG ALBERT
WHITTED
27.76472
-82.6275
2
-5
8
2019
10
-------
Surface
WBAN
Surface
Call
Ice Free
Call
Ice Free
Date
Name
Surface
Latitude
Surface
Longitude
Surface
Elevation
UTC
Anenom.
Height
Year
92809
KHWO
HWO
80109
HOLLYWOOD NORTH PERRY AP
25.99889
-80.2411
2
-5
10
2019
93805
KTLH
TLH
50907
TALLAHASSEE RGNL AP
30.39306
-84.3533
17
-5
10
2019
3813
KMCN
MCN
72407
MACON MIDDLE GA RGNL AP
32.6847
-83.6527
105
-5
10
2019
3820
KAGS
AGS
51209
AUGUSTA BUSH FLD AP
33.3644
-81.9633
40
-5
10
2019
3822
KSAV
SAV
21209
SAVANNAH INTL AP
32.13
-81.21
14
-5
10
2019
3888
KFTY
FTY
32007
ATLANTA FULTON CO AP
33.77917
-84.5214
256
-5
8
2019
13837
KDNL
DNL
50409
AUGUSTA DANIEL FLD AP
33.46694
-82.0386
125
-5
10
2019
13869
KABY
ABY
53107
ALBANY SW GA RGNL AP
31.53556
-84.1944
58
-5
10
2019
13870
KAMG
AMG
10609
ALMA BACON CO AP
31.5358
-82.5067
59
-5
10
2019
13873
KAHN
AHN
51007
ATHENS BEN EPPS AP
33.948
-83.3275
239
-5
8
2019
13874
KATL
ATL
32707
ATLANTA HARTSFIELD INTL AP
33.6301
-84.4418
308
-5
10
2019
13878
KSSI
SSI
30707
BRUNSWICK MALCOLM
MCKINNON AP
31.1522
-81.3908
5
-5
10
2019
53819
KFFC
FFC
20306
PEACHTREE CITY FALCON FLD
33.35528
-84.5669
243
-5
10
2019
53838
KGVL
GVL
41207
GAINESVILLE GILMER AP
34.27194
-83.8303
387
-5
8
2019
53863
KPDK
PDK
32107
ATLANTA PEACHTREE AP
33.875
-84.3022
303
-5
10
2019
53873
KVPC
VPC
32207
CARTERSVILLE AP
34.12306
-84.8486
228
-5
10
2019
93801
KRMG
RMG
51707
ROME R B RUSSELL AP
34.34778
-85.1611
195
-5
10
2019
93842
KCSG
CSG
52407
COLUMBUS METRO AP
32.5161
-84.9422
119
-5
10
2019
93845
KVLD
VLD
60707
VALDOSTA RGNL AP
30.7825
-83.2767
60
-5
10
2019
21504
PHTO
ITO
62603
HILO INTL AP
19.7191
-155.053
12
-10
10
2019
21510
PHKO
KOA
50809
KAILUA KONA KE-AHOLE AP
19.73556
-156.049
9
-10
10
2019
22516
PHOG
OGG
62909
KAHULUI AP
20.89972
-156.429
16
-10
10
2019
22521
PHNL
HNL
51209
HONOLULU INTLAP
21.324
-157.929
2
-10
8
2019
22534
PHMK
MKK
61509
MOLOKAI AP
21.1545
-157.096
135
-10
10
2019
22536
PHLI
LIH
61109
LIHUE WSO AP 1020.1
21.98389
-159.341
30
-10
10
2019
22551
PHJR
NA
EWA KALAELOA AP
21.31667
-158.067
15
-10
10
2019
14931
KBRL
BRL
41707
BURLINGTON MUNI AP
40.78333
-91.1253
211
-6
10
2019
14933
KDSM
DSM
41807
DES MOINES INTLAP
41.5338
-93.653
292
-6
10
2019
11
-------
Surface
WBAN
Surface
Call
Ice Free
Call
Ice Free
Date
Name
Surface
Latitude
Surface
Longitude
Surface
Elevation
UTC
Anenom.
Height
Year
14937
KIOW
IOW
102005
IOWA CITY MUNI AP
41.63278
-91.5431
198
-6
10
2019
14940
KMCW
MCW
51707
MASON CITY MUNI AP
43.1544
-93.3269
373
-6
10
2019
14943
KSUX
SUX
43009
SIOUX CITY GATEWAY AP
42.3913
-96.3791
334
-6
10
2019
14950
KOTM
OTM
61307
OTTUMWA INDUSTRIAL AP
41.1077
-92.4466
257
-6
10
2019
14972
KSPW
SPW
110705
SPENCER MUNI AP
43.16444
-95.2017
407
-6
10
2019
14990
KCID
CID
41807
CEDAR RAPIDS MUNI AP
41.8833
-91.7166
265
-6
8
2019
94908
KDBQ
DBQ
42007
DUBUQUE RGNLAP
42.39778
-90.7036
322
-6
10
2019
94910
KALO
ALO
62507
WATERLOO MUNI AP
42.5544
-92.4011
265
-6
10
2019
94971
KEST
EST
50207
ESTHERVILLE MUNICIPAL AP
43.40111
-94.7472
401
-6
10
2019
94982
KDVN
DVN
102005
DAVENPORT MUNI AP
41.61389
-90.5914
229
-6
10
2016
94988
KMIW
MIW
102005
MARSHALLTOWN MUNICIPAL
AP
42.11056
-92.9161
297
-6
10
2019
94989
KAMW
AMW
102705
AMES MUNICIPAL AP
41.99056
-93.6189
291
-6
10
2019
94991
KLWD
LWD
52907
LAMONI MUNICIPAL AP
40.6306
-93.9008
346
-6
10
2019
4110
KJER
JER
50406
JEROME CO AP
42.72667
-114.456
1223
-7
8
2019
4114
KLLJ
LU
10203
CHALLIS AP
44.52278
-114.215
1534
-7
8
2016
24131
KBOI
BOI
10907
BOISE AIR TERMINAL
43.5666
-116.241
858
-7
10
2019
24133
KBYI
BYI
110705
BURLEY MUNI AP
42.5416
-113.766
1266
-7
10
2019
24145
KIDA
IDA
13007
IDAHO FALLS FANNING FLD
43.51639
-112.067
1441
-7
8
2019
24149
KLWS
LWS
41107
LEWISTON NEZ PERCE CO AP
46.3747
-117.016
438
-8
8
2019
24154
KMLP
MLP
70306
MULLAN PASSVOR DME
47.45694
-115.645
1837
-8
8
2019
24156
KPIH
PIH
30607
POCATELLO RGNLAP
42.9202
-112.571
1357
-7
10
2019
94178
KTWF
TWF
71807
TWIN FALLS SUN VLY RGNL AP
42.48194
-114.487
1261
-7
10
2019
94182
KMYL
MYL
60706
MCCALL AP
44.88889
-116.102
1528
-7
10
2019
94194
KRXE
RXE
102005
REXBURG MADISON CO AP
43.83389
-111.804
1481
-7
10
2019
3887
KDEC
DEC
32007
DECATUR AP
39.83444
-88.8656
206
-6
10
2019
3960
KCPS
CPS
51109
CAHOKIA ST LOUIS AP
38.57139
-90.1572
126
-6
10
2019
4808
KARR
ARR
120602
CHICAGO AURORA MUNI AP
41.77
-88.4814
216
-6
8
2019
4838
KPWK
PWK
62607
CHICAGO PALWAUKEE AP
42.12083
-87.9047
194
-6
8
2019
12
-------
Surface
WBAN
Surface
Call
Ice Free
Call
Ice Free
Date
Name
Surface
Latitude
Surface
Longitude
Surface
Elevation
UTC
Anenom.
Height
Year
13809
KLWV
LWV
110705
LAWRENCEVILLE INTLAP
38.76417
-87.6056
131
-6
10
2019
14819
KMDW
MDW
61307
CHICAGO MIDWAY AP
41.78611
-87.7522
187
-6
10
2019
14842
KPIA
PIA
91806
PEORIA GTR PEORIA AP
40.6675
-89.6839
198
-6
10
2019
14880
KUGN
UGN
52407
CHICAGO WAUKEGAN RGNL AP
42.41667
-87.8667
216
-6
10
2019
14923
KMLI
MLI
41607
MOLINE QUAD CITY INTL AP
41.46528
-90.5233
180
-6
10
2019
53802
KMTO
MTO
102005
MATTOON COLES CO AP
39.47806
-88.2803
217
-6
8
2019
93810
KMDH
MDH
52407
CARBONDALE SOUTHERN IL AP
37.77972
-89.2497
124
-6
10
2019
93822
KSPI
SPI
92506
SPRINGFIELD CAPITAL AP
39.8447
-89.6839
181
-6
10
2019
93989
KUIN
UIN
92006
QUINCY RGNL AP
39.93694
-91.1919
234
-6
8
2019
94822
KRFD
RFD
52207
ROCKFORD GTR ROCKFORD AP
42.1927
-89.093
223
-6
10
2019
94846
KORD
ORD
62707
CHICAGO OHARE INTL AP
41.995
-87.9336
202
-6
10
2019
94870
KCMI
CMI
51007
CHAMPAIGN WILLARD AP
40.03972
-88.2778
229
-6
8
2019
94892
KDPA
DPA
62907
WEST CHICAGO DUPAGE AP
41.91444
-88.2464
230
-6
8
2019
3868
KHUF
HUF
11603
TERRE HAUTE HULMAN RGNL
AP
39.45194
-87.3089
175
-5
8
2019
3893
KBMG
BMG
52507
BLOOMINGTON MONROE CO
AP
39.13333
-86.6167
257
-5
8
2019
4846
KVPZ
VPZ
110705
VALPARAISO PORTER CO MUNI
AP
41.4525
-87.0058
232
-6
8
2019
14827
KFWA
FWA
92106
FT WAYNE INTLAP
40.9705
-85.2063
241
-5
10
2019
14829
KGSH
GSH
121605
GOSHEN MUNI AP
41.5333
-85.7833
253
-5
10
2019
14835
KLAF
LAF
91406
LAFAYETTE PURDUE UNIV AP
40.41222
-86.9369
183
-5
10
2019
14848
KSBN
SBN
92706
S BEND AP
41.7072
-86.3163
236
-5
10
2019
53842
KEYE
EYE
100405
INDIANAPOLIS EAGLE CREEK AP
39.825
-86.2958
249
-5
10
2019
53866
KGEZ
GEZ
100405
SHELBYVILLE MUNI AP
39.57806
-85.8033
245
-5
10
2019
93817
KEVV
EVV
92606
EVANSVILLE REGIONAL AP
38.0441
-87.5205
122
-6
8
2019
93819
KIND
IND
52207
INDIANAPOLIS INTLAP
39.7318
-86.2788
241
-5
10
2019
94895
KMIE
MIE
52907
MUNCIE DELAWARE CO AP
40.23417
-85.3936
286
-5
10
2019
13
-------
Surface
WBAN
Surface
Call
Ice Free
Call
Ice Free
Date
Name
Surface
Latitude
Surface
Longitude
Surface
Elevation
UTC
Anenom.
Height
Year
3928
KICT
ICT
100605
WICHITA DWIGHTD
EISENHOWER NA
37.6475
-97.43
403
-6
10
2019
3936
KMHK
MHK
22107
MANHATTAN MUNI AP
39.1346
-96.6788
322
-6
8
2018
3967
KOJC
OJC
42007
OLATHE JOHNSON CO EXEC AP
38.85
-94.7392
326
-6
10
2019
3974
KAAO
AAO
102005
WICHITA COLONEL JAMES
JABARA A
37.74611
-97.2211
431
-6
10
2019
3997
KLWC
LWC
100705
LAWRENCE MUNI AP
39.00778
-95.2117
254
-6
10
2019
3998
KPPF
PPF
40307
PARSONS TRI CITYAP
37.32778
-95.5042
265
-6
8
2019
13920
KFOE
FOE
90706
TOPEKA FORBES FLD
38.95028
-95.6639
325
-6
10
2019
13932
KWLD
WLD
101305
WINFIELD STROTHER FLD AP
37.16806
-97.0369
351
-6
8
2019
13981
KCNU
CNU
60706
CHANUTE MARTIN JOHNSON
AP
37.67028
-95.4842
300
-6
10
2019
13984
KCNK
CNK
11007
CONCORDIA MUNI AP
39.5514
-97.6508
448
-6
10
2019
13985
KDDC
DDC
91306
DODGE CITY RGNL AP
37.7686
-99.9678
787
-6
8
2019
13986
KHUT
HUT
12907
HUTCHINSON MUNI AP
38.06528
-97.8606
463
-6
10
2019
13989
KEMP
EMP
40207
EMPORIA MUNI AP
38.32917
-96.1947
365
-6
10
2019
13996
KTOP
TOP
101702
TOPEKA MUNI AP
39.0725
-95.6261
267
-6
8
2019
23064
KGCK
GCK
12407
GARDEN CITY RGNL AP
37.92722
-100.725
878
-6
8
2019
23065
KGLD
GLD
102705
GOODLAND RENNER FLD
39.36722
-101.693
1114
-7
10
2019
93909
KIXD
IXD
12607
OLATHE JOHNSON CO AP
38.83167
-94.8897
327
-6
10
2019
93990
KHLC
HLC
20607
HILL CITY MUNI AP
39.37556
-99.8297
667
-6
10
2019
93997
KRSL
RSL
90606
RUSSELL MUNI AP
38.87611
-98.8092
568
-6
10
2019
3816
KPAH
PAH
22207
PADUCAH BARKLEY RGNL AP
37.0563
-88.7744
126
-6
10
2019
3849
KLOZ
LOZ
110705
LONDON CORBIN AP
37.08722
-84.0769
362
-5
10
2019
3889
KJKL
JKL
102005
JACKSON JULIAN CARROLL AP
37.59139
-83.3144
416
-5
10
2019
13810
KLOU
LOU
52307
LOUISVILLE BOWMAN FLD
38.22806
-85.6636
165
-5
10
2019
53841
KFFT
FFT
102005
FRANKFORT CAPITAL CITY AP
38.18472
-84.9033
238
-5
10
2019
93808
KBWG
BWG
110705
BOWLING GREEN WARREN CO
AP
36.9647
-86.4238
161
-6
8
2019
14
-------
Surface
WBAN
Surface
Call
Ice Free
Call
Ice Free
Date
Name
Surface
Latitude
Surface
Longitude
Surface
Elevation
UTC
Anenom.
Height
Year
93814
KCVG
CVG
42407
CINCINNATI NORTHERN KYAP
39.04306
-84.6717
265
-5
10
2019
93820
KLEX
LEX
92706
LEXINGTON BLUEGRASS AP
38.0408
-84.6058
299
-5
10
2019
93821
KSDF
SDF
61407
LOUISVILLE INTL AP
38.1811
-85.7391
149
-5
10
2019
3937
KLCH
LCH
41907
LAKE CHARLES RGNLAP
30.12472
-93.2283
3
-6
10
2019
3996
KTVR
TVR
60507
TALLULAH VICKSBURG AP
32.35
-91.0278
26
-6
10
2019
12884
KBVE
BVE
41807
BOOTHVILLE ASOS
29.333
-89.4075
1
-6
10
2018
12916
KMSY
MSY
32307
NEW ORLEANS INTL AP
29.9933
-90.2511
1
-6
10
2019
13942
KMLU
MLU
100908
MONROE REGIONAL AP
32.5155
-92.0405
24
-6
8
2019
13957
KSHV
SHV
60707
SHREVEPORT RGNLAP
32.4472
-93.8244
77
-6
10
2019
13970
KBTR
BTR
92606
BATON ROUGE RYAN AP
30.5372
-91.1469
20
-6
10
2019
13976
KLFT
LFT
60107
LAFAYETTE RGNLAP
30.205
-91.9875
12
-6
8
2018
53865
KASD
ASD
30207
SLIDELL AP
30.34333
-89.8222
8
-6
10
2016
53905
KDTN
DTN
41907
SHREVEPORT DWTN AP
32.54278
-93.745
55
-6
10
2019
53915
KARA
ARA
61207
NEW IBERIA AP - ACAD IAN A
RGNL
30.0375
-91.8839
7
-6
8
2019
53917
KNEW
NEW
10803
NEW ORLEANS LAKEFRONT AP
30.0494
-90.0288
3
-6
8
2019
93915
KAEX
AEX
42607
ALEXANDRIA INTLAP
31.33472
-92.5586
26
-6
8
2019
4780
KFIT
FIT
102005
FITCHBURG MUNI AP
42.55194
-71.7558
101
-5
10
2019
14702
KBED
BED
52907
BEDFORD HANSCOM FLD
42.47
-71.2894
38
-5
10
2019
14739
KBOS
BOS
100506
BOSTON LOGAN INTLAP
42.3606
-71.0106
4
-5
8
2019
14756
KACK
ACK
51909
NANTUCKET MEM AP
41.25306
-70.0608
10
-5
10
2019
14763
KPSF
PSF
102705
PITTSFIELD MUNI AP
42.42722
-73.2892
351
-5
10
2019
14775
KBAF
BAF
100208
WESTFIELD BARNES MUNI AP
42.15778
-72.7161
80
-5
10
2019
54704
KOWD
OWD
111808
NORWOOD MEM AP
42.19083
-71.1736
15
-5
8
2019
54733
KBVY
BVY
100808
BEVERLY MUNI AP
42.58417
-70.9175
29
-5
10
2019
54756
KORE
ORE
102005
ORANGE MUNI AP
42.57
-72.2911
167
-5
10
2019
54768
KAQW
AQW
110705
NORTH ADAMS HARRIMAN AP
42.7
-73.1667
200
-5
10
2019
54769
KPYM
PYM
102005
PLYMOUTH MUNI AP
41.90972
-70.7294
44
-5
10
2019
54777
KTAN
TAN
100206
TAUNTON MUNI AP
41.87556
-71.0211
9
-5
10
2019
15
-------
Surface
WBAN
Surface
Call
Ice Free
Call
Ice Free
Date
Name
Surface
Latitude
Surface
Longitude
Surface
Elevation
UTC
Anenom.
Height
Year
94624
KCQX
CQX
30107
CHATHAM MUNI AP
41.6875
-69.9933
17
-5
10
2019
94720
KHYA
HYA
43009
HYANNIS BARNSTABLE MUNI
AP
41.66861
-70.28
14
-5
8
2019
94723
KLWM
LWM
110408
LAWRENCE MUNI AP
42.71722
-71.1239
42
-5
10
2019
94724
KMVY
MVY
51109
VINEYARD HAVEN AP
41.39306
-70.615
19
-5
10
2019
94726
KEWB
EWB
103008
NEW BEDFORD MUNI AP
41.67639
-70.9583
22
-5
8
2019
94746
KORH
ORH
32807
WORCESTER RGNLAP
42.2706
-71.8731
305
-5
10
2019
93706
KHGR
HGR
40307
HAGERSTOWN WASHINGTON
CO AP
39.70778
-77.7297
213
-5
8
2019
93720
KSBY
SBY
41107
SALISBURY WICOMICO RGNLAP
38.34056
-75.5103
15
-5
8
2019
93721
KBWI
BWI
92006
BALTIMORE WASH INTL AP
39.1666
-76.6833
48
-5
10
2019
93786
KOXB
OXB
40507
OCEAN CITY MUNI AP
38.30833
-75.1239
4
-5
10
2019
4836
KFVE
FVE
121605
FRENCHVILLE AROOSTOOK AP
47.28556
-68.3133
302
-5
10
2019
14605
KAUG
AUG
102705
AUGUSTA STATE AP
44.3155
-69.7972
107
-5
8
2019
14606
KBGR
BGR
92706
BANGOR INTLAP
44.7978
-68.8185
45
-5
10
2019
14607
KCAR
CAR
92602
CARIBOU MUNI AP
46.8705
-68.0173
190
-5
8
2019
14609
KHUL
HUL
102705
HOULTON INTL AP
46.1236
-67.7928
145
-5
10
2019
14610
KMLT
MLT
101305
MILLINOCKET MUNI AP
45.6477
-68.6925
124
-5
10
2019
14764
KPWM
PWM
100606
PORTLAND INTLJETPORT
43.6497
-70.3002
14
-5
8
2019
54772
KIZG
IZG
91206
FRYEBURG E SLOPES AP
43.99056
-70.9475
136
-5
10
2019
94623
KIWI
IWI
91306
WISCASSET AP
43.96361
-69.7117
13
-5
10
2019
4839
KBIV
BIV
111705
HOLLAND TULIP CITY AP
42.74611
-86.0967
206
-5
10
2019
4847
KADG
ADG
100705
ADRIAN LENAWEE CO AP
41.86778
-84.0794
243
-5
10
2019
4854
KGLR
GLR
100705
GAYLORD OTSEGO CO AP
45.01333
-84.7014
407
-5
10
2019
14815
KBTL
BTL
22007
BATTLE CREEK KELLOGG AP
42.3075
-85.2511
283
-5
10
2019
14822
KDET
DET
101206
DETROIT CITY AP
42.40917
-83.01
191
-5
10
2019
14826
KFNT
FNT
91808
FLINT BISHOP INTLAP
42.9666
-83.7494
235
-5
10
2019
14833
KJXN
JXN
41607
JACKSON REYNOLDS FLD
42.2667
-84.4667
304
-5
8
2019
14836
KLAN
LAN
90706
LANSING CAPITAL CITY AP
42.78028
-84.5789
256
-5
10
2019
16
-------
Surface
WBAN
Surface
Call
Ice Free
Call
Ice Free
Date
Name
Surface
Latitude
Surface
Longitude
Surface
Elevation
UTC
Anenom.
Height
Year
14840
KMKG
MKG
32007
MUSKEGON CO AP
43.17111
-86.2367
191
-5
10
2019
14841
KPLN
PLN
100705
PELLSTON RGNL AP
45.5644
-84.7927
215
-5
8
2019
14845
KMBS
MBS
53107
SAGINAW MBS INTL AP
43.53306
-84.0797
201
-5
10
2019
14847
KANJ
ANJ
91406
SAULT STE MARIE SANDERSON
FLD
46.4794
-84.3572
220
-5
10
2019
14850
KTVC
TVC
51707
TRAVERSE CITY CHERRY CPTL AP
44.74083
-85.5825
188
-5
8
2019
14853
KYIP
YIP
92608
DETROIT WILLOW RUN AP
42.23333
-83.5333
237
-5
10
2019
14858
KCMX
CMX
112602
HANCOCK HOUGHTON CO AP
47.16861
-88.4889
325
-5
8
2019
94814
KHTL
HTL
32907
HOUGHTON LK ROSCOMMON
AP
44.3591
-84.6738
351
-5
10
2019
94815
KAZO
AZO
83106
KALAMAZOO BATTLE CK INTL
AP
42.23472
-85.5519
265
-5
10
2019
94817
KPTK
PTK
42407
PONTIAC OAKLAND CO INTL AP
42.665
-83.4181
297
-5
10
2019
94847
KDTW
DTW
60707
DETROIT METRO AP
42.2313
-83.3308
192
-5
10
2019
94849
KAPN
APN
50407
ALPENA CO RGNL AP
45.0716
-83.5644
208
-5
10
2019
94860
KGRR
GRR
22107
GRAND RAPIDS INTLAP
42.8825
-85.5239
245
-5
10
2019
94871
KBEH
BEH
121605
BENTON HARBOR AIRPORT
ASOS
42.1256
-86.4284
196
-5
10
2019
94889
KARB
ARB
92408
ANN ARBOR MUNI AP
42.22278
-83.7444
253
-5
10
2019
94893
KIMT
IMT
110705
IRON MTN FORD AP
45.81833
-88.1144
338
-6
8
2019
14910
KAXN
AXN
102705
ALEXANDRIA MUNI AP
45.8679
-95.3941
432
-6
10
2019
14913
KDLH
DLH
72507
DULUTH INTLAP
46.8369
-92.1833
437
-6
10
2019
14918
KINL
INL
90706
INTL FALLS INTLAP
48.5614
-93.3981
361
-6
10
2019
14922
KMSP
MSP
91306
MINNEAPOLIS ST PAUL AP
44.8831
-93.2289
266
-6
10
2019
14925
KRST
RST
53007
ROCHESTER INTLAP
43.9041
-92.4916
397
-6
10
2019
14926
KSTC
STC
70306
ST CLOUD RGNL AP
45.5433
-94.0513
308
-6
10
2019
14927
KSTP
STP
91206
ST PAUL DOWNTOWN AP
44.93194
-93.0556
213
-6
10
2019
14992
KRWF
RWF
102005
REDWOOD FALLS MUNI AP
44.5483
-95.0804
311
-6
10
2019
17
-------
Surface
WBAN
Surface
Call
Ice Free
Call
Ice Free
Date
Name
Surface
Latitude
Surface
Longitude
Surface
Elevation
UTC
Anenom.
Height
Year
94931
KHIB
HIB
102005
HIBBING CHISHOLM HIBBING
AP
47.38639
-92.8389
411
-6
10
2019
94938
KBRD
BRD
102705
BRAINERD CROW WING CO AP
46.40472
-94.1308
372
-6
10
2019
94960
KMIC
MIC
41807
MPLS CRYSTAL AP
45.06194
-93.3508
262
-6
10
2019
94961
KBDE
BDE
91206
BAUDETTE INTLAP
48.71667
-94.6
330
-6
10
2019
94963
KFCM
FCM
42507
MPLS FLYING CLOUD AP
44.8321
-93.4705
276
-6
10
2019
94967
KPKD
PKD
41807
PARK RAPIDS MUNI AP
46.90056
-95.0678
437
-6
10
2019
3935
KCGI
CGI
121605
CAPE GIRARDEAU MUNI AP
37.2252
-89.5705
102
-6
8
2019
3945
KCOU
COU
62007
COLUMBIA RGNL AP
38.8169
-92.2183
272
-6
10
2019
3947
KMCI
MCI
91906
KANSAS CITY INTL AP
39.2972
-94.7306
306
-6
10
2019
3963
KJEF
JEF
71107
JEFFERSON CITY MEM AP
38.59111
-92.1558
175
-6
10
2019
3966
KSUS
SUS
43007
ST LOUIS SPRT OF SLAP
38.65722
-90.6558
141
-6
8
2019
3975
KPOF
POF
121605
POPLAR BLUFF MUNI AP
36.7725
-90.3247
100
-6
8
2019
3994
KDMO
DMO
81406
SEDALIA MEM AP
38.70417
-93.1833
274
-6
10
2019
13987
KJLN
JLN
92706
JOPLIN REGIONAL AIRPORT
37.1466
-94.5022
299
-6
10
2019
13988
KMKC
MKC
91306
KANSAS CITY DOWNTOWN AP
39.1208
-94.5969
226
-6
8
2019
13993
KSTJ
STJ
30507
ST JOSEPH ROSECRANS AP
39.7736
-94.9233
249
-6
10
2019
13994
KSTL
STL
92606
ST LOUIS LAMBERT INTL AP
38.7525
-90.3736
162
-6
10
2019
13995
KSGF
SGF
92006
SPRINGFIELD RGNL AP
37.2397
-93.3897
384
-6
10
2019
13997
KVIH
VIH
12407
VICHY ROLLA NATIONAL AP
38.13111
-91.7683
344
-6
10
2019
14938
KIRK
IRK
100705
KIRKSVILLE RGNL AP
40.09722
-92.5433
293
-6
10
2019
53879
KLXT
LXT
41805
LEES SUMMIT MUNI AP
38.95972
-94.3714
304
-6
10
2019
53901
KUNO
UNO
101305
WEST PLAINS MUNI AP
36.87806
-91.9025
373
-6
10
2019
53904
KSET
SET
102705
ST CHARLES CO AP
38.92861
-90.4281
133
-6
10
2019
3940
KJAN
JAN
52207
JACKSON INTLAP
32.3205
-90.0777
101
-6
10
2019
13833
KHBG
HBG
22707
HATTIESBURG CHAIN MUNI AP
31.28194
-89.2531
46
-6
10
2019
13865
KMEI
MEI
60607
MERIDIAN KEY FLD
32.3347
-88.7442
90
-6
10
2019
13927
KHKS
HKS
22807
JACKSON HAWKINS FLD
32.33667
-90.2214
104
-6
10
2019
13939
KGLH
GLH
13007
GREENVILLE ASOS
33.4825
-90.9853
39
-6
10
2019
18
-------
Surface
WBAN
Surface
Call
Ice Free
Call
Ice Free
Date
Name
Surface
Latitude
Surface
Longitude
Surface
Elevation
UTC
Anenom.
Height
Year
13978
KGWO
GWO
30607
GREENWOOD LEFLORE AP
33.4963
-90.0866
41
-6
10
2019
53858
KPQL
PQL
32607
PASCAGOULA LOTT INTL AP
30.46361
-88.5319
5
-6
8
2019
93862
KTUP
TUP
91708
TUPELO RGNLAP
34.2622
-88.7713
110
-6
10
2019
93874
KGPT
GPT
30607
GULFPORT- BILOXI AP
30.4119
-89.0808
13
-6
10
2019
93919
KMCB
MCB
41607
MCCOMB_PIKE COJOHN E
LEWIS AP
31.1827
-90.4708
126
-6
10
2019
24033
KBIL
BIL
90506
BILLINGS INTL AP
45.8069
-108.542
1091
-7
10
2019
24036
KLWT
LWT
41706
LEWISTOWN MUNI AP
47.0492
-109.458
1263
-7
10
2019
24037
KMLS
MLS
102705
MILES CITYAP
46.4266
-105.883
800
-7
10
2019
24132
KBZN
BZN
42507
BOZEMAN GALLATIN FLD
45.788
-111.161
1349
-7
10
2019
24135
KBTM
BTM
21306
BUTTE BERTMOONEY AP
45.9647
-112.501
1678
-7
10
2019
24137
KCTB
CTB
20306
CUT BANK MUNI AP
48.6033
-112.375
1170
-7
10
2019
24138
KDLN
DLN
13006
DILLON AP
45.2575
-112.554
1585
-7
10
2019
24143
KGTF
GTF
32607
GREAT FALLS INTLAP
47.4733
-111.382
1117
-7
10
2019
24144
KHLN
HLN
53107
HELENA RGNLAP
46.6056
-111.964
1167
-7
10
2019
24146
KGPI
GPI
91306
KALISPELL GLACIER AP
48.3042
-114.264
901
-7
8
2019
24150
KLVM
LVM
102005
LIVINGSTON AP
45.6983
-110.441
1415
-7
10
2019
24153
KMSO
MSO
53107
MISSOULA INTLAP
46.9208
-114.093
973
-7
10
2019
94008
KGGW
GGW
41405
GLASGOW INTL AP
48.2138
-106.621
696
-7
10
2019
94012
KHVR
HVR
110705
HAVRE CITY CO AP
48.5428
-109.763
788
-7
10
2019
94017
KOLF
OLF
102005
WOLF POINT INTLAP
48.09444
-105.574
605
-7
10
2019
94055
KBHK
BHK
102705
BAKER MUNI AP
46.3583
-104.25
906
-7
10
2019
3810
KHKY
HKY
110608
HICKORY FAA AP
35.7425
-81.3819
348
-5
8
2019
3812
KAVL
AVL
101608
ASHEVILLE RGNLAP
35.43194
-82.5375
645
-5
8
2019
13722
KRDU
RDU
70809
RALEIGH DURHAM INTLAP
35.8923
-78.7819
127
-5
10
2019
13723
KGSO
GSO
63009
PIEDMONT TRIAD INTLAP
36.0969
-79.9432
271
-5
10
2019
13748
KILM
ILM
41307
WILMINGTON INTLAP
34.2675
-77.8997
10
-5
10
2016
13754
KNKT
NA
CHERRY POINT MCAS
34.88333
-76.8667
30
-5
10
2019
13776
KLBT
LBT
41907
LUBERTION REGIONAL AP
34.608
-79.0591
37
-5
10
2018
19
-------
Surface
WBAN
Surface
Call
Ice Free
Call
Ice Free
Date
Name
Surface
Latitude
Surface
Longitude
Surface
Elevation
UTC
Anenom.
Height
Year
13786
KECG
ECG
32607
ELIZABETH CITYCGAS
36.26056
-76.175
4
-5
10
2019
13881
KCLT
CLT
60209
CHARLOTTE DOUGLAS AP
35.2236
-80.9552
222
-5
10
2019
53870
KAKH
AKH
112008
GASTONIA MUNI AP
35.19667
-81.1558
242
-5
10
2019
53872
KEQY
EQY
52009
MONROE AP
35.01694
-80.6206
204
-5
10
2019
93719
KEWN
EWN
92206
NEW BERN CRAVEN CO AP
35.0677
-77.048
6
-5
8
2019
93729
KHSE
HSE
31907
CAPE HATTERAS AP
35.2326
-75.6219
3
-5
10
2019
93740
KFAY
FAY
60209
FAYETTEVILLE RGNLAP
34.99139
-78.8803
57
-5
10
2019
93759
KRWI
RWI
51507
ROCKY MT WILSON AP
35.855
-77.8931
45
-5
8
2019
93765
KMRH
MRH
20807
BEAUFORT MICHAEL J SMITH
FLD
34.73361
-76.6606
2
-5
10
2019
93782
KMEB
MEB
60209
LAURINBURG MAXTON AP
34.79167
-79.3661
66
-5
10
2019
93783
KBUY
BUY
60507
BURLINGTON ALAMANCE AP
36.04667
-79.4769
187
-5
10
2019
93785
KIGX
IGX
42905
CHAPEL HILL WILLIAMS AP
35.93333
-79.0642
152
-5
10
2016
93807
KINT
INT
62509
WINSTON SALEM RYNLDS AP
36.13361
-80.2222
292
-5
8
2019
14914
KFAR
FAR
92606
FARGO HECTOR INTL AP
46.92528
-96.8111
274
-6
10
2019
14916
KGFK
GFK
101702
GRAND FORKS INTL AP
47.9428
-97.1839
257
-6
8
2019
14919
KJMS
JMS
11007
JAMESTOWN MUNI AP
46.9258
-98.6691
455
-6
8
2019
24011
KBIS
BIS
50107
BISMARCK MUNI AP
46.7825
-100.757
503
-6
10
2019
24012
KDIK
DIK
11707
THEODORE ROOSEVELT AP
46.7994
-102.797
786
-7
10
2019
24013
KMOT
MOT
90606
MINOT INTL AP
48.2552
-101.273
507
-6
8
2019
94014
KISN
ISN
40407
WILLISTON SLOULIN INTL AP
48.1738
-103.637
580
-6
10
2018
94038
KHEI
HEI
100405
HETTINGER MUNI AP
46.01389
-102.655
824
-7
10
2018
14935
KGRI
GRI
91906
GRAND ISLAND AP
40.9611
-98.3136
561
-6
8
2019
14939
KLNK
LNK
32607
LINCOLN MUNI AP
40.8508
-96.7475
363
-6
10
2019
14941
KOFK
OFK
102005
NORFOLK KARL STEFAN AP
41.9855
-97.4352
473
-6
10
2019
14942
KOMA
OMA
91306
OMAHA EPPLEY AIRFIELD
41.3102
-95.8991
299
-6
10
2019
24017
KCDR
CDR
100708
CHADRON MUNI AP
42.8374
-103.098
1004
-7
10
2019
24023
KLBF
LBF
102005
NORTH PLATTE RGNLAP
41.1213
-100.669
847
-6
8
2019
24028
KBFF
BFF
22003
SCOTTSBLUFF HEILIG AP
41.8705
-103.593
1202
-7
8
2019
20
-------
Surface
WBAN
Surface
Call
Ice Free
Call
Ice Free
Date
Name
Surface
Latitude
Surface
Longitude
Surface
Elevation
UTC
Anenom.
Height
Year
24030
KSNY
SNY
92508
SIDNEY MUNI AP
41.0993
-102.986
1309
-7
10
2019
24032
KVTN
VTN
91306
VALENTINE MILLER FLD
42.8783
-100.55
789
-6
10
2019
24044
KAIA
AIA
92908
ALLIANCE MUNI AP
42.0573
-102.802
1198
-7
10
2019
24091
KIML
IML
60507
IMPERIAL MUNI AP
40.51
-101.62
996
-7
10
2019
94040
KMCK
MCK
22107
MCCOOK MUNI AP
40.20639
-100.591
779
-6
10
2019
94946
KBBW
BBW
100705
BROKEN BOW MUNI AP
41.43333
-99.6333
771
-6
10
2019
94949
KHSI
HSI
110705
HASTINGS MUNI AP
40.6005
-98.4258
598
-6
8
2019
94957
KFNB
FNB
91406
FALLS CITY BRENNER FLD
40.08028
-95.5919
299
-6
10
2019
94958
KODX
ODX
100405
ORD EVELYN SHARP FLD
41.62333
-98.9483
629
-6
8
2019
94978
KTQE
TQE
102005
TEKAMAH MUNI AP
41.76361
-96.1778
313
-6
10
2019
14710
KMHT
MHT
51309
MANCHESTER AP
42.93333
-71.4383
69
-5
10
2019
14745
KCON
CON
102005
CONCORD MUNI AP
43.1952
-71.5011
105
-5
8
2019
54728
KHIE
HIE
102005
WHITEFIELD MT WASHINGTON
AP
44.3675
-71.545
320
-5
8
2016
54770
KAFN
AFN
111705
JAFFREY MUNI AP
42.805
-72.0036
317
-5
10
2019
54791
KDAW
DAW
102005
ROCHESTER SKYHAVEN AP
43.27806
-70.9222
96
-5
8
2019
94700
KBML
BML
102705
BERLIN MUNI AP
44.57611
-71.1786
342
-5
10
2019
94765
KLEB
LEB
11007
LEBANON MUNI AP
43.62639
-72.3047
182
-5
10
2019
13735
KMIV
MIV
92606
MILLVILLE MUNI AP
39.3667
-75.0667
21
-5
8
2019
14734
KEWR
EWR
70809
NEWARK INTL AP
40.6825
-74.1694
2
-5
10
2019
14792
KTTN
TTN
91708
TRENTON MERCER CO AP
40.27694
-74.8158
56
-5
8
2019
54743
KCDW
CDW
60909
CALDWELL ESSEX CO AP
40.87639
-74.2831
53
-5
8
2019
54785
KSMQ
SMQ
100708
SOMERVILLE SOMERSET AP
40.62389
-74.6694
33
-5
10
2019
54793
KFWN
FWN
92408
SUSSEX AP
41.20028
-74.6231
123
-5
10
2019
93730
KACY
ACY
112906
ATLANTIC CITY INTLAP
39.4494
-74.5672
18
-5
8
2019
93780
KVAY
VAY
91206
MT HOLLY S JERSEY AP
39.94917
-74.8417
14
-5
10
2019
94741
KTEB
TEB
51909
TETERBORO AP
40.85
-74.0614
2
-5
8
2019
3027
KCQC
CQC
32007
CLINES CORNERS
35.00278
-105.663
2160
-7
10
2019
23009
KROW
ROW
41607
ROSWELLIND AIRPK
33.3075
-104.508
1112
-7
8
2019
21
-------
Surface
Surface
Ice Free
Ice Free
Surface
Surface
Surface
Anenom.
WBAN
Call
Call
Date
Name
Latitude
Longitude
Elevation
UTC
Height
Year
23048
KTCC
TCC
40507
TUCUMCARI MUNI AP
35.18222
-103.603
1234
-7
10
2019
23049
KSAF
SAF
52307
SANTA FE CO MUNI AP
35.61694
-106.089
1923
-7
8
2018
23050
KABQ
ABQ
52207
ALBUQUERQUE INTL AP
35.0419
-106.616
1618
-7
10
2019
23051
KCAO
CAO
50307
CLAYTON MUNI AIR PK
36.4486
-103.154
1512
-7
10
2019
23052
KRTN
RTN
31407
RATON MUNI CREWS AP
36.74139
-104.502
1934
-7
10
2019
23054
KLVS
LVS
42607
LAS VEGAS MUNI AP
35.65417
-105.142
2092
-7
10
2019
23078
KDMN
DMN
92706
DEMING MUNI AP
32.26222
-107.721
1311
-7
10
2019
23081
KGUP
GUP
51507
GALLUP MUNI AP
35.5144
-108.794
1972
-7
10
2019
23090
KFMN
FMN
40407
FARMINGTON RGNL AP
36.74361
-108.229
1675
-7
10
2019
93033
KCNM
CNM
51007
CARLSBAD CAVERN CITY AP
32.3335
-104.258
985
-7
10
2019
93045
KTCS
TCS
31907
TRUTH OR CONSEQUENCE AP
33.23667
-107.268
1470
-7
8
2019
3160
KDRA
DRA
13006
MERCURY DESERT ROCK AP
36.6206
-116.028
985
-8
10
2019
23153
KTPH
TPH
20306
TONOPAH
38.0511
-117.09
1644
-8
10
2019
23154
KELY
ELY
121605
ELYYELLAND FLD AP
39.2952
-114.847
1909
-8
10
2019
23169
KLAS
LAS
42507
LAS VEGAS MCCARRAN AP
36.0719
-115.163
664
-8
10
2019
23185
KRNO
RNO
51507
RENOTAHOE INTLAP
39.4838
-119.771
1344
-8
10
2019
24121
KEKO
EKO
62807
ELKO RGNL AP
40.8288
-115.789
1533
-8
10
2019
24128
KWMC
WMC
111705
WINNEMUCCA MUNI AP
40.9017
-117.808
1309
-8
10
2019
24172
KLOL
LOL
101305
LOVELOCK DERBY FLD
40.0681
-118.569
1189
-8
10
2019
53123
KVGT
VGT
42607
LAS VEGAS AIR TERMINAL
36.21167
-115.196
670
-8
10
2019
4725
KBGM
BGM
21307
BINGHAMTON GREATER AP
42.2068
-75.98
486
-5
8
2019
4781
KISP
ISP
ISLIP LONG IS MACARTHUR AP
40.79389
-73.1017
26
-5
8
2019
4789
KMGJ
MGJ
71509
MONTGOMERY ORANGE AP
41.50917
-74.265
106
-5
10
2019
14719
KFOK
FOK
21109
WESTHAMPTN GABRESKI AP
40.84361
-72.6322
18
-5
10
2019
14732
KLGA
LGA
70609
NEW YORK LAGUARDIA AP
40.77944
-73.8803
3
-5
10
2019
14733
KBUF
BUF
60409
BUFFALO NIAGARA INTLAP
42.9408
-78.7358
218
-5
10
2019
14735
KALB
ALB
90806
ALBANY AP
42.7431
-73.8092
95
-5
10
2019
14747
KDKK
DKK
91908
DUNKIRK CHAUTAUQUA AP
42.49333
-79.2722
203
-5
10
2019
14748
KELM
ELM
42507
ELMIRA CORNING RGNL AP
42.15944
-76.8919
288
-5
10
2019
22
-------
Surface
WBAN
Surface
Call
Ice Free
Call
Ice Free
Date
Name
Surface
Latitude
Surface
Longitude
Surface
Elevation
UTC
Anenom.
Height
Year
14750
KGFL
GFL
102705
GLENS FALLS AP
43.35
-73.6167
98
-5
10
2019
14757
KPOU
POU
91206
POUGHKEEPSIE DUTCHESS CO
AP
41.6266
-73.8842
51
-5
8
2019
14768
KROC
ROC
102008
ROCHESTER GTR INTL AP
43.1167
-77.6767
164
-5
10
2019
14771
KSYR
SYR
20107
SYRACUSE HANCOCK INTL AP
43.1111
-76.1038
126
-5
10
2019
54757
KELZ
ELZ
110705
WELLSVILLE MUNI AP
42.10944
-77.9919
647
-5
10
2019
54773
KFZY
FZY
102705
FULTON OSWEGO CO AP
43.34972
-76.3847
141
-5
8
2019
54778
KPEO
PEO
110705
PENNYAN AP
42.6425
-77.0564
263
-5
10
2019
54787
KFRG
FRG
51809
FARMINGDALE AP
40.73417
-73.4169
24
-5
10
2019
54790
KHWV
HWV
120308
SHIRLEY BROOKHAVEN AP
40.82167
-72.8689
20
-5
10
2019
64775
KRME
RME
32807
ROME GRIFFISS AIRFIELD
43.23389
-75.4117
147
-5
10
2019
64776
KPBG
PBG
110705
PLATTSBURGH INTL AP
44.65
-73.4667
71
-5
10
2019
94704
KDSV
DSV
92408
DANSVILLE MUNI AP
42.57083
-77.7133
196
-5
10
2019
94725
KMSS
MSS
111705
MASSENA INTL AP
44.93583
-74.8458
65
-5
10
2019
94728
KNYC
NYC
91806
NEW YORK CNTRL PK TWR
40.77889
-73.9692
40
-5
10
2017
94740
KSLK
SLK
111805
SARANAC RGNL AP
44.38528
-74.2067
501
-5
8
2019
94745
KHPN
HPN
52209
WESTCHESTER CO AP
41.06694
-73.7075
116
-5
10
2019
94789
KJFK
JFK
63009
NEW YORK JFK INTL AP
40.63861
-73.7622
3
-5
10
2019
94790
KART
ART
110705
WATERTOWN INTLAP
43.9922
-76.0217
97
-5
8
2019
4804
KOSU
OSU
61107
COLUMBUS OHIO STATE UNIV
AP
40.07806
-83.0781
276
-5
8
2019
4842
KBJJ
BJJ
100406
WOOSTER WAYNE CO AP
40.87306
-81.8867
336
-5
10
2019
4848
KTDZ
TDZ
82008
TOLEDO METCALF FLD
41.56306
-83.4764
189
-5
10
2019
4849
KLPR
LPR
120606
ELYRIA LORAIN CO AP
41.34611
-82.1794
241
-5
10
2019
4850
KAOH
AOH
91906
LIMA ALLEN COUNTY AP
40.7075
-84.0272
297
-5
10
2019
4851
KDFI
DFI
42407
DEFIANCE AP
41.3375
-84.4289
215
-5
10
2019
4852
KPHD
PHD
90706
NEW PHILADELPHIA FLD
40.47194
-81.4236
272
-5
10
2019
4853
KBKL
BKL
81808
CLEVELAND BURKE AP
41.5175
-81.6836
177
-5
8
2019
4855
KMNN
MNN
120606
MARION MUNI AP
40.61611
-83.0636
301
-5
10
2019
23
-------
Surface
WBAN
Surface
Call
Ice Free
Call
Ice Free
Date
Name
Surface
Latitude
Surface
Longitude
Surface
Elevation
UTC
Anenom.
Height
Year
4857
KHZY
HZY
100606
ASHTABULA CO AP
41.77806
-80.6958
278
-5
10
2019
4858
KVTA
VTA
120606
NEWARK HEATH AP
40.02278
-82.4625
267
-5
10
2019
13841
KILN
ILN
102005
WILMINGTON AIRBORNE PARK
39.42028
-83.8217
321
-5
10
2019
14813
KAKR
AKR
100306
AKRON FULTON INTL AP
41.0375
-81.4642
318
-5
10
2019
14820
KCLE
CLE
52207
CLEVELAND HOPKINS INTL AP
41.405
-81.8528
235
-5
10
2019
14821
KCMH
CMH
53007
COLUMBUS PORT COLUMBUS
INTL AP
39.99139
-82.8808
247
-5
10
2019
14825
KFDY
FDY
100506
FINDLAY AP
41.01361
-83.6686
244
-5
10
2019
14852
KYNG
YNG
90808
YOUNGSTOWN RGNL AP
41.25444
-80.6739
360
-5
10
2016
14891
KMFD
MFD
52307
MANSFIELD LAHM MUNI AP
40.82028
-82.5178
395
-5
10
2019
14895
KCAK
CAK
70709
AKRON CANTON RGNL AP
40.91667
-81.4333
368
-5
10
2019
53844
KLHQ
LHQ
61207
LANCASTER FAIRFIELD AP
39.75556
-82.6572
259
-5
10
2019
53855
KHAO
HAO
50807
HAMILTON BUTLER CO RGNL AP
39.36444
-84.5247
189
-5
10
2019
53859
KMGY
MGY
43007
DAYTON WRIGHT BROS AP
39.59361
-84.2264
290
-5
8
2019
93812
KLUK
LUK
42007
CINCINNATI LUNKEN AP
39.10333
-84.4189
149
-5
10
2019
93815
KDAY
DAY
50907
DAYTON INTL AP
39.90611
-84.2186
305
-5
10
2019
93824
KZZV
ZZV
30907
ZANESVILLE MUNI AP
39.94444
-81.8922
268
-5
8
2019
94830
KTOL
TOL
12007
TOLEDO EXPRESS AP
41.58861
-83.8014
204
-5
10
2019
3030
KGUY
GUY
90606
GUYMON MUNI AP
36.68167
-101.505
950
-6
8
2019
3932
KCSM
CSM
12209
CLINTON-SHERMAN AP
35.3568
-99.2042
586
-6
10
2019
3950
KLAW
LAW
22509
LAWTON MUNI AP
34.5584
-98.4172
326
-6
10
2019
3954
KPWA
PWA
10709
OKLAHOMA CITY POST AP
35.53417
-97.6469
395
-6
8
2019
3959
KBVO
BVO
21309
BARTLESVILLE F P FLD
36.7683
-96.0261
218
-6
10
2019
3965
KSWO
SWO
12009
STILLWATER RGNL AP
36.1624
-97.0894
300
-6
10
2019
3981
KFDR
FDR
21809
FREDERICK MUNI AP
34.21
-98.59
383
-6
10
2019
13967
KOKC
OKC
11409
OKLAHOMA CITY WILL ROGERS
AP
35.3889
-97.6006
392
-6
8
2019
13968
KTUL
TUL
42209
TULSA INTL AP
36.1994
-95.8872
198
-6
10
2019
13969
KPNC
PNC
20609
PONCA CITY MUNI AP
36.73667
-97.1019
305
-6
10
2019
24
-------
Surface
Surface
Ice Free
Ice Free
Surface
Surface
Surface
Anenom.
WBAN
Call
Call
Date
Name
Latitude
Longitude
Elevation
UTC
Height
Year
13975
KGAG
GAG
12109
GAGE AP
36.2967
-99.7689
668
-6
10
2019
53908
KRVS
RVS
21209
TULSA R L JONES JR AP
36.03944
-95.9844
190
-6
10
2019
53913
KGOK
GOK
72607
GUTHRIE MUNI AP
35.8517
-97.4142
326
-6
10
2019
93950
KMLC
MLC
43009
MCALESTER RGNLAP
34.8822
-95.783
235
-6
10
2019
93953
KMKO
MKO
20907
MUSKOGEE DAVIS FLD
35.65667
-95.3614
184
-6
10
2019
93986
KHBR
HBR
80107
HOBARTMUNI AP
34.9894
-99.0525
474
-6
10
2018
4113
KHRI
HRI
41907
HERMISTON MUNI AP
45.82583
-119.261
196
-8
10
2019
4201
KSPB
SPB
22706
SCAPPOOSE IND AP
45.77278
-122.861
16
-8
10
2019
24130
KBKE
BKE
50806
BAKER CITY MUNI AP
44.8428
-117.809
1024
-8
10
2019
24152
KMEH
MEH
92806
M EACH AM
45.51139
-118.425
1135
-8
10
2018
24155
KPDT
PDT
42607
PENDLETON E OR RGNLAP
45.6983
-118.855
453
-8
10
2019
24162
KONO
ONO
50806
ONTARIO MUNI AP
44.02056
-117.013
668
-7
10
2019
24221
KEUG
EUG
40507
EUGENE MAHLON SWEET AP
44.1278
-123.221
108
-8
10
2019
24225
KMFR
MFR
40607
MEDFORD ROGUE VLYAP
42.3811
-122.872
395
-8
10
2019
24229
KPDX
PDX
20107
PORTLAND INTL AP
45.5958
-122.609
6
-8
10
2019
24230
KRDM
RDM
32207
REDMOND ROBERTS FLD
44.2558
-121.139
928
-8
10
2019
24231
KRBG
RBG
22706
ROSEBURG RGNLAP
43.23889
-123.355
158
-8
10
2019
24232
KSLE
SLE
51507
SALEM MCNARY FLD
44.905
-123.001
62
-8
10
2019
24235
KSXT
SXT
121605
SEXTON SUMMIT
42.6003
-123.364
1168
-8
10
2019
24242
KTTD
TTD
40407
PORTLAND TROUTDALE AP
45.55111
-122.409
9
-8
10
2019
94185
KBNO
BNO
50806
BURNS MUNI AP
43.595
-118.956
1262
-8
10
2019
94224
KAST
AST
32006
ASTORIA RGNLAP
46.1569
-123.883
3
-8
10
2019
94236
KLMT
LMT
52507
KLAMATH FALLS INTL AP
42.14694
-121.724
1245
-8
10
2019
94261
KHIO
HIO
31607
PORTLAND-HILLSBORO AP
45.54056
-122.949
63
-8
10
2019
94273
KMMV
MMV
40306
MCMINNVILLE MUNI AP
45.19472
-123.134
48
-8
10
2019
94281
KUAO
UAO
11503
AURORA STATE AP
45.24861
-122.769
60
-8
8
2019
4726
KJST
J ST
82506
JOHNSTOWN CAMBRIA CO AP
40.31611
-78.8339
695
-5
8
2019
4751
KBFD
BFD
111705
BRADFORD RGNLAP
41.8
-78.6333
653
-5
8
2019
4787
KDUJ
DUJ
40207
DUBOIS JEFFERSON CO AP
41.17833
-78.8989
553
-5
8
2019
25
-------
Surface
WBAN
Surface
Call
Ice Free
Call
Ice Free
Date
Name
Surface
Latitude
Surface
Longitude
Surface
Elevation
UTC
Anenom.
Height
Year
4843
KGKJ
GKJ
10606
PORT MEADVILLE AP
41.62639
-80.215
426
-5
10
2019
13739
KPHL
PHL
73009
PHILADELPHIA INTL AP
39.8683
-75.2311
3
-5
8
2019
14711
KMDT
MDT
82208
MIDDLETOWN HARRISBURG
INTL AP
40.1962
-76.7724
95
-5
8
2019
14712
KRDG
RDG
111908
READING SPAATZ FLD
40.36667
-75.9667
87
-5
8
2019
14736
KAOO
AOO
82108
ALTOONA BLAIR CO AP
40.29639
-78.3203
451
-5
8
2019
14737
KABE
ABE
93008
ALLENTOWN INTL AP
40.65083
-75.4492
119
-5
8
2019
14751
KCXY
CXY
71207
HARRISBURG CPTLCY AP
40.21722
-76.8514
104
-5
8
2019
14762
KAGC
AGC
40307
PITTSBURGH ALLEGHENY CO AP
40.35472
-79.9217
380
-5
8
2019
14770
KSEG
SEG
81808
SELINSGROVE PENN VLY AP
40.82056
-76.8642
135
-5
10
2019
14777
KAVP
AVP
32807
WILKES-BARRE INTLAP
41.3336
-75.7269
283
-5
10
2019
14778
KIPT
IPT
53007
WILLIAMSPORT
41.2433
-76.9217
158
-5
8
2019
14860
KERI
ERI
82108
ERIE INTLAP
42.08
-80.1825
223
-5
10
2019
54737
KLNS
LNS
81508
LANCASTER AP
40.12028
-76.2944
122
-5
8
2019
54782
KPTW
PTW
90408
POTTSTOWN LIMERICK AP
40.23833
-75.5572
90
-5
8
2019
54786
KDYL
DYL
90208
DOYLESTOWN AP
40.33
-75.1225
119
-5
10
2019
54789
KMPO
MPO
91908
MT POCONO MOUNTAINS AP
41.13889
-75.3794
578
-5
10
2019
54792
KFIG
FIG
102705
CLEARFIELD LAWRENCE AP
41.04667
-78.4117
462
-5
10
2019
93778
KTHV
THV
31505
YORKAP
39.91806
-76.8742
141
-5
10
2019
94732
KPNE
PNE
71207
PHILADELPHIA NEAP
40.08194
-75.0111
30
-5
10
2019
94823
KPIT
PIT
72809
PITTSBURGH INTLAP
40.4846
-80.2144
367
-5
10
2019
11641
TJSJ
SJU
12709
SAN JUAN LM MARIN AP
18.4325
-66.0108
3
-4
10
2019
14765
KPVD
PVD
71709
PROVIDENCE T F GREEN AP
41.7219
-71.4325
18
-5
10
2019
14787
KUUU
UUU
92606
NEWPORT STATE AP
41.53333
-71.2833
43
-5
8
2019
14794
KWST
WST
32107
WESTERLY STATE AP
41.34972
-71.7989
21
-5
8
2019
3870
KGSP
GSP
110508
GRNVL SPART AP
34.8842
-82.2209
287
-5
8
2019
13744
KFLO
FLO
41707
FLORENCE RGNLAP
34.1852
-79.7238
45
-5
10
2019
13880
KCHS
CHS
61009
CHARLESTON INTLAP
32.8986
-80.0402
12
-5
10
2019
13883
KCAE
CAE
51209
COLUMBIA METRO AP
33.9419
-81.1181
69
-5
10
2019
26
-------
Surface
Surface
Ice Free
Ice Free
Surface
Surface
Surface
Anenom.
WBAN
Call
Call
Date
Name
Latitude
Longitude
Elevation
UTC
Height
Year
13886
KGMU
GMU
102808
GREENVILLE DWTN AP
34.84611
-82.3461
309
-5
8
2019
53850
KCEU
CEU
100208
CLEMSON OCONEE CO AP
34.67194
-82.8864
265
-5
8
2019
53854
KOGB
OGB
42909
ORANGEBURG MUNI AP
33.46167
-80.8581
55
-5
10
2019
53867
KCUB
CUB
42809
COLUMBIA OWENS DWTN AP
33.97056
-80.9958
55
-5
8
2019
53871
KUZA
UZA
51909
ROCK HILL YORK CO AP
34.98694
-81.0575
200
-5
10
2019
53874
KGRD
GRD
101508
GREENWOOD CO AP
34.24861
-82.1592
192
-5
10
2019
93718
KCRE
CRE
42007
N MYRTLE BCH AP
33.81167
-78.7239
10
-5
10
2019
93846
KAND
AND
102108
ANDERSON CO AP
34.4977
-82.7097
232
-5
10
2019
14929
KABR
ABR
102705
ABERDEEN RGNLAP
45.4433
-98.413
395
-6
8
2019
14936
KHON
HON
42809
HURON RGNLAP
44.3981
-98.2231
390
-6
10
2019
14944
KFSD
FSD
60706
SIOUX FALLS FOSS FLD
43.5778
-96.7539
435
-6
10
2019
14946
KATY
ATY
110705
WATERTOWN RGNLAP
44.9047
-97.1494
533
-6
8
2019
24024
KPHP
PHP
51507
PHILIP AP
44.05111
-101.601
672
-7
10
2019
24025
KPIR
PIR
111705
PIERRE RGNLAP
44.3813
-100.286
531
-6
10
2019
24090
KRAP
RAP
92806
RAPID CITY REGIONAL AP
44.0433
-103.054
963
-7
10
2019
94032
KCUT
CUT
102005
CUSTER CO AP
43.73306
-103.611
1690
-7
10
2019
94039
KIEN
IEN
102908
PINE RIDGE AP
43.02056
-102.518
1005
-7
10
2019
94052
KMBG
MBG
10907
MOBRIDGE MUNI AP
45.54639
-100.408
517
-6
10
2019
94950
KMHE
MHE
20306
MITCHELL MUNI AP
43.7743
-98.0384
396
-6
8
2019
94990
KICR
ICR
82108
WINNER WILEY FLD
43.39056
-99.8422
619
-6
10
2019
3811
KMKL
MKL
93008
JACKSON MCKELLAR AP
35.593
-88.9167
132
-6
10
2019
3847
KCSV
CSV
41007
CROSSVILLE MEM AP
35.9509
-85.0813
569
-6
8
2019
3894
KCKV
CKV
41907
CLARKSVILLE OUTLAW AP
36.62389
-87.4194
171
-6
10
2019
13877
KTRI
TRI
42307
BRISTOL TRI CITY AP
36.4731
-82.4044
457
-5
10
2019
13882
KCHA
CHA
32707
CHATTANOOGA LOVELL AP
35.0311
-85.2014
205
-5
10
2019
13891
KTYS
TYS
41707
KNOXVILLE MCGHEE TYSON AP
35.8181
-83.9858
293
-5
10
2019
13893
KMEM
MEM
100608
MEMPHIS INTL AP
35.0564
-89.9865
77
-6
10
2019
13897
KBNA
BNA
40507
NASHVILLE INTL AP
36.11889
-86.6892
183
-6
10
2019
53868
KOQ.T
OQT
32207
OAK RIDGE ASOS
36.0236
-84.2375
277
-5
10
2019
27
-------
Surface
Surface
Ice Free
Ice Free
Surface
Surface
Surface
Anenom.
WBAN
Call
Call
Date
Name
Latitude
Longitude
Elevation
UTC
Height
Year
3024
KBGD
BGD
90506
BORGER HUTCHINSON CO AP
35.695
-101.395
925
-6
10
2019
3031
KODO
ODO
32907
ODESSA SCHLEMEYER FLD
31.92056
-102.387
906
-6
10
2019
3901
KGGG
GGG
100808
LONGVIEW ETX RGNL AP
32.38472
-94.7117
111
-6
10
2019
3904
KCLL
CLL
62309
COLLEGE STN
30.58917
-96.3647
93
-6
8
2019
3927
KDFW
DFW
52709
DAL-FTW WSCMO AP
32.8978
-97.0189
171
-6
10
2019
3971
KRBD
RBD
30707
DALLAS REDBIRD AP
32.68083
-96.8681
201
-6
10
2019
3991
KDTO
DTO
22707
DENTON MUNI AP
33.20611
-97.1989
197
-6
10
2019
3999
KBMQ
BMQ
90308
BURNET MUNI AP
30.7406
-98.2354
393
-6
10
2019
12904
KHRL
HRL
50907
HARLINGEN RIO GRANDE AP
26.22806
-97.6542
10
-6
10
2019
12912
KVCT
VCT
22307
VICTORIA RGNL AP
28.8614
-96.9303
35
-6
10
2019
12917
KBPT
BPT
62007
PORT ARTHUR SE TX AP
29.95056
-94.0206
5
-6
10
2019
12918
KHOU
HOU
62707
HOUSTON HOBBY AP
29.63806
-95.2819
13
-6
10
2019
12919
KBRO
BRO
40607
BROWNSVILLE INTL AP
25.9141
-97.423
7
-6
10
2019
12921
KSAT
SAT
102208
SAN ANTONIO INTL AP
29.5443
-98.4839
240
-6
10
2019
12923
KGLS
GLS
53007
GALVESTON SCHOLES FLD
29.2733
-94.8592
2
-6
8
2019
12924
KCRP
CRP
21306
CORPUS CHRISTI INTL AP
27.7742
-97.5122
13
-6
10
2019
12932
KALI
ALI
22707
ALICE INTLAP
27.74111
-98.0247
53
-6
10
2019
12935
KPSX
PSX
61107
PALACIOS MUNI AP
28.72472
-96.2536
4
-6
10
2019
12947
KCOT
COT
22607
COTULLA LA SALLE CO AP
28.45667
-99.2183
145
-6
10
2019
12957
KPIL
PIL
40307
PORT ISABEL CAMERON AP
26.16583
-97.3458
4
-6
10
2019
12959
KMFE
MFE
51507
MCALLEN MILLER INTLAP
26.18389
-98.2539
30
-6
10
2019
12960
KIAH
IAH
61109
HOUSTON INTERCONT AP
29.98
-95.36
29
-6
10
2019
12962
KHDO
HDO
50207
HONDO MUNI AP
29.3601
-99.1742
280
-6
10
2019
12970
KSSF
SSF
90908
SAN ANTONIO STINSON AP
29.3389
-98.472
174
-6
10
2019
12971
KBAZ
BAZ
51305
NEW BRAUNFELS MUNI AP
29.7089
-98.0458
197
-6
10
2019
12972
KRKP
RKP
30107
ROCKPORT ARANSAS CO AP
28.08361
-97.0464
7
-6
10
2019
12975
KLVJ
LVJ
60607
HOUSTON CLOVER FLD
29.51889
-95.2417
13
-6
8
2019
12976
KLBX
LBX
62107
ANGLETON BRAZORIA AP
29.10972
-95.4619
8
-6
10
2019
12977
KSGR
SGR
62207
HOUSTON SUGARLAND MEM
29.62194
-95.6567
26
-6
8
2019
28
-------
Surface
Surface
Ice Free
Ice Free
Surface
Surface
Surface
Anenom.
WBAN
Call
Call
Date
Name
Latitude
Longitude
Elevation
UTC
Height
Year
13904
KAUS
AUS
103008
AUSTIN BERGSTROM AP
30.1831
-97.6799
146
-6
10
2019
13958
KATT
ATT
30807
AUSTIN-CAMP MABRY
30.3208
-97.7604
204
-6
10
2019
13959
KACT
ACT
30207
WACO RGNL AP
31.61889
-97.2283
152
-6
10
2019
13960
KDAL
DAL
52809
DALLAS FAA AP
32.8519
-96.8555
134
-6
10
2019
13961
KFTW
FTW
21003
FT WORTH MEACHAM FLD
32.81917
-97.3614
209
-6
8
2019
13962
KABI
ABI
12909
ABILENE RGNL AP
32.4105
-99.6822
546
-6
10
2019
13966
KSPS
SPS
32409
WICHITA FALLS MUNI AP
33.9786
-98.4928
310
-6
10
2019
13972
KTYR
TYR
103008
TYLER POUNDS FLD
32.35417
-95.4025
166
-6
10
2019
13973
KJCT
JCT
32807
JUNCTION KIMBLE CO AP
30.51083
-99.7664
524
-6
10
2019
22010
KDRT
DRT
60707
DEL RIO INTL AP
29.3784
-100.927
304
-6
10
2019
23007
KCDS
CDS
42607
CHILDRESS MUNI AP
34.4272
-100.283
595
-6
10
2019
23023
KMAF
MAF
41107
MIDLAND INTLAP
31.9475
-102.209
872
-6
8
2019
23034
KSJT
SJT
52307
SAN ANGELO MATHIS FLD
31.35167
-100.495
584
-6
10
2019
23040
KINK
INK
40307
WINKLER CO AP
31.7801
-103.202
856
-6
10
2019
23042
KLBB
LBB
41107
LUBBOCK INTLAP
33.6656
-101.823
992
-6
10
2019
23044
KELP
ELP
111308
EL PASO INTLAP
31.81111
-106.376
1194
-7
10
2019
23047
KAMA
AMA
82506
AMARILLO INTLAP
35.2295
-101.704
1098
-6
10
2019
23055
KGDP
GDP
52207
PINE SPRINGS NP
31.83111
-104.809
1663
-6
6
2019
23091
KFST
FST
50107
FT STOCKTON PECOS AP
30.91194
-102.917
917
-6
10
2016
53902
KCXO
CXO
52609
CONROE MONTGOMERY CO AP
30.35667
-95.4139
71
-6
8
2019
53903
KUTS
UTS
61909
HUNTSVILLE MUNI AP
30.74389
-95.5861
106
-6
10
2019
53907
KGKY
GKY
22607
ARLINGTON MUNI AP
32.66361
-97.0939
188
-6
10
2019
53909
KAFW
AFW
22007
FT WORTH ALLIANCE AP
32.97333
-97.3181
209
-6
10
2019
53910
KDWH
DWH
73107
HOUSTON HOOKS MEM AP
30.0675
-95.5561
47
-6
10
2019
53911
KTRL
TRL
30607
TERRELL MUNI AP
32.71
-96.2672
144
-6
8
2019
53912
KCRS
CRS
22207
CORSICANA CAMPBELL FLD
32.03111
-96.3989
136
-6
10
2019
53914
KTKI
TKI
30507
MCKINNEY MUNI AP
33.19028
-96.5914
179
-6
10
2019
93042
KDHT
DHT
90506
DALHART FAA AP
36.0167
-102.55
1216
-6
10
2019
93985
KMWL
MWL
22106
MINERAL WELLS AP
32.7816
-98.0602
283
-6
10
2019
29
-------
Surface
WBAN
Surface
Call
Ice Free
Call
Ice Free
Date
Name
Surface
Latitude
Surface
Longitude
Surface
Elevation
UTC
Anenom.
Height
Year
93987
KLFK
LFK
112008
LUFKIN ANGELINA CO AP
31.23611
-94.7544
88
-6
8
2019
23159
KBCE
BCE
91906
BRYCE CANYON AP
37.70639
-112.146
2307
-7
10
2019
23176
KMLF
MLF
21306
MILFORD MUNI AP
38.41667
-113.017
1538
-7
10
2017
24127
KSLC
SLC
52307
SALT LAKE CITY INTL AP
40.7781
-111.969
1288
-7
10
2019
93075
KCNY
CNY
50307
MOABCANYONLAND AP
38.75
-109.763
1390
-7
10
2019
93129
KCDC
CDC
120606
CEDAR CITY MUNI AP
37.7086
-113.094
1703
-7
8
2019
93141
KPUC
PUC
110705
PRICE CARBON CO AP
39.60917
-110.755
1779
-7
10
2019
94030
KVEL
VEL
32707
VERNAL MUNI AP
40.44278
-109.513
1606
-7
10
2019
94128
KLGU
LGU
110705
LOGAN CACHE AP
41.78722
-111.853
1356
-7
10
2019
13728
KDAN
DAN
101508
DANVILLE RGNLAP
36.5728
-79.3361
174
-5
10
2019
13733
KLYH
LYH
103008
LYNCHBURG RGNLAP
37.3208
-79.2067
287
-5
10
2019
13737
KORF
ORF
32707
NORFOLK INTL AP
36.9033
-76.1922
9
-5
10
2019
13740
KRIC
RIC
32807
RICHMOND INTL AP
37.505
-77.3202
50
-5
10
2019
13741
KROA
ROA
102308
ROANOKE RGNLAP
37.3169
-79.9741
358
-5
10
2019
13743
KDCA
DCA
92606
WASHINGTON REAGAN AP
38.8483
-77.0341
3
-5
8
2019
93736
KCHO
CHO
42307
CHARLOTTESVILLE AP
38.13861
-78.4531
188
-5
8
2019
93738
KIAD
IAD
100306
WASHINGTON DC DULLES AP
38.9408
-77.4636
88
-5
10
2019
93739
KWAL
WAL
41107
WALLOPS ISLAND FLIGHT FAC
37.9372
-75.4708
14
-5
10
2019
93741
KPHF
PHF
32007
NEWPORT NEWS INTL AP
37.13194
-76.4931
11
-5
10
2019
93773
KAKQ
AKQ
31307
WAKEFIELD MUNI AP
36.98389
-77.0072
33
-5
8
2019
93775
KOFP
OFP
32907
ASHLAND HANOVER CO MUNI
AP
37.70806
-77.4344
63
-5
10
2016
14742
KBTV
BTV
92402
BURLINGTON INTL AP
44.4683
-73.1499
101
-5
8
2019
54740
KVSF
VSF
110705
SPRINGFIELD HARTNESS AP
43.34361
-72.5178
176
-5
8
2019
54771
KMVL
MVL
110705
MORRISVILLE STOWE STATE AP
44.53444
-72.6144
225
-5
8
2019
54781
KDDH
DDH
110705
BENNINGTON MORSE ST AP
42.89139
-73.2469
243
-5
10
2019
94705
KMPV
MPV
102705
BARRE MONTPELIER AP
44.2035
-72.5623
343
-5
8
2019
24110
KMWH
MWH
81507
MOSES LAKE GRANT CO AP
47.20778
-119.319
357
-8
10
2019
24141
KEPH
EPH
111705
EPHRATA MUNI AP
47.3078
-119.515
382
-8
10
2019
30
-------
Surface
WBAN
Surface
Call
Ice Free
Call
Ice Free
Date
Name
Surface
Latitude
Surface
Longitude
Surface
Elevation
UTC
Anenom.
Height
Year
24157
KG EG
GEG
61407
SPOKANE INTL AP
47.6216
-117.528
717
-8
10
2019
24160
KALW
ALW
82207
WALLA WALLA RGNL AP
46.09472
-118.287
355
-8
10
2019
24217
KBLI
BLI
40307
BELLINGHAM INTLAP
48.79389
-122.537
45
-8
10
2016
24219
KDLS
DLS
62206
THE DALLES MUNI AP
45.6194
-121.166
72
-8
10
2016
24220
KELN
ELN
71107
ELLENSBURG BOWERS FLD
47.03389
-120.53
535
-8
10
2019
24222
KPAE
PAE
32907
EVERETT SNOHOMISH AP
47.90778
-122.28
181
-8
10
2019
24227
KOLM
OLM
51007
OLYMPIA AP
46.9733
-122.903
57
-8
8
2019
24233
KSEA
SEA
51707
SEATTLE TACOMA INTL AP
47.4444
-122.314
113
-8
10
2019
24234
KBFI
BFI
51707
SEATTLE BOEING FLD
47.53028
-122.301
5
-8
8
2016
24243
KYKM
YKM
80807
YAKIMA AIR TERMINAL
46.5683
-120.543
324
-8
10
2019
94119
KDEW
DEW
91206
DEER PARK AP
47.97417
-117.428
668
-8
10
2019
94129
KPUW
PUW
110705
PULLMAN MOSCOW RGNL AP
46.74389
-117.109
772
-8
10
2019
94176
KSFF
SFF
62607
SPOKANE FELTS FLD
47.68306
-117.321
595
-8
10
2019
94197
KOMK
OMK
62707
OMAK
48.46444
-119.517
396
-8
10
2016
94225
KHQM
HQM
41006
HOQUIAM BOWERMAN AP
46.9727
-123.93
4
-8
10
2019
94227
KSHN
SHN
111705
SHELTON SANDERSON FLD
47.238
-123.141
83
-8
10
2019
94239
KEAT
EAT
121605
WENATCHEE PANGBORN AP
47.3977
-120.201
375
-8
10
2019
94240
KUIL
UIL
52506
QUILLAYUTESTATE AP
47.9375
-124.555
56
-8
10
2019
94248
KRNT
RNT
33007
RENTON MUNI AP
47.49333
-122.214
7
-8
8
2019
94266
KCLM
CLM
50806
PORT ANGELES INTL AP
48.12028
-123.498
79
-8
10
2019
94274
KTIW
TIW
51607
TACOMA NARROWS AP
47.2675
-122.576
89
-8
8
2019
94276
KFHR
FHR
121605
FRIDAY HARBOR AP
48.52222
-123.023
27
-8
8
2019
94298
KVUO
VUO
21306
VANCOUVER PEARSON AP
45.62083
-122.657
8
-8
8
2019
4803
KRHI
RHI
100705
RHINELANDER ONEIDA AP
45.6314
-89.4823
495
-6
8
2019
4826
KISW
ISW
100405
WISCONSIN RAPIDS ALEXANDER
FLD
44.35917
-89.8369
311
-6
10
2019
4840
KFLD
FLD
102005
FOND DU LAC CO AP
43.76944
-88.4908
246
-6
10
2019
4841
KSBM
SBM
100405
SHEBOYGAN CO MEM AP
43.76944
-87.8506
227
-6
10
2019
4845
KENW
ENW
32607
KENOSHA RGNL AP
42.595
-87.9381
225
-6
10
2019
31
-------
Surface
Surface
Ice Free
Ice Free
Surface
Surface
Surface
Anenom.
WBAN
Call
Call
Date
Name
Latitude
Longitude
Elevation
UTC
Height
Year
14837
KMSN
MSN
51007
MADISON DANE RGNLAP
43.1405
-89.3452
264
-6
10
2019
14839
KMKE
MKE
91406
MILWAUKEE MITCHELL AP
42.955
-87.9044
204
-6
10
2019
14897
KAUW
AUW
100405
WAUSAU ASOS
44.9288
-89.6277
366
-6
10
2019
14898
KGRB
GRB
82605
GREEN BAY AS INTL AP
44.4794
-88.1366
209
-6
10
2019
14920
KLSE
LSE
92006
LA CROSSE MUNI AP
43.8788
-91.2527
199
-6
10
2019
14921
KLNR
LNR
50907
LONE ROCK TRI CO AP
43.21194
-90.1814
219
-6
10
2019
14991
KEAU
EAU
32007
EAU CLAIRE RGNLAP
44.8665
-91.4879
270
-6
10
2019
94818
KRAC
RAC
102705
RACINE BATTEN AP
42.76111
-87.8136
202
-6
10
2019
94855
KOSH
OSH
110402
OSHKOSH WITTMAN AP
43.98444
-88.5569
238
-6
8
2019
94929
KASX
ASX
111705
ASHLAND KENNEDY MEM AP
46.54861
-90.9189
251
-6
10
2019
94973
KHYR
HYR
110705
HAYWARD MUNI AP
46.02611
-91.4442
367
-6
10
2019
94985
KMFI
MFI
100405
MARSHFIELD MUNI AP
44.63806
-90.1875
383
-6
10
2019
94994
KOVS
OVS
100705
BOSCOBEL AP
43.15611
-90.6775
203
-6
10
2019
3802
KCKB
CKB
52207
CLARKSBURG BENEDUM AP
39.29556
-80.2289
361
-5
8
2018
3804
KPKB
PKB
50307
PARKERSBURG WOOD CO AP
39.2
-81.27
253
-5
10
2018
3859
KBLF
BLF
100308
BLUEFIELD MERCER CO AP
37.2958
-81.2077
875
-5
8
2019
3860
KHTS
HTS
12607
HUNTINGTON TRI STATE AP
38.365
-82.555
251
-5
8
2018
3872
KBKW
BKW
30907
BECKLEY RALEIGH CO AP
37.7836
-81.123
766
-5
10
2019
13729
KEKN
EKN
92206
ELKINS RANDOLPH CO AP
38.8853
-79.8528
603
-5
8
2017
13734
KMRB
MRB
31407
MARTINSBURG E WV RGNL AP
39.4019
-77.9844
163
-5
8
2019
13736
KMGW
MGW
22807
MORGANTOWN HART FLD
39.64278
-79.9164
378
-5
8
2019
13866
KCRW
CRW
91906
CHARLESTON YEAGERAP
38.3794
-81.59
277
-5
8
2018
14894
KHLG
HLG
22707
WHEELING OHIO CO AP
40.17639
-80.6472
359
-5
10
2019
4111
KEVW
EVW
102005
EVANSTON BURNS FLD
41.27306
-111.031
2175
-7
10
2019
24018
KCYS
CYS
92606
CHEYENNE MUNI AP
41.15
-104.817
1868
-7
10
2019
24021
KLND
LND
111705
LANDER HUNT FLD AP
42.8154
-108.726
1704
-7
10
2019
24022
KLAR
LAR
92806
LARAMIE AP
41.3167
-105.683
2215
-7
10
2019
24027
KRKS
RKS
52907
ROCK SPRINGS AP
41.5944
-109.053
2055
-7
10
2019
24029
KSHR
SHR
83006
SHERIDAN AP
44.7694
-106.969
1202
-7
10
2019
32
-------
Surface
Surface
Ice Free
Ice Free
Surface
Surface
Surface
Anenom.
WBAN
Call
Call
Date
Name
Latitude
Longitude
Elevation
UTC
Height
Year
24048
KGEY
GEY
71807
GREYBULLS BIG HORN AP
44.51694
-108.082
1194
-7
10
2019
24057
KRWL
RWL
90408
RAWLINS AP
41.8025
-107.206
2053
-7
10
2019
24061
KRIW
RIW
110705
RIVERTON RGNL AP
43.06417
-108.459
1660
-7
10
2019
24062
KWRL
WRL
53107
WORLAND
43.96583
-107.951
1272
-7
10
2019
24089
KCPR
CPR
41107
CASPER NATRONA CO AP
42.8977
-106.474
1619
-7
10
2019
24164
KBPI
BPI
61107
BIG PINEYMARBLETON AP
42.58444
-110.108
2124
-7
10
2019
94023
KGCC
GCC
12407
GILLETTE CAMPBELL AP
44.33944
-105.542
1327
-7
8
2019
94053
KTOR
TOR
91306
TORRINGTON MUNI AP
42.0613
-104.158
1280
-7
10
2019
94054
KBYG
BYG
90606
BUFFALO JOHNSON CO AP
44.38139
-106.721
1506
-7
10
2019
94057
KDGW
DGW
92308
CONVERSE CO AP ASOS
42.79611
-105.38
1504
-7
10
2019
33
-------
Surface
WBAN
Type Station
State
tif file
snow
arid
moisture
state
Airport
AERMOD File
Upper Air
WMO
Upper Air
UTC
25308
AIRW AYS, ASOS,COOP
AK
Y
N
A
AK
Y
AK25308 2016
70398
-9
25309
AIRW AYS, ASOS,COOP
AK
Y
N
A
AK
Y
AK25309 2019
71964
-8
25323
AIRWAYS,ASOS,COOP
AK
Y
N
A
AK
Y
AK25323 2019
71964
-8
25325
AIRWAYS,ASOS,COOP
AK
Y
N
A
AK
Y
AK25325 2019
70398
-9
25331
AIRWAYS,ASOS,COOP
AK
Y
N
A
AK
Y
AK25331 2019
70273
-9
25333
ASOS,COOP
AK
Y
N
A
AK
Y
AK25333 2019
71964
-8
25335
AIRWAYS,ASOS,COOP
AK
Y
N
A
AK
Y
AK25335 2019
71964
-8
25501
AIRWAYS,ASOS,COOP
AK
Y
N
A
AK
Y
AK25501 2019
70350
-9
25503
ASOS,COOP
AK
Y
N
A
AK
Y
AK25503 2019
70326
-9
25506
AIRWAYS,ASOS,COOP
AK
Y
N
A
AK
Y
AK25506 2019
70326
-9
25507
ASOS,COOP
AK
Y
N
A
AK
Y
AK25507 2019
70273
-9
25516
AIRW AYS, ASOS
AK
Y
N
A
AK
Y
AK25516 2019
70350
-9
25624
AIRWAYS,ASOS,COOP
AK
Y
N
A
AK
Y
AK25624 2019
70316
-9
25628
AIRW AYS, ASOS
AK
Y
N
A
AK
Y
AK25628 2019
70308
-9
25713
AIRWAYS,ASOS,COOP
AK
Y
N
A
AK
Y
AK25713 2019
70308
-9
26409
AIRW AYS, ASOS
AK
Y
N
A
AK
Y
AK26409 2019
70273
-9
26410
ASOS,COOP
AK
Y
N
A
AK
Y
AK26410 2019
70273
-9
26411
ASOS,COOP
AK
Y
N
A
AK
Y
AK26411 2019
70261
-9
26412
AIRWAYS,ASOS,COOP
AK
Y
N
A
AK
Y
AK26412 2018
70361
-9
26415
AIRWAYS,ASOS,COOP
AK
Y
N
A
AK
Y
AK26415 2019
70261
-9
26425
AIRWAYS,ASOS,COOP
AK
Y
N
A
AK
Y
AK26425 2019
70261
-9
26435
ASOS
AK
Y
N
A
AK
Y
AK26435 2019
70261
-9
26438
AIRW AYS, ASOS
AK
Y
N
A
AK
Y
AK26438 2019
70273
-9
26451
AIRWAYS,ASOS,COOP
AK
Y
N
A
AK
Y
AK26451 2019
70273
-9
26492
ASOS,COOP,WXSVC
AK
Y
N
A
AK
N
AK26492 2019
70273
-9
26510
AIRWAYS,ASOS,COOP
AK
Y
N
A
AK
Y
AK26510 2016
70231
-9
26523
AIRWAYS,ASOS,COOP
AK
Y
N
A
AK
Y
AK26523 2019
70273
-9
26528
ASOS,COOP
AK
Y
N
A
AK
Y
AK26528 2019
70273
-9
26529
AIRWAYS,ASOS,COOP
AK
Y
N
A
AK
Y
AK26529_2018
70231
-9
34
-------
Surface
WBAN
Type Station
State
tif file
snow
arid
moisture
state
Airport
AERMOD File
Upper Air
WMO
Upper Air
UTC
26533
AIRW AYS, ASOS,COOP
AK
Y
N
A
AK
Y
AK26533 2019
70261
-9
26615
AIRW AYS, ASOS,COOP
AK
Y
N
A
AK
Y
AK26615 2019
70219
-9
26616
AIRWAYS,ASOS,COOP
AK
Y
N
A
AK
Y
AK26616 2019
70133
-9
26617
AIRWAYS,ASOS,COOP
AK
Y
N
A
AK
Y
AK26617 2019
70200
-9
27406
AIRWAYS,ASOS,COOP
AK
Y
N
A
AK
Y
AK27406 2019
70261
-9
27502
ASOS,COOP
AK
Y
N
A
AK
Y
AK27502 2019
70026
-9
27503
AIRW AYS, ASOS
AK
Y
N
A
AK
Y
AK27503 2019
70026
-9
27515
AIRW AYS, ASOS
AK
Y
N
A
AK
Y
AK27515 2019
70026
-9
3856
ASOS,COOP
AL
N
N
A
AL
Y
AL03856 2019
72230
-6
3878
ASOS
AL
N
N
A
AL
Y
AL03878 2019
72230
-6
13838
AIRW AYS, ASOS
AL
N
N
A
AL
Y
AL13838 2019
72233
-6
13839
AIRWAYS,ASOS,COOP
AL
N
N
A
AL
Y
AL13839 2019
72214
-5
13871
AIRWAYS,ASOS,COOP
AL
N
N
A
AL
Y
AL13871 2019
72230
-6
13876
AIRSAMPLE,AIRWAYS,ASOS,
COOP
AL
N
N
A
AL
Y
AL13876 2019
72230
-6
13894
ASOS,COOP,UPPERAIR
AL
N
N
A
AL
Y
AL13894 2019
72233
-6
13895
AIRSAMPLE,ASOS,COOP
AL
N
N
A
AL
Y
AL13895 2019
72230
-6
13896
AIRW AYS,ASOS,COOP,USHCN
AL
N
N
A
AL
Y
AL13896 2019
72230
-6
53820
AIRW AYS, ASOS
AL
N
N
A
AL
Y
AL53820 2019
72230
-6
53852
AIRW AYS, ASOS
AL
N
N
A
AL
Y
AL53852 2019
72230
-6
63872
ASOS, MILITARY
AL, GA
N
N
A
AL
Y
AL63872 2016
72215
-5
63874
ASOS, MILITARY
AL
N
N
A
AL
Y
AL63874 2018
72230
-6
93806
AIRWAYS,ASOS,COOP
AL
N
N
A
AL
Y
AL93806 2019
72230
-6
3953
AIRW AYS, ASOS
AR
N
N
A
AR
Y
AR03953 2019
72340
-6
3962
ASOS,COOP
AR
N
N
A
AR
Y
AR03962 2019
72340
-6
13963
AIRWAYS,ASOS,COOP
AR
N
N
A
AR
Y
AR13963 2019
72340
-6
13964
AIRSAMPLE,ASOS,COOP
AR
N
N
A
AR
Y
AR13964 2019
72440
-6
13971
ASOS,COOP
AR
N
N
A
AR
Y
AR13971 2019
72440
-6
13977
ASOS,COOP
TX, AR
N
N
A
AR
Y
AR13977_2019
72248
-6
35
-------
Surface
WBAN
Type Station
State
tif file
snow
arid
moisture
state
Airport
AERMOD File
Upper Air
WMO
Upper Air
UTC
53869
AIRWAYS,ASOS
AR
N
N
A
AR
Y
AR53869 2019
72340
-6
53918
AIRWAYS,ASOS
AR
N
N
A
AR
Y
AR53918 2019
72440
-6
53919
ASOS,COOP
AR
N
N
A
AR
Y
AR53919 2016
72340
-6
53920
AIRWAYS,ASOS
AR
N
N
A
AR
Y
AR53920 2019
72340
-6
53921
AIRW AYS, ASOS,COOP
AR
N
N
A
AR
N
AR53921 2016
72340
-6
53922
AIRWAYS,ASOS
AR
N
N
A
AR
Y
AR53922 2019
72440
-6
53925
ASOS,COOP
AR
N
N
A
AR
Y
AR53925 2019
72248
-6
93988
ASOS
AR
N
N
A
AR
Y
AR93988 2019
72340
-6
93992
AIRWAYS,ASOS,COOP
AR
N
N
A
AR
Y
AR93992 2019
72248
-6
93993
ASOS,COOP
AR
N
N
A
AR
Y
AR93993 2019
72440
-6
3029
ASOS
AZ,
NM
N
Y
A
AZ
Y
AZ03029 2019
72365
-7
3103
ASOS,COOP
AZ
N
Y
A
AZ
Y
AZ03103 2019
72376
-7
3124
ASOS
AZ
N
Y
A
AZ
Y
AZ03124 2016
72274
-7
3162
ASOS
AZ
N
Y
A
AZ
Y
AZ03162 2019
72376
-7
3184
AIRW AYS, ASOS
AZ
N
Y
A
AZ
Y
AZ03184 2019
72376
-7
3192
AIRW AYS, ASOS
AZ
N
Y
A
AZ
Y
AZ03192 2019
72376
-7
3195
AIRW AYS, ASOS
AZ
N
Y
A
AZ
Y
AZ03195 2019
72376
-7
3196
AIRW AYS, ASOS
AZ
N
Y
A
AZ
Y
AZ03196 2019
72274
-7
23160
ASOS,COOP
AZ
N
Y
A
AZ
Y
AZ23160 2019
72274
-7
23183
ASOS,COOP
AZ
N
Y
A
AZ
Y
AZ23183 2019
72274
-7
23184
AIRW AYS, ASOS
AZ
N
Y
A
AZ
Y
AZ23184 2019
72376
-7
23194
ASOS,COOP
AZ
N
Y
A
AZ
Y
AZ23194 2019
72376
-7
93026
ASOS,COOP
AZ
N
Y
A
AZ
Y
AZ93026 2019
72274
-7
93027
AIRW AYS, ASOS
AZ
N
Y
A
AZ
Y
AZ93027 2019
72376
-7
93084
AIRW AYS, ASOS
AZ
N
Y
A
AZ
Y
AZ93084 2019
72274
-7
93167
ASOS,COOP
AZ
N
Y
A
AZ
Y
AZ93167 2019
72388
-8
3102
AIRW AYS, ASOS
CA
N
N
A
CA
Y
CA03102 2019
72293
-8
3104
ASOS
CA
N
N
A
CA
Y
CA03104_2019
72293
-8
36
-------
Surface
WBAN
Type Station
State
tif file
snow
arid
moisture
state
Airport
AERMOD File
Upper Air
WMO
Upper Air
UTC
3131
AIRWAYS,ASOS
CA
N
N
A
CA
Y
CA03131 2019
72293
-8
3144
ASOS
CA
N
Y
A
CA
Y
CA03144 2019
72293
-8
3159
ASOS,COOP
CA
N
N
A
CA
Y
CA03159 2019
72293
-8
3166
AIRWAYS, ASOS
CA
N
N
A
CA
Y
CA03166 2019
72293
-8
3167
AIRWAYS, ASOS
CA
N
N
A
CA
Y
CA03167 2019
72293
-8
3171
AIRWAYS, ASOS
CA
N
N
A
CA
Y
CA03171 2019
72293
-8
3177
AIRWAYS, ASOS
CA
N
N
A
CA
Y
CA03177 2019
72293
-8
3178
ASOS
CA
N
N
A
CA
Y
CA03178 2019
72293
-8
3179
AIRWAYS, ASOS
CA
N
N
A
CA
Y
CA03179 2019
72293
-8
23129
ASOS,COOP
CA
N
N
A
CA
Y
CA23129 2019
72293
-8
23130
AIRWAYS, ASOS
CA
N
N
A
CA
Y
CA23130 2019
72293
-8
23136
AIRWAYS, ASOS
CA
N
N
A
CA
Y
CA23136 2019
72293
-8
23152
AIRWAYS, ASOS
CA
N
N
A
CA
Y
CA23152 2019
72293
-8
23155
ASOS,COOP
CA
N
N
A
CA
Y
CA23155 2019
72293
-8
23157
ASOS,COOP
CA
N
N
A
CA
Y
CA23157 2019
72489
-8
23158
ASOS,COOP
CA
N
Y
A
CA
Y
CA23158 2019
72293
-8
23161
ASOS,COOP
CA
N
N
A
CA
Y
CA23161 2019
72388
-8
23174
ASOS,COOP
CA
N
N
A
CA
Y
CA23174 2019
72293
-8
23179
ASOS,COOP,USHCN,WXSVC
CA
N
Y
A
CA
Y
CA23179 2019
72388
-8
23182
AIRWAYS, ASOS
CA
N
N
A
CA
Y
CA23182 2019
72293
-8
23187
ASOS,COOP
CA
N
N
A
CA
N
CA23187 2018
72293
-8
23188
ASOS,COOP
CA
N
N
A
CA
Y
CA23188 2019
72293
-8
23190
AIRWAYS,ASOS,COOP
CA
N
N
A
CA
Y
CA23190 2019
72293
-8
23191
ASOS
CA
N
N
A
CA
Y
CA23191 2019
72293
-8
23199
ASOS
CA
N
Y
A
CA
N
CA23199 2019
72293
-8
23213
AIRWAYS,ASOS,COOP
CA
N
N
A
CA
Y
CA23213 2019
72493
-8
23225
ASOS,COOP
CA
N
Y
A
CA
Y
CA23225 2019
72489
-8
23230
ASOS,UPPERAIR
CA
N
N
A
CA
Y
CA23230 2019
72493
-8
23232
AIRWAYS,ASOS,COOP
CA
N
N
A
CA
Y
CA23232_2019
72493
-8
37
-------
Surface
WBAN
Type Station
State
tif file
snow
arid
moisture
state
Airport
AERMOD File
Upper Air
WMO
Upper Air
UTC
23233
ASOS,COOP
CA
N
N
A
CA
Y
CA23233 2019
72493
-8
23234
ASOS,COOP
CA
N
N
A
CA
Y
CA23234 2019
72493
-8
23237
ASOS,COOP
CA
N
N
A
CA
Y
CA23237 2019
72493
-8
23244
ASOS
CA
N
N
A
CA
Y
CA23244 2016
72493
-8
23254
AIRW AYS, ASOS,COOP
CA
N
N
A
CA
Y
CA23254 2019
72493
-8
23257
AIRWAYS, ASOS
CA
N
N
A
CA
Y
CA23257 2019
72493
-8
23258
AIRWAYS,ASOS,COOP
CA
N
N
A
CA
Y
CA23258 2019
72493
-8
23259
AIRW AYS, ASOS
CA
N
N
A
CA
Y
CA23259 2019
72493
-8
23273
ASOS,COOP
CA
N
N
A
CA
Y
CA23273 2019
72493
-8
23275
AIRW AYS, ASOS
CA
N
N
A
CA
Y
CA23275 2019
72493
-8
23277
AIRW AYS, ASOS
CA
N
N
A
CA
Y
CA23277 2019
72493
-8
23285
AIRWAYS,ASOS,COOP
CA
N
N
A
CA
Y
CA23285 2019
72493
-8
23293
ASOS,COOP
CA
N
N
A
CA
Y
CA23293 2019
72493
-8
24215
ASOS
CA
N
N
A
CA
N
CA24215 2018
72597
-8
24216
ASOS,COOP
CA
N
N
A
CA
Y
CA24216 2019
72597
-8
24257
ASOS,COOP,USHCN
CA
N
N
A
CA
Y
CA24257 2019
72597
-8
24259
ASOS
CA
N
N
A
CA
Y
CA24259 2019
72597
-8
24283
AIRW AYS, ASOS
CA
N
N
A
CA
Y
CA24283 2019
72597
-8
24286
ASOS
CA
N
N
A
CA
Y
CA24286 2019
72597
-8
53119
AIRW AYS, ASOS
CA
N
N
A
CA
Y
CA53119 2019
72493
-8
53120
ASOS
CA
N
N
A
CA
Y
CA53120 2019
72293
-8
53121
AIRW AYS, ASOS
CA
N
N
A
CA
Y
CA53121 2019
72293
-8
93110
AIRW AYS, ASOS
CA
N
N
A
CA
Y
CA93110 2019
72293
-8
93115
ASOS
CA
N
N
A
CA
Y
CA93115 2018
72293
-8
93134
ASOS,COOP
CA
N
N
A
CA
N
CA93134 2018
72293
-8
93138
AIRW AYS, ASOS
CA
N
N
A
CA
Y
CA93138 2019
72293
-8
93184
AIRW AYS, ASOS
CA
N
N
A
CA
Y
CA93184 2019
72293
-8
93193
ASOS,COOP,USHCN
CA
N
N
A
CA
Y
CA93193 2019
72493
-8
93197
ASOS
CA
N
N
A
CA
Y
CA93197_2019
72293
-8
38
-------
Surface
WBAN
Type Station
State
tif file
snow
arid
moisture
state
Airport
AERMOD File
Upper Air
WMO
Upper Air
UTC
93205
ASOS,COOP
CA
N
N
A
CA
Y
CA93205 2019
72493
-8
93206
ASOS
CA
N
N
A
CA
Y
CA93206 2019
72493
-8
93209
ASOS,COOP
CA
N
N
A
CA
Y
CA93209 2019
72493
-8
93210
AIRWAYS, ASOS
CA
N
N
A
CA
Y
CA93210 2019
72489
-8
93227
AIRWAYS, ASOS,COOP
CA
N
N
A
CA
Y
CA93227 2019
72493
-8
93228
AIRWAYS,ASOS,COOP
CA
N
N
A
CA
Y
CA93228 2019
72493
-8
93230
ASOS,COOP
CA
N
Y
A
CA
Y
CA93230 2019
72489
-8
93241
ASOS,COOP
CA
N
N
A
CA
Y
CA93241 2019
72493
-8
93242
AIRWAYS, ASOS
CA
N
N
A
CA
Y
CA93242 2019
72493
-8
94299
ASOS
CA
N
Y
A
CA
Y
CA94299 2019
72489
-8
3013
AIRWAYS, ASOS
CO
N
Y
A
CO
Y
C003013 2019
72451
-6
3017
ASOS,COOP
CO
N
N
A
CO
Y
C003017 2019
72469
-7
3026
AIRWAYS, ASOS
CO
N
Y
A
CO
Y
C003026 2019
72562
-6
23061
ASOS,COOP,WXSVC
CO
N
Y
A
CO
Y
CO23061 2019
72365
-7
23066
ASOS,COOP
CO
N
Y
A
CO
Y
CO23066 2019
72476
-7
23067
AIRWAYS,ASOS,COOP
CO
N
N
A
CO
Y
CO23067 2019
72469
-7
23070
AIRWAYS,ASOS,COOP
CO
N
N
A
CO
Y
C023070 2019
72469
-7
24015
AIRWAYS, ASOS
CO
Y
N
A
CO
Y
CO24015 2019
72469
-7
24046
AIRWAYS,ASOS,COOP
CO
N
Y
A
CO
Y
CO24046 2019
72476
-7
93005
AIRWAYS,ASOS,COOP
CO
N
Y
A
CO
Y
C093005 2019
72476
-7
93009
ASOS
CO
Y
N
A
CO
Y
C093009 2019
72469
-7
93010
ASOS
CO
N
N
A
CO
N
C093010 2019
72469
-7
93013
AIRWAYS,ASOS,COOP
CO
N
Y
A
CO
Y
CO93013 2019
72476
-7
93037
ASOS,COOP
CO
N
N
A
CO
Y
CO93037 2019
72469
-7
93058
ASOS,COOP,WXSVC
CO
N
N
A
CO
Y
CO93058 2019
72469
-7
93067
AIRWAYS, ASOS
CO
N
N
A
CO
Y
CO93067 2019
72469
-7
93069
AIRWAYS,ASOS,COOP
CO
N
Y
A
CO
Y
CO93069 2019
72476
-7
93073
AIRWAYS,ASOS,COOP
CO
N
Y
A
CO
Y
CO93073 2019
72476
-7
94050
AIRWAYS,ASOS,COOP
CO
N
Y
A
CO
Y
C094050_2019
72476
-7
39
-------
Surface
WBAN
Type Station
State
tif file
snow
arid
moisture
state
Airport
AERMOD File
Upper Air
WMO
Upper Air
UTC
14707
ASOS
CT
Y
N
A
CT
Y
CT14707 2019
72501
-5
14740
ASOS,COOP
CT
Y
N
A
CT
Y
CT14740 2019
72501
-5
14752
ASOS
CT
Y
N
A
CT
Y
CT14752 2019
72501
-5
14758
AIRWAYS, ASOS
CT
Y
N
A
CT
Y
CT14758 2019
72501
-5
54734
AIRWAYS, ASOS
CT
Y
N
A
CT
Y
CT54734 2019
72501
-5
54767
AIRWAYS, ASOS
CT
Y
N
A
CT
Y
CT54767 2019
72501
-5
54788
AIRWAYS, ASOS
CT
Y
N
A
CT
Y
CT54788 2019
72501
-5
94702
ASOS,COOP
CT
Y
N
A
CT
Y
CT94702 2019
72501
-5
13764
AIRWAYS, ASOS
DE
N
N
A
DE
Y
DE13764 2018
72402
-5
13781
ASOS,COOP
DE
Y
N
A
DE
Y
DE13781 2019
72403
-5
3818
AIRWAYS, ASOS
FL
N
N
A
FL
Y
FL03818 2019
72214
-5
12812
AIRWAYS, ASOS
FL
N
N
A
FL
Y
FL12812 2019
72210
-5
12815
ASOS,COOP
FL
N
N
A
FL
Y
FL12815 2019
72210
-5
12816
AIRWAYS,ASOS,COOP
FL
N
N
A
FL
Y
FL12816 2019
72206
-5
12818
AIRWAYS, ASOS
FL
N
N
A
FL
Y
FL12818 2019
72210
-5
12819
AIRWAYS, ASOS
FL
N
N
A
FL
Y
FL12819 2019
72210
-5
12832
ASOS
FL
N
N
A
FL
Y
FL12832 2019
72214
-5
12834
ASOS,COOP
FL
N
N
A
FL
Y
FL12834 2019
72206
-5
12835
AIRWAYS,ASOS,COOP,USHCN
FL
N
N
A
FL
Y
FL12835 2019
72210
-5
12836
AIRSAMPLE,AIRWAYS,ASOS,
COOP,USHCN
FL
N
N
A
FL
Y
FL12836 2019
72201
-5
12838
ASOS,COOP
FL
N
N
A
FL
Y
FL12838 2019
72210
-5
12839
ASOS,COOP
FL
N
N
A
FL
Y
FL12839 2019
72202
-5
12841
AIRWAYS, ASOS
FL
N
N
A
FL
Y
FL12841 2019
72210
-5
12842
ASOS,COOP
FL
N
N
A
FL
Y
FL12842 2019
72210
-5
12843
ASOS,COOP
FL
N
N
A
FL
Y
FL12843 2019
72210
-5
12844
ASOS,COOP,UPPERAIR
FL
N
N
A
FL
Y
FL12844 2019
72202
-5
12849
AIRWAYS,ASOS,COOP
FL
N
N
A
FL
Y
FL12849 2019
72202
-5
12854
AIRWAYS, ASOS
FL
N
N
A
FL
Y
FL12854_2019
72210
-5
40
-------
Surface
WBAN
Type Station
State
tif file
snow
arid
moisture
state
Airport
AERMOD File
Upper Air
WMO
Upper Air
UTC
12871
AIRWAYS,ASOS
FL
N
N
A
FL
Y
FL12871 2019
72210
-5
12873
AIRWAYS,ASOS
FL
N
N
A
FL
Y
FL12873 2019
72210
-5
12876
AIRWAYS,ASOS
FL
N
N
A
FL
Y
FL12876 2019
72210
-5
12882
AIRWAYS,ASOS
FL
N
N
A
FL
Y
FL12882 2019
72202
-5
12885
AIRWAYS,ASOS
FL
N
N
A
FL
Y
FL12885 2019
72202
-5
12888
AIRW AYS, ASOS
FL
N
N
A
FL
Y
FL12888 2019
72202
-5
12894
AIRW AYS, ASOS
FL
N
N
A
FL
Y
FL12894 2019
72210
-5
12895
AIRW AYS, ASOS
FL
N
N
A
FL
Y
FL12895 2019
72210
-5
12896
AIRW AYS, ASOS
FL
N
N
A
FL
Y
FL12896 2019
72201
-5
12897
AIRW AYS, ASOS,COOP
FL
N
N
A
FL
Y
FL12897 2019
72202
-5
13884
AIRW AYS, ASOS,COOP
FL
N
N
A
FL
Y
FL13884 2019
72214
-5
13889
ASOS,COOP
FL
N
N
A
FL
Y
FL13889 2019
72206
-5
13899
AIRW AYS, ASOS,COOP, USHCN
FL
N
N
A
FL
Y
FL13899 2019
72233
-6
53847
ASOS, MILITARY
FL
N
N
A
FL
Y
FL53847 2016
72214
-5
53853
AIRW AYS, ASOS
FL
N
N
A
FL
Y
FL53853 2019
72214
-5
53860
AIRWAYS,ASOS,COOP
FL
N
N
A
FL
Y
FL53860 2019
72206
-5
73805
AIRW AYS, ASOS
FL
N
N
A
FL
Y
FL73805 2018
72214
-5
92805
AIRW AYS, ASOS
FL
N
N
A
FL
Y
FL92805 2019
72202
-5
92806
AIRW AYS, ASOS
FL
N
N
A
FL
Y
FL92806 2019
72210
-5
92809
AIRW AYS, ASOS
FL
N
N
A
FL
Y
FL92809 2019
72202
-5
93805
ASOS,COOP,USHCN
FL
N
N
A
FL
Y
FL93805 2019
72214
-5
3813
ASOS,COOP
GA
N
N
A
GA
Y
GA03813 2019
72215
-5
3820
ASOS,COOP
GA, SC
N
N
A
GA
Y
GA03820 2019
72208
-5
3822
ASOS,COOP,USHCN
GA
N
N
A
GA
Y
GA03822 2019
72208
-5
3888
AIRW AYS, ASOS
GA
N
N
A
GA
Y
GA03888 2019
72215
-5
13837
ASOS
GA
N
N
A
GA
Y
GA13837 2019
72208
-5
13869
ASOS
GA
N
N
A
GA
Y
GA13869 2019
72214
-5
13870
AIRWAYS,ASOS,COOP
GA
N
N
A
GA
Y
GA13870_2019
72206
-5
41
-------
Surface
WBAN
Type Station
State
tif file
snow
arid
moisture
state
Airport
AERMOD File
Upper Air
WMO
Upper Air
UTC
13873
AIRSAMPLE,AIRWAYS,ASOS,
COOP
GA
N
N
A
GA
Y
GA13873 2019
72215
-5
13874
ASOS,COOP
GA
N
N
A
GA
Y
GA13874 2019
72215
-5
13878
ASOS,COOP
GA
N
N
A
GA
Y
GA13878 2019
72206
-5
53819
AIRWAYS,ASOS,UPPERAIR
GA
N
N
A
GA
Y
GA53819 2019
72215
-5
53838
AIRWAYS, ASOS
GA
N
N
A
GA
Y
GA53838 2019
72215
-5
53863
AIRWAYS, ASOS
GA
N
N
A
GA
Y
GA53863 2019
72215
-5
53873
ASOS
GA
N
N
A
GA
Y
GA53873 2019
72215
-5
93801
AIRWAYS, ASOS
GA
N
N
A
GA
Y
GA93801 2019
72215
-5
93842
AIRSAMPLE,AIRWAYS,ASOS,
COOP
AL, GA
N
N
A
GA
Y
GA93842 2019
72215
-5
93845
ASOS
GA
N
N
A
GA
Y
GA93845 2019
72214
-5
21504
ASOS,COOP
HI
N
N
A
HI
Y
HI21504 2019
91285
-10
21510
AIRWAYS, ASOS
HI
N
N
A
HI
Y
HI21510 2019
91285
-10
22516
AIRWAYS,ASOS,COOP
HI
N
N
A
HI
Y
HI22516 2019
91285
-10
22521
ASOS,COOP
HI
N
N
A
HI
Y
HI22521 2019
91165
-10
22534
ASOS,COOP
HI
N
N
A
HI
Y
HI22534 2019
91165
-10
22536
ASOS,COOP
HI
N
N
A
HI
Y
HI22536 2019
91165
-10
22551
AIRWAYS, ASOS
HI
N
N
A
HI
Y
HI22551 2019
91165
-10
14931
ASOS
IA, IL
Y
N
A
IA
Y
IA14931 2019
74455
-6
14933
ASOS,COOP
IA
Y
N
A
IA
Y
IA14933 2019
72558
-6
14937
AIRWAYS, ASOS
IA
Y
N
A
IA
Y
IA14937 2019
74455
-6
14940
ASOS,COOP
IA
Y
N
A
IA
Y
IA14940 2019
72649
-6
14943
AIRWAYS,ASOS,COOP
IA, NE
Y
N
A
IA
Y
IA14943 2019
72558
-6
14950
ASOS,COOP
IA
Y
N
A
IA
Y
IA14950 2019
74455
-6
14972
AIRWAYS, ASOS
IA
Y
N
A
IA
Y
IA14972 2019
72558
-6
14990
AIRWAYS,ASOS,COOP
IA
Y
N
A
IA
Y
IA14990 2019
74455
-6
94908
ASOS,COOP
IA
Y
N
A
IA
Y
IA94908 2019
74455
-6
94910
AIRWAYS,ASOS,COOP,WXSVC
IA
Y
N
A
IA
Y
IA94910_2019
74455
-6
42
-------
Surface
WBAN
Type Station
State
tif file
snow
arid
moisture
state
Airport
AERMOD File
Upper Air
WMO
Upper Air
UTC
94971
AIRW AYS, ASOS,COOP
IA
Y
N
A
IA
Y
IA94971 2019
72649
-6
94982
AIRWAYS,ASOS
IA
Y
N
A
IA
Y
IA94982 2016
74455
-6
94988
AIRW AYS, ASOS,COOP
IA
Y
N
A
IA
Y
IA94988 2019
74455
-6
94989
AIRWAYS,ASOS,COOP
IA
Y
N
A
IA
Y
IA94989 2019
72558
-6
94991
AIRWAYS,ASOS,COOP
IA
Y
N
A
IA
Y
IA94991 2019
72456
-6
4110
AIRW AYS, ASOS
ID
N
Y
A
ID
Y
ID04110 2019
72681
-7
4114
ASOS
ID
Y
N
A
ID
Y
ID04114 2016
72681
-7
24131
ASOS,COOP
ID
N
N
A
ID
Y
ID24131 2019
72681
-7
24133
ASOS,COOP
ID
N
Y
A
ID
Y
ID24133 2019
72572
-7
24145
AIRW AYS, ASOS
ID
N
Y
A
ID
Y
ID24145 2019
72572
-7
24149
AIRSAMPLE,ASOS,COOP,
USHCN
WA,
ID
N
N
A
ID
Y
ID24149 2019
72786
-8
24154
AIRW AYS, ASOS
MT, ID
Y
N
A
ID
N
ID24154 2019
72786
-8
24156
ASOS,COOP
ID
N
Y
A
ID
Y
ID24156 2019
72572
-7
94178
AIRW AYS, ASOS
ID
N
Y
A
ID
Y
ID94178 2019
72681
-7
94182
AIRW AYS, ASOS
ID
Y
N
A
ID
Y
ID94182 2016
72681
-7
94194
AIRW AYS, ASOS
ID
N
Y
A
ID
Y
ID94194 2019
72572
-7
3887
AIRW AYS, ASOS
IL
Y
N
A
IL
Y
IL03887 2019
74560
-6
3960
AIRW AYS, ASOS
IL, MO
N
N
A
IL
Y
IL03960 2019
74560
-6
4808
AIRW AYS, ASOS
IL
Y
N
A
IL
Y
IL04808 2019
74560
-6
4838
AIRW AYS, ASOS
IL
Y
N
A
IL
Y
IL04838 2019
72645
-6
13809
AIRW AYS, ASOS
IL
Y
N
A
IL
Y
IL13809 2019
74560
-6
14819
AIRW AYS, ASOS, WXSVC
IL
Y
N
A
IL
Y
IL14819 2019
74560
-6
14842
AIRSAMPLE,ASOS,COOP,
WXSVC
IL
Y
N
A
IL
Y
IL14842 2019
74560
-6
14880
ASOS
IL
Y
N
A
IL
Y
IL14880 2019
72645
-6
14923
ASOS,COOP,WXSVC
IL
Y
N
A
IL
Y
IL14923 2019
74455
-6
53802
AIRW AYS, ASOS
IL
Y
N
A
IL
Y
IL53802 2019
74560
-6
93810
AIRW AYS, ASOS
IL
N
N
A
IL
Y
IL93810_2019
74560
-6
43
-------
Surface
WBAN
Type Station
State
tif file
snow
arid
moisture
state
Airport
AERMOD File
Upper Air
WMO
Upper Air
UTC
93822
AIRSAMPLE,AIRWAYS,ASOS,
COOP
IL
Y
N
A
IL
Y
IL93822 2019
74560
-6
93989
ASOS,COOP
IL
N
N
A
IL
Y
IL93989 2019
74455
-6
94822
ASOS,COOP,WXSVC
IL
Y
N
A
IL
Y
IL94822 2019
74455
-6
94846
ASOS,COOP,WXSVC
IL
Y
N
A
IL
Y
IL94846 2019
74560
-6
94870
AIRWAYS, ASOS
IL
Y
N
A
IL
Y
IL94870 2019
74560
-6
94892
AIRWAYS, ASOS
IL
Y
N
A
IL
Y
IL94892 2019
74560
-6
3868
AIRWAYS, ASOS
IN
Y
N
A
IN
Y
IN03868 2019
74560
-6
3893
AIRWAYS, ASOS
IN
Y
N
A
IN
Y
IN03893 2019
72426
-5
4846
AIRWAYS, ASOS
IN
Y
N
A
IN
Y
IN04846 2019
74560
-6
14827
AIRWAYS,ASOS,COOP
IN
Y
N
A
IN
Y
IN14827 2019
72426
-5
14829
AIRWAYS,ASOS,COOP
IN
Y
N
A
IN
Y
IN14829 2019
72632
-5
14835
AIRWAYS, ASOS, WXSVC
IN
Y
N
A
IN
Y
IN14835 2019
74560
-6
14848
AIRWAYS,ASOS,COOP
IN
Y
N
A
IN
Y
IN14848 2019
72632
-5
53842
AIRWAYS, ASOS
IN
Y
N
A
IN
Y
IN53842 2019
72426
-5
53866
AIRWAYS, ASOS
IN
Y
N
A
IN
Y
IN53866 2019
72426
-5
93817
AIRSAMPLE,AIRWAYS,ASOS,
COOP
IN
N
N
A
IN
Y
IN93817 2019
72327
-6
93819
ASOS,COOP,WXSVC
IN
Y
N
A
IN
Y
IN93819 2019
72426
-5
94895
AIRWAYS, ASOS
IN
Y
N
A
IN
Y
IN94895 2019
72426
-5
3928
ASOS,COOP,WXSVC
KS
N
N
A
KS
Y
KS03928 2019
74646
-6
3936
AIRWAYS,ASOS,COOP,WXSVC
KS
Y
N
A
KS
Y
KS03936 2018
72456
-6
3967
AIRWAYS, ASOS
KS
N
N
A
KS
Y
KS03967 2019
72456
-6
3974
ASOS
KS
N
N
A
KS
Y
KS03974 2019
74646
-6
3997
AIRWAYS,ASOS,COOP
KS
Y
N
A
KS
Y
KS03997 2019
72456
-6
3998
AIRWAYS, ASOS
KS
Y
N
A
KS
Y
KS03998 2019
72456
-6
13920
AIRWAYS, ASOS
KS
N
N
A
KS
Y
KS13920 2019
72456
-6
13932
AIRWAYS, ASOS
KS
N
N
A
KS
Y
KS13932 2019
74646
-6
13981
ASOS
KS
Y
N
A
KS
Y
KS13981_2019
72456
-6
44
-------
Surface
WBAN
Type Station
State
tif file
snow
arid
moisture
state
Airport
AERMOD File
Upper Air
WMO
Upper Air
UTC
13984
ASOS,COOP
KS
Y
N
A
KS
Y
KS13984 2019
72456
-6
13985
ASOS,COOP,WXSVC
KS
N
N
A
KS
Y
KS13985 2019
72451
-6
13986
ASOS
KS
N
N
A
KS
Y
KS13986 2019
74646
-6
13989
AIRWAYS,ASOS,BASIC,COOP,
WXSVC
KS
N
N
A
KS
Y
KS13989 2019
72456
-6
13996
ASOS,COOP,WXSVC
KS
N
N
A
KS
Y
KS13996 2019
72456
-6
23064
AIRWAYS, ASOS
KS
N
N
A
KS
Y
KS23064 2019
72451
-6
23065
ASOS,COOP,WXSVC
KS
N
Y
A
KS
Y
KS23065 2019
72562
-6
93909
AIRWAYS, ASOS
KS
N
N
A
KS
Y
KS93909 2019
72456
-6
93990
AIRWAYS, ASOS
KS
Y
N
A
KS
Y
KS93990 2019
72451
-6
93997
ASOS
KS
N
N
A
KS
Y
KS93997 2019
72451
-6
3816
AIRSAMPLE,ASOS,COOP,
WXSVC
KY
Y
N
A
KY
Y
KY03816 2019
72327
-6
3849
AIRWAYS,ASOS,COOP
KY
N
N
A
KY
Y
KY03849 2019
72426
-5
3889
ASOS,COOP
KY
N
N
A
KY
Y
KY03889 2019
72426
-5
13810
AIRWAYS, ASOS
KY
Y
N
A
KY
Y
KY13810 2019
72327
-6
53841
AIRWAYS, ASOS
KY
Y
N
A
KY
Y
KY53841 2019
72426
-5
93808
AIRWAYS,ASOS,COOP,USHCN
KY
N
N
A
KY
Y
KY93808 2019
72327
-6
93814
ASOS,COOP
OH,
KY
Y
N
A
KY
Y
KY93814 2019
72426
-5
93820
ASOS,COOP,WXSVC
KY
Y
N
A
KY
Y
KY93820 2019
72426
-5
93821
ASOS,COOP,WXSVC
KY
Y
N
A
KY
Y
KY93821 2019
72327
-6
3937
ASOS,COOP,UPPERAIR
LA
N
N
A
LA
Y
LA03937 2019
72240
-6
3996
AIRWAYS,ASOS,COOP
MS,
LA
N
N
A
LA
Y
LA03996 2019
72235
-6
12884
ASOS,COOP
LA
N
N
A
LA
N
LAI2884 2018
72233
-6
12916
ASOS,COOP
LA
N
N
A
LA
Y
LA12916 2019
72233
-6
13942
ASOS,COOP
LA
N
N
A
LA
Y
LA13942 2019
72248
-6
13957
AIRSAMPLE,ASOS,COOP
LA
N
N
A
LA
Y
LA13957_2019
72248
-6
45
-------
Surface
WBAN
Type Station
State
tif file
snow
arid
moisture
state
Airport
AERMOD File
Upper Air
WMO
Upper Air
UTC
13970
ASOS,COOP,USHCN
LA
N
N
A
LA
Y
LA13970 2019
72233
-6
13976
AIRW AYS, ASOS,COOP, USHCN
LA
N
N
A
LA
Y
LA13976 2018
72240
-6
53865
ASOS,COOP
LA
N
N
A
LA
Y
LA53865 2016
72233
-6
53905
AIRWAYS,ASOS,COOP
LA
N
N
A
LA
Y
LA53905 2019
72248
-6
53915
AIRWAYS,ASOS,COOP
LA
N
N
A
LA
Y
LA53915 2019
72240
-6
53917
AIRWAYS,ASOS,COOP
LA
N
N
A
LA
Y
LA53917 2019
72233
-6
93915
AIRW AYS, ASOS
LA
N
N
A
LA
Y
LA93915 2019
72240
-6
4780
AIRW AYS, ASOS
MA
Y
N
A
MA
Y
MA04780 2019
72518
-5
14702
AIRW AYS, ASOS
MA
Y
N
A
MA
Y
MA14702 2019
74494
-5
14739
ASOS,COOP
MA
Y
N
A
MA
Y
MA14739 2019
74494
-5
14756
AIRW AYS, ASOS
MA
Y
N
A
MA
Y
MA14756 2019
74494
-5
14763
AIRW AYS, ASOS
MA
Y
N
A
MA
Y
MA14763 2019
72518
-5
14775
AIRW AYS, ASOS
MA
Y
N
A
MA
Y
MA14775 2019
72518
-5
54704
AIRW AYS, ASOS
MA
Y
N
A
MA
Y
MA54704 2019
74494
-5
54733
AIRW AYS, ASOS
MA
Y
N
A
MA
Y
MA54733 2019
74494
-5
54756
AIRW AYS, ASOS
MA
Y
N
A
MA
Y
MA54756 2019
72518
-5
54768
AIRWAYS,ASOS,COOP
VT,
MA
Y
N
A
MA
Y
MA54768 2019
72518
-5
54769
AIRW AYS, ASOS
MA
Y
N
A
MA
Y
MA54769 2019
74494
-5
54777
AIRW AYS, ASOS
MA
Y
N
A
MA
Y
MA54777 2019
74494
-5
94624
AIRW AYS, ASOS
MA
Y
N
A
MA
Y
MA94624 2019
74494
-5
94720
AIRW AYS, ASOS
MA
Y
N
A
MA
Y
MA94720 2019
74494
-5
94723
AIRW AYS, ASOS
MA
Y
N
A
MA
Y
MA94723 2019
74389
-5
94724
AIRW AYS, ASOS
MA
Y
N
A
MA
Y
MA94724 2019
74494
-5
94726
AIRW AYS, ASOS
MA
Y
N
A
MA
Y
MA94726 2019
74494
-5
94746
ASOS,COOP
MA
Y
N
A
MA
Y
MA94746 2019
72518
-5
93706
AIRW AYS, ASOS
PA,
MD
N
N
A
MD
N
MD93706 2019
72403
-5
93720
AIRWAYS,ASOS,COOP
MD
N
N
A
MD
Y
MD93720_2019
72402
-5
46
-------
Surface
WBAN
Type Station
State
tif file
snow
arid
moisture
state
Airport
AERMOD File
Upper Air
WMO
Upper Air
UTC
93721
ASOS,COOP
MD
N
N
A
MD
Y
MD93721 2019
72403
-5
93786
AIRWAYS,ASOS
MD
N
N
A
MD
Y
MD93786 2019
72402
-5
4836
AIRWAYS,ASOS
ME
Y
N
A
ME
Y
ME04836 2019
72712
-5
14605
ASOS,COOP
ME
Y
N
A
ME
Y
ME14605 2019
74389
-5
14606
AIRW AYS, ASOS,COOP
ME
Y
N
A
ME
Y
ME14606 2019
74389
-5
14607
ASOS,COOP
ME
Y
N
A
ME
Y
ME14607 2019
72712
-5
14609
AIRW AYS, ASOS,COOP
ME
Y
N
A
ME
Y
ME14609 2019
72712
-5
14610
AIRW AYS, ASOS
ME
Y
N
A
ME
Y
ME14610 2019
72712
-5
14764
ASOS,COOP,USHCN
ME
Y
N
A
ME
Y
ME14764 2019
74389
-5
54772
AIRW AYS, ASOS
ME,
NH
Y
N
A
ME
Y
ME54772 2019
74389
-5
94623
AIRW AYS, ASOS
ME
Y
N
A
ME
Y
ME94623 2019
74389
-5
4839
AIRW AYS, ASOS
Ml
Y
N
A
Ml
Y
M104839 2019
72634
-5
4847
AIRW AYS, ASOS
Ml
Y
N
A
Ml
Y
M104847 2019
72632
-5
4854
AIRW AYS, ASOS
Ml
Y
N
A
Ml
Y
M104854 2019
72634
-5
14815
AIRW AYS, ASOS
Ml
Y
N
A
Ml
Y
MI14815 2019
72632
-5
14822
ASOS
Ml
Y
N
A
Ml
Y
MI14822 2019
72632
-5
14826
ASOS,COOP,WXSVC
Ml
Y
N
A
Ml
Y
MI14826 2019
72632
-5
14833
ASOS,COOP
Ml
Y
N
A
Ml
Y
MI14833 2019
72632
-5
14836
ASOS,COOP,WXSVC
Ml
Y
N
A
Ml
Y
MI14836 2019
72632
-5
14840
ASOS,COOP,WXSVC
Ml
Y
N
A
Ml
Y
Ml 14840 2019
72634
-5
14841
AIRWAYS,ASOS,COOP
Ml
Y
N
A
Ml
Y
Ml 14841 2019
72634
-5
14845
ASOS
Ml
Y
N
A
Ml
Y
Ml 14845 2019
72632
-5
14847
ASOS,COOP,WXSVC
Ml
Y
N
A
Ml
Y
Ml 14847 2019
72634
-5
14850
AIRWAYS,ASOS,COOP
Ml
Y
N
A
Ml
Y
MI14850 2019
72634
-5
14853
AIRW AYS, ASOS
Ml
Y
N
A
Ml
Y
MI14853 2019
72632
-5
14858
ASOS
Ml
Y
N
A
Ml
Y
MI14858 2019
72645
-6
94814
ASOS,COOP,WXSVC
Ml
Y
N
A
Ml
Y
M194814 2019
72634
-5
94815
AIRW AYS, ASOS, WXSVC
Ml
Y
N
A
Ml
Y
MI94815_2019
72632
-5
47
-------
Surface
WBAN
Type Station
State
tif file
snow
arid
moisture
state
Airport
AERMOD File
Upper Air
WMO
Upper Air
UTC
94817
AIRWAYS,ASOS
Ml
Y
N
A
Ml
Y
MI94817 2019
72632
-5
94847
ASOS,COOP,WXSVC
Ml
Y
N
A
Ml
Y
M194847 2019
72632
-5
94849
ASOS,COOP
Ml
Y
N
A
Ml
Y
M194849 2019
72634
-5
94860
ASOS,COOP,WXSVC
Ml
Y
N
A
Ml
Y
M194860 2019
72632
-5
94871
ASOS,COOP
Ml
Y
N
A
Ml
Y
MI94871 2019
72645
-6
94889
AIRWAYS,ASOS
Ml
Y
N
A
Ml
Y
M194889 2019
72632
-5
94893
AIRWAYS, ASOS
Wl,
Ml
Y
N
A
Ml
Y
M194893 2019
72645
-6
14910
AIRWAYS, ASOS,COOP
MN
Y
N
A
MN
Y
MN14910 2019
72649
-6
14913
AIRW AYS, ASOS,COOP, WXSVC
MN
Y
N
A
MN
Y
MN14913 2019
72747
-6
14918
ASOS,COOP,WXSVC
MN
Y
N
A
MN
Y
MN14918 2019
72747
-6
14922
ASOS, COOP, USHCN, WXSVC
MN
Y
N
A
MN
Y
MN14922 2019
72649
-6
14925
ASOS,COOP,WXSVC
MN
Y
N
A
MN
Y
MN14925 2019
72649
-6
14926
AIRW AYS,ASOS,COOP,WXSVC
MN
Y
N
A
MN
Y
MN14926 2019
72649
-6
14927
ASOS,COOP
MN
Y
N
A
MN
Y
MN14927 2019
72649
-6
14992
ASOS,COOP
MN
Y
N
A
MN
Y
MN14992 2019
72649
-6
94931
ASOS
MN
Y
N
A
MN
Y
MN94931 2019
72747
-6
94938
AIRWAYS,ASOS
MN
Y
N
A
MN
Y
MN94938 2019
72649
-6
94960
AIRWAYS,ASOS,COOP
MN
Y
N
A
MN
Y
MN94960 2019
72649
-6
94961
AIRWAYS, ASOS
MN
Y
N
A
MN
Y
MN94961 2019
72747
-6
94963
ASOS,COOP
MN
Y
N
A
MN
Y
MN94963 2019
72649
-6
94967
AIRWAYS, ASOS
MN
Y
N
A
MN
Y
MN94967 2019
72747
-6
3935
AIRWAYS,ASOS,COOP,WXSVC
MO
N
N
A
MO
Y
MO03935 2019
74560
-6
3945
AIRWAYS,ASOS,COOP
MO
Y
N
A
MO
Y
MO03945 2019
72440
-6
3947
AIRWAYS,ASOS,COOP
MO
Y
N
A
MO
Y
MO03947 2019
72456
-6
3963
AIRWAYS, ASOS
MO
Y
N
A
MO
Y
MO03963 2019
72440
-6
3966
AIRWAYS, ASOS
MO
N
N
A
MO
Y
MO03966 2019
74560
-6
3975
AIRWAYS, ASOS
MO
N
N
A
MO
Y
MO03975 2019
72340
-6
3994
AIRWAYS, ASOS
MO
N
N
A
MO
Y
MO03994_2019
72440
-6
48
-------
Surface
WBAN
Type Station
State
tif file
snow
arid
moisture
state
Airport
AERMOD File
Upper Air
WMO
Upper Air
UTC
13987
ASOS,COOP
MO
N
N
A
MO
Y
M013987 2019
72440
-6
13988
AIRW AYS, ASOS,COOP
KS,
MO
Y
N
A
MO
Y
M013988 2019
72456
-6
13993
AIRW AYS, ASOS,COOP
KS,
MO
Y
N
A
MO
Y
M013993 2019
72456
-6
13994
ASOS,COOP,WXSVC
MO
N
N
A
MO
Y
M013994 2019
74560
-6
13995
ASOS,COOP,WXSVC
MO
N
N
A
MO
Y
M013995 2019
72440
-6
13997
ASOS,COOP
MO
N
N
A
MO
Y
M013997 2019
72440
-6
14938
ASOS
MO
Y
N
A
MO
Y
M014938 2019
74455
-6
53879
AIRW AYS, ASOS
MO
Y
N
A
MO
Y
M053879 2019
72456
-6
53901
AIRW AYS, ASOS
MO
N
N
A
MO
Y
MO53901 2019
72440
-6
53904
AIRW AYS, ASOS
IL, MO
N
N
A
MO
Y
MO53904 2019
74560
-6
3940
ASOS,COOP,UPPERAIR
MS
N
N
A
MS
Y
MS03940 2019
72235
-6
13833
ASOS,COOP
MS
N
N
A
MS
Y
MS13833 2019
72233
-6
13865
ASOS,COOP
MS
N
N
A
MS
Y
MS13865 2019
72235
-6
13927
ASOS,COOP
MS
N
N
A
MS
Y
MS13927 2019
72235
-6
13939
ASOS,COOP
MS
N
N
A
MS
Y
MS13939 2019
72235
-6
13978
AIRWAYS,ASOS,COOP
MS
N
N
A
MS
Y
MS13978 2019
72235
-6
53858
AIRW AYS, ASOS
MS
N
N
A
MS
Y
MS53858 2019
72233
-6
93862
AIRSAMPLE,ASOS,COOP
MS
N
N
A
MS
Y
MS93862 2019
72230
-6
93874
AIRWAYS,ASOS,COOP
MS
N
N
A
MS
Y
MS93874 2019
72233
-6
93919
AIRWAYS,ASOS,COOP
MS
N
N
A
MS
Y
MS93919 2019
72233
-6
24033
ASOS,COOP
MT
N
Y
A
MT
Y
MT24033 2019
72672
-7
24036
AIRWAYS,ASOS,COOP
MT
N
Y
A
MT
Y
MT24036 2019
72776
-7
24037
ASOS,COOP,USHCN
MT
N
Y
A
MT
Y
MT24037 2019
72768
-7
24132
AIRWAYS,ASOS,COOP
MT
N
Y
A
MT
Y
MT24132 2019
72776
-7
24135
ASOS,COOP
MT
N
Y
A
MT
Y
MT24135 2019
72776
-7
24137
ASOS,COOP,USHCN
MT
Y
N
A
MT
Y
MT24137 2019
72776
-7
24138
AIRWAYS,ASOS,COOP
MT
N
Y
A
MT
Y
MT24138_2019
72776
-7
49
-------
Surface
WBAN
Type Station
State
tif file
snow
arid
moisture
state
Airport
AERMOD File
Upper Air
WMO
Upper Air
UTC
24143
ASOS,COOP,USHCN
MT
Y
N
A
MT
Y
MT24143 2019
72776
-7
24144
ASOS,COOP,USHCN
MT
N
Y
A
MT
Y
MT24144 2019
72776
-7
24146
ASOS,COOP,USHCN
MT
Y
N
A
MT
Y
MT24146 2019
72776
-7
24150
ASOS,COOP
MT
N
Y
A
MT
Y
MT24150 2019
72776
-7
24153
ASOS,COOP
MT
N
Y
A
MT
Y
MT24153 2019
72776
-7
94008
ASOS, COOP, USHCN
MT
N
Y
A
MT
Y
MT94008 2019
72768
-7
94012
ASOS,COOP
MT
Y
N
A
MT
Y
MT94012 2019
72776
-7
94017
AIRWAYS, ASOS
MT
N
Y
A
MT
Y
MT94017 2019
72768
-7
94055
ASOS,COOP
MT
Y
N
A
MT
Y
MT94055 2019
72662
-7
3810
AIRWAYS, ASOS,COOP
NC
N
N
A
NC
Y
NC03810 2019
72317
-5
3812
ASOS,COOP,WXSVC
NC
N
N
A
NC
Y
NC03812 2019
72317
-5
13722
ASOS,COOP
NC
N
N
A
NC
Y
NC13722 2019
72317
-5
13723
ASOS,COOP
NC
N
N
A
NC
Y
NC13723 2019
72317
-5
13748
ASOS,COOP
NC
N
N
A
NC
Y
NC13748 2016
72305
-5
13754
ASOS
NC
N
N
A
NC
Y
NC13754 2019
72305
-5
13776
AIRWAYS, ASOS,COOP
NC
N
N
A
NC
Y
NC13776 2018
72317
-5
13786
AIRWAYS, ASOS
NC
N
N
A
NC
Y
NC13786 2019
72305
-5
13881
ASOS,COOP
NC
N
N
A
NC
Y
NC13881 2019
72317
-5
53870
AIRWAYS, ASOS
NC, SC
N
N
A
NC
Y
NC53870 2019
72317
-5
53872
AIRWAYS, ASOS
NC
N
N
A
NC
Y
NC53872 2019
72317
-5
93719
AIRWAYS,ASOS,COOP
NC
N
N
A
NC
Y
NC93719 2019
72305
-5
93729
ASOS,COOP,USHCN
NC
N
N
A
NC
Y
NC93729 2019
72305
-5
93740
AIRWAYS, ASOS
NC
N
N
A
NC
Y
NC93740 2019
72317
-5
93759
ASOS
NC
N
N
A
NC
Y
NC93759 2019
72305
-5
93765
ASOS
NC
N
N
A
NC
Y
NC93765 2019
72305
-5
93782
AIRWAYS, ASOS
NC
N
N
A
NC
Y
NC93782 2019
72317
-5
93783
AIRWAYS, ASOS
NC
N
N
A
NC
Y
NC93783 2019
72317
-5
93785
AIRWAYS, ASOS
NC
N
N
A
NC
Y
NC93785 2016
72317
-5
93807
AIRWAYS, ASOS
NC
N
N
A
NC
Y
NC93807_2019
72317
-5
50
-------
Surface
WBAN
Type Station
State
tif file
snow
arid
moisture
state
Airport
AERMOD File
Upper Air
WMO
Upper Air
UTC
14914
AIRW AYS, ASOS,COOP
ND,
MN
Y
N
A
ND
Y
ND14914 2019
72659
-6
14916
AIRW AYS, ASOS,COOP, WXSVC
ND
Y
N
A
ND
Y
ND14916 2019
72659
-6
14919
ASOS,COOP,WXSVC
ND
Y
N
A
ND
Y
ND14919 2019
72659
-6
24011
ASOS,COOP,WXSVC
ND
Y
N
A
ND
Y
ND24011 2019
72764
-6
24012
ASOS,COOP,WXSVC
ND
Y
N
A
ND
Y
ND24012 2019
72764
-6
24013
AIRWAYS,ASOS,COOP
ND
Y
N
A
ND
Y
ND24013 2019
72764
-6
94014
ASOS,COOP,WXSVC
ND
Y
N
A
ND
Y
ND94014 2018
72768
-7
94038
AIRW AYS, ASOS
ND
Y
N
A
ND
Y
ND94038 2018
72662
-7
14935
AIRW AYS,ASOS,COOP,WXSVC
NE
Y
N
A
NE
Y
NE14935 2019
72558
-6
14939
AIRWAYS,ASOS,COOP
NE
N
N
A
NE
Y
NE14939 2019
72558
-6
14941
ASOS,COOP,WXSVC
NE
N
Y
A
NE
Y
NE14941 2019
72558
-6
14942
AIRWAYS,ASOS,COOP
IA, NE
Y
N
A
NE
Y
NE14942 2019
72558
-6
24017
ASOS,COOP
NE
N
Y
A
NE
Y
NE24017 2019
72662
-7
24023
ASOS,COOP,WXSVC
NE
Y
N
A
NE
Y
NE24023 2019
72562
-6
24028
ASOS,COOP,WXSVC
NE
Y
N
A
NE
Y
NE24028 2019
72662
-7
24030
AIRWAYS,ASOS,COOP
NE
Y
N
A
NE
Y
NE24030 2019
72562
-6
24032
ASOS,COOP,WXSVC
NE
N
Y
A
NE
Y
NE24032 2019
72562
-6
24044
AIRWAYS,ASOS,COOP
NE
Y
N
A
NE
Y
NE24044 2019
72662
-7
24091
ASOS
NE
Y
N
A
NE
Y
NE24091 2019
72562
-6
94040
AIRW AYS, ASOS
NE
Y
N
A
NE
Y
NE94040 2019
72562
-6
94946
AIRW AYS, ASOS
NE
N
N
A
NE
Y
NE94946 2019
72562
-6
94949
AIRWAYS,ASOS,COOP
NE
N
N
A
NE
Y
NE94949 2019
72558
-6
94957
ASOS
NE
Y
N
A
NE
Y
NE94957 2019
72456
-6
94958
ASOS,COOP
NE
Y
N
A
NE
Y
NE94958 2019
72562
-6
94978
AIRW AYS, ASOS
NE
Y
N
A
NE
Y
NE94978 2019
72558
-6
14710
AIRW AYS, ASOS
NH
Y
N
A
NH
Y
NH14710 2019
74389
-5
14745
ASOS,COOP
NH
Y
N
A
NH
Y
NH14745 2019
74389
-5
54728
AIRW AYS, ASOS
NH
Y
N
A
NH
Y
NH54728_2016
74389
-5
51
-------
Surface
WBAN
Type Station
State
tif file
snow
arid
moisture
state
Airport
AERMOD File
Upper Air
WMO
Upper Air
UTC
54770
AIRWAYS,ASOS
NH
Y
N
A
NH
Y
NH54770 2019
72518
-5
54791
AIRWAYS,ASOS
ME,
NH
Y
N
A
NH
Y
NH54791 2019
74389
-5
94700
AIRWAYS,ASOS
NH
Y
N
A
NH
Y
NH94700 2019
74389
-5
94765
AIRWAYS,ASOS
VT,
NH
Y
N
A
NH
Y
NH94765 2019
72518
-5
13735
AIRWAYS, ASOS,COOP
NJ
Y
N
A
NJ
Y
NJ13735 2019
72402
-5
14734
ASOS,COOP
NJ
Y
N
A
NJ
Y
NJ14734 2019
72501
-5
14792
AIRWAYS,ASOS
PA, NJ
Y
N
A
NJ
Y
NJ14792 2019
72501
-5
54743
ASOS
NJ
Y
N
A
NJ
Y
NJ54743 2019
72501
-5
54785
ASOS
NJ
Y
N
A
NJ
Y
NJ54785 2019
72501
-5
54793
ASOS
NJ
Y
N
A
NJ
Y
NJ54793 2019
72501
-5
93730
ASOS,COOP
NJ
N
N
A
NJ
Y
NJ93730 2019
72402
-5
93780
AIRWAYS, ASOS
NJ
Y
N
A
NJ
Y
NJ93780 2019
72501
-5
94741
ASOS
NJ
Y
N
A
NJ
Y
NJ94741 2019
72501
-5
3027
ASOS
NM
N
Y
A
NM
N
NM03027 2019
72365
-7
23009
AIRSAMPLE,AIRWAYS,ASOS,
COOP,USHCN
NM
N
Y
A
NM
Y
NM23009 2019
72364
-7
23048
ASOS
NM
N
Y
A
NM
Y
NM23048 2019
72363
-6
23049
AIRWAYS, ASOS
NM
N
Y
A
NM
Y
NM23049 2018
72365
-7
23050
AIRSAMPLE,AIRWAYS,ASOS,
COOP
NM
N
Y
A
NM
Y
NM23050 2019
72365
-7
23051
AIRSAMPLE,ASOS,COOP,
USHCN
NM
N
Y
A
NM
Y
NM23051 2019
72363
-6
23052
ASOS
NM
N
Y
A
NM
Y
NM23052 2019
72365
-7
23054
ASOS
NM
N
Y
A
NM
Y
NM23054 2019
72365
-7
23078
AIRWAYS, ASOS
NM
N
Y
A
NM
Y
NM23078 2019
72364
-7
23081
ASOS,COOP
NM
N
Y
A
NM
Y
NM23081 2019
72365
-7
23090
AIRWAYS, ASOS
NM
N
Y
A
NM
Y
NM23090_2019
72365
-7
52
-------
Surface
WBAN
Type Station
State
tif file
snow
arid
moisture
state
Airport
AERMOD File
Upper Air
WMO
Upper Air
UTC
93033
AIRW AYS, ASOS,COOP, WXSVC
NM
N
Y
A
NM
Y
NM93033 2019
72265
-6
93045
ASOS
NM
N
Y
A
NM
Y
NM93045 2019
72364
-7
3160
ASOS,COOP
NV
N
Y
A
NV
Y
NV03160 2019
72388
-8
23153
AIRWAYS,ASOS,COOP
NV
N
Y
A
NV
Y
NV23153 2019
72388
-8
23154
AIRWAYS,ASOS,COOP
NV
N
Y
A
NV
Y
NV23154 2019
72582
-8
23169
AIRSAMPLE,AIRWAYS,ASOS,
COOP
NV
N
Y
A
NV
Y
NV23169 2019
72388
-8
23185
ASOS,COOP,USHCN
NV
N
Y
A
NV
Y
NV23185 2019
72489
-8
24121
AIRW AYS,ASOS,COOP,USHCN
NV
N
Y
A
NV
Y
NV24121 2019
72582
-8
24128
AIRW AYS,ASOS,COOP,USHCN
NV
N
Y
A
NV
Y
NV24128 2019
72582
-8
24172
ASOS,COOP
NV
N
Y
A
NV
Y
NV24172 2019
72489
-8
53123
ASOS
NV
N
Y
A
NV
Y
NV53123 2019
72388
-8
4725
AIRW AYS,ASOS,COOP,USHCN
NY
Y
N
A
NY
Y
NY04725 2019
72518
-5
4781
AIRWAYS,ASOS,COOP
NY
Y
N
A
NY
Y
NY04781 2019
72501
-5
4789
AIRW AYS, ASOS
NY
Y
N
A
NY
Y
NY04789 2019
72501
-5
14719
AIRW AYS, ASOS
NY
Y
N
A
NY
Y
NY14719 2019
72501
-5
14732
ASOS,COOP
NY
Y
N
A
NY
Y
NY14732 2019
72501
-5
14733
ASOS,COOP,USHCN
NY
Y
N
A
NY
Y
NY14733 2019
72528
-5
14735
ASOS,COOP,USHCN
NY
Y
N
A
NY
Y
NY14735 2019
72518
-5
14747
AIRW AYS, ASOS
NY
Y
N
A
NY
Y
NY14747 2019
72528
-5
14748
AIRW AYS, ASOS
NY
Y
N
A
NY
Y
NY14748 2019
72528
-5
14750
ASOS,COOP
NY
Y
N
A
NY
Y
NY14750 2019
72518
-5
14757
ASOS,COOP
NY
Y
N
A
NY
Y
NY14757 2019
72501
-5
14768
ASOS,COOP,USHCN
NY
Y
N
A
NY
Y
NY14768 2019
72528
-5
14771
ASOS,COOP,USHCN
NY
Y
N
A
NY
Y
NY14771 2019
72518
-5
54757
AIRW AYS, ASOS
NY
Y
N
A
NY
Y
NY54757 2019
72528
-5
54773
AIRW AYS, ASOS
NY
Y
N
A
NY
Y
NY54773 2019
72528
-5
54778
AIRW AYS, ASOS
NY
Y
N
A
NY
Y
NY54778 2019
72528
-5
54787
AIRW AYS, ASOS
NY
Y
N
A
NY
Y
NY54787_2019
72501
-5
53
-------
Surface
WBAN
Type Station
State
tif file
snow
arid
moisture
state
Airport
AERMOD File
Upper Air
WMO
Upper Air
UTC
54790
AIRWAYS,ASOS
NY
Y
N
A
NY
Y
NY54790 2019
72501
-5
64775
AIRWAYS,ASOS
NY
Y
N
A
NY
Y
NY64775 2019
72518
-5
64776
ASOS
NY
Y
N
A
NY
Y
NY64776 2019
72518
-5
94704
ASOS
NY
Y
N
A
NY
Y
NY94704 2019
72528
-5
94725
ASOS
NY
Y
N
A
NY
Y
NY94725 2019
71722
-5
94728
ASOS, COOP, USHCN
NY, NJ
Y
N
A
NY
N
NY94728 2017
72501
-5
94740
AIRWAYS, ASOS
NY
Y
N
A
NY
Y
NY94740 2019
72518
-5
94745
AIRWAYS,ASOS,COOP
NY, CT
Y
N
A
NY
Y
NY94745 2019
72501
-5
94789
ASOS,COOP
NY
Y
N
A
NY
Y
NY94789 2019
72501
-5
94790
ASOS,COOP
NY
Y
N
A
NY
Y
NY94790 2019
71722
-5
4804
ASOS
OH
Y
N
A
OH
Y
OH04804 2019
72426
-5
4842
AIRWAYS, ASOS
OH
Y
N
A
OH
Y
OH04842 2019
72520
-5
4848
AIRWAYS, ASOS
OH
Y
N
A
OH
Y
OH04848 2019
72632
-5
4849
AIRWAYS, ASOS
OH
Y
N
A
OH
Y
OH04849 2019
72632
-5
4850
AIRWAYS,ASOS,COOP
OH
Y
N
A
OH
Y
OH04850 2019
72426
-5
4851
ASOS,COOP
OH
Y
N
A
OH
Y
OH04851 2019
72632
-5
4852
AIRWAYS, ASOS
OH
Y
N
A
OH
Y
OH04852 2019
72520
-5
4853
AIRWAYS, ASOS
OH
Y
N
A
OH
Y
OH04853 2019
72520
-5
4855
AIRWAYS, ASOS
OH
Y
N
A
OH
Y
OH04855 2019
72426
-5
4857
AIRWAYS, ASOS
OH
Y
N
A
OH
Y
OH04857 2019
72520
-5
4858
AIRWAYS, ASOS
OH
Y
N
A
OH
Y
OH04858 2019
72426
-5
13841
ASOS
OH
Y
N
A
OH
Y
OH13841 2019
72426
-5
14813
AIRWAYS, ASOS
OH
Y
N
A
OH
Y
OH14813 2019
72520
-5
14820
ASOS,COOP
OH
Y
N
A
OH
Y
OH14820 2019
72520
-5
14821
ASOS,COOP
OH
Y
N
A
OH
Y
OH14821 2019
72426
-5
14825
ASOS,COOP
OH
Y
N
A
OH
Y
OH14825 2019
72426
-5
14852
ASOS,COOP
OH
Y
N
A
OH
Y
OH14852 2016
72520
-5
14891
ASOS,COOP
OH
Y
N
A
OH
Y
OH14891 2019
72426
-5
14895
ASOS,COOP
OH
Y
N
A
OH
Y
OH14895_2019
72520
-5
54
-------
Surface
WBAN
Type Station
State
tif file
snow
arid
moisture
state
Airport
AERMOD File
Upper Air
WMO
Upper Air
UTC
53844
AIRWAYS,ASOS
OH
Y
N
A
OH
Y
OH53844 2019
72426
-5
53855
AIRWAYS,ASOS
OH
Y
N
A
OH
Y
OH53855 2019
72426
-5
53859
AIRWAYS,ASOS
OH
Y
N
A
OH
Y
OH53859 2019
72426
-5
93812
AIRW AYS, ASOS,COOP
OH,
KY
Y
N
A
OH
Y
OH93812 2019
72426
-5
93815
ASOS,COOP
OH
Y
N
A
OH
Y
OH93815 2019
72426
-5
93824
ASOS,COOP
OH
Y
N
A
OH
Y
OH93824 2019
72520
-5
94830
ASOS,COOP
OH
Y
N
A
OH
Y
OH94830 2019
72632
-5
3030
ASOS
OK
N
Y
A
OK
Y
OK03030 2019
72363
-6
3932
ASOS,COOP
OK
N
N
A
OK
Y
OK03932 2019
72357
-6
3950
AIRWAYS,ASOS,COOP
OK
N
N
A
OK
Y
OK03950 2019
72357
-6
3954
AIRW AYS, ASOS
OK
N
N
A
OK
Y
OK03954 2019
72357
-6
3959
AIRW AYS,ASOS,COOP,USHCN
OK
N
N
A
OK
Y
OK03959 2019
74646
-6
3965
ASOS,COOP
OK
N
N
A
OK
Y
OK03965 2019
74646
-6
3981
ASOS,COOP
OK
N
N
A
OK
Y
OK03981 2019
72357
-6
13967
ASOS,COOP
OK
N
N
A
OK
Y
OK13967 2019
72357
-6
13968
AIRSAMPLE,ASOS,COOP
OK
N
N
A
OK
Y
OK13968 2019
74646
-6
13969
ASOS,COOP
OK
N
N
A
OK
Y
OK13969 2019
74646
-6
13975
AIRWAYS,ASOS,COOP
OK
N
N
A
OK
Y
OK13975 2019
72451
-6
53908
AIRW AYS, ASOS
OK
N
N
A
OK
Y
OK53908 2019
74646
-6
53913
AIRWAYS,ASOS,COOP
OK
N
N
A
OK
Y
OK53913 2019
72357
-6
93950
AIRWAYS,ASOS,COOP
OK
N
N
A
OK
Y
OK93950 2019
72357
-6
93953
AIRW AYS, ASOS
OK
N
N
A
OK
Y
OK93953 2019
72357
-6
93986
AIRW AYS,ASOS,COOP,USHCN
OK
N
N
A
OK
Y
OK93986 2018
72357
-6
4113
AIRW AYS, ASOS
OR
N
N
A
OR
Y
OR04113 2019
72786
-8
4201
AIRW AYS, ASOS
OR
N
N
A
OR
Y
OR04201 2019
72694
-8
24130
AIRW AYS,ASOS,COOP,USHCN
OR
Y
N
A
OR
Y
OR24130 2019
72681
-7
24152
ASOS
OR
Y
N
A
OR
N
OR24152 2018
72786
-8
24155
ASOS,COOP
OR
N
N
A
OR
Y
OR24155_2019
72786
-8
55
-------
Surface
WBAN
Type Station
State
tif file
snow
arid
moisture
state
Airport
AERMOD File
Upper Air
WMO
Upper Air
UTC
24162
AIRW AYS, ASOS,COOP
OR
N
N
A
OR
Y
OR24162 2019
72681
-7
24221
ASOS,COOP
OR
N
N
A
OR
Y
OR24221 2019
72694
-8
24225
ASOS,COOP
OR
N
N
A
OR
Y
OR24225 2019
72597
-8
24229
ASOS,COOP
WA,
OR
N
N
A
OR
Y
OR24229 2019
72694
-8
24230
AIRWAYS,ASOS,COOP
OR
N
N
A
OR
Y
OR24230 2019
72694
-8
24231
AIRW AYS, ASOS
OR
N
N
A
OR
Y
OR24231 2019
72597
-8
24232
ASOS,COOP
OR
N
N
A
OR
Y
OR24232 2019
72694
-8
24235
ASOS,COOP
OR
N
N
A
OR
N
OR24235 2019
72597
-8
24242
AIRW AYS, ASOS
WA,
OR
N
N
A
OR
Y
OR24242 2019
72694
-8
94185
ASOS,COOP
OR
N
N
A
OR
Y
OR94185 2019
72681
-7
94224
ASOS,COOP,USHCN
OR
N
N
A
OR
Y
OR94224 2019
72694
-8
94236
AIRWAYS,ASOS,COOP
OR
N
N
A
OR
Y
OR94236 2019
72597
-8
94261
AIRW AYS, ASOS
OR
N
N
A
OR
Y
OR94261 2019
72694
-8
94273
AIRW AYS, ASOS
OR
N
N
A
OR
Y
OR94273 2019
72694
-8
94281
AIRW AYS, ASOS
OR
N
N
A
OR
Y
OR94281 2019
72694
-8
4726
ASOS
PA
Y
N
A
PA
Y
PA04726 2019
72520
-5
4751
AIRWAYS,ASOS,COOP
PA
Y
N
A
PA
Y
PA04751 2019
72528
-5
4787
ASOS,COOP
PA
Y
N
A
PA
Y
PA04787 2019
72520
-5
4843
AIRW AYS, ASOS
PA
Y
N
A
PA
Y
PA04843 2019
72520
-5
13739
ASOS,COOP
PA, NJ
Y
N
A
PA
Y
PA13739 2019
72403
-5
14711
AIRWAYS,ASOS,COOP
PA
Y
N
A
PA
Y
PA14711 2019
72403
-5
14712
ASOS
PA
Y
N
A
PA
Y
PA14712 2019
72403
-5
14736
ASOS
PA
Y
N
A
PA
Y
PA14736 2019
72403
-5
14737
ASOS,COOP,USHCN
PA
Y
N
A
PA
Y
PA14737 2019
72501
-5
14751
AIRW AYS, ASOS
PA
Y
N
A
PA
Y
PA14751 2019
72403
-5
14762
AIRW AYS, ASOS, WXSVC
PA
N
N
A
PA
Y
PA14762 2019
72520
-5
14770
AIRW AYS, ASOS
PA
Y
N
A
PA
Y
PA14770_2019
72403
-5
56
-------
Surface
WBAN
Type Station
State
tif file
snow
arid
moisture
state
Airport
AERMOD File
Upper Air
WMO
Upper Air
UTC
14777
ASOS,COOP
PA
Y
N
A
PA
Y
PA14777 2019
72518
-5
14778
ASOS,COOP,USHCN
PA
Y
N
A
PA
Y
PA14778 2019
72528
-5
14860
ASOS,COOP,USHCN
PA
Y
N
A
PA
Y
PA14860 2019
72520
-5
54737
AIRWAYS,ASOS
PA
Y
N
A
PA
Y
PA54737 2019
72403
-5
54782
AIRWAYS,ASOS
PA
Y
N
A
PA
Y
PA54782 2019
72403
-5
54786
AIRWAYS, ASOS
PA
Y
N
A
PA
Y
PA54786 2019
72501
-5
54789
AIRWAYS, ASOS
PA
Y
N
A
PA
Y
PA54789 2019
72501
-5
54792
ASOS
PA
Y
N
A
PA
Y
PA54792 2019
72520
-5
93778
AIRWAYS, ASOS
PA
Y
N
A
PA
Y
PA93778 2019
72403
-5
94732
ASOS
PA
Y
N
A
PA
Y
PA94732 2019
72501
-5
94823
ASOS,COOP
PA
Y
N
A
PA
Y
PA94823 2019
72520
-5
11641
ASOS,COOP
PR
N
N
A
PR
Y
PR11641 2019
78526
-4
14765
ASOS, COOP, USHCN
Rl
Y
N
A
Rl
Y
RI14765 2019
74494
-5
14787
AIRWAYS, ASOS
Rl
Y
N
A
Rl
Y
Rl 14787 2019
74494
-5
14794
AIRWAYS, ASOS
CT, Rl
Y
N
A
Rl
Y
Rl 14794 2019
72501
-5
3870
ASOS,COOP,USHCN
SC
N
N
A
SC
Y
SC03870 2019
72317
-5
13744
AIRWAYS,ASOS,COOP
SC
N
N
A
SC
Y
SC13744 2019
72208
-5
13880
ASOS,COOP,UPPERAIR
SC
N
N
A
SC
Y
SC13880 2019
72208
-5
13883
ASOS,COOP
SC
N
N
A
SC
Y
SC13883 2019
72208
-5
13886
AIRWAYS, ASOS
SC
N
N
A
SC
Y
SC13886 2019
72215
-5
53850
ASOS
SC
N
N
A
SC
Y
SC53850 2019
72215
-5
53854
AIRWAYS, ASOS
SC
N
N
A
SC
Y
SC53854 2019
72208
-5
53867
AIRWAYS, ASOS
SC
N
N
A
SC
Y
SC53867 2019
72208
-5
53871
AIRWAYS, ASOS
SC
N
N
A
SC
Y
SC53871 2019
72317
-5
53874
ASOS
SC
N
N
A
SC
Y
SC53874 2019
72208
-5
93718
ASOS,COOP
SC
N
N
A
SC
Y
SC93718 2019
72208
-5
93846
AIRWAYS,ASOS,COOP
SC
N
N
A
SC
Y
SC93846 2019
72215
-5
14929
ASOS, COOP, USHCN, WXSVC
SD
Y
N
A
SD
N
SD14929 2019
72659
-6
14936
ASOS,COOP,WXSVC
SD
Y
N
A
SD
Y
SD14936_2019
72659
-6
57
-------
Surface
WBAN
Type Station
State
tif file
snow
arid
moisture
state
Airport
AERMOD File
Upper Air
WMO
Upper Air
UTC
14944
ASOS,COOP,WXSVC
SD
Y
N
A
SD
Y
SD14944 2019
72558
-6
14946
AIRW AYS, ASOS,COOP, USHCN
SD
Y
N
A
SD
Y
SD14946 2019
72659
-6
24024
ASOS
SD
N
Y
A
SD
Y
SD24024 2019
72662
-7
24025
AIRW AYS,ASOS,COOP,USHCN
SD
N
Y
A
SD
Y
SD24025 2019
72659
-6
24090
ASOS,COOP
SD
N
Y
A
SD
Y
SD24090 2019
72662
-7
94032
ASOS
SD
N
Y
A
SD
Y
SD94032 2019
72662
-7
94039
AIRW AYS, ASOS
SD, NE
N
Y
A
SD
Y
SD94039 2019
72662
-7
94052
AIRW AYS, ASOS
SD
Y
N
A
SD
Y
SD94052 2019
72764
-6
94950
AIRWAYS,ASOS,COOP
SD
Y
N
A
SD
Y
SD94950 2019
72659
-6
94990
AIRW AYS, ASOS
SD
N
Y
A
SD
Y
SD94990 2019
72562
-6
3811
AIRWAYS,ASOS,COOP
TN
N
N
A
TN
Y
TN03811 2019
72327
-6
3847
ASOS,COOP
TN
N
N
A
TN
Y
TN03847 2019
72327
-6
3894
ASOS,COOP
KY, TN
N
N
A
TN
Y
TN03894 2019
72327
-6
13877
AIRSAMPLE,ASOS,COOP
TN
N
N
A
TN
Y
TN13877 2019
72318
-5
13882
AIRSAMPLE,AIRWAYS,ASOS,
COOP,WXSVC
TN,
GA
N
N
A
TN
Y
TN13882 2019
72215
-5
13891
AIRSAMPLE,AIRWAYS,ASOS,
COOP
TN
N
N
A
TN
Y
TN13891 2019
72215
-5
13893
AIRSAMPLE,ASOS,COOP
TN
N
N
A
TN
Y
TN13893 2019
72340
-6
13897
AIRSAMPLE,ASOS,COOP
TN
N
N
A
TN
Y
TN13897 2019
72327
-6
53868
AIRSAMPLE,ASOS,COOP
TN
N
N
A
TN
N
TN53868 2019
72327
-6
3024
AIRW AYS, ASOS
TX
N
Y
A
TX
Y
TX03024 2019
72363
-6
3031
AIRW AYS, ASOS
TX
N
Y
A
TX
Y
TX03031 2019
72265
-6
3901
AIRWAYS,ASOS,COOP
TX
N
N
A
TX
Y
TX03901 2019
72248
-6
3904
AIRWAYS,ASOS,COOP
TX
N
N
A
TX
Y
TX03904 2019
72249
-6
3927
ASOS,COOP
TX
N
N
A
TX
Y
TX03927 2019
72249
-6
3971
AIRW AYS, ASOS
TX
N
N
A
TX
Y
TX03971 2019
72249
-6
3991
AIRW AYS, ASOS
TX
N
N
A
TX
Y
TX03991 2019
72249
-6
3999
AIRWAYS,ASOS,COOP
TX
N
N
A
TX
Y
TX03999_2019
72249
-6
58
-------
Surface
WBAN
Type Station
State
tif file
snow
arid
moisture
state
Airport
AERMOD File
Upper Air
WMO
Upper Air
UTC
12904
AIRWAYS,ASOS
TX
N
Y
A
TX
Y
TX12904 2019
72250
-6
12912
ASOS,COOP
TX
N
N
A
TX
Y
TX12912 2019
72251
-6
12917
ASOS,COOP
TX
N
N
A
TX
Y
TX12917 2019
72240
-6
12918
AIRW AYS, ASOS,COOP
TX
N
N
A
TX
Y
TX12918 2019
72240
-6
12919
AIRW AYS, ASOS,COOP,
UPPERAIR
TX
N
Y
A
TX
Y
TX12919 2019
72250
-6
12921
ASOS,COOP,USHCN
TX
N
N
A
TX
Y
TX12921 2019
72251
-6
12923
AIRWAYS,ASOS,COOP
TX
N
N
A
TX
Y
TX12923 2019
72240
-6
12924
ASOS, COOP, USHCN, WXSVC
TX
N
N
A
TX
Y
TX12924 2019
72251
-6
12932
AIRWAYS,ASOS,COOP
TX
N
N
A
TX
Y
TX12932 2019
72251
-6
12935
ASOS,COOP
TX
N
N
A
TX
Y
TX12935 2019
72251
-6
12947
ASOS,COOP
TX
N
N
A
TX
Y
TX12947 2019
72251
-6
12957
AIRW AYS, ASOS
TX
N
Y
A
TX
Y
TX12957 2019
72250
-6
12959
AIRW AYS,ASOS,COOP,WXSVC
TX
N
Y
A
TX
Y
TX12959 2019
72250
-6
12960
ASOS,COOP
TX
N
N
A
TX
Y
TX12960 2019
72240
-6
12962
AIRWAYS,ASOS,COOP
TX
N
N
A
TX
Y
TX12962 2019
72261
-6
12970
AIRWAYS,ASOS,COOP
TX
N
N
A
TX
Y
TX12970 2019
72251
-6
12971
AIRWAYS,ASOS,COOP
TX
N
N
A
TX
Y
TX12971 2019
72251
-6
12972
AIRWAYS,ASOS,COOP
TX
N
N
A
TX
Y
TX12972 2019
72251
-6
12975
AIRW AYS, ASOS
TX
N
N
A
TX
Y
TX12975 2019
72240
-6
12976
AIRW AYS, ASOS
TX
N
N
A
TX
Y
TX12976 2019
72251
-6
12977
ASOS,COOP
TX
N
N
A
TX
Y
TX12977 2019
72240
-6
13904
AIRWAYS,ASOS,COOP
TX
N
N
A
TX
Y
TX13904 2019
72251
-6
13958
ASOS,COOP
TX
N
N
A
TX
N
TX13958 2019
72249
-6
13959
ASOS,COOP
TX
N
N
A
TX
Y
TX13959 2019
72249
-6
13960
ASOS,COOP
TX
N
N
A
TX
Y
TX13960 2019
72249
-6
13961
AIRWAYS,ASOS,COOP
TX
N
N
A
TX
Y
TX13961 2019
72249
-6
13962
AIRSAMPLE,ASOS,COOP
TX
N
Y
A
TX
Y
TX13962 2019
72249
-6
13966
ASOS,COOP
TX
N
N
A
TX
Y
TX13966_2019
72357
-6
59
-------
Surface
WBAN
Type Station
State
tif file
snow
arid
moisture
state
Airport
AERMOD File
Upper Air
WMO
Upper Air
UTC
13972
AIRW AYS, ASOS,COOP, WXSVC
TX
N
N
A
TX
Y
TX13972 2019
72248
-6
13973
AIRWAYS,ASOS
TX
N
Y
A
TX
Y
TX13973 2019
72261
-6
22010
ASOS,COOP
TX
N
Y
A
TX
Y
TX22010 2019
72261
-6
23007
AIRWAYS,ASOS,COOP
TX
N
Y
A
TX
Y
TX23007 2019
72363
-6
23023
ASOS,COOP,WXSVC
TX
N
Y
A
TX
Y
TX23023 2019
72265
-6
23034
AIRSAMPLE,ASOS,COOP
TX
N
Y
A
TX
Y
TX23034 2019
72265
-6
23040
ASOS,COOP,WXSVC
TX
N
Y
A
TX
Y
TX23040 2019
72265
-6
23042
AIRWAYS,ASOS,COOP
TX
N
Y
A
TX
Y
TX23042 2019
72363
-6
23044
ASOS,COOP,USHCN
TX
N
Y
A
TX
Y
TX23044 2019
72364
-7
23047
ASOS,COOP
TX
N
Y
A
TX
Y
TX23047 2019
72363
-6
23055
ASOS
TX
N
Y
A
TX
N
TX23055 2019
72364
-7
23091
AIRW AYS, ASOS
TX
N
Y
A
TX
Y
TX23091 2016
72265
-6
53902
AIRW AYS, ASOS
TX
N
N
A
TX
Y
TX53902 2019
72240
-6
53903
AIRW AYS, ASOS
TX
N
N
A
TX
Y
TX53903 2019
72240
-6
53907
AIRW AYS, ASOS
TX
N
N
A
TX
Y
TX53907 2019
72249
-6
53909
AIRW AYS, ASOS
TX
N
N
A
TX
Y
TX53909 2019
72249
-6
53910
AIRW AYS, ASOS
TX
N
N
A
TX
Y
TX53910 2019
72240
-6
53911
AIRW AYS, ASOS
TX
N
N
A
TX
Y
TX53911 2019
72249
-6
53912
AIRW AYS, ASOS
TX
N
N
A
TX
Y
TX53912 2019
72249
-6
53914
AIRWAYS,ASOS,COOP
TX
N
N
A
TX
Y
TX53914 2019
72249
-6
93042
ASOS,COOP
TX
N
Y
A
TX
Y
TX93042 2019
72363
-6
93985
ASOS,COOP
TX
N
Y
A
TX
Y
TX93985 2019
72249
-6
93987
ASOS,COOP
TX
N
N
A
TX
Y
TX93987 2019
72248
-6
23159
ASOS
UT
N
Y
A
UT
Y
UT23159 2019
72376
-7
23176
ASOS
UT
N
Y
A
UT
Y
UT23176 2017
72572
-7
24127
ASOS,COOP
UT
N
Y
A
UT
Y
UT24127 2019
72572
-7
93075
AIRWAYS,ASOS,COOP
UT
N
Y
A
UT
Y
UT93075 2019
72476
-7
93129
AIRWAYS,ASOS,COOP
UT
N
Y
A
UT
Y
UT93129 2019
72388
-8
93141
AIRW AYS, ASOS
UT
N
Y
A
UT
Y
UT93141_2019
72572
-7
60
-------
Surface
WBAN
Type Station
State
tif file
snow
arid
moisture
state
Airport
AERMOD File
Upper Air
WMO
Upper Air
UTC
94030
ASOS,COOP
UT
N
Y
A
UT
Y
UT94030 2019
72476
-7
94128
AIRWAYS,ASOS
UT
N
Y
A
UT
Y
UT94128 2019
72572
-7
13728
ASOS,COOP
VA,
NC
N
N
A
VA
Y
VA13728 2019
72317
-5
13733
ASOS,COOP
VA
N
N
A
VA
Y
VA13733 2019
72318
-5
13737
ASOS, COOP, USHCN
VA
N
N
A
VA
Y
VA13737 2019
72402
-5
13740
ASOS,COOP
VA
N
N
A
VA
Y
VA13740 2019
72403
-5
13741
ASOS,COOP
VA
N
N
A
VA
Y
VA13741 2019
72318
-5
13743
ASOS,COOP
MD,
DC,
VA
N
N
A
VA
Y
VA13743 2019
72403
-5
93736
AIRWAYS, ASOS
VA
N
N
A
VA
Y
VA93736 2019
72403
-5
93738
ASOS,COOP
VA
N
N
A
VA
Y
VA93738 2019
72403
-5
93739
ASOS,COOP
VA
N
N
A
VA
Y
VA93739 2019
72402
-5
93741
ASOS
VA
N
N
A
VA
Y
VA93741 2019
72402
-5
93773
AIRWAYS, ASOS
VA
N
N
A
VA
Y
VA93773 2019
72402
-5
93775
AIRWAYS, ASOS
VA
Y
N
A
VA
Y
VA93775 2016
72403
-5
14742
ASOS, COOP, USHCN
VT
Y
N
A
VT
Y
VT14742 2019
72518
-5
54740
AIRWAYS, ASOS
VT
Y
N
A
VT
Y
VT54740 2019
72518
-5
54771
AIRWAYS, ASOS
VT
Y
N
A
VT
Y
VT54771 2019
72518
-5
54781
AIRWAYS, ASOS
VT, NY
Y
N
A
VT
Y
VT54781 2019
72518
-5
94705
AIRWAYS,ASOS,COOP
VT
Y
N
A
VT
Y
VT94705 2019
72518
-5
24110
AIRWAYS, ASOS
WA
N
N
A
WA
Y
WA24110 2019
72786
-8
24141
AIRWAYS,ASOS,COOP
WA
Y
N
A
WA
Y
WA24141 2019
72786
-8
24157
AIRSAMPLE,ASOS,COOP,
USHCN
WA
Y
N
A
WA
Y
WA24157 2019
72786
-8
24160
AIRWAYS, ASOS
WA
N
N
A
WA
Y
WA24160 2019
72786
-8
24217
AIRWAYS, ASOS
WA
N
N
A
WA
Y
WA24217_2016
72797
-8
61
-------
Surface
WBAN
Type Station
State
tif file
snow
arid
moisture
state
Airport
AERMOD File
Upper Air
WMO
Upper Air
UTC
24219
AIRW AYS, ASOS,COOP
WA,
OR
Y
N
A
WA
Y
WA24219 2016
72694
-8
24220
AIRWAYS,ASOS
WA
N
N
A
WA
Y
WA24220 2019
72786
-8
24222
AIRW AYS, ASOS
WA
N
N
A
WA
Y
WA24222 2019
72797
-8
24227
ASOS,COOP
WA
N
N
A
WA
Y
WA24227 2019
72797
-8
24233
ASOS,COOP
WA
N
N
A
WA
Y
WA24233 2019
72797
-8
24234
AIRW AYS, ASOS
WA
N
N
A
WA
Y
WA24234 2016
72797
-8
24243
ASOS,COOP
WA
N
N
A
WA
Y
WA24243 2019
72694
-8
94119
AIRWAYS,ASOS,COOP
WA
Y
N
A
WA
Y
WA94119 2019
72786
-8
94129
AIRW AYS, ASOS
WA
N
N
A
WA
Y
WA94129 2019
72786
-8
94176
AIRW AYS, ASOS
WA
Y
N
A
WA
Y
WA94176 2019
72786
-8
94197
ASOS
WA
Y
N
A
WA
N
WA94197 2016
71203
-8
94225
ASOS,COOP
WA
N
N
A
WA
Y
WA94225 2019
72797
-8
94227
ASOS,COOP
WA
N
N
A
WA
Y
WA94227 2019
72797
-8
94239
ASOS,COOP
WA
Y
N
A
WA
Y
WA94239 2019
72786
-8
94240
ASOS,COOP
WA
N
N
A
WA
Y
WA94240 2019
72797
-8
94248
AIRW AYS, ASOS
WA
N
N
A
WA
Y
WA94248 2019
72797
-8
94266
AIRW AYS, ASOS
WA
N
N
A
WA
Y
WA94266 2019
72797
-8
94274
AIRW AYS, ASOS
WA
N
N
A
WA
Y
WA94274 2019
72797
-8
94276
AIRW AYS, ASOS
WA
N
N
A
WA
Y
WA94276 2019
72797
-8
94298
AIRW AYS, ASOS
WA,
OR
N
N
A
WA
Y
WA94298 2019
72694
-8
4803
AIRWAYS,ASOS,COOP
Wl
Y
N
A
Wl
Y
WI04803 2019
72645
-6
4826
AIRW AYS, ASOS
Wl
Y
N
A
Wl
Y
WI04826 2019
72645
-6
4840
AIRW AYS, ASOS
Wl
Y
N
A
Wl
Y
WI04840 2019
72645
-6
4841
AIRW AYS, ASOS
Wl
Y
N
A
Wl
Y
WI04841 2019
72645
-6
4845
AIRW AYS, ASOS
Wl
Y
N
A
Wl
Y
WI04845 2019
72645
-6
14837
ASOS,COOP,WXSVC
Wl
Y
N
A
Wl
Y
Wl 14837 2019
72645
-6
14839
AIRSAMPLE,ASOS,COOP
Wl
Y
N
A
Wl
Y
WI14839_2019
72645
-6
62
-------
Surface
WBAN
Type Station
State
tif file
snow
arid
moisture
state
Airport
AERMOD File
Upper Air
WMO
Upper Air
UTC
14897
ASOS,COOP
Wl
Y
N
A
Wl
Y
Wl 14897 2019
72645
-6
14898
ASOS,COOP,WXSVC
Wl
Y
N
A
Wl
Y
Wl 14898 2019
72645
-6
14920
AIRW AYS, ASOS,COOP
Wl,
MN
Y
N
A
Wl
Y
WI14920 2019
74455
-6
14921
ASOS
Wl
Y
N
A
Wl
Y
WI14921 2019
74455
-6
14991
ASOS,COOP,WXSVC
Wl
Y
N
A
Wl
Y
WI14991 2019
72649
-6
94818
AIRW AYS, ASOS
Wl
Y
N
A
Wl
Y
WI94818 2019
72645
-6
94855
AIRW AYS, ASOS
Wl
Y
N
A
Wl
Y
WI94855 2019
72645
-6
94929
AIRW AYS, ASOS
Wl
Y
N
A
Wl
Y
WI94929 2019
72649
-6
94973
AIRW AYS, ASOS
Wl
Y
N
A
Wl
Y
WI94973 2019
72649
-6
94985
AIRW AYS, ASOS
Wl
Y
N
A
Wl
Y
WI94985 2019
72645
-6
94994
AIRW AYS, ASOS
Wl
Y
N
A
Wl
Y
WI94994 2019
74455
-6
3802
AIRW AYS, ASOS
WV
Y
N
A
WV
Y
WV03802 2018
72520
-5
3804
ASOS,COOP
WV
Y
N
A
WV
Y
WV03804 2018
72520
-5
3859
ASOS,COOP
WV,
VA
Y
N
A
WV
Y
WV03859 2019
72318
-5
3860
ASOS,COOP
WV,
OH,
KY
N
N
A
WV
Y
WV03860 2018
72426
-5
3872
ASOS,COOP
WV
Y
N
A
WV
Y
WV03872 2019
72318
-5
13729
ASOS,COOP
WV
Y
N
A
WV
Y
WV13729 2017
72520
-5
13734
ASOS,COOP,USHCN
WV
N
N
A
WV
Y
WV13734 2019
72403
-5
13736
AIRWAYS,ASOS,COOP
WV
Y
N
A
WV
Y
WV13736 2019
72520
-5
13866
ASOS,COOP
WV
Y
N
A
WV
Y
WV13866 2018
72318
-5
14894
AIRW AYS, ASOS
OH,
WV
N
N
A
WV
Y
WV14894 2019
72520
-5
4111
AIRW AYS, ASOS
WY,
UT
N
Y
A
WY
Y
WY04111 2019
72572
-7
24018
ASOS,COOP,USHCN,WXSVC
WY
Y
N
A
WY
Y
WY24018_2019
72469
-7
63
-------
Surface
WBAN
Type Station
State
tif file
snow
arid
moisture
state
Airport
AERMOD File
Upper Air
WMO
Upper Air
UTC
24021
ASOS,COOP,WXSVC
WY
N
Y
A
WY
Y
WY24021 2019
72672
-7
24022
ASOS,COOP,USHCN
WY
Y
N
A
WY
Y
WY24022 2019
72469
-7
24027
AIRW AYS, ASOS,AWOS,COOP,
USHCN,WXSVC
WY
N
Y
A
WY
Y
WY24027 2019
72672
-7
24029
ASOS,COOP
WY
N
Y
A
WY
Y
WY24029 2019
72672
-7
24048
AIRW AYS, ASOS
WY
N
Y
A
WY
Y
WY24048 2019
72672
-7
24057
ASOS,COOP
WY
N
Y
A
WY
Y
WY24057 2019
72672
-7
24061
AIRW AYS, ASOS, WXSVC
WY
Y
N
A
WY
Y
WY24061 2019
72672
-7
24062
AIRW AYS, ASOS
WY
Y
N
A
WY
Y
WY24062 2019
72672
-7
24089
AIRW AYS,ASOS,COOP,WXSVC
WY
N
Y
A
WY
Y
WY24089 2019
72672
-7
24164
AIRW AYS, ASOS
WY
N
Y
A
WY
Y
WY24164 2019
72672
-7
94023
AIRW AYS, ASOS
WY
N
Y
A
WY
Y
WY94023 2019
72662
-7
94053
AIRWAYS,ASOS,COOP
WY
N
N
A
WY
Y
WY94053 2019
72662
-7
94054
AIRW AYS, ASOS
WY
N
Y
A
WY
Y
WY94054 2019
72672
-7
94057
AIRWAYS,ASOS,COOP
WY
N
Y
A
WY
Y
WY94057_2019
72662
-7
64
-------
Appendix 4
Dispersion Model Receptor Revisions and Additions
-------
Dispersion Model Receptor Revisions and Additions
SOCMI Source Category
To estimate ambient concentrations for evaluating long-term exposures, the HEM4 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 this source category, the table below contains each census block for which we
changed the centroid location because it was on facility property or was otherwise not
representative of the residential locations in the block. The table also contains the locations of
additional receptors that were included to represent residential locations nearer to the facility
than the block centroid.
-------
Dispersion Model Receptor Revisions:
CENSUS Block
Updated
Rec Lat
Updated
Rec Long
Note
480396627002026
29.084941
-95.75106
Relocate receptor to
represent house
locations
480396627002018
29.089818
-95.757954
Relocate receptor to
represent house
locations
482013205001049
29.705007
-95.242179
Relocate receptor to
represent house
locations
482013114001021
29.720037
-95.276043
Relocate receptor to
represent house
locations
484090107001245
27.918043
-97.23186
Relocate receptor to
represent house
locations
481830015004055
32.452473
-94.703679
Relocate receptor to
represent house
locations
481830015004056
32.453286
-94.704889
Relocate receptor to
represent house
locations
481830015004057
32.455618
-94.704426
Relocate receptor to
represent house
locations
481830015004058
32.455202
-94.703865
Relocate receptor to
represent house
locations
482030206043061
32.45343
-94.679932
Relocate receptor to
represent house
locations
483610205001005
30.067959
-93.751198
Relocate receptor to
represent house
locations
483610205001005
30.067959
-93.751198
Relocate receptor to
represent house
locations
482013416003090
29.590017
-95.026482
Relocate receptor to
represent house
locations
482013431001011
29.651976
-95.085045
Relocate receptor to
represent house
locations
-------
CENSUS Block
Updated
Rec Lat
Updated
Rec Long
Note
482013402031000
29.631615
-95.105607
Relocate receptor to
represent house
locations
482013430003032
29.653099
-95.090192
Relocate receptor to
represent house
locations
482013431002019
29.652789
-95.07129
Relocate receptor to
represent house
locations
482013432002007
29.65273
-95.07432
Relocate receptor to
represent house
locations
482013431001000
29.653116
-95.076446
Relocate receptor to
represent house
locations
480570003002016
28.679148
-96.549526
Relocate receptor to
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-------
CENSUS Block
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-------
CENSUS Block
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-------
CENSUS Block
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480396627002037
Make Zero Population
and Remove
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Make Zero Population
and Remove
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Make Zero Population
and Remove
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and Remove
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Make Zero Population
and Remove
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Make Zero Population
and Remove
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Make Zero Population
and Remove
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Make Zero Population
and Remove
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Make Zero Population
and Remove
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Make Zero Population
and Remove
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and Remove
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Make Zero Population
and Remove
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Make Zero Population
and Remove
-------
CENSUS Block
Updated
Rec Lat
Updated
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482979501001130
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and Remove
540390122001003
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and Remove
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Make Zero Population
and Remove
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Make Zero Population
and Remove
220510275011011
Make Zero Population
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Make Zero Population
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-------
CENSUS Block
Updated
Rec Lat
Updated
Rec Long
Note
482012534001240
Make Zero Population
and Remove
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Make Zero Population
and Remove
220510275011058
Make Zero Population
and Remove
484090107002082
Make Zero Population
and Remove
220510275011057
Make Zero Population
and Remove
-------
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7228511, 9588611, 17640311
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7228511, 9588611, 17640311
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7228511, 9588611, 17640311
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-------
Appendix 5:
Technical Support Document for Acute Risk Screening Assessment
1
-------
Table of Contents
I. Analysis of Data on Short-Term Emission Rates Relative
to Long-Term Emission Rates 3
II. Basis for Reasonable Worst-Case Air Dispersion Conditions 20
2
-------
I. Analysis of Data on Short-Term Emission Rates Relative to Long-Term
Emission Rates
Ted Palma
Roy Smith
EPA/OAQPS/ATAG
Revised September 19, 2011
1. Introduction
1.1. The problem
The process of listing hazardous air pollutants (HAPs) provided by the Clean Air Act (CAA,
section 112(b)(2)) explicitly includes acute toxicity as a listing criterion. For this reason, in
addition to chronic exposures, EPA considers acute exposures in risk-based decision-making for
the HAP regulatory program. Estimating acute exposures via dispersion modeling requires input
data on hourly meteorological conditions (available for most areas of the US) and short-term
emission rates of individual facilities (almost universally absent from the National Emissions
Inventory (NEI), the Toxic Release Inventory (TRI), and state emission databases).
Lacking short-term emission rates, we must estimate peak short-term rates based on annual
average rates, which are available. For Risk and Technology Review (RTR) rulemakings, we
have assumed that the 1-hour emission rate for each facility could exceed the annual average
hourly emission rate by up to tenfold for most sources, and further assumed that this emission
spike could coincide with worst-case meteorological conditions and the presence of a human
receptor at the facility boundary, as a means of screening for potentially significant acute
exposures.
In a consultation on the "RTR Assessment Plan", a panel of the EPA's Science Advisory Board
(SAB), several reviewers questioned the appropriateness of these factors; some even suggested
that this tenfold assumption may underestimate actual maximum short-term emissions for some
facilities, and thereby also underestimate maximum acute risks. The SAB recommended an
analysis of available short-term emissions data for HAP to test this assumption. This analysis
responds to that SAB recommendation and attempts to test the protectiveness of the two-tenfold
assumption using a database of "event emissions" collected from facilities in the Houston-
Galveston area, to compare events representative of HAP releases to long-term release rates. We
welcome comments from the public on the methods used and the conclusions reached by this
analysis.
2. Methods
2.1. Texas Commission on Environmental Quality event emissions database
The Texas Commission on Environmental Quality (TCEQ) collects emissions data using online
reporting required of any facility releasing 100 pounds or more of a listed chemical (primarily
ozone-forming VOCs) during a non-routine event. The TCEQ data are intended to improve the
state's knowledge of how short-term releases affect tropospheric ozone levels in that area. The
database we utilized in our analysis was a subset of the TCEQ data covering emission events that
3
-------
occurred in an eight-county area in eastern Texas during a 756-day period between January 31,
2003, and February 25, 2005.
The complete emissions event data were obtained in April 2007 from Cynthia Folsom Murphy, a
research scientist with the University of Texas at Austin (UTA) Center for Energy and
Environmental Resources. The data were provided in four Excel spreadsheets generated from an
original MS Access file. We used these Excel files to reconstruct a MS Access database in order
to facilitate selection of a representative subset of records for this analysis.
Although some of the released substances were HAPs, this was incidental to the database's
primary purpose of enhancing the TCEQ's knowledge of photochemical activity. Thus, more
than 80% of the released mass was ethene and propene, neither of which are HAPs. The database
included release events caused by accidents, equipment failures, maintenance, startup, and
shutdown. It also contained facility names, information on amounts of individual compounds
released. To provide a basis for comparing the event releases with "typical" emissions, the UTA
staff included total VOC emissions data for each facility for calendar year 2004, obtained from
the EPA Toxic Release Inventory (TRI). The database did not contain any records for facilities
that did not experience any reportable events during this period.
2.2. Data filtering
Because the event release data were intended for modeling short-term releases of ozone-
producing VOCs, the database includes releases from accidents (which are regulated under
section 112(r) of the CAA and are therefore not considered in residual risk assessments) and
releases of light hydrocarbon compounds that are not HAPs and are much more volatile than
most HAPs. This intent of this analysis, on the other hand, was to evaluate short-term releases of
HAPs due to normal process variability or scheduled startups, shutdowns, and maintenance,
relative to long-term release rates. Because the full emission events database was not
representative of likely HAP emissions normally considered under the residual risk program, we
filtered the release data as follows in an attempt to improve its representativeness:
1. Hydrocarbons of C5 or less were dropped, except that all HAPs (including non-VOCs)
were retained regardless of molecular structure;
2. Accidental releases were dropped, but all others (including startup, shutdown, and
maintenance) were retained;
3. Only facilities whose long-term VOC releases exceeded 0.068 tons per day (25 tons per
year) were retained, to approximate the population of facilities likely to be subject to
residual risk standards (i.e., major facilities);
4. A few release records had to be dropped because their facility numbers did not link to any
facility in the database;
5. A few facilities had to be dropped because the database did not include their 2004 TRI
VOC release information.
4
-------
2.3. Analysis
Annual VOC emissions and emission event release data were both converted to lb/hr. In order to
conform to our atmospheric dispersion models, which estimate ambient concentrations for
periods of 1 hour or more, amounts released during events shorter than 1 hour were assigned to
the whole hour. For example, a release of 100 lb in ten minutes was converted to 100 lb/hr.
Events longer than 1 hour were converted normally, e.g., a release of 100 lb in 120 minutes was
converted to 50 lb/hr. The event release rates for individual compounds were summed, yielding a
total release rate for each event. This total release rate for each event was divided by the annual
VOC release rate for the facility to derive the ratio of peak-to-mean emission rate for the event.
3. Results and Discussion
3.1. Database filtering
The original database contained 505 individual contaminants, including multiple redundancies.
These redundancies did not affect this analysis, so we did not resolve them. After filtering out
light, non-HAP, VOCs, 317 contaminants remained (Table 1).
The database contained release records for 150 unique facilities. Of these, 48 facilities (Table 2)
were major VOC emitters that reported releases of at least one of the contaminants in Table 1.
The database contained 3641 individual release events reported by the original 150 facilities. Of
these, 319 events involved a Table 1 contaminant released by a Table 2 facility during startup,
shutdown, or maintenance. For evaluating short-term releases for residual risk assessments, these
319 events comprise the most representative subset of the full database.
3.2. Descriptive statistics
For this subset of emission events, ratios of event release rate to long-term release rate varied
from 0.00000004 to 74. Distribution statistics appear in Tables 3 and 4. The 99th percentile ratio
was 9 (i.e., an event release rate nine times the long-term average). Only 3 ratios exceeded a
factor of 10, and of these only one exceeded 11. The full cumulative probability density of the
ratios is shown in Figure 1.
Figure 2 shows the relationship between ratio and event duration. As expected, the ratio
declined as duration increased. Only 18 events lasted less than 2 hours, but these events
produced the three highest ratios. Figure 3 is a similar ratio vs. duration plot, but with duration
as a percentage of total time. Only 35 events exceeded 1% of the total period covered by the
database. Figure 4 shows the relationship between ratio and total amount released, and suggests
that the highest ratios were produced by facilities whose routine VOC emissions were relatively
small (all less than 200 lbs/hr). Thus, the events themselves also tended to be relatively small in
absolute terms. This suggests that for larger emitting facilities that a factor of ten may be overly
protective and for at least the key source sectors represented by the study (petroleum and
chemical sectors), that a factor of twofold for facilities with VOC emissions greater than 200
lb/hr may be more appropriate.
3.3. Discussion
5
-------
These results suggest that the tenfold ratio assumption for short-term releases is protective, and
that the facilities for which it may underestimate event releases may tend to be smaller emitters.
However, this analysis is limited in the following ways by the nature of the database and the
filtering that we applied:
1. The only long-term release data available from the database were total VOC emissions
for 2004. Ideally, we would have preferred to have routine release rates for each
individual contaminant. However, retrieving these data from other sources and linking
them to this database was not feasible.
2. Removing VOCs that are not representative of HAPs, and comparing the releases against
all VOCs, would tend to underestimate the true ratios. This effect could be quantitatively
large.
3. Retaining HAPs that are not VOCs (such as toxic metals) and including them in the total
to be compared against all VOCs would tend to overestimate the true ratios. The size of
this effect is not known but seems likely to be less than for (2) above.
4. The database contains only facilities that had at least one release event during the
reporting period. The number of facilities in the statistical population that did not
experience an event is not known. The lack of data for these facilities (whose ratios in
this analysis would have been zero) would cause the descriptive statistics to be skewed
toward an overestimate. The size of this effect is unknown.
Table 1. Event emissions in the Houston-Galveston area.
Representative contaminants included in the analysis, selected because
they are either HAPs or VOCs with more than 5 carbon atoms. (These
data were retrieved directly from the original database, which included
multiple redundancies that did not affect the analysis and were left
intact.)
Contaminant
HAP
CAS
SAROAD
2-Methyloctane
I No
3221-61-2
90008
2-Methylpentane
No
107-83-5
43229
2-methylhexane
No
591-76-4
43263
2-Methylpentane
No
107-83-5
43229
2,2,3-T rimethylpentane
No
564-02-3
2,2,4-T rimethylpentane
Yes
540-84-1
43250
dimethyl butane
No
75-83-2
43291
2,3-Dimethylbutane
No
79-29-8
43276
2,3,4-T rimethylpentane
No
565-75-3
43252
2,3-Dimethylbutane
No
79-29-8
43276
2,4-Dimethylpentane
No
108-08-7
43247
2-methylheptane
No
592-27-8
43296
2-methylhexane
No
591-76-4
43263
2-Methylpentane
No
107-83-5
43229
3-Methylhexane
No
589-34-4
43295
3-Methylpentane
No
96-14-0
43230
6
-------
Table 1. Event emissions in the Houston-Galveston area.
Representative contaminants included in the analysis, selected because
they are either HAPs or VOCs with more than 5 carbon atoms. (These
data were retrieved directly from the original database, which included
multiple redundancies that did not affect the analysis and were left
intact.)
Contaminant
HAP
CAS
SAROAD
3-Methylhexane
No
589-34-4
43295
3-Methylpentane
No
96-14-0
43230
3-Methylheptane
No
589-81-1
43253
3-Methylhexane
No
589-34-4
43295
3-Methylpentane
No
96-14-0
43230
Acetaldehyde
Yes
75-07-0
43503
Acetic Acid
No
64-19-7
43404
Acetonitrile
Yes
75-05-8
70016
Acetophenone
Yes
98-86-2
Acrolein
Yes
107-02-8
43505
Acrylic acid
Yes
79-10-7
43407
Acrylonitrile
Yes
107-13-1
43704
alkylphenol
No
none
Benzene
Yes
71-43-2
45201
Benzofalanthracene
Yes
56-55-3
46716
Benzo[a]pyrene
Yes
50-32-8
46719
Benzofblfluoranthene
Yes
205-99-2
46717
Biphenyl
Yes
92-52-4
45226
Butanol
No
35296-72-1
Butyl Acrylate
No
141-32-2
43440
t-Butyl Alcohol
No
75-65-0
43309
butylcyclohexane
No
1678-93-9
90101
Butyraldehyde
No
123-72-8
43510
C9 Aromatics
No
none
Naphthalene
Yes
91-20-3
46701
Nonane
No
111-84-2
43235
C9+
No
none
Carbon tetrachloride
Yes
56-23-5
43804
Carbonyl Sulfide
Yes
463-58-1
43933
Chloral
No
75-87-6
Trichloromethane
Yes
67-66-3
43803
Chlorothalonil
No
1897-45-6
Petroleum
No
8002-05-9
Petroleum
No
8002-05-9
Cumene
Yes
98-82-8
45210
Cyclohexane
No
110-82-7
43248
Cyclohexanol
No
108-93-0
43317
Cyclohexanone
No
108-94-1
43561
Cyclohexanone
No
108-94-1
43561
Decane
No
124-18-5
43238
Decane
No
124-18-5
43238
-------
Table 1. Event emissions in the Houston-Galveston area.
Representative contaminants included in the analysis, selected because
they are either HAPs or VOCs with more than 5 carbon atoms. (These
data were retrieved directly from the original database, which included
multiple redundancies that did not affect the analysis and were left
intact.)
Contaminant
HAP
CAS
SAROAD
1,2-Dichloroethane
No
107-06-2
43815
Diethylbenzene (mixture)
No
25340-17-4
45106
Methyl Ether
No
115-10-6
43350
Dimethylcyclohexane
No
27195-67-1
98059
Dimethylcyclopentane
No
28729-52-4
90064
Dimethylcyclopentane
No
28729-52-4
90064
Dimethyl formamide
Yes
68-12-2
43450
Dimethylhexane
No
28777-67-5
90067
Dimethyl pentane
No
38815-29-1
90063
Epichlorohydrin
Yes
106-89-8
43863
Ethyl Alcohol
No
64-17-5
43302
Ethyl Acrylate
Yes
140-88-5
43438
Ethyl Alcohol
No
64-17-5
43302
Ethyl Benzene
Yes
100-41-4
45203
Ethyl Chloride
Yes
75-00-3
43812
Ethylcyclohexane
No
1678-91-7
43288
ethylacetylene
No
107-00-6
43281
Ethyl Benzene
Yes
100-41-4
45203
Ethylene Oxide
Yes
75-21-8
43601
ethylmethylbenzene
No
25550-14-5
45104
formaldehyde
Yes
50-00-0
43502
Furfural
No
98-01-1
45503
straight-run middle distillate
No
64741-44-2
Gasoline
No
86290-81-5
Gasoline
No
86290-81-5
Heavy Olefins
No
none
n-Heptane
No
142-82-5
43232
n-Heptane
No
142-82-5
43232
Heptylene
No
25339-56-4
hexane
Yes
110-54-3
43231
hexane
Yes
110-54-3
43231
2-Methyl pentane
No
107-83-5
43229
hexane
Yes
110-54-3
43231
Hexene
No
25264-93-1
43289
lndeno[1,2,3-cd]pyrene
Yes
193-39-5
46720
Isobutyraldehyde
No
78-84-2
43511
2-Methyl-1-propanol
No
78-83-1
43306
2-Methyl-1-propanol
No
78-83-1
43306
Isobutyraldehyde
No
78-84-2
43511
Isoheptanes (mixture)
No
31394-54-4
43106
2-Methyl pentane
No
107-83-5
43229
-------
Table 1. Event emissions in the Houston-Galveston area.
Representative contaminants included in the analysis, selected because
they are either HAPs or VOCs with more than 5 carbon atoms. (These
data were retrieved directly from the original database, which included
multiple redundancies that did not affect the analysis and were left
intact.)
Contaminant
HAP
CAS
SAROAD
2,2,4-T rimethylpentane
No
540-84-1
43250
2,2,4-T rimethylpentane
No
540-84-1
43250
Isopar E
No
Isoprene
No
78-79-5
43243
2-Propanol
No
67-63-0
43304
2-Propanol
No
67-63-0
43304
Cumene
Yes
98-82-8
45210
Isopropylcyclohexane
No
696-29-7
90128
Diisopropyl ether
No
108-20-3
85005
Kerosene
No
64742-81-0
Methyl ethyl ketone
No
78-93-3
43552
Methyl isobutenyl ketone
Yes
141-79-7
Methanol
Yes
67-56-1
43301
Methyl Acetylene
No
74-99-7
43209
Cresol
Yes
1319-77-3
45605
Methyl Chloride
Yes
74-87-3
43801
methyl cyclohexane
No
108-87-2
43261
Methyl ethyl ketone
No
78-93-3
43552
lodomethane
No
74-88-4
86025
Methyl Mercaptan
No
74-93-1
43901
methyl cyclohexane
No
108-87-2
43261
Methylcyclopentane
No
96-37-7
43262
2-Methyldecane
No
6975-98-0
98155
Methylheptane
No
50985-84-7
90045
2-methylheptane
No
592-27-8
43296
2-Methyl nonane
No
871-83-0
90047
Tert-butyl methyl ether
No
1634-04-4
43376
meta-xylene
No
108-38-3
45205
Nonane
No
111-84-2
43235
Naphtha
No
8030-30-6
45101
Naphthalene
Yes
91-20-3
46701
Naphtha
No
8030-30-6
45101
Naphthalene
No
91-20-3
46701
Butyl acetate
No
123-86-4
43435
Butyraldehyde
No
123-72-8
43510
Nonane
No
111-84-2
43235
Nonane
No
111-84-2
43235
Octadecene
No
27070-58-2
n-Octane
No
111-65-9
43233
Octene (mixed isomers)
No
25377-83-7
ortho-xylene
No
95-47-6
45204
-------
Table 1. Event emissions in the Houston-Galveston area.
Representative contaminants included in the analysis, selected because
they are either HAPs or VOCs with more than 5 carbon atoms. (These
data were retrieved directly from the original database, which included
multiple redundancies that did not affect the analysis and were left
intact.)
Contaminant
HAP
CAS
SAROAD
Parathion
Yes
56-38-2
4-Aminohippuric Acid
No
61-78-9
Phenol
Yes
108-95-2
45300
Silicone
No
63148-62-9
Naphtha
No
8030-30-6
45101
Naphtha
No
8030-30-6
45101
Polyethylene
No
9002-88-4
Poly(lsobutylene)
No
9003-27-4
Chloromethyl pivalate
No
18997-19-8
Process fuel gas
No
none
Propionic Acid
No
79-09-4
43405
Propylene oxide
No
75-56-9
43602
para-xylene
No
106-42-3
45206
Styrene
Yes
100-42-5
45220
Sulfolane
No
126-33-0
t-Butyl Alcohol
No
75-65-0
43309
t-Butyl Alcohol
No
75-65-0
43309
tert-butyl hydroperoxide
No
75-91-2
Toluene
Yes
108-88-3
45202
Aqualyte(TM), LSC cocktail
No
25551-13-7
45107
1,3,4-Trimethylbenzene
No
95-63-6
45208
trimethylcyclopentane
No
30498-64-7
98058
trimethylpentane
No
29222-48-8
90092
Undecane
No
1120-21-4
43241
Vinyl acetate
Yes
108-05-4
43453
Vinyl acetate
Yes
108-05-4
43453
Vinyl chloride
Yes
75-01-4
43860
vinyl resin
No
none
Vinylcyclohexane
No
695-12-5
xylenes
Yes
1330-20-7
45102
xylenes
Yes
1330-20-7
45102
meta-xylene
Yes
108-38-3
45205
ortho-xylene
Yes
95-47-6
45204
para-xylene
Yes
106-42-3
45206
Mineral spirits
No
64475-85-0
43118
Propylene glycol
No
57-55-61
43369
Vinyl chloride
Yes
75-01-4
43860
1-Decene
No
872-05-9
90014
2-Ethyl-1-hexanol
No
104-76-7
43318
2-Pyrrolidone
No
616-45-5
Aromatic
No
none
-------
Table 1. Event emissions in the Houston-Galveston area.
Representative contaminants included in the analysis, selected because
they are either HAPs or VOCs with more than 5 carbon atoms. (These
data were retrieved directly from the original database, which included
multiple redundancies that did not affect the analysis and were left
intact.)
Contaminant
HAP
CAS
SAROAD
Decene
No
25339-53-1
90014
2-N,N-Dibutylaminoethanol
No
102-81-8
86007
Diisopropanolamine
No
110-97-4
86004
N,N-Dimethylethanolamine
No
108-01-0
84004
trifluoroethane
No
27987-06-0
2,2'-Oxybisethanol
No
111-46-6
43367
Hydrocarbons
No
none
Methyl Formate
No
107-31-3
43430
Isopropylamine
No
75-31-0
86014
n-Butanol
No
71-36-3
43305
Polypropylene glycol ether
No
N-Vinyl-2-Pyrrolidinone
No
88-12-0
1,1 -Di(t-Amylperoxy)
Cyclohexane
No
15667-10-4
1,2,3-Trimethyl-4-ethyl benzene
No
none
2-Methyldecane
No
6975-98-0
98155
2-methylheptane
No
592-27-8
43296
2-Methyl nonane
No
871-83-0
90047
2,5-Dimethylhexane-2,5-
dihydroperoxide
No
3025-88-5
Butyl ether
No
142-96-1
43372
1,2-Dichloroethane
Yes
107-06-2
43815
Hydrindene
No
496-11-7
98044
Methylheptane
No
50985-84-7
90045
methyl methacrylate
No
80-62-6
43441
Naphtha
No
8030-30-6
45101
hexane
Yes
110-54-3
43231
tert-amyl hydroperoxide
No
3425-61-4
1,3,4-Trimethylbenzene
No
95-63-6
45208
n-Butanol
No
71-36-3
43305
2-Butoxy ethanol
Yes
111-76-2
43308
hexane
Yes
110-54-3
43231
cycloheptane
No
291-64-5
43115
n-Heptane
No
142-82-5
43232
n-Octane
No
111-65-9
43233
Hexyl Carbitol
No
112-59-4
Nonene
No
27215-95-8
Silane, ethenyltrimethoxy
No
2768-02-7
tetrahydrofuran
No
109-99-9
70014
Vinyl chloride
Yes
75-01-4
43860
Methyl Formate
No
107-31-3
43430
Phenyl ether
No
101-84-8
-------
Table 1. Event emissions in the Houston-Galveston area.
Representative contaminants included in the analysis, selected because
they are either HAPs or VOCs with more than 5 carbon atoms. (These
data were retrieved directly from the original database, which included
multiple redundancies that did not affect the analysis and were left
intact.)
Contaminant
HAP
CAS
SAROAD
phosgene
Yes
75-44-5
1,2-Dichloroethane
No
107-06-2
43815
2-Butoxy ethanol
Yes
111-76-2
43308
Gasoline
No
86290-81-5
1-Tridecanol
No
112-70-9
1,2,4-Trichlorobenzene
Yes
120-82-1
45208
2- (2- B utoxyet h oxy) ethanol
Yes
112-34-5
43312
2,3,4-trihydroxybenzophenone
Ester
No
1143-72-2
Methyl n-amyl ketone
No
110-43-0
43562
4,4-Cyclohexylidenebis[phenoll
No
843-55-0
Anisole
No
100-66-3
2-Butoxy ethanol
Yes
111-76-2
43308
Cresol-Formaldehyde novolac
Resin
No
proprietary
Decane
No
124-18-5
43238
gamma-Butyrolactone
No
96-48-0
Dimethyl pentane
No
38815-29-1
90063
Dodecyl Benzenesulfonic Acid
No
27176-87-0
Ethanol Amine
No
141-43-5
43777
ethyl lactate
No
687-47-8
Hexamethyldisilazane
No
999-97-3
Methyl ethyl ketone
No
78-93-3
43552
Cresol
Yes
1319-77-3
45605
Naphthalene Sulfonic Acid Resin
No
Naphthalene Sulfonic Acid Resin
No
n-Butanol
No
71-36-3
43305
Decane
No
124-18-5
43238
1 -Methyl-2-pyrrolidinone
No
872-50-4
70008
Pentyl Ester Acetic Acid
No
Phenol Formaldehyde Resin,
Novolac
No
Phenol Formaldehyde Resin,
Novolac
No
Propylene Glycol Monomethyl
Ether
No
107-98-2
70011
Pyrocatechol
No
120-80-9
Carbon Disulfide
Yes
75-15-0
43934
Hexene
No
592-41-6
43245
VOC
No
none
Methacrylic acid
No
79-41-4
84009
Methyl 3-hydroxybutyrate
No
1487-49-6
t-Butyl Alcohol
No
75-65-0
43309
12
-------
Table 1. Event emissions in the Houston-Galveston area.
Representative contaminants included in the analysis, selected because
they are either HAPs or VOCs with more than 5 carbon atoms. (These
data were retrieved directly from the original database, which included
multiple redundancies that did not affect the analysis and were left
intact.)
Contaminant HAP CAS SAROAD
methyl valeraldehyde
No
123-15-9
Butyl Methacrylate
No
97-88-1
85008
dipropyl ether
No
111-43-3
n-Propanol
No
71-23-8
43303
Propyl propionate
No
106-36-5
86052
1,2-Epoxybutane
Yes
106-88-7
Methylamine
No
74-89-5
1,1-Dimethylcyclohexane
No
590-66-9
1,1-Dimethylcyclopentane
No
1638-26-2
2-Methylpentane
No
107-83-5
43229
dimethyl butane
No
75-83-2
43291
2,3,3-T rimethylpentane
No
560-21-4
2,3-Dimethylhexane
No
584-94-1
2,3-Dimethylpentane
No
565-59-3
2,4-Dimethylhexane
No
589-43-5
2,5-Dimethyl-hexane
No
592-13-2
2-Butoxy ethanol
Yes
111-76-2
43308
2-mercaptoethanol
No
60-24-2
Bisphenol A
No
80-05-7
straight-run middle distillate
No
64741-44-2
4-Vinylcyclohexene
No
100-40-3
straight-run middle distillate
No
64741-44-2
Allyl alcohol
No
107-18-6
xylenes
Yes
1330-20-7
45102
Naphthalene
Yes
91-20-3
46701
3-Methylethylcyclohexane
No
VOC
No
none
Gasoline
No
86290-81-5
Butyl ether
No
142-96-1
dimethyl butane
No
75-83-2
Dodecene
No
25378-22-7
Styrene
Yes
100-42-5
45220
tetrahydrofuran
No
109-99-9
70014
hexane
Yes
110-54-3
43231
2-Propanol
No
67-63-0
43304
liquified petroleum gas
No
68476-85-7
Methyl acetylene propadiene
No
methyl isobutyl ketone
Yes
108-10-1
Methyl n-amyl ketone
No
110-43-0
43562
Methyl pentane
No
43133-95-5
Tert-butyl methyl ether
Yes
1634-04-4
43376
13
-------
Table 1. Event emissions in the Houston-Galveston area.
Representative contaminants included in the analysis, selected because
they are either HAPs or VOCs with more than 5 carbon atoms. (These
data were retrieved directly from the original database, which included
multiple redundancies that did not affect the analysis and were left
intact.)
Contaminant
HAP
CAS
SAROAD
Toluene
Yes
108-88-3
45202
Mineral oil
No
8012-95-1
Gasoline
No
86290-81-5
2,2-Dimethylpropane
No
463-82-1
43222
n-propylbenzene
No
103-65-1
propylcyclohexane
No
1678-92-8
n-Octane
No
111-65-9
43233
ortho-xylene
No
95-47-6
45204
Gasoline
No
86290-81-5
propylenimine
No
75-55-8
Gasoline
No
86290-81-5
Technical White Oil
No
Total Alkylate - non-speciated
No
Trichloroethylene
Yes
79-01-6
Di(2-ethylhexyl)
peroxydicarbonate
No
16111-62-9
trimethylcyclopentane
No
30498-64-7
98058
Ultraformate
No
4-Vinylcyclohexene
No
100-40-3
14
-------
Table 2. Event emissions in the Houston-Galveston area. Major emitters
reporting at least one release event of a representative substance.
2004 VOC Emission
Company Name Rate (Ib/h)
ATOFINA PETROCHEMICALS LA PORTE PLANT 47.88
BALL METAL BEVERAGE CONTAINER CONROE 24.18
FACILITY
BASF FREEPORT SITE 46.47
BELVIEU ENVIRONMENTAL FUELS 112.3
BOC GROUP CLEAR LAKE BOC GASES PLANT 9.52
BP AMOCO CHEMICAL CHOCOLATE BAYOU PLANT 130.4
BP AMOCO CHEMICAL PASADENA PLANT 36.92
BP AMOCO POLYMERS 57.18
BP PRODUCTS NORTH AMERICA TEXAS CITY 737.4
BP TEXAS CITY CHEMICAL PLANT B 112.2
CELANESE BAY CITY PLANT 17.12
CELANESE CLEAR LAKE PLANT 53.11
CELANESE PASADENA PLANT 5.934
CHEVRON PHILLIPS CEDAR BAYOU PLANT 105.3
CHEVRON PHILLIPS CHEMICAL SWEENY COMPLEX 106.7
CHEVRON PHILLIPS HOUSTON CHEMICAL COMPLEX 215.7
CROWN BEVERAGE PACKAGING 18.05
CROWN CENTRAL PETROLEUM PASADENA PLANT 114.3
CROWN CORK & SEAL 18.10
DEER PARK LIQUID STORAGE TERMINAL 124.8
DOW CHEMICAL LA PORTE SITE 5.902
DOW TEXAS OPERATIONS FREEPORT 203.2
E I DUPONT DE NEMOURS AND COMPANY - LA 51.30
PORTE PLANT
EQUISTAR CHEMICALS CHANNELVIEW COMPLEX 275.4
EQUISTAR CHEMICALS CHOCOLATE BAYOU 84.87
COMPLEX
EQUISTAR CHEMICALS LA PORTE COMPLEX 90.97
EXXON MOBIL CHEMICAL BAYTOWN OLEFINS PLANT 84.73
EXXONMOBIL CHEMICAL BAYTOWN CHEMICAL 313.7
PLANT
EXXONMOBIL CHEMICAL MONT BELVIEU PLASTICS 40.64
PLANT
GOODYEAR HOUSTON CHEMICAL PLANT 85.68
ISP TECHNOLOGIES TEXAS CITY PLANT 22.12
KANEKA TEXAS CORPORATION 20.55
KINDER MORGAN LIQUID TERMINALS PASADENA 913.9
KINDER MORGAN LIQUIDS TERMINALS 132.7
LBC HOUSTON BAYPORT TERMINAL 12.83
LYONDELL CHEMICAL BAYPORT PLANT 30.04
LYONDELL CHEMICAL CHANNELVIEW 74.15
MARATHON ASHLAND PETROLEUM TEXAS CITY 111.8
REFINERY
MOBIL CHEMICAL HOUSTON OLEFINS PLANT 26.29
MORGANS POINT PLANT 31.03
PASADENA PLANT 13.40
15
-------
Table 2. Event emissions in the Houston-Galveston area. Major emitters
reporting at least one release event of a representative substance.
Company Name
2004 VOC Emission
Rate (Ib/h)
SHELL OIL DEER PARK
405.2
SOLUTIA CHOCOLATE BAYOU PLANT
53.09
STOLTHAVEN HOUSTON TERMINAL
7.347
SWEENY COMPLEX
157.1
UNION CARBIDE TEXAS CITY OPERATIONS
174.4
VALERO REFINING TEXAS CITY
260.1
WHARTON GAS PLANT
7.552
Table 3. Frequency distribution for ratio of event
emission rate to long-term emission rate
Bin
Cumulative
Frequency Frequency
1.00E-08
0
0
3.16E-08
0
0
1.00E-07
2
2
3.16E-07
1
3
1.00E-06
0
3
3.16E-06
2
5
1.00E-05
1
6
3.16E-05
2
8
1.00E-04
5
13
3.16E-04
9
22
1.00E-03
15
37
3.16E-03
28
65
1.00E-02
33
98
3.16E-02
41
139
1.00E-01
59
198
3.16E-01
38
236
1.00E+00
33
269
3.16E+00
31
300
1.00E+01
16
316
3.16E+01
2
318
1.00E+02
1
319
3.16E+02
0
319
-------
Table 4. Statistics for ratio of event emission
rate to long-term emission rate
Statistic for Ratio Value
Median 0.043923
75th %ile 0.342655
90th %ile 2.204754
95th %ile 3.344422
96th %ile 3.400832
97th %ile 3.8126
98th %ile 4.790098
99th %ile 8.973897
Max 74.37138
Average 0.815352
Figure 1. Cumulative probability density for ratio of event to routine emission rates.
Cumulative probability of event ratios
250
1.E-06 1.E-05 1.E-04 1.E-03 1.E-02 1.E-01 1.E+00 1.E+01
Ratio of event emission rate to long-term emission rate
17
-------
Figure 2. Relationship between ratio of event to duration emission rate and emission
duration.
Event ratio vs. duration
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Event duration (min)
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Figure 3. Relationship between ratio of event to duration emission rate and emission duration,
percentage of total time.
Event ratio vs. duration
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-------
Figure 4. Relationship between ratio of event to duration emission rate and total amount emitted
during the event.
Event ratio vs. 2004 VOC releases - by event
1.E+02
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E
^ 1.E-05
I
O
S. 1.E-07
10 100
Long-term VOC releases (Ib/hr)
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II. Basis for Reasonable Worst-Case Air Dispersion Conditions
Matthew Woody
EPA/OAQPS/ATAG
May 16, 2019
Introduction
In developing an acute exposure scenario, we estimate 1-hour exposure concentrations through
air dispersion modeling during hours of peak emissions. However, hourly emissions data are not
typically available, and the exact hours of peak emissions are often unknown, making it difficult
to determine the air dispersion conditions to model with the peak emissions. Therefore, we make
assumptions about when peak hourly emissions occur. In a worst-case scenario, peak hourly
emissions would occur during the one hour of the year with the worst-case air dispersion
conditions (i.e., low, continuous wind speeds blowing in a specific direction). However, the
probability of these two events occurring simultaneously is, in most cases, extremely low. For
example, if we select, from the set of data presented in Section I of this document (which
represent accidents, equipment failures, maintenance, startup, and shutdown for facilities in one
state and may not representative all types of peak emission events, e.g., batch processes, across
different source categories), the facility with the greatest number of hours of peak emission
events (i.e., hours where the ratio of the peak short-term emission rate to the long-term emission
rate is greater than 1), we find that the probability of these peak emission events occurring at the
same hour as the worst-case air dispersion conditions is 1 in 200,000 (or a 0.0005% chance).
Alternatively, if we use the average number of hours from all facilities where the ratio of the
peak short-term emission rate to the long-term emission rate is greater than 1, the probability
decreases to 1 in 1,000,000. Finally, if we use only hours when the ratio of the peak short-term
emission rate to the long-term emission rate is 10 or more, the probability decreases further to 1
in 15,000,000. Therefore, using the one hour of worst-case air dispersion conditions would
reflect an exposure scenario with little probability of occurring and therefore likely overestimate
potential exposure events (i.e., estimate false positive acute exposures).
As an alternative approach, we could assume peak hourly emissions occur during mean or
median air dispersion conditions; however, this would likely underestimate potential exposure
events, as approximately half of all modeled acute exposures would be higher than estimated
with this assumption. This scenario would have a much higher probability of occurring (a 1 in 50
chance for the one facility where the ratio of the peak short-term emission rate to the long-term
emission rate is greater than 1 from the set of data in Section I of this document) but would not
necessarily be health protective.
This points to a need to identify air dispersion conditions that would estimate an acute exposure
scenario that: 1) is health protective without overestimating acute exposures (i.e., false positives),
and 2) has a reasonable probability of occurrence.
Methods
To identify reasonable worst-case air dispersion conditions that satisfy the two criteria described
above, air dispersion modeling was performed with AERMOD (vl8081). Unit emissions from a
model plant were modeled along with meteorological input from 824 ASOS meteorological
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stations (see Appendix 3 for a full list of stations), which are located throughout the United
States. The model plant consisted of a single 1 m2 area source located at ground level. Modeling
was performed for two sets of meteorological data, one for the year 2014 and the other for the
year 2016.
Hourly modeling results were then analyzed to determine the distribution of values, with analysis
performed both on each individual model plant output as well as the entire dataset. As emissions
were constant, differences in concentrations are directly attributed to differences in
meteorological (i.e., air dispersion) conditions.
Results
Table 1 provides the average concentrations estimated for all model scenarios, normalized by the
mean. When comparing the hours with the maximum concentrations (i.e., worst-case dispersion)
to the average, the data indicate that the 1-hour worst-case air dispersion conditions, which in
most cases occurred in winter months and the hours just before sunrise (i.e., 6-8 AM LST),
predict a concentration 22.5 times higher than the average. The 99th percentile worst-case
dispersion conditions (i.e., the 88th highest value for a year, out of 8,760 hours) is 11.4 times
higher than the mean.
Table 1. Average metrics for concentrations modeled across all model plants and years, normalized by the
mean concentration.
Metric
Value
Mean
1
Standard Deviation
2.2
90% Percentile
2.8
95% Percentile
5.3
98% Percentile
9.0
99% Percentile
11.4
Maximum
22.5
Upon examining the concentrations at individual model plants, we found that for each model
scenario the distribution was skewed right (Figure 1). The maximum concentration estimated
using the worst-case air dispersion conditions was significantly higher than the most commonly
occurring concentrations and is an extreme value compared to the rest of the distribution.
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2000 4000 6000 8000 10000 12000 14000 16000
Y
0 2000 4000 6000 6000 10000 12000 14000
Y
0.0012
0.0010
0.0008
0.0006
0.00200
0.00175
0.00150
0.00125
0.00100
0.00075
0.00050
0.00025
0.00000
2000 4000 6000 8000 10000 12000 14000 16000
Figure 1. Representative histograms and probability density functions for model scenarios performed with
meteorological inputs from meterological stations located in Tennessee (top left), California (top right),
Michigan (bottom left), and Alaska (bottom right). The x-axis is the relative concentration based on unit
emissions and the y-axis is the probability of that concentration occurring. Note that all model scenarios
produced similar results.
To provide a statistical basis for identifying the maximum concentration as an extreme value, we
used the adjusted boxplot for skewed distributions.1 This tool was specifically designed for
skewed distributions and able to identify extreme values in the distribution (i.e., outliers). The
results of that analysis indicated that in all modeled cases, the maximum concentration was
always statistically identified as an extreme value. For comparison, the 99th percentile highest
concentration was found to be an extreme value in 99 percent of scenarios, the 98th percentile
would be an extreme value in 92 percent of cases, the 95th percentile would be an extreme value
in 22 percent of cases, and the 90th percentile was never identified as an extreme value. For
reference, Figure 2 shows representative adjusted boxplots for 4 randomly selected model plants.
1 Hubert, M. and Vandervieren, E., 2008. An adjusted boxplot for skewed
distributions. Computational statistics & data analysis, 52(12), pp.5186-5201.
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Figure 2. Representative adjusted box and whisker plots for model scenarios performed with meteorological
inputs from meterological stations located in Alabama (top left), Iowa (top right), New York (bottom left),
and Oregon (bottom right). The x-axis is the relative concentration based on unit emissions. Extreme values
(i.e., outliers) are represented by open circles. Note that all model scenarios produced similar results.
Discussion
As discussed in the introduction to this section, our goal was to identify air dispersion conditions
that provided an exposure scenario that was 1) health protective without overestimating acute
exposures (i.e., false positives), and 2) has a reasonable probability of occurring. We also
previously noted that neither the worst-case hour nor the mean hour fits this description.
Therefore, we considered other meteorological hours and corresponding air dispersion conditions
to use to estimate exposure scenarios. Hours initially considered included the 90th percentile, 95th
percentile, 98th percentile, and 99th percentile air dispersion conditions.
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Figure 3 illustrates the probability density function and adjusted boxplot for a representative
model scenario and locates each of these hours on the plots.
Mean
90th
percentile 95th
percentile
98th
percentile
99th
percentile
Max
° Max
O
O
99th
9Rth
percentile
percentile
95th
90th
percentile
percentile
1 Mpan
Figure 3. Histogram and probability density function (left) and adjusted box and whisker (right) plots for a
representative model scenario. The location of the mean, 90th, 95th, 98th, 99th, and max concentrations are
identified on each plot. Extreme values (i.e., outliers) on the box and whisker plot are represented by open
circles.
Conclusion
Based on this analysis, we selected the 99th percentile air dispersion conditions as the value to
represent a reasonable worst-case air dispersion. The 99th percentile value has a higher
probability of occurring (approximately 88 in 200,000, or 1 in 2,273 (a 0.044% chance) for the
one facility with the most hours with a peak short-term emission rate to long-term emission rate
greater than 1 from Part I of this Appendix) but is still considered an extreme value in essentially
all the modeled cases (i.e., reasonable worst-case air dispersion conditions), and therefore health
protective. Thus, the HEM-3 acute analysis will utilize the 99th percentile highest hourly ambient
concentration (the 88th highest concentration for a 1-year simulation) when estimating acute level
noncancer risks.
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Appendix 6
Technical Support Document for the TRIM-Based
Multipathway Tiered Screening Methodology for RTR
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Technical Support Document
for the TRIM-Based Multipathway
Tiered Screening Methodology for RTR
February 2021
Prepared For:
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Research Triangle Park, NC 27711
Prepared By:
ICF
2635 Meridian Parkway
Suite 200
Durham, NC 27713
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TRIM-Based Tiered Screening Methodology for RTR
Contents
Exhibits v
Acronyms vii
1. Introduction 1
1.1 Purpose of RTR Multipathway Screens 1
1.2 Overview of Multimedia Ingestion Screening Methods 2
1.3 Tiered Approach 3
1.4 Chemicals of Potential Concern 5
1.5 Report Organization 7
2. Tier 1 Screen 7
2.1 Overview of Tier 1 8
2.2 Conceptual Exposure Scenario 10
2.2.1 Approach to Development of the Tier 1 Scenario 12
2.2.2 Fate and Transport Modeling (TRIM.FaTE) 14
2.2.3 Exposure Modeling and Risk Characterization 14
2.2.4 Implementation of Risk-based Equivalency Factors for POM
and Dioxin Congeners 15
2.3 Description of Environmental Modeling Scenario 22
2.3.1 Chemical Properties 22
2.3.2 Spatial Layout 22
2.3.3 Watershed and Water Body Parameterization 24
2.3.4 Meteorology 27
2.3.5 Aquatic Food Web 29
2.3.6 Using TRIM.FaTE Media Concentrations 30
2.4 Description of Human Exposure and Risk Estimates 32
2.4.1 Calculating Concentrations in Farm Foods 32
2.4.2 Ingestion Exposure 32
2.4.3 Calculating Risk 36
2.4.4 Summary of Tier 1 Assumptions 37
2.5 Evaluation of Screening Scenario 41
2.5.1 Arsenic 42
2.5.2 Cadmium Compounds 46
2.5.3 Mercury Compounds 47
2.5.4 Dioxins 50
2.5.5 Polycyclic Aromatic Hydrocarbons and Other Polycyclic
Organic Matter 51
2.5.6 Summary 55
3. Tier 2 Screen 55
3.1 Overview of Approach 56
3.1.1 Tier 2 Environmental Assumptions 56
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3.1.2 Tier 2 Exposure Assumptions 57
3.1.3 Implementation of Tier 2 57
3.2 Library of Tier 2 Screening Threshold Emission Rates 60
3.2.1 Meteorological Data 60
3.2.2 Locations of Lakes and Farms/Gardens 62
3.2.3 Gardener Exposure Scenario 65
3.2.4 Development of Library of Tier 2 Screening Threshold
Emission Rates, REFs, and Mixing Height Refinements 66
3.3 Implementing the Tier 2 Multipathway Screen 67
3.3.1 Accounting for Sustainable Fishing 70
3.3.2 Refined-fisher Scenario 73
3.3.3 Considering Inhalation Risks at Hypothetical Garden Locations 75
3.3.4 Outputs 75
4. Tier 3 Screen 77
4.1 Overview of Approach 77
4.2 Lake Screen 77
4.3 Farmer Scenario Evaluation 78
4.4 Gardener Scenario Evaluation 80
4.5 Plume-rise Screen 81
4.6 Screen Using Hourly Time-series Meteorological Data and Effective
Release Heights 82
5. References 83
Attachment A. TRIM.FaTE Inputs A-1
Attachment B. Multimedia Ingestion Risk Methodology Used for RTR
Exposure and Risk Estimates B-1
Attachment C. Dermal Risk Screening C-1
Attachment D. Summary of TRIM.FaTE Parameters Considered for Inclusion
in Tier 2 Assessment D-1
Attachment E. Analysis of Lake Size and Sustainable Fish Population E-1
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TRIM-Based Tiered Screening Methodology for RTR
Exhibits
Exhibit 1. Overview of Ingestion Exposure and Risk Screening Evaluation
Method 2
Exhibit 2. Conceptual Decision Tree for Evaluating Non-Inhalation Exposures for
PB-HAPs 4
Exhibit 3. OAQPS PB-HAP Compounds 6
Exhibit 4. Conceptual Decision Tree for Tier 1 Evaluation of Multimedia Ingestion
Exposures to PB-HAPs 9
Exhibit 5. Screening Threshold Emission Rates for Multimedia Ingestion
Exposures 9
Exhibit 6. Overview of Multimedia Ingestion Risk Calculations for RTR 15
Exhibit 7. Exposure, Toxicity, and Risk Equivalency Factors Relative to
Benzo[a]pyrene for POM Congeners Currently Evaluated in the
Screens 17
Exhibit 8. Relationship between Ingestion Exposure and Kowfor POM Chemicals 19
Exhibit 9. LogKow Values for POM Chemical Congeners 19
Exhibit 10. Exposure and Toxicity Equivalency Factors Relative to TCDD for
Modeled Dioxin Congeners 21
Exhibit 11. TRIM.FaTE Surface Parcel Layouts 23
Exhibit 12. Summary of Key Meteorological Parameter Inputs 28
Exhibit 13. Aquatic Biota Parameter Values for the TRIM.FaTE Screening
Scenario 30
Exhibit 14. Spatial Considerations—TRIM.FaTE Results Selected for Calculating
Farm-food and Fish Media Concentrations and Receptor Exposures 31
Exhibit 15. Summary of Ingestion Exposure Pathways 33
Exhibit 16. Overview of Exposure Factors Used for RTR Tier 1 Ingestion Screen 34
Exhibit 17. Dose-response Values for PB-HAPs in RTR Ingestion Screening
Scenario 36
Exhibit 18. Summary of RTR Tier 1 Screening Scenario Assumptions 37
Exhibit 19. Estimated Media Contributions to Arsenic Ingestion Exposures and
Lifetime Cancer Risks 45
Exhibit 20. Estimated Media Contributions to Cadmium Ingestion Exposures and
HQs 47
Exhibit 21. Estimated Media Contributions to Methyl Mercury Ingestion
Exposures and HQs 49
Exhibit 22. Estimated Media Contributions to Dioxin Ingestion Exposures 52
Exhibit 23. Estimated Media Contributions to Polycyclic Organic Matter Ingestion
Exposures 54
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Exhibit 24. Estimated Contributions of Modeled Food Types to Additional POM
Chemical Ingestion Exposures 55
Exhibit 25. Basic Process for Implementing the Tier 2 Multipathway Screen 58
Exhibit 26. The Locations of Meteorological Stations Used in RTR Modeling, and
Locations of NATA 2011 Point-Source Facilities for Reference 61
Exhibit 27. Values for Meteorological Parameters Used to Develop the Tier 2
Screening Threshold Emission Rates and REFs 62
Exhibit 28. Distances Used to Develop the Tier 2 REFs and Screening Threshold
Emission Rates 63
Exhibit 29. TRIM.FaTE Surface Layouts for the Tier 2 Multipathway Screen,
Using Alternative Distances Between the Facility and the Fishable
Lake or Farm/Garden 64
Exhibit 30. Ingested Media for Farmer and Gardener Scenarios 65
Exhibit 31. Estimated Maximum Fish Ingestion Rate (g/d) Associated with
Sustainable Fishing 72
Exhibit 32. Example of Source Category Summary Results Output from Tier 2
Tool 76
Exhibit 33. Example of Facility-level Results Output from Tier 2 Tool 76
Exhibit 34. Example of the Refined-fisher Output for Facility and PB-HAP from
Tier 2 Tool 77
Exhibit 35. Example of Lake Removed from Screening—Likely Evaporated or
Drained 79
Exhibit 36. Example of Lake Removed from Screening—Likely an Industrial Lake 80
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Acronyms
3MRA Multimedia, Multipathway, and Multireceptor Risk Assessment Modeling System
ADAF age-dependent adjustment factor
ADD average daily dose
AERMOD American Meteorological Society/Environmental Protection Agency Regulatory
Model
AT averaging time
BAF bioaccumulation factor
BaP benzo[a]pyrene
BC benthic carnivore (fish)
BCF bioconcentration factor
Bl benthic invertebrate
BMF biomagnification factor
BO benthic omnivore (fish)
BSAF biota-sediment accumulation factor
BW body weight
CSF cancer slope factor
DDE dichlorodiphenyldichloroethylene
ED exposure duration
EEF exposure equivalency factor
EF exposure frequency
EPA U.S. Environmental Protection Agency
ER emission rate
ESRI Environmental Systems Research Institute
FC fraction contaminated
HAP hazardous air pollutant
HHRAP Human Health Risk Assessment Protocol for Hazardous Waste Combustion
Facilities
HQ hazard quotient
IR ingestion rate
IRIS Integrated Risk Information System
LADD lifetime average daily dose
MACT maximum achievable control technology
MVP minimum viable population
NAAQS National Ambient Air Quality Standards
NATA National Air Toxics Assessment
NCDC National Climatic Data Center
NEI National Emissions Inventory
OAQPS Office of Air Quality Planning and Standards (U.S. EPA)
ORD Office of Research and Development (U.S. EPA)
PAH polycyclic aromatic hydrocarbon
PB persistent and bioaccumulative
PB-HAP persistent and bioaccumulative hazardous air pollutant
PCB polychlorinated biphenyl
PCDD polychlorinated dibenzodioxin
PCDF polychlorinated dibenzofuran
POM polycyclic organic matter
REF risk equivalency factor
RfD reference dose
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RGP
reactive gaseous phase
RTR
Risk and Technology Review
RZ
root zone
SAB
Science Advisory Board
SV
screening value
TCDD
2,3,7,8-tetrachlorodibenzo-p-dioxin, termed "dioxin" in this report
TEF
toxic equivalency factor
TL
trophic level
TL2
trophic level 2
TL3
trophic level 3
TL3.5
between trophic level 3 and 4
TL4
trophic level 4
TPY
short tons per a year
TRIM
Total Risk Integrated Methodology
TRIM.FaTE
TRIM'S Fate, Transport, and Ecological Exposure module
TSD
Technical Support Document
USGS
U.S. Geological Survey
USLE
universal soil loss equation
WBAN
Weather Bureau-Army-Navy
WCC
water-column carnivore (fish)
WCH
water-column herbivore (fish)
WCO
water-column omnivore (fish)
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1. Introduction
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. These risk assessments are a major component of EPA's Risk
and Technology Review (RTR) program. One aspect of human health that EPA must consider
under RTR is the potential for health effects resulting from exposures to HAPs via non-
inhalation pathways, namely ingestion and dermal exposure. These non-inhalation human
health risks are considered in combination with estimated inhalation human health risks,
potential ecological impacts, and other factors to support RTR decisions. This report documents
the technical bases and methods used for RTR non-inhalation human health risk screens.
This section introduces the reader to the Total Risk Integrated Methodology (TRIM)-Based
Multipathway Tiered Screening Methodology. It describes the purpose of the RTR program and
this Technical Support Document (TSD, Section 1.1) and provides an overview of the
multipathway screening approach (Section 1.2). This section also provides an overview of the
tiered implementation of the screen (Section 1.3), the chemicals that are evaluated in the RTR
mulitpathway screen (Section 1.4), and the organization of the remainder of the TSD
(Section 1.5). The subsequent main sections, 2 through 4 of this report, describe Tiers 1
through 3 of the screen in greater detail. References are listed in Section 5.
1.1 Purpose of RTR Multipathway Screens
As noted above, Section 112 of the CAA directs EPA to assess the residual risk from emissions
of HAPs following the implementation of MACT standards for emission sources. Facilities are
grouped into source categories, and each source category is evaluated independently. As part
of this program, EPA considers additional emission controls for a source category if the current
MACT does not provide an "ample margin of safety" to protect human health.
EPA's Office of Air Quality Planning and Standards (OAQPS) has identified specific persistent
and bioaccumulative HAPs (PB-HAPs) for which it must consider all possible routes of
exposure—inhalation, ingestion, and dermal. EPA must evaluate potential ingestion and dermal
exposures to PB-HAPs deposited from air to ground-level surfaces, considering subsequent
transport and fate of those chemicals in the environment. For PB-HAPs, exposures via ingestion
have been shown to be much higher than exposures via dermal absorption (see Attachment C.
EPA OAQPS has developed an iterative, tiered approach to screen exposure and risk
specifically for multimedia ingestion of PB-HAPs for its RTR program. The iterative, tiered
screening approach described in this document allows EPA to efficiently gauge the largest
potential exposures and health risks from non-inhalation exposure to emitted PB-HAPs in a
source category. If the conclusion of a screen is that exposures and health risks above levels of
concern cannot be ruled out, EPA can conduct refined, complex, site-specific modeling of
potential risks (which is not discussed in this document).
EPA evaluates human inhalation exposures to HAPs separately using other tools. For each
source category, EPA OAQPS considers risks to humans from ingestion exposures along with
risks from inhalation, potential ecological and other environmental impacts, and other factors
when deciding if regulatory action is needed.
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1.2 Overview of Multimedia Ingestion Screening Methods
The screening approach and tools are summarized in Exhibit 1 and described below.
1. We use TRIM.FaTE—the Fate, Transport, and Ecological Exposure module of TRIM—to
model fate and transport of air emissions of PB-HAPs using a base emission rate of 1 g/d
This modeling includes chemical partitioning across soil, water, and other environmental
media (including fish). TRIM.FaTE outputs include chemical concentrations in fish (mg/kg
wet weight), soil (|jg/g dry weight), and water (mg/L), and deposition rates for chemicals
from air via wet and dry deposition.
Exhibit 1. Overview of Ingestion Exposure and Risk Screening
Evaluation Method
2. We use TRIM.FaTE outputs (e.g., chemical air deposition and environmental media
concentrations) as inputs to multimedia ingestion risk calculations that include ingestion of
PB-HAPs in locally raised foods (e.g., produce, livestock, and dairy products). The
multimedia risk estimation methods are based on EPA's Human Health Risk Assessment
Protocol for Hazardous Waste Combustion Facilities (U.S. EPA 2005a).
3. The calculated chemical concentrations in the ingested media, along with food ingestion
rates and other exposure factors, are used to estimate ingestion exposures from the
selected media for hypothetical human receptors. Specifically, estimates are made of
average daily doses (ADDs) for the noncarcinogenic chemicals assessed (i.e., for cadmium
and mercury) and lifetime ADDs (LADDs) for the carcinogenic chemicals (i.e., for arsenic,
dioxins/furans [abbreviated in this document as dioxins]), and polycyclic organic matter
[POM]).
4. Chemical-specific lifetime cancer risk or chronic noncancer hazard (expressed as a hazard
quotient [HQ]) are estimated for each PB-HAP at a modeled emission rate of 1 g/d.
5. For each PB-HAP, based on the estimated cancer risk or HQ at the 1 g/d emission rate, we
determine the emission rate at which the excess lifetime cancer risk equals 1-in-one million
or the noncancer HQ equals 1. These emission rates are termed "screening threshold
emission rates."
6. We then compare a facility's PB-HAP emission rate to the screening threshold emission rate
for each PB-HAP emitted (e.g., a facility's actual cadmium emission rate would be compared
to the screening threshold emission rate for cadmium). The resulting ratio of a facility's
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TRIM-Based Tiered Screening Methodology for RTR
actual emission rate to the screening threshold emission rate is termed a "screening value"
orSV.
1.3 Tiered Approach
EPA developed the tiered approach to screen out PB-HAP emissions unlikely to pose health
risks above levels of concern, allowing the Agency focus on facilities and chemicals of greatest
concern within a source category. Sensitivity analyses and model testing revealed 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) influence estimated chemical concentrations
in air, soil, water, sediment, and fish more than physical/chemical parameter values of the
PB-HAPs. As discussed in detail below, the Tier 1 assumptions about meteorological data and
lake location are refined with more site-specific data in subsequent tiers. In addition, if a facility
does not screen out, we further evaluate the surrounding land use to determine if the exposure
scenario is realistic, and if not, we remove the exposure scenario from evaluation. For example,
if a farmer scenario does not screen out, and we determine that exposures are in an urban
setting and it is unlikely that a full-scale farming operation will operate in the vicinity, we will
remove the farmer exposure scenario. The iterative approach is divided into three tiers of
increasing refinement as illustrated in Exhibit 2 and described below.
. Tier 1 compares facility-specific PB-HAP emissions to the screening threshold
emission rates for a hypothetical scenario in which an individual eats locally caught
fish; consumes only homegrown produce, livestock, and livestock products (e.g., eggs,
meat, dairy products); and incidentally ingests local soil. The ingestion rate for each
ingested medium was set to an upper percentile value. This approach overestimates
total chemical exposure for a single hypothetical individual, but it will not miss an
important exposure pathway. The screening scenario represents a "worst-case"
ingestion exposure that is unlikely to be exceeded at any actual facility evaluated for
the RTR program. For a facility, if the emission rate of each PB-HAP is less than the
Tier 1 screening threshold emission rate (i.e., if the SVs are less than or equal to 2,
when rounded to one significant figure), no additional multipathway screening is done.
If, however, the emission rate of any PB-HAP exceeds the Tier 1 screening threshold
emission rate (i.e., an SV of 2 or more, when rounded to one significant figure), the
facility can be evaluated further in Tier 2.
. In Tier 2, the actual location of each modeled facility is used to refine some
assumptions associated with the environmental scenario. Combinations of
meteorological conditions, lake locations, and farming locations were systematically
varied, and TRIM.FaTE and multimedia exposure algorithms were used to calculate
screening threshold emission rates for PB-HAPs for each combination, or bin (see
Section 3.2). For each facility, an algorithm identifies the predefined bin that most
closely matches the local weather conditions and relative location(s) of fishable lakes
for that facility. Multiple hypothetical farming locations also are evaluated for each
facility. The facility's emissions are compared to the screening threshold emission
rates for the best-match bin for each PB-HAP to determine SVs. Unlike Tier 1, which
considers combined ingestion of fish, farm foods, and soil, Tier 2 separately screens a
hypothetical person consuming fish and a hypothetical person consuming farm foods
and soil (i.e., the Tier 1 ingestion scenario is disaggregated into separate hypothetical
subsistence farmer and subsistence fisher exposure scenarios). In addition, a
gardener exposure scenario is added in Tier 2 to represent exposures to individuals
who garden and eat eggs from home-raised chickens, but who do not raise animals for
meat or dairy ingestion. Hypothetical gardeners and hypothetical farmers are
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evaluated at identical locations (and thus use the same calculated media
concentrations). The gardener's ingestion rates for consumed media depend upon
whether or not the facility is located in an urban or rural area (lower ingestion rates are
assumed for gardeners in urban areas compared with rural areas, see: Section 3.2.3).
If the resulting SVs are all less than 2 (when rounded to one significant figure), no
additional screening is needed. Facilities with SVs greater than or equal to 2 (when
rounded to one significant figure) for one or more PB-HAPs, for any of the exposure
scenarios, can be further analyzed in Tier 3.
Exhibit 2. Conceptual Decision Tree for Evaluating Non-Inhalation Exposures
for PB-HAPs
oc
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• In Tier 3, further site-specific refinements are included in the screen.
- To further evaluate a fisher scenario SV exceedance (i.e., an SV of 2 or greater),
nearby lakes are examined more closely for suitability for fishing; unsuitable lakes
are removed from the lake database, and the facility is rescreened (using Tier 2
methods) with the revised lake database.
- To further evaluate a farmer SV exceedance, EPA uses census data, aerial
imagery, and other available data to further assess the likelihood of subsistence
farmer operations within 50 km of the facility. If, based on the additional analysis
and review, it cannot be determined that subsistence farming operations are in the
area, then the farmer scenario is not used in Tier 3 and only gardener SVs are
reported.
- To further evaluate a gardener exceedance, EPA will examine information such as
Census data, aerial imagery, and land-use data to determine the likelihood that
people reside at the location of the gardener exceedance. If EPA determines that
people likely reside at that location, the Tier 2 gardener SV will be retained in
Tier 3. Otherwise, EPA will report the highest gardener SV for locations at which
EPA determines people likely reside.
- Each of the next two refinements (i.e., plume-rise and hourly weather data) can
result in different Tier 3 screening threshold emission rates. Facilities having
emissions that exceed the refined screening threshold emission rates for Tier 3
(i.e., SVs are 2 or more as described previously) may require additional analysis.
If, based on results of the screens, a risk assessor concludes that there is a reasonable
probability that individual humans could be adversely affected by the facility emissions, a refined
site-specific multipathway assessment can be performed. The land parcels are defined using
geographic features around a facility that define the magnitude of runoff and erosion. The lake
parcels follow the general shapes of the actual lakes. 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, location of local farms/gardens and water bodies, types of land use, soil properties,
erosion and runoff rates with slope features, surface water and sediment properties, water
transfer rates, and aquatic ecosystem information. If available, other site-specific information
could be included (e.g., crops grown, local fish ingestion rates, typical growing season).
1.4 Chemicals of Potential Concern
EPA's assessment of multipathway human exposures for RTR focuses on PB-HAPs that
OAQPS has identified as candidates for multipathway risk assessments. OAQPS developed a
list of 14 chemicals and chemical groups considered to be PB-HAPs using two criteria:
. Their presence on three 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
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Assessment (ATRA) Reference Library (U.S. EPA 2004a). Exhibit 3, below, presents the 14
PB-HAP chemicals and chemical groups, with the addition of arsenic, which was not in the
original list (see Exhibit endnote). TRIM.FaTE is not parameterized to evaluate risk for all
PB-HAPs on the list. Currently, TRIM.FaTE includes chemical-specific parameter values
required for modeling exposure and risk for four of the 14 PB-HAPs (as indicated in Exhibit 3)
plus arsenic. These five PB-HAPs are the focus of the RTR multimedia screen 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.1
The five PB-HAPs assessed under RTR include:
. Arsenic compounds,
. Cadmium compounds,
. Chlorinated dibenzodioxins and furans (dioxins),
. Mercury compounds, and
. Polycyclic organic matter (POM2).
Exhibit 3. OAQPS PB-HAP Compounds
PB-HAP Compound3
Addressed by Screening Scenario?
Arsenicb
Yes
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
1 Potential impacts on human health from non-inhalation exposures to lead are evaluated for RTR using the National
Ambient Air Quality Standard (NAAQS) for lead, which accounts for 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.
2Although POM (polycyclic organic matter) is the HAP listed in the Clean Air Act, most of the POM chemicals
evaluated are "polycyclic aromatic hydrocarbons" or PAHs. Throughout this document, PAH and POM can generally
be considered interchangeable. There are, however, instances where the discussion is specific to one or the other
group of chemicals; for example, when discussing regulatory chemical groups or properties that are specific to a
specific chemical class, or when providing information from a referenced source we use the chemical class specified
in that source.
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PB-HAP Compound3
Addressed by Screening Scenario?
Toxaphene
No
Trifluralin
No
aSource of list: U.S. EPA (2004a).
bArsenic was not in the OAQPS initial list of PB-HAPs because its bioaccumulation potential is limited. It was recently
added, however, because it is carcinogenic at very low doses, is persistent in the environment, and is emitted from
many source categories. We refer to it as one of five PB-HAPs in the RTR multipathway assessment.
1.5 Report Organization
The remainder of this document is organized into four sections. Section 2 describes the Tier 1
screen, including the spatial layout of the hypothetical facility environment, the Tier 1 exposure
scenario, and derivation of Tier 1 screening threshold emission rates.
Section 3 describes use of readily available site-specific information to refine estimates of
screening threshold emission rates for Tier 2, and other aspects of the Tier 2 assessment.
Section 4 discusses additional refinements that can be applied, sequentially, in Tier 3.
References are listed in Section 5.
2. Tier 1 Screen
EPA's multimedia risk screen for RTR focuses on PB-HAPs that OAQPS identified as
candidates for multimedia ingestion risk assessments (Section 1.4). Sources that are "screened
out" at Tier 1 are assumed to pose no risks to human health. For sources that do not pass the
Tier 1 screen, more refined screens, up to and including site-specific assessments, can be
conducted as appropriate.
Using a worst-case hypothetical screening scenario for Tier 1 minimizes the chance that a
facility that actually poses a risk to human health screens out. However, the scenario is not so
biased that it never screens out facilities. An abundance of "false positives" would not help EPA
focus on the facilities and PB-HAPs with emissions of actual concern.
This section describes the technical basis for Tier 1 of EPA's human multimedia ingestion
screen of PB-HAP emissions from RTR sources. Specifically, the scenarios, models,
configurations, and inputs used to derive screening threshold emission rates are described in
detail in four subsections:
. Section 2.1 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 2.2 and 2.3 present technical descriptions of the hypothetical environmental
setting, the exposure scenario used in Tier 1, and the models used in the screen.
. Section 2.4 provides a brief discussion of the screening threshold emission rates for
each of the chemicals assessed.
Finally, Section 2.5 provides evaluations of the screening scenario for each of the modeled
chemicals or chemical groups.
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2.1 Overview of Tier 1
An ideal screening approach strikes a balance between being health-protective—to ensure that
risks above levels of concern are identified (i.e., no false negatives)— and being accurate—to
minimize false positives (i.e., results suggesting that additional analysis is required when, in
fact, the actual risk is low). Typically, gains in accuracy in environmental modeling
(i.e., reductions of both false positives and false negatives) require additional resources.
The Tier 1 hypothetical watershed includes a farm homestead and a fishable lake near the
facility, 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 health-protective;
i.e., designed to maximize PB-HAP chemical concentrations in the food sources. The many
environmental and chemical-specific properties governing fate and transport of PB-HAPs are
parameterized with either conservative (i.e., health-protective) values or central-tendency
values. Health-protective values (e.g., upper-percentile values from a national distribution) are
used for parameters that most influence exposure and risk. Including central-tendency values
for the remaining parameters should help limit the number of false positives. False positives
(i.e., results that suggest more assessment is required when in fact the actual risk is low) waste
resources by leading to additional, unnecessary analysis. The Tier 1, TRIM.FaTE-based,
multipathway fate and transport modeling scenario, or "Tier 1 scenario," is used to determine
the Tier 1 screening threshold emission rates for comparison with individually reported facility
emissions. The Tier 1 scenario includes the Tier 1 spatial layout for a hypothetical watershed
and the assumptions and input values for a health-protective exposure and risk screen. The
Tier 1 scenario is a static configuration that calculates a Tier 1 screening threshold emission
rate for each of the five PB-HAP chemical groups.
The Tier 1 approach for evaluating multimedia ingestion exposures to PB-HAPs for RTR is
diagrammed in Exhibit 4. Air toxics emitted by a source under consideration are reviewed to
determine, first, whether emissions are reported for any of the five PB-HAPs of concern for non-
inhalation pathways. If such emissions are reported, the emission rates are compared to Tier 1
screening threshold emission rates derived for each PB-HAP as described in this section. A
screening threshold emission rate is the rate that corresponds to a cancer risk of 1-in-one
million or an HQ of 1.
Exhibit 5 presents those rates for the five PB-HAP groups.3
As depicted in Exhibit 4, the final decision point in the Tier 1 evaluation for a given facility has
two possible outcomes:
. Emissions are equal to or less than the threshold emission rate of concern and
therefore the facility screens out from further evaluation (SV <1); or
. Emissions are above the threshold emission rate of concern (SV >1), and risks from
ingestion exposures cannot be ruled out (the facility does not screen out).
3For chemicals known to cause both cancer and chronic noncancer impacts, and for which acceptable quantitative
dose-response values are available for both cancer and noncancer endpoints, the endpoint that results in the lower
screening threshold emission rate is used for screening (i.e., the screening threshold emission rate will be based on
the effect that occurs at the lower exposure level). For the set of PB-HAPs for which screening threshold emission
rates have been derived, arsenic and chlorinated dibenzo-dioxins and -furans cause both types of effects. Because
the cancer dose-response value at a risk of 1-in-one million is lower than that for the noncancer reference toxicity
dose, the screening threshold emission rate is based on the cancer endpoint.
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Exhibit 4. Conceptual Decision Tree for Tier 1 Evaluation of Multimedia Ingestion
Exposures to PB-HAPs
Exhibit 5. Screening Threshold Emission Rates for Multimedia Ingestion Exposures
Chemical
Screening
Threshold Emission
Rate (TPY)
Basis of Threshold
(Type of Health Endpoint)
Arsenic
2.08E-04
Cancer
Cadmium
2.38E-03
Noncancer
Mercury (as divalent mercury emissions)
1.46E-04
Noncancer
POM (as benzo[a]pyrene equivalents)3
9.58E-04
Cancer
Dioxins (as 2,3,7,8-TCDD equivalents)3
2.65E-10
Cancer
Note: TPY = U.S. short tons per year.
aSee Section 2.2.4 for a discussion on the derivation of equivalent emissions.
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,
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accounting for the exposure and fate and transport calculations included in the model. Because
the models used in this assessment are not designed to run "backwards," the rates instead were
derived from regression equations established following a series of TRIM.FaTE and
exposure/risk model runs spanning a wide range of emission rates for each chemical. The
estimated screening threshold emission rates are verified by performing model runs using the
estimated screening threshold emission rate to confirm that the emission rates result in a cancer
risk of 1-in-one million or an HQ of 1.0. Actual risks for each screening threshold emission rate
would be lower than the levels of concern in nearly all circumstances, given the health
protective nature of the Tier 1 scenario configuration.
Tier 1 screening threshold emission rates were developed individually for elemental and divalent
mercury. Both were based on the lower of the screening threshold emission rates associated
with multimedia ingestion exposures to divalent mercury and methyl mercury.4 Only 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 emission rate. Moreover, elemental mercury has a high vapor pressure and generally
remains in air; deposited only during precipitation events and rapidly revolatilizing.
2.2 Conceptual Exposure Scenario
A conceptual model for exposure pathways describes the movement of chemicals from the point
of release to the points where exposure occurs. An exposure model generally includes several
elements:
. Release to the environment (i.e., emissions);
. A receiving medium (e.g., air);
. Transport processes within and between media;
. Transformation to other chemicals via one or more physical, chemical, or biological
processes;
. Continued tracking of a transformed chemical, if of concern (e.g., methyl mercury), or
loss of chemical from the modeling domain via degradation;
. Estimates of chemical concentrations in human exposure media (e.g., air, foods, soils);
and
. Human uptake of chemicals from those media by specific routes of exposure
(i.e., inhalation, ingestion, dermal absorption).
PB-HAPs can persist in the environment for many years and, therefore, can build up in soils and
lakes (sediments) and accumulate in biota, including fish, fruits and vegetables, and animal
products (e.g., meat, dairy, eggs). For this reason, ingestion of foods grown near facilities that
release PB-HAPs to air can be an important source of exposure.
Previously, to assess risks from hazardous waste combustion facilities, EPA identified several
hypothetical receptor scenarios, noting that the scenarios can be appropriate for a broad range
of situations where emissions to air are evaluated. The scenarios are described in EPA's
Human Health Risk Assessment Protocol for Hazardous Waste Combustion Facilities, or
4Note 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.
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HHRAP (U.S. EPA 2005a). In HHRAP, EPA recommends assessing several hypothetical
receptors: Farmer, Farmer Child, Resident, Resident Child, Fisher, Fisher Child, Acute
Receptor, and Nursing Infant. These receptors are distinguished by their pathways of exposure
and contact rates (e.g., food ingestion rates, hand-to-mouth soil ingestion, skin surface area).
EPA further notes in HHRAP that some exposure settings might warrant including additional
exposure pathways, such as fish ingestion by the Farmer.
For the RTR Tier 1 screen, ingestion exposure is estimated for a single hypothetical receptor
who ingests both locally caught fish and home-raised or home-produced farm foods. The
ingestion exposure scenario for the PB-HAP Tier 1 screen includes several media:
. Soil,
. Farm-grown fruits and vegetables,
. Farm-raised beef,
. Dairy products from local farm-raised cows,
. Farm-raised poultry and eggs,
. Farm-raised pork,
. Locally caught fish, and
. For children less than 1 year old, breast milk from a woman exposed via the media
listed above (for dioxins only).5
As discussed in detail in Section 2.4.2, aside from ingestion of breast milk, ingestion exposure
for all other media is assessed for adults and several age categories of children.
Other non-inhalation exposures possible for PB-HAPs discussed in HHRAP include using
surface water or groundwater as a drinking water source and dermal exposure to chemicals in
surface water and in soils; however, those exposure pathways are not evaluated for RTR. First,
farmers are unlikely to use untreated surface water for drinking (or other household water
uses).6 HHRAP also recommends that exposure to groundwater not be evaluated because EPA
found that groundwater is an insignificant exposure pathway for airborne combustion emissions
(U.S. EPA 2005a). In addition, based on numerous evaluations of groundwater concentrations
developed during RTR evaluations using TRIM.FaTE, we have confirmed that exposure from
groundwater ingestion is a small fraction of overall exposure. Dermal absorption of deposited
PB-chemicals that are originally airborne generally is relatively minor compared with other
exposures (U.S. EPA 2006, Cal/EPA 2012). Preliminary calculations of estimated dermal
exposure and risk of PB-HAPs, presented in Attachment C, show that the dermal exposure
route is not a significant risk pathway relative to ingestion exposures. In addition, HHRAP
recommends that dermal exposure not be assessed because available data indicate that the
contribution to overall risk from dermal exposure to soils typically is small (U.S. EPA 2005a).
5Breast milk ingestion is an important exposure pathway for lipophilic compounds like dioxins. Breast milk does not
contribute meaningfully to exposures to the other PB-HAPs assessed. See Section 2.4.2.2 below and Attachment B,
Section B.3.4 for full discussions of infant exposures via breast milk ingestion.
6An exception to this generality would be reservoirs used for drinking water supplies, although treatment facilities
would remove some proportion of PB-HAPs prior to water distribution. Such a situation might be worthy of additional
analysis, if warranted for a given assessment (e.g., several facilities close to a reservoir).
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2.2.1 Approach to Development of the Tier 1 Scenario
The TRIM-based Tier 1 scenario described in this document does not represent any particular
facility. The Tier 1 scenario is hypothetical and designed to estimate screening threshold
emission rates 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 human, any potential long-term exposures for any given geographic region would be
unlikely to exceed those estimated for the Tier 1 configuration.
The development and application of the Tier 1 scenario for residual risk evaluations considered
EPA's technical and policy guidelines presented in the Residual Risk Report to Congress (U.S.
EPA 1999); Volumes I and II of the Air Toxics Risk Assessment Reference Library (U.S. EPA
2004a, 2005a); and other EPA publications (e.g., U.S. EPA 2003a, 2005a).The scenario
described herein is the culmination of assessments completed since 2005. It allows an efficient
and scientifically defensible screen of multipathway human health risk and provides a solid
baseline from which to perform Tier 2 and Tier 3 screens, as described in Sections 3 and 4,
respectively. All attributes of this scenario should not be considered "final," however. Some will
continue to evolve based on feedback from the scientific community and Agency reviewers, on
lessons learned as the scenario is further applied for RTR, and on future changes in legislated
requirements.
2.2.1.1 Modeling Framework
The approach for multimedia ingestion risk screening and evaluation for RTR can be divided
into four steps as shown in Exhibit 1:
1. Model the fate and transport of PB-HAPs emitted to air including partitioning to soil, water,
and other environmental media (including fish7);
2. Estimate uptake of PB-HAPs by farm-grown foods (e.g., produce, livestock, dairy products)
from soil and air and calculate the resulting concentrations in each food category;
3. Estimate human ingestion of PB-HAPs in farm-grown foods and in fish and through
incidental ingestion of soils; and
4. Calculate estimates of lifetime cancer risk or chronic noncancer HQs, as appropriate, for
each PB-HAP and compare these to selected evaluation criteria.
As shown in Exhibit 1, EPA's TRIM.FaTE model provides multimedia fate and transport
modeling. Subsequent uptake of chemicals into farm foods and human ingestion exposures and
risk is estimated using the multimedia exposure algorithms.
EPA's TRIM 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
7Concentrations in fish calculated by TRIM.FaTE are used to estimate ingestion exposures for humans consuming
fish (except for arsenic, as provided below). Modeling offish concentrations is therefore discussed herein as part of
the TRIM.FaTE fate and transport modeling. TRIM.FaTE media concentration outputs are used to calculate the
uptake of PB-HAPs into all other biotic media assumed to be ingested as part of the second step of the modeling
framework.
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screening ingestion risk (Exhibit 1).8 TRIM.FaTE—the fate and transport module—is available
for application. EPA has completed some development activities for TRIM.Expo-lngestion and
TRIM.Risk-Human Health, two additional modules that cover the other three steps. Software
development, however, is not yet complete for these modules. Thus, the RTR screening
approach uses separate multimedia exposure and risk calculations to estimate PB-HAP
concentrations in farm-grown foods, average daily ingestion doses, and cancer risks and
chronic noncancer HQs. TRIM.FaTE plus the exposure and risk algorithms that are used are
conceptually identical to the ingestion exposure and risk assessments that TRIM is intended to
cover.
TRIM.FaTE outputs that are used as inputs to exposure and risk calculations 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, and
. PB-HAP concentrations in surface soil and root zone soil.
Using the exposure and risk algorithms, the RTR screening approach then estimates chemical
concentrations in crop products based on deposition from air and uptake from soils, ingestion of
PB-HAPs by farm animals via plant and soil ingestion and transfer to livestock products that are
consumed by humans (e.g., eggs, milk, meat), and ingestion of PB-HAPs through these media
by humans at various age groups from toddlers to adults (breast milk ingestion is also
considered for infants for dioxins). The screening approach sums cancer risks across different
age-groups to calculate a total lifetime cancer risk and calculates HQs for each age group for
noncancer effects.
2.2.1.2 Model Configuration and Parameterization
The Tier 1 scenario is intended to minimize the chance that EPA would underestimate potential
human multimedia ingestion risks. Although the health-protective approach likely overestimates
risk for any given facility, it is appropriate for an initial screen. As in the 2006 preliminary
multipathway screening for RTR (U.S. EPA 2006), exposures are modeled for a hypothetical
farm homestead and fishable lake located adjacent to an emissions source. The hypothetical
individual for which exposures are calculated derives all foods and soil from potentially
contaminated adjacent locations and food and soil ingestion rates are from the upper ends of a
nationally representative distribution of values (e.g., from EPA's 2011 Exposure Factors
Handbook).
The physical/chemical environment represented in the screening scenario was parameterized
with two types of values: typical and health-protective. In general, the spatial layout and the
components of the scenario that influence air concentrations and deposition rates (which in turn
affect PB-HAP concentrations in all other media) are defined or set to be health protective.
Properties of environmental media are set with either typical or health protective values, as
further discussed below and provided in Attachment A.
information about the current status of TRIM modules and comprehensive documentation of modules developed
thus far can be accessed on EPA's Fate, Exposure, and Risk Analysis website (https://www.epa.gov/fera/total-risk-
intearated-methodoloav-trim-trimfate).
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Calculated TRIM.FaTE concentrations generally are more sensitive to attributes of the spatial
layout and the meteorological data than to other attributes of the scenario. For example, the
dominant wind direction influences the direction of greatest deposition of emissions from a
source, 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. Thus, health-protective values for meteorological data and a spatial layout that
maximizes PB-HAP concentrations in the farm and lake are used for TRIM.FaTE in Tier 1.
2.2.2 Fate and Transport Modeling (TRIM.FaTE)
In developing the Tier 1 scenario, Version 3.6.2 of TRIM.FaTE was used to model the fate and
transport of emitted PB-HAPs and to estimate concentrations in relevant environmental media.
Additional information about TRIM.FaTE, including support documentation, software, and the
TRIM.FaTE public reference library, is available at https://www.epa.gov/fera/total-risk-
inteqrated-methodoloqy-trim-trimfate.
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.
Algorithms used to model mercury species and POM are described in Volume II of the
TRIM.FaTE Technical Support Document (U.S. EPA 2002a). A comprehensive evaluation of the
performance of TRIM.FaTE for modeling mercury was documented in Volumes I and II of the
TRIM.FaTE Evaluation Report (U.S. EPA 2002b, 2005b). Algorithms specific to the fate and
transport of chlorinated dibenzo-dioxin and -furan congeners are documented in the third
volume of the TRIM.FaTE Evaluation Report (U.S. EPA 2004b).
Since 2005, the TRIM.FaTE master library was updated to include cadmium and, most recently,
arsenic. In general, many of the algorithms and properties that are used to model mercury
(except for the mercury transformation algorithms) are also applicable to cadmium and arsenic,
although different empirical data are used for chemical-specific parameters values.
Comprehensive technical documentation of TRIM.FaTE algorithms specific to cadmium and
arsenic have not yet been compiled; however, all chemical-specific properties used by
TRIM.FaTE to model cadmium and arsenic (as well as POM, mercury, and dioxins) are
documented in Attachment A.
Based on a thorough 2011 evaluation of TRIM.FaTE performance in modeling the aquatic food
web, a zooplankton compartment was added to TRIM.FaTE's aquatic compartment to facilitate
comparison of TRIM.FaTE results for organic chemicals to those from other aquatic food-web
models which include zooplankton separately from phytoplankton. Performance of the model
was then recalibrated for mercury comparing the ratio and concentrations of inorganic and
methylated mercury in each component of the aquatic food web with data from field studies.
Parameterization of the TRIM.FaTE scenario used for RTR screening is described in more
detail in Section 2.3.
2.2.3 Exposure Modeling and Risk Characterization
The algorithms that calculate chemical concentrations in farm-grown foods and ingestion
exposures for hypothetical individuals are from EPA's Human Health Risk Assessment Protocol
for Hazardous Waste Combustion Facilities, or HHRAP (U.S. EPA 2005a). An overview of the
input data and flow of computations for these calculations is presented in Exhibit 6. This exhibit
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demonstrates the general relationships between the relevant TRIM.FaTE outputs (i.e., chemical
concentrations in environmental media and fish) and the calculations of ingestion exposure and
risk. 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.
Exhibit 6. Overview of Multimedia Ingestion Risk Calculations for RTR
Two of the five PB-HAPs for which screening threshold emission rates have been developed for
RTR—POM and dioxins—are chemical groups comprising numerous individual compounds.
The members of these groups as 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 because they have a similar mode of toxic action and because they
share attributes of environmental behavior.
2.2.4 Implementation of Risk-based Equivalency Factors for POM and Dioxin Congeners
To facilitate application of the multimedia ingestion screening methods for RTR, reported
emissions of POM and dioxins are compared with a single reference (or index) chemical for
each group: benzo[a]pyrene for POM and 2,3,7,8-TCDD for dioxins. These reference chemicals
are both relatively well-studied and among the most toxic compounds within each group.
Derivation of equivalency factors begins with the basic relationship used to characterize health
risk:
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TRIM-Based Tiered Screening Methodology for RTR
multiplying by relative toxicity equivalency factors (TEFs) and relative exposure equivalency
factors (EEFs). Using the dioxin group as an example, and 2,3,7,8-TCDD as the reference
compound, scaling emissions follows Equation 1:
Emiss,
Dioxin(i)
:tcdd ~ Emiss,
Dioxin(i)
x EEF,
Dioxin(i)
:TCDD x TEF,
Dioxin(i):TCDD
Eqn. 1
where:
EmissDj0Xjn(j):TCDD -
Emiss,
Dioxin(i)
EEF,
Dioxin(i):TCDD
TEF
Dioxin(i):TCDD
Risk-weighted emissions of Dioxin® (weighted according to cancer risk
relative to 2,3,7,8-TCDD for oral exposures)
= Emission rate of Dioxin®
Exposure equivalency factor accounting for the ratio of final Dioxin® exposure
= dose compared with initial Dioxin® emissions relative to the final BaP dose
compared with initial 2,3,7,8-TCDD emissions
Toxicity equivalency factor accounting for the toxicity of Dioxin® relative to the
= toxicity of 2,3,7,8-TCDD via ingestion
After all the emissions of all congeners of dioxins (/'... z) have been converted to TCDD-
equivalent emissions, they can be summed to a total TCDD-equivalent emissions rate. This
TCDD-equivalent emission rate is compared with the TCDD screening threshold emission rate
to develop a screening value (to determine if there is a possibility of adverse health effects.)
The oral TEF for each POM and dioxin compound is based on the compound's oral toxicity
relative to the oral toxicity of the index chemical for the group. The oral TEFs for POMs and
dioxins were obtained from previous EPA analyses (U.S. EPA 2008b and 2017a, respectively).
For POM, oral toxicity values for individual compounds have been derived following the same
approach used to develop inhalation toxicity values (U.S. EPA 2017a). For dioxins, oral TEFs
are those published by U.S. EPA (2008b), which were adopted from the values developed for
the World Health Organization for its 2005 TEF reevaluation (van den Berg et al. 2006). Refer to
Attachment B, Section B.4 for more information on these values.
The EEFs are calculated for each individual chemical that is modeled. TRIM.FaTE is
parameterized for 14 POM and 17 dioxin congeners. For these chemicals, EEFs were
calculated using the Tier 1 screening scenario described in this document. A release rate of 1
g/sec was modeled for each of the congeners. A chemical's EEF equals the ratio of its exposure
concentration or dose to that of the index chemical for the group, also modeled with an emission
rate of 1 g/sec. Emissions of several additional POM chemicals, however, are reported in the
NEI. The determination of EEFs for these chemicals, and chemical groups, are discussed in the
subsections below.
The product of the EEF and TEF for a given substance is called its "risk equivalency factor"
(REF) for the purposes of RTR evaluations. POM (or dioxin) emissions from a facility can be
quickly evaluated by summing the products of chemical-specific REFs and chemical-specific
emission rates.
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2.2.4.1 Calculation of Exposure, Toxicity, and Risk Equivalency Factors for POM
Congeners
There is a large universe of POM chemicals, though only a subset of 36 POMs traditionally are
reported to the NEI. Of those 36 POMs, 14 are parameterized in TRIM.FaTE; that is, values for
all chemical-specific parameters required by TRIM.FaTE (e.g., solubility, vapor pressure,
octanol-water partition coefficient [Kow], Henry's law constant) for its multimedia transport and
fate algorithms are included in the TRIM.FaTE library. The other 22 POMs (or POM groups) in
this subset of 36 are reported to the NEI less frequently than the 14 and are not currently
parameterized in TRIM.FaTE (their chemical and physical parameter values are not included).
The calculated EEFs, TEFs, and REFs for the 14 POM congeners that are parameterized in
TRIM.FaTE, plus the 22 others, are shown in Exhibit 7. To determine appropriate exposure
surrogates for chemicals not parameterized in TRIM.FaTE, EPA evaluated the relationships
between chemical-specific properties (e.g., Kow, Henry's law constant), intermediate modeled
values (e.g., deposition, soil concentration), and exposures in terms of lifetime average daily
dose (LADD), 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 possible
mutagenic mode of action of POMs (U.S. EPA 2005c,d,e). The correlation between Kow and
LADD is stronger than any other chemical-specific property and a power regression was
developed to estimate LADD based on congener-specific Kow. Based on this analysis, total
LADD (age-adjusted) for each congener is calculated based on the congener's Kow and the
power regression of the modeled POMs, as provided in Exhibit 8. Exhibit 8 shows that, in
general, as Kow increases, so too does exposure.
Exhibit 7. Exposure, Toxicity, and Risk Equivalency Factors Relative to Benzo[a]pyrene
for POM Congeners Currently Evaluated in the Screens
PB-HAP3
Fully
Characterized
for TRIM.FaTE
Modeling?15
Tier 1
Exposure-
equivalency
Factor (EEF)
Toxic-
equivalency
Factor (TEF)C
Tier 1 Risk-
equivalency
Factor (REF)
2-Methylnaphthalene
Yes
0.003
0.05
0.0001
7,12-Dimethylbenz[a]anthracene
Yes
1.3
250
314
Acenaphthene
Yes
0.004
0.05
0.0002
Acenaphthylene
Yes
0.006
0.05
0.0003
Benz[a]anthracene
Yes
0.07
0.1
0.007
Benzo[a]pyrene
Yes
1
1
1
Benzo[b]fluoranthene
Yes
3.6
0.1
0.4
Benzo[ghi]perylene
Yes
2.9
0.05
0.1
Benzo[k]fluoranthene
Yes
5.5
0.01
0.05
Chrysene
Yes
0.2
0.001
0.0002
Dibenzo[a,h]anthracene
Yes
4
1
4
Fluoranthene
Yes
0.01
0.05
0.0007
Fluorene
Yes
0.005
0.05
0.0002
lndeno[1,2,3-c,d]pyrene
Yes
2.9
0.1
0.3
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PB-HAP3
Fully
Characterized
for TRIM.FaTE
Modeling?15
Tier 1
Exposure-
equivalency
Factor (EEF)
Toxic-
equivalency
Factor (TEF)C
Tier 1 Risk-
equivalency
Factor (REF)
1 -Methylnaphthalene
No
0.003
0.05
0.0001
2-Acetylaminofluorene
No
0.0006
1
0.0006
3-Methylcholanthrene
No
2.6
22
56.4
Anthracene
No
0.01
0
0
Benz[a]anthracene/Chrysene
No
3.2
0.05
0.2
Benzo[a]fluoranthene
No
1.1
0.05
0.06
Benzo[b+k]fluoranthene
No
5.5
0.01
0.05
Benzo[c]phenanthrene
No
0.2
0.05
0.01
Benzo[e]pyrene
No
2.7
0.05
0.1
Benzo[g,h,i]fluoranthene
No
0.2
0.05
0.01
Benzo[j]fluoranthene
No
2.4
0.1
0.2
Benzofluoranthenes
No
5.4
0.05
0.3
beta-Chloronaphthalene
No
0.006
0.05
0.0003
Carbazole
No
0.002
0.02
0.00004
Dibenz[aj]acridine
No
0.3
0.1
0.03
Dibenzo[a,i]pyrene
No
25.5
10
255
PAH, total
No
3.2
0.05
0.2
Perylene
No
0.5
0.05
0.03
Phenanthrene
No
0.01
0
0
Polycyclic organic matter
No
3.2
0.05
0.2
Pyrene
No
0.04
0
0
Retene
No
2.1
0.05
0.1
Notes: Rounding artifacts present. HAP = hazardous air pollutant; PB-HAP = persistent and bioaccumulative HAP;
TRIM.FaTE = Total Risk Integrated Methodology (Fate and Transport Ecological model); POM = polycyclic organic matter;
BaP = benzo[a]pyrene; RTR = Risk and Technology Review program; Kow = octanol-water partition coefficient; PAH = polycyclic
aromatic hydrocarbon.
aNaphthalene 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/oria189.html1. Although naphthalene is a POM as defined in the Clean Air Act, unlike the other POM
chemicals modeled in the multipathway assessment, naphthalene remains primarily (>98-99%) in vapor phase at ambient
temperatures; thus, it disperses far away from a facility in air with negligible local deposition. Given its volatility (solid phase
sublimates to vapor phase at ambient temperatures), it does not accumulate in localized environmental media over time (ATSDR
2005). Additionally, based on a log Kow of 3.29, it has a low affinity for lipids compared with other POMs. For these reasons, EPA
does not consider naphthalene to be a persistent and bioaccumulative POM; inhalation is the only pathway of concern for RTR
assessment of naphthalene.
bSome POM congeners are not fully characterized in TRIM.FaTE (with their chemical properties, partition coefficients, etc.) and so
cannot be modeled directly. As discussed in the text, EEFs for these uncharacterized POM congeners are estimated based on
Kow.
°Sources: U.S. EPA (2017a); professional judgment.
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Exhibit 8. Relationship between Ingestion Exposure and Kow
for POM Chemicals
For POM reported as undefined groups (i.e., "PAH, total" and "Polycyclic Organic Matter"), EPA
assigned 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 (logKow = 6.5) (see Exhibit 9). This assignment is
assumed to be health protective and is unlikely to under predict exposure.
For POM chemicals that are not fully parameterized in TRIM.FaTE ("No" in Exhibit 7), we use
the regression equation in Exhibit 8 with the Kow values listed in Exhibit 9 to extrapolate the
EEFs in Exhibit 7. Thus, all POM have EEF, TEF, and REF values relative to BaP.
Exhibit 9. LogKow Values for POM Chemical Congeners
Chemical
LogKow (Kow)
Source
1 -Methylnaphthalene
3.87 (7.41 E+03)
Mackay et al. 2006a
2-Acetylaminofluorene
3.28 (1.91 E+03)
Montgomery 2007e
3-Methylcholanthrene
6.42 (2.63E+06)
Mackay et al. 2006a
Anthracene
4.45 (2.82E+04)
Mackay et al. 2006a
Benzo[a]fluoranthene
6.11 (1.29E+06)
U.S. EPA 2012a (EPI Suite, estimate)
Benzo[c]phenanthrene
5.52 (3.31 E+05)
U.S. EPA 2012a (EPI Suite, estimate)
Benzo[e]pyrene
6.44 (2.75E+06)
Mackay et al. 2006b
Benzo[g,h,i]fluoranthene
5.52 (3.31 E+05)
U.S. EPA 2012a (EPI Suite, estimate)
Benzo[j]fluoranthene
6.40 (2.51 E+06)
Mackay et al. 2006f
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Chemical
LogKow (Kow)
Source
Benzofluoranthenes
6.70 (5.01 E+06)
U.S. EPA 2012a (EPI Suite, estimate)
beta-Chloronaphthalene
4.14 (1.38E+04)
Mackay et al. 2006a
Carbazole
3.72 (5.25E+03)
U.S. EPA 2012a (EPI Suite)3
Dibenz[aj]acridine
5.63 (4.27E+05)
U.S. EPA 2012a (EPI Suite)0
Dibenzo[a,i]pyrene
7.28 (1.91E+07)
U.S. EPA 2012a (EPI Suite, estimate)
PAH, totald
6.50 (3.16E+06)
EPA assigned
Perylene
5.82 (6.61 E+05)
Mackay et al. 2006a
Phenanthrene
4.46 (2.88E+04)
Mackay et al. 2006a
Polycyclic organic matter01
6.50 (3.16E+06)
EPA assigned
Pyrene
4.88 (7.59E+04)
Mackay et al. 2006a
Retene
6.35 (2.24E+06)
U.S. EPA 2012a (EPI Suite, estimate)
Note: Benz[a]anthracene/chrysene and benzo[b+k]fluoranthene are not provided in this exhibit because
benz[a]anthracene/chrysene is modeled as "polycyclic organic matter" and benzo[b+k]fluoranthene is modeled as
benzo[k]fluoranthene) for RTR screens due to data limitations.
aOriginal source is Hansch et al. 1995.
bOriginal source is Sangster 1993.
°Original source is Helweg et al. 1997.
dFor 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 (logKow = 6.5). This assignment is assumed to be health protective and likely will not under predict
exposure.
eOriginal source is Mercer et al. 1990.
'Original source is Bayona et al. 1991.
One POM chemical that is not evaluated for ingestion exposure is naphthalene, which 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 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/oriq189.html).
While naphthalene is a POM, as defined in the Clean Air Act, unlike the other POM chemicals
modeled in the multipathway assessment, at ambient temperatures, naphthalene remains in
vapor phase; generally, only 2-3 percent of naphthalene emitted to air deposits to ground level
(ATSDR 2005). Naphthalene in other environmental media is short-lived due to its tendency to
volatilize. Thus, it does not build up in soils, sediments, water, or biota over time (ATSDR 2005)
With a logKow of 3.29, naphthalene has a moderate affinity for lipids and can accumulate in
some tissues over the short term; however, it is rapidly exhaled or metabolized to other readily
eliminated chemicals. For these reasons, EPA is not including naphthalene in its multipathway
risk assessment.
2.2.4.2 Calculation of Scaling Factors for Dioxin Congeners
The calculated EEFs, TEFs, and REFs for the 17 dioxin congeners that are chlorinated in the
lateral 2, 3, 7, and 8 positions are presented in Exhibit 10.
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Exhibit 10. Exposure and Toxicity Equivalency Factors Relative to TCDD for Modeled
Dioxin Congeners
PB-HAP
Tier 1 Exposure-
equivalency
Factor (EEF)
Toxic-
equivalency
Factor (TEF)a
Tier 1 Risk-
equivalency
Factor (REF)
1,2,3,4,6,7,8-Heptachlorodibenzo-p-dioxin
1.2
0.01
0.01
1,2,3,4,6,7,8-Heptachlorodibenzofuran
3.0
0.01
0.03
1,2,3,4,7,8,9-Heptachlorodibenzofuran
6
0.01
0.06
1,2,3,4,7,8-Hexachlorodibenzo-p-dioxin
1.3
0.1
0.1
1,2,3,4,7,8-Hexachlorodibenzofuran
1.2
0.1
0.1
1,2,3,6,7,8-Hexachlorodibenzo-p-dioxin
0.6
0.04a
0.03
1,2,3,6,7,8-Hexachlorodibenzofuran
0.3
0.1
0.03
1,2,3,7,8,9-Hexachlorodibenzo-p-dioxin
0.6
0.04a
0.03
1,2,3,7,8,9-Hexachlorodibenzofuran
0.6
0.1
0.06
2,3,4,6,7,8-Hexachlorodibenzofuran
0.3
0.1
0.03
1,2,3,4,6,7,8,9-Octachlorodibenzo-p-dioxin
0.7
0.0003
0.0002
1,2,3,4,6,7,8,9-Octachlorodibenzofuran
0.4
0.0003
0.0001
1,2,3,7,8-Pentachlorodibenzo-p-dioxin
2
1
2
1,2,3,7,8-Pentachlorodibenzofuran
0.8
0.03
0.02
2,3,4,7,8-Pentachlorodibenzofuran
1
0.3
0.3
2,3,7,8-Tetrachlorodibenzo-p-dioxin
1
1
1
2,3,7,8-Tetrachlorodibenzofuran
0.6
0.1
0.06
Notes: Rounding artifacts present; HAP = hazardous air pollutant; PB-HAP = persistent and bioaccumulative HAP;
TCDD = tetrachlorodibenzo-p-dioxin; IRIS = EPA's Integrated Risk Information System; CSF = cancer slope factor.
aSources: van den Berg et al. (2006), except for 1,2,3,6,7,8-hexachlorodibenzo-p-dioxin and 1,2,3,7,8,9-hexachlorodibenzo-p-
dioxin, which are calculated based on the ratio of the IRIS-based CSF for the respective congener to the IRIS-based CSF for
2,3,7,8-TCDD (available at U.S. EPA 2017b)
As provided in Exhibit 10, WHO TEFs from van den Berg et al. (2006) are used except for two
congeners for which EPA's IRIS program has developed a CSF—1,2,3,6,7,8-
hexachlorodibenzo-p-dioxin and 1,2,3,7,8,9-hexachlorodibenzo-p-dioxin. Collectively across
RTR assessments that EPA has conducted in recent years, these two congeners together
constitute roughly 4 percent of total dioxin emissions from point sources. When the dioxin
emissions are weighted by TEFs (to calculate TEQs), the two congeners constitute about 4
percent of the total dioxin TEQ emissions from point sources using TEF=0.1 from van den Berg
et al. (2006) and about 2 percent using TEF=0.04 derived from the IRIS-based CSF. Therefore,
the impact of changing the TEFs of the two congeners is small.
Some 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 2,3,7,8-TCDD in the environment. We assume that the toxicity
of unspecified total "Dioxins" equals that of the same quantity of 2,3,7,8-TCDD. This approach
could be improved by obtaining information on the speciation of dioxin emissions for each
facility or data that might allow calculation of an average speciation profile that could be applied
to all facilities in a source category.
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2.3 Description of Environmental Modeling Scenario
As described in Section 2.2.1.2, the physical configuration of the RTR Screening Scenario was
designed to encompass the upper end of possible long-term PB-HAP exposures. Values for
environmental and chemical-specific properties were selected to be either health protective or
central-tendency. Scenario configuration and parameterization, the rationale for selecting
particular property values, and model uncertainties are presented in the sections that follow.
Comprehensive documentation of TRIM.FaTE property values for the Tier 1 screening scenario
is provided in Attachment A.
2.3.1 Chemical Properties
The chemical-specific chemical/physical properties that TRIM.FaTE requires to simulate
transport and fate through multiple environmental media (e.g., Henry's law constant, molecular
weight, melting point) were obtained from peer-reviewed and standard chemical reference
sources. Numerous other chemical-specific properties also depend on the particular abiotic or
biotic compartment type; those properties are discussed generally in the sections that follow and
are documented in Attachment A.
2.3.2 Spatial Layout
The Tier 1 spatial layout for TRIM.FaTE, provided in Exhibit 11, represents a farm homestead
with a fishable lake (and its surrounding watershed) located near a facility emissions source.
The source parcel is a square with sides of 250 m to represent 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 emissions plume.
The modeled wedge extends 10 km downwind (eastward) from the facility. Air dispersion
modeling indicates the maximum air concentration and deposition rates occur relatively close to
the facility (probably within a few hundred meters, with the exact location varying with stack
height and other parameters), which is well within a 10-km radius. TRIM.FaTE modeling also
indicates that at 10 km downwind of a source, deposition rates for the PB-HAPs are expected to
be lower by about two orders of magnitude than deposition rates at a few hundred meters of the
source. Extending the modeling layout beyond 10-km downwind 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. Moreover, the additional mass
deposited beyond 10 km is expected to cause a negligible increase in total ingestion exposure.9
Given these conditions, a downwind length of 10 km is appropriate for the screening scenario.
9Mass 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 TRIM.FaTE runs supporting Tier 1 (discussed in this section) indicate that average chemical deposition rates at
the parcel farthest from the emission source (e.g., parcel 5 in the farm layout of Exhibit 11, which is 5-10 km from the
emission source) are between 1 and 2 orders of magnitude smaller than those within 1 km of the source (e.g., parcels
1 and 2 in the farm layout of Exhibit 11), depending on the chemical. The large distance from the eastern edge of the
watershed to the lake or farm attenuates transport of chemical mass by erosion and runoff, dampening the effect of
including additional deposition beyond 10 km.
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Exhibit 11. TRIM.FaTE Surface Parcel Layouts
10.125 km extent (10.0 km from source center point)
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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 (oy, 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).10 For a relatively neutral atmosphere (stability class D), o at 10
km is about 550 m using this estimation. In a Gaussian distribution, about 99.6 percent of the
plume spread area is contained within 3o of the median line. Therefore, the plume o was set at
3 times 550 m, or approximately 1.75 km north and south from the centerline at a distance of 10
km. The total plume width at 10 km is twice that 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 area of each parcel would encompass similar chemical mass (i.e., larger area for a parcel
farther from the source would encompass a similar total chemical mass because concentrations
per unit area would be lower than for a smaller parcel closer to the source with higher
concentrations per unit area).
The depth of the surface soil compartments was set to 1 cm, except for the farm parcel, 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 air parcel layouts mirror those of the surface parcel layout, except that the air parcels over
the lake and farm encompass the areas north and south of the lake and the farm.
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
discusses the selection of values used for key properties of the soil, water, and sediment
compartments. Also discussed are chemical properties related to watershed and water body
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 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, parameter values were set to satisfy two equations to balance water
volume. The first equation (Equation 2) maintains a balance of water entering and leaving the
terrestrial portion of the scenario:
10http://www.epa.qov/scram001/userq/reqmod/isc3v2.pdf
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[total precipitation] = [evapotranspiration] + [total runoff] Eqn. 2
In Equation 2, total runoff is equal to the sum of overland runoff to the lake and seepage to the
lake via groundwater. Evapotranspiration represents that water released to air from plants in
vegetated parcels.
Equation 3 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] Eqn. 3
Note that TRIM.FaTE uses all these properties with the exception of evapotranspiration, which
is part of the water balance calculation outside TRIM.FaTE. 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 facility 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/year based on data reported by Morton (1986) for various lakes. The
runoff rate was defined to be both spatially 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/year, 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 Erosion and Runoff
Erosion and runoff are important surface transport algorithms for modeling chemical transport in
TRIM.FaTE. Model input parameters for erosion include (1) parcel-specific erosion rates (in
kg/m2-day); and (2) inter-parcel erosion links (directing erosion to a specific parcel or parcels).
Model input parameters for runoff include (1) parcel-specific runoff rates (in m3/m2-day); and (2)
inter-parcel runoff links (as defined above). TRIM.FaTE uses those properties for chemical
transport only; movement of soil and water into and out of parcels are assumed to balance so
that there is no net change.
To establish soil erosion and runoff rates into the lake and onto the farm parcel, mean values,
as estimated or measured in several studies, were used (Bajracharya et al., 1998; Gaspar et al.,
2013; Schimmack et al., 2002; Young et al., 2014). Separate sites and measurement methods
across the studies were treated as distinct observations, for a total of eight mean deposition
rates to represent a distribution of values for varying landscapes. Use of mean values from
multiple data sets limits the influence of extreme measured values within any one data set.
Combining observations from different sites and measurement methods effectively combines
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variability and uncertainty distributions. To ensure an appropriate level of health protectiveness
in this context, the 90th percentile of the mean soil deposition rate was used in the RTR
screening scenario for all chemicals and for all tiers. This corresponds to a soil deposition rate
of 3 mm/year onto the farm parcel, which is achieved in the Tier 1 layout by setting erosion and
runoff onto the farm parcel from neighboring parcels at 60 percent. Runoff and erosion patterns
were exactly aligned, instead of setting distinct values for the two processes. For the lake only
scenario, 100 percent of the erosion and runoff from neighboring parcels enters the lake. This
assumption is both health protective and physically plausible in terms watershed dynamics and
based on the lake flush rate it implies.
2.3.3.3 Sediment Balance
A simplified balance of sediment transfers between the watershed and the lake also was
maintained for the scenario via parameterization of sediment-related properties. As with water,
TRIM.FaTE does not internally balance sediment mass; calculations external to TRIM.FaTE
balanced sediment gains and losses to set relevant parameter values. The sediment balance
maintained is described by Equation 4, 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] Eqn. 4
The second term (removal of sediment from the water column via outflow) is represented in
TRIM.FaTE by the lake flush (or turnover) rate. The third term (sediment burial) is the transfer of
sediment from the unconsolidated benthic sediment to the consolidated sediment layer below.
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 in the
lake is assumed to remain constant, and the flush rate of the lake (calculated via the water
balance approach described above) allows an estimate of sediment removal from the modeling
domain via lake water outflow. The difference between these sediment fluxes equals the
sediment burial rate, which 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. TRIM.FaTE calculates burial rate as 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 top,
unconsolidated benthic sediment layer). TRIM.FaTE keeps a constant volume of particles in the
unconsolidated sediment layer. The density of solid particles is the same for both particles
suspended in the water column and for benthic sediments; therefore, the mass of solid 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 0.0026 kg/m2-day, based on calculations using the USLE. The
HHRAP documentation notes that the USLE equation sometimes overestimates sediment
loading to a lake from the surrounding watershed (U.S. EPA 2005a). For the Tier 1 scenario,
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however, this possible bias is appropriate because it is health protective.11 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
move directly to a sink and, therefore, do not enter the Tier 1 scenario lake. The overland
transport of sediment to the lake from Parcel 4 also occurs via a direct link; however, in reality,
the overland runoff and erosion would be attenuated by the intervening soil parcels. That
attenuation is simulated by using a lower sediment delivery ratio in the USLE as applied to
Parcel 4.
Using the calculated surface soil erosion rates for the scenario, the total average daily sediment
load to the lake from the watershed is about 12,050 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 eventually
transferred to the sediment burial sink.
2.3.4 Meteorology
Meteorological properties used in TRIM.FaTE algorithms include air temperature, air 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 to 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 homegrown farm foods and self-caught fish, with
all farm foods and fish obtained from locations receiving chemicals emitted from the local
source). Ensuring that the meteorological parameters were not overly protective of health, such
as always having the wind blow toward the location of interest, however, was also important to
avoid too many false positives.
The meteorological data for the screening scenario are intended to represent a location with a
low wind speed, a wind direction primarily over the simulated watershed, a low mixing height,
and a relatively high amount of total precipitation falling on the watershed. The values used
were based on the distribution of values for U.S. locations as specified in Exhibit 12, but an
artificial data set was compiled for this screen and not linked to any real location (for example,
temporally variable meteorological parameters were changed only on a daily basis). Using a
daily time step instead of an hourly time step substantially reduces required model run time.
Meteorological inputs are summarized in Exhibit 12.
11 Based on sensitivity analysis, a higher erosion rate will both increase surface water concentrations and decrease
surface soil concentrations; the relative impact on resulting concentrations, however, will be proportionally greater in
the water body.
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Exhibit 12. Summary of Key Meteorological Parameter Inputs
Parameter
Selected Value
Justification
Air
temperature
Constant at 298
Kelvin
Recommended default value listed in HHRAP (U.S. EPA 2005a).
Value is similar to the mean maximum daily temperatures in May
and September in much of the U.S. mid-Atlantic, mid-West, and
Great Plains, according to 1981-2010 climatology.3
Mixing height
Constant at 226 m
Value is ~5th percentile of median hourly mixing heights recorded
at 824 meteorological stations across the United States from
January 1-December 31, 2016.
Wind
direction
Blows from source
parcel into scenario
domain (west to
east) 3 days per
week (roughly 43%
of the week); during
other times does not
blow into domain
Wnd blowing toward a location of interest (e.g., toward a lake or
farm) will move more emitted chemical mass over the location of
interest than wind blowing in other directions. For much of the U.S.
mid-Atlantic and western regions, the wind generally blows
eastward.3 Among the NOAA 1981-2010 normalized wind-vector
data, the average wind direction had a strong eastward component
at over one-third of the stations.13 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% of the time (review of wind direction data
compiled by the National Weather Service; NCDC 1995). This
pattern is approximated in the RTR scenario with a configuration in
which the modeled domain is downwind of the source 3 out of
every 7 days.
Horizontal
wind speed
Constant at 1.6
m/sec
Set to ~5th percentile of median hourly wind speeds, partitioned by
eight wind directions, recorded at 824 meteorological stations
across the United States from January 1-December 31, 2016.
Precipitation
frequency
Precipitation occurs
3 days per week
(roughly 43% of the
week); wind
direction blows into
domain 2 of these
days (roughly 29%
of the week)
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, according to 1961-1990 climatology.0
Total
Precipitation
1.47 m/yr
1.47 m/yr approximates the 95th percentile of annual average
precipitation for 824 meteorological stations across the United
States. Where available (813 meteorological stations), annual
precipitation is the 30-year normal valueb; where normal values
were unavailable, annual average precipitation was calculated from
precipitation measured at the station from January 1-December
31, 2016.
National Oceanic and Atmospheric Administration U.S. Climate Atlas for 1981-2010: https://www.ncdc.noaa.gov/climateatlas/.
bNational Oceanic and Atmospheric Administration 1981-2010 Climate Normals; https://www.ncdc.noaa.gov/data-access/land-
based-station-data/land-based-datasets/climate-normals/1981-2010-normals-data.
°A graphical view of U.S. rainfall for 1981-2010 climate normals was available for precipitation amount but not precipitation
frequency. Instead, we used a map of precipitation frequency based on 1961-1990 climate normals;
https://www.ncdc.noaa.gov/cgi-bin/climaps/climaps.pl. Regional patterns of rainfall frequency could have changed between
1961-1990 and 1981-2010.
The sensitivity of modeled PB-HAPs to changes in these meteorological variables was tested.
Lower wind speeds and mixing heights affected concentrations the most. Lower wind speeds
should increase localized pollutant deposition onto the soil and lower mixing heights reduces the
volume of air in which emissions are mixed and diluted. The wind speed and mixing height used
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for the screening scenario were 1.6 m/s and 226 m, respectively, approximating the 5th
percentile values among 824 meteorological stations in the contiguous United States.
2.3.5 Aquatic Food Web
The lake aquatic food web is an important part of the screening scenario because 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 for all chemicals except arsenic,
for which water-biota and sediment-biota bioaccumulation factors were used instead.
For the biokinetic approach, primary producers (first trophic level) in the TRIM.FaTE lake are
algae and macrophytes in the water column and detritus in the sediments (the latter simulated
as sediment particles). Algae are represented as a phase in the water column and macrophytes
are represented in a single but separate compartment. Zooplankton (another compartment) feed
on algae in the water column, while benthic invertebrates (a separate compartment) consume
detritus that settles to the sediment compartment. In the water column, small young-of-the-year
fish and minnows that feed on zooplankton and phytoplankton are represented by a single
water-column herbivore (WCH) compartment. The small fish are in turn consumed by larger or
"pan" fish (e.g., bluegills, white perch), represented by a single water-column omnivore (WCO)
compartment, which are in turn consumed by the top consumers (e.g., gar, pickerel)
represented as a single water-column carnivore (WCC) compartment. The invertebrates in the
sediments of the benthic environment support bottom-feeding fish, or benthic omnivores (BO),
of small to moderate size, which in turn are consumed by large bottom-feeding fish (e.g.,
catfish) in the benthic carnivore (BC) compartment. For TRIM.FaTE to provide reasonable
predictions of the distribution of a chemical mass (and thereby chemical concentrations) across
biotic and abiotic compartments in aquatic systems, the biomass of the biotic compartments
must represent all biota in the system and the distribution of biomass among trophic groups (or
compartments) 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). As expected, 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). That initial
research suggested that a lake of approximately 50 hectares (ha) or 120 acres could support
high trophic level predatory fish (i.e., WCC).
The RTR Tier 1 scenario includes a 47-hectare (116-acre) lake, given the lake parcel shape and
overall size of the defined watershed in the screening scenario. The fish types, biomass, diet
fractions, and average individual body weights for the Tier 1 scenario are listed in Exhibit 13.
The total biomass for all fish compartments was assumed to be 5.7 grams wet weight per
square meter based on Kelso and Johnson (1991) for clear-water lakes in Ontario. That
assumption yields health protective (i.e., higher) estimates of chemical concentrations in fish
than would the assumption of higher standing fish biomass and fish productivity for lakes
characteristic of warmer climates.
For arsenic, freshwater fish bioaccumulation factors (BAFs) and biota-sediment accumulation
factors (BSAFs) were identified from the literature (see Attachment A).
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Exhibit 13. Aquatic Biota Parameter Values for the TRIM.FaTE Screening Scenario
TRIM.FaTE
Compartment
Organisms in
Compartment
Biomass
Diet
Avg. Body
Weight (kg)
Areal density
(gwetweight/m^)
Fraction of Total
Fish Biomass
Algae3
green algae,
diatoms, blue-
green algae
7.95
Autotrophic
Zooplankton
water fleas,
rotifers,
protozoans
6.36
100% algae3
5.7E-8
Macrophyte
hydrilla, milfoil
500
-
-
-
Water column
planktivore/
herbivore (WCH)
young-of-the-
year, minnows
2.0
0.35
100% zooplankton
0.025
Water column
omnivore (WCO)
bluegill, white
perch
0.5
0.08
100% water
column planktivore
0.25
Water column
carnivore (WCC)
largemouth
bass, walleye
0.2
0.035
100% water
column omnivore
2.0
Benthic
invertebrates
(Bl)b
aquatic insect
larvae,
crustaceans
20
detritus in
sediments
0.000255
Benthic
omnivore (BO)
small catfish,
rock bass
2.0
0.351
100% benthic
invert.
0.25
Benthic
carnivore (BC)
large catfish,
sculpins
1.0
0.175
50% benthic invert.
50% benthic
omniv.
2.0
Total Fish Biomassc
5.7
1.00
-
-
Acronyms and abbreviations: Avg. = average; invert. = invertebrate; omniv. = omnivore.
aAigae is modeled as a phase of surface water in TRIM.FaTE (i.e., surface water has three phases: aqueous, particulate, and algal).
bBenthic invertebrates include aquatic insects (e.g., nymphs of mayflies, caddisflies, dragonflies, and other species that emerge from
the water when they become adults), crustaceans (e.g., amphipods, crayfish), and mollusks (e.g., snails, mussels).
Total fish biomass does not include algae, macrophytes, zooplankton, or benthic invertebrates.
2.3.6 Using TRIM.FaTE Media Concentrations
The Tier 1 scenario TRIM.FaTE outputs include average PB-HAP concentrations in air and air
deposition rates for each year and for each air parcel of the model scenario. In each surface
parcel of the scenario, TRIM.FaTE models wet and dry deposition of chemicals to surface soil
compartments (and surface water). From surface soils, for each parcel TRIM.FaTE estimates
transport of chemicals downward through root-zone and vadose-zone soils as well as runoff
and erosion to the lake. For each air parcel, air concentrations are provided. For the lake, the
multimedia risk screening approach uses TRIM.FaTE-estimated concentrations in the BC and
WCC fish compartments along with adult human fish-ingestion rates to estimate an adult's
exposure via local fish consumption. For the farm ingestion exposure calculations, the RTR
multimedia screening approach calculates direct exposures via incidental soil ingestion and
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indirect exposures via transfers from soil and air into various types of produce, livestock, and
animal products, which then are ingested by humans.
To ensure health protective calculations, the locations (i.e., parcels) with the highest chemical
concentrations predicted by TRIM.FaTE provide the input data for the multimedia exposure
calculations. For Tier 1, we assume that the highest air concentrations and deposition rates
occur in the parcels closest to the source. For the farm, those locations also receive the majority
of chemical from the rest of the simulated watershed by erosion and runoff. The assumptions
are summarized in Exhibit 14.
Exhibit 14. Spatial Considerations—TRIM.FaTE Results Selected for Calculating Farm-
food and Fish Media Concentrations and Receptor Exposures
TRIM.FaTE Output Used in Human
Exposure Calculations
Representative Compartment
Layout3
Concentration in air, for uptake by
plants via vapor transfer
Air compartment in air Parcel 2A (air
over tilled soil)
Farm (bottom of
Exhibit 11)
Deposition rates, for uptake by farm
produce
Deposition to surface soil compartment
in surface Parcel Farm (tilled soil)
Farm (bottom of
Exhibit 11)
Concentration in surface soil, for
incidental ingestion by humans and
farm animals
Surface soil compartment in surface
Parcel Farm (tilled soil)
Farm (bottom of
Exhibit 11)
Concentration in soil, for uptake by
farm produce and animal feed
Surface soil compartment in surface
Parcel Farm (tilled soil)
Farm (bottom of
Exhibit 11)
Concentration in fish consumed by
fisher
Water column carnivore compartment in
lake (50% offish consumed) and benthic
carnivore in lake (50% offish consumed)
Lake (top of
Exhibit 11)
aThe Tier 1 screening scenario is based on the combination of exposures from soil, farm produce, and farm animals (from the
farmer scenario, spatial layout shown at the bottom of Exhibit 11) and from fish (from the fisher scenario, spatial layout shown at
the top of Exhibit 11). Both the farm and the lake are located 0.5 km from the facility.
TRIM.FaTE can output "instantaneous" chemical concentrations at the end of a short, user-
specified time step (e.g., 1 hour, 4 hours, 1 day) and also can be configured to calculate
temporal averages (e.g., annual averages). For the Tier 1 scenario, TRIM.FaTE results are
saved for each 24-hour period, because wind direction and precipitation input to TRIM.FaTE
change on a daily (not hourly) basis. The annual average concentration equals the average of
the 365 daily estimates. The simulation runs for 50 years, and the concentrations at the end of
year 50 are used to estimate human exposures (i.e., we do not use earlier or time-weighted
concentrations for PB-HAPs in soils and fish over the duration of the facility operation to
estimate human exposures).
For arsenic, cadmium, POM, and dioxins, TRIM.FaTE-estimated concentrations in
environmental media are close to steady state (i.e., almost constant from year to year) by year
50. Although mercury concentrations are continuing to increase by year 50 in the screening
scenario, the rate of increase in mercury concentrations in soils and fish is much slower by year
50 than in the first 3-4 decades
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2.4 Description of Human Exposure and Risk Estimates
This section describes the approach for estimating chemical concentrations in farm-food
products (Section 2.4.1); estimating human exposures associated with ingestion of those
products, incidental ingestion of soil, ingestion offish, and infant consumption of breast milk
(Section 2.4.2); and characterizing screening-level human health ingestion risks (Section 2.4.3).
The multimedia risk screening approach uses calculates partitioning of PB-HAPs into farm
produce using TRIM.FaTE-estimated chemical concentrations in soil, air concentrations, and
wet and dry chemical deposition rates. It also computes total ingestion exposure as described in
this section. Attachment B describes the multimedia exposure and risk calculations further.
Section 2.4.4 summarizes the Tier 1 assumptions.
2.4.1 Calculating Concentrations in Farm Foods
As discussed above and shown Exhibit 6, the RTR multimedia risk screening approach
estimates PB-HAPs concentrations in farm foods, including:
. Exposed and protected fruit,
. Exposed and protected vegetables,
. Root vegetables,
. Beef,
. Dairy products,
. Pork, and
. Poultry and eggs.
PB-HAP concentrations in these products are calculated with algorithms from HHRAP (U.S.
EPA 2005a). HHRAP also provides plant- and animal-specific parameter values that can be
used to calculate media concentrations, including chemical-specific transfer factors.
2.4.2 Ingestion Exposure
The multimedia risk screening approach estimates average daily doses (ADDs) of ingested
chemical, normalized to body weight, for the exposure pathways listed in Exhibit 15.
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 a 99th percentile value). All media are from locations receiving the highest rate
of deposition from the modeled source. Although this approach could overestimate total
chemical exposure for an individual (i.e., total food ingestion rate is extremely high with an
upper-percentile rate for each food type), it avoids underestimating exposure for any single
farm-food type. The exposure characteristics selected for the Tier 1 scenario are summarized in
Exhibit 16.
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Exhibit 15. Summary of Ingestion Exposure Pathways
Ingestion
Exposure
Pathway
Medium
Ingested
Intermediate Exposure
Pathway - Farm
Animals3
Environmental Uptake Route
Medium
Process15
Incidental
ingestion of soil
Unfilled surface
soil
NA
Surface soil
Deposition; transfer via
erosion and runoff0
Consumption of
fish
Fish from local
water body
NA
Fish tissue
Direct uptake from water and
consumption of food
compartments modeled in
TRIM.FaTE0
Consumption of
breast milk,
infants onlyd
Breast milk
NA
Breast milk
Contaminant ingested by
mother partitions to breast
milk
Consumption of
produce
Aboveg round
produce,
exposed fruits
and vegetables
NA
Air
Air
RZ soil
Deposition on leaves/plants
Vapor transfer to leaves
Root uptake
Above- and
belowg round
produce,
protected fruits
and vegetables
NA
RZ soil
Root uptake
Consumption of
farm animals
and related
food products
Beef
Ingestion of forage
Air
Air
RZ soil
Direct deposition on plant
Vapor transfer to plant
Root uptake
Ingestion of silage
Ingestion of grain
RZ soil
Root uptake
Ingestion of soil
Surface soil
Ingestion while grazing
Dairy (milk)3
Ingestion of forage
Air
Air
RZ Soil
Direct deposition on plant
Vapor transfer to plant
Root uptake
Ingestion of silage
Ingestion of grain
RZ Soil
Root uptake
Ingestion of soil
Surface soil
Ingestion while grazing
Pork
Ingestion of silage
Air
Air
RZ soil
Direct deposition on plant
Vapor transfer to plant
Root uptake
Ingestion of grain
RZ soil
Root uptake
Ingestion of soil
Surface soil
Ingestion from surface
Poultry
Ingestion of grain
RZ soil
Root uptake
Ingestion of soil
Surface soil
Ingestion while foraging on
grains spread on ground
Poultry (eggs)3
Ingestion of grain
RZ soil
Root uptake
Ingestion of soil
Surface soil
Ingestion while foraging
Abbreviations: NA = not applicable; RZ = root-zone.
Calculation of intermediate exposure concentrations were required only for the farm animal/animal product ingestion pathways.
bProcess by which HAP enters medium ingested by humans.
°Modeled in TRIM.FaTE.
dThe infant consumption of breast milk pathway is discussed in Section 2.4.2.2.
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Exhibit 16. Overview of Exposure Factors Used for RTR Tier 1 Ingestion Screen3'"
Exposure Factor
Selection for Screen
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 up to 70 years)
Body weight (BW; varies by age)
Weighted mean of national distribution (from
Chapter 8 of U.S. EPA 2011 a; see Exhibit B-14 in
Attachment B).
Ingestion rate (IR) for farm produce and animal
products other than fish (varies by age and food
type)
90th percentile of distribution of consumers who
produce own food (see Exhibit B-16 in
Attachment B); values from Chapter 13 of U.S.
EPA (2011 a) not adjusted for proportion of those
surveyed who did not eat food type during the
week covered by the survey.
Ingestion rate for fish
For adults, 99th percentile as-prepared ingestion
rate representative of subsistence fisher woman.
For children, based on 99th percentile, as-
prepared, consumer-only, national ingestion rates
- adjusted (see Exhibit B-17 in Attachment B).
Exposure frequency (EF)
350 days/year (i.e., 2 weeks away from home per
year) (from Chapter 6 of U.S. EPA 2005a).
Exposure duration (ED)
For carcinogens: 70-yr lifetime.
For noncancer effects: varies by chemical
(i.e., whether effect occurs during critical window in
development or effect requires chronic exposure
(i.e., more than 7 years of a human lifespan).
Fraction contaminated (FC) (could vary by media
consumed)0
1.0 (i.e., all ingested fish and farm foods and soils
are from most contaminated parcel).
Cooking lossesd
Assumed to be "typical"; varies by food product
(see Exhibit B-24 in Attachment B). Cooking
losses were not considered for fish consumption
because ingestion rates are "as prepared" values.
Chemical concentration adjustment factors due to
fish cookinge
Arsenic = 1.5
Cadmium = 1.5
Mercury = 1.5
Dioxin = 0.7
POM = 1.0
aData for exposure characteristics are presented in Attachment B. Exposure parameter values were based on data obtained
primarily from the Exposure Factors Handbook (U.S. EPA 2011a). See Attachment B for details.
bExposure factor inputs are used in calculating ADD estimates for each exposure pathway. ADD equations for each pathway
evaluated in this screen are provided in Attachment B.
^'Fraction contaminated" represents the fraction of food product that is from the contaminated parcels in the screening scenario.
Because ingestion rates reflect intake of home-produced foods, a fraction contaminated of 1.0 is used.
dCooking 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.
eBecause "as consumed," fish consumption rates are used with whole-fish concentrations, adjustment factors are applied to the fish
tissue concentrations to reflect changes in concentrations due to cooking. See Attachment B, Section B.6.4.4 for additional
discussion.
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2.4.2.1 Calculating Average Daily Doses
The multimedia risk screening approach 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 in EPA's Multimedia, Multipathway, and Multireceptor Risk
Assessment (3MRA) Modeling System (U.S. EPA 2003a), with the exception of values for
exposure factors, which were updated using EPA's 2011 Exposure Factors Handbook. The
ingestion exposure modeling approach in 3MRA is conceptually similar to that presented in
HHRAP and frequently used in risk assessments (Equation 5).
(
ADD(yi) ~
where:
C(j) x IR,„,, x FCm x ED,
(y,i)
(0
"Yy)
Y
BW(y) /AT(y)
EF,
(y)
365 days
Eqn. 5
ADD(yi) —
C(i) -
Average daily dose for age group y from food type or ingestion medium /' (mg
chemical/kg body weight-day)
Concentration of chemical in food type /' harvested from the contaminated area
(mg chemical/kg food or mg food/L water)
IR(yj) = Ingestion rate for age group y of food type /' (kg/day or L/day)
FCo) = Fraction of food type /' 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)
Averaging time for calculation of daily dose (years) for age group y, set equal to
AT(y) -
ED
EF(y) = Annual exposure frequency for age group y (days)
A discussion of exposure dose estimation and the equations to calculate ADDs for each
ingestion pathway are provided in Attachment B.
2.4.2.2 Infant Ingestion of Breast Milk
A nursing mother exposed to contaminants by ingestion can pass the contaminants to her infant
through breast milk (ATSDR 1998). The nursing infant's exposure is estimated from chemical
concentrations in breast milk, which are estimated based on the mother's chemical intake rates.
Reports of bioaccumulation of lipophilic compounds, such as polychlorinated biphenyls (PCBs),
polychlorinated dibenzofurans (PCDFs), and dioxins (PCDDs), are prevalent in the scientific
literature. Due to their high lipophilicity, these compounds partition almost exclusively into body
fats, which include the high-fat content of a mother's breast milk (U.S. EPA 1998). PCBs,
PCDFs, and PCDDs are frequently reported as contaminants in human breast milk, usually at
concentrations resulting in higher daily doses to infants than their mothers were likely to have
ingested (Trapp et al. 2008). Lipophilic compounds accumulated overtime in maternal fat
reserves can be mobilized into the fats of breast milk, and lactation is a mode of excreting the
compounds. Once ingested by an infant, they can accumulate in their body fats. Other organic
compounds, with lower octanol-water partition coefficients, such as phenol, benzene,
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halobenzenes, and POM, are found in both the fat and the aqueous phases of breast milk.
Those compounds accumulate to a limited degree in body fats and are excreted from the
mother in bile (to feces) and in aqueous phase in urine. In addition, humans can metabolize
many POMs to polar metabolites which also are excreted in urine (ATSDR 1995). Inorganic
forms of heavy metals, such as arsenic, lead, cadmium, and mercury, tend not to partition to
body fats and are excreted from the body in urine, although they also have detected in the
aqueous phase of the breast milk. Thus, for the PB-HAPs assessed for RTR, only for PCDFs
and PCDDs is it possible for a substantial proportion of an individual's total lifetime cancer risk
to result from breast feeding in the first year of life; therefore, dioxins are the only PB-HAP
evaluated for infant exposures via breast milk for RTR at this time. This approach is consistent
with EPA's HHRAP (U.S. EPA 2005a). Methyl mercury is evaluated for RTR using its RfD for
pregnant women.
The breast-milk ingestion pathway is included in computing total exposure of a person to dioxins
over their lifetime for developing the screening threshold emission rate for dioxins. In the
absence of congener-specific data, all dioxin congeners were assumed to accumulate in breast
milk to the same degree as 2,3,7,8-TCDD.
2.4.3 Calculating Risk
The multimedia risk screening approach calculates excess lifetime cancer risk and noncancer
hazard (expressed as the hazard quotient or HQ) using the calculated ADDs and oral cancer
slope factors (CSFs) and toxicity reference doses (RfDs), respectively. The CSFs and RfDs for
the PB-HAPs included in the RTR tiered screening approach are presented in Exhibit 17 and
are discussed in more detail in Attachment B.
Exhibit 17. Dose-response Values for PB-HAPs in RTR Ingestion Screening Scenario
PB-HAP
CSF
([mg/kg-day]-1)
Source
RfD
(mg/kg-day)
Source
Inorganics
Arsenic compounds (as As)a
1.5
IRIS
not critical health endpoint
Cadmium compounds (as Cd)ab
not available
1E-3
IRIS
Elemental mercury0
not available
not available
Divalent mercury30
not available
3E-4
IRIS
Methyl mercury3
not available
1E-4
IRIS
Organics
Benzo[a]pyrene (BaP)3 d
1.0
IRIS
not critical health endpoint and no RfD
2,3,7,8-TCDDe
1.5E+5
ORD
not critical health endpoint
Notes: CSF = cancer slope factor; RfD = reference dose; IRIS = EPA's Integrated Risk Information System; ORD = EPA's Office
of Research and Development; TCDD = tetrachlorodibenzo-p-dioxin; POM = polycyclic organic matter; PB-HAP = persistent and
bioaccumulative hazardous air pollutant.
aSource: U.S. EPA Integrated Risk Information System (see U.S. EPA 2017b).
bRfD for cadmium in food (not water).
°Exposure to elemental mercury is not assessed in the multipathway screening due to limited information on oral dose-response.
Exposure to divalent mercury is not assessed in the multipathway screening due to its higher (i.e., less stringent) RfD and lower
bioaccumulation potential in the ingested food products in the screening, relative to methyl mercury.
dEPA considers BaP to be a mutagenic carcinogen (IRIS).
eSource: U.S. EPA (1997a).
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The equations used to estimate cancer risk and noncancer hazard also are provided in
Attachment B. Exposure and risk estimation follows the age-groupings EPA recommends for
estimating cancer risks for each life-stage (U.S. EPA 2005c), and total lifetime cancer risk is the
sum of those age-specific cancer risks. The approach also conforms with EPA guidance on
estimating cancer risks for chemicals with a demonstrated mutagenic mode of action, applying,
as appropriate, age-adjustment factor to account for the higher sensitivity of developing children
to mutagens compared with adults (U.S. EPA 2005c,d,e).
Accordingly, estimated individual cancer risks for BaP (and other POM), which has a mutagenic
mode of carcinogenesis, were adjusted upward to account for the stronger mutagenic potency
of these compounds during childhood, as specified by EPA in its supplemental guidance for
cancer risk assessment (U.S. EPA 2005c). Specifically, cancer potency for BaP (and all POM)
is assumed to be tenfold greater for the first 2 years of life and threefold greater for the next
14 years (U.S. EPA 2005c,e). The cancer potency adjustment for chemicals with a mutagenic
mode of action is discussed in Attachment B, Section B.5.1.
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 out. The degree to which the scenario is health protective overall depends on the
combination of parameters for which "upper-end" percentile or health protective values are used
instead of nationwide mean values. Exhibit 18 summarizes influential parameter values for this
scenario and indicates the likely degree of health protectiveness associated with each. Although
this summary does not quantify overall health-protective bias, it does demonstrate qualitatively
that the scenario generally overestimates exposure and therefore is unlikely to screen out
facilities that might pose risks to human health.
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 influences soil and air
concentrations and deposition rates used
to calculate chemical levels in farm foods.
Lake location
375 m from source;
generally downwind
Health Protective
Location influences contamination levels in
fish.
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 (i.e., not
"diluted" by averaging with less
contaminated areas farther from the
source).
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Characteristic
Value
Neutral or Health
Protective?
Comments on Assumptions
Size of lake
47 ha; about 3 m
average depth
Health Protective
Lake is just large enough to support an
aquatic ecosystem with high trophic-level
fish. The higher water content of larger or
deeper lakes would provide more dilution
of chemicals received.
Meteorological Inputs
Total precipitation
1.47 m/yr
Health Protective
Reflects particularly rainy areas of the
United States (see Exhibit 12 for source).
Higher precipitation rates result in more
wet deposition over the modeled domain.
Precipitation
frequency (with
respect to impacted
farm/lake)
Two thirds of total
precipitation fall on
farm/lake and
watershed
Health Protective
Most precipitation occurs when the
farm/lake are downwind of the source (see
Exhibit 12 for additional justification).
Wind direction
Farm/lake are
downwind 40% of
the time
Health Protective
Reflects areas of the United States with
particularly persistent wind flows (see
Exhibit 12 for additional justification).
Farm/lake located in the predominantly
downwind direction. Chemical deposition
over the watershed increases when winds
blow from the facility into the watershed.
Wind speed
1.6 m/sec
Health Protective
Reflects areas of the United States with
particularly low wind speeds (see
Exhibit 12 for source). Slower wind speeds
lead to more chemical deposition closer to
the facility (i.e., over the farm/lake).
Air temperature
298 K
Neutral
Recommended default value listed in
HHRAP (U.S. EPA 2005a). See Exhibit 12
for additional context.
Mixing height
226 m
Health Protective
Reflects areas of the United States with
particularly low mixing heights (see
Exhibit 12 for source). Lower mixing
heights decrease the volume of air in
which chemical mixing occurs, resulting in
higher chemical concentrations in air and
higher chemical deposition rates to the
watershed.
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 over-estimate
concentration in surface water.
Surface runoff into
lake, onto farm
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/farm.
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Characteristic
Value
Neutral or Health
Protective?
Comments on Assumptions
Surface water
turnover rate in
lake
About 12 turnovers
per year
Neutral
Consistent with calculated water balance;
reasonable in light of published values for
small lakes. Might overestimate flushing
rate if water inputs are also overestimated.
Note that after evapotranspiration,
remaining water volume added via
precipitation is assumed to flow into or
through lake.
Soil erosion from
surface soil into
lake
Varies by parcel;
ranges from 0.002 to
0.005 kg/m2-day
Health Protective
Erosion rates calculated using the
universal soil loss equation (USLE); input
parameter values were selected to favor
higher erosion rates (i.e., to move more
chemical from the watershed into the lake).
Might underestimate erosion for locations
with steeper slopes or more exposed soils.
Soil erosion from
surface soil onto
farm
About 0.003 kg/m2-
day
Health Protective
Erosion rates calculated using the USLE;
inputs parameter values were selected to
favor higher erosion rates. Might
underestimate erosion for locations
susceptible to high erosion rates; might
overestimate erosion for locations where a
farm is not an erosion sink in the
watershed. Higher erosion increases
concentration in soil (and farm foods).
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
bioaccumulation of chemicals in consumed
fish. Linear food-chains (instead of more
realistic food webs) maximizes
concentration of bioaccumulative
chemicals in higher trophic-level fish.
Parameters for Estimating Concentrations in Farm-Food-Chain Media12
Fraction of plants
and soil ingested
by farm animals
that is
contaminated
1.0 (all food and soil
from contaminated
areas)
Health Protective
Assumes all livestock feed sources
(including grains and silage) are derived
from land parcel with highest chemical
concentrations.
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.
12 The terms "farm foods" and "farm-food-chain" or "FFC" generally are interchangeable. In specific context, the farm-
food-chain includes soil in addition to farm foods.
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Characteristic
Value
Neutral or Health
Protective?
Comments on Assumptions
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.
Human Ingestion Exposure Assumptions
Combined
ingestion of farm-
food-chain media
and fish
High-end,
subsistence
ingestion rates for
both (i.e., 90th to
99th percentile)
Health Protective
Assuming combined high-end
consumption, consistent with subsistence
farming and fishing, likely overestimates
exposure to any single individual.
Ingestion rates for
all farm foods
All ingested foods
are home-grown
from impacted farm;
90th percentile
ingestion rate for
each of 10 foods
Health Protective
All food from contaminated farm; total food
ingestion rate (across 10 food categories)
for individual exceeds expected body
weight-normalized ingestion rates
(prevents underestimating any individual
food type). See Exhibit 16 for source.
Fish ingestion rate
Adult: 373 g/day
Child age groups:
1-2: 108 g/day
3-5: 159 g/day
6-11: 268 g/day
12-19: 331 g/day
Health Protective
The adult rate, the 99th percentile value for
adult females from Burger (2002), is
considered representative of subsistence
fishers.
Rates for children are based on the 99th
percentile, consumer-only fish ingestion
rates from EPA's 2002c Estimated Per
capita Fish Consumption in the United
States. Rates were adjusted to represent
the age groups used in the screening
scenario. See Exhibit B-17 in
Attachment B for a detailed discussion.
Exposure
frequency
Consumption of
contaminated food
items occurs 350
days/yr
Health Protective
All meals from local farm or fish products,
except for two weeks per year when
consumer is elsewhere. See Exhibit 16 for
source.
Body weight
Mean of national
distribution
Neutral
Note that this does not affect the body-
weight-normalized ingestion rates for
produce and animal products. See
Exhibit 16 for source.
Chemical-Specific Characteristics
General chemical
properties used in
fate and transport
modeling (Henry's
law, Kow, etc.)
Depends on
chemical
Neutral
Obtained from peer-reviewed sources;
intended to be representative of typical
behavior and characteristics. See
Attachment A and Attachment B for
additional information.
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Characteristic
Value
Neutral or Health
Protective?
Comments on Assumptions
"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. See
Attachment A and Attachment B for
additional information.
Dose-response
values
Neutral to Health
Protective
Values used are those determined to be
appropriate for risk assessment by
OAQPS; values are developed to be
health protective. See Exhibit 17 for
source.
Tier 1 screening threshold emission rates were calculated by conducting iterative model
simulations using the screening scenario described above to determine emission rates for
arsenic, cadmium, mercury, dioxins, and POM that correspond to a cancer risk of 1-in-one
million or a chronic noncancer HQ of 1. Given the generally health protective nature of the
scenario inputs, these screening threshold emission rates are appropriate for a Tier 1 screen.
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 emission rate for any
PB-HAP can be scrutinized further.
2.5 Evaluation of Screening Scenario
For a given source category, all facilities are reviewed to determine if emissions of any of five
PB-HAPs are reported. If any facility emits one or more of the PB-HAPs, the Tier 1 screen is
applied. Facility emissions of each PB-HAP are compared with the Tier 1 screening threshold
emission rate to determine the resulting SV. In Tier 1, the magnitude of an SV has a limited
implication for relative risk. For example, exceeding the screening threshold emission rate by a
factor of 60 for dioxins does not imply an actual cancer risk of 60-in-one million. Rather, an SV
of 60 implies that it is highly unlikely that the actual risk would exceed 60-in-one million, and
likely would be much lower.
The Tier 1 methods evaluate congener-specific differences in fate and transport and in toxicity
for dioxins and POMs. The final results are reported in 2,3,7,8-TCDD equivalents and
benzo[a]pyrene equivalents, respectively.
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 arsenic (Section 2.5.1),
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 focused on assessing the
behavior of these HAP categories in the environment, accumulation of these chemicals in fish
and farm foods, and the exposure pathways and chemicals that contributed most to human
risks.
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2.5.1 Arsenic
Arsenic is a natural component of the earth's crust and is found naturally in minerals, most often
as a compound with sulfide (HSDB 2009). Some of the largest anthropogenic sources of arsenic
to air are nonferrous metal mining and smelting, pesticide application, coal combustion, wood
combustion, and waste incineration (ATSDR, 2007). Inorganic arsenic has two stable oxidation
states, +3 and +5 (arsenite and arsenate, respectively).
2.5.1.1 Behavior in the Environment
Depending on its chemical form and source, arsenic can undergo a variety of transformations,
including oxidation or reduction, ligand exchange, precipitation out of solution when arsenate or
arsenite combine with iron, sulfur, or chloride in water, and biotransformation to or from organic
forms. Arsenic released to air from sources evaluated by the RTR program is primarily in
particulate form as highly soluble oxides (pentavalent arsenate or As(V) as the arsenite ions
H2ASO4" and HASO42"; trivalent arsenite or As(lll) as arsenous acid, H3AsC>3).Trivalent arsenite
predominates in releases to air from industrial processes. Compounds detected in air include
arsenic trisulfide from coal combustion, organic arsines from oil combustion, and arsenic
trichloride from waste incineration (ATSDR 2007).
Arsenic is found in soil as a result of natural processes and anthropogenic sources including
ash residue from power plants, smelting facilities, mining wastes, and industrial waste. Arsenic
is found in mixtures of mineral phases (e.g., co-precipitates, sorbed to soil particles). Arsenic
adsorbs to particulate matter in soils and sediments and tends to concentrate in the upper
layers of soil. Iron content strongly affects arsenic adsorption to soil particles (ATSDR 2007).
Arsenic has low to moderate mobility in clay soils, in which particles are small and total particle
surface area high, and much higher mobility in loamy and sandy soils, for which particle sizes
are larger, with less surface area per particle and per unit weight solid material.
Potential volatilization of arsenic from soil depends on its original form when deposited from air.
Some microorganisms can methylate some inorganic arsenicals, with a proportion of dimethyl
and trimethyl arsenic volatilizing to air. Soil particles adsorb other arsenic compounds,
depending on iron oxide and organic carbon content, limiting bioavailability and future
volatilization (HSDB 2009).
Arsenic exists primarily in the pentavalent form under oxidizing conditions, such as found in
surface water, and in the trivalent form in reducing conditions, such as found in groundwater
(ATSDR 2007). Arsenic transport in groundwater is determined by the chemical form of arsenic
and adsorption is based on the other materials present in the aquifer, as well as the pH of the
water (ATSDR 2007). Arsenic strongly sorbs onto sediments, and bacteria and fungi methylate
arsenic compounds to form dimethyl and trimethylarsines (HSDB 2009).
Bioaccumulation of arsenic in plants and organisms in water depends on factors including type
of water body, organism type, status in the food chain, concentration, and route of uptake.
Bioconcentration of arsenic occurs primarily in algae and invertebrates; however, bottom
feeders and predatory fish might accumulate arsenic from ingestion of sediments along with
prey. Arsenic does not appear to biomagnify in the aquatic food chain (ATSDR 2007). In a study
of bioaccumulation data for fish and invertebrates, bioconcentration factor (BCF) values ranged
from 0.048 to 1,390 (U.S. EPA 2003b).
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2.5.1.2 Arsenic Speciation Modeling Approach
Although inorganic arsenic exists in the environment as two predominant species—trivalent
arsenic (arsenite) and pentavalent arsenic (arsenate)—with distinct characteristics, the
modeling approach in TRIM.FaTE aggregated the two species for several reasons:
. EPA's Integrated Risk Information System (IRIS) provides one oral reference dose for
inorganic arsenic (i.e., an undifferentiated species). Although some investigators report
that trivalent arsenic is more toxic than pentavalent, the studies are in the context of
aquatic toxicity to fish and invertebrates, not human ingestion toxicity.
. All sources of arsenic biotransfer factors (which represent the ratio of chemical
concentration in produce to the chemical concentration in soil) and bioaccumulation
factors (which represent the ratio of chemical concentration in various aquatic trophic
levels to the chemical concentration in water) that we reviewed report values for total
inorganic arsenic, without specifying oxidation state.
. NEI emissions data usually are reported in unspeciated terms, like "total inorganic
arsenic." Estimating speciation for those emissions would require substantial research
or simplifying assumptions.
. Other EPA programs also model a single inorganic arsenic species (e.g., the recent
Risk Assessment of Spent Foundry Sands in Soil-Related Applications, U.S. EPA
2014c).
Modeling a single form of arsenic was implemented in both TRIM.FaTE and the multimedia
exposure and risk calculations:
. For parameters for which different values are available for trivalent and pentavalent
arsenic, the more health-protective value is used. If we could not predict which value
would be more protective a priori, the screening approach was performed with each
value, and the value that resulted in higher risk was chosen.
. Organic arsenic is not explicitly modeled. Ignoring potential methylation of inorganic
arsenic in soils and sediments is a health-protective assumption because inorganic
arsenic has been considered more toxic than organic forms (ATSDR 2007), although
some recent studies suggest that a portion of ingested organic arsenic might be
converted back to inorganic forms in animals (Carlin et al. 2005; U.S. EPA 2003b). For
farm produce and livestock, biotransfer factors are reported for total arsenic only.
Similarly, BAF and BSAF factors for fish are based on studies primarily of trivalent or
unspecified inorganic arsenic in water and sediments, respectively. The BAF and
BSAF factors are presumably based on total arsenic in the fish compared with
dissolved inorganic arsenic in the environmental medium (U.S. EPA 2003b).
2.5.1.3 Arsenic Aquatic Bioaccumulation Modeling Approach
In modeling transfers of arsenic through the aquatic food web, empirical BAFs and BSAFs were
used instead of the biokinetic approach, which is used for the other PB-HAPs in TRIM.FaTE.
Fish tissue concentrations of arsenic are calculated as the product of water column and
sediment arsenic concentrations (from TRIM.FaTE) and the empirical BAFs and BSAFs,
respectively, for freshwater. The screening approach, therefore, estimates arsenic
concentrations in fish in much the same way as in produce. Estimation of water-column fish
concentration using the BAF approach requires, as an input, the concentration of dissolved
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chemical in surface water. Because TRIM.FaTE outputs the total water-column concentration
(i.e., as both dissolved and suspended solids), this total water-column concentration is
multiplied by the fraction of mass dissolved (which is available from TRIM.FaTE HTML outputs)
to estimate the dissolved chemical concentration. This dissolved concentration is then multiplied
by the empirical BAFs to estimate water-column fish concentrations.
This approach can save time and effort because several arsenic-specific parameter values
(e.g., gill uptake rate, metabolic transformations, absorption efficiency across the gut, form-
specific elimination rates) are needed to implement the TRIM.FaTE aquatic food chain.
Moreover, those arsenic values would need to be coded in the TRIM.FaTE Java library for the
aquatic invertebrate and fish compartments. Although the empirical BAF approach is not mass-
balanced (i.e., does not remove the arsenic transferred to fish compartments from the sediment
and water-column compartments), we believe that the approach is adequate for the RTR screen
for three reasons: (1) total chemical mass in the fish compartments typically is small compared
with chemical mass in the surface water and sediment compartments; (2) RTR models focus on
concentrations at year 50, by which time simulated environmental concentrations are typically
close to steady-state; and (3) in past applications, the biokinetic food-chain models have been
calibrated using measured chemical concentrations in algae, zooplankton, and fish at different
trophic levels.
Arsenic concentrations tend to be the same or lower at successive trophic levels (e.g., it
biodiminishes instead of bioaccumulates; Williams et al. 2006). BAF values for arsenic also tend
to decrease with increasing water concentrations, indicating some physiological regulation by
fish (Williams et al. 2006). The BAF/BSAF-based approach developed for arsenic can be
applied to other chemicals in future.
2.5.1.4 Arsenic—Bioavailability in Soils Ingested by Humans
At the screening level, contaminants ingested with foods and with soils are assumed to be 100
percent bioavailable (i.e., all of the chemical ingested is absorbed from the exposure medium).
Although unlikely to be true (e.g., a few percent is expected to be eliminated with feces, and
some might be excreted in bile), the assumption is health protective and close to accurate for
many organic chemicals. Inorganic chemicals, on the other hand, might or might not be well
absorbed, particularly from soils with minerals and organic carbon to which inorganic chemicals
adsorb. For example, the RfD for cadmium is higher (less stringent) for its ingestion with food
(1E-03) than cadmium ingested with water (5E-04), meaning it is less bioavailable in food than
in water. For arsenic, the CSF is based on ingestion in drinking water. EPA's Superfund
Program has therefore investigated arsenic bioavailability from incidental ingestion of arsenic-
contaminated soils compared with its bioavailability in water to assist in setting target
concentrations in soils for site remediation.
Swine have served as an in vivo animal model by which to evaluate the bioavailability of
chemicals in soils for many years (primates also have been used on occasion; U.S. EPA
2012b). In the past decade, in vivo animal models have been extended to mice. Examining the
results across species, EPA developed an estimate of the bioavailability of arsenic in soils to
mammals, finding the upper 95th percentile value to approximate 0.60. The upper 95th
percentile values for swine, monkeys, and mice were estimated as 0.609, 0.327, and 0.502,
respectively (U.S. EPA 2012b). Thus, the value of 0.60 is likely to be a health-protective value
for humans and is used to estimate exposure from ingestion of soil.
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2.5.1.5 Arsenic Concentrations in Ingestible Products
Most non-inhalation exposure to arsenic outside of occupational settings is through dietary
intake. Arsenic in agricultural soils is largely immobile and remains in the upper soil levels
(ATSDR 2007). Most plants would accumulate arsenic initially released to air from stationary
facilities by uptake by the roots from the soil or by arsenic deposition on the leaves. Larsen et al.
(1992) found that arsenic emitted from burning arsenic-treated wood was taken up by kale from
arsenic deposited on leaves and that arsenic in potatoes and carrots came from both
atmospheric deposition to leaves and root uptake. In general, arsenic accumulation by plants
depends on the form(s) of arsenic in the environment and the species of plant. In the United
States, seafood (i.e., marine and estuarine fish and shellfish), meat, and rice have been
reported to contain the highest levels of arsenic. Arsenic has also been detected at low levels in
other foods (ATSDR 2007).
For the RTR screening scenario, the relative arsenic concentration estimates were consistent
with relative concentrations reported for soils, produce, and other farm-grown meat products.
2.5.1.6 Lifetime Average Daily Dose (LADD)
Exhibit 19 presents the contribution of the various ingested media to overall arsenic exposure
for different age groups. Ingestion of freshwater fish contributes approximately 8 percent of total
exposure, whereas direct soil ingestion contributes 5 percent. The remaining exposure is fairly
evenly distributed across farm foods such as fruit, vegetables, dairy, eggs, and meat. That
distribution appears reasonable, given that data (U.S. EPA 2012b; Williams et al. 2006) indicate
that freshwater fish accumulate substantially less arsenic from water than marine or estuarine
fish species.
Exhibit 19. Estimated Media Contributions to
Arsenic Ingestion Exposures and Lifetime Cancer Risks
¦ Freshwater Fish
¦ Fruits & Vegetables
¦ Meat, Dairy, & Eggs
¦ Soil
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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). Cadmium has one stable oxidation (or valence) state, +2.
2.5.2.1 Behavior in the Environment
Once emitted to air, cadmium in or on small airborne particles can travel long distances before
being deposited; however, most cadmium released to air is found in soils near facilities that
released it (ATSDR 2008).
The mobility of cadmium in soil depends strongly on soil pH, clay content, and availability of
organic matter—factors that determine whether the cadmium is dissolved or adsorbed 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 highly organic soils.
Nonetheless, some plant species absorb cadmium efficiently via their roots, thus providing an
entry point for cadmium into the terrestrial food chain (ATSDR 2008).
Cadmium in air can enter surface waters directly via wet and dry deposition and indirectly from
runoff and erosion of cadmium deposited to soil. Most cadmium compounds entering the water
column are quickly removed through adsorption to suspended particles or algae, with eventual
sedimentation. Cadmium that remains in the water column is expected to exist primarily as
dissolved cations, which are readily bioavailable to aquatic organisms.
Freshwater fish accumulate cadmium primarily through direct uptake of dissolved cadmium
through the gills, but also can accumulate cadmium ingested with their foods (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, bioaccumulation in freshwater
systems occurs mainly at lower trophic levels (Chen et al. 2000), primarily in phytoplankton and
zooplankton and in filter-feeding macroinvertebrates (e.g., bivalves; 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 can biodiminish in fish from one trophic
level to the next (Chen et al. 2000; Mason et al. 2000).
For the RTR screening scenario, the partitioning behavior modeled in TRIM.FaTE was
consistent with monitoring data for cadmium in the environment.
2.5.2.2 Concentrations in Foods
Most 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). Measured cadmium levels in
foods vary 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).
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The cadmium concentrations estimated with the RTR screening approach were consistent with
values reported in the literature. The products with higher reported cadmium levels in the
literature also contained the higher modeled concentrations.
2.5.2.3 Average Daily Dose
Exhibit 20 presents the average daily dose (ADD) received through each of the ingested media,
by age category, at a unit emission rate of 1-gram cadmium per day. This chart can be used to
evaluate the relative contributions of ingested media to the chemical HQ. (Using the cadmium
screening threshold emission rate would simply change the y-axis ADD and HQ labels; the
media contributions relative to each other would be unchanged). Fish ingestion dominates risk
for cadmium across all age categories, accounting for about 90 percent or more of the ADD for
all groups. The combined contribution from all other ingested media accounts for less than 10
percent of the total ADD for all age groups. Most of the additional exposure was from ingestion
of fruits and vegetables. The highest ADD is for children aged 1-2 years because of their high
food ingestion rate relative to body weight; thus, the exposure corresponding to this group
determines the screening threshold emission rate for cadmium (i.e., the rate at which the HQ for
this age category equals 1.0).
Exhibit 20. Estimated Media Contributions to
Cadmium Ingestion Exposures and HQs
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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, but with some divalent forms bound to
particles (U.S. EPA 1997b).
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2.5.3.1 Behavior in the Environment
Once emitted to air, mercury form and valence state can change in the atmosphere. Elemental
mercury (Hg2°) vapor is the most prevalent species of mercury in the atmosphere at ambient
temperatures. Due to its high vapor pressure, generally more than 98 percent of elemental
mercury remains in the atmosphere, where it is dispersed long distances on a global scale.
Divalent mercury can sorb to particles, but a large proportion is in vapor phase and quite
reactive with surfaces. Divalent mercury therefore deposits from air relatively quickly, more so in
its reactive gaseous phase (RGP) than in particulate phase (Landis et al. 2004; Cohen 2005).
Thus, divalent mercury deposits locally via wet, dry, or reactive deposition, where it adsorbs
tightly to soil particles (U.S. EPA 1997b). Divalent mercury also deposits to surface waters,
where it sorbs to particulate or dissolved organic carbon (Driscoll et al. 2007). Divalent mercury
in soil also can be methylated by microbes or reduced to elemental mercury, which volatilizes
back into the atmosphere. Most divalent mercury from atmospheric deposition will remain in the
soil profile, however, in the form of inorganic compounds bound to soil organic matter (ATSDR
1999). Although complexing with organic matter and several minerals in soil significantly limits
further aqueous mercury transport (e.g., via leaching or runoff), the tendency of mercury to form
these complexes depends on soil conditions such as pH, temperature, and soil humic content
(U.S. EPA 1997b). 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). More mercury
in soil is likely to reach surface waters via erosion than via runoff or leaching.
Mercury also deposits to lakes directly from air. Once in the water body, microbes can methylate
divalent mercury, particularly in the sediments. In addition, divalent and methyl mercury can be
further reduced to elemental mercury, which can volatilize to the atmosphere. Solid forms of
inorganic mercury compounds could adsorb to particulates in the water column or partition to
the sediment bed (U.S. EPA 1997b).
The solubility of mercury in water depends on the species and form of mercury present as well
as properties of the water such as pH and chloride ion concentration (ATSDR 1999). Low pH
favors methylation of mercury in the water column and sediments, typically performed by sulfur-
reducing bacteria in anaerobic conditions (e.g., anaerobic layer of sediments). Methyl mercury is
typically of greatest concern because it readily bioaccumulates and biomagnifies in aquatic
organisms. Once ingested by fish, methylmercury distributes to all tissues and binds to proteins,
thereby sequestering large amounts in muscle.
A considerable amount (25-60 percent) of both divalent mercury compounds and methyl
mercury is strongly bound to particulates in the water column (U.S. EPA 1997b). 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 Foods
Available data indicate that mercury bioaccumulates in plants, aquatic organisms, and terrestrial
animals, providing multiple ingestion exposure pathways (U.S. EPA 1997b; ATSDR 1999). Low
levels of mercury are found in plants, with leafy vegetables containing higher concentrations
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than potatoes, grains, legumes, and other vegetables and fruits (ATSDR 1999; U.S. EPA
1997b). 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 relatively low (ATSDR 1999). Concentrations of methyl mercury in fish are generally
highest in larger, older fish at the higher trophic levels (U.S. EPA 1997b).
Although data on mercury in foods other than fish are not abundant in the literature, estimated
relative mercury concentrations across food types are generally consistent with available
environmental monitoring data. The ingested media that most influenced the mercury HQs in
the model are presented in Exhibit 21. As shown, the dominant exposure pathway for all age
groups is ingestion of fish. In top trophic level fish, methyl mercury accounts for more than 95
percent of total mercury.
Exhibit 21. Estimated Media Contributions to Methyl Mercury Ingestion Exposures
and HQs
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2.5.3.3 Average Daily Dose
Exhibit 21 presents the ADD received through each ingested medium, by age category, at a unit
emission rate of 1 gram of divalent mercury per day. This chart can be used to judge the relative
contribution of ingested media to the methyl mercury HQ. (Using the divalent mercury screening
threshold emission rate would simply change the y-axis ADD and HQ labels; the media
contributions relative to each other would be unchanged). 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 ingested media 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 is used to determine the screening threshold emission rate for
mercury.
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2.5.4 Dioxins
Incineration and combustion processes are believed to be the primary sources for emissions of
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. Forest fires and agricultural field
burning also account for a large proportion of dioxins in soils and in ambient air (ATSDR 1998).
2.5.4.1 Behavior in the Environment
Dioxins emitted to the atmosphere can be transported long distances in vapor form or bound to
small particulates, depositing to soils and water bodies primarily via precipitation events in
otherwise pristine locations far from all sources. Although airborne dioxins are susceptible to
wet and dry deposition, most dioxins emitted to the atmosphere through incineration/combustion
processes that vent from tall stacks 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 in the subsurface soil profile, with erosion of contaminated soil
particles the only significant mechanism for transport to water bodies. Dioxin volatilization from
the soil surface can contribute to plant uptake via foliage because of the very high
bioaccumulation potential for TCDD (and presumably the other dioxins/furans) by plant leaves
from air (Trapp 1995).
Dry deposition of dioxins from the atmosphere to water bodies is another important transport
process. Because of their hydrophobic nature, most dioxins entering the water column are
expected to adsorb to suspended organic particles or partition to bed sediment, which appears
to be the primary environmental sink for this chemical group (U.S. EPA 2004c). Although dioxins
bound to aquatic sediment particles eventually become buried in consolidated sediments, some
resuspension and remobilization of congeners can occur if sediments are disturbed (e.g., by
benthic organisms; ATSDR 1998).
Bioaccumulation factors in fish are high as a result of the lipophilic nature of chlorinated dioxins.
Although the processes by which freshwater fish accumulate dioxins are not well understood,
both fish and invertebrates bioaccumulate congeners that have partitioned to sediment or have
become suspended in water (U.S. EPA 2004c). Because most dioxins in the aquatic
environment are adsorbed to suspended particles, however, direct uptake from the water is
unlikely to be the primary route of exposure for most aquatic organisms at higher trophic levels
(ATSDR 1998). At lower trophic levels, the primary route of exposure appears to be through
uptake of water in contaminated sediment pores, and the primary route of exposure in the
higher trophic levels appears to be through food chain transfer. Following ingestion, some fish
can slowly metabolize certain congeners, such as 2,3,7,8-TCDD, and release the polar
metabolites in bile. This process ultimately might limit bioaccumulation at higher trophic levels
(ATSDR 1998).
For the RTR screening scenario, the partitioning behavior modeled in TRIM.FaTE was
consistent with the behavior of 2,3,7,8-TCDD expected in the natural environment. Dioxins also
readily partition into breast milk, which has a high fat content, due to the lipophilic nature of
these compounds.
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2.5.4.2 Concentrations in Foods
The primary source of non-inhalation exposure to dioxins outside of occupational settings is
through dietary intake (ATSDR 1998). Available data indicate that dioxins concentrate in plants,
aquatic organisms, and animals, offering multiple ingestion exposure pathways. Actual
congener levels in foods, 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 uptake by plant foliage, which then can be consumed by larger animals.
Another major source of animal exposure to dioxins is through ingestion of contaminated soil.
Observed trends indicate that meat, dairy, and fish consumption are the dominant exposure
pathways for environmental dioxins, comprising 90 percent of dioxin dietary intake (ATSDR
1998). Consistent with the literature, the modeled concentration of 2,3,7,8-TCDD in the fish
compartment for the screening scenario was at least one order of magnitude greater than
concentrations in the other compartments. Among the compartments with the lowest
concentrations were fruits and vegetables, which do not readily accumulate 2,3,7,8-TCDD.
Ingestion of breast milk during infancy and fish ingestion contribute to over 97 percent of lifetime
dioxin exposure for 2,3,7,8-TCDD-equivalents 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 (ATSDR 1998). 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 screen.
2.5.4.3 Lifetime Average Daily Dose (LADD)
The contributions of the various ingested media to the lifetime average daily dose (l_ADD) (and
thus lifetime cancer risk) for the modeled dioxin congeners are presented in Exhibit 22. Based
on the models and assumptions used, exposure via breast milk ingestion during the first year of
life accounts for approximately 30 percent of the lifetime exposure for all congeners, while
exposure via ingestion of fish, soil, and the various farm foods varies across congeners largely
because of differences in physiochemical properties that drive environmental transport
processes (e.g., Kow, molecular weight). Some differences are also likely due to different
biological half-lives of congeners in plants and animals. The relative contribution of farm-raised
livestock and produce to total congener exposure is higher for the more highly chlorinated
dioxins, which are less soluble in water.
2.5.5 Polycyclic Aromatic Hydrocarbons and Other Polycyclic Organic Matter
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 anthropogenic PAH emissions (ATSDR 1995). 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 22. Estimated Media Contributions to Dioxin Ingestion Exposures
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2.5.5.1 Behavior in the Environment
PAHs and other POM emitted to the atmosphere can travel long distances in vapor form or
attached to small particles, or they can deposit relatively close to an emission source by wet or
dry deposition onto surface waters, soils, and vegetation. In the atmosphere, PAHs occur
primarily in the particle-bound phase, and climatic conditions and the size of the particles to
which they are bound highly influence atmospheric residence time and transport distances
(ATSDR 1995). Lower molecular weight PAHs are more volatile than higher molecular weight
compounds. The smallest PAH, two-ringed naphthalene, is highly volatile and remains largely
(e.g., 98 percent) in vapor phase in air. Thus, naphthalene is not evaluated as a PB-HAP.
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,
which limits 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 of the smaller molecules
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 invertebrates, smaller fish),
biomagnification in fish does not occur because fish can rapidly metabolize PAHs (ATSDR
1995). As a result, concentrations of PAHs in fish have generally been observed to decrease
with increasing trophic levels (ATSDR 1995). Sediment-dwelling invertebrates can accumulate
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PAHs via filter feeding, consumption of detritus, and direct uptake from sediment pore water
(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 Food
The primary source of non-inhalation exposure to benzo[a]pyrene and other PAHs and POM,
outside of occupational settings, is through dietary intake. Exposure concentrations depend on
the origin of the food (higher values are often recorded at contaminated sites). Moreover,
because PAHs and POM are created by combustion of organic materials, cooking foods at high
temperatures (e.g., grilling or broiling to a surface char) or smoking meats can create PAHs in
and on foods from any location.
PAHs can bioaccumulate in aquatic organisms and terrestrial animals through uptake of
contaminated water, soil, and food. These compounds are readily metabolized by vertebrates,
however, so bioaccumulation in fish and livestock generally is considered to be insignificant
(ATSDR 1995). Plants can accumulate vapor-phase PAHs through open leaf stomata during the
day, and some particulate-phase POM deposited to leaf surfaces might transfer through the leaf
cuticle. Monitored PAH concentrations in plants, however, tend to be below detection limits.
PAH concentrations in meat can vary widely, from below detection levels to high concentrations,
particularly in smoked meats. 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 meat and fish products preserved or prepared using these
processes. It is possible that concentrations of PAHs in foods grown near facilities that emit
PAHs to air are lower than in foods grilled or smoked.
For the RTR screening scenario, estimated BaP concentrations in foods grown near a facility
are generally lower than the reported ranges for BaP in cooked or smoked fish and meat
products and generally are predicted to be near or below likely detection limits.
2.5.5.3. Lifetime Average Daily Dose
The contributions of the various ingested media to the LADD (and thus lifetime cancer risk) for
various PAHs frequently reported in NEI for RTR source categories and that are fully
parameterized in TRIM.FaTE are presented in Exhibit 23. As shown, the contribution of different
ingested media to total ingestion of each compound varies, although fish and dairy comprise
between 67 and 99 percent of exposure for different PAHs (with beef, fruits, and vegetables
comprising nearly all the remainder).
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 by plants and animals. The variability across
exposure pathways is consistent with information provided in the literature. The PAHs with lower
molecular weights tend to be more volatile and more soluble in water than the PAHs with higher
molecular weights; hence fish ingestion is a more important pathway for lighter-weight PAHs.
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Exhibit 24 shows the contribution of the various ingested media to the LADD (and thus lifetime
cancer risk) for each of the additional 22 POM chemicals evaluated for RTR, but not fully
parameterized in TRIM.FaTE. The same trend with lower to higher molecular weight POM
observed in Exhibit 23 is evident in Exhibit 24.
Exhibit 23. Estimated Media Contributions to Polycyclic Organic Matter Ingestion
Exposures3
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Exhibit 24. Estimated Contributions of Modeled Food
Types to Additional POM Chemical Ingestion Exposures3
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data limitations).
2.5.6 Summary
Trends in the predominant media contributing to exposure for the PB-HAP categories assessed
to date by EPA's RTR Program are generally consistent with trends in measured data and
partitioning behavior reported in the literature. This assessment reveals that fish ingestion is a
major route of exposure for cadmium, mercury, dioxins, and the lower molecular weight POM
chemicals. For arsenic, farm-raised produce and livestock also contribute to ingestion
exposures, with freshwater fish ingestion accounting for a comparatively small percentage of
total exposure. For the lipophilic organic PB-HAPs (i.e., dioxins and POM), farm foods also
contribute substantially to ingestion exposures, with beef and dairy contributing significantly to
the LADD.
3. Tier 2 Screen
This section describes the Tier 2 screening methods and assumptions. Section 3.1 provides an
overview and compares and contrasts the Tier 2 approach with the Tier 1 approach.
Construction of the library of Tier 2 screening threshold emission rates is presented in
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Section 3.2. Finally, Section 3.3 describes implementation of Tier 2: the one-time set of runs to
define the library of emission thresholds values, and the facility-specific implementation of Tier 2
screens.
3.1 Overview of Approach
In Tier 2, some of the conservative assumptions in the Tier 1 screen are replaced with more
site-specific information. These fall into two general categories: environmental assumptions and
exposure assumptions. The remainder of this section describes in detail the Tier 2
environmental assumptions (Section 3.1.1), exposure assumptions (Section 3.1.2), and
implementation of Tier 2 (Section 3.1.3).
3.1.1 Tier 2 Environmental Assumptions
In Tier 2, location-specific data on five environmental variables comprising two general types of
data are evaluated:
. Meteorological characteristics: (1) the fraction of time the wind blows toward each farm
and lake (based on wind direction), (2) wind speed, (3) precipitation rate, and (4) air
mixing height; and
. (5) Distance from facility: Locations of farms/gardens and fishable lake(s) relative to
the facility13 (including the absence of a fishable lake).
Those inputs were selected for Tier 2 modifications based on:
. Relative influence on estimated risks,
. Ease of implementation in TRIM.FaTE (e.g., can the parameter be modified as a user
input, or must the model code be modified and tested?), and
. Ease of obtaining reliable parameter values more representative of specific locations.
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 listed above (i.e., 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 in eight octants. The values of each of
the four variables were varied independently from one another (i.e., other variable values held
constant). The values (i.e., four to six different values 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 per variable
resulted in a reasonable number of total runs and condition combinations.
For distance from the facility, lakes and farms/gardens were modeled at five different distances.
In implementing Tier 2, actual locations of fishable lakes near the facility are determined and
these locations are used in selecting the appropriate distance from the facility. For the fisher
scenario, the Tier 2 screen allows for aggregate contributions from multiple facilities within a
source category that are located near actual lakes. Actual farm/garden locations near the facility
are not known in Tier 2 because there is no known national database of locations of farms and
13The lake size also changes with each lake distance allowing for a constant ratio between watershed and erosion
area compared with lake area within the TRIM.FaTE modeling structure.
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home gardens. Therefore, each of the hypothetical locations in the modeling is evaluated (with
no multi-facility aggregate contributions of deposited chemical), and the location with the highest
SV is identified.
3.1.2 Tier 2 Exposure Assumptions
In Tier 2, several aspects of the exposure scenario are reevaluated. Although subsistence fisher
and farmer scenarios still are evaluated (if needed based on the results of the Tier 1 screen),
the ingestion is disaggregated into two exposure scenarios that represent a subsistence farmer
(who ingests fruits and vegetables, meat and dairy products, eggs, and soil) and a subsistence
fisher (who ingests only fish).
In addition, another exposure scenario is introduced in Tier 2: a gardener scenario. In many
settings, it is unrealistic to assume that a farm that provides all exposure media for the farmer
scenario can exist in the area surrounding a facility. The gardener scenario often will provide a
more realistic exposure scenario where the resident grows and eats fruits and vegetables from
a home garden and eats eggs from home-raised hens but does not produce and eat meat or
dairy products. The gardener also is assumed to incidentally ingest the same high-end amount
of soil as a farmer. The gardener scenario is further defined as either an urban gardener or a
rural gardener based on Census data for the location being assessed. If the census block
closest to the facility is in a Census-defined urbanized area based on population density, the
area around the facility is considered urban (otherwise, it is considered rural). The rural
gardener consumes produce at the same 90th percentile ingestion rates as the famer; however,
the urban gardener consumes less food from the garden (i.e., mean ingestion rates) because
they likely also consume store-bought produce and have a smaller footprint in which to grow
produce. The gardener scenario uses the same media concentration data that are developed for
the farmer scenario.
Tier 2 differs in two additional ways from the Tier 1 exposure scenario:
. Incidental soil ingestion and farm-food ingestion is evaluated at each of 40 locations
around the facility (five distances and eight directions), not just at one location close to
the source. The results reported for Tier 2 for the farmer and the gardener are from the
location with the maximum SV (i.e., which corresponds to the area with maximum
deposition).
. Fish are harvested at a sustainable rate based on lake size, so that the fisher might
need to fish from multiple lakes to meet the subsistence fish ingestion rate.
3.1.3 Implementation of Tier 2
The overall implementation of the Tier 2 multipathway screen is illustrated in Exhibit 25. The
steps on the left in Exhibit 25, which are discussed in Section 3.2, need only to be conducted
once. The "one-time" steps include running 64 combinations of meteorological parameters
through TRIM.FaTE for each of five separate lake-distance scenarios and five separate
farm/garden-distance scenarios (i.e., distance from the facility). Resultant concentrations are
processed assuming separate fisher, farmer, and gardener (both rural and urban gardener)
ingestion scenarios. These runs result in Tier 2 REFs and screening threshold emission rates
for all Tier 2 exposure scenarios.
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Exhibit 25. Basic Process for Implementing the Tier 2 Multipathway Screen
DEVELOPMENT OF TIER 2
FACILITY SCREEN
Identify Meteorological Statistics,
Distance Values for Lakes &
Farms/Gardens
(Reflecting expected range of
conditions and residences near most
U.S. facilities) .
APPLICATION OF TIER 2
FACILITY SCREEN
Facility's Tier 1 Screening Results
f \
Identify Facility's Closest Meteorological
Station, All Qualifying Lakes,
Urban/Rural Designation
Perform Model Runs to Create
Library of Tier 2 Screening
Threshold Emission Rates and Risk
Equivalency Factors
(Reflecting all combinations of
meteorological statistics &
distances to lakes & farms/gardens)
v_
Identify and Use Tier 2 Screening
Threshold Emission Rates, Risk
Equivalency Factors, and Mixing
Height and Wind Direction
Refinements. Accumulate Screening
Values for All POM Congeners and
All Dioxin Congeners
Diagram Key
Process
Outcome
Tools/
Data
Use Refined Fisher Approach to Identify
Lakes for Fish Consumption. Identify
Location for Farmer and Gardener
Exposure.
Aggregate PB-HAP-specific Screening
Values for Lakes Assessed for
Multiple Facilities
Final Results:
Screening Values for
Each of the 5 PB-HAPs,
Separately for Fisher,
Farmer, and Gardener
The steps on the right in Exhibit 25 are conducted for each facility using a Microsoft® Access™
tool developed for RTR screens. For each facility, the tool identifies the same meteorology
station used in RTR inhalation assessments, and it records the values for the four relevant
meteorological parameters at that station. The tool also computes distances from the facility to
real lakes in the RTR lake dataset within a user-specified distance (default is 50 km) and
matches the lakes to their respective directional "octant" relative to the facility. These five
parameter values become the set of facility-specific inputs for Tier 2.
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The lake dataset for RTR is based on a U.S. Geological Survey (USGS) database, which
includes information on location, surface area, use or type designation, and name (if available)
for all lakes in the United States. The dataset 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. In general, smaller lakes and lakes closer to a facility
are likely to be the most highly contaminated by air emissions from that facility.
Very large lakes (i.e., those larger than 100,000 acres) are not considered because their large
volumes significantly dilute air deposition from point sources. Such large lakes, including the
Great Lakes, the Great Salt Lake, Lake Okeechobee, Lake Pontchartrain, and Lake Champlain,
also dilute contaminants in the vast biomass of fish in the large aquatic food webs.
Contaminants derived from emissions to air by a point source would be distributed among
populations of millions of fish resulting in negligible increases in fish tissue concentrations
attributable to the point source. Also, very large lakes are rare (only 35 such lakes in the
conterminous United States). Moreover, for facilities near large lakes, there usually are other,
smaller lakes that we do consider for which contaminant dilution would be lower, and therefore
risks likely higher. Thus, we do model exposure via fish consumption for populations that are
near large lakes in a manner that generally will be more health protective than modeling the
very large lake. If, on the other hand, multiple point sources from the same source category
were clustered along several miles of shoreline of a very large lake, with no smaller lakes
nearby, a health protective, simplified model of the near-shore environment could be simulated
for a site-specific assessment.
Bays where rivers enter the ocean, such as Galveston Bay and San Francisco Bay, are not only
very large, but also have complex patterns of tidal flow, sediment deposition, and fish migration
between the oceanic, estuarine, and upstream river systems. Air deposition from air emissions
from a given point source would be widely dispersed and diluted among large populations of
many different estuarine and migratory fish species. Thus bays/estuaries are not considered
relevant for estimating risks from point source air emissions.
Finally, very large lakes can have notable contamination from current and historical pollution
produced by various industries as well as from agricultural and other land-use practices. The
RTR program, however, regulates HAPs at the source-category level and does so by evaluating
category facilities' contributions to incremental, localized risk; cumulative risk from all sources
and previous contamination is not relevant to the RTR program.
For the purposes of Tier 2, a "relevant" lake meets the size and designation criteria discussed in
the previous paragraphs. Second, the lake names are reviewed, and lakes with names
suggesting uses related to disposal, evaporation, or treatment may be removed from the
dataset (sometimes the name indicates one of these uses while the USGS designations do not;
for example, the Gavin Fly Ash Impoundment may 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 screen for the fisher.
For the farmer and gardener scenarios, each farm/garden-distance scenario is evaluated from
the "one-time" modeling, and the facility's emissions are compared with the Tier 2 screening
threshold emission rates for those farm/garden-distance values and site-specific meteorological
values. As noted previously, media concentrations developed for the farmer scenario are used
for the gardener scenarios.
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As with Tier 1, a facility screens out if none of the chemical specific facility emissions exceed the
applicable Tier 2 screening threshold emission rates; otherwise, additional evaluation might be
needed (i.e., Tier 3; see Section 4).
3.2 Library of Tier 2 Screening Threshold Emission Rates
This section describes the "one-time" steps presented on the left side of Exhibit 25.
Section 3.2.1 discusses use of site-specific meteorological data, and Section 3.2.2 discusses
the potential locations of lakes, farms, and gardens. Section 3.2.3 discusses the Gardener
exposure scenario, which is introduced to the screening assessment during the Tier 2
screening. Finally, Section 3.2.4 describes the creation of a library of Tier 2 screening threshold
emission rates, REFs, and mixing height refinements.
Attachment D provides information on all the TRIM.FaTE variables considered for the Tier 2
screen.14 Using the criteria above, we ranked variables as high, medium, or low priority.
Meteorological parameters and lake location were high priority and feasible to implement with
input from public databases.
3.2.1 Meteorological Data
We created a database of the relevant U.S. meteorological data for 824 surface stations paired
with their closest upper-air stations located throughout the country. The hourly surface data
cover 2016 and are the same AERMOD-ready data used for RTR inhalation modeling. To
provide a general sense of where these stations are relative to facilities that might be screened
in the RTR program, Exhibit 26 shows the surface and upper-air meteorological stations
represented in this database along with the locations of U.S. point-source facilities from the
2011 National Air Toxics Assessment (NATA; U.S. EPA 2015). Generally, the spatial density of
the surface meteorological stations is similar to the spatial density of the 2011 NATA facilities—
i.e., more stations and facilities in areas with more people: in the Great Lakes region, along the
East and West Coasts, and in the Southern Plains; and fewer stations and facilities in the
Rockies (except Colorado) and Northern Plains. We expect that an image reflecting a more
recent NATA (e.g., the NATA released in 2018 and representing the 2014 facility inventory)
would look very similar.
The meteorological database includes annual summary statistics on wind direction, wind speed,
precipitation, and mixing heights. We gathered wind information in directional octants that could
be linked to the direction (with respect to the facility location) of the relevant lakes and of the
hypothetical locations of farmers/gardeners (facility screening is discussed in Section 3.3). The
area around a facility is divided into the eight octants shown below, representing possible wind
directions (e.g., N is north, NE is northeast).
N:
>337.5 to 360 or >0 to 22.5 degrees
S:
>157.5 to
202.5
degrees
NE:
>22.5 to 67.5 degrees
SW:
>202.5 to
247.5
degrees
E:
>67.5 to 112.5 degrees
W:
>247.5 to
292.5
degrees
SE:
>112.5 to 157.5 degrees
NW
>292.5 to
337.5
degrees
14Only TRIM.FaTE parameters were considered for site-specific refinements in Tier 2. Exposure characteristics are
considered to be generally consistent across different locations and facilities.
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Exhibit 26. The Locations of Meteorological Stations Used in RTR Modeling, and
Locations of NATA 2011 Point-Source Facilities for Reference
Note: The NATA inventory is a comprehensive, finalized dataset of nationwide point source emitters of hazardous air pollutants. The
2011 NATA (released in 2015 and representing the 2011 facility inventory) is used here only for illustrative purposes, and we expect
that a more recent NATA (e.g., the one released in 2018 and representing the 2014 facility inventory) would result in a very similar
image. The meteorology locations shown here are those used in the RTR modeling described in this report; NATA used a different
meteorology dataset.
From the hourly weather data, we calculated or gathered the annual statistics listed below for
each of the 824 surface stations.
. Number of hourly observations,
. Number of hours with calm winds or no wind data reported,
. Fraction of time the wind blows into each octant (after excluding missing and calm
wind hours),
. Median wind speed blowing into each octant (after excluding calm winds), and
. Median mixing height (irrespective of wind octant).
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• Average annual precipitation (irrespective of wind octant and using 30-year normal
data15 if available, to avoid biasing the screening results in favor of any precipitation
anomalies that existed in 2016)
We selected median instead of mean values because medians were usually smaller than mean
values and because lower wind speed and mixing height are more health protective
(i.e., typically lead to higher chemical deposition in areas near the emission source). We
evaluated the distributions of median wind speeds, median mixing heights, and average annual
precipitation across all 824 stations. From those distributions, we identified values to represent
reasonable low, mid-range low-end, mid-range high-end, and high values across all sites
(i.e., roughly 5th, 35th, 65th, and 95th percentile values). The values shown in Exhibit 27 are
those used in TRIM.FaTE model runs as part of developing the library of Tier 2 screening
threshold emission rates, REFs, and mixing height refinements (the library is discussed in
Section 3.2.4).
Exhibit 27. Values for Meteorological Parameters Used to Develop the Tier 2 Screening
Threshold Emission Rates and REFs
Parameter
Value
Risk Direction
Wind Speed (m/s)
1.6
As wind speed increases, it carries more airborne chemical out of
the modeling domain and decreases risk. Slower wind speeds
lead to more chemical deposition closer to the facility.
2.8
3.7
5.4
Precipitation (mm/yr)
240
As precipitation amounts increase, so does wet deposition over
the modeled domain.
706
1,069
1,474
Mixing Height (m)
226
At higher mixing heights, pollutants released to air mix with larger
volumes of air, resulting in lower air concentrations and modeled
deposition to surfaces and, consequently, lower ingestion
exposures.
351
454
674
Notes: Bold font indicates the value used in Tier 1. Also, we do not show wind direction here because it has a linear effect on
exposure and risk modeled in TRIM.FaTE (using the scenario design of the screens). Use of site-specific wind direction data is
discussed in Section 3.3.
3.2.2 Locations of Lakes and Farms/Gardens
We model lakes and hypothetical farms/gardens within a 50-km radius around a facility. We
believe that a 50-km domain places a reasonable restriction on how far a nearby resident will
travel to catch and consume fish from area lakes on a routine basis. Although extending the
modeling domain beyond 50 km would increase the amount of deposition "captured" by the
modeled watershed, the incremental chemical mass expected to accumulate in the watershed
diminishes rapidly with distance. Areas beyond 50 km from the emission source are expected to
15We obtained 30-year-average annual precipitation for the period of 1981-2010, from the National Oceanic and
Atmospheric Administration, https://www.ncdc.noaa.gov/data-access/land-based-station-data/land-based-
datasets/climate-normals/1981-2010-normals-data.
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contribute relatively negligible amounts of chemical to the watershed, based on air-to-soil
deposition values produced by TRIM.FaTE in the Tier 2 scenarios.16
As indicated in Exhibit 28, within the 50-km radius, we evaluate lake and farm/garden impacts at
five distances in Tier 2: three distances within a 10-km radius where most chemical deposition
occurs (i.e., at 0.5, 5, and 10 km from the facility), and two additional distances beyond 10 km
(i.e., at 20 and 40 km from the facility). All farm/garden locations are hypothetical, so potential
exposure is evaluated in Tier 2 at each possible distance and octant. Note if an actual lake
distance is in between two of the distances in Exhibit 28, that lake will be assigned the closer
location (i.e., the distance expected to yield the greatest risk). For example, if an actual lake is
7 km from the analyzed facility, that lake will be set 5 km from the facility for screening
purposes.
Exhibit 28. Distances Used to Develop the Tier 2 REFs and Screening
Threshold Emission Rates
Parameter
Value
Risk Direction
Lake and Farm/Garden Distances,
measured from the inside geographic
centroid of the feature (km)
0.5
With increased distance from the source,
chemical deposition typically is reduced and,
consequently, exposures are reduced.
5
10
20
40
Notes: Bold font indicates the value is equal to the value used in Tier 1.
Exhibit 29 depicts the spatial layouts of each lake- and farm/garden-distance scenario in Tier 2
for a single octant.17 The 0.5-km-distance scenario is the same layout as the Tier 1 scenario
shown in Exhibit 11 and is not repeated in Exhibit 29. The runoff and erosion characteristics are
unchanged from the Tier 1 screen.
In resituating the lake and farm/garden at these alternative locations, we maintained ratios
consistent with those included in the Tier 1 screening scenario for (1) lake or farm area to total
land area in the modeled domain, (2) runoff watershed area to lake or farm area, and
(3) erosion watershed area to lake or farm area. We used "thin" lake and farm shapes
(i.e., downwind width much shorter than the cross-wind length) to minimize distance from the far
end of the lake or farm to the facility, resulting in higher and more health protective media
concentrations. Situating the lakes or farm/gardens farther from the stack required expansion of
the modeled domain. For example, the modeling domain in the top parcel layouts in Exhibit 29
16Mass deposited at the outer edge of the watershed (50 km) is expected to result in a negligible increase in
estimated exposure via the fisher or farmer scenarios. The TRIM.FaTE runs supporting Tier 2 indicate that chemical
deposition rates at 43-45 km from the emission source (at parcel 6 in the Farm at 40 km layout shown in Exhibit 29)
are between 1 and 3 orders of magnitude smaller than those at 0.5-2 km from the source (at parcel 2), depending on
the chemical and meteorological parameters. Although additional chemical mass could be transported to the lake and
farm through erosion and runoff, the amount of chemical deposited beyond 50 km from a facility is less per unit area
and would runoff or erode over longer distances, further attenuating the mass that might reach a farm or lake. Wind
speeds of 13 m/s (approximately 29 mph) or greater must be sustained for a full hour for the chemical plume to travel
farther than 50 km. Wind speeds of that magnitude are unlikely to occur consistently for many days or weeks 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.
17The lake and farm surface areas also were changed for each new distance layout, which allowed for the simulations
to maintain a constant ratio between watershed and erosion area compared with lake and farm areas.
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extends 10 km from the middle of the source parcel, while the modeling domain in the bottom
parcel layouts extend 45 km from the source (although the diagrams are the same size in
Exhibit 29).
Exhibit 29. TRIM.FaTE Surface Layouts for the Tier 2 Multipathway Screen, Using
Alternative Distances Between the Facility and the Fishable Lake or Farm/Garden (base
layout is the same as Tier 1 shown in Exhibit 11 and not shown here)
the bottom diagrams). Heavy, black arrows depict the direction of chemical runoff and erosion.
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Maintaining the same overall ratio of land area to lake or farm area in each domain resulted in
scenarios with surface areas for lakes and farms increasing with distance from the source. The
changes in lake size between these configurations are not expected to have a substantial
independent effect on exposure and risk because the effect of increased lake size (i.e., dilution
of chemical due to greater volume) is offset by greater watershed area for total deposition and
runoff. Furthermore, lake depth was not changed. The changes in farm size between these
configurations also are not expected to have a substantial independent effect on exposure and
risk because chemical concentrations in soil are estimated as mass per unit area (this also
allows us to use the exposure media concentrations developed for the farmer scenario with the
gardener scenarios, which have a smaller surface area than the farm). As noted above, we set
up the configurations to ensure that the lakes and farms in the different scenarios received
runoff and erosion from equivalent watersheds on a per-surface-area basis.
3.2.3 Gardener Exposure Scenario
The gardener scenario comprises the exposure pathways through which individuals might be
exposed in an urban or non-farm rural setting. Notably, the gardener exposure scenario is
analogous to the HHRAP "Resident" exposure scenario (U.S. EPA 2005a). Similar to the
resident scenario in HHRAP, the gardener ingests a subset of the media that the subsistence
farmer exposure scenario ingests, namely:
. Soil,
. Exposed fruits and vegetables,
. Protected fruits and vegetables,
. Root vegetables,
. Eggs, and
. Breastmilk (as an infant).
Exhibit 30 compares the ingested media for the gardener exposure scenario to those of the
farmer exposure scenario.
Exhibit 30. Ingested Media for Farmer and Gardener Scenarios
Scenario
Soil
Protected
Fruit
Exposed
Fruit
Protected
Vegetable
Exposed
Vegetable
Root
Vegetable
Dairy
Beef
Pork
Poultry
(A
O)
O)
LU
Breast Milk
Farmer
V
V
V
V
V
V
V
V
V
V
V
V
Gardener
V
V
V
V
V
V
V
V
For the RTR inhalation risk assessment, receptor locations are designated as rural or urban,
and the gardener in the multipathway screen is designated the same way. For a gardener in a
rural environment, we use the same ingestion rates (IRs) as used for the farmer for the produce
that the gardener ingests. As discussed above, this is a subset of the media that a farmer would
ingest (see Exhibit 30). A reasonable assumption is that a gardener in a rural setting would be
more likely to have sufficient land to support a garden large enough to provide for the assumed
90th percentile IRs and would tend to consume larger amounts of home-produced foods than
would gardeners in urban settings. Gardeners in urban settings likely would grow produce on
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smaller plots and, in general, would likely supplement homegrown produce with store-bought
produce, particularly during the non-growing season (ingestion of produce that is not
homegrown is not assessed in the screens). For gardeners in an urban setting, therefore, the
mean instead of 90th percentile IR for homegrown produce from EPA's (2011) Exposure
Factors Handbook is used. The IRs for the urban gardener generally are between one-third to
one-half of those for the farmer and rural gardener, as shown in more detail in Attachment B.
Soil ingestion for the urban and rural gardeners is the same high-end rate as for the farmer, and
the farmer and gardener (both rural and urban) have higher soil IRs than the general population.
A central-tendency soil IR could underestimate soil ingestion for gardeners.
To be health-protective, the gardener scenarios assume concentrations, and thus, the same
transfers of chemical from adjacent modeling areas through runoff and erosion as the farming
scenario. In a more refined site-specific assessment, an urban garden scenario might assume a
raised garden bed or garden boxes that does not receive chemicals through runoff or erosion.
3.2.4 Development of Library of Tier 2 Screening Threshold Emission Rates, REFs, and
Mixing Height Refinements
We conducted a large set of modeling runs based on unit emissions of 1 gram per day and
taking into account: (1) wind speed, mixing height, and precipitation rate values shown in
Exhibit 27; (2) lake and farm distances shown in Exhibit 28; and (3) spatial layouts shown in
Exhibit 11 and Exhibit 29. These runs systematically varied each of these parameters so that all
possible combinations were evaluated.18 The resulting matrix of screening-level risk estimates
represented each unique combination of PB-HAP and values for wind speed, mixing height,
precipitation rate, and distance from the facility to a lake or farm. From these screening-level
risk estimates, we calculated Tier 2 REFs and screening threshold emission rates for all the
combinations stated above (note that for POMs and dioxins, screening threshold emission rates
are only calculated for benzo[a]pyrene and 2,3,7,8-TCDD, respectively).
As in the Tier 1 screen, the Tier 2 screening threshold emission rate is defined as the emission
rate that corresponds to a 1-in-one million excess lifetime cancer risk or an HQ of 1 for a given
PB-HAP. As in Tier 1, for those chemicals that are not fully parameterized in TRIM.FaTE, the
REFs in the Tier 2 screen reflect an individual POM or dioxin chemical's fate, transport, and
toxicity relative to the index chemical for each group (BaP for POM and 2,3,7,8-TCDD for dioxin;
see Section 2.2.4).
Mixing height has a direct effect on chemical concentrations, and therefore its exposure level.
When a chemical is released to air, it mixes with air in the lower atmosphere (i.e., in the mixing
layer). With the assumption of instantaneous and complete mixing (which is the assumption
used in TRIM.FaTE), there is an inverse linear relationship between changes in mixing height
and changes in chemical air concentrations. A lower mixing height (boundary) means that there
is a smaller volume of air available for mixing, meaning less dilution and higher chemical
concentrations within the mixing layer than when the mixing height is higher. At a given location,
higher chemical air concentrations lead to higher deposition and higher chemical concentrations
in environmental media, and ultimately higher exposure. Precipitation, on the other hand, affects
chemical concentrations in two opposing processes: more precipitation results in more wet
chemical deposition, but it also dilutes the deposited chemical in leachate and runoff. Wnd
speed influences air concentration with distance from an emission source—lower wind speeds
result in higher chemical concentrations in air and more deposition closer to the source than
18There were 640 independent modeling runs per PB-HAP chemical (including each dioxin/furan congener).
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farther away, while higher wind speeds result in relatively lower concentrations and deposition
near the source.
Because of the direct and predictable effects of mixing height on exposure, and because the
range of median mixing heights across the 824 meteorological stations is substantial (i.e., less
than 200 m to more than 2,000 m), we used the matrix of Tier 2 screening threshold emission
rates described above to further quantify the relationship between mixing height and exposure.
The screening threshold emission rates decrease linearly with decreasing mixing height; put
another way, SVs increase linearly with decreasing mixing height. The linear relationships are
specific to each combination of PB-HAP, distance from facility to the lake or farm/garden, wind
speed, and precipitation rate. Therefore, for each combination of those parameter values, we
derived a linear regression to relate changes in mixing height (i.e., the four mixing height values
used in the modeling) to changes in screening threshold emission rate. Using the regression
coefficients, we are able to estimate the influence of mixing height on Tier 2 SV estimates with a
continuous function based on reported mixing heights near a facility.
After developing the mixing height regression coefficients, we condensed the Tier 2 matrix into a
Tier 2 library containing REFs, screening threshold emission rates, and mixing height regression
coefficients for each unique combination of PB-HAP, wind speed, precipitation rate, and
distance from the facility to a lake or farm/garden. With respect to mixing height, the screening
threshold emission rates and REFs are derived using the mixing height value of 226 m (i.e., the
Tier 1 mixing height) and are then adjusted using the actual mixing height near the facility being
screened.19
Unlike Tier 1, the Tier 2 screen assesses potential risk from fish ingestion separately from
homegrown produce, animal products, and soil ingestion; therefore, there is a distinct library of
screening threshold emission rates, REFs, and mixing height regressions for each of the
exposure scenarios (fisher, farmer, and rural and urban gardeners).
Section 3.3 discusses how the screening threshold emission rates, REFs, and regression
coefficients discussed in this section are used to estimate potential multipathway risk.
3.3 Implementing the Tier 2 Multipathway Screen
To implement the Tier 2 multipathway screen, we developed a Microsoft® Access™ tool that is
pre-loaded with the (1) U.S. lake location data; (2); U.S. meteorological database; and (3)
libraries of Tier 2 screening threshold emission rates, REFs, and mixing height regression
coefficients described above.
As noted in Section 3.2.2, the database of lakes used in the Tier 2 screen is available from
ESRI® and based on USGS data. The database includes information on the location, surface
area, use or type designation, and name (if available) of all lakes (including ponds and
reservoirs) in the United States. To focus on lakes that can support harvest of upper-trophic-
level fish, we excluded lakes used for disposal, evaporation, or treatment, and we included only
lakes greater than 25 acres in area (see Section 3.3.1 below for more detail). We did not include
19The Tier 2 screening threshold emission rates are all based on a mixing height of 226 m. However, the SVs that are
calculated from these screening threshold emission rates will be refined based on a regression equation that
accounts for the actual mixing height around the facility. For example, if a facility has an SV of 3 assuming a mixing
height of 226, and the mixing height refinement is a factor of 0.8 (based on the regression equation and a site specific
median mixing height of 400 m), then the refined Tier 2 SV for this facility would be 2.4 (i.e., 3 * 0.8). This calculation
is further described in Section 3.3.
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lakes larger than 100,000 acres in area (Section 3.1.3). The database of lakes contains
approximately 433,000 fishable lakes for evaluating Tier 2 impacts.
The Tier 2 screening tool identifies all qualifying lakes in the area surrounding a screened facility
and determines their distances and directions with respect to the facility; each of these distance
values are subsequently matched to the closest lake distance "bin" in the Tier 2 library (fish in
lakes closer to the emission source generally accumulate more chemical). The user can vary
the radial distance and area limits of qualifying lakes (defaults are set at 50 km and 25 to
<100,000 acres, respectively), and the user can also review the nearby lakes and exclude ones
that would not be used to harvest fish (e.g., based on names indicating industrial, waste, or
treatment purposes). The tool records any excluded lakes to omit them from subsequent
screens.
Unlike lake locations, farm and garden locations are not site-specific, so the tool calculates the
Tier 2 farmer and gardener SVs at all distances available in the Tier 2 library and in all
directions from the facility. SVs are calculated for both the rural and gardener scenarios and the
appropriate scenario is selected based on whether the census block nearest to the facility is in
an urbanized area based on population density.
Each facility being screened is then matched with the same surface meteorological station used
in the RTR inhalation risk assessment (i.e., the closest station). This process currently utilizes
over 800 meteorological stations nationwide.20 From the selected meteorological station, the
tool identifies the appropriate precipitation and wind speed bins. The tool matches the
meteorological station's annual precipitation amount to the next higher precipitation amount in
the Tier 2 library (higher precipitation rates generally lead to greater wet deposition resulting in
increased chemical accumulation in fish and surface soil). The tool identifies the annual median
wind speed blowing toward each lake or farm/garden location at the facility and matches it to the
next lower wind speed in the Tier 2 library (lower wind speeds generally lead to greater near-
field chemical accumulation in fish and surface soil).
Given the matching meteorological (wind speed and precipitation) and distance (for
farms/gardens and lakes) values, the tool identifies the appropriate Tier 2 screening threshold
emission rate and REF from the Tier 2 library for each emitted chemical. The annual median
mixing height for the facility's matching meteorological station is then used with the mixing
height regression coefficients from the Tier 2 library to account for the impact of mixing height
using Equation 6:
RefMixT2=(M x S) + i Eqn. 6
where:
20The process of pairing dozens or hundreds of facilities with meteorological data has precedent. 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 for the 1996 NATA (U.S. EPA 2001b). In a separate 2009 report to the SAB on the RTR program,
EPA described using 158 meteorological stations nationwide, with a standard practice of selecting the station closest
to a facility unless the facility provides onsite meteorological data (U.S. 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 the
approach for modeling large numbers of facilities, although it 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 (U.S. EPA 2001a; U.S. EPA 2010b).
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RefMixT2 = mixing height multiplier for Tier 2
/' = intercept coefficient of the linear regression
M = median mixing height (in meters) associated with the facility
S = slope coefficient of the linear regression
After the appropriate Tier 2 screening threshold emission rate, REF, and mixing height
refinement factor are identified, the final site-specific factor is considered: the frequency that
winds blow toward an evaluated lake or hypothetical farm/garden location. In the Tier 2
modeling runs, as in the Tier 1 modeling runs, winds are modeled as blowing toward the lake
and farm/garden 43 percent of the time (i.e., three days per week—an unusually consistent, but
feasible, long-term wind pattern; e.g., similar to wind direction patterns in Yakima, Washington).
The screening threshold emission rates in the Tier 2 library correspond to that direction
frequency. Using the Tier 2 database of meteorological data, the Tier 2 screening tool accounts
for the percentage of time that the wind actually blows in the direction of the lake or farm/garden
being evaluated in the Tier 2 screen using Equation 7:
RefWDT2=p^R Eqn. 7
'z FreqWDn ^
where:
RefWDT2 = Tier 2 wind direction multiplier
FreqWDr2 = percent of time winds blow toward the Tier 2 lake or farm/garden
FreqWDn = percent of time winds blow toward the Tier 1 lake and farm/garden
(i.e., 43%).
Finally, for each chemical emitted by a facility and for each lake, farm, and garden, the tool
calculates the Tier 2 SV for the facility's emissions using Equation 8:
SVT2= (ER 1*EFt2 ) x RefMixT2 * RefWDT2 Eqn. 8
V '"72 '
where:
SVt2 = Facility- and chemical-specific Tier 2 SV (i.e., ratio of facility emissions to
threshold for adverse health effects for the chemical)
ER = Chemical-specific facility emission rate
REFt2 = Tier 2 REF (for individual dioxins or POM; = 1 for other chemicals)
77772 = Tier 2 screening threshold emission rate for the PB-HAP (arsenic,
cadmium, mercury, 2,3,7,8-TCDD, or BaP) and the lake, farm, and garden.
As with the Tier 1 screen, the Tier 2 SVs for all emitted POM chemicals are summed to a total
SV of POM as BaP-equivalents, and the Tier 2 SVs for all emitted dioxin/furan chemicals are
summed to a total dioxin SV as 2,3,7,8-TCDD-equivalents.
At this stage of the Tier 2 screen, the Tier 2 fisher-scenario SVs reflect subsistence fishing at
each individual lake, regardless of lake size (i.e., whether or not that harvest rate might overfish
top trophic level fish in a lake). As discussed in Sections 3.3.1 and 3.3.2 below, we further refine
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the Tier 2 fisher scenario to better reflect sustainable fish withdrawals. The Tier 2 farmer and
gardener exposure scenarios assess exposure at each hypothetical farm and garden location,
and the hypothetical farm and garden location with the largest SV for a facility and PB-HAP is
identified.
3.3.1 Accounting for Sustainable Fishing
Early in the process of compiling the Tier 2 lake database, we encountered the question: "What
size 'lake' is fishable for the purposes of this assessment?" The Tier 2 screen should focus on
lakes large enough to support a fish harvest rate that would meet the high-end fish ingestion
rates assumed for the exposure scenario (i.e., 373 g wet-weight fish fillet/day).
In the TRIM.FaTE model screening scenario, WCCs are modeled at the top of the water-column
food chain (e.g., pickerel, pike, walleye, largemouth bass), with all of their diet consisting of
smaller "prey" or "pan" fish in the water column (e.g., sunfish, crappie, perch). In the assumed
linear water-column food chain for the screening scenario, those fish in turn consume smaller
fish that are planktivorous (WCH; e.g., minnows, young-of-the-year fish). Thus, the WCCs can
be called trophic level 4 (TL4) if the smallest fish are considered trophic level 2 (TL2). The BCs
in TRIM.FaTE are modeled to represent an intermediate trophic level between 3 and 4
(i.e., TL3.5) in the benthic food web. Benthic carnivores (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, which consume benthic invertebrates only. Together, we refer to
the WCC and BC fish compartments as piscivorous fish.
To identify sustainable fish harvest rates by lake size, we made the eight key assumptions listed
below. Information and citations to peer-reviewed literature that support these assumptions are
provided in Attachment E.
1. Ponds or lakes must exceed a certain size to sustain a population of WCC over the long
term (i.e., smaller ponds/lakes might support only two or three trophic levels given limits on
total lake productivity per unit area and the 80-90 percent loss of food energy per trophic
transfer).
2. In lakes with stable fish communities including a reliable WCC fish population, piscivorous
fish (i.e., WCC TL4 and BC TL3.5) might comprise approximately 20-22 percent of the total
fish biomass (references in Attachment E).
3. Productivity in most lakes of small to moderate size depends substantially on the benthos,
with benthic invertebrates consuming detritus derived from both in-lake algae and
macrophytes and from plant litter eroded into the lake from terrestrial sources across the
watershed. We expect more biomass in the BC than in the WCC compartment. Assuming 21
percent of the standing biomass of fish are piscivorous, BC fish might account for 17.5
percent of the total standing fish biomass, and WCC fish might account for 3.5 percent of
the total fish biomass (Attachment E). The remaining 79 percent would include "pan" fish
(e.g., sunfish, perch), minnows, young-of-the-year of piscivorous fish. This set of
assumptions represents a "point estimate" offish biomass distribution across different
compartments.
4. Humans consume fish from the BC compartment and the WCC compartment, with a 50:50
split, reflecting fishing and consumption preferences rather than relative abundance of fish in
the BC and WCC compartments. Depending on the chemical, bioaccumulation over 4.0
trophic transfers might result in higher concentrations in the WCC fish compartment than
bioaccumulation over 3.5 trophic transfers in the BC fish compartment. On the other hand,
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for chemicals that partition primarily to the sediment compartment, benthic invertebrates
might accumulate more chemical, resulting in higher concentrations in the BC than the WCC
compartment. Because we could not predict, a priori, which fish compartment, the BC or the
WCC, would have higher chemical concentrations for any PB-HAPs, we assumed the 50:50
split in fish harvested from the WCC and BC compartments.
5. The total fish standing biomass is assumed to be 40 g wet weight/m2, which might be
relatively high for natural ponds and lakes across much of the United States; however, it is a
mean value for reservoirs. Overestimates of lake productivity would bias results to be more
health protective, because more fish could be harvested from contaminated lakes closer to a
facility.
6. We assume that the minimum viable population (MVP) size for a single fish species is at
least 50 adult fish for a local population to survive over several decades. Interbreeding
populations of 500 or more adults (with 50:50 male:female ratio) should be sustainable
without adverse effects from inbreeding. Actual MVP for a population genome depends on
many factors and varies substantially across different species and landscapes. To model
MVP for a given species and location, one should specify the timeframe of concern
(e.g., 50 years, 100 years) and a target probability of local extirpation (e.g., less than
5 percent). Population modeling for individual species is beyond the scope of RTR screens;
we therefore use the estimate of at least 50 breeding individuals to maintain a fish species in
a lake.
7. Humans can harvest 10 percent of any single fish compartment without threatening the
population due to overharvesting. Although sustainable harvest rates vary with species life
history characteristics, for top carnivores, data suggest that 10 percent harvest rates should
prevent overfishing (Attachment E).
8. 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 that must be harvested for human
consumption of fillet only.
Using the above assumptions, we estimated fish-fillet ingestion rates as a function of total
standing fish biomass and lake area. Because we assume a 50:50 harvest of BC and WCC fish,
and because the standing biomass of WCC fish is approximately one fifth of the standing
biomass of BC fish, we focus on lakes that can provide the MVP of 50 breeding individuals for
the WCC fish compartment. Attachment E presents the calculations and steps required to
estimate which combinations of lake size and productivity could sustain at least 50 individual
WCC fish, and the human fish ingestion rates that could be supported for those combinations.
The grey shading in Exhibit 31 indicates combinations of lake size and lake productivity that
would not support a MVP of 50 individual adult WCC fish. The white, or unshaded, cells in
Exhibit 31 indicate combinations of lake area and productivity that could sustain the listed fish-
ingestion rates for WCC plus BC fish over several decades, but might not be sufficient to
prevent inbreeding depression. Finally, the yellow shading in Exhibit 31 indicates combinations
of lake productivity and lake size that are likely to provide long-term sustainability of WCC fish in
the lake.
Once we had established which cells of Exhibit 31 were in the grey, white, and yellow zones, we
calculated the fish ingestion rates associated with each cell.
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Exhibit 31. Estimated Maximum Fish Ingestion Rate (g/d) Associated with Sustainable Fishing3
Total Fish
Biomass (g
ww/m2)b
Area 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
Note: Calculated using a series of basic assumptions and equations discussed in this section and in Attachment E.
aDark gray shading indicates combinations of lake productivity and size that could support 50 or more adult WCC fish over a few decades, the minimum viable population size; yellow-
shaded cells indicate a long-term self-sustaining population of WCC with at least 500 adult fish for one (or more) species is likely; no shading (white) indicates medium-term
sustainability.
bRepresents the total fish standing biomass. The biomass of WCC fish is 3.5% of the total. Reading from the table, at the assumed fish standing biomass of 40 g ww/m2, 25 acres
could support a water-column WCC fish population but would provide at most 26 grams of fillet (wet weight) per day for a single fisher over a full year (intersection of the vertical and
horizontal red lines). A lake of 100 acres with 40 g ww/m2 total fish standing biomass could provide as much as 102 g/d of fish fillet. Reading straight across the row at 40 g ww/m2
total fish biomass, the WCC plus BC fish-fillet-ingestion rate associated with lakes of different sizes turned out to be 1 g ww/acre. Thus, as a rule of thumb, we estimated lake
productivity in grams offish fillet [WCC & BC] per person per day as equal to the lake surface area in acres.
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At the assumed standing fish biomass of 40 g wet weight (ww)/m2, a 25-acre lake is the smallest
lake that might sustain a population of 50 or more WCC (smallest lake with unshaded cells).
Therefore, 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 and Tier 3 screens. In addition, we did not consider lakes
larger than 100,000 acres (Section 3.1.3).
As shown in Exhibit 31, the fish-ingestion rate associated with a 25-acre lake and the assumed
fish biomass of 40 g ww/m2 is 26 g/day, or approximately 1 gram fish/acre/day. Thus, a 25-acre
lake cannot by itself support the adult human ingestion rate used in the multipathway screens
(i.e., 373 g ww fillet per day) with a 50:50 mix of WCC and BC fish. However, a fisher could fish
multiple lakes, totaling 373 acres, to achieve that ingestion rate. In Section 3.3.2 below, we
discuss the refined-fisher scenario, whereby a fisher withdraws and consumes fish at an
assumed sustainable rate of 1 gram fish/acre/day from as many acres of lake(s) as necessary
to harvest 373 grams of fish (wet weight fillet) per day. The refined-fisher scenario also
aggregates SVs at lakes impacted by emissions from more than one facility in the source
category.
Lakes smaller than 25 acres could be stocked annually to support substantial fish withdrawals.
However, we assume that when introduced to the lake, the stocked fish would be
uncontaminated by the chemicals of interest. Moreover, the period over which accumulation of
chemical from the lake could occur would be approximately three to six months (i.e., the fishing
season) for the majority of the fish stocked as large juveniles or adults, instead of several years
for fish hatched in or born into the lake. We believe that not taking stocked fish into
consideration is a reasonable assumption.
We could have used other assumptions about human fishing behavior. For example, fishers
could harvest BC and WCC in proportion to their relative abundance (i.e., 80:20); however, it is
not clear which fish compartment might have higher chemical concentrations. Alternatively,
fishers could consume "pan" fish like sunfish and small perch to meet their daily fish ingestion
rates fishing smaller lakes than predicted in Exhibit 31. Pan fish, however, represent TL3 fish in
the water column. Therefore, chemical concentrations in the tissues of pan fish would likely be
lower than in the TL3.5 BC or the TL4 WCC fish compartments for mercury, cadmium, dioxins,
and POM.
3.3.2 Refined-fisher Scenario
In the Tier 2 screen, the refined-fisher scenario is based on the idea that an adult fisher might
fish from multiple lakes if the first fished lake is unable to provide an adequate catch to satisfy
the assumed ingestion rate (i.e., 373 g ww-fish/day for adults). The scenario assumes that lake
fish productivity supports a long-term sustainable harvest of no more than 1 gram of fish fillet
(wet weight) of top trophic level fish per acre of lake (Attachment E). That means the fisher must
harvest fish from 373 acres of lakes to fulfill the assumed ingestion rate, provided the
assumptions in Section 3.3.1. Which lakes are fished, and in what order, must be methodically
determined.
We determine the Tier 2 refined-fisher lake fishing order using estimates of chemical
concentrations in fish in each lake within 50 km of a facility, and those concentrations include
the contributions from source-category emissions from all facilities within 50 km of that lake.
Thus, if a lake is 50 km or less from two facilities (e.g., Facility A and B) in the source category,
two SV values are calculated for a given PB-HAP using Equation 8 (in Section 3.3): one SVt2
value corresponding to Facility A's emissions and another SVt2 value corresponding to Facility
B's emissions. In this example, the two SVt2 values are summed into one aggregated SVt2
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value for the lake and PB-HAP. The aggregate SVt2 value accounts for emissions from both
facilities.
In the refined-fisher scenario, a fisher travels to each relevant lake within 50 km of a facility
(Section 3.2.2), in order of highest to lowest chemical concentration in fish (of a given PB-HAP),
until the fisher catches fish from 373 acres of lake(s). Ordering of fished lakes can be different
for different PB-HAPs because fate-and-transport characteristics vary by chemical. Thus, the
most contaminated lake (of at least 25 acres) for one PB-HAP might differ from the most
contaminated lake (of at least 25 acres) for another PB-HAP. In this situation, the order of lakes
fished would be different for the two PB-HAPs. The final PB-HAP-specific Tier 2 SV for the
fisher can be expressed as the sum of the SV from each lake that is fished (which is based on
the amount of fish ingested from each lake multiplied by the PB-HAP concentration in fish).
If there are no lakes within 50 km of a facility, then there is no fisher scenario (the fisher SV
would be 0). Otherwise, there are three possible lake fishing scenarios, discussed below: (1) the
highest-concentration lake (for a given PB-HAP) within 50 km of the facility can provide 373 g/d
fish (is 373 acres or larger, though we limit ingestion to 373 g/d); (2) the highest-concentration
lakes within 50 km of the facility individually are smaller than 373 acres and unable to provide
373 g/d fish, but together can provide a total of 373 g/d; or (3) all lakes within 50 km cannot
supply a total of 373 g fish/d.
1. If the first lake fished is 373 acres or larger, the fisher is assumed to catch 373 g ww-fish/day
from that lake. The refined-fisher SV is equal to the value obtained from Equation 8,
summed across all source-category facilities within 50 km of the lake.
2. If the first lake fished is smaller than 373 acres, then multiple lakes must be fished. If n lakes
are fished, where the total surface area of lakes 1 to n-1 is less than 373 acres (and the total
area of lakes 1 to n is 373 acres or more), the refined-fisher SV of each lake 1 to n-1 is
calculated using Equation 9 below.
For lakes 1 to n-1 which total less than 373 acres:
SVT2RefFish, Lake = SVT2Fish, Lake x (373^^5) Eqn. 9
where:
SVT2RefFish,Lake = lake's SV for the Tier 2 refined-fisher scenario;
SVr2Fish,Lake = lake's Tier 2 SV from Equation 8, summed across all source-category
facilities within 50 km of the lake; and
ALake = lake's surface area (acres).
Then, the refined fisher SV for lake n is calculated using Equation 10 below. As discussed in
the preceding paragraphs, these SVs incorporate deposition from multiple source-category
facilities.
For lake n, where lakes 1 to n total 373 acres or more:
cm, _o,, JsTSacres-ZTJr^LakeO] ran1Q
^>vT2RefFish, Lake n ~ &vT2Fish, Lake n x I 373acres / ^
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Finally, the cumulative Tier 2 SV for the refined fisher is calculated as shown in Equation 11:
SVT2RefFishjotai = I (Eqn. 9) + Eqn. 10 Eqn. 11
or
i = (n-1)
SVT2RefFish,Total = ^ (SVT2RefFish, Lake /) + SVT2RefFish, Lake n
i=1
3. If there are n total lakes in the modeling domain to assess, and their total surface area is
smaller than 373 acres, then we use Equation 9 above to calculate the refined Tier 2 fisher
SV for each lake, and then Equation 12 below calculates the final fisher SV for all fished
lakes combined. As discussed in the preceding paragraphs, these SVs incorporate
deposition from multiple facilities within a source category.
SVT2RefFjsh Totai = I (Eq. 9) Eqn. 12
or
t,— IL
SVT2RefFjsh Total = ^ (SVT2RefFish , Lake /)
i= 1
3.3.3 Considering Inhalation Risks at Hypothetical Garden Locations
To further prioritize the next steps in risk evaluations and risk management decisions, the
screening tool incorporates the RTR total-cancer inhalation risk value at the closest residential
receptor (according to the inhalation modeling) in each of the eight primary directions. Each
inhalation-receptor location is matched to the closest hypothetical garden location, and then the
garden total-cancer SV (i.e., the sum of the arsenic, POM, and dioxin SVs) is summed with the
total-cancer inhalation risk (i.e., the sum of cancer risks from all emitted HAPs, normalized to a
"1-in-one million" convention) and the location of the largest total cancer-risk is identified. The
combination of inhalation risk and ingestion SV is used to better understand the potential total
cancer risk that might exist for individuals living near a facility emitting PB-HAPs. That
information guides selection of next steps in the risk assessment of the source category.
3.3.4 Outputs
The screening tool generates several output tables, including an intermediate table that
provides information on each lake, farm, and garden, including the amount offish ingested and
SV associated with each lake.
Finally, the tool generates the final screening tables for each facility and PB-HAP group.
Summary tables identify the number of facilities exceeding the Tier 2 screening threshold
emission rate of each PB-HAP group (separately for the fisher, farmer, and gardener), and
which facilities have the largest SVs. All intermediate and final results tables present the Tier 1
and Tier 2 SVs side-by-side for comparison. Exhibit 32 through Exhibit 34 provide screen shots
of the primary tool output tables.
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Facilities with PB-HAP emissions that do not exceed any Tier 2 screening threshold emission
rate are assumed to pose risks below levels of concern and no additional multipathway
assessment is required. Facilities having emissions that exceed any of the Tier 2 screening
threshold emission rates could be assessed further with Tier 3.
Exhibit 32. Example of Source Category Summary Results Output from Tier 2 Tool
Src Cat Info
Tier 1
Src Cat -
PB-HAP Grp •
Num Facil in Src
Cat (Emitting
Any HAP)
Num Facil
Emitting this
Grp
Num
Facil SV
>2
MaxSV -
Max Facil ID
Num Facil SV > 2
Fisher
Farmer | Inh+Farmer
Fisher
With Agg
Impacts
Without Agg
Impacts
SV -
Agg
Impacts?
SV Before Agg
Impacts (If
Applicable)
1
1
Facil ID 1 SV
Src Cat A
Arsenic
91
90
55
l.E+01
96058
0
0
io i2.E-01
Y
3.E-02
12785
Src Cat A
Cadmium
91
90
0
4.E-01
88187
0
0
0 10 I7.E-02
N
78624 I2.E-02
Src Cat A
Dioxin
91
68
50
7.E+02
40172
45
14
30 BlO
N
89662 I3.E+01
Src Cat A
Methyl Mercury
(Hg2)
91
90
68
2.E+02
55584
52
24
0 10
1 i
I 1
Y
3.E+00
80221 j9.E-02
Src Cat A
POM
91
0
0
0.E+00
30537
0
0
0 10
0.E+00
N
37979 I0.E+0C
Src Cat A
Total Cancer
(Arsenic+POM+
Dioxin)
91
90
70
7.E+02
34211
45
19
36 j 36
l.E+02
N
49159 I3.E+01
i
i
Src Cat B
Arsenic
91
90
84
3.E+01
75443
0
0
20 T|0
3.E-01
Y
2.E-01
38743
Src Cat B
Cadmium
91
90
0
1.E+00
66297
0
0
0 10 ll.E-01
Y
7.E-02
19292 I4.E-02
Src Cat B
Dioxin
91
68
68
7.E+02
51927
72
29
jo j l.E+02
N
60856 I3.E+0]
Src Cat B
Methyl Mercury
(Hg2)
91
90
70
5.E+02
68523
67
34
0 10
Y
3.E+01
11903 jl.E-01
Src Cat B
POM
91
0
0
0.E+00
19060
0
0
0 10 I0.E+00
N
48528 I0.E+0C
Src Cat B
Total Cancer
(Arsenic+POM+
Dioxin)
91
90
89
7.E+02
63574
72
35
63 163 ll.E+02
j |
N
37307
If '
1
1
li
Note: Only a portion of this table is shown due to space limitations; information not shown here includes: numbers of facilities with
gardener SVs > 2, numbers of facilities where sums of inhalation risk values with gardener SVs are > 2, largest farmer and gardener
SVs and their corresponding facility IDs, and largest sums of inhalation risk values with farmer/gardener SVs and their
corresponding facility IDs. The facility IDs shown here are not real (they are only for illustration purposes). Red shading and font
indicate where facilities did not screen out (i.e., the SV or sum of SV and inhalation risk rounded to 2 or higher). "Src Cat" = source
category, "HAP" = hazardous air pollutant. "PB-HAP Grp" = persistent and bioaccumulative HAP group. "Num" = number.
"Facil" = facility. "SV" = screening value (i.e., ratio of facility emission to screening threshold emission rate). "Max" = maximum,
"Inh" = inhalation. "ID" = identifier. "Agg" = aggregate (as in emissions from multiple source category facilities impacting the feature
of interest).
Exhibit 33. Example of Facility-level Results Output from Tier 2 Tool
Facil Info
Tier 1
Src Cat • 1 Facil ID
Facil Lat -
Facil Long
Facil Met
WBAN
PB-HAP Gr •
SV
Fisher i Farmer
Farmer + Inhalation
SV -
Agg
Impacts?
SV Before Agg |
Impacts (If i
Applicable) j SV F
Oct
E
III
S
Total
Value -
Value -
Inhalation
Value
Oct
Inh
DiS
Fa<
Src Cat A 167248
34.3134
-70.7310
13876
Arsenic
5.E+00
7.E-02
Y
7.E-02
2.E+00
W
5.E-01
Src Cat A 167248
34.3134
-70.7310
13876
Cadmium
2.E-01
2.E-02
Y
2.E-02
7.E-03
w
5.E-01
Src Cat A 167248
34.3134
-70.7310
13876
Dioxin
l.E+02
2.E+01
N
6.E+00
w
5.E-01
Src Cat A 167248
34.3134
-70.7310
13876
Methyl
Mercury (Hg2)
2.E+00
4.E-01
Y
4.E-01
8.E-04
w
5.E-01
Src Cat A 167248
34.3134
-70.7310
13876
POM
0.E+00
O.E+OO
N
O.E+OO
NW
4.E+01
Src Cat A 167248
34.3134
-70.7310
13876
Total Cancer
(Arsenic+POM
+Dioxin)
2.E+02
2.E+01
Y
2.E+01
8.E+00
W
5.E-01
8.E+00
8.E+00
O.E+OO
W
O.E+C
Src Cat A 141870
49.9141
-105.7870
93806
Arsenic
7.E-01
l.E-02
N
3.E-01
S
5.E-01
Src Cat A 141870
49.9141
-105.7870
93806
Cadmium
l.E-02
2.E-03
N
7.E-04
S
5.E-01
Src Cat A 141870
49.9141
-105.7870
93806
Dioxin
0.E+00
O.E+OO
N
O.E+OO
NW
4.E+01
Src Cat A 141870
49.9141
-105.7870
93806
Methyl
Mercury (Hg2)
6.E-04
l.E-04
N
3.E-07
s
5.E-01
Src Cat A 141870
49.9141
-105.7870
93806
POM
0.E+00
O.E+OO
N
O.E+OO
NW
4.E+01
Src Cat A 141870
49.9141
-105.7870
93806
Total Cancer
7.E-01
l.E-02
N
3.E-01
s
5.E-01
3.E-01
3.E-01
O.E+OO
s
O.E+C
Note: Only a portion of this table is shown for space limitations; information not shown completely here includes: gardener SVs and
sums of inhalation risk values with farmer/gardener SVs. The facility IDs, locations, and meteorology station assignments shown
here are not real (they are only for illustration purposes). Red shading and font indicate where facilities did not screen out (i.e., the
SV or sum of SV and inhalation risk rounded to 2 or higher). "Src Cat" = source category. "Facil'' = facility. "ID" = identifier. "Lat" and
"Long" = latitude and longitude. "Met WBAN" = Weather Bureau-Army-Navy identifier for the meteorology station.
"HAP" = hazardous air pollutant. "PB-HAP Grp" = persistent and bioaccumulative HAP group. "SV" = screening value (i.e., ratio of
facility emission to screening threshold emission rate). "Oct" = one of the eight primary directional octants. "Dist" = distance,
"km" = kilometers. "Agg" = aggregate (as in emissions from multiple source category facilities impacting the feature of interest).
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Exhibit 34. Example of the Refined-fisher Output for Facility and PB-HAP from
Tier 2 Tool
Facil Info
Tier 1
Tier 2
1
Lake Info
! Unrefined Fisher SV
Refined Fisher Assessment
1 Fisher Order
Fisher Fraction' SV Fisher
Src Cat
Facil ID
PB-HAP
Grp
SV
!
Object ID
(USGS)
,r., -
Facility-Lake
Dist (km)
Lat
Agg 'Independent
pacts? I Facil
10 = fisher did
• 1 not fish
Ingestion
(based on
lake area)
1
I Independent
] Facil
Agg Facil
Src Cat A
89341
Arsenic
5.E+00
W
'Lake A
278
37
12.8
28.4090
-90.2940
Y
¦8.E-02
9.E-02 |1
0.10
j 8.E-03
9.E-03
Src Cat A
89341
Arsenic
5.E+00
SW
j Lake C
845
974
12.9
28.4380
-90.8240
Y
'7.E-02
7.E-02
j2
0.90
J6.E-02
6.E-02
Src Cat A
89341
Arsenic
5.E+00
sw
'LakeW
454
59
10.8
28.6720
-90.6420
Y
"7.E-02
7.E-02
jo
0.00
jO.E+OO
0.E+00
Src Cat A
89341
Arsenic
5.E+00
SW
! Lake 1
260
54
10.0
28.0140
-90.7200
Y
[7.E-02
7.E-02
>0
0.00
jO.E+00
0.E+00
Src Cat A
89341
Arsenic
5.E+00
S j Lake U
789
198
8.1
28.9810
-90.1110
Y
'6.E-02
7.E-02
jo
0.00
jO.E+00
O.E+00
Src Cat A
89341
Arsenic
5.E+00
S
' Lake E
660
40
8.9
28.3370
-90.7690
Y
J6.E-02
7.E-02
jo
0.00
jO.E+OO
0.E+00
Src Cat A
89341
Arsenic
5.E+00
NW
j Lake Q
601
32
6.9
28.4590
-90.9130
Y
[6.E-02
7.E-02
jo
0.00
JO.E+00
0.E+00
Src Cat A
89341
Arsenic
5.E+00
SE
j Lake B
113
40
9.1
28.1480
-90.3060
Y
[4.E-02
5.E-02
jo
0.00
jO.E+00
O.E+00
Src Cat A
89341
Arsenic
5.E+00
S
'Lake D
673
40
19.2
28.3470
-90.3000
Y
J4.E-02
5.E-02
jo
0.00
jO.E+OO
O.E+OO
Src Cat A
89341
Arsenic
5.E+00
S
j Lake F
119
30
11.4
28.7060
-90.4190
Y
[4.E-02
5.E-02
jo
0.00
JO.E+00
0.E+00
Src Cat A
89341
Arsenic
5.E+00
w
J Lake G
369
44
39.0
28.8280
-90.6500
N
[4.E-02
4.E-02
jo
0.00
JO.E+00
0.E+00
Src Cat A
89341
Arsenic
5.E+00
E
j Lake J
412
37
5.5
28.1330
-90.0920
Y
'4.E-02
6.E-02
jo
0.00
jO.E+OO
O.E+00
Src Cat A
89341
Arsenic
5.E+00
sw
J Lake V
942
114
20.6
28.9080
-90.4110
Y
"3.E-02
4.E-02
jo
0.00
JO.E+00
0.E+00
Src Cat A
89341
Arsenic
5.E+00
sw
J Lake M
894
94
22.7
28.5680
-90.6640
Y
J 3.E-02
4.E-02
jo
0.00
jO.E+00
0.E+00
Src Cat A
89341
Arsenic
5.E+00
sw
j Lake K
754
86
27.9
28.6740
-90.1760
Y
J3.E-02
4.E-02
jo
0.00
'0.E+00
O.E+00
Src Cat A
89341
Arsenic
5.E+00
sw
j LakeS
425
84
32.1
28.1320
-90.8510
Y
[3.E-02
4.E-02
jo
0.00
jO.E+OO
O.E+00
Src Cat A
89341
Arsenic
5.E+00
sw
[Lake T
838
82
23.9
28.5560
-90.4330
Y
J 3.E-02
4.E-02
jo
0.00
jO.E+00
0.E+00
Src Cat A
89341
Arsenic
5.E+00
sw
j Lake H
756
74
38.6
28.9480
-90.3070
Y
j3.E-02
4.E-02
jo
0.00
j0.E+00
O.E+00
Src Cat A
89341
Arsenic
5.E+00
sw
j Lake N
210
69
30.8
28.8000
-90.4880
Y
"3.E-02
4.E-02
jo
0.00
jO.E+OO
0.E+00
Src Cat A
89341
Arsenic
5.E+00
sw
jLakeO
515
32
39.1
28.8470
-90.9710
Y
J 3.E-02
5.E-02
jo
0.00
j0.E+00
0.E+00
Src Cat A
89341
Arsenic
5.E+00
sw
j Lake Z
818
27
26.1
28.2140
-90.8050
Y
13.E-02
4.E-02
jo
0.00
jO.E+00
O.E+00
Note: The facility IDs and lake information shown here are not real (they are only for illustration purposes). "Src Cat" = source
category. Red shading and font indicate where SVs rounded to 2 or higher. "Facil" = facility. "ID" = identifier. "HAP" = hazardous air
pollutant. "PB-HAP Grp" = persistent and bioaccumulative HAP group. "SV" = screening value (i.e., ratio of facility emission to
screening threshold emission rate). "Oct" = one of the eight primary directional octants. "USGS" = U.S. Geological Survey.
"Dist" = distance, "km" = kilometers. "Lat" and "Long" = latitude and longitude. "Agg" = aggregate (as in emissions from multiple
source category facilities impacting the feature of interest).
4. Tier 3 Screen
This section describes the methods and assumptions for the Tier 3 screen. We provide an
overview of the approach (Section 4.1); description of the lake screen refinement (Section 4.2);
evaluations of the farmer and gardener exposure scenarios (Sections 4.3 and 4.4); refinement
of the plume rise evaluation (Section 4.5); and a final time-series assessment using hourly
instead of annual average meteorological data (Section 4.6).
4.1 Overview of Approach
Tier 3 multipathway screens can be conducted on facilities that do not screen out in Tier 2.
Tier 3 consists of five possible individual refinements (described in more detail below) that are
based on additional site-specific data. These refinements are applied in sequence because all
might not be needed; potential ingestion risk is evaluated at the end of each refinement.
Because the Tier 3 screens introduce additional site-specificity to the screening scenario, it can
require a higher level of effort than the Tier 2 screen, but still a much lower level of effort than
required for a full site-specific assessment. One of the Tier 3 screens (i.e., the lake screen)
potentially results in the rescreening of facilities' emissions using the Tier 2 methods described
in Section 3 and using a revised lake dataset. The other screens each may result in a
refinement of the Tier 2 screening value. The hourly time-series screen, if conducted, supplants
the plume-rise screen because it calculates plume rise on an hourly basis.
4.2 Lake Screen
A Tier 3 lake evaluation is conducted if the Tier 2 screen for the fisher scenario indicates a
potential for risk. During this evaluation, we examine: (1) whether or not a given lake used in the
Tier 2 screen truly exists; (2) the intended purpose of the lake (e.g., recreation, industrial
disposal); (3) lake accessibility; and (4) whether or not the lake is likely fishable. This evaluation
is conducted because the USGS database of lakes and reservoirs used in the Tier 2 screen
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does not indicate lake accessibility or which lakes are likely fishable. In addition, USGS
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.
Using aerial imagery and other data sources, non-fishable "lakes" are identified, removed from
the Tier 3 screen, and removed from the RTR lake dataset. If one or more lakes are removed
from a facility's screen, the facility's emissions are rescreened using the revised lake database
and the Tier 2 methods described in Section 3. If removing a lake(s) causes the originally fished
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 screen 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 screen.
We use aerial and street-view imagery and internet searches to ascertain if an assessed lake
actually exists and whether or not it is likely to be fished. The assessed lakes are those from
which the fisher harvests fish according to the Tier 2 methods discussed in Section 3.3.1. Lakes
that appear swampy or covered in algae or used for industrial or waste disposal/treatment
purposes are not fishable. Lakes adjacent or connected to a river or saltwater body (estuaries
and rivers) are likely to have high outflow with limited chemical retention.
Based on the evaluation described above, we remove from the RTR lake dataset any lakes that
are unsuitable for the RTR fisher scenario. For example, the area outlined in blue in Exhibit 35
identifies an area that the USGS dataset originally identified as a lake. However, aerial imagery
(current and historical) shows that it is mostly or entirely dried up and not suitable for fishing.
The area outlined in blue in Exhibit 36 identifies a lake from the USGS dataset that originally
qualified for Tier 2 screening based on that dataset; however, aerial imagery shows that it is
directly adjacent to an industrial facility and likely used only for on-site industrial purposes. Both
lakes would be permanently removed from the RTR lake dataset and not considered in future.
If we remove a lake during the Tier 3 screen, we often need to include an additional lake for fish
harvest to reach 373 fishable acres. We assess the additional lake(s) using the same criteria
and searches discussed in this section. After all lakes fished in the scenario (for the facilities not
screening out in Tier 2) have been evaluated, we rerun the Tier 2 screen (using the tool
discussed in Section 3) with the revised RTR lake dataset, producing revised screening results.
Lakes removed during this step of Tier 3 could affect screening results for other facilities in the
source category. For example, if an assessed facility is within 100 km of another assessed
facility, removing a lake might affect the screening results for both facilities. For this reason, we
rerun the Tier 2 screen with the revised lake dataset for all facilities in the source category,
including lakes contaminated by multiple facilities in the same source category. Screening
results for the farmer and gardener scenarios are not affected by the lake screen.
4.3 Farmer Scenario Evaluation
In many settings, based on local land use, population density, and other factors, the existence
of a full-scale farm capable of providing all of the ingested media that are assumed for the
farmer scenario might be unrealistic. If the farmer exposure scenario does not screen out at
Tier 2, additional information can be evaluated on the likelihood that full-scale farming
operations exist within the modeling domain.
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Exhibit 35. Example of Lake Removed from Screening—Likely
Evaporated or Drained
Note: Aerial imagery from ESRI World Imagery (2014).
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Exhibit 36. Example of Lake Removed from Screening—Likely an Industrial Lake
Note: Aerial imagery from ESRI World Imagery (2014).
If, after Tier 2, a farmer SV is above a level of concern, EPA will use census data, aerial
imagery, and other available data to further assess the likelihood of subsistence farmer
operations within 50 km of the facility. If, based on the additional analysis and review, it is
determined that no subsistence farming operations are in the area, then the farmer scenario will
no longer be used in Tier 3 and only the gardener SVs are reported. That is, EPA will assume
that subsistence farming operations are not likely within 50 km of the facility, and only gardener
SVs will be evaluated and reported. If information obtained suggests that subsistence farming
operations likely exist, then in Tier 3, EPA will identify the farmer SV at the modeled location(s)
that best matches the locational data obtained, and EPA will evaluate and report the largest of
these SVs. Such location(s) may not be at the location of maximum SV as indicated in the Tier 2
screen. The gardener SVs will continue to be evaluated and reported, even if farmer results are
used, because EPA considers the gardener scenario to be possible in all RTR evaluations.
4.4 Gardener Scenario Evaluation
Unlike the farmer exposure scenario, the gardener exposure scenario does not require a large
geographic footprint (i.e., relatively small gardens could provide the fruits and vegetables to
satisfy the gardener ingestion rates); this is especially true for the urban gardener scenario.
Nonetheless, it does require that human receptors be present in the area.
If, after Tier 2, a gardener SV is above a level of concern, in Tier 3 EPA will evaluate whether
there likely are residential areas near the location of the gardener SV. EPA will use information
such as Census data, aerial imagery, and land-use data to determine whether people are likely
to live near the SV location. If EPA determines that people likely live there, the Tier 2 gardener
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SV is retained. If EPA determines people likely do not live there, then in Tier 3 EPA will report
the next-highest gardener SV at a location where people likely reside.
4.5 Plume-rise Screen
If, after the lake screen, the Tier 3, an SV is still above a level of concern, the risk assessor may
choose to conduct a plume-rise screen. Atmospheric conditions coupled with the physical
parameters of the chemical release point can cause the chemical plume to rise substantially
higher than the physical release height. Plume rise is not explicitly modeled by TRIM.FaTE but
can substantially reduce ground-level chemical exposure if the plume frequently rises above the
air mixing height. The plume-rise screen varies chemical release height over time to simulate
the effect of 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 air mixing height for a given hour, then in the
TRIM.FaTE modeling system there is no chemical deposition or exposure for that hour.
In TRIM.FaTE modeling, the chemical mass reaching 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). Depending on ambient conditions, the top of the air mixing layer can fall below
the top of tall stacks 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 well above the
source height and mixing height. If this occurs across many hours, it will substantially reduce
total PB-HAP exposure and reduce the screening value. The plume-rise refinement 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 value, thus lowering
the screening value.
The Tier 3 plume-rise screen uses 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 use hourly meteorological data (e.g., air temperature
and wind speed) from the closest weather station, 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
(needed to estimate the plume height at the estimated edge of the facility).
We use EPA guidance (U.S. EPA 2000) to calculate wind speed at the stack height. We use
equations reproduced in Seinfeld and Pandis (1998) to calculate plume rise with the above data
and the assumed average vertical gradients of temperature and potential temperature (a
calculation that normalizes temperature measurements for differences in height and pressure)
corresponding to the stability class (e.g., neutral stability, slight or extreme instability—
atmospheric conditions that affect how an air parcel moves vertically).
For each relevant emission source, we compare estimates of the hourly effective release height
(i.e., sum of actual release height and plume rise) to the hourly mixing height to determine the
mass of chemical remaining in the mixing layer when winds in that layer blow toward the lake or
farm/garden of interest. We compare the mass of chemical remaining in the mixing layer,
summed across all sources at a given facility, to the total emitted mass of the chemical—this
ratio is the plume-rise retention factor. The screening tool described in Section 3 multiplies that
factor by the appropriate farmer, gardener, and fisher SVs following the Tier 3 analyses
discussed above (Equation 13):
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SVT3pR-SVxx [-
HrsjW and E
-------
TRIM-Based Tiered Screening Methodology for RTR
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 estimation, leads directly to a
screening-level cancer risk or HQ (i.e., a revised screening value). For simplicity in the software
implementation of the Tiers 2 and 3 screens, the result of this Tier 3 time-series screen is
converted to a time-series refinement factor—the revised SV divided by the SV after the Tier 3
lake screen. This ratio can then be multiplied by the SV after the Tier 3 lake screen, yielding the
revised SV accounting for the time-series screen.
For a Tier 3 time-series screen, we use the facility's emissions, a time series of hourly effective
release heights, a time series of hourly meteorological data (i.e., wind speed and direction,
mixing height, temperature, and precipitation), and the Tier 2 spatial scenario that best matches
each lake fished by the simulated subsistence fisher (or the relevant farm/garden locations if the
farmer or gardener scenario is of concern). The site-specific hourly data and Tier 2 spatial
layout are input to TRIM.FaTE, which provides estimated PB-HAP concentrations in
environmental media that subsequently are used to estimate exposures and risk. If multiple
lakes are fished (to allow for the subsistence fish ingestion rate), the percent of daily-ingested
fish caught at each lake is multiplied by the screening level risk or HQ value for that lake. The
PB-HAP-specific results are summed across all lakes (i.e., the refined-fisher calculations
discussed in Section 3.3.1 are applied to the modeling results, using the screening tool
described in Section 3).
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 2005. Toxicological Profile for Naphthalene, 1-Methylnaphthalene, and
2-Methylnaphthalene. U.S. Department of Health and Human Services, Public Health
Service, Agency for Toxic Substances and Disease Registry.
https://www.atsdr.cdc.gov/ToxProfiles/tp67.pdf
ATSDR 2007. Toxicological Profile for Arsenic. U.S. Department of Health and Human Services,
Public Health Service, Agency for Toxic Substances and Disease Registry.
https://www.atsdr.cdc.gov/ToxProfiles/tp2.pdf
ATSDR. 2008. Toxicological profile for Cadmium. Atlanta, GA: U.S. Department of Health and
Human Services, Public Health Service.
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Attachment A. TRIM.FaTE Inputs
Technical Support Document
A-1
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Exhibits
Exhibit A-1. TRIM.FaTE Simulation Parameters for the TRIM.FaTE Screening
Scenario A-5
Exhibit A-2. Meteorological Input Values for the TRIM.FaTE Screening Scenario A-6
Exhibit A-3. Air Parameter Values for the TRIM.FaTE Screening Scenario A-7
Exhibit A-4. Soil and Groundwater Parameter Values for the TRIM.FaTE
Screening Scenario A-7
Exhibit A-5. Runoff Assumptions for TRIM.FaTE Base Lake (L) Screening
Scenario A-10
Exhibit A-6. Runoff Assumptions for TRIM.FaTE Base Farm (F) Screening
Scenario A-10
Exhibit A-7. USLE Erosion Parameter Values for the TRIM.FaTE Base Lake (L)
Screening Scenario A-11
Exhibit A-8. USLE Erosion Parameter Values for the TRIM.FaTE Base Farm (F)
Screening Scenario A-12
Exhibit A-9. Terrestrial Plant Placement for the TRIM.FaTE Base Lake (L)
Screening Scenario A-13
Exhibit A-10. Terrestrial Plant Placement for the TRIM.FaTE Base Farm (F)
Screening Scenario A-13
Exhibit A-11. Terrestrial Plant Parameter Values for the TRIM.FaTE Screening
Scenario A-14
Exhibit A-12. Surface Water Parameters for the TRIM.FaTE Screening Scenario A-17
Exhibit A-13. Sediment Parameter Values for the TRIM.FaTE Screening
Scenario A-18
Exhibit A-14. Aquatic Animals Food Chain, Density, and Biomass for the
TRIM.FaTE Screening Scenario A-19
Exhibit A-15. Arsenic Chemical-Specific Parameter Values for the TRIM.FaTE
Screening Scenario A-20
Exhibit A-16. Cadmium Chemical-Specific Parameter Values for the TRIM.FaTE
Screening Scenario A-20
Exhibit A-17. Mercury Chemical-Specific Parameter Values for the TRIM.FaTE
Screening Scenario A-21
Exhibit A-18. POM Chemical-Specific Parameter Values for the TRIM.FaTE
Screening Scenario A-22
Exhibit A-19. Dioxin Chemical-Specific Parameter Values for the TRIM.FaTE
Screening Scenario A-24
Exhibit A-20. Arsenic Chemical-Specific Parameter Values for Abiotic
Compartments in the TRIM.FaTE Screening Scenario A-26
Attachment A A-3 February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-21. Cadmium Chemical-Specific Parameter Values for Abiotic
Compartments in the TRIM.FaTE Screening Scenario A-26
Exhibit A-22. Mercury Chemical-Specific Parameter Values for Abiotic
Compartments in the TRIM.FaTE Screening Scenario A-27
Exhibit A-23. POM Chemical-Specific Parameter Values for Abiotic
Compartments in the TRIM.FaTE Screening Scenario A-31
Exhibit A-24. Dioxin Chemical-Specific Parameters for Abiotic Compartments in
the TRIM.FaTE Screening Scenario A-35
Exhibit A-25. Arsenic Chemical-Specific Parameters for Plant Compartments in
the TRIM.FaTE Screening Scenario A-39
Exhibit A-26. Cadmium Chemical-Specific Parameters for Plant Compartments in
the TRIM.FaTE Screening Scenario A-39
Exhibit A-27. Mercury Chemical-Specific Parameter Values for Plant
Compartments in TRIM.FaTE Screening Scenario A-40
Exhibit A-28. POM Chemical-Specific Parameter Values for Plant Compartments
in TRIM.FaTE Screening Scenario A-41
Exhibit A-29. Dioxin Chemical-Specific Parameter Values for Plant
Compartments in the TRIM.FaTE Screening Scenario A-43
Exhibit A-30. Arsenic Chemical-Specific Parameter Values for Aquatic Species in
the RTR Screening Scenario A-44
Exhibit A-31. Cadmium Chemical-Specific Parameter Values for Aquatic Species
in TRIM.FaTE Screening Scenario A-45
Exhibit A-32. Mercury Chemical-Specific Parameter Values for Aquatic Species
in TRIM.FaTE Screening Scenario A-47
Exhibit A-33. POM Chemical-Specific Parameter Values for Aquatic Species in
TRIM.FaTE Screening Scenario A-48
Exhibit A-34. Dioxin Chemical-Specific Parameter Values for Aquatic Species in
TRIM.FaTE Screening Scenario A-51
Attachment A A-4 February 2021
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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 the
meteorological and air parameter values, respectively, entered into the model.
Exhibit A-4 presents parameter values for soil and groundwater. Exhibit A-5 and Exhibit A-6
present runoff assumptions for the lake and farm scenarios, respectively; while Exhibit A-7 and
Exhibit A-8 present parameter values for the universal soil loss equation (USLE) for calculating
erosion for the lake and farm scenarios, respectively. Exhibit A-9 and Exhibit A-10 indicate the
vegetation type assumed for each terrestrial parcel for the lake and farm scenarios,
respectively, while Exhibit A-11 lists parameter values used terrestrial vegetation. Lake-
parameter values for abiotic compartments are included in Exhibit A-12 for the surface water
column and in Exhibit A-13 for the unconsolidated sediment layer (above the sediment sink).
Parameter values for the biotic compartments in the lake (e.g., fish, invertebrates, algae) are
included in Exhibit A-14.
Chemical-specific parameter values for the TRIM.FaTE scenarios (e.g., molecular weight,
diffusion rate constants, Henry's law constant) are provided in Exhibit A-15 for arsenic,
Exhibit A-16 for cadmium, Exhibit A-17 for mercury, Exhibit A-18 for POM, and Exhibit A-19 for
dioxins. Chemical-specific parameter values for the TRIM.FaTE abiotic compartments (e.g., air,
surface water, sediment, surface soil, root-zone soil) are presented in Exhibit A-20 for arsenic,
Exhibit A-21 for cadmium, Exhibit A-22 for mercury, Exhibit A-23 for POM, and Exhibit A-24 for
dioxins. Chemical-specific parameter values for the plant compartments (e.g., leaf, stem, root)
are presented in Exhibit A-25 for arsenic, Exhibit A-26 for cadmium, Exhibit A-27 for mercury,
Exhibit A-28 for POM, Exhibit A-29 for dioxins. Finally, chemical-specific parameter values for
aquatic species are presented in Exhibit A-30 for arsenic, Exhibit A-31 for cadmium,
Exhibit A-32 for mercury, Exhibit A-33 for POM, and Exhibit A-34 for dioxins.
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 step3
hr
4
Selected value.
aOutput time step is set in TRIM.FaTE using the scenario properties "simulationStepsPerOutputStep" and "simulationTimeStep."
Attachment A
A-5
February 2021
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-2. Meteorological Input Values for the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value Used
Reference
Air temperature
degrees K
298
U.S. EPA 2005a.
Horizontal wind speed
m/sec
1.6
or varies
Tiers 1 and 2, and Tier 3 lake screen: The ~5th percentile of median annual wind
speeds, partitioned by 8 wind directions, recorded at 824 MET stations across
the United States in 2016.
Tier 3 plume-rise and time-series screens: Varies by the hour; site-specific hourly
meteorological data.
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
or varies
Tiers 1 and 2, and Tier 3 lake screen: "On" is defined as time during which wind
is blowing across the source into the model domain. The weekly split was
determined to be a health-protective setting by evaluating archived meteorology
data (NCDC 1995).
Tier 3 plume-rise and time-series screens: Varies by the hour; site-specific hourly
meteorological data.
Rainfall rate
m3[rain]/m2
[surface area]-
day
0.0041
or
varies
Tiers 1 and 2, and Tier 3 lake screen: The ~95th percentile of the annual average
precipitation for 824 MET stations across the United States was approximately
1.5 m/yr or 0.0041 m/day, based on 1981-2010 normals where available (812
MET stations) and based on 2016 values otherwise (11 MET stations).
Tier 3 plume-rise and time-series screens: Varies by the hour; site-specific hourly
meteorological data.
Mixing height (used to set air
VE property named "top")
m
226
or varies
Tiers 1 and 2, and Tier 3 lake screen: The ~5th percentile of annual median
mixing heights recorded at 824 MET stations across the United States in 2016.
Tier 3 plume-rise and time-series screens: Varies by the hour; site-specific hourly
meteorological data.
isDay_SteadyState_forAir
unitless
-
Value not used in current dynamic runs (would need to be reevaluated if steady-
state runs are needed).
isDay_SteadyState_forOther
unitless
-
Note: MET = meteorological.
Attachment A
A-6
February 2021
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-3. Air Parameter Values for the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value Used
Reference
Atmospheric dust load
kg[dust]/m3[air]
6.15E-08
Bidleman 1988.
Density of air
g/cm3
0.0012
U.S. EPA 1997.
Dust density
kg[dust]/m3[dust]
1,400
Bidleman 1988.
Fraction organic matter on particulates
unitless
0.2
Harnerand Bidleman 1998.
Exhibit A-4. Soil and Groundwater Parameter Values for the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value Used
Reference
Surface Soil Compartment Type
Air content
m3[gas]/m3[compartment]
0.28a
McKone et al. 2001.
Average vertical velocity of water
(percolation)
m3[water]/m2[surface soil]-day (or m/day)
8.08E-04
Assumed as 0.2 times average precipitation for
New England in McKone et al. 2001.
Boundary layer thickness above
surface soil
m
0.005
Thibodeaux 1996; McKone et al. 2001 (Table 3).
Density of soil solids (dry weight)
kg[solid]/m3[solid]
2600
Default in McKone et al. 2001 (Table 3).
Thickness - untilledb
m
0.01
McKone et al. 2001 (p. 30).
Thickness - tilledb
m
0.20
U.S. EPA 2005a.
Erosion fraction
unitless
varies0
See Exhibit A-5 and Exhibit A-6.
Fraction of area available for erosion
m2[area available]/m2[total]
1
Area assumed rural.
Fraction of area available for runoff
m2[area available]/m2[total]
1
Area assumed rural.
Fraction of area available for vertical
diffusion
m2[area available]/m2[total]
1
Area assumed rural.
Fraction sand
unitless
0.25
Assumption.
Organic carbon fraction
kg[organic carbon]/kg[solids wet wt]
0.008a
U.S. average in McKone et al. 2001 (Tables 16
and A-3).
PH
unitless
6.8
Assumption.
Runoff fraction
unitless
varies0
See Exhibit A-5 and Exhibit A-6
Total erosion rate
kg[soil]/m2[surface soil]-day
varies0
See Exhibit A-7 and Exhibit A-8
Attachment A
A-7
February 2021
-------
TRIM-Based Tiered Screening Methodology for RTR
Parameter Name
Units
Value Used
Reference
Total runoff rate
m3[water]/m2[surface soil]-day
1.62E-033
Calculated using scenario-specific precipitation
rate and assumptions associated with water
balance.
Water content
volume[water]/volume[compartment]
0.19a
McKone et al. 2001 (Table 15).
Root Zone Soil Compartment Type
Air content
m3[gas]/m3[compartment]
0.25a
McKone et al 2001 (Table 16).
Average vertical velocity of water
(percolation)
m3[water]/m2[surface soil]-day (or m/day)
8.08E-04
Assumed as 0.2 times average precipitation for
New England in McKone et al. 2001.
Density of soil solids (dry weight)
kg[solid]/m3[solid]
2,600
McKone et al. 2001 (Table 3).
Fraction sand
unitless
0.25
Assumption.
Thickness - untilledb
m
0.79
McKone et al. 2001 (Table 16, U.S. average).
Thickness - tilledb
m
0.6
Adjusted from McKone et al. 2001 (Table 16).
Organic carbon fraction
kg[organic carbon]/kg[solids wet wt]
0.008a
McKone et al. 2001 (Tables 16 and A-3, U.S.
average).
PH
unitless
6.8 a
Assumption.
Water content
volume[water]/volume[compartment]
0.21a
McKone et al. 2001 (Table 16).
Vadose Zone Soil Compartment Type
Air content
m3[gas]/m3[compartment]
0.22a
McKone et al. 2001 (Table 17).
Average vertical velocity of water
(percolation)
m3[water]/m2[surface soil]-day (or m/day)
8.08E-04a
Assumed as 0.2 times average precipitation for
New England in McKone et al. 2001.
Density of soil solids (dry weight)
kg[solid]/m3[solid]
2,600
Default in McKone et al. 2001 (Table 3).
Fraction sand
unitless
0.35
Assumption.
Thickness13
m
1.4
McKone et al. 2001 (Table 17).
Organic carbon fraction
kg[organic carbon]/kg[solids wet wt]
0.003a
McKone et al. 2001 (Tables 16 and A-3, U.S.
average).
PH
unitless
6.8
Assumption.
Water content
m3[liquid]/m3[compartment]
0.21a
McKone et al. 2001 (Table 17, U.S. average).
Attachment A
A-8
February 2021
-------
TRIM-Based Tiered Screening Methodology for RTR
Parameter Name
Units
Value Used
Reference
Groundwater Compartment Type
Thicknessb
m
3
McKone et al. 2001 (Table 3).
Fraction sand
unitless
0.4
Assumption.
Organic carbon fraction
kg[organic carbon]/kg[solids wet wt]
0.004
Assumption.
PH
unitless
6.8
Assumption.
Porosity
L[total pore space]/L [total compartment]
0.2
Default in McKone et al. 2001 (Table 3).
Density of solid material
kg[solid]/m3[solid]
2,600
Default in McKone et al. 2001 (Table 3).
aScenario-specific parameters.
bSet using the volume element properties file.
°See Exhibit A-5, Exhibit A-6, Exhibit A-7, and Exhibit A-8 for erosion/runoff fractions and total erosion rates.
Attachment A
A-9
February 2021
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-5. Runoff Assumptions for TRIM.FaTE Base Lake (L) Screening Scenario
Originating Compartment
Destination Compartment
Runoff/Erosion Fraction
SurfSoil_Source
Sink
1.0
SurfSoil_Parcel1
0
SurfSoil_Parcel1
SurfSoil_Source
0
Lake
1.0
SurfSoil_Parcel2N
Lake
1.0
SurfSoil_Parcel3
0
SurfSoil_Parcel2S
Lake
1.0
SurfSoil_Parcel3
0
Lake
SurfSoil_Parcel1
0
SurfSoil_Parcel2N
0
SurfSoil_Parcel2S
0
Lake
1.0
SurfSoil_Parcel3
0
SurfSoil_Parcel3
SurfSoil_Parcel2N
0
SurfSoil_Parcel2S
0
Lake
1.0
SurfSoil_Parcel4
0
SurfSoil_Parcel4
SurfSoil_Parcel3
1.0
Exhibit A-6. Runoff Assumptions for TRIM.FaTE Base Farm (F) Screening Scenario
Originating Compartment
Destination Compartment
Runoff/Erosion Fraction
SurfSoil_Source
Sink
1.0
SurfSoil_Parcel1
0
SurfSoil_Parcel1
Sink
0.4
SurfSoil_Source
0
SurfSoil_Farm
0.6
SurfSoil_Parcel2
0
SurfSoil_Farm
Sink
1.0
SurfSoil_Parcel1
0
SurfSoil_Parcel2
0
SurfSoil_Parcel2
Sink
0.4
SurfSoil_Parcel1
0
SurfSoil_Farm
0.6
SurfSoil_Parcel3
0
SurfSoil_Parcel3
SurfSoil_Parcel2
1.0
SurfSoil_Parcel4
0
SurfSoil_Parcel4
SurfSoil_Parcel3
1.0
SurfSoil_Parcel5
0
SurfSoil_Parcel5
SurfSoil_Parcel4
1.0
Attachment A
A-10
February 2021
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-7. USLE Erosion Parameter Values for the TRIM.FaTE Base Lake (L) Screening Scenario
Soil
Parcel
Code:
Area
m2
Rainfall/
Erosivity
Index
R (100
ft-ton/ac)
Soil
Erodibility
Index
K [ton/ac/(100
ft-ton/acre)]
Length-
Slope
Factor
Land Use
Cover
Mgmt.
Factor
Supporting
Practices
Factor
Unit Soil Loss
Sediment
Delivery
RatioJ
Calculated
(Adjusted)
Erosion Rate
LS
(USCS)
type
C
(USCS)
P
A
(ton/ac/yr)
A
(kg/m2/d)
SDRa
kg/m2/d
Source
62,500
300
0.39
1.5
untilled soil
0.2
1
35.1
0.02156
0.5281
0.01138
Parcel 1
116,891
300
0.39
1.5
grass
0.1
1
17.55
0.01078
0.4884
0.005264
Parcel2N
232,594
300
0.39
1.5
grass
0.1
1
17.55
0.01078
0.4481
0.004830
Parcel2S
232,594
300
0.39
1.5
grass
0.1
1
17.55
0.01078
0.4481
0.0048301
Parcel3
4,082,258
300
0.39
1.5
coniferous
forest
0.1
1
17.55
0.01078
0.2088
0.002251
Parcel4
13,386,064
300
0.39
1.5
coniferous
forest
0.1
1
17.55
0.01078
0.1800
0.001940
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-11
February 2021
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-8. USLE Erosion Parameter Values for the TRIM.FaTE Base Farm (F) Screening Scenario
Soil
Parcel
Code:
Area
m2
Rainfall/
Erosivity
Index
R (100
ft-ton/ac)
Soil Erodibility
Index
K (ton/ac/(100
ft-ton/acre))
Length-
Slope
Factor
Land Use
Cover
Mgmt.
Factor
Supporting
Practices
Factor
Unit Soil Loss
Sediment
Delivery
RatioJ
Calculated
(Adjusted)
Erosion Rate
LS
(USCS)
Type
C
(USCS)
P
A
(ton/ac/yr
)
A
(kg/m2/d)
SDRa
kg/m2/d
Source
62,500
300
0.39
1.5
untilled soil
0.2
1
35.1
0.02156
0.5281
0.01138
Parcel 1
116,891
300
0.39
1.5
grass
0.1
1
17.55
0.01078
0.4884
0.005264
Farm
40,633
300
0.39
1.5
tilled soil
0.2
1
35.1
0.02156
0.5573
0.01201
Parcel2
281,012
300
0.39
1.5
grass
0.1
1
17.55
0.01078
0.3960
0.004268
Parcel3
608,730
300
0.39
1.5
grass
0.1
1
17.55
0.01078
0.3595
0.003875
Parcel4
4,082,258
300
0.39
1.5
coniferous
forest
0.1
1
17.55
0.01078
0.2088
0.002251
Parcel5
13,386,064
300
0.39
1.5
coniferous
forest
0.1
1
17.55
0.01078
0.1800
0.001940
Calculated using SDR = a* (AL) b, where a is the empirical intercept coefficient (based on the size of the watershed
empirical slope coefficient (always 0.125).
AL is the total watershed area receiving deposition (m2), and b is the
Attachment A
A-12
February 2021
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-9. Terrestrial Plant Placement for the TRIM.FaTE Base Lake (L) Screening
Scenario
Surface Soil Volume
Element
Surface Soil Depth
(m)
Coniferous
Forest
Grasses/
Herbs
None
Source
0.01 (unfilled)
X
ParceH
0.01
X
Parcel2N
0.01
X
Parcel2S
0.01
X
Parcel3
0.01
X
Parcel4
0.01
X
Exhibit A-10. Terrestrial Plant Placement for the TRIM.FaTE Base Farm (F) Screening
Scenario
Surface Soil Volume
Element
Surface Soil Depth (m)
Coniferous
Forest
Grasses/
Herbs
None
Source
0.01 (untilled soil)
X
ParceH
0.01
X
Farm
0.2 (tilled soil)
X
Parcel2
0.01
X
Parcel3
0.01
X
Parcel4
0.01
X
Parcel5
0.01
X
Attachment A
A-13
February 2021
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-11. Terrestrial Plant Parameter Values for the TRIM.FaTE Screening Scenario
Parameter Name
Units
Coniferous3
Grass/Herb3
Value
Used
Reference
Value Used
Reference
Leaf Compartment Type
Allow exchange
1 = yes, 0 = no
1
Seasonal13
Growing season: for screening
scenario, begins March 9 (set to
1) and ends November 7 (set to
0). Nationwide 80th percentile.
Average leaf area index
m2[total leaf area]/
m2[underlying soil
area]
5
Representative value for
conifers, N. Nikolov, Oak Ridge
National Laboratory
5
Mid-range of 4-6 for old fields,
R.J. Luxmoore, Oak Ridge
National Laboratory.
Calculate wet deposition
interception fraction
(Boolean)
1 = yes, 0 = no
1
Selected setting.
1
Selected setting.
Correction exponent, octanol
to lipid
unitless
0.76
From roots, Trapp 1995.
0.76
From roots, Trapp 1995.
Degree stomatal opening
unitless
1
Set to 1 for daytime (when
stomata are open; stomatal
diffusion is turned off at night
using a different property,
IsDay).
1
Set to 1 for daytime (stomatal
diffusion is turned off at night
using a different property,
IsDay).
Density of wet leaf
kg [leaf wet
wt]/m3[leaf wet]
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, Miillerand
Prohl 1993.
3.00E-04
1 E-04 to 6E-04 for different
crops and elements, Muller and
Prohl 1993.
Length of leaf
m
0.01
Professional judgment.
0.05
Professional judgment.
Lipid content
kg[lipid]/kg[leaf
wet weight]
0.00224
European beech, Riederer
1995.
0.00224
European beech, Riederer 1995.
Litter fall rate
1/day
0.0021
Value assumes first-order
relationship and that 99% of
leaves fall in 6 years.
Seasonal13
Leaf fall: for screening scenario
begins November 7 and ends
December 6; rate = 0.15/day
during this time (value assumes
99% of leaves fall in 30 days).
Attachment A
A-14
February 2021
-------
TRIM-Based Tiered Screening Methodology for RTR
Parameter Name
Units
Coniferous3
Grass/Herb3
Value
Used
Reference
Value Used
Reference
Stomatal area normalized
effective diffusion path length
1/m
200
Wilmer and Fricker 1996.
200
Wlmer 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
kg[water]/kg[leaf
wet wt]
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[plant part wet
wt]/ m2[surface
soil]
2
Calculated from leaf area index,
leaf thickness (Simonich and
Hites, 1994), density of wet
foliage.
0.6
Calculated from leaf area index
and Leith 1975a,b in Leith and
Whitaker 1975.
Particle on Leaf Compartment Type
Allow exchange
1 = yes, 0 = no
1
-
Seasonal13
See leaf compartment.
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
Seasonal13
See leaf compartment.
Correction exponent, octanol
to lipid
unitless
0.76
Trapp 1995.
Lipid content of root
kg[lipid]/kg[root
wet wt]
0.011
From bean root, Trapp 1995.
Water content of root
kg[water]/kg[root
wet wt]
0.8
Professional judgment.
Wet density of root
kg [root wet
wt]/m3[root wet]
820
Soybean, Paterson et al. 1991.
Wet mass per soil area
kg[plant part wet
wt]/m2[surface soil]
1.4
Temperate grassland, Jackson
et al. 1996.
Attachment A
A-15
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Parameter Name
Units
Coniferous3
Grass/Herb3
Value
Used
Reference
Value Used
Reference
Stem Compartment Type - Nonwoody Only
Allow exchange
1 = yes, 0 = no
Seasonal13
See leaf compartment
Correction exponent, octanol
to lipid
unitless
0.76
From roots, Trapp 1995; in
Trapp and McFarlane, eds.
1995.
Density of phloem fluid
kg[phloem]/m3
[phloem]
1,000
Professional judgment.
Density of xylem fluid
kg[xylem
fluid]/m3[xylem
fluid]
900
Professional judgment.
Flow rate of transpired water
per leaf area
m3[water]/m2
[stem]-day
0.0048
Crank et al. 1981, as cited by
Paterson et al. 1991.
Fraction of transpiration flow
rate that is phloem rate
unitless
0.05
Paterson et al. 1991.
Lipid content of stem
kg[lipid]/kg[stem
wet wt]
0.00224
Leaves of European beech,
Riederer 1995.
Water content of stem
kg[water]/kg [stem
wet wt]
0.8
Paterson et al. 1991.
Wet density of stem
kg[stem wet wt]/
m3[stem wet]
830
Professional judgment.
Wet mass per soil area
kg[plant part wet
wt]/m2[surface soil]
0.24
Calculated from leaf and root
biomass density based on
professional judgment.
aSee Exhibit A-9 and Exhibit A-10 for assignment of plant types.
bSeasonal values; leaves must be present for exchanges with leaves.
Attachment A
A-16
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-12. Surface Water Parameters for the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value Used
Reference
Algae carbon content (mass
fraction; dry wt basis)
unitless
0.465
APHA 1995.
Algae density in water
column
g[algae wet wt]/L[water]
0.0025a
Millard et al. 1996.
Algae growth rate
1/day
0.7
Hudson et al. 1994, in Watras and
Huckabee, eds. 1994. Also cited
in Mason et al. 1995b.
Algae radius
jjm
2.5
Mason et al. 1995b.
Algae water content (mass
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 [ch I o rid e/L [s u rface
water]
8a
Kaushal et al. 2005.
Chlorophyll concentration
mg[chlorophyll]/L[surface
water]
0.0029a
Nurnberg 1996.
.a
.C
<
Q_
CD
a
m
3.12a
Wl DNR 2007- calculation based
on relationship between drainage
basin and lake area size.b
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
12.17a
Calculated based on pond
dimensions and flow calculations.
Fraction sand
unitless
0.25
Assumption.
Organic carbon fraction in
suspended sediments
kg[organic
carbon]/kg[solids wet wt]
0.02a
Professional judgment.
PH
unitless
7.3a
Professional judgment.
Suspended sediment
deposition velocity
m/day
2a
Assumption (in sediment balance
calculations).
Total suspended sediment
concentration
kg[sediment]/m3[water
column]
0.05a
Assumption (in sediment balance
calculations).
Water temperature
degrees K
298a
U.S. EPA 2005a.
aScenario-specific parameters, values provided are for RTR screens.
bSet using the volume element properties named "top" and "bottom." If not set, depth computed via: d/(A*F), where d is the annual
discharge (in m3/year), A is the lake area (in m2), and F is the flush rate per year.
Attachment A
A-17
February 2021
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-13. Sediment Parameter Values for the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value Used
Reference
Depth3
m
0.05
McKone et al. 2001
(Table 3).
Fraction sand
unitless
0.25
Assumption.
Organic carbon fraction
kg[organic carbon]/kg[solids
wet wt]
0.02b
McKone et al. 2001
(Table 3).
Porosity of the sediment
zone
volume[total pore
space]/volume[sediment
compartment]
0.6
Assumption.
Solid material density in
sediment
kg[sediment]/m3[sediment]
2,600
McKone et al. 2001
(Table 3).
PH
unitless
.a
CO
Assumption.
Sediment resuspension
velocity
m/day
7.62E-05b
Calculated from sediment
balance model.
Unconsolidated sediment layer just below surface water column.
bScenario-specific parameters; values provided are for Tier 1 screenings.
Attachment A
A-18
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-14. Aquatic Animals Food Chain, Density, and Biomass for the TRIM.FaTE Screening Scenario
Fraction Diet3
£
J*
£
ra
Q.
o
o
N
Benthic
Invertebral
Water Coli
Herbivore
Benthic
Omnivore
Water Coli
Omnivore
Benthic
Carnivore
Water Coli
Carnivore
Biomass
(kg/m2)a
Body
Weight
(kg)b
Reference
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
0.25
Assumption.
Water column
0%
0%
0%
100%
0%
0%
0%
0%
0.001
0.25
Assumption.
omnivore
Benthic carnivore
0%
0%
50%
0%
50%
0%
0%
0%
0.001
2.0
Assumption.
Water column
0%
0%
0%
0%
0%
100%
0%
0%
0.0002
2.0
Assumption.
carnivore
Zooplankton
100%
0%
0%
0%
0%
0%
0%
0%
0.0064
5.70E-08
Assumption.
aScenario-specific parameters; values provided are for RTR screening.
bAssumption across all scenarios.
Attachment A
A-19
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-15. Arsenic Chemical-Specific Parameter Values
for the TRIM.FaTE Screening Scenario
Parameter Name3
Units
Value
Reference
CAS numberb
-
7440-38-2
-
Diffusion coefficient in pure
air
m2[air]/day
0.92
U.S. EPA 1996 as cited in U.S. EPA
1999.
Diffusion coefficient in pure
water
m2[water]/day
1.07E-04
U.S. EPA 1996 as cited in U.S. EPA
1999.
Henry's Law constant
Pa-m3/mol
1.00E-37
U.S. EPA 1999.
Melting point
degrees K
1093
U.S. EPA 2004 as cited in U.S. EPA
2005a.
Molecular weight
g/mol
77.922
NCBI 2017
Octanol-air partition
coefficient (Koa)
m3[air]/m3[octanol]
-
-
Octanol-water partition
coefficient (Kow)
L[water]/kg[octanol]
-
-
aAII parameters in this table are TRIM.FaTE chemical properties.
bThis CAS number applies to elemental As.
Exhibit A-16. Cadmium Chemical-Specific Parameter Values
for the TRIM.FaTE Screening Scenario
Parameter Name3
Units
Value
Reference
CAS numberb
-
7440-43-9
-
Diffusion coefficient in pure
air
m2[air]/day
0.71
U.S. EPA 1996 as in U.S. EPA 1999,
Table A-2-35.
Diffusion coefficient in pure
water
m2[water]/day
8.16E-05
U.S. EPA 1996 as cited in U.S.
EPA1999, Table A-2-35).
Henry's Law constant
Pa-m3/mol
1.00E-37
U.S. EPA 1999 (Table A-2-35;
assumed to be zero).
Melting point
degrees K
593.15
U.S. EPA 2004 as cited in U.S. EPA
2005a.
Molecular weight
g/mol
112.41
NCBI 2017 (rounded to five
significant digits)
Octanol-air partition
coefficient (Koa)
m3[air]/m3[octanol]
-
-
Octanol-water partition
coefficient (Kow)
L[water]/kg[octanol]
-
-
aAII parameters in this table are TRIM.FaTE chemical properties.
This CAS number applies to elemental Cd; however, the cations of cadmium are being modeled.
Attachment A
A-20
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-17. Mercury Chemical-Specific Parameter Values
for the TRIM.FaTE Screening Scenario
Parameter Name3
Units
Value
Reference
Hg(0)b
Hg(2)b
MHgb
CAS number
unitless
7439-97-6
14302-87-5
22967-92-6
ChemFinder
Diffusion coefficient
in pure air
m2[air]/day
0.478
0.478
0.456
U.S. EPA 1997.
Diffusion coefficient
in pure water
m2[water]/day
5.54E-05
5.54E-05
5.28E-05
U.S. EPA 1997.
Henry's Law
constant
Pa-m3/mol
719
7.19E-05
0.0477
U.S. EPA 1997.
Melting point
degrees K
234c
5.50E+02d
443e
See endnotes.
Molecular weightf
g/mol
201
201
216
U.S. EPA 1997.
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
U.S. EPA 1997,
based on
Petersen et al.
1995.
aAII parameters in this table are TRIM.FaTE chemical properties.
bOn this and all following tables, Hg(0) = elemental mercury, Hg(2) = divalent mercury, and MHg = methyl mercury.
CU.S. EPA (2004) as cited in U.S. EPA (2005a).
dSRC (2005) as cited in U.S. EPA (2005a).
eUSDHHS (1992) as cited in CARB (1994).
fNCBI (2017), rounded to 3 significant figures.
Attachment A
A-21
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-18. POM Chemical-Specific Parameter Values for the TRIM.FaTE Screening Scenario
Value
Parameter Name
Units
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
0.00864
0.388
0.441
0.372
0.00864
0.19
Diffusion coefficient
in pure water
m2/day
6.70E-05
6.91 E-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
0.203
18.5
12.7
1.22
0.074
0.0485
0.0278
Melting point
degrees K
307.6
395.5
366.4
364.8
433.5
452.1
441
545.5
Molecular weight
g/mol
142.2
256.34
154.21
152.2
228.29
252.31
252.31
276.33
Octanol-water
partition coefficient
(Kow)
L[water]/L[octanol]
7.24E+03
6.31 E+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
0.00864
0.00864
0.00864
0.00864
0.00864
0.00864
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
0.043
0.53
0.0076
1.96
9.81
0.029
Melting point
degrees K
490
528.5
542.5
383.19
387.77
435
Molecular weight
g/mol
252.31
228.29
278.36
202.25
166.22
276.33
Octanol-water
partition coefficient
(Kow)
L[water]/L[octanol]
8.71 E+05
5.37E+05
3.16E+06
1.45E+05
1.51E+04
5.25E+06
Attachment A
A-22
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Parameter Name
Units
Reference
CAS number
unitless
Chemfinder database
Diffusion coefficient in pure
air
m2/day
U.S. EPA 2005a. Exceptions include U.S. EPA 1995a (7,12-dimethylbenz[a]anthracene)
and U.S. EPA 2004 as cited in U.S. EPA 2005b (2-methylnapthalene, acenaphthylene,
and benzo(ghi)perylene).
Diffusion coefficient in pure
water
m2/day
U.S. EPA 2005a. Exceptions include U.S. EPA 1995a (7,12-dimethylbenz[a]anthracene)
and U.S. EPA 2004 as cited in U.S. EPA 2005b (2-methylnapthalene, acenaphthylene,
and benzo[ghi]perylene).
Henry's Law constant
Pa-m3/mol
All values cited in Mackay et al. 2006, with exception of 7,12-dimethylbenz[a]anthracene,
which is from ToxNet HSDB, derived from Meylen 1991 ,a [Original studies, cited by
Mackay et al. 2006 but not in the reference list for this attachment, include Bamford et al.
1999, Yaws et al. 1991, Staudinger and Roberts 2001, Altschuh et al. 1999, Hulscher et
al. 1992, and Eastcott et al. 1988.]
Melting point
degrees K
Lide 2003 as cited in Mackay et al. 2006.
Molecular weight
g/mol
Mackay et al. 2006.
Octanol-water partition
coefficient (Kow)
L[water]/L[octanol]
All values cited in Mackay et al. 2006. [Original studies, cited by Mackay et al. 2006 but
not in the reference list for this attachment, include Hansch et al. 1995, Passivirta et al.
1999, and Sangster 1993.]
Attachment A
A-23
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-19. Dioxin Chemical-Specific Parameter Values 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
6.91 E-05
6.91 E-05
6.91 E-05
6.91 E-05
6.91 E-05
6.91 E-05
6.91 E-05
Henry's Law constant
Pa-m3/mol
0.68
0.19
1.28
1.43
1.42
1.08
1.449
Melting point
degrees K
598.7
532.2
537.7
509.7
495.2
547.2
499.2
Molecular weight
g/mol
460.76
443.76
425.31
409.31
409.31
390.87
374.87
Octanol-water partition
coefficient (Kow)
L[water]/L[octanol]
1.58E+08
1.00E+08
1.00E+08
2.51 E+07
7.94E+06
6.31 E+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
6.91 E-05
6.91 E-05
6.91 E-05
6.91 E-05
6.91 E-05
6.91 E-05
6.91 E-05
Henry's Law constant
Pa-m3/mol
1.11
0.741
1.11
1.115
0.26
0.507
1.115
Melting point
degrees K
558.7
505.7
516.7
520.7
513.7
499.2
512.7
Molecular weight
g/mol
390.87
374.87
390.87
374.87
356.42
340.42
374.87
Octanol-water partition
coefficient (Kow)
L[water]/L[octanol]
1.62E+08
8.31 E+07
1.62E+08
3.80E+07
1.86E+07
6.17E+06
8.31 E+07
Attachment A
A-24
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Value
Parameter Name
Units
2,3,4,7,8-
PeCDF
2,3,7,8-
TCDD
2,3,7,8-
TCDF
Reference
CAS number
unitless
57117-31-4
1746-01-6
51207-31-9
ChemFinder
Diffusion coefficient in
pure air
m2/day
0.192
0.899
0.203
U.S. EPA 2000b cited in U.S. EPA 2005a. Exception: U.S. EPA
2004 cited in U.S. EPA 2005a (for 2,3,7,8-TCDD and
2,3,7,8-TCDF).
Diffusion coefficient in
pure water
m2/day
6.91 E-05
4.84E-05
5.19E-05
U.S. EPA 1995b cited in U.S. EPA 2005a. Exception: U.S. EPA
2004 cited in U.S. EPA 2005a (for 2,3,7,8-TCDD and
2,3,7,8-TCDF).
Henry's Law constant
Pa-m3/mol
0.505
3.33
1.459
Mackay et al. 1992 cited in U.S. EPA 2000b. Exceptions: Sijm
et al. 1989 cited in U.S. EPA 2000b (1,2,3,7,8-PeCDD); and
U.S. EPA 2000b cited in U.S. EPA 2005a (for
1.2.3.6.7.8-HxCDD; 1,2,3,7,8,9-HxCDD; 1,2,3,4,7,8-HxCDF;
1.2.3.7.8.9-HxCDF; 2,3,4,6,7,8-HxCDF; OCDF; and
1,2,3,7,8 PeCDF).
Melting point
degrees K
469.4
578.7
500.7
Rordorf 1987 cited in U.S. EPA 2000b. Exception: Friesen et al.
1985 cited in U.S. EPA 2000b (for OCDD).
Molecular weight
g/mol
340.42
321.98
305.98
U.S. EPA 2000b cited in U.S. EPA 2005a.
Octanol-water partition
coefficient (Kow)
L[water]/L[octanol]
3.16E+06
6.31 E+06
1.26E+06
Mackay et al. 1992 cited in U.S. EPA 2000b. Exceptions:
Passivirta et al. 1999 cited in Mackay et al. 2006
(1,2,3,7,8-PeCDD); U.S. EPA 2000a (for 1,2,3,6,7,8-HxCDD,
1,2,3,7,8,9-HxCDD, 1,2,3,6,7,8-HxCDF, 1,2,3,7,8,9-HxCDF and
2,3,4,6,7,8-HxCDF); Sijm et al. 1989 cited in U.S. EPA 2000b
(for 1,2,3,7,8-PeCDF); and Broman etal. 1991 cited in Mackay
2006 (for 1,2,3,4,7,8,9-HpCDF).
Attachment A
A-25
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-20. Arsenic Chemical-Specific Parameter Values for Abiotic Compartments
in the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value
Reference
Air Compartment Type
Particle dry deposition velocity
(vdep)
m/day
500
McKone et al. 2001.
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
Set to no.
Root Zone Soil Compartment Type
Use input characteristic depth
(Boolean)
0 = no, else = yes
0
Set to no.
Vadose Zone Soil Compartment Type
Use input characteristic depth
(Boolean)
0 = no, else = yes
0
Set to no.
Surface Water Compartment Type
Ratio of concentration in water to
concentration in algae to
concentration dissolved in water
L[water]/g[algae wet wt]
0.155
Mean value from Table 5.5
of Crompton 1998.
Exhibit A-21. Cadmium Chemical-Specific Parameter Values for Abiotic Compartments
in the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value
Reference
Air Compartment Type
Particle dry deposition velocity
(vdep)
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
Set to no.
Root Zone Soil Compartment Type
Use input characteristic depth
(Boolean)
0 = no, else = yes
0
Set to no.
Vadose Zone Soil Compartment Type
Use input characteristic depth
(Boolean)
0 = no, else = yes
0
Set to no.
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-26
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-22. Mercury Chemical-Specific Parameter Values 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 (vdep)
m/day
500
500
500
CalTOX value cited in McKone et al. 2001.
Demethylation rate
1/day
NA
NA
0
Assumption.
Methylation rate
1/day
NA
0
NA
Assumption.
Oxidation rate
1/day
0.00385
NA
NA
Low end of half-life range (6 months to
2 years) in U.S. EPA 1997.
Reduction rate
1/day
NA
0
NA
Assumption.
Washout ratio
m3[air]/m3[rain]
2E+5
2E+5
2E+5
Mackay et al. 1986.
Surface Soil Compartment Type
Use input characteristic depth
(Boolean)
0 = no, else = yes
0
0
0
Set to no.
Soil-water partition coefficient (Kd)
L[water]/kg[soil
wet wt]
1,000
58,000
7,000
U.S. EPA 1997.
Vapor dry deposition velocity
m/day
50
2,500
NA
Hg(0) - from Lindberg et al. 1992; Hg(2) -
estimate by EPA using the Industrial Source
Complex (ISC) Model - [See Vol. Ill, App. A of
the Mercury Study Report (U.S. EPA 1997)];
MHg not emitted from source.
Demethylation rate
1/day
NA
NA
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
NA
0.001
NA
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
NA
NA
Value assumed in U.S. EPA 1997.
Reduction rate
1/day
NA
1.25E-05
NA
Value used for unfilled surface soil (2 cm),
10% moisture content, in U.S. EPA 1997;
general range is 0.0013-0.0001/day *
moisture_content for forested region (Lindberg
1996; Carpi and Lindberg 1997).
Attachment A
A-27
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
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
Set to no.
Soil-water partition coefficient (Kd)
L[water]/kg[soil
wet wt]
1,000
58,000
7,000
U.S. EPA 1997.
Demethylation rate
1/day
NA
NA
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
NA
0.001
NA
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
NA
NA
Value assumed in U.S. EPA 1997.
Reduction rate
1/day
NA
3.25E-06
NA
Value used for tilled surface soil (20 cm), 10%
moisture content, in U.S. EPA 1997 (Lindberg
1996; Carpi and Lindberg 1997).
Vadose Zone Soil Compartment Type
Use input characteristic depth
(Boolean)
0 = no, else = yes
0
0
0
Set to no.
Soil-water partition coefficient (Kd)
L[water]/kg[soil
wet wt]
1,000
58,000
7,000
U.S. EPA 1997.
Demethylation rate
1/day
NA
NA
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
NA
0.001
NA
Range reported in Porvari and Verta 1995 is
2E-4 to 1 E-3 /day; value is average
maximum potential methylation rate constant
under anaerobic conditions.
Oxidation rate
1/day
0
NA
NA
Value assumed in U.S. EPA 1997.
Attachment A
A-28
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Parameter Name
Units
Value
Reference
Hg(0)
Hg(2)
MHg
Reduction rate
1/day
NA
3.25E-06
NA
Value used for tilled surface soil (20 cm), 10%
moisture content, in U.S. EPA 1997 (Lindberg
1996; Carpi and Lindberg 1997).
Groundwater Compartment Type
Soil-water partition coefficient
L[water]/kg[soil
wet wt]
1,000
58,000
7,000
U.S. EPA 1997.
Demethylation rate
1/day
NA
NA
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
NA
0.001
NA
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
NA
NA
Small default nonzero value (0 assumed in
U.S. EPA 1997).
Reduction rate
1/day
NA
3.25E-06
NA
Value used for tilled surface soil (20 cm), 10%
moisture content, in U.S. EPA 1997 (Lindberg
1996; Carpi and Lindberg 1997).
Surface Water Compartment Type
Algal surface area-specific uptake
rate constant
nmol/[|jm2-day-
nmol]
0
2.04E-10
3.60E-10
Assumes radius = 2.5 mm, 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]
1E+3
1E+5
1 E+5
U.S. EPA 1997.
Vapor dry deposition velocity
m/day
NA
2,500
NA
U.S. EPA 1997 (Vol. Ill, App. A).
Demethylation rate
1/day
NA
NA
0.013
Average range of 1 E-3 to 2.5E-2/day from
Gilmourand Henry 1991.
Methylation rate
1/day
NA
0.001
NA
Value used in U.S. EPA 1997; range is 1E-4
to 3E-4/day (Gilmour and Henry 1991).
Oxidation rate
1/day
0
NA
NA
Assumption.
Attachment A
A-29
February 2021
-------
TRIM-Based Tiered Screening Methodology for RTR
Parameter Name
Units
Value
Reference
Hg(0)
Hg(2)
MHg
Reduction rate
1/day
NA
0.0075
NA
Value used in U.S. EPA 1997; reported values
range from less than 5E-3/day for depths
greater than 17 m, up to 3.5/day (Xiao et al.
1995; Vandal et al. 1995; Mason et al. 1995a;
Amyot et al. 1997).
Sediment Compartment Type
Solids-water partition coefficient (Kd)
L[water]/kg[solids
wet wt]
3,000
50,000
3,000
U.S. EPA 1997.
Demethylation rate
1/day
NA
NA
0.0501
Average range of 2E-4 to 1E-1/day from
Gilmourand Henry 1991.
Methylation rate
1/day
NA
1.00E-04
NA
Value used in U.S. EPA 1997; range is 1E-5
to 1E-3/day, Gilmourand Henry 1991.
Oxidation rate
1/day
0
NA
NA
Assumption.
Reduction rate
1/day
NA
1.00E-06
NA
Inferred value based on presence of Hg(0) in
sediment porewater (U.S. EPA 1997; Vandal
et al. 1995).
Note: NA = not applicable.
aTRIM.FaTE Formula Property, which varies from 0.025 to 1.625, depending on pH and chloride concentration.
bTRIM.FaTE Formula Property, which varies from 0.075 to 1.7, depending on pH and chloride concentration.
Attachment A
A-30
February 2021
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-23. POM Chemical-Specific Parameter Values for Abiotic Compartments in the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value
2Methyl
712DMB
Acenaph-
thene
Acenaph-
thylene
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
m3[air]/m3[rain]
2E+5
2E+5
2E+5
2E+5
2E+5
2E+5
2E+5
2E+5
2E+5
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 cone in algae to cone
dissolved in water
(g [ch e m]/kg [a Ig a e])/
(g [eh em]/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-31
February 2021
-------
TRIM-Based Tiered Screening Methodology for RTR
Parameter Name
Units
Value
Reference
Chr
DahA
Fluoran-
thene
Fluor-
ene
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); U.S. EPA 1998
(7,12-dimethylbenz[a]anthracene, benzo[ghi]perylene,
and fluoranthene)/average of range; HSDB 2001 d
(acenaphthene); HSDB 2001b (acenaphthylene); and
Spero et al. 2000 (fluorene).
Washout ratio
m3[air]/m3[rain]
2E+5
2E+5
2E+5
2E+5
2E+5
Mackay et al. 1986 (for chemicals primarily or entirely in
particle form).
Surface Soil Compartment Type
User input characteristic
depth (Boolean)
0 = No, Else = Yes
0
0
0
0
0
Set to no.
Half-life
day
1000
940
275
33
730
MacKay et al. 2000/average of range. Exceptions include
ATSDR 2005 (2-methylnaphthalene = value recorded for
napthalene); U.S. EPA 1998
(7,12-dimethylbenz[a]anthracene, benzo[ghi]perylene,
and fluoranthene)/average of range; HSDB 2001 d
(acenaphthene); HSDB 2001b (acenaphthylene); and
HSDB 2001 e (fluorene).
Root Zone Soil Compartment Type
User input characteristic
depth (Boolean)
0 = No, Else = Yes
0
0
0
0
0
Set to no.
Half-life
day
1000
940
275
33
730
Howard et al. 1991/upper bound measured or estimated
value. Exceptions include ATSDR 2005
(2-methylnaphthalene = value recorded for napthalene);
U.S. EPA 1998 (7,12-dimethylbenz[a]anthracene,
benzo[ghi]perylene, and fluoranthene)/average of range;
HSDB 2001d (acenaphthene); HSDB 2001b
(acenaphthylene); and HSDB 2001 e (fluorene).
Attachment A
A-32
February 2021
-------
TRIM-Based Tiered Screening Methodology for RTR
Parameter Name
Units
Value
Reference
Chr
DahA
Fluoran-
thene
Fluor-
ene
IcdP
Vadose Zone Soil Compartment Type
User input characteristic
depth (Boolean)
0 = No, else = Yes
0
0
0
0
0
Assumption.
Half-life
day
2000
1880
550
66
1460
Howard et al. 1991/upper bound measured or estimated
value. Exceptions include ATSDR 2005
(2-methylnaphthalene = value recorded for napthalene);
U.S. EPA 1998 (7,12-dimethylbenz[a]anthracene,
benzo[ghi]perylene, and fluoranthene)/twice average of
range; HSDB2001d (acenaphthene)/multiplied by 2;
HSDB 2001b (acenaphthylene)/multiplied by 2; and
HSDB 2001 e (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 = value recorded for napthalene);
U.S. EPA 1998 (7,12-dimethylbenz[a]anthracene,
benzo[ghi]perylene, and fluoranthene)/twice average of
range; HSDB2001d (acenaphthene)/multiplied by 2;
HSDB 2001b (acenaphthylene)/multiplied by 2; and
HSDB 2001 e (fluorene)/multiplied by 2.
Surface Water Compartment Type
Ratio of cone in algae to
cone dissolved in water
(g [ch e m]/kg [a Ig a e])/
(g [eh em]/L [water])
280
1388
67.4
5.8
1653
Calculated from Kowfrom 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[ghi]perylene); Montgomery 2000
(fluoranthene); and Boyle 1985 (fluorene).
Attachment A
A-33
February 2021
-------
TRIM-Based Tiered Screening Methodology for RTR
Parameter Name
Units
Value
Reference
Chr
DahA
Fluoran-
thene
Fluor-
ene
IcdP
Sediment Compartment Type
Half-life
day
2290
2290
2290
2290
2290
Mackay et al. 1992/POM 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-34
February 2021
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-24. Dioxin Chemical-Specific Parameters for Abiotic
Compartments in the TRIM.FaTE Screening Scenario
Parameter Name
Units
Va
ue
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
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 cone in algae to
cone 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-35
February 2021
-------
TRIM-Based Tiered Screening Methodology for RTR
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 cone in algae to
cone 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-36
February 2021
-------
TRIM-Based Tiered Screening Methodology for RTR
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 cone in algae to cone
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-37
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Parameter Name
Reference
Air Compartment Type
Deposition velocity
McKone et al. 2001.
Half-life
Atkinson 1996 as cited in U.S. EPA 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)
Set to no.
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
Set to no.
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)
Set to no.
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 cone in algae to cone dissolved in
water
Estimated from Kow value using model from DelVento and
Dachs 2002.
Half-life
Kim and O'Keefe 1998, as cited in U.S. EPA 2000b.
Sediment Compartment Type
Half-life
Estimation based on Adriaens and Grbic-Galic 1992,1993
and Adriaens et al. 1995, as cited in U.S. EPA 2000b.
Attachment A
A-38
February 2021
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-25. Arsenic 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 (assume 1% of
transfer factor from leaf particle
to leaf).
Particle on Leaf Compartment Type
Transfer factor to leaf
1/day
0.2
Assumption.
Root Compartment Type - Grasses and Herbsa
Root-to-root soil partition -
alpha of steady state
unitless
0.95
Selected value.
Root-to-root soil partition -
partitioning coefficient
m3[bulk root
soil]/m3[root]
0.05
Bergqvist 2013.
Root-to-root soil partition -
time to reach alpha
day
10
Iriel 2015 (time to reach 95% of
equilibrium).
Stem Compartment Type - Grasses and Herbsa
Transpiration stream
concentration factor (TSCF)
m3[soil pore
water]/m3[xylem
fluid]
0.24
Zhao 2008.
aRoots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE.
Exhibit A-26. 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 (assume 1% of
transfer factor from leaf particle
to leaf).
Particle on Leaf Compartment Type
Transfer factor to leaf
1/day
0.2
Assumption.
Root Compartment Type - Grasses and Herbsa
Root-to-root soil partition -
alpha of steady state
unitless
0.95
Selected value.
Root-to-root soil partition -
partitioning coefficient
m3[bulk root
soil]/m3[root]
0.23
Nriagu 1980; based on average
value calculated from various
agricultural plant species.
Root-to-root soil partition -
time to reach alpha
day
28
Henning et al. 2001 (time to
reach 95% of equilibrium).
Stem Compartment Type - Grasses and Herbsa
Transpiration stream
concentration factor (TSCF)
m3[soil pore water]/
m3[xylem fluid]
0.45
Tsiros et al. 1999.
aRoots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE.
Attachment A A-39 February 2021
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-27. Mercury Chemical-Specific Parameter Values for Plant Compartments in 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
NA
NA
0.03
Calculated from Bache et al. 1973.
Methylation rate
1/day
NA
0
NA
Assumed from Gay 1975, Bache et al. 1973.
Oxidation rate
1/day
1.0E+06
NA
NA
Assumed to be nearly instantaneous.
Reduction rate
1/day
NA
0
NA
Assumption.
Particle on Leaf Compartment Type
Transfer factor to leaf
1/day
0.2
0.2
0.2
Assumption.
Demethylation rate
1/day
NA
NA
0
Assumption.
Methylation rate
1/day
NA
0
NA
Assumption.
Oxidation rate
1/day
0
NA
NA
Assumption.
Reduction rate
1/day
NA
0
NA
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
Hg(0) assumption; Hg(2) is geometric mean of values
from Leonard et al. 1998, John 1972, and Hogg et al.
1978; MHg is based on Hogg et al. 1978.
t-alpha for root-root zone bulk soil
day
21
21
21
Assumption.
Demethylation rate
1/day
NA
NA
0
Assumption.
Methylation rate
1/day
NA
0
NA
Assumption.
Oxidation rate
1/day
0
NA
NA
Assumption.
Reduction rate
1/day
NA
0
NA
Assumption.
Attachment A
A-40
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Parameter Name
Units
Value
Reference
Hg(0)
Hg(2)
MHg
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 and Scots pine,
Bishop et al. 1998.
Demethylation rate
1/day
NA
NA
0.03
Calculated from Bache et al. 1973.
Methylation rate
1/day
NA
0
NA
Assumption.
Oxidation rate
1/day
0
NA
NA
Assumption.
Reduction rate
1/day
NA
0
NA
Assumption.
Note: NA = not applicable.
aRoots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE.
Exhibit A-28. POM Chemical-Specific Parameter Values for Plant Compartments in TRIM.FaTE Screening Scenario
Parameter Name
Units
Value
2Methyl
712DMB
Acenaph-
thene
Acenaph-
thylene
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
3.5
3.5
3.5
3.5
3.5
3.5
3.5
3.5
3.5
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
17.8
Root Compartment Type - Grasses and Herbsa
Half-life
day
34.6
34.6
34.6
34.6
34.6
34.6
34.6
34.6
34.6
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
3.5
3.5
3.5
3.5
3.5
3.5
3.5
3.5
3.5
Attachment A
A-41
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Parameter Name
Units
Value
Reference
Chr
DahA
Fluoran-
thene
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
3.5
3.5
3.5
3.5
3.5
Approximated from data reported by Edwards
1988 and from unpublished research (McKone
1997).
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
17.8
2.31
2.31
17.8
Calculated as 2 times the measured photolysis
half-life from Mackay et al. 1992. Exceptions:
value of 2.31 for BaP used for
2-methylnaphthalene,
7,12-dimethylbenz[a]anthracene,
acenaphthene, acenaphthylene, BghiP,
fluoranthene, and fluorene.
Root Compartment Type - Grasses and Herbsa
Half-life
day
34.6
34.6
34.6
34.6
34.6
Approximated from data reported by Edwards
1988 (in Cooke and Dennis, eds.,1988); for
bush beans in nutrient solution.
Root-soil-water
interaction - alpha
unitless
0.95
0.95
0.95
0.95
0.95
Selected value.
Stem Compartment Type - Grasses and Herbsa
Half-life
day
3.5
3.5
3.5
3.5
3.5
Approximated from data reported by Edwards
1988 (in Cooke and Dennis, eds.,1988); for
bush beans in nutrient solution.
aRoots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE.
Attachment A
A-42
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-29. Dioxin Chemical-Specific Parameter Values 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% of transfer factor to leaf; highly uncertain.
Half-life
day
70
Arjmand and Sandermann 1985, as cited in Komoba et al. 1995 (in
Trapp and McFarlane, eds., 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 U.S. EPA 2000c (an estimate for mercury) and
Trapp 1995; highly uncertain.
Half-life
day
4.4
McCrady and Maggard 1993; photodegradation; particles sorbed to
grass foliage in sunlight; assumed 10% direct sunlight per day.
Root Compartment Type - Grasses and Herbsa
Half-life
day
70
Arjmand and Sandermann 1985, as cited in Komoba et al. 1995 (in
Trapp and McFarlane, eds., 1995); soybean root cell culture
metabolism test data for DDE.
Root-soil-water interaction - alpha
unitless
0.95
Selected value.
Stem Compartment Type - Grasses and Herbsa
Half-life
day
70
Arjmand and Sandermann 1985, as cited in Komoba et al. 1995 (in
Trapp and McFarlane, eds., 1995); soybean root cell culture
metabolism test data for DDE.
aRoots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE.
Attachment A
A-43
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-30. Arsenic Chemical-Specific Parameter Values for Aquatic Species in the RTR Screening Scenario3
Parameter Name
Units
Value
Reference
Benthic Invertebrate (Bl) Compartment Type
Biota - Sediment accumulation factor
(BSAF)
kg[bulk dry sed]/kg[fish - benthic
invertebrate wet wt]
8.5E-02
BJC 1998. Mean of as-sampled BSAF (0.329) and
depurated BSAF (0.240), in units of kg[bulkdry
sed]/kg[fish - benthic invertebrate dry wet], then
multiplied by fraction dry weight (0.30).
Benthic Omnivore (BO) Compartment Type
Biota - Sediment accumulation factor
(BSAF)
kg[bulk dry sed]/kg[fish - benthic
omnivore wet wt]
6.5E-04
Davis et al. 1996.
Benthic Carnivore (BC) Compartment Type
Biota - Sediment accumulation factor
(BSAF)
kg[bulk dry sed]/kg[fish - benthic
carnivore wet wt]
6.5E-04
Davis et al. 1996.
Water-column Herbivore (WCH) Compartment Type
Bioaccumulation factor (BAF)
L[water]/kg [fish - water-column
herbivore wet wt]
71
U.S. EPA 2003b, Table 3.3, highest value for
freshwater carp.
Water-column Omnivore (WCO) Compartment Type
Bioaccumulation factor (BAF)
L[water]/kg [fish - water-column
omnivore wet wt]
95
U.S. EPA 2003b, Tables 3.3 and 3.9, highest
value for Trophic Level 3 fish, alewife.
Water-column Carnivore (WCC) Compartment Type
Bioaccumulation factor (BAF)
L[water]/kg [fish - water-column
carnivore wet wt]
46
U.S. EPA 2003b,Tables 3.4 and 3.9, highest value
for Trophic Level 4, largemouth bass.
aArsenic tends not to bioaccumulate from one trophic level to the next in freshwater ecosystems. Instead, concentrations in top predatory fish tend to be somewhat lower than
concentrations in their prey (U.S. EPA 2003). As a result, the biokinetic model of food-web bioaccumulation simulated in TRIM.FaTE was not used for arsenic. Instead, biota-sediment
accumulation factors (BSAF) and biota-water bioaccumulation factors (BAF) were sought for freshwater fish. Other investigators have reported different BAF values for arsenic in fish
from specific studies than presented by EPA [e.g., Williams et al. (2006) reported the wet-weight BAF for alewife in the Upper Mystic Lake study by Chen and Folt (2000) was 46, not
95],
Attachment A
A-44
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-31. Cadmium Chemical-Specific Parameter Values for Aquatic Species in TRIM.FaTE Screening Scenario
Parameter Name
Units
Value
Reference
Zooplankton Compartment Type
Absorption rate constant
L[water]/kg[plankton 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
Selected value.
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
1.23a
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
0.66a
Calculated based on body weight from regression
in Hendriks and Heikens 2001.
Elimination rate constant
unitless
1,68E-03b
Computed based on empirical equation.
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
2.46a
Calculated based on body weight from regression
in Hendriks and Heikens 2001.
Attachment A
A-45
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Parameter Name
Units
Value
Reference
Elimination rate constant
unitless
1.73E-02
Assumption.
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
1.23a
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
0.66a
Calculated based on body weight from regression
in Hendriks and Heikens 2001.
Elimination rate constant
unitless
1.73E-02
Assumption
aFormula used: 10**(-0.30*log10(compartment.BW)-0.09).
bFormula used: 10**(-0.25*log10(compartment.BW)-2.7).
Attachment A
A-46
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-32. Mercury Chemical-Specific Parameter Values for Aquatic Species in TRIM.FaTE Screening Scenario
Parameter Name
Units
Value
Reference
Hg(0)
Hg(2)
MHg
Zooplankton Compartment Type
Assimilation efficiency from algae
unitless
0.015
0.2
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
NA
0
NA
Assumption.
Oxidation rate
1/day
1.0E+06
NA
NA
Assumption.
Reduction rate
1/day
NA
0
NA
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[in-
vertebrate wet wt]
0.0824
0.0824
5.04
Hg(0) value assumed based on
Hg(2) value; Hg(2) and MHg
from 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
NA
NA
0
Assumption.
Methylation rate
1/day
NA
0
NA
Assumption.
Oxidation rate
1/day
1.0E+06
NA
NA
Assumption.
Reduction rate
1/day
NA
0
NA
Assumption.
Water-column Herbivore Compartment Type
Assimilation efficiency from plankton
unitless
0.06
0.06
0.5
Williams et al. 2010.
Note: NA = not applicable.
Screening scenario includes: benthic omnivore, benthic carnivore, water-column herbivore, water-column omnivore, and water-column carnivore.
Attachment A
A-47
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-33. POM Chemical-specific Parameter Values for Aquatic Species in TRIM.FaTE Screening Scenario
Parameter
Name
Units
Value
2Methyl
712DMB
Acenaph-
thene
Acenaph-
thylene
BaA
BaP
BbF
BghiP
BkF
Zooplankton Compartment Type
Absorption rate
constant
L[water]/kg[plank-
ton wet wt]-day
790
42650
42230
42300
42650
42653
42650
42656
42652
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
170
2.03
148
123
2.07
1.389
2.12
0.33
1.48
Half-life
day
0.00779
17
0.00239
0.00239
1.28
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
2
2
2
2
Attachment A
A-48
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Parameter Name
Units
Value
Reference
Chr
DahA
Fluoran-
thene
Fluorene
IcdP
Zooplankton Compartment Type
Absorption rate
constant
L[water]/kg[fish
wet wt]-day
42650
42656
142000
15000
42656
AQUAWEB-estimated based on Kow
(Arnot et al. 2004). Exceptions:
2-methylnaphthalene, fluoranthene, and
fluorene from Berrojalbiz et al. 2009.
Assimilation
efficiency (AE) from
algae
unitless
0.46
0.25
0.49
0.5
0.25
AQUAWEB-estimated based on Kow
(Arnot et al. 2004). Exceptions: Value of
0.25, the maximum AE for copepods
exposed to BaP (Wang and Wang 2006),
is assumed for all higher molecular
weight POM (i.e.,
7,12-dimethylbenz[a]anthracene, BaA,
BaP, BbF, BghiP, DahA, and IcdP).
Elimination rate
constant
1/day
2.375
0.4331
8.678
81.87
0.269
AQUAWEB-estimated based on Kow
(Arnot et al. 2004).
Half-life
day
0.495
17
0.00239
0.00025
17
McElroy 1990. Exceptions:
2-methylnaphthalene, fluoranthene, and
fluorene from Berrojalbiz et al. 2009;
BaA, BaP, and chrysene from Moermond
et al. 2007.
Benthic Invertebrate Compartment Type
Clearance constant
unitless
100.6
100.6
100.6
100.6
100.6
Stehly et al. 1990; estimated for mayfly,
120-day-old nymphs.
Vd (ratio of
concentration
in benthic
invertebrates to
concentration in pore
water)
mL/g
7235
7235
7235
7235
7235
Stehly et al. 1990; estimated for mayfly,
120-day-old nymphs.
Half-life
day
0.495
17
0.722
0.722
17
Moermond et al. 2007.
Attachment A
A-49
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Parameter Name
Units
Value
Reference
Chr
DahA
Fluoran-
thene
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:
Barber 2008 (for
2-methylnaphthalene and
acenaphthene); Niimi and Palazzo
1986 (for acenaphthylene,
fluoranthene, and fluorene).
Half-life
day
0.533
2
0.165
0.2
2
Moermond et al. 2007. Exceptions
see note.b
Screening scenario includes: benthic omnivore, benthic carnivore, water-column herbivore, water-column omnivore, and water-column carnivore.
bMoermond et al. (2007) calculated metabolic degradation rate constants for fluoranthene, chrysene, BaA, BeP, and BaP from experiments conducted on fish. Value of 0.2days is
assumed for the lower molecular weight POM based on the value for fluoranthene rounded to one significant digit. Value of 2 days is assumed for the higher molecular weight POM
based on the value for BaP rounded to one significant digit.
Attachment A
A-50
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-34. Dioxin Chemical-Specific Parameter Values for Aquatic Species in TRIM.FaTE Screening Scenario
Value
Parameter Name
Units
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
L[water cleared]/kg[benthic
invertebrate wet wt]-hr
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)
L[water]/kg[benthic
invertebrate wet wt]
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
Fish 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-51
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
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
l
CO
CM X
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
2
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
L[water cleared]/kg[benthic
invertebrate wet wt]-hr
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)
L[water]/kg[benthic
invertebrate wet wt]
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-52
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Parameter Name
Units
Reference
Zooplankton Compartment
Absorption rate constant
L[water]/kg[fish
wet wt]-day
Zhang et al. 2011, copepod ku value.
Assimilation efficiency from algae
unitless
Morrison et al. 1999. Exceptions: Niimi and Oliver 1986 (for
1,2,3,4,6,7,8,9-OCDD, 1,2,3,4,6,7,8,9-OCDF); Berntssen et al. 2007 (for
1,2,3,4,6,7,8-HpCDD, 1,2,3,4,6,7,8-HpCDF); and value for 1,2,3,4,7,8,9-HpCDF
set by 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., 0.2 interpolated from 0.3 and
0.1).
Elimination rate constant
1/day
AQUQWEB-estimated based on Kow (Arnot and Gobas 2004).
Half-life
day
Morrison et al. 1999, metabolic rates for invertebrates.
Benthic Invertebrate Compartment
Clearance constant
L[water cleared]
/kg[benthic
invertebrate wet
wt]-hr
Assumption.
Sediment partitioning partition coefficient
kg/kg
Rubinstein et al. 1990 (used TCDD data forsandworm) and U.S. EPA 1999.
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)
L[water]/kg[benthic
invertebrate wet
wt]
Assumption.
Half-life
day
Rubinstein et al. 1990, TCDD value for sandworm; same value assumed for all
other congeners.
All Fish Compartmentsa
Assimilation efficiency (AE) from food
unitless
Morrison et al. 1999. Exceptions: Niimi and Oliver 1986 (OCDD, OCDF); value for
1,2,3,4,7,8,9-HpCDF set by linear interpolation between values for
Attachment A
A-53
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Parameter Name
Units
Reference
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., 0.2
interpolated from 0.3 and 0.1); and two exceptions in notes b and c.
Chemical uptake rate via gill
L[water]/kg[fish
wet wt]-day
Muir et al. 1985 (explicit or interpolated based on congener-specific differences in
relative assimilation efficiencies from food). 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, representative of 2,3,7,8-substituted dioxins and furans fed
to large salmon (calculated half-lives ranged from 36 to 99 days with no trend
apparent with degree of chlorination).
Screening scenario includes: benthic omnivore, benthic carnivore, water-column herbivore, water-column omnivore, and water-column carnivore.
bAE value of 0.21 from Berntssen et al. (2007) (for fish smaller than 1 kg body weight) used for water-column herbivore, water-column omnivore, and benthic omnivore. AE values of
0.13 used for the two carnivore fish compartments (2 kg body weight) based on van den Berg et al. (1984).
°AE value of 0.37 from van den Berg et al. (1984) (for smallest fish species) used for water-column herbivore. AE value of 0.31 used for remaining fish compartments based on
Morrison et al. (1999).
Attachment A
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TRIM-Based Tiered Screening Methodology for RTR
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Trudel, M., and J.B. Rasmussen. 1997. Modeling the elimination of mercury by fish.
Environmental Science and Technology 31:1716-1722.
Tsiros, I.X.; R.B. Ambrose, and A. Chronopoulou-Sereli. 1999. Air-vegetation partitioning of toxic
chemicals in environmental simulation modeling. Global NEST: The International Journal
1(3): 177-184.
U.S. EPA (U.S. Environmental Protection Agency). 2007. Draft risk assessment for the Siemens
Water Technologies Corp. carbon reactivation facility Parker, Arizona. Appendix F.
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http://www.epa.gov/reqion9/waste/siemens/pdf/RiskAssessment/siemens-
riskassessAppxF.pdf.
U.S. EPA. 2005a. Human Health Risk Assessment Protocol for Hazardous Waste Combustion
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U.S. EPA. 2005c. Empirical Models of Pb and Cd Partitioning Using Data from 13 Soils,
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Attachment A
A-63
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
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divalent mercury in aqueous solution. Water, Air, and Soil Pollution 80:789-798.
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Zn in marine fish Lutjanus argentimaculatus. Marine Ecology Progress Series 238: 173-186.
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plants. New Phytologist 181: 777-79
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Attachment B. Multimedia Ingestion Risk Methodology Used for RTR
Exposure and Risk Estimates
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Contents
B.1 Introduction B-9
B. 1.1 Purpose and Overview B-9
B. 1.2 Organization of This Attachment B-9
B.2 Methodology Overview B-9
B.2.1 Exposure Pathways B-10
B.2.2 Receptor Groups B-11
B.3 Exposure Algorithms B-13
B.3.1 Farm Food Algorithms B-13
B.3.2 Chemical Intake Calculations for Adults and Non-infant
Children B-23
B.3.3 Total Chemical Intake B-32
B.3.4 Chemical Intake Calculations for Nursing Infants B-33
B.4 Dose-response Values B-41
B.4.1 Arsenic B-45
B.4.2 Cadmium B-45
B.4.3 Mercury B-46
B.4.4 Dioxins B-46
B.4.5 Polycyclic Organic Matter B-47
B.5 Risk Estimation B-49
B.5.1 Cancer Risks B-50
B.5.2 Noncancer Hazard Quotients B-52
B.6 Assessment Data and Parameter Values B-53
B.6.1 Environmental Concentrations B-54
B.6.2 Farm Foods Parameter Values B-55
B.6.3 Exposure Parameter Values for Adults and Non-infants B-74
B.6.4 Other Exposure Factor Values B-87
B.6.5 Breast-Milk Infant Exposure Pathway Parameter Values B-91
B.7 Summary of Default Exposure Parameter Values B-98
B.7.1 Default Ingestion Rates B-98
B.7.2 Default Screening-Level Population-Specific Parameter Values B-103
B.7.3 Default Chemical-Specific Parameter Values for Screening
Analysis B-103
B.7.4 Screening-Level Parameter Values for Nursing Infant
Exposure B-105
B.8 References B-106
Attachment B B-3 February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Exhibits
Exhibit B-1. Transfer Pathways for Farm Foods B-10
Exhibit B-2. Chemical-transfer Pathways for Produce B-13
Exhibit B-3. Estimating Chemical Concentration in Aboveground Produce B-14
Exhibit B-4. Chemical-transfer Pathways for Animal Products B-20
Exhibit B-5. Oral Dose-response Values B-42
Exhibit B-6. Toxic Equivalency Factors for Dioxin Congeners B-47
Exhibit B-7. Oral Dose-response Values for POM Groups B-48
Exhibit B-8. Parameters Used to Estimate Chemical Concentrations in Farm
Foods B-55
Exhibit B-9. Chemical-specific Inputs for Produce Parameters B-58
Exhibit B-10. Chemical-specific Inputs by Plant Type B-60
Exhibit B-11. Non-chemical-specific Produce Inputs B-70
Exhibit B-12. Animal Product Chemical-specific Inputs B-72
Exhibit B-13. Soil and Plant Ingestion Rates for Animals B-74
Exhibit B-14. Mean and Percentile Estimates of Body Weight B-75
Exhibit B-15. Estimated Daily Consumer-only Mean and Percentile Water
Ingestion Rates B-76
Exhibit B-16. Summary of Age-group-specific Ingestion Rates for Farm Foods B-77
Exhibit B-17. Ingestion Rates for Fish, as used in the Screening Scenario B-81
Exhibit B-18. Daily Mean and Percentile Consumer-only Fish Ingestion Rates
(IRco.y) B-83
Exhibit B-19. Fraction of Population Consuming Freshwater/Estuarine Fish on a
Single Day (Fpc,y) B-84
Exhibit B-20. Long-term Mean and Percentile Per-capita Fish Ingestion Rates
(IRpc.y) B-84
Exhibit B-21. Mean and 90th Percentile Per-capita Fish Ingestion Rates for
Populations of Recreational Fishers (IRpc.y) B-85
Exhibit B-22. Daily Mean and Percentile Soil and Dust Ingestion Rates B-86
Exhibit B-23. Daily Mean and Percentile Per Capita Total Food Intake B-86
Exhibit B-24. Fraction Weight Losses from Preparation of Various Foods B-88
Exhibit B-25. Scenario- and Receptor-specific Input Parameter Values Used to
Estimate Infant Exposures via Breast Milk B-91
Exhibit B-26. Average Body Weight for Infants B-92
Exhibit B-27. Time-weighted Average Body Weight for Mothers B-93
Exhibit B-28. Infant Breast Milk Intake Rates B-94
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit B-29. Chemical-specific Input Parameter Values for Breast Milk Exposure
Pathway B-95
Exhibit B-30. Farm-food Category Ingestion Rates for Health Protective
Screening Scenario for Farming Households B-100
Exhibit B-31. Ingestion Rates for Rural Gardeners B-102
Exhibit B-32. Ingestion Rates for Urban Gardeners B-102
Exhibit B-33. Mean Body-weight Estimates B-103
Exhibit B-34. Chemical-Specific Parameter Values B-104
Exhibit B-35. Chemical and Animal-Type Specific Biotransfer Factor (Ba) Values B-105
Equations
Equation B-1. Chemical Concentration in Aboveground Produce B-14
Equation B-2. Chemical Concentration in Aboveground Produce Due to Root
Uptake B-15
Equation B-3. Chemical Concentration in Aboveground Produce Due to
Deposition of Particle-phase Chemical B-15
Equation B-4. Chemical Concentration in Aboveground Produce Due to Air-to-
plant Transfer of Vapor-phase Chemical B-16
Equation B-5. Conversion of Aboveground Produce Chemical Concentration from
Dry- to Wet-weight Basis B-17
Equation B-6. Chemical Concentration in Belowground Produce: Nonionic
Organic Chemicals B-18
Equation B-7. Chemical Concentration in Belowground Produce: Inorganic
Chemicals B-18
Equation B-8. Conversion of Belowground Produce Chemical Concentration from
Dry- to Wet-weight Basis B-19
Equation B-9. Chemical Concentration in Beef, Pork, or Total Dairy B-20
Equation B-10. Chemical Concentration in Poultry or Eggs B-21
Equation B-11. Incidental Ingestion by Livestock of Chemical in Soil B-21
Equation B-12. Ingestion by Livestock of Chemical in Feed B-22
Equation B-13. Chemical Concentration in Livestock Feed (All Aboveground) B-22
Equation B-14. Chemical Concentration in Livestock Feed Due to Root Uptake B-23
Equation B-15. Average Daily Dose for Specified Age Group and Food Type B-23
Equation B-16. Chemical Intake from Soil Ingestion B-25
Equation B-17. Chemical Intake from Fish Ingestion B-25
Equation B-18. Consumption-weighted Chemical Concentration in Fish B-25
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TRIM-Based Tiered Screening Methodology for RTR
Equation B-19. Concentrations in Aquatic Biota based on Empirical
Bioaccumulation Factors B-27
Equation B-20. Concentrations in Aquatic Biota based on Empirical Biota-
sediment Accumulation Factors B-27
Equation B-21. Chemical Intake from Consumption of Exposed Fruits B-27
Equation B-22. Chemical Intake from Consumption of Protected Fruits B-27
Equation B-23. Chemical Intake from Exposed Vegetables B-28
Equation B-24. Chemical Intake from Protected Vegetables B-28
Equation B-25. Chemical Intake from Root Vegetables B-28
Equation B-26. Chemical Intake from Ingestion of Beef B-29
Equation B-27. Chemical Intake from Dairy Ingestion B-30
Equation B-28. Chemical Intake from Pork Ingestion B-30
Equation B-29. Chemical Intake from Poultry Ingestion B-31
Equation B-30. Chemical Intake from Egg Ingestion B-31
Equation B-31. Chemical Intake from Drinking-water Ingestion B-32
Equation B-32. Total Average Daily Dose of Chemical for Infants less than One
Year, from Ingestion of Breast Milk (mg/kg-day) B-32
Equation B-33. Total Average Daily Dose of Chemical from All Ingestion Sources
for Children Ages 1 through 2 Years (mg/kg-day) B-32
Equation B-34. Total Average Daily Dose of Chemical from All Ingestion Sources
for Children Ages 3 through 5 Years (mg/kg-day) B-32
Equation B-35. Total Average Daily Dose of Chemical from All Ingestion Sources
for Children Ages 6 through 11 Years (mg/kg-day) B-32
Equation B-36. Total Average Daily Dose of Chemical from All Ingestion Sources
for Children Ages 12 through 19 Years (mg/kg-day) B-33
Equation B-37. Total Average Daily Dose of Chemical from All Ingestion Sources
for Adults Ages 20 up to 70 years (mg/kg-day) B-33
Equation B-38. Lifetime Average Daily Dose B-33
Equation B-39. Average Daily Dose of Chemical to the Nursing Infant B-34
Equation B-40. Chemical Concentration in Breast-milk Fat B-35
Equation B-41. Daily Maternal Absorbed Intake B-36
Equation B-42. Biological Elimination Rate Constant for Chemicals for Non-
lactating Women B-37
Equation B-43. Biological Elimination Rate Constant for Lipophilic Chemicals for
Lactating Women B-37
Equation B-44. Chemical Concentration in Aqueous Phase of Breast Milk B-38
Equation B-45. Fraction of Total Chemical in Body in the Blood-plasma
Compartment B-39
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TRIM-Based Tiered Screening Methodology for RTR
Equation B-46. Biological Elimination Rate Constant for Hydrophilic Chemicals B-40
Equation B-47. Infant Average Daily Absorbed Dose of Methyl Mercury B-41
Equation B-48. Excess Lifetime Cancer Risk B-50
Equation B-49. Risk from Chemical Ingestion in First Year of Life (chemicals with
a mutagenic mode of action) B-51
Equation B-50. Risk from Chemical Ingestion during Ages 1 through 2 Years
(chemicals with a mutagenic mode of action) B-51
Equation B-51. Risk from Chemical Ingestion during Ages 3 through 5 Years
(chemicals with a mutagenic mode of action) B-51
Equation B-52. Risk from Chemical Ingestion during Ages 6 through 11 Years
(chemicals with a mutagenic mode of action) B-51
Equation B-53. Risk from Chemical Ingestion during Ages 12 through 19 Years
(chemicals with a mutagenic mode of action) B-51
Equation B-54. Risk from Chemical Ingestion during Ages 20 up to 70 Years
(chemicals with a mutagenic mode of action) B-51
Equation B-55. Total Extra Lifetime Cancer Risk (chemicals with a mutagenic
mode of action) B-51
Equation B-56. Hazard Quotient for Chemicals with a Chronic RfD B-52
Equation B-57. Hazard Index for Chemicals with Chronic RfDs B-53
Equation B-58. Age-group-specific and Food-specific Ingestion Rates B-79
Equation B-59. Alternative Age-group-specific Fish Ingestion Rates B-82
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B.1 Introduction
B.1.1 Purpose and Overview
For persistent and bioaccumulative hazardous air pollutants (PB-HAPs), risks from direct
inhalation of the chemical can be much less than risks from ingestion of the chemical in water,
fish, and food products grown in an area of chemical deposition. For example, 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. This attachment provides a detailed description of the multimedia ingestion
risk estimation methodology developed by EPA's Office of Air Quality Planning and Standards
(OAQPS) for use in Risk and Technology Review (RTR) multimedia risk assessments.
The methodology described in this attachment uses equations, assumptions, and default
parameter values previously published by EPA and approaches consistent with EPA guidance
on human exposure and risk estimation. In particular, the methodology complies with EPA's
latest guidelines for exposure and risk assessment, including Human Health Risk Assessment
Protocol for Hazardous Waste Combustion Facilities (HHRAP; U.S. EPA 2005a); 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 (U.S. EPA 2005c,d,b, respectively); and its Exposure Factors
Handbook (EFH; U.S. EPA 2008a, 2011a).
B.1.2 Organization of This Attachment
The RTR multimedia ingestion risk estimation methodology (hereafter referred to as "the
methodology") is described in Sections B.2 through B.5 of this attachment. Section B.2 identifies
the exposure pathways and receptors included in the scope of the methodology. Section B.3
describes the exposure algorithms used in the methodology, including how average daily doses
(ADDs) are calculated. Section B.4 presents the toxicity reference values the methodology uses
to calculate risks. Section B.5 describes the risk characterization algorithms. Section B.6
describes the data requirements of the methodology, and Section B.7 identifies default
parameter assumptions EPA uses for RTR screening assessments. Section B.8 provides
references.
Note that EPA used the default parameter values described in Section B.7 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. For some assessments, it may be appropriate to use values other than the
defaults to better represent a specific exposure scenario. This attachment provides tables of
alternate values for some parameter values and assumptions (e.g., exposure factors) from
previously published EPA sources.
B.2 Methodology Overview
The RTR multimedia ingestion risk methodology provides screening-level estimates of
exposures and risks associated with fishing activities and farming and gardening activities in the
vicinity of a source of PB chemical emissions to air. The methodology can assess human
exposures via ingestion pathways, including drinking water consumption, incidental soil
ingestion, fish ingestion, and ingestion of 10 types of farm foods: exposed fruits, protected fruits,
exposed vegetables, protected vegetables, root vegetables, beef, total dairy, pork, poultry, and
Attachment B
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TRIM-Based Tiered Screening Methodology for RTR
eggs. It also includes breast milk ingestion and risk estimates 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.
Sections B.2.1 and B.2.2 below summarize the ingestion exposure pathways and receptor age
categories, respectively, included in the methodology.
B.2.1 Exposure Pathways
The methodology estimates the concentrations of chemicals in farm foods grown in an area of
airborne chemical deposition using algorithms and parameter values provided in HHRAP (U.S.
EPA 2005a). Ten categories of farm foods are examined: exposed fruit, protected fruit, exposed
vegetables, protected vegetables, root vegetables, beef, total dairy, pork, poultry, and eggs.
Exhibit B-1 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 estimated,
as is the case for EPA's RTR calculation of screening threshold emission rates for PB-HAPs
(U.S. EPA 2008b).
Farm foods 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
(RCF) is applied. The algorithms used to estimate produce concentrations are presented in
Section B.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. Diet options for
farm animals include forage (plants grown on-site for 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.
Exhibit B-1. Transfer Pathways for Farm Foods
Farm Foods
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 grain3
• Soil ingestion
Attachment B
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TRIM-Based Tiered Screening Methodology for RTR
Farm Foods
Chemical-transfer Pathways
Pork
• Ingestion of silage and grain3
• Soil ingestion
Poultry and eggs
• Ingestion of grain3
• Soil ingestion
aChemical concentrations in forage, silage, and grain are estimated via intermediate calculations analogous to those used for
aboveground produce.
The algorithms in the methodology 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 B.3.1.2 of this attachment.
B.2.2 Receptor Groups
As noted in EPA risk assessment guidelines (U.S. EPA 2005b,c,d, 2008a), exposures of
children are expected to differ from exposures of adults due to differences in body weights
(BWs), ingestion rates (IRs), 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.
EPA's HHRAP (Chapter 4, U.S. 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. Overtime, different EPA programs have used
different child age groupings to evaluate BWs, IRs, 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 (U.S. 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 IRs per unit BW, with the highest IRs occurring for the youngest, most
rapidly growing, age groups.
For purposes of RTR assessments using this methodology, the selection of age categories is
limited by the categories for which most of the farm food IRs have been calculated. In Chapter
13 of both its EFH (U.S. EPA 2011a) and its Child-Specific Exposure Factors Handbook
(CSEFH; U.S. EPA 2008a), EPA summarized homegrown/raised food IRs 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.
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TRIM-Based Tiered Screening Methodology for RTR
Although the age groupings used to estimate farm food IRs do not match precisely the
groupings that EPA recommended in 2005 for Agency exposure assessments (U.S. 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
(NFCS; USDA 1992, 1993, 1994a) remains the most recent survey of IRs for homegrown 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 homegrown produce and animal
products are the primary exposure pathways for which the multipathway risk methodology was
developed, those are the age groupings used for all child parameter values used to estimate
exposure and risk.
Thus, values for each exposure parameter were estimated for adults (20 up to 70 years of age)
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 B.5.1 and B.5.2 for descriptions of the risk characterization algorithms used to
calculate cancer and noncancer 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.
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 (U.S. 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 (U.S. 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 (U.S. EPA 2005c,d) also are included in the
methodology:
. 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, the only
PB-HAPs included in RTR assessments that have a mutagenic mode of carcinogenesis are the
carcinogenic POMs. See Section B.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.
Attachment B
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B.3 Exposure Algorithms
The exposure algorithms are described below in four sections. Section B.3.1 presents the
algorithms used to estimate chemical concentrations in farm 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 B.3.2, and total chemical intake
calculations are described in Section B.3.3. Finally, the sets of algorithms used to estimate
chemical intake via consumption of breast milk by nursing infants are described in Section
B.3.4. As noted previously, the exposure algorithms used in in this methodology are based on
those presented in HHRAP (U.S. EPA 2005b). Any differences from HHRAP are explained in
this section.
B.3.1 Farm Food Algorithms
The algorithms and parameters used to estimate chemical concentrations in produce and
animal products are described in Sections B.3.1.1 and B.3.1.2, respectively. Discussions of the
parameter value options and the values selected as defaults for RTR risk assessment are
provided in Section B.6.2. The use of TRIM.FaTE to model chemical fate and transport in the
environment prior to farm food calculations drives the most significant difference between the
farm food 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 farm foods
algorithms to calculate concentrations in those 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 farm foods algorithms to estimate food media concentrations.
B.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-2 lists the pathways by which chemicals are transferred to the 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 PB-HAPs in its RTR
assessments (U.S. EPA 2008b), and as described in the Technical Support Document. The two
sections below (Aboveground Produce and Belowground Produce) describe the transfer
pathways and algorithms for aboveground and belowground produce, respectively.
Exhibit B-2. Chemical-transfer Pathways for Produce
Farm Foods
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
Attachment B
B-13
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit B-3. Estimating Chemical
Concentration in Aboveground Produce
\
ftx\
t
Deposition
of Particles
(Pd)
Vapor
Transfer
(Pv)
Root Uptake
from Soil
(PrAG-produce)
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-3. 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 BCF (BrAG-Produce) that relates the chemical concentration in the plant to the
average chemical concentration in the soil at the root-zone depth in the produce-growing area
( CSroot-zone_produce) ¦
The edible portions of aboveground protected produce are not subject to contamination via
particle deposition (Pd) or vapor transfer (Pv). Therefore, root uptake of chemicals is the primary
mechanism through which aboveground protected produce becomes contaminated. As shown
below, the chemical concentration in the aboveground plant due to root uptake from soil
(PrAG-produce- dw) is estimated using an empirical BCF (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.
Chemical Concentration in
Aboveground Produce
Equation B-1. Chemical Concentration in Aboveground Produce
¦' AG-produce-DW(i)
P'" AG-produce-DW (/')
Pv
(')
Eqn. B-1
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)
Chemical concentration in edible portion of aboveground produce type i due to
Pd(i) = deposition of particles (mg/kg produce DW); for protected aboveground produce,
Pd equals zero (Equation B-3)
Chemical concentration in edible portion of aboveground produce type i, exposed
PrAG-produce-DW(i) = or protected, due to root uptake from soil at the root-zone depth of the produce
growing area (mg/kg produce DW) (Equation B-2)
Attachment B
B-14
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Chemical concentration in edible portion of aboveground produce type i due to
Pv(i) = air-to-plant transfer (|jg/g [or mg/kg] produce DW); for protected aboveground
produce, Pv equals zero (Equation B-4)
Equation B-2. Chemical Concentration in Aboveground Produce Due to Root Uptake
^^AG-produce-DW(/') — ^^root-zone produce X ®^AG-produce-DW(i) ECjtl B-2
where:
Concentration of chemical in edible portion of aboveground produce type /',
PrAG -produce-DW(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)
_ _ Average chemical concentration in soil at root-zone depth in produce-growing area
CSroot-zone_produce - (mg/kg s0|| DW)
R _ Chemical-specific plant/soil chemical bioconcentration factor for edible portion of
rAG-Produce-Dwo) - aboveground produce type /', exposed or protected (g soil DW/g produce DW)
Equation B-3. Chemical Concentration in Aboveground Produce Due to Deposition of Particle-
phase Chemical
UCF x (Drdp + (Fw * Drwp)) * Rp(j) x (1 - rp(/)))
Pdm= v " ---(f)
(i) Ypm x kp
where:
(0 -^(i)
Eqn. B-3
Chemical concentration in aboveground produce type /' on a dry-weight (DW) basis due to
particle deposition (mg/kg produce DW); set equal to zero for protected aboveground produce
Pd0)
UCF = Units conversion factor of 1,000 mg/kg
Drdp = Average annual dry deposition of particle-phase chemical (g/m2-yr)
l~w _ 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)
Rpo) = Interception fraction of the edible portion of plant type /' (unitless)
kpo) = Plant surface loss coefficient for plant type/'(yr1)
Tpo) = Length of exposure to deposition in the field per harvest of the edible portion of plant type /' (yr)
Ypo) = Yield or standing crop biomass of the edible portion of plant type /' (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 Qx(1 - Fv) to indicate the emissions rate, in g/sec, of
Attachment B
B-15
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
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.
The mulitpathway ingestion risk methodology uses 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 Equation B-3.
Equation B-4. Chemical Concentration in Aboveground Produce Due to
Air-to-plant Transfer of Vapor-phase Chemical
a,, _ Ca x Fv x Bvag(/) x VGag(/)
(/)" p
Pa Eqn. B-4
where:
Concentration of chemical in edible portion of aboveground produce type /' from air-to-
plant transfer of vapor-phase chemical on a dry-weight (DW) basis (|jg/g produce DW);
set equal to zero for protected aboveground produce
Average annual total chemical concentration in air (jjg/m3)
Fraction of airborne chemical in vapor phase (unitless)
Air-to-plant biotransfer factor for aboveground produce type /' for vapor-phase chemical in
air ([mg/g produce DW]/[mg/g air], i.e., g air/ g produce DW)
Empirical correction factor for aboveground exposed produce type /' to address possible
overestimate of the diffusive transfer of chemical from the outside to the inside of bulky
produce, such as fruit (unitless)
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 QxFv 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 vapor-phase
chemical in units of |jg-sec/g-m3. However, the multimedia ingestion risk methodology uses the
average annual total air concentration of the chemical, Ca, for a specific location relative to the
source in units of |jg/m3. 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 |jg/m3.
PV(i) =
Ca =
Fv =
BVAG(i) =
VGago) =
pa =
Attachment B
B-16
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
With the publication of HHRAP, EPA provided a companion database that includes default
chemical-specific values for Fv, as well as certain other parameters.22
The calculations of chemical concentration in aboveground produce, (CAG-Produce-Dw), shown in
Equation B-1 above, are on a DW basis. The farm food IRs, on the other hand, are on a fresh-
or wet-weight (WW) basis. Therefore the concentration in aboveground produce must be
calculated on a WW basis, Cag -produce-WW, using Equation B-5 and the moisture adjustment factor
(MAF) of the farm food category.
Equation B-5. Conversion of Aboveground Produce Chemical Concentration from
Dry- to Wet-weight Basis
Cag-produce-WW(/) ~ ^AG-produce-DW(i) x
(100 -MAF{i))]
100
Eqn. B-5
where:
CAG-produce-WW(i)
CAG-produce-DW(i)
Chemical concentration in edible portion of aboveground produce type i on a wet-
weight (WW) basis (mg/kg produce WW)
Chemical concentration in edible portion of aboveground produce type i on a dry-
weight (DW) basis (mg/kg produce DW)
Moisture adjustment factor for aboveground produce type /' to convert the chemical
MAFo) = concentration estimated for dry-weight produce to the corresponding chemical
concentration for full-weight fresh produce (percent water)
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.
(a) 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
RCF and the average chemical concentration in the soil at the root-zone depth in the produce-
growing area (Csr0ot-zone_produce), as shown in Equation B-6. The RCF relates the chemical
concentration in the plant on a WW basis to the average chemical concentration in the root-
zone soil (Csr0ot-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.
22The HHRAP Companion Database is available at
https://archive.epa.aov/epawaste/hazard/tsd/td/web/html/riskvol.html
Attachment B
B-17
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TRIM-Based Tiered Screening Methodology for RTR
Equation B-6. Chemical Concentration in Belowground Produce: Nonionic Organic Chemicals
_ CSroot-zone_produce x RCF X VGrootveg
^BG-produce-WW ~~ T7~\ , #/-vi-
Kds x UCF
Eqn. B-6
where:
CBG-produce-WW =
CSroot-zone_produce =
RCF =
VGrootveg =
Concentration of chemical in belowground (BG) produce (i.e., tuber or root
vegetable) on a wet-weight (WW) basis (mg chemical/kg produce WW)*
Average chemical concentration in soil at root-zone depth in produce-growing
area, on a dry-weight (DW) basis (mg chemical/kg soil DW)
Chemical-specific root concentration factor for tubers and root produce (L soil
pore water/kg root WW)*
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)
*Note that there is only one type of BG produce; hence there are no plant-type-specific subscripts.
The RCF, as developed by Briggs etal. (1982), is the ratio of the chemical concentration in the
edible root on a WW 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 octanol-water partition
coefficient (Kow) from empirically derived regression models. Those models are shown in
HHRAP Appendix A-2, Equations A-2-14 and A-2-15 (RCF) and in Equations A-29 and A-2-10
(Kds). The RCF and Kds values calculated for many of the chemicals in HHRAP are included in
the HHRAP Companion Database (including the values for POMs and dioxins).
(b) 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
BCF must be used. The root/soil BCF, 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
_7 nna rtmrlima -*• B fQfl_nrnHi ir>t=t—D I A/ x VG„
r* _ - Jroot-zone_ produce LJ' BG-ptoduce-DW rootveg
^BG-ptoduce-DW ~ Z
Eqn. B-7
Attachment B
B-18
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
where:
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 dry-
weight (DW) basis (mg/kg produce DW)
Average chemical concentration in soil at root-zone depth in produce-growing
area (mg/kg soil DW)
Chemical-specific root/soil chemical bioconcentration factor for edible portion
of belowground produce (g soil DW/g produce DW)
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
r(100 -MAFbg))
uBG-produce-WW ~ uBG-produce-DW x TTTTj
^ luu J Eqn. B-8
where:
Chemical concentration in edible portion of belowground produce on a weight-
weight (WW) basis (mg/kg produce WW)
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 dry-
weight (DW) basis (mg/kg produce DW)
Moisture adjustment factor (as in Equation B-5, but single value for below
ground produce) (percent water)
B.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 /' (Plantch-mtakeo.m>) and
incidental ingestion of soil for ground-foraging animals (SoilCh-mtake(m))¦ Exhibit B-4 summarizes
the pathways by which chemicals are transferred to these home- or farm-raised animal food
products. Note that for a general screening-level assessment, all of the pathways can be
modeled, as is done for EPA's RTR calculation of screening threshold emission rates for
PB-HAPs (U.S. EPA 2008b).
The feed options for farm animals in the mulitpathway ingestion risk methodology 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-4, the algorithms for chemical intake with plant feeds
(Plantch-mtake(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.
C BG-produce-DW —
CSroot-zone_produce =
BrBG -produce-DW —
VGrootveg —
C BG-produce-WW
CBG-produce-DW
MAF(bg)
Attachment B
B-19
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit B-4. Chemical-transfer Pathways for Animal Products
Farm Foods
Chemical-transfer Pathways
Animal
Products
Beef and total dairy (including milk)
• Ingestion of forage, silage, and grain3
• Incidental soil ingestion
Pork
• Ingestion of silage and grain3
• Incidental soil ingestion
Poultry and eggs
• Ingestion of grain3
• Incidental soil ingestion
aChemical 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. The multimedia ingestion risk methodology 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
where:
Cmammal(m)
Ba(m)
Soilch -lntake(m)
Plantch -intake(i,m)
Ba{m) x MF x
Soilr
Ch-lntake(m)
\ <=1
n \
Ch-lntake(i ,m)
X Plant c
/
Eqn. B-9
MF =
Concentration of chemical in mammalian animal product m, where m = beef,
pork, or total dairy (mg chemical/kg animal product WW)
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)
Chemical-specific mammalian metabolism factor that accounts for endogenous
degradation of the chemical (unitless)
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
For livestock (animal product) type m, ingestion of chemical from plant feed
type /' (mg chemical/kg livestock WW); see Equation B-12 below
Attachment B
B-20
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
(If m =beef or total dairy, then n = 3 and /' = forage, silage, and grain; m = pork,
then n = 2 and /' = silage and grain; m = poultry, then n = 1 and I = grain.)
Equation B-10. Chemical Concentration in Poultry or Eggs
Cpoultry(m) ®®(m) x
(Soil,
Ch-lntake(m)
Plant,
Ch-lntake(i,m))
Eqn. B-10
where:
Cpoultry(m)
Ba(m)
Soilch -lntake(m)
Plantch -intake(i,m)
Concentration of chemical in food product m, where m = poultry or eggs (mg
chemical/kg animal product WW)
Chemical-specific biotransfer factor for food product m, where m = poultry or
eggs (day/kg animal product WW)
Incidental ingestion of chemical in surface soils by consumption of food on the
ground (mg chemical/day) where m = poultry; see Equation B-11
For poultry (and eggs), animal m, ingestion of the chemical in plant feed type /'
(mg chemical/day), which for poultry is limited to grain; see Equation B-12
The incidental ingestion of the chemical in soils by livestock during grazing or consumption of
feed placed on the ground (Soiich-mtakem) is estimated using empirical soil IRs (Qs) and a soil
bioavailability factor for livestock (Bs), as shown in Equation B-11. The default value for Bs 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. The surface soil concentration in Equation B-11, Css-nvestock, is for areas
where livestock forage, which may be distinct from the surface soil concentration in areas where
produce are grown and where humans might incidentally ingest soils (see Section B.6.1 of this
attachment).
Equation B-11. Incidental Ingestion by Livestock of Chemical in Soil
S°ilch-lntake(m)
Qs x Css_ljvestock x Bs
Eqn. B-11
where:
Soilch -lntake(m)
QS(m)
CSs-livestock
Bs
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)
Quantity of soil, on a dry-weight basis (DW), eaten by animal type m each day
(kg soil DW/day)
Chemical concentration in surface soil in contaminated area where livestock
feed (mg chemical/kg soil DW)
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: /' = 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
Attachment B
B-21
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
chemical with each feed type, /', Plantch-mtakeo.m), is calculated separately according to
Equation B-12. Note that the animal feed IRs are on a DW basis; hence, no DW to WW
conversion is needed.
Equation B-12. Ingestion by Livestock of Chemical in Feed
Plantch Intake(im) F(l m)
Qp,
(i,m) Weec/(/')
Eqn. B-12
where:
Plant Ch-intake(i,m)
F(i,m)
QP(i,m)
Cfeed(i)
Ingestion of chemical in plant feed type /' (mg chemical/day), where /' = forage,
silage, or grain, for livestock type m
Fraction of plant feed type /' obtained from contaminated area used to grow
animal feed, where I = forage, silage, or grain (unitless) for livestock type m
Quantity, on a dry-weight (DW) basis, of plant feed type /' consumed per animal
per day (kg plant feed DW/day), where /' = forage, silage, or grain, for livestock
type m
Concentration of chemical in ingested plant feed type /' (mg chemical/kg plant
feed DW), where /' = 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)
c - Pr
^feed(i) ~ feed(i)
Pd(i)+Pv
0)
Eqn. B-13
where:
Cfeed(i)
Prfeed(i)
Concentration of chemical in plant feed type /' on a dry-weight (DW) basis (mg
chemical/kg plant feed DW), where /' = forage, silage, or grain
Concentration of chemical in plant feed type /' due to root uptake from soil (mg/kg
DW), where /'= forage, silage, or grain; see Equation B-14 below
Concentration of chemical in plant feed type /' due to wet and dry deposition of
Pd(i) = particle-phase chemical (mg/kg DW), where /' = forage, silage, or grain; when /' =
grain, the Pd term equals zero
Concentration of chemical in plant feed type /' due to air-to-plant transfer of vapor-
Pv(i) = phase chemical (|jg/g [or mg/kg] DW) where /' = forage, silage, or grain; when /' =
grain, the Pd term equals zero
The chemical concentration in animal feed due to root uptake from the soil is calculated with
Equation B-14. The equation is the same as Equation B-2, except that a Br value appropriate to
grasses is used and different soil concentrations could be used for the area used to grow animal
Attachment B
B-22
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
feed and the area used to grow produce for human consumption (see Section B.6.1 of this
attachment). Note that for feed type /' = grains, the Pd and Pv terms do not apply (are set to
zero), because the feed products (i.e., corn kernels, soybeans) are protected from the air
(i.e., by husks, pods).
Equation B-14. Chemical Concentration in Livestock Feed Due to Root Uptake
^^feed(i) ~ root-zone_feed(i) * ^^feed(i) f_
where:
Concentration of chemical in plant feed type /' due to root uptake from soil on a
dry-weight (DW) basis (mg chemical/kg plant feed DW), where /' = forage, silage,
or grain
Average chemical concentration in soil at root-zone depth in area used to grow
plant feed type /' (mg chemical/kg soil DW), where /' = forage, silage, or grain
Chemical-specific plant-soil bioconcentration factor for plant feed type /' (kg soil
DW/kg plant feed DW), where /' = forage, silage, or grain
Prfeed(i)
CSroot-zone_feed(i)
Brfeed(i)
The algorithms used to calculate Pd^ and Pv^ when plant feed type /' = forage and silage are
identical to those used to calculate Pd^ and Pv^ 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 IRs for livestock are based
on DW feed.
B.3.2 Chemical Intake Calculations for Adults and Non-infant Children
The multimedia ingestion risk methodology calculates human chemical intake rates from the
ingestion of homegrown foods as ADDs normalized to BW 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 BW per day (mg/kg-day).
Equation B-15. Average Daily Dose for Specified Age Group and Food Type
ADD(yj) ~
( x IR(yj) x FC(jj x ED(y) ^
BW(y) x AT(y)
EF,
(y)
365 days
Eqn. B-15
where:
ADD(yi) =
Average daily dose for age group y from food type or ingestion medium /' (mg chemical/kg
body weight-day)
Concentration of chemical in food type /' harvested from the contaminated area (mg
chemical/kg food or mg food/L water)
Cw
IR(yj) = Ingestion rate for age group y of food type /' (kg/day or L/day)
Attachment B B-23 February 2021
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TRIM-Based Tiered Screening Methodology for RTR
FC(o = Fraction of food type /' 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
EF(y) = Annual exposure frequency for age group y (days)
Equation B-15 accounts for the chemical concentration in each food type /' (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 BW (U.S. EPA 2011a,
2003a). ADDs are calculated separately for each chemical, homegrown food type, and
consumer age group.
ADD values, expressed as intakes, not absorbed doses, are appropriate for comparison with
reference doses (RfDs) and for use with cancer slope factors (CSFs) to estimate risk, as
discussed in Section B.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 B.3.4 of this attachment.
For screening-level assessments, all components of Equation B-15 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 accounted for). To calculate an ADD(yj) from the contaminated area for
food group /' over an entire lifetime of exposure, age-group-specific IRs and BWs are used for
the age groups described in Section B.2.2 of this attachment. The averaging time (AT) 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(yj) is estimated as the sum of chemical intake from all ingestion pathways
combined: Note that the last exposure pathway is limited to infants.
. Incidental soil ingestion;
. Ingestion of fish;
. Ingestion of homegrown fruits (exposed and protected);
. Ingestion of homegrown vegetables (exposed, protected, and root);
. Ingestion of animal products from home-raised animals:
- Milk and other dairy products from cows,
- Beef products,
- Pork products, and
- Poultry and eggs;
. Ingestion of drinking water from specified source; and
. Ingestion of breast milk by infants.
Attachment B
B-24
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
The algorithms for the first six exposure pathways listed above are described in Sections
B.3.2.1 through B.3.2.6 of this attachment. The algorithms for the breast milk ingestion pathway
are described in Section B.3.4.
B.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:
ADD.
Soil(y)
^Soil x ^Soil(y) x FCSoll X 0.001
mg
BW,
(y)
EF
365 days
Eqn. B-16
ADDsoil(y) -
Csoil =
IRsoil(y) =
FCsoii =
BW(y) =
EF =
Average daily chemical intake from incidental ingestion of soil or ingestion by child in
age group y (mg chemical/kg body weight-day)
Concentration of chemical in soil from contaminated area on a dry-weight (DW) basis
(|jg/g soil DW)
Soil ingestion rate for age group y (g DW/day)
Fraction of soil ingested that is from contaminated area (unitless)
Body weight for age group y (kg)
Exposure frequency; number of days per year of exposure for family(ies) as specified
for scenario (< 365 days)
B.3.2.2 Chemical Intake from Fish Ingestion
The multimedia ingestion risk methodology includes ingestion of locally caught fish as a
possible exposure pathway (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.
Equation B-17. Chemical Intake from Fish Ingestion
(
ADDFjSh(y) - (1 - Z-1FjSh ) x (1 - L2Fjsh ):
CFish x ^Fish(y) x 0-001 ^ x FCFjsh
y
A
BW,
(y)
EF
365 days
Eqn. B-17
Equation B-18. Consumption-weighted Chemical Concentration in Fish
CFish = (CFishTL3xFiL3) + (CFishTL4xFiL4)
Eqn. B-18
where:
Attachment B
B-25
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Average daily chemical intake from ingestion of local fish for age group y (mg/kg-
day)
Weight offish brought into home that is discarded during preparation (e.g., head,
bones, liver, other viscera, belly fat, skin with fat) (unitless)
Loss of weight during cooking, such as evaporation and loss of fluids into pan
(unitless)
Chemical concentration in whole fish for trophic level 3.5 (TL3) fish on a wet-
weight (WW) basis (mg/kg WW)
Chemical concentration in whole fish for trophic level 4 (TL4) fish (mg/kg WW)
Fraction offish intake that is from TL3 (unitless)
Fraction offish intake that is from TL4 (unitless)
Consumption-weighted mean chemical concentration in total fish (i.e., as
specified by Equation B-18) (mg/kg WW)
Fraction of local fish consumed derived from contaminated area (unitless)
Body weight for age y (kg)
Local fish ingestion rate for age y (g WW/day)
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 ingestion rates for homegrown food products, which
are based on the products as brought into the home from the field (see Section B.6.3.3), the fish ingestion rates are on
an "as consumed" basis (i.e., after preparation and cooking losses), and L1 and L2 therefore are set equal to zero. If an
assessment will include local fish ingestion rates on an "as harvested" basis, L1 and L2 values also should be included
as specified in Section B.6.4.3.
When whole fish are prepared for cooking, it is usual for the viscera, head, and fins to be
removed, particularly for larger fish. Many persons also remove (or do not eat) the skin, bones,
and belly fat. EPA has, therefore, estimated the proportion of the weight of whole fish that tends
to be lost during preparation and cooking across a variety offish species (EFH; U.S. EPA
2011a) and included those losses in its HHRAP algorithms for chemical intake from fish (LUish
and L2Fish in Equation B-17).
For arsenic, TRIM.FaTE-calculated water and sediment concentrations are multiplied by
empirical bioaccumulation factors (BAFs) and biota-sediment accumulation factors (BSAFs) to
estimate fish tissue concentrations in the water-column communities and benthic communities,
respectively. (Fish tissue concentrations for other PB-HAPs are calculated in TRIM.FaTE's
biokinetic food web model.) Estimation of water-column fish tissue concentration using the BAF
approach requires, as an input, the concentration of dissolved chemical in surface water.
Because TRIM.FaTE outputs the total water-column concentration (i.e., as both dissolved and
suspended solids), this total water-column concentration is multiplied by the fraction of mass
dissolved (which is available from TRIM.FaTE HTML outputs) to estimate the dissolved
chemical concentration. This dissolved concentration is then multiplied by the empirical BAF to
estimate water column fish concentrations. The BAF/BSAF approach to aquatic
bioaccumulation could easily be extended to other inorganic chemicals included in an
assessment. Equation B-19 presents the algorithm for estimating aquatic biota concentrations
based on BAFs. Equation B-20 presents the algorithm for estimating aquatic biota
concentrations based on BSAFs and is appropriate for sediment-dwelling fish and organisms.
ADDFish(y) —
LlFish* =
L2Fish* =
CFishTL3 =
CFishTL4 =
FtL3 =
FtL4 =
CFish =
FCFish =
BW(y) =
IRFish(y)* =
EF =
Attachment B
B-26
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Equation B-19. Concentrations in Aquatic Biota based on Empirical Bioaccumulation Factors
CHsh=BAF x CsurfaceWater x FMD Eqn. B-19
where:
CFish = Chemical concentration in whole fish on a wet-weight (WW) basis (mg/kg WW)
Csurface water = Chemical concentration in surface water (mg/L)
BAF = Bioaccumulation Factor (L/kg WW fish)
FMD = Fraction mass of chemical dissolved in the water column (unitless)
Equation B-20. Concentrations in Aquatic Biota based on Empirical Biota-sediment
Accumulation Factors
CFish=BSAF x Csediment Eqn. B-20
where:
CFish = Chemical concentration in whole fish on a wet-weight (WW) basis (mg/kg WW)
Csediment = Chemical concentration in sediment on a dry-weight (DW) basis (mg/kg DW)
BSAF = Biota-sediment accumulation factor (kg DW sediment/kg WW fish)
B.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-21 and Equation B-22, respectively).
Equation B-21. Chemical Intake from Consumption of Exposed Fruits
ADDExpFruit(y) = 0 ~ ^ExpFruit) x 0 ~ ^ExpFruit)x l^ExpFruit x ^ExpFruit(y) x 0.001 — X FCExpFrujt j X (jgyg j
Eqn. B-21
Equation B-22. Chemical Intake from Consumption of Protected Fruits
( ^ \ ( EF \
ADDProFruit(y) - (l LA proFrUjt)
nnn-1^9 i=n
CproFruit x '"ProFruit(y) x 0.001 X rCProFru/f
9
j
v365 daysy
Eqn. B-22
where:
ADDExpFwit(y) _ Average daily chemical intake from ingestion of exposed fruit or protected fruit
ADDproFruit(y) (depending on subscript) (mg chemical/kg body weight-day)
Mean reduction in fruit weight resulting from removal of skin or peel, core or pit,
LlExpFruit = stems or caps, seeds and defects, and from draining liquids from canned or
frozen forms (unitless)
. 1 _ Mean reduction in fruit weight that results from paring or other preparation
proFwit techniques for protected fruits (unitless)
Attachment B
B-27
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
L2ExpFruit —
CExpFruit _
CproFruit
EF =
FCExpFruit _
FCproFruit
IRExpFruit(y) _
IRproFruit(y)
Mean reduction in fruit weight that results from draining liquids from cooked
forms of the fruit (unitless)
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)
Exposure frequency; number of days per year of exposure for family(ies) as
specified for scenario (< 365 days)
Fraction of exposed fruits or protected fruits (depending on subscript) obtained
from contaminated area (unitless)
Ingestion rate of homegrown exposed fruits or protected fruits (depending on
subscript) for age y (g WW/kg body weight-day)
Fruit IRs 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 {L1 ExpFrmtand LIp^Fiun, 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 (L2ExpFmit)¦ EPA has assumed that cooking of protected fruit results in no loss
of weight for the fruit.
B.3.2.4 Chemical Intake from Vegetable Ingestion
The methodology includes three separate algorithms for homegrown vegetables adapted from
EPA's HHRAP (U.S. EPA 2005a): one for exposed vegetables such as asparagus, broccoli,
lettuce, and tomatoes (although they are actually a fruit); one for protected vegetables such as
corn, cabbage, soybeans, and peas; and one for root vegetables such as carrots, beets, and
potatoes (see Equation B-23, Equation B-24, and Equation B-25, respectively).
Equation B-23. Chemical Intake from Exposed Vegetables
ADDExpVegfy) 0 ^ExpVeg )x ( ^ExpVeg x
IRExpVegMxO-OO^xFC,
'Exp Veg
J
EF
365 days
Eqn. B-23
Equation B-24. Chemical Intake from Protected Vegetables
ADDProVeg(y) - (1 L\proVeg )x [ CproVeg x ^ ProVeg (y) x 0.001 X FCI
\ f ef ^
ProVeg
365 days
Eqn. B-24
Equation B-25. Chemical Intake from Root Vegetables
ADD,
365 days
Eqn. B-25
Attachment B
B-28
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
where:
ADDExp Veg(y)
ADDproVeg(y) =
ADDRootVeg(y)
LlExpVeg =
LlproVeg =
LlRootVeg =
L2RootVeg =
CExpVeg
CproVeg =
CpootVeg
EF =
F CExpVeg
F CproVeg =
FCpootVeg
IRExpVeg(y)
IRproVeg(y) =
IRRootVeg(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)
Mean net preparation and cooking weight loss for exposed vegetables (unitless);
includes removing stalks, paring skins, discarding damaged leaves
Mean net cooking weight loss for protected vegetables (unitless); includes
removing husks, discarding pods of beans and peas, removal of outer leaves
Mean net cooking weight loss for root vegetables (unitless); includes losses from
removal of tops and paring skins
Mean net post cooking weight loss for root vegetables from draining cooking
liquids and removal of skin after cooking (unitless)
Chemical concentration in exposed vegetables, protected vegetables, or root
vegetables (depending on subscript) on a wet-weight (WW) basis (mg
chemical/kg vegetable WW)
Exposure frequency; number of days per year of exposure for family(ies) as
specified for scenario (<365 days)
Fraction of exposed vegetables, protected vegetables, or root vegetables
(depending on subscript) obtained from contaminated area (unitless)
Ingestion rate of exposed vegetables, protected vegetables, or root vegetables
(depending on subscript) for age group y (g vegetable WW/kg body weight-day)
B.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-26 through Equation B-30 for homegrown beef, dairy (milk),
pork, poultry, and eggs, respectively.
Equation B-26. Chemical Intake from Ingestion of Beef
( \ ( £1=
ADDBeef(y) - (l ^Beef )x 0 L2 Beef)
x CBeef x IRBeef(y) x 0.001 —x FCgggf
g
365 days
Eqn. B-26
where:
ADDBeef(y) —
Average daily chemical intake from ingestion of beef for age group y
(mg/kg-day)
LlBeef = 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))
_ Exposure frequency; number of days per year of exposure for family(ies) as
specified for scenario (< 365 days)
Attachment B B-29 February 2021
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TRIM-Based Tiered Screening Methodology for RTR
IRBeem = Ingestion rate of contaminated beef for age group y (g WW/kg-day)
Fraction of beef consumed raised on contaminated area or fed contaminated
FCBeef =
silage and grains (unitless)
Equation B-27. Chemical Intake from Dairy Ingestion
(
ADDD3jry(y) ~
_ .... kg _ _
CDairy x Dairyfy) x 0.001 —X FCDajry
y
W EF A
365 days
Eqn. B-27
where:
ADDoairy(y)
CDairy
EF
IRDairy(y)
F CDairy
Average daily chemical intake from ingestion of total dairy for age group y
(mg/kg-day)
Average concentration of contaminant in total dairy (mg/kg WW)
Exposure frequency; number of days per year of exposure for family(ies) as
specified for scenario (< 365 days)
Ingestion rate of contaminated total dairy for age group y (g WW/kg-day)
Fraction of total dairy products from contaminated area (unitless)
Equation B-28. Chemical Intake from Pork Ingestion
ADDPork(y) - 0 ^ Pork) 7 0 L2Pork)>
(
Cpork x IRpork(y) x 0.001 —X FCPork
g
\f EF ^
y lv365daysy
Eqn. B-28
where:
ADDpork(y)
Llpork -
L2pork -
Cpork
EF
IRpork(y)
FCpork
Average daily chemical intake from ingestion of pork for age group y
(mg/kg-day)
Mean net cooking loss for pork (unitless); includes dripping and volatile losses
during cooking; averaged over various cuts and preparation methods
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
= Concentration of contaminant in pork (mg/kg WW)
Exposure frequency; number of days per year of exposure for family(ies) as
specified for scenario (<365 days)
= Ingestion rate of contaminated pork for age y (g WW/kg-day)
= Fraction of pork obtained from contaminated area (unitless)
Attachment B
B-30
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Equation B-29. Chemical Intake from Poultry Ingestion
/ADDD™,iw„i - (l ^-Vou/fry )x 0 ^Poultry )x | ^Poultry x IRPoultry(y) x 0-001 X EC,
'Poultry(y)
kg
g
W £F A
Poultry
365 days
Eqn. B-29
where:
ADDPoultry(y)
L1 Poultry
L2Poultry
CPoultry
EF
IRPoultry(y)
FC Poultry
Average daily dose (chemical intake) from ingestion of poultry (mg/kg-day)
Mean net cooking loss for poultry (unitless)
Mean net post cooking loss for poultry (unitless)
Concentration of chemical in poultry (mg/kg WW)
Exposure frequency; number of days per year of exposure for family(ies) as
specified for scenario (<365 days)
Ingestion rate of poultry for age group y (g WW/kg-day)
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-30. Chemical Intake from Egg Ingestion
(
ADD,
Egg(y)
CEgg x IREgg(y) x 0.001^xFCe?s
y
W £F A
365 days
Eqn. B-30
where:
ADDEgg(y)
^Egg
EF
IREgg(y)
FCEgg
Average daily chemical intake from ingestion of eggs for age group y
(mg/kg-day)
= Concentration of contaminant in eggs (mg/kg WW)
Exposure frequency; number of days per year of exposure for family(ies) as
specified for scenario (< 365 days)
= Ingestion rate of contaminated eggs for age group y (g WW/kg-day)
= Fraction of eggs obtained from contaminated area (unitless)
B.3.2.6 Chemical Intake from Drinking-water Ingestion
Assessments that evaluate chemical ingestion via drinking water use chemical concentration in
g/L (equivalent to mg/ml_), which could represent water from groundwater wells, community
Attachment B
B-31
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
water, nearby surface waters, or other source. For this exposure pathway, IRs are in units of
mL/day (see Equation B-31).
Equation B-31. Chemical Intake from Drinking-water Ingestion
ADD,
DW(y)
CdW x ^DW(y) x FCdw ^ ^
BW,
(y)
EF
365 days
Eqn. B-31
where:
ADDdw(y)
Cdw =
IRdw(y) =
FCdw =
BW(y) =
EF =
Average daily chemical intake from ingestion of drinking water from local
residential water source for age group y (mg/kg-day)
Concentration of contaminant in drinking water (g/L)
Drinking water ingestion rate for age group y (mL/day)
Fraction of drinking water obtained from contaminated area (unitless)
Body weight of age group y (kg)
Exposure frequency; number of days per year of exposure for family(ies) as
specified for scenario (<365 days)
B.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-32 through Equation B-37, where /' represents
the ith food type or ingestion medium and n equals the total number of food types or ingestion
media.
Equation B-32. Total Average Daily Dose of Chemical for Infants &DD{ 1} ADDbreastmilk
less than One Year, from Ingestion of Breast Milk (mg/kg-day)
Equation B-33. Total Average Daily Dose of Chemical from All ADD0 2) = 1ADD^ 2 j)
Ingestion Sources for Children Ages 1 through 2 Years (mg/kg- '
day)
Equation B-34. Total Average Daily Dose of Chemical from All ADD{3_5) = 1 ADD(3 5 ()
Ingestion Sources for Children Ages 3 through 5 Years (mg/kg- '
day)
Equation B-35. Total Average Daily Dose of Chemical from All ADD{6_11} = 1 ADD{6_11 ()
Ingestion Sources for Children Ages 6 through 11 Years (mg/kg- '
day)
Attachment B
B-32
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Equation B-36. Total Average Daily Dose of Chemical from All ADD(A2 19) = 1 ADD(A2 19()
Ingestion Sources for Children Ages 12 through 19 Years (mg/kg- '
day)
Equation B-37. Total Average Daily Dose of Chemical from All ADD(adult) = ADD(adultJ)
Ingestion Sources for Adults Ages 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-38).
Equation B-38. Lifetime Average Daily Dose
LADD = ADD,.,,^ + + ADDmiS[j^) * ADD^<«{^} + ADDW">(^}
Eqn. B-38
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.
B.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 breast milk
and absorption of the chemical. This exposure pathway is included in the multimedia ingestion
risk methodology with adapted exposure algorithms and default assumptions from EPA's
Methodology for Assessing Health Risks Associated with Multiple Pathways of Exposure to
Combustor Emissions (U.S. 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.
B.3.4.1 Infant Average Daily Absorbed Dose
The ADD of chemical absorbed by the infant (DAIm) is estimated with Equation B-39. This basic
exposure equation relies on the concentration of the chemical in the breast milk, the infant's
breast-milk IR (IRmnk), the absorption efficiency of the chemical by the oral route of exposure
(AEim), the bodyweight of the infant {BWini), and the duration of breast feeding (ED).
Equation B-39 is EPA's (U.S. EPA 1998) modification of an ADD for the infant model first
published by Smith (1987) and includes variables for both the concentration of the chemical in
the breast milk fat (Cmiikfat) and the concentration of the chemical in the aqueous phase of breast
milk (Caqueous)¦ The remainder of the DAImrassociated 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 assume that either Cm-mat or Caqueous is equal to zero and hence drops out of the
equation.
Attachment B
B-33
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
For the parameters in Equation B-39 (and the equations that follow) that are not calculated from
another equation, an EPA default value and options for other values for the infant breast-milk-
exposure pathway are described in Section B.6.4 of this attachment.
Equation B-39. Average Daily Dose of Chemical to the Nursing Infant
_ [i^milkfat x ^mbm) + (paqueous x 0 ~ ^mbm))Jx ^milk x AEjnf X ED
BW» *AT Eqn. B-39
Average daily dose of chemical absorbed by infant (mg chemical/kg body
weight-day)
Concentration of chemical in lipid phase of maternal milk (mg chemical/kg milk
lipid; calculated using Equation B-40)
Fraction of fat in breast milk (unitless)
Concentration of chemical in aqueous phase of maternal milk (mg chemical/kg
aqueous phase milk; calculated using Equation B-44)
Infant milk ingestion rate over the duration of nursing (kg milk/day)
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)
Exposure duration, i.e., duration of breast feeding (days)
Body weight of infant averaged over the duration of nursing (kg)
Averaging time associated with exposure of interest; equal to ED (days)
As mentioned above, Equation B-39 includes terms for the chemical in both the lipid- and non-
lipid 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 Cmmat (described in Section B.3.4.2) and Caqueous (described in Section B.3.4.3).
B.3.4.2 Chemical Concentration in Breast-milk Fat
When developing the MPE (U.S. EPA 1998), EPA reviewed three first-order kinetics models for
estimating chemical concentration in breast-milk fat. The model selected for use with the
multimedia ingestion risk methodology is the model selected for MPE. The other two models
were not selected for use 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 used in the multimedia ingestion risk methodology is a changing-concentration
model that EPA adapted from a model by Sullivan et al. (1991). The model, shown in
Equation B-40, 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-40 accounts for the changing concentration in breast-milk fat but estimates
Attachment B B-34 February 2021
where:
DAIinf =
Cmilkfat =
fmbm =
Caqueous =
IR milk =
AEinf =
ED =
BWinf =
AT =
-------
TRIM-Based Tiered Screening Methodology for RTR
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 (U.S. EPA 2001a).
Equation B-40. Chemical Concentration in Breast-milk Fat
'milkfat
where:
DAImat x ff
kelim x ffm
Is
nelim
kfat elac ^fat elac x ^bf
—k t
«| 0 Ae//'mlpn
Is
nelim
*fat elac
g kfat _e!ac^bf ^
Eqn. B-40
Cmiikfat = Concentration of chemical in lipid phase of maternal milk (mg chemical/kg lipid)
DAImat
Daily absorbed maternal chemical dose (mg chemical/kg maternal body weight-
day; calculated using Equation B-41)
Fraction of total maternal body burden of chemical that is stored in maternal fat
ft = (mg chemical in body fat/mg total chemical in whole body; value from literature
or EPA default - see Section B.6.5 of this attachment)
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-42)
kelim
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-43)
tbf = Duration of breast feeding (days)
tpn
Duration of mother's exposure prior to parturition and initiation of breastfeeding
(days)
Equation B-40 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 (/y) to determine the amount (i.e., mass) of chemical in the
fat. This product, divided by the chemical-specific elimination rate constant (keiim) for non-
lactating 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 (DAImatfflkeiim 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 (ED), which are the chemicals of concern in the applications of this
methodology to date, an additional term is needed to determine the average concentration in
the milk fat over the duration of her exposure.
Attachment B B-35 February 2021
-------
TRIM-Based Tiered Screening Methodology for RTR
To account for breast feeding losses and longer chemical half-lives in the mother than the ED,
an additional term is included in Equation B-40. This term includes a fraction dependent on two
rate constants, kenm and the elimination constant for a lipophilic chemical in lactating women via
the lipid phase of breast milk (kfat_eiac), the duration of the mother's chemical exposure prior to
nursing (tpn), and the duration of breast feeding (fez). The whole-body concentration (DAImatff/keiim
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-40, see Appendix B of MPE (U.S. EPA 1998).
To estimate an ADD absorbed by an infant's mother, or DAImat, the ADD (in mg/kg-day) for the
chemical from all sources calculated for adults (ADDaduit), described in Section B.3.3 of this
attachment, Equation B-37), is multiplied by an absorption efficiency (AEmat) or fraction of the
chemical absorbed by the oral route of exposure, as shown in Equation B-41. 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-41.
Equation B-41. Daily Maternal Absorbed Intake
DAImat - ADD(adult) x AE
mat
Eqn. B-41
where:
DAImat = Daily maternal dose of chemical absorbed from medium /' (mg/kg-day)
ADD(adult)
Average daily dose to the mother (mg/kg-day) (see Section B.3.3 of this
attachment, Equation B-37)
Absorption efficiency of the chemical by the oral exposure route (i.e., chemical-
AEmat = specific fraction of ingested chemical that is absorbed) by the mother (unitless)
(value from literature or EPA default - see Section B.6.4 of this attachment)
Equation B-37, used to calculate ADDaduit), is based on many medium-specific IRs that are
normalized to BW. The adult BWs to which the homegrown food IRs are normalized are the
BWs of the consumers in the original USDA survey (see Section B.6.3.3 of this attachment),
which included both males and females. An assumption in the breast-milk exposure pathway is
that those IRs also are applicable to nursing mothers. The original data for IRs for soil, drinking
water, and fish are on a per person basis for males and females combined. This methodology
divides those chemical intakes by an adult BWfor males and females combined (i.e., 71.4 kg
mean value) to estimate the ADD normalized to BW from those sources. If the assessor finds
that those exposure media contribute the majority of the chemical intake for the risk scenario
under consideration, they may use alternative IRs for those media and alternative BWs for
nursing women, as described in Section B.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,
Attachment B
B-36
February 2021
-------
TRIM-Based Tiered Screening Methodology for RTR
the first being more rapid than the second. For screening risks for PB-HAPs, 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-42 is used to convert a measured
half-life for elimination of a chemical for adults or non-lactating women to an elimination rate
constant (U.S. EPA 1998). The equation can be used to estimate any kind of chemical loss rate
constant from a measured chemical half-life.
Equation B-42. Biological Elimination Rate Constant for Chemicals
for Non-lactating Women
^elim ~
In 2
h Eqn. B-42
where:
. _ Chemical-specific elimination rate constant for elimination of the chemical for
elim non-lactating women (per day; e.g., via urine, bile to feces, exhalation)
In2 = 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-43 to estimate the total
chemical elimination rate for lactating women, /Oaf_e/ac(U.S. EPA 1998).
Equation B-43. Biological Elimination Rate Constant for Lipophilic Chemicals
for Lactating Women
^fat elac ^elim
IRmilk x ff x fmbm
ffm x BWmat Eqn. B-43
where:
Rate constant for total elimination of chemical during nursing (per day); accounts for
kfat_eiac = 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-42)
IRmiik = Infant milk ingestion rate over the duration of nursing (kg/d)
ft
fmbm = Fraction of fat in breast milk (unitless)
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)
Attachment B
B-37
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
ftm = Fraction of maternal body weight that is fat stores (unitless)
di/i/ _ Maternal body weight over the entire duration of the mother's exposure to the chemical
mat ~ including during pregnancy and lactation (kg)
Equation B-43 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_eiac is estimated
by adding an estimate of the first-order elimination constant for breast feeding losses to kenm,
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 (IRmnk), the
fraction of the total maternal body burden of the chemical that is stored in maternal body fat (/>),
the fraction of the mother's breast milk that consists of fat (lipids) (fmbm), the mother's BW
(BWmat), and the fraction of the mother's weight that is body fat (ffm). In Equation B-43, the value
for BWmat should be specific to women of child-bearing age, as opposed to a BW value for both
males and females that is used to estimate an adult ADD and the mother's absorbed daily
intake in Equation B-41. BW values for the mother are described in Section B.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 (U.S.
EPA 1998).
B.3.4.3 Chemical Concentration in Aqueous Phase of Breast Milk
When developing MPE (U.S. 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 Cm//waf and developed the Caqueous model shown
in Equation B-44 (U.S. 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-44. Chemical Concentration in Aqueous Phase of Breast Milk
p _ ^^1 mat x ^pi x P°bm
^aqueous ~ # r
aq_elac xT pm Eqtl. B-44
where:
Caqueous
DAI mat
fpl -
PCbm —
Concentration of chemical in aqueous phase of maternal milk (mg/kg)
Daily absorbed maternal chemical dose (mg/kg-day; calculated by Equation B-41)
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-45)
Partition coefficient for chemical between the plasma and breast milk in the
aqueous phase (unitless); assumed to equal 1.0
Attachment B
B-38
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Chemical-specific rate constant for total elimination of chemical in the aqueous
kaq_elac = phase of milk during nursing (per day; value from literature or calculated in
Equation B-46)
fPm = Fraction of maternal weight that is blood plasma (unitless)
Equation B-44 is a steady-state concentration model that, like the Equation B-40 for Cmmat, is
dependent on the maternal absorbed daily intake (DAImat). In Equation B-44, DAImat is multiplied
by the fraction of the absorbed chemical that is circulating in the blood-plasma compartment (fpi)
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_eiac) and the fraction of the mother's weight that is plasma (fpi). 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 (MeHg), 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 (U.S. EPA 2001a). Due to this
limitation, the model may over- or underestimate exposure to the infant.
Because Equation B-44 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 (fpi) is required. Ideally, an empirical value for fpi should be used. If
empirical values are not available, fpi can be estimated from Equation B-45, 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 (fbi, U.S. EPA 1998).
Equation B-45. Fraction of Total Chemical in Body in the Blood-plasma Compartment
, _ Fraction of chemical in body (based on absorbed intake) that is in the blood-
p/ plasma compartment (unitless); chemical-specific
, _ Fraction of chemical in body (based on absorbed intake) in the whole-blood
bl 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
/
Eqn. B-45
where:
Attachment B
B-39
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
If the fraction of the total chemical in the body that is in the whole-blood compartment {fbi) 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-44 is the total chemical
elimination rate for lactating women for hydrophilic chemicals, kaq_eiac. As for kfat_eiac for lipophilic
chemicals, kaq_eiac for hydrophilic chemicals would be equal to kenm 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 kenm, the elimination of the chemical from a non-lactating woman, as shown in Equation
B-42. The extent to which kenm is an underestimate of kaq_eiac for a given chemical will determine
the extent of health protective bias in kaq_elac-
Equation B-46. Biological Elimination Rate Constant for Hydrophilic Chemicals
kaq_elac = kelim Eqtl. B-46
where:
Chemical-specific rate constant for total elimination of chemical by lactating
women for hydrophilic chemicals (per day)
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-42)
B.3.4.4 Alternative Model for Infant Intake of Methyl Mercury
EPA has not fully parameterized 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 (fpi). This parameter could be estimated using
Equation B-45 if a suitable chemical-specific fraction of chemical in the body that is in the whole
blood (fbi) could be found. However, the value found for fbi is based on a single-dose study and
is not considered reliable for use in chronic exposure calculations.
A literature search was conducted to identify existing physiologically based toxicokinetic (PBTK)
models of lactational transfer of MeHg in humans. Most PBTK models identified focused on
gestational transfer of mercury between mother and fetus, including a PBTK dynamic
compartmental model for gestational transfer of MeHg in humans developed by Gearhart et at.
(1995, 1996), and reparameterized by Clewell etal. (1999).
Byczkowski and Lipscomb (2001) added a lactational transfer module to the Clewell etal.
(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
kaq_elac —
kelim —
Attachment B
B-40
February 2021
-------
TRIM-Based Tiered Screening Methodology for RTR
Iraq (Amin-Zaki etal. 1976) and from 34 mother-nursing-infant 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 (standard deviation) |jg/kg-day and the
average infant intake of MeHg to be 0.80 ± 0.38 |jg/kg-day. Therefore, for purposes of this
methodology, the DAImfOf MeHg is estimated to be the same as the maternal intake per unit BW
(Equation B-47).
Equation B-47. Infant Average Daily Absorbed Dose of Methyl Mercury
DAIinf_MeHg ~ DAImat_MeHg ECjtl B-47
where:
DAIinf_ MeHg —
Average daily dose of methyl mercury (MeHg) absorbed by infant from breast
milk (mg/kg-day)
DAI mat MeHg = Average daily dose of MeHg absorbed by the mother, predominantly from fish
(mg/kg-day)
B.4 Dose-response Values
The chemical dose-response values used with the multimedia ingestion risk methodology
include ingestion carcinogenic potency slope factors (CSFs) and noncancer oral RfDs for
chronic exposures. The dose-response values currently used for RTR assessments are shown
in Exhibit B-5. OAQPS identified dose-response values for use in RTR based on the following
hierarchy of sources: EPA's Integrated Risk Information System (IRIS); the Centers for Disease
Control's Agency for Toxic Substances and Disease Registry (ATSDR); and the California
Environmental Protection Agency's (CalEPA's) Office of Environmental Health Hazard
Assessment (OEHHA) Toxicity Criteria Database. For PB-HAPs without OAQPS-identified
dose-response values, alternative methods for deriving values were used (see Sections B.4.4
and B.4.5).
As provided in Exhibit B-5, TEFs from van den Berg et al. (2006) are used except for two
congeners for which EPA's IRIS program has developed a CSF—1,2,3,6,7,8-
hexachlorodibenzo-p-dioxin and 1,2,3,7,8,9-hexachlorodibenzo-p-dioxin. Collectively across
RTR assessments that EPA has conducted in recent years, these two congeners together
constitute roughly 4 percent of total dioxin emissions from point sources. When the dioxin
emissions are weighted by TEFs (to calculate TEQs), the two congeners constitute about 4
percent of the total dioxin TEQ emissions from point sources using TEF=0.1 from van den Berg
et al. (2006) and about 2 percent using TEF=0.04 derived from the IRIS-based CSF. Therefore,
Attachment B
B-41
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
the impact of changing the TEFs of the two congeners is small. Moreover, as noted above, IRIS
is the most preferred source for toxicity data.
Exhibit B-5. Oral Dose-response Values
Chemical
CAS No.
Cancer Slope Factor
(CSF)a
Reference Dose (RfD)a
Value
(mg/kg-
day)-1
Source
Value
(mg/kg-
day)
Source
Inorganics
Arsenic
7440-38-2
1.5
IRIS
(last updated 6/1/1995)
0.0003
IRIS
(last updated 9/1/1991)
Cadmium compounds in foodb
7440-43-9
not available
1.0E-03
IRIS
(last updated 10/1/1989)
Mercury (elemental)
7439-97-6
not available
not available
Mercuric chloride
7487-94-7
not available
3.0E-04
IRIS
(last updated 5/1/1995)
Methyl mercury (MeHg)
22967-92-6
not available
1.0E-04
IRIS
(last updated 7/27/2001)
Dioxins
1,2,3,4,6,7,8-
Heptachlorodibenzo-p-dioxin
35822-46-9
1.5E+03
not available0
7.0E-08
not available0
1,2,3,4,6,7,8-
Heptachlorodibenzofuran
67562-39-4
1.5E+03
not available0
7.0E-08
not available0
1,2,3,4,7,8,9-
Heptachlorodibenzofuran
55673-89-7
1.5E+03
not available0
7.0E-08
not available0
1,2,3,4,7,8-
Hexachlorodibenzo-p-dioxin
39227-28-6
1.5E+04
not available0
7.0E-09
not available0
1,2,3,4,7,8-
Hexachlorodibenzofuran
70648-26-9
1.5E+04
not available0
7.0E-09
not available0
1,2,3,6,7,8-
Hexachlorodibenzo-p-dioxin
57653-85-7
6.2E+03
IRIS
(last updated 3/31/1987)
1.8E-08
not available0
1,2,3,6,7,8-
Hexachlorodibenzofuran
57117-44-9
1.5E+04
not available0
7.0E-09
not available0
1,2,3,7,8,9-
Hexachlorodibenzo-p-dioxin
19408-74-3
6.2E+03
IRIS
(last updated 3/31/1987)
1.8E-08
not available0
1,2,3,7,8,9-
Hexachlorodibenzofuran
72918-21-9
1.5E+04
not available0
7.0E-09
not available0
2,3,4,6,7,8-
Hexachlorodibenzofuran
60851-34-5
1.5E+04
not available0
7.0E-09
not available0
1,2,3,4,6,7,8,9-
Octachlorodibenzo-p-dioxin
3268-87-9
4.5E+01
not available0
2.3E-06
not available0
1,2,3,4,6,7,8,9-
Octachlorodibenzofuran
39001-02-0
4.5E+01
not available0
2.3E-06
not available0
Attachment B
B-42
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Chemical
CAS No.
Cancer Slope Factor
(CSF)a
Reference Dose (RfD)a
Value
(mg/kg-
day)-1
Source
Value
(mg/kg-
day)
Source
1,2,3,7,8-
Pentachlorodibenzo-p-dioxin
40321-76-4
1.5E+05
not available0
7.0E-10
not available0
1,2,3,7,8-
Pentachlorodibenzofuran
57117-41-6
4.5E+03
not available0
2.3E-08
not available0
2,3,4,7,8-
Pentachlorodibenzofuran
57117-31-4
4.5E+04
not available0
2.3E-09
not available0
2,3,7,8-
Tetrachlorodibenzo-p-dioxin
1746-01-6
1.5E+05
EPA ORD
7.0E-10
IRIS
(last updated 2/17/2012)
2,3,7,8-
Tetrachlorodibenzofuran
51207-31-9
1.5E+04
not available0
7.0E-09
not available0
Polycyclic Organic Matter (POM)
1 -Methylnaphthalene
90-12-0
5.0E-02
POM Group
72002d
7.0E-02
ATSDR
(minimum risk level; last
updated 8/2005)
2-Acetylaminofluorene
53-96-3
1.0E+00
POM Group
75002d
not available
2-Methylnaphthalene
91-57-6
5.0E-02
POM Group
72002d
4.0E-3
IRIS
(last updated 12/22/2003)
3-Methylcholanthrene
56-49-5
2.2E+01
CalEPA
(last updated 2011)
not available
7,12-
Dimethylbenz[a]anthracene
57-97-6
2.5E+02
CalEPA
(last updated 2011)
not available
Acenaphthene
83-32-9
5.0E-02
POM Group
72002d
6.0E-02
IRIS
(last updated 11/1/1990)
Acenaphthylene
208-96-8
5.0E-02
POM Group
72002d
not available
Anthracene
120-12-7
0e
IRIS
3.0E-01
IRIS
(last updated 9/1/1990)
Benz[a]anthracene
56-55-3
1.0E-01
IRIS CSF for
BaP with EPA
CPF
not available
Benz[a]anthracene/Chrysene
NA
5.0E-02
POM Group
71002d
not available
Benzo[a]pyrene
50-32-8
1.0E+00
IRIS
(last updated 1/19/2017)
3.0E-04
IRIS
(last updated 1/19/2017)
Benzo[a]fluoranthene
203-33-8
5.0E-02
POM Group
72002d
not available
Benzo[b]fluoranthene
205-99-2
1.0E-01
IRIS CSF for
BaP with EPA
CPF
not available
Attachment B
B-43
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Chemical
CAS No.
Cancer Slope Factor
(CSF)a
Reference Dose (RfD)a
Value
(mg/kg-
day)-1
Source
Value
(mg/kg-
day)
Source
Benzo[b+k]fluoranthene
NA
1.0E-01
POM Group
76002d
not available
Benzo[c]phenanthrene
195-19-7
5.0E-02
POM Group
72002d
not available
Benzo[e]pyrene
192-97-2
5.0E-02
POM Group
72002d
not available
Benzo[ghi]fluoranthene
203-12-3
5.0E-02
POM Group
72002d
not available
Benzo[ghi]perylene
191-24-2
5.0E-02
POM Group
72002d
not available
Benzo[j]fluoranthene
205-82-3
1.0E-01
IRIS CSF for
BaP with
California CPF
not available
Benzo[k]fluoranthene
207-08-9
1.0E-02
IRIS CSF for
BaP with EPA
CPF
not available
Benzofluoranthenes
56832-73-6
5.0E-02
POM Group
72002d
not available
beta-Chloronaphthalene
91-58-7
5.0E-02
POM Group
72002d
8.0E-02
IRIS
(last updated 11/1/1990)
Carbazole
86-74-8
2.0E-02
EPA ORD
not available
Chrysene
218-01-9
1.0E-03
IRIS CSF for
BaP with EPA
CPF
not available
Dibenzo[a,h]anthracene
53-70-3
1.0E+00
IRIS CSF for
BaP with EPA
CPF
not available
Dibenzo[a,i]pyrene
189-55-9
1.0E+01
IRIS CSF for
BaP with
California CPF
not available
Dibenzo[aj]acridine
224-42-0
1.0E-01
IRIS CSF for
BaP with
California CPF
not available
Fluoranthene
206-44-0
5.0E-02
POM Group
72002d
4.0E-02
IRIS
(last updated 9/1/1990)
Fluorene
86-73-7
5.0E-02
POM Group
72002d
4.0E-02
IRIS
(last updated 11/1/1990)
lndeno[1,2,3-c,d]pyrene
193-39-5
1.0E-01
IRIS CSF for
BaP with EPA
CPF
not available
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Chemical
CAS No.
Cancer Slope Factor
(CSF)a
Reference Dose (RfD)a
Value
(mg/kg-
day)-1
Source
Value
(mg/kg-
day)
Source
PAH, total
NA
5.0E-02
POM Group
71002d
not available
Perylene
198-55-0
5.0E-02
POM Group
72002d
not available
Phenanthrene
85-01-8
0e
IRIS
not available
Polycyclic organic matter
NA
5.0E-02
POM Group
71002d
not available
Pyrene
129-00-0
0e
IRIS
3.0E-02
IRIS
(last updated 9/1/1990)
Retene
483-65-8
5.0E-02
POM Group
72002d
not available
Abbreviations and data sources: NA = not applicable; CAS No. = Chemical Abstracts Service Registry Number, IRIS = Integrated
Risk Information System (U.S. EPA 2017a), EPA ORD = EPA's Office of Research and Development (U.S. EPA 1997b), ATSDR
= Agency for Toxic Substances and Disease Registry, CalEPA = California EPA (CalEPA 2019); CPF = cancer potency factor
(EPA CPF = U.S. EPA 2015, California CPF [also called PEF] = CalEPA 2015), PAH = polycyclic aromatic hydrocarbon, BaP =
benzo[a]pyrene.
Note: The "last updated" indicators refer to the date the agency posted the value.
aValues as of February 2021; these values may be updated as newer ones become available.
bThere are RfDs for both water ingestion and food ingestion for cadmium—the RfD for food is used.
°Dose-response values for these dioxin congeners are not available from EPA sources. CSFs and/or RfDs for these congeners
were derived as discussed in Section B.4.4 of this attachment.
dThe method to assign oral cancer slope factors to POM without CSFs available from other EPA sources is the same as that
used in the 1999 National Air Toxics Assessment [see: U.S. EPA (1999a)]. This method also is summarized in Section B.4.5 of
this attachment.
eWeight of evidence evaluations indicated that the available data were adequate to determine that this chemical was not
carcinogenic (U.S. EPA 2010).
B.4.1 Arsenic
EPA has developed a CSF of 1.5 per mg/kg-day for arsenic compounds based on data and
analysis reported in IRIS. The data derived from 40,000 persons exposed to arsenic in drinking
water and 7,500 relatively unexposed controls. A multistage model with time was used to predict
dose-specific and age-specific skin cancer prevalence rates associated with ingestion of
inorganic arsenic. IRIS also reports an RfD for arsenic; the RfD, however, was not considered
because the use of the CSF is more health protective regardless of emission scenario.
B.4.2 Cadmium
EPA has developed two chronic RfDs for cadmium, 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 for RTR assessments is the higher RfD value for cadmium compounds in food (as
described in Section B.3.2.6, 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 this methodology who assess exposures via drinking
water would need to use the RfD for cadmium compounds in water (i.e., 5.0E-04 mg/kg-day).
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B.4.3 Mercury
The RfD applies to the pregnant mother as well as young children. EPA has not specified the
minimum ED at the RfD level of exposure that is appropriate to use in characterizing risk. For
this methodology, EPA assumes 10 years for women of childbearing age and 1 year for infants.
EPA notes that human exposures to MeHg are primarily through the consumption of fish and
shellfish (U.S. 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 (U.S.
EPA 2001b).
B.4.4 Dioxins
For chemicals for which the critical health effect is developmental, either in utero and/or during
the first months or years of life, the ED and timing of exposure for comparison with the RfD (or
comparable values) require special consideration. The most sensitive health endpoints for both
mercury and dioxins (e.g., 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 ED 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 BWfor 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-Tetrachlorodibenzo-p-dioxin (2,3,7,8-TCDD) is the most widely
studied and considered to be one of the most toxic congeners. It is therefore assigned a TEF of
1, with the other dioxin congener TEQ concentrations scaled relative to 2,3,7,8-TCDD
concentrations on the basis of toxicity. For risk assessment of dioxins for RTR, the TEFs
presented in Exhibit B-6 were used to derive the CSFs and RfDs (shown in Exhibit B-5) for
dioxin congeners without available EPA dose-response values. These TEFs are from the World
Health Organization (WHO) 2005 dioxin reevaluation (van den Berg et al. 2006), with the
exception of 1,2,3,7,8,9-hexachlorodibenzo-p-dioxin and 1,2,3,6,7,8-hexachlorodibenzo-p-
dioxin—their CSFs are from IRIS, so their TEFs are calculated as the ratio of their CSFs to the
CSF of 2,3,7,8-TCDD.
The TEF values from the WHO 2005 reevaluation are based on effects mediated by dioxins
binding to the aryl hydrocarbon receptor (AhR) (van den Berg et al. 2006). Blocking AhR
receptors contributes to several health effects in mammals, including impaired immune
response, reproduction, development (e.g., cleft palate), and liver function and a variety of
neoplastic lesions. Some in vitro studies of dioxin congeners compared with the same type of
study with 2,3,7,8-TCDD (e.g., specific enzyme induction in mammalian tissue cultures)
contribute to the weight of evidence used to estimate TEFs. The TEFs can therefore be
multiplied by the CSF for 2,3,7,8-TCDD to estimate the CSF for other dioxins or the RfD for
2,3,7,8-TCDD can be divided by the TEF to estimate RfDs for the other dioxins.
As provided in Exhibit B-6, WHO TEFs from van den Berg et al. (2006) are used except for two
congeners for which EPA's IRIS program has developed a CSF: 1,2,3,6,7,8-hexachlorodibenzo-
p-dioxin and 1,2,3,7,8,9-hexachlorodibenzo-p-dioxin. Collectively across RTR assessments that
EPA has conducted in recent years, these two congeners together constitute roughly 4 percent
of total dioxin emissions from point sources. When the dioxin emissions are weighted by TEFs
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TRIM-Based Tiered Screening Methodology for RTR
(to calculate TEQs), the two congeners constitute about 4 percent of the total dioxin TEQ
emissions from point sources using TEF=0.1 from van den Berg et al. (2006) and about 2
percent using TEF=0.04 derived from the IRIS-based CSF. Therefore, the impact of changing
the TEFs of the two congeners is small.
Exhibit B-6. Toxic Equivalency Factors for Dioxin Congeners
Dioxiri Congener
CAS No.
Toxic Equivalency
Factor (TEF)
1,2,3,4,6,7,8-Heptachlorodibenzo-p-dioxin
35822-46-9
0.01
1,2,3,4,6,7,8-Heptachlorodibenzofuran
67562-39-4
0.01
1,2,3,4,7,8,9-Heptachlorodibenzofuran
55673-89-7
0.01
1,2,3,4,7,8-Hexachlorodibenzo-p-dioxin
39227-28-6
0.1
1,2,3,4,7,8-Hexachlorodibenzofuran
70648-26-9
0.1
1,2,3,6,7,8-Hexachlorodibenzo-p-dioxin
57653-85-7
0.04a
1,2,3,6,7,8-Hexachlorodibenzofuran
57117-44-9
0.1
1,2,3,7,8,9-Hexachlorodibenzo-p-dioxin
19408-74-3
0.04a
1,2,3,7,8,9-Hexachlorodibenzofuran
72918-21-9
0.1
2,3,4,6,7,8-Hexachlorodibenzofuran
60851-34-5
0.1
1,2,3,4,6,7,8,9-Octachlorodibenzo-p-dioxin
3268-87-9
3E-04
1,2,3,4,6,7,8,9-Octachlorodibenzofuran
39001-02-0
3E-04
1,2,3,7,8-Pentachlorodibenzo-p-dioxin
40321-76-4
1
1,2,3,7,8-Pentachlorodibenzofuran
57117-41-6
0.03
2,3,4,7,8-Pentachlorodibenzofuran
57117-31-4
0.3
2,3,7,8-Tetrachlorodibenzo-p-dioxin
1746-01-6
1
2,3,7,8-Tetrachlorodibenzofuran
51207-31-9
0.1
Source: van den Berg et al. (2006), except as noted in footnote a, below.
Note: CAS No. = Chemical Abstracts Service Registry Number.
aFor 1,2,3,7,8,9-HexCDD and 1,2,3,6,7,8-HexCDD, OAQPS identified an oral CSF from IRIS. For RTR multipathway
assessments, EPA uses the TEF derived from this IRIS oral CSF (6200 per mg/kg-d, equaling a TEF of 0.04) rather than the van
den Berg et al. (2006) TEF of 0.1.
B.4.5 Polycyclic Organic Matter
Dose-response values for some of the of polycyclic organic matter (POM) chemicals that are
included in the screens 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 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. Exhibit B-7 presents each POM group (with all its
member POM species reported in NEI, not just those currently evaluated in this assessment)
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and the corresponding CSFs using this methodology. 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-7. Oral Dose-response Values for POM Groups
Individual POM Species or POM Group
CAS No.
Cancer Slope Factor3'15
(mg/kg-day)"1
POM Group 71002
Benz[a]anthracene/Chrysene
NA
PAH, Total
NA
Polycyclic organic matter
NA
0.05
16-PAH
NA
16-PAH-7-PAH
NA
POM Group 72002
Anthracene
120-12-7
Pyrene
129-00-0
Benzo[ghi]perylene
191-24-2
Benzo[e]pyrene
192-97-2
Benzo[c]phenanthrene
195-19-7
Perylene
198-55-0
Benzo[g,h,i]fluoranthene
203-12-3
Benzo[a]fluoranthene
203-33-8
Fluoranthene
206-44-0
Acenaphthylene
208-96-8
1-Methylpyrene
2381-21-7
12-Methylbenz[a]anthracene
2422-79-4
0.05
Methylbenzopyrenes
NA
Methylanthracene
26914-18-1
Retene
483-65-8
Benzofluoranthenes
56832-73-6
9-Methylbenz[a]anthracene
NA
1 -Methylphenanthrene
832-69-9
Acenaphthene
83-32-9
Phenanthrene
85-01-8
Fluorene
86-73-7
1 -Methylnaphthalene
90-12-0
2-Methylnaphthalene
91-57-6
beta-Chloronaphthalene
91-58-7
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Individual POM Species or POM Group
CAS No.
Cancer Slope Factor3'15
(mg/kg-day)"1
POM Group 73002
7,12-Dimethylbenz[a]anthracene
57-97-6
100
POM Group 74002
Dibenzo[a,i]pyrene
189-55-9
10
Dibenzo[a,h]pyrene
189-64-0
3-Methylcholanthrene
56-49-5
POM Group 75002
Dibenzo[a,e]pyrene
192-65-4
1
Methylchrysene
NA
5-Methylchrysene
3697-24-3
Benzo[a]pyrene
50-32-8
Dibenzo[a,h]anthracene
53-70-3
2-Acetylaminofluorene
53-96-3
POM Group 76002
Benzo[b+k]fluoranthene
NA
0.1
lndeno[1,2,3-c,d]pyrene
193-39-5
Benzo[j]fluoranthene
205-82-3
Benzo[b]fluoranthene
205-99-2
Dibenz[a,j]acridine
224-42-0
Benz[a]anthracene
56-55-3
POM Group 77002
Benzo[k]fluoranthene
207-08-9
0.01
Chrysene
218-01-9
Carbazole
86-74-8
POM Group 78002
7-PAH
NA
0.5
Notes: CAS No. = Chemical Abstracts Service Registry Number; NA = not applicable.
aThese group CSFs are used only when OAQPS has not identified a CSF specific to the individual chemical.
bThe method to assign oral cancer slope factors to POM groups was the same as that used in the 1999 National Air Toxics
Assessment (U.S. EPA 1999a). A complete description of the methodology is available at:
http://archive.epa.qov/nata2002/web/pdf/pom approach.pdf.
B.5 Risk Estimation
For 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 MOA at low
doses is described in Section B.5.1 of this attachment. Risk characterization for chemicals likely
to exhibit a threshold for response (e.g., noncancer hazards) is described in Section B.5.2.
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B.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 MOA, the estimated
ELCR is calculated as the product of the LADD and the CSF:
Equation B-48. Excess Lifetime Cancer Risk
ELCR = LADD x CSF Eqn. B-48
where:
Estimated excess lifetime cancer risk from a chemical summed across all
exposure pathways and media (unitless)
Lifetime average total daily dose from all exposure pathways and media (mg/kg-
day)
Oral carcinogenic potency slope factor for chemical (per mg/kg-day)
As described in Section B.3.3, 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 (U.S. EPA 2005b,c). Where data allow, EPA
recommends development of lifestage-specific cancer potency CSFs. To date, EPA has
developed a separate CSF for early lifestage exposure for only one chemical
(i.e., 1,1,1-trichloroethane; U.S. EPA 2007a), 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 MOA for carcinogenesis for which EPA has adequate
data to develop a reasonable quantitative default approach is mutagenesis (U.S. 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 (U.S. EPA 2005c). These conclusions are represented by 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 homegrown food IRs in the
multimedia ingestion risk methodology. As a consequence, ADAFs for the age groups are
adapted as time-weighted average values as follows:
ELCR =
LADD =
CSF =
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ADAF^ = 10 ADAF^y = 3
ADAF(Kt = (10x1 yr) + (3 x 1 yr) = 6 5 ADAF(n.w> = (3 " 4 yrs) + (1 x 4 yrs).
8
ADAF'3-5 j - 3 ADAF(adult) - 1
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 /' = 1 to 6 age groups.
Lifetime Cancer Risk: Chemicals with a Mutagenic MOA for Cancer
Equation B-49. Risk from Chemical Ingestion Risk( = Average daily dose for infants under one year of age (mg/kg-day)
ADD(i-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-ii> = Average daily dose from age 6 through 11 years of age (mg/kg-day)
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ADD(12-19) = Average daily dose from age 12 through 19 years of age (mg/kg-day)
ADD(aduit) = Average daily dose for adults age 20 to up to 70 years of age (mg/kg-day)
CSF = Oral carcinogenic potency slope factor for chemical (per mg/kg-day)
Risko) = Risk from chemical ingestion for the ith age group
n = Number of age groups (i.e., 6)
(XJ70) = Number of years in that age group (X) divided by a 70-year lifetime (weighting
factor)
B.5.2 Noncancer Hazard Quotients
Noncancer risks are presented as hazard quotients (HQs), that is, the ratio of the estimated
daily intake (i.e., ADD) to the RfD. If the HQ for a chemical is equal to or less than 1, EPA
believes there is no appreciable risk that noncancer 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 an adverse health effect may occur.
B.5.2.1 Hazard Quotients for Chemicals with a Chronic RfD
For chemicals with a chronic RfD, HQs are calculated for each age group separately using
Equation B-56 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 BW (i.e., ADD) to the RfD for that chemical. HQs are threshold
effects and are not additive across age groups.
Equation B-56. Hazard Quotient for Chemicals with a Chronic RfD
ADD
HQ = ¦
RfD Eqn. B-56
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)
B.5.2.2 Hazard Quotients for Chemicals with an RfD Based on Developmental
Effects
For chemicals for which the toxicity reference value is an RfD based on developmental effects,
a shorter ED and AT may be required. For this type of chemical (e.g., MeHg, 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 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).
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B.5.2.3 Hazard Index for Chemicals with Chronic RfDs
When conducting screening-level assessments for multiple chemicals, it can be informative to
calculate a hazard index (HI) for toxicologically similar chemicals (U.S. EPA 2000). The HI is the
sum of HQs across chemicals (not age groups) as shown in Equation B-57. 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 MOA and target organ(s) for the chemicals with the highest HQs to develop an appropriate
approach to assessing their potential joint action.
Equation B-57. Hazard Index for Chemicals with Chronic RfDs
HI = HQ-i + HQ2... HQn Eqn. B-57
where:
HI = Hazard index (unitless)
HQi = 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" (U.S. 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 (U.S.
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 (U.S. EPA 2000).
Users of the multimedia ingestion risk methodology are responsible for determining how to
interpret HQs for multiple chemicals for each assessment.
B.6 Assessment Data and Parameter Values
This section describes the types of data and parameter values required for the multimedia
ingestion risk methodology. Where applicable, default parameter values recommended by EPA
are presented and discussed. In general, parameter values recommended by EPA were
identified from HHRAP and EPA publications. For HHRAP parameters, including chemical-
specific parameters, values were originally provided in the HHRAP Companion Database (U.S.
EPA 2005a).
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Required data for environmental media concentrations and air deposition rates, such as those
predicted by (output of) TRIM.FaTE, are described in Section B.6.1 of this attachment. Values
for farm food parameters for specific types of produce and animal products are discussed in
Section B.6.2. Receptor characterization parameters are described in Section B.6.3, including
age-group-specific parameter values for BW, water ingestion, and food ingestion by food type.
Other exposure parameter values, such as exposure frequency and loss of chemical during
food preparation and cooking, are discussed in Section B.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
data presented in this section were reviewed and used by EPA 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 section, are discussed in Section B.7.
B.6.1 Environmental Concentrations
As noted in Section B.2, the multimedia ingestion risk methodology is intended to estimate
exposures and risks to farming, gardening, and fishing families from ingestion of contaminated
media in an area of airborne chemical deposition.
Accordingly, the following values, specific to the air pollutant of concern, are required:
. 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) (only if drinking water exposure is assessed);
and
. four chemical concentrations in soil (in |jg/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 methodology as described in this attachment includes algorithms 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 B.3.2.6, drinking water
exposure is not estimated 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.
For RTR assessments, EPA uses media concentrations output by TRIM.FaTE: EPA provides no
default values for the data requirements listed above.
Attachment B
B-54
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
B.6.2 Farm Foods Parameter Values
Using the chemical information specified in Section B.6.1, above, chemical concentrations are
calculated for 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.
B.6.2.1 List of Farm Foods Parameters
The multimedia ingestion risk methodology uses algorithms from HHRAP (U.S. EPA 2005a) to
estimate chemical concentrations in the produce identified above, as described in Section B.3.2.
Parameters required for these HHRAP algorithms, including chemical-specific media transfer
factors (e.g., soil-to-plant transfer coefficients) and plant- and animal-specific properties (e.g.,
plant interception fraction, quantity of forage consumed by cattle) are described in Exhibit B-8.
As described in Section B.7, the default values recommended by EPA for RTR assessments are
HHRAP-recommended parameter values, where available.
Exhibit B-8. Parameters Used to Estimate Chemical Concentrations in Farm Foods
Parameter
Description
Units
Plants/Produce
BrAG-produce-
DW(i)
Chemical-specific plant/soil chemical bioconcentration
factor for edible portion of aboveground produce type /',
exposed or protected
Unitless (g soil DW/g
produce DW)
BVAG(i)
Chemical-specific air-to-plant biotransfer factor for
aboveground produce type /' 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
kpti)
Plant-specific surface loss coefficient for aboveground
exposed produce and animal forage and silage
yr1
MAF(i)
Moisture adjustment factor for aboveground produce type
/' to convert the chemical concentration estimated for dry-
weight produce to the corresponding chemical
concentration for full-weight fresh produce
Percent water
RCF
Chemical-specific root concentration factor for tubers and
root produce
L soil pore water/kg root
WW
RPd)
Plant-specific interception fraction for the edible portion of
aboveground exposed produce or animal forage and
silage
Unitless
Tpa)
Length of plant exposure to deposition per harvest of the
edible portion of aboveground exposed produce or animal
forage and silage
Year
VGago)
Empirical correction factor for aboveground exposed
produce type /' to address possible overestimate of the
diffusive transfer of chemical from the outside to the
inside of bulky produce, such as fruit
Unitless
Attachment B
B-55
February 2021
-------
TRIM-Based Tiered Screening Methodology for RTR
Parameter
Description
Units
VGrootveg
Empirical correction factor for belowground produce
(i.e., tuber or root vegetable) to account for possible
overestimate of the diffusive transfer of chemicals from
the outside to the inside of bulky tubers or roots (based
on carrots and potatoes)
Unitless
Ypo)
Plant-specific yield or standing crop biomass of the edible
portion of produce or animal feed
kg produce DW/m2
Animal Products
Bs
Soil bioavailability factor for livestock
Unitless
MF
Chemical-specific mammalian metabolism factor that
accounts for endogenous degradation of the chemical
Unitless
Ba
-------
TRIM-Based Tiered Screening Methodology for RTR
calculation of RCF values from log Kow (see equation A-2-14 of U.S. EPA 2005a). The RCF
values as presented in HHRAP had been converted from wet-weight to dry-weight by dividing
by a moisture adjustment factor of 0.13. For some chemicals, the Kow values differ between
HHRAP and those used in TRIM.FaTE; to align with TRIM.FaTE Kow values, we recalculated
the RCFs for nonionic organic chemicals using the regressions mentioned above and the
TRIM.FaTE Kow values; we divided the regression output values by the same moisture
adjustment factor of 0.13, which similarly resulted in RCF values on a DW basis. This moisture
adjustment factor was reapplied when using the RCF to calculate the concentration of chemical
in belowground produce on a wet-weight (WW) basis. All RCF values in Exhibit B-9 are in units
of say L soil pore water/kg root DW. 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.
As discussed in HHRAP (U.S. EPA 2005a), Appendix A, the Kds (Exhibit B-9) 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 Section B 1.2.1.3 of U.S. EPA (1997c). Kds
for cadmium compounds was calculated using the equation for cadmium presented in the
abstract of U.S EPA (2005f). The Kds value for arsenic compounds was obtained from Table 3
of U.S. EPA (2005g). For all POM and dioxins, Kds was calculated by multiplying Koc times the
screening scenario's fraction organic carbon content (0.008), as specified in Section A2-2.10 of
U.S. EPA (2005b). Empirical information for Koc was available for acenaphthene,
benz[a]anthracene, benzo[a]pyrene, dibenz[a,h]anthracene, fluoranthene, and fluorene in U.S.
EPA (1996). For all other organic compounds, the Koc was calculated using the correlation
equation A-2-7 presented in Section A2-2.9.2 of U.S. EPA (2005a).
As discussed in HHRAP (U.S. EPA 2005a), Appendix A (Section A2-2.12.4), the chemical air-
to-plant biotransfer factor (BvAG(i), Exhibit B-9) value for mercuric chloride was obtained
from U.S. EPA (1997c). Bvagco values for POM in HHRAP were calculated using the correlation
equation (Equation A-2-19 in HHRAP Appendix A, Section A2-2.12) 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. However, the Bacci equation uses Kow and H (Henry's Law Constant), and
for some chemicals the values of these parameters differ between HHRAP and those used in
TRIM.FaTE; to align with TRIM.FaTE Kow and H values, we recalculated the Bvag(o values for
POM using the Bacci equation, the TRIM.FaTE Kow and H values, and the *100 reduction
factor mentioned above. The values for dioxins in HHRAP were obtained from Lorber and
Pinsky (2000). It is assumed that metals, with the exception of vapor-phase elemental mercury,
do not transfer significantly from air into leaves. Speciation and fate and transport of mercury
from emissions suggest that Bvag(i) values for elemental and methyl mercury are likely to be
zero (U.S. EPA 2005a).
Attachment B
B-57
February 2021
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit B-9. Chemical-specific Inputs for Produce Parameters
Chemical
Fraction of
Wet
Deposition
(Fw)
(unitless)
Root
Concentration
Factor (RCF)
(belowground)
(L soil pore
water/kg root
dry weight)
Soil-Water
Partition
Coefficient
(Kds)
(L/kg)
Chemical Air-to-
Plant
Biotransfer
Factor (Bvago))
(unitless)
Inorganics
Arsenic compounds
0.6
NA
2.5E+03
NAa
Cadmium compounds
0.6
NA
3.1E+02
NAa
Mercury (elemental)
0.6
NA
1.0E+03
0b
Mercuric chloride
0.6
NA
5.8E+04
1.8E+03
Methyl mercury
0.6
NA
7.0E+03
0b
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
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
POMs
2-Methylnaphthalene
0.6
2.2E+02
5.0E+01
1.4E+00
7,12-
Dimethylbenz[a]anthracene
0.6
6.8E+03
4.0E+03
4.2E+04
Acenaphthene
0.6
2.4E+02
3.9E+01
4.6E+00
Acenaphthylene
0.6
2.8E+02
6.8E+01
8.1E+00
Benz[a]anthracene
0.6
6.7E+03
2.9E+03
6.8E+03
Benzo[a]pyrene
0.6
9.2E+03
7.8E+03
1.7E+05
Benzo[b]fluoranthene
0.6
6.6E+03
3.8E+03
1.7E+05
Benzo[ghi]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
Attachment B
B-58
February 2021
-------
TRIM-Based Tiered Screening Methodology for RTR
Chemical
Fraction of
Wet
Deposition
(Fw)
(unitless)
Root
Concentration
Factor (RCF)
(belowground)
(L soil pore
water/kg root
dry weight)
Soil-Water
Partition
Coefficient
(Kds)
(L/kg)
Chemical Air-to-
Plant
Biotransfer
Factor (Bvag(o)
(unitless)
Dibenzo[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
lndeno[1,2,3-c,d]pyrene
0.6
3.5E+04
3.2E+04
2.8E+06
Sources: HHRAP (U.S. EPA 2005a); Table 3 of U.S. EPA (2005g) for Kds of arsenic; equation for cadmium presented in the
abstract of U.S EPA (2005f) for Kds of cadmium.
Note: NA = not applicable; CDD = chlorodibenzo-p-dioxin; CDF = chloridibenzofuran.
alt is assumed that metals, with the exception of vapor-phase elemental mercury, do not transfer significantly from air into leaves.
bSpeciation and fate and transport of mercury from emissions suggest that Bvago) values for elemental and methyl mercury are
likely to be zero (U.S. EPA 2005a).
As discussed in HHRAP (U.S. EPA 2005a), Appendix A Section A2-2.12, the plant-soil
bioconcentration factor (BrAG-produce-Dwpy, Exhibit B-10) for aboveground produce, grain, silage,
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).
However, those correlation equations (shown as A-2-17 and A-2-18 in the reference) use Kow
(octanol-water partitioning coefficient), and for some chemicals, the Kow values differ between
HHRAP and those used in TRIM.FaTE; to align with TRIM.FaTE Kow values, we recalculated
the Brvalues for organics using the correlation equations mentioned above and the TRIM.FaTE
Kow values. For cadmium and arsenic, Brvalues in HHRAP were derived from uptake slope
factors provided in U.S. 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 in HHRAP for mercuric chloride and MeHg were calculated using methodology and data
from Baes et al. (1984). Br forage values in HHRAP for mercuric chloride and MeHg (on a dry
weight basis) were obtained from U.S. EPA (1997c), and Br forage and silage values for these
chemicals are 0. The HHRAP methodology assumes that elemental mercury does not deposit
onto soils; therefore, it is assumed there is no plant uptake through the soil. The BrAG-Produce-DW(i)
for root produce account for the uptake from soil. The Br root values in HHRAP for organics
were calculated using the RCF divided by the Kds—we recalculated these values using the
RCF and Kds values shown in Exhibit B-9. The Br root values in HHRAP for cadmium and
arsenic were calculated from the same U.S. EPA (1992) upslope factor methodology noted
above. The Br root values in HHRAP for mercuric chloride and MeHg were obtained from U.S.
EPA (1997c).
As discussed in HHRAP (U.S. EPA 2005a), Section 5.3.3 and Appendix B, the empirical
correction factor for belowground produce (VGrootveg; Exhibit B-10) 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 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
Attachment B
B-59
February 2021
-------
TRIM-Based Tiered Screening Methodology for RTR
provided in U.S. EPA (1994b). We used the Kow values from TRIM.FaTE in applying these
recommendations, to remain consistent with that model. In developing these values, U.S. 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).
As discussed in HHRAP (U.S. EPA 2005a), Sections 5.3.2.1 and 5.4.2.1, as well as
Appendix B, the empirical correction factor for aboveground produce (VGag; Exhibit B-10)
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 reduce residues. For aboveground
produce, HHRAP 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 U.S. EPA (1994b). We used the Kow values from TRIM.FaTE in applying these
recommendations, to remain consistent with that model. In developing these values, U.S. EPA
(1994b) assumed the following: (1) translocation of compounds deposited on the surface of
aboveground vegetation to inner parts of aboveground produce would be insignificant
(potentially underestimating the concentration of PB-HAP in aboveground produce due to air-to-
plant transfer); (2) the density of the skin and the whole vegetable are equal (potentially
overestimating the concentration of PB-HAP in aboveground produce due to air-to-plant
transfer); and (3) the thickness of vegetable skin and broadleaf tree skin are equal (effects on
the concentration of PB-HAP in aboveground produce due to air-to-plant transfer unknown). For
forage, HHRAP recommends a VGag value of 1.0, also based on information provided in U.S.
(EPA 1994b). A VGag value for silage is not provided in U.S. (EPA 1994b); the VGag value for
silage of 0.5 was obtained from NC DEHNR (1997); however, NC DEHNR does not present a
specific rationale for this recommendation. Depending on the composition of the site-specific
silage, this value may under- or overestimate the actual value.
Exhibit B-10. Chemical-specific Inputs by Plant Type
Compound Name
Plant Part
Plant-Soil Bio-
Concentration
Factor
(BrA G-produce-DW(ij)
(unitless)
Empirical
Correction
Factor:
Belowground
Produce
(VGrootveg)
(unitless)
Empirical
Correction
Factor:
Aboveground
Produce
(VGago))
(unitless)
Inorganics
Arsenic compounds
Exposed Fruit
6.3E-03
-
1.0E+00
Exposed Vegetables
6.3E-03
-
1.0E+00
Forage
3.6E-02
-
1.0E+00
Grain
4.0E-03
-
-
Protected Fruit
6.3E-03
-
-
Protected Vegetables
6.3E-03
-
-
Root Vegetables
8.0E-03
1.0E+00
-
Silage
3.6E-02
-
5.0E-01
Attachment B
B-60
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Compound Name
Plant Part
Plant-Soil Bio-
Concentration
Factor
(BrA G-produce-DW(i))
(unitless)
Empirical
Correction
Factor:
Belowground
Produce
(VGrootveg)
(unitless)
Empirical
Correction
Factor:
Aboveground
Produce
(VGago))
(unitless)
Cadmium compounds
Exposed Fruit
1.3E-01
-
1.0E+00
Exposed Vegetables
1.3E-01
-
1.0E+00
Forage
3.6E-01
-
1.0E+00
Grain
6.2E-02
-
-
Protected Fruit
1.3E-01
-
-
Protected Vegetables
1.3E-01
-
-
Root Vegetables
6.4E-02
1.0E+00
-
Silage
3.6E-01
-
5.0E-01
Mercury (elemental)
Exposed Fruit
-
-
1.0E+00
Exposed Vegetables
-
-
1.0E+00
Forage
-
-
1.0E+00
Grain
-
-
-
Protected Fruit
-
-
-
Protected Vegetables
-
-
-
Root Vegetables
-
1.0E+00
-
Silage
-
-
5.0E-01
Mercuric chloride
Exposed Fruit
1.5E-02
-
1.0E+00
Exposed Vegetables
1.5E-02
-
1.0E+00
Forage
0.0E+00
-
1.0E+00
Grain
9.3E-03
-
-
Protected Fruit
1.5E-02
-
-
Protected Vegetables
1.5E-02
-
-
Root Vegetables
3.6E-02
1.0E+00
-
Silage
0.0E+00
-
5.0E-01
Methyl mercury
Exposed Fruit
2.9E-02
-
1.0E-02
Exposed Vegetables
2.9E-02
-
1.0E-02
Forage
0.0E+00
-
1.0E+00
Grain
1.9E-02
-
-
Protected Fruit
2.9E-02
-
-
Protected Vegetables
2.9E-02
-
-
Root Vegetables
9.9E-02
1.0E-02
-
Silage
0.0E+00
-
5.0E-01
Attachment B
B-61
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Compound Name
Plant Part
Plant-Soil Bio-
Concentration
Factor
(BrA G-produce-DW(i))
(unitless)
Empirical
Correction
Factor:
Belowground
Produce
(VGrootveg)
(unitless)
Empirical
Correction
Factor:
Aboveground
Produce
(VGago))
(unitless)
PAHs
2-Methylnaphthalene
Exposed Fruit
2.3E-01
-
1.0E+00
Exposed Vegetables
2.3E-01
-
1.0E+00
Forage
2.3E-01
-
1.0E+00
Grain
2.3E-01
-
-
Protected Fruit
2.3E-01
-
-
Protected Vegetables
2.3E-01
-
-
Root Vegetables
4.4E+00
1.0E+00
-
Silage
2.3E-01
-
5.0E-01
7,12-
Dimethylbenz[a]anthracene
Exposed Fruit
1.7E-02
-
1.0E-02
Exposed Vegetables
1.7E-02
-
1.0E-02
Forage
1.7E-02
-
1.0E+00
Grain
1.7E-02
-
-
Protected Fruit
1.7E-02
-
-
Protected Vegetables
1.7E-02
-
-
Root Vegetables
1.7E+00
1.0E-02
-
Silage
1.7E-02
-
5.0E-01
Acenaphthene
Exposed Fruit
2.1E-01
-
1.0E+00
Exposed Vegetables
2.1E-01
-
1.0E+00
Forage
2.1E-01
-
1.0E+00
Grain
2.1E-01
-
-
Protected Fruit
2.1E-01
-
-
Protected Vegetables
2.1E-01
-
-
Root Vegetables
6.2E+00
1.0E+00
-
Silage
2.1E-01
-
5.0E-01
Acenaphthylene
Exposed Fruit
1.9E-01
-
1.0E-02
Exposed Vegetables
1.9E-01
-
1.0E-02
Forage
1.9E-01
-
1.0E+00
Grain
1.9E-01
-
-
Protected Fruit
1.9E-01
-
-
Protected Vegetables
1.9E-01
-
-
Root Vegetables
4.1E+00
1.0E-02
-
Silage
1.9E-01
-
5.0E-01
Attachment B
B-62
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Compound Name
Plant Part
Plant-Soil Bio-
Concentration
Factor
(BrA G-produce-DW(i))
(unitless)
Empirical
Correction
Factor:
Belowground
Produce
(VGrootveg)
(unitless)
Empirical
Correction
Factor:
Aboveground
Produce
(VGago))
(unitless)
Benz[a]anthracene
Exposed Fruit
1.7E-02
-
1.0E-02
Exposed Vegetables
1.7E-02
-
1.0E-02
Forage
1.7E-02
-
1.0E+00
Grain
1.7E-02
-
-
Protected Fruit
1.7E-02
-
-
Protected Vegetables
1.7E-02
-
-
Root Vegetables
2.3E+00
1.0E-02
-
Silage
1.7E-02
-
5.0E-01
Benzo[a]pyrene
Exposed Fruit
1.4E-02
-
1.0E-02
Exposed Vegetables
1.4E-02
-
1.0E-02
Forage
1.4E-02
-
1.0E+00
Grain
1.4E-02
-
-
Protected Fruit
1.4E-02
-
-
Protected Vegetables
1.4E-02
-
-
Root Vegetables
1.2E+00
1.0E-02
-
Silage
1.4E-02
-
5.0E-01
Benzo[b]fluoranthene
Exposed Fruit
1.8E-02
-
1.0E-02
Exposed Vegetables
1.8E-02
-
1.0E-02
Forage
1.8E-02
-
1.0E+00
Grain
1.8E-02
-
-
Protected Fruit
1.8E-02
-
-
Protected Vegetables
1.8E-02
-
-
Root Vegetables
1.7E+00
1.0E-02
-
Silage
1.8E-02
-
5.0E-01
Benzo[ghi]perylene
Exposed Fruit
5.7E-03
-
1.0E-02
Exposed Vegetables
5.7E-03
-
1.0E-02
Forage
5.7E-03
-
1.0E+00
Grain
5.7E-03
-
-
Protected Fruit
5.7E-03
-
-
Protected Vegetables
5.7E-03
-
-
Root Vegetables
1.1E+00
1.0E-02
-
Silage
5.7E-03
-
5.0E-01
Attachment B
B-63
February 2021
-------
TRIM-Based Tiered Screening Methodology for RTR
Compound Name
Plant Part
Plant-Soil Bio-
Concentration
Factor
(BrA G-produce-DW(i))
(unitless)
Empirical
Correction
Factor:
Belowground
Produce
(VGrootveg)
(unitless)
Empirical
Correction
Factor:
Aboveground
Produce
(VGago))
(unitless)
Benzo[k]fluoranthene
Exposed Fruit
1.4E-02
-
1.0E-02
Exposed Vegetables
1.4E-02
-
1.0E-02
Forage
1.4E-02
-
1.0E+00
Grain
1.4E-02
-
-
Protected Fruit
1.4E-02
-
-
Protected Vegetables
1.4E-02
-
-
Root Vegetables
1.6E+00
1.0E-02
-
Silage
1.4E-02
-
5.0E-01
Chrysene
Exposed Fruit
1.9E-02
-
1.0E-02
Exposed Vegetables
1.9E-02
-
1.0E-02
Forage
1.9E-02
-
1.0E+00
Grain
1.9E-02
-
-
Protected Fruit
1.9E-02
-
-
Protected Vegetables
1.9E-02
-
-
Root Vegetables
1.7E+00
1.0E-02
-
Silage
1.9E-02
-
5.0E-01
Dibenzo[a,h]anthracene
Exposed Fruit
6.8E-03
-
1.0E-02
Exposed Vegetables
6.8E-03
-
1.0E-02
Forage
6.8E-03
-
1.0E+00
Grain
6.8E-03
-
-
Protected Fruit
6.8E-03
-
-
Protected Vegetables
6.8E-03
-
-
Root Vegetables
1.6E+00
1.0E-02
-
Silage
6.8E-03
-
5.0E-01
Fluoranthene
Exposed Fruit
4.0E-02
-
1.0E-02
Exposed Vegetables
4.0E-02
-
1.0E-02
Forage
4.0E-02
-
1.0E+00
Grain
4.0E-02
-
-
Protected Fruit
4.0E-02
-
-
Protected Vegetables
4.0E-02
-
-
Root Vegetables
5.6E+00
1.0E-02
-
Silage
4.0E-02
-
5.0E-01
Attachment B
B-64
February 2021
-------
TRIM-Based Tiered Screening Methodology for RTR
Compound Name
Plant Part
Plant-Soil Bio-
Concentration
Factor
(BrA G-produce-DW(i))
(unitless)
Empirical
Correction
Factor:
Belowground
Produce
(VGrootveg)
(unitless)
Empirical
Correction
Factor:
Aboveground
Produce
(VGago))
(unitless)
Fluorene
Exposed Fruit
1.5E-01
-
1.0E-02
Exposed Vegetables
1.5E-01
-
1.0E-02
Forage
1.5E-01
-
1.0E+00
Grain
1.5E-01
-
-
Protected Fruit
1.5E-01
-
-
Protected Vegetables
1.5E-01
-
-
Root Vegetables
6.2E+00
1.0E-02
-
Silage
1.5E-01
-
5.0E-01
lndeno[1,2,3-c,d]pyrene
Exposed Fruit
5.1E-03
-
1.0E-02
Exposed Vegetables
5.1E-03
-
1.0E-02
Forage
5.1E-03
-
1.0E+00
Grain
5.1E-03
-
-
Protected Fruit
5.1E-03
-
-
Protected Vegetables
5.1E-03
-
-
Root Vegetables
1.1E+00
1.0E-02
-
Silage
5.1E-03
-
5.0E-01
Dioxins
OctaCDD, 1,2,3,4,6,7,8,9-
Exposed Fruit
7.1E-04
-
1.0E-02
Exposed Vegetables
7.1E-04
-
1.0E-02
Forage
7.1E-04
-
1.0E+00
Grain
7.1E-04
-
-
Protected Fruit
7.1E-04
-
-
Protected Vegetables
7.1E-04
-
-
Root Vegetables
6.1E-01
1.0E-02
-
Silage
7.1E-04
-
5.0E-01
OctaCDF, 1,2,3,4,6,7,8,9-
Exposed Fruit
9.2E-04
-
1.0E-02
Exposed Vegetables
9.2E-04
-
1.0E-02
Forage
9.2E-04
-
1.0E+00
Grain
9.2E-04
-
-
Protected Fruit
9.2E-04
-
-
Protected Vegetables
9.2E-04
-
-
Root Vegetables
6.8E-01
1.0E-02
-
Silage
9.2E-04
-
5.0E-01
Attachment B
B-65
February 2021
-------
TRIM-Based Tiered Screening Methodology for RTR
Compound Name
Plant Part
Plant-Soil Bio-
Concentration
Factor
(BrA G-produce-DW(i))
(unitless)
Empirical
Correction
Factor:
Belowground
Produce
(VGrootveg)
(unitless)
Empirical
Correction
Factor:
Aboveground
Produce
(VGago))
(unitless)
HeptaCDD, 1,2,3,4,6,7,8-
Exposed Fruit
9.2E-04
-
1.0E-02
Exposed Vegetables
9.2E-04
-
1.0E-02
Forage
9.2E-04
-
1.0E+00
Grain
9.2E-04
-
-
Protected Fruit
9.2E-04
-
-
Protected Vegetables
9.2E-04
-
-
Root Vegetables
6.8E-01
1.0E-02
-
Silage
9.2E-04
-
5.0E-01
HeptaCDF, 1,2,3,4,6,7,8-
Exposed Fruit
2.0E-03
-
1.0E-02
Exposed Vegetables
2.0E-03
-
1.0E-02
Forage
2.0E-03
-
1.0E+00
Grain
2.0E-03
-
-
Protected Fruit
2.0E-03
-
-
Protected Vegetables
2.0E-03
-
-
Root Vegetables
9.4E-01
1.0E-02
-
Silage
2.0E-03
-
5.0E-01
HeptaCDF, 1,2,3,4,7,8,9-
Exposed Fruit
4.0E-03
-
1.0E-02
Exposed Vegetables
4.0E-03
-
1.0E-02
Forage
4.0E-03
-
1.0E+00
Grain
4.0E-03
-
-
Protected Fruit
4.0E-03
-
-
Protected Vegetables
4.0E-03
-
-
Root Vegetables
1.2E+00
1.0E-02
-
Silage
4.0E-03
-
5.0E-01
HexaCDD, 1,2,3,4,7,8-
Exposed Fruit
1.2E-03
-
1.0E-02
Exposed Vegetables
1.2E-03
-
1.0E-02
Forage
1.2E-03
-
1.0E+00
Grain
1.2E-03
-
-
Protected Fruit
1.2E-03
-
-
Protected Vegetables
1.2E-03
-
-
Root Vegetables
7.6E-01
1.0E-02
-
Silage
1.2E-03
-
5.0E-01
Attachment B
B-66
February 2021
-------
TRIM-Based Tiered Screening Methodology for RTR
Compound Name
Plant Part
Plant-Soil Bio-
Concentration
Factor
(BrA G-produce-DW(i))
(unitless)
Empirical
Correction
Factor:
Belowground
Produce
(VGrootveg)
(unitless)
Empirical
Correction
Factor:
Aboveground
Produce
(VGago))
(unitless)
HexaCDF, 1,2,3,4,7,8-
Exposed Fruit
3.5E-03
-
1.0E-02
Exposed Vegetables
3.5E-03
-
1.0E-02
Forage
3.5E-03
-
1.0E+00
Grain
3.5E-03
-
-
Protected Fruit
3.5E-03
-
-
Protected Vegetables
3.5E-03
-
-
Root Vegetables
1.2E+00
1.0E-02
-
Silage
3.5E-03
-
5.0E-01
HexaCDD, 1,2,3,6,7,8-
Exposed Fruit
7.0E-04
-
1.0E-02
Exposed Vegetables
7.0E-04
-
1.0E-02
Forage
7.0E-04
-
1.0E+00
Grain
7.0E-04
-
-
Protected Fruit
7.0E-04
-
-
Protected Vegetables
7.0E-04
-
-
Root Vegetables
6.1E-01
1.0E-02
-
Silage
7.0E-04
-
5.0E-01
HexaCDF, 1,2,3,6,7,8-
Exposed Fruit
1.0E-03
-
1.0E-02
Exposed Vegetables
1.0E-03
-
1.0E-02
Forage
1.0E-03
-
1.0E+00
Grain
1.0E-03
-
-
Protected Fruit
1.0E-03
-
-
Protected Vegetables
1.0E-03
-
-
Root Vegetables
7.1E-01
1.0E-02
-
Silage
1.0E-03
-
5.0E-01
HexaCDD, 1,2,3,7,8,9-
Exposed Fruit
7.0E-04
-
1.0E-02
Exposed Vegetables
7.0E-04
-
1.0E-02
Forage
7.0E-04
-
1.0E+00
Grain
7.0E-04
-
-
Protected Fruit
7.0E-04
-
-
Protected Vegetables
7.0E-04
-
-
Root Vegetables
6.1E-01
1.0E-02
-
Silage
7.0E-04
-
5.0E-01
Attachment B
B-67
February 2021
-------
TRIM-Based Tiered Screening Methodology for RTR
Compound Name
Plant Part
Plant-Soil Bio-
Concentration
Factor
(BrA G-produce-DW(i))
(unitless)
Empirical
Correction
Factor:
Belowground
Produce
(VGrootveg)
(unitless)
Empirical
Correction
Factor:
Aboveground
Produce
(VGago))
(unitless)
HexaCDF, 1,2,3,7,8,9-
Exposed Fruit
1.6E-03
-
1.0E-02
Exposed Vegetables
1.6E-03
-
1.0E-02
Forage
1.6E-03
-
1.0E+00
Grain
1.6E-03
-
-
Protected Fruit
1.6E-03
-
-
Protected Vegetables
1.6E-03
-
-
Root Vegetables
8.5E-01
1.0E-02
-
Silage
1.6E-03
-
5.0E-01
HexaCDF, 2,3,4,6,7,8-
Exposed Fruit
1.0E-03
-
1.0E-02
Exposed Vegetables
1.0E-03
-
1.0E-02
Forage
1.0E-03
-
1.0E+00
Grain
1.0E-03
-
-
Protected Fruit
1.0E-03
-
-
Protected Vegetables
1.0E-03
-
-
Root Vegetables
7.1E-01
1.0E-02
-
Silage
1.0E-03
-
5.0E-01
PentaCDD, 1,2,3,7,8-
Exposed Fruit
2.4E-03
-
1.0E-02
Exposed Vegetables
2.4E-03
-
1.0E-02
Forage
2.4E-03
-
1.0E+00
Grain
2.4E-03
-
-
Protected Fruit
2.4E-03
-
-
Protected Vegetables
2.4E-03
-
-
Root Vegetables
1.0E+00
1.0E-02
-
Silage
2.4E-03
-
5.0E-01
PentaCDF, 1,2,3,7,8-
Exposed Fruit
4.6E-03
-
1.0E-02
Exposed Vegetables
4.6E-03
-
1.0E-02
Forage
4.6E-03
-
1.0E+00
Grain
4.6E-03
-
-
Protected Fruit
4.6E-03
-
-
Protected Vegetables
4.6E-03
-
-
Root Vegetables
1.3E+00
1.0E-02
-
Silage
4.6E-03
-
5.0E-01
Attachment B
B-68
February 2021
-------
TRIM-Based Tiered Screening Methodology for RTR
Compound Name
Plant Part
Plant-Soil Bio-
Concentration
Factor
(BrA G-produce-DW(i))
(unitless)
Empirical
Correction
Factor:
Belowground
Produce
(VGrootveg)
(unitless)
Empirical
Correction
Factor:
Aboveground
Produce
(VGago))
(unitless)
PentaCDF, 2,3,4,7,8-
Exposed Fruit
6.8E-03
-
1.0E-02
Exposed Vegetables
6.8E-03
-
1.0E-02
Forage
6.8E-03
-
1.0E+00
Grain
6.8E-03
-
-
Protected Fruit
6.8E-03
-
-
Protected Vegetables
6.8E-03
-
-
Root Vegetables
1.5E+00
1.0E-02
-
Silage
6.8E-03
-
5.0E-01
TetraCDD, 2,3,7,8-
Exposed Fruit
4.5E-03
-
1.0E-02
Exposed Vegetables
4.5E-03
-
1.0E-02
Forage
4.5E-03
-
1.0E+00
Grain
4.5E-03
-
-
Protected Fruit
4.5E-03
-
-
Protected Vegetables
4.5E-03
-
-
Root Vegetables
1.3E+00
1.0E-02
-
Silage
4.5E-03
-
5.0E-01
TetraCDF, 2,3,7,8-
Exposed Fruit
1.2E-02
-
1.0E-02
Exposed Vegetables
1.2E-02
-
1.0E-02
Forage
1.2E-02
-
1.0E+00
Grain
1.2E-02
-
-
Protected Fruit
1.2E-02
-
-
Protected Vegetables
1.2E-02
-
-
Root Vegetables
1.9E+00
1.0E-02
-
Silage
1.2E-02
-
5.0E-01
Source: HHRAP (U.S. EPA 2005a).
Note: - = not applicable; CDD = chlorodibenzo-p-dioxin; CDF = chloridibenzofuran.
Baes etal. (1984) used an empirical relationship developed by Chamberlain (1970) to identify a
correlation between initial interception fraction (Rp; Exhibit B-11) 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.
Attachment B
B-69
February 2021
-------
TRIM-Based Tiered Screening Methodology for RTR
The plant surface loss coefficient (kp; Exhibit B-11) is a measure of the amount of chemical
that is lost to natural physical processes (e.g., wind, water) overtime. The HHRAP-
recommended value of 18 yr1 [also recommended by U.S. EPA (1994a, 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 yr1, plant
concentrations could range from about 1.8 times higher to about 5 times lower than the plant
concentrations estimated in farm food media using the midpoint kp value of 18.
For length of plant exposure to deposition (Tp, Exhibit B-11), HHRAP (U.S. EPA 2005a)
recommends using a value of about 0.16 years for aboveqround produce and cattle silage. This
is consistent with earlier reports by U.S. 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;
and (2) the concentration of chemical in aboveground produce due to direct (wet and dry)
deposition (Pd) will be underestimated if the site-specific value of Tp is less than 60 days, or
overestimated if the site-specific value of Tp is more than 60 days.
Values for yield or standing crop biomass (Yp; Exhibit B-11) 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 ith 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.
The plant tissue-specific MAF (Exhibit B-11) converts dry-weight concentrations into WW
concentrations (which are lower owing to the dilution by water compared with dry-weight
concentrations). Values obtained from Section 10.3.2.1.4 of U.S. EPA (1999b), which
references U.S. EPA (1997d).
Exhibit B-11. Non-chemical-specific Produce Inputs
Plant Part
Interception
Fraction
(RP(o)
(unitless)
Plant
Surface
Loss
Coefficient
(*Pffl)
(1/year)
Length of
Plant
Exposure to
Deposition
(Tp(i))
(year)
Yield or
Standing
Crop
Biomass
(Vpffl)
(kg/m2)
Plant Tissue-
specific
Moisture
Adjustment
Factor (MAFg))
(percent)
Exposed
Vegetables
0.982
18
0.164
5.66
92
Protected Fruit
NA
NA
NA
NA
90
Protected
Vegetables
NA
NA
NA
NA
80
Forage (animal
feed)
0.5
18
0.12
0.24
NAa
Attachment B
B-70
February 2021
-------
TRIM-Based Tiered Screening Methodology for RTR
Plant Part
Interception
Fraction
(RP(o)
(unitless)
Plant
Surface
Loss
Coefficient
(*Pffl)
(1/year)
Length of
Plant
Exposure to
Deposition
(TP(i))
(year)
Yield or
Standing
Crop
Biomass
(YP(i))
(kg/m2)
Plant Tissue-
specific
Moisture
Adjustment
Factor (MAFo>)
(percent)
Exposed Fruit
0.053
18
0.164
0.25
85
Root Vegetables
NA
NA
NA
NA
87
Silage (animal feed)
0.46
18
0.16
0.8
NAa
Grain (animal feed)
NA
NA
NA
NA
NAa
Source: HHRAP (U.S. EPA 2005a).
Note: NA = not applicable.
aMAFs were not implemented for animal feed groups as the calculations for chemical concentration are based on dry weight not
wet weight. Previous values used for these groups were 92, 92, and 90 respectively; however, 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.
B.6.2.3 Animal Product Parameter Values
The multimedia ingestion risk methodology requires chemical-specific inputs for many of the
animal product algorithms. The relevant values are shown in Exhibit B-12 for the PB-HAP
chemicals included in RTR multipathway assessments 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 IRs recommended in HHRAP
for beef cattle, dairy cattle, swine, and chicken are provided in Exhibit B-13.
As discussed in HHRAP, Appendix A (Section A2-2.13) (U.S. EPA 2005a), biotransfer factors
(Bam; Exhibit B-12) for mercury compounds were obtained from U.S. EPA (1997c). 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. Also as discussed in HHRAP, Appendix A (Section A2-2.13), biotransfer factors
for cadmium compounds were obtained from U.S. EPA (1995b), and those for arsenic were
obtained from Baes et al. (1984) for beef and dairy. Biotransfer factors for arsenic into eggs,
pork, and poultry were obtained from Appendix K of CalEPA (2012). HHRAP calculated
biotransfer factors for dioxins and POM using a regression equation that accounted for Kow and
then adjusted for fat content (Equation A-2-21 of HHRAP Appendix A, Section A2-2.13).
However, for some chemicals, the Kow values differ between HHRAP and those used in
TRIM.FaTE. To align with TRIM.FaTE Kow values, we recalculated the biotransfer factors for
dioxins and POM using the regression mentioned above, the TRIM.FaTE Kow values, and the
fat contents noted in HHRAP.
As discussed in HHRAP (U.S. EPA 2005a), U.S. EPA (1995c) recommends using an MF
(Exhibit B-12) to account for metabolism by mammals of some chemicals, offsetting 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 (U.S. EPA 1995d). An MF of
0.01 is also used to calculate concentrations of POM in food products from mammalian species
based on the work of Hofelt et al. (2001). This factor accounts for the P450-mediated
metabolism of POM 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
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit B-12. Animal Product Chemical-specific Inputs
Compound Name
Biotransfer Factors (Bam) (day/kg fresh-weight tissue)
and Metabolism Factors (MF) (unitless)
Mammal
Non-mammal
Beef
{Babeef)
Dairy
(Ba dairy)
Pork
(Bapork)
MF
Eggs
(Bdeggs)
Poultry
(Bdpoultry)
MF
Inorganics
Arsenic compounds
2.0E-03
6.0E-05
1.0E-02
1
7.0E-02
3.0E-02
NA
Cadmium compounds
1.2E-04
6.5E-06
1.9E-04
1
2.5E-03
1.1E-01
NA
Mercury (elemental)
0
0
0
1
0
0
NA
Mercuric chloride
1.1E-04
1.4E-06
3.4E-05
1
2.4E-02
2.4E-02
NA
Methyl mercury
1.2E-03
1.7E-05
5.1E-06
1
3.6E-03
3.6E-03
NA
Dioxins
OctaCDD, 1,2,3,4,6,7,8,9-
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-
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-
8.8E-03
1.8E-03
1.1E-02
1
3.7E-03
6.5E-03
NA
HeptaCDF, 1,2,3,4,6,7,8-
1.6E-02
3.5E-03
2.0E-02
1
6.9E-03
1.2E-02
NA
HeptaCDF, 1,2,3,4,7,8,9-
2.4E-02
5.1E-03
3.0E-02
1
1.0E-02
1.8E-02
NA
HexaCDD, 1,2,3,4,7,8-
1.1E-02
2.3E-03
1.3E-02
1
4.6E-03
8.1E-03
NA
HexaCDF, 1,2,3,4,7,8-
2.3E-02
4.8E-03
2.8E-02
1
9.6E-03
1.7E-02
NA
HexaCDD, 1,2,3,6,7,8-
6.8E-03
1.4E-03
8.2E-03
1
2.9E-03
5.0E-03
NA
HexaCDF, 1,2,3,6,7,8-
9.7E-03
2.0E-03
1.2E-02
1
4.1E-03
7.1E-03
NA
HexaCDD, 1,2,3,7,8,9 -
6.8E-03
1.4E-03
8.2E-03
1
2.9E-03
5.0E-03
NA
HexaCDF, 1,2,3,7,8,9-
1.4E-02
2.9E-03
1.7E-02
1
5.8E-03
1.0E-02
NA
HexaCDF, 2,3,4,6,7,8-
9.6E-03
2.0E-03
1.2E-02
1
4.1E-03
7.1E-03
NA
PentaCDD, 1,2,3,7,8-
1.8E-02
3.9E-03
2.2E-02
1
7.8E-03
1.4E-02
NA
PentaCDF, 1,2,3,7,8-
2.6E-02
5.5E-03
3.2E-02
1
1.1E-02
1.9E-02
NA
PentaCDF, 2,3,4,7,8-
3.1E-02
6.5E-03
3.8E-02
1
1.3E-02
2.3E-02
NA
TetraCDD, 2,3,7,8-
2.6E-02
5.5E-03
3.2E-02
1
1.1E-02
1.9E-02
NA
TetraCDF, 2,3,7,8-
3.6E-02
7.7E-03
4.4E-02
1
1.5E-02
2.7E-02
NA
POMs
2-Methylnaphthalene
2.4E-02
5.0E-03
2.9E-02
0.01
1.0E-02
1.7E-02
NA
7,12-
Dimethylbenz[a]anthracene
3.9E-02
8.3E-03
4.8E-02
0.01
1.7E-02
2.9E-02
NA
Acenaphthene
2.5E-02
5.2E-03
3.0E-02
0.01
1.0E-02
1.8E-02
NA
Acenaphthylene
2.6E-02
5.5E-03
3.1E-02
0.01
1.1E-02
1.9E-02
NA
Benz[a]anthracene
3.9E-02
8.3E-03
4.8E-02
0.01
1.7E-02
2.9E-02
NA
Benzo[a]pyrene
3.8E-02
8.0E-03
4.6E-02
0.01
1.6E-02
2.8E-02
NA
Benzo[b]fluoranthene
3.9E-02
8.3E-03
4.8E-02
0.01
1.7E-02
2.9E-02
NA
Benzo[ghi]perylene
2.9E-02
6.1E-03
3.5E-02
0.01
1.2E-02
2.1E-02
NA
Benzo[k]fluoranthene
3.8E-02
8.0E-03
4.6E-02
0.01
1.6E-02
2.8E-02
NA
Chrysene
4.0E-02
8.4E-03
4.8E-02
0.01
1.7E-02
2.9E-02
NA
Dibenzo[a,h]anthracene
3.1E-02
6.5E-03
3.8E-02
0.01
1.3E-02
2.3E-02
NA
Attachment B
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February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Compound Name
Biotransfer Factors (Bam) (day/kg fresh-weight tissue)
and Metabolism Factors (MF) (unitless)
Mammal
Non-mammal
Beef
{Babeef)
Dairy
(Bddairy)
Pork
[Bdpork)
MF
Eggs
(Bdeggs)
Poultry
(Bdpoultry)
MF
Fluoranthene
4.0E-02
8.5E-03
4.9E-02
0.01
1.7E-02
3.0E-02
NA
Fluorene
2.9E-02
6.1E-03
3.5E-02
0.01
1.2E-02
2.1E-02
NA
lndeno[1,2,3-c,d]pyrene
2.7E-02
5.8E-03
3.3E-02
0.01
1.2E-02
2.0E-02
NA
Source: CalEPA (2012) for arsenic into pork, poultry, and eggs; HHRAP (U.S. EPA 2005a) for all other values.
Note: NA = not applicable; CDD = chlorodibenzo-p-dioxin; CDF = chloridibenzofuran.
NC DEHNR (1997) and U.S. EPA (1994b) recommended a soil IR (Qsfm/, Exhibit B-13) 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 BW. This results in a dry matter intake rate of 11.8 kg DW/day
and a daily soil IR of about 0.5 kg/day. NC DEHNR (1997) and U.S. EPA (1994b) recommended
a Qs(m) 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 BW. This resulted in a daily dry matter intake rate of 20 kg/day
DW, and a daily soil IR of approximately 0.4 kg/day. Uncertainties associated with Qs include
the lack of current empirical data to support soil IRs for dairy cattle and the assumption of
uniform contamination of soil ingested by cattle. NC DEHNR (1997) recommended a QS(m) for
swine of 0.37, estimated by assuming a soil intake that is 8 percent of the plant IR of 4.3 kg
DW/day. Uncertainties include the lack of current empirical data to support soil IRs and the
assumption of uniform contamination of the soil ingested by swine. HHRAP 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 IRs for chicken and the assumption of uniform
contamination of soil ingested by chicken.
The beef cattle IRs of forage, silage, and grain (Qpo,m); Exhibit B-13) 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 BW of 590 kg) and are supported by NC
DEHNR (1997), U.S. 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 IRs for
cattle. The dairy cattle Qp values are based on the total daily intake rate of about 20 kg DW/day
(NAS 1987; U.S. 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. Swine are not
grazing animals and are assumed not to eat forage (U.S. EPA 1998). U.S. EPA (1994b and
1998) and NC DEHNR (1997) recommended including only silage and grains in the diet of
swine. EPA (1995c) recommended an IR of 4.7 kg DW/day for a swine, referencing NAS (1987).
Assuming a diet of 70 percent grain and 30 percent silage (U.S. EPA 1990), HHRAP estimated
Qp values 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. 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
Attachment B
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February 2021
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TRIM-Based Tiered Screening Methodology for RTR
(1987). Uncertainties associated with this variable include the variability of actual grain IRs from
site to site. In addition, assuming uniform contamination of plant materials consumed by chicken
introduces some uncertainty.
Exhibit B-13. Soil and Plant Ingestion Rates for Animals
Animal
Soil Ingestion Rate -
Qs(m) (kg/day)a
Plant Part Consumed
by Animal
Plant Ingestion Rate -
Qpo,m) (kg/day)b
Beef cattle
0.5
Silage
2.5
Forage
8.8
Grain
0.47
Dairy cattle
0.4
Silage
4.1
Forage
13.2
Grain
3.0
Swine
0.37
Silage
1.4
Grain
3.3
Chicken (eggs)
0.022
Grain
0.2
Source: HHRAP (U.S. EPA 2005a) (Chapter 5).
B.6.3 Exposure Parameter Values for Adults and Non-infants
The exposure parameters included in the multimedia ingestion risk methodology and their
default values are summarized in the following subsections. EPA selected the default values to
result in a highly health-protective screening scenario. Also presented are alternatives to the
default values (e.g., typically based on other percentiles from the distribution), which may be
appropriate on a case-by-case basis. These parameter value options were primarily obtained or
estimated from EPA's 2011 Exposure Factors Handbook (EFH) (U.S. EPA 2011a) and 2008
Child-specific EFH (CSEFH) (U.S. EPA 2008a). Where values were reported for age groupings
other than those used in the methodology (see Section B.2.2), time-weighted average values
were estimated for the methodology's age groups from the available data.
IRs for home-produced farm food items were identified for exposed fruit, protected fruit,
exposed vegetables, protected vegetables, root vegetables, beef, total dairy, pork, poultry, and
eggs. Those IRs are already normalized to BW (i.e., gww/kg-day) (U.S. EPA 2011a). The BW
parameter values presented in Exhibit B-14, therefore, are not applied in the chemical intake
(ADD) equations for these food types.
IRs also are identified for drinking water (mL/day), soil (mg/day), and fish (g/day). These IRs,
however, are on a per-person basis (i.e., not normalized for BW). The BW parameter values
presented in Exhibit B-14, therefore, are applied in the chemical intake (ADD) equations for
these media.
B.6.3.1 Body Weights
BW options 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 screen, EPA uses the mean BWfor each age group. The BW values are listed in
Exhibit B-14.
Attachment B
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit B-14. Mean and Percentile Estimates of Body Weight
Lifestage
(years)
Duration
(years)
Body Weight (kg)
Mean
5th
10th
50th
90th
95th
Adult 20 up to 70a
50
80.0
53.6
57.9
79.0
108
119
Child <1b
1
7.83
6.03
6.38
7.76
9.24
9.66
Child 1-2C
2
12.6
9.90
10.4
12.5
14.9
15.6
Child 3-5
3
18.6
13.5
14.4
17.8
23.6
26.2
Child 6-11d
6
36.0
22.1
24.0
33.5
51.2
58.6
Child 12-19e
8
64.2
41.1
44.6
60.9
88.5
98.4
Source, unless otherwise noted: Table 8-3 of U.S. EPA (2011 a) (EFH), which derived the values from 1999-2006 National Health
and Nutrition Examination Survey data. In some cases, as indicated in the footnotes below, the age groupings in the EFH differ
from those shown in this table; in these cases, we used time-weighted averages of the values from the EFH. These time-weighted
averages have uncertainties in cases where the EFH age groupings extend beyond the age group the data were used for (e.g., the
estimation of BWs for Child 6-11 years was estimated using EFH age categories 6 to <11 and 11 to <16 years, as shown below).
Original sample sizes are provided in the EFH table.
aThe adult mean body weight (BW) represents the recommended value for adults from Table 8-1 of the EFH. The EFH defines
adults as 21 years and older, while the methodology used here defines adults as 20 up to 70 years, which we estimate leads to
minimal discrepancies (i.e., less than 1 % BW). For the remaining percentiles for the adult, BW represents a time-weighted average
of BWs for age categories 16 to <21, 21 to <30, 30 to <40, 40 to <50, 50 to <60, and 60 to <70 years (Table 8-3 of the EFH).
bFor Child <1 year, each BW represents a time-weighted average of BWs for age groups birth to <1 month, 1 to <3 months, 3 to <6
months, and 6 to <12 months.
°For Child 1-2 years, each BW represents a time-weighted average of BWs for age groups 1 to <2 years and 2 to <3 years.
dFor Child 6-11 years, each BW represents a time-weighted average of BWs for age groups 6 to <11 years and 11 to <16 years.
eFor Child 12-19 years, each BW represents a time-weighted average BWs for age groups 11 to <16 years and 16 to <21 years.
B.6.3.2 Ingestion Rates for Water
Although exposure through ingestion of contaminated drinking water is not evaluated for RTR
assessments (see Section 2.2 of the main document), the methodology allows for calculation of
chemical ingestion via drinking water obtained from surface-water sources or from wells (i.e.,
from groundwater) in the contaminated area. The 2011 EFH-recommended values for drinking-
water IRs for children are based on a study reported by Kahn and Stralka (2008). Table 3-33 of
the EFH provides consumer-only estimates of community water IRs by age categories, based
on EPA analysis of the 2003-2006 National Health and Nutrition Examination Survey
(NHANES). 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. EPA concluded that some
of these NHANES values were less statistically reliable due to small sample sizes, particularly
for children under 3 years of age. Table 3-15 of the EFH provides consumer-only estimates of
community water IRs by age category, based on the 1994-1996 and 1998 U.S. Department of
Agriculture's (USDA's) Continuing Survey of Food Intakes by Individuals (CSFII) (USDA 2000),
and also based on EPA (2004a) for adults 65 years and older. Although these Table 3-15
values are from an older survey relative to Table 3-33, the values for younger children were
determined to be more statistically reliable in Table 3-15. The recommended values shown in
Exhibit B-15 come from Table 3-15 for the Child <1 and Child 1-2 age groups, and Table 3-33
for the other age groups, with time-average weighting as needed to conform to the required age
groups.
Attachment B
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February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit B-15. Estimated Daily Consumer-only Mean and Percentile Water Ingestion Rates*
Lifestage (years)
Ingestion Rates, Community Water (mL/day)
Mean
50th
90th
95th
99th
Child <1a
504
482
969
1113
1440
Child 1-2b
332
255
687
903
1318
Child 3-5c
382
316
778
999
1592
Child 6-11d
532
417
1149
1499
2274
Child 12—19e
698
473
1641
2163
3467
Adult 20 up to 70f
1219
981
2534
3087
4567
*As discussed in Section 2.2 of the main document, chemical intake from water ingestion is not evaluated for RTR because it is
assumed that individuals are unlikely to use untreated surface water for drinking (or other household water uses). Also, HHRAP
recommends that exposure to groundwater not be evaluated because EPA found that groundwater is an insignificant exposure
pathway for airborne combustion emissions
Source: 2011 EFH (U.S. EPA 2011a), Table 3-15 for Child <1 and Child 1-2 (based on Kahn and Stralka [2008] examination of
the 1994-1996 and 1998 Continuing Survey of Food Intakes by Individuals [USDA 2000]), and Table 3-33 for all other ages
(based on EPA analysis of the 2003-2006 National Health and Nutrition Examination Survey). For some of the age groupings
presented, the values are based on the time-weighted average value for 2 or more age ranges from the source table, as indicated
below. 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.
aEach 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.
bEach IR represents a time-weighted average of ingestion rates for age groups 1 to <2 years and 2 to <3 years.
°Each IR represents the ingestion rate for age group 3 to <6 years.
dEach IR represents a time-weighted average of ingestion rates for age groups 6 to <11 years and 11 to <16 years. Note that
estimated values include children older than 11 years, which contributes to uncertainty in the estimates for 6 to 11 years.
eEach IR represents a time-weighted average of ingestion rates for age groups 11 to <16 years, 16 to <18, and 18 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.
'Each IR represents a time-weighted average of ingestion rates for age groups 18 to <21 years and >21 years. Note that estimated
values include people ages 18-19 years, which contributes to uncertainty in the estimates for people 20 years and older.
B.6.3.3 Ingestion Rates for Local Food
Exhibit B-16 presents mean, median, 90th, 95th, and 99th percentile food-specific IRs for
consumers-only of farm foods for adults and children. The mean and percentile values are from
EPA's analysis of data from the USDA's 1987-1988 NFCS (USDA 1993), as presented in
Chapter 13 of the Agency's 2011 EFH (i.e., Intake of Home-Produced Foods) (U.S. 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 IRs 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.
For the adult age group, data were compiled on food-specific IRs separately for two types of
households as indicated in the "Response to Questionnaire" (U.S. EPA 2011a, Chapter 13):
(1) households that farm (F) and (2) households that garden or raise animals (HG, for home
gardener). This division reflects EPA's data analysis. EPA tabulated IRs for fruits and
vegetables separately for F households and HG households. Similarly, EPA tabulated IRs for
animals and animal products for F households and HG households. Thus, F households
represent farmers who may both grow crops and raise animals and who are likely to consume
more homegrown/raised foods than HG households. HG households represent the non-farming
households that may consume lower amounts of homegrown or home-raised foods (i.e., HG
encompasses both households that garden and households that raise animals). The food-
Attachment B
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TRIM-Based Tiered Screening Methodology for RTR
specific IRs are based on the amount of each food type that each household produced and
brought into their homes for consumption and the number of persons consuming the food. EPA
averaged the actual IRs for homegrown foods over the 1-week survey period.
For children, EPA estimated food-specific IRs for four age categories (U.S. 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-16. Summary of Age-group-specific Ingestion Rates for Farm Foods
Product
Child (age in years)
Adult
(20 up to 70
years)
<1
1-2
3-5
6-11
12-19
Mean ingestion rates
(g/kg-day)
Beef
NA
4.14
4.00
3.77
1.72
1.93
Dairy
NA
91.64
50.91
27.36
13.63
2.96
Eggs
NA
2.46
1.42
0.86
0.58
0.61
Exposed Fruit
NA
6.14
2.60
2.52
1.33
1.19
Exposed Vegetable
NA
3.48
1.74
1.39
1.07
1.38
Pork
NA
2.23
2.15
1.50
1.28
1.10
Poultry
NA
3.57
3.35
2.14
1.50
1.37
Protected Fruit
NA
16.64
12.36
8.50
2.96
5.19
Protected Vegetable
NA
2.46
1.30
1.10
0.78
0.86
Root Vegetable
NA
2.52
1.28
1.32
0.94
1.03
Median ingestion rates (g/kg-day)
Beef
NA
2.51
2.49
2.11
1.51
1.55
Dairy
NA
124.63
65.98
34.43
15.46
2.58
Eggs
NA
1.51
0.83
0.56
0.43
0.47
Exposed Fruit
NA
5.03
1.82
1.11
0.61
0.68
Exposed Vegetable
NA
1.89
1.16
0.64
0.66
0.81
Pork
NA
1.80
1.49
1.04
0.89
0.80
Poultry
NA
3.01
2.90
1.48
1.30
0.92
Protected Fruit
NA
7.59
5.94
3.63
1.23
2.08
Protected Vegetable
NA
1.94
1.04
0.79
0.58
0.56
Root Vegetable
NA
0.92
0.46
0.52
0.57
0.63
90th percentile ingestion rates (g/kg-day)
Beef
NA
9.49
8.83
11.40
3.53
4.41
Dairy
NA
185.34
92.45
57.37
30.92
6.16
Eggs
NA
4.90
3.06
1.90
1.30
1.31
Exposed Fruit
NA
12.70
5.41
6.98
3.41
2.37
Exposed Vegetable
NA
10.70
3.47
3.22
2.35
3.09
Attachment B
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TRIM-Based Tiered Screening Methodology for RTR
Product
Child (age in years)
Adult
(20 up to 70
years)
<1
1-2
3-5
6-11
12-19
Pork
NA
4.90
4.83
3.72
3.69
2.23
Poultry
NA
7.17
6.52
4.51
3.13
2.69
Protected Fruit
NA
44.80
32.00
23.31
7.44
15.14
Protected Vegetable
NA
3.88
2.51
2.14
1.85
1.81
Root Vegetable
NA
7.25
4.26
3.83
2.26
2.49
95th percentile ingestion rates (g/kg-day)
Beef
NA
12.86
12.47
12.50
3.57
5.83
Dairy
NA
166.67
89.94
55.97
32.25
7.80
Eggs
NA
5.38
3.62
2.37
1.43
1.59
Exposed Fruit
NA
14.60
6.07
11.70
4.78
3.38
Exposed Vegetable
NA
11.90
6.29
5.47
3.78
4.46
Pork
NA
6.52
6.12
4.73
6.39
2.60
Poultry
NA
8.10
7.06
5.07
3.51
3.93
Protected Fruit
NA
48.28
35.11
26.86
11.40
19.16
Protected Vegetable
NA
9.42
5.10
3.12
2.20
2.83
Root Vegetable
NA
10.40
4.73
5.59
3.32
3.37
99th percentile ingestion rates (g/kg-day)
Beef
NA
20.90
19.76
13.30
4.28
6.84
Dairy
NA
180.48
87.17
54.83
34.70
9.20
Eggs
NA
16.17
11.24
8.19
4.77
1.83
Exposed Fruit
NA
25.15
32.50
15.70
5.90
12.96
Exposed Vegetable
NA
12.10
7.36
13.30
5.67
8.42
Pork
NA
8.71
9.74
6.61
4.29
3.87
Poultry
NA
9.63
10.24
6.12
4.60
4.93
Protected Fruit
NA
109.30
71.20
58.17
19.10
34.42
Protected Vegetable
NA
9.42
5.31
5.40
2.69
5.56
Root Vegetable
NA
10.40
4.73
7.47
5.13
7.57
Notes: NA = not applicable; the 90th percentile values are the default ingestion rates for RTR screening assessments and
chemical threshold calculations.
Sources: 2011 EFH (U.S. EPA 2011a). Tables 13-25 (dairy), 13-33 (beef), 13-40 (eggs), 13-51 (pork), 13-52 (poultry), 13-58
(exposed fruit), 13-59 (protected fruit), 13-60 (exposed vegetable), 13-61 (protected vegetable), and 13-62 (root vegetable). The
primary source for values was the 1987-1988 Nationwide Food Consumption Survey (USDA 1993). For all but dairy, 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 the Continuing Survey of Food Intakes by Individuals (USDA 2000)—see Tables
3-23a (beef), 3-6a (eggs), 3-24a (pork), 3-25a (poultry), 3-14a (exposed fruit), 3-15a (protected fruit), 3-11a (exposed vegetable),
3-12a (protected vegetable), and 3-13a (root vegetable). For dairy, 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 Table
11 -4 (based on the 2003-2006 National Health and Nutrition Examination Survey).
For some food types and age categories, there were insufficient data for EPA to provide
consumer-only IRs (i.e., the dataset for the subpopulation consisted of fewer than 20
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observations). The HHRAP methodology, Section 6.2.2.2 (U.S. EPA 2005a), recommends a
method by which to calculate the "missing" age-specific consumer-only IRs, as explained below.
Food-specific 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, 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 (U.S. 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 U.S. EPA 2003c)—IRPC_total.
The ratio of IRpc, age_group_x to IRpc totai from the CSFII data shows the IR of a particular food type
by a specific age group relative to the IR for that food type for the population as a whole. The
ratio of IRco, age_group_x to IRcojotai, that is the IR of a particular food type by a specific age group
(consumers only) relative to the IR 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-58 was used to calculate the "missing" age-specific consumer-only IRs\
Equation B-58. Age-group-specific and Food-specific Ingestion Rates
ip _ IRCOJotal x 'RFC, age_group_x
'~CO, age_group_x ~ .p
PC_total
where:
Mean or percentile-specific consumer-only intake of the food type from all
sources, as consumed, for the specific child age group X
Mean or percentile-specific consumer-only intake of the farm food, as brought
into the home, for the total Nationwide Food Consumption Survey population
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
Mean or percentile-specific per capita intake of the farm food item for the total
CSFII survey population
IRco, age_group_x ~
IRco_ total —
IRpc, age_group_x ~
IRpc_ total —
In this discussion, per capita (as opposed to consumer-only) indicates the IRs 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 IRs is 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 IR for a food type for a
specific age group (consumers only) has the same shape as the distribution of the IR 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
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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 IRs based on
only a few respondents in the tails of the distributions. The default IRs for this methodology are
the 90th percentiles, which are likely more reliable than the 95th or 99th percentile estimates in
this particular calculation.
B.6.3.4 Ingestion Rates for Local Fish
Screening Scenario
The USDA's 1987-1988 NFCS (USDA 1993, 1994a), as presented in Chapter 13 of the
Agency's 2011 EFH (i.e., Intake Rates for Various Home Produced Food Items) (U.S. EPA
2011a), includes IRs by age category for family-caught fish. There are several disadvantages,
however, to using that data source to estimate fish IRs. First, due to inadequate sample sizes,
EPA did not report fish IRs for children less than 6 years of age. Second, the NFCS data were
collected more than three decades ago. Third, the reported fish IRs are for ages 6 to 11 and 12
to 19 and are based on 29 and 21 individuals in each age category, respectively (U.S. EPA
2011a, Table 13-20). Finally, the IRs from NFCS data are based on total weight offish as
brought into the home, and do not include losses from preparation of the fish (i.e., removal of
inedible parts and, possibly, the skin). Estimates of preparation losses for fish, intended to apply
to the NFCS fish IR data, are very uncertain and are based on squid and a wide variety of
freshwater, estuarine, and marine fish (U.S. 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, IRs that are reflective of subsistence fisher IRs are desired.
Therefore, a more recent survey was sought that included larger sample sizes, data for children
younger than six years, IRs for the parts of fish actually consumed, and IRs reflective of
subsistence fishers.
Taking all of these issues into consideration, the selected default IR of fish for adults is 373
g/day, which is the estimated 99th percentile offish IRs 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 IRs 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 IR for this group of subsistence fishers is not the highest fish IR available for use by EPA, 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
IR 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 childbearing age are located throughout the United States.
Finally, using a high-end (subsistence) fish IR is consistent with section 112 of the CAA, which
focuses on risks associated with maximally exposed individuals.
Because Burger (2002) did not estimate fish IRs for children, another data source was needed
to develop IRs for the child age categories. The child IRs need to be consistent with the Burger
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IR for adults, reflective of subsistence-fisher IRs, and based on adequate sample sizes. To
satisfy these requirements, data on IRs for children from EPA's Estimated Per Capita Fish
Consumption in the United States (U.S. EPA 2002) were selected for use. Specifically, the
estimated 99th percentile of as-prepared, consumer-only IRs for finfish plus shellfish were
selected (see Section 4.2.1.1, Table 5 of U.S. EPA 2002). The original data were collected as
part of the 1994-1996 and 1998 CSFII (USDA 2000) and do not require additional consideration
of cooking and preparation losses.
Because the child age categories used in the methodology differ from the CSFII age categories
presented in U.S. EPA (2002), the CSFII data were adjusted. The CSFII data did not provide
IRs for the 1-2-year age category. To estimate IRs for this age group, EPA used the IR for the
3-5-year age category, scaled downward by the ratio of the mean BW of the 1-2-year age
category to the mean BW of the 3-5-year age category. Because the methodology 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-year age categories, time-weighted-average IRs were calculated based on the
CSFII IRs. Exhibit B-17 provides the fish IRs used in the screen.
Exhibit B-17. Ingestion Rates for Fish, as used in the Screening Scenario
Fish Ingestion Rates (g/day)
Infants
<1 year
Child
1-2 years
Child
3-5 years
Child
6-11 years
Child
12-19 years
Adult
20 up to 70 years
NA
107.703
158.99b
268.19c
331.01c
373d
Sources: Ages up through 19 years: U.S. EPA (2002) (Section 4.2.1.1 Tables 4 and 5 [freshwater/estuarine habitat]); ages 20 years
and above: Burger (2002).
Note: NA = not applicable (it is assumed that children <1 year of age do not consume fish).
aA fish-ingestion rate for ages 1-2 years was not available. The value represents the consumer-only fish-ingestion rate for ages 3-5
years from U.S. EPA (2002), scaled down by the ratio of the mean Child 1-2 body weight to the mean Child 3-5 body weight.
This value represents the consumer-only fish-ingestion rate for ages 3-5 years from U.S. EPA (2002).
These values represent time-weighted-average consumer-only fish ingestion rates based on ingestion rates from U.S. EPA (2002).
This value represents the 99th percentile ingestion rate of wild-caught fish for women, as reported by Burger (2002).
Alternative Values
EPA's 2002 analysis of freshwater and estuarine fish ingestion data from the CSFII for the years
1994-96 and 1998 was chosen to provide fish IR options by age category (U.S. EPA 2002).
Although the fish consumption rates reported in the CSFII include all sources (commercial and
self-caught), for purposes of screening-level assessments of risk, it was assumed that all
freshwater and estuarine fish consumed are self-caught. The inclusion of commercially obtained
and estuarine fish could overestimate IRs of locally caught freshwater fish for most populations
in the United States; however, these IRs also could underestimate IRs of locally caught fish 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,
assessors are encouraged to use more locally relevant data when available.
For children, EPA identified values for the mean and the 90th, 95th, and 99th percentile fish per-
capita IRs (freshwater and estuarine fish only) based on EPA's analysis of 1994-96 and 1998
CSFII data (U.S. EPA 2002, 2008a). Those rates include individuals who eat fish and those who
do not eat fish.
As shown in Table 10-7 of EPA's 2008 CSEFH (U.S. EPA 2008a), the 90th percentile per-capita
IRs estimated from the two-day CSFII recall period are zero for some child age groups.
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Although not presented in CSEFH Table 10-7, median IRs for all child age groups would be
zero (considering the "consumer-only" sample sizes [CSEFH Table 10-9] relative to the "per-
capita" sample sizes in Table 10-7). The high-percentile fish IRs that are zero result from the
short duration of the CSFII recall period (two days) compared with the AT of interest (a year)
and the relatively infrequent consumption of fish (e.g., on the order of once a week to once a
month or less) compared with the near daily ingestion of other types of food products (e.g.,
dairy, produce, meat).
Use of zero for fish IRs, however, is not useful. As a result, an alternative method was used to
estimate fish IRs for children and adults that could provide reasonable, non-zero values for all
age groups and percentiles. The alternative, age-group-specific fish IRs were derived using
values for each age group, y:
. Mean or other appropriate percentile consumer-only fish IRs for age group y, IRco.y,
from EPA's Estimated Per Capita Fish Consumption in the United States (U.S. EPA
2002, Section 5.2.1.1, Table 5, for freshwater/estuarine habitat).23
. 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 (U.S. EPA 2008a) and U.S.
EPA (2002).
Equation B-59 was used to calculate the alternative, per-capita fish IRs by age group (IRpc.y):
Equation B-59. Alternative Age-group-specific Fish Ingestion Rates
'RpC,y = '^CO,y X FpC,y
Per-capita fish ingestion rate for age group y (g/day)
Consumer-only fish ingestion rates for age group y (g/day) (U.S. EPA 2002,
Section 5.2.1.1, Table 5, for freshwater/estuarine habitat)
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) (U.S. EPA 2008a, 2002)
where:
IRpc.y =
IRco.y =
Fpc.y =
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 fish. Here, per-capita
ingestions are recommended by the HHRAP methodology because no consumer-only
percentile-specific intakes are provided for the different age groups.
23Most of these data also are provided in Table 10-9 of the CSEFH; the median values, however, are not presented in
the CSEFH, and values for the mean and all other percentiles are slightly different due to rounding.
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The mean and percentile consumer-only fish IRs for children and adults and the fraction of the
population consuming freshwater/estuarine fish used in calculating long-term per capita fish IRs
by age group are presented in Exhibit B-18 and Exhibit B-19. The mean and percentile per-
capita fish IRs estimated using the methodology are summarized in Exhibit B-20. The fish IRs
provided in Exhibit B-20 are intended to represent the harvest and consumption of fish in
surface waters in a hypothetical depositional area. For site-specific assessments, more
localized survey data may be more appropriate to estimate fish IRs. 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 B-17, in developing the screening threshold
emission rates, health-protective fish IRs for child and adult fish consumers that more closely
represent exposures of a high-end recreational fisher were used.
As noted in Section B.6.4.3, if the fish IRs shown in Exhibit B-20 are replaced with fresh-weight
as-caught values (e.g., values obtained from a local creel survey), the assessor 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 B.6.4.3.
Exhibit B-18. Daily Mean and Percentile Consumer-only Fish Ingestion Rates
(IRco,y)
Lifestage (years)
Ingestion Rates, All Fish (g/day)
Mean
50th
90th
95th
99th
Child <1
NA
NA
NA
NA
NA
Child 1-2a
27.31
15.61
64.46
87.60
138.76*
Child 3-5b
40.31
23.04
95.16
129.31
204.84*
Child 6-11c
61.49
28.46
156.86*
247.69*
385.64*
Child 12-19d
79.07
43.18
181.40*
211.15*
423.38*
Adult 20 up to 70e
81.08
47.39
199.62*
278.91
505.65*
Sources: U.S. EPA (2002) (Section 5.2.1.1 Table 5 [freshwater/estuarine habitat]), 2008 CSEFH (U.S. EPA 2008a).
Notes: NA = not applicable (it is assumed that children <1 year of age do not consume fish). Per-capita fish-ingestion rates (IRs)
for children by age group are available from Chapter 10 of the 2008 CSEFH (U.S. EPA 2008a); however, all 50th and some 90th
percentile IRs are zero. Per-capita fish IRs were therefore estimated as described in Equation B-59 to provide reasonable, non-
zero values for all age groups and percentiles.
"The sample size for this value does not meet minimum reporting requirements as described in U.S. EPA (2002). Owing to the
small sample sizes, these upper-percentiles value are highly uncertain.
aA fish IR for ages 1-2 years was not available. The value represents the consumer-only fish IR for ages 3-5 years from U.S. EPA
(2002), scaled down by the ratio of the mean Child 1-2 body weight to the mean Child 3-5 body weight.
bThese values represent the consumer-only fish IR for ages 3-5 years from U.S. EPA (2002). Sample size = 442.
These values represent the consumer-only fish IR for ages 6-10 years from U.S. EPA (2002). Sample size = 147.
dThese values represent the time-weighted-average per-capita fish IR for ages 11-15 and 16-17 years from U.S. EPA (2002); the
value may underestimate ingestion rate for ages 12-19 years. Sample size = 135.
eThese values represent the consumer-only fish IR for individuals 18 years and older from U.S. EPA (2002). Sample size = 1,633.
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit B-19. Fraction of Population Consuming Freshwater/Estuarine Fish
on a Single Day (Fpc,y)
Lifestage (years)
Fraction Consuming Fish
Child 3-5
0.0503
Child 6-11
0.0440
Child 12-19
0.0493
Adult 20 up to 70
0.08509
Sources: U.S. EPA (2002) (Section 5.1.1.1 Table 4), 2008 CSEFH (U.S. EPA 2008a).
Note: Values were calculated using the sample size for consumers only of the age group 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. For the Child 12-19 lifestage, the
calculation uses the sum of the ages 11-15 and 16-17. For the Adult lifestage, the calculation uses ages 18 and older.
Exhibit B-20. Long-term Mean and Percentile Per-capita Fish Ingestion Rates (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
Adult 20 up to 70e
6.90
4.03
16.99
23.73
43.02
Sources: U.S. EPA (2002, 2008a).
NA = not applicable (it is assumed that children <1 year of age do not consume fish).
aValues were calculated as (consumer-only IR for Child 1-2) * (fraction of population consuming fish for Child 3-5).
bValues were calculated as (consumer-only IR for Child 3-5) * (fraction of population consuming fish for Child 3-5).
°Values were calculated as (consumer-only IR for Child 6-11) * (fraction of population consuming fish for Child 6-11).
dValues were calculated as (consumer-only IR estimated for Child 12-19) * (fraction of population estimated to consume fish for
Child 12-19).
eValues were calculated as (consumer-only IR for Adults) * (fraction of population consuming fish for Adults).
Exhibit B-21 provides mean and the 90th percentile fish IRs for recreational fishers, black and
female recreational fishers, and fishers of Hispanic, Laotian, and Vietnamese descent. 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 (U.S. EPA 2011a). IRs
for black and female recreational fishers are presented in Burger (2002). The fish IRs 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. They reported
mean and 95th percentile IRs for each subpopulation. In part due to the low sample size in the
Shilling study (n = 30-45), 95th percentile values were believed to be unrealistically high. The
90th percentile IR estimates presented in Exhibit B-21 for Hispanic, Laotian, and Vietnamese
fishers were derived by EPA using information from Shilling etal. (2010) and U.S. EPA (2010).
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Exhibit B-21. Mean and 90th Percentile Per-capita Fish Ingestion Rates for Populations of
Recreational Fishers (IRpc,y)
Subpopulation
Percentile
Units
Recrea-
tional
Fisher3
Female
Recrea-
tional
Fisherb
Black
Recrea-
tional
Fisherb
Hispanic
Recrea-
tional
Fisher0
Laotian
Recrea-
tional
Fisher0
Vietnamese
Recrea-
tional
Fisher0
Mean
g/day
8
39.1
171
25.8
47.2
27.1
90th
g/day
11
123
446
98
144.8
99.1
aSource: 1997 EFH (U.S. EPA 1997a)
bBurger (2002) weights are "as consumed" for locally caught fish.
°Source: Shilling et al. (2010).
RTR multipathway assessments to date have used whole-fish concentrations estimated by
TRIM.FaTE (or by application of BAF and BSAF values for arsenic). 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 fish fillets equal to 2.6 percent for TL3 and 3.0
percent for TL4 (Table 6-9 in U.S. EPA 2003b). If an assessor wishes to account for reduced
chemical concentration in fillet compared with whole fish for lipophilic chemicals, they can use a
"preparation" loss of chemical (see Section B.6.4).
For lipophilic chemicals (e.g., log Kow greater than 4), which partition primarily into the fatty
tissues of fish, much of the higher concentration tissues might be stripped from the fish during
preparation (e.g., belly fat, viscera which includes fat in liver, etc., fat under skin). The degree to
which the concentration of chemical in a fillet is less than the average total concentration in the
whole fish is chemical specific. Assuming 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.
B.6.3.5 Soil Ingestion Rates
Adult gardeners and farmers may incidentally ingest soils from gardening activities and from soil
particles that adhere to exposed fruits and exposed and belowground 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. Both adults and children also may incidentally
ingest indoor dust. Exhibit B-22 includes soil and dust IR options by age group for these types of
exposures. Exhibit B-22 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), nor does it include options for geophagy, or the intentional ingestion of earths, which
is usually associated with cultural practices (i.e., on the order of 50,000 mg/day) (U.S. EPA
2008a, 2017b).
Data on soil and dust IRs are sparse; the soil and dust IRs listed in Exhibit B-22 are based on
limited data. The studies evaluated by EPA for children generally focused on children between
the ages of 1 and 6 years and were not specific to families that garden or farm. To be health-
protective, the default IRs are the EFH General Population values for Soil + Dust (Table 5.1,
U.S. EPA 2017b).
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Applying the Soil + Dust IR for the general population better reflects the risk associated with
chronic exposure than applying a daily-peak IR associated with soil pica or geophagy. EPA's
soil pica and geophagy IRs are likely to represent acute, high-end soil-ingestion episodes or
behaviors at an unknown point on the high end of the distribution of soil ingestion. Moreover,
most of the key studies used to develop the soil IRs were tracer-element studies that might not
represent long-term behavior. EPA's HHRAP (U.S. EPA 2005a) excluded soil pica, in part,
because the behavior is "temporary."
Exhibit B-22. Daily Mean and Percentile Soil and Dust Ingestion Rates
Age Group (years)
Soil and Dust Ingestion Rate (mg/day)
Mean3
50tha
90thb
95thb
99thb
Child <1c
NA
Child 1-2
90
90
200
200
200
Child 3-5
60
60
200
200
200
Child 6-11
60
60
200
200
200
Child 12-19
30
30
200d
200d
200d
Adult 20 up to 70
30
30
200d
200d
200d
NA = not applicable
Sources: 2017 EFH (U.S. EPA 2017b). Child 1-2 values here are taken from the Table 5-1 category of 1 to <2 years; Child 3-5 from
the category of 2 to <6 years; Child 6-11 from the category of 6 to <12 years; Child 12-19 and Adult from the category of >=12.
aFor mean and 50th percentile soil ingestion rates, value represents a "central tendency" estimate for soil + dust ingestion from EPA's
2017 EFH, Chapter 5, Table 5-1.
bValues are the recommended "upper percentile" estimate for soil + dust ingestion from EPA's 2017 EFH, Chapter 5, Table 5-1.
Estimates for children <1 year in the 2017 EFH are not based on measured tracers and so are not included because of the high-level
of uncertainty associated with these IRs. The EFH considered biokinetic modeling for 4 children <6 months and biokinetic modeling
blood lead levels in 31 children 6 months to 1 year from one location near a lead smelting facility.
dValue represents "adults following a traditional rural or wilderness lifestyle", as described in footnote j to EPA's 2017 EFH, Chapter 5,
Table 5-1. This value was selected to better represent potentially higher ingestion rates for the farmer and gardener scenarios.
B.6.3.6 Total Food Ingestion Rates
Although the multimedia ingestion risk methodology was developed to perform deterministic
screening-level exposure and risk assessments, total food IRs could be included if the
methodology is adapted for a probabilistic assessment. In particular, the total food IRs
presented in Exhibit B-23 could be used to normalize or to truncate the sum of food-specific IRs
to ensure reasonable values. This procedure is particularly important when chemical intake from
multiple upper-percentile food IRs for different types of food are added together. Individuals
representing the upper-percentile IR for one food category might not be the same individuals
who reported high-percentile IRs for one or any of the other food categories.
Exhibit B-23. Daily Mean and Percentile Per Capita Total Food Intake
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
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Lifestage (years)
Percent of Group
Consuming Food
Mean
50th
90th
95th
99th
Adult 20 up to 70f
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
Adult 20 up to 70s
100%
14.8
13.9
23.7
27.6
35.5
Sources: U.S. EPA (2005e), 2008 CSEFH (U.S. EPA 2008a).
aThese 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.
bThese 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.
These values were obtained from Table 14-3 of the 2008 CSEFH (age group 3 to <6 years, N = 4,112).
dThese values were obtained from Table 14-3 of the 2008 CSEFH (age group 6 to <11 years, N = 1,553). These values represent
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.
eThese 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.
'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 the Continuing Survey of Food Intakes by Individuals.
9These 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 the Continuing Survey of Food Intakes by Individuals.
hPercents consuming foods from Table 14-3 of the 2008 CSEFH include: 67.0% (birth to <1 month); 74.7% (1 to <3 months);
93.7% (3 to <6 months); and 99.7% (6 to <12 months). Infants under the age of 1 that consume breast milk are classified as "non-
consumers" of food.
B.6.4 Other Exposure Factor Values
The other exposure parameters included in the algorithms are exposure frequency (Section
B.6.4.1), fraction of the food type obtained from the contaminated area (Section B.6.4.2), and
reduction in the weight of the food types during preparation and cooking (Section B.6.4.3). For
the breast milk ingestion pathway, additional exposure parameters are included in the
algorithms (Section B.6.5).
B.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.
The default value for EF is 350 days/year for all exposure sources and all potential receptors.
This assumption is consistent with the food IRs (i.e., daily intake rates equivalent to annual
totals divided by 365 days) and does not imply that residents necessarily consume home-
produced food products every day of the year.
If an assessor wishes to evaluate daily intake rates based on shorter ATs, they can replace both
the food-specific IRs and the EF for each homegrown food product. For example, they may
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 homegrown produce is seasonal.
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B.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 assessment scenario.
For RTR screening assessments, the default FC is 1, assuming that households that farm,
garden, or raise animals produce 100 percent of the food product consumed, and 100 percent
of the fish consumed is home caught. The assessor can vary this default FC value for individual
food products to tailor the assessment to a particular exposure scenario.
B.6.4.3 Preparation and Cooking Losses
Food preparation and cooking losses are included in the calculations of exposure to farm foods
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 IRs calculated from the USDA 1987-1988 NFCS (USDA
1993, 1994a) are based on the weight of homegrown 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: (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]). The
methodology uses 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-24. 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 EFHs (U.S. EPA 1997a, 2011a).
Exhibit B-24. Fraction Weight Losses from Preparation of Various Foods
Product
Mean Cooking, Paring, or
Preparation Loss
(Cooking Loss Type 1 [L1 ]) (unitless)a
Mean Net Post Cooking
(Cooking Loss Type 2 [L2])
(unitless)b
Exposed Fruit0
0.244
0.305
Exposed Vegetable
0.162d
NA
Protected Fruit
0.29e
NA
Protected Vegetable
CO
CO
o
o
NA
Root Vegetable9
0.075
0.22
Beef
0.27
0.24
Pork
0.28
0.36
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Product
Mean Cooking, Paring, or
Preparation Loss
(Cooking Loss Type 1 [L1 ]) (unitless)3
Mean Net Post Cooking
(Cooking Loss Type 2 [L2])
(unitless)b
Poultry
0.32
0.295h
Fish'
0.0
0.0
Note: NA = Not Available.
aFor 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.
bFor 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.
These values represent averages of means for all fruits with available data (except oranges) [Table 13-6 of 1997 EFH: U.S. EPA
(1997a)].
dThis value represents an average of means for all exposed vegetables with available data (Table 13-7 of 1997 EFH). Exposed
vegetables include asparagus, broccoli, cabbage, cucumber, lettuce, okra, peppers, snap beans, and tomatoes.
eThis value was set equal to the value for oranges (Table 13-6 of 1997 EFH).
this value represents an average of means for all protected vegetables with available data (Table 13-7 of 1997 EFH). Protected
vegetables include pumpkin, corn, peas, and lima beans.
9These values represent averages of means for all root vegetables with available data (Table 13-7 of 1997 EFH). Root vegetables
include beets, carrots, onions, and potatoes.
This value represents an average of means for chicken and turkey (Table 13-5 of 1997 EFH).
'If the assessor 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
[Table 13-69 of 2011 EFH: U.S. EPA (2011 a)].
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 IRs is not the USDA's 1987-1988 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 are based on parts actually consumed, and so no loss processes for
preparation are needed.
If the assessor uses fish IRs 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), they should also use
non-zero values for the L1 and L2 parameters.
B.6.4.4 Food Preparation/Cooking Adjustment Factor 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 IRs 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, a food preparation/cooking adjustment factor (FPCAF) can be applied to the
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concentration in uncooked fish to estimate a concentration in cooked fish. The following
sections discuss FPCAFs for each of the four PB-HAPs.
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 (U.S. EPA 2011b),
an FPCAF of 1.5 was used to adjust MeHg concentrations in consumed fish (i.e., a 50-percent
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).
Arsenic
Similar to mercury, arsenic 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 arsenic.
Cadmium
Similar to mercury and arsenic, 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.
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 pentachlorodibenzo-p-dioxin (PCDD) and
pentachlorodibenzofuran (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 64-72 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
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with 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.
Polycyclic Organic Matter
While it is reasonable to assume that there might be losses of lipophilic POM during the cooking
process, there is insufficient information to distinguish what the net loss (or gain) during cooking
might be because cooking can create POM 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 POM.
B.6.5 Breast-Milk Infant Exposure Pathway Parameter Values
Values used for parameters in the breast-milk exposure pathway algorithms (see Section B.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
scenario-specific or for which empirical values are required, the default values are listed. For
parameters for which algorithms calculate values, the appropriate equation is listed. Scenario-
and receptor-specific parameters are discussed in Section B.6.5.1 and chemical-specific
parameters are discussed in Section B.6.5.2.
B.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-25 lists the parameters and their default values. The text that follows
describes the recommended value or alternative values for each exposure parameter needed to
calculate the infant absorbed chemical intake rate, or DAImf. For parameter values that can be
estimated when empirical values are not available, see the equation description in Section B.3.4
of this attachment.
Exhibit B-25. Scenario- and Receptor-specific Input Parameter Values Used to Estimate
Infant Exposures via Breast Milk
Parameter
Description
Default Value
AT
Averaging time for infant's exposure via breast milk, i.e., duration of
nursing (days)
= ED
BWinf
Body weight of infant (kg) averaged over duration of nursing exposure
7.8
BWmat
Body weight of mother (kg) averaged over duration of mother's exposure
66
DAI mat
Daily absorbed intake of chemical by mother (mg/kg-day)
Equation B-38
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
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Parameter
Description
Default Value
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
AT and 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 ADD equation.
Infant BW (BWmf). The assessor selects a value for BH/W, the time-weighted average BW of the
infant over the 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 assessor should select the BW
for an infant that is averaged from birth to the first birthday. Similarly, if an infant breast feeds for
6 months, the assessor should select the BW for an infant that is averaged from birth to six
months. Because the default breast feeding duration (fc/) is one year (i.e., 365 days), the default
infant BW is 7.8 kg, which is the time-weighted average for the mean infant BW between birth
and the first birthday from EPA's 2008 CSEFH (U.S. EPA 2008a). Exhibit B-26 presents
additional percentile values for the infant BW parameter that may be appropriate for some
assessments.
Exhibit B-26. Average Body Weight for Infants
Statistic
Oto <6
months (kg)
Oto <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
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.
aDefault value used for RTR assessments.
Maternal BW (BWmat). This parameter represents the BW of the mother averaged over the
entire duration of the mother's exposure to the chemical of concern. The maternal BW is
needed to calculate the biological elimination constant for the lipophilic chemical in lactating
women (kfat_eiac). The methodology assumes that the mother will be pregnant for 9 months
(i.e., 0.75 year) and will be lactating for 1 year. The recommended default maternal BW 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 default
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BWmat of 66 kg is based on CSFII data compiled by EPA for non-lactating and non-pregnant
women between the ages of 15 and 44 (i.e., women of child-bearing age), lactating women, and
pregnant women (U.S. EPA 2004a). Exhibit B-27 presents additional values for the maternal
BW parameter which might be appropriate for some assessments. The BWmat value is not the
value that the methodology uses to estimate the mother's absorbed daily intake (DAImat). The
daily IRs for homegrown/raised food products are for men and women combined, with the rates
normalized to BW. The IRs for soil, water, and fish are not normalized to BW but are based on
both men and women. For those IRs, the methodology uses an average BW value for males
and females to estimate the ADD (intake) of the chemical in mg/kg-day. These values are
subject to the assumption that the body-weight normalized IRs and resulting ADD values are
applicable to nursing mothers.
Exhibit B-27. 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: U.S. EPA (2004a).
aDefault value.
Exposure duration (ED). See discussion of AT and ED above.
Fraction of mother's whole blood that is plasma (fhn). Steinbeck (1954) reported that plasma
volume accounts for approximately 60 percent of the total blood volume in non-lactating human
females (U.S. EPA 1998). Harrison (1967) and Ueland (1976) reported plasma volumes
between 63-70 percent in postpartum women (U.S. EPA 1998). The default value of 65 percent
(0.65) is the value recommended by EPA in its MPE (U.S. EPA 1998).
Fraction of mother's BW that is fat (ftm). 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-40, 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 (U.S. 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 (U.S. EPA 2001a). Studies cited in ATSDR's toxicological profile for chlorinated dibenzo-
p-dioxins 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) (U.S. EPA 1998). A fraction of 0.3 indicates that 30
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percent of the mother's BW is fat, which is a health protective value (U.S. EPA 2001a). To
establish a health protective screening scenario, a default value for frm of 0.30 is used.
Fraction of fat in mother's breast milk (fmbm). The Cm-mat model (Equation B-40) 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 etal. 1994, McLachlan 1993, Bates etal. 1994, NAS 1991, Butte
etal. 1984, Maxwell and Burmaster 1993, U.S. EPA 2011a, Smith 1987, Sullivan etal. 1991).
The default value for fmbm of 0.04 (i.e., 4 percent) is the value EPA recommended for MPE (U.S.
EPA 1998).
Fraction of maternal weight that is plasma (fnm). Altmann and Dittmer (1964) estimated that
plasma volume for adult women ranged from 37 to 60 ml_/kg of BW and averaged about 45
ml_/kg. Ueland (1976) observed that the average plasma volume of women 6 weeks postpartum
was 45 ml_/kg of BW. 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 (U.S.
EPA 1998). The default value for fpm therefore is 0.046.
Infant breast milk IR (IRmiik). Milk IRs 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 IRs for infants that nurse for six
and for twelve months (U.S. 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 IRs from those (U.S. EPA 2011a). EPA estimated an
upper-percentile (upper-bound) value as the mean plus two standard deviations. The IRs,
measured volumetrically (mL/day), are converted 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 U.S.
EPA 2011a). The resulting values are shown in the first two rows of Exhibit B-28. The
screening-level default value of 980 mL/day is an upper-bound estimate based on a one-year
nursing period.
Exhibit B-28. Infant Breast Milk Intake Rates
Age Category
Average
(mL/d)
Average
(kg/d)
"Upper Bound"
(mL/d)
"Upper Bound"
(kg/d)
Reference
1 to 6 months
742
0.764
1,033
1.064
U.S. EPA 2011ab
0 to <12 months
688
0.709
980a
1.01a
U.S. EPA 2011ab
0 to <1 month
510
0.525
950
0.979
U.S. EPA 2008ac
1 to <3 months
690
0.711
980
1.01
U.S. EPA 2008ab
3 to <6 months
770
0.793
1,000
1.03
U.S. EPA 2008ab
6 to <12 months
620
0.639
1,000
1.03
U.S. EPA 2008ab
aDefault; bBased on review of multiple studies; °Based on a single study.
Exhibit B-28 also includes the recommended values for four non-overlapping age categories
from the CSEFH (U.S. EPA 2008a, Table 15-1). The values demonstrate that although infants
grow substantially from birth to one year of age, the "upper bound" estimates of their milk IRs
are very close to 1 liter per day at all stages of development in the first year.
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TRIM-Based Tiered Screening Methodology for RTR
Duration of breast feeding (thf). This parameter is equal to the infant's ED and the infant's 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) (U.S. 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 (U.S. EPA 2001a). Nonetheless, to establish a
health protective screening scenario, the default value for fa is 365 days.
Duration of the mother's exposure to the chemical of concern prior to nursing (fa). The model
shown as Equation B-40 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 ED (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-40 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-40 with the tpn term (U.S. 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, the methodology uses an ED equal to the
default half-life for dioxins, or 10 years. Only 3,285 days of that period are pre-natal (i.e., 3,650
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.
B.6.5.2 Chemical-Specific Parameter Values
The chemical-specific parameters in the breast-milk pathway are listed in Exhibit B-29. 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.
Exhibit B-29. Chemical-specific Input Parameter Values for
Breast Milk Exposure Pathway
Parameter arid 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
ft
Fraction of steady-state lipophilic chemical body
burden in mother that is stored in body fat (unitless)
>0.90 (ATSDR
1992)
NA
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TRIM-Based Tiered Screening Methodology for RTR
Parameter and Description
2,3,7,8-TCDD
MeHg
fpi
Fraction of steady-state total hydrophilic chemical body
burden in mother that is in the blood-plasma
compartment (unitless)
NA
Not yet identified13
h
Biological half-life for chemical in non-lactating women
(days)
3650 (U.S. 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 non-
lactating women (per day; related to chemical half-life)
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-43
NA
E
.a
O
Q_
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 etal.
1975)
NA = not applicable; ND = not yet determined from literature.
aThis value is based on a single-dose study and may not be appropriate for a chronic exposure model.
bAn empirical value for this variable is currently missing for application of model.
This value was calculated from biological half-life (h) using Equation B-42.
Absorption efficiency of the chemical by the oral route of exposure for the infant (AEmt)¦ The
models included in the methodology assume that the AEmt from the lipid phase of breast milk is
equal to the AEmt from the aqueous phase of the milk. Reviewers of the model stated that this
assumption may not be valid and that ideally, the equation DAimt would include variables for the
AEjnf from the breast milk fat and the AEinr from the aqueous phase of breast milk (U.S. EPA
2001a). However, since the methodology assumes that chemicals will partition to either the lipid
or aqueous phase of milk, it is not necessary at this time to have multiple AEm 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 AEmt. 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 (U.S. EPA 2001a). If chemical-
specific data are not available for adults or infants, a health protective default value for AEmt for
a screening level assessment is 1.0, which assumes 100 percent absorption (U.S. EPA 1998).
The default value for AEinf for both MeHg and dioxin is 1.0. For ingested lipophilic chemicals, it is
reasonable to assume that absorption will be high (U.S. EPA 2004b). 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 (U.S. EPA 2004b). MeHg also is well
absorbed, with measured values as high as 95 percent, and so a value of 100 percent is used
(U.S. 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 (fhj). The default value
for MeHg, 0.059, is from Kershaw etal. (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 |jg Hg/kg BW. The fraction of the dose deposited in the blood volume after mercury
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TRIM-Based Tiered Screening Methodology for RTR
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 (ft). Based on ATSDR's
Toxicological Profile for Selected PCBs (ATSDR 1992) and Sullivan etal. (1991), EPA
concluded that the "fraction of ingested contaminant stored in fat may be >90%" for lipophilic
chemicals such as PCBs and dioxins (U.S. 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 (fnl). 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 fpi may be available for some chemicals in the
scientific literature. No value for this parameter for MeHg has been identified from the literature
at this time. A value can be calculated using Equation B-45. However, this equation requires a
reliable value for fbi, and the value found for mercury may not be appropriate for a chronic
exposure analysis (see above).
Chemical half-life in non-lactatinq women (/?). 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 (U.S. 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 default half-life for dioxins is set to 10 years or 3650 days.
The half-life for MeHg is on the order of weeks, not years. Greenwood etal. (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 default for MeHg is set
to the longer average half-life of 50 days.
Chemical elimination rate constant for lactating women - aqueous (ka„ Pian). The parameter
kaq_eiac is equal to keiim plus the loss rate for the chemical in the aqueous phase of breast-milk
during lactation. EPA has yet to propose a term for the additional elimination of a chemical in
the aqueous phase of milk from breast feeding. In the absence of empirical values, a
reasonable assumption for water soluble chemicals is that kaq_eiac is equal to kenm as discussed
for Equation B-45. The extent to which kenm is an underestimate of kaq_eiac for a given chemical
will determine the extent of health protective bias in kaq_elac-
Chemical elimination rate constant for non-lactating women (keiim). Although values for this
parameter often are reported directly in the literature, the methodology estimates kenm from
chemical half-life assuming first-order kinetics as shown in Equation B-42. For example, for a
biological half-life of 3,650 days for dioxins, kenm is estimated to be 1.9E-04 per day. Assuming a
biological half-life of 50 days for MeHg, the value for kenm is estimated to be 0.014 per day.
Rate constant for total elimination of lipophilic chemicals by lactating women (kfat man). Although
values for this parameter might be found in the scientific literature for some chemicals, kfat_eiac for
dioxins is calculated from Equation B-43. When the parameters in that equation use the default
values for dioxins, the estimated value of kfat_eiac. is 0.0015 per day
Partition coefficient for chemical between maternal blood plasma and agueous phase of breast
milk (PChm). The aqueous model, presented in Equation B-44, assumes that the concentrations
Attachment B
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TRIM-Based Tiered Screening Methodology for RTR
in the plasma and aqueous phase of breast milk are directly proportional (U.S. EPA 1998).
Therefore, the default value for this parameter for MeHg 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 (U.S. EPA
1998). For MeHg, the methodology uses 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).
B.7 Summary of Default Exposure Parameter Values
The default parameter values used in the multimedia ingestion risk methodology 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. EPA used these default parameter
values 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 for
parameter values as described in Section B.6 of this attachment. This section summarizes the
default parameter values used to calculate screening thresholds.
This section is organized to present the chemical- and scenario-specific parameters by data
type. The screening-level analysis uses the following IRs for each ingestion scenario of interest
and population-specific characteristic assumptions (presented in Section B.7.1), that are
generally health protective in nature:
• Fisher Scenario: 99th percentile IRs for fish (presented in Section B.7.1.1)
• Farmer Scenario: 90th percentile IRs for soil, breast milk, and farm foods (presented in
Section B.7.1.2)
• Gardener Scenario: Urban gardener uses mean IRs for fruits and vegetables and eggs
and 90th percentile IRs for soil and breast milk. Rural gardener uses 90th percentile
rates for fruits and vegetables, eggs, soil, and breast milk (presented in Section
B.7.1.3).
Screening threshold emission rates were derived for five RTR chemical groups: arsenic
compounds, cadmium compounds, MeHg, 2,3,7,8-TCDD, and benzo[a]pyrene. Section B.7.3
presents chemical-specific parameter inputs for these five chemicals. Finally, Section B.7.4
presents default parameter values for the nursing infant exposure scenario, which applied only
to dioxin and MeHg as discussed in Section B.3.4.
B.7.1 Default Ingestion Rates
The screening-level (or default) values for IRs for soil, breast milk, and for each farm-food item
are set to the 90th percentile or mean of the distribution of national data for that medium based
on the exposure scenario of interest. In general, these values were obtained from the 2011 EFH
or the 2008 CSEFH (see Exhibit B-16). Fish IRs also are available from these sources;
however, as described in Section B.6.3.4, other sources were used to obtain fish IRs.
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TRIM-Based Tiered Screening Methodology for RTR
B.7.1.1 Fisher Ingestion Scenario
The adult fish IR was obtained from Burger (2002), a study that examined daily consumption of
wild-caught fish for high-end recreationalists (white, black, 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-17 and discussed in Section
B.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 B.6.3.4, the baseline fish IRs for the screening
scenario are based on "as prepared" total freshwater/estuarine fish IRs at the 99th percentile of
the distribution for the consumer-only population (i.e., inclusive only of people who consume
fish, rather than per-capita rates, which include both consumers and non-consumers), as
estimated in U.S. 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 IRs are
available) do not all directly correspond to the age groups in the U.S. EPA (2002) report. As
described in Section B.6.3.4, these adjustments convert the available age-specific data on fish
IRs to the age-specific values needed for the methodology.
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. Estimated exposures are intended to encompass those of a
subsistence fisher whose diet comprises a substantial proportion offish. The scenario is not,
however, intended to represent the maximum possible exposure an individual subsistence fisher
might experience.
Although the fish IRs presented here are representative of the 99th percentile of the evaluated
data set, the use of these values (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 IRs 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 IR 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 IR also is consistent
with section 112 of the CAA, which focuses on risks associated with maximally exposed
individuals.
B.7.1.2 Farmer Ingestion Scenario
The default parameter values assume that all food types are obtained from the area of chemical
deposition specified by the assessment scenario (i.e., fraction of food from contaminated area =
1.0).
For estimates of screening threshold emission rates for PB-HAPS, environmental
concentrations and air deposition rates were estimated using TRIM.FaTE for the area of
maximal deposition in the vicinity of a hypothetical facility, and thus represent risks estimated for
a maximally exposed individual/farm/family.
Attachment B
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit B-30 also includes a sum of the 90th percentile IRs for homegrown food categories and
99th percentile fish ingestion to show the implied total food IR associated with setting multiple
food-type-specific IRs 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 IRs 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 IRs from the USDA's 1994-96 and 1998 CSFII (USDA 2000) data sets, as analyzed
by EPA (U.S. EPA 2005e). The total IR for the farming households (third row from bottom)
accounts for the cooking losses typical of each food category to provide a better comparison
with the 90th percentile individual total food IRs from CSFII (which are based on consumption of
prepared foods). The final row of Exhibit B-30 shows the likely magnitude of the overestimates
by age category by presenting the ratio of the two preceding rows. The values in this row
demonstrate the potential for overestimating intake by using upper-percentile values for all food
groups. This bias may be considered when evaluating the results estimated with the
methodology.
Exhibit B-30. Farm-food Category Ingestion Rates for Health Protective Screening
Scenario for Farming Households
Product
Screening-Level Consumer Ingestion Rate
Units
Infants
<1 yr
Child
1-2 yrs
Child
3-5 yrs
Child
6-11 yrs
Child
12-19 yrs
Adult
20 up to
70 yrs
Farm Foods
Beef3
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
Eggs3
NA
4.90
3.06
1.90
1.30
1.31
g/kg-day
Exposed Fruit3
NA
12.7
5.41
6.98
3.41
2.37
g/kg-day
Exposed Vegetable3
NA
10.7
3.47
3.22
2.35
3.09
g/kg-day
Pork3
NA
4.90
4.83
3.72
3.69
2.23
g/kg-day
Poultry3
NA
7.17
6.52
4.51
3.13
2.69
g/kg-day
Protected Fruit3
NA
44.8
32.0
23.3
7.44
15.1
g/kg-day
Protected Vegetable3
NA
3.88
2.51
2.14
1.85
1.81
g/kg-day
Root Vegetable3
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/
NA
107.79
159.09
268.2h
331.0h
373
g/day
Total Food Ingestion Rates (for comparison only not, used in RTR screening; excludes soil and
water)
Total Food: Homegrown
only'
NA
259
142
99
51
35.5
g/kg-day
Attachment B
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TRIM-Based Tiered Screening Methodology for RTR
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 up to
70 yrs
Total Food: All Sources'
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: U.S. EPA 2011a, 2008a, unless otherwise noted.
NA = not applicable.
aPrimary source for values was the 1987-1988 Nationwide Food Consumption Survey (USDA 1993); compiled results are
presented in Chapter 13 of the 2011 Exposure Factors Handbook (U.S. 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 the Continuing Survey of Food Intakes by Individuals.
bPrimary source for values was the 1987-1988 Nationwide Food Consumption Survey (USDA 1993), compiled results are
presented in Chapter 13 of the 2011 Exposure Factors Handbook (U.S. 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 a National Health and Nutrition Examination Survey 2003-2006 analysis in Chapter 11 of the Exposure Factors
Handbook.
°lnfants are assumed to consume only breast milk for one year.
dThese 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.
eThese values are 90th percentile adult ingestion rates calculated in Stanek et al. 1997, and they are used to represent older
children and adults.
'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.
9The fish ingestion rate for children aged 3-5 years was obtained directly from Section 4.2.1.1, Table 5 in the U.S. EPA (2002)
report (value presented is rounded); for these children, the RTR age-group range matches the U.S. 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.
hTime-weighted average ingestion rates were calculated using the U.S. 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.
'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-Li) * (1 -L2), where U and L2 are the loss rates from Exhibit B-24. The rows are then summed to
get the total post-cooking ingestion rate.
'90th percentile total food intake rates from U.S. EPA (2008a, 2005e) based on Continuing Survey of Food Intakes by Individuals
data 1994-96 and 1998; see Section B.6.3.6 of this document.
B.7.1.3 Gardener Ingestion Scenario
As discussed in Section 3.2.3, two potential gardener scenarios can be evaluated depending on
the surrounding land use: a rural gardener and an urban gardener. Similar to the farmer
ingestion scenario, 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);
however, an urban gardener is expected to have a lower IR for home produced goods than a
rural gardener. The rural gardener is assumed to have the same 90th percentile IR as the
farmer for the produce consumed. Exhibit B-31 and Exhibit B-32 provide IRs for the rural
gardener and urban gardener, respectively.
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 resident in an urban or rural setting.
Attachment B
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit B-31. Ingestion Rates for Rural Gardeners
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 up to
70 yrs
Exposed Fruit3
NA
12.7
5.41
6.98
3.41
2.37
g/kg-day
Exposed Vegetable3
NA
10.7
3.47
3.22
2.35
3.09
g/kg-day
Protected Fruit3
NA
44.8
32.0
23.3
7.44
15.1
g/kg-day
Protected Vegetable3
NA
3.88
2.51
2.14
1.85
1.81
g/kg-day
Root Vegetable3
NA
7.25
4.26
3.83
2.26
2.49
g/kg-day
Eggs3
NA
4.90
3.06
1.90
1.30
1.31
g/kg-day
Other
Soil
NA
200b
200b
201c
201c
201c
mg/day
Note: NA = not applicable.
aPrimary source for values was the 1987-1988 Nationwide Food Consumption Survey (USDA 1993); compiled results are presented in
Chapter 13 of the 2011 Exposure Factors Handbook (U.S. 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 the
Continuing Survey of Food Intakes by Individuals. Ingestion rates presented are the 90th percentile values.
bThese 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.
These values are 90th percentile adult ingestion rates calculated in Stanek et al. (1997), and they are used to represent older children
and adults.
Exhibit B-32. Ingestion Rates for Urban Gardeners
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 up to 70
yrs
Exposed Fruit3
NA
6.14
2.60
2.52
1.33
1.19
g/kg-day
Exposed Vegetable3
NA
3.48
1.74
1.39
1.07
1.38
g/kg-day
Protected Fruit3
NA
16.6
12.4
8.50
2.96
5.19
g/kg-day
Protected Vegetable3
NA
2.46
1.30
1.10
0.78
0.86
g/kg-day
Root Vegetable3
NA
2.52
1.28
1.32
0.94
1.03
g/kg-day
Eggs3
NA
2.46
1.42
0.86
0.58
0.606
g/kg-day
Other
Soil
NA
200b
200b
201c
201c
201c
mg/day
Note: NA = not applicable.
aPrimary source for values was the 1987-1988 Nationwide Food Consumption Survey (USDA 1993); compiled results are presented in
Chapter 13, Table 13-58 to Table 13-62, of the 2011 Exposure Factors Handbook (U.S. 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 the Continuing Survey of Food Intakes by Individuals. Ingestion rates presented are the mean values.
bThese 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.
These values are 90th percentile adult ingestion rates calculated in Stanek et al. (1997), and they are used to represent older children
and adults.
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TRIM-Based Tiered Screening Methodology for RTR
B.7.2 Default Screening-Level Population-Specific Parameter Values
The screening-level values for BWs for the RTR screening threshold analysis, which serve as
the default values, are mean values and are presented in Exhibit B-33. As stated in Section
B.6.3.1 of this attachment, EPA recommends using the mean BWfor each age group when
using upper-percentile values for IRs. Use of the mean BWs introduces no bias toward over- or
underestimating risk. The default ED for each age group also is presented in Exhibit B-33.
Exhibit B-33. Mean Body-weight Estimates3
Lifestage (years)
Duration (years)
Mean Body Weight (kg)
Adultb (20 up to 70)
50
80.0
Child <1c
1
7.83
Child 1-2C
2
12.6
Child 3-5d
3
18.6
Child 6-11e
6
36.0
Child 12-19f
8
64.2
Sources: U.S. EPA (1997, 2008a).
bEPA-recommended value (U.S. EPA 2011 a).
dThese values were obtained directly from Table 8-3 of the 2008 CSEFH.
eEach BW represents a time-weighted average of BWs for age groups 6 to <11 years and 11 to <16 years from Table 8-3 of the
2008 CSEFH. Original sample sizes for each of these age groups can also be found in Table 8-3.
'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 ofthe possible bias is unknown. The values match the estimate based on Table 8-22 of the
National Health and Nutrition Examination Survey IV data as presented by Portier et al. (2007).
B.7.3 Default Chemical-Specific Parameter Values for Screening Analysis
Exhibit B-34 presents chemical-specific parameter values for the screening-level analysis.
Values for bioavailability when ingested in soil (6s), mammalian MFs, correction factors for
belowground produce (VGmotveg), wet deposition fractions (Fw), air to plant transfer factors
(Bvag), RCFs, and Kds are presented.
Only single estimates were developed for each of these parameters for HHRAP (U.S. 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-
type-specific, as presented in Exhibit B-10, are not repeated here. Finally, Exhibit B-35 presents
biotransfer factors for each of the chemicals and animal types for which screening threshold
emissions were calculated.
Attachment B
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit B-34. Chemical-Specific Parameter Values3
Parameter
Description
BaP
Cadmium
Mercuric
chloride
Methyl
mercury
2,3,7,8-
TCDD
Arsenic
Units
Bs
Soil bioavailability factor for
livestock
1
1
1
1
1
1
unitless
SoilAdjFactor
Soil bioavailability factor
1
1
1
1
1
.a
CD
O
unitless
MF
Mammalian metabolism factor
0.01
1
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
1
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
0.6
unitless
BvAG
Air-to-plant biotransfer factor for
aboveground produce for vapor-
phase chemical in air
174,523
0
1,800
0
65,500
0
[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
0
L soil pore
water/kg
root WW
Kds
Chemical-specific soil/water
partition coefficient
7,750
332
58,000
7,000
31,126
2,512
L soil pore
water/kg
soil DW
aValues 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 screen are provided here.
bFrom U.S. EPA (2012). Compilation and Review of Data on Relative Bioavailability of Arsenic in Soil. Relative bioavailability (RBA) of arsenic in soils compared with arsenate
dissolved in water. Fewer than 5% of 103 estimates of RBA of arsenic exceeded 0.60 (in vivo studies of juvenile swine, n = 64; monkeys, n = 24; and mice, n = 15).
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit B-35. Chemical and Animal-Type Specific Biotransfer Factor (Ba) Values3
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
2.6E-02
5.5E-03
3.2E-02
1.1E-02
1.9E-02
Arsenic
2.0E-03
6.0E-05
1.0E-02
7.0E-02
3.0E-02
a([mg chemical/kg WW tissue ordairy]/[mg chemical intake/day] = day/kg WW tissue or dairy).
B.7.4 Screening-Level Parameter Values for Nursing Infant Exposure
For dioxins, chemical intake via breast milk by nursing infants was estimated using the model
presented in EPA's MPE (U.S. EPA 1998). The assumption that lactational transfer of dioxins to
the infant occurs via the lipid-phase of milk appears reasonable. The following screening-level
assumptions used in that model should bias the results toward health-protective 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 B.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 (1-5 percent).
. The maternal chemical intake is estimated using upper-percentile IRs for the different
homegrown foods (see discussion for Exhibit B-30); this assumption might
overestimate total ingestion of homegrown foods by a factor of more than 2 (see
Exhibit B-30).
. If the fraction of the maternal body burden of dioxin that is in the body fat compartment
is greater than 90 percent, as suggested by ATSDR (1998), then actual exposures of
the infant may be less than estimated.
There also are parameter values and 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 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 ED for the mother equal to the
assumed half-life for dioxins, 10 years, may underestimate the duration of exposure of the
mother.
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TRIM-Based Tiered Screening Methodology for RTR
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Volume V. External Review Draft. U.S. EPA Region 5, Chicago, Illinois.
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Response, Washington D.C. EPA/540/R-96/018, April 1996.
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Development, Washington, D.C. EPA-600-P-95-002Fa,b,c. August. Available at:
https://cfpub.epa.gov/ncea/efp/recordisplay.cfm?deid=12464.
U.S. EPA. 1997b. Health Effects Assessment Summary Tables (HEAST). U.S. Environmental
Protection Agency, Washington, D.C., 1997. EPA-540/R-97-036. Available at:
https://cfpub.epa. gov/ncea/risk/recordisplay.cfm?deid=2877.
U.S. EPA. 1997c. Mercury Study Report to Congress. Volume III: Fate and Transport of
Mercury in the Environment. Office of Air Quality Planning and Standards and Office of
Research and Development. EPA-452/R-97-005. December.
U.S. EPA. 1997d. Parameter Guidance Document. National Center for Environmental
Assessment, NCEA-0238.
U.S. EPA. 1998. Methodology for Assessing Health Risks Associated with Multiple Pathways of
Exposure to Combustor Emissions. National Center for Environmental Assessment,
Cincinnati, OH. EPA-600-R-98-137. Available at:
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U.S. EPA. 1999a. 1999 National-Scale Air Toxics Assessment Results; Approach for Modeling
POM. Available at: http://archive.epa.gov/nata2002/web/pdf/pom approach.pdf.
U.S. EPA. 1999b. Data Collection for the Hazardous Waste Identification Rule. Office of Solid
Waste. October. Available at: https://archive.epa.gov/epawaste/hazard/web/html/risk.html.
U.S. EPA 2000. Supplementary Guidance for Conducting Health Risk Assessment of Chemical
Mixtures. EPA/630/R-00/002. Risk Assessment Forum, U.S. Environmental Protection
Attachment B
B-111
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TRIM-Based Tiered Screening Methodology for RTR
Agency, Washington, DC. August. Available at
https://cfpub.epa.qov/ncea/risk/recordisplav.cfm?deid=20533
U.S. EPA. 2001a. Peer Review of EPA's Hazardous Waste Identification Rule Risk Assessment
Model: Breast milk exposure model for the HWIR 3MRA Model. Prepared by Eastern
Research Group for EPA Office of Solid Wastes. 68-W5-0057.
U.S. EPA. 2001b. Water Quality Criterion for the Protection of Human Health: Methylmercury.
Office of Water, Office of Science and Technology. Washington D.C. EPA-823-R-01-001.
January. Available at:
http://water.epa.qov/scitech/swquidance/standards/criteria/health/upload/2009 01 15 criteri
a methylmercury mercurv-criterion.pdf.
U.S. EPA. 2002. Estimated Per Capita Fish Consumption in the United States. Office of Water,
Office of Science and Technology, Washington, D.C. EPA-821- C- 02-003. August.
Available at:
http://water.epa.qov/scitech/swquidance/standards/criteria/health/upload/consumption repor
t.pdf.
U.S. EPA. 2003a. Chapter 10 In: Multimedia, Multipathway, and Multireceptor Risk Assessment
(3MRA) Modeling System, Volume II: Site-based, Regional, and National Data. SAB Review
Draft. EP-530/D-03-001b. Office of Research and Development, Athens, GA, and Research
Triangle Park, NC, and Office of Solid Waste, Washington, D.C. July. Available at:
https://archive.epa.gov/epawaste/hazard/web/html/risk03.html.
U.S. EPA. 2003b. Methodology for Deriving Ambient Water Quality Criteria for the Protection of
Human Health (2000): Technical Support Document. Volume 2: Development of National
Bioaccumulation Factors. Office of Water, Office of Science and Technology, Washington,
D.C. EPA-822-R-03-030. December. Available at:
http://water.epa.qov/scitech/swquidance/standards/criteria/health/methodoloqy/.
U.S. EPA. 2003c. CSFII Analysis of Food Intake Distributions. Office of Research and
Development, National Center for Environmental Assessment, Washington, D.C. EPA-600-
R-03-29. Available at: http://cfpub.epa.qov/ncea/cfm/recordispIav.cfm?deid=56610.
U.S. EPA. 2004. Estimated Per Capita Water Ingestion and Body Weight in the United States -
An Update. Office of Water, Office of Science and Technology, Washington, D.C. EPA-822-
R-00-001. October. Available at:
http://water.epa.gov/action/advisories/drinkinq/upload/2005 05 06 criteria drinking percapi
ta 2004.pdf.
U.S. EPA. 2004b. 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. Available at:
https://www.epa.qov/risk/risk-assessment-quidance-superfund-raqs-part-e.
U.S. EPA. 2005a. Human Health Risk Assessment Protocol for Hazardous Waste Combustion
Facilities. Office of Solid Waste and Emergency Response, Washington, DC. EPA-530-R-
05-006. September. Available at:
https://archive.epa.gov/epawaste/hazard/tsd/td/web/html/risk.html.
Attachment B
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TRIM-Based Tiered Screening Methodology for RTR
U.S. 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://www2.epa.qov/sites/production/files/2013-09/documents/aqeqroups.pdf.
U.S. EPA. 2005c. Guidelines for Carcinogen Risk Assessment. Risk Assessment Forum,
Washington, DC. EPA/630/P-03/001F. March. Available from:
http://www2.epa.qov/sites/production/files/2013-09/documents/cancer guidelines final 3-
25-05.pdf.
U.S. EPA. 2005d. Supplemental Guidance for Assessing Susceptibility from Early-Life Exposure
to Carcinogens. Risk Assessment Forum: Washington, D.C. EPA-630/R-03-003F. March.
Available at: http://www.epa.gov/ttn/atw/childrens supplement final.pdf.
U.S. 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/recordisplav.cfm?deid=132173.
U.S. EPA. 2005f. Empirical Models of Pb and Cd Partitioning Using Data from 13 Soils,
Sediments, and Aquifer Materials. Available at:
https://cfpub.epa.gov/si/si public record report.cfm?Lab=NERL&dirEntrvld=136786&subiec
t=Air%20Research&showCriteria=0&searchAII=Air%20and%20Exposure&actTvpe=Product
&TIMSTvpe=PUBLISHED+REPORT&sortBy=revisionDate.
U.S. EPA. 2005g. Partition Coefficients for Metals in Surface Water, Soil, and Waste. National
Exposure Research Laboratory. Athens, GA. EPA/600/R-05/074. July. Available at:
https://cfpub.epa.gov/si/si public record report.cfm?Lab=NERL&dirEntryld=135783.
U.S. EPA. 2007a. Toxicological Review of 1,1,1-Trichloroethane (CAS No. 71-55-6) In Support
of Summary Information on the Integrated Risk Information System (IRIS). Office of
Research and Development, Washington, DC. EPA/635/R-03/006. August. Available at:
http://www.epa.gov/iris.
U.S. 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/recordisplav.cfm?deid=199243.
U.S. EPA. 2008b. Draft Report on EPA OAQPS Risk and Technology Review Methodologies:
For Review by the EPA Science Advisory Board; Case Studies - MACT I Petroleum
Refining Sources, Portland Cement Manufacturing. Office of Air Quality Planning and
Standards, Office of Air and Radiation, Research Triangle Park, NC. July 14, 2008.
U.S. 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/recordisplav.cfm?deid= 194584.
U.S. 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/recordisplav.cfm?deid=236252.
Attachment B
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TRIM-Based Tiered Screening Methodology for RTR
U.S. 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.
U.S. EPA (2012). Compilation and Review of Data on Relative Bioavailability of Arsenic in Soil.
Office of Solid Waste and Emergency Response. OSWER 9200.1-113
https://semspub.epa.qov/work/11/175339.pdf.
U.S. EPA. 2015. Estimated Order of Potential Potencies of Selected PAH Based on Mouse Skin
Carcinogenesis. Available at: https://www.epa.gov/sites/production/files/2015-
11/documents/pah-rpfs.pdf.
U.S. EPA. 2017a. Integrated Risk Information System. Available at: https://www.epa.gov/iris.
U.S. EPA. 2017b. Update for Chapter 5 of the Exposure Factors Handbook. Soil and Dust
Ingestion. Office of Research and Development, Washington, D.C. EPA/600/R-17/384F.
September. Available at: https://www.epa.gov/expobox/exposure-factors-handbook-chapter-
5.
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 offish fillets harvested from the Great Lakes. Bulletin of Environmental
Contamination and Toxicology. 55:264-269.
Attachment B
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TRIM-Based Tiered Screening Methodology for RTR
Attachment C. Dermal Risk Screening
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TRIM-Based Tiered Screening Methodology for RTR
Contents
C.1 Hazard Identification and Dose Response Assessment C-5
C.2 Dermal Exposure Estimation C-6
C.2.1 Equations for Estimating Dermal Exposure C-6
C.2.2 Exposure Factors and Assumptions C-7
C.2.3 Receptor-Specific Parameters C-8
C.2.4 Scenario-Specific Parameters C-9
C.2.5 Chemical-Specific Parameters C-9
C.3 Screening-Level Cancer Risks and Noncancer Hazard Quotients C-10
C.3.1 Dermal Cancer Risk C-10
C.3.2 Dermal Hazard Quotient C-11
C.4Dermal Screening Results C-11
C.5References C-12
Exhibits
Exhibit C-1. Cancer Slope Factors and Reference Doses Based on Absorbed
Dose C-6
Exhibit C-2. Receptor-Specific Body Surface Area Assumed to be Exposed to
Chemicals C-8
Exhibit C-3. Scenario-Specific Exposure Values for Water and Soil Contact C-9
Exhibit C-4. Chemical-Specific Dermal Exposure Values for Water and Soil
Contact C-10
Exhibit C-5. Dermal Noncancer HQs - Summed for Water and Soil Exposures C-11
Exhibit C-6. Dermal Cancer Risks - Summed for Water and Soil Exposures C-12
Exhibit C-7. Comparison of Ingestion Risk/HQ to Dermal Risk/HQ C-12
Attachment C C-3 February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Non-inhalation exposure to PB-HAPs can occur by dermal contact with PB-HAP-contaminated
soil and water. Although dermal absorption of chemicals that are originally airborne generally is
considered a relatively minor pathway of exposure compared to other exposure pathways, in
certain settings it can be significant (U.S. EPA 2006, Cal/EPA 2012). 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 compared with the ingestion
pathway. In general, the RTR dermal assessment follows the protocol for evaluating a
reasonable maximum exposure as described in EPA's Risk Assessment Guidance for
Superfund (RAGS), Volume I: Human Health Evaluation Model, Part E, Supplemental Guidance
for Dermal Risk Assessment (U.S. EPA 2004)
C.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 noncancer effects for the five PB-HAPs currently
assessed in the multipathway screening evaluation for RTR: arsenic, cadmium, divalent
mercury, 2,3,7,8-TCDD, and benzo[a]pyrene. EPA has not developed carcinogenic potency
slope factors (CSFs) and noncancer 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 (Gl) absorption. EPA recommends making this adjustment only when Gl
absorption of the chemical is significantly less than 100 percent (i.e., less than 50 percent).
Otherwise, a default value of complete (100 percent) oral absorption is assumed, and no
adjustment is made (U.S. EPA 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:
CSFfl0c = CSF°
ABS absgi
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.
RfDABS = RfDo x ab$gi
where:
RfDABs = Absorbed reference dose (mg/kg-day)
RfDo = Oral reference dose (mg/kg-day)
ABSgi = Fraction of chemical absorbed in gastrointestinal tract (unitless)
Attachment C
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TRIM-Based Tiered Screening Methodology for RTR
The Gl absorptions for arsenic, 2,3,7,8-TCDD, and all polycyclic aromatic hydrocarbons (PAHs)
(which includes benzo[a]pyrene) are estimated to be greater than 50 percent based on data
provided in RAGS Part E, Exhibit 4-1. Therefore, as shown in Exhibit C-1, no adjustments to the
available oral toxicity values (RfD or CSF) were required for these chemicals. For cadmium and
divalent mercury, adjustments were made based on absorption data provided in RAGS Part E,
Exhibit 4-1. The absorbed RfDs for cadmium and divalent mercury, adjusted to account for Gl
absorption, also are provided in Exhibit C-1.
Exhibit C-1. Cancer Slope Factors and Reference Doses Based on Absorbed Dose
PB-HAP
Fraction of Contaminant
Absorbed in Gl Tract (ABSgi)
(unitless)
Absorbed Cancer
Slope Factor (CSFabs)
(mg/kg-day)"1
Absorbed Reference
Dose (RfDABs)
(mg/kg-day)
Arsenic
No adjustment
required3
1.5E+00
3.0E-04
Cadmium
Compounds
0.05
NA
2.5E-05b
Divalent
Mercury
0.07
NA
2.1 E-05c
2,3,7,8-TCDD
No adjustment
Required3
1.5E+05
7.0E-10
Benzo[a]pyrene
No adjustment
Required3
1.0E+00
3.0E-04
NA = Not applicable.
According to RAGS Part E, Exhibit 4-1, Gl absorption is expected to be greater than 50%.
"Cadmium RfD for water = 5.0E-4.
°Divalent mercury RfD for = 3.0E-4.
C.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 (Farm) 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.
C.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.
DAD _ Prevent * EV X ED X EF X SA
BWxAT
Attachment C
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February 2021
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TRIM-Based Tiered Screening Methodology for RTR
where:
Absorbed dose per event; chemical-specific; equation for DAevent also differs
depending on water or soil contact (mg/cm2-event)
Event frequency (events/day)
Exposure duration (years)
Exposure frequency (days/year)
Skin surface area available for contact (cm2)
Body weight (kg)
Averaging time; for noncancer 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).
16 X T X t
Water - Organic Chemicals: DAevent = CW / 2 / FA / Kp J —'¦
Water - Inorganic Chemicals: DAevent =CwxKpx tevent
Soil - All Chemicals: DAevent = Cs x AF x ABS x CF
where:
DAevent
=
Absorbed dose per event (mg/cm2-event)
Cw
Cs
=
Chemical concentration in water (mg/cm3) or soil (mg/kg)
KP
=
Chemical-specific dermal permeability coefficient of compound in water (cm/hr)
FA
Chemical-specific fraction absorbed; accounts for loss due to the regular
shedding of skin cells of some chemical originally dissolved into skin (unitless)
Tevent
=
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)
C.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
DAevent —
EV =
ED =
EF =
S4 =
BW =
AT =
Attachment C
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TRIM-Based Tiered Screening Methodology for RTR
conservative estimate of exposure (i.e., exposures are likely overestimated). Parameter values
were primarily obtained or estimated from RAGS Part E (U.S. EPA 2004) and the Child-Specific
Exposure Factors Handbook (CSEFH, U.S. 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.
C.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 Group3
Surface Area for
Surface Area for
(years)
Water Exposure (cm2)
Soil Exposure (cm2)
Adult 20 up to 70
18,1509
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
aSources for the child groups included Table 7-1 (total body surface area for SA-Water), and Table 7-2
(fraction of total body surface area for SA-Soil) of the 2008 CSEFH.
bRepresents a time-weighted average for age groups birth to <1 month, 1 to <3 months, 3 to <6 months, and
6 to <12 months.
Represents a time-weighted average for age groups 1 to <2 years and 2 to <3 years.
dValues for age group 3 to <6 years in the 2008 CSEFH.
eValues for age group 6 to <11 years in the 2008 CSEFH. Represents a conservative (i.e., slightly low)
estimate for ages 6 through 11 years because 11-year-olds are not included in this CSEFH age group.
'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.
9Represents the average total surface area of adults from Table C-1 of RAGS Part E.
Represents the average surface area of adults for head, forearms, hands, lower legs, and feet from
Table C-1 of RAGS Part E.
Attachment C
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TRIM-Based Tiered Screening Methodology for RTR
C.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.
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
from RAGS Part E, Exhibit 3-2
Adult
0.58
Soil Contact
Soil Adherence Factor (AF)
(mg/cm2)
Child
0.2
For children, value is geometric
mean value for children playing
(wet soil) and for adults, value is
geometric mean value for an
adult farmer from RAGS Part E,
Exhibit 3-3
Adult
0.1
Both Media
Event Frequency (EV)
(events/day)
All
1
Reasonable maximum exposure
scenario from RAGS Part E,
Exhibits 3-2 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 up to 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 noncancer 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.
C.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 Farm (tilled 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.
Attachment C
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit C-4. Chemical-Specific Dermal Exposure Values for Water and Soil Contact
PB-HAP
Arsenic
Cadmiu
m
Divalent
Mercury
2,3,7,8-
TCDD
BaP
Source
Chemical
concentration in
Water (Cw) (mg/cm3)
4.19E-10
7.48E-09
3.85E-10
9.03E-18
1.01E-11
TRIM.FaTE modeled
concentration in
screening scenario lake
Chemical
concentration in Soil
(Cs) (mg/kg)
1.66E-02
1.57E-01
3.36E-02
7.41E-10
9.77E-04
TRIM.FaTE modeled
concentration in
surface soil in farm in
screening scenario
Permeability
coefficient in water
(Kp) (cm/hour)
0.001
0.001
0.001
0.81
0.7
Values from RAGS Part
E, Exhibits B-3
(organics) and B-4
(inorganics)
Fraction absorbed
water (FA) (unitless)
NA
NA
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
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)
0.03
0.001
0.045a
0.03
0.13
Values from RAGS Part
E, Exhibit 3-4, unless
otherwise noted
aValue obtained from Bioavailability in Environmental Risk Assessment (Hrudey et al. 1996).
Dermal absorption of chemicals in water is based on the use of a dermal permeability coefficient
(Kp), which measures the rate that a chemical penetrates the skin. Dermal absorption of soil-
bound chemicals is based on the use of a dermal absorption fraction (ABS), which is a measure
of how much of a chemical the skin absorbs through contact with soil.
C.3 Screening-Level Cancer Risks and Noncancer Hazard Quotients
Toxicity values were used in conjunction with exposure information to evaluate the potential for
cancer risks and noncancer health hazards. Risk estimation methods are presented below.
C.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 x CSFabs Eqn. C-1
where:
DAD = Dermal Absorbed Dose (mg/kg-day)
CSFabs = Absorbed cancer slope factor (mg/kg-day)1
Attachment C
C-10
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
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).
C.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 Eqn. C-2
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. Noncancer hazard is not additive
across the age groups evaluated here.
C.4 Dermal Screening Results
Exhibit C-5 provides estimated dermal noncancer hazards by age group and Exhibit C-6
provides estimated lifetime cancer risks from dermal exposures. Risks and hazards are
summed for exposure to both water and soil. Soil and water concentrations that resulted in the
Tier 1 threshold emissions rates are used as the media concentrations; these concentrations
resulted in cancer risks of 1E-6 or HQs of 1.0 for a combined farmer and fisher receptor. The
highest HQ value for dermal exposures was 0.003, representing divalent mercury exposure for
children aged 1 to 2. This is approximately 320 times less than the estimated ingestion HQs
associated with the screening scenario (i.e., emissions of divalent mercury in the screening
scenario resulted in an ingestion HQ of 1, based on the ingestion of methyl mercury). The
highest estimated individual lifetime cancer risk associated with potential dermal exposures was
1 .OE-8 for arsenic; this value is approximately 100 times smaller than the estimated Tier 1
ingestion risk (i.e., 1E-06). Exhibit C-7 provides the estimated magnitude of difference between
the ingestion risks or HQs and those for dermal exposure for each of the five PB-HAPs. These
were calculated as the ratio of the risk or HQ from exposure through ingestion to the risk or HQ
from dermal exposures. Although As, BaP, and TCDD have RfDs, as noted in Exhibit C-1, their
cancer exposure assessments are more of concern; therefore, only lifetime cancer risks and not
HQs are presented in Exhibit C-6.
Exhibit C-5. Dermal Noncancer HQs - Summed for Water and Soil Exposures
PB-HAP
HQ Child 1
HQ Child 2
HQ Child 3
HQ Child 4
HQ Child 5
HQ Adult
Max HQ
Cd
0.0004
0.0004
0.0003
0.0002
0.0002
0.00009
0.0004
Hg2+
0.003
0.003
0.002
0.002
0.001
0.0006
0.003
Attachment C
C-11
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit C-6. Dermal Cancer
Risks - Summed for Water and Soil Exposures
PB-HAP
Lifetime Risk
As
1.0E-08
BaP
8.4E-09
TCDD
8.7E-10
Exhibit C-7. Comparison of
Ingestion Risk/HQ to Dermal Risk/HQ
PB-HAP
Magnitude of Difference
As
99
BaP
119
Cd
2,400
Hg2+
319
TCDD
1,150
Based on these results and taking into consideration the extremely conservative nature of the
dermal exposure calculations, EPA has determined 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.
C.5 References
Cal/EPA (California Environmental Protection Agency) Office of Environmental Health Hazard
Assessment (OEHHA). 2012. Air Toxics Hot Spots Program Risk Assessment Guidelines:
Technical Support Document for Exposure Assessment and Stochastic Analysis. Section 6,
Dermal Exposure Assessment. September. Available at:
https://oehha.ca.gov/media/downloads/crnr/chapter62012.pdf.
Hrudey, S.E., W. Chen, and C.G. Roussex, 1996. Bioavailability in environmental risk
assessment. CRC Press, Inc, Lewis publishers.
U.S. EPA (Environmental Protection Agency). 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://www2.epa.gov/risk/risk-
assessment-quidance-superfund-raqs-part-e.
U.S. EPA. 2006. Risk and Technology Review (RTR) Assessment Plan. Office of Air and
Radiation. November 20. Available at:
http://www.epa.gov/sab/panels/consul risk and tech assessment plan.htm.
Attachment C
C-12
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
U.S. EPA. 2008. Child-Specific Exposure Factors Handbook. Office of Research and
Development, Washington, D.C. EPA/600/R-06/096F. September. Available at:
http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=199243.
Attachment C C-13 February 2021
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Attachment D. Summary of TRIM.FaTE Parameters Considered for
Inclusion in Tier 2 Assessment
D-1
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Exhibit D-1. TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Assessment
Parameter
Mechanism of Potential
Influence in TRIM
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
annually-averaged 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-3
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Parameter
Mechanism of Potential
Influence in TRIM
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 PB-HAPs (POM,
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
annually-averaged 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
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-4
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Parameter
Mechanism of Potential
Influence in TRIM
Uncertainty in Site-Specific
Data for Facilities
Priority for
Inclusion
Configurational Parameters
Distance of lake
from stack
Deposition is known to decrease
with distance from stack,
although this relationship also
depends on meteorological
parameters such as wind speed
and wind direction.
Significance Difficult to Determine:
Limited results from previous TRIM
model runs show an inconclusive
relationship between risk and distance
from stack, possibly as a result of
limited statistical power. Some studies
in the literature show a definite
decreasing risk gradient with distance
but others report too many
confounding factors to isolate the
precise relationship.
Moderate Effort to Implement: This
variable requires updates to the layout
coordinates and requires more effort to
vary in the Tier 2 screening scenarios
than the meteorological parameters.
Low: The lakes within a given
radius of each facility can be
found using ArcGIS™.
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-5
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Parameter
Mechanism of Potential
Influence in TRIM
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 particle-attached chemicals
into the lake. But the associated
higher flush rate will likely reduce
this effect.
Significance Difficult to Determine:
Changes in the watershed to lake ratio
affect risk, but the interaction depends
on other variables involved in the
water balance.
Moderate Effort to Implement: In
implementing changes in the
watershed:lake ratios in TRIM, care
must be taken to also preserve the
overall water balance in the model.
High: The portion of land
serving as a watershed to a
particular lake is difficult to
determine.
Medium
Area and depth of
lake
A higher lake area would capture
more deposition but this effect
might be counterbalanced by the
ensuing larger volume of water,
which reduces chemical
concentration. Similarly, a
deeper lake would also reduce
concentrations, but this effect
might be counterbalanced by the
ensuing lower flush rates at a
constant level of
precipitation/runoff.
Significance Difficult to Determine:
The impact of these parameters is
inconclusive based on current studies
using the TRIM model.
Moderate Effort to Implement: The
lake area variable requires updates to
the layout coordinates and requires
more effort to vary in the Tier 2
screening scenarios than the
meteorological parameters. In
implementing changes in these
variables in TRIM, care must be taken
to also preserve the overall water
balance in the model.
High: While the area of lakes
near a facility can be
determined using GIS, the
depth cannot.
Medium
Attachment D
D-6
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Parameter
Mechanism of Potential
Influence in TRIM
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-7
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Parameter
Mechanism of Potential
Influence in TRIM
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 offish.
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-8
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Attachment E. Analysis of Lake Size
and Sustainable Fish Population
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TRIM-Based Tiered Screening Methodology for RTR
Contents
E.1 Purpose E-5
E. 1.1 Methods - Literature Searches E-5
E.1.2 Methods - Food Web Simulated in TRIM.FaTE E-6
E. 1.3 Organization of This Report E-8
E.2 Angler Behavior E-8
E.2.1 Consumption of Top-trophic-level Fish E-8
E.2.2 Sustainable Fish Harvest Rates E-8
E.2.3 Other Assumptions about Angler Behavior E-9
E.3 Fish Populations E-9
E.3.1 Fish Biology E-10
E.3.2 Fish Population Modeling E-11
E.3.3 General Estimates of MVP E-12
E.3.4 Sustainable Fish Harvest Rates E-13
E.4 Lake Fish Productivity E-14
E.4.1 Lake Characteristics E-14
E.4.2 Predicted Lake Productivity - Nutrient Status and Fish
Biomass E-16
E.4.3 Measured Total Fish Standing Biomass E-20
E.5 Proportion of Fish Biomass by Trophic Level E-22
E.5.1 Principles of Trophic Pyramids E-22
E.5.2 Models of Fish Biomass in Different Trophic Groups E-23
E.5.3 Measured Biomass of Fish in Different Trophic Groups E-25
E.5.4 Conclusion E-29
E.6 Derivation of Lake Sizes for Sustainable WCC Harvest E-29
E.6.1 Minimum Lake Size for Self-sustaining Population of WCC E-29
E.6.2 Maximum Fish Ingestion Rate by Lake Size E-33
E.7 References E-35
Exhibits
Exhibit E-1. Aquatic Food Web Simulated in TRIM.FaTE E-6
Exhibit E-2. Distribution of Biomass in Aquatic Compartments E-7
Exhibit E-3. Characteristics of 72 Lakes in Eastern North America E-14
Exhibit E-4. One Trophic Classification Standard for Lakes E-15
Exhibit E-5. Predictions of Biomass (B) of Biotic Components of Lakes with
Different Total Phosphorus (TP) Concentrations E-17
Exhibit E-6. Reported Compared with Predicted Fish Biomass for Five Reservoirs E-19
Exhibit E-7. Comparison of Predictions of Total Fish Biomass from Total
Phosphorus (TP) E-19
Attachment E E-3 February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit E-8. Total Fish Biomass in Reservoirs of the United States by Drainage
Area E-20
Exhibit E-9. Total Fish Biomass Density in Reservoirs and Lakes from Different
Studies E-22
Exhibit E-10. Distribution of Standing Biomass Among Aquatic Compartments
Simulated in the Comprehensive Aquatic Systems Model (CASM) for
a Florida lake E-24
Exhibit E-11. Estimated Biomass by Aquatic Compartment in LakeToya, Japan E-25
Exhibit E-12. Carrying Capacity, Biomass (g ww/m2) of Fish Supported by Each
Food Compartment Across 61 Reservoirs by Drainage Area E-26
Exhibit E-13. Proportion of Total Carrying Capacity, Proportion Fish Biomass
Supported by each Food Compartment by Drainage Area E-26
Exhibit E-14. Total Fish Biomass by Trophic Level in Wupper Reservoir,
Germany E-27
Exhibit E-15. Number of WCC Adult Fish Supported by Lake Size (surface area
in acres) and by Total Fish Biomass (TFB) E-31
Exhibit E-16. Total Standing Biomass of WCC Fish (kg) by Lake Size and Total
Fish Biomass (TFB) E-32
Exhibit E-17. Estimated Maximum Fish-fillet-ingestion Rate (g/day) Associated
with Sustainable Fishing of WCC by Lake Size and Total Standing
Fish Biomass (TFB) E-34
Acronyms
BC
benthic carnivore (fish, e.g., large catfish)
Bl
benthic (sediment-dwelling) invertebrate
BO
benthic omnivore (fish, e.g., smaller bottom feeding fish)
CASM
Comprehensive Aquatic Systems Model
CPUE
catch per unit effort
DOC
dissolved organic carbon
EPA
U.S. Environmental Protection Agency
HAP
hazardous air pollutant
MEI
morphoedaphic index
MVP
minimum viable population (self-sustaining population over decades)
PB-HAP
persistent and bioaccumulative hazardous air pollutant
RTR
Risk and Technology Review
SD
standard deviation
TLn
trophic level n (e.g., TL1, TL2, TL2.5 ... TL4.5)
TN
total nitrogen
TP
total phosphorus
WCC
water-column carnivore (fish, e.g., walleye, pike)
WCO
water-column omnivore (fish, e.g., yellow perch, sunfish)
WCH
water-column herbivore (fish, e.g., young-of-the-year fish, minnows)
Attachment E
E-4
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
As stated in Section 1.4.1.1 of the TSD, the tiered risk screen for Risk and Technology Review
(RTR) persistent and bioaccumulative hazardous air pollutants (PB-HAPs) includes a
subsistence fisher who consumes fish from one or more lakes near a facility. The fish at the top
of the aquatic food web in a lake should have substantially higher tissue concentrations of
PB-HAPs than concentrations in the water or sediments due to bioaccumulation of the PB-HAPs
through the food web links. For the Tier 1 screening scenario, we include a single hypothetical
lake near the facility. For Tiers 2 and 3, we include actual lakes near the facility. This attachment
provides supporting information for Section 3.5.1 of the Technical Support Document (TSD)—
Processing Lake Data for Tier 2 Screen. For the remainder of this attachment, the word "angler"
is used to refer to the "human fisher" consuming fish at subsistence levels in order to distinguish
it from the mammalian "fisher" (Maries pennanti), which is used in some ecological risk
assessments.
E.1 Purpose
To develop the screening scenarios with an angler, we needed to address two questions:
1. How large does a lake need to be to provide a self-sustaining population(s) of top-trophic-
level fish?
2. How much fish can be harvested sustainably from lakes of different sizes?
The assumed high-end fish ingestion rate for an adult angler is 373 grams fish fillet per day (see
Section 1.4.1.2 of TSD). A health-protective assumption is that the angler consumes top-trophic-
level fish (allows maximal bioaccumulation). Thus, we needed to estimate, in essence, the fish
ingestion rates near trophic level 4 (TL4) supported by lakes of different sizes.
Addressing the first question ensured we did not model an angler harvesting more fish than a
lake could provide (e.g., removing several pounds per day, 365 days per year, a rate at which
the entire fish population would be fished out within weeks or months). The second question
estimates how many lakes of what size(s) would be required to meet the angler's daily fish
ingestion rate.
E.1.1 Methods - Literature Searches
ICF conducted two searches through online bibliographic databases for information on aquatic
food webs and biomass distribution within those webs: one in 2005 and one in 2014. In 2005,
using standard literature/citation databases (e.g., Elsevier BIOBASE, Enviroline), ICF's
information specialist searched citations for articles published from 1975 to 2005. The following
search terms and logical variations of these words were used:
. Aquatic, aquatic ecosystem, fish, fisheries, fisheries population
. Lake, river, reservoir, pond, stream (not marine or estuarine)
. Trophic, pyramid, food web, food chain, trophic community structure
. Biomass, bioaccumulation, biomagnification, accumulation
Fugacity, mass-balance, model
The results of this search yielded an initial list of more than 400 publication titles. These titles
were reviewed to develop a list of about 100 articles for which abstracts were retrieved. The
abstracts were reviewed and used to select 33 publications for retrieval and review. Some of
these publications cited additional relevant literature that we retrieved for review. Where we
Attachment E
E-5
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
have used a secondary source to describe the findings of an original source, we cite both the
primary and secondary sources.
We conducted a similar literature search in 2014 to identify relevant references published since
2005. We found an initial list of more than 200 publication titles that we reviewed. We retrieved
more than 60 abstracts and selected 31 for retrieval. We reviewed those studies to supplement
this documentation and to determine if any literature contradicted key assumptions we made in
2005. Where we have used a secondary source from our 2014 search to describe the findings
of an original source, we provide both the original and secondary citations.
E.1.2 Methods - Food Web Simulated in TRIM.FaTE
The food web simulated in TRIM.FaTE is reproduced in Exhibit E-1. Values for attributes of
each biotic compartment in the TRIM.FaTE-simulated lake are listed in Exhibit E-2. The total
fish biomass per unit area simulated by TRIM.FaTE is 5.7 grams fish wet weight/square meter
[g ww/m2], which is typical of lakes in Maine and southern Ontario. The final two columns in
Exhibit E-2 show fish biomass and numbers for purposes of evaluating fish harvesting by
anglers. The total fish standing biomass is higher, 40 g ww/m2, to be more representative of
lakes across the United States as described in Section E.6. The lower fish biomasses were
used for TRIM.FaTE so that the fish compartments did not sequester (remove) large quantities
of chemical mass from the water column (and sediments).
Wth limited removal of chemical mass from water and sediments, the TRIM.FaTE simulation is
more similar to other aquatic food-web models that assume bioaccumulation in fish and other
biota does not change the concentrations of chemical in water or sediments [e.g., Arnot and
Gobas (2004); U.S. EPA (2009) KABAM for predicting pesticide bioaccumulation potential in
aquatic systems].
Exhibit E-1. Aquatic Food Web Simulated in TRIM.FaTE
Percentage of Consumer's Diet
Aquatic Biota Compartments
(Consumer Groups)
Algae
Macrophytes
Zooplankton
Benthic
Invertebrates
Water Column
Herbivores (WCH)
Benthic Omnivore
(BO)
Water Column
Omnivore (WCO)
Benthic Carnivore
(BC)
Water Column
Carnivore (WCC)
Sum of
% Total
Diet
Zooplankton
100
0
0
0
0
0
0
0
0
100
Benthic Invertebrate (Bl)
0
0
0
0
0
0
0
0
0
100
Water Column Herbivore (WCH)
0
0
100
0
0
0
0
0
0
100
Benthic Omnivore (BO)
0
0
0
100
0
0
0
0
0
100
Water Column Omnivore (WCO)
0
0
0
0
100
0
0
0
0
100
Benthic Carnivore (BC)
0
0
0
50
0
50
0
0
0
100
Water Column Carnivore (WCC)
0
0
0
0
0
0
100
0
0
100
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Exhibit E-2. Distribution of Biomass in Aquatic Compartments
Organism
Weight Per
Individual
(kg)
Percent
of Total
Fish
Biomass
For TRIM.FaTE
For Fish Harvesting
Biomass
(g ww/m2)
No. Fish
Per
Hectare
Biomass
(g ww/m2)
No. Fish
Per
Hectare
Macrophytes
NA
NA
500
NA
NA
NA
Zooplankton
5.70E-08
NA
6.4
NA
NA
NA
Benthic Invertebrate (Bl)
2.55E-04
NA
20
NA
NA
NA
Water Column Herbivore
(WCH)
0.025
35
2
800
14
5614
Benthic Omnivore (BO)
0.25
35
2
80
14
561
Water Column Omnivore
(WCO)
0.25
8.8
0.5
20
3.5
140
Benthic Carnivore (BC)
2
17.5
1
5
7
35
Water Column Carnivore
(WCC)
2
3.5
0.2
1
1.4
7
Total Biomass of All Fish
NA
100
5.7
NA
40
NA
Abbreviations: 1 hectare = 10,000 m2; NA = not applicable; ww = wet weight.
Of the trophic compartments in Exhibit E-1, two compartments represent top-trophic-level fish:
benthic carnivores (BC) and water-column carnivores (WCC). Benthic carnivores are relatively
large (e.g., 2 kg) bottom-feeding fish (e.g., catfish, chub) that consume benthic invertebrates
and small benthic fish. The BC compartment thus represents the top-trophic-level fish exposed
via trophic transfers to chemicals from the sediment compartment. Water-column carnivores are
relatively large (e.g., 2 kg) pelagic piscivores (e.g., walleye, lake trout, northern pike), or "game"
fish, that feed primarily on smaller fish in the water column. The WCC thus represents the top-
trophic-level fish exposed to chemicals dissolved in the water column or adsorbed to suspended
sediment particles and algae.
As shown in Exhibit E-1, for the BC compartment, we assume a diet of 50-percent benthic
invertebrates (TL2) and 50-percent smaller benthic fish (TL3) that feed on benthic invertebrates
(TL2) that feed on detritus in sediments (TL1, not included in Exhibit E-1). That diet composition
averages to TL2.5, which means that the BC compartment represents TL3.5. For the WCC
compartment (TL4.5), our simplified food web assumes that 100 percent of the WCC diet
consists of water column omnivores (WCO, TL3.5, e.g., "pan" fish such as bluegill, other
sunfish, white perch). The diet of the WCO can include various types of prey, but for a simplified
food web we assume the diet of WCO is 100-percent minnow-sized fish species and young-of-
the-year fish (TL2.5) that feed on zooplankton (TL2) and algae (TL1—treated as a phase of the
surface water column) in the water column. In reality, many fish species (e.g., rainbow trout)
feed on smaller fish and on invertebrates in both the water column and at the sediment surface.
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E.1.3 Organization of This Report
Given the necessity of answering the two questions posed in Section E.1 for purposes of RTR
screening, we evaluated several factors and used several simplifying assumptions. For factors
with high natural variability, and for which we could predict whether high-end or low-end values
would increase the angler's exposure to PB-HAPs, we selected values that likely would increase
the angler's risk. For factors with high natural variability for which we could not predict which
end of the range might result in more or less risk, we selected data or made an informed
assumption that we thought would represent a central tendency in conditions across the
country. We emphasize, however, that lake productivity, fish predator-prey relationships, and
species' population dynamics in lakes across the United States are highly variable.
The remainder of this attachment is organized in six sections:
E.2 Assumptions about Angler Behavior
E.3 Assumptions about Fish Biology
E.4 Lake Fish Productivity
E.5 Proportion of Fish Biomass by Trophic Level
E.6 Lake Size for Sustainable WCC Harvest
E.7 References
E.2 Angler Behavior
Assumptions regarding angler behavior drove some of our data selections and assumptions
used to answer our two questions.
E.2.1 Consumption of Top-trophic-level Fish
As stated in Section 3.3.1, Exhibit 31, of the TSD, the angler consumes only top-trophic-level
fish. Although the angler might prefer to catch and consume the WCC (TL4.5) game fish
species, individual fish in that group are the least abundant and account for the lowest group
biomass of all the fish compartments (Exhibit E-2). Fish in the BC (TL3.5) compartment are
more abundant and account for more biomass than the WCC compartment (Exhibit E-2) in most
lakes of moderate size (as discussed in Section E.5, excludes Great Lakes).
We could not predict a priori whether chemical concentrations in the WCC or in the BC
compartment would be higher for any given PB-HAP. Depending on chemical Kow (octanol-
water partitioning coefficient) and Kd (soil/sediment-water partitioning coefficient), TRIM.FaTE
might estimate higher or lower concentrations in the TL3.5 BC fish than in the TL4.5 WCC fish.
Given that unknown, we assumed that the angler catches and consumes a 50:50 ratio of fish
from the WCC and BC compartments.
E.2.2 Sustainable Fish Harvest Rates
The angler lives in the same location for 50 to 70 years. The lake(s) must support fish
harvesting by the angler over that period. In other words, the lake should not be "fished out" by
the harvest rate required to meet the angler's 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 sustainably for human consumption are difficult values to estimate.
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Models to predict sustainable harvests of different fisheries are numerous and complex.
Species-specific parameters key to such 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. We discuss some of those issues later in
Section E.3-Fish Populations, Section E.3.4-Sustainable Fish Harvest Rates. Angler behavior
related to sustainable fishing is discussed below.
Angler fishing pressure is a product of the number of anglers fishing a lake and the time each
angler is willing to spend per unit catch. In reality, those factors are not independent of fish
abundance per unit area and total number of fish per lake. For purposes of the RTR
assessment, however, we assume a single angler is fishing the lake(s) near a facility. We also
assume that the angler harvests fish at a subsistence level. In Tier 1 of the human health risk
assessment, we assume the single lake provides fish at that level. In Tiers 2 and 3 of the human
health risk assessment, if the lake with the maximum chemical concentrations in fish is too small
to provide a sustainable harvest at that level, the angler moves to the next lake with the next
highest chemical concentrations, and so on, until the desired harvest is met.
Other influences of angler behavior on fish population density and abundance are not included
in RTR assessment. For example, fishing "pressure" does not change the abundance of fish in
the lake. In actual lakes, as fishing pressure increases, fish abundance generally decreases.
For example, in Wolfe Lake in Alberta, Canada, overfishing of walleye has resulted in a
decrease of catch-per-unit-effort or time (CPUE) from 0.25 fish/hour in the early 1980s to 0.02
fish/hr in the mid-1990s (Post et al. 2002). In 1969, catching a pike in Lake Kehiwin took
approximately 2.5 hours, whereas in 1995 an estimated 25 hours was required (Post et al.
2002). Stocking lakes has been the solution to allow harvesting at levels well above what wild
populations could sustain in many locations. For the RTR screen, interactions among angler
effort, fish population size and biomass density, and fishing success are not considered.
Instead, we assume certain constants for fish harvesting.
E.2.3 Other Assumptions about Angler Behavior
Another assumption about angler behavior is that anglers consume only the fillet portion of a
fish. According to Ebert et al. (1993), the edible fraction offish 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 using
0.30 for the consumable fraction of fish (U.S. EPA 1989). For this assessment, we assume that
the edible fraction for top-trophic-level fish is 0.33 (i.e., some proportion offish consumed are
salmon-like). The edible fraction of 0.33 is used in the analyses in Section E.6 to estimate total
fish biomass required to support specified human fish consumption rates.
A final assumption is that the angler consumes 373 g/day of fish fillet. The value is from
Burger's (2002) report on fish ingestion rates for avid sport fishers interviewed at the Palmetto
Sportsmen's Classic in South Carolina in March 1998. The ingestion rate of 373 g/person-day is
the 99th percentile ingestion rate reported by 107 females. EPA used that value in its 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 (U.S. EPA 2011).
E.3 Fish Populations
Our initial question in Section E.1 was what is the minimum size of a lake that can support a
self-sustaining population of top-trophic-level fish? As stated in Section E.2, the RTR screening
scenario assumes that an angler consumes 373 g ww fish fillet/day (50:50 ratio of BC to WCC)
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for 50 to 70 years without stocking to maintain the fish population. This section provides
background information required for the lake size analyses in Section E.6.
First, some basic principles of fish biology are reviewed (Section E.3.1). Next, a brief overview
offish population modeling is presented (Section E.3.2). To support calculation of the minimum
lake acreage required to support a self-sustaining WCC fish population, an assumption for the
minimum viable population (MVP) size is presented (Section E.3.3.). Finally, a sustainable adult
fish harvest rate is proposed (Section E.3.4).
E.3.1 Fish Biology
For persons familiar with human health risk assessment or assessment of risks to terrestrial
populations of wildlife (e.g., birds, mammals), some important attributes of fish biology are worth
stating.
Fish are cold-blooded (i.e., poikilothermic). Their internal body temperatures vary
considerably, particularly with the temperature of ambient water in which they live. Few fish
(e.g., open-ocean tuna) are sufficiently active swimmers to maintain a core temperature above
ambient water. Nor do fish have significant control over absorption of heat from incident
sunlight. Thus, fish growth and reproduction vary considerably with latitude and general climatic
factors.
Fish are gape feeders. They consume their prey whole, and thus cannot eat fish larger than
their "gape," or mouth opening. This results in the typical aquatic "food chain" of smaller fish
being consumed by larger fish, which are consumed by still larger fish. The top piscivorous fish
(e.g., walleye, pike) in the water column also tend to have wider or longer gapes, or both, for a
given body weight compared to lower trophic-level fish (e.g., perch, sunfish) with a smaller gape
relative to their body size.
Fish continue to grow over their lifespan. In northern temperate (and southern temperate)
regions like the United States, fish tend to reproduce seasonally (once per year). The fastest
growth occurs during the summer months. For all fish species, body size increases with age.
For the longer-lived species, growth continues over the lifespan, and the age at first
reproduction might be delayed for several years. As growth continues after sexual maturity,
larger females can produce more eggs than younger, smaller females.
Many attributes offish populations are density dependent. Survivorship of young
("recruitment") tends to decrease with increasing abundance of adults and other predatory fish
species; conversely, higher mortality among adults can release the young and juveniles from
predation and competition for food, allowing higher recruitment and growth rates. Individual fish
growth rates depend on density to some extent; growth rates tend to decrease with increasing
fish numerical and biomass density due to increasing competition for food.
Approximately 10 percent of energy is lost between tropic levels. Limits to surface water
primary productivity and inputs of organic materials from terrestrial ecosystems limit the overall
fish carrying capacity (K) of any given lake. Losses of energy from one trophic level of fish to the
next tend to be on the order of 90 percent (85-95 percent) (UM 2016); loss of energy from one
level to the next for warm blooded animals (birds and mammals) is even higher (95-99 percent)
because of the energy spent in maintaining body temperature. Thus, ingestion of 10 grams of
fish biomass by another fish usually leads to a 1-gram increase in body weight or in egg
production in the consumer fish. Fish standing biomass, therefore, tends to decrease with
increasing trophic level.
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E.3.2 Fish Population Modeling
Population modeling often is used in predicting fisheries responses to management options,
including sustainable rates of exploitation. A variety of types of population models have long
been used in fisheries management (Vaughan et al. 1984): (1) surplus production models
(Shaffer 1968); (2) yield models (Gulland 1969; Ricker 1975); (3) stock-recruitment models
(Ricker 1975; DeAngelis and Christensen 1979); (4) Leslie Matrix models (Leslie 1945;
Goodyear and Christensen 1984); and (5) bioenergetics models, which examine factors that
affect growth of individual fish (Ursin 1967; Stewart 1980). Leslie matrix models have the
advantage of incorporating age-specific survivorship, growth, age at sexual maturity, and
fecundity rates for females of a population, which is important for longer-lived top-trophic-level
fish.
Use of population models in the field of ecological risk assessment began in the 1990s, but it
faces many challenges (Barnthouse et al. 2008). One particularly difficult characteristic of
natural populations is variation in key life-history parameter values with changes in population
density (i.e., density-dependent population regulation) and fish community structure. In general,
some additional adult mortality (e.g., fish harvesting) can be compensated by increased growth
rates and increased survival of the young to maturity. Estimating MVP and sustainable harvest
rates, given density-dependent compensation in populations, is difficult. Density-dependent
predator-prey interactions among fish species in the same lake compound the difficulty. For
example, Post et al. (2002) found that in lakes with high walleye harvest rates, populations of
cyprinids and other TL3 fish increased. The TL3 fish eventually outcompeted juvenile walleye
for food, resulting in loss of walleye altogether (Post et al. 2002).
An example of the Leslie matrix-approach is the Purchase et al. (2005) study of harvest rates of
walleye and lake trout compatible with sustained fishing of those species in Lake Erie and in the
Upper Kesagami Lake in Ontario, Canada. Purchase and colleagues used a modified age-
structured Leslie matrix model (Leslie 1945, Caswell 1989, Hayes 2000) to estimate population
sustainability under different fishing pressures. The basic equation using the Leslie matrix can
be specified by Equation E-1:
1 = Ixq=i !xmx e- Eqn.E-1
where:
lx = age-specific survival rates (per year)
mx = age-specific fecundity (birth rates, per year)
r = Malthusian parameter (per capita population growth rate)
x = age (years)
q = lifespan (years)
With population- and species-specific life-history data, the maximum value of r(rmax) can be
estimated. That value corresponds, in theory, with a sustainable harvest rate assuming
relatively constant environmental conditions and density-independent values for the specified
parameters. The realized value of r for a population must exceed zero for long-term existence.
Purchase et al. (2005) analyzed fisheries data for walleye and lake trout in the two lakes, using
published data for age of maturity, relative fecundity, and natural mortality from previous studies
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of the populations. The annual natural adult mortality rates ranged from 0.11 for walleye in
Upper Kesagami to 0.35 for walleye in Lake Erie, while reports of early mortality (for eggs
through year 1) ranged from 0.99985 for walleye to 0.9957 for lake trout. Purchase et al. (2005)
found that estimates of rmax were sensitive to estimates of early mortality, adult mortality, and
growth rates. Purchase et al. (2005) found larger differences in modeled population growth rates
between two populations of the same species in two different lakes than between the two
different species in the same lake. This level of site-specificity is inappropriate for a screening
level, nationwide, risk assessment for thousands of facilities.
Post et al. (2008) demonstrated use of a fish production and harvest model [based on the
Gordon-Schaefer model included in Clark (2006)], which also depends on the logistic population
growth function. The model integrates the density dependence of birth and death rates into the
single parameter, r. The value of r declines with increasing density, approach the carrying
capacity of a lake, K, at a rate that is density-dependent. The productivity of an environment
(and the abiotic characteristics) and species life histories determine K. This approach, however,
requires knowledge of carrying capacity, which depends on overall lake productivity and size.
We therefore moved on to other approaches to estimating MVP (Section E.3.3) and lake
productivity (Section E.4).
E.3.3 General Estimates of MVP
The MVP, a concept used frequently in conservation biology for animals, is defined as the
smallest population that will persist for a specified duration (e.g., 100, 250, 1,000 years) with a
given probability (e.g., 95 percent). To estimate an MVP, one must specify a timeframe of
interest and an "acceptable" probability of extinction within that period (e.g., Soule 1987;
Ak?akaya et al. 1999).
MVP for any given species and location depends on many attributes of the species' biology
(e.g., body size, reproductive rate, home range size, habitat patches, connectivity between
habitats, variability in environmental characteristics that impact fecundity and survival,
probability of local catastrophes). At lower numbers of breeding individuals, the chance that a
local population would go extinct because of random environmental and demographic events is
higher (Menzie et al. 2008).
Many textbooks and advanced degrees are dedicated to applied ecology and population
modeling to inform conservation or resource management efforts. Much of the initial work on
MVP investigated the genetic minima required for short-term survival, continuing adaptation to
environmental change, and ultimately, long-term evolution. Consequences of inbreeding have
been considered the primary threat to short-term population survival, and genetic drift is the
principal threat to losing the genetic variation required for adaptation (Shaffer 1987). Several
analyses (Senner 1980; Franklin 1980; Soule 1980; Frankel and Soule 1981; Lande and
Barrowclough 1987) have led to the conclusion that a minimum "effective" population size of
about 50 is required for short-term survival (e.g., several generations, decades). Effective
population sizes of approximately 500 are necessary to provide adequate genetic variation for
continuing adaptation over the longer term (e.g., tens of generations, centuries for some
animals) (Shaffer 1981, 1987; FAO/UNEP 1980).
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
Allendorf 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, random mating between individuals, and no overlap between
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generations (see studies cited in NRC 1986). For animals with 50:50 sex ratios, the effective
population size is close to the actual breeding adult population size (Ewens et al. 1987).
The Food and Agriculture Organization of the United Nations Environment Programme
(FAO/UNEP 1980) pointed out that if a population is held in check at Ne = 50, it will lose about
one-fourth of its genetic variation after 20 to 30 generations. Thus, to maintain a particular stock
for longer than that, its Ne must be increased. As stated in the report, "a rough rule of thumb is
that G is approximately equal to Ne, G being the number of generations the stock is likely to
retain its fitness at a relatively high level" (FAO/UNEP 1980).
We therefore concluded that a minimum of 50 adult fish of one species in the WCC
compartment would be needed for a population to be self-sustaining. Given the large number of
factors that influence MVP, Ewens et al. (1987) cautioned against using a "rule of thumb" across
circumstances.
E.3.4 Sustainable Fish Harvest Rates
In addition to identifying an MVP, we needed to estimate what additional adult mortality might be
tolerated by a WCC population due to harvesting by the angler in the RTR screening scenario.
This introduces additional density-dependent interactions between the angler and the fish
population. From an evaluation of 3,500 rainbow trout populations in British Columbia, Post et
al. (2008) concluded that fish population abundance depends on the relationship between
fishing effort and fish CPUE for four reasons: (1) harvest equals fishing effort multiplied by catch
rate; (2) catch rate correlates with fish abundance; (3) abundance depends on the outcome of
the fish population interaction with harvesting; and (4) fishing effort is a function offish
abundance.
Modeling the relationships between angler and fish population would require site-specific data,
which is not appropriate for a nationwide screening-level assessment. We therefore searched
the literature to find estimates of fish harvest rates that are sufficiently conservative to be
tolerated by most fish species.
Allen et al. (2009) used an age-structured model and existing fisheries data to evaluate
sustainable recreational harvesting of Murray cod (Maccullochella peelii peelii), one of the
world's largest freshwater fish in southeastern Australia. They concluded that fishing could be
sustained if the exploitation rate is maintained under 0.15 (for the current regulation of 50 cm
minimum length to take home) to prevent overfishing. At a higher exploitation rate of 0.30, the
minimum fish length would need to be at least 70 cm to be sustainable (i.e., for adequate annual
spawning).
Johnson (1980) found that an annual exploitation rate of 0.11 (11 percent) of anadromous arctic
charr (Salvelinus alpinus) by Inuit in northern Canada led to a steady decline in the size of fish.
Based on those data, VanGerwen-Toyne & Tallman (2010) recommended that to ensure
sustainability, a harvest rate <0.05 per year was needed in this very cold environment (Roux et
al. 2011).
In a survey offish communities in 122 lakes in northern Europe, Hakanson and Boulion (2004)
concluded that a typical loss from fishing by birds, mammals, and humans approximates 10
percent of the fish biomass in the prey fish compartment (TL3) and 10 percent of the biomass in
the predator fish compartment (TL4).
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For our lake size analysis, we assumed that anglers could harvest 10 percent of the biomass of
pelagic WCC (TL4.5) adult fish each year without diminishing the WCC fish population size or
annual productivity. This harvest rate is low enough to allow density-dependent increased
survival and growth rates of young and juvenile fish to balance (compensate for) the additional
adult mortality.
E.4 Lake Fish Productivity
The first question in Section E.1 is: How large does a lake need to be to provide a self-
sustaining population(s) of top-trophic-level fish? To phrase the question in another way, what
are the combinations of (a) minimum lake size and (b) fish productivity per unit area that could
maintain an MVP of 50 adult breeding fish in the WCC compartment? This section focuses on
(b) lake fish productivity per unit area.
We emphasize that lake productivity varies with surface area, depth, temperature, latitude,
altitude, nutrient status, local hydrogeology, weather extremes, and other factors. Fish
population sustainability also depends on lake primary productivity, inputs of organic materials
from land, the relative abundance and diversity of invertebrates and other fish species and their
feeding relationships, among other factors. Thus, no "single" answer to either question would be
"representative" of lakes across the United States for a screening-level risk assessment.
Nonetheless, for the RTR screen, we established one (Tier 1) or possibly more lake(s) (Tiers 2
and 3) and estimated a WCC harvest in those lakes. As background, we first describe general
lake characteristics (Section E.4.1). Empirical models of lake productivity as it relates to
measurable lake attributes are presented next (Section E.4.2). Finally, some of the studies that
measured fish productivity in specific locations are included to emphasize similarities and
differences among lakes (Section E.4.3). All three subsections discuss total fish productivity; we
conclude this section with our selection of one lake productivity estimate to use for the RTR
screen. Fish productivity by trophic level is investigated in Section E.5.
E.4.1 Lake Characteristics
Exhibit E-3 provides one summary of physical and chemical characteristics of natural lakes in
North America based on a sample of 72 lakes of at least 5 hectares in size, located from the
Precambrian shield in Central Ontario through sedimentary basin lakes in the eastern United
States (Nurnberg 1996). In this sample, lake surface area ranges over 5 orders of magnitude
and the mean depth for each lake ranges from 1.8 to 200 m. Exhibit E-3 is not meant to
summarize the characteristics of lakes across all regions of the United States.
Exhibit E-3. Characteristics of 72 Lakes in Eastern North America
Variable
Units
Median
Minimum
Maximum
n
Surface Area (A)
ha
64
5
8.2 x 106
72
km2
0.64
0.05
8.2 x 104
m2
640,000
50,000
8.2 x 1010
Depth, mean (D)
m
7.6
1.8
200
72
D/A
m/km2
8.0
0.14
48.1
72
Total Phosphorus (TP)
|jg/L
8.1
3.3
107
72
Total Nitrogen (TN)
|jg/i
324
149
1,000
63
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TRIM-Based Tiered Screening Methodology for RTR
Variable
Units
Median
Minimum
Maximum
n
TN/TP
34
11.6
79
63
Chlorophyll
ijg/L
2.9
1.0
40
43
Dissolved Organic Carbon (DOC)
mg/L
3.5
1.5
12.0
62
Note: Lakes from central Ontario in the Precambrian shield, from southern Ontario and Quebec, and from the eastern
United States in sedimentary basins, n = number of lakes.
Source: Nurnberg (1996).
Although the maximum total phosphorus (TP) concentration in Exhibit E-3 is 107 |jg/L for this
sample of lakes, TP concentrations in some lakes are much higher.
Some attributes of lakes vary by latitude. For example, lakes in the southeastern United States
are considered monomictic, that is, they turn over24 once per year in the autumn, whereas
northeastern lakes also turn over in the spring when the winter ice cover melts (Osidele and
Beck 2003). In addition, the longer growing season in the south promotes higher total
phytoplankton and microbial production (and higher turnover rates), which can support higher
total biomasses of both non-fish and fish trophic groups (Osidele and Beck 2003).
Lakes have been categorized from a biological perspective into three categories generally
related to available nutrients and consequent primary productivity: oligotrophic, mesotrophic,
and eutrophic (see text box below). Values for several chemical/physical characteristics of lakes
that are associated with these categories have been quantified. For example, Exhibit E-4
presents one lake classification standard and associated values for TP, TN, chlorophyll, and
water transparency associated with the three lake trophic categories in Canada (colder than
most regions in the United States).
Exhibit E-4. One Trophic Classification Standard for Lakes
Trophic Status
Total
Phosphorous
(mg/m3)
Total Nitrogen
(mg/m3)
Chlorophyll a
(mg/m3)
Transparency
(m)
Oligotrophic
<15
<400
<3
>4.0
Mesotrophic
15-25
400-600
3-7
2.5-4.0
Eutrophic
>25
>600
>7
<2.5
Measurements are average, epilimnetic (layer of water above the thermocline), summer values, in Canadian lakes.
Source: Forsberg and Ryding (1980) as modified by Canfield et al. (1983).
24During summer, a thermocline generally develops as the surface layer of water warms, becomes less dense, and
therefore floats above the bottom layer of colder water (in lakes deep enough to develop a thermocline). In the fall,
the surface water layer cools, becomes similar in density to the bottom layer, and they can mix (turn over) with the
nutrient-laden bottom waters mixing with the nutrient-depleted surface water. In northern freshwater lakes, ice cover
keeps water at the surface colder than in the remainder of the lake; when the ice cover melts, the dense colder
surface layer again mixes with the remaining lake waters. TRIM.FaTE does not simulate lake turnovers.
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In most lakes, nitrogen concentrations are more than adequate to support maximal primary
production; TP tends to be the limiting nutrient. Thus,
the inorganic parameter most often related to lake
trophic status is TP concentration. Definitions of TP
concentration "cutoffs" between lake trophic
categories vary slightly among investigators. Using
data from several classification cutoffs reported by
Nurnberg (1996), we summarize the definitions of
trophic categories for lakes with respect to
epilimnetic summer values for TP as:
. Oligotrophic: TP <10-15 |jg/L
. Mesotrophic: TP 10-15 to 25-30 |jg/L
. Eutrophic: TP 25-30 to 100 |jg/L
. Hypertrophic: TP >100 |jg/L
Shallow lakes with large stands of macrophytes can
show different relationships between TP and
phytoplankton, oxygen, and transparency because of the phosphorus tied up in the
macrophytes (Canfield etal. 1983).
The biomass offish (and the number of trophic levels supported) depends on lake size and the
general productivity of a lake per unit area. Lake productivity depends on many factors,
including latitude, seasonal temperatures, nutrients supporting algae, and inputs of organic
materials (e.g., leaf litter) from terrestrial habitats and from emergent vegetation (allochthonous
inputs). For example, in sub-catchments within a 275-hectare watershed in Ontario, Canada,
Tanentzap et al. (2014) found that near-shore forested and wetland sub-catchment areas
around Daisy Lake export more organic material to the lake than other sub-catchments. They
estimated that at least 34 percent of yellow perch (Perca flavescens) biomass in the lake is
supported by terrestrial primary production via organic inputs that enhance bacterial biomass
that enhances biomass in larger zooplankton, which enhances production of young-of-the-year
fish. In areas with high forest cover, they estimated that up to 66 percent of fish biomass was
supported by organic loading from terrestrial primary production. TRIM.FaTE does not simulate
export of organic materials from terrestrial parcels to the lake(s).
E.4.2 Predicted Lake Productivity - Nutrient Status and Fish Biomass
As stated above, climatic factors play a large role on a global or hemispheric scale, but at
regional scales, many researchers have found "morphometric" (e.g., surface area, maximum
depth, mean depth) and "edaphic" (e.g., nutrient content, dissolved oxygen, acidity) indicators
for lakes correlate with overall fish productivity. Several versions of the morphoedaphic index
(MEI) were developed starting in the 1960s and 1970s to combine lake morphology and nutrient
status to estimate fish yields (Cote et al. 2011).
The literature on productivity and standing crop (biomass) of fish and other trophic groups in
lakes is extensive and is not reviewed here. As stated in Section E.4.1, one physical/chemical
attribute of lakes that provides high predictive power for biomass in aquatic ecosystems is the
often-limiting nutrient TP. Other characteristics, such as total lake surface area, ratio of surface
area to mean depth, dissolved organic carbon (DOC), macrophyte biomass, transparency, and
an MEI based on several abiotic and biotic measures, have also been examined for their
Lake Trophic Classification -
Definitions
Oligotrophic: Waters lacking in plant
nutrients and plants and generally rich in
oxygen.
Mesotrophic: Stage between oligotrophic
and eutrophic with respect to plant
nutrients, plant productivity, and water
oxygen content.
Eutrophic: Waters rich in mineral and
organic nutrients that promote abundant
plant life, particularly algae. As the plant
material turns over and decays, dissolved
oxygen can decline to levels that support
few fish.
Attachment E
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TRIM-Based Tiered Screening Methodology for RTR
predictive power. The simplest relationship with high predictive powers, however, relates total
fish biomass to lake TP.
Peters (1986) evaluated empirical relationships between TP and biomass in various categories
of organisms in lakes developed by other researchers (e.g., Bird and Kalff 1984; Hanson and
Legget 1982; Pace 1986). Categories included bacteria, nanoplankton, "net" plankton,
microzooplankton (e.g., rotifers and flagellated or ciliated protozoa), and macrozooplankton
(e.g., Daphnia, copepods, amphipods, fish larvae). Peters converted all biomass to units of
grams wet weight per square meter (g ww/m2). Exhibit E-5 presents those models along with
predictions of total biomass for each group for 5, 10, and 50 |jg [TP]/L.
Relationships for bacteria and plankton were initially reported in biomass per unit volume.
Peters (1986) converted them to biomass per unit area by assuming that bacteria and
planktonic organisms occur only in the euphotic zone, the depth of which is given by Equation
E-2 from Peters (1986):
Depth_of_Euphotic_Zone (m) = 24 * TP (mg/m3)'028 Eqn. E-2
Note that this equation indicates that the more abundant plankton of more eutrophic lakes
should be concentrated in a shallower euphotic zone (the depth of light penetration decreases
with increasing concentrations of algae at the surface of more eutrophic lakes). Peters (1986)
converted zooplankton dry weight to wet weight assuming a 1:10 ratio and converted bacterial
cell counts to wet weight (ww) assuming 0.1 g ww per 1012 cells (Peters 1986).
Exhibit E-5. Predictions of Biomass (B) of Biotic Components of Lakes with Different
Total Phosphorus (TP) Concentrations
Group
Equation
Biomass (B) (g wet weight/m2)
TP = 5 Mg/L
TP = 10 Mg/L
TP = 50 Mg/L
Bacteria
B = 2.1 x TP037
3.8
4.9
8.9
Nanoplankton
B = 0.40 x TP10
2.0
4.0
20
Net plankton
B = 0.20 x TP14
1.9
5.0
48
Microzooplankton
B = 4.1 x TP0-29
6.5
8.0
13
Macrozooplankton
B = 4.6 x TP037
8.3
11
20
Benthos
B = 0.81 x jp°7i
2.5
4.2
13
Fish
B = 0.59 x TP0-71
1.8
3.0
9.5
Source: Adapted from Peters (1986).
In a regression analysis of data on TP and fish biomass for 31 lakes across North America,
Europe, and Russia, Nurnberg (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 |jg/L) = 1.9 g ww/m2
Meso-eutro (TP = 30 |jg/L) = 3.7 g ww/m2
Eutro-hypereutro (TP = 100 |jg/L) = 8.5 g ww/m2
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TRIM-Based Tiered Screening Methodology for RTR
Nurnberg (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 |jg/L) = 7.4 g ww/m2
Meso-eutro (TP = 30 |jg/L) = 10.6 g ww/m2
Eutro-hypereutro (TP = 100 |jg/L) = 15.6 g ww/m2
As 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) evaluated data for 43 lakes ranging in surface area from 0.1 to
82,414 km2 (10 ha to 8 million ha; 25 acres to 20 million acres), with TP concentrations of
8-540 |jg/L and macrobenthos standing crop of 0.48-61.1 g/m2, and located between 42° and
62° N latitude and 17° E to 117° W longitude. Based on a subset of 21 lakes sampled at the
same time, the best univariate predictor offish yield was TP; the regression correlation
coefficient (r2) was 0.84 (Equation E-3):
FY = 0.792 + 0.072 (TP) Eqn. E-3
where:
FY = total fish yield (kg/hectare)
TP = total phosphorous (jjg/L)
Logarithmic transformation did not improve the predictive power. All but five of the lakes had TP
under 100 |jg/L and fish yield of less than 1 g ww/m2. At a 10-percent harvest rate, that would
equal 10 g ww biomass/m2.
Hanson and Legget (1982) also estimated the relationship between macrobenthos biomass and
TP and fish standing crop from a sample of 18 to 20 lakes drawn from the same set of 43 lakes.
The relationship between TP and total fish standing biomass is shown in Equation E-4 and
between standing biomass of benthic invertebrates and fish biomass is shown in Equation E-5.
log-io(FSB) = 0.708 log10(TP) + 0.774 (r2 = 0.75, n= 18) Eqn. E-4
log10(FSB) = 5.692 (M/z) + 28.7 (r2 = 0.83, n = 20) Eqn. E-5
where:
total fish standing crop or biomass (kg/ha)
total phosphorus (jjg/L)
macrobenthos biomass (kg/ha) divided by mean lake depth (z)
(meters)
FSB
TP =
M/z
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TRIM-Based Tiered Screening Methodology for RTR
Hanson and Leggett (1982) compared the predictions of Equation E-4 with Taylor's (1971) data
on average TP and total fish biomass from five Tennessee Valley Authority reservoirs following
rotenone poisoning. The comparison, presented in Exhibit E-6, produced a reasonable match.
Yurk and Ney (1989) examined the relationship between TP and standing stock of fish in 22
reservoirs in southern Appalachia sampled in 1973. The reservoirs ranged in surface area from
445 to 53,400 hectares, had TP concentrations ranging from 8 to 81 |jg/L, with total fish
biomass ranging from 3.4 to 232 g ww/m2. Their logarithmic regression relating total fish
standing crop or biomass (FSB) to TP is presented as Equation E-6.
log10(FSB) = 1.07+ 1.14 \og10(TP) (r2 = 0.75, n = 22) Eqn. E-6
Predictions of total fish biomass from TP from the equation of Yurk and Ney (1989) are
compared with the predictions from the equation of Hanson and Legget (1982) in Exhibit E-7. At
intermediate TP concentrations, predictions of total fish biomass are similar between the two
models.
Exhibit E-6. Reported Compared with Predicted Fish Biomass for Five Reservoirs
Reservoir
Average Total
Phosphorus
(mq/l)
Reported Fish
Biomass (g
ww/m2)a
Predicted Fish
Biomass
(g ww/m2)
Percent
Predicted/Reported
Kentucky
270
28
26
92.5
Cherokee
160
23
19
83.9
N orris
20
15
11
73.3
Nottley
50
14.3
12.8
85.5
Douglas
110
12.5
16.4
131.2
aTotal fish biomass following rotenone kill as reported by Taylor (1971).
Source: Hanson and Leggett (1982), Table 5; original units for biomass density = kg/hectare; changed to g wet weight biomass/m2
by dividing by 10.
Exhibit E-7. Comparison of Predictions of Total Fish Biomass from Total Phosphorus
(TP)
TP (Mg/L)
Total Fish Biomass (g ww/m2)
Hanson and Legget (1982)
Yurk and Ney (1989)
10
3.0
1.6
30
6.6
5.7
80
13.2
17.4
100
15.5
22.4
200
25.4
-
500
48.7
-
indicates that TP is much higher than the TP range for data used to derive the model; thus, estimating fish biomass for those TP
values with the Yurk and Ney (1989) model is not appropriate.
For a site-specific, refined risk assessment, one could use these regressions and measured TP
concentrations in the lake(s) to predict total fish standing crop or biomass per unit area. For
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TRIM-Based Tiered Screening Methodology for RTR
Tiers 1 through 3 of the RTR screening risk assessment, however, we need to assume a single
value for fish productivity per unit area where TP concentration is an unknown.
E.4.3 Measured Total Fish Standing Biomass
The empirical models provided in Section E.4.2 are based on lake data sets for which the
original data are only partially published. In this section, we present some studies that measured
total fish biomass in lakes of different sizes and from different climates. In reviewing studies of
aquatic communities, we excluded data from the Great Lakes, because the size of those
systems allows for substantially longer food chains and a more complete segregation between
pelagic and benthic food webs than occurs in most freshwater ecosystems of North America.
We also excluded lakes less than 5 hectares from our assessment, because they are unlikely to
support stable fish communities and therefore generally are not evaluated for bioaccumulative
chemicals.
In general, for small lakes in cold climates, relatively low fish productivity is likely. For example,
Demers et al. (2001) found total fish standing biomass 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. Across 48 lakes in
Newfoundland ranging in size from 3.56 hectares to 1,909 hectares, Cote et al. (2011) found
that benthivorous salmonid biomass per unit area varied by more than an order of magnitude
(minimum 0.045 g ww/m2; maximum 1.0 g ww/m2; mean: 0.40 g ww/m2). Brook trout (Salvelinus
fontinalis) biomass was almost 76 percent of total salmonids but varied by almost two orders of
magnitude across lakes.
Bronmark and Weisner (1996) reported fish communities from 44 small ponds in southern
Sweden (most were less than 5 hectares, or about 12 acres). All small ponds were dominated
by periphyton (algae growing on rock surfaces), which was heavily grazed by freshwater snails.
The TL3 fish consumed the snails. The piscivorous fish found in some ponds were all bottom
feeders that ate both snails and small fish. Similarly, De Leeuw et al. (2003) found that most
Scandinavian and Dutch lakes are dominated by benthivorous fish. The biomass and proportion
of benthivores increased significantly with TP primarily due to increase of benthivorous bream (a
species of sunfish/cyprinid) >25 cm in length.
The largest freshwater data set from more temperate climates of which we are aware is that of
Leidy and Jenkins (1977). They analyzed several large data sets to support modeling offish
productivity and carrying capacity in reservoirs across the United States for the National
Reservoir Research Program. The analyses derived from data for 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 total fish
biomass density was 41.3 (± 30.4 standard deviation) g ww/m2 (Exhibit E-8).
Exhibit E-8. Total Fish Biomass in Reservoirs of the United States by Drainage Area
Drainage Area
Number of
Reservoirs
Total Fish Biomass (g wet weight/m2)
Mean
SD
Middle Atlantic
1
14.2
Gulf and South Atlantic
9
18.3
6.2
Ohio Basin
13
26.4
16.3
Lower Mississippi
5
41.1
19.9
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TRIM-Based Tiered Screening Methodology for RTR
Drainage Area
Number of
Reservoirs
Total Fish Biomass (g wet weight/m2)
Mean
SD
Arkansas (Arkansas)
19
68.7
35.1
White (Arkansas)
6
33.4
8.4
Red (Arkansas)
6
30.9
24.6
Rio Grande and Gulf
1
28.3
Missouri Basin
1
74.1
All Reservoirs
61
41.3
30.4
Abbreviations: SD = standard deviation.
Source: Appendix B in Leidy and Jenkins (1977).
The minimum and maximum total fish biomass densities were 3.2 and 133.2 g ww/m2,
respectively, and the median value was 30.9 g ww/m2 (Exhibit E-8). Thus, fish standing biomass
per unit area in the reservoirs varied by more than three orders of magnitude.
The fish were sampled using rotenone poisoning of coves ranging in size from 1 to 5 acres after
separating the coves from the reservoir using nets, similar to the method of Taylor (1971). To
estimate the percentage of fish actually present that were recovered, marked fish were placed in
the segregated coves prior to treatment with rotenone. In some cases, divers collected fish that
did not float to the surface. All fish collected were identified to species and weighed. Most cove
sampling was performed one time per year in August. Most reservoirs were sampled at least
once for 2 or more years between 1952 and 1975, with some being sampled 10 to 20 years
during that interval.
Leidy and Jenkins (1977) applied adjustment factors to correct for non-recovery bias
(i.e., bottom fish that tend not to float to the surface; small fish that are not recovered) and
habitat preference bias (i.e., fish that are more or less abundant in the coves compared with the
open water). The combined adjustments for sampling bias ranged from a factor of 0.88 for
sunfishes (cyprinids), which were over-represented by sampling in coves, to factors of 3.08 and
3.36 for catostomids and freshwater drum, respectively, which were estimated to be about
2.4 times more prevalent in the open water than in the coves. The use of adjustment factors for
some species indicates the uncertainties in the data; however, unadjusted biomass estimates
are very likely to be biased.
Exhibit E-9 summarizes the data on total fish biomass in reservoirs and lakes from the literature
we reviewed. The table suggests that average fish biomass density for reservoirs, although
quite variable, is generally higher than that for lakes. TP concentrations in the reservoirs might
be higher on average than TP concentrations in the natural lakes; however, the data are
insufficient to test that hypothesis for the studies reviewed. Reservoirs in general might support
higher fish biomass densities for a given TP level than do natural lakes because of extensive
littoral zones with macrophytes or high quantities of detritus to fuel the Bl component of the
aquatic food web.
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit E-9. Total Fish Biomass Density in Reservoirs and Lakes from Different Studies
Water Body (Source)
N
Total Fish Biomass (g ww/m2)
Mean TP
(mq/l)
Mean
Min
Max
Med.
Reservoirs of the U.S. > 202 ha (a)
61
41.3
3.2
133
30.9
NR
Appalachian Reservoirs, U.S. (b)
22
64.2
3.4
232
55.0
32
DeGray Lake, Arkansas, U.S. (c)
1
7.5
-
-
-
NR
Ranger & Mouse Lakes, Ontario (d)
2
3.3
2.7
3.8
-
NR
Lakes in U.S. (e)
18
9.4
NR
NR
NR
NR
Abbreviations: NR = not reported;indicates not relevant; TP = total phosphorus.
Sources: (a) Leidy and Jenkins (1977); (b) Yurk and Ney (1989); (c) Ploskey and Jenkins (1982); (d) Demers et al. (2001);
(e) Randall et al. (1995) as reanalyzed by Nash et al. (1999).
To estimate 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 Nurnberg (1996) at a high TP of 100 |jg/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.
E.5 Proportion of Fish Biomass by Trophic Level
Much of the literature on fish communities comes from research on the effects of different
trophic elements on aquatic food web structure and consequent productivity of fisheries. Several
hypotheses have been developed over the years to explain relationships among trophic levels in
lakes and rivers using fundamental ecological concepts.
E.5.1 Principles of Trophic Pyramids
As a "rule of thumb" in ecology, 10 percent 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) (UM 2016). With different species having different energy assimilation
efficiencies, with fat providing approximately twice as many calories as muscle, and with smaller
animal species generally having higher turnover rates than larger species, however, 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 TL4.5 fish).
Further complicating prediction of standing biomass at different trophic levels are the
relationships among trophic groups. For example, a "classic" trophic cascade hypothesis
associated with managing lakes for top-trophic-level fish predicts that increasing piscivore
biomass in a lake will result in: (a) decreasing biomass of their prey, including planktivorous fish;
(b) increasing biomass of zooplankton, and (c) decreasing biomass of phytoplankton (Carpenter
etal. 1985; Carpenter and Kitchell 1996).
An alternative hypothesis about trophic structure is the "top-down/bottom-up" hypothesis, which
predicts that the top-down effects of piscivores are strongest at the top of the food web,
weakening in trophic groups closer to the primary producers, whereas the phytoplankton are
most strongly influenced by nutrient availability (bottom-up). Drenner and Hambright (2002)
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TRIM-Based Tiered Screening Methodology for RTR
reported that as of 2002, over 1,900 reports had been published on the effects of fish in lakes.
They reviewed 33 experiments and 6 surveys to test these hypotheses, of which only 17 did not
include confounding factors. Of those, they concluded that 7 supported the trophic cascade
hypothesis and 10 did not.
Drenner and Hambright (2002) found a general pattern of lower chlorophyll concentrations for
given TP concentrations in systems containing piscivores (4-link systems) relative to systems
with only planktivorous fish (3-link systems). The trophic cascade appears to work where
herbivorous fish are dominated by small (vulnerable to predation) species rather than larger
herbivores (e.g., shad, carp) that are not vulnerable to predation after reaching larger sizes.
Given the diversity of lake ecosystems and competing hypotheses for fish community structure
by trophic level, we investigated two lines of evidence: models offish biomass at different
trophic levels (Section E.5.2) and measurements of fish biomass in different trophic groups
(Section E.5.3). Bioenergetic simulation models of fish community structure are useful because
a model can include several species, predator-prey relationships, and age/size classes at one
time, using measured values to parameterize the model initially. Measurement of biomass at
different trophic levels is difficult because different species and sizes of fish are best caught via
different methods. Rotenone killing of all fish in a lake, which can yield the most accurate
measurement, is feasible only in relatively small lakes or ponds and is wasteful.
E.5.2 Models of Fish Biomass in Different Trophic Groups
Of the recent models that simulate bioaccumulation of toxic chemicals in aquatic food webs
identified in the literature search, the one that appeared most similar to the TRIM.FaTE
approach in compartmentalizing the fish compartments of the food web is the Comprehensive
Aquatic Systems Model (CASM, Version 2.0) developed for Quebec, Canada (DeAngelis et al.
1989). This detailed food-web model includes data sets that provide parameter values for four
Canadian aquatic ecosystems: (1) northern lakes/reservoirs, (2) northern rivers, (3) southern
lakes/reservoirs, and (4) southern rivers. Northern is defined as between 48° latitude and
55° latitude, and southern is defined as between 44° and 48° latitude. The parameterization of
the model for "southern" locations would apply only to the more northern areas of the United
States.
For each aquatic ecosystem, CASM includes three data sets derived from the primary literature:
(1) data for the primary producer and consumer populations; (2) definitions of the grazing and
predator-prey interactions (diet preferences and assimilation efficiencies); and (3) data on daily
incident solar radiation, water temperature, and nutrient inputs. Using those three data sets,
CASM can be used to estimate the baseline biomass values in 10 biotic compartments based
on factors that affect primary productivity and trophic transfers.
Although CASM and its databases are not publicly available, Bartell et al. (1999) have published
baseline biomass estimates in the open literature for a northern river and for a Florida lake. We
totaled those biomass estimates for each compartment type and then determined the proportion
of the total biomass represented in each compartment type, shown in Exhibit E-10 for the lake.
The diets assigned to each species were not reported in the publications, so cannot be
evaluated.
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit E-10. Distribution of Standing Biomass Among Aquatic Compartments Simulated
in the Comprehensive Aquatic Systems Model (CASM) for a Florida lake
Biotic Compartment
Total Biomass
Percent Biomass
g C/m2
Percent
Animal
Fish
Phytoplankton
1.38
13
NA
NA
Periphyton
0.70
6
NA
NA
Macrophytes (e.g., Elodea, Ceratophyllum)
7.6
70
NA
NA
Zooplankton
0.07
1
0.06
NA
Benthic Invertebrates
0.44
4
0.39
NA
Pelagic Omnivore (e.g., shiners, sunfish)
0.263
2
0.23
0.42
Pelagic Piscivore (e.g., gar, pickerel)
0.059
1
0.05
0.10
Benthic Omnivore (e.g., bullhead, warmouth)
0.275
3
0.24
0.44
Benthic Piscivore (i.e., largemouth bass)
0.022
2
0.02
0.04
Note: We did not identify data for converting dry carbon to wet-weight biomass for the compartments listed.
Abbreviations: C = carbon, NA = not applicable.
Source: Bartell et al. (1999).
The lake clearly is dominated by macrophytes, including the invasive species from the aquarium
trade, rooted or free-floating Elodea sp. and Ceratophyllum sp., which, unlike phytoplankton,
grow in length without harvesting by most fish species (an exception is carp, which can
consume both macrophytes). The macrophytes and plankton undoubtedly contribute to detritus
in the benthos; however, Bartell et al. (1999) did not report the carbon content of detritus per
unit area. The pelagic and benthic omnivores comprise 86 percent of the total fish biomass,
while the pelagic and benthic carnivores comprise 14 percent of the total fish biomass. For a
lake in Florida without large quantities of invasive macrophytes, the trophic pyramid might look
substantially different.
Hossain et al. (2010) evaluated fish biomass and harvest rates for an oligotrophic lake (low
productivity) in Southern Hokkaido, Japan (latitude 42°36' N, longitude 140°51' E). The lake is
volcanic in origin, with surface area 70 km2, maximum depth 179 m, and mean depth 116 m. A
monomictic system, its annual average TP concentration is 3 |jg/L and TN is 150 |jg/L. Hossain
et al. (2010) used the mass-balance modeling software Ecopath and Ecosim (EwE) (e.g.,
Christensen and Walters 2004, Christensen et al. 2005), built to simulate coastal fisheries, to
investigate whether the level of fish harvests reported for the late 1990s (masu salmon harvest
of 2.64 kg/km2-year and sockeye salmon harvest of 24.45 kg/km2-yr) are likely sustainable.
Exhibit E-11 lists the estimated biomass, trophic level, annual production/biomass ratio (except
for detritus and organic matter), and the percentage of total fish biomass represented in each of
their fish compartments. Values in Exhibit E-11 are in line with other estimates (see Exhibit E-1
and Section E.5.3): 5.8 percent of total fish biomass estimated at a trophic level higher than 4.0
(masu salmon, Oncorhynchus masou), 12 percent of adult sockeye salmon (Oncorhynchus
nerka) estimated to be at TL3.75 in their model, and 81 percent of fish near TL3 (smelt,
Hypomesus transpacificus nipponensis, and juvenile sockeye salmon). None of the fish groups
are TL2; fish fry are probably represented in the zooplankton compartment. Difficulties
interpreting these simulations, however, come from the continual stocking of salmon, fish
harvesting above levels that might be sustainable for the sockeye salmon, the complex food
web simulated, and migration of some of the fish into and from the lake (anadromous).
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Exhibit E-11. Estimated Biomass by Aquatic Compartment in Lake Toya, Japan
Aquatic Compartment
Biomass
(kg/km2)
Biomass
(g ww/m2)
Trophic
Level3
Production/
Biomass (kg/kg)
Percent Total
Fish Biomass
Masu Salmon
22.7
0.023
4.12
0.54
5.8%
Adult Sockeye Salmon
45.5
0.046
3.75
0.33
12%
Juvenile Sockeye Salmon
14.1
0.014
3.16
1.72
4%
Japanese Smelt
303
0.30
3.17
1.24
77%
Other Fish
5.8
0.0058
3.07
1.50
1.5%
Shrimp
5.9
0.0059
2.27
1.83
NA
Amphipods
136
0.14
2.32
6.0
NA
Insects
110
0.11
2.11
4.2
NA
Zooplankton
162
0.16
2.05
33.5
NA
Phytoplankton
50.2
0.050
1
365
NA
Organic Materials
2000
2.0
1
NA
NA
Detritus
1000
1.0
1
NA
NA
Abbreviation: NA = not applicable.
aTrophic level estimated by Hossain et al. (2010) given the food web they characterized.
Source: Hossain et al. (2010).
Exhibit E-11 does illustrate well, however, the relatively low standing biomass of phytoplankton
(0.050 g ww/m2) compared with the other compartments but its very high annual productivity
(365 g/g production/standing biomass) and turnover rates compared with other aquatic
compartments. Zooplankton shows the next highest annual productivity rate (33.5 g/g), even
though its standing biomass (0.16 g ww/m2) is less than that of the smelt (0.3 g ww/m2), which
produce 1.24 g/g annually.
Rather than work further with fish biomass and production simulation models, which require
substantial data and are not readily transparent, we investigated measurements of fish biomass
in different trophic groups (see Section E.5.3).
E.5.3 Measured Biomass of Fish in Different Trophic Groups
A popular measure of fish productivity for game fish species across lakes is the angler effort
required to catch each fish (or catch per unit effort, CPUE). The measure, used in numerous
studies (e.g., Gorman et al. 2014; Quiros 1990), provides valuable information for commercial
and recreational fisheries applications. It, however, does not provide information on the numeric
"trophic pyramid" in lakes or the relative standing biomass of each trophic group needed for
TRIM.FaTE modeling. Specifically, CPUE usually misses the smaller fish and untargeted
species.
A key difficulty with sampling lakes for total standing fish biomass and for fish biomass at
different trophic levels is capturing and measuring the fish in the first place (see Section E.4.3).
Some lakes have been sampled by killing with rotenone all fish in a lake, which then can be
collected and measured. This practice is feasible for relatively small lakes for which state fish
and game officials might want to start the lake's trophic structure "over"; however, for larger
lakes, it is both impractical and wasteful. Other approaches to making an inventory offish
standing stock include combinations of seine fishing, electroshocking, and other methods;
Attachment E
E-25
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
however, each includes some biases against certain species and age-classes that require
"correction factors" (e.g., based on total kill inventory methods) or at least acknowledgment of
the possible magnitude and direction of biases (Leidy and Jenkins 1977).
Leidy and Jenkins (1977) estimated the biomass offish supported by various food
compartments in the 61 reservoirs included in their survey (Exhibit E-12). Only reservoirs at
least 500 acres (202 hectares) in size were included. They did not separate the piscivorous fish
species (i.e., the biomass of fish supported by "Fish" in Exhibit E-12) by benthic or pelagic
feeding habits. We pulled the data in Exhibit E-12 from Appendix G in Leidy and Jenkins (1977).
Exhibit E-12. Carrying Capacity, Biomass (g ww/m2) of Fish Supported by Each Food
Compartment Across 61 Reservoirs by Drainage Area
Drainage Area
Plants &
Detritus
Benthic
Inverts.
Zoo-
plankton
Fish
Terrest.
Inverts.
Total
Gulf and South Atlantic
5.12
3.77
0.55
2.77
0.45
12.67
Green and Cumberland Rivers
and Dewey Reservoir
10.60
6.03
1.61
3.09
0.39
21.74
Lower Mississippi Valley
11.54
4.81
4.89
6.77
0.31
28.36
Blue Mountain, Nimrod, and
Wister Reservoirs
22.64
8.53
16.03
9.26
0.33
56.72
Arkansas River Basin
25.78
9.63
6.50
7.79
0.44
50.10
Red River Basin
9.01
7.32
0.46
4.40
0.84
22.08
White River Basin
10.46
7.32
2.01
3.43
0.48
23.65
Average
13.59
6.77
4.58
5.36
0.46
30.76
Standard Deviation
7.59
2.05
5.53
2.57
0.18
16.27
Abbreviations: Terrest. Inverts. = terrestrial invertebrates, primarily insects that lay eggs at the water surface or that fall into the
reservoir from emergent and terrestrial plants.
Source: Appendix G in Leidy and Jenkins (1977).
We calculated from Exhibit E-12 that, on average, 18 percent of the fish biomass across the 61
reservoirs they examined was piscivorous (minimum of 14 percent and maximum 24 percent,
including both benthic and pelagic species; see bold values in Exhibit E-13).
Exhibit E-13. Proportion of Total Carrying Capacity, Proportion Fish Biomass Supported
by each Food Compartment by Drainage Area
Drainage Area
Plants &
Detritus
Benthic
Inverts.
Zoo-
plankton
Fish
Terrest.
Inverts.
Total
Gulf and South Atlantic
0.40
0.30
0.04
0.22
0.04
1.00
Green and Cumberland Rivers
and Dewey Reservoir
0.49
0.28
0.07
0.14
0.02
1.00
Lower Mississippi Valley
0.41
0.17
0.17
0.24
0.01
1.00
Blue Mountain, Nimrod, and
Wister Reservoirs
0.40
0.15
0.28
0.16
0.01
1.00
Arkansas River Basin
0.51
0.19
0.13
0.16
0.01
1.00
Attachment E
E-26
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Drainage Area
Plants &
Detritus
Benthic
Inverts.
Zoo-
plankton
Fish
Terrest.
Inverts.
Total
Red River Basin
0.41
0.33
0.02
0.20
0.04
1.00
White River Basin
0.44
0.31
0.08
0.15
0.02
1.00
Average
0.44
0.25
0.12
0.18
0.02
Standard Deviation
0.05
0.07
0.09
0.04
0.01
Minimum
0.40
0.15
0.02
0.14
0.01
Maximum
0.51
0.33
0.28
0.24
0.04
Median
0.41
0.28
0.08
0.16
0.02
Source: Calculated from Exhibit E-12; data from Appendix G in Leidy and Jenkins (1977).
Hakanson 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 |jg/L, the proportion offish represented by piscivores declined to less than 20
percent. The piscivores included both benthic and pelagic species. We note that most benthic
piscivores also consume benthic macroinvertebrates.
Scharf (2008) evaluated the biomass of top predatory fish (TL4 to TL4.5, pike > 20 cm,
pikeperch > 40 cm) in a large, deep stratifying reservoir in Germany (Exhibit E-14). Scharf found
that over the 20 years of the reservoir's existence, the standing biomass of those fish never
exceeded 10 percent of total fish biomass despite stocking and protection efforts. We assigned
a TRIM.FaTE compartment (WCC, WCO, WCH, BC, BO) or combination of two compartments
to describe the feeding habitat of each fish age/size-class and species and assigned a likely
trophic level to each age/size-class based on our experience with estimating fish trophic levels
(U.S. EPA 2000). Those compartments and trophic levels also are listed in Exhibit E-14. Our
estimate is that the WCC compartment of fish at TL4.5 is 3.4 percent of the total fish biomass
and that the combined WCC/BC TL3.5 (perch > 16 cm) is 17.6 percent of the total fish biomass.
Based on data from the reservoir over 20 years, Scharf (2008) concluded that introduction of
pikeperch in 1988, which became self-reproducing, helped release perch from competition,
which allowed perch to grow larger than >16 cm. At this size, they can consume other fish and
become more abundant, accounting for 17.6 percent of the total fish biomass.
Exhibit E-14. Total Fish Biomass by Trophic Level in Wupper Reservoir, Germany
Fish Age-class and Species
Compartment
Trophic Level3
Biomass Density
Individual Abundance
kg ww/ha
Percent
Individuals/ha
Percent
Total Fish Biomass
NA
93.6
100
4025
100
Piscivorous Fish Biomass (large
pike, pikeperch, perch)
NA
25.7
27.5
NA
NA
Total Fish Biomass without YOY
NA
79.4
100
NA
NA
Piscivorous Fish Biomass
NA
25.7
32.4
NA
NA
Pike > 20 cm in length
WCC 4.5
0.5
0.5
1
0.02
Attachment E
E-27
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Fish Age-class and Species
Compartment
Trophic Level3
Biomass Density
Individual Abundance
kg ww/ha
Percent
Individuals/ha
Percent
Pikeperch YOY (<12 cm)
WCH 2.5
0.2
0.2
30
-
Pikeperch (12 to 40 cm)b
WCO 3.5
2.3
2.7
15.5
-
Pikeperch >40 cm
WCC 4.5
2.7
2.9
2.5
-
Perch YOY (<10 cm)
WCH 2
12.5
13.4
2.24
56
Perch 1 -yr old (10 to <16 cm)
WCO 3
18.6
19.9
677
17
Perch older (>16 cm)
WCC/BC 3.5
16.5
17.6
90
2.2
Cyprinids YOY
WCH 2
1.7
1.8
374
9.3
Cyprinids 1 -yr old
WCH 2.5
7
7.5
296
7.4
Cyprinids older (>16 cm)
WCO/BO 3
28.1
30
292
7.3
Eel (benthic carnivore)
BC 3.5
3.5
3.7
6
-
Total of Age Classes
94
100%
1834
100%
Water Column Carnivore (WCC)
4.5
3.2
3.4
NA
NA
Water Column Carnivore/Benthic
Carnivore (WCC/BC) (except eels)
3.5
18.8
17.6
NA
NA
Water Column Omnivore
(WCO/BO)
3.0
46.7
52.6
NA
NA
Water Column Herbivore (WCH)
2.0-2.5
21.4
22.9
NA
NA
Benthic Carnivore (BC) (eel)
3.5
3.5
3.7
NA
NA
Abbreviations:not calculated in Scharf (2008) because body weight distribution across age classes uncertain; NA = not
applicable (body size varies); YOY = young-of-year (from hatching to <1 yr).
aWe assigned trophic levels to the group based on general feeding characteristics.
bPikeperch 12 cm to <40 cm in length calculated from row for total pikeperch minus the smaller and larger pikeperch in Table 1 of
Scharf (2008).
Source: Scharf (2008), Table 1.
We investigated other studies offish biomass in lakes; however, most had limitations that meant
we could not use them to estimate biomass distribution across fish trophic levels. Moreover, a
disproportionate number of studies are for areas with colder climates than most of the
continental United States, for which we expect total fish standing biomass to be less than the
value of 5.7 g ww/m2 used for the state of Maine. We list three examples below.
Post et al. (2008) estimated the carrying capacity of south-central British Columbia lakes to be
500 rainbow trout per hectare based on other studies. Individual trout body weight, however,
was not reported.
Examining 78 lowland lakes in Germany, Emmrich et al. (2011) found that lake area is positively
correlated with the number of fish size classes, with a wider range of fish body size, and with
more of the larger sized fish in larger lakes. Raw data were not reported.
For 31 lakes in Newfoundland, Cote et al. (2011) reported a mean brook trout biomass of
0.474 g ww/m2 (range 0.069-1.01 g ww/m2) and a mean total salmonid biomass of
0.54 g ww/m2 (range 0.113-1.01 g ww/m2).
Attachment E
E-28
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
To summarize, several studies of fish biomass by trophic level indicate that top-trophic-level
fish, combining pelagic and benthic carnivorous fish, might comprise approximately 20 percent
of the standing fish biomass in many lakes. Ploskey and Jenkins (1982) estimated that
piscivorous fish, both those that are generally free swimming or pelagic (e.g., pike, gar, walleye,
TL4.5) and those that forage primarily in the benthos (e.g., various species of catfish, suckers,
TL3.5) comprise 22 percent of the total fish biomass in DeGray Lake, Arkansas (averaged
across several years). Using data from 122 lakes in Europe and North America, Hakanson and
Boulion (2004) estimated 27 percent piscivorous fish biomass/total fish biomass for oligotrophic
and mesotrophic lakes, declining to 20 percent in lakes with more than 100 |jg/L TP. We
interpret the data from Leidy and Jenkins (1977) as indicating 18 percent (range 14-24 percent)
of the total standing fish biomass in reservoirs to be piscivorous fish (pelagic and benthic).
Finally, Scharf (2008) provided data suggesting that 21 percent of the total standing fish
biomass represented piscivores, with only 3.4 percent pelagic piscivores (WCC) at TL4.5.
E.5.4 Conclusion
Based on the studies listed above, we assume that 3.5 percent of fish standing biomass is in the
WCC compartment for purposes of TRIM.FaTE modeling and for simulating angler harvest of
WCC from lakes. The remaining distribution of biomass across biotic compartments in
TRIM.FaTE, as presented in Exhibit E-2, also is consistent with the data presented here.
E.6 Derivation of Lake Sizes for Sustainable WCC Harvest
As stated in Section E.1, this attachment provides supporting information for Section 3.3.1 of the
TSD—Accounting for Sustainable Fishing. To develop the screening scenarios with an angler,
we needed to address two questions. Question 1—How large does a lake need to be to provide
a self-sustaining population(s) of top-trophic-level fish?—is answered in Section E.6.1. Question
2—How much fish can be harvested sustainably from lakes of different sizes?—is answered in
Section E.6.2.
E.6.1 Minimum Lake Size for Self-sustaining Population of WCC
As stated in Section E.3.3, we assume that at least 50 adult breeding WCC are needed for a
self-sustaining population of WCC in an isolated lake. We derive the minimum lake size from
two equations: Equations E-7 and E-8. The standing biomass of WCC in a lake is calculated
using Equation E-7. The assumption that the WCC fish compartment represents approximately
3.5 percent of the total fish standing biomass was documented in Section E.5.
WCC_SB = Standing biomass of WCC fish (g ww/m2)
Total_SB = Total standing biomass of all fish (g ww/m2)
Fraction WCC = Fraction WCC fish biomass of total fish biomass (i.e., 0.035)
Using WCC_SB calculated from Equation E-7 and the size of the lake (Lake_Size), the total
number of WCC fish supported in the lake is calculated using Equation E-8:
WCC SB = Total SB x Fraction WCC
Eqn. E-7
Number_WCC = (Lake_Size x WCC_SB x CFi)/BWWcc
Eqn. E-8
Attachment E
E-29
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
where:
Number_WCC = Total number of adult breeding WCC fish in lake
Lake_Size = Size of lake (acres)
WCC_SB = Standing biomass of WCC fish (g ww/m2; from Equation E-7)
CFi = Unit conversion factor (4047 m2/acre)
BWwcc = Body weight of adult WCC fish (2000 g ww per individual; assumed)
Based on those two equations, we created a matrix that predicted the Number_WCC in a lake
as a function of both fish biomass per unit area and the overall lake size in Exhibit E-15. The
first vertical column presents the range of total fish biomass found by Leidy and Jenkins (1977)
across 61 reservoirs in the United States. The interval between total fish biomass values from
one row to the next is not monotonic; finer resolution is provided for the less productive lakes.
The second vertical column in Exhibit E-15 presents the corresponding range of WCC biomass
estimates assuming that WCC comprises 3.5 percent of the total fish biomass. The remaining
columns in Exhibit E-15 present lakes of increasing size (from left to right). Again, the interval in
lake size from one column to the next is not monotonic; finer resolution is presented for the
smaller lakes. The numbers in each cell of Exhibit E-15 are the number of individual WCC fish
predicted for each combination of total fish biomass and lake size.
In Exhibit E-15, all combinations of lake productivity and overall size that would not support a
population of at least 50 WCC fish are shaded in gray. All combinations of lake productivity and
size that might support 500 or more WCC fish, and therefore might be self-sustaining for a
century or more, are highlighted in yellow. The unshaded cells represent the number of WCC
between 50 and 500 individuals (2 kg each) that might be sustainable for an angler's lifetime.
Attachment E
E-30
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit E-15. Number of WCC Adult Fish Supported by Lake Size (surface area in acres) and by Total Fish Biomass (TFB)
TFB
WCC
Number of Adult Water-column Carnivores (WCC) (by lake surface area from 1 to 250 acres)
(g ww/m2)
1
2
3
4
5
7.5
10
15
25
35
40
50
60
70
80
90
100
125
150
175
200
225
250
2
0.070
0
0
0
1
1
1
1
2
4
5
6
7
8
10
11
13
14
18
21
25
28
32
35
3
0.105
0
0
1
1
1
2
2
3
5
7
8
11
13
15
17
19
21
27
32
37
42
48
53
4
0.140
0
1
1
1
1
2
3
4
7
10
11
14
17
20
23
25
28
35
42
50
57
64
71
5.7
0.200
0
1
1
2
2
3
4
6
10
14
16
20
24
28
32
36
40
50
61
71
81
91
101
10
0.350
1
1
2
3
4
5
7
11
18
25
28
35
42
50
57
64
71
89
106
124
142
159
177
15
0.525
1
2
3
4
5
8
11
16
27
37
42
53
64
74
85
96
106
133
159
186
212
239
266
20
0.700
1
3
4
6
7
11
14
21
35
50
57
71
85
99
113
127
142
177
212
248
283
319
354
30
1.05
2
4
6
8
11
16
21
32
53
74
85
106
127
149
170
191
212
266
319
372
425
478
531
35
1.225
2
5
7
10
12
19
25
37
62
87
99
124
149
174
198
223
248
310
372
434
496
558
620
40
1.40
3
6
8
11
14
21
28
42
71
99
113
142
170
198
227
255
283
354
425
496
567
637
708
50
1.75
4
7
11
14
18
27
35
53
89
124
142
177
212
248
283
319
354
443
531
620
708
797
885
60
2.10
4
8
13
17
21
32
42
64
106
149
170
212
255
297
340
382
425
531
637
744
850
956
1062
70
2.45
5
10
15
20
25
37
50
74
124
174
198
248
297
347
397
446
496
620
744
868
992
1115
1239
80
2.80
6
11
17
23
28
42
57
85
142
198
227
283
340
397
453
510
567
708
850
992
1133
1275
1416
90
3.15
6
13
19
25
32
48
64
96
159
223
255
319
382
446
510
574
637
797
956
1115
1275
1434
1594
100
3.50
7
14
21
28
35
53
71
106
177
248
283
354
425
496
567
637
708
885
1062
1239
1416
1594
1771
110
3.85
8
16
23
31
39
58
78
117
195
273
312
390
467
545
623
701
779
974
1169
1363
1558
1753
1948
120
4.20
8
17
25
34
42
64
85
127
212
297
340
425
510
595
680
765
850
1062
1275
1487
1700
1912
2125
130
4.55
9
18
28
37
46
69
92
138
230
322
368
460
552
644
737
829
921
1151
1381
1611
1841
2072
2302
Fish standing biomass for all fish (TFB) and for the WCC fish are provided in the first two columns. The TFB spans 2 to 130 acres in line with Leidy and Jenkins's (1977) estimates of
total fish standing biomass per unit area across 61 reservoirs in the United States. The total standing biomass for WCC fish = TFB * 0.035.
Grey shaded area indicates that 50 or fewer WCC fish would be supported at the specified combination of lake size (acres) and TFB. Clear cells represent numbers of individual WCC
fish that might be sustainable for an angler's lifetime of 50 to 70 years for lakes of different productivities and size. Yellow cells have populations of WCC that exceed 500, which might
be self-sustaining for a century or more.
Note: Exhibit E-16 and Exhibit E-17 retain the same cell shading as Exhibit E-15, which presents the number of individual WCC that might be supported by the combinations of TFB
and lake size. Each WCC fish weighs 2 kg.
Attachment E
E-31
February 2021
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit E-16. Total Standing Biomass of WCC Fish (kg) by Lake Size and Total Fish Biomass (TFB)
TFB
WCC
Total Standing Biomass of Water-column Carnivores (WCC) (kg) (by lake surface area from 1 to 250 acres)
(g ww/m2)
1
2
3
4
5
7.5
10
15
25
35
40
50
60
70
80
90
100
125
150
175
200
225
250
2
0.070
0
1
1
1
1
2
3
4
7
10
11
14
17
20
23
25
28
35
42
50
57
64
71
3
0.105
0
1
1
2
2
3
4
6
11
15
17
21
25
30
34
38
42
53
64
74
85
96
106
4
0.140
1
1
2
2
3
4
6
8
14
20
23
28
34
40
45
51
57
71
85
99
113
127
142
5.7
0.200
1
2
2
3
4
6
8
12
20
28
32
40
48
57
65
73
81
101
121
141
161
182
202
10
0.350
1
3
4
6
7
11
14
21
35
50
57
71
85
99
113
127
142
177
212
248
283
319
354
15
0.525
2
4
6
8
11
16
21
32
53
74
85
106
127
149
170
191
212
266
319
372
425
478
531
20
0.700
3
6
8
11
14
21
28
42
71
99
113
142
170
198
227
255
283
354
425
496
567
637
708
30
1.050
4
8
13
17
21
32
42
64
106
149
170
212
255
297
340
382
425
531
637
744
850
956
1062
35
1.225
5
10
15
20
25
37
50
74
124
174
198
248
297
347
397
446
496
620
744
868
992
1115
1239
40
1.40
6
11
17
23
28
42
57
85
142
198
227
283
340
397
453
510
567
708
850
992
1133
1275
1416
50
1.75
7
14
21
28
35
53
71
106
177
248
283
354
425
496
567
637
708
885
1062
1239
1416
1594
1771
60
2.10
8
17
25
34
42
64
85
127
212
297
340
425
510
595
680
765
850
1062
1275
1487
1700
1912
2125
70
2.45
10
20
30
40
50
74
99
149
248
347
397
496
595
694
793
892
992
1239
1487
1735
1983
2231
2479
80
2.80
11
23
34
45
57
85
113
170
283
397
453
567
680
793
907
1020
1133
1416
1700
1983
2266
2550
2833
90
3.15
13
25
38
51
64
96
127
191
319
446
510
637
765
892
1020
1147
1275
1594
1912
2231
2550
2868
3187
100
3.50
14
28
42
57
71
106
142
212
354
496
567
708
850
992
1133
1275
1416
1771
2125
2479
2833
3187
3541
110
3.85
16
31
47
62
78
117
156
234
390
545
623
779
935
1091
1246
1402
1558
1948
2337
2727
3116
3506
3895
120
4.20
17
34
51
68
85
127
170
255
425
595
680
850
1020
1190
1360
1530
1700
2125
2550
2975
3399
3824
4249
130
4.55
18
37
55
74
92
138
184
276
460
644
737
921
1105
1289
1473
1657
1841
2302
2762
3222
3683
4143
4603
Note: Each WCC fish is assumed to weigh 2 kg. The total fish standing biomass used in TRIM.FaTE was 5.7 g ww/m2 (see Exhibit E-2). Total fish standing biomass of 40 g ww/m2 (red
text) used to assess angler behavior is based on the mean fish standing biomass for 61 reservoirs of 41 g ww/m2 (Leidy and Jenkins 1977). With a WCC proportion of the total fish
biomass of 0.035, the assumed WCC standing fish biomass for the screen is 1.4 g ww/m2. For example, a 25-acre pond (101,175 m2) might support an annual average standing
biomass of 142 kg WCC at a total fish biomass of 40 g ww/m2.
Attachment E
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TRIM-Based Tiered Screening Methodology for RTR
E.6.2 Maximum Fish Ingestion Rate by Lake Size
The likely annual productivity of WCC fish (kg/year) in a lake is estimated using Equation E-9.
Productivity_WCC = (Lake_Size x WCC_SB x CF1)/CF2 Eqn. E-9
where:
Productivity_WCC = Likely annual productivity of WCC fish (kg/year)
Lake_Size = Size of lake (acres)
WCC_SB = Standing biomass of WCC fish (g ww/m2; from Equation E-7)
CF1 = Unit conversion factor 1 (4047 m2/acre)
CF2 = Unit conversion factor 2 (1000 g/kg)
The maximum daily fish ingestion rate (g/day) for fillet of WCC plus BC associated with
sustainable fishing can be predicted using Equation E-10. The equation assumes the angler
consumes 50 percent WCC and 50 percent BC, represented by the factor of 2 in Equation E-10:
Max_IR(Bc+wcc) = 2 x (Productivity_WCC x FF x HFx CFi)/CF2 Eqn. E-10
where:
Max_IR(Bc+wcc) = Predicted maximum sustainable ingestion rate for BC and WCC fish (g/day)
Productivity_WCC = Annual productivity of WCC fish in the lake (kg/year; from Equation E-9)
FF = Fillet fraction; represents the assumed edible portion offish (0.33; unitless)
HF = Annual harvest fraction (0.10; unitless)
CF2 = Unit conversion factor 2 (1000 g/kg)
CF3 = Unit conversion factor 3 (365 days/year)
Exhibit E-17 lists the fish-fillet-ingestion rates that could be supported for each combination of
lake productivity (standing fish biomass per unit area) and lake size. Exhibit E-17 is similar to
Exhibit 26 in the TSD, except that a different series of lake sizes is presented in the columns. At
the assumed total fish standing biomass of 40 g ww/m2, the ingestion rate offish fillet (including
both WCC and BC fish in a 50:50 ratio) supported by a lake is approximately 1 gram per day per
acre. With this assumption, the angler needs to fish from at least 373 acres of lake to support a
fish-fillet-ingestion rate of 373 g ww/day.
Attachment E
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit E-17. Estimated Maximum Fish-fillet-ingestion Rate (g/day) Associated with Sustainable Fishing of WCC by Lake
Size and Total Standing Fish Biomass (TFB)
TFB
WCC
Maximum Fish-fillet-ingestion Rate (g/day) for a Diet of 50% BC Plus 50% WCC Fish
(by lake surface area from 1 to 250 acres)
(g ww/m2)
1
2
3
4
5
7.5
10
15
25
35
40
50
60
70
80
90
100
125
150
175
200
225
250
2
0.070
0
0
0
0
0
0
1
1
1
2
2
3
3
4
4
5
5
6
8
9
10
12
13
3
0.105
0
0
0
0
0
1
1
1
2
3
3
4
5
5
6
7
8
10
12
13
15
17
19
4
0.140
0
0
0
0
1
1
1
2
3
4
4
5
6
7
8
9
10
13
15
18
20
23
26
5.7
0.200
0
0
0
1
1
1
1
2
4
5
6
7
9
10
12
13
15
18
22
26
29
33
36
10
0.350
0
1
1
1
1
2
3
4
6
9
10
13
15
18
20
23
26
32
38
45
51
58
64
15
0.525
0
1
1
2
2
3
4
6
10
13
15
19
23
27
31
35
38
48
58
67
77
86
96
20
0.700
1
1
2
2
3
4
5
8
13
18
20
26
31
36
41
46
51
64
77
90
102
115
128
30
1.050
1
2
2
3
4
6
8
12
19
27
31
38
46
54
61
69
77
96
115
134
154
173
192
35
1.225
1
2
3
4
4
7
9
13
22
31
36
45
54
63
72
81
90
112
134
157
179
202
224
40
1.40
1
2
3
4
5
8
10
15
26
36
41
51
61
72
82
92
102
128
154
179
205
231
256
50
1.75
1
3
4
5
6
10
13
19
32
45
51
64
77
90
102
115
128
160
192
224
256
288
320
60
2.10
2
3
5
6
8
12
15
23
38
54
61
77
92
108
123
138
154
192
231
269
307
346
384
70
2.45
2
4
5
7
9
13
18
27
45
63
72
90
108
126
143
161
179
224
269
314
359
403
448
80
2.80
2
4
6
8
10
15
20
31
51
72
82
102
123
143
164
184
205
256
307
359
410
461
512
90
3.15
2
5
7
9
12
17
23
35
58
81
92
115
138
161
184
207
231
288
346
403
461
519
576
100
3.50
3
5
8
10
13
19
26
38
64
90
102
128
154
179
205
231
256
320
384
448
512
576
640
110
3.85
3
6
8
11
14
21
28
42
70
99
113
141
169
197
225
254
282
352
423
493
563
634
704
120
4.20
3
6
9
12
15
23
31
46
77
108
123
154
184
215
246
277
307
384
461
538
615
692
768
130
4.55
3
7
10
13
17
25
33
50
83
117
133
166
200
233
266
300
333
416
499
583
666
749
832
Note: We assume a 10% sustainable WCC fish harvest rate for the values in Exhibit E-13. Those values divided by 365 days/year = kg fish harvested/day. Multiplied by 0.33 edible
fraction = kg fish fillet/day for one person. The BC fish are more abundant; therefore, if the angler can consume 0.013 kg WCC fish/day, the angler also can consume 0.013 kg BC
fish/day. Thus, at a total fish standing biomass of 40 g ww/m2, a 25-acre lake can support ingestion of 26 g total fish fillet/day (see Equations E-9 and E-10), or 1 g total fish fillet can be
harvested per lake acre.
Attachment E
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TRIM-Based Tiered Screening Methodology for RTR
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Populations with High Consumption of Self-caught Freshwater Fish, in Support of the
Attachment E
E-39
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TRIM-Based Tiered Screening Methodology for RTR
Appropriate and Necessary Finding for Coal- and Oil-fired Electric Generating Units.
Research Triangle Park, NC: Office of Air Quality Planning and Standards, Health and
Environmental Impacts Division. December. EPA-452/D-11-002. March. Available from:
https://www3.epa.gov/ttn/atw/utilitv/pro/hq risk tsd 3-17-11.pdf.
VanGerwen-Toyne, M., and Tallman, R. (2010). Information in support of an exploratory fishery
protocol - Nunavut and Northwest Territories anadromous Arctic charr. Canadian Science
Advisory Secretariat, Research Document 2010/077. (As cited in Roux et al. 2011).
Vaughan, D.S., Yoshiyama, R.M., Breck, J.E., and DeAngelis, D.L. (1984). Chapter 17-
Modeling approaches for assessing the effects of stress on fish populations. In: V.W. Cairns,
P.V. Hodson, and J.O. Nriagu (eds.) Contaminant Effects in Fisheries. John Wiley and Sons,
Inc.
Yurk, J.J., and Ney, J.J. (1989). Phosphorus-fish community biomass relationships in southern
Appalachian reservoirs: can lakes be too clean for fish? Lake Reserv. Manage. 5(2): 83-90.
Attachment E
E-40
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Appendix 7 Protocol for Site-Specific Multipathway Risk Assessment
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Protocol for Developing a TRIM.FaTE Model Scenario to
Support a Site-specific Multipathway Risk Assessment in the
RTR Program
July 27, 2018
Prepared For:
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Research Triangle Park, NC 27711
Contract No. EP-W-12-010
Prepared By:
ICF
2635 Meridian Parkway
Suite 200
Durham, NC 27713
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CONTENTS
1. Introduction 1
1.1 Regulatory Context and Approach to Risk Assessment for PB-HAPs 1
1.2 Purpose of this Protocol 2
1.3 Scope and Limitations 2
1.4 Caveats 3
1.5 Protocol Road Map 3
2. A Brief Introduction to TRIM.FaTE Input Requirements 4
2.1 TRIM.FaTE Input Files and Contents 4
2.2 Recommended Sequence of Activities for TRIM.FaTE Set Up 5
3. Meteorological Data Development 5
4. Air and Surface-parcel Design 7
4.1 The Role of Spatial Layouts in TRIM.FaTE 7
4.2 Recommended Best Practices for Air- and Surface-parcel Design 8
5. Air Modeling Using AERMOD 9
5.1 Rationale for AERMOD Deposition Inputs 9
5.2 AERMOD Deposition Modeling 9
5.2.1 AERMOD Receptor Grid 10
5.3 Incorporating AERMOD Results into TRIM.FaTE 10
6. Surface Hydrology and Erosion Property Definitions 10
6.1 Surface-parcel Chemical Transfer Dynamics in TRIM.FaTE 10
6.2 Estimating Runoff and Erosion Fractions without Sophisticated GIS
Software 11
6.3 Estimating Runoff and Erosion Fractions with Sophisticated GIS
Software 12
7. Developing Values of Compartment Properties 13
7.1 The Role of Properties in TRIM.FaTE 13
7.2 Approach to Prioritizing Properties for Site-specific Parameterization 13
7.3 Elimination of Properties from Site-specific Parameterization 14
7.3.1 Process-based Elimination of Parameters 14
7.3.2 Data Availability-based Elimination of Parameters 15
7.3.3 Combination of Data- and Sensitivity-based Elimination of
Parameters 15
7.3.4 Elimination of Physical and Chemical Characteristics 16
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7.4 Properties Recommended for Site-specific Parameterization 16
7.5 Properties Recommended for Values Based on Land Use 17
7.6 Properties Recommended for National Values 18
8. Potential Future Improvements 19
References 20
Appendix A. Documentation of Empirical Analyses Used to Prioritize TRIM.FaTE
Properties A-1
Appendix B. AERMOD-to-TRIM.FaTE Input Requirements B-1
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Protocol for Developing a TRIM.FaTE Model Scenario to Support RTR Site-specific Multipathway Risk Assessments
TABLES
Table 3-1. Meteorological Parameters Required for Meteorology Input File for
TRIM.FaTE 6
Table 7-1. TRIM.FaTE Properties Recommended for Site-specific
Parameterization 17
Table 7-2. TRIM.FaTE Properties Recommended for Parameterization Based on
Land Use 18
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Protocol for Developing a TRIM.FaTE Model Scenario to Support RTR Site-specific Multipathway Risk Assessments
1. Introduction
This document presents a protocol for developing TRIM.FaTE scenarios in support of site-specific
multipathway risk assessments conducted within the Risk and Technology Review (RTR) program using
the TRIM.FaTE environmental fate-and-transport model.
This section describes the regulatory context, intended purpose of the protocol, the scope and limitations
of the protocol, and some caveats to its use. It also presents a road map to the content and structure of
this document.
1.1 Regulatory Context and Approach to Risk Assessment for PB-HAPs
Section 112 of the Clean Air Act (CAA) directs the U.S. Environmental Protection Agency (EPA) to assess
the risk remaining (residual risk) from emissions of hazardous air pollutants (HAPs) following the
implementation of maximum achievable control technology (MACT) standards for emission sources. Such
risk assessments for various emission source categories are a major component of EPA's RTR program.
To evaluate multipathway exposures and human health risks for RTR on a source category basis, EPA
currently employs an iterative approach. The approach enables EPA to confidently screen out emissions
of persistent and bioaccumulative HAPs (PB-HAPs) unlikely to pose health risks above levels of concern
and to focus additional resources on sources of greater concern within the category.
Three models are used to estimate multipathway exposure and multipathway risk in the RTR program, as
noted below.
• AERMOD is EPA's preferred near-field dispersion model for regulatory applications, and EPA uses it
for RTR site-specific multipathway assessments to model the transport of pollutants in air and
subsequent dry and wet deposition to soil, plant surfaces, and water.
• EPA uses the Fate, Transport, and Ecological Exposure module of EPA's Total Risk Integrated
Methodology (TRIM.FaTE) to model the fate and transport of pollutants deposited to soil, plant
surfaces, and water.
• EPA uses the RTR multimedia ingestion risk estimation methodology1 to estimate transfer and
uptake into the farm food chain and exposure to receptors consuming contaminated fish, farm foods,
and soil. A subset of media-concentration estimates from AERMOD and TRIM.FaTE serve as inputs
for estimating risk, which also depends on other exposure and biotransfer-related input parameters.
(This document focuses on the TRIM.FaTE modeling with inputs from AERMOD).
The RTR approach to multipathway assessments is divided into four steps of increasing refinement,
which are described below.
1. Tier 1 of the approach identifies facility-level emissions of PB-HAPs within a source category and
compares them to the screening threshold emission rates.
2. Tier 2 uses the actual location of the facility emitting PB-HAPs to refine a subset of the assumptions
associated with the modeled Tier 1 environmental scenario while maintaining the Tier 1 multipathway
exposure-scenario assumptions.
3. Tier 3 uses Web searches on local lakes to determine their fishability and suitability for the approach,
and it also uses facility stack parameters and local hourly meteorology to estimate the impacts on
potential exposure from plume rise and hour-by-hour variations in meteorological conditions.
1The multimedia ingestion risk estimation methodology used for RTR risk assessments is discussed in detail in
Attachment B of Appendix 6 to the Risk Report.
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Protocol for Developing a TRIM.FaTE Model Scenario to Support RTR Site-specific Multipathway Risk Assessments
4. The final step, for facilities that cannot be screened out based on the Tiers 1-3 screens, is to conduct
a more refined, site-specific multipathway risk assessment. A site-specific risk assessment (the
subject of this Protocol document) is intended to 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. Site-specific risk
assessments require more time and resources to complete than the screens. Unlike the screens, site-
specific assessments utilize AERMOD to conduct air-dispersion and deposition modeling of chemical
emissions, the results of which are input into TRIM.FaTE.
The methodology that EPA employs for the Tiers 1-3 screening is documented in Technical Support
Document for the TRIM-Based Multipathway Tiered Screening Methodology for RTR, which is an
appendix to the Risk Report.
1.2 Purpose of this Protocol
The site-specific protocol presented in this document is intended to serve as a guiding framework to set
up and parameterize scenarios in TRIM.FaTE that support accurate and cost-effective site-specific risk
assessments as part of the RTR framework.
The purpose of the protocol is to develop a standard set of guidelines and recommendations for
conducting site-specific assessments, providing a streamlined and replicable framework for configuring
and parameterizing the TRIM.FaTE model. The protocol aims to balance modeling accuracy with cost-
effectiveness in implementation, and to facilitate consistency and transparency across diverse
assessments. This protocol is also intended to function as part of the technical documentation for site-
specific residual risk assessments by providing a clear and transparent description of the approach to
parameterization and some of the relevant sources. Deviations from this protocol would need to be
documented on a case-by-case basis.
1.3 Scope and Limitations
The site-specific protocol presented in this document focuses on the fundamental aspects of setting up a
scenario in TRIM.FaTE from an RTR perspective. While the TRIM.FaTE User's Guide (U.S. EPA 2005)
provides guidance on the mechanistic aspects of designing a simulation, the protocol focuses on
identifying best practices that optimize model set-up efficiency while maintaining a high level of model
precision in the RTR context. Chemical uptake into the farm food chain, average daily doses from
ingestion of contaminated media, and subsequent health risks are calculated using the RTR multimedia
ingestion risk estimation methodology and are not discussed here1.
These best practices have been developed with a focus on the impact of alternative model-configuration
and model-parameterization approaches on multipathway risk in the RTR process. Thus, if two alternative
model-configuration approaches are estimated to have similar impacts on risk estimates in the RTR
process, the protocol will recommend the less effort-intensive approach where appropriate. For instance,
the protocol identifies only a limited set of TRIM.FaTE model properties as requiring site-specific
parameterization, while proposing land-use-specific or nationally representative or health-protective
values for others based on the finding that relatively few model parameters substantially influence risk in
the RTR context.
However, the protocol is not driven exclusively by considerations of cost-effectiveness. In some
instances, the protocol aims to provide superior methods of model configuration based on model
accuracy and scientific considerations that were previously not clearly articulated in available TRIM.FaTE
guidance and that have a focus on the RTR program.
This protocol is not step-by-step guide to running the model. It is not intended to serve as a substitute for
the TRIM.FaTE User's Guide (U.S. EPA 2005) or the TRIM.FaTE Technical Support Document (U.S.
EPA 2002), but it is recommended that the protocol be read in conjunction with those documents to
provide a holistic perspective on how the model should be used in site-specific RTR applications.
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1.4 Caveats
The findings and recommendations presented in this document are subject to the caveats given below.
• Some of the conclusions presented in this protocol are based on a combination of available
empirical evidence, theoretical considerations, and expert judgment. A "brute-force" empirical
approach to test an extensive range of scenarios and parameters was not feasible.
• For some model parameters, ICF relied on sensitivity analyses performed on previous configurations
of the model. It is possible that the results of previous sensitivity analyses differ somewhat from the
current Tier 1 screen model configuration.
• ICF did not test the sensitivity of model parameters in alternative model configurations.
• ICF did not research and identify land use-specific parameter values for soil properties values as
part of this protocol, although it recommends their use.
Despite these limitations, the current recommendations are expected to meet the objectives of providing a
cost-effective and accurate approach to site-specific multipathway risk assessment in the RTR program.
However, users are encouraged to extend site-specific model design and parameterization beyond the
levels proposed here as circumstances permit.
1.5 Protocol Road Map
This protocol contains
• best practices for TRIM.FaTE model configuration for use in site-specific RTR applications;
• documentation of the rationale for best-practice recommendations;
• nationally representative or health-protective model parameter values for site-specific applications of
TRIM.FaTE; and
• documentation on using AERMOD deposition outputs in TRIM.FaTE.
These distinct elements are woven together in the structure noted below.
• Section 2 sets the context with a summary of TRIM.FaTE input files and their content.
• Section 3 discusses the model's meteorological data requirements, potential data sources,
approaches to address missing data, data-processing requirements, and the issue of plume rise.
• Section 4 presents recommendations and rationale for best practices for designing air and surface
parcels in TRIM.FaTE.
• Section 5 presents methodology for air-dispersion modeling in AERMOD and incorporation of
deposition outputs into TRIM.FaTE.
• Section 6 presents recommendations and rationale for best practices for defining surface hydrology
and erosion parameters required by TRIM.FaTE.
• Section 7 identifies parameters recommended for site-specific parameterization.
Section 7.5 identifies parameters recommended for land-use specific parameterization.
Section 7.6 identifies parameters recommended for national-default parameterization.
• Section 8 discusses potential future improvements and enhancements to the protocol.
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Protocol for Developing a TRIM.FaTE Model Scenario to Support RTR Site-specific Multipathway Risk Assessments
2. A Brief Introduction to TRIM.FaTE Input Requirements
TRIM.FaTE is a spatially and temporally explicit multimedia environmental fate-and-transport model that
estimates the concentrations of emitted chemicals in biotic and abiotic environmental media. The model
uses a compartmental box-model approach to track the movement of chemicals in environmental media.
The model is based on representing environmental media as compartments, moving chemical mass
between interacting compartments consistent with a set of governing mathematical algorithms that
describe physical and chemical processes in the environment, and assuming instantaneous mixing within
each compartment.
2.1 TRIM.FaTE Input Files and Contents
TRIM.FaTE requires a variety of inputs from users to define the modeled environment and to quantify the
various environmental mass-transfer processes. These inputs are provided to the model in the form of the
files noted below.
• A "volume elements" file defines the spatial layout of the modeled domain in terms of three-
dimensional abiotic compartments. Each volume element provides a frame of reference for one or
more biotic compartments within it.
• A "compartments" file places biotic and abiotic compartments (modeling units containing chemical
mass) within the volume elements.
• A "library" file contains all the model algorithms, properties, and emission-source information.
Examples of the kinds of properties that are defined in the library file include
- scenario characteristics (e.g., start/stop time, modeling time parameters, output options);
- source characteristics (e.g., chemicals emitted, location, emission rate);
chemical-specific properties, including physiochemical (e.g., molecular weight, Kow) and
abiotic (e.g., degradation half-life);
nonchemical-specific characteristics of biota (e.g., body weight, food intake rate);
- site-specific ecological setting data and characteristics of biota (e.g., type of species present,
population and density information, food web relationships); and
abiotic environmental setting data such as abiotic media characteristics (e.g., air/water
content of soil, pH of surface water, suspended sediment density), runoff/erosion fractions for
adjacent surface soil compartments, and water flow between connected surface water
compartments.
• A properties file typically contains: (i) simulation- and site-specific property values that are used to
overwrite default library values, and (ii) the location of time-varying input files for parameters such as
meteorological and vegetation parameters.
For RTR site-specific applications of TRIM.FaTE, EPA uses AERMOD to model the air dispersion of
facility emissions and subsequent chemical deposition, rather than the simpler air-transport and
deposition algorithms used in TRIM.FaTE (that is, TRIM.FaTE is run without a facility emission rate). The
rationale for this is provided in Section 5.
The input files listed above must be developed using syntax that is consistent with TRIM.FaTE
requirements. Further detail on the required syntax of the input files, and the process of setting up and
running the model using these input files, is available in the TRIM.FaTE User's Guide (U.S. EPA, 2005).
Supplemental input files are generated that set up AERMOD deposition rates as emission sources for
TRIM.FaTE; further details on these input files can be found in Section 5 and Appendix B.
Much of the challenge in a site-specific TRIM.FaTE application lies in designing a spatial layout that is
consistent with the nature of the governing algorithms and that reflects the environmental dynamics of the
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Protocol for Developing a TRIM.FaTE Model Scenario to Support RTR Site-specific Multipathway Risk Assessments
modeled domain, researching and estimating numerous environmental properties that serve as inputs
into the model, finding and preparing appropriate meteorological and climate-related data, and setting up
the input files. The following sections discuss the optimal methods of performing these tasks from the
perspective of a site-specific RTR application.
2.2 Recommended Sequence of Activities for TRIM.FaTE Set Up
The following sections of this document focus on various aspects of TRIM.FaTE set up as discrete
elements in the model-configuration process. There are, however, interconnections between the research
required to guide various components of the set-up process. Although there are no firm rules governing
the order in which the model's input files must be developed, this protocol recommends the sequence of
activities numbered below as a means to enhance efficiency and accuracy in the model-configuration
process.
1. Perform qualitative spatial analyses of topography, aerial imagery, hydrography (boundaries of
watersheds, flow lines), and land cover around the site. This will aid in identifying meteorology data,
modeled lakes and farms or potential farmland, potential residences where home gardening might
occur, and the shape of the model domain.
2. Identify meteorological data based on RTR considerations (e.g., what meteorology did the RTR
inhalation risk assessment use for the site?), data availability, data quality, and the
representativeness of the data and instrument siting with respect to the modeled facility. Create the
meteorological file needed for modeling.
3. Identify lakes to model based on lakes evaluated in the Tier 2 screen, lake size, and a preliminary
assessment of risk potential and data availability.
4. Identify farms or potential farmland and/or home gardens to model based on a preliminary
assessment of risk potential and data availability.
5. Create the modeling spatial layout, including a receptor grid for AERMOD to calculate air
concentrations and deposition rates.
6. Run AERMOD with chemical-specific properties and source characteristics to get receptor-specific air
concentrations and deposition rates.
7. Estimate and define surface hydrology and erosion dynamics within the layout.
8. Gather data on site-specific properties per the protocol.
9. Generate TRIM.FaTE input files, including the supplemental files that set up surface-deposition rates
transformed from AERMOD deposition outputs.
10. Run TRIM.FaTE.
3. Meteorological Data Development
RTR site-specific multipathway assessments use AERMOD to model the air dispersion and deposition to
the ground of chemicals emitted by the facility (see Section 5), and then TRIM.FaTE models the re-
suspension and re-deposition of chemical (which typically affects a small fraction of the chemical initially
deposited) as well as the chemical transfer between terrestrial and aquatic media. Both models in general
are highly sensitive to meteorological parameters such as wind speed, wind direction, mixing height, and
rainfall rate, among others, and to the interactive effects between those parameters (see U.S. EPA 2009
for a sensitivity analysis for TRIM.FaTE). However, with this configuration of TRIM.FaTE using outputs of
AERMOD, meteorological parameters have a greater impact on the results of AERMOD modeling (on the
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Protocol for Developing a TRIM.FaTE Model Scenario to Support RTR Site-specific Multipathway Risk Assessments
airtransport and initial deposition of facility emissions) than on TRIM.FaTE modeling (on chemical re-
suspension and re-deposition, and transfer through compartments).
For these reasons, it is recommended that all meteorological parameters for AERMOD be site-specific at
an hourly resolution (averaging to coarser time steps can obscure real trends in the data). For
consistency, the TRIM.FaTE meteorological parameters should match those of AERMOD.
The development of meteorological data for the AERMOD modeling should follow established EPA
guidance, and the selected surface and upper-air meteorological stations typically should match those
used for the facility in the RTR inhalation assessment (which are typically the stations closest to the
facility). This section discusses best practices in reformatting the data used in the AERMOD modeling for
use in TRIM.FaTE.
TRIM.FaTE meteorology data must include the fields in Table 3-1. The TRIM.FaTE meteorology data file
does not require hourly time steps (larger time steps will shorten model run time), although hourly data
are used in site-specific assessments.
Table 3-1. Meteorological Parameters Required for Meteorology Input File for TRIM.FaTE
Parameter
Format
Units
Further Description and Notes
Date
M/D/YYYY
NA
NA
Hour
Numeric
NA
NA
Time Zone
e.g., "EST"
NA
NA
Horizontal Wind
Speed
Numeric
m/s
NA
Wind Direction
Numeric degrees
degrees clockwise
from north; blowing
from
e.g., from north is 360 degrees; from
east is 90 degrees; from south is 180
degrees; and so on. 0 degrees is
reserved for calm winds (e.g., wind
speed = 0 m/s), which cannot be
used in TRIM.FaTE (minimum wind
speed for TRIM.FaTE is 0.75 m/s).
Air Temperature
Numeric
K
NA
Mixing Height
Numeric
m
NA
Rain Rate
Numeric
m/day
NA
Cumulative Rain
Numeric
m
Total precipitation in a precipitation
event. A multi-hour event will have
equal cumulative rainfall values for
each hour.
Is Day
Boolean (i.e., 1 or
0)
NA
Daytime (value of 1; after sunrise) or
nighttime (value of 0; after sunset).
Calculated using U.S. EPA's SR-
SS.exe program, available with
TRIM.FaTE.
The AERMOD meteorology file produces two different calculations of mixing height—one based on
convective turbulence, the other based on mechanically-generated turbulence. For use in TRIM.FaTE,
these values should be condensed to one mixing height per hour: during convective conditions (when
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sensible heat flux is positive) it should be the larger of the two values from the AERMOD meteorology file,
and during all other times it should be the mechanical mixing height.
TRIM.FaTE requires that there be no missing data in its meteorology fields. U.S. EPA's recommended
guidance for replacing missing meteorology data (U.S. EPA 1992) has a series of objective data
replacement steps as a first pass, but those steps might not fill in all missing data. The guidance suggests
some subjective procedures for filling in remaining missing data; however, these are manual steps and do
not cover all possible cases of missing data (e.g., if more than a few contiguous hours of data are
missing). A meteorologist or experienced air quality modeler should perform these subjective data-fill
procedures. The user should expect that the quality of substituted values will be worse for longer
contiguous periods of missing data versus only a few contiguous hours of missing data. However, as long
as the amount of data originally missing is no more than 10 percent, and as long as the substituted values
are not out of normal bounds, then substituted data will have only a small impact on modeling results—
especially for RTR assessments where (1) AERMOD models primary air dispersion and deposition and
(2) the desired TRIM.FaTE outputs are final accumulated media concentrations after several decades of
modeling.
ICF developed a tool (AERMET2TRIM), based in Microsoft® Access™, that uses the meteorology file
from the AERMOD modeling, fills in missing data in all meteorology fields needed by TRIM.FaTE (based
on methods from U.S. EPA 1992), and reformats the data into the format required by TRIM.FaTE.
Previous site-specific assessments conducted using TRIM.FaTE typically have used 50-year modeling
periods, so the reformatting performed by AERMET2TRIM includes duplicating the one- or several-year
meteorology file into a 50-year data period. For example, if the meteorology data represent years 2013-
2016, that four-year period is repeated to create 50 years of data (e.g., 1990-2039).
If TRIM.FaTE time steps greater than 1 hour are desired (though not recommended), the user should
aggregate the data to conform to the time step. Values of wind speed, air temperature, mixing height, and
rain rates should be averaged. For wind direction, the hourly values of wind speed and wind direction
should be used to calculate the vector components of the wind (u and v values), those vector components
should be averaged, and the averaged vectors should be used to calculate the average wind direction.
Calculate new cumulative rain values after averaging the rain rates. Use professional judgment to
determine appropriate values for the "Is Day" parameter.
4. Air and Surface-parcel Design
4.1 The Role of Spatial Layouts in TRIM.FaTE
One of the primary inputs required by the TRIM.FaTE model is the specification of a spatial layout using
Cartesian coordinates to define the vertices of surface and air parcels and volume elements. This
information is input into the model via the "volume elements" input file. To construct the volume elements
input file, users are required to divide the modeled domain into two-dimensional air parcels and surface
parcels. Air parcels need not line up with surface parcels in all cases. Each parcel is also associated with
a height, which may vary in time. The parcel coordinates and height are combined to define three-
dimensional abiotic volume elements that contain biotic and abiotic compartments used to model the
movement of chemical mass in TRIM.FaTE.
In a site-specific multipathway assessment, the spatial layout should capture the features of interest
(farms, home gardens, and/or lakes) at the surface level and also specify how the overlying air domain is
to be divided to produce accurate and informative estimates. Although the TRIM.FaTE User's Guide (U.S.
EPA 2005) provides useful mechanistic guidelines and rules of thumb on the design of air and surface
parcels, those recommendations are not specific to the RTR context and are not based on a multipathway
risk perspective. The following guidelines, as noted in the introduction to this document, are intended to
support site-specific multipathway risk assessments in the RTR program (utilizing AERMOD estimates of
air dispersion and deposition of facility emissions) and should be considered in addition to the instructions
and recommendations provided in the TRIM.FaTE User's Guide and TRIM.FaTE Technical Support
Document (U.S. EPA 2002).
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4.2 Recommended Best Practices for Air- and Surface-parcel Design
The following recommendations for air- and surface-parcel design are intended to maintain a high degree
of modeling accuracy while reducing design effort and potentially optimizing computer run time. While
these guidelines are intended to facilitate optimal parcel design, every scenario is unique and might
require site-specific adjustments beyond the suggested approach provide here.
Step 1: Identify Features of Interest
Several steps are recommended for identifying features of interest, as described below.
• Use geospatial data (e.g., aerial imagery, data on watersheds and water bodies, and remotely-
sensed land cover and crop growth) to identify features of potential interest from the RTR
perspective, such as farms and lakes.
Geospatial data can include Google Earth (Google 2013), the National Hydrography Dataset
(USGS 2013), the National Land Cover Database (MRLC 2013), and the Cropland Data
Layer (USDA 2013).
• Use the guidance below to finalize the selection of lakes, gardens, and farms for modeling.
Features should be within 50 km of the emitting facility.
Prefer features closer to the emission source versus those farther away.
Prefer features that are frequently downwind from the emission source, if they exist, based on
the meteorology data selected for modeling.
Prefer features that potentially receive elevated levels of chemical input via runoff and
erosion from surrounding areas.
Prefer lakes for which preliminary research suggests good availability of modeling data (e.g.,
flush rates, depth, pH, total phosphorus levels, suspended sediment concentration).
Prefer the lake(s) selected in the Tier 2 and Tier 3 screens (all of which are between 25 and
100,000 acres in size).
Prefer features that are not very close to other features.
Hypothetical home gardens are most appropriately modeled at potential residential locations.
Draw a simplified surface-parcel polygon for each feature, reasonably capturing its true or intended
surface area and location while minimizing unneeded complexity (complex feature boundaries will
increase model run time while typically not improving modeling accuracy in a significant way).
Step 2: Identify Areas Potentially Impacting Features of Interest
Use geospatial data (elevation contours and watershed boundaries) to identify areas potentially providing
non-negligible amounts of chemical runoff and erosion to the features of interest. These areas will include
watersheds or sub-watersheds in which the features are located, as well as watersheds or sub-
watersheds upslope from those features. These impacting areas should be within 50 km of the emitting
facility.
Draw simplified surface-pa reel polygons for these impacting areas. It is typically appropriate that they be
delineated along watershed or sub-watershed boundaries, which the U.S. Geological Survey (USGS) or
other agencies have estimated based on local elevation contours and water-body features that define the
flow of rainfall and surface water across the local region. Because TRIM.FaTE requires that surface
parcels be characterized based on land cover, it may also be appropriate to further subdivide the
polygons based on primary land cover. These polygons should use appropriately simplified boundary
definitions to minimize unneeded complexity.
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Step 3: Draw Air Parcels
To improve the accuracy of TRIM.FaTE-modeled chemical re-suspension and re-deposition, it is
recommended that the air parcels be co-located with surface parcels. This is chiefly important for the
features of interest. If model run time is of concern, then air parcels away from the features of interest
may be larger than the underlying surface parcels; professional judgment should be used in these cases,
as fewer and larger air parcels will degrade the accuracy of calculations of re-suspension and re-
deposition.
5. Air Modeling Using AERMOD
AERMOD (the AMS/EPA Regulatory Model) is the preferred model for near-field (less than 50 km)
dispersion and it is used in RTR inhalation assessments. Its calculations of chemical air dispersion and
deposition are more sophisticated than those of TRIM.FaTE; therefore, RTR site-specific multipathway
assessments use AERMOD outputs as inputs into TRIM.FaTE.
5.1 Rationale for AERMOD Deposition Inputs
AERMOD uses a Gaussian plume approach based on a mathematical solution of the advection-
dispersion equation with numerous meteorological, thermodynamic, and terrain considerations to
calculate air concentrations. The model then calculates deposition using calculated or user-provided
deposition velocities; calculated velocities are based on information about meteorological parameters,
land cover, viscosities, resistances, particle sizes and densities (for particulate deposition), and pollutant-
specific properties (for vapor deposition). It then computes deposition as the product of air concentration
and deposition velocity. AERMOD assumes instantaneous steady-state concentrations and is not mass
balanced.
The transfer factors in TRIM.FaTE are similar to the algorithms used by AERMOD for some types of
deposition. TRIM.FaTE is a dynamic (non-steady-state), system-wide mass-balanced model. AERMOD
and TRIM.FaTE are based on very different theoretical assumptions. Although the deposition factors
translating air concentrations into deposition rates are approximately similar in some respects, the models
are based on entirely different methods of computing air concentrations. Furthermore, in AERMOD, the
user defines the particulate- and vapor-phase speciation in the emissions stream (and this distribution is
assumed to remain constant during dispersion). By contrast, TRIM.FaTE phase speciation is computed
within each air compartment based on chemical equilibration between the solid, aqueous, and air phases
of the air compartments. Therefore, it is reasonable to conclude that overall net deposition is computed in
very dissimilar ways by AERMOD and TRIM.FaTE.
The pseudo-source method, detailed in this section and Appendix B, combines air-deposition estimates
from AERMOD with the surface modeling capabilities of TRIM.FaTE. The method introduces mass
deposited from the air (based on AERMOD estimates) directly into the underlying soil, water, and
vegetation compartments in the TRIM.FaTE model. Small amounts of chemical re-suspension from the
surface, and subsequent re-deposition, are captured by the TRIM.FaTE algorithms; there is potentially a
mass-imbalance issue with this approach, although it remains to be evaluated if it involves a significant
loss of accuracy.
5.2 AERMOD Deposition Modeling
For the selected facilities of interest, AERMOD is used to estimate deposition rates for input into
TRIM.FaTE. AERMOD modeling is completed using guidance from the Guideline on Air Quality Models,
also published as Appendix W of 40 CFR Part 51, to determine model set up and application including
• four years of recent, representative meteorological data—the same surface meteorology data used to
develop the meteorology file for TRIM.FaTE;
• local terrain elevations for sources and receptors;
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• chemical properties and information collection request (ICR) emissions data specific to PB-HAPs of
concern; and
• use of urban dispersion-modeling settings when appropriate.
5.2.1 AERMOD Receptor Grid
Deposition rates are calculated for a grid of receptors covering all surface parcels being utilized in
TRIM.FaTE. The receptors may use uniform or non-uniform spacing between points. All TRIM.FaTE
surface parcels should have at least one corresponding AERMOD receptor; parcels larger than about 1
acre should have at least two AERMOD receptors, and receptor spacing should not exceed 1,000 m.
5.3 Incorporating AERMOD Results into TRIM.FaTE
AERMOD produces deposition estimates for each receptor within a TRIM.FaTE surface compartment.
AERMOD deposition rates are calculated on an hourly basis by the model, and then the model
aggregates across the entire simulation period to produce a period-total deposition rate (g/m2/period) for
each modeled receptor. To aggregate these outputs for input to TRIM.FaTE, an average deposition rate
for each TRIM.FaTE surface parcel is computed from the receptor estimates within each parcel. Area-
weighted averages are used to ensure that varying receptor densities between and within parcels are
properly accounted for. The units are converted to g/m2/day for use in TRIM.FaTE (which assumes
constant deposition with time).
In order to apply AERMOD average deposition rates directly to soil, plants, and surface water in
TRIM.FaTE, several modifications to the model scenarios and libraries are required. A "pseudo-source" is
created for each surface parcel to represent the deposition rate for the parcel as derived from AERMOD
modeling, with an emission rate (in units g/day) equal to the product of the parcel surface area (m2) and
the spatially averaged deposition rate (g/m2/day). Separate pseudo-sources are created for each
deposition type: wet and dry, vapor-phase and particulate. In this way, the TRIM.FaTE processes of air
transport and deposition are replaced by "emissions" directly into surface soil, plant, and surface water
compartments. More details on how to input AERMOD deposition values in TRIM.FaTE can be found in
Appendix B.
It should be noted that outputs from AERMOD also are used in the multimedia ingestion risk estimation
methodology algorithms to estimate exposure and risk. Among other parameters, chemical air
concentrations above features of interest and chemical-deposition rates to those features of interest are
used. When site-specific RTR multipathway risk assessments use AERMOD for primary dispersion and
deposition modeling, the values of air concentration and deposition in TRIM.FaTE reflect only the
chemical re-suspended and re-deposited within TRIM.FaTE. Parcel-average AERMOD air concentrations
must be calculated and included in estimating exposure and risk, and the parcel-average AERMOD
deposition rates input to TRIM.FaTE must also be included in the equations used to estimate exposure
and risk.
6. Surface Hydrology and Erosion Property Definitions
6.1 Surface-parcel Chemical Transfer Dynamics in TRIM.FaTE
The TRIM.FaTE model incorporates the ability to account for chemical transfers between adjacent
surface parcels via runoff and erosion. The algorithms that model surface runoff and erosion in
TRIM.FaTE simulate the advective chemical transfer dynamics between surface parcels without requiring
spatial elevation information or land cover details as inputs. Instead, the algorithms depend on inputs
explicitly specifying the destination of erosion and runoff from a specific parcel. In other words, for each
surface parcel, users must specify the proportion of the erosion and runoff originating in that parcel that
reaches specific adjacent parcels. These inputs are known as link properties in TRIM.FaTE and are
typically specified in the TRIM.FaTE "properties" file discussed in Section 2. Users must also separately
specify the average runoff and erosion rate for each surface parcel. These inputs are combined internally
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with estimates of the chemical concentration in surface soil and soil water to estimate mass transfers that
occur in conjunction with erosion and runoff processes.
The inter-parcel runoff and erosion parameter inputs in TRIM.FaTE are inherently site-specific because
there is no logical default value for the percentage of runoff and erosion from one parcel that reaches an
adjacent parcel. Simulations indicate that multipathway risk in the RTR process is sensitive to the choice
of these values (refer to Appendix A). This section discusses options for parameterizing these inputs in
site-specific TRIM.FaTE applications for RTR.
For users not having access to (or expertise in using) geographical information systems (GIS) software
with features to quantitatively analyze surface hydrology and erosion, the recommended method of
estimating parcel-to-pa reel runoff/erosion fractions is summarized in Section 6.2. If sophisticated GIS
software with features to analyze surface hydrology and erosion based on elevation is to be used, the
recommended method is summarized in Section 6.3.
6.2 Estimating Runoff and Erosion Fractions without Sophisticated GIS Software
Without a license for sophisticated GIS software, the user can still obtain free GIS viewing tools that allow
the user to display multiple layers of geospatial data and that have limited interaction with the data,
including querying the data and measuring distances. With such viewing software, the method for
estimating runoff/erosion fractions provided in Module 11 of the TRIM.FaTE User's Guide (U.S. EPA
2005) is appropriate. This method is summarized briefly here, with some additional tips not provided in
the User's Guide.
Step 1: Assemble Hydrological and Elevation Data
The user should obtain geospatial data indicating boundaries of hydrological units relevant to the
modeling domain. These hydrological data are available from the USGS National Hydrography Dataset
(NHD; USGS 2013). These hydrography data should already have been obtained and used to inform the
design of the modeling parcels. The NHD offers several levels of hydrological units, typically from regions
(the most spatially coarse) to sub-watersheds (typically the highest spatial resolution). Considering that
the typical site-specific TRIM.FaTE modeling domain has a radius less than 50 km and is divided into
several surface parcels, watersheds or sub-watersheds will usually offer the most appropriate resolution
for use in configuring parcels and estimating runoff/erosion fractions. The NHD also offers directional flow
lines of streams, rivers, and other hydrographic features.
The user should also obtain elevation data for the modeling domain. High resolution data are available
from the USGS National Elevation Dataset (NED; USGS 2006). These elevation data should already
have been obtained and used to help construct the modeling parcels. The data with the highest spatial
resolution are not necessary; 30-m resolution usually is appropriate.
Step 2: Relate Model Surface Parcels to Each Other and to Hydrological Units
The user should display the modeling surface parcels along with the appropriate hydrologic unit
boundaries from the NHD. For each parcel ("sending parcel"), follow the steps below.
1. For each hydrologic unit that occupies at least part of the sending parcel, estimate (or calculate, if
able) the ratio [surface area of the part of the hydrologic unit that is inside the sending parcel] to
[surface area of the sending parcel],
2. Identify each neighboring parcel ("receiving parcel"), including sinks where appropriate for the sides
of the sending parcel that lie along the outer boundary of the modeling.
3. For each hydrologic unit that occupies at least part of the sending parcel, estimate or calculate the
length of each interface between the hydrologic unit and each receiving parcel (not discussed in the
TRIM.FaTE User's Guide).
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4. Estimate or calculate the fraction of the sending parcel's perimeter that interfaces with each receiving
parcel (not discussed in the TRIM.FaTE User's Guide).
Step 3: Estimate Fraction of Runoff and Erosion
For each hydrologic unit that occupies at least part of a sending parcel, one should use NED elevation
data and NHD flow lines to estimate the fraction of runoff that will flow from the hydrologic unit into each
receiving parcel and, where appropriate, into sinks outside the modeling domain. A fraction might be 0 if
the elevation and flow lines suggest that all water in the hydrologic unit flows away from a receiving
parcel.
The TRIM.FaTE User's Guide (U.S. EPA 2005) Section A.5 discusses runoff/erosion fractions. Although
not discussed there, the NHD flow lines can help estimate the relative distribution of runoff from a sending
parcel to its receiving parcels, or from a hydrologic unit in the sending parcel to a receiving parcel. One
can examine the flow lines along each sending-receiving boundary to get a sense how much of the
boundary has flows from the sending area to the receiving area. This information can be combined with
information on how much of the sending area's perimeter interfaces with the receiving area in question,
aiding the user in developing runoff/erosion fractions.
As discussed in the TRIM.FaTE User's Guide Sections A.5 and A.6—separately for each hydrologic unit
in a sending parcel, multiply [the fraction of sending parcel's area covered by the hydrologic unit] by [the
runoff/erosion fraction from the hydrologic unit to the receiving parcel] for each of the sending parcel's
receiving parcels. Then, for each of these receiving parcels, sum this product across the hydrologic units.
This sum provides the final fraction of runoff/erosion from each sending parcel to each receiving parcel.
For each sending parcel, the fractions will sum to 1 when sinks are included as appropriate.
Another option is to estimate the runoff and erosion fractions based on visual inspection. This approach
does not explicitly relate the area of each hydrologic unit to each sending parcel. Therefore, it does not
explicitly assume that water cannot cross the boundaries of hydrologic units. Like the methods described
above, this option uses flow lines and the interfacial length between adjacent parcels. In this option, for
each sending parcel, the user visually examines the NHD flow lines to see where (if at all) flow lines cross
each interfacial boundary and into the receiving parcels. For each sending-receiving pair of parcels, the
user should estimate (or measure, if possible) the length of the part of the interfacial boundary that has
flow lines crossing into the receiving parcel. Then, divide that length by the total perimeter length of the
sending parcel. This ratio provides the fraction of runoff/erosion from the sending parcel into the receiving
parcel. Some professional judgment is required to subjectively adjust these fractions based on the relative
magnitude of runoff across the various interfacial boundaries. These relative magnitudes can consider the
overall terrain and flow patterns throughout the sending parcel (a flow into the receiving parcel with a
relatively small fetch will likely carry less chemical into the receiving parcel than a flow with a relatively
long fetch).
6.3 Estimating Runoff and Erosion Fractions with Sophisticated GIS Software
The method discussed in this section requires the use of ESRI® ArcGIS™ software. The software license
must enable the "Spatial Analyst" extension.
In ArcGIS, select the "Flow Direction" tool of the "Spatial Analyst" extension. Given a raster elevation
dataset (such as the NED), this tool will determine the flow direction of each raster cell to the steepest
downhill neighboring raster cell. The output of this tool will be a raster, where the value of each raster cell
will indicate the flow direction.
Then, select the "Flow Accumulation" tool of the "Spatial Analyst" extension. The input to the "Flow
Accumulation" tool is the output of the "Flow Direction" tool described above. Separately for each input
raster cell, the "Flow Accumulation" tool will follow the flow direction into the appropriate neighboring cell,
and continue following the flow direction of that cell into a third cell, and so on, "connecting the dots" of
the flow vectors until an endpoint is reached. This creates flow lines across the raster. Then, the tool
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calculates the number of these flow lines that cross each raster cell. This is the "flow accumulation"
number produced by this tool. The flow accumulation is unitless, as it does not represent an actual
amount of water or chemical flowing from one place to another; the accumulation values should be
viewed relative to each other.
For each sending parcel, the user would use the combination of flow-direction and flow-accumulation data
from the above tools to calculate the total flow (unitless) from the sending parcel to each receiving parcel.
The runoff/erosion fraction from the sending parcel into receiving parcel "A" would be the accumulated
flow from the sending parcel to receiving parcel "A" divided by the total accumulated flow from the
sending parcel to all its receiving parcels.
7. Developing Values of Compartment Properties
7.1 The Role of Properties in TRIM.FaTE
The TRIM.FaTE model is dependent on hundreds of user-specified properties that describe the biotic and
abiotic environments being modeled. Properties in TRIM.FaTE can be broadly divided into the types listed
below.
• Nonchemical-specific properties that define biotic compartments (e.g., biomass of game fish in a
lake, the length of a leaf on a deciduous plant).
• Nonchemical-specific properties that define abiotic compartments (e.g., porosity of surface soil, the
total suspended solids concentration in a lake).
• Chemical-specific properties (including system-wide chemical properties such as the Henry's Law
constant, the octanol-water partition coefficient, and compartment-specific chemical properties such
as reaction and degradation rate constants in various environmental media).
• Simulation-specific properties (e.g., model run time, model time step).
All user-defined (e.g., non-formula) properties in a TRIM.FaTE scenario can be assigned simulation- or
site-specific values. In theory, the more properties that are assigned site-specific values, the more
accurately the simulation will represent chemical fate and transport at that location. Following this logic,
the user should try to find site-specific values for as many properties as possible. However, although each
model property is potentially important in defining a particular environmental fate-and-transport process, it
is apparent based on theoretical considerations and empirical evidence (analysis of model results and
model evaluations) that there is a subset of model properties that more significantly influences the
environmental concentrations that drive the risks of importance in the exposure scenarios evaluated in
RTR assessments. The fact that some parameters are more influential on results is true for complex
models in general. This is the focus of sensitivity analyses.
In previous site-specific risk assessments using TRIM.FaTE, which were conducted for RTR and in other
regulatory applications, a substantial portion of the level of effort required to perform the assessments
was directed toward site-specific property parameterization. One of the specific objectives of this protocol
is to take advantage of the results of sensitivity analyses and model evaluations conducted of
TRIM.FaTE. Based on these results, we have identified those compartment properties that are a high
priority for site-specific parameterization, those that can be adequately represented by regional or land-
use-specific default values, and those for which nationally representative or health-protective values are
adequate. This classification scheme is intended to reduce the level of effort required to adequately
parameterize site-specific multipathway assessments while maintaining a high level of accuracy in risk
estimates for RTR.
7.2 Approach to Prioritizing Properties for Site-specific Parameterization
ICF relied on a combination of theoretical reasoning and empirical evidence to prioritize TRIM.FaTE
properties for the purposes of this protocol. In this way, ICF was able to limit the need for "brute-force"
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empirical evaluations (e.g., comprehensive sensitivity analyses that systematically vary all or most of the
user-defined inputs, such as those conducted prior to the 2009 EPA Science Advisory Board review of
RTR assessments (U.S. EPA 2009)) and additional resource-intensive literature searches. ICF's
justification for determining that properties were not high priority was based on the three lines of evidence
discussed below.
1. ICF followed a "process"-based approach to rule out a large subset of TRIM.FaTE properties from
the need for site-specific parameterization. This approach was founded on the idea that the
TRIM.FaTE model produces greater than necessary resolution (in terms of the number of
concentrations that are calculated for different environmental media types) when viewed from the
RTR perspective. The individual human multipathway exposure scenarios evaluated for RTR rely
most directly on results from TRIM.FaTE for surface soil compartments at the location of a farm or
garden and fish compartments in a lake of interest. All fate-and-transport processes—and the
properties that exclusively define those processes—that do not strongly influence these
concentrations can reasonably be ruled out from requiring site-specific parameterization. The
implications of this approach will be discussed in greater detail below.
2. ICF also used practical considerations regarding data availability to rule out certain properties from
site-specific parameterization. Over the course of numerous site-specific assessments and the
parameterization of the screening scenarios, ICF has conducted literature searches on numerous
TRIM.FaTE properties. ICF used the insight gained from these exercises to identify certain sets of
parameters as being too data-scarce to parameterize on a site-specific basis at this time without
expending a substantial amount of time and money (for possibly uncertain results).
3. Physical constants and physicochemical properties of the modeled PB-HAPs were also ruled out
from site-specific parameterization based on their largely unchanging nature in the environment for
the chemicals considered for RTR.
ICF evaluated the parameters not eliminated by the above considerations to determine which properties
should be the focus of data collection efforts during site-specific TRIM.FaTE modeling for RTR. ICF
conducted a limited number of evaluations and used the results of previous sensitivity analyses to decide
which of these shortlisted parameters should be prioritized for site-specific parameterization, for land-use-
based parameterization, or for regional parameterization.
Other scenario properties, such as emission period and the model's numerical integration time step, are
typically not varied between site-specific assessments.
7.3 Elimination of Properties from Site-specific Parameterization
7.3.1 Process-based Elimination of Parameters
The operative principle in the process-based elimination of parameters is that fate-and-transport
processes that do not substantially influence concentrations of interest from an RTR perspective are less
important to parameterize. ICF used theoretical considerations based on the evaluation of the underlying
TRIM.FaTE algorithms, combined with empirical evidence from TRIM.FaTE simulations, to identify the
less important fate-and-transport processes and eliminate the need to parameterize those processes on a
site-specific basis. The specific processes identified as being of less importance in the RTR context and
the underlying justification for ruling them out from site-specific consideration are listed below:
• Chemical transported via water percolation through the sub-surface soil layers (not including surface
soil) does not affect surface soil or lake water concentrations. Theoretical considerations suggest
that chemical, once transported into the lower soil layers, will not substantially make its way back to
the surface compartments of interest.
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• Chemical transport via sub-surface soil diffusive processes, although having the potential to transfer
mass upwards, is not sizeable in comparison to advective transfer processes. An evaluation of
relative mass rate in the Tier 1 screen supports this assertion for all the chemicals evaluated.
• Chemical transport to the lake via horizontal groundwater flow and recharge is negligibly small
compared to other advective chemical inputs into the lake. The relative mass rate for this process
compared to other advective transfer processes carrying chemical into the lake in the Tier 1
screening scenario supports this assertion for all chemicals evaluated.
Because the RTR user has no intrinsic interest in the concentrations prevailing in the lower soil layers, all
of the above processes have been ruled out from consideration for site-specific parameterization. As a
consequence, it is possible to rule out all sub-surface soil compartment properties from requiring site-
specific parameterization.
7.3.2 Data Availability-based Elimination of Parameters
Chemical-Specific Aquatic Biota Properties: The aquatic biota compartments in TRIM.FaTE—currently
including benthic invertebrates and five types offish—are characterized by several potentially site-specific
properties that control algorithms influencing the uptake, degradation, and elimination of chemicals in the
aquatic organisms. These chemical-specific properties include the absorption rates of chemical into each
type offish from surface water, elimination rates from fish digestive systems, degradation rates within the
fish, and other parameters. In the course of parameterizing TRIM.FaTE for the screens and conducting
extensive evaluations of parameter sensitivity, it has become apparent that only a limited number of
studies are available for several of these properties for most combinations of chemicals and organisms.
Although these properties may potentially differ in alternative climates and conditions, it appears unlikely
that additional literature searches and evaluations would yield better, more appropriate site-specific
values than the current defaults. Until such time as more studies on these properties are available,
practical considerations suggest that these chemical-specific aquatic biota properties be ruled out from
site-specific parameterization.
Chemical-Specific Abiotic Compartment Properties: TRIM.FaTE algorithms model chemical reaction
and degradation processes in several abiotic compartments (e.g., surface soil). These algorithms depend
on chemical-specific parameters such as degradation rates (or half-lives), transformation rates, and other
properties. Literature searches conducted during previous site-specific assessments in the RTR process
and other regulatory applications using TRIM.FaTE have suggested that data are limited for these
properties.
These chemical properties (with the exception of the oxidation, reduction, and methylation and
demethylation rates influencing mercury) therefore are currently ruled out from site-specific consideration.
The mercury transformation properties have been shown to be highly risk-influential as well as variable
across different ecosystem types and conditions, and these properties are reserved for site- or land-use-
specific parameterization in the future, subject to greater data availability and the results of additional
evaluations.
7.3.3 Combination of Data- and Sensitivity-based Elimination of Parameters
Terrestrial Vegetation: The terrestrial vegetation compartments in TRIM.FaTE—currently including
grass, coniferous forest, deciduous forest, wetland grass, and wetland forest—are not directly part of the
RTR risk assessment calculations (chemical concentrations in these compartments are not used as
inputs to the ingestion exposure estimation). However, these compartments act as sinks for chemicals
and also transfer chemicals from air to soil via leaf litterfall. In this way, the choice of terrestrial vegetation
influences surface soil concentrations and, ultimately, risk.
The terrestrial vegetation compartments depend on properties such as the lipid content of leaves, wet
density of leaves, area indices of leaves, etc. Although it is possible that these properties differ on a site-
specific basis—for instance, the characteristics of coniferous trees in Oregon are different from those of
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coniferous trees in North Carolina—these differences are not expected to have a substantial influence on
risk. ICF's simulations indicate that the impact on risk of alternative vegetation scenarios is limited after
accounting for differences in erosion regimes specific to land-use type (see Appendix A). It is expected
that site-specific differences within a single vegetation type would be even lower.
Literature searches during previous site-specific multipathway assessments in the RTR process have
indicated that highly intensive literature search would be required to parameterize the full range of
terrestrial vegetation parameters required by the TRIM.FaTE algorithms. Based on the limited risk impact
of terrestrial vegetation properties, and limited data availability at the site-specific level, these properties
are currently ruled out from site-specific parameterization.
7.3.4 Elimination of Physical and Chemical Characteristics
Algorithms in the TRIM.FaTE model frequently depend on physical and chemical parameters, such as the
Henry's law constant and the octanol-water partition coefficient, to partition chemicals between phases
within a compartment. These properties are, by their nature, relatively unchanging across most standard
environmental conditions for the non-ionic organic compounds currently evaluated (dioxins/furans and
polycyclic organic matter)2. These properties are thus ruled out from requiring site-specific
parameterization for the time being.
7.4 Properties Recommended for Site-specific Parameterization
Following the elimination process described above, ICF identified a set of parameters for further
evaluation based theoretical considerations as well as higher sensitivity potential displayed in previous
sensitivity analyses (e.g., U.S. EPA 2009). To estimate the risk influence of these parameters, ICF
performed a limited set of additional sensitivity analyses. The evaluated parameters are listed below,
grouped by compartment type.
• Air: dust load, fraction of organic matter.
• Surface Soil: unit soil loss, inter-compartment drainage and erosion fractions, soil-particle density,
soil-air fraction, soil organic content, soil pH, soil water content.
• Surface Water and Sediment: suspended solids concentration, bed-sediment density, suspended
solids density, bed-sediment porosity.
• Aquatic Biota: biomass of various aquatic biota compartments.
• Terrestrial Vegetation: "Allow exchange" and "Litterfall" file inputs.
Unlike previous analyses, these sensitivity analyses were not based on fixed perturbations from the
default values but instead used reasonable high and/or low bounds approximately corresponding to the
range found in the environment. The impacts on risk were computed with respect to the Tier 1 screen
results at equivalent emission rates. AERMOD was not incorporated into these runs.
ICF extended the scope of the current analyses by also using the results of TRIM.FaTE sensitivity
analyses conducted in previous regulatory applications and pertaining to air, surface soil, and surface
water and sediment. Although these analyses were performed on a different version of the Tier 1 screen
setup, the results are considered informative.
The specific details of the analyses conducted as part of this protocol development are reported in
Appendix A, while other supporting evidence has been drawn from previous reports (e.g., U.S. EPA
2009). Based on the results of these analyses, Table 7-1 contains TRIM.FaTE properties recommended
for site-specific parameterization in the RTR process. These properties have been further classified as
2 For mercury, some analogous properties, such as the partition coefficient for mercury in the aqueous phase, do vary
according to pH; these relationships are incorporated into the model as formula properties.
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high, medium, and low priority to facilitate an appropriate allocation of available resources in the
parameterization process.
Table 7-1. TRIM.FaTE Properties Recommended for Site-specific Parameterization
Compartment
Property
Priority
Remark
Surface water
Depth
High
Having depth as well as flush rate helps serve as
a check on surface hydrology assumptions.
Flush rate
High
Suspended solids
concentration
High
Attempt to find a column-averaged value.
PH
Moderate
Important for metals.
Algae density
Moderate
May be estimated from total phosphorus
concentrations in the absence of measured
values.
Organic-carbon
fraction
Moderate
Important for 2,3,7,8-tetrachlorodibenzo-p-dioxin
(U.S. EPA 2009). Data availability may be
limited.
Water temperature
Moderate
Sensitive but unlikely to manifest wide range.
Aquatic biota
Biomass
Moderate
May be estimated from total phosphorus
concentrations in absence of measured values.
Surface soil
PH
Moderate
Important for metals.
Terrestrial biota
"Allow Exchange"
and "Litterfall" data
files
Low
These files govern how long leaves remain open
for stomatal exchange during different times of
the year and also when the leaves fall off the
trees onto the surface soil. Although the impact
of these properties has not been empirically
tested, theoretical considerations suggest they
will have a low impact when estimating average
annual risks.
In addition to these values, meteorology parameters, surface hydrology and erosion-related parameters,
and the spatial layout are fundamentally site-specific elements of a TRIM.FaTE simulation, as noted in
the previous sections.
7.5 Properties Recommended for Values Based on Land Use
In addition to the properties identified in Section 7.4 as desirable for site-specific parameterization, we
identified properties that also influence risk substantially but for which the impacts on risk are expected to
be largely captured by land-use-specific parameters. In other words, for these properties, accounting for
variations that correspond to land use is expected to adequately account for any variation in these
parameters (to the extent that they influence risk). Additional variation in parameter values resulting from
site-specific variations within a particular land-use category is not expected to be significant. For example,
differences in surface soil erosion (as expressed by the unit soil loss rate property in TRIM.FaTE) are
expected to be larger between the average deciduous forest and the average parcel of tilled soil than
between different types of deciduous forest or between different types of tilled soil. The use of land-use-
specific values for such properties is expected, therefore, to adequately capture their impact on risk
estimates in the RTR process.
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The rationale for identifying properties as land-use-based in this protocol is a combination of risk
sensitivity analysis (Appendix A and U.S. EPA 2009), professional judgment about the range exhibited in
the environment, and expected data availability at the site-specific level. Table 7-2 lists the TRIM.FaTE
parameters that are recommended for land-use-specific parameterization. These parameters are all
related to the surface soil parcel and assume distinct values for each of the land-use types modeled in
TRIM.FaTE. These land-use types currently include deciduous forest, coniferous forest, grass,
agricultural soil, unfilled soil, forested wetlands, and grassy wetlands. Land-use type is not an explicit
input in TRIM.FaTE but is implicitly reflected in the TRIM.FaTE property values corresponding to each
surface parcel.
Table 7-2. TRIM.FaTE Properties Recommended for Parameterization Based on Land Use
Property
Remark
Organic-carbon fraction
Fraction of dry-soil solids that is organic in origin.
Water content
The sum of the water and air content fractions of a soil determines its
porosity.
Air content
Particle density
Refers to the dry density of the average soil particle.
Rainfall/erosivity index
Universal Soil Loss Equation (USLE) properties used to compute each
surface soil compartment's average erosion rate.
Soil-erodibility index
Topographical (LS) factor
Cover/management factor
Supporting-practices factor
Fraction of precipitation that
evapotranspires
Water-balance-related property used to compute each surface soil
compartment's average runoff rate.
Fraction of precipitation subject
to overland runoff
7.6 Properties Recommended for National Values
Nationally representative or health-protective values are recommended for all TRIM.FaTE properties that
are not identified for site-specific or land-use-based parameterization in Sections 7.4 and 7.5 above.
These properties are expected either to (1) not substantially influence risk in the RTR process, (2) not
have adequate data to support site-specific parameterization, or (3) be relatively constant in the
environment, as discussed in greater detail in the approach described earlier in Section 7. These
properties have been previously characterized in the RTR Tier 1 and Tier 2 screening threshold derivation
analyses by either nationally representative values or health-protective values. The same values are
recommended for these properties in site-specific analyses. The national values are documented in the
Technical Support Document for the TRIM-Based Multipathway Tiered Screening Methodology for RTR,
which is an appendix to the Risk Report.
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Protocol for Developing a TRIM.FaTE Model Scenario to Support RTR Site-specific Multipathway Risk Assessments
8. Potential Future Improvements
This protocol, documenting the current state of knowledge related to conducting site-specific
environmental modeling in support of RTR multipathway risk assessments, could be enhanced in the
future by documenting best practices and developing recommendations regarding the issues listed below
(among others).
• Identification of land-use-specific parameters for the identified soil properties based on literature
review.
• Application of enhanced technical approaches, such as the use of a sensitivity-score approach, to
identify the most influential model properties.
• Additional sensitivity tests utilizing the combined AERMOD-TRIM.FaTE approach as well as the
current TRIM.FaTE Master Library (now including arsenic) and screening configuration.
• Potential development of regional parameters for a subset of model properties based on the results
of further sensitivity analysis and data-availability assessments.
• Greater use of graphics and figures to illustrate model set-up concepts.
• Enhanced technical editing to help the protocol be more self-explanatory and independent of other
TRIM.FaTE support documents in its scope.
• Researching the potential for geographically variable biotransfer factors and other parameters used
in estimating concentrations of ingested food products.
• Further research and development of GIS-based approaches to surface hydrology and erosion-
property parameterization.
19 July 2018
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Protocol for Developing a TRIM.FaTE Model Scenario to Support RTR Site-specific Multipathway Risk Assessments
References
ESRL (Earth System Research Laboratory). 2011. NOAA/ESRL Radiosonde Database Access.
Available online at: http://www.esrl.noaa.gov/raobs/. Accessed March 3, 2011.
Google. 2013. Google Earth. Available at http://www.qooqle.com/earth/index.html. Accessed
July 24, 2013.
Multi-resolution Land Characterization (MRLC) Consortium. 2013. National Land Cover
Database. Available online at http://www.mrlc.gov/index.php. Web page last updated
February 7, 2013.
U.S. Department of Agriculture (USDA). 2013. CropScape - Cropland Data Layer. Available
online at http://nassgeodata.gmu.edu/CropScape/.
U.S. EPA (U.S. Environmental Protection Agency). 1992. Procedures for Substituting Values for
Missing NWS Meteorological Data for Use in Regulatory Air Quality Models (Dennis
Atkinson and Russell F. Lee). July 7, 1992. Available online at
http://www.epa.gov/ttn/scram/surface/missdata.txt. Last accessed March 07, 2011.
U.S. EPA. 2002. TRIM.FaTE Technical Support Document. U.S. EPA Office of Air Quality
Planning and Standards. Available at:
http://www.epa.gov/ttn/fera/trim fate.html#current user.
U.S. EPA. 2005. TRIM.FaTE User's Guide. Office of Air Quality Planning and Standards.
September 2005. Available at: http://www.epa.gov/ttn/fera/trim fate.html#current user.
U.S. EPA. 2009. Risk and Technology Review (RTR) Risk Assessment Methodologies: For
Review by the EPA's Science Advisory Board. Attachment C-3 in Appendix C. (EPA-
452/R-09-006).
USGS (U.S. Geological Survey). 2006. National Elevation Dataset. Available at:
http://ned.usgs.gov/. Web page last updated August 2006.
USGS. 2013. National Hydrography Dataset. Available at http://nhd.usgs.gov/. Web page last
updated July 19, 2013.
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Protocol for Developing a TRIM.FaTE Model Scenario to Support RTR Site-specific Multipathway Risk Assessments
Appendix A. Documentation of Empirical Analyses Used to Prioritize
TRIM.FaTE Properties
A.1. Introduction
Several years ago, ICF performed a series of empirical analyses to prioritize TRIM.FaTE model properties
for site-specific parameterization. These analyses were based on changing the value of one or more
model properties relative to the Tier 1 screen and measuring the relative impact on risk. Unlike in a
traditional sensitivity analysis, this analysis changed property values to approximate high- and low-end
values within the environmental range of the property of interest, instead of using a fixed perturbation.
The measured impacts on risk, the expected range in the environment, and data availability were
considered in prioritizing model properties for site-specific parameterization, as discussed in Section 7.
These model runs did not utilize AERMOD and did not include arsenic, which was added to RTR
multipathway assessments in 2016. The TRIM.FaTE Master Library and Tier 1 configuration used in
these empirical analyses may be different from the current Library and configuration.
Table A-1 summarizes the various empirical analyses that were conducted, the risk impact of the scenario
modifications, and conclusions from the analyses.
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Protocol for Developing a TRIM.FaTE Model Scenario to Support RTR Site-specific Multipathway Risk Assessments
Table A-1. Results and Conclusions from Empirical Analyses Used to Prioritize TRIM.FaTE Properties
Scenario
Name
Scenario Description
Normalized Risk Relative to
Tier 1 Screening Scenario
Risk Impact of Scenario
Modification
Conclusions
(with respect to Tier
1 Screening
Scenario)
2,3,7,8-
tetrachloro
dibenzo-p-
dioxin
Benzo(a)
pyrene
Cadmium
Methyl
Mercury
Tier 1 SS
Tier 1 Screening
Scenario.
1.00
1.00
1.00
1.00
Designed to produce
most conservative risk
estimate.
All relative risks for
modified scenarios are
measured relative to the
Tier 1 screening scenario.
WF1
Reduce watershed
flows (erosion and
runoff) to half
screening scenario
levels. Redirect
remainder to sink.
Maintain same flow
directions as
screening scenario.
0.56
0.69
0.21
0.38
Reducing the quantity of
runoff and erosion
reaching receiving
compartments reduces
chemical inputs into those
compartments, including
the lake, and reduces
risk.
Surface hydrology and
erosion flows (where and
how much of the erosion
and runoff from a
compartment reaches) are
potentially highly sensitive
properties in the model
(influencing risk by up to a
factor of 10) and are
recommended for site-
specific parameterization.
WF2
Reduce watershed
flows (erosion and
runoff) to 1/10
screening scenario
levels. Redirect
remainder to sink.
Maintain same flow
directions as
screening scenario.
0.34
0.68
0.11
0.25
Turning off erosion
Although erosion is a
reduces chemical inputs
relatively important
ERO
Switch off erosion.
0.39
1.02
1.10
0.43
into the lake and reduces
process, its maximum
chemical removal off the
impact on risk is less than
farm.
a factor of 3, even when
A-2
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Protocol for Developing a TRIM.FaTE Model Scenario to Support RTR Site-specific Multipathway Risk Assessments
Scenario Description
Normalized Risk Relative to
Tier 1 Screening Scenario
Scenario
Name
(with respect to Tier
1 Screening
Scenario)
2,3,7,8-
tetrachloro
dibenzo-p-
dioxin
Benzo(a)
pyrene
Cadmium
Methyl
Mercury
Risk Impact of Scenario
Modification
Conclusions
ER1
Double erosion rates.
1.09
0.99
0.86
0.86
Increasing erosion
produces competing
effects: while it increases
chemical inputs into the
lake, it also increases the
burial rate of sediment
and increases chemical
removal from the farm.
accounting for variable
runoff rates. A land-use-
specific parameterization
approach is recommended
for the average erosion
rates of surface soil
compartments.
ER-RUN1
Double erosion and
runoff rates (same
flush rate; higher lake
depth).
0.94
0.96
0.74
0.89
Increasing the runoff rate
increases the input of
soluble chemicals into the
lake and decreases the
removal of those
chemicals from the farm.
ER-RUN2
Double erosion and
runoff rates (higher
flush rate; same lake
depth).
1.09
0.99
0.74
0.85
Increased runoff rates
can be accommodated by
means of increased lake
depths or increased flush
rates.
RUN1
Switch off runoff;
maintain flush rate and
depth.
0.99
1.00
0.70
0.96
Nullifying chemical
transfer through runoff
reduces chemical input
into the lake and reduces
chemical removal from
the farm.
Runoff rates have a limited
impact on risk. A land-use-
specific parameterization
approach is recommended
for average runoff rates
from surface soil
compartments.
RUN2
Implement cumulative
runoff regime.
1.02
1.00
1.14
1.10
Assumes runoff from one
compartment does not
evaporate but contributes
to runoff from the
receiving compartment.
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Protocol for Developing a TRIM.FaTE Model Scenario to Support RTR Site-specific Multipathway Risk Assessments
Scenario Description
Normalized Risk Relative to
Tier 1 Screening Scenario
Scenario
Name
(with respect to Tier
1 Screening
Scenario)
2,3,7,8-
tetrachloro
dibenzo-p-
dioxin
Benzo(a)
pyrene
Cadmium
Methyl
Mercury
Risk Impact of Scenario
Modification
Conclusions
FR1
Double lake depth,
half flush rate, same
rainfall, and same
runoff fraction.
0.71
0.96
1.00
1.15
Doubling depth reduces
concentrations but
halving the flush rate
reduces chemical output
from the lake.
Lake depth and flush rate
have a modest impact on
risk. However, knowledge
of both these parameters
can help guide the surface
hydrology and erosion
direction flows in the
watershed which can more
substantially influence risk.
Site-specific
parameterization is
recommended for lake
depth and flush rate.
FR2
Half lake depth,
double flush rate,
same rainfall, and
same runoff fraction.
1.28
1.07
1.00
0.92
Halving depth increases
concentrations but
doubling the flush rate
increases chemical output
from the lake.
FR3
Double depth, same
flush rate, same
rainfall, same runoff
fraction (violate water
balance in screening
scenario).
0.69
0.96
0.58
1.02
Doubling depth reduces
lake concentrations for
most chemicals.
FR4
Double flush rate,
same depth, same
rainfall, and same
runoff fraction (violate
water balance in
screening scenario).
0.95
1.00
0.58
0.89
Doubling flush rate
reduces lake
concentrations.
PERC1
Implement balanced
percolation regime.
0.99
1.00
0.62
0.99
Assumes runoff from one
compartment does not
evaporate but percolates
in the receiving
compartment.
Percolation rate (the
fraction of rainfall that is
subject to percolation into
the sub-surface) has a
modest impact on risk.
Land-use-based
parameterization is
recommended for this
property.
A-4
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Protocol for Developing a TRIM.FaTE Model Scenario to Support RTR Site-specific Multipathway Risk Assessments
Scenario Description
Normalized Risk Relative to
Tier 1 Screening Scenario
Scenario
Name
(with respect to Tier
1 Screening
Scenario)
2,3,7,8-
tetrachloro
dibenzo-p-
dioxin
Benzo(a)
pyrene
Cadmium
Methyl
Mercury
Risk Impact of Scenario
Modification
Conclusions
R1
Reduce rainfall down
to 1 /3rd SS value;
same lake depth;
runoff rates and flush
rate down to 1 /3rd.
0.64
0.59
0.92
0.58
Reducing rainfall reduces
chemical washout from
air.
This run, when combined
with earlier runs focusing
on the impacts of flush
rate, suggests that the
chemical washout impact
of rainfall has more
influence on risk than the
impact of rainfall levels on
hydrological properties like
flush rate. This reinforces
the argument for site-
specific meteorological
parameters.
V_C
Set all surface
compartments except
farm to coniferous
forests.
0.79
0.75
0.40
0.87
The choice of vegetation
in surface soil
compartments impacts
risk by absorbing
chemicals from air and
soil and then redepositing
them onto the surface soil
via litterfall.
Land-use type has a
limited impact on risk.
Based on these results,
terrestrial vegetation
parameters are
recommended for land-
use-specific
parameterization. In
interpreting these results, it
is important to note that
these runs have not been
normalized for erosion
rates. Therefore, the
impacts on risk presented
here are from a
combination of impacts
from differential erosion
rates and vegetation types.
V_D
Set all surface
compartments except
farm to deciduous
forests.
0.34
0.92
0.49
0.39
V_G
Set all surface
compartments except
farm to grassland.
0.88
0.82
0.45
0.92
v_u
Set all surface
compartments except
farm to unfilled soil.
0.42
0.73
0.37
0.81
v_ww
Set all surface
compartments except
farm to forested
wetlands.
0.36
0.92
0.49
0.47
A-5
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Protocol for Developing a TRIM.FaTE Model Scenario to Support RTR Site-specific Multipathway Risk Assessments
Normalized Risk Relative to
Scenario Description
T
ier 1 Screening Scenario
Scenario
Name
(with respect to Tier
1 Screening
Scenario)
2,3,7,8-
tetrachloro
dibenzo-p-
dioxin
Benzo(a)
pyrene
Cadmium
Methyl
Mercury
Risk Impact of Scenario
Modification
Conclusions
Set all surface
V_WG
compartments except
farm to grassy
wetlands.
0.86
0.83
0.46
0.94
Risk is sensitive to the
aquatic biomass levels.
These properties are
therefore recommended
BM1
Increase aquatic
biomass uniformly by
a factor of 10.
0.84
0.39
0.90
0.99
Increasing aquatic
biomass reduces
for site-specific
parameterization. In
interpreting the results of
these runs, it may be noted
that all biomass levels
were uniformly raised. In
real applications, the
chemical concentration in
biomass as the same
amount of chemical is
distributed in a higher
amount of biomass.
biomass levels of the
upper trophic levels may
constitute a lower
BM2
Increase aquatic
biomass uniformly by
a factor of 100.
0.35
0.32
0.29
0.79
percentage of the total
biomass as total biomass
increases, suggesting
slightly lower risk
sensitivity than apparent
here.
Air_DL1
Increase air dust load
by a factor of 10.
2.34
2.31
0.50
0.98
Increasing the dust load
in air increases
Although these runs
indicate that air dust load
A-6
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Protocol for Developing a TRIM.FaTE Model Scenario to Support RTR Site-specific Multipathway Risk Assessments
Normalized Risk Relative to
Scenario Description
T
ier 1 Screening Scenario
Scenario
Name
(with respect to Tier
1 Screening
Scenario)
2,3,7,8-
tetrachloro
dibenzo-p-
dioxin
Benzo(a)
pyrene
Cadmium
Methyl
Mercury
Risk Impact of Scenario
Modification
Conclusions
particulate deposition to
the surface.
moderately influences risk,
literature search indicated
that the range manifested
Air_DL2
Increase air dust load
by a factor of 100.
4.14
2.71
0.50
0.90
by this property is relatively
small and the default value
used is already in the high
end of the observed range
in the U.S. Therefore, this
property is not
recommended for site-
specific parameterization.
Halve the fraction of
Although these runs
Air_FOM1
organic matter in air
0.87
0.66
0.50
1.00
indicate that the fraction of
solids.
The organic content of air
solids can differentially
influence chemical
adherence to the solid
phase.
organic matter in air solids
moderately influences risk,
literature search indicated
Air_FOM2
Double the fraction of
organic matter in air
1.23
1.43
0.50
1.00
that site-specific data may
be difficult to obtain. This
solids.
property is not
recommended for site-
specific parameterization.
Increasing the soil-air
fraction reduces soil
Although these runs
indicate that air dust load
solids, which distributes
moderately influences risk,
Soil_Air
Double the soil-air
1.21
1.29
0.50
1.14
the same amount of
literature search indicated
content.
chemical over a lower
solids content, thereby
increasing soil
concentrations.
that the range manifested
by this property is relatively
small and the default value
used is already in the high
Soil_FOC
Increase the soil
organic fraction
1.04
1.01
0.60
1.00
Increasing soil organic
content increases
end of the observed range
in the U.S. Therefore, this
content by a factor of
10.
chemical adherence to
soil for some chemicals.
property is not
recommended for site-
A-7
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Protocol for Developing a TRIM.FaTE Model Scenario to Support RTR Site-specific Multipathway Risk Assessments
Scenario Description
Normalized Risk Relative to
Tier 1 Screening Scenario
Scenario
Name
(with respect to Tier
1 Screening
Scenario)
2,3,7,8-
tetrachloro
dibenzo-p-
dioxin
Benzo(a)
pyrene
Cadmium
Methyl
Mercury
Risk Impact of Scenario
Modification
Conclusions
Soil_pH1
Set soil pH at 4.
1.00
1.00
0.39
1.00
Soil pH can influence
chemical adherence to
soil solids for some
chemicals.
specific parameterization.
Soil_pH2
Set soil pH at 10.
1.00
1.00
0.66
1.00
Soil_Rho
Set soil solids density
at 1000 kg/m3.
1.41
1.43
0.50
1.16
Decreasing soil particle
density increases soil
concentrations when
normalized by soil weight.
Soil_Water
Double the soil water
content.
1.00
1.00
0.50
1.00
Increasing soil water
content increases
chemical removal by
percolation for some
chemicals.
SusSed TS
S1
Increase lake
suspended solids
concentration by a
factor of 2.
0.73
0.98
1.01
0.74
Increasing suspended
solids in water causes
more chemical to be
deposited to sediment.
Suspended solids
concentration in lakes has
a moderate influence on
risk. Due to the wide range
potentially exhibited by this
property, it has been
recommended for site-
specific parameterization.
SusSed TS
S2
Increase lake
suspended solids
concentration by a
factor of 10.
0.33
0.98
0.46
0.38
Sed_Bur
Halve sediment-burial
rate; same erosion
rate (violate solids
balance in screening
scenario).
1.11
1.00
1.12
1.31
Decreasing the burial rate
reduces the removal of
chemicals from the
sediment layer.
Sediment properties have
a moderate impact on risk,
given the limited range of
values assumed by them
in the environment.
A-8
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Protocol for Developing a TRIM.FaTE Model Scenario to Support RTR Site-specific Multipathway Risk Assessments
Scenario Description
Normalized Risk Relative to
Tier 1 Screening Scenario
Scenario
Name
(with respect to Tier
1 Screening
Scenario)
2,3,7,8-
tetrachloro
dibenzo-p-
dioxin
Benzo(a)
pyrene
Cadmium
Methyl
Mercury
Risk Impact of Scenario
Modification
Conclusions
Sed_Rho
Decrease bed-
sediment-particle
density to 1000 kg/m3.
1.36
1.00
0.63
2.74
The lower the sediment-
particle density, the lower
the volumetric re-
suspension rate from
sediment and the higher
the volumetric burial rate.
Sed_Por
Halve sediment-bed
porosity.
0.85
1.00
0.42
0.78
The lower the sediment
porosity, the lower the
volumetric re-suspension
rate from sediment and
the lower the volumetric
burial rate.
A-9
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Protocol for Developing a TRIM.FaTE Model Scenario to Support RTR Site-specific Multipathway Risk Assessments
Appendix B. AERMOD-to-TRIM.FaTE Input Requirements
As mentioned in Section 5.3, following the pseudo-source methodology of incorporation AERMOD
deposition outputs into TRIM.FaTE, pseudo-sources are assigned to placeholder volume elements and
linked to transfer mass to the appropriate surface compartments. For water parcels, the entire mass is
transferred to the surface water compartment. For land parcels, algorithms apportioning mass between
surface soil and leaves were derived from the existing FaTE algorithms for air-soil and air-plant transfers.
The mass-transfer rates were time-varying because they rely on factors such as hours of daylight and
fraction leaf coverage. The placeholder volume elements are designed to prevent transfer to any
compartment other than those prescribed, and each contains only one pseudo-source. These four
pseudo-sources (representing dry/wet and vapor/particle deposition) for each parcel, along with the
corresponding placeholder compartments, links, and algorithms, are set up in the input files described in
Table B-1.
Table B-1. Supplemental Files to Parameterize AERMOD Outputs for TRIM.FaTE
File Description
Contents
Purpose
Pseudo-source deposition-rate
properties
Assigns surface-deposition rates
(g/m2/day) to each of the
placeholder volume elements (dry
particle, dry vapor, wet particle,
and wet vapor deposition as
needed) for each surface parcel
and chemical.
This file serves as TRIM.FaTE
input file to parameterize the
surface-deposition rates for the
subsequent fate-and-transport
modeling.
Pseudo-source volume elements
Coordinates specifying the spatial
dimensions of the placeholder
volume elements for each surface
parcel.
This file serves as a TRIM.FaTE
input file to supplement the
defined spatial layout of the
modeled domain to include
pseudo-source compartments.
Pseudo-source library
Defines supplemental
compartment types, property
types, and algorithms used in
linking placeholder volume
elements to targeted surface
compartments. This file also
includes the definitions and
locations of pseudo-sources with
emission-rate formulas
accounting for parcel surface
area.
This file serves as a TRIM.FaTE
input file to define additional
properties, compartments, and
algorithms to initialize the
pseudo-source methodology.
This file must be manually
imported through the TRIM.FaTE
graphical interface and saved as
a library.
Pseudo-source link properties
Defines the actual links that
connect the placeholder elements
to their surface targets. This
includes determining which water,
soil, and/or plant compartments
are present on the surface of
each parcel.
This file serves as a TRIM.FaTE
input file to define links between
different compartments,
specifically to initiate mass
transfer from pseudo-source to
compartments on the surface of
each parcel.
B-1
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Appendix 8
Dose-Response Values Used in the RTR Risk Assessments
-------
Appendix 8. Dose-Response Values Used in the RTR Risk Assessments
The dose-response values presented in Table 1 (chronic) and Table 2 (acute) are values used
in the Risk and Technology Review program as of June 2018. In some cases, a value in Table
1 or 2 may reflect an update made after a source category-specific risk assessment was
conducted. The values used in this risk assessment are presented in Table 3.1-1 in the body of
the report.
Definition for Chronic Values
URE (unit risk estimate) = the upper-bound excess lifetime cancer risk estimated to result from
continuous exposure to an agent over a lifetime at a concentration of 1 |jg/m3 in air.
RfC (reference concentration) = an estimate of a continuous inhalation exposure to the human
population (including sensitive subgroups) that is likely to be without an appreciable risk of
harmful noncancer health effects during a lifetime.
Cancer Slope Factor = an upper-bound estimate of the increased cancer risk from a lifetime
oral exposure to an agent.
RfD (reference dose) = an estimate of a continuous oral exposure to the human population
(including sensitive subgroups) that is likely to be without an appreciable risk of harmful
noncancer health effects during a lifetime.
Sources:
IRIS = EPA Integrated Risk Information System
ATSDR = US Agency for Toxic Substances Disease Registry
CAL = California EPA Office of Environmental Human Health Assessment
HEAST = EPA Health Effects Assessment Tables
EPA OAQPS = EPA Office of Air Quality Planning and Standards
EPA ORD = EPA Office of Research & Development
Definition of Acute Values
AEGL-1 (acute exposure guideline level 1) = 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 (acute exposure guideline level 2) = 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.
ERPG-1 (emergency response planning guideline 1) = the maximum airborne concentration
below which nearly all individuals could be exposed for up to 1 hour without experiencing more
than mild, transient health effects or without perceiving a clearly defined objectionable odor.
ERPG-2 (emergency response planning guideline 2) = the maximum airborne concentration
below which nearly all individuals could be exposed for up to 1 hour without experiencing or
developing irreversible or other serious adverse health effects or symptoms that could impair an
individual's ability to take protective action.
REL (reference exposure level) = the concentration level at or below which no adverse health
effects are anticipated for a specified exposure duration. RELs are based on the most sensitive,
relevant, adverse health effect reported in the medical and toxicological literature and are
designed to protect the most sensitive individuals in the population by the inclusion of margins
of safety.
Sources:
National Advisory Committee on Acute Exposure Guideline Level for Hazardous Substances,
reviewed and published by the National Research Council - AEGL
American Industrial Hygiene Association - ERPG
California EPA Office of Environmental Human Health Assessment - REL
-------
Table 1. Chronic Cancer and Noncancer Inhalation and Oral Dose-Response Values and
the Source of Those Values
Inhalation
Oral (ingestion)3
Cancer
URE
URE
RfC
RfC
Slope
Factor
RfD
Pollutant
CAS No.
1/(ug/m3)
Source
(mg/m3)
Sourc
(1/(mg/kg/d))
(mg/kg/d)
1,1,1-Trichloroethane
71-55-6
5
IRIS
1,1,2-Trichloroethane
79-00-5
0.000016
IRIS
Hexachlorocyclohexanes
alpha-Hexachlorocyclohexane
(a- HCH)
319-84-6
0.0018
IRIS
beta-Hexachlorocyclohexane
(b- HCH)
319-85-7
0.00053
IRIS
Lindane (gamma-HCH)
58-89-9
0.00031
CAL
technical Hexachlorocyclohexane
(HCH)
608-73-1
0.00051
IRIS
1,2,4-T richlorobenzene
120-82-1
0.2
1,2-Dibromo-3-chloropropane
96-12-8
0.002
CAL
0.0002
IRIS
1,2-Diphenylhydrazine
122-66-7
0.00022
IRIS
1,2-Epoxybutane
106-88-7
0.02
IRIS
1,3-Butadiene
106-99-0
0.00003
IRIS
0.002
IRIS
1,3-Dichloropropene
542-75-6
0.000004
IRIS
0.02
IRIS
1,3-Propane sultone
1120-71-4
0.00069
CAL
p-Dichlorobenzene
106-46-7
0.000011
CAL
0.06
ATSDR
p-Dimethylaminoazobenzene
60-11-7
0.0013
CAL
1,4-Dioxane
123-91-1
0.000005
IRIS
0.03
IRIS
2,4,6-T richlorophenol
88-06-2
0.0000031
IRIS
2,4-Dinitrotoluene
121-14-2
0.000089
CAL
2,4-Toluene diamine
95-80-7
0.0011
CAL
2,4/2,6-Toluene
diisocyanate mixture (TDI)
26471-62-5
0.000011
CAL
0.00007
IRIS
2,4-Toluene diisocyanate
584-84-9
0.000011
CAL
0.00007
IRIS
2-Chloroacetophenone
532-27-4
0.00003
IRIS
EPA
2-Nitropropane
79-46-9
0.0000056
OAQPS
0.02
IRIS
3,3'-Dichlorobenzidine
91-94-1
0.00034
CAL
4,4'-Methylene bis(2-chloroaniline)
101-14-4
0.00043
CAL
4,4'-Methylenedianiline
101-77-9
0.00046
CAL
0.02
CAL
Methylene diphenyl diisocyanate
101-68-8
0.0006
IRIS
Acetaldehyde
75-07-0
0.0000022
IRIS
0.009
IRIS
Acetamide
60-35-5
0.00002
CAL
Acetonitrile
75-05-8
0.06
IRIS
Acrolein
107-02-8
0.00035
CAL
Acrylamide
79-06-1
0.00016
IRIS
0.006
IRIS
Acrylic acid
79-10-7
0.001
IRIS
Acrylonitrile
107-13-1
0.000068
IRIS
0.002
IRIS
Allyl chloride
107-05-1
0.000006
CAL
0.001
IRIS
Aniline
62-53-3
0.0000016
CAL
0.001
IRIS
Antimony Compounds
Antimony compounds
7440-36-0
0.0002
IRIS
Antimony oxide
1327-33-9
0.0002
IRIS
Antimony pentafluoride
7783-70-2
0.0002
IRIS
Antimony pentoxide
1314-60-9
0.0002
IRIS
Antimony potassium tartrate
304-61-0
0.0002
IRIS
Antimony tetroxide
1332-81-6
0.0002
IRIS
Antimony trihydride
7803-52-3
0.0002
IRIS
Antimony trioxide
1309-64-4
0.0002
IRIS
-------
Inhalation
Oral (ingestion)3
Pollutant
CAS No.
URE
1/(ug/m3)
URE
Source
RfC
(mg/m3)
RfC
Sourc
Cancer
Slope
Factor
(1/(mg/kg/d))
RfD
(mg/kg/d)
Arsenic Compounds
Arsenic acid
7778-39-4
0.0043
IRIS
0.000015
CAL
Arsenic as lead arsenate
7784-40-9
0.0043
IRIS
0.000015
CAL
Arsenic chloride
7784-34-1
0.0043
IRIS
0.000015
CAL
Arsenic compounds
7440-38-2
0.0043
IRIS
0.000015
CAL
1.5
Arsenic pentoxide
1303-28-2
0.0043
IRIS
0.000015
CAL
Arsenic trioxide
1327-53-3
0.0043
IRIS
0.000015
CAL
Arsine
7784-42-1
0.00005
IRIS
Benzene
71-43-2
0.0000078d
IRIS
0.03
IRIS
Benzidine
92-87-5
0.1072
IRIS
Benzyl chloride
100-44-7
0.000049
CAL
Beryllium Compounds
Beryllium chloride
7787-47-5
0.0024
IRIS
0.00002
IRIS
Beryllium compounds
7440-41-7
0.0024
IRIS
0.00002
IRIS
Beryllium fluoride
7787-49-7
0.0024
IRIS
0.00002
IRIS
Beryllium nitrate
13597-99-4
0.0024
IRIS
0.00002
IRIS
Beryllium oxide
1304-56-9
0.0024
IRIS
0.00002
IRIS
Bis(2-ethylhexyl) phthalate
117-81-7
0.0000024
CAL
Bis(chloromethyl)ether
542-88-1
0.062
IRIS
Bromoform
75-25-2
0.0000011
IRIS
Cadmium Compounds
Cadmium acetate
543-90-8
0.0018
IRIS
0.00001
ATSDR
Cadmium compounds
7440-43-9
0.0018
IRIS
0.00001
ATSDR
0.001
Cadmium as cadmium
cyanamide
20654-10-8
0.0018
IRIS
0.00001
ATSDR
Cadmium nitrate
10325-94-7
0.0018
IRIS
0.00001
ATSDR
Cadmium oxide
1306-19-0
0.0018
IRIS
0.00001
ATSDR
Cadmium stearate
2223-93-0
0.0018
IRIS
0.00001
ATSDR
Carbon disulfide
75-15-0
0.7
IRIS
Carbon tetrachloride
56-23-5
0.000006
IRIS
0.1
IRIS
Carbonyl sulfide
463-58-1
0.163f
EPA ORD
Chlordane
57-74-9
0.0001
IRIS
0.0007
IRIS
Chlorine
7782-50-5
0.00015
ATSDR
Chlorobenzene
108-90-7
1
CAL
Chlorobenzilate
510-15-6
0.000078
HEAST
Chloroform
67-66-3
0.098
ATSDR
Chloroprene
126-99-8
0.00048
IRIS
0.02
IRIS
Chromium Compounds
Ammonium chromate
7788-98-9
0.012
IRIS
0.0001
IRIS
Ammonium dichromate
7789-09-5
0.012
IRIS
0.0001
IRIS
Barium chromate
10294-40-3
0.012
IRIS
0.0001
IRIS
Calcium chromate
13765-19-0
0.012
IRIS
0.0001
IRIS
Chromic acid (VI)
7738-94-5
0.012
IRIS
0.0001
IRIS
Chromic sulfuric acid
13530-68-2
0.012
IRIS
0.0001
IRIS
Chromium (VI) as lead chromate
7758-97-6
0.012
IRIS
0.0001
IRIS
Chromium (VI) as lead chromate
oxide
18454-12-1
0.012
IRIS
0.0001
IRIS
Chromium (VI) compounds
18540-29-9
0.012
IRIS
0.0001
IRIS
Chromium (VI) trioxide, chromic
acid mist
11115-74-5
0.012
IRIS
0.000008
IRIS
Chromium compounds
7440-47-3
0.012
IRIS
0.0001
IRIS
Chromium dioxide
12018-01-8
0.012
IRIS
0.0001
IRIS
Potassium chromate
7789-00-6
0.012
IRIS
0.0001
IRIS
Potassium dichromate
7778-50-9
0.012
IRIS
0.0001
IRIS
Sodium chromate
7775-11-3
0.012
IRIS
0.0001
IRIS
Sodium dichromate
10588-01-9
0.012
IRIS
0.0001
IRIS
-------
Inhalation
Oral (ingestion)3
Pollutant
CAS No.
URE
1/(ug/m3)
URE
Source
RfC
(mg/m3)
RfC
Source
Cancer
Slope
Factor
(1/(mg/kg/d))
RfD
(mg/kg/d)
Strontium chromate
7789-06-2
0.012
IRIS
0.0001
IRIS
Zinc chromate
13530-65-9
0.012
IRIS
0.0001
IRIS
Zinc potassium chromate
11103-86-9
0.012
IRIS
0.0001
IRIS
Cobalt Compounds
Cobalt aluminate
1345-16-0
0.0001
ATSDR
Cobalt bromide
7789-43-7
0.0001
ATSDR
Cobalt carbonate
513-79-1
0.0001
ATSDR
Cobalt carbonyl
10210-68-1
0.0001
ATSDR
Cobalt chloride
7646-79-9
0.0001
ATSDR
Cobalt compounds
7440-48-4
g
0.0001
ATSDR
Cobalt hydrocarbonyl
16842-03-8
0.0001
ATSDR
Cobalt naphtha
61789-51-3
0.0001
ATSDR
Cobalt nitrate
Co Nitrate
0.0001
ATSDR
Cobalt oxide
1307-96-6
0.0001
ATSDR
Cobalt oxide (II, III)
1308-06-1
0.0001
ATSDR
Hexanoic acid, 2-ethyl-
cobalt (2+) salt
136-52-7
0.0001
ATSDR
Coke Oven Emissions
Benzene soluble organics (BSO)
141
0.00099
IRIS
Coke oven emissions
8007-45-2
0.00099
IRIS
Methylene chloride soluble
organics (MCSO)
142
0.00099
IRIS
Cresols
Cresols (mixed)
1319-77-3
0.6
CAL
m-Cresol (3-methylphenol)
108-39-4
0.6
CAL
o-Cresol
95-48-7
0.6
CAL
p-Cresol (4-methy phenol)
106-44-5
0.6
CAL
Cumene
98-82-8
0.4
IRIS
Cyanide Compounds
Acetone cyanohydrin
75-86-5
0.01
HEAST
Barium cyanide
542-62-1
0.0008
IRIS
Calcium cyanamide
156-62-7
0.0008
IRIS
Calcium cyanide
592-01-8
0.0008
IRIS
Copper cyanide
544-92-3
0.0008
IRIS
Cyanazine
21725-46-2
0.0008
IRIS
Cyanide as Cadmium cyanamide
20654-10-8
0.0008
IRIS
Cyanide compounds
57-12-5
0.0008
IRIS
Cyanogen
460-19-5
0.0008
IRIS
Cyanogen bromide
506-68-3
0.0008
IRIS
Cyanogen chloride
506-77-4
0.0008
IRIS
Cyanogen iodide
506-78-5
0.0008
IRIS
Cyanophos
2636-26-2
0.0008
IRIS
Cyanuric fluoride
675-14-9
0.0008
IRIS
Ethylene cyanohydrin
109-78-4
0.0008
IRIS
Hydrogen cyanide
74-90-8
0.0008
IRIS
Isopropyl cyanide
78-82-0
0.0008
IRIS
Potassium cyanide
151-50-8
0.0008
IRIS
Potassium silver cyanide
506-61-6
0.0008
IRIS
Potassium thiocyanate
333-20-0
0.0008
IRIS
Silver cyanide
506-64-9
0.0008
IRIS
Sodium cyanide
143-33-9
0.0008
IRIS
Thiocyanate
Thiocyanate
0.0008
IRIS
Thiocyanic acid
21564-17-0
0.0008
IRIS
Zinc cyanide
557-21-1
0.0008
IRIS
Dichloroethyl ether
111-44-4
0.00033
IRIS
Dichlorvos
62-73-7
0.0005
IRIS
Diesel engine emissions
Diesel emis
0.005
IRIS
-------
Inhalation
Oral (ingestion)3
Pollutant
CAS No.
URE
1/(ug/m3)
URE
Source
RfC
(mg/m3)
RfC
Source
Cancer
Slope
Factor
(1/(mq/kq/d))
RfD
(mg/kg/d)
Diethanolamine
111-42-2
0.003
CAL
Dimethyl formamide
68-12-2
0.03
CAL
Dioxins and Furans
1,2,3,4,6,7,8,9-
Octachlorodibenzo-p-dioxin
3268-87-9
0.0099
EPA
ORD
0.00013
CAL
45
1,2,3,4,6,7,8,9-
Octachlorodibenzofuran
39001-02-0
0.0099
EPA
ORD
0.00013
CAL
45
1,2,3,4,6,7,8-
Heptachlorodibenzo-p-dioxin
35822-46-9
0.33
EPA
ORD
0.000004
CAL
1500
1,2,3,4,6,7,8-
Heptachlorodibenzofuran
67562-39-4
0.33
EPA
ORD
0.000004
CAL
1500
1,2,3,4,7,8,9-
Heptachlorodibenzofuran
55673-89-7
0.33
EPA
ORD
0.000004
CAL
1500
1,2,3,4,7,8-Hexach lorodibenzo-p-
dioxin
39227-28-6
3.3
EPA
ORD
0.0000004
CAL
15000
1,2,3,4,7,8-
Hexachlorodibenzofuran
70648-26-9
3.3
EPA
ORD
0.0000004
CAL
15000
1,2,3,6,7,8-Hexachlorodibenzo-p-
dioxin
57653-85-7
3.3
EPA
ORD
0.0000004
CAL
6200
1,2,3,6,7,8-
Hexachlorodibenzofuran
57117-44-9
3.3
EPA
ORD
0.0000004
CAL
15000
1,2,3,7,8,9-Hexach lorodibenzo-p-
dioxin
19408-74-3
3.3
EPA
ORD
0.0000004
CAL
6200
1,2,3,7,8,9-
Hexachlorodibenzofuran
72918-21-9
3.3
EPA
ORD
0.0000004
CAL
15000
1,2,3,7,8-Pentachlorodibenzo-p-
dioxin
40321-76-4
33
EPA
ORD
0.00000004
CAL
150000
1,2,3,7,8-
Pentachlorodibenzofuran
57117-41-6
0.99
EPA
ORD
0.0000013
CAL
4500
2,3,4,6,7,8-
Hexachlorodibenzofuran
60851-34-5
3.3
EPA
ORD
0.0000004
CAL
15000
2,3,4,7,8-
Pentachlorodibenzofuran
57117-31-4
9.9
EPA
ORD
0.00000013
CAL
45000
2,3,7,8-Tetrachlorodibenzo-p-
dioxin
1746-01-6
33
EPA
ORD
0.00000004
CAL
150000
2,3,7,8-Tetrachlorodibenzofuran
51207-31-9
3.3
EPA
ORD
0.0000004
CAL
15000
Hexachlorodibenzo-p-dioxin
34465-46-8
3.3
EPA
ORD
0.0000004
CAL
15000
Epichlorohydrin
106-89-8
0.0000012
IRIS
0.001
IRIS
Ethyl benzene
100-41-4
0.0000025
CAL
0.3
ATSDR
Ethyl carbamate
51-79-6
0.000464
CAL
Ethyl chloride
75-00-3
10
IRIS
Ethylene dibromide
106-93-4
0.0006
IRIS
0.009
IRIS
Ethylene dichloride
107-06-2
0.000026
IRIS
2.4
ATSDR
Ethylene glycol
107-21-1
0.4
CAL
Ethylene oxide
75-21-8
0.005
IRIS
0.03
CAL
Ethylene thiourea
96-45-7
0.000013
CAL
Ethylidene dichloride
75-34-3
0.0000016
CAL
0.5
HEAST
Formaldehyde
50-00-0
0.000013
IRIS
0.0098
ATSDR
Glycol ethers
1,2-Dimethoxyethane
110-71-4
0.02
IRIS
2-Butoxyethyl acetate
112-07-2
0.02
IRIS
2-(Hexyloxy)ethanol
112-25-4
0.02
IRIS
2-Propoxyethyl acetate
20706-25-6
0.02
IRIS
Butyl carbitol acetate
124-17-4
0.02
IRIS
Carbitol acetate
112-15-2
0.02
IRIS
Diethylene glycol diethyl ether
112-36-7
0.02
IRIS
Diethylene glycol dimethyl ether
111-96-6
0.02
IRIS
-------
Inhalation
Oral (ingestion)3
Pollutant
CAS No.
URE
1/(ug/m3)
URE
Source
RfC
(mg/m3)
RfC
Source
Cancer
Slope
Factor
(1/(mg/kg/d))
RfD
(mg/kg/d)
Diethylene glycol ethyl methyl
ether
1002-67-1
0.02
IRIS
Diethylene glycol monobutyl
ether
112-34-5
0.02
HEAST
Diethylene glycol monoethyl
ether
111-90-0
0.02
IRIS
Diethylene glycol monomethyl
ether
111-77-3
0.02
IRIS
Ethoxytriglycol
112-50-5
0.02
IRIS
Ethylene glycol diethyl ether
629-14-1
0.02
IRIS
Ethylene glycol ethyl ether
110-80-5
0.2
IRIS
Ethylene glycol ethyl ether
acetate
111-15-9
0.3
CAL
Ethylene glycol methyl ether
109-86-4
0.02
IRIS
Ethylene glycol methyl ether
acetate
110-49-6
0.09
CAL
Ethylene glycol mono-sec-butyl
ether
7795-91-7
0.02
IRIS
Glycol ethers
171
0.02
IRIS
Methoxytriglycol
112-35-6
0.02
IRIS
Methyl Cellosolve Acrylate
3121-61-7
0.02
IRIS
N-Hexyl carbitol
112-59-4
0.02
IRIS
Phenyl cellosolve
122-99-6
0.02
IRIS
Propyl cellosolve
2807-30-9
0.02
IRIS
Triethylene glycol dimethyl ether
112-49-2
0.02
IRIS
Triethylene glycol monohexyl ether
25961-89-1
0.02
IRIS
Triglycol monobutyl ether
143-22-6
0.02
IRIS
Heptachlor
76-44-8
0.0013
IRIS
Hexachlorobenzene
118-74-1
0.00046
IRIS
Hexachlorobutadiene
87-68-3
0.000022
IRIS
Hexachlorocyclopentadiene
77-47-4
0.0002
IRIS
Hexachloroethane
67-72-1
0.03
IRIS
Hexamethylene-1,6-diisocyanate
822-06-0
0.00001
IRIS
n-Hexane
110-54-3
0.7
IRIS
Hydrazine
302-01-2
0.0049
IRIS
0.0002
CAL
Hydrochloric acid
7647-01-0
0.02
IRIS
Hydrofluoric acid
7664-39-3
0.014
CAL
Isophorone
78-59-1
2
CAL
Lead Compoundse
Lead (II) oxide
1317-36-8
0.00015
EPA
OAQPS
Lead acetate
301-04-2
0.00015
EPA
OAQPS
Lead as lead arsenate
7784-40-9
0.00015
EPA
OAQPS
Lead as lead chromate
7758-97-6
0.00015
EPA
OAQPS
Lead as lead chromate oxide
18454-12-1
0.00015
EPA
OAQPS
Lead chloride
7758-95-4
0.00015
EPA
OAQPS
Lead compounds
7439-92-1
0.00015
EPA
OAQPS
Lead compounds (otherthan
inorganic)
603
0.00015
EPA
OAQPS
Lead dioxide
1309-60-0
0.00015
EPA
OAQPS
Lead nitrate
10099-74-8
0.00015
EPA
OAQPS
-------
Inhalation
Oral (ingestion)3
Pollutant
CAS No.
URE
1/(ug/m3)
URE
Source
RfC
(mg/m3)
RfC
Source
Cancer
Slope
Factor
(1/(mg/kg/d))
RfD
(mg/kg/d)
Lead subacetate
1335-32-6
0.00015
EPA
OAQPS
Lead sulfate
7446-14-2
0.00015
EPA
OAQPS
Tetraethyl lead
78-00-2
0.00015
EPA
OAQPS
Tetramethyl lead
75-74-1
0.00015
EPA
OAQPS
Maleic anhydride
108-31-6
0.0007
CAL
Manganese Compounds
Manganese chloride
2145-07-6
0.0003
ATSDR
Manganese compounds
7439-96-5
0.0003
ATSDR
Manganese dioxide
1313-13-9
0.0003
ATSDR
Manganese nitrate
10377-66-9
0.0003
ATSDR
Manganese oxide
1317-35-7
0.0003
ATSDR
Manganese sulfate
7785-87-7
0.0003
ATSDR
Manganese tetroxide
1317-35-7
0.0003
ATSDR
Manganese tricarbonyl (eta.5-
2,4-cyclopentadien-1-yl)-
12079-65-1
0.0003
ATSDR
Manganese trioxide
1317-34-6
0.0003
ATSDR
Mercury compounds
Gaseous divalent mercury
201
0.0003
IRIS
0.0001
Mercuric acetate
1600-27-7
0.0003
IRIS
0.0001
Mercuric chloride
7487-94-7
0.0003
IRIS
0.0001
Mercuric nitrate
10045-64-0
0.0003
IRIS
0.0001
Mercuric oxide
21908-53-2
0.0003
IRIS
0.0001
Mercury (elemental)
7439-97-6
0.0003
IRIS
c
Mercury (organic)
22967-92-6
0.0003
IRIS
c
Mercury compounds
HGCMPDS
0.0003
IRIS
c*
Methoxyethylmercuric acetate
151-38-2
0.0003
IRIS
0.0001
Methyl mercury
22967-92-6
0.0003
IRIS
c
Methylmercuric dicyanamide
502-39-6
0.0003
IRIS
0.0001
Particulate divalent mercury
202
0.0003
IRIS
0.0001
Phenylmercuric acetate
62-38-4
0.0003
IRIS
0.0001
Methanol
67-56-1
20
IRIS
Methyl bromide
74-83-9
0.005
IRIS
Methyl chloride
74-87-3
0.09
IRIS
Methyl isobutyl ketone
108-10-1
3
IRIS
Methyl isocyanate
624-83-9
0.001
CAL
Methyl methacrylate
80-62-6
0.7
IRIS
Methyl tert-butyl ether
1634-04-4
0.00000026
CAL
3
IRIS
Methylene chloride
75-09-2
0.000000016
IRIS
0.6
IRIS
Naphthalene
91-20-3
0.000034
CAL
0.003
IRIS
Nickel compounds
Nickel (II) sulfate hexahydrate
10101-97-0
0.00048
EPA
OAQPS
0.00009
ATSDR
Nickel acetate
373-02-4
0.00048
EPA
OAQPS
0.00009
ATSDR
Nickel carbonyl
13463-39-3
0.00048
EPA
OAQPS
0.00009
ATSDR
Nickel chloride
7718-54-9
0.00048
EPA
OAQPS
0.00009
ATSDR
Nickel compounds
7440-02-0
0.00048
EPA
OAQPS
0.00009
ATSDR
Nickel nitrate
13138-45-9
0.00048
EPA
OAQPS
0.00009
ATSDR
Nickel oxide
1313-99-1
0.00048
EPA
OAQPS
0.00002
CAL
-------
Inhalation
Oral (ingestion)3
Pollutant
CAS No.
URE
1/(ug/m3)
URE
Source
RfC
(mg/m3)
RfC
Source
Cancer
Slope
Factor
(1/(mg/kg/d))
RfD
(mg/kg/d)
Nickel refinery dust
Nl DUST
0.00024
IRIS
Nickel subsulfide
12035-72-2
0.00048
IRIS
0.00009
ATSDR
Nickel sulfamate
13770-89-3
0.00048
EPA
OAQPS
0.00009
ATSDR
Nickel sulfate
7786-81-4
0.00048
EPA
OAQPS
0.00009
ATSDR
Nitrobenzene
98-95-3
0.00004
IRIS
0.009
IRIS
Nitrosodimethylamine
62-75-9
0.022
IRIS
N-Nitrosomorpholine
59-89-2
0.0019
CAL
o-Toluidine
95-53-4
0.000051
CAL
Pentachlorophenol
87-86-5
0.0000051
CAL
Phenol
108-95-2
0.2
CAL
Phosgene
75-44-5
0.0003
IRIS
Phosphine
7803-51-2
0.0003
IRIS
Phthalic anhydride
85-44-9
0.02
CAL
Polychlorinated Biphenyls
Aroclor 1016
12674-11-2
0.0001
IRIS
Aroclor 1221
11104-28-2
0.0001
IRIS
Aroclor 1242
53469-21-9
0.0001
IRIS
Aroclor 1248
12672-29-6
0.0001
IRIS
Aroclor 1254
11097-69-1
0.0001
IRIS
Aroclor 1260
11096-82-5
0.0001
IRIS
Polychlorinated biphenyls
1336-36-3
0.0001
IRIS
2-Chlorobiphyenyl
15999-91-1
0.0001
IRIS
2,4,4'-Trichlorobiphenyl (PCB-28)
7012-37-5
0.0001
IRIS
4,4'-Dichlorobiphenyl (PCB-15)
2050-68-2
0.0001
IRIS
Decachlorobiphenyl (PCB-209)
2051-60-7
0.0001
IRIS
Heptachlorobiphenyl
28655-71-2
0.0001
IRIS
Hexachlorobiphenyl
26601-64-9
0.0001
IRIS
Pentachlorobiphenyl
25429-29-2
0.0001
IRIS
Tetrachlorobiphenyl
26914-33-0
0.0001
IRIS
Polycyclic Organic Matter
POM 71002
0.000048
CAL
16-PAH
40
0.000048
CAL
PAH, total
234
0.000048
CAL
0.05
Polycyclic organic matter
246
0.000048
CAL
0.05
POM 72002
0.000048
CAL
1-Methylnaphthalene
90-12-0
0.000048
CAL
0.05
1-Methylphenanthrene
832-69-9
0.000048
CAL
2-Methylphenanthrene
2531-84-2
0.000048
CAL
1-Methylpyrene
2381-21-7
0.000048
CAL
2-Methylnaphthalene
91-57-6
0.000048
CAL
0.05
2-Naphthylamine
91-59-8
0.000048
CAL
12-Methylbenz(a)anthracene
2422-79-9
0.000048
CAL
beta-Chloronaphthalene
91-58-7
0.000048
CAL
0.05
Acenaphthene
83-32-9
0.000048
CAL
0.05
Acenaphthylene
208-96-8
0.000048
CAL
0.05
Anthracene
120-12-7
b
IRIS
Benzo(a)fluoranthene
203-33-8
0.000048
CAL
0.05
Benzo(g,h,i)fluoranthene
203-12-3
0.000048
CAL
0.05
Benzo(c)phenanthrene
195-19-7
0.000048
CAL
0.05
Benzo(e)pyrene
192-97-2
0.000048
CAL
0.05
Benzofluoranthenes
56832-73-6
0.000048
CAL
0.05
Benzo(ghi)perylene
191-24-2
0.000048
CAL
0.05
Coal tar
8007-45-2
0.00099
CAL
Coronene
191-07-1
0.000048
CAL
Extractable organic matter (EOM)
284
0.000048
CAL
Fluoranthene
206-44-0
0.000048
CAL
0.05
Fluorene
86-73-7
0.000048
CAL
0.05
-------
Inhalation
Oral (ingestion)3
Pollutant
CAS No.
URE
1/(ug/m3)
URE
Source
RfC
(mg/m3)
RfC
Source
Cancer
Slope
Factor
(1/(mg/kg/d))
RfD
(mg/kg/d)
Indene
95-13-6
0.000048
CAL
Methylanthracene
26914-18-1
0.000048
CAL
9-Methylanthracene
779-02-2
0.000048
CAL
Methylbenzopyrene
65357-69-9
0.000048
CAL
Octabromodiphenyl ether
32536-52-0
0.000048
CAL
Perylene
198-55-0
0.000048
CAL
0.05
Phenanthrene
85-01-8
b
IRIS
Pyrene
129-00-0
b
IRIS
POM 73002
0.096
CAL
7,12-Dimethylbenz[a]anthracene
57-97-6
0.1136
CAL
250
POM 74002
0.0096
CAL
1,6-Dinitropyrene
42397-64-8
0.0096
CAL
3-Methylcholanthrene
56-49-5
0.01008
CAL
22
6-Nitrochrysene
7496-02-8
0.0096
CAL
Dibenzo[a,h]pyrene
189-64-0
0.0096
CAL
Dibenzo[a,i]pyrene
189-55-9
0.0096
CAL
10
Dibenzo[a,l]pyrene
191-30-0
0.0096
CAL
POM 75002
0.00096
CAL
1,8-Dinitropyrene
42397-65-9
0.00096
CAL
2-Acetylaminofluorene
53-96-3
0.00208
CAL
1
Methylchrysene
41637-90-5
0.00096
CAL
5-Methylchrysene
3697-24-3
0.00096
CAL
7H-Dibenzo[c,g]carbazole
194-59-2
0.00096
CAL
Benzo[a]pyrene
50-32-8
0.00096
EPA
1
Dibenzo[a,e]pyrene
192-65-4
0.00096
CAL
Dibenzo[a,h]anthracene
53-70-3
0.00096
EPA
1
Polycyclic aromatic
hydrocarbon as B(a)P TEQ
0.00096
CAL
POM 76002
0.000096
CAL
1-Nitropyrene
5522-43-0
0.000096
CAL
4-Nitropyrene
57835-92-4
0.000096
CAL
5-Nitroacenaphthene
602-87-9
0.0000592
CAL
Benz[a]anthracene
56-55-3
0.000096
EPA
0.1
Benzo[b]fluoranthene
205-99-2
0.000096
EPA
0.1
Benzo[b+k]fluoranthene
102
0.000096
CAL
0.1
Benzo[j]fluoranthene
205-82-3
0.000096
CAL
0.1
Dibenz[a,h]acridine
226-36-8
0.000096
CAL
Dibenz[a,j]acridine
224-42-0
0.000096
CAL
0.1
lndeno[1,2,3-c,d]pyrene
193-39-5
0.000096
EPA
0.1
POM 77002
0.0000096
CAL
Benzo[k]fluoranthene
207-08-9
0.0000096
EPA
0.01
2-Aminoanthraquinone
117-79-3
0.000015
CAL
2-Nitrofluorene
607-57-8
0.0000096
CAL
Carbazole
86-74-8
0.0000096
CAL
0.02
Chrysene
218-01-9
0.00000096
EPA
0.001
POM 78002
0.000176
CAL
7-PAH
75
0.000176
CAL
0.05
Propionaldehyde
123-38-6
0.008
IRIS
Propylene dichloride
78-87-5
0.004
IRIS
Propylene oxide
75-56-9
0.0000037
IRIS
0.03
IRIS
Radionuclides
Uranium, insoluble
7440-61-1
0.0008
ATSDR
Uranium (IV) dioxide
1344-57-6
0.0008
ATSDR
Uranium compounds
7440-61-1
0.0008
ATSDR
Uranium hexafluoride
7783-81-5
0.00004
ATSDR
Uranium oxide
1344-59-8
0.0008
ATSDR
Uranium, soluble
UranSol
0.00004
ATSDR
-------
Inhalation
Oral (ingestion)3
Pollutant
CAS No.
URE
1/(ug/m3)
URE
Source
RfC
(mg/m3)
RfC
Source
Cancer
Slope
Factor
(1/(mg/kg/d))
RfD
(mg/kg/d)
Uranyl acetate dihydrate
541-09-3
0.00004
ATSDR
Uranyl nitrate hexahydrate
13520-83-7
0.00004
ATSDR
Selenium Compounds
Hydrogen selenide
7783-07-5
Potassium selenate
7790-59-2
0.02
CAL
Selenious acid
7783-00-8
0.02
CAL
Selenium compounds
7782-49-2
0.02
CAL
Selenium dioxide
7446-08-4
0.02
CAL
Selenium disulfide
7488-56-4
0.02
CAL
Selenium hexafluoride
7783-79-1
0.02
CAL
Selenium oxide
12640-89-0
0.02
CAL
Selenium oxychloride
7791-23-3
0.02
CAL
Selenium sulfide
7446-34-6
0.02
CAL
Selenourea
630-10-4
0.02
CAL
Sodium selenate
13410-01-0
0.02
CAL
Sodium selenite
10102-18-8
0.02
CAL
Styrene
100-42-5
1
IRIS
Styrene oxide
96-09-3
Tetrachloroethene
127-18-4
0.00000026
IRIS
0.04
IRIS
Titanium tetrachloride
7550-45-0
0.0001
ATSDR
Toluene
108-88-3
5
IRIS
Toxaphene
8001-35-2
0.00032
IRIS
Trichloroethylene
79-01-6
0.0000048
IRIS
0.002
IRIS
Triethylamine
121-44-8
0.007
IRIS
Vinyl acetate
108-05-4
0.2
IRIS
Vinyl bromide
593-60-2
0.000032
HEAST
0.003
IRIS
Vinyl chloride
75-01-4
0.0000088
IRIS
0.1
IRIS
Vinylidene chloride
75-35-4
0.2
IRIS
Xylenes
m-Xylene
108-38-3
0.1
IRIS
o-Xylene
95-47-6
0.1
IRIS
p-Xylene
106-42-3
0.1
IRIS
Xylenes (mixed)
1330-20-7
0.1
IRIS
8/2021
Notes:
a Benchmark values are provided only for those PB-HAPs for which multipathway risk is assessed (via TRIM). There
may be other PB-HAPs in this table, even though no benchmark is presented,
k IRIS has determined this POM to be not carcinogenic.
c The predominant form of mercury assessed in our multipathway risk screening is methyl mercury, which is a
transformation product of divalent mercury and accumulates in fish. While elemental mercuryemissions can convert to
divalent mercury in the atmosphere, such transformations generally occurbeyond the 50 km modeling domain around
the emissions sources in our assessment. *Emissions reported as "mercury compounds" is speciated into elemental,
particulate divalent, and gaseous divalent and modeled accordingly in the multipathway screening assessment.
^ The EPA IRIS assessment for benzene provides a range of plausible UREs. This assessment usedthe highest
value in that range, 7.8E-06 |jg/m3. The low end of the range is 2.2E-06 |jg/m3.
e There is no reference concentration for lead. In considering noncancer hazards for lead in thisassessment, we
compared rolling three-month average exposure estimates to the National Ambient Air Quality Standard (NAAQS)
for lead (0.15 |jg/m3). The primary (health-based) standard is a maximumor 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.
f A chronic screening level of 0.163 mg/m3 was developed for carbonyl sulfide by EPA ORD from a No Observed
Adverse Effects Level of 200 ppm based on brain lesions and neurophysiological alterations in rodents.
9 Based on examination of California EPA's cancer inhalation unit risk factor for cobalt compounds, and taking into
account aspects of the methodology used in the derivation of the value, we have decided not to use this value to
support EPA's risk and technology review rules.
-------
Table 2. Acute Dose-Response Values
AEGL-1
AEGL-2
REL
(1-hr)
(1-hr)
ERPG-1
ERPG-2
MRL
(1-hr)
Pollutant
CAS No.
(mg/m3)
(mg/m3)
(mg/m3)
(mg/m3)
(mg/m3)
(mg/m3)
1,1,1-Trichloroethane
71-55-6
1300
3300
1900
3800
11
68
1,1,2-Trichloroethane
79-00-5
0.16
1,1 -Dimethylhydrazine
57-14-7
7.4
1,2-Epoxybutane
106-88-7
210
410
1,2-Propyleneimine
75-55-8
28
1,3-Butadiene
106-99-0
1500
12000
22
1100
a
1,4-Dichlorobenzene
106-46-7
12
1,4-Dioxane
123-91-1
61
1200
7.2
3
2,4/2,6-Toluene diisocyanate mixture
26471-62-5
0.14
0.59
0.071
1.1
0.000071
0.002
2,4-Toluene diisocyanate
584-84-9
0.14
0.59
0.071
1.1
0.000071
0.002
Methylene diphenyl diisocyanate
101-68-8
5
0.012
Acetaldehyde
75-07-0
81
490
18
360
0.47
Acetonitrile
75-05-8
22
84
Acrolein
107-02-8
0.069
0.23
0.11
0.34
0.0069
0.0025
Acrylic acid
79-10-7
4.4
140
2.9
150
6
Acrylonitrile
107-13-1
3.7
22
76
0.22
Allyl chloride
107-05-1
00
CO
170
9.4
130
Aniline
62-53-3
30
46
Antimony Compounds
Antimony compounds
7440-36-0
0.001
Antimony oxide
1327-33-9
0.001
Antimony pentafluoride
7783-70-2
0.001
Antimony pentoxide
1314-60-9
0.001
Antimony potassium tartrate
304-61-0
0.001
Antimony tetroxide
1332-81-6
0.001
Antimony trihydride
7803-52-3
7.7
2.6
0.001
Antimony trioxide
1309-64-4
0.001
Arsenic Compounds
Arsenic acid
7778-39-4
0.0002
Arsenic as lead arsenate
7784-40-9
0.0002
Arsenic chloride
7784-34-1
0.0002
Arsenic compounds
7440-38-2
0.0002
Arsenic pentoxide
1303-28-2
0.0002
Arsenic trioxide
1327-53-3
3.0
0.0002
Arsine
7784-42-1
0.54
1.6
0.0002
Benzene
71-43-2
170
2600
160
480
0.029
b
Benzyl chloride
100-44-7
5.2
52
0.24
Beryllium Compounds
Beryllium compounds
7440-41-7
0.025
Biphenyl
92-52-4
61
Bis(chloromethyl)ether
542-88-1
0.21
0.47
Cadmium compounds
7440-43-9
0.1
0.76
0.00003
Carbon disulfide
75-15-0
40
500
3.1
160
6.2
Carbon tetrachloride
56-23-5
82
130
630
1.9
Carbonyl sulfide
463-58-1
140
a
Chlorine
7782-50-5
1.5
5.8
2.9
8.7
0.17
0.21
Chloroacetic acid
79-11-8
26
Chlorobenzene
108-90-7
46
690
Chloroform
67-66-3
310
240
0.49
0.15
Chloromethyl methyl ether
107-30-2
1.6
3.3
Cobalt Compounds
Cobalt hydrocarbonyl
16842-03-8
0.9
Cumene
98-82-8
250
1500
-------
AEGL-1
AEGL-2
REL
(1"hr)
(1"hr)
ERPG-1
ERPG-2
MRL
(1"hr)
Pollutant
CAS No.
(mg/m3)
(mg/m3)
(mg/m3)
(mg/m3)
(mg/m3)
(mg/m3)
Cyanide Compounds
Acetone cyanohydrin
75-86-5
7
25
Calcium cyanide
592-01-8
3.8
13
Cyanogen
460-19-5
4.3
18
Cyanogen chloride
506-77-4
0.13
Hydrogen cyanide
74-90-8
2.2
7.8
11
0.34
Isopropyl cyanide
78-82-0
5.7
85
Potassium cyanide
151-50-8
5.3
19
Sodium cyanide
143-33-9
4.0
14
Dichlorvos
62-73-7
0.99
5.1
0.018
Dimethyl formamide
68-12-2
270
6
300
Dimethyl sulfate
77-78-1
0.12
0.62
Epichlorohydrin
106-89-8
6.4
91
19
76
1.3
Ethyl acrylate
140-88-5
34
150
0.041
120
Ethyl benzene
100-41-4
140
4800
22
Ethyl chloride
75-00-3
40
Ethylene dibromide
106-93-4
130
180
Ethylene dichloride
107-06-2
200
810
Ethylene glycol
107-21-1
2
Ethylene imine (aziridine)
151-56-4
8.1
Ethylene oxide
75-21-8
81
90
0.72
Formaldehyde
50-00-0
1.1
17
1.2
12
0.049
0.055
Glycol ethers
1,2-Dimethoxyethane
110-71-4
0.093
2-Butoxyethyl acetate
112-07-2
0.093
2-(Hexyloxy)ethanol
112-25-4
0.093
2-Propoxyoethyl acetate
20706-25-6
0.093
Butyl carbitol acetate
124-17-4
0.093
Carbitol acetate
112-15-2
0.093
Diethylene glycol diethyl ether
112-36-7
0.093
Diethylene glycol dimethyl ether
111-96-6
0.093
Diethylene glycol ethyl methyl ether
1002-67-1
0.093
Diethylene glycol monobutyl ether
112-34-5
0.093
Diethylene glycol monoethyl ether
111-90-0
0.093
Diethylene glycol monomethyl ether
111-77-3
0.093
Ethoxytriglycol
112-50-5
0.093
Ethylene glycol diethyl ether
629-14-1
0.093
Ethylene glycol ethyl ether
110-80-5
0.37
Ethylene glycol ethyl ether acetate
111-15-9
0.14
Ethylene glycol methyl ether
109-86-4
0.093
Ethylene glycol methyl ether acetate
110-49-6
0.093
Ethylene glycol mono-sec-butyl
7795-91-7
0.093
Glycol ethers
171
0.093
Methoxytriglycol
112-35-6
0.093
Methyl cellosolve acrylate
3121-61-7
0.093
N-Hexyl carbitol
112-59-4
0.093
Phenyl cellosolve
122-99-6
0.093
Propyl cellosolve
2807-30-9
0.093
Triethylene glycol dimethyl ether
112-49-2
0.093
Triethylene glycol monohexyl ether
25961-89-1
0.093
Triglycol monobutyl ether
143-22-6
0.093
Hexachlorobutadiene
87-68-3
11
32
Hexachloroethane
67-72-1
58
Hexamethylene-1,6-diisocyanate
822-06-0
a
n-Hexane
110-54-3
10000
Hydrazine
302-01-2
0.13
17
0.66
6.6
-------
Hydrochloric acid
7647-01-0
2.7
33
4.5
30
2.1
Pollutant
CAS No.
AEGL-1
(1"hr)
(mg/m3)
AEGL-2
(1"hr)
(mg/m3)
ERPG-1
(mg/m3)
ERPG-2
(mg/m3)
MRL
(mg/m3)
REL
(1"hr)
(mg/m3)
Hydrofluoric acid
7664-39-3
0.82
20
1.6
16
0.016
0.24
Maleic anhydride
108-31-6
0.8
20
Mercury compounds
Mercury (elemental)
7439-97-6
1.7
2
0.0006
Methanol
67-56-1
690
2700
260
1300
28
Methyl bromide
74-83-9
820
190
0.19
3.9
Methyl chloride
74-87-3
1900
310
2100
1
Methyl hydrazine
60-34-4
1.7
Methyl iodide
74-88-4
130
480
150
290
Methyl isocyanate
624-83-9
0.16
0.058
0.58
Methyl methacrylate
80-62-6
70
490
Methyl tert-butyl ether
1634-04-4
180
2100
180
3600
7.2
Methylene chloride
75-09-2
690
1900
1000
2600
2.1
14
Nickel compounds
C
Nickel carbonyl
13463-39-3
0.25
Parathion
56-38-2
1.5
Phenol
108-95-2
58
89
38
190
5.8
Phosgene
75-44-5
1.2
2.0
0.004
Phosphine
7803-51-2
2.8
0.7
Phosphorus
7723-14-0
3.7
11
Propionaldehyde
123-38-6
110
620
Propylene dichloride
78-87-5
0.092
Propylene oxide
75-56-9
170
690
120
590
3.1
Radionuclides
Uranium (IV) dioxide
1344-57-6
10
Uranium hexafluoride
7783-81-5
3.6
9.6
5
15
Uranium oxide
1344-59-8
10
Selenium Compounds
Hydrogen selenide
7783-07-5
0.36
0.66
0.005
Selenium hexafluoride
7783-79-1
0.42
0.69
Styrene
100-42-5
85
550
210
1100
21
21
Tetrachloroethene
127-18-4
240
1600
680
1400
0.041
20
Titanium tetrachloride
7550-45-0
7.8
5
20
Toluene
108-88-3
250
2100
190
1100
7.5
a
Trichloroethylene
79-01-6
700
2400
540
2700
Triethylamine
121-44-8
2.8
Vinyl acetate
108-05-4
24
130
18
260
Vinyl chloride
75-01-4
640
3100
1300
13000
1.3
180
Vinylidene chloride
75-35-4
2000
Xylenes
m-Xylene
108-38-3
22
o-Xylene
95-47-6
22
p-Xylene
106-42-3
22
xylenes (mixed)
1330-20-7
560
4000
8.7
22
8/2021
Notes:
a Based on examination of California EPA's acute (1-hour) REL for this pollutant and considering aspects of the
methodology used in the derivation of the value, we have decided not to use this value to support EPA's risk and
technology review rules.
k Based on examination of California EPA's acute (1-hour) REL for benzene and considering aspects of the
methodology used in the derivation of the value and how this assessment stands in comparison to the ATSDR
toxicological assessment, we have decided not to use this value to support EPA's risk and technology review rules.
-------
c Based on an in-depth examination of the available acute value for nickel [California EPA's acute (1-hour) REL], we
have concluded that this value is not appropriate to use to support EPA's risk and technology review rules. This
conclusion considers: the effect on which the acute REL is based; aspects of the methodology used in its derivation;
and how this assessment stands in comparison to the ATSDR toxicological assessment, which considered the
broader nickel health effects database. (79 FR 60247-8; October 6, 2014)
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Appendix 9
Technical Support Document for Environmental Risk Screening Assessment
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Technical Support Document
for the Environmental Risk Screen for RTR
July 2017
Prepared For:
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Research Triangle Park, NC 27711
Prepared By:
ICF
2635 Meridian Parkway
Suite 200
Durham, NC 27713
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CONTENTS
FIGURES iv
TABLES iv
ACRONYMS/ABBREVIATIONS v
1 Overview 1
2 Key Components of the Environmental Risk Screen 2
2.1 Environmental HAPs 2
2.2 Assessment Endpoints 7
2.3 Environmental Risk Screening Approach 9
2.3.1 PB-HAPs 10
2.3.2 Lead 12
2.3.3 Acid Gases 13
3 Effects Assessment 14
3.1 Benchmarks for PB-HAPs 17
3.1.1 Types of Benchmarks for PB-HAPs 18
3.1.2 Preferred Sources for PB-HAP Benchmark Values 21
3.1.3 Selected PB-HAP Benchmarks 25
3.2 Benchmarks for Acid Gases 26
3.2.1 Hydrogen Chloride 26
3.2.2 Hydrogen Fluoride 28
4 Exposure Assessment 31
4.1 Environmental Risk Screen for PB-HAPs 32
4.1.1 Tier 1 Exposure Assessment 34
4.1.2 Tier 2 Environmental Risk Screen 38
4.1.3 Tier 3 Exposure Assessment 40
4.2 Environmental Risk Screen for Lead and Acid Gases 43
4.2.1 Lead 43
4.2.2 Acid Gases 43
5 Environmental Risk Characterization/Screening Results 45
5.1 Environmental Risk Screen Metrics for PB-HAPs 45
5.1.1 Tier 1 45
5.1.2 Tiers 2 and 3 46
5.2 Environmental Risk Screen Metrics for Acid Gases 48
5.3 Environmental Risk Screen Metrics for Lead 49
6 References 49
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FIGURES
Figure 2-1. Overview of the Environmental Risk Screen for PB-HAPs 11
Figure 2-2. Overview of the Environmental Risk Screen for Acid Gases 14
Figure 4-1. Approach for Tier 1 Environmental Risk Screen for PB-HAPs 34
Figure 4-2. TRIM.FaTE Lake-centric (Top) and Farm-centric (Bottom) Surface
Layouts for the Tier 1 Screen 36
Figure 4-3. Approach for Tier 2 Environmental Risk Screen for PB-HAPs 39
Figure 4-4. Approach for Tier 3 Environmental Risk Screen for PB-HAPs 42
TABLES
Table 2-1. Summary of HAPs Considered for Inclusion in Environmental Risk
Screen 4
Table 2-2. Generic Ecological Assessment Endpoints Used in the Environmental
Risk Screen 8
Table 3-1. Ecological Benchmarks Used in the Environmental Risk Screen for each
PB-HAP and Assessment Endpoint 26
Table 5-1. Summary of PB-HAP Environmental Risk Screen Metrics 47
Table 5-2. Summary of Acid Gas Environmental Risk Screen Metrics 49
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ACRONYMS/ABBREVIATIONS
AERMOD
American Meteorological Society/EPA Regulatory Model
AWQB
ambient water quality benchmarks
AWQC
ambient water quality criteria
BaP
benzo[a]pyrene
BM
benchmark
BW
body weight
CAA
Clean Air Act
DOE
Department of Energy
EcoEEF
ecological exposure equivalency factor
Eco-SSL
ecological soil screening level (Superfund)
EcoTEF
ecological toxic equivalency factor
EEF
exposure equivalency factor
EPA
Environmental Protection Agency
GEAE
generic ecological assessment endpoints
GLWQI
Great Lakes Water Quality Initiative
HAP
hazardous air pollutant
HC1
hydrogen chloride
HEM
Human Exposure Model
HF
hydrogen fluoride
LOAEL
lowest observed adverse effect level
LOEL
lowest observed effect level
MACT
maximum achievable control technology
NAAQS
national ambient air quality standards
NAWQC-ALC
national ambient water quality criteria-aquatic life criteria
NEL
no effect level
NOAEL
no observed adverse effect level
OAQPS
Office of Air Quality Planning and Standards (U.S. EPA)
ORNL
Oak Ridge National Laboratory
OSWER
Office of Solid Waste and Emergency Response (U.S. EPA) (currently Office of
Land and Emergency Management
PAH
polycyclic aromatic hydrocarbon
PB-HAP
persistent and bioaccumulative HAP
PEL
probable effect level
POM
polycyclic organic matter
RAIS
Risk Assessment Information System (ORNL)
RTR
Risk and Technology Review
SAB
Science Advisory Board
SEB
soil ecotoxicity benchmark
SQB
sediment quality benchmark
SV
screening value
TCDD
2,3,7,8-tetrachlorodibenzo-p-dioxin, termed "dioxin" in this report
TCE
trichloroethylene
TEF
toxic equivalency factor
TEL
threshold effect level
TRIM.FaTE
TRIM's Fate, Transport, and Ecological Exposure module
TRV
toxicity reference value
UF
uncertainty factor
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1 Overview
The environmental risk screen was developed to examine the potential for "adverse
environmental effects" as required under Section 112(f)(2)(A) of the Clean Air Act (CAA).
Section 112(a)(7) of the CAA defines an "adverse environmental effect" as:
"any significant and widespread adverse effect, which may reasonably be anticipated, to
wildlife, aquatic life, or other natural resources, including adverse impacts on
populations of endangered or threatened species or significant degradation of
environmental quality over broad areas. "
The environmental risk screen was developed as a systematic, scientifically defensible, and
efficient approach that the U.S Environmental Protection Agency (EPA) can use to screen for
potential adverse environmental effects associated with emissions of hazardous air pollutants
(HAPs) from facilities in Risk and Technology Review (RTR) source categories. The
environmental risk screen is designed so it can be used effectively for large source categories,
some with more than one thousand facilities, and for facilities located in any part of the United
States.
The screen can be run quickly and with minimal additional data gathering by drawing on existing
data, models, and modeling results, including those developed for the human health
multipathway risk screen. The environmental risk screen uses the same TRIM.FaTE (Total Risk
Integrated Methodology's Fate, Transport, and Ecological Exposure module) multipathway
modeling and American Meteorological Society/EPA Regulatory Model (AERMOD) air
dispersion modeling used for the human health risk assessment. In addition, the environmental
risk screen applies ecological assessment endpoints and ecological health benchmarks to the
same tiered screen design used for the human multipathway screen.
The environmental risk screen was developed to ensure consistency with the following EPA
guidance and peer-review comments:
• EPA's 1998 Guidelines for Ecological Risk Assessment (U.S. EPA 1998)
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• EPA's Scientific Advisory Board Comments (U.S. EPA SAB 2010) on the Portland
Cement manufacturing case study and the Petroleum Refining case study provided to the
panel for review of RTR methods (U.S. EPA 2009)
• Participant comments and feedback provided to EPA during the Office of Air Quality
Planning and Standards (OAQPS) Workshop on Ecological Risk Assessment of Air
Toxics, June 2006 (ICF 2006).
Below, we summarize the design and key features of the environmental risk screen. In Section 2,
we present the environmental risk screen conceptual model, the HAPs included in the screen, and
the endpoints for which environmental risk are screened. Section 3 presents the approach used to
identify ecological benchmarks for each HAP for each assessment endpoint. Section 4 describes
the methods used to estimate HAP exposures in the environment. This section also describes
how we used the benchmarks identified in Section 3 to calculate "screening threshold emission
rates" and how we compared those thresholds to exposure estimates to screen for adverse
environmental effects. Section 5 presents the outputs and analyses of the risk screening results.
2 Key Components of the Environmental Risk Screen
2.1 ENVIRONMENTAL HAPS
When considering which HAPs should be included in the environmental risk screen, we
narrowed the list of 189 HAPs to the 31 environmental HAPs suggested in EPA's 2006
Ecological Risk Workshop. The workshop participants developed a list of 31 suggested
environmental HAPs by starting with the 14 PB-HAPs identified for the RTR program (See the
second column of Table 2-1) and then adding the following 17 pollutants for the reasons indicted
below (OAQPS Workshop on Ecological Risk Assessment of Air Toxics June of 2006; ICF
2006):
• Hydrogen chloride (HC1), hydrogen fluoride (HF), and trichloroethylene (TCE) - toxicity
to plants
• Hexachlorobutadiene and pentachlorophenol - toxicity to plants and aquatic species
• Phthalates - dibutyl phthalate, dimethyl phthalate, and bis-(2-ethylhexyl) phthalate
(DEHP) - endocrine disruptors
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• HAP metal compounds - antimony compounds, arsenic compounds, beryllium
compounds, chromium compounds, cobalt compounds, manganese compounds, nickel
compounds, and selenium compounds - persistence
• Cyanide compounds - highly toxic.
We evaluated the 31 suggested environmental HAPs for inclusion in the environmental screen
based on the criteria shown in Table 2-1:
• Persistence and bioaccumulation potential
• Inclusion in the TRIM.FaTE multipathway model
• Magnitude of emissions
• Relative environmental toxicity - based on toxicity to wildlife, soil communities, and
aquatic biota.
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Table 2-1. Summary of HAPs Considered for Inclusion in Environmental Risk Screen
Pollutant
RTR
PB-HAP
In Multi-
pathway
Model
2005 NEI
Point Source
Emissions
(TPY)
Environmental Criteria
Included/Excluded - Rationale
Wildlife NOAEL
for Mink
(mg/kg/d)a
Soil
Screening
BM (mg/kg)b
Water Quality
Criteria
(Mg/L)°
Antimony compounds
54
0.052
0.142d
80d
Excluded - persistent, but not bioaccumulative.
Arsenic compounds
X
181
0.052
100
150
Included - persistent but not bioaccumulative;
low toxicity to aquatic biota and soil
communities; but high relative wildlife toxicity.
Beryllium compounds
12
0.51
1.06d
3.6d
Excluded - persistent, but not bioaccumulative.
Bis-(2-ethylhexyl) phthalate
(DEHP)
266
7.6
0.925"
0.3d
Excluded - not bioaccumulative; low relative
wildlife toxicity.
Cadmium compounds
X
X
34
0.742
20
0.25
Included - PB-HAP in multipathway model;
moderate wildlife and aquatic toxicity.
Chlordane
X
0.01
14
0.22"
0.0043
Excluded - PB-HAP, but very low emissions.
Chlorinated dibenzodioxins and
furans (2,3,7,8-TCDD)
X
X
NA
0.0000008
1.2E-06e
1.0E-05e
Included - PB-HAP in multipathway model, high
relative toxicity.
Chromium compounds
4,025
2.52 (Cr6)
2,105 (Cr3)
10
11 (Cr6)
74 (Cr3)
Excluded - persistent, but not bioaccumulative;
low relative wildlife and water toxicity.
Cobalt compounds
77
7.339
0.14d
24d
Excluded - persistent, but not bioaccumulative.
Cyanide compounds
290
49.7
1.33d
5.2
Excluded - not PB-HAP.
DDE
dichlorodiphenyldichloroethylene
X
0.005
0.62
0.596d
4.5E-9d
Excluded - PB-HAP, but very low emissions.
Dibutyl phthalate
89
229
0.15d
9.7d
Excluded - not PB-HAP, low relative wildlife
toxicity.
Dimethyl phthalate
248
NA
734d
CD
O
CO
CO
Excluded - not PB-HAP; low relative toxicity.
Heptachlor
X
0.002
0.1
0.0060d
0.0038
Excluded - very low emissions.
Hexachlorobenzene
X
0.61
0.08
0.20d
0.0003d
Excluded - PB-HAP, but low emissions.
Hexachlorobutadiene
0.77
NA
0.040d
0.053d
Excluded - not PB-HAP, low emissions.
Hexachlorocyclohexane (all
isomers)
X
0.01
NA
NA
NA
Excluded - PB-HAP, but low emissions, no BM.
Hydrogen chloride
396,069
NA
NA
NA
Included - high vapor emissions and high
toxicity to terrestrial plants.
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Pollutant
RTR
PB-HAP
In Multi-
pathway
Model
2005 NEI
Point Source
Emissions
(TPY)
Environmental Criteria
Included/Excluded - Rationale
Wildlife NOAEL
for Mink
(mg/kg/d)a
Soil
Screening
BM (mg/kg)b
Water Quality
Criteria
(M9/L)c
Hydrogen fluoride
60,238
NA
NA
NA
Included - high vapor emissions and high
toxicity to terrestrial plants.
Lead compounds
X
307
6.15
900
2.5
Included - PB-HAP, Secondary NAAQS
Standard.
Manganese compounds
1,386
68
100
120h
Excluded - persistent, but not bioaccumulative;
low relative toxicity.
Mercury compounds
X
X
33
1.0
30
0.77
Included - PB-HAP, in multipathway model;
methylmercury highly bioaccumulative and toxic.
Methoxychlor
X
0.001
3.1
NA
0.03
Excluded - PB-HAP, but very low emissions.
Nickel compounds
566
30.77
90
52
Excluded - persistent, but not bioaccumulative;
low relative toxicity.
Pentachlorophenol
3
0.185
400
15
Excluded - not PB-HAP, low emissions.
Polychlorinated biphenyls
X
0.6
0.14J
0.000332"
0.014
Excluded - PB-HAP, but low emissions.
Polycyclic organic matter (BaP)
X
X
181
0.42
1.52"
0.0149
Included - PB-HAP, in multipathway model,
and high relative toxicity.
Selenium compounds
496
0.154
100
5
Excluded - not PB-HAP; low relative toxicity.
Toxaphene
X
0.003
6.2
0.119"
0.0002
Excluded - very low emissions.
Trichloroethylene
4,291
0.291
12.4"
47"
Excluded - not PB-HAP; low relative toxicity.
Trifluralin
X
1
NA
NA
0.2h
Excluded - PB-HAP, but low relative toxicity to
wildlife and no BM for soils.
Acronyms and abbreviations: BaP - benzoapyrene; BM - benchmark, NA - Not Available; NAAQS - National Ambient Air Quality Standards; PB-HAP - persistent bioaccumulative
hazardous air pollutant, NEI = National Emissions Inventory, TPY = short tons per a year
aSample et al. (1996). U.S. Department of Energy. ES/ER/TM-86/R3.
bEfroymson et al. (1997a,b). U.S. Department of Energy. ES/ER/TM-126/R2.
°U.S. EPA (2016b) National Aquatic Life Criteria Table, http://water.epa.gov/scitech/swguidance/standards/criteria/current/index.cfm
dU.S. EPA (2003a) Region 5 "RCRA [Resource Conservation and Recovery Act] Ecological Screening Levels" for soil and water.
eU.S. EPA Region 6 recommends using Texas Natural Resource Conservation Commission values (TNRCC 2001).
'U.S. EPA (2005c) "Ecological Soil Screening Levels for Cobalt, Interim Final" OSWER Directive 9285.7-67
9Suter and Tsao (1996). U.S. Department of Energy. ES/ER/TM-96/R2.
hU.S. EPA (2006). Region 3 Biological Technical Assistance Group (BTAG) Freshwater Sediment Screening Benchmarks.
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The far right column of Table 2-1 shows the rationale for each HAP's inclusion or exclusion
from the current environmental HAP risk screen. The following eight environmental HAPs are
included in the environmental risk screen:
• Six persistent and potentially bioaccumulative HAPs:
- arsenic
- cadmium
- dioxins/furans
- polycyclic organic matter
- mercury (both inorganic mercury and methyl mercury)
- lead
• Two acid gases:
- hydrogen chloride
- hydrogen fluoride.
HAPs that persist in the environment and bioaccumulate through food chains are of particular
environmental concern. They can accumulate in soils and sediments, with subsequent releases to
pore water and surface waters where they can be taken up by plants or by animals (e.g., small
fish) near the base of food webs, with possible further concentration by animals at higher trophic
levels. Table 2-1 shows that cadmium, dioxins/furans, mercury, and POM all have relatively
high environmental toxicity values (i.e., threshold-for-effect benchmarks are relatively low).
Lead was included in the screen because it is a PB-HAP and because we can use the secondary
lead National Ambient Air Quality Standards (NAAQS) as a reasonable measure for determining
whether an adverse environmental effect occurs. According to the 2011 National Air Toxics
Assessment of stationary sources, the six PB-HAPs we include in the screening analysis (arsenic,
cadmium, mercury, lead, dioxins, POM) account for 99.9 percent of national emissions of
PB-HAPs (the 14 in the RTR list cited above plus arsenic).
The acid gases HC1 and HF were included due to their well-documented potential to cause direct
damage to terrestrial plant foliage. In addition, when HF concentrations are above those at which
plant damage is first seen, HF can cause fluorosis in livestock feeding on exposed forage.
According to the 2005 National Emissions Inventory, HC1 and HF account for about 99 percent
(on a mass basis) of national acid gas emissions from stationary sources. We acknowledge that
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other HAPs beyond the eight discussed above might have potential to cause adverse
environmental effects. Therefore, EPA might add other HAPs to its environmental risk screen in
the future, as risk assessment methods and resources allow.
2.2 ASSESSMENT ENDPOINTS
For the RTR environmental risk screen, we use conventional generic ecological assessment
endpoints (GEAEs) (U.S. EPA 2003b, 2016b; Suter et al. 2004). EPA's 1998 Guidelines for
Ecological Risk Assessment defines an ecological assessment endpoint as "an explicit expression
of the environmental value to be protected and is defined operationally as an ecological entity
(e.g., individual organisms, specified populations of species, biological communities or
assemblages, and ecosystems) and its attributes (e.g., frequency of mortality, average fecundity,
species abundance, community diversity)" (U.S. EPA 1998). Although EPA developed GEAEs
to improve the scientific basis for ecological risk management decisions at EPA, GEAEs are
used frequently for ecological risk assessments conducted outside the Agency.
For the RTR assessment, all emissions of HAPs are to the air from point sources (i.e., facilities)
in the evaluated source categories. Consequently, all environmental media can be exposed to the
HAPs. For the ecological HAPs that partition primarily to air (e.g., HC1, HF), we evaluate risks
to the environment from direct contact with the airborne HAPs. For HAPs that can deposit on
and partition to ground-level environmental media, and from there partition to other media and
accumulate along biological food chains (i.e., PB-HAPs), we evaluate multimedia risks to the
environment.
In the environmental risk screen, we evaluate the following four exposure media: terrestrial soils,
surface water bodies, fish consumed by wildlife, and air. Within these four exposure media, we
evaluate the nine GEAEs shown in Table 2-2. The GEAEs reflect the overall "health" of aquatic
and terrestrial ecosystems and any important biota or community types that could be exposed in
those ecosystems. For PB-HAPs, the generic set of receptors includes both community-level and
population-level endpoints. For acid gases, the receptors are terrestrial plant communities.
Selection of species for the population-level assessments for PB-HAPs was based on those
organisms likely to be the most highly exposed due to bioaccumulation of the PB-HAP through
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aquatic and terrestrial food chains. Exposure scenarios assumed for all GEAEs are chronic. For
each GEAE listed in Table 2-2, we identified ecotoxicity benchmarks as discussed in Section 3.
Table 2-2. Generic Ecological Assessment Endpoints Used in the Environmental Risk
Screen
Exposure
Media
No.
Assessment Endpoint
Entities
Relevant Attributes
Benchmark3
1
Maintain structure/function of
soil invertebrate communities
(e.g., for nutrient recycling,
soil aeration)
Assemblages of
earthworms,
insect grubs,
nematodes
Species abundance and diversity;
species composition; and survival
and reproduction of those species'
populations
Soil ecotoxicity
benchmark (SEB):
Invertebrates
Terrestrial
Soils
2
Maintain structure/function of
terrestrial plant communities
(e.g., for food and habitat for
wildlife)
Assemblages of
plant species:
trees, herbs,
grasses
Species abundance and diversity;
survival, growth, and productivity
of those species
SEB: Plants
3
Maintain local bird
populations that feed on soil
invertebrates
Woodcock,
robins,
thrashers
Individual survival, growth,
reproduction and development;
area contaminated
SEB: Birds
4
Maintain local mammal
populations that feed on soil
invertebrates
Shrews, moles,
voles
Individual survival, growth,
reproduction and development;
area contaminated
SEB: Mammals
Surface
Water Bodies
5
Maintain benthic community
structure/function (sediment-
dwelling organisms)
Assemblages of
aquatic insects,
amphipods,
isopods,
crayfish
Species abundance and diversity;
survival, growth, development, and
reproduction of those species
Sediment quality
benchmark (SQB)
6
Maintain aquatic community
structure/function (water-
column community to support
fisheries)
Assemblages of
fish and
invertebrates in
water column
Species abundance and diversity;
survival, growth, development, and
reproduction of those species
Ambient water
quality benchmark
(AWQB)
Fish
(consumed
by wildlife)
7
Maintain local populations of
birds that feed on fish and
other aquatic prey
Common
merganser,
belted
kingfisher,
herons, gulls,
loons
Individual survival, growth and
development, reproduction; area
contaminated
Wildlife Toxicity
Reference Value
(TRV)
8
Maintain local populations of
mammals that feed on fish
and other aquatic prey
Mink, otter,
raccoon
Survival, growth and development,
reproduction at the individual level;
proportion habitat contaminated
Wildlife TRV
Air
9
Maintain community structure/
function of plants with foliage
exposed to HAPs in air (e.g.,
food and habitat for wildlife)
Trees, shrubs,
herbs, grasses,
crops
Abundance; productivity
Air ecotoxicity
benchmark: Plants
aA soil ecotoxicity benchmark (SEB) is a generic term used here to indicate any type of soil benchmark for ecological risk
assessment. A sediment quality benchmark (SQB) also is a generic term, as is the term ambient water quality benchmark
(AWQB). Different agencies, states, and offices have named and defined their own particular SEBs, SQBs, and AWQBs in
different ways.
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As mentioned above, the GEAEs used in the environmental risk screen include both population-
level and community-level endpoints. Column 3 ("Assessment Endpoint") of Table 2-2 indicates
whether an endpoint is population based or community based.
An assessment population is a group of organisms belonging to the same species that occupy the
area defined as relevant to the ecological risk assessment (U.S. EPA 2003b). For the RTR risk
screen, that area is defined as the modeling domain surrounding each facility. Endpoints 3, 4, 7,
and 8 in Table 2-2 represent population-based GEAEs. Specifically, these population endpoints
include bird and mammal populations that feed on soil invertebrates and aquatic prey (e.g., fish).
Impairment of individual growth, development, reproduction, or survival could reduce
population size and productivity and increase the probability of local extirpation if the
impairment occurs in a significant proportion of the exposed or local population.
An assessment community is a multispecies group of organisms occupying the area defined as
relevant to the assessment (U.S. EPA 2003b). For the RTR risk screen, that area is defined as the
modeling domain surrounding each facility. Endpoints 1, 2, 5, 6, and 9 in Table 2-2 represent
community-based GEAEs. Specifically, these community endpoints include the following
communities: soil invertebrate, terrestrial plant, benthic, and aquatic.
2.3 ENVIRONMENTAL RISK SCREENING APPROACH
EPA conducts the environmental risk screen if any facilities in the source category emit any of
the eight environmental HAPs. Specifically, if one or more of the eight environmental HAPs are
emitted by at least one facility in the source category, the Agency conducts the environmental
risk screen. Because of the unique properties and environmental effects of the HAPs, the
environmental risk screen differs for three groups of the eight environmental HAPs:
• PB-HAPs - arsenic, cadmium, mercury, POM, and dioxin/furans
• Lead
• Acid gases - HC1 and HF.
An overview of the environmental risk screen for each group is provided below.
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2.3.1 PB-HAPs
For the five PB-HAPs—arsenic, cadmium, mercury, POM, and dioxins/furans—the
environmental risk screen consists of three tiers (Figure 2-1). The tiered design used for the
environmental risk screen is the same as that used for the human multipathway screen described
Appendix 6 of the Risk Report.1 Each tier uses a different conceptual model for the spatial
relationship of the facility to surface waters and terrestrial environments, and each tier uses
different parameter inputs. All three tiers of the environmental risk screen for PB-HAPs use the
same assessment endpoint benchmarks (see Section 2.2).
The first tier of the screen is based on a hypothetical facility for which the surrounding
environment was designed to encompass a health-protective environmental layout that would
maximize PB-HAP concentrations in fish and in terrestrial environments in the immediate
vicinity of a facility. This conceptual model is the same as used for the Tier 1 human health
screen.
Section 4 provides further description of the conceptual model as applied in the environmental
risk screen. TRIM.FaTE simulations were used to back-calculate Tier 1 screening threshold
emission rates that correspond to the assessment endpoint benchmarks for each PB-HAP. In
other words, each Tier 1 screening threshold emission rate represents the emission rate in tons
per year that results in media concentrations at the hypothetical facility that equal the relevant
ecological benchmarks.2
The Tier 1 environmental risk screen is performed by comparing the reported emission rate for
each facility in tons per year to the Tier 1 screening threshold emission rate in tons per year for
each PB-HAP, GEAE, and effect level if more than one is identified. If none of the emissions
from a facility exceed these chemical-specific Tier 1 screening threshold emission rates, the
facility "screens out" and therefore is not evaluated further under the environmental risk screen.
1 Appendix 6 to the Risk Report is the Technical Support Document for the TRIM-Based Multipathway Tiered
Screening Methodology for RTR.
9
See Section 3, Effects Assessment, for discussion of the ecological benchmarks and wildlife toxicity reference
values used for all three tiers of the environmental assessment.
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If emissions from a facility exceed any of the Tier 1 screening threshold emission rates, the
facility could be further evaluated in Tier 2.
Figure 2-1. Overview of the Environmental Risk Screen for PB-HAPs
Are As, Cd, Hg, Pb, POM, or dioxin/furans emitted [screening for persistent
and bioaccumulative HAPs only]?
YES
Tier 1:
Compare the actual total facility emissions for each facility to Tier 1
screening threshold emission rates for each PB-HAP and assessment
endpoint. The Tier 1 screening threshold emission rates use the same
health-protective settings for the facility as used in the Tier 1 human health
risk assessment.
Do emissions exceed the Tier I thresholds for any endpoint?
YES
Tier 2:
Compare the actual total facility emissions for each facility to Tier 2 refined
screening threshold emission rates for each PB-HAP and assessment
endpoint. The Tier 2 screening threshold emission rates use the same
facility-specific meteorology and lake location data as Tier 2 for the human
health risk assessment.
Do emissions exceed the Tier 2 thresholds?
YES
NO
NO
NO
Tier 3:
Compare facility emissions to Tier 3 screening threshold emission rates for
each PB-HAP and assessment endpoint. The Tier 3 multipathway screen
includes additional evaluations of lake data, plume rise, and time-series
meteorological and plume-rise data. Refine screening with additional
considerations, such as proportion of facilities exceeding the thresholds,
degree to which the thresholds are exceeded, geographic setting, and total
areal extent exceeding the benchmark per facility.
Does the refined screen indicate the potential for widespread and significant
adverse environmental effects?
NO
Widespread, significant
adverse environmental
effects unlikely
Widespread significant
adverse environmental
effects unlikely
Widespread significant
adverse environmental
effects unlikely
Widespread significant
adverse environmental
effects unlikely
YES
Potential for widespread and significant adverse environmental effects
In Tier 2 of the environmental risk screen, the screening threshold emission rates are refined to
account for facility-specific meteorology and the actual location of lakes near facilities that did
not pass the Tier 1 screen. If emissions from a facility do not exceed the Tier 2 screening
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threshold emission rates, the facility "screens out" and is not evaluated further under the
environmental risk screen. If emissions from a facility exceed the Tier 2 screening threshold
emission rates, the facility could be further evaluated in Tier 3.
In Tier 3 of the environmental risk screen, the screening threshold emission rates are refined to
account for lake data and time-series meteorological and plume-rise data (see Section 4 of
Appendix 6 of the Risk Report for more information on the Tier 3 methods). If emissions from a
facility do not exceed the Tier 3 screening threshold emission rates, the facility "screens out" and
is not evaluated further. If emissions from a facility exceed the Tier 3 screening threshold
emission rates, the facility could be further evaluated to consider the degree to which the
screening threshold emission rates are exceeded, which endpoints and effect levels are exceeded,
the geographic setting, and the total area exceeding the screening threshold emission rates. If,
after additional refinement, the facility still exceeds the screening threshold emission rates, the
facility might cause adverse environmental effects.
As with the multipathway human health risk assessment, a site-specific assessment could be
conducted if the Tier 3 screening results indicate a potential for adverse environmental effects.
The site-specific assessment uses model parameter values and scenario designs intended to better
represent the modeled facility—aspects such as local terrain (influencing runoff and erosion
patterns), watersheds, actual lake boundaries and water retention rates, soil types, and land cover.
Site-specific assessments are not presented in this report.
2.3.2 Lead
The environmental risk screen for lead consists of one tier. For lead compounds, we currently do
not have the ability to calculate concentrations in soils, surface waters, and sediments using the
TRIM.FaTE model. Therefore, to evaluate the potential for adverse environmental effects from
lead compounds, we compare the Human Exposure Model (HEM)/AERMOD-modeled air
concentrations of lead for each facility in the source category to the level of the secondary
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NAAQS for lead.3 We consider values below the level of the secondary lead NAAQS to be
unlikely to cause adverse environmental effects.4
2.3.3 Acid Gases
For HF and HC1, the environmental risk screen evaluates potential phytotoxicity (i.e., poisonous
to plants) and reduced productivity of plants due to chronic exposure. For each acid gas, the
environmental risk screen compares the HEM/AERMOD-modeled ambient air concentrations in
the modeling domain around each facility to ecological benchmarks (Figure 2-2). If the average
concentration of a given HAP in the modeling domain around a facility exceeds the ecological
benchmark, the facility does not pass the screen and, therefore, might cause adverse
environmental effects.
The secondary lead NAAQS is a reasonable measure of determining whether an adverse environmental effect exists
because it was established considering "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."
4On October 18, 2016, EPA issued its final decision to retain the 2008 NAAQS for lead (U.S. EPA 2016a).
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Figure 2-2. Overview of the Environmental Risk Screen for Acid Gases
For each facility, compare the HEM/AERMOD-modeled ambient air
concentration for each individual data point to the ecological
benchmarks. Do any of the individual data point concentrations
exceed any of the ecological benchmarks?
NO
YES
Calculate the area-weighted average concentration of all data points
in the modeling domain for each facility and for each HAP
exceeding the ecological benchmark. Does the area-weighted
average concentration for any facility exceed the ecological
benchmarks?
NO
YES
Refine screening results as necessary with additional site-specific
data and modeling refinements. Consider the proportion of facilities
that exceed the benchmark and the magnitude of exceedance.
Does the refined screen indicate the potential for widespread and
significant adverse environmental effects?
NO
YES
Potential for Widespread and Significant Adverse
Environmental Effects
Adverse
environmental effects
from acid gases
unlikely
Adverse
environmental effects
from acid gases
unlikely
Adverse
environmental effects
from acid gases
unlikely
Adverse
environmental effects
from acid gases
unlikely
3 Effects Assessment
To assess effects, we identified appropriate ecological benchmarks to compare to exposure
concentrations. As indicated in Section 2.2, we searched for available ecological benchmarks for
each assessment endpoint listed in Table 2-2. Specifically, we sought benchmarks for chronic
exposure to each HAP included in the environmental risk screen, except for lead, which was
screened using the secondary NAAQS.
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Ecological benchmarks represent a level of exposure to a chemical in the environment that has
been linked to a particular environmental effect level (e.g., a no-effect level or a threshold effect
level) through scientific study. The three general metrics for ecological benchmarks are listed
below.
• Dose-based - Dose-based benchmarks are expressed as a dose of chemical ingested per
day per kg of animal body weight, typically mg/kg-day, which has been linked to a
particular effect level. This type of benchmark usually is used when evaluating risks to
wildlife via ingestion pathways. TRVs for terrestrial animals (e.g., wildlife) are an
example of a dose-based benchmark.
• Concentration-based - Concentration-based benchmarks are expressed as the
concentration of a chemical in an environmental medium (e.g., |ig of HAP per liter of
water) that has been linked to a particular environmental effect level. Concentration-based
benchmarks usually are used when evaluating risks to receptors that have direct contact
with the contaminated medium (e.g., fish in water, plant roots in soil, plant foliage in air).
• Tissue-based - Tissue-based benchmarks are expressed in units of the amount of
chemical per mass of tissue in the exposed receptor (e.g., mg of cadmium per kg of
tissue). This type of benchmark can be used to assess almost any type of consumer animal
(e.g., fish, benthic invertebrates, birds, mammals).
To evaluate risk in the RTR program, we use reported emissions data that include the mass of
HAPs emitted from each facility in the source category being examined. The emissions data are
used as inputs to the TRIM.FaTE multipathway model to estimate HAP concentrations in soil,
surface water bodies, and fish, and using the HEM/AERMOD model to estimate HAP
concentrations in air. These estimates are best suited to the use of dose-based or concentration-
based benchmarks. Tissue-based benchmarks have little utility for the RTR program because
site-specific data for the concentrations of HAPs in animal tissues (e.g., liver, kidney) are not
available. Therefore, the identification of benchmarks for the environmental risk screen focused
entirely on dose-based and concentration-based benchmarks.
Based on a review of available ecological benchmarks, where possible, we identified existing
ecological benchmarks at three generic effect levels:
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• Probable effect level (PEL): The level above which adverse effects at both population
and community levels are expected to occur frequently. In general, local extirpation or
absence of populations of key community species is likely, compromising community
structure and function.
• Threshold effect level (TEL): The level at which some adverse effects might occur in a
minority fraction (e.g., up to 20 percent) of the exposed proportion of a specified
population (e.g., mink, merganser) or at which few species (e.g., 5 percent aquatic animal
species) might be lost from a community. Losses are not expected to influence either
population survival over its range at the county or state level or the overall structure and
function of the community near the facility. To screen risks to wildlife populations, we use
lowest-observed-adverse-effect levels (LOAELs) from scientific toxicity tests that assess
survival, growth, reproduction, and development to calculate assessment population
benchmarks from the same taxonomic class that represents TELs. LOAELs are the lowest
test exposure level at which statistically significant adverse effects on survival, growth,
reproduction, or development occurred in the test organisms of the toxicity study
considered key.5
• No effect level (NEL): The highest exposure level at which no biologically significant
increases occur in the frequency or severity of (1) adverse effects on community structure
or (2) adverse effects on assessment populations. To screen risks to wildlife populations,
we use no-observed-adverse-effect levels (NOAELs) from a key toxicity test6 that
assessed growth, reproduction, or survival species from the same taxonomic class to
calculate assessment population benchmarks that represent NELs.7
We identified preferred benchmark sources to allow selection of benchmarks for each
environmental HAP for each ecological assessment endpoint. In general, we used EPA sources at
a programmatic level (e.g., Office of Water, Superfund Program), if available. If not, we used
5Many ecological risk assessors use the geometric mean of the LOAEL and NOAEL to represent a "threshold"
acceptable exposure level. For the RTR assessment, we use the LOAEL to represent a threshold for potential
"significant" (biologically) adverse effect in keeping with Section 112(a)(7) of the CAA.
6 A key toxicity test is one selected from the set of adequately conducted and documented tests to represent a
sensitive species and sensitive endpoint, given the experimental data set as a whole.
H
No-effect-level benchmarks are generally used to assess risks to threatened and endangered species (e.g., U.S. EPA
2004), although additional "safety" factors might be applied to account for species-to-species variation in chemical
sensitivity and for extrapolation from laboratory to field conditions.
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EPA benchmarks used in regional programs (e.g., region-specific Superfund). If benchmarks
were not available at a programmatic or regional level, we used benchmarks developed by other
federal agencies (e.g., National Oceanic and Atmospheric Administration), state agencies, or
Canada. Section 3.1.2 discusses the preferred benchmark sources in detail.
Benchmarks for all effect levels are not available for all combinations of environmental HAPs
and assessment endpoints. In cases where benchmarks representing multiple effect levels, as
defined above, were available for a particular environmental HAP and assessment endpoint, we
used all three available effect levels. We believe this best informs conclusions regarding whether
ecological risks exist and, if so, whether the risks could be considered significant and
widespread. Probable-effect-level benchmarks generally are not available except for benthic
community sediment benchmarks for some chemicals.
We have organized the remainder of this section into two sections: Section 3.1 - Benchmarks for
PB-HAPs and Section 3.2 - Benchmarks for Acid Gases. Attachment A contains additional
discussion about the ecological benchmarks, wildlife toxicity reference values, and toxic
equivalency factors (TEFs). Attachment A also includes additional tables and citations to those
presented in this section.
3.1 BENCHMARKS FOR PB-HAPS
This section identifies ecological toxicity (ecotoxicity) benchmarks, expressed as concentrations
of chemicals in environmental exposure media, for the five PB-HAPs included in the
environmental risk screen (Note, lead is the sixth PB-HAP. It is screened using the secondary
NAAQS level for lead). It also includes TRVs for wildlife. The PB-HAPs included in the
ecological effects assessment (i.e., benchmark assessment) are mercury (as methyl mercury or
inorganic divalent mercury), cadmium, POM, arsenic, and dioxins/furans. We evaluated POM
and dioxins/furans by relating each compound to an "index" compound within the group.
Specifically, we identified both toxicity benchmarks for the "index" chemicals (i.e.,
benzo[a]pyrene, or BaP, and 2,3,7,8-tetrachlorodibenzo-p-dioxin or TCDD) and TEFs for the
remaining chemicals in each category relative to the appropriate index chemical.
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3.1.1 Types of Benchmarks for PB-HAPs
In this section, we define the benchmarks selected for the combinations of assessment
populations and communities and exposure media listed in Table 2-2 (Section 2.2), focusing on
PB-HAPs. We also briefly reiterate the three generic effect levels for which we sought
benchmarks.
3.1.1.1 Surface Soil Benchmarks
Across the Agency, up to two distinct types of soil communities and two groups of wildlife
species have been used to derive soil ecotoxicity benchmarks (SEBs): (1) invertebrate
community, (2) plant community, (3) birds that feed on soil invertebrates, and (4) mammals that
feed on soil invertebrates. The latter two assessment endpoints are included specifically for
PB-HAPs because the soil invertebrates might bioaccumulate these chemicals, resulting in higher
exposures for the ground-feeding birds and mammals compared with chemicals that do not
bioaccumulate.
SEBs are expressed as milligrams (mg) or micrograms (|ig) of chemical per kilogram (kg) dry
soil. To screen a location for possible risks to one or more of the soil assessment endpoints,
estimates of surface soil contamination of PB-HAPs are compared with available corresponding
benchmark values. TRIM.FaTE estimates concentrations for surface soil compartments at several
successive distances from the source up to 10 kilometers (km). The TRIM.FaTE estimate of
surface soil compartment chemical mass per unit volume is converted to a dry weight soil
concentration by multiplying the volume of the compartment by the fraction of the volume that is
in solid phase (0.57) and dividing the volume of the compartment by the mass-density of soil
particles (2.6 kg/L soil).
For SEBs for avian or mammalian wildlife that EPA already has calculated for the Superfund or
Resource Conservation and Recovery Act programs, we accepted the SEB as is. Implicit in the
SEB is the TRY for the bird or mammal used by the office to back-calculate the SEB.8
o
EPA "back-calculates" an SEB for a ground-feeding bird (e.g., woodcock) or mammal (e.g., shrew) as a
concentration of chemical in soil that would result in the bird or mammal ingesting an amount of chemical equal to
its TRV in mg/kg-day. A chemical-specific bioaccumulation factor relates the concentration in the food (e.g.,
earthworms) to the concentration in the soil. For PB-HAPs, the SEBs are lowest for wildlife species that ingest soil
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For its derivation of ecological soil screening levels (Eco-SSLs), EPA's Superfund Office uses
both bounded and unbounded NOAELs to establish a TRV for birds and for mammals based on
the geometric mean of NOAELs across different toxicity tests for growth and reproductive
effects for each taxon (U.S. EPA 2003c). The method also uses bounded LOAELs to check the
final geometric mean NOAEL for plausibility. The geometric mean calculation gives equal
weight to each result from multiple studies of the same endpoint (e.g., clutch size) for the same
species (e.g., chicken) as for single studies of a different endpoint (e.g., weight gain by chicks)
with a different species (e.g., mallard). We therefore conclude that the final geometric mean
NOAEL does not account for interspecies variation in sensitivity (i.e., NOAEL is biased toward
the species tested most often) and does not necessarily correspond to the most sensitive effect
(i.e., NOAELs are averaged across growth and reproduction endpoints even if reproduction is the
most sensitive endpoint).
3.1.1.2 Surface Water Body Benchmarks
Some EPA programs and regions (e.g., Superfund, Office of Water, Office of Pesticide
Programs, various EPA Regions) also have developed aquatic benchmarks for two
environmental "compartments" of aquatic ecosystems that might be in disequilibrium with each
other: benthic sediments and the water column. Thus, benchmarks have been derived for aquatic
communities in both compartments: the benthic community and the water-column community
(Endpoints 5 and 6, respectively, in Table 2-2). The benthic community consists primarily of
macroinvertebrates in contact with the sediments that consume detritus or graze on algae (e.g.,
amphipods, annelid worms, snails, aquatic larval stages of many insect species), but also can
include filter feeders (e.g., mussels), predatory invertebrates (e.g., dragonfly nymphs), and
invertebrate scavengers (e.g., crayfish). Benthic organisms are exposed through direct contact
with contaminants in sediments and sediment pore water and by consumption of contaminated
detritus/prey in the sediments. Benchmarks for the benthic community generally are called
sediment quality benchmarks (SQB) and usually are expressed in units of mg chemical per
invertebrates (e.g., earthworms); other chemicals might be accumulated more by plants than by soil invertebrates. To
calculate an SEB, EPA uses species-specific values for wildlife body weight, diet, food ingestion rate, and incidental
soil ingestion as described in its guidance (U.S. EPA 2003c).
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kilogram (kg) dry-weight sediment (Jones et al. 1997). Some SQBs are normalized to the total
organic carbon content of the sediments (Jones et al. 1997).
The "aquatic" biota in the water-column compartment include plankton (i.e., microscopic algal
cells and zooplankton such as water fleas and copepods) and free-swimming fish and some larger
invertebrates (e.g., shrimp-like crustaceans). The water-column organisms are exposed by direct
contact with the water (and water through their gills for respiration) and by ingestion of
chemicals in their food. The food of free-swimming animals can be obtained from the water-
column, the benthos, or both, depending on species of consumer. For that reason, the two
compartments are not strictly separable when considering aquatic food webs. EPA Office of
Water benchmarks for the water-column community of organisms generally are called ambient
water quality benchmarks (AWQB) or criteria (AWQC) for the protection of aquatic life and are
expressed as water concentrations in micrograms per liter of water.
3.1.1.3 Benchmarks for Wildlife that Feed on Contaminated Fish
For bioaccumulative chemicals, animals that feed at the top of food webs (i.e., top predators) are
likely to experience the highest exposures of animal species in geographic area/ecosystem. For
chemicals that bioaccumulate through aquatic food chains, the top predators in many geographic
areas are wildlife that feed on aquatic prey. Thus, for PB-HAPs, EPA usually assesses risks to
fish-eating (i.e., piscivorous) birds and mammals when evaluating ecological risks (e.g., see
Great Lakes Water Quality Initiative, U.S. EPA 1995b).
EPA selected the American merganser (Mergus merganser americana), a bird of intermediate
body size that regularly consumes relatively larger fish (up to 36 cm, Mallory and Mertz 1999),
to represent highly exposed piscivorous birds. Many species of birds are piscivorous (Table 2-2,
Endpoint 7). The belted kingfisher often is evaluated in ecological risk assessments; however,
the maximum size of fish (and hence the top trophic level of fish they can consume) that belted
kingfishers can consume is relatively small (generally no larger than 18 cm, Salyer and Lagler
1946).
EPA selected mink for screening of piscivorous mammals. Few mammals (see Table 2-2,
Endpoint 8) are piscivores. Both river otters and mink commonly are assessed for risks from
persistent and bioaccumulative chemicals (e.g., DDT [dichlorodiphenyltrichloroethane], DDE
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[dichlorodiphenyldichloroethylene], PCBs [polychlorinated biphenyls], and other chemicals
released directly to surface waters). Mink in some locations consume fish almost exclusively,
and their smaller body size (i.e., 0.68-1.4 kg) compared with otters (i.e., 4.5-11 kg) (Burt and
Grossenheider 1980) means that mink have a higher metabolic rate and so consume more fish
per unit body weight than do otters. Both species consume primarily trophic level 3 fish (i.e.,
minnows, shiners, small trout, perch), although river otters capture larger fish on occasion. In
addition, mink tend to be more abundant than otters and have smaller home ranges (U.S. EPA
1993a,b).
Note that geographic range was not a criterion that distinguished one species from another for
the options listed above. The overall range of belted kingfishers and the common merganser
spans North America from coast to coast, although the summer breeding ranges generally are
more northerly while the overwintering ranges are more southerly. Similarly, the overall range of
mink and river otters spans North America from coast to coast.
To assess risks to piscivorous wildlife from consuming contaminated fish for the environmental
risk screen, we calculated TRVs, expressed as a dose, to compare with the total chemical intake
of each wildlife species from its aquatic prey. To estimate exposures as total chemical intake, we
used the Tier 1 (or Tier 2) screening TRIM.FaTE scenario to estimate the concentration of
chemicals in the aquatic biota (compartments) included. Species-specific data for the mink and
common merganser were used to estimate their food ingestion rates and the proportion of their
diets likely obtained from each biotic compartment. For the latter, literature on the size of fish
captured was consulted for both mink and merganser.
3.1.2 Preferred Sources for PB-HAP Benchmark Values
Available community-level benchmarks for sediments, surface waters, and soils were identified
using the Oak Ridge National Laboratory (ORNL) Risk Assessment Information System (RAIS)
(http://rai s. ornl. gov/). The Department of Energy (DOE) maintains the ORNL RAIS database for
use in its risk assessments at hazardous waste sites. RAIS identifies virtually all toxicity
reference values and benchmarks developed to date by federal and some state agencies in the
United States and by other countries (e.g., Canada) for human health and ecological risk
assessment. RAIS therefore allows quick identification of available ecotoxicity benchmarks.
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RAIS includes all screening-level ecological benchmarks available from Suter and Tsao (1996;
benchmarks developed at ORNL for use at DOE Superfund sites), which was a key source of
benchmarks for the Coke Oven MACT (maximum achievable control technology) Residual Risk
Assessment (U.S. EPA 2003d). RAIS also includes the other sources of benchmarks used in that
assessment (e.g., U.S. EPA National Ambient Water Quality Criteria, EPA Region 4 values,
National Oceanic and Atmospheric Administration benchmarks, Florida Department of
Environmental Protection benchmarks).
We established a hierarchy of preferred benchmark sources to allow selection of benchmarks for
chemicals and environmental media for which numerous benchmarks are listed in RAIS. In
general, EPA benchmarks used at a programmatic level (e.g., Office of Water, Superfund
Program) are preferred, if available. If not, EPA Regional benchmarks as used in regional
programs (e.g., Superfund) are used, if available. If benchmarks are not available from EPA at a
regional level, we consider the benchmarks developed by other agencies (e.g., DOE), by states,
or by Canada.
In all cases, we reviewed available benchmarks to find one to represent each of the three levels
of effect specified above (i.e., NEL, TEL, PEL). For some media/chemical combinations, we
could identify benchmarks for all three effect levels, but for most, we could not. In several cases,
only a single benchmark was available, generally a threshold for effects.
3.1.2.1 Soil Ecotoxicity Benchmarks (SEB)
For soils, EPA's national Superfund Program Eco-Soil Screening Levels (Eco-SSLs, U.S. EPA
2005a) were selected, if available, as the SEBs for the RTR environmental risk screen. These
Superfund Eco-SSLs (from EPA's Office of Solid Waste and Emergency Response, OSWER
[currently Office of Land and Emergency Management) are the only peer-reviewed and EPA-
vetted ecological toxicity screening benchmarks for soils established for use by the Agency
nationwide. For chemicals for which no Eco-SSLs were available, EPA Regional sources of soil
ecotoxicity benchmarks (SEBs) were reviewed (e.g., Regions 4, 5, and 6). The general methods
for deriving those benchmarks can differ from the methods EPA used to derive Eco-SSLs.
For some chemicals, the Regions use SEBs developed by other agencies such as DOE or by a
state within the region. If not specified in published information, we assumed that whichever
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group of organisms was most sensitive to the chemical in soil (e.g., earthworms, insect larvae,
plant roots, ground-feeding wildlife consuming soil invertebrates, and in some cases herbivorous
animals consuming plants grown in the contaminated soil) was likely to have been the basis for
the criterion. If an EPA Region and another non-EPA agency use the same numeric benchmark,
all sources that designated that value are acknowledged in the tables presenting the RTR
ecotoxicity benchmarks. Finally, if the only source providing a screening-level benchmark for
soils was not an EPA office or Region (e.g., DOE, Environment Canada, or a state), that value is
used.
3.1.2.2 Aquatic Sediment Quality Benchmarks
For the benthic community residing in and on the sediments of a water body, the preferred
benchmarks were the national-level sediment quality criteria published by EPA's Office of
Water (U.S. EPA 1993a, 2001b, 2003e, 2008), if they were available or readily usable.
If national sediment quality benchmarks were not available from EPA's Office of Water, we
selected sediment benchmarks from those available from EPA's Superfund Program and Regions
4 and 5, as available. If EPA-vetted sediment benchmarks were not available, other benchmarks
were used (e.g., from the State of Florida, ORNL, and MacDonald et al. [2000]).
3.1.2.3 Ambient Water-Column Benchmarks
For organisms that live primarily in the water-column of aquatic ecosystems, EPA's National
Ambient Water Quality Criteria, Aquatic Life Criteria (NAWQC-ALC) were used, as available
(Stephan et al. 1985, U.S. EPA 2002). According to Suter and Tsao (1996), the acute
NAWQC-ALC are considered "upper" screening levels in EPA's Superfund program—which
we interpret to mean probable effect levels if associated with continuous long-term (chronic)
exposures. The chronic NAWQC-ALC are considered "lower" screening-level benchmarks in
EPA's Superfund program (Suter and Tsao 1996). Given the methods by which both acute and
chronic NAWQC-ALC are derived, we interpret the chronic NAWQC-ALC to represent a
threshold for adverse effects in aquatic communities (water-column compartment) rather than a
no-effect level. At the NAWQC-ALC, 5 percent of species typical of the ecosystem might be
lost; however, substantial changes in aquatic community structure and function are not expected
because of functional redundancies among species in aquatic communities.
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For chemicals for which NAWQC-ALC and Tier II secondary values were not available, we
turned to benchmarks developed by EPA Regions 4, 5, or 6.
3.1.2.4 Avian and Mammalian Toxicity Reference Values
To assess risks to piscivorous (i.e., fish-eating) wildlife, one must identify a TRV for the wildlife
species, expressed as an oral dose, and estimate dietary exposure via the chemical in prey (i.e., in
fish and invertebrates consumed). The estimated total chemical intake via all types of prey in the
diet, expressed as mg[chemical]/kg[wildlife body weight]/day (mg[chem]/kg bw-day), then can
be compared with the TRV (expressed in the same units). An emission rate, back-calculated to
match the TRV, then is used to screen facilities in Tiers 1, 2, and 3 environmental risk screens.
Two types of avian and mammalian TRVs were included in the environmental risk screen. The
first type of TRV is incorporated into the EPA OSWER derivation of the Eco-SSLs intended to
protect wildlife that feed on soil invertebrates (see Section 3.1.1). We indirectly use those TRVs
by using the Eco-SSLs as soil benchmarks. We calculated separate TRVs to use for wildlife that
consume fish using an approach similar to that developed for the EPA Great Lakes Water
Quality Initiative (GLWQI, U.S. EPA 1995b). Those calculations are presented in Attachment A,
Section A.3.
EPA OSWER developed TRVs for the EcoSSLs using an approach unique to those benchmarks.
The EcoSSL TRVs are based on NOAELs, and they are calculated as the geometric mean of all
NOAELs from adequately performed and reported studies for growth and for reproductive
effects across all species of birds (or mammals). Thus, even unbounded NOAELs, which might
be well below an effect level (because no effect level was identified), are included in calculating
the geometric mean. That method of calculating a wildlife TRV has some limitations, as
discussed by several investigators (e.g., Allard et al. 2010; Mayfield and Fairbrother 2013;
Sample et al. 2014a,b).
For purposes of the RTR assessment of fish-eating wildlife, we prefer the GLWQI approach to
developing a TRV for wildlife (U.S. EPA 1995b), which is to select a key study that represents a
sensitive species and endpoint from among the available, adequately conducted and reported,
studies. Moreover, we prefer to scale doses between experimental animals and wildlife species
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based on relative body weight (U.S. EPA 2011). The derivation of TRVs for PB-HAPs for
piscivorous wildlife are presented in Attachment A, Section A.3.1.
For the GLWQI approach, the available toxicity data are examined to determine the magnitude
of uncertainty factors (UFs) that might be needed for three types of data gaps: to estimate a
NOAEL from a LOAEL, to extrapolate from subchronic to chronic exposure, or to account for
differences in sensitivity of test species. For most chemicals (including PB-HAPs, particularly
dioxins and POM), only a few species of birds (e.g., quail, mallard, chicken, pheasant) and a few
species of mammals (e.g., mice, rats, hamster, mink) have been tested sufficiently to provide
both a LOAEL and a NOAEL for effects resulting from chronic exposures. Uncertainty factors
can range from 1 to 10 for each type of uncertainty listed above, depending on the apparent
magnitude of the data gap. A joint uncertainty value (the product of all three types of UF)
exceeding 100 indicates that a TRV cannot be derived (U.S. EPA 1995b). Typically, a value of
1, 3, or 10 (not values in between) is used for each UF. The appropriate UFs are applied as
divisors of the original toxicity value (e.g., LOAEL).
To estimate TRVs for piscivorous wildlife, we used the LOAELs and NOAELs from a single
key study (most sensitive effect and species). If only an unbounded LOAEL were available (no
NOAEL), the LOAEL could be divided by a factor of 10 to extrapolate to a NOAEL or an EPA-
derived UF could be applied. The subchronic-to-chronic uncertainty factor was not applied,
because all TRVs calculated for the PB-HAPs are based on chronic or gestational exposures.
Neither was an interspecies UF used, except for the case of methyl mercury, for which EPA had
already published a joint LOAEL-to-NOAEL and an interspecies UF for birds (Attachment A,
Section A.3.2). For the other PB-HAPs, doses were scaled between a test species and the
assessment species based on relative body weight to the % power (U.S. EPA 2011).
3.1.3 Selected PB-HAP Benchmarks
Table 3-1 shows the ecological benchmarks used in the environmental risk screen for each
PB-HAP and assessment endpoint. A discussion of the TEFs used to adjust each POM chemical
relative to BaP and to adjust each dioxin congener relative to TCDD is presented in
Attachment A, Section A.4.
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3.2 BENCHMARKS FOR ACID GASES
3.2.1 Hydrogen Chloride
For HC1, EPA identified chronic benchmark concentrations as described in Appendix K to
EPA's (2009) Risk and Technology Review (RTR) Risk Assessment Methodologies: For Review
by the EPA 's Science Advisory Board. Case Studies - MACTI Petroleum Refining Sources,
Portland Cement Manufacturing. Substantial data were available for short-term exposures of
plants to HC1; however, data to relate chronic exposures of plants to adverse effects on growth
and productivity were lacking.
The chronic benchmark for HC1 was based on a lowest-observed-effect level (LOEL) from a
short-term exposure (20 minutes) that related HC1 concentration to "changes" in the leaves of 7
of 8 plant species as reported by Lerman et al. (1976). The benchmark was the lowest exposure
concentration at which effects of any type were observed (visible injury to some proportion of
leaves). Haber's law (see Attachment A, Section A.2.3.2) was used to extrapolate the 1.5-mg/m3
LOEL concentration after 20 minutes of exposure to a 0.5-mg/m3 concentration expected to
produce the same effect after 1 hour. To extrapolate from a 1-hr estimated LOEL to a chronic
benchmark, they divided by a factor of 10 to yield 0.050 mg/m3 or 50 |ig/m3.
We recognize that the uncertainty associated with extrapolating from a 20-minute exposure with
minimally defined visual effects on foliage to a chronic exposure scenario with plant
productivity as the assessment endpoint is very high. Thus, 50 [j,g/m3 cannot be assumed to
represent a benchmark with a known effect level for chronic exposures. EPA does consider the
benchmark, however, to likely represent a NEL for exposures of plants to HC1.
Table 3-1. Ecological Benchmarks Used in the Environmental Risk Screen for each
PB-HAP and Assessment Endpoint
Eco-HAP
Assessment Endpoint
Benchmark Effects Level
Benchmark Value
Benchmark Source
Cadmium
Fish-eating birds feeding
from lake
NOAEL-common merganser
0.7 (mg/kg BW/day)
CA DTSC HERD 2009
LOAEL-common merganser
1 (mg/kg BW/day)
Fish-eating mammals
feeding from lake
NOAEL-mink
0.742 (mg/kg BW/day)
Sample etal. 1996 from
Sutou etal. 1980
LOAEL-mink
7.42 (mg/kg BW/day)
Cadmium
Lake benthic sediment
community
No-effect level
0.33 (mg/kg dry sediment)
CCME 1999a
Threshold level
1.2 (mg/kg dry sediment)
U.S. EPA 1996a
Probable-effect level
3.5 (mg/kg dry sediment)
CCME 1999a
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Eco-HAP
Assessment Endpoint
Benchmark Effects Level
Benchmark Value
Benchmark Source
Surface soil - birds and
Threshold-shrew
0.36 (mg/kg dry soil)
Cadmium
mammals that consume
soil invertebrates; soil
plant and invertebrate
communities
Threshold-woodcock
0.77 (mg/kg dry soil)
U.S. EPA 2005d, OSWER
Threshold-plant community
32 (mg/kg dry soil)
Eco-SSLs
Threshold-invert, community
140 (mg/kg dry soil)
Cadmium
Water-column community
Threshold level (chronic)
0.72 (|jg/L)
U.S. EPA 2001b, revised
Frank-effect level (acute)
1.8 ((jg/L)
2016b
Arsenic
Fish-eating birds feeding
NOAEL-common merganser
0.15 (mg/kg BW/day)
Sample etal. 1996 from
from lake
LOAEL-common merganser
1.5 (mg/kg BW/day)
Camardese etal. 1990
Fish-eating mammals
feeding from lake
NOAEL-mink
0.052 (mg/kg BW/day)
Sample etal. 1996 from
Arsenic
LOAEL-mink
0.52 (mg/kg BW/day)
Schroeder and Mitchener
1971
Arsenic
Lake benthic sediment
Threshold level
8.2 (mg/kg dry sediment)
U.S. EPA 1996a
community
Probable-effect level
33 (mg/kg dry sediment)
U.S. EPA 1996b
Surface soil - birds and
Threshold-shrew
46 (mg/kg dry soil)
Arsenic
mammals that consume
soil invertebrates; soil
plant community
Threshold-woodcock
43 (mg/kg dry soil)
U.S. EPA 2005b, OSWER
Eco-SSLs
Threshold-plant community
18 (mg/kg dry soil)
Arsenic
Water-column community
Threshold level (chronic)
150 (|jg/L)
U.S. EPA 1995a OW
Frank-effect level (acute)
340 (|jg/L)
2,3,7,8-TCDD
Fish-eating birds feeding
NOAEL-common merganser
0.0000014 (mg/kg BW/day)
U.S. EPA 1995b, GLWQI,
from lake
LOAEL-common merganser
0.000014 (mg/kg BW/day)
from Nosek et al. 1992a,b
2,3,7,8-TCDD
Fish-eating mammals
NOAEL-mink
0.000000771 (mg/kg
BW/day)
U.S. EPA 1995b, GLWQI,
feeding from lake
LOAEL-mink
0.00000771 (mg/kg
BW/day)
from Murray etal. 1979
2,3,7,8-TCDD
Lake benthic sediment
community
Threshold level
0.00000116
(mg/kg dry sediment)
Average of U.S. EPA
2001a, 2003a, 2006
(Regions 3, 4, and 5)
Surface soil - mammals
2,3,7,8-TCDD
that consume soil
invertebrates
Threshold - shrew
0.0000002 (mg/kg dry soil)
U.S. EPA 2003a, Region 5
2,3,7,8-TCDD
Water-column community
Threshold level (chronic)
0.000012 ([jg/L)
U.S. EPA 2001a, Region 4
Frank-effect level (acute)
0.1 ftjg/L)
Mercuric
Lake benthic sediment
Threshold level
0.16 (mg/kg dry sediment)
Average of 8*
chloride
community
Probable-effect level
0.84 (mg/kg dry sediment)
Average of 4"
Mercuric
chloride
Surface soil plant and
invertebrate communities
Threshold-plant community
0.3 (mg/kg dry soil)
U.S. EPA Region 6 cites
Efroymson etal. 1997a
Threshold-invert, community
0.1 (mg/kg dry soil)
U.S. EPA 2015, Region 4
Mercuric
Water-column community
Threshold level (chronic)
0.77 ftjg/L)
U.S. EPA 1993c, 1995a,
chloride
Frank-effect level (acute)
1.4 ((jg/L)
2015, OW
Mercury
(methyl)
Fish-eating birds feeding
from lake
NOAEL-common merganser
0.013 (mg/kg BW/day)
U.S. EPA 1995b from
LOAEL-common merganser
0.078 (mg/kg BW/day)
Heinz 1974, 1975,
1976a,b, 1979
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Eco-HAP
Assessment Endpoint
Benchmark Effects Level
Benchmark Value
Benchmark Source
Mercury
(methyl)
Fish-eating mammals
feeding from lake
NOAEL-mink
0.0247 (mg/kg BW/day)
Sample etal. 1996 from
Verschuuren etal. 1976
LOAEL-mink
0.123 (mg/kg BW/day)
Mercury
(methyl)
Lake benthic sediment
community
Threshold level
0.2 (mg/kg dry sediment)
MacDonald et al. 2000
Probable-effect level
1 (mg/kg dry sediment)
Mercury
(methyl)
Surface soil - birds and
mammals that consume
soil invertebrates; soil
plant and invertebrate
communities
Threshold-montane shrew
0.0068 (mg/kg dry soil)
U.S. EPA 2015, Region 4
Threshold-American robin
0.0011 (mg/kg dry soil)
Threshold-plant community
0.3 (mg/kg dry soil)
U.S. EPA Region 6 cites
Efroymson etal. 1997a
Threshold-invert, community
0.1 (mg/kg dry soil)
U.S. EPA 2015, Region 4
Mercury
(methyl)
Water-column community
Threshold level (chronic)
0.0028 (|jg/L)
U.S. EPA 2015, Region 4,
cites Suter and Tsao 1996
Frank-effect level (acute)
0.099 (|jg/L)
Benzo[a]-
pyrene
Fish-eating mammals
feeding from lake
NOAEL-mink
0.417 (mg/kg BW/day)
Sample etal. 1996, from
Mackenzie and Angevine
1981
LOAEL-mink
4.17 (mg/kg BW/day)
Benzo[a]-
pyrene
Lake benthic sediment
community
No-effect level
0.032 (mg/kg dry sediment)
CCME 2012
Threshold level
0.15 (mg/kg dry sediment)
U.S. EPA 1996b, 2006
Probable-effect level
1.45 (mg/kg dry sediment)
Benzo[a]-
pyrene
Surface soil - mammals
that consume soil
invertebrates
Threshold-masked shrew
1.52 (mg/kg dry soil)
U.S. EPA 2003a, Region 5
Benzo[a]-
pyrene
Water-column community
Threshold level (chronic)
0.014 (|jg/L)
U.S. EPA 2003a, Region
5, from Suter and Tsao
1996
Frank-effect level (acute)
0.24 ftjg/L)
Suter and Tsao 1996
Lead
Ambient Air
NAAQS Secondary Standard
0.15 (|jg/m3)
U.S. EPA 2016a
Acronyms/abbreviations: BW = avian or mammalian body weight; invert. = invertebrates; CCME = Canadian Council of Ministers of
the Environment; GLWQI = Great Lakes Water Quality Initiative; NAAQS = National Ambient Air Quality Standards; LOAEL =
lowest-observed-adverse-effect level; NOAEL = no-observed-adverse-effect-level; OW = EPA's Office of Water; TCDD = 2,3,7,8-
tetrachlorodibenzo-p-dioxin.
'Average of 8 threshold-effect levels: U.S. EPA(1996b) 0.18 mg/kg dry sediment; MacDonald et al. (2000) 0.18 mg/kg; Florida
Department of Environmental Protection (FDEP, MacDonald 1994) 0.13 mg/kg; U.S. EPA (1996a) 0.15 mg/kg; U.S. EPA (2015)
0.13 mg/kg; U.S. EPA (2006) 0.18 mg/kg; U.S. EPA (2003a) 0.174 mg/kg); and Region 6 (TNRCC 2001) 0.174 mg/kg.
** Average of 4 probable-effect levels: U.S. EPA (1996b) 1.06 mg/kg; MacDonald et al. (2000) 1.06 mg/kg; FDEP (MacDonald 1994)
0.70 mg/kg; and CCME (2001) 0.486 mg/kg.
3.2.2 Hydrogen Fluoride
HF is one of the most phytotoxic air pollutants. It is 10 to 1000 times more toxic to plants than
ozone, and many species of plants are more sensitive to the chronic effects of HF than are
humans (APIS 2010). Reports from decades ago document commercially significant injuries to
plants near facilities that emitted fluoride. The damages included "commercially significant"
reductions in crops of citrus fruits (Wander and McBride 1956); grapes (Brewer et al. 1957;
Wann 1953); Italian prunes (Miller et al. 1948; Wann 1953); peaches (Daines et al. 1952);
ponderosa pine (Adams et al. 1956); apricots (Wann 1946; De Ong 1946); and many varieties of
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gladioli (Johnson et al. 1950; Miller et al. 1953) (examples cited by Hill and Pack 1983). In an
area around one industrial emitter of HF, before installation of control equipment, a high
proportion of the ponderosa pine trees surrounding the facility had died (Adams et al. 1956).
Incidents like this in the United States, however, have declined; no publications describing
similar events in the past few decades were identified in our literature search.
Atmospheric fluoride ion accumulates in the leaves of plants, entering through stomata on the
underside of leaves. Atmospheric fluoride deposition to soils also can occur, but most soil
fluoride changes to insoluble forms that are not readily bioavailable to plants. Several researchers
have concluded that the limited amounts of fluoride that reach soils from contaminated
atmospheres do not affect plant uptake overall (Maclntire et al. 1949; Hansen et al. 1958).
Researchers also have demonstrated that leaves can absorb the fluoride from soluble fluoride
particles (such as calcium fluoride, which yields a fluoride ion), particularly when the leaves are
moist with dew. Nonetheless, fluoride as gaseous HF is the most bioavailable and causes much
greater injuries to plants (Hill and Pack 1983).
Gas-phase HF is particularly hazardous to plants because of its tendency to accumulate over time
in foliar tissue. Plants can accumulate HF to concentrations 1,000,000 times higher than ambient
atmospheric concentrations. Thus, unlike many pollutants, HF is expected to cause injury to
plants primarily from exposures over weeks to months, and the longer the exposure, the more
severe the effects (Hill 1969).
Susceptibility to HF varies widely among plant species and varieties. Species known to be
sensitive to HF exposure include gladioli, apricots, prunes, sorghum, corn, grapes, and conifers
(Hill and Pack 1983). Species that are relatively insensitive to HF exposure include cotton,
celery, alfalfa, and tomatoes (Hill and Pack 1983). Relatively low air concentrations can damage
sensitive species, while less sensitive species can exhibit little to no damage at somewhat higher
concentrations (TCEQ 2009; CEPA/FPAC WGAQOG 1996; Hill 1969). Several
monocotyledons rank among the most sensitive taxa, including the genera Gladiolus, Allium,
Crocus, Tulipa, Lilium, and Polygonatum (APIS 2010, citing Weinstein et al. 1998).
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Attachment A, Section A.2.3 contains the following background sections.
• Section A.2.3.1: Methods for Establishing HF Benchmarks - presents three bases that can
be used to establish HF regulatory standards.
• Section A.2.3.2: HF Regulatory Levels - summarizes atmospheric (air concentration)
criteria and regulatory levels that states and other countries have established for HF for the
protection of vegetation and other endpoints.
• Section A.2.3.3: Studies Showing the Effects of HF Exposure on Plants - discusses the
bulk of readily available data relating HF exposures to plant responses based on
atmospheric concentrations. Those data are presented to assist EPA risk managers in
interpreting the results of screens of HF emissions. Comparisons of the criteria for
protecting productivity of agricultural plants and livestock from fluorosis to those
available for protecting human health indicate that air concentration benchmarks for HF
developed for plants are lower than those developed to protect livestock and human
health.
Two HF benchmarks are used for the environmental risk screen. The value of 0.5 [j,g HF/m3 is
based on the Washington State criterion for gaseous HF. The value of 0.4 jag HF/m3, which is 20
percent lower, is based on the Environment Canada criterion. Both criteria were developed for
90-day averaging periods during the growing season.
For HF, we model annual estimates of facility emissions in HEM/AERMOD to obtain average
annual HF air concentrations. When screening for chronic HF risks to plants in the
environmental risk screen, we compare the average annual HF air concentrations from the
HEM/AERMOD runs to the 90-day criteria. If exposures are not the same during the growing
season and the nongrowing season, the use of annual average exposures could underestimate or
overestimate risks. An additional uncertainty in evaluating chronic HF risks to plants is the wide
variation in plant sensitivity to airborne HF and the relatively few nonagricultural plants that
have been tested (Attachment A, Section A.2.3.2).
Empirical models that relate exposure concentration, exposure duration, and plant response for
different plant groups are not simple mathematical relationships (e.g., see McCune et al. (1991)
equations to predict severity and incidence of foliar injury from HF exposure concentration and
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duration). In other words, although plant foliage accumulates fluoride from HF in air over time,
effects on plants are not proportional to air concentration only, nor are they proportional to the
simple product of average exposure concentration and duration (e.g., a time-weighted average
exposure concentration). This lack of proportionality could be due to factors such as more
frequent periods of rain wash-off that can leach fluoride from leaves over longer exposure
periods and slower fluoride absorption rates as fluoride concentrations in plant leaves increase.
Short-term exposure data and criteria were not used to assess risk to plant communities from HF
for several reasons. Characterizing possible adverse effects on the assessment endpoints of plant
productivity and community structure (e.g., as habitat for wildlife, agricultural productivity) over
the long term from data on species-specific effects on plants from short-term exposures (or short-
term exposure criteria) would require many assumptions and include major uncertainties. Data
are lacking to link effects like "foliar markings" and mild leaf necrosis to plant reproduction and
productivity over the long term. Also lacking are data on the recovery of plants after short-term
exposures and the frequency of high short-term exposures that could be tolerated if time needed
for recovery is adequate. In addition, some long-term effects (e.g., annual seed production) that
might result from short-term exposures would occur only if a short-term peak in HF
concentration occurred during the few days of a sensitive life-stage of the plant (e.g., flowering).
4 Exposure Assessment
This section presents the models and methods used to estimate HAP exposures in the
environment. We describe how to use the effect levels to calculate emission "screening
thresholds" and how these thresholds are compared to facility emissions to screen for adverse
environmental effects.
The first step in the ecological exposure assessment is to determine whether any facilities in the
source category of interest emit any of the eight environmental HAPs (see Figure 2-1 and Figure
2-2 in Section 2.3). This step is performed by querying the emissions data for the source category
in question. Typically, emissions data are obtained from the National Emissions Inventory,
section 114 surveys of the industry, or from facility stack emissions tests. Emissions data for
facilities identified in this step are used to perform the environmental risk screen, as described in
this section. The approach for the overall environmental risk screen uses separate methods to
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assess ecological exposures to PB-HAPs, lead, and acid gases. Section 4.1 details the exposure
assessment methods for PB-HAPs. Section 4.2 details the exposure assessment methods for lead
and the acid gases.
4.1 ENVIRONMENTAL RISK SCREEN FOR PB-HAPS
Figure 2-1 in Section 2.3 provides an overview of the approach for the environmental risk screen
for PB-HAPs. This approach includes three tiers of assessment designed for implementation with
a minimum of required site-specific or other assessment-specific inputs. The Tier 1, Tier 2, and
Tier 3 approaches are discussed in further detail in Sections 4.1.1, 4.1.2, and 4.1.3. See Section 5
for further discussion of outputs from an environmental risk screen.
Possible exposure pathways from facility air emissions to biological receptors of concern were
identified from HAP-specific chemical properties, the conceptual model of multimedia fate and
transport, and the GEAEs in Table 2-2. The wildlife populations most highly exposed to
PB-HAPs would be those that consume aquatic or terrestrial biota that have bioaccumulated the
chemical along food chains. Thus, we assumed that some local populations of birds or mammals
could be exposed to PB-HAPs that have bioaccumulated in food chains to relatively high
concentrations in fish and in terrestrial prey. Additionally, persistent HAPs could accumulate
over time in surface soils and reach concentrations toxic to terrestrial plants and to invertebrate
communities in soils (e.g., earthworms).
The biotic compartments in the lake(s) for which TRIM.FaTE simulates whole-organism
contaminant concentrations in Tiers 1, 2, and 3 are described below.
1. Phytoplankton, suspended algae in the water column, is modeled as a "phase" of the water
column.
2. Zooplankton are modeled as a compartment in the water column that is in chemical
equilibrium with the phases in the water column, including aqueous and algal phases.
3. Macrophytes in a lake can accumulate and "sequester" some chemicals and are modeled as a
separate compartment in the water column.
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4. Benthic invertebrates such as mollusks, Crustacea, and aquatic insect nymphs that consume
periphyton and detritus are modeled as a compartment in chemical equilibrium with bottom
sediments.
5. Benthivorous fish are bottom-feeding fish (e.g., young catfish) that consume primarily
benthic invertebrates.
6. Bottom-feeding carnivores (e.g., adult catfish) consume both benthic invertebrates and young
benthivorous fish.
7. Water-column planktivores, such as young-of-the-year for many species and other small fish
(e.g., shiners, minnows), consume primarily planktonic organisms.
8. Water-column omnivores are larger fish that consume invertebrates and smaller fish from
both the benthic and pelagic environments (e.g., "panfish" like bluegill, yellow perch, and
young age classes of the game species).
9. Water-column piscivores are larger game-fish species that primarily consume smaller fish in
pelagic or benthic environments (e.g., walleye, largemouth bass).
The same aquatic food webs developed in TRIM.FaTE for the human health screen for fish
ingestion are used to estimate doses to fish-eating wildlife species chosen as assessment
populations for the environmental risk screen. The parameterization of those compartments is
described in Appendix 6 to the Risk Report.
For wildlife exposed to PB-HAPs via consumption of aquatic life, we assume that the assessment
populations obtain 100 percent of their diet from the appropriate biotic compartments
corresponding to the different types of aquatic prey they consume. Parameterization of the
wildlife diets and other relevant exposure factors (e.g., body weight) is described in Attachment
A, Section A.6.
We also assumed that ground-feeding birds and mammals that consume primarily soil
invertebrates (e.g., earthworms, grubs) could be exposed to PB-HAPs that have bioaccumulated
in the invertebrates from ingestion of or contact with soils. We assumed that the assessment
populations obtained 100 percent of their diet from the assessment area (radius of 10 km). We
did not assess risks to higher-level carnivores (e.g., wolves, eagles) because their feeding ranges
generally are large and difficult to link to specific facilities.
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For benthic and water-column aquatic communities, we estimate exposure to PB-HAPs using the
TRIM.FaTE-model-estimated concentrations in sediments and the water column, respectively,
for the lake(s) situated in Tiers 1, 2, and 3.
4.1.1 Tier 1 Exposure Assessment
Figure 4-1 summarizes the Tier 1 screening approach for PB-HAPs. The Tier 1 assessments for
all source categories use ecological screening threshold emission rates for each GEAE and
PB-HAP. The screening threshold emission rates (in tons per year) yield concentrations in
environmental media at receptor locations in the hypothetical TRIM.FaTE environmental setting
that equal the ecological benchmarks. The ratio of a facility's PB-HAP emissions to the
corresponding screening threshold emission rate is called the "screening value" (SV). When
rounded to one significant figure, SVs greater than 1 indicate that adverse ecological effects
within 10 km of the facility cannot be ruled out, and further assessment (e.g., Tier 2, see Section
4.1.2) might be needed.
Figure 4-1. Approach for Tier 1 Environmental Risk Screen for PB-HAPs
The hypothetical environmental settings are the same as used in the human health risk screen.
The lake-centric setting (top panel of Figure 4-2) is used to assess fish and other biota in surface
water and sediment. The nonfarm (i.e., grass and forest) parcels in the farm-centric setting
(bottom panel of Figure 4-2) are used for the environmental risk screen related to soil.9 Both
9The farm itself is not used in the environmental risk screen.
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spatial layouts include an emission source on the west side and several modeling compartments
extending to 10 km east of the source. The compartments are shown with arbitrary names (e.g.,
1, 2, 3) and are modeled with the indicated land-cover properties and runoff patterns. The
assessment of aquatic-related endpoints uses modeled concentrations for water, sediment, fish
tissue, and benthic invertebrates at a lake close to the facility (see top panel of Figure 4-2). The
assessment of soil-related endpoints uses the modeled surface soil concentrations at five
distances from the facility, up to 7.5 km (see bottom panel of Figure 4-2), not including the
farming parcel.
The Tier 1 environmental modeling scenario was parameterized to include hypothetical
environmental conditions that would provide conservatively high PB-HAP concentration
estimates. For example, in the Tier 1 scenario, emissions blow from the facility into the narrow
wedge depicted for both settings in Figure 4-2 for 3 days per week, or 43 percent of the time—an
unusually consistent long-term wind pattern but not unrealistic (e.g., similar to wind direction
patterns in Yakima, Washington). Model settings maximize runoff from terrestrial parcels into
the hypothetical lake (for aquatic-related assessment), which in turn maximizes the chemical
concentrations in the water, sediments, and fish. The lake situated near the facility also would
receive relatively high levels of direct air-to-surface wet and dry deposition. Further details of
the Tier 1 TRIM.FaTE environmental modeling scenario, including a description of the aquatic
food web, are available in Appendix 6 to the Risk Report. EPA's Science Advisory Board
reviewed the approach to parameterizing the hypothetical environmental setting, and other
aspects of the TRIM-based modeling used to develop screening threshold emission rates, in
2009/2010.
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Figure 4-2. TRIM.FaTE Lake-centric (Top) and Farm-centric (Bottom) Surface Layouts
for the Tier 1 Screen
Note: For the environmental risk screen, the iake-centric layout is used for fish, surface water,
and sediment endpoints, while the grass and forest parcels of the farm-centric layout are used for
soil endpoints.
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To calculate the environmental screening threshold emission rates, we ran TRIM.FaTE with a
standardized emission rate of 1 g/day for each PB-HAP and saved the resulting PB-HAP
concentrations in media at receptor locations throughout the hypothetical environment. We then
calculated the environmental screening threshold emission rates by multiplying the 1 g/day
emission rate by the ratio of ecological benchmark concentrations to modeled media
concentrations. This approach is possible because, for any single period and location (all things
being held constant), changes in TRIM.FaTE-predicted PB-HAP concentrations are linear with
changes in emission rate. Attachment A provides the final Tier 1 environmental screening
threshold emission rates.
Two of the six PB-HAPs for which environmental screening threshold emission rates have been
developed (POM and dioxins) are chemical groups comprising numerous individual compounds.
For example, for POM, emissions reported include various chemicals, such as
benz[a]anthracene, 2-methylnaphthalene, and chrysene, and a few nonspecific entries, such as
"PAH, total." As explained below, the results for individual compounds in the POM and dioxin
groups are summed, using a TEF approach (see Appendix 6 of the Risk Report for additional
information) and an exposure equivalency factor (EEF) approach (described below), to provide
one POM result in BaP-equivalents and one dioxin result in 2,3,7,8-TCDD-equivalents.
For POM and dioxins, ecological exposure equivalency factors (EcoEEFs) are calculated for
surface water, soil, and sediment by dividing the media concentrations predicted by TRIM.FaTE
for each chemical by the predicted concentration of the reference (index) chemical for each
group. For example, the EcoEEF for chrysene in soil is calculated as the TRIM.FaTE-estimated
concentration of chrysene in soil divided by the estimated concentration of BaP in soil at the
same location.
Application of EcoEEFs for POMs and dioxins for piscivorous wildlife differs from the approach
described above for surface water, soil, and sediment because TRIM.FaTE does not model
PB-HAP exposure doses for the representative animal fish-eating wildlife (i.e., mink, American
merganser). The exposure doses for each individual chemical (arsenic, cadmium, mercury, each
congener of the POM and dioxin groups) are calculated outside of TRIM.FaTE using the
TRIM.FaTE-estimated concentrations in fish and using fish ingestion rates and body weights
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specific to the mink and merganser. Each chemical's EcoEEF then is calculated as the ratio of its
exposure dose to the exposure dose of the index chemical. The wildlife exposure doses vary
across chemicals because the relative concentrations of individual chemicals in each food type
consumed (e.g., different fish compartments) vary across chemicals relative to the index
chemical due to the variation in chemical-specific assimilation efficiencies, among other factors,
for a given fish compartment. The wildlife-specific characteristics influencing the types and
quantity of aquatic biota consumed are described in Attachment A, Section A.6, including the
data used to assess ingestion of chemicals from each dietary component for mink and common
mergansers.
For wildlife-consuming aquatic biota, no adjustments were needed for variation in chemical
assimilation efficiency among POM and dioxin/furan congeners, respectively. All toxicity data
used to estimate TRVs for POM and dioxin/furan congeners for birds and mammals were based
on "administered" doses (the amount of chemical ingested with food, not the amount absorbed
into the blood stream). Thus, no adjustments for absorption are needed; differences in absorption
among congeners are reflected in the TRVs. That is in contrast to the aquatic food chain
modeling, for which congener-specific absorption, metabolic degradation, and elimination rates
were estimated for fish and invertebrates and incorporated into the TRIM.FaTE compartment
models to estimate bioaccumulation through the aquatic food chains more accurately when
calculating EEFs.
The Tier 1 SV for a chemical's emissions from a facility is calculated as Emissions x ecological
toxic equivalency factor (EcoTEF) x EcoEEF Screening Threshold Emission Rate. For each
assessment endpoint and benchmark, the SVs are summed for all POM congeners at a facility
(into a total BaP-equivalent SV), and the SVs are summed for all dioxin congeners at a facility
(into a total 2,3,7,8-TCDD-equivalent SV).
4.1.2 Tier 2 Environmental Risk Screen
After reviewing the results of the Tier 1 environmental risk screen, EPA might choose to
evaluate sources with HAP emissions above the Tier 1 screening threshold emission rates (with
SVs of 2 or more when rounded to one significant figure). The Tier 2 environmental screening
approach, summarized in Figure 4-3, consists of the following steps.
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Figure 4-3. Approach for Tier 2 Environmental Risk Screen for PB-HAPs
First, TRIMFaTE is used to estimate environmental concentrations associated with an emission
rate of 1-g/day for 64 combinations of meteorological conditions (see Section 3 of Appendix 6 of
the Risk Report for more information). We assess five different distances of the lake from the
facility (see Section 3 in Appendix 6 of the Risk Report for a discussion on modeling domain
sizes, including modeled lake location values). For the soil endpoints, we use the Tier 1 farm-
centric layout (locations of soil endpoints are unchanged from Tier 1). All other attributes of the
TRIM.FaTE runs for the Tier 2 environmental risk screen are identical to those of Tier 1. The
Tier 2 TRIM.FaTE runs are performed once, for use in both the human health and ecological risk
screening.
Second, for aquatic-related endpoints, each lake near the facility that meets inclusion criteria is
identified by its location relative to the facility and by its surface area (see Section 3 of Appendix
6 of the Risk Report for more information). Section 3 of Appendix 6 of the Risk Report also
describes the lake database used to identify appropriate lakes. Several lake-selection criteria used
in the human health assessment (not swampy or covered in algae, not closed to public access) are
not used as criteria for the environmental assessments. Facility-specific meteorology and lake
location data are used to identify which combination of meteorological conditions and lake
distance is most similar to that of the facility and each individual lake.
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Third, for soil endpoints, facility-specific meteorological data are used to identify which
combination of meteorological conditions is most similar to that of the facility, and the
corresponding chemical-specific environmental screening threshold emission rates and EcoEEFs
are identified for each of the five soil locations.
The second-pass Tier 2 SV is based on additional adjustments for how frequently the wind blows
toward the lake or soil locations of interest (compared with Tier 1) and for the relationship
between site-specific air mixing height.
The third-pass Tier 2 screen accounts for multifacility chemical loading to lakes (e.g., two
facilities from the same source category located within 100 km of each other, each contributes
chemical mass to the same lake). For each ecological assessment endpoint and benchmark effects
level, the SVs are summed for all POM congeners (into a total BaP-equivalent SV) and the SVs
are summed for all dioxin congeners (into a total 2,3,7,8-TCDD-equivalent SV).
For each facility, for each assessment endpoint, benchmark, and PB-HAP (with POM and
dioxins summed to BaP- and 2,3,7,8-TCDD equivalents, respectively), we identify the lake with
the largest Tier 2 SV—the final Tier 2 SV for that facility, endpoint, benchmark, and PB-HAP.
For each facility, endpoint, benchmark, and PB-HAP (with POM and dioxins summed to BaP-
and 2,3,7,8-TCDD equivalents, respectively), we average the Tier 2 SVs across all 40 soil
locations (8 directional octants x 5 soil distances). Each estimate is area weighted (points distant
from the source represent larger soil areas than nearer points in the radial domain) to obtain an
area-weighted average soil SV.
If Tier 2 SVs are less than or equal to 1, after rounding to one significant figure, the facility
screens out (the emissions are below environmental screening threshold emission rates), and it is
typically not evaluated further. If Tier 2 SVs after rounding to one significant figure are greater
than 1, the facility might be evaluated further with additional site-specific data and modeling
refinements as described for Tier 3.
4.1.3 Tier 3 Exposure Assessment
A Tier 3 screen can be conducted on facilities that do not screen out in Tier 2. The Tier 3
screening approach consists of three individual assessments (shown in Figure 4-4 and described
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in more detail in Section 4 of Appendix 6 of the Risk Report) that further refine the screening
scenario (beyond Tier 2) based on additional site-specific data and evaluations. The refinements
are conducted in a step-wise fashion, and all three might not always be needed (e.g., a facility
might screen out after the first refinement in Tier 3).
In the first step of the Tier 3 assessment (the lake assessment), we investigate further the lakes
assessed in Tier 2 (the lake at each facility associated with the largest aquatic-related SVs per
PB-HAP). If we modify, add, or remove any lakes from the assessment, we also modify the lake
database and rerun the Tier 2 assessment (e.g., identify a new, more appropriate lake for
assessment). If SVs still exceed 1, in the second step of the Tier 3 assessment (i.e., the plume-rise
assessment), we estimate how often the chemical plume rises above the mixing layer and,
therefore, disperses out of the modeling domain (no ground-level exposures). Finally, if SVs still
exceed 1, in the third step of the Tier 3 assessment (the time-series-meteorology assessment), we
conduct new runs of TRIM.FaTE and the Multimedia Ingestion Risk Calculator with time-series
data for meteorology and plume rise. This last set of SVs typically is smaller than those produced
by the Tier 3 plume-rise assessment.
Information about the number and proportion of facilities in a source category exceeding the
environmental screening threshold emission rates (SVs >1), proportion and absolute area over
which soil-based screening threshold emission rates are exceeded, and magnitude of those SVs
help EPA decide whether adverse ecological effects are potentially widespread and significant. If
a facility exceeds Tier 3 screening threshold emission rates, it could be further evaluated to
consider the degree to which the emission rates are exceeded, which endpoints and effect levels
are exceeded, the geographic setting (e.g., proximity to protected areas and resources), and the
total area exceeding the screening threshold emission rates. If, after additional refinement, the
facility still exceeds the screening threshold emission rates, a site-specific assessment could be
conducted. The site-specific assessment uses model parameter values and scenario designs
intended to better represent the modeled facility—aspects such as local terrain (influencing
runoff and erosion patterns), watersheds, actual lake boundaries and water retention rates, soil
types, and land cover. Site-specific environmental assessments are not presented in this report.
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Figure 4-4. Approach for Tier 3 Environmental Risk Screen for PB-HAPs
Tier 3 Lake
Assessment
Forthe lake(s) associated with a facility's highest Tier 2 screening
values for aquatic endpoints, use aerial imager/and Web
searches to determine if the lake is suitable for the models and
methods used inthemultipathwayscreeningassessment
If the lake is unsuitable, remove it and evaluate the lake with the
next-highest screening value. Continue until a suitable lake is
identified
Rerun the Tier 2 screening by matchingthe lake location with the
appropriate adjustmentfactorsforscreeningthresholdsand
EcoEEFs
< Thresh old: Risk
below level of
concern; Stop here
If emissions > thresh old, make risk management decision or proceed
to Tier 3 plume-rise assessment
If emissions > thresh old, make risk management decision or proceed
to Tier 3 time-series assessment
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4.2 ENVIRONMENTAL RISK SCREEN FOR LEAD AND ACID GASES
4.2.1 Lead
The level of the primary and secondary NAAQS for lead, 0.15 |ig/m3, is intended to protect
humans from both excess inhalation and ingestion exposures and, secondarily, to protect the
environment from adverse effects (U.S. EPA 2016a).10 Therefore, RTR multipathway
assessments evaluate modeled air concentrations of lead compounds against the NAAQS level
directly, without additional fate, transport, and exposure modeling. We compare the AERMOD-
modeled air concentrations of lead for each individual emission point for each facility in the
source category to the 0.15-|ig/m3 level of the secondary NAAQS for lead. The environmental
risk screen for lead consists of this single tier. We consider air concentrations below the level of
the secondary lead NAAQS unlikely to cause adverse environmental effects.
4.2.2 Acid Gases
We needed a separate approach for exposure modeling for acid gases because TRIM.FaTE does
not explicitly model gas-phase dispersion in ambient air around a source and the estimated
ground-level ambient concentrations are uncertain, particularly with respect to relatively fine
spatial resolution. Based on the nature of the GEAE selected for acid gases and the mode of
exposure for these chemicals (direct contact of plant foliage with acid gases present in ambient
air), EPA used AERMOD (an air dispersion model), which is the same model used in the human
inhalation risk assessment. The typical defaults for AERMOD are to model 13 concentric rings
at various distances from the facility with 16 concentration data points equally spaced across
each ring for 208 modeled air concentrations.
Relative to the PB-HAPs exposure estimates, the acid gas exposure estimates are less health
protective and more facility specific, primarily due to the characteristics of the acid gas analysis:
• Only one environmental medium assessed (air only in contrast to air, soil, and water)
• Direct contact of the chemical in air with plant foliage, which eliminates the need for
multimedia modeling of chemical transfers
10
The secondary lead NAAQS (U.S. EPA 2016a) is a reasonable measure of determining whether an adverse
environmental effect is present because it was established considering "effects on soils, water, crops, vegetation,
man-made materials, animals, wildlife, weather, visibility and climate, damage to and deterioration of property, and
hazards to transportation, as well as effects on economic values and on personal comfort and well-being."
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• Refined air modeling approach, using hourly meteorology data and multiple emission
sources.
The environmental risk screen for acid gases includes a single tier. Screening compares modeled
ambient air concentrations of each acid gas, HF and HC1, to the air concentration benchmarks for
terrestrial plants. For HF, we assume that all HF emitted by facilities would remain in the
atmosphere in vapor phase; none would be adsorbed to particles that also might be emitted by the
facility. That assumption could substantially overestimate the HF concentrations to which
terrestrial plant foliage might be exposed.
Because modeled air concentrations are compared directly to the acid-gas ecological benchmarks
expressed as air concentrations, emission-based screening thresholds are not calculated for acid
gases as they are in the environmental risk screen for PB-HAPs.
For HF, the exposure durations for the available benchmarks (Section 3.2.2) do not correspond
precisely to the exposure averaging times of the HEM/AERMOD results. The benchmarks for
HF are equivalent to the 90-day Washington State criterion (0.5 |ig HF/m3) and the 90-day
Canadian Ambient Air Quality Objective for the growing season (0.4 |ig HF/m3). Although some
risk assessors would consider those two values to be essentially equivalent, and might propose
using the more health-protective (lower) value, others might consider the 20-percent difference
between the two values an important distinction and propose using a value applied within the
United States. We therefore have retained both benchmarks for now. The exposure averaging
time output from HEM/AERMOD for chronic scenarios is an annual average. Given that 90 days
(the approximate growing season when foliage is present and exposed to air) is the longest
duration for which HF criteria are available for jurisdictions within North America, the 90-day
criteria are considered the best available benchmarks for direct comparison to annual average
concentrations from HEM/AERMOD.
For HC1, our calculations to estimate a chronic benchmark for terrestrial plants expressed as air
concentrations are the same as described in Appendix K of the 2009 SAB report (U.S. EPA
2009). Specifically, as summarized on page 3-23 of that report:
"We extrapolated the LOEL andLOAEL exposures to 1-hour equivalent concentrations
of 0.5 and 1 mg/m3, respectively using the common application ofHaber 's law, as
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modified by Ten Berge et al. [1986]. Lacking long-term study data, we applied an
additional uncertainty factor of 10 to extrapolate the lower of the two acute thresholds
(0.5 mg/m3) from a 1-hour to a 1-year exposure threshold of 0.05 mg/m3. "
Therefore, in our environmental risk screen, we compare the annual average HEM/AERMOD
concentrations to the HC1 benchmark of 0.05 mg/m3 (50 |ig/m3).
5 Environmental Risk Characterization/Screening
Results
In this section, we discuss the outputs and analyses generated as part of the environmental risk
screen.
5.1 ENVIRONMENTAL RISK SCREEN METRICS FOR PB-HAPS
5.1.1 Tier 1
The modeling domain for Tier 1 consists of a health-protective set of conditions (see Section
4.1.1). The modeled area for Tier 1 does not fully extend around the facility but, rather, is a
single, downwind wedge. The wedge includes point locations (centroids for modeled surface soil
compartments) for estimating chemical concentrations in untitled surface soils at five locations
(at 312 m, 850 m, 1500 m, 3500 m, and 7500 m from the facility; measured from the facility
center point to the center point of the parcel). The wedge contains one freshwater lake or pond at
approximately 500 m from the facility (with one compartment each for surface water, sediment,
benthic invertebrates, and five categories of fish). Therefore, for water, sediment, and fish
tissues, the Tier 1 environmental risk screen for a PB-HAP is based on the TRIM.FaTE-modeled
chemical concentration in the lake water-column compartment, in the lake sediment
compartment, and in each of the six aquatic animal compartments in the one lake, respectively.
For surface soils, the Tier 1 environmental risk screen is based on the location with the highest
soil concentration. Use of the highest soil concentration for Tier 1 is consistent with Tier 1 being
a health-protective scenario.
The Tier 1 environmental risk screen for a PB-HAP is performed with a computational tool that
automates steps from assembling emissions data to presenting results in preformatted
spreadsheet tables. The tool calculates the Tier 1 SV, which is the emissions of the PB-HAP
from a facility (adjusted to the BaP and 2,3,7,8-TCDD index chemicals for POM and dioxins,
respectively) divided by the environmental screening threshold emission rate for that PB-HAP.
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An SV less than or equal to 1 (after rounding to 1 significant figure) indicates that the facility
screened out; an SV greater than 1 indicates the potential for adverse environmental effects
cannot be ruled out. Outputs provided by this tool include the Tier 1 SVs, the number of facilities
that did not screen out in the Tier 1 screen, and the highest Tier 1 SV. The SVs also can be
presented for each facility, PB-HAP, assessment endpoint, and benchmark effects level and can
be summarized across all facilities. See Table 5-1 for a summary of PB-HAP environmental risk
screen metrics.
Facilities not passing the Tier 1 screen for any PB-HAP, assessment endpoint, or benchmark
effects level are evaluated in Tier 2. Facilities that screened out of the Tier 1 screen are not
evaluated further for potential environmental effects.
5.1.2 Tiers 2 and 3
For Tier 2, TRIM.FaTE was run once with hundreds of combinations of meteorological
conditions (from 823 meteorological stations) and lake locations [five distances in eight octants
(wedges) that together fully surround the source]. For each combination, environmental
screening threshold emission rates are calculated for each PB-HAP and assessment endpoint. For
a Tier 2 assessment for a given source category, each facility not ruled out by Tier 1 can be
evaluated. First, a computational tool identifies which combination of meteorological conditions
and lake location best matches the facility. The SVs for the facility equal the ratio of the
facility's emissions to the environmental screening threshold emission rate for that combination
from the Tier 2 TRIM.FaTE runs. Tier 2 soil calculations use the same five facility-to-soil
distances as in Tier 1, but in all eight directional octants.
As in Tier 1, the Tier 2 environmental risk screen for PB-HAPs uses a computational tool that
automates the steps described above. The Tier 2 environmental SVs are tabulated by facility, PB-
HAP group, and assessment endpoint. For aquatic assessment endpoints, the final Tier 2 SVs are
for the lake with the highest chemical concentrations (a protective setting). For soil-based
assessment endpoints, the final Tier 2 SV is the average of the area-weighted SVs across all 40
surface soil compartments (5 distances in each of 8 octants). Facility-level results include the
percentage of the total modeled soil area not passing the screen for each facility and each PB-
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HAP. The tool also identifies lake names, sizes (acres), and locations. Table 5-1 summarizes the
PB-HAP environmental risk screen metrics.
Table 5-1. Summary of PB-HAP Environmental Risk Screen Metrics
Tier
Modeling Domain
Source Category Results
Tier 1
Soils and Lake
• Tier 1 emission screening value (SV) for each facility for each combination of PB-HAP,
assessment endpoint, and benchmark effects level. [For soils, the SV is based on the
highest concentration from among the five soil locations.]
• Number of facilities that do not screen out (for each combination of PB-HAP, assessment
endpoint, and benchmark effects level; associated with SVs of 2 or more).
• Highest Tier 1 screening ratio for the category (for each combination of PB-HAP,
assessment endpoint, and benchmark effects level).
Tier 2
Soils
• Tier 2 SVs for each facility for each combination of PB-HAP, assessment endpoint, and
benchmark effects level. [Overall SV is based on the area-weighted average for all 40
calculated soil concentrations within a 7.5-km radius.]
• Number of facilities that do not screen out (for each combination of PB-HAP, assessment
endpoint, and benchmark effects level; associated with SVs of 2 or more).
• Highest Tier 2 SV for the category (for each combination of PB-HAP, assessment endpoint,
and benchmark effects level).
• Percentage of the total soil area with an SV of 2 or more for each facility (if at all).
Lakes
• Tier 2 SVs for each facility for each combination of PB-HAP, assessment endpoint, and
benchmark effects. [SV is based on the highest lake concentrations, after accounting for
possible multifacility chemical loading.]
• Number of facilities that do not screen out (for each combination of PB-HAP, assessment
endpoint, and benchmark effects level; associated with SVs of 2 or more).
• Highest Tier 2 SV for the category (for each combination of PB-HAP, assessment endpoint,
and benchmark effects level).
• For each modeled lake: lake name, lake surface area (acres), facility-to-lake distance, and
latitude/longitude of the lake.
Tier 3
Same as Tier 2.
Facilities that screen out of the Tier 2 screen for all assessment endpoints are not evaluated
further. Facilities that do not screen out might be evaluated further with additional site-specific
data and modeling refinements as described for Tier 3 (see Section 4.1.3).
The Tier 3 screening approach consists of three individual assessments (shown in Figure 4-4 and
described in more detail in Section 4 of Appendix 6 of the Risk Report) that further refine the
screening scenario (beyond Tier 2) based on additional site-specific data and evaluations. The
refinements are conducted in a step-wise fashion, and all three might not always be needed (e.g.,
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a facility might screen out after the first refinement in Tier 3). The three tier 3 assessments
include the lake assessment, plume-rise assessment, and time-series meteorological assessment.
The environmental risk screen metrics for Tier 3 are the same as for Tier 2.
As with the multipathway human health risk assessment, a site-specific assessment could be
conducted if the Tier 3 screening results indicate a potential for adverse environmental effects.
The site-specific assessment uses model parameter values and scenario designs intended to better
represent the modeled facility—aspects such as local terrain (influencing runoff and erosion
patterns), watersheds, actual lake boundaries and water retention rates, soil types, and land cover.
This report does not present site-specific assessments.
5.2 ENVIRONMENTAL RISK SCREEN METRICS FOR ACID GASES
The HEM/AERMOD domain extends 50 km from the center of the facility. The
HEM/AERMOD approach includes 13 concentric rings at various distances (out to 50 km) from
the facility with 16 locations, each separated by 22.5 degrees on each ring. Therefore, the
HEM/AERMOD model generates 208 point estimates of acid gas concentration.
Although an SV could be calculated for all 208 point estimates of air concentration, an SV for a
single data point would have little meaning in the context of assessing "significant and
widespread" effects over "broad areas" as specified in the CAA definition of "adverse
environmental effects." For example, the area of a parcel close to the facility is only a few acres
in size. Therefore, in the context of the statutory definition of adverse environmental effects, we
use the metrics shown in Table 5-2 to identify effects that are significant and widespread
(covering broad areas).
For acid gases, we report the following:
• If individual locations with an SV of 2 or more are present around a facility, we indicate
the percentage of the modeling area that had an SV of 2 or more.
• If all locations (i.e., 208 modeled locations) for which HEM/AERMOD estimated acid gas
concentrations had SVs less than 2, we indicate that all estimated concentrations around
the facility are below the ecological benchmarks for acid gases in air.
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Table 5-2. Summary of Acid Gas Environmental Risk Screen Metrics
Metric
Description
Facility
Modeled area exceeding the
ecological benchmarks, in
acres and km2
• All 208 modeled acid gas concentrations in air are compared with the
ecological benchmarks (concentration/benchmark = screening value). Those
SVs of 2 or more do not screen out.
• The total modeled area with an SV of 2 or more.
Percentage of the modeled
area exceeding the
ecological benchmarks
• The total modeled area with an SV of 2 or more divided by the total area of
the 50-km (radius) modeling domain.
Area-weighted average SV
• The area-weighted average concentration of all 208 modeled data points
divided by the ecological benchmark.
Source
Category
Number of facilities with
exceedances
• The number of facilities in the category that did not screen out according to
area-weighted averaging.
5.3 ENVIRONMENTAL RISK SCREEN METRICS FOR LEAD
For lead compounds, we currently have no ability to calculate concentrations in multiple
environmental media using the TRIM.FaTE model. Therefore, to evaluate the potential for
adverse environmental effects from lead compounds, we compare the HEM/AERMOD air
concentrations of lead around each facility in the source category to the 0,15-|ig/m3 level of the
secondary NAAQS for lead (U.S. EPA 2016a). The environmental risk screen for lead consists
of one tier. We consider values below the level of the secondary lead NAAQS unlikely to cause
adverse environmental effects.
6 References
Adams, D.F., Shaw, C.G., and Yerkes, D.Jr. (1956). Relationship of injury indexes and
fumigation fluoride levels. Phytopath. 46: 587-591. (As cited in CEPA/FPAC WGAQOG
1996.)
Allard, P., Fairbrother, A., Hope, B.K., Hull, R.N., Johnson, M.S., Kaputska, L., Mann, G.,
McDonald, B., and Sample, B.E. (2010). Recommendations for the development and
application of wildlife toxicity reference values. Int. Environ. Assess. Manage. 6: 28-37.
APIS (Air Pollution Information System) (2010). Halogens: Inorganic Fluorides - HF. Available
online at http://www.apis.ac.uk/overview/pollutants/overview_halogens.htm. Accessed
July 16, 2012.
Brewer, R.F., McColloch, R.C., and Sutherland, F.H. (1957). Fluoride accumulation in foliage
and fruit of wine grapes growing in the vicinity of heavy industry. Proc. Amer. Soc. Hort.
Sci. 70: 183-188. (As cited in Hill and Pack 1983.)
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Wander, I.W., and McBride, J.J. (1956). Chlorosis produced by fluorine on citrus in Florida.
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Attachment A. Environmental Effects Assessment
CONTENTS
TABLES A-2
ACRONYMS A-3
A. 1 Introduction A-5
A.2 Ecological Benchmarks A-6
A.2.1 Population-level and Community-level Benchmarks A-7
A.2.2 Ecological Benchmarks for Persistent and Bioaccumulative
Hazardous Air Pollutants (PB-HAPs) A-13
A.2.3 Hydrogen Fluoride (HF) Air Benchmarks for Terrestrial Plants A-28
A.3 Wildlife Toxicity Reference Values A-41
A.3.1 Derivation of TRVs for Piscivorous Wildlife in RTR Assessment A-42
A.3.2 Chemical-specific Wildlife TRVs for PB-HAPs A-44
A.4 Derivation of Ecological TEFs for POM and Dioxin Benchmarks A-50
A.4.1 TEFs for POM for Surface Water, Sediments, and Soils A-50
A.4.2 TEFs for Dioxins for Surface Water, Sediments, and Soils A-62
A. 5 TEFs for Wildlife TRVs A-62
A.5.1 TEFs for Wildlife for POM A-62
A.5.2 TEFs for Wildlife for Dioxins A-66
A.6 Piscivorous Wildlife Exposure Factors A-66
A.6.1 Mink Exposure Factor Values and Assumed Diet A-68
A.6.2 Merganser Exposure Factor Values and Assumed Diet A-70
A.7 Derivation of Bioaccumulation Factors for Arsenic A-71
A.7.1 Differences between Freshwater and Marine Fish A-72
A.7.2 BAFs for Arsenic in Freshwater Fish A-74
A.7.3 BSAFs for Arsenic in Freshwater Benthic Invertebrates and Fish A-80
A.8 Environmental Screening Threshold Emission Rates A-81
A.9 References A-86
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TABLES
Table A-l. Generic Ecological Assessment Endpoints Not Used in the Nationwide
RTR Environmental Risk Screen A-12
Table A-2. Ecological Freshwater Benchmarks for Dissolved Inorganic Arsenic
(ug/I.) A-15
Table A-3. Sediment Screening Benchmarks Identified in ORNL RAIS Database A-18
Table A-4. Ecological Soil Benchmarks for Inorganic Arsenic, CAS No. 7440-38-2 A-23
Table A-5. Screening Soil Benchmarks for Cadmium, Thresholds for Effect A-25
Table A-6. Soil Screeing Benchmarks for Methyl Mercury A-26
Table A-7. Soil Ecological Screening Levels for Methyl Mercury from Los Alamos
National Laboratory A-27
Table A-8. Overview of Three Approaches to Hydrogen Fluoride (HF)
Environmental Standards (Hill 1969) A-30
Table A-9. Governmental Air Criteria for Hydrogen Fluoride (HF) to Protect Plants A-32
Table A-10. Adverse Effects in Terrestrial Plants Following Short-term Exposures to
HF A-3 5
Table A-l 1. Adverse Effects in Terrestrial Plants Following Longer-term Exposure
to HF A-3 7
Table A-12. Toxicity Equivalency Factors (TEFs) for Surface Waters, Soils,
Sediments, and Mammalian Wildlife—POM Compounds Relative to BaP A-51
Table A-13. Toxicity Equivalency Factors (TEFs) for Surface Waters, Soils,
Sediments, and Mammalian and Avian Wildlife—Dioxins Relative to
2,3,7,8-TCDD A-63
Table A-14. TEFs for Oral Exposures of Mammalian Wildlife—POM Congeners
Relative to BaP A-64
Table A-l5. Mink Exposure Factor Values A-68
Table A-16. Mink Diet Assumptions3 A-69
Table A-17. Common Merganser Exposure Factor Values A-70
Table A-18. Common Merganser Diet Assumptions51 A-70
Table A-19. Marine and Freshwater Fish Tissue Concentrations A-73
Table A-20. BAF/BCF Values for Freshwater Fish Exposed to Different Water
Concentrations of Arsenic A-77
Table A-21. Tier 1 Environmental Screening Threshold Emission Rates (ESTER) for
each PB-HAP and each Benchmark Assessed in the Environmental Risk
Screen A-82
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ACRONYMS
AE
assimilation efficiency
GMATC
geometric mean maximum
ALC
aquatic life criteria
acceptable toxic concentration
ARCS
Assessment and Remediation of
HAP
hazardous air pollutant
Contaminated Sediments
HC1
hydrogen chloride
As
arsenic
HF
hydrogen fluoride
BaF
benzo [a] fluoranthene
Hg
mercury
BAF
bioaccumulation factor
Hg++
divalent mercury
BaP
benzo [a]pyrene
HMW
high-molecular-weight
BeP
benzo [e]pyrene
ISQG
interim sediment quality
BCF
bioconcentration factor
guideline
BbF
benzo [b] fluoranthene
Koc
organic carbon partitioning
BjF
benzo [j ] fluoranthene
factor
BMD
benchmark dose
Kow
octanol-water partitioning
BSAF
biota-sediment accumulation
coefficient
factor
LANL
Los Alamos National
BTAG
Biological Technical Assistance
Laboratory
Group
LC50
lethal concentration for 50
bw
body weight
percent of animals tested
CAS
Chemical Abstracts Service
LEL
lowest-effect level
CCME
Canadian Council of Ministers
LOAEL
lowest-observed-adverse-effect
of the Environment
level
Cd
cadmium
LOEC
lowest-observed-effect
DaP
dibenzo [a,i] pyrene
concentration
DMA
dimethylarsenic acid
LOEL
lowest-observed-effect level
DOE
Department of Energy
MACT
maximum achievable control
dw
dry weight
technology
ECxx
effective concentration (xx-
MATC
maximum allowable toxicant
percent response)
concentration
Eco-TEFs
ecological toxicity equivalency
MATL
maximum allowable toxicant
factors
level
EPA
U.S. Environmental Protection
ME
metabolizable energy
Agency
MeHg
methyl mercury
EqP
equilibrium partitioning
MOA
mode of action
ESL
ecological screening level
MMA
monomethylarsonic acid
ESTER
environmental screening
NAWQC
National Ambient Water
threshold emission rate
Quality Criteria
F
fluorine
NEC
no-effect concentration
FIR
food ingestion rate
NEL
no-effect level
FMR
free-living metabolic rate
NOAEL
no-observed-adverse-effect
GE
gross energy
level
GEAE
generic ecological assessment
NOEC
no-observed-effect
endpoint
concentration
GLNPO
Great Lakes National Program
NOEL
no-observed-effect level
Office
OEHHA
California Office of
GLWQI
Great Lakes Water Quality
Environment and Health Hazard
Initiative
Assessment
GMAT
geometric mean
OME
Ontario Ministry for the
Environment
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ORNL
Oak Ridge National Laboratory
OSWER
Office of Solid Waste and
Emergency Response
OW
Office of Water
PAH
polycyclic aromatic
hydrocarbon
PB-HAP
persistent bioaccumulative HAP
PCDD
polychlorinated dibenzo-p-
dioxin
PCDF
polychlorinated dibenzofurans
PEL
probable-effect level
PHE
phenanthrene
PMC
photomodification
POM
polycyclic organic matter
PSC
photosensitization
PYR
pyrene
QSAR
quantitative structure-activity
relationship
RAIS
Risk Assessment Information
System
RCRA
Resource Conservation and
Recovery Act
RfD
reference dose
RTR
Risk Technology and Review
SAV
secondary acute value
scv
secondary chronic value
SESL
soil ecological screening level
SQB
sediment quality benchmark
TCDD
2,3,7,8-tetrachlorodibenzo-p-
dioxin
TCEQ
Texas Commission on
Environmental Quality
TEC
threshold-effects concentration
TEF
toxicity equivalency factor
TEL
threshold-effect level
TL
trophic level
TMA
trimethylarsenic
TOC
total organic carbon
TPY
tons per year
TRIM.FaTE
Total Risk Integrated
Methodology, Environmental
Fate, Transport, and Ecological
Exposure
TRV
toxicity reference value
UF
uncertainty factor
WEFH
Wildlife Exposure Factors
Handbook
WHO
World Health Organization
WQB
water quality benchmark
WQG
water quality guideline
WW
wet weight
YOY
young of the year
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A.1 Introduction
Pursuant to the Clean Air Act, the U.S. Environmental Protection Agency (EPA) developed both
human health and environmental risk screens under its Risk Technology and Review (RTR)
program. The program assesses 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 attachment provides materials supporting EPA's
approach to the effects assessment, as described in Section 3 of the main report.
EPA developed the environmental risk screen to examine the potential for adverse environmental
effects as required under Section 112(f)(2)(A) of the Clean Air Act. Section 112(a)(7) of the Act
defines "adverse environmental effect" as "any significant and widespread adverse effect, which
may reasonably be anticipated, to wildlife, aquatic life, or other natural resources, including
adverse impacts on populations of endangered or threatened species or significant degradation of
environmental quality over broad areas."
The environmental risk screen includes eight HAPs, which we refer to as "environmental
HAPs": six persistent bioaccumulative HAPs (PB-HAPs) and two acid gases. The six PB-HAPs
are arsenic; cadmium; mercury (both inorganic mercury and methyl mercury); dioxins/furans
(referred to herein as dioxins); polycyclic organic matter (POM); and lead. The two acid gases
are hydrogen chloride (HC1) and hydrogen fluoride (HF). The remainder of this attachment is
organized in eight sections.
Section A.2. We first provide supplemental information for the derivation of ecological
benchmarks for surface waters, sediment, surface soils, and air. Benchmarks are
expressed as the concentrations of individual chemicals in the environmental media listed
above. The benchmarks are compared with exposure estimates to screen for risks to
generic ecological assessment endpoints (GEAEs).
Section A.3. For POM and dioxins, we discuss derivation of toxicity equivalency factors
(TEFs) for each group relative to their index chemicals, benzo[a]pyrene (BaP) and
2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), respectively, for surface waters, sediment,
and surface soils.
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Section A.4. We describe the derivation of toxicity reference values (TRVs) for two
wildlife species—mink and common (American) merganser—intended to represent fish-
eating mammals and fish-eating birds, respectively. In contrast to ecological benchmarks,
which are expressed as concentrations of chemicals in environmental media, TRVs are
expressed as ingested doses in milligrams chemical ingested per kilogram wildlife body
weight per day. TRVs are calculated for mink and American merganser based on key
toxicity studies in the literature.
Section A.5. We discuss derivation of ecological toxicity equivalency factors (Eco-TEFs)
for POM and dioxins relative to their index chemicals, BaP and TCDD, respectively, for
TRVs for birds and mammals.
Section A.6. Data on dietary habits and values for exposures factors (e.g., ingestion rates,
body weight) are provided for mink and American merganser. Those data are used to
estimate exposure doses for wildlife from estimates of chemical concentrations in smaller
and larger fish made with the Total Risk Integrated Methodology, Environmental Fate,
Transport, and Ecological Exposure module (TRIM.FaTE).
Section A.7. Empirical data by which a bioaccumulation factor (BAF) was derived for
arsenic in the water column and in benthic sediments are presented.
Section A.8. Screening emission rate thresholds, expressed as tons of chemical per year
(TPY) released by a facility, are presented for each chemical, assessment endpoint, and
environmental medium evaluated in the environmental risk screen.
Section A.9. This attachment concludes with a list of the references cited.
A.2 Ecological Benchmarks
Benchmark concentrations are derived for several GEAEs (U.S. EPA 2003a, 2016a) that are
relevant to the different environmental media. GEAEs can be defined for individual organisms,
specified populations of species, biological communities or assemblages, and ecosystems.
Effects at the population or community level usually are inferred from scientific measurement of
adverse effects at the individual or population level, respectively. Table 2-2 in the main report
presents a list of GEAEs used in the RTR screen. We assess both populations (e.g., mink,
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merganser) and communities (e.g., sediment benthic invertebrates, soil communities, water
column communities) for the RTR assessment.
In this section, we provide supplemental information for the ecological benchmarks described in
Section 3 of the main report. Section A.2.1 describes differences between "population-level" and
"community-level" benchmarks in more detail than in the main report. Section A.2.2 provides
supplemental information supporting the derivation of ecological benchmarks for PB-HAPs.
Section A.2.3 provides background information and data from original studies used to derive the
air concentration benchmarks for plants exposed to HF in air. Derivation of air concentration
benchmarks for plants exposed to HC1 was presented in materials prepared for the previous 2009
EPA Science Advisory Board review of the RTR assessment risk screens (U.S. EPA 2009a) and
is not repeated here.
A.2.1 Population-level and Community-level Benchmarks
For readers familiar with EPA human health risk assessment, for which EPA identifies
benchmarks and TRVs intended to protect individual humans from adverse health effects (e.g.,
noncancer effects) or to ensure risks (e.g., of cancer) are no higher than 1-in-ten thousand to
1-in-one million, the basis of ecological benchmarks and TRVs can be confusing. Federal risk
assessments for endangered or threatened species might be conducted with individual-level
TRVs, as is done for humans.
For nonthreatened wildlife, risks of losing local populations of economically important,
"ecological indicator" species or most "exposed species" often are assessed (Section A.2.1.1).
For other biota, such as invertebrates in aquatic sediments or in soils, community assemblages
generally are assessed for their ability to provide habitat or other ecosystems services (Section
A.2.1.2). Three of EPA's twelve GEAEs (USEPA 2003a) were not used because established
benchmarks are not available (Section A.2.1.3).
Similar to the situation for human health risk assessment, we prefer to use previously established
and peer-reviewed ecological benchmarks and TRVs (Section A.2.1.4). We also considered three
effect levels that could assist EPA decision-makers in interpreting the results of RTR
environmental risk screens (Section A.2.1.5).
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A.2.1.1 Population-level Benchmarks
In general, population-level effects are inferred from available single-species toxicity tests for
the assessment species (or the most closely related species as data allow). The results of single-
species chronic toxicity tests with animals usually have been reported as NOELs (no-observed-
effect levels) and LOELs (lowest-observed-effect levels) for a specified effect. The NOEL and
LOEL (NOEC and LOEC where the C stands for "concentration" instead of level) are identified
by hypothesis testing. The LOEL is the lowest exposure level at which the test-group response
differs from the response of the control group with a probability,/? (usually <0.05), that the
difference is due to chance alone. The NOEL is the highest exposure level at which the test
group response does not statistically differ from that of the control group.
For nonhuman biota, "health" usually is assessed at the population level (Biddinger et al. 2008).
Therefore, generally only effects that readily can be linked to negative population-level
consequences (or higher level impacts such as on communities or ecosystems) have been
considered to represent lowest-observed-aJverse-effect levels (LOAELs) in ecological risk
assessments. Four effect categories for individual-level effects are considered closely linked with
population-level effects: survival, reproduction, development, and growth (Rodier and Zeeman
1994; U.S. EPA 1998). When using both the statistical and biological definitions of "significant"
effects, distinguishing biological significance (e.g., average weight loss of the test group of 10
percent is considered biologically significant) from statistical significance (i.e., less than a
5-percent chance that the difference from the control or reference area is due to chance alone) is
important.
For a given species, if different sensitivities are associated with different lifestages, results from
tests of the most sensitive lifestage are used to represent the species in chronic exposure
scenarios. If some effects occur at lower concentrations than others (e.g., impaired reproductive
success compared with growth), the most sensitive effect is used. If multiple studies on the same
species' most sensitive lifestage report the same most sensitive effect, the geometric mean of the
no-observed-adverse-effect level (NOAEL) values and the geometric mean of the LOAEL values
across tests can be used to represent the NOAEL and LOAEL, respectively, for the species and
endpoint. Otherwise, well-tested species could be over-represented in criteria or benchmark
development (Stephan et al. 1985; U.S. EPA 1999).
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Because of costs, fewer exposure levels typically are used in chronic toxicity tests than are used
in acute toxicity tests. That has resulted in many reports of tests in which the LOAEL is the
lowest exposure level tested or in which the NOAEL is the highest concentration tested (i.e.,
"unbounded" LOAEL and NOAEL values, respectively). The numeric values for unbounded
LOAELs and NOAELs generally have the "<" and ">" signs, respectively, included. Tests in
which both a NOAEL and a LOAEL are identified provide "bounded" values amenable to
evaluating toxicity to the species used in that test.
A recent trend with the advent of the benchmark dose (BMD) approach is to evaluate the
response at all chronic exposure concentrations. The BMD approach now is preferred to establish
points of departure for toxicity when deriving reference values protective of human health,
provided that available data are adequate to use the approach (U.S. EPA 2012a). Similarly, for
ecotoxicity testing, particularly as reported in peer-reviewed journals, the trend is to report
several points along the exposure-response curve for sublethal effects of chronic exposures, for
example an ECos or ECio, an EC20, EC25, or EC30, as well as an EC50. An ECxx is the "effective
concentration" at which a specified effect is observed in xx percent of the test animals.
When EC values are available or can be calculated, and when the lower percent-effect
concentrations have not been extrapolated "too far" below the range of observed responses, risk
assessors consider an EC05 or EC10 to be roughly equivalent to historical NOECs or NOAECs in
aquatic animal toxicity testing (SETAC 1994, p. 6; Sijm et al. 2002, p. 234). The effect level
considered equivalent to LOECs or LOAECs is greater than an EC10, with some risk assessors
citing an EC20 (Anderson and Norberg-King 1991; Sijm et al. 2002, p. 235) and others indicating
that LOAECs can be equivalent to EC25 or higher EC values, depending on many factors (e.g.,
number of animals per exposure group, number of exposure groups, spacing of exposure
concentrations or doses) (Suter et al. 2000, 2003). The advantages of using all exposure-response
data to fit exposure-response models to estimate low-effect levels instead of using NOAELs and
LOAELs determined by hypothesis testing have been discussed in several texts and EPA
guidance documents (e.g., Efroymson et al. 1997a,b; Suter 1993; U.S. EPA 1998, 2005a).
Assuming the availability of a robust toxicity test for a species of similar or greater sensitivity
than the assessment species, usually a NOAEL (or EC05-EC15) and a LOAEL (or EC15-EC25)
can be defined. For environmental screens, some EPA program offices prefer to use a NOAEL-
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based benchmark (e.g., Superfund). Other offices have preferred using a GMAT—the geometric
mean of the NOAEL and LOAEL, often referred to as a maximum allowable toxicant level
(MATL) or concentration (MATC). The MATC is roughly equivalent to a "threshold for
effects." The LOAEC often is associated with an effect level (e.g., 20-25 percent) that might not
be sustainable for a local population, depending on species, its life history, sample sizes in the
toxicity experiment, and other factors (Suter et al. 2000, 2003). Generally, however, the NOAEL
and LOAEL are within one order of magnitude of each other in chronic experiments; hence, the
utility of calculating the geometric mean between them is limited.
A.2.1.2 Community-level Benchmarks
Usually, ecological communities are valued by humans for the services they provide to humans,
to wildlife, to valued species, to landscapes, or to functioning of ecosystems in general (Daily
1997; NRC 2004). For example, soil invertebrate communities are needed to recycle nutrients
and to aerate soils. Measureable attributes of a soil invertebrate community that might influence
its provision of those services include the presence and abundance of one (or more) key
organism(s) (e.g., earthworms) or a diversity of organisms. Benthic (sediment-dwelling)
invertebrates in lakes and rivers are important for recycling detritus and in providing food for
fish communities.
Protection of ecosystem services provided by ecological communities usually requires an
adequate number, abundance, and diversity of different species present to perform key ecological
functions despite natural variation in local conditions (e.g., weather). For example, soil
invertebrate communities generally require earthworms for soil aeration and conditioning to
support plant life adequately; however, a diversity of other soil invertebrates assist. Benthic
communities often require invertebrates that graze on algae or detritus to support higher tropic
levels. To support fisheries, surface waters require a diversity of potential prey species, including
smaller fish (e.g., minnows), young-of-year fish, and invertebrates (e.g., aquatic insect larvae
such as midge and mayfly larvae).
For most ecological communities to provide an appropriate structure (e.g., tree canopy with
understory) and to serve various functions (e.g., as bird habitat, flood protection), not all species
in the community are required. In most ecosystems, several species perform similar or
overlapping functions, and loss of one does not necessarily mean loss of the ecological service it
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provides (this is particularly true of benthic invertebrates and plant communities). Some
keystone species, however, are critical to community structure and function. Loss of those (e.g.,
sea otters consuming sea urchins in kelp beds, blue mussels occupying space in the intertidal
zone, wolves feeding on other mammals on the prairies) can profoundly change the presence and
abundance of other major species and thus profoundly change the structure of the ecosystem.
For sediments, exposure-effect data for some chemicals directly relate measures of benthic
community structure (e.g., related to species diversity and abundance) to the concentration of
specific chemicals. For water-column and soil-based communities, on the other hand, exposure-
response functions generally are not available for community structure or function. Thus, EPA
has used the premise that community structure (and therefore function) is unlikely to be affected
if fewer than 5 percent of species (Office of Water [OW], U.S. EPA 1998; Stephan et al. 1985)
or 10 percent of species (Solomon and Takacs 2002; Efroymson et al. 1997a,b) in the community
might be locally extirpated. The rationale for allowing 5 or 10 percent of species to be affected,
and potentially to disappear from a local community, is the concept of ecosystem resiliency, that
is, the functional redundancy of groups of species (Solomon and Takacs 2002; van Straalen and
van Leeuwen 2002).
Functional redundancy in most ecosystems has evolved owing to natural fluctuations in
environmental conditions and has been demonstrated in several experimental multispecies tests
(Solomon and Takacs 2002). In general, such experiments suggest that the 5-percent species-
protection level does protect ecosystem structure and function against significant changes
(Posthuma et al. 2002). Identifying upper percentile species "protection" benchmarks, however,
requires testing of many phylogenetically distinct species; therefore, derivation of community-
level benchmarks often is precluded for chemicals for which few species have been tested.
A.2.1.3 Assessment Endpoints Not Used in RTR Environmental Screen
Nine GEAEs (U.S. EPA 2003a) used in the RTR environmental screen are listed in Table 2-2 of
the main report. We evaluated, but did not use, the remaining three EPA GEAEs for the
environmental risk screen (Table A-l):
• Animals exposed to airborne HAPs by inhalation,
• Microbial community in soils, and
• Amphibians and reptiles in their respective habitats.
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Table A-l. Generic Ecological Assessment Endpoints Not Used in the Nationwide RTR
Environmental Risk Screen
Exposure
Media
No.
Assessment Endpoint
Entities
Relevant Attributes
Benchmark
Air
10
Maintain local populations of
wildlife and aboveground
invertebrates exposed to
airborne HAPs via inhalation
Birds, mammals,
bees, butterflies,
etc.
Individual survival,
growth and development;
area contaminated
No avian or invertebrate
data available
11
Maintain microbial function in
soils (e.g., nitrogen fixation,
decomposition of detritus to
nutrients)
Assemblages of
bacteria, fungi
Species diversity;
decomposition rate for
leaf litter; "soil" oxygen
consumption rates; area
contaminated
No consensus
benchmarks available
Other
12
Maintain local populations of
amphibians and reptiles
(aquatic-stage amphibia should
be covered by ambient water
criteria)
Frogs,
salamanders,
toads, turtles,
lizards
Individual survival,
growth and development;
area contaminated
No consensus
benchmarks available;
cold blooded, food
ingestion rates
substantially lower than
for birds and mammals
A.2.1.4 Preferred Sources of Benchmarks
We prefer to use established and peer-reviewed ecological benchmarks when available.
Benchmarks for sediments, surface waters, and soils initially were identified using the Oak
Ridge National Laboratory (ORNL) Risk Assessment Information System (RAIS)
(http://rais.ornl. gov/). The ORNL RAIS database is maintained by the Department of Energy (DOE)
for use in its risk assessments at hazardous waste sites. It includes virtually all TRVs and
benchmarks developed to date that might be used by federal agencies in the United States and
several other countries to assess risks to human health and the environment (ecological
receptors). RAIS therefore provides "one-stop shopping" to identify the availability of and
values for ecotoxicity benchmarks for chemicals of concern to U.S. regulatory communities.
All screening-level benchmarks available from Suter and Tsao (1996), which was a key source
of benchmarks for the Coke Oven MACT Residual Risk Assessment (U.S. EPA 2003b), are
included in RAIS, as are the other sources of benchmarks used in that assessment (e.g., U.S. EPA
National Ambient Water Quality Criteria [NAWQA], EPA Region 4 values, National Oceanic
and Atmospheric Administration benchmarks, Florida Department of Environmental Protection
benchmarks). Once we identified ecological benchmarks in RAIS, we obtained the original
sources to confirm values. Our most recent query of RAIS was in August 2016, to check for
updates and possibly new benchmarks; we found both.
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Finally, we established a hierarchy of preferred benchmark sources to enable selection of
benchmarks for each environmental HAP for each ecological assessment endpoint. In general,
we used EPA sources at a programmatic level (e.g., OW, Superfund Program), if available. If
not, we used EPA benchmarks used in regional programs (e.g., region-specific Superfund). If
benchmarks were not available at a programmatic or regional level, we used benchmarks
developed by other federal agencies (e.g., DOE), state agencies, or Canada.
A.2.1.5 Effect Levels
In our review of existing benchmarks derived by EPA program offices, EPA regions, other
agencies, and states, we found that for some environmental media, notably sediments,
benchmarks had been established for two or three different effect levels, not just a "threshold for
effects." Several physical attributes of sediments can modify the response of biota living in them.
These include pH, sediment particle size, interstitial pore size, organic carbon content, acid
volatile sulfide, content, sediment depth, and characteristics of benthic organisms (e.g., sizes,
method of feeding, depth of burial, mobility). Therefore, over the past several decades, sediment
benchmarks often have been defined at three different levels of effect: no-effect level (NEL: low
probability of changes in the structure or function of the benthic community); threshold-effect
level (TEL: concentrations above threshold might cause adverse effects in structure and function
of benthic community); and probable-effect level (PEL: high probability of frank changes in
community structure, function, and provision of ecosystem services).
We therefore decided to look for benchmarks that might represent all three effect levels (i.e.,
NEL, TEL, and PEL) for each exposure medium/GEAE/chemical combination. Only TELs were
available for most benchmarks; we included NEL and PEL values, if available, to provide more
information to EPA decision-makers who need to consider whether adverse ecological effects are
significant and widespread.
A.2.2 Ecological Benchmarks for Persistent and Bioaccumulative
Hazardous Air Pollutants (PB-HAPs)
Ecological benchmarks for PB-HAPs are needed for three environmental media: the water
column in lakes (Section A.2.2.1), the sediment bed in lakes (Section A.2.2.2), and surface soils
in terrestrial environments (Section A.2.2.3).
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A.2.2.1 Water-Column Benchmarks
For organisms that live primarily in the water-column of aquatic ecosystems, EPA's NAWQC-
ALC (aquatic life criteria) are used as available (Stephan 1985, 2002; U.S. EPA 2002, 2016b).
According to Suter and Tsao (1996), the acute NAWQC-ALC are considered "upper" screening
levels in EPA's Superfund program—which we interpret to mean probable effect levels if
associated with continuous long-term (chronic) exposures. The chronic NAWQC-ALC are
considered "lower" screening-level benchmarks in EPA's Superfund program (Suter and Tsao
1996). Given the methods by which both acute and chronic NAWQC-ALC are derived, we
interpret the chronic NAWQC-ALC to represent a threshold for adverse effects in aquatic
communities (water-column compartment) rather than an NEL.
For chemicals for which available data do not cover the taxonomic groups required to establish
NAWQC, EPA's OW established a Tier II approach (not to be confused with the RTR ecological
or human health Tier 2 assessment) allowing derivation of a secondary acute value (SAV) and a
secondary chronic value (SCV) based on toxicity data for fewer taxonomic groups than the eight
specified for NAWQC. The Tier II approach was developed for the Great Lakes Water Quality
Initiative (GLWQI) (U.S. EPA 1993a). Depending on the number of taxa for which acute
toxicity data are available, a sliding scale of uncertainty factors (UFs) is applied to the lowest
acute and chronic toxicity values to estimate the Tier II SAVs and SCVs. EPA's Superfund
program adopted the Tier II SAV methodology from the GLWQI, but on occasion varies its
approach to calculating SCVs from SAVs when chronic aquatic toxicity data are limited.
For chemicals for which NAWQC-ALC and Tier II secondary values were not available, we
turned to benchmarks developed by EPA Regions 3, 4, 5, or 6.
We describe the sources of the TELs and PELs (acute and chronic criteria) for the PB-HAPs
below. For arsenic, we present our review of available data in detail to document our approach.
For cadmium, mercury (divalent and methyl), POM, and dioxins, we simply present the
benchmarks selected based on the preferred hierarchy of sources.
Arsenic (As) Surface Water Column Screening Benchmarks
EPA derived NAWQC-ALC for arsenic (III). No data are available to determine whether the
effects of arsenic (III) and (IV) are additive (U.S. EPA 1995a). Therefore, the values are applied
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to total dissolved inorganic arsenic. The multiple freshwater benchmarks identified in DOE
ORNL RAIS are listed in Table A-2.
Table A-2. Ecological Freshwater Benchmarks for Dissolved Inorganic Arsenic (jig/L)
Name of Benchmark
Arsenic (III)
Arsenic,
Inorganic
Canadian WQG Surface Water Screening Benchmark
NA
5
U.S. EPA Region 4 Acute Surface Water Screening Benchmark
360
360
U.S. EPA Region 4 Chronic Surface Water Screening Benchmark
190
190
U.S. EPA NAWQC Acute Criterion
NA
340
U.S. EPA NAWQC Chronic Criterion
NA
150
U.S. EPA OSWER (Superfund) Water Quality Screening Level
NA
190
U.S. EPA Region 5 ESL Surface Water Screening Benchmark
NA
148
U.S. EPA Region 6 FW Surface Water Screening Benchmark
NA
190
Abbreviations and Acronyms: ESL = ecological screening level; FW = freshwater; NAWQC = National Ambient Water
Quality Criteria (U.S. EPA, for the protection of aquatic life); OSWER = Office of Solid Waste and Emergency
Response (Superfund, U.S. EPA); NA = not available; |jg/L = micrograms per liter; WQG = water quality guideline
Source: Department of Energy (DOE) Oak Ridge National Laboratory (ORNL) Risk Assessment Information System
(RAIS) Ecological Benchmark Tool. Listed in order of RAIS output. Marine values excluded.
The acute and chronic NAWQC for freshwater aquatic life, 340 and 150 |ig/L, respectively, are
applicable nationwide. Therefore, they were selected as the PEL and TEL freshwater
benchmarks for arsenic (listed in Table 5-1 of the main report).
Most benchmarks identified by RAIS are similar for acute and chronic exposures; an exception is
the Canadian water quality guideline (WQG) of 5 |ig/L. It was derived from the 50-|ig/L arsenic
concentration that reduced growth in one algal species by 50 percent (Vocke et al. 1980). That
value was multiplied by a safety factor of 0.1 to calculate the Canadian WQG (Canadian Council
of Ministers of the Environment [CCME] 1991). In surface waters, many different algal species
can provide the same ecological services. Thus, in the field, the loss of a single algal species does
not necessarily alter the ecological structure or function of the aquatic community. We therefore
considered the Canadian WQG to be too conservative for the RTR assessment.
Cadmium (Cd) Surface Water Column Screening Benchmarks
Cadmium is one chemical for which we found a 2016 revision to the NAWQC in our review of
benchmarks in RAIS: chronic criterion (TEL) of 0.72 |ig/L and acute criterion (PEL) of 1.8 |ig/L
dissolved Cd assuming water hardness of 100 mg/L as CaCCb (in Table 3-1 of the main report;
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U.S. EPA 2016c). EPA's NAWQC for the protection of aquatic life for cadmium depend on
water hardness (U.S. EPA 2001a).
Divalent Mercury (Hg++) Surface Water Column Screening Benchmarks
For inorganic, divalent mercury (e.g., dissolved mercuric chloride), EPA's NAWQC are
0.77 |ig/L for the chronic criterion and 1.4 |ig/L for the acute criterion (U.S. EPA 2016b) (listed
in Table 3-1 of the main report). The 1995 criteria (U.S. EPA 1995b) were updated by
multiplying the criteria by 0.85 to account for the fraction dissolved in water, as per guidance
(U.S. EPA 1993b) that was not widely available in 1995.
Methyl Mercury (MeHg) Surface Water Column Screening Benchmarks
Facilities in RTR source categories emit inorganic mercury, which deposits to surface waters and
soils, and from soils, runoff and erosion transport it to the lake, where it enters sediments.
Although the divalent mercury is methylated primarily in sediments, some net methylation also
occurs in surface soils. TRIM.FaTE estimates bioaccumulation of MeHg through the aquatic
food chain, predicting concentrations in the various biotic compartments, particularly fish.
EPA's OW decided to publish its NAWQC criteria for MeHg as concentrations in fish rather
than as concentrations in water, because measured BAFs for MeHg in surface waters vary
substantially across lakes. Thus, we could have compared TRIM.FaTE-estimated concentrations
of MeHg in fish with the NAWQC MeHg concentrations in fish. Instead, we chose to identify
MeHg concentrations in the water column to serve as benchmarks for the aquatic community.
EPA Region 4 cites Suter and Tsao (1996) as its source for a Tier II SCV (chronic, TEL) of
0.0028 |ig/L and Tier II SAV (acute or PEL) of 0.099 |ig/L (U.S. EPA 2015) (listed in Table 5-1
of the main report). Suter and Tsao (1996) followed the EPA GLWQI guidance for deriving Tier
II SCV and SAV values (U.S. EPA 1995b).
POM—Benzo[a]pyrene (BaP) Surface Water Column Screening Benchmarks
Data available for BaP are insufficient for deriving an EPA NAWQC. BaP is highly lipophilic;
thus, toxicity testing for aquatic organisms is difficult because toxicity might not be reached at
the limit of solubility. Suter and Tsao (1996) calculated a Tier II SCV and SAV using EPA
GLWQI (1993a) guidance, and other groups have adopted their values. The SCV (chronic TEL)
of 0.014 |ig/L has been adopted by EPA Region 5 (U.S. EPA 2003c) and the State of Texas
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(TNRCC 2001), and Region 6 recommends its use to its risk assessors (ORNL RAIS).11 The
SAV (acute, PEL) of 0.24 |ig/L, calculated by Suter and Tsao (1996) has not been adopted by the
EPA regions, but is included in the RTR ecological benchmarks rather than having no PEL
freshwater benchmark.
Dioxins—2,3,7,8-TCDD Surface Water Column Screening Benchmarks
Dioxins also are lipophilic and difficult to test for aquatic toxicity; thus, no NAWQC are
available for TCDD. Nonetheless, EPA Region 4 developed chronic and acute freshwater
screening values for TCDD of 1E-05 |ig/L and 0.1 |ig/L, respectively (U.S. EPA 2001b) (in
Table 3-1 of the main report).
A.2.2.2 Sediment Benchmarks
This section describes the selection of sediment benchmarks for arsenic, cadmium, divalent
mercury, methyl mercury, BaP for POM, and 2,3,7,8-TCDD for dioxins. We demonstrate our
approach using arsenic, and provide briefer accounts for the remaining five PB-HAPs.
Arsenic (As) Sediment Screening Benchmarks
Many groups and investigators have developed chronic sediment quality criteria for arsenic,
including those for freshwater sediments listed in Table A-3. Further, many different acronyms
and terms are used to describe the same concepts within sediment benchmark terminology. For
example, some sediment criteria experts consider a TEL or threshold-effects concentration
(TEC) to be a level below which adverse effects are unlikely to occur (MacDonald et al. 2000),
while others define a lowest-effect level (LEL) or a minimal effect threshold as the 15th
percentile of species-specific threshold concentrations across diverse taxa (Jones et al. 1997).
Few studies have examined the success/failure rate of sediment benchmarks to predict sediment
toxicity accurately in the field. The Canadian Council of Ministers of the Environment (CCME
1999a,b) reported that the incidence of effects in sediment samples below the Canadian interim
sediment quality guideline (ISQG) concentration for arsenic (i.e., 5.9 mg/kg dry sediment) is
only 3 percent, which is close to a no-effect incidence rate (CCME 1999a,b).
UEPA recently merged Regional Screening Levels for Chemical Contaminants at Superfund Sites for Regions 3, 6,
and 9 at a single website (https://www.epa. eov/risk/redonal-screening-levels-rsls).
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Table A-3. Sediment Screening Benchmarks Identified in ORNL RAIS Database
Sediment Screening Benchmark
mg/kg dw
Rationale for Not Using*
U.S. EPA ARCS highest NEC (similar to Washington State
MAEL)
92.9
biological meaning of highest NEC for
sediment communities is unclear
U.S. EPA ARCS PEC
33
*selected for use for RTR as probable
effect level
U.S. EPA ARCS TEC
9.79
lower "threshold" available
Canadian ISQG
5.9
Canadian
Canadian PEL
17
lowest PEL
Consensus PEC (MacDonald et al. 2000)
33
*selected for use for RTR as probable
effect level
Consensus TEC (MacDonald et al. 2000)
9.79
lower "threshold" available
Florida Department of Environmental Protection PEL
41.6
Florida conditions unusual
Florida Department of Environmental Protection TEL
7.24
Florida conditions unusual
Ontario Low (Persaud et al. 1993)
6
Canadian
Ontario Severe (Persaud et al. 1993)
33
*selected for use for RTR as probable
effect level
U.S. EPA OSWER (Superfund) ERL
8.2
*selected for use for RTR as threshold for
effect
U.S. EPA Region 4 TEL
7.24
could not verify online
U.S. EPA Region 5 RCRA ESL
9.79
lower "threshold" available
U.S. EPA Region 6 freshwater
5.9
could not verify online
Washington State freshwater MAEL
93
higher than PELs & PECs
Washington State freshwater NEL
57
higher than other threshold levels
U.S. EPA Region 3 BTAG, freshwater
9.8
lower "threshold" available
Acronyms: ARCS = Assessment and Remediation of Contaminated Sediments (Program); BTAG = Biological
Technical Assistance Group (Superfund); EPA = Environmental Protection Agency; ERL = effects range - low; ESL =
ecological screening level; ISQG = interim sediment quality guideline; MAEL = Sediment Impact Zone Maximum
Level; NEC = no-effects concentration; NEL = no-effect level; ORNL = Oak Ridge National Laboratory; PEC =
probable effects concentration; PEL = probable effect level; RCRA = Resource Conservation and Recovery Act; RAIS
= risk assessment information system (Department of Energy); TEC = threshold effects concentration
Abbreviations: mg/kg dw = milligrams arsenic per kilogram dry weight sediment
* Value selected for use in RTR screens; see text.
For purposes of RTR assessments, we selected a threshold-effects benchmark of 8.2 mg[As]/kg
dry weight sediment from EPA's Superfund program, because it is an EPA benchmark (preferred
over DOE ORNL and state and Canadian benchmarks), and we could verify its derivation (U.S.
EPA 1996a). Not all benchmarks included in RAIS can be verified using original sources,
because several sources do not explain derivation of the benchmarks.
Values in Table A-3 associated with benchmark names suggesting that adverse effects are
"probable," likely to be "frequent," or likely to be "severe" range from 33 to 93 mg[As]/kg[dry
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weight (dw) sediment]. With three different groups identifying 33 mg/kg dw sediment as a
probable or severe effects level [i.e., U.S. EPA Assessment and Remediation of Contaminated
Sediments (ARCS program); MacDonald et al. 2000; Ontario (Persaud et al. 1993)], we
recommend 33 mg[As]/kg dw sediment to represent the probable-effect benchmark.
Cadmium (Cd) Sediment Screening Benchmarks
Although we would prefer to have NEL, TEL, and PEL benchmarks from the same source for
sediments; that was not possible for cadmium. EPA has recommended only a TEL (U.S. EPA
1996a, OSWER - Superfund Program) of 1.2 mg[Cd]/kg dw sediment. The CCME (1999b) had
recommended an ISQG of 0.6 mg[Cd]/kg dw sediment, but more recently defined an even lower
effect level, called the rare-effect level (EC & MDQuebec 2007) of 0.33 mg[Cd]/kg dw
sediment. The CCME (1999b) PEL is 3.5 mg[Cd]/kg dw sediment.
Divalent Mercury (Hg++) Sediment Screening Benchmarks
For Hg++, we found no benchmarks representing an NEL but many benchmarks that could be
interpreted as TELs and PELs. Given the similarity of the benchmarks, we could not clearly
recommend one over another and decided, in this case, to average the values across sources to
develop a TEL and a PEL for sediments.
For a TEL, we calculated the arithmetic mean of eight available benchmarks for inorganic (or
total) mercury. That approach gives equal weight to the eight sediment benchmarks:
• U.S. EPA (1996b) - Great Lakes National Program Office (GLNPO) Assessment and
Remediation of Contaminated Sediments (ARCS) program - 0.18 mg/kg[dry weight
sediment] (mg/kg dw);
• MacDonald et al. (2000) - Consensus Threshold Effects Concentration - 0.18 mg/kg dw;
• Florida Department of Environmental Protection (MacDonald 1994) - sediment
screening benchmark -0.13 mg/kg dw;
• U.S. EPA (1996a) OSWER - Ecotox Threshold sediment screening level - 0.15 mg/kg
dw;
• U.S. EPA (2015) Region 4 - sediment screening benchmark - 0.13 mg/kg dw;
• U.S. EPA (2006a) Region 3 Biological Technical Assistance Group (BTAG) - sediment
screening benchmark - 0.18 mg/kg dw;
• U.S. EPA (2003c) Region 5 - Resource Conservation and Recovery Act (RCRA) -
sediment screening benchmark - 0.174 mg/kg dw; and
• U.S. EPA Region 6 (TNRCC 2001) - sediment screening benchmark - 0.174 mg/kg dw.
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The benchmarks listed above range from 0.13 mg/kg dw sediment to 0.18 mg/kg dw, with
arithmetic mean 0.16 mg/kg dw sediment (in Table 3-1 of the main report).
For a PEL, we averaged the four values available for freshwater sediment probable effect levels:
• U.S. EPA (1996b) - GLNPO ARCS probable effects concentration - 1.06 mg/kg dw;
• MacDonald et al. (2000) - Consensus probable effects concentration - 1.06 mg/kg dw;
• Florida Department of Environmental Protection (MacDonald 1994) - PEL - 0.70 mg/kg
dw; and
• CCME (2001) - PEL - 0.486 mg/kg dw sediment.
The benchmarks listed above range from 0.486 mg[Hg]/kg dry sediments to 1.06 mg[Hg]/kg dw,
with arithmetic mean 0.84 mg [Hg]/kg dry sediments (in Table 3-1 of the main report).
Methyl Mercury (Xlellg) Sediment Screening Benchmarks
We identified no benchmarks for MeHg in sediments. MacDonald et al. (2000) estimated a
consensus TEC of 0.2 mg[total Hg]/kg dry sediments and a PEC of 1 mg[total Hg]/kg dry
sediments (rounded to one significant digit). MeHg generally is 4 percent (range 1 to 11 percent)
of total Hg in sediments (Krabbenhoft et al. 1999). Thus, we could have set benchmarks at 0.005
and 0.04 mg[MeHg]/kg dry sediments if we had confidence in the proportion of MeHg in
sediments. TRIM.FaTE, however, estimates mercury transformations between Hg++ and MeHg
for the environmental input parameters (e.g., pH, chloride ions, fraction organic carbon) and
empirical values for equilibrium partitioning between aqueous phase and particulate phase
chemical. Thus, over-riding those calculations based on the data reported by Krabbenhoft et al.
(1999) would not have been reasonable. We therefore kept the TEC and PEC values estimated by
MacDonald et al. (2000). Because the TEC and PEC values for Hg++ (see previous paragraph)
are lower than for MeHg, and because most Hg in sediments is likely to be inorganic, the
sediment benchmarks for Hg++ are the limiting benchmarks. Effectively, we have no benchmarks
for MeHg in sediments.
POM—Benzo[a]pyrene [BaP] Sediment Screening Benchmarks
Several freshwater sediment benchmarks are available for BaP for the NEL, TEL, and PEL. For
the NEL, we used the value of 0.032 mg[BaP]/kg dry sediments, which is recommended by
CCME (1999b) and Region 6 (TNRCC 2001). Three sources recommend a TEL of
0.15 mg[BaP]/kg dry sediments: GLNPO ARCS (U.S. EPA 1996b); Region 3 BTAG (U.S. EPA
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2006); and MacDonald et al. (2000). The same three sources recommend a PEL of
1.5 mg[BaP]/kg dry sediments (in Table 3-1 of the main report).
Dioxins—2,3,7,8-TCDD Sediment Screening Benchmarks
Dioxins are difficult to test for aquatic toxicity, because they basically do not partition to the
water column or to sediment pore water. In addition, they are toxic at very low concentrations
that are difficult to measure. We did identify TELs for 2,3,7,8-TCDD in sediments of 8.5E-07
mg/kg dry sediment (U.S. EPA 2006, Region 3), 2.5E-06 mg/kg dw (U.S. EPA 2001b, Region
4), and 1.2E-07 mg/kg dw (U.S. EPA 2003c, Region 5). The arithmetic mean of those three
benchmarks rounded to two significant digits is 1.2E-06 mg/kg dw (in Table 3-1 of the main
report). A geometric mean would be more conservative; however, the Los Alamos National
Laboratory (LANL) recently has made its database of benchmarks available via the internet,
recommending a screening LOAEL value of 8.5E-06 mg/kg dw (LANL 2015). We attempted to
verify the derivation of that value; however, the references are to previous LANL versions of the
database (e.g., LANL 2012 and earlier), rather than to original toxicity studies. Thus, we retain
the arithmetic mean of three EPA TCDD benchmarks for sediments.
Initially, we found no benchmarks for an NEL or a PEL for TCDD. In 2016, we found a NOAEL
of 8.5E-07 mg/kg dw in the LANL (2015) database and a PEL of 0.022 (rounded to two
significant digits) mg/kg dw for Canadian sediments (CCME 2001; previously overlooked). We
have not verified the derivation of the NEL or PEL; therefore, they each represent a single point-
estimate of a sediment benchmark, in contrast to the TEL, which represents three separate point-
estimates of a sediment benchmark.
A.2.2.3 Soil Benchmarks
For soils, EPA's national Superfund Program (formerly called the Office of Solid Waste and
Emergency Response or OSWER) Eco-Soil Screening Levels (Eco-SSLs, U.S. EPA 2005c) were
selected, if available, as the soil ecological benchmarks for the ecological risk environmental
screens for the RTR assessment. The OSWER Eco-SSLs are the only EPA-vetted ecological
toxicity screening benchmarks for soils established for use by the Agency nationwide. For
chemicals for which no Eco-SSLs were available, EPA regional sources of soil ecotoxicity
benchmarks were sought (e.g., Regions 4, 5, and 6). The general methods for deriving those
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benchmarks differ from the methods EPA used to derive Eco-SSLs, and some are not available
via the internet.
For some chemicals, EPA regions use soil ecological benchmarks developed by other agencies
such as DOE or one of the states in the region. If not specified in published information, we
assumed that whichever group of organisms was most sensitive to the chemical in soil (e.g.,
earthworms, insect larvae, plant roots, and in some cases herbivorous animals consuming plants
grown in the contaminated soil) was likely to have been the basis for a soil screening criterion. If
an EPA region and another non-EPA agency were identified as using the same numeric
benchmark value, the sources that designated that value are acknowledged. Finally, if the only
source providing a screening-level benchmark for soils was not an EPA office or region (e.g.,
DOE, ORNL, Environment Canada, a state), the value was used as last priority.
Arsenic (As) Soil Screening Benchmarks
Arsenic has not been demonstrated to bioaccumulate significantly in soil invertebrates. Data
compiled to develop and validate bioaccumulation models for earthworms indicate that arsenic
concentrations in earthworms tend to be approximately one order of magnitude lower than the
concentration in soils on a mg/kg dry weight basis (i.e., both soils and earthworm arsenic
concentrations measured per unit dry weight; Sample et al. 1998). Thus, for arsenic, the Eco-SSL
for plants is lower than the Eco-SSLs for ground-feeding birds and mammals that ingest soil
invertebrates. In contrast, the most appropriate Eco-SSLs for bioaccumulative substances (e.g.,
mercury, cadmium) are for birds or mammals consuming soil invertebrates. The lowest arsenic
Eco-SSL value for plants, 18 mg[As]/kg[dry weight soil] (Table A-4), is the geometric mean of
the maximum allowable toxicant concentration (MATC) for three plant studies (with ryegrass,
cotton, and rice) that EPA judged to have appropriate arsenic bioavailability.
The three studies included both a low pH (5.6) and organic matter content (0.7%) and a higher
pH (7.9) and organic matter content (1.1%) (Table 3.1 in U.S. EPA 2005b). For each of the three
plant species, the MATC represents the geometric mean of the experimentally determined
LOAEL and the NOAEL for plant growth.
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Table A-4. Ecological Soil Benchmarks for Inorganic Arsenic, CAS No. 7440-38-2
Name of Benchmark
mg/kg dw soil
U.S. EPA OSWER Eco-SSL Plants
18
U.S. EPA OSWER Eco-SSL Avian
43
U.S. EPA OSWER Eco-SSL Invertebrate
NA
U.S. EPA OSWER Eco-SSL Mammalian
46
U.S. EPA Region 6 Earthworms Surface Soil Screening Benchmark
60
U.S. EPA Region 6 Plants Surface Soil Screening Benchmark
37
U.S. DOE ORNL Invertebrates Soil Screening Benchmark
60
U.S. DOE ORNL Microbes Soil Screening Benchmark
100
U.S. DOE ORNL Plants Screening Benchmark
10
Abbreviations and Acronyms: CAS = Chemical Abstracts Service; dw = dry weight; Eco-SSL = U.S. EPA Ecological
Soil Screening Level (Superfund); ORNL = Oak Ridge National Laboratory; DOE =Department of Energy; mg/kg dw
soil = milligrams arsenic per kilogram dry weight soil; NA = not available
The avian Eco-SSL (woodcock) is based on one of four toxicity experiments that both met
EPA's criteria for study acceptability and examined growth and reproduction in birds. Of those,
only one experiment identified NOAELs for both growth and reproduction at 2.24 mg[As]/kg
[body weight]-day (arsenate oxide) in domestic chickens (Holcman and Stibilj 1997, as cited in
U.S. EPA 2005b). Camardese et al. (1990) identified a lower LOAEL of 1.49 mg/kg-day
(arsenate) for growth for mallard duck; however, because that study did not identify a NOAEL,
EPA used 2.24 mg/kg-day as a TRV for birds (U.S. EPA 2005b). Using that TRV and back-
calculating a soil concentration based on woodcock consumption of arsenic with a diet of
earthworms yields an Eco-SSL for ground-feeding birds of 43 mg/kg dw soil (U.S. EPA 2005b).
More toxicity studies of acceptable quality were available for mammals than for birds. From 55
mammalian studies, over 100 toxicity values were identified. EPA calculated the geometric mean
of 27 bounded12 NOAELs for reproduction and growth to be 2.47 mg[As]/kg-day. One study
using beagle dogs (initially 7-8 months old) identified a bounded LOAEL of 1.66 mg/kg-day
(Neiger and Osweiler 1989, as cited in U.S. EPA 2005b), which is lower than 2.47 mg/kg-day.
EPA therefore used the NOAEL associated with the dog study, 1.04 mg/kg-day, to calculate a
12
A bounded NOAEL is one from a study in which a LOAEL was identified. A bounded LOAEL is one from a
study in which a NOAEL was identified.
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TRV for mammals. Back-calculation of a soil concentration for a shrew that consumes
invertebrates in soils yielded an Eco-SSL for ground-feeding mammals of 46 mg[As]/kg dw soil.
Five other LOAELs are from studies [two in mice measuring growth and reproduction (total four
LOAELs), and one in Guinea pigs measuring growth] that did not identify a NOAEL and for
which the LOAELs for reproduction, growth, or survival were lower than 2.47 mg[As]/kg-day
(see Figure 6.1 in USEPA 2005b). Those were not considered in deriving the Eco-SSL for
mammals because they were not bounded by a NOAEL identified in the same experiment. Thus,
the Eco-SSL for soils for shrews might be based on a NOAEL that is not necessarily protective
of some sensitive species or sensitive effect endpoints.
Cadmium (Cd) Soil Screening Benchmarks
EPA has derived four Eco-SSLs for cadmium (Table A-5). As is often the case for Eco-SSLs for
bioaccumulative substances, the benchmarks protective of birds and mammals that feed on soil
invertebrates are lower (more restrictive) than those for plants and invertebrates. That is because
chemicals bioaccumulate from soils to the soil invertebrates that then are consumed by the
wildlife. Although nominally based on NOAELs for adverse effects on reproduction and growth,
the Eco-SSLs for insectivorous wildlife are based on the geometric mean of NOAELs across
both types of effect and across all species for which data are available within each group, birds or
mammals, respectively.
For the cadmium Eco-SSL for birds, most (15/20) NOAELs used to derive the geometric mean
NOAEL came from toxicity tests using chickens and quail (Order Galliformes) with a minority
(4/20) of toxicity values from mallard duck and one value from wood duck (Order Anseriformes
includes ducks, mergansers, and other waterfowl). The avian geometric mean NOAEL calculated
for the Eco-SSL is 1.47 mg[Cd]/kg bw-day. Back-calculating a soil concentration that
corresponds to the avian TRV for woodcock consuming 100% earthworms yields an Eco-SSL of
0.77 mg[Cd]/kg dw soil (U.S. EPA 2005d) (Table A-5). We calculated a NOAEL and LOAEL
for piscivorous birds (in Section A.4 below) as 1.0 and 0.7 mg[Cd]/kg bw-day, respectively. We
conclude that the avian Eco-SSL, based on the geometric mean of NOAELs across species and
endpoints, is similar to a LOAEL for ducks and mergansers as discussed in Section A.4.
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Table A-5. Screening Soil Benchmarks for Cadmium, Thresholds for Effect
Benchmark Type
Value
Units
Benchmark
Reference
Mammals (shrew)
0.36
mg [total Cd]/kg dry
weight soil
Eco-SSL for four soil
communities specified
under Benchmark Type
U.S. EPA 2005d,
OSWER
Birds (American woodcock)
0.77
Plants
32
Invertebrates
140
Acronym: Eco-SSL = U.S. EPA Ecological Soil Screening Level (Superfund)
For the cadmium Eco-SSL for mammals, the geometric mean of 23 NOAELs for reproduction
(21 from rats and 2 from mice) and 59 NOAELs for growth (most from rats, but a few from
mice, cattle, sheep, pigs, dogs, and voles) of 1.86 mg[Cd]/kg bw-day turned out to be higher than
the highest bounded NOAEL (0.77 mg cadmium/kg bw-day) below the lowest bounded LOAEL.
EPA therefore set the TRV used to calculate the Eco-SSL for mammals to 0.77 mg cadmium/kg
bw-day. Back-calculating the corresponding soil concentrations for shrews that consume 100%
earthworms resulted in an Eco-SSL of 0.36 mg[Cd]/kg dry soil (U.S. EPA 2005d). The values
we identified as the LOAEL and NOAEL for mammals for a sensitive species and endpoint (in
Section A.4) are 7.42 and 0.742 mg[Cd]/kg bw-day, respectively. Thus, in this case, the
mammalian Eco-SSL is based on a TRV that is similar to a NOAEL for a sensitive mammalian
species and endpoint.
Divalent Mercury (Hg++) Soil Screening Benchmarks
EPA has not estimated Eco-SSLs for divalent mercury in soils. Inorganic mercury is not
expected to bioaccumulate. Thus, the only soil screening levels that we identified were the EPA
Region 6 recommendation of 0.3 mg[Hg]/kg dry soil for plants (Efroymson et al. 1997a) and the
EPA Regions 4 and 6 recommendation of 0.1 mg[Hg]/kg dry soil for earthworms in soil (U.S.
EPA 2015 and Efroymson et al. 1997b, respectively). See Table 3-1 of the main report.
Methyl Mercury (MeHg) Soil Screening Benchmarks
Methyl mercury is expected to bioaccumulate; however, its concentrations in soils that receive
air deposition of divalent mercury are expected to be low. Nonetheless, some methylation of
mercury can occur in soils, so in 2016, we sought benchmarks for MeHg in soils (Table A-6).
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Table A-6. Soil Screeing Benchmarks for Methyl Mercury
Benchmark Type
Units
Value
Source: Benchmark Name [Comment]
Mammals (shrew)
mg/kg dry soil
0.0068
GMATC values calculated from U.S. EPA (2015) Region 4 SESLs for
mammals and birds [LANL (2012) ECORISK Database Version 3.2]
Birds (robin)
0.0011
Plants
0.3
U.S. EPA (2015) Region 4 cites Efroymson et al. (1997a)
Invertebrates
0.1
U.S. EPA (2015) Region 4
Acronyms: GMATC = geometric mean maximum allowable toxicant concentration = geometric mean of LOAEL and
NOAEL, SESL = soil ecological screening level
In a recent update of its ecological screening benchmarks, Region 4 cited the September 2012
release of the LANL ECORISK (Version 3.2) database as its source of estimated soil-screening
levels for MeHg protective of ground-feeding birds and mammals (U.S. EPA 2015). As of
August 20, 2016, a more recent version of the ECORISK database (Version 3.3) was available
(LANL 2015), which we checked for MeHg soil ecological screening levels (SESLs). We
provide a summary of the derivation of the LANL ECORISK SESLs below. Unlike the EPA
Eco-SSLs, which use a geometric mean of all NOAELs from all studies and all avian species of
acceptable quality for growth and reproduction for which both a NOAEL and LOAEL were
identified, the LANL ECORISK SESLs are based on a single critical study, a sensitive species,
and sensitive endpoints (i.e., according to U.S. EPA 1995b GLWQI Guidelines). After LANL
has selected TRVs for sensitive endpoints and species from the available data, it uses the TRVs
to back-calculate SESLs, as does EPA when deriving Eco-SSLs.
For birds, LANL uses American robin (wide habitat and geographic range) instead of woodcock
as the ground-feeding bird for which to back-calculate a soil concentration. As shown in Table
A-7, the lowest SESLs for American robin are associated with a diet consisting entirely (100%)
of soil invertebrates. That is the same diet assumed for woodcock for U.S. EPA (2007) Eco-
SSLs. Both LANL and EPA assume that the soil invertebrates are earthworms, which
bioaccumulate MeHg from the soils.
LANL (2015) cited the Heinz et al. (1979) study of mallard duck exposed to MeHg in the diet
for three generations. A significant decrease in egg and duckling production was observed at that
0.5 ppm in the diet. Sample et al. (1996) used the food consumption rate from Heinz et al. (1979)
and typical body weights for growing mallards from another data source to convert the 0.5-ppm
MeHg concentration in food to a TRV dose of 0.064 mg/kg-day. Using a LOAEL-to-NOAEL
UF of 10, Sample et al. (1996) estimated a NOAEL of 0.0064 mg/kg-day. Back-calculating the
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corresponding soil concentration for a robin consuming 100 percent earthworms that had
bioaccumulated MeHg from the soil, LANL (2015) estimated a NOAEL and LOAEL of 0.00035
and 0.0035 mg/kg dry soil, respectively (Table A-7).
Table A-7. Soil Ecological Screening Levels for Methyl Mercury from
Los Alamos National Laboratory
Species
Diet
Soil Ecological Screening Level
mg/kg dry soil)
SESL NOAEL
SESL LOAEL
GMATC
American robin
(avian ground-
feeding bird)
100% plants (berries)
0.075
0.75
0.2372
100% soil invertebrates
0.00035
0.0035
0.0011
50:50 plants/soil invertebrates
0.00071
0.0071
0.0022
American kestrel
(avian top carnivore)
100% small mammal flesh
0.0078
0.078
0.0247
50:50 small mammals and soil invertebrates
0.0017
0.017
0.0054
Deer mouse
50:50 soil invertebrates and seeds
0.0063
0.031
0.0140
Montane shrew
100% soil invertebrates
0.0031
0.015
0.0068
Acronyms: GMATC = geometric mean maximum acceptable toxic concentration; calculated in this table as the
geometric mean of the SESL NOAEL and LOAEL. SESL = soil ecological screening level
Because the EPA Superfund Eco-SSLs provide a single SSL for each assessment endpoint, and
because we are using each Eco-SSL as a TEL, having two different LANL SESLs (i.e., a
NOAEL and a LOAEL) would be inconsistent. We therefore calculated the geometric mean of
the NOAEL and LOAEL SESLs (i.e., the GMATC) to represent a TEL for the robin (0.0011
mg/kg dw soil, value in bold in Table A-7).
For mammals, LANL used a montane shrew to represent ground-feeding small mammals. LANL
cited the Verschuuren et al. (1976) toxicity study of rat exposed to MeHg in the diet for three
generations at three dietary concentrations—0.1-, 0.5-, and 2.5-ppm MeHgCl, where Hg makes
up 79.9% of the compound. Reduced pup viability was observed in the 2.5-ppm MeHgCl, and no
adverse effects were observed in the other two groups. LANL (2015) cited Sample et al. (1996)
for the conversion of dietary concentrations to ingested doses based on rat food ingestion rates
(FIRs) and body weights: the chronic TRV NOAEL is 0.032 mg[Hg]/kg bw-day and the chronic
TRV LOAEL is 0.16 mg[Hg]/kg bw-day. Back-calculating the corresponding soil concentrations
for montane shrew consuming 100-percent earthworms that had bioaccumulated MeHg from the
soil, LANL (2015) estimated a NOAEL of 0.0031 mg[Hg]/kg dry soil and a LOAEL of 0.015
mg[Hg]/kg dry soil (listed in the last row of Table A-6). Again, we calculated the geometric
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mean of the NOAEL and LOAEL (i.e., the GMATC) to represent a TEL for the shrew, 0.0068
mg[Hg]/kg dry soil (in bold in Table A-7).
POM—Benzo[a]pyrene (BaP) Soil Screening Benchmark
EPA has developed no Eco-SSLs for BaP, although it has estimated an Eco-SSL for mammals
and an Eco-SSL for invertebrates for HMW polycyclic aromatic hydrocarbons (PAHs) (i.e., 4 or
more fused benzene rings) of 1.1 mg/kg dry soil and 18 mg/kg dry soil, respectively (U.S. EPA
2007). EPA Region 5 has developed a soil screening value for BaP for masked shrew of 1.52
mg/kg dry soil. Because we are using the toxicity equivalency approach to evaluate the joint
toxicity of POM based on their BaP-toxic equivalents, we use the EPA Region 5 value for BaP.
Dioxins—2,3,7,8-TCDD Soil Screening Benchmark
EPA Region 5 estimated an ESL for soils of 2.0E-07 mg/kg dry soil to protect masked shrews
that consume earthworms contaminated with TCDD from soils (U.S. EPA 2003c). LANL (2015)
lists its soil screening levels for montane shrew as a NOAEL of 2.9E-07 mg/kg dry soil and
LOAEL of 1.9E-06 mg/kg dry soil. Those values bracket the Region 5 ESL; therefore, we use
the Region 5 value to represent a TEL for the shrew.
No screening benchmarks were identified for birds or plants exposed to TCDD in soils. For
invertebrates, LANL (2015) calculated a NOAEL of 5 mg/kg dry soil and a LOAEL of 10 mg/kg
dry soil for SELS for TCDD. The geometric mean of 5 and 10 equals 7.1 mg/kg dry soil, which
we use for the soil invertebrate community TEL benchmark.
A.2.3 Hydrogen Fluoride (HF) Air Benchmarks for Terrestrial Plants
Gaseous fluorides, such as HF, are phytotoxic (i.e., toxic to plants). Gaseous HF enters leaves of
plants through the stomata, which generally are open during daylight hours and closed at night.
Gaseous HF is much more rapidly absorbed than fluoride associated with particulates, which do
not diffuse through the stomata. Fluoride absorption is fairly uniform over the entire leaf under-
surface. It readily dissolves and is then transported in ionic form through the apoplastic aqueous
spaces of the mesophyll cell walls driven by transpiration. Thus, fluoride moves via translocation
to the leaf tip and edges where cell necrosis occurs first (Hill and Pack 1983). Leaf tips can
contain up to 100 times more fluoride than the leaf basal section after long-term exposure (Hill
1969; Hill and Pack 1983).
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The most common initial symptoms of fluoride injury are necrotic lesions at leaf tips and edges,
extending toward the leaf base as exposure continues (Hill and Pack 1983). In a few species,
including corn and citrus, chlorosis (i.e., loss of chlorophyll and green color) is evident before
necrosis appears. Although loss of functional leaf area can reduce growth and yield in many
species of plants, a few species show little effect on yield depending on the part of the plant
harvested and the stages at which exposures occurred (e.g., some species are most sensitive
during rapid growth of seedlings or during flowering) (Hill and Pack 1983).
Susceptibility to HF also varies with lifestage of the plant and abiotic factors. For broadleaf
plants, several studies indicate that damage from HF exposure is more pronounced when plant
tissues are expanding or elongating (WHO 2002; Hill 1969). Some pine species are included
among species of concern due to their sensitivity to HF during needle growth (Adams et al.
1956; APIS 2010). Abiotic factors, such as humidity, air temperature, wind (speed and
direction), and soil water content, can influence exposure by modifying the rate of HF absorption
by plants. For example, dry conditions reduce HF absorption due to reduced transpiration and
stomatal conductance (APIS 2010). Excessive rain also can reduce exposure due to "washing,"
while light rain can effectively increase the amount of fluoride deposited on the leaves (Hill
1969). Abiotic factors also can affect inherent plant sensitivity to HF exposures. In the field,
some plants stressed by unfavorable conditions of low fertility and limited water are more
sensitive to HF exposure than the same species grown under more favorable conditions (Hill and
Pack 1983).
The remainder of this section provides background information for the derivation of HF air
concentration benchmarks for terrestrial plant communities (Section 3.2.2 of the main report).
Section A.2.3.1 discusses three distinct approaches to setting limits for plant exposures to HF.
Section A.2.3.2 lists existing regulatory benchmarks for HF in the United States and other
countries. Section A.2.3.3 summarizes exposure-response data for effects of air HF on plants,
both for short-term exposures (e.g., 1-day maximum concentration) and over the longer term
(e.g., average 4-month concentration).
A.2.3.1 Methods of Establishing HF Benchmarks
In theory, environmental standards for HF effects on vegetation could be defined in at least three
ways (Hill 1969): atmospheric fluoride concentrations, vegetation fluoride concentrations, or the
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presence of leaf necrosis or chlorosis. Table A-8 presents the pros and cons of each method
outlined by Hill (1969).
Table A-8. Overview of Three Approaches to Hydrogen Fluoride (HF) Environmental
Standards (Hill 1969)
Approach
Traditional Use/Benefit
Complicating Factors with Hydrogen Fluoride
Atmospheric
concentration
Used frequently in air quality
standards
Simple
Ease of use for control
programs
Inter- and intraspecies variation in effects (lack of data for levels that are
protective of large majority of species for site-specific assessments)
Need to understand contribution of exposure duration
Variation in responses associated with abiotic factors (e.g., rainfall,
humidity, temperature)
Atmospheric fluoride includes total soluble inorganic fluorides (speciation
data and effects data for various species lacking) and might include
fluoride adsorbed to particles in the air
Vegetation
concentration
Useful for protecting wildlife (or
livestock) via plant
consumption
Leaves accumulate most HF
(compared with other plant
parts)
Leaf sampling relatively simple
and cost effective
Need for standardization in:
Leaf age (at time of exposure)
Lifestage (at time of exposure, e.g., fast growth, flower set)
Time of sampling
Species/varieties sampled
Random selection of leaves
Method of analysis
Need to remove F from plant surfaces without leaching F from leaf interior
Fluoride content concentrated along leaf margins and tips
Leaf
appearance
(necrosis or
chlorosis)
Time effective
Summary outcome (no detailed
analysis of complex
variables)
Need qualified/trained personnel
Leaf appearance can be influenced by other (non-HF-related) factors
Need fluoride analysis to confirm
Most existing HF standards are based on plant concentration data collected for what have been
identified thus far as particularly sensitive species and for livestock forage. Hill (1969) noted that
adequate data generally are not available to develop site-specific HF air benchmarks for the
protection of plants. To estimate fluoride concentrations in plants, however, TRIM.FaTE would
need to be parameterized for plant uptake of fluoride from the air and possibly from uptake
through the roots. That level of effort is beyond a Tier 1 or 2 screen for ecological risks.
For RTR ecological risk screens of acid gases, which are conducted using modeled estimates of
ambient air concentrations based on emissions from the regulated source, the most expedient
expression of an air benchmark for HF for plants is as an air concentration. The remaining
sections of this document, therefore, focus on the relationship between HF air concentrations and
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adverse effects in plants. In addition, for purposes of the RTR ecological risk screen, chronic
benchmarks are relevant to the chronic exposure scenarios evaluated.
A.2.3.2 HF Regulatory Levels
Although EPA has not established environmental criteria for HF, at least 13 other countries have
established national environmental criteria or standards (Newman 1984). In the United States, at
least 12 states have established criteria or standards, most based on protecting forage grasses
from accumulating fluoride to concentrations exceeding 35-40 mg[F]/kg dry weight plant. Some
data suggest higher concentrations in forage result in development of fluorosis in cattle/calves
(Newman 1984).
Most of the available criteria or standards are expressed as concentrations in plants, not as
atmospheric concentrations, particularly if the intent is to protect livestock from fluorosis from
fluoride in their forage. Use of plant-based HF concentrations would require a plant-fluoride
uptake model. At this time, TRIM.FaTE is not parameterized for HF uptake in plant
compartments. The remaining discussion focuses on criteria and standards expressed as
concentrations of gas-phase HF in air, not total fluoride in plants. The criteria or standards that
were readily available from Canada and several U.S. states are summarized in Table A-9.
Guidelines to protect vegetation from exposures to HF expressed as air concentrations were first
developed in Canada under the Canadian Environmental Protection Act by Bourgeau and
colleagues in 1996 (EC 1996). To protect vegetation from adverse effects resulting from HF
exposure, CCME (1999c) recommends HF concentrations not exceed 0.4 |ig/m3 air over 30 to
90 days (Alberta Environment 2006; HF concentrations can be higher for shorter exposures).
Environment Canada (EC 1996; CCME 1999c) defined the criteria as:
"The level above which there are demonstrated effects on human health and/or the
environment. It is scientifically based and defines the boundary between the LOAEL and
the NOAEL. It is considered to be the level of exposure just below that most likely to
result in a defined and identifiable but minimal effect. The reference levels have no safety
factors applied to them, as they are related directly to the LOAEL, and are the most
conservative estimates of the effect level." (emphasis added; CCME 1999c)
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Table A-9. Governmental Air Criteria for Hydrogen Fluoride (HF) to Protect Plants
Reference
Specific
Information3
Air Criteria for Hydrogen Fluoride (|jg HF/m3) for Specified Duration
(Averaging Period)
30 min
12 h
24 h
7 d
30 d
70 d
90 d
Canada (EC 1996; CCME
1999c)
Gaseous, growing
season
-
-
1.1
0.5
0.4
-
0.4
Alberta and Manitoba
(Alberta Environment 2006)
Gaseous
-
-
0.85
0.55
0.35
0.2
0.2
Ontario (OME 2004)
Gaseous, growing
season
4.3
-
0.86
-
0.34
-
-
Ontario (OME 2004)
Total, growing
season
8.6
-
1.72
-
0.69
-
-
Ontario (OME 2004)
Total, nongrowing
season
17.2
-
3.44
-
1.38
-
-
Texas Commission on
Environmental Quality
(TCEQ 2009)b
Gaseous
-
-
3.0
-
-
-
0.6
Kentucky, Jefferson County0
Gaseous
-
3.68
2.86
0.80
0.50
-
-
New York Stated
Gaseous
-
3.7
2.85
1.65
0.80
-
-
Washington Statee
Gaseous
-
2.9
1.7
0.84
0.5
0.5
Tennessee'
Not specified
-
3.7
2.9
1.6
1.2
-
-
Abbreviations: min = minutes; h = hours; d = days;means no criterion for that exposure duration
a"Total" atmospheric HF includes both gaseous and particulate-bound HF.
bAir quality standards for the State of Texas were removed in 2000.
cSee http://www.epa. gov/region4/air/sips/kv/lou/3.04 .pdf.
dSee http://www.dec.nv.gov/regs/4146.html.
eSee https://fortress.wa.gov/ecv/publications/publications/wacl73481 .pdf: the bold highlighted values are HF
benchmarks for RTR environmental risk screening (see text).
fSee http://www.state.tn.us/sos/rules/1200/1200-03/1200-03-03.pdf.
The Environment Canada criteria were based on regression analysis of exposure-concentration
vs. exposure-duration data from the studies shown in Section A.2.3.3 and from additional
unpublished studies.13 The linear regression model used log(exposure concentration x duration),
specified as "dose," as the dependent variable. Log(exposure duration) was the independent
variable. Environment Canada pointed out, however, the selection of data to include in the
regression was based on expert judgment, and the data set used did not meet some assumptions
associated with estimating confidence intervals for the regression equation. Also, the value for
1 3
References cited by Environment Canada (1996) from conference proceedings abstracts or other nonpeer-
reviewed/nonpublished sources are not included in this report.
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"dose" is not independent of the duration value, violating a key assumption for simple regression
analyses.
Most investigators plot a specified effect level (e.g., initial evidence of leaf necrosis) for each
study using log(exposure concentration) for the y-axis and log(exposure duration) for the x-axis.
If Haber's rule applies, a straight line with a slope of 1.0 would result across all exposure
durations. Haber's rule states that response is directly proportional to the exposure duration
multiplied by the exposure concentration. Over the short term (i.e., a few days), the accumulation
of HF in plants generally follows Haber's rule (data presented in McCune 1969a). The slope of
the relationship decreases (becomes more horizontal; more dependent on concentration and less
dependent on exposure duration) as exposure duration increases beyond 1 or 2 days (McCune
1969a). Thus, for chronic exposures, only exposure concentration need be specified.
Provincial guidelines for Alberta include a 30-day average limit of 0.35 |ig HF/m3 and 70- and
90-day average limits of 0.20 |ig HF/m3. Although Alberta Environment did not specify the level
of effect associated with 0.2 |ig HF/m3 (see Table A-l 1), given the available data, only
grapevines might be expected to show some evidence of injury at that concentration, and the
significance of that injury to grape productivity is unknown. Thus, we conclude that the
provincial guidelines for Alberta are similar to an NEL for plant communities and populations,
including the most HF-sensitive commercial crops.
The Ontario Ministry for the Environment (OME 2004) has established provincial guidelines for
Ontario that distinguish between the growing season and the nongrowing season and between
total HF in air (including particulates) and gaseous HF only. The 30-day criterion for gas-phase
HF during the growing season is 0.34 |ig HF/m3; longer-duration criteria were not established.
This criterion and other air concentration criteria for HF established in Canada are listed in Table
A-9. The criteria are based on studies of agricultural crops, horticultural plants, and coniferous
trees, as described in Section A.2.3.3.
In the United States, for the states having ambient air quality standards or criteria for gaseous
HF, the values are generally less than 1.0 |ig/m3 as a 30-day limit. Examples for several states
are included in Table A-9. The Texas Commission on Environmental Quality (TCEQ)
established effect screening levels for the protection of vegetation, cattle, and human health
(TCEQ 2009, Table A-9). The TCEQ chronic (90-day) criterion was based on a LOAEL for
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soybean productivity; nonagricultural plants were not evaluated. The other state and county
standards or criteria included in Table A-9 are similar in magnitude to the TCEQ values for
90-day durations.
For purposes of the RTR environmental risk screen, the two benchmarks for HF were evaluated
as representing an LEL: the 90-day criterion from Washington State of 0.5 |ig HF/m3 and the
Environment Canada 90-day criterion of 0.4 |ig HF/m3. Both criteria are presented in bold and
highlighted in Table A-9. Section A.2.3.3 below includes summaries of original data on HF
toxicity to plants.
For comparison with long-term human health criteria, the California Office of Environment and
Health Hazard Assessment (OEHHA) has recommended a chronic inhalation reference exposure
limit for humans of 14 |ig/m3 based on the occurrence of skeletal fluorosis.14 Thus, the 90-day
criteria for plants are lower than the reference exposure limit to protect human health from
inhalation toxicity.
A.2.3.3 Hydrogen Fluoride (HF) Exposure-Response for Plants
Critical concentrations cited in criteria documents often are based on the prevention of visible
injury to plants by HF rather than on measured reductions in plant productivity as measured by
vegetative growth and seed yield, for two reasons. First, data on effects of HF on plant growth
and productivity are limited. Second, concentrations inducing visible injury are lower than those
affecting growth and are therefore protective of both endpoints (APIS 2010).
Short-term Exposures
Short-term exposure to HF typically results in leaf lesions and necrosis along the tips and
margins of leaves where fluoride has accumulated. Table A-10 summarizes information on the
phytotoxic effects of short-term exposure to HF available from the literature. Consequently, a
longer averaging time (e.g., 24 hours) is more relevant than a shorter averaging time (e.g., 30
minutes, 1 hour).
14See http://oehha.ca.gov/air/chronic rels/HvFluoCREL.html.
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Table A-10. Adverse Effects in Terrestrial Plants Following Short-term Exposures to HF
Species
Tested
Study Protocol3
Results
LOEL
(Mg/m3)
C * D
(jjg/m3-d)b
Reference
Ponderosa
pine (Pinus
ponderosa)
1.46 [jg F/m3for24 h
Leaf injury index = 0.5, that is 50%
of the length of needles injured
1.46
1.46
Adams et al.
(1956)
Jerusalem
cherry
(¦Solannum
pseudo-
capsicum)
0.9 or 4.0 |jg F/m3 for 4 d in
dark
Mild leaf necrosis in "sensitive"
clone during exposure in dark,
which became "severe" (40-60%),
leaf necrosis after plant was
exposed to light
0.9
0.9
MacLean et al.
(1982)
Gladiolus sp.
0.17 [jg F/m3 for 9 d
Necrotic leaf tips (% not specified)
0.17
1.53
Hitchcock et al.
(1962), as cited in
WHO (2002)
Wheat
(Triticum
aestivum)
0.9 |jg or 2.9 |jg F/m3 for 4 d
Reduced mean yield by 25% dry
weight in grain spikes when
exposure occurs during anthesis
(i.e., flowering)
0.9
3.6
MacLean and
Schneider (1981)
Sorghum sp.
Northrup King
22A hybrid
"0"c,1.6, 2.2, 2.8, or 3.3 |jg/m3
(mean concentration over 9 d);
experiment varied the order in
which different exposure
concentrations (1.5,1.8, 3.2, or
3.6 |jg F/m3) were applied over
three successive 3-day periods
Reduced total dry weight biomass
at harvest by 20% after 72-d
exposure and reduced grain dry
weight yield by 9% with exposures
at 1.5, 3.2, then 1.8 |jg/m3 for
three successive 3-d periods
2.2
20
MacLean et al.
(1984)
Black spruce
(P/'cea
mariana); 2
years old
0.3, 2.3, 4.2, or 8.1 [jg F/m3 for
78 h, observed 20 d after
exposure ceased
At 2.3 |jg/m3, 23% of trees
exhibited slight (12%), moderate
(10%), or severe (1 %) injury to
needles. At 4.2 |jg/m3, 61 % of
trees exhibited needle injury. At
8.1 |jg/m3, 96% of trees exhibited
needle injury, and 72% of injury
was moderate to severe.
2.3
7.5
McCune et al.
(1991)
White spruce
(P/'cea
glauca); 3
years old
0.3,2.6, 5.2, or 11.1 [jg F/m3
for 50 h, observed 20 d after
exposure ceased
At 5.2 |jg/m3, 9% of trees
categorized with needle injury. At
11 |jg/m3, 40% of trees with
needle injury: 32% categorized
with moderate to severe needle
necrosis; remaining 8% with slight
needle necrosis
5.2
11
McCune et al.
(1991)
Tobacco
(.Nicotiana
tabacum L.)
0.5 or 45 |jg HF/m3for 1 d
Growth (plant height) reduced by
50% at 45 |jg/m3 compared with
controls, accompanied by 63%
reduction in chlorophyll content
45
45
Dogeroglu et al.
(2003)
Tobacco
(.Nicotiana
tabacum)
0.5 or 45 |jg HF/m3for3d
Growth (plant height) reduced by
70% at 45 |jg/m3 compared with
controls, accompanied by 85%
reduction in chlorophyll
45
135
Dogeroglu et al.
(2003)
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Species
Tested
Study Protocol3
Results
LOEL
(Mg/m3)
C * D
(jjg/m3-d)b
Reference
"Conifers"
Summary of dose-response
relationships from the available
literature based on 24-h
average HF concentrations
Increased foliar markings
3.0d
3.0
McCune (1969b)
"Fruit Trees"
Increased foliar markings
4.5d
4.5
Gladiolus
Reduced growth or yield
6.0d
6.0
Corn
Reduced growth or yield
10.5d
10.5
Tomato
Increased foliar markings
12d
12
Concentrations can be reported for hydrogen fluoride (HF) or the fluoride ion (F) only. Atomic weight of H = 1 g/mole,
and F = 19 g/mole. Thus, the difference in an air concentration expressed as |jg HF/m3 and an air concentration
expressed as |jg F/m3 is only 5%. For comparison with other measurements of HF concentrations in air, note that 1
|jg/m3 of fluoride (F) is equal to 0.874 ppb (parts per billion) fluoride by weight or 1.33 ppb by volume of any gas
containing 1 fluorine atom per molecule. These conversions hold true at an atmospheric pressure of 29.9 inches of
Hg and 60 °F (Hill and Pack 1983).
bC x D = exposure concentration multiplied by exposure duration, assuming Haber's rule applies over short-term
exposures.
cThe authors stated that "no HF" exposure occurred for this group, but a background concentration around 0.01-0.03
|jg/m3 likely was used for this group.
dValues reported by McCune (1969) are 24-hour mean threshold concentrations based on an evaluation of the
available literature (exposure concentration, duration, and plant-response data plotted with curves).
Concentrations listed in the LOEL column of Table A-10 represent the lowest concentration at
which statistically significant effects on growth, yield, or leaf necrosis were evident when the test
group exposed at the LOEL was compared with the control group of plants. We use LOEL
instead of LOAEL terminology because the significance of low levels of leaf necrosis and
several other types of effects on plant productivity has not been quantified. The study protocol
column includes a list of the exposure concentrations tested. In some cases, the lowest
concentration listed is the "background" concentration the control plants experience. The highest
concentration listed in the study-protocol column that is lower than the concentration listed in the
LOEL column represents a NOEL. Effects, if present, at a NOEL were not statistically different
from effects shown in the control plants (or the NOEL represents the control plants). Table A-10
indicates that effects evident after short-term exposures include foliar chlorosis and necrosis and,
in some tests, reduced plant growth rates.
Longer-term Exposures
Longer-term (i.e., greater than 30 days) exposures of plants to HF usually result in leaf chlorosis
and necrosis and can result in reduced growth and productivity even when leaf damage is not
apparent. More data are available for longer-term exposures of plants to HF (Table A-l 1) than
for short-term exposures.
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Table A-ll. Adverse Effects in Terrestrial Plants Following Longer-term Exposure to HF
Species Tested
Study Protocol
Results
LOEL
(Mg/m3)
Reference
Tendergreen bean
0.6 |jg F/m3 for 43 d
Reduced number and mass of
marketable pods by 20% and
25%, respectively; no influence
on growth or foliar appearance
0.6
MacLean etal. (1977)
Tomato (Fireball
861 VR)
0.6 |jg F/m3 for 93 d
No effect on growth or fruiting
-
MacLeanetal. (1977)
Soybean
0.64, 2.1, or 5.0 [jg F/m3,10-16 h/d
for 98 d
Reduced number of fruit per
pot by more than 90%
<0.64
Pack and Sulzbach
(1976)
Bell pepper
0.01,0.63,2.2, 4.5, or 10 |jg F/m3,
10-16 h/d fori 12 d
Reduced number of peppers
by more than 65%
2.2
Pack and Sulzbach
(1976)
Sorghum
0.01,0.53,2.2, 4.7, or 10.6 [jg F/m3,
10-16 h/d fori 14 d
Slightly reduced weight per
seed; at 4.7 |jg F/m3, number
of seeds reduced by 85%
2.2
Pack and Sulzbach
(1976)
Sweet corn
0.01,0.54,2.0,2.3, or 8.7 [jg F/m3,
10-16 h/d for 97 d
Seed production totally (100%)
inhibited (after anthers
released, ears and seeds did
not develop)
2.0
Pack and Sulzbach
(1976)
Cucumber
0.01,0.61,2.3, 4.4, 4.6, 5.5,7.8, or
8.9 |jg F/m3,10-16 h/d for 104 d
Reduced number of fruit by
24%
4.6
Pack and Sulzback
(1976)
Pea
0.01,2.1,4.4, 5.3, or 9.0 [jg F/m3,
10-16 h/d for 56 d
Reduced number of seeds per
fruit by approximately 5%
4.4
Pack and Sulzback
(1976)
Wheat
0.01,5.0, or 8.2 [jg F/m3,10-16 h/d
for 130 d
Reduced number of seeds by
50%; reduced weight per seed
by 18%
8.2
Pack and Sulzback
(1976)
Oat
0.01,2.2, 4.3, or 9.1 [jg F/m3,
10-16 h/d for 147 d
Reduced seed production
(proportion not specified)
9.1
Pack and Sulzbach
(1976)
Cotton
0.01,3.1,5.0, or 8.0 |jg F/m3,
10-16 h/d for 164 d
No significant differences for all
measured parameters
>8
Pack and Sulzbach
(1976)
Snow princess
gladiolus
(Gladiolus
grandiflorus)
0.03,0.35,0.36,0.41,0.44,0.50
and higher up to 1.85 |jg F/m3 for up
to 117 d
Leaf necrosis (65% of leaves);
117 d
0.36
Hill and Pack (1983)
Freesia sp.
(commercial
flower)
Continuous fumigation at 0.5 |jg
HF/m3 for 5 mo OR intermittent
fumigation with 0.3 |jg HF/m3 (6 h/d,
3 or 4 times/wk) for 18 wk
Leaf necrosis over 30% of
exposed leaf surface area
compared with 5% in control
plants
0.3
Wolting (1975)
Gladiolus sp.
(commercial
flower)
0.35 or 0.76 |jg F/m3 for 40 d
Increased necrosis by 46%
and increased respiration by
39%
0.76
Hill etal. (1959)
Apple {Malis
domestica Borkh)
0.03,0.44,0.82 |jg HF/m3 for 164 d
Slightly reduced growth and
necrosis (see text)
0.44
Hill and Pack (1983)
Pole bean
(.Phaseolus
vulgaris)
0.03,0.54,0.79 |jg HF/m3 for 83 d
Fruit set reduced by 80%
0.54
Hill and Pack (1983)
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Species Tested
Study Protocol
Results
LOEL
(Mg/m3)
Reference
Grapevine {Vitis
vinifera)
0.07 (control), 0.17, or 0.27 |jg/m3
for 189 d
Foliar necrosis after 99 and
83 d at 0.17 and 0.27 |jg/m3,
respectively; reduced
chlorophyll a and total
chlorophyll noted
0.17
Murray (1984)
Grapevine
(3 varieties)
0.37 |jg F/m3 to 6.0 |jg F/m3 for four
growing seasons (season duration
varied from 54-159 d) for 12
different exposure
concentration/duration combinations
No substantial effects up to 1.5
|jg F/m3 for 54 d; exposure at
2.2 |jg F/m3 for 60 d reduced
leaf area by up to 45%
2.2
Doley (1986)
Wheat (Triticum
aestivum)
0.03 or 0.38 |jg HF/m3 for 90 d
No effects on yield
-
Murray and Wilson
(1988a)
Barley (Hordeum
vulgare)
0.03 or 0.38 |jg HF/m3 for 90 d
Increase in grain protein
concentration; not necessarily
an adverse effect
0.38
Murray and Wilson
(1988a)
Tendergreen bean
plant
0.58, 2.1, 9.1, or 10.5 |jg F/m3
seedling to maturity to next
generation
At 2.1 |jg F/m3, lower starch
content of seeds (15-21 %)
compared with controls (35%
starch) resulting in reduced Fi
generation plant height (-17%)
and leaf surface area (-23%)
and increased (+137%)
proportion abnormal trifoliate
leaves
2.1a
Pack (1971)
Eucalyptus
(Eucalyptus
tereticornis)
0.03 or 0.38 |jg F/m3 for 90 d in
open-top chambers
Reduced leaf surface area and
weight in mature and immature
leaves
0.38
Murray and Wilson
(1988b)
Marri (£.
calophylla)
0.03 or 0.39 |jg F/m3 for 120 d
Reduced leaf surface area and
weight in immature leaves,
reduced surface area in mature
leaves
0.39
Murray and Wilson
(1988c)
Tuart (£.
gomphocephala)
0.03 or 0.39 |jg F/m3 for 120 d
Reduced leaf surface area and
weight in mature and immature
leaves
0.39
Murray and Wilson
(1988c)
Jarrah (£.
marginata)
0.03 or 0.39 |jg F/m3 for 120 d
Reduced leaf surface area and
weight in immature leaves only
0.39
Murray and Wilson
(1988c)
aPrimary leaves of some F1 progeny noted as being severely stunted and distorted at 2.1 |jg/m3 (dosing protocol
unclear).
Although many plant species do not exhibit adverse effects from short-term exposures at ambient
air concentrations less than 1 |ig F/m3, several do show effects after longer-term exposures at
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concentrations of 0.5 |ig F/m3 or less (Table A-l 1).15 Several studies of plants of agricultural
importance are described in more detail below.
Pack and Sulzbach (1976) fumigated nine species of agricultural crops with HF gas from seed
through flowering to the time of harvest. Table A-l 1 lists the exposure concentrations and the
exposure durations associated with the LOEL concentration for each crop. The crop that was
most sensitive to HF was soybean, with a 90-percent reduction in the number of bean pods at the
lowest exposure concentration tested (0.64 |ig F/m3). The next most sensitive crop appears to be
sweet corn. Although no effects other than brown streaks through the plant leaves were observed
at 0.54 |ig F/m3, at the next higher exposure concentration (2.0 |ig F/m3), ears and seeds failed to
develop in all corn plants. Cotton was the most resistant to fumigation with HF of the plants
tested, with no effects observed at a concentration of 8.0 |ig F/m3 for 164 days.
Hill and Pack (1983) grew apples (1-year-old whips of the delicious variety) in three
greenhouses, starting HF exposures 5 weeks after planting and continuing for 164 days.
Air was filtered in two greenhouses to remove gaseous (and particulate) fluoride. One of those
greenhouses served as a "clean air" control (0.03 |ig HF/m3), while HF was added to another
greenhouse to achieve an air concentration of 0.44 |ig HF/m3. The third greenhouse received
ambient air with an average concentration of 0.83 |ig HF/m3. The group exposed at
0.44 |ig HF/m3 exhibited an 11-percent reduction in leaf length and a 6-percent reduction in leaf
width (p < 0.01) compared with the control. In addition, leaves exposed during their expansion
sporadically exhibited leaf tip necrosis and chlorosis, with leaf growth ceasing once necrosis was
visible. Leaf injury often was apparent soon after 24-h air sample readings of up to
0.99 |ig HF/m3.
Hill and Pack (1983) also examined the response of Chinese apricot trees fumigated with HF
during three growing seasons (Experiments A, B, and C). Experiments A and B used higher
exposure concentrations over shorter durations than did experiment C. In trials B and C, both
ambient air and test air HF concentrations were 0.35 |ig HF/m3, and the "clean" air greenhouse
(at 0.03 |ig HF/m3) served as the control. Trees were exposed as soon as they began to develop
15Air concentrations are variously reported as |ig HF/m3 or |ig F/m3. We report the original units without adjusting
one to the other. The atomic weight of F is approximately 95% of the molecular weight of HF.
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leaves. Necrosis of leaf tips and edges, necrotic spots on leaves, leaf curling, and increased leaf
drop were observed. In Experiment C, after 117 days of exposure at 0.35 |ig HF/m3, leaf drop
averaged 18 percent, average tree trunk diameters were 53 percent that of controls, and average
shoot length was 54 percent that of controls.
Peach trees exposed to gaseous HF under conditions similar to those described above appeared to
be even more sensitive to HF. Specifically, leaves of HF-exposed peach trees tended to be
smaller than those of controls and also tended to drop prematurely (Hill and Pack 1983). In one
part of the study, leaves on trees exposed at 0.41 |ig HF/m3 for 73 days were 24-percent smaller
than leaves on control trees. In another part of the study, 1,768 leaves dropped from trees
exposed at 0.34 |ig HF/m3 for 110 days, while only 102 leaves dropped from the control trees.
Pack (1971) evaluated effects on tendergreen bean plants grown from seeding to maturity under
continuous exposure to HF gas at 0.58, 2.1, 9.1, or 10.5 |ig F/m3. No significant growth or yield
effects were observed at any test concentration, with the exception of a 15- to 21-percent
reduction in bean starch content at the three highest concentrations tested. Beans from the
exposed parental generation (F0) then were planted and grown in "clean" air to produce the F1
generation. For the plants exposed at 2.1 |ig F/m3, the F1 generation plants exhibited a
17-percent reduction in plant height and a 23-percent reduction in leaf surface area. Subsequent
plantings of F2 and F3 generations (grown in clean air) indicated that the traits exhibited in the
F1 generation were not heritable.
Murray and Wilson (1988c) evaluated adverse effects from 120 days of HF exposure for three
eucalyptus species by conducting an analysis of variance for the exposed (0.39 |ig HF/m3) versus
control plants (background concentration of 0.03 |ig HF/m3) for several parameters. For
immature leaves, reduced leaf area and reduced leaf weight were significant at/? = 0.001 for
Eucalyptus calophylla. For is. marginata, reduced immature leaf area was significant at p = 0.01,
and reduced immature leaf weight was significant at/? = 0.05. For /•]. gomphocephala, reduced
immature leaf area was significant at/? = 0.05, and reduced immature leaf weight was significant
at/? = 0.01. In contrast, for mature leaves, only E. gomphocephala showed both significantly
reduced leave area (p = 0.01) and weight (p = 0.001).
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Murray and Wilson (1988c) also estimated visible foliar injury for three eucalyptus species using
two factors: "A" (the proportion of necrotic leaf area on damaged leaves) and "Z" (the proportion
of all damaged leaves). The injury index (7) formula then was calculated using Equation A-l:
I = (A x L)0-5 Eq. A-l
Analysis of variance for the exposed plants compared with control plants indicated that E.
calophylla was significantly affected at 0.39 |ig/m3 (p = 0.001). Murray and Wilson (1988c) did
not report actual measurements for leaf area, weight, or necrotic leaves.
Considering the data as a whole, a benchmark of 0.4 or 0.5 |ig HF/m3 air would appear
protective of most plant species included in the table, but not some species of commercial
flowers or ornamental plants (see gladiolus Table A-10) and freesia, grapevine, and eucalyptus
(Table A-l 1). Streaking of leaves is an adverse effect for plants bred for their appearance. Thus,
air HF concentration benchmark of 0.4 or 0.5 jug HF/m3 air appears consistent with a TEL for
assessing plant communities for wildlife food and habitat and for agricultural crops. Some
species of HF-sensitive ornamental plants would not be protected at that level.
A.3 Wildlife Toxicity Reference Values
To assess risks to piscivorous wildlife, a TRV for wildlife, expressed as an oral dose, is needed
for comparison with estimated dietary exposures via the chemical in prey (i.e., in fish and
invertebrates consumed). The estimated total chemical intake via all types of prey in the diet,
expressed as mg[chemical]/kg[wildlife body weight]/day (mg/kg-day), can be compared with the
TRV (expressed in the same units) to estimate a hazard quotient. An emission rate that
corresponds to a hazard quotient of 1.0 (i.e., the emissions screening threshold rate) then is used
to screen facilities in Tiers 1 through 3 of the RTR ecological risk environmental screens.
Avian and mammalian TRVs are included in the RTR ecological assessment in two contexts.
The first is in OSWER's derivation of Eco-SSLs, expressed as chemical concentrations in soil, to
protect wildlife that feed on soil invertebrates (Section A.2.2.3 and Section A.3.2). The second is
use of TRVs, expressed as chemical doses to avian and mammalian wildlife (mg/kg-day), to
compare with their estimated ingestion of chemicals in fish from the onsite lake. These TRVs are
calculated in this section using an approach similar to that used for the EPA GLWQI (U.S. EPA
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1995b) (Section A.2.2.1). One exception is allometric scaling of dose from a test animal to dose
for the wildlife species based on relative body weights instead of using an interspecies UF of 10.
An interspecies UF generally has two components: a toxicokinetic component and a
toxicodynamic component (U.S. EPA 2005a, 201 la). The toxicokinetic component generally can
be represented by scaling the toxicity value for the test species to the assessment species on the
basis of relative body weights to the 3/4 power. That scaling is based on the allometric
relationship of metabolic rate to body weight for mammals in general (U.S. EPA 1993c) and
assumes that much of the toxicokinetic difference among species scales to metabolic rate.
Toxicodynamic differences are associated with taxonomic differences in physiology between the
test and assessment species that might affect sensitivity to a toxicant. Such differences generally
increase in magnitude with increasing taxonomic distance (Brown et al. 2000); for example,
rodents might be more sensitive to some plant toxins than ungulates such as cows or goats or
other herbivores that have evolved metabolic pathways to detoxify those compounds.
Given the maximum value for the interspecies UF is 10, a common recent EPA practice has been
to assign each component, toxicokinetic and toxicodynamic, aUF of 3 (U.S. EPA 2011). Thus, if
toxicokinetic differences are accounted for by scaling to relative body weight, the maximum
value of the remaining UF would equal 3 (i.e., 3x3=9; close to 10). The approach is consistent
with that used most recently by EPA to estimate reference doses (RfDs) for humans (U.S. EPA
2011) and that EPA has used for some time in estimating cancer potency factors for humans
from animal data (U.S. EPA 2005a). For purposes of clarity and simplicity, however, we did not
apply a UF of 3 if the test species taxon differs from the assessment species taxon at the level of
order (e.g., test species is a rodent [rat] and assessment species is a carnivore [mink]; both are
mammals).
A.3.1 Derivation of TRVs for Piscivorous Wildlife in RTR Assessment
As described in Section 3.1.1 of the main report, we selected mink (Mustela vison, recently
renamed Neovison vison based on cytogenetic and biochemical data that distinguish it from other
members of the genus Mustela, Wozencraft 2005) to represent fish-eating mammals and
common (American) merganser (Mergus merganser americana) to represent fish-eating birds.
These two species are of moderate size (moderate metabolic rate and FIR per kg body weight),
but can catch and consume larger fish than other moderate-sized mammals or birds, respectively.
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The TRVs used to assess risk to piscivorous wildlife for Tiers 1 through 3 of the RTR ecological
risk screen were calculated for the RTR assessment using the methods developed for the GLWQI
(U.S. EPA 1995b). In the GLWQI approach, the most sensitive type of effect of the most
sensitive of the species tested is used to identify a LOAEL and NOAEL, which then can be used
to calculate TRVs. For most chemicals, only a few (e.g., 2-7) species of birds (e.g., quail,
mallard, chicken, pheasant) and a few species of mammals (e.g., mice, rats, hamster, mink) have
been tested sufficiently to provide both a LOAEL and a NOAEL for effects resulting from
chronic exposures. Thus, using toxicity data from the most sensitive of the few species tested is
not necessarily an overly protective approach.
For wildlife, chronic TRVs were derived after reviewing the following sources of toxicity study
summaries:
• chronic (or reproductive) toxicity studies of mammals and birds as compiled by EPA for
the GLWQI (U.S. EPA 1995b),
• studies compiled by Sample et al. (1996),
• studies reported in Eco-SSL documents for individual chemicals (i.e., cadmium, U.S.
EPA 2005d), or
• studies identified by a literature search for TRVs or toxicity benchmarks for the six PB-
HAPs for birds and mammals (e.g., CA DTSC HERD 2009).
For each source listed above, the author(s) had evaluated the individual toxicity study reports for
scientific adequacy. We used the study for the most sensitive species showing an adverse effect
on survival, growth and development, or reproduction to identify the lowest LOAEL and lowest
NOAEL for use as wildlife TRVs. When not available, a NOAEL was set equal to the LOAEL
divided by a UF of 10 (U.S. EPA 1995b). For TRVs obtained from the GLWQI documentation,
we used the LOAELs and NOAELs from the key study without application of any UFs, which is
consistent with Sample et al. (1996), maximizes clarity, and minimizes the number of
assumptions used in developing the TRVs.
To estimate TRVs for the RTR piscivorous wildlife risk screen, we used the LOAELs and
NOAELs from a single key study (most sensitive effect and species). Doses were scaled between
a test species and the assessment species on the basis of relative body weight to the % power
(U.S. EPA 2011), as described below if the difference in body weight was more than 20 percent.
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For mammals, for which the test species, usually rats (350 g) or mice (30 g), is of smaller body
size and higher metabolic rate than mink (1,000 g), dose conversions from the test animal to
mink were based on allometric scaling of metabolic rate between mammalian species (U.S. EPA
1993c, 1995b; Equation A-2 below):
DoSewildlife DoSetest-species X (BJVtest-species/BWwildlife) EC]. A-2
where
Dose = chemical ingestion (mg[chemical]/kg[wildlife BW]-day)
BW = body weight
For birds, given the similarity of the body weight of test species (e.g., chicken, pheasant, mallard
duck about 1 kg) to American merganser (1.27 kg), no dose conversions were performed.
A.3.2 Chemical-specific Wildlife TRVs for PB-HAPs
In the main report, Table 3-1 lists the TRVs, both the NOAEL and the LOAEL, used for fish-
eating birds and mammals for each PB-HAP. Further details are provided below. The discussion
for arsenic demonstrates our approach. The remaining derivations are described more briefly.
A.3.2.1 Arsenic (As) Wildlife TRVs
Data were available to calculate a TRV for both (1) mink (Mustela vison, or Neovison vison) and
(2) American merganser (Mergus merganser americana).
Mink Arsenic (As) TRV
We based our wildlife TRV for arsenic toxicity to mink on a three-generation study of mice.
Schroeder and Mitchener (1971) administered a soluble arsenite (AsCb"3) salt in drinking water
of mice at 3 ppm (or 5 mg[As]/L). They found a statistically significantly reduced litter size
(25 percent, 8 percent, and 23 percent for generations 1, 2, and 3, respectively) for female mice
ingesting the arsenite in drinking water. Because arsenic is naturally occurring, feed for both
control and experimental mice contained 0.06 ppm arsenic. Sample et al. (1996) calculated the
dose at the LOAEL to be 1.26 mg[As]/kg[mouse body weight]-d. To estimate a NOAEL, the
LOAEL is divided by a UF of 10 (GLWQI, U.S. EPA 1995b).
LOAEL for mouse = 1.26 mg[As]/kg-day
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NOAEL for mouse = LOAEL/IO
= 0.126 mg[As]/kg-day
LOAEL for mink = daily dose to mouse x (mouse body weight/mink body
weight)114
= 1.26 mg/kg mouse/day x (0.03 kg[mouse]/l kg[mink])1/4
= 0.52 mg[As]/kg-day
NOAEL for mink = 0.052 mg[As]/kg-day
American Merganser Arsenic (As) TRV
As described above for the Eco-SSL for birds, EPA identified an arsenic TRV for birds based on
one of four toxicity studies of acceptable quality that examined growth and reproduction (U.S.
EPA 2005b). Of those four studies, one reported NOAELs for both reproduction and growth at
2.24 mg/kg[body weight]-day in domestic chickens (Holcman and Stibilj 1997, cited by U.S.
EPA 2005b). Although a study of Camardese et al. (1990) identified a lower LOAEL of
1.49 mg/kg-day for growth for mallard duck, EPA did not use it to estimate a TRV because a
NOAEL was not determined in that study (U.S. EPA 2005b).
We are concerned that mallards might be more sensitive to arsenic than domestic chickens.
Camardese et al. (1990) exposed mallard ducklings to arsenic in food from days 1 through 14
after hatching. Although four doses were administered, effects on growth were seen at the lowest
dose tested (1.49 mg/kg-day). For purposes of RTR screening, we use the mallard duck LOAEL.
The NOAEL is estimated as the LOAEL divided by a UF of 10 (and rounded to 2 significant
digits).
LOAEL for mallard duck = 1.5 mg[As]/kg-day
NOAEL for mallard duck = 0.15 mg[As]/kg-day
Given the similarity in size between mallard (1 kg) and American merganser (1.27 kg), no dose
conversions were estimated:
LOAEL for merganser = 1.5 mg[As]/kg-day
NOAEL for merganser = 0.15 mg[As]/kg-day
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A.3.2.2 Cadmium (Cd) Wildlife TRVs
Data were available to calculate a TRY for both mink and American merganser.
Mink Cadmium (Cd) TRV
Sample et al (1996) identified the study of Sutou et al. (1980), which reported a NOAEL and a
LOAEL for reproduction in rats. Sutou et al. (1980) exposed female rats (mean body weight 0.30
kg) to cadmium for 6 weeks (from mating through gestation) by oral gavage adjusted to body
weight to attain three doses: 0.1, 1.0, and 10.0 mg[Cd]/kg[body weight]-day. At the LOAEL of
10 mg/kg-day, fetal implantations were reduced 28 percent, fetal survivorship was reduced by 50
percent, and fetal resorptions increased by 400 percent; the NOAEL in the experiment was
0.1 mg/kg-day.
NOAEL for mink = daily dose to rat x (rat body weight / mink body weight)114
= 1 mg/kg[rat]-day x (0.30 kg[rat] /I kg[mink])1/4
= 0.74 mg/kg-day
LOAEL for mink = 10 x NOAEL
= 7.4 mg/kg-day
Common Merganser Cadmium (Cd) TRV
For the Eco-SSLs to protect ground-feeding birds, EPA calculated the geometric mean of all
bounded NOAELs for reproduction and growth across several species of birds to estimate a TRV
of 1.47 mg/kg-day (U.S. EPA 2005d). In 2009, the EPA Region 9 BTAG reevaluated the
Eco-SSL TRV for cadmium considering data published after 2004 and using revised allometric
equations (Nagy 2001) to estimate FIRs rather than the earlier equations (Nagy 1987) used for
the Eco-SSL TRVs.
Based on kidney toxicity in mallards (Cain et al. 1983), EPA Region 9 and the California
Department of Toxic Substances Control, Human and Ecological Risk Division recommended an
avian LOAEL of 1.0 mg/kg-day (U.S. EPA 2009b; CA DTSC HERD 2009). Cain et al. (1983)
reported mild-to-severe kidney degeneration in four growing mallard ducklings fed 14.6 ppm
cadmium in their diet for 12 weeks, which they calculated to equal an ingested dose of
1.0 mg/kg-day. Other studies also identified potential reproductive effects near that dose (White
et al. 1978; Leach et al. 1979). EPA Region 9 and CA DTSC HERD identified a NOAEL of
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0.7 mg/kg-day from a study by Mayack et al. (1981) that identified kidney damage in wood
ducks after 3 months of exposure to 7 mg/kg-day, but not in wood ducks exposed at
0.68 mg/kg-day (which we round to 0.7 mg/kg-day).
We follow EPA Region 9 and use the lowest LOAEL of 1 mg/kg-day from Cain et al. (1993) and
the highest NOAEL of 0.7 mg/kg-day from Mayack et al. (1981) for merganser TRVs for
cadmium (Table 5-1 in the main report). Given the similarity in size between mallard (1 kg) and
American merganser (1.27 kg), no dose conversions were estimated.
A.3.2.3 Divalent Mercury (Hg++) Wildlife TRVs
We did not calculate TRVs for mink and American merganser for divalent mercury because it is
not bioaccumulative. Instead, we focused on methyl mercury for fish-eating wildlife (Section
A.3.2.4).
A.3.2.4 Methyl Mercury (MeHg) Wildlife TRVs
Data were available to calculate a TRV for both (1) mink and (2) American merganser.
Mink Methyl Mercury (MeHg) TR V
Verschuuren et al. (1976) exposed rats to methyl mercury chloride (which is 79.89% Hg by
weight) at doses of 0.1, 0.5, and 2.5 ppm in the diet for three generations. Reduced pup survival
was observed at 2.5 ppm MeHgCl, but not at the lower dietary concentrations. The exposure
level of 0.5 ppm MeHgCl, or 0.4 mg[Hg]/kg[diet], is considered the NOAEL. Sample et al.
(1996) calculated the doses for the rat assuming a rat body weight of 0.35 kg and FIR of 28 g/day
(U.S. EPA 1988a). The calculations below are based on doses expressed in mg Hg, not MeHg or
MeHgCl, per kg body weight per day.
NOAEL for rat = (concentration in food x FIR/day) / body weight
= (0.4 mg[Hg]/kg[food] x 28 g[food]/day) / 0.35 kg[ratbody
weight]
= 0.032 mg[Hg]/kg-day
LOAEL for rat = (2.0 mg[Hg]/kg[food] x 28 g[food]/day) / 0.35 kg[rat body
weight]
= 0.16 mg[Hg]/kg-day
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NOAEL for mink = daily dose to rat x (rat body weight / mink body weight)114
= 0.032 mg[Hg]/kg[rat]-day x (0.35 kg[rat] / 1 kg[mink])1/4
= 0.0246 mg[Hg]/kg-day
LOAEL for mink = 0.16 mg[Hg]/kg[rat]-day x (0.35 kg[rat] / 1 kg[mink])1/4
= 0.123 mg[Hg]/kg-day
American Merganser Methyl Mercury (MeHg) TRV
Heinz (1974, 1975, 1976a,b, and 1979) identified a LOAEL for reduced production of eggs and
ducklings for mallards exposed for up to three generations to MeHg added to the diet as methyl
mercury dicyandiamide at 0.5 ppm Hg. EPA estimated the average daily dose to the mallards at
that dietary concentration to be 0.078 mg[Hg]/kg bw-day (U.S. EPA 1995b). Sample et al.
(1996) also calculated a dose to mallards from 0.5 ppm Hg in the diet to be
0.064 mg[Hg]/kg bw-day based on slightly different assumptions about body weight and FIR
than used by EPA. To estimate a NOAEL, EPA used a compound UF of 6: a UF of 2 for the
LOAEL-to-NOAEL extrapolation, because the effect level at the LOAEL was slight, and a UF of
3 for interspecies extrapolation (U.S. EPA 1995b).
LOAEL for mallard = 0.078 mg[Hg]/kg-day
NOAEL for mallard = 0.078 mg[Hg]/kg-day / 6 (UF)
= 0.013 mg[Hg]/kg-day
LOAEL for merganser = 0.078 mg[Hg]/kg-day
NOAEL for merganser = 0.013 mg[Hg]/kg-day
A.3.2.5 POM Index Chemical—Benzo[a]pyrene (BaP) Wildlife TRV
Data were available to calculate a TRV only for mink; insufficient data were identified to
estimate a TRV for birds.
Mink Benzofajpyrene (BaP) TRV
Mackenzie and Angevine (1981) exposed female mice to BaP during days 7 to 16 of gestation
via oral intubation. Exposure doses were 10, 40, and 160 mg/kg-day. Total sterility occurred in
97 percent of offspring in the 40- and 160-mg/kg-day groups; fertility was impaired in offspring
at the lowest exposure dose tested of 10 mg/kg-day (Sample et al. 1996). To estimate a NOAEL
for the mouse from the unbounded LOAEL, the LOAEL is divided by a UF of 10 (U.S. EPA
1995b).
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LOAEL for mouse = 10 mg/kg-day
NOAEL for mouse
LOAEL for mink
= 1 mg/kg-day
NOAEL for mink
daily dose to mouse x (mouse body weight/mink body
weight)114
10 mg/kg[mouse]/day x (0.03 kg[mouse] / 1 kg[mink])1/4
4.17 mg/kg-day
0.417 mg/kg-day
Common Merganser Benzo[a]pyrene (BaP) TRV
In the absence of data for BaP toxicity to birds, we did not estimate TRVs for birds for BaP.
A.3.2.6 Dioxins Index Chemical—2,3,7,8-TCDD Wildlife TRV
Data were available to calculate a TRV for both (1) mink and (2) American merganser.
Mink 2,3,7,8-TCDD TRV
Using a three-generation study design, Murray et al. (1979) identified a NOAEL and LOAEL for
reproductive effects of 2,3,5,8-TCDD in rats of 0.001 and 0.01 |ig/kg-day (U.S. EPA 1995b,
Sample et al. 1996):
= 0.001 |ig/kg-day
= 0.01 |ig/kg-day
= daily dose to rat x (rat body weight / mink body weight)114
= 0.000001 mg/kg[rat]/day x (0.35 kg[rat] / 1 kg[mink])1/4
= 7.71E-07 mg/kg-day
LOAEL for mink = 7.71E-06 mg/kg-day
American Merganser 2,3,7,8-TCDD TRV
No avian toxicity studies are available for TCDD administered orally. We did identify an
intraperitoneal (i.p.) injection study. Nosek et al. (1992, 1993) dosed female ring-necked
pheasants (Phasianus colchicus) one time per week for 10 weeks by i.p. injection. The
equivalent average daily doses were 0.14, 0.014, and 0.0014 |ig/kg-day. This route of
administration ensures "uptake" of the complete dose and avoids the "first pass through the
liver." We investigated, however, the possibility that oral administration can result in lower
uptake.
NOAEL for rat
LOAEL for rat
NOAEL for mink
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Available data indicate no differences in gastrointestinal tract absorption of dioxins across
taxonomic groups of mammals and some birds (van den Berg et al. 1984). Moreover, uptake of
2,3,7,8-TCDD by mammals following oral administration appears high, ranging from 75 percent
(hamster) to >86 percent (humans), with absorption depending on the oil content of the vehicle
(van den Berg et al. 1984). In mammals, the tissue distribution of administered 2,3,7,8-
substituted polychlorinated dibenzo-p-dioxins (PCDDs) and 2,3,7,8-substituted polychlorinated
dibenzofurans (PCDFs) following i.p. and subcutaneous administration is similar to that
following oral administration, with the highest proportion of the dose retained in the liver and in
adipose tissues (van den Berg et al. 1984). Based on that information, we conclude that i.p.
administration can represent ingestion toxicity.
The NOAEL and LOAEL values for egg production by pheasants administered 2,3,7,8-TCDD
were 0.014 and 0.14 |ig/kg-day, respectively (Nosek et al. 1992, 1993, U.S. EPA 1995b). Given
the similarity in size between female ring-necked pheasant (0.9 to 1.1 kg) and American
merganser (1.3 kg), no dose conversions were estimated:
NOAEL for merganser = 1.4E-05 mg/kg-day
LOAEL for merganser = 1.40E-04 mg/kg-day
A.4 Derivation of Ecological TEFs for POM and Dioxin Benchmarks
Section A.4.1 covers the derivation of TEFs for POM relative to BaP for the benchmarks for
surface waters, sediments, and soils. If the POM is more toxic than BaP, the POM's benchmark
would be lower than the benchmark for BaP, and the TEF would be greater than 1.0. If the POM
is less toxic than BaP, the POM's benchmark would be higher than the benchmark for BaP, and
the TEF would be less than 1.0
Section A.4.2 covers the derivation of TEFs for dioxins relative to 2,3,7,8-TCDD. If the
congener is less toxic than TCDD, the TEF would be less than 1. All TEFs for dioxins are less
than or equal to 1.
A.4.1 TEFs for POM for Surface Water, Sediments, and Soils
Table A-12 lists the TEFs for POM compounds relative to BaP. Physical and chemical properties
of the unsubstituted PAHs, and their toxic mode of action (MO A), tend to be similar, with values
for some parameters, including toxic potency, changing predictably with the number of aromatic
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rings and the configuration of those rings (e.g., compact, elongated). With any substitutions (e.g.,
alkyl groups, alcohol groups, chlorine or bromine atoms) or with noncarbon atoms (e.g.,
nitrogen) included in five-carbon non-aromatic rings, the MOA can change for some groups of
organisms (e.g., crustaceans, insects, algae, terrestrial plants) in ways that would not be predicted
on the basis of other groups (e.g., aquatic or terrestrial vertebrates).
Table A-12. Toxicity Equivalency Factors (TEFs) for Surface Waters, Soils, Sediments, and
Mammalian Wildlife—POM Compounds Relative to BaP
Polyaromatic Organic Matter
CAS RN
Surface Water TEF
Soil TEF
Sediment TEF
Mammalian TEF
1-Methylnaphthalene
90-12-0
0.000042
0.47
7.4
0.014
2-Acetylaminofluorene
53-96-3
0.000026
2.55
9.79
1
2-Chloronaphthalene [beta]
91-58-7
0.0354
125
0.36
1
2-Methylnaphthalene
91-57-6
0.000042
0.470
7.4
0.014
3-Methylcholanthrene
56-49-5
0.16
19.5
0.000018
1
7,12-Dimethylbenz[a]anthracene
57-97-6
0.026
0.093
0.002
1
Acenaphthene
83-32-9
0.00037
0.002
22.4
0.0057
Acenaphthylene
208-96-8
0.000003
0.002
25.6
0.056
Anthracene
120-12-7
0.40
0.001
2.62
0.001
Benz[a]anthracene
56-55-3
0.56
0.292
1.39
1
Benz[a]anthracene/Chrysene
NA
0.1
0.1
0.1
0.1
Benzo[a]fluoranthene
203-33-8
0.0015
0.025
0.014
7.5
Benzo[c]phenanthrene
195-19-7
0.42
0.292
1.4
1
Benzo[g,h,i]fluoranthene
203-12-3
0.71
0.292
1.39
1
Benzo(g,h,i)perylene
191-24-2
0.0018
0.013
0.882
1
Benzo[a]pyrene
50-32-8
1
1
1
1
Benzo[b]fluoranthene
205-99-2
0.0015
0.025
0.014
4.4
Benzo[b+k]fluoranthene
NA
0.0015
0.010
0.625
7.5
Benzo[e]Pyrene
192-97-2
3
1
1
1
Benzo[j]fluoranthene
205-82-3
0.0015
0.01
0.625
7.5
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Polyaromatic Organic Matter
CAS RN
Surface Water TEF
Soil TEF
Sediment TEF
Mammalian TEF
Benzo[k]fluoranthene
207-08-9
0.0015
0.010
0.625
7.5
Carbazole
86-74-8
0.0054
0.2
0.36
0.008
Chrysene
218-01-9
0.0020
0.321
0.904
6
Dibenzo[a,h]anthracene
53-70-3
0.0028
0.083
4.54
2
Dibenzo[a,i]pyrene
189-55-9
0.003
0.14
0.8
1
Dibenzo[a,j]acridine
224-42-0
0.43
0.29
1.4
0.1
Fluoranthene
206-44-0
0.0074
0.012
0.355
0.0067
Fluorene
86-73-7
0.00074
0.012
1.94
0.008
lndeno[1,2,3-c,d]pyrene
193-39-5
0.0032
0.014
0.75
1
PAH, Total
NA
0.1
0.1
0.1
0.1
Perylene
198-55-0
2
1
1
1
Phenanthrene
85-01-8
0.0039
0.033
0.735
0.001
Polycyclic organic matter
NA
0.1
0.1
0.1
0.1
Pyrene
129-00-0
0.047
0.019
0.77
0.013
Retene
483-65-8
0.015
0.093
0.002
1
Note: If the TEF is less than 1, the chemical is not as toxic to organisms in that medium as is BaP (in bold). If the TEF
is equal to or greater than 1, the chemical is as toxic or more toxic to organisms in that medium as BaP.
Most research on toxic effects of PAHs and similar POM in the United States has focused on
their mutagenic and carcinogenic potential in mammals, with several being known human
carcinogens. As stated in the introduction, cancer is not an endpoint pursued for wildlife and
nonvertebrate animal risk assessment (CCME 2010); thus, TEFs based on carcinogenic potency
of POM relative to BaP are not applicable to ecological risk assessments.
General considerations for deriving TEFs for POM for surface water, soil, and sediment in Table
A-12 are described in Sections A.4.1.1 through A.4.1.5. Chemical-specific derivations are
described in Section A.4.1.6.
A.4.1.1 Data Retrieval and Comparison to Estimate TEFs for POM
To develop TEFs for POM relative to BaP, we first consulted ORNL RAIS to identify any
benchmarks available for POM compounds. We used the same hierarchy of preferred data
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sources as described in Section A.2.1.4. When available, if the source was the same source that
we used for the BaP benchmark for the particular environmental medium (e.g., surface water,
sediment, soil), we could compare the POM benchmark to the BaP benchmark to calculate a
TEF.
For some POM chemicals, EPA OW or an EPA region had calculated sediment quality criteria
using the using the equilibrium partitioning (EqP) approach. That approach includes several
assumptions about environmental characteristics, as explained below.
The EqP approach estimates pore-water concentrations of a chemical assuming an equilibrium
between the chemical adsorbed to organic carbon in the sediments and the chemical freely
dissolved in the pore water. The model uses a chemical-specific surface water quality benchmark
(WQB), such as an NAWQC-ALC, and an organic carbon partitioning factor (Koc), which is
based on experimentally measured value(s) or can be estimated using Equation A-3 from an
experimentally measured octanol-water partitioning coefficients) (Kow) for the specified
chemical (provided suitable empirical data are available):
SOB = foc x Koc x WQB Eq. A-3
where
SQB = chemical-specific sediment quality benchmark
foc = fraction total organic carbon (TOC) in sediments
Koc = chemical-specific organic carbon/water partition coefficient
WQB =
chemical-specific water-quality benchmark for the protection of water-column
biota
The EqP model requires the risk assessor to assign a total organic carbon (TOC) concentration in
sediments using site-specific measurements or to use values typical of certain types of water
bodies [e.g., data presented in U.S. EPA (2003d) are from sediments with 0.201 to 15.2 percent
organic carbon]. For a regional or nationwide environmental screen, however, a common
approach is to assume a relatively low TOC value to maximize the chemical's bioavailability.
Several EPA regions and Environment Canada have adopted the 1-percent TOC value used by
Jones et al. (1997) for DOE. Using that assumption, one can calculate a chemical-specific SQB
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as the total concentration of the chemical in sediment (on a dry-weight basis) that would produce
a sediment pore-water concentration equal to the WQB.
TRIM.FaTE assumes a TOC for sediments of 2 percent. Had a TOC of 2 percent been used
instead of 1 percent with the EqP model to estimate SQBs for the non-ionic organic chemicals,
the EPA-calculated SQB values would have been higher (i.e., less conservative) by a factor of 2.
In that case, the ratio of TRIM.FaTE-predicted sediment concentrations to the sediment
benchmarks would have been lower, meaning that more facilities might have passed the tiered
screening (i.e., be removed from further consideration).
For many POM chemicals, however, no benchmarks are included in RAIS. We next consulted
the EPA NAWQC, the Eco-SSLs, and the EPA regional benchmark compilations to identify
benchmarks for POM that might not have been included in ORNL RAIS. When those efforts
failed, we finally sought individual toxicity study entries in EPA's ECOTOX database. Data
presentation in ECOTOX is difficult to interpret because the results from one experiment are
presented as separate records depending on the endpoint (ECio, ECso, NOEC, LOEC, LCso) and
sometimes experimental conditions that are not coded into ECOTOX (e.g., presence or absence
of UV radiation). We therefore used ECOTOX primarily to identify original publications with
titles indicating a focus on endpoints and chemicals of interest. Finally, for chemicals not
included in ECOTOX, we conducted web searches by chemical name or CAS Registry Number
for ecotoxicity tests, revealing additional original study reports for several POM.
A.4.1.2 General Characteristics of RTR POM
Most POM included for RTR multipathway assessment are PAHs. Excluding naphthalene, PAHs
have relatively high melting and boiling points and low water solubility. Their water solubility
increases with decreasing molecular weight. Most PAHs are highly lipophilic, with lipophilicity
(i.e., Kow) increasing with increasing molecular weight. Overall, aquatic invertebrates (e.g.,
annelids, insect larvae, daphnids) are more sensitive than fish, and benthic fish are among the
least sensitive species to PAHs (Wang et al. 2013, 2014, as cited by Wu et al. 2016). Four
characteristics of POM challenge attempts to develop new TEFs as discussed in the paragraphs
that follow:
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• Aquatic toxicity testing is complicated by low solubility of high-K0W chemicals.
• Quantitative structure-activity relationships (QSARs) are not available for PAH modes of
action.
• Sunlight can modify the toxicity of PAHs to plants and invertebrates.
• Bioaccumulation depends on taxonomy.
A.4.1.3 Aquatic Toxicity Testing Limited by Low Solubility of High-Kow Chemicals
Aquatic toxicity testing for PAHs, including BaP, and other POM has been limited by the low
solubility of high-K0W chemicals. Typical endpoints often are not reached at the limit of
solubility for the high-molecular-weight (HMW) polycyclics (e.g., four or more aromatic rings,
225 grams per mole or higher). In an early-lifestage study of BaP toxicity to rainbow trout
(Oncorhynchus mykiss), a NOEC of 1.5 |ig/L and an ECio of 2.9 |ig/L were identified for
developmental abnormalities (Hannah et al. 1982, ECHA 2016). Danio rerio (zebra danio)
exposed for 28 days, on the other hand, showed no abnormalities at 4 |ig/L (Hooftman and
Evers-de Ruiter 1992, as cited by ECHA 2016), which is at or above the water solubility of BaP
(approximately 3.8 |ig/L). As a consequence, many of the high Kow POM have not been tested
for aquatic toxicity. Fish in bodily contact with sediments (e.g., salmon eggs, other fish eggs and
fry, flatfish) can be exposed both dermally and via desorption from sediment particles; thus
embryo toxicity tests with spiked sediments are available for some HMW POM.
A.4.1.4 QSAR Applicability is Limited for High Kow Chemicals
QSAR models that predict the aquatic toxicity of HMW non-ionic organic chemicals (e.g.,
ECOSAR in EPA EPI-Suite, U.S. EPA 2012b) are generally not valid for high-K0W chemicals
because HMW chemicals with logKow values over 6 are too large to readily penetrate membranes
despite their lipophilicity. For example, the maximum logKow for which ECOSAR estimates of a
fish 96-hour LCso, a daphnid 48-hour LCso, or a mysid 96-hour LCso are valid is 5.0, and the
maximum for an ECOSAR prediction of an earthworm LCso is 6.0.
The maximum logKow for which chronic values for fish and invertebrates might be valid is 8.0.
Droge et al. (2006) demonstrated, however, that PAHs can induce mortality via narcosis
(corresponding to Kow at concentrations for which the PAH is soluble), whereas reproductive
effects did not follow Kow. We do not recommend a QSAR based on narcosis as a valid predictor
of POM-induced endocrine disruption or developmental abnormalities. Sverdrup et al. (2001)
demonstrated that some PAHs are more or less toxic than predicted on the basis of neutral
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organic QSAR models. For example, carbazole and acridine are more toxic to springtail
reproduction than predicted on the basis of Kow for nonpolar organics, while fluoranthene is less
toxic than predicted on the basis of Kow values and estimated pore-water ECio values (Sverdrup
etal. 2001,2002a).
We conclude that the QSAR models available for aquatic or earthworm toxicity, such as those
included in EPA's ECOSAR model, are unlikely to provide reasonable predictions of POM
toxicity to aquatic organisms, acute or chronic. Instead, for POM chemicals without toxicity
data, we used chemical structure (e.g., elongated versus compact, locations of substitutions) in
addition to Kow and number of benzene rings to identify which parameterized POM appeared
most similar, as described in Section A.4.1.6.
A.4.1.5 Photomodification of PAH Toxicity
Another attribute of PAHs that complicates interpretation of aquatic and surface soil toxicity
testing is that sunlight can increase the toxicity of many congeners. For PAHs in surface waters
and surface soils, two or more conjugated benzene rings facilitate absorption of UV-A and
UV-B, and, in some instances, visible light (i.e., wavelengths of 400-700 nm) (Lampi et al.
2006). PAHs strongly absorb photons in the UV-B (290-320 nm) and UV-A (320-400 nm)
wavelength regions (both regions are in sunlight). The toxicity of PAHs can be enhanced in the
presence of UV radiation; however, lighting in laboratory settings usually is in the visible range
only. Photosensitization (PSC) reactions result from generation of singlet-state oxygen (Krylov
et al. 1997). Photomodification (PMC) results from photooxidation or photolysis (Huang et al.
1997).
Krylov et al. (1997) examined the phytotoxicity of 16 PAHs to Lemna gibba (duckweed). All 16
PAHs exhibited half-lives in simulated sunlight including UV-A and UV-B of 100 hours or less.
Anthracene was by far the most toxic of the PAHs examined. Intact anthracene is not a strong
photosensitizer; perhaps its degradation products cause its toxicity. Kow values do not predict
photoinduced toxicity for PAHs. Krylov et al. (1997) provided a QSAR model based on the 16
PAHs that includes both PSC and PMC. The predictive model indicates that PSC and PMC
contribute additively to toxicity. We consider attempts to use this model beyond the scope of
identifying TEFs for aquatic plants for PAHs. Moreover, because plants generally are less
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sensitive to PAHs than are invertebrates and fish, we did not use aquatic plant toxicity tests to
estimate TEFs for surface water.
PSC and PMC reactions can affect PAH toxicity to aquatic invertebrates, such as Daphnia
magna. Lampi et al. (2006) found that toxicity increased (EC50 decreased) approximately
threefold in simulated sunlight compared with visible-plus-UVA (no UV-B). Again, Kow does
not predict PSC or PMC reactions. Lampi et al. (2006) demonstrated that some PAHs absorb
UV-A poorly (e.g., chrysene, fluorene), while decreases in EC50 values were substantial for
PAHs that strongly absorb in the UV-B region (benzo[e]pyrene, benzo[g]pyrene,
dibenzo[a,i]pyrene). Accounting for PSC and PMC reactions and toxicity to invertebrates is
beyond the scope of this work. To estimate TEFs by matching individual toxicity tests of a
congener with BaP, however, we did attempt to match the lighting conditions (e.g., simulated
sunlight or visible light only).
A.4.1.6 TEFs for POM for Surface Waters, Soils, and Sediments
For POM chemicals for which we identified appropriate benchmarks to compare with the BaP
benchmarks for surface water, sediment, or soil, we used the ratio of the benchmarks to calculate
a TEF appropriate for the ecological assessment endpoint and environmental medium.
Benchmarks were not available, however, for many individual POM chemicals. We therefore
needed to use original toxicity tests to estimate TEFs.
To calculate TEFs from individual toxicity tests requires a different approach than estimating
TEFs from community benchmarks already calculated on the basis of many different species'
tests and possibly some "uncertainty" factors based on available data. We compared individual
ecotoxicity tests for POM only to the same type of study for BaP. We therefore also used
ECOTOX and web searches to compile individual toxicity test data for BaP to enable those
comparisons. Study types and endpoints include 96-hour algal EC50 values, 48-hour daphnid
LC50 values, 96-hour fish LC50 values, 2- to 4-week early-lifestage studies with fish (considered
chronic), and 4-week earthworm or springtail survival and reproduction tests (soil toxicity tests),
although only one or two study types/endpoints were available for most of the POM not included
in RAIS. A compilation of those data is available on request. We document below the
comparisons on which we based individual POM TEFs, presented in Table A-12, where
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benchmarks were not already established. For each POM, we include its CAS Registry Number,
its logKow, and a schematic of its chemical structure to help the reader follow our rationale.
Benzo[a]pyrene (BaP; CAS # 50-32-8): LogKow = 5.97. Index chemical for POM TEFs. The
average of six 4-day and one 2-day daphnid LC50 values is 4.0 |ig/L (range 1.0-6.1 |ig/L) (Lampi
et al. 2006; Wu et al. 2016; Trucco et al. 1983; Ikenaka et al. 2013). With
sunlight (which includes UV-A and UV-B), BaP is more toxic to daphnids than
under visible light plus UV-A, which is more toxic than under laboratory lighting
with no UV-A or UV-B. When fed to Chlorella sp., BaP is less toxic than it is to
daphnids not fed during the exposure period (Ikenaka et al. 2013). Nonetheless, the range of
LC50 values across those conditions is limited (i.e., 1.3-6.1 |ig/L). For bluegill fish (Lepomis
macrochirus), the 4-day LC50 is 5 |ig/L (Wu et al. 2016). For rainbow trout (Oncorhynchus
mykiss, formerly Salmo gairdneri), a 36-day EC 10 for abnormalities in development of early
lifestages is 2.9 |ig/L (NOEC is 1.5 |ig/L) (ECHA 2016 calculated from exposure-response data
reported by Hannah et al. 1982). Algae are not as sensitive as daphnids or fish by two or more
orders of magnitude (Warshawsky et al. 1995). The aquatic toxicity benchmarks border on the
limit of solubility of 0.0063 |imol/L (Pearlman et al. 1984) or about 1.6 |ig/L (BaP molecular
weight = 252.3 g/mole). These toxicity data are compared to available toxicity test data for other
POM chemicals below.
1-Methylnaphthalene (CAS # 90-12-0): LogKow = 3.87. In freshwater, the 4-day
LC50 for fathead minnow (static test) is 9,000 |ig/L (Mattson et al.
1976), compared with the BaP-exposed bluegill (Lepomis
macrochirus) 4-day LC50 of 5 |ig/L (Wu et al. 2016). The ratio of
those values yields an acute TEF of 0.00056 |ig/L. Those studies, however, do not predict
chronic toxicity of 1-methylnaphthalene to aquatic organisms or to soil communities and birds
and mammals. Therefore, we assigned the same TEFs as for 2-methylnaphthalene (CAS # 91-57-
6) (figure above right) to 1-methylnaphthalene.
2-Acetylaminofluorene (CAS # 53-96-3): An aromatic amine with LogKow =
3.28. TEFs for surface water, soil, and sediment are from Region 5 ESL
benchmarks relative to Region 5 ESLs for BaP for each medium, respectively (U.S.
EPA 2003c).
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2-Chloronaphthalene (CAS # 91-48-7): LogKow = 4.14. TEFs for surface water,
crXDO soil, and sediment are from Region 5 ESLs relative to Region 5 ESLs for BaP for
each medium, respectively (U.S. EPA 2003c).
3-Methylcholanthrene (CAS # 56-49-5): LogKow = 6.42. TEFs for surface
water, soil, and sediment are from Region 5 ESLs relative to Region 5 ESLs for
BaP for each medium, respectively (U.S. EPA 2003c).
Benzo[e]pyrene (BeP, CAS # 192-97-2): More compact and with a higher logKow (6.44) than
BaP. Under visible light plus UV-A, the 48-hour daphnid immobility ECso
(= LCso) = 1.43 |ig/L, whereas under simulated sunlight spectrum, the EC50 =
oor
0.325 |ig/L (Lampi et al. 2006). For BaP, under visible light plus UV-A, the 48-
hour daphnid immobility EC50 = 1.62 |ig/L, whereas under simulated sunlight
spectrum, the EC50 = 0.98 |ig/L (Lampi et al. 2006). No other data on freshwater organisms was
identified. Freshwater TEF for BeP = under simulated sunlight = 0.98/0.325 = 3.0. No
ecotoxicity data were identified for sediments or soils; therefore, we set the remaining TEFs to
1.0 assuming similarity to BaP.
Benzo[a]fluoranthene (BaF, CAS # 203-33-8): LogKow = 6.11, which is higher
than the logKow for benzo[b]fluoranthene (BbF, CAS # 205-99-2, logKow =5.78,
figure to the right). BaF also has a higher logKow than
jQP benzo[k]fluoranthene (BkF, logKow 5.94) and a lower logKow than
benzo[j]fluoranthene (BjF, logKow 6.4). No ecotoxicity data were identified for BaF for water,
sediment, or soils. TEFs were set equal to those for BbF (figure right).
Benzo[j]fluoranthene (BjF, CAS # 205-82-3): LogKow = 6.4, which is higher
~ than for the other benzofluorenes. All benzo[x]fluorenes have the same
OQ) molecular weight (252.3 g/mole). No toxicity data were identified for
BjF for water, sediment, or soils. TEFs were set equal to those for BbF (figure right).
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Benzo[g,h,i]fluoranthene (CAS # 203-12-3): LogKow= 5.52 (molecular weight 226.3 g/mole).
With logKow close to 5.5, we used EPA EPI-Suite ECOSAR Version 4.11 (U.S. EPA 2012b) to
estimate four freshwater TEFs: 48-hour LCso for daphnids, 96-hour LCso value
for fish, chronic value for daphnids, and chronic value for fish. Toxicity to algae
(V0
is not evaluated because algae are generally less sensitive than invertebrates to
PAHs and because less sensitive species can replace more sensitive species in the
water column community. The four toxicity values were compared with the same values from
EPI-Suite ECOSAR for BaP to calculate the four corresponding TEFs. The highest of the four
corresponding TEFs (chronic fish TEF of 0.71) represents the surface water TEF (range of four
TEFs from 0.60 to 0.71). For the remaining TEFs for sediment, soil, birds, and mammals, for
which no toxicity data were identified, we set TEFs equal to those for benz[a]anthracene which
has a logKow of 5.79, a similar molecular weight (226.3 g/mole), and a similar TEF (0.56) for
surface waters.
Benzo[c]phenanthrene (CAS # 195-19-7): LogKow = 5.52. With logKow close to 5.5, we used
EPA's EPI-Suite ECOSAR Version 4.11 to estimate four freshwater TEFs as explained for
benzo[g,h,i]fluoranthene above. The highest of the four corresponding TEFs, chronic
fish TEF of 0.42, represents the surface water TEF (range of four TEFs from 0.32 to
i]
0.42). For the remaining TEFs for sediment and soil, for which no toxicity data were
identified, we set TEFs equal to those for benz[a]anthracene, which has a logKow of 5.79, the
same molecular weight (228.29 g/mole), and a similar TEF (0.56) for surface waters.
Carbazole (CAS # 86-74-8): LogKow = 3.72. In freshwater, fathead minnow 4-day LCso (flow-
through design) = 930 |ig/L (Brooke 1991) compared with BaP exposed bluegill (Lepomis
mcicrochirus) 4-day LCso of 5 |ig/L (Wu et al. 2016) for a TEF of 0.0054. For
soils, earthworm 28-day LCso = 106/2 (division by 2 because endpoint is 50
percent lethality) = 53 mg/kg dry soil and ECso for growth = 54 mg/kg dry soil
(Sverdrup et al. 2002b) compared with a BaP 28-day LOEC for earthworm
survival of 10 mg/kg dry soil (Achazi et al. 1995 as cited by Sverdrup et al. 2007) for TEF of
0.20. For sediments, no data were found; therefore, we assign a sediment TEF of 7.5, which is
similar to the other PAHs with logKow values between 3.28 and 3.87. Carbazole is structurally
similar to fluorene, except the nitrogen atom is at the apex of a five-member nonaromatic ring in
center.
CK>
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^ Dibenzo[a,i]pyrene (DaP, CAS #189-55-9): LogKow = 7.28. Extremely low
solubility. We assigned the same TEFs as indeno[l,2,3-c,d]pyrene, which has a Kow
value of 6.72. Aquatic toxicity benchmarks are higher than the limit of solubility.
Dibenzo[a,j]acridine (CAS # 224-42-0): LogKow= 5.63. With logKowunder 6.0,
we used EPA EPI-Suite ECOSAR Version 4.11 to estimate four freshwater TEFs
as explained for benzo[c]phenanthrene (above). The highest of the four
corresponding TEFs (chronic fish TEF of 0.43) represents the surface water TEF
(range of four TEFs from 0.35 to 0.43). That aquatic toxicity benchmarks are not reached at the
limit of solubility is possible.
Perylene (CAS # 198-55-0): LogKow = 5.82 (although the MSDS, http://datasheets.scbt.com/sc-
206007.pdf. states 6.25 for logKow). Fish 4-day LC50 values range from 1.1 to 5.0 (MSDS). In a
daphnid acute test, 0.6 |ig/L kills 50% of individuals in 0.764 days (LT50 in
renewal system). Thus, perylene appears to be more toxic in the water column to
both fish and daphnids than is BaP. Based on those data, we set the TEF for
freshwater to 2.0 compared with BaP. We set the remaining TEFs for perylene
for soil and sediments to 1.0 compared with BaP.
Phenanthrene (PHE, CAS # 85-01-8): LogKow = 4.46. TEFs for surface water,
soil, and sediment are from Region 5 ESLs relative to Region 5 ESLs for BaP for
each medium, respectively (U.S. EPA 2003c).
Pyrene (PYR, CAS # 129-00-0): LogKow = 4.88. TEFs for surface water, soil, and
sediment are from Region 5 ESLs relative to Region 5 ESLs for BaP for each
medium, respectively (U.S. EPA 2003c).
Retene (CAS # 483-65-8): LogKow = 6.35. In freshwater, the 14-day LC50 (flow-
through design) for zebra Danio fish = 353 |ig/L (Billiard et al. 1999) compared
with bluegill 4-day LC50 of 5 |ig/L for BaP (Wu et al. 2016). Comparing those
two studies as "acute" values, the TEF equals 0.014. A zebra Danio early lifestage
42-day LOEC (flow-through) =180 |ig/L (NOEC = 100 |ig/L; Billiard et al. 1999). Compared
with the LOEC of 2.9 |ig/L for developmental abnormalities in a 36-day BaP exposure of early
lifestage Oncorhynchus mykiss (rainbow trout, ECHA 2016, exposure-response calculation from
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data in Hannah et al. 1982), the chronic TEF equals 0.016. Surface water TEFs based on both
acute and chronic exposures of fish equal 0.015. No relevant data were found for sediments or
soils; therefore, those TEFs for retene were assigned based on 7,12-dimethylbenz[a]anthracene,
which has a similar freshwater TEF of 0.026 |ig/L, a similar logKow of 5.8, and two alkyl groups
attached to its rings (figure above right).
A.4.2 TEFs for Dioxins for Surface Water, Sediments, and
Soils
Table A-13 presents the TEFs for dioxins for surface water, soils, and sediments.
Surface water TEFs are based on 1998 World Health Organization (WHO) TEFs
for fish (from Van den Berg et al. (1998) as presented in Table 4 of Framework for Application
of the Toxicity Equivalence Methodology for Polychlorinated Dioxins, Furans, and Biphenyls in
Ecological Risk Assessment (U.S. EPA (2008). For sediments, we set the TEF for each congener
to the TEF for surface water based on the concept of equilibrium partitioning as per the Canadian
ISQG for the protection of aquatic life (CCME 2001).
For soils, we adopted a different approach. Plants and most invertebrate groups are not adversely
affected by dioxins, because they lack the aryl hydrocarbon (Ah) receptor that mediates the
adverse effects in vertebrates, including birds and mammals (UKDTER 1999).We concluded that
the soil TEFs should be based on relative toxicity to birds or to mammals to reflect possible
toxicity to ground-feeding birds and mammals. We therefore set the soil TEFs for dioxins to the
TEF for mammalian or avian wildlife, whichever of the two was higher.
A.5 TEFs for Wildlife TRVs
This section describes the derivation of TEFs for the wildlife TRVs for POM and for dioxins.
A.5.1 TEFs for Wildlife for POM
Most POM in the RTR multipathway list have been screened in vitro (cell cultures) for
carcinogenic potential; however, cancer is not an endpoint evaluated for wildlife risk
assessments (CCME 2010). Most animals die from starvation, disease, extreme weather, or
predation before tumors can develop. Disruption of vertebrate endocrine systems, immune
effects, and fetal abnormalities are endpoints of concern for wildlife (CCME 2010).
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Table A-13. Toxicity Equivalency Factors (TEFs) for Surface Waters, Soils, Sediments, and
Mammalian and Avian Wildlife—Dioxins Relative to 2,3,7,8-TCDD
Congener
CAS RN
Surface
Water TEF
Soil TEF
Sediment
TEF
Mammalian
TEFa
Avian TEF
1,2,3,4,6,7,8,9-OCDD
3268-87-9
0.0001
0.0003
0.0001
0.0003
0.0001
1,2,3,4,6,7,8,9-OCDF
39001-02-0
0.0001
0.0003
0.0001
0.0003
0.0001
1,2,3,4,6,7,8-HpCDD
35822-46-9
0.001
0.01
0.001
0.01
0.0005
1,2,3,4,6,7,8-HpCDF
67562-39-4
0.01
0.01
0.01
0.01
0.01
1,2,3,4,7,8,9-HpCDF
55673-89-7
0.01
0.01
0.01
0.01
0.01
1,2,3,4,7,8-HxCDD
39227-28-6
0.5
0.1
0.5
0.1
0.05
1,2,3,4,7,8-HxCDF
70648-26-9
0.1
0.1
0.1
0.1
0.1
1,2,3,6,7,8-HxCDD
57653-85-7
0.01
0.1
0.01
0.1
0.01
1,2,3,6,7,8-HxCDF
57117-44-9
0.1
0.1
0.1
0.1
0.1
1,2,3,7,8,9-HxCDD
19408-74-3
0.01
0.1
0.01
0.1
0.1
1,2,3,7,8,9-HxCDF
72918-21-9
0.1
0.1
0.1
0.1
0.1
2,3,4,6,7,8-HxCDF
60851-34-5
0.1
0.1
0.1
0.1
0.1
1,2,3,7,8-PeCDD
40321-76-4
1
1
1
1
1
1,2,3,7,8-PeCDF
57117-41-6
0.05
0.1
0.05
0.03
0.1
2,3,4,7,8-PeCDF
57117-31-4
0.5
1
0.5
0.3
1
2,3,7,8-TCDD
1746-01-6
1
1
1
1
1
2,3,7,8-TCDF
51207-31-9
0.05
1
0.05
0.1
1
Abbreviations: CDD = chlorinated dibenzo-p-dioxins; CDF = chlorinated dibenzo-p-furans. Hp = hepta (seven); Hx =
hexa (six); O = octa (eight), Pe = penta (five); T = tetra (four)
Note: If the TEF is less than 1, the chemical is not as toxic to organisms as is 2,3,7,8-TCDD (in bold). If the TEF is
equal to or greater than 1, the chemical is as toxic or more toxic to organisms as 2,3,7,8-TCDD.
a Source: Van den Berg et al. (2006).
As was the case for the benchmarks for surface water, soils, and sediments, we found no
additional avian or mammalian toxicity data for many of the recently added POM. Although
EPA's ECOTOX database does include avian and toxicity data in addition to aquatic toxicity
information, we found that few of the new POM are included in ECOTOX.
We found avian embryo toxicity data for several POM chemicals based on egg injection studies.
Brunstrom et al. (1990) reported the proportion of eider duck embryos that died or were
malformed after a single injected dose (2 mg/kg egg) for several POM (BaP, 30% mortality;
BkF, 100%; fluoranthene, 20%; benzo[g,h,i]perylene, 15%; and indeno[ 1,2,3-c,d]pyrene, 85%
mortality). Many of the POM chemicals were not toxic to eggs at 2 mg/kg egg (i.e., anthracene,
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fluorene, pyrene, BeP, perylene, benzo[g,h,i]perylene). These data are insufficient to estimate
avian toxicity TEFs for most RTR POM; we therefore did not estimate avian TEFs.
For mammals, we checked the Canadian TRV data for wildlife in CCME (2010) for
unsubstituted PAHs to identify original toxicity study data that focused on endpoints other than
carcinogenicity and mutagenicity. For other POM, we checked EPA's Integrated Risk
Information System. We considered subchronic or chronic oral administration studies for which
the concentration of chemical in the diet had been converted to a dose in mg/kg body weight per
day. In addition, for several POM, we compared the immunocompetence of mice following a
single intraperitoneal dose of the POM with the immunocompetence of mice following a single
dose of BaP (Silkworth et al. 1995). We did not consider LD50 data for mammals to be
appropriate for estimating chronic TEFs because the MO A of acute lethality and chronic effects
on immunity, reproduction, or development likely differs. The variety of chemical compounds
included in the 2016 list of POM also suggests that several different MO As might be relevant.
Table A-14 presents the data used to estimate TEFs for the RTR POM for mammalian wildlife.
Insufficient data were available to calculate TEFs for POM for birds. The mammalian TEFs also
are included in Table A-12 for comparison with other benchmark TEFs.
Table A-14. TEFs for Oral Exposures of Mammalian Wildlife—POM
Congeners Relative to BaP
Compound
(surrogate PAH)
CAS RN
N0AEL/L0AEL3
(mg/kg-d)
Species/Effect
(exposure regimen)
[notes]
NOAEL-
based TEF
Reference
1-Methylnaphthalene
(Naphthalene
surrogate)
90-12-0 (91-20-3)
71/143
Rats/decreased body wt
(5 d/wk for 13 wk)
[dose is time adjusted]
0.014
BCL (1980) in
CCME (2010)
2-Acetylaminofluorene
53-96-3
ND
Set = BaP reproduction
1
none identified
2-Chloronaphthalene
91-58-7
ND
Set = BaP reproduction
1
none identified
2-Methylnaphthalene
(Naphthalene
surrogate)
91-57-6 (91-20-3)
71/143
Rats/decreased body wt
(5 d/wk for 13 wk)
[dose is time adjusted]
0.014
BCL (1980) in
CCME (2010)
3-Methylcholanthrene
56-49-5
ND
Set = BaP reproduction
1
none identified
7,12-Dimethylbenz[a]
anthracene
57-97-6
ND
Set = BaP reproduction
1
none identified
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Compound
(surrogate PAH)
CAS RN
N0AEL/L0AEL3
(mg/kg-d)
Species/Effect
(exposure regimen)
[notes]
NOAEL-
based TEF
Reference
Acenaphthene
83-32-9
175/350
Mouse/liver wt (13 wk)
0.0057
U.S. EPA (1989a)
in CCME (2010)
and ATSDR (1995)
Acenaphthylene
208-96-8
18/180
Mouse/immunocompetence
(12 d) TDLo (NEL =
LEL/10)
0.056
RTECS (1999
Toxicologist 48:13,
in CCME 2010)
Anthracene
120-12-7
1000/
>1000
Mouse/multiple systems
examined (13 wk)
0.001
U.S. EPA (1989b)
in CCME (2010)
and ATSDR (1995)
Benz[a]anthracenea
56-55-3
-8% at 1
Mouse/immunocompetence
(1 dose)
1
Silkworth et al.
(1995)
Benzo[a]fluoranthene
203-33-8
ND
Set = BkF immune
7.5
none identified
Benzo[c]phenanthrene
195-19-7
ND
Set = BaP reproduction
1
none identified
Benzo[g,h,i]
fluoranthene
203-12-3
ND
Set = BaP reproduction
1
none identified
Benzo[g,h,i]perylene
191-24-2
ND
Set = BaP reproduction
1
none identified
Benzo[a]pyrenea
50-32-8
-8% at 1
By definition of index
chemical
1
Silkworth et al.
(1995)
Benzo[a]pyrene
50-32-8
1/10
Mouse/reduced fertility of
progeny of exposed
animals (gd 7-16)
1
Mackenzie &
Angevine (1981)
Benzo[b]fluoranthenea
205-99-2
-35% at 1
Mouse/immuno-
competence (1 dose)
4.4
Silkworth et al.
(1995)
Benzo[e]pyrene
192-97-2
ND
Set = BaP reproduction
1
none identified
Benzo[j]fluoranthene
205-82-3
ND
Set = BkF immune
7.5
none identified
Benzo[k]fluoranthenea
207-08-9
-60% at 1
Mouse/immunocompetence
(1 dose)
7.5
Silkworth et al.
(1995)
Carbazole
86-74-8
ND
Set = fluorene
0.008
none identified
Chrysenea
218-01-9
-48% at 1
Mouse/immunocompetence
(1 dose)
6
Silkworth et al.
(1995)
Dibenzo[a,h]
anthracene3
53-70-3
-15% at 1
Mouse/immunocompetence
(1 dose)
2
Silkworth et al.
(1995)
Dibenzo[a,i]pyrene
189-55-9
ND
Set = BaP reproduction
1
none identified
Dibenzo[a,j]acridine
224-42-0
ND
Less toxic than BaP
0.1
none identified
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Compound
(surrogate PAH)
CAS RN
NOAEL/LOAEL3
(mg/kg-d)
Species/Effect
(exposure regimen)
[notes]
NOAEL-
based TEF
Reference
Fluoranthene
206-44-0
150/250
Rat/increased liver wt
(13 wk)
0.0067
U.S. EPA (1988b);
Knuckles et al.
(2004)
Fluorene
86-73-7
125/250
Mouse/liver wt hemato-
logical effects (13 wk)
0.008
U.S. EPA (1989c)
in CCME (2010)
lndeno[1,2,3-c,d]
pyrene
193-39-5
ND
Set = BaP reproduction
1
none identified
Perylene
198-55-0
ND
Set = BaP reproduction
1
none identified
Phenanthrene
85-01-8
1000/
>1000
Set = anthracene
0.001
none identified
Pyrene
129-00-0
75/125
Mouse/nephrotoxicity
(90 d, gavage)
0.013
U.S. EPA (1989d)
in CCME (2010)
Retene
483-65-8
ND
Set = BaP reproduction
1
none identified
Abbreviations: Ah = aromatic hydrocarbon; d = day; gd = gestation day; LEL = lowest-effect level; NEL = no effect
level; ND = no data found; TDLo = threshold dose-lowest observed effect level; wk = week
aFor Silkworth et al. (1995), data in NOAEL/LOAEL column are the percent decrease in ability to suppress the
antibody response in Ah+/+ mice immunized 12 hours after administration of one dose of chemical at 1 mg/kg bw.
A.5.2 TEFs for Wildlife for Dioxins
To estimate TEFs for dioxins mammals and birds (listed in Table A-13 along with TEFs for
soils, sediments, and surface waters), we used the 1998 and 2005 WHO TEFs for dioxins and
furans as presented in EPA's Framework for Application of Toxicity Equivalency Methodology
(U.S. EPA 2008; Van den Berg et al. 1998; Van den Berg et al. 2006). The dioxin TEFs apply to
both cancer and noncancer (e.g., reproductive) endpoints, and therefore we did not need to look
for noncancer toxicity tests for individual dioxin congeners.
A.6 Piscivorous Wildlife Exposure Factors
To calculate wildlife exposures via fish ingestion, a series of exposure factor values and an
assumed diet are required for the representative species: mink and American merganser. Those
values are then used with the TRIM.FaTE estimates of chemical concentrations in fish in the
most contaminated lake to estimate mink and merganser chemical intake, in mg/kg-day, via fish
ingestion.
Although conceptually considered part of the "exposure assessment" described in the main
report, the values selected to parameterize the wildlife exposures via consumption of aquatic
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prey are used to backcalculate facility emission screening threshold rates that correspond to the
TRVs. Therefore, the input data used for the piscivorous wildlife exposure assessments,
calculated outside of TRIM.FaTE, are described in this section.
For the RTR environmental screen, the wildlife are assumed to consume their entire diet from the
lake located near the emissions source in the screening scenarios. To calculate the wildlife
exposure for each TRIM.FaTE screening scenario, the TRIM.FaTE estimates of chemical
concentrations in various compartments of the aquatic biota were calculated first. Then, wildlife
exposure based on those data and values for the wildlife exposure factors were calculated. The
wildlife exposure factors include an estimated FIR, the caloric energy of the food ingested, the
ability of the wildlife species to assimilate calories from the food, and the proportion of the
animal's diet consisting of each food type. Food ingestion rates were either obtained from
measured values in the open literature or calculated from estimates of free-living metabolic rate
(FMR) using allometric equations developed by Nagy (1987). Measured data were selected from
the information presented in EPA's (1993c,d) Wildlife Exposure Factors Handbook (WEFH) to
be "representative" of the data available for the species across its range.
Estimates of FMRs across animals of varying body size within numerous taxa have become
available with modern techniques using labeled oxygen measurements. Nagy and his colleagues
used the empirical data to develop allometric equations relating FMR to body weight for
numerous taxonomic groups (Nagy 1987; Nagy 2001). Estimates of FMR with body weight
within a taxon allows estimates of the required daily caloric intake from food. As described in
EPA's WEFH (U.S. EPA 1993c,d), with additional information on the caloric content of
different types of food and the food habits of a wildlife species, one can estimate the total weight
of different foods (e.g., different trophic levels of prey) ingested.
Information on the diets of wildlife species are obtained from field studies in which animals are
captured and their gut contents removed or from studies of animals found dead in the field. In
general, even the most specialized of feeders must adjust its food sources based on circumstance.
For piscivorous wildlife, consumption of fish and invertebrate species varies with availability
according to location and season. Nonetheless, comparisons of studies of the same species across
years and locations have revealed some consistent patterns that can be used as default
assumptions in an ecological risk screening scenario.
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The next two subsections describe the exposure parameter values and assumed diets used to
estimate consumption of fish for both mink (Section A.6.1) and American merganser (Section
A.6.2).
A.6.1 Mink Exposure Factor Values and Assumed Diet
For mink (Mustela vison or Neovison vison), none of the measured FIRs available for captive
animals were considered representative of free-living animals. Caged animals might not be as
active as free-ranging animals that must catch their prey in cold waters and escape predators. We
therefore used Nagy's (1987) allometric model for nonherbivorous mammals to estimate an
FMR first, which was converted to units of kcal/day as recommended by EPA (U.S. EPA
1993c). The FMR then was normalized to body weight (Table A-15). The estimate of a FMR of
245 kcal/kg bw/day in Table A-12 is similar to the estimated metabolic rate of 258 kcal/kg
bw/day for farm-raised (ranch cage) female mink as estimated by Farrell and Wood (1968).
Table A-15. Mink Exposure Factor Values
Parameter
Value
Comments/References
Body weight (kg)
0.8
Average of male and female body weights in summer in
Montana (Mitchell 1961)
Free-living metabolic rate (FMR):
Estimated for 1.0-kg mink using Nagy's (1987) allometric
equation for nonherbivorous mammals
FMR (k Joules/day)
821
FMR (kJoules/day) = 2.582 x BW (g)0862 (Nagy 1987)
FMR (kcal/day)
196
FRM (kcal/day = 0.6167 x BW (g)°-«2 (U.S. EPA 1993c)
FMR normalized to BW (kcal/kg-day)
245
FMR normalized to body weight (kcal/kg-day) = FMR
(kcal/day) / BW (kg)
Gross energy (GE) of fish (kcal/g ww)
1.20
Table 4-1 of U.S. EPA (1993c)
Food assimilation efficiency (AE) for mammal
consuming fish
0.91
U.S. EPA (1993c), Table 3-1
Metabolizable energy (ME) in fish (kcal/g ww)
1.09
ME (kcal/g ww) = GE (kcal/g wet weight) x AE
Normalized food ingestion rate (FIR)
(g/g-day)
0.225
FIR (g/g-day) = FMR (kcal/kg-day) x 0.001 kg/gram / ME
(kcal/g wet weight)
FIR (percent total body weight)
22.5%
(see previous cell)
FIR per animal (g/d)
180
assuming an 800-g mink
Acronyms: BW = body weight
The gross energy (GE) content for fish and a caloric assimilation efficiency (AE) for a mammal
consuming fish were obtained from the WEFH to estimate the metabolizable energy (ME) for the
diet on a wet-weight basis (Table A-15). Based on the energy requirements (FMR) of mink and
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the ME per unit wet-weight prey, an FIR then could be calculated as the FMR/ME (with units
corrected), which in this case equals 22.5 percent of the adult mink's body weight daily. For an
individual mink weighing 800 grams, that would be 180 grams of fish, wet weight, ingested per
day. To determine chemical ingestion rates, the proportion of the diet obtained by mink from
each aquatic biotic compartment in TRIM.FaTE must be specified. All data summarized in
EPA's 1993 WEFH, Volume 2, Appendices (U.S. EPA 1993d) were consulted to generalize the
dietary assumptions for the RTR environmental screen and to maximize the higher trophic level
components of the diet. Those assumptions are listed in Table A-16 (Diet Composition column).
The total daily FIR of 180 grams of fish could then be divided among the TRIM.FaTE aquatic
biota compartments. Table A-16 shows the resulting FIR in three different units.
Table A-16. Mink Diet Assumptions3
Food Type
Percent Diet
Composition
Food Ingestion Rate
(g/day)
Food Ingestion Rate
(kg/day)
Food Ingestion Rate
(kg/kg bw-day)
Benthic invertebratesb
25
44.9
0.0449
0.0561
Benthivorous fish (consuming benthic
invertebrates only)
25
44.9
0.0449
0.0561
Bottom-feeding carnivores)
0
0.0
0.0000
0.0000
Water-column herbivore (planktivore)
25
44.9
0.0449
0.0561
Water-column omnivore
25
44.9
0.0449
0.0561
Water-column carnivore
0
0.0
0.0000
0.0000
TOTAL
100
179.6
0.1796
0.2245
aDietary studies provided in U.S. EPA (1993d) were reviewed to develop assumptions in this table.
The gross energy (GE) and assimilation efficiency (AE) for invertebrates are not identical to the GE and AE for fish;
however, assuming that they are the same should have negligible effects on the overall results of the screen.
To evaluate the spatial extent of chemical contamination above a level that would be toxic to
mink consuming fish from a water body, the home range of a mink or a mink family is
important. Home range size depends on the location, type of habitat, season, and type of water
body. In the prairie potholes region of the United States, mink home ranges of 259-380 hectares
have been reported (U.S. EPA 1993 d). In the pothole region of Manitoba, Canada, Arnold and
Fritzell (1987) reported breeding home ranges of 770 hectares per mink or mink family. Along
rivers and very large lakes, home ranges generally are expressed as length of river or shoreline.
In Sweden, Gerell (1970) reported home ranges between 1.0 and 5.0 km in length depending on
age and sex.
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A.6.2 Merganser Exposure Factor Values and Assumed Diet
For American merganser (Mergus merganser americanus), a few measured FIRs were available
from the literature (Salyer and Lagler 1940; Gooders and Boyer 1986; Alexander 1977) that
suggested FIR values between 33 and 50 percent of the bird's body weight daily. Using Nagy's
(1987) allometric equation for nonpasserine birds,16 we estimated a FIR of 20 percent daily for a
1.27-kg American merganser. Based on that broad range of possible values, we selected 33
percent as the normalized FIR to use in the RTR screening scenarios. Assuming the body weight
of mergansers in Michigan, the FIR equals 419 grams of fish, wet weight, per day per merganser
(Table A-17).
Table A-17. Common Merganser Exposure Factor Values
Parameter
Value
References, Comments
Body weight (kg)
1.27
Salyer and Lagler (1940), Michigan
Normalized food ingestion rate (FIR) (g/g-day)
0.33
Salyer and Lagler (1940), Alexander (1977), Gooders and
Boyer (1986), and estimated from Nagy (1987)
FIR (percent total body weight)
33%
(see previous cell)
FIR per animal (g/d)
419
Assuming a 1.27-kg American merganser
Estimates of the diet of American merganser, shown in Table A-18, are based on the reported
lengths of fish caught in Michigan (Alexander 1977), with some consideration of studies from
other locations (e.g., White 1936, 1937; Huntington and Roberts 1959) and experimental choice
studies (Latta and Sharkey 1966).
Table A-18. Common Merganser Diet Assumptions"
Food Type
Diet
Composition (%)
Food Ingestion
Rate (g/day)
Food Ingestion
Rate (kg/day)
Food Ingestion Rate
(kg/kg bw-day)
Benthic invertebrates
0
0.0
0.000
0.00
Benthivorous fish (consuming benthic
invertebrates only)
35
146.7
0.147
0.1155
Bottom-feeding carnivores (e.g., eel)
0
0.0
0.000
0.00
Water-column planktivore (YOY fish,
shiners, 1-5 inches)
35
146.7
0.147
0.1155
Water-column omnivore (perch, young
trout; 6-10 inches)
25
104.8
0.105
0.0825
16Groups of birds that generally are larger with slower metabolic rates per unit body weight than are birds in the
Order Passeriformes, which includes the song birds such as warblers, robins, thrushes.
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Food Type
Diet
Composition (%)
Food Ingestion
Rate (g/day)
Food Ingestion
Rate (kg/day)
Food Ingestion Rate
(kg/kg bw-day)
Water-column piscivore (e.g., largemouth
bass >12 inches)
5
21.0
0.021
0.0165
TOTAL
100
419.1
0.419
0.33
Acronyms: YOY = young of the year
aDiet consumption compartmentalized into TRIM.FaTE biotic compartments is based on the lengths offish reported
caught in Michigan by Alexander (1977), with some consideration of studies from other locations (e.g., White
1936,1937; Huntington and Roberts 1959) and experimental choice studies (Latta and Sharkey 1966)
Most fish consumed are 10-30 cm long, although American merganser will choose larger fish in
higher proportion than their availability relative to smaller fish (Mallory and Metz 1999). Fish up
to 36 cm long are commonly consumed; mergansers have been reported to eat eels up to 55 cm
long. The size of fish consumed apparently is determined by fish girth not length.
American merganser is not territorial. Groups of several females might nest together near
productive water bodies during the breeding season, while in winter, large flocks often travel
together from one body of water to another. In the Canadian Clay Belt Region (north of the Great
Lakes), breeding densities of 7.2 pairs/100 km2 (7.2 pairs /10,000 hectares) have been reported.
Overall breeding densities in Atlantic Canada range from 0 to 81 pairs/10,000 hectares, with
densities of 9-10 pairs/10,000 hectares typical of Newfoundland and Nova Scotia (Mallory and
Mertz 1999). Along California rivers, 0.5-4.7 birds per linear km have been reported throughout
the year (Mallory and Mertz 1999).
A.7 Derivation of Bioaccumulation Factors for Arsenic
Use of BAFs or biota-sediment accumulation factors (BSAFs) "depends on the assumption that
the concentration of chemicals in organisms is a linear no thresholdfunction of the
concentration in sediment. This will not be the case if uptake or depuration of the chemical in
question is well-regulated by the organism, either because it is an essential nutrient or because it
is a toxicant for which the organism has inducible mechanisms for metabolism or excretion"
(BJC 1998). Thus, for several metals, aqueous concentrations are not good predictors of
concentrations in fish (BJC 1998; Chen and Folt 2000; Williams et al. 2006).
In addition, bioaccumulation of ionic inorganic chemicals that dissolve in water is different in
marine vs. freshwater ecosystems. Because cations and anions are abundant in marine waters,
they compete with chemical contaminant ions for transport through gills, although the overall
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concentration of "salts" in fish blood and tissues is similar to that in ocean water. In freshwaters,
aquatic organisms must osmoregulate, retaining cations and anions at higher concentrations in
blood and tissues than in the surrounding water. Physiological mechanisms, therefore, differ
between saltwater and freshwater fish and among species that can tolerate excess salinity or that
live in estuarine environments.
We therefore conducted a literature search for studies of arsenic bioaccumulation in freshwater
fish only, looking for field-measured BAFs for both pelagic and benthic feeding fish (many
freshwater species feed in both habitats). Of particular concern was the possibility that bottom-
feeding carnivorous fish might accumulate more arsenic than pelagic carnivorous fish. The
bottom-feeding fish could ingest arsenic from both their prey and from sediment particles. We
first present BAFs that relate dissolved arsenic concentrations in the water column to arsenic
concentrations in top trophic-level fish. We then present data for BSAFs for bottom-dwelling
freshwater fish.
The next three subsections discuss differences between freshwater and marine fish-tissue arsenic
concentrations (Section A.7.1), BAFs (Section A.7.2), and biota-sediment accumulation factors
(Section A.7.3).
A.7.1 Differences between Freshwater and Marine Fish
Differences between marine and freshwater organisms are evident from the concentrations of
inorganic arsenic in water that produce acute lethality. For As(III) in saltwater, acute toxicity
ranges from 250 |ig/L for invertebrates (crabs and copepods) to more than 1,500 |ig/L for filter-
feeding mollusks and for fish (U.S. EPA 1985, 2003e). For As(III) in freshwaters, however,
acute toxicity values range from 1,000 to 3,000 |ig/L for invertebrates (amphipods and
cladocerans) to more than 10,000 |ig/L for most freshwater fish.
Marine fish usually contain more arsenic (0.19-65 mg[As]/kg[fish dry weight]) than freshwater
fish (0.007-1.46 mg[As]/kg[fish dw]) (Donohue and Abernathy 1999). Table A-19 summarizes
arsenic concentration data for marine and freshwater fish. As reported by ATSDR (2007), Hellou
et al. (1996) measured 8-37 mg[As]/kg[fish fillet dw] in yellowtail flounder from the Northwest
Atlantic in 1993. Assuming fish to be 75-percent water, the tissue concentration on a wet-weight
(ww) basis would be approximately 2-9.3 mg/kg ww. Buchet and Lison (1998) measured total
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arsenic concentrations in several fish species in Belgian markets; they found total arsenic at
concentrations from 2.4 to 19.8 mg[total As]/kg[fish dw], which would equal approximately
0.6-5.0 mg/kg ww. They also found that inorganic arsenic contributed only a small fraction
(0.003-0.2 mg[As]/kg[fish dw]) to the total arsenic.
Table A-19. Marine and Freshwater Fish Tissue Concentrations
Habitat
mg[As]/kg[fish
dry weight]
mg[As]/kg[fish
wet weight]
Species/location
Reference
Marine
0.190—65a
0.048-16
fish, marine
Donohue and Abernathy
(1999)
Marine
8—37a
2-9.3
yellowtail flounder, Northwest
Atlantic Ocean
Hellou etal. (1996)
Marine
2.4-19.8
(inorganic:
0.003—0.2)a
0.6-5
several species in Belgian fish
market
Buchet and Lison (1998)
Freshwater
0.007-1,46a
0.028-5.8
fish, freshwater
Donohue and Abernathy
(1999)
Freshwater
6.4
0.16 ± 0.23a
bottom feeding
Kidwell etal. (1995)
Freshwater
6.4
0.16 ± 0.14a
predatory fish
Kidwell etal. (1995)
Freshwater
<0.4
<0.1a
several, Savanna River
Burger et al. (2002)
Freshwater
1.3
0.32 ± 0.040a
bowfin, Savanna River
Burger et al. (2002)
Freshwater
NC
0.01-0.03
bluegill, yellow perch,
largemouth bass
Chen and Folt (2000)
Freshwater
NC
0.017
(0.012.5-0.028)
6 fish species, California
CAOEHHA (2012)
Freshwater
<0.005-0.2a
<0.001-0.05
mixed, Candamo River, Peru
Gutleb etal. (2002)
Acronyms: NC = not calculated
aFish tissue concentrations reported as wet weight were converted to dry weight (and the reverse) assuming 75%
moisture content in fresh fish.
Arsenic concentrations in freshwater fish are much lower. As reported in ATSDR (2007),
Kidwell et al. (1995) analyzed data from the National Contaminant Biomonitoring Program
(1984-1985, 112 stations) and found similar concentrations in bottom-feeding fish
(0.16 ± 0.23 mg[As]/kg[fish ww]; n = 2,020) and in "predatory" fish (0.16 ± 0.14 mg[As]/kg[fish
ww]; n = 12). In fish from the Savannah River below DOE's Savanna River Site, Burger et al.
(2002) found concentrations less than 0.1 mg[As]/kg[fish fillet ww] for bass, channel catfish,
pickerel, yellow perch, black crappie, American eel, bluegill, and other fish, with only the
bowfin showing higher concentrations—0.32 ± 0.04 mg[As]/kg[fish fillet ww]. Similarly, Gutleb
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et al. (2002) found concentrations in freshwater fish from the unpolluted Candamo River in Peru
from <0.005 to 0.2 mg[As]/kg[fish fillet dw], which would approximate <0.001-0.05 mg/kg ww.
In marine, estuarine, and freshwater bodies, inorganic arsenic (As), predominates (U.S. EPA
2003e). In fish, however, organoarsenical compounds predominate, with arsenobetaine,
arsenocholine, monomethylarsonic acid (MMA), dimethylarsenic acid (DMA), and
trimethylarsenic (TMA) identified in various species (U.S. EPA 2003e). In marine fish and
shellfish, only 10-15 percent of arsenic is inorganic (U.S. EPA 2003e). In freshwater fish,
limited field data suggest that organoarsenical compounds might predominate, but laboratory
data indicate a wide range of organic-to-total arsenic ratios (U.S. EPA 2003e). Kaise et al. (1997)
reported 88-99 percent organic arsenic in six freshwater fish species caught in a river, with more
than half as TMA and most of the remainder as DMA. On the other hand, in laboratory studies in
which fish were exposed to As(III) or As(V) in water, the fraction of total arsenic comprising
organic arsenic compounds varied substantially from 0 to 94 percent (U.S. EPA 2003e).
Laboratory data on measured bioconcentration factor (BCF) values in saltwater and freshwater
fish species are too sparse to allow comparison. For selecting BAFs and BSAFs, preference is
given to field studies that are adequately conducted, with concentrations measured in water,
sediments, and fish that are sampled at the same locations on the same dates. Those data for
freshwater fish are described in the next section.
A.7.2 BAFs for Arsenic in Freshwater Fish
For the RTR Tiers 1 and 2 environmental screens for arsenic, the screening scenario assumes that
people catch and consume fish from an onsite pond and that they eat 50-percent trophic level 4
(TL4) fish from the water column and 50-percent trophic level 3 (TL3) fish from the benthic
environment. For arsenic modeling, EPA chose not to use the biokinetic model of aquatic food
chain bioaccumulation (or trophic transfers) included in TRIM.FaTE. Instead, EPA uses arsenic-
specific BAFs and BSAFs applied to TRIM.FaTE-estimated water and sediment concentrations,
respectively. The BAF/BSAF approach should require fewer empirical data to estimate values
for fewer model parameters than the biokinetic approach, which requires values for parameters
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related to uptake and elimination via gills and ingestion with food for six components of an
aquatic food web.
As of February 2016, EPA has not published BAFs for arsenic in fish that could be used to
estimate bioaccumulation and risk at a national level. The current EPA NAWQC for arsenic are
based on a BAF of 44, with the value for fish 1.0 and the value for oysters 350 (U.S. EPA 1985;
Williams et al. 2006). Recently, EPA published BAF values and other data related to arsenic in
organisms in marine and freshwaters (U.S. EPA 2003e). For BAFs, EPA separated data by
habitat (marine, freshwater) and by trophic level (i.e., TLs 2, 3, and 4). The water-column fish
consumed by people in the screening scenario for RTR assessments is assumed TL4. We
therefore recommended using the highest BAF reported, 46.1 L/kg, for a freshwater carnivorous
fish, largemouth bass, in the compilation for freshwater lentic ecosystems (see Table 3-4 in U.S.
EPA 2003e). That value rounds to 46 L/kg for the arsenic BAF for the water-column carnivore
for use in RTR environmental screens.
More recently, the State of California Office of Environmental Health Hazard Assessment (CA
OEHHA 2012) derived a freshwater fish BAF of 17 L/kg[fish ww], calculated as the arithmetic
mean arsenic BAF from six species of freshwater fish (based on Baker and King 1994, Huang et
al. 2003, Lin et al. 2001, Liao et al. 2003, and Skinner 1985) (range of field-measured BAFs in
natural lakes 12.5-28). California OEHHA concluded that a BAF of 44 is too high for its
freshwater fish risk assessments and now uses the calculated value of 17 L/kg[fish ww] instead.
Given the variation in arsenic BAFs (and BSAFs) in the data presented by EPA (2003e), we
decided to investigate arsenic bioaccumulation in more detail to provide additional information
for consideration by EPA's Office of Air Quality Planning and Standards. In its 2003 technical
review, EPA concluded that arsenic BAF values were too variable to allow the Agency to
recommend a single BAF that would apply nationwide (U.S. EPA 2003e). Arsenic
concentrations tend to be higher in estuarine and marine fish than in freshwater fish (Table A-19)
and higher in filter-feeding invertebrates, including oysters and mussels, than in fish. Arsenic
does not bioaccumulate in food chains (U.S. EPA 2003e, Section 1.2). In its grouping of BAF
data in 2003, EPA calculated BAFs for animals in trophic level 2 (TL2), TL3, and TL4 for lakes,
rivers, and estuaries separately. Thus, BAFs can potentially differ for TL2 lakes, TL2 rivers, and
TL2 estuaries; TL3 lakes, TL3 rivers, and TL3 estuaries; and TL4 lakes, TL4 rivers, and TL4
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estuaries (U.S. EPA 2003e). The Agency grouped organisms from different phyla (e.g., fish,
insect larvae, mussels) if their food habits indicated the same or similar trophic level in the same
habitat (e.g., TL3 lakes). We believe that including BAFs for species from different phyla for a
specified habitat and trophic level contributed to the variation among BAFs within each
habitat/trophic level group.
We found one study that appears to have identified a parameter that explains much of the
variation in the freshwater BAF data reviewed by EPA (U.S. EPA 2003e). Williams et al. (2006)
focused on field and lab studies of arsenic bioaccumulation and bioconcentration in freshwater
fish only. They found an inverse relationship between field BAFs and arsenic concentrations in
water, a trend observed for other metals (McGeer et al. 2003). Overall, measured concentrations
of arsenic in the fillet or in the whole body of fish collected in the field were relatively constant
(i.e., 51-370 |ig[As]/kg[fish ww]),17 although most freshwater fish contained less than 200
|ig[As]/kg[fish ww] across fish species, trophic levels, and sizes (Table A-19).
In contrast, measured arsenic concentrations in the water ranged over roughly 3.5 orders of
magnitude (0.02-56 |ig[As]/L[freshwater]) (Williams et al. 2006). The measured BAFs ranged
from 0.5 to 1,600 L/kg. Measured BAFs in waters with the highest concentrations (56 |ig[As]/L)
were 6.1 L/kg ww or less (one exception), while waters with the lowest arsenic concentration
(0.085 |ig[As]/L) yielded the highest BAF (1,600 L/kg, bluegill) as shown in Table A-20. The
inverse correlation between the magnitude of field-measured BAFs and arsenic concentrations in
water suggests some degree of internal regulation of arsenic by the fish at typical environmental
concentrations (Williams et al. 2006).
BCFs measured in the laboratory, with higher arsenic concentrations in water than in the field
studies, ranged from 0.1 to 15 L/kg at water concentrations ranging from 10 to 18,100 |ig/L;
whole-fish concentrations ranged from 100 to 11,700 |ig[As]/kg[fish ww]. The laboratory BCF
values are presented after the BAF values in Table A-20.
17
From over 50 separate fish species/sizes sampled over 6 field studies, Table 1, in Williams et al. (2006). Four
unidentified composite samples and one measurement from creek chub of 2,360 j.ig| As|/kg| fish ww] excluded.
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Table A-20. BAF/BCF Values for Freshwater Fish Exposed to Different Water
Concentrations of Arsenic
Fish species, condition
Study Type
M9[As]/
L[water]
BAF or BCF
(L/kg)
Location
Reference
Bluegill
Mixed salmonids
Smallmouth bass
Smallmouth bass
White perch
Pumpkinseed
Largemouth bass
Field BAF
0.085
0.022
0.107
0.107
0.367
0.113
0.409
1600
3091
542
533
322
265
46
20 lakes in
northeastern
United States for
U.S. EPA EMAP
Chen et al. (2000)
Mottled sculpin
Blacknose dace
Brook trout, small
Brook trout, large
Field BAF
0.37
811
541
541
270
Blacklick Run,
MD
Mason et al. (2000), as
cited in Williams et al.
(2006) [incorrectly cited as
2002 in Table 1]
White sucker
Brook trout, large
Brook trout, small
Creek chub
Field BAF
0.67
448
299
299
299
Harrington Creek,
MD
Mason et al. (2000), as
cited in Williams et al.
(2006) [incorrectly cited as
2002 in Table 1]
Alewife
Killifish
Yellow perch
Largemouth bass
Bluegill
Black crappie
Field BAF
0.78
46
41
28
23
22
19
Upper Mystic
Lake, MA
Chen and Folt (2000)
Miscellaneous "omnivores"
Field BAF
5.1
5.1
Moon Lake, MS
Cooper and Gillespie
(2001)
Carp (n = 5)
Channel catfish (n = 4)
Flathead catfish
Field BAF
12
20
20
12
9.7
6.3
Upper Gila River,
AZ
Baker and King (1994)
Amphidormous goby
Goby
Fatminnow
Japanese dace
Sweet fish
Field BAF
30
12
11
8.9
3.3
1.7
Haya-kawa River,
Japan
Kaise etal. (1997)
Creek chub
Pumpkinseed
Golden shiner
White sucker
Rock bass
Banded killifish
Largemouth bass
Yellow perch
Walleye
Bluntnose minnow
Longnose gar
Emerald shiner
Spottail shiner
Northern pike
Field BAF
56
42*
6.1
3.0
2.4
2.3
1.8
1.5
1.4
1.4
0.9
0.9
0.6
0.5
0.4
Moira Lake,
Ontario Canada
Azcue and Dixon (1994)
"considered an outlier
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Fish species, condition
Study Type
M9[As]/
L[water]
BAF or BCF
(L/kg)
Location
Reference
Bluegill juvenile
Bluegill adult
juvenile
adult
juvenile
adult
juvenile
adult
Lab BCF
10
10
50
50
260
260
610
610
12
14
10
7.8
2.5
2.0
2.5
1.9
Laboratory
mesocosm, 16
weeks
Gilderhus (1966)
Rainbow trout 5 °C
Rainbow trout 15 °C
5 °C
15 °C
15 °C
5 °C
15 °C
Lab BCF
10
10
1,400
1,400
8,400
16,300
18,100
15
15
0.2
0.2
0.2
0.1
0.2
Lab, 11-week
exposure,
Ontario
groundwater, at 5
and 15 °C
McGeachy and Dixon
(1990)
Rainbow trout
Lab BCF
<20
760
2,480
15
0.3
0.2
Lab, 181-day
exposure
Rankin and Dixon (1994)
Acronyms and abbreviations: BAF = bioaccumulation factor (i.e., arsenic accumulation from both water and food);
BCF = bioconcentration factor (i.e., arsenic accumulation from water via the gills); EMAP = U.S. EPA Environmental
Monitoring and Assessment Program; Lab = laboratory
Source: Williams etal. (2006).
Williams et al. (2006) demonstrated an inverse relationship between arsenic concentrations in
water and in fish for low, environmentally common arsenic concentrations in surface waters (i.e.,
0.02-56 |ig/L), The relationship (Equation A-4) is close, with an r2 of 0.82.
Field BAF (L/kg) = 87.4 * Water Concentration (|ig/L)~0-925 Eq. A-4
At higher arsenic concentrations in water (e.g., 10-12,000 |ig/L), the laboratory BCFs were still
inversely related to water concentration; however, the exponent was smaller (Equation A-5;
Williams et al. 2006). The relationship is close (r2 = 0.79).
Lab BCF (L/kg) = 78.7 * Water Concentration (|ig/L)~0-669 Eq. A-5
The smaller exponent suggests that internal arsenic regulation might be impaired at higher water
concentrations.
The trends shown in Table A-20 are apparent despite grouping fish that feed at different trophic
levels. In fact, some evidence indicates that arsenic concentrations in fish decrease slightly with
increasing trophic level. For example, Chen and Folt (2000) measured arsenic and lead
concentrations in Upper Mystic Lake, Massachusetts, in small and large zooplankton and in six
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species of fish in three different seasons. The lake, designated as a Superfund site, had been
contaminated by past leather and chemical manufacturing upstream. Arsenic was elevated in the
zooplankton relative to zooplankton in uncontaminated lakes. Arsenic decreased, however, with
increasing trophic level. Fish from Mystic Lake contained the same arsenic concentrations as fish
from uncontaminated lakes in the northeastern United States. The highest arsenic concentrations
were in planktivorous fish that consumed zooplankton that were high in arsenic. Subsequent
consumers in the food chain had lower tissue concentrations of arsenic, leading to the idea that
arsenic "biodiminishes" with increasing trophic level in fish. Chen and Folt (2000) found that
arsenic concentrations in fish were 10-20 times lower than in the zooplankton, and
concentrations in larger plankton (>202 jam) were less than in smaller plankton (45-202 |im).
Arsenic concentrations in all fish sampled (planktivores—alewife and killifish; omnivores—
black crappie, bluegill sunfish, and yellow perch; and piscivores—largemouth bass) were
between 0.01 and 0.03 |ig/g wet weight.
Based on the analysis of Williams et al. (2006), for refined site-specific RTR assessments, we
recommend using the two equations above (Equation A-4 and Equation A-5) to estimate
bioaccumulation of arsenic in water-column fish (water-column carnivore). Application of the
equations would be conditional on the TRIM.FaTE-estimated arsenic concentration in the water
column being less than or more than 10 |ig/L. A warning flag should alert the user if the
estimated arsenic concentrations in water are less than 0.01 |ig/L or more than 20,000 |ig/L,
which are concentrations beyond the observed data upon which the empirical models are based.
For simplicity, however, we applied a BAF for the water-column carnivore of
46 L[water]/kg[fish wet weight] (USEPA 2003e, Tables 3.4 and 3.9, highest value for TL4 fish,
largemouth bass). That BAF is below 1,000 L/kg, which is a typical criterion for a chemical to be
considered bioaccumulative. The BAF values for TL3 fish (alewife) and TL2 fish (carp) were
95 L/kg and 71 L/kg, respectively (USEPA 2003e).
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A.7.3 BSAFs for Arsenic in Freshwater Benthic Invertebrates and
Fish
As discussed in detail in Appendix 6 of the Risk Report,18 for the RTR Tiers 1, 2, and 3 human
health screens for arsenic, EPA relies on the BSAF/BAF approach rather than biokinetic
modeling of aquatic food chain bioaccumulation (or trophic transfers). Predicting
bioaccumulation of metals and transition elements requires chemical-specific empirical data; no
chemical property, such as Kow, predicts bioaccumulation of these elements across organisms in
aquatic food chains.
Bechtel Jacobs Company (BJC 1998) assembled data to estimate freshwater BSAFs for benthic
invertebrates (predominantly the aquatic larval stage of several groups of insects) for use in risk
assessments on DOE properties. As for most estimates of BSAFs for metals published in the
literature, BJC (1998) reported BSAFs as the ratio of dry-weight biota concentration to dry-
weight sediment concentration (i.e., kg[dry weight sediments]/kg[dry weight biota]. For a dataset
of 55 sediment-invertebrate BSAFs, BJC (1998) found a mean value of 0.329 kg[dw]/kg[dw].
For 49 of those studies for which the organisms had not been depurated (i.e., moved to clean
sediments and allowed to eliminate the chemical), the mean BSAF was 0.240 kg[dw]/kg[dw],
TRIM.FaTE calculates both invertebrate and fish concentrations on a wet-weight basis. For the
benthic invertebrates reviewed by BJC, typically 70-percent water, the fresh-weight BSAF
would be lower. The BSAF multiplied by 0.30 (fraction dry weight) yields BSAFs of 0.1 and
0.07 kg[dry sediment]/kg[wet weight invertebrates] for the set of 55 and set of 49 studies,
respectively.
The data described above could be used to parameterize the beginning of the benthic food chain
in TRIM.FaTE for arsenic. For the RTR human health and environmental screens, however, we
are not employing the TRIM.FaTE biokinetic food-web model to estimate bioaccumulation.
Thus, we needed to find a BSAF value for freshwater fish that consume benthic invertebrates and
small bottom fish to calculate their tissue concentrations relative to sediment concentrations.
We found a single study that measured a BSAF for freshwater fish in the field. Davis et al.
(1996) measured arsenic concentrations in fish and sediments in a holding pond at the
1 R
Appendix 6 to the Risk Report is the Technical Support Document for the TRIM-Based Multipathway Tiered
Screening Methodology for RTR.
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Industriplex Superfund Site north of Boston, Massachusetts, that had been contaminated with
arsenic in the 1970s. At a depth of 45 cm in the sediments, they measured approximately
500 |ig[As]/L[pore water] and 1,000 mg[As]/kg[dry weight sediment]. They found increasing
arsenic concentrations with decreasing depth in the sediment column: 1,700 |ig[As]/L[pore
water] and 1,200 mg[As]/kg[sediment dw] at a depth of 30 cm; and 5,500 |ig[As]/L[pore water]
and 3,000 mg[As]/kg[sediment dw] at the surface (the top few cm). They calculated a sediment-
water Kd for arsenic of 560 L/kg. Arsenic near the surficial sediments was 1,700 |ig[total As]/L,
with <1.0 |ig[As]/L MMA, <1.9 |ig[As]/L DMA, 1,100 |ig/L as As(III), and 610 |ig/L as As(V).
Davis et al. (1996) measured arsenic in the fillet portion of bottom-feeding fish (brown bullhead
and white sucker) and in nearby surficial sediments. Although they did not describe their
methods for estimating arsenic concentrations in the fish or in bulk sediments in detail, their goal
was to report a BSAF that could predict wet-weight fish concentrations of arsenic. They reported
1.19 mg[As]/kg[ww fish fillet] and a surficial sediment concentration of 1,830 mg[As]/
kg[sediment]. Those data indicate a BSAF of 6.5 x 10"4 kg[bulk sediment]/kg[ww fish fillet],
which we have adopted for RTR analyses.
We have only a single estimate of a BSAF for freshwater fish. This BSAF might be lower than is
typical in most surface water bodies for two reasons. First, the exposure concentration is
relatively high. Based on the findings of Williams et al. (2006), high exposure concentrations
would likely result in low bioaccumulation for arsenic. Second, Davis et al. (1996) measured a
relatively high sediment-water Kd for arsenic of 560 L/kg, which is higher than the median value
of 316 L/kg (logKd of 2.5 L/kg, range of logKd 1.6-4.3 L/kg) reported by EPA for a sediment-
water Kd (U.S. EPA 2005b). Thus, the bioavailability of arsenic in sediments at the Superfund
site investigated by Davis et al. (1996) might have been lower than at most locations.
A.8 Environmental Screening Threshold Emission Rates
As described in the main report, the Tier 1 environmental screening thresholds are expressed as
chemical- and assessment-endpoint-specific emission rates (in tons per year). They are
backcalculated from media-specific benchmarks or TRVs for fish-eating birds and mammals
using TRIM.FaTE. Those screening emission thresholds are listed in Table A-21. The methods
of changing thresholds for Tiers 2 and 3 also are described in the main report.
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Table A-21. Tier 1 Environmental Screening Threshold Emission Rates (ESTER) for each
PB-HAP and each Benchmark Assessed in the Environmental Risk Screen
PB-HAP
Assessment Endpoint
Benchmark and Effect Level3
Tier 1 ESTER (TPY)
Fish-consuming birds
NOAEL (American merganser)
6.20E+00
LOAEL (American merganser)
6.20E+01
Fish-consuming mammals
NOAEL (mink)
6.57E-01
LOAEL (mink)
6.57E+00
Sediment Community
Threshold Level
5.97E-01
Probable-effect Level
2.40E+00
Threshold - Mammalian Insectivores (shrew)
1.92E+00
Surface Soil - Dist. 1 - 312 m
Threshold - Avian Insectivores (woodcock)
1.80E+00
Threshold Level - Plant Community
7.53E-01
Threshold - Mammalian Insectivores (shrew)
3.63E-01
Surface Soil - Dist. 2 - 850 m
Threshold - Avian Insectivores (woodcock)
3.39E-01
Arsenic
Threshold Level - Plant Community
1.42E-01
Threshold - Mammalian Insectivores (shrew)
7.25E-01
Surface Soil - Dist. 3 -1,500 m
Threshold - Avian Insectivores (woodcock)
6.77E-01
Threshold Level - Plant Community
2.84E-01
Threshold - Mammalian Insectivores (shrew)
3.35E+00
Surface Soil - Dist. 4 - 3,500 m
Threshold - Avian Insectivores (woodcock)
3.13E+00
Threshold Level - Plant Community
1.31E+00
Threshold - Mammalian Insectivores (shrew)
1.55E+01
Surface Soil - Dist. 5 - 7,500 m
Threshold - Avian Insectivores (woodcock)
1.45E+01
Threshold Level - Plant Community
6.06E+00
Water-column Community
Threshold Level (chronic)
7.24E+01
Frank-effect Level (acute)
1.64E+02
Fish-consuming birds
NOAEL (American merganser)
2.22E-02
LOAEL (American merganser)
3.17E-02
Fish-consuming mammals
NOAEL (mink)
4.43E-02
LOAEL (mink)
4.44E-01
No-effect Level
1.04E-01
Cadmium
Sediment Community
Threshold Level
3.77E-01
Probable-effect Level
1.10E+00
Threshold - Mammalian Insectivores (shrew)
3.28E-02
Surface Soil - Dist. 1 - 312 m
Threshold - Avian Insectivores (woodcock)
7.01 E-02
Threshold Level - Plant Community
2.91 E+00
Threshold Level - Invertebrate Community
1.27E+01
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PB-HAP
Assessment Endpoint
Benchmark and Effect Level3
Tier 1 ESTER (TPY)
Threshold - Mammalian Insectivores (shrew)
7.46E-03
Surface Soil - Dist. 2 - 850 m
Threshold - Avian Insectivores (woodcock)
1.60E-02
Threshold Level - Plant Community
6.64E-01
Threshold Level - Invertebrate Community
2.90E+00
Threshold - Mammalian Insectivores (shrew)
1.51E-02
Surface Soil - Dist. 3 -1,500 m
Threshold - Avian Insectivores (woodcock)
3.23E-02
Threshold Level - Plant Community
1.34E+00
Threshold Level - Invertebrate Community
5.88E+00
Threshold - Mammalian Insectivores (shrew)
8.52E-02
Surface Soil - Dist. 4 - 3,500 m
Threshold - Avian Insectivores (woodcock)
1.82E-01
Threshold Level - Plant Community
7.58E+00
Threshold Level - Invertebrate Community
3.31 E+01
Threshold - Mammalian Insectivores (shrew)
3.99E-01
Surface Soil - Dist. 5 - 7,500 m
Threshold - Avian Insectivores (woodcock)
8.53E-01
Threshold Level - Plant Community
3.54E+01
Threshold Level - Invertebrate Community
1.55E+02
Water-column Community
Threshold Level (chronic)
2.41 E-01
Frank-effect Level (acute)
6.02E-01
Sediment Community
Threshold Level
3.64E-03
Probable-effect Level
1.91E-02
Surface Soil - Dist. 1 - 312 m
Threshold Level - Plant Community
1.96E-03
Threshold Level - Invertebrate Community
6.54E-04
Surface Soil - Dist. 2 - 850 m
Threshold Level - Plant Community
9.15E-04
Mercury -
divalent mercury
(Hg++)
Threshold Level - Invertebrate Community
3.05E-04
Surface Soil - Dist. 3 -1,500 m
Threshold Level - Plant Community
2.20E-03
emissions and
exposures
Threshold Level - Invertebrate Community
7.35E-04
Surface Soil - Dist. 4 - 3,500 m
Threshold Level - Plant Community
1.15E-02
Threshold Level - Invertebrate Community
3.83E-03
Surface Soil - Dist. 5 - 7,500 m
Threshold Level - Plant Community
7.23E-02
Threshold Level - Invertebrate Community
2.41 E-02
Water-column Community
Threshold Level (chronic)
2.91 E-01
Frank-effect Level (acute)
5.30E-01
Mercury - Hg++
Fish-consuming birds
NOAEL (American merganser)
3.37E-03
emissions, but
exposure to
MeHg
LOAEL (American merganser)
2.02E-02
Fish-consuming mammals
NOAEL (mink)
1.79E-02
LOAEL (mink)
8.89E-02
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PB-HAP
Assessment Endpoint
Benchmark and Effect Level3
Tier 1 ESTER (TPY)
Sediment Community
Threshold Level
2.08E+00
Probable-effect Level
1.04E+01
Surface Soil - Dist. 1 - 312 m
Threshold Level - Invertebrate Community
3.94E-02
Surface Soil - Dist. 2 - 850 m
Threshold Level - Invertebrate Community
1.84E-02
Surface Soil - Dist. 3 - 1,500 m
Threshold Level - Invertebrate Community
4.42E-02
Surface Soil - Dist. 4 - 3,500 m
Threshold Level - Invertebrate Community
2.32E-01
Surface Soil - Dist. 5 - 7,500 m
Threshold Level - Invertebrate Community
1.45E+00
Water-column Community
Threshold Level (chronic)
1.48E-01
Frank-effect Level (acute)
5.23E+00
Fish-consuming mammals
NOAEL (mink)
1.33E+02
LOAEL (mink)
1.33E+03
No-effect Level
1.32E+00
Sediment Community
Threshold Level
6.20E+00
Probable-effect Level
5.99E+01
BaP-equivalents
Surface Soil - Dist. 1 - 312 m
Threshold - Mammalian Insectivores (shrew)
6.56E-01
Surface Soil - Dist. 2 - 850 m
Threshold - Mammalian Insectivores (shrew)
8.17E-01
Surface Soil - Dist. 3 - 1,500 m
Threshold - Mammalian Insectivores (shrew)
1.43E+00
Surface Soil - Dist. 4 - 3,500 m
Threshold - Mammalian Insectivores (shrew)
5.06E+00
Surface Soil - Dist. 5 - 7,500 m
Threshold - Mammalian Insectivores (shrew)
1.83E+01
Water-column Community
Threshold Level (chronic)
5.16E+00
Frank-effect Level (acute)
8.84E+01
Fish-consuming birds
NOAEL (American merganser)
6.61 E-06
LOAEL (American merganser)
6.61 E-05
Fish-consuming mammals
NOAEL (mink)
8.58E-06
LOAEL (mink)
8.58E-05
Sediment Community
Threshold Level
6.68E-06
2,3,7,8-TCDD
Surface Soil - Dist. 1 - 312 m
Threshold - Mammalian Insectivores (shrew)
1.17E-07
equivalents
Surface Soil - Dist. 2 - 850 m
Threshold - Mammalian Insectivores (shrew)
5.04E-08
Surface Soil - Dist. 3 - 1,500 m
Threshold - Mammalian Insectivores (shrew)
8.33E-08
Surface Soil - Dist. 4 - 3,500 m
Threshold - Mammalian Insectivores (shrew)
2.80E-07
Surface Soil - Dist. 5 - 7,500 m
Threshold - Mammalian Insectivores (shrew)
8.78E-07
Water-column Community
Threshold Level
6.67E-04
Frank-effect Level
6.67E+00
Lead
Ambient Air
NAAQS Secondary Standard
NA
Acronyms and abbreviations: BaP = benzo[a]pyrene; Dist. = distance; TCDD = tetrachlorodibenzo-p-dioxin; Hg =
mercury; Hg++ = divalent mercury; MeHg = methyl mercury; NA = not applicable; NAAQS = National Ambient Air
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Quality Standards; NOAEL = no-observed-adverse-effect level; LOAEL = lowest-observed-adverse-effect level; TPY
= tons per year
alnsectivore means diet of insects; however, here insectivore means specifically feeding on both insects (larvae and
adults) and other invertebrates (e.g., earthworms) that dwell in surface soil, as the named species (shrew and
woodcock) suggest.
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Environmental Risk Screen for RTR
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Technical Support Document
A-102
July 2017
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Appendix 10
Detailed Risk Modeling Results
-------
Table 1. Facility Identification Information
Facility EIS ID1
Facility Name
Address
City
State
949811
AKZO NOBEL FUNCTIONAL
CHEMICALS LLC
13440 HWY 43 NORTH
AXIS
AL
1020111
SABIC INNOVATIVE PLASTICS US
LLC (FORMERLY GE PLASTICS)
ONE PLASTICS DR
BURKVILLE
AL
985511
ASCEND PERFORMANCE
MATERIALS LLC
1050 CHEMSTRAND AVE
DECATUR
AL
999511
3M COMPANY
1400 STATE DOCKS RD
DECATUR
AL
999411
INDORAMA VENTURES XYLENES
AND PTA, LLC (FORMERLY
AMOCO CHEMICAL)
1401 FINLEY ISLAND RD
DECATUR
AL
949911
EVONIK CORPORATION
4201 EVONIK ROAD
THEODORE
AL
1072711
INEOS PHENOL
7770 RANGELINE ROAD
THEODORE
AL
973911
GEORGIA PACIFIC CHEMICALS
124 PAPER MILL RD
CROSSETT
AR
993411
GREAT LAKES CHEMICAL,
SOUTH PLANT
324 SOUTHFIELD CUTOFF
EL DORADO
AR
976011
ALBEMARLE CORPORATION,
SOUTH PLANT
2270 HIGHWAY 79 SOUTH
MAGNOLIA
AR
588311
MOTIVA - DELAWARE CITY
REFINERY
4550 WRANGLE HILL RD
DELAWARE CITY
DE
751411
ASCEND PERFORMANCE
MATERIALS OPERATIONS
3000 OLD CHEMSTRAND RD
CANTONMENT
FL
-------
Facility EIS ID1
Facility Name
Address
City
State
2491711
TAMINCO US INC (SUBSIDIARY
OF EASTMAN CHEMICAL
COMPANY) / AIR PRODUCTS
AND CHEMICALS, INC.
4575 HIGHWAY 90 EAST
PACE
FL
946711
SNF - RICEBORO
ONE CHEMICAL PLANT ROAD
RICEBORO
GA
18929011
NEW HEAVEN CHEMICALS, INC.
1575 380TH ST
MANLY
IA
10716511
BP AMOCO CHEMICAL CO
23425 W AMOCO RD
CHANNAHON
IL
8137811
KOPPERSINC
3900 S LARAMIE AVE
CICERO
IL
7972911
STEPAN CO
22500 WMILLSDALE RD
ELWOOD
IL
5386211
DYNACHEM INC
MAPLE GROVE RD
GEORGETOWN
IL
8209411
AKZO NOBEL SURFACE
CHEMISTRY LLC
8005 NTABLER RD
MORRIS
IL
7940411
CONOCOPHILLIPS CO / SHELL
OIL CO. WOOD RIVER MFG.
COMPLEX
900 S CENTRAL AVE
ROXANA
IL
7338711
AFTON CHEMICAL (FORMERLY
ETHYL PETROLEUM ADDITIVES
INC.)
501 MONSANTO AVE
SAUGET
IL
7972111
VERTELLUS INTEGRATED
PYRIDINES LLC
1500 STIBBS AVE
INDIANAPOLIS
IN
7246511
EVONIK CORPORATION -
TIPPECANOE LABORATORIES
1650 LILLY RD
LAFAYETTE
IN
7364611
SABIC INNOVATIVE PLASTICS
MT VERNON, LLC
1 LEXAN LN
MOUNT VERNON
IN
8067211
CHS MCPHERSON REFINERY
INC.
1391 IRON HORSE ROAD
MCPHERSON
KS
-------
Facility EIS ID1
Facility Name
Address
City
State
8059311
OCCIDENTAL CHEMICAL CORP
(FORMERLY OXYCHEM -
WICHITA PLANT AND BASIC
CHEMICALS COMPANY, LLC AND
VULCAN CHEMICALS)
6200 S RIDGE RD
WICHITA
KS
7351811
MONUMENT CHEMICAL
KENTUCKY LLC/ARCH
CHEMICALS
2450 OLIN RD
BRANDENBURG
KY
5929411
WESTLAKE VINYLS INC
2468 INDUSTRIAL PKWY
CALVERT CITY
KY
8096711
CELANESE
408 N MAIN ST
CALVERT CITY
KY
7366811
ARKEMA INC
4444 INDUSTRIAL PKWY
CALVERT CITY
KY
8194311
THE DOW CHEMICAL CO
(FORMERLY DOW CORNING
CORP)
4770 US 42 E
CARROLLTON
KY
7331911
MARATHON PETROLEUM CO
LLC - CATLETTSBURG REFINING
11631 US 23
CATLETTSBURG
KY
10695411
CHEMOURS, CO. (FORMERLY E 1
DU PONT DE NEMOURS & CO.)
4200 CAMP GROUND RD
LOUISVILLE
KY
7367811
AMERICAN SYNTHETIC RUBBER
CO
4500 CAMP GROUND RD
LOUISVILLE
KY
7368011
HEXION, INC.
6200 CAMP GROUND RD
LOUISVILLE
KY
7226311
DELTECH CORPORATION,
BATON ROUGE FACILITY
11911 SCENIC HWY
BATON ROUGE
LA
16972411
ECO SERVICES OPERATIONS LLC
-SULFURIC ACID PLANT/
SOLVAY USA INC - CATHYVAL
PLANT / RHODIA
1275 AIRLINE HWY
BATON ROUGE
LA
-------
Facility EIS ID1
Facility Name
Address
City
State
8215111
ECO SERVICES OPERATIONS LLC
-SULFURIC ACID PLANT/
SOLVAY USA INC - CATHYVAL
PLANT / RHODIA
1301 AIRLINE HWY
BATON ROUGE
LA
7226611
EXXONMOBIL BATON ROUGE
CHEMICAL PLANT
4999 SCENIC HWY
BATON ROUGE
LA
8467611
HONEYWELL INTERNATIONAL
INC - BATON ROUGE PLANT
2966 LUPINE AVE
BATON ROUGE
LA
7354711
FORMOSA PLASTICS CORP
LOUISIANA
N END OF GULF STATES RD
BATON ROUGE
LA
7203711
PHILLIPS 66 CO - ALLIANCE
REFINERY
15551 HWY 23 S
BELLE CHASSE
LA
5505011
TOTAL PETROCHEMICALS &
REFINING USA INC-CARVILLE
POLYSTYRENE PLANT
6225 HWY 75
CARVILLE
LA
7448011
COS-MAR STYRENE MONOMER
PLANT (FORMERLY TOTAL
PETROCHEMICALS & REFINING
USA INC-COS-MAR CO)
6325 HWY 75
CARVILLE
LA
8020411
CHALMETTE REFINING
500 W ST BERNARD HWY
CHALMETTE
LA
7915011
WESTLAKE VINYLS CO LP
36045 HWY 30
GEISMAR
LA
7365611
LION COPOLYMER GEISMAR LLC
- GEISMAR FACILITY
36191 HWY 30
GEISMAR
LA
7367211
BASF CORP - DNT PLANT
36637 B HWY 30
GEISMAR
LA
16966011
METHANEX USA LLC - GEISMAR
METHANOL PLANT
4171 HWY 73 (FORMER GATE
AT 4279)
GEISMAR
LA
-------
Facility EIS ID1
Facility Name
Address
City
State
7368811
HEXION INC - FORMALDEHYDE
PLANT
4338 HWY 73
GEISMAR
LA
7445611
SHELL CHEMICAL LP - GEISMAR
PLANT
7594 HWY 75
GEISMAR
LA
7445711
OCCIDENTAL CHEMICAL
CORPORATION - GEISMAR
PLANT
8318 ASHLAND RD
GEISMAR
LA
8465611
BASF CORP - GEISMAR SITE
8404 RIVER RD (HWY 75)
GEISMAR
LA
8465711
PRAXAIR INC - GEISMAR PLANT
9154 HWY 75
GEISMAR
LA
8465311
RUBICON LLC - GEISMAR PLANT
9156 HWY 75
GEISMAR
LA
17640111
DENKA PERFORMANCE
ELASTOMER LLC
586 HWY 44
LAPLACE
LA
7204811
DUPONT SPECIALTY PRODUCTS
USA LLC - PONTCHARTRAIN SITE
586 HWY 44
LAPLACE
LA
8020011
AMERICAS STYRENICS LLC - ST
JAMES PLANT
9901 HWY 18
ST. JAMES
LA
8020811
SHELL (FORMERLY MOTIVA
ENTERPRISES LLC - NORCO
REFINERY)
15536 RIVER RD
NORCO
LA
8239511
SHELL CHEMICAL, NORCO
CHEMICAL PLANT EAST SITE
15536 RIVER RD
NORCO
LA
8018911
SHELL CHEMICALS, NORCO
CHEMICAL PLANT WEST SITE
16122 RIVER RD
NORCO
LA
8026211
SHELL CHEMICALS, NORCO
WEST SITE FACILITY
16122 RIVER RD
NORCO
LA
-------
Facility EIS ID1
Facility Name
Address
City
State
8467311
THE DOW CHEMICAL CO-
LOUISIANA OPERATIONS
21255 HWY 1
PLAQUEMINE
LA
5520211
INEOS OXIDE - A DIVISION OF
INEOS AMERICAS LLC
21255A HWY 1
PLAQUEMINE
LA
7227011
AXIALL LLC - PLAQUEMINE
FACILITY
26100 HWY 405 S
PLAQUEMINE
LA
13610611
SHINTECH LOUISIANA LLC-
SHINTECH PLAQUEMINE PLANT
26270 HWY 405 S
PLAQUEMINE
LA
15639911
FLOPAM INC - FLOPAM FACILITY
26790 HWY 405
PLAQUEMINE
LA
7226711
ANGUS CHEMICAL CO
350 HWY 2
STERLINGTON
LA
7380611
WESTLAKE STYRENE LLC-
MARINE TERMINAL
1820 PAKTANK RD
SULPHUR
LA
7928911
WESTLAKE PETROCHEMICALS LP
- ETHYLENE MANUFACTURING
COMPLEX (PETRO l/PETRO II),
POLY III
900 HWY 108
SULPHUR
LA
7929111
WESTLAKE PETROCHEMICALS LP
- ETHYLENE MANUFACTURING
COMPLEX (PETRO l/PETRO II),
WESTLAKE PETROCHEMICAL
COMPLEX
900 HWY 108
SULPHUR
LA
8465211
WESTLAKE PETROCHEMICALS LP
- ETHYLENE MANUFACTURING
COMPLEX (PETRO l/PETRO II),
STYRENE MONOMER
PRODUCTION FACILITY
900 HWY 108
SULPHUR
LA
-------
Facility EIS ID1
Facility Name
Address
City
State
7202911
UNION CARBIDE CORP-ST
CHARLES OPERATIONS
355 HWY3142
HAHNVILLE
LA
7354911
EAGLE US 2 LLC - LAKE CHARLES
COMPLEX
1300 PPG DR
WESTLAKE
LA
8361111
AXIALL LLC (FORMERLY
GEORGIA GULF LAKE CHARLES
LLC)
1600 VCM PLANT RD
WESTLAKE
LA
8468011
SASOL CHEMICALS (USA) LLC -
LAKE CHARLES CHEMICAL
COMPLEX
2201 OLD SPANISH TRAIL
WESTLAKE
LA
17640311
EVONIK CYRO LLC - MMA PLANT
10800 RIVER RD
WAGGAMAN
LA
7228511
EVONIK CYRO LLC - MMA PLANT
10800 RIVER RD
WAGGAMAN
LA
9588611
EVONIK CYRO LLC - MMA PLANT
10800 RIVER RD
WESTWEGO
LA
17640911
BLUE CUBE OPERATIONS LLC -
PLAQUEMINE SITE
21255 HWY 1 S
PLAQUEMINE
LA
5719311
CITGO PETROLEUM
CORPORATION, LAKE CHARLES
TRUCK LOADING FACILITY
4401 HWY 108 S
SULPHUR
LA
7380411
CITGO PETROLEUM
CORPORATION, LAKE CHARLES
MANUFACTURING COMPLEX
4401 HWY 108 S
SULPHUR
LA
13614411
KEMIRA WATER SOLUTIONS -
ACRYLAMIDE UNIT - AMD UNIT
10800 RIVER RD
WAGGAMAN
LA
17905711
LOTTE CHEMICAL LOUISIANA
LLC
2200 BAYOU DINDE PASS
WESTLAKE
LA
17055211
TRINSEO LLC-MI OPERATIONS
1604 BUILDING
MIDLAND
Ml
-------
Facility EIS ID1
Facility Name
Address
City
State
18982311
CORTEVA AGRISCIENCE LLC
701 WASHINGTON STREET
MIDLAND
Ml
8384311
CHEVRON PRODUCTS CO,
PASCAGOULA REFINERY
250 INDUSTRIAL ROAD
PASCAGOULA
MS
7984011
GEORGIA PACIFIC CHEMICALS
(WOOD PRODUCTS),
TAYLORSVILLE
HIGHWAY 28 WEST
TAYLORSVILLE
MS
7984111
GEORGIA PACIFIC CHEMICALS,
TAYLORSVILLE
HIGHWAY 28 WEST
TAYLORSVILLE
MS
7302511
HEXION INC
3670 GRANT CREEK RD
MISSOULA
MT
8006811
GEORGIA-PACIFIC CHEMICALS,
LLC-CONWAY
200 AMPAC ROAD
CONWAY
NC
8135311
HEXION INC. - FAYETTEVILLE
FACILITY
1411 INDUSTRIAL DRIVE
FAYETTEVILLE
NC
16856611
OAK-BARK CORP / HEXION INC.
- ACME OPERATIONS / WRIGHT
CORP / SILAR LLC
333 NEILS EDDY ROAD
RIEGELWOOD
NC
8447711
OAK-BARK CORP / HEXION INC.
- ACME OPERATIONS / WRIGHT
CORP/SILAR LLC
333 NEILS EDDY ROAD
RIEGELWOOD
NC
8086711
DAKOTA GASIFICATION
COMPANY-GREAT PLAINS
SYNFUELS PLANT
420 COUNTY ROAD 26
BEULAH
ND
7311911
POLYMER ADDITIVES INC
(FORM: FERRO CORP - DE RIV.
PLANT)
US RT 130 SOUTH
BRIDGEPORT
NJ
8107111
SI GROUP INC - ROTTERDAM
JUNCT FACIL
1000 MAIN ST|ST RTE 5S
ROTTERDAM
JUNCTION
NY
-------
Facility EIS ID1
Facility Name
Address
City
State
8434411
VON ROLL USA INC
200 VON ROLL DR
SCHENECTADY
NY
8105111
SHPP US LLC (FORMERLY SABIC
INNOVATIVE PLASTICS US LLC)
1 NORYL AVE
SELKIRK
NY
8123911
MOMENTIVE PERFORMANCE
MATERIALS
260 HUDSON RIVER RD
WATERFORD
NY
8130511
KRATON POLYMERS U.S. LLC
2419 STATE ROUTE 618
BELPRE
OH
7937511
PMC SPECIALTIES GROUP, INC.
(FORMERLY CINCINNATI
SPECIALITIES)
501 MURRAY ROAD
ST. BERNARD
OH
8148211
ALTIVIA PETROCHEMICALS, LLC
1019 HAVERHILL-OHIO
FURNACE ROAD
HAVERHILL
OH
8007011
LIMA REFINING COMPANY
1150 SOUTH METCALF
STREET
LIMA
OH
9308811
ISP
1220 SOUTH METCALF
STREET
LIMA
OH
13431911
TOLEDO REFINING COMPANY,
LLC.
1819 WOODVILLE ROAD
OREGON
OH
7319811
TOLEDO REFINING COMPANY,
LLC.
1819 WOODVILLE ROAD
OREGON
OH
8418011
BP-HUSKY REFINING LLC
4001 CEDAR POINT ROAD
OREGON
OH
8262411
SUNOCO PARTNERS
MARKETING & TERMINALS LP
TOLEDO TERM
1601 WOODVILLE ROAD
TOLEDO
OH
8263111
PERSTOP POYOLS, INC.
600 MATZINGER ROAD
TOLEDO
OH
15077311
MONTGOMERY CHEM
LTD/CONSHOHOCKEN
901 CONSHOHOCKEN RD
CONSHOHOCKEN
PA
-------
Facility EIS ID1
Facility Name
Address
City
State
4950811
ADVANSIX INC
4700 BERMUDA ST
PHILADELPHIA
PA
9177911
CELANESE LTD ENOREE PLANT
14355 HWY221
ENOREE
SC
4041311
DAK AMERICAS LLC COLUMBIA
SITE
570 K AVE
GASTON
SC
4965811
BP AMOCO CHEMICAL, COOPER
RIVER PLT
1306 AMOCO DR
WANDO
SC
5611111
CHATTEM CHEMICALS, INC.
3708 ST. ELMO AVENUE
CHATTANOOGA
TN
3982311
EASTMAN CHEMICAL
COMPANY, TENNESSEE
OPERATIONS
200 SOUTH WILCOX DRIVE
KINGSPORT
TN
6194311
LUCITE INTERNATIONAL INC
2665 FITE ROAD
MEMPHIS
TN
16871811
MONSANTO CO/ASCEND
PERFORMANCE MATERIALS
CHOCOLATE BAYOU PLANT
(FORMERLY AMOCO AKA
EASTMAN)
ON FM 2917 8 MILES SOUTH
OF INTERSECTION OF HWY35
AND FM 2917
ALVIN
TX
5632411
MONSANTO CO/ASCEND
PERFORMANCE MATERIALS
CHOCOLATE BAYOU PLANT
(FORMERLY AMOCO AKA
EASTMAN)
3 MS OF FM 2004 & FM
2917 INTERX
ALVIN
TX
5633311
MONSANTO CO/ASCEND
PERFORMANCE MATERIALS
CHOCOLATE BAYOU PLANT
(FORMERLY AMOCO AKA
EASTMAN)
FM 2917; 8 Ml S OF INTXOF
HWY35 & FM 2917
ALVIN
TX
13411911
OQ CHEMICALS BAY CITY PLANT
2001 FM 3057 ADMIN BLDG
BAY CITY
TX
-------
Facility EIS ID1
Facility Name
Address
City
State
4924411
EXXON MOBIL BAYTOWN
REFINERY
2800 DECKER DR
BAYTOWN
TX
4056511
EXXON MOBIL CHEMICAL
BAYTOWN OLEFINS PLANT
3525 DECKER DR
BAYTOWN
TX
6421811
EXXONMOBIL CHEMICAL
BAYTOWN CHEMICAL PLANT
5000 BAYWAY DR
BAYTOWN
TX
5729211
COVESTRO INDUSTRIAL PARK
BAYTOWN AKA BAYER
MATERIAL SCIENCE
8500 W BAY RD
BAYTOWN
TX
9175811
LANXESS CORP BAYTOWN
W SIDE OF CHAMBER
COUNTY NEXT TO
CHAMBER/HARRIS CTY LINE,
ON CEDAR BAYOU TAKE ST
HWY 146 S FROM 1-10
BAYTOWN
TX
5653011
GOODYEAR TIRE & RUBBER
BEAUMONT CHEMICAL PLANT
ON WEST 1H10 SMITH ROAD
EXIT
BEAUMONT
TX
4930211
EXXONMOBIL OIL BEAUMONT
CHEMICAL PLANT
2775 GULF STATES RD
BEAUMONT
TX
17912111
THE DOW CHEMICAL CO -
ANILINE FACILITY (FORMERLY
CHEMOURS BEAUMONT
ANILINE FACILITY)
5470 N TWIN CITY HWY
NEDERLAND
TX
6362111
THE DOW CHEMICAL CO -
ANILINE FACILITY (FORMERLY
CHEMOURS BEAUMONT
ANILINE FACILITY)
HWY 347 .5 Ml S OF INTERX
OF HWYS 69 & 347
BEAUMONT
TX
-------
Facility EIS ID1
Facility Name
Address
City
State
6386311
THE DOW CHEMICAL CO-
ANILINE FACILITY (FORMERLY
CHEMOURS BEAUMONT
ANILINE FACILITY)
5470 N TWIN CITY HWY
NEDERLAND
TX
5651911
ALON USA BIG SPRING
REFINERY
IH 20 AT REFINERY RD
BIG SPRING
TX
13389511
OXEA BISHOP FACILITY, BISHOP
PLANT
FROM BISHOP TX NORTH
CITY LIMITS TAKE BUS HWY
77 SOUTH TO FM 4 TURN
RIGHT TO PLANT ENTRANCE
BISHOP
TX
4929511
OXEA BISHOP FACILITY, TICONA
POLYMERS BISHOP FACILITY
1 M S OF BISHOP ON US HWY
77 BUS
BISHOP
TX
6157311
CHEVRON PHILLIPS CHEMICAL
BORGER PLANT
2 Ml. N.E. OF BORGER ON
STATE SPUR 119
BORGER
TX
4861611
BORGER REFINERY
NORTH SIDE OF PHILLIPS
(UNINCORP)
BORGER
TX
4941411
LYONDELL CHEMICAL
CHANNELVIEW PLANT
2502 SHELDON RD
CHANNELVIEW
TX
4925111
EQUISTAR CHEMICALS
CHANNELVIEW COMPLEX
8280 SHELDON RD
CHANNELVIEW
TX
4945611
HUNTSMAN PETROCHEMICAL
CONROE PLANT
5451 JEFFERSON CHEMICAL
RD
CONROE
TX
17974911
VALERO CORPUS CHRISTI
REFINERY EAST PLANT
1300 CANTWELL LN
CORPUS CHRISTI
TX
5656011
VALERO CORPUS CHRISTI
REFINERY EAST PLANT
1300 CANTWELL LN
CORPUS CHRISTI
TX
-------
Facility EIS ID1
Facility Name
Address
City
State
5862111
EQUISTAR CHEMICALS
(OXYCHEM), CORPUS CHRISTI
PLANT
1501 MCKINZIE RD
CORPUS CHRISTI
TX
4031311
FLINT HILLS RESOURCES EAST
REFINERY
1607 NUECES BAY BLVD
CORPUS CHRISTI
TX
4945411
CITGO CORPUS CHRISTI
REFINERY EAST PLANT
1801 NUECES BAY BLVD
CORPUS CHRISTI
TX
4205511
FHR CORPUS CHRISTI WEST
PLANT, WEST REFINERY
SUNTIDE & UP RIVER ROADS
CORPUS CHRISTI
TX
17985111
VALERO REFINING COMPANY
WEST PLANT, VALERO
PARTNERS CORPUS CHRISTI
WEST
5900 UP RIVER RD
CORPUS CHRISTI
TX
4929811
VALERO REFINING COMPANY
WEST PLANT, CORPUS CHRISTI
WEST PLANT
5900 UP RIVER RD
CORPUS CHRISTI
TX
9115811
VALERO REFINING COMPANY
WEST PLANT, CORPUS CHRISTI
LP TANK FARM
5900 UP RIVER RD
CORPUS CHRISTI
TX
4167811
KMCO, CROSBY FACILITY
16503 RAMSEY RD
CROSBY
TX
4778311
ROHM AND HAAS TEXAS DEER
PARK PLANT
6600 LA PORTE FWY
DEER PARK
TX
4168511
HEXION DEER PARK/SHELL
DEER PARK REFINERY
HWY 225 W OF
BATTLEGROUND RD
DEER PARK
TX
4982011
HEXION DEER PARK/SHELL
DEER PARK REFINERY
5900 LA PORTE FWY
DEER PARK
TX
-------
Facility EIS ID1
Facility Name
Address
City
State
4778211
OXYVINYLS (OCCIDENTAL
CHEMICAL CORP) VCM PLANT
1000 TIDAL RD INSIDE SHELL
DEER PARK MFG COMPLEX
DEER PARK
TX
6641411
GEO SPECIALTY CHEMICAL DEER
PARK
739 INDEPENDENCE PKWY
DEER PARK
TX
5632711
DOW TEXAS OPERATIONS
FREEPORT
2301 N BRAZOSPORT BLVD
CLUTE
TX
4897511
BASF FREEPORT SITE
602 COPPER RD
FREEPORT
TX
5019011
PHILLIPS 66 CHEMICAL PE SITE /
FREEPORT TERMINAL
(FORMERLY CHEVRON PHILLIPS
CHEMICAL PE SITE / FREEPORT
TERMINAL)
21441 LOOP 419
OLD OCEAN
TX
4762811
HOUSTON REFINING
2 Ml E OF THE 610 & 225
INTERX; 12000 LAWN DALE
HOUSTON
TX
4941211
GOODYEAR HOUSTON
CHEMICAL PLANT
2000 GOODYEAR DR
HOUSTON
TX
4168611
TPC GROUP HOUSTON PLANT
8600 PARK PLACE
HOUSTON
TX
4778711
ECO SERVICES OPERATIONS AKA
RHODIA AKA SOLVAY
8615 MANCHESTER RD
HOUSTON
TX
5746611
OCCIDENTAL CHEMICAL
CORPORATION/INGLESIDE /
OXYCHEM INGLESIDE PLANT
ON F.M. 361, S. OF S.H. 35
INGLESIDE
TX
4055111
LA PORTE METHANOL, LA
PORTE COMPLEX
11603 STRANG RD
LA PORTE
TX
4167411
LA PORTE METHANOL, LA
PORTE COMPLEX
11603 STRANG RD
LA PORTE
TX
-------
Facility EIS ID1
Facility Name
Address
City
State
9103411
INVISTA S A R L LA PORTE PLANT
12455 STRANGE RD
LA PORTE
TX
6671911
LYONDELLBASELL ACETYLS, LA
PORTE COMPLEX
1350 MILLER CUT OFF RD
LA PORTE
TX
6534811
CLEAN HARBORS DEER PARK
2027 BATTLEGROUND RD
LA PORTE
TX
4057611
OXYVINYLS (GEON), LA PORTE
VCM PLANT
2400 MILLER CUT OFF RD
LA PORTE
TX
4941511
EASTMAN CHEMICAL TEXAS
OPERATIONS
HWY 149 KODAK BLVD
LONGVIEW
TX
7908711
EASTMAN CHEMICAL TEXAS
OPERATIONS, COGENERATION
FACILITY
CALLAHAN RD
LONGVIEW
TX
5631411
GEORGIA-PACIFIC RESINS
LUFKIN PLANT
1429 E LUFKIN AVE
LUFKIN
TX
6385211
LUCITE BEAUMONT SITE
6350 N TWIN CITY HWY
NEDERLAND
TX
5018711
CHEVRON PHILLIPS CHEMICAL
SWEENY REFINERY PETROCHEM
COMPLEX
HWY 35 AND 524 AT OLD
OCEAN
SWEENY
TX
4190211
DUPONT SABINE RIVER WORKS
FM 1006
ORANGE
TX
10678011
INVISTA SARL SABINE RIVER
SITE
3055A FM 100
ORANGE
TX
3737011
GULF COAST WASTE DISPOSAL
BAYPORT FACILITY
10800 BAY AREA BLVD
PASADENA
TX
4924311
LYONDELL CHEMICAL BAYPORT
CHOATE PLANT
10801 CHOATE RD
PASADENA
TX
4980911
KURARAY COMPANY INC
11500 BAY AREA BLVD BAY
AREA & CHOATE RD
PASADENA
TX
-------
Facility EIS ID1
Facility Name
Address
City
State
6534611
INEOS STYROLUTION AMERICA
BAYPORT FACILITY
12222 PORT RD
PASADENA
TX
4055511
THE GOODYEAR TIRE & RUBBER
CO BAYPORT CHEMICAL PLANT
13441 BAY AREA BLVD
PASADENA
TX
6510111
SEKISUI SPECIALTY CHEMICALS
AMERICA, LLC.
1423 HWY 225
PASADENA
TX
6510311
SEKISUI SPECIALTY CHEMICALS
AMERICA, LLC.
1423 HWY 225
PASADENA
TX
6421511
INEOS AMERICAS PASADENA
PLANT
3503 PASADENA FWY
PASADENA
TX
4926611
EQUISTAR BAYPORT
UNDERWOOD PLANT
5761 UNDERWOOD RD
PASADENA
TX
10679911
CELANESE CLEAR LAKE PLANT
9502 BAYPORT BLVD
PASADENA
TX
13411211
CELANESE CLEAR LAKE
OPERATIONS
9502 B BAYPORT BLVD B
PASADENA
TX
14997411
CELANESE CLARIANT CLEAR
LAKE PLANT
9502 BAYPORT BLVD
PASADENA
TX
4057911
CELANESE CLEAR LAKE PLANT
9502 BAYPORT BLVD
PASADENA
TX
5633411
FORMOSA POINT COMFORT
PLANT
201 FORMOSA DR
POINT COMFORT
TX
6158411
CHEVRON CHEMICAL
COMPANY, PORT ARTHUR
PLANT
2001 SOUTH GULFWAY
DRIVE (IN THE OLD CHEVRON
REFINERY)
PORT ARTHUR
TX
4863111
PORT ARTHUR REFINERY
32NDST& HWY 366
PORT ARTHUR
TX
6430411
FLINT HILLS RESOURCES PORT
ARTHUR CHEMICALS FACILITY
4241 SAVANNAH AVE
PORT ARTHUR
TX
-------
Facility EIS ID1
Facility Name
Address
City
State
6444911
VEOLIA ES TECHNICAL
SOLUTIONS, HAZARDOUS
WASTE DISPOSAL
3 Ml W OF TAYLORS BAYOU
ON HW 73
PORT ARTHUR
TX
6445411
BASF TOTAL NAFTA REGION
OLEFINS COMPLEX
INTX OF HWY 366 & HWY 87
PORT ARTHUR
TX
5632511
INEOS GREEN LAKE PLANT
6.5 M SON SH 185 FROM
BLOOMINGTON
PORT LAVACA
TX
5651611
LION ELASTOMERS, PORT
NECHES SYNTHETIC RUBBER
PLANT
1615 MAIN STAT INTERX
WITH SPUR 136
PORT NECHES
TX
4945211
INDORAMA (FORMERLY
HUNTSMAN PORT NECHES)
2102 SPUR 136
PORT NECHES
TX
13407911
PORT NECHES OPERATIONS C4
PLANT
SPUR 136 & HWY 366
PORT NECHES
TX
5846511
UCC SEADRIFT OPERATIONS
HIGHWAY 185
SEADRIFT
TX
6615111
EASTMAN CHEMICAL TEXAS
CITY OPERATIONS
201 BAY ST S
TEXAS CITY
TX
4835311
BLANCHARD REFINING
GALVESTON BAY REFINERY / BP
/AMOCO
2401 5TH AVE SIN TEXAS
CITY
TEXAS CITY
TX
10722911
TEXAS CITY CHEMICAL PLANT /
INEOS STYRENICS TEXAS CITY
PLANT / BP AMOCO
2800 FM 519 E
TEXAS CITY
TX
3930711
TEXAS CITY CHEMICAL PLANT /
INEOS STYRENICS TEXAS CITY
PLANT / BP AMOCO
2800 FM 519 E
TEXAS CITY
TX
-------
Facility EIS ID1
Facility Name
Address
City
State
3967011
UNION CARBIDE TEXAS CITY
PLANT
3301 5TH AVE S
TEXAS CITY
TX
17909311
DIAMOND SHAMROCK CORP
(THREE RIVERS REFINERY)/
VALERO
301 W LEROY ST
THREE RIVERS
TX
6152811
DIAMOND SHAMROCK CORP
(THREE RIVERS REFINERY)/
VALERO
301 LEROY ST
THREE RIVERS
TX
5679711
INVISTA SARL, VICTORIA SITE
8.5 Ml SE OFF FM 404
VICTORIA
TX
17735411
OLIN BLUE CUBE OPERATIONS,
LLC (FREEPORT)
2301 N BRAZOSPORT BLVD
FREEPORT
TX
17058711
KURARAY AMERICA VINYLS, LA
PORTE PLANT
12342 STRANG RD & SENS RD
LA PORTE
TX
18973611
CORTEVA AGRISCIENCES LLC
TEXAS OPERATIONS FREEPORT
2301 N BRAZOSPORT BLVD
BLDG A3210
FREEPORT
TX
18893911
BEAUMONT GAS TO GASOLINE
PLANT
2366 SULFUR PLANT RD
BEAUMONT
TX
5768911
HERCULES INC AQUALON DIV
1111 HERCULES RD
HOPEWELL
VA
5769011
ADVANSIX (FORMERLY
HONEYWELL RESINS AND
CHEMICALS LLC -HOPEWELL)
905 E RANDOLPH RD
HOPEWELL
VA
4004311
CELANESE ACETATE LLC / DUKE
ENERGY GENERATION SERVICES
OF NARROWS, LLC
3520 VIRGINIA AVE
NARROWS
VA
5748611
US ARMY - RADFORD ARMY
AMMUNITION PLANT
4050 PEPPERS FERRY ROAD
RADFORD
VA
-------
Facility EIS ID1
Facility Name
Address
City
State
4783711
EMERALD KALAMA CHEMICAL
LLC
1296 THIRD STREET NW
KALAMA
WA
6884211
DUPONT (BELLE SITE)/
CHEMOURS
901 WEST DUPONT AVE.
BELLE
WV
6234511
KOPPERS INC., FOLLANSBEE
810 KOPPERS ROAD
FOLLANSBEE
WV
4878911
THE CHEMOURS COMPANY FC,
LLC, DUPONT WASHINGTON
WORKS
ROUTE 892
WASHINGTON
WV
19250811
ALTIVIA- INSTITUTE
250 CARBIDE RD
DUNBAR
WV
'BOLD indicates where multiple facility Emissions Inventory System (EIS) IDs represent a single facility.
-------
Inhalation Risk Modeling Results
Chronic Inhalation Risks
Table 2. Maximum Predicted HEM-4 Chronic Inhalation Risk - Actual and Allowable Emissions
Facility EIS ID
Source Category Chronic Risk'
Whole Facility Chronic Risk'
% Source
Contribution
(Cancer MIR)
Cancer MIR
Non-cancer
Max H I
Target Organ
Cancer MIR
Non-cancer
Max I II
Target Organ
4945211
2000
0.02
neurological
2000
0.02
neurological
100%
7202911
700
0.1
respiratory
700
0.2
respiratory
100%
5846511
600
0.006
respiratory
600
0.05
respiratory
100%
4941511
500
0.2
respiratory
500
0.3
respiratory
100%
7445611
400
0.07
respiratory
500
0.08
respiratory
80%
4926611
200
0.002
reproductive
200
0.002
reproductive
100%
8467311
200
1
respiratory
200
1
respiratory
100%
4057911
200
0.009
respiratory
200
0.009
respiratory
100%
8465611
100
0.03
respiratory
200
0.04
respiratory
50%
8468011
100
0.03
reproductive
600
3
respiratory
17%
985511
100
1
respiratory
100
1
respiratory
100%
4945611
100
0.0009
neurological
2000
0.02
respiratory
5%
7351811
100
0.003
respiratory
100
0.01
respiratory
100%
5929411
100
1
respiratory
100
1
respiratory
100%
4941411
90
0.2
respiratory
200
0.2
respiratory
45%
5632711
90
0.4
respiratory
100
0.6
respiratory
90%
-------
Facility EIS ID
Source Category Chronic Risk'
Whole Facility Chronic Risk'
% Source
Contribution
(Cancer MIR)
Cancer MIR
Non-cancer
Max HI
Target Organ
Cancer MIR
Non-cancer
Max HI
Target Organ
17640111
90
0.1
respiratory
600
0.3
respiratory
15%
8137811
70
0.8
respiratory
70
0.8
respiratory
100%
13610611
60
0.8
respiratory
60
3
respiratory
100%
976011
50
0.9
respiratory
80
0.9
respiratory
63%
7915011
50
0.08
respiratory
50
0.09
respiratory
100%
946711
30
0.7
respiratory
30
0.7
respiratory
100%
4924411
30
0.6
respiratory
100
0.7
respiratory
30%
8020811
30
0.2
respiratory
30
0.2
respiratory
100%
13407911
30
0.4
reproductive
30
0.4
reproductive
100%
5651611
30
0.4
reproductive
30
0.4
reproductive
100%
993411
30
0.8
respiratory
200
0.8
respiratory
15%
7354911
30
2
respiratory
30
2
respiratory
100%
5520211
20
0.08
respiratory
20
0.08
respiratory
100%
7228511
20
0.2
respiratory
20
0.2
respiratory
100%
5653011
20
0.3
reproductive
20
0.3
reproductive
100%
7354711
20
0.1
respiratory
20
0.1
respiratory
100%
14997411
20
0.0001
neurological
20
0.0001
neurological
100%
7226711
20
0.1
liver
60
0.5
respiratory
33%
3982311
20
0.2
respiratory
20
0.8
respiratory
100%
7940411
10
0.06
immunological
20
0.07
immunological
50%
8239511
10
0.2
reproductive
70
0.4
respiratory
14%
-------
Facility EIS ID
Source Category Chronic Risk'
Whole Facility Chronic Risk'
% Source
Contribution
(Cancer MIR)
Cancer MIR
Non-cancer
Max HI
Target Organ
Cancer MIR
Non-cancer
Max HI
Target Organ
17055211
10
0.09
reproductive
10
0.09
reproductive
100%
5633411
10
0.4
respiratory
10
0.7
respiratory
100%
6152811
10
0.05
immunological
10
0.09
neurological
100%
4168611
10
0.2
reproductive
10
0.2
reproductive
100%
4925111
10
0.2
reproductive
30
0.3
reproductive
33%
17640911
10
0.7
respiratory
10
2
respiratory
100%
15639911
10
0.6
respiratory
10
0.6
respiratory
100%
4762811
10
0.04
respiratory
10
0.04
respiratory
100%
8465311
10
0.2
spleen
10
0.2
spleen
100%
13431911
9
0.04
immunological
9
0.04
immunological
100%
7203711
9
0.04
respiratory
10
0.04
respiratory
90%
8384311
8
0.03
respiratory
9
0.09
neurological
89%
6444911
8
0.004
respiratory
8
0.004
respiratory
100%
4778211
7
0.05
respiratory
7
0.05
respiratory
100%
4835311
7
0.03
respiratory
80
0.09
respiratory
9%
7984011
6
0.2
respiratory
6
0.2
respiratory
100%
4778711
6
0.06
respiratory
6
0.06
respiratory
100%
7445711
6
0.2
respiratory
6
0.2
respiratory
100%
4205511
5
0.1
respiratory
5
0.2
respiratory
100%
999411
5
0.3
respiratory
5
0.3
respiratory
100%
4168511
5
0.05
reproductive
10
0.1
reproductive
50%
-------
Facility EIS ID
Source Category Chronic Risk'
Whole Facility Chronic Risk'
% Source
Contribution
(Cancer MIR)
Cancer MIR
Non-cancer
Max HI
Target Organ
Cancer MIR
Non-cancer
Max HI
Target Organ
8026211
5
1
respiratory
5
1
respiratory
100%
4941211
5
0.08
reproductive
5
0.08
reproductive
100%
13614411
5
0.03
respiratory
5
0.03
respiratory
100%
5632511
5
0.04
respiratory
5
0.04
respiratory
100%
5633311
5
0.04
respiratory
10
0.09
respiratory
50%
6445411
5
0.07
reproductive
5
0.07
reproductive
100%
7338711
4
0.04
respiratory
4
0.04
respiratory
100%
7368011
4
0.06
respiratory
4
0.06
respiratory
100%
4945411
4
0.02
immunological
5
0.3
neurological
80%
8020011
4
0.02
immunological
5
0.02
immunological
80%
7368811
4
0.03
respiratory
4
0.03
respiratory
100%
8020411
4
0.02
respiratory
20
0.1
neurological
20%
8018911
4
0.003
reproductive
100
0.8
respiratory
4%
4167811
4
0.1
respiratory
4
0.1
respiratory
100%
7226611
4
0.1
respiratory
20
0.2
respiratory
20%
17909311
4
0.01
immunological
4
0.02
immunological
100%
7227011
3
0.02
respiratory
4
0.02
respiratory
75%
5018711
3
0.2
respiratory
20
0.8
respiratory
15%
8006811
3
0.03
respiratory
6
0.05
respiratory
50%
7331911
3
0.02
immunological
3
0.02
immunological
100%
5631411
3
0.04
respiratory
3
0.04
respiratory
100%
-------
Facility EIS ID
Source Category Chronic Risk'
Whole Facility Chronic Risk'
% Source
Contribution
(Cancer MIR)
Cancer MIR
Non-cancer
Max HI
Target Organ
Cancer MIR
Non-cancer
Max HI
Target Organ
7380411
3
0.03
respiratory
3
0.2
respiratory
100%
8361111
3
0.05
respiratory
3
0.05
respiratory
100%
17735411
3
1
respiratory
3
1
respiratory
100%
8130511
3
0.04
reproductive
3
0.04
reproductive
100%
8263111
3
0.02
respiratory
3
0.02
respiratory
100%
7984111
2
0.02
respiratory
2
0.02
respiratory
100%
7319811
2
0.009
immunological
2
0.01
immunological
100%
5651911
2
0.03
respiratory
4
0.05
neurological
50%
6234511
2
0.01
developmental
2
0.01
developmental
100%
8107111
2
0.01
respiratory
2
0.02
respiratory
100%
17912111
2
0.1
spleen
2
0.1
spleen
100%
5729211
2
0.2
respiratory
2
0.2
respiratory
100%
7972111
2
0.03
respiratory
3
0.03
respiratory
67%
6386311
2
0.06
spleen
2
0.06
spleen
100%
7302511
2
0.01
respiratory
2
0.01
respiratory
100%
5656011
2
0.02
respiratory
2
0.03
respiratory
100%
6430411
2
0.007
immunological
6
0.03
reproductive
33%
4863111
2
0.05
respiratory
2
0.2
neurological
100%
6884211
2
0.5
respiratory
2
0.5
respiratory
100%
5019011
1
0.02
respiratory
9
0.08
reproductive
11%
17974911
1
0.005
immunological
1
0.005
immunological
100%
-------
Facility EIS ID
Source Category Chronic Risk'
Whole Facility Chronic Risk'
% Source
Contribution
(Cancer MIR)
Cancer MIR
Non-cancer
Max HI
Target Organ
Cancer MIR
Non-cancer
Max HI
Target Organ
7226311
1
0.005
immunological
1
0.005
immunological
100%
4057611
1
0.08
respiratory
2
0.08
respiratory
50%
7204811
1
0.2
spleen
1
0.2
spleen
100%
8465711
1
0.009
respiratory
1
0.009
respiratory
100%
4031311
1
0.004
immunological
2
0.04
neurological
50%
8067211
1
0.004
immunological
1
0.02
respiratory
100%
6534611
1
0.004
immunological
1
0.004
immunological
100%
4861611
1
0.1
neurological
2
0.1
neurological
50%
6157311
1
0.02
reproductive
1
0.02
reproductive
100%
10678011
1
0.1
respiratory
3
0.2
respiratory
33%
4190211
1
0.1
respiratory
6
0.4
respiratory
17%
8465211
1
0.002
immunological
1
0.002
immunological
100%
8209411
1
0.007
respiratory
1
0.01
respiratory
100%
7929111
1
0.009
reproductive
10
0.1
reproductive
10%
3737011
1
0.004
respiratory
1
0.004
respiratory
100%
8447711
1
0.006
respiratory
1
0.006
respiratory
100%
9103411
1
0.006
respiratory
1
0.006
respiratory
100%
5746611
1
0.01
respiratory
1
0.01
respiratory
100%
4929511
1
0.008
respiratory
1
0.01
respiratory
100%
19250811
1
0.006
respiratory
2
0.01
kidney
50%
7448011
1
0.0008
kidney
7
0.02
immunological
14%
-------
Facility EIS ID
Source Category Chronic Risk'
Whole Facility Chronic Risk'
% Source
Contribution
(Cancer MIR)
Cancer MIR
Non-cancer
Max HI
Target Organ
Cancer MIR
Non-cancer
Max HI
Target Organ
4783711
1
0.003
immunological
1
0.004
immunological
100%
8434411
1
0.006
respiratory
1
0.006
respiratory
100%
17058711
1
0.04
respiratory
1
0.04
respiratory
100%
17985111
1
0.002
immunological
1
0.003
immunological
100%
973911
1
0.8
respiratory
1
0.8
respiratory
100%
18973611
1
0.02
respiratory
1
0.02
respiratory
100%
4929811
1
0.002
immunological
1
0.2
neurological
100%
5505011
0
0.0006
neurological
0
0.0006
neurological
0%
8007011
0
0.005
reproductive
1
0.02
developmental
0%
9115811
0
0.002
respiratory
0
0.002
respiratory
0%
8467611
0
0.3
respiratory
1
0.4
respiratory
0%
6158411
0
0.002
reproductive
0
0.002
reproductive
0%
6421811
0
0.02
neurological
7
0.1
neurological
0%
4041311
0
0.2
respiratory
10
0.9
respiratory
0%
3930711
0
0.004
neurological
0
0.004
neurological
0%
4056511
0
0.005
reproductive
20
0.2
reproductive
0%
10722911
0
0.0005
immunological
0
0.0005
immunological
0%
4878911
0
0.002
respiratory
1
0.01
respiratory
0%
4897511
0
0.02
respiratory
1
0.1
respiratory
0%
4924311
0
0.003
respiratory
0
0.003
respiratory
0%
5632411
0
0.004
reproductive
5
0.07
reproductive
0%
-------
Facility EIS ID
Source Category Chronic Risk'
Whole Facility Chronic Risk'
% Source
Contribution
(Cancer MIR)
Cancer MIR
Non-cancer
Max HI
Target Organ
Cancer MIR
Non-cancer
Max HI
Target Organ
4980911
0
0.004
reproductive
0
0.004
reproductive
0%
6421511
0
0.001
immunological
0
0.001
immunological
0%
4982011
0
0.2
respiratory
0
0.2
respiratory
0%
8096711
0
0.03
respiratory
0
0.03
respiratory
0%
6362111
0
0.001
neurological
0
0.001
neurological
0%
8262411
0
0.001
respiratory
0
0.002
neurological
0%
8135311
0
0.001
respiratory
1
0.01
respiratory
0%
6534811
0
0.0007
respiratory
0
0.0007
respiratory
0%
4004311
0
0.002
reproductive
0
0.002
reproductive
0%
7246511
0
0.02
respiratory
0
0.02
respiratory
0%
6641411
0
0.001
respiratory
0
0.001
respiratory
0%
4965811
0
0.003
respiratory
0
0.003
respiratory
0%
8059311
0
0.1
respiratory
1
0.1
respiratory
0%
6194311
0
0.03
neurological
0
0.03
neurological
0%
7367211
0
0.02
respiratory
0
0.02
respiratory
0%
13389511
0
0.005
respiratory
0
0.005
respiratory
0%
7380611
0
0.0005
neurological
0
0.0006
neurological
0%
7367811
0
0.002
neurological
7
0.1
reproductive
0%
1020111
0
0.03
respiratory
0
0.03
respiratory
0%
588311
0
0.0005
reproductive
0
0.1
neurological
0%
5862111
0
0.0005
reproductive
10
0.1
reproductive
0%
-------
Facility EIS ID
Source Category Chronic Risk'
Whole Facility Chronic Risk'
% Source
Contribution
(Cancer MIR)
Cancer MIR
Non-cancer
Max HI
Target Organ
Cancer MIR
Non-cancer
Max HI
Target Organ
4055511
0
0.0003
immunological
0
0.0003
immunological
0%
7972911
0
2
respiratory
70
2
respiratory
0%
999511
0
0.01
respiratory
1
0.02
respiratory
0%
10716511
0
0.04
respiratory
0
0.04
respiratory
0%
13411211
0
0.03
respiratory
0
0.03
respiratory
0%
9177911
0
0.002
respiratory
9
0.5
respiratory
0%
8086711
0
0.007
whole body
2
0.007
whole body
0%
6510111
0
0.005
respiratory
0
0.005
respiratory
0%
6671911
0
0.003
respiratory
0
0.03
respiratory
0%
8215111
0
0.0002
neurological
0
0.006
respiratory
0%
5679711
0
0.0001
respiratory
1
0.02
neurological
0%
17905711
0
0.00003
immunological
0
0.00003
immunological
0%
4930211
0
0.0000005
neurological
2
0.1
respiratory
0%
5719311
0
0.00003
neurological
0
0.00004
neurological
0%
13411911
0
0.0009
respiratory
0
0.003
respiratory
0%
949911
0
0.2
respiratory
0
0.3
respiratory
0%
6615111
0
0.2
respiratory
0
0.2
respiratory
0%
7928911
0
0.02
respiratory
0
0.02
respiratory
0%
4778311
0
0.002
respiratory
0
0.09
neurological
0%
1072711
0
0.001
liver
0
0.001
liver
0%
16871811
0
0.00006
liver
0
0.00006
liver
0%
-------
Facility EIS ID
Source Category Chronic Risk'
Whole Facility Chronic Risk'
% Source
Contribution
(Cancer MIR)
Cancer MIR
Non-cancer
Max HI
Target Organ
Cancer MIR
Non-cancer
Max HI
Target Organ
7366811
0
0.3
respiratory
0
0.4
respiratory
0%
6385211
0
0.1
neurological
0
0.1
neurological
0%
5748611
0
0.00004
respiratory
0
0.005
respiratory
0%
5611111
0
0.004
respiratory
0
0.005
respiratory
0%
7364611
0
0.0000002
respiratory
1
0.3
respiratory
0%
7365611
0
0.00002
neurological
0
0.02
neurological
0%
16856611
0
0.00004
neurological
0
0.00004
neurological
0%
5386211
0
0.0000001
neurological
0
0.007
respiratory
0%
16966011
0
0.00002
developmental
0
0.3
respiratory
0%
16972411
0
0.0001
neurological
1
0.004
respiratory
0%
17640311
0
0.0008
respiratory
2
0.05
respiratory
0%
9588611
0
0.01
respiratory
0
0.0002
developmental
0%
9175811
0
0.002
respiratory
0
0.01
kidney
0%
3967011
0
0.00009
respiratory
0
0.03
respiratory
0%
10679911
0
0.000000001
developmental
0
0.00002
developmental
0%
4055111
0
0.000009
developmental
0
0.0001
neurological
0%
4167411
0
0.002
respiratory
0
0.0002
respiratory
0%
6510311
0
0.000003
developmental
0
0.003
respiratory
0%
7908711
0
0.04
respiratory
0
0.01
respiratory
0%
18893911
0
0.000003
developmental
4
0.05
respiratory
0%
949811
0
0.007
respiratory
5
0.02
respiratory
0%
-------
Facility EIS ID
Source Category Chronic Risk'
Whole Facility Chronic Risk'
% Source
Contribution
(Cancer MIR)
Cancer MIR
Non-cancer
Max HI
Target Organ
Cancer MIR
Non-cancer
Max HI
Target Organ
751411
0
0.1
respiratory
1
0.05
respiratory
0%
2491711
0
0.0000006
developmental
0
0.1
respiratory
0%
18929011
0
0.0002
developmental
0
0.002
neurological
0%
8194311
0
0
—
3
2
developmental
0%
10695411
0
0.0002
skeletal
0
0.0007
kidney
0%
18982311
0
0.01
respiratory
0
0.00001
developmental
0%
7311911
0
0
—
1
0.08
liver
0%
8105111
0
0.00000009
neurological
0
0.002
respiratory
0%
8123911
0
0.007
neurological
1
0.08
respiratory
0%
9308811
0
0.1
respiratory
0
0.00002
developmental
0%
7937511
0
0.0005
neurological
0
0.00001
developmental
0%
8418011
0
0.00008
neurological
0
0.002
respiratory
0%
8148211
0
0.0006
kidney
0
0.00006
kidney
0%
15077311
0
0.00001
developmental
0
0.04
respiratory
0%
4950811
0
0.007
kidney
0
0.000003
developmental
0%
5768911
0
0.0002
developmental
80
0.002
respiratory
0%
5769011
0
0
—
3
0.01
spleen
0%
'BOLD indicates a cancer Maximum Individual Risk (MIR) value greater than 100-in-l million or chronic non-cancer maximum Hazardous Index
(HI) value greater than 1
-------
Table 3. Maximum Predicted HEM-4 Cancer Inhalation Risk - SOCMI Source Category Baseline & Post Control
Facility EIS ID
Baseline Cancer Risks'
Post Control Cancer Risks'
MIR
Incidence
MIR
Incidence
4945211
2000
0.5
100
0.02
7202911
700
0.1
90
0.01
5846511
600
0.02
80
0.002
4941511
500
0.1
100
0.02
7445611
400
0.04
50
0.004
4926611
200
0.3
90
0.1
8467311
200
0.02
80
0.005
4057911
200
0.2
40
0.05
8465611
100
0.04
30
0.004
8468011
100
0.004
20
0.0005
985511
100
0.01
100
0.01
4945611
100
0.009
40
0.003
7351811
100
0.02
20
0.001
5929411
100
0.009
100
0.003
4941411
90
0.003
40
0.01
5632711
90
0.01
30
0.005
17640111
90
0.006
90
0.006
8137811
70
0.02
70
0.02
13610611
60
0.0009
60
0.0009
976011
50
0.001
50
0.001
7915011
50
0.007
50
0.007
-------
Facility EIS ID
Baseline Cancer Risks'
Post Control Cancer Risks'
MIR
Incidence
MIR
Incidence
946711
30
0.0008
30
0.0008
4924411
30
0.007
30
0.007
8020811
30
0.001
30
0.001
13407911
30
0.003
30
0.003
5651611
30
0.0008
30
0.0008
993411
30
0.0006
30
0.0006
7354911
30
0.002
30
0.002
5520211
20
0.0009
6
0.0002
7228511
20
0.003
20
0.003
5653011
20
0.001
20
0.001
7354711
20
0.005
20
0.005
14997411
20
0.02
10
0.02
7226711
20
0.0002
20
0.0002
3982311
20
0.0008
20
0.0008
7940411
10
0.001
10
0.001
8239511
10
0.001
10
0.001
17055211
10
0.0005
10
0.0005
5633411
10
0.0005
9
0.0003
6152811
10
0.00003
10
0.00003
4168611
10
0.005
10
0.005
4925111
10
0.006
10
0.006
17640911
10
0.0005
10
0.0005
-------
Facility EIS ID
Baseline Cancer Risks'
Post Control Cancer Risks'
MIR
Incidence
MIR
Incidence
15639911
10
0.0002
10
0.0002
4762811
10
0.006
10
0.006
8465311
10
0.001
7
0.0008
13431911
9
0.0002
9
0.0002
7203711
9
0.0005
9
0.0005
8384311
8
0.001
8
0.001
6444911
8
0.003
8
0.003
4778211
7
0.003
7
0.003
4835311
7
0.002
7
0.002
7984011
6
0.00003
6
0.00003
4778711
6
0.01
6
0.01
7445711
6
0.001
6
0.001
4205511
5
0.0002
5
0.0002
999411
5
0.0004
5
0.0004
4168511
5
0.003
5
0.003
8026211
5
0.00005
5
0.00005
4941211
5
0.005
5
0.005
13614411
5
0.001
5
0.001
5632511
5
0.0003
5
0.0003
5633311
5
0.002
5
0.002
6445411
5
0.001
5
0.001
7338711
4
0.0004
4
0.0004
-------
Facility EIS ID
Baseline Cancer Risks'
Post Control Cancer Risks'
MIR
Incidence
MIR
Incidence
7368011
4
0.0001
4
0.0001
4945411
4
0.0004
4
0.0004
8020011
4
0.0001
4
0.0001
7368811
4
0.0002
4
0.0002
8020411
4
0.001
4
0.001
8018911
4
0.00006
4
0.00006
4167811
4
0.0004
1
0.00007
7226611
4
0.001
4
0.001
17909311
4
0.00002
4
0.00002
7227011
3
0.0005
3
0.0005
5018711
3
0.00004
3
0.00004
8006811
3
0.00003
3
0.00003
7331911
3
0.0003
3
0.0003
5631411
3
0.0001
3
0.0001
7380411
3
0.0006
3
0.0006
8361111
3
0.0002
3
0.0002
17735411
3
0.0006
3
0.0006
8130511
3
0.00006
3
0.00006
8263111
3
0.0002
3
0.0002
7984111
2
0.00002
2
0.00002
7319811
2
0.0001
2
0.0001
5651911
2
0.00007
2
0.00007
-------
Facility EIS ID
Baseline Cancer Risks'
Post Control Cancer Risks'
MIR
Incidence
MIR
Incidence
6234511
2
0.0001
2
0.0001
8107111
2
0.00008
2
0.00008
17912111
2
0.0003
2
0.0003
5729211
2
0.0004
2
0.0004
7972111
2
0.0001
2
0.0001
6386311
2
0.0003
2
0.0003
7302511
2
0.0003
2
0.0003
5656011
2
0.0002
2
0.0002
6430411
2
0.00009
2
0.00009
4863111
2
0.0002
2
0.0002
6884211
2
0.00006
2
0.00006
5019011
1
0.00003
1
0.00003
17974911
1
0.0001
1
0.0001
7226311
1
0.0002
1
0.0002
4057611
1
0.003
1
0.003
7204811
1
0.0003
1
0.0003
8465711
1
0.00006
1
0.00006
4031311
1
0.0001
1
0.0001
8067211
1
0.00005
1
0.00005
6534611
1
0.0006
1
0.0006
4861611
1
0.00006
1
0.00006
6157311
1
0.00009
1
0.00009
-------
Facility EIS ID
Baseline Cancer Risks'
Post Control Cancer Risks'
MIR
Incidence
MIR
Incidence
10678011
1
0.00007
1
0.00007
4190211
1
0.00007
1
0.00007
8465211
1
0.00003
1
0.00003
8209411
0.9
0.0002
0.9
0.0002
7929111
0.9
0.00006
0.9
0.00006
3737011
0.8
0.0009
0.8
0.0009
8447711
0.8
0.00004
0.8
0.00004
9103411
0.8
0.0002
0.8
0.0002
5746611
0.8
0.0003
0.8
0.0003
4929511
0.7
0.0001
0.7
0.0001
19250811
0.7
0.00002
0.7
0.00002
7448011
0.6
0.00002
0.6
0.00002
4783711
0.6
0.00001
0.6
0.00001
8434411
0.6
0.00006
0.6
0.00006
17058711
0.5
0.0001
0.5
0.0001
17985111
0.5
0.00002
0.5
0.00002
973911
0.5
0.00002
0.5
0.00002
18973611
0.5
0.00004
0.5
0.00004
4929811
0.5
0.00003
0.5
0.00003
5505011
0.4
0.00002
0.4
0.00002
8007011
0.4
0.00002
0.4
0.00002
9115811
0.4
0.000007
0.4
0.000007
-------
Facility EIS ID
Baseline Cancer Risks'
Post Control Cancer Risks'
MIR
Incidence
MIR
Incidence
8467611
0.4
0.00002
0.4
0.00002
6158411
0.4
0.0002
0.4
0.0002
6421811
0.4
0.0001
0.4
0.0001
4041311
0.3
0.00008
0.3
0.00008
3930711
0.3
0.0002
0.3
0.0002
4056511
0.3
0.0002
0.3
0.0002
10722911
0.3
0.0001
0.3
0.0001
4878911
0.3
0.00001
0.3
0.00001
4897511
0.3
0.00006
0.3
0.00006
4924311
0.3
0.0004
0.3
0.0004
5632411
0.3
0.0004
0.3
0.0004
4980911
0.3
0.0002
0.3
0.0002
6421511
0.2
0.0006
0.2
0.0006
4982011
0.2
0.00007
0.2
0.00007
8096711
0.2
0.000002
0.2
0.000002
6362111
0.2
0.00004
0.2
0.00004
8262411
0.2
0.00002
0.2
0.00002
8135311
0.2
0.00002
0.2
0.00002
6534811
0.2
0.0007
0.2
0.0007
4004311
0.1
0.000002
0.1
0.000002
7246511
0.1
0.00001
0.1
0.00001
6641411
0.1
0.0002
0.1
0.0002
-------
Facility EIS ID
Baseline Cancer Risks'
Post Control Cancer Risks'
MIR
Incidence
MIR
Incidence
4965811
0.1
0.00007
0.1
0.00007
8059311
0.1
0.00003
0.1
0.00003
6194311
0.1
0.00001
0.1
0.00001
7367211
0.1
0.00006
0.1
0.00006
13389511
0.1
0.00001
0.1
0.00001
7380611
0.1
0.000003
0.1
0.000003
7367811
0.09
0.00001
0.09
0.00001
1020111
0.09
0.00004
0.09
0.00004
588311
0.07
0.00003
0.07
0.00003
5862111
0.07
0.00002
0.07
0.00002
4055511
0.06
0.00002
0.06
0.00002
7972911
0.05
0.00003
0.05
0.00003
999511
0.05
0.000005
0.05
0.000005
10716511
0.05
0.0001
0.05
0.0001
13411211
0.04
0.0001
0.04
0.0001
9177911
0.03
0.000002
0.03
0.000002
8086711
0.03
0.0000005
0.03
0.0000005
6510111
0.02
0.00001
0.02
0.00001
6671911
0.02
0.00004
0.02
0.00004
8215111
0.01
0.000002
0.01
0.000002
5679711
0.009
0.000003
0.009
0.000003
17905711
0.007
0.0000004
0.007
0.0000004
-------
Facility EIS ID
Baseline Cancer Risks'
Post Control Cancer Risks'
MIR
Incidence
MIR
Incidence
4930211
0.005
0.000002
0.005
0.000002
5719311
0.005
0.000001
0.005
0.000001
13411911
0.005
0.0000003
0.005
0.0000003
949911
0.005
0.0000008
0.005
0.0000008
6615111
0.004
0.0000003
0.004
0.0000003
7928911
0.004
0.0000005
0.004
0.0000005
4778311
0.002
0.000009
0.002
0.000009
1072711
0.002
0.0000007
0.002
0.0000007
16871811
0.001
0.0000004
0.001
0.0000004
7366811
0.0006
0.00000005
0.0006
0.00000005
6385211
0.0006
0.0000001
0.0006
0.0000001
5748611
0.0004
0.00000003
0.0004
0.00000003
5611111
0.00005
0.000000004
0.00005
0.000000004
7364611
0.00003
0.000000001
0.00003
0.000000001
7365611
0.000006
8E-10
0.000006
8E-10
16856611
0.0000002
9E-12
0.0000002
9E-12
949811
0
0
0
0
751411
0
0
0
0
2491711
0
0
0
0
5386211
0
0
0
0
18929011
0
0
0
0
8194311
0
0
0
0
-------
Facility EIS ID
Baseline Cancer Risks'
Post Control Cancer Risks'
MIR
Incidence
MIR
Incidence
10695411
0
0
0
0
16966011
0
0
0
0
16972411
0
0
0
0
17640311
0
0
0
0
9588611
0
0
0
0
18982311
0
0
0
0
7311911
0
0
0
0
8105111
0
0
0
0
8123911
0
0
0
0
9308811
0
0
0
0
7937511
0
0
0
0
8418011
0
0
0
0
8148211
0
0
0
0
15077311
0
0
0
0
4950811
0
0
0
0
9175811
0
0
0
0
3967011
0
0
0
0
10679911
0
0
0
0
4055111
0
0
0
0
4167411
0
0
0
0
6510311
0
0
0
0
7908711
0
0
0
0
-------
Facility EIS ID
Baseline Cancer Risks'
Post Control Cancer Risks'
MIR
Incidence
MIR
Incidence
18893911
0
0
0
0
5768911
0
0
0
0
5769011
0
0
0
0
'BOLD indicates a cancer Maximum Individual Risk (MIR) value greater than 100-in-l million
Table 4. Maximum Predicted HEM-4 Cancer Inhalation Risk - Whole Facility Baseline & Post Control
Facility EIS ID
Baseline Cancer Risks'
Post Control Cancer Risks'2
MIR
Incidence
MIR
Incidence
4945211
2000
0.5
100
0.02
4945611
2000
0.09
1000
0.07
7202911
700
0.1
100
0.01
5846511
600
0.02
80
0.002
8468011
600
0.07
500
0.06
17640111
600
0.06
200
0.02
4941511
500
0.1
100
0.02
7445611
500
0.05
200
0.01
4926611
200
0.3
90
0.1
8467311
200
0.02
90
0.007
4057911
200
0.2
40
0.05
8465611
200
0.06
90
0.02
-------
Facility EIS ID
Baseline Cancer Risks'
Post Control Cancer Risks':
MIR
Incidence
MIR
Incidence
4941411
200
0.05
100
0.02
993411
200
0.004
200
0.004
985511
100
0.01
100
0.01
7351811
100
0.009
20
0.001
5929411
100
0.003
100
0.003
5632711
100
0.02
50
0.01
4924411
100
0.08
100
0.08
8018911
100
0.0009
100
0.0009
976011
80
0.002
80
0.002
4835311
80
0.004
80
0.004
5768911
80
0.01
80
0.01
8137811
70
0.02
70
0.02
8239511
70
0.007
70
0.007
7972911
70
0.02
70
0.02
13610611
60
0.001
60
0.001
7226711
60
0.0003
60
0.0003
7915011
50
0.007
50
0.007
946711
30
0.0008
30
0.0008
8020811
30
0.001
30
0.001
13407911
30
0.003
30
0.003
5651611
30
0.0009
30
0.0009
7354911
30
0.002
30
0.002
-------
Facility EIS ID
Baseline Cancer Risks'
Post Control Cancer Risks':
MIR
Incidence
MIR
Incidence
4925111
30
0.02
30
0.02
5520211
20
0.0009
6
0.0002
7228511
20
0.003
20
0.003
5653011
20
0.001
20
0.001
7354711
20
0.005
20
0.005
14997411
20
0.02
10
0.02
3982311
20
0.003
20
0.003
7940411
20
0.002
20
0.002
8020411
20
0.002
20
0.002
7226611
20
0.004
20
0.004
5018711
20
0.0004
20
0.0004
4056511
20
0.01
20
0.01
17055211
10
0.0005
10
0.0005
5633411
10
0.0006
10
0.0004
6152811
10
0.00003
10
0.00003
4168611
10
0.006
10
0.006
17640911
10
0.0005
10
0.0005
15639911
10
0.0005
10
0.0005
4762811
10
0.007
10
0.007
8465311
10
0.002
9
0.002
7203711
10
0.001
10
0.001
4168511
10
0.01
10
0.01
-------
Facility EIS ID
Baseline Cancer Risks'
Post Control Cancer Risks':
MIR
Incidence
MIR
Incidence
5633311
10
0.003
10
0.003
7929111
10
0.0003
10
0.0003
4041311
10
0.001
10
0.001
5862111
10
0.0004
10
0.0004
13431911
9
0.0002
9
0.0002
8384311
9
0.002
9
0.002
5019011
9
0.0001
9
0.0001
9177911
9
0.00004
9
0.00004
6444911
8
0.003
8
0.003
4778211
7
0.003
7
0.003
7448011
7
0.0003
7
0.0003
6421811
7
0.001
7
0.001
7367811
7
0.001
7
0.001
7984011
6
0.00004
6
0.00004
4778711
6
0.01
6
0.01
7445711
6
0.001
6
0.001
8006811
6
0.00008
6
0.00008
6430411
6
0.0004
6
0.0004
4190211
6
0.0008
6
0.0008
4205511
5
0.0002
5
0.0002
999411
5
0.0005
5
0.0005
8026211
5
0.00005
5
0.00005
-------
Facility EIS ID
Baseline Cancer Risks'
Post Control Cancer Risks':
MIR
Incidence
MIR
Incidence
4941211
5
0.005
5
0.005
13614411
5
0.001
5
0.001
5632511
5
0.0003
5
0.0003
6445411
5
0.001
5
0.001
4945411
5
0.0008
5
0.0008
8020011
5
0.0002
5
0.0002
5632411
5
0.003
5
0.003
8105111
5
0.0003
5
0.0003
7338711
4
0.0004
4
0.0004
7368011
4
0.0001
4
0.0001
7368811
4
0.0003
4
0.0003
4167811
4
0.0004
1
0.00009
17909311
4
0.00002
4
0.00002
7227011
4
0.0006
4
0.0006
5651911
4
0.0002
4
0.0002
7311911
4
0.0005
4
0.0005
7331911
3
0.0003
3
0.0003
5631411
3
0.0001
3
0.0001
7380411
3
0.0007
3
0.0007
8361111
3
0.0002
3
0.0002
17735411
3
0.0006
3
0.0006
8130511
3
0.00007
3
0.00007
-------
Facility EIS ID
Baseline Cancer Risks'
Post Control Cancer Risks':
MIR
Incidence
MIR
Incidence
8263111
3
0.0003
3
0.0003
7972111
3
0.0009
3
0.0009
10678011
3
0.0005
3
0.0005
8418011
3
0.005
3
0.005
5769011
3
0.0003
3
0.0003
7984111
2
0.00002
2
0.00002
7319811
2
0.0003
2
0.0003
6234511
2
0.0001
2
0.0001
8107111
2
0.00008
2
0.00008
17912111
2
0.0003
2
0.0003
5729211
2
0.0004
2
0.0004
6386311
2
0.0003
2
0.0003
7302511
2
0.0003
2
0.0003
5656011
2
0.0003
2
0.0003
4863111
2
0.0003
2
0.0003
6884211
2
0.00007
2
0.00007
4057611
2
0.004
2
0.004
4031311
2
0.0002
2
0.0002
4861611
2
0.0001
2
0.0001
19250811
2
0.00007
2
0.00007
8086711
2
0.00004
2
0.00004
4930211
2
0.0008
2
0.0008
-------
Facility EIS ID
Baseline Cancer Risks'
Post Control Cancer Risks':
MIR
Incidence
MIR
Incidence
5386211
2
0.00002
2
0.00002
17974911
1
0.0001
1
0.0001
7226311
1
0.0003
1
0.0003
7204811
1
0.0003
1
0.0003
8465711
1
0.00006
1
0.00006
8067211
1
0.00006
1
0.00006
6534611
1
0.0006
1
0.0006
6157311
1
0.00009
1
0.00009
8465211
1
0.00003
1
0.00003
8209411
1
0.0004
1
0.0004
3737011
1
0.0009
1
0.0009
8447711
1
0.00004
1
0.00004
9103411
1
0.0002
1
0.0002
4929511
1
0.0002
1
0.0002
4783711
1
0.00004
1
0.00004
8434411
1
0.00006
1
0.00006
17058711
1
0.0001
1
0.0001
17985111
1
0.00003
1
0.00003
973911
1
0.00003
1
0.00003
18973611
1
0.00004
1
0.00004
4929811
1
0.0002
1
0.0002
8007011
1
0.0002
1
0.0002
-------
Facility EIS ID
Baseline Cancer Risks'
Post Control Cancer Risks':
MIR
Incidence
MIR
Incidence
8467611
1
0.00004
1
0.00004
4878911
1
0.00005
1
0.00005
4897511
1
0.0001
1
0.0001
8135311
1
0.0001
1
0.0001
8059311
1
0.0003
1
0.0003
999511
1
0.0001
1
0.0001
5679711
1
0.0002
1
0.0002
7364611
1
0.0001
1
0.0001
2491711
1
0.00003
1
0.00003
8123911
1
0.00002
1
0.00002
4950811
1
0.0004
1
0.0004
3967011
1
0.00009
1
0.00009
5746611
0.8
0.0003
0.8
0.0003
5505011
0.4
0.00002
0.4
0.00002
9115811
0.4
0.000007
0.4
0.000007
6158411
0.4
0.0002
0.4
0.0002
8194311
0.4
0.00001
0.4
0.00001
3930711
0.3
0.0002
0.3
0.0002
10722911
0.3
0.0001
0.3
0.0001
4924311
0.3
0.0004
0.3
0.0004
4980911
0.3
0.0002
0.3
0.0002
6421511
0.3
0.0007
0.3
0.0007
-------
Facility EIS ID
Baseline Cancer Risks'
Post Control Cancer Risks':
MIR
Incidence
MIR
Incidence
588311
0.3
0.0008
0.3
0.0008
4982011
0.2
0.00007
0.2
0.00007
8096711
0.2
0.000002
0.2
0.000002
6362111
0.2
0.00004
0.2
0.00004
8262411
0.2
0.00002
0.2
0.00002
6534811
0.2
0.0007
0.2
0.0007
4004311
0.2
0.000003
0.2
0.000003
7246511
0.2
0.00002
0.2
0.00002
4778311
0.2
0.0004
0.2
0.0004
6641411
0.1
0.0002
0.1
0.0002
4965811
0.1
0.00008
0.1
0.00008
6194311
0.1
0.00001
0.1
0.00001
7367211
0.1
0.00006
0.1
0.00006
13389511
0.1
0.00001
0.1
0.00001
7380611
0.1
0.000003
0.1
0.000003
1020111
0.1
0.00004
0.1
0.00004
7366811
0.1
0.00001
0.1
0.00001
5611111
0.1
0.000002
0.1
0.000002
751411
0.1
0.0001
0.1
0.0001
13411211
0.08
0.0001
0.08
0.0001
13411911
0.08
0.000003
0.08
0.000003
4055511
0.07
0.00002
0.07
0.00002
-------
Facility EIS ID
Baseline Cancer Risks'
Post Control Cancer Risks':
MIR
Incidence
MIR
Incidence
10716511
0.05
0.0002
0.05
0.0002
7908711
0.04
0.00003
0.04
0.00003
6615111
0.03
0.000009
0.03
0.000009
6510111
0.02
0.00001
0.02
0.00001
6671911
0.02
0.00004
0.02
0.00004
8215111
0.02
0.000008
0.02
0.000008
5748611
0.02
0.000005
0.02
0.000005
18929011
0.01
0.0000003
0.01
0.0000003
6385211
0.008
0.000005
0.008
0.000005
8148211
0.008
0.000001
0.008
0.000001
17905711
0.007
0.0000004
0.007
0.0000004
949911
0.007
0.000007
0.007
0.000007
5719311
0.006
0.000002
0.006
0.000002
6510311
0.005
0.00002
0.005
0.00002
7928911
0.004
0.0000005
0.004
0.0000005
7365611
0.003
0.0000003
0.003
0.0000003
10695411
0.003
0.000005
0.003
0.000005
17640311
0.003
0.000002
0.003
0.000002
1072711
0.002
0.0000007
0.002
0.0000007
16871811
0.002
0.0000005
0.002
0.0000005
4167411
0.001
0.000006
0.001
0.000006
16972411
0.0002
0.00000003
0.0002
0.00000003
-------
Facility EIS ID
Baseline Cancer Risks'
Post Control Cancer Risks':
MIR
Incidence
MIR
Incidence
10679911
0.0002
0.0000003
0.0002
0.0000003
949811
0.0001
0.00000004
0.0001
0.00000004
9175811
0.00009
0.00000003
0.00009
0.00000003
18982311
0.00005
0.000000009
0.00005
0.000000009
16966011
0.00001
0.000000003
0.00001
0.000000003
16856611
0.0000002
9.00E-12
0.0000002
9.00E-12
9588611
0
0
0
0
9308811
0
0
0
0
7937511
0
0
0
0
15077311
0
0
0
0
4055111
0
0
0
0
18893911
0
0
0
0
'BOLD indicates a cancer Maximum Individual Risk (MIR) value greater than 100-in-l million
2The post control whole facility results include proposed controls for the Neoprene Production source category for the
SOCMI facility that has collocated emissions from the Neoprene Production source category.
-------
Acute Inhalation Risks
Table 5. Maximum Predicted Acute Inhalation Risks (HEM-4)
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
17640111
chloroprene
—
—
—
—
—
17640111
glycol ethers
8E-03
—
—
—
—
17640111
triethylamine
1E-06
—
—
—
—
17640111
xylenes (mixed)
4E-07
2E-08
2E-09
—
—
17640111
hydrochloric acid
4E-02
3E-02
2E-03
2E-02
3E-03
17640111
chlorine
5E-02
8E-03
2E-03
4E-03
1E-03
17640111
1,3-butadiene
—
1E-05
2E-06
9E-04
2E-05
17640111
toluene
—
6E-04
8E-05
8E-04
1E-04
17640111
methylene chloride
3E-07
6E-09
2E-09
4E-09
2E-09
1020111
2,2,4-trimethylpentane
—
—
—
—
—
1020111
beryllium compounds
—
—
—
—
2E-07
1020111
chromium (iii) compounds
—
—
—
—
—
1020111
chromium (vi) compounds
—
—
—
—
—
1020111
cobalt compounds
—
—
—
—
—
1020111
ethyl chloride
—
—
—
—
—
1020111
gaseous divalent mercury
—
—
—
—
—
1020111
lead compounds
—
—
—
—
—
1020111
manganese compounds
—
—
—
—
—
1020111
methyl isobutyl ketone
—
—
—
—
—
1020111
naphthalene
—
—
—
—
—
1020111
nickel compounds
—
—
—
—
—
1020111
p-dichlorobenzene
—
—
—
—
—
1020111
pah, total
—
—
—
—
—
1020111
particulate divalent
mercury
—
—
—
—
—
1020111
selenium compounds
—
—
—
—
—
1020111
triethylamine
4E-01
—
—
—
—
1020111
arsenic compounds
6E-05
—
—
—
—
1020111
carbonyl sulfide
—
—
5E-03
—
—
1020111
phosgene
4E-03
—
1E-05
—
8E-06
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
1020111
ethyl benzene
—
3E-04
7E-06
—
—
1020111
xylenes (mixed)
7E-04
3E-05
4E-06
—
—
1020111
phosphine
—
—
3E-06
—
1E-05
1020111
cumene
—
4E-06
7E-07
—
—
1020111
n-hexane
—
—
2E-07
—
—
1020111
cadmium compounds
—
6E-07
8E-08
—
—
1020111
mercury (elemental)
2E-05
—
6E-09
—
5E-09
1020111
phenol
1
1E-01
9E-02
2E-01
4E-02
1020111
carbon disulfide
9E-02
1E-02
1E-03
2E-01
4E-03
1020111
methylene chloride
5E-01
1E-02
4E-03
7E-03
3E-03
1020111
hydrochloric acid
5E-03
4E-03
3E-04
2E-03
3E-04
1020111
styrene
2E-03
5E-04
7E-05
2E-04
3E-05
1020111
formaldehyde
3E-03
IE-04
8E-06
IE-04
1E-05
1020111
toluene
—
6E-05
7E-06
8E-05
1E-05
1020111
carbon tetrachloride
4E-03
—
IE-04
7E-05
1E-05
1020111
chlorine
8E-04
IE-04
3E-05
6E-05
2E-05
1020111
acrylonitrile
—
—
2E-04
3E-05
1E-05
1020111
benzene
—
2E-05
1E-06
2E-05
7E-06
10678011
cresols (mixed)
—
—
—
—
—
10678011
manganese compounds
—
—
—
—
—
10678011
hydrogen cyanide
3E-02
5E-03
1E-03
—
1E-03
10678011
chlorobenzene
—
2E-04
1E-05
—
—
10678011
biphenyl
—
—
3E-06
—
—
10678011
acetonitrile
—
6E-06
1E-06
—
—
10678011
chlorine
1E-01
2E-02
4E-03
8E-03
3E-03
10678011
1,3-butadiene
—
7E-06
9E-07
5E-04
1E-05
10678011
hydrochloric acid
2E-04
2E-04
1E-05
IE-04
2E-05
10678011
benzene
—
IE-04
9E-06
IE-04
5E-05
10678011
methanol
2E-04
7E-06
2E-06
2E-05
4E-06
10678011
acrylonitrile
—
—
9E-05
1E-05
4E-06
10678011
phenol
6E-05
6E-06
4E-06
9E-06
2E-06
10678011
toluene
—
7E-07
8E-08
9E-07
2E-07
10679911
methanol
9E-06
4E-07
1E-07
1E-06
2E-07
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
10695411
hydrofluoric acid
2E-02
5E-03
2E-04
3E-03
3E-04
10716511
benzo[a]pyrene
—
—
—
—
—
10716511
beryllium compounds
—
—
—
—
3E-06
10716511
chromium (iii) compounds
—
—
—
—
—
10716511
chromium (vi) compounds
—
—
—
—
—
10716511
cobalt compounds
—
—
—
—
—
10716511
gaseous divalent mercury
—
—
—
—
—
10716511
lead compounds
—
—
—
—
—
10716511
manganese compounds
—
—
—
—
—
10716511
naphthalene
—
—
—
—
—
10716511
nickel compounds
—
—
—
—
—
10716511
particulate divalent
mercury
—
—
—
—
—
10716511
phthalic anhydride
—
—
—
—
—
10716511
selenium compounds
—
—
—
—
—
10716511
arsenic compounds
4E-03
—
—
—
—
10716511
o-xylene
1E-03
—
—
—
—
10716511
m-xylene
2E-04
—
—
—
—
10716511
p-xylene
2E-07
—
—
—
—
10716511
methyl bromide
2E-03
—
1E-05
—
4E-05
10716511
ethyl benzene
—
4E-06
1E-07
—
—
10716511
cadmium compounds
—
5E-07
6E-08
—
—
10716511
xylenes (mixed)
4E-06
2E-07
2E-08
—
—
10716511
cumene
—
1E-07
2E-08
—
—
10716511
n-hexane
—
—
6E-09
—
—
10716511
mercury (elemental)
2E-05
—
6E-09
—
5E-09
10716511
maleic anhydride
—
—
—
4E-02
2E-03
10716511
formaldehyde
2E-02
1E-03
7E-05
1E-03
1E-04
10716511
acrylic acid
5E-04
7E-04
2E-05
1E-03
2E-05
10716511
methanol
3E-04
1E-05
4E-06
4E-05
7E-06
10716511
acetaldehyde
4E-04
2E-06
3E-07
9E-06
5E-07
10716511
styrene
7E-05
2E-05
3E-06
7E-06
1E-06
10716511
benzene
—
5E-06
3E-07
5E-06
2E-06
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
10716511
toluene
—
1E-06
2E-07
2E-06
3E-07
10716511
hydrochloric acid
2E-06
2E-06
1E-07
9E-07
1E-07
10722911
biphenyl
—
—
—
—
—
10722911
ethylene glycol
—
—
—
—
—
10722911
naphthalene
—
—
—
—
—
10722911
ethyl benzene
—
1E-03
3E-05
—
—
10722911
xylenes (mixed)
4E-04
2E-05
2E-06
—
—
10722911
styrene
2E-02
4E-03
6E-04
2E-03
3E-04
10722911
toluene
—
2E-04
2E-05
3E-04
4E-05
10722911
benzene
—
2E-04
1E-05
2E-04
7E-05
10722911
1,3-butadiene
—
3E-09
4E-10
2E-07
4E-09
1072711
acetophenone
—
—
—
—
—
1072711
methyl isobutyl ketone
—
—
—
—
—
1072711
cumene
—
2E-02
4E-03
—
—
1072711
xylenes (mixed)
3E-06
1E-07
2E-08
—
—
1072711
ethyl benzene
—
4E-07
1E-08
—
—
1072711
phenol
7E-01
7E-02
4E-02
1E-01
2E-02
1072711
methanol
2E-02
8E-04
2E-04
2E-03
4E-04
1072711
acetaldehyde
6E-03
3E-05
6E-06
2E-04
8E-06
1072711
benzene
—
4E-07
2E-08
4E-07
1E-07
1072711
toluene
—
3E-07
3E-08
3E-07
6E-08
13389511
acetaldehyde
3E-01
2E-03
3E-04
8E-03
4E-04
13407911
methyl isobutyl ketone
—
—
—
—
—
13407911
xylenes (mixed)
—
—
—
—
—
13407911
ethyl benzene
—
5E-05
2E-06
—
—
13407911
n-hexane
—
—
1E-07
—
—
13407911
1,3-butadiene
—
3E-05
4E-06
2E-03
4E-05
13407911
chlorine
1E-03
2E-04
5E-05
IE-04
3E-05
13407911
methanol
2E-04
7E-06
2E-06
2E-05
4E-06
13407911
methyl tert-butyl ether
—
2E-05
1E-06
2E-05
9E-07
13407911
benzene
—
5E-06
3E-07
6E-06
2E-06
13407911
styrene
1E-05
3E-06
5E-07
1E-06
3E-07
13407911
toluene
—
1E-06
1E-07
1E-06
3E-07
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
13411211
ethylene glycol
—
—
—
—
—
13411211
hydroquinone
—
—
—
—
—
13411211
manganese compounds
—
—
—
—
—
13411211
ethylene oxide
—
—
1E-07
—
9E-08
13411211
methyl methacrylate
—
7E-08
1E-08
—
—
13411211
n-hexane
—
—
7E-10
—
—
13411211
ethyl acrylate
—
9E-04
2E-04
8E-01
3E-04
13411211
acrylic acid
1E-02
2E-02
5E-04
2E-02
5E-04
13411211
acrolein
2E-02
6E-04
2E-04
4E-04
1E-04
13411211
formaldehyde
6E-03
3E-04
2E-05
3E-04
3E-05
13411211
vinyl acetate
—
1E-05
2E-06
2E-05
1E-06
13411211
methanol
IE-04
6E-06
2E-06
2E-05
3E-06
13411211
acetaldehyde
2E-06
1E-08
2E-09
5E-08
3E-09
13411211
methyl iodide
—
3E-08
8E-09
3E-08
1E-08
13411211
benzene
—
8E-10
5E-11
8E-10
3E-10
13411911
ethylene glycol
—
—
—
—
—
13411911
propionaldehyde
—
IE-04
2E-05
—
—
13411911
n-hexane
—
—
4E-06
—
—
13411911
acrolein
2E-02
9E-04
3E-04
6E-04
2E-04
13411911
acetaldehyde
2E-02
IE-04
2E-05
5E-04
2E-05
13411911
vinyl acetate
—
6E-05
1E-05
8E-05
6E-06
13411911
toluene
—
1E-06
2E-07
2E-06
3E-07
13411911
methanol
2E-05
7E-07
2E-07
2E-06
3E-07
13411911
benzene
—
4E-08
2E-09
4E-08
1E-08
13431911
xylenes (mixed)
3E-03
IE-04
1E-05
—
—
13431911
benzene
—
7E-04
5E-05
8E-04
3E-04
13431911
toluene
—
3E-04
3E-05
4E-04
6E-05
13610611
1,1,2,2-tetrachloroethane
—
—
—
—
—
13610611
1,1,2-trichloroethane
—
—
—
—
—
13610611
1,2,3,4,6,7,8,9-
octachlorodibenzo-p-
dioxin
13610611
1,2,3,4,6,7,8,9-
octachlorodibenzofuran
—
—
—
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
13610611
1,2,3,4,6,7,8-
heptachlorodibenzo-p-
dioxin
13610611
1,2,3,4,6,7,8-
heptachlorodibenzofuran
—
—
—
—
—
13610611
1,2,3,4,7,8,9-
heptachlorodibenzofuran
—
—
—
—
—
13610611
1,2,3,4,7,8-
hexachlorodibenzo-p-
dioxin
13610611
1,2,3,4,7,8-
hexachlorodibenzofuran
—
—
—
—
—
13610611
1,2,3,6,7,8-
hexachlorodibenzo-p-
dioxin
13610611
1,2,3,6,7,8-
hexachlorodibenzofuran
—
—
—
—
—
13610611
1,2,3,7,8,9-
hexachlorodibenzo-p-
dioxin
13610611
1,2,3,7,8,9-
hexachlorodibenzofuran
—
—
—
—
—
13610611
1,2,3,7,8-
pentachlorodibenzo-p-
dioxin
13610611
1,2,3,7,8-
pentachlorodibenzofuran
—
—
—
—
—
13610611
2,3,4,6,7,8-
hexachlorodibenzofuran
—
—
—
—
—
13610611
2,3,4,7,8-
pentachlorodibenzofuran
—
—
—
—
—
13610611
chloroprene
—
—
—
—
—
13610611
ethyl chloride
—
—
—
—
—
13610611
ethylene glycol
—
—
—
—
—
13610611
ethylidene dichloride
—
—
—
—
—
13610611
vinyl bromide
—
—
—
—
—
13610611
vinylidene chloride
—
—
—
—
7E-09
13610611
chloroform
2E-03
—
1E-06
—
1E-06
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
13610611
cumene
—
6E-07
1E-07
—
—
13610611
chlorine
1E-01
2E-02
4E-03
8E-03
3E-03
13610611
hydrochloric acid
6E-03
5E-03
4E-04
3E-03
4E-04
13610611
ethylene dichloride
—
—
—
8E-05
2E-05
13610611
methanol
4E-04
1E-05
4E-06
4E-05
8E-06
13610611
vinyl chloride
IE-04
3E-05
7E-06
2E-05
2E-06
13610611
carbon tetrachloride
2E-04
—
6E-06
3E-06
7E-07
13610611
formaldehyde
2E-05
1E-06
7E-08
1E-06
1E-07
13610611
acetaldehyde
3E-05
2E-07
3E-08
7E-07
4E-08
13610611
vinyl acetate
—
2E-07
3E-08
2E-07
2E-08
13610611
methyl chloride
—
—
5E-09
3E-08
5E-09
13610611
benzene
—
3E-08
2E-09
3E-08
1E-08
13610611
1,3-butadiene
—
4E-10
5E-11
3E-08
6E-10
13614411
acrylamide
—
—
—
—
—
13614411
acrylonitrile
—
—
2E-02
3E-03
8E-04
14997411
ethylene oxide
—
—
8E-05
—
7E-05
14997411
methanol
2E-04
7E-06
2E-06
2E-05
4E-06
15077311
methanol
2E-02
7E-04
2E-04
2E-03
4E-04
15639911
acrylamide
—
—
—
—
—
15639911
acrylic acid
2E-02
2E-02
7E-04
3E-02
7E-04
15639911
acrylonitrile
—
—
4E-03
6E-04
2E-04
15639911
chlorine
4E-03
6E-04
2E-04
3E-04
1E-04
16856611
1,1,2,2-tetrachloroethane
—
—
—
—
—
16856611
triethylamine
4E-06
—
—
—
—
16856611
n-hexane
—
—
8E-07
—
—
16856611
methyl methacrylate
—
4E-07
6E-08
—
—
16856611
xylenes (mixed)
4E-07
2E-08
2E-09
—
—
16856611
vinyl acetate
—
1E-06
2E-07
2E-06
1E-07
16856611
toluene
—
5E-07
6E-08
6E-07
1E-07
16856611
methanol
5E-06
2E-07
5E-08
6E-07
1E-07
16856611
methylene chloride
3E-07
6E-09
2E-09
4E-09
2E-09
16871811
phenol
2E-02
2E-03
1E-03
3E-03
7E-04
16871811
benzene
—
4E-06
2E-07
4E-06
1E-06
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
16966011
methanol
8E-03
3E-04
8E-05
9E-04
2E-04
16972411
catechol
—
—
—
—
—
16972411
ethyl chloride
—
—
—
—
—
16972411
hydroquinone
—
—
—
—
—
16972411
methyl isobutyl ketone
—
—
—
—
—
16972411
methanol
2E-03
9E-05
2E-05
2E-04
5E-05
16972411
phenol
6E-04
6E-05
4E-05
IE-04
2E-05
16972411
methyl chloride
—
—
3E-06
2E-05
3E-06
17055211
ethylene glycol
—
—
—
—
—
17055211
ethyl benzene
—
4E-04
1E-05
—
—
17055211
methyl methacrylate
—
4E-05
6E-06
—
—
17055211
ethyl acrylate
—
7E-05
2E-05
6E-02
2E-05
17055211
1,3-butadiene
—
3E-05
4E-06
2E-03
4E-05
17055211
acrylonitrile
—
—
8E-03
1E-03
4E-04
17055211
acrylic acid
3E-04
4E-04
1E-05
7E-04
1E-05
17055211
styrene
3E-03
7E-04
IE-04
3E-04
6E-05
17055211
vinyl acetate
—
4E-05
8E-06
6E-05
4E-06
17055211
dimethyl formamide
—
—
6E-07
3E-05
6E-07
17055211
methanol
IE-04
6E-06
1E-06
1E-05
3E-06
17058711
ethylene glycol
—
—
—
—
—
17058711
hydroquinone
—
—
—
—
—
17058711
methyl methacrylate
—
7E-04
9E-05
—
—
17058711
vinyl acetate
—
6E-02
1E-02
8E-02
6E-03
17058711
methanol
5E-02
2E-03
5E-04
6E-03
1E-03
17058711
acetaldehyde
2E-01
1E-03
2E-04
5E-03
3E-04
17058711
acrolein
5E-02
2E-03
5E-04
1E-03
3E-04
17058711
chlorine
4E-04
5E-05
1E-05
3E-05
9E-06
17640311
methanol
6E-04
2E-05
6E-06
6E-05
1E-05
17640311
chlorine
7E-05
1E-05
3E-06
5E-06
2E-06
17640911
1,1,2,2-tetrachloroethane
—
—
—
—
—
17640911
1,1,2-trichloroethane
—
—
—
—
—
17640911
1,3-dichloropropene
—
—
—
—
—
17640911
bromoform
—
—
—
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
17640911
ethyl chloride
—
—
—
—
—
17640911
ethylidene dichloride
—
—
—
—
—
17640911
hexachlorobenzene
—
—
—
—
—
17640911
hexachlorocyclopentadien
e
—
—
—
—
—
17640911
hexachloroethane
—
—
—
—
—
17640911
nickel compounds
—
—
—
—
—
17640911
propylene dichloride
—
—
—
—
—
17640911
vinylidene chloride
—
—
—
—
3E-10
17640911
chloroform
6E-02
—
3E-05
—
4E-05
17640911
xylenes (mixed)
4E-05
2E-06
2E-07
—
—
17640911
ethyl benzene
—
2E-06
6E-08
—
—
17640911
methyl bromide
4E-08
—
2E-10
—
7E-10
17640911
hydrochloric acid
1E-02
9E-03
8E-04
6E-03
9E-04
17640911
chlorine
4E-02
6E-03
1E-03
3E-03
1E-03
17640911
ethylene dichloride
—
—
—
2E-04
4E-05
17640911
carbon tetrachloride
7E-03
—
2E-04
IE-04
2E-05
17640911
tetrachloroethene
2E-03
2E-04
3E-05
7E-05
3E-05
17640911
benzene
—
6E-05
4E-06
7E-05
2E-05
17640911
methyl chloride
—
—
3E-06
2E-05
3E-06
17640911
hexachlorobutadiene
—
—
—
1E-05
5E-06
17640911
toluene
—
9E-06
1E-06
1E-05
2E-06
17640911
styrene
8E-05
2E-05
3E-06
8E-06
2E-06
17640911
allyl chloride
—
4E-06
2E-07
4E-06
3E-07
17640911
methylene chloride
2E-04
3E-06
1E-06
2E-06
9E-07
17640911
propylene oxide
6E-05
1E-06
3E-07
1E-06
3E-07
17640911
methanol
1E-05
4E-07
1E-07
1E-06
2E-07
17640911
trichloroethylene
—
5E-07
2E-07
7E-07
1E-07
17640911
vinyl chloride
8E-07
2E-07
5E-08
1E-07
1E-08
17735411
1,1,2,2-tetrachloroethane
—
—
—
—
—
17735411
1,1,2-trichloroethane
—
—
—
—
—
17735411
1,2,3,4,6,7,8,9-
octachlorodibenzo-p-
dioxin
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
17735411
1,2,3,4,6,7,8,9-
octachlorodibenzofuran
—
—
—
—
—
17735411
1,2,3,4,6,7,8-
heptachlorodibenzo-p-
dioxin
17735411
1,2,3,4,6,7,8-
heptachlorodibenzofuran
—
—
—
—
—
17735411
1,2,3,4,7,8,9-
heptachlorodibenzofuran
—
—
—
—
—
17735411
1,2,3,4,7,8-
hexachlorodibenzo-p-
dioxin
17735411
1,2,3,4,7,8-
hexachlorodibenzofuran
—
—
—
—
—
17735411
1,2,3,6,7,8-
hexachlorodibenzo-p-
dioxin
17735411
1,2,3,6,7,8-
hexachlorodibenzofuran
—
—
—
—
—
17735411
1,2,3,7,8,9-
hexachlorodibenzo-p-
dioxin
17735411
1,2,3,7,8,9-
hexachlorodibenzofuran
—
—
—
—
—
17735411
1,2,3,7,8-
pentachlorodibenzo-p-
dioxin
17735411
1,2,3,7,8-
pentachlorodibenzofuran
—
—
—
—
—
17735411
1,3-dichloropropene
—
—
—
—
—
17735411
2,3,4,6,7,8-
hexachlorodibenzofuran
—
—
—
—
—
17735411
2,3,4,7,8-
pentachlorodibenzofuran
—
—
—
—
—
17735411
2,3,7,8-
tetrachlorodibenzo-p-
dioxin
17735411
2,3,7,8-
tetrachlorodibenzofuran
—
—
—
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
17735411
acetophenone
—
—
—
—
—
17735411
diethanolamine
—
—
—
—
—
17735411
ethyl chloride
—
—
—
—
—
17735411
ethylidene dichloride
—
—
—
—
—
17735411
hexachlorobenzene
—
—
—
—
—
17735411
hexachlorobutadiene
—
—
—
—
—
17735411
hexachloroethane
—
—
—
—
—
17735411
methyl isobutyl ketone
—
—
—
—
—
17735411
propylene dichloride
—
—
—
—
—
17735411
vinylidene chloride
—
—
—
—
1E-05
17735411
glycol ethers
IE-04
—
—
—
—
17735411
chloroform
5E-02
—
2E-05
—
3E-05
17735411
cumene
—
IE-04
2E-05
—
—
17735411
biphenyl
—
—
9E-07
—
—
17735411
xylenes (mixed)
5E-05
2E-06
3E-07
—
—
17735411
1,2-epoxybutane
—
6E-07
3E-07
—
—
17735411
ethyl benzene
—
5E-06
1E-07
—
—
17735411
chlorobenzene
—
2E-08
2E-09
—
—
17735411
propionaldehyde
—
9E-09
2E-09
—
—
17735411
n-hexane
—
—
7E-11
—
—
17735411
chlorine
4E-02
5E-03
1E-03
3E-03
9E-04
17735411
hydrochloric acid
2E-03
2E-03
2E-04
1E-03
2E-04
17735411
epichlorohydrin
9E-03
2E-03
IE-04
6E-04
2E-04
17735411
phenol
2E-03
2E-04
2E-04
4E-04
7E-05
17735411
acrolein
1E-02
4E-04
IE-04
2E-04
7E-05
17735411
allyl chloride
—
2E-04
1E-05
2E-04
1E-05
17735411
formaldehyde
4E-03
2E-04
1E-05
2E-04
2E-05
17735411
ethylene dichloride
—
—
—
3E-05
7E-06
17735411
methylene chloride
1E-03
2E-05
8E-06
2E-05
6E-06
17735411
carbon tetrachloride
6E-04
—
1E-05
9E-06
2E-06
17735411
methanol
5E-05
2E-06
5E-07
6E-06
1E-06
17735411
methyl chloride
—
—
6E-07
4E-06
6E-07
17735411
trichloroethylene
—
3E-06
7E-07
3E-06
7E-07
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
17735411
vinyl chloride
9E-06
3E-06
5E-07
1E-06
1E-07
17735411
toluene
—
7E-07
9E-08
1E-06
2E-07
17735411
benzene
—
1E-06
8E-08
1E-06
4E-07
17735411
tetrachloroethene
2E-05
2E-06
3E-07
7E-07
3E-07
17735411
acetaldehyde
4E-06
2E-08
4E-09
1E-07
5E-09
17735411
1,1,1-trichloroethane
1E-07
6E-09
3E-09
4E-09
2E-09
17735411
propylene oxide
5E-08
9E-10
2E-10
1E-09
3E-10
17905711
pah, total
—
—
—
—
—
17905711
cumene
—
4E-08
6E-09
—
—
17905711
ethyl benzene
—
2E-07
5E-09
—
—
17905711
toluene
—
7E-06
8E-07
9E-06
2E-06
17905711
benzene
—
2E-06
1E-07
2E-06
7E-07
17909311
2,2,4-trimethylpentane
—
—
—
—
—
17909311
cresols (mixed)
—
—
—
—
—
17909311
naphthalene
—
—
—
—
—
17909311
phenanthrene
—
—
—
—
—
17909311
xylenes (mixed)
3E-03
IE-04
2E-05
—
—
17909311
n-hexane
—
—
3E-06
—
—
17909311
ethyl benzene
—
4E-05
1E-06
—
—
17909311
cumene
—
1E-06
2E-07
—
—
17909311
biphenyl
—
—
7E-08
—
—
17909311
toluene
—
4E-04
4E-05
5E-04
8E-05
17909311
benzene
—
5E-04
3E-05
5E-04
2E-04
17909311
phenol
5E-05
5E-06
3E-06
8E-06
2E-06
17909311
styrene
3E-05
6E-06
1E-06
3E-06
5E-07
17909311
1,3-butadiene
—
8E-10
1E-10
6E-08
1E-09
17912111
nitrobenzene
—
—
—
—
—
17912111
p-phenylenediamine
—
—
—
—
—
17912111
aniline
—
1E-02
9E-03
—
—
17912111
benzene
—
8E-05
5E-06
9E-05
3E-05
17912111
phenol
1E-06
1E-07
9E-08
2E-07
4E-08
17974911
2,2,4-trimethylpentane
—
—
—
—
—
17974911
cresols (mixed)
—
—
—
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
17974911
diethanolamine
—
—
—
—
—
17974911
naphthalene
—
—
—
—
—
17974911
phenanthrene
—
—
—
—
—
17974911
n-hexane
—
—
2E-06
—
—
17974911
xylenes (mixed)
4E-04
2E-05
2E-06
—
—
17974911
ethyl benzene
—
1E-05
3E-07
—
—
17974911
biphenyl
—
—
2E-07
—
—
17974911
cumene
—
1E-06
2E-07
—
—
17974911
toluene
—
9E-05
1E-05
IE-04
2E-05
17974911
benzene
—
IE-04
8E-06
IE-04
4E-05
17974911
styrene
7E-05
2E-05
3E-06
7E-06
1E-06
17974911
phenol
4E-05
4E-06
3E-06
6E-06
1E-06
17974911
hydrofluoric acid
4E-06
1E-06
5E-08
7E-07
7E-08
17985111
2,2,4-trimethylpentane
—
—
—
—
—
17985111
cresols (mixed)
—
—
—
—
—
17985111
naphthalene
—
—
—
—
—
17985111
phenanthrene
—
—
—
—
—
17985111
n-hexane
—
—
2E-06
—
—
17985111
xylenes (mixed)
2E-04
8E-06
1E-06
—
—
17985111
ethyl benzene
—
5E-06
2E-07
—
—
17985111
cumene
—
3E-07
5E-08
—
—
17985111
biphenyl
—
—
2E-08
—
—
17985111
benzene
—
9E-05
6E-06
IE-04
3E-05
17985111
toluene
—
4E-05
5E-06
5E-05
9E-06
17985111
styrene
2E-05
4E-06
6E-07
2E-06
3E-07
17985111
phenol
8E-06
8E-07
6E-07
1E-06
3E-07
18893911
methanol
5E-03
2E-04
5E-05
5E-04
1E-04
18929011
methanol
2E-01
9E-03
2E-03
2E-02
5E-03
18973611
2,4,6-trichlorophenol
—
—
—
—
—
18973611
pentachlorophenol
—
—
—
—
—
18973611
hydrochloric acid
2E-02
1E-02
1E-03
8E-03
1E-03
18973611
chlorine
5E-02
7E-03
2E-03
4E-03
1E-03
18973611
carbon tetrachloride
1E-01
—
3E-03
2E-03
4E-04
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
18973611
phenol
2E-03
2E-04
IE-04
3E-04
7E-05
18973611
tetrachloroethene
1E-03
IE-04
2E-05
4E-05
2E-05
18973611
vinyl chloride
2E-05
6E-06
1E-06
3E-06
3E-07
18973611
toluene
—
2E-06
3E-07
3E-06
5E-07
18973611
benzene
—
7E-07
5E-08
8E-07
3E-07
18982311
hydrochloric acid
1E-01
8E-02
6E-03
5E-02
7E-03
19250811
acrylamide
—
—
—
—
—
19250811
chromium (iii) compounds
—
—
—
—
—
19250811
chromium (vi) compounds
—
—
—
—
—
19250811
isophorone
—
—
—
—
—
19250811
manganese compounds
—
—
—
—
—
19250811
methyl isobutyl ketone
—
—
—
—
—
19250811
nickel compounds
—
—
—
—
—
19250811
biphenyl
—
—
4E-04
—
—
19250811
ethyl benzene
—
IE-04
4E-06
—
—
19250811
toluene
—
6E-06
7E-07
8E-06
1E-06
19250811
formaldehyde
2E-04
9E-06
6E-07
8E-06
8E-07
19250811
hydrochloric acid
1E-05
1E-05
9E-07
6E-06
1E-06
19250811
methanol
2E-05
8E-07
2E-07
2E-06
4E-07
2491711
lead compounds
—
—
—
—
—
2491711
methanol
2E-04
9E-06
2E-06
3E-05
5E-06
3737011
1,1,2-trichloroethane
—
—
—
—
—
3737011
acetophenone
—
—
—
—
—
3737011
bis(2-ethylhexyl)phthalate
—
—
—
—
—
3737011
ethylene glycol
—
—
—
—
—
3737011
methyl isobutyl ketone
—
—
—
—
—
3737011
naphthalene
—
—
—
—
—
3737011
pah, total
—
—
—
—
—
3737011
propylene dichloride
—
—
—
—
—
3737011
glycol ethers
5E-05
—
—
—
—
3737011
acetonitrile
—
5E-04
IE-04
—
—
3737011
phosgene
2E-03
—
6E-06
—
4E-06
3737011
hydrogen cyanide
IE-04
2E-05
6E-06
—
4E-06
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
3737011
chloroform
1E-02
—
5E-06
—
7E-06
3737011
xylenes (mixed)
9E-04
3E-05
5E-06
—
—
3737011
propionaldehyde
—
2E-05
4E-06
—
—
3737011
methyl bromide
5E-04
—
3E-06
—
1E-05
3737011
cumene
—
1E-05
2E-06
—
—
3737011
1,4-dioxane
2E-04
1E-05
5E-07
—
—
3737011
n-hexane
—
—
2E-07
—
—
3737011
ethyl benzene
—
9E-07
3E-08
—
—
3737011
methyl methacrylate
—
3E-09
4E-10
—
—
3737011
chlorobenzene
—
4E-09
3E-10
—
—
3737011
ethyl acrylate
—
4E-06
9E-07
3E-03
1E-06
3737011
acrylic acid
9E-04
1E-03
4E-05
2E-03
4E-05
3737011
formaldehyde
1E-02
7E-04
5E-05
7E-04
7E-05
3737011
methanol
6E-03
2E-04
6E-05
6E-04
1E-04
3737011
acetaldehyde
8E-03
4E-05
7E-06
2E-04
1E-05
3737011
dimethyl formamide
—
—
6E-07
3E-05
5E-07
3737011
maleic anhydride
—
—
—
2E-05
7E-07
3737011
methyl iodide
—
3E-05
7E-06
2E-05
1E-05
3737011
benzene
—
1E-05
8E-07
1E-05
4E-06
3737011
toluene
—
6E-06
7E-07
8E-06
1E-06
3737011
methylene chloride
3E-04
6E-06
2E-06
4E-06
2E-06
3737011
1,3-butadiene
—
5E-08
7E-09
4E-06
7E-08
3737011
propylene oxide
IE-04
2E-06
6E-07
3E-06
7E-07
3737011
acrylonitrile
—
—
1E-05
2E-06
6E-07
3737011
styrene
1E-05
4E-06
6E-07
1E-06
3E-07
3737011
epichlorohydrin
2E-05
4E-06
2E-07
1E-06
3E-07
3737011
ethylene dichloride
—
—
—
2E-07
4E-08
3737011
carbon tetrachloride
2E-05
—
4E-07
2E-07
5E-08
3737011
methyl tert-butyl ether
—
6E-08
5E-09
6E-08
3E-09
3930711
naphthalene
—
—
—
—
—
3930711
xylenes (mixed)
8E-03
3E-04
4E-05
—
—
3930711
ethyl benzene
—
IE-04
3E-06
—
—
3930711
cumene
—
2E-07
4E-08
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
3930711
n-hexane
—
—
3E-08
—
—
3930711
styrene
5E-03
1E-03
2E-04
5E-04
9E-05
3930711
benzene
—
IE-04
7E-06
IE-04
4E-05
3930711
toluene
—
6E-05
7E-06
8E-05
1E-05
3930711
1,3-butadiene
—
1E-09
2E-10
9E-08
2E-09
3967011
nickel compounds
—
—
—
—
—
3967011
propionaldehyde
—
8E-07
1E-07
—
—
3967011
n-hexane
—
—
9E-10
—
—
3982311
anisidine
—
—
—
—
—
3982311
beryllium compounds
—
—
—
—
5E-03
3982311
bis(2-ethylhexyl)phthalate
—
—
—
—
—
3982311
chromium (iii) compounds
—
—
—
—
—
3982311
chromium (vi) compounds
—
—
—
—
—
3982311
ethylene glycol
—
—
—
—
—
3982311
hydroquinone
—
—
—
—
—
3982311
lead compounds
—
—
—
—
—
3982311
manganese compounds
—
—
—
—
—
3982311
methyl isobutyl ketone
—
—
—
—
—
3982311
nickel compounds
—
—
—
—
—
3982311
o-toluidine
—
—
—
—
—
3982311
pah, total
—
—
—
—
—
3982311
phthalic anhydride
—
—
—
—
—
3982311
arsenic compounds
5
—
—
—
—
3982311
triethylamine
8E-03
—
—
—
—
3982311
biphenyl
—
—
3E-02
—
—
3982311
propionaldehyde
—
1E-02
2E-03
—
—
3982311
aniline
—
2E-03
1E-03
—
—
3982311
xylenes (mixed)
1E-01
5E-03
7E-04
—
—
3982311
cadmium compounds
—
7E-04
9E-05
—
—
3982311
methyl bromide
7E-03
—
3E-05
—
2E-04
3982311
1,4-dioxane
7E-03
3E-04
2E-05
—
—
3982311
ethyl benzene
—
IE-04
3E-06
—
—
3982311
n-hexane
—
—
5E-08
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
3982311
methanol
6E-01
2E-02
6E-03
6E-02
1E-02
3982311
acetaldehyde
1
7E-03
1E-03
3E-02
2E-03
3982311
toluene
—
5E-03
6E-04
7E-03
1E-03
3982311
1,3-butadiene
—
2E-05
3E-06
2E-03
3E-05
3982311
benzene
—
6E-04
4E-05
6E-04
2E-04
3982311
hydrochloric acid
2E-04
2E-04
1E-05
IE-04
2E-05
3982311
tetrachloroethene
1E-05
9E-07
1E-07
3E-07
2E-07
4004311
gaseous divalent mercury
—
—
—
—
—
4004311
particulate divalent
mercury
—
—
—
—
—
4004311
acetonitrile
—
2E-04
4E-05
—
—
4004311
mercury (elemental)
9E-04
—
3E-07
—
3E-07
4004311
1,3-butadiene
—
1E-05
1E-06
7E-04
1E-05
4004311
methylene chloride
4E-02
9E-04
3E-04
6E-04
2E-04
4031311
2,2,4-trimethylpentane
—
—
—
—
—
4031311
cresols (mixed)
—
—
—
—
—
4031311
diethanolamine
—
—
—
—
—
4031311
naphthalene
—
—
—
—
—
4031311
xylenes (mixed)
3E-04
1E-05
2E-06
—
—
4031311
biphenyl
—
—
1E-06
—
—
4031311
carbonyl sulfide
—
—
1E-06
—
—
4031311
n-hexane
—
—
8E-07
—
—
4031311
ethyl benzene
—
1E-05
3E-07
—
—
4031311
cumene
—
9E-07
1E-07
—
—
4031311
toluene
—
3E-05
4E-06
4E-05
7E-06
4031311
benzene
—
4E-05
3E-06
4E-05
1E-05
4031311
carbon disulfide
2E-05
2E-06
2E-07
3E-05
6E-07
4031311
1,3-butadiene
—
2E-08
3E-09
1E-06
3E-08
4031311
phenol
6E-06
6E-07
4E-07
9E-07
2E-07
4031311
formaldehyde
2E-05
8E-07
5E-08
7E-07
7E-08
4031311
styrene
4E-06
9E-07
1E-07
4E-07
7E-08
4031311
tetrachloroethene
1E-05
8E-07
1E-07
3E-07
1E-07
4031311
methylene chloride
2E-07
4E-09
1E-09
3E-09
1E-09
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
4041311
2-methylnaphthalene
—
—
—
—
—
4041311
antimony compounds
—
—
—
—
—
4041311
chromium (iii) compounds
—
—
—
—
—
4041311
chromium (vi) compounds
—
—
—
—
—
4041311
cobalt compounds
—
—
—
—
—
4041311
ethylene glycol
—
—
—
—
—
4041311
fluoranthene
—
—
—
—
—
4041311
fluorene
—
—
—
—
—
4041311
gaseous divalent mercury
—
—
—
—
—
4041311
lead compounds
—
—
—
—
—
4041311
manganese compounds
—
—
—
—
—
4041311
naphthalene
—
—
—
—
—
4041311
nickel compounds
—
—
—
—
—
4041311
p-dichlorobenzene
—
—
—
—
—
4041311
particulate divalent
mercury
—
—
—
—
—
4041311
phenanthrene
—
—
—
—
—
4041311
pyrene
—
—
—
—
—
4041311
p-xylene
2E-02
—
—
—
—
4041311
arsenic compounds
2E-05
—
—
—
—
4041311
biphenyl
—
—
1E-02
—
—
4041311
methyl bromide
5E-02
—
2E-04
—
1E-03
4041311
1,4-dioxane
IE-04
6E-06
3E-07
—
—
4041311
cadmium compounds
—
3E-07
4E-08
—
—
4041311
n-hexane
—
—
4E-09
—
—
4041311
mercury (elemental)
5E-06
—
2E-09
—
2E-09
4041311
acetaldehyde
4E-02
2E-04
4E-05
1E-03
5E-05
4041311
methanol
1E-03
5E-05
1E-05
IE-04
3E-05
4041311
benzene
—
3E-05
2E-06
4E-05
1E-05
4041311
toluene
—
1E-05
1E-06
2E-05
3E-06
4041311
formaldehyde
3E-05
2E-06
1E-07
2E-06
2E-07
4055111
glycol ethers
IE-04
—
—
—
—
4055111
methanol
8E-03
3E-04
8E-05
8E-04
2E-04
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
4055511
hydroquinone
—
—
—
—
—
4055511
methyl isobutyl ketone
—
—
—
—
—
4055511
cumene
—
5E-05
8E-06
—
—
4055511
ethyl benzene
—
1E-08
4E-10
—
—
4055511
n-hexane
—
—
2E-10
—
—
4055511
styrene
1E-03
3E-04
5E-05
IE-04
3E-05
4055511
benzene
—
1E-05
8E-07
1E-05
4E-06
4056511
cobalt compounds
—
—
—
—
—
4056511
hydrochloric acid
—
—
—
—
—
4056511
methyl isobutyl ketone
—
—
—
—
—
4056511
tetrachloroethene
—
—
—
—
—
4056511
n-hexane
—
—
1E-07
—
—
4056511
1,3-butadiene
—
2E-05
2E-06
1E-03
2E-05
4056511
methanol
4E-06
2E-07
4E-08
4E-07
9E-08
4057611
1,1,2,2-tetrachloroethane
—
—
—
—
—
4057611
1,1,2-trichloroethane
—
—
—
—
—
4057611
asbestos
—
—
—
—
—
4057611
chlorobenzene
—
—
—
—
—
4057611
chloroprene
—
—
—
—
—
4057611
dichloroethyl ether
—
—
—
—
—
4057611
ethyl chloride
—
—
—
—
—
4057611
ethylene glycol
—
—
—
—
—
4057611
ethylidene dichloride
—
—
—
—
—
4057611
hexachlorobenzene
—
—
—
—
—
4057611
hexachloroethane
—
—
—
—
—
4057611
lead compounds
—
—
—
—
—
4057611
methyl isobutyl ketone
—
—
—
—
—
4057611
naphthalene
—
—
—
—
—
4057611
vinylidene chloride
—
—
—
—
6E-07
4057611
chloroform
1E-02
—
6E-06
—
8E-06
4057611
chlorine
2E-01
3E-02
9E-03
2E-02
6E-03
4057611
ethylene dichloride
—
—
—
2E-03
5E-04
4057611
hydrochloric acid
2E-03
1E-03
IE-04
8E-04
1E-04
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
4057611
vinyl chloride
7E-04
2E-04
4E-05
IE-04
1E-05
4057611
carbon tetrachloride
7E-04
—
2E-05
1E-05
2E-06
4057611
tetrachloroethene
4E-05
3E-06
5E-07
1E-06
5E-07
4057611
acetaldehyde
3E-05
2E-07
3E-08
7E-07
4E-08
4057611
trichloroethylene
—
5E-08
1E-08
6E-08
1E-08
4057611
benzene
—
1E-08
7E-10
1E-08
4E-09
4057911
ethyl chloride
—
—
—
—
—
4057911
ethylene glycol
—
—
—
—
—
4057911
glycol ethers
7E-04
—
—
—
—
4057911
ethylene oxide
—
—
2E-04
—
2E-04
4057911
n-hexane
—
—
1E-06
—
—
4057911
vinyl acetate
—
1E-02
2E-03
2E-02
1E-03
4057911
acetaldehyde
8E-02
5E-04
8E-05
2E-03
1E-04
4057911
methanol
7E-04
3E-05
7E-06
7E-05
1E-05
4057911
formaldehyde
1E-03
5E-05
3E-06
4E-05
4E-06
4057911
acrolein
8E-04
3E-05
8E-06
2E-05
6E-06
4057911
methyl iodide
—
5E-06
1E-06
4E-06
2E-06
4057911
benzene
—
3E-06
2E-07
3E-06
9E-07
4057911
vinyl chloride
1E-08
4E-09
9E-10
2E-09
2E-10
4167411
n-hexane
—
—
5E-08
—
—
4167411
chlorine
1E-03
2E-04
5E-05
IE-04
3E-05
4167411
methanol
1E-03
5E-05
1E-05
IE-04
3E-05
4167811
bis(2-ethylhexyl)phthalate
—
—
—
—
—
4167811
cresols (mixed)
—
—
—
—
—
4167811
diethanolamine
—
—
—
—
—
4167811
ethylene glycol
—
—
—
—
—
4167811
naphthalene
—
—
—
—
—
4167811
1,2-epoxybutane
—
4E-04
2E-04
—
—
4167811
chlorobenzene
—
1E-03
6E-05
—
—
4167811
ethylene oxide
—
—
1E-05
—
1E-05
4167811
xylenes (mixed)
4E-04
1E-05
2E-06
—
—
4167811
chloroform
9E-04
—
4E-07
—
5E-07
4167811
ethyl benzene
—
8E-06
2E-07
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
4167811
cumene
—
2E-07
4E-08
—
—
4167811
maleic anhydride
—
—
—
2E-01
6E-03
4167811
ethyl acrylate
—
6E-05
1E-05
5E-02
2E-05
4167811
propylene oxide
1E-02
2E-04
6E-05
3E-04
7E-05
4167811
phenol
2E-04
2E-05
1E-05
2E-05
5E-06
4167811
toluene
—
3E-06
3E-07
4E-06
7E-07
4167811
methyl chloride
—
—
1E-07
8E-07
1E-07
4167811
styrene
4E-06
9E-07
1E-07
4E-07
7E-08
4167811
tetrachloroethene
3E-06
2E-07
4E-08
9E-08
4E-08
4168511
2,2,4-trimethylpentane
—
—
—
—
—
4168511
acetophenone
—
—
—
—
—
4168511
anthracene
—
—
—
—
—
4168511
benzo(ghi)perylene
—
—
—
—
—
4168511
carbon disulfide
—
—
—
—
—
4168511
carbonyl sulfide
—
—
—
—
—
4168511
catechol
—
—
—
—
—
4168511
cobalt compounds
—
—
—
—
—
4168511
cresols (mixed)
—
—
—
—
—
4168511
diethanolamine
—
—
—
—
—
4168511
ethylene glycol
—
—
—
—
—
4168511
methyl isobutyl ketone
—
—
—
—
—
4168511
naphthalene
—
—
—
—
—
4168511
nickel compounds
—
—
—
—
—
4168511
phenanthrene
—
—
—
—
—
4168511
polycyclic organic matter
—
—
—
—
—
4168511
glycol ethers
2E-02
—
—
—
—
4168511
acetonitrile
—
2E-04
6E-05
—
—
4168511
cumene
—
2E-04
3E-05
—
—
4168511
xylenes (mixed)
1E-03
4E-05
6E-06
—
—
4168511
ethyl benzene
—
5E-05
1E-06
—
—
4168511
n-hexane
—
—
8E-07
—
—
4168511
propionaldehyde
—
3E-08
6E-09
—
—
4168511
phenol
7E-03
7E-04
4E-04
1E-03
2E-04
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
4168511
benzene
—
2E-04
1E-05
2E-04
8E-05
4168511
1,3-butadiene
—
3E-06
4E-07
2E-04
4E-06
4168511
epichlorohydrin
2E-03
4E-04
3E-05
IE-04
3E-05
4168511
formaldehyde
2E-03
IE-04
7E-06
9E-05
9E-06
4168511
toluene
—
4E-05
5E-06
5E-05
9E-06
4168511
hydrochloric acid
IE-04
7E-05
6E-06
4E-05
7E-06
4168511
methanol
8E-05
3E-06
9E-07
9E-06
2E-06
4168511
acetaldehyde
IE-04
9E-07
1E-07
4E-06
2E-07
4168511
methylene chloride
2E-04
4E-06
2E-06
3E-06
1E-06
4168511
hydrazine
—
1E-05
8E-08
2E-06
2E-07
4168511
tetrachloroethene
1E-05
1E-06
2E-07
4E-07
2E-07
4168511
styrene
4E-06
1E-06
2E-07
4E-07
8E-08
4168511
vinyl acetate
—
2E-08
4E-09
3E-08
2E-09
4168611
1,3-butadiene
—
1E-05
2E-06
9E-04
2E-05
4168611
dimethyl formamide
—
—
2E-05
8E-04
2E-05
4168611
methyl tert-butyl ether
—
3E-04
2E-05
3E-04
1E-05
4168611
methanol
8E-04
3E-05
8E-06
8E-05
2E-05
4168611
styrene
3E-05
7E-06
1E-06
3E-06
6E-07
4190211
cresols (mixed)
—
—
—
—
—
4190211
naphthalene
—
—
—
—
—
4190211
p-phenylenediamine
—
—
—
—
—
4190211
chloroform
1E-02
—
6E-06
—
7E-06
4190211
acetonitrile
—
8E-06
2E-06
—
—
4190211
biphenyl
—
—
3E-08
—
—
4190211
acrylic acid
6E-03
9E-03
3E-04
1E-02
3E-04
4190211
chlorine
6E-02
8E-03
2E-03
4E-03
1E-03
4190211
vinyl acetate
—
9E-04
2E-04
1E-03
8E-05
4190211
hydrochloric acid
2E-03
1E-03
IE-04
8E-04
1E-04
4190211
ethyl acrylate
—
1E-07
2E-08
9E-05
3E-08
4190211
methanol
2E-04
7E-06
2E-06
2E-05
4E-06
4190211
maleic anhydride
—
—
—
7E-06
3E-07
4190211
toluene
—
4E-06
5E-07
5E-06
9E-07
4190211
dimethyl formamide
—
—
9E-08
4E-06
9E-08
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
4190211
benzene
—
3E-06
2E-07
3E-06
9E-07
4190211
styrene
2E-06
5E-07
7E-08
2E-07
4E-08
4205511
2,2,4-trimethylpentane
—
—
—
—
—
4205511
cresols (mixed)
—
—
—
—
—
4205511
diethanolamine
—
—
—
—
—
4205511
gaseous divalent mercury
—
—
—
—
—
4205511
lead compounds
—
—
—
—
—
4205511
mercury (elemental)
—
—
—
—
—
4205511
naphthalene
—
—
—
—
—
4205511
pah, total
—
—
—
—
—
4205511
particulate divalent
mercury
—
—
—
—
—
4205511
biphenyl
—
—
7E-05
—
—
4205511
xylenes (mixed)
6E-03
3E-04
4E-05
—
—
4205511
cumene
—
4E-05
7E-06
—
—
4205511
n-hexane
—
—
4E-06
—
—
4205511
ethyl benzene
—
8E-05
2E-06
—
—
4205511
carbonyl sulfide
—
—
6E-07
—
—
4205511
chlorine
3E-03
4E-04
IE-04
2E-04
6E-05
4205511
benzene
—
2E-04
1E-05
2E-04
8E-05
4205511
toluene
—
IE-04
1E-05
2E-04
3E-05
4205511
hydrofluoric acid
IE-04
3E-05
1E-06
2E-05
2E-06
4205511
phenol
7E-05
7E-06
4E-06
1E-05
2E-06
4205511
carbon disulfide
6E-06
9E-07
7E-08
1E-05
2E-07
4205511
1,3-butadiene
—
2E-07
2E-08
1E-05
2E-07
4205511
formaldehyde
IE-04
7E-06
4E-07
6E-06
6E-07
4205511
styrene
2E-05
4E-06
6E-07
2E-06
3E-07
4205511
methylene chloride
6E-09
1E-10
4E-11
8E-11
3E-11
4205511
tetrachloroethene
2E-09
1E-10
2E-11
5E-11
2E-11
4762811
coal tar
—
—
—
—
—
4762811
diethanolamine
—
—
—
—
—
4762811
ethylene glycol
—
—
—
—
—
4762811
gaseous divalent mercury
—
—
—
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
4762811
mercury (elemental)
—
—
—
—
—
4762811
naphthalene
—
—
—
—
—
4762811
particulate divalent
mercury
—
—
—
—
—
4762811
phenol
—
—
—
—
—
4762811
polycyclic organic matter
—
—
—
—
—
4762811
glycol ethers
2E-06
—
—
—
—
4762811
xylenes (mixed)
1E-03
4E-05
6E-06
—
—
4762811
n-hexane
—
—
5E-06
—
—
4762811
biphenyl
—
—
2E-06
—
—
4762811
ethyl benzene
—
3E-05
8E-07
—
—
4762811
cumene
—
3E-07
5E-08
—
—
4762811
formaldehyde
5E-03
3E-04
2E-05
2E-04
2E-05
4762811
toluene
—
9E-05
1E-05
IE-04
2E-05
4762811
benzene
—
IE-04
7E-06
IE-04
4E-05
4762811
1,3-butadiene
—
1E-06
1E-07
7E-05
1E-06
4762811
methanol
6E-05
2E-06
6E-07
6E-06
1E-06
4762811
methyl tert-butyl ether
—
3E-06
3E-07
3E-06
2E-07
4762811
styrene
1E-05
3E-06
5E-07
1E-06
3E-07
4778211
1,1,2,2-tetrachloroethane
—
—
—
—
—
4778211
1,1,2-trichloroethane
—
—
—
—
—
4778211
1,2,3,4,6,7,8,9-
octachlorodibenzo-p-
dioxin
4778211
1,2,3,4,6,7,8,9-
octachlorodibenzofuran
—
—
—
—
—
4778211
1,2,3,4,6,7,8-
heptachlorodibenzo-p-
dioxin
4778211
1,2,3,4,6,7,8-
heptachlorodibenzofuran
—
—
—
—
—
4778211
1,2,3,4,7,8,9-
heptachlorodibenzofuran
—
—
—
—
—
4778211
1,2,3,4,7,8-
hexachlorodibenzo-p-
dioxin
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
4778211
1,2,3,4,7,8-
hexachlorodibenzofuran
—
—
—
—
—
4778211
1,2,3,6,7,8-
hexachlorodibenzo-p-
dioxin
4778211
1,2,3,6,7,8-
hexachlorodibenzofuran
—
—
—
—
—
4778211
1,2,3,7,8,9-
hexachlorodibenzo-p-
dioxin
4778211
1,2,3,7,8-
pentachlorodibenzo-p-
dioxin
4778211
1,2,3,7,8-
pentachlorodibenzofuran
—
—
—
—
—
4778211
2,3,4,6,7,8-
hexachlorodibenzofuran
—
—
—
—
—
4778211
2,3,4,7,8-
pentachlorodibenzofuran
—
—
—
—
—
4778211
2,3,7,8-
tetrachlorodibenzo-p-
dioxin
4778211
2,3,7,8-
tetrachlorodibenzofuran
—
—
—
—
—
4778211
ethyl chloride
—
—
—
—
—
4778211
hexachloroethane
—
—
—
—
—
4778211
chloroform
1E-03
—
7E-07
—
9E-07
4778211
chlorobenzene
—
7E-07
5E-08
—
—
4778211
hydrochloric acid
1E-02
9E-03
8E-04
6E-03
8E-04
4778211
acetaldehyde
4E-02
2E-04
4E-05
1E-03
5E-05
4778211
chlorine
1E-02
2E-03
4E-04
8E-04
3E-04
4778211
ethylene dichloride
—
—
—
5E-04
1E-04
4778211
vinyl chloride
2E-04
5E-05
9E-06
2E-05
2E-06
4778211
tetrachloroethene
4E-06
3E-07
5E-08
1E-07
6E-08
4778211
trichloroethylene
—
4E-09
1E-09
5E-09
1E-09
4778311
methyl methacrylate
—
2E-06
3E-07
—
—
4778311
acetonitrile
—
4E-07
1E-07
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
4778311
acrylic acid
9E-03
1E-02
4E-04
2E-02
4E-04
4778311
formaldehyde
9E-04
4E-05
3E-06
4E-05
4E-06
4778311
acrolein
2E-04
9E-06
3E-06
5E-06
2E-06
4778311
methanol
2E-05
9E-07
2E-07
2E-06
5E-07
4778311
acrylonitrile
—
—
6E-06
1E-06
3E-07
4778311
1,1,1-trichloroethane
2E-05
9E-07
4E-07
6E-07
3E-07
4778311
styrene
6E-07
1E-07
2E-08
6E-08
1E-08
4778311
acetaldehyde
2E-06
1E-08
2E-09
4E-08
2E-09
4778311
toluene
—
5E-09
5E-10
6E-09
1E-09
4778311
benzene
—
4E-09
3E-10
4E-09
1E-09
4778711
antimony compounds
—
—
—
—
—
4778711
beryllium compounds
—
—
—
—
2E-03
4778711
chromium (iii) compounds
—
—
—
—
—
4778711
chromium (vi) compounds
—
—
—
—
—
4778711
dimethyl formamide
—
—
—
—
—
4778711
ethyl chloride
—
—
—
—
—
4778711
ethylene glycol
—
—
—
—
—
4778711
gaseous divalent mercury
—
—
—
—
—
4778711
hydroquinone
—
—
—
—
—
4778711
lead compounds
—
—
—
—
—
4778711
n-hexane
—
—
—
—
—
4778711
nickel compounds
—
—
—
—
—
4778711
o-toluidine
—
—
—
—
—
4778711
p-cresol (4-methy phenol)
—
—
—
—
—
4778711
particulate divalent
mercury
—
—
—
—
—
4778711
phenol
—
—
—
—
—
4778711
selenium compounds
—
—
—
—
—
4778711
styrene
—
—
—
—
—
4778711
toluene
—
—
—
—
—
4778711
xylenes (mixed)
—
—
—
—
—
4778711
arsenic compounds
7E-01
—
—
—
—
4778711
triethylamine
IE-04
—
—
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
4778711
cadmium compounds
—
4E-04
6E-05
—
—
4778711
mercury (elemental)
2E-03
—
6E-07
—
5E-07
4778711
carbon disulfide
6E-04
9E-05
8E-06
1E-03
2E-05
4778711
acrolein
3E-02
1E-03
4E-04
8E-04
2E-04
4778711
chlorine
7E-03
1E-03
3E-04
5E-04
2E-04
4778711
acetaldehyde
2E-02
IE-04
2E-05
5E-04
3E-05
4778711
vinyl acetate
—
2E-04
3E-05
2E-04
2E-05
4778711
hydrochloric acid
3E-05
2E-05
2E-06
1E-05
2E-06
4778711
methanol
9E-05
3E-06
9E-07
9E-06
2E-06
4778711
1,3-butadiene
—
6E-08
7E-09
4E-06
8E-08
4778711
benzene
—
5E-07
3E-08
5E-07
2E-07
4783711
2,2,4-trimethylpentane
—
—
—
—
—
4783711
ethyl chloride
—
—
—
—
—
4783711
propionaldehyde
—
—
—
—
—
4783711
n-hexane
—
—
3E-06
—
—
4783711
biphenyl
—
—
4E-07
—
—
4783711
xylenes (mixed)
4E-05
2E-06
2E-07
—
—
4783711
ethyl benzene
—
3E-06
1E-07
—
—
4783711
toluene
—
4E-04
5E-05
5E-04
9E-05
4783711
benzene
—
2E-04
2E-05
3E-04
9E-05
4783711
1,3-butadiene
—
2E-06
3E-07
2E-04
3E-06
4783711
methanol
1E-03
4E-05
1E-05
IE-04
2E-05
4783711
styrene
7E-05
2E-05
3E-06
7E-06
1E-06
4783711
acetaldehyde
2E-04
1E-06
2E-07
6E-06
3E-07
4783711
methyl chloride
—
—
1E-08
7E-08
1E-08
4783711
methylene chloride
2E-06
3E-08
1E-08
2E-08
8E-09
4835311
2,2,4-trimethylpentane
—
—
—
—
—
4835311
2,4-dinitrophenol
—
—
—
—
—
4835311
biphenyl
—
—
—
—
—
4835311
cresols (mixed)
—
—
—
—
—
4835311
naphthalene
—
—
—
—
—
4835311
nickel compounds
—
—
—
—
—
4835311
p-dichlorobenzene
—
—
—
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
4835311
pah, total
—
—
—
—
—
4835311
pentachlorophenol
—
—
—
—
—
4835311
phenanthrene
—
—
—
—
—
4835311
chloroform
9E-02
—
4E-05
—
6E-05
4835311
xylenes (mixed)
2E-03
6E-05
8E-06
—
—
4835311
carbonyl sulfide
—
—
5E-06
—
—
4835311
cumene
—
3E-05
5E-06
—
—
4835311
ethyl benzene
—
IE-04
4E-06
—
—
4835311
n-hexane
—
—
1E-06
—
—
4835311
aniline
—
2E-08
1E-08
—
—
4835311
benzene
—
3E-04
2E-05
3E-04
1E-04
4835311
toluene
—
7E-05
9E-06
IE-04
2E-05
4835311
formaldehyde
6E-04
3E-05
2E-06
3E-05
3E-06
4835311
hydrofluoric acid
IE-04
4E-05
1E-06
2E-05
2E-06
4835311
methyl tert-butyl ether
—
1E-05
9E-07
1E-05
5E-07
4835311
1,3-butadiene
—
1E-07
2E-08
8E-06
2E-07
4835311
styrene
2E-05
4E-06
6E-07
2E-06
3E-07
4835311
phenol
3E-07
3E-08
2E-08
4E-08
8E-09
4835311
tetrachloroethene
2E-07
2E-08
3E-09
7E-09
3E-09
4861611
2,2,4-trimethylpentane
—
—
—
—
—
4861611
antimony compounds
—
—
—
—
—
4861611
beryllium compounds
—
—
—
—
1E-06
4861611
chromium (iii) compounds
—
—
—
—
—
4861611
chromium (vi) compounds
—
—
—
—
—
4861611
cresols (mixed)
—
—
—
—
—
4861611
diethanolamine
—
—
—
—
—
4861611
ethylene glycol
—
—
—
—
—
4861611
gaseous divalent mercury
—
—
—
—
—
4861611
lead compounds
—
—
—
—
—
4861611
manganese compounds
—
—
—
—
—
4861611
naphthalene
—
—
—
—
—
4861611
nickel compounds
—
—
—
—
—
4861611
pah, total
—
—
—
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
4861611
particulate divalent
mercury
—
—
—
—
—
4861611
phenanthrene
—
—
—
—
—
4861611
selenium compounds
—
—
—
—
—
4861611
arsenic compounds
7E-04
—
—
—
—
4861611
hydrogen cyanide
4E-02
6E-03
2E-03
—
1E-03
4861611
xylenes (mixed)
1E-03
4E-05
6E-06
—
—
4861611
n-hexane
—
—
3E-06
—
—
4861611
ethyl benzene
—
5E-05
1E-06
—
—
4861611
carbonyl sulfide
—
—
5E-07
—
—
4861611
biphenyl
—
—
4E-07
—
—
4861611
cadmium compounds
—
3E-06
4E-07
—
—
4861611
cumene
—
3E-06
4E-07
—
—
4861611
mercury (elemental)
4E-05
—
1E-08
—
1E-08
4861611
hydrofluoric acid
6E-03
2E-03
7E-05
9E-04
9E-05
4861611
toluene
—
2E-04
2E-05
2E-04
4E-05
4861611
methanol
2E-03
6E-05
2E-05
2E-04
3E-05
4861611
chlorine
1E-03
2E-04
5E-05
IE-04
3E-05
4861611
benzene
—
9E-05
6E-06
IE-04
3E-05
4861611
acrolein
2E-03
6E-05
2E-05
4E-05
1E-05
4861611
formaldehyde
2E-04
1E-05
8E-07
1E-05
1E-06
4861611
hydrochloric acid
7E-06
6E-06
5E-07
3E-06
5E-07
4861611
1,3-butadiene
—
2E-08
3E-09
1E-06
3E-08
4861611
acetaldehyde
6E-06
4E-08
6E-09
2E-07
8E-09
4861611
phenol
6E-07
6E-08
4E-08
9E-08
2E-08
4861611
styrene
3E-07
8E-08
1E-08
3E-08
6E-09
4861611
tetrachloroethene
4E-07
4E-08
5E-09
1E-08
6E-09
4861611
methyl tert-butyl ether
—
8E-10
7E-11
8E-10
4E-11
4861611
methylene chloride
2E-08
5E-10
2E-10
3E-10
1E-10
4861611
1,1,1-trichloroethane
6E-10
3E-11
1E-11
2E-11
1E-11
4863111
2,2,4-trimethylpentane
—
—
—
—
—
4863111
diethanolamine
—
—
—
—
—
4863111
gaseous divalent mercury
—
—
—
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
4863111
hydrochloric acid
—
—
—
—
—
4863111
hydrogen cyanide
—
—
—
—
—
4863111
mercury (elemental)
—
—
—
—
—
4863111
naphthalene
—
—
—
—
—
4863111
pah, total
—
—
—
—
—
4863111
particulate divalent
mercury
—
—
—
—
—
4863111
xylenes (mixed)
2E-03
8E-05
1E-05
—
—
4863111
n-hexane
—
—
3E-06
—
—
4863111
ethyl benzene
—
3E-05
8E-07
—
—
4863111
cumene
—
2E-06
3E-07
—
—
4863111
biphenyl
—
—
3E-08
—
—
4863111
toluene
—
2E-04
2E-05
2E-04
3E-05
4863111
benzene
—
IE-04
9E-06
2E-04
5E-05
4863111
1,3-butadiene
—
5E-07
7E-08
4E-05
7E-07
4863111
1,1,1-trichloroethane
5E-05
3E-06
1E-06
2E-06
9E-07
4863111
methanol
2E-05
8E-07
2E-07
2E-06
4E-07
4878911
antimony compounds
—
—
—
—
—
4878911
gaseous divalent mercury
—
—
—
—
—
4878911
lead compounds
—
—
—
—
—
4878911
mercury (elemental)
—
—
—
—
—
4878911
particulate divalent
mercury
—
—
—
—
—
4878911
biphenyl
—
—
9E-05
—
—
4878911
aniline
—
9E-09
6E-09
—
—
4878911
n-hexane
—
—
3E-14
—
—
4878911
formaldehyde
2
1E-01
8E-03
1E-01
1E-02
4878911
toluene
—
3E-06
4E-07
4E-06
7E-07
4878911
methanol
3E-06
1E-07
3E-08
3E-07
7E-08
4878911
acrylonitrile
—
—
2E-07
3E-08
9E-09
4878911
acetaldehyde
1E-06
6E-09
9E-10
3E-08
1E-09
4878911
benzene
—
1E-08
8E-10
1E-08
5E-09
4897511
antimony compounds
—
—
—
—
—
4897511
beryllium compounds
—
—
—
—
1E-05
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
4897511
chromium (iii) compounds
—
—
—
—
—
4897511
chromium (vi) compounds
—
—
—
—
—
4897511
cobalt compounds
—
—
—
—
—
4897511
dimethyl phthalate
—
—
—
—
—
4897511
gaseous divalent mercury
—
—
—
—
—
4897511
lead compounds
—
—
—
—
—
4897511
manganese compounds
—
—
—
—
—
4897511
nickel compounds
—
—
—
—
—
4897511
particulate divalent
mercury
—
—
—
—
—
4897511
phthalic anhydride
—
—
—
—
—
4897511
selenium compounds
—
—
—
—
—
4897511
arsenic compounds
7E-03
—
—
—
—
4897511
biphenyl
—
—
1E-03
—
—
4897511
phosphorus
—
2E-06
5E-07
—
—
4897511
n-hexane
—
—
3E-07
—
—
4897511
cadmium compounds
—
3E-07
4E-08
—
—
4897511
propionaldehyde
—
1E-07
2E-08
—
—
4897511
mercury (elemental)
4E-05
—
1E-08
—
1E-08
4897511
methyl methacrylate
—
1E-09
1E-10
—
—
4897511
acrylic acid
4E-03
6E-03
2E-04
9E-03
2E-04
4897511
formaldehyde
4E-02
2E-03
IE-04
2E-03
2E-04
4897511
acrolein
2E-02
6E-04
2E-04
4E-04
1E-04
4897511
methanol
1E-03
4E-05
1E-05
IE-04
2E-05
4897511
phenol
6E-04
6E-05
4E-05
9E-05
2E-05
4897511
benzene
—
5E-05
3E-06
5E-05
2E-05
4897511
hydrochloric acid
8E-05
6E-05
5E-06
4E-05
6E-06
4897511
chlorine
4E-04
5E-05
1E-05
3E-05
9E-06
4897511
maleic anhydride
—
—
—
1E-05
5E-07
4897511
acetaldehyde
7E-06
4E-08
7E-09
2E-07
1E-08
4924311
acetamide
—
—
—
—
—
4924311
glycol ethers
3E-01
—
—
—
—
4924311
propionaldehyde
—
5E-07
9E-08
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
4924311
n-hexane
—
—
6E-09
—
—
4924311
propylene oxide
1E-02
3E-04
7E-05
4E-04
8E-05
4924311
methanol
4E-04
2E-05
4E-06
4E-05
9E-06
4924311
acetaldehyde
2E-04
9E-07
1E-07
4E-06
2E-07
4924411
1,1,2,2-tetrachloroethane
—
—
—
—
—
4924411
2,2,4-trimethylpentane
—
—
—
—
—
4924411
antimony compounds
—
—
—
—
—
4924411
benzo[a]pyrene
—
—
—
—
—
4924411
beryllium compounds
—
—
—
—
7E-04
4924411
bis(2-ethylhexyl)phthalate
—
—
—
—
—
4924411
chromium (iii) compounds
—
—
—
—
—
4924411
chromium (vi) compounds
—
—
—
—
—
4924411
cobalt compounds
—
—
—
—
—
4924411
dibutylphthalate
—
—
—
—
—
4924411
gaseous divalent mercury
—
—
—
—
—
4924411
lead compounds
—
—
—
—
—
4924411
manganese compounds
—
—
—
—
—
4924411
methyl isobutyl ketone
—
—
—
—
—
4924411
naphthalene
—
—
—
—
—
4924411
nickel compounds
—
—
—
—
—
4924411
pah, total
—
—
—
—
—
4924411
particulate divalent
mercury
—
—
—
—
—
4924411
phenanthrene
—
—
—
—
—
4924411
selenium compounds
—
—
—
—
—
4924411
tetrachloroethene
—
—
—
—
—
4924411
arsenic compounds
1
—
—
—
—
4924411
hydrogen cyanide
9E-02
1E-02
4E-03
—
3E-03
4924411
cadmium compounds
—
4E-03
6E-04
—
—
4924411
mercury (elemental)
3E-01
—
9E-05
—
8E-05
4924411
xylenes (mixed)
1E-03
4E-05
6E-06
—
—
4924411
methyl bromide
IE-04
—
7E-07
—
3E-06
4924411
ethyl benzene
—
2E-05
6E-07
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
4924411
cumene
—
4E-06
6E-07
—
—
4924411
n-hexane
—
—
3E-07
—
—
4924411
propionaldehyde
—
5E-07
9E-08
—
—
4924411
carbonyl sulfide
—
—
3E-09
—
—
4924411
formaldehyde
1E-01
7E-03
5E-04
7E-03
7E-04
4924411
phenol
4E-02
4E-03
2E-03
5E-03
1E-03
4924411
acrolein
1E-01
4E-03
1E-03
3E-03
8E-04
4924411
chlorine
3E-02
4E-03
9E-04
2E-03
6E-04
4924411
hydrochloric acid
3E-03
2E-03
2E-04
1E-03
2E-04
4924411
toluene
—
6E-04
7E-05
8E-04
1E-04
4924411
acetaldehyde
1E-02
6E-05
1E-05
3E-04
1E-05
4924411
1,3-butadiene
—
3E-06
3E-07
2E-04
4E-06
4924411
benzene
—
9E-05
6E-06
IE-04
3E-05
4924411
carbon disulfide
2E-05
4E-06
3E-07
5E-05
1E-06
4924411
methanol
3E-04
1E-05
3E-06
4E-05
7E-06
4924411
methylene chloride
IE-04
3E-06
9E-07
2E-06
7E-07
4924411
styrene
3E-06
7E-07
1E-07
3E-07
5E-08
4924411
trichloroethylene
—
6E-08
2E-08
8E-08
2E-08
4924411
methyl tert-butyl ether
—
3E-08
2E-09
3E-08
1E-09
4925111
naphthalene
—
—
—
—
—
4925111
pah, total
—
—
—
—
—
4925111
cumene
—
6E-06
1E-06
—
—
4925111
xylenes (mixed)
6E-05
2E-06
3E-07
—
—
4925111
acetonitrile
—
7E-07
2E-07
—
—
4925111
ethyl benzene
—
2E-06
5E-08
—
—
4925111
n-hexane
—
—
3E-08
—
—
4925111
1,2-epoxybutane
—
9E-09
5E-09
—
—
4925111
propionaldehyde
—
1E-09
2E-10
—
—
4925111
1,3-butadiene
—
6E-05
7E-06
4E-03
8E-05
4925111
maleic anhydride
—
—
—
6E-05
2E-06
4925111
styrene
2E-04
6E-05
9E-06
2E-05
4E-06
4925111
methyl tert-butyl ether
—
2E-05
2E-06
2E-05
1E-06
4925111
benzene
—
2E-05
1E-06
2E-05
5E-06
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
4925111
toluene
—
1E-05
1E-06
1E-05
3E-06
4925111
methanol
IE-04
5E-06
1E-06
1E-05
3E-06
4925111
propylene oxide
3E-07
5E-09
1E-09
8E-09
2E-09
4926611
ethyl chloride
—
—
—
—
—
4926611
ethylene glycol
—
—
—
—
—
4926611
glycol ethers
3E-01
—
—
—
—
4926611
ethylene oxide
—
—
5E-04
—
5E-04
4926611
1,4-dioxane
3E-05
2E-06
8E-08
—
—
4926611
acetaldehyde
5E-03
3E-05
4E-06
IE-04
6E-06
4926611
methanol
3E-04
1E-05
3E-06
3E-05
6E-06
4926611
toluene
—
9E-06
1E-06
1E-05
2E-06
4926611
propylene oxide
2E-04
3E-06
8E-07
5E-06
9E-07
4929511
chlorine
—
—
—
—
—
4929511
ethylene glycol
—
—
—
—
—
4929511
triethylamine
3E-04
—
—
—
—
4929511
n-hexane
—
—
1E-05
—
—
4929511
formaldehyde
1
5E-02
3E-03
5E-02
5E-03
4929511
methanol
4E-02
2E-03
5E-04
5E-03
9E-04
4929511
hydrochloric acid
8E-03
6E-03
5E-04
4E-03
5E-04
4929511
acrolein
1E-01
4E-03
1E-03
3E-03
8E-04
4929511
acetaldehyde
1E-01
7E-04
IE-04
3E-03
2E-04
4929511
phenol
6E-04
6E-05
4E-05
9E-05
2E-05
4929511
benzene
—
5E-05
3E-06
5E-05
2E-05
4929511
titanium tetrachloride
—
—
9E-06
1E-05
3E-06
4929511
methylene chloride
2E-07
4E-09
1E-09
3E-09
1E-09
4929811
2,2,4-trimethylpentane
—
—
—
—
—
4929811
anthracene
—
—
—
—
—
4929811
antimony compounds
—
—
—
—
—
4929811
benzo(ghi)perylene
—
—
—
—
—
4929811
chromium (iii) compounds
—
—
—
—
—
4929811
chromium (vi) compounds
—
—
—
—
—
4929811
cobalt compounds
—
—
—
—
—
4929811
cresols (mixed)
—
—
—
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
4929811
ethylene glycol
—
—
—
—
—
4929811
lead compounds
—
—
—
—
—
4929811
manganese compounds
—
—
—
—
—
4929811
naphthalene
—
—
—
—
—
4929811
nickel compounds
—
—
—
—
—
4929811
phenanthrene
—
—
—
—
—
4929811
polycyclic organic matter
—
—
—
—
—
4929811
selenium compounds
—
—
—
—
—
4929811
arsenic compounds
2E-04
—
—
—
—
4929811
n-hexane
—
—
6E-06
—
—
4929811
carbonyl sulfide
—
—
1E-06
—
—
4929811
xylenes (mixed)
2E-04
1E-05
1E-06
—
—
4929811
ethyl benzene
—
1E-05
3E-07
—
—
4929811
cadmium compounds
—
1E-06
2E-07
—
—
4929811
biphenyl
—
—
4E-08
—
—
4929811
cumene
—
3E-07
4E-08
—
—
4929811
toluene
—
5E-05
6E-06
7E-05
1E-05
4929811
benzene
—
3E-05
2E-06
4E-05
1E-05
4929811
carbon disulfide
2E-05
3E-06
3E-07
4E-05
9E-07
4929811
tetrachloroethene
2E-04
2E-05
3E-06
6E-06
3E-06
4929811
formaldehyde
8E-05
4E-06
3E-07
4E-06
4E-07
4929811
styrene
1E-05
3E-06
5E-07
1E-06
2E-07
4929811
phenol
5E-06
5E-07
3E-07
8E-07
2E-07
4929811
hydrofluoric acid
4E-06
1E-06
5E-08
6E-07
6E-08
4929811
methanol
4E-06
2E-07
4E-08
4E-07
9E-08
4929811
1,3-butadiene
—
1E-09
2E-10
1E-07
2E-09
4930211
pah, total
—
—
—
—
—
4930211
xylenes (mixed)
3E-05
1E-06
2E-07
—
—
4941211
polycyclic organic matter
—
—
—
—
—
4941211
ethyl benzene
—
9E-06
3E-07
—
—
4941211
xylenes (mixed)
5E-05
2E-06
3E-07
—
—
4941211
styrene
1E-01
2E-02
4E-03
1E-02
2E-03
4941211
1,3-butadiene
—
5E-05
6E-06
3E-03
6E-05
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
4941211
toluene
—
2E-06
2E-07
3E-06
5E-07
4941411
acetophenone
—
—
—
—
—
4941411
anthracene
—
—
—
—
—
4941411
catechol
—
—
—
—
—
4941411
methyl isobutyl ketone
—
—
—
—
—
4941411
naphthalene
—
—
—
—
—
4941411
phenanthrene
—
—
—
—
—
4941411
polycyclic organic matter
—
—
—
—
—
4941411
acetonitrile
—
2E-03
4E-04
—
—
4941411
ethyl benzene
—
6E-04
2E-05
—
—
4941411
ethylene oxide
—
—
1E-05
—
1E-05
4941411
cumene
—
5E-06
8E-07
—
—
4941411
propionaldehyde
—
4E-06
7E-07
—
—
4941411
n-hexane
—
—
2E-07
—
—
4941411
xylenes (mixed)
3E-05
1E-06
1E-07
—
—
4941411
1,2-epoxybutane
—
2E-08
1E-08
—
—
4941411
acetaldehyde
1E-01
8E-04
IE-04
4E-03
2E-04
4941411
maleic anhydride
—
—
—
1E-03
4E-05
4941411
propylene oxide
2E-02
3E-04
8E-05
5E-04
9E-05
4941411
methanol
5E-03
2E-04
5E-05
5E-04
1E-04
4941411
styrene
3E-03
8E-04
IE-04
3E-04
6E-05
4941411
benzene
—
2E-04
2E-05
3E-04
8E-05
4941411
toluene
—
3E-05
4E-06
4E-05
7E-06
4941411
phenol
7E-05
7E-06
5E-06
1E-05
2E-06
4941411
methyl tert-butyl ether
—
1E-05
8E-07
1E-05
5E-07
4941411
tetrachloroethene
7E-05
6E-06
9E-07
2E-06
1E-06
4941411
vinyl acetate
—
8E-07
1E-07
1E-06
7E-08
4941411
1,3-butadiene
—
5E-09
6E-10
3E-07
6E-09
4941511
anthracene
—
—
—
—
—
4941511
beryllium compounds
—
—
—
—
8E-05
4941511
chromium (iii) compounds
—
—
—
—
—
4941511
chromium (vi) compounds
—
—
—
—
—
4941511
coal tar
—
—
—
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
4941511
dimethyl phthalate
—
—
—
—
—
4941511
ethyl chloride
—
—
—
—
—
4941511
ethylene glycol
—
—
—
—
—
4941511
gaseous divalent mercury
—
—
—
—
—
4941511
lead compounds
—
—
—
—
—
4941511
methyl isobutyl ketone
—
—
—
—
—
4941511
naphthalene
—
—
—
—
—
4941511
particulate divalent
mercury
—
—
—
—
—
4941511
glycol ethers
5E-01
—
—
—
—
4941511
arsenic compounds
8E-03
—
—
—
—
4941511
hydrogen cyanide
3E-02
4E-03
1E-03
—
8E-04
4941511
ethylene oxide
—
—
3E-04
—
3E-04
4941511
chloroform
5E-01
—
2E-04
—
3E-04
4941511
biphenyl
—
—
7E-05
—
—
4941511
propionaldehyde
—
2E-04
3E-05
—
—
4941511
cadmium compounds
—
IE-04
2E-05
—
—
4941511
n-hexane
—
—
3E-06
—
—
4941511
mercury (elemental)
5E-03
—
2E-06
—
1E-06
4941511
xylenes (mixed)
8E-05
3E-06
5E-07
—
—
4941511
ethyl benzene
—
1E-05
3E-07
—
—
4941511
acetonitrile
—
4E-07
9E-08
—
—
4941511
acetaldehyde
4E-01
2E-03
4E-04
1E-02
6E-04
4941511
hydrochloric acid
1E-02
1E-02
9E-04
7E-03
1E-03
4941511
methyl chloride
—
—
3E-04
2E-03
2E-04
4941511
chlorine
2E-02
3E-03
7E-04
1E-03
5E-04
4941511
hydrofluoric acid
2E-03
6E-04
3E-05
3E-04
3E-05
4941511
1,3-butadiene
—
4E-06
5E-07
3E-04
6E-06
4941511
methanol
2E-03
7E-05
2E-05
2E-04
4E-05
4941511
carbon tetrachloride
4E-03
—
9E-05
6E-05
1E-05
4941511
benzene
—
1E-05
1E-06
2E-05
5E-06
4941511
methylene chloride
1E-03
2E-05
8E-06
1E-05
6E-06
4941511
styrene
IE-04
3E-05
4E-06
1E-05
2E-06
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
4941511
toluene
—
1E-05
1E-06
1E-05
2E-06
4941511
formaldehyde
7E-05
3E-06
2E-07
3E-06
3E-07
4941511
ethylene dichloride
—
—
—
2E-08
6E-09
4941511
1,1,1-trichloroethane
1E-08
6E-10
2E-10
4E-10
2E-10
4945211
2,2,4-trimethylpentane
—
—
—
—
—
4945211
bromoform
—
—
—
—
—
4945211
diethanolamine
—
—
—
—
—
4945211
ethyl chloride
—
—
—
—
—
4945211
ethylene glycol
—
—
—
—
—
4945211
methyl isobutyl ketone
—
—
—
—
—
4945211
naphthalene
—
—
—
—
—
4945211
propylene dichloride
—
—
—
—
—
4945211
glycol ethers
8E-03
—
—
—
—
4945211
ethylene oxide
—
—
9E-04
—
8E-04
4945211
n-hexane
—
—
1E-05
—
—
4945211
chloroform
1E-02
—
6E-06
—
7E-06
4945211
acetonitrile
—
1E-05
3E-06
—
—
4945211
ethyl benzene
—
5E-05
1E-06
—
—
4945211
1,4-dioxane
3E-04
1E-05
7E-07
—
—
4945211
chlorobenzene
—
6E-06
4E-07
—
—
4945211
xylenes (mixed)
5E-06
2E-07
3E-08
—
—
4945211
cumene
—
1E-07
2E-08
—
—
4945211
methyl tert-butyl ether
—
1E-03
9E-05
1E-03
5E-05
4945211
propylene oxide
6E-03
IE-04
2E-05
IE-04
3E-05
4945211
1,3-butadiene
—
1E-06
2E-07
IE-04
2E-06
4945211
formaldehyde
1E-03
7E-05
5E-06
7E-05
7E-06
4945211
carbon disulfide
2E-05
4E-06
3E-07
5E-05
9E-07
4945211
styrene
2E-04
4E-05
7E-06
2E-05
3E-06
4945211
methanol
2E-04
9E-06
2E-06
2E-05
5E-06
4945211
benzene
—
2E-05
1E-06
2E-05
7E-06
4945211
toluene
—
1E-06
1E-07
2E-06
3E-07
4945211
acetaldehyde
8E-05
5E-07
8E-08
2E-06
1E-07
4945211
methyl chloride
—
—
8E-09
5E-08
7E-09
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
4945211
methylene chloride
3E-06
6E-08
2E-08
4E-08
1E-08
4945211
ethylene dichloride
—
—
—
7E-09
2E-09
4945211
vinyl chloride
2E-08
4E-09
9E-10
2E-09
2E-10
4945411
2,2,4-trimethylpentane
—
—
—
—
—
4945411
diethanolamine
—
—
—
—
—
4945411
lead compounds
—
—
—
—
—
4945411
phenol
—
—
—
—
—
4945411
polycyclic organic matter
—
—
—
—
—
4945411
cumene
—
5E-05
8E-06
—
—
4945411
n-hexane
—
—
5E-06
—
—
4945411
xylenes (mixed)
6E-04
2E-05
3E-06
—
—
4945411
ethyl benzene
—
2E-05
5E-07
—
—
4945411
carbonyl sulfide
—
—
2E-07
—
—
4945411
hydrofluoric acid
7E-03
2E-03
9E-05
1E-03
1E-04
4945411
benzene
—
6E-04
4E-05
7E-04
2E-04
4945411
toluene
—
2E-04
3E-05
3E-04
5E-05
4945411
hydrochloric acid
6E-05
5E-05
4E-06
3E-05
5E-06
4945411
1,3-butadiene
—
1E-07
1E-08
8E-06
2E-07
4945411
carbon disulfide
3E-06
4E-07
3E-08
5E-06
1E-07
4945411
chlorine
4E-05
6E-06
1E-06
3E-06
1E-06
4945611
diethanolamine
—
—
—
—
—
4945611
ethylene glycol
—
—
—
—
—
4945611
nickel compounds
—
—
—
—
—
4945611
glycol ethers
1E-01
—
—
—
—
4945611
ethylene oxide
—
—
2E-04
—
2E-04
4945611
1,2-epoxybutane
—
1E-05
5E-06
—
—
4945611
methanol
5E-04
2E-05
5E-06
5E-05
1E-05
4945611
hydrochloric acid
3E-05
2E-05
2E-06
1E-05
2E-06
4945611
propylene oxide
3E-04
6E-06
2E-06
9E-06
2E-06
4945611
formaldehyde
2E-05
8E-07
5E-08
8E-07
8E-08
4945611
acrylonitrile
—
—
5E-07
8E-08
2E-08
4950811
acetophenone
—
—
—
—
—
4950811
cumene
—
3E-03
5E-04
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
4950811
phenol
6E-02
6E-03
4E-03
9E-03
2E-03
4950811
methanol
4E-05
1E-06
4E-07
4E-06
8E-07
4965811
p-xylene
4E-02
—
—
—
—
4965811
methyl bromide
1E-03
—
7E-06
—
3E-05
4965811
methanol
7E-01
3E-02
7E-03
7E-02
1E-02
4965811
formaldehyde
5E-02
2E-03
2E-04
2E-03
2E-04
4965811
acetaldehyde
3E-03
2E-05
3E-06
7E-05
3E-06
4965811
toluene
—
4E-05
4E-06
5E-05
8E-06
4965811
benzene
—
3E-05
2E-06
3E-05
1E-05
4980911
n-hexane
—
—
3E-07
—
—
4980911
ethyl benzene
—
1E-06
3E-08
—
—
4980911
vinyl acetate
—
5E-03
8E-04
6E-03
4E-04
4980911
methanol
2E-02
6E-04
2E-04
2E-03
3E-04
4980911
1,3-butadiene
—
1E-05
1E-06
7E-04
1E-05
4980911
styrene
4E-04
IE-04
2E-05
4E-05
8E-06
4980911
acetaldehyde
2E-05
1E-07
2E-08
5E-07
2E-08
4982011
1,3-dichloropropene
—
—
—
—
—
4982011
propylene dichloride
—
—
—
—
—
4982011
glycol ethers
3E-04
—
—
—
—
4982011
n-hexane
—
—
1E-08
—
—
4982011
phenol
2E-02
2E-03
2E-03
4E-03
7E-04
4982011
epichlorohydrin
2E-02
4E-03
3E-04
1E-03
3E-04
4982011
chlorine
2E-05
2E-06
6E-07
1E-06
4E-07
5018711
2,2,4-trimethylpentane
—
—
—
—
—
5018711
benzo(ghi)perylene
—
—
—
—
—
5018711
carbon disulfide
—
—
—
—
—
5018711
carbonyl sulfide
—
—
—
—
—
5018711
diethanolamine
—
—
—
—
—
5018711
gaseous divalent mercury
—
—
—
—
—
5018711
naphthalene
—
—
—
—
—
5018711
particulate divalent
mercury
—
—
—
—
—
5018711
polycyclic organic matter
—
—
—
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
5018711
tetrachloroethene
—
—
—
—
—
5018711
phosphorus
—
1E-02
3E-03
—
—
5018711
n-hexane
—
—
1E-05
—
—
5018711
xylenes (mixed)
1E-03
4E-05
6E-06
—
—
5018711
mercury (elemental)
7E-03
—
2E-06
—
2E-06
5018711
ethyl benzene
—
3E-05
1E-06
—
—
5018711
cumene
—
6E-07
1E-07
—
—
5018711
biphenyl
—
—
2E-08
—
—
5018711
chlorine
6E-01
8E-02
2E-02
4E-02
1E-02
5018711
1,3-butadiene
—
2E-05
3E-06
2E-03
3E-05
5018711
hydrofluoric acid
1E-03
3E-04
1E-05
2E-04
2E-05
5018711
benzene
—
2E-04
1E-05
2E-04
7E-05
5018711
toluene
—
IE-04
1E-05
IE-04
2E-05
5018711
methyl tert-butyl ether
—
2E-05
1E-06
2E-05
9E-07
5018711
styrene
4E-05
1E-05
2E-06
4E-06
8E-07
5018711
methanol
2E-05
1E-06
3E-07
3E-06
5E-07
5018711
phenol
2E-06
2E-07
1E-07
3E-07
6E-08
5018711
methylene chloride
3E-07
6E-09
2E-09
4E-09
2E-09
5019011
diethanolamine
—
—
—
—
—
5019011
naphthalene
—
—
—
—
—
5019011
n-hexane
—
—
3E-07
—
—
5019011
ethyl benzene
—
4E-06
1E-07
—
—
5019011
xylenes (mixed)
3E-05
1E-06
1E-07
—
—
5019011
benzene
—
IE-04
9E-06
IE-04
5E-05
5019011
1,3-butadiene
—
1E-06
2E-07
9E-05
2E-06
5019011
toluene
—
2E-05
2E-06
2E-05
4E-06
5019011
methanol
2E-04
6E-06
2E-06
2E-05
3E-06
5019011
styrene
IE-04
3E-05
5E-06
1E-05
2E-06
5386211
xylenes (mixed)
9E-08
4E-09
5E-10
—
—
5386211
styrene
3E-07
7E-08
1E-08
3E-08
5E-09
5505011
ethyl benzene
—
4E-04
1E-05
—
—
5505011
xylenes (mixed)
2E-04
6E-06
9E-07
—
—
5505011
cumene
—
2E-06
3E-07
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
5505011
n-hexane
—
—
2E-07
—
—
5505011
styrene
7E-03
2E-03
3E-04
7E-04
1E-04
5505011
toluene
—
2E-04
3E-05
3E-04
6E-05
5505011
chlorine
4E-05
6E-06
1E-06
3E-06
1E-06
5505011
methanol
3E-05
1E-06
3E-07
3E-06
6E-07
5505011
benzene
—
2E-06
1E-07
2E-06
7E-07
5520211
diethanolamine
—
—
—
—
—
5520211
ethylene glycol
—
—
—
—
—
5520211
methoxytriglycol
2E-01
—
—
—
—
5520211
ethylene glycol methyl
ether
4E-02
—
—
—
—
5520211
ethylene glycol ethyl ether
5E-03
—
—
—
—
5520211
ethylene oxide
—
—
3E-05
—
3E-05
5520211
1,4-dioxane
3E-04
2E-05
8E-07
—
—
5520211
methanol
9E-04
4E-05
9E-06
IE-04
2E-05
5520211
propylene oxide
3E-04
6E-06
1E-06
9E-06
2E-06
5520211
acetaldehyde
1E-05
9E-08
1E-08
4E-07
2E-08
5611111
chloroacetic acid
—
—
9E-06
—
—
5611111
n-hexane
—
—
1E-09
—
—
5611111
hydrochloric acid
3E-03
2E-03
2E-04
1E-03
2E-04
5611111
methanol
4E-03
2E-04
4E-05
4E-04
9E-05
5611111
formaldehyde
1E-05
7E-07
4E-08
6E-07
6E-08
5631411
cresols (mixed)
—
—
—
—
—
5631411
glycol ethers
4E-02
—
—
—
—
5631411
biphenyl
—
—
IE-04
—
—
5631411
n-hexane
—
—
1E-08
—
—
5631411
formaldehyde
6E-01
3E-02
2E-03
3E-02
3E-03
5631411
methanol
5E-02
2E-03
5E-04
5E-03
1E-03
5631411
maleic anhydride
—
—
—
2E-03
1E-04
5631411
phenol
8E-03
8E-04
5E-04
1E-03
2E-04
5631411
acetaldehyde
1E-05
7E-08
1E-08
3E-07
2E-08
5632411
anthracene
—
—
—
—
—
5632411
naphthalene
—
—
—
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
5632411
acetonitrile
—
1E-03
3E-04
—
—
5632411
n-hexane
—
—
2E-06
—
—
5632411
xylenes (mixed)
IE-04
4E-06
6E-07
—
—
5632411
1,3-butadiene
—
6E-06
8E-07
4E-04
9E-06
5632411
benzene
—
9E-05
6E-06
IE-04
3E-05
5632411
toluene
—
2E-05
2E-06
3E-05
4E-06
5632411
styrene
8E-05
2E-05
3E-06
8E-06
2E-06
5632411
methanol
4E-05
2E-06
4E-07
4E-06
9E-07
5632511
acetaldehyde
—
—
—
—
—
5632511
hydrogen cyanide
9E-03
1E-03
4E-04
—
3E-04
5632511
acetonitrile
—
1E-03
3E-04
—
—
5632511
acrolein
3E-01
1E-02
3E-03
7E-03
2E-03
5632511
acrylonitrile
—
—
2E-02
3E-03
1E-03
5632511
methanol
2E-05
7E-07
2E-07
2E-06
4E-07
5632711
1,1,2,2-tetrachloroethane
—
—
—
—
—
5632711
1,1,2-trichloroethane
—
—
—
—
—
5632711
1,3-dichloropropene
—
—
—
—
—
5632711
2,4,6-trichlorophenol
—
—
—
—
—
5632711
2-methylnaphthalene
—
—
—
—
—
5632711
4,4'-methylenedianiline
—
—
—
—
—
5632711
acetophenone
—
—
—
—
—
5632711
chromium (iii) compounds
—
—
—
—
—
5632711
chromium (vi) compounds
—
—
—
—
—
5632711
cobalt compounds
—
—
—
—
—
5632711
ethyl chloride
—
—
—
—
—
5632711
ethylene glycol
—
—
—
—
—
5632711
ethylidene dichloride
—
—
—
—
—
5632711
fluorene
—
—
—
—
—
5632711
gaseous divalent mercury
—
—
—
—
—
5632711
hexachlorobenzene
—
—
—
—
—
5632711
hexachloroethane
—
—
—
—
—
5632711
lead compounds
—
—
—
—
—
5632711
manganese compounds
—
—
—
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
5632711
methyl isobutyl ketone
—
—
—
—
—
5632711
naphthalene
—
—
—
—
—
5632711
nickel compounds
—
—
—
—
—
5632711
o-toluidine
—
—
—
—
—
5632711
p-dichlorobenzene
—
—
—
—
—
5632711
pah, total
—
—
—
—
—
5632711
particulate divalent
mercury
—
—
—
—
—
5632711
pentachlorophenol
—
—
—
—
—
5632711
phenanthrene
—
—
—
—
—
5632711
polychlorinated biphenyls
—
—
—
—
—
5632711
propylene dichloride
—
—
—
—
—
5632711
pyrene
—
—
—
—
—
5632711
vinylidene chloride
—
—
—
—
1E-08
5632711
glycol ethers
8E-05
—
—
—
—
5632711
arsenic compounds
5E-05
—
—
—
—
5632711
phosgene
2E-01
—
7E-04
—
4E-04
5632711
ethylene oxide
—
—
1E-05
—
1E-05
5632711
aniline
—
8E-06
5E-06
—
—
5632711
cumene
—
3E-05
4E-06
—
—
5632711
hydrogen cyanide
5E-05
7E-06
2E-06
—
1E-06
5632711
biphenyl
—
—
1E-06
—
—
5632711
propionaldehyde
—
6E-06
1E-06
—
—
5632711
1,2-epoxybutane
—
1E-06
5E-07
—
—
5632711
chlorobenzene
—
6E-06
4E-07
—
—
5632711
chloroform
6E-04
—
3E-07
—
3E-07
5632711
n-hexane
—
—
1E-07
—
—
5632711
cadmium compounds
—
6E-07
7E-08
—
—
5632711
mercury (elemental)
5E-05
—
2E-08
—
1E-08
5632711
ethyl benzene
—
3E-07
9E-09
—
—
5632711
xylenes (mixed)
5E-07
2E-08
3E-09
—
—
5632711
1,4-dioxane
6E-07
3E-08
1E-09
—
—
5632711
chlorine
1E-02
2E-03
5E-04
1E-03
4E-04
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
5632711
hydrochloric acid
2E-03
1E-03
IE-04
9E-04
1E-04
5632711
propylene oxide
9E-03
2E-04
4E-05
2E-04
5E-05
5632711
epichlorohydrin
9E-04
2E-04
1E-05
6E-05
1E-05
5632711
carbon tetrachloride
3E-03
—
8E-05
5E-05
1E-05
5632711
phenol
IE-04
1E-05
8E-06
2E-05
4E-06
5632711
1,3-butadiene
—
3E-07
4E-08
2E-05
4E-07
5632711
formaldehyde
3E-04
2E-05
1E-06
1E-05
1E-06
5632711
2,4-toluene diisocyanate
4E-04
5E-06
1E-06
1E-05
7E-07
5632711
acetaldehyde
4E-04
2E-06
4E-07
1E-05
5E-07
5632711
benzene
—
7E-06
5E-07
7E-06
2E-06
5632711
methanol
6E-05
2E-06
6E-07
6E-06
1E-06
5632711
acrylic acid
3E-06
4E-06
1E-07
6E-06
1E-07
5632711
hexachlorobutadiene
—
—
—
4E-06
1E-06
5632711
acrylonitrile
—
—
3E-05
4E-06
1E-06
5632711
styrene
4E-05
1E-05
2E-06
4E-06
8E-07
5632711
toluene
—
2E-06
2E-07
2E-06
4E-07
5632711
ethylene dichloride
—
—
—
1E-06
4E-07
5632711
tetrachloroethene
5E-05
4E-06
6E-07
1E-06
7E-07
5632711
allyl chloride
—
1E-06
6E-08
1E-06
8E-08
5632711
methylene chloride
6E-05
1E-06
4E-07
8E-07
3E-07
5632711
carbon disulfide
2E-07
3E-08
3E-09
4E-07
9E-09
5632711
vinyl chloride
1E-06
4E-07
7E-08
2E-07
2E-08
5632711
trichloroethylene
—
9E-08
3E-08
1E-07
2E-08
5632711
methyl chloride
—
—
4E-09
2E-08
3E-09
5632711
vinyl acetate
—
1E-08
3E-09
2E-08
1E-09
5632711
1,1,1-trichloroethane
5E-09
3E-10
1E-10
2E-10
9E-11
5633311
acetamide
—
—
—
—
—
5633311
acrylamide
—
—
—
—
—
5633311
antimony compounds
—
—
—
—
—
5633311
dibenzofuran
—
—
—
—
—
5633311
ethylene glycol
—
—
—
—
—
5633311
hydroquinone
—
—
—
—
—
5633311
hydrogen cyanide
6E-02
1E-02
3E-03
—
2E-03
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
5633311
acetonitrile
—
9E-05
2E-05
—
—
5633311
n-hexane
—
—
2E-10
—
—
5633311
formaldehyde
2E-01
1E-02
7E-04
1E-02
1E-03
5633311
acrolein
2E-01
9E-03
3E-03
5E-03
2E-03
5633311
acrylonitrile
—
—
9E-03
1E-03
4E-04
5633311
hydrofluoric acid
4E-03
1E-03
5E-05
7E-04
7E-05
5633311
hydrochloric acid
5E-04
4E-04
3E-05
2E-04
4E-05
5633311
methanol
3E-04
1E-05
3E-06
4E-05
7E-06
5633311
acrylic acid
1E-05
1E-05
4E-07
2E-05
4E-07
5633311
benzene
—
4E-06
3E-07
4E-06
1E-06
5633311
phenol
1E-05
1E-06
7E-07
2E-06
3E-07
5633311
toluene
—
4E-07
5E-08
6E-07
1E-07
5633311
acetaldehyde
4E-06
2E-08
4E-09
1E-07
5E-09
5633411
1,1,2,2-tetrachloroethane
—
—
—
—
—
5633411
1,1,2-trichloroethane
—
—
—
—
—
5633411
1,2,3,4,6,7,8,9-
octachlorodibenzo-p-
dioxin
5633411
1,2,3,4,6,7,8,9-
octachlorodibenzofuran
—
—
—
—
—
5633411
1,2,3,4,6,7,8-
heptachlorodibenzo-p-
dioxin
5633411
1,2,3,4,6,7,8-
heptachlorodibenzofuran
—
—
—
—
—
5633411
1,2,3,4,7,8,9-
heptachlorodibenzofuran
—
—
—
—
—
5633411
1,2,3,4,7,8-
hexachlorodibenzo-p-
dioxin
5633411
1,2,3,4,7,8-
hexachlorodibenzofuran
—
—
—
—
—
5633411
1,2,3,6,7,8-
hexachlorodibenzo-p-
dioxin
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
5633411
1,2,3,6,7,8-
hexachlorodibenzofuran
—
—
—
—
—
5633411
1,2,3,7,8,9-
hexachlorodibenzo-p-
dioxin
5633411
1,2,3,7,8,9-
hexachlorodibenzofuran
—
—
—
—
—
5633411
1,2,3,7,8-
pentachlorodibenzo-p-
dioxin
5633411
1,2,3,7,8-
pentachlorodibenzofuran
—
—
—
—
—
5633411
2,2,4-trimethylpentane
—
—
—
—
—
5633411
2,3,4,6,7,8-
hexachlorodibenzofuran
—
—
—
—
—
5633411
2,3,4,7,8-
pentachlorodibenzofuran
—
—
—
—
—
5633411
2,3,7,8-
tetrachlorodibenzofuran
—
—
—
—
—
5633411
2,4-dinitrophenol
—
—
—
—
—
5633411
acetophenone
—
—
—
—
—
5633411
antimony compounds
—
—
—
—
—
5633411
bis(2-ethylhexyl)phthalate
—
—
—
—
—
5633411
carbon disulfide
—
—
—
—
—
5633411
chloroprene
—
—
—
—
—
5633411
chromium (iii) compounds
—
—
—
—
—
5633411
chromium (vi) compounds
—
—
—
—
—
5633411
ethyl chloride
—
—
—
—
—
5633411
ethylene glycol
—
—
—
—
—
5633411
ethylidene dichloride
—
—
—
—
—
5633411
hydroquinone
—
—
—
—
—
5633411
manganese compounds
—
—
—
—
—
5633411
naphthalene
—
—
—
—
—
5633411
titanium tetrachloride
—
—
—
—
—
5633411
vinyl bromide
—
—
—
—
—
5633411
vinylidene chloride
—
—
—
—
3E-10
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
5633411
glycol ethers
5E-06
—
—
—
—
5633411
ethylene oxide
—
—
5E-06
—
4E-06
5633411
n-hexane
—
—
6E-08
—
—
5633411
xylenes (mixed)
1E-05
4E-07
6E-08
—
—
5633411
ethyl benzene
—
2E-06
5E-08
—
—
5633411
chloroform
9E-05
—
4E-08
—
5E-08
5633411
biphenyl
—
—
1E-08
—
—
5633411
chlorobenzene
—
1E-08
8E-10
—
—
5633411
cumene
—
2E-09
3E-10
—
—
5633411
chlorine
8E-02
1E-02
3E-03
6E-03
2E-03
5633411
hydrochloric acid
1E-02
9E-03
7E-04
5E-03
8E-04
5633411
acetaldehyde
1E-02
8E-05
1E-05
4E-04
2E-05
5633411
vinyl acetate
—
7E-05
1E-05
9E-05
6E-06
5633411
ethylene dichloride
—
—
—
3E-05
7E-06
5633411
formaldehyde
7E-04
4E-05
2E-06
3E-05
3E-06
5633411
vinyl chloride
IE-04
3E-05
6E-06
1E-05
1E-06
5633411
benzene
—
7E-06
5E-07
8E-06
3E-06
5633411
1,3-butadiene
—
1E-07
1E-08
7E-06
1E-07
5633411
phenol
3E-05
3E-06
2E-06
4E-06
9E-07
5633411
toluene
—
2E-06
2E-07
2E-06
4E-07
5633411
methanol
8E-07
3E-08
9E-09
9E-08
2E-08
5633411
carbon tetrachloride
1E-06
—
3E-08
2E-08
3E-09
5633411
styrene
2E-07
4E-08
6E-09
2E-08
3E-09
5633411
trichloroethylene
—
2E-08
5E-09
2E-08
5E-09
5633411
methylene chloride
3E-07
7E-09
2E-09
5E-09
2E-09
5633411
methyl chloride
—
—
3E-10
2E-09
3E-10
5633411
1,1,1-trichloroethane
3E-08
2E-09
7E-10
1E-09
6E-10
5651611
cumene
—
2E-05
3E-06
—
—
5651611
n-hexane
—
—
3E-08
—
—
5651611
xylenes (mixed)
5E-07
2E-08
3E-09
—
—
5651611
ethyl benzene
—
5E-08
2E-09
—
—
5651611
1,3-butadiene
—
9E-05
1E-05
6E-03
1E-04
5651611
styrene
1E-02
4E-03
6E-04
1E-03
3E-04
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
5651611
formaldehyde
2E-04
1E-05
7E-07
9E-06
9E-07
5651911
1,3-butadiene
—
—
—
—
—
5651911
2,2,4-trimethylpentane
—
—
—
—
—
5651911
antimony compounds
—
—
—
—
—
5651911
bis(2-ethylhexyl)phthalate
—
—
—
—
—
5651911
chlorine
—
—
—
—
—
5651911
cresols (mixed)
—
—
—
—
—
5651911
gaseous divalent mercury
—
—
—
—
—
5651911
lead compounds
—
—
—
—
—
5651911
mercury (elemental)
—
—
—
—
—
5651911
naphthalene
—
—
—
—
—
5651911
nickel compounds
—
—
—
—
—
5651911
pah, total
—
—
—
—
—
5651911
particulate divalent
mercury
—
—
—
—
—
5651911
phenanthrene
—
—
—
—
—
5651911
ethyl benzene
—
2E-04
6E-06
—
—
5651911
xylenes (mixed)
7E-04
3E-05
4E-06
—
—
5651911
n-hexane
—
—
3E-07
—
—
5651911
cumene
—
8E-07
1E-07
—
—
5651911
phenol
7E-03
7E-04
5E-04
1E-03
2E-04
5651911
benzene
—
5E-04
3E-05
5E-04
2E-04
5651911
toluene
—
3E-04
4E-05
4E-04
8E-05
5651911
hydrofluoric acid
3E-03
7E-04
3E-05
4E-04
4E-05
5651911
methylene chloride
1E-03
2E-05
8E-06
2E-05
6E-06
5651911
formaldehyde
IE-04
5E-06
3E-07
5E-06
5E-07
5651911
methanol
4E-05
1E-06
4E-07
4E-06
8E-07
5653011
benzo(ghi)perylene
—
—
—
—
—
5653011
naphthalene
—
—
—
—
—
5653011
polycyclic organic matter
—
—
—
—
—
5653011
glycol ethers
4E-04
—
—
—
—
5653011
acetonitrile
—
3E-03
8E-04
—
—
5653011
n-hexane
—
—
2E-04
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
5653011
ethyl benzene
—
6E-04
2E-05
—
—
5653011
xylenes (mixed)
1E-05
4E-07
5E-08
—
—
5653011
1,3-butadiene
—
2E-04
3E-05
1E-02
3E-04
5653011
hydrofluoric acid
3E-02
8E-03
3E-04
4E-03
4E-04
5653011
chlorine
8E-03
1E-03
3E-04
6E-04
2E-04
5653011
styrene
2E-03
6E-04
9E-05
2E-04
4E-05
5653011
carbon disulfide
7E-06
1E-06
8E-08
1E-05
3E-07
5653011
benzene
—
5E-06
3E-07
5E-06
2E-06
5653011
hydrochloric acid
3E-06
3E-06
2E-07
2E-06
2E-07
5653011
toluene
—
4E-07
5E-08
6E-07
1E-07
5656011
2,2,4-trimethylpentane
—
—
—
—
—
5656011
benzo(ghi)perylene
—
—
—
—
—
5656011
cresols (mixed)
—
—
—
—
—
5656011
diethanolamine
—
—
—
—
—
5656011
naphthalene
—
—
—
—
—
5656011
phenanthrene
—
—
—
—
—
5656011
polycyclic organic matter
—
—
—
—
—
5656011
acetonitrile
—
3E-05
9E-06
—
—
5656011
n-hexane
—
—
6E-06
—
—
5656011
xylenes (mixed)
1E-03
4E-05
5E-06
—
—
5656011
ethyl benzene
—
4E-05
1E-06
—
—
5656011
cumene
—
1E-06
2E-07
—
—
5656011
biphenyl
—
—
3E-08
—
—
5656011
carbonyl sulfide
—
—
2E-08
—
—
5656011
benzene
—
3E-04
2E-05
3E-04
1E-04
5656011
toluene
—
IE-04
2E-05
2E-04
3E-05
5656011
styrene
2E-05
5E-06
7E-07
2E-06
4E-07
5656011
phenol
1E-05
1E-06
7E-07
2E-06
3E-07
5656011
carbon disulfide
7E-07
1E-07
8E-09
1E-06
3E-08
5656011
hydrochloric acid
2E-06
1E-06
1E-07
8E-07
1E-07
5656011
formaldehyde
8E-06
4E-07
2E-08
3E-07
3E-08
5656011
1,3-butadiene
—
4E-10
4E-11
2E-08
5E-10
5679711
cresols (mixed)
—
—
—
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
5679711
hydrogen cyanide
IE-04
2E-05
6E-06
—
4E-06
5679711
acetonitrile
—
4E-06
1E-06
—
—
5679711
n-hexane
—
—
8E-08
—
—
5679711
biphenyl
—
—
5E-08
—
—
5679711
ethyl acrylate
—
3E-07
6E-08
2E-04
8E-08
5679711
vinyl acetate
—
6E-05
1E-05
8E-05
6E-06
5679711
acrylic acid
2E-05
3E-05
1E-06
5E-05
9E-07
5679711
1,3-butadiene
—
3E-07
4E-08
2E-05
4E-07
5679711
phenol
8E-05
8E-06
5E-06
1E-05
2E-06
5679711
benzene
—
2E-06
1E-07
2E-06
8E-07
5679711
toluene
—
9E-07
1E-07
1E-06
2E-07
5679711
methanol
6E-06
3E-07
7E-08
7E-07
1E-07
5679711
acrylonitrile
—
—
1E-07
2E-08
7E-09
5719311
2,2,4-trimethylpentane
—
—
—
—
—
5719311
methyl isobutyl ketone
—
—
—
—
—
5719311
naphthalene
—
—
—
—
—
5719311
xylenes (mixed)
5E-03
2E-04
3E-05
—
—
5719311
n-hexane
—
—
3E-06
—
—
5719311
ethyl benzene
—
2E-05
6E-07
—
—
5719311
cumene
—
4E-06
6E-07
—
—
5719311
toluene
—
5E-04
5E-05
6E-04
1E-04
5719311
benzene
—
IE-04
8E-06
IE-04
4E-05
5729211
2,4-toluene diamine
—
—
—
—
—
5729211
4,4'-methylene bis(2-
chloroaniline)
—
—
—
—
—
5729211
4,4'-methylenedianiline
—
—
—
—
—
5729211
4-aminobiphenyl
—
—
—
—
—
5729211
dimethyl formamide
—
—
—
—
—
5729211
ethylene glycol
—
—
—
—
—
5729211
ethylene glycol methyl
ether
—
—
—
—
—
5729211
hexamethylene-1,6-
diisocyanate
—
—
—
—
—
5729211
methyl isobutyl ketone
—
—
—
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
5729211
nitrobenzene
—
—
—
—
—
5729211
o-toluidine
—
—
—
—
—
5729211
p-dichlorobenzene
—
—
—
—
—
5729211
pah, total
—
—
—
—
—
5729211
methylene diphenyl
diisocyanate
3E-03
—
—
—
8E-06
5729211
phosgene
1E-01
—
4E-04
—
2E-04
5729211
aniline
—
2E-04
IE-04
—
—
5729211
chlorobenzene
—
IE-04
8E-06
—
—
5729211
chloroform
5E-03
—
2E-06
—
3E-06
5729211
ethyl benzene
—
4E-06
1E-07
—
—
5729211
xylenes (mixed)
6E-06
2E-07
3E-08
—
—
5729211
hydrochloric acid
2E-03
2E-03
IE-04
1E-03
2E-04
5729211
chlorine
1E-02
1E-03
4E-04
7E-04
2E-04
5729211
2,4-toluene diisocyanate
8E-04
1E-05
3E-06
2E-05
2E-06
5729211
toluene
—
9E-06
1E-06
1E-05
2E-06
5729211
benzene
—
1E-05
7E-07
1E-05
4E-06
5729211
phenol
4E-05
4E-06
3E-06
6E-06
1E-06
5729211
carbon tetrachloride
3E-04
—
7E-06
4E-06
9E-07
5729211
methylene chloride
3E-04
5E-06
2E-06
4E-06
1E-06
5729211
methyl chloride
—
—
3E-07
2E-06
2E-07
5729211
formaldehyde
2E-05
1E-06
7E-08
9E-07
9E-08
5729211
maleic anhydride
—
—
—
4E-07
2E-08
5729211
methanol
2E-06
8E-08
2E-08
2E-07
4E-08
5746611
1,1,2,2-tetrachloroethane
—
—
—
—
—
5746611
1,1,2-trichloroethane
—
—
—
—
—
5746611
chloroprene
—
—
—
—
—
5746611
dichloroethyl ether
—
—
—
—
—
5746611
ethyl chloride
—
—
—
—
—
5746611
ethylidene dichloride
—
—
—
—
—
5746611
hexachlorobenzene
—
—
—
—
—
5746611
hexachlorobutadiene
—
—
—
—
—
5746611
methanol
—
—
—
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
5746611
n-hexane
—
—
—
—
—
5746611
polychlorinated biphenyls
—
—
—
—
—
5746611
styrene
—
—
—
—
—
5746611
vinylidene chloride
—
—
—
—
1E-09
5746611
acetonitrile
—
2E-05
4E-06
—
—
5746611
chloroform
5E-03
—
2E-06
—
3E-06
5746611
1,2-epoxybutane
—
4E-07
2E-07
—
—
5746611
chlorobenzene
—
9E-07
6E-08
—
—
5746611
chlorine
3E-02
4E-03
1E-03
2E-03
7E-04
5746611
hydrochloric acid
5E-03
4E-03
3E-04
2E-03
3E-04
5746611
ethylene dichloride
—
—
—
2E-04
4E-05
5746611
vinyl chloride
2E-04
7E-05
1E-05
3E-05
3E-06
5746611
carbon tetrachloride
3E-05
—
7E-07
4E-07
9E-08
5746611
tetrachloroethene
2E-06
1E-07
2E-08
5E-08
2E-08
5746611
allyl chloride
—
6E-08
3E-09
5E-08
4E-09
5746611
trichloroethylene
—
6E-10
2E-10
8E-10
2E-10
5748611
2,4-dinitrotoluene
—
—
—
—
—
5748611
chlorine
—
—
—
—
—
5748611
dibutylphthalate
—
—
—
—
—
5748611
lead compounds
—
—
—
—
—
5748611
methylene chloride
1E-03
2E-05
9E-06
2E-05
7E-06
5768911
ethyl chloride
—
—
—
—
—
5768911
ethylene glycol
—
—
—
—
—
5768911
chloroacetic acid
—
—
8E-04
—
—
5769011
polycyclic organic matter
—
—
—
—
—
5846511
acetophenone
—
—
—
—
—
5846511
chromium (iii) compounds
—
—
—
—
—
5846511
chromium (vi) compounds
—
—
—
—
—
5846511
cresols (mixed)
—
—
—
—
—
5846511
diethanolamine
—
—
—
—
—
5846511
ethyl chloride
—
—
—
—
—
5846511
ethylene glycol
—
—
—
—
—
5846511
naphthalene
—
—
—
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
5846511
glycol ethers
2E-01
—
—
—
—
5846511
ethylene oxide
—
—
8E-04
—
7E-04
5846511
chlorobenzene
—
7E-04
5E-05
—
—
5846511
1,4-dioxane
3E-03
2E-04
8E-06
—
—
5846511
cumene
—
5E-06
9E-07
—
—
5846511
n-hexane
—
—
6E-07
—
—
5846511
ethyl benzene
—
3E-07
9E-09
—
—
5846511
ethyl acrylate
—
9E-06
2E-06
8E-03
3E-06
5846511
acrolein
2E-01
6E-03
2E-03
4E-03
1E-03
5846511
vinyl acetate
—
1E-03
2E-04
2E-03
1E-04
5846511
formaldehyde
4E-02
2E-03
IE-04
2E-03
2E-04
5846511
acetaldehyde
3E-02
IE-04
2E-05
7E-04
3E-05
5846511
hydrochloric acid
IE-04
IE-04
8E-06
6E-05
9E-06
5846511
propylene oxide
1E-03
2E-05
6E-06
3E-05
7E-06
5846511
phenol
7E-05
7E-06
4E-06
1E-05
2E-06
5846511
ethylene dichloride
—
—
—
1E-06
3E-07
5846511
1,3-butadiene
—
1E-08
2E-09
1E-06
2E-08
5846511
methanol
5E-06
2E-07
5E-08
5E-07
1E-07
5846511
toluene
—
2E-07
3E-08
3E-07
5E-08
5846511
benzene
—
1E-07
8E-09
1E-07
5E-08
5846511
vinyl chloride
3E-07
9E-08
2E-08
4E-08
4E-09
5862111
xylenes (mixed)
9E-05
3E-06
5E-07
—
—
5862111
ethyl benzene
—
8E-06
2E-07
—
—
5862111
n-hexane
—
—
1E-08
—
—
5862111
toluene
—
1E-05
2E-06
2E-05
3E-06
5862111
benzene
—
2E-05
1E-06
2E-05
7E-06
5862111
1,3-butadiene
—
2E-07
3E-08
1E-05
3E-07
588311
acenaphthene
—
—
—
—
—
588311
acenaphthylene
—
—
—
—
—
588311
anthracene
—
—
—
—
—
588311
arsenic compounds
—
—
—
—
—
588311
benz[a]anthracene
—
—
—
—
—
588311
benzo(ghi)perylene
—
—
—
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
588311
benzo[a]pyrene
—
—
—
—
—
588311
benzo[b]fluoranthene
—
—
—
—
—
588311
benzo[k]fluoranthene
—
—
—
—
—
588311
beryllium compounds
—
—
—
—
—
588311
biphenyl
—
—
—
—
—
588311
cadmium compounds
—
—
—
—
—
588311
carbonyl sulfide
—
—
—
—
—
588311
chromium (vi) compounds
—
—
—
—
—
588311
chrysene
—
—
—
—
—
588311
cresols (mixed)
—
—
—
—
—
588311
cumene
—
—
—
—
—
588311
dibenzo[a,h]anthracene
—
—
—
—
—
588311
fluoranthene
—
—
—
—
—
588311
fluorene
—
—
—
—
—
588311
gaseous divalent mercury
—
—
—
—
—
588311
hydrogen cyanide
—
—
—
—
—
588311
indeno[l,2,3-c,d]pyrene
—
—
—
—
—
588311
lead compounds
—
—
—
—
—
588311
manganese compounds
—
—
—
—
—
588311
mercury (elemental)
—
—
—
—
—
588311
methanol
—
—
—
—
—
588311
naphthalene
—
—
—
—
—
588311
nickel compounds
—
—
—
—
—
588311
particulate divalent
mercury
—
—
—
—
—
588311
phenanthrene
—
—
—
—
—
588311
phenol
—
—
—
—
—
588311
pyrene
—
—
—
—
—
588311
styrene
—
—
—
—
—
588311
tetrachloroethene
—
—
—
—
—
588311
xylenes (mixed)
1E-03
5E-05
7E-06
—
—
588311
ethyl benzene
—
5E-05
1E-06
—
—
588311
n-hexane
—
—
3E-07
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
588311
1,3-butadiene
—
2E-05
2E-06
1E-03
2E-05
588311
benzene
—
7E-04
4E-05
7E-04
2E-04
588311
toluene
—
IE-04
2E-05
2E-04
3E-05
5929411
1,1,2,2-tetrachloroethane
—
—
—
—
—
5929411
1,1,2-trichloroethane
—
—
—
—
—
5929411
1,2,4-trichlorobenzene
—
—
—
—
—
5929411
anthracene
—
—
—
—
—
5929411
benzo(ghi)perylene
—
—
—
—
—
5929411
chloroprene
—
—
—
—
—
5929411
chromium (iii) compounds
—
—
—
—
—
5929411
chromium (vi) compounds
—
—
—
—
—
5929411
dichloroethyl ether
—
—
—
—
—
5929411
ethyl chloride
—
—
—
—
—
5929411
ethylidene dichloride
—
—
—
—
—
5929411
hexachlorobenzene
—
—
—
—
—
5929411
hexachlorobutadiene
—
—
—
—
—
5929411
hexachloroethane
—
—
—
—
—
5929411
methanol
—
—
—
—
—
5929411
methylene chloride
—
—
—
—
—
5929411
naphthalene
—
—
—
—
—
5929411
p-dichlorobenzene
—
—
—
—
—
5929411
phenanthrene
—
—
—
—
—
5929411
polycyclic organic matter
—
—
—
—
—
5929411
vinylidene chloride
—
—
—
—
2E-10
5929411
chloroform
5
—
2E-03
—
3E-03
5929411
biphenyl
—
—
IE-04
—
—
5929411
xylenes (mixed)
8E-03
3E-04
5E-05
—
—
5929411
chlorobenzene
—
4E-04
3E-05
—
—
5929411
ethyl benzene
—
6E-05
2E-06
—
—
5929411
chlorine
6
8E-01
2E-01
4E-01
1E-01
5929411
hydrochloric acid
6E-01
5E-01
4E-02
3E-01
4E-02
5929411
ethylene dichloride
—
—
—
7E-02
2E-02
5929411
styrene
2E-01
5E-02
8E-03
2E-02
4E-03
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
5929411
benzene
—
2E-02
1E-03
2E-02
8E-03
5929411
vinyl chloride
9E-02
3E-02
5E-03
1E-02
1E-03
5929411
carbon tetrachloride
5E-01
—
1E-02
7E-03
1E-03
5929411
toluene
—
3E-03
4E-04
4E-03
7E-04
5929411
1,3-butadiene
—
6E-05
8E-06
4E-03
9E-05
5929411
tetrachloroethene
1E-03
IE-04
1E-05
3E-05
2E-05
5929411
methyl chloride
—
—
6E-09
3E-08
5E-09
5929411
trichloroethylene
—
7E-10
2E-10
1E-09
2E-10
6152811
benzo(ghi)perylene
—
—
—
—
—
6152811
diethanolamine
—
—
—
—
—
6152811
hydrofluoric acid
—
—
—
—
—
6152811
naphthalene
—
—
—
—
—
6152811
phenanthrene
—
—
—
—
—
6152811
polycyclic organic matter
—
—
—
—
—
6152811
tetrachloroethene
—
—
—
—
—
6152811
cumene
—
4E-04
7E-05
—
—
6152811
xylenes (mixed)
6E-03
2E-04
3E-05
—
—
6152811
carbonyl sulfide
—
—
1E-05
—
—
6152811
ethyl benzene
—
IE-04
4E-06
—
—
6152811
n-hexane
—
—
2E-06
—
—
6152811
phenol
7E-02
7E-03
5E-03
1E-02
2E-03
6152811
benzene
—
3E-03
2E-04
4E-03
1E-03
6152811
toluene
—
5E-04
6E-05
7E-04
1E-04
6152811
carbon disulfide
4E-05
6E-06
5E-07
7E-05
1E-06
6152811
chlorine
7E-04
9E-05
2E-05
5E-05
2E-05
6152811
1,3-butadiene
—
4E-08
5E-09
3E-06
6E-08
6152811
hydrochloric acid
8E-07
6E-07
5E-08
4E-07
6E-08
6157311
ethyl chloride
—
—
—
—
—
6157311
lead compounds
—
—
—
—
—
6157311
phenol
—
—
—
—
—
6157311
xylenes (mixed)
4E-04
2E-05
2E-06
—
—
6157311
n-hexane
—
—
1E-06
—
—
6157311
ethyl benzene
—
4E-07
1E-08
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
6157311
ethylene dibromide
—
2E-08
1E-08
—
—
6157311
1,3-butadiene
—
IE-04
1E-05
7E-03
1E-04
6157311
toluene
—
9E-04
IE-04
1E-03
2E-04
6157311
methyl tert-butyl ether
—
5E-05
4E-06
5E-05
2E-06
6157311
methanol
6E-05
2E-06
6E-07
6E-06
1E-06
6157311
carbon disulfide
8E-07
1E-07
1E-08
2E-06
3E-08
6157311
ethylene dichloride
—
—
—
1E-08
3E-09
6158411
1,2,4-trichlorobenzene
—
—
—
—
—
6158411
ethylene glycol
—
—
—
—
—
6158411
naphthalene
—
—
—
—
—
6158411
glycol ethers
3E-05
—
—
—
—
6158411
1,2-propyleneimine
—
—
3E-07
—
—
6158411
xylenes (mixed)
3E-05
1E-06
2E-07
—
—
6158411
n-hexane
—
—
9E-08
—
—
6158411
ethyl benzene
—
7E-07
2E-08
—
—
6158411
benzene
—
IE-04
8E-06
IE-04
4E-05
6158411
1,3-butadiene
—
2E-06
3E-07
IE-04
3E-06
6158411
toluene
—
6E-06
8E-07
8E-06
1E-06
6158411
methanol
2E-05
8E-07
2E-07
2E-06
4E-07
6158411
styrene
1E-05
3E-06
4E-07
1E-06
2E-07
6158411
phenol
7E-06
7E-07
4E-07
1E-06
2E-07
6194311
methyl methacrylate
—
9E-01
1E-01
—
—
6194311
hydrogen cyanide
3E-02
5E-03
1E-03
—
1E-03
6194311
methanol
9E-02
4E-03
9E-04
1E-02
2E-03
6194311
toluene
—
5E-03
6E-04
6E-03
1E-03
6194311
benzene
—
4E-04
3E-05
4E-04
1E-04
6234511
3-methylcholanthrene
—
—
—
—
—
6234511
7,12-
dimethylbenz[a]anthracen
e
6234511
acenaphthene
—
—
—
—
—
6234511
acenaphthylene
—
—
—
—
—
6234511
anthracene
—
—
—
—
—
6234511
benz[a]anthracene
—
—
—
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
6234511
benzo(ghi)perylene
—
—
—
—
—
6234511
benzo[a]pyrene
—
—
—
—
—
6234511
benzo[b]fluoranthene
—
—
—
—
—
6234511
benzo[j]fluoranthene
—
—
—
—
—
6234511
benzo[k]fluoranthene
—
—
—
—
—
6234511
chromium (iii) compounds
—
—
—
—
—
6234511
chromium (vi) compounds
—
—
—
—
—
6234511
chrysene
—
—
—
—
—
6234511
cumene
—
—
—
—
—
6234511
dibenzo[a,h]anthracene
—
—
—
—
—
6234511
dibenzo[a,h]pyrene
—
—
—
—
—
6234511
dibenzofuran
—
—
—
—
—
6234511
fluoranthene
—
—
—
—
—
6234511
fluorene
—
—
—
—
—
6234511
indeno[l,2,3-c,d]pyrene
—
—
—
—
—
6234511
m-cresol (3-methylphenol)
—
—
—
—
—
6234511
naphthalene
—
—
—
—
—
6234511
nickel compounds
—
—
—
—
—
6234511
o-cresol
—
—
—
—
—
6234511
p-cresol (4-methy phenol)
—
—
—
—
—
6234511
phenanthrene
—
—
—
—
—
6234511
pyrene
—
—
—
—
—
6234511
quinoline
—
—
—
—
—
6234511
p-xylene
IE-04
—
—
—
—
6234511
m-xylene
IE-04
—
—
—
—
6234511
o-xylene
5E-05
—
—
—
—
6234511
biphenyl
—
—
7E-06
—
—
6234511
n-hexane
—
—
2E-06
—
—
6234511
ethyl benzene
—
4E-06
1E-07
—
—
6234511
formaldehyde
1E-02
6E-04
4E-05
5E-04
5E-05
6234511
benzene
—
4E-04
3E-05
4E-04
1E-04
6234511
toluene
—
6E-05
7E-06
7E-05
1E-05
6234511
styrene
3E-04
8E-05
1E-05
3E-05
6E-06
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
6234511
phenol
IE-04
1E-05
9E-06
2E-05
4E-06
6362111
chloroform
2E-01
—
9E-05
—
1E-04
6362111
carbon tetrachloride
8E-02
—
2E-03
1E-03
2E-04
6362111
tetrachloroethene
7E-03
6E-04
9E-05
2E-04
1E-04
6362111
trichloroethylene
—
7E-06
2E-06
9E-06
2E-06
6362111
vinyl chloride
3E-05
7E-06
2E-06
4E-06
4E-07
6362111
benzene
—
2E-07
1E-08
2E-07
7E-08
6362111
ethylene dichloride
—
—
—
3E-08
7E-09
6362111
methylene chloride
6E-07
1E-08
5E-09
9E-09
3E-09
6385211
hydroquinone
—
—
—
—
—
6385211
hydrogen cyanide
9E-02
1E-02
4E-03
—
3E-03
6385211
methyl methacrylate
—
2E-04
3E-05
—
—
6385211
n-hexane
—
—
6E-08
—
—
6385211
acetonitrile
—
7E-08
2E-08
—
—
6385211
xylenes (mixed)
6E-07
3E-08
4E-09
—
—
6385211
methanol
7E-05
3E-06
7E-07
8E-06
2E-06
6385211
chlorine
IE-04
1E-05
4E-06
7E-06
2E-06
6385211
benzene
—
3E-07
2E-08
3E-07
1E-07
6385211
toluene
—
2E-07
2E-08
2E-07
4E-08
6385211
acrylonitrile
—
—
4E-08
6E-09
2E-09
6386311
nitrobenzene
—
—
—
—
—
6386311
aniline
—
2E-03
1E-03
—
—
6386311
benzene
—
5E-04
3E-05
5E-04
2E-04
6421511
naphthalene
—
—
—
—
—
6421511
cumene
—
7E-03
1E-03
—
—
6421511
ethyl benzene
—
2E-06
7E-08
—
—
6421511
n-hexane
—
—
4E-09
—
—
6421511
phenol
3E-03
3E-04
2E-04
5E-04
1E-04
6421511
benzene
—
2E-04
1E-05
2E-04
8E-05
6421511
1,3-butadiene
—
2E-08
3E-09
2E-06
3E-08
6421511
toluene
—
6E-08
7E-09
8E-08
1E-08
6421811
carbon disulfide
—
—
—
—
—
6421811
chlorine
—
—
—
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
6421811
cobalt compounds
—
—
—
—
—
6421811
tetrachloroethene
—
—
—
—
—
6421811
xylenes (mixed)
1E-02
5E-04
6E-05
—
—
6421811
n-hexane
—
—
3E-06
—
—
6421811
cumene
—
9E-08
1E-08
—
—
6421811
ethyl benzene
—
1E-07
3E-09
—
—
6421811
benzene
—
5E-05
3E-06
5E-05
2E-05
6421811
1,3-butadiene
—
1E-07
1E-08
7E-06
1E-07
6421811
toluene
—
2E-06
2E-07
3E-06
4E-07
6421811
methyl chloride
—
—
8E-08
5E-07
8E-08
6421811
methanol
1E-07
4E-09
1E-09
1E-08
2E-09
6430411
biphenyl
—
—
—
—
—
6430411
bromoform
—
—
—
—
—
6430411
naphthalene
—
—
—
—
—
6430411
polycyclic organic matter
—
—
—
—
—
6430411
glycol ethers
2E-05
—
—
—
—
6430411
n-hexane
—
—
7E-08
—
—
6430411
ethyl benzene
—
2E-06
5E-08
—
—
6430411
xylenes (mixed)
1E-05
4E-07
5E-08
—
—
6430411
chloroform
2E-05
—
1E-08
—
1E-08
6430411
cumene
—
4E-10
6E-11
—
—
6430411
benzene
—
3E-04
2E-05
3E-04
1E-04
6430411
toluene
—
6E-05
7E-06
8E-05
1E-05
6430411
chlorine
4E-05
5E-06
1E-06
3E-06
9E-07
6430411
styrene
4E-06
9E-07
1E-07
4E-07
7E-08
6430411
1,3-butadiene
—
2E-09
3E-10
2E-07
3E-09
6444911
1,1,2,2-tetrachloroethane
—
—
—
—
—
6444911
1,1,2-trichloroethane
—
—
—
—
—
6444911
1,2,4-trichlorobenzene
—
—
—
—
—
6444911
1,2-diphenylhydrazine
—
—
—
—
—
6444911
2,4,5-trichlorophenol
—
—
—
—
—
6444911
2,4,6-trichlorophenol
—
—
—
—
—
6444911
2,4-d, salts and esters
—
—
—
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
6444911
2,4-dinitrophenol
—
—
—
—
—
6444911
2,4-dinitrotoluene
—
—
—
—
—
6444911
2,4-toluene diamine
—
—
—
—
—
6444911
2-acetylaminofluorene
—
—
—
—
—
6444911
2-nitropropane
—
—
—
—
—
6444911
3,3'-dichlorobenzidine
—
—
—
—
—
6444911
3,3'-dimethoxybenzidine
—
—
—
—
—
6444911
3,3'-dimethylbenzidine
—
—
—
—
—
6444911
4,4'-methylenedianiline
—
—
—
—
—
6444911
4,6-dinitro-o-cresol
—
—
—
—
—
6444911
4-nitrophenol
—
—
—
—
—
6444911
acetamide
—
—
—
—
—
6444911
acetophenone
—
—
—
—
—
6444911
acrylamide
—
—
—
—
—
6444911
anisidine
—
—
—
—
—
6444911
antimony compounds
—
—
—
—
—
6444911
benzidine
—
—
—
—
—
6444911
benzo(ghi)perylene
—
—
—
—
—
6444911
benzotrichloride
—
—
—
—
—
6444911
beryllium compounds
—
—
—
—
5E-05
6444911
bis(2-ethylhexyl)phthalate
—
—
—
—
—
6444911
captan
—
—
—
—
—
6444911
catechol
—
—
—
—
—
6444911
chlordane
—
—
—
—
—
6444911
chlorobenzilate
—
—
—
—
—
6444911
chromium (iii) compounds
—
—
—
—
—
6444911
chromium (vi) compounds
—
—
—
—
—
6444911
cobalt compounds
—
—
—
—
—
6444911
cresols (mixed)
—
—
—
—
—
6444911
dibutylphthalate
—
—
—
—
—
6444911
diethanolamine
—
—
—
—
—
6444911
diethyl sulfate
—
—
—
—
—
6444911
dimethyl phthalate
—
—
—
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
6444911
dimethylcarbamoyl
chloride
—
—
—
—
—
6444911
ethyl carbamate
—
—
—
—
—
6444911
ethyl chloride
—
—
—
—
—
6444911
ethylene glycol
—
—
—
—
—
6444911
ethylene thiourea
—
—
—
—
—
6444911
ethylidene dichloride
—
—
—
—
—
6444911
gaseous divalent mercury
—
—
—
—
—
6444911
heptachlor
—
—
—
—
—
6444911
hexachlorobenzene
—
—
—
—
—
6444911
hexachloroethane
—
—
—
—
—
6444911
hexamethylphosphoramid
e
—
—
—
—
—
6444911
hydroquinone
—
—
—
—
—
6444911
lead compounds
—
—
—
—
—
6444911
lindane (gamma-hch)
—
—
—
—
—
6444911
manganese compounds
—
—
—
—
—
6444911
methoxychlor
—
—
—
—
—
6444911
methyl isobutyl ketone
—
—
—
—
—
6444911
n-nitroso-n-methylurea
—
—
—
—
—
6444911
naphthalene
—
—
—
—
—
6444911
nickel compounds
—
—
—
—
—
6444911
nitrobenzene
—
—
—
—
—
6444911
o-toluidine
—
—
—
—
—
6444911
p-dichlorobenzene
—
—
—
—
—
6444911
P-
dimethylaminoazobenzen
e
6444911
p-phenylenediamine
—
—
—
—
—
6444911
particulate divalent
mercury
—
—
—
—
—
6444911
pentachlorophenol
—
—
—
—
—
6444911
phenanthrene
—
—
—
—
—
6444911
phthalic anhydride
—
—
—
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
6444911
polychlorinated biphenyls
—
—
—
—
—
6444911
polycyclic organic matter
—
—
—
—
—
6444911
propylene dichloride
—
—
—
—
—
6444911
quinoline
—
—
—
—
—
6444911
quinone
—
—
—
—
—
6444911
selenium compounds
—
—
—
—
—
6444911
toxaphene
—
—
—
—
—
6444911
trifluralin
—
—
—
—
—
6444911
vinylidene chloride
—
—
—
—
1E-09
6444911
glycol ethers
2E-02
—
—
—
—
6444911
arsenic compounds
7E-04
—
—
—
—
6444911
triethylamine
6E-06
—
—
—
—
6444911
bis(chloromethyl)ether
—
—
8E-03
—
3E-03
6444911
chloromethyl methyl ether
—
—
1E-03
—
5E-04
6444911
methyl hydrazine
—
—
1E-03
—
—
6444911
hydrogen cyanide
6E-03
9E-04
3E-04
—
2E-04
6444911
1,1-dimethylhydrazine
—
—
2E-04
—
—
6444911
ethylene imine (aziridine)
—
—
2E-04
—
—
6444911
1,2-propyleneimine
—
—
6E-05
—
—
6444911
ethylene oxide
—
—
2E-05
—
2E-05
6444911
acetonitrile
—
5E-06
1E-06
—
—
6444911
n-hexane
—
—
3E-07
—
—
6444911
chlorobenzene
—
3E-06
2E-07
—
—
6444911
cadmium compounds
—
1E-06
2E-07
—
—
6444911
chloroform
2E-04
—
1E-07
—
1E-07
6444911
xylenes (mixed)
2E-05
7E-07
1E-07
—
—
6444911
dimethyl sulfate
—
6E-07
1E-07
—
—
6444911
aniline
—
2E-07
1E-07
—
—
6444911
mercury (elemental)
2E-04
—
9E-08
—
7E-08
6444911
biphenyl
—
—
2E-08
—
—
6444911
ethyl benzene
—
2E-07
5E-09
—
—
6444911
methyl methacrylate
—
3E-08
4E-09
—
—
6444911
chloroacetic acid
—
—
3E-09
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
6444911
1,4-dioxane
1E-06
6E-08
3E-09
—
—
6444911
cumene
—
4E-09
6E-10
—
—
6444911
ethylene dibromide
—
5E-10
4E-10
—
—
6444911
propionaldehyde
—
6E-10
1E-10
—
—
6444911
titanium tetrachloride
—
—
6E-04
1E-03
2E-04
6444911
hydrochloric acid
1E-03
1E-03
9E-05
6E-04
9E-05
6444911
2,4-toluene diisocyanate
9E-04
1E-05
3E-06
3E-05
2E-06
6444911
hydrofluoric acid
2E-04
6E-05
2E-06
3E-05
3E-06
6444911
ethyl acrylate
—
3E-08
8E-09
3E-05
1E-08
6444911
chlorine
3E-04
5E-05
1E-05
2E-05
8E-06
6444911
formaldehyde
3E-04
1E-05
9E-07
1E-05
1E-06
6444911
methyl isocyanate
—
—
2E-06
6E-06
6E-07
6444911
methanol
6E-05
2E-06
6E-07
6E-06
1E-06
6444911
maleic anhydride
—
—
—
5E-06
2E-07
6444911
acrolein
IE-04
5E-06
2E-06
3E-06
1E-06
6444911
ethylene dichloride
—
—
—
2E-06
4E-07
6444911
benzyl chloride
4E-05
—
—
2E-06
2E-07
6444911
toluene
—
2E-06
2E-07
2E-06
4E-07
6444911
vinyl acetate
—
1E-06
2E-07
1E-06
9E-08
6444911
dimethyl formamide
—
—
3E-08
1E-06
3E-08
6444911
acrylic acid
7E-07
1E-06
3E-08
1E-06
3E-08
6444911
hexachlorobutadiene
—
—
—
8E-07
3E-07
6444911
phenol
3E-06
3E-07
2E-07
5E-07
1E-07
6444911
benzene
—
3E-07
2E-08
3E-07
1E-07
6444911
carbon disulfide
8E-08
1E-08
1E-09
2E-07
3E-09
6444911
methylene chloride
8E-06
2E-07
6E-08
1E-07
4E-08
6444911
trichloroethylene
—
8E-08
2E-08
1E-07
2E-08
6444911
carbon tetrachloride
6E-06
—
1E-07
9E-08
2E-08
6444911
tetrachloroethene
3E-06
3E-07
4E-08
9E-08
4E-08
6444911
acrylonitrile
—
—
4E-07
7E-08
2E-08
6444911
epichlorohydrin
8E-07
2E-07
1E-08
6E-08
1E-08
6444911
acetaldehyde
2E-06
1E-08
2E-09
6E-08
3E-09
6444911
1,1,1-trichloroethane
8E-07
4E-08
2E-08
3E-08
1E-08
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
6444911
1,3-butadiene
—
4E-10
5E-11
3E-08
6E-10
6444911
methyl tert-butyl ether
—
2E-08
2E-09
2E-08
1E-09
6444911
styrene
1E-07
3E-08
5E-09
1E-08
3E-09
6444911
allyl chloride
—
2E-08
8E-10
1E-08
1E-09
6444911
methyl chloride
—
—
1E-09
6E-09
9E-10
6444911
propylene oxide
2E-08
4E-10
1E-10
6E-10
1E-10
6444911
vinyl chloride
1E-09
3E-10
7E-11
2E-10
2E-11
6445411
polycyclic organic matter
—
—
—
—
—
6445411
n-hexane
—
—
IE-04
—
—
6445411
1,2-epoxybutane
—
6E-05
3E-05
—
—
6445411
xylenes (mixed)
4E-03
2E-04
2E-05
—
—
6445411
chloroform
9E-03
—
4E-06
—
6E-06
6445411
ethyl benzene
—
2E-06
5E-08
—
—
6445411
1,3-butadiene
—
4E-04
4E-05
2E-02
5E-04
6445411
benzene
—
6E-03
4E-04
6E-03
2E-03
6445411
toluene
—
1E-03
2E-04
2E-03
3E-04
6445411
styrene
4E-03
1E-03
2E-04
4E-04
8E-05
6445411
tetrachloroethene
3E-03
2E-04
3E-05
8E-05
4E-05
6510111
vinyl acetate
—
2E-03
4E-04
3E-03
2E-04
6510111
hydrochloric acid
3E-03
3E-03
2E-04
2E-03
2E-04
6510111
methanol
4E-03
IE-04
4E-05
4E-04
8E-05
6510111
acetaldehyde
2E-02
IE-04
2E-05
4E-04
2E-05
6510311
methanol
3E-04
1E-05
3E-06
3E-05
7E-06
6534611
biphenyl
—
—
—
—
—
6534611
ethylene glycol
—
—
—
—
—
6534611
naphthalene
—
—
—
—
—
6534611
ethyl benzene
—
3E-04
1E-05
—
—
6534611
xylenes (mixed)
5E-08
2E-09
3E-10
—
—
6534611
benzene
—
4E-04
3E-05
5E-04
2E-04
6534611
styrene
2E-03
4E-04
6E-05
2E-04
3E-05
6534611
toluene
—
2E-05
3E-06
3E-05
5E-06
6534611
1,3-butadiene
—
2E-08
2E-09
1E-06
3E-08
6534811
1,1,2,2-tetrachloroethane
—
—
—
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
6534811
1,1,2-trichloroethane
—
—
—
—
—
6534811
2,4-d, salts and esters
—
—
—
—
—
6534811
antimony compounds
—
—
—
—
—
6534811
asbestos
—
—
—
—
—
6534811
beryllium compounds
—
—
—
—
4E-05
6534811
chromium (iii) compounds
—
—
—
—
—
6534811
chromium (vi) compounds
—
—
—
—
—
6534811
dibutylphthalate
—
—
—
—
—
6534811
ethyl chloride
—
—
—
—
—
6534811
ethylene glycol
—
—
—
—
—
6534811
gaseous divalent mercury
—
—
—
—
—
6534811
hexachloroethane
—
—
—
—
—
6534811
hydroquinone
—
—
—
—
—
6534811
lead compounds
—
—
—
—
—
6534811
lindane (gamma-hch)
—
—
—
—
—
6534811
methoxychlor
—
—
—
—
—
6534811
methyl isobutyl ketone
—
—
—
—
—
6534811
naphthalene
—
—
—
—
—
6534811
nickel compounds
—
—
—
—
—
6534811
nitrobenzene
—
—
—
—
—
6534811
o-cresol
—
—
—
—
—
6534811
p-dichlorobenzene
—
—
—
—
—
6534811
particulate divalent
mercury
—
—
—
—
—
6534811
polycyclic organic matter
—
—
—
—
—
6534811
trifluralin
—
—
—
—
—
6534811
arsenic compounds
2E-02
—
—
—
—
6534811
acetonitrile
—
7E-04
2E-04
—
—
6534811
mercury (elemental)
3E-02
—
9E-06
—
7E-06
6534811
n-hexane
—
—
5E-06
—
—
6534811
chloroform
9E-03
—
4E-06
—
5E-06
6534811
chlorobenzene
—
5E-05
3E-06
—
—
6534811
cadmium compounds
—
2E-05
2E-06
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
6534811
xylenes (mixed)
2E-04
8E-06
1E-06
—
—
6534811
aniline
—
2E-06
1E-06
—
—
6534811
ethyl benzene
—
8E-06
2E-07
—
—
6534811
biphenyl
—
—
1E-07
—
—
6534811
formaldehyde
3E-01
1E-02
9E-04
1E-02
1E-03
6534811
hydrochloric acid
5E-04
4E-04
3E-05
2E-04
3E-05
6534811
methanol
2E-03
7E-05
2E-05
2E-04
4E-05
6534811
toluene
—
9E-05
1E-05
IE-04
2E-05
6534811
benzene
—
IE-04
8E-06
IE-04
4E-05
6534811
methyl tert-butyl ether
—
9E-05
8E-06
9E-05
5E-06
6534811
maleic anhydride
—
—
—
8E-05
3E-06
6534811
2,4-toluene diisocyanate
3E-03
4E-05
1E-05
8E-05
5E-06
6534811
dimethyl formamide
—
—
2E-07
1E-05
2E-07
6534811
methylene chloride
6E-04
1E-05
5E-06
9E-06
3E-06
6534811
tetrachloroethene
3E-04
3E-05
4E-06
9E-06
5E-06
6534811
acrylonitrile
—
—
5E-05
8E-06
2E-06
6534811
carbon tetrachloride
4E-04
—
1E-05
7E-06
1E-06
6534811
vinyl acetate
—
5E-06
1E-06
7E-06
5E-07
6534811
acrylic acid
2E-06
3E-06
9E-08
5E-06
9E-08
6534811
ethylene dichloride
—
—
—
4E-06
1E-06
6534811
chlorine
5E-05
7E-06
2E-06
3E-06
1E-06
6534811
trichloroethylene
—
2E-06
6E-07
3E-06
5E-07
6534811
vinyl chloride
1E-05
4E-06
8E-07
2E-06
2E-07
6534811
allyl chloride
—
2E-06
1E-07
2E-06
1E-07
6534811
1,1,1-trichloroethane
2E-05
9E-07
4E-07
6E-07
3E-07
6534811
titanium tetrachloride
—
—
2E-07
3E-07
7E-08
6534811
phenol
1E-06
1E-07
1E-07
2E-07
5E-08
6534811
styrene
1E-06
3E-07
5E-08
1E-07
2E-08
6534811
1,3-butadiene
—
1E-09
1E-10
8E-08
2E-09
6615111
xylenes (mixed)
2E-05
7E-07
1E-07
—
—
6615111
ethyl benzene
—
6E-07
2E-08
—
—
6615111
chlorine
4E-03
5E-04
IE-04
3E-04
9E-05
6615111
methanol
2E-03
7E-05
2E-05
2E-04
4E-05
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
6615111
toluene
—
2E-06
2E-07
3E-06
5E-07
6615111
methyl iodide
—
2E-06
7E-07
2E-06
1E-06
6641411
hydrogen cyanide
4E-03
6E-04
2E-04
—
1E-04
6641411
formaldehyde
4E-01
2E-02
1E-03
2E-02
2E-03
6671911
hydroquinone
—
—
—
—
—
6671911
vinyl acetate
—
2E-02
3E-03
2E-02
2E-03
6671911
acrolein
3E-01
1E-02
3E-03
6E-03
2E-03
6671911
acetaldehyde
4E-02
3E-04
4E-05
1E-03
6E-05
6671911
methanol
2E-03
7E-05
2E-05
2E-04
4E-05
6671911
methyl iodide
—
2E-04
4E-05
IE-04
7E-05
6671911
chlorine
IE-04
2E-05
4E-06
9E-06
3E-06
6671911
benzene
—
1E-07
7E-09
1E-07
4E-08
6884211
methyl isobutyl ketone
—
—
—
—
—
6884211
methyl methacrylate
—
2E-02
3E-03
—
—
6884211
dimethyl sulfate
—
4E-03
8E-04
—
—
6884211
acetonitrile
—
2E-03
6E-04
—
—
6884211
hydrogen cyanide
6E-03
9E-04
3E-04
—
2E-04
6884211
chloroform
1E-02
—
5E-06
—
7E-06
6884211
formaldehyde
10
6E-01
4E-02
6E-01
6E-02
6884211
methanol
5E-01
2E-02
5E-03
5E-02
1E-02
6884211
dimethyl formamide
—
—
1E-03
5E-02
1E-03
6884211
chlorine
4E-02
6E-03
2E-03
3E-03
1E-03
6884211
methyl chloride
—
—
4E-05
2E-04
4E-05
6884211
hydrochloric acid
4E-04
3E-04
3E-05
2E-04
3E-05
6884211
carbon tetrachloride
2E-03
—
6E-05
3E-05
7E-06
7202911
chromium (iii) compounds
—
—
—
—
—
7202911
chromium (vi) compounds
—
—
—
—
—
7202911
diethanolamine
—
—
—
—
—
7202911
dimethyl phthalate
—
—
—
—
—
7202911
ethylene glycol
—
—
—
—
—
7202911
hydroquinone
—
—
—
—
—
7202911
lead compounds
—
—
—
—
—
7202911
manganese compounds
—
—
—
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
7202911
naphthalene
—
—
—
—
—
7202911
phthalic anhydride
—
—
—
—
—
7202911
vinyl chloride
—
—
—
—
—
7202911
ethylene glycol methyl
ether
7E-02
—
—
—
—
7202911
methoxytriglycol
6E-02
—
—
—
—
7202911
ethylene oxide
—
—
5E-04
—
4E-04
7202911
1,4-dioxane
2E-03
9E-05
4E-06
—
—
7202911
propionaldehyde
—
3E-06
5E-07
—
—
7202911
xylenes (mixed)
4E-06
2E-07
2E-08
—
—
7202911
cumene
—
9E-09
2E-09
—
—
7202911
n-hexane
—
—
1E-09
—
—
7202911
ethyl benzene
—
2E-08
5E-10
—
—
7202911
ethyl acrylate
—
4E-04
8E-05
3E-01
1E-04
7202911
acrolein
4E-01
2E-02
5E-03
1E-02
3E-03
7202911
formaldehyde
2E-01
8E-03
5E-04
7E-03
7E-04
7202911
acrylic acid
2E-03
3E-03
8E-05
4E-03
8E-05
7202911
acetaldehyde
1E-01
7E-04
IE-04
3E-03
2E-04
7202911
hydrochloric acid
5E-03
4E-03
3E-04
2E-03
3E-04
7202911
methanol
8E-04
3E-05
8E-06
8E-05
2E-05
7202911
1,3-butadiene
—
3E-07
4E-08
2E-05
4E-07
7202911
benzene
—
1E-06
9E-08
1E-06
5E-07
7202911
toluene
—
6E-07
7E-08
8E-07
1E-07
7202911
ethylene dichloride
—
—
—
2E-07
6E-08
7202911
phenol
1E-06
1E-07
1E-07
2E-07
5E-08
7202911
styrene
2E-06
6E-07
9E-08
2E-07
5E-08
7203711
2,2,4-trimethylpentane
—
—
—
—
—
7203711
antimony compounds
—
—
—
—
—
7203711
chromium (iii) compounds
—
—
—
—
—
7203711
chromium (vi) compounds
—
—
—
—
—
7203711
cobalt compounds
—
—
—
—
—
7203711
cresols (mixed)
—
—
—
—
—
7203711
gaseous divalent mercury
—
—
—
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
7203711
hexamethylene-1,6-
diisocyanate
—
—
—
—
—
7203711
lead compounds
—
—
—
—
—
7203711
manganese compounds
—
—
—
—
—
7203711
naphthalene
—
—
—
—
—
7203711
nickel compounds
—
—
—
—
—
7203711
p-dichlorobenzene
—
—
—
—
—
7203711
pah, total
—
—
—
—
—
7203711
particulate divalent
mercury
—
—
—
—
—
7203711
selenium compounds
—
—
—
—
—
7203711
ethylene glycol methyl
ether
2E-02
—
—
—
—
7203711
arsenic compounds
1E-02
—
—
—
—
7203711
xylenes (mixed)
9E-04
4E-05
5E-06
—
—
7203711
cadmium compounds
—
2E-05
3E-06
—
—
7203711
cumene
—
2E-05
3E-06
—
—
7203711
ethyl benzene
—
7E-05
2E-06
—
—
7203711
biphenyl
—
—
1E-06
—
—
7203711
mercury (elemental)
4E-03
—
1E-06
—
1E-06
7203711
hydrogen cyanide
2E-05
3E-06
8E-07
—
6E-07
7203711
n-hexane
—
—
7E-07
—
—
7203711
chlorobenzene
—
7E-09
4E-10
—
—
7203711
hydrofluoric acid
5E-03
1E-03
6E-05
7E-04
7E-05
7203711
benzene
—
7E-04
4E-05
7E-04
2E-04
7203711
acrolein
2E-02
8E-04
2E-04
5E-04
2E-04
7203711
formaldehyde
1E-02
6E-04
4E-05
5E-04
5E-05
7203711
toluene
—
2E-04
2E-05
3E-04
5E-05
7203711
hydrochloric acid
IE-04
IE-04
9E-06
6E-05
1E-05
7203711
1,3-butadiene
—
5E-07
6E-08
3E-05
7E-07
7203711
phenol
2E-04
2E-05
1E-05
2E-05
5E-06
7203711
acetaldehyde
5E-04
3E-06
5E-07
1E-05
7E-07
7203711
styrene
4E-06
9E-07
1E-07
4E-07
7E-08
7203711
methanol
4E-08
2E-09
4E-10
5E-09
9E-10
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
7203711
methylene chloride
8E-08
2E-09
6E-10
1E-09
4E-10
7203711
1,1,1-trichloroethane
2E-09
1E-10
4E-11
7E-11
4E-11
7203711
carbon disulfide
4E-12
6E-13
5E-14
8E-12
1E-13
7204811
4-aminobiphenyl
—
—
—
—
—
7204811
antimony compounds
—
—
—
—
—
7204811
beryllium compounds
—
—
—
—
4E-06
7204811
chromium (iii) compounds
—
—
—
—
—
7204811
chromium (vi) compounds
—
—
—
—
—
7204811
cobalt compounds
—
—
—
—
—
7204811
ethylene glycol
—
—
—
—
—
7204811
gaseous divalent mercury
—
—
—
—
—
7204811
lead compounds
—
—
—
—
—
7204811
manganese compounds
—
—
—
—
—
7204811
nickel compounds
—
—
—
—
—
7204811
p-phenylenediamine
—
—
—
—
—
7204811
particulate divalent
mercury
—
—
—
—
—
7204811
selenium compounds
—
—
—
—
—
7204811
arsenic compounds
8E-04
—
—
—
—
7204811
aniline
—
1E-03
9E-04
—
—
7204811
cadmium compounds
—
1E-03
2E-04
—
—
7204811
biphenyl
—
—
1E-05
—
—
7204811
mercury (elemental)
IE-04
—
4E-08
—
3E-08
7204811
formaldehyde
4E-03
2E-04
1E-05
2E-04
2E-05
7204811
benzene
—
6E-05
4E-06
6E-05
2E-05
7204811
hydrochloric acid
3E-05
2E-05
2E-06
1E-05
2E-06
7226311
naphthalene
—
—
—
—
—
7226311
ethyl benzene
—
3E-04
9E-06
—
—
7226311
xylenes (mixed)
1E-03
4E-05
6E-06
—
—
7226311
cumene
—
1E-06
2E-07
—
—
7226311
n-hexane
—
—
4E-09
—
—
7226311
benzene
—
9E-04
6E-05
1E-03
3E-04
7226311
toluene
—
6E-04
7E-05
8E-04
1E-04
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
7226311
styrene
1E-03
3E-04
4E-05
IE-04
2E-05
7226311
methanol
6E-06
2E-07
6E-08
6E-07
1E-07
7226611
2,2,4-trimethylpentane
—
—
—
—
—
7226611
bromoform
—
—
—
—
—
7226611
chromium (iii) compounds
—
—
—
—
—
7226611
chromium (vi) compounds
—
—
—
—
—
7226611
cobalt compounds
—
—
—
—
—
7226611
cresols (mixed)
—
—
—
—
—
7226611
ethylene glycol
—
—
—
—
—
7226611
manganese compounds
—
—
—
—
—
7226611
naphthalene
—
—
—
—
—
7226611
nickel compounds
—
—
—
—
—
7226611
pah, total
—
—
—
—
—
7226611
phthalic anhydride
—
—
—
—
—
7226611
ethylene glycol methyl
ether
2E-03
—
—
—
—
7226611
n-hexane
—
—
IE-04
—
—
7226611
acetonitrile
—
8E-05
2E-05
—
—
7226611
hydrogen cyanide
3E-04
4E-05
1E-05
—
9E-06
7226611
xylenes (mixed)
1E-03
4E-05
5E-06
—
—
7226611
ethyl benzene
—
2E-05
5E-07
—
—
7226611
cumene
—
5E-07
8E-08
—
—
7226611
phosphorus
—
1E-07
5E-08
—
—
7226611
maleic anhydride
—
—
—
9E-03
4E-04
7226611
hydrochloric acid
1E-02
9E-03
8E-04
6E-03
8E-04
7226611
chlorine
1E-02
2E-03
4E-04
9E-04
3E-04
7226611
methanol
2E-03
IE-04
3E-05
3E-04
5E-05
7226611
dimethyl formamide
—
—
7E-06
3E-04
6E-06
7226611
1,3-butadiene
—
2E-06
3E-07
2E-04
3E-06
7226611
methyl chloride
—
—
2E-05
IE-04
2E-05
7226611
benzene
—
8E-05
5E-06
9E-05
3E-05
7226611
toluene
—
4E-05
5E-06
5E-05
9E-06
7226611
methyl tert-butyl ether
—
2E-05
1E-06
2E-05
9E-07
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
7226611
styrene
4E-05
1E-05
2E-06
4E-06
8E-07
7226611
phenol
1E-05
1E-06
1E-06
2E-06
5E-07
7226611
1,1,1-trichloroethane
4E-07
2E-08
8E-09
1E-08
7E-09
7226611
methylene chloride
5E-08
1E-09
4E-10
7E-10
3E-10
7226711
2-nitropropane
—
—
—
—
—
7226711
nickel compounds
—
—
—
—
—
7226711
triethylamine
6E-05
—
—
—
—
7226711
acetonitrile
—
1E-03
3E-04
—
—
7226711
propionaldehyde
—
2E-06
3E-07
—
—
7226711
formaldehyde
4E-01
2E-02
1E-03
2E-02
2E-03
7226711
acetaldehyde
6E-02
3E-04
5E-05
1E-03
7E-05
7226711
chlorine
2E-03
3E-04
9E-05
2E-04
6E-05
7226711
methanol
1E-03
4E-05
1E-05
IE-04
2E-05
7227011
1,1,2,2-tetrachloroethane
—
—
—
—
—
7227011
1,1,2-trichloroethane
—
—
—
—
—
7227011
1,2,3,4,6,7,8,9-
octachlorodibenzo-p-
dioxin
7227011
1,2,3,4,6,7,8,9-
octachlorodibenzofuran
—
—
—
—
—
7227011
1,2,3,4,6,7,8-
heptachlorodibenzo-p-
dioxin
7227011
1,2,3,4,6,7,8-
heptachlorodibenzofuran
—
—
—
—
—
7227011
1,2,3,4,7,8,9-
heptachlorodibenzofuran
—
—
—
—
—
7227011
1,2,3,4,7,8-
hexachlorodibenzo-p-
dioxin
7227011
1,2,3,4,7,8-
hexachlorodibenzofuran
—
—
—
—
—
7227011
1,2,3,6,7,8-
hexachlorodibenzo-p-
dioxin
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
7227011
1,2,3,6,7,8-
hexachlorodibenzofuran
—
—
—
—
—
7227011
1,2,3,7,8,9-
hexachlorodibenzo-p-
dioxin
7227011
1,2,3,7,8,9-
hexachlorodibenzofuran
—
—
—
—
—
7227011
1,2,3,7,8-
pentachlorodibenzo-p-
dioxin
7227011
1,2,3,7,8-
pentachlorodibenzofuran
—
—
—
—
—
7227011
2,3,4,6,7,8-
hexachlorodibenzofuran
—
—
—
—
—
7227011
2,3,4,7,8-
pentachlorodibenzofuran
—
—
—
—
—
7227011
2,3,7,8-
tetrachlorodibenzo-p-
dioxin
7227011
2,3,7,8-
tetrachlorodibenzofuran
—
—
—
—
—
7227011
acetophenone
—
—
—
—
—
7227011
asbestos
—
—
—
—
—
7227011
chloroprene
—
—
—
—
—
7227011
chromium (iii) compounds
—
—
—
—
—
7227011
chromium (vi) compounds
—
—
—
—
—
7227011
cobalt compounds
—
—
—
—
—
7227011
dibutylphthalate
—
—
—
—
—
7227011
ethyl chloride
—
—
—
—
—
7227011
ethylene glycol
—
—
—
—
—
7227011
ethylidene dichloride
—
—
—
—
—
7227011
hexachlorobenzene
—
—
—
—
—
7227011
hexachloroethane
—
—
—
—
—
7227011
manganese compounds
—
—
—
—
—
7227011
naphthalene
—
—
—
—
—
7227011
nickel compounds
—
—
—
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
7227011
p-dichlorobenzene
—
—
—
—
—
7227011
pah, total
—
—
—
—
—
7227011
polychlorinated biphenyls
—
—
—
—
—
7227011
propylene dichloride
—
—
—
—
—
7227011
vinylidene chloride
—
—
—
—
9E-08
7227011
ethylene glycol methyl
ether
1E-03
—
—
—
—
7227011
cumene
—
1E-03
2E-04
—
—
7227011
xylenes (mixed)
2E-03
8E-05
1E-05
—
—
7227011
chloroform
8E-03
—
4E-06
—
5E-06
7227011
ethyl benzene
—
8E-05
2E-06
—
—
7227011
cadmium compounds
—
4E-07
6E-08
—
—
7227011
n-hexane
—
—
5E-08
—
—
7227011
ethylene oxide
—
—
3E-08
—
3E-08
7227011
hydrochloric acid
2E-02
2E-02
2E-03
1E-02
2E-03
7227011
formaldehyde
2E-02
1E-03
7E-05
1E-03
1E-04
7227011
acrolein
4E-02
1E-03
4E-04
9E-04
3E-04
7227011
chlorine
8E-03
1E-03
3E-04
6E-04
2E-04
7227011
methanol
4E-03
2E-04
4E-05
4E-04
8E-05
7227011
ethylene dichloride
—
—
—
3E-04
8E-05
7227011
vinyl chloride
4E-04
IE-04
2E-05
5E-05
5E-06
7227011
phenol
3E-04
3E-05
2E-05
5E-05
1E-05
7227011
acetaldehyde
2E-03
1E-05
2E-06
5E-05
2E-06
7227011
benzene
—
6E-06
4E-07
6E-06
2E-06
7227011
toluene
—
4E-06
4E-07
5E-06
8E-07
7227011
carbon tetrachloride
2E-04
—
4E-06
2E-06
5E-07
7227011
1,3-butadiene
—
3E-08
4E-09
2E-06
5E-08
7227011
methyl chloride
—
—
2E-07
1E-06
1E-07
7227011
tetrachloroethene
2E-05
1E-06
2E-07
5E-07
3E-07
7227011
trichloroethylene
—
2E-07
5E-08
2E-07
4E-08
7227011
methylene chloride
2E-06
4E-08
2E-08
3E-08
1E-08
7227011
1,1,1-trichloroethane
4E-07
2E-08
7E-09
1E-08
6E-09
7228511
acrylamide
—
—
—
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
7228511
cobalt compounds
—
—
—
—
—
7228511
ethylene glycol
—
—
—
—
—
7228511
nickel compounds
—
—
—
—
—
7228511
hydrogen cyanide
3E-02
5E-03
1E-03
—
1E-03
7228511
acetonitrile
—
IE-04
4E-05
—
—
7228511
methyl methacrylate
—
7E-05
1E-05
—
—
7228511
n-hexane
—
—
9E-08
—
—
7228511
acrylonitrile
—
—
3E-02
5E-03
2E-03
7228511
acrolein
4E-02
1E-03
4E-04
9E-04
3E-04
7228511
hydrochloric acid
2E-04
2E-04
1E-05
IE-04
1E-05
7228511
methanol
9E-04
4E-05
9E-06
9E-05
2E-05
7228511
formaldehyde
7E-04
4E-05
2E-06
3E-05
3E-06
7228511
acetaldehyde
IE-04
7E-07
1E-07
3E-06
2E-07
7228511
toluene
—
1E-07
2E-08
2E-07
3E-08
7228511
benzene
—
5E-09
4E-10
6E-09
2E-09
7246511
2-methylnaphthalene
—
—
—
—
—
7246511
anthracene
—
—
—
—
—
7246511
benz[a]anthracene
—
—
—
—
—
7246511
beryllium compounds
—
—
—
—
—
7246511
chromium (iii) compounds
—
—
—
—
—
7246511
chromium (vi) compounds
—
—
—
—
—
7246511
chrysene
—
—
—
—
—
7246511
cobalt compounds
—
—
—
—
—
7246511
ethylene glycol
—
—
—
—
—
7246511
fluoranthene
—
—
—
—
—
7246511
fluorene
—
—
—
—
—
7246511
gaseous divalent mercury
—
—
—
—
—
7246511
lead compounds
—
—
—
—
—
7246511
manganese compounds
—
—
—
—
—
7246511
methyl isobutyl ketone
—
—
—
—
—
7246511
naphthalene
—
—
—
—
—
7246511
nickel compounds
—
—
—
—
—
7246511
pah, total
—
—
—
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
7246511
particulate divalent
mercury
—
—
—
—
—
7246511
phenanthrene
—
—
—
—
—
7246511
pyrene
—
—
—
—
—
7246511
selenium compounds
—
—
—
—
—
7246511
arsenic compounds
5E-02
—
—
—
—
7246511
triethylamine
3E-02
—
—
—
—
7246511
acetonitrile
—
3E-02
7E-03
—
—
7246511
cadmium compounds
—
6E-04
8E-05
—
—
7246511
n-hexane
—
—
2E-05
—
—
7246511
methyl methacrylate
—
IE-04
2E-05
—
—
7246511
xylenes (mixed)
2E-04
9E-06
1E-06
—
—
7246511
1,4-dioxane
7E-05
3E-06
2E-07
—
—
7246511
mercury (elemental)
2E-05
—
7E-09
—
5E-09
7246511
toluene
—
2E-03
2E-04
2E-03
4E-04
7246511
chlorine
2E-02
2E-03
6E-04
1E-03
4E-04
7246511
hydrochloric acid
2E-03
2E-03
IE-04
1E-03
1E-04
7246511
methanol
1E-02
5E-04
IE-04
1E-03
3E-04
7246511
methyl tert-butyl ether
—
8E-04
7E-05
8E-04
4E-05
7246511
methylene chloride
1E-03
2E-05
7E-06
1E-05
5E-06
7246511
formaldehyde
2E-04
1E-05
8E-07
1E-05
1E-06
7246511
benzene
—
7E-07
4E-08
7E-07
2E-07
7246511
acetaldehyde
1E-06
6E-09
1E-09
3E-08
1E-09
7302511
formaldehyde
1
5E-02
4E-03
5E-02
5E-03
7302511
methanol
3E-03
IE-04
3E-05
3E-04
6E-05
7302511
phenol
5E-04
5E-05
3E-05
8E-05
2E-05
7311911
lead compounds
—
—
—
—
—
7319811
2,2,4-trimethylpentane
—
—
—
—
—
7319811
2-methylnaphthalene
—
—
—
—
—
7319811
3-methylcholanthrene
—
—
—
—
—
7319811
7,12-
dimethylbenz[a]anthracen
e
7319811
acenaphthene
—
—
—
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
7319811
acenaphthylene
—
—
—
—
—
7319811
anthracene
—
—
—
—
—
7319811
benz[a]anthracene
—
—
—
—
—
7319811
benzo(ghi)perylene
—
—
—
—
—
7319811
benzo[a]pyrene
—
—
—
—
—
7319811
benzo[b]fluoranthene
—
—
—
—
—
7319811
benzo[k]fluoranthene
—
—
—
—
—
7319811
beryllium compounds
—
—
—
—
7E-09
7319811
chromium (iii) compounds
—
—
—
—
—
7319811
chromium (vi) compounds
—
—
—
—
—
7319811
chrysene
—
—
—
—
—
7319811
cobalt compounds
—
—
—
—
—
7319811
cresols (mixed)
—
—
—
—
—
7319811
dibenzo[a,h]anthracene
—
—
—
—
—
7319811
fluoranthene
—
—
—
—
—
7319811
fluorene
—
—
—
—
—
7319811
gaseous divalent mercury
—
—
—
—
—
7319811
indeno[l,2,3-c,d]pyrene
—
—
—
—
—
7319811
lead compounds
—
—
—
—
—
7319811
manganese compounds
—
—
—
—
—
7319811
naphthalene
—
—
—
—
—
7319811
nickel compounds
—
—
—
—
—
7319811
particulate divalent
mercury
—
—
—
—
—
7319811
phenanthrene
—
—
—
—
—
7319811
phenol
—
—
—
—
—
7319811
pyrene
—
—
—
—
—
7319811
selenium compounds
—
—
—
—
—
7319811
tetrachloroethene
—
—
—
—
—
7319811
arsenic compounds
3E-05
—
—
—
—
7319811
xylenes (mixed)
9E-03
4E-04
5E-05
—
—
7319811
n-hexane
—
—
6E-07
—
—
7319811
ethyl benzene
—
1E-05
4E-07
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
7319811
cumene
—
3E-07
6E-08
—
—
7319811
cadmium compounds
—
3E-07
4E-08
—
—
7319811
mercury (elemental)
3E-06
—
9E-10
—
7E-10
7319811
toluene
—
3E-03
3E-04
4E-03
7E-04
7319811
benzene
—
IE-04
9E-06
2E-04
5E-05
7319811
acrolein
2E-04
8E-06
2E-06
5E-06
2E-06
7319811
methanol
3E-05
1E-06
3E-07
3E-06
7E-07
7319811
formaldehyde
4E-05
2E-06
1E-07
2E-06
2E-07
7319811
1,3-butadiene
—
3E-08
4E-09
2E-06
4E-08
7331911
1,3-butadiene
—
—
—
—
—
7331911
2,2,4-trimethylpentane
—
—
—
—
—
7331911
benzo(ghi)perylene
—
—
—
—
—
7331911
carbon disulfide
—
—
—
—
—
7331911
cobalt compounds
—
—
—
—
—
7331911
ethylene glycol
—
—
—
—
—
7331911
gaseous divalent mercury
—
—
—
—
—
7331911
naphthalene
—
—
—
—
—
7331911
nickel compounds
—
—
—
—
—
7331911
particulate divalent
mercury
—
—
—
—
—
7331911
polycyclic organic matter
—
—
—
—
—
7331911
cumene
—
2E-03
4E-04
—
—
7331911
n-hexane
—
—
7E-05
—
—
7331911
carbonyl sulfide
—
—
2E-05
—
—
7331911
mercury (elemental)
6E-03
—
2E-06
—
2E-06
7331911
xylenes (mixed)
2E-04
8E-06
1E-06
—
—
7331911
ethyl benzene
—
1E-07
3E-09
—
—
7331911
hydrofluoric acid
7E-02
2E-02
9E-04
1E-02
1E-03
7331911
benzene
—
1E-02
8E-04
1E-02
4E-03
7331911
hydrochloric acid
4E-03
3E-03
3E-04
2E-03
3E-04
7331911
styrene
6E-03
1E-03
2E-04
6E-04
1E-04
7331911
toluene
—
2E-04
2E-05
2E-04
4E-05
7331911
phenol
8E-04
8E-05
5E-05
IE-04
2E-05
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
7331911
methanol
2E-04
8E-06
2E-06
2E-05
4E-06
7331911
tetrachloroethene
2E-06
2E-07
3E-08
7E-08
3E-08
7338711
ethyl benzene
—
—
—
—
—
7338711
lead compounds
—
—
—
—
—
7338711
methyl isobutyl ketone
—
—
—
—
—
7338711
naphthalene
—
—
—
—
—
7338711
triethylamine
—
—
—
—
—
7338711
xylenes (mixed)
—
—
—
—
—
7338711
n-hexane
—
—
1E-05
—
—
7338711
maleic anhydride
—
—
—
1E-02
4E-04
7338711
hydrochloric acid
1E-02
1E-02
8E-04
6E-03
9E-04
7338711
carbon disulfide
4E-04
6E-05
5E-06
8E-04
1E-05
7338711
methanol
2E-03
IE-04
2E-05
3E-04
5E-05
7338711
hydrazine
—
2E-03
1E-05
3E-04
3E-05
7338711
chlorine
2E-03
3E-04
8E-05
2E-04
5E-05
7338711
benzene
—
1E-05
6E-07
1E-05
3E-06
7351811
2,4-toluene diisocyanate
—
—
—
—
—
7351811
benzene
—
—
—
—
—
7351811
diethanolamine
—
—
—
—
—
7351811
ethylene glycol
—
—
—
—
—
7351811
hexamethylene-1,6-
diisocyanate
—
—
—
—
—
7351811
hydrazine
—
—
—
—
—
7351811
n-hexane
—
—
—
—
—
7351811
toluene
—
—
—
—
—
7351811
xylenes (mixed)
—
—
—
—
—
7351811
ethylene glycol methyl
ether
2
—
—
—
—
7351811
ethylene oxide
—
—
2E-03
—
2E-03
7351811
ethyl benzene
—
4E-04
1E-05
—
—
7351811
acrylonitrile
—
—
2E-02
3E-03
7E-04
7351811
formaldehyde
5E-02
2E-03
2E-04
2E-03
2E-04
7351811
propylene oxide
5E-02
1E-03
2E-04
1E-03
3E-04
7351811
hydrochloric acid
3E-04
2E-04
2E-05
IE-04
2E-05
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
7351811
methanol
2E-04
9E-06
2E-06
2E-05
5E-06
7354711
1,1,2,2-tetrachloroethane
—
—
—
—
—
7354711
1,1,2-trichloroethane
—
—
—
—
—
7354711
1,2,3,4,6,7,8,9-
octachlorodibenzo-p-
dioxin
7354711
1,2,3,4,6,7,8,9-
octachlorodibenzofuran
—
—
—
—
—
7354711
1,2,3,4,6,7,8-
heptachlorodibenzo-p-
dioxin
7354711
1,2,3,4,6,7,8-
heptachlorodibenzofuran
—
—
—
—
—
7354711
1,2,3,4,7,8,9-
heptachlorodibenzofuran
—
—
—
—
—
7354711
1,2,3,4,7,8-
hexachlorodibenzo-p-
dioxin
7354711
1,2,3,4,7,8-
hexachlorodibenzofuran
—
—
—
—
—
7354711
1,2,3,6,7,8-
hexachlorodibenzo-p-
dioxin
7354711
1,2,3,6,7,8-
hexachlorodibenzofuran
—
—
—
—
—
7354711
1,2,3,7,8,9-
hexachlorodibenzo-p-
dioxin
7354711
1,2,3,7,8,9-
hexachlorodibenzofuran
—
—
—
—
—
7354711
1,2,3,7,8-
pentachlorodibenzo-p-
dioxin
7354711
1,2,3,7,8-
pentachlorodibenzofuran
—
—
—
—
—
7354711
2,3,4,6,7,8-
hexachlorodibenzofuran
—
—
—
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
7354711
2,3,4,7,8-
pentachlorodibenzofuran
—
—
—
—
—
7354711
2,3,7,8-
tetrachlorodibenzo-p-
dioxin
7354711
2,3,7,8-
tetrachlorodibenzofuran
—
—
—
—
—
7354711
acetophenone
—
—
—
—
—
7354711
bromoform
—
—
—
—
—
7354711
chloroprene
—
—
—
—
—
7354711
ethyl chloride
—
—
—
—
—
7354711
ethylene glycol
—
—
—
—
—
7354711
ethylidene dichloride
—
—
—
—
—
7354711
chloroform
3E-01
—
2E-04
—
2E-04
7354711
chlorobenzene
—
8E-07
6E-08
—
—
7354711
n-hexane
—
—
1E-09
—
—
7354711
hydrochloric acid
2E-02
1E-02
1E-03
9E-03
1E-03
7354711
ethylene dichloride
—
—
—
2E-03
4E-04
7354711
chlorine
3E-02
4E-03
1E-03
2E-03
7E-04
7354711
vinyl chloride
1E-03
4E-04
8E-05
2E-04
2E-05
7354711
carbon tetrachloride
9E-03
—
2E-04
IE-04
3E-05
7354711
formaldehyde
1E-03
5E-05
3E-06
5E-05
5E-06
7354711
benzene
—
2E-05
1E-06
2E-05
7E-06
7354711
acetaldehyde
6E-04
4E-06
6E-07
2E-05
8E-07
7354711
tetrachloroethene
4E-04
3E-05
4E-06
1E-05
5E-06
7354711
methyl chloride
—
—
9E-07
5E-06
8E-07
7354711
trichloroethylene
—
2E-06
7E-07
3E-06
6E-07
7354711
methylene chloride
IE-04
3E-06
1E-06
2E-06
7E-07
7354711
1,1,1-trichloroethane
5E-05
2E-06
1E-06
2E-06
8E-07
7354711
methanol
2E-05
9E-07
2E-07
2E-06
5E-07
7354911
1,1,2,2-tetrachloroethane
—
—
—
—
—
7354911
1,1,2-trichloroethane
—
—
—
—
—
7354911
1,2,3,4,6,7,8,9-
octachlorodibenzo-p-
dioxin
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
7354911
1,2,3,4,6,7,8,9-
octachlorodibenzofuran
—
—
—
—
—
7354911
1,2,3,4,6,7,8-
heptachlorodibenzo-p-
dioxin
7354911
1,2,3,4,6,7,8-
heptachlorodibenzofuran
—
—
—
—
—
7354911
1,2,3,4,7,8-
hexachlorodibenzofuran
—
—
—
—
—
7354911
1,2,3,6,7,8-
hexachlorodibenzo-p-
dioxin
7354911
1,2,3,6,7,8-
hexachlorodibenzofuran
—
—
—
—
—
7354911
1,2,3,7,8,9-
hexachlorodibenzo-p-
dioxin
7354911
1,2,3,7,8,9-
hexachlorodibenzofuran
—
—
—
—
—
7354911
1,2,3,7,8-
pentachlorodibenzo-p-
dioxin
7354911
1,2,3,7,8-
pentachlorodibenzofuran
—
—
—
—
—
7354911
1,2,4-trichlorobenzene
—
—
—
—
—
7354911
l,2-dibromo-3-
chloropropane
—
—
—
—
—
7354911
2,3,4,7,8-
pentachlorodibenzofuran
—
—
—
—
—
7354911
2,3,7,8-
tetrachlorodibenzo-p-
dioxin
7354911
2,3,7,8-
tetrachlorodibenzofuran
—
—
—
—
—
7354911
2,4,6-trichlorophenol
—
—
—
—
—
7354911
2-nitropropane
—
—
—
—
—
7354911
acetophenone
—
—
—
—
—
7354911
antimony compounds
—
—
—
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
7354911
beryllium compounds
—
—
—
—
4E-03
7354911
bis(2-ethylhexyl)phthalate
—
—
—
—
—
7354911
bromoform
—
—
—
—
—
7354911
chloroprene
—
—
—
—
—
7354911
cobalt compounds
—
—
—
—
—
7354911
dichloroethyl ether
—
—
—
—
—
7354911
ethyl chloride
—
—
—
—
—
7354911
ethylene glycol
—
—
—
—
—
7354911
ethylidene dichloride
—
—
—
—
—
7354911
gaseous divalent mercury
—
—
—
—
—
7354911
hexachlorobenzene
—
—
—
—
—
7354911
hexachlorocyclopentadien
e
—
—
—
—
—
7354911
hexachloroethane
—
—
—
—
—
7354911
lead compounds
—
—
—
—
—
7354911
manganese compounds
—
—
—
—
—
7354911
methyl isobutyl ketone
—
—
—
—
—
7354911
naphthalene
—
—
—
—
—
7354911
nickel compounds
—
—
—
—
—
7354911
nitrobenzene
—
—
—
—
—
7354911
p-dichlorobenzene
—
—
—
—
—
7354911
particulate divalent
mercury
—
—
—
—
—
7354911
polychlorinated biphenyls
—
—
—
—
—
7354911
selenium compounds
—
—
—
—
—
7354911
vinylidene chloride
—
—
—
—
1E-05
7354911
arsenic compounds
3E-02
—
—
—
—
7354911
biphenyl
—
—
4E-04
—
—
7354911
chloroform
3E-01
—
IE-04
—
2E-04
7354911
1,2-epoxybutane
—
1E-05
5E-06
—
—
7354911
cadmium compounds
—
3E-05
4E-06
—
—
7354911
mercury (elemental)
8E-03
—
3E-06
—
2E-06
7354911
xylenes (mixed)
1E-05
4E-07
5E-08
—
—
7354911
chlorobenzene
—
9E-08
6E-09
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
7354911
hydrochloric acid
3E-01
2E-01
2E-02
1E-01
2E-02
7354911
chlorine
2E-01
3E-02
8E-03
2E-02
5E-03
7354911
ethylene dichloride
—
—
—
7E-04
2E-04
7354911
tetrachloroethene
1E-02
9E-04
IE-04
3E-04
2E-04
7354911
hexachlorobutadiene
—
—
—
2E-04
8E-05
7354911
carbon tetrachloride
1E-02
—
3E-04
2E-04
4E-05
7354911
trichloroethylene
—
IE-04
4E-05
2E-04
4E-05
7354911
1,1,1-trichloroethane
1E-03
8E-05
3E-05
5E-05
3E-05
7354911
vinyl chloride
IE-04
4E-05
8E-06
2E-05
2E-06
7354911
carbon disulfide
7E-06
1E-06
9E-08
1E-05
3E-07
7354911
methanol
6E-05
2E-06
6E-07
6E-06
1E-06
7354911
styrene
3E-05
9E-06
1E-06
3E-06
7E-07
7354911
toluene
—
1E-06
1E-07
2E-06
3E-07
7354911
1,3-butadiene
—
3E-08
3E-09
2E-06
4E-08
7354911
benzene
—
7E-07
4E-08
7E-07
2E-07
7354911
methylene chloride
3E-05
7E-07
2E-07
5E-07
2E-07
7354911
methyl chloride
—
—
3E-08
2E-07
2E-08
7354911
acrylonitrile
—
—
5E-07
9E-08
3E-08
7354911
allyl chloride
—
2E-08
9E-10
2E-08
1E-09
7354911
acetaldehyde
4E-07
2E-09
4E-10
1E-08
5E-10
7354911
phenol
3E-09
3E-10
2E-10
4E-10
9E-11
7364611
formaldehyde
8E-04
4E-05
2E-06
4E-05
4E-06
7364611
benzene
—
7E-09
5E-10
8E-09
3E-09
7365611
ethyl chloride
—
—
—
—
—
7365611
pah, total
—
—
—
—
—
7365611
n-hexane
—
—
2E-05
—
—
7365611
xylenes (mixed)
3E-08
1E-09
2E-10
—
—
7365611
acrolein
9E-05
3E-06
1E-06
2E-06
7E-07
7365611
formaldehyde
5E-05
3E-06
2E-07
2E-06
2E-07
7365611
acetaldehyde
4E-06
2E-08
4E-09
1E-07
5E-09
7365611
benzene
—
1E-08
9E-10
1E-08
5E-09
7365611
toluene
—
4E-09
5E-10
5E-09
9E-10
7365611
1,3-butadiene
—
6E-11
8E-12
4E-09
9E-11
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
7366811
1,1,2,2-tetrachloroethane
—
—
—
—
—
7366811
1,1,2-trichloroethane
—
—
—
—
—
7366811
1,2,4-trichlorobenzene
—
—
—
—
—
7366811
antimony compounds
—
—
—
—
—
7366811
ethyl benzene
—
—
—
—
—
7366811
ethyl chloride
—
—
—
—
—
7366811
ethylidene dichloride
—
—
—
—
—
7366811
p-dichlorobenzene
—
—
—
—
—
7366811
toluene
—
—
—
—
—
7366811
chloroform
5E-04
—
2E-07
—
3E-07
7366811
1,4-dioxane
5E-06
3E-07
1E-08
—
—
7366811
chlorobenzene
—
2E-07
1E-08
—
—
7366811
xylenes (mixed)
1E-08
6E-10
8E-11
—
—
7366811
hydrochloric acid
9E-01
7E-01
6E-02
4E-01
6E-02
7366811
chlorine
2
3E-01
7E-02
1E-01
4E-02
7366811
hydrofluoric acid
3E-01
8E-02
3E-03
4E-02
4E-03
7366811
acrolein
4E-04
2E-05
5E-06
1E-05
3E-06
7366811
1,1,1-trichloroethane
1E-05
7E-07
3E-07
5E-07
2E-07
7366811
carbon disulfide
1E-07
2E-08
2E-09
3E-07
6E-09
7366811
carbon tetrachloride
5E-06
—
1E-07
8E-08
2E-08
7366811
acrylonitrile
—
—
3E-07
6E-08
2E-08
7366811
trichloroethylene
—
3E-08
8E-09
4E-08
7E-09
7366811
benzyl chloride
7E-07
—
—
3E-08
3E-09
7366811
ethylene dichloride
—
—
—
2E-08
5E-09
7366811
benzene
—
5E-09
3E-10
6E-09
2E-09
7366811
vinyl chloride
4E-09
1E-09
2E-10
5E-10
5E-11
7367211
2,4-dinitrotoluene
—
—
—
—
—
7367211
ethylene glycol
—
—
—
—
—
7367211
methylene diphenyl
diisocyanate
3
—
—
—
8E-03
7367211
triethylamine
4E-06
—
—
—
—
7367211
hydrogen cyanide
1E-01
2E-02
5E-03
—
3E-03
7367211
chloroform
2E-04
—
1E-07
—
1E-07
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
7367211
toluene
—
2E-04
3E-05
3E-04
5E-05
7367811
toluene
—
8E-02
9E-03
1E-01
2E-02
7367811
styrene
1E-02
3E-03
5E-04
1E-03
3E-04
7367811
acrylonitrile
—
—
5E-03
8E-04
2E-04
7368011
2-methylnaphthalene
—
—
—
—
—
7368011
7,12-
dimethylbenz[a]anthracen
e
7368011
beryllium compounds
—
—
—
—
7E-09
7368011
chromium (iii) compounds
—
—
—
—
—
7368011
chromium (vi) compounds
—
—
—
—
—
7368011
cobalt compounds
—
—
—
—
—
7368011
cresols (mixed)
—
—
—
—
—
7368011
ethylene glycol
—
—
—
—
—
7368011
gaseous divalent mercury
—
—
—
—
—
7368011
lead compounds
—
—
—
—
—
7368011
manganese compounds
—
—
—
—
—
7368011
methyl isobutyl ketone
—
—
—
—
—
7368011
naphthalene
—
—
—
—
—
7368011
nickel compounds
—
—
—
—
—
7368011
p-dichlorobenzene
—
—
—
—
—
7368011
particulate divalent
mercury
—
—
—
—
—
7368011
phenanthrene
—
—
—
—
—
7368011
selenium compounds
—
—
—
—
—
7368011
methylene diphenyl
diisocyanate
1E-01
—
—
—
2E-04
7368011
glycol ethers
1E-03
—
—
—
—
7368011
arsenic compounds
2E-05
—
—
—
—
7368011
triethylamine
8E-06
—
—
—
—
7368011
biphenyl
—
—
1E-06
—
—
7368011
cadmium compounds
—
2E-07
2E-08
—
—
7368011
n-hexane
—
—
3E-09
—
—
7368011
mercury (elemental)
1E-06
—
5E-10
—
4E-10
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
7368011
formaldehyde
2E-01
1E-02
7E-04
1E-02
1E-03
7368011
hydrofluoric acid
3E-02
8E-03
3E-04
4E-03
4E-04
7368011
phenol
1E-02
1E-03
1E-03
2E-03
5E-04
7368011
methanol
3E-03
IE-04
4E-05
4E-04
7E-05
7368011
styrene
6E-06
2E-06
2E-07
6E-07
1E-07
7368011
hydrochloric acid
5E-09
4E-09
3E-10
2E-09
3E-10
7368011
toluene
—
2E-10
2E-11
3E-10
5E-11
7368011
benzene
—
2E-10
1E-11
2E-10
7E-11
7368811
ethylene glycol
—
—
—
—
—
7368811
biphenyl
—
—
7E-05
—
—
7368811
formaldehyde
2
1E-01
7E-03
1E-01
1E-02
7368811
methanol
1E-02
5E-04
IE-04
1E-03
2E-04
7380411
2,2,4-trimethylpentane
—
—
—
—
—
7380411
cobalt compounds
—
—
—
—
—
7380411
cresols (mixed)
—
—
—
—
—
7380411
diethanolamine
—
—
—
—
—
7380411
ethylene glycol
—
—
—
—
—
7380411
lead compounds
—
—
—
—
—
7380411
naphthalene
—
—
—
—
—
7380411
pah, total
—
—
—
—
—
7380411
carbonyl sulfide
—
—
2E-04
—
—
7380411
xylenes (mixed)
3E-03
IE-04
2E-05
—
—
7380411
n-hexane
—
—
4E-06
—
—
7380411
cumene
—
2E-05
4E-06
—
—
7380411
biphenyl
—
—
2E-06
—
—
7380411
ethyl benzene
—
7E-05
2E-06
—
—
7380411
carbon disulfide
2E-03
2E-04
2E-05
3E-03
6E-05
7380411
chlorine
5E-03
8E-04
2E-04
4E-04
1E-04
7380411
toluene
—
3E-04
3E-05
3E-04
6E-05
7380411
phenol
1E-03
IE-04
7E-05
2E-04
3E-05
7380411
benzene
—
2E-04
1E-05
2E-04
6E-05
7380411
methanol
1E-03
5E-05
1E-05
IE-04
2E-05
7380411
methyl tert-butyl ether
—
7E-05
6E-06
7E-05
4E-06
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
7380411
hydrochloric acid
IE-04
8E-05
7E-06
5E-05
7E-06
7380411
1,3-butadiene
—
4E-07
6E-08
3E-05
6E-07
7380411
carbon tetrachloride
6E-04
—
1E-05
9E-06
2E-06
7380411
ethylene dichloride
—
—
—
4E-06
9E-07
7380411
tetrachloroethene
3E-05
3E-06
4E-07
9E-07
5E-07
7380611
styrene
1E-02
3E-03
4E-04
1E-03
2E-04
7380611
benzene
—
3E-04
2E-05
3E-04
1E-04
7445611
cobalt compounds
—
—
—
—
—
7445611
ethyl chloride
—
—
—
—
—
7445611
ethylene glycol
—
—
—
—
—
7445611
nickel compounds
—
—
—
—
—
7445611
ethylene oxide
—
—
4E-04
—
3E-04
7445611
n-hexane
—
—
4E-07
—
—
7445611
xylenes (mixed)
7E-09
3E-10
4E-11
—
—
7445611
acetaldehyde
2E-01
9E-04
IE-04
4E-03
2E-04
7445611
chlorine
7E-03
1E-03
3E-04
5E-04
2E-04
7445611
formaldehyde
1E-02
5E-04
3E-05
4E-04
4E-05
7445611
allyl chloride
—
3E-05
1E-06
2E-05
2E-06
7445611
methanol
4E-05
2E-06
4E-07
5E-06
9E-07
7445611
benzene
—
3E-07
2E-08
3E-07
9E-08
7445611
vinyl chloride
4E-08
1E-08
2E-09
5E-09
5E-10
7445611
methyl chloride
—
—
9E-10
5E-09
8E-10
7445611
toluene
—
2E-09
3E-10
3E-09
5E-10
7445711
1,1,2,2-tetrachloroethane
—
—
—
—
—
7445711
1,1,2-trichloroethane
—
—
—
—
—
7445711
antimony compounds
—
—
—
—
—
7445711
beryllium compounds
—
—
—
—
5E-07
7445711
chromium (iii) compounds
—
—
—
—
—
7445711
chromium (vi) compounds
—
—
—
—
—
7445711
ethyl chloride
—
—
—
—
—
7445711
ethylene glycol
—
—
—
—
—
7445711
ethylidene dichloride
—
—
—
—
—
7445711
gaseous divalent mercury
—
—
—
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
7445711
hexachlorobenzene
—
—
—
—
—
7445711
hexachloroethane
—
—
—
—
—
7445711
lead compounds
—
—
—
—
—
7445711
manganese compounds
—
—
—
—
—
7445711
naphthalene
—
—
—
—
—
7445711
nickel compounds
—
—
—
—
—
7445711
pah, total
—
—
—
—
—
7445711
particulate divalent
mercury
—
—
—
—
—
7445711
polychlorinated biphenyls
—
—
—
—
—
7445711
propylene dichloride
—
—
—
—
—
7445711
selenium compounds
—
—
—
—
—
7445711
vinylidene chloride
—
—
—
—
2E-06
7445711
arsenic compounds
1E-03
—
—
—
—
7445711
chloroform
3E-01
—
IE-04
—
2E-04
7445711
n-hexane
—
—
7E-08
—
—
7445711
cadmium compounds
—
2E-07
3E-08
—
—
7445711
chlorobenzene
—
2E-07
1E-08
—
—
7445711
mercury (elemental)
2E-05
—
8E-09
—
6E-09
7445711
ethylene dibromide
—
2E-09
1E-09
—
—
7445711
hydrochloric acid
5E-02
4E-02
3E-03
2E-02
4E-03
7445711
chlorine
9E-02
1E-02
3E-03
7E-03
2E-03
7445711
ethylene dichloride
—
—
—
1E-03
3E-04
7445711
carbon tetrachloride
1E-01
—
2E-03
1E-03
3E-04
7445711
hexachlorobutadiene
—
—
—
4E-04
1E-04
7445711
tetrachloroethene
6E-03
5E-04
7E-05
2E-04
8E-05
7445711
methyl chloride
—
—
3E-05
2E-04
2E-05
7445711
methanol
1E-03
4E-05
1E-05
IE-04
2E-05
7445711
methylene chloride
3E-03
5E-05
2E-05
4E-05
1E-05
7445711
formaldehyde
6E-04
3E-05
2E-06
3E-05
3E-06
7445711
propylene oxide
7E-04
1E-05
3E-06
2E-05
4E-06
7445711
hydrofluoric acid
IE-04
4E-05
2E-06
2E-05
2E-06
7445711
vinyl chloride
5E-05
1E-05
3E-06
7E-06
7E-07
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
7445711
trichloroethylene
—
6E-06
2E-06
7E-06
1E-06
7445711
1,1,1-trichloroethane
IE-04
6E-06
2E-06
4E-06
2E-06
7445711
benzene
—
5E-09
3E-10
5E-09
2E-09
7448011
ethyl benzene
—
6E-03
2E-04
—
—
7448011
n-hexane
—
—
5E-11
—
—
7448011
xylenes (mixed)
3E-09
1E-10
1E-11
—
—
7448011
cumene
—
4E-11
7E-12
—
—
7448011
styrene
3E-02
7E-03
1E-03
3E-03
6E-04
7448011
toluene
—
2E-04
2E-05
2E-04
4E-05
7448011
benzene
—
8E-05
5E-06
9E-05
3E-05
7448011
chlorine
3E-05
4E-06
1E-06
2E-06
6E-07
751411
biphenyl
—
—
3E-03
—
—
751411
maleic anhydride
—
—
—
2
6E-02
7908711
chlorine
8E-02
1E-02
3E-03
6E-03
2E-03
7915011
1,1,2,2-tetrachloroethane
—
—
—
—
—
7915011
1,1,2-trichloroethane
—
—
—
—
—
7915011
1,2,3,4,6,7,8,9-
octachlorodibenzo-p-
dioxin
7915011
1,2,3,4,6,7,8,9-
octachlorodibenzofuran
—
—
—
—
—
7915011
1,2,3,4,6,7,8-
heptachlorodibenzo-p-
dioxin
7915011
1,2,3,4,6,7,8-
heptachlorodibenzofuran
—
—
—
—
—
7915011
1,2,3,4,7,8,9-
heptachlorodibenzofuran
—
—
—
—
—
7915011
1,2,3,4,7,8-
hexachlorodibenzofuran
—
—
—
—
—
7915011
1,2,3,6,7,8-
hexachlorodibenzofuran
—
—
—
—
—
7915011
1,2,3,7,8-
pentachlorodibenzofuran
—
—
—
—
—
7915011
2,3,4,6,7,8-
hexachlorodibenzofuran
—
—
—
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
7915011
2,3,4,7,8-
pentachlorodibenzofuran
—
—
—
—
—
7915011
2,3,7,8-
tetrachlorodibenzofuran
—
—
—
—
—
7915011
bromoform
—
—
—
—
—
7915011
chloroprene
—
—
—
—
—
7915011
dichloroethyl ether
—
—
—
—
—
7915011
ethyl chloride
—
—
—
—
—
7915011
ethylidene dichloride
—
—
—
—
—
7915011
polycyclic organic matter
—
—
—
—
—
7915011
propylene dichloride
—
—
—
—
—
7915011
vinylidene chloride
—
—
—
—
4E-06
7915011
chloroform
3
—
1E-03
—
2E-03
7915011
chlorobenzene
—
1E-05
8E-07
—
—
7915011
ethyl benzene
—
1E-11
4E-13
—
—
7915011
hydrochloric acid
3E-02
2E-02
2E-03
1E-02
2E-03
7915011
ethylene dichloride
—
—
—
5E-03
1E-03
7915011
chlorine
5E-02
7E-03
2E-03
4E-03
1E-03
7915011
methanol
4E-03
2E-04
5E-05
5E-04
9E-05
7915011
phenol
1E-03
IE-04
7E-05
2E-04
3E-05
7915011
1,1,1-trichloroethane
3E-03
2E-04
7E-05
IE-04
6E-05
7915011
vinyl chloride
6E-04
2E-04
4E-05
9E-05
9E-06
7915011
benzene
—
4E-05
3E-06
4E-05
1E-05
7915011
trichloroethylene
—
2E-05
6E-06
3E-05
5E-06
7915011
carbon tetrachloride
1E-03
—
2E-05
1E-05
3E-06
7915011
tetrachloroethene
2E-04
2E-05
2E-06
5E-06
3E-06
7915011
methyl chloride
—
—
4E-07
2E-06
3E-07
7915011
methylene chloride
6E-05
1E-06
5E-07
9E-07
3E-07
7915011
toluene
—
5E-07
6E-08
6E-07
1E-07
7928911
naphthalene
—
—
—
—
—
7928911
pah, total
—
—
—
—
—
7928911
ethyl benzene
—
6E-07
2E-08
—
—
7928911
xylenes (mixed)
2E-08
8E-10
1E-10
—
—
7928911
n-hexane
—
—
4E-11
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
7928911
maleic anhydride
—
—
—
IE-04
5E-06
7928911
chlorine
1E-03
2E-04
5E-05
IE-04
3E-05
7928911
formaldehyde
1E-03
6E-05
4E-06
5E-05
5E-06
7928911
acrolein
6E-05
2E-06
6E-07
1E-06
4E-07
7928911
acetaldehyde
5E-06
3E-08
5E-09
1E-07
6E-09
7928911
benzene
—
5E-08
3E-09
5E-08
2E-08
7928911
toluene
—
1E-08
1E-09
2E-08
3E-09
7929111
2,2,4-trimethylpentane
—
—
—
—
—
7929111
bromoform
—
—
—
—
—
7929111
chromium (iii) compounds
—
—
—
—
—
7929111
chromium (vi) compounds
—
—
—
—
—
7929111
hydroquinone
—
—
—
—
—
7929111
manganese compounds
—
—
—
—
—
7929111
naphthalene
—
—
—
—
—
7929111
nickel compounds
—
—
—
—
—
7929111
chloroform
2E-03
—
1E-06
—
1E-06
7929111
xylenes (mixed)
5E-05
2E-06
3E-07
—
—
7929111
ethyl benzene
—
8E-06
2E-07
—
—
7929111
n-hexane
—
—
2E-08
—
—
7929111
1,3-butadiene
—
7E-06
8E-07
5E-04
9E-06
7929111
benzene
—
7E-05
5E-06
8E-05
3E-05
7929111
toluene
—
2E-05
3E-06
3E-05
5E-06
7929111
styrene
2E-04
5E-05
7E-06
2E-05
4E-06
7929111
methyl tert-butyl ether
—
2E-05
2E-06
2E-05
9E-07
7929111
methanol
8E-06
3E-07
8E-08
9E-07
2E-07
7937511
manganese compounds
—
—
—
—
—
7940411
antimony compounds
—
—
—
—
—
7940411
cresols (mixed)
—
—
—
—
—
7940411
diethanolamine
—
—
—
—
—
7940411
ethylene glycol
—
—
—
—
—
7940411
tetrachloroethene
—
—
—
—
—
7940411
o-xylene
5E-05
—
—
—
—
7940411
biphenyl
—
—
2E-04
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
7940411
hydrogen cyanide
2E-03
4E-04
IE-04
—
8E-05
7940411
xylenes (mixed)
2E-02
6E-04
9E-05
—
—
7940411
carbonyl sulfide
—
—
2E-05
—
—
7940411
n-hexane
—
—
5E-06
—
—
7940411
ethyl benzene
—
2E-05
5E-07
—
—
7940411
carbon disulfide
2E-03
3E-04
2E-05
4E-03
7E-05
7940411
benzene
—
3E-03
2E-04
3E-03
9E-04
7940411
toluene
—
1E-03
2E-04
2E-03
3E-04
7940411
hydrochloric acid
1E-03
9E-04
7E-05
5E-04
8E-05
7940411
styrene
2E-03
5E-04
7E-05
2E-04
3E-05
7940411
methanol
2E-03
7E-05
2E-05
2E-04
4E-05
7972111
hydrogen cyanide
—
—
—
—
—
7972111
lead compounds
—
—
—
—
—
7972111
acetonitrile
—
7E-04
2E-04
—
—
7972111
propionaldehyde
—
6E-05
1E-05
—
—
7972111
xylenes (mixed)
3E-05
1E-06
2E-07
—
—
7972111
formaldehyde
1E-01
6E-03
4E-04
6E-03
6E-04
7972111
acetaldehyde
3E-02
2E-04
3E-05
7E-04
3E-05
7972111
toluene
—
7E-05
8E-06
9E-05
2E-05
7972111
benzene
—
7E-05
5E-06
8E-05
3E-05
7972111
hydrochloric acid
IE-04
9E-05
7E-06
5E-05
8E-06
7972111
methanol
9E-05
4E-06
1E-06
1E-05
2E-06
7972911
acenaphthene
—
—
—
—
—
7972911
acenaphthylene
—
—
—
—
—
7972911
anthracene
—
—
—
—
—
7972911
benz[a]anthracene
—
—
—
—
—
7972911
benzo(ghi)perylene
—
—
—
—
—
7972911
benzo[a]pyrene
—
—
—
—
—
7972911
benzo[b]fluoranthene
—
—
—
—
—
7972911
benzo[k]fluoranthene
—
—
—
—
—
7972911
beryllium compounds
—
—
—
—
1E-07
7972911
chromium (iii) compounds
—
—
—
—
—
7972911
chromium (vi) compounds
—
—
—
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
7972911
chrysene
—
—
—
—
—
7972911
cobalt compounds
—
—
—
—
—
7972911
dibenzo[a,h]anthracene
—
—
—
—
—
7972911
fluoranthene
—
—
—
—
—
7972911
fluorene
—
—
—
—
—
7972911
gaseous divalent mercury
—
—
—
—
—
7972911
indeno[l,2,3-c,d]pyrene
—
—
—
—
—
7972911
manganese compounds
—
—
—
—
—
7972911
naphthalene
—
—
—
—
—
7972911
nickel compounds
—
—
—
—
—
7972911
particulate divalent
mercury
—
—
—
—
—
7972911
phenanthrene
—
—
—
—
—
7972911
phthalic anhydride
—
—
—
—
—
7972911
pyrene
—
—
—
—
—
7972911
selenium compounds
—
—
—
—
—
7972911
o-xylene
4E-04
—
—
—
—
7972911
arsenic compounds
3E-04
—
—
—
—
7972911
xylenes (mixed)
2E-03
7E-05
1E-05
—
—
7972911
ethyl benzene
—
6E-05
2E-06
—
—
7972911
cadmium compounds
—
3E-06
4E-07
—
—
7972911
n-hexane
—
—
5E-08
—
—
7972911
mercury (elemental)
6E-05
—
2E-08
—
2E-08
7972911
maleic anhydride
—
—
—
5E-01
2E-02
7972911
benzyl chloride
1E-03
—
—
6E-05
6E-06
7972911
acrolein
2E-03
7E-05
2E-05
4E-05
1E-05
7972911
methanol
4E-04
2E-05
4E-06
4E-05
9E-06
7972911
formaldehyde
4E-04
2E-05
1E-06
2E-05
2E-06
7972911
acetaldehyde
1E-05
6E-08
1E-08
3E-07
1E-08
7972911
toluene
—
4E-09
4E-10
5E-09
8E-10
7972911
benzene
—
3E-09
2E-10
4E-09
1E-09
7984011
1,1,1-trichloroethane
—
—
—
—
—
7984011
methyl isobutyl ketone
—
—
—
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
7984011
naphthalene
—
—
—
—
—
7984011
p-dichlorobenzene
—
—
—
—
—
7984011
pah, total
—
—
—
—
—
7984011
styrene
—
—
—
—
—
7984011
propionaldehyde
—
2E-05
4E-06
—
—
7984011
xylenes (mixed)
8E-05
3E-06
4E-07
—
—
7984011
n-hexane
—
—
6E-09
—
—
7984011
formaldehyde
3
1E-01
1E-02
1E-01
1E-02
7984011
acrolein
2
7E-02
2E-02
5E-02
2E-02
7984011
acetaldehyde
1
7E-03
1E-03
3E-02
1E-03
7984011
methanol
1E-01
5E-03
1E-03
1E-02
3E-03
7984011
phenol
2E-02
2E-03
1E-03
3E-03
5E-04
7984011
toluene
—
6E-05
7E-06
8E-05
1E-05
7984011
benzene
—
1E-05
8E-07
1E-05
4E-06
7984111
arsenic compounds
—
—
—
—
—
7984111
benzene
—
—
—
—
—
7984111
cadmium compounds
—
—
—
—
—
7984111
chromium (iii) compounds
—
—
—
—
—
7984111
chromium (vi) compounds
—
—
—
—
—
7984111
cobalt compounds
—
—
—
—
—
7984111
cresols (mixed)
—
—
—
—
—
7984111
ethylene glycol
—
—
—
—
—
7984111
gaseous divalent mercury
—
—
—
—
—
7984111
manganese compounds
—
—
—
—
—
7984111
mercury (elemental)
—
—
—
—
—
7984111
naphthalene
—
—
—
—
—
7984111
nickel compounds
—
—
—
—
—
7984111
particulate divalent
mercury
—
—
—
—
—
7984111
biphenyl
—
—
3E-03
—
—
7984111
n-hexane
—
—
6E-09
—
—
7984111
formaldehyde
1
6E-02
4E-03
6E-02
6E-03
7984111
methanol
8E-03
3E-04
8E-05
9E-04
2E-04
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
7984111
phenol
4E-03
4E-04
2E-04
6E-04
1E-04
7984111
toluene
—
5E-10
6E-11
6E-10
1E-10
8006811
cresols (mixed)
—
—
—
—
—
8006811
n-hexane
—
—
3E-07
—
—
8006811
formaldehyde
1
5E-02
3E-03
5E-02
5E-03
8006811
phenol
4E-02
4E-03
3E-03
7E-03
1E-03
8006811
methanol
1E-02
5E-04
IE-04
1E-03
3E-04
8006811
acetaldehyde
7E-05
4E-07
7E-08
2E-06
1E-07
8006811
toluene
—
2E-08
3E-09
3E-08
5E-09
8006811
benzene
—
2E-08
1E-09
2E-08
7E-09
8007011
cresols (mixed)
—
—
—
—
—
8007011
lead compounds
—
—
—
—
—
8007011
biphenyl
—
—
5E-06
—
—
8007011
ethyl benzene
—
IE-04
3E-06
—
—
8007011
cumene
—
3E-06
5E-07
—
—
8007011
1,3-butadiene
—
4E-06
5E-07
2E-04
5E-06
8007011
methanol
9E-06
4E-07
1E-07
1E-06
2E-07
8007011
styrene
7E-06
2E-06
3E-07
7E-07
1E-07
8018911
1,3-dichloropropene
—
—
—
—
—
8018911
antimony compounds
—
—
—
—
—
8018911
chromium (iii) compounds
—
—
—
—
—
8018911
chromium (vi) compounds
—
—
—
—
—
8018911
cobalt compounds
—
—
—
—
—
8018911
cresols (mixed)
—
—
—
—
—
8018911
diethanolamine
—
—
—
—
—
8018911
ethyl chloride
—
—
—
—
—
8018911
gaseous divalent mercury
—
—
—
—
—
8018911
lead compounds
—
—
—
—
—
8018911
manganese compounds
—
—
—
—
—
8018911
methyl isobutyl ketone
—
—
—
—
—
8018911
naphthalene
—
—
—
—
—
8018911
nickel compounds
—
—
—
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
8018911
particulate divalent
mercury
—
—
—
—
—
8018911
phthalic anhydride
—
—
—
—
—
8018911
propylene dichloride
—
—
—
—
—
8018911
selenium compounds
—
—
—
—
—
8018911
arsenic compounds
8E-04
—
—
—
—
8018911
aniline
—
2E-06
2E-06
—
—
8018911
acetonitrile
—
4E-06
1E-06
—
—
8018911
mercury (elemental)
9E-04
—
3E-07
—
3E-07
8018911
biphenyl
—
—
1E-07
—
—
8018911
chlorobenzene
—
2E-06
1E-07
—
—
8018911
1,4-dioxane
2E-05
1E-06
6E-08
—
—
8018911
phosphorus
—
2E-07
6E-08
—
—
8018911
cadmium compounds
—
4E-07
5E-08
—
—
8018911
cumene
—
3E-07
5E-08
—
—
8018911
ethyl benzene
—
6E-07
2E-08
—
—
8018911
xylenes (mixed)
4E-06
2E-07
2E-08
—
—
8018911
n-hexane
—
—
3E-10
—
—
8018911
acrolein
3E-02
1E-03
3E-04
7E-04
2E-04
8018911
ethylene dichloride
—
—
—
IE-04
4E-05
8018911
hydrazine
—
6E-04
4E-06
IE-04
1E-05
8018911
hydrochloric acid
IE-04
9E-05
7E-06
5E-05
8E-06
8018911
1,3-butadiene
—
7E-08
9E-09
5E-06
1E-07
8018911
phenol
3E-05
3E-06
2E-06
4E-06
9E-07
8018911
epichlorohydrin
6E-05
1E-05
8E-07
4E-06
1E-06
8018911
methanol
7E-06
3E-07
8E-08
8E-07
2E-07
8018911
benzene
—
6E-07
4E-08
6E-07
2E-07
8018911
methyl tert-butyl ether
—
5E-07
4E-08
5E-07
2E-08
8018911
toluene
—
4E-07
4E-08
5E-07
8E-08
8018911
styrene
4E-06
9E-07
1E-07
4E-07
7E-08
8018911
methylene chloride
5E-06
1E-07
4E-08
7E-08
3E-08
8018911
methyl chloride
—
—
1E-11
8E-11
1E-11
8020011
ethyl benzene
—
5E-04
2E-05
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
8020011
cumene
—
1E-05
2E-06
—
—
8020011
xylenes (mixed)
4E-05
2E-06
2E-07
—
—
8020011
styrene
3E-02
9E-03
1E-03
3E-03
7E-04
8020011
benzene
—
2E-03
IE-04
2E-03
6E-04
8020011
toluene
—
6E-05
7E-06
8E-05
1E-05
8020011
methanol
4E-05
2E-06
4E-07
5E-06
9E-07
8020411
2,2,4-trimethylpentane
—
—
—
—
—
8020411
cresols (mixed)
—
—
—
—
—
8020411
methyl isobutyl ketone
—
—
—
—
—
8020411
naphthalene
—
—
—
—
—
8020411
pah, total
—
—
—
—
—
8020411
quinoline
—
—
—
—
—
8020411
biphenyl
—
—
7E-05
—
—
8020411
xylenes (mixed)
7E-03
3E-04
4E-05
—
—
8020411
ethyl benzene
—
2E-04
6E-06
—
—
8020411
n-hexane
—
—
4E-06
—
—
8020411
cumene
—
8E-06
1E-06
—
—
8020411
acetonitrile
—
9E-09
2E-09
—
—
8020411
carbon disulfide
3E-04
4E-05
3E-06
5E-04
1E-05
8020411
toluene
—
2E-04
3E-05
3E-04
5E-05
8020411
benzene
—
2E-04
2E-05
3E-04
9E-05
8020411
methanol
1E-03
6E-05
2E-05
2E-04
3E-05
8020411
phenol
6E-04
6E-05
4E-05
9E-05
2E-05
8020411
1,3-butadiene
—
3E-07
3E-08
2E-05
4E-07
8020411
methyl tert-butyl ether
—
4E-06
4E-07
4E-06
2E-07
8020411
styrene
3E-05
7E-06
1E-06
3E-06
6E-07
8020411
trichloroethylene
—
7E-07
2E-07
9E-07
2E-07
8020411
tetrachloroethene
2E-05
1E-06
2E-07
5E-07
3E-07
8020411
methylene chloride
3E-05
5E-07
2E-07
4E-07
1E-07
8020811
2,2,4-trimethylpentane
—
—
—
—
—
8020811
antimony compounds
—
—
—
—
—
8020811
chromium (iii) compounds
—
—
—
—
—
8020811
chromium (vi) compounds
—
—
—
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
8020811
cobalt compounds
—
—
—
—
—
8020811
cresols (mixed)
—
—
—
—
—
8020811
diethanolamine
—
—
—
—
—
8020811
gaseous divalent mercury
—
—
—
—
—
8020811
lead compounds
—
—
—
—
—
8020811
manganese compounds
—
—
—
—
—
8020811
methyl isobutyl ketone
—
—
—
—
—
8020811
naphthalene
—
—
—
—
—
8020811
nickel compounds
—
—
—
—
—
8020811
pah, total
—
—
—
—
—
8020811
particulate divalent
mercury
—
—
—
—
—
8020811
xylenes (mixed)
8E-03
3E-04
4E-05
—
—
8020811
n-hexane
—
—
1E-05
—
—
8020811
ethyl benzene
—
2E-04
6E-06
—
—
8020811
cumene
—
2E-05
3E-06
—
—
8020811
biphenyl
—
—
8E-07
—
—
8020811
mercury (elemental)
IE-04
—
4E-08
—
3E-08
8020811
acetonitrile
—
1E-08
3E-09
—
—
8020811
hydrochloric acid
1E-02
9E-03
7E-04
5E-03
8E-04
8020811
toluene
—
5E-04
7E-05
7E-04
1E-04
8020811
benzene
—
6E-04
4E-05
7E-04
2E-04
8020811
1,3-butadiene
—
9E-06
1E-06
6E-04
1E-05
8020811
styrene
2E-04
6E-05
9E-06
2E-05
4E-06
8020811
formaldehyde
5E-05
3E-06
2E-07
2E-06
2E-07
8020811
methyl tert-butyl ether
—
2E-06
2E-07
2E-06
1E-07
8020811
tetrachloroethene
9E-06
8E-07
1E-07
3E-07
1E-07
8020811
methanol
1E-06
5E-08
1E-08
1E-07
3E-08
8020811
acetaldehyde
3E-06
2E-08
3E-09
9E-08
5E-09
8020811
phenol
5E-07
5E-08
4E-08
8E-08
2E-08
8026211
1,3-dichloropropene
—
—
—
—
—
8026211
bromoform
—
—
—
—
—
8026211
ethyl chloride
—
—
—
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
8026211
naphthalene
—
—
—
—
—
8026211
propylene dichloride
—
—
—
—
—
8026211
chloroform
7E-06
—
3E-09
—
4E-09
8026211
chlorobenzene
—
3E-08
2E-09
—
—
8026211
xylenes (mixed)
2E-07
6E-09
9E-10
—
—
8026211
n-hexane
—
—
5E-11
—
—
8026211
acrolein
4
1E-01
4E-02
9E-02
3E-02
8026211
epichlorohydrin
2E-01
5E-02
3E-03
2E-02
4E-03
8026211
allyl chloride
—
2E-02
9E-04
2E-02
1E-03
8026211
hydrochloric acid
3E-02
2E-02
2E-03
1E-02
2E-03
8026211
toluene
—
1E-07
1E-08
1E-07
2E-08
8026211
benzene
—
1E-07
7E-09
1E-07
4E-08
8026211
1,3-butadiene
—
2E-10
3E-11
2E-08
3E-10
8026211
carbon disulfide
6E-09
1E-09
8E-11
1E-08
2E-10
8026211
carbon tetrachloride
2E-07
—
4E-09
2E-09
5E-10
8026211
methylene chloride
7E-08
1E-09
5E-10
9E-10
4E-10
8026211
methyl chloride
—
—
1E-10
7E-10
1E-10
8026211
tetrachloroethene
3E-09
3E-10
4E-11
9E-11
5E-11
8026211
methyl tert-butyl ether
—
5E-11
4E-12
5E-11
3E-12
8026211
methanol
3E-10
1E-11
3E-12
4E-11
7E-12
8059311
asbestos
—
—
—
—
—
8059311
tetrachloroethene
—
—
—
—
—
8059311
chloroform
4E-02
—
2E-05
—
2E-05
8059311
n-hexane
—
—
3E-08
—
—
8059311
hydrochloric acid
4E-01
3E-01
3E-02
2E-01
3E-02
8059311
chlorine
6E-01
9E-02
2E-02
5E-02
2E-02
8059311
carbon tetrachloride
4E-02
—
1E-03
6E-04
1E-04
8059311
formaldehyde
3E-04
1E-05
9E-07
1E-05
1E-06
8059311
methyl chloride
—
—
1E-07
8E-07
1E-07
8059311
methylene chloride
5E-05
1E-06
4E-07
7E-07
3E-07
8059311
methanol
1E-06
4E-08
1E-08
1E-07
2E-08
8059311
ethylene dichloride
—
—
—
3E-09
6E-10
8067211
2,2,4-trimethylpentane
—
—
—
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
8067211
carbon disulfide
—
—
—
—
—
8067211
carbonyl sulfide
—
—
—
—
—
8067211
naphthalene
—
—
—
—
—
8067211
phenol
—
—
—
—
—
8067211
xylenes (mixed)
2E-02
7E-04
IE-04
—
—
8067211
n-hexane
—
—
4E-05
—
—
8067211
ethyl benzene
—
7E-04
2E-05
—
—
8067211
cumene
—
7E-05
1E-05
—
—
8067211
benzene
—
5E-03
3E-04
6E-03
2E-03
8067211
toluene
—
3E-03
3E-04
4E-03
6E-04
8067211
methanol
3E-02
1E-03
3E-04
3E-03
6E-04
8067211
formaldehyde
8E-03
4E-04
3E-05
4E-04
4E-05
8067211
hydrochloric acid
7E-04
6E-04
5E-05
3E-04
5E-05
8067211
hydrofluoric acid
1E-03
4E-04
2E-05
2E-04
2E-05
8067211
acetaldehyde
2E-03
1E-05
2E-06
5E-05
2E-06
8067211
ethylene dichloride
—
—
—
4E-05
9E-06
8067211
1,3-butadiene
—
2E-07
3E-08
1E-05
3E-07
8067211
tetrachloroethene
IE-04
1E-05
1E-06
3E-06
2E-06
8086711
acetonitrile
—
5E-01
1E-01
—
—
8086711
methanol
1
5E-02
1E-02
1E-01
3E-02
8086711
toluene
—
5E-04
6E-05
7E-04
1E-04
8086711
benzene
—
7E-04
4E-05
7E-04
2E-04
8096711
vinyl acetate
—
7E-01
1E-01
9E-01
6E-02
8096711
methanol
4
2E-01
4E-02
4E-01
9E-02
8096711
acetaldehyde
1
8E-03
1E-03
4E-02
2E-03
8105111
toluene
—
2E-05
2E-06
2E-05
4E-06
8107111
m-cresol (3-methylphenol)
—
—
—
—
—
8107111
naphthalene
—
—
—
—
—
8107111
o-cresol
—
—
—
—
—
8107111
p-cresol (4-methy phenol)
—
—
—
—
—
8107111
xylenes (mixed)
7E-03
3E-04
4E-05
—
—
8107111
ethyl benzene
—
4E-04
1E-05
—
—
8107111
cumene
—
3E-09
5E-10
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
8107111
formaldehyde
2
9E-02
6E-03
8E-02
8E-03
8107111
phenol
3E-02
3E-03
2E-03
4E-03
9E-04
8107111
methanol
8E-03
3E-04
8E-05
8E-04
2E-04
8107111
toluene
—
2E-07
2E-08
2E-07
4E-08
8107111
benzene
—
1E-08
7E-10
1E-08
4E-09
8123911
dimethyl sulfate
—
—
—
—
—
8123911
ethyl benzene
—
—
—
—
—
8123911
ethyl chloride
—
—
—
—
—
8123911
diethylene glycol
monobutyl ether
9E-04
—
—
—
—
8123911
hydrochloric acid
3E-02
3E-02
2E-03
2E-02
2E-03
8123911
methyl chloride
—
—
1E-03
6E-03
9E-04
8123911
toluene
—
6E-04
7E-05
8E-04
1E-04
8123911
methanol
4E-03
2E-04
4E-05
4E-04
8E-05
8130511
ethylene glycol
—
—
—
—
—
8130511
ethyl benzene
—
2E-04
5E-06
—
—
8130511
1,3-butadiene
—
9E-05
1E-05
6E-03
1E-04
8130511
methanol
8E-03
3E-04
8E-05
9E-04
2E-04
8130511
styrene
4E-03
1E-03
2E-04
4E-04
8E-05
8135311
triethylamine
7E-06
—
—
—
—
8135311
formaldehyde
4E-02
2E-03
IE-04
2E-03
2E-04
8135311
methanol
3E-06
1E-07
4E-08
4E-07
7E-08
8137811
cresols (mixed)
—
—
—
—
—
8137811
dibenzofuran
—
—
—
—
—
8137811
naphthalene
—
—
—
—
—
8137811
phthalic anhydride
—
—
—
—
—
8137811
quinoline
—
—
—
—
—
8137811
o-xylene
3E-03
—
—
—
—
8137811
biphenyl
—
—
6E-06
—
—
8137811
xylenes (mixed)
3E-04
1E-05
2E-06
—
—
8137811
ethyl benzene
—
4E-05
1E-06
—
—
8137811
maleic anhydride
—
—
—
9E-03
4E-04
8137811
phenol
1E-02
1E-03
7E-04
2E-03
4E-04
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
8137811
benzene
—
6E-04
4E-05
7E-04
2E-04
8137811
toluene
—
IE-04
1E-05
IE-04
2E-05
8137811
styrene
2E-05
4E-06
7E-07
2E-06
3E-07
8148211
acetophenone
—
—
—
—
—
8148211
cumene
—
3E-03
6E-04
—
—
8148211
phenol
7E-03
7E-04
5E-04
1E-03
2E-04
8148211
methanol
1E-02
5E-04
IE-04
1E-03
3E-04
8194311
dimethyl sulfate
—
—
—
—
—
8194311
lead compounds
—
—
—
—
—
8209411
formaldehyde
3E-01
1E-02
9E-04
1E-02
1E-03
8209411
methyl chloride
—
—
2E-04
1E-03
2E-04
8209411
acrylonitrile
—
—
1E-03
2E-04
6E-05
8209411
methanol
2E-03
8E-05
2E-05
2E-04
4E-05
8209411
benzyl chloride
4E-04
—
—
2E-05
2E-06
8209411
styrene
7E-05
2E-05
3E-06
7E-06
1E-06
8215111
2,2,4-trimethylpentane
—
—
—
—
—
8215111
diethyl sulfate
—
—
—
—
—
8215111
dimethyl sulfate
—
—
—
—
—
8215111
ethyl chloride
—
—
—
—
—
8215111
ethylene glycol
—
—
—
—
—
8215111
gaseous divalent mercury
—
—
—
—
—
8215111
maleic anhydride
—
—
—
—
—
8215111
mercury (elemental)
—
—
—
—
—
8215111
methyl isobutyl ketone
—
—
—
—
—
8215111
naphthalene
—
—
—
—
—
8215111
o-toluidine
—
—
—
—
—
8215111
particulate divalent
mercury
—
—
—
—
—
8215111
phthalic anhydride
—
—
—
—
—
8215111
triethylamine
6E-05
—
—
—
—
8215111
hydrogen cyanide
3E-04
5E-05
1E-05
—
9E-06
8215111
carbonyl sulfide
—
—
1E-07
—
—
8215111
chloroform
6E-05
—
3E-08
—
4E-08
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
8215111
ethyl benzene
—
3E-07
9E-09
—
—
8215111
methyl methacrylate
—
7E-08
9E-09
—
—
8215111
n-hexane
—
—
8E-09
—
—
8215111
cumene
—
2E-08
3E-09
—
—
8215111
1,4-dioxane
8E-07
4E-08
2E-09
—
—
8215111
xylenes (mixed)
3E-07
1E-08
2E-09
—
—
8215111
methyl bromide
7E-08
—
3E-10
—
1E-09
8215111
carbon disulfide
6E-04
9E-05
7E-06
1E-03
2E-05
8215111
acrolein
2E-03
7E-05
2E-05
5E-05
1E-05
8215111
hydrochloric acid
4E-05
3E-05
2E-06
2E-05
3E-06
8215111
acetaldehyde
3E-04
2E-06
3E-07
7E-06
4E-07
8215111
vinyl acetate
—
4E-06
7E-07
5E-06
4E-07
8215111
benzene
—
4E-06
2E-07
4E-06
1E-06
8215111
chlorine
4E-05
5E-06
1E-06
3E-06
9E-07
8215111
1,3-butadiene
—
1E-08
1E-09
8E-07
2E-08
8215111
allyl chloride
—
7E-07
4E-08
7E-07
5E-08
8215111
acrylonitrile
—
—
3E-06
5E-07
1E-07
8215111
toluene
—
2E-07
3E-08
3E-07
5E-08
8215111
methanol
3E-06
1E-07
3E-08
3E-07
6E-08
8215111
acrylic acid
2E-07
2E-07
7E-09
3E-07
6E-09
8215111
benzyl chloride
4E-06
—
—
2E-07
2E-08
8215111
carbon tetrachloride
4E-06
—
9E-08
6E-08
1E-08
8215111
methyl chloride
—
—
9E-09
6E-08
8E-09
8215111
phenol
2E-07
2E-08
2E-08
4E-08
7E-09
8215111
styrene
1E-07
2E-08
4E-09
1E-08
2E-09
8215111
trichloroethylene
—
9E-09
2E-09
1E-08
2E-09
8215111
tetrachloroethene
3E-07
2E-08
3E-09
8E-09
4E-09
8239511
2,2,4-trimethylpentane
—
—
—
—
—
8239511
cresols (mixed)
—
—
—
—
—
8239511
diethanolamine
—
—
—
—
—
8239511
naphthalene
—
—
—
—
—
8239511
pah, total
—
—
—
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
8239511
ethylene glycol methyl
ether
2E-06
—
—
—
—
8239511
acetonitrile
—
2E-02
5E-03
—
—
8239511
xylenes (mixed)
2E-03
9E-05
1E-05
—
—
8239511
n-hexane
—
—
7E-06
—
—
8239511
ethyl benzene
—
2E-04
7E-06
—
—
8239511
biphenyl
—
—
6E-06
—
—
8239511
cumene
—
5E-06
8E-07
—
—
8239511
1,3-butadiene
—
2E-04
2E-05
1E-02
2E-04
8239511
benzene
—
5E-04
3E-05
5E-04
2E-04
8239511
toluene
—
2E-04
3E-05
3E-04
6E-05
8239511
styrene
2E-03
4E-04
6E-05
2E-04
3E-05
8239511
methanol
1E-03
5E-05
1E-05
IE-04
3E-05
8239511
phenol
3E-04
3E-05
2E-05
5E-05
9E-06
8239511
hydrochloric acid
2E-06
2E-06
1E-07
1E-06
2E-07
8239511
methyl tert-butyl ether
—
3E-07
3E-08
3E-07
2E-08
8262411
2,2,4-trimethylpentane
—
—
—
—
—
8262411
benzo(ghi)perylene
—
—
—
—
—
8262411
naphthalene
—
—
—
—
—
8262411
polycyclic organic matter
—
—
—
—
—
8262411
xylenes (mixed)
4E-04
1E-05
2E-06
—
—
8262411
n-hexane
—
—
3E-07
—
—
8262411
ethyl benzene
—
1E-05
3E-07
—
—
8262411
cumene
—
6E-07
9E-08
—
—
8262411
toluene
—
3E-05
4E-06
4E-05
7E-06
8262411
benzene
—
4E-06
3E-07
5E-06
2E-06
8263111
formaldehyde
4
2E-01
1E-02
2E-01
2E-02
8263111
methanol
2E-03
7E-05
2E-05
2E-04
4E-05
8361111
1,1,2,2-tetrachloroethane
—
—
—
—
—
8361111
1,1,2-trichloroethane
—
—
—
—
—
8361111
1,2,3,4,6,7,8,9-
octachlorodibenzo-p-
dioxin
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
8361111
1,2,3,4,6,7,8,9-
octachlorodibenzofuran
—
—
—
—
—
8361111
1,2,3,4,6,7,8-
heptachlorodibenzo-p-
dioxin
8361111
1,2,3,4,6,7,8-
heptachlorodibenzofuran
—
—
—
—
—
8361111
1,2,3,4,7,8,9-
heptachlorodibenzofuran
—
—
—
—
—
8361111
1,2,3,4,7,8-
hexachlorodibenzo-p-
dioxin
8361111
1,2,3,4,7,8-
hexachlorodibenzofuran
—
—
—
—
—
8361111
1,2,3,6,7,8-
hexachlorodibenzo-p-
dioxin
8361111
1,2,3,6,7,8-
hexachlorodibenzofuran
—
—
—
—
—
8361111
1,2,3,7,8,9-
hexachlorodibenzo-p-
dioxin
8361111
1,2,3,7,8-
pentachlorodibenzo-p-
dioxin
8361111
1,2,3,7,8-
pentachlorodibenzofuran
—
—
—
—
—
8361111
1,3-dichloropropene
—
—
—
—
—
8361111
2,3,4,6,7,8-
hexachlorodibenzofuran
—
—
—
—
—
8361111
2,3,4,7,8-
pentachlorodibenzofuran
—
—
—
—
—
8361111
2,3,7,8-
tetrachlorodibenzo-p-
dioxin
8361111
2,3,7,8-
tetrachlorodibenzofuran
—
—
—
—
—
8361111
chloroprene
—
—
—
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
8361111
dichloroethyl ether
—
—
—
—
—
8361111
ethyl chloride
—
—
—
—
—
8361111
ethylene glycol
—
—
—
—
—
8361111
ethylidene dichloride
—
—
—
—
—
8361111
naphthalene
—
—
—
—
—
8361111
pah, total
—
—
—
—
—
8361111
polychlorinated biphenyls
—
—
—
—
—
8361111
vinylidene chloride
—
—
—
—
5E-09
8361111
chloroform
2E-02
—
1E-05
—
1E-05
8361111
chlorobenzene
—
1E-05
1E-06
—
—
8361111
ethylene oxide
—
—
7E-08
—
6E-08
8361111
xylenes (mixed)
1E-06
5E-08
7E-09
—
—
8361111
ethyl benzene
—
8E-08
2E-09
—
—
8361111
hydrochloric acid
2E-03
1E-03
IE-04
8E-04
1E-04
8361111
chlorine
5E-03
7E-04
2E-04
3E-04
1E-04
8361111
ethylene dichloride
—
—
—
IE-04
3E-05
8361111
carbon tetrachloride
2E-03
—
4E-05
2E-05
5E-06
8361111
tetrachloroethene
8E-04
7E-05
1E-05
2E-05
1E-05
8361111
vinyl chloride
6E-05
2E-05
3E-06
8E-06
8E-07
8361111
benzene
—
3E-06
2E-07
4E-06
1E-06
8361111
toluene
—
6E-07
7E-08
8E-07
1E-07
8361111
styrene
4E-07
1E-07
2E-08
4E-08
8E-09
8361111
trichloroethylene
—
3E-08
9E-09
4E-08
8E-09
8384311
2,2,4-trimethylpentane
—
—
—
—
—
8384311
anthracene
—
—
—
—
—
8384311
benzo(ghi)perylene
—
—
—
—
—
8384311
beryllium compounds
—
—
—
—
—
8384311
chromium (iii) compounds
—
—
—
—
—
8384311
chromium (vi) compounds
—
—
—
—
—
8384311
cobalt compounds
—
—
—
—
—
8384311
cresols (mixed)
—
—
—
—
—
8384311
diethanolamine
—
—
—
—
—
8384311
ethylene glycol
—
—
—
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
8384311
gaseous divalent mercury
—
—
—
—
—
8384311
lead compounds
—
—
—
—
—
8384311
m-cresol (3-methylphenol)
—
—
—
—
—
8384311
manganese compounds
—
—
—
—
—
8384311
naphthalene
—
—
—
—
—
8384311
nickel compounds
—
—
—
—
—
8384311
o-cresol
—
—
—
—
—
8384311
p-cresol (4-methy phenol)
—
—
—
—
—
8384311
pah, total
—
—
—
—
—
8384311
particulate divalent
mercury
—
—
—
—
—
8384311
phenanthrene
—
—
—
—
—
8384311
selenium compounds
—
—
—
—
—
8384311
arsenic compounds
8E-04
—
—
—
—
8384311
xylenes (mixed)
9E-02
3E-03
5E-04
—
—
8384311
n-hexane
—
—
2E-04
—
—
8384311
carbonyl sulfide
—
—
9E-05
—
—
8384311
ethyl benzene
—
1E-03
4E-05
—
—
8384311
cumene
—
IE-04
2E-05
—
—
8384311
chloroform
8E-03
—
4E-06
—
5E-06
8384311
ethylene dibromide
—
4E-06
3E-06
—
—
8384311
cadmium compounds
—
8E-06
1E-06
—
—
8384311
biphenyl
—
—
5E-07
—
—
8384311
mercury (elemental)
5E-05
—
2E-08
—
1E-08
8384311
hydrochloric acid
2E-02
2E-02
1E-03
1E-02
1E-03
8384311
toluene
—
1E-02
1E-03
1E-02
2E-03
8384311
benzene
—
6E-03
4E-04
7E-03
2E-03
8384311
chlorine
3E-02
4E-03
9E-04
2E-03
6E-04
8384311
1,3-butadiene
—
3E-05
4E-06
2E-03
4E-05
8384311
methanol
1E-02
6E-04
IE-04
1E-03
3E-04
8384311
phenol
1E-03
IE-04
7E-05
2E-04
3E-05
8384311
formaldehyde
1E-03
6E-05
4E-06
6E-05
6E-06
8384311
tetrachloroethene
9E-05
8E-06
1E-06
3E-06
1E-06
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
8384311
acetaldehyde
4E-05
2E-07
4E-08
1E-06
5E-08
8384311
methyl tert-butyl ether
—
6E-07
5E-08
6E-07
3E-08
8384311
propylene oxide
8E-06
1E-07
4E-08
2E-07
4E-08
8418011
cresols (mixed)
—
—
—
—
—
8418011
lead compounds
—
—
—
—
—
8418011
carbonyl sulfide
—
—
1E-05
—
—
8418011
n-hexane
—
—
4E-06
—
—
8418011
cumene
—
1E-05
2E-06
—
—
8418011
carbon disulfide
5E-04
8E-05
6E-06
1E-03
2E-05
8418011
methanol
1E-03
4E-05
1E-05
IE-04
2E-05
8418011
styrene
7E-06
2E-06
3E-07
7E-07
1E-07
8434411
catechol
—
—
—
—
—
8434411
dibutylphthalate
—
—
—
—
—
8434411
ethylene glycol
—
—
—
—
—
8434411
hydroquinone
—
—
—
—
—
8434411
methyl isobutyl ketone
—
—
—
—
—
8434411
naphthalene
—
—
—
—
—
8434411
phthalic anhydride
—
—
—
—
—
8434411
quinone
—
—
—
—
—
8434411
ethylene glycol methyl
ether
4E-04
—
—
—
—
8434411
propyl cellosolve
4E-05
—
—
—
—
8434411
diethylene glycol
monobutyl ether
2E-06
—
—
—
—
8434411
p-xylene
9E-07
—
—
—
—
8434411
xylenes (mixed)
6E-04
2E-05
3E-06
—
—
8434411
ethyl benzene
—
2E-05
7E-07
—
—
8434411
ethylene oxide
—
—
2E-07
—
2E-07
8434411
cumene
—
3E-08
4E-09
—
—
8434411
formaldehyde
4E-02
2E-03
IE-04
2E-03
2E-04
8434411
maleic anhydride
—
—
—
1E-03
4E-05
8434411
toluene
—
4E-05
4E-06
5E-05
8E-06
8434411
methanol
4E-05
2E-06
4E-07
4E-06
9E-07
8434411
styrene
4E-06
1E-06
2E-07
4E-07
8E-08
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
8434411
vinyl acetate
—
2E-07
4E-08
3E-07
2E-08
8434411
acrylic acid
5E-08
7E-08
2E-09
1E-07
2E-09
8434411
benzene
—
1E-09
7E-11
1E-09
4E-10
8447711
formaldehyde
2
1E-01
7E-03
1E-01
1E-02
8447711
methanol
3E-01
1E-02
3E-03
3E-02
6E-03
8465211
naphthalene
—
—
—
—
—
8465211
ethyl benzene
—
1E-03
4E-05
—
—
8465211
cumene
—
3E-06
5E-07
—
—
8465211
xylenes (mixed)
6E-05
2E-06
3E-07
—
—
8465211
chloroform
5E-04
—
2E-07
—
3E-07
8465211
n-hexane
—
—
1E-11
—
—
8465211
styrene
8E-03
2E-03
3E-04
8E-04
2E-04
8465211
benzene
—
4E-04
3E-05
4E-04
1E-04
8465211
toluene
—
2E-04
2E-05
2E-04
4E-05
8465211
formaldehyde
IE-04
5E-06
3E-07
5E-06
5E-07
8465211
acrolein
2E-05
7E-07
2E-07
4E-07
1E-07
8465211
acetaldehyde
4E-07
3E-09
4E-10
1E-08
6E-10
8465311
2,4-dinitrophenol
—
—
—
—
—
8465311
4,4'-methylenedianiline
—
—
—
—
—
8465311
4-nitrophenol
—
—
—
—
—
8465311
dibutylphthalate
—
—
—
—
—
8465311
diethanolamine
—
—
—
—
—
8465311
methyl isobutyl ketone
—
—
—
—
—
8465311
nitrobenzene
—
—
—
—
—
8465311
o-toluidine
—
—
—
—
—
8465311
p-dichlorobenzene
—
—
—
—
—
8465311
methylene diphenyl
diisocyanate
1E-01
—
—
—
3E-04
8465311
aniline
—
1E-02
7E-03
—
—
8465311
phosgene
8E-01
—
3E-03
—
2E-03
8465311
chlorobenzene
—
3E-02
2E-03
—
—
8465311
acetonitrile
—
1E-03
4E-04
—
—
8465311
chloroform
4E-02
—
2E-05
—
3E-05
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
8465311
ethylene oxide
—
—
1E-05
—
1E-05
8465311
xylenes (mixed)
5E-04
2E-05
3E-06
—
—
8465311
ethyl benzene
—
3E-05
8E-07
—
—
8465311
n-hexane
—
—
2E-07
—
—
8465311
maleic anhydride
—
—
—
7E-02
3E-03
8465311
acrylic acid
1E-02
2E-02
5E-04
3E-02
5E-04
8465311
hydrochloric acid
1E-02
9E-03
8E-04
6E-03
8E-04
8465311
formaldehyde
7E-02
4E-03
2E-04
3E-03
3E-04
8465311
carbon tetrachloride
1E-01
—
3E-03
2E-03
4E-04
8465311
benzene
—
6E-04
4E-05
6E-04
2E-04
8465311
methanol
2E-03
7E-05
2E-05
2E-04
4E-05
8465311
phenol
8E-04
8E-05
5E-05
IE-04
2E-05
8465311
methyl tert-butyl ether
—
5E-05
4E-06
5E-05
3E-06
8465311
methylene chloride
2E-03
4E-05
2E-05
3E-05
1E-05
8465311
propylene oxide
9E-04
2E-05
4E-06
2E-05
5E-06
8465311
toluene
—
1E-05
1E-06
2E-05
3E-06
8465311
1,1,1-trichloroethane
1E-05
8E-07
3E-07
5E-07
3E-07
8465611
1,1,2-trichloroethane
—
—
—
—
—
8465611
2,4-dinitrotoluene
—
—
—
—
—
8465611
4,4'-methylenedianiline
—
—
—
—
—
8465611
ethyl chloride
—
—
—
—
—
8465611
ethylene glycol
—
—
—
—
—
8465611
naphthalene
—
—
—
—
—
8465611
nickel compounds
—
—
—
—
—
8465611
nitrobenzene
—
—
—
—
—
8465611
o-toluidine
—
—
—
—
—
8465611
pah, total
—
—
—
—
—
8465611
propylene dichloride
—
—
—
—
—
8465611
methylene diphenyl
diisocyanate
2E-01
—
—
—
4E-04
8465611
aniline
—
IE-04
9E-05
—
—
8465611
ethylene oxide
—
—
6E-05
—
6E-05
8465611
chloroform
9E-02
—
5E-05
—
6E-05
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
8465611
chlorobenzene
—
6E-04
4E-05
—
—
8465611
phosgene
5E-03
—
2E-05
—
1E-05
8465611
hydrogen cyanide
IE-04
2E-05
5E-06
—
3E-06
8465611
xylenes (mixed)
5E-04
2E-05
3E-06
—
—
8465611
ethyl benzene
—
1E-05
3E-07
—
—
8465611
n-hexane
—
—
4E-08
—
—
8465611
formaldehyde
1E-01
5E-03
3E-04
5E-03
5E-04
8465611
hydrochloric acid
6E-03
5E-03
4E-04
3E-03
4E-04
8465611
chlorine
4E-03
6E-04
2E-04
3E-04
1E-04
8465611
methanol
3E-03
IE-04
3E-05
3E-04
7E-05
8465611
styrene
2E-03
5E-04
8E-05
2E-04
4E-05
8465611
toluene
—
2E-04
2E-05
2E-04
4E-05
8465611
2,4-toluene diisocyanate
4E-03
6E-05
1E-05
IE-04
7E-06
8465611
propylene oxide
1E-03
2E-05
5E-06
3E-05
6E-06
8465611
benzene
—
2E-05
1E-06
2E-05
8E-06
8465611
acetaldehyde
7E-04
4E-06
6E-07
2E-05
9E-07
8465611
carbon tetrachloride
9E-04
—
2E-05
1E-05
3E-06
8465611
phenol
4E-05
4E-06
2E-06
5E-06
1E-06
8465611
1,3-butadiene
—
3E-08
4E-09
2E-06
4E-08
8465611
ethylene dichloride
—
—
—
1E-06
3E-07
8465611
methyl chloride
—
—
6E-08
4E-07
5E-08
8465611
vinyl chloride
2E-06
5E-07
1E-07
3E-07
3E-08
8465611
1,1,1-trichloroethane
1E-06
5E-08
2E-08
4E-08
2E-08
8465611
methylene chloride
2E-06
5E-08
2E-08
3E-08
1E-08
8465611
tetrachloroethene
1E-06
1E-07
1E-08
3E-08
2E-08
8465611
trichloroethylene
—
1E-08
3E-09
1E-08
3E-09
8465711
formaldehyde
6
3E-01
2E-02
3E-01
3E-02
8465711
methanol
7E-02
3E-03
7E-04
7E-03
1E-03
8467311
1,1,2,2-tetrachloroethane
—
—
—
—
—
8467311
1,1,2-trichloroethane
—
—
—
—
—
8467311
2,2,4-trimethylpentane
—
—
—
—
—
8467311
anthracene
—
—
—
—
—
8467311
antimony compounds
—
—
—
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
8467311
diethanolamine
—
—
—
—
—
8467311
ethyl chloride
—
—
—
—
—
8467311
ethylene glycol
—
—
—
—
—
8467311
ethylene glycol methyl
ether
—
—
—
—
—
8467311
gaseous divalent mercury
—
—
—
—
—
8467311
hexachlorobenzene
—
—
—
—
—
8467311
hexachloroethane
—
—
—
—
—
8467311
naphthalene
—
—
—
—
—
8467311
pah, total
—
—
—
—
—
8467311
particulate divalent
mercury
—
—
—
—
—
8467311
propylene dichloride
—
—
—
—
—
8467311
selenium compounds
—
—
—
—
—
8467311
arsenic compounds
7E-03
—
—
—
—
8467311
ethylene oxide
—
—
IE-04
—
9E-05
8467311
propionaldehyde
—
8E-06
2E-06
—
—
8467311
1,4-dioxane
3E-04
1E-05
7E-07
—
—
8467311
chloroform
7E-04
—
3E-07
—
4E-07
8467311
biphenyl
—
—
1E-07
—
—
8467311
mercury (elemental)
4E-04
—
1E-07
—
1E-07
8467311
ethyl benzene
—
4E-06
1E-07
—
—
8467311
xylenes (mixed)
2E-05
9E-07
1E-07
—
—
8467311
n-hexane
—
—
7E-08
—
—
8467311
hydrochloric acid
9E-03
7E-03
6E-04
4E-03
6E-04
8467311
chlorine
3E-02
4E-03
1E-03
2E-03
7E-04
8467311
propylene oxide
4E-02
7E-04
2E-04
1E-03
2E-04
8467311
acetaldehyde
2E-02
IE-04
2E-05
6E-04
3E-05
8467311
benzene
—
3E-04
2E-05
3E-04
1E-04
8467311
methanol
1E-03
5E-05
1E-05
IE-04
3E-05
8467311
1,3-butadiene
—
1E-06
2E-07
IE-04
2E-06
8467311
methyl chloride
—
—
1E-05
7E-05
1E-05
8467311
toluene
—
3E-05
3E-06
3E-05
6E-06
8467311
epichlorohydrin
2E-04
5E-05
3E-06
2E-05
4E-06
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
8467311
tetrachloroethene
3E-04
3E-05
4E-06
1E-05
5E-06
8467311
formaldehyde
3E-04
1E-05
9E-07
1E-05
1E-06
8467311
methyl tert-butyl ether
—
5E-06
4E-07
5E-06
3E-07
8467311
ethylene dichloride
—
—
—
6E-07
2E-07
8467311
carbon disulfide
3E-07
4E-08
4E-09
6E-07
1E-08
8467311
carbon tetrachloride
3E-05
—
8E-07
5E-07
1E-07
8467311
styrene
5E-06
1E-06
2E-07
5E-07
9E-08
8467311
hexachlorobutadiene
—
—
—
3E-07
9E-08
8467311
trichloroethylene
—
2E-07
4E-08
2E-07
4E-08
8467311
methylene chloride
1E-06
2E-08
8E-09
2E-08
6E-09
8467311
vinyl chloride
6E-08
2E-08
4E-09
9E-09
9E-10
8467611
1,1,2,2-tetrachloroethane
—
—
—
—
—
8467611
1,1,2-trichloroethane
—
—
—
—
—
8467611
antimony compounds
—
—
—
—
—
8467611
pah, total
—
—
—
—
—
8467611
vinylidene chloride
—
—
—
—
2E-12
8467611
phosgene
6E-03
—
2E-05
—
1E-05
8467611
chloroform
1E-02
—
6E-06
—
8E-06
8467611
methyl bromide
2E-09
—
9E-12
—
4E-11
8467611
hydrochloric acid
8E-02
6E-02
5E-03
4E-02
6E-03
8467611
hydrofluoric acid
2E-01
6E-02
2E-03
3E-02
3E-03
8467611
chlorine
5E-02
7E-03
2E-03
4E-03
1E-03
8467611
carbon tetrachloride
8E-03
—
2E-04
IE-04
2E-05
8467611
methanol
9E-04
3E-05
9E-06
9E-05
2E-05
8467611
tetrachloroethene
1E-03
IE-04
1E-05
3E-05
2E-05
8467611
1,1,1-trichloroethane
5E-04
3E-05
1E-05
2E-05
9E-06
8467611
formaldehyde
3E-04
2E-05
1E-06
2E-05
2E-06
8467611
methyl chloride
—
—
1E-06
7E-06
1E-06
8467611
ethylene dichloride
—
—
—
3E-07
6E-08
8467611
vinyl chloride
4E-07
1E-07
2E-08
6E-08
6E-09
8467611
trichloroethylene
—
4E-09
1E-09
5E-09
1E-09
8467611
methylene chloride
2E-07
3E-09
1E-09
2E-09
9E-10
8468011
2,2,4-trimethylpentane
—
—
—
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
8468011
chromium (iii) compounds
—
—
—
—
—
8468011
chromium (vi) compounds
—
—
—
—
—
8468011
ethylene glycol
—
—
—
—
—
8468011
gaseous divalent mercury
—
—
—
—
—
8468011
lead compounds
—
—
—
—
—
8468011
mercury (elemental)
—
—
—
—
—
8468011
naphthalene
—
—
—
—
—
8468011
particulate divalent
mercury
—
—
—
—
—
8468011
methoxytriglycol
1
—
—
—
—
8468011
ethylene oxide
—
—
6E-05
—
5E-05
8468011
n-hexane
—
—
7E-08
—
—
8468011
xylenes (mixed)
7E-06
3E-07
4E-08
—
—
8468011
ethyl benzene
—
2E-07
6E-09
—
—
8468011
biphenyl
—
—
9E-10
—
—
8468011
hydrofluoric acid
2E-02
5E-03
2E-04
3E-03
3E-04
8468011
benzene
—
5E-05
3E-06
5E-05
2E-05
8468011
acetaldehyde
2E-04
1E-06
2E-07
6E-06
3E-07
8468011
toluene
—
4E-07
5E-08
5E-07
9E-08
8468011
methanol
3E-06
1E-07
3E-08
3E-07
7E-08
8468011
styrene
6E-09
2E-09
2E-10
6E-10
1E-10
9103411
ethylene glycol
—
—
—
—
—
9103411
nickel compounds
—
—
—
—
—
9103411
formaldehyde
1
6E-02
4E-03
5E-02
5E-03
9103411
methanol
8E-02
3E-03
9E-04
9E-03
2E-03
9115811
2,2,4-trimethylpentane
—
—
—
—
—
9115811
cresols (mixed)
—
—
—
—
—
9115811
naphthalene
—
—
—
—
—
9115811
phenanthrene
—
—
—
—
—
9115811
n-hexane
—
—
7E-07
—
—
9115811
xylenes (mixed)
7E-05
3E-06
4E-07
—
—
9115811
ethyl benzene
—
3E-06
9E-08
—
—
9115811
cumene
—
2E-07
4E-08
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
9115811
biphenyl
—
—
2E-08
—
—
9115811
benzene
—
1E-05
7E-07
1E-05
4E-06
9115811
phenol
6E-05
6E-06
4E-06
9E-06
2E-06
9115811
toluene
—
7E-06
8E-07
9E-06
2E-06
9115811
styrene
7E-06
2E-06
3E-07
7E-07
1E-07
9175811
dibutylphthalate
—
—
—
—
—
9175811
chlorine
7E-03
9E-04
2E-04
5E-04
2E-04
9175811
maleic anhydride
—
—
—
9E-05
4E-06
9175811
acrylic acid
7E-06
1E-05
3E-07
1E-05
3E-07
9177911
vinyl acetate
—
7E-04
IE-04
1E-03
7E-05
9177911
acetaldehyde
1E-02
9E-05
1E-05
4E-04
2E-05
9308811
n-hexane
—
—
2E-07
—
—
9308811
acrylic acid
2E-03
3E-03
9E-05
4E-03
8E-05
9308811
methanol
2E-04
8E-06
2E-06
2E-05
4E-06
946711
acrylamide
—
—
—
—
—
946711
methyl methacrylate
—
2E-04
3E-05
—
—
946711
n-hexane
—
—
1E-05
—
—
946711
acrylic acid
4E-02
5E-02
2E-03
8E-02
2E-03
946711
acrylonitrile
—
—
5E-02
8E-03
2E-03
946711
formaldehyde
1E-01
6E-03
4E-04
5E-03
5E-04
946711
methyl chloride
—
—
IE-04
8E-04
1E-04
946711
methanol
5E-03
2E-04
5E-05
5E-04
1E-04
949811
chloroacetic acid
—
—
4E-05
—
—
949811
carbon disulfide
7E-01
1E-01
8E-03
1
3E-02
949811
chlorine
6E-02
8E-03
2E-03
4E-03
1E-03
949911
acetonitrile
—
—
—
—
—
949911
allyl chloride
—
—
—
—
—
949911
cumene
—
—
—
—
—
949911
ethyl chloride
—
—
—
—
—
949911
ethylene glycol
—
—
—
—
—
949911
glycol ethers
—
—
—
—
—
949911
isophorone
—
—
—
—
—
949911
methyl isobutyl ketone
—
—
—
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
949911
n-hexane
—
—
—
—
—
949911
naphthalene
—
—
—
—
—
949911
phthalic anhydride
—
—
—
—
—
949911
propylene dichloride
—
—
—
—
—
949911
xylenes (mixed)
—
—
—
—
—
949911
triethylamine
4E-05
—
—
—
—
949911
hydrogen cyanide
4E-01
6E-02
2E-02
—
1E-02
949911
methyl methacrylate
—
6E-04
9E-05
—
—
949911
chloroform
5E-03
—
2E-06
—
3E-06
949911
propionaldehyde
—
1E-07
2E-08
—
—
949911
hydrochloric acid
1E-01
8E-02
7E-03
5E-02
7E-03
949911
chlorine
2E-01
3E-02
9E-03
2E-02
6E-03
949911
acrolein
2E-01
6E-03
2E-03
4E-03
1E-03
949911
formaldehyde
9E-02
4E-03
3E-04
4E-03
4E-04
949911
methanol
2E-03
8E-05
2E-05
2E-04
4E-05
949911
methyl chloride
—
—
4E-06
2E-05
3E-06
949911
toluene
—
7E-06
8E-07
9E-06
2E-06
949911
acetaldehyde
3E-04
2E-06
3E-07
8E-06
4E-07
949911
acrylic acid
3E-06
4E-06
1E-07
6E-06
1E-07
9588611
ethylene glycol
—
—
—
—
—
9588611
methyl methacrylate
—
2E-03
3E-04
—
—
9588611
hydrogen cyanide
2E-03
3E-04
9E-05
—
6E-05
9588611
methanol
2E-03
9E-05
2E-05
2E-04
5E-05
9588611
chlorine
3E-04
4E-05
1E-05
2E-05
8E-06
973911
ethylene glycol
—
—
—
—
—
973911
o-cresol
—
—
—
—
—
973911
pah, total
—
—
—
—
—
973911
chloroform
1
—
5E-04
—
6E-04
973911
cadmium compounds
—
2E-06
3E-07
—
—
973911
n-hexane
—
—
2E-08
—
—
973911
chlorine
1
2E-01
4E-02
9E-02
3E-02
973911
formaldehyde
1
5E-02
3E-03
5E-02
5E-03
973911
hydrochloric acid
2E-02
2E-02
1E-03
1E-02
2E-03
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
973911
phenol
1E-02
1E-03
7E-04
2E-03
3E-04
973911
methanol
1E-02
4E-04
IE-04
1E-03
2E-04
973911
epichlorohydrin
9E-03
2E-03
IE-04
6E-04
2E-04
973911
acetaldehyde
9E-03
5E-05
9E-06
2E-04
1E-05
973911
maleic anhydride
—
—
—
4E-05
2E-06
973911
acrolein
3E-04
1E-05
3E-06
7E-06
2E-06
976011
bromoform
—
—
—
—
—
976011
ethyl chloride
—
—
—
—
—
976011
ethylene glycol
—
—
—
—
—
976011
ethylidene dichloride
—
—
—
—
—
976011
phthalic anhydride
—
—
—
—
—
976011
chloroform
7E-01
—
3E-04
—
4E-04
976011
methyl bromide
3E-02
—
IE-04
—
6E-04
976011
xylenes (mixed)
7E-03
3E-04
4E-05
—
—
976011
ethyl benzene
—
8E-04
2E-05
—
—
976011
ethylene dibromide
—
2E-06
2E-06
—
—
976011
1,2-epoxybutane
—
2E-06
1E-06
—
—
976011
n-hexane
—
—
4E-10
—
—
976011
chlorine
4E-01
5E-02
1E-02
3E-02
8E-03
976011
hydrochloric acid
2E-02
1E-02
1E-03
8E-03
1E-03
976011
hydrazine
—
4E-02
3E-04
8E-03
8E-04
976011
acetaldehyde
2E-01
1E-03
2E-04
6E-03
3E-04
976011
methanol
4E-02
2E-03
4E-04
5E-03
9E-04
976011
acrolein
7E-02
3E-03
8E-04
2E-03
5E-04
976011
toluene
—
6E-04
7E-05
8E-04
1E-04
976011
ethylene dichloride
—
—
—
6E-04
1E-04
976011
methyl chloride
—
—
6E-05
4E-04
5E-05
976011
benzene
—
3E-04
2E-05
3E-04
1E-04
976011
methylene chloride
1E-02
2E-04
8E-05
2E-04
6E-05
976011
phenol
IE-04
1E-05
8E-06
2E-05
4E-06
976011
1,3-butadiene
—
3E-07
3E-08
2E-05
3E-07
985511
gaseous divalent mercury
—
—
—
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
E.RPG1
ERPG2
985511
particulate divalent
mercury
—
—
—
—
—
985511
cadmium compounds
—
3E-03
4E-04
—
—
985511
acetonitrile
—
3E-04
8E-05
—
—
985511
mercury (elemental)
2E-04
—
8E-08
—
7E-08
985511
acrylonitrile
—
—
4
7E-01
2E-01
985511
styrene
1E-03
3E-04
4E-05
IE-04
2E-05
985511
hydrochloric acid
IE-04
IE-04
9E-06
6E-05
1E-05
985511
benzene
—
6E-05
4E-06
6E-05
2E-05
993411
ethylene dichloride
—
—
—
—
—
993411
phthalic anhydride
—
—
—
—
—
993411
ethylene dibromide
—
1E-03
9E-04
—
—
993411
methyl bromide
2E-02
—
9E-05
—
4E-04
993411
chlorine
6
8E-01
2E-01
4E-01
1E-01
993411
hydrochloric acid
5E-01
4E-01
3E-02
2E-01
3E-02
993411
toluene
—
6E-03
7E-04
8E-03
1E-03
993411
styrene
6E-02
2E-02
2E-03
6E-03
1E-03
993411
propylene oxide
1E-01
2E-03
4E-04
2E-03
5E-04
993411
phenol
5E-03
5E-04
3E-04
8E-04
2E-04
999411
chromium (iii) compounds
—
—
—
—
—
999411
chromium (vi) compounds
—
—
—
—
—
999411
cobalt compounds
—
—
—
—
—
999411
ethylene glycol
—
—
—
—
—
999411
lead compounds
—
—
—
—
—
999411
manganese compounds
—
—
—
—
—
999411
naphthalene
—
—
—
—
—
999411
nickel compounds
—
—
—
—
—
999411
xylenes (mixed)
9E-02
3E-03
5E-04
—
—
999411
methyl bromide
2E-02
—
9E-05
—
4E-04
999411
ethyl benzene
—
1E-03
3E-05
—
—
999411
cumene
—
1E-06
2E-07
—
—
999411
1,4-dioxane
2E-05
9E-07
4E-08
—
—
999411
n-hexane
—
—
1E-08
—
—
-------
Facility EIS ID
Pollutant
Maximum Hazard Quotient (HQ)'2
REL
AEGL1
AEGL2
ERPG1
ERPG2
999411
biphenyl
—
—
1E-08
—
—
999411
formaldehyde
7E-01
3E-02
2E-03
3E-02
3E-03
999411
methanol
3E-02
1E-03
3E-04
4E-03
7E-04
999411
1,3-butadiene
—
1E-05
2E-06
1E-03
2E-05
999411
benzene
—
3E-04
2E-05
4E-04
1E-04
999411
toluene
—
IE-04
1E-05
IE-04
2E-05
999411
acetaldehyde
4E-03
2E-05
4E-06
IE-04
5E-06
999511
ethylene glycol
—
—
—
—
—
999511
lead compounds
—
—
—
—
—
999511
phthalic anhydride
—
—
—
—
—
999511
methyl methacrylate
—
7E-05
1E-05
—
—
999511
n-hexane
—
—
1E-06
—
—
999511
hydrochloric acid
7E-02
6E-02
5E-03
3E-02
5E-03
999511
acetaldehyde
3E-01
1E-03
2E-04
7E-03
3E-04
999511
formaldehyde
1E-02
6E-04
4E-05
6E-04
6E-05
'BOLD indicates
2 "—" indicates a
a maximum Hazard Quotient (HQ)
benchmark was not available
va
ue greater than 1
-------
Refined Acute Modeling Approach and Results
Initial acute screening risk calculations were performed with the HEM-4 model using the
maximum hourly emissions estimates described in Section 3.1 in the main body of this report. HEM-4
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.
This appendix addresses refinements to determine the maximum offsite values by plotting the
HEM-4 polar grid results on aerial photographs of the facilities for those facilities and pollutants that
exceeded short-term health benchmarks (i.e., original acute Hazard Quotient is greater than 1). These
photographs were examined to determine off-site locations that may be accessible to the public (e.g.,
public roadways, public buildings, and public waterways). The attached figures present the estimated
hazard quotient values (the modeled 1-hour concentration of a pollutant divided by its short-term health
benchmark) at the polar receptors near the facilities. Table 4 provides the resulting change in acute risks
taking into account these off-site locations for the baseline modeling scenario.
Table 6. Refined acute modeling results
Facility EIS ID
Pollutant
Criteria
Original
Acute
Hazard
Quotient
Refined
(Off-site)
Hazard
Quotient1
Notes
6884211
formaldehyde
REL
10
2
See Figure 1
8465711
formaldehyde
REL
6
0.7
See Figure 2
5929411
chlorine
REL
6
3
See Figure 3
993411
chlorine
REL
6
0.5
See Figure 4
3982311
arsenic compounds
REL
5
0.9
See Figure 5
5929411
chloroform
REL
5
2
See Figure 6
8096711
methanol
REL
4
0.2
See Figure 7
8026211
acrolein
REL
4
3
See Figure 8
8263111
formaldehyde
REL
4
0.7
See Figure 9
7367211
methylene diphenyl
diisocyanate
REL
3
0.8
See Figure 10
7984011
formaldehyde
REL
3
0.7
See Figure 11
7915011
chloroform
REL
3
0.4
See Figure 12
-------
4878911
formaldehyde
REL
2
0.3
See Figure 13
8447711
formaldehyde
REL
2
0.5
See Figure 14
7368811
formaldehyde
REL
2
1
See Figure 15
7984011
acrolein
REL
2
0.8
See Figure 16
8107111
formaldehyde
REL
2
1
See Figure 17
7366811
chlorine
REL
2
0.09
See Figure 18
7351811
ethylene glycol methyl
ether
REL
2
0.05
See Figure 19
751411
maleic anhydride
ERPG1
2
0.07
See Figure 20
985511
acrylonitrile
AEGL2
4
0.1
See Figure 21
'BOLD indicates a refined (off-site) maximum Hazard Quotient (HQ) value greater than 1
-------
Figure 1. Aerial photo of Facility 6884211 and corresponding acute RELs for formaldehyde at HEM-4
polar receptor locations.
-------
Figure 2. Aerial photo of Facility 8465711 and corresponding acute RELs for formaldehyde at FIEM-4
polar receptor locations.
-------
Figure 3. Aerial photo of Facility 5929411 and corresponding acute RELs for chlorine at FIEM-4 polar
receptor locations.
-------
Figure 4. Aerial photo of Facility 993411 and corresponding acute RELs for chlorine at HEM-4 polar
receptor locations.
-------
Figure 5. Aerial photo of Facility 3982311 and corresponding acute RELs for arsenic compounds at
HEM-4 polar receptor locations.
-------
Figure 6. Aerial photo of Facility 5929411 and corresponding acute RELs for chloroform at FIEM-4 polar
receptor locations.
-------
Figure 7. Aerial photo of Facility 8096711 and corresponding acute RELs for methanol at HEM-4 polar
receptor locations.
-------
Figure 8. Aerial photo of Facility 8026211 and corresponding acute RELs for acrolein at FIEM-4 polar
receptor locations.
-------
Figure 9. Aerial photo of Facility 8263 111 and corresponding acute RELs for formaldehyde at HEM-4
polar receptor locations.
-------
Figure 10. Aerial photo of Facility 7367211 and corresponding acute RELs for methylene diphenyl
diisocyanate at HEM-4 polar receptor locations.
-------
Figure 11. Aerial photo of Facility 7984011 and corresponding acute RELs for formaldehyde at HEM-4
polar receptor locations.
-------
Figure 12. Aerial photo of Facility 7915011 and corresponding acute RELs for chloroform at HEM-4
polar receptor locations.
-------
Figure 13. Aerial photo of Facility 4878911 and corresponding acute RELs for formaldehyde at HEM-4
polar receptor locations.
-------
Figure 14. Aerial photo of Facility 8447711 and corresponding acute RELs for formaldehyde at HEM-4
polar receptor locations.
-------
Figure 15. Aerial photo of Facility 7368811 and corresponding acute RELs for formaldehyde at HEM-4
polar receptor locations.
-------
Figure 16. Aerial photo of Facility 7984011 and corresponding acute RELs for acrolein at HEM-4 polar
receptor locations.
-------
Figure 17. Aerial photo of Facility 8107111 and corresponding acute RELs for formaldehyde at HEM-4
polar receptor locations.
-------
Figure 18. Aerial photo of Facility 7366811 and corresponding acute RELs for chlorine at HEM-4 polar
receptor locations.
-------
Figure 19. Aerial photo of Facility 7351811 and corresponding acute RELs for ethylene glycol methyl
ether at HEM-4 polar receptor locations.
-------
Figure 20. Aerial photo of Facility 751411 and corresponding acute ERPGls formaleic anhydride at
HEM-4 polar receptor locations.
-------
Figure 21. Aerial photo of Facility 985511 and corresponding acute AEGL2s for acrylonitrile atHEM-4
polar receptor locations.
-------
Table 7. Multipathway Cancer Tier 1 and Tier 2 Screen Values
I'acililN LIS II)
Screening Values
Screening Values
Tier 1
Tier 2
¦isherman Scenario
(iaixlener Scenario
l)io\in
POM
Arsenic
1 Ol;ll
l)io\in
POM
Arsenic
Toial
Diomii
POM
Arsenic
Toial
1020111
—
3E-04
2E-02
2E-02
—
9E-06
4E-04
4E-04
—
2E-05
4E-03
4E-03
1072711
—
—
—
—
—
—
—
—
—
—
—
—
949811
—
—
—
—
—
—
—
—
—
—
—
—
949911
—
—
—
—
—
—
—
—
—
—
—
—
985511
—
—
—
—
—
—
—
—
—
—
—
—
999411
—
—
—
—
—
—
—
—
—
—
—
—
999511
—
—
—
—
—
—
—
—
—
—
—
—
973911
—
2E-04
—
2E-04
—
5E-06
—
5E-06
—
7E-06
—
7E-06
976011
—
—
—
—
—
—
—
—
—
—
—
—
993411
—
—
—
—
—
—
—
—
—
—
—
—
588311
—
—
—
—
—
—
—
—
—
—
—
—
751411
—
—
—
—
—
—
—
—
—
—
—
—
2491711
—
—
—
—
—
—
—
—
—
—
—
—
946711
—
—
—
—
—
—
—
—
—
—
—
—
8137811
—
—
—
—
—
—
—
—
—
—
—
—
8209411
—
—
—
—
—
—
—
—
—
—
—
—
7940411
—
—
—
—
—
—
—
—
—
—
—
—
7338711
—
—
—
—
—
—
—
—
—
—
—
—
5386211
—
—
—
—
—
—
—
—
—
—
—
—
10716511
—
3E-02
3
3
—
5E-05
2E-02
2E-02
—
4E-04
1E-01
1E-01
7972911
—
1E-03
1E-01
1E-01
—
1E-05
6E-04
6E-04
—
1E-05
5E-03
5E-03
7972111
—
—
—
—
—
—
—
—
—
—
—
—
7364611
—
—
—
—
—
—
—
—
—
—
—
—
7246511
—
8E-06
2E-01
2E-01
—
1E-07
1E-03
1E-03
—
6E-08
1E-02
1E-02
18929011
—
—
—
—
—
—
—
—
—
—
—
—
-------
I;acilil\ LIS II)
Screening Values
Screening Values
Tier 1
Tier 2
¦'ishernian Scenario
(iaalener Scenario
DlOMIl
POM
Arsenic
1 olal
Diomii
POM
Arsenic
Tolal
Diomii
POM
Arsenic
Tolal
8067211
—
—
—
—
—
—
—
—
—
—
—
—
8059311
—
—
—
—
—
—
—
—
—
—
—
—
7331911
—
4
—
4
—
1E-02
—
1E-02
—
1E-01
—
1E-01
8194311
—
—
—
—
—
—
—
—
—
—
—
—
10695411
—
—
—
—
—
—
—
—
—
—
—
—
7367811
—
—
—
—
—
—
—
—
—
—
—
—
7368011
—
2E-02
4E-03
3E-02
—
4E-04
2E-05
4E-04
—
9E-05
2E-04
3E-04
5929411
—
—
—
—
—
—
—
—
—
—
—
—
7366811
—
—
—
—
—
—
—
—
—
—
—
—
8096711
—
—
—
—
—
—
—
—
—
—
—
—
7351811
—
—
—
—
—
—
—
—
—
—
—
—
16966011
—
—
—
—
—
—
—
—
—
—
—
—
7365611
—
2E-04
—
2E-04
—
3E-06
—
6E-07
—
1E-05
—
1E-05
7367211
—
—
—
—
—
—
—
—
—
—
—
—
7368811
—
—
—
—
—
—
—
—
—
—
—
—
7445611
—
—
—
—
—
—
—
—
—
—
—
—
7445711
—
100
2E-01
100
—
2
1E-03
3E-01
—
8
4E-02
8
7915011
20
9E-02
—
20
4E-02
1E-03
—
4E-02
3E-02
6E-03
—
4E-02
8465311
—
—
—
—
—
—
—
—
—
—
—
—
8465611
—
10
—
10
—
2E-01
—
4E-02
—
9E-01
—
9E-01
8465711
—
—
—
—
—
—
—
—
—
—
—
—
17905711
—
9E-03
—
9E-03
—
9E-05
—
1E-04
—
1E-04
—
1E-04
5719311
—
—
—
—
—
—
—
—
—
—
—
—
7354911
20
—
20
40
3E-01
—
2E-01
5E-01
8E-03
—
9E-01
9E-01
7380411
—
80
—
80
—
1
—
6E-01
—
1
—
1
7380611
—
—
—
—
—
—
—
—
—
—
—
—
7928911
—
1E-02
—
1E-02
—
1E-04
—
7E-05
—
2E-04
—
2E-04
-------
I;acilil\ LIS II)
Screening Values
Screening Values
Tier 1
Tier 2
¦'ishernian Scenario
(iaalener Scenario
DlOMIl
POM
Arsenic
1 olal
Diomii
POM
Arsenic
Tolal
Diomii
POM
Arsenic
Tolal
7929111
—
—
—
—
—
—
—
—
—
—
—
—
8361111
200
7E-03
—
200
6
7E-05
—
6
6E-02
9E-05
—
6E-02
8465211
—
—
—
—
—
—
—
—
—
—
—
—
8468011
—
—
—
—
—
—
—
—
—
—
—
—
16972411
—
—
—
—
—
—
—
—
—
—
—
—
7226311
—
—
—
—
—
—
—
—
—
—
—
—
7226611
—
9E-02
—
9E-02
—
2E-04
—
2E-03
—
2E-03
—
2E-03
7354711
1000
—
—
1000
20
—
—
20
2
—
—
2
8215111
—
—
—
—
—
—
—
—
—
—
—
—
8467611
—
2E-03
—
2E-03
—
6E-06
—
4E-05
—
4E-05
—
4E-05
13610611
20
—
—
20
3E-01
—
—
3E-01
3E-02
—
—
3E-02
15639911
—
—
—
—
—
—
—
—
—
—
—
—
17640911
—
—
—
—
—
—
—
—
—
—
—
—
5505011
—
—
—
—
—
—
—
—
—
—
—
—
5520211
—
—
—
—
—
—
—
—
—
—
—
—
7227011
70
1
—
80
8E-01
9E-03
—
8E-01
1E-01
7E-02
—
2E-01
7448011
—
—
—
—
—
—
—
—
—
—
—
—
8467311
—
9E-02
1
2
—
2E-04
4E-02
7E-03
—
2E-03
2E-01
2E-01
13614411
—
—
—
—
—
—
—
—
—
—
—
—
17640311
—
—
—
—
—
—
—
—
—
—
—
—
7228511
—
—
—
—
—
—
—
—
—
—
—
—
9588611
—
—
—
—
—
—
—
—
—
—
—
—
7226711
—
—
—
—
—
—
—
—
—
—
—
—
7203711
—
1E-01
2
2
—
3E-04
7E-03
6E-03
—
2E-03
1E-01
1E-01
8020411
—
30
—
30
—
7E-02
—
7E-02
—
2E-01
—
2E-01
7202911
—
—
—
—
—
—
—
—
—
—
—
—
8018911
—
—
3E-01
3E-01
—
—
8E-04
9E-04
—
—
1E-02
1E-02
-------
I;acilil\ LIS II)
Screening Values
Screening Values
Tier 1
Tier 2
¦'ishernian Scenario
(iaalener Scenario
DlOMIl
POM
Arsenic
1 olal
Diomii
POM
Arsenic
Tolal
Diomii
POM
Arsenic
Tolal
8020811
—
4E-02
—
4E-02
—
4E-04
—
4E-04
—
4E-04
—
4E-04
8026211
—
—
—
—
—
—
—
—
—
—
—
—
8239511
—
30
—
30
—
3E-01
—
3E-01
—
3E-01
—
3E-01
8020011
—
—
—
—
—
—
—
—
—
—
—
—
17640111
—
—
—
—
—
—
—
—
—
—
—
—
7204811
—
—
5E-01
5E-01
—
—
2E-03
2E-03
—
—
4E-02
4E-02
17055211
—
—
—
—
—
—
—
—
—
—
—
—
18982311
—
—
—
—
—
—
—
—
—
—
—
—
8384311
—
1000
5E-01
1000
—
6
3E-03
6
—
100
1E-01
100
7984011
—
2E-01
—
2E-01
—
2E-03
—
2E-03
—
7E-03
—
7E-03
7984111
—
—
—
—
—
—
—
—
—
—
—
—
7302511
—
—
—
—
—
—
—
—
—
—
—
—
7311911
—
—
—
—
—
—
—
—
—
—
—
—
8105111
—
—
—
—
—
—
—
—
—
—
—
—
8123911
—
—
—
—
—
—
—
—
—
—
—
—
8107111
—
—
—
—
—
—
—
—
—
—
—
—
8434411
—
—
—
—
—
—
—
—
—
—
—
—
16856611
—
—
—
—
—
—
—
—
—
—
—
—
8447711
—
—
—
—
—
—
—
—
—
—
—
—
8135311
—
—
—
—
—
—
—
—
—
—
—
—
8006811
—
—
—
—
—
—
—
—
—
—
—
—
8086711
—
—
—
—
—
—
—
—
—
—
—
—
8007011
—
—
—
—
—
—
—
—
—
—
—
—
9308811
—
—
—
—
—
—
—
—
—
—
—
—
7937511
—
—
—
—
—
—
—
—
—
—
—
—
13431911
—
—
—
—
—
—
—
—
—
—
—
—
7319811
—
5E-01
2E-01
7E-01
—
1E-02
1E-03
2E-02
—
1E-03
5E-03
6E-03
-------
I;acilil\ LIS II)
Screening Values
Screening Values
Tier 1
Tier 2
¦'ishernian Scenario
(iaalener Scenario
DlOMIl
POM
Arsenic
1 olal
Diomii
POM
Arsenic
Tolal
Diomii
POM
Arsenic
Tolal
8262411
—
5E-02
—
5E-02
—
2E-04
—
2E-04
—
4E-04
—
4E-04
8263111
—
—
—
—
—
—
—
—
—
—
—
—
8418011
—
—
—
—
—
—
—
—
—
—
—
—
8148211
—
—
—
—
—
—
—
—
—
—
—
—
8130511
—
—
—
—
—
—
—
—
—
—
—
—
15077311
—
—
—
—
—
—
—
—
—
—
—
—
4950811
—
—
—
—
—
—
—
—
—
—
—
—
4965811
—
—
—
—
—
—
—
—
—
—
—
—
4041311
—
1E-07
2E-02
2E-02
—
2E-08
6E-04
6E-04
—
7E-09
8E-03
8E-03
9177911
—
—
—
—
—
—
—
—
—
—
—
—
5611111
—
—
—
—
—
—
—
—
—
—
—
—
6194311
—
—
—
—
—
—
—
—
—
—
—
—
3982311
—
9
30
40
—
9E-02
2E-01
3E-01
—
4E-01
4
4
5631411
—
—
—
—
—
—
—
—
—
—
—
—
16871811
—
—
—
—
—
—
—
—
—
—
—
—
17735411
200
—
—
200
2
—
—
2
1E-01
—
—
1E-01
18973611
—
—
—
—
—
—
—
—
—
—
—
—
4897511
—
—
20
20
—
—
1E-01
4E-01
—
—
3
3
5018711
—
6
—
6
—
2E-03
—
2E-03
—
3E-01
—
3E-01
5019011
—
—
—
—
—
—
—
—
—
—
—
—
5632411
—
—
—
—
—
—
—
—
—
—
—
—
5632711
—
2E-07
5E-02
5E-02
—
8E-10
2E-04
1E-03
—
9E-10
4E-03
4E-03
5633311
—
—
—
—
—
—
—
—
—
—
—
—
5632511
—
—
—
—
—
—
—
—
—
—
—
—
5633411
2000
—
—
2000
10
—
—
10
7E-01
—
—
7E-01
5846511
—
—
—
—
—
—
—
—
—
—
—
—
5729211
—
1E-01
—
1E-01
—
2E-04
—
4E-04
—
1E-03
—
1E-03
-------
I;acilil\ LIS II)
Screening Values
Screening Values
Tier 1
Tier 2
¦'ishernian Scenario
(iaalener Scenario
DlOMIl
POM
Arsenic
1 olal
Diomii
POM
Arsenic
Toial
Diomii
POM
Arsenic
Tolal
9175811
—
—
—
—
—
—
—
—
—
—
—
—
10722911
—
—
—
—
—
—
—
—
—
—
—
—
3930711
—
—
—
—
—
—
—
—
—
—
—
—
3967011
—
—
—
—
—
—
—
—
—
—
—
—
4835311
—
3E-01
—
3E-01
—
9E-04
—
6E-04
—
2E-03
—
2E-03
6615111
—
—
—
—
—
—
—
—
—
—
—
—
10679911
—
—
—
—
—
—
—
—
—
—
—
—
13411211
—
—
—
—
—
—
—
—
—
—
—
—
14997411
—
—
—
—
—
—
—
—
—
—
—
—
17058711
—
—
—
—
—
—
—
—
—
—
—
—
3737011
—
200
—
200
—
5
—
9E-01
—
3
—
3
4055111
—
—
—
—
—
—
—
—
—
—
—
—
4055511
—
—
—
—
—
—
—
—
—
—
—
—
4056511
—
—
—
—
—
—
—
—
—
—
—
—
4057611
—
—
—
—
—
—
—
—
—
—
—
—
4057911
—
—
—
—
—
—
—
—
—
—
—
—
4167411
—
—
—
—
—
—
—
—
—
—
—
—
4167811
—
—
—
—
—
—
—
—
—
—
—
—
4168511
—
10
—
10
—
5E-02
—
1E-01
—
1E-01
—
1E-01
4168611
—
—
—
—
—
—
—
—
—
—
—
—
4762811
—
30
—
30
—
3E-02
—
3E-01
—
4E-01
—
4E-01
4778211
100
—
—
100
2
—
—
2
4E-02
—
—
4E-02
4778311
—
—
—
—
—
—
—
—
—
—
—
—
4778711
—
—
700
700
—
—
5
5
—
—
70
70
4924311
—
—
—
—
—
—
—
—
—
—
—
—
4924411
—
80
80
200
—
2E-01
4E-01
7E-01
—
9E-01
4
5
4925111
—
3
—
3
—
7E-03
—
6E-02
—
4E-02
—
4E-02
-------
I;acilil\ LIS II)
Screening Values
Screening Values
Tier 1
Tier 2
¦'ishernian Scenario
(iaalener Scenario
DlOMIl
POM
Arsenic
1 olal
Diomii
POM
Arsenic
Tolal
Diomii
POM
Arsenic
Tolal
4926611
—
—
—
—
—
—
—
—
—
—
—
—
4941211
—
—
—
—
—
—
—
—
—
—
—
—
4941411
—
9E-02
—
9E-02
—
2E-04
—
8E-04
—
1E-03
—
1E-03
4980911
—
—
—
—
—
—
—
—
—
—
—
—
4982011
—
—
—
—
—
—
—
—
—
—
—
—
6421511
—
—
—
—
—
—
—
—
—
—
—
—
6421811
—
—
—
—
—
—
—
—
—
—
—
—
6510111
—
—
—
—
—
—
—
—
—
—
—
—
6510311
—
—
—
—
—
—
—
—
—
—
—
—
6534611
—
—
—
—
—
—
—
—
—
—
—
—
6534811
—
3E-02
20
20
—
1E-04
2E-01
2E-01
—
3E-04
8E-01
8E-01
6641411
—
—
—
—
—
—
—
—
—
—
—
—
6671911
—
—
—
—
—
—
—
—
—
—
—
—
9103411
—
—
—
—
—
—
—
—
—
—
—
—
4941511
—
—
1
1
—
—
2E-02
2E-02
—
—
1E-01
1E-01
7908711
—
—
—
—
—
—
—
—
—
—
—
—
5651911
—
40
—
40
—
8E-02
—
8E-02
—
3E-01
—
3E-01
4861611
—
2
4
7
—
3E-03
7E-03
1E-02
—
2E-02
1E-01
1E-01
6157311
—
—
—
—
—
—
—
—
—
—
—
—
13407911
—
—
—
—
—
—
—
—
—
—
—
—
17912111
—
—
—
—
—
—
—
—
—
—
—
—
18893911
—
—
—
—
—
—
—
—
—
—
—
—
4863111
—
6
—
6
—
2E-03
—
2E-03
—
1E-01
—
1E-01
4930211
—
2
—
2
—
1E-03
—
6E-04
—
2E-02
—
2E-02
4945211
—
—
—
—
—
—
—
—
—
—
—
—
5651611
—
—
—
—
—
—
—
—
—
—
—
—
5653011
—
4E-01
—
4E-01
—
2E-04
—
1E-04
—
8E-03
—
8E-03
-------
I;acilil\ LIS II)
Screening Values
Screening Values
Tier 1
Tier 2
¦'ishernian Scenario
(iaalener Scenario
DlOMIl
POM
Arsenic
1 olal
Diomii
POM
Arsenic
Tolal
Diomii
POM
Arsenic
Tolal
6158411
—
—
—
—
—
—
—
—
—
—
—
—
6362111
—
—
—
—
—
—
—
—
—
—
—
—
6385211
—
—
—
—
—
—
—
—
—
—
—
—
6386311
—
—
—
—
—
—
—
—
—
—
—
—
6430411
—
4E-01
—
4E-01
—
2E-04
—
3E-04
—
4E-03
—
4E-03
6444911
—
5E-01
5E-01
1
—
7E-04
8E-04
9E-04
—
1E-02
4E-02
5E-02
6445411
—
—
—
—
—
—
—
—
—
—
—
—
17909311
—
—
—
—
—
—
—
—
—
—
—
—
6152811
—
9
—
9
—
6E-02
—
6E-02
—
2E-01
—
2E-01
13411911
—
—
—
—
—
—
—
—
—
—
—
—
4945611
—
—
—
—
—
—
—
—
—
—
—
—
13389511
—
—
—
—
—
—
—
—
—
—
—
—
17974911
—
—
—
—
—
—
—
—
—
—
—
—
17985111
—
—
—
—
—
—
—
—
—
—
—
—
4031311
—
—
—
—
—
—
—
—
—
—
—
—
4205511
—
2E-01
—
2E-01
—
2E-03
—
2E-03
—
9E-04
—
9E-04
4929511
—
—
—
—
—
—
—
—
—
—
—
—
4929811
—
—
5E-01
5E-01
—
—
3E-03
3E-03
—
—
1E-02
1E-02
4945411
—
—
—
—
—
—
—
—
—
—
—
—
5656011
—
6E-01
—
6E-01
—
4E-03
—
4E-03
—
4E-03
—
4E-03
5862111
—
—
—
—
—
—
—
—
—
—
—
—
9115811
—
—
—
—
—
—
—
—
—
—
—
—
10678011
—
—
—
—
—
—
—
—
—
—
—
—
4190211
—
—
—
—
—
—
—
—
—
—
—
—
5746611
—
—
—
—
—
—
—
—
—
—
—
—
5679711
—
—
—
—
—
—
—
—
—
—
—
—
4004311
—
—
—
—
—
—
—
—
—
—
—
—
-------
l:acilil\ LIS II)
Screening \ allies
Screening \ allies
Tier 1
Tier:
¦isherman Scenario
(iai'dener Scenario
l.)io\m
POM
Arsenic
loial
l)io\in
POM
Arsenic
1 olal
l.)io\in
POM
Arsenic
Tolal
5748611
—
—
—
—
—
—
—
—
—
—
—
—
5768911
—
—
—
—
—
—
—
—
—
—
—
—
5769011
—
—
—
—
—
—
—
—
—
—
—
—
4783711
—
—
—
—
—
—
—
—
—
—
—
—
6234511
—
2
—
2
—
7E-03
—
7E-03
—
9E-03
—
9E-03
19250811
—
—
—
—
—
—
—
—
—
—
—
—
6884211
—
—
—
—
—
—
—
—
—
—
—
—
4878911
—
—
—
—
—
—
—
—
—
—
—
—
'BOLD indicates a screening value greater than 1
2"—" indicates the pollutant was not emitted by the facility and therefore a Tier 2 screen was not performed
-------
Table 8. Multipathway Cancer Tier 2 vs. Tier 3 Screen Values
Facility EIS ID
POM Cancer
Screening Values'
Tier 3 POM Cancer Screen Values
(Gardener Scenario)1
Comments
Tier l2
Tier 23
Rural
Gardener
Screen
Plume-rise
TRIM
Hourly
Run
280598384311
1000
100
20
NA
NA
See Appendix
11 for Tier 3
Results
'BOLD indicates a screening value greater than 10
2 Tier 1 screening values based upon a combined fish and farmer ingestion rate
3 MIR Site for Rural Gardener Screen
-------
Table 9. Multipathway Non-Cancer Tier 1 and Tier 2 Screen Values
l;acilil\ LIS II)
Screening Value
Tier 1
Tier:
I'i sherman Scenario
Gardener Scenario
\lercur\
( admium
\lercur\
Cadmium
\lercur\
Cadmium
1020111
0.009
0.009
0.002
0.0002
0.000004
0.0004
1072711
—
—
—
—
—
—
949811
—
—
—
—
—
—
949911
—
—
—
—
—
—
985511
0.09
0.2
0.01
0.02
0.00001
0.002
999411
—
—
—
—
—
—
999511
—
—
—
—
—
—
973911
—
0.01
—
0.002
—
0.0003
976011
—
—
—
—
—
—
993411
—
—
—
—
—
—
588311
—
—
—
—
—
—
751411
—
—
—
—
—
—
2491711
—
—
—
—
—
—
946711
—
—
—
—
—
—
8137811
—
—
—
—
—
—
8209411
—
—
—
—
—
—
7940411
—
—
—
—
—
—
7338711
—
—
—
—
—
—
5386211
—
—
—
—
—
—
10716511
0.1
0.07
0.02
0.003
0.00002
0.0006
7972911
0.1
0.05
0.01
0.003
0.00001
0.0005
7972111
—
—
—
—
—
—
7364611
—
—
—
—
—
—
7246511
0.1
0.1
0.009
0.006
0.00001
0.001
18929011
—
—
—
—
—
—
8067211
—
—
—
—
—
—
8059311
—
—
—
—
—
—
7331911
20
—
1
—
0.007
—
8194311
—
—
—
—
—
—
10695411
—
—
—
—
—
—
7367811
—
—
—
—
—
—
7368011
0.006
0.002
0.0004
0.00009
0.0000008
0.00002
5929411
—
—
—
—
—
—
7366811
—
—
—
—
—
—
8096711
—
—
—
—
—
—
7351811
—
—
—
—
—
—
16966011
—
—
—
—
—
—
7365611
—
—
—
—
—
—
7367211
—
—
—
—
—
—
-------
I;acilil\ LIS II)
Screening Value
Tier 1
Tier:
I'isherman Scenario
(iardener Scenario
\lercur\
Cadmium
\lercur\
Cadmium
\lercur\
Cadmium
7368811
—
—
—
—
—
—
7445611
—
—
—
—
—
—
7445711
0.006
0.001
0.00005
0.00002
0.000003
0.00008
7915011
—
—
—
—
—
—
8465311
—
—
—
—
—
—
8465611
—
—
—
—
—
—
8465711
—
—
—
—
—
—
17905711
—
—
—
—
—
—
5719311
—
—
—
—
—
—
7354911
0.7
0.6
0.1
0.07
0.00009
0.007
7380411
—
—
—
—
—
—
7380611
—
—
—
—
—
—
7928911
—
—
—
—
—
—
7929111
—
—
—
—
—
—
8361111
—
—
—
—
—
—
8465211
—
—
—
—
—
—
8468011
—
—
—
—
—
—
16972411
—
—
—
—
—
—
7226311
—
—
—
—
—
—
7226611
—
—
—
—
—
—
7354711
—
—
—
—
—
—
8215111
—
—
—
—
—
—
8467611
—
—
—
—
—
—
13610611
—
—
—
—
—
—
15639911
—
—
—
—
—
—
17640911
—
—
—
—
—
—
5505011
—
—
—
—
—
—
5520211
—
—
—
—
—
—
7227011
—
0.0008
—
0.00002
—
0.00004
7448011
—
—
—
—
—
—
8467311
1
—
0.3
—
0.0003
—
13614411
—
—
—
—
—
—
17640311
—
—
—
—
—
—
7228511
—
—
—
—
—
—
9588611
—
—
—
—
—
—
7226711
—
—
—
—
—
—
7203711
0.9
0.2
0.05
0.003
0.0002
0.003
8020411
—
—
—
—
—
—
7202911
—
—
—
—
—
—
8018911
6
0.006
1
0.0004
0.0006
0.00005
8020811
0.08
—
0.02
—
0.000009
—
-------
I;acilil\ LIS II)
Screening Value
Tier 1
Tier:
I'isherman Scenario
(iardener Scenario
\lercur\
Cadmium
\lercur\
Cadmium
\lercur\
Cadmium
8026211
—
—
—
—
—
—
8239511
—
—
—
—
—
—
8020011
—
—
—
—
—
—
17640111
—
—
—
—
—
—
7204811
0.3
30
0.02
2
0.00006
0.6
17055211
—
—
—
—
—
—
18982311
—
—
—
—
—
—
8384311
0.5
0.3
0.03
0.02
0.0003
0.02
7984011
—
—
—
—
—
—
7984111
—
—
—
—
—
—
7302511
—
—
—
—
—
—
7311911
—
—
—
—
—
—
8105111
—
—
—
—
—
—
8123911
—
—
—
—
—
—
8107111
—
—
—
—
—
—
8434411
—
—
—
—
—
—
16856611
—
—
—
—
—
—
8447711
—
—
—
—
—
—
8135311
—
—
—
—
—
—
8006811
—
—
—
—
—
—
8086711
—
—
—
—
—
—
8007011
—
—
—
—
—
—
9308811
—
—
—
—
—
—
7937511
—
—
—
—
—
—
13431911
—
—
—
—
—
—
7319811
0.2
0.08
0.03
0.005
0.00002
0.0005
8262411
—
—
—
—
—
—
8263111
—
—
—
—
—
—
8418011
—
—
—
—
—
—
8148211
—
—
—
—
—
—
8130511
—
—
—
—
—
—
15077311
—
—
—
—
—
—
4950811
—
—
—
—
—
—
4965811
—
—
—
—
—
—
4041311
0.02
0.01
0.005
0.003
0.00001
0.0009
9177911
—
—
—
—
—
—
5611111
—
—
—
—
—
—
6194311
—
—
—
—
—
—
3982311
—
0.2
—
0.01
—
0.004
-------
I;acilil\ LIS II)
Screening Value
Tier 1
Tier:
I'isherman Scenario
(iardener Scenario
\lercur\
Cadmium
\lercur\
Cadmium
\lercur\
Cadmium
5631411
—
—
—
—
—
—
16871811
—
—
—
—
—
—
17735411
—
—
—
—
—
—
18973611
—
—
—
—
—
—
4897511
2
0.04
0.03
0.001
0.0008
0.002
5018711
1
—
0.008
—
0.0005
—
5019011
—
—
—
—
—
—
5632411
—
—
—
—
—
—
5632711
0.5
0.03
0.008
0.0006
0.0001
0.0004
5633311
—
—
—
—
—
—
5632511
—
—
—
—
—
—
5633411
—
—
—
—
—
—
5846511
—
—
—
—
—
—
5729211
—
—
—
—
—
—
9175811
—
—
—
—
—
—
10722911
—
—
—
—
—
—
3930711
—
—
—
—
—
—
3967011
—
—
—
—
—
—
4835311
—
—
—
—
—
—
6615111
—
—
—
—
—
—
10679911
—
—
—
—
—
—
13411211
—
—
—
—
—
—
14997411
—
—
—
—
—
—
17058711
—
—
—
—
—
—
3737011
—
—
—
—
—
—
4055111
—
—
—
—
—
—
4055511
—
—
—
—
—
—
4056511
—
—
—
—
—
—
4057611
—
—
—
—
—
—
4057911
—
—
—
—
—
—
4167411
—
—
—
—
—
—
4167811
—
—
—
—
—
—
4168511
—
—
—
—
—
—
4168611
—
—
—
—
—
—
4762811
—
—
—
—
—
—
4778211
—
—
—
—
—
—
4778311
—
—
—
—
—
—
4778711
3
20
0.08
0.4
0.0008
0.5
4924311
—
—
—
—
—
—
-------
I;acilil\ LIS II)
Screening Value
Tier 1
Tier:
I'isherman Scenario
(iardener Scenario
\lercur\
Cadmium
\lercur\
Cadmium
\lercur\
Cadmium
4924411
30
10
6
2
0.004
0.1
4925111
—
—
—
—
—
—
4926611
—
—
—
—
—
—
4941211
—
—
—
—
—
—
4941411
—
—
—
—
—
—
4980911
—
—
—
—
—
—
4982011
—
—
—
—
—
—
6421511
—
—
—
—
—
—
6421811
—
—
—
—
—
—
6510111
—
—
—
—
—
—
6510311
—
—
—
—
—
—
6534611
—
—
—
—
—
—
6534811
300
0.5
50
0.05
0.04
0.006
6641411
—
—
—
—
—
—
6671911
—
—
—
—
—
—
9103411
—
—
—
—
—
—
4941511
5
0.3
1
0.05
0.002
0.008
7908711
—
—
—
—
—
—
5651911
—
—
—
—
—
—
4861611
0.4
0.8
0.01
0.01
0.00004
0.005
6157311
—
—
—
—
—
—
13407911
—
—
—
—
—
—
17912111
—
—
—
—
—
—
18893911
—
—
—
—
—
—
4863111
—
—
—
—
—
—
4930211
—
—
—
—
—
—
4945211
—
—
—
—
—
—
5651611
—
—
—
—
—
—
5653011
—
—
—
—
—
—
6158411
—
—
—
—
—
—
6362111
—
—
—
—
—
—
6385211
—
—
—
—
—
—
6386311
—
—
—
—
—
—
6430411
—
—
—
—
—
—
6444911
3
0.04
0.8
0.0006
0.0007
0.0008
6445411
—
—
—
—
—
—
17909311
—
—
—
—
—
—
6152811
—
—
—
—
—
—
13411911
—
—
—
—
—
—
-------
l:acilil\ LIS II)
Screening Value
Tier 1
Tier 2
I'lsheinian Scenario
(iai'denei' Scenario
\leicur\
( adnuum
\le ivu in
( adnuum
\ leicn in
( adnuum
4945611
—
—
—
—
—
—
13389511
—
—
—
—
—
—
17974911
—
—
—
—
—
—
17985111
—
—
—
—
—
—
4031311
—
—
—
—
—
—
4205511
—
—
—
—
—
—
4929511
—
—
—
—
—
—
4929811
—
0.2
—
0.01
—
0.0008
4945411
—
—
—
—
—
—
5656011
—
—
—
—
—
—
5862111
—
—
—
—
—
—
9115811
—
—
—
—
—
—
10678011
—
—
—
—
—
—
4190211
—
—
—
—
—
—
5746611
—
—
—
—
—
—
5679711
—
—
—
—
—
—
4004311
4
—
0.2
—
0.001
—
5748611
—
—
—
—
—
—
5768911
—
—
—
—
—
—
5769011
—
—
—
—
—
—
4783711
—
—
—
—
—
—
6234511
—
—
—
—
—
—
19250811
—
—
—
—
—
—
6884211
—
—
—
—
—
—
4878911
—
—
—
—
—
—
'BOLD indicates a screening value greater than 1
2"—" indicates the pollutant was not emitted by the facility and therefore a Tier 2 screen was not
performed
-------
Table 10. Multipathway Non-Cancer Tier 2 vs. Tier 3 Screen Values
Facility EIS ID
PB-HAP
Non-Cancer
Screening
Values'
Tier 3 Non-Cancer Screen Values
(Fisher Scenario)'
Comments
Tier l2
Tier 23
Lake
Assessment
Plume-
rise
TRIM
Hourly
Run
482016534811
Methyl
Mercury
300
60
60
NA
NA
See Appendix
11 for Tier 3
Results
220957204811
Cadmium
30
2
2
NA
NA
See Appendix
11 for Tier 3
Results
'BOLD indicates a screening value equal to or greater than 2
2 Tier 1 screening values based upon a combined fish and farmer ingestion rate
3 Tier 2 and Tier 3 screening values are based upon aggregate lake impacts
-------
Table 11. Maximum Modeled Lead Concentrations
Facility EIS ID
Maximum Lead
Concentration
(Hg/m3)
Max
Concentration
Latitude
Max
Concentration
Longitude
Refined Lead
Concentration
(Hg/m3)
3982311
0.058
36.52235
-82.5428
0.0041
5651911
0.0032
32.28742
-101.418
NA
4941511
0.0017
32.42917
-94.6807
NA
4924411
0.0011
29.73197
-95.0045
NA
7380411
0.00042
30.19594
-93.3245
NA
4778711
0.00015
29.72441
-95.273
NA
7338711
0.00013
38.60228
-90.1686
NA
7203711
9.2E-05
29.67673
-89.9818
NA
7354911
7.1E-05
30.2335
-93.2935
NA
999411
6.9E-05
34.63707
-87.059
NA
8384311
3.1E-06
30.3348
-88.4937
NA
4205511
2.4E-06
27.83158
-97.5359
NA
5632711
8.9E-07
28.97124
-95.4007
NA
6444911
8.1E-07
29.85589
-94.0953
NA
7445711
2.3E-07
30.18342
-90.9833
NA
7319811
2.0E-07
41.63994
-83.4926
NA
7368011
1.5E-07
38.1968
-85.8696
NA
8020811
9.5E-08
29.99341
-90.3979
NA
7202911
0
29.53219
-90.4559
NA
8468011
0
29.79908
-93.2776
NA
4945411
0
27.35965
-97.4308
NA
8018911
0
29.55486
-90.4334
NA
7972111
0
39.29271
-86.2236
NA
See Appendix 11 for additional details on the refined lead concentration
-------
Environmental Risk Screening Site-specific Results
In the screening-level evaluation for potential adverse environmental risks there were Tier 2
exceedances for methyl mercury emissions, specifically a screening value of 5 for the fish-eating
birds No-Observed-Adverse-Effect-Level (NOAEL) benchmark (specifically for the small duck
called the merganser), a screening value of 2 for the maximum allowable toxicant level for the
merganser, and a screening value of 3 for avian ground insectivores (woodcock). There were also
Tier 2 screening values exceedances for divalent mercury emissions, specifically a screening
value of 4 for a sediment threshold level and a screening value of 2 for an invertebrate threshold
level. All of the Tier 2 exceedances for the merganser and sediment benchmarks are the result of
emissions from 3 facilities acting on the same lake. The invertebrate and insectivore soil
benchmarks are the result of emissions from 1 facility.
Since there were Tier 2 exceedances, we conducted a Tier 3 environmental risk screen. In the
Tier 3 environmental risk screen, we looked at aerial photos of the lake being impacted by
mercury emissions from the three HON-subject facilities. The aerial photos show that the "lake"
is located in an industrialized area, has been channelized, and largely filled/drained (Figure 1).
Therefore, it was determined that this "lake" would not support a fish population.
Figure 1. Lake impacted by mercury emissions in the environmental risk screen.
-------
We also looked at aerial photos of the facility that was driving the invertebrate and insectivore
Tier 2 soil exceedances due to mercury emissions. The aerial photos show that the facility is
located in a heavily industrialized area with the nearest "natural areas" being located more than
1500 meters from the facility (Figure 2). We re-calculated the soil screening values with the
industrial areas removed and calculated a maximum Tier 3 soil screening value of 1 for mercury.
Figure 2. Distance to natural soil.
-------
Appendix 11
Site-Specific Human Health Multipathway Residual Risk Assessment Report
-------
Site-Specific Human Health Multipathway Residual Risk Assessment Report
SOCMI Source Category
For facilities where a Tier 2 multipathway screening value(s) indicate a potential health risk to
the public, we conduct a Tier 3 multipathway screening assessment. The Tier 3 screening
assessment adds additional site-specific features into the Tier 2 screening, such as examining if
lakes are fishable in the fisher scenario. In the Tier 2 screening, mercury and cadmium screening
values for the fisher scenario were 60 and 2, respectively, and a lake analysis was performed as
described below. The Tier 2 gardener screening assessment indicated the maximum Tier 2 cancer
screening value for POM was 100 and a refinement was also performed as described below.
Finally, in the adverse environmental risk screening analysis, there were Tier 2 exceedances for
divalent mercury emissions (a screening value of 4 for a sediment threshold level and a screening
value of 2 for an invertebrate threshold level) and methyl mercury (a screening value of 4 for a
sediment threshold level and a screening value of 2 for an invertebrate threshold level).
Therefore, a Tier 3 refinement was performed for the adverse environmental risk screening as
detailed below.
For the Tier 2 exceedances of the fisher scenario (cadmium and methyl mercury), we examined
the fished lakes from Tier 2 and evaluated the existence, the potential purpose, the accessibility
and fishability, and the suitability of the lakes for the models and methods used in the screening
assessments. We do not reasonably expect a subsistence fisher to catch and consume fish from
lakes or ponds that are for industrial or wastewater disposal; are covered in thick plant growth
(e.g., swamps or marshes); are clearly closed to public use; or no longer exist (i.e., filled or
drained). TRIM.FaTE is not configured to model chemical processes and environmental fate and
transport mechanisms in saltwater or brackish waters, nor is it configured to model the very large
watersheds and water dynamics of rivers, bays or very large lakes (e.g., larger than 100,000
acres)1. We use aerial imagery and web inquires to evaluate whether any Tier 2 fished lakes meet
these disqualifying criteria and, if so, remove those lakes from all future screening assessments.
If we remove a lake from a facility's assessment, and the total acres of fished lakes drops below
the target of 373 acres, we evaluate the previously unfished lake with the highest chemical
concentration, and so on, until the sizes of the qualifying lakes collectively comprise at least 373
acres or all lakes have been evaluated. We then rerun the fisher screening scenario with the
revised lake data set.
For methyl mercury, we reviewed the lakes associated with the maximum Tier 2 screening value
of 30. A total of 5 lakes were required to reach 373 acres. One of those lakes (located at 29.7761,
-95.0689) was deemed inaccessible (see Figure 1 below). That lake, which was 180 acres, was
removed from the fisher scenario and we recalculated the screening value assuming the fish from
1 Very large lakes and bays (i.e., those larger than 100,000 acres) are not included because their watersheds are too
large and 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.
-------
that lake would instead be caught from the 5th lake in the scenario (which was 788 acres). Using
this approach, the Tier 3 screening value remained the same as the Tier 2 value, which was 30.
Figure 1. Inaccessible lake removedfrom the methyl mercury Tier 2 screen.
For cadmium, we reviewed the lakes associated with the maximum Tier 2 screening value of 2.
Two lakes were required to reach 373 acres. One of those lakes (located at 30.0893, -90.5521)
was deemed inaccessible (see Figure 2 below). That lake, which was 42 acres, was removed
from the fisher scenario and we recalculated the fisher scenario screening value assuming the
fish from that lake would instead be caught from the 2nd lake in the scenario (which was 15,797
acres). Using this approach, the Tier 3 screening value remained the same as the Tier 2 value,
which was 2.
-------
Figure 2. Inaccessible lake removed from the cadmium Tier 2 screen
For POM and the gardener scenario, we refined the Tier 2 screening value by using the screening
value that corresponded to the same distance and direction of the highest concentrations of
modeled POM where a person lives in the inhalation risk assessment. This replaced the original
Tier 2 screening value which was based on the maximum screening value for any direction and
distance. The Tier 2 screening value of 100 was based on the value to the south of the facility
wi thin 0.5 km. Based on the distance (2 km) and direction (northwest) of the highest POM
concentrations (see Figure 3), the Tier 3 screening value was 20.
-------
Figure 3. Distance and direction to highest modeled POM concentration for the gardener
scenario.
Maximum Ambient Lead Concentration Refinement
For modeled annual lead concentrations that, when multiplied by 4, would exceed the NAAQS,
we conduct a site-specific analysis to determine if the maximum concentration is ambient air
{i.e., outside the facility's fenceline). For the SOCMI source category, the maximum lead
concentration was 0.058 (ig/m3 (or 0.23 jag/m3 when multiplied by 4) at facility 471633982311.
When examining aerial imagery for the facility, it was determined this concentration was within
the facility's fenceline (Figure 4). We therefore refined the maximum ambient lead concentration
to the next highest value that was outside of the facility's fenceline, which was 0.0038 (ig/m3 (or
0.015 |.ig/m3 when multiplied by 4).
-------
Figure 4. Site-specific analysis for the maximum ambient lead concentration (ug mf. Map is
showing the ambient lead concentrations at polar receptor locations around the facility. The
concentrations shown in the map have been multiplied by 4 as part of the screening
methodology.
-------
Attachment A. Application of the Lead NAAQS for RTR Risk Assessments
Attachment A
A-l
February 2022
-------
Application of the Lead NAAQS for RTR Risk Assessments
Background and Issue
For each facility, EPA's Human Exposure Model (HEM) calculates a target organ-specific hazard
index (TOSHI) for all hazardous air pollutants (HAP) associated with chronic noncancer effects to the
same target organ; this is based on the highest annual exposure concentration for each HAP occurring
where a person resides.
We take a different approach for assessing lead compounds than we do for other HAP. In evaluating the
potential multipathway risks from emissions of lead compounds, rather than calculating a TOSHI, we
multiply the maximum annual estimated atmospheric concentration by 4, to represent a "worst case" 3-
month concentration and compare it to the national ambient air quality standard (NAAQS) for lead
(0.15 ug/m3, 3-month rolling average). Values below the NAAQS are considered to have a low potential
for multipathway risks. Where values exceed the NAAQS, further assessment is performed. Where data
are available to support doing so, we calculate 3-month rolling averages based on modeling and/or
monitoring information. Any 3-month rolling average concentration that is above 0.15 ug/m3 indicates
a potential public health concern.
Since the lead primary NAAQS is based on a 3-month period and compared to the HEM annual
concentration of lead, it is important to determine how best to address these differences and evaluate the
potential noncancer hazard posed by lead.
The lead NAAQS is designed to be compared with monitoring data that meet certain acceptability and
siting criteria (40 CFR Part 50), so care should be taken when using modeled concentrations to identify
potential health impacts of lead emissions.
1) How should we evaluate modeled annual exposure concentrations for lead against the lead
NAAQS to determine whether lead emissions could pose a potential noncancer hazard?
Since the lead NAAQS is based on a 3-month rolling average concentration, annual concentrations at the
monitor would be lower than the maximum 3-month rolling average for monitor or model assessments.
Also, monitor concentrations may not capture areas that represent the highest concentrations based on
modeling due to emissions and their respective release characteristics (stack height, stack temperature,
exit velocity, process fugitive emissions, etc.) from a facility.
A review of monitoring, modeling, and engineering methods to convert an annual concentration of lead to
a 3-month rolling average (hereinafter "AC3") was conducted and resulted in a conservative screening
multiplier of 4.
The first method for calculating an AC3 was based on an engineering approach. Since the Pb NAAQS
value is based on a 3-month exposure period and the modeled concentrations from HEM4 are derived
from annual emissions, we assume the annual emissions occur over a 3-month period. When we make this
assumption of increased emissions occuring over a shorter time, this provides an upper-bound worst-case
screening AC3 multiplier of 4. This is the most simplistic and conservative method based solely upon
emission increases being directly proportional to modeled concentration increases.
The second method for calculating an AC3 was based on comparisons of monthly AERMOD runs and
monitoring data over a 3-month rolling period with their respective annual lead concentrations. These
comparisons have some level of uncertainty, i) monitored levels may be impacted by additional
Attachment A
A-2
February 2022
-------
"background" sources that are difficult to account for; ii) the quality of the emission inventory does not
account for fugitives, re-entrained particles, and daily intermittent releases that are associated with lead
emitting sources; iii) extrapolation of low concentrations at the monitor site just at or below the detection
limit (DL) to the modeled maximum individual risk (MIR) location may result in a skewed comparison;
and iv) any errors in emissions locations, release heights, meteorological data, the effect of building
downwash, etc. may cause under- or over-estimates at the monitor location. Refer to Table 1 for a list of
Pb NAAQS monitors that were evaluated based upon a review of monitoring or modeling data to compare
their respective annual concentrations with their 3-month max values (AC3).
A review of Pb modeling data from a Primary Copper and a Lead Acid Battery (LAB) facility indicated
an upper-bound AC3 value of 2. A review of the model inputs for these sites do not account for any
temporal or seasonal variations in emissions based upon production increases or decreases. The modeled
hourly emission rate was the same throughout the year for all emission sources. The upper-bound AC3
value of 2 was solely based on meteorological/seasonal variations through the year; refer to Table 2
(Ratio Column).
A review of 8 Pb NAAQS monitoring sites from 4 different source categories indicated an upper-bound
AC3 multiplier between 2 and 4. The selected monitors represent the locations with the highest 3-month
design values (DV) used to assess compliance with the Pb NAAQS. The ambient Pb levels from monitors
not only depict monthly/seasonal changes associated with the meteorology but also potential changes
associated with emissions from both process-specific units and fugitive emissions.
Based on this review of engineering, modeling, and monitoring data, it seems reasonable to utilize a
screening AC3 multiplier of 4 to adjust annual modeled concentrations to 3-month max concentrations for
lead. In the event the facility modeled has a valid lead NAAQS monitor on site, the actual monitored data
may be applied to calculate an AC3 multiplier for purposes of estimating the maximum 3-month modeled
concentrations at the MIR location.
Approach:
Step 1: Screen for potential lead impacts by multiplying the maximum off-site annual concentration for
lead by 4; this effectively assumes a "worst case" where the modeled annual lead concentration represents
the highest off-site modeled exposure concentration for a max 3-month period. If the resulting
concentration is less than the NAAQS (0.15 (.ig/nr1). no further assessment is needed. If the resulting
concentration for lead is greater than the NAAQS, further assessment is needed as described in Step 2.
Step 2: A modeled concentration greater than the lead NAAQS indicates a potential for multipathway
risk. If the facility does not have a site-specific lead monitor to help characterize the relationship between
modeling and monitoring data, then we need to estimate the maximum 3-month average concentration for
lead. These refinements include the use of a post-processer (Lead-POST) in AERMOD that calculates the
maximum 3-month lead concentration for each off-site receptor to directly compare to the current lead
NAAQS standard (0.15 (.ig/nr1).1 Any off-site 3-month rolling average concentration that is above 0.15
(ig/m3 indicates a potential public health concern, requiring coordination with the Lead NAAQS program.
1 EPA SCRAM site to access LEADPOST, which is utilized for the Pb NAAQS program, is
httpsi//www. epa.gov/scram/air-qualitv-dispersion-modeling-preferred-and-recommended-models.
Attachment A
A-3
February 2022
-------
Table 1: AERMOD Modeling and Pb NAAQS Monitor Sites
Pb NAAQS
Monitors (ID)
Source Category
X1
Y1
Year Assessed and Method
40078000
Primary Copper
515833
3697752
2019 (monitor and modeling)
420110022
Lead Acid Batteries
435841
4480985
2017 (monitor and modeling)
211510005
Lead Acid Batteries
738647
4180016
2017 monitor
ng
201690004
Lead Acid Batteries
621047
4292897
2017 monitor
ng
60371403
Lead Acid Batteries
402449
3757657
2017 monitor
ng
420110020
Lead Acid Batteries
422519
4470999
2017 monitor
ng
290990027
Primary Lead Smelting
729228
4238299
2017 monitor
ng
290930034
Secondary Lead Smelting
666118
4168888
2017 monitor
ng
1. Represents the Ideational coordinates for the Pb NAAQS monitors used in the model runs
Table 2: Pb NAAQS Monitor and Model Results (Annual vs 3-month Concentrations)
Monitor Results 1
Model Results
Monitor ID
Annual Cone.
(ug/m3)
Max 3-month
Cone, (ug/m3)
(AC3)
Multiplier
Annual Cone.
(ug/m3)
Max 3-month Cone.
(ug/m3)
(AC3)
Multiplier
40078000
0.022
0.038
1.7
0.026
0.045
1.7
420110020
0.012
0.044
3.7
0.002
0.0027
1.4
420110022
0.013
0.016
1.2
0.047
211510005
0.023
0.044
1.9
0.005
201690004
0.069
0.134
1.9
0.004
60371403
0.045
0.09
2.0
0.0011
290990027
0.11
0.2
1.8
290930034
0.069
0.13
1.9
1. EPA AirNow/AQS monitoring data: https://www.epa.gov/outdoor-air-quality-data/download-daily-data
Note: When available, monitored and modeled concentrations at the monitoring site should be compared; in some
cases, there will be a significant difference. In this case, the model underestimates the monitored concentration by a
factor of 40 (Monitor 60371403). This monitor was within 60 meters of the modeled source. These near-field
comparisons of the monitored and modeled lead levels may be impacted due to unaccounted for lead emissions as
well as the effect of building downwash.
2) How should lead and other HAP associated with neurological effects be considered as part of a
facility-specific risk assessment?
It is not appropriate to combine the lead NAAQS with chronic noncancer toxicity values for other HAP
(due to the 3-month time scale and the application criteria for the lead NAAQS).
Approach:
For the AMOS determination, it is important to qualitatively consider the additional contribution of lead
emissions to a chronic HI calculated for other HAP for which the nervous system is the target organ.
Attachment A
A-4
February 2022
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