Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category
1
Residual Risk Assessment for the
Aerospace Manufacturing and Rework Facilities Source Category
in Support of the January, 2015 Risk and Technology Review
Proposal
EPA's Office of Air Quality Planning and Standards
Office of Air and Radiation
January 2015
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Table of Contents
1 Introduction 5
2 Methods 5
2.1 Emissions and source data 5
2.2 Dispersion modeling for inhalation exposure assessment 6
2.3 Estimating chronic human inhalation exposure 9
2.4 Acute Risk Screening and Refined Assessments 10
2.5 Multipathway human health and environmental risk analysis 11
2.6 Dose-response assessment 14
2.6.1 Sources of chronic dose-response information 14
2.6.2 Sources of acute dose-response information 25
2.7 Risk characterization 30
2.7.1 General 30
2.7.2 Mixtures 31
2.7.3 MACT-Allowable Emissions and Risks 32
3 Risk results for the Aerospace Manufacturing and Rework facilities 32
3.1 Source Category Emissions 32
3.2 Risk characterization 38
3.2.1 Baseline Actual Emission Risks 38
MACT-level Inhalation Assessment Results (Actual) 39
MACT-level Multipathway Assessment Results (Actual) 42
MACT-level Environmental Assessment Results (Actual) 43
Facility-wide Inhalation Assessment Results (Actual) 43
3.2.2 Baseline Allowable Emission Risks 44
MACT-level Inhalation Assessment Results (Allowable) 44
4 General discussion of uncertainties and how they have been addressed 45
4.1 Exposure modeling uncertainties 45
4.2 Uncertainties in the dose-response relationships 45
5 References 55
Index of Tables
Table 2-1 AERMOD version 14134 model options for RTR modeling 7
Table 2.6-1 Dose-Response Values for Chronic Inhalation Exposure to Carcinogens 18
Table 2.6-2 (A) Dose-Response Values for Chronic Inhalation Exposure to Noncarcinogens
21
Table 2.6-2 (B) Dose-Response Values for Chronic Oral Exposure to Noncarcinogens 25
Table 2.6-3 Dose-Response Values for Acute Exposures 27
Table 3.1-1 Baseline Actual Emissions from Aerospace and Availability of Dose-Response
Values 33
Table 3.2-1. Risk Summary for the Aerospace Manufacturing 38
Table 3.2-2. MACT Level Actual Emissions Chronic Inhalation Risks for 39
Aerospace Manufacturing and Rework Source Category 39
Table 3.2-3. MACT Level Actual Emissions Acute Inhalation Risks for 41
Aerospace Manufacturing and Rework Source Category 41
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Table 3.2-4. MACT Level Actual Emissions - Summary of Multipathway Screening for 42
Aerospace Manufacturing and Rework Source Category 42
Table 3.2-5 MACT Level Actual Emissions - Summary of Environmental Risk Screen
Results for the Aerospace Manufacturing and Rework Facilities Source Category 43
Table 3.2-6 Source Category Contribution to Facility-Wide 44
Chronic Cancer Risks (Actual Emissions) 44
Appendices
Appendix 1 Aerospace Manufacturing and Rework Facilities RTR Modeling File
Preparation
Appendix 2 Technical Support Document for HEM3 Modeling
Appendix 3 Meteorological Data for HEM3 Modeling
Appendix 4 Dispersion Model Receptor Revisions and Additions for the Aerospace Source
Category
Appendix 5 Technical Support Document for TRIM-Based Multipathway Tiered Screening
Methodology for RTR
Appendix 6 Environmental Risk Screen
Appendix 7 Detailed Risk Modeling Results
Appendix 8 Acute Impacts Refined Analysis
Index of Acronyms
AERMOD
American Meteorological Society/EPA Regulatory Model
AEGL
Acute exposure guideline level
ASTDR
US Agency for Toxic Substances and Disease Registry
CalEPA
California Environmental Agency
CTE
Central Tendency Estimate
ERPG
Emergency Response Planning Guideline
HAP
Hazardous Air Pollutant
HEM
Human Exposure Model
HI
Hazard index
HQ
Hazard quotient
IRIS
Integrated Risk Information System
MACT
Maximum Achievable Control Technology
MIR
Maximum Individual Risk
MOA
Mode of action
NAC
National Advisory Committee
NAAQS
National Ambient Air Quality Standards
NATA
National Air Toxics Assessment
NEI
National Emissions Inventory
NPRM
Notice of Proposed Rulemaking
PB-HAP
Persistent and Bioaccumulative - HAP
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POM
Polycyclic organic matter
REL
Reference exposure level
RfC
Reference concentration
RfD
Reference dose
RME
Reasonable Maximum Exposure
RTR
Risk and Technology
TOSHI
Target-organ-specific hazard index
URE
Unit risk estimate
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1 Introduction
Section 112 of the Clean Air Act (CAA) establishes a two-stage regulatory process for
addressing emissions of hazardous air pollutants (HAP) from stationary sources. In the first
stage, section 112(d) requires the Environmental Protection Agency (EPA, or the Agency) to
develop technology-based standards for categories of sources (e.g., petroleum refineries, pulp
and paper mills, etc.) [7], EPA has largely completed the initial Maximum Achievable
Control Technology (MACT) standards as required under this provision. Under section
112(d)(6), EPA must review each of these technology-based standards at least every eight
years and revise a standard, as necessary, "taking into account developments in practices,
processes and control technologies." In the second stage, EPA is required under section
112(f)(2) to assess the health and environmental risks that remain after implementation of the
MACT standards. If additional risk reductions are necessary to protect public health with an
ample margin of safety or to prevent an adverse environmental effect, EPA must develop
standards to address these remaining risks. This second stage of the regulatory process is
known as the residual risk stage. For each source category for which EPA issued MACT
standards, the residual risk stage must be completed within eight years of promulgation of the
initial technology-based standard.
In December of 2006 we consulted with a panel from the EPA's Science Advisory Board
(SAB) on the "Risk and Technology Review (RTR) Assessment Plan," and in June of 2007,
we received a letter with the results of that consultation. Subsequent to the consultation, in
June of 2009, a meeting was held with an SAB panel for a formal peer review of the "Risk
and Technology Review (RTR) Assessment Methodologies" [2], We received the final SAB
report on this review in May of 2010 [3], Where appropriate, we have responded to the key
messages from this review in developing our current risk assessments and we will be
continuing our efforts to improve our assessments by incorporating updates based on the SAB
recommendations as they are developed and become available. Our responses to the key
recommendations of the SAB are outlined in a memo entitled, "EPA's Actions in Response to
Key Recommendations from the SAB Review of RTR risk Assessment Methodologies" [4].
This document contains the methods and the results of baseline risk assessments (i.e., after the
implementation of the MACT standard) performed for the Aerospace Manufacturing and
Rework Facilities source category. The methods discussion includes descriptions of the
methods used to develop refined estimates of chronic inhalation exposures and human health
risks for cancer and noncancer endpoints, as well as descriptions of the methods used to
screen for acute health risks, chronic non-inhalation health risks, and adverse environmental
effects.
2 Methods
2.1 Emissions and source data
The Aerospace Manufacturing and Rework facilities source category includes all facilities
that manufacture aerospace vehicles and/or vehicle components and all facilities that rework
or repair these items. An aerospace vehicle or component is any fabricated, processed, or
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assembled set of parts or complete unit of any aircraft including, but not limited to, airplanes,
helicopters, missiles, rockets, and space vehicles.
Organic and inorganic HAP emissions in aerospace facilities originate from cleaning, primer
application, topcoat application, paint stripping, and chemical milling maskant application
among other secondary emission points. Organic HAP emissions from primer, topcoat, and
chemical milling maskant application operations are the result of the evaporation of solvent
contained in the coatings. These applications can take place in large open areas, such as
hangars, or in partially or fully enclosed spaces, such as within spray booths. Primer and
topcoat applications can also produce by-product inorganic HAP emissions. Cleaning and
paint stripping operations can result in the release of organic HAP emissions due to the
evaporation of the volatile portion of the cleaning solvents and chemical paint strippers,
respectively. Cleaning emissions are nearly always fugitive in nature and occur at essentially
every processing step. A complete description of the aerospace manufacturing and rework
facilities source category can be found in the text of the Notice of Proposed Rulemaking
(NPRM).
Based on 2011 survey data from an information collection request (ICR) as well as emission
data updated in 2013 and 2014, we currently estimate that there are 144 Aerospace
Manufacturing and Rework facilities operating in the U.S. and 87 of these facilities are
identified as major Based on 2011 survey data from an information collection request (ICR)
as well as emission data updated in 2013 and 2014, we currently estimate that there are 144
Aerospace Manufacturing and Rework facilities operating in the U.S. and 87 of these facilities
are identified as major sources.
Details on the development of the emissions and source data for this source category can be
found in Appendix 1. Section 3 below provides a summary of the emissions.
2.2 Dispersion modeling for inhalation exposure assessment
Both long- and short-term inhalation exposure concentrations and associated health risks from
each facility in the source category were estimated using the Human Exposure Model in
combination with the American Meteorological Society/EPA Regulatory Model dispersion
modeling system (HEM-AERMOD, or HEM3). The approach used in applying this modeling
system is outlined below, and further details are provided in Appendix 2 to this document
(.Technical Support Document for HEM3 Modeling). The HEM3 performs three main
operations: atmospheric dispersion modeling, estimation of individual human exposures and
health risks, and estimation of population risks. This section focuses on the dispersion
modeling component. The exposure and risk characterization components are discussed in
other subsections of Sections 2 and 3.
The dispersion model in the HEM3 system, AERMOD version 14134 (the latest version of
AERMOD that was available at the time of the risk analysis), is a state-of-the-science
Gaussian plume dispersion model that is preferred by EPA for modeling point, area, and
volume sources of continuous air emissions from facility applications [5], Further details on
AERMOD can be found in the AERMOD Users Guide [6], The model is used to develop
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annual average ambient concentrations through the simulation of hour-by-hour dispersion
from the emission sources into the surrounding atmosphere. Hourly emission rates used for
this simulation are generated by evenly dividing the total annual emission rate from the
inventory into the 8,760 hours of the year.
The first step in the application of the HEM3 modeling system is to predict ambient
concentrations at locations of interest. The AERMOD model options employed are
summarized in Table 2.2-1 and are discussed further below.
Table 2-1 AERMOD version 14134 model options for RTR modeling
Modeling Option
Selected Parameter for chronic exposure
Type of calculations
Hourly Ambient Concentration
Source type
Point, area represented as pseudo point source
Receptor orientation
Polar (13 rings and 16 radials)
Discrete (census block centroids) and user-supplied receptors
Terrain characterization
Actual from USGS 1-degree DEM data
Building downwash
Not Included
Plume deposition/depletion
Not Included
Urban source option
No
Meteorology
1 year representative NWS from nearest site ( 824 stations)
In HEM3, meteorological data are ordinarily selected from a list of over 800 National
Weather Service (NWS) surface observation stations across the continental United States,
Alaska, Hawaii, and Puerto Rico. In most cases the nearest station is selected as
representative of the conditions at the subject facility. When considering off-site
meteorological data most site specific dispersion, EPA modeling guidelines suggest that
modeling efforts employ up to five years of data to capture variability in weather patterns
from year to year. However, because dispersion model runtimes using five years of
meteorological data would be too long for RTR source categories with many sources, we
modeled only a single year, 2011. While the selection of a single year may result in under -
prediction of long-term ambient levels at some locations, likewise it may result in over-
prediction at others. For each facility identified by its characteristic latitude and longitude
coordinates, the closest meteorological station was used in the dispersion modeling. The
average distance between a modeled facility and the applicable meteorological station was 18
miles. Appendix 3 to this document (Meteorological Data Processing Using AERMETfor
HEMS) provides a complete listing of stations and assumptions along with further details used
in processing the data through AERMET, the AERMOD meteorological data processing
program. The document "Risk and Technology Review (RTR) Risk Assessment
Methodologies: For Review by the EPA's Science Advisory Board with Case Studies -
MACT I Petroleum Refining Sources and Portland Cement Manufacturing" (US EPA, 2009)
provides more details on these modeling approaches.
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Beginning with version 12345, AERMET allows for a minimum wind speed of 0.5 m/s to be
utilized when processing monitoring stations that are equipped with sonic anemometers and
for an adjustment to the surface friction velocity (u*). The public version of AERMET
available at the time we conducted the AERMET processing did not include the surface
friction velocity adjustment. Also at the time, an updated version of AERMET (and
AERMOD) was under development that incorporated further surface friction velocity
adjustments to AERMET version 12345. In anticipation of the update, EPA processed
meteorological data using a pre-public release version of AERMET and incorporated the
updated surface friction velocity adjustment in the output. It was EPA's judgement that the
pre-public release version of AERMET would generate AERMOD ready meteorological data
very close to that generated with the final public release version.
EPA has posted the AERMET meteorological data used in the analysis on the EPA's FERA
(Fate, Exposure, and Risk Analysis) website under the HEM model page. AERMET and
AERMOD (version 13350) were released to the public in late December 2013 on the EPA's
Support Center for Regulatory Atmospheric Modeling (SCRAM) website.
The HEM3 system estimates ambient concentrations at the geographic centroids of census
blocks (using the 2010 Census) and at other receptor locations that can be specified by the
user. See Appendix 4 of this document (Dispersion Model Receptor Revisions and Additions
for the Petroleum Refining Sector) for a discussion of user receptors and centroid location
changes specific to this source category. The model accounts for the effects of multiple
facilities when estimating concentration impacts at each block centroid. When evaluating the
risks associated with a particular source category we combined the impacts of all facilities
within the same source category, and assessed chronic exposure and risk for all census blocks
with at least one resident (i.e., locations where people may reasonably be assumed to reside
rather than receptor points at the fenceline of a facility). Chronic ambient concentrations were
calculated as the annual average of all estimated short-term (one-hour) concentrations at each
block centroid. Possible future residential use of currently uninhabited areas was not
considered. Census blocks, the finest resolution available in the census data, are typically
comprised of approximately 40 people or about ten households.
In contrast to the development of ambient concentrations for evaluating long-term exposures,
which was performed only for occupied census blocks, worst-case short-term (one-hour)
concentrations were estimated both at the census block centroids and at points nearer the
facility that represent locations where people may be present for short periods, but generally
no nearer than 100 meters from the center of the facility (note that for large facilities, this
100-meter ring could still contain locations inside the facility property).
Short-term emission rates were needed to screen for the potential for hazard via acute
exposures. The average hourly emissions rate used for chronic risk modeling is defined as the
total emissions for a year divided by the total number of operating hours in the year. If a
source operated 8,760 hours per year this emissions rate would be appropriate for predicting
potential acute impacts. However, many sources in this source sector emit on a more
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intermittent basis and thus specific hourly emissions may be higher than those utilized for
annual or chronic risks modeling. While hourly emissions data for each source are not
available, in this assessment, we utilized source category specific engineering judgment to
determine what the peak hourly emissions for any given process could be. For the aerospace
category this factor was estimated to be 1.2 times the annual emissions level. Further
information on this factor is provided in the emissions memo presented in Appendix 1 of this
report.
Census block elevations for HEM3 modeling were determined nationally from the US
Geological Service 1-degree digital elevation model (DEM) data files, which have a spatial
resolution of about 90 meters. Elevations of polar grid points used in estimating short- and
long-term ambient concentrations were assumed to be equal to the highest elevation of any
census block falling within the polar grid sector corresponding to the grid point. If a sector
does not contain any blocks, the model defaults the elevation to that of the nearest block. If
an elevation is not provided for the emission source, the model uses the average elevation of
all sectors within the innermost model ring.
In addition to using receptor elevation to determine plume height, AERMOD adjusts the
plume's flow if nearby elevated hills are expected to influence the wind patterns. For details
on how hill heights were estimated and used in the AERMOD modeling see Appendix 2 of
this document.
2.3 Estimating chronic human inhalation exposure
We used the estimated annual average ambient air concentration of each HAP, at each census
block centroid, as a surrogate for the lifetime inhalation exposure concentration of all the
people who reside in the census block. That is, the risk analysis did not consider either the
short-term or long-term behavior (mobility) of the exposed populations and its potential
influence on their exposure.
We did not address short-term human activity, including indoor air concentrations, for two
reasons. First, our experience with the NATA assessments (which modeled daily activity
using EPA's HAPEM model) suggests that, given our current understanding of
microenvironment concentrations and daily activities, modeling short-term activity would, on
average, reduce risk estimates about 25 percent for particulate HAP; it will also reduce risk
estimates for gaseous HAP, but typically by much less. Second, basing exposure estimates on
average ambient concentrations at census block centroids may underestimate or overestimate
actual exposure concentrations at some residences.
We did not address long-term migration nor population growth or decrease over 70 years,
instead basing the assessment on the assumption that each person's predicted exposure is
constant over the course of their lifetime which is assumed to be 70 years. In assessing cancer
risk, we generally estimated three metrics; the maximum individual risk (MIR), which is
defined as the risk associated with a lifetime of exposure at the highest concentration where
people are likely to live; the population risk distribution; and the cancer incidence. The
assumption of not considering short or long-term population mobility does not bias the
estimate of the theoretical MIR (assumes a person stays in one location for 70 years) nor does
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it affect the estimate of cancer incidence since the total population number remains the same.
It does, however, affect the shape of the distribution of individual risks across the affected
population, shifting it toward higher estimated individual risks at the upper end and reducing
the number of people estimated to be at lower risks, thereby increasing the estimated number
of people at specific risk levels.
When screening for potentially significant acute exposures, we used an estimate of the highest
hourly ambient concentration at any off-site location as the surrogate for the maximum
potential acute exposure concentration for any individual. This was generally the facility
fenceline.
2.4 Acute Risk Screening and Refined Assessments
In establishing a scientifically defensible approach for the assessment of potential health risks
due to acute exposures to HAP, we followed the same general approach that has been used for
developing chronic health risk assessments under the residual risk program. That is, we
developed a tiered, iterative approach. This approach to risk assessment was endorsed by the
National Academy of Sciences in its 1993 publication "Science and Judgment in Risk
Assessment" and subsequently was adopted in the EPA's "Residual Risk Report to Congress"
in 1999.
The assessment methodology is designed to eliminate from further consideration those
facilities for which we have confidence that no acute adverse health effects of concern will
occur. To do so, we use what is called a tiered, iterative approach to the assessment. This
means that we began with a screening assessment, which relies on readily available data and
uses conservative assumptions that in combination approximate a worst-case exposure. The
result of this screening process is that either the facility being assessed poses no potential
acute health risks (i.e., it "screens out"), or that it requires further, more refined assessment.
A more refined acute assessment could use industry- or site-specific data on the temporal
pattern of emissions, the layout of emission points at the facility, the boundaries of the
facility, and/or the local meteorology. In some cases, all of these site-specific data are used to
refine the assessment; in others, lesser amounts of site-specific data can be used to determine
that acute exposures are not a concern, and significant additional data collection is not
necessary.
Acute health risk screening was performed for each facility as the first step. We used
conservative assumptions for emission rates, meteorology, and exposure location. We used
the following worst-case assumptions in our screening approach:
• Peak 1-hour emissions were obtained from the ICR and based on the operating
characteristics and engineering judgement of aerospace facility emission as described
in Appendix 1.
• We assumed that the peak emissions occur at all emission points at the same time.
• For facilities with multiple emission points, 1 -hour concentrations at each receptor
were assumed to be the sum of the maximum concentrations due to each emission
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point, regardless of whether those maximum concentrations occurred during the same
hour.
• Worst-case meteorology (from one year of local meteorology) was assumed to occur
at the same time the peak emission rates occur. The recommended EPA local-scale
dispersion model, AERMOD, is used for simulating atmospheric dispersion.
• A person was assumed to be located downwind at the point of maximum modeled
impact during this same worst-case 1-hour period, but no nearer to the source than 100
meters.
• The maximum impact was compared to multiple short-term health benchmarks for the
HAP being assessed to determine if a possible acute health risk might exist. These
benchmarks are described in section 2.6 of this report.
As mentioned above, when we identify acute impacts which exceed their relevant
benchmarks, we pursue refining our acute screening estimates to the extent possible.
In some cases, this may entail determining the actual physical layout and boundaries of a
facility to more accurately gauge where people might reasonably be exposed for an hour.
For the aerospace source category, the refinement included a review of the layout of the
emission points at the facilities with the facility boundaries to determine the maximum off-site
acute impact for the facilities that did not screen out during the initial run. Refer to
Appendices 5 and 6 for the screening and refined acute results, respectively.
2.5 Multipathway human health and environmental risk analysis
The EPA conducted a screening analysis examining the potential for significant human health
risks due to exposures via routes other than inhalation (i.e., ingestion). We first determined
whether any sources in the source categories emitted any hazardous air pollutants known to be
persistent and bio-accumulative in the environment (PB-HAP)1. The PB-HAP compounds or
compound classes are identified for the screening from the EPA's Air Toxics Risk
Assessment Library [7], With respect to PB-HAP emissions other than lead, emissions were
evaluated for potential non-inhalation risks impacts using a Tiered screening approach. We
first determined whether the facility-specific emission rates of each of the emitted PB-HAP
were large enough to create the potential for significant non-inhalation human health risks
under reasonable worst-case conditions. To facilitate this step, we developed emissions rate
thresholds for each PB-HAP using a hypothetical upper-end screening exposure scenario
developed for use in conjunction with the EPA's Total Risk Integrated Methodology. Fate,
Transport, and Ecological Exposure (TRIM.FaTE[S]) model. We conducted a sensitivity
analysis on the screening scenario to ensure that its key design parameters would represent the
upper end of the range of possible values, such that it would represent a conservative but not
impossible scenario. The facility-specific emissions rates of each of the PB-HAP were
compared to the emission rate threshold values for each of the PB-HAP identified to assess
1 Although the two-letter chemical symbol for lead is Pb, in this assessment PB-HAP refers to the many air
pollutants known to be persistent and bioaccumulative in the environment. In instances where the report is
specifically referring to lead, it is spelled out (i.e., the two-letter chemical symbol for lead is not used in this
document).
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the potential for significant human health risks via non-inhalation pathways. We call this
application of the TRIM.FaTE model the Tier 1 TRIM- Screen.
For the purpose of developing emissions rates for our Tier 1 TRIM-Screen, we derived
emission levels for each PB-HAP (other than lead) at which the maximum excess lifetime
cancer risk would be 1-in-l million or, for HAP that cause non-cancer health effects, the
maximum HQ would be 1. If the emissions rate of any PB-HAP exceeds the Tier 1 screening
emissions rate for any facility, we conduct a Tier 2 multi-pathway screen. In the Tier 2 screen,
the location of each facility that exceeded the Tier 1 emission rate is used to refine the
assumptions associated with the environmental scenario while maintaining the exposure
scenario assumptions. We then adjust the risk-based Tier 1 screening level for each PB-HAP
for each facility based on an understanding of how exposure concentrations estimated for the
screening scenario change with meteorology and environmental assumptions. PB-HAP
emissions that do not exceed these new Tier 2 screening levels are considered to pose no
unacceptable risks. When facilities exceed the Tier 2 screening levels, it does not mean that
multi-pathway impacts are significant, only that we cannot rule out that possibility based on
the results of the screen. These facilities, if needed, may be further evaluated for multi-
pathway risks using the TRIM.FaTE model and more site specific information. Appendix 5
of this document (Technical Support Document for TRIM-Based Multipathway Tiered
Screening Methodology for RTR) contains a complete discussion of the multipathway Tier 1
and 2 screening approach.
In evaluating the potential multipathway risks from emissions of lead compounds, rather than
developing a screening emission rate for them, we compared maximum estimated chronic
atmospheric concentrations with the current National Ambient Air Quality Standard
(NAAQS) for lead. Values below the NAAQS were considered to have a low potential for
multipathway risks of any significance.
The NAAQS value, a public health policy judgment, incorporated the Agency's most recent
health evaluation of air effects of lead exposure for the purposes of setting a national ambient
air quality standard. In setting this value, the Administrator promulgated a standard that was
requisite to protect public health with an adequate margin of safety. We consider values
below the level of the primary NAAQS to protect against multipathway risks because as
mentioned above, the primary NAAQS is set as to protect public health with an adequate
margin of safety. However, ambient air lead concentrations above the NAAQS are
considered to pose the potential for increased risk to public health. We consider this NAAQS
assessment to be a refined analysis given: 1) the numerous health studies, detailed risk and
exposure analyses, and level of external peer and public review that went into the
development of the primary NAAQS for lead, combined with: 2) the site-specific dispersion
modeling used in this assessment to estimated ambient lead concentrations due to the source
category emissions. It should be noted, however, that this comparison does not account for
possible population exposures to lead from sources other than the one being modeled; for
example, via consumption of water from untreated local sources or ingestion of locally grown
food. Nevertheless, the Administrator judged that such a standard would protect, with an
adequate margin of safety, the health of children and other at-risk populations against an array
of adverse health effects, most notably including neurological effects, particularly
neurobehavioral and neurocognitive effects, in children (73 FR 67007). The Administrator, in
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setting the standard, also recognized that no evidence-or risk based bright line indicated a
single appropriate level. Instead a collection of scientific evidence and other information was
used to select the standard from a range of reasonable values (73 FR 67006).
We further note that comparing ambient lead concentrations to the NAAQS for lead,
considering the level, averaging time, form and indicator, also informs whether there is the
potential for adverse environmental effects. This is because the secondary lead NAAQS, set
to protect against adverse welfare effects (including adverse environmental effects), has the
same averaging time, form, and level as the primary standard. Thus, ambient lead
concentrations above the NAAQS for lead also indicate the potential for adverse
environmental effects.
Adverse environmental effects
The EPA has developed a screening approach to examine the potential for adverse
environmental effects as required under section 112(f)(2)(A) of the CAA. The environmental
screen focuses on the following seven environmental HAP:
• Five persistent bioaccumulative HAP (PB-HAP) - cadmium, dioxins/furans,
polycyclic organic matter (POM), mercury (both inorganic mercury and
methyl mercury) and lead.
• Two acid gases - hydrogen chloride (HC1), and hydrogen fluoride (HF).
HAP that persist and bioaccumulate are of particular environmental concern because they
accumulate in the soil, sediment and water. The acid gases, HC1 and HF, were included due to
their well-documented potential to cause direct damage to terrestrial plants.
For the environmental risk screening analysis, EPA first determined whether any facilities in
the source category emitted any of the seven environmental HAP. If one or more of the seven
environmental HAP evaluated are emitted by at least one facility in the source category we
proceed to the second step of the evaluation.
For cadmium, mercury, POM and dioxins/furans, the environmental screening analysis
consists of two tiers. In the first tier, TRIM.FaTE modeling was conducted under worst-cast
environmental conditions to determine whether the facility-specific off-site emission rates of
each of the emitted environmental HAP exceeded ecological benchmarks represented as
emissions threshold values. If off-site emissions from a facility do not exceed the Tier 1
thresholds the facility "passes" the screen, and therefore, is not evaluated further under the
screening approach. If off-site emissions from a facility exceed the Tier 1 thresholds, we
evaluate the facility further in Tier II.
In Tier 2 of the environmental screening analysis, the screening emission thresholds are
adjusted to account for local meteorology and the actual location of lakes in the vicinity of
facilities that did not pass the Tier 1 screen. If off-site emissions from a facility do not exceed
the Tier 2 thresholds, the facility passes the screen, and is typically not evaluated further. If
off-site emissions from a facility exceed the Tier 2 thresholds, the facility does not pass the
screen and, therefore, may have the potential to cause adverse environmental effects. Such
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facilities are evaluated further to investigate factors such as the magnitude and characteristics
of the area of exceedance.
For acid gases, the environmental screening analysis evaluates the potential phytotoxicity and
reduced productivity of plants due to chronic exposure to acid gases. The environmental risk
screening methodology for acid gases is a single-tier screen that compares the average off-site
ambient air concentration over the modeling domain to ecological benchmarks for each of the
acid gases. For purposes of ecological risk screening, EPA identifies a potential for adverse
environmental effects to plant communities from exposure to acid gases when the average
concentration of the HAP around a facility exceeds the LOAEL ecological benchmark. In
such cases, we further investigate factors such as the magnitude and characteristics of the area
of exceedance (e.g., land use of exceedance area, size of exceedance area) to determine if
there is an adverse environmental effect.
For lead compounds, we currently do not have the ability to calculate media concentrations
using the TRIM.FaTE model. However, air concentrations of lead are already calculated as
part of the human health exposure and risk analysis using the HEM-3 AERMOD air
dispersion model. To evaluate the potential for adverse environmental effects from lead, we
compare the average modeled air emission concentrations of lead from each facility in the
source category emissions to the level of the secondary National Ambient Air Quality
Standard (NAAQS) for lead. The secondary lead NAAQS is a reasonable means of evaluating
environmental risk because it is set to provide substantial protection against adverse welfare
effects which can include "effects on soils, water, crops, vegetation, man-made materials,
animals, wildlife, weather, visibility and climate, damage to and deterioration of property, and
hazards to transportation, as well as effects on economic values and on personal comfort and
well-being."2
Ecological benchmarks included in the environmental risk screen are presented in Appendix
6.
2.6 Dose-response assessment
2.6.1 Sources of chronic dose-response information
Dose-response assessment (carcinogenic and non-carcinogenic) for chronic exposure (either
by inhalation or ingestion) for the HAP reported in the emissions inventory for the aerospace
source category were based on the EPA Office of Air Quality Planning and Standards'
(OAQPS) existing recommendations for HAP [9], which were also used for NATA [70], This
information has been obtained from various sources and prioritized according to (1)
conceptual consistency with EPA risk assessment guidelines and (2) level of peer review
2 A secondary standard, as defined in Section 109(b)(2), must "specify a level of air quality the attainment and
maintenance of which, in the judgment of the Administrator, based on criteria, is requisite to protect the public
welfare from any known or anticipated adverse effects associated with the presence of [the] pollutant in the
ambient air." Welfare effects as defined in section 302(h) (42 U.S.C. 7602(h)) include, but are not limited to,
"effects on soils, water, crops, vegetation, man-made materials, animals, wildlife, weather, visibility and climate,
damage to and deterioration of property, and hazards to transportation, as well as effects on economic values and
on personal comfort and well-being."
-------
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 15
received. The prioritization process was aimed at incorporating into our assessments the best
available science with respect to dose-response information. The recommendations are based
on the following sources, in order of priority:
1) US Environmental Protection Agency (EPA). EPA has developed dose-response
assessments for chronic exposure for many of the pollutants in this study. These
assessments typically provide a qualitative statement regarding the strength of scientific
data and specify a reference concentration (RfC, for inhalation) or reference dose (RfD,
for ingestion) to protect against effects other than cancer and/or a unit risk estimate (URE,
for inhalation) or slope factor (SF, for ingestion) to estimate the probability of developing
cancer. The RfC is defined as an "estimate (with uncertainty spanning perhaps an order of
magnitude) of a continuous inhalation exposure to the human population (including
sensitive subgroups) that is likely to be without an appreciable risk of deleterious effects
during a lifetime." The RfD is "an estimate (with uncertainty spanning perhaps an order
of magnitude) of a daily oral exposure to the human population (including sensitive
subgroups) that is likely to be without an appreciable risk of deleterious effects during a
lifetime." The URE is defined as "the upper-bound excess cancer risk estimated to result
from continuous lifetime exposure to an agent at a concentration of 1 |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) [77] is an EPA database that contains scientific
health assessment information, including dose-response information. All IRIS assessments
since 1996 have also undergone independent external peer review. The current IRIS
process includes review by EPA scientists, interagency reviewers from other federal
agencies, and the public, and peer review by independent scientists external to EPA. New
IRIS values are developed and old IRIS values are updated as new health effects data
become available. Refer to the "IRIS Track" website3 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 (MO A) 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 MO A.
2) US Agency for Toxic Substances and Disease Registry (ATSDRY ATSDR, which is part
of the US Department of Health and Human Services, develops and publishes Minimum
Risk Levels (MRLs) [12] for inhalation and oral exposure to many toxic substances. As
stated on the ATSDR web site: "Following discussions with scientists within the
Department of Health and Human Services (HHS) and the EPA, ATSDR chose to adopt a
practice similar to that of the EPA's Reference Dose (RfD) and Reference Concentration
(RfC) for deriving substance specific health guidance levels for non-neoplastic
endpoints." The MRL is defined as "an estimate of daily human exposure to a substance
3http://cfbub.epa.gov/ncea/iristrac/
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Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 16
that is likely to be without an appreciable risk of adverse effects (other than cancer) over a
specified duration of exposure." ATSDR describes MRLs as substance-specific estimates
to be used by health assessors to select environmental contaminants for further evaluation.
Exposures above an MRL do not necessarily represent a threat, and MRLs are therefore
not intended for use as predictors of adverse health effects or for setting cleanup levels.
3) California Environmental Protection Agency (CalEPA). The CalEPA Office of
Environmental Health Hazard Assessment has developed dose-response assessments for
many substances, based both on carcinogenicity and health effects other than cancer. The
process for developing these assessments is similar to that used by EPA to develop IRIS
values and incorporates significant external scientific peer review. As cited in the
CalEPA Technical Support Document for developing their chronic assessments4: "The
guidelines for developing chronic inhalation exposure levels incorporate many
recommendations of the U.S. EPA [73] and NAS [7¥]." The non-cancer information
includes available inhalation health risk guidance values expressed as chronic inhalation
reference exposure levels (RELs) [75], CalEPA defines the REL as "the concentration
level at or below which no health effects are anticipated in the general human population."
CalEPA's quantitative dose-response information on carcinogenicity by inhalation
exposure is expressed in terms of the URE [7(5], defined similarly to EPA's URE.
In developing chronic risk estimates, we adjusted dose-response values for some HAP based
on professional judgment, as follows:
1) In the case of manganese (Mn), consistent with Agency policy supported by the SAB (as
mentioned above), the EPA considers the ATSDR MRL for manganese the most
appropriate value to be used in RTR assessments. There is an existing IRIS RfC for
manganese (Mn) published in 1993.5 Also, ATSDR published an assessment of Mn
toxicity (2012) which includes a chronic inhalation reference value (i.e., an ATSDR
Minimal Risk Level or MRL).6 Both the 1993 IRIS RfC and the 2012 ATSDR MRL were
based on the same study (Roels et al., 1993), however, ATSDR used updated 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 value to use
in RTR risk assessments.
2) In the case of nickel compounds, to provide a conservative estimate of the potential cancer
risks, the EPA considers the IRIS URE value for nickel subsulfide (which is considered
the most potent carcinogen among all nickel compounds) to be the most appropriate value
to be used in RTR assessments. Based on consistent views of major scientific bodies (i.e.,
4 Air Toxics Hot Spots Program, Risk Assessment Guidelines, Part III - Technical Support Document
for the Determination of Non-cancer Chronic Reference Exposure Levels. Air Toxicology and Epidemiology
Section, Office of Environmental Health Hazard Assessment, California Environmental Protection Agency.
February 2000 (http://www.oehha.ca.gov/air/chronic rels/pdf/relsP32k.pdf)
5 USEPA Integrated Risk Information System Review of Manganese (1993) available at
http://www.epa.gov/iris/subst/0373.htm
6 Agency for Toxic Substances & Disease Registry Toxicological Profile for Manganese (2012) available at
http://www.atsdr.cdc. gov/toxprofiles/tp.asp?id= 102&tid=23
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Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 17
National Toxicology Program (NTP) in their 12th Report of the Carcinogens (RoC),7
International Agency for Research on Cancer (IARC),8 and other international agencies9
that consider all nickel compounds to be carcinogenic, we currently consider all nickel
compounds to have the potential of being carcinogenic to humans. The 12th RoC states
that the "combined results of epidemiological studies, mechanistic studies, and
carcinogenic studies in rodents support the concept that nickel compounds generate nickel
ions in target cells at sites critical for carcinogenesis, thus allowing consideration and
evaluation of these compounds as a single group." Although the precise nickel compound
(or compounds) responsible for carcinogenic effects in humans is not always clear, studies
indicate that nickel sulfate and the combinations of nickel sulfides and oxides encountered
in industrial emissions of nickel mixtures cause cancer in humans (these studies are
summarized in a review by Grimsrud et al., 2010).10 The major scientific bodies
mentioned above have also recognized that there may be differences in the toxicity and/or
carcinogenic potential across the different nickel compounds. For this reason, and given
that there are two additional URE values11 derived for exposure to mixtures of nickel
compounds (as a group) that are 2-3 fold lower than the IRIS URE for nickel subsulfide,
the EPA considers it reasonable, in some instances (e.g., when high quality data is
available on the composition of nickel emissions from a specific source category), to use a
value that is 50 percent of the IRIS URE for nickel subsulfide for providing an estimate of
the lower end of the plausible range of cancer potency values for different mixtures of
nickel compounds.
For this specific assessment, based on the lack of information on the composition of nickel
mixtures emitted by the Aerospace Manufacturing and Rework source category and to
provide a conservative estimate of potential cancer risks, the EPA considers the IRIS URE
for nickel (which is considered the most potent carcinogen among all nickel compounds)
to be the most appropriate value to be used to estimate the risks associated with nickel
emissions from this source category.
3) In the case of emissions of unspecified mixtures of HAP categories such as metal
compounds and POM, we apply category-specific chemical speciation profiles appropriate
to the source category. For POM emissions that are not speciated into individual
compounds, we apply the same simplifying assumptions to assessments that are used in
7 National Toxicology Program (NTP), 2011. Report on Carcinogens (RoC). 12th ed. Research Triangle Park,
NC: US Department of Health and Human Services (DHHS), Public Health Service. Available online at
http://ntp.niehs.nih.gov/ntp/roc/twelfth/rocl2.pdf
8 International Agency for Research on Cancer (IARC), 1990. IARC monographs on the evaluation of
carcinogenic risks to humans. Chromium, nickel, and welding. Vol.49. Lyons, France: International Agency
for Research on Cancer, World Health Organization Vol. 49:256.
9 World Health Organization (WHO, 1991) and the European Union's Scientific Committee on Health and
Environmental Risks (SCHER, 2006).
10 Grimsrud TK and Andersen A. Evidence of carcinogenicity in humans of water-soluble nickel salts. J Occup
Med Toxicol 2010, 5:1-7. Available online at http://www.occup-med.com/content/5/1/7
11 Two UREs (other than the current IRIS values) have been derived for nickel compounds as a group: one
developed by the California Department of Health Services
(http://www.arb.ca.gov/toxics/id/summarv/nickel tech b.pdf) and the other by the Texas Commission on
Environmental Quality (http://www.epa.gov/ttn/atw/natal999/99pdfs/healthefFectsinfo.pdf).
-------
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 18
NAT A.12 The NATA approach partitions POM into eight different non-overlapping
"groups" that are modeled as separate pollutants. Each POM group includes POM species
of similar carcinogenic potency, for which we can apply the same URE.
4) In the case of formaldehyde, the EPA determined in 2004 that the Chemical Industry
Institute of Toxicology (CUT) cancer dose-response value (5.5 x 10"9 per (j,g/m3) was
based on better science than the IRIS cancer dose-response value (1.3 x 10"5 per (J,g/m3),
and the EPA decided to switch from using the IRIS value to the CUT value in risk
assessments supporting regulatory actions. Subsequent research published by EPA
suggested that the CUT model was not appropriate and in 2010 EPA returned to using the
1991 IRIS value. The EPA is currently revising the formaldehyde IRIS assessment and in
the interim, we will present findings using the 1991 IRIS value as a primary estimate.
EPA may also consider other information as the science evolves.
The emissions inventory for the aerospace source category includes emissions of HAP with
available chronic quantitative inhalation dose-response values. Of these, 42 are classified as
known, probably, or possible carcinogens, with quantitative cancer dose-response values
available. These 42 HAP, their quantitative inhalation chronic cancer dose-response values,
and the source of each value are listed in Table 2.6-1. One hundred and eleven HAP have
quantitative inhalation chronic noncancer threshold values available; two of these one
hundred and eleven HAP (cadmium and mercury), for which a screening multipathway
assessment was performed, also have quantitative oral chronic noncancer threshold values
available. These HAP, their threshold values, and the source of the value are listed in Table
2.6-2(A) and Table 2.6-2(B).
Table 2.6-1 Dose-Response Values for Chronic Inhalation Exposure to Carcinogens
URE (unit risk estimate for cancer)13 = cancer risk per [j,g/m3 of average lifetime exposure.
Sources: IRIS = EPA Integrated Risk Information System, EPA ORD = EPA Office of Research &
Development, EPA OAQPS = EPA Office of Air Quality Planning & Standards, CAL = California
EPA Office of Environmental Health Hazard Assessment.
Polllllillll
CAS Number14
i ui:
Source
(l/nji/m5)
1,3-Butadiene
106990
0.00003
IRIS
1,4-Dioxane
123911
0.000005
IRIS
2,4-Toluene diisocyanate
584849
0.000011
CAL
2-Nitropropane
79469
0.0000056
EPA OAQPS
3,3 '-Dichlorobenzidine
91941
0.00034
CAL
4,4'-Methylene bis(2-chloroaniline)
101144
0.00043
CAL
12 US EPA, 2006. Approach for modeling POM. Technical support information for the 1999 National Air
Toxics Assessment, http://www.epa.gov/ttn/atw/natal999/99pdfs/pomapproachian.pdf
13 The URE is the upper-bound excess cancer risk estimated to result from continuous lifetime exposure to an
agent at a concentration of 1 (ig/m3 in air. URE's are considered upper bound estimates meaning they represent
a plausible upper limit to the true value.
14 Chemical Abstract Services identification number. For groups of compounds that lack a CAS number we
have used a surrogate 3-digit identifier corresponding to the group's position on the CAA list of HAPs.
-------
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 19
Table 2.6-1 Dose-Response Values for Chronic Inhalation Exposure to Carcinogens
URE (unit risk estimate for cancer)13 = cancer risk per [j,g/m3 of average lifetime exposure.
Sources: IRIS = EPA Integrated Risk Information System, EPA ORD = EPA Office of Research &
Development, EPA OAQPS = EPA Office of Air Quality Planning & Standards, CAL = California
EPA Office of Environmental Health Hazard Assessment.
Poll ul mil
CAS Number14
I UK
Source
(l/fig/nr')
4,4'-Methylenedianiline
101779
0.00046
CAL
p-Dichlorobenzene
106467
0.000011
CAL
Acetaldehyde
75070
0.0000022
IRIS
Acrylamide
79061
0.00016
IRIS
Acrylonitrile
107131
0.000068
IRIS
Aniline
62533
0.0000016
CAL
Arsenic compounds
7440382
0.0043
IRIS
Benzene15
71432
0.0000078
IRIS
Beryllium compounds
7440417
0.0024
IRIS
Bis(2-ethylhexyl)phthalate
117817
0.0000024
CAL
Cadmium compounds
7440439
0.0018
IRIS
Carbon tetrachloride
56235
0.000006
IRIS
Chromium & Chromium compounds
Barium chromate
10294403
0.012
IRIS
Calcium chromate
13765190
0.012
IRIS
Chromic acid (VI)
7738945
0.012
IRIS
Chromium (VI) as lead chromate
7758976
0.012
IRIS
Chromium (VI) compounds
18540299
0.012
IRIS
Chromium (VI) trioxide, chromic acid
mist
11115745
0.012
IRIS
Sodium chromate
7775113
0.012
IRIS
Sodium dichromate
10588019
0.012
IRIS
Strontium chromate
7789062
0.012
IRIS
Zinc chromate
13530659
0.012
IRIS
Zinc potassium chromate
11103869
0.012
IRIS
Epichlorohydrin (l-Chloro-2,3-
epoxypropane)
106898
0.0000012
IRIS
Ethyl benzene
100414
0.0000025
CAL
Ethylene oxide
75218
0.000088
CAL
Formaldehyde16
50000
0.000013
IRIS
Hexachlorobenzene
118741
0.00046
IRIS
Methylene chloride
75092
0.000000016
IRIS
Naphthalene
91203
0.000034
CAL
15 The EPA IRIS assessment for benzene provides a range of plausible UREs. This assessment used the highest
value in that range, 7.8E-06 per ug/m3. The low end of the range is 2.2E-06 per ug/m3.
16 The EPA has used the CUT URE value, 5.5X10"9 per mg/m3, to characterize formaldehyde cancer risk in
some instances.
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Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 20
Table 2.6-1 Dose-Response Values for Chronic Inhalation Exposure to Carcinogens
URE (unit risk estimate for cancer)13 = cancer risk per pg/m3 of average lifetime exposure.
Sources: IRIS = EPA Integrated Risk Information System, EPA ORD = EPA Office of Research &
Development, EPA OAQPS = EPA Office of Air Quality Planning & Standards, CAL = California
EPA Office of Environmental Health Hazard Assessment.
Poll ul mil
CAS Number14
I UK
Source
(l/fig/nr')
Nickel & Nickel Compounds17
Nickel compounds
7440020
0.00048
IRIS
Nickel oxide
1313991
0.00048
IRIS
Propylene oxide
75569
0.0000037
IRIS
T etrachloroethene
127184
0.00000026
IRIS
T richloroethylene
79016
0.0000048
IRIS
Vinyl chloride
75014
0.0000088
IRIS
17 The EPA IRIS assessments for nickel compounds provide a range of plausible UREs. This assessment used
the highest value in that range which is equal to the URE for nickel subsulfide, 4.8E-04 per ug/m3. The low end
of the range is equal to 50% of the URE for nickel subsulfide, 2.4E-04 per ug/m3.
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Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 21
Table 2.6-2 (A) Dose-Response Values for Chronic Inhalation Exposure to Noncarcinogens
RfC (reference inhalation concentration) =
an estimate (with uncertainty spanning perhaps an order
of magnitude) of a continuous inhalation exposure to the human population (including sensitive
subgroups) that is likely to be without an appreciable risk of deleterious effects during a lifetime.
Sources: IRIS = EPA Integrated Risk Information System, CAL = California EPA Office of
Environmental Human Health Assessment, ATSDR = US Agency for Toxic Substances Disease
Registry, HEAST = EPA Health Effects Assessment Tables, EPA OAQPS = EPA Office of Air
Quality Planning & Standards, EPA ORD
= EPA Office of Research and Development
Pol In la ill
CAS Number1*
UK
Source1''
(mg/nr')
1,1,1-Trichloroethane (methyl chloroform)
71556
5
IRIS -M
1,2-Epoxybutane
106887
0.02
IRIS -M
1,3-Butadiene
106990
0.002
IRIS -M
1,4-Dioxane
123911
0.03
IRIS -M
2,4-Toluene diisocyanate
584849
0.00007
IRIS -M
2-Nitropropane
79469
0.02
IRIS -L
4,4'-Methylenedianiline
101779
0.02
CAL
n-Hexane
110543
0.7
IRIS -M
p-Dichlorobenzene
106467
0.8
IRIS -M
Acetaldehyde
75070
0.009
IRIS -L
Acrylamide
79061
0.006
IRIS -M
Acrylic acid
79107
0.001
IRIS -M
Acrylonitrile
107131
0.002
IRIS -M
Aniline
62533
0.001
IRIS -L
Antimony & Antimony Compounds
Antimony compounds
7440360
0.0002
IRIS -L
Antimony oxide
1327339
0.0002
IRIS -L
Antimony pentoxide
1314609
0.0002
IRIS -L
Antimony trioxide
1309644
0.0002
IRIS -L
Arsenic compounds
7440382
0.000015
CAL
Benzene
71432
0.03
IRIS -M
Beryllium compounds
7440417
0.00002
IRIS -M
Bis(2-ethylhexyl)phthalate
117817
0.01
CAL
Cadmium compounds
7440439
0.00001
ATSDR
Carbon tetrachloride
56235
0.1
IRIS -M
Chlorobenzene
108907
1
CAL
Chloroform
67663
0.098
ATSDR
Chromium & Chromium Compounds
18 Chemical Abstract Services identification number. For groups of compounds that lack a CAS number we
have used a surrogate 3-digit identifier corresponding to the group's position on the CAA list of HAPs.
19 The descriptors L (low), M (medium), and H (high) have been added for IRIS RfC values to indicate the
overall level of confidence in the RfC value, as reported in IRIS.
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Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 22
Table 2.6-2 (A) Dose-Response Values for Chronic Inhalation Exposure to Noncarcinogens
RfC (reference inhalation concentration) =
an estimate (with uncertainty spanning perhaps an order
of magnitude) of a continuous inhalation exposure to the human population (including sensitive
subgroups) that is likely to be without an appreciable risk of deleterious effects during a lifetime.
Sources: IRIS = EPA Integrated Risk Information System, CAL = California EPA Office of
Environmental Human Health Assessment, ATSDR = US Agency for Toxic Substances Disease
Registry, HEAST = EPA Health Effects Assessment Tables, EPA OAQPS = EPA Office of Air
Quality Planning & Standards, EPA ORD
= EPA Office of Research and Development
Pol In la ill
CAS Number1*
UK
Source1''
(mg/nr')
Barium chromate
10294403
0.0001
IRIS -M
Calcium chromate
13765190
0.0001
IRIS -M
Chromic acid (VI)
7738945
0.0001
IRIS -M
Chromium (VI) as lead chromate
7758976
0.0001
IRIS -M
Chromium (VI) compounds
18540299
0.0001
IRIS -M
Chromium (VI) trioxide, chromic
acid mist
11115745
0.000008
IRIS -L
Sodium chromate
7775113
0.0001
IRIS -M
Sodium dichromate
10588019
0.0001
IRIS -M
Strontium chromate
7789062
0.0001
IRIS -M
Zinc chromate
13530659
0.0001
IRIS -M
Zinc potassium chromate
11103869
0.0001
IRIS -M
Cobalt & Cobalt Compounds
Cobalt aluminate
1345160
0.0001
ATSDR
Cobalt compounds
7440484
0.0001
ATSDR
Cobalt naphtha
61789513
0.0001
ATSDR
Cobalt oxide
1307966
0.0001
ATSDR
Hexanoic acid, 2-ethyl-, cobalt(2+)
salt
136527
0.0001
ATSDR
Cresols (mixed)
1319773
0.6
CAL
m-Cresol
108394
0.6
CAL
o-Cresol
95487
0.6
CAL
p-Cresol
106445
0.6
CAL
Cumene
98828
0.4
IRIS -M
Cyanide compounds20
57125
0.0008
IRIS - L/M
Diethanolamine
111422
0.003
CAL
Dimethyl formamide
68122
0.03
IRIS -M
Epichlorohydrin (l-Chloro-2,3
epoxypropane)
106898
0.001
IRIS -M
Ethyl benzene
100414
1
IRIS -L
Ethylene glycol
107211
0.4
CAL
Ethylene oxide
75218
0.03
CAL
Formaldehyde
50000
0.0098
ATSDR
Glycol Ethers21
20 The value for hydrogen cyanide was used as a surrogate for all cyanide compounds without an RfC.
21 The RfC value for ethylene glycol methyl ether was used as a surrogate for all glycol ethers without an RfC.
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Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 23
Table 2.6-2 (A) Dose-Response Values for Chronic Inhalation Exposure to Noncarcinogens
RfC (reference inhalation concentration) =
an estimate (with uncertainty spanning perhaps an order
of magnitude) of a continuous inhalation exposure to the human population (including sensitive
subgroups) that is likely to be without an appreciable risk of deleterious effects during a lifetime.
Sources: IRIS = EPA Integrated Risk Information System, CAL = California EPA Office of
Environmental Human Health Assessment, ATSDR = US Agency for Toxic Substances Disease
Registry, HEAST = EPA Health Effects Assessment Tables, EPA OAQPS = EPA Office of Air
Quality Planning & Standards, EPA ORD
= EPA Office of Research and Development
Pol In la ill
CAS Number1*
UK
Source1''
(mg/nr')
1,2-Dimethoxyethane
110714
0.02
IRIS -M
2-(Hexyloxy)ethanol
112254
0.02
IRIS -M
2-Butoxyethyl acetate
112072
0.02
IRIS -M
Butyl Carbitol acetate
124174
0.02
IRIS -M
Carbitol acetate
112152
0.02
IRIS -M
Diethylene glycol dimethyl ether
111966
0.02
IRIS -M
Diethylene glycol monobutyl ether
112345
0.02
HEAST
Diethylene glycol monoethyl ether
111900
0.02
IRIS -M
Diethylene glycol monomethyl ether
111773
0.02
IRIS -M
Ethylene glycol ethyl ether
110805
0.2
IRIS -M
Ethylene glycol ethyl ether acetate
111159
0.3
CAL
Ethylene glycol methyl ether
109864
0.02
IRIS -M
Ethylene glycol methyl ether acetate
110496
0.09
CAL
Glycol ethers
181
0.02
IRIS -M
Propyl cellosolve
2807309
0.02
IRIS -M
Hexachlorobenzene
118741
0.003
CAL
Hexamethylene-1,6-diisocyanate
822060
0.00001
IRIS -L
Hydrochloric acid (hydrogen chloride)
7647010
0.02
IRIS -L
Hydrofluoric acid (hydrogen fluoride)
7664393
0.014
CAL
Isophorone
78591
2
CAL
Lead and Lead Compounds
Lead (II) oxide
1317368
0.00015
EPA OAQPS
Lead as lead chromate
7758976
0.00015
EPA OAQPS
Lead compounds
7439921
0.00015
EPA OAQPS
Lead dioxide
1309600
0.00015
EPA OAQPS
Maleic anhydride
108316
0.0007
CAL
Manganese and Manganese Compounds
Manganese compounds
7439965
0.0003
ATSDR
Manganese dioxide
1313139
0.0003
ATSDR
Manganese tetroxide
1317357
0.0003
ATSDR
Manganese trioxide
1317346
0.0003
ATSDR
Mercury and Mercury Compounds
Gaseous divalent mercury
201
0.0003
IRIS -M
Mercury (elemental)
7439976
0.0003
IRIS -M
Particulate divalent mercury
184
0.0003
IRIS -M
Methanol
67561
20
IRIS - M/H
-------
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 24
Table 2.6-2 (A) Dose-Response Values for Chronic Inhalation Exposure to Noncarcinogens
RfC (reference inhalation concentration) = an estimate (with uncertainty spanning perhaps an order
of magnitude) of a continuous inhalation exposure to the human population (including sensitive
subgroups) that is likely to be without an appreciable risk of deleterious effects during a lifetime.
Sources: IRIS = EPA Integrated Risk Information System, CAL = California EPA Office of
Environmental Human Health Assessment, ATSDR = US Agency for Toxic Substances Disease
Registry, HEAST = EPA Health Effects Assessment Tables, EPA OAQPS = EPA Office of Air
Quality Planning & Standards, EPA ORD = EPA Office of Research and Development
Pol In la ill
CAS Number1*
UK
(mg/nr')
Source1''
Methyl isobutyl ketone
108101
3
IRIS - L/M
Methyl methacrylate
80626
0.7
IRIS - M/H
Methylene chloride
75092
0.6
IRIS - M/H
Methylene diphenyl diisocyanate
101688
0.0006
IRIS -M
Naphthalene
91203
0.003
IRIS -M
Nickel and Nickel Compounds
Nickel compounds
7440020
0.00009
ATSDR
Nickel oxide
1313991
0.00002
CAL
Phenol
108952
0.2
CAL
Phthalic anhydride
85449
0.02
CAL
Propylene oxide
75569
0.03
IRIS -M
Selenium and Selenium Compounds
Selenious acid
7783008
0.02
CAL
Selenium compounds
7782492
0.02
CAL
Styrene
100425
1
IRIS -M
T etrachloroethene
127184
0.04
IRIS -M
Toluene
108883
5
IRIS -H
T richloroethylene
79016
0.002
IRIS -H
Triethylamine
121448
0.007
IRIS -L
Vinyl acetate
108054
0.2
IRIS -H
Vinyl chloride
75014
0.1
IRIS -M
Xylenes (mixed)22
1330207
0.1
IRIS -M
m-Xylene
108383
0.1
IRIS -M
o-Xylene
95476
0.1
IRIS -M
p-Xylene
106423
0.1
IRIS -M
22 The RfC for mixed xylene was used as a surrogate for each of the isomers.
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Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 25
Table 2.6-2 (B) Dose-Response Values for Chronic Oral Exposure to Noncarcinogens
RfD (reference dose) = an estimate (with uncertainty spanning perhaps an order of magnitude) of a
continuous oral exposure to the human population (including sensitive subgroups) that is likely to
be without an appreciable risk of deleterious effects during a lifetime.
Sources: IRIS = EPA Integrated Risk Information System, ATSDR
= US Agency for Toxic
Substances and Disease Registry.
Pol In la ill
CAS Number2"'
m i)
L- '4
Source
(mg/kg/d))
Cadmium compounds
7440439
0.0005
IRIS-H
Mercuric chloride25
7439976
0.0003
IRIS-H
2.6.2 Sources of acute dose-response information
Hazard identification and dose-response assessment information for preliminary acute
inhalation exposure assessments are based on the existing recommendations of OAQPS for
HAP [77], Depending on availability, the results from screening acute assessments are
compared to both "no effects" reference levels for the general public, such as the California
Reference Exposure Levels (RELs), as well as emergency response levels, such as Acute
Exposure Guideline Levels (AEGLs) and Emergency Response Planning Guidelines
(ERPGs), with the recognition that the ultimate interpretation of any potential risks associated
with an estimated exceedance of a particular reference level depends on the definition of that
level and any limitations expressed therein. Comparisons among different available
inhalation health effect reference values (both acute and chronic) for selected HAP can be
found in an EPA document of graphical arrays [75],
California Acute Reference Exposure Levels (RELs). The California Environmental
Protection Agency (CalEPA) has developed acute dose-response reference values for many
substances, expressing the results as acute inhalation Reference Exposure Levels (RELs).
The acute REL (http://www.oehha.ca. gov/air/pdf/acuterel.pdf) is defined by CalEPA as
"the concentration level at or below which no adverse health effects are anticipated for a
specified exposure duration. [79], RELs are based on the most sensitive, relevant,
adverse health effect reported in the medical and toxicological literature. RELs are
designed to protect the most sensitive individuals in the population by the inclusion of
margins of safety. Since margins of safety are incorporated to address data gaps and
uncertainties, exceeding the REL does not automatically indicate an adverse health
23 Chemical Abstract Services identification number. For groups of compounds that lack a CAS number we
have used a surrogate 3-digit identifier corresponding to the group's position on the CAA list of HAPs.
24 The descriptors L (low), M (medium), and H (high) have been added for IRIS RfC values to indicate the
overall level of confidence in the RfC value, as reported in the IRIS file.
25 The multipathway exposure assessment for mercury included fate and transport analysis, that included
separate oral exposure estimates for divalent mercury and methylmercury.
-------
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 26
impact." Acute RELs are developed for 1-hour (and 8-hour) exposures. The values
incorporate uncertainty factors similar to those used in deriving EPA's Inhalation
Reference Concentrations (RfCs) for chronic exposures (and, in fact, California also has
developed chronic RELs).
Acute Exposure Guideline Levels (AEGLs). AEGLs are developed by the National Advisory
Committee (NAC) on Acute Exposure Guideline Levels (NAC/AEGL) for Hazardous
Substances, and then reviewed and published by the National Research Council. As described
in the Committee's "Standing Operating Procedures (SOP)"
(http://www.epa.gov/opptintr/aegl/pubs/sop.pdf). AEGLs "represent threshold exposure limits
for the general public and are applicable to emergency exposures ranging from 10 min to 8 h."
Their intended application is "for conducting risk assessments to aid in the development of
emergency preparedness and prevention plans, as well as real time emergency response
actions, for accidental chemical releases at fixed facilities and from transport carriers." The
document states that "the primary purpose of the AEGL program and the NAC/AEGL
Committee is to develop guideline levels for once-in-a-lifetime, short-term exposures to
airborne concentrations of acutely toxic, high-priority chemicals." In detailing the intended
application of AEGL values, the document states that, "It is anticipated that the AEGL values
will be used for regulatory and nonregulatory purposes by U.S. Federal and State agencies,
and possibly the international community in conjunction with chemical emergency response,
planning, and prevention programs. More specifically, the AEGL values will be used for
conducting various risk assessments to aid in the development of emergency preparedness and
prevention plans, as well as real-time emergency response actions, for accidental chemical
releases at fixed facilities and from transport carriers."
The NAC/AEGL defines AEGL-1 and AEGL-2 as:
"AEGL-1 is the airborne concentration (expressed as ppm or mg/m3) of a substance above
which it is predicted that the general population, including susceptible individuals, could
experience notable discomfort, irritation, or certain asymptomatic nonsensory effects.
However, the effects are not disabling and are transient and reversible upon cessation of
exposure."
"AEGL-2 is the airborne concentration (expressed as ppm or mg/m3) of a substance above
which it is predicted that the general population, including susceptible individuals, could
experience irreversible or other serious, long-lasting adverse health effects or an impaired
ability to escape."
"Airborne concentrations below AEGL-1 represent exposure levels that can produce mild
and progressively increasing but transient and nondisabling odor, taste, and sensory
irritation or certain asymptomatic, nonsensory effects. With increasing airborne
concentrations above each AEGL, there is a progressive increase in the likelihood of
occurrence and the severity of effects described for each corresponding AEGL. Although
the AEGL values represent threshold levels for the general public, including susceptible
subpopulations, such as infants, children, the elderly, persons with asthma, and those with
other illnesses, it is recognized that individuals, subject to unique or idiosyncratic
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Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 27
responses, could experience the effects described at concentrations below the
corresponding AEGL."
Emergency Response Planning Guidelines (ERPGs). The American Industrial Hygiene
Association (AIHA) has developed Emergency Response Planning Guidelines (ERPGs) [20]
for acute exposures at three different levels of severity. These guidelines represent
concentrations for exposure of the general population (but not particularly sensitive persons)
for up to 1 hour associated with effects expected to be mild or transient (ERPG-1),
irreversible or serious (ERPG-2), and potentially life-threatening (ERPG-3).
ERPG values https://www.aiha.org/get-
involved/aihaguidelinefoundation/emergencvresponseplanningguidelines/Pages/default.aspx
are described in their supporting documentation as follows: "ERPGs are air concentration
guidelines for single exposures to agents and are intended for use as tools to assess the
adequacy of accident prevention and emergency response plans, including transportation
emergency planning, community emergency response plans, and incident prevention and
mitigation."
ERPG-1 and ERPG-2 values are defined by AIHA as follows:
"ERPG-1 is the maximum airborne concentration below which it is believed that nearly all
individuals could be exposed for up to 1 hour without experiencing other than mild
transient adverse health effects or without perceiving a clearly defined, objectionable
odor."
"ERPG-2 is the maximum airborne concentration below which it is believed that nearly all
individuals could be exposed for up to 1 hour without experiencing or developing
irreversible or other serious health effects or symptoms which could impair an individual's
ability to take protective action."
The emissions inventory for the aerospace source category includes emissions of 59 HAP
with relevant and available quantitative acute dose-response threshold values. These HAP,
their acute threshold values, and the source of the value are listed below in Table 2.6-3.
Table 2.6-3 Dose-Response Values for Acute Exposures
Pollutant
(AS
Nil m her'"
ai-:<;i -i
(1-lir)
(mg/iir')
ai:(;i -2
(l-lir)
(nig/in )
i:im;-i
(ni»/in4)
(nig/m')
ri:l
1,1,1 -Trichloroethane
(methyl chloroform)
71556
1300
3300
1900
3800
68
1,2-Epoxybutane
106887
210
410
26 Chemical Abstract Services identification number. For groups of compounds that lack a CAS number we
have used a surrogate 3-digit identifier corresponding to the group's position on the CAA list of HAPs.
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Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 28
Table 2.6-3 Dose-Response Values for Acute Exposures
ai:(;i -i
ai-:<;i -2
(AS
(1-hr)
(i-in* >
i:im;-i
Pollutant
Number'"
(mg/nr')
(mg/iir')
(in»/m)
(iiig/m'')
ri:i.
1,3-Butadiene
106990
1500
12000
22
440
1,4-Dioxane
123911
61
1200
3
2,4-Toluene
diisocyanate
584849
0.14
0.59
0.071
1.1
n-Hexane
110543
12000
Acetaldehyde
75070
81
490
18
360
0.47
Acrylic acid
79107
4.4
140
2.9
150
6
Acrylonitrile
107131
10
120
22
77
Aniline
62533
30
46
Arsenic compounds
7440382
0.0002
Benzene
71432
170
2600
160
480
1.3
Beryllium compounds
7440417
0.025
Carbon tetrachloride
56235
280
1200
130
630
1.9
Chlorobenzene
108907
46
690
Chloroform
67663
310
240
0.15
Cumene
98828
250
1500
Dimethyl formamide
68122
270
6
300
Epichlorohydrin (1-
Chloro-2,3-
epoxypropane)
106898
22
91
19
76
1.3
Ethyl acrylate
140885
34
150
0.041
120
Ethyl benzene
100414
140
4800
Ethylene oxide
75218
81
90
Formaldehyde
50000
1.1
17
1.2
12
0.055
Glycol Ethers27
1,2-Dimethoxyethane
110714
0.093
2-(Hexyloxy)ethanol
112254
0.093
2-Butoxyethyl
acetate
112072
0.093
Butyl carbitol acetate
124174
0.093
Carbitol acetate
112152
0.093
Diethylene glycol
dimethyl ether
111966
0.093
Diethylene glycol
monobutyl ether
112345
0.093
Diethylene glycol
monoethyl ether
111900
0.093
Diethylene glycol
111773
0.093
27 The acute REL for ethylene glycol methyl ether was used as a surrogate for glycol ether compounds without
an acute REL.
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Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 29
Table 2.6-3 Dose-Response Values for Acute Exposures
ai-:<;i -i
ai-:<;i -2
C AS
(1-hr)
(i-in* >
i:im;-i
Pollutant
Number'"
(mg/nr')
(mg/iir')
(in»/m)
(iiig/m'')
ri:i.
monomethyl ether
Ethylene glycol ethyl
ether
110805
0.37
Ethylene glycol ethyl
ether
acetate
111159
0.14
Ethylene glycol
methyl
ether
109864
0.093
Ethylene glycol
methyl
ether acetate
110496
0.093
Glycol ethers
181
0.093
Propyl cellosolve
2807309
0.093
Hydrochloric acid
7647010
2.7
33
4.5
30
2.1
Hydrofluoric acid
7664393
0.82
20
1.6
16
0.24
Maleic anhydride
108316
0.8
8
Mercury (elemental)
7439976
1.7
2
0.0006
Methanol
67561
690
2700
260
1300
28
Methyl methacrylate
80626
70
490
Methylene chloride
75092
690
1900
1000
2600
14
Methylene diphenyl
diisocyanate
101688
5
Phenol
108952
58
89
38
190
5.8
Propylene oxide
75569
170
690
120
590
3.1
Styrene
100425
85
550
210
1100
21
T etrachloroethene
127184
240
1600
680
1400
20
Toluene
108883
750
4500
190
1100
37
T richloroethylene
79016
700
2400
540
2700
Triethylamine
121448
2.8
Vinyl acetate
108054
24
630
18
260
Vinyl chloride
75014
640
3100
1300
13000
180
Xylenes (mixed)28
1330207
560
4000
22
m-Xylene
108383
22
o-Xylene
95476
22
p-Xylene
106423
22
28 The REL for mixed xylenes was used as a surrogate for each isomer.
-------
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 30
2.7 Risk characterization
2.7.1 General
The final product of the risk assessment is the risk characterization, in which the information
from the previous steps is integrated and an overall conclusion about risk is synthesized that is
complete, informative, and useful for decision makers. In general, the nature of this risk
characterization depends on the information available, the application of the risk information
and the resources available. In all cases, major issues associated with determining the nature
and extent of the risk are identified and discussed. Further, the EPA Administrator's March
1995 Policy for Risk Characterization [21] specifies that a risk characterization "be prepared
in a manner that is clear, transparent, reasonable, and consistent with other risk
characterizations of similar scope prepared across programs in the Agency." These principles
of transparency and consistency have been reinforced by the Agency's Risk Characterization
Handbook [22], in 2002 by the Agency's information quality guidelines [23], and in the
OMB/OSTP September 2007 Memorandum on Updated Principles for Risk Analysis29, and
are incorporated in these assessments.
Estimates of health risk are presented in the context of uncertainties and limitations in the data
and methodology. Through our tiered, iterative analytical approach, we have attempted to
reduce both uncertainty and bias to the greatest degree possible in these assessments, within
the limitations of available time and resources. We provide summaries of risk metrics
(including maximum individual cancer risks and noncancer hazards, as well as cancer
incidence estimates) along with a discussion of the major uncertainties associated with their
derivation to provide decision makers with the fullest picture of the assessment and its
limitations.
For each carcinogenic HAP included in an assessment that has a potency estimate available,
individual and population cancer risks were calculated by multiplying the corresponding
lifetime average exposure estimate by the appropriate URE. This calculated cancer risk is
defined as the upper-bound probability of developing cancer over a 70-yr period (i.e., the
assumed human lifespan) at that exposure. Because UREs for most HAP are upper-bound
estimates, actual risks at a given exposure level may be lower than predicted, and could be
zero.
For EPA's list of carcinogenic HAP that act by a mutagenic mode-of-action [24], we applied
EPA's Supplemental Guidance for Assessing Susceptibility from Early-Life Exposure to
Carcinogens [25], This guidance has the effect of adjusting the URE by factors of 10 (for
children aged 0-1), 3 (for children aged 2-15), or 1.6 (for 70 years of exposure beginning at
birth), as needed in risk assessments. In this case, this has the effect of increasing the
estimated life time risks for these pollutants by a factor of 1.6. In addition, although only a
small fraction of the total POM emissions may be reported as individual compounds, EPA
29Memorandum for the Heads of Executive Departments and Agencies - Updated Principles for Risk Analysis
(September 19, 2007), From Susan E. Dudley, Administrator, Office of Information and Regulatory Affairs,
Office of Management and Budget; and Sharon L. Hays, Associate Director and Deputy Director for Science,
Office of Science and Technology Policy
http://www.whitehouse.gov/sites/default/files/omb/memoranda/fV2007/m07-24.pdf
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Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 31
expresses carcinogenic potency for compounds in this group in terms of benzo[a]pyrene
equivalence, based on evidence that carcinogenic POM have the same mutagenic mechanism
of action as does benzo[a]pyrene. For this reason, EPA implementation policy [26]
recommends applying the Supplemental Guidance to all carcinogenic PAHs for which risk
estimates are based on relative potency. Accordingly, we applied the Supplemental Guidance
to all unspeciated POM mixtures.
Increased cancer incidence for the entire receptor population within the area of analysis was
estimated by multiplying the estimated lifetime cancer risk for each census block by the
number of people residing in that block, then summing the results for the entire modeled
domain. This lifetime population incidence estimate was divided by 70 years to obtain an
estimate of the number of cancer cases per year.
Unlike linear dose-response assessments for cancer, noncancer health hazards generally are
not expressed as a probability of an adverse occurrence. Instead, "risk" for noncancer effects
is expressed by comparing an exposure to a reference level as a ratio. The "hazard quotient"
(HQ) is the estimated exposure divided by a reference level (e.g., the RfC). For a given HAP,
exposures at or below the reference level (HQ < 1) are not likely to cause adverse health
effects. As exposures increase above the reference level (HQs increasingly greater than 1),
the potential for adverse effects increases. For exposures predicted to be above the RfC, the
risk characterization includes the degree of confidence ascribed to the RfC values for the
compound(s) of concern (i.e., high, medium, or low confidence) and discusses the impact of
this on possible health interpretations.
The risk characterization for chronic effects other than cancer is expressed in terms of the HQ
for inhalation, calculated for each HAP at each census block centroid. As discussed above,
RfCs incorporate generally conservative uncertainty factors in the face of uncertain
extrapolations, such that an HQ greater than one does not necessarily suggest the onset of
adverse effects. The HQ cannot be translated to a probability that adverse effects will occur,
and is unlikely to be proportional to adverse health effect outcomes in a population.
Screening for potentially significant acute inhalation exposures also followed the HQ
approach. We divided the maximum estimated acute exposure by each available short-term
threshold value to develop an array of HQ values relative to the various acute endpoints and
thresholds. In general, when none of these HQ values are greater than one, there is no
potential for acute risk. In those cases where HQ values above one are seen, additional
information is used to determine if there is a potential for significant acute risks.
2.7.2 Mixtures
Since most or all receptors in these assessments receive exposures to multiple pollutants
rather than a single pollutant, we estimated the aggregate health risks associated with all the
exposures from a particular source category combined.
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Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 32
To combine risks across multiple carcinogens, our assessments use the mixtures guidelines'
[27,28] default assumption of additivity of effects, and combine risks by summing them using
the independence formula in the mixtures guidelines.
In assessing noncancer hazard from chronic exposures, in cases where different pollutants
cause adverse health effects via completely different modes of action, it may be inappropriate
to aggregate HQs. In consideration of these mode-of-action differences, the mixtures
guidelines support aggregating effects of different substances in specific and limited ways.
To conform to these guidelines, we aggregated non-cancer HQs of HAP that act by similar
toxic modes of action, or (where this information is absent) that affect the same target organ.
This process creates, for each target organ, a target-organ-specific hazard index (TOSHI),
defined as the sum of hazard quotients for individual HAP that affect the same organ or organ
system. All TOSHI calculations presented here were based exclusively on effects occurring
at the "critical dose" (i.e., the lowest dose that produces adverse health effects). Although
HQs associated with some pollutants have been aggregated into more than one TOSHI, this
has been done only in cases where the critical dose affects more than one target organ.
Because impacts on organs or systems that occur above the critical dose have not been
included in the TOSHI calculations, some TOSHIs may have been underestimated. As with
the HQ, the TOSHI should not be interpreted as a probability of adverse effects, or as strict
delineation of "safe" and "unsafe" levels. Rather, the TOSHI is another measure of the
potential for adverse health outcomes associated with pollutant exposure, and needs to be
interpreted carefully by health scientists and risk managers.
Because of the conservative nature of the acute inhalation screening and the variable nature of
emissions and potential exposures, acute impacts were screened on an individual pollutant
basis, not using the TOSHI approach.
2.7.3 MACT-Allowable Emissions and Risks
The emissions data in the dataset for the aerospace source category are estimates of actual
emissions on an annual basis. The risk results presented in the following sections are based
on these actual emissions. For the aerospace source category, estimates of MACT-allowable
emissions were also estimated, and the risk results based on those emissions are presented
below as well. For more detail, please refer to the emissions memo in Appendix 1 to this
document.
3 Risk results for the Aerospace Manufacturing and Rework
facilities
3.1 Source Category Emissions
-------
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 33
Based on 2011 survey data from an information collection request (ICR) as well as emission
data updated in 2013 and 2014, we currently estimate that there are 144 Aerospace
Manufacturing and Rework facilities operating in the U.S. and 87 of these facilities are
identified as major sources.
The baseline actual emissions for the Aerospace Manufacturing and Rework facilities source
category NPRM data set (of 144 facilities) are summarized in Table 3.1-1. The total HAP
emissions for the source category are approximately 1,130 tons per year. Based on these data,
the HAP emitted in the largest quantities across these 144 facilities are: toluene,
tetrachloroethene, methyl isobutyl ketone, xylenes (mixed), methylene chloride, phenol, N-
hexane, ethylene glycol ethyl ether, ethyl benzene, methanol, diethylene glycol monobutyl
ether, diethanolamine, propyl cello solve, 2-butoxyethyl acetate, 1,1,1-trichloroethane, styrene,
manganese compounds, ethylene glycol methyl ether, glycol ethers, ethylene glycol,
methylene diphenyl diisocyanate, butyl carbitol acetate and dimethyl formamide. These 23
HAP account for 99 percent of the mass of the total emissions by mass. Persistent and
bioaccumulative HAP (PB HAP)30 reported as emissions from these facilities include
cadmium, mercury, lead compounds and hexachlorobenzene. The following environmental
HAP are emitted from the Aerospace Manufacturing and Rework Facilities source category
and are included in the environmental risk screen: lead, mercury (mercuric chloride and
methyl mercury), cadmium, hydrogen chloride and hydrogen fluoride.
Table 3.1-1 Baseline Actual Emissions from Aerospace and Availability of Dose-Response Values
Number of
Facilities
Prioritized Inhalation Dose-Response Value
Identified by OAQPSb
Health
Benchmark
Values for
Acute
Noncancer?
PB-
HAP
1
IIAI"
Emissions
(tpy)
Reporting
HAP (144
facilities in
Unit Risk
Estimate
for
Reference
Concentration
for
data set)
Cancer?
Noncancer?
Toluene
348
133
V
V
T etrachloroethene
139
17
V
V
V
Methyl isobutyl ketone
136
129
V
Xylenes (mixed)
115
131
V
V
Methylene chloride
111
56
V
V
S
Phenol
100
62
V
S
n-Hexane
36
50
V
S
Ethylene glycol ethyl ether
27
20
V
S
Ethyl benzene
16
129
V
V
S
Methanol
14
90
S
S
30 Persistent and bioaccumulative HAP are defined in the EPA's Air Toxics Risk Assessment Library, Volume 1,
EPA-453K-04-001A, as referenced in the NPRM and provided on the EPA's Technology Transfer Network
website for Fate, Exposure, and Risk Assessment at http://www.epa.gov/ttn/fera/risk atra voll.html.
-------
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 34
Table 3.1-1 Baseline Actual Emissions from Aerospace and Availability of Dose-Response Values
Number of
Facilities
Prioritized Inhalation Dose-Response Value
Identified by OAQPSb
Health
Benchmark
Values for
Acute
Noncancer?
PB-
HAP
1
IIAI"
Emissions
(tpy)
Reporting
HAP (144
facilities in
Unit Risk
Estimate
for
Reference
Concentration
for
data set)
Cancer?
Noncancer?
Diethylene glycol monobutyl
ether
14
28
V
V
Diethanolamine
12
12
V
Propyl cellosolve
11
20
V
V
2-Butoxyethyl acetate
8
41
V
S
1,1,1 -Trichloroethane
6
8
V
S
Styrene
5
27
S
S
Manganese compounds
3
36
S
Ethylene glycol methyl ether
3
20
S
V
Glycol ethers
3
19
S
S
Ethylene Glycol
3
34
S
Methylene diphenyl
diisocyanate
3
42
V
~
Butyl carbitol acetate
2
23
S
S
Dimethyl formamide
1
4
S
S
Ethylene glycol ethyl ether
acetate
1
22
V
~
Cumene
1
66
S
S
T richloroethylene
0.7
9
V
V
V
Naphthalene
0.7
23
S
V
Strontium chromate
0.6
94
S
V
Manganese dioxide
0.6
13
V
Hexamethylene-1,6-diisocyanate
0.6
83
V
Bis(2-ethylhexyl)phthalate
0.6
17
V
V
1,2-Epoxybutane
0.4
6
V
V
Cyanide compounds
0.4
6
V
Dibutylphthalate
0.4
31
Methyl methacrylate
0.4
21
V
V
Acrylic acid
0.3
12
V
V
Nickel compounds
0.3
43
V
V
Triethylamine
0.3
16
V
V
4,4'-Methylenedianiline
0.2
3
V
V
Formaldehyde
0.2
61
V
V
V
Diethylene glycol monoethyl
ether
0.2
7
V
S
o-Xylene
0.2
4
S
S
Benzene
0.1
33
V
S
S
Diethylene glycol dimethyl ether
0.1
5
S
S
2,4-Toluene diisocyanate
0.1
25
V
S
S
-------
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 35
Table 3.1-1 Baseline Actual Emissions from Aerospace and Availability of Dose-Response Values
Number of
Facilities
Prioritized Inhalation Dose-Response Value
Identified by OAQPSb
Health
Benchmark
Values for
Acute
Noncancer?
PB-
HAP
1
IIAI"
Emissions
(tpy)
Reporting
HAP (144
facilities in
Unit Risk
Estimate
for
Reference
Concentration
for
data set)
Cancer?
Noncancer?
Chlorobenzene
0.1
7
~
~
Cobalt compounds
0.1
38
V
Lead compounds0
0.1
41
V
V
Chromium (iii) compounds
0.07
49
Diethylene glycol monomethyl
ether
0.07
6
V
Isophorone
0.06
5
S
Cadmium compounds
0.05
31
V
V
~
Barium chromate
0.05
36
V
V
Chromium (vi) compounds
0.05
60
V
V
2-Nitropropane
0.05
17
S
S
Cresols (mixed)
0.05
11
S
Hydroquinone
0.04
17
Ethyl acrylate
0.04
13
~
Antimony compounds
0.03
41
V
p-Xylene
0.03
10
S
~
Antimony trioxide
0.03
26
S
Epichlorohydrin
0.02
11
V
V
V
Zinc chromate
0.02
38
S
V
2,4-Dinitrophenol
0.02
1
4-Nitrophenol
0.02
2
N,N-Dimethylaniline
0.01
9
Selenium compounds
0.009
14
V
Hydrochloric acid
0.007
1
V
V
p-Cresol (4-Methyl phenol)
0.006
2
V
1,3-Butadiene
0.006
4
V
V
V
o-Cresol
0.005
5
V
Lead (II) oxidec
0.004
2
V
Ethylene oxide
0.004
7
V
V
V
Acrylonitrile
0.003
5
V
V
S
m-Cresol (3-Methyl phenol)
0.003
3
V
Calcium chromate
0.003
20
V
V
Carbitol acetate
0.003
6
V
V
m-Xylene
0.002
5
V
S
Chromic oxide
0.002
17
Ethylene glycol methyl ether
acetate
0.002
1
V
S
Carbon tetrachloride
0.002
9
V
S
S
-------
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 36
Table 3.1-1 Baseline Actual Emissions from Aerospace and Availability of Dose-Response Values
Number of
Facilities
Prioritized Inhalation Dose-Response Value
Identified by OAQPSb
Health
Benchmark
Values for
Acute
Noncancer?
PB-
HAP
1
IIAI"
Emissions
(tpy)
Reporting
HAP (144
facilities in
Unit Risk
Estimate
for
Reference
Concentration
for
data set)
Cancer?
Noncancer?
Sodium chromate
0.002
1
~
~
Arsenic compounds
0.002
27
V
V
V
Chromic acid (vi)
0.001
20
V
V
Chromium (vi) trioxide,
Chromic acid mist
0.001
13
S
V
p-Dichlorobenzene
0.001
2
V
V
Acetophenone
0.001
3
2-(Hexyloxy)ethanol
0.001
2
V
V
Acetaldehyde
0.001
9
V
S
S
1,4-Dioxane
0.001
6
S
S
S
Aniline
0.0008
5
S
S
S
Hydrofluoric acid
0.0008
11
S
S
Vinyl acetate
0.0007
1
S
S
Propylene oxide
0.0007
8
V
S
S
2,4,5-Trichlorophenol
0.0005
1
Catechol
0.0004
2
1,2-Dimethoxyethane
0.0004
1
V
~
Dimethyl phthalate
0.0003
1
Zinc potassium chromate
0.0002
10
V
V
Maleic anhydride
0.0002
1
V
V
Mercury (elemental)
0.0002
6
V
V
V
Cobalt naphtha
0.0001
2
V
p-Phenylenediamine
0.0001
1
Gaseous divalent mercury
0.0001
6
V
V
4,4'-Methylene bis(2-
chloroaniline)
0.0001
1
V
Particulate divalent mercury
0.00008
6
V
V
2,2,4-Trimethylpentane
0.00005
1
Sodium dichromate
0.00003
5
V
V
Antimony oxide
0.00003
1
V
Phthalic anhydride
0.00003
1
V
Antimony pentoxide
0.00003
4
V
Cobalt aluminate
0.00002
5
V
Manganese trioxide
0.00002
1
V
Cobalt oxide
0.00001
1
S
Chromium (vi) As lead
chromate
0.000006
6
V
V
Lead As lead chromate0
0.000006
6
S
V
Nickel oxide
0.000003
5
V
S
-------
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 37
Table 3.1-1 Baseline Actual Emissions from Aerospace and Availability of Dose-Response Values
Number of
Facilities
Prioritized Inhalation Dose-Response Value
Identified by OAQPSb
Health
Benchmark
Values for
Acute
Noncancer?
PB-
HAP
1
IIAI"
Emissions
(tpy)
Reporting
HAP (144
facilities in
Unit Risk
Estimate
for
Reference
Concentration
for
data set)
Cancer?
Noncancer?
Chloroform
0.000002
1
~
~
Zinc chromite
0.000002
2
Selenious acid
0.000001
1
V
Chromium zinc oxide
0.000001
1
Vinyl chloride
0.000001
1
~
V
V
Acrylamide
0.0000005
1
V
V
Hexanoic acid, 2-ethyl-,
Cobalt(2+) salt
0.0000003
2
V
Beryllium compounds
0.0000002
11
V
S
V
3,3 '-Dichlorobenzidine
0.0000001
1
S
Lead dioxide0
0.00000007
1
V
~
Hexachlorobenzene
0.00000005
1
V
S
Manganese tetroxide
0.00000002
1
S
a Notes for how HAP were speciated for risk assessment:
• For most metals, emissions reported as the elemental metal are combined with metal compound emissions (e.g.,
"cadmium" emissions modeled as "cadmium & compounds"). In the absence of speciation information, we assume
the reported mass is 100 percent metal.
• For emissions reported generically as "chromium" or "chromium & compound," emissions are speciated
"chromium (III) compounds" and "chromium (VI) compounds" according to the individual emitting process
speciation profile for this source category. Chromium speciation profiles can be found on the EPA's Technology
Transfer Network website for emissions inventories at http://www.epa.gov/ttn/chief/net/2005inventorv.html
• For emissions reported generically as "mercury" or "mercury & compounds," emissions are speciated for this
category as "mercury (elemental)" and "mercuric chloride." Mercury speciation profiles can be found on the
EPA's Technology Transfer Network website for emissions inventories at
http://www.epa.gov/ttn/chief/net/2005inventorv.html.
• For emissions of any chemicals or chemical groups classified as POM, emissions were grouped into POM
subgroups as found on EPA's Technology Transfer Network website for the 2005 National-Scale Air Toxics
Assessment at http://www.epa.gOv/ttn/atw/nata2005/methods.html#pom (Approach to Modeling POM).
• For emissions reported generically as "Glycol Ethers" or specific glycol ethers not found on EPA's Technology
Transfer Network for air toxics (see footnote b), emissions were treated as ethylene glycol methyl ether.
b Specific dose-response values for each chemical are identified on EPA's Technology Transfer Network website for air
toxics at
http://www2.epa.gov/fera/dose-response-assessment-assessing-health-risks-associated-exposure-hazardous-air-pollutants
c There is no reference concentration for lead. In considering noncancer hazards for lead in this assessment, we compared
rolling three-month average exposure estimates to the National Ambient Air Quality Standard (NAAQS) for lead (0.15
(ig/m3). These NAAQS for lead were recently reviewed with revisions adopted in October 2008
rhttp://www.epa.gov/air/lead/actions.html'l. The primary (health-based) standard is a maximum or not-to-be-exceeded,
rolling three-month average, measured as total suspended particles (TSP). The secondary (welfare-based) standard is
identical to the primary standard.
-------
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 38
3.2 Risk characterization
This section presents the results of the risk assessment for Aerospace Manufacturing and
Rework source category based on the modeling methods described in the previous sections.
Tables 3.2-1 presents the summary of the risks from for the Aerospace Manufacturing and
Rework Source Category. The maximum individual lifetime cancer risk (MIR) for both
baseline actual emissions and baseline allowable emissions is 10 in a million. When
considering whole facility baseline actual emissions the predicted MIR is 20 in a million.
The maximum chronic hazard index is predicted to be below 1 considering all emissions
scenarios. The acute inhalation impact screening assessment predicted the maximum hazard
quotients is 2. Both multipathway and ecological screening risks are below their respective
screening thresholds. More details on each component of the risks assessment are presented
in the sections and tables below.
Table 3.2-1. Risk Summary for the Aerospace Manufacturing
Inhalation Cancer Risk
Population Cancer Risk
Max Chronic Individual Non-
Cancer Risk
Max Acute
Non-Cancer
Risk
Multipathway
Analysis
MIR
(in 1
million)
Risk Driver
Cancer
Incidence
(cases per
year)
> 1 in 10
in 1
million
>1 inl
million
HI
(TO SHI)
Risk Driver
HQ
Risk
Driver
Max PB-HAP
Emissions /
Screening Level
Baseline Actual Emissions
Source
Category
10
Strontium
chromate
0.02
1,500
180,000
0.5
Cadmium
compounds
2
Ethylene
glycol
ethyl
ether
acetate
(REL)
Cadmium
Compounds: 1
(Tier 1)
Divalent
Mercury: 1 (Tier
i)
Whole
Facility
20
Arsenic,
Chromium
compounds
0.04
3,200
270,000
0.5
Hexamethylene-1,6-
diisocyanate
...
...
Baseline Allowable Emissions
Source
Category
10
Strontium
chromate
0.02
2,000
180,000
0.5
Cadmium
Compounds
...
...
...
3.2.1 Baseline Actual Emission Risks
All baseline risk results are developed using the best estimates of actual HAP emissions
summarized in the previous section. The basic chronic inhalation risk estimates presented
here are the maximum individual lifetime cancer risk, the maximum chronic hazard index,
and the cancer incidence. We also present results from our acute inhalation impact screening
assessment in the form of maximum hazard quotients, as well as the results of our preliminary
screen for potential non-inhalation risks from PB-HAP. Also presented are the HAP
"drivers," which are the HAP that collectively contribute 90 percent of the maximum cancer
risk or maximum hazard at the highest exposure location, Definitions of these and other
-------
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 39
relevant risk-related terms can be found on the 2005 National-Scale Air Toxics Assessment
Glossary Web page. A detailed summary of the facility-specific inhalation and multipathway
risk assessment results is available in Appendix 7 of this document {Detailed Risk Modeling
Results).
MACT-level Inhalation Assessment Results (Actual)
Tables 3.2-2 and 3.2-3 summarize the chronic and acute inhalation risk results for this source
category based upon baseline actual emissions. The results of the chronic inhalation cancer
risk assessment are that the maximum lifetime individual cancer risk posed by the 144
facilities could be 10 in a million considering both the maximum facility, and the entire
source sector. Emissions of strontium chromate, from coating operations are the major
contributors to this risk. The total estimated cancer incidence from this source category is
0.02 excess cancer cases per year, or one excess case in every 50 years. Approximately 1,500
people were estimated to have cancer risks above 10 in a million and approximately 180,000
people were estimated to have cancer risks above 1 in a million considering the 144 facilities
in this source category. The maximum chronic noncancer TOSHI value for the source
category could be up to 0.5 (kidney) driven by emissions of cadmium compounds from blast
depainting.
Worst-case acute hazard quotients (HQs) were calculated for every HAP shown in
Table 3.1-1 that has an acute benchmark, and the highest refined screening acute HQ value of
2 (based on the acute REL for ethylene glycol ethyl ether acetate) is shown in Table 3.2-2.
This value includes a refinement of determining the highest HQ value that is outside facility
boundaries. It's also important to note that the highest HQ assumes that the primary source of
the ethylene glycol ethyl ether acetate emissions from coating operations, was modeled with
an hourly emissions multiplier of 1.2 times the annual emissions rate. Further, this
exceedance was only predicted to occur in a remote, non-inhabited area just adjacent to the
facility fence line for 2 hours a year. No facilities are estimated to have an AEGL or an EPRG
greater than 1. Acute estimates for each plant and pollutant are provided in Appendix 7 of
this document, while refined estimates are provided in Appendix 8 of this document (Acute
Impacts Refined Analysis).
Table 3.2-2. MACT Level Actual Emissions Chronic Inhalation Risks for
Aerospace Manufacturing and Rework Source Category
Result
HAP "Drivers"
Facilities in Source Category
Number of Facilities Estimated to be in
Source Category
144
n/a
Number of Facilities Identified by ICR and
Modeled in Risk Assessment
144
n/a
Cancer Risks
-------
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 40
Table 3.2-2. MACT Level Actual Emissions Chronic Inhalation Risks for
Aerospace Manufacturing and Rework Source Category
Result
HAP "Drivers"
Maximum Individual Lifetime Cancer Risk
(in 1 million)
10
strontium chromate, tetracholoroethene,
chromium (VI) compounds, zinc
chromate, barium chromate
Number of Facilities with Maximum Individual Lifetime Cancer Risk:
Greater than or equal to 100 in 1 million
0
n/a
strontium chromate, tetracholoroethene,
Greater than or equal to 10 in 1 million
2
chromium (VI) compounds, zinc
chromate, barium chromate
strontium chromate, tetracholoroethene,
chromium (VI) compounds, zinc
chromate, barium chromate, cadmium
Greater than or equal to 1 in 1 million
27
compounds, ethyl benzene, sodium
chromate, 4,4'-methylenedianiline, nickel
compounds, chromium (VI) trioxide
(chromic acid mist), methylene chloride,
zinc potassium chromate,
trichloroethylene, calcium chromate,
arsenic compounds, naphthalene, chromic
acid (VI), bis(2-ethylhexyl)phthalate, 2,4-
toluene diisocyanate, formaldehyde, p-
dichlorobenzene
Chronic Noncancer Risks
Maximum Kidney Hazard Index
0.5
cadmium compounds
Number of Facilities with Maximum Respiratory Hazard Index:
Greater than 1
0
n/a
Acute Noncancer Screening Results
Maximum Acute Hazard Quotient31
2
0.03
0.02
0.01
0.005
Ethylene glycol ethyl ether acetate (REL)
Phenol (ERPG-l)
Phenol (AEGL-1)
Phenol (AEGL-2)
Phenol (ERPG-2)
Number of Facilities With Potential for
Acute Effects
1
Ethylene glycol ethyl ether acetate
Population Exposure
Number of People Living Within 50
Kilometers of Facilities Modeled
130,000,000
n/a
Number of People Exposed to Cancer Risk:
Greater than or equal to 100 in 1
0
n/a
million
Greater than or equal to 10 in 1
million
1,500
n/a
31 As shown in Tables 2.6.3 and 3.2-2 there are no AEGL or ERPG values for nickel or arsenic compounds.
Additionally, there are no short-term occupational values for these metals to compare to the REL values.
-------
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 41
Table 3.2-2. MACT Level Actual Emissions Chronic Inhalation Risks for
Aerospace Manufacturing and Rework Source Category
Result
HAP "Drivers"
Greater than or equal to 1 in 1
million
180,000
n/a
Number of People Exposed to Noncancer Respiratory Hazard Index:
Greater than 1
0
n/a
Estimated Cancer Incidence (excess cancer
cases per year)
0.02
n/a
Contribution of HAP to Cancer Incidence
strontium chromate
66%
n/a
chromium (VI) compounds
15%
n/a
4,4'-methylenedianiline
3%
n/a
nickel compounds
3%
n/a
cadmium compounds
3%
n/a
Contribution of Process to Cancer Incidence
Coatings (CT)
46%
n/a
Specialty Coatings (SC)
44%
n/a
Blast Depainting (BD)
8%
n/a
Solvent Cleaning (CL)
2%
n/a
Table 3.2-3. MACT Level Actual Emissions Acute Inhalation Risks for
Aerospace Manufacturing and Rework Source Category
Refined Results
Maximum Acute Hazard Quotients
Acute Dose-Response Values
HAP
Max. 1-
hr. Air
Cone.
(mg/m3)
Based on
REL
Based on
AEGL-1/
ERPG-1
Based on
AEGL-2/
ERPG-2
REL
(mg/m3)
AEGL-1
(1-hr)
(mg/m3)
ERPG-1
(mg/m3)
AEGL-2
(1-hr)
(mg/m3)
ERPG-2
(mg/m3)
Ethylene
glycol ethyl
ether acetate
0.3
2
0.14
Notes on Refined Process:
1) The initial screening was performed for all emitted HAP with available acute dose-response values. Only those pollutants whose
screening HQs were greater than 1 for at least one acute threshold value are shown in the table.
2) HAP with available acute dose-response values which are not in the table do not carry any potential for posing acute health risks,
based on an analysis of currently available emissions data.
Notes on Acute Dose-Response Values:
REL - California EPA reference exposure level for no adverse effects. Most, but not all, RELs are for 1 -hour exposures.
AEGL - Acute exposure guideline levels represent emergency exposure (1 -hour) limits for the general public.
-------
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 42
AEGL-1 is the exposure level above which it is predicted that the general population, including susceptible individuals, could
experience effects that are notable discomfort, but which are transient and reversible upon cessation of exposure.
AEGL-2 is the exposure level above which it is predicted that the general population, including susceptible individuals, could
experience irreversible or other serious, long-lasting adverse health effects of an impaired ability to escape.
EPRG - Emergency Removal Program guidelines represent emergency exposure (1-hour) limits for the general public.
ERPG-1 is the maximum level below which it is believed that nearly all individuals could be exposed for up to 1 hour without
experiencing other than mild, transient adverse health effects.
ERPG-2 is the maximum exposure below which it is believed that nearly all individuals could be exposed for up to 1 hour without
experiencing or developing irreversible or other serious health effects or symptoms which could impair an individual's ability to
take protective action.
MACT-level Multipathway Assessment Results (Actual)
To identify potential multipathway health risks from PB-HAP other than lead, we first
performed a screening analysis (Tier 1 and 2) that compared emissions of PB-HAP emitted
from the petroleum refining source sector (based on actual emissions) to screening emission
rates (see section 2.5). The PB-HAP emitted by facilities in the subject source category
include cadmium compounds (31 facilities) and mercury compounds (6 facilities).
Following the above approach, no facility emitted cadmium or mercury compounds above the
Tier 1 screening level. Table 3.2-4 summarizes the results of this screening (Tier 1)
comparison. Due to the theoretical construct of the screening model, these factors are not
directly translatable into estimates of risk or hazard quotients for these facilities.
Table 3.2-4. MACT Level Actual Emissions - Summary of Multipathway Screening for
Aerospace Manufacturing and Rework Source Category
Tier 1
Tier 2
PB-HAP
Nil m
Facilities
Emitting PB-
HAP
(144 in source
category)
Max
PB-HAP
Emissions
(TPY)
Max PB-HAP
Emissions /
Screening
Level
Nil m
Facilities
Exceeding
PB-HAP
Screening
Level
Max
PB-HAP
Emissions /
Screening
Level
Nu m
Facilities
Exceeding
PB-HAP
Screening
Level
Cadmium Compounds
31
0.015
1
0
na
na
Divalent Mercury
6
0.00019
1
0
na
na
In evaluating the potential for multipathway effects from emissions of lead, modeled
maximum annual lead concentrations were compared to the NAAQS for lead (0.15 |ig/m3).
Lead emissions were reported from 103 facilities. Results of this analysis estimate that with
the exception of one facility, the NAAQS for lead would not be exceeded by any facility. At
the one facility, the lead NAAQS is exceeded 1.5 times at an on-site location. Off-site at this
facility, the concentrations are below the NAAQS.
-------
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 43
MACT-level Environmental Assessment Results (Actual)
We conducted a screening-level evaluation of the potential adverse environmental risks
associated with emissions of the following environmental HAP from the Aerospace
Manufacturing and Rework Facilities source category: lead, mercury, cadmium, hydrogen
chloride and hydrogen fluoride. The results of the environmental screening analysis are
summarized in Table 3.2-5.
In the Tier 1 screening analysis for PB-HAP (other than lead, which was evaluated
differently) the individual modeled Tier 1 concentrations for mercury and cadmium did not
exceed any ecological benchmark for any facility in the source category. For lead, we did not
estimate any exceedances of the secondary lead NAAQS.
For hydrogen fluoride and hydrogen chloride, the average modeled concentration around each
facility (i.e., the average concentration of all off-site data points in the modeling domain) did
not exceed the ecological benchmarks. In addition, each individual modeled concentration of
hydrogen chloride and hydrogen fluoride (i.e., each off-site data point in the modeling
domain) was below the ecological benchmarks for all facilities.
Table 3.2-5 MACT Level Actual Emissions - Summary of Environmental Risk Screen Results
for the Aerospace Manufacturing and Rework Facilities Source Category
Environmental
HAP
Number of Facilities In Category
Exceeding
Percent of Modeled
Area in Category
Exceeding2
Tier 1 Screen
Tier 2 Screen1
NOAEL
LOAEL
NOAEL
LOAEL
NOAEL
LOAEL
PB-
HAP
Pb
None
None
NA
NA
0%
0%
Hg
NA
None
NA
NA
0%
0%
Cd
None
None
NA
NA
0%
0%
Acid
Gases
HF3
NA
None
-
-
NA
0%
HCL4
NA
None
-
-
NA
0%
NA - Not Applicable.
1- Tier 2 screen is performed for PB-HAP when there are exceedances of the Tier 1 screen. The acid gas screen
is a one tier screen.
2 - A value of 0% indicates that none of the modeled data points exceeded the benchmark. For PB-HAP the
percent area is based on the Tier 2 results, if a Tier 2 analysis is performed. Otherwise, the percent area is based
on the Tier 1 results.
3 - For HF, we evaluated two benchmarks, one from Canada and the other from the State of Washington.
Although, they are both considered to be LOELs - the level between a NOAEL and a LOAEL, we have listed the
results under the LOAEL column.
4 - For HCL, we evaluated one benchmark at the LOAEL level.
Facility-wide Inhalation Assessment Results (Actual)
-------
Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 44
The facility-wide chronic MIR and TOSHI, available in Appendix 7, are based on emissions
from all sources at the identified facilities (both MACT and non MACT sources). The results
of the facility-wide assessment for cancer risks are summarized in Table 3.2-6. The results
indicate that 44 facilities with aerospace manufacturing and rework processes have a facility-
wide cancer MIR greater than or equal to 1 in a million. The maximum facility-wide cancer
MIR is 20 in a million, mainly driven by arsenic, and chromium (VI) compounds, from
internal combustion engines. The maximum facility-wide TOSHI for the source category is
estimated to be less than 1, mainly driven by emissions of hexamethylene-l,6-diisocyanate
from non MACT specialty coatings operations.
Table 3.2-6 Source Category Contribution to Facility-Wide
Chronic Cancer Risks (Actual Emissions)
Petroleum Refining Source
Number of Facilities Binned by Facility-Wide
Sector
MIR (in 1 million)
Source Sector MIR Contribution
<1
1< MIR<10
10< MIR<100
> 100
Total
to Facility-Wide MIR
> 90%
49
22
1
0
72
50-90%
16
1
2
0
19
10-50%
14
2
1
0
17
< 10%
21
15
0
0
36
Total
100
40
4
0
144
3.2.2 Baseline Allowable Emission Risks
Baseline risk results were also developed using the best estimates of allowable HAP
emissions under the current MACT standards. The basic chronic inhalation risk estimates
presented here are the maximum individual lifetime cancer risk, the maximum chronic hazard
index, and the cancer incidence. Acute, multipathway or ecological risks are not determined
for allowable emissions.
MACT-level Inhalation Assessment Results (Allowable)
Analysis of potential differences between actual emissions levels and the maximum emissions
allowable under the MACT standards (i.e., MACT-allowable emissions) were also calculated
for the source category. Risk results from the inhalation risk assessment using the MACT -
allowable emissions indicate that the maximum lifetime individual cancer risk could as high
as 10 in a million, and that the maximum chronic noncancer Hazard Index value is about 0.5.
The total estimated cancer incidence from this source category considering allowable
emissions is expected to be about 0.02 excess cancer cases per year or one excess case in
every 50 years. Based on allowable emission rates approximately 2,000 people were
estimated to have cancer risks above 10 in a million and approximately 180,000 people were
estimated to have cancer risks above 1.
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Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 45
4 General discussion of uncertainties and how they have been
addressed
4.1 Exposure modeling uncertainties
Although every effort has been made to identify all the relevant facilities and emission points,
as well as to develop accurate estimates of the annual emission rates for all relevant HAP, the
uncertainties in our emission inventory likely dominate the uncertainties in the exposure
analysis. The chronic ambient modeling uncertainties are considered relatively small in
comparison, since we are using EPA's refined local dispersion model with site-specific
parameters and reasonably representative meteorology. If anything, the population exposure
estimates are biased high by not accounting for short- or long-term population mobility, and
by neglecting processes like deposition, plume depletion, and atmospheric degradation.
Additionally, estimates of the MIR contain uncertainty, because they are derived at census
block centroid locations rather than actual residences. This uncertainty is known to create
potential underestimates and overestimates of the actual MIR values for individual facilities,
but, overall, it is not thought to have a significant impact on the estimated MIR for a source
category. Finally, we did not factor in the possibility of a source closure occurring during the
70-year chronic exposure period, leading to a potential upward bias in both the MIR and
population risk estimates; nor did we factor in the possibility of population growth during the
70-year chronic exposure period, leading to a potential downward bias in both the MIR and
population risk estimates.
A sensitivity analysis performed for the 1999 NATA found that the selection of the
meteorology dataset location could result in a range of chronic ambient concentrations which
varied from as much as 17 percent below the predicted value to as much as 84 percent higher
than the predicted value. This variability translates directly to the predicted exposures and
risks in our assessment, indicating that the actual risks could vary from 17 percent lower to 84
percent higher than the predicted values.
We have purposely biased the acute screening results high, considering that they depend upon
the joint occurrence of independent factors, such as hourly emissions rates, meteorology and
human activity patterns. Furthermore, in cases where multiple acute threshold values are
considered scientifically acceptable we have chosen the most conservative of these threshold
values, erring on the side of overestimating potential health risks from acute exposures. In the
cases where these results indicated the potential for exceeding short-term health thresholds,
we refined our assessment by developing a better understanding of the geography of the
facility relative to potential exposure locations.
4.2 Uncertainties in the dose-response relationships
In the sections that follow, separate discussions are provided on uncertainty associated with
cancer potency factors and for noncancer reference values. Cancer potency values are derived
for chronic (lifetime) exposures. Noncancer reference values are generally derived for
chronic exposures (up to a lifetime), but may also be derived for acute (D<24 hours), short-
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term (>24 hours up to 30 days), and subchronic (>30 days up to 10 percent of lifetime)
exposure durations, all of which are derived based on an assumption of continuous exposure
throughout the duration specified. For the purposes of assessing all potential health risks
associated with the emissions included in an assessment, we rely on both chronic (cancer and
noncancer) and acute (noncancer) benchmarks, which are described in more detail below.
Although every effort is made to identify peer-reviewed dose-response values for all HAP
emitted by the source category included in an assessment, some HAP have no peer-reviewed
cancer potency values or reference values for chronic non-cancer or acute effects (inhalation
or ingestion). Since exposures to these pollutants cannot be included in a quantitative risk
estimate, an understatement of risk for these pollutants at environmental exposure levels is
possible.
Additionally, chronic dose-response values for certain compounds included in the assessment
may be under EPA IRIS review and revised assessments may determine that these pollutants
are more or less potent than currently thought. We will re-evaluate risks if, as a result of these
reviews, a dose-response metric changes enough to indicate that the risk assessment may
significantly mischaracterize human health risk
Cancer assessment
The discussion of dose-response uncertainties in the estimation of cancer risk below focuses
on the uncertainties associated with the specific approach currently used by the EPA to
develop cancer potency factors. In general, these same uncertainties attend the development
of cancer potency factors by CalEPA, the source of peer-reviewed cancer potency factors
used where EPA-developed values are not yet available. To place this discussion in context,
we provide a quote from the EPA's Guidelines for Carcinogen Risk Assessment [29] (herein
referred to as Cancer Guidelines). "The primary goal of EPA actions is protection of human
health; accordingly, as an Agency policy, risk assessment procedures, including default
options that are used in the absence of scientific data to the contrary, should be health
protective." The approach adopted in this document is consistent with this approach as
described in the Cancer Guidelines.
For cancer endpoints EPA usually derives an oral slope factor for ingestion and a unit risk
value for inhalation exposures. These values allow estimation of a lifetime probability of
developing cancer given long-term exposures to the pollutant. Depending on the pollutant
being evaluated, EPA relies on both animal bioassay and epidemiological studies to
characterize cancer risk. As a science policy approach, consistent with the Cancer
Guidelines, EPA uses animal cancer bioassays as indicators of potential human health risk
when other human cancer risk data are unavailable.
Extrapolation of study data to estimate potential risks to human populations is based upon
EPA's assessment of the scientific database for a pollutant using EPA's guidance documents
and other peer-reviewed methodologies. The EPA Cancer Guidelines describes the Agency's
recommendations for methodologies for cancer risk assessment. EPA believes that cancer
risk estimates developed following the procedures described in the Cancer Guidelines and
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outlined below generally provide an upper bound estimate of risk. That is, EPA's upper
bound estimates represent a "plausible upper limit to the true value of a quantity" (although
this is usually not a true statistical confidence limit).32 In some circumstances, the true risk
could be as low as zero; however, in other circumstances the risk could also be greater.33
When developing an upper bound estimate of risk and to provide risk values that do not
underestimate risk, EPA generally relies on conservative default approaches.34 EPA also
uses the upper bound (rather than lower bound or central) estimates in its assessments,
although it is noted that this approach can have limitations for some uses (e.g. priority setting,
expected benefits analysis).
Such health risk assessments have associated uncertainties, some which may be considered
quantitatively, and others which generally are expressed qualitatively. Uncertainties may vary
substantially among cancer risk assessments associated with exposures to different pollutants,
since the assessments employ different databases with different strengths and limitations and
the procedures employed may differ in how well they represent actual biological processes for
the assessed substance. EPA's Risk Characterization Handbook also recommends that risk
characterizations present estimates demonstrating the impact on the assessment of alternative
choices, data, models and assumptions [30], Some of the major sources of uncertainty and
variability in deriving cancer risk values are described more fully below.
(1) The qualitative similarities or differences between tumor responses observed in
experimental animal bioassays and those which would occur in humans are a source of
uncertainty in cancer risk assessment. In general, EPA does not assume that tumor sites
observed in an experimental animal bioassay are necessarily predictive of the sites at which
tumors would occur in humans.35 However, unless scientific support is available to show
otherwise, EPA assumes that tumors in animals are relevant in humans, regardless of target
organ concordance. For a specific pollutant, qualitative differences in species responses can
lead to either under-estimation or over-estimation of human cancer risks.
(2) Uncertainties regarding the most appropriate dose metric for an assessment can also lead
to differences in risk predictions. For example, the measure of dose is commonly expressed
32 IRIS glossary (www.epa.gov/NCEA/iris/help_gloss.htm).
33 The exception to this is the URE for benzene, which is considered to cover a range of values, each end of
which is considered to be equally plausible, and which is based on maximum likelihood estimates.
34 According to the NRC report Science and Judgment in Risk Assessment (NRC, 1994) "[Default] options are
generic approaches, based on general scientific knowledge and policy judgment, that are applied to various
elements of the risk-assessment process when the correct scientific model is unknown or uncertain." The 1983
NRC report Risk Assessment in the Federal Government: Managing the Process defined default option as "the
option chosen on the basis of risk assessment policy that appears to be the best choice in the absence of data to
the contrary" (NRC, 1983a, p. 63). Therefore, default options are not rules that bind the agency; rather, the
agency may depart from them in evaluating the risks posed by a specific substance when it believes this to be
appropriate. In keeping with EPA's goal of protecting public health and the environment, default assumptions
are used to ensure that risk to chemicals is not underestimated (although defaults are not intended to overtly
overestimate risk). See EPA 2004 An Examination of EPA Risk Assessment Principles and Practices,
EPA/100/B-04/001 available at: http://www.epa.gov/osa/pdfs/ratf-final.pdf.
35 Per the EPA Cancer Guidelines: "The default option is that positive effects in animal cancer studies indicate
that the agent under study can have carcinogenic potential in humans." and "Target organ concordance is not a
prerequisite for evaluating the implications of animal study results for humans."
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in units of mg/kg/d ingested or the inhaled concentration of the pollutant. However, data may
support development of a pharmacokinetic model for the absorption, distribution, metabolism
and excretion of an agent, which may result in improved dose metrics (e.g., average blood
concentration of the pollutant or the quantity of agent metabolized in the body). Quantitative
ncertainties result when the appropriate choice of a dose metric is uncertain or when dose
metric estimates are themselves uncertain (e.g., as can occur when alternative
pharmacokinetic models are available for a compound). Uncertainty in dose estimates may
lead to either over or underestimation of risk.
(3) For the quantitative extrapolation of cancer risk estimates from experimental animals to
humans, EPA uses scaling methodologies (relating expected response to differences in
physical size of the species), which introduce another source of uncertainty. These
methodologies are based on both biological data on differences in rates of process according
to species size and empirical comparisons of toxicity between experimental animals and
humans. For a particular pollutant, the quantitative difference in cancer potency between
experimental animals and humans may be either greater than or less than that estimated by
baseline scientific scaling predictions due to uncertainties associated with limitations in the
test data and the correctness of scaled estimates.
(4) EPA cancer risk estimates, whether based on epidemiological or experimental animal data,
are generally developed using a benchmark dose (BMD) analysis to estimate a dose at which
there is a specified excess risk of cancer, which is used as the point of departure (or POD) for
the remainder of the calculation. Statistical uncertainty in developing a POD using a
benchmark dose (BMD) approach is generally addressed though use of the 95 percent lower
confidence limit on the dose at which the specified excess risk occurs (the BMDL),
decreasing the likelihood of understating risk. EPA has generally utilized the multistage
model for estimation of the BMDL using cancer bioassay data (see further discussion below).
(5) Extrapolation from high to low doses is an important, and potentially large, source of
uncertainty in cancer risk assessment. EPA uses different approaches to low dose risk
assessment (i.e., developing estimates of risk for exposures to environmental doses of an
agent from observations in experimental or epidemiological studies at higher dose) depending
on the available data and understanding of a pollutant's mode of action (i.e., the manner in
which a pollutant causes cancer). EPA's Cancer Guidelines express a preference for the use
of reliable, compound-specific, biologically-based risk models when feasible; however, such
models are rarely available. The mode of action for a pollutant (i.e., the manner in which a
pollutant causes cancer) is a key consideration in determining how risks should be estimated
for low-dose exposure. A reference value is calculated when the available mode of action
data show the response to be nonlinear (e.g., as in a threshold response). A linear low-dose
(straight line from POD) approach is used when available mode of action data support a linear
(e.g., nonthreshold response) or as the most common default approach when a compound's
mode of action is unknown. Linear extrapolation can be supported by both pollutant-specific
data and broader scientific considerations. For example, EPA's Cancer Guidelines generally
consider a linear dose-response to be appropriate for pollutants that interact with DNA and
induce mutations. Pollutants whose effects are additive to background biological processes in
cancer development can also be predicted to have low-dose linear responses, although the
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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 has encouraged the exploration of sigmoidal type functions (e.g., log-probit
models) in representing dose response relationships due to the variability in response within
human populations. Another National Research Council (NRC) report [37] suggests that
models based on distributions of individual thresholds are likely to lead to sigmoidal-shaped
dose-response functions for a population. This report notes sources of variability in the
human population: "One might expect these individual tolerances to vary extensively in
humans depending on genetics, coincident exposures, nutritional status, and various other
susceptibility factors..." Thus, if a distribution of thresholds approach is considered for a
carcinogen risk assessment, application would depend on ability of modeling to reflect the
degree of variability in response in human populations (as opposed to responses in bioassays
with genetically more uniform rodents). Note also that low dose linearity in risk can arise for
reasons separate from population variability: due to the nature of a mode of action and
additivity of a chemical's effect on top of background chemical exposures and biological
processes.
As noted above, EPA's current approach to cancer risk assessment typically utilizes a straight
line approach from the BMDL. This is equivalent to using an upper confidence limit on the
slope of the straight line extrapolation. The impact of the choice of the BMDL on bottom line
risk estimates can be quantified by comparing risk estimates using the BMDL value to central
estimate BMD values, although these differences are generally not a large contributor to
uncertainty in risk assessment (Subramaniam et. al., 2006) [32], It is important to note that
earlier EPA assessments, including the majority of those for which risk values exist today,
were generally developed using the multistage model to extrapolate down to environmental
dose levels and did not involve the use of a POD. Subramaniam et. al. (2006) also provide
comparisons indicating that slopes based on straight line extrapolation from a POD do not
show large differences from those based on the upper confidence limit of the multistage
model.
(6) Cancer risk estimates do not generally make specific adjustments to reflect the variability
in response within the human population — resulting in another source of uncertainty in
assessments. In the diverse human population, some individuals are likely to be more
sensitive to the action of a carcinogen than the typical individual, although compound-specific
data to evaluate this variability are generally not available. There may also be important life
stage differences in the quantitative potency of carcinogens and, with the exception of the
recommendations in EPA's Supplemental Cancer Guidance for carcinogens with a mutagenic
mode of action, risk assessments do not generally quantitatively address life stage differences.
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However, one approach used commonly in EPA assessments that may help address variability
in response is to extrapolate human response from results observed in the most sensitive
species and sex tested, resulting typically in the highest URE which can be supported by
reliable data, thus supporting estimates that are designed not to underestimate risk in the face
of uncertainty and variability.
Chronic noncancer assessment
Chronic noncancer reference values represent chronic exposure levels that are intended to be
health-protective. That is, EPA and other organizations which develop noncancer reference
values (e.g., the Agency for Toxic Substances and Disease Registry - ATSDR) utilize an
approach that is intended not to underestimate risk in the face of uncertainty and variability.
When there are gaps in the available information, uncertainty factors (UFs) are applied to
derive reference values that are intended to be protective against appreciable risk of
deleterious effects. Uncertainty factors are commonly default values36 e.g., factors of 10 or 3,
used in the absence of compound-specific data; where data are available, uncertainty factors
may also be developed using compound-specific information. When data are limited, more
assumptions are needed and more default factors are used. Thus there may be a greater
tendency to overestimate risk—in the sense that further study might support development of
reference values that are higher (i.e., less potent) because fewer default assumptions are
needed. However, for some pollutants it is possible that risks may be underestimated.
For non-cancer endpoints related to chronic exposures, EPA derives a Reference Dose (RfD)
for exposures via ingestion, and a Reference Concentration (RfC) for inhalation exposures.
These values provide an estimate (with uncertainty spanning perhaps an order of magnitude)
of daily oral exposure (RfD) or of a continuous inhalation exposure (RfC) to the human
population (including sensitive subgroups) that is likely to be without an appreciable risk of
deleterious effects during a lifetime.37 To derive values that are intended to be "without
appreciable risk," EPA's methodology relies upon an uncertainty factor (UF) approach [33],
[.34] which includes consideration of both uncertainty and variability.
EPA begins by evaluating all of the available peer-reviewed literature to determine non-
cancer endpoints of concern, evaluating the quality, strengths and limitations of the available
studies. EPA typically chooses the relevant endpoint that occurs at the lowest dose, often
36 According to the NRC report Science and Judgment in Risk Assessment (NRC, 1994) "[Default] options are
generic approaches, based on general scientific knowledge and policy judgment, that are applied to various
elements of the risk-assessment process when the correct scientific model is unknown or uncertain." The 1983
NRC report Risk Assessment in the Federal Government: Managing the Process defined default option as "the
option chosen on the basis of risk assessment policy that appears to be the best choice in the absence of data to
the contrary" (NRC, 1983a, p. 63). Therefore, default options are not rules that bind the agency; rather, the
agency may depart from them in evaluating the risks posed by a specific substance when it believes this to be
appropriate. In keeping with EPA's goal of protecting public health and the environment, default assumptions
are used to ensure that risk to chemicals is not underestimated (although defaults are not intended to overtly
overestimate risk). See EPA 2004 An examination of EPA Risk Assessment Principles and Practices,
EPA/100/B-04/001 available at: http://www.epa.gov/osa/pdfs/ratf-final.pdf.
37 See IRIS glossary
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using statistical modeling of the available data, and then determines the appropriate point of
departure (POD) for derivation of the reference value. A POD is determined by (in order of
preference): (1) a statistical estimation using the benchmark dose (BMD) approach; (2) use of
the dose or concentration at which the toxic response was not significantly elevated (no
observed adverse effect level— NOAEL); or (3) use of the lowest observed adverse effect
level (LOAEL).
A series of downward adjustments using default UFs is then applied to the POD to estimate
the reference value [35], While collectively termed "UFs", these factors account for a number
of different quantitative considerations when utilizing observed animal (usually rodent) or
human toxicity data in a risk assessment. The UFs are intended to account for: (1) variation
in susceptibility among the members of the human population (i.e., inter-individual
variability); (2) uncertainty in extrapolating from experimental animal data to humans (i.e.,
interspecies differences); (3) uncertainty in extrapolating from data obtained in a study with
less-than-lifetime exposure (i.e., extrapolating from subchronic to chronic exposure);
(4) uncertainty in extrapolating from a LOAEL in the absence of a NOAEL; and
(5) uncertainty when the database is incomplete or there are problems with applicability of
available studies. When scientifically sound, peer-reviewed assessment-specific data are not
available, default adjustment values are selected for the individual UFs. For each type of
uncertainty (when relevant to the assessment), EPA typically applies an UF value of 10 or 3
with the cumulative UF value leading to a downward adjustment of 10-3000 fold from the
selected POD. An UF of 3 is used when the data do not support the use of a 10-fold factor. If
an extrapolation step or adjustment is not relevant to an assessment (e.g., if applying human
toxicity data and an interspecies extrapolation is not required) the associated UF is not used.
The major adjustment steps are described more fully below.
1) Heterogeneity among humans is a key source of variability as well as uncertainty.
Uncertainty related to human variation is considered in extrapolating doses from a subset or
smaller-sized population, often of one sex or of a narrow range of life stages (typical of
occupational epidemiologic studies), to a larger, more diverse population. In the absence of
pollutant-specific data on human variation, a 10-fold UF is used to account for uncertainty
associated with human variation. Human variation may be larger or smaller; however, data to
examine the potential magnitude of human variability are often unavailable. In some
situations, a smaller UF of 3 may be applied to reflect a known lack of significant variability
among humans.
2) Extrapolation from results of studies in experimental animals to humans is a
necessary step for the majority of chemical risk assessments. When interpreting animal data,
the concentration at the POD (e.g. NOAEL, BMDL) in an animal model (e.g. rodents) is
extrapolated to estimate the human response. While there is long-standing scientific support
for the use of animal studies as indicators of potential toxicity to humans, there are
uncertainties in such extrapolations. In the absence of data to the contrary, the typical
approach is to use the most relevant endpoint from the most sensitive species and the most
sensitive sex in assessing risks to the average human. Typically, compound specific data to
evaluate relative sensitivity in humans versus rodents are lacking, thus leading to uncertainty
in this extrapolation. Size-related differences (allometric relationships) indicate that typically
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humans are more sensitive than rodents when compared on a mg/kg/day basis. The default
choice of 10 for the interspecies UF is consistent with these differences. For a specific
chemical, differences in species responses may be greater or less than this value.
Pharmacokinetic models are useful to examine species differences in pharmacokinetic
processing and associated uncertainties; however, such dosimetric adjustments are not always
possible. Information may not be available to quantitatively assess toxicokinetic or
toxicodynamic differences between animals and humans, and in many cases a 10-fold UF
(with separate factors of 3 for toxicokinetic and toxicodynamic components) is used to
account for expected species differences and associated uncertainty in extrapolating from
laboratory animals to humans in the derivation of a reference value. If information on one or
the other of these components is available and accounted for in the cross-species
extrapolation, a UF of 3 may be used for the remaining component.
3) In the case of reference values for chronic exposures where only data from shorter
durations are available (e.g., 90-day subchronic studies in rodents) or when such data are
judged more appropriate for development of an RfC, an additional UF of 3 or 10-fold is
typically applied unless the available scientific information supports use of a different value.
4) Toxicity data are typically limited as to the dose or exposure levels that have been
tested in individual studies; in an animal study, for example, treatment groups may differ in
exposure by up to an order of magnitude. The preferred approach to arrive at a POD is to use
BMD analysis; however, this approach requires adequate quantitative results for a meaningful
analysis, which is not always possible. Use of a NOAEL is the next preferred approach after
BMD analysis in determining a POD for deriving a health effect reference value. However,
many studies lack a dose or exposure level at which an adverse effect is not observed (i.e., a
NOAEL is not identified). When using data limited to a LOAEL, a UF of 10 or 3 -fold is
often applied.
5) The database UF is intended to account for the potential for deriving an
underprotective RfD/RfC due to a data gap preventing complete characterization of the
chemical's toxicity. In the absence of studies for a known or suspected endpoint of concern, a
UF of 10 or 3-fold is typically applied.
There is no RfD or other comparable chronic health benchmark value for lead compounds.
Thus, to address multipathway human health and environmental risks associated with
emissions of lead from this facility, ambient lead concentrations were compared to the
NAAQS for lead. In developing the NAAQS for lead, EPA considered human health
evidence reporting adverse health effects associated with lead exposure, as well as an EPA
conducted multipathway risk assessment that applied models to estimate human exposures to
air-related lead and the associated risk (73FR at 66979). EPA also explicitly considered the
uncertainties associated with both the human health evidence and the exposure and risk
analyses when developing the NAAQS for lead. For example, EPA considered uncertainties
in the relationship between ambient air lead and blood lead levels (73FR at 66974), as well as
uncertainties between blood lead levels and loss of IQ points in children (73FR at 66981). In
considering the evidence and risk analyses and their associated uncertainties, the EPA
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Administrator noted his view that there is no evidence- or risk-based bright line that indicates
a single appropriate level. Instead, he noted, there is a collection of scientific evidence and
judgments and other information, including information about the uncertainties inherent in
many relevant factors, which needs to be considered together in making this public health
policy judgment and in selecting a standard level from a range of reasonable values (73FR at
66998). In so doing, the Administrator decided that, a level for the primary lead standard of
0.15 (J,g/m3, in combination with the specified choice of indicator, averaging time, and form,
is requisite to protect public health, including the health of sensitive groups, with an adequate
margin of safety (73FR at 67006). A thorough discussion of the health evidence, risk and
exposure analyses, and their associated uncertainties can be found in EPA's final rule revising
the lead NAAQS (73 FR 66970-66981, November 12, 2008).
We also note the uncertainties associated with the health-based (i.e., primary) NAAQS are
likely less than the uncertainties associated with dose-response values developed for many of
the other HAP, particularly those HAP for which no human health data exist. In 1988, EPA's
IRIS program reviewed the health effects data regarding lead and its inorganic compounds
and determined that it would be inappropriate to develop an RfD for these compounds,
saying, "A great deal of information on the health effects of lead has been obtained through
decades of medical observation and scientific research. This information has been assessed in
the development of air and water quality criteria by the Agency's Office of Health and
Environmental Assessment (OHEA) in support of regulatory decision-making by the Office
of Air Quality Planning and Standards (OAQPS) and by the Office of Drinking Water
(ODW). By comparison to most other environmental toxicants, the degree of uncertainty
about the health effects of lead is quite low. It appears that some of these effects, particularly
changes in the levels of certain blood enzymes and in aspects of children's neurobehavioral
development, may occur at blood lead levels so low as to be essentially without a threshold.
The Agency's RfD Work Group discussed inorganic lead (and lead compounds) at two
meetings (07/08/1985 and 07/22/1985) and considered it inappropriate to develop an RfD for
inorganic lead." EPA's IRIS assessment for Lead and compounds (inorganic) (CASRN 7439-
92-1), http://www.epa.gov/iris/subst/0277.htm.
We note further that because of the multi-pathway, multi-media impacts of lead, the risk
assessment supporting the NAAQS considered direct inhalation exposures and indirect air-
related multi-pathway exposures from industrial sources like primary and secondary lead
smelting operations. It also considered background lead exposures from other sources (like
contaminated drinking water and exposure to lead-based paints). In revising the NAAQS for
lead, we note that the Administrator placed more weight on the evidence-based framework
and less weight on the results from the risk assessment, although he did find the risk estimates
to be roughly consistent with and generally supportive of the evidence-based framework
applied in the NAAQS determination (73FR at 67004). Thus, when revising the NAAQS for
lead to protect public health with an adequate margin of safety, EPA considered both the
evidence-based framework and the risk assessment, albeit to different extents.
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Acute noncancer assessment
Many of the UFs used to account for variability and uncertainty in the development of acute
reference values are quite similar to those developed for chronic durations, but more often
using individual UF values that may be less than 10. UFs are applied based on chemical-
specific or health effect-specific information (e.g., simple irritation effects do not vary
appreciably between human individuals, hence a value of 3 is typically used), or based on the
purpose for the reference value (see the following paragraph). The UFs applied in acute
reference value derivation include: 1) heterogeneity among humans; 2) uncertainty in
extrapolating from animals to humans; 3) uncertainty in LOAEL to NOAEL adjustments; and
4) uncertainty in accounting for an incomplete database on toxic effects of potential concern.
Additional adjustments are often applied to account for uncertainty in extrapolation from
observations at one exposure duration (e.g., 4 hours) to arrive at a POD for derivation of an
acute reference value at another exposure duration (e.g., 1 hour).
Not all acute reference values are developed for the same purpose and care must be taken
when interpreting the results of an acute assessment of human health effects relative to the
reference value or values being exceeded. Where relevant to the estimated exposures, the
lack of threshold values at different levels of severity should be factored into the risk
characterization as potential uncertainties.
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5 References
7. US EPA. National Emission Standards for Hazardous Air Pollutants.
http ://www. epa. gov/ttn/ atw/mactfnlalph. html.
2. US EPA, 2009. Risk and Technology Review (RTR) Risk Assessment Methodologies: For
Review by the EPA's Science Advisory Board with Case Studies - MACTI Petroleum
Refining Sources and Portland Cement Manufacturing. EPA-452/R-09-006.
http J/www. epa. gov/ttn/ atw/rrisk/rtrpg. html.
3. US EPA, 2010. SAB's Response to EPA's RTR Risk Assessment Methodologies.
http://vosemite.epa. gov/sab/sabproduct.nsf/4AB3966E263D943A8525771F00668381/$File/E
PA- SAB-10-007-unsigned, pdf
4. US EPA, 2010. Memorandum from Dave Guinnup to Docket EPA-HQ-OAR-2010-0600,
entitled, "EPA's Actions in Response to Key Recommendations of the SAB Review of RTR
Risk Assessment Methodologies".
5. US EPA, 2005. Revision to the Guideline on Air Quality Models: Adoption of a Preferred
General Purpose (Flat and Complex Terrain) Dispersion Model and Other Revisions; Final
Rule. 40CFRPart51. http://www.epa.gov/EPA-AIR/2005/November/Dav-09/a21627.htm
6. US EPA, 2004. Users' guide for the AMS/EPA regulatory model - AERMOD. EPA-454/B-
03-001. http://www.epa.gov/ttn/scram/models/aermod/aermod userguide.zip
7. US EPA, 2004. Air Toxics Risk Assessment Reference Library, Volume 1. EPA-453-K-04-
001 A. http://www.epa.gov/ttn/fera/risk_atra_voll .html.
8. EPA's Total Risk Integrated Methodology (General Information)
http:// epa. gov/ttn/ fera/trimgen, html
9. US EPA, 2014. Table 1. Prioritized Chronic Dose-Response Values (5/9/14). Office of
Air Quality Planning and Standards, http://www2.epa.gov/fera/dose-response-assessment-
assessing-health-risks-associated-exposure-hazardous-air-pollutants
10. US EPA, 2005. 1999 National Air Toxics Risk Assessment.
http://www.epa. gov/ttn/atw/natal 999
77. US EPA, 2006. Integrated Risk Information System, http://www.epa.gov/iris/index.html.
12. US Agency for Toxic Substances and Disease Registry. 2006. Minimum Risk Levels (MRLs)
for Hazardous Substances, http J/www.atsdr.cdc.gov/mrls/index.html.
13. US EPA, 1994. US Environmental Protection Agency. Methods for Derivation of
Inhalation Reference Concentrations and Application of Inhalation Dosimetry.
EPA/600/8-90/066F. Office of Research and Development. Washington, DC: U.S.EPA.
14. NRC, 1994. National Research Council. Science and Judgment in Risk Assessment.
Washington, DC: National Academy Press.
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Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 56
15. CA Office of Environmental Health Hazard Assessment, 2014. Chronic Reference
Exposure Levels Adopted by OEHHA as of January 2014.
http://oehha.ca.gov/air/allrels.html
16. CA Office of Environmental Health Hazard Assessment, 2005. Technical Support Document
for Describing Available Cancer Potency Factors, May 2005.
http://www.oehha.ca.gov/air/hot spots/pdf/mav2005Hotspots.pdf
17. US EPA, 2005. Table 2. Acute Dose-Response Values for Screening Risk Assessments
(6/02/2005). Office of Air Quality Planning and Standards, http://www2.epa.gov/fera/dose-
response-assessment-assessing-health-risks-associated-exposure-hazardous-air-pollutants
18. U.S. EPA, 2009. Graphical Arrays of Chemical-Specific Health Effect Reference Values for
Inhalation Exposures [Final Report], EPA/600/R-09/061, 2009.
http://cfpub.epa. gov/ncea/cfm/recordisplav. cfm?deid=211003
19. CA Office of Environmental Health Hazard Assessment, 2014. All Acute Reference
Exposure Levels developed by OEHHA as of January 2014.
http://oehha.ca.gov/air/allrels.html
20. American Industrial Hygiene Association, 2013. Current AULA ERPG Values.
https://www. aiha. org/ get-
involved/aihaguidelinefoundation/emergencvresponseplanningguidelines/Pages/default.aspx
27. US EPA, 1995. Guidance for Risk Characterization. Science Policy Council.
http://www.epa.gov/OSA/spc/pdfs/rcguide.pdf.
22. US EPA, 2000. Risk Characterization Handbook. EPA 100-B-00-002.
23. US EPA, 2002. EPA's Guidelines for Ensuring and Maximizing the Quality, Objectivity,
Utility, and Integrity of Information Disseminated by the Environmental Protection Agency.
EPA Office of Environmental Information. EPA/260R-02-008.
http://www.epa.gov/qualitv/informationguidelines/documents/EPA_InfoOualitvGuidelines.pdf
24. US EPA, 2006. Performing risk assessments that include carcinogens described in the
Supplemental Guidance as having a mutagenic mode of action. Science Policy Council Cancer
Guidelines Implementation Workgroup Communication II: Memo from W.H. Farland dated
14 June 2006. http://www.epa.gov/osa/spc/pdfs/CGIWGCommunication_II.pdf
25. US EPA, 2005. Supplemental guidance for assessing early-life exposure to carcinogens.
EPA/630/R-03003F. http://www.epa.gov/ttn/atw/childrens_supplement_final.pdf.
26. US EPA, 2005. Science Policy Council Cancer Guidelines Implementation Workgroup
Communication I: Memo from W.H. Farland dated 4 October 2005 to Science Policy
Council, http://www.epa.gov/osa/spc/pdfs/canguidl.pdf
27. US EPA, 1986. Guidelines for the Health Risk Assessment of Chemical Mixtures. EPA-630-
R-98-002. http://cfpub.epa.gov/ncea/cfm/recordisplav.cfm?deid=22567
28. US EPA, 2000. Supplementary Guidance for Conducting Health Risk Assessment of
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Risk Assessment for the Aerospace Manufacturing and Rework Facilities Source Category 57
Chemical Mixtures. EPA-630/R-00-002.
http://www.epa.gov/raf/publications/pdfs/CHEM_MIX_08_2001.PDF
29. US EPA, 2005. Guidelines for Carcinogen Risk Assessment (2005). U.S. Environmental
Protection Agency, Washington, DC, EPA/630/P-03/001F, 2005.
http://cfpub.epa.gov/ncea/raf/recordisplav. cfm?deid=l 16283
30. US EPA. 2000. Risk Characterization Handbook. EPA 100-B-00-002.
31. NRC (National Research Council) 2006. Assessing the Human Health Risks of
Trichloroethylene. National Academies Press, Washington DC.
32. R.P. Subramaniam, P. White and V.J. Cogliano. 2006. Comparison of cancer slope factors
using different statistical approaches, Risk Anal. Vol 26, p. 825-830.
33. US EPA. 1993. Reference Dose (RfC): Description and Use in Health Risk Assessments.
http://www.epa.gov/iris/rfd.htm.
34. US EPA. 1994. Methods for Derivation of Inhalation Reference Concentrations and
Application of Inhalation Dosimetry.
http://cfpub.epa. gov/ncea/cfm/recordisplav. cfm?deid=71993.
35. US EPA. 2002. A Review of the Reference Dose and Reference Concentration Processes.
http://www.epa.gov/ncea/iris/RFC FINAL1 .pdf
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Appendix 1
Aerospace Manufacturing and Rework Facilities RTR Modeling File Preparation
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www.erg.com
MEMORANDUM
TO:
Anne Pope and Kim Teal, U.S. EPA
FROM:
Stacie Enoch, Jason Huckaby, Ashley Penrod, and Darcy Wilson, ERG
DATE:
December 17, 2014
SUBJECT: Aerospace Manufacturing and Rework Facilities RTR Modeling File Preparation
I.
Introduction
The purpose of this memorandum is to provide information on the U.S. Environmental Protection
Agency (EPA) Risk and Technology Review (RTR) database used in estimating residual risk for the
Aerospace Manufacturing and Rework Facilities National Emission Standards For Hazardous Air
Pollutants (NESHAP) source category. This NESHAP covers only major sources.
Section 112 of the Clean Air Act (CAA) establishes a two-stage regulatory process to address
emissions of hazardous air pollutants (HAPs) from stationary sources. In the first stage, section
112(d) requires the EPA to develop technology-based standards for categories of industrial sources.
In the second stage, known as the residual risk stage, section 112(f)(2) requires EPA to assess the
health and environmental risks that remain after sources come into compliance with standards
based on the Maximum Achievable Control Technology (MACT). If additional risk reductions are
necessary to protect public health with an ample margin of safety or to prevent adverse
environmental effects, EPA must develop standards to address these remaining risks. As part of this
second stage, data were gathered to assess the residual risks from the Aerospace Manufacturing
NESHAP source category.
The 2002 National Emissions Inventory (NEI) data served as the starting point for the initial phase of
this assessment. Using the process regulatory code,1 a subset of the NEI was developed that
contained facility, process, and emissions data for each facility potentially subject to the Aerospace
Manufacturing and Rework Facilities NESHAP (40 CFR 63, subpart GG) and in the Aerospace
Manufacturing source category (regulatory code 63GG). The 2002 NEI data for Aerospace
Manufacturing facilities were released through an Advanced Notice of Proposed Rulemaking (ANPR)
in March of 2007, where NEI revisions for facilities in the MACT source category were solicited from
State and local agencies, industry representatives, and EPA. The revisions that were provided to EPA
were incorporated into the 2005 NEI.
The 2005 NEI data then served as the starting point for the next phase of the RTR assessment.
1 Assigning data with regulatory codes allows EPA to determine reductions attributable to the MACT program. The
NEI associates regulatory codes corresponding to MACT source categories with stationary major and area source
data.
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On February 4, 2011, EPA sent out an information collection request (ICR) requesting data on
aerospace-related processes and general information about each facility. Information was requested
on operations subject to 40 CFR 63, subpart GG (coatings, blast depainting operations, solvent
depainting operations, and solvent cleaning operations) as well as specialty coatings, chemical
milling and metal finishing operations, composite processing, storage tanks, and wastewater
treatment. Information was also requested on booth characteristics and control devices and
locational coordinates.
On October 2, 2012, EPA sent out an ICR requesting stack emissions testing data for select coating
operations and spray booths and blast depainting, composite processing and metal finishing
operations.
On May 23, 2013, EPA provided the estimated facility-wide emissions rates for major sources and
synthetic minor sources based on the results of the ICR data collection effort for industry review.
On September 18, 2013, EPA issued a request for supplemental coatings analyses for select facilities.
On May 15, 2014, EPA provided industry the opportunity for a final review of the estimated
emissions (and emission estimation methods) for processes subject to 40 CFR 63, subpart GG and
other processes included in the February 4, 2011 ICR request, as well as other facility operations
included in the 2011 NEI. Review comments were also solicited on stack parameters (including
fugitive area dimensions) and locational coordinates.
The following sections detail how EPA calculated emissions and conducted quality assurance/quality
control (QA/QC) of the data.
II. Category and Specialty Coatings Actual Emissions Development
a. Regulated processes included coatings, blast depainting, solvent depainting, and
solvent cleaning. Non-regulated processes included specialty coatings, chemical
milling and metal finishing, composite processing, storage tanks, and wastewater
treatment.
b. February 4, 2011 ICR Data Compilation
The ICR requested data on coating usage and physical property data, emissions, booth
characteristics, and locational coordinates. Forms A-l and A-2 requested facility
address and contact information. Form B-l requested paint spray-booth-specific
information. Form C-l (and C-2 in metric units) requested coating usage and physical
property data that EPA then used in calculating HAP emission estimates. Forms D-l,
E-l through E-3, G-l, and H-l requested HAP emission estimates from tanks used for
chemical milling or metals finishing operations, blast depainting/cleaning operations,
solvent depainting operations, solvent cleaning operations, storage tanks, and
wastewater treatment. Form F-l requested resins usage and physical property data
that EPA then used in calculating HAP emission estimates from composites processing
operations. Forms J-l through J-10 requested information specific to each control
device used.
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c. Development of Actual Emissions Estimates
The following equations and assumptions were used to develop actual emissions
estimates from the ICR data.
Coating Operations (CI)
Emissions were calculated for each HAP within a coating and for each booth in which
the coating was used. The following equations were used to calculate coating
emissions in tons per year.
• Organic HAP stack emissions from coating usage inside a booth:
Volume * Density * HAP Mass Fraction *
(Coating Usage Pet inside booth/100) * (Capture Efficiency/100) *
(1 — (Control Efficiency/100)) * (1/2000)
• Organic HAP fugitive emissions from coating usage inside a booth:
Volume * Density * HAP Mass Fraction *
(Coating Usage Pet inside booth/100) * (1 — Capture Efficiency/100) *
(1/2000)
• Organic HAP fugitive emissions from coating usage outside of a booth:
Volume * Density * HAP Mass Fraction
* (Coating Usage Pet outside booth/100) * (1/2000)
• Inorganic HAP stack emissions:
Volume * Density * HAP Mass Fraction *
(Coating Usage Pet inside booth/100) * Adjustment Factor *
(Capture Efficiency/100) * (1 — (Filter Control Efficiency/100)) *
(1/2000)
• Inorganic HAP fugitive emissions:
Volume * Density * HAP Mass Fraction *
(Coating Usage Pet inside booth/100) * Adjustment Factor * (1 —
Capture Efficiency/100) * (1/2000)
• Inorganic HAP fugitive emissions (out of booth usage):
Volume * Density * HAP Mass Fraction *
(Coating Usage Pet outside booth/100) * Adjustment Factor *(1/2000)
Where:
Volume = total coating volume used (gal/yr);
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Density = density of the coating (lb coating/gal coating);
HAP Mass Fraction = the fraction of a specific HAP in the coating (lb HAP/lb
coating);
Coating Usage Pet = the portion of the annual coating usage at the specified
location (i.e., in the identified booth, or outside of booth usage);
Adjustment Factor = factor to account for the transfer of coating solids to the
part being coated (assumed 65%) and fallout for coatings applied within a
booth (assumed 50%);
Capture Efficiency = the percent of emissions captured by the booth;
Control Efficiency = the percent reduction achieved by the control device
(organic);
Filter control efficiency = the percent reduction achieved by the booth filter
(inorganic HAP).
Assumptions
• Due to the overall lack of control efficiency data, all control efficiency values
were assumed based on the type of control device (95% emission control for
carbon adsorbers and scrubbers and 98% control for thermal incinerators). If no
control device was listed, the control efficiency was assumed to be zero.
• All materials applied inside a booth were assumed to be spray applied.
• If no filter control efficiency was provided, a value of 98% was assumed.
• If no capture efficiency was provided, the capture efficiency was assumed based
on the booth type and pollutant type.
o For organic pollutants, the capture efficiencies were assumed to be as
follows: (open/open face/other: 90%, permanent total enclosure (PTE):
100%; enclosed but not a PTE: 98%); if the facility did not provide a
capture efficiency or booth type, a value of 90% was assumed,
o For inorganic pollutants, the capture efficiencies were assumed to be as
follows: (open/open face/other: 99%, permanent total enclosure (PTE):
100%; enclosed but not a PTE: 99.5%); if the facility did not provide a
capture efficiency or booth type, a value of 99% was assumed.
• It was assumed that inorganic fugitive emissions from application of coatings
outside of the booth are zero if the coatings were not spray applied. If the
coatings were spray applied, the transfer efficiency was assumed to be 65%. If
the application method for coatings applied outside of the booth was not
specified, it was assumed to be spray applied.
Chemical Milling and Metal Finishing Operations (Dl)
Facilities conducting these types of operations were required to report emissions
directly instead of providing the usage/composition data required to calculate
emissions (such as in CI). Therefore, the only calculation required was to convert
emissions from pounds per year to tons per year.
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Blast Depainting/Cleaning Operations (El). Solvent Depainting Operations (E2). and
Storage Tanks (Gl)
Facilities conducting these types of operations were required to report emissions
directly instead of providing the usage/composition data required to calculate
emissions (such as in CI). However; it was assumed that the emission values reported
(Emislbyr in equations below) were stack emissions after capture and control, and it
was therefore necessary to back-calculate the fugitive emissions from the stack
emissions. The following equations were used to calculate emissions for El, E2, and
Gl operations in tons per year.
• Stack emissions:
Emislbyr * (1/2000)
• Fugitive emissions for units not equipped with a control device:
(Emislbyr/(Capture Efficiency/100)) * (1
— (Capture Efficiency/100)) * (1/2000)
• Fugitive emissions for units equipped with a control device:
(Emislbyr/((Capture Efficiency/100) * (1 — (Control Efficiency/
100)))) * (1 — (Capture Efficiency/100)) * (1/2000)
Assumptions
• As stated above, it was assumed that the emission values reported in the
survey are stack emissions after capture and control.
• If no capture efficiency was provided, a value of 70% was used for El and E2
operations, and zero was used for Gl operations.
• All control efficiency values were assumed based on the type of control device,
as outlined for CI operations.
• If a facility indicated that total mass balance emissions were reported instead
of stack emissions after control, then the emissions were adjusted to take into
account the capture and control efficiencies as requested.
Solvent Cleaning Operations - Point and Fugitive Sources (E3)
Facilities conducting solvent cleaning operations were required to report emissions
directly as either stack or fugitive emissions. Therefore, the only calculation required
was to convert emissions from pounds per year to tons per year.
Composite Processing Operations (F1 Resins)
Emissions were calculated for each HAP within the resin and for each booth in which
the resin was used. The following equations were used to calculate emissions from
composite processing operations in tons per year.
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• Stack emissions:
Volume * Density * HAP Mass Fraction * (Pet HAP emitted/100) *
(Resin Usage Pet inside booth/100) * (Capture Efficiency/100) * (1 —
(Control Efficiency/100)) * (1/2000)
• Fugitive emissions:
Volume * Density * HAP Mass Fraction * (Pet HAP emitted/100) *
(Resin Usage Pet inside booth/100) * (1 — (Capture Efficiency/100)) *
(1/2000)
• Fugitive emissions (out of booth usage):
Volume * Density * HAP Mass Fraction * (Pet HAP emitted/100) *
(Resin Usage Pet outside booth/100) * (1/2000)
Assumptions
• All control efficiency values were assumed based on the type of control device,
as outlined for CI operations.
• All materials applied inside a booth were assumed to be spray applied.
• If no capture efficiency was provided, the capture efficiency was assumed
based on the booth type and pollutant type.
o For organic pollutants, the capture efficiencies are assumed to be as
follows: (open/open face/other: 90%, permanent total enclosure (PTE):
100%; enclosed but not a PTE: 98%); if the facility did not provide a
capture efficiency or booth type, a value of 90% was assumed,
o For inorganic pollutants, the capture efficiencies are assumed to be as
follows: (open/open face/other: 99%, permanent total enclosure (PTE):
100%; enclosed but not a PTE: 99.5%); if the facility did not provide a
capture efficiency or booth type, a value of 99% was assumed.
• For outside the booth application, if the percent HAP emitted was not
provided, a value of 100 percent was assumed for organic pollutants. For
inorganic pollutants from resins that are applied outside of a booth that are
not spray applied, it was assumed that 0% was emitted. If the out of booth
resins are spray applied, the transfer efficiency was assumed to be 65% (or 35%
emitted). If the application method for resins applied outside of the booth was
not specified, it was assumed to be spray applied.
• For inorganic pollutants from resins applied inside a booth, the percent HAP
emitted was adjusted to account for an assumed transfer efficiency of 65% and
50% fallout within the booth (see adjustment factor used in equations for CI).
Wastewater Treatment Operations (HI)
Facilities conducting wastewater treatment operations were required to report
emissions directly as either stack or fugitive emissions. Therefore, the only calculation
required was to convert emissions from pounds per year to tons per year.
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Assumptions
It was assumed that the reported stack emission values are after capture and control.
Therefore, no capture or control efficiencies were applied. If a facility did not report
whether an emission value was fugitive or stack, it was assumed to be stack.
d. Regulatory Code and Emission Process Group Assignments
i. The regulatory code "63GG" was assigned for processes that are currently
subject to 40 CFR 63, Subpart GG: coatings, blast depainting, solvent
depainting, and solvent cleaning.
ii. Emission process group and source classification code (SCC) were assigned as
shown below based on information provided in the ICR response:
Table 1. Aerospace Manufacturing and Rework Emission Process Groups and SCCs
EMISSION PROCESS GROUP
SCC
SCC Description
Blast Depainting
30900201
Industrial Processes; Fabricated Metal Products; Abrasive
Blasting of Metal Parts; General
Blast Depainting
68241001
MACT Source Categories; Miscellaneous Processes; Paint
Stripper Users - Non-chemical Strippers; Media Blasting
Coatings
40202401
Petroleum and Solvent Evaporation; Surface Coating
Operations; Large Aircraft; Prime Coating Operation
Coatings
40202406
Petroleum and Solvent Evaporation; Surface Coating
Operations; Large Aircraft; Topcoat Operation
Coatings
40202499
Petroleum and Solvent Evaporation; Surface Coating
Operations; Large Aircraft; Other Not Classified
Solvent Cleaning
40202402
Petroleum and Solvent Evaporation; Surface Coating
Operations; Large Aircraft; Cleaning/Pretreatment
Solvent Depainting
68240030
MACT Source Categories; Miscellaneous Processes; Paint
Stripper Users - Chemical Strippers; Application,
Degradation, and Coating Removal Steps
Specialty Coatings
40200801
Petroleum and Solvent Evaporation; Surface Coating
Operations; Coating Oven - General; General
Specialty Coatings
40202401
Petroleum and Solvent Evaporation; Surface Coating
Operations; Large Aircraft; Prime Coating Operation
Specialty Coatings
40202406
Petroleum and Solvent Evaporation; Surface Coating
Operations; Large Aircraft; Topcoat Operation
Specialty Coatings
40202499
Petroleum and Solvent Evaporation; Surface Coating
Operations; Large Aircraft; Other Not Classified
Chemical Milling and Metal
Finishing
30901018
Industrial Processes; Fabricated Metal Products;
Electroplating Operations; Hard Chromium - Electroplating
Tank
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Table 1. Aerospace Manufacturing and Rework Emission Process Groups and SCCs
EMISSION PROCESS GROUP
see
SCC Description
Chemical Milling and Metal
Finishing
30901038
Industrial Processes; Fabricated Metal Products;
Electroplating Operations; Chromic Acid Anodizing -
Anodizing Tank
Chemical Milling and Metal
Finishing
30901042
Industrial Processes; Fabricated Metal Products;
Electroplating Operations; Copper (cyanide, including strike)
- Electroplating Tank
Chemical Milling and Metal
Finishing
30901048
Industrial Processes; Fabricated Metal Products;
Electroplating Operations; Copper (general) - Electroplating
Tank 1000 amp-hr current applied
Chemical Milling and Metal
Finishing
30901052
Industrial Processes; Fabricated Metal Products;
Electroplating Operations; Cadmium (cyanide) -
Electroplating Tank 1000 amp-hr current applied
Chemical Milling and Metal
Finishing
30901058
Industrial Processes; Fabricated Metal Products;
Electroplating Operations; Cadmium (general) -
Electroplating Tank 1000 amp-hr current applied
Chemical Milling and Metal
Finishing
30901065
Industrial Processes; Fabricated Metal Products;
Electroplating Operations; Nickel (sulfamate or watts) -
Electroplating Tank 1000 amp-hr current
Chemical Milling and Metal
Finishing
30901068
Industrial Processes; Fabricated Metal Products;
Electroplating Operations; Nickel (general) - Electroplating
Tank
Chemical Milling and Metal
Finishing
30901098
Industrial Processes; Fabricated Metal Products;
Electroplating Operations; Other Not Classified
Chemical Milling and Metal
Finishing
30901101
Industrial Processes; Fabricated Metal Products; Conversion
Coating of Metal Products; Alkaline Cleaning Bath
Chemical Milling and Metal
Finishing
30901102
Industrial Processes; Fabricated Metal Products; Conversion
Coating of Metal Products; Acid Cleaning Bath (Pickling)
Chemical Milling and Metal
Finishing
30901103
Industrial Processes; Fabricated Metal Products; Conversion
Coating of Metal Products; Anodizing Kettle
Chemical Milling and Metal
Finishing
30901199
Industrial Processes; Fabricated Metal Products; Conversion
Coating of Metal Products; Other Not Classified
Chemical Milling and Metal
Finishing
30901501
Industrial Processes; Fabricated Metal Products; Chemical
Milling of Metal Products; Milling Tank
Chemical Milling and Metal
Finishing
30904001
Industrial Processes; Fabricated Metal Products; Metal
Deposition Processes; Metallizing: Wire Atomization and
Spraying
Chemical Milling and Metal
Finishing
30904010
Industrial Processes; Fabricated Metal Products; Metal
Deposition Processes; Thermal Spraying of Powdered Metal
Chemical Milling and Metal
Finishing
30999999
Industrial Processes; Fabricated Metal Products; Other Not
Classified; Other Not Classified
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Table 1. Aerospace Manufacturing and Rework Emission Process Groups and SCCs
EMISSION PROCESS GROUP
see
SCC Description
Composite Processing
30800720
Industrial Processes; Rubber and Miscellaneous Plastics
Products; Fiberglass Resin Products; General
Storage Tanks
40100550
Petroleum and Solvent Evaporation; Organic Solvent
Evaporation; Solvent Storage; General Processes: Drum
Storage - Pure Organic Chemicals
Wastewater Treatment
40282001
Petroleum and Solvent Evaporation; Surface Coating
Operations; Wastewater, Aggregate; Process Area Drains
III. Testing ICR
a. The test data were reviewed to confirm there were no additional source ID or
pollutant matches that were missed when incorporating test data into the modeling
file.
b. The methodology that was used to include (or not include) emissions test data in
the modeling file was reviewed. Findings were as follows:
¦ Test data were compiled for 15 facilities.
¦ The compiled test data for one facility were not included for modeling file
updates, because the units of measure could not be converted to a pound per
hour or other comparable emissions rate.
¦ One test point for one facility was excluded from the modeling file updates,
because it overlapped with a different test point that was included in the
modeling file updates.
¦ With the exception of the test point noted above, test data for the remaining
14 facilities were added to the modeling file if the data met three criteria:
• There was at least one test run where emissions were not below
detection limits. For the purpose of the aerospace source category risk
analysis, test points where all three runs were below detection limits
were assumed to not emit that HAP from that process type and these
emissions were not included in the risk modeling file. This assumption
was made for this source category because of the variability in
processes and input materials used at aerospace facilities (for example,
some coatings do not contain certain HAP and, therefore, are not
expected to be sources of those HAP).
• The data that could be mapped to source IDs were already included in
the modeling file.
• The test pollutants were not reported in the original ICR data. If the test
pollutants were already reported in the original ICR data for the
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corresponding source ID, then the stack test data were not added to the
modeling file.
¦ If the test data met the three criteria, then the average emissions from the test
runs were added to the modeling file. If at least one run was above the
detection limit, one-half of the detection limit was used for any runs that were
below the detection limit. If all three runs were below the detection limit, then
emissions of that HAP were assumed to not be present for that process in the
aerospace source category. Stack test data were added to the modeling file for
11 out of 14 facilities. (Although it is common practice to use one half of the
detection limit for below detection limit in other source categories, the
methodology used in developing the aerospace risk modeling file reflects the
fact that some HAP are not present in the coatings and other raw materials used
in aerospace processes and are not expected to be emitted.)
c. Industry-supplied materials and prior EC/R analysis of emissions test data and
capture efficiencies were reviewed to determine whether any calculations or
assumptions currently used should be revised.
d. Emissions test data amendments and results of coatings test data were reviewed to:
1) identify which pollutants in the emissions test data were not being reported
under material balance-based coatings records already (i.e., should be added to the
coatings category records); 2) which pollutants were measured in the emission
tests, but were not found in coatings tests, and therefore should not be allocated to
the coatings process group; 3) confirm that those pollutants in the emissions test
data that were not flagged for appending were reflected in material balance-based
coatings records; and 4) provide analytical test data instead of MSDS-supplied
metals content values where the tested coating could be matched up to coatings
reportedly being used.
IV. Speciation
a. Chromium (Cr) emissions from coatings and specialty coatings were speciated into
its two most prevalent oxidation states: trivalent chromium (Cr III) and hexavalent
chromium (Cr VI). Chromium from ICR processes were speciated using a profile of
20.6% Cr VI and 79.4% Cr III. The coatings test results showed that strontium
chromate is the predominant compound. Therefore, as a first step, a 25.5% weight
fraction was applied to estimate how much of the compound is chromium. The
25.5% weight fraction was then multiplied by 0.807 to adjust this factor downward,
because the test results indicated that not all of the Cr in the coatings is Cr VI. The
average ratio of Cr VI to Cr in the coatings test data is 0.807, as can be seen in the Cr
VI to Cr ratio calculation from coatings test data found in Appendix A. Records that
were previously speciated using a 25% Cr VI/75% Cr III were re-speciated using the
profile described above for ICR processes.
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b. Mercury (Hg) emissions were speciated into three forms (elemental gaseous,
particulate divalent, and gaseous divalent) using SCC-based NEI defaults. If a SCC
from the modeling file did not match the NEI defaults, the generic 20% particulate
divalent, 30% gaseous divalent, and 50% elemental gaseous profile was used.
V. Industry Reviews
a. Draft modeling file records were provided to all parent companies (in the EPA
modeling file format) on May 15, 2014, for review. The file included the coating
emission calculations in addition to modeling data. EPA provided review files to
171 facilities. Responses were received for 84 facilities. Nine facilities were not
included in the draft modeling file as of May 2014 and were added after the industry
review period.
b. Revisions to the modeling file records included changes to emission values, control
device types, stack parameters, locational coordinates, fugitive area dimensions,
pollutant information, and removal of units or processes that are shutdown or
otherwise not part of the facility. Revisions to the coating calculations included
adding missing coatings and pollutants, removing pollutants no longer emitted, and
revising the coating calculation inputs (volume, density, HAP mass fraction, coating
usage percent, booth type and capture percent, control efficiency, filter control
efficiency, and the adjustment factor).
VI. Development of Allowable Emissions for Category
Facilities were asked to provide a multiplier in the ICR survey to scale up average hourly
emissions to maximum hourly emissions for air dispersion modeling. As there are typically a
large number of emission points at a facility, determining the maximum hourly emissions
from each and every emission point can be difficult. Therefore, EPA chose to use a single
multiplier of 1.02 to scale average annual emissions to allowable annual emissions. The
allowable emissions multiplier is based on the difference between 2008 production
utilization rate of 83.1 and the 20-year historical maximum production utilization rate of
85.0 (i.e., 85 h-83.1= 1.02).
VII. Development of Acute Emissions for Category
EPA determined that an acute factor of 1.2 was appropriate for the aerospace
manufacturing industry. This multiplier is based on the ratio of the 2008 production
utilization rate (83.1) and the maximum production rate of 100 (i.e., 100 4- 83.1 = 1.2).
VIII. Non-Category Aerospace Manufacturing Facility Emissions
a. Non-category emissions for aerospace manufacturing facilities were obtained from
the 2011 NEI. Non-category emissions were not available in the 2011 NEI or 2008 NEI
for three facilities; emissions were pulled from the 2005 National-Scale Air Toxics
Assessment (NATA) inventory for these three facilities.
11
-------
b. Appendix B shows a list of SCCs that were included in the non-category records.
Appendix C shows a list of additional SCCs that were included in the non-category
records only if the record had the specific unit description shown. Individual
chromium records were excluded if they were found to overlap with a regulated
process.
c. Speciation
i. Chromium
SCC-based NEI Cr speciation defaults were used for non-ICR processes. If a SCC
from the modeling file did not match the NEI default, the Cr profile for ICR
processes was used.
ii. Mercury
Mercury (Hg) emissions were speciated into three forms (elemental gaseous,
particulate divalent, and gaseous divalent) using SCC-based NEI defaults. If a SCC
from the modeling file did not match the NEI defaults, the generic 20%
particulate divalent, 30% gaseous divalent, and 50% elemental gaseous profile
was used.
d. Industry Review
The 2014 industry review responses described in Section V above included
changes for non-category records from NEI.
Quality Assurance/Quality Control (QA/QC)
All of the QA/QC procedures discussed in the revised Quality Assurance Project Plan (QAPP)
dated June 6, 2014 were implemented for the aerospace manufacturing and rework
emissions estimates, locational coordinates, stack parameters, fugitive parameters, and
non-category emissions records. Manual and computerized verification by a team member
who did not perform the original data entry or calculations was conducted to ensure that all
ICR data were correctly incorporated, and that all automated emission calculations were
implemented correctly. Additional computerized QA/QC checks were then implemented,
thereby conducting checks on close to 100% of the records.
a. Aerospace Manufacturing and Rework Facilities Emission Estimates
The Aerospace Manufacturing and Rework Facilities emission records included in the
modeling file were reviewed to verify that no emissions were double counted, and
that the emitted pollutants were consistent with the reported SCCs/unit or process
descriptions. HAPs not typically emitted from the processes were removed. For
example, EPA removed homopolymers from coating emissions where facilities
reported both the homopolymer and hexamethylene diisocyanate (HMDI)
components of a coating material and adjusted HMDI estimates that were
overestimated using an average ratio from the study "Determination of 1,6-
12
-------
Hexamethyiene Diisocyanate (HDI) Emissions from Spray Booth Operations2." Outliers
(e.g., HAPs emitted in very large quantities relative to similar processes at other
facilities) were flagged for investigation. For example, one facility reported specialty
coating emissions in pounds per year instead of reporting coating inputs; EPA adjusted
these emissions to tons per year. EPA contacted four facilities and made revisions for
two facilities as needed based on responses. For example, specialty coating emissions
were reassigned to non-category processes and duplicate emission values were
removed for one facility, and fugitive chromium emissions from surface coating were
removed for the other facility.
b. Locational Coordinates
Location coordinates (X_COQRDINATE and Y_COORDINATE) were reviewed using
ArcGIS®. Coordinates that placed stack emission release points over facility buildings,
stacks, hangers, etc., were considered suitable. If coordinates placed an emission
release point over parking lots, vegetation, residential housing, or other non-facility
building, new coordinates were assigned. Fugitive emission release points are
represented in modeling files as rectangles or squares. Coordinates of these release
points should be located in the southwest corner of the fugitive area to be modeled.
Fugitive coordinates were also QA'ed based on the fugitive length (y), width (x), and
angle associated with the fugitive area or building. These parameters are discussed
below and an example is displayed in Figure 1. If a fugitive area was shown to be over
vegetation, residential housing, etc., the coordinates were reassigned to the
southwest corner of the facility building based on the fugitive length, width, and
angle.
NORTH
Angle = O0
x
v
Angle = 45°
Angle = 0°
x
Figure 1. Depiction of Fugitive Area Source Parameters3
2 Ontario Ministry of the Environment, April 2006.
3 Figure created by ICF International.
13
-------
c. Stack Parameters
Stack parameters included in the ICR submittals and the modeling file include stack
diameter, stack height, exhaust stream temperature, and stream flow rate. Stream
velocity is provided in the modeling file and was calculated based on stack diameter
and flow rate. If one or more of the ICR stack parameters was not provided, aerospace
stack parameter defaults were assigned using the average of stack parameters
reported in the ICR for other aerospace release points. The average aerospace stack
parameters assigned as defaults are:
Stack height: 48.43091684 ft
Stack temperature: 76.27270788°F
Stack diameter: 4.846738822 ft
Stack velocity: 50.7294305266868 ft/sec
Stack flow rate: 978.086679026503 ft3/sec
The QA ranges for stack parameters for the aerospace modeling file are listed below.
One exception to the QA ranges is for engine testing and internal combustion engines
where the temperature can exceed the normal QA maximum. If either diameter or
velocity was out of range for category records, diameter, velocity and flow rate were
all set to the aerospace stack parameter defaults.
Table 2. Aerospace Stack Parameter QA Ranges
QA Range for Category
QA Range for Non-
Parameter
Records
Category Records
Units
Min
Max
Min
Max
Stack Height
0.1
1,200
0.1
1,200
ft
Exhaust Gas
Temperature
50
1,800
50
1,800
°F
Stack Diameter
0.1
40
0.1
50
ft
Exhaust Gas Velocity
0.1
200
0.1
200
ft/sec
Exhaust Gas Flow Rate
ft3/sec
d. Fugitive Parameters
Fugitive parameters in the modeling file include the same stack parameters as well as
fugitive length, width, and angle. The fugitive area is comprised of the southwest
corner of the representative building, or latitude and longitude, the length and width,
or x and y depicted in Figure 1, and the angle at which the area is rotated in relation to
the southwest corner. The fugitive angle must be between 0 and 90 degrees. The
national defaults for fugitive parameters are listed below.
14
-------
Table 3. Fugitive Stack Parameter Defaults Applied
National Defaults
Units
Stack Height
10
ft
Exhaust Gas
Temperature
72
°F
Stack Diameter
0.003
ft
Exhaust Gas Velocity
0.0003
ft/sec
Exhaust Gas Flow Rate
0
ft3/sec
e. Addition of new HAPs to RTR file
There were several new HAPs provided in the facility ICRs that were not yet included
in the RTR (or NEI) pollutant look-up tables. The HAPs added to the RTR look-up table
are provided in the following table.
Table 4. Additional Aerospace HAPs
Pollutant
Pollutant Code
Cadmium Selenide Sulfide
12626367
Cobalt Neodecanoate
27253312
Cobalt(ll) Nitrate
10141056
Cobalt(ll) Phosphate Octahydrate
10294505
Coronene
191071
Dibasic Lead Phosphate
15845520
Iron Chromite Brown Spinel (C.I. Pigment Brown 35)
68187097
Lead Chromate Molybdate Sulfate (C.I. Pigment Red 104)
12656858
Manganese Ferrite Black Spinel (C.I. Pigment Black 26)
68186947
Magnesium Dichromate
14104859
Phosphoric Acid, Reaction Products with Aluminum Hydroxide
and Chromium Oxide (Cr03)
92203026
Zinc Potassium Chromate
37224570
f. Non-category Emission Estimates
The NEI non-aerospace emission records included in the modeling file were
reviewed to verify that no emissions were double counted, and that the emitted
pollutants were consistent with the reported SCCs/unit or process descriptions.
HAPs not typically emitted from the processes were removed. Outliers (e.g., HAPs
emitted in very large quantities relative to similar processes at other facilities) were
flagged for investigation. For example, the NEI emissions from a wastewater
treatment process at one facility were adjusted. EPA contacted three facilities and
made revisions as needed based on responses. For example, combustion emissions
15
-------
were corrected at two facilities, and tetrachloroethylene emissions were removed
from the third facility.
16
-------
Appendix A. Coatings Test Data where Chromium and Chromium (VI) Data are Available
Facility
MSDS Coating ID
Avg CrVI Content (mg/kg)
Avg Chromium Content (mg/kg)
Fraction ChromeVI
Adjusted Fraction ChromeVI
1
Black Super Koropon base
#528x310
31.1
48.6
0.639917695
0.639917695
1
Eco Prime Yellow-Green #
EWDE072 A Base
19590
21005
0.932635087
0.932635087
2
515X349 BASE COMPONENT
25033.33333
30015.33333
0.834018168
0.834018168
2
BR 127 Corrosion Inhibiting
Primer
2080.666667
2699.333333
0.770807607
0.770807607
5
10P20-44_High Solids Epoxy
Primer
36912
49047.96411
0.752569463
0.752569463
6
10P8-10NF; Fluid Resistant
Epoxy Primer
14814.66667
21178.15975
0.699525683
0.699525683
6
515X346 Room Temperature
Bonding Primer
15944.5
37971.39558
0.41990819
0.41990819
8
463-6-4; Aluminized Epoxy
Primer Base
639.5
2801
0.228311317
0.228311317
8
BMS10-11AA-TYPE l-CLASS A
GRADE E BASE
64922.5
65226
0.995346948
0.995346948
9
10P8-11LC Primer
18874.5
22901
0.824177983
0.824177983
10
MIL-PRF-85582D, TYPE 1, CLASS
C2 BASE
34477
40365
0.854131054
0.854131054
10
MIL-PRF-85582D-TYPE l-CLASS
C2-CATALYST
2.53
1.92
1.317708333a
1
11
MIL-PRF-23377K TYPE 1 CLASS
C2 PART A BASE
35750
36691
0.974353384
0.974353384
11
MIL-PRF-23377K TYPE 1 CLASS
C2 PART B CATALYST
3.07
3.03
1.01320132a
1
11
MIL-PRF-85582D, TYPE 1, CLASS
C2, 44GN072 BASE
66158
84002
0.787576486
0.787576486
13
10P8-10NMF Base
19021.5
21092.5
0.901813441
0.901813441
13
BMS10-11A Type 1 Class A Cat
7.54
3.8
1.984210526a
1
A-l
-------
Appendix A. Coatings Test Data where Chromium and Chromium (VI) Data are Available
Facility
MSDS Coating ID
Avg CrVI Content (mg/kg)
Avg Chromium Content (mg/kg)
Fraction ChromeVI
Adjusted Fraction ChromeVI
BMS10-11A Type 1 Class A
13
Primer
64112.5
67715.5
0.946792093
0.946792093
AQUAPRIME PRIMER, MIL-PRF-
85582D, TYPE 1, CLASS C2,
14
PART A
30680
39440
0.777890467
0.777890467
Average:
0.807
a Measured Cr VI was greater than Cr; all reported Cr is assumed to be Cr VI.
A-2
-------
Appendix B. Non-category SCCs
see
SCC Description
10100224
External Combust
on Boilers
Electric Generation; Subbituminous Coal; Boiler, Spreader Stoker
10100501
External Combust
on Boilers
Electric Generation; Distillate Oil - Grades 1 and 2; Boiler, Normal firing
10100602
External Combust
on Boilers
Electric Generation; Natural Gas; Boiler < 100 Million BTU, except tangential
10102101
External Combust
on Boilers
Electric Generation; Other Oil; All
10200204
External Combust
on Boilers
Industrial; Bituminous Coal; Spreader Stoker
10200206
External Combust
on Boilers
Industrial; Bituminous Coal; Underfeed Stoker
10200212
External Combust
on Boilers
Industrial; Bituminous Coal; Pulverized Coal: Dry Bottom (Tangential)
10200401
External Combust
on Boilers
Industrial; Residual Oil - Grade 6; Boiler
10200404
External Combust
on Boilers
Industrial; Residual Oil; Grade 5 Oil
10200501
External Combust
on Boilers
Industrial; Distillate Oil - Grades 1 and 2; Boiler
10200502
External Combust
on Boilers
Industrial; Distillate Oil; 10-100 Million BTU/hr **
10200503
External Combust
on Boilers
Industrial; Distillate Oil; < 10 Million BTU/hr **
10200601
External Combust
on Boilers
Industrial; Natural Gas; > 100 Million BTU/hr
10200602
External Combust
on Boilers
Industrial; Natural Gas; 10-100 Million BTU/hr
10200603
External Combust
on Boilers
Industrial; Natural Gas; < 10 Million BTU/hr
10200799
External Combust
on Boilers
Industrial; Process Gas; Other: Specify in Comments
10300203
External Combust
on Boilers
Commercial/Institutional
Bituminous Coal; Cyclone Furnace
10300209
External Combust
on Boilers
Commercial/Institutional
Bituminous Coal; Spreader Stoker
10300402
External Combust
on Boilers
Commercial/Institutional
Residual Oil; 10-100 Million BTU/hr **
10300403
External Combust
on Boilers
Commercial/Institutional
Residual Oil; < 10 Million BTU/hr **
10300404
External Combust
on Boilers
Commercial/Institutional
Residual Oil; Grade 5 Oil
10300501
External Combust
on Boilers
Commercial/Institutional
Distillate Oil - Grades 1 and 2; Boiler
10300502
External Combust
on Boilers
Commercial/Institutional
Distillate Oil; 10-100 Million BTU/hr **
10300503
External Combust
on Boilers
Commercial/Institutional
Distillate Oil; < 10 Million BTU/hr **
10300504
External Combust
on Boilers
Commercial/Institutional
Distillate Oil; Grade 4 Oil
10300601
External Combust
on Boilers
Commercial/Institutional
Natural Gas; > 100 Million BTU/hr
10300602
External Combust
on Boilers
Commercial/Institutional
Natural Gas; 10-100 Million BTU/hr
10300603
External Combust
on Boilers
Commercial/Institutional
Natural Gas; < 10 Million BTU/hr
B-l
-------
Appendix B. Non-category SCCs
see
SCC Description
10301002
External Combustion Boilers; Commercial/Institutional; Liquified Petroleum Gas (LPG); Propane
10500105
External Combustion Boilers; Space Heaters; Industrial; Distillate Oil
10500106
External Combustion Boilers; Space Heaters; Industrial; Natural Gas
10500206
External Combustion Boilers; Space Heaters; Commercial/Institutional; Natural Gas
20100102
Internal Combust
on Engines
Electric Generation; Distillate Oil (Diesel); Reciprocating
20100107
Internal Combust
on Engines
Electric Generation; Distillate Oil (Diesel); Reciprocating: Exhaust
20100201
Internal Combust
on Engines
Electric Generation; Natural Gas; Turbine
20100202
Internal Combust
on Engines
Electric Generation; Natural Gas; Reciprocating
20100901
Internal Combust
on Engines
Electric Generation; Kerosene/Naphtha (Jet Fuel); Turbine
20200102
Internal Combust
on Engines
Industrial; Distillate Oil (Diesel); Reciprocating
20200103
Internal Combust
on Engines
Industrial; Distillate Oil (Diesel); Turbine: Cogeneration
20200104
Internal Combust
on Engines
Industrial; Distillate Oil (Diesel); Reciprocating: Cogeneration
20200201
Internal Combust
on Engines
Industrial; Natural Gas; Turbine
20200202
Internal Combust
on Engines
Industrial; Natural Gas; Reciprocating
20200203
Internal Combust
on Engines
Industrial; Natural Gas; Turbine: Cogeneration
20200252
Internal Combust
on Engines
Industrial; Natural Gas; 2-cycle Lean Burn
20200253
Internal Combust
on Engines
Industrial; Natural Gas; 4-cycle Rich Burn
20200254
Internal Combust
on Engines
Industrial; Natural Gas; 4-cycle Lean Burn
20200401
Internal Combust
on Engines
Industrial; Large Bore Engine; Diesel
20200402
Internal Combust
on Engines
Industrial; Large Bore Engine; Dual Fuel (Oil/Gas)
20200901
Internal Combust
on Engines
Industrial; Kerosene/Naphtha (Jet Fuel); Turbine
20201001
Internal Combust
on Engines
Industrial; Liquified Petroleum Gas (LPG); Propane: Reciprocating
20201012
Internal Combust
on Engines
Industrial; Liquified Petroleum Gas (LPG); Reciprocating Engine
20300101
Internal Combust
on Engines
Commercial/Institutional; Distillate Oil (Diesel); Reciprocating
20300102
Internal Combust
on Engines
Commercial/Institutional; Distillate Oil (Diesel); Turbine
20300201
Internal Combust
on Engines
Commercial/Institutional; Natural Gas; Reciprocating
20300203
Internal Combust
on Engines
Commercial/Institutional; Natural Gas; Turbine: Cogeneration
20300301
Internal Combust
on Engines
Commercial/Institutional; Gasoline; Reciprocating
B-2
-------
Appendix B. Non-category SCCs
see
SCC Description
20301001
Internal Combustion Engines
Commercial/Institutional; Liquified Petroleum Gas (LPG); Propane: Reciprocating
20400101
Internal Combustion Engines
Engine Testing; Aircraft Engine Testing; Turbojet
20400102
Internal Combustion Engines
Engine Testing; Aircraft Engine Testing; Turboshaft
20400110
Internal Combustion Engines
Engine Testing; Aircraft Engine Testing; Jet A Fuel
20400199
Internal Combustion Engines
Engine Testing; Aircraft Engine Testing; Other Not Classified
20400201
Internal Combustion Engines
Engine Testing; Rocket Engine Testing; Rocket Motor: Solid Propellant
20400302
Internal Combustion Engines
Engine Testing; Turbine; Diesel/Kerosene
20400305
Internal Combustion Engines
Engine Testing; Turbine; Kerosene/Naphtha
20400402
Internal Combustion Engines
Engine Testing; Reciprocating Engine; Diesel/Kerosene
20400406
Internal Combustion Engines
Engine Testing; Reciprocating Engine; Kerosene/Naphtha (Jet Fuel)
30101602
Industrial Processes
Chemical Manufacturing; Phosphoric Acid: Wet Process; Gypsum Pond
30188801
Industrial Processes
Chemical Manufacturing; Fugitive Emissions; Specify in Comments Field
30400360
Industrial Processes
Secondary Metal Production; Grey Iron Foundries; Castings Finishing
30400401
Industrial Processes
Secondary Metal Production; Lead; Pot Furnace
30400409
Industrial Processes
Secondary Metal Production; Lead; Casting
30400867
Industrial Processes
Secondary Metal Production; Zinc; Kettle (Pot) Melting Furnace
30400873
Industrial Processes
Secondary Metal Production; Zinc; Casting
30402201
Industrial Processes
Secondary Metal Production; Metal Heat Treating; Furnace: General
30499999
Industrial Processes
Secondary Metal Production; Other Not Classified; Specify in Comments Field
30501050
Industrial Processes
Mineral Products; Coal Mining, Cleaning, and Material Handling; Vehicle Traffic: Light/Medium Vehicles
30600101
Industrial Processes
Petroleum Industry; Process Heaters; Oil-fired **
30601301
Industrial Processes
Petroleum Industry; Coke Handling System; Storage and Transfer
30622003
Industrial Processes
Petroleum Industry; Underground Storage Remediation & Other Remediation; Soil: Natural Gas
30700752
Industrial Processes
Natural Gas-Fired, 1-
Pulp and Paper and Wood Products; Plywood Operations; Softwood Plywood, Veneer Dryer, Direct
eated Zones
30900500
Industrial Processes; Fabricated Metal Products; Welding; General
30901003
Industrial Processes; Fabricated Metal Products; Electroplating Operations; Entire Process: Nickel
30901006
Industrial Processes; Fabricated Metal Products; Electroplating Operations; Entire Process: Chrome
B-3
-------
Appendix B. Non-category SCCs
see
SCC Description
30901018
Industrial Processes
Fabr
cated Metal Products
Electroplating Operations; Hard Chromium - Electroplating Tank
30901028
Industrial Processes
Fabr
cated Metal Products
Electroplating Operations; Decorative Chromium - Electroplating Tank
30901098
Industrial Processes
Fabr
cated Metal Products
Electroplating Operations; Other Not Classified
30901102
Industrial Processes
Fabr
cated Metal Products
Conversion Coating of Metal Products; Acid Cleaning Bath (Pickling)
30901103
Industrial Processes
Fabr
cated Metal Products
Conversion Coating of Metal Products; Anodizing Kettle
30901104
Industrial Processes
Fabr
cated Metal Products
Conversion Coating of Metal Products; Rinsing/Finishing
30901199
Industrial Processes
Fabr
cated Metal Products
Conversion Coating of Metal Products; Other Not Classified
30905100
Industrial Processes
Fabr
cated Metal Products
Shielded Metal Arc Welding (SMAW); General
30905200
Industrial Processes
Fabr
cated Metal Products
Gas Metal Arc Welding (GMAW); General
30990003
Industrial Processes
Fabr
cated Metal Products
Fuel Fired Equipment; Natural Gas: Process Heaters
30999999
Industrial Processes
Fabr
cated Metal Products
Other Not Classified; Other Not Classified
31299999
Industrial Processes
Machinery, Miscellaneous
Miscellaneous Machinery; Other Not Classified
31399999
Industrial Processes
Electrical Equipment; Other Not Classified; Other Not Classified
31499999
Industrial Processes
Transportation Equipment; Other Not Classified; Other Not Classified
39000699
Industrial Processes
In-process Fuel Use
Natural Gas; General
39001299
Industrial Processes
In-process Fuel Use
Solid Waste; General
39090001
Industrial Processes
In-process Fuel Use
Fuel Storage - Fixed Roof Tanks; Residual Oil: Breathing Loss
39090003
Industrial Processes
In-process Fuel Use
Fuel Storage - Fixed Roof Tanks; Distillate Oil (No. 2): Breathing Loss
39090004
Industrial Processes
In-process Fuel Use
Fuel Storage - Fixed Roof Tanks; Distillate Oil (No. 2): Working Loss
39090011
Industrial Processes
In-process Fuel Use
Fuel Storage - Fixed Roof Tanks; Dual Fuel (Gas/Oil): Breathing Loss
39091004
Industrial Processes
In-process Fuel Use
Fuel Storage - Floating Roof Tanks; Distillate Oil (No. 2): Withdrawal Loss
39091011
Industrial Processes
In-process Fuel Use
Fuel Storage - Floating Roof Tanks; Dual Fuel (Gas/Oil): Standing Loss
39900601
Industrial Processes
Miscellaneous Manufacturing Industries; Process Heater/Furnace; Natural Gas
39990003
Industrial Processes
Heaters
Miscellaneous Manufacturing Industries; Miscellaneous Manufacturing Industries; Natural Gas: Process
39999992
Industrial Processes; Miscellaneous Manufacturing Industries; Miscellaneous Industrial Processes; Other Not Classified
39999993
Industrial Processes; Miscellaneous Manufacturing Industries; Miscellaneous Industrial Processes; Other Not Classified
39999999
Industrial Processes; Miscellaneous Manufacturing Industries; Miscellaneous Industrial Processes; Other Not Classified
B-4
-------
Appendix B. Non-category SCCs
see
SCC Description
40100198
Petroleum and Solvent Evaporation; Organic Solvent Evaporation; Dry Cleaning; Other Not Classified
40200801
Petroleum and Solvent Evaporation; Surface Coating Operations; Coating Oven - General; General
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes)
Gasoline RVP
40301001
13: Breathing Loss (67000 Bbl. Tank Size)
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes)
Gasoline RVP
40301005
10: Breathing Loss (250000 Bbl. Tank Size)
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes)
Gasoline RVP
40301007
13: Working Loss (Tank Diameter Independent)
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes)
Jet Kerosene:
40301016
Breathing Loss (67000 Bbl. Tank Size)
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes)
Jet Kerosene:
40301017
Breathing Loss (250000 Bbl. Tank Size)
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes)
Jet Kerosene:
40301018
Working Loss (Tank Diameter Independent)
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes)
Distillate Fuel
40301019
#2: Breathing Loss (67000 Bbl. Tank Size)
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes)
Distillate Fuel
40301020
#2: Breathing Loss (250000 Bbl. Tank Size)
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes)
Distillate Fuel
40301021
#2: Working Loss (Tank Diameter Independent)
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes)
Specify Liquid:
40301098
Breathing Loss (250000 Bbl. Tank Size)
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes)
Specify Liquid:
40301099
Working Loss (Tank Diameter Independent)
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Floating Roof Tanks (Varying Sizes); Jet Kerosene:
40301114
Standing Loss (250000 Bbl. Tank Size)
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Floating Roof Tanks (Varying Sizes); Jet Kerosene:
40301119
Withdrawal Loss
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 10: Breathing Loss
40400102
(67000 Bbl Capacity) - Fixed Roof Tank
40400108
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 10: Working Loss
B-5
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Appendix B. Non-category SCCs
see
SCC Description
(Diameter Independent) - Fixed Roof Tank
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 13: Standing Loss -
40400131
Ext. Floating Roof w/ Primary Seal
40400250
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Loading Racks
40400254
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Tank Truck Vapor Losses
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Specify Liquid: Standing Loss - Int.
40400270
Floating Roof w/ Secondary Seal
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Oil and Gas Field Storage and Working Tanks;
40400316
Fixed Roof Tank, Diesel, working+breathing+flashing losses
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products - Underground Tanks;
40400402
Gasoline RVP 13: Working Loss
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products - Underground Tanks; Jet
40400411
Kerosene: Breathing Loss
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products - Underground Tanks; Jet
40400412
Kerosene: Working Loss
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products - Underground Tanks;
40400497
Specify Liquid: Breathing Loss
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products - Underground Tanks;
40400498
Specify Liquid: Working Loss
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank Cars and Trucks; Gasoline:
40600131
Submerged Loading (Normal Service)
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank Cars and Trucks; Jet Naphtha:
40600133
Submerged Loading (Normal Service)
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank Cars and Trucks; Gasoline:
40600136
Splash Loading (Normal Service)
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline Retail Operations - Stage 1;
40600301
Splash Filling
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline Retail Operations - Stage 1;
40600302
Submerged Filling w/o Controls
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline Retail Operations - Stage 1;
40600306
Balanced Submerged Filling
B-6
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Appendix B. Non-category SCCs
see
SCC Description
40600307
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline Retail Operations - Stage 1;
Underground Tank Breathing and Emptying
40600401
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Filling Vehicle Gas Tanks - Stage II;
Vapor Loss w/o Controls
40600402
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Filling Vehicle Gas Tanks - Stage II;
Liquid Spill Loss w/o Controls
40600503
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Pipeline Petroleum Transport -
General - All Products; Pump Station
40600707
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Consumer (Corporate) Fleet
Refueling - Stage 1; Underground Tank Breathing and Emptying
40799997
Petroleum and Solvent Evaporation; Organic Chemical Storage; Miscellaneous; Specify in Comments
41082001
Petroleum and Solvent Evaporation; Dry Cleaning; Petroleum Solvent - Wastewater, Aggregate; Process Area Drains
42500101
Petroleum and Solvent Evaporation; Fixed Roof Tanks (210 Bbl Size) Breathing Loss
42500102
Petroleum and Solvent Evaporation; Fixed Roof Tanks (210 Bbl Size) Working Loss
50100402
Waste Disposal; Solid Waste Disposal - Government; Landfill Dump; Fugitive Emissions
50100404
Waste Disposal; Solid Waste Disposal - Government; Landfill Dump; Trench Method
50100406
Waste Disposal; Solid Waste Disposal - Government; Landfill Dump; Gas Collection System: Other
50100601
Waste Disposal; Solid Waste Disposal - Government; Fire Fighting; Structure: Jet Fuel
50200103
Waste Disposal; Solid Waste Disposal - Commercial/Institutional; Incineration; Controlled Air
50200602
Waste Disposal; Solid Waste Disposal - Commercial/Institutional; Landfill Dump; Municipal: Fugitive Emissions ** (Use 5-01-
004-02)
50300205
Waste Disposal; Solid Waste Disposal - Industrial; Open Burning; Rocket Propellant
50300702
Waste Disposal; Solid Waste Disposal - Industrial; Liquid Waste; Waste Treatment: General
B-7
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Appendix C. Additional Non-Category SCCs with Unit Descriptions
Emission Unit Description
see
SCC Description
Building 1665 - open top
Industrial Processes; Miscellaneous Manufacturing Industries; Miscellaneous Industrial Processes;
chemical tank
39999995
Other Not Classified
Petroleum and Solvent Evaporation; Organic Solvent Evaporation; Miscellaneous Volatile Organic
Explosives Manufacturing
49099998
Compound Evaporation; Identify the Process and Solvent in Comments
FIRING RANGE TRAINING
Petroleum and Solvent Evaporation; Surface Coating Operations; Surface Coating Application -
OPERATIONS
40200101
General; Paint: Solvent-base
Industrial Processes; Miscellaneous Manufacturing Industries; Miscellaneous Industrial Processes;
Solids Handling
39999994
Other Not Classified
Autoclave (from facility ID
5555)
40201001
Petroleum and Solvent Evaporation; Surface Coating Operations; Coating Oven Heater; Natural Gas
Autoclave (NTR)
40201001
Petroleum and Solvent Evaporation; Surface Coating Operations; Coating Oven Heater; Natural Gas
Boilers and Humidifiers (1
and S)
40201001
Petroleum and Solvent Evaporation; Surface Coating Operations; Coating Oven Heater; Natural Gas
Clean Line - Heating Units
(1 and S)
40201001
Petroleum and Solvent Evaporation; Surface Coating Operations; Coating Oven Heater; Natural Gas
Coating Ovens (NTR)
40201001
Petroleum and Solvent Evaporation; Surface Coating Operations; Coating Oven Heater; Natural Gas
GAS OVENS-PAINT
CURING
40201001
Petroleum and Solvent Evaporation; Surface Coating Operations; Coating Oven Heater; Natural Gas
HEATERS (land Sand
NTR)
40201001
Petroleum and Solvent Evaporation; Surface Coating Operations; Coating Oven Heater; Natural Gas
Ovens 1-10 (from facility
ID 5555)(l and S)
40201001
Petroleum and Solvent Evaporation; Surface Coating Operations; Coating Oven Heater; Natural Gas
Industrial Processes; Miscellaneous Manufacturing Industries; Miscellaneous Industrial Processes;
Fire training pit
39999994
Other Not Classified
Building No. 4035 - hard
chrome plating tank
Industrial Processes; Miscellaneous Manufacturing Industries; Miscellaneous Industrial Processes;
[MACT, Subpart N]
39999994
Other Not Classified
CADMIUM
Industrial Processes; Miscellaneous Manufacturing Industries; Miscellaneous Industrial Processes;
ELECTROPLATING LINE
39999995
Other Not Classified
Industrial Processes; Miscellaneous Manufacturing Industries; Miscellaneous Industrial Processes;
Wastewater Treatment
39999995
Other Not Classified
C-l
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Appendix C. Additional Non-Category SCCs with Unit Descriptions
Emission Unit Description
see
SCC Description
Wastewater treatment
facility
40188898
Petroleum and Solvent Evaporation; Organic Solvent Evaporation; Fugitive Emissions; Specify in
Comments Field
Welding Sources
39999995
Industrial Processes; Miscellaneous Manufacturing Industries; Miscellaneous Industrial Processes;
Other Not Classified
C-2
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Appendix 2
Technical Support Document for HEM-AERMOD Modeling
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Modeling for the Residual Risk and Technology Review
Using the Human Exposure Model 3 - AERMOD Version
Updated 01/08/2014
Technical Support Document
Prepared for:
U.S. Environmental Protection Agency
Office of Air and Radiation
Office of Air Quality Planning and Standards
Health and Environmental Impacts Division
Air Toxics Assessment Group
Research Triangle Park, NC 27711
Prepared by:
EC/R Incorporated
501 Eastowne Drive, Suite 250
Chapel Hill, North Carolina 27514
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Disclaimer
The research described in this document has been funded by the United States
Environmental Protection Agency contracts 68-D-01-076, EP-D-06-119, and
currently EP-W-12-011 to EC/R Incorporated. Although it has been subject to the
Agency's review, it does not necessarily reflect the views of the Agency, and no
official endorsement should be inferred.
111
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1. Introduction
Contents
1
2. Overview of the HEM-3 - AERMOD System 2
2.1 Preparation of Dispersion Modeling Inputs 3
2.1.1 Compiling Emission Source Data 3
2.1.2 Defining the Modeling Domain and Receptors 4
Treatment of Nearby Census Blocks and Screening for Overlapping Blocks 4
Polar receptor network 5
Elevations and hill heights for model receptors 5
2.1.3 Selection of Meteorological Data 6
2.2 Running of AERMOD 6
2.2.1 AERMOD Dispersion Options Used by HEM-3 6
2.2.2 Use of Dilution Factors 7
2.3 Postprocessing of AERMOD Results in HEM-3 7
2.3.1 Calculation of Impacts at Modeled Receptors 8
2.3.2 Interpolation of Impacts at Outer Census Blocks 9
2.3.3 Calculation of Population Exposures and Incidence 10
2.3.4 Model Outputs 11
2.4 Data Libraries Used in HEM-3 12
2.4.1 Chemical Health Effects Information 12
2.4.2 Census Block Locations and Elevation Data 13
2.4.3 Meteorological Data 14
2.4.4 Gaseous Deposition Parameters 14
3. Modeling for the Residual Risk Technology Review 15
3.1 Emission Source Inputs 15
3.2 Pollutant Cross-Referencing 15
3.3 Meteorological Data 16
3.4 Model Options Selected 19
3.4.1 Urban or Rural Dispersion Characteristics 19
iv
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3.4.2 Deposition and Plume Depletion 19
3.4.3 Cutoff Distance for Modeling of Individual Blocks 20
3.4.4 Facility Boundary Assumptions 20
3.5 Modeling of Multiple Facilities 22
4. Quality Assurance 23
4.1 Engineering Review 23
4.2 Geographic Pre-Modeling Checks 23
4.3 Geographic Post-Modeling Checks 24
5. Uncertainties 26
6. References 32
Figures
Figure 3-1. AERMOD Meteorological Stations 18
Tables
Table 2-1. Parameters Used to Delineate the Modeling Domain in HEM-3 4
Table 3-1. HEM-3 Domain and Set-Up Options As Used in the Residual Risk and Technology
Review Assessments 21
Table 5-1. Summary of General Uncertainties Associated with Risk and Technology Review
Risk Assessments 28
v
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1. Introduction
This document describes the general modeling approach used to estimate the risks to
human populations in support of the Residual Risk and Technology Review (RTR) currently
being carried out by the U.S. Environmental Protection Agency (EPA). It is important to note
that risk characterizations of individual source categories under the RTR program may not
follow every item/approach noted in this document. The reader is referred to the main body of
the risk assessment document for more details on source category specific approaches that may
have been included in the analysis.
The model used in these risk assessments is the Human Exposure Model, Version 3
(HEM-3). HEM-3 incorporates AERMOD, a state of the art air dispersion model developed
under the direction of the American Meteorological Society / Environmental Protection Agency
Regulatory Model Improvement Committee (AERMIC).
Section 2 of this report provides an overview of the HEM-3-AERMOD system; and
Section 3 describes inputs and choices made in implementing the model for the RTR program.
Quality assurance efforts undertaken in the modeling effort are discussed in Section 4, and
uncertainties associated with the modeling effort are discussed in Section 5.
1
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2. Overview of the HEM-3 - AERMOD System
HEM-3 performs three main operations: dispersion modeling, estimation of population
exposure, and estimation of human health risks. The state-of-the-art American Meteorological
Society (AMS) EPA Regulatory Model (AERMOD)1'2 is used for dispersion modeling.
AERMOD can handle a wide range of different source types which may be associated with an
industrial source complex, including stack (point) sources, area and polygon sources, and volume
sources.
To prepare dispersion modeling inputs and carry out risk calculations, HEM-3 draws on
four data libraries, which are provided with the model. The first is a library of meteorological
data for over 824 stations, which are used for dispersion calculations. A second library of Census
block ("centroid") internal point locations and populations provides the basis of human exposure
calculations. The Census library also includes the elevations of every Census block, which are
used in the dispersion calculations for the RTR assessments. A third library of pollutant unit risk
estimates and reference concentrations is used to calculate population risks. These unit risk
estimates and reference concentrations are based on the latest values recommended by EPA for
hazardous air pollutants (HAP) and other toxic air pollutants. The fourth data library, which
provides deposition parameters for gaseous pollutants, is used only when the user opts to
compute deposition and plume depletion (not computed for the RTR assessments to date).
HEM-3 has been implemented in two versions: a single facility version, and a multiple
facility version ("Multi HEM-3"). Multi HEM-3 is used in the RTR risk assessment modeling.
Both versions operate under the same general principles. In essence, Multi HEM-3 provides a
platform for running the single facility version multiple times. In both versions, source location
and emissions data are input through a set of Excel™ spreadsheets. The main difference is in the
user interface for other model inputs. Single HEM-3 includes a graphical user interface (GUI) for
the selection of various dispersion modeling options. In Multi HEM-3, a control file replaces
many of these GUI inputs.
The model estimates cancer risks and noncancer adverse health effects due to inhalation
exposure at Census block internal point locations (or "centroids"), at concentric rings
surrounding the facility center, and at other receptor locations that can be specified by the user.
Cancer risks are computed using EPA's recommended unit risk estimates for Hazardous Air
Pollutants (HAP) and other toxic air pollutants. The resulting estimates reflect the excess cancer
risk for an individual breathing the ambient air at a given receptor site 24-hours per day over a
70-year lifetime. The model estimates the numbers of people exposed to various cancer risk
levels. In addition, HEM-3 estimates the total incremental cancer risks for people living within
different distances of the modeled emission sources.
Potential noncancer health effects due to chronic exposures are quantified using hazard
quotients and hazard indices for various target organs. The "hazard quotient" (HQ) for a given
chemical and receptor site is the ratio of the ambient concentration of the chemical to the
reference concentration. The "hazard index" (HI) for a given organ is the sum of hazard
quotients for substances that affect that organ. HEM-3 computes target-organ-specific hazard
indices (TOSHI) for HAPs and other toxic air pollutants, and estimates the numbers of people
2
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exposed to different hazard index levels. In addition, maximum short term ("acute")
concentrations are computed for all pollutants, and concentrations are compared with threshold
levels for acute health effects.
The following sections outline the methodologies used in the HEM-3-AERMOD system.
Section 2.1 describes the preparation of dispersion modeling inputs, Section 2.2 describes the
running of AERMOD, Section 2.3 describes calculations performed by HEM-3 to calculate risks
and exposures, and Section 2.4 details the sources and methods used to produce HEM-3 's data
libraries. The HEM-3 User's Manuals - for single HEM-3 and Multi HEM-3 - provide
additional details on the input data and algorithms used in the model.3 Specific model options
used in the RTR assessments are discussed in Chapter 3.
2.1 Preparation of Dispersion Modeling Inputs
HEM-3 compiles data that will be needed for dispersion modeling, and prepares an input
file suitable for running AERMOD. The dispersion modeling inputs can be divided into three
main components: emission source data, information on the modeling domain and receptors for
which impacts will be computed, and meteorological data.
2.1.1 Compiling Emission Source Data
A series of Excel™ spreadsheet files are used to specify the emissions and configuration
of the facility to be modeled. At a minimum, two files are needed: a pollutant emission file, and
an emission location file. The emission file includes an emission source identification code for
each emission source at the facility, the names of pollutants emitted by each source, and the
emission rate for each pollutant. In addition, if the model run is to incorporate deposition or
plume depletion, the emission file must also specify the percentage of each pollutant that is in the
form of particulate matter. The balance is assumed to be in vapor form.
The emission location file includes the coordinates of each source, as well as information
on the configuration and other characteristics of the source. HEM-3 can analyze point sources,
area and polygon sources, and volume sources - configurations that are described in AERMOD's
documentation.1'2 For stack (point) sources, the location file must provide the stack height, stack
diameter, emission velocity, and emission temperature. The file must also provide dimensions
for each area or volume source, as well as the height of the source above the ground. For area
sources, the angle of rotation from north can also be specified. The user can also provide the
terrain elevation at the base of each source. (The controlling hill height is also used in
AERMOD's flow calculations. Calculation of the controlling hill height by HEM-3 is discussed
in Section 2.4.2.) If the terrain elevations are not provided by the user, HEM-3 will calculate
elevations and controlling hill heights based on elevations and hill heights provided by the
Census database for the Census blocks nearest to the facility.
If particulate deposition and plume depletion are to be considered, then HEM-3 requires a
third input file to specify the particle size distribution. This input file must include the average
particle diameter, the mass fraction percentage, and the average particle density for each size
range emitted. Another optional file can be used to specify building dimensions if building wake
effects are to be modeled.
3
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2.1.2 Defining the Modeling Domain and Receptors
HEM-3 defines a modeling domain for each facility that is analyzed based on parameters
specified by the model user or calculated by the model. These parameters are summarized in
Table 2-1. The modeling domain is circular, and is centered on the facility, with a radius
specified by the user. For the RTR analysis, the radius of the modeling domain is 50 kilometers
(km). HEM-3 identifies all of the Census block locations in the modeling domain from its
Census database, and divides the blocks into two groups based on their distance from the facility.
For the inner group of Census blocks (closest to the facility), each block location is modeled as a
separate receptor in AERMOD. The cutoff distance for modeling individual Census blocks is
generally set to 3 km for the RTR assessments, although it can be set differently by the model
user. The model user can also provide an Excel™ spreadsheet specifying additional locations to
be included as model receptors in AERMOD. These additional discrete "user receptors" may
include facility boundary locations, monitoring sites, individual residences, schools, or other
locations of interest.
For Census blocks in the outer group,
beyond this modeling cutoff distance,
emission impacts are interpolated based on
modeling results for a polar receptor
network. The user also specifies an
"overlap" distance, within which Census
block coordinates will be considered to be
on facility property. The following
paragraphs provide more details on the
treatment of blocks near the facility, on the
polar receptor network, and on the
determination of receptor elevations and
controlling hill heights to be used in
AERMOD.
Treatment of Nearby Census Blocks and
Screening for Overlapping Blocks
Census block locations near the
facility are modeled as separate receptors within AERMOD. The cutoff distance for modeling of
individual Census blocks may be chosen by the user, but is typically 3000 meters for the RTR
assessments. This distance is not measured from the center of the facility, but is the minimum
distance from any source at the facility. Therefore, any Census block location that is within the
cutoff distance from any emission source is treated as a discrete AERMOD receptor.
HEM-3 checks Census blocks that are very close to the facility in order to assess whether
they overlap any point, area or volume emission sources. In addition, the user can specify an
overlap distance, within which receptors will be considered to be on facility property. The
default value for the overlap distance is 30 meters, or approximately equal to the width of a
narrow buffer and a roadway. HEM-3 tests each nearby receptor to determine whether it is
within this distance from any stack or from the perimeter of any area or volume source. If a
Table 2-1. Parameters Used to Delineate the
Modeling Domain in HEM-3
Typical
Parameter
\ a 1 nc
Modeling domain size - maximum
radial distance to be modeled from
50 km
facility center
Cutoff distance for modeling of
individual blocksa
3,000 m
Overlap distance - where receptors
are considered on facility property21
30 m
Polar receptor network:
Distance to the innermost ringb
>100 m
Number of concentric rings
13
Number of radial directions
16
a Measured from each stack at the facility, and from the
edges of each area or volume source
b Generally model-calculated to encompass all emission
sources but not less than 100 meters from the facility
center
4
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receptor falls within this distance, HEM-3 will not calculate risks based on the location of that
receptor, but will instead assume that the risks associated with the receptor are the same as the
highest predicted value for any receptor that is not overlapping. The location for calculating the
default impact may be either another Census block, one of the polar grid receptors, or one of the
additional discrete user-specified receptor locations.
Polar receptor network
The polar receptor network used in HEM-3 serves three functions. First, it is used to
estimate default impacts if one or more Census locations are inside the overlap cutoff distance
used to represent the facility boundary. Second, it is used to evaluate potential acute effects that
may occur due to short-term exposures in locations outside the facility boundary. Third, the polar
receptor network is used to interpolate long-term and short-term impacts at Census block
locations that are outside the cutoff distance for modeling of individual blocks.
Generally, the model calculates the inner radius (or first ring distance) for the polar
receptor network to be just outside the emission source locations, but not less than 100 meters
from the facility center. However, the user can override the default distance calculated by the
model to fit the size and shape of the facility properties to be modeled. Likewise, the model will
also use default values for the number of concentric rings to be analyzed (13 rings by default),
and the number of radial directions (16 radials by default), although these default values can also
be changed by the user to meet the needs of a specific modeling study. The inner radius of the
polar network should be the minimum distance from the facility center that is generally outside
of facility property. (For complex facility shapes, it is sometimes useful to specify an inner ring
that encroaches on facility property in some directions.) HEM-3 will distribute the radial
directions evenly around the facility. For the concentric rings, the model will generate a
logarithmic progression of distances starting at the inner ring radium and ending at the outer
radium of the modeling domain.
Elevations and hill heights for model receptors
HEM-3 includes terrain elevations by default for the RTR assessments, but the user can
choose to exclude terrain effects when running AERMOD. If the default terrain option is used,
HEM-3 obtains elevations and controlling hill heights for Census block receptors from its
internal Census location library. Section 2.4.2 describes the derivation of these elevations and
hill heights.
Elevations and controlling hill heights for the polar grid receptors are also estimated
based on values from the Census library. HEM-3 divides the modeling domain into sectors based
on the polar receptor network, with each Census block assigned to the sector corresponding to
the closest polar grid receptor. Each polar grid receptor is then assigned an elevation based on
the highest elevation for any Census block in its sector. The controlling hill height is also set to
the maximum hill height within the sector. If a sector does not contain any blocks, the model
defaults to the elevation and controlling hill height of the nearest block outside the sector.
5
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2.1.3 Selection of Meteorological Data
In addition to source and receptor information, AERMOD requires surface and upper air
meteorological observations in a prescribed format. The model user can select a meteorological
station from the HEM-3 meteorological data library, or add new files to the library if site-specific
data are available. If the user does not specify a meteorological station, HEM-3 will select the
closest station to the center of the modeling domain, as is generally done for the RTR
assessments.
2.2 Running of AERMOD
Based on the user input data and other data described in the previous section, HEM-3
produces an input file suitable for AERMOD. HEM-3 then runs AERMOD as a compiled
executable program. No changes have been made from the version of AERMOD released to the
public by EPA. The following sections give additional information on how AERMOD is used
within HEM-3.
2.2.1 AERMOD Dispersion Options Used by HEMS
AERMOD provides a wide array of options for controlling dispersion modeling
calculations. In general, HEM-3 uses the regulatory default options when running AERMOD.1
These options include the following:
• Use stack-tip downwash (except for Schulman-Scire downwash);
• Use buoyancy-induced dispersion (except for Schulman-Scire downwash);
• Do not use gradual plume rise (except for building downwash);
• Use the "calms processing" routines;
• Use upper-bound concentration estimates for sources influenced by building downwash
from super-squat buildings;
• Use default wind profile exponents;
• Use low wind speed threshold;
• Use default vertical potential temperature gradients;
• Use of missing-data processing routines; and
• Consider terrain effects.
The following additional AERMOD options are available to the HEM-3 user:
• Calculation of wet and dry deposition rates for vapor and particulate matter;
• Consideration of plume depletion (due to deposition) when calculating air concentrations;
• Consideration of building wake effects;
• Calculation of short term (acute) impacts; and
• Use of the FASTALL option, which conserves model runtime by simplifying the
AERMOD algorithms used to represent meander of the pollutant plume.
6
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As noted in Section 2.1, the calculation of deposition or depletion and the consideration of
building wake effects require additional user inputs.
The user can opt to analyze short term impacts on a number of different time scales (i.e.,
1 hour, 6 hours, 8 hours, or 24 hours) however only one short term time scale can be selected per
run. If the user chooses to analyze short term impacts, a multiplier must be specified to reflect
the ratio between the maximum short term emission rate and the long term average emission rate.
The default multiplier for short term emissions is a factor of 10. This means that in the default
case the maximum short term emission rate is assumed to be 10 times the long term average
emission rate. The multiplier can be set to 1.0 if emissions from the facility are known to be
constant. For RTR assessments, acute impacts are generally included in the modeling and the
default multiplier of 10 is used, unless more source-specific information is available upon which
to base the acute factor for the source category being modeled.
2.2.2 Use of Dilution Factors
To save computer run time when analyzing the impacts of multiple pollutants, HEM-3
does not model each pollutant separately. Instead, AERMOD is used to compute a series of
dilution factors, specific to each emission source and receptor. The dilution factor for a particular
emission source and receptor is defined as the predicted ambient impact from the given source
and at the given receptor, divided by the emission rate from the given source.
If the user chooses not to analyze deposition and plume depletion, the dilution factor does
not vary from pollutant to pollutant. If deposition and depletion is chosen as a model option,
separate dilution coefficients must be computed for each gaseous pollutant. In addition, separate
dilution factors must be computed for different components of particulate matter if the
components do not have the same particle size distribution. In the current version of HEM-3, this
can be done by creating a separate emission record for each pollutant emitted by from each
source. (Common location data and source configurations can be used for different pollutant
records representing the same emission source.)
2.3 Postprocessing of AERMOD Results in HEM-3
HEM-3 estimates total excess cancer risks and potential chronic noncancer health effects
for all Census block locations in the modeling domain, all user-defined receptors, and all points
in the polar receptor network. Potential chronic noncancer health effects are expressed in terms
of TOSHI. Based on the results for Census blocks and other receptors, HEM-3 estimates the
maximum individual risk (MIR) and maximum TOSHI for populated receptors, and determines
the locations of these maximum impacts. The model also determines the concentrations of
different pollutants at the sites of the maximum risks, and the contributions of different emission
sources to these maximum estimated risks. It should be noted that the locations may differ for the
maximum individual cancer risk and for the hazard indices for different target organs.
For acute impacts, HEM-3 calculates the maximum short term concentrations for all
pollutants emitted by the facility. These maximum short term concentrations are compared with
various threshold levels for acute health effects (e.g., the California EPA reference exposure
level [REL] for no adverse effects).
7
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At the option of the model user, HEM-3 will also compute the long term and short term
predicted ambient concentrations of all pollutants emitted by the facility at all of the receptors in
the modeling domain. In addition, pollutant contributions from each emission source at the
facility are computed under this option.
Section 2.3.1. describes methods used to calculate cancer risks and hazard indices for
receptors that are explicitly modeled using AERMOD. Section 2.3.2 describes the interpolation
approach used to estimate cancer risks and hazard indices at Census blocks that are not explicitly
modeled.
2.3.1 Calculation of Impacts at Modeled Receptors
As noted in Section 2.2.2, HEM-3 does not model each pollutant separately unless
deposition or depletion is being analyzed. Instead, AERMOD is used to compute a series of
dilution factors, specific to each emission source and receptor. HEM-3 also conserves computer
memory by computing cancer risks and hazard indices directly, without recording the
concentration of each pollutant at each receptor. The following algorithms are used to compute
cancer risks and TOSHI for chronic noncancer health effects.
For cancer risk:
For TOSHI:
where:
CRt =
I» =
CRy =
DFy =
CF =
2> =
E;,k =
UREk =
TOSHIx =
TOSHIy =
RfCk =
CRt Xy CRij
CRi,j = DFy x CF x Xk [E,.k x UREk]
TOSHIx = ly TOSHIy
TOSHIy = DFy xCFx^k [E,.k / RfCk]
total cancer risk at a given receptor (probability for one person)
the sum over all sources i and pollutant types j (particulate or gas)
cancer risk at the given receptor for source i and pollutant type j
dilution factor [(|ig/m3) / (g/sec)] at the given receptor for source i and pollutant
typej
conversion factor, 0.02877 [(g/sec) / (ton/year)]
sum over all pollutants k within pollutant type j (particulate or gas)
emissions of pollutant k from source i and in pollutant type j
cancer unit risk factor for pollutant k
total target-organ-specific hazard index at a given receptor
target-organ-specific hazard index at the given receptor for source i and pollutant
typej
non-cancer health effect reference concentration for pollutant k
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The above equations are equivalent to the following simpler equations:
CRx = I,.k ACi.k x UREk
HIt = I,.k ACi,k / RCk
where:
ACi,k = ambient concentration (|ig/m3) for pollutant k at the given receptor. This is the same
as [E,.k x DFi.j 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 is not available for a given chemical, then that chemical is
not included in the calculation of cancer risk. Likewise, if the noncancer reference concentration
is not available for a given chemical, that chemical is not included in the calculation of hazard
indices. Note also that separate reference concentrations are used for acute and chronic hazard
indices.
HEM-3 computes short term concentrations and records the highest short term
concentration for each pollutant. In addition, the user can opt to compute and record the short
term and long concentrations at each receptor. Concentrations are computed as follows.
Long term concentrations:
ACT,k = £i ACi,k
ACi,k = EytxDFij x CF
Short term concentrations:
ACt = £i ACi,k
ACi,k = EytxDFij x CF x M
where:
ACx.k = total estimated ambient concentration for pollutant k at a given receptor
£i = the sum over all sources i (|ig/m3)
ACi,k = estimated ambient concentration of pollutant k at the given receptor as a result of
emissions from source i (|ig/m3)
M = ratio between the estimated maximum short term emission rate and the long term
average emission rate (dimensionless)
2.3.2 Interpolation of Impacts at Outer Census Blocks
For Census blocks outside of the cutoff distance for individual block modeling, HEM-3
estimates cancer risks and hazard indices by interpolation from the polar receptor network.
Impacts at the polar grid receptors are estimated using AERMOD modeling results and the
algorithms described in Section 2.3.1. If terrain elevation is part of the modeling, then an
elevation is estimated for each polar receptor. HEM-3 estimates elevations and controlling hill
heights for the polar grid receptors based on values from the census library. HEM-3 divides the
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modeling domain into sectors based on the polar grid receptor network, with each census block
assigned to the sector corresponding to the closest polar grid receptor.
HEM-3 then assigns each polar grid receptor an elevation based on the highest elevation
for any census block in its sector. The controlling hill height is also set to the maximum hill
height within the sector. If a sector does not contain any blocks, the model defaults to the
elevation and controlling hill height of the nearest block outside the sector.
The impacts at each outer Census block are interpolated from the four nearest polar grid
receptors. The interpolation is linear in the angular direction, and logarithmic in the radial
direction, as summarized in the following equations:
Ia,r= lAl,r+ (lA2,r ~ lAl,r) x (a - Al) / (A2 - Al)
lAi,r= exp{(ln (Iai,ri) + [(In (Iai,r2) - In (Iai,ri)] x [(In r) - ln(Rl)] / [ln(R2) - ln(Rl)]}
Ia2,t= exp{(ln (Ia2,ri) + [(In (Ia2,r2) - In (Ia2,ri)] x [(In r) - ln(Rl)] / [ln(R2) - ln(Rl)]}
where:
Ia,r= the impact (cancer risk, hazard index, or concentration) at an angle, a, from north,
and radius, r, from the center of the modeling domain
a = the angle of the target receptor, from north
r = the radius of the target receptor, from the center of the modeling domain
Al = the angle of the polar network receptors immediately counterclockwise from the
target receptor
A2 = the angle of the polar network receptors immediately clockwise from the target
receptor
R1 = the radius of the polar network receptors immediately inside the target receptor
R2 = the radius of the polar network receptors immediately outside the target receptor
2.3.3 Calculation of Population Exposures and Incidence
Using the predicted impacts for Census blocks, HEM-3 estimates the numbers of people
exposed to various cancer risk levels and TOSHI levels. This is done by adding up the
populations for receptors that have predicted cancer risks or TOSHI above the given threshold.
The model also estimates the total annual excess cancer risk (incidence) for the entire
modeling region. The following equation is used:
TCR = Zm [CRm x Pm ] / LT
where:
TCR = the estimated total annual cancer risk, or incidence, (cancers/year) to the
population living within the modeling domain
Xm = the sum over all Census blocks m within distance the modeling domain
CRm = the total lifetime cancer risk (from all modeled pollutants and emission sources)
at Census block m
Pm the population at Census block m
LT = the average lifetime used to develop the cancer unit risk factor, 70 years
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Furthermore, HEM-3 estimates the contributions of different chemicals and emission sources to
total annual cancer incidence for the overall modeling domain using the following equations:
TCRij = [(Ik Ei.k x UREk) x DF,.,m x CF / LT]
TCRi,k = TCRij x E,.k x UREk / (Ik E,.k x UREk)
where:
TCRij = the estimated total annual cancer incidence (cancers/year) to the population in the
modeling domain due to emissions from pollutant type j (1 = particulate, 2 = gas) and
emission source i
Xn, = the sum over all Census blocks m within distance the modeling domain
Xk = the sum over all pollutant k, within pollutant type j
Ei.k = emissions of pollutant k from source i (tons/year)
UREk = unit risk factor for pollutant k
DFj j ni = dilution factor at receptor m, for emissions of pollutant type j (which includes
pollutant k), from source i
CF = conversion factor, 0.02877 [(g/sec) / (ton/year)]
TCRi,k = the estimated annual cancer incidence (cancers/year) of the population in the
modeling domain due to emissions of pollutant k (in pollutant type j) from emission
source i
2.3.4Model Outputs
The following is a summary of the outputs produced by HEM-3. These are written to a
collection of files in Excel™ and dBase™ format (dbf).
• Maximum long term impacts at populated locations
o maximum lifetime individual cancer risk (MIR)
o maximum TO SHI for the following health effects
- respiratory system effects
- liver effects
- neurological system effects
- developmental effects
- reproductive system effects
- kidney effects
- ocular system effects
- endocrine system effects
- hematological system effects
- immunological system effects
- skeletal system effects
- spleen effects
- thyroid effects
- whole body effects
o locations of the maximum cancer risk and TOSHI
o Census block identification codes for the maximum cancer risk and TOSHI, and
number of people in the Census block
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o contributions of different chemicals and emission sources to the maximum risk and
TOSHI
• Maximum acute impacts
o maximum short term ambient concentration for each chemical
o threshold levels for acute health effects of each chemical (compared with the
maximum short term concentrations)
o locations of the maximum impacts for different chemicals (often polar receptors)
o Census block identification codes at the locations of maximum concentration, and
number of people in the block
o contribution of each emission source at the facility to the maximum short term
concentration of each chemical
• Outputs for all receptors
o maximum individual cancer risk and TOSHI (all target organs) for each Census
block and each user-specified discrete receptor (monitoring sites, etc.)
o maximum individual cancer risk and TOSHI (all target organs) for each polar grid
receptor
o estimated deposition (optional)
o predicted ambient concentration resulting from each emission source at each
Census block and polar grid receptor (optional)
• Population exposures and total cancer risk, or incidence
o estimated numbers of people exposed to different levels of lifetime individual
cancer risk (1 in a million, 1 in 100,000, etc.)
o estimated numbers of people exposed to different levels of TOSHI (1, 2, 10, etc.)
o total cancer risk, or incidence, in estimated cancer deaths per year, over the entire
modeling domain, and for each pollutant and source combination
2.4 Data Libraries Used in HEM-3
2.4.1 Chemical Health Effects Information
HEM-3 includes a library of available health effects data for HAPs. For each pollutant,
the library includes the following parameters, where available:
• unit risk estimate (URE) for cancer
• reference concentration (RfC) for chronic noncancer health effects
• reference concentrations for acute health effects
• target organs affected by the chemical for chronic noncancer health effects
Unit risk estimates and reference concentrations included in the HEM-3 chemical library have
been taken from EPA's database of recommended dose-response factors for HAPs, which is
updated periodically, consistent with continued research on these parameters.4 The URE
represents the upper-bound excess lifetime cancer risk estimated to result from continuous
exposure to an agent at a concentration of 1 microgram per cubic meter (|ig/m3) in air (e.g., if the
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URE = 1.5 x 10"6 per (j,g/m3, 1.5 excess tumors are expected to develop per 1 million people if all
1 million people were exposed daily for a lifetime to 1 microgram of the chemical in 1 cubic
meter of air).5
The RfC is a concentration estimate of a continuous inhalation exposure to the human
population that is likely to be without an appreciable "risk" of deleterious non-cancer health
effects during a lifetime. No adverse effects are expected as a result of exposure if the ratio of the
potential exposure concentration to the RfC, defined as the hazard quotient (HQ), is less than l.5
The reference benchmark concentration for acute health effects, similar to the chronic
RfC, is the concentration below which no adverse health effects are anticipated when an
individual is exposed to the benchmark concentration for 1 hour (or 8 hours, depending on the
specific acute benchmark used and the formulation of that benchmark). Target organs are those
organs (e.g., kidney) or organ systems (e.g., respiratory) which may be impacted with chronic
non-cancer health effects by exposure to the chemical in question. A more in-depth discussion of
the development and use of these parameters for estimating cancer risk and non-cancer hazard
may be found in the EPA's Air Toxics Risk Assessment Library.6
The model user can add pollutants and associated health effects to HEM-3's chemical
health effects (dose-response) library, as needed.
2.4.2 Census Block Locations and Elevation Data
The HEM-3 Census library includes Census block identification codes, locations,
populations, elevations, and controlling hill heights for all of the over 6 million Census blocks
identified in the 2010 Census and the over 5 million Census blocks identified in the 2000
Census. The model user may choose to use either Census database according to their modeling
needs. The location coordinates reflect the internal "centroid" of the block, which is a point
selected by the Census to be roughly in the center of the block. For complex shapes, the internal
point may not be in the geographic center of the block. Locations and population data for Census
blocks in the 50 states and Puerto Rico were extracted from the Land View® database For the
2000 Census7 and from the U.S. Census Bureau website for the 2010 Census.8 Locations and
populations for blocks in the Virgin Islands were obtained from the U.S. Census Bureau website.
U.S. Geological Survey data was used to estimate the elevation of each census block in
the continental U.S. and Hawaii. The data used for the 2000 Census elevations have a resolution
of 3 arc seconds, or about 90 meters.9 The data used for the 2010 Census elevations have a
resolution of 1/3 of an arc second, or about 10 meters.10 Using analysis tools (ArcGIS® 9.1
software application for the 2000 Census, and ArcGIS® 10 for the 2010 Census), elevation was
estimated for each census block in Alaska and the U.S. Virgin Islands. The point locations of the
census blocks in Alaska and the U.S. Virgin Islands were overlaid with a raster layer of North
American Digital Elevation Model (DEM) elevations (in meters).9 An elevation value was
assigned to each census block point based on the closest point in the ArcGIS elevation raster file.
An algorithm used in AERMAP, the AERMOD terrain processor, is used to determine
controlling hill heights.11'12 These values are used for flow calculations within AERMOD.
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To save run time and resources, the HEM-3 census block elevation database is substituted for the
DEM data generally used in AERMAP. As noted above, the census block elevations were
originally derived from the DEM database. To determine the controlling hill height for each
census block, a cone is projected away from the block centroid location, representing a 10%
elevation grade. The controlling hill height is selected based on the highest elevation above that
10% grade (in accordance with the AERMAP methodology). The distance cutoff for this
calculation is 100 km. (This corresponds to an elevation difference at a 10% grade of 10,000 m,
which considerably exceeds the maximum elevation difference in North America.)
2.4.3 Meteorological Data
HEM-3 includes an extensive library of meteorological data to support the AERMOD
dispersion model. Currently 824 meteorological stations have been preprocessed for AERMOD
as part of the RTR effort. Section 3.3 includes a depiction of these meteorological stations and
Appendix 2 discusses the preparation of meteorological data for the RTR in more detail.
2.4.4 Gaseous Deposition Parameters
HEM-3 provides options to compute the deposition of air pollutants, and to take into
account the impacts of plume depletion due to deposition of gaseous and particulate pollutants. If
the deposition and depletion option is selected by the model user for gaseous pollutants, a
number of pollutant properties are required by AERMOD. (These include the diffusivity of the
pollutant in air, the diffusivity of the pollutant in water, the Henry's Law constant, and a
parameter reflecting the cuticular resistance to uptake of the pollutant by leaves rci.).13 HEM-3
includes a library of these parameters for most gaseous HAPs. This library is based on a
compendium of gaseous deposition parameters developed by Argonne National Laboratories.14 It
should be noted, however, that the deposition and depletion option of HEM-3 and AERMOD
have not been used to date for the RTR assessments.
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3. Modeling for the Residual Risk Technology Review
This section discusses the general approach used to implement the HEM-3 AERMOD
system for the RTR modeling analyses. Separate reports have been prepared for each of the
emission source categories analyzed to date. These reports provide information on the emissions
inputs and results for specific emission categories.
3.1 Emission Source Inputs
HEM-3 and AERMOD require detailed data on emissions from each emission source
included in the modeling analysis. These data include:
• pollutants emitted;
• emission rate for each pollutant;
• emission source coordinates;
• stack height (or emission height for fugitive and other area sources);
• stack diameter (or configuration of fugitive and other area sources);
• emission velocity; and
• emission temperature.
Emissions data for the RTR assessments are compiled form a variety of data sources (i.e.,
the 2005 National Emissions Inventory (NEI)15 information data requests). Each source
category under the RTR program, in most cases, utilizes the latest best available data. These
data include HAP emission rates, emission source coordinates, stack heights, stack diameters,
flow rates, and exit temperatures. EPA performs an engineering review of the NEI data. In cases
where new or better data were known to exist for a particular source category, that information is
integrated into the data used in modeling for that category. For each source category, the
emissions are summarized in the source category specific report. Detailed computer files
containing all emission and release characteristics are available in the docket prepared for the
specific RTR source category proposed or final rule.
As noted in the previous section, industrial emission sources can be characterized in
AERMOD as point, area, polygon, or volume sources. Fugitive emissions are generally
characterized as low point sources with minimal exit velocities. For some categories, additional
information was available on the configuration of fugitive emission sources. This information
was incorporated into the emissions database as part of the engineering review. Thus, fugitive
emission sources were characterized as area or volume sources when sufficient configuration
information was available.
3.2 Pollutant Cross-Referencing
Because the NEI is developed from a number of different data sources, a single chemical
may be listed in the inventory under different names (i.e. a "common name" and one or more
structure-based names). In addition, pollutant groupings such as polycyclic organic matter
(POM), can be listed in the NEI under the names of individual member compounds, and under
different synonyms (e.g. polynuclear aromatic hydrocarbons). HEM-3 requires an exact match in
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the chemical name in order to link emissions to the appropriate dose-response factors. The model
will not process any pollutant that is not specifically listed in the chemical library. Therefore, all
of the HAP names used in the NEI were linked to the appropriate chemical names in the HEM-3
reference file.
Pollutant-specific dose response values are used in the HEM-3 modeling whenever
available, including when modeling POM pollutants. Pollutant groupings, such as POM
groupings, are used for POMs without a chemical-specific unit URE's. These POMs are assigned
a URE associated with various POM compounds having similar characteristics. "An Overview of
Methods for EPA's National-Scale Air Toxics Assessment" 2011 document16 provides more
details regarding POM modeling, including:
[S]ome emissions of POM were reported in [the] NEI as "7-PAH" or "16-PAH,"
representing subsets of certain POM, or simply as "total PAH" or "polycyclic organic
matter." In other cases, individual POM compounds are reported for which no
quantitative cancer dose-response value has been published in the sources used for
NAT A. As a result, simplifying assumptions that characterize emissions reported as POM
are applied so that cancer risk can be quantitatively evaluated for these chemicals without
substantially under- or overestimating risk (which can occur if all reported emissions of
POM are assigned the same URE). To accomplish this, POM emissions as reported in
NEI are grouped into categories. EPA assigns dose-response values based on the known
or estimated toxicity for POM within each group and on information for the POM
speciation of emission sources, such as wood fires and industrial processes involving
combustion.
Emissions of metal compounds are also adjusted using algorithms developed for the
Emissions Modeling System for Hazardous Air Pollutants (EMS-HAP) under the National-scale
Air Toxics Assessment (NATA). A mass adjustment factor was applied to the emissions of metal
compounds to account for a particular portion (e.g., the lead portion of lead sulfate) or to
partition them among multiple pollutant categories (e.g. chromium arsenate into chromium VI
compounds and arsenic compounds). In addition, where no specific compound information was
available, metals were speciated into appropriate oxidation states (e.g. chromium compounds
into chrome VI and chrome III) based on factors that have been developed for specific source
categories and average factors that have been developed for the inventory as a whole. The
adjustment factors and speciation factors were taken from the HAP Table module of EMS-
HAP.1718
3.3 Meteorological Data
Nationwide meteorological data files are accessed by HEM-3 and used for the RTR
modeling. The current HEM-3 AERMOD Meteorological Library includes over 800 nationwide
locations, depicted in Figure 3-1. This library contains surface and upper air meteorological data
from National Weather Service (NWS) observation stations, which are named beginning with the
state abbreviation for the state in which the station is located. AERMOD requires surface and
upper air meteorological data that meet specific format requirements.19'20 Appendix 2 discusses
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the preprocessing performed on the meteorological data used by AERMOD and includes a
detailed listing of the 824 meteorological station pairs.
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Figure 3-1. AERMQD Meteorological Stations
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3.4 Model Options Selected
HEM-3 presents a number of options for characterizing the modeling domain and data
sources. As many sources are generally modeled in RTR assessments, established defaults and
common practices are relied on to make these choices. The choices available to a HEM-3 user
and the selections that are made in most RTR assessments are presented in Table 3-1. Some of
the key selections are discussed in more detail in the paragraphs below.
It should be noted that although routine emissions are not expected to vary significantly
with time, nonroutine (upset) emissions can be significant relative to routine emissions. Upset
emissions occur during periods of startup, shutdown, and malfunction. Upset emissions are not
likely for equipment or storage tanks, but do result from malfunctioning control devices and
leaks in cooling tower heat exchangers. There is some limited data on upset emissions
available,21 but no facility-specific analyses of these data were performed to characterize short-
term emissions from these emission sources, and upset emissions are generally not modeled for
the RTR risk assessments.
3.4.1 Urban or Rural Dispersion Characteristics
Current RTR source category assessments which use the 2010 Census are based on either
urban or rural dispersion characteristics, depending on the land characteristics surrounding each
modeled facility. The EPA provides guidance on whether to select urban or rural dispersion
coefficients in its Guideline on Air Quality Models.22 In general, the urban option is used if (1)
the land use is classified as urban for more than 50% of the land within a 3-kilometer radius of
the emission source, or (2) the population density within a 3-kilometer radius is greater than 750
people per square kilometer. Of these two criteria, the land use criterion is more definitive.
Using the 2010 Census, the HEM-3 model determines, by default, whether to use rural or
urban dispersion characteristics. HEM-3 will find the nearest census block to the facility center
and determine whether that census block is in an urban area, as designated by the 2010 Census.23
The population of the designated urban area will be used to specify the population input for
AERMOD's urban mode. (Alternatively, a user may select the rural or urban option to override
determination by the model. If a user selects an urban dispersion environment, then the user must
provide the urban population as well.)
For the 2008 and prior screening-level RTR assessments of 51 source categories, the rural
option was chosen to be most conservative (i.e., more likely to overestimate risk results). The
rural option is also chosen by default by the HEM-3 model whenever the 2000 Census is selected
by the user.
3.4.2 Deposition and Plume Depletion
The RTR modeling analysis to date has not taken into account the depletion of pollutant
concentrations in the plume due to wet or dry deposition, although HEM-3 can model deposition
and depletion using AERMOD. In addition, reactivity and decay have not been considered. It is
possible that this approach may overestimate air concentrations and therefore risk. However, one
of the main metrics used by EPA in the residual risk program is the risk to the individual most
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exposed (the maximum individual risk, or MIR). Because the maximum risk usually occurs at a
receptor very close to the emission source, it is unlikely to be influenced by altered plume
dispersion characteristics of this type. For more refined, multipathway assessments, EPA may
consider deposition and depletion.
3.4.3 Cutoff Distance for Modeling of Individual Blocks
The cutoff distance for modeling individual Census blocks is initially set to 3 km by
default. This distance generally ensures that the maximum individual cancer risk and the
maximum TOSHI are modeled explicitly and not interpolated. Following a modeling run, the
results for each facility are checked to determine whether the maximum impacts are located
inside the modeling cutoff distance. If the maximum impacts are outside the cutoff distance, and
if any of the impacts are significant, then HEM-3 is rerun for the facility with a cutoff distance
greater than 3 km. In general, this is done if the cancer risk exceeds 1 in 1 million or any TOSHI
exceeds 1. However, the risks for such facilities are generally very low, since the maximum
impacts are in most cases only interpolated when the nearest Census block is more than 3 km
from the facility (i.e., in sparsely populated areas).
3.4.4 Facility Boundary Assumptions
The main input mechanisms for incorporating facility boundary information in HEM-3
are the overlap distance, the distance to the innermost polar receptor ring, and user-specified
receptor locations. The NEI does not provide information on facility boundaries. However,
satellite/aerial images are used to locate residential populations that are closer to a facility than
the Census block centroid. User-specified receptor locations are used in such assessments to
avoid underestimating risk. Conservative default assumptions are used for the overlap distance
and the innermost polar receptor ring. However, these are adjusted for some categories where
facility sites are known to be large. In addition, satellite imagery is used to check the facility
boundary assumptions for facilities with large projected impacts. These checks are discussed
further in the section on Quality Assurance (Section 4).
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Table 3-1. HEM-3 Domain and Set-Up Options As Used in the Residual Risk and
Technology Review Assessments
Option
Selection
Dispersion model
AERMOD
Census database: 2010 or 2000
Based on date of
analysis
Type of analysis: chronic, acute, or both
Both
Averaging time for short term impacts
1-hour
Multiplier for short term emissions
10 generally, although
multiple source type-
specific factors are
also used if available
Dispersion characteristics: urban or rural, as determined by model,
based on closest 2010 Census block to each facility (when using
2010 Census). Rural by default, when using the 2000 Census.
Urban or Rural based
on facility location;
(Rural for 2000
Census)
Include terrain impacts
Yes
Include building wake effects
No
Calculate deposition (wet, dry, or both) & include impacts of plume
depletion
No
User-specified receptor locations (for residential population
locations, facility boundary sites, or other sites of interest)
Yes, for some
facilities
Modeling domain size - maximum distance to be modeled
50 km
Cutoff distance for modeling of individual blocks
3 kma
Overlap distance where receptors are considered to be on facility
property - measured from each source measured from each source
30 mb
Polar receptor network specifications:
Distance from the facility center to the innermost ring
> 100 mc
Number of rings
13
Number of directions
16
Meteorology data
Closest site
a The individual block modeling cutoff is increased for categories and for some facilities to ensure that the
maximum individual risk values are not interpolated.
b The overlap distance is adjusted for some facilities to avoid modeling locations that are on facility property
(see section 4.2).
0 HEM-3 sets the innermost ring distance to be just outside the emission sources but not < 100 m.
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3.5 Modeling of Multiple Facilities
HEM-3 models one facility at a time. However, clusters of nearby facilities may impact
the same people, resulting in higher risk to those people. To account for this situation, risks are
summed at each Census block for all facilities affecting the Census block.
As described earlier (Section 2.3.4), HEM-3 produces detailed output tables containing
the risk and population for every Census block in the modeling domain. These detailed tables are
combined for all facilities in a source category and the risk for each Census block is summed,
using the RTR Summary Program add-on module to the Multi HEM-3 model.3 Thus, the effect
of multiple facilities in the same source category on the same receptor are estimated. The
resulting "combined facility" or "cluster-effect" Census block risks are used to calculate
population exposure to different cancer risk levels, noncancer hazard indices, and source
category incidence.
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4. Quality Assurance
The National Emissions Inventory (NEI) is subject to an extensive program of quality
assurance (QA) and quality control (QC). The QA/QC program for the point source component
of the NEI is documented in a separate report, available from the NEI website.24 This section
describes QA activities carried out under the RTR modeling analysis.
4.1 Engineering Review
In addition to the standardized QA steps taken for the entire NEI, EPA performs an
engineering review of NEI data for the emission source categories included in the RTR analysis.
This engineering review includes two main components. The first component addresses the list
of facilities included in each source category. EPA engineers review independent sources of
information to identify all sources in the category that are included in the NEI. In addition, EPA
reviews the list of sources represented as part of each category in the NEI to make sure that the
facilities actually manufacture products characteristic of the source category.
The second component of the engineering review focuses on the appropriateness of
facility emissions. EPA reviews the list of HAPs reportedly emitted by each facility to make sure
that the pollutants are appropriate to the source category. In addition, EPA engineers review the
magnitude of those HAP emissions. In cases where new or better data are known to exist for a
particular source category, that information is integrated into the data used in modeling for that
category. In these cases, the source category specific documents provide additional details on the
emissions inputs used.
4.2 Geographic Pre-Modeling Checks
The NEI QA process includes some basic checks on location data for point sources. The
coordinates for each source are checked to ensure that they are in the county that has been
specified for the source. If this is not the case, or if no geographic coordinates are available for
the emission source, then the coordinates are set to a default location based on the nature of the
emission source category.25 In addition, coordinates for all emission sources at a given facility
are checked to ensure that they are within 3 km of one another. These Q A checks happen prior to
HEM-3 modeling and the results of such checks are reflected in the HEM-3 input files.
Another pre-modeling geographic QA check regards the location of the census block
receptors. As noted above, to estimate ambient concentrations for evaluating long-term
exposures, the HEM-3 model uses the census block centroids as dispersion model receptors. The
census block centroids are often good surrogates for where people live within a census block. A
census block generally encompasses about 40 people or 10-15 households. However, in cases
where a block centroid is located on industrial facility property, or where a census block is large
and the centroid less likely to be representative of the block's residential locations, the block
centroid may not be an appropriate surrogate.
Census block centroids that are on facility property can sometimes be identified by their
proximity to emission sources. In cases where a census block centroid is within 300 meters of
23
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any emission source, aerial images of the facility are reviewed to determine whether the block
centroid is likely located on facility property. The selection of the 300-meter distance reflects a
compromise between too few and too many blocks identified as being potentially on facility
property. Distances smaller than 300 meters would identify only block centroids very near the
emission sources and could exclude some block centroids that are still within facility boundaries,
particularly for large facilities. Distances significantly larger than 300 meters would identify
many block centroids that are outside facility boundaries, particularly for small facilities. Block
centroids confirmed to be located on facility property are moved to a location that best represents
the residential locations in the block.
In addition, census block centroids for blocks with large areas may not be representative
of residential locations. Risk estimates based on such centroids can be understated if there are
residences nearer to a facility than the centroid, and overstated if the residences are farther from
the facility than the centroid. To avoid understating the maximum individual risk associated with
a facility, block centroids are relocated in some cases, or additional user-specified receptors are
added to a block. Aerial images of all large census blocks within one kilometer of any emission
source are examined. Experience from previous risks characterizations show that in most cases
the MIR is generally located within 1 km of the facility boundary. If the block centroid does not
represent the residential locations, it is relocated in the HEM-3 input files to better represent
them. If residential locations cannot be represented by a single receptor (that is, the residences
are spread out over the block), additional user-specified receptors are included in the HEM-3
input files to represent residences nearer to the facility than the centroid.
4.3 Geographic Post-Modeling Checks
As part of the RTR modeling analysis, additional geographical QA checks are made for
some facilities, after initial HEM-3 modeling results are reviewed. Facilities subjected to these
additional checks include:
• cases where the initial estimates of maximum risks are particularly high
o maximum individual cancer risk of over 1 in 10,000
o any maximum TO SHI above 10
• cases where no Census blocks are identified by the model within 3 km of the facility
HEM-3 produces a detailed Google Earth™ map of the modeled point and area emission
sources and surrounding receptors (including Census block centroids, polar receptors and user-
specified receptors) overlaying Google Earth™'s satellite imagery. This map allows a QA check
of the specific source locations, as well as an approximate check of the facility boundaries. The
emission source coordinates are reviewed for each of these facilities and compared with the
address reported for the facility. If the address and the coordinates represent the same location,
then the coordinates are taken to be correct. For more recent modeling of source categories, the
emission coordinates initially modeled by HEM-3 tend to be correct, as they undergo pre-
modeling scrutiny and QA checks (as discussed in Section 4.2).
More rarely, the modeled emission coordinates will be determined post initial modeling
not to be located on facility property. If the facility and emission coordinate locations are
24
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different, then the satellite imagery for the address and the coordinate location are reviewed to
determine whether either photograph includes an industrial facility. Generally for the 2008 and
prior screening-level RTR assessments of 51 source categories, where the two locations were
different, the facility address was found to be correct (and the emission source coordinates
required correction). In some cases, this comparison could not be made because the reported
address was a Post Office box or a headquarters address. Where this occurred, the aerial
photograph for the coordinate location was reviewed to determine whether an industrial facility
was located at or near the location. If emission source coordinates are found to be incorrect,
HEM-3 is rerun using corrected coordinates. These changes are described in the source category
documents.
For the high-risk facilities, the coordinates used to represent the most impacted Census
blocks are also reviewed. This review draws on detailed Census block boundary maps and
satellite imagery. Large industrial facilities will frequently occupy one or more entire Census
blocks. However, these blocks may also include one or more residences on the periphery of the
industrial land. Generally, the centroid coordinates listed for a Census block are near the center
of the block. In these cases of mixed industrial and residential blocks, the coordinates may be on
facility property.
In general, block coordinates are considered to be on facility property if they are located
between the different emission source locations listed for the facility. In these situations, HEM-3
is rerun with an expanded overlap distance, in order to exclude the Census block coordinates that
appear to be located on facility property. The distance to the innermost polar receptor ring is also
adjusted to ensure that this ring is not on facility property, but as close to the apparent facility
boundaries as possible.
25
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5. Uncertainties
The RTR risk assessments using HEM-3 and AERMOD are subject to a number of
uncertainties. For instance, model verification studies for AERMOD show predicted maximum
annual concentrations ranging from 0.3 to 1.6 times measured values, with an average of 0.9.
Predicted maximum short term (1 to 24 hours) concentrations were 0.25 to 2.5 times measured
values, with an average of 1.0.25
In addition, a number of simplifying assumptions are made in these modeling analyses.
First, the coordinates reported by the Department of Census for Census block internal points
("centroids") have been used as a surrogate for long-term population exposures. Locations of
actual residences have not been modeled. In addition, the current version of HEM-3 does not
take into account the movement of people from one Census block to another during the course of
their lives, or commuting patterns during a given day. Nor does the model take into account the
attenuation of pollutant from outside emission sources in indoor air. Ideally, risks to individuals
would be modeled as they move through their communities and undertake different activities.
However, such modeling is time-and resource-intensive and can only capture a portion of the
uncertainty associated with the full range of human activities. In general, it is expected that long-
term exposures will be overstated for high-end estimates (as most individuals will not spend all
their time at their highly affected residences), but may understate the total population exposed
(as some individuals living outside the modeled area may regularly commute into the area for
work or school).
When considering long-term or lifetime exposures, it should be noted that relatively few
people in the United States reside in one place for their entire lives. For the purposes of this
assessment, cancer risk estimates are based on a lifetime exposure at the Census-identified place
of residence. While it is impossible to know how this assumption affects the risk experiences by
a particular individual (as people can move into higher- or lower-risk areas), it is likely that this
assumption will overstate the exposure to those most exposed (i.e., people already living in high
exposure areas are unlikely to move to yet higher exposure areas). However, this assumption will
also tend to underestimate the total number of people exposed and population risk (i.e.,
incidence) because population levels are generally increasing.
In the current analyses, only direct inhalation is modeled. Other pathways such as the
deposition of pollutants to drinking water, and to bioaccumulation of deposited pollutants in the
food supply may be a significant source of exposure for persistent and bioaccumulative
hazardous air pollutants (PB HAP). Screening level evaluations of the potential human health
risks associated with emissions of PB HAP from the modeled facilities are used to determine if
additional analyses are needed, but these analyses are outside the scope of this document.
Because the HEM-3 AERMOD analyses are restricted to the inhalation pathway, the impacts of
plume depletion due to deposition are not taken into account. Thus, inhalation impacts may be
overestimated for some pollutants, but exposures through other pathways would be
underestimated.
A number of other simplifications are made in the dispersion modeling analyses, as noted
in Table 3-1. For instance, building wake effects are not considered. In addition, meteorological
26
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observations are based on the closest station in the HEM-3 meteorological library (see Figure
3-1). Alternative meteorological stations may be more appropriate for some facilities. Ideally,
facility-specific meteorological observations would be used. A single year of meteorological data
(2011) is currently used for AERMOD's dispersion modeling. (The 2008 and prior screening-
level RTR assessments of 51 source categories used meteorological data based on the year
1991.) When considering off-site meteorological data most site specific dispersion modeling
efforts will employ up to five years of data to capture variability in weather patterns from year to
year. However, because of the large number of facilities in the analyses and the extent of the
dispersion modeling analysis (national scale), it is not practical to model five years of data. Other
national studies such as NAT A also consider only a single year of meteorological data. A
sensitivity analyses performed by the NATA assessment found that variability attributable to the
selection of the meteorology location/time (both temporal and spatial) resulted in a 17-84%
variation in predicted concentrations at a given station.26
Finally, risk and exposure factors are also subject to uncertainty. Not all individuals
experience the same degree of exposure or internal dose of a given pollutant due to individual-
specific parameters such as weight, age, and gender. While the health benchmarks used in the
analyses crudely account for sensitive populations, a prototypical human (e.g., body weight,
ventilation rate) is used to define the benchmark. Because of the variability of these parameters
in the population, this factor will result in a degree of uncertainty in the resulting risk estimate.
Table 5-1 summarizes the general sources of uncertainty for the RTR modeling analyses.
The table also gives a qualitative indication of the potential direction of bias on risk estimates.
The sources of uncertainty in Table 5-1 are divided into four categories, based on the major
components of the analyses:
• emissions inventory;
• fate and transport modeling;
• exposure assessment; and
• toxicity assessment.
It must also be noted that individual source categories may be subject to additional uncertainties.
These are discussed in separate reports which are prepared for each emission source category
included in the RTR assessments.
27
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Table 5-1. Summary of General Uncertainties Associated with Risk and Technology Review Risk Assessments
Paramclcr
Assumption
I iH'c'rl;iin(\/V;iri;il)ili(\ Discussion
I'olenlial Direct ion ol° liias on
Risk I'lslimalcs
Emissions Inventory
Individual HAP emissions
rates and facility
characteristics (stack
parameters, property
boundaries)
Emissions and facility characteristics
from the NEI provide an accurate
characterization of actual source
emissions.
Our current emissions inventory is based on the 2005
NEI, our internal review, and public comments
received. The degree to which the data in our
inventory represents actual emissions is likely to vary
across sources. For the 2008 screening level
assessments, nearly half of the sources in a given
source category submitted a review of their
emissions and facility characteristics data. Some
detailed data, such as property boundary information
is not available for most facilities. This is an
important consideration in determining acute
impacts.
Unbiased overall, magnitude
variable
Multiplier for short-term
emission rates
Generally, maximum short term
emission rates are estimated by
applying a simple multiplier (a factor
of 10) to average annual emissions.
The ratio between short-term and long-term average
emission rates may vary among the different
emission sources at a facility. In addition, the use of a
simple multiplier means that impacts of maximum
short term emissions are modeled for all
meteorological conditions, including the worst-case
conditions for population exposure.
Potential overestimate due to
the fact that worst-case
emissions are assumed to
occasionally coincide with
worst-case meteorology.
Overestimate due to lack of
actual information on short-
term emission rates.
Fate and Transport Modeling
Atmospheric dispersion
model choice
AERMOD is EPA's recommended
dispersion model for assessing
pollutant concentrations from
industrial facilities
Field testing of dispersion models, including
AERMOD, have shown results to generally be within
a factor of 2 of measured concentrations.
Unbiased overall
Building downwash
Not included in assessments
Use of this algorithm in AERMOD could improve
the dispersion calculations at individual facilities.
However, data are not readily available to utilize this
option.
Potential underestimate of
maximum risks near facility.
No effect on risks further out.
Plume deposition and
depletion
Not included in assessments
Ignoring these impacts for pollutants that deposit
minimally, and whose risks derive predominantly
from inhalation, should have minimal effect on risk
estimates.
Unbiased or minimal
overestimate.
28
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Table 5-1. Summary of General Uncertainties Associated with Risk and Technology Review Risk Assessments
(continued)
Piii'iiiiKMor
Assumption
1 iH'c'rl;iin(\/V;iri;il)ili(\ Discussion
Polcnlhil Direct ion of lihis on
Risk llsliniiiles
Meteorology
One year of meteorological data from
the nearest weather station (selected
from 824 nationwide) is representative
of long-term weather conditions at the
facility.
The use of one year of data rather than the five or
more adds uncertainty based on whether that year is
representative of each location's climatology. Use of
weather station data rather than on-site data can add
to uncertainty. Additionally, the use of default
surface parameters in the generation of the
meteorological datasets imparts uncertainty to the
results from any individual facility.
Minimal underestimate or
overestimate.
Reactivity
Not included in the assessments
Chemical reactions and transformations of individual
HAP into other compounds due to solar radiation and
reactions with other chemicals happens in the
atmosphere. However, in general, the HAP in this
assessment do not react quickly enough for these
transformations to be important near the sources,
where the highest individual risks are estimated.
Further, most of the HAP do not react quickly
enough for these transformations to be important to
risk estimates in the entire modeled domain (i.e.,
within 50 km of the source).
No impact on maximum risk
estimates. Minimal impact on
population risks and incidence.
Maximum modeling
distance
50 kilometers from center of facility
This distance is considered to be the maximum
downwind distance for a Gaussian plume model such
as AERMOD. This is because, in general, winds
cannot be considered to follow straight line
trajectories beyond this distance.
No effect on maximum
individual risks. Minimal
underestimation of incidence.
Exposure Assessment
Locations and short-term
movements of individuals
Ambient concentration at centroid of
each off-site census block is equal to
the exposure concentration for all
people living in that census block.
Effect of human activity patterns on
exposures is not included in the
assessment.
People live at different areas within block that may
have higher or lower exposures than at the centroid.
Individuals also move from outdoors to indoors and
from home to school/work to recreation, etc., and this
can affect their total exposure from these sources.
Unbiased across population for
most pollutants and individuals,
likely overestimate for most
exposed and underestimate for
least exposed persons.
29
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Table 5-1. Summary of General Uncertainties Associated with Risk and Technology Review Risk Assessments
(continued)
Piii'iiiiKMor
Assumption
1 nccrl;iin(\/Y;iri;il)ili(\ Discussion
Polcnlhil Direct ion of lihis on
Risk llsliniiilcs
Long-term movements of
individuals
MIR individual is exposed
continuously to the highest exposure
concentration for a 70-year lifetime.
Maximum individual risk (MIR) is defined in this
way to be a maximum theoretical risk at a point
where a person can actually reside.
Unbiased for most individuals,
likely overestimate for the
actual individual most exposed
and likely underestimate for the
least exposed. Incidence
remains unbiased unless
population around facilities
increases or decreases over 70
years.
Toxicity Assessment
Reference concentrations
(RfC)
Consistent with EPA guidance, RfCs
are developed including uncertainty
factors to be protective of sensitive
subpopulations. Additionally, RfCs
are developed based on the level
producing an effect in the most
sensitive target organ or system.
While other organ systems may be impacted at
concentrations above the RfC, these are not included
in the calculation of target organ-specific hazard
indices.
In general, EPA derives RfCs
using procedures whose goal is
to avoid underestimating risks
in light of uncertainty and
variability. The greater the
uncertainties, the greater the
potential for overestimating
risks.
Unit Risk Estimate
(URE)
Use of unit risk estimates developed
from dose-response models such as
linear low-dose extrapolation.
Uncertainty in extrapolating the impacts from short-
duration, high-dose animal or work-related exposures
to longer duration, lower-dose environmental
impacts.
Overestimate of risks for
nonlinear carcinogens and for
linear carcinogens with sparse
health effects data. In general,
EPA derives URE values using
procedures aimed at
overestimating risks in light of
uncertainty and variability.
Toxicity of mixtures
Cancer risks and noncancer hazard
quotients were calculated for each
HAP individually and then summed
into a total risk or hazard index
(assumption of additivity).
Concurrent exposures to multiple chemicals may
result in either increased or decreased toxicity due to
chemical interactions but the data needed to quantify
these effects are generally not available.
Unbiased overall. Some
mixtures may have
underestimated risks, some
overestimated, and some
correctly estimated.
30
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Table 5-1. Summary of General Uncertainties Associated with Risk and Technology Review Risk Assessments
(continued)
Piii'iiiiKMor
Assumption
1 iH'c'rl;iin(\/V;iri;il)ili(\ Discussion
Polcnlhil Direct ion of lihis on
Risk I'lsliniiilcs
Surrogate dose- response
values for HAPs without
values
In the case of groups of HAPs such as
glycol ethers, the most conservative
dose-response value of the chemical
group was used as a surrogate for
missing dose-response values in the
group. For others, such as unspeciated
metals, we have applied speciation
profiles appropriate to the source
category to develop a composite dose-
response value for the group.
For HAP which are not in a group and
for which no URE's or RfC's are
available from credible sources, no
assessment of risk is made.
Rather than neglecting the assessment of risks from
some HAPs lacking dose response values,
conservative assumptions allow the examination of
whether these HAPs may pose an unacceptable risk
and require further examination, or whether the
conservative level examination with surrogates
screens out the HAPs from further assessment.
Overestimate where most
conservative values used.
Unbiased where category-
specific profiles applied.
There is the potential to
underestimate risks for
pollutants which are not
included in the assessment.
31
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6. References
1. EPA. 2004. AERMOD: Description of Model Formulation. EPA-454/R-03-004, U.S.
Environmental Protection Agency, Research Triangle Park, NC.
http://www.epa.gov/ttn/scram/7thconf/aermod/aermod mfd.pdf
2. EPA. 2004. User's Guide for the AMS/EPA Regulatory Model - AERMOD. EPA-454/B-
03-001, U.S. Environmental Protection Agency, Research Triangle Park, NC.
www.epa. gov/scramOO 1 /dispersion prefrec.htm#aermod
3. EPA. 2013. The Human Exposure Model (HEM): Single-facility HEM-3 User's Guide;
and Multi-facility HEM-3 and RTR Summary Programs User's Guide. Prepared for the
U.S. Environmental Protection Agency, Research Triangle Park, NC, by EC/R Inc.,
Chapel Hill, NC. February 2013. http://www.epa.gov/ttn/fera/human hem.html
4. EPA. 2012. Dose-Response Assessment for Assessing Health Risks Associated With
Exposure to Hazardous Air Pollutants. U.S. Environmental Protection Agency, Research
Triangle Park, NC. Available at: http://www.epa.gov/ttn/atw/toxsource/summary.html
5. EPA. 2011. Glossary of Key Terms. Technology Transfer Network Air Toxics, 2005
National-Scale Air Toxics Assessment. U.S. Environmental Protection Agency.
http ://www. epa. gov/ttn/atw/natamain/ gloss 1 .html.
6. EPA. 2004. Air Toxics Risk Assessment Reference Library, EPA-453-K-04-001 A, U.S.
Environmental Protection Agency, Research Triangle Park, NC. April 2004.
www, epa. gov/ttn/ fera/risk_atra_vol 1. html
7. Census. 2000. LandView® 5 on DVD. U.S. Census Bureau, Washington, DC.
8. Census. 2010. Census Summary File 1 - United States:
http://www2.census.gov/census_2010/04-Summarv_File_l/ prepared by the U.S. Census
Bureau, Washington, DC, 2011. See also Technical Documentation for the 2010 Census
Summary File 1.
9. USGS. 2006. US GeoData Digital Elevation Models - Fact Sheet 040-00 (April 2000).
U.S. Department of the Interior - U.S. Geological Survey, Washington, DC.
http://data.geocomm.com/sdts/fsQ4000.pdf
10. USGS. 2010. USGS Seamless Data Warehouse. U.S. Department of the Interior - U.S.
Geological Survey, Washington, DC. http://nationalmap.gov/viewer.html
32
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11. EPA. 2004. User's Guide for the AERMOD Terrain Preprocessor (AERMAP). EPA-
454/B-03-003, U.S. Environmental Protection Agency, Research Triangle Park, NC.
http://www.epa. gov/scramOO 1 /dispersion related.htm#aermap
12. EPA. 2011. Addendum to the User's Guide for the AERMOD Terrain Preprocessor
(AERMAP) (EPA-454/B-03-003, October 2004), U.S. Environmental Protection Agency,
Research Triangle Park, NC. March 2011.
http://www.epa. gov/scramOO 1 /dispersion related.htm#aermap
13. EPA. 2012. Addendum: User's Guide for the AMS/EPA Regulatory Model - AERMOD
(EPA-454/B-03-001, September 2004) (Section 2.2 related to deposition). U.S.
Environmental Protection Agency, Research Triangle Park, NC. December 2012.
http://www.epa.gov/scram001/dispersion prefrec.htm#aermod. See also the AERMOD
Deposition Algorithms - Science Document (Revised Draft)" at
http://www.epa.gov/ttn/scram/7thconf/aermod/aer scid.pdf
14. Wesely, M.L., P.V. Doskey, and J.D. Shannon. 2002. Deposition Parameterizations for
the Industrial Source Complex (ISC3) Model. ANL/ER/TR-01/003, Argonne National
Laboratory, Argonne, Illinois 60439. See "AERMOD Deposition Parameterizations
Document" pdf link under "Model Supporting Documents" section of TTN's
Preferred/Recommended Models webpage at
http://www.epa. gov/scramOO 1 /dispersion prefrec.htm#aermod
15. EPA. 2012. 2005 National Emissions Inventory Data & Documentation. U.S.
Environmental Protection Agency, Research Triangle Park, NC.
http://www.epa.gOv/ttn/chief/net/2005inventorv.html#inventorvdata
16. EPA. 2011. An Overview of Methods for EPA's National-Scale Air Toxics Assessment.
Prepared for EPA's Office of Air Quality, Planning, and Standards, Research Triangle
Park, NC, by ICF International, Durham, NC. January 2011, p 55.
http://www.epa.gov/ttn/atw/nata2005/05pdf/nata_tmd.pdf.
17. EPA. 2004. EMS-HAP Modeling System. Environmental Protection Agency, Research
Triangle Park, NC. http://www.epa.gOv/scram001/dispersion_related.htm#ems-hap
18. Strum, Madeleine, Richard Mason, and James Thurman. 2004. User's Guide for the
Emissions Modeling System for Hazardous Air Pollutants (EMS-HAP) Version 3.0.
EPA-454/B-03-006, Environmental Protection Agency, Research Triangle Park, NC.
August 2004. http://www.epa.gov/ttn/scram/userg/other/emshapv3ug.pdf
33
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19. EPA. 2004. User's Guide for the AERMOD Meteorological Preprocessor (AERMET),
EPA-454/B-03-002, U.S. Environmental Protection Agency, Research Triangle Park,
NC. http://www.epa.gov/scramOO 1 /7thconf/aermod/aermetugb.pdf
20. EPA. 2012. Addendum to the User's Guide for the AERMOD Meteorological
Preprocessor (AERMET) (EPA-454/B-03-002, November 2004), U.S. Environmental
Protection Agency, Research Triangle Park, NC. December 2012.
http://www.epa.gov/ttn/scram/metobsdata procaccprogs.htm#aermet
21. Texas Commission on Environmental Quality. 2006. Reports on Air Emission Events.
http://www.tceq.texas.gov/field/eventreporting
22. EPA. 2005. Revision to the Guideline on Air Quality Models: Adoption of a Preferred
General Purpose (Flat and Complex Terrain) Dispersion Model and Other Revisions.
Appendix W of 40 CFR Part 51.
http://www.epa. gov/ttn/scram/guidance/guide/appw 05.pdf
23. Federal Register, 2012. Qualifying Urban Areas for the 2010 Census. FR 77:59 (27
March 2012). p. 18652. http://www.gpo.gov/fdsvs/pkg/FR-2012-03-27/pdf/2012-69Q3.pdf
24. EPA. 2006. NEI Quality Assurance and Data Augmentation for Point Sources. U.S.
Environmental Protection Agency, Emission Inventory Group, RTP, NC.
http://www.epa.gov/ttn/atw/nata2005/05pdf/20Q2nei qa augmentation report0206.pdf
25. EPA. 2003. AERMOD: Latest Features and Evaluation Results. EPA-454/R-03-003,
U.S. Environmental Protection Agency, Research Triangle Park, NC.
http ://www. epa. gov/ scramOO 1 /7thconf/aermod/eval. pdf
26. EPA, 2001, National-Scale Air Toxics Assessment for 1996; EPA-453/R-01-003; EPA
454/B-03-003, U.S. Environmental Protection Agency, Research Triangle Park, NC.
http ://www. epa. gov/ttn/atw/nata/peer. html
34
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DRAFT
METEOROLOGICAL DATA PROCESSING
USING AERMET
FOR HEM3
US EPA
Air Toxic Assessment Group
RTP NC 27711
September, 2014
-------
Meteorological Data Processing using AERMET
For HEM3
December 2013
BACKGROUND
The AERMOD meteorological processor, AERMET, and its supporting modeling system
(AERSURFACE and AERMINUTE) were used to process one year of meteorological
data for over 800 observation stations across the continental United States, Alaska,
Hawaii, and Puerto Rico.
METEOROLOGICAL DATA
To estimate the boundary layer parameters required by AERMOD, AERMET requires
hourly surface weather observations (which may include hourly values calculated from 1-
minute data) and the full (i.e., meteorological variables reported at all levels) twice-daily
upper air soundings. The surface and upper air stations are paired to produce the required
input data for AERMOD.
USEPA meteorologists obtained calendar year 2011 Integrated Surface Hourly Data
(ISHD) for over 800 Automated Surface Observation System (ASOS) stations spanning
the entire US, as well as Puerto Rico and the US Virgin Islands, from the National
Climatic Data Center (NCDC). To support AERMET, ASOS 1-minute data for each
surface station were also obtained from NCDC in a DSI 6405 format.
Further, upper air sounding data for the same time period for over 80 observation sites
were obtained from the "NOAA/ESRL" online Radiosonde Database. These datasets
were produced by ESRL in Forecast Systems Laboratory (FSL) format. Appendix 1 lists
the surface stations, as well as the location, ground elevation, and anemometer height for
each station. Figure 1 is a map that shows the locations of all the surface stations.
Appendix 2 lists the upper air stations and their locations.
AERMET PROCESSING
Utilizing the AERMET meteorological data preprocessor, and the ASOS surface and FSL
upper air stations, surface and profile files for input into AERMOD were generated
nationwide. The surface stations were paired with representative upper air stations by
taking the upper air station closest to each surface station. The AERSURFACE tool was
used to estimate the surface characteristics for input into AERMET utilizing land cover
data surrounding the surface station. In addition, the AERMINUTE preprocessor was
used to process 1-minute ASOS wind data for input into AERMET. Table 1 outlines the
approach/inputs each of the data preprocessors and tools used to generate the AERMOD
meteorological data.
1
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Meteorological Data Processing using AERMET
For HEMS
December 2013
Figure 1. Surface Stations
2
-------
Meteorological Data Processing using AERMET
For HEM3
December 2013
Table 1. AERMET Processing Options
AERMET Options
Version
13350 1
ASOS Site
Yes
Surface Data Format
NCDC TD-3505 (ISHD)
Upper Air Data Format
FSL, all levels, tenths m/s
Wind Speed Threshold
0.5 m/s
Beta Option (U*)
Yes
AERMINUTE Options
Version
11325
Include 1 minute ASOS
Data
Yes, where available TD-6405
format
AERSURFACE Options
Version
13016
Landcover data
USGS NLCD92 Geo TIFF format
(except Alaska and Hawaii which
used 2001 landcover data)
Radius for Surface
Roughness Calculations
1 km
Site Characteristics
Airport Site (where applicable)
Arid Regions for all sites in AZ,
NM, UT only
Temporal resolution
Monthly, 12 sectors
Site Surface Moisture
Average
Snow Cover
Late Winter/Winter without
continuous snow cover - all states
(except Alaska where we used
continuous snow)
1 : generated with the pre-public release version of A
ERMET 13350.
RESULTS
Beginning with version 12345, AERMET allows for a minimum wind speed of 0.5 m/s to
be utilized when processing monitoring stations that are equipped with sonic
anemometers and for an adjustment to the surface friction velocity (u*). The public
version of AERMET available at the time we conducted the processing of this data did
not include the surface friction velocity adjustment. Also at the time, an updated version
of AERMET (and AERMOD) was under development that incorporated further surface
friction velocity adjustments to AERMET version 12345. In anticipation of the update,
EPA processed meteorological data using a pre-public release version of AERMET and
incorporated the updated surface friction velocity adjustment in the output. It was EPA's
judgement that the pre-public release version of AERMET would generate AERMOD
ready meteorological data very close to that generated with the 13350 version of
AERMET. The AERMET meteorological data posted on the EPA's FERA (Fate,
Exposure, and Risk Analysis) website under the HEM model page was generated with the
pre-public release version of AERMET 13350.
3
-------
Meteorological Data Processing using AERMET
For HEM3
December 2013
ly, USEPA meteorologists ran AERMOD using a model plant. Further, the surface files
were examined for completeness. If more than 10 percent of the data were missing, the
station was not considered suitable for the HEM-3 meteorological database. In all, 824
met station pairs ran successfully and will be included in the HEM-3 meteorological
library. A summary of these station pairs are presented in Appendix 3 of this document.
4
-------
Appendix 4
Dispersion Model Receptor Revisions and Additions
for the Aerospace Source Category
-------
Dispersion Model Receptor Revisions and Additions
To estimate ambient concentrations for evaluating long-term exposures, the HEM-3
model uses the geographic centroids of census blocks (currently utilizing the 2010 Census) as
dispersion model receptors. The census block centroids are generally good surrogates for where
people live within a census block. A census block generally encompasses about 40 people or 10-
15 households. However, in cases where a block centroid is located on industrial property, or
where a census block is large and the centroid less likely to be representative of the block's
residential locations, the block centroid may not be an appropriate surrogate.
Census block centroids that are on facility property can sometimes be identified by their
proximity to emission sources. In cases where a census block centroid was within 300 meters of
any emission source, we viewed aerial images of the facility to determine whether the block
centroid was likely located on facility property. The selection of the 300-meter distance reflects a
compromise between too few and too many blocks identified as being potentially on facility
property. Distances smaller than 300 meters would identify only block centroids near the
emission sources and could exclude some block centroids that are still within facility boundaries,
particularly for large facilities. Distances significantly larger than 300 meters would identify
many block centroids that are outside facility boundaries, particularly for small facilities. Where
we confirmed a block centroid on facility property, we moved the block centroid to a location
that best represents the residential locations in the block.
In addition, census block centroids for blocks with large areas may not be representative
of residential locations. Risk estimates based on such centroids can be understated if there are
residences nearer to a facility than the centroid, and overstated if the residences are farther from
the facility than the centroid. To avoid understating the maximum individual risk associated
with a facility, in some cases we relocated block centroids, or added dispersion model receptors
other than the block centroid. We examined aerial images of all large census blocks within one
kilometer of any emission source. Experience from previous risks characterizations show that in
most cases the MIR is generally located within 1 km of the facility boundary. If the block
centroid did not represent the residential locations, we relocated it to better represent them. If
residential locations could not be represented by a single receptor (that is, the residences were
spread out over the block), we added additional receptors for residences nearer to the facility
than the centroid.
For these source categories, the table below contains each census block for which we
changed the centroid location because it was on facility property or was otherwise not
representative of the residential locations in the block. The table also contains the locations of
additional receptors that were included to represent residential locations nearer to the facility
than the block centroid.
-------
Dispersion IV
odeling Receptor Revisions and Addiitons
Block ID
NEIID
New Latitude
New
Longitude
Comment
010730003003000
33.551805
-86.759025
On plant property
020900018002007
64.674932
-147.069107
Centroid doesn't represent population
051139501003052
34.549923
-94.20052
Centroid doesn't represent population
060375742022013
33.797825
-118.148638
On plant property
090076802002008
41.559581
-72.606323
Centroid doesn't represent population
090076802002030
41.530871
-72.568172
Centroid doesn't represent population
090035106001020
41.743352
-72.631209
On plant property
120090671001045
28.218591
-80.604812
On plant property
120310138002003
30.389219
-81.398452
Centroid doesn't represent population
130099705003027
33.083926
-83.306343
Centroid doesn't represent population
150030087012030
21.392368
-157.979934
Centroid doesn't represent population
191751903001185
41.050938
-94.330568
Centroid doesn't represent population
191751903001184
41.057247
-94.336577
Centroid doesn't represent population
200354933001017
37.183071
-97.024316
Centroid doesn't represent population
201259507002092
37.164787
-95.764402
Centroid doesn't represent population
201730073022000
37.683842
-97.235703
Centroid doesn't represent population
201730092002069
37.670818
-97.435695
Centroid doesn't represent population
201730096031008
37.628536
-97.425269
Centroid doesn't represent population
201730058001012
37.616576
-97.29574
Centroid doesn't represent population
240054515003002
39.326719
-76.438174
Centroid doesn't represent population
240010020003030
39.565437
-78.855722
Centroid doesn't represent population
240010020001059
39.567312
-78.853521
Centroid doesn't represent population
240150305034001
39.608694
-75.862987
Centroid doesn't represent population
250092544023011
42.641297
-71.193259
On plant property
270530251003004
44.848346
-93.232734
Centroid doesn't represent population
280890302041010
32.513103
-90.115196
Centroid doesn't represent population
291833102022024
Remove - no apparent population
291833104002011
38.798652
-90.472628
Centroid doesn't represent population
330110026003011
42.947922
-71.445989
Centroid doesn't represent population
330110026002029
42.941096
-71.420333
Centroid doesn't represent population
361119505001033
42.023843
-74.103
Centroid doesn't represent population
371330010001078
Remove - no apparent population
390210104003056
40.113107
-83.756062
Centroid doesn't represent population
390210106003038
40.095165
-83.758549
Centroid doesn't represent population
390210115045026
40.090555
-83.760095
Centroid doesn't represent population
390610232011004
39.231316
-84.441067
Centroid doesn't represent population
391093653021008
40.022496
-84.222607
On plant property
391336009012020
41.163415
-81.25601
Centroid doesn't represent population
401091074032002
35.417778
-97.364281
Centroid doesn't represent population
401430059001112
36.173684
-95.850039
Centroid doesn't represent population
401430059001102
36.169252
-95.852922
Centroid doesn't represent population
420034511052004
40.507363
-80.22897
Centroid doesn't represent population
420550104002034
Remove - no apparent population
470370157001012
36.112564
-86.694519
Centroid doesn't represent population
481210203083090
32.997107
-97.326708
Centroid doesn't represent population
482013413012014
29.56424
-95.106548
Centroid doesn't represent population
482319611003051
33.072415
-96.086581
Centroid doesn't represent population
482319611003064
33.057274
-96.077758
Centroid doesn't represent population
490111252002238
41.114114
-112.003497
On plant property
490111255022016
Remove - no apparent population
490351139071081
Remove - no apparent population
page 1 of 3
-------
Dispersion IV
odeling Receptor Revisions and Addiitons
Block ID
NEIID
New Latitude
New
Longitude
Comment
530330253021004
47.502702
-122.203415
On plant property
530330309011001
47.281439
-122.246982
On plant property
530530714063015
47.075226
-122.345873
Centroid doesn't represent population
530530731082001
47.085115
-122.335884
Centroid doesn't represent population
530610419032005
47.910351
-122.259637
Centroid doesn't represent population
530610419011004
47.888713
-122.280334
On plant property
530610413042008
47.92782
-122.286347
Centroid doesn't represent population
530610419031003
47.922014
-122.258728
Centroid doesn't represent population
550870126013002
44.246606
-88.500529
On plant property
050272001
33.279137
-93.24732
Additional receptor
01015223
33.636798
-85.917779
Additional receptor
01015223
33.64429
-85.922
Additional receptor
010515017
32.483183
-85.891947
Additional receptor
05113377
34.553318
-94.201272
Additional receptor
05143331
36.163941
-94.129957
Additional receptor
05143331
36.164968
-94.134175
Additional receptor
05143331
36.162945
-94.133167
Additional receptor
06037429
33.799406
-118.155953
Additional receptor
06037490
33.921879
-118.378028
Additional receptor
06037490
33.921247
-118.378083
Additional receptor
06065520
33.946768
-117.464655
Additional receptor
06065520
33.943259
-117.468022
Additional receptor
06065520
33.942524
-117.462057
Additional receptor
06065520
33.943152
-117.460244
Additional receptor
06065520
33.946694
-117.45886
Additional receptor
09001688
41.230118
-73.188762
Additional receptor
09001688
41.229658
-73.181106
Additional receptor
09001693
41.250318
-73.102572
Additional receptor
09001693
41.254451
-73.100727
Additional receptor
09003626
41.859277
-72.699027
Additional receptor
09003626
41.860102
-72.696426
Additional receptor
090032025
41.702315
-72.861
Additional receptor
090075021
41.583854
-72.709395
Additional receptor
090075021
41.584044
-72.712449
Additional receptor
13067506
33.93187
-84.521904
Additional receptor
13067506
33.928956
-84.541948
Additional receptor
13153431
32.63233
-83.606056
Additional receptor
132332017
34.000155
-85.037232
Additional receptor
19163356
41.545325
-90.610764
Additional receptor
20035486
37.174104
-97.022622
Additional receptor
20173193
37.665652
-97.376544
Additional receptor
24015282
39.611523
-75.873373
Additional receptor
24015282
39.60952
-75.870575
Additional receptor
25009450
42.641822
-71.190422
Additional receptor
25009450
42.639385
-71.195099
Additional receptor
25009450
42.634538
-71.187894
Additional receptor
25009450
42.635331
-71.179925
Additional receptor
25009450
42.636161
-71.19115
Additional receptor
25009450
42.631334
-71.184271
Additional receptor
25009450
42.631605
-71.178394
Additional receptor
25009477
42.456095
-70.970509
Additional receptor
page 2 of 3
-------
Dispersion IV
odeling Receptor Revisions and Addiitons
Block ID
NEIID
New Latitude
New
Longitude
Comment
29183329
38.802311
-90.477323
Additional receptor
29183329
38.80376
-90.479095
Additional receptor
33011652
42.945817
-71.445529
Additional receptor
36103521
40.70906
-73.39568
Additional receptor
36103521
40.710546
-73.396134
Additional receptor
36111630
42.025358
-74.104536
Additional receptor
39017343
39.300248
-84.445037
Additional receptor
391335027
41.162827
-81.25431
Additional receptor
40143460
36.170512
-95.849741
Additional receptor
401432003
36.204015
-95.90583
Additional receptor
401432013
36.2485
-95.92349
Additional receptor
42003603
40.509554
-80.23638
Additional receptor
4703758
36.121575
-86.697288
Additional receptor
4703758
36.123731
-86.697024
Additional receptor
482015020
29.564051
-95.07879
Additional receptor
48231376
33.063178
-96.079797
Additional receptor
483672020
32.774411
-97.80383
Additional receptor
484392009
32.807578
-97.154854
Additional receptor
53033532
47.271577
-122.237263
Additional receptor
54067418
38.226333
-80.582851
Additional receptor
54067418
38.229495
-80.584709
Additional receptor
550876001
44.258407
-88.506004
Additional receptor
2017365
37.68934
-97.207254
Additional receptor
2017365
37.687577
-97.207454
Additional receptor
15003346
21.334582
-157.95132
Additional receptor
page 3 of 3
-------
APPENDIX 5: Technical Support Document for the TRIM-Based
Multipathway Tiered Screening Methodology for RTR
-------
Technical Support Document
for the TRIM-Based Multipathway
Tiered Screening Methodology for RTR
October 2014
Prepared For:
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Research Triangle Park, NC 27711
Prepared By:
ICF International
2635 Meridian Parkway
Suite 200
Durham, NC 27713
-------
[This page intentionally left blank.]
-------
TRIM-Based Tiered Screening Methodology for RTR
CONTENTS
1. Introduction and Background 1
1.1 Tier 1 2
1.1.1 Chemicals of Concern 2
1.1.2 Development of Emission Thresholds 4
1.2 Tier 2 7
1.3 Tier 3 10
1.4 Site-Specific Assessment 11
2. Tier 1 Methodology 12
2.1 Introduction 12
2.2 Summary of Approach 12
2.2.1 Overview 12
2.2.2 Chemicals of Potential Concern 15
2.2.3 Conceptual Exposure Scenario 16
2.2.4 Approach to Development of the Tier 1 Scenario 18
2.2.5 Fate and Transport Modeling (TRIM.FaTE) 20
2.2.6 Exposure Modeling and Risk Characterization (MIRC) 21
2.2.7 Implementation of Risk-based Emission Scaling Factors for
POM and Dioxin Emissions 22
2.3 Description of Environmental Modeling Scenario 26
2.3.1 Chemical Properties 26
2.3.2 Spatial Layout 27
2.3.3 Watershed and Water Body Parameterization 28
2.3.4 Meteorology 30
2.3.5 Aquatic Food Web 32
2.3.6 Using TRIM.FaTE Media Concentrations 33
2.4 Description of Exposure and Risk Modeling Scenario 34
2.4.1 Calculating Concentrations in Farm Food Chain Media 35
2.4.2 Ingestion Exposure 35
2.4.3 Calculating Risk 38
2.4.4 Summary of Tier 1 Assumptions 39
2.5 Evaluation of Screening Scenario 42
2.5.1 Introduction 42
2.5.2 Cadmium Compounds 42
2.5.3 Mercury Compounds 44
2.5.4 Dioxins 46
2.5.5 Polycyclic Aromatic Hydrocarbons 48
2.5.6 Summary 50
3. Tier 2 Methodology 52
3.1 Overview of Approach 52
3.2 Selection of Site-Specific Characteristics for the Tier 2 Assessment 55
3.3 Estimation of Adjustment Factors for Selected Site-Specific
Parameters 55
-------
TRIM-Based Tiered Screening Methodology for RTR
3.3.1 Selection Values for Variables of Interest 56
3.3.2 Estimation of Adjustment Factors 59
3.4 Preparing National Databases of Lake and Meteorological Data 64
3.4.1 Processing Lake Data for Tier 2 Assessment 64
3.4.2 Processing Meteorological Data for Tier 2 Assessment 68
3.5 Implementation of Tier 2 Assessment 71
4. Tier 3 Methodology 74
4.1 Overview of Approach 74
4.2 Lake Assessment 74
4.3 Plume-rise Assessment 76
4.4 Assessment of Time-series Meteorology and Effective Release
Heights 76
5. References 78
Attachment A, TRIM.FaTE Inputs................................................... A-1
Attachment B. Description of Multimedia Ingestion Risk Calculator (MIRC)
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
-------
TRIM-Based Tiered Screening Methodology for RTR
1. Introduction and Background
Section 112 of the Clean Air Act (CAA) directs the U.S. Environmental Protection Agency (EPA)
to assess the risk remaining (i.e., residual risk) from emissions of hazardous air pollutants
(HAPs) following the implementation of maximum achievable control technology (MACT)
standards for emission sources. This risk assessment is a major component of EPA's Risk and
Technology Review (RTR) program. As part of this program, EPA must consider additional
emission standards for a source category if the current emission standards—with MACT
regulations in place—do not provide an "ample margin of safety" for human health. One aspect
of human health that EPA must consider under RTR is the potential for health effects resulting
from exposures to persistent and bioaccumulative HAPs (PB-HAPs) via non-inhalation
pathways, namely ingestion and dermal exposure. EPA's assessment for RTR focuses on
specific PB-HAPs that the Office of Air Quality Planning and Standards (OAQPS) has identified
as candidates for multipathway risk assessments (selection of the PB-HAPs is discussed in
Section 2.2.2). These non-inhalation human health risks are considered in combination with
estimated inhalation human health risks, potential ecological impacts, and other factors to
support decisions about residual risk for RTR source categories. For PB-HAPs, exposures via
ingestion are anticipated to be significantly higher than any dermal exposures that might occur
as a result of the same emissions (see Attachment C). Consequently, a methodology has been
developed to evaluate ingestion exposure and risk for PB-HAPs efficiently in the context of
EPA's RTR program.
To evaluate ingestion exposures and human health risks for RTR on a source category basis,
an iterative approach was developed that enables EPA to confidently screen out PB-HAP
emissions unlikely to pose health risks above levels of concern (i.e., a cancer risk of 1 in
1 million or a noncancer hazard of 1.0) and to focus additional resources on sources of greater
concern within the category. To estimate exposure and risk, two models are used: the Fate,
Transport, and Ecological Exposure module of EPA's Total Risk Integrated Methodology
(TRIM.FaTE) to model the fate and transport of pollutants released to the environment and the
Multimedia Ingestion Risk Calculator (MIRC) to estimate transfer and uptake into the food chain
and exposure to receptors consuming contaminated food products and soil. This approach is
divided into three tiers of increasing refinement, as follows.
Tier 1 of the approach begins by identifying the facility-level emissions of PB-HAPs
within a source category and comparing them to risk-based emission thresholds. The
risk-based thresholds are derived using the aforementioned models applied for a
hypothetical environmental and exposure scenario, assuming ingestion of locally caught
fish, locally grown produce and livestock, and local soil. This hypothetical "screening
scenario" is intended to represent a situation in which the ingestion exposure is unlikely
to be exceeded at any actual facility evaluated through the RTR program. The
thresholds for Tier 1 are derived by estimating the emission rate that corresponds to a
lifetime cancer risk of 1 in 1 million or a chronic non-cancer hazard quotient (HQ) of 1 for
an individual exposed according to the characteristics associated with the screening
scenario. For a facility, if the emission rate of each PB-HAP is less than the Tier 1
threshold emission rate, risks are assumed to be low and no additional multipathway
screening is done. If, however, the emission rate of any PB-HAP exceeds the Tier 1
threshold emission rate, the facility can be evaluated further in Tier 2.
In Tier 2, the actual location of the facility is used to refine some assumptions associated
with the environmental scenario while maintaining the Tier 1 ingestion exposure scenario
assumptions. The environmental scenario assumptions are refined by incorporating
binned site-specific meteorological data and and locations of fishable lakes near the
facility (see below). The risk-based threshold for each PB-HAP is then adjusted for that
Introduction and Background
1
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
facility based on an understanding of how exposure concentrations estimated for the
screening scenario change with meteorology and lake location. PB-HAP emissions that
do not exceed the adjusted threshold are assumed to pose risks below levels of concern
and no additional multipathway assessment is required. Facilities having emissions that
exceed the adjusted thresholds for Tier 2 may require additional analysis.
• A Tier 3 screening assessment can be conducted on facilities that do not screen out with
the Tier 2 assessment, at the discretion of the risk assessor. The Tier 3 screening
approach consists of three individual assessments that further refine the Tier 2
screening scenario based on additional site-specific data and evaluations. One of the
Tier 3 assessments (i.e., the lake assessment) results in the rescreening of the facility's
emissions using the Tier 2 methods and using a revised lake database. The other two
assessments (i.e., the plume-rise and time-series assessments) each result in an
adjustment factor to be applied to the screening result reflecting the Tier 3 lake
assessment. Facilities having emissions that exceed the adjusted thresholds for Tier 3
may require additional analysis.
For facilities emitting PB-HAPs at levels that cannot be ruled out as being above levels
of concern based on the screening assessments, a refined, site-specific, multipathway
risk assessment can be conducted. Such an assessment would incorporate location- or
facility-specific characteristics regarding the environment to which PB-HAPs are emitted,
relevant exposure pathways, ingestion rates or other exposure factors, and other
parameters. A range of exposure scenarios could be evaluated as part of a site-specific
assessment, resulting in a range of risk estimates.
The key processes and decisions that make up this approach are summarized in Exhibit 1. In
the remainder of this document, each of the tiers in the multipathway assessment approach is
described in additional detail. The attachments provide a comprehensive record of the
characteristics and methods associated with Tier 1 and Tier 2 assessments. If a site-specific
assessment is conducted, a separate report detailing that assessment will be prepared.
1.1 Tier 1
The methods used in Tier 1 are intended to enable EPA to evaluate PB-HAP emissions from
multiple facilities in a source category quickly and efficiently and to remove from consideration
those that are unlikely to pose risks above levels of concern, while also minimizing the
possibility of EPA's failing to identify risks that exceed levels of concern. The hypothetical
scenario used to estimate Tier 1 thresholds is designed to be health-protective in estimating
exposures and risks; specifically, it is intended to avoid underestimating exposures to PB-HAPS
that might be encountered for any location throughout the United States. The scenario also is
intended to avoid grossly overestimating risk to the point where no emissions screen out (i.e.,
overprotective, resulting in too many "false positives").
1.1.1 Chemicals of Concern
The assessment of risk from multipathway exposures begins with a review of data for sources in
a particular category to determine if emissions of any of the following PB-HAPs are reported:
Cadmium compounds,
Chlorinated dibenzodioxins and furans (dioxins),
Mercury compounds, and
• Polycyclic organic matter (POM).
Introduction and Background
2
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit 1. Conceptual Decision Tree for Evaluating Non-Inhalation Exposures
for PB-HAPs
Emssian& Inventory
Date
Evaluate HAP amtennas bv facility
£
u
§L
VY
2
C/>
\ H&alfh'pfoteciivft \
h
) Configuration & ]—
EC
/ Moifcorologiral D3te J
a
E
T3
C
J5
\ Mjiallh Protective \
ExpofiLre
/ Assumptions /
l.
Does the erraaaan rata
for any P8-H^P exceed
Tim 1 ihr&shold?
ratfnfy screens out - no
farther assrstmrnr
nctusaar^
vts
Tier 1
CcmHgur
-------
TRIM-Based Tiered Screening Methodology for RTR
Based on current emissions, bioaccumulation potential, and toxicity considerations, emissions
of these four PB-HAPs are expected to pose the majority of the non-inhalation risks to humans
from air emissions at sources subject to residual risk provisions of the CAA1 Thus, although
EPA has identified nine other PB-HAPs that should be evaluated as part of residual risk
assessments, the methods for multipathway assessment described here encompass only these
four at this time.
If emissions of any of the four PB-HAPs listed above are reported for a facility, the emission rate
for that PB-HAP is compared to the threshold emissions rate derived for that chemical using the
hypothetical TRIM-based screening scenario. This threshold is the emission rate that, when
input to the models used in evaluating multipathway risk for RTR, results in a specified cancer
risk or non-cancer HQ threshold level of concern. For the screening scenario, threshold
emission rates were calculated for a cancer risk of 1 in 1 million or an HQ of 1.0, depending on
the more sensitive health effect of the PB-HAP. However, due to the hypothetical nature of the
screening scenario, exceeding the threshold emissions rate by 60 times, for dioxins for
example, does not imply a resulting cancer risk of 60 in 1 million. Rather, exceeding the
threshold emissions rate by 60 times for dioxin implies that it is highly unlikely that the actual
risk would exceed 60 in 1 million.
Important to note for dioxins and POM is that the screening methodology assesses individual
congeners, taking into account differences in both the fate and transport and the toxicity among
the various congeners. The details of the methods for assessing dioxins and POM are provided
in Section 2.
1.1.2 Development of Emission Thresholds
Generally, the approach used to assess ingestion exposures and resulting risks for RTR has
four components (Exhibit 2):
1. Fate and transport modeling of PB-HAPs emitted to air that partition into soil, water, and
other environmental media (including fish uptake);
2. Modeling of uptake of PB-HAPs by farm food chain media from soil and air;
3. Estimating ingestion exposures in terms of average daily dose for consumption of farm
food items by a hypothetical exposed human; and
4. Calculating lifetime cancer risk estimates or chronic non-cancer HQs for each HAP
and corresponding screening threshold emission rates.
The TRIM.FaTE model is used in the first component, and the MIRC model is used to conduct
calculations for the other three components.2 To derive the emission thresholds used in Tier 1,
these models are used to estimate the emission rate corresponding to a cancer risk of 1 in
1 Potential impacts on human health from non-inhalation exposures to lead are evaluated for RTR using the National
Ambient Air Quality Standard for lead, which takes into account multipathway risks.
2 EPA's TRIM methodology was conceived as a comprehensive modeling framework for evaluating risks from air
toxics. It was designed to address each of the four steps in screening ingestion risk; however, only the fate and
transport module currently is available for use. For the RTR screening scenario, the Multimedia Ingestion Risk
Calculator (MIRC) was constructed to complete the calculations required for estimating PB-HAP concentrations in
farm food chain media, average daily ingestion doses, and cancer risks and chronic non-cancer HQs. The framework
is conceptually identical to the ingestion exposure and risk analyses that TRIM intended to cover. Information about
the current status of TRIM modules and documentation of modules developed thus far can be accessed on EPA's
Technology Transfer Network (TTN) on the Fate, Exposure, and Risk Analysis website (http://www.epa.gov/ttn/fera/).
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TRIM-Based Tiered Screening Methodology for RTR
1 million or a noncancer HQ of 1 (depending on the PB-HAP) for each PB-HAP within the
hypothetical environmental scenario, as described in more detail in the following sections.
Exhibit 2. Overview of Ingestion Exposure and Risk Screening
Evaluation Method
Chemical Emissionsto Air
Cancer Risk
Hazard Quotient
1.1.2.1 Modeling Fate and Transport
To model chemical fate and transport in the environment when deriving emission thresholds for
Tier 1, the TRIM.FaTE module of the TRIM system was used.3 The two main components of
the fate and transport modeling are (1) the modeled domain, including the meteorological data
and (2) the environmental and chemical-specific properties associated with fate and transport
through the environment. The hypothetical modeled domain includes a farm homestead and a
fishable lake near (i.e., 2 km) an emissions source, which are assumed to be the primary food
sources for exposed individuals. The spatial layout and other physical aspects of the modeled
domain configuration are designed to be health-protective, which results in an ingestion
exposure situation that is unlikely to be exceeded at any actual facility evaluated under the RTR
program. The environmental and chemical-specific properties governing fate and transport of
PB-HAPs are parameterized with either conservative (i.e., health protective) values or central-
tendency values. The mix of health protective and central-tendency assumptions and
parameterization is expected to result in a scenario configuration that, on average, is likely to
overpredict environmental concentrations of PB-HAPs in media of interest for this evaluation.
The inclusion of central-tendency values where warranted is intended to minimize the number of
false positives.
Based on sensitivity analyses and model testing it is generally recognized that the spatial layout
of the modeled domain (e.g, distance to a fishable lake) and the meteorological data used (or a
combination of these two factors) are more influential than physical/chemical parameters in
dictating the resulting chemical concentrations in air, soil, water, sediment, and fish within
TRIM.FaTE. The Tier 1 assumptions about these two components of fate and transport
modeling are refined with relatively more site-specific data in subsequent tiers. The spatial
layout used to develop the threshold emission levels in Tier 1 and other details of the Tier 1
methodology are presented in Section 2.
htt p 7/www. e pa. g ov/ttn/fe ra/tri m_fate .html
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1.1.2.2 Modeling Transfer and Uptake
MIRC was developed to conduct the required calculations involving farm food chain transfer,
ingestion exposure, and risk. TRIM.FaTE outputs that are used as inputs to MIRC include:
PB-HAP concentrations in air,
• Air-to-surface deposition rates for PB-HAPs in both particle and vapor phases,
PB-HAP concentrations in fish tissue for fish consumed, and
• PB-HAP concentrations in surface soil and root zone soil.
From these inputs, MIRC calculates the transfer and uptake of PB-HAPs through the farm food
chain using algorithms based on those included in EPA's Human Health Risk Assessment
Protocol for Hazardous Waste Combustion Facilities (EPA 2005) and biotransfer factors (e.g.,
soil-to-plant factors, which are the ratios of the concentrations in plants to concentrations in
soil). The outputs of MIRC are PB-HAP concentrations in contaminated food items.
1.1.2.3 Estimating Ingestion Exposure
MIRC is also used to estimate exposure in terms of average daily doses (ADDs), normalized to
body weight for the following exposure pathways:
Incidental ingestion of soil • Ingestion of homegrown poultry/eggs
Ingestion of homegrown produce • Ingestion of homegrown pork
Ingestion of homegrown beef • Ingestion of fish
• Ingestion of milk from homegrown • Ingestion of breast milk (children <1 year
cows old; dioxins only)4
Chemicals are modeled separately to evaluate the potential for risks, with exposures (in terms
of ADD) for each PB-HAP summed across all ingestion exposure pathways. For the screening
scenario used in Tiers 1 and 2, exposure characteristics were selected that result in a highly
health protective estimate of total exposure. The ingestion rate for each exposure pathway
listed above was set (as feasible) equal to an upper percentile value (99th percentile for fish and
90th percentile for all other food types) based on EPA's Exposure Factors Handbook (EPA
2011a) or other sources as appropriate. All media were assumed to be obtained from a location
impacted by the modeled source. This approach results in an overestimate of total chemical
exposure for a hypothetical exposure scenario. For example, the resulting total food ingestion
rate is extremely high for a hypothetical consumer, with ingestion rates at the 99th percentile for
fish and the 90th percentile for every other farm food type. These health protective exposure
assumptions can be replaced in a site-specific assessment as appropriate (e.g., with
distributions of the data for key exposure factors).
Dermal absorption of chemicals that are originally airborne is generally relatively minor and this
pathway was not included in the scenario used to calculate Tier 1 emission thresholds (this topic
is discussed further in Section 2 and Attachment C).
4 Breast milk ingestion is an important exposure pathway for lipophilic compounds like dioxins and has been shown
not to contribute meaningfully for exposures to mercury, cadmium, and POM. See Section 2.4.2.3 and Section 3.4 of
Attachment B for full discussions of infant exposures via breast milk ingestion.
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1.1.2.4 Calculating Lifetime Cancer Risk and Non-Cancer HQs
Lifetime cancer risks and the potential for chronic non-cancer effects are estimated using
chemical-specific oral cancer slope factors and oral reference doses. Lifetime cancer risk
estimates are calculated separately for each PB-HAP. As provided in Section 5.1 of Attachment
B, age-group specific cancer risk estimates are calculated and the estimated lifetime cancer risk
equals the sum of these age-group specific risks. Similarly, HQs are calculated separately for
each PB-HAP and for each age group. However, as detailed in Section 5.2 of Attachment B, the
HQ for the most sensitive age group is used to determine the screening threshold emission rate.
1.1.2.5 Determining Threshold Emission Rates
Tier 1 emission thresholds were calculated by conducting iterative model simulations in
TRIM.FaTE and MIRC using the screening scenario described above to determine emission
rates for cadmium, mercury, dioxins, and POM that correspond to a cancer risk of 1 in 1 million
or a chronic non-cancer HQ of 1. Given the generally health protective nature of the scenario
inputs, these thresholds are assumed to be appropriate for screening facilities emitting these
PB-HAPs.
1.2 Tier 2
The Tier 1 screening approach is, by design, generic and health protective. It was constructed
for quick application to a large number of facilities in a source category with the least chance of
returning false negatives for risk potential. Once the Tier 1 screen is complete, however,
facilities whose emissions exceed the emission screening threshold for any PB-HAP can be
scrutinized further. Based on screening assessments conducted for RTR to date, many
facilities might not screen out of the Tier 1 assessment for some source categories. However,
conducting a full site-specific assessment of all facilities that cannot be screened out in Tier 1
would not be practical.
Site-specific values for some influential variables, however, can be determined without intensive
effort during the assessment. The use of some site-specific characteristics instead of the
generic characteristics used in Tier 1 can justify adjusting the threshold emission rate for a given
PB-HAP at that facility, potentially screening out the facility while maintaining a high degree of
confidence that risks above levels of concern have not been overlooked. In addition, using a
scenario in which a fisher fishes from multiple nearby lakes, catching an amount of fish from
each lake that is limited by the lake's theoretical fish productivity, creates a more site-specific
approach while also maintaining health protectiveness.
In selecting the scenario characteristics to modify in Tier 2, a balance was struck between the
degree of impact on the risk estimate, the ease of implementation, and the ease of obtaining
relatively certain site-specific values for all facilities that might be evaluated under the RTR
program. As discussed in the remainder of this section, some of these characteristics affect the
PB-HAP concentrations in environmental media estimated by TRIM.FaTE. Other characteristics
affect the sources of ingested fish and, potentially, the fish ingestion rates.
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1.2.1.1 Site Specific Considerations
For Tier 2, location-specific data on two types of variables are taken into account:
Meteorological characteristics, including the fraction of time the wind blows toward each
farm and lake (using wind direction), wind speed, precipitation rate, and mixing height;
and
• Locations of fishable lake(s) relative to the facility5 (including the absence of a fishable
lake).
For efficient Tier 2 evaluation of the impacts these parameters could have on specific facilities, a
series of TRIM.FaTE simulations was performed that systematically varied the values used in
the screening scenario for four of the five selected variables (lake location, wind speed,
precipitation rate, and mixing height). Wind direction affects only whether the chemical mass
advects toward the farms and lakes, so the effect of site-specific wind directions can be
evaluated outside of the TRIM.FaTE simulations. The values of each of the four variables were
changed, independent of any other changes. The values (four to six for each variable, including
the original, Tier 1 scenario values) were selected using statistics on U.S. meteorological data
or professional judgment to capture the expected range in the data. Four to six values were
selected to result in a total number of runs that was reasonable.
Based on the TRIM.FaTE results of these simulations (and the subsequent exposure and risk
characterization, conducted using MIRC), threshold adjustment factors were calculated for each
unique combination of the five parameters, for each PB-HAP. These adjustment factors
represent the ratio between the risk metric (i.e., cancer risk or HQ) obtained using the Tier 1
screening scenario and the risk metric obtained from the Tier 2 run. For a given facility, for each
PB-HAP it emits and each nearby lake evaluated, an adjusted Tier 2 emission threshold can be
estimated by dividing the Tier 1 emission threshold by the adjustment factor that best
corresponds to the meteorological conditions present at the site and the location of the lake
(hypothetical farms are assessed for every facility, at a constant distance from the facility). The
contribution of each exposure media toward each scenario's Tier 2 risk metric (i.e., the
individual contributions of fish ingestion, soil ingestion, beef ingestion, etc. toward the total Tier
2 risk metric) was included in the matrix because the Tier 2 assessment separates chemical
exposure from fish ingestion from exposure from farm food chain ingestion.
To facilitate the implementation of this approach without requiring facility-specific data searches
for each new source category evaluated, databases of the relevant U.S. meteorological and
lake data were created that could be accessed readily during a Tier 2 evaluation. These
databases are described in more detail in Section 3.4. The meteorological database is based on
the same hourly meteorology data used for RTR inhalation assessments. The meteorology
database includes annual summary statistics on wind direction, wind speed, precipitation, and
mixing heights for more than 800 surface stations, paired with their closest upper-air station with
available data, located throughout the United States (data available from the National Oceanic
and Atmospheric Administration). The lake database, based on U.S. Geological Survey
(USGS) data and including location and size information, consists of hundreds of thousands of
water bodies classified as "Lake/Pond" or "Reservoir" but not designated for disposal,
evaporation, or treatment. To focus on lakes that can support fishing of upper trophic level fish,
a minimum lake surface area of 25 acres is recommended. Very large lakes and bays (i.e.,
those larger than 100,000 acres) are not included because their watersheds are too large and
5The lake size also was changed for each lake distance allowing for a constant ratio between watershed and erosion
area compared with lake area within the TRIM modeling structure.
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their lake dynamics are too complex to realistically model in the TRIM.FaTE system. Lakes and
bays larger than 100,000 acres include the Great Lakes, the Great Salt Lake, Lake
Okeechobee, Lake Pontchartrain, Lake Champlain, Green Bay, and Galveston Bay.
When the Tier 2 screening is conducted, three additional processing steps are completed for
each facility and PB-HAP that will be analyzed in Tier 2 before looking up the appropriate
adjustment factors. First, a proximity analysis for each lake is conducted, whereby each
relevant lake within 50 km of the facility is identified and matched to its respective directional
"octant" relative to the facility. For the purposes of Tier 2, a "relevant" lake meets the size and
designation criteria discussed in the previous paragraph. Second, the lakes are manually
evaluated to remove lakes whose names suggest uses related to disposal, evaporation, or
treatment (sometimes the name indicates one of these uses while the USGS designations do
not; for example, the Gavin Fly Ash Impooundment would not be included in the screening
process). Third, the lakes around the facility that remain after the first two processing steps are
ranked in order of highest to lowest PB-HAP concentrations in fish. These rankings are then
used to refine the Tier 2 risk metric for the fisher, as discussed in the next section.
To perform Tier 2 assessments, a Microsoft® Access™ tool was created that merges Tier 1
screening results with the Tier 2 adjustment factors and the lake and meteorology information
relevant to a specific facility. In the tool, each facility is matched with the same meteorology
station used in RTR inhalation assessments, and the values for the four relevant meteorological
parameters at that station are recorded. The distances from the facility to relevant lakes are
computed in the tool as well. These five parameter values become the set of facility-specific
inputs in Tier 2. Then, the Tier 2 adjustment factors are determined for each PB-HAP for the
combination of these five variables. As described above, the Tier 1 screening emission
threshold is then divided by the appropriate adjustment factor to obtain a revised, Tier 2
emission threshold for that PB-HAP. More information about Tier 2 assessment methods can be
found in Section 3.
1.2.1.2 Refined Tier 2 Fisher Assessment
The refined fisher scenario is based on the idea that an adult fisher might travel to multiple lakes
if the first lake (i.e., the lake with the highest concentrations in fish for a given PB-HAP) is
unable to provide him an adequate catch to satisfy the assumed ingestion rate (i.e., 373 g/d for
adults). This refined fisher assessment uses the assumption that the biological productivity
limitation of each lake is 1 gram of fish (wet weight) per acre of lake; meaning that in order to
fulfill the adult ingestion rate, the fisher will need to fish from 373 total acres of lakes.
In the refined fisher scenario, a fisher travels to each relevant lake in order of highest to lowest
chemical concentration in fish (of a given PB-HAP) and catches fish up to the lake's assumed
biological productivity limitation. A maximum travel radius of 50 km relative to the facility is used
to maintain a realistic scenario. The final Tier 2 screening result for the fisher can be expressed
as the sum of the screening result from each lake that is fished (which is based on the amount
of fish ingested for each lake multiplied by the PB-HAP concentration in fish). If the highest-
concentration lake is at least 373 acres, the ingestion rate is not altered (i.e., remains 373 g/d
for adults). If the cumulative size of multiple visited lakes exceeds 373 acres, the fisher catches
from the final lake only the amount of fish necessary to satisfy the ingestion rate (i.e., to reach
the 373 g/d). If there are not 373 total acres of lakes, the risk reflects a reduced ingestion rate
based on the cumulative lake acreage.
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1.3 Tier 3
A Tier 3 screening assessment can be conducted on facilities that do not screen out with the
Tier 2 assessment, at the discretion of the risk assessor. The Tier 3 screening approach
consists of three individual assessments that further refine the screening scenario (beyond the
refinements in Tier 2) based on additional site-specific data and evaluations. Because the Tier 3
assessment introduces additional site-specificity to the screening scenario, it requires a
potentially higher level of effort than the Tier 2 assessment, but still a much lower level of effort
than the full site-specific assessment (discussed in Section 1.4). One of the Tier 3 assessments
(i.e., the lake assessment) potentially results in the rescreening of the facility's emissions using
the Tier 2 methods described in Section 1.2 and using a revised lake dataset. The other two
assessments (i.e., the plume-rise and time-series assessments) each result in an adjustment
factor to be applied to the screening result reflecting the Tier 3 lake assessment. The
adjustment factors of the plume-rise and time-series assessments cannot be compounded (i.e.,
the time-series assessment, if conducted, supplants the plume-rise assessment already
conducted).
One component of the Tier 3 assessment is evaluating the fishability of the lakes used in Tier 2.
The USGS dataset occasionally includes lakes that appear to be misclassified, no longer exist,
or are estuarian by nature. These lakes are removed from the Tier 3 assessment after
evaluating their validity using aerial imagery and other available data. These additional data
sources are not used in the Tier 2 screening. Examples of lakes removed from Tier 3 source
category assessments are provided in Section 4.2. If one or more lakes are removed from a
facility's assessment, the facility's emissions are rescreened using the revised lake database
and the Tier 2 methods described in Section 1.2. If removing a lake(s) causes the 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 assessment
is conducted on the newly added lake(s), and another rescreening is conducted, and so on until
no further lakes are removed or added to the assessment. The Tier 3 lake assessment is more
thoroughly described in Section 4.2.
If, after the lake assessment, the Tier 3 screening result is still above a level of concern, the risk
assessor may choose to conduct a plume-rise assessment. Atmospheric conditions coupled
with the physical parameters of the chemical release point can cause the chemical plume to rise
substantially beyond the physical release height. This process is not explicitly modeled by
TRIM.FaTE but can substantially reduce ground-level chemical exposure if the plume frequently
rises above the mixing height. This assessment uses a scenario in which the chemical release
height varies over time due to hourly meteorological conditions and the parameters associated
with the chemical release point (i.e., physical release height and diameter, exit velocity, and gas
temperature). If the resulting "effective release height" is above the mixing height for a given
hour, then in the TRIM.FaTE modeling system there is no chemical deposition or exposure for
that hour. If this occurs across many hours, it will substantially reduce total PB-HAP exposure
and reduce the screening result. The plume-rise adjustment factor—the number of hours when
the effective release height remains below the mixing height, divided by the number of total
modeled hours—is multiplied by the Tier 2 screening result, thus lowering the screening result.
A more thorough description is described in Section 4.3.
If the Tier 3 screening result after the lake and plume-rise assessments still is above a level of
concern, the risk assessor may choose to conduct a time-series assessment. This assessment
utilizes hourly effective release heights (computed in the plume-rise assessment above) along
with the hourly meteorology data associated with the facility (i.e., the meteorology data that was
summarized and used in Tier 2). Using hourly meteorology data adds additional site-specificity
compared to the summarized, binned meteorology statistics used in Tier 2. Using these data in
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combination with hourly effective release heights is a more complete evaluation of hourly
chemical losses due to plume rise compared with the Tier 3 plume-rise assessment described
above. These time-varying release height and meteorology files are used in a run of TRIM.FaTE
that also uses the facility's PB-HAP emissions and the Tier 2 spatial scenario associated with
the lake being assessed. The TRIM.FaTE modeling, and subsequent exposure and risk
characterization conducted using MIRC, leads directly to a screening-level cancer risk or HQ
(i.e., a revised screening result). For simplicity in the software implementation of the Tiers 2 and
3 screening assessments, the result of this Tier 3 time-series assessment is converted to a
time-series adjustment factor—the revised screening result divided by the screening result after
the Tier 3 lake assessment. This ratio can then be multiplied by the screening result after the
Tier 3 lake assessment, yielding the revised screening result accounting for the time-series
assessment.
1.4 Site-Specific Assessment
If, based on results of the screening assessments, a risk assessor concludes that there is a
reasonable probability that individuals could be adversely affected by the facility emissions, a
refined site-specific multipathway assessment might be performed. Examples of recent refined
multipathway assessments include residual risk assessments of a ferroalloys production facility
(EPA 2014a), petroleum refinery facility (EPA 2014b), two secondary lead smelting facilities
(EPA 2011b), assessments of two coal-fired electric utility units conducted in support of EPA's
utility rule (EPA 2011c), and a case study evaluation of a Portland cement facility included with
other RTR materials presented to the Science Advisory Board for review (Appendix I of EPA
2009).
Whereas Tier 2 and Tier 3 assessments incorporate some site-specific and regional information
on meteorology and water bodies, a refined multipathway assessment uses detailed site-
specific data to parameterize more accurately (to the extent possible) each important parameter
that affects pollutant fate and transport. These site-specific properties are incorporated into
model scenarios configured in TRIM.FaTE and MIRC. Important site-specific data likely would
include emission release height and plume buoyancy, hourly meteorology (e.g., wind flow,
temperature, mixing height, and precipitation), surface compartments based on watershed and
terrain data, local farms and water bodies, land use, soil properties, erosion and runoff rates,
surface water and sediment properties, water transfer rates, and aquatic ecosystem information.
The outputs from the site-specific run of TRIM.FaTE (i.e., chemical concentrations in
environmental media and fish) are used in MIRC to produce estimates of exposure and health
risk (i.e., risk and/or HQ values). Additional analyses of the media concentrations, exposure
estimates, and risk estimates for the various ingested media using a range of ingestion rates for
each modeled PB-HAP allows the risk assessor to understand, based on TRIM.FaTE and
MIRC, the sources and pathways of possible human health risk from emissions of PB-HAPs.
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2. Tier 1 Methodology
2.1 Introduction
As discussed in Section 1, the U.S. Environmental Protection Agency (EPA) implements a
tiered approach to evaluate multipathway exposures and human health risks for the Risk and
Technology Review (RTR) program. EPA's assessment for RTR focuses on persistent and
bioaccumulative hazardous air pollutants (PB-HAPs) that the Office of Air Quality Planning and
Standards (OAQPS) has identified as candidates for multipathway risk assessments (selection
of the PB-HAPs is discussed in Section 2.2.2). In the first tier, a screen is conducted that
focuses on the identity and magnitude of emissions of PB-HAPs from a given facility to
determine whether a facility passes certain human health risk-based criteria. Sources that are
"screened out" in the Tier 1 assessment are assumed to pose no risks to human health above
levels of concern and and are not considered in further assessments. For sources that do not
pass the Tier 1 screen, more refined assessments, up to and including site-specific
multipathway assessments, can be conducted as appropriate.
This section describes the technical basis for the first screening-level tier of EPA's multipathway
human health evaluation of PB-HAP emissions from RTR sources. Specifically, the scenarios,
models, configurations, and inputs used to derive screening threshold emission rates in the first
tier of the approach are described in detail in the following sections.
Section 2.2 presents an overview of how screening is conducted in Tier 1, the chemicals
and exposure scenario evaluated in Tier 1, and the models and methods used to
conduct the screen.
Sections 2.3 and 2.4 present technical descriptions of the hypothetical environmental
setting and the exposure modeling scenario used in Tier 1 as well as the models used in
the screen.
• Section 2.5 provides a brief discussion of the screening threshold emissions for each of
the chemicals assessed.
The Tier 2 and Tier 3 screening methods are discussed in Sections 3 and 4, respectively, and
references cited are provided in Section 5.
2.2 Summary of Approach
2.2.1 Overview
The Tier 1 approach for evaluating non-inhalation, multipathway exposures to PB-HAPs for RTR
is diagrammed in Exhibit 3. Air toxics emitted by a source under consideration are reviewed to
determine, first, whether emissions are reported for any of the four PB-HAPs of concern for non-
inhalation pathways. If such emissions are reported, the emission rates are compared to Tier 1
threshold-screening emission rates that have been derived using the TRIM-based Tier 1
scenario described in this document (see Exhibit 4 for threshold screening emission rates).
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Exhibit 3. Conceptual Decision Tree for Tier 1 Evaluation of Non-Inhalation
Exposures of PB-HAPs
Diagram Key
Process
Outcome
Are any PB-HAPs
emitted?
<
Z>
_l
£
LU
C£
LU
YES
A Are PB-HAPs
of primary concern
for RTR emitted?
(Cd, dioxins, Hg,
POM)?
YES
Non-inhalation risks not likely,
but evaluate on a case-by-case
basis
Does the emission
rate for any PB-
HAP exceed Tier
1 threshold?
Facility screens out; no concern for
multipathway risk
(check emissions of PB-HAPs other than
Cd, dioxins, Hg, POM, Pb on a case-by-
case basis)
YES
Tier 1 Methodology
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TRIM-Based Tiered Screening Methodology for RTR
The TRIM-based multipathway modeling configuration, referred to in this document as the Tier 1
scenario, forms the technical basis for determining the Tier 1 emission thresholds. The term,
Tier 1 scenario, is used to refer collectively to the specific TRIM.FaTE and exposure modeling
configuration described herein, including the set of assumptions and input values associated
with a hypothetical watershed and the exposure and risk scenarios evaluated for this watershed.
The Tier 1 scenario is a static configuration, and its primary purpose is as a modeling tool to
calculate the Tier 1 emission rate thresholds for PB-HAPs of concern.
The two potential outcomes of the Tier 1 evaluation are:
Non-inhalation exposures are unlikely to pose a human health problem (i.e., the
emissions evaluated "pass" the screen); or
• Risks above the levels of concern from non-inhalation exposures cannot be ruled out.
An ideal screening approach strikes a balance between being health-protective—to ensure that
risks above levels of concern are identified, and being accurate—to minimize results suggesting
that additional analysis is required when in fact the actual risk is low. Typically, gains in
accuracy in environmental modeling are accompanied by additional resource requirements.
Stated another way, a suitable approach minimizes both false negatives and false positives.
False negatives (i.e., results that suggest that the risk is acceptable when in fact the actual risk
is high) can lead to inappropriate and non-protective health or environmental policy decisions.
False positives (i.e., results that suggest more assessment is required when in fact the actual
risk is low) can result in wasted resources by leading to additional, unnecessary analysis. For
the evaluation of multipathway human health exposures to PB-HAPs, the methods for screening
described in this document are intended to achieve this balance.
Because the Tier 1 evaluation enables EPA to confidently eliminate from consideration those
facilities where risks from non-inhalation exposures are projected to be minimal, resources can
be targeted toward those facilities that do not pass the screening test. For facilities that do not
pass the Tier 1 screening, in additional tiers of analysis, some of the Tier 1 parameters are
reassessed, and if appropriate, are changed to more accurately reflect site-specific
characteristics. With each successive tier of the assessment, additional Tier 1 assumptions are
evaluated and refined to better reflect site-specific characteristics of the facility being modeled.
The Tier 1 screening evaluation for RTR compares reported air emission rates of PB-HAPs
(summed by PB-HAP for each facility) to screening threshold emission rates derived using the
Tier 1 scenario. A threshold emission rate is the level that, when input to a risk model using
emissions as a parameter, corresponds to a specified cancer risk or non-cancer hazard quotient
(HQ) that, for the purposes of the evaluation being conducted, is assumed to be below a level of
concern. Tier 1 threshold emission rates were calculated for a cancer risk of 1 in 1 million or an
HQ of 1.0 and are presented in Exhibit 4.6 Conceptually, a threshold level for the RTR
multipathway screening evaluation could be obtained by back-calculating the emission rate that
results in the specified cancer risk or HQ level, taking into account the exposure and fate and
transport calculations included in the model. Because the models used in this assessment are
not designed to run "backwards," the rates instead were derived from regression equations
established following a series of TRIM.FaTE and exposure/risk model runs spanning a wide
6For chemicals known to cause both cancer and chronic non-cancer impacts, and for which acceptable quantitative
dose-response values are available for both cancer and non-cancer endpoints, the endpoint that results in the lower
threshold emission level is used for screening (i.e., the threshold will be based on the effect that occurs at the lower
exposure level). For the set of PB-HAPs for which screening threshold levels have been derived, only chlorinated
dibenzo-dioxins and -furans meet both criteria. Because the cancer dose-response value is lower than that for
non-cancer effects, the screening threshold value is based on the cancer endpoint.
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TRIM-Based Tiered Screening Methodology for RTR
range of emission rates for each chemical. The estimated screening-level emission rates are
verified by performing model runs using the estimated threshold emission rate to confirm that
the emission rates result in a cancer risk of 1 in 1 million or an HQ of 1.0.
Exhibit 4. Emission Thresholds for Screening of Multipathway Exposures
Chemical
Screening
Threshold
Emission Rate
(TPY)
Basis of Threshold
(Type of Health Endpoint)
POM (as benzo(a)pyrene equivalents)3
2.58E-03
Cancer
Dioxins (as 2,3,7,8-TCDD equivalents)3
2.81 E-09
Cancer
Cadmium
1.18E-02
Non-cancer
Mercury (as divalent mercury emissions)
3.16E-04
Non-cancer
TPY = U.S. short tons per year
aSee Section 2.2.7 for a discussion on the derivation of equivalent emissions.
The more probable risk for each emission rate would be lower than the level corresponding to
the screening threshold risk quantities in nearly all circumstances, given the health protective
and hypothetical nature of the Tier 1 screening configuration. It is considered very unlikely that
the estimated risk at a real site would be greater than the estimated risk for the simulated Tier 1
scenario at equivalent emission rates. This is because the Tier 1 scenario assumes, for many of
the most risk influential parameters in the model, parameter values that result in high-end risk
estimates. In the real world, the probability of such risk-maximizing conditions prevailing across
multiple parameters is very low. For example, the Tier 1 scenario assumes a fishable lake
approximately 2 km from any given facility, when in reality, a lake may be more than 50 km
away. Additional conservative assumptions used in the Tier 1 screen are described in Section
2.4.4.
Tier 1 emission thresholds were developed individually for elemental and divalent mercury.
Both were based on the lower of the thresholds associated with multipathway exposures to
divalent mercury and methyl mercury.7 Only speciated emissions of divalent mercury are
screened because the sum of elemental mercury emissions across all National Emission
Inventory (NEI) facilities is less than the elemental mercury screening threshold level. See
Section 2.5.3 for a detailed discussion of mercury.
2.2.2 Chemicals of Potential Concern
EPA's assessment of multipathway human exposures for RTR focuses on PB-HAPs8 that the
Office of Air Quality Planning and Standards (OAQPS) has identified as candidates for
multipathway risk assessments. OAQPS developed a list of 14 chemicals and chemical groups
that are PB-HAPs based on a two-step process taking into account the following (EPA 2004a):
7Note that TRIM.FaTE models the transformation of mercury within the environment; thus, emissions of both divalent
and elemental mercury will result in multipathway exposures to elemental mercury, methyl mercury, and divalent
mercury.
8Although POM (polycyclic organic matter) is the name of the HAP listed in the Clean Air Act, the term "polycyclic
aromatic hydrocarbons" or PAHs is used in many cases. Much of the literature regarding toxicity and fate and
transport of this chemical group refers to PAHs rather than POM. In addition, the individual POM species that are of
concern with respect to health risk for RTR evaluations are all PAHs (i.e., there are no POM species explicitly
evaluated for RTR that do not include an aromatic ring). The terms are used interchangeably throughout this text.
Tier 1 Methodology
15
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TRIM-Based Tiered Screening Methodology for RTR
their presence on three existing EPA lists of persistent, bioaccumulative, and toxic
substances, and
• a semiquantitative ranking of toxicity and bioaccumulation potential of the entire list of
HAPs.
The list's development and utility in hazard identification for multipathway risk assessment are
explained further in Chapter 14 and Appendix D of Volume I of EPA's Air Toxics Risk
Assessment (ATRA) Reference Library (EPA 2004a). Exhibit 5 presents the 14 chemicals and
groups that are PB-HAPs.
Exhibit 5. OAQPS PB-HAP Compounds
PB-HAP Compound3
Addressed by Screening Scenario?
Cadmium compounds
Yes
Chlordane
No
Chlorinated dibenzodioxins and furans
Yes
DDE (1,1-dichloro-2,2-bis(p-chlorophenyl) ethylene)
No
Heptachlor
No
Hexachlorobenzene
No
Hexachlorocyclohexane (all isomers)
No
Lead compounds
No
Mercury compounds
Yes
Methoxychlor
No
Polychlorinated biphenyls
No
Polycyclic organic matter (POM)
Yes
Toxaphene
No
Trifluralin
No
aSource of list: EPA (2004a).
The screening scenario described in this document is not configured for evaluating the risk
potential for all 14 PB-HAPs on the list. Currently, the scenario can only be used to estimate
exposures and risks quantitatively for 4 of the 14 PB-HAPs (as indicated in Exhibit 5). These
four PB-HAPs are the focus of the current scenario because, based on current emissions,
bioaccumulation potential, and toxicity considerations, they are expected to pose the vast
majority of the non-inhalation risks to humans from air emissions at sources subject to residual
risk provisions of the Clean Air Act.9
2.2.3 Conceptual Exposure Scenario
A conceptual exposure scenario was developed that encompasses the specific exposure routes
and pathways of interest for the four PB-HAPs that are assessed in the Tier 1 assessment.
Exposure routes and pathways describe the movement of air toxics from the point of release to
the point where exposure occurs and generally consist of the following elements:
Release to the environment (i.e., emissions);
9Potential impacts on human health from non-inhalation exposures to lead are evaluated for RTR using the National
Ambient Air Quality Standard for lead, which takes into account multipathway risks. Non-inhalation exposures to the
other nine PB-HAPs not addressed by the modeling scenario discussed in this report will be evaluated on an
individual facility or source category basis as appropriate.
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TRIM-Based Tiered Screening Methodology for RTR
• A retention medium, or a transport mechanism and subsequent retention medium in
cases involving media transfer of chemicals;
• A point of potential human contact with the contaminated medium; and
• An exposure route.
An exposure route is the particular means of entry into the body. Receptors are exposed to
chemicals emitted to the atmosphere via two primary routes: either directly via inhalation, or
indirectly via ingestion or dermal contact with various media that have been contaminated with
the emitted PB-HAPs. (Inhalation pathways are assessed separately and are not considered in
the Tier 1 assessment presented here.)
PB-HAPs can persist in the environment for long periods of time and also build up in soil and in
the food chain, including fish, fruits and vegetables, and animal products (e.g., meat, dairy,
eggs). For this reason, ingestion of foods grown within an area impacted by RTR sources can
be an important source of exposure to PB-HAPs.
To assess risks from hazardous waste combustion facilities, EPA identified several hypothetical
receptor scenarios, noting that these scenarios are considered appropriate for a broad range of
situations, rather than to represent any actual scenario. These scenarios are described in
EPA's Human Health Risk Assessment Protocol for Hazardous Waste Combustion Facilities, or
HHRAP (EPA 2005a). In this document, EPA recommends assessment of the following
hypothetical receptors: a Farmer, Farmer Child, Resident, Resident Child, Fisher, Fisher Child,
Acute Receptor, and Nursing Infant. These receptors are distinguished by their pathways of
exposures. EPA further notes in HHRAP that some exposure settings might warrant including
additional exposure pathways; such as including exposure through fish ingestion for the farmer
receptor. For the RTR screening scenario, risks are assessed for a single hypothetical receptor.
Based on the guidance provided in HHRAP, a health protective exposure scenario was
developed whereby the hypothetical receptor receives ingestion exposure via both the farm food
chain and the fish ingestion pathways. The exposure scenario for the RTR Tier 1 assessment
includes the following ingestion pathways:
Incidental ingestion of soil,
Ingestion of homegrown fruits and vegetables,
Ingestion of homegrown beef,
Ingestion of dairy products from homegrown cows,
Ingestion of homegrown poultry and eggs,
Ingestion of homegrown pork,
Ingestion of locally caught fish, and
• Ingestion of breast milk (for children less than 1 year old and for dioxins only).10
As discussed in detail in Section 2.4.2, exposure via these pathways is assessed for adults,
various age categories for children, and nursing infants (for dioxins only).
10 Breast milk ingestion is an important exposure pathway for lipophilic compounds like dioxins and has been shown
not to contribute meaningfully for exposures to mercury, cadmium, and POM. See Section 2.4.2.3 below and Section
3.4 of Attachment B for full discussions of infant exposures via breast milk ingestion.
Tier 1 Methodology
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TRIM-Based Tiered Screening Methodology for RTR
Other non-inhalation exposure routes of possible concern for PB-HAPs discussed in HHRAP
include the use of surface waters as a drinking water source and dermal exposure. These
exposure routes, however, are not evaluated in the current assessment. The drinking water
exposure pathway is not likely for the modeling scenario developed for this assessment
because the likelihood that humans would use a lake as a drinking water source was assumed
to be low.11 Dermal absorption of chemicals that are originally airborne has been shown to a
relatively minor pathway of exposure compared to other exposure pathways (EPA 2006,
Cal/EPA 2000). Preliminary calculations of estimated dermal exposure and risk of PB-HAPs,
presented in Attachment C, showed that the dermal exposure route is not a significant risk
pathway relative to ingestion exposures.
2.2.4 Approach to Development of the Tier 1 Scenario
The TRIM-based Tier 1 scenario described in this document is used to provide a means to
qualitatively estimate the potential for non-inhalation risks above the levels of concern for PB-
HAPs emissions from facilities in the context of residual risk assessments conducted as part of
RTR. The Tier 1 scenario used to derive the threshold emission rates is not intended to be
representative of any particular situation. Rather, it was developed for the purpose of RTR to
portray a hypothetical exposure scenario that will generate emissions screening levels that are
health protective for any potential exposure situation that might plausibly be encountered in the
United States. A range of conditions was assessed when conceptualizing and developing the
screening scenario. The final configuration was chosen so that for a given individual, any
potential long-term exposure levels for a given geographic region would be reasonably unlikely
to exceed those of the Tier 1 configuration. These criteria were met by constructing a
hypothetical scenario that would be health-protective in key aspects, including spatial
orientation, meteorology, types of exposures, and ingestion rates. The overall result is a
scenario that is unlikely to occur at any one location but has a high likelihood of representing the
upper end of all potential exposures. This latter aspect accomplishes the goal of striking a
balance between health protectiveness and the level of accuracy called for in the ideal
screening approach previously discussed.
The development and application of the Tier 1 scenario for residual risk evaluations considered
EPA's technical and policy guidelines presented in the Residual Risk Report to Congress (EPA
1999); Volumes I and II of the Air Toxics Risk Assessment Reference Library (EPA 2004a,
2005a); and other EPA publications. The scenario described herin is the culmination of
assessments completed since 2005; it provides the basis for an efficient and scientifically
defensible method for screening multipathway human health risk and provides a solid baseline
from which to perform Tier 2 and Tier 3 assessments, as described in Sections 3 and 4,
respectively. Nevertheless, this scenario should not be considered "final" but rather a product
that can continue to evolve based on feedback from the scientific community and Agency
reviewers, lessons learned as the scenario is further applied for RTR, variations in EPA's needs
and requirements, and other factors.
2.2.4.1 Modeling Framework
The approach for multipathway risk screening and evaluation for RTR can be divided into four
steps:
11 An exception to this generality would be reservoirs used for drinking water supplies. This situation might be worthy
of additional analysis, if warranted by the characteristics of a given assessment (e.g., to estimate PB-HAP
concentrations in treated drinking water derived from reservoirs).
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TRIM-Based Tiered Screening Methodology for RTR
1. Fate and transport modeling of PB-HAPs emitted to air by the source that partition into soil,
water, and other environmental media (including fish12);
2. Modeling of transfer and uptake of PB-HAPs into farm food chain media (e.g., produce,
livestock, dairy products) from soil and air, and calculation of FFC media concentrations;
3. Estimating exposures from ingestion of selected media and estimating average daily
ingestion doses for a hypothetical human receptor; and
4. Calculating lifetime cancer risk estimates or chronic non-cancer HQs, as appropriate, for
each PB-HAP and comparing these to selected evaluation criteria.
The relationship among these four processes is shown in Exhibit 6.
Exhibit 6. Overview of Ingestion Exposure and
Risk Screening Evaluation Method
Chemical Emissions to Air
/"
Chemical fate and
transport:
Physical environment
and aquatic ecosystem
TRIM.FaTE
Uptake fit transfer
into produce and
livestock
Human
ingestion
exposure
Risk & hazard
estimation
Multimedia Ingestion Risk Calculator (MIRC)
Cancer Risk
Hazard Quotient
As shown in Exhibit 6, two models are used to evaluate the four steps outlined above. EPA's
TRIM methodology was conceived as a comprehensive modeling framework for evaluating risks
from air toxics, and the TRIM system was designed to address each of the four steps involved in
screening ingestion risk.13 Currently, however, only one component corresponding to the first
step included in Exhibit 6—the fate and transport module—is available for application in an
ingestion risk assessment. EPA has completed some development activities for
TRIM.Expo-lngestion and TRIM.Risk-Human Health, two additional modules that cover the
other three steps. Modeling software, however, is not currently available for these modules.
For the RTR screening scenario, the Multimedia Ingestion Risk Calculator (MIRC), a Microsoft®
Excel™-based computing framework, was constructed to complete the calculations required for
estimating PB-HAP concentrations in farm food chain media, average daily ingestion doses, and
12As discussed below, concentrations in fish calculated by the TRIM.FaTE model were used to estimate ingestion
exposures for humans consuming fish. Modeling of fish concentrations is therefore discussed herein as part of the
fate and transport modeling. Uptake of PB-HAPs into all other biotic media assumed to be ingested is modeled in the
second step of the modeling framework.
13lnformation about the current status of TRIM modules and comprehensive documentation of modules developed
thus far can be accessed on EPA's Technology Transfer Network (TTN) on the Fate, Exposure, and Risk Analysis
website (http://www.epa.gov/ttn/fera/).
Tier 1 Methodology
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TRIM-Based Tiered Screening Methodology for RTR
cancer risks and chronic non-cancer HQs. This framework is conceptually identical to the
ingestion exposure and risk assessments that TRIM is intended to cover.
2.2.4.2 Model Configuration and Parameterization
The Tier 1 scenario is intended to reduce the possibility that EPA would underestimate potential
multipathway human health risks. Although the health protective approach likely overestimates
risk, EPA determined that this approach is appropriate for the purposes of an initial
multipathway screening assessment. As was done with the preliminary multipathway screening
for RTR conducted in 2006 (EPA 2006), exposures were modeled for a hypothetical farm
homestead and fishable lake located adjacent to an emissions source. The hypothetical
individual for which exposures were calculated was assumed to derive all potentially
contaminated foodstuffs from adjacent locations, and many of the exposure/activity assumptions
(e.g., amount of food consumed per day) were selected from the upper ends of representative
exposure parameter distributions.
The physical/chemical environment represented in the screening scenario was parameterized
with two types of values. One type is typical values, such as national averages. The second
type is health-protective values, or values that would tend to overestimate concentrations in
media driving ingestion exposures for humans, based on knowledge of exposure patterns. In
general, the spatial and temporal aspects of the scenario and the components of the scenario
that influence air concentrations and deposition rates (which in turn affect all other exposures)
were defined to be health protective. Chemical-specific and non-chemical-specific properties of
the environmental media were parameterized with either typical or health protective values;
properties having greater uncertainty were assigned a greater level of health protective bias.
The spatial layout of the Tier 1 scenario and the meteorological data (or a combination of these
two factors) are generally more influential than physical/chemical parameters in dictating the
screening model outcomes, taking into account the potential range of variation in possible
values. For example, where and how the layout is spatially oriented relative to the dominant
wind direction can dramatically affect the concentrations in air, thereby driving estimated
concentrations of PB-HAPs in soil, water, and biota. In contrast, relatively large changes in soil
characteristics within the range of possible values (e.g., organic carbon content, water content)
typically result in relatively small changes in media concentrations.
The mix of health protective and central-tendency assumptions and parameterization is
expected to result in a scenario configuration that, on average, is likely to overpredict
environmental concentrations of PB-HAPs in media of interest for this evaluation. Given the
intended application of this scenario as a screening tool, this health protective bias was
deliberate, because of the desire to ensure that risks above levels of potential concern are not
overlooked (i.e., to minimize false negatives). Although the inclusion of central-tendency values
where warranted is intended to minimize the number of false positives, some false positives are
to be expected from a screening scenario. False positives are addressed in subsequent tiers of
the screening evaluation for a particular source.
2.2.5 Fate and Transport Modeling (TRIM.FaTE)
The fate and transport modeling step depicted in the first box in Exhibit 6 is implemented for
RTR using the Fate, Transport, and Ecological Exposure module of the TRIM modeling system
(TRIM.FaTE).14 In developing the Tier 1 scenario, Version 3.6.2 of TRIM.FaTE was used to
14TRIM.FaTE is a spatially explicit, compartmental mass balance model that describes movement and transformation
of pollutants over time, through a user-defined, bounded system that includes both biotic and abiotic compartments.
Outputs include pollutant concentrations in multiple environmental media and biota.
Tier 1 Methodology
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TRIM-Based Tiered Screening Methodology for RTR
model the fate and transport of emitted PB-HAPs and to estimate concentrations in relevant
media. Additional information about TRIM.FaTE, including support documentation, software,
and the TRIM.FaTE public reference library, is available at http://www.epa.gov/ttn/fera/.
The algorithms used to model mercury species and polyaromatic hydrocarbons (PAHs) are
described in Volume II of the TRIM.FaTE Technical Support Document (EPA 2002a). A
comprehensive evaluation of the performance of TRIM.FaTE for modeling mercury was
documented in Volumes I and II of the TRIM.FaTE Evaluation Report (EPA 2002b, 2005b).
Algorithms specific to the fate and transport of chlorinated dibenzo-dioxin and -furan congeners
were added following the addition of those for mercury and PAHs. Documentation of the
application of TRIM.FaTE for dioxin emissions is contained in the third volume of the
TRIM.FaTE Evaluation Report (EPA 2004). More recently, the TRIM.FaTE public reference
library was updated to include information on modeling for cadmium. In general, many of the
algorithms and properties included in the public reference library that are used to model mercury
(except for the mercury transformation algorithms) are also applicable to cadmium.
Comprehensive technical documentation of TRIM.FaTE algorithms specific to cadmium has not
yet been compiled; however, all chemical-specific properties used by TRIM.FaTE to model
cadmium (as well as PAHs, mercury, and dioxins) are documented in Attachment A. Based on
a thorough 2011 evaluation of TRIM.FaTE performance in modeling mercury's fate, transport,
and transformation in the aquatic food web, a zooplankton compartment was added to
TRIM.FaTE's aquatic compartment to increase the resolution and accuracy of the aquatic food
web modeling. Parameterization of the TRIM.FaTE scenario used for RTR screening is
described in more detail in Section 2.3.
2.2.6 Exposure Modeling and Risk Characterization (MIRC)
The algorithms included in MIRC that calculate chemical concentrations in farm food chain
media and ingestion exposures for hypothetical individuals were obtained from EPA's Human
Health Risk Assessment Protocol for Hazardous Waste Combustion Facilities, or HHRAP (EPA
2005a).15 These algorithms, and the required exposure factors and other parameter values,
were compiled into a database. An overview of the computational processes this tool carries
out and the types of input data it requires is presented in Exhibit 7. This exhibit demonstrates
the general relationships between the relevant TRIM.FaTE outputs (i.e., chemical
concentrations in environmental media and fish) and the ingestion exposure and risk
calculations carried out using MIRC. Additional discussion of exposure and risk calculations for
the Tier 1 scenario is presented in Section 2.4 and Attachment B, and all inputs required by
these calculations are documented in Attachment B.
15The farm food chain calculations and ingestion exposure equations to be included in the TRIM.Expo software are
expected to be very similar to those included in HHRAP.
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit 7. Overview of Process Carried Out in the
Multimedia Ingestion Risk Calculator
£
Farm Food Chain
Concentration
Calculator
i
w
r
Chemical-
specific uptake/
transfer factors
TRIM.FaTE
outputs
x
FFC media
concentrations
Plant- and
animal-specific
parameters
Ingestion
Exposure
Calculator
Average daily
doses
Risk and Hazard
Calculator
Cancer risks and
hazard quotients
Human activity/
exposure factors
Ingestion
dose-response
values
Key to symbols:
Access db
process
L
input
data
2.2.7 Implementation of Risk-based Emission Scaling Factors for POM and Dioxin
Emissions
Two of the four PB-HAPs for which screening emission thresholds have been developed for
RTR—POM and dioxins—are chemical groups comprising numerous individual entities. The
members of these categories reported in NEI include both specific chemicals and groups
containing multiple chemicals. For example, for POM, emissions reported in NEI include
various species, such as benz[a]anthracene, 2-methylnaphthalene, and chrysene, as well as
non-specific entries, such as "PAH, total." The constituents included in the POM and dioxin
PB-HAP categories are grouped together not only because they are types of the "same" HAP,
but also because members of these groups are assumed to have similar characteristics with
respect to toxicity and behavior in the environment.
To facilitate a practical application of the multipathway screening methods for RTR, reported
emissions of POM and dioxins are normalized or scaled to a single reference chemical for each
group. The reference chemicals used in RTR for POM and dioxins are benzo[a]pyrene and
2,3,7,8-TCDD, respectively. These compounds were selected because they are relatively well-
studied among the members of the two groups and are also among the most toxic species
within each group.
Derivation of appropriate scaling factors begins with an evaluation of the basic relationship used
to characterize health risk:
Risk (Exposure Concentration) x (Toxicity)
For a given air pollutant, the incremental exposure concentration is directly proportional to the
emissions of that substance. That is, as the emissions increase, so too does the exposure to
Tier 1 Methodology 22 October 2014
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TRIM-Based Tiered Screening Methodology for RTR
that substance. Furthermore, toxicity is assumed to increase linearly with concentration.
Consequently, emissions of one substance (e.g., chrysene) can be scaled proportional to a
reference compound (e.g., benzo(a)pyrene or BaP) by applying weighting factors corresponding
to the relative differences in exposure behavior and toxicity. Using the POM group as an
example and BaP as the reference compound, this scaling can be expressed through an
equation as follows:
EmiSSpAHi.BaP = EmiSSpAHi x EEFpAHi.BaP x TEFpAHi-.BaP
where:
Risk-weighted emissions of PAHi (weighted according to cancer risk
relative to BaP for oral exposures)
Emission rate of PAHi
Exposure equivalency (weighting) factor accounting for difference in
relative oral exposure between PAHi and BaP
Toxicity equivalency (weighting) factor accounting for difference in relative toxicity via
oral route between PAHi and BaP
In combination, the product of the EEF and TEF for a given substance is considered to be a
"risk equivalency factor" for the purposes of RTR evaluations that enables scaling of emissions
of a given substance for a given exposure scenario.
The TEF for each PAH and dioxin species can be calculated on the basis of relative toxicities.
Toxicities were not evaluated separately for RTR but are based on analyses conducted by EPA
elsewhere. For PAHs, oral toxicity values for individual species have been derived following the
same approach used to develop inhalation toxicity values. For dioxins, TEFs are based on the
relative toxicities developed by EPA recently and are ultimately based on the values developed
by the World Health Organization (van den Berg et al. 2006). Refer to Section 4 of
Attachment B for more information on these values.
The EEFs can be calculated directly for each individual chemical that can be modeled in
TRIM.FaTE and MIRC. TRIM.FaTE is configured for 14 POM congeners and 17 dioxin/furan
congeners. For these substances, EEFs were calculated directly using the modeling approach
and parameterization scheme for the Tier 1 scenario described in this document. Several other
POM and dioxin emissions, however, are reported in the NEI. For these, exposure surrogates
must be assigned after evaluating the correlation between chemical properties of the POM or
dioxin congener and exposure quantified as lifetime average daily dose. The specific
calculations for EEFs and exposure surrogates for each chemical group are discussed in the
sections that follow.
2.2.7.1 Calculation of Scaling Factors for POM Congeners
The calculated EEFs, TEFs, and total REFs for the 14 POM congeners that are configured in
TRIM.FaTE, plus 15 others not configured in TRIM.FaTE, are shown in Exhibit 8. To determine
appropriate exposure surrogates for chemicals not parameterized in TRIM.FaTE, EPA
evaluated the relationships between chemical-specific properties (e.g., KoW and Henry's law
constant, kh) and intermediate modeled values (e.g., deposition, soil concentration) and
exposure in terms of lifetime average daily dose (LADDs) where the average daily doses
(ADDs) for the youngest two age groups were adjusted by the age-dependent adjustment
factors (ADAFs) to account for the mutagenic mode of action of PAHs. The correlation between
Kow and exposure is stronger than for any other chemical-specific property. Exposure
EmiSSPAHuBaP -
EmisspAHi =
EEFpAHiBaP =
TEFpAHiBaP =
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TRIM-Based Tiered Screening Methodology for RTR
surrogates were thus identified for each congener by calculating Total Lifetime Average Daily
Dose (Age Adjusted) for each based on the congener's Kow and the power regression of the
modeled PAHs. Exhibit 9 shows that as Kow increases, so too does exposure.
Exhibit 8. Exposure, Toxicity, and Risk Equivalency Factors Relative to BaP
for POM Congeners Currently Evaluated in Tiers 1 and 2 Assessments
Chemical
Fully
Parameterized
in
TRIM.FaTE?
Exposure
Equivalency
Factor (EEF)
Toxicity
Equivalency
Factor
(TEF)a
Risk
Equivalency
Factor
(REF)
Dibenzo(a,i)pyrene
n
27.5
16.4
452
7,12-Dimethylbenz(a)anthracene
Y
5.4
34.2
186
3-Methylcholanthrene
n
4.3
3.0
12.9
Dibenz(a,h)anthracene
Y
8.0
0.6
4.5
Benzo(k)fluoranthene
Y
18.0
0.2
3.0
Benzo(a)pyrene
Y
1.0
1.00
1.00
Benzo(b)flouranthene
Y
11.4
0.2
1.9
lndeno(1,2,3-cd)pyrene
Y
4.5
0.2
0.7
PAH, total
n
5.1
0.07
0.3
Polycyclic Organic Matter
n
5.1
0.07
0.3
Benzo(g,h,i)perylene
Y
4.3
0.07
0.3
Benzo(e)pyrene
n
4.5
0.07
0.3
Retene
n
3.7
0.07
0.3
Dibenzo(a,j)acridine
n
0.78
0.2
0.1
Perylene
n
1.2
0.07
0.08
Benzo(a)anthracene
Y
0.09
0.16
0.01
Chrysene
Y
0.25
0.02
0.004
2-Acetyl am i n of I u o re n e
n
0.005
1.4
0.01
Fluoranthene
Y
0.04
0.07
0.003
Acenaphthylene
Y
0.04
0.07
0.003
2-Chloronaphthalene
n
0.03
0.07
0.002
Fluorene
Y
0.03
0.07
0.002
Acenaphthene
Y
0.02
0.07
0.002
1 -Methylnaphthalene
n
0.02
0.07
0.001
2-Methylnaphthalene
Y
0.02
0.07
0.001
Carbazole
n
0.01
0.003
0.00003
Anthracene
n
0.06
ob
0
Phenanthrene
n
0.06
ob
0
Pyrene
n
0.15
ob
0
aTEFs are calculated as the ratio of the cancer slope factor (CSF) for each specific POM congener
to the CSF for benzo(a)pyrene. Dose response values, including CSFs, that are used in the
screening assessment are discussed in Section 4 of Attachment B.
bWeight of evidence evaluations indicated that the available data were adequate to determine that
three PAHs (anthracene, phenanthrene, and pyrene) were not carcinogenic (EPA 2010a),
For POMs reported as unspeciated groups (i.e., "PAH, total" and "Polycyclic Organic Matter")
EPA assigned surrogates with Kow values near the upper end of the range of all of the Kow
values, corresponding to an exposure near the upper end of the range (log Kow = 6.5). This
assignment is assumed to be health protective and likely will not under predict exposure.
Tier 1 Methodology
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit 9. Relationship between Exposure and K0
for POM Congeners
5.E+02
.E-06
5.E+03
5.E+04
5.E+05
5.E+06
5.E+07
o
1
D
>>
CO
O
CD
01
U>
m
V
a>
<
"O
<
QJ
aj
E
U)
<
o>
<*_
Zj
IS
+->
o
t-
1
¦ Modeled PAHs
~ PAHs NOT Parameterized
—Power (Modeled PAHs)
E-07
E-08
E-09
.E-10
1.E-11
~#
y = 4E-14X0 8929
R2 = 0.848
Kn
It is worthy of noting that naphthalene is not included in the POM category for the RTR
multipathway (i.e., non-inhalation) analyses. Naphthalene is listed individually as a HAP under
Section 112(b) of the Clean Air Act. POM also is listed as a HAP under Section 112(b) and is
defined as organic compounds with more than one benzene ring and a boiling point greater than
or equal to 100° C (see http://www.epa.gov/ttn/atw/orig189.html). Under this definition,
naphthalene could be considered as part of the POM listing. However, naphthalene is short-
lived in environmental media due to its tendency to volatilize and biodegrade and, consequently,
will not build up in environmental media overtime (ATSDR 2005). Additionally, based on a log
Kow of 3.29, it has a moderate affinity for lipids and will undergo short-term bioaccumulation in
tissues; however, biochemical processes lead to its biodegradation and elimination. Because it
is neither persistent nor bioaccumulative, it is not considered a PB-HAP, and EPA believes that
its inclusion in the POM category would result in artificially inflated media concentrations and
multipathway risk estimates for POM.
2.2.7.2 Calculation of Scaling Factors for Dioxin Congeners
The calculated EEFs, TEFs, and REFs for the 17 dioxin congeners that are configured in
TRIM.FaTE are presented in Exhibit 10. Although there are many dioxins reported in the NEI
other than the 17 configured for TRIM.FaTE, to date, none of them have been included in
emissions datasets that have been screened. Therefore, no surrogate EEF's have been
developed for dioxins. In future screening assessments, if surrogate EEFs are needed, an
approach similar to that used for POM will be used to develop surrogate EEFs for dioxins.
Many facilities report dioxins as "Dioxins, Total, without Individual Isomers Reported," "Dioxins,"
or as "2,3,7,8-TCDD TEQ," and in these cases, we do not adjust or scale the emissions. That is,
we assume that they behave like and possess the toxic characteristics of TCDD. This approach
could be improved by obtaining information on the speciation of dioxin emissions for each
facility or an average speciation profile that could be assumed to apply to all facilities in a source
category.
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit 10. Exposure and Toxicity Equivalency Factors Relative to TCDD for Modeled
Dioxin Congeners
Chemical
Exposure
Equivalency
Factor (EEF)
Toxicity
Equivalency
Factor (TEF)a
Risk
Equivalency
Factor (REF)
PentaCDD, 1,2,3,7,8-
3.8
1
3.8
TetraCDD, 2,3,7,8-
1
1
1
Dioxins, Total, w/o Indiv. Isomers Rptd.
1
1
1
Dioxins
1
1
1
2,3,7,8-TCDD TEQ
1
1
1
HexaCDD, 1,2,3,4,7,8-
1.6
0.1
0.2
PentaCDF, 2,3,4,7,8-
0.4
0.3
0.1
HexaCDD, 1,2,3,6,7,8-
1.0
0.1
0.1
HexaCDF, 1,2,3,7,8,9-
0.5
0.1
0.05
HexaCDF, 1,2,3,6,7,8-
0.5
0.1
0.05
HexaCDD, 1,2,3,7,8,9 -
1.0
0.04
0.04
HexaCDF, 1,2,3,4,7,8-
0.3
0.1
0.03
HexaCDF, 2,3,4,6,7,8-
0.2
0.1
0.02
PentaCDF, 1,2,3,7,8-
0.4
0.03
0.01
TetraCDF, 2,3,7,8-
0.1
0.1
0.01
HeptaCDD, 1,2,3,4,6,7,8-
1.0
0.01
0.01
HeptaCDF, 1,2,3,4,6,7,8-
0.2
0.01
0.002
HeptaCDF, 1,2,3,4,7,8,9-
0.2
0.01
0.002
OctaCDD, 1,2,3,4,6,7,8,9-
1.1
0.0003
0.0003
OctaCDF, 1,2,3,4,6,7,8,9-
0.2
0.0003
0.0001
a Values from Van den Berg et al. (2006), except for 1,2,3,7,8,9-hexaCDD, which is calculated based on the ratio of the IRIS-
based CSF for 1,2,3,7,8,9-hexaCDD to the IRIS-based CSF for 2,3,7,8-TCDD. Dose response values, including CSFs, that are
used in the screening assessment are discussed in Section 4 of Attachment B.
2.3 Description of Environmental Modeling Scenario
As described in Section 2.2.4.2, the physical configuration of the RTR Screening Scenario was
designed to encompass the upper end of potential long-term PB-HAP exposures, and the
environmental and chemical-specific properties were parameterized with either health protective
or central-tendency values. Information regarding the scenario configuration and important
aspects of the parameterization process, justifications for selecting particular property values,
and model uncertainties is presented in the sections that follow. Comprehensive documentation
of TRIM.FaTE property values for this scenario is provided in Attachment A.
2.3.1 Chemical Properties
The general chemical/physical properties that TRIM.FaTE requires, such as Henry's law
constant, molecular weight, and melting point, were obtained from peer-reviewed and standard
reference sources. Numerous other chemical-specific properties are related more specifically to
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TRIM-Based Tiered Screening Methodology for RTR
a particular abiotic or biotic compartment type; these properties are discussed generally in the
sections that follow and are documented in Attachment A.
2.3.2 Spatial Layout
For the purpose of estimating media concentrations, the TRIM.FaTE scenario is intended to
represent a farm homestead and a fishable lake (and its surrounding watershed) located near
the emissions source of interest. A diagram of the surface parcel layout is presented in
Exhibit 11. The source parcel is parameterized as a square with sides of 250 m, which is
assumed to be a fair estimation for the size of a relatively small-to-medium facility at the fence
line. With a predominant wind direction toward the east, the modeled layout is generally
symmetric about an east-west line and is wedge-shaped to reflect Gaussian dispersion of the
emission plume.
A lateral, downwind distance of 10 km was established for the watershed included in the
scenario. Based on the results of dispersion modeling, the location of the maximum air
concentration and deposition rate is expected to occur relatively close to the facility (probably
within a few hundred meters, with the exact location varying with stack height and other
parameters) and well within a 10-km radius. Additionally, deposition rates for the PB-HAPs for
which this screening scenario is applicable is expected to decrease by about two orders of
magnitude relative to the predicted maximum rate within a 10-km radius. Extending the
modeling layout beyond a 10-km downwind distance would increase the amount of deposition
"captured" by the modeled watershed, but the incremental chemical mass expected to
accumulate in the watershed diminishes rapidly with distance. In addition, the impact of this
additional deposited mass on ingestion exposures is expected to be negligible.16 Given these
conditions, a downwind length of 10 km was determined to be appropriate for the screening
scenario.
Exhibit 11. TRIM.FaTE Surface Parcel Layout
3.5 km
10 km
| Tilled Soil
] Vegetation (Grasses & Herbs)
| Vegetation (Coniferous Forest)
—> Runoff
Source N6
16Mass deposited at the outer edge of the watershed is expected to result in only a very small increase in estimated
exposure via fish consumption by increasing the chemical mass transported to the lake through erosion and runoff.
The distance from these more distant locations to the lake would attenuate transport of chemical mass by erosion
and runoff, dampening the effect of including additional deposition beyond 10 km. (Other exposure pathways largely
would be unaffected; the soil concentrations used to calculate exposures for the farm food scenario are derived from
soil parcels located close to the source and unaffected by deposition to the far reaches of the watershed.)
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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).17 For a relatively neutral atmosphere (stability class D), o at
10km is about 550 m using this estimation. In a Gaussian distribution, about 99.6 percent of the
plume spread area is contained within 3o of the median line. Therefore, the plume o was set at
3 times 550 m, or approximately 1.75 km from the centerline at a distance of 10 km. The plume
width for these conditions is expected to be about twice this distance, or 3.5 km. These
dimensions were used to define the dimensions of the overall air and surface parcel layouts for
the screening scenario.
The surface (land and surface water) modeling area was initially divided into five pairs of parcels
the areas of which increase with distance from the source, which approximately corresponds to
the spatial gradient that is expected in the downwind direction from the source. The second
north parcel from the source was divided further into two parts; one of them tilled soil (Parcel
N6) to represent agricultural conditions and the other (Parcel N7) unfilled to represent pasture.
The depth of the surface soil compartments was set to 1 cm, except for Parcel N6, for which the
depth was set to 20 cm to simulate the effect of tillage. Characteristics of the soil layers (e.g.,
organic carbon content, air and water content, and subsoil depth) generally were set to
represent typical or national averages as summarized by McKone et al. (2001).
The overall shape and boundaries of the air parcel layout mirror those of the surface parcel
layout. A single air parcel (N2) overlies surface Parcels N6 and N7, and the air over the lake is
divided into air Parcels S2 and S3 (mirroring the analogous parcels on the north side of the
lake).
2.3.3 Watershed and Water Body Parameterization
Properties associated with the watershed soil and lake determine how pollutants in the system
are transported through and accumulate in various media compartments. These properties
describe the physical characteristics of the environmental media included in the modeled region,
as well as the assumed connections and relationships between media types and modeled
spatial components that in turn affect chemical transport via water runoff, ground infiltration,
deposition of suspended sediments in the water column, and other processes. This section
presents the justification for setting the key properties of the soil, water, and sediment
compartments. Also discussed are some of the chemical properties related to watershed and
waterbody processes (chemical-specific compartment properties in TRIM.FaTE) and the
configuration of terrestrial plants included in the scenario.
2.3.3.1 Water Balance
Water-related properties of the lake and related watershed characteristics (e.g., runoff rates
from each surface soil compartment) were set so that a simplified water balance is achieved.
Although TRIM.FaTE maintains a chemical mass balance, the model does not calculate or
maintain media mass balances (e.g., for water) except where specified in certain formulas. For
the Tier 1 scenario, the parameters were set to satisfy two equations relating water volume.
The first equation maintains a balance of water entering and leaving the terrestrial portion of the
scenario:
17http://www.epa.gov/scram001/userg/regmod/isc3v2.pdf
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[total precipitation] = [evapotranspiration] + [total runoff]
In this equation, total runoff is equal to the sum of overland runoff to the lake and seepage to the
lake via groundwater.
The second equation describes the volumetric balance of transfers of water to and from the
lake:
[total runoff] + [direct precipitation to the lake] = [evaporation from the lake surface] +
[outflow from the lake]
Note that TRIM.FaTE actually uses only some of these properties (e.g., precipitation rate and
surface runoff, but not evapotranspiration). The water characteristics assumed for the Tier 1
scenario are meant to represent a relatively wet and moderately warm location in the United
States (USGS 1987). Following are the assumptions for this scenario:
• 35 percent of the total precipitation leaves the scenario through evapotranspiration.
• 25 percent of total precipitation infiltrates into the groundwater and eventually flows into
the lake.
• 40 percent of total precipitation contributes to overland runoff.
For these calculations, the source parcel was considered to be outside the watershed and
therefore was not included in the water balance. The evaporation rate from the lake was
assumed to be 700 mm/yr based on data reported by Morton (1986) for various lakes. The
runoff rate was defined to be spatially constant and temporally constant (i.e., it is not linked to
precipitation events) throughout the modeled domain. Based on these assumptions, the outflow
of water from the lake is about 18 million m3/yr, which translates to a volumetric turnover rate of
about 12.2 lake volumes per year.
Other quantitative water body and watershed characteristics TRIM.FaTE uses are listed in
Attachment A.
2.3.3.2 Sediment Balance
A simplified balance of sediment transfers between the watershed and the lake was also
maintained for the screening scenario via the parameterization of sediment-related properties.
As with water, the model does not internally balance sediment mass; these calculations were
performed externally for the purposes of setting parameter values. The sediment balance
maintained is described by the following equation, where terms represent mass of sediment:
[total surface soil transfers to the lake via erosion] =
[removal of sediment from the water column via outflow] + [sediment burial]
where the second term (removal of sediment from the water column via outflow) is accounted
for in TRIM.FaTE by lake flushing rate and the third term (sediment burial) is the transfer of
sediment from the unconsolidated benthic sediment compartment to the consolidated sediment
layer.
To maintain the sediment balance, erosion rates were calculated for each surface soil
compartment using the universal soil loss equation (USLE, Wischmeier and Smith 1978),
assuming a relatively high rate of erosion. The total suspended sediment concentration is
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TRIM-Based Tiered Screening Methodology for RTR
assumed to remain constant in TRIM.FaTE, and the flushing rate of the lake (calculated via the
water balance approach described above) was then used to estimate the removal of sediment
from the modeling domain via lake water outflow. The difference between these sediment
fluxes was taken to be the sediment burial rate. The sediment burial rate is the rate at which
sediment particles in the unconsolidated benthic sediment layer are transported to the
consolidated sediment, where the particles can no longer freely interact with the water column.
In TRIM.FaTE, the consolidated sediment layer is represented with a sediment sink; as with all
sinks in TRIM.FaTE, chemical mass sorbed to buried sediment that is transported to the sink
cannot be returned to the modeling domain. The burial rate is a formula property calculated by
the model according to the difference between user-specified values for sediment deposition
velocity (from the water column to the benthic sediment) and sediment resuspension velocity
(back into the water column from the benthic sediments). These formula properties assume a
constant volume of particles in the sediment layer (because the densities for benthic and
suspended sediment particles were defined to the same value, the mass of particles in the
sediment is also constant).
For the Tier 1 scenario described here, the average sediment delivery rate (i.e., transfer of
sediment mass from watershed surface soil to the lake due to erosion) for the entire watershed
was estimated to be about 0.0036 kg/m2-day, based on calculations using the USLE. The
HHRAP documentation notes that using the USLE to calculate sediment load to a lake from the
surrounding watershed sometimes leads to overestimates (EPA 2005a). For the Tier 1
scenario, however, this approach was considered to be appropriate in that health protective
assumptions are a goal of the screening scenario.18 Surface soil compartments adjacent to the
lake are linked directly to the lake for the purposes of estimating erosion and runoff transfers
(see layout in Exhibit 11). Erosion and runoff from the source parcel are linked directly to a sink
and do not enter the Tier 1 scenario lake. The transport of sediment to the lake via overland is
thus assumed to be efficient. Note that erosion from parcels not directly adjacent to the lake is
assumed to be somewhat attenuated, effected by using a lower sediment delivery ratio in the
USLE.
Using the calculated surface soil erosion rates for the scenario, the total average daily sediment
load to the lake from the watershed is about 16,600 kg/day. About 15 percent of this load is
removed from the lake via outflow of suspended sediments (based on a calculated flush rate of
12.2 volume turnovers per year) with the remainder of the sediment input to the lake transferred
to the sediment burial sink.
2.3.4 Meteorology
Meteorological properties used in TRIM.FaTE algorithms include air temperature, mixing height,
wind speed and direction, and precipitation rate. These properties, which can vary significantly
among geographic locations, and seasonally and hourly for a single location, greatly influence
the chemical concentrations predicted in media of interest. Because the screening scenario is
intended to be generally applicable for any U.S. location, and to minimize the frequency of false
negatives, a health protective configuration was used. The meteorology of the screening
scenario was defined to ensure that (when used in combination with the selected spatial layout)
the maximum exposures that might be encountered for the scenarios of interest would be
encompassed (i.e., consumption of home-grown farm foodstuffs and self-caught fish, with all
farm foods and fish obtained from locations impacted by chemicals emitted from the local
18Based on sensitivity analysis, a higher erosion rate will both increase surface water concentrations and decrease
surface soil concentrations; the relative impact on resulting concentrations, however, will be proportionally greater in
the waterbody.
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TRIM-Based Tiered Screening Methodology for RTR
source). Ensuring that the meteorological parameters were not overly protective of health, such
as always having the wind blow toward the location of interest, however, was also important to
avoid too many false positives.
The meteorological data for the screening scenario are intended to be representative of a
location with a low wind speed, a wind direction that strongly favors the watershed, and a
relatively high amount of total precipitation falling on the watershed. The values used were
based on actual data trends for U.S. locations as specified in Exhibit 12 but an artificial data set
was compiled for this assessment (for example, temporally variable meteorological parameters
were made to vary only on a daily basis). This simplified approach allowed for greater control
(relative to selecting a data set for an actual location) so that desired trends or outcomes could
be specified. Also, using a meteorological data set with values varying on a daily basis rather
than a shorter period (such as hourly, which is the typical temporal interval for meteorological
measurements) reduced required model run time. Meteorological inputs are summarized in
Exhibit 12.
The sensitivity of modeled PB-HAPs to changes in these meteorological variables was tested.
Lower wind speeds and mixing heights affected concentrations the most. This sensitivity is not
unexpected because lower wind speeds should increase pollutant deposition onto the soil and
lower mixing heights should reduce the volume through which pollutants disperse. The wind
speed used for the screening scenario was 2.8 m/s, the 5th percentile of the annual averages
among 239 stations; by comparison, the mean annual average wind speed is approximately 4
m/s in the contiguous United States). The mixing height (mean heights from 4 states) used was
710 m (the 5th percentile of annual averages among all 40 states in the SCRAM database).
Exhibit 12. Summary of Key Meteorological Inputs
Meteorological
Property
Selected Value
Justification
Air temperature
Constant at 298 Kelvin
Recommended default value listed in HHRAP (EPA 2005a).
Value is similar to the mean daily June temperature in much of the
U.S. Southern Plains and Southeast.3
Mixing height
Constant at 710 m
Value is 5th percentile of annual average mixing heights for 75
U.S. locations, using data obtained from EPA's SCRAM Web
site.b Value is the approximate U.S. median for periods without
precipitation, based on data compiled by Holzworth (1972). Value
is conservative compared to the 1- to 2-km typical mid-latitude
daytime value (Stull 1988).
Wind direction
Blows from source
parcel into scenario
domain (west to east) 3
days per week; during
other times does not
blow into domain
A wind direction that favors the location of interest (for example, a
watershed downwind of a source of concern) will tend to result in
more emitted mass accumulating in the location of interest. For
much of the U.S. mid-Atlantic and western regions, the wind tends
to favor the eastward direction.3 Among the NCDC 1981-2010
normalized wind vector data, the average wind direction had a
strong eastward component at over one-third of the stations.0 For
the hypothetical RTR scenario, a more extreme example of this
pattern is represented by conditions in Yakima, Washington,
where the wind blows eastward approximately 40 percent of the
time based on a review of wind direction data compiled by the
National Weather Service (NCDC 1995). This pattern was
approximated in the RTR scenario with a configuration in which
the modeled domain is downwind of the source 3 out of 7 days.
Horizontal wind
speed
Constant at 2.8 m/sec
Set to 5th percentile of annual average speed for 239 stations
across the contiguous United States (about 50 years of data per
station). Value is similar to the annual average wind speeds of
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TRIM-Based Tiered Screening Methodology for RTR
Meteorological
Property
Selected Value
Justification
many areas of the U.S. east coast and west coast.3
Precipitation
frequency
Precipitation occurs 3
days per week; wind
direction blows into
domain 2 of these days
This value was selected so that two-thirds of the total precipitation
occurs when the domain is downwind of the modeled source.
This pattern approximates that for rainy U.S. locations, where
precipitation occurs 35-40% of the time (Holzworth 1972). These
locations include parts of the U.S. Northeast and Northwest.3
Total
Precipitation
1.5 m/yr
Assumed to represent rainy conditions for the United States. This
annual precipitation amount is experienced in parts of the U.S.
Deep South and parts of the U.S. northwest coast.3 Conditional
precipitation rate (rainfall rate when precipitation is occurring) is
9.59 mm/d, which is similar to conditions in many areas along the
U.S. east coast and in the Midwest and Plains.3
National Climatic Data Center CliMaps (NCDC-CliMaps) (2007). http://cdo.ncdc.noaa.gov/cqi-bin/climaps/climaps.pl.
bSupport Center for Regulatory Atmospheric Modeling; http://www.epa.qov/ttn/scram/.
°National Climatic Data Center 1981-2010 Climate Normals; http://www.ncdc.noaa.gov/oa/climate/normals/usnormals.html
2.3.5 Aquatic Food Web
The aquatic food web is an important part of the screening scenario because the chemical
concentrations modeled in fish are used to calculate human ingestion exposure and risks
associated with eating contaminated local fish. A biokinetic approach to modeling
bioaccumulation in fish is used in the RTR screening scenario. The primary producers (first
trophic level) in the TRIM.FaTE aquatic ecosystems are algae and macrophytes in the water
column and detritus in the sediments (the latter simulated as sediment particles). Zooplankton
feed on algae in the water column, while benthic invertebrates, represented as a single
compartment, consume detritus that settles to the sediment compartment. In the water column,
small young-of-the-year fish and minnows feed on zooplankton and phytopolankton. The small
fish are in turn consumed by larger or "pan" fish (e.g., bluegills, white perch), which are in turn
consumed by the top consumers (e.g., gar, pickerel). The invertebrates in the sediments of the
benthic environment support small bottom-feeding fish (young-of-the-year fish for many
species), which in turn are consumed by larger bottom-feeding fish (e.g., catfish). For
TRIM.FaTE to provide reasonable predictions of the distribution of a chemical across biotic and
abiotic compartments in aquatic systems, the biomass of the aquatic biotic compartments must
represent all biota in the system and the distribution of biomass among the trophic levels and
groups must be as realistic as possible.
To support the development of a relatively generic freshwater aquatic ecosystem in which to
model bioaccumulation in fish, a literature search, review, and analysis was conducted
(ICF 2005). This research demonstrated that the diversity of species and food webs across
U.S. aquatic ecosystems is substantial, reflecting the wide range of sizes, locations, and
physical/chemical attributes of both flowing (rivers, streams) and low-flow water bodies (ponds,
lakes, reservoirs). In general, lentic bodies of water (lakes and ponds) can accumulate higher
levels of contaminants in both sediments and biota than lotic systems (rivers, streams). Also,
that initial research (ICF 2005) suggests that a lake of at least 60 hectares (ha) or 150 acres
could support higher trophic level predatory fish, with some fraction of their diet comprising
smaller fish.
The RTR Tier 1 scenario includes a generic aquatic ecosystem with a 47-hectare (116-acre)
lake. Although slightly smaller than the size suggested by the previous review (ICF 2005), a 47-
ha lake is large enough to support higher trophic level fish given appropriate conditions (e.g.,
high productivity given a sufficient nutrient base and average temperature and growing season).
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Also, this size was compatible with the overall size of the defined watershed in the screening
scenario. The fish types, biomass, diet fractions, and body weights recommended for fish
compartments for the Tier 1 scenario are listed in Exhibit 13. Biomass is based on an
assumption that the total fish biomass (wet-weight) for the aquatic ecosystem is 5.7 grams per
square meter (gw/m2, ICF 2005). That assumption yields health protective (i.e., higher)
estimates of chemical concentrations in fish than would the assumption of higher standing
biomass and fish productivity.
In general, the food web implemented in the Tier 1 scenario is consistent with aquatic food webs
that support trophic level 4 fish (to maximize bioaccumulation), and is intended to be generally
health protective.
Exhibit 13. Aquatic Biota Parameters for the TRIM.FaTE Screening Scenario
TRIM.FaTE
Compartment
Type
Organisms
Represented by
Compartment
Biomass
Diet
Average
Body Weight
(kg)
Areal density
(gw/m2)
Fraction of
Total Fish
Biomass
Algae
green algae,
diatoms, blue-
green algae
7.95
-
Autotrophic
-
Zooplankton
crustaceans,
rotifers,
protozoans
6.36
-
100% algae3
5.7 E-8
Macrophyte
hydrilla, milfoil
500
-
-
-
Water column
planktivore
young-of-the-
year, minnows
2.0
35.1%
100% zooplankton
0.025
Water column
omnivore
bluegill, white
perch
0.5
8.8%
100% water column
planktivore
0.25
Water column
carnivore
largemouth bass,
walleye
0.2
3.5%
100% water column
omnivore
2.0
Benthic
invertebrate
aquatic insect
larvae,
crustaceans,
mollusksb
20
-
detritus in sediments
0.000255
Benthic
omnivore
small catfish,
rock bass
2.0
35.1%
100% benthic invert.
0.25
Benthic
carnivore
large catfish,
sculpins
1.0
17.5%
50% benthic invert.
50% benthic omniv.
2.0
Total Fish Biomassc
5.7
aAlgae is modeled as a phase of surface water in TRIM.FaTE.
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 outputs include average PB-HAP concentrations and deposition rates for
each year and for each parcel of the model scenario. In each surface parcel, deposition rates to
the soil are provided as are soil concentrations for the surface, root, and vadose zones and
grass or leaf concentrations as appropriate for the plants. For each air parcel, air
concentrations are provided. For the lake, surface water concentrations and concentrations in
the various levels of the aquatic food chain are provided. For the ingestion exposure
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TRIM-Based Tiered Screening Methodology for RTR
calculations, some concentrations are used to calculate direct exposure (e.g., soil ingestion),
and some are used to perform the farm food chain concentration calculations in the various
media that humans can ingest.
The locations that determine exposures were selected to be health protective. Decisions
regarding which TRIM.FaTE outputs to use in calculating exposures for the Tier 1 scenario
assume exposure at locations very close to the modeled source. These locations are predicted
to have amongst the highest media concentrations consistent with the specified spatial layout,
thereby resulting in higher exposures to the emitted chemicals. These assumptions are
summarized in Exhibit 14.
Exhibit 14. Spatial Considerations - TRIM.FaTE Results Selected for Calculating Farm
Food Chain Media Concentrations and Receptor Exposures
TRIM.FaTE Output Used in Exposure
Calculations
Representative Compartment
Concentration in air, for uptake by plants via vapor
transfer
Air compartment in air Parcel N2 (air over tilled soil)
Deposition rates, for uptake by farm produce
Deposition to surface soil compartment in surface
Parcel N6 (tilled soil)
Concentration in surface soil, for incidental
ingestion by humans and farm animals
Surface soil compartment in surface Parcel N1
(untilled soil, closest to facility)
Concentration in soil, for uptake by farm produce
and animal feed
Surface soil compartment in surface Parcel N6
(tilled soil)
Concentration in fish consumed by fisher
Water column carnivore compartment in lake (50%
offish consumed) and benthic carnivore in lake
(50% offish consumed)
TRIM.FaTE can output instantaneous chemical concentrations for a user-specified time step
and also can be configured to calculate temporal averages (e.g., annual averages). For the
Tier 1 scenario, the model is set up to output results on a daily basis, largely because daily is
the smallest time step over which input data change (i.e., wind direction and precipitation rate).
Daily concentration results were averaged to obtain annual average concentrations. The default
assumption is annual average concentrations for media during the fiftieth year of emissions.
For the chemicals modeled in this scenario, long-term concentrations in environmental media
will be relatively constant at 50 years. Aside from mercury, chemicals modeled for RTR
approach steady state before 50 years. And, although mercury concentrations do not achieve
steady state after 50 years in the modeled screening scenario configuration, the rate of change
in mercury concentrations shows a decreasing trend.
2.4 Description of Exposure and Risk Modeling Scenario
This section describes the approach for modeling chemical concentrations in farm food chain
(FFC) media (Section 2.4.1); estimating human exposures associated with ingestion of FFC
media, incidental ingestion of soil, ingestion of fish, and infant consumption of breast milk
(Section 2.4.2); and calculating human health screening risk metrics associated with these
exposure pathways (Section 2.4.3). All of these calculations are conducted using MIRC. For
this multipathway screening evaluation, partitioning of PB-HAPs into FFC media is modeled with
MIRC, not as a part of the TRIM.FaTE modeling. Consequently, processes and inputs related
to estimating chemical levels in FFC media are summarized in this section and discussed in
detail in Attachment B.
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2.4.1 Calculating Concentrations in Farm Food Chain Media
As was shown in Exhibit 7, MIRC was compiled to calculate concentrations of PB-HAPs in
foodstuffs that are part of the farm food chain. The FFC media included in this screening
scenario include:
exposed and protected fruit,
exposed and protected vegetables,
root vegetables,
beef,
dairy products,
pork, and
• poultry and eggs.
The algorithms used in MIRC were obtained from EPA's Human Health Risk Assessment
Protocol for Hazardous Waste Combustion Facilities (HHRAP; EPA 2005a). These algorithms
model the transfer of concentrations of PB-HAPs in FFC media using biotransfer factors.
Environmental media concentrations (i.e., the chemical source terms in these algorithms) are
obtained from TRIM.FaTE. As noted in Section 1.1.2.2, the TRIM.FaTE outputs included as
inputs to MIRC are the following:
PB-HAP concentrations in air;
air-to-surface deposition rates for PB-HAPs in both particle and vapor phases;
PB-HAP concentrations in fish tissue for water column carnivores and benthic
carnivores; and
PB-HAP concentrations in surface soil and root zone soil.
In general, plant- and animal-specific parameter values, including chemical-specific transfer
factors for FFC media, were obtained from the Hazardous Waste Companion Database
included in HHRAP (EPA 2005a). Attachment B provides parameter values used in MIRC for
the Tier 1 assessment.
2.4.2 Ingestion Exposure
MIRC was used to estimate ingestion rates as ADDs, normalized to body weight for a range of
exposure pathways. Exposure pathways included are incidental ingestion of soil, consumption
of fish, produce, farm animals and related products, and ingestion of breast milk by infants. The
ingestion exposure pathways included in the screening evaluation and the environmental media
through which these exposures occur are summarized in Exhibit 15.
2.4.2.1 Exposure Scenarios and Corresponding Inputs
Specific exposure scenarios are developed by defining the ingestion activity patterns (i.e.,
estimating how much of each medium is consumed and the fraction of the consumed medium
that is grown in or obtained from contaminated areas) and the characteristics of the hypothetical
human exposed (e.g., age and body weight). MIRC computes exposure doses and risks for
each ingestion pathway separately, enabling the pathway(s) of interest for each PB-HAP to be
determined. Data related to exposure factors and receptor characteristics were obtained
primarily from EPA's Exposure Factors Handbook (EPA 2011).
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Exhibit 15. Summary of Ingestion Exposure Pathways and Routes of Uptake
Ingestion
Exposure
Pathway
Intermediate
Environmental Uptake Route
Medium Ingested
Exposure
Pathway - Farm
Animals3
Medium
Process15
Incidental
ingestion of soil
Unfilled surface soil
N/A
Surface soil
Deposition; transfer via
erosion and runoff0
Direct uptake from water and
Consumption of
fish
Fish from local water
body
N/A
Fish tissue
consumption of food
compartments modeled in
TRIM.FaTE0
Consumption of
breast milkd
Breast milk
N/A
Breast milk
Contaminant ingested by
mother partitions to breast
milk
Aboveground
produce, exposed
fruits and vegetables
N/A
Air
Air
Soil
Deposition to leaves/plants
Vapor transfer
Root uptake
Consumption of
produce
Aboveground
produce, protected
fruits and vegetables
N/A
Soil
Root uptake
Belowg round
produce
N/A
Soil
Root uptake
Ingestion of forage
Air
Direct deposition to plant
Beef
Ingestion of silage
Air
Soil
Vapor transfer to plant
Root uptake
Ingestion of grain
Soil
Root uptake
Ingestion of soil
Soil
Ingestion from surface
Ingestion of forage
Air
Direct deposition to plant
Dairy (milk)
Ingestion of silage
Air
Soil
Vapor transfer to plant
Root uptake
Consumption of
Ingestion of grain
Soil
Root uptake
farm animals
and related
food products
Ingestion of soil
Soil
Ingestion from surface
Pork
Ingestion of silage
Air
Air
Soil
Direct deposition to plant
Vapor transfer to plant
Root uptake
Ingestion of grain
Soil
Root uptake
Ingestion of soil
Soil
Ingestion from surface
Poultry
Ingestion of grain
Soil
Root uptake
Ingestion of soil
Soil
Ingestion from surface
Eggs
Ingestion of grain
Soil
Root uptake
Ingestion of soil
Soil
Ingestion from surface
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 consumption of breast milk exposure scenario is discussed in Section 2.4.2.3.
For the Tier 1 scenario described here, exposure characteristics that would result in a highly
health protective estimate of total exposure were selected. The ingestion rate for each medium
was set at high-end values (equal to the 90th percentile values for all food types except for fish,
which was set at 99th percentile values). All media were assumed to be obtained from locations
impacted by the modeled source. Although this approach could result in an overestimate of
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TRIM-Based Tiered Screening Methodology for RTR
total chemical exposure for a hypothetical exposure scenario (for example, note that the total
food ingestion rate that results is extremely high for a hypothetical consumer with ingestion
rates in the upper percentile for every food type), it was selected to avoid underestimating
exposure for any single farm food type. The exposure characteristics selected for the Tier 1
scenario are summarized in Exhibit 16.
Exhibit 16. Overview of Exposure Factors Used for RTR Multipathway Screening313
Exposure Factor
Selection for Screening Assessment
Age group evaluated
Infants under 1 year (breast milk only)
Children 1-2 years of age
Children 3-5 years of age
Children 6-11 years of age
Children 12-19 years of age
Adult (20-70 years)
Body weight (BW; varies by age)
Weighted mean of national distribution or
recommended value
Ingestion rate (IR) for farm produce and animal
products other than fish (varies by age and medium)
90th percentile of distribution of consumers who
produce own food
Ingestion rate for fish
For adults, 99th percentile as-prepared ingestion
rate representative of subsistence fisher woman.
For children, based on 99th percentile, as-
prepared, consumer-only, national ingestion rates
- adjusted.
Exposure frequency (EF)
365 days/year
Exposure duration
Lifetime, for estimating cancer risk; varies by
chemical for chronic non-cancer evaluation
Fraction contaminated (FC) (varies by media
consumed)0
1
Cooking lossd
Assumed to be "typical"; varies depending on food
product (see Attachment B). Cooking losses were
not considered for fish consumption because
intake rates represent "as prepared" values.
Food preparation/cooking adjustment factor for fishe
Mercury = 1.5
Cadmium = 1.5
Dioxin = 0.7
PAH = 1.0
aData for exposure characteristics are presented in Attachment B. Exposure parameter values were based on data obtained
primarily from the Exposure Factors Handbook (EPA 2011). See Attachment B for details.
bExposure factor inputs are used in calculating ADD estimates for each exposure pathway. ADD equations for each pathway
evaluated in this screening assessment are provided in Attachment B.
°Fraction contaminated represents the fraction of food product that is derived from the environment included in the screening
scenario (e.g., produce grown on soil impacted by PB-HAPs). This parameter is defined separately for each FFC medium;
however, for the Tier 1 scenario, all ingested media are assumed to be impacted.
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, adjustments might be appropriate to
adjust the fish tissue concentrations to reflect concentrations after food preparation. See Attachment B, Section 6.4.4 for additional
discussion.
2.4.2.2 Calculating Average Daily Doses
MIRC calculates chemical-specific ADDs normalized to body weight (mg PB-HAP per kg of
body weight per day). Equations used to calculate ADDs were adapted from the algorithms
provided in the technical documentation of EPA's Multimedia, Multipathway, and Multireceptor
Risk Assessment (3MRA) Modeling System (EPA 2003), which derived much of its input data
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TRIM-Based Tiered Screening Methodology for RTR
from the Exposure Factors Handbook (EPA 2011). The ingestion exposure modeling approach
embodied by 3MRA is conceptually similar to that presented in HHRAP. A discussion of
exposure dose estimation and the equations to calculate ADDs for each ingestion pathway are
provided in Attachment B.
2.4.2.3 Infant Ingestion of Breast Milk
A nursing mother exposed to contaminants by any ingestion pathway described above can pass
the contaminants to her infant through breast milk (ATSDR 1998). The nursing infant's
exposure can be estimated from the levels of chemical concentrations in the breast milk, which
in turn can be estimated based on the mother's chemical intake.
Reports of bioaccumulation of lipophilic compounds such as polychlorinated biphenyls (PCBs),
polychlorinated dibenzofurans (PCDFs), and dioxins are prevalent in the scientific literature.
Due to their high lipophilicity, these compounds partition almost exclusively to the milk fat of
breast milk rather than the aqueous phase (EPA 1998). PCBs, PCDFs, and PCDDs are the
most documented groups of contaminants found in breast milk. Other compounds with lower
octanol-water partition coefficients, such as phenol, benzene, halobenzenes, and PAHs, are
found in both the milk fat and the aqueous phase of breast milk. Heavy metals such as arsenic,
lead, cadmium, and mercury have been found in the aqueous phase of the breast milk.
However, given their chemical and physical characteristics (and the impact such qualities have
on partitioning within the body and pharmacokinetics), substances that do not partition as
strongly to the lipophilic phase of breast milk tend to be of lower concern with regard to
exposures to nursing infants. Because of the greater concern with regard to dioxins for this
exposure pathway, it is the only PB-HAP included in the breast milk exposure pathway for RTR
at this time. This approach is consistent with the risk assessment procedures included in EPA's
Human Health Risk Assessment Protocol (EPA 2005a).
Exposure via the breast milk consumption pathway is estimated in MIRC for dioxins only. This
pathway is included in computing total exposure for developing the screening threshold for
dioxins. In the absence of congener-specific data, dioxin congeners were assumed to manifest
the same tendency to accumulate in breast milk as 2,3,7,8-TCDD.
2.4.3 Calculating Risk
MIRC was used to calculate excess lifetime cancer risk and non-cancer hazard (expressed as
the hazard quotient or HQ) using the calculated ADDs and ingestion dose-response values.
Chemical dose-response data include cancer slope factors (CSFs) for ingestion and non-cancer
oral RfDs. The CSFs and RfDs for the PB-HAPs included in the Tier 1 scenario are presented
in Exhibit 17 and are discussed in more detail in Attachment B. Equations used to estimate
cancer risk and non-cancer hazard also are provided in Attachment B.
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit 17. Dose-response Values for PB-HAPs Addressed
by the Screening Scenario
PB-HAP
CSF
([mg/kg-day]"1)
Source
RfD
(mg/kg-day)
Source
Inorganics
Cadmium compounds (as Cd)
not available
1E-3
IRIS
Elemental mercury
not available
not available
Divalent mercury
not available
3E-4
IRIS
Methyl mercury
not available
1E-4
IRIS
Organics
Benzo[a]pyrenea
7.3
IRIS
not available
2,3,7,8-TCDD
1.5E+5
EPA ORD
7E-10
IRIS
Source: EPA (2007).
CSF = cancer slope factor; RfD = reference dose; IRIS = EPA's Integrated Risk Information System; Cal/EPA = California
Environmental Protection Agency; EPA ORD = EPA's Office of Research and Development
aFor consistency with the overall approach for dose-response assessment of PAHs, the CSF listed in IRIS for benzo[a]pyrene ([7.3
mg/kg-day]"1) was adjusted due to its mutagenic mode of action as discussed below (see also Attachment B).
Estimated individual cancer risks for the PAHs included in the screening scenario were adjusted
upward to account for the mutagenic cancer potency of these compounds during childhood, as
specified by EPA in supplemental guidance for cancer risk assessment (EPA 2005c).
Specifically, cancer potency for PAHs is assumed to be tenfold greater for the first 2 years of life
and threefold greater for the next 14 years. These factors were incorporated into a time-
weighted total increase in potency over a lifetime of 70 years. The cancer potency adjustment
for chemicals with a mutagenic mode of action is discussed in Attachment B.
2.4.4 Summary of Tier 1 Assumptions
As emphasized previously, the screening scenario created for evaluating PB-HAP emissions
from RTR facilities is intended to be health protective to prevent underestimating risk. The
scenario also is intended to avoid grossly overestimating risk to the point where no emissions
screen. The overall degree to which the scenario is health protective is the sum of the multiple
assumptions that affect the outputs of the fate and transport, exposure, and risk modeling.
Exhibit 18 summarizes important characteristics that influence exposure and risk estimates for
this scenario and indicates the general degree of health protectiveness associated with the
values for each assumption. Although this summary does not provide a quantitative estimate of
the output uncertainty or the degree to which exposures and risks estimated using the scenario
would be overestimated, it does demonstrate qualitatively that the scenario generally
overestimates exposure and thus favors a health-protective risk output.
Exhibit 18. Summary of RTR Tier 1 Screening Scenario Assumptions
Characteristic
Value
Neutral or
Health
Protective?
Comments on Assumptions
General Spatial Attributes
Farm location
375 m from
source; generally
downwind
Health
Protective
Location dictates soil and air concentrations
and deposition rates used to calculate
chemical levels in farm produce.
Lake location
375 m from
source; generally
downwind
Health
Protective
Location dictates where impacted fish
population is located.
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Characteristic
Value
Neutral or
Health
Protective?
Comments on Assumptions
Surface soil
properties
Typical values or
national averages
Neutral
Based on existing EPA documentation and
other references.
Size of farm parcel
About 4 ha
Health
Protective
Relatively small parcel size results in higher
chemical concentration.
Size of lake
47 ha; about 3 m
average depth
Health
Protective
Lake is large enough to support an aquatic
ecosystem with higher trophic-level fish, but
is relatively small and shallow (thus
increasing surface area-to-volume ratio).
Meteorological Inputs
Total precipitation
1.5 m/yr
Health
Protective
Intended to represent rainy U.S. location;
set to highest state-wide average for the
contiguous United States.
Precipitation
frequency (with
respect to impacted
farm/lake)
2/3 of total
precipitation fall on
farm/lake and
watershed
Health
Protective
Most of total precipitation occurs when the
farm/lake are downwind of the source.
Wind direction
Farm/lake are
downwind 40% of
the time
Health
Protective
Farm/lake located in the predominantly
downwind direction. Temporal dominance
of wind direction based on data from
Yakima, Washington, where wind is
predominantly from the west.
Wind speed
2.8 m/sec
Health
Protective
Low wind speed (5th percentile of long-term
averages for contiguous United States);
increases net deposition to lake/watershed.
Air temperature
298 K
Neutral
Typical for summer temperatures in central
and southern United States.
Mixing height
710 m
Health
Protective
Relatively low long-term average mixing
height (5th percentile of long-term averages
for contiguous United States); increases
estimated air concentration.
Watershed and Water Body Characteristics
Evaporation of lake
surface water
700 mm/yr
Neutral
Based on sensitivity analyses, value is not
expected to under- or overestimate
concentration in surface water.
Surface runoff into
lake
Equal to 40% of
total precipitation
Health
Protective
Based on typical water flow in wetter U.S.
locations; higher runoff results in greater
transfer of chemical to lake.
Surface water
turnover rate in lake
About 12 turnovers
per year
Neutral
Consistent with calculated water balance;
reasonable in light of published values for
small lakes. Might overestimate flushing
rate if water inputs are also overestimated.
Note that after evapotranspiration,
remaining water volume added via
precipitation is assumed to flow into or
through lake.
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TRIM-Based Tiered Screening Methodology for RTR
Characteristic
Value
Neutral or
Health
Protective?
Comments on Assumptions
Soil erosion from
surface soil into lake
Varies by parcel;
ranges from 0.002
to 0.01 kg/m2-day
Neutral
Erosion rates calculated using the universal
soil loss equation (USLE); inputs to USLE
were selected to be generally conservative
with regard to concentration in the pond
(i.e., higher erosion rates were favored).
Might underestimate erosion for locations
susceptible to high erosion rates. Note that
higher erosion increases concentration in
lake (and fish) but decreases levels in
surface soil (and farm products).
Aquatic food web
structure and
components
Multilevel; includes
large, upper
trophic-level fish
Health
Protective
Inclusion of upper trophic-level fish and
absence of large-bodied
herbivore/detritivore fish favor higher
concentrations of bioaccumulative
chemicals and result in higher
concentrations in consumed fish. Linear
food-chain maximizes concentration of
bioaccumulative chemicals in higher
trophic-level fish.
Parameters for Estimating Concentrations in Farm Food Chain Media
Fraction of plants
and soil ingested by
farm animals that is
contaminated
1.0 (all food and
soil from
contaminated
areas)
Health
Protective
Assumes livestock feed sources (including
grains and silage) are derived from most
highly impacted locations.
Soil- and air-to-plant
transfer factors for
produce and related
parameters
Typical (see
Attachment B for
details)
Neutral
Obtained from peer-reviewed and standard
EPA reference sources.
Biotransfer factors
for efficiency of
uptake by animal of
chemical in food/soil
Typical (see
Attachment B for
details)
Neutral
Obtained from peer-reviewed and standard
EPA reference sources.
Bioavailability of
chemicals in soil (for
soil ingested by
animals)
1.0 (relative to
bioavailability of
chemical in plant
matter)
Health
Protective
Probably overestimates bioavailability in
soil; many chemicals are less bioavailable
in soil than in plants.
Ingestion Exposure Assumptions
Ingestion rates for all
farm
produce/livestock
types
Person obtains all
food sources from
local farm;
ingestion rate is
90th percentile of
rates for home-
produced food
items
Health Protective
All food derived from impacted farm; total
food ingestion rate would exceed expected
body weight-normalized ingestion rates
(prevents underestimating any individual
food type).
Fish ingestion rate
Adult: 373 g/day
Child age groups:
1 to 2: 108 g/day
3 to 5: 159 g/day
6 to 11: 268 g/day
12 to 19: 331 g/day
Health Protective
The adult rate is the 99th percentile value
for adult females from Burger (2002) and is
considered representative of subsistence
fishers.
Rates for children are based on the 99th
percentile, consumer-only fish ingestion
rates from EPA 2002. Rates were adjusted
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Characteristic
Value
Neutral or
Health
Protective?
Comments on Assumptions
to be representative of the age groups used
in the screening scenario. See Attachment
B for a detailed discussion.
Exposure frequency
Consumption of
contaminated food
items occurs 365
days/yr
Health Protective
All meals from local farm products.
Body weight
Mean of national
distribution
Neutral
Note that this does not affect the body-
weight-normalized rates for produce and
animal products.
Other Chemical-Specific Characteristics
General chemical
properties used in
fate and transport
modeling (Henry's
law, Kow, etc.)
Varies
Neutral
Obtained from peer-reviewed sources;
intended to be representative of typical
behavior and characteristics.
"General" physical
properties (plant
matter density,
aquatic life biomass,
algal growth rate,
etc.)
Varies
Neutral
Obtained from peer-reviewed sources;
intended to be representative of typical
behavior and characteristics.
Dose-response
values
Varies
Neutral to Health
Protective
Values used are those determined to be
appropriate for risk assessment by OAQPS;
values are developed to be health
protective.
2.5 Evaluation of Screening Scenario
2.5.1 Introduction
The screening scenario developed for assessing multipathway human health risk for EPA's Risk
and Technology Review has been subjected to a series of evaluations. As described previously
the major PB-HAP categories of concern for this assessment are cadmium compounds (Section
2.5.2), mercury compounds (Section 2.5.3), dioxins (Section 2.5.4), and POM (Section 2.5.5).
The scenario evaluations were focused primarily on assessing the behavior of these HAP
categories in the environment, the accumulation of these chemicals in ingestible food products,
and the predominant pathways of human exposure.
2.5.2 Cadmium Compounds
Some of the largest anthropogenic sources of cadmium to air are facilities that process, mine, or
smelt cadmium-zinc ores or cadmium-zinc-lead ores, coal- and oil-fired boilers, other urban and
industrial facilities, phosphate fertilizer manufacturing facilities, road dust, and municipal sewage
sludge incinerators (ATSDR 2008).
2.5.2.1 Behavior in the Environment
Once emitted into the environment, airborne cadmium particles can be transported over long
distances before being deposited. Cadmium has been observed to partition primarily to soil
when released to the environment (ATSDR 2008). The mobility of cadmium in soil depends
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TRIM-Based Tiered Screening Methodology for RTR
strongly on soil pH, clay content, and availability of organic matter—factors that determine
whether the cadmium is dissolved or sorbed in surface soil. In general, cadmium adsorbs to soil
particles in the surface layers of the soil profile, but to a lesser degree than many other heavy
metals (HSDB 2005). Cadmium also binds strongly to organic matter, rendering the metal
relatively immobile in some soils. Nonetheless, some plants still can take up cadmium
efficiently, thus providing an entry point for cadmium into the food chain (ATSDR 2008).
Cadmium also enters surface waters, which can occur via atmospheric deposition, runoff and
erosion, or wastewater streams. Most cadmium compounds entering the water column are
quickly removed through adsorption to organic matter in sediment or to other suspended
compounds. Cadmium that remains in the water column is expected to exist primarily in the
dissolved state where it is available for uptake by aquatic organisms.
Freshwater fish accumulate cadmium primarily through direct uptake of the dissolved form
through the gills and secondarily through the diet, which plays a variable role in total cadmium
uptake (Reinfelder et al. 1998; Chen et al. 2000; Saiki et al. 1995). Although some
biomagnification of cadmium has been reported for aquatic food chains in saltwater systems,
biomagnification in freshwater systems appears to be present only at lower trophic levels (Chen
et al. 2000) and in narrowly defined niches (e.g., plankton/macroinvertebrate food chains;
Croteau et al. 2005). Biomagnification factors (BMFs) of less than 1 generally have been
reported for fish at higher trophic levels, indicating that cadmium concentrations generally
biodiminish from lower to higher trophic levels (Chen et al. 2000; Mason et al. 2000).
For the RTR screening scenario, the partitioning behavior modeled in TRIM.FaTE was
consistent with the behavior of cadmium expected in the natural environment.
2.5.2.2 Concentrations in Ingestible Products
Most non-inhalation exposure to cadmium outside of occupational settings is through dietary
intake. Available data indicate that cadmium accumulates in plants, aquatic organisms, and
terrestrial animals, offering multiple ingestion exposure pathways (ATSDR 2008). Actual
cadmium levels in ingestible products, however, varies based on type of food, agricultural and
cultivation practices, atmospheric deposition rates, characteristics of environmental media, and
presence of other anthropogenic pollutants. Meat and fish generally contain lower amounts of
cadmium overall, but cadmium can be highly concentrated in certain organ meats, such as
kidney and liver (ATSDR 2008). In a study of cadmium concentrations in 14 food groups
(including prepared foods), meat, cheese, and fruits generally contained low levels of cadmium
(ATSDR 2008).
For the RTR screening scenario, the cadmium concentrations output by MIRC were consistent
with reported values in all ingestible media products. The products with higher reported
cadmium levels in the literature, including soil, plants, and fish, also contained the higher
modeled concentrations.
2.5.2.3 Average Daily Dose (ADD)
To determine the media most relevant to exposure and risk, the ingestion exposure factors must
be considered in addition to the estimated media concentrations (i.e., a higher concentration for
a particular medium does not necessarily mean higher risk). In Exhibit 19, the contributions of
ingestion exposure pathways to the average daily dose (ADD) (and thus the HQ) for the
different age categories are presented. As shown in the exhibit, fish ingestion is the dominant
exposure pathway across all age categories, accounting for nearly 100 percent of the ADD for
all groups. The combined contribution from all other exposure pathways accounts for less than
0.7 percent of the total ADD for all age groups. Most of the additional exposure was from
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ingestion of fruits and vegetables. The highest ADD corresponds to children aged 1-2 years;
thus, the exposure corresponding to this group was used to determine the emission threshold
for cadmium. In other words, the threshold emissions rate for cadmium is set at the level where
the HQ for this age category is equal to 1.0.
Exhibit 19. Estimated Contributions of Modeled Food Types to
Cadmium ingestion Exposures and Hazard Quotients
4 0E-05
3 5E-05
3 0E-05
5" 2.5E-05
~ 5
Q cn
I 0E-05
1.5E-Q5
I 0E-05
5.0E-06
0.0E+00
• Fish
• Fruite & Vegetables
Meat, Dairy, 8. Eggs
¦ Soil
08
06
0 4
0.2
Child 1-2
Child 3-5
Child 6-11
Child 12-19
Adult
2.5.3 Mercury Compounds
Some of the largest anthropogenic sources of mercury to air are facilities that process, mine, or
smelt mercury ores; industrial/commercial boilers; fossil fuel combustion activities (primarily
coal); cement production facilities; other urban and industrial facilities; and medical and
municipal waste incinerators (ATSDR 1999). These facilities can emit a mixture of elemental
and divalent mercury, mostly in the gaseous phase, with some divalent forms in particle-bound
phases (EPA 1997).
2.5.3.1 Behavior in the Environment
Once emitted into the environment, mercury undergoes changes in form and species as it
moves through environmental media. Elemental mercury is the most prevalent species of
mercury in the atmosphere. Due to the long residence time of elemental mercury in the
atmosphere, this compound is relatively well distributed, even on a global scale.
Divalent mercury is removed from the atmosphere at a faster rate than elemental mercury, and
it can be transferred to the surface near the emission source via wet or dry deposition where it
appears to adsorb tightly to soil particles (EPA 1997) or dissolved organic carbon. Divalent
mercury in soil also can be methylated by microbes or reduced to elemental mercury and
revolatilized back into the atmosphere. Most divalent mercury from atmospheric deposition will
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TRIM-Based Tiered Screening Methodology for RTR
remain in the soil profile, however, in the form of inorganic compounds bound to soil organic
matter. Although this complexing behavior with organic matter significantly limits mercury
transport, the ability of mercury to form these complexes greatly depends on soil conditions
such as pH, temperature, and soil humic content. For example, mercury strongly adsorbs to
humic materials and sesquioxides in soil at pH > 4 and in soils with high iron and aluminum
content (ATSDR 1999). Small amounts of mercury in soil can be transported to surface water
via runoff or leaching.
Mercury could also enter the water column through atmospheric fallout. Once in the water
body, divalent mercury can be methylated through microbial activity. In addition, divalent and
methyl mercury can be further reduced to elemental mercury, which can volatilize and reenter
the atmosphere. Solid forms of inorganic mercury compounds could adsorb to particulates in
the water column or partition to the sediment bed (EPA 1997).
The solubility of mercury in water depends on the species and form of mercury present as well
as properties of the water such as water pH and chloride ion concentration (ATSDR 1999). Low
pH favors the methylation of mercury in the water column, typically performed by sulfur-reducing
bacteria in anaerobic conditions. Methyl mercury is typically of greatest concern because it
readily bioaccumulates and efficiently biomagnifies in aquatic organisms. A considerable
amount (25-60 percent) of both divalent mercury compounds and methyl mercury is strongly
bound to particulates in the water column (EPA 1997). The remaining mercury is dissolved.
Most of the elemental mercury produced as a result of reduction of divalent mercury volatilizes
back into the atmosphere.
For the screening scenario, the partitioning behavior modeled in TRIM.FaTE generally was
consistent with trends noted in the literature. Divalent mercury was the most prevalent species
in modeled surface soil, surface water, and sediment compartments, while methyl mercury was
the dominant species in fish.
2.5.3.2 Concentrations in Ingestible Products
Available data indicate that mercury bioaccumulates in plants, aquatic organisms, and terrestrial
animals, providing multiple ingestion exposure pathways (EPA 1997; ATSDR 1999). Low levels
of mercury are found in plants, with leafy vegetables containing higher concentrations than
potatoes, grains, legumes, and other vegetables and fruits (ATSDR 1999; EPA 1997). Cattle
demethylate mercury in the rumen and, therefore, store very little of the mercury they ingest by
foraging or consuming silage or grain. Thus, mercury content in meat and cow's milk is low
(ATSDR 1999). Concentrations of methyl mercury in fish are generally highest in larger, older
specimens at the higher trophic levels (EPA 1997).
Although data on mercury in foods other than fish are not abundant in the literature, total
mercury concentrations output by MIRC were generally consistent with the reported values that
were available. The exposure pathways that most influenced the mercury HQs in the model are
presented in Exhibit 20. As shown, the dominant exposure pathway for all age groups is
ingestion of fish. Relative to divalent mercury, methyl mercury concentrations in fish were very
high (approximately 95 percent of total mercury).
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Exhibit 20. Estimated Contributions of Modeled Food Types
to Methyl Mercury Ingestion Exposures
1.5E-04
1.3E-04
1.1E-04
9.0E-05
O 5> 7.0E-05
5.0E-05
3.0E-05
1.0E-05
-1.0E-05
1
0.8
0.6
o
3
o
0.4
0.2
0
2.5.3.3 Average Daily Dose
In Exhibit 20, the contributions of ingestion exposure pathways to the ADD (and thus the HQ) for
methyl mercury across the different age categories are presented. As shown, fish is the
dominant exposure pathway across all age categories, accounting for nearly 100 percent of the
ADD for each group. The combined contribution of all other exposure pathways accounts for
less than 1 percent of the total ADD for all age groups. The high degree of exposure to methyl
mercury through fish ingestion is attributed to the ease with which this compound
bioaccumulates and biomagnifies in fish and to the health protective ingestion assumptions
used in the screening scenario. The highest ADD corresponds to children aged 1-2 years;
thus, the exposure corresponding to this group was used to determine the emission threshold
for mercury.
2.5.4 Dioxins
Incineration and combustion processes are believed to be the primary emission sources for
chlorinated dioxins (ATSDR 1998). The five stationary source categories that generate the vast
majority of 2,3,7,8-TCDD emissions in the United States are municipal waste incineration,
medical waste incineration, hazardous waste kilns from Portland cement manufacturing,
secondary aluminum smelting, and biological incineration.
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2.5.4.1 Behavior in the Environment
Dioxins emitted to the atmosphere can be transported long distances in vapor form or bound to
particulates, depositing in soils and water bodies in otherwise pristine locations far from the
source. Although airborne dioxins are susceptible to wet and dry deposition, most dioxins
emitted to the atmosphere through incineration/combustion processes are not deposited close
to the source (ATSDR 1998).
In soil, dioxins strongly adsorb to organic matter and show very little vertical movement,
particularly in soils with a high organic carbon content (ATSDR 1998). Most dioxins deposited
in soil are expected to remain buried in the soil profile, with erosion of contaminated soil
particles the only significant mechanism for transport to water bodies.
The dry deposition of dioxins from the atmosphere to water bodies is another important
transport process. Because of the hydrophobic nature of dioxins, most dioxins entering the
water column are expected to adsorb to suspended organic particles or partition to bed
sediment, which appears to be the primary environmental sink for this chemical group (EPA
2004c). Although dioxins bound to aquatic sediment primarily become buried in the sediment
compartment, some resuspension and remobilization of congeners can occur if sediments are
disturbed by benthic organisms (ATSDR 1998).
Bioaccumulation factors (BAFs) in fish are high as a result of the lipophilic nature of chlorinated
dioxins. Although the processes by which freshwater fish accumulate dioxins are not well
understood, both fish and invertebrates bioaccumulate congeners that have partitioned to
sediment or have become suspended in water (EPA 2004c). Because most dioxins in the
aquatic environment are adsorbed to suspended particles, however, direct uptake from the
water is unlikely to be the primary route of exposure for most aquatic organisms at higher
trophic levels (ATSDR 1998). At lower trophic levels, the primary route of exposure appears to
be through uptake of water in contaminated sediment pores, and the primary route of exposure
in the higher trophic levels appears to be through food chain transfer. Following ingestion,
some fish can slowly metabolize certain congeners, such as 2,3,7,8-TCDD, and release the
polar metabolites in bile. This process ultimately might limit bioaccumulation at higher trophic
levels (ATSDR 1998).
For the RTR screening scenario, the partitioning behavior modeled in TRIM.FaTE was
consistent with the behavior of 2,3,7,8-TCDD expected in the natural environment. Also of note
is that dioxins readily partition into breast milk due to the lipophilic nature of these compounds.
2.5.4.2 Concentrations in Ingestible Products
The primary source of non-inhalation exposure to dioxins outside of occupational settings is
through dietary intake, which accounts for more than 90 percent of daily dioxin exposure
(ATSDR 1998). Available data indicate that dioxins concentrate in plants, aquatic organisms,
and animals, offering multiple ingestion exposure pathways. Actual congener levels in
ingestible products, however, can vary based on type of food, agricultural and cultivation
practices, atmospheric deposition rates, characteristics of environmental media, and presence
of other anthropogenic pollutants. Dioxins appear to enter the terrestrial food chain primarily
through vapor-phase deposition onto surfaces of plants, which are then consumed by larger
animals. Another major source of exposure to dioxins is through ingestion of contaminated soil
by animals.
Observed trends indicate that meat, dairy, and fish consumption are the dominant exposure
pathways, comprising 90 percent of dioxin dietary intake (ATSDR 1998). Consistent with the
literature, the modeled concentration of 2,3,7,8-TCDD in the fish compartment for the screening
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scenario was at least one order of magnitude greater than concentrations in the other
compartments. Among the compartments with the lowest concentrations were fruits and
vegetables, which do not readily accumulate 2,3,7,8-TCDD.
Ingestion of breast milk during infancy and fish ingestion contribute to over 97 percent of lifetime
dioxin exposure for 2,3,7,8-TCDD in the screening scenario. Daily intakes of 2,3,7,8-TCDD
from cow's milk, produce, and fish have been reported in the literature to comprise 27 percent,
11 percent, and 10 percent, respectively, of the total daily intake in the general population.
Some studies note that specific subpopulations, such as subsistence farmers and fishers,
however, might have very different exposure profiles in which fish, meat, and dairy drive
congener exposure (ATSDR 1998). Given the subsistence diet modeled in the RTR screening
scenario, the high exposure from consumption of fish is appropriate within the context of this
assessment.
2.5.4.3 Lifetime Average Daily Dose (LADD)
The contributions of ingestion exposure pathways to the lifetime average daily dose (LADD)
(and thus lifetime cancer risk) for the modeled dioxin congeners are presented in Exhibit 21.
Based on the modeling methodology and assumptions used, exposures via the breast milk
pathway consistently account for approximately 30 percent of the lifetime exposure for all
congeners, while exposure via fish, soil, and the various farm food chain pathways is highly
variable across congeners. This variability can be explained in part by differences in the
physiochemical properties that drive the environmental transport processes of these congeners
(e.g., Kow, molecular weight). The differences are also likely attributed to differences in the
congener-specific half-life in abiotic media and the degree to which the congener is metabolized
in biotic media.
2.5.5 Polycyclic Aromatic Hydrocarbons
PAHs can enter the atmosphere as a result of a variety of combustion processes, both natural
and anthropogenic. Stationary emission sources account for approximately 80 percent of total
annual PAH emissions. Although the primary source of stationary source PAH emissions is
thought to be residential wood burning, other processes such as power generation; incineration;
coal tar, coke, and asphalt production; and petroleum catalytic cracking are also major
contributors (ATSDR 1995).
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Exhibit 21. Estimated Contributions of Modeled Food
Types to Dioxin Ingestion Exposures
100%
90%
80%
70%
60%
50%
40%
30%
20%
Sreastmilk
Soil
Pork
¦ Rah
¦ Fruits & Vegetables
¦ Poultry & Eggs
¦ Total Dairy
Bee*
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fy sV ~i- n?- ^ n>- o-* ^
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" & . -V5' .0?"
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TRIM-Based Tiered Screening Methodology for RTR
exposure to PAHs through association (e.g., direct uptake, consumption) with contaminated
sediment (ATSDR 1995).
For the screening scenario, the partitioning behavior of benzo(a)pyrene is generally consistent
with trends reported in the literature.
2.5.5.2 Concentrations in Ingestible Products
The primary source of non-inhalation exposure to benzo(a)pyrene outside of occupational
settings is through dietary intake. Exposure can depend on the origin of the food (higher values
are often recorded at contaminated sites) and the method of food preparation (higher values
have been reported for food that is smoked or grilled). PAHs have been observed to
bioaccumulate in aquatic organisms and terrestrial animals through uptake of contaminated
water, soil, and food. These compounds are readily metabolized by higher trophic level
organisms, including humans, however, so biomagnification is not considered to be significant
(ATSDR 1995). Plants accumulate PAHs primarily through atmospheric deposition, but
chemical concentrations tend to be below detection levels. PAHs in meat have been observed
at concentrations below detection levels up to higher concentrations when the meat is smoked.
Similar concentrations have been reported for fish, with smoked fish concentrations sometimes
quadruple those found in terrestrial animals. Because PAH concentrations are highest in
products that are smoked or grilled, most of the available data for benzo(a)pyrene in food is for
products that have been prepared using these processes. As a result, reported values might be
significantly higher than those output by MIRC.
For the RTR screening scenario, concentrations output by MIRC were generally lower than the
reported ranges for benzo(a)pyrene in ingestible products. This trend is likely the result of
background exposure in reported measurements and available data that are skewed toward
concentrations in highly contaminated products. Considering these mitigating factors, the RTR
screening scenario output concentrations are within the range of plausible values for PAHs in
ingestible products.
2.5.5.3 Lifetime Average Daily Dose
The contributions of ingestion exposure pathways to the l_ADD (and thus lifetime cancer risk) for
various PAHs are presented in Exhibit 22. As shown, the variability in the driving exposure
pathways across PAHs is significant, with fish, beef, dairy, fruits, and vegetables comprising
between 90 and 99 percent of exposure for different PAHs.
This variability can be accounted for in part by differences in the physiochemical properties that
drive the environmental fate and transport processes of these PAHs (e.g., Kow, molecular
weight, chemical structure), differences in the PAH-specific half-life in abiotic media, and the
degree to which the PAHs are metabolized in biotic media. The variability in exposure
pathways is consistent with information provided in the literature.
2.5.6 Summary
This assessment provides a summary of the fate and transport processes and the major routes
of exposure for the PB-HAP categories of interest to EPA's RTR Program, as modeled in
TRIM.FaTE. In general, the modeled behavior of the compounds is consistent with data found
in the literature.
This assessment reveals that fish ingestion is a major route of exposure for cadmium, mercury,
dioxins, and PAHs. For organics (i.e., dioxins and PAHs), the farm-food-chain also is a major
route of exposure, with beef and dairy contributing significantly to the l_ADD.
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Exhibit 22. Estimated Contributions of Modeled Food
Types to PAH Ingestion Exposures
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3. Tier 2 Methodology
3.1 Overview of Approach
The Tier 1 screening scenario is, by design, generic and health-protective. It was constructed to
allow for quick application to a large number of facilities in a source category with a minimal
chance of returning false negatives for risk. Based on screening assessments conducted for
RTR to date, many facilities might not screen out of the Tier 1 assessment for some source
categories. Because the Tier 1 screen uses numerous health-protective assumptions, the
assessment must be refined to determine whether the facility is actually expected to pose a risk
above levels of concern.
One way to refine the risk estimates is to conduct a site-specific assessment where the Tier 1
model values and layout are replaced with site-specific values and layouts wherever possible.
However, this approach would not be feasible given the resource intensive nature of a site-
specific assessment and the number of facilities that might not screen out of the Tier 1
assessment in some source categories.
As an intermediate approach, we instead refine our Tier 1 screening estimates to Tier 2
screening estimates. This is done by replacing some of the more health-protective assumptions
in a Tier 1 screen with more site-appropriate values. Specifically, for Tier 2, the following
parameter values are varied from their Tier 1 values:
Meteorological characteristics, including the fraction of time the wind blows in the
direction of each farm and lake (using wind direction), the wind speed, the precipitation
rate, and the mixing height; and
• Locations of fishable lake(s) relative to the facility (including the absence of a fishable
lake).
In addition, a refined fisher scenario is used to model risks associated with nearby lakes. The
refined fisher scenario is based on the idea that an adult fisher might fish from multiple lakes if
the first (i.e., highest-concentration) lake is unable to provide him an adequate catch to satisfy
the assumed ingestion rate (i.e., 373 g/d for adults). This assessment uses the assumption that
the biological productivity limitation of each lake is 1 gram of fish per acre of water: meaning that
in order to fulfill the adult ingestion rate, the fisher will need to fish from 373 total acres of lakes.
In selecting the fate and transport variables listed above to include in Tier 2 adjustments, a
balance was struck between: 1) the degree of impact on the potential risk estimate; 2) the ease
of implementation in TRIM.FaTE; and 3) the ease of obtaining site-specific values on a facility-
by-facility basis. Because of the expected variability in exposure parameters amongst any
population living around a given facility, the exposure parameters pertaining to farm ingestion
pathways remain fixed at their health-protective Tier 1 levels. However, the fish ingestion rates
might vary on a site-specific level using the refined fisher scenario based on the availability of
lakes to fish.
Tier 2 screening assessments are performed for those facilities that do not screen out during the
Tier 1 assessment. The overall implementation of Tier 2 is shown in Exhibit 23. The starting
point (shown in green) is the ratio of the facility emission rate for the PB-HAP of concern to the
Tier 1 threshold for that PB-HAP. Next, the facility-specific estimates of the Tier 2
meteorological and lake-location parameters listed above must be gathered for each facility
(shown in red). Then, the associated TRIM.FaTE and MIRC risk estimates must be calculated
(shown in orange). Because of the volume of facilities that need to be evaluated in Tier 2, the
implementation focuses on estimating refined risk using pre-calculated databases (discussed
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TRIM-Based Tiered Screening Methodology for RTR
below) rather than gathering the input data and performing TRIM.FaTE and MIRC modeling
speartely for each facility.
Exhibit 23. Basic Process for Implementing Tier 2
For Each Facility
One Time Assessment
First, databases of the relevant U.S. meteorological and lake data were created that can be
accessed during a Tier 2 evaluation (shown in red in the figure). These meteorological and lake
data are discussed in more detail in Section 3.5. The meteorological database includes annual-
average summary statistics on wind direction, wind speed, and precipitation for more than 800
surface stations paired with their closest upper-air stations located throughout the United States.
These surface data cover year 2011 and are the same AERMOD-ready data used by EPA
OAQPS for RTR inhalation modeling. As discussed below, in most cases the 2011 precipitation
data were not used, and instead, the 30-year average annual precipitation data for each station
were used. The database of lakes, available from ESRI® and based on U.S. Geological Survey
data, includes information on the location, size, use or type designation, and name (if available)
of all lakes in the United States. To focus on lakes that can support angling of upper trophic
level fish, lakes used for disposal, evaporation, or treatment were excluded, and only lakes
greater than 25 acres in area are included. Lakes larger than 100,000 acres in area are not
included because the sizes of their watersheds and the complexity of their lake dynamics are
not feasible to model with the TRIM.FaTE modeling system.
In parallel to the meterological and lake data collection, a series of TRIM.FaTE simulations was
performed that systematically varied the values for four of the five selected fate and transport
variables (shown in orange in the figure, consisting of lake location, wind speed, precipitation
rate, and mixing height). Wind direction affects only whether the chemical mass advects toward
the farm and lake, so the effect of site-specific wind directions can be evaluated outside
TRIM.FaTE simulations. These simulations do not simulate specific facilities; instead, four or
five alternative values for each of the four variables were selected using statistics on U.S.
meteorological data or professional judgment to capture the expected range in the facility data.
TRIM.FaTE simulations were performed for every possible combination of these variable values
to enable the estimation of appropriate site-specific threshold adjustment factors for scenarios
with the corresponding characteristics. Based on the TRIM.FaTE results of these simulations
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TRIM-Based Tiered Screening Methodology for RTR
(and the subsequent exposure and risk characterization, conducted using MIRC), a matrix of
Tier 2 threshold adjustment factors was calculated, with each element of the matrix
corresponding to a unique combination of values from each of the selected variables. The
contribution of each exposure media toward each scenario's Tier 2 risk metric (i.e., the
individual contributions offish ingestion, soil ingestion, beef ingestion, etc. toward the total
Tier 2 risk metric) was included in the matrix because the Tier 2 assessment separates
chemical exposure from fish ingestion from exposure from farm food chain ingestion.
These TRIM.FaTE and MIRC simulations are used to estimate part of the Tier 2 threshold
adjustment factors. Wind direction is the other part of the Tier 2 threshold adjustment factors.
The wind direction adjustment for a given facility is the ratio of the frequency that winds blow
toward the Tier 1 farm and lake (43 percent of the time) and the frequency that winds blow
toward the facility-specific farm and lake in Tier 2. These Tier 2 threshold adjustment factors are
multiplied by each other and represent the ratio between the risk metric (i.e., cancer risk or HQ)
obtained using the Tier 1 screening scenario and the risk metric obtained from the Tier 2
TRIM.FaTE runs. For a given facility, an adjusted Tier 2 ratio (emissions compared to the
emission threshold) can be estimated by dividing the Tier 1 emission ratio (the output of the Tier
1 screen) by the adjustment factor that best corresponds to the meteorological conditions
present at the site and the presence and location of lakes at the site:
Tier 2 Ratio = Tier 1 Ratio + Tier 2 Adjustment Factor
Matrices of threshold adjustment factors from the TRIM.FaTE and MIRC simulations were
separately developed for the four PB-HAPs that currently have screening emission thresholds in
the Tier 1 process: benzo(a)pyrene (BaP, representative of PAHs), cadmium, divalent mercury
and 2,3,7,8-Tetrachlorodibenzo-p-Dioxin (TCDD, representative of dioxins). The wind direction
threshold adjustment factors were irrespective of chemical (specific only to the facility). In
implementing the Tier 2 process, a risk equivalency approach was used to convert speciated
emissions of PAHs and dioxins into BaP and TCDD equivalents, respectively, similar to the Tier
1 screening approach.
Finally, to implement the Tier 2 screen, a Microsoft® Access™ tool was created that includes
the database of meteorological data and lake data as well as the Tier 2 adjustment factors for
the combinations of variables simulated in the Tier 2 TRIM.FaTE simulations. In the tool, each
facility is matched with the same surface meteorological station used in the RTR inhalation risk
assessment, and the values for the four relevant parameters at that station are recorded (wind
speed, wind direction, precipitation rate, and mixing height). The tool also facilitates the
identification of all qualifying lakes in the area surrounding the facility and their distances and
directions relative to the facility. These five values become the set of facility-specific
parameters. The threshold adjustment factor corresponding to this set of site-specific data is
then estimated using the matrix of adjustment factors and the wind direction adjustment. Wind
direction values are used as-is with no rounding or binning. If one of the four facility matrix
variables (wind speed, precipitation rate, mixing height, or lake location) is between two of the
computed levels available for that variable in the simulation matrix, the more health-protective of
the two levels is selected (i.e., the level that resulted in the smaller adjustment to the Tier 1
exposure). The Tier 1 screening emission threshold for a PB-HAP is then divided by the
appropriate adjustment factors to obtain an updated Tier 2 emission threshold for that PB-HAP
at that facility. A facility then screens out in Tier 2 if the emissions are below the Tier 2 threshold
for the fisher scenario and for the farmer scenario.
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3.2 Selection of Site-Specific Characteristics for the Tier 2 Assessment
The screening scenario used to derive Tier 1 thresholds incorporates assumptions regarding
meteorological conditions, the spatial configuration of the hypothetical exposure setting,
physical parameters of the environment, and chemical-specific parameters that result in
generally health-protective results. In Tier 2, selected assumptions used in the fate and
transport modeling conducted using TRIM.FaTE are modified to reflect more site-specific
information for the facilities being evaluated.19 To determine which scenario characteristics
should be incorporated into the Tier 2 assessment, the following issues were considered for
TRIM.FaTE model parameters:
How sensitive are the modeled risks to a specific user-input model parameter (e.g., wind
direction, wind speed)?
Do the plausible values for a given parameter span a large range when comparing
different RTR facilty locations?
Which site-specific characteristics can be found easily and reliably for facilities with
emissions exceeding Tier 1 thresholds?
In general, is the uncertainty associated with the parameter high or low?
• How complicated or time-consuming is the incorporation of a given parameter (e.g., wind
speed) into the screening scenario set-up?
Attachment D provides an exhibit showing all the TRIM.FaTE variables considered for the Tier 2
assessment. These variables were evaluated qualitatively using the criteria above to determine
whether the variable was of high, medium, or low priority. The following five "high priority"
variables were selected for implementation in the current Tier 2 assessment:
Wind direction (the percent of time the wind blows toward the lake and farm),
Wind speed,
Precipitation,
Mixing height, and
• Location of fishable lakes relative to the facility.
These parameters were judged to represent a balance between range of potential variability,
ease of implementation within the modeling scheme used for RTR, and ease of obtaining site-
specific values with a relatively high level of confidence.
3.3 Estimation of Adjustment Factors for Selected Site-Specific Parameters
The purpose of including site-specific detail for a facility evaluated in Tier 2 is to develop a more
realistic estimate of risk associated with facility emissions. This purpose is achieved within the
assessment by generating revised emission thresholds of potential concern specific to a given
PB-HAP on a facility-by-facility basis. However, instead of performing full-fledged, site-specific
model runs for each facility that does not "screen out" in Tier 1, a set of generally applicable
threshold adjustment factors for each PB-HAP was developed based on a set of model runs.
19Only TRIM.FaTE parameters were considered for inclusion in Tier 2 adjustments because of the difficulty in
identifying substantial location-related differences in values for exposure factors (and other inputs to MIRC). The
exposure characteristics used in MIRC are considered to be generally consistent across different locations and
facilities.
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For each PB-HAP, these runs corresponded to unique combinations of values for wind speed,
precipitation rate, mixing height, and lake location (wind direction is assessed separately).
These adjustment factors were based on a set of runs in which the values for these parameters
were varied systematically. The wind direction adjustment factor is the ratio of the frequency
that winds blow toward the lake and farm in the Tier 1 screening scenario (43 percent) and the
frequency that winds blow toward the facility's farm and lake in Tier 2, with a separate wind
direction adjustment factor for each directional octant (i.e., separate factors for the farm and
lakes in the northern octant, the farm and lakes in the northeastern octant, etc.). The
appropriate adjustment factors are then applied to the Tier 1 threshold for that PB-HAP when
evaluating a facility in Tier 2.
The analyses conducted to select the parameters to derive the threshold adjustment factors are
described in this section. Section 3.3.2 also describes the development of appropriate "bins" for
the selected parameters. These bins correspond to the subset of parameter values for which
adjustment factors were calculated, based on the anticipated range of plausible values for
facilities evaluated in RTR.
3.3.1 Selection Values for Variables of Interest
For each site-specific parameter that is assessed in the Tier 2 assessment, other than wind
direction, adjustment factors were estimated that correspond to a set of four to six particular
values for the parameter. The adjustment factor for wind direction directly relates Tier 1 and
site-specific Tier 2 wind direction frequencies, with respect to the directions of the lake and farm.
These individual adjustment factors can the be multiplied for a particular variable combination to
get an overall adjustment factor. To facilitate this, bins were created for each parameter of
interest and a relevant range of values, with the exception of wind direction (as described below,
representative bins were not necessary for this parameter). The rationale for selecting the
range for each bin for each parameter of interest is described below.
Wind Direction: Within the context of the hypothetical exposure scenario used in Tier 1 (and
under actual conditions), when the frequency with which the wind blows towards the modeled
domain (i.e., where the hypothetical farm and lake are located) increases, greater pollutant
deposition will occur over and around the farm and lake. The percentage of time the wind blows
toward the farm and lake is therefore positively correlated with ingestion exposure and risk. In
the screening scenario used to estimate Tier 1 thresholds, the wind is assumed to blow toward
the modeled domain 3 days a week, or 43 percent of the time. This assumption is intended to
approximate an unusually consistent long-term wind pattern and is representative of wind
direction patterns in Yakima, Washington (where the wind blows eastward approximately 40
percent of the time, based on a review of wind direction data compiled by the National Weather
Service).
In the model runs to develop the Tier 2 bins, this factor was changed to 1, 2, and 4 days per
week, corresponding to 14 percent, 29 percent, and 57 percent of the time. This range of
values was chosen to obtain a good understanding of the impact of wind direction on risk for the
range of wind direction patterns likely to be present at real facilities. Review of these results
indicated that, within this modeling scenario, estimated ingestion exposure varies directly with
percentage of time the wind blows toward the farm and lake. Given the exactly linear nature of
the relationship observed in model results obtained from these runs, the adjustment of the
threshold for wind direction in Tier 2 is a direct, linear adjustment using the actual site value
rather than an incremental, bin-based approach. In other words, the Tier 1 threshold is adjusted
for wind direction in direct proportion to the difference between conditions for the actual facility
location and the wind direction parameters included in the Tier 1 screening scenario (i.e.,
blowing toward the lake/farm 43 percent of the time on average).
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Wind Speed: Although the impact of wind speed on non-inhalation risks also is likely to depend
on configurational parameters such as the location of farms and lakes, in general it is
reasonable to assume that higher wind speeds lead to more rapid chemical transfer out of the
modeled domain, allowing less time for chemical deposition and, therefore, less total near-field
deposition and a lower exposure and risk. The Tier 1 screening assessment assumed a wind
speed of 2.8 m/s, corresponding to the 5th percentile (i.e., slower) of annual average speed for
the contiguous United States (distribution was based on data from a climate publication from the
National Oceanic and Atmospheric Administration [NOAA], which used data from over 200
stations nationwide).20 This value is similar to the annual average wind speeds of the U.S.
Deep South.21 In the model runs to develop the Tier 2 threshold adjustment factors, we
calculated the change to exposure resulting from increasing this value to 3.5 m/s, 4 m/s, and 5
m/s (values also shown in Exhibit 24; 5 m/s is the 88th percentile in the NOAA data). Based on
these values, the bins used to classify wind speed are: 2.8-3.5 m/s, 3.5-4 m/s, 4-5 m/s and
above 5 m/s. In all modeled cases, increasing the wind speed while maintaining constant lake
location, wind direction, mixing height, and precipitation led to Tier 2 estimations of high-end risk
or hazard that were smaller than that of Tier 1. To ensure that the approach is health-protective,
a facility was assigned the lower end value of the bin into which it was placed. Facilities with
wind speeds less than 2.8 m/s were assumed to have a wind speed of 2.8 m/s.
Precipitation: Higher levels of precipitation over the modeled domain are expected to increase
non-inhalation risks by increasing particulate and gaseous wet deposition near-field to the
source. The screening scenario used in Tier 1 assessment assumed an annual precipitation
rate of 1,500 mm/year. This level of annual precipitation was estimated to represent rainy
conditions in the U.S., such as parts of the U.S. Deep South and parts of the U.S. Northwest
Coast22. Though the rate was an estimate, it does correspond to approximately the 95th
percentile (i.e., higher rate) precipitation in the National Climatic Data Center's (NCDC) 30-year
(1981-2010) data from U.S. stations.23 To estimate adjustment factors in the Tier 2
assessment, model simulations were conducted with the parameter value set to three lower
values (i.e., 1,187 mm/year, 924 mm/year and 512 mm/year; values also shown in Exhibit 24),
corresponding to the 75th, 50th and 25th percentiles, respectively, of the NCDC data. Locations
with lower precipitation levels were assumed to have a minimum precipitation of 512 mm/year.
Based on these levels, the bins used to classify precipitation were: 0-512 mm/year, 512-924
mm/year, 924-1,187 mm/year, 1,187-1,500 mm/year and above 1,500 mm/year. In nearly all
modeled cases, decreasing the precipitation rate while maintaining constant lake location, wind
speed, wind direction, and mixing height led to Tier 2 estimations of high-end risk or hazard that
were smaller than that of Tier 1. To be health-protective, a facility was assigned the upper end
value of the bin in which it was placed. Facilities with precipitation levels above 1,500 mm/year
were assumed to experience precipitation of 1,500 mm/year.
20http://ols.nndc.noaa.aov/plolstore/plsal/olstore.prodspecific?prodnum=C00095-PUB-A0001#TABLES - this website
is updated yearly, so its current data may not match the data used to develop wind speeds for screening analyses.
21National Climatic Data Center CliMaps (NCDC-CliMaps) (2007).
22National Climatic Data Center Historical Climate Series (NCDC-HCS) (2007).
23http://www.ncdc.noaa.aov/oa/climate/normals/usnormals.html
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit 24. Lake Distance and Meteorological Parameter Values for which Adjustment
Factors were Developed in Tier 2a b
Parameter
Value
Wind Speed (m/s)
2.8
3.5
4
5
Precipitation (mm/yr)
512
924
1,187
1,500
Mixing Height (m)
710
865
1,097
1,537
Lake Distance (km)
No lake
2
5
10
20
40
aBold indicates the value is equal to the value used in Tier 1.
bWind direction is not shown here because its effect on modeled exposure and risk in TRIM.FaTE is linear.
Mixing Height: Greater mixing heights can dilute the concentration of pollutants in air,
resulting in lower deposition and other transfer rates from air to surfaces and consequently also
lower ingestion exposures. The Tier 1 screening assessment assumed a mixing height of 710
meters. This value is the 5th percentile (i.e., lower) of annual average mixing heights for 463
U.S. locations, based on data obtained from EPA's SCRAM Web site.24 To estimate adjustment
factors in the Tier 2 assessment, mdoel simulations were conducted with the parameter value
set to three larger values (i.e., 865 m, 1,079 m, and 1,537 m; values also shown in Exhibit 24).
These values correspond to North Little Rock, Arkansas; Boise, Idaho; and Tucson, Arizona;
and they are intended to encompass the range of annual average mixing heights experienced in
different parts of the United States. Based on these levels, the following bins were selected for
categorization of mixing height: 710-865 m, 865-1,079 m, 1,079-1,537 m, and above 1,537.
In all modeled cases, increasing the mixing heights while maintaining constant lake location,
wind direction, wind speed, and precipitation rate led to Tier 2 estimations of high-end risk or
hazard that were smaller than that of Tier 1. To be health-protective, a facility was assigned the
lower end value of the bin into which it was placed. Facilities with mixing heights above 1,537 m
and those below 710 m were assumed to have mixing heights of 1,537 m and 710 m,
respectively.
Lake Location: Moving the lake included in the hypothetical Tier 1 scenario to a location
farther from the actual source in the modeled domain will reduce modeled (TRIM.FaTE)
deposition to the lake and its watershed and consequently reduce exposures associated with
24Support Center for Regulatory Atmospheric Modeling; http://www.epa.aov/scram001/tt24.htm
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TRIM-Based Tiered Screening Methodology for RTR
the fish consumption pathway. For the scenario modeled in Tier 1, the center of the lake was
situated approximately 2 km from the source. To estimate lake location adjustment factors for
use in Tier 2, we completed a series of model runs in which the lake was located 5 km, 10 km,
20 km, and 40 km from the source, as well as runs with no lake (values also shown in Exhibit
24). Accordingly, the bins used to classify lake location relative to the facility are: no lake, 2-5
km, 5-10 km, 10-20 km, 20-40 km, and 40-50 km (lakes farther than 50 km are not
considered). To be health-protective, a facility was assigned the lower end value of the bin into
which it was placed. A facility with a lake closer than 2 km was assumed to have a lake at 2 km.
In resituating the lake at these alternative locations, we maintained ratios consistent with those
included in the Tier 1 screening scenario for (1) lake area to total land area in the modeled
domain, (2) runoff watershed area to lake area, and (3) erosion watershed area to lake area.
Exhibit 25 provides a diagram of the TRIM.FaTE layout in each of the alternate lake location
simulations except no-lake. We used a "thin" lake shape (i.e., downwind width much smaller
than the cross-wind length) that minimized the potential effect of declining deposition with
distance from stack that might affect lakes that are long in the downwind direction. By
controlling for these potentially confounding effects, we could isolate the effect of lake location
on risk appropriately. Moving the lakes to increasing distances from the stack required
expansion of the modeled domain. Maintaining the same overall ratio of land area to lake area
in each domain resulted in scenarios with increasingly large lakes, with surface area increasing
with distance from the source. This approach also was taken for reasons of modeling
convenience (i.e., taking into account resource requirements associated with reconfiguring the
TRIM.FaTE spatial layout). The changes in lake size between these four runs are not expected
to have a substantial independent effect on exposure and risk because the effect of increased
lake size is offset by greater total deposition and runoff. Furthermore, the lake depth was not
changed, which might be as important a parameter as lake surface area in determining the
chemical concentrations in the water column and sediment. As noted above, we set up the
configurations to ensure that the lakes in the different scenarios received runoff and erosion
from equivalent watersheds on a per surface area basis.
3.3.2 Estimation of Adjustment Factors
Adjustment factors were estimated for each variable described above and applied as divisors to
the Tier 1 emissions thresholds. The resulting Tier 2 emissions thresholds are used to assess
whether facilities with corresponding configurations carry some potential for significant
multipathway health risks by comparing their actual emissions to the Tier 2 thresholds.
3.3.2.1 General Approach
The core principle in the estimation of adjustment factors is the assumption of direct
proportionality of risk and emissions in the modeling approach used for RTR involving
TRIM.FaTE and MIRC. Although not strictly present across all variations due to feedback
mechanisms and other processes encompassed by the TRIM.FaTE model, a generally linear
relationship between risk and emissions has been observed across model simulations
conducted for RTR. This suggests that the ratio of total estimated exposures (and consequently
risks) obtained for the screening scenario and any alternative configuration could be used as an
adjustment factor to scale emissions for that specific alternative configuration. The risk in the
alternative configuration following such a scaling of emissions would be equal to the risk in the
screening scenario (which in Tier 1 was set at a risk level of 1E-6 incremental lifetime cancer
risk or an incremental HQ of 1, depending on the toxic effect of the chemical in question).
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit 25. Layouts for Tier 2 TRIM.FaTE Simulations Using Alternate Distances
Between the Facility and the Fishable Lake3
aThe no~!ake scenario is not shown.
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To account for potential interactions between the chosen Tier 2 variables, matrix adjustment
factors were estimated by performing TRIM.FaTE runs for each unique combination of the
specified variable values (that is, all permutations of the selected values for the wind speed,
precipitation, mixing height, and lake location). Adjustment factors for each configuration were
then estimated as the ratio of risks in the Tier 1 assessment to the estimated risk for the
particular TRIM.FaTE configuration. This approach results in a large matrix of adjustment
factors and has the advantage of accounting for all possible interaction effects between the
variables. The contribution of each exposure media toward each scenario's Tier 2 risk metric
(i.e., the individual contributions offish ingestion, soil ingestion, beef ingestion, etc. toward the
total Tier 2 risk metric) was included in the matrix because the Tier 2 assessment separates
chemical exposure from fish ingestion from exposure from farm food chain ingestion.
The adjustment factor for wind direction is handled separately. Based on TRIM.FaTE test runs,
the fraction of the time the wind blows in the direction of the farm and lake was observed to
have a direct, linear effect on the resulting risk estimates. For this reason, it was not necessary
to include this variable in the TRIM runs conducted to create the matrix of adjustment factors, as
described above. Instead, the adjustment factor is calculated with a linear factor that divides the
Tier 1 value (0.43, or 43% of the time in the direction of the farm and lake) by the site-specific
facility value, as follows:
Adjustment Factor wind Direction = 0.43 / (fraction of time wind blows towards domain)
The adjustment factor for wind direction is then multiplied by the matrix adjustment factor
discussed above to obtain a consolidated threshold adjustment factor that accounts for all the
five variables considered in Tier 2.
3.3.2.2 Incorporation of the Risk Equivalency Approach
The adoption of a risk-equivalency approach to convert speciated emissions of PAHs and
dioxins to BaP and TCDD equivalents, respectively, in Tier 1 required the development of risk
equivalency factors (REFs) for each reported species in these groups. The REFs for PAHs and
dioxins represent the ratio of the risk posed by a particular species to the risk posed by BaP and
TCDD, respectively, at equivalent emissions rates in a given scenario.
The REFs can be represented as the product of exposure equivalency factors (EEFs) and
toxicity equivalency factors (TEFs). For the PAHs, this can be expressed as:
REFpah = EEFpah x TEFpah
The EEFs for PAHs represent the ratio of the exposure to a particular species to the exposure
to BaP (and similarly for dioxins and TCDD) at equivalent emission rates. These ratios are thus
specific to the TRIM.FaTE layout and input assumptions. The TEFs for PAHs and dioxins
represent the ratio of the oral cancer slope factor (CSF) for a particular species to the CSF for
BaP and TCDD, respectively, and are the same in Tier 1 and in Tier 2.
The EEFs depend on the TRIM.FaTE configuration, including layout and meteorlogical input
values. For example, the exposure profile (i.e., how the different ingestion pathways contribute
to total exposure and risk) is different for each PAH. For a PAH where fish is a dominant driver
of risk, moving the lake will have a large effect on the overall risk; however, if produce is the
dominant driver of risk, moving the lake will have a much smaller effect on overall risk. Thus, for
Tier 2, EEFs were recomputed for each of the representative scenarios modeled in Tier 2
separately for the PAH species currently evaluated (some based on direct TRIM.FaTE
modeling, others 15 based on Kow-based regression estimates) and the dioxin species currently
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TRIM-Based Tiered Screening Methodology for RTR
evaluated (most based on direct TRIM.FaTE modeling, and a small number assumed to behave
like TCDD).
The following mathematical formulas demonstrate how the Tier 2 adjustment factors are
estimated for the PAH and dioxin species. The formulas presented below are for a
representative PAH species, but they are also applied to dioxin species.
For a given PAH congener emitted at a rate Epah at a facility, the risk-equivalent BaP emission
level can be expressed as:
Ebapeqiv - EpAH SPECIES x EEFPAH SPECIES x TEFpah SPECIES
Then, the Tier 1 screening result is estimated by comparing the risk-equivalent BaP emissions
to the BaP emission threshold:
_ _ l=TIER 1_BAPEQUIV
Hanotier 1 pah species - T, A„. H
ThresholdTiER ^3AP
If the ratio is less than 1, the facility "screens out" of the Tier 1 assessment. Similarly, for Tier 2,
the ratio of risk-equivalent BaP emissions to the Tier 2 BaP threshold may be expressed as:
_ _ EtIER 2_BAPEQUIV
Hanotier 2 pah species - T, A„. H
ThresholdTIER2_BAP
Using the definition of the risk-equivalent BaP emissions, this can be re-expressed for a given
PAH species as:
_ _ EpAH SPECIES x EEFTiER 2_PAH SPECIES x TEFpah SPECIES
kanotier 2 pah species T. , ¦
ThresholdTIER2_BAP
This expression may be further reconfigured, after some algebraic rearrangement, in terms of
the Tier 1 ratio as:
_ Thresho!dTjER i_bap EEFTiER 2_pah species
KailOtier 2 PAH SPECIES ~ ^atl0TIER 1 PAH SPECIES x T. , ¦ x ~EEE
' nresnoiaTiER 2 bap i=i=i~tier 1 pah species
„. „ . t t ThresholdTiER1 BAP EEFTjER2 PAh species
Tier 2 Adjustment FactorPAH species = = x ~EcE =
/ nresnoiaTiER 2_bap i=i=i~tier i_pah species
These equations simply state that to adjust the Tier 1 threshold to a Tier 2 threshold for a
particular PAH species, the adjustment factor must include the ratio of the BaP Tier 1 and 2
thresholds (as is true for cadmium, mercury, and TCDD, as discussed above) and the ratio of
the EEFs for the particular PAH species in Tier 2 and Tier 1. This additional EEF factor is
needed to account for the fact that the EEFs are different for each Tier 2 TRIM.FaTE
configuration.
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TRIM-Based Tiered Screening Methodology for RTR
Finally, the ratio of total BaP equivalents contributed by all PAH species at a given facility to the
BaP Tier 2 threshold may be expressed, by summing the above expression, as:
ALLPAhls
i nresnoiaTiER 1 BAP ttrtier 2 pah species
X
RatiOTIER 2_ALL PAHs ~ ^ RdtiOTlER 1_PAH SPECIES X
Threshold tier 2 bap EEF tieri pah species
If the ratio of total BaP equivalents contributed by all PAH species to the BaP Tier 2 threshold is
greater than 1, the facility would not have screened out of the Tier 2 assessment for the PAH
group.
3.3.2.3 Refined Fisher Scenario
The refined fisher scenario is based on the idea that an adult fisher might fish from multiple
lakes if the first (i.e., highest-concentration) lake is unable to provide an adequate catch to
satisfy the assumed ingestion rate (i.e., 373 g/d for adults). This assessment uses the
assumption that the biological productivity limitation of each lake is 1 gram of fish per acre of
water: meaning that in order to fulfill the adult ingestion rate, the fisher will need to fish from 373
total acres of lakes (see Attachment E for further discussion on lake productivity).
Under the refined fisher scenario, a fisher fishes from each surrounding lake in order of highest
chemical concentration in fish to lowest concentration and catches fish up to the biological
productivity limit. A maximum travel radius is used to maintain a realistic scenario. The total
screening result to the fisher can be expressed as the sum of all screening results for each lake
(which is based on the ingestion rate and fish concentration for each respective lake).
A 50 km radius of lakes is used to maintain a realistic scenario. Depending on the model
configuration, very large modeling configurations put a strain on computing resources, and
TRIM.FaTE can crash if the domain is very large or if the domain is divided into too many
compartments. In most scenarios, the vast majority of chemical deposition occurs across
distances smaller than 50 km, so the added computing resources needed to model a domain
larger than 50 km will usually not be offset by additional, substantial chemical deposition and
exposure. Indeed, wind speeds must exceed 13 m/s (approximately 29 mph) sustained across
an hour in order for the chemical plume to travel farther than 50 km; those wind speeds are
unlikely to occur across the many days or weeks needed to substantially affect chronic
exposure. In addition, a 50-km limit also puts a reasonable constraint on the domain of lakes for
the fisher scenario.
In order for the Tier 2 screening to remain health protective, it is assumed that the fisher visits
lakes in order of highest to lowest with respect to risk. If the first lake is 373 acres or greater, the
fisher will only travel to that lake. If the acreage is smaller than 373 acres, then the fisher will
travel to the next highest risk lake, and continues to travel to subsequent lakes until a total
acreage of 373 acres is achieved, or until all qualifying lakes have been visited.
The associated risk for each lake is then scaled by the total adult ingestion rate (equations 1
and 2).
/Lake SA(acs)\
(1) Scaled T2 Fish risk = T2 Fish risk x
\ J 7 J acs /
f Lake SA(acs')\
Equation 1: Scaled fish risk for each lake 1 to n-1
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TRIM-Based Tiered Screening Methodology for RTR
acs— YiSAs of Previously Visited Lakes lacs))\
iled. T2 risk = T2 Fish risk x I
y 373 acs /
Equation 2: Scaled fish risk for nth lake
The risk associated with each lake visited is then summed together for a final risk (equation 3).
(3) Total Scaled T2 Fish risk = (Eq, 2) + ,1)
Equation 3; Total fish risk for scenario
If the total acreage of fishable lakes surrounding the facility fails to exceed 373 acres, then the
risk for each lake is found using equation 1, and the total risk is the sum of all scaled risks
(equation 4).
(4) Total Scaled T2 Fish risk [. 1)
Equation 4: Total fish risk if total acreage of lakes does not reach 373 acres
3.4 Preparing National Databases of Lake and Meteorological Data
To facilitate the effective application of the Tier 2 screening procedures, databases were
prepared containing national-scale information about lakes (locations and sizes) and
meteorological data at available surface stations (including wind direction, wind speed,
precipitation, and estimated mixing height). The development and content of these two
databases are discussed in the following sections.
3.4.1 Processing Lake Data for Tier 2 Assessment
The lake database was built using a geospatial file (U.S. Water Bodies) provided by ESRI® for
their ArcGIS™ products.25 Because this geospatial file excluded water bodies in Alaska, Puerto
Rico, and the U.S. Virgin Islands, it was augmented with water body information (directly from
the USGS National Hydrography Dataset) for those other locations. The data generally have an
estimated horizontal accuracy of 50 m. For the Tier 2 assessment, we focused on the hundreds
of thousands of water bodies classified as "Lake/Pond" or "Reservoir" but not designated for
disposal, evaporation, or treatment. We refer to these water bodies simply as "lakes" in the
remainder of this document. The approximately 100,000 other water bodies (classified as
canal/ditch, ice mass, inundation area, playa, stream/river, swamp marsh, or unclassified) were
not included. In a more subjective step during the processing of the lake database for a specific
source category (when Tier 2 is run "operationally"), the lake names (when provided) are
scrutinized manually, and lakes are removed from the assessment when their names suggest
industrial or treatment use (e.g., wastewater treatment ponds, sludge ponds, fly ash ponds,
paper mill ponds, sewage pools, etc.). In this respect, the lake dataset is never truly final; lake
names are scrutinized each time a new set of facilities is assessed using the Tier 2 methods
and tools, which can lead to the permanent removal of some lakes from the dataset.
Early in the process of compiling this database, we encountered the question: "What size water
body qualifies as a 'lake' for the purposes of this assessment?" The Tier 2 assessment must
focus on lakes large enough to support relatively intensive angling pressure to be compatible
25 Specifically, the geospatial file in the ESRI® Data & Maps 2009 Data Update for ArcGIS™ version 9.3.1. It was
derived by the United States Geological Survey (USGS), EPA, and ESRI® from the USGS National Hydrography
Dataset (USGS 2012).
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TRIM-Based Tiered Screening Methodology for RTR
with the assumed exposure scenario. To estimate the relationship between high fish
consumption rates, harvest rates, and lake size, the following five key assumptions were made.
Information and citations to peer-reviewed literature that support these assumptions are
provided in Attachment E. Note that in the TRIM.FaTE model screening scenario, water-column
carnivores (WCCs) are modeled as trophic level 4 (TL4) fish (e.g., pickerel, largemouth bass),
with all of their diet consisting of smaller "prey" or "pan" fish in the water column that are
simulated as trophic level 3 (TL3). The benthic carnivores (BCs) in TRIM.FaTE are modeled to
represent an intermediate trophic level between 3 and 4, i.e., TL3.5. The BCs (e.g., catfish)
obtain half of their diet from TL2 (benthic invertebrates that feed on detritus at the sediment
surface) and half from TL3 fish in the benthic environment. For the screening scenario, we
assume that fishers consume fish biomass in a ratio of 50:50 from the BC and WCC
compartments, respectively. Together, these two fish compartments are referred to as
piscivorous fish.
1. Piscivorous fish, when present, comprise approximately 21 percent of the standing biomass
of fish. BC fish represent 17.5 percent of the standing fish biomass in natural lakes; WCC
fish account for 3.5 percent of this total fish biomass. Thus, WCC (or TL4) fish represent the
limiting compartment for fish harvesting and consumption.
2. Humans can harvest 10 percent of any single fish compartment without threatening the
population due to overharvesting.
3. The minimum viable effective population size for a single fish species is at least 50 adult fish
for a local population to survive over the short term (e.g., more than a decade).
4. Only 33 percent of the fish caught for consumption is edible fillet muscle. A 0.33 edible
fraction is used to estimate total fish biomass associated with human consumption.
5. A total fish standing biomass of 40 g wet weight/m2 represents an approximate upper bound
for natural ponds and lakes in the United States.
Using the above assumptions and a series of equations (see Attachment E), the maximum fish
ingestion rates as a function of standing biomass and lake size were estimated. Exhibit 26
presents these findings, where the grey shading indicating when WCC fish would probably not
be present, the white (unshaded) cells represent combinations of lake size and productivity that
could sustain the listed fish ingestion rates over some time, and the yellow cells represent likely
long-term sustainability associated with more than 500 adult WCC fish in the lake (see Exhibit
notes).
The Tier 2 assessment is intended to retain most of the health-protective attributes of the
screening scenario used in Tier 1 so that no facilities of potential concern erroneously "pass" the
screen. For a given facility, the smaller the lake size threshold, the greater the number of lakes
and the greater the probability that a lake is closer to the facility. Lakes closer to a facility will
result in higher chemical concentrations in fish compared with lakes farther from the facility.
Thus, Exhibit 26 was used to determine the smallest lake size that would support a TL4
population. At the assumed upper-limit standing fish biomass of 40 g ww/m2, this corresponds
to 25 acres (the first "white" box when moving from smaller to larger lakes).
The fish ingestion rate supported by a 25-acre lake is not as high as the adult ingestion rate
used in the Tier 1 and Tier 2 assessments (i.e., 373 g ww fillet per day). Exhibit 26 indicates
that at a total fish biomass productivity of 40 g ww/m2, the maximum sustainable fish harvest
from the WCC compartment would correspond to an ingestion rate of only 26 g/day for those
fish, with an additional 26 g/day from the benthic carnivores (which are not the limiting
population), for a total of 52 g per day. However, to be health protective and to ensure that small
lakes that might be more highly contaminated than estimated by the TRIM.FaTE screens were
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TRIM-Based Tiered Screening Methodology for RTR
not eliminated, we selected 25 acres as the "cutoff" for the minimum size for an actual lake near
a facility to be included in the Tier 2 assessment. In addition, larger lakes (larger than 100,000
acres) were not considered since they cannot be readily modeled in TRIM.FaTE. For the
purposes of proximity matching lakes to emitting facilities, the location of each lake is identified
as the geographic centroid inside the lake.
It should be noted that an individual lake as small as 25 acres could be stocked annually at a
rate adequate to support the assumed fish ingestion rate. For stocked fish, however, we would
have to assume that when introduced to the lake, the fish were uncontaminated by the
chemicals of interest. Moreover, the period over which accumulation of chemical from the lake
could occur would be roughly three to six months (fishing season) for the majority of the fish
stocked as adults (i.e., at approximately 2 kg), instead of several years for fish hatched in the
lake.
To place an upper bound on the radial distance from the source up to which lake-derived risks
need to be assessed, we took into account the limitations of TRIM.FaTE. We limited the lake
analysis to an outward radial bound of 50 km from the stack. For facilities with no lakes within
50 km, lake-derived risk is assumed 0.
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit 26. Maximum Fish Ingestion Rate (g/day) Associated with Sustainable Fishing3
Standing
Biomass
(g ww/m2)b
Size of Pond or Lake (acres)
1
2
3
4
5
7.5
10
15
25
35
50
75
100
150
200
400
2
0
0
0
0
0
0
1
1
1
2
3
4
5
8
10
20
3
0
0
0
0
0
1
1
1
2
3
4
6
8
12
15
31
4
0
0
0
0
1
1
1
2
3
4
5
8
10
15
20
41
5.7
0
0
0
1
1
1
1
2
4
5
7
11
15
22
29
58
10
0
1
1
1
1
2
3
4
6
9
13
19
26
38
51
102
15
0
1
1
2
2
3
4
6
10
13
19
29
38
58
77
154
20
1
1
2
2
3
4
5
8
13
18
26
38
51
77
102
205
30
1
2
2
3
4
6
8
12
19
27
38
58
77
115
154
307
35
1
2
3
4
4
7
9
13
22
31
45
67
90
134
179
359
40
1
2
3
4
5
8
10
15
26
36
51
77
102
154
205
410
50
1
3
4
5
6
10
13
19
32
45
64
96
128
192
256
512
60
2
3
5
6
8
12
15
23
38
54
77
115
154
231
307
615
70
2
4
5
7
9
13
18
27
45
63
90
134
179
269
359
717
80
2
4
6
8
10
15
20
31
51
72
102
154
205
307
410
820
90
2
5
7
9
12
17
23
35
58
81
115
173
231
346
461
922
100
3
5
8
10
13
19
26
38
64
90
128
192
256
384
512
1025
110
3
6
8
11
14
21
28
42
70
99
141
211
282
423
563
1127
120
3
6
9
12
15
23
31
46
77
108
154
231
307
461
615
1229
130
3
7
10
13
17
25
33
50
83
117
166
250
333
499
666
1332
aDark gray shading indicates insufficient population size for TL4 (WCC) fish (<50 adults) to be sustainable for more than a decade; yellow-shaded cells indicate the likelihood to provide long-term
sustainable fish populations with at least 500 TL4 adult fish present; white area indicates medium-term sustainability.
bRepresents the standing biomass of TL4 fish. At the upper-limit standing biomass of 40 g ww/ m2estimated for natural lakes, 25 acres could support a water-column TL4 fish population, but
would provide for no more than 26 grams of fillet per day for a single fisher over a full year.
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3.4.2 Processing Meteorological Data for Tier 2 Assessment
In addition to the lake database, a database of U.S. surface stations with complete data was
created, so that each source category facility can be paired with the closest meteorological
station data. This process of pairing dozens or hundreds of facilities with meteorological data is
not unprecedented. In their report to the Science Advisory Board (SAB) on the 1996 NATA,
EPA described pairing each facility with the closest meteorological station in an inventory of
over 350 meteorological stations nationwide, creating an average facility-to-station distance of
less than 50 km in the 1996 NATA (EPA 2001b). In a separate 2009 report to the SAB on the
RTR program, EPA described using 158 meteorological stations to choose from nationwide,
with a standard practice of selecting the station nearest to each facility unless the facility
provides onsite meteorological data (EPA 2009). Using 156 petroleum refineries as a sample
data set, the average facility-to-station distance was 72 km. In both instances, the SAB
accepted this matching as standard practice when modeling large numbers of sources, although
they recommended providing high-level siting maps (e.g., meteorological stations overlaid with
terrain gradients or regional climate regimes) to qualify some of the uncertainties related to
meteorological data in air dispersion modeling (EPA 2001a; EPA 2010b). The current effort
builds on this practice but increases the number of available meteorological stations as
described below.
3.4.2.1 Sources of Meteorological Data
To construct a database of meteorological statistics for all available U.S. surface stations for use
in multipathway screening assessments, EPA started with the same U.S. meteorology dataset
used in RTR inhalation assessments. RTR inhalation assessments use data from 824 ASOS
(Automated Surface Observing System) stations that record hourly and sub-hourly
measurements. These data represent the year 2011, and the surface stations are paired with
their closest, regularly-reporting upper-air stations. This number of stations is far greater than
the 350 and 158 stations, respectively, used in the 1996 NATA report (EPA 2001b) and the
2009 RTR report (EPA 2009).
The 2011 precipitation measurements reflect 2011 weather conditions, and, like any other year,
some areas of the country experienced rainfall that was significantly less than normal, and some
areas received much more rainfall than normal. To reduce this bias in precipitation data, we
used average annual precipitation data from the 1981-2010 National Climatic Data Center 30-
year normal dataset wherever possible. If 30-year normal precipitation data were not available
for a station, as was the case for a few stations in the dataset, we used the ASOS precipitation
data as-is.
3.4.2.2 Coverage of Meteorological Stations Compared with Facility Locations
Exhibit 27 shows the proximity of the evaluated meteorology sites to the locations of U.S. point
source facilities from the 2005 NATA. Generally, the spatial density of the surface
meteorological stations in this dataset was similar to the spatial density of the 2005 NATA
facilities. That is, the density tends to be greatest in the Great Lakes region, along the East and
West Coasts, and in the Southern Plains, and tends to be lowest in the Rockies (except
Colorado) and Northern Plains.
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3.4.2.3 Data Processing
To facilitate application of the meteorological data to the Tier 2 assessment, EPA gathered wind
information in directional octants that could be linked to the direction of the relevant lakes (see
Introduction and Section 3.5). EPA divided the periphery around a meteorological station into
eight octants representing the direction toward which the wind was blowing:
N: >337.5 to 22.5 degrees
NE: >22.5 to 67.5 degrees
E: >67.5 to 112.5 degrees
SE: >112.5 to 157.5 degrees
S: >157.5 to 202.5 degrees
SW: >202.5 to 247.5 degrees
W: >247.5 to 292.5 degrees
NW: >292.5 to 337.5 degrees
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit 27. The Locations of Meteorological Stations and Point Source Facilities3
aThe 2005 NATA was the most recent, comprehensive, finalized dataset of nationwide point source emitters of hazardous air pollutants, and it is used here only for illustrative
purposes. The 2005 NATA used a meteorological dataset different from the one used in this report.
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A software program was developed to calculate the following statistics for each of these
stations:
Number of hourly observations,
Number of hours with calm winds or missing winds,
Percentage of time the wind blows into each octant (after excluding missing wind hours),
Median wind speed blowing into each octant,
Median mixing height used (if heat flux > 0, convective mixing height was used,
otherwise mechanical mixing height was used), and
• Average annual precipitation (irrespective of wind octant and preferring 30-year normal
data if available).
Total annual precipitation data in the 2011 meteorological data included anomalies (relative to
normal conditions) in areas that experienced extreme drought conditions or large rainfall
surplusses. To address this, the 30-year (1981-2010) average annual precipitation was used
instead.26
The choice of using median values for wind speed and mixing height was based on a
comparison of median and mean values. For nearly all stations, the median value was smaller
than the mean value; because smaller values are more health protective, we selected the
median statistic for all stations.
3.5 Implementation of Tier 2 Assessment
The Tier 2 screening assessment is conducted using a Microsoft® Access™ tool that was
developed specifically for this purpose. Similar to the Tier 1 screening assessment tool, the Tier
2 tool was created so that facilities that do not screen out during the Tier 1 assessment can be
screened concurrently, if desired.
The Tier 2 tool requires three inputs to run: the results of the Tier 1 screening assessment, a list
of facilities to model, associated meteorology stations that can be matched to using a proximity
analysis, and the matrix of adjustments factors that are used to adjust the Tier 1 screening
thresholds to the Tier 2 screening levels. The matrix is used to standardize the scenario based
on the chemical and on the lake distance and meteorology characteristics of the assessed
facility (refer to Section 3.3 for a discussion of these adjustment factors).
The tool contains procedures that match each facility to all lakes within a user-defined radius
(currently 50 km) that are within user-defined size limitations (currently 25 acres to 100,000
acres), and based on water body type (e.g., they are not waste disposal or treatment facilities,
retention ponds, etc). In addition, each facility is matched to the same meteorology station used
in RTR inhalation assessments, which is usually the nearest meteorology station; when the
meteorology station match is not known from the RTR inhalation assessment, the tool conducts
a proximity analysis. The tool allows the user to review matching lakes and exclude ones that
are not considered suitable for modeling (e.g., based on names indicating industrial, waste, or
treatment purposes, as discussed in Section 3.4.1.) Any excluded lakes are recorded by the tool
so that they are omitted from subsequent screening assessments.
26 30-year average annual precipitation was obtained from the National Climatic Data Center (NCDC).
http://vwvw.ncdc.noaa.aov/oa/climate/normals/usnormals.html.
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Per facility and PB-HAP, the tool evaluates the screening result at each lake and hypothetical
farm. For each lake, the initial calculations produce screening results consistent with the full
screening scenario ingestion rate (i.e., 373 g/day for adults, regardless of assumed lake fish
productivity), and then additional calculations adjust the screening results for the lakes based on
the initial screening results and areas of the lakes (see Section 3.3.2.3). Each modeling
scenario consists of a farm and a lake (or, in some cases, no lake). Although the distance from
the facility to the modeled farm remains fixed for each octant, the location of the lake affects
chemical concentrations in some farm food chain media due to modeled runoff and erosion
patterns that are directed mostly toward the lake. Therefore, there can be multiple different farm
screening results in the same octant for the same facility, each corresponding to a different lake-
distance scenario. The tool identifies the largest farm screening result from all scenarios
evaluated for a facility. The tool presents the final Tier 2 screening results for the fisher and
farmer separately, including results specific to individual farm food chain media, for each
chemical, and then summed together for each PB-HAP group.
The tool also can incorporate the results of Tier 3 assessments. Since the tool allows the user to
manually exclude lakes, if any lakes are removed from the assessment in Tier 3, those lakes
can be removed in the tool and the Tier 2 screening can be repeated with the revised lake
database. If Tier 3 plume-rise or time-series assessments are conducted, the resulting Tier 3
adjustment factors can be entered into the tool and it will update the Tier 2 screening results to
produce Tier 3 screening results.
The tool outputs several results tables, including an intermediate table that provides information
on each lake and farm, including the fish ingestion and screening-level risk associated with each
lake. The final screening results for each facility and PB-HAP group are also output. A summary
table identifies the facilities exceeding the Tier 2 emission screening threshold of each PB-HAP
group (separately for the fisher and farmer) and the largest screening results. All intermediate
and final results tables present the Tier 1 and Tier 2 screening results side-by-side for
comparison. Exhibit 28 through Exhibit 30 provide screen shots of tool output tables.
Exhibit 28, Example of the Summary Output Table from Tier 2 Tool3
Src Cat Info
Tier 1
Tier 2
Num Facil
[
in Src Cat
Num Facil
Num Facil
Num Facil
Facil with
Facil with
(Emitting
Emitting
Num Facil
Larges
Fisher SV 2
Farmer SV
Largest Fisher
Largest
Largest Farmer
Largest
PB-HAP 6rp »
Any HAI *
thisGr »
sv>|T
Facil with Largest S' ~
tfT
2 Pi -
>2 '
SV
Fisher S
SV .! *¦
Farmer •
Cadmium
12
12
6
3.E+01
0
0
l.E+OO
3.E-01
Dioxin
13
13
13
2.E+02
12
12
4.E+01
2.E+01
Mercury
13
13
9
l.E+Ol
0
0
l.E+OO
8.E-04
(methyl)
PAH
13
13
13
2.E+03
10
9
6.E401
1.E+Q2
PAH + Dioxin
13
13
13
2.E+03
13
12
7.E-H51
2.E+02
Cadmium
13
13
7
3.E+01
1
0
2.E+O0
4.E-01
Dioxin
14
14
14
3.E+02
13
13
5.E+01
2.E+01
Mercury
14
14
9
1.E-KJ1
1
0
2.E+00
8.E-04
(methyl)
PAH
14
14
14
4.E+03
11
11
3.E+02
2.E-KJ2
PAH + Dioxin
14
14
14
5.E+03
14
14
4.E+02
2.E+02
aThe screen shot examples shown in this section are for actual facilities in actual source categories; however their source
categories, NEI IDs, and coordinates have been altered or masked so the data are not linked to specific facilities. "SV" means
"screening value", which is the ratio of emissions to emission screening threshold.
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit 29. Example of the Facility Output Table from Tier 2 Tool3'b
Facjil Info
Tierl
—
I
1
1
1
1
Farmer SV,
SticCst n -
NEI ID | -r
Lat
Long F
Met WBAN
] PB-HAP Grp -
SV ^
FisherS »¦
Farmer SV »
Soil
94725
Mercury (methyl 2.E-KX)
2.E-01
2.E-04
3.E-05
94725
Dioxin
8.E+01
6.E+0G
4.E+00
4.E-02
94725
Cadmium
1.E+00
9.E-02
8.E-03
2.E-04
4725
PAH
2.E+01
9.E-01
2.E+00
l.E-01
3880
Dioxin
l.E+02
3.E+01
4.E+00
5.E-02
3880
Cadmium
3.E+00
9.E-01
2.E-02
4.E-04
3880
Mercury (methyl 5.E+O0
1.E+00
3.E-04
5.E-05
3880
PAH
4.E+01
6.E+00
2.E+00
2.E-01
94239
Mercury (methyl 2.E+00
3.E-01
l.E-04
2.E-05
94239
PAH
2.E+01
9.E-01
1.E+O0
7.E-02
94239
Dioxin
l.E+02
l.E+01
6.E+00
7.E-02
94239
Cadmium
9.E-01
9.E-02
5.E-03
9.E-05
94725
PAH
2.E+03
6.E+01
l.E+02
l.E+01
94725
Cadmium
8.E-01
4.E-02
6.E-03
l.E-04
94725
Dioxin
2.E+02
l.E+01
2.E+01
2.E-01
4725
Mercury (methyl 5.E-01
2.E-02
4.E-05
6.E-06
13937
Dioxin
2.E+00
5.E-01
8.E-02
l.E-03
>3937
Cadmium
2.E-02
6.E-03
2.E-04
4.E-06
03937
Mercury (methyl 7.E-01
2.E-01
6.E-05
l.E-05
aThe screen shot examples shown in this section are for actual facilities in actual source categories; however their source
categories, NEI IDs, and coordinates have been altered or masked so the data are not linked to specific facilities. "SV" means
"screening value", which is the ratio of emissions to emission screening threshold.
b Only a portion of the table is shown. Individual farm ingestion media are in the table but only "soil" is displayed for space reasons.
Exhibit 30. Example of the Lake Assessment Output Table from Tier 2 Tool
a,b
Facil Info
Tierl
Tm
Lake Info
Facility-
Object ID
Size
Lake Dist
SrcCat -l
i.WBlD '.P*
PB-HAP Grp •
SV
Oct •
Name
(acres;
ISir?
Lat™
I : Long '
Cadmium
2.E+Q0
N
40
3.1
Cadmium
2.E+00
N
42
5.2
Cadmium
2.E+O0
NE
59
33.2
Cadmium
2.E+00
NE
54
32.5
Cadmium
2.E+00
NE
44
33.0
Cadmium
2.E+O0
NE
44
28.8
Cadmium
2.E+00
NE
42
36.9
Cadmium
2.E+00
SW
72
23.5
Cadmium
2-E+OO
E
27
21.7
Cadmium
2.E+Q0
E
27
30.6
Cadmium
2.E+00
E
32
22.9
Cadmium
2.E+Q0
E
30
22.7
Cadmium
2-E+OO
N
32
47.3
Cadmium
2.E+00
N
62
45.9
Cadmium
2.E+00
N
32
45.1
Cadmium
2.E+00
N
27
46.5
Cadmium
2-E+OO
N
54
49.8
NF
94
41.8
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Trn«lin| Flihn Asvc^ncnt
Timl fhte
1(avel[«« Fithei Oniet
fraction
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4.E-01
3.C-03
1
0.105996783
TRUE
0 650
0.69003637^
4.E01
s.eos
0.U2M1SS2
TRUE
0-WJ
0.690036J7*
2.E-Q1
6.C-03
0.158995174
true
0.650
0,690036379i
l.t- trt
8.E-OJ
TRUE
B.MH
2.C-01
fi.C-OJ
0.119246381
TRUE
0.690
0,690015 3t
2-E 01
0.JWH&BI
tRue
iktm
2.C-01
6.E-03
0.112621582
true
0.690
0.69003531s
LEDJ
J.tBi
%
D.1Z&W6S47
TRUE
If-WjG
9.E-02
5.C-03
n/»
0
TRUE
0.69
0.6900363791
S.MB
n/»
0
tRUB
0-b9
0.6900363 >9
9Z-02
5C-0J
"/•
0
TRUE
0.69
0.69003631*
9-6-02
s.l-aj
n/a
a
mut-
aw
D.6S005USW
7Z-0Z
e.t-os
n/i
0
THUE
&W
0,69CC36"L
7.E-02
8,€-03
n/a
0
TRUE
0.69
0.69Q0354-W.
T.I'M
SE03
n/a
0
TRUE
0.69
0.690035AU.
7.E-02
a. £-03
n/»
0
TRUE
0.69
0.69003M-H-
7.MM
aeoj
n/a
0
IWJf
().«9
O.fWOCiVfcW.
R.F-0?
ft cm
nbt
fl
Tib IF
aThe screen shot examples shown in this section are for actual facilities; however their source categories, NEI IDs, and coordinates
have been altered or masked so the data are not linked to specific facilities. Only a portion of the table might be shown. The headers
"Stationary Fisher" and "Traveling Fisher" are shorthand references to the initial fisher scenario (i.e., catches and consumes 373 g/d
per lake, regardless of lake area) and the refined fisher scenario (i.e., assumes a 1 g/acre/d lake productivity), respectively. "SV"
means "screening value", which is the ratio of emissions to emission screening threshold.
b The table displayed reflects a Tier 3 Screening Assesment having been conducted. In a Tier 2 Screening Assesssment, the Add'l
Lake Assessment, Plume Rise Adj Factors, and Time Series Adj Factors would not be shown.
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TRIM-Based Tiered Screening Methodology for RTR
4. Tier 3 Methodology
4.1 Overview of Approach
The Tier 3 screening assessment is an additional assessment that can be conducted, at the
discretion of the risk assessor, on facilities that do not screen out with the Tier 2 assessment.
The Tier 3 screening approach consists of three individual assessments that further refine the
screening scenario (beyond the refinements in Tier 2) based on additional site-specific data and
evaluations. The assessments introduce additional site-specificity to the screening scenario,
requiring a potentially higher level of effort than the Tier 2 assessment, but still a much lower
level of effort than the full site-specific assessment. One of the Tier 3 assessments (i.e., the lake
assessment) results in the rescreening of the facility's emissions using the Tier 2 methods and
using a revised lake database. The other two assessments (i.e., the plume-rise and time-series
assessments) each result in an adjustment factor to be applied to the screening result reflecting
the Tier 3 lake assessment. The adjustment factors of the plume-rise and time-series
assessments cannot be compounded (i.e., the time-series assessment, if conducted, supplants
the plume-rise assessment already conducted).
The remainder of this section discusses each of these assessments.
4.2 Lake Assessment
Other than verifying a facility's emission rates, one of the simplest assessments that can be
conducted (beyond the Tier 2 methods) is to assess the existence, the potential purpose, the
accessibility and fishability, and the suitability of lakes for the models and methods used in
Tier 2. The full USGS dataset of lakes and reservoirs nationwide that is used in Tier 2 does not
contain information on lake accessibility or fishability. In addition, it occasionally identifies a lake
that no longer exists (e.g., has evaporated or been drained) or it uses a classification that might
not accurately reflect the lake's purpose or type. Aerial and street-view imagery and internet
searches can be used to quickly ascertain if an assessed lake actually exists, if it is likely not
fished (e.g., appears swampy or covered in algae), if it is likely used for industrial or waste
disposal/treatment purposes, and/or if it is adjacent to or connected to a river or saltwater body
(estuaries, rivers, and saltwater areas are not ideal for the assessment models and methods).
For example, the blue outline in Exhibit 31 identifies a lake from the USGS dataset that
originally qualified for Tier 2 based on the information provided in that dataset, but aerial
imagery shows it is likely evaporated or drained. Similarly, the outline in Exhibit 32 identifies a
lake that originally qualified, but aerial imagery shows that it is directly adjacent to an industrial
facility and likely used only for on-site industrial purposes.
For facilities undergoing Tier 3 screening, all lakes are assessed from which the fisher catches
and consumes fish according to the refined Tier 2 fisher methods discussed in Section 3. All
lakes meeting the criteria discussed in the previous paragraphs are permantly removed from the
screening assessment so that they are no longer used in any screening assessment. If a lake is
removed from the facility's assessment, it might be necessary for the hypothetical fisher to catch
and consume fish from additional lake(s) in order to fish a total of 373 acres. In this case,
additional lakes that were not fished previously are evaluated, using the methods discussed in
Section 3, in descending order of initial Tier 2 fisher screening result and lake area.
If any lakes are removed from the screening, the screening assessment is conducted again
(using the Tier 2 methods) with the revised lake dataset. Although this additional lake
assessment is conducted based on the Tier 2 screening results of a particular set of facilities,
lakes removed during that assessment could affect the screening results of other facilities in the
Tier 3 Methodology
74
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TRIM-Based Tiered Screening Methodology for RTR
source category beyond that original set. This can happen if an assessed facility is within 50 km
of another assessed facility, whereby removing a lake could affect the screening results of both
facilities. It should be noted, however, that screening results across different facilities are not
summed.
Exhibit 31. Example of Lake Removed from Screening—Likely Evaporated or Drained
Note: Aerial imagery from ESRI World Imagery (2014).
Exhibit 32. Example of Lake Removed from Screening—Likely an Industrial Lake
Note: Aerial imagery from ESRI World Imagery (2014). This map was created during the
assessment of a specific source category, thus the reference to "the assessment in this report" in
the text box.
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TRIM-Based Tiered Screening Methodology for RTR
4.3 Plume-rise Assessment
In Tiers 1 and 2, all chemicals are emitted inside the mixing layer and are available for ground-
level exposure. In reality, the physical height of an emission source, in combination with the
temperature and velocity of the chemical plume as it leaves the source, can cause some of the
chemical plume to reach areas above the mixing layer. In TRIM.FaTE modeling, the chemical
mass deposited above the mixing layer (i.e., the model's upper-air layer) is unavailable for
ground-level exposure (i.e., the upper-air layer functions as a chemical sink). Many emission
sources in RTR are physically taller than the mixing height during some hours, and hot exit gas
temperatures (i.e., buoyancy) and/or high exit gas velocities (i.e., momentum) can further
elevate the chemical plume.
The Tier 3 methodology utilizes methods summarized by Seinfeld and Pandis (1998) to
estimate how often a facility's emissions reach the upper-air sink, which decreases availability
for ground-level exposure. The methods to estimate the amount of chemical lost to the upper-air
sink require use of the facility's corresponding meteorology data (e.g., air temperature and
vertical temperature gradient, wind speed, and atmospheric stability), the mass of the PB-HAP
emitted from each source, the physical characteristics of the sources (i.e., release height, inside
diameter at the release point, and exit gas temperature and velocity), and an estimate of the
size of the facility.
For each relevant emission source, estimates of the hourly effective release height (i.e., sum of
actual release height and plume rise) are compared to the hourly mixing height to determine the
mass of chemical remaining in the mixing layer. The mass of chemical remaining in the mixing
layer, summed across the sources, is compared to the total emitted mass of the chemical. This
results in an adjustment factor expressing the fraction of emissions remaining in the mixing layer
after accounting for release height and plume rise. This final plume-rise adjustment factor is
then applied to the Tier 2 screening results.
4.4 Assessment of Time-series Meteorology and Effective Release Heights
As discussed in Section 3, the Tier 2 screening results are based on the average meteorological
conditions prevailing at the facility being assessed. The use of time-series meteorology data,
which captures hour-by-hour changes in each of the assessed meteorological parameters
instead of using constant average values, increases the accuracy of the estimates of potential
risk by accounting for potential statistical interactions between the meteorology parameters and
by improving the method of accounting for mass advected into the modeling domain.
Tier 2 screening results also are based on a constant emission release height that is always
within the mixing layer. Including hourly effective release heights along with hourly meteorology
data further increases the accuracy of the screening results.
For a facility undergoing a time-series assessment, a time series of hourly effective release
heights is developed and the Tier 2 spatial scenario that best matches each lake from which the
hypothetical fisher fishes is identified. Using these data along with the facility's matching hourly
meteorology data, the facility's emission rates, and the fish ingestion rates, chemical fate and
transport is modeled in TRIM.FaTE and corresponding screening-level risks are calculated
using MIRC. This results in a screening-level risk value for each lake. If the farmer scenario is
specifically being assessed, the modeling will also result in a screening-level risk value for each
farm. The screening-level risk associated with each lake is multiplied by the percent of daily
ingested fish caught from the lake; summing those products across all modeled lakes results in
the screening-level fisher risk (i.e., fisher screening result) associated with the evaluated PB-
Tier 3 Methodology
76
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TRIM-Based Tiered Screening Methodology for RTR
HAP emitted by the facility. The time-series-adjusted Tier 3 screening results are compared to
the Tier 2 screening results calculated previously.
Tier 3 Methodology
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TRIM-Based Tiered Screening Methodology for RTR
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.
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TRIM-Based Tiered Screening Methodology for RTR
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http://www. epa. gov/ttn/fera/data/risk/voM /chapter_14. pdf.
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Facilities (including the Hazardous Waste Companion Database of chemical-specific
parameter values). U.S. Environmental Protection Agency, Office of Solid Waste and
Emergency Response, Washington, DC. EPA-530-R-05-006. September.
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Carcinogens. EPA-630/R-03-003F. Risk Assessment Forum: Washington, DC. March.
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November 20, 2006. Available at:
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1). Office of Air Quality Planning and Standards; June 12, 2007. Available at:
http://www.epa.gov/ttn/atw/toxsource/summary.html.
EPA 2009. Risk and Technology Review (RTR) Risk Assessment Methodologies: For Review
by the EPA's Science Advisory Board with Case Studies - MACT I Petroleum Refining
Sources and Portland Cement Manufacturing. EPA Office of Air Quality Planning and
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Tier 3 Methodology
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TRIM-Based Tiered Screening Methodology for RTR
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Hydrocarbon (PAH) Mixtures (External Review Draft). Washington, DC, EPA/635/R-08/012A
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Assessment Methodologies: For Review by the EPA's Science Advisory Board with Case
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EPA 2011b Human Health Multipathway Residual Risk Assessment for the Secondary Lead
Smelting Source Category. Draft Report. Prepared by ICF International for EPA Risk and
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Health Risks Associated with Mercury Emissions from Electric Utility Steam-generating
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Margaret McVey and Rebecca Kauffman, ICF Consulting. October 18.
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mercury, methylmercury, arsenic, selenium, and cadmium by freshwater invertebrates and
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TRIM-Based Tiered Screening Methodology for RTR
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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
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Attachment A. TRIM.FaTE Inputs
Attachment A
TRIM.FaTE Inputs
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Exhibits, Attachment A
Exhibit A-1. TRIM.FaTE Simulation Parameters for the TRIM.FaTE Screening
Scenario A-1
Exhibit A-2. Meteorological Inputs for the TRIM.FaTE Screening Scenario A-2
Exhibit A-3. Air Parameters for the TRIM.FaTE Screening Scenario A-3
Exhibit A-4. Soil and Groundwater Parameters for the TRIM.FaTE Screening
Scenario A-4
Exhibit A-5. Runoff Assumptions for the TRIM.FaTE Screening Scenario A-6
Exhibit A-6. USLE Erosion Parameters for the TRIM.FaTE Screening Scenario A-7
Exhibit A-7. Terrestrial Plant Placement for the TRIM.FaTE Screening Scenario A-8
Exhibit A-8. Terrestrial Plant Parameters for the TRIM.FaTE Screening Scenario A-9
Exhibit A-9. Surface Water Parameters for the TRIM.FaTE Screening Scenario A-11
Exhibit A-10. Sediment Parameters for the TRIM.FaTE Screening Scenario A-12
Exhibit A-11. Aquatic Animals Food Chain, Density, and Mass for the
TRIM.FaTE Screening Scenario A-13
Exhibit A-12. Cadmium Chemical-Specific Parameters for the TRIM.FaTE
Screening Scenario A-14
Exhibit A-13. Mercury Chemical-Specific Parameters for the TRIM.FaTE
Screening Scenario A-15
Exhibit A-14. PAH Chemical-Specific Parameters for the TRIM.FaTE Screening
Scenario A-16
Exhibit A-15. Dioxin Chemical-Specific Parameters for the TRIM.FaTE
Screening Scenario A-18
Exhibit A-16. Cadmium Chemical-Specific Parameters for Abiotic
Compartments in the TRIM.FaTE Screening Scenario A-20
Exhibit A-17. Mercury Chemical-Specific Parameters for Abiotic Compartments
in the TRIM.FaTE Screening Scenario A-21
Exhibit A-18. PAH Chemical-Specific Parameters for Abiotic Compartments in
the TRIM.FaTE Screening Scenario A-25
Exhibit A-19. Dioxin Chemical-Specific Parameters for Abiotic Compartments in
the TRIM.FaTE Screening Scenario A-28
Exhibit A-20. Cadmium Chemical-Specific Parameters for Plant Compartments
in the TRIM.FaTE Screening Scenario A-32
Exhibit A-21. Mercury Chemical-Specific Parameters for Plant Compartments in
the TRIM.FaTE Screening Scenario A-33
Exhibit A-22. PAH Chemical-Specific Parameters for Plant Compartments in the
TRIM.FaTE Screening Scenario A-34
Exhibit A-23. PAH Chemical-Specific Parameters for Plant Compartments in the
TRIM.FaTE Screening Scenario A-35
Attachment A /'/'/ October 2014
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-24. Cadmium Chemical-Specific Parameters for Aquatic Species in the
TRIM.FaTE Screening Scenario A-37
Exhibit A-25. Mercury Chemical-Specific Parameters for Aquatic Species in the
TRIM.FaTE Screening Scenario A-39
Exhibit A-26. PAH Chemical-Specific Parameters for Aquatic Species in the
TRIM.FaTE Screening Scenario A-40
Exhibit A-27. Dioxin Chemical-Specific Parameters for Aquatic Species in the
TRIM.FaTE Screening Scenario A-43
Attachment A iv October 2014
TRIM.FaTE Inputs
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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
meteorological and air parameters, respectively, entered into the model. Exhibit A-4. ,
Exhibit A-5, and Exhibit A-6 present the parameters for soil and groundwater, runoff
assumptions, and the USLE (universal soil loss equation) erosion parameters, respectively, for
the screening scenario. Exhibit A-7 and Exhibit A-8 present terrestrial parameters. Exhibit A-9
through Exhibit A-11 present lake parameters, and Exhibit A-12 through Exhibit A-27 present
parameters specific to the chemicals modeled in the scenario.
Exhibit A-1. TRIM.FaTE Simulation Parameters
for the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value Used
Reference
Start of simulation
date/time
1/1/1990, midnight
Consistent with met data.
End of simulation
date/time
1/1/2040, midnight
Consistent with met data set; selected to
provide a 50-year modeling period.
Simulation time step
hr
1
Selected value.
Output time step3
hr
4
Selected value.
aOutput time step is set in TRIM.FaTE using the scenario properties "simulationStepsPerOutputStep" and "simulationTimeStep."
Attachment A
TRIM.FaTE Inputs
A-1
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-2. Meteorological Inputs for the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value Used
Reference
Meteorological Inputs
Air temperature
degrees K
298
USEPA 2005.
Horizontal wind speed
m/sec
2.8
5th percentile annual average value for contiguous United States, calculated from
30 yrs of annual normal temperature values.
Vertical wind speed
m/sec
0.0
Assumption; vertical wind speed not used by any of the algorithms in the version
of the TRIM.FaTE library used for screening.
Wind direction
degrees
clockwise from
N (blowing
from)
3-days-on
4-days-off
On is defined as time during which wind is blowing into the model domain. A
conservative estimate of time during which wind should blow into the modeling
domain was determined by evaluating HUSWO; it was concluded that a
conservative estimate would be approximately 42% of the time.
Rainfall rate
m3[rain]/m2
[surface area]-
day
varies daily
1.5 m/yr is the maximum statewide 30-year (1971-2000) average for the
contiguous United States, excluding Rhode Island because of extreme weather
conditions on Mt. Washington. Data obtained from the National Climatic Data
Center at http://www.ncdc.noaa.gov/oa/climate/online/ccd/nrmpcp.txt. The
precipitation frequency was 3-days-on:4-days-off based on data from Holzworth,
1972.
Mixing height (used to set air
VE property named "top")
m
710
5th percentile annual average mixing heights (calculated from daily morning and
afternoon values), for all stations on SCRM (40 state, 70 stations).
isDay_SteadyState_forAir
unitless
-
Value not used in current dynamic runs (would need to be reevaluated if steady-
state runs are needed).
isDay_SteadyState_forOther
unitless
-
Attachment A
TRIM.FaTE Inputs
A-2
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-3. Air Parameters for the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value Used
Reference
Atmospheric dust load
kg[dust]/m3[air]
6.15E-08
Bidleman 1988
Density of air
g/cm3
0.0012
USEPA 1997b
Dust density
kg[dust]/m3[dust]
1,400
Bidleman 1988
Fraction organic matter
on particulates
unitless
0.2
Harnerand Bidleman 1998
Attachment A
TRIM.FaTE Inputs
A-3
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-4. Soil and Groundwater Parameters for the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value Used
Reference
Surface Soil Compartment Type
Air content
volume[air]/volume[compartment]
0.28
McKone et al. 2001.
Average vertical velocity of water
(percolation)
m/day
8.22E-04
Assumed to be 0.2 times average precipitation
for site.
Boundary layer thickness above
surface soil
m
0.005
Thibodeaux 1996; McKone et al. 2001 (Table
3).
Density of soil solids (dry weight)
kg [soil]/m3[soil]
2600
Default in McKone et al. 2001 (Table 3).
Thickness - unfilled3
m
0.01
McKone et al. 2001 (p. 30).
Thickness - tilled3
m
0.20
USEPA 2005.
Erosion fraction
unitless
variesb
See Exhibit 5.
Fraction of area available for
erosion
m2[area available]/m2[total]
1
Assumption ; area assumed rural.
Fraction of area available for runoff
m2[area available]/m2[total]
1
Assumption ; area assumed rural.
Fraction of area available for vertical
diffusion
m2[area available]/m2[total]
1
Assumption ; area assumed rural.
Fraction sand
unitless
0.25
Assumption.
Organic carbon fraction
unitless
0.008
U.S. average in McKone et al. 2001 (Table 16
and A-3).
PH
unitless
6.8
Assumption.
Runoff fraction
unitless
variesb
See Exhibit A-5.
Total erosion rate
kg [soil]/m2/day
variesb
See Exhibit A-6.
Total runoff rate
m3[water]/m2/day
1.64E-03
Calculated using scenario-specific
precipitation rate and assumptions associated
with water balance.
Water content
volume[water]/volume[compartment]
0.15
McKone et al. 2001
Root Zone Soil Compartment Type
Air content
volume[air]/volume[compartment]
0.25
McKone et al 2001 (Table 16).
Average vertical velocity of water
(percolation)
m/day
8.22E-04
Assumed as 0.2 times average precipitation
for New England in McKone et al. 2001.
Density of soil solids (dry weight)
kg [soil]/m3[soil]
2,600
McKone et al. 2001 (Table 3).
Fraction sand
unitless
0.25
Assumption.
Attachment A
TRIM.FaTE Inputs
A-4
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-4. Soil and Groundwater Parameters for the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value Used
Reference
Root Zone Soil Compartment Type, continued
Thickness - unfilled3
m
0.79
McKone et al. 2001 (Table 16 - U.S. average).
Thickness - tilled3
m
0.6
Adjusted from McKone et al. 2001 (Table 16).
Organic carbon fraction
unitless
0.008
McKone et al. 2001 (Tables 16 and A-3, U.S.
average).
PH
unitless
6.8
Assumption.
Water content
volume[water]/volume[compartment]
0.15
McKone et al. 2001
Vadose Zone Soil Compartment Type
Air content
volume[air]/volume[compartment]
0.22
McKone et al. 2001 (Table 17).
Average vertical velocity of water
(percolation)
m/day
8.22E-04
Assumed as 0.2 times average precipitation
for New England in McKone et al. 2001.
Density of soil solids (dry weight)
kg [soil]/m3[soil]
2,600
Default in McKone et al. 2001 (Table 3).
Fraction sand
unitless
0.35
Assumption.
Thickness3
m
1.4
McKone et al. 2001 (Table 17).
Organic carbon fraction
unitless
0.003
McKone et al. 2001 (Tables 16 and A-3, U.S.
average).
PH
unitless
6.8
Assumption.
Water content
volume[water]/volume[compartment]
0.21
McKone et al. 2001 (Table 17 - national
average).
Groundwater Compartment Type
Thickness3
m
3
McKone et al. 2001 (Table 3).
Fraction sand
unitless
0.4
Assumption.
Organic carbon fraction
unitless
0.004
Assumption.
PH
unitless
6.8
Assumption.
Porosity
volume[total pore
space]/volume[compartment]
0.2
Default in McKone et al. 2001 (Table 3).
Density of solid material in aquifer
kg [soil]/m3[soil]
2,600
Default in McKone et al. 2001 (Table 3).
aSet using the volume element properties file.
bSee separate tables (Exhibit A-5 and Exhibit A-6) for erosion/runoff fractions and total erosion rates.
Attachment A
TRIM.FaTE Inputs
A-5
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-5. Runoff Assumptions for the TRIM.FaTE Screening Scenario
Originating Compartment
Destination Compartment
Runoff/Erosion Fraction
SurfSoil Source
SurfSoil N1
0.0
SurfSoil S1
0.0
sink
1.0
SurfSoil N1
SW Pond
1.0
SurfSoil Source
0.0
SurfSoil N6
0.0
SurfSoil S1
0.0
sink
0.0
SurfSoil S1
SW Pond
1.0
SurfSoil Source
0.0
SurfSoil N1
0.0
sink
0.0
SurfSoil N6
SW Pond
1.0
SurfSoil N1
0.0
SurfSoil N7
0.0
sink
0.0
SurfSoil N7
SW Pond
1.0
SurfSoil N6
0.0
SurfSoil N3
0.0
sink
0.0
SurfSoil N3
SW Pond
1.0
SurfSoil N7
0.0
SurfSoil N4
0.0
sink
0.0
SurfSoil N4
SW Pond
1.0
SurfSoil N3
0.0
SurfSoil N5
0.0
SurfSoil S4
0.0
sink
0.0
SurfSoil S4
SW Pond
1.0
SurfSoil N4
0.0
SurfSoil S5
0.0
sink
0.0
SurfSoil N5a
SW Pond
0.0
SurfSoil N4
0.5
SurfSoil S5
0.5
sink
0.0
SurfSoil S5a
SW Pond
0.0
SurfSoil N5
0.0
SurfSoil S4
1.0
sink
0.0
aAssumes that N5 is higher ground than S5, and half of the runoff flows into N4, and the other half into S5. Assumes all runoff from S5
flows into S4.
Attachment A
TRIM.FaTE Inputs
A-6
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-6. USLE Erosion Parameters for the TRIM.FaTE Screening Scenario
Soil
Parcel
Area
Rainfall/Erosivity
Index
Soil
Erodibility
Index
Length-
Slope
Factor
Land
Use
Cover
Mgmt
Factor
Supporting
Practices
Factor
Unit Soil Loss
Sediment
Delivery
Ratio3
Calculated
(Adjusted)
Erosion
Rate
Code
m2
R (100 ft-ton/ac)
K
(ton/ac/(100
ft-ton/acre))
LS
(USCS)
type
C
(USCS)
P
A
(ton/ac/yr)
CM
< E
O)
SDRa
kg/m2/d
N1
5.8E+04
300
0.39
1.5
grass
0.1
1
17.55
0.010779
0.533
0.005740
N6
4.1E+04
300
0.39
1.5
crops
0.2
1
35.1
0.021557
0.557
0.012014
N7
7.3E+04
300
0.39
1.5
grass
0.1
1
17.55
0.010779
0.518
0.005580
N3
3.5E+05
300
0.39
1.5
grass
0.1
1
17.55
0.010779
0.385
0.004151
N4
2.0E+06
300
0.39
1.5
forest
0.1
1
17.55
0.010779
0.309
0.003331
N5
6.7E+06
300
0.39
1.5
forest
0.1
1
17.55
0.010779
0.196
0.002116
S1
5.8E+04
300
0.39
1.5
grass
0.1
1
17.55
0.010779
0.533
0.005740
S4
2.0E+06
300
0.39
1.5
forest
0.1
1
17.55
0.010779
0.309
0.003331
S5
6.7E+06
300
0.39
1.5
forest
0.1
1
17.55
0.010779
0.196
0.002116
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
TRIM.FaTE Inputs
A-7
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-7. Terrestrial Plant Placement for the TRIM.FaTE Screening Scenario
Surface Soil Volume
Element
Surface Soil Depth
(m)
Coniferous
Forest
Grasses/
Herbs
None
Source
0.01
X
N1
0.01
X
N6
0.20 (tilled)
X
N7
0.01
X
N3
0.01
X
N4
0.01
X
N5
0.01
X
S1
0.01
X
S4
0.01
X
S5
0.01
X
Attachment A
TRIM.FaTE Inputs
A-8
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-8. Terrestrial Plant Parameters 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
-
Average leaf area index
m2[leaf]/
m2[area]
5.0
Harvard Forest, dom. red oak and
red maple, CDIAC website
5.0
Mid-range of 4-6 for old fields, R.J.
Luxmoore, ORNL.
Calculate wet deposition
interception fraction (Boolean)
1=yes, 0=no
0
Assumption.
0
Assumption.
Correction exponent, octanol to
lipid
unitless
0.76
From roots, Trapp 1995.
0.76
From roots, Trapp 1995.
Degree stomatal opening
unitless
1
Assumed value of 1 for daytime
(stomatal diffusion is turned off at
night using a different property,
IsDay).
1
Assumed value of 1 for daytime
(stomatal diffusion is turned off at night
using a different property, IsDay).
Density of wet leaf
kg/m3
820
Paterson et al. 1991.
820
Paterson et al. 1991.
Leaf wetting factor
m
3.00E-04
1E-04 to 6E-04 for different crops
and elements, Mullerand Prohl
1993.
3.00E-04
1E-04 to 6E-04 for different crops and
elements, Mullerand Prohl 1993.
Length of leaf
m
0.01
Assumption.
0.05
Assumption.
Lipid content
kg/kg wet weight
0.00224
European beech, Riederer 1995.
0.00224
European beech, Riederer 1995.
Litter fall rate
1/day
0.0021
value assumes 1st-order
relationship and that 99% of leaves
fall over 6 years
Seasonal0
Stomatal area normalized
effective diffusion path length
1/m
200
Wilmer and Fricker 1996.
200
Wilmer and Fricker 1996.
Vegetation attenuation factor
m2/kg
2.9
Grass/hay, Baes et al. 1984.
2.9
Grass/hay, Baes et al. 1984.
Water content
unitless
0.8
Paterson et al. 1991.
0.8
Paterson et al. 1991.
Wet deposition interception
fraction
unitless
0.2
Calculated based on 5 years of
local met data, 1987-1991.
0.2
Calculated based on 5 years of local met
data, 1987-1991.
Wet mass of leaf per soil area
kg [fresh
leaf]/m2[area]
2.0
Calculated from leaf area index,
leaf thickness (Simonich and Hites,
1994), density of wet foliage.
0.6
Calculated from leaf area index and Leith
1975.
Particle on Leaf Compartment Type
Allow exchange
1=yes, 0=no
1
-
Seasonal13
-
Attachment A
TRIM.FaTE Inputs
A-9
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-8. Terrestrial Plant Parameters for the TRIM.FaTE Screening Scenario
Parameter Name
Units
Coniferous3
Grass/Herb3
Value
Used
Reference
Value
Used
Reference
Particle on Leaf Compartment Type, continued
Volume particle per area leaf
m3[leaf
particles]/m2[leaf]
1.00E-09
Based on particle density and size
distribution for atmospheric
particles measured on an adhesive
surface, Coe and Lindberg 1987.
1.00E-09
Based on particle density and size
distribution for atmospheric particles
measured on an adhesive surface, Coe
and Lindberg 1987.
Root Compartment Type - Nonwoody Only
Allow exchange
1=yes, 0=no
Seasonal13
-
Correction exponent, octanol to
lipid
unitless
0.76
Trapp 1995.
Lipid content of root
kg/kg wet weight
0.011
Calculated.
Water content of root
kg/kg wet weight
0.8
Assumption.
Wet density of root
kg/m3
820
Soybean, Paterson et al. 1991.
Wet mass per soil area
kg/m2
1.4
Temperate grassland, Jackson et al.
1996.
Stem Compartment Type - Nonwoody Only
Allow exchange
1=yes, 0=no
Seasonal13
-
Correction exponent, octanol to
lipid
unitless
0.76
Trapp 1995.
Density of phloem fluid
kg/m3
1,000
Assumption.
Density of xylem fluid
kg/cm3
900
Assumption.
Flow rate of transpired water per
leaf area
m3[water]/m2[leaf
]
0.0048
Crank et al. 1981.
Fraction of transpiration flow
rate that is phloem rate
unitless
0.05
Paterson et al. 1991.
Lipid content of stem
kg/kg wet weight
0.00224
Leaves of European beech, Riederer
1995.
Water content of stem
unitless
0.8
Paterson et al. 1991
Wet density of stem
kg/m3
830
Assumption.
Wet mass per soil area
kg/m2
0.24
Calculated from leaf and root biomass
density.
aSee Exhibit A-7 for assignment of plant types to surface soil compartments.
bBegins March 9 (set to 1), ends November 7 (set to 0). Nationwide 80th percentile.
°Begins November 7, ends December 6; rate = 0.15/day during this time (value assumes 99 percent of leaves fall in 30 days).
Attachment A
TRIM.FaTE Inputs
A-10
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-9. Surface Water Parameters for the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value Used
Reference
Algae carbon content
(fraction)
unitless
0.465
APHA 1995.
Algae density in water
column
g[algae]/L[water]
0.0025
Millard et al. 1996 as cited in ICF
2005.
Algae growth rate
1/day
0.7
Hudson et al. 1994 as cited in
Mason et al. 1995b.
Algae radius
um
2.5
Mason et al. 1995b.
Algae water content (fraction)
unitless
0.9
APHA 1995.
Average algae cell density
(per volume cell, not water)
g[algae]/m3[algae]
1,000,000
Mason et al. 1995b, Mason et al.
1996.
Boundary layer thickness
above sediment
m
0.02
Cal EPA 1993.
Chloride concentration
mg/L
8.0
Kaushal et al. 2005.
Chlorophyll concentration
mg/L
0.0029
ICF 2005.
Depth3
m
3.18
Wl DNR 2007 - calculation based
on relationship between drainage
basin and lake area size.
Dimensionless viscous
sublayer thickness
unitless
4
Ambrose et al. 1995.
Drag coefficient for water
body
unitless
0.0011
Ambrose et al. 1995.
Flush rate
1/year
12.17
Calculated based on pond
dimensions and flow calculations.
Fraction Sand
unitless
0.25
Assumption.
Organic carbon fraction in
suspended sediments
unitless
0.02
Assumption.
PH
unitless
7.3
Assumption.
Suspended sediment
deposition velocity
m/day
2
USEPA 1997b.
Total suspended sediment
concentration
kg[sediment]/m3[water
column]
0.05
USEPA 2005.
Water temperature
degrees K
298
USEPA 2005.
aSet using the volume element properties named "top" and "bottom."
Attachment A
TRIM.FaTE Inputs
A-11
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-10. Sediment Parameters 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
unitless
0.02
McKone et al. 2001 (Table
3).
Porosity of the sediment
zone
volume[total pore
space]/volume[sediment
compartment]
0.6
USEPA 2005.
Solid material density in
sediment
kg[sediment]/m3[sediment]
2,600
McKone et al. 2001 (Table
3).
PH
unitless
7.3
Assumption.
Sediment resuspension
velocity
m/day
6.69E-05
Calculated from water
balance model.
aSet using the volume element properties named "top" and "bottom."
Attachment A
TRIM.FaTE Inputs
A-12
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-11. Aquatic Animals Food Chain, Density, and Mass for the TRIM.FaTE Screening Scenario
Aquatic Biota
(Consuming
Organism)
Fraction Diet
Biomass
(kg/m2)
Body
Weight
(kg)
Reference
Algae
Zooplankto
n
Benthic
Invertebrate
Water
Column
Herbivore
Benthic
Omnivore
Water
Column
Omnivore
Benthic
Carnivore
Water
Column
Carnivore
Benthic
Invertebrate
0%
0%
0%
0%
0%
0%
0%
0%
0.020
2.55E-04
Assumption.
Water Column
Herbivore
0%
100%
0%
0%
0%
0%
0%
0%
0.002
0.025
Assumption.
Benthic Omnivore
0%
0%
100%
0%
0%
0%
0%
0%
0.002
2.50E-01
Assumption.
Water Column
Omnivore
0%
0%
0%
100%
0%
0%
0%
0%
0.001
0.25
Assumption.
Benthic Carnivore
0%
0%
50%
0%
50%
0%
0%
0%
0.001
2.0
Assumption.
Water Column
Carnivore
0%
0%
0%
0%
0%
100%
0%
0%
0.0002
2.0
Assumption.
Zooplankton
100%
0%
0%
0%
0%
0%
0%
0%
0.0064
5.70E-08
Assumption.
Attachment A
TRIM.FaTE Inputs
A-13
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-12. Cadmium Chemical-Specific Parameters
for the TRIM.FaTE Screening Scenario
Parameter Name3
Units
Value
Reference
CAS numberb
unitless
7440-43-9
-
Diffusion coefficient in pure
air
m2[air]/day
0.71
USEPA 1999 (Table A-2-
35).
Diffusion coefficient in pure
water
m2[water]/day
8.16E-05
USEPA 1999 (Table A-2-
35).
Henry's Law constant
Pa-m3/mol
1.00E-37
USEPA 1999 (Table A-2-
35; assumed to be zero).
Melting point
degrees K
594
ATSDR 1999.
Molecular weight
g/mol
112.41
ATSDR 1999.
Octanol-air partition
coefficient (Koa)
m3[air]/m3[octanol]
-
-
Octanol-carbon partition
coefficient (Koc)
-
-
Octanol-water partition
coefficient (K0w)
L[water]/kg[octanol]
-
-
aAII parameters in this table are TRIM.FaTE chemical properties.
This CAS numbers applies to elemental Cd; however, the cations of cadmium are being modeled.
Attachment A
TRIM.FaTE Inputs
A-14
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-13. Mercury Chemical-Specific Parameters
for the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value
Reference
Hg(0)b
Hg(2)b
MHgb
CAS number
unitless
7439-97-6
14302-87-5
22967-92-6
-
Diffusion coefficient
in pure air
m2[air]/day
0.478
0.478
0.456
USEPA 1997b.
Diffusion coefficient
in pure water
m2[water]/day
5.54E-05
5.54E-05
5.28E-05
USEPA 1997b.
Henry's Law
constant
Pa-m3/mol
719
7.19E-05
0.0477
USEPA 1997b.
Melting point
degrees K
234
5.50E+02
443
CARB 1994.
Molecular weight
g/mol
201
201
216
USEPA 1997b.
Octanol-water
partition coefficient
(Kow)
L[water]/kg[octanol]
4.15
3.33
1.7
Mason et al. 1996.
Vapor washout ratio
m3[air]/m3[rain]
1,200
1.6E+06
0
USEPA 1997b,
based on Petersen
et al. 1995.
aAII parameters in this table are TRIM.FaTE chemical properties.
b0n this and all following tables, Hg(0) = elemental mercury, Hg(2) = divalent mercury, and MHg = methyl mercury.
Attachment A
TRIM.FaTE Inputs
A-15
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-14. PAH Chemical-Specific Parameters for the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value
2Methyl
712DMB
Acenaphthene
Acenaphthylene
BaA
BaP
BbF
BghiP
CAS number
unitless
91-57-6
57-97-6
83-32-9
208-96-8
56-55-3
50-32-8
205-99-2
191-24-2
Diffusion coefficient
in pure air
m2/day
0.451
0.691
0.009
0.388
0.441
0.372
0.009
0.190
Diffusion coefficient
in pure water
m2/day
6.70E-05
6.91 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
0.20
18.50
12.70
1.22
0.07
0.05
0.03
Melting point
degrees K
307.75
396.65
366.15
365.65
433
452
441
550.15
Molecular weight
g/mol
142.20
256.35
154.21
152.20
228.29
252.32
252.32
276.34
Octanol-water
partition coefficient
(Kow)
L[water]/L[octanol]
7.24E+03
6.31 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
0.009
0.009
0.009
0.009
0.009
0.009
Diffusion coefficient
in pure water
m2/day
8.64E-05
8.64E-05
8.64E-05
8.64E-05
8.64E-05
8.64E-05
Henry's Law
constant
Pa-m3/mol
0.04
0.53
0.0076
1.96
9.81
0.03
Melting point
degrees K
490
531
539
383.15
383.15
437
Molecular weight
g/mol
252.32
228.29
278.33
202.26
166.20
276.34
Octanol-water
partition coefficient
(Kow)
L[water]/L[octanol]
8.71 E+05
5.37E+05
3.16E+06
1.45E+05
1.51E+04
5.25E+0
6
Attachment A
TRIM.FaTE Inputs
A-16
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-14. PAH Chemical-Specific Parameters for the TRIM.FaTE Screening Scenario
Parameter Name
Units
Reference
CAS number
unitless
-
Diffusion coefficient in pure
air
m2/day
USEPA 2005. Exceptions include USEPA 1997a (7,12-Dimethylbenz(a)anthracene), and
USEPA 2007 (2-Methylnaphthalene, Acenaphthylene, and Benzo(g,h,i)perylene)
Diffusion coefficient in pure
water
m2/day
USEPA 2005. Exceptions include USEPA 1997a (7,12-Dimethylbenz(a)anthracene), and
USEPA 2007 (2-Methylnaphthalene, Acenaphthylene, and Benzo(g,h,i)perylene)
Henry's Law constant
Pa-m3/mol
USEPA 2005. Exceptions include USEPA 2003 (2-Methylnaphthalene) HSDB 2001a
(7,12-Dimethylbenz(a)anthracene), HSDB 2001b (Acenaphthylene), and HSDB 2001c
(Benzo(g,h,i)perylene)
Melting point
degrees K
Budavari 1996. Exceptions include USEPA 2003 (2-Methylnaphthalene), HSDB 2001a
(7,12-Dimethylbenz(a)anthracene), HSDB 2001b (Acenaphthylene), HSDB 2001c
(Benzo(g,h,i)perylene), and USEPA 2005 (Acenaphthene, Fluoranthene, and Fluorene)
Molecular weight
g/mol
Budavari 1996. Exceptions include USEPA 2003 (2-Methylnaphthalene), HSDB 2001a
(7,12-Dimethylbenz(a)anthracene), HSDB 2001b (Acenaphthylene), HSDB 2001c
(Benzo(g,h,i)perylene), and USEPA 2005 (Acenaphthene, Fluoranthene, and Fluorene)
Octanol-water partition
coefficient (K0w)
L[water]/L[octanol]
Hansch et al. 1995. Exceptions include Passivirta et al. 1999 (Acenaphthylene,
Benzo(k)fluoranthene, and lndeno(1,2,3-cd)pyrene), and Sangster1993
(Benzo(b)fluoranthene)
Attachment A
TRIM.FaTE Inputs
A-17
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-15. Dioxin Chemical-Specific Parameters for the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value
1,2,3,4,6,7,8,9-
OCDD
1,2,3,4,6,7,8,9-
OCDF
1,2,3,4,6,7,8-
HpCDD
1,2,3,4,6,7,8-
HpCDF
1,2,3,4,7,8,9-
HpCDF
I
CO _
CM X
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
0.684
0.19
1.22
1.43
1.42
1.08
1.45
Melting point
degrees K
603.0
259.0
538.0
236.5
222.0
546.0
499.0
Molecular weight
g/mol
460
443.76
425.2
409.31
409.31
391
374.87
Octanol-water partition
coefficient (K0w)
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
1.11
0.263
0.507
1.11
Melting point
degrees K
558.0
506.0
517.0
509.0
513.0
499.0
512.5
Molecular weight
g/mol
390.84
374.87
390.84
374.87
356.4
340.42
374.87
Octanol-water partition
coefficient (K0w)
L[water]/L[octanol]
1.62E+08
8.24E+07
1.62E+08
3.80E+07
1.86E+07
6.17E+06
8.31 E+07
Attachment A
TRIM.FaTE Inputs
A-18
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-15. Dioxin Chemical-Specific Parameters for the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value
Reference
2,3,4,7,8-
PeCDF
2,3,7,8-
TCDD
2,3,7,8-
TCDF
CAS number
unitless
57117-31-4
1746-01-6
51207-31-9
-
Diffusion coefficient in
pure air
m2/day
0.192
0.899
0.203
US EPA 2005
Diffusion coefficient in
pure water
m2/day
6.91 E-05
4.84E-05
5.19E-05
US EPA 2005
Henry's Law constant
Pa-m3/mol
0.505
3.33
1.46
US EPA 2005
Melting point
degrees K
469.3
578.0
500.0
Mackay et al. 2000. Exceptions include USEPA 2000a
(1,2,3,6,7,8-HxCDD, 1,2,3,7,8,9-HxCDF, and 1,2,3,7,8-
PeCDD), ATSDR 1998 (1,2,3,6,7,8-HxCDF, 1,2,3,7,8-
PeCDF, and 2,3,4,6,7,8-HxCDF), and NLM 2002
(1,2,3,7,8,9-HxCDD)
Molecular weight
g/mol
340.42
322
306
Mackay et al. 2000. Exceptions include ATSDR 1998
(1,2,3,6,7,8-HxCDF, 1,2,3,7,8,9-HxCDF, 1,2,3,7,8-PeCDD,
1,2,3,7,8-PeCDF, and 2,3,4,6,7,8-HxCDF) and NLM 2002
(1,2,3,6,7,8-HxCDD and 1,2,3,7,8,9-HxCDD)
Octanol-water partition
coefficient (K0w)
L[water]/L[octanol]
3.16E+06
6.31 E+06
1.26E+06
Mackay et al. 1992 as cited in USEPA 2000b. Exceptions
include Mackay et al. 2000 (1,2,3,4,7,8,9-HpCDF), USEPA
2000a (1,2,3,6,7,8-HxCDD, 1,2,3,6,7,8-HxCDF, 1,2,3,7,8,9-
HxCDD, 1,2,3,7,8,9-HxCDF, and 2,3,4,6,7,8-HxCDF), and
Sijm et al. 1989 as cited in USEPA 2000b (1,2,3,7,8-
PeCDD)
Attachment A
TRIM.FaTE Inputs
A-19
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-16. Cadmium Chemical-Specific Parameters for Abiotic Compartments
in the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value
Reference
Air Compartment Type
Particle dry deposition velocity
m/day
260
Calculated from
Muhlbaier and Tissue
1980.
Washout ratio
m3[air]/m3[rain]
200,000
MacKay et al. 1986.
Surface Soil Compartment Type
Use input characteristic depth
(Boolean)
0 = no, Else = yes
0
Assumption.
Root Zone Soil Compartment Type
Use input characteristic depth
(Boolean)
0 = no, Else = yes
0
Assumption.
Vadose Zone Soil Compartment Type
Use input characteristic depth
(Boolean)
0 = no, Else = yes
0
Assumption.
Surface Water Compartment Type
Ratio of concentration in water to
concentration in algae to
concentration dissolved in water
L[water]/g[algae
wet wt]
1.87
McGeer et al. 2003.
Attachment A
TRIM.FaTE Inputs
A-20
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-17. Mercury Chemical-Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value
Reference
Hg(0)
Hg(2)
MHg
Air Compartment Type
Particle dry deposition velocity
m/day
500
500
500
CalTOX value cited in McKone et al. 2001.
Demethylation rate
1/day
N/A
N/A
0
Assumption.
Methylation rate
1/day
0
0
0
Assumption.
Oxidation rate
1/day
0.00385
0
0
Low end of half-life range (6 months to 2 years) in
USEPA 1997b.
Reduction rate
1/day
0
0
0
Assumption.
Washout ratio
m3[air]/m3[rain]
200,000
200,000
200,000
Assumption.
Surface Soil Compartment Type
Use input characteristic depth
(Boolean)
0 = no, Else = yes
0
0
0
Assumption.
Soil-water partition coefficient
L[water]/kg[soil
wet wt]
1,000
58,000
7,000
USEPA 1997b.
Vapor dry deposition velocity
m/day
50
2500
0
Hg(0) - from Lindberg et al. 1992; Hg(2) - estimate
by USEPA using the Industrial Source Complex
(ISC) Model - [See Vol. Ill, App. A of the Mercury
Study Report (USEPA 1997b)].
Demethylation rate
1/day
N/A
N/A
0.06
Range reported in Porvari and Verta 1995 is 3E-2
to 6E-2 /day; value is average maximum potential
demethylation rate constant under anaerobic
conditions.
Methylation rate
1/day
0
0.001
0
Range reported in Porvari and Verta 1995 is 2E-4
to 1E-3 /day; value is average maximum potential
methylation rate constant under anaerobic
conditions.
Oxidation rate
1/day
0
0
0
Value assumed in USEPA 1997b.
Reduction rate
1/day
0
1.25E-05
0
Value used for unfilled surface soil (2cm), 10%
moisture content, in USEPA 1997b; general range
is (0.0013/day)*moisture content to
(0.0001/day)*moisture content for forested region
(Lindberg 1996; Carpi and Lindberg 1997).
Attachment A
TRIM.FaTE Inputs
A-21
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-17. Mercury Chemical-Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value
Reference
Hg(0)
Hg(2)
MHg
Root Zone Soil Compartment Type
Use input characteristic depth (Boolean)
0 = no, Else = yes
0
0
0
Assumption.
Soil-water partition coefficient
L[water]/kg[soil
wet wt]
1,000
58,000
7,000
USEPA 1997b.
Demethylation rate
1/day
N/A
N/A
0.06
Range reported in Porvari and Verta 1995 is 3E-2
to 6E-2 /day; value is average maximum potential
demethylation rate constant under anaerobic
conditions.
Methylation rate
1/day
0
0.001
0
Range reported in Porvari and Verta 1995 is 2E-4
to 1E-3 /day; value is average maximum potential
methylation rate constant under anaerobic
conditions.
Oxidation rate
1/day
0
0
0
Value assumed in USEPA 1997b.
Reduction rate
1/day
0
3.25E-06
0
Value used for tilled surface soil (20cm), 10%
moisture content, in USEPA 1997b (Lindberg
1996; Carpi and Lindberg 1997).
Vadose Zone Soil Compartment Type
Use input characteristic depth (Boolean)
0 = no, Else = yes
0
0
0
Assumption.
Soil-water partition coefficient
L[water]/kg[soil
wet wt]
1,000
58,000
7,000
USEPA 1997b.
Demethylation rate
1/day
N/A
N/A
0.06
Range reported in Porvari and Verta 1995 is 3E-2
to 6E-2 /day; value is average maximum potential
demethylation rate constant under anaerobic
conditions.
Methylation rate
1/day
0
0.001
0
Range reported in Porvari and Verta 1995 is 2E-4
to 1 E-3 /day; value is average maximum potential
methylation rate constant under anaerobic
conditions.
Oxidation rate
1/day
0
0
0
Value assumed in USEPA 1997b.
Reduction rate
1/day
0
3.25E-06
0
Value used for tilled surface soil (20cm), 10%
moisture content, in USEPA 1997b (Lindberg
1996; Carpi and Lindberg 1997).
Attachment A
TRIM.FaTE Inputs
A-22
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-17. Mercury Chemical-Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value
Reference
Hg(0)
Hg(2)
MHg
Groundwater Compartment Type
Soil-water partition coefficient
L[water]/kg[soil
wet wt]
1,000
58,000
7,000
USEPA 1997b.
Demethylation rate
1/day
N/A
N/A
0.06
Range reported in Porvari and Verta 1995 is 3E-2
to 6E-2 /day; value is average maximum potential
demethylation rate constant under anaerobic
conditions.
Methylation rate
1/day
0
0.001
0
Range reported in Porvari and Verta 1995 is 2E-4
to 1E-3 /day; value is average maximum potential
methylation rate constant under anaerobic
conditions.
Oxidation rate
1/day
1.00E-08
0
0
Small default nonzero value (0 assumed in USEPA
1997 b).
Reduction rate
1/day
0
3.25E-06
0
Value used for tilled surface soil (20cm), 10%
moisture content, in USEPA 1997b (Lindberg
1996; Carpi and Lindberg 1997).
Surface Water Compartment Type
Algal surface area-specific uptake rate
constant
nmol/[|jm2-day-nmol]
0
2.04E-10
3.60E-10
Assumes radius = 2.5mm, Mason et al. 1995b,
Mason et al. 1996; Hg(0) assumed same as Hg(2).
Dow ("overall K0w")
L[water]/kg[octanol]
0
-a
-b
Mason et al. 1996.
Solids-water partition coefficient
L[water]/kg[solids
wet wt]
1,000
100,000
100,000
USEPA 1997b.
Vapor dry deposition velocity
m/day
N/A
2500
USEPA 1997b (Vol. Ill, App. A).
Demethylation rate
1/day
N/A
N/A
0.013
Average range of 1E-3 to 2.5E-2/day from Gilmour
and Henry 1991.
Methylation rate
1/day
0
0.001
0
Value used in EPA 1997; range is 1E-4 to 3E-
4/day (Gilmour and Henry 1991).
Oxidation rate
1/day
0
0
0
Assumption.
Reduction rate
1/day
0
0.0075
0
Value used in USEPA 1997b; reported values
range from less than 5E-3/day for depths greater
than 17m, up to 3.5/day (Xiao et al. 1995; Vandal
et al. 1995; Mason et al. 1995a; Amyot et al.
1997).
Attachment A A-23 October 2014
TRIM.FaTE Inputs
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-17. Mercury Chemical-Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value
Reference
Hg(0)
Hg(2)
MHg
Sediment Compartment Type
Solids-water partition coefficient
L[water]/kg[solids
wet wt]
3,000
50,000
3,000
USEPA 1997b.
Demethylation rate
1/day
N/A
N/A
0.0501
Average range of 2E-4 to 1 E-1/day from Gilmour
and Henry 1991.
Methylation rate
1/day
0
1.00E-04
0
Value used in EPA 1997b; range is 1E-5 to
1 E-3/day, Gilmour and Henry 1991.
Oxidation rate
1/day
0
0
0
Assumption.
Reduction rate
1/day
0
1.00E-06
0
Inferred value based on presence of Hg(0) in
sediment porewater (USEPA 1997b; Vandal et al.
1995).
aTRIM.FaTE Formula Property, which varies from 0.025 to 1.625 depending on pH and chloride concentration.
TRIM.FaTE Formula Property, which varies from 0.075 to 1.7 depending on pH and chloride concentration.
Attachment A
TRIM.FaTE Inputs
A-24
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-18. PAH Chemical-Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value
2Methyl
712DMB
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
200000
200000
200000
200000
200000
200000
200000
200000
200000
Surface Soil Compartment Type
User input characteristic depth
(Boolean)
0 = No, Else = Yes
0
0
0
0
0
0
0
0
0
Half-life
day
18
24
56
66.5
680
530
610
415
2140
Root Zone Soil Compartment Type
User input characteristic depth
(Boolean)
0 = No, Else = Yes
0
0
0
0
0
0
0
0
0
Half-life
day
18
24
56
66.5
680
530
610
415
2140
Vadose Zone Soil Compartment Type
User input characteristic depth
(Boolean)
0 = No, Else = Yes
0
0
0
0
0
0
0
0
0
Half-life
day
36
48
112
133
1360
1060
1220
830
4280
Groundwater Compartment Type
Half-life
day
36
48
112
133
1360
1060
1220
830
4280
Surface Water Compartment Type
Ratio Of Cone In Algae To
Cone Dissolved In Water
(g[chem]/kg[algae]) /
(g[chem]/L[water])
2.6
333.4
3
3.7
325
510
317
1539
473
Half-life
day
78
216
25
184
0.375
0.138
90
1670
62.4
Sediment Compartment Type
Half-life
day
2290
2290
2290
2290
2290
2290
2290
2290
2290
Attachment A
TRIM.FaTE Inputs
A-25
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-18. PAH Chemical-Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value
Reference
Chr
DahA
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), USEPA 1998 (7,12-
Dimethylbenz(a)anthracene, Benzo(g,h,i)perylene,
and Fluoranthene) / average of range, HSDB 2001 d
(Acenaphthene), HSDB 2001b (Acenaphthylene), and
Spero et al. 2000 (Fluorene).
Washout ratio
200000
200000
200000
200000
200000
Mackay et al. 1986.
Surface Soil Compartment Type
User input characteristic depth
(Boolean)
0 = No,
Else = Yes
0
0
0
0
0
Assumption.
Half-life
day
1000
940
275
33
730
MacKay et al. 2000 / average of range. Exceptions
include ATSDR 2005 (2-Methylnaphthalene), USEPA
1998 (7,12-Dimethylbenz(a)anthracene,
Benzo(g,h,i)perylene, and Fluoranthene) / average of
range, HSDB 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
Assumption.
Half-life
day
1000
940
275
33
730
Howard et al. 1991 / upper bound measured or
estimated value. Exceptions include ATSDR 2005 (2-
Methylnaphthalene), USEPA 1998 (7,12-
Dimethylbenz(a)anthracene, Benzo(g,h,i)perylene,
and Fluoranthene) / average of range, HSDB 2001 d
(Acenaphthene), HSDB 2001b (Acenaphthylene), and
HSDB 2001 e (Fluorene).
Vadose Zone Soil Compartment Type
User input characteristic depth
(Boolean)
0 = No, Else =
Yes
0
0
0
0
0
Assumption.
Attachment A
TRIM.FaTE Inputs
A-26
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-18. PAH Chemical-Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value
Reference
Chr
DahA
Fluora
n-thene
Fluor-
ene
IcdP
Vadose Zone Soil Compartment Type, continued
Half-life
day
2000
1880
550
66
1460
Howard et al. 1991 / upper bound measured or
estimated value. Exceptions include ATSDR 2005 (2-
Methylnaphthalene), USEPA 1998 (7,12-
Dimethylbenz(a)anthracene, Benzo(g,h,i)perylene, and
Fluoranthene) / twice average of range, HSDB 2001 d
(Acenaphthene) / multiplied by 2, HSDB 2001 b
(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), USEPA 1998 (7,12-
Dimethylbenz(a)anthracene, Benzo(g,h,i)perylene, and
Fluoranthene) / twice average of range, HSDB 2001 d
(Acenaphthene) / multiplied by 2, HSDB 2001 b
(Acenaphthylene) / multiplied by 2, and HSDB 2001 e
(Fluorene) / multiplied by 2.
Surface Water Compartment Type
RatioOfConclnAlgaeToConcD
issolvedlnWater
(g[chem]/kg[algae])/
(g[chem]/L[water])
280
1388
67.4
5.8
1653
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 2001 d
(Acenaphthene), HSDB 2001b (Acenaphthylene), and
HSDB 2001c (Benzo(g,h,i)perylene), Montgomery 2000
(Fluoranthene), and Boyle 1985 (Fluorene).
Sediment Compartment Type
Half-life
day
2290
2290
2290
2290
2290
Mackay et al. 1992 / PAH values are the mean half-life
of the log class that Mackay et al. assigned for
sediment, except for BbF and IcdP, which were not in
Table 2.3 of Mackay et. al.
Attachment A
TRIM.FaTE Inputs
A-27
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-19. Dioxin Chemical-Specific Parameters for Abiotic
Compartments in the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value
1,2,3,4,6,7,8,9
-OCDD
1,2,3,4,6,7,8,9
-OCDF
1,2,3,4,6,7,8-
HpCDD
1,2,3,4,6,7,8-
HpCDF
1,2,3,4,7,8,9-
HpCDF
1,2,3,4,7,8-
HxCDD
Air Compartment Type
Deposition Velocity
m/day
500
500
500
500
500
500
Half-life
day
162
321
64
137
122
42
Washout Ratio
m3[air]/m3[rain]
91000
22000
64000
32000
32000
9000
Surface Soil Compartment Type
Input characteristic depth
m
0.08
0.08
0.08
0.08
0.08
0.08
Use input characteristic
depth (Boolean)
0 = No, Else = Yes
0
0
0
0
0
0
Ha If 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
TRIM.FaTE Inputs
A-28
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-19. Dioxin Chemical-Specific Parameters for Abiotic
Compartments in the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value
1,2,3,4,7,8-
HxCDF
1,2,3,6,7,8-
HxCDD
1,2,3,6,7,8-
HxCDF
1,2,3,7,8,9-
HxCDD
1,2,3,7,8,9-
HxCDF
1,2,3,7,8-
PeCDD
Air Compartment Type
Deposition velocity
m/day
500
500
500
500
500
500
Half-life
day
78
28
55
28
51
18
Washout ratio
m3[air]/m3[rain]
10000
9000
10000
9000
10000
18000
Surface Soil Compartment Type
Input characteristic depth
m
0.08
0.08
0.08
0.08
0.08
0.08
Use input characteristic
depth (Boolean)
0 = No, Else = Yes
0
0
0
0
0
0
Half-life
day
3650
3650
3650
3650
3650
3650
Root Zone Soil Compartment Type
Input characteristic depth
m
0.08
0.08
0.08
0.08
0.08
0.08
Use input characteristic
depth
0 = No, Else = Yes
0
0
0
0
0
0
Half-life
day
3650
3650
3650
3650
3650
3650
Vadose Zone Soil Compartment Type
Input characteristic depth
m
0.08
0.08
0.08
0.08
0.08
0.08
Use input characteristic
depth (Boolean)
0 = No, Else = Yes
0
0
0
0
0
0
Half-life
day
1008
1008
1008
1008
1008
1008
Groundwater Compartment Type
Half-life
day
1008
1008
1008
1008
1008
1008
Surface Water Compartment Type
Ratio Of 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
TRIM.FaTE Inputs
A-29
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-19. Dioxin Chemical-Specific Parameters for Abiotic
Compartments in the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value
1,2,3,7,8-
PeCDF
2,3,4,6,7,8-
HxCDF
2,3,4,7,8-
PeCDF
2,3,7,8-TCDD
2,3,7,8-TCDF
Air Compartment Type
Deposition velocity
m/day
500
500
500
500
500
Half-life
day
31
59
33
12
19
Washout ratio
m3[air]/m3[rain]
13000
10000
14000
18000
19000
Surface Soil Compartment Type
Input characteristic depth
m
0.08
0.08
0.08
0.08
0.08
Use input characteristic depth
(Boolean)
0 = No, Else = Yes
0
0
0
0
0
Half-life
day
3650
3650
3650
3650
3650
Root Zone Soil Compartment Type
Input characteristic depth
m
0.08
0.08
0.08
0.08
0.08
Use input characteristic depth
0 = No, Else = Yes
0
0
0
0
0
Half-life
day
3650
3650
3650
3650
3650
Vadose Zone Soil Compartment Type
Input characteristic depth
m
0.08
0.08
0.08
0.08
0.08
Use input characteristic depth
(Boolean)
0 = No, Else = Yes
0
0
0
0
0
Half-life
day
1008
1008
1008
1008
1008
Groundwater Compartment Type
Half-life
day
1008
1008
1008
1008
1008
Surface Water Compartment Type
Ratio Of 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
TRIM.FaTE Inputs
A-30
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-19. Dioxin Chemical-Specific Parameters for Abiotic
Compartments in the TRIM.FaTE Screening Scenario
Parameter Name
Reference
Air Compartment Type
Deposition velocity
McKone et al. 2001.
Half-life
Atkinson 1996 as cited in USEPA 2000b; vapor phase
reaction with hydroxyl radical.
Washout ratio
Vulykh et al. 2001.
Surface Soil Compartment Type
Input characteristic depth
Not used (model set to calculate value).
Use input characteristic depth (Boolean)
Assumption.
Half-life
Mackay et al. 2000; the degradation rate was cited by
multiple authors, value is for2,3,7,8-TCDD.
Root Zone Soil Compartment Type
Input characteristic depth
Not used (model set to calculate value).
Use input characteristic depth
Assumption.
Half-life
Mackay et al. 2000; the degradation rate was cited by
multiple authors, value is for2,3,7,8-TCDD.
Vadose Zone Soil Compartment Type
Input characteristic depth
Not used (model set to calculate value).
Use input characteristic depth (Boolean)
Assumption.
Half-life
Average value of the range presented in Mackay et al. 2000;
based on estimated unacclimated aerobic biodegradation
half-life, value is for2,3,7,8-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 for2,3,7,8-TCDD.
Surface Water Compartment Type
Ratio Of Cone In Algae To Cone Dissolved
In Water
Estimated from K0w value using model from DelVento and
Dachs 2002
Half-life
Kim and O'Keefe 1998, as cited in USEPA 2000b.
Sediment Compartment Type
Half-life
Estimation based on Adriaens and Grbic-Galic 1992,1993
and Adriaens et al. 1995 as cited in USEPA 2000b.
Attachment A
TRIM.FaTE Inputs
A-31
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-20. Cadmium Chemical-Specific Parameters for
Plant Compartments in the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value
Reference
Leaf Compartment Type
Transfer factor to leaf particle
1/day
0.002
Assumption.
Particle on Leaf Compartment Type
Transfer factor to leaf
1/day
0.200
Assumption.
Root Compartment Type - Grasses and Herbsa
Root to Root Soil Partition-
Alpha of Steady State
unitless
0.95
Henning et al. 2001.
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.
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
TRIM.FaTE Inputs
A-32
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-21. Mercury Chemical-Specific Parameters for Plant Compartments in the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value
Reference
Hg(0)
Hg(2)
MHg
Leaf Compartment Type
Transfer factor to leaf particle
1/day
0.002
0.002
0.002
Assumed based on 1% of transfer factor
from leaf particle to leaf.
Demethylation rate
1/day
N/A
N/A
0.03
Calculated from Bache et al. 1973.
Methylation rate
1/day
0
0
0
Assumed from Gay 1975, Bache et al.
1973.
Oxidation rate
1/day
1.0E+06
0
0
Assumed to be nearly instantaneous
Reduction rate
1/day
0
0
0
Assumption.
Particle on Leaf Compartment Type
Transfer factor to leaf
1/day
0.2
0.2
0.2
Assumption.
Demethylation rate
1/day
N/A
N/A
0
Assumption.
Methylation rate
1/day
0
0
0
Assumption.
Oxidation rate
1/day
0
0
0
Assumption.
Reduction rate
1/day
0
0
0
Assumption.
Root Compartment Type - Grasses and Herbsa
Alpha for root-root zone bulk soil
unitless
0.95
0.95
0.95
Selected value.
Root/root-zone-soil-water partition
coefficient
m3[bulk root soil]/ m3[root]
0
0.18
1.2
Hg2- geometric mean Leonard et al. 1998,
John 1972, Hogg et al. 1978; MHg-
assumed, based on Hogg et al. 1978.
t-alpha for root-root zone bulk soil
day
21
21
21
Assumption.
Demethylation rate
1/day
N/A
N/A
0
Assumption.
Methylation rate
1/day
0
0
0
Assumption.
Oxidation rate
1/day
0
0
0
Assumption.
Reduction rate
1/day
0
0
0
Assumption.
Stem Compartment Type - Grasses and Herbsa
Transpiration stream concentration
factor (TSCF)
m3[soil pore
water]/m3[xylem fluid]
0
0.5
0.2
Calculation from Norway spruce, Scots
pine, Bishop et al. 1998.
Demethylation rate
1/day
N/A
N/A
0.03
Calculated from Bache et al. 1973.
Methylation rate
1/day
0
0
0
Assumption.
Oxidation rate
1/day
0
0
0
Assumption.
Reduction rate
1/day
0
0
0
Assumption.
aRoots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE.
Attachment A
TRIM.FaTE Inputs
A-33
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-22. PAH Chemical-Specific Parameters for Plant Compartments in the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value
2Methyl
712DMB
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
3.50
3.50
3.50
3.50
3.50
3.50
3.50
3.50
3.50
Particle on Leaf Compartment Type
Transfer factor to
leaf
1/day
1.00E-04
1.00E-04
1.00E-04
1.00E-04
1.00E-04
1.00E-04
1.00E-04
1.00E-04
1.00E-04
Half-life
day
2.31
2.31
2.31
2.31
1.84
2.31
3.56
2.31
17.80
Root Compartment Type - Grasses and Herbsa
Half-life
day
34.60
34.60
34.60
34.60
34.60
34.60
34.60
34.60
34.60
Root soil-water
interaction - alpha
unitless
0.95
0.95
0.95
0.95
0.95
0.95
0.95
0.95
0.95
Stem Compartment Type - Grasses and Herbsa
Half-life
day
3.50
3.50
3.50
3.50
3.50
3.50
3.50
3.50
3.50
aRoots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE.
Attachment A
TRIM.FaTE Inputs
A-34
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-23. PAH Chemical-Specific Parameters for Plant Compartments in the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value
Reference
Chr
DahA
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
3.50
3.50
3.50
3.50
3.50
Edwards 1988 (calculated from metabolic rate
constant).
Particle on Leaf Compartment Type
Transfer factor to
leaf
1/day
1.00E-04
1.00E-04
1.00E-04
1.00E-04
1.00E-04
Assumption.
Half-life
day
4.12
17.80
2.31
2.31
17.80
Calculated as 2 times the measured photolysis
half-life from Mackay et al. 1992. Exceptions
include values that have been set equal to
Benzo(a)pyrene (2-Methylnaphthalene; 7,12-
Dimethylbenz(a)anthracene; Acenaphthene;
Acenaphthylene; Benzo(ghi)perylene;
Fluoranthene; and Fluorene).
Root Compartment Type - Grasses and Herbsa
Half-life
day
34.60
34.60
34.60
34.60
34.60
Edwards 1988 (calculated from metabolic rate
constant).
Root soil water
interaction - alpha
unitless
0.95
0.95
0.95
0.95
0.95
Assumption.
Stem Compartment Type - Grasses and Herbsa
Half-life
day
3.50
3.50
3.50
3.50
3.50
Edwards 1988 (calculated from metabolic rate
constant).
aRoots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE.
Attachment A
TRIM.FaTE Inputs
A-35
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-23. Dioxin Chemical-Specific Parameters for Plant Compartments in the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value
Reference
All Dioxiris
Leaf Compartment Type
Transfer factor to leaf particle
1/day
0.003
Calculated as 1 percent of transfer factor to leaf; highly uncertain.
Half-life
day
70
Arjmand and Sandermann 1985, as cited in Komoba et al. 1995;
soybean root cell culture metabolism test data for DDE.
Particle on Leaf Compartment Type
Transfer factor to leaf
1/day
0.3
Assumption based on USEPA 2000b (an estimate for mercury) and
Trapp 1995; highly uncertain.
Half-life
day
4.4
McCrady and Maggard 1993; photodegradation sorbed to grass
foliage in sunlight; assumed 10% sunlight per day.
Root Compartment Type - Grasses and Herbsa
Half-life
day
70
Arjmand and Sandermann 1985, as cited in Komoba et al. 1995;
soybean root cell culture metabolism test data for DDE.
Root soil-water interaction - alpha
unitless
0.95
Assumption.
Stem Compartment Type - Grasses and Herbsa
Half-life
day
70
Arjmand and Sandermann 1985, as cited in Komoba et al. 1995;
soybean root cell culture metabolism test data for DDE.
aRoots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE.
Attachment A
TRIM.FaTE Inputs
A-36
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-24. Cadmium Chemical-Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value
Reference
Zooplankton Compartment Type
Absorption rate constant
L[water]/kg[fish wet wt]-day
1500
Goulet 2007.
Assimilation efficiency from algae
unitless
0.5
Goulet 2007.
Elimination rate constant
1/day
0.03
Goulet 2007.
Benthic Invertebrate Compartment Type
Sediment partitioning - alpha of
equilibrium
unitless
0.95
Assumption.
Sediment partitioning - partition
coefficient
kg[bulk sed/kg[invertebrate wet wt]
0.27
Assumption.
Sediment partitioning - time to reach
alpha of equilibrium
day
21
Hare et al. 2001.
Benthic Omnivore Compartment Type
Assimilation efficiency from food
unitless
0.1
Assumption based on Yan and Wang 2002.
Absorption rate constant
unitless
1.23
Calculated based on body weight from regression
in Hendriks and Heikens 2001.
Elimination rate constant
unitless
1.73E-02
Assumption.
Benthic Carnivore Compartment Type
Assimilation efficiency from food
unitless
0.1
Assumption based on Yan and Wang 2002.
Absorption rate constant
unitless
0.66
Calculated based on body weight from regression
in Hendriks and Heikens 2001.
Elimination rate constant
unitless
1.68E-03
Assumption.
Water-column Herbivore Compartment Type
Assimilation efficiency from food
unitless
0.1
Assumed value based on Yan and Wang 2002.
Assimilation efficiency from plants
unitless
0.1
Assumed value based on Yan and Wang 2002.
Absorption rate constant
unitless
2.46
Calculated based on body weight from regression
in Hendriks and Heikens 2001.
Elimination rate constant
unitless
1.73E-02
Assumption.
Attachment A
TRIM.FaTE Inputs
A-37
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-24. Cadmium Chemical-Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value
Reference
Water-column Omnivore Compartment Type
Assimilation efficiency from food
unitless
0.1
Assumption based on Yan and Wang 2002.
Assimilation efficiency from plants
unitless
0.1
Assumption based on Yan and Wang 2002.
Absorption rate constant
unitless
1.23
Calculated based on body weight from regression
in Hendriks and Heikens 2001.
Elimination rate constant
unitless
1.73E-02
Assumption.
Water-column Carnivore Compartment Type
Assimilation efficiency from food
unitless
0.1
Assumption based on Yan and Wang 2002.
Absorption rate constant
unitless
0.66
Calculated based on body weight from regression
in Hendriks and Heikens 2001.
Elimination rate constant
unitless
1.73E-02
Assumption
Attachment A
TRIM.FaTE Inputs
A-38
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-25. Mercury Chemical-Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value
Reference
Hg(0)
Hg(2)
MHg
Zooplankton Compartment Type
Assimilation Efficiency From Algae
unitless
0.2
0.015
0.5
Environment Canada 2002.
Half-life
day
1.0E+09
1.0E+09
1.0E+09
Assumption.
How Much Faster Hg Elimination Is Than
For MHg
unitless
3
3
1
Assumption.
Methylation rate
1/day
0
0
0
Assumption.
Oxidation rate
1/day
0
1.0E+06
0
Assumption.
Reduction rate
1/day
0
0
0
Assumption.
Benthic Invertebrate Compartment Type
Alpha of equilibrium for sediment
partitioning
unitless
0.95
0.95
0.95
Selected value (i.e., proportion of
equilibrium achieved by time "t").
Benthic invertebrate-bulk sediment partition
coefficient
kg[bulk
sediment]/kg[invertebrate
wet wt]
0.0824
0.0824
5.04
Hg(0) - assumed based on Hg(2)
value; Hg(2) and MHg - Saouter
etal. 1991.
t-alpha for equilibrium for sediment
partitioning
day
14
14
14
Experiment duration from
Saouter et al. 1991.
All Fish Compartments Typesa
Elimination adjustment factor
unitless
3
3
1
Trudel and Rasmussen 1997.
Assimilation efficiency from food
unitless
0.06
0.06
0.5
Williams et al. 2010.
Demethylation rate
1/day
N/A
N/A
0
Assumption.
Methylation rate
1/day
0
0
0
Assumption.
Oxidation rate
1/day
1.0E+06
0
0
Assumption.
Reduction rate
1/day
0
0
0
Assumption.
Water-column Herbivore Compartment Type
Assimilation efficiency from plankton
unitless
0.06
0.06
0.5
Williams et al. 2010.
Screening scenario includes: Benthic Omnivore, Benthic Carnivore, Water-column Herbivore, Water-column Omnivore, and Water-column Carnivore.
Attachment A
TRIM.FaTE Inputs
A-39
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-26. PAH Chemical-Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario
Parameter
Name
Value
Units
2Methyl
712DMB
Acenaph-
thene
Acenaph-
thylene
BaA
BaP
BbF
BghiP
BkF
Zooplankton Compartment Type
Absorption rate
constant
L[water]/kg[fish
wet wt]-day
790
42650.94
42231
42302.18
42650.81
42652.78
42650.68
42655.77
42652.5
Assimilation
efficiency from
algae
unitless
0.5
0.25
0.5
0.5
0.46
0.25
0.25
0.25
0.25
Elimination
rate constant
1/day
169.68
2.03
148.07
123.44
2.073
1.3864
2.12
0.33
1.48
Half-life
day
0.007788
17
0.00239
0.00239
1.284
16.5
17
17
17
Benthic Invertebrate Compartment Type
Clearance
constant
unitless
100.6
100.6
100.6
100.6
100.6
100.6
100.6
100.6
100.6
Vd (ratio of
concentration
in benthic
invertebrates
to
ml/g
7235
7235
7235
7235
7235
7235
7235
7235
7235
concentration
in water)
Half-life
day
0.722
17
0.722
0.722
1.284
16.5
17
17
17
All Fish Compartment Typesa
Gamma fish
unitless
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
Assimilation
efficiency from
food
unitless
0.5
0.15
0.5
0.32
0.15
0.15
0.15
0.15
0.15
Half-life
day
0.2
2
0.2
0.2
0.408
1.925
2
2
2
Screening scenario includes: Benthic Omnivore, Benthic Carnivore, Water-column Herbivore, Water-column Omnivore, and Water-column Carnivore.
Attachment A
TRIM.FaTE Inputs
A-40
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-26. PAH Chemical-Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value
Reference
Chr
DahA
Fluoran-
thene
Fluorene
IcdP
Zooplankton Compartment Type
Absorption rate
constant
L[water]/kg[fish
wet wt]-day
42649.95
42655.48
142000
15000
42655.93
Kow from Arnot et al. 2004.
Exception is Berrojalbiz et al. 2009
(2-Methylnaphthalene,
Fluoranthene, and Fluorene).
Assimilation
efficiency from algae
unitless
0.46
0.25
0.49
0.5
0.25
Kow from Arnot et al. 2004.
Exception is maximum value from
Wang and Wang 2006 (7,12-
Dimethylbenz(a)anthracene,
Benzo(a)pyrene,
Benzo(b)fluoranthene,
Benzo(g,h,i)perylene,
Benzo(k)fluoranthene,
Dibenz(a,h)anthracene, and
lndeno(1,2,3-cd)pyrene).
Elimination rate
constant
1/day
2.3746
0.4331
8.678
81.87
0.269
Kow from Arnot et al. 2004.
Half-life
day
0.495
17
0.00239
0.0002476
17
McElroy 1990. Exceptions include
Berrojalbiz et al. 2009 (2-
Methylnaphthalene, Fluoranthene,
and Fluorene) and Moermond et al.
2007 (Benz(a)anthracene and
Benzo(a)pyrene).
Benthic Invertebrate Compartment Type
Clearance constant
unitless
100.6
100.6
100.6
100.6
100.6
Stehly et al. 1990.
Vd (ratio of
concentration
in benthic
invertebrates to
concentration in
water)
ml/g
7235
7235
7235
7235
7235
Stehly et al. 1990.
Half-life
day
0.495
17
0.722
0.722
17
Moermond et al. 2007.
Attachment A
TRIM.FaTE Inputs
A-41
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-26. PAH Chemical-Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value
Reference
Chr
DahA
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
Lemairetal. 1992. Exceptions
include Barber 2008 (2-
Methylnaphthalene and
Acenaphthene) and Niimi and
Palazzo 1986 (Acenaphthylene,
Fluoranthene, and Fluorene).
Half-life
day
0.533
2
0.165
0.2
2
Moermond et al. 2007.
Screening scenario includes: Benthic Omnivore, Benthic Carnivore, Water-column Herbivore, Water-column Omnivore, and Water-column Carnivore.
Attachment A
TRIM.FaTE Inputs
A-42
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-27. Dioxin Chemical-Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value
1,2,3,4,6,7,8,9-
OCDD
1,2,3,4,6,7,8,9-
OCDF
1,2,3,4,6,7,8-
HpCDD
1,2,3,4,6,7,8-
HpCDF
1,2,3,4,7,8,9-
HpCDF
1,2,3,4,7,8-
HxCDD
1,2,3,4,7,8-
HxCDF
1,2,3,6,7,8-
HxCDD
1,2,3,6,7,8-
HxCDF
Zooplankton Compartment
Absorption rate constant
L[water]/kg[fish
wet wt]-day
8640
8640
8640
8640
8640
8640
8640
8640
8640
Assimilation efficiency from algae
unitless
0.08
0.05
0.21
0.09
0.2
0.31
0.31
0.31
0.31
Elimination rate constant
1/day
0.0102
0.016
0.016
0.0616
0.1829
0.0252
0.1474
0.0099
0.0194
Half-life
day
7E+06
7E+06
7E+06
7E+06
7E+06
7E+06
7E+06
7E+06
7E+06
Benthic Invertebrate Compartment
Clearance constant
unitless
0
0
0
0
0
0
0
0
0
Sediment partitioning partition
coefficient
kg/kg
0.0013
0.0017
0.0055
0.0012
0.042
0.033
0.0081
0.013
0.02
Sediment partitioning alpha of
equilibrium
unitless
0.95
0.95
0.95
0.95
0.95
0.95
0.95
0.95
0.95
Sediment partitioning time to
reach alpha of equilibrium
days
120
42
120
42
42
120
42
120
42
Vd (ratio of concentration
in benthic invertebrates to
concentration in water)
ml/g
0
0
0
0
0
0
0
0
0
Half-life
day
5776.2
5776.2
5776.2
5776.2
5776.2
5776.2
5776.2
5776.2
5776.2
All Fish Compartmentsa
Assimilation efficiency from food
unitless
0.08
0.05
0.21b
0.09
0.2
0.31c
0.31
0.31
0.31
Chemical uptake rate via gill
L[water]/kg[fish
wet wt]-day
11
6
56
25
50
102
200
300
200
Half-life
day
70
70
70
70
70
70
70
70
70
Attachment A
TRIM.FaTE Inputs
A-43
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-27. Dioxin Chemical-Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario
Parameter Name
Units
Value
1,2,3,7,8,9-
HxCDD
1,2,3,7,8,9-
HxCDF
1,2,3,7,8-
PeCDD
1,2,3,7,8-
PeCDF
2,3,4,6,7,8-
HxCDF
2,3,4,7,8-
PeCDF
2,3,7,8-TCDD
2,3,7,8-TCDF
Zooplankton Compartment
Absorption Rate Constant
L[water]/kg[fish wet
wt]-day
8640
8640
8640
8640
8640
8640
8640
8640
Assimilation Efficiency from Algae
unitless
0.31
0.31
0.42
0.42
0.31
0.42
0.41
0.51
Elimination Rate Constant
1/day
0.0099
0.0413
0.0819
0.2316
0.0192
0.4331
0.2268
1.0375
Half-life
day
7E+06
7E+06
7E+06
7E+06
7E+08
7E+08
7E+06
7E+08
Benthic Invertebrate Compartment
Clearance Constant
unitless
0
0
0
0
0
0
0
0
Sediment Partitioning Partition
Coefficient
kg/kg
0.015
0.067
0.098
0.024
0.072
0.17
0.205
0.056
Sediment Partitioning Alpha of
Equilibrium
unitless
0.95
0.95
0.95
0.95
0.95
0.95
0.95
0.95
Sediment Partitioning Time to
Reach Alpha of Equilibrium
days
120
42
120
42
42
42
120
42
Vd (ratio of concentration
in benthic invertebrates to
concentration in water)
ml/g
0
0
0
0
0
0
0
0
Half-life
day
5776.2
5776.2
5776.2
5776.2
5776.2
5776.2
5776.2
5776.2
All Fish Compartmentsa
Assimilation efficiency from food
unitless
0.31
0.31
0.42
0.42
0.31
0.42
0.41
0.51
Chemical uptake rate via gill
L[water]/kg[fish wet
wt]-day
300
200
700
300
200
400
600
400
Half-life
day
70
70
70
70
70
70
70
70
Attachment A
TRIM.FaTE Inputs
A-44
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit A-27. Dioxin Chemical-Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario
Parameter Name
Units
Reference
Zooplankton Compartment
Absorption rate constant
L[water]/kg[fish
wet wt]-day
Zhang et al. 2011; used copepod ku value.
Assimilation efficiency from algae
unitless
Morrison et al. 1999. Exceptions include Niimi and Oliver 1986 (1,2,3,4,6,7,8,9-
OCDD, 1,2,3,4,6,7,8,9-OCDF), Berntssen et al. 2007 (1,2,3,4,6,7,8-HpCDD,
1,2,3,4,6,7,8-HpCDF), and 1,2,3,4,7,8,9-HpCDF set conservatively as approximate
linear interpolation between values for 1,2,3,4,7,8-HxCDD and 1,2,3,4,6,7,8-HpCDD
/1,2,3,4,6,7,8-HpCDF (i.e., 0.3 to 0.1-0.2).
Elimination rate constant
1/day
Arnot and Gobas 2004; used K0w value.
Half-life
day
Morrison et al. 1999; used metabolic rates for invertebrates.
Benthic Invertebrate Compartment
Clearance constant
unitless
Assumption.
Sediment partitioning partition coefficient
kg/kg
Rubinstein et al. 1990; used TCDD data forsandworm.
Sediment partitioning alpha of equilibrium
unitless
Rubinstein et al. 1990.
Sediment Partitioning Time to Reach
Alpha of Equilibrium
days
Rubinstein et al. 1990.
Vd (ratio of concentration in benthic
invertebrates to concentration in water)
ml/g
Assumption.
Half-life
day
Rubinstein et al. 1990; used TCDD data forsandworm.
All Fish Compartmentsa
Assimilation Efficiency from Food
unitless
Morrison et al. 1999. Exceptions include Niimi and Oliver 1996 (1,2,3,4,6,7,8,9-
OCDD, 1,2,3,4,6,7,8,9-OCDF), Van den Berg et al. 1994 (1,2,3,4,6,7,8-HpCDD),
Berntssen et al. 2007 ( 1,2,3,4,6,7,8-HpCDF), and 1,2,3,4,7,8,9-HpCDF set
conservatively as approximate linear interpolation between values for 1,2,3,4,7,8-
HxCDD and 1,2,3,4,6,7,8-HpCDD /1,2,3,4,6,7,8-HpCDF (i.e., 0.3 to 0.1-0.2).
Chemical Uptake Rate Via Gill
L[water]/kg[fish
wet wt]-day
Muiretal. 1985. Exception is Opperhuizen et al. 1986 (1,2,3,7,8,9-HxCDF, 1,2,3,7,8-
PeCDD, 1,2,3,7,8-PeCDF, 2,3,4,7,8-PeCDF, 2,3,7,8-TCDD, 2,3,7,8-TCDF).
Half-life
day
Berntssen et al. 2007.
Screening scenario includes: Benthic Omnivore, Benthic Carnivore, Water-column Herbivore, Water-column Omnivore, and Water-column Carnivore.
b0.21 used for benthic omnivore, water column omnivore, and water column herbivore; 0.13 used for benthic carnivore and water column carnivore.
°0.31 used for benthic omnivore, water column omnivore, benthic carnivore, and water column carnivore; 0.37 used for water column herbivore.
Attachment A
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A-45
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
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Porvari, P., and M. Verta. 1995. Methylmercury production in flooded soils: A laboratory study. Water,
Air, and Soil Pollution 80:765-773.
Riederer, M. 1995. Partitioning and transport of organic chemicals between the atmospheric
environment and leaves. In: Trapp, S. and J. C. McFarlane, eds. Plant contamination: Modeling and
simulation of organic chemical processes. Boca Raton, FL: Lewis Publishers, pp. 153-190.
Rubinstein, N.I., R.J. Pruell, B.K. Taplin, J.A. LiVoIsi, and C.B. Norwood. 1990. Bioavailability of 2,3,7,8-
TCDD, 2,3,7,8-TCDF and PCBs to marine benthos from Passaic river sediments. Chemosphere
20:1097-1102.
Sangster, J. (1993) LOGKOW, A Databank of Evaluated Octanol-Water Partition Coefficients. 1st
Edition, Montreal, Quebec, Canada.
Saouter, E., F. Ribeyre, A. Boudou, and R. Maurybrachet. 1991. Hexagenia rigida (Ephemeroptera) as
a biological model in aquatic ecotoxicology - Experimental studies on mercury transfers from
sediment. Environmental Pollution 69:51-67.
Attachment A A-51 October 2014
TRIM.FaTE Inputs
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TRIM-Based Tiered Screening Methodology for RTR
Sijm, D.T.H.M., H. Wever, P.J. de Vries, and A. Opperhuizen. 1989. Octan-1-ol/water partition
coefficients of polychlorinated dibenzo-p-dioxins and dibenzofurans: experimental values
determined with a stirring method. Chemosphere 19(1-6):263-266.
Simonich, S.L., and R.A. Hites. 1994. Importance of vegetation in removing polycyclic aromatic
hydrocarbons from the atmosphere. Nature 370:49-51.
Spero, J., B. DeVito, and L. Theodore, [eds.]. 2000. Regulatory chemicals handbook. New York, NY:
CRC Press, p. 1072.
Stehly, G.R., P.F. Landrum, M.G. Henry, and C. Klemm. 1990. Toxicokinetics of PAHs in Hexagenia
Environmental Toxicology and Chemistry 9:167-174.
Thibodeaux, L.J. 1996. Environmental chemodynamics: Movement of chemicals in air, water, and soil.
New York, NY: John Wiley and Sons, Inc.
Thomann, R.V. 1989. Bioaccumulation model of organic-chemical distribution in aquatic food-chains.
Environmental Science and Technology 23(6):699-707.
Trapp, S. 1995. Model for uptake of xenobiotics into plants. In: Trapp, S. and J. C. McFarlane, eds.
Plant contamination: Modeling and simulation of organic chemical processes. Boca Raton, FL:
Lewis Publishers, pp. 107-151.
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.
USEPA (U.S. Environmental Protection Agency). 2007. Draft risk assessment for the Siemens Water
Technologies Corp. carbon reactivation facility Parker, Arizona. Appendix F. Chemical-physical
parameters for compounds not in USEPA's HHRAP. Available at:
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USEPA. 2005. Human Health Risk Assessment Protocol for Hazardous Waste Combustion Facilities.
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(OPPT). Available at: http://www.epa.gov/oppt/exposure/docs/episuitedl.htm.
USEPA. 2000b. Draft exposure and human health reassessment of 2,3,7,8-tetrachlorodibenzo-p-dioxin
TCDD) and related compounds, Volume 3: Properties, environmental levels, and background
exposures, Chapter 2: Physical and chemical properties and fate and Appendix A. EPA/600/P-
00/001 Be. Available at: http://www.epa.gov/ncea/pdfs/dioxin/part1/volume3/chap-2.pdf.
USEPA. 1999. Screening level ecological risk assessment protocol for hazardous waste combustion
facilities. Peer Review Draft, November. Available at:
http://www.epa.gov/epaoswer/hazwaste/combust/ecorisk.htm
Attachment A
TRIM.FaTE Inputs
A-52
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
USEPA. 1998. Chemical fate half-lives for Toxics Release Inventory (TRI) chemicals. July. SRC TR 98-
008.
USEPA. 1997a. Supplemental background document; Nongroundwater pathway risk assessment;
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USEPA. 1997b. U.S. Environmental Protection Agency. 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.
Van den Berg, M., J. De Jongh, H. Poiger, and J.R. Olson. 1994. The toxicokinetics and metabolism of
polychlorinated dibenzo-p-dioxins (PCDDs) and dibenzofurans (PCDFs) and their relevance for
toxicity. CRC Critical Reviews in Toxicology 24(1): 1-74.
Vandal, G.M., W.F. Fitzgerald, K.R. Rolfhus, and C.H.Lamborg. 1995. Modeling the elemental mercury
cycle in Pallette Lake, Wisconsin, USA. Water, Air, and Soil Pollution 80:789-798.
Vulykh, N., and V. Shatalov. 2001. Investigation of Dioxin/Furan Composition in Emissions and in
Environmental Media: Selection of Congeners for Modeling. Technical Note 6/2001. Meteorological
Synthesizing Center - East.
Wang, X., and W.X. Wang. 2006. Bioaccumulation and transfer of benzo (a) pyrene in a simplified
marine food chain. Marine Ecology Progress Series 312:101-111.
Williams, J.J., J. Dutton, C.Y. Chen, and N.S. Fisher. 2010. Metal (As, Cd, Hg, and CHaHg)
bioaccumulation from water and food by the benthic amphipod Leptocherius plumulosus.
Environmental Toxicology and Chemistry 29(8): 1755-1761.
Wilmer, C., and M. Fricker. 1996. Stomata. Second ed. New York, NY: Chapman and Hall. p. 121.
Wl DNR (Wisconsin Department of Natural Resources) 2007. A Guide to Understanding the Hydrologic
Condition of Wisconsin's Lake Superior Watersheds. Wisconsin Department of Natural Resources
(DNR).
Xiao, Z.F., D. Stromberg, and O. Lindqvist. 1995. Influence of humic substances on photolysis of
divalent mercury in aqueous solution. Water, Air, and Soil Pollution 80:789-798.
Yan, X; and Wang, WX. 2002. Exposure and potential food chain transfer factor of Cd, Se and Zn in
marine fish Lutjanus argentimaculatus. Marine Ecology Progress Series 238: 173-186.
Zhang, Q., L. Yang, and W.X. Wang. 2011. Bioaccumulation and trophic transfer of dioxins in marine
copepods and fish. Environmental Pollution 159(12):3390-3397.
Attachment A
TRIM.FaTE Inputs
A-53
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TRIM-Based Tiered Screening Methodology for RTR
Attachment B. Description of Multimedia Ingestion Risk Calculator
(MIRC) Used for RTR Exposure and Risk Estimates
Attachment B
Description of MIRC
i
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CONTENTS, ATTACHMENT B
1. Introduction B-1
1.1. Purpose and Overview B-1
1.2. Scope of MlRC B-1
1.3. Use in EPA's Air Toxics Program B-2
1.4. MIRC Highlights B-2
1.5. Organization of This Attachment B-3
2. MIRC Overview B-3
2.1. Software B-4
2.2. Exposure Pathways B-6
2.3. Receptor Groups B-7
3. Exposure Algorithms B-9
3.1. Farm Food Chain Algorithms B-9
3.1.1. Estimating Chemical Concentrations in Produce B-10
3.1.2. Estimating Chemical Concentrations in Animal Products B-16
3.2. Chemical Intake Calculations for Adults and Non-Infant Children B-19
3.2.1. Chemical Intake from Soil Ingestion B-21
3.2.2. Chemical Intake from Fish Ingestion B-21
3.2.3. Chemical Intake from Fruit Ingestion B-23
3.2.4. Chemical Intake from Vegetable Ingestion B-23
3.2.5. Chemical Intake from Animal Product Ingestion B-25
3.2.6. Chemical Intake from Drinking Water Ingestion B-27
3.3. Total Chemical Intake B-27
3.4. Chemical Intake Calculations for Nursing Infants B-28
3.4.1. Infant Average Daily Absorbed Dose B-29
3.4.2. Chemical Concentration in Breast Milk Fat B-30
3.4.3. Chemical Concentration in Aqueous Phase of Breast Milk B-33
3.4.4. Alternative Model for Infant Intake of Methyl Mercury B-35
4. Dose-Response Values Used for Assessment B-36
4.1. Cadmium B-39
4.2. Dioxins (2,3,7,8-TCDD) B-39
4.3. Mercury B-40
4.4. Polycyclic Organic Matter B-40
5. Risk Estimation B-42
5.1. Cancer Risks B-42
5.2. Non-cancer Hazard Quotients B-44
5.2.1. Hazard Quotients for Chemicals with a Chronic RfD B-45
Attachment B /'/'/ October 2014
Description of MIRC
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TRIM-Based Tiered Screening Methodology for RTR
5.2.2. Hazard Quotients for Chemicals with RfD Based on
Developmental Effects B-45
5.2.3. Hazard Index for Chemicals with RfDs B-45
6. Model Input Options B-46
6.1. Environmental Concentrations B-47
6.2. Farm-Food-Chain Parameter Values B-47
6.2.1. List of Farm-Food-Chain (FFC) Parameters B-47
6.2.2. Produce Parameter Values B-49
6.2.3. Animal Product Parameter Values B-62
6.3. Adult and Non-Infant Exposure Parameter Values B-64
6.3.1. Body Weights B-65
6.3.2. Water Ingestion Rates B-66
6.3.3. Local Food Ingestion Rates B-66
6.3.4. Local Fish Ingestion Rates B-70
6.3.5. Soil Ingestion Rates B-77
6.3.6. Total Food Ingestion Rates B-77
6.4. Other Exposure Factor Values B-78
6.4.1. Exposure Frequency B-78
6.4.2. Fraction Contaminated B-79
6.4.3. Preparation and Cooking Losses B-79
6.4.4. Food Preparation/Cooking Adjustment Factor (FPCAF) for
Fish B-81
6.5. Breast-Milk Infant Exposure Pathway Parameter Values B-82
6.5.1. Receptor-specific Parameters B-82
6.5.2. Chemical-Specific Parameter Values B-86
7. Summary of MIRC Default Exposure Parameter Settings B-89
7.1. Default Ingestion Rates B-89
7.1.1. Fish Ingestion Rates B-90
7.1.2. Farm Food Chain Ingestion B-90
7.2. Default Screening-Level Population-Specific Parameter Values B-92
7.3. Default Chemical-Specific Parameter Values for Screening Analysis B-93
7.4. Screening-Level Parameter Values for Nursing Infant Exposure B-94
7.4.1. DioxinsB-94
7.4.2. Methyl Mercury B-95
8. References B-96
Attachment B iv October 2014
Description of MIRC
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TRIM-Based Tiered Screening Methodology for RTR
Exhibits, Attachment B
Exhibit B-1. Overview of MIRC Software Application for Performing Farm-Food-
Chain Ingestion Exposure and Risk Calculations B-5
Exhibit B-2. Transfer Pathways for Modeled Farm Food Chain (FFC) Media B-7
Exhibit B-3. Chemical Transfer Pathways for Produce B-10
Exhibit B-4. Estimating Chemical Concentration in Aboveground Produce B-10
Exhibit B-5. Chemical Transfer Pathways for Animal Products B-16
Exhibit B-6. Oral Dose-response Values Used to Calculate RTR Screening
Threshold Emission Rates for PB-HAP Chemicals3 B-37
Exhibit B-7. WHO 2005 Toxic Equivalency Factors (TEFs) for Dioxin Congeners B-40
Exhibit B-8. Oral Dose-response Values for Polycyclic Organic Matter (POM)
Groups3 B-41
Exhibit B-9. MIRC Parameters Used to Estimate Chemical Concentrations in
Farm Foods B-48
Exhibit B-10. Chemical-Specific Inputs for Produce Parameters for Chemicals
Included in MIRC B-49
Exhibit B-11. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC B-51
Exhibit B-12. Non-Chemical-Specific Produce Inputs B-61
Exhibit B-13. Animal Product Chemical-specific Inputs for Chemicals Included in
MIRC B-62
Exhibit B-14. Soil and Plant Ingestion Rates for Animals B-64
Exhibit B-15. Mean and Percentile Body Weight Estimates for Adults and
Children B-65
Exhibit B-16. Estimated Daily Per Capita Mean and Percentile Water Ingestion
Rates for Children and Adults® B-66
Exhibit B-17. Summary of Age-Group Specific Food Ingestion Rates for Farm
Food Items B-67
Exhibit B-18. Fish Ingestion Rates Used in Screening Assessment B-72
Exhibit B-19. Daily Mean and Percentile Consumer-Only Fish Ingestion Rates
for Children and Adults (IRco,y)a B-74
Exhibit B-20. Fraction of Population Consuming Freshwater/Estuarine Fish on a
Single Day (Fpc,y) B-75
Exhibit B-21. Calculated Long-term Mean and Percentile per capita Fish
Ingestion Rates for Children and Adults (IRpc.y) B-75
Exhibit B-22. Calculated Mean and 90th Percentile Per capita Fish Ingestion
Rates for Populations of Recreational Fishers (IRpc,y) B-76
Exhibit B-23. Daily Mean and Percentile Soil Ingestion Rates for Children and
Adults B-77
Exhibit B-24. Daily Mean and Percentile Per Capita Total Food Intake for
Children and Adults B-78
Attachment B v October 2014
Description of MIRC
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit B-25. Fraction Weight Losses from Preparation of Various Foods B-80
Exhibit B-26. Scenario- and Receptor-Specific Input Parameter Values Used to
Estimate Infant Exposures via Breast Milk B-83
Exhibit B-27. Average Body Weight for Infants B-83
Exhibit B-28. Time-weighted Average Body Weight for Mothers B-84
Exhibit B-29. Infant Breast Milk Intake Rates B-86
Exhibit B-30. Chemical-specific Input Parameter Values for Breast Milk
Exposure Pathway B-87
Exhibit B-31. Farm Food Category Ingestion Rates for Health Protective
Screening Scenario for Farming Households B-91
Exhibit B-32. Mean Body Weight Estimates for Adults and Children3 B-92
Exhibit B-33. Chemical-Specific Parameter Values for Input to MIRCa B-93
Exhibit B-34. Chemical and Animal-Type Specific Biotransfer Factor (Ba) Values
for Input to MIRC B-94
Attachment B vi October 2014
Description of MlRC
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TRIM-Based Tiered Screening Methodology for RTR
Equations, Attachment B
Equation B-1. Chemical Concentration in Aboveground Produce B-11
Equation B-2. Chemical Concentration in Aboveground Produce Due to Root
Uptake B-11
Equation B-3. Chemical Concentration in Aboveground Produce Due to
Deposition of Particle-phase Chemical B-12
Equation B-4. Chemical Concentration in Aboveground Produce Due to Air-to-
Plant Transfer of Vapor-phase Chemical B-12
Equation B-5. Conversion of Aboveground Produce Chemical Concentration from
Dry- to Wet-Weight Basis B-13
Equation B-6. Chemical Concentration in Belowground Produce: Nonionic
Organic Chemicals B-14
Equation B-7. Chemical Concentration in Belowground Produce: Inorganic
Chemicals B-15
Equation B-8. Conversion of Belowground Produce Chemical Concentration from
Dry- to Wet-Weight Basis B-15
Equation B-9. Chemical Concentration in Beef, Pork, or Total Dairy B-16
Equation B-10. Chemical Concentration in Poultry or Eggs B-17
Equation B-11. Incidental Ingestion of Chemical in Soil by Livestock B-17
Equation B-12. Ingestion of Chemical in Feed by Livestock B-18
Equation B-13. Chemical Concentration in Livestock Feed (All Aboveground) B-18
Equation B-14. Chemical Concentration in Livestock Feed Due to Root Uptake B-19
Equation B-15. Average Daily Dose for Specified Age Group and Food Type B-19
Equation B-16. Chemical Intake from Soil Ingestion B-21
Equation B-17. Chemical Intake from Fish Ingestion B-22
Equation B-18. Consumption-weighted Chemical Concentration in Fish B-22
Equation B-19. Chemical Intake from Consumption of Exposed Fruits B-23
Equation B-20. Chemical Intake from Consumption of Protected Fruits B-23
Equation B-21. Chemical Intake from Exposed Vegetables B-24
Equation B-22. Chemical Intake from Protected Vegetables B-24
Equation B-23. Chemical Intake from Root Vegetables B-24
Equation B-24. Chemical Intake from Ingestion of Beef B-25
Equation B-25. Chemical Intake from Dairy Ingestion B-25
Equation B-26. Chemical Intake from Pork Ingestion B-25
Equation B-27. Chemical Intake from Poultry Ingestion B-26
Equation B-28. Chemical Intake from Egg Ingestion B-26
Equation B-29. Chemical Intake from Drinking Water Ingestion B-27
Attachment B vii October 2014
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TRIM-Based Tiered Screening Methodology for RTR
Equation B-30 B-27
Equation B-31 B-27
Equation B-32 B-27
Equation B-33 B-28
Equation B-34 B-28
Equation B-35 B-28
Equation B-36. Lifetime Average Daily Dose (LADD) B-28
Equation B-37. Average Daily Dose of Chemical to the Nursing Infant B-29
Equation B-38. Chemical Concentration in Breast Milk Fat B-30
Equation B-39. Daily Maternal Absorbed Intake B-31
Equation B-40. Biological Elimination Rate Constant for Chemicals for Non-
lactating Women B-32
Equation B-41. Biological Elimination Constant for Lipophilic Chemicals for
Lactating Women B-32
Equation B-42. Chemical Concentration in Aqueous Phase of Breast Milk B-33
Equation B-43. Fraction of Total Chemical in Body in the Blood Plasma
Compartment B-34
Equation B-44. Biological Elimination Rate Constant for Hydrophilic Chemicals B-35
Equation B-45. Calculation of Infant Average Daily Absorbed Dose of Methyl
Mercury B-36
Equation B-46. Calculation of Excess Lifetime Cancer Risk B-43
Equation B-47 B-44
Equation B-48 B-44
Equation B-49 B-44
Equation B-50 B-44
Equation B-51 B-44
Equation B-52 B-44
Equation B-53 B-44
Equation B-54. Hazard Quotient for Chemicals with a Chronic RfD B-45
Equation B-55. Hazard Index Calculation B-46
Equation B-56. Calculation of Age-Group-Specific and Food-Specific Ingestion
Rates B-70
Equation B-57. Calculation of Alternative Age-Group-Specific Fish Ingestion
Rates B-73
Attachment B viii October 2014
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TRIM-Based Tiered Screening Methodology for RTR
1. Introduction
1.1. Purpose and Overview
This document provides a detailed description of the Multimedia Ingestion Risk Calculator
(MIRC), a modeling tool and database designed to assist in estimating risks via multiple
ingestion pathways, particularly for food products grown or raised at home or on a farm.27 MIRC
was designed to estimate risks to humans from ingestion of produce or animal products, fish,
and water in the vicinity of a source of chemical emissions to air. The user can evaluate either
generalized (e.g., health protective default) or more site-specific scenarios using the same tool.
MIRC includes a database of exposure parameter values, offering the user the option of
selecting mean, median, and upper percentile values for many parameters, data permitting.
Generally health protective default values were assigned to each parameter in the tool and the
default configuration is used for initial risk screening efforts by EPA's Office of Air Quality
Planning and Standards' (OAQPS) for Risk and Technology Review (RTR) multimedia risk
assessments. MIRC also allows the user to define the farm food chain (FFC) parameter values
and receptor characteristics to better represent a site-specific scenario.
With user-input concentrations for one or more chemicals in air and soil and air-to-surface
deposition rates, MIRC calculates the chemical's concentrations in home- or farm-grown
produce and animal food products using FFC algorithms adapted from EPA's Human Health
Risk Assessment Protocol for Hazardous Waste Combustion Facilities (hereafter referred to as
HHRAP; EPA 2005a). MIRC uses these calculated concentrations, along with user-input
chemical concentrations for fish and drinking water, to estimate chemical intake rates, as
average daily doses (ADDs), for adults, children, and nursing infants. Users can obtain
chemical input concentrations and deposition rates from measurements at an actual site or from
a transport and fate model, such as TRIM.FaTE, as is done for RTR risk assessment.
For a specified set of chemical concentrations and MIRC parameter options, MIRC calculates
ADDs separately for adults, four age groups of children, and infants to reflect differences in food
ingestion rates and diet at different lifestages. MIRC estimates age-specific hazard quotients
(HQs) as the ratio of age-specific ADDs to the reference dose (RfD) for a chemical. The most
appropriate HQ for a chemical depends on its toxic mode of action and the duration of exposure
required to produce an effect. MIRC also estimates average lifetime ADDs and compares those
to cancer slope factors (CSFs) to estimate cancer risks. A breast milk ingestion pathway is
available to estimate exposure and risks to nursing infants.
MIRC was developed to be a flexible, transparent application. The tool includes chemical
transfer and ingestion exposure algorithms and a database of parameter values, many with
several options, used by these equations. The MIRC database includes values for the relevant
physiochemical properties and toxicity reference values for more than 500 chemicals, including
approximately 60 inorganics taken primarily from a database developed for HHRAP (EPA
2005a). Although designed for OAQPS' RTR assessments for sources of hazardous air
pollutants (HAPs), the tool is flexible in its design and can be used to assess risks in many other
contexts where soil and air concentrations are predicted or measured.
1.2. Scope of MIRC
For persistent and bioaccumulative (PB) chemicals, risks from direct inhalation of the chemical
can be much less than risks from ingestion of the chemical in water, fish, and food products
27Fully functional versions of MIRC have been developed in both Access™-based and Excel™-based formats;
however, MIRC currently is not publicly available.
Attachment B
Description of MIRC
B-1
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
grown in an area of chemical deposition. Vegetables and fruits in such areas can become
contaminated directly by deposition of the airborne chemical to foliage, fruits, and vegetables or
indirectly by root uptake of the chemical deposited to soils. Livestock can be exposed to the PB
chemicals via ingestion of contaminated forage and incidental ingestion of contaminated soils.
For PB chemicals, evaluation of the inhalation pathway for air pollutants may reveal only a
portion of the risk to individuals in such populations. Households that consume high quantities
of self-caught fish or locally grown produce and animal products may be particularly susceptible
to ingestion of chemicals transferred from air in the vicinity of an air emissions source. For PB
chemicals in particular, therefore, EPA developed methods of estimating risk from indirect
exposure pathways associated with the deposition of airborne chemicals to gardens and farms,
as described in HHRAP (EPA 2005a).
1.3. Use in EPA's Air Toxics Program
For PB-HAPs, indirect exposure pathways, such as ingestion, might contribute more to total risk
than the inhalation pathway. EPA therefore developed several computer software tools to assist
in evaluating exposure and risk from non-inhalation pathways. EPA developed the Total Risk
Integrated Methodology (TRIM) Environmental Fate, Transport, and Ecological Exposure
(TRIM.FaTE) computer program to simulate the release, transport, and fate of HAPs from a
specific source throughout the area in which local (non-source) chemical deposition is likely to
be a concern. TRIM.FaTE models the transport of individual chemicals from the source through
air by advection (wind) of particle- and vapor-phase chemical and deposition of the chemical
from air to terrestrial and aquatic ecosystems by wet and dry deposition. Movement of the
chemical through a watershed via erosion and runoff, uptake by plants, and other abiotic and
biotic transfer processes also are simulated. For the chemical that reaches surface waters,
TRIM.FaTE models uptake and bioaccumulation to trophic level (TL) 3 and 4 fish (i.e., pan fish
and game fish, respectively).
MIRC was developed to process TRIM.FaTE results, in particular, air deposition rates and the
concentrations of a chemical, after a specified duration of emissions, in several spatially explicit
environmental compartments, including air, surface and root-zone soils, surface and ground
waters, and fish. MIRC uses those results to calculate exposure to the chemical through
ingestion of locally grown foods, including various types of fruits and vegetables, poultry, swine,
and dairy (and beef) cattle. MIRC also calculates the associated risks for individuals who
consume those foods. MIRC was designed to use specific TRIM.FaTE results to estimate FFC
concentrations, ingestion exposures, and human health risks for OAQPS' RTR assessments. It
uses the same approach that OAQPS intends to implement directly in its TRIM system via three
modules beyond TRIM.FaTE: TRIM Farm Food Chain, TRIM.Expoingestion, and TRIM.Risk.28
1.4. MIRC Highlights
Although designed to assist EPA OAQPS in its RTR assessments, MIRC is a stand-alone
software application that can be used in other contexts. A user can supply either measured or
estimated chemical concentrations for soil, air, water, and fish, and also can provide air
deposition rates likely for the location(s) of interest based on local meteorology. The user can
accept the default values for many exposure parameters and screen for small possibilities of
risk, or the user can select other options or overwrite parameter values to tailor the estimates to
a specific scenario or location.
28 General information about the TRIM system is available at http://www.epa.gov/ttn/fera/trim_gen.html.
Attachment B B-2 October 2014
Description of MIRC
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TRIM-Based Tiered Screening Methodology for RTR
MIRC complies with EPA's latest guidelines for exposure and risk assessment, including
HHRAP; the Agency's 2005 Guidelines for Carcinogen Risk Assessment (Cancer Guidelines),
Supplemental Guidance for Assessing Susceptibility from Early-Life Exposure to Carcinogens
(Supplemental Guidance), and Guidance on Selecting Age Groups for Monitoring and
Assessing Childhood Exposures to Environmental Contaminants (EPA 2005b,c,d); and its 2008
Child-Specific Exposure Factors Handbook (EPA 2008a). In particular, MIRC provides several
important capabilities:
When provided air and soil concentrations, the MIRC software package allows rapid
calculation of screening-level exposures and risks associated with household
consumption of locally grown/raised foods.
MIRC can calculate exposures and risks associated with incidental ingestion of surface
soils, fish consumption, and drinking water.
The tool calculates ADDs (i.e., chemical intake rates) for six "built-in" age groups to allow
use of age-group-specific body weights, ingestion rates, food preferences, and
susceptibility to toxic effects.
Its database of chemical information covers plant- and animal-specific transfer factors
and other inputs that determine concentrations in farm food stuffs.
Value options for receptor characteristics in the database include the mean and 50th,
90th, 95th, and 99th percentile values where data permit.
For carcinogens with a mutagenic mode of action, MIRC estimates a lifetime ADD using
the three lifestages and potency adjustment factors recommended in EPA's 2005
Cancer Guidelines and Supplemental Guidance.
• The data for exposure parameters in the tool have been updated to include the latest
recommended values for children issued September 30, 2008, in the Agency's Child-
Specific Exposure Factors Handbook (CSEFH) (EPA 2008a).
1.5. Organization of This Attachment
Sections 2 through 5 of this attachment describe the exposure and risk models implemented in
MIRC. Section 2 provides an overview of the FFC exposure scenario and indicates options
available to a user to tailor the scenario to specific applications. Section 3 describes the
exposure algorithms used in MIRC, including how ADDs are calculated. Section 4 presents the
toxicity reference values included in MIRC to calculate risks. Section 5 describes the risk
characterization algorithms in MIRC. Section 6 of this document describes data input options
for the model. Section 7 describes the default parameterization of MIRC for application to
health protective risk screening assessments, and Section 8 provides the references.
Note that the default parameterization described in Section 7 was used to estimate Tier 1
screening threshold emission rates of PB-HAPs from RTR facilities. These emissions levels are
assumed to pose negligible risk to subsistence communities in the vicinity of a facility emitting
the PB-HAPs to air. Users of MIRC can modify the default values for many of the parameters to
better represent a specific exposure scenario.
2. MIRC Overview
The Multimedia Ingestion Risk Calculator (MIRC) software package is designed to allow rapid
calculation of screening-level exposures and risks associated with subsistence and recreational
farmer/fisher populations in the vicinity of a source of chemical emissions to air. The tool allows
a user to assess human exposures via ingestion pathways, including drinking water
Attachment B
Description of MIRC
B-3
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
consumption, incidental soil ingestion, fish ingestion, and ingestion often types of farm food
chain (FFC) products: exposed fruits, protected fruits, exposed vegetables, protected
vegetables, root vegetables, beef, total dairy, pork, poultry, and eggs. The tool also includes a
breast milk ingestion and risk module for nursing infants. For fruits and vegetables, the terms
"exposed" and "protected" refer to whether the edible portion of the plant is exposed to the
atmosphere.
The remainder of this overview consists of three sections. The first (Section 2.1 of this
attachment) provides an overview of the MIRC software package. The second and third
sections summarize the ingestion exposure pathways included in the tool and the "built-in"
receptor age categories, respectively (Sections 2.2 and 2.3 of this attachment).
2.1. Software
The MIRC application includes the following components:
• A graphical user interface through which the user locates and accesses various input
and output tables.
Input tables in which the user can enter environmental concentrations of a chemical
estimated for air, soil, drinking water, and fish tissue.
Internal chemical transfer and exposure algorithms and database of options for FFC
algorithm parameter value, chemical-specific inputs, and exposure factors.
Tabulated outputs of calculated chemical concentrations in the various farm food
products (e.g., fruits, vegetables, beef, eggs) and ADDs for those foods and for water
and fish ingestion for each receptor category.
• Output tables with estimated cancer risks and non-cancer hazard estimates associated
with total ingestion exposure to each chemical for each receptor category.
Exhibit B-1 provides a flowchart displaying the types of required and optional inputs and the
general flow of calculations carried out by the tool.
Attachment B
Description of MIRC
B-4
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit B-1. Overview of MIRC Software Application for
Performing Farm-Food-Chain Ingestion Exposure and Risk Calculations
User Selects Chemical(s) of Concern: For Each, MIRC Calculates Risks
User Specifies Environmental Concentrations for Chemical of Concern
(j\ir^ CBoot-zone and Surface SoHs^> (^Fish^) C^rTnking Wate^>
Vegetables, Fruits
Grains
Hay, Grass
Animal Products
Farm Food Chain Biotransfer Calculations
User Selects Receptor Characteristics
(^ody Weighj)
From Options or Over-write
Home Grown
Food Product
ngestion Rat
Fish and Water
Ingestion Rates,
Average Daily Dose (ADD) for Age Group y; y =1 to 5
I
Chemical Intake with Food/
Medium Type i; i = 1 to 10
Exposure Module
Lifetime Average
Daily Dose
J
Adult ADD x
absorption efficiency
User Option to Add Breast Milk Pathway
©
Chemical
Toxicity
Reference
Values:
SF and RfDs
User Selects BMP
Parameter Values
Duration Breast Feeding; maternal
jand infant characteristics
Risk Characterization Module
Age-specific & Lifetime Exposure Doses
Lifetime Cancer Risks
Age-Specific Hazard Quotients
Breast Milk Exposure
Module
c
ADD Maternal
[C] in milk
Infant Dose
J
Attachment B
Description of MIRC
B-5
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
A form within the graphical user interface enables the user to construct specific scenarios by
choosing ingestion sources, receptor ages, and other input choices (e.g., diet composition, body
weight percentiles). This feature facilitates the assessment of various exposure scenarios. To
begin an assessment, the user must supply values for the following chemical-specific
parameters for the scenario being evaluated:
• Air concentration of total chemical,
Fraction chemical in air in vapor-phase,
Wet and dry deposition rates for particle-phase chemical,
Drinking water concentration,
Chemical concentration in surface soils (two locations; can be tilled and unfilled),
Chemical concentration in root-zone soils (two locations; can be tilled and unfilled), and
• Chemical concentrations in upper trophic-level fish.
Users can input measured values or values estimated by TRIM.FaTE or other models for these
parameters.
The MIRC application uses the input data and a variety of empirical transfer factor values
(included in its database) to estimate chemical concentrations in nine categories of FFC food
types (see Section 2.2 of this attachment). The FFC algorithms and transfer factor values
included in MIRC are based on those presented in Chapter 5 of EPA's HHRAP (EPA 2005a).
For outputs, MIRC is designed to calculate individual cancer risk and non-cancer hazard
quotients for one chemical at a time. It is up to the risk assessor to determine if cancer risks or
hazard quotients may be additive across two or more chemicals (i.e., if they cause toxic effects
in the same target organ by the same mode of action, such as multiple polycyclic aromatic
hydrocarbons (PAHs) that are carcinogenic by a mutagenic mode of action).
The tool assumes that an individual is exposed via all of the pathways specified (e.g, fruits and
vegetables, animal products, soil, etc.). The tool therefore is useful in estimating risk to the
maximally exposed individuals in a risk assessment. To evaluate other receptor populations,
the user must specify the each exposure scenario separately.
2.2. Exposure Pathways
MIRC estimates the concentrations of chemicals in FFC food categories grown in an area of
airborne chemical deposition using algorithms and parameter values provided in HHRAP (EPA
2005a). FFC foods are evaluated in ten categories: exposed fruit, protected fruit, exposed
vegetables, protected vegetables, root vegetables, beef, total dairy, pork, poultry, and eggs.
Exhibit B-2 summarizes the pathways by which chemicals are transferred to these food media.
Note that for a general Tier 1 screening-level assessment, all of the pathways can be modeled,
as is the case for EPA's Risk and Technology Review (RTR) calculation of screening threshold
emission rates for persistent and bioaccumulative hazardous air pollutants (PB-HAPs) (EPA
2008b).
Attachment B
Description of MIRC
B-6
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit B-2. Transfer Pathways for Modeled
Farm Food Chain (FFC) Media
Farm Food Media
Chemical Transfer Pathways
Exposed fruit and vegetables
• Direct deposition from air of particle-bound chemical
• Air-to-plant transfer of vapor phase chemical
• Root uptake from soil
Protected fruit and vegetables
(including root vegetables)
• Root uptake from soil
Beef and total dairy
(including milk)
• Ingestion of forage, silage, and grain3
• Soil ingestion
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.
Produce types included in the FFC can accumulate a chemical directly from air and/or soil. For
exposed produce, chemical mass is assumed to be transferred to plants from the air in two
ways. First, particle-bound chemical can deposit directly on the plant surface. Second, the
uptake of vapor-phase chemicals by plants through their foliage can occur. For both exposed
and protected produce, the concentration in the plant derived from exposure to the chemical in
soil is estimated using an empirical bioconcentration factor (BCF) that relates the concentration
in the plant to the concentration present in the soil. For belowground root vegetables, a root
concentration factor is applied. The algorithms used to estimate produce concentrations are
presented in Section 3.1.1 of this attachment.
Chemical concentrations in animal products are estimated based on the amount of chemical
consumed through the diet, including incidental ingestion of soil while grazing. The diet options
for farm animals in MIRC include forage (plants grown on-site for animal grazing, such as
grass), silage (wet forage grasses, fresh-cut hay, or other fresh plant material that has been
stored and fermented), and feed grain products grown on the farm (e.g., corn, soybeans). All
three animal feed products are assumed to accumulate chemical via root uptake from the soil.
Forage and silage also can accumulate chemical via direct deposition of particle-bound
chemical and vapor transfer.
The algorithms in MIRC are based on the assumptions that beef and dairy cattle consume all
three feed products, while pigs consume only silage and grain and chickens consume only
grain. The incidental ingestion of the chemical in soils during grazing or consumption of foods
placed on the ground is estimated using empirical soil ingestion values. For secondary animal
products (dairy products and eggs), chemical concentrations are estimated by applying a
biotransfer factor to the estimated concentration in the "source" animal (cows and chickens,
respectively). The algorithms used to estimate animal product concentrations are described in
Section 3.1.2 of this attachment.
2.3. Receptor Groups
As noted in EPA risk assessment guidelines (EPA 2005b,c,d, 2008a), exposures of children are
expected to differ from exposures of adults due to differences in body weights, ingestion rates,
dietary preferences, and other factors. It is important, therefore, to evaluate the contribution of
exposures during childhood to total lifetime risk using appropriate exposure factor values.
Attachment B
Description of MIRC
B-7
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
EPA's HHRAP (Chapter 4, EPA 2005a) recommends assessing exposures for children and
adults separately, but considers all non-infant children in one category. Specifically, HHRAP
recommends eight categories of receptor: farmer, child farmer, resident, child resident, fisher,
child fisher, acute receptor, and nursing infant. Overtime, different EPA programs have used
different child age groupings to evaluate body weights, ingestion rates, and other parameter
values needed to estimate chemical exposures and risks to children.
To improve the match between age groups used to estimate values across exposure
parameters, in 2005, EPA recommended a standard set of child age categories for exposure
and risk assessments (EPA 2005b). EPA recommended four age groups for infants: birth to < 1
month; 1 to < 3 months; 3 to < 6 months; and 6 to < 12 months. For young children, EPA
recommended an additional four age groups: 1 to < 2 years; 2 to < 3 years; 3 to < 6 years; and
6 to < 11 years. Two age groupings were recommended for teenagers and young adults: 11 to
<16 years; and 16 to < 21 years. These age groupings correspond to different developmental
stages and reflect different food ingestion rates per unit body weight, with the highest ingestion
rates occurring for the youngest, most rapidly growing, age groups.
For purposes of RTR assessment using MIRC, the selection of age categories is limited by the
categories for which most of the FFC food ingestion rates have been calculated. In Chapter 13
of both its Exposure Factors Handbook (EFH; EPA 2011a) and its Child-Specific Exposure
Factors Handbook (CSEFH; EPA 2008a), EPA summarized home-grown/raised food ingestion
rates for four children's age groups: 1 to < 3 years; 3 to < 6 years; 6 to < 12 years; and 12 to <
20 years. Intake rates were not calculated for children younger than 1 year because infants are
unlikely to consume those foods. They are more likely to be nursing or to be fed formula and
other commercial baby-food products.
Although the age groupings used to estimate FFC ingestion rates do not match precisely the
groupings that EPA recommended in 2005 for Agency exposure assessments (EPA 2005b),
they are the only age-groupings for which such data are available. The U.S. Department of
Agriculture's (USDA's) 1987-1988 Nationwide Food Consumption Survey (USDA 1992, 1993,
1994a) remains the most recent survey of ingestion rates for home-grown foods, and EPA's
analysis of those data, published in its 2011 EFH, remains the most recently published major
analysis of those data. Because ingestion of home-grown produce and animal products are the
primary exposure pathways for which MIRC was developed, those are the age groupings used
for all child parameter values used to estimate exposure and risk in MIRC.
Thus, in MIRC, values for each exposure parameter were estimated for adults (20 to 70 years)
and five children's age groups:
infants under 1 year (i.e., 0 to < 1 year);
children ages 1 through 2 years (i.e., 1 to < 3 years);
children ages 3 through 5 years (i.e., 3 to < 6 years);
children ages 6 through 11 years (i.e., 6 to < 12 years); and
• children ages 12 through 19 years (i.e., 12 to < 20 years).
See Sections 5.1 and 5.2 for descriptions of the risk characterization algorithms used to
calculate cancer and non-cancer effects, respectively, for the above age groupings. Exposure
and risks to infants under 1 year of age are estimated only for the breast-milk-ingestion
pathway.
Attachment B
Description of MIRC
B-8
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
For assessment of cancer risks from early-life exposure, EPA recognizes that infants and
children may be more sensitive to a carcinogenic chemical than adults, with cancers appearing
earlier in life or with lower doses experienced during childhood (EPA 2005c, d). Thus, the
"potency" of a carcinogen might be higher for infants and children than for adults. To date,
however, data by which to evaluate the relative sensitivity of children and adults to the same
daily dose of a carcinogen remain limited. Based on analyses of radioactive and other
carcinogenic chemicals, EPA recommends evaluating two lifestages for children separately from
adults for chemicals that cause cancer by a mutagenic mode of action (MOA): from birth to < 2
years and from 2 to < 16 years (EPA 2005c,d). EPA also suggests that, as data become
available regarding carcinogens with a mutagenic MOA, further refinements of these age
groupings may be considered.
For assessing risks from exposures to carcinogenic chemicals that act via a mutagenic MOA,
the two early lifestages recommended by EPA (EPA 2005c,d) also are included in MIRC:
children under the age of 2 years (i.e., 0 to < 2 years); and
• children from 2 through 15 years (i.e., 2 to < 16 years).
Different age groupings are needed for the assessment of risks from carcinogenic chemicals
with a mutagenic MOA and other carcinogens with other or unknown MOAs. Currently in MIRC,
the only PB-HAPs with a mutagenic mode of carcinogenesis are the carcinogenic PAHs. See
Section 5.1 for a description of the age-dependent adjustment factors (ADAFs) that are used to
calculate cancer risks for chemicals with a mutagenic MOA.
3. Exposure Algorithms
The exposure algorithms in MIRC are described below in four sections. Section 3.1 of this
attachment presents the algorithms used to estimate chemical concentrations in FFC foods from
chemical concentrations in soil and air. Pathway-specific algorithms used to estimate chemical
intakes by adults and non-infant children are described in Section 3.2, and total chemical intake
calculations are described in Section 3.3. Finally, the sets of algorithms used to estimate
chemical intake via consumption of breast milk by nursing infants are described in Section 3.4.
As noted previously, the exposure algorithms used in MIRC are based on those presented in
HHRAP (EPA 2005b). Any differences between MIRC and HHRAP are explained in this
section.
3.1. Farm Food Chain Algorithms
The algorithms and parameters used to estimate chemical concentrations in produce and
animal products are described in Sections 3.1.1 and 3.1.2 of this attachment, respectively.
Discussions of the parameter value options and the values selected as defaults in MIRC for
RTR risk assessment are provided in Section 6.2. The use of TRIM.FaTE to model chemical
fate and transport in the environment prior to FFC calculations drives the most significant
difference between the FFC algorithms included in HHRAP and the equations used for RTR.
The approach in HHRAP uses estimated ambient air concentrations and deposition rates from
dispersion model simulations that use unit emission rates. Chemical-specific emission rates
(adjusted for vapor and particle-bound fractions) are then incorporated into some of the HHRAP
FFC algorithms to calculate concentrations in FFC media. Soil concentrations are calculated
using a similar approach in HHRAP. For assessment of multipathway exposures for RTR,
TRIM.FaTE is used to estimate air concentrations, air-to-surface deposition rates, and soil
concentrations, and these outputs are used in the FFC algorithms.
Attachment B
Description of MIRC
B-9
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
3.1.1. Estimating Chemical Concentrations in Produce
Produce (vegetables and fruits) can become contaminated directly by deposition of airborne
chemicals to foliage and fruits or indirectly by uptake of chemicals deposited to the soil. Given
these two contamination processes, produce is divided into two main groups: aboveground and
belowground produce. Aboveground produce is divided into fruits and vegetables. These
groups are further subdivided into "exposed" and "protected" depending on whether the edible
portion of the plant is exposed to the atmosphere or is protected by a husk, hull, or other outer
covering.
Exhibit B-3 lists the pathways by which chemicals are transferred to the FFC produce
categories. Note that for a general screening-level assessment, all of the pathways can be
modeled, as was done for EPA's calculation of Tier 1 screening threshold emission rates for PB-
HAPs in its RTR assessments (EPA 2008b), and as described in the Technical Support
Document. Sections 3.1.1.1 and 3.1.1.2 below describe the transfer pathways and algorithms
for aboveground and belowground produce, respectively.
Exhibit B-3. Chemical Transfer Pathways for Produce
Farm Food Media
Chemical Transfer Pathways
Aboveground
Produce
Exposed fruits and
vegetables
Direct deposition from air of
particle-bound chemical
Air-to-plant transfer of vapor
phase chemical
Root uptake from soil
Protected fruits and
vegetables
Root uptake from soil
Belowground
Produce
Root vegetables
Root uptake from soil
3.1.1.1. Aboveground Produce
For aboveground exposed produce, Exhibit B-4. Estimating Chemical
chemical mass is assumed to be Concentration in Aboveground Produce
transferred to plants from the air in three
ways, as illustrated in Exhibit B-4. First,
particle-bound chemical can deposit
directly on the plant surface via deposition
(.Pd). The amount of chemical
accumulated is estimated based on the
areal fraction of chemical deposition
intercepted by the plant surface, minus a
loss factor that is intended to account for
removal of deposited chemical by wind and
rain and changes in concentration due to
growth dilution. Second, for chemical
present in air in the vapor phase, the
concentration of chemical accumulated by
the plant's foliage is estimated using an
empirical air-to-plant biotransfer factor (Pv).
Third, the chemical concentration in the plant due to root uptake from the soil (PrAG-produce) is
estimated using an empirical bioconcentration factor (BrAG-produce) that relates the chemical
¦9
Ifti
Attachment B
Description of MlRC
B-10
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
concentration in the plant to the average chemical concentration in the soil at the root-zone
depth in the produce-growing area (Csr0ot- zone_produce) ¦
The edible portions of aboveground protected produce are not subject to contamination via
particle deposition (Pd) or vapor transfer (Pv). Therefore, root uptake of chemicals is the
primary mechanism through which aboveground protected produce becomes contaminated. As
shown below, the chemical concentration in the aboveground plant due to root uptake from soil
{PrAG-produce-dw) is estimated using an empirical bioconcentration factor (BrAG-produce-Dw) that
relates the chemical concentration in the plant to the average chemical concentration in the soil
at the root-zone depth in the produce-growing area (Csr0ot-zone_produce). These equations all
assume measurements on a dry-weight (DW) basis.
Equation B-1. Chemical Concentration in Aboveground Produce
^AG-produce-DW(/') — AG-produce-DW (i) ^^(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
deposition of particles (mg/kg produce DW); for protected aboveground
produce, Pd equals zero
Chemical concentration in edible portion of aboveground produce type i,
exposed or protected, due to root uptake from soil at the root-zone depth of the
produce growing area (mg/kg produce DW)
Chemical concentration in edible portion of aboveground produce type i due to
air-to-plant transfer (|jg/g [or mg/kg] produce DW); for protected aboveground
produce, Pv equals zero
Equation B-2. Chemical Concentration in Aboveground Produce Due to Root Uptake
^^AG-produce-DW(/') — ^^root-zone produce X ®^AG-produce-DW(i)
Concentration of chemical in edible portion of aboveground produce type /',
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 (mg/kg soil DW)
Chemical-specific plant/soil chemical bioconcentration factor for edible portion
of aboveground produce type /', exposed or protected (g soil DW / g produce
DW)
where:
OAG-produce-DW(i) =
Pd(i) =
PrAG -produce-DW(i) ~
Pvni =
where:
PrAG -produce-DW(i) ~
CSroot-zone_produce
Brag -produce-DW(i) —
Attachment B
Description of MlRC
B-11
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
Equation B-3. Chemical Concentration in Aboveground Produce Due to Deposition of
Particle-phase Chemical
1,000 X (Drdp + (Fw X Diwp)) x Rp{j) x (1 - e(-kp(iyTp(i)))
Pd(i) =
Yp().) x kp{i)
where:
Chemical concentration in aboveground produce type /' on a dry-weight (DW)
Pd(i) = basis due to particle deposition (mg/kg produce DW); set equal to zero for
protected aboveground produce
Drdp = Average annual dry deposition of particle-phase chemical (g/m2-yr)
Pw _ Fraction of wet deposition that adheres to plant surfaces; 0.2 for anions, 0.6
for cations and most organics (unitless)
Drwp = Average annual wet deposition of particle-phase chemical (g/m2-yr)
Rp(i) = Interception fraction of the edible portion of plant type /' (unitless)
kp(i) = Plant surface loss coefficient for plant type /' (yr1)
Tpo) =
Yp(i) =
Length of exposure to deposition in the field per harvest of the edible portion of
plant type /' (yr)
Yield or standing crop biomass of the edible portion of plant type i (kg produce
DW/m2)
Note that Equation B-3 differs from Equation 5-14 in HHRAP, from which it is derived. In
HHRAP, Equation 5-14 includes the term Qx(1 - Fv) to indicate the emissions rate, in g/sec, of
chemical from the source and the proportion of the chemical that remains in, or partitions to, the
particle-phase in the air. Also in HHRAP, the dry and wet particle phase deposition rates, Dydp
and Dywp, respectively, are normalized to the emission rate and are expressed in units of
sec/m2-yr.
With MIRC, the user inputs both the dry and wet particle-phase deposition rates, Drdp and
Drwp, respectively, in units of g/m2-yr for a specific location relative to an emissions source.
Those deposition rates might be values measured near that location or estimated using a fate
and transport model, such as TRIM.FaTE, in conjunction with local meteorological information
and emissions rate data. The chemical emissions term used in HHRAP, Q, therefore, is not
used in MIRC's Equation B-3. In addition, in MIRC, Drdp and Drwp, the average annual dry-
and wet-particle-phase deposition rates, respectively, are in units of g/m2-yr. Users of
TRIM.FaTE should note that the dry- and wet-particle-deposition rates output from TRIM.FaTE
are in units of g/m2-day; therefore, users must adjust the TRIM.FaTE output values to units of
g/m2-yr (i.e., multiply by 365 days/yr) before inputting values for Drdp and Drwp into MIRC.
Equation B-4. Chemical Concentration in Aboveground Produce Due to
Air-to-Plant Transfer of Vapor-phase Chemical
D., _CaxFvx Bvag(/) x VGag(/)
Pv"'' 7.
Attachment B
Description of MIRC
B-12
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
where:
Concentration of chemical in edible portion of aboveground produce type /'
Pv(i) = 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
Ca = Average annual total chemical concentration in air (g/m3)
Fv = Fraction of airborne chemical in vapor phase (unitless)
_ Air-to-plant biotransfer factor for aboveground produce type /' for vapor-phase
AG(i) 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
VGago) = possible overestimate of the diffusive transfer of chemical from the outside to
the inside of bulky produce, such as fruit (unitless)
pa = 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. For MIRC, the user inputs the average annual total air
concentration of the chemical, Ca, for a specific location relative to the source in units of g/m3;
MIRC includes a chemical-specific default value for Fv for chemicals included in its database.
The air concentration might be a value measured near that location or a value estimated by a
fate and transport model such as TRIM.FaTE. Users of TRIM.FaTE should note that the
average annual concentration of the total chemical in air (i.e., total of both vapor and particulate
phases), Ca, output from TRIM.FaTE is in units |jg/m3; therefore, the user must adjust the value
to units of g/m3 (i.e., divide by 1,000 |jg/g) before entering it in MIRC.
The calculations of chemical concentration in aboveground produce, (CAG-Produce-Dw), shown
above, are on a dry-weight (DW) basis. The family FFC food ingestion rates, on the other hand,
are on a fresh- or wet-weight (WW) basis. MIRC therefore calculates the concentration in
aboveground produce on a wet-weight basis, Cag -produce-WW, using Equation B-5 and the moisture
content (MAF) of the FFC food category.
Equation B-5. Conversion of Aboveground Produce Chemical Concentration from
Dry- to Wet-Weight Basis
CAG-produce-WW(i) ^AG-produce-DW(i) x
((100 - MAF^)
V
100
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 i to convert the chemical
MAF(i) = concentration estimated for dry-weight produce to the corresponding chemical
concentration for full-weight fresh produce (percent water)
Attachment B
Description of MIRC
B-13
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
3.1.1.2. Belowground Produce
The equations by which chemical concentrations are estimated in belowground produce are
different for nonionic organic chemicals than for inorganic chemicals and ionic organic
chemicals.
3.1.1.2.1. Nonionic Organic Chemicals
For belowground produce, the nonionic organic chemical concentration in the tuber or root
vegetable is derived from exposure to the chemical in soil and is estimated using an empirical
root concentration factor (RCF) and the average chemical concentration in the soil at the 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 wet-weight 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.
Equation B-6. Chemical Concentration in Belowground Produce: Nonionic Organic
Chemicals
where:
_ CSroot-zone_produce x RCF X VGrootveg
^BG-produce-WW ~~ T7~\ , ir\ r-
Kds x UCF
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) *
Chemical-specific soil/water partition coefficient (L soil pore water/kg soil DW)
Units conversion factor of 1 kg/L
*Note that there is only one type of BG produce; hence there are no plant-type-specific subscripts.
The RCF, as developed by Briggs et al. (1982), is the ratio of the chemical concentration in the
edible root on a wet-weight basis to its concentration in the soil pore water. RCFs are based on
experiments with growth solutions (hydroponic) instead of soils; therefore, it is necessary to
divide the soil concentration by the chemical-specific soil/water partition coefficient (Kds). There
is no conversion of chemical concentrations in belowground produce from DW to WW because
the values are already on a WW basis.
For nonionic organic chemicals, it is possible to predict RCF values and Kds values (for a
specified soil organic carbon content) from an estimate of the chemical's Kow from empirically
Attachment B B-14 October 2014
Description of MlRC
Cbg -produce-WW
CSroot-zone_produce
RCF
VG,
rootveg
Kds
UCF
-------
TRIM-Based Tiered Screening Methodology for RTR
derived regression models. Those models are shown in HHRAP Appendix A-2, Equations
A-2-14 and A-2-15 (RCF) and in Equations A-29 and A-2-10 (Kds). The RCF and Kds values
so calculated for many of the chemicals in HHRAP are included in the MIRC database
(including the values for PAHs and dioxins).
3.1.1.2.2.
Inorganic and Ionic Organic Chemicals
For inorganic chemicals and ionized organic chemicals, it is not possible to predict RCF or Kds
values from Kow . For inorganic chemicals, chemical specific empirical values for the root/soil
bioconcentration factor must be used. The root/soil bioconcentration factor, now specified as
BtbG-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
'B G-produce-D l/V
root-zone produce x ^^BG-produce-DW r0otveg
where:
Cbg -produce-DW ~
CSroot-zone_produce ~
B/bg -produce-DW ~~
VGi
rootveg
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
CBG-produce-WW ~ CBG-produce-DW x
(100 - MAFBG)
100
where:
Cbg -produce-WW
Cl3G-produce-DW
MAF,
(BG)
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)
Attachment B
Description of MIRC
B-15
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
3.1.2. Estimating Chemical Concentrations in Animal Products
Chemical concentrations in animal products are estimated based on the amount of chemical
consumed by each animal group m through each plant feed type /' (Plantch-mtakeo.m>) and
incidental ingestion of soil for ground-foraging animals (Soilch-mtake(m•>)¦ Exhibit B-5 summarizes
the pathways by which chemicals are transferred to these home- or farm-raised animal food
products. Note that for a general screening-level assessment, all of the pathways can be
modeled, as is done for EPA's RTR calculation of screening threshold emission rates for PB-
HAPs (EPA 2008b).
The feed options for farm animals in MIRC include forage (plants grown on-site for animal
grazing, such as grass), silage (wet forage grasses, fresh-cut hay, or other fresh plant material
that has been stored and fermented), and grain products grown on the farm. As seen in
Exhibit B-5, the algorithms in MIRC for chemical intake with plant feeds (Plantch-mtakeam>) are
based on the assumptions that beef and dairy cattle consume all three plant feed products,
while pigs consume only silage and grain, and chickens consume only grain.
Exhibit B-5. Chemical Transfer Pathways for Animal Products
Farm Food Media
Chemical Transfer Pathways
Animal Products
Beef and total dairy
(including milk)
• Ingestion of forage, silage, and 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. MIRC uses Equation B-9 to calculate the
concentration of chemical in beef, pork, or total dairy and Equation B-10 to calculate the
concentration of chemical in poultry or eggs. The chemical concentration in mammalian farm
animals (i.e., beef and pigs) is adjusted using a metabolism factor (MF) that accounts for
endogenous degradation of the chemical (see Equation B-9). MF is set to 1.0 for chemicals that
are not metabolized and for chemicals for which the metabolic degradation rate is unknown.
Although other vertebrates, including birds, are likely to have similar metabolic pathways for
most chemicals, the health protective assumption is that birds do not metabolize any chemicals;
therefore, the MF is omitted from Equation B-10 for poultry and eggs.
Equation B-9. Chemical Concentration in Beef, Pork, or Total Dairy
Ba(m) X MF X
Soil
Ch-lntake(m)
¦£¦Plant,
Ch-lntake(i,m)
/=1
Attachment B
Description of MIRC
B-16
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
where:
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
(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
^poultry(m) ~ ®®(m) x {Soilch-lntake(m) Pl&HtCh-lntake(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
In MIRC, the incidental ingestion of the chemical in soils by livestock during grazing or
consumption of feed placed on the ground (Soilch-mtake(m)) is estimated using empirical soil
ingestion rates (Qs) and a soil bioavailability factor for livestock (Bs), as shown in
Equation B-11. At this time, the default value for Bs in MIRC for all chemicals is 1.0 (i.e., the
chemical in soil is assumed to be 100 percent bioavailable to the animal). This assumption may
be reasonably accurate for the soil surface to which airborne chemical is deposited. MIRC
allows the user to enter a surface soil concentration for areas where livestock forage, Css-nvestock,
that is distinct from the surface soil concentration input for areas where produce may be grown
and where humans might incidentally ingest soils (see Section 6.1 of this attachment).
Equation B-11. Incidental Ingestion of Chemical in Soil by Livestock
Soilch Intake(m) ~ x S-liVestock x
Omammal(m)
Ba(m)
MF =
Soilch -lntake(m) ~
Plantch -intake(i,m) —
where:
Cpoultry(m)
Ba(m)
Soilch -lntake(m)
Piantch -!ntake(i,m)
Attachment B
Description of MIRC
B-17
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
where:
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 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
chemical with each feed type, /', Plantch-imakeo.m), is calculated separately according to
Equation B-12. Note that the animal feed ingestion rates are on a dry-weight (DW) basis;
hence, no DW to wet weight (WW) conversion is needed.
Equation B-12. Ingestion of Chemical in Feed by Livestock
P/a^^Ch-/nfate(/,m) — X QP(i,m) x ^feed(i)
where:
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 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)
Cfeed(i) = Prfeed(i) + ^ (i) + (i)
where:
£ _ Concentration of chemical in plant feed type /' on a dry-weight (DW) basis (mg
feed(i) - Chemica|/kg plant feed DW), where /' = forage, silage, or grain
Soilch -lntake(m) ~
QS(m) ~
CSs-livestock =
Bs =
Plant Ch-intake(i,m) ~
F(i,m) =
QP(i,m) =
Ofeed(i) =
Attachment B
Description of MlRC
B-18
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
pr _ Concentration of chemical in plant feed type /' due to root uptake from soil
feed(i) - (mg/kg qw) 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
Pv(i) = vapor-phase chemical (|jg/g [or mg/kg] DW) where /' = forage, silage, or grain;
when /' = grain, the Pd term equals zero
MIRC calculates the chemical concentration in animal feed due to root uptake from the soil
using Equation B-14. The equation is the same as Equation B-2, except that a Br value
appropriate to grasses is used and MIRC allows for different soil concentrations in the area
used to grow animal feed than in the area used to grow produce for human consumption (see
Section 6.1 of this attachment, user inputs). Note that for feed type /' = grains, the Pd and Pv
terms do not apply (are set to zero), because the feed products (i.e., corn kernels, soy beans)
are protected from the air (i.e., by husks, pods).
Equation B-14. Chemical Concentration in Livestock Feed Due to Root Uptake
^l"feed(i) root-zone _ feed (i) x ^^feed(i)
where:
Concentration of chemical in plant feed type /' due to root uptake from soil on a
Prfeed(i) = dry-weight (DW) basis (mg chemical/kg plant feed DW), where /' = forage, silage,
or grain
c _ Average chemical concentration in soil at root-zone depth in area used to grow
root-zonejeed(i) p|gnt type ^mg chemjca|/kg soil DW), where /' = forage, silage, or grain
gr _ Chemical-specific plant-soil bioconcentration factor for plant feed type /' (kg soil
feed(i) - pyy/kg p|ant feec| pyy) where /' = forage, silage, or grain
The algorithms used to calculate Pd® and Pvp) 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 ingestion rates for livestock
are based on DW feed.
3.2. Chemical Intake Calculations for Adults and Non-Infant Children
MIRC calculates human chemical intake rates from the ingestion of home-grown foods as
average daily doses (ADDs) normalized to body weight for each age group, chemical, and food
type separately. ADDs, calculated using Equation B-15, are expressed in milligrams of
chemical per kilogram of receptor body weight per day (mg/kg-day).
Equation B-15. Average Daily Dose for Specified Age Group and Food Type
ADD(yj) ~
( Cm x IR,„ „ x FCm x ED/tA Y EF,
'(i) A " (y,i) A ' ~(i) A "-"(y)
BW(y) / AT(y)
(y)
365 days
Attachment B
Description of MIRC
B-19
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
where:
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)
Ingestion rate for age group y of food type /' (kg/day or L/day)
Fraction of food type /' that was harvested from contaminated area (unitless)
Exposure duration for age group y (years)
Body weight for age group y (kg)
Averaging time for calculation of daily dose (years) for age group y, set equal to
ED in MIRC
Annual exposure frequency for age group y (days)
Equation B-15 takes into account 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 body weight (EPA 2011a,
2003a). In MIRC, ADDs are calculated separately for each chemical, home-grown food type,
and consumer age group.
ADD values, expressed as intakes, not absorbed doses, are appropriate for comparison with
RfDs and for use with cancer slope factors (CSFs) to estimate risk, as discussed in Section 5 of
this attachment. An exception is for the breast-milk exposure pathway, where calculating the
dose available to and absorbed by the nursing infant is related to the dose absorbed by the
mother as discussed in Section 3.4 of this attachment.
MIRC evaluates only one exposure scenario at a time. For screening-level assessments, all
components of this equation are assumed to remain constant for consumers in a given age
group over time (e.g., seasonal and annual variations in diet are not explicitly taken into
account). To calculate an ADD(yj) from the contaminated area for food group /' over an entire
lifetime of exposure, age-group-specific ingestion rates and body weights are used for the age
groups described in Section 2.3 of this attachment. In MIRC, the averaging time used to
calculate the daily dose for an age group (ATy) is equal to the exposure duration for that group
(EDy); therefore these variables drop out of Equation B-15.
For each chemical included in a screening scenario, total average daily exposure for age
group y (ADD(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,
ADD(yJ) —
C(i) =
IR(yJ) =
FC(i) =
ED(y) =
BW(y) =
A T(y) =
EF(y) =
Attachment B
Description of MIRC
B-20
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
- Beef products,
- Pork products, and
- Poultry and eggs;
Ingestion of drinking water from specified source; and
• Ingestion of breast milk by infants.
The algorithms for the first six exposure pathways listed above are described in Sections 3.2.1
through 3.2.6 of this attachment. The algorithms for the breast-milk ingestion pathway are
described in Section 3.4.
3.2.1. Chemical Intake from Soil Ingestion
Equation B-16 shows the equation used to estimate chemical intake through incidental ingestion
of soil.
Equation B-16. Chemical Intake from Soil Ingestion
ADD
Soil (y)
Cs0„ x IRsoil(y) X FCqnii X 0.001
'Soil
mg
V9
BW,
(y)
EF
365 days
where:
ADDsoil(y)
Csoii
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)
Note: MIRC saves soil ingestion rates in units of mg/day (not g/day); therefore, there is an additional 0.001 g/mg conversion unit in
the actual MIRC algorithm than shown here.
3.2.2. Chemical Intake from Fish Ingestion
Ingestion of locally caught fish is included as a possible exposure pathway in MIRC (Equation
B-17). Two types of fish are included in the exposure algorithm: trophic level 3.5 (abbreviated
as TL3) fish, equivalent to benthic carnivores such as catfish and trophic level 4 (TL4) fish in the
water column, equivalent to game fish such as lake trout and walleye. The chemical
concentration in fish in Equation B-17 is estimated as the consumption-weighted chemical
concentration using Equation B-18.
Attachment B
Description of MIRC
B-21
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Equation B-17. Chemical Intake from Fish Ingestion
( Irr, \
CFish x ^Fish(y) x 0-001 ^ X ECpjsh
y
j
ADDpjshfy) - (1 Llpish)7 (1 ^-2F/s/)) x x
BW(y)
( EF
365 days
Equation B-18. Consumption-weighted Chemical Concentration in Fish
^Fish = (^FishTL3 x ^TLs) + (CFishTL4 x ?TL4)
where:
ADDFish(y) =
L1Fish* =
L2Fish* =
CFishTL3 =
CFishTL4 =
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 on a wet-weight
(WW) basis (mg/kg WW)
Ftl3 = Fraction offish intake that is from TL3 (unitless)
Ftl4 = Fraction offish intake that is from TL4 (unitless)
r _ Consumption-weighted mean chemical concentration in total fish (i.e., as
Flsh specified by Equation B-18) (mg/kg WW)
FCFish = Fraction of local fish consumed derived from contaminated area (unitless)
BW(y) = Body weight for age y (kg)
IRFish(y)* = Local fish ingestion rate for age y (g WW/day)
EF =
Exposure frequency; number of days per year of exposure for family(ies) as
specified for scenario (< 365 days)
'Parameter values must be internally consistent. In contrast to the tables included in MIRC for ingestion rate options for
homegrown food products, which are based on the products as brought into the home from the field (see Section 6.3.3
of this attachment), the tables offish ingestion rate options included in MIRC are on an "as consumed" basis (i.e., after
preparation and cooking losses), and L1 and L2 therefore are set equal to zero. If the user wishes to enter local fish
ingestion rates on an "as harvested" basis, the user also should enter L1 and L2 values as specified in Section 6.4.3 of
this attachment.
When whole fish are prepared for cooking, it is usual for the viscera, head, and fins to be
removed, particularly for larger fish. Many persons also remove (or do not eat) the skin, bones,
and belly fat. EPA has, therefore, estimated the proportion of the weight of whole fish that tends
to be lost during preparation and cooking across a variety of fish species (EFH; EPA 2011a) and
included those losses in its HHRAP algorithms for chemical intake from fish (L1nsh and L2nSh in
Equation B-17).
Attachment B
Description of MIRC
B-22
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
3.2.3. Chemical Intake from Fruit Ingestion
Average daily doses of a chemical from homegrown exposed fruits are calculated separately for
exposed and protected fruits (Equation B-19 and Equation B-20, respectively).
Equation B-19. Chemical Intake from Consumption of Exposed Fruits
ADD,
ExpFruit(y)
0 LAExpFruit) x 0 L2ExpFruit) x I ^ExpFruit x ^ExpFruit(y) x 0.001-^- X FCi
\
ExpFruit
EF
365 days
Equation B-20. Chemical Intake from Consumption of Protected Fruits
ADD,
ProFruit(y)
- (1 Z.1 proprujt ) >
(
nnn-1^9 pr
ProFruit x '^ProFruit(y) x 0.001—- X ProFruit
\ f
X
EF
365 days
where:
ADDExpFruit(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,
L1 ExpFruit = stems or caps, seeds and defects, and from draining liquids from canned or
frozen forms (unitless)
L1 ProFruit
L2ExpFruit
OExpFruit
OproFruit
EF =
FCExpFruit
FC ProFruit
IRExpFruit(y)
IRproFruit(y)
Mean reduction in fruit weight that results from paring or other preparation
techniques for protected fruits (unitless)
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 home-grown exposed fruits or protected fruits (depending on
subscript) for age y (g WW/kg body weight-day)
Fruit ingestion rates in the survey were based on weights of unprepared fruits (e.g., one apple;
one pear) or the weight of a can of fruit (e.g., 8 oz can). The weight of the fruit ingested is less
than the initial weight owing to common preparation actions {L1 ExpFruit and LIproFmt, e.g., coring
apples and pears; peeling apples; pitting cherries). Cooking of exposed fruit (e.g., berries,
apples, peaches) often results in further weight loss that results from liquids lost during cooking
and drained from the cooking vessel (L2ExPFruit). EPA has assumed that cooking of protected
fruit results in no loss of weight for the fruit.
3.2.4. Chemical Intake from Vegetable Ingestion
MIRC includes three separate algorithms for homegrown vegetables adapted from EPA's
HHRAP Modeling System (EPA 2005a): one for exposed vegetables such as asparagus,
broccoli, lettuce, and tomatoes (although they are actually a fruit); one for protected vegetables
Attachment B B-23 October 2014
Description of MIRC
-------
TRIM-Based Tiered Screening Methodology for RTR
such as corn, cabbage, soybeans, and peas; and one for root vegetables such as carrots,
beets, and potatoes (see Equation B-21, Equation B-22, and Equation B-23, respectively).
Equation B-21. Chemical Intake from Exposed Vegetables
( \ ( kg ^ f EF ^
^^^ExpVeg(y) ~ V ~~ ^ExpVeg /x ^ExpVeg x ^ExpVeg(y) x 0.001——X FC^p^gg
g
/
V
365 days
Equation B-22. Chemical Intake from Protected Vegetables
( \ ( kg ^ f EF ^
ADDproVeg(y) = (1 - /-1proi/eg jx | ^ProVeg x ^ProVeg(y) x 0.001 —X FCproVeg X 355 days
Equation B-23. Chemical Intake from Root Vegetables
kg W EF
ADDRoofvegfy) 0 ^RootVeg )x 0 ^-^-RootVeg )x ^^Roofl/eg1 x ^RootVeg(y) x 0.001 ^ ^ ^^RootVeg
where:
/
V
365 days
ADDexpveg(y) Average chemical intake from ingestion of exposed vegetables, protected
ADDproveg(y) = vegetables, or root vegetables (depending on subscript) for age group y (mg
ADDRootveg(y) chemical/kg body weight-day)
L1 ExpVeg =
LlproVeg ~
L1 RootVeg ~
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
R°°tveg liquids and removal of skin after cooking (unitless)
CExpVeg Chemical concentration in exposed vegetables, protected vegetables, or root
Cproveg = vegetables (depending on subscript) on a wet-weight (WW) basis (mg
CRootveg chemical/kg vegetable WW)
EF =
Exposure frequency; number of days per year of exposure for family(ies) as
specified for scenario (<365 days)
FCExpVeg
Fr*
proveg (depending on subscript) obtained from contaminated area (unitless)
it-'RootVeg
Fraction of exposed vegetables, protected vegetables, or root vegetables
IRExpVeg(y)
IRproVeg(y)
IRpootVeg(y)
Ingestion rate of exposed vegetables, protected vegetables, or root vegetables
(depending on subscript) for age group y (g vegetable WW/kg body weight-day)
Attachment B
Description of MlRC
B-24
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
3.2.5. Chemical Intake from Animal Product Ingestion
Calculations of chemical intake from the consumption of farm animals and related food products
are provided below in Equation B-24 through Equation 2-28 for homegrown beef, dairy (milk),
pork, poultry, and eggs, respectively.
Equation B-24. Chemical Intake from Ingestion of Beef
ADDBeef(y) - (l L~\Beef)x (l L2Beef)>
(
CBeef x ^Beef(y) x 0.001 X FCBeef
g
~\ f
X
EF
365 days
where:
ADDie-e-: —
L1 Beef ~
L2[}eef =
Oseef =
EF =
Average daily chemical intake from ingestion of beef for age group y (mg/kg-
day)
Mean net cooking loss for beef (unitless)
Mean net post cooking loss for beef (unitless)
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)
IRseefM = Ingestion rate of contaminated beef for age group y (g WW/kg-day)
FCBeef = fraction beef consumed raised on contaminated area or fed contaminated
silage and grains (unitless)
Equation B-25. Chemical Intake from Dairy Ingestion
(
ADD,
Dairy(y)
_ .... kg _ _
CDairy x Dairyfy) x 0.001 — X FCDajry
y
^ f
X
EF
365 days
where:
ADDoairy(y)
C Dairy
EF
IRDairy(y)
FC Dairy
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-26. Chemical Intake from Pork Ingestion
ADDporky - 0 Llpork)'^ (1 L2p0rk)>
(
Cpork x IRpork(y) x 0.001 X FCpork
g
V EF ^
365 days
Attachment B
Description of MlRC
B-25
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
where:
ADDporkfy)
Llpork
L2p0rk ~
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)
Equation B-27. Chemical Intake from Poultry Ingestion
ADD0„„/W„, - (l LI poultry") x (l L2Poultry)x | CPoultry x IRPouitry(y) x 0.001 x FC
Poultry(y)
kg
g
A
Poultry
EF
365 days
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-28. Chemical Intake from Egg Ingestion
(
ADD,
Egg(y)
CEgg X IREgg(y) X 0.001 ""^X FC £gg
y
V EF ^
365 days
where:
nnn - Average daily chemical intake from ingestion of eggs for age group y (mg/kg-
ADDEgg(y) - dgy)
Attachment B
Description of MlRC
B-26
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
^Egg
EF
IREgg(y)
FCEgg
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)
3.2.6. Chemical Intake from Drinking Water Ingestion
If the user chooses to evaluate chemical ingestion via drinking water, the user specifies a
chemical concentration in g/L (equivalent to mg/ml_) based on their particular scenario. The
chemical concentration could represent water from groundwater wells, community water, nearby
surface waters, or other source. For this exposure pathway, ingestion rates are in units of
milliliters of water per day (mL/day) (see Equation B-29).
Equation B-29. Chemical Intake from Drinking Water Ingestion
ADD,
DW(y)
'DW
xIR,
DW(y)
xFC,
DW
BW,
(y)
EF
365 days
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)
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 home-
grown 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(yj) is estimated as the sum of chemical intake from all ingestion pathways
combined, as illustrated in Equation B-30 through Equation B-35 below.
Equations B-30 to B-35. Total Average Daily Dose of a Chemical for Different Age
Groups
Equation B-30. ADD(<1) = ADDbreastmilk
Equation B-31.
Equation B-32. ADD,, 5| = J™ 1 ADD,, 5;l
Attachment B
Description of MlRC
B-27
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
Equation B-33. ADD{6_U) = J]"=/DD(6_ni-)
Equation B-34. ADD(12_19) = £"=1 ADD{,2_Wi)
Equation B-35. ADD{aM) = £"=1 ADD
(adult,i)
where /' represents the Ith food type or ingestion medium and n equals the total number of food
types or ingestion media, and ADD parameters are defined below:
Total average daily dose of chemical for infants less than one year from
ingestion of breast milk (mg/kg-day)
Total average daily dose of chemical from all ingestion sources for children
ages 1 through 2 years (mg/kg-day)
Total average daily dose for children ages 3 through 5 years (mg/kg-day)
Total average daily dose for children ages 6 through 11 years (mg/kg-day)
Total average daily dose for children ages 12 through 19 years (mg/kg-day)
Total average daily dose for adult age 20 up to 70 years (mg/kg-day)
The lifetime average daily dose (LADD) is calculated as the time-weight average of the ADD
values for each age group (Equation B-36).
Equation B-36. Lifetime Average Daily Dose (LADD)
LADD = ADD, + ADD„.2)^ j + ADDI3_5)^ j + ADD^,,^ + ADDln.^ | + ADD,
The time-weighting factors simply equal the duration of exposure for the specified age category
in years divided by the total lifespan, assumed to be 70 years.
3.4. Chemical Intake Calculations for Nursing Infants
The scientific literature indicates that infants can be exposed to some chemicals via their
mothers' breast milk. The magnitude of the exposure can be estimated from information on the
mother's exposure, data on the partitioning of the chemical into various compartments of the
mother's body and into breast milk, and information on the infant's consumption of milk and
absorption of the chemical. To add this exposure pathway to the MIRC application, we adapted
exposure algorithms and default assumptions from EPA's Methodology for Assessing Health
Risks Associated with Multiple Pathways of Exposure to Combustor Emissions (EPA 1998),
hereafter referred to as MPE, as explained below.
Note that this pathway generally is of most concern for lipophilic bioaccumulative chemicals
(e.g., dioxins) that can cause developmental effects. The period of concern for the more
hydrophilic chemicals that cause developmental effects generally is earlier, that is, from
conception to birth. Hydrophilic chemicals generally exchange well between the maternal and
fetal blood supplies at the placenta.
ADD(<1) -
ADD (1-2) =
ADD (3-5) =
ADD (6-11) =
ADD (12-19) =
ADDfadult) =
Attachment B
Description of MIRC
B-28
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
3.4.1. Infant Average Daily Absorbed Dose
The average daily dose of chemical absorbed by the infant (DAI mi) is estimated in MIRC with
Equation B-37. This basic exposure equation relies on the concentration of the chemical in the
breast milk, the infant's breast-milk ingestion rate (IRmiik), the absorption efficiency of the
chemical by the oral route of exposure (AEm), the bodyweight of the infant (BWW), and the
duration of breast feeding (ED). Equation B-37 is EPA's (EPA 1998) modification of an average
daily dose for the infant model first published by Smith (1987) and includes variables for both
the concentration of the chemical in the breast milk fat (Cmmat) 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 in MIRC assume that either Cmmat or Caqueous is
equal to zero and hence drops out of the equation.
For the parameters in Equation B-37 (and the equations that follow) that are not calculated from
another equation, an EPA default value and options for other values available in MIRC for the
infant breast-milk-exposure pathway are described in Section 6.4 of this attachment. The user
also can overwrite those parameter values with a different value from the literature as
appropriate.
Equation B-37. Average Daily Dose of Chemical to the Nursing Infant
_ [(Cmilkfat x fmbm) + (paqueous x Q ~ tmbm))]x ^milk x AEjnf X ED
inf~ BWjnf x AT
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-38)
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-42)
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-37 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 Cmiikfat (described in Section 3.4.2 of this attachment) and Caqueous (described in
Section 3.4.3 of this attachment).
L
where:
DAI)n f =
Cmiikfat =
fmbm ~
Caqueous ~
IRmiik =
AEinf =
ED =
BWinf =
AT =
Attachment B
Description of MIRC
B-29
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
3.4.2. Chemical Concentration in Breast Milk Fat
When developing the Methodology for Assessing Health Risks Associated with Multiple
Pathways of Exposure to Combustor Emissions (MPE) (EPA 1998), EPA reviewed three first-
order kinetics models for estimating chemical concentration in breast milk fat. The model
selected for use in MIRC is the model selected in MPE. The other two models were not
considered in MPE because one used a biotransfer factor (BTF) approach considered more of a
screening model than a predictive tool (Travis et al. 1988) and the other assumed that the
contaminant concentration in the maternal fat compartment is at steady state and that the
concentration in breast milk fat is the same as in maternal body fat (Smith 1987). The model in
MIRC is a changing-concentration model that EPA adapted from a model by Sullivan et al.
(1991). The model, shown in Equation B-38, estimates the average chemical concentration in
the breast milk over the entire period of breast feeding by reference to a maximum theoretical
steady-state concentration. Studies of lipophilic chemicals such as dioxins suggest that
concentrations in the maternal milk are highest during the first few weeks of breast feeding and
then decrease over time (ATSDR 1998). Equation B-38 accounts for the changing
concentration in breast milk fat, but estimates one average value to represent the concentration
over the entire duration of breast feeding. The model is dependent on the maternal body
burden of the chemical and assumes that the chemical concentration in breast milk fat is the
same as the concentration in general maternal body fat. According to reviewers of the model,
this assumption warrants further investigation because milk fat appears to be synthesized in the
mammary glands and may have lower chemical concentrations than general body fat stores
(EPA 2001a).
Equation B-38. Chemical Concentration in Breast Milk Fat
'milkfat
DA Imat x ff
kelim x ffm
Is
elim
*fat elac
kfat elac x ^bf
— U t
«| 0 Ae//'mlpn
elim
*fat elac
g kfat _e!ac^bf ^
where:
Cmukfat = Concentration of chemical in lipid phase of maternal milk (mg chemical/kg lipid)
DAI mat
Daily absorbed maternal chemical dose (mg chemical/kg maternal body weight-
day; calculated using Equation B-39)
Fraction of total maternal body burden of chemical that is stored in maternal fat
ff = (mg chemical in body fat / mg total chemical in whole body; value from literature
or EPA default - see Section 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-40)
kelim
ftm = Fraction of maternal body weight that is fat stores (unitless)
kfat elac
Chemical-specific rate constant for total elimination of chemical in the lipid
phase of milk during nursing (per day; value from literature or calculated using
Equation B-41)
tbf = Duration of breast feeding (days)
tpn —
Duration of mother's exposure prior to parturition and initiation of breast feeding
(days)
Attachment B
Description of MIRC
B-30
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
Equation B-38 relies on the daily maternal absorbed intake (DAImat) to determine the
concentration of the chemical in the breast milk fat. DAImat is multiplied by the fraction of the
chemical that is stored in maternal fat (/y) to determine the amount (i.e., mass) of chemical in the
fat. This product, divided by the chemical-specific elimination rate constant (kenm) 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 (DAImat ft I keiim ffm) also assumes that the
biological half-life of the chemical in the mother's breast milk fat is small relative to the duration
of the mother's exposure. However, for chemicals with half-lives that are longer than the
exposure duration, which are the chemicals of concern in the applications of MIRC to date, an
additional term is needed to determine the average concentration in the milk fat over the
duration of her exposure.
To account for breast feeding losses and longer chemical half-lives in the mother than the
exposure duration, an additional term is included in Equation B-38. This term includes a fraction
dependent on two rate constants, 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 (DAImat ft I kenm ffm), the maximum theoretical steady-state concentration, is
multiplied by the rate of elimination averaged over the duration of the mother's exposure,
including her exposure prior to and during lactation. To review the derivation of Equation B-38,
see Appendix B of MPE (EPA 1998).
To estimate an average daily dose absorbed by an infant's mother, or DAImat, the average daily
dose (ADD) (in mg/kg-day) for the chemical from all sources that MIRC calculates for adults
(ADD(aduit), described in Section 3.3 of this attachment, Equation B-35), is multiplied by an
absorption efficiency (AEmat) or fraction of the chemical absorbed by the oral route of exposure,
as shown in Equation B-39. The value for AEmat can be estimated from absorption efficiencies
for adults in general. Available data for some chemicals, in particular some inorganic
compounds, indicate AE values for ingestion exposures of substantially less than 100 percent.
For a few of these chemicals, data also indicate lower AEs for the chemical when ingested in
food or in soil than when ingested in water (e.g., cadmium). For a screening level assessment,
however, it is reasonable to either assume 100 percent for the AEmat or to use the higher AEmat
of the food and water AEmat values if available; hence, a single AEmat parameter is included in
Equation B-39.
Equation B-39. Daily Maternal Absorbed Intake
DAImat = ADD(aduit) X AE
mat
where:
= Daily maternal dose of chemical absorbed from medium /' (mg/kg-day)
Average daily dose to the mother (mg/kg-day) (calculated by MIRC - see
Section 3.3 of this attachment, Equation B-35)
Absorption efficiency of the chemical by the oral exposure route (i.e., chemical-
= specific fraction of ingested chemical that is absorbed) by the mother (unitless)
(value from literature or EPA default - see Section 6.4 of this attachment)
DAImat
ADD(adult)
AEmat
Attachment B
Description of MIRC
B-31
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
Equation B-35, used to calculate ADD(aduit), is based on many medium-specific ingestion rates
that are normalized to body weight. The adult body weights to which the homegrown food
ingestion rates are normalized are the body weights of the consumers in the original USDA
survey (see Section 6.3.3 of this attachment), which included both males and females. An
assumption in the breast-milk exposure pathway is that those ingestion rates also are applicable
to nursing mothers. The original data for ingestion rates for soil, drinking water, and fish are on
a per person basis for males and females combined. MIRC divides those chemical intakes by
an adult body weight for males and females combined as specified by the user (e.g., 71.4 kg
mean value) to estimate the ADD normalized to body weight from those sources. If the user
finds that those exposure media contribute the majority of the chemical intake for the risk
scenario under consideration, the user may use alternative ingestion rates for those media and
alternative body weights for nursing women, as described in Section 6.5 of this attachment.
Elimination rates for chemicals often are reported as the half-life of the chemical in the body
following a known dose of chemical. Many chemicals exhibit a two-phase elimination process,
the first being more rapid than the second. For screening risks for persistent and
bioaccumulative chemicals, the half-life of the slower phase of elimination, presumably from
non-blood compartments of the body, is the more important of the two. Assuming first-order
kinetics, Equation B-40 is used to convert a measured half-life for elimination of a chemical for
adults or non-lactating women to an elimination rate constant (EPA 1998). The equation can be
used to estimate any kind of chemical loss rate constant from a measured chemical half-life.
Equation B-40. Biological Elimination Rate Constant for Chemicals
for Non-lactating Women
In 2
elim ~ ^
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-41 to estimate the total
chemical elimination rate for lactating women, kfat_eiac (EPA 1998).
Equation B-41. Biological Elimination Constant for Lipophilic Chemicals
for Lactating Women
is — is
A/af elac Aelim
IRmilk KffXf,
mbm
ffm X BWmat
where:
Attachment B B-32 October 2014
Description of MIRC
-------
TRIM-Based Tiered Screening Methodology for RTR
Rate constant for total elimination of chemical during nursing (per day); accounts
kfat_eiac = for both elimination by adults in general and the additional chemical elimination
via the lipid phase of milk in nursing women
*elim
Elimination rate constant for chemical from adults, including non-lactating
women (per day; e.g., via urine, bile to feces, exhalation; chemical-specific;
value from literature or calculated from half-life using Equation B-40)
IRmiik = Infant milk ingestion rate over the duration of nursing (kg/d)
Fraction of total maternal body burden of chemical that is stored in maternal fat
ft = (mg chemical in body fat / mg chemical total in body; value from literature or
EPA default)
fmbm = Fraction of fat in breast milk (unitless)
ffm = Fraction of maternal body weight that is fat stores (unitless)
di/i/ _ Maternal body weight over the entire duration of the mother's exposure to the
mat ~ chemical including during pregnancy and lactation (kg)
Equation B-41 is based on a model from Smith (1987) and accounts for the additional
elimination pathway for lipophilic chemicals via the breast milk fat. The term Kfat_eiac is
estimated by adding an estimate of the first-order elimination constant for breast feeding losses
to keiim, 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 (IRmiik), 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 body
weight (BWmat), and the fraction of the mother's weight that is body fat (frm). In Equation B-41,
the value for the mother's body weight should be specific to women of child-bearing age, as
opposed to a body weight value for both males and females that is used to estimate an adult
average daily dose and the mother's absorbed daily intake in Equation B-39. Body weight
values for the mother are described in Section 6.5 of this attachment. Smith's (1987) model
assumes that the chemical partitions to the lipid-phase of breast milk to the same degree that it
partitions into the mother's body fat. For highly lipophilic compounds, losses from breast
feeding can be larger than losses by all other pathways (EPA 1998).
3.4.3. Chemical Concentration in Aqueous Phase of Breast Milk
When developing MPE (EPA 1998), EPA also considered models to estimate chemical
concentrations in the aqueous phase of breast milk (CaqUeous). EPA adapted Smith's (1987)
steady state concentration model for estimating Cmiikfatand developed the CaqUeous model shown
in Equation B-42 (EPA 1998). Chemicals that would partition to the aqueous phase of human
milk include water-soluble chemicals, such as salts of metals, and other hydrophilic chemicals
that may be in equilibrium with bound forms of the chemical in different tissues. The CaqUeous
equation assumes that the chemical concentration in the aqueous phase of milk is directly
proportional to the chemical concentration in the mother's blood plasma. The portion of
chemical sequestered in red blood cells (e.g., bound to RBC proteins) is assumed to be
unavailable for direct transfer to breast milk.
Equation B-42. Chemical Concentration in Aqueous Phase of Breast Milk
p _ DAImat x fpi x PCbm
Uaqueous ~ . r
aq_elac pm
Attachment B
Description of MlRC
B-33
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
where:
Caqueous = Concentration of chemical in aqueous phase of maternal milk (mg/kg)
DAImat = Daily absorbed maternal chemical dose (mg/kg-day; calculated by Equation B-39)
Fraction of chemical in the body (based on absorbed intake) that is in the blood
fpi = plasma compartment (unitless; value from literature or calculated by Equation
B-43)
pc _ Partition coefficient for chemical between the plasma and breast milk in the
bm aqueous phase (unitless); assumed to equal 1.0
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-44)
fpm = Fraction of maternal weight that is blood plasma (unitless)
Equation B-42 is a steady-state concentration model that, like the Equation B-38 for Cmmat, is
dependent on the maternal absorbed daily intake (DAImat). In Equation B-42, DAImat is multiplied
by the fraction of the absorbed chemical that is circulating in the blood plasma compartment (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, lead, and cadmium that
partition into body compartments such as red blood cells and bone. While these latter
chemicals or forms of these chemicals are water-soluble when free, they have relatively long
half-lives because they are in equilibrium with the chemical bound to macromolecules in some
tissue compartments. Lead is of particular concern because it can be released from the bone
into the blood during lactation, and thus into the breast milk (EPA 2001a). Due to this limitation,
the model may over- or underestimate exposure to the infant.
Because Equation B-42 is based on the relationship between the chemical concentrations in the
aqueous phase of breast milk and the blood plasma, a value for the fraction of the chemical in
the mother's blood plasma (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-43, provided that an
empirical value can be found for the fraction of the chemical in the body that is in the mother's
whole blood compartment (fa, EPA 1998).
Equation B-43. Fraction of Total Chemical in Body in the Blood Plasma Compartment
where:
Fraction of chemical in body (based on absorbed intake) that is in the blood
plasma compartment (unitless); chemical-specific
Attachment B
Description of MlRC
B-34
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
, _ 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)
d~od^ Partition coefficient for chemical between red blood cells and plasma (unitless);
nCKoLf - I ¦
chemical-specific
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-42 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-40. 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-44. Biological Elimination Rate Constant for Hydrophilic Chemicals
Is — Is
rxaq_elac elim
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-40)
3.4.4. Alternative Model for Infant Intake of Methyl Mercury
In this version of MIRC, we were unable to fully parameterize the aqueous model for mercury. In
particular, no empirical value could be found for the steady-state fraction of total hydrophilic
chemical body burden in the mother that is in the blood plasma (fpi, see Exhibit B-29). This
parameter could be estimated using Equation B-43 if a suitable chemical-specific fraction of
chemical in the body that is in the whole blood (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.
We therefore conducted a literature search to identify existing physiologically based
toxicokinetic (PBTK) models of lactational transfer of methylmercury (MeHg) in humans. Most
PBTK models that we identified focused on gestational transfer of mercury between mother and
Attachment B B-35 October 2014
Description of MIRC
kaq_elac ~
kelim —
-------
TRIM-Based Tiered Screening Methodology for RTR
fetus, including a PBTK dynamic compartmental model for gestational transfer of MeHg in
humans developed by Gearhart et al. (1995, 1996), and reparameterized by Clewell et al.
(1999).
We did find, however, that Byczkowski and Lipscomb (2001) had added a lactational transfer
module to the Clewell et al. (1999) model. Byczkowski and Lipscomb compared their model's
predictions to epidemiological data from mother-nursing-infant pairs obtained following an
accidental high-dose poisoning in Iraq (Amin-Zaki et al. 1976) and from 34 mother-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 (SD) |jg/kg-day and the average infant intake of MeHg to be 0.80 ± 0.38 |jg/kg-day.
Therefore, for purposes of MIRC, the DAimt of MeHg is estimated to be the same as the
maternal intake per unit body weight (Equation B-42).
Equation B-45. Calculation of Infant Average Daily Absorbed Dose of Methyl Mercury
DAI,
inf_MeHg
= DAI,
mat_MeHg
where:
DAIjnf_ MeHg ~~ Average d3ily dos© of MeHg absorbed by infant from br63St milk (mg/Kg-day)
nAI _ Average daily dose of methyl mercury absorbed by the mother, predominantly
mat_MeHg - from fjs(l (mg/kg-day)
4. Dose-Response Values Used for Assessment
Chemical dose-response values included in MIRC include carcinogenic potency slope factors
for ingestion and non-cancer oral reference doses (RfDs) for chronic exposures. The dose-
response values currently used in MIRC for RTR assessments are shown in Exhibit B-6 For
some chemicals, OAQPS has identified dose-response values for use in RTR (EPA 2007a), and
these dose-response values are used in MIRC for RTR assessments. In general, OAQPS chose
these values based on the following hierarchy of sources: EPA's Integrated Risk Information
System (IRIS); the Centers for Disease Control's Agency for Toxic Substances and Disease
Registry (ATSDR); and the California Environmental Protection Agency's (CalEPA's) Office of
Environmental Health Hazard Assessment (OEHHA) Toxicity Criteria Database. For PB-HAP
chemicals that are currently evaluated in MIRC but do not currently have dose-response values
identified by OAQPS for RTR, alternative methods for deriving values were used (see Sections
4.2 and 4.4 of this attachment).
Attachment B
Description of MIRC
B-36
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit B-6. Oral Dose-response Values Used to Calculate RTR Screening Threshold
Emission Rates for PB-HAP Chemicals3
Chemical
CAS No.
Cancer Slope Factor
Reference Dose
Value
(mg/kg-day)-1
Source
Value
(mg/kg-
day)
Source
Inorganics
Cadmium compounds in foodb
7440439
not available
1.0E-03
IRIS
Mercury (elemental)
7439976
not available
not available
Mercuric chloride
7487947
not available
3.0E-04
IRIS
Methyl mercury (MeHg)
22967926
not available
1.0E-04
IRIS
Dioxins
1,2,3,4,6,7,8-
Heptachlorodibenzo-p-dioxin
35822469
1.50E+03
Derived from WHO
2005 TEFsc
not available
1,2,3,4,6,7,8-
Heptachlorodibenzofuran
67562394
1.50E+03
Derived from WHO
2005 TEFsc
not available
1,2,3,4,7,8,9-
Heptachlorodibenzofuran
55673897
1.50E+03
Derived from WHO
2005 TEFsc
not available
1,2,3,4,7,8-
Hexachlorodibenzo-p-dioxin
39227286
1.50E+04
Derived from WHO
2005 TEFsc
not available
1,2,3,4,7,8-
Hexachlorodibenzofuran
70648269
1.50E+04
Derived from WHO
2005 TEFsc
not available
1,2,3,6,7,8-
Hexachlorodibenzo-p-dioxin
57653857
1.50E+04
Derived from WHO
2005 TEFsc
not available
1,2,3,6,7,8-
Hexachlorodibenzofuran
57117449
1.50E+04
Derived from WHO
2005 TEFsc
not available
1,2,3,7,8,9-
Hexachlorodibenzo-p-dioxin
19408743
6.20E+03
IRIS
not available
1,2,3,7,8,9-
Hexachlorodibenzofuran
72918219
1.50E+04
Derived from WHO
2005 TEFsc
not available
2,3,4,6,7,8-
Hexachlorodibenzofuran
60851345
1.50E+04
Derived from WHO
2005 TEFsc
not available
1,2,3,4,6,7,8,9-
Octachlorodibenzo-p-dioxin
3268879
4.50E+01
Derived from WHO
2005 TEFsc
not available
1,2,3,4,6,7,8,9-
Octachlorodibenzofuran
39001020
4.50E+01
Derived from WHO
2005 TEFsc
not available
1,2,3,7,8-
Pentachlorodibenzo-p-dioxin
40321764
1.50E+05
Derived from WHO
2005 TEFsc
not available
1,2,3,7,8-
Pentachlorodibenzofuran
57117416
4.50E+03
Derived from WHO
2005 TEFsc
not available
2,3,4,7,8-
Pentachlorodibenzofuran
57117314
4.50E+04
Derived from WHO
2005 TEFsc
not available
2,3,7,8-
Tetrachlorodibenzo-p-dioxin
1746016
1.50E+05
EPA ORD
7E-10
IRIS
2,3,7,8-
Tetrachlorodibenzofuran
51207319
1.50E+04
Derived from WHO
2005 TEFsc
not available
Attachment B
Description of MlRC
B-37
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit B-6. Oral Dose-response Values Used to Calculate RTR Screening Threshold
Emission Rates for PB-HAP Chemicals3
Chemical
CAS No.
Cancer Slope Factor
Reference Dose
Value
(mg/kg-day)-1
Source
Value
(mg/kg-
day)
Source
Polycyclic Organic Matter
1 -Methylnaphthalene
90120
5.0E-01
EPA 1999, POM
Group 72002d
7.0E-02
ATSDR
2-Acetylaminofluorene
53963
1.0E+01
EPA 1999, POM
Group 75002d
not available
2-Chloronaphthalene
91587
5.0E-01
EPA 1999, POM
Group 72002d
8.0E-02
IRIS
2-Methylnaphthalene
91576
5.0E-01
EPA 1999, POM
Group 72002d
5.0E-02
ATSDR
3-Methylcholanthrene
56495
2.2E+1
CalEPA
not available
7,12-Dimethylbenz(a)anthracene
57976
2.5E+02
CalEPA
not available
Acenaphthene
83329
5.0E-01
EPA 1999, POM
Group 72002d
6.0E-02
IRIS
Acenaphthylene
208968
5.0E-01
EPA 1999, POM
Group 72002d
not available
Anthracene
120127
0e
IRIS
3.0E-01
IRIS
Benz(a)anthracene
56553
1.2E+00
CalEPA
not available
Benzo(a)pyrene
50328
7.3E+00
IRIS
not available
Benzo(b)fluoranthene
205992
1.2E+00
CalEPA
not available
Benzo(e)pyrene
192972
5.0E-01
EPA 1999, POM
Group 72002d
not available
Benzo(g,h,i)perylene
191242
5.0E-01
EPA 1999, POM
Group 72002d
not available
Benzo(k)fluoranthene
207089
1.2E+00
CalEPA
not available
Carbazole
86748
2.0E-02
HEAST
not available
Chrysene
218019
1.2E-01
CalEPA
not available
Dibenzo(a,h)anthracene
53703
4.1E+00
CalEPA
not available
Dibenzo(a,i)pyrene
189559
1.2E+02
CalEPA
not available
Dibenzo(a,j)acridine
224420
1.2E+00
CalEPA
not available
Fluoranthene
206440
5.0E-01
EPA 1999, POM
Group 72002d
4.0E-02
IRIS
Fluorene
86737
5.0E-01
EPA 1999, POM
Group 72002d
4.0E-02
IRIS
lndeno(1,2,3-c,d)pyrene
193395
1.2E+00
CalEPA
not available
PAH, total
234
5.0E-01
EPA 1999, POM
Group 71002d
not available
Perylene
198550
5.0E-01
EPA 1999, POM
Group 72002d
not available
Phenanthrene
85018
0e
IRIS
not available
Polycyclic Organic Matter
246
5.0E-01
EPA 1999, POM
Group 71002d
not available
Attachment B
Description of MlRC
B-38
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit B-6. Oral Dose-response Values Used to Calculate RTR Screening Threshold
Emission Rates for PB-HAP Chemicals3
Chemical
CAS No.
Cancer Slope Factor
Reference Dose
Value
(mg/kg-day)"1
Source
Value
(mg/kg-
day)
Source
Pyrene
129000
0e
IRIS
3.0E-02
IRIS
Retene
483658
5.0E-01
EPA 1999, POM
Group 72002d
not available
ATSDR = Agency for Toxic Substances and Disease Registry, IRIS = Integrated Risk Information System, EPA OAQPS = EPA's
Office of Air Quality Planning and Standards, CalEPA = California EPA, EPA ORD = EPA's Office of Research and Development,
HEAST = EPA Health Effects Assessment Tables, TEF = toxic equivalency factor
aValues as of June 2012; these values may be updated as newer ones become available.
There 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 for these congeners were derived as
discussed in Section 4.2 of this attachment.
dThe method to assign oral cancer slope factors to polycyclic organic matter (POM) without CSFs available from other EPA sources is
the same as that used in the 1999 National Air Toxics Assessment (EPA 1999). A complete description of the methodology is
available at: http://www.epa.gov/ttn/atw/nata1999/99pdfs/pomapproachjan.pdfand is summarized in Section 4.4 of this attachment.
Weight of evidence evaluations indicated that the available data were adequate to determine that this chemical was not carcinogenic
(EPA 2010a),
4.1. Cadmium
EPA has developed two chronic RfDs for cadmium (Cd), one for food and one for water, based
on data in IRIS indicating a lower absorption efficiency of cadmium from food than from water.
The default RfD set in MIRC is the higher RfD for Cd compounds in food (as described in
Section A.2.3, the drinking water exposure pathway is not modeled in the screening scenario
because the likelihood that humans would use a lake as a drinking water source is assumed to
be low). Users of MIRC who assess exposures via drinking water would need to use the RfD
for Cd compounds in water (i.e., 5.0E-04 mg/kg-day).
4.2. Dioxins (2,3,7,8-TCDD)
For chemicals for which the critical health effect is developmental, either in utero and/or during
the first months or years of life, the exposure duration and timing of exposure for comparison
with the RfD (or comparable values) require special consideration. The most sensitive health
endpoints for both mercury and 2,3,7,8-TCDD are neurological effects during development that
have long-lasting effects on learning and social behaviors. To ensure a protective risk
characterization for these chemicals, it is important to use the shortest exposure duration
appropriate, at the appropriate life stage, for comparison with the toxicity reference values. This
approach avoids "dilution" of an estimated average ADD that would result from averaging the
lower daily chemical intake rates normalized to body weight for older children and adults with
the potentially higher daily intake rates of infants over a longer exposure averaging period.
The convention for assessing risk from mixtures of dioxins is by application of toxic equivalency
factors (TEFs) to dioxin concentrations, which are then expressed as toxic equivalents (TEQs).
Of the dioxin congeners, 2,3,7,8-TCDD is the most widely studied and considered to be one of
the most toxic congeners. It is therefore assigned a TEF of 1, with the other dioxin congener
TEQ concentrations scaled relative to 2,3,7,8-TCDD concentrations on the basis of toxicity. For
risk assessment of dioxins for RTR (with one exception), the World Health Organization (WHO)
2005 TEFs presented in Exhibit B-7 were used to derive the CSFs (shown in Exhibit B-6) for
dioxin congeners without available EPA dose response values. The one exception is
1,2,3,7,8,9-Hexachlorodibenzo-p-dioxin, whose TEF was based on data from IRIS.
Attachment B
Description of MIRC
B-39
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit B-7. WHO 2005 Toxic Equivalency Factors (TEFs) for Dioxin
Congeners
Dioxin Congener
CAS No.
WHO 2005 Toxic
Equivalency Factor3
1,2,3,4,6,7,8-Heptachlorodibenzo-p-dioxin
35822469
0.01
1,2,3,4,6,7,8-Heptachlorodibenzofuran
67562394
0.01
1,2,3,4,7,8,9-Heptachlorodibenzofuran
55673897
0.01
1,2,3,4,7,8-Hexachlorodibenzo-p-dioxin
39227286
0.1
1,2,3,4,7,8-Hexachlorodibenzofuran
70648269
0.1
1,2,3,6,7,8-Hexachlorodibenzo-p-dioxin
57653857
0.1
1,2,3,6,7,8-Hexachlorodibenzofuran
57117449
0.1
1,2,3,7,8,9-Hexachlorodibenzo-p-dioxinb
19408743
0.04
1,2,3,7,8,9-Hexachlorodibenzofuran
72918219
0.1
2,3,4,6,7,8-Hexachlorodibenzofuran
60851345
0.1
1,2,3,4,6,7,8,9-Octachlorodibenzo-p-dioxin
3268879
3E-04
1,2,3,4,6,7,8,9-Octachlorodibenzofuran
39001020
3E-04
1,2,3,7,8-Pentachlorodibenzo-p-dioxin
40321764
1
1,2,3,7,8-Pentachlorodibenzofuran
57117416
0.03
2,3,4,7,8-Pentachlorodibenzofuran
57117314
0.3
2,3,7,8-Tetrachlorodibenzo-p-dioxin
1746016
1
2,3,7,8-Tetrachlorodibenzofuran
51207319
0.1
aSource: van den Berg et al. 2006, with the one exception in the next footnote.
bFor 1,2,3,7,8,9-HexCDD, OAQPS identified an oral cancer slope factor from IRIS. For the purposes of
these multipathway assessments, EPA uses the TEF derived from this IRIS oral CSF (6200 1/mg/kd/d,
equaling a TEF of 0.041) rather than the WHO 2005 TEF of 0.1.
4.3. Mercury
The RfD applies to the pregnant mother as well as young children. EPA has not specified the
minimum exposure duration at the RfD level of exposure that is appropriate to use in
characterizing risk; we assume 10 years for women of childbearing age and 1 year for infants.
We note that human exposures to MeHg are primarily through the consumption of fish and
shellfish (EPA 2001b). EPA found that, on average, approximately 76 percent of the exposure
to MeHg for women of childbearing age could be attributed to ingestion of mercury in freshwater
and estuarine fish and shellfish, with the remaining 24 percent derived from marine fish and
shellfish. Other sources accounted for less than 0.06 percent of total exposures (EPA 2001b).
4.4. Polycyclic Organic Matter
Dose-response values for some of the POM species that are included in the screening
assessment were not identified by OAQPS; for these POM species, an alternative methodology
for identifying CSFs was needed. Previously, for risk assessment of inhalation exposures to
polycyclic organic matter (POM) for EPA's National-Scale Air Toxics Assessments (NATA) and
for RTR, OAQPS developed an approach for characterizing risks associated with the individual
POM species and POM groups reported in the National Emissions Inventory (NEI). Individual
POMs were assigned to one of eight POM groups according to cancer potencies derived by
EPA for IRIS and by CalEPA, and based on assumptions regarding relative carcinogenicity.
OAQPS then estimated an inhalation CSF for each POM group. The same approach was used
to derive oral CSFs for POMs without available CSFs. Each POM group (with all its member
Attachment B
Description of MlRC
B-40
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
POM species reported in NEI, not just the species currently evaluated in this assessment) and
the corresponding CSFs using this methodology are presented in Exhibit B-8. These group
CSFs are used only when OAQPS has not, for the purposes of RTR, identified a CSF specific to
the individual chemical.
Exhibit B-8. Oral Dose-response Values for
Polycyclic Organic Matter (POM) Groups3
Individual POM or POM Group
CAS No.
Cancer Slope Factor15
(mg/kg-day)"1
POM Group 71002
Benz(a)anthracene/chrysene (7-PAH)
103
Total PAH
234
Polycyclic organic matter
246
0.5
16-PAH
40
16-PAH-7-PAH
75040
POM Group 72002
Anthracene
120127
Pyrene
129000
Benzo(g,h,i) perylene
191242
Benzo(e)pyrene
192972
Benzo(c)phenanthrene
195197
Perylene
198550
Benzo(g,h,i)fluoranthene
203123
Benzo(a)fluoranthene
203338
Fluoranthene
206440
Acenaphthylene
208968
1-Methylpyrene
2381217
12-Methylbenz(a)anthracene
2422794
0.5
Methylbenzopyrenes
247
Methylchrysene
248
Methylanthracene
26914181
Benzofluoranthenes
56832736
9-Methylbenz(a)anthracene
779022
1 -Methylphenanthrene
832699
Acenaphthene
83329
Phenanthrene
85018
Fluorene
86737
2-Methylnaphthalene
91576
2-Chloronaphthalene
91587
POM Group 73002
7,12-Dimethylbenz(a)anthracene
57976
1000
POM Group 74002
Dibenzo(a,i)pyrene
189559
100
Dibenzo(a,h)pyrene
189640
Attachment B
Description of MlRC
B-41
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit B-8. Oral Dose-response Values for
Polycyclic Organic Matter (POM) Groups3
Individual POM or POM Group
CAS No.
Cancer Slope Factor15
(mg/kg-day)"1
POM Group 75002
3-Methylcholanthrene
56495
Dibenzo(a,e)pyrene
192654
5-Methylchrysene
3697243
10
Benzo(a)pyrene
50328
Dibenzo(a,h)anthracene
53703
POM Group 76002
Benzo(b+k)fluoranthene
102
lndeno(1,2,3-c,d)pyrene
193395
Benzo(j)fluoranthene
205823
Benzo(b)fluoranthene
205992
1
Benzo(k)fluoranthene
207089
Dibenzo(a,j)acridine
224420
Benz(a)anthracene
56553
POM Group 77002
Chrysene
218019
0.1
POM Group 78002
7-PAH
75
0.5
aThese group CSFs are used only when OAQPS has not, for the purposes of RTR, 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 (EPA 1999). A complete description of the methodology is available at:
http://www.epa.gov/ttn/atw/nata1999/99pdfs/pomapproachjan.pdf.
5. Risk Estimation
For persistent and bioaccumulative hazardous air pollutants (PB-HAPs), risks from inhalation of
a chemical directly from air generally will be negligible compared with risks from ingestion of the
chemical from foodstuffs grown in an area subject to air deposition of the chemical. Risk
characterization for carcinogens with a linear mode of action at low doses is described in
Section 5.1 of this attachment. Risk characterization for chemicals likely to exhibit a threshold
for response (e.g., non-cancer hazards) is described in Section 5.2.
5.1. Cancer Risks
The estimated risk of developing cancer from exposure to a chemical from a specified source is
characterized as the excess lifetime cancer risk (ELCR). The ELCR represents the incremental
probability of an individual developing cancer over a lifetime as a result of lifetime exposure to
the chemical. For a known or suspected carcinogen with a low-dose linear mode of action, the
estimated ELCR is calculated as the product of the lifetime average daily dose (LADD) and the
cancer slope factor (CSF):
Attachment B
Description of MlRC
B-42
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
Equation B-46. Calculation of Excess Lifetime Cancer Risk
ELCR = LADD x CSF
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 3.3 of this attachment, the LADD (in mg/kg-day) for a chemical is
calculated to reflect age-related differences in exposure rates that are experienced by a
hypothetical individual throughout his or her lifetime of exposure. The total chemical intake is
normalized to a lifetime, which for the purposes of this assessment is assumed to be 70 years.
EPA considers the possibility that children might be more sensitive than adults to toxic
chemicals, including chemical carcinogens (EPA 2005b,c). Where data allow, EPA
recommends development of lifestage-specific cancer potency CSFs. To date, EPA has
developed a separate slope factor for early lifestage exposure for only one chemical (i.e., 1,1,1-
trichloroethane; EPA 2007b), and current data availability for most chemicals preclude this
approach. EPA has, therefore, examined options for default adjustments of the CSF to protect
children. To date, the only mode of action (MOA) for carcinogenesis for which EPA has
adequate data to develop a reasonable quantitative default approach is mutagenesis (EPA
2005b,c). For carcinogens with a mutagenic MOA for cancer, EPA concluded that the
carcinogenic potency of a chemical may be approximately tenfold greater for the first 2 years of
life (i.e., birth up to second birthday) and threefold greater for the next 14 years of life (i.e., ages
2 through 15) than for adults (EPA 2005c). These conclusions are represented by age-
dependent adjustment factors (ADAFs) of 10, 3, and 1 for the first two lifestages and for adults,
respectively.
These three lifestages do not match the age categories for the home-grown food ingestion
rates, the age categories in MIRC. As a consequence, ADAFs for the age groups in MIRC are
adapted as time-weighted average values as follows:
where:
ELCR
LADD
CSF
ADAF(<1) = 10 ADAF(6_iy = 3
ADAF(V2) = (lO"1yrH3x1yr) = 6s ADAFm,al = (3x4yrsHlx4yrs)= 2
ADAF,3_5) = 3 ADAF(aduit) = 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.
Attachment B B-43 October 2014
Description of MIRC
-------
TRIM-Based Tiered Screening Methodology for RTR
Equations B-47 to B-53. Lifetime Cancer Risk: Chemicals with a
Mutagenic MOA for Cancer
Equation
B-47.
Risk(
In other words, Equation B-53 indicates that the total excess lifetime cancer risk (ELCR) equals
the sum of the age-group-specific risks estimated by Equation B-47 through Equation B-52,
where:
Risk(
-------
TRIM-Based Tiered Screening Methodology for RTR
5.2.1. Hazard Quotients for Chemicals with a Chronic RfD
For chemicals with a chronic RfD, MIRC calculates an HQ for each age group separately using
Equation B-54 to indicate the potential for adverse health effects associated with chronic
exposure via ingestion pathways. The HQ is the ratio of a long-term, daily average exposure
normalized to the receptor's body weight (i.e., ADD) to the RfD for that chemical. HQs are
threshold effects and are not additive across age groups.
Equation B-54. Hazard Quotient for Chemicals with a Chronic RfD
hq = add
RfD
where:
Hazard quotient for chemical (unitless)
Average daily ingested dose of chemical (mg/kg-day) from all food types and
ingested media for the age group
Chronic oral reference dose for chemical (mg/kg-day)
5.2.2. Hazard Quotients for Chemicals with RfD Based on Developmental
Effects
For chemicals for which the toxicity reference value is an RfD based on developmental effects,
a shorter exposure duration (ED) and averaging time (AT) may be required. For this type of
chemical (e.g., methylmercury, 2,3,7,8-TCDD), the appropriate ED/AT and sensitive lifestage for
exposure may need to be estimated from the information provided in the critical developmental
study(ies) from which the RfD was derived (e.g., in consultation with the RfD documentation in
EPA's IRIS or in a toxicological profile developed for the chemical). For screening-level risk
assessments, however, a health protective approach is to compare the highest ADD from
among the child age categories provided in MIRC to the RfD, as is done for all PB-HAPs. This
approach ensures that the highest exposure from among the various age groups evaluated is
taken into consideration, regardless of which age group might be most relevant to the health
effect of interest (i.e., the age group on which the RfD is based).
5.2.3. Hazard Index for Chemicals with RfDs
When conducting screening-level assessments for multiple chemicals, it can be informative to
calculate a hazard index (HI) for toxicologically similar chemicals (EPA 2000). The HI is the
sum of HQs across chemicals (not age groups) as shown in Equation B-55. As with the HQ, if
the HI value is less than 1, adverse health effects are not expected for that suite of chemicals. If
the screening level HI exceeds 1, however, the risk assessor may in some instances, evaluate
the assumptions of the screening-level assessment to determine if more realistic local values
are available for parameters that drive risk. In addition, the risk assessor may need to examine
the mode of action (MOA) and target organ(s) for the chemicals with the highest HQs to develop
an appropriate approach to assessing their potential joint action.
HQ =
ADD =
RfD =
Attachment B B-45 October 2014
Description of MIRC
-------
TRIM-Based Tiered Screening Methodology for RTR
Equation B-55. Hazard Index Calculation
HI = HQ1 + HQ2... HQn
where:
HI = Hazard index (unitless)
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" (EPA 2000; p
79). If a receptor is exposed to multiple chemicals that affect different target organs or that
operate by different MOAs, and if more than one HQ is close to 1, the risk assessor in some
circumstances, may consider whether chemical interactions play a role in chemical toxicity (EPA
2000). Exposures to more than one chemical can result in a greater or lesser toxic response
than might be predicted on the basis of one or the other chemical acting alone (toxicologically
independent) or acting in concert (toxicologically similar chemicals). Users are referred to
EPA's Supplementary Guidance for Conducting Health Risk Assessment of Chemical Mixtures
for approaches to assessing the potential for adverse health effects from exposure to multiple
chemicals (EPA 2000).
Note that users of MIRC are responsible for determining how to interpret HQs for multiple
chemicals.
6. Model Input Options
This section describes the input options currently included in MIRC. Required user inputs for
environmental media concentrations and air deposition rates, such as those predicted by
(output of) TRIM.FaTE, are described in Section 6.1 of this attachment. Values for farm-food-
chain (FFC) parameters for specific types of produce and animal products are discussed in
Section 6.2. Options for parameterizing receptor characteristics are described in Section 6.3,
including age-group-specific values for body weight, water ingestion, and food ingestion by food
type. Options for other exposure parameter values in MIRC, such as exposure frequency and
loss of chemical during food preparation and cooking, are provided in Section 6.4.
Where values for chemical-specific parameters are presented, values are presented only for
PB-HAP chemicals currently evaluated using the TRIM-based RTR screening scenario. The
database included with MIRC contains chemical-specific parameter values for a large number of
chemicals (more than 500), because all of the chemical-specific input data compiled by EPA for
use in HHRAP were uploaded into MIRC. However, only chemicals that are PB-HAPs
evaluated for RTR are discussed in this document (see Sections 1.1.1 and 2.2.2), and the
HHRAP inputs provided for other chemicals have not been reviewed or verified. The data
presented in this chapter were reviewed and used to develop the set of modeling defaults used
to calculate screening threshold emission rates for RTR. Note that the default values used to
estimate RTR screening thresholds, and the justification for selecting a specific value from the
data sets described in this chapter, are discussed in Chapter 7.
Attachment B
Description of MIRC
B-46
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
6.1. Environmental Concentrations
As noted in Section 2 of this attachment, MIRC is intended to estimate exposures and risks to
self-sufficient farming and fishing families from ingestion of FFC media in an area of airborne
chemical deposition. The tool analyzes one exposure scenario at a time (e.g., adult farmer
exposed to dioxin from ingestion of beef); therefore, it is best used to evaluate a maximally
exposed individual (MEI) or family when MIRC is used to screen for possible risks.
The following values specific to the air pollutant of concern are required inputs to MIRC:
a single air concentration (in g/m3);
the fraction of chemical in the air that is in the vapor phase;
air-to-surface deposition rates for both vapor- and particle-phase chemical in the air (in
g/m2-yr);
two fish tissue concentrations, one each for forage and game fish (i.e., fish in TL 3 and
TL 4) (in mg/kg wet weight);
concentrations in drinking water (in g/L); and
• four chemical concentrations in soil (in |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 MIRC software is configured to estimate ingestion exposures via drinking water for a
specified chemical concentration in the drinking water source (e.g., groundwater well).
However, no exposure via drinking water is assumed to occur when calculating the Tier 1
screening thresholds. As discussed in Section 2.2.3, the drinking water exposure pathway is
not modeled for the scenario developed for the Tier 1 assessment because the likelihood that
humans would use a lake as a drinking water source is assumed to be low.
The user must provide the inputs listed above; no default values are included for these
parameters in MIRC. Media concentrations output by TRIM.FaTE can be entered into the tool
manually from model output files or can be imported directly from the TRIM.FaTE output files.
For RTR evaluations, a tool to facilitate this process was developed using a Microsoft® Excel™
routine written in Visual Basic.
6.2. Farm-Food-Chain Parameter Values
Using the chemical information specified in Section 6.1 above as inputs, MIRC calculates
chemical concentrations in foods that are commonly grown or raised on family farms: exposed
and protected fruits; exposed and protected vegetables; root vegetables; beef; total dairy
products; pork; and poultry and eggs.
6.2.1. List of Farm-Food-Chain (FFC) Parameters
MIRC estimates chemical concentrations in the produce identified above using algorithms from
HHRAP (EPA 2005a) as described in Section 3.2 of this attachment. Parameter values
required for these HHRAP algorithms, including chemical-specific media transfer factors (e.g.,
Attachment B B-47 October 2014
Description of MIRC
-------
TRIM-Based Tiered Screening Methodology for RTR
soil-to-plant transfer coefficients) and plant- and animal-specific properties (e.g., plant
interception fraction, quantity of forage consumed by cattle), are included in tables in MIRC. As
described in Section 7 of this attachment, the HHRAP-recommended parameter values are the
default values in MIRC; however, these and other inputs in MIRC can be revised as needed.
Exhibit B-9 describes the parameters that are included in the algorithms used to estimate
chemical concentrations in the farm food categories. The parameter names and symbols are
referenced in this section for plants/produce and animal products.
Exhibit B-9. MIRC Parameters Used to Estimate Chemical Concentrations
in Farm Foods
Parameter
Description
Units
Plants/Produce
BrAG-produce-
DW(i)
Chemical-specific plant/soil chemical bioconcentration factor for
edible portion of aboveground produce type i, exposed or
protected
Unitless (g soil DW / g
produce DW)
BVAG(i)
Chemical-specific air-to-plant biotransfer factor for aboveground
produce type i for vapor-phase chemical in air
Unitless ([mg chemical /
g DW plant] / [mg
chemical / g air])
Fw
Fraction of wet deposition that adheres to plant surfaces; 0.2 for
anions, 0.6 for cations and most organics
Unitless
Kds
Chemical-specific soil/water partition coefficient
L soil pore water / kg soil
DW
kp(i)
Plant-specific surface loss coefficient for aboveground exposed
produce and animal forage and silage
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 on a wet-weight (WW) basis
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
Tpo)
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 i to address possible overestimate of the diffusive transfer of
chemical from the outside to the inside of bulky produce, such as
fruit
Unitless
VGrootveg
Empirical correction factor for belowground produce (i.e., tuber or
root vegetable) to account for possible overestimate of the
diffusive transfer of chemicals from the outside to the inside of
bulky tubers or roots (based on carrots and potatoes)
Unitless
Ypa)
Plant-specific yield or standing crop biomass of the edible portion
of produce or animal feed
kg produce DW/m2
Animal Products
Bs
Soil bioavailability factor for livestock
Unitless
MF
Chemical-specific mammalian metabolism factor that accounts
for endogenous degradation of the chemical
Unitless
Attachment B
Description of MIRC
B-48
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit B-9. MIRC Parameters Used to Estimate Chemical Concentrations
in Farm Foods
Parameter
Description
Units
Ba)
(unitless)d
Inorganics
Cadmium compounds
0.6
NA
7.5E+01
NAe
Mercury (elemental)
0.6
NA
1.0E+03
0f
Mercuric chloride
0.6
NA
5.8E+04
1.8E+03
Methyl mercury
0.6
NA
7.0E+03
0f
PAHs
2-Methylnaphthalene
0.6
2.2E+02
5.0E+01
1.4E+00
7,12-
Dimethylbenz(a)anthrace
ne
0.6
6.8E+03
4.0E+03
4.2E+04
Acenaphthene
0.6
2.4E+02
3.9E+01
4.6E+00
Acenaphthylene
0.6
2.8E+02
6.8E+01
8.1E+00
Benz(a)anthracene
0.6
6.7E+03
2.9E+03
6.8E+03
Benzo(a)pyrene
0.6
9.2E+03
7.8E+03
1.7E+05
Attachment B
Description of MIRC
B-49
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit B-10. Chemical-Specific Inputs for Produce Parameters
for Chemicals Included in MIRC
Chemical
Fraction of
Wet
Deposition
(Fw)
(unitless)3
Root
Concentration
Factor (RCF)
(belowground)
(L/kg)b
Soil-Water
Partition
Coefficient
(Kc/s)
(L/kg)c
Chemical Air-to-
Plant
Biotransfer
Factor (Bvago))
(unitless)d
Benzo(b)fluoranthene
0.6
6.6E+03
3.8E+03
1.7E+05
Benzo(g,h,i)perylene
0.6
3.0E+04
2.6E+04
2.3E+06
Benzo(k)fluoranthene
0.6
8.7E+03
5.5E+03
2.8E+05
Chrysene
0.6
6.0E+03
3.4E+03
1.4E+04
Dibenz(a,h)anthracene
0.6
2.3E+04
1.4E+04
6.2E+06
Fluoranthene
0.6
2.2E+03
3.9E+02
9.0E+02
Fluorene
0.6
3.8E+02
6.2E+01
1.6E+01
lndeno(1,2,3-cd)pyrene
0.6
3.5E+04
3.2E+04
2.8E+06
Dioxins
OctaCDD,
1,2,3,4,6,7,8,9-
0.6
4.8E+05
7.8E+05
2.4E+06
OctaCDF, 1,2,3,4,6,7,8,9-
0.6
3.4E+05
4.9E+05
2.3E+06
HeptaCDD, 1,2,3,4,6,7,8-
0.6
3.4E+05
4.9E+05
9.1E+05
HeptaCDF, 1,2,3,4,6,7,8-
0.6
1.2E+05
1.2E+05
8.3E+05
HeptaCDF, 1,2,3,4,7,8,9-
0.6
4.8E+04
3.9E+04
8.3E+05
HexaCDD, 1,2,3,4,7,8-
0.6
2.4E+05
3.1E+05
5.2E+05
HexaCDF, 1,2,3,4,7,8-
0.6
5.7E+04
4.9E+04
1.6E+05
HexaCDD, 1,2,3,6,7,8-
0.6
4.9E+05
8.0E+05
5.2E+05
HexaCDF, 1,2,3,6,7,8-
0.6
2.9E+05
4.1E+05
1.6E+05
HexaCDD, 1,2,3,7,8,9 -
0.6
4.9E+05
8.0E+05
5.2E+05
HexaCDF, 1,2,3,7,8,9-
0.6
1.6E+05
1.9E+05
1.6E+05
HexaCDF, 2,3,4,6,7,8-
0.6
2.9E+05
4.1E+05
1.6E+05
PentaCDD, 1,2,3,7,8-
0.6
9.2E+04
9.2E+04
2.4E+05
PentaCDF, 1,2,3,7,8-
0.6
3.9E+04
3.0E+04
9.8E+04
PentaCDF, 2,3,4,7,8-
0.6
2.3E+04
1.6E+04
9.8E+04
Attachment B
Description of MIRC
B-50
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit B-10. Chemical-Specific Inputs for Produce Parameters
for Chemicals Included in MIRC
Chemical
Fraction of
Wet
Deposition
(Fw)
(unitless)3
Root
Concentration
Factor (RCF)
(belowground)
(L/kg)b
Soil-Water
Partition
Coefficient
(Kc/s)
(L/kg)c
Chemical Air-to-
Plant
Biotransfer
Factor (Bvago))
(unitless)d
TetraCDD, 2,3,7,8-
0.6
4.0E+04
3.1E+04
6.6E+04
TetraCDF, 2,3,7,8-
0.6
1.2E+04
6.2E+03
4.6E+04
Source: EPA 2005a. NA = not applicable.
a6E-01 is the value for cations and most organic chemicals. As described in HHRAP (EPA 2005a), Appendix B (available at
http://www.epa.gov/osw/hazard/tsd/td/combust/finalmact/ssra/05hhrapapb.pdf), EPA estimated this value (EPA 1994a, 1995a)
from a study by Hoffman et al. (1992) in which soluble gamma-emitting radionuclides and insoluble particles tagged with gamma-
emitting radionuclides were deposited onto pasture grass via simulated rain. Note that the values developed experimentally for
pasture grass may not accurately represent all aboveground produce-specific values. Also note that values based on the
behavior of insoluble particles tagged with radionuclides may not accurately represent the behavior of organic compounds under
site-specific conditions.
bFor nonionic organic chemicals, as described in HHRAP (EPA 2005a), Appendix A (available at
http://www.epa.gov/osw/hazard/tsd/td/combust/finalmact/ssra/05hhrapapa.pdf), RCF is used to calculate the below-ground
transfer of contaminants from soil to a root vegetable on a wet-weight basis as shown in Equation B-6. EPA estimated chemical-
specific values for RCF from empirical regression equations developed by Briggs et al. (1982) based on their experiments
measuring uptake of compounds into barley roots from growth solution. Briggs' regression equations allow calculation of RCF
values from log Kow. For metals and mercuric compounds, empirical values for soil to root vegetable transfer on a dry-weight
basis are available in the literature, thus the RCF was not needed.
°As discussed in HHRAP (EPA 2005a), Appendix A, Kds describes the partitioning of a compound between soil pore-water and
soil particles and strongly influences the release and movement of a compound into the subsurface soils and underlying aquifer.
Kds values for mercuric compounds were obtained from EPA (1997b). Kds for cadmium compounds were obtained from EPA
1996. For all PAHs and dioxins, Kds was calculated by multiplying Koc times the screening scenario's fraction organic carbon
content (0.008). Empirical information for Koc was available for acenaphthene, benz(a)anthracene, benzo(a)pyrene,
dibenz(a,h)anthracene, fluoranthene, and fluorene in USEAP 1996. For all other organic compounds, the Koc was calculated
using the correlation equations presented in USEAP 2005a.
dAs discussed in HHRAP (EPA 2005a), Appendix A, the value for mercuric chloride was obtained from EPA 1997b. BvAG(i)
values for PAHs were calculated using the correlation equation derived for azalea leaves as cited in Bacci et al. (1992), then
reducing this value by a factor of 100, as suggested by Lorber (1995), who concluded that the Bacci factor reduced by a factor of
100 was similar to his own observations in various studies. The values for dioxins were obtained from Lorber and Pinsky (2000).
elt is assumed that metals, with the exception of vapor-phase elemental mercury, do not transfer significantly from air into leaves.
fSpeciation and fate and transport of mercury from emissions suggest that BvAG(i) values for elemental and methyl mercury are
likely to be zero (EPA 2005a).
Exhibit B-11. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC
Compound Name
Plant Part
Plant-Soil Bio-
Concentration
Factor
(BfA G-produce-DW(ij)
(unitless)3
Empirical Correction
Factor- Belowground
Produce
{VG root veg) (unitless)b
Empirical
Correction Factor-
Aboveground
Produce
(VGago)) (unitless)c
Inorganics
Cadmium compounds
Exp. Fruit
1.3E-01
-
1.0E+00
Exp. Veg.
1.3E-01
-
1.0E+00
Forage
3.6E-01
-
1.0E+00
Grain
6.2E-02
-
-
Prot. Fruit
1.3E-01
-
-
Prot. Veg.
1.3E-01
-
-
Root
6.4E-02
1.0E+00
-
Silage
3.6E-01
-
5.0E-01
Attachment B
Description of MIRC
B-51
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit B-11. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC
Compound Name
Plant Part
Plant-Soil Bio-
Concentration
Factor
(BrA G-produce-DW(ij)
(unitless)3
Empirical Correction
Factor- Belowground
Produce
(VGrootveg) (unitless)b
Empirical
Correction Factor-
Aboveground
Produce
(VGago)) (unitless)c
Mercury (elemental)
Exp. Fruit
-
-
1.0E+00
Exp. Veg.
-
-
1.0E+00
Forage
-
-
1.0E+00
Grain
-
-
-
Prot. Fruit
-
-
-
Prot. Veg.
-
-
-
Root
-
1.0E+00
-
Silage
-
-
5.0E-01
Mercuric chloride
Exp. Fruit
1.5E-02
-
1.0E+00
Exp. Veg.
1.5E-02
-
1.0E+00
Forage
0.0E+00
-
1.0E+00
Grain
9.3E-03
-
-
Prot. Fruit
1.5E-02
-
-
Prot. Veg.
1.5E-02
-
-
Root
3.6E-02
1.0E+00
-
Silage
0.0E+00
-
5.0E-01
Methyl mercury
Exp. Fruit
2.9E-02
-
1.0E-02
Exp. Veg.
2.9E-02
-
1.0E-02
Forage
0.0E+00
-
1.0E+00
Grain
1.9E-02
-
-
Prot. Fruit
2.9E-02
-
-
Prot. Veg.
2.9E-02
-
-
Root
9.9E-02
1.0E-02
-
Silage
0.0E+00
-
5.0E-01
PAHs
2-Methylnaphthalene
Exp. Fruit
2.3E-01
-
1.0E+00
Exp. Veg.
2.3E-01
-
1.0E+00
Forage
2.3E-01
-
1.0E+00
Grain
2.3E-01
-
-
Prot. Fruit
2.3E-01
-
-
Prot. Veg.
2.3E-01
-
-
Root
4.4E+00
1.0E+00
-
Silage
2.3E-01
-
5.0E-01
7,12-
Dimethylbenz(a)anthra
cene
Exp. Fruit
1.7E-02
-
1.0E-02
Exp. Veg.
1.7E-02
-
1.0E-02
Forage
1.7E-02
-
1.0E+00
Grain
1.7E-02
-
-
Prot. Fruit
1.7E-02
-
-
Prot. Veg.
1.7E-02
-
-
Root
1.7E+00
1.0E-02
-
Silage
1.7E-02
-
5.0E-01
Attachment B
Description of MIRC
B-52
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit B-11. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC
Compound Name
Plant Part
Plant-Soil Bio-
Concentration
Factor
(BrA G-produce-DW(ij)
(unitless)3
Empirical Correction
Factor- Belowground
Produce
(VGrootveg) (unitless)b
Empirical
Correction Factor-
Aboveground
Produce
(VGago)) (unitless)c
Acenaphthene
Exp. Fruit
2.1E-01
-
1.0E+00
Exp. Veg.
2.1E-01
-
1.0E+00
Forage
2.1E-01
-
1.0E+00
Grain
2.1E-01
-
-
Prot. Fruit
2.1E-01
-
-
Prot. Veg.
2.1E-01
-
-
Root
6.2E+00
1.0E+00
-
Silage
2.1E-01
-
5.0E-01
Acenaphthylene
Exp. Fruit
1.9E-01
-
1.0E-02
Exp. Veg.
1.9E-01
-
1.0E-02
Forage
1.9E-01
-
1.0E+00
Grain
1.9E-01
-
-
Prot. Fruit
1.9E-01
-
-
Prot. Veg.
1.9E-01
-
-
Root
4.1E+00
1.0E-02
-
Silage
1.9E-01
-
5.0E-01
Benz(a)anthracene
Exp. Fruit
1.7E-02
-
1.0E-02
Exp. Veg.
1.7E-02
-
1.0E-02
Forage
1.7E-02
-
1.0E+00
Grain
1.7E-02
-
-
Prot. Fruit
1.7E-02
-
-
Prot. Veg.
1.7E-02
-
-
Root
2.3E+00
1.0E-02
-
Silage
1.7E-02
-
5.0E-01
Benzo(a)pyrene
Exp. Fruit
1.4E-02
-
1.0E-02
Exp. Veg.
1.4E-02
-
1.0E-02
Forage
1.4E-02
-
1.0E+00
Grain
1.4E-02
-
-
Prot. Fruit
1.4E-02
-
-
Prot. Veg.
1.4E-02
-
-
Root
1.2E+00
1.0E-02
-
Silage
1.4E-02
-
5.0E-01
Attachment B
Description of MIRC
B-53
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit B-11. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC
Compound Name
Plant Part
Plant-Soil Bio-
Concentration
Factor
(BrA G-produce-DW(ij)
(unitless)3
Empirical Correction
Factor- Belowground
Produce
(VGrootveg) (unitless)b
Empirical
Correction Factor-
Aboveground
Produce
(VGago)) (unitless)c
Benzo(b)fluoranthene
Exp. Fruit
1.8E-02
-
1.0E-02
Exp. Veg.
1.8E-02
-
1.0E-02
Forage
1.8E-02
-
1.0E+00
Grain
1.8E-02
-
-
Prot. Fruit
1.8E-02
-
-
Prot. Veg.
1.8E-02
-
-
Root
1.7E+00
1.0E-02
-
Silage
1.8E-02
-
5.0E-01
Benzo(g,h,i)perylene
Exp. Fruit
5.7E-03
-
1.0E-02
Exp. Veg.
5.7E-03
-
1.0E-02
Forage
5.7E-03
-
1.0E+00
Grain
5.7E-03
-
-
Prot. Fruit
5.7E-03
-
-
Prot. Veg.
5.7E-03
-
-
Root
1.1E+00
1.0E-02
-
Silage
5.7E-03
-
5.0E-01
Benzo(k)fluoranthene
Exp. Fruit
1.4E-02
-
1.0E-02
Exp. Veg.
1.4E-02
-
1.0E-02
Forage
1.4E-02
-
1.0E+00
Grain
1.4E-02
-
-
Prot. Fruit
1.4E-02
-
-
Prot. Veg.
1.4E-02
-
-
Root
1.6E+00
1.0E-02
-
Silage
1.4E-02
-
5.0E-01
Chrysene
Exp. Fruit
1.9E-02
-
1.0E-02
Exp. Veg.
1.9E-02
-
1.0E-02
Forage
1.9E-02
-
1.0E+00
Grain
1.9E-02
-
-
Prot. Fruit
1.9E-02
-
-
Prot. Veg.
1.9E-02
-
-
Root
1.7E+00
1.0E-02
-
Silage
1.9E-02
-
5.0E-01
Attachment B
Description of MIRC
B-54
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit B-11. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC
Compound Name
Plant Part
Plant-Soil Bio-
Concentration
Factor
(BrA G-produce-DW(ij)
(unitless)3
Empirical Correction
Factor- Belowground
Produce
(VGrootveg) (unitless)b
Empirical
Correction Factor-
Aboveground
Produce
(VGago)) (unitless)c
Dibenz(a,h)anthracene
Exp. Fruit
6.8E-03
-
1.0E-02
Exp. Veg.
6.8E-03
-
1.0E-02
Forage
6.8E-03
-
1.0E+00
Grain
6.8E-03
-
-
Prot. Fruit
6.8E-03
-
-
Prot. Veg.
6.8E-03
-
-
Root
1.6E+00
1.0E-02
-
Silage
6.8E-03
-
5.0E-01
Fluoranthene
Exp. Fruit
4.0E-02
-
1.0E-02
Exp. Veg.
4.0E-02
-
1.0E-02
Forage
4.0E-02
-
1.0E+00
Grain
4.0E-02
-
-
Prot. Fruit
4.0E-02
-
-
Prot. Veg.
4.0E-02
-
-
Root
5.6E+00
1.0E-02
-
Silage
4.0E-02
-
5.0E-01
Fluorene
Exp. Fruit
1.5E-01
-
1.0E-02
Exp. Veg.
1.5E-01
-
1.0E-02
Forage
1.5E-01
-
1.0E+00
Grain
1.5E-01
-
-
Prot. Fruit
1.5E-01
-
-
Prot. Veg.
1.5E-01
-
-
Root
6.2E+00
1.0E-02
-
Silage
1.5E-01
-
5.0E-01
lndeno(1,2,3-
cd)pyrene
Exp. Fruit
5.1E-03
-
1.0E-02
Exp. Veg.
5.1E-03
-
1.0E-02
Forage
5.1E-03
-
1.0E+00
Grain
5.1E-03
-
-
Prot. Fruit
5.1E-03
-
-
Prot. Veg.
5.1E-03
-
-
Root
1.1E+00
1.0E-02
-
Silage
5.1E-03
-
5.0E-01
Attachment B
Description of MIRC
B-55
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit B-11. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC
Compound Name
Plant Part
Plant-Soil Bio-
Concentration
Factor
(BrA G-produce-DW(ij)
(unitless)3
Empirical Correction
Factor- Belowground
Produce
{VG root veg) (unitless)b
Empirical
Correction Factor-
Aboveground
Produce
(VGago)) (unitless)c
Dioxins
OctaCDD,
1,2,3,4,6,7,8,9-
Exp. Fruit
7.1E-04
-
1.0E-02
Exp. Veg.
7.1E-04
-
1.0E-02
Forage
7.1E-04
-
1.0E+00
Grain
7.1E-04
-
-
Prot. Fruit
7.1E-04
-
-
Prot. Veg.
7.1E-04
-
-
Root
6.1E-01
1.0E-02
-
Silage
7.1E-04
-
5.0E-01
OctaCDF,
1,2,3,4,6,7,8,9-
Exp. Fruit
9.2E-04
-
1.0E-02
Exp. Veg.
9.2E-04
-
1.0E-02
Forage
9.2E-04
-
1.0E+00
Grain
9.2E-04
-
-
Prot. Fruit
9.2E-04
-
-
Prot. Veg.
9.2E-04
-
-
Root
6.8E-01
1.0E-02
-
Silage
9.2E-04
-
5.0E-01
HeptaCDD,
1,2,3,4,6,7,8-
Exp. Fruit
9.2E-04
-
1.0E-02
Exp. Veg.
9.2E-04
-
1.0E-02
Forage
9.2E-04
-
1.0E+00
Grain
9.2E-04
-
-
Prot. Fruit
9.2E-04
-
-
Prot. Veg.
9.2E-04
-
-
Root
6.8E-01
1.0E-02
-
Silage
9.2E-04
-
5.0E-01
HeptaCDF,
1,2,3,4,6,7,8-
Exp. Fruit
2.0E-03
-
1.0E-02
Exp. Veg.
2.0E-03
-
1.0E-02
Forage
2.0E-03
-
1.0E+00
Grain
2.0E-03
-
-
Prot. Fruit
2.0E-03
-
-
Prot. Veg.
2.0E-03
-
-
Root
9.4E-01
1.0E-02
-
Silage
2.0E-03
-
5.0E-01
Attachment B
Description of MIRC
B-56
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit B-11. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC
Compound Name
Plant Part
Plant-Soil Bio-
Concentration
Factor
(BrA G-produce-DW(ij)
(unitless)3
Empirical Correction
Factor- Belowground
Produce
(VGrootveg) (unitless)b
Empirical
Correction Factor-
Aboveground
Produce
(VGago)) (unitless)c
HeptaCDF,
1,2,3,4,7,8,9-
Exp. Fruit
4.0E-03
-
1.0E-02
Exp. Veg.
4.0E-03
-
1.0E-02
Forage
4.0E-03
-
1.0E+00
Grain
4.0E-03
-
-
Prot. Fruit
4.0E-03
-
-
Prot. Veg.
4.0E-03
-
-
Root
1.2E+00
1.0E-02
-
Silage
4.0E-03
-
5.0E-01
HexaCDD, 1,2,3,4,7,8-
Exp. Fruit
1.2E-03
-
1.0E-02
Exp. Veg.
1.2E-03
-
1.0E-02
Forage
1.2E-03
-
1.0E+00
Grain
1.2E-03
-
-
Prot. Fruit
1.2E-03
-
-
Prot. Veg.
1.2E-03
-
-
Root
7.6E-01
1.0E-02
-
Silage
1.2E-03
-
5.0E-01
HexaCDF, 1,2,3,4,7,8-
Exp. Fruit
3.5E-03
-
1.0E-02
Exp. Veg.
3.5E-03
-
1.0E-02
Forage
3.5E-03
-
1.0E+00
Grain
3.5E-03
-
-
Prot. Fruit
3.5E-03
-
-
Prot. Veg.
3.5E-03
-
-
Root
1.2E+00
1.0E-02
-
Silage
3.5E-03
-
5.0E-01
HexaCDD, 1,2,3,6,7,8-
Exp. Fruit
7.0E-04
-
1.0E-02
Exp. Veg.
7.0E-04
-
1.0E-02
Forage
7.0E-04
-
1.0E+00
Grain
7.0E-04
-
-
Prot. Fruit
7.0E-04
-
-
Prot. Veg.
7.0E-04
-
-
Root
6.1E-01
1.0E-02
-
Silage
7.0E-04
-
5.0E-01
Attachment B
Description of MIRC
B-57
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit B-11. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC
Compound Name
Plant Part
Plant-Soil Bio-
Concentration
Factor
(BrA G-produce-DW(ij)
(unitless)3
Empirical Correction
Factor- Belowground
Produce
(VGrootveg) (unitless)b
Empirical
Correction Factor-
Aboveground
Produce
(VGago)) (unitless)c
HexaCDF, 1,2,3,6,7,8-
Exp. Fruit
1.0E-03
-
1.0E-02
Exp. Veg.
1.0E-03
-
1.0E-02
Forage
1.0E-03
-
1.0E+00
Grain
1.0E-03
-
-
Prot. Fruit
1.0E-03
-
-
Prot. Veg.
1.0E-03
-
-
Root
7.1E-01
1.0E-02
-
Silage
1.0E-03
-
5.0E-01
HexaCDD, 1,2,3,7,8,9-
Exp. Fruit
7.0E-04
-
1.0E-02
Exp. Veg.
7.0E-04
-
1.0E-02
Forage
7.0E-04
-
1.0E+00
Grain
7.0E-04
-
-
Prot. Fruit
7.0E-04
-
-
Prot. Veg.
7.0E-04
-
-
Root
6.1E-01
1.0E-02
-
Silage
7.0E-04
-
5.0E-01
HexaCDF, 1,2,3,7,8,9-
Exp. Fruit
1.6E-03
-
1.0E-02
Exp. Veg.
1.6E-03
-
1.0E-02
Forage
1.6E-03
-
1.0E+00
Grain
1.6E-03
-
-
Prot. Fruit
1.6E-03
-
-
Prot. Veg.
1.6E-03
-
-
Root
8.5E-01
1.0E-02
-
Silage
1.6E-03
-
5.0E-01
HexaCDF, 2,3,4,6,7,8-
Exp. Fruit
1.0E-03
-
1.0E-02
Exp. Veg.
1.0E-03
-
1.0E-02
Forage
1.0E-03
-
1.0E+00
Grain
1.0E-03
-
-
Prot. Fruit
1.0E-03
-
-
Prot. Veg.
1.0E-03
-
-
Root
7.1E-01
1.0E-02
-
Silage
1.0E-03
-
5.0E-01
Attachment B
Description of MIRC
B-58
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit B-11. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC
Compound Name
Plant Part
Plant-Soil Bio-
Concentration
Factor
(BrA G-produce-DW(ij)
(unitless)3
Empirical Correction
Factor- Belowground
Produce
(VGrootveg) (unitless)b
Empirical
Correction Factor-
Aboveground
Produce
(VGago)) (unitless)c
PentaCDD, 1,2,3,7,8-
Exp. Fruit
2.4E-03
-
1.0E-02
Exp. Veg.
2.4E-03
-
1.0E-02
Forage
2.4E-03
-
1.0E+00
Grain
2.4E-03
-
-
Prot. Fruit
2.4E-03
-
-
Prot. Veg.
2.4E-03
-
-
Root
1.0E+00
1.0E-02
-
Silage
2.4E-03
-
5.0E-01
PentaCDF, 1,2,3,7,8-
Exp. Fruit
4.6E-03
-
1.0E-02
Exp. Veg.
4.6E-03
-
1.0E-02
Forage
4.6E-03
-
1.0E+00
Grain
4.6E-03
-
-
Prot. Fruit
4.6E-03
-
-
Prot. Veg.
4.6E-03
-
-
Root
1.3E+00
1.0E-02
-
Silage
4.6E-03
-
5.0E-01
PentaCDF, 2,3,4,7,8-
Exp. Fruit
6.8E-03
-
1.0E-02
Exp. Veg.
6.8E-03
-
1.0E-02
Forage
6.8E-03
-
1.0E+00
Grain
6.8E-03
-
-
Prot. Fruit
6.8E-03
-
-
Prot. Veg.
6.8E-03
-
-
Root
1.5E+00
1.0E-02
-
Silage
6.8E-03
-
5.0E-01
TetraCDD, 2,3,7,8-
Exp. Fruit
4.5E-03
-
1.0E-02
Exp. Veg.
4.5E-03
-
1.0E-02
Forage
4.5E-03
-
1.0E+00
Grain
4.5E-03
-
-
Prot. Fruit
4.5E-03
-
-
Prot. Veg.
4.5E-03
-
-
Root
1.3E+00
1.0E-02
-
Silage
4.5E-03
-
5.0E-01
Attachment B
Description of MIRC
B-59
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit B-11. Chemical-Specific Inputs by Plant Type for Chemicals in MIRC
Compound Name
Plant Part
Plant-Soil Bio-
Concentration
Factor
(BrA G-produce-DW(ij)
(unitless)3
Empirical Correction
Factor- Belowground
Produce
(VGrootveg) (unitless)b
Empirical
Correction Factor-
Aboveground
Produce
(VGago)) (unitless)c
TetraCDF, 2,3,7,8-
Exp. Fruit
1.2E-02
-
1.0E-02
Exp. Veg.
1.2E-02
-
1.0E-02
Forage
1.2E-02
-
1.0E+00
Grain
1.2E-02
-
-
Prot. Fruit
1.2E-02
-
-
Prot. Veg.
1.2E-02
-
-
Root
1.9E+00
1.0E-02
-
Silage
1.2E-02
-
5.0E-01
aAs discussed in HHRAP (EPA 2005a), the BrAG.praJuce.Dw(i) for aboveground produce and forage accounts for the uptake from soil
and the subsequent transport of contaminants through the roots to the aboveground plant parts. For organics, correlation
equations to calculate values for Br on a dry weight basis were obtained from Travis and Arms (1988). For cadmium, Brvalues
were derived from uptake slope factors provided in EPA 1992. Uptake slope is the ratio of contaminant concentration in dry weight
plant tissue to the mass of contaminant applied per hectare soil. Br aboveground values for mercuric chloride and methyl mercury
were calculated using methodology and data from Baes, et al. (1984). Brforage values for mercuric chloride and methyl mercury
(on a dry weight basis) were obtained from EPA 1997b. The HHRAP methodology assumes that elemental mercury doesn't
deposit onto soils. Therefore, it's assumed that there is no plant uptake through the soil.
bAs discussed in HHRAP (EPA 2005a), Appendix B, VGrootveg represents an empirical correction factor that reduces produce
concentration. Because of the protective outer skin, size, and shape of bulky produce, transfer of lipophilic chemicals (i.e., log K™,
greater than 4) to the center of the produce is not likely. In addition, typical preparation techniques, such as washing, peeling, and
cooking, further reduce the concentration of the chemical in the vegetable as consumed by removing the high concentration of
chemical on and in the outer skin, leaving the flesh with a lower concentration than would be the case if the entire vegetable were
pureed without washing. For belowground produce, HHRAP (EPA 2005a) recommends using a VGrootveg value of 0.01 for PB-HAP
with a log Kow greater than 4 and a value of 1.0 for PB-HAP with a log Kow less than 4 based on information provided in EPA
1994b. In developing these values, EPA (1994b) assumed that the density of the skin and the whole vegetable are equal
(potentially overestimating the concentration of PB-HAP in belowground produce due to root uptake).
°As discussed in HHRAP (EPA 2005a), Appendix B, VGag represents an empirical correction factor that reduces aboveground
produce concentration and was developed to estimate the transfer of PB-HAP into leafy vegetation versus bulkier aboveground
produce (e.g., apples). Because of the protective outer skin, size, and shape of bulky produce, transfer of lipophilic PB-HAP (log
Kow greater than 4) to the center of the produce is not likely. In addition, typical preparation techniques, such as washing, peeling,
and cooking, further reduces residues. For aboveground produce, HHRAP (EPA 2005a) recommends using a VGag value of 0.01
for PB-HAP with a log Kow greater than 4 and a value of 1.0 for PB-HAP with a log K™, less than 4 based on information provided in
EPA 1994b. In developing these values, EPA (1994b) assumed the following: (1) translocation of compounds deposited on the
surface of aboveground vegetation to inner parts of aboveground produce would be insignificant (potentially underestimating the
concentration of PB-HAP in aboveground produce due to air-to-plant transfer); (2) the density of the skin and the whole vegetable
are equal (potentially overestimating the concentration of PB-HAP in aboveground produce due to air-to-plant transfer); and (3) the
thickness of vegetable skin and broadleaf tree skin are equal (effects on the concentration of PB-HAP in aboveground produce
due to air-toplant transfer unknown).
For forage, HHRAP recommends a VGag value of 1.0, also based on information provided in EPA 1994b.
A VGag value for silage is not provided in EPA 1994b; the VGag value for silage of 0.5 was obtained from NC DEHNR (1997);
however, NC DEHNR does not present a specific rationale for this recommendation. Depending on the composition of the site-
specific silage, this value may under- or overestimate the actual value.
Attachment B
Description of MIRC
B-60
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
Exhibit B-12. Non-Chemical-Specific Produce Inputs
Plant Part
Interception
Fraction
(RP(o)
(unitless)3
Plant
Surface
Loss
Coefficient
(kPffl)
(1/year)b
Length of
Plant
Exposure to
Deposition
(Tpo))
(year)c
Yield or
Standing
Crop
Biomass
(YP(0)
(kg/m2)d
Plant Tissue-
Specific
Moisture
Adjustment
Factor (MAFo>)
(percent)"5
Exposed Vegetable
0.982
18
0.16
5.66
92
Protected Fruit
NA
NA
NA
NA
90
Protected
Vegetable
NA
NA
NA
NA
80
Forage (animal
feed)
0.5
18
0.12
0.24
92
Exposed Fruit
0.053
18
0.16
0.25
85
Root Vegetables
NA
NA
NA
NA
87
Silage (animal feed)
0.46
18
0.16
0.8
92
Grain (animal feed)
NA
NA
NA
NA
90
Source: EPA 2005a. NA = not applicable.
aBaes et al. (1984) used an empirical relationship developed by Chamberlain (1970) to identify a correlation between initial Rp
values and pasture grass productivity (standing crop biomass [Vp]) 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.
bThe term kp 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 EPA 1994a and 1998) represents the midpoint of a range of values
reported by Miller and Hoffman (1983). There are two key uncertainties associated with using these values for kp: (1) The
recommended equation for calculating kp includes a health protective bias in that it does not consider chemical degradation
processes. (2) Given the reported range of kp values from 7.44 to 90.36 yr"1, plant concentrations could range from about 1.8
times higher to about 5 times lower than the plant concentrations estimated in FFC media using the midpoint kp value of 18.
°HHRAP (EPA 2005a) recommends using a Tp value of 0.16 years for aboveground produce and cattle silage. This is consistent
with earlier reports by EPA (1994a, 1998) and NC DEHNR (1997), which recommended treating Tp as a constant based on the
average period between successive hay harvests. Belcher and Travis (1989) estimated this period at 60 days. Tp is calculated
as 60 days 365 days/year = 0.16 years. For forage, the average of the average period between successive hay harvests (60
days) and the average period between successive grazing (30 days) is used (that is, 45 days), and Tp is calculated as (60 days +
30 days)/ 2 365 days/yr = 0.12 yr. Two key uncertainties are associated with use of these values for Tp\ (1) The average period
between successive hay harvests (60 days) may not reflect the length of the growing season or the length between successive
harvests for site-specific aboveground produce crops. The concentration of chemical in aboveground produce due to direct (wet
and dry) deposition (Pd) will be underestimated if the site-specific value of Tp is less than 60 days, or overestimated if the site-
specific value of Tp is more than 60 days.
dYp values for aboveground produce and forage were calculated using an equation presented in Baes et al. (1984) and Shor et al.
(1982): Yp = Yhjl Ahi, where Yhi= Harvest yield of th crop (kg DW) and Ahi = Area planted to /'"'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.
"MAP represents the plant tissue-specific moisture adjustment factor to convert dry-weight concentrations into wet-weight
concentrations (which are lower owing to the dilution by water compared with dry-weight concentrations). Values obtained from
Chapter 10 of EPA's 2003 SAB Review materials for 3MRA Modeling System, Volume II, "Farm Food Chain and Terrestrial Food
Web Data" (EPA 2003a), which references EPA 1997c. Note that the value for grain used as animal feed is based on corn and
soybeans, not seed grains such as barley, oats, or wheat.
Attachment B
Description of MlRC
B-61
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
6.2.3. Animal Product Parameter Values
MIRC also requires chemical-specific inputs for many of the animal product algorithms. The
relevant values are shown in Exhibit B-13 for the chemicals included in MIRC to date. The
HHRAP algorithms require additional inputs for the animal products calculations that are not
specific to PB-HAPs, but are specific to the animal and animal product type. The soil and plant
ingestion rates recommended in HHRAP for beef cattle, dairy cattle, swine, and chicken are
provided in Exhibit B-14.
Exhibit B-13. Animal Product Chemical-specific Inputs
for Chemicals Included in MIRC
Compound Name
Soil Bio-
Availability
Factor (Ss)
(unitless)
Biotransfer Factors (Bam) (day/kg FW tissue)3
and Metabolism Factors (MF) (unitless)b
Mammal
Non-mammal
Beef
[Babeef)
Dairy
(Bd dairy)
Pork
[Bdpork)
MF
Eggs
(Bdeggs)
Poultry
(Bd poultry)
MF
Inorganics
Cadmium compounds
1
1.2E-04
6.5E-06
1.9E-04
1
2.5E-03
1.1E-01
NA
Mercury (elemental)
1
0
0
0
1
0
0
NA
Mercuric chloride
1
1.1E-04
1.4E-06
3.4E-05
1
2.4E-02
2.4E-02
NA
Methyl mercury
1
1.2E-03
1.7E-05
5.1E-06
1
3.6E-03
3.6E-03
NA
PAHs
2-Methylnaphthalene
1
2.4E-02
5.0E-03
2.9E-02
0.01
1.0E-02
1.7E-02
NA
7,12-
Dimethylbenz(a)anthra
cene
1
3.9E-02
8.3E-03
4.8E-02
0.01
1.7E-02
2.9E-02
NA
Acenaphthene
1
2.5E-02
5.2E-03
3.0E-02
0.01
1.0E-02
1.8E-02
NA
Acenaphthylene
1
2.6E-02
5.5E-03
3.1E-02
0.01
1.1E-02
1.9E-02
NA
Benz(a)anthracene
1
3.9E-02
8.3E-03
4.8E-02
0.01
1.7E-02
2.9E-02
NA
Benzo(a)pyrene
1
3.8E-02
8.0E-03
4.6E-02
0.01
1.6E-02
2.8E-02
NA
Benzo(b)fluoranthene
1
3.9E-02
8.3E-03
4.8E-02
0.01
1.7E-02
2.9E-02
NA
Benzo(g,h,i)perylene
1
2.9E-02
6.1E-03
3.5E-02
0.01
1.2E-02
2.1E-02
NA
Benzo(k)fluoranthene
1
3.8E-02
8.0E-03
4.6E-02
0.01
1.6E-02
2.8E-02
NA
Chrysene
1
4.0E-02
8.4E-03
4.8E-02
0.01
1.7E-02
2.9E-02
NA
Dibenz(a,h)anthracen
e
1
3.1E-02
6.5E-03
3.8E-02
0.01
1.3E-02
2.3E-02
NA
Fluoranthene
1
4.0E-02
8.5E-03
4.9E-02
0.01
1.7E-02
3.0E-02
NA
Fluorene
1
2.9E-02
6.1E-03
3.5E-02
0.01
1.2E-02
2.1E-02
NA
lndeno(1,2,3-
cd)pyrene
1
2.7E-02
5.8E-03
3.3E-02
0.01
1.2E-02
2.0E-02
NA
Dioxins
OctaCDD,
1,2,3,4,6,7,8,9-
1
6.9E-03
1.4E-03
8.3E-03
1
2.9E-03
5.1E-03
NA
OctaCDF,
1,2,3,4,6,7,8,9-
1
8.8E-03
1.8E-03
1.1E-02
1
3.7E-03
6.5E-03
NA
HeptaCDD,
1,2,3,4,6,7,8-
1
8.8E-03
1.8E-03
1.1E-02
1
3.7E-03
6.5E-03
NA
Attachment B
Description of MIRC
B-62
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit B-13. Animal Product Chemical-specific Inputs
for Chemicals Included in MIRC
Compound Name
Soil Bio-
Availability
Factor (Bs)
(unitless)
Biotransfer Factors (Bam) (day/kg FW tissue)3
and Metabolism Factors (MF) (unitless)b
Mammal
Non-mammal
Beef
{BSbeef)
Dairy
(Bd dairy)
Pork
[Bdpork)
MF
Eggs
(Bdeggs)
Poultry
(Bd poultry)
MF
HeptaCDF,
1,2,3,4,6,7,8-
1
1.6E-02
3.5E-03
2.0E-02
1
6.9E-03
1.2E-02
NA
HeptaCDF,
1,2,3,4,7,8,9-
1
2.4E-02
5.1E-03
3.0E-02
1
1.0E-02
1.8E-02
NA
HexaCDD, 1,2,3,4,7,8-
1
1.1E-02
2.3E-03
1.3E-02
1
4.6E-03
8.1E-03
NA
HexaCDF, 1,2,3,4,7,8-
1
2.3E-02
4.8E-03
2.8E-02
1
9.6E-03
1.7E-02
NA
HexaCDD, 1,2,3,6,7,8-
1
6.8E-03
1.4E-03
8.2E-03
1
2.9E-03
5.0E-03
NA
HexaCDF, 1,2,3,6,7,8-
1
9.7E-03
2.0E-03
1.2E-02
1
4.1E-03
7.1E-03
NA
HexaCDD, 1,2,3,7,8,9
1
6.8E-03
1.4E-03
8.2E-03
1
2.9E-03
5.0E-03
NA
HexaCDF, 1,2,3,7,8,9-
1
1.4E-02
2.9E-03
1.7E-02
1
5.8E-03
1.0E-02
NA
HexaCDF, 2,3,4,6,7,8-
1
9.6E-03
2.0E-03
1.2E-02
1
4.1E-03
7.1E-03
NA
PentaCDD, 1,2,3,7,8-
1
1.8E-02
3.9E-03
2.2E-02
1
7.8E-03
1.4E-02
NA
PentaCDF, 1,2,3,7,8-
1
2.6E-02
5.5E-03
3.2E-02
1
1.1E-02
1.9E-02
NA
PentaCDF, 2,3,4,7,8-
1
3.1E-02
6.5E-03
3.8E-02
1
1.3E-02
2.3E-02
NA
TetraCDD, 2,3,7,8-
1
2.6E-02
5.5E-03
3.2E-02
1
1.1E-02
1.9E-02
NA
TetraCDF, 2,3,7,8-
1
3.6E-02
7.7E-03
4.4E-02
1
1.5E-02
2.7E-02
NA
Source: EPA 2005a, unless otherwise indicated. NA = not applicable.
aAs discussed in HHRAP (EPA 2005a), Appendix A, biotransfer factors for mercury compounds were obtained from EPA 1997b. Considering
speciation, fate, and transport of mercury from emission sources, elemental mercury is assumed to be vapor-phase and hence is assumed not to
deposit to soil or transfer into aboveground plant parts. As a consequence, there is no transfer of elemental mercury into animal tissues.
Biotransfer factors for cadmium compounds were obtained from EPA 1995b. Biotransfer factors for dioxins and PAHs were calculated from
chemical octanol-water partitioning coefficients (Kow values) using the correlation equation from RTI (2005) and assuming the following fat contents:
milk - 4%; beef -19%; pork - 23%; poultry -14%; and eggs - 8%.
bAs discussed in HHRAP (EPA 2005a), EPA (1995c) recommends using a metabolism factor (MF) to account for metabolism of PAHs by mammals
to offset the amount of bioaccumulation suggested by biotransfer factors. EPA has recommended an MF of 0.01 for bis(2-ethylhexyl)phthalate
(BEHP) and 1.0 for all other chemicals (EPA 1995d). For MIRC, an MF of 0.01 is also used to calculate concentrations of PAHs in food products
from mammalian species based on the work of Hofelt et al. (2001). This factor takes into account the P450-mediated metabolism of PAHs in
mammals; applying this factor in our approach reduced the concentrations of chemicals in beef, pork, and dairy by two orders of magnitude.
Attachment B
Description of MIRC
B-63
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit B-14. Soil and Plant Ingestion Rates for Animals
Animal
Soil Ingestion Rate -
Qs(m) (kg/day)a
Plant Part Consumed
by Animal
Plant Ingestion Rate -
Qpo.m) (kg/day)
Beef cattleb
0.5
Silage
2.5
Forage
8.8
Grain
0.47
Dairy cattle0
0.4
Silage
4.1
Forage
13.2
Grain
3.0
Swined
0.37
Silage
1.4
Grain
3.3
Chicken (eggs)e
0.022
Grain
0.2
Source: EPA 2005a HHRAP (Chapter 5).
aBeef cattle: NC DEHNR (1997) and EPA (1994b) recommended a soil ingestion rate for subsistence beef cattle of 0.5 kg/day
based on Fries (1994) and NAS (1987). As discussed in HHRAP, Fries (1994) reported soil ingestion to be 4 percent of the total
dry matter intake. NAS (1987) cited an average beef cattle weight of 590 kg, and a daily dry matter intake rate (non-lactating
cows) of 2 percent of body weight. This results in a daily dry matter intake rate of 11.8 kg DW/day and a daily soil ingestion rate of
about 0.5 kg/day.
Dairy cattle: NC DEHNR (1997) and EPA (1994b) recommended a soil ingestion rate for dairy cattle of 0.4 kg/day based on Fries
(1994) and NAS (1987). As discussed in HHRAP, Fries (1994) reported soil ingestion to be 2 percent of the total dry matter
intake. NAS (1987) cited an average beef cattle weight of 630 kg and a daily dry matter intake rate (non-lactating cows) of 3.2
percent of body weight. This resulted in a daily dry matter intake rate of 20 kg/day DW, and a daily soil ingestion rate of
approximately 0.4 kg/day. Uncertainties associated with Qs include the lack of current empirical data to support soil ingestion
rates for dairy cattle and the assumption of uniform contamination of soil ingested by cattle.
Swine: NC DEHNR (1997) recommended a soil ingestion rate for swine of 0.37, estimated by assuming a soil intake that is 8% of
the plant ingestion rate of 4.3 kg DW/day. Uncertainties include the lack of current empirical data to support soil ingestion rates
and the assumption of uniform contamination of the soil ingested by swine.
Chicken: HHRAP (EPA 2005a) assumes that chickens consume 10 percent of their total diet (which is approximately 0.2 kg/day
grain) as soil, a percentage that is consistent with the study from Stephens et al. (1995). Uncertainties include the lack of current
empirical data to support soil ingestion rates for chicken and the assumption of uniform contamination of soil ingested by chicken.
The beef cattle ingestion rates of forage, silage, and grain are based on the total daily intake rate of about 12 kg DW/day (based
on NAS [1987] reporting a daily dry matter intake that is 2 percent of an average beef cattle body weight of 590 kg) and are
supported by NC DEHNR (1997), EPA (1994b and 1990), and Boone et al. (1981). The principal uncertainty associated with
these Qp values is the variability between forage, silage, and grain ingestion rates for cattle.
The dairy cattle ingestion rates of forage, silage, and grain are based on the total daily intake rate of about 20 kg DW/day (NAS
1987; EPA 1992) as recommended by NC DEHNR (1997). Uncertainties include the proportion of each food type in the diet,
which varies from location to location. Assuming uniform contamination of plant
materials consumed by cattle also introduces uncertainty.
dSwine are not grazing animals and are assumed not to eat forage (EPA 1998). EPA (1994b and 1998) and NC DEHNR (1997)
recommended including only silage and grains in the diet of swine. EPA (1995c) recommended an ingestion rate of 4.7 kg
DW/day for a swine, referencing NAS (1987). Assuming a diet of 70 percent grain and 30 percent silage (EPA 1990), HHRAP
estimated ingestion rates of 3.3 kg DW/day (grain) and 1.4 kg DW/day (silage). Uncertainties associated with Qp include
variability of the proportion of grain and silage in the diet, which varies from location to location.
eChickens consume grain provided by the farmer. The daily quantity of grain feed consumed by chicken is assumed to be 0.2
kg/day (Ensminger (1980), Fries (1982), and NAS (1987). Uncertainties associated with this variable include the variability of
actual grain ingestion rates from site to site. In addition, assuming uniform contamination of plant materials consumed by chicken
introduces some uncertainty.
6.3. Adult and Non-Infant Exposure Parameter Values
The exposure parameters included in MIRC and their default and other value options are
summarized in the following subsections. The default values were selected to result in a highly
health protective screening scenario. Parameter value options were primarily obtained or
estimated from EPA's Exposure Factors Handbook (EFH; EPA 2011a) and Child-Specific
Exposure Factors Handbook (CSEFH; EPA 2008a). Where values were reported for age
groupings other than those used in MIRC (see Section 2.3 above for MIRC age groups), time-
weighted average values were estimated for the MIRC age groups from the available data.
Attachment B
Description of MIRC
B-64
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
In MIRC, ingestion rates for home-produced farm food items are included for exposed fruit,
protected fruit, exposed vegetables, protected vegetables, root vegetables, beef, total dairy,
pork, poultry, and eggs. Those ingestion rates are already normalized to body weight (i.e., gwet
weight/kg-day) (EPA 2011a). The body weight parameter values presented in Exhibit B-15,
therefore, are not applied in the chemical intake (ADD) equations for these food types.
In MIRC, ingestion rates also are included for drinking water (mL/day), soil (mg/day), and fish
(g/day). These ingestion rates, however, are on a per person basis (i.e., not normalized for
body weight). The body weight parameter values presented in Exhibit B-15, therefore, are
applied in the chemical intake (ADD) equations for these media.
6.3.1. Body Weights
Body weight (BW) options included in MIRC include mean, 5th, 10th, 50th, 90th, and 95th
percentile values for adults and the five children's age groups: <1 year; 1-2 years; 3-5 years;
6-11 years; and 12-19 years. For its default screening assessment, EPA uses the mean BW
for each age group. The BWs currently in the MIRC database are listed in Exhibit B-15.
In general, BW values for the five children's age groups were calculated from the summary data
provided in Table 8-3 of EPA's 2008 CSEFH. For purposes of comparison, alternative BW
values for children ages 12 through 19 years also were estimated using data from Portier et al.
(2007). These values are listed in the last row of Exhibit B-15, but are not included in MIRC.
The means calculated using the two methods for children ages 12 through 19 years were
essentially identical at 64 kg. The other percentile values for this age group using the two
methods differed by approximately 10 percent or less.
Exhibit B-15. Mean and Percentile Body Weight Estimates
for Adults and Children
Lifestage
(years)
Duration
(years)
Body Weight (kg)
Mean
5th
10th
50th
90th
95th
Adult3 (20-70)
50
80.0a
52.9
56
69.3
89.7
97.6
Child < 1b
1
7.83
6.03
6.38
7.76
9.24
9.66
Child 1-2C
2
12.6
9.9
10.4
12.5
14.9
15.6
Child 3-5d
3
18.6
13.5
14.4
17.8
23.6
26.2
Child 6-11e
6
36.0
22.1
24.0
33.5
51.2
58.6
Child 12-19f
8
64.2
39.5
45
64.2
83.5
89
[Child 12-193
8
64.3
41.1
44.6
60.9
88.5
98.4]
aBW represents the recommended body weight from EPAs 2011 EFH. Although the 18 to 74 year age category in EPA's EFH
does not match exactly the age 20 to 70 year categorization of adults in MIRC, the magnitude of error in the mean and percentile
body weights is likely to be very small (i.e., less than 1%).
bEach BW represents a time-weighted average of body weights for age groups birth to <1 month, 1 to <3 months, 3 to <6
months, and 6 to <12 months from Table 8-3 of the 2008 CSEFH. Original sample sizes for each of these age groups can also
be found in Table 8-3.
°Each BW represents a time-weighted average of body weights for age groups 1 to <2 years and 2 to <3 years from Table 8-3 of
the 2008 CSEFH. Original sample sizes for each of these age groups can also be found in Table 8-3.
dBWs obtained directly from Table 8-3 of the 2008 CSEFH (age group 3 to <6 years).
eEach BW represents a time-weighted average of body weights for age groups 6 to <11 years and 11 to <16 years from Table 8-
3 of the 2008 CSEFH. Original sample sizes for each of these age groups can also be found in Table 8-3.
'Mean BW estimated using Table 8-22 of the 2008 CSEFH, which is based on NHANES IV data as presented in Portier et al.
(2007). This estimate was calculated as the average of the 8 single-year age groups from 12 to 13 years through 19 to 20 years.
Values for the other percentiles were estimated using Portier et al., 2007.
9Each BW represents a time-weighted average of body weights for age groups 11 to <16 years and 16 to <21 years from Table
8-3 of the 2008 CSEFH. Estimated values include 11-year-olds and individuals through age 20, which contributes to uncertainty
in the estimates for 12 to 19 years. Those values are provided for comparison purposes only and are not included in MIRC.
Attachment B
Description of MIRC
B-65
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
6.3.2. Water Ingestion Rates
MIRC also includes the option of calculating chemical ingestion via drinking water obtained from
surface-water sources or from wells (i.e., from groundwater) in the contaminated area. Users
have the option in MIRC to set drinking water ingestion rates to zero or to revise the drinking
water ingestion rates in MIRC to better reflect site-specific water uses. The 2008 CSEFH
recommends values for drinking water ingestion rates for children based on a study reported by
Kahn and Stralka (2008). Table 3-4 of the CSEFH provides per capita estimates of community
water ingestion rates by age categories. Community water ingestion includes both direct and
indirect ingestion of water from the tap. Direct ingestion is defined as direct consumption of
water as a beverage, while indirect ingestion includes water added during food or beverage
preparation. The source of these data is the 1994-1996 and 1998 U.S. Department of
Agriculture's (USDA's) Continuing Survey of Food Intakes by Individuals (CSFII) (USDA 2000).
Exhibit B-16 includes the drinking water ingestion rates for children that are included in MIRC.
Mean and percentile adult drinking water ingestion rates were obtained from EPA (2004b),
which presents estimated per capita water ingestion rates for various age categories based on
data collected by the USDA's 1994-1996 and 1998 CSFII (USDA 2000). Adult ingestion rates,
presented in Exhibit B-16, represent community water ingestion, both direct and indirect as
defined above, for males and females combined, ages 20 years and older.
Exhibit B-16. Estimated Daily Per Capita Mean and Percentile Water Ingestion Rates for
Children and Adults3
Lifestage (years)
Ingestion Rates, Community Water (mL/day)
Mean
50th
90th
95th
99th
Child <1b
504
482
969
1113
1440
Child 1-2C
332
255
687
903
1318
Child 3-5d
382
316
778
999
1592
Child 6-11e
532
417
1149
1499
2274
Child 12-19f
698
473
1641
2163
3467
Adult9
1219
981
2534
3087
4567
Sources: EPA 2004, 2008a
"The sample size does not meet minimum reporting requirements as described in EPA 2008a. For some of these MIRC age
groupings, the values are based on the time-weighted average value for 2 or more age ranges from CSEFH Table 3-4. One or
more age ranges within the group may not meet the minimum reporting requirements, but not necessarily all of them fall within this
category.
aSource is Kahn and Stralka 2008, also presented in the CSEFH (EPA 2008a).
bEach IR represents a time-weighted average of ingestion rates for age groups birth to <1 month, 1 to <3 months, 3 to <6 months,
and 6 to <12 months from Table 3-4 of the 2008 CSEFH.
°Each IR represents a time-weighted average of ingestion rates for age groups 1 to <2 years and 2 to <3 years from Table 3-4 of
the 2008 CSEFH.
dEach IR represents the ingestion rate for age group 3 to <6 years from Table 3-4 of the 2008 CSEFH.
eEach IR represents the ingestion rate for age group 6 to <11 years from Table 3-4 of the 2008 CSEFH. This value represents a
health protective (i.e., slightly low) estimate of IR for ages 6 through 11 years since 11-year olds are not included in this CSEFH
age group.
'Each IR represents a time-weighted average of ingestion rates for age groups 11 to <16 years, 16 to <18, and 18 to <21 years
from Table 3-4 of the 2008 CSEFH. Note that estimated values include 11-year-olds and individuals through age 20, which
contributes to uncertainty in the estimates for 12 to 19 years.
9Adult drinking water ingestion rates were obtained from EPA (2004b), Appendix E, Part I, Table A1 for community water, both
sexes (ages 20+), direct plus indirect water ingestion.
6.3.3. Local Food Ingestion Rates
MIRC includes mean, median, 90th, 95th, and 99th percentile food-specific ingestion rates (IRs)
for consumers-only of farm food chain (FFC) media for adults and children. The mean and
Attachment B
Description of MIRC
B-66
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TRIM-Based Tiered Screening Methodology for RTR
percentile values are from EPA's analysis of data from the USDA's 1987 to 1988 Nationwide
Food Consumption Survey (NFCS) (USDA 1993), as presented in Chapter 13 of the Agency's
Exposure Factors Handbook (i.e., Intake Rates for Various Home Produced Food Items) (EPA
2011a). Consumers-only means that individuals who did not report eating a specified type of
food during the three-day period covered by the food ingestion part of the survey were not
included in the analysis of ingestion rates for that food type. The questionnaire included the
options for a household to self-identify in one or more of five categories: as a household that
gardens, raises animals, hunts, fishes, or farms. As of September 2008, that survey was the
most recent NFCS available (EPA 2008a, CSEFH), and we are not aware of any that might be
more recent.29
For the adult age group in MIRC, data were compiled on food-specific IRs separately for two
types of households as indicated in the "Response to Questionnaire" (EPA 2011a, Chapter 13):
(1) households that farm (F) and (2) households that garden or raise animals (HG for
homegrown). This division reflects EPA's data analysis. EPA tabulated IRs for fruits and
vegetables separately for households that farm and households that garden. Similarly, EPA
tabulated IRs for animals and animal products for households that farm and for households that
raise animals. Thus, the first type of household, F, represents farmers who may both grow
crops and raise animals and who are likely to consume more homegrown/raised foods than the
second type of household. The second type of household, HG, represents the non-farming
households that may consume lower amounts of home-grown or raised foods (i.e., HG
encompasses both households that garden and households that raise animals).
The food-specific ingestion rates are based on the amount of each food type that households
that farm (F) or households that garden and raise animals (HG) produced and brought into their
homes for consumption and the number of persons consuming the food. EPA averaged the
actual consumption rate for home-grown foods over the 1-week survey period.
The default food-specific ingestion rates in MIRC for adults are those for farming households (F)
in Exhibit B-17. The user can specify use of the non-farming household (HG) ingestion rates if
they are more appropriate for the user's exposure scenario.
Exhibit B-17. Summary of Age-Group Specific Food Ingestion Rates for
Farm Food Items
Product
Child (age in yr)
Adult
(20-70
yrs)
<1
1-2
3-5
6-11
12-19
Mean ingestion rates (g/kg-day)
S-
CD
CD
CO
N/A
4.14
4.00
3.77
1.72
1.93
Dairyb
N/A
91.6
50.9
27.4
13.6
2.96
Eggs3
N/A
2.46
1.42
0.86
0.588
0.606
Exposed Fruit3
N/A
6.14
2.60
2.52
1.33
1.19
Exposed Vegetable3
N/A
3.48
1.74
1.39
1.07
1.38
Pork3
N/A
2.23
2.15
1.50
1.28
1.10
Poultry3
N/A
3.57
3.35
2.14
1.50
1.37
29Note that EPA's 2008 CSEFH does not distinguish between exposed and protected fruits and vegetables when
recommending food ingestion rates based on the same data set for the same age categories. EPA's 1997 analysis
for its EFH therefore remains the most appropriate data source for use in MIRC.
Attachment B
Description of MIRC
B-67
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit B-17. Summary of Age-Group Specific Food Ingestion Rates for
Farm Food Items
Product
Child (age in yr)
Adult
(20-70
yrs)
<1
1-2
3-5
6-11
12-19
Protected Fruit3
N/A
16.6
12.4
8.50
2.96
5.19
Protected Vegetable3
N/A
2.46
1.30
1.10
0.78
0.862
Root Vegetable3
N/A
2.52
1.28
1.32
0.94
1.03
Water (mL/day)c
N/A
332
382
532
698
1218
Median ingestion rates (g/kg-day)
2-
CD
CO
N/A
2.51
2.49
2.11
1.51
1.55
Dairyb
N/A
125
66.0
34.4
15.5
2.58
Eggs3
N/A
1.51
0.83
0.561
0.435
0.474
Exposed Fruit3
N/A
1.82
1.11
0.61
0.62
0.593
Exposed Vegetable3
N/A
1.89
1.16
0.64
0.66
0.812
Pork3
N/A
1.80
1.49
1.04
0.89
0.802
Poultry3
N/A
3.01
2.90
1.48
1.30
0.922
Protected Fruit3
N/A
7.59
5.94
3.63
1.23
2.08
Protected Vegetable3
N/A
1.94
1.04
0.79
0.58
0.564
Root Vegetable3
N/A
0.46
0.52
0.57
0.56
0.59
Water (mL/day)c
N/A
255
316
417
473
981
90th percentile ingestion rates (g/kg-day)d
2-
CD
CO
N/A
9.49
8.83
11.4
3.53
4.41
Dairyb
N/A
185
92.5
57.4
30.9
6.16
Eggs3
N/A
4.90
3.06
1.90
1.30
1.31
Exposed Fruit3
N/A
12.7
5.41
6.98
3.41
2.37
Exposed Vegetable3
N/A
10.7
3.47
3.22
2.35
3.09
Pork3
N/A
4.90
4.83
3.72
3.69
2.23
Poultry3
N/A
7.17
6.52
4.51
3.13
2.69
Protected Fruit3
N/A
44.8
32.0
23.3
7.44
15.1
Protected Vegetable3
N/A
3.88
2.51
2.14
1.85
1.81
Root Vegetable3
N/A
7.25
4.26
3.83
2.26
2.49
Water (mL/day)c
N/A
687
778
1149
1640
2534
95th percentile ingestion rates (g/kg-day)
2-
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit B-17. Summary of Age-Group Specific Food Ingestion Rates for
Farm Food Items
Product
Child (age in yr)
Adult
(20-70
yrs)
<1
1-2
3-5
6-11
12-19
Water (mL/day)c
N/A
903
999
1499
2163
3087
99th percentile ingestion rates (g/kg-day)
S-
CD
CD
CO
N/A
20.9
19.8
13.3
4.28
6.84
Dairyb
N/A
180
87.2
54.8
34.7
9.20
Eggs3
N/A
16.2
11.2
8.19
4.77
1.83
Exposed Fruit3
N/A
25.2
32.5
15.7
5.9
13.0
Exposed Vegetable3
N/A
12.1
7.36
13.3
5.67
8.42
Pork3
N/A
8.71
9.74
6.61
4.29
3.87
Poultry3
N/A
9.63
10.24
6.12
4.60
4.93
Protected Fruit3
N/A
109
71.2
58.2
19.1
34.4
Protected Vegetable3
N/A
9.42
5.31
5.40
2.69
5.56
Root Vegetable3
N/A
10.4
4.73
7.47
5.13
7.57
Water (mL/day)c
N/A
1318
1592
2274
3467
4567
aPrimary source for values was the 1987-1988 NFCS survey; compiled results are presented in Chapter 13 of 2011 Exposure
Factors Handbook (EPA, 2011 a). When data were unavailable for a particular age group, intake rate for all age groups was used
multiplied by the age-specific ratio of intake based on national population intake rates from CSFII.
bPrimary source for values was 1987-1988 NFCS survey, compiled results presented in Chapter 13 of 2011 Exposure Factors
Handbook (EPA, 2011a). When data were unavailable for a particular age group, intake rate for all age groups was used multiplied
by the age-specific ratio of intake based on national population intake rates from an NHANES 2003-2006 analysis in Chapter 11 of
the Exposure Factors Handbook.
°Primary source for children less than 3 years of age was a Kahn and Stralka (2008) analysis of CSFII data, and from EPA's
analysis of NHANES 2003-2006 data for children and adults greater than three. All data tables that were used and justifications for
data sources are presented in Chapter 3 of the 2011 Exposure Factors' Handbook.
dDefault ingestion rate percentile used in MIRC for Tier 1 assessments and chemical threshold calculations.
For children, EPA estimated food-specific IRs for four age categories (EPA 2011a): 1-2 years,
3-5 years, 6-11 years, and 12-19 years. Sample sizes were insufficient to distinguish IRs for
children in different types of households; hence, for children, a single IR value represents both F
and HG households for a given food type and age category (Exhibit B-17). For some food types
and age categories, there were insufficient data for EPA to provide consumer-only intake rates
(i.e., data set for the subpopulation consisted of fewer than 20 observations). The HHRAP
methodology, Section 6.2.2.2, recommends a method by which to calculate the "missing" age-
specific consumer-only ingestion rates, as explained below. Food-specific intake rates (IRs) for
those child age groups and food items not included in Chapter 13 of the 2011 EFH, that is
IRage_grouP_x, were derived using the following information:
Mean or percentile-specific consumer-only intake of the farm food item, as brought into
the home, for the total NFCS survey population (from EFH Chapter 13) - IRCO_total;
Mean or percentile-specific per capita intake of the food type from all sources, as
consumed, for the specific child age group, from Chapter 3 of the CSFII Analysis of Food
Intake Distributions (EPA 2003c) - IRPC, age_group_x; and
• Mean or percentile-specific per capita intake of the farm food item for the total CSFII
survey population (from Chapter 3 of EPA 2003c) - IRPC_total.
Attachment B
Description of MIRC
B-69
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
The ratio of IRpc, age_group_x to IRpc totai from the CSFII data shows the consumption rate of a
particular food type by a specific age group relative to the consumption rate for that food type for
the population as a whole. The ratio of IRco, age_group_x to IRcojotai, that is the consumption rate
of a particular food type by a specific age group (consumers only) relative to the consumption
rate for that food type for the NFCS survey population as a whole (consumers only), should be
approximately the same. Given the assumption that the two ratios are equal, Equation B-56
was used to calculate the "missing" age-specific consumer-only IRs\
Equation B-56. Calculation of Age-Group-Specific and Food-Specific Ingestion Rates
ip _ IRcojotai x IRpc, age_group_x
'~CO, age_group_x ~
PCJotal
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 item, as
brought into the home, for the total NFCS 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
In this discussion, per capita (as opposed to consumer-only) indicates the intake rates are
based on the entire population rather than the subset of the population that ingests the
particular food category (i.e., consumers). Here, the use of per capita ingestion rates are
recommended by the HHRAP methodology because no consumer-only percentile-specific
intakes are provided for the different age groups.
The above calculation implicitly assumes that the distribution of the consumption rate for a food
type for a specific age group (consumers only) has the same shape as the distribution of the
consumption rate for a food type for a specific age group in the general population (per capita).
Otherwise, the separate calculation of each percentile might yield intake estimates that
decrease as the percentile increases. This calculation artifact could occur if the shapes of the
two distributions differ in the upper percentiles (or "tails") of the distributions.
In the instances where the above calculations were used to fill data gaps in the above exhibit,
only the dairy child-specific age group intake estimates are not strictly increasing with increasing
percentile. The distributions likely track better (and thus the above assumption of equal ratios is
more reasonable) for lower percentiles, with deviations occurring due to outlier ingestion rates
based on only a few respondents in the tails of the distributions. The MIRC defaults use the 90th
percentile ingestion estimates, which are likely more reliable than the 95th or 99th percentile
estimates in this particular calculation.
6.3.4. Local Fish Ingestion Rates
6.3.4.1. Screening Scenario
The USDA's 1987 to 1988 Nationwide Food Consumption Survey (NFCS) (USDA 1993, 1994a),
as presented in Chapter 13 of the Agency's Exposure Factors Handbook (i.e., Intake Rates for
IRco, age_group_x
IRco_ total
IRpc, age_group_x
IRpc total
Attachment B
Description of MIRC
B-70
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
Various Home Produced Food Items) (EPA 2011a), includes family-caught fish ingestion rates
by age category. There are several disadvantages, however, to using that data source to
estimate fish ingestion rates. First, due to inadequate sample sizes, EPA did not report fish
ingestion rates for children less than 6 years of age. Second, the NFCS data were collected
more than two decades ago. Third, the reported fish ingestion rates are for ages 6 to 11 and 12
to 19 and are based on 29 and 21 individuals in each age category, respectively (EPA 2011a,
Table 13-20). Finally, the ingestion rates from NFCS data are based on total weight of fish as
brought into the home, and do not include losses from preparation of the fish (i.e., removal of
inedible parts and, possibly, the skin). Estimates of preparation losses for fish intended to apply
to the NFCS fish ingestion rate data are very uncertain and are based on a wide variety of
freshwater, estuarine, and marine fish, and squid (EPA 2011a, Table 13-69). Additionally, when
considering the multipathway screening methodology, it is important that potential health effects
to those individuals who are most likely to have the greatest PB-HAP exposure are not
underestimated and, therefore, ingestion rates that are reflective of subsistence fisher ingestion
rates are desired. Therefore, a more recent survey was sought that included larger sample
sizes, data for children younger than six years, ingestion rates for the parts of fish actually
consumed, and ingestion rates reflective of subsistence fisher ingestion rates.
Taking all of these issues into consideration, the default adult fish ingestion rate selected for use
in MIRC is 373 g/day, which is the the estimated 99th percentile of fish ingestion rates for
woman fishers as reported by Burger (2002). This rate is based upon ingestion of "wild-caught"
fish, which includes freshwater, estuarine, and marine species, while our screening scenarios
focus only on freshwater fish from lakes. This is notable because a number of studies indicate
that fish ingestion rates are limited by species and habitat (i.e., lake, river, estuary, and ocean)
and that the majority of the fish consumed in the United States are from river, marine and
estuarine habitats versus lakes. Thus, although the fish ingestion rate for this group of
subsistence fishers is not the highest fish ingestion rate available for use by EPA, we do believe
it strikes the appropriate balance between being health protective and having screening
scenarios so conservative that they are of limited use in the decision making process. This
high-end fish ingestion rate is appropriate in the context of the conservative screening scenario
used in the RTR process. This methodology is particularly applicable for national rulemakings
given that it is very likely that subsistence woman fishers of child bearing age are located
throughout the United States. Finally, we note that using a high-end subsistence fish ingestion
rate is consistent with section 112 of the CAA, which focuses on risks associated with maximally
exposed individuals.
Because Burger (2002) did not estimate fish ingestion rates for children, another data source
was needed to develop ingestion rates for the child age categories that are used in MIRC. The
child ingestion rates need to be consistent with the Burger adult ingestion rate, reflective of
subsistence fisher ingestion rates, and based on adequate sample sizes. To satisfy these
requirements, data on child ingestion rates from EPA's Estimated Per Capita Fish Consumption
in the United States (EPA 2002) were selected for use. Specifically, the estimated 99th
percentile of as-prepared, consumer-only ingestion rates for finfish and shellfish were selected
(see Section 4.2.1.1 Table 5 of EPA 2002).The original data were collected as part of the 1994-
96 and 1998 USDA Continuing Survey of Intakes by Individuals (CSFII; USDA 2000). Values
reflect "as prepared" ingestion rates for which cooking and preparation losses (L1 and L2) did
not need to be considered. "Total" fish as reported in this source represents consumption of
finfish plus shellfish.
Because the child age categories used in MIRC differ from the CSFII age categories presented
in EPA 2002, the CSFII data were adjusted for use in MIRC. The CSFII data did not provide
ingestion rates for the 1-2 year age category. To estimate ingestion rates for this age group,
Attachment B
Description of MIRC
B-71
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
EPA used the ingestion rate for the 3-5 year age category scaled downward by the ratio of the
mean body weight of the 1-2 year age group to the mean body weight of the 3-5 year age
group. Because MIRC uses a 3-5 year age category, no adjustment was needed for CSFII data
from that age category. For the 6-11 and 12-19 age categories, time-weighted average
ingestion rates were calculated based on the CSFII ingestion rates. Exhibit B-18 provides the
fish ingestion rates used in the screening assessment.
Exhibit B-18. Fish Ingestion Rates Used in Screening Assessment
Ingestion Rates (g/day)
Infants
Child
Child
Child
Child
Adult
<1 yr
1-2 yrs
3-5 yrs
6-11 yrs
12-19 yrs
20-70 yrs
NA
107.7a
159.0b
268.2°
331,0C
373d
aA fish ingestion rate for ages 1 -2 years was not available. The value represents the consumer-only fish ingestion rate for
ages 3 to 5 from EPA (2002) (Section 4.2.1.1 Table 5 [freshwater/estuarine habitat]), scaled down by the ratio of the mean
Child 1-2 body weight to the mean Child 3-5 body weight.
This value represents the consumer-only fish ingestion rate for ages 3 to 5 from EPA (2002), Section 4.2.1.1 Table 5
(freshwater/estuarine habitat) rounded to the nearest full number.
¦These values represent time-weighted average consumer-only fish ingestion rates based on ingestion rates from EPA
(2002), Section 4.2.1.1 Table 5 (freshwater/estuarine habitat).
dThis value represents the 99th percentile ingestion rate of wild caught fish for women as reported by Burger (2002).
6.3.4.2. Other Values Available in MIRC
EPA's (2002) analysis of freshwater and estuarine fish consumption from the USDA's
Continuing Survey of Food Intake by Individuals (CSFII) for 1994-96 and 1998 was chosen to
provide fish ingestion rate options by age category in MIRC. Although the fish consumption
rates reported in the CSFII include all sources, commercial and self-caught, for purposes of
screening level risk assessments, it was assumed that all freshwater and estuarine fish
consumed are self-caught. The inclusion of commercially obtained and estuarine fish will
overestimate locally caught freshwater fish ingestion rates for most populations in the United
States; however, it also might underestimate locally caught fish ingestion rates for some
populations (e.g., Native Americans, Asian and Pacific Island communities, rural African
American communities). Because consumption of locally caught fish varies substantially from
region to region in the United States and from one population or ethnic group to the next, users
of MIRC are encouraged to use more locally relevant data whenever available.
For children, MIRC also includes values for the mean and the 90th, 95th, and 99th percentile
fish per-capita ingestion rates (freshwater and estuarine fish only) based on EPA's analysis of
1994-96 and 1998 CSFII data (EPA 2002, 2008a). Those rates include individuals who eat fish
and those who do not eat fish. As shown in EPA's 2008 CSEFH, Table 10-7, the 90th percentile
per capita ingestion rates estimated from the two-day CSFII recall period are zero for some child
age groups. Although not presented in CSEFH Table 10-7, median ingestion rates for all child
age groups would be zero (considering the "consumer only" sample sizes [CSEFH Table 10-9]
relative to the "per-capita" sample sizes in Table 10-7).
The high percentile fish ingestion rates that are zero result from the short duration of the CSFII
recall period (two days) compared with the averaging time of interest (a year) and the relatively
infrequent consumption of fish (e.g., on the order of once a week to once a month or less)
compared with the near daily ingestion of other types of food products (e.g., dairy, produce,
meat). Use of zero for fish ingestion rates, however, is not useful in MIRC. As a result, an
alternative method was used to estimate fish ingestion rates for children and adults that could
provide reasonable, non-zero values for all age groups and percentiles.
Attachment B
Description of MIRC
B-72
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
The alternative, age-group-specific fish ingestion rates were derived using values for each age
group, y:
Mean or other appropriate percentile consumer-only fish ingestion rates for age group y,
IRCO,y, from EPA's Estimated Per Capita Fish Consumption in the United States (EPA
2002), Section 5.2.1.1, Table 5, for freshwater/estuarine habitat.30
• Fraction of the population consuming freshwater/estuarine fish, FPC,y, calculated as
consumer-only sample size / U.S. population sample for age group y. The data to
calculate those fractions are available in the 2008 CSEFH and EPA 2002.
Equation B-57 was used to calculate the alternative, per capita fish ingestion rates by age group
CIRpc.y):
Equation B-57. Calculation of Alternative Age-Group-Specific Fish Ingestion Rates
'RpC,y = '^CO,y X Fpc.y
where:
Per capita fish ingestion rate for age group y (g/day)
Consumer-only fish ingestion rates for age group y (g/day) (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) (2008 CSEFH, EPA 2002)
In the above, per capita (as opposed to consumer-only) indicates the intake rates are based on
the entire population rather than the subset of the population that ingests the particular food
category. Here, per capita ingestions are recommended by the HHRAP methodology because
no consumer-only percentile-specific intakes are provided for the different age groups.
The mean and percentile consumer-only fish ingestion rates for children and adults and the
fraction of the population consuming freshwater/estuarine fish used in calculating long-term per
capita fish ingestion rates by age group are presented in Exhibit B-19 and Exhibit B-20. The
mean and percentile per capita fish ingestion rates estimated using this methodology are
summarized in Exhibit B-21 and are available in MIRC. The fish ingestion rates provided in
Exhibit B-21 and included in MIRC are intended to represent the harvest and consumption of
fish in surface waters in a hypothetical depositional area. For site-specific application of this
tool, users should consider using more localized survey data to estimate more appropriate fish
ingestion rates. The fishing season varies substantially across the United States by latitude,
and fish consumption patterns also vary by type of water body (e.g., ponds, lakes, rivers,
streams, estuaries, coastal marine), cultural heritage, and general geographic area. Therefore,
use of more localized information is encouraged. Note that as indicated in Exhibit 16 and
Exhibit 18, in developing the screening threshold emission rates, health protective fish ingestion
rates for child and adult fish consumers that more closely represent exposures of a high-end
recreational fisher were used.
30Most of these data also are provided in Table 10-9 of the CSEFH; the median values, however, are not presented in
the CSEFH, and values for the mean and all other percentiles are slightly different due to rounding.
IRpc.y -
IRcO.y =
Fpc.y =
Attachment B
Description of MIRC
B-73
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
As noted in Section 6.4.3 of this attachment, if the user overwrites the fish IRs shown in Exhibit
B-21 with fresh-weight as caught values (e.g., values obtained from a local creel survey), the
user is advised to set non-zero values for the preparation and cooking loss factors L1 and L2 in
Equation B-15. Suggested values are presented in Section 6.4.3.
Exhibit B-19. Daily Mean and Percentile Consumer-Only Fish Ingestion Rates
for Children and Adults (IRco,y)a
Lifestage (years)
Ingestion Rates, All Fish (g/day)
Mean
50th
90th
95th
99th
Child <1
NA
NA
NA
NA
NA
Child 1-2b
27.31
15.61
64.46
87.60
138.76*
Child 3-5c
40.31
23.04
95.16
129.31
204.84*
Child 6-11d
61.49
28.46
156.86*
247.69*
385.64*
Child 12-19e
79.07
43.18
181.40*
211.15*
423.38*
Adultf
81.08
47.39
199.62*
278.91
505.65*
Sources: EPA 2002, 2008a
NA = not applicable; it is assumed that children < 1 year of age do not consume fish.
'Indicates that the sample size does not meet minimum reporting requirements as described in EPA 2002. Owing to the small
sample sizes, these upper percentiles values are highly uncertain.
sPer capita fish ingestion (Fl) rates for children by age group are available from Chapter 10 of the CSEFH (EPA 2008a); however,
all 50th and some 90th percentile ingestion rates are zero. Per capita Fl rates were therefore estimated as described in Equation
B-57 to provide reasonable, non-zero values for all age groups and percentiles.
bA fish IR for ages 1 -2 years was not available. The value represents the consumer-only fish ingestion rate for ages 3 to 5 from
EPA (2002) (Section 5.2.1.1 Table 5 [freshwater/estuarine habitat]), scaled down by the ratio of the mean Child 1-2 body weight to
the mean Child 3-5 body weight.
These values represent the consumer-only fish ingestion rate for ages 3 to 5 from EPA (2002), Section 5.2.1.1 Table 5
(freshwater/estuarine habitat). Sample size = 442.
dThese values represent the consumer-only fish ingestion rate for ages 6 to 10 from EPA (2002), Section 5.2.1.1 Table 5
(freshwater/estuarine habitat). Sample size = 147.
eThese values represent the time-weighted average per capita fish ingestion rate for ages 11 to 15 and 16 to 17 years from EPA
(2002), Section 5.1.1.1 Table 5 (freshwater/estuarine habitat); the value may underestimate ingestion rate for ages 12 to 19 years.
Sample size = 135.
These values represent the consumer-only fish ingestion rate for individuals 18 years and older from EPA (2002), Section 5.2.1.1
Table 4 (freshwater/estuarine habitat). Sample size = 1,633.
Attachment B
Description of MlRC
B-74
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit B-20. Fraction of Population Consuming Freshwater/Estuarine Fish on a Single
Day (FPC,y)
Lifestage (years)
Fraction Consuming Fish
Child 3-5
0.0503a
Child 6-11
0.0440b
Child 12-19
0.0493c
Adult
0.08509d
Sources: EPA 2002, 2008a
aThis value was calculated using the ages 3 to 5 sample size for consumers only divided by the sample size for the U.S.
population divided by 2 to represent the proportion consuming fish on a single day (the consumers-only group includes individuals
who consumed fish on at least one of two survey days) to match the one-day ingestion rate.
bAs in footnote a, the value was calculated using the ages 6 to 10 sample size for consumers only divided by the sample size for
U.S. population divided by 2.
The value was calculated by summing the ages 11 to 15 and 16 to 17 sample sizes for consumers only and dividing by both by
the sum of the sample sizes for U.S. population and by a factor of 2.
dThe value was calculated using the ages 18 and older sample size for consumers only divided by the sample size for U.S.
population from Section 5.1.1.1 Table 4. The result was divided by 2 to represent a one-day sampling period in order to match the
one-day ingestion rate.
Exhibit B-21. Calculated Long-term Mean and Percentile per capita Fish Ingestion Rates
for Children and Adults [IRpc,y)
Lifestage (years)
Ingestion Rates, All Fish (g/day)
Mean
50th
90th
95th
99th
Child <1
NA
NA
NA
NA
NA
Child 1-2a
1.37
0.79
3.24
4.41
6.98
Child 3-5b
2.03
1.16
4.79
6.51
10.3
Child 6-11c
2.71
1.25
6.90
10.9
17.0
Child 12-19d
3.90
2.13
8.95
10.4
20.9
Adulte
6.90
4.03
16.99
23.73
43.02
Sources: EPA 2002, 2008a
NA = not applicable; it is assumed that children < 1 year of age do not consume fish.
aValues were calculated as (consumer-only IR for Child 1-2) x (fraction of population consuming fish for Child 3-5).
bValues were calculated as (consumer-only IR for Child 3-5) x (fraction of population consuming fish for Child 3-5).
¦Values were calculated as (consumer-only IR for Child 6-11) x (fraction of population consuming fish for Child 6-11).
dValues were calculated as (consumer-only IR estimated for Child 12-19) x (fraction of population estimated to consume fish for
Child 12-19).
eValues were calculated as (consumer-only IR for Adults) x (fraction of population consuming fish for Adults).
Attachment B
Description of MlRC
B-75
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
MIRC also includes values for the mean and the 90th percentile fish ingestion rates for
recreational fishers, black and female recreational fishers, and fishers of Hispanic, Laotian, and
Vietnamese descent which are shown in Exhibit B-22. These latter three populations are
culturally or economically disposed to higher rates of fish ingestion than the general population.
Recreational fisher values are from the EFH (EPA, 2011a). Black and female recreational
fishers ingestion rates are presented in Burger (2002). The fish ingestion rates for Hispanic,
Laotian, and Vietnamese populations were derived from a study by Shilling et al. (2010) of
contaminated fish consumption in California's Central Valley Delta. Shilling et al. (2010)
reported mean and 95th percentile ingestion rates for each subpopulation. In part due to the low
sample size in the Shilling study (n of 30 to 45), 95th percentile values were believed to be
unrealistically high. The 90th percentile ingestion rate estimates presented in Exhibit B-22 for
Hispanic, Laotian, and Vietnamese fishers were derived by EPA using information from Shilling
et al. (2010; EPA, 2010).
Exhibit B-22. Calculated Mean and 90th Percentile Per capita Fish Ingestion Rates for
Populations of Recreational Fishers (IRpc,y)
Subpopulation
Percentile
Units
Recrea-
tional
Fisher3
Female
Recrea-
tional
Fisherb
Black
Recrea-
tional
Fisherb
Hispanic
Recrea-
tional
Fisherc
Laotian
Recrea-
tional
Fisherc
Vietnamese
Recrea-
tional
Fisherc
Ingestion of Fish
Mean
g/day
8
39.1
171
25.8
47.2
27.1
90th
g/day
11
123
446
98
144.8
99.1
a1997 Exposure Factors Handbook (USEPA, 1997a)
bBurger (2002) weights are "as consumed" for locally caught fish.
°Shilling, F., A. White, L. Lippert, and M. Lubell. 2010. Contaminated fish consumption in California's Central Valley Delta.
Environmental Research 110:334-344.
Applications to date of MIRC have used whole fish concentrations estimated by TRIM.FaTE.
The proportion of lipid in TL3 and TL4 fish in TRIM.FaTE is assumed to be 5.7 percent (by
weight) for the whole fish, based on information provided by Thomann (1989). The lipid content
of the part(s) of the fish normally consumed is likely to be less than 5.7 percent. For example,
EPA estimated a consumption-weighted mean lipid value for fillets of fish from TL3 to be 2.6
percent and from TL4 to be 3.0 percent (Table 6-9 in EPA 2003b). If a user of MIRC wishes to
account for reduced chemical concentration in fillet compared with whole fish for lipophilic
chemicals, the user can specify a "preparation" loss of chemical (see Section 6.4 of this
attachment).
For lipophilic chemicals (e.g., log Kow greater than 4), which partition primarily into the fatty
tissues of fish, much of the higher concentration tissues might be stripped from the fish during
preparation (e.g., belly fat, viscera which includes fat in liver, etc., fat under skin). The degree to
which the concentration of chemical in a fillet is less than the average total concentration in the
whole fish is chemical specific. Assuming the chemical concentration in the fillet is the same as
in the whole fish may result in a health protective bias for highly lipophilic chemicals. For
persons who prefer to consume fillets with the skin on and do not discard belly fat, assuming the
same concentration of chemical in the fish consumed as in the whole fish also is protective.
Attachment B
Description of MIRC
B-76
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
6.3.5. Soil Ingestion Rates
Adult gardeners and farmers may incidentally ingest soils from gardening activities, and
gardening and farming families might ingest soil particles that adhere to exposed fruits and
exposed and belowground vegetables. Soils that are re-suspended in the air by wind can
resettle on exposed fruits and vegetables. Children may incidentally ingest soils in those ways,
but in addition, children playing outdoors may ingest soils directly or by hand-to-mouth activities
during play. MIRC includes soil ingestion rate options by age group for these types of
exposures. MIRC does not include options for children who may exhibit pica, or the recurrent
ingestion of unusually high amounts of soil (i.e., on the order of 1,000 - 5,000 mg/day or more)
(EPA 2008a).
Data on soil ingestion rates are sparse; the soil ingestion rates listed in Exhibit B-23 and
included in MIRC are based on very limited data, as is evident from the values listed. The
studies evaluated by EPA for children generally focused on children between the ages of 1 and
3 to 6 years and were not specific to families that garden or farm. The default ingestion rates in
MIRC are the 90th percentile values, as for other ingestion rate parameters.
Exhibit B-23. Daily Mean and Percentile Soil Ingestion Rates
for Children and Adults
Age Group
(years)
Soil Ingestion Rate (mg/day)
Mean3
50th a
90th
95th
99th
Child < 1
NA
Child 1-2
50
50
200b
200b
200b
Child 3-5
50
50
200b
200b
200b
Child 6-11
50
50
201c
331d
331d
Child 12-19
50
50
201c
331d
331d
Adult 20-70
20
20
201c
331d
331d
Sources: EPA 2008a, EPA 2011a
aFor mean and 50th percentile soil ingestion rates for children, value represents a "central tendency" estimate from EPA's 2008
CSEFH, Table 5-1. For adults, value is the recommended mean value for adults from EPA's 2011 EFH, Chapter 5, Table 5-1.
bValues 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.
"Values are 90th percentile adult ingestion rates calculated in Stanek et al. 1997; used to represent older children and adults.
dValues are 95th percentile adult ingestion rates calculated in Stanek et al. 1997; used to represent older children and adults.
6.3.6. Total Food Ingestion Rates
Although not included in MIRC for deterministic screening-level exposure and risk assessments,
total food ingestion rates would be included in any probabilistic module developed for MIRC.
The total food ingestion rates presented in Exhibit B-24 will be used to normalize or to truncate
the sum of food-specific ingestion rates to reasonable values. This procedure is particularly
important when chemical intake from multiple upper-percentile food ingestion rates for different
types of food are added together. Individuals representing the upper percentile ingestion rate
for one food category might not be the same individuals who reported high percentile ingestion
rates for one or any of the other food categories.
Attachment B
Description of MIRC
B-77
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit B-24. Daily Mean and Percentile Per Capita Total Food Intake for
Children and Adults
Lifestage (years)
Percent of Group
Consuming Food
Mean
50th
90th
95th
99th
Total Food Intake (g/day, as consumed)
Child < 1a
67.0% - 99.7% h
322
270
599
779
1152
Child 1-2b
100%
1,032
996
1537
1703
2143
Child 3-5c
100%
1,066
1,020
1,548
1,746
2,168
Child 6-11d
100%
1,118
1,052
1,642
1,825
2,218
Child 12-19e
100%
1,197
1,093
1,872
2,231
2,975
Adultf
100%
1,100
1,034
1,738
2,002
2,736
Total Food Intake (g/kg-day, as consumed)
Child < 1a
67.0% - 99.7% h
39
34
72
95
147
Child 1-2b
100%
82
79
125
144
177
Child 3-5c
100%
61
57
91
102
132
Child 6-11d
100%
40
38
61
70
88
Child 12-19e
100%
21
19
34
40
51
Adult9
100%
14.8
13.9
23.7
27.6
35.5
Sources: EPA 2005e, 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.
These values represent a time-weighted average for age groups 1 to <2 years (N=1,002) and 2 to <3 years (N=994) from Table
14-3 of the 2008 CSEFH.
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 represents
a health protective (i.e., slightly low) estimate for ages 6 through 11 years since 11-year olds are not included in this CSEFH age
group.
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 CSFII.
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 CSFII.
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.
6.4. Other Exposure Factor Values
The other exposure parameters included in the MIRC algorithms are exposure frequency
(Section 6.4.1), fraction of the food type obtained from the contaminated area (Section 6.4.2),
and reduction in the weight of the food types during preparation and cooking (Section 6.4.3).
For the breast milk ingestion pathway, additional exposure parameters are included in the FFC
algorithms (Section 6.5).
6.4.1. Exposure Frequency
The exposure frequency (EF) represents the number of days per year that an individual
consumes home-produced food items that are contaminated with the chemical being evaluated.
In MIRC, the default value for EF is 365 days/year for all exposure sources and all potential
receptors. This assumption is consistent with the food ingestion rates used in MIRC (i.e., daily
intake rates equivalent to annual totals divided by 365 days) and does not imply that residents
necessarily consume home-produced food products every day of the year.
Attachment B B-78 October 2014
Description of MIRC
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TRIM-Based Tiered Screening Methodology for RTR
If the user wishes to evaluate daily intake rates based on shorter averaging times, the user can
overwrite both the food-specific ingestion rates and the EF for each home-grown food product.
Users of MIRC might want to specify a lower EF values for various food types where residents
obtain some of their diet from commercial sources and where consumption of home grown
produce is seasonal.
6.4.2. Fraction Contaminated
The fraction contaminated (FC) represents the fraction of each food product consumed that is
contaminated by the chemical at a level consistent with environmental concentrations in the
area of concern (e.g., area with maximum deposition rates). Obviously, the most health
protective assumption is that all food products consumed (i.e., 100 percent) are from the
location represented by the chemical concentrations input into MIRC.
For non-infant children and the adult age cohorts, MIRC includes the default FC of 1, assuming
that 100 percent of the food product consumed is produced by households that farm, garden, or
raise animals. The user can vary this default FC value for individual food products to tailor the
assessment to a particular exposure scenario.
6.4.3. Preparation and Cooking Losses
Food preparation and cooking losses are included in the FFC exposure calculations to account
for the amount of a food product as brought into the home that is not ingested due to loss during
preparation, cooking, or post-cooking. These losses need to be accounted for in the ADD
equations because the food ingestion rates calculated from the USDA 1987 to 1988 NFCS are
based on the weight of home grown produce and animal products brought from the field into the
house prior to any type of preparation. Not all of the produce or products were eventually
ingested. In general, some parts of the produce and products are discarded during preparation
while other parts might not be consumed even after cooking (e.g., bones). Thus, the actual food
ingested is generally less than the amount brought into the home.
Three distinct types of preparation and cooking losses are included in the ingestion exposure
algorithms in MIRC: (1) loss of parts of the food type from paring (i.e., removing the skin from
vegetables and fruits) or other types of preparation (e.g., removing pits, coring, deboning), (2)
additional loss of weight for the food type during cooking (e.g., evaporation of water), and (3)
post-cooking losses (e.g., non-consumption of bones, draining cooking liquid [e.g., spinach]).
MIRC includes mean values for these three types of preparation and cooking losses for
exposed fruit, protected fruit, exposed vegetables, protected vegetables, root vegetables, beef,
pork, poultry, and fish. Different types of losses apply to different types of foods. Therefore, the
losses can be represented by only two parameters, L1 and L2, the definitions of which vary
according to the food type as explained in the endnotes in Exhibit B-25. All preparation and
cooking loss parameter values were estimated as specified in the exhibit's endnotes from data
presented in Chapter 13 of the EPA's 1997 and 2011 EFH (EPA 1997a and 2011a).
Attachment B
Description of MIRC
B-79
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit B-25. Fraction Weight Losses from Preparation of Various Foods
Product
Mean Cooking, Paring, or
Preparation Loss
(Cooking Loss Type 1 [L1])
(unitless)3
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
0.088f
NA
Root Vegetables
0.075
0.22
Beef
0.27
0.24
Pork
0.28
0.36
Poultry
0.32
0.295h
Fish'
0.0
0.0
Source: EPA 1997a and 2011a
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).
dThis value represents an average of means for all exposed vegetables with available data (Table 13-7). Exposed vegetables
include asparagus, broccoli, cabbage, cucumber, lettuce, okra, peppers, snap beans, and tomatoes.
eThis value was set equal to the value for oranges (Table 13-6).
'This value represents an average of means for all protected vegetables with available data (Table 13-7). Protected vegetables
include pumpkin, corn, peas, and lima beans.
9These values represent averages of means for all root vegetables with available data (Table 13-7). Root vegetables include
beets, carrots, onions, and potatoes.
This value represents an average of means for chicken and turkey (Table 13-5).
'If the user changes fish ingestion rates to match a survey of the whole weight offish brought into the home from the field (divided
by the consumers of the fish), an appropriate value for LI would be 0.31 and an appropriate L2 would be 0.11 (EPA 2011a).
There are substantial uncertainties associated with the L1 and L2 parameters, including the
wide variation in values across produce types that were averaged together to recommend a
central tendency value for each. For example, the L2 factor does not distinguish between
weight loss during cooking by water evaporation, which might leave the chemical in the food
(chemical not lost), and pouring the cooking liquid down the drain (chemical lost) or using the
liquid to create a sauce (chemical not lost). In addition, the concentration of chemical might be
highest in the skin, which often is discarded, and lower in the consumed portion of many bulky
fruits and vegetables. Finally, the data EPA used to evaluate L1 included negative losses (i.e.,
weight gains) due to hydration of dried vegetables (e.g., peas and lima beans), which increases
the range of L1 values across different vegetables.
Note that the default L1 and L2 values for fish are set to zero. That is because the data source
for the fish ingestion rates is not the USDA's 1987 to 1988 NFCS (USDA 1993, 1994a) as
reported in EPA's EFH, which reported food as brought into the home, as is the case for the
other food categories. Instead, the fish IR data included in MIRC are based on parts actually
consumed, and so no loss processes for preparation are needed.
If the user manually changes fish ingestion rates to match a local survey of the whole weight of
fish brought into the home from the field (divided by number of persons consuming the fish), the
user should also set the L1 and L2 parameter to non-zero values.
Attachment B B-80 October 2014
Description of MIRC
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TRIM-Based Tiered Screening Methodology for RTR
6.4.4. Food Preparation/Cooking Adjustment Factor (FPCAF) for Fish
In addition to estimating the weight of the food that is lost to preparation and cooking, there also
can be changes in the chemical concentrations due to cooking. Because the fish consumption
rates are "as consumed" and the fish concentration is based on uncooked fish, adjustments
should be made to reflect the chemical concentrations in fish after cooking. In order to account
for this phenomenon, an FPCAF can be applied to the uncooked fish concentration to estimate
a concentration in cooked fish. The following sections discuss FPCAFs for each of the 4 PB-
HAPs.
6.4.4.1. Mercury
In the U.S. EPA Revised Technical Support Document: National-Scale Assessment of Mercury
Risk to Populations with High Consumption of Self-caught Freshwater Fish (EPA 2011b), a food
preparation/cooking adjustment factor (FPCAF) of 1.5 was used to adjust methyl-mercury
(MeHg) concentrations in consumed fish (i.e., a 50% increase in MeHg concentration due to
cooking). Cooking fish typically increases MeHg levels per unit fish (as consumed) because
MeHg concentrates in the muscle, while preparation involves removal primarily of non-muscle
elements of the fish. The value is based on a study by Morgan et al. (1997).
6.4.4.2. Cadmium
Similar to mercury, cadmium will bind to muscle and will be retained during the cooking process.
As such, the same FPCAF of 1.5 that is used for mercury is assumed for cadmium.
6.4.4.3. Dioxin
Dioxins are lipophilic and have been demonstrated to be lost during cooking. Based on a
literature review, an FPCAF of 0.7 to is applied to account for these losses during the cooking
process. A brief summary of supporting literature follows.
Schecter et al. (1998) found that the mass of PCDD and PCDF in fresh catfish fillet (skin
on) decreased by about 50 percent per serving portion during cooking. Given the
simultaneous losses of moisture/fats during broiling of the catfish, the PCDDs and
PCDFs concentrations decreased by 33 percent (i.e., multiply uncooked concentration in
fresh fish by a factor of 0.66 = 0.70 to one significant digit).
Reinert et al. (1972) reported higher losses of another highly lipophilic chemical, DDT,
from cooking fish fillets of bloaters, yellow perch, lake trout, and coho salmon.
Concentrations of DDT in fish fillet portions for lake trout and coho salmon, top
predators, were reduced by 64 to 72 percent by frying or broiling, primarily through
preferential loss of fat (and lipophilic DDT) during cooking. The investigators did not
report skin on or off; however, they used steak cuts instead of flat fillets, which provide a
smaller ratio of skin to muscle than is the case for fillets that constitute one side of the
fish.
• Zabik and Zabik (1995) quantified the reduction in TCDD concentration of cooked, with
the skin off, fillets compared with uncooked fillet with skin for fish harvested from the
Great Lakes. Concentrations in the cooked fish with the skin off were reduced relative to
the raw fillet with the skin on by approximately 44 percent for walleye, 80 percent for
white bass, and 61 percent for lake trout. Comparing losses of TCDD for fillets cooked
with the skin on versus fillets that were both skinned and cooked, Zabik and Zabik
(1995) found reductions in TCDD concentrations of approximately 43 percent for
Chinook Salmon cooked with the skin on and 57 percent for Chinook salmon cooked with
Attachment B
Description of MlRC
B-81
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
the skin off. They found a 37 percent reduction of TCDD concentration for carp fillets
cooked with the skin on and 54 percent reduction if the skin was removed.
The three studies listed above indicate that the 0.7 factor is not likely to overestimate loss of
PCDD/PCDFs from fish during cooking (pan frying, broiling, grilling). Reductions in TCDD
concentrations could be much higher with skin removal and trimming of fat.
6.4.4.4. PAHs
While it is reasonable to assume that there might be losses of lipophilic PAHs during the
cooking process, there is insufficient information to distinguish what the net loss (or gain) during
cooking might be because cooking can create PAHs from proteins in the tissue. The literature
acknowledges these competing forces, but does not provide information to disentangle the gain
and loss mechanisms. As such, a neutral approach was taken, which is to assume an
adjustment factor of 1.0 (i.e., no adjustment) for PAHs.
6.5. Breast-Milk Infant Exposure Pathway Parameter Values
Values used for parameters in the breast-milk exposure pathway algorithms (see Section 3.4 of
this attachment) can be scenario-specific, receptor-specific, and/or chemical-specific and might
be empirically derived or estimated by an appropriate model. For parameters that are scenario-
specific or for which empirical values are required, the default values provided in MIRC are
listed. For parameters for which MIRC calculates values, the appropriate equation is listed.
Scenario- and receptor-specific parameters are discussed in Section 6.5.1 and chemical-
specific parameters are discussed in Section 6.5.2.
6.5.1. Receptor-specific Parameters
Receptor-specific values are needed for parameters that describe the characteristics or
activities of the exposed individual. In this context, there are two relevant receptors: the mother
and the infant. Exhibit B-26 lists the parameters and their default values. The text that follows
describes the input value or value options for each exposure parameter required by MIRC to
calculate the infant absorbed chemical intake rate, or DAImf. For parameter values that can be
estimated when empirical values are not available, see the equation description in Section 3.4
of this attachment.
Attachment B
Description of MIRC
B-82
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit B-26. Scenario- and Receptor-Specific Input Parameter Values Used to
Estimate Infant Exposures via Breast Milk
Parameter
Description
Default Value
AT
Averaging time for infant's exposure via breast milk, i.e., duration of
nursing (days)
=ED
BWinf
Body weight of infant (kg) averaged over duration of nursing exposure
7.8
BWmat
Body weight of mother (kg) averaged over duration of mother's
exposure
66
DAImat
Daily absorbed intake of chemical by mother (mg/kg-day)
Equation B-36
ED
Exposure duration for infant, i.e., duration of breast feeding (days)
=AT
AT/ED
Averaging time divided by exposure duration
1.0
fbp
Fraction of mother's whole blood that is plasma (unitless)
0.65
ffm
Fraction of mother's body weight that is fat (unitless)
0.30
fmbm
Fraction of fat in mother's breast milk (unitless)
0.04
fpm
Fraction of mother's body weight that is plasma (unitless)
0.046
IRmilk
Mean infant milk ingestion rate over duration of nursing (kg/day)
0.709
tbf
Duration of breast feeding (days)
365
tpn
Duration of maternal chemical exposure prior to nursing (days)
3285
Averaging time (AT) and exposure duration (ED). AT refers to the time over which the infant's
exposure to the chemical of concern is averaged. ED refers to the duration of the infant's
exposure. For the exposure scenario considered for this age group, both AT and ED equal the
duration of the nursing period, and they therefore cancel each other out in the infant average
daily dose equation.
Infant body weight (BWmf). The user selects a value for BWm, the time-weighted average body
weight of the infant over the entire duration of breast feeding, based on the age at which the
infant stops breast feeding. For example, if the infant breast feeds for one year, the user should
select the body weight for an infant that is averaged from birth to the first birthday. Similarly, if
an infant breast feeds for 6 months, the user should select the body weight for an infant that is
averaged from birth to six months. Because the default breast feeding duration (tbf) is one year
(i.e., 365 days), the default infant body weight is 7.8 kg, which is the time-weighted average for
the mean infant body weight between birth and the first birthday from EPA's 2008 Child Specific
Exposure Factors Handbook (CSEFH; EPA 2008a). Exhibit B-27 presents additional values for
the infant body weight parameter that the user can select instead of the MIRC default.
Exhibit B-27. Average Body Weight for Infants
Statistic
0 to < 6 months
(kg)
0 to < 12 months
(kg)
0 to < 18 months
(kg)
0 to < 24 months
(kg)
Mean
6.5
7.8a
9.0
9.6
5th percentile
5.0
6.0
7.0
7.5
10th percentile
5.3
6.4
7.4
7.8
15th percentile
5.5
6.7
7.7
8.2
25th percentile
5.8
7.0
8.1
8.7
50th percentile
6.4
7.8
8.9
9.5
75th percentile
7.1
8.6
9.9
10.5
85th percentile
7.4
9.0
10.3
11.0
Attachment B B-83 October 2014
Description of MIRC
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit B-27. Average Body Weight for Infants
Statistic
0 to < 6 months
(kg)
0 to < 12 months
(kg)
0 to < 18 months
(kg)
0 to < 24 months
(kg)
Mean
6.5
7.8a
9.0
9.6
90th percentile
7.7
9.2
10.6
11.3
95th percentile
8.0
9.7
11.1
11.8
Source: EPA (2008a); each value is the time-weighted average from the data summaries presented in the CSEFH, Table 8-3.
aMIRC default
Maternal body weight (BWmat). This parameter represents the body weight of the mother
averaged over the entire duration of the mother's exposure to the chemical of concern. The
maternal body weight is needed to calculate the biological elimination constant for the lipophilic
chemical in lactating women (kfat_eiac). MIRC assumes that the mother will be pregnant for 9
months (i.e., 0.75 year) and will be lactating for 1 year. The MIRC default maternal body weight
also assumes that the mother has been exposed for 10 years total. For 8.25 years, she is not
pregnant or lactating, for 0.75 year she is pregnant, and for 1 year she is lactating. The MIRC
default BWmat of 66 kg is based on CSFII data compiled by EPA for non-lactating and non-
pregnant women between the ages of 15 and 44 (i.e., women of child-bearing age), lactating
women, and pregnant women (EPA 2004). Exhibit B-28 presents additional values for the
maternal body weight parameter which the user may choose to use instead of the MIRC default.
The BWmat value is not the value that MIRC uses to estimate the mother's absorbed daily intake
(DAImat). The daily ingestion rates for home-grown/raised food products in MIRC are for men
and women combined, with the rates normalized to body weight. The ingestion rates for soil,
water, and fish included in MIRC are not normalized to body weight but are based on both men
and women. For those ingestion rates, MIRC uses an average body weight value for males and
females to estimate the average daily dose (intake) of the chemical in mg/kg-day. These values
are subject to the assumption that the body-weight normalized ingestion rates and resulting
ADD values are applicable to nursing mothers.
Exhibit B-28. Time-weighted Average
Body Weight for Mothers
Statistic
Weight (kg)
Mean
66.0a
5th
47.1
10th
50.2
25th
54.3
50th
62.0
75th
72.0
90th
85.7
95th
97.0
Source: EPA 2004
aMIRC default value
Exposure duration (ED). See discussion of AT and ED above.
Fraction of mother's whole blood that is plasma (fbo). Steinbeck (1954) reported that plasma
volume accounts for approximately 60 percent of the total blood volume in non-lactating human
females (EPA 1998). Harrison (1967) and Ueland (1976) reported plasma volumes between 63
to 70 percent in postpartum women (EPA 1998). The default value in MIRC of 65 percent (0.65)
Attachment B
Description of MIRC
B-84
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
is the value recommended by EPA in its Methodology for Assessing Health Risks Associated
with Multiple Pathways of Exposure to Combustor Emissions (MPE, EPA 1998).
Fraction of mother's body weight that is fat (ffm). A limitation of using a steady-state, instead of a
dynamic partitioning, model for lactational transfer of chemicals is that several key parameters
change over the course of exposure. For example, Equation B-38, used to estimate the
concentration of a lipophilic chemical in breast milk fat, assumes that the mother's body fat will
remain constant over the entire duration of breast feeding (W), which is unlikely to be true (EPA
2001a). Another limitation of the single analytic model is that chemical transfer rates from blood
to milk are unlikely to be the same as the rate of mobilization of the chemical from fat stores to
the blood (EPA 2001a). Studies cited in ATSDR's toxicological profile for chlorinated dibenzo-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) (EPA 1998). A fraction of 0.3 indicates that 30 percent
of the mother's body weight is fat, which is a health protective value (EPA 2001a). To establish
a health protective screening scenario, the MIRC default value for frm is 0.30.
Fraction of fat in mother's breast milk (fmhm). The Cmmat model (Equation B-38) assumes that a
constant fraction of breast milk is fat, even though there is evidence that indicates variation in
the fat content of breast milk throughout lactation (Sim and McNeil 1992). Different studies
suggest a fat content of breast milk in humans of between 1 and 5 percent (Jensen 1987,
Schecter et al. 1994, Hong et al. 1994, McLachlan 1993, Bates et al. 1994, NAS 1991, Butte et
al. 1984, Maxwell and Burmaster 1993, EPA 2011a, Smith 1987, Sullivan etal. 1991). The
MIRC default value for fmbm of 0.04 (i.e., 4 percent) is the value EPA recommended for MPE
(EPA 1998).
Fraction of maternal weight that is plasma (fom). Altmann and Dittmer (1964) estimated that
plasma volume for adult women ranged from 37 to 60 ml_/kg of body weight and averaged
about 45 ml_/kg. Ueland (1976) observed that the average plasma volume of women 6 weeks
postpartum was 45 ml_/kg of body weight. Using a value of 1.026 for the specific gravity of
plasma from Conley (1974), EPA estimated a value of 0.046 for the fraction of maternal weight
that is plasma (EPA 1998). The MIRC default for fpm therefore is 0.046.
Infant breast milk ingestion rate (IRmuk). Milk ingestion rates vary with several factors, including
the age and size of the infant and use of other foods such as formula. Based on its review of a
several studies, EPA recommended time-weighted average and upper percentile milk ingestion
rates for infants that nurse for six and for twelve months (EPA 2011a, Table 15-3). To estimate
an "average" value, EPA first estimated study-sample-size weighted average values for 1
through 12 months of age and then developed time-weighted average milk ingestion rates from
those (EPA 2011a). EPA estimated an upper percentile (upper bound) value as the mean plus
two standard deviations. MIRC converts the ingestion rates measured volumetrically (mL/day)
to mass-based estimates (kg/day) assuming the density of human milk to be 1.03 g/mL
(reported by NAS 1991 and recommended by EPA 2011a). The resulting values are shown in
the first two rows of Exhibit B-29. The MIRC screening-level default of 980 mL/day is an upper-
bound estimate based on a one-year nursing period.
Exhibit B-29 also includes the recommended values for four non-overlapping age categories
from the CSEFH (EPA 2008a, Table 15-1). The values demonstrate that although infants grow
substantially from birth to one year of age, the "upper bound" estimates of their milk ingestion
rates are very close to 1 liter per day at all stages of development in the first year.
Attachment B
Description of MIRC
B-85
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit B-29. Infant Breast Milk Intake Rates
Age Category
Average
(mL/d)
Average
(kg/d)
"Upper Bound"
(mL/d)
"Upper Bound"
(kg/d)
Reference
1 to 6 months
742
0.764
1,033
1.064
EPA 2011 at
Oto < 12
months
688
0.709
980a
1.01a
EPA 2011 at
0 to < 1 month
510
0.525
950
0.979
EPA 2008att
1 to < 3 months
690
0.711
980
1.01
EPA 2008at
3 to < 6 months
770
0.793
1,000
1.03
EPA 2008at
6 to < 12
months
620
0.639
1,000
1.03
EPA 2008at
aMIRC default;f Based on review of multiple studies;ft Based on a single study
Duration of breast feeding (thf). This parameter is equal to the infant's exposure duration (ED)
and the infant's averaging time (AT). In its MPE Methodology, EPA asserts a health protective
value for the duration of breast feeding of 1 year (i.e., 365 days) and a central tendency
estimate of 6 months (180 days) (EPA 1998). Reviewers of MPE noted that 365 days may be
overly health protective, given that only 20 percent of infants are breast fed for 6 months, at
which point alternative foods are introduced, at least in addition to breast milk (EPA 2001a).
Nonetheless, to establish a health protective screening scenario, the MIRC default for fa is 365
days.
Duration of the mother's exposure to the chemical of concern prior to nursing (ton). The model
shown as Equation B-38 includes this parameter to reduce the over-estimate of chemical
concentration in milk fat that occurs if the model is applied to a chemical with a long biological
half-life (e.g., many years). The factor is needed for applications of the model to scenarios with
a brief exposure duration (e.g., beginning a few months prior to the start of nursing) relative to
the chemical half-life. As the duration of an exposure scenario increases to meet and exceed
the chemical half-life, however, the overestimate that occurs without this parameter is reduced.
For example, assume a chemical biological half-life of 8 years and a nursing period of 1 year. If
exposure of the mother starts at the beginning of nursing, using Equation B-38 without the tpn
term results in an over-estimate of the concentration of the chemical in breast milk by a factor of
28.1 compared with the prediction using Equation B-38 with the tpn term (EPA 1998, Table 9-6).
However, at longer pre-natal exposures of the mother, the magnitude of the over-estimate is
reduced: for a 10-year exposure, the magnitude of the overestimate without the tpn term is 2.28,
and for a 30-year exposure, the overestimate is reduced to 1.39.
For purposes of the screening-level assessment, we assume an exposure duration equal to the
MIRC default half-life for dioxins, or 10 years. Only 3,285 days of that period are pre-natal (i.e.,
3650 minus 365 days, assuming 1 year lactation period). Although longer exposure periods are
possible for the screening scenario, there is sufficient uncertainty in the model to merit
accepting a health protective bias for this parameter value.
6.5.2. Chemical-Specific Parameter Values
The chemical-specific parameters in the breast-milk pathway in MIRC are listed in Exhibit B-30.
Note that the parameters for which values are needed are different for the lipophilic chemicals
(i.e., dioxins), for which lactational transfer is assumed to occur via milk fat, and inorganic
chemicals, for which the transfer is assumed to occur via the aqueous phase of breast milk (i.e.,
mercury). All dioxin congeners were assumed to manifest identical values as TCDD in regard to
breast milk-related parameters.
Attachment B B-86 October 2014
Description of MIRC
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit B-30. Chemical-specific Input Parameter Values for
Breast Milk Exposure Pathway
Parameter and Description
2,3,7,8-
TCDD
MeHg
AEinf
Infant absorption efficiency of the chemical by the
oral route of exposure (i.e., fraction of ingested
chemical that is absorbed by the infant; unitless)
1.0 (default)
1.0 (default)
AEmat
Maternal absorption efficiency of the chemical by
the oral route of exposure (i.e., fraction of ingested
chemical that is absorbed by the mother; unitless)
1.0 (default)
1.0 (default)
fbl
Fraction of steady-state total body burden of
hydrophilic chemical in mother that is in the
mother's whole blood compartment (unitless)
NA
0.059 (Kershaw et
al. 1980)a
ft
Fraction of steady-state lipophilic chemical body
burden in mother that is stored in body fat (unitless)
>0.90 (ATSDR
1992)
NA
fpl
Fraction of steady-state total hydrophilic chemical
body burden in mother that is in the blood plasma
compartment (unitless)
NA
Not yet identified13
h
Biological half-life for chemical in non-lactating
women (days)
3650 (EPA
1994c)
50 (Sherlock et al.
1984)
kaq_elac
Rate constant for total elimination of hydrophilic
chemicals by lactating women (per day)
NA
— kelim
kelim
Rate constant for elimination of chemical for 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-41
NA
E
.a
O
o_
Partition coefficient for hydrophilic chemical
between maternal blood plasma and aqueous
phase of breast milk (g milk/g plasma; model
assumption)
NA
1.0 (model
assumption)
Pcrbc
Partition coefficient for hydrophilic or protein-bound
chemical between red blood cells (RBC) and
plasma in maternal blood (mL whole blood/mL
RBC)
NA
40 (Hollins et al.
1975)
NA = not applicable. ND = not yet determined from literature.
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-40.
Absorption efficiency of the chemical by the oral route of exposure for the infant (AEmt). The
models included in MIRC assume that the AEmfrom the lipid phase of breast milk is equal to the
AEm 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 AEmt from
the breast milk fat and the AEmt from the aqueous phase of breast milk (EPA 2001a). However,
since the MIRC assumption is that chemicals will partition to either the lipid or aqueous phase of
milk, it is not necessary at this time to have multiple AEmt 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 (EPA 2001a). If chemical-specific data are not available
Attachment B
Description of MIRC
B-87
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
for adults or infants, a health protective default value for/\£,nffor a screening level assessment
is 1.0, which assumes 100 percent absorption (EPA 1998).
The default value for AEmf in MIRC for both MeHg and dioxin is 1.0. For ingested lipophilic
chemicals, it is reasonable to assume that absorption will be high (EPA 2004c). ATSDR (1998)
reported that dioxins are well absorbed by the oral route of exposure, with one human
experiment indicating more than 86 percent absorption. It is EPA policy to assume 100 percent
absorption for chemicals with reported AEs of 50 percent or higher (EPA 2004c). MeHg also is
well absorbed, with measured values as high as 95 percent, and so a value of 100 percent is
used in MIRC (EPA 2001b).
Absorption efficiency of the chemical by the oral route of exposure for the mother (AEmat). The
default value for both dioxins and MeHg is 1.0, as described in the previous paragraph.
Fraction of total maternal chemical body burden that is in the whole blood (fbi). The default
value for MeHg in MIRC, 0.059, is from Kershaw et al. (1980), which reported kinetics of blood
deposition and clearance of MeHg in humans. Individuals consumed one meal of fish that
contained between 18 and 22 |jg Hg/kg body weight. The fraction of the dose deposited in the
blood volume after mercury was fully distributed in tissues was 5.9 percent or 0.059. This study
used a single-dose and thus may not be appropriate for a chronic exposure analysis.
Fraction of total maternal chemical body burden that is in body fat (ft). Based on ATSDR's
Toxicological Profile for Selected PCBs (ATSDR 1992) and Sullivan et al. (1991), EPA
concluded that the "fraction of ingested contaminant stored in fat may be >90%" for lipophilic
chemicals such as PCBs and dioxins (EPA 1998). This statement was interpreted to mean that
90 percent of the maternal body burden of chemical at "steady state" is located in body fat for
dioxins at steady state.
Fraction of total maternal chemical body burden that is in blood plasma (fni). 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 methyl mercury has been identified from the
literature at this time. A value can be calculated using Equation B-43. However, this equation
requires a reliable value for fbi, and the value found for mercury may not be appropriate for a
chronic exposure analysis (see above).
Chemical half-life in non-lactating 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 (EPA 1994a). ATSDR estimates that the half-life for 2,3,7,8-TCDD in particular
may be as long as 12 years (ATSDR 1998). To establish a health protective screening
scenario, the MIRC default half-life for dioxins is set to 10 years or 3650 days.
The half-life for methylmercury is on the order of weeks, not years. Greenwood et al. (1978)
measured blood clearance rates for MeHg in lactating Iraqi women exposed accidentally to
MeHg via bread prepared from wheat treated with a fungicide that contained MeHg. The data
indicated a mean half-life for MeHg of approximately 42 days. Sherlock et al. (1984) reported
an average measured half-life for MeHg of 50 days with a range of 42-70 days. The MIRC
default for MeHg is set to the longer average half-life of 50 days.
Chemical elimination rate constant for lactating women - aqueous (ka„ Piac). 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
Attachment B B-88 October 2014
Description of MIRC
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TRIM-Based Tiered Screening Methodology for RTR
reasonable assumption for water soluble chemicals is that kaq_eiac is equal to keum as discussed
for Equation B-43. 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 (keum). Although values for this
parameter often are reported directly in the literature, MIRC estimates kenm from chemical half-
life assuming first-order kinetics as shown in Equation B-40. 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 lactatinq women (kfat eiar). Although
values for this parameter might be found in the scientific literature for some chemicals, in MIRC,
kfat_eiac for dioxins is calculated from Equation B-41. When the parameters in that equation are
set to the default values in MIRC for dioxins, MIRC estimates a value of 0.0015 per day for
kfat_elac-
Partition coefficient for chemical between maternal blood plasma and aqueous phase of breast
milk (PChm). The aqueous model, presented in Equation B-42, assumes that the concentrations
in the plasma and aqueous phase of breast milk are directly proportional (EPA 1998).
Therefore, the default value for this parameter for MeHg in MIRC is 1.0.
Partition coefficient for chemical between red blood cells and plasma in maternal blood (Pcrbc).
Chemical-specific values for this parameter should be located in the scientific literature. If
chemical-specific values are unavailable and it is assumed that there is equal distribution of the
chemical in the plasma and red blood cells, EPA suggests a default value of 1.0 (EPA 1998).
For MeHg, MIRC includes a value of 40 based on Hollins et al. (1975) study of cats exposed to
MeHg, which reported a ratio of radio-labeled mercury in red blood cells to plasma of 97.7 to 2.3
(i.e., ratio of 42.5).
7. Summary of MIRC Default Exposure Parameter Settings
The default settings included in MIRC are intended to be characteristic of a health protective
(but plausible) exposure scenario that results in a negligible or extremely low chance of
underestimating risk. These default parameter values were used to derive the screening
threshold emission rates used for screening emissions of PB-HAPs from sources included in
RTR risk assessments. These values are the default, or initial setting, for parameter values in
MIRC as described in Section 6 of this attachment. This section summarizes the default
parameter values used to calculate screening thresholds.
This chapter is organized to present the chemical- and scenario-specific inputs to MIRC by data
type. The screening-level analysis uses 90th percentile ingestion rates for soil, breast milk, and
farm food items and 99th percentile ingestion rates for fish (presented in Section 7.1) and
population-specific characteristic assumptions (presented in Section 7.2), that are generally
health protective in nature. Screening thresholds were derived for five RTR chemical species:
benzo(a)pyrene, cadmium, mercuric chloride, methyl mercury, and 2,3,7,8-TCDD; Section 7.3
presents chemical-specific parameter inputs for these five chemicals. Finally, Section 7.4
presents default parameter values for the nursing infant exposure scenario, which applied only
to dioxin and methyl mercury as discussed in Section 3.4.
7.1. Default Ingestion Rates
The screening-level (or default) values for ingestion rates for soil, breast milk, and for each farm
food item are equal to the 90th percentile of the distribution of national data for that ingestion
Attachment B
Description of MIRC
B-89
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
medium. In general, these values were obtained from the 2011 Exposure Factors Handbook or
the 2008 Child-Specific Exposure Factors Handbook (see Exhibit B-31). Fish ingestion rates
are also available from these sources; however, as described in Section 6.3.4, these sources
were not used to obtain fish ingestion rates.
7.1.1. Fish Ingestion Rates
The adult fish ingestion rate was obtained from Burger (2002), a study that examined daily
consumption of wild-caught fish for high-end recreationalists (white, black and female) in South
Carolina. For female high-end consumers of wild-caught fish, Burger identified average and
higher-percentile consumption rates as follows: 39.1 g/day (mean), 123 g/day (90th percentile),
172 g/day (95th percentile), and 373 g/day (99th percentile). As shown in Exhibit B-31 and
discussed in Section 6.3.4, for adults, the rate of fish ingestion assumed in the screening
scenario is 373 g/day, which corresponds to the 99th percentile value estimated by Burger for
adult females. This value was selected to be representative of subsistence fishers.
For the child age groups, as discussed in Section 6.3.4, the baseline fish ingestion rates for the
screening scenario are based on "as prepared" total freshwater/estuarine fish ingestion rates at
the 99th percentile of the distribution for the consumer-only population (i.e., inclusive only of
people who consume fish, rather than per-captita rates, which include both consumers and non-
consumers), as estimated in EPA (2002), Section 4.2.1.1. Some adjustments were necessary
because the age groups evaluated for RTR (which correspond to the age groups for which farm
food ingestion rates are available) do not all directly correspond to the age groups in the EPA
(2002) report. As described in Section 6.3.4, these adjustments convert the available age-
specific data on fish ingestion rates to the age-specific values needed for MIRC.
For the screening-level fish ingestion exposure scenario, the consumer evaluated is an
individual who regularly consumes a large amount of fish that he or she has caught locally over
the course of a 70-year lifetime. Modeled exposures are intended to encompass those of a
subsistance fisher whose diet comprises a substantial proportion offish. The scenario is not,
however, intended to represent the maximum possible exposure an individual subsistence fisher
might experience.
Although the fish ingestion rates presented here are representative of the 99th percentile of the
evaluated data set, the use of these inputs (compared with 90th percentile values used for other
food types) is not considered to be inconsistent. This is due to the idiosyncrasies of the survey
data on fish consumption, the fact that the data sets for homegrown foods and fish are not
parallel, and the consideration of rates appropriate for subsistence fishers, as described above.
As discussed above, EPA believes that use of these fish ingestion rates strikes the appropriate
balance between being health protective and having screening scenarios so conservative that
they are of limited use in the decision making process. This high-end fish ingestion rate is
appropriate in the context of the conservative screening scenario used in the RTR process and
is applicable for national rulemakings given that it is very likely that subsistence woman fishers
of child bearing age are located throughout the United States. Using a high-end subsistence fish
ingestion rate also is consistent with section 112 of the CAA, which focuses on risks associated
with maximally exposed individuals.
7.1.2. Farm Food Chain Ingestion
The default settings assume that all food types are obtained from the area of chemical
deposition specified by the user (i.e., fraction of food from contaminated area = 1.0).
Attachment B
Description of MIRC
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October 2014
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TRIM-Based Tiered Screening Methodology for RTR
For estimates of screening threshold emission rates for PB-HAPS, environmental
concentrations and air deposition rates were estimated using TRIM.FaTE for the area of
maximal deposition in the vicinity of a hypothetical facility, and thus represent risks estimated for
a maximally exposed individual/farm/family.
Exhibit B-31 also includes a sum of the 90th percentile ingestion rates for homegrown food
categories and 99th percentile fish ingestion to show the implied total food ingestion rate
associated with setting multiple food-type-specific ingestion rates at upper percentiles. Because
these upper percentile values for each farm food category are likely to reflect different
individuals, it is likely that addition of multiple upper percentile intake values will exceed the total
food ingestion rates expected for the general population. This sum is shown on the third row
from the bottom (Total Food: Homegrown Only).
The second row from the bottom presents the 90th percentile of the distribution of individual total
food ingestion rates from the USDA's 1994-96 and 1998 Continuing Survey of Food Intakes by
Individuals (CSFII) (USDA 2000) data sets, as analyzed by EPA (EPA 2005e). The total
ingestion rate for the farming households (third row from bottom) takes into account the cooking
losses typical of each food category to provide a better comparison with the 90th percentile
individual total food ingestion rates from CSFII (which are based on consumption of prepared
foods). The final row of Exhibit B-31 shows the likely magnitude of the overestimates by age
category by presenting the ratio of the two preceding rows. The values in this row demonstrate
the potential for overestimating intake by using upper percentile values for all food groups. This
bias may be considered when evaluating the results estimated by MIRC.
Exhibit B-31. Farm Food Category Ingestion Rates for Health Protective Screening Scenario
for Farming Households
Product
Screening-Level Consumer Ingestion Rate
Units
Infants
<1 yr
Child
1-2 yrs
Child
3-5 yrs
Child
6-11 yrs
Child
12-19 yrs
Adult
20-70 yrs
Farm Food Item
2-
CD
CO
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
107.70
159.00
268.2h
331,0h
373
g/day
Attachment B
Description of MIRC
B-91
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit B-31. Farm Food Category Ingestion Rates for Health Protective Screening Scenario
for Farming Households
Product
Screening-Level Consumer Ingestion Rate
Units
Infants
<1 yr
Child
1-2 yrs
Child
3-5 yrs
Child
6-11 yrs
Child
12-19 yrs
Adult
20-70 yrs
Total Food Ingestion Rates for Comparison Only (not in MIRC; excludes soil and water)
Total Food: Homegrown
only'
NA
259
142
99
51
35.5
g/kg-day
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: EPA 2011 a, EPA 2008a, unless otherwise noted.
NA = not applicable
aPrimary source for values was the 1987-1988 NFCS survey; compiled results are presented in Chapter 13 of the 2011 Exposure
Factors Handbook (EPA, 2011a). When data were unavailable for a particular age group, the intake rate for all age groups was
multiplied by the age-specific ratio of intake based on national population intake rates from CSFII.
bPrimary source for values was 1987-1988 NFCS survey, compiled results are presented in Chapter 13 of the 2011 Exposure Factors
Handbook (EPA, 2011a). When data were unavailable for a particular age group, the intake rate for all age groups was multiplied by the
age-specific ratio of intake based on national population intake rates from an NHANES 2003-2006 analysis in Chapter 11 of the
Exposure Factors Handbook.
°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 EPA (2002) report (value
presented is rounded); for these children, the RTR age-group range matches the EPA (2002) age category. Fish ingestion rates for
children less than 3 years old, however, were not provided. Therefore, for children aged 1-2 years, the fish ingestion rate was calculated
using the ingestion rate for children aged 3-5 years scaled downward by the ratio of the mean body weight of children aged 1-2 years
to the mean body weight of children aged 3-5-years.
Time-weighted average ingestion rates were calculated using the EPA (2002) fish ingestion estimates in order to adjust for the
differences between the age group ranges used for the RTR screening and those presented in the 2002 EPA report.
'Sum of post-cooking food ingestion rates. This estimate is calculated by multiplying the food ingestion rates on previous rows (excluding
soil and water) by (1-L1)x(1-L2), where U and L2 are the loss rates from Exhibit B-25. The rows are then summed to get the total post-
cooking ingestion rate.
j90,h percentile total food intake rates from EPA 2008a and 2005e based on CSFII data 1994-96 and 1998; see Section 6.3.6 of this
document.
7.2. Default Screening-Level Population-Specific Parameter Values
The screening-level values for body weights (BWs) for the RTR screening threshold analysis,
which serve as the default values in MIRC, are mean values and are presented in Exhibit B-32.
As stated in Section 6 of this attachment, EPA recommends using the mean BW for each age
group when using upper percentile values for medium ingestion rates. Use of the mean body
weights introduces no bias toward over- or underestimating risk.
Exhibit B-32. Mean Body Weight Estimates for Adults and Children3
Lifestage (years)
Duration (years)
Mean Body Weight (kg)
Adultb (20-70)
50
80.0
Child < 1c
1
7.83
Child 1-2C
2
12.6
Child 3-5d
3
18.6
Attachment B
Description of MIRC
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit B-32. Mean Body Weight Estimates for Adults and Children3
Lifestage (years)
Duration (years)
Mean Body Weight (kg)
Child 6-11e
6
36.0
Child 12-19f
8
64.2
aSources: EPA 1997, 2008a
b EPA-recommended value (EPA 2011a).
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 of the possible bias is unknown. The values match the estimate based on Table 8-22 of the
NHANES IV data as presented by Portier et al. (2007).
7.3. Default Chemical-Specific Parameter Values for Screening Analysis
Exhibit B-33 presents chemical-specific parameter values for input to MIRC for the screening-
level analysis. Values for bioavailability when ingested in soil (6s), mammalian metabolism
factors (MF), correction factors for belowground produce (VGrootveg), wet deposition fractions
(Fw), air to plant transfer factors (Bvag), root concentration factors (RCF), and soil-water
partition coefficient (Kds) are presented in Exhibit B-33.
Exhibit B-33. Chemical-Specific Parameter Values for Input to MIRCa
Parameter
Description
Benzo(a)-
pyrene
Cadmium
Mercuric
chloride
Methyl
mercury
2,3,7,8-
TCDD
Units
Bs
Soil bioavailability
factor for livestock
1
1
1
1
1
unitless
MF
Mammalian
metabolism factor
0.01
1
1
1
1
unitless
VGrootveg
Empirical correction
factor for
belowground
produce, i.e., tuber
or root vegetable, to
account for possible
overestimate of the
transfer of chemicals
from the outside to
the inside of bulky
tubers or roots
(based on carrots
and potatoes)
0.01
1
1
0.01
0.01
unitless
Fw
Fraction of wet
deposition that
adheres to plant
surfaces; 0.2 for
anions, 0.6 for
cations and most
organics
0.6
0.6
0.6
0.6
0.6
unitless
Attachment B
Description of MIRC
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit B-33. Chemical-Specific Parameter Values for Input to MIRCa
Parameter
Description
Benzo(a)-
pyrene
Cadmium
Mercuric
chloride
Methyl
mercury
2,3,7,8-
TCDD
Units
BvAG
Air-to-plant
biotransfer factor for
aboveground
produce for vapor-
phase chemical in
air
174,523
0
1,800
0
65,500
[mg/g
produce
DW]/[mg/g
air]
RCF
Chemical-specific
root concentration
factor for tubers and
root produce
9,180
0
0
0
40,002
L soil pore
water/kg root
WW
Kds
Chemical-specific
soil/water partition
coefficient
7,750
75
58,000
7,000
31,126
L soil pore
water/kg soil
DW
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 screening assessment are provided here.
Only single estimates were developed for each of these parameters for HHRAP (EPA 2005a),
and the potential direction and magnitude of bias toward over- or underestimating risks were not
investigated in this assessment. The inputs that are both chemical-specific and plant-type-
specific, as presented in Exhibit B-11, are not repeated here. Again, only single estimates were
developed for these parameters and the potential direction and magnitude of bias toward over-
or underestimating risks were not investigated. Finally, Exhibit B-34 presents biotransfer factors
for each of the chemicals and animal types for which screening threshold emissions were
calculated.
Exhibit B-34. Chemical and Animal-Type Specific Biotransfer Factor (Ba) Values for
Input to MIRC
([mg chemical/kg WW tissue or dairy] / [mg chemical intake/day] = day/kg WW tissue or dairy)
Chemical
Beef
Dairy
Pork
Eggs
Poultry
Benzo(a)pyrene
3.8E-02
8.0E-03
4.6E-02
1.6E-02
2.8E-02
Cadmium
1.2E-04
6.5E-06
1.9E-04
2.5E-03
1.1E-01
Mercuric chloride
1.1E-04
1.4E-06
3.4E-05
2.4E-02
2.4E-02
Methyl mercury
1.2E-03
1.7E-05
5.1E-06
3.6E-03
3.6E-03
2,3,7,8-TCDD
3.6E-02
7.7E-03
4.4E-02
1.5E-02
2.7E-02
7.4. Screening-Level Parameter Values for Nursing Infant Exposure
MIRC is configured to evaluate risk to nursing infants exposed to dioxins and to methylmercury
(MeHg) in their mother's milk for a family farming and catching fish in the area of maximal air
deposition of chemical. Input values were summarized in Section 6.5 of this attachment.
7.4.1. Dioxins
For dioxins, chemical intake via breast milk by nursing infants was estimated using the model
presented in EPA's Methodology for Assessing Health Risks Associated with Multiple Pathways
of Exposure to Combustor Emissions (MPE, EPA 1998). The assumption that lactational
transfer of dioxins to the infant occurs via the lipid-phase of milk appears reasonable. The
Attachment B
Description of MIRC
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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 6.5.2).
The fat content of human milk is assumed to be 4 percent, a value toward the high end
of the reported range of values (1 to 5 percent).
The maternal chemical intake is estimated using upper percentile ingestion rates for the
different homegrown foods (see discussion for Exhibit B-31); this assumption might
overestimate total ingestion of homegrown foods by a factor of more than 2 (see Exhibit
B-31).
• If the fraction of the maternal body burden of dioxin that is in the body fat compartment is
greater than 90 percent, as suggested by ATSDR (1998), then actual exposures of the
infant may be less than estimated.
There also are parameter values and model assumptions for the lipid-phase breast-milk
pathway for which possible bias is unknown.
The accuracy of the model is unknown; it has not been verified or validated with
empirical data.
• Using a half-life of 10 years for dioxins may over- or under-estimate risks.
Finally, there is one assumption that might possibly introduce some bias toward underestimating
risks. The model results are sensitive to the biological half-life of the chemical in the mother
relative to the length of her exposure prior to the lactation period. Using an exposure duration
for the mother equal to the assumed half-life for dioxins, 10 years, may underestimate the
duration of exposure of the mother.
7.4.2. Methyl Mercury
For MeHg, empirical data from a single human study (Fujita and Takabatake 1977) was used in
conjunction with a physiologically based pharmacokinetic (PBPK) model of lactational transfer of
MeHg developed and partially validated by Byczkowski and Lipscomb (2001) to support a very
simple predictive model. Both the human data and the PBPK model indicated that for relatively
low MeHg exposures, the concentration of MeHg in the nursing infant's blood is similar to its
concentration in the mother's blood. The PBPK model suggested in addition that the average
daily dose of MeHg absorbed from milk by the nursing infant (DAhni) is indistinguishable from the
dose of MeHg absorbed by its mother from her food (DAImat). The data are limited, and the
model includes various assumptions; however, there is no known directional bias in the
estimates.
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8. References
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Attachment B
Description of MlRC
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TRIM-Based Tiered Screening Methodology for RTR
Clewell, H.J, J.M. Gearhart, R.P. Gentry, T.R Covington, C.B. VanLandingham, C.B. Crump,
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Attachment B
Description of MlRC
B-97
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TRIM-Based Tiered Screening Methodology for RTR
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Description of MlRC
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TRIM-Based Tiered Screening Methodology for RTR
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National Center for Environmental Assessment, Washington, D.C. EPA-600-R-03-29.
Available at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=56610.
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-
Attachment B
Description of MlRC
B-101
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
00-001. October. Available at:
http://www.epa.gov/waterscience/criteria/drinking/percapita/2004.pdf
EPA. 2004c. Risk Assessment Guidance for Superfund. Volume I: Human Health Evaluation
Manual (Part E, Supplemental Guidance for Dermal Risk Assessment), Final. Office of
Superfund Remediation and Technology Innovation, Washington, D.C. EPA/540/R/99/005;
OSWER 9285.7-02EP; NTIS PB99-963312. July.
EPA. 2005a. Human Health Risk Assessment Protocol for Hazardous Waste Combustion
Facilities. Office of Solid Waste and Emergency Response, Washington, DC. EPA-530-R-
05-006. September. Available at: http://www.epa.gov/osw/hazard/tsd/td/combust/risk.htm.
EPA. 2005b. Guidance on Selecting Age Groups for Monitoring and Assessing Childhood
Exposures to Environmental Contaminants. Risk Assessment Forum. Washington, DC.
November. EPA/630/P-03/003F. Available at:
http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=146583.
EPA. 2005c. Guidelines for Carcinogen Risk Assessment. Risk Assessment Forum,
Washington, DC. EPA/630/P-03/001F. March. Available from:
http://www.epa.gov/IRIS/cancer032505-final.pdf.
EPA. 2005d. Supplemental Guidance for Assessing Susceptibility from Early-Life Exposure to
Carcinogens. Risk Assessment Forum: Washington, D.C. EPA-630/R-03-003F. March.
Available at: http://www.epa.gov/ttn/atw/childrens_supplement_final.pdf.
EPA. 2005e. Analysis of Total Food Intake and Composition of Individual's Diet Based on the
U.S. Department of Agriculture's 1994-96, 1998 Continuing Survey of Food Intakes By
Individuals (CSFII) (Final). Office of Research and Development, National Center for
Environmental Assessment, Washington, D.C. EPA/600/R-05/062F. Available at:
http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=132173.
EPA. 2007a. Prioritized Chronic Dose-Response Values for Screening Risk Assessments
(Table 1). Office of Air Quality Planning and Standards; June 12, 2007. Available at:
http://www. epa. gov/ttn/atw/toxsource/sum m ary. htm I.
EPA. 2007b. Toxicological Review of 1,1,1-Trichloroethane (CAS No. 71-55-6) In Support of
Summary Information on the Integrated Risk Information System (IRIS). Office of Research
and Development, Washington, DC. EPA/635/R-03/006. August. Available at:
http://www.epa.gov/iris
EPA. 2008a. Child-Specific Exposure Factors Handbook. Office of Research and Development,
Washington, D.C. EPA/600/R-06/096F. September. Available at:
http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=199243 .
EPA. 2008b. Draft Report on EPA OAQPS Risk and Technology Review Methodologies: For
Review by the EPA Science Advisory Board; Case Studies - MACT I Petroleum Refining
Sources, Portland Cement Manufacturing. Office of Air Quality Planning and Standards,
Office of Air and Radiation, Research Triangle Park, NC. July 14, 2008.
EPA. 2010. U.S. EPA. Development of a Relative Potency Factor (RPF) Approach for Polycyclic
Aromatic Hydrocarbon (PAH) Mixtures (External Review Draft). Washington, DC,
EPA/635/R-08/012A February. Available at:
http://cfpub.epa.gov/ncea/iris drafts/recordisplay.cfm?deid=194584
Attachment B
Description of MlRC
B-102
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
EPA. 2011a. Exposure Factors Handbook: 2011 Edition. Office of Research and Development,
Washington, D.C. EPA/600/R-090/052F. September. Available at:
http://cfpub.epa. qov/ncea/risk/recordisplav.cfm?deid=236252.
EPA. 2011b. Revised Technical Support Document: National-Scale Assessment of Mercury
Risk to Populations with High Consumption of Self-caught Freshwater Fish; In Support of
the Appropriate and Necessary Finding for Coal- and Oil-Fired Electric Generating Units.
Office of Air Quality Planning and Standards, Research Triangle Park, NC. EPA-452/R-11-
009. December.
Van den Berg, M., L.S. Birnbaum, M. Denison, M. De vito, W. Farlans, M. Feeley, H. Fiedler, H.
Hakansson, A. Hanberg, L.. Haws, M. Rose, S. Safe, D. Schrenk, C. Tohyama, A. Tritscher,
J. tuomisto, M. Tysklind, N. Walker, and R.E. Peterson. 2006. The 2005 World Health
Organization reevaluation of human and mammalian toxic equivalency factors for dioxins
and dioxin-like compounds. Toxicol Sci. 93(2): 223-41.
Zabik, ME; Zabik, MJ. 1995. Tetra-chlorodibenzo-p-dioxin residue reduction by
cooking/processing offish fillets harvested from the Great Lakes. Bulletin of Environmental
Contamination and Toxicology. 55:264-269.
Attachment B
Description of MlRC
B-103
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
Attachment C. Dermal Risk Screening
Attachment C
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i
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TRIM-Based Tiered Screening Methodology for RTR
CONTENTS, ATTACHMENT C
1. Hazard Identification and Dose Response Assessment C-1
2. Dermal Exposure Estimation C-2
2.1. Equations for Estimating Dermal Exposure C-2
2.2. Exposure Factors and Assumptions C-3
2.3. Receptor-Specific Parameters C-4
2.4. Scenario-Specific Parameters C-4
2.5. Chemical-Specific Parameters C-5
3. Screening-Level Cancer Risks and Non-Cancer Hazard Quotients C-6
3.1. Dermal Cancer Risk C-6
3.2. Dermal Hazard Quotient C-7
4. Dermal Screening Results C-7
5. References C-10
Attachment C /'/'/ October 2014
Dermal Risk Screening
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TRIM-Based Tiered Screening Methodology for RTR
Exhibits, Addendum 3
Exhibit C-1. Cancer Slope Factors and Reference Doses Based on Absorbed
Dose C-2
Exhibit C-2. Receptor-Specific Body Surface Area Assumed to be Exposed to
Chemicals C-4
Exhibit C-3. Scenario-Specific Exposure Values for Water and Soil Contact C-5
Exhibit C-4. Chemical-Specific Dermal Exposure Values for Water and Soil
Contact C-6
Exhibit C-5. Summary of Dermal Non-Cancer Hazards C-8
Exhibit C-6. Summary of Dermal Cancer Risks C-9
Attachment C iv October 2014
Dermal Risk Screening
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TRIM-Based Tiered Screening Methodology for RTR
Non-inhalation exposure to PB-HAPs can occur by way of the dermal pathway through contact
with PB-HAP-contaminated soil and water. However, dermal absorption of chemicals that are
originally airborne is generally a relatively minor pathway of exposure compared to other
exposure pathways (EPA 2006, Cal/EPA 2000). This section demonstrates that for the
conservative tiered screening scenario developed for RTR multipathway evaluation, the dermal
exposure route is not a significant risk pathway when compared to the ingestion pathway. In
general, the RTR multipathway assessment followed the protocol for evaluating a reasonable
maximum exposure as described in EPA's Risk Assessment Guidance for Superfund (RAGS),
Volume I: Human Health Evaluation Model, Part E, Supplemental Guidance for Dermal Risk
Assessment (EPA 2004).
1. Hazard Identification and Dose Response Assessment
To assess the potential contribution of dermal exposure to non-inhalation exposure, we
evaluated the potential for cancer and chronic non-cancer effects for the four PB-HAPs currently
assessed in the multipathway screening evaluation for RTR: cadmium, divalent mercury,
2,3,7,8-TCDD, and benzo(a)pyrene. EPA has not developed carcinogenic potency slope
factors (CSFs) and non-cancer reference doses (RfDs) specifically for evaluating potential
human health concerns associated with dermal exposure to PB-HAPs. Instead, dermal toxicity
values can be derived from oral toxicity values via route-to-route extrapolation by adjusting for
gastrointestinal (Gl) absorption. EPA recommends making this adjustment only when Gl
absorption of the chemical is significantly less than 100% (i.e., less than 50 percent).
Otherwise, a default value of complete (100 percent) oral absorption is assumed, and no
adjustment is made (EPA 2004).
The absorbed cancer slope factor (CSFabs) is based on the oral cancer slope factor (CSFo) and
the fraction of the contaminant absorbed in the gastrointestinal track (ABSgi), as follows:
CSF - 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 = RfD0 x ABSGI
where:
RfDABs = Absorbed reference dose (mg/kg-day)
RfD0 = Oral reference dose (mg/kg-day)
ABSgi = Fraction of chemical absorbed in gastrointestinal tract (unitless)
Attachment C
Dermal Risk Screening
C-1
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
The Gl absorptions for 2,3,7,8-TCDD and all polycyclic aromatic hydrocarbons (PAHs) (which
includes benzo[a]pyrene) were estimated to be greater than 50 percent. Therefore, as shown in
Exhibit C-1, no adjustments to the available oral CSFs were required. For cadmium and
divalent mercury, adjustments were made based on absorption data provided in RAGS Part E,
Exhibit 4-1. The RfDs for dermal exposure to cadmium and divalent mercury are also shown in
Exhibit C-1.
Exhibit C-1. Cancer Slope Factors and Reference Doses Based on Absorbed Dose
PB-HAP
Fraction of Contaminant
Absorbed in Gl Tract (ABSgi)
(unitless)
Absorbed Cancer
Slope Factor (CSFabs)3
(mg/kg-day)"1
Absorbed Reference
Dose (RfDABs)a
(mg/kg-day)
Cadmium
Compounds
0.05
NA
2.5E-05
Divalent
Mercury
0.07
NA
2.1E-05
2,3,7,8-TCDD
No adjustment
required b
1.5E+05
1.0E-09
Benzo[a]pyrene
No adjustment
required b
1.0E+01
NA
NA = Not applicable
aOral dose response values are presented in Appendix 2. Only the resulting adjusted dose response values are presented in
this table.
bAccording to RAGS Part E, Exhibit 4-1, Gl absorption is expected to be greater than 50%.
2. Dermal Exposure Estimation
Dermal exposures and risks resulting from absorption of the chemical through the skin from
contact with contaminated water and soil were evaluated for the RTR screening scenario.
Individuals were assumed to be exposed on a fraction of their bodies (i.e., their head, forearms,
hands, lower legs, and feet) to contaminated soil from the TRIM.FaTE surface soil parcel with
the highest concentration (N1) on a daily basis. For the water evaluation, individuals were
assumed to be exposed to contaminated surface water with the same PB-HAP concentration as
the TRIM.FaTE screening scenario lake over their entire bodies on a daily basis.
2.1. Equations for Estimating Dermal Exposure
The general equation used to estimate dermal absorbed dose (DAD) for water or soil is shown
below, and is expressed in milligrams of PB-HAP per kilogram of receptor body weight per day
(mg/kg-day). DADs are calculated separately for the water and soil pathways and then added
together for each age group.
CAC DAeventxEVxEDxEFxSA
BWxAT
where:
_ Absorbed dose per event; chemical-specific; equation for DAevent also differs
event depending on water or soil contact (mg/cm2-event)
EV = Event frequency (events/day)
ED = Exposure duration (years)
Attachment C C-2 October 2014
Dermal Risk Screening
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TRIM-Based Tiered Screening Methodology for RTR
Exposure frequency (days/year)
Skin surface area available for contact (cm2)
Body weight (kg)
Averaging time; for non-cancer effects, equals ED x 365 days/year; for cancer
effects, equals 70 years x 365 days/year (days)
DAevent is estimated to be the total dose absorbed through the skin at the end of exposure and
the equation for calculation is different for organic and inorganic chemicals in water and for soil
The equations for calculating these chemical-specific DAeVent values for water contact are
provided in RAGS Part E, Chapter 3 (see Equations 3.2 - 3.4). For soil, the equation for
calculating these chemical-specific DAevent values is provided in RAGS Part E, Chapter 3 (see
Equation 3.12).
6 X T X t
Water - Organic Chemicals: DAevent = CW / 2 / FA / Kp J —'¦
Water - Inorganic Chemicals: DAevent =CW xKpx 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)
2.2. Exposure Factors and Assumptions
The exposure parameters included in this assessment and their default and other value options
are summarized in this subsection. Default values were selected to result in a highly
conservative estimate of exposure (i.e., exposures are likely overestimated). Parameter values
were primarily obtained or estimated from RAGS Part E (EPA 2004) and the Child-Specific
Exposure Factors Handbook (CSEFH, EPA 2008). Receptor-and scenario-specific exposure
assumptions are discussed first, and a discussion of chemical-specific parameters values
follows. Estimated water and soil exposure concentrations are presented at the end of this
subsection.
EF =
S4 =
BW =
AT =
Attachment C
Dermal Risk Screening
C-3
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
2.3. Receptor-Specific Parameters
Dermal exposures and risks were estimated for the same age groups used in the ingestion
exposure assessment: adults (ages 20 to 70 years) and five child age groups: <1 year; 1 to 2
years; 3 to 5 years; 6 to 11 years; and 12 to 19 years. The body weight values used in the
ingestion exposure assessment were used in the dermal exposure assessment.
Body surface areas (SAs) for water and soil exposures for adults were calculated using
Appendix C, Exhibit C-1, of RAGS Part E. For children, SAs for water and soil exposures for
the five children's age groups were estimated using Tables 7-1 and 7-2 of the CSEFH,
respectively. For SA (water), individuals were assumed to shower or bathe in the water with
100 percent of their body exposed. For SA (soil), it was assumed that individuals were exposed
on a fraction of their total body, specifically their head, forearms, hands, lower legs, and feet.
Based on information provided in RAGS Part E, the SA for forearms was calculated using the
SA for arms and assuming a forearm-to-arm ratio of 0.45, and the SA for lower legs was
estimated using the SA for legs and assuming a lower leg-to-leg ratio of 0.4.
Values for body SA by age group are summarized in Exhibit C-2.
Exhibit C-2. Receptor-Specific Body Surface Area Assumed to be
Exposed to Chemicals
Age Group3
Surface Area for
Surface Area for
(years)
Water Exposure (cm2)
Soil Exposure (cm2)
Adult 20-70
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 since 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.
2.4. Scenario-Specific Parameters
Exhibit C-3 summarizes the exposure values related to frequency and duration of contact. In
general, these are the recommended defaults for calculating a reasonable maximum exposure
(RME) for a residential scenario as proposed by EPA in RAGS Part E, Chapter 3.
Attachment C
Dermal Risk Screening
C-4
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit C-3. Scenario-Specific Exposure Values for Water and Soil Contact
Exposure Parameter
Receptor
Value
Source
Water Contact
Event Duration (tevent)
(hr/event)
Child
1
Reasonable maximum exposure
scenario for showering/bathing
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 20-70
50
Averaging Time (AT) (days)
For cancer assessment, an AT equal to a lifetime (70 years) * 365
days/year is used. Same value used in ingestion exposure calculations.
For non-cancer assessment, an AT equal to the exposure duration (ED)
x 365 days/year is used, so AT will vary by receptor group. Same value
used in ingestion exposure calculations.
2.5. Chemical-Specific Parameters
The chemical-specific parameters required to quantitatively evaluate dermal pathway exposures
are listed in Exhibit C-4. For the water concentration in the dermal analysis, the modeled
TRIM.FaTE chemical concentration in the screening scenario pond at the screening threshold
emission rate was used. For the soil concentration, the modeled TRIM.FaTE chemical
concentration in surface soil in parcel N1 (unfilled soil, closest to facility) of the screening
scenario at threshold emission rate was used. This same soil concentration was also used in
ingestion exposure calculations for soil ingestion.
Dermal absorption of chemicals in water is based on the use of a dermal permeability coefficient
(Kp), which measures the rate that a chemical penetrates the skin. Dermal absorption of 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.
Attachment C
Dermal Risk Screening
C-5
October 2014
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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
Cadmium
Divalent
Mercury
2,3,7,8-
TCDD
Benzo[a]
pyrene
Source
Chemical concentration in
Water (Cw) (mg/cm3)
2.4E-08
1.9E-09
2.6E-18
2.1E-13
TRIM.FaTE modeled
concentration in screening
scenario pond
Chemical concentration in
Soil (Cs) (mg/kg)
6.9E-02
6.3E-02
2.2E-10
1.4E-04
TRIM.FaTE modeled
concentration in surface soil
in parcel N1 in screening
scenario
Permeability coefficient in
water (Kp) (cm/hour)
0.001
0.001
0.81
0.7
Values from RAGS Part E,
Exhibits B-3 (organics) and B-
4 (inorganics)
Fraction absorbed water
(FA) (unitless)
NA
NA
0.5
1.00
Values from RAGS Part E,
Exhibits B-3; only used for
organic chemicals
Lag time per event (event)
(hr/event)
NA
NA
6.82
2.69
Values from RAGS Part E,
Exhibits B-3; only used for
organic chemicals
Dermal absorption fraction
(ABS) from soil (unitless)
0.001
0.045a
0.03
0.13
Values from RAGS Part E,
Exhibit 3-4, unless otherwise
noted
aValue obtained from Bioavailability in Environmental Risk Assessment (Hrudey et al. 1996).
3. Screening-Level Cancer Risks and Non-Cancer Hazard Quotients
Toxicity values were used in conjunction with exposure information to evaluate the potential for
cancer risks and non-cancer health hazards. Risk estimation methods are presented below.
3.1. Dermal Cancer Risk
Cancer risk for the dermal route was calculated as the product of the age-specific DADs and the
absorbed CSF for each chemical, as follows:
Dermal Cancer Risk = DAD x CSFabs
where:
DAD = Dermal Absorbed Dose (mg/kg-day)
CSFabs = Absorbed cancer slope factor (mg/kg-day)-1
Lifetime dermal cancer risks were calculated for 2,3,7,8-TCDD and benzo[a]pyrene. The total
risk accounts for dermal exposures that an individual might receive from these PB-HAPs in
water plus soil over his or her lifetime (70 years).
Attachment C
Dermal Risk Screening
C-6
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
3.2. Dermal Hazard Quotient
Dermal hazard quotient (HQ) was estimated as the ratio of age-specific DADs to the absorbed
RfD for each chemical, as shown below:
Dermal HQ = DAD / RfDabs
where:
DAD = Dermal Absorbed Dose (mg/kg-day)
RfDABs = Absorbed reference dose (mg/kg-day)
The aggregate HQ accounts for exposures that an individual in a receptor group may receive
from the PB-HAP in water and soil over the exposure duration. Non-cancer hazard is not
additive across the age groups evaluated here.
4. Dermal Screening Results
Exhibit C-5 presents a summary of estimated dermal non-cancer hazards by age group. A
summary of estimated lifetime dermal cancer risks is provided in Exhibit C-6. The highest HQ
value was 0.006 (representing divalent mercury exposure for children less than 1 year of age) or
less. This is approximately 170 times less than the potential ingestion hazard quotients
associated with the screening scenario (i.e. emissions of divalent mercury in the screening
scenario resulted in an ingestion hazard quotient of 1). The highest estimated individual lifetime
cancer risk associated with potential dermal exposures was 4.1E-09 for benzo[a]pyrene; this
value is approximately 240 times smaller than the ingestion risk (i.e., 1E-06) estimated for the
same screening threshold emission rate.
Attachment C
Dermal Risk Screening
C-7
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit C-5. Summary of Dermal Non-Cancer Hazards
Attachment C
Dermal Risk Screening
C-8
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit C-6. Summary of Dermal Cancer Risks
PB-HAP
Dermal Lifetime
Cancer Risk
Magnitude of Difference3
2,3,7,8-TCDD
Water
2.64E-10
>3,700
Soil
1.49E-11
>67,300
Total
2.79E-10
>3,500
Benzo[a]pyrene
Water
1.50E-09
>600
Soil
2.63E-09
>300
Total
4.12E-09
>200
Represents the magnitude of difference between the estimated dermal risk and
the potential ingestion risk associated with the screening scenario.
Based on these results and taking into consideration the extremely conservative nature of the
dermal exposure calculations, it was assumed that it is not necessary to incorporate dermal
exposures in calculating multipathway screening threshold levels. Specifically, the daily
exposure durations of 0.58 hour for adults and 1 hour for children used to calculate dermal
exposure from water are highly conservative and assume that the individual is bathing in
surface water taken directly from a contaminated lake or is swimming in the lake for 350 days of
the year. The exposure frequency of 350 days and corresponding skin surface area available
for contact with contaminated soils (i.e., head, hands, arms, legs, and feet) likely also grossly
overestimates dermal exposure to soil.
Attachment C
Dermal Risk Screening
C-9
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
5. References
Cal/EPA (California Environmental Protection Agency) Office of Environmental Health Hazard
Assessment (OEHHA). 2000. Air Toxics Hot Spots Program Risk Assessment Guidelines;
Part IV, Exposure Assessment and Stochastic Analysis Technical Support Document.
Section 6, Dermal Exposure Assessment. September. Available at:
http://www.oehha.ca.gov/air/hot_spots/pdf/chap6.pdf.
Hrudey, S.E., W. Chen, and C.G. Roussex, 1996. Bioavailability in environmental risk
assessment. CRC Press, Inc, Lewis publishers.
EPA (Environmental Protection Agency). 2004. Risk Assessment Guidance for Superfund
Volume 1: Human Health Evaluation Manual (Part E, Supplemental Guidance for Dermal
Risk Assessment). EPA/540/R99/005. Available at:
http://www.epa.gov/oswer/riskassessment/ragse/index.htm
EPA. 2006. Risk and Technology Review (RTR) Assessment Plan. Office of Air and Radiation.
November 20. Available at:
http://www.epa.gov/sab/panels/consul_risk_and_tech_assessment_plan.htm.
EPA. 2008. Child-Specific Exposure Factors Handbook. Office of Research and Development,
Washington, D.C. EPA/600/R-06/096F. September. Available at:
http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=199243 .
Attachment C
Dermal Risk Screening
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October 2014
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TRIM-Based Tiered Screening Methodology for RTR
Attachment D. Summary of TRIM.FaTE Parameters Considered for
Inclusion in Tier 2 Assessment
Attachment D
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TRIM-Based Tiered Screening Methodology for RTR
Exhibits, Addendum 1
Exhibit Add B1-1. TRIM.FaTE Parameters Considered for Inclusion in Tier 2
Analysis D-1
Attachment B, Addendum 1 /'/'/ October 2014
Tier 2 TRIM.FaTE Parameters
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit D-1. TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Assessment
Parameter
Mechanism of Potential
Influence in TRIM
Assessment of Parameter
Significance and Ease in
Implementation
Uncertainty in
Site-Specific
Data for
Facilities
Priority
for
Inclusion
Meteorological Parameters
Wind direction
(% of time wind
blows toward
the lake and
farm)
In previous runs, direct
deposition accounted for the
bulk of chemical input onto
farms and into lakes.
Because wind direction is
strongly correlated to direct
deposition in a given
location, media
concentrations are
potentially highly sensitive to
this parameter. Also,
because the percentage of
time the prevailing wind
blows in the direction of
lakes and farms can vary
considerably across
locations, differences in this
parameter might also result
in significant changes in
important environmental
concentrations.
Highly Significant: Previous
sensitivity analyses have
confirmed this to be a very
sensitive parameter in the Tier
1 Screening modeling set-up.
Changing the fraction of time
the wind blows toward the lake
and farm by a factor of two
corresponds to a change in
the risk by a factor of two.
Low Effort to Implement:
This variable is relatively
straightforward to vary in the
Tier 2 screening scenarios.
Low to
Moderate: The
average fraction
of time the wind
blows in a given
direction can be
estimated for any
surface
meteorological
station. Then,
facilities can be
linked to the
closest surface
meteorological
station.
High
Wind speed
Wind speed can affect the
location of the "peak"
concentration and deposition
patterns in a given model
configuration, as well as the
risk-distance profile.
Highly Significant: Previous
sensitivity analyses have
confirmed this to be a very
sensitive parameter. However,
wind speed does not vary
widely across U.S. locations
which could reduce its
potential influence.
Low Effort to Implement: This
variable is relatively
straightforward to vary in
the Tier 2 screening
scenarios.
Low to Moderate:
The 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
Tier 2 TRIM.FaTE Parameters
D-1
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit D-1. TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Assessment
Parameter
Mechanism of Potential
Influence in TRIM
Assessment of Parameter
Significance and Ease in
Implementation
Uncertainty in
Site-Specific
Data for
Facilities
Priority
for
Inclusion
Precipitation
Chemicals for which wet
vapor or wet particle
deposition processes are
important are likely to be
sensitive to the assumed
level of precipitation.
Highly Significant: Previous
sensitivity analyses have
indicated a relatively high
sensitivity of risk to
precipitation for most PB-
HAPs (PAHs, cadmium, and
mercury).
Moderate Effort to
Implement: In implementing
changes in precipitation in
TRIM, care must be taken to
also preserve the overall water
balance in the model.
Low to
Moderate: The
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
Attachment D
Tier 2 TRIM.FaTE Parameters
D-2
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit D-1. TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Assessment
Parameter
Mechanism of Potential
Influence in TRIM
Assessment of Parameter
Significance and Ease in
Implementation
Uncertainty in
Site-Specific
Data for
Facilities
Priority
for
Inclusion
Mixing height
Greater mixing heights
increase the dispersion of
pollutants in the atmosphere
and consequently reduce
deposition to the ground in
the areas around the stack.
This is likely to be a highly
sensitive parameter if there
is a sizeable variation in
mixing heights between
facilities.
Highly Significant: Previous
sensitivity analyses have
shown risk to be very sensitive
to mixing height.
Low Effort to Implement:
This variable is relatively
straightforward to vary in the
Tier 2 screening scenarios.
Moderate to
High: Mixing
height estimates
are available for
upper air
meteorological
stations, and this
set of stations is
more limited than
the set of surface
meteorological
stations. Each
surface station
can be linked to
the closest upper
air station to
estimate the
average mixing
height. Then,
facilities can be
linked to the
closest surface
meteorological
station. The
relative
uncertainty in
mixing height for
a given facility is
high, given
diurnal variations
in mixing height
and the smaller
number of upper
air stations.
High
Attachment D
Tier 2 TRIM.FaTE Parameters
D-3
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit D-1. TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Assessment
Parameter
Mechanism of Potential
Influence in TRIM
Assessment of Parameter
Significance and Ease in
Implementation
Uncertainty in
Site-Specific
Data for
Facilities
Priority
for
Inclusion
Configurational Parameters
Distance of
lake from
stack
Deposition is known to
decrease with distance from
stack, although this
relationship also depends on
meteorological parameters
such as wind speed and
wind direction.
Significance Difficult to
Determine: Limited results
from previous TRIM model
runs show an inconclusive
relationship between risk and
distance from stack, possibly
as a result of limited statistical
power. Some studies in the
literature show a definite
decreasing risk gradient with
distance but others report too
many confounding factors to
isolate the precise
relationship.
Moderate Effort to
Implement: This variable
requires updates to the layout
coordinates and requires more
effort to vary in the Tier 2
screening scenarios than the
meteorological parameters.
Low: The lakes
within a given
radius of each
facility can be
found using
ArcGIS™ (see
section 4).
High
Distance of
farm from
stack
Deposition is known to
decrease with distance from
stack, although this
relationship also depends on
meteorological parameters
such as wind speed and
wind direction.
Significance Difficult to
Determine: Limited results
from previous TRIM model
runs show an inconclusive
relationship between risk and
distance from stack, possibly
as a result of limited statistical
power. Some studies in the
literature show a definite
decreasing risk gradient with
distance but others report too
many confounding factors to
isolate the precise
relationship.
Moderate Effort to
Implement: This variable
requires updates to the layout
coordinates and requires more
effort to vary in the Tier 2
screening scenarios than the
meteorological parameters.
High: Although
the distance to
the farm will likely
affect risk, it is
difficult to
determine the
precise land
parcels near
each facility that
are actually used
for farming now
or in the future.
Medium
Attachment D
Tier 2 TRIM.FaTE Parameters
D-4
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit D-1. TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Assessment
Parameter
Mechanism of Potential
Influence in TRIM
Assessment of Parameter
Significance and Ease in
Implementation
Uncertainty in
Site-Specific
Data for
Facilities
Priority
for
Inclusion
Watershed:
lake area ratio
A higher watershed:lake
area ratio potentially
increases the chemical input
of water-soluble or 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
Tier 2 TRIM.FaTE Parameters
D-5
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit D-1. TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Assessment
Parameter
Mechanism of Potential
Influence in TRIM
Assessment of Parameter
Significance and Ease in
Implementation
Uncertainty in
Site-Specific
Data for
Facilities
Priority
for
Inclusion
Physical Parameters
Flush rate
A higher flush rate out of the
lake would result in a higher
rate of chemical output from
the lake, assuming constant
inflow and volume.
Significance Difficult to
Determine: The impact of this
parameter is inconclusive
based on current studies using
the TRIM model.
Moderate Effort to
Implement: In implementing
changes in the flush rate in
TRIM, care must be taken to
also preserve the overall water
balance in the model.
High: The flush
rate of a lake
cannot be
determined easily
for any lake
found near a
facility. In
addition, erosion
rates, watershed
information, and
lake depth
needed to
estimate the
flushing rate are
not readily
available.
Medium
Runoff rate
and fraction
A higher runoff rate (or
fraction) would likely result in
greater chemical input into
the lake for some chemicals
but also potentially a higher
flush rate out of the lake.
Significance Difficult to
Determine: The impact of this
parameter is inconclusive
based on current studies using
the TRIM model.
Moderate Effort to
Implement: In implementing
changes in the runoff rate and
fraction in TRIM, care must be
taken to also preserve the
overall water balance in the
model.
High: As with the
flush rate, the
runoff rate and
fraction for any
lake near a
facility cannot be
readily
determined.
Medium
Erosion rate
and fraction
A higher erosion rate would
likely result in greater
chemical input into the lake
for particle-bound chemicals.
It would also result in greater
chemical transport onto
farmlands, but this might be
counterbalanced by equally
greater erosion off farmland.
Highly Significant: Previous
analyses have shown risk to
be sensitive to this parameter
for some chemicals.
Moderate Effort to
Implement: In implementing
changes in the erosion rate
and fraction in TRIM, care
must be taken to also preserve
the overall water balance in
the model.
High: As with the
flush rate, the
erosion rate and
fraction for any
lake near a
facility cannot be
readily
determined.
Medium
Attachment D
Tier 2 TRIM.FaTE Parameters
D-6
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
Exhibit D-1. TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Assessment
Parameter
Mechanism of Potential
Influence in TRIM
Assessment of Parameter
Significance and Ease in
Implementation
Uncertainty in
Site-Specific
Data for
Facilities
Priority
for
Inclusion
Chemical Parameters
Methylation/
demethylation
rates (Hg)
For Hg, methylation and
demethylation rates in lake
sediment and surface water
are potentially sensitive
parameters affecting risk. A
literature survey has
indicated a relatively high
range for rate constants
describing these processes.
Highly Significant: Previous
analyses run in TRIM have
confirmed the high sensitivity
of these parameters for Hg.
Low Effort to Implement:
This variable is relatively
straightforward to vary in the
Tier 2 screening scenarios.
High: The
specific
methylation /
demethylation
rates for mercury
in the vicinity of a
specific facility
cannot be readily
determined.
Low
Total
phosphorus
levels in the
lake
The total phosphorus
content of a lake is used as
part of the TRIM.FaTE
parameterization process to
estimate the biomass
content of different trophic
levels. These biomass levels
affect the biomagnification of
chemicals up the food chain
and potentially risk to human
consumers 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
Tier 2 TRIM.FaTE Parameters
D-7
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
Attachment E. Analysis of Lake Size and
Sustainable Fish Population
Attachment E
Lake Size Analysis
i
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
CONTENTS, ADDENDUM 2
1. Introduction E-1
2. Angler Behavior E-1
3. Fish Biology E-2
3.1. Lake Productivity E-2
3.2. Proportion of Fish Biomass by Trophic Level E-4
3.3. Minimum Viable Population Size E-5
4. Summary of Assumptions for the Lake Size Analysis E-6
5. Equations Used to Determine Lake Fish Populations E-6
6. References E-8
Attachment E /'/'/ October 2014
Lake Size Analysis
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TRIM-Based Tiered Screening Methodology for RTR
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Attachment E
Lake Size Analysis
iv
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
1. Introduction
Identifying the smallest size of a lake that might maintain self-sustaining populations of fish from
trophic levels (TL) 3 and 4 and is sufficient to support at least one fisher at a specified fish
ingestion rate requires consideration of many factors. Some factors depend on assumptions
about the behavior of fishers who consume fish from a lake (see Section 2 below). Other
factors depend upon the general biology of fish populations in North American ecoregions (see
Section 3). Based on evaluation of these factors, a set of assumptions was developed to
support the estimation of minimum lake sizes that are needed to sustain a particular total human
ingestion rate in grams/day (see Section 4). Then, equations were developed (see Section 5)
that were used to create Exhibit 26 (in Section 3.4 of the main body of the report), which was
used to determine the threshold lake size of 25 acres.
2. Fisher Behavior
Several assumptions regarding fisher behavior are important for estimating a minimum lake size
that is fishable. The first is a conservative assumption that fishers (and their family members)
consume about 50:50 top carnivore fish from both the benthos and from the water column.
Benthic carnivores (BC), such as catfish and chub that consume benthic invertebrates and small
benthic fish, can grow to large sizes (e.g., 2 kg). Assuming a diet of 50 percent benthic
invertebrates and 50 percent small benthic fish (e.g., young of the year that feed on algae or
detritus), the BC fish category would represent trophic level (TL) 3.5. Pelagic piscivores—water
column carnivores (WCC)—include species such as largemouth bass, lake trout, pickerel, and
walleye. The WCC are modeled as TL 4, with 100 percent of their diet comprised of water
column omnivores (WCO, e.g., pan fish such as bluegill and white perch), and the diet of the
WCO is assumed to be 100 percent minnow-sized fish that feed on zooplankton and algae . The
assumption of 50:50 WCC and BC consumption is conservative, because smaller, pan fish, are
not included. Although fishers might prefer to catch and consume the pelagic (TL4) game fish
species, their generally lower abundance compared with fish that also consume benthic
invertebrates dictates that fishers will more often capture the benthic (TL3.5) carnivores. Some
TL2 (herbivorous) fish such as carp attain "catchable" size, but they generally are not popular
fish for consumption and are not considered here. .
A second assumption is that fishers and their family members consume only the fillet portion of
a fish. According to Ebert et al. (1993), the edible fraction of fish as a proportion of total fresh
body weight is 0.4 for salmon, 0.78 for smelt, and 0.3 for all other species. EPA recommends
use of 0.30 for the consumable fraction of fish (USEPA, 1989). For this assessment, a 0.33
edible fraction for TL4 fish was assumed. That factor is roughly one-third, which we considered
preferable to 0.30 to account for some proportion of salmon likely in the diet. This factor is used
in the lake size analysis to estimate total fish biomass required to support specified human fish
consumption rates.
A third assumption relates to ingestion rates of the fisher or fisher family. Fish ingestion rates
used for the purpose of the Tier 2 assessment are the same as those in Tier 1 and are
consistent with subsistence fisher ingestion rates (see Exhibit 18. Summary of RTR Tier 1
Screening Scenario Assumptions).
The final assumption is that the lake must support a sustainable fishery despite fish harvesting
by humans; in other words, the lake shouldn't be "fished out" by the harvest rate required to
meet the subsistence fisher fish ingestion rate. The productivity of any particular fishery (local
population of a species of fish) and the proportion of adult fish that can be harvested for human
consumption are difficult values to estimate, and models to predict sustainable harvests of
different fisheries are numerous and complex. Species-specific parameters of key to such
Attachment E E-1 October 2014
Lake Size Analysis
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TRIM-Based Tiered Screening Methodology for RTR
models include fecundity with age and size; survivorship of eggs, fry, and juveniles to sexual
maturity (recruitment); natural predation pressures; and temporal variation in food availability.
For the purpose of this analysis, simplifying assumptions are required. In the analysis by
Hakanson and Boulion (2004), which included a survey of 122 lakes, the authors noted that a
typical loss from fishing by birds, mammals, and humans would be approximately 10 percent of
the fish biomass in the prey fish compartment (TL3) and 10 percent of the biomass in the
predator fish compartment (TL4). The authors also found that as overall lake productivity
increased, the biomass of prey (TL3) fish increased more rapidly than the biomass of predator
(TL4) fish. For our lake size analysis, we assumed that fishers can harvest 10 percent of the
biomass of pelagic WCC fish without diminishing the fish population size.
3. Fish Biology
Fish life histories also are key to estimating the minimum surface area of a pond or lake that
could support a sustainable fish population of WCC. The productivity and trophic structure of
fish communities in ponds and lakes across the United States are varied. Thus, any set of
assumptions is unlikely to all hold true at any given location. Nonetheless, three factors are
important to any estimate of a minimum lake surface area for sustainable pelagic TL4 fishing:
the general productivity of a lake (expressed as grams of fish wet weight per meter squared, g
ww/m2); the maximum likely proportion of the total fish biomass in a lake that is comprised of the
top trophic level fish; and the minimum viable population (MVP) size required for the fish
species to be self-sustaining in the short term (for at least a few decades).
3.1. Lake Productivity
The general productivity of a lake depends on many factors, including latitude, seasonal
temperatures, and nutrients supporting the base of the food web. For lakes at approximately
the same latitude in the same climate, nutrients play a key role in the total fish biomass that a
lake might support. In a regression analysis of data on total phosphorus (TP) and fish biomass
for 31 lakes across North America, Europe, and Russia, Nurnberg (1996) summarized the
"limits" among three TP-defined lake trophic status categories with respect to total fish wet
weight biomass per unit area:
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:
As might be expected, for the same TP concentrations, standing fish biomass per unit area in
the Florida lakes is two to three times higher than standing fish biomass for more northerly lakes
with shorter growing seasons.
Hanson and Legget (1982) estimated the relationship between TP and standing stock of fish
using a regression model based on samples from 21 lakes ranging in surface area from
0.1-25,000 hectares (-0.25-62,000 acres) and located between 0° and 56° N latitude and
Attachment E E-2 October 2014
Lake Size Analysis
Oligo-meso (TP = 10 pg/L)
Meso-eutro (TP = 30 pg/L)
Eutro-hypereutro (TP = 100 pg/L)
1.9 g ww/m2
3.7 g ww/m2
8.5 g ww/m2
Oligo-meso (TP = 10 pg/L)
Meso-eutro (TP = 30 pg/L)
Eutro-hypereutro (TP = 100 pg/L)
1A g ww/m2
10.6 g ww/m2
15.6 g ww/m2
-------
TRIM-Based Tiered Screening Methodology for RTR
121° E to 122° W longitude. Their linear regression relating TP to total fish standing biomass
(B) had a correlation coefficient (r2) of 0.84:
B = 0.792 + 0.072 (TP)
where:
B = total fish biomass (kg/hectare)
jp = total phosphorous (jjg/L)
The regression model of Hanson and Legget (1982) predicted total fish biomass densities in
lakes of 3.0-9.5 g ww/m2 for TP concentrations ranging from 10-50 |jg/L for oligo-mesotrophic
to mid-range eutrophic lakes. Another regression model from Hoyer and Canfield (1991)
predicted fish biomass densities in streams of 2.6-6.6 g ww/m2 over the same range of TP
concentrations.
In general, for very small lakes, relatively low fish productivity is likely. For example, Demers et
al. (2001) found fish standing biomass values of 2.73 and 3.81 g ww/m2 in two lakes of 27 and
22 acres (11 and 9 hectares), respectively, in south-central Ontario. Bronmark and Weisner
(1996) reported on aquatic communities from a sample of 44 small ponds in southern Sweden
(most were less than 5 hectares « 12 acres). They found no fish in 5 of the smaller ponds
(mean surface area of 0.20 ± 0.097 acres)—which also exhibited lower TP concentrations than
the larger ponds—and no piscivorous fish in another 11 of the 44 ponds (mean surface area of
0.46 ± 0.27 acres). For the 28 ponds with piscivorous (TL4) fish present, the mean pond
surface area was 1.4 (±1.3 SD) acres.
Scientists have also examined the relationship between TP and total fish biomass in reservoirs.
Yurk and Ney (1989) examined the relationship between TP and standing stock of fish in 22
reservoirs in southern Appalachia sampled in 1973. Their logarithmic regression relating TP to
total fish standing biomass (B) used the following equation and had an r2 of 0.75:
Log10 (B) = 1.07+ 1.14 * Log10 (TP)
Use of the equations from Hanson and Legget (1982) and Yurk and Ney (1989) yielded similar
predications of total fish biomass at low to intermediate TP concentrations. At low TP (e.g., 10
|jg/L), predictions of total fish biomass were 3.0 g ww/m2 (Yurk and Ney, 1989) and 1.6 g ww/m2
(Hanson and Legget, 1982); at high TP (e.g., 100 jjg/L), fish biomass predicted by the two
models were 15.5 and 22.4 g ww/m2, respectively.
Ideally, one would have data indicating TP levels in lakes in the vicinity of facilities for a Tier 2
assessment. Such data, however, are rarely readily available. For purposes of the screening
assessment, therefore, we assume that fish productivity per unit area is independent of lake
size over a wide range of lake sizes and that TP levels are unknown.
Leidy and Jenkins (1977) reported analyses of several large data sets to support modeling of
fish productivity and carrying capacity in reservoirs across the United States for the National
Reservoir Research Program. The analyses included studies of fish standing biomass by
species in 61 reservoirs across the midwestern and eastern United States sampled at different
times between 1952 and 1975. Only reservoirs of at least 500 acres (202 hectares) in size were
included, with some exceeding 65,000 acres (in the Missouri drainage basin). Considering all
61 reservoirs, the mean biomass density of fish was 41.3 (± 30.4 SD) g ww/m2. The minimum
Attachment E
Lake Size Analysis
E-3
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
and maximum total fish biomass densities were 3.2 and 133.2 g ww/m2, respectively, and the
median value was 30.9 g ww/m2. Reservoirs typically have large drainage basins, which in
some areas can receive excess TP from large expanses of agricultural areas.
In summary, the fish productivity in lakes and reservoirs can vary by more than three orders of
magnitude. The reservoirs surveyed by Leidy and Jenkins (1977) in general were much larger
(and were often more shallow and nutrient rich) than the natural lakes surveyed by others
discussed above. The mean standing fish biomass of approximately 41.3 g ww/m2 from the
reservoir survey is likely to be higher than a mean value for representative samples of natural
lakes in the United States. For the purpose of estimating the minimum lake size that would
support a sustainable WCC fishery, we rounded that value down to a single significant digit of
40 g ww/m2 as the upper limit for total fish biomass in a lake. That standing biomass is higher
than predicted by the regression models of Hanson and Legget (1982), Yurk and Ney (1989),
and Nurnberg (1996) at a high total phosphorus 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.
3.2. Proportion of Fish Biomass by Trophic Level
As indicated previously, for the Tier 2 assessment, the proportion of fish in a fisher's diet that
consists of WCC (TL4) and BC (TL3.5) is assumed to be 50:50 by biomass (not numbers) for
lakes that support the four trophic levels. In smaller lakes, TL4 fish are likely to be missing or
rare, with TL3 fish in the water column (e.g., sunfish) being the highest trophic level supported
by the primary productivity (algal/plant production) in some lakes. As a "rule of thumb" in
ecology, 10 percent or less of the energy produced at one trophic level usually can be converted
to biomass in the next trophic level (i.e., approximately 90 percent loss of energy) per trophic
step. However, with different species having different energy assimilation efficiencies and with
smaller species generally having higher turnover rates than larger species, the 10 percent
energy rule does not necessarily translate into a standing biomass pyramid of similar
proportions. In this section, the proportion of fish (based on biomass) that might be expected in
the WCC and the BC fish compartments relative to total standing fish biomass are examined
assuming that the lake is large enough to support WCC (pelagic TL4 fish).
Examination of several studies of fish biomass by trophic level indicated that top trophic level
fish might comprise up to 20 percent of the standing fish biomass in many locations. Ploskey
and Jenkins (1982) estimated that piscivorous fish, both those that are generally free-swimming
or pelagic (e.g., pike, gar, walleye) and those that rest and forage primarily in the benthos (e.g.,
various species of catfish, suckers) comprise 22 percent of the total fish biomass in DeGray
Lake, Arkansas (averaged across several years). Leidy and Jenkins (1977) estimated that 18
percent of the fish biomass across the 61 reservoirs they examined was piscivorous (minimum
of 14 percent and maximum 24 percent). Demers et al. (2001) categorized 2 percent and 15
percent of the total fish biomass in two small lakes of 27 and 22 acres in size, respectively, as
piscivorous/benthivorous fish (e.g., largemouth bass, creek chub); primary benthivores (e.g.,
catfish, suckers) dominated at >70 percent in both lakes.
One of the more recent food web models for freshwater lakes is that of Hakanson and Boulion
(2004). They designed their model to predict productivity and standing crop of prey and
predatory fishes in lakes of northern Europe. The authors acknowledged that fish feeding
patterns are complicated by the fact that fish change their feeding preferences as they
age. Some fish species consume zooplankton or benthic invertebrates in their first year, and
switch to small fish and then to larger fish as they mature and grow in size.
Attachment E
Lake Size Analysis
E-4
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
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 of fish represented by piscivores declines to less than 20
percent. The piscivores included both benthic and pelagic species. We note that most benthic
piscivores also consume benthic macroinvertebrates.
Based on the information above, the top trophic level fish are assumed to comprise 21 percent
of the total fish biomass. With the bulk of productivity in lakes originating from detritus in the
benthos, the total biomass of strictly pelagic game fish is expected to be less than that of
benthic fish. Therefore, for purpose of this lake size analysis, the piscivorous/benthivorous fish
were separated into two compartments, with 17.5 percent of the total fish biomass in a benthic
carnivore (TL3.5 or BC) compartment and 3.5 percent of the total in a pelagic piscivore (WCC)
compartment. Thus, the TL4 fish, when present, represent the limiting compartment for fish
harvesting and consumption.
3.3. Minimum Viable Population Size
The final step in estimating the minimum lake size that can support sustainable fishing of its
WCC fish species is to invoke the concept of minimum viable population (MVP) size. MVP is a
concept used frequently in conservation biology for animals and is defined as the smallest
population that will persist for a specified duration (e.g., 100 years) with a given probability (e.g.,
95 percent). MVP for any given species and location depends on many attributes of the species
biology (e.g., body size, reproductive rate, home range size, corridors between populations,
variability in environmental characteristics that impact fecundity and survival). At lower
numbers, the likelihood of population extinction increases due to environmental and
demographic stochasticity (Menzie et al., 2008). As for fisheries biology, entire text books have
been dedicated to applied population ecology with population simulations incorporating
demographic and life-history characteristics, spatial separation of habitat patches and
metapopulations, the probability of local catastrophes, genetic variation (e.g., drift), and other
factors with predictions of time-to-extinction or probability of extinction within specified time
periods (e.g., Soule, 1987; Ak?akaya et al., 1999). Consideration of such models in population-
level ecological risk assessment has begun, but faces many challenges (Barnthouse et al.,
2008). Moreover, that level of effort is beyond the resources available for screening-level
assessments.
Much of the initial work on MVP investigated the genetic minima required for short-term survival,
continuing adaptation to environmental change, and ultimately evolution. Inbreeding has been
considered the primary threat to short-term survival and genetic drift the principal threat to losing
the genetic variation required for adaptation (Shaffer, 1987). Several analyses (Senner, 1980;
Franklin, 1980; Soule, 1980; Frankel and Soule, 1981; Lande and Barrowclough, 1987) have led
to the conclusion that minimum effective population sizes on the order of 50 are required for
short-term survival (e.g., several generations, decades), while effective population sizes on the
order of 500 are necessary to provide adequate genetic variation for continuing adaptation over
the long term (e.g., tens of generations, centuries for some animals) (Shaffer, 1981; 1987).
Effective population size, Ne, is a measure of the rate of genetic drift (loss of genetic diversity or
inbreeding), and its definition generally depends on the population in question (Rieman and
Allendorft, 2001). Ne can be estimated mathematically based on stochastic behavior of gene
frequencies in a diploid population. Simple models assume a fixed population size, constant
fecundity, specified sex ratio, and no overlap between generations (see studies cited in NRC,
1986). For animals with 50:50 sex ratios, the effective population size is essentially the same
as the actual breeding adult population size (Ewens et al., 1987). One of the most extensive
Attachment E E-5 October 2014
Lake Size Analysis
-------
TRIM-Based Tiered Screening Methodology for RTR
population viability analyses in the United States has been conducted on the spotted owl
(Boyce, 1993). Given the number and complexity of factors that influence MVP, however,
including the definitions of time horizon (e.g., 100 years) and probability of survival (e.g., 95
percent), population biologists caution against using a "rule of thumb" for MVP across
circumstances (Ewens et al., 1987).
Note that the MVP is appropriate for a single species of fish, not for generic categories of fish
such as WCC or TL4. For this Tier 2 assessment, the MVP of 50 associated with short-term
population survival was assumed for a TL4 fish species isolated in a lake. In reality, short-term
extirpations from a lake can be countered by purposeful introductions from other lakes or during
flooding events. Thus, an MVP of 500 was not considered necessary for game fish in lakes.
4. Summary of Assumptions for the Lake Size Analysis
The following assumptions were used in processing lake data for the Tier 2 assessment and in
estimating the relationship between fish ingestion and sustainable harvest rates and lake size
(see Section 3.4.1 of Attachment B).
1. Piscivorous fish (WCC and BC), when present, comprise approximately 21 percent of the
standing biomass of fish (ignoring seasonal changes). The BC fish represent 17.5 percent
of the standing fish biomass; WCCs account for 3.5 percent of the total fish biomass. Thus,
WCC fish, when present, represent the limiting compartment for fish harvesting and
consumption.
2. Humans can harvest 10 percent of the biomass of a fish compartment without threatening
the population due to overharvesting.
3. The MVP size for a single WCC species is at least 50 adult fish for a local population to
survive over the short term (more than a decade).
4. Only 33 percent of the fish is edible fillet muscle.
5. Equations Used to Determine Lake Fish Populations
The standing biomass of WCC (TL4) fish supported in Lake X can be calculated as the total
standing biomass of fish (Total SB) multiplied by 0.035, based on the assumption that TL4 fish
represent approximately 3.5 percent of the standing biomass in Lake X.
WCC SB = Total SB x Fraction WCC (Equation 1)
where:
WCC SB = Standing biomass of WCC fish (g wet weight [ww]/m2) in Lake X
Total SB = Total standing biomass of fish (g ww/m2) in Lake X
Fraction WCC = Fraction of WCC fish in Lake X (i.e., 0.035)
Using WCC SB and the size of Lake X (Lake Size), the total number of WCC fish supported in
Lake X can be calculated using Equation 2 below.
No WCC ~ Lake Size x WCC SB x CF
Bwa (Equation 2)
where:
Attachment E E-6 October 2014
Lake Size Analysis
-------
TRIM-Based Tiered Screening Methodology for RTR
No. WCC = Total number of WCC fish in Lake X
Lake Size = Size of Lake X (acres)
WCC SB = Standing biomass of WCC fish (g ww/m2; from Equation 1)
CF = 4047 (unit conversion factor m2/acre)
BWa = Body weight of adult TL4 fish (2000 g; assumed)
The likely annual productivity of WCC fish (kg/year) in Lake X can be estimated using
Equation 3.
Productivity WCC =
Lake Size x WCC SB x CF 1
(Equation 3)
CF 2
where:
Productivity WCC= Likely annual productivity of WCC fish in Lake X (kg/year)
Lake Size = Size of the Lake X (acres)
WCC SB = Standing biomass of WCC fish (g ww/m2; from Equation 1)
CF1 = 4047 (unit conversion factor 1, m2/acre)
CF2 = 1000 (unit conversion factor 2, g/kg)
The maximum fish ingestion rate (g/day) for WCCs plus BCs associated with sustainable fishing
can be predicted using Equation 4. It assumes that the fishers consume 50 percent WCC and
50 percent BC, represented by the factor of 2 in Equation 4.
Max Sustain IR (BC + WCC) = Predicted maximum sustainable ingestion rate for BC and WCCfish (g/day)
Productivity WCC = Likely annual productivity of WCC fish in Lake X (kg/year; from Equation 3)
Max Sustain IR (BC + WCC) =
2 x Productivity WCC xFF x HF x CF 1
CF2
(Equation 4)
where:
FF = Fillet fraction; represents the assumed edible portion offish (0.33; unitless)
HF = Annual harvest fraction (0.10; unitless)
CF1 = 1000 (unit conversion factor 1, g/kg)
CF2 = 365 (unit conversion factor 2, days/year)
Attachment E
Lake Size Analysis
E-7
October 2014
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TRIM-Based Tiered Screening Methodology for RTR
6. References
Ak?akaya, H.R., Burgman, M.A., and Ginzburg, L.R. 1999. Applied Population Ecology:
Principles and Computer Exercises using RAMAS EcoLab. Sunderland, MA: Sinauer
Associates, Inc. Publishers.
Bachmann, RW; Jones, BL; Fox, DD; Hoyer, M; Bull, l_A; Canfield, DE. 1996. Relations
between trophic state indicators and fish in Florida (USA) lakes. Canadian Journal of
Fisheries and Aquatic Sciences. 53:842-855.
Barnthouse, L.W., Munns, W.R. Jr., and Sorensen, M.T. 2008. Population-Level Ecological Risk
Assessment. Boca Raton, FL: CRC Taylor & Francis Group; Pnesacola, FL: Society of
Environmental Toxicology and Chemistry (SETAC).
Boyce, M.S. 1993. Population viability analysis: adaptive management for threatened and
endangered species. In: Wildlife Management Institute, Transactions of the 58th North
American Wildlife and Natural Resources Conferences. Proceedings WWRC-93-19, pp.
520-527.
Bronmark, C. and S.E.B. Weisner. 1996. Decoupling of cascading trophic interactions in a
freshwater benthic food chain. Oecologia 108: 534-541.
Demers, E., McQueen, D.J., Ramcharan, C.W., and A. Perez-Fuentetaja. 2001. Did piscivores
regulate changes in fish community structure? Advanc. Limnol. 56: 49-80.
Ebert, E., N. Harrington, K. Boyle, J. Knight, and R. Keenan. 1993. Estimating consumption of
freshwater fish among Maine anglers. N. Am. J. Fisheries Manage. 13: 737-745.
Ewens, W.J., Brockwell, P.J., Gani, J.M., and Resnick, S.I. 1987. Minimum viable population
sizes in the presence of catastrophes. In: M.E. Soule (ed.) Viable Populations for
Conservation. United Kingdom, Cambridge: Cambridge University Press, pp. 59-68.
Frankel, O.H., and Soule, M.E. 1981. Conservation and evolution. United Kingdom, Cambridge:
Cambridge University Press.
Franklin, I.R. 1980. Evolutionary change in small populations. In: M.E. Soule and B.A. Wilcox
(eds.) Conservation Biology: An Evolutionary-Ecological Perspective. Sunderland, MA:
Sinauer Associates; pp. 135-150. As cited by Shaffer, 1987.
Hakanson, L. and V.V. Boulion. 2004. Modeling production and biomasses of prey and
predatory fish in lakes. Hydrobiologia 511: 125-150.
Hanson, J.M., and W.C. Leggett. 1982. Empirical prediction offish biomass and yield. Can. J.
Fish. Aquat. Sci. 39: 257-263.
Hoyer, M.V., and D.E. Canfield, Jr. 1991. A phosphorus-fish standing crop relationship for
streams? Lake Reserv. Manage. 7: 25-32.
Lande, R., and Barrowclough, G.F. 1987. Effective population size, genetic variation, and their
use in population management. In: M.E. Soule (ed.) Viable Populations for Conservation.
United Kingdom, Cambridge: Cambridge University Press, pp. 87-123.
Leidy, G.R., and Jenkins, R.M. 1977. The development of fishery compartments and population
rate coefficients for use in reservoir ecosystem modeling. Contract Report Y-77-1 prepared
Attachment E
Lake Size Analysis
E-8
October 2014
-------
TRIM-Based Tiered Screening Methodology for RTR
by the National Reservoir Research Program, U.S. Fish and Wildlife Service, for the U.S.
Army Corps of Engineers, Waterways Experiment Station, Vicksburg, Mississippi, USA.
Menzie, C., Bettinger, N., Fritz, A., Kapustka, L., Regan, H., Moller, V., and Noel, H. 2008.
Population protection goals. In: Barnthouse, L.W., Munns, W.R. Jr., and Sorensen, M.T.
2008. Population-Level Ecological Risk Assessment. Boca Raton, FL: CRC Taylor & Francis
Group; Pnesacola, FL: Society of Environmental Toxicology and Chemistry (SETAC), pp.
41-68.
NRC (National Research Council). 1986. Ecological Knowledge and Environmental Problem
Solving. Committee on the Applications of Ecological Theory to Environmental Problems.
Washington, DC: National Academies of Science Press.
Nurnberg, G.K. 1996. Trophic state of clear and colored, soft- and hardwater lakes with special
consideration of nutrients, anoxia, phytoplankton and fish. Journal of Lake and Reservoir
Management 12(4): 432-447.
Ploskey, G.R., and R.M. Jenkins. 1982. Biomass model of reservoir fish and fish-food
interactions, with implications for management. North American Journal of Fisheries
Management 2(2): 105-121.
Rieman, B.E., and Allendorf, F.W. 2001. Effective population size and genetic conservation
criteria for bull trout. N. A. J. Fisheries Manage. 21:756-764.
Senner, J.W. 1980. Inbreeding depression and the survival of zoo populations. In: M.E. Soule
and B.A. Wilcox (eds.) Conservation Biology: An Evolutionary-Ecological Perspective.
Sunderland, MA: Sinauer Associates; pp. 209-244. As cited by Shaffer, 1987.
Scaffer, M.L. 1981. Minimum population sizes for species conservation. Bioscience 31: 131-
134.
Shaffer, M. 1987. Minimum viable populations: coping with uncertainty. In: M.E. Soule (ed.)
Viable Populations for Conservation. United Kingdom, Cambridge: Cambridge University
Press, pp. 69-86.
Soule, M.E. 1980. Thresholds for survival: maintaining fitness and evolutionary potential. In:
M.E. Soule and B.A. Wilcox (eds.) Conservation Biology: An Evolutionary-Ecological
Perspective. Sunderland, MA: Sinauer Associates; pp. 151-170. As cited by Shaffer, 1987.
Soule, M.E. (ed.) 1987. Viable Populations for Conservation. United Kingdom, Cambridge:
Cambridge University Press.
USEPA (US Environmental Protection Agency). 1989. Assessing human health risks from
chemically contaminated fish and shellfish: a guidance manual. EPA-503/8-89-002.
Washington, DC.
Yurk, J.J., and J.J. Ney. 1989. Phosphorus-fish community biomass relationships in southern
Appalachian reservoirs: can lakes be too clean for fish? Lake and Reservoir Management
5(2): 83-90.
Attachment E
Lake Size Analysis
E-9
October 2014
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[This page intentionally left blank.]
-------
Appendix 6 - Environmental Risk Screen
-------
Table 1 - PB-HAP Benchmarks Included in the Environmental Risk Screen
EcoHAP
Assessment Endpoint
Benchmark Effects Level
Benchmark Value
Benchmark
Source
Tier 1 Eco
Screening
Threshold
(TPY)
Cadmium
Fish - Avian Piscivores
NOAEL (merganser)
0.7 (mg/kg BW/day)
d
2.03E-01
Cadmium
Fish - Avian Piscivores
LOAEL (merganser)
1 (mg/kg BW/day)
d
2.90E-01
Cadmium
Fish - Mammalian Piscivores
NOAEL (mink)
0.742 (mg/kg BW/day)
e
4.06E-01
Cadmium
Fish - Mammalian Piscivores
LOAEL (mink)
7.42 (mg/kg BW/day)
e
4.06E+00
Cadmium
Sediment Community
No-effect Level
0.6 (mg/kg dry wt)
b
1.73E+00
Cadmium
Sediment Community
Threshold Level
1.2 (mg/kg dry wt)
a
3.45E+00
Cadmium
Sediment Community
Probable-effect Level
3.5 (mg/kg dry wt)
b
1.01E+01
Cadmium
Surface Soil - Dist. 1 - 312 m
Threshold - Mammalian Insectivores (shrew)
0.36 (mg/kg dry wt)
a
3.64E-01
Cadmium
Surface Soil - Dist. 1 - 312 m
Threshold - Avian Ground Insectivores (woodcock)
0.77 (mg/kg dry wt)
a
7.79E-01
Cadmium
Surface Soil - Dist. 1 - 312 m
Threshold Level - Plant Community
32 (mg/kg dry wt)
a
3.24E+01
Cadmium
Surface Soil - Dist. 1 - 312 m
Threshold Level - Invertebrate Community
140 (mg/kg dry wt)
a
1.42E+02
Cadmium
Surface Soil - Dist. 2 - 850 m
Threshold - Mammalian Insectivores (shrew)
0.36 (mg/kg dry wt)
a
5.21E-01
Cadmium
Surface Soil - Dist. 2 - 850 m
Threshold - Avian Ground Insectivores (woodcock)
0.77 (mg/kg dry wt)
a
1.11E+00
Cadmium
Surface Soil - Dist. 2 - 850 m
Threshold Level - Plant Community
32 (mg/kg dry wt)
a
4.63E+01
Cadmium
Surface Soil - Dist. 2 - 850 m
Threshold Level - Invertebrate Community
140 (mg/kg dry wt)
a
2.03E+02
Cadmium
Surface Soil - Dist. 3 - 1,500 m
Threshold - Mammalian Insectivores (shrew)
0.36 (mg/kg dry wt)
a
7.53E-01
Cadmium
Surface Soil - Dist. 3 - 1,500 m
Threshold - Avian Ground Insectivores (woodcock)
0.77 (mg/kg dry wt)
a
1.61E+00
Cadmium
Surface Soil - Dist. 3 - 1,500 m
Threshold Level - Plant Community
32 (mg/kg dry wt)
a
6.69E+01
Cadmium
Surface Soil - Dist. 3 - 1,500 m
Threshold Level - Invertebrate Community
140 (mg/kg dry wt)
a
2.93E+02
Cadmium
Surface Soil - Dist. 4 - 3,500 m
Threshold - Mammalian Insectivores (shrew)
0.36 (mg/kg dry wt)
a
1.05E+00
Cadmium
Surface Soil - Dist. 4 - 3,500 m
Threshold - Avian Ground Insectivores (woodcock)
0.77 (mg/kg dry wt)
a
2.24E+00
Cadmium
Surface Soil - Dist. 4 - 3,500 m
Threshold Level - Plant Community
32 (mg/kg dry wt)
a
9.32E+01
Cadmium
Surface Soil - Dist. 4 - 3,500 m
Threshold Level - Invertebrate Community
140 (mg/kg dry wt)
a
4.08E+02
Cadmium
Surface Soil - Dist. 5 - 7,500 m
Threshold - Mammalian Insectivores (shrew)
0.36 (mg/kg dry wt)
a
2.99E+00
Cadmium
Surface Soil - Dist. 5 - 7,500 m
Threshold - Avian Ground Insectivores (woodcock)
0.77 (mg/kg dry wt)
a
6.40E+00
Cadmium
Surface Soil - Dist. 5 - 7,500 m
Threshold Level - Plant Community
32 (mg/kg dry wt)
a
2.66E+02
Cadmium
Surface Soil - Dist. 5 - 7,500 m
Threshold Level - Invertebrate Community
140 (mg/kg dry wt)
a
1.16E+03
Cadmium
Water-column Community
Threshold Level
0.25 (ug/L)
c
7.66E-01
Cadmium
Water-column Community
Frank-effect Level
2 (ug/L)
c
6.12E+00
-------
-05
-04
-05
-04
-05
-07
-07
-06
-06
-06
-03
-01
-03
-02
-03
-03
-02
-03
-02
-03
-02
-03
-02
-02
-01
-00
-03
-02
-02
-01
-00
-01
-00
Table 1 - PB-HAP Benchmarks Included in the Environmental Risk Screen
Fish - Avian Piscivores
NOAEL (merganser)
0.0000014 (mg/kg BW/day)
Fish - Avian Piscivores
LOAEL (merganser)
0.000014 (mg/kg BW/day)
Fish - Mammalian Piscivores
NOAEL (mink)
0.000000771 (mg/kg BW/day)
Fish - Mammalian Piscivores
LOAEL (mink)
0.00000771 (mg/kg BW/day)
Sediment Community
Threshold Level
0.00000116 (mg/kg dry wt)
Surface Soil - Dist. 1 - 312 m
Threshold - Mammalian Insectivores (shrew)
0.0000002 (mg/kg dry wt)
Surface Soil - Dist. 2 - 850 m
Threshold - Mammalian Insectivores (shrew)
0.0000002 (mg/kg dry wt)
Surface Soil - Dist. 3 - 1,500 m
Threshold - Mammalian Insectivores (shrew)
0.0000002 (mg/kg dry wt)
Surface Soil - Dist. 4 - 3,500 m
Threshold - Mammalian Insectivores (shrew)
0.0000002 (mg/kg dry wt)
Surface Soil - Dist. 5 - 7,500 m
Threshold - Mammalian Insectivores (shrew)
0.0000002 (mg/kg dry wt)
Water-column Community
Threshold Level
0.000012 (ug/L)
Water-column Community
Frank-effect Level
0.1 (ug/L)
Sediment Community
Threshold Level
0.15 (mg/kg dry wt)
Sediment Community
Probable-effect Level
0.7 (mg/kg dry wt)
Surface Soil - Dist. 1 - 312 m
Threshold Level - Plant Community
0.3 (mg/kg dry wt)
Surface Soil - Dist. 1 - 312 m
Threshold Level - Invertebrate Community
0.1 (mg/kg dry wt)
Surface Soil - Dist. 2 - 850 m
Threshold Level - Plant Community
0.3 (mg/kg dry wt)
Surface Soil - Dist. 2 - 850 m
Threshold Level - Invertebrate Community
0.1 (mg/kg dry wt)
Surface Soil - Dist. 3 - 1,500 m
Threshold Level - Plant Community
0.3 (mg/kg dry wt)
Surface Soil - Dist. 3 - 1,500 m
Threshold Level - Invertebrate Community
0.1 (mg/kg dry wt)
Surface Soil - Dist. 4 - 3,500 m
Threshold Level - Plant Community
0.3 (mg/kg dry wt)
Surface Soil - Dist. 4 - 3,500 m
Threshold Level - Invertebrate Community
0.1 (mg/kg dry wt)
Surface Soil - Dist. 5 - 7,500 m
Threshold Level - Plant Community
0.3 (mg/kg dry wt)
Surface Soil - Dist. 5 - 7,500 m
Threshold Level - Invertebrate Community
0.1 (mg/kg dry wt)
Water-column Community
Threshold Level
0.77 (ug/L)
Water-column Community
Frank-effect Level
1.4 (ug/L)
Fish - Avian Piscivores
NOAEL (merganser)
0.013 (mg/kg BW/day)
Fish - Avian Piscivores
LOAEL (merganser)
0.078 (mg/kg BW/day)
Fish - Mammalian Piscivores
NOAEL (mink)
0.0247 (mg/kg BW/day)
Fish - Mammalian Piscivores
LOAEL (mink)
0.123 (mg/kg BW/day)
Sediment Community
Threshold Level
0.2 (mg/kg dry wt)
Sediment Community
Probable-effect Level
1 (mg/kg dry wt)
Surface Soil - Dist. 1 - 312 m
Threshold Level - Invertebrate Community
0.67 (mg/kg dry wt)
-------
Table 1 - PB-HAP Benchmarks Included in the Environmental Risk Screen
Mercury (methyl)
Surface Soil - Dist. 2 - 850 m
Threshold Level - Invertebrate Community
0.67 (mg/kg dry wt)
i
1.82E+00
Mercury (methyl)
Surface Soil - Dist. 3 - 1,500 m
Threshold Level - Invertebrate Community
0.67 (mg/kg dry wt)
i
2.40E+00
Mercury (methyl)
Surface Soil - Dist. 4 - 3,500 m
Threshold Level - Invertebrate Community
0.67 (mg/kg dry wt)
i
3.62E+00
Mercury (methyl)
Surface Soil - Dist. 5 - 7,500 m
Threshold Level - Invertebrate Community
0.67 (mg/kg dry wt)
i
9.71E+00
Mercury (methyl)
Water-column Community
Threshold Level
0.0028 (ug/L)
c
3.30E-01
Mercury (methyl)
Water-column Community
Frank-effect Level
0.099 (ug/L)
c
1.17E+01
PAH
Fish - Mammalian Piscivores
NOAEL (mink)
0.417 (mg/kg BW/day)
e
4.72E+02
PAH
Fish - Mammalian Piscivores
LOAEL (mink)
4.17 (mg/kg BW/day)
e
4.72E+03
PAH
Sediment Community
No-effect Level
0.032 (mg/kg dry wt)
b
4.73E+00
PAH
Sediment Community
Threshold Level
0.15 (mg/kg dry wt)
1, h
2.22E+01
PAH
Sediment Community
Probable-effect Level
1.45 (mg/kg dry wt)
1
2.14E+02
PAH
Surface Soil - Dist. 1 - 312 m
Threshold - Mammalian Insectivores (shrew)
1.52 (mg/kg dry wt)
h
3.45E+00
PAH
Surface Soil - Dist. 2 - 850 m
Threshold - Mammalian Insectivores (shrew)
1.52 (mg/kg dry wt)
h
5.09E+00
PAH
Surface Soil - Dist. 3 - 1,500 m
Threshold - Mammalian Insectivores (shrew)
1.52 (mg/kg dry wt)
h
8.22E+00
PAH
Surface Soil - Dist. 4 - 3,500 m
Threshold - Mammalian Insectivores (shrew)
1.52 (mg/kg dry wt)
h
2.02E+01
PAH
Surface Soil - Dist. 5 - 7,500 m
Threshold - Mammalian Insectivores (shrew)
1.52 (mg/kg dry wt)
h
5.06E+01
PAH
Water-column Community
Threshold Level
0.014 (ug/L)
a, h, m
1.84E+01
PAH
Water-column Community
Frank-effect Level
0.24 (ug/L)
a
3.16E+02
Lead
Ambient Air
NAAQS Secondary Standard
0.15 ug/m3
n
NA
References:
a. U.S. EPA OSWER
b. Environment Canada
c. U.S. EPA OW
d. CA DTSC HERD (2009)
e. Sample, et. al, 1996
f. U.S. EPA 1995
g. U.S. EPA Region 3, 4, and 5.
h. U.S. EPA Region
i. U.S. EPA Region 4
j. Florida DEP, NOAA
k. ORNL and EPA R4, R6
1. MacDonald, et. al. 2000
m. U.S. EPA Region 6
n. NAAQS for Lead, Secondary
Standard
-------
Table 2 - Acid Gas Benchmarks Included in the Environmental Risk Screen
Acid Gas
Chronic 90-day Benchmark in jig/m3
Hydrochloric acid - LOEL
50a
Hydrofluoric acid - Plant Community LOEL
0.5
Hydrofluoric acid - Plant Community LOEL
0.4
a- Note that the human health RfC is 20 |ig/m3, which is lower than the ecological benchmark.
-------
Appendix 7
Detailed Risk Modeling Results
-------
Table 1 - Facility Identification Information
Facility NEI ID
Facility Name
Address
City
State
01015223
Anniston Army Depot, Anniston Alabama
7 Frankford Avenue
Anniston
AL
01045482
U.S. Army Aviation Center of Excellence and Fort
Rucker
Dilly Branch Road, Bldg. 1120, Att
Fort Rucker
AL
010515017
GKN WESTLAND AEROSPACE, INC.
3951 ALABAMA HWY 229
TALLASSEE
AL
01073446
Alabama Aircraft Industries Incorporated -
Birmingham
1943 50th Street North
Birmingham
AL
01083198
Pratt & Whitney Automation
15091 AL HWY 20
Madison
AL
010892023
George C Marshall Space Flight Center
Marshall Space Flight Center
Huntsville
AL
02090348
Eielson Air Force Base
2310 Central Avenue, Suite 100
Eielson AFB
AK
02090529
United States Army Garrison Fort Wainwright,
Alaska
1060 Gaffney Road #6000
Fairbanks North Star
AK
02150683
U.S. Coast Guard Base Support Unit Kodiak
Bldg. N38 Rezanof Hwy and Cape
Kodiak
AK
04013510
Ohlinger Industries Inc.
2111 West Melinda Lane
Phoenix
AZ
04013602
US Airways Maintenance and Technical
Operations Center
4000 East Sky Harbor Boulevard
Phoenix
AZ
04019300
Learjet Inc.
1255 East Aero Park Boulevard
Tucson
AZ
04019433
Davis-Monthan Air Force Base, Tucson, AZ
3791 S. Third Street
Tucson
AZ
050272001
American Fuel Cells and Coated Fabrics
601 Firestone Drive
Magnolia
AR
05051409
Triumph Fabrications Hot Springs
1923 Central Ave
Hot Springs
AR
05113377
Crider Aircraft Painting, Inc.
104 Airport Lane
Mena
AR
05143331
Pratt & Whitney PSD Inc.
275 E. Robinson Ave.
Springdale
AR
060292024
Naval Air Weapons Station
1 Administration Circle
China Lake
CA
06029545
Edwards Air Force Base
5 Popson Ave., Building 2650A
Edwards Air Force B
CA
060372022
Palmdale
3520 East Avenue M
Palmdale
CA
06037429
The Boeing Company - C-17 Facility
2401 E Wardlow Rd
Long Beach
CA
06037490
El Segundo
500 N Douglas Ave
El Segundo
CA
060376004
Gulfstream Aerospace Corporation
4150 Donald Douglas Drive
Long Beach
CA
06059538
DUCOMMUN AEROSTRUCTURES INC
1885 N BATAVIA ST
ORANGE
CA
06065520
Rohr, Inc.
8200 Arlington Avenue
Riverside
CA
06071540
Ducommun AeroStructures El Mirage Facility
4001 El Mirage Road
El Mirage
CA
06073306
Naval Air Station North Island
NAS North Island
San Diego
CA
060812015
United Airlines Technical Operations
Maintenance Center
800 S. Airport Blvd
San Francisco
CA
09001684
Sikorsky Aircraft - Bridgeport 1 Facility
1210 South Avenue
Bridgeport
CT
09001688
Helicopter Support Inc.
116/124 Quarry Road
Trumbull
CT
09001693
Sikorsky Aircraft Corporation - Stratford Facility
6900 Main Street
Stratford
CT
090032025
EDAC Technologies
1798 New Britain Avenue
Farmington
CT
09003626
Kaman Aerospace Corporation - Helicopters
Division
50 Old Windsor Road
Bloomfield
CT
1 of 5
-------
Table 1 - Facility Identification Information
Facility NEI ID
Facility Name
Address
City
State
090075021
Kaman Aerospace Corporation, Precision
Products Division
217 Smith Street
Middletown
CT
10003349
DASSAULT FALCON JET - WILMINGTON
CORP
191 North Dupont Hwy
New Castle
DE
12005313
Tyndall AFB
325 FW/Tyndall AFB
Panama City
FL
12009358
Patrick Air Force Base
1201 Edward H. White II Street
Patrick Air Force Bas
FL
12023284
TIMCO Aviation Services, Inc. - Lake City Base
Maintenance Operations
102 SE Academic Avenue
Lake City
FL
12031634
U.S. Naval Station Mayport
Old Mayport Road
Jacksonville
FL
12061380
Piper Aircraft, Inc.
2926 Piper Drive
Vera Beach
FL
12083270
LOCKHEED MARTIN MISSILES AND FIRE
CONTROL
498 OAK ROAD
OCALA
FL
120862016
AAR LANDING GEAR SERVICES
9371 NW 100 STREET
MIAMI
FL
12099695
Sikorsky Aircraft Corporation - Jupiter, Florida
Facility
17900 Beeline Highway (SR-710)
JUPITER
FL
13009224
Triumph Aerostructures - Vought Integrated
Programs Division
90 Hwy 22 W
Milledgeville
GA
130516005
Gulfstream Aerospace Corporation
P.O. Box 2206
Savannah
GA
13063388
Delta Air Lines, Inc. Technical Operations Center
1775 Aviation Blvd.
Atlanta
GA
13067506
U.S. Air Force Plant 6 Lockheed Martin
Aeronautics Company
86 South Cobb Drive
Marietta
GA
131276002
Gulfstream Aerospace Corporation
550 Connole Street
Brunswick
GA
13153431
Robins Air Force Base
Highway 247 and Watson Boulevc
Robins Air Force Baj
GA
132332017
Meggitt (Rockmart), Inc. (formerly Engineered
Fabrics Corp)
669 Goodyear Street
Rockmart
GA
15003346
Joint Base Pearl Harbor Hickam
Commander Navy Region Hawaii
Pearl Harbor
HI
18097487
Rolls-Royce Corporation
2001/2355 S. Tibbs Avenue
Indianapolis
IN
181035004
Grissom Air Reserve Base
7207 South Grissom Avenue
Grissom Air Reserve
IN
181412011
Honeywell International Inc; South Bend
3520 Westmoor Street
South Bend
IN
19163356
Carleton Life Support Systems Inc.
2734 Hickory Grove Road
Davenport
IA
20035486
GE Engine Services, LLC
7577 4th and A St.
Arkansas City
KS
201252005
Cessna Aircraft Company
One Cessna Boulevard
Independence
KS
20173193
Globe Engineering Company, Inc.
1539 South St. Paul
Wichita
KS
201732006
Cessna Aircraft Company - Mid-Continent Facility
One Cessna Boulevard
Wichita
KS
201732007
Cessna Aircraft Company
5800 E. Pawnee Street
Wichita
KS
201732027
Spirit Aerosystems, Inc.
3801 South Oliver
Wichita
KS
20173453
Learjet, Inc
One Learjet Way
Wichita
KS
2017365
Hawker Beechcraft Corporation
9709 East Central
Wichita
KS
24005458
Middle River Aircraft Systems
103 Chesapeake Park Plaza
Baltimore
MD
2 of 5
-------
Table 1 - Facility Identification Information
Facility NEI ID
Facility Name
Address
City
State
24015282
Alliant Techsystems Inc.
55 Thiokol Road
Elkton
MD
24017525
NAVFAC Washington, PWD South Potomac,
Naval Support Facility Indian Head
Environmental Division, 3972 War
Indian Head
MD
25009450
Raytheon Integrated Air Defense Center
350, 358 and 362 Lowell St
Andover
MA
25009477
General Electric Aviation
1000 Western Avenue
Lynn
MA
26065644
Pratt & Whitney AutoAir, Inc.
5640 Enterprise Drive
Lansing
Ml
27053389
Delta Air Lines, Inc. MSP TechOps
7500 Airline Drive
Minneapolis
MN
280475024
1108 TASMG
Hangar 1, Hewes Ave.
Gulf port
MS
28089367
L-3 Vertex Aerospace
555 Industrial Drive South
Madison
MS
29183329
The Boeing Company-St. Charles
2600 North 3rd Street
St. Charles
MO
29189332
The Boeing Company - St. Louis
Airport and McDonnell Blvd.
St. Louis
MO
29189513
GKN Aerospace - St. Louis
142 James S. McDonnell Bouleva
Hazelwood
MO
33011652
PGM of New England LLC
5 Perimeter Road
Manchester
NH
36103518
EDO Corporation Antenna Products &
Technologies
585 Johnson Ave
Bohemia
NY
36103521
EDO Marine and Aircraft Systems
1250-1500 New Horizon Blvd
North Amityville
NY
36111630
Ametek Rotron, Inc.
55 Hasbrouck Lane
Woodstock
NY
370492008
Dept. of the Navy, Fleet Readiness Center East
A Street - Marine Corps Air Statior
Cherry Point
NC
37049483
Marine Corps Air Station - Cherry Point
NC Highway 101 at US 70 West
Cherry Point
NC
37051340
Headquarters XVIII Airborne Corps and Fort
Bragg
2175 Reilly Road, Stop A
Fort Bragg
NC
37119600
US Airways, Inc. - CLT (Charlotte, NC)
5020 Hangar Road
Charlotte
NC
37133455
US Marine Corps Base Camp Lejeune
1 Camp Lejeune
Camp Lejeune
NC
39017343
CTL Aerospace, Inc.
5616 Spellmire Drive
Cincinnati
OH
39021672
Honeywell Aerospace
550 State Route 55
Urbana
OH
39057430
Wright-Patterson AFB
1450 Littrell Road
Dayton
OH
39061524
GE Aviation, Evendale
One Neumann Way
Cincinnati
OH
39109419
Goodrich
101 Waco St
Troy
OH
391335027
SAINT-GOBAIN PERFORMANCE PLASTICS
CORPORATION
335 N. DIAMOND STREET
RAVENNA
OH
39151479
PCC Airfoils, LLC
3860 Union Ave. S.E.
Minerva
OH
401092026
Tinker Air Force Base, OK
72 ABW CEAN, 7535 5th Street, E
Tinker AFB
OK
401432003
Bizjet International
1925 N. Sheridan
Tulsa
OK
401432013
Nordam l&S and NTRSD Divisions
6910 and 6911 N. Whirlpool Dr.
Tulsa
OK
401432014
Quality Plating Co. of Tulsa
2665 N. Darlington
Tulsa
OK
401432021
American Airlines Maintenance and Engineering
Center
3800 N MINGO RD
TULSA
OK
40143460
NORDAM Repair Division
11200 East Pine
Tulsa
OK
40143494
Spirit AeroSystems
3330 North Mingo Road
Tulsa
OK
40143523
Southwest United Industries, Inc.
422 S. St. Louis Ave.
Tulsa
OK
3 of 5
-------
Table 1 - Facility Identification Information
Facility NEI ID
Facility Name
Address
City
State
42003603
US Airways Pittsburgh International Airport
500 Tower Road
Coraopolis
PA
42029689
Sikorsky Global Helicopters
110 E. Stewart Huston Drive
Coatesville
PA
42045473
The Boeing Company, Defense, Space &
Security, Rotorcraft Programs, Philadelphia
Stewart Avenue and Route 291
Ridley Township
PA
42055233
Letterkenny Army Depot
1 Overcash Avenue
Chambersburg
PA
42081660
Avco Corporation d/b/a Lycoming Engines
652 Oliver Street
Williamsport
PA
45045417
Lockheed Martin Aeronautics Company
244 Terminal Road
Greenville
SC
45063428
Eagle Aviation Inc
2700 Aviation Way
West Columbia
SC
4703758
Triumph Aerostructures, LLC
1431 Vultee Blvd
Nashville
TN
48027213
Department of the Army, U.S. Army Garrison-Fort
Hood
4622 Engineer Dr.
Fort Hood
TX
48029468
The Boeing Company - San Antonnio
375 Airlift Drive 2nd Floor/C43
San Antonio
TX
480295025
Randolph Air Force Base
1 Washington Circle
Randolph AFB
TX
481136003
Dallas Love Field
P.O. Box 7415
Dallas
TX
481215032
American Airlines - AFW
2000 Eagle Parkway
Fort Worth
TX
482015019
NASA Ellington Field
12400 Brantly Ave.
Houston
TX
482015020
NASA Lyndon B. Johnson Space Center
2101 NASA Pkwy
Houston
TX
48231376
L-3 Communications Intergrated Systems MID
10001 Jack Finney Blvd
Greenville
TX
483553000
US Army Corpus Christi Army Depot
308 Crecy St.
Corpus Christi
TX
483672020
WEATHERFORD AEROSPACE INC.
610 W. THIRD ST.
WEATHERFORD
TX
484392009
Bell Helicopter Textron Plant 1
600 East Hurst BLVD
Ft. Worth
TX
484392010
Bell Helicopter Textron Plant 5
1700 N Highway 360
Grand Prairie
TX
48439416
Lockheed Martin Corporation d.b.a. Lockheed
Martin Aeronautics Company
1 Lockheed Boulevard
Fort Worth
TX
48439508
Goodrich Interiors Fort Worth
1201 Forum Way South
Fort Worth
TX
49003696
ATK Launch Systems, Promontory
9160 North Highway 83
Promontory
UT
490115010
Alliant Techsystems Aerospace Structures
Building C-14 Clearfield Freeport (
Clearfield
UT
490115012
ATK Launch Systems, Promontory
Buildings H-7, H-5, G-12, G-13 Fr<
Clearfield
UT
49011663
Hill Air Force Base
75 CEG/CEV
Hill Air Force Base
UT
490355013
ATK Launch Systems, Bacchus
5000 South 8400 West
West Valley City
UT
516505023
LANGLEY AIR FORCE BASE JOINT BASE
LANGLEY-EUSTIS
37 SWEENEY BOULEVARD
LANGLEY AFB
VA
51700334
Joint Base Langley-Eustis
1407 Washington Blvd.
Fort Eustis
VA
51710293
Naval Station Norfolk, Norfolk Virginina
Naval Station Norfolk
Norfolk
VA
51810292
Naval Air Station Oceana, Virginia Beach, VA
1750 Tomcat Blvd
Virginia Beach
VA
53029438
Naval Air Station Whidbey Island (Ault Field)
1115 W. Lexington Street (buildinc
Oak Harbor
WA
53033383
Boeing Commercial Airplane Group - Renton
800 Logan Ave. N
Renton
WA
53033384
Boeing Commercial Airplane Group-North Boeing
Field
7700 E. Marginal Way
Seattle
WA
53033406
Hexcel Corporation
19819 84th Avenue South
Kent
WA
53033532
Boeing Auburn
700 15th St. SW
Auburn
WA
4 of 5
-------
Table 1 - Facility Identification Information
Facility NEI ID
Facility Name
Address
City
State
53053447
Boeing Commercial Airplanes Fabrication
Division
18001 Canyon Rd
Puyallup
WA
53061398
Boeing Everett
3003 West Casino Road
Everett
WA
530615014
AVIATION TECHNICAL SERVICES, INC.
3121 109TH ST SW
EVERETT
WA
54033246
Pratt and Whitney Engine Services
1525 Midway Park Drive
Bridgeport
WV
540572002
Allegany Ballistics Laboratory
210 State Route 956
Rocket Center
WV
54067418
BE Aerospace / SMR Technologies
93 Nettie Fenwick Road
Fenwick
WV
55075288
Goodrich Cabin Seating Systems
701 Maple Street
Peshtiqo
Wl
55079687
Derco Repair Services
8065 West Fairlane Avenue
Milwaukee
Wl
550876001
Gulfstream Aerospace Corporation
W6365 Discovery Drive
Appleton
Wl
5 of 5
-------
Table 2a - Maximum Predicted HEM-3 Chronic Risks
Actual Emissions
Category Chronic Risk 1
Facility Chronic Risk 1
SC % of
Cancer
Cancer
Noncancer
Target
Cancer
Noncancer
Target
Facility-wide
Facility NEI ID
MIR
Incidence
Max HI
Organ
MIR
Max HI
Organ
Cancer Risk
01015223
2.10E-12
3.92E-10
2.46E-07
respiratory
3.50E-06
4.00E-01
neurological
0%
01045482
5.55E-10
3.39E-08
1.49E-02
neurological
1.18E-06
4.32E-02
kidney
0%
01073446
4.00E-10
1.05E-07
5.62E-05
neurological
7.01 E-09
1.51 E-04
kidney
6%
01083198
1.51E-09
1.42E-07
6.61 E-05
respiratory
1.51 E-09
6.61 E-05
respiratory
100%
2017365
4.25E-07
3.87E-05
7.60E-03
reproductive
5.70E-07
7.60E-03
reproductive
75%
02090348
7.08E-11
2.61 E-09
1.95E-04
respiratory
7.45E-08
9.35E-02
developmental
0%
02090529
5.17E-07
1.87E-05
4.47E-04
respiratory
5.17E-07
4.48E-04
respiratory
100%
02150683
1.17E-07
1.51 E-07
9.70E-05
respiratory
1.17E-07
4.86E-03
neurological
100%
04013510
0.00E+00
0.00E+00
5.82E-04
neurological
8.70E-06
2.49E-02
respiratory
0%
04013602
5.41 E-09
3.64E-06
1.54E-05
neurological
5.41 E-09
1.54E-05
neurological
100%
04019300
1.49E-07
4.30E-05
3.47E-04
neurological
1.49E-07
3.47E-04
neurological
100%
04019433
2.10E-07
9.98E-05
2.62E-02
respiratory
2.04E-07
2.62E-02
respiratory
100%
4703758
2.09E-06
5.43E-04
1.10E-02
respiratory
2.09E-06
1.11E-02
respiratory
100%
05051409
1.07E-05
4.29E-04
4.47E-02
respiratory
1.23E-05
4.59E-02
respiratory
87%
05113377
1.69E-07
8.05E-07
1.49E-01
respiratory
1.78E-07
1.56E-01
respiratory
95%
05143331
3.11E-10
1.62E-08
1.69E-05
neurological
3.24E-10
1.76E-05
neurological
96%
06029545
6.30E-09
6.78E-07
6.63E-06
respiratory
1.09E-08
9.07E-05
kidney
58%
06037429
2.51 E-08
4.69E-05
3.17E-04
respiratory
3.31 E-08
9.51 E-04
respiratory
76%
06037490
1.97E-08
7.08E-06
1.79E-04
neurological
5.94E-08
3.02E-04
respiratory
33%
06059538
2.87E-08
1.49E-05
2.76E-03
neurological
1.29E-07
6.31 E-03
neurological
22%
06065520
2.77E-07
4.19E-05
8.04E-03
respiratory
2.87E-07
1.24E-02
respiratory
96%
06071540
3.05E-07
3.79E-06
2.98E-02
neurological
3.05E-07
2.98E-02
neurological
100%
06073306
1.86E-09
1.16E-07
2.95E-03
respiratory
1.14E-06
2.40E-02
respiratory
0%
09001684
0.00E+00
0.00E+00
5.81 E-07
developmental
0.00E+00
5.37E-07
developmental
-
09001688
0.00E+00
0.00E+00
7.30E-07
neurological
0.00E+00
8.55E-07
neurological
-
09001693
1.84E-07
7.03E-05
9.18E-03
respiratory
6.18E-06
4.00E-02
respiratory
3%
09003626
3.65E-08
2.38E-06
3.74E-03
respiratory
3.28E-07
3.78E-03
respiratory
11%
10003349
5.56E-08
1.12E-05
8.92E-05
neurological
3.13E-07
3.24E-04
respiratory
18%
010515017
6.48E-09
3.50E-07
7.65E-03
respiratory
6.48E-09
7.65E-03
respiratory
100%
010892023
9.35E-08
3.98E-05
3.59E-03
liver
1.53E-06
6.88E-03
kidney
6%
12005313
5.22E-09
9.91 E-07
3.70E-03
respiratory
5.23E-09
3.70E-03
respiratory
100%
12009358
1.55E-09
9.77E-08
3.00E-05
respiratory
7.65E-09
3.00E-05
respiratory
20%
12023284
3.15E-07
4.12E-06
5.12E-03
respiratory
3.10E-07
5.12E-03
respiratory
100%
12031634
2.25E-07
3.00E-05
1.60E-03
respiratory
2.26E-07
1.60E-03
respiratory
100%
12061380
7.06E-07
4.67E-05
4.70E-03
respiratory
7.06E-07
4.70E-03
respiratory
100%
12083270
2.36E-10
1.41 E-08
8.20E-07
respiratory
2.75E-10
9.50E-04
neurological
86%
12099695
0.00E+00
0.00E+00
4.42E-10
developmental
4.24E-10
9.44E-06
kidney
0%
13009224
1.12E-07
1.49E-05
2.44E-02
respiratory
1.15E-07
2.54E-02
respiratory
97%
13063388
5.32E-07
3.56E-04
1.28E-02
respiratory
8.61 E-07
1.68E-02
kidney
62%
13067506
1.07E-06
9.57E-04
5.22E-03
respiratory
1.09E-06
5.27E-03
respiratory
99%
13153431
3.47E-06
3.32E-04
2.06E-01
respiratory
3.71 E-06
2.12E-01
respiratory
93%
15003346
1.31 E-08
1.62E-06
2.65E-04
respiratory
1.31 E-08
2.65E-04
respiratory
100%
18097487
2.21 E-08
5.06E-06
2.85E-04
liver
1.16E-07
2.05E-03
respiratory
19%
19163356
1.30E-07
1.01E-05
1.20E-03
respiratory
1.30E-07
1.20E-03
respiratory
100%
20035486
3.79E-08
1.34E-06
8.02E-02
respiratory
9.78E-07
8.50E-02
respiratory
4%
20173193
1.34E-08
4.50E-07
1.07E-05
respiratory
1.34E-08
3.42E-05
skeletal
100%
20173453
1.03E-07
7.74E-06
2.57E-03
respiratory
6.29E-07
6.37E-03
respiratory
16%
24005458
5.14E-06
1.64E-03
1.43E-02
reproductive
5.31 E-06
1.48E-02
reproductive
97%
24015282
1.97E-07
1.32E-05
1.44E-03
neurological
2.00E-07
1.77E-02
respiratory
99%
24017525
2.83E-10
4.05E-07
4.03E-06
neurological
2.22E-08
7.45E-03
respiratory
1%
25009450
1.50E-10
3.02E-08
1.89E-05
neurological
3.53E-09
5.25E-05
kidney
4%
25009477
1.73E-11
1.84E-09
2.51 E-04
reproductive
3.82E-08
1.01 E-03
kidney
0%
26065644
2.37E-08
5.80E-06
3.93E-03
respiratory
2.37E-08
3.93E-03
respiratory
100%
27053389
1.69E-08
1.79E-05
2.39E-03
respiratory
4.12E-08
2.42E-03
respiratory
41%
28089367
7.71 E-08
1.90E-06
1.41 E-04
reproductive
7.71 E-08
1.41 E-04
reproductive
100%
29183329
1.12E-07
5.68E-06
1.75E-03
respiratory
1.26E-07
1.85E-03
respiratory
89%
29189332
1.65E-07
4.77E-05
3.37E-02
respiratory
2.23E-06
3.44E-02
respiratory
7%
29189513
3.91 E-07
1.78E-04
1.23E-02
respiratory
7.07E-07
1.24E-02
respiratory
55%
33011652
2.94E-09
9.45E-07
1.99E-04
respiratory
2.94E-09
1.99E-04
respiratory
100%
36103518
0.00E+00
0.00E+00
4.15E-03
neurological
0.00E+00
4.15E-03
neurological
-
36103521
3.45E-09
6.61 E-07
1.08E-04
neurological
3.57E-09
1.19E-04
neurological
97%
36111630
6.00E-08
5.39E-07
5.63E-03
neurological
6.00E-08
5.63E-03
neurological
100%
37049483
6.67E-10
4.79E-08
1.37E-04
respiratory
5.77E-06
9.22E-02
developmental
0%
37051340
5.58E-09
6.43E-07
1.01E-03
respiratory
1.24E-06
9.74E-03
respiratory
0%
1 of 3
-------
Table 2a - Maximum Predicted HEM-3 Chronic Risks
Actual Emissions
Category Chronic Risk 1
Facility Chronic Risk 1
SC % of
Cancer
Cancer
Noncancer
Target
Cancer
Noncancer
Target
Facility-wide
Facility NEI ID
MIR
Incidence
Max HI
Organ
MIR
Max HI
Organ
Cancer Risk
37119600
9.99E-09
5.83E-06
2.79E-04
respiratory
1.02E-08
2.81 E-04
respiratory
98%
37133455
5.44E-09
3.52E-07
2.63E-04
respiratory
1.12E-06
1.28E-01
respiratory
0%
39017343
1.53E-07
3.35E-05
1.30E-04
respiratory
1.53E-07
1.30E-04
respiratory
100%
39021672
4.71 E-07
3.04E-05
1.02E-01
respiratory
4.78E-07
1.03E-01
respiratory
98%
39057430
2.08E-10
2.28E-08
5.09E-05
respiratory
8.55E-08
4.49E-03
developmental
0%
39061524
3.10E-06
8.06E-04
4.14E-02
respiratory
3.11 E-06
4.14E-02
respiratory
100%
39109419
9.17E-08
1.02E-06
6.00E-02
respiratory
8.83E-07
6.03E-02
respiratory
10%
39151479
5.44E-06
2.18E-04
1.07E-01
respiratory
5.44E-06
1.07E-01
respiratory
100%
40143460
2.21 E-06
2.30E-04
8.11E-03
respiratory
2.21 E-06
8.11 E-03
respiratory
100%
40143494
9.09E-08
4.20E-05
6.55E-03
respiratory
9.24E-08
6.56E-03
respiratory
98%
40143523
9.49E-06
3.18E-04
4.58E-01
kidney
1.16E-05
4.58E-01
respiratory
82%
42003603
1.05E-07
1.14E-05
2.77E-03
respiratory
1.07E-07
2.78E-03
respiratory
98%
42029689
0.00E+00
0.00E+00
2.32E-06
developmental
0.00E+00
2.59E-06
developmental
-
42045473
6.69E-08
4.23E-05
5.26E-03
respiratory
2.96E-07
6.11 E-03
respiratory
23%
42055233
3.25E-11
1.15E-08
9.49E-07
neurological
4.12E-08
3.50E-04
kidney
0%
42081660
2.05E-06
1.51E-04
7.99E-02
kidney
2.36E-06
3.95E-01
neurological
87%
45045417
2.26E-08
1.02E-05
4.70E-04
respiratory
2.91 E-08
5.41 E-04
respiratory
78%
45063428
1.07E-07
3.96E-06
2.38E-03
liver
1.32E-08
2.38E-03
liver
100%
48027213
1.64E-10
4.12E-08
3.42E-04
respiratory
2.98E-07
3.77E-03
kidney
0%
48029468
3.67E-06
3.45E-04
1.07E-01
respiratory
3.56E-06
1.04E-01
respiratory
100%
48231376
9.07E-08
1.18E-05
5.51 E-04
neurological
9.26E-08
6.27E-04
neurological
98%
48439416
2.90E-07
1.84E-05
1.55E-01
respiratory
3.07E-06
1.57E-01
respiratory
9%
48439508
2.16E-11
2.07E-08
1.97E-06
reproductive
2.16E-11
1.97E-06
reproductive
100%
49003696
7.05E-08
8.21 E-07
8.24E-03
respiratory
8.51 E-08
8.24E-03
respiratory
83%
49011663
6.56E-06
7.58E-04
9.20E-03
respiratory
6.68E-06
1.63E-02
respiratory
98%
050272001
2.35E-07
1.53E-06
3.75E-03
neurological
2.84E-07
3.81 E-03
neurological
83%
51700334
7.29E-11
4.41 E-08
6.07E-08
respiratory
1.67E-07
3.35E-03
kidney
0%
51710293
8.47E-06
2.54E-03
2.86E-02
respiratory
8.48E-06
2.86E-02
respiratory
100%
51810292
1.87E-06
4.41 E-04
4.89E-02
respiratory
1.87E-06
4.89E-02
respiratory
100%
53029438
9.78E-07
4.63E-05
9.62E-04
respiratory
1.05E-06
1.46E-03
respiratory
93%
53033383
2.37E-06
7.21 E-04
1.30E-02
reproductive
2.38E-06
1.30E-02
reproductive
100%
53033384
2.67E-06
3.76E-04
2.60E-02
respiratory
2.65E-06
2.59E-02
respiratory
100%
53033406
8.75E-08
1.17E-05
2.17E-04
neurological
8.85E-08
7.42E-03
respiratory
99%
53033532
4.11 E-07
1.87E-04
1.30E-02
respiratory
8.07E-07
1.36E-02
respiratory
51%
53053447
5.30E-07
1.83E-04
1.63E-03
respiratory
5.40E-07
1.71 E-03
respiratory
98%
53061398
6.03E-07
1.05E-03
1.01E-02
respiratory
6.16E-07
1.03E-02
respiratory
98%
54033246
2.27E-10
3.96E-08
1.89E-07
respiratory
2.30E-10
4.96E-06
neurological
99%
54067418
1.21E-08
1.45E-08
4.34E-04
neurological
3.49E-08
5.52E-04
kidney
35%
55075288
4.95E-09
1.09E-07
4.20E-05
neurological
3.26E-08
2.97E-04
kidney
15%
55079687
0.00E+00
0.00E+00
5.55E-09
neurological
0.00E+00
5.46E-09
neurological
-
060292024
4.27E-11
6.75E-09
1.96E-07
reproductive
3.28E-06
1.32E-02
kidney
0%
060372022
8.36E-08
9.93E-06
2.40E-03
respiratory
2.96E-07
3.60E-02
respiratory
28%
060376004
6.59E-08
1.15E-04
5.52E-04
neurological
7.51 E-08
5.40E-04
neurological
88%
060812015
2.65E-08
1.34E-05
1.08E-02
respiratory
4.02E-07
1.54E-02
respiratory
7%
090032025
7.42E-11
3.75E-09
2.21 E-06
neurological
7.42E-11
2.21 E-06
neurological
100%
090075021
1.36E-07
1.83E-06
1.02E-02
neurological
1.67E-07
1.02E-02
neurological
81%
120862016
1.02E-07
9.40E-05
2.38E-04
neurological
2.89E-07
2.38E-04
respiratory
35%
130516005
9.79E-07
7.27E-05
1.22E-02
respiratory
9.80E-07
1.22E-02
respiratory
100%
131276002
2.47E-08
8.77E-07
1.23E-04
reproductive
2.47E-08
1.23E-04
reproductive
100%
132332017
2.32E-06
5.00E-05
5.97E-02
neurological
2.32E-06
5.97E-02
neurological
100%
181035004
1.88E-08
1.60E-06
9.61 E-03
respiratory
1.62E-06
9.30E-03
respiratory
1%
181412011
1.90E-07
2.18E-05
1.58E-04
respiratory
2.68E-07
1.20E-03
kidney
71%
201252005
1.70E-08
5.06E-07
5.28E-04
respiratory
1.71 E-08
5.31 E-04
respiratory
99%
201732006
3.12E-08
1.03E-05
3.86E-02
neurological
5.17E-08
3.91 E-02
neurological
60%
201732007
2.54E-06
3.93E-04
4.90E-02
respiratory
2.56E-06
4.90E-02
respiratory
99%
201732027
1.42E-05
5.25E-03
1.09E-01
neurological
1.49E-05
1.13E-01
neurological
96%
280475024
5.60E-10
1.30E-07
5.17E-05
reproductive
9.36E-06
7.80E-03
respiratory
0%
370492008
7.20E-06
4.92E-04
3.09E-02
respiratory
1.82E-05
1.28E-01
developmental
40%
391335027
1.46E-08
6.18E-07
1.36E-03
neurological
1.46E-08
1.36E-03
neurological
100%
401092026
1.54E-06
4.96E-04
8.44E-02
respiratory
1.54E-06
8.44E-02
respiratory
100%
401432003
5.82E-09
2.16E-06
5.40E-04
respiratory
5.82E-09
5.40E-04
respiratory
100%
401432013
1.19E-06
6.15E-05
8.73E-02
respiratory
1.19E-06
8.73E-02
respiratory
100%
401432014
5.42E-06
1.87E-04
3.35E-01
respiratory
5.44E-06
3.36E-01
respiratory
100%
401432021
2.80E-06
5.36E-04
2.27E-01
respiratory
3.00E-06
2.27E-01
respiratory
93%
2 of 3
-------
Table 2a - Maximum Predicted HEM-3 Chronic Risks
Actual Emissions
Category Chronic Risk 1
Facility Chronic Risk 1
SC % of
Cancer
Cancer
Noncancer
Target
Cancer
Noncancer
Target
Facility-wide
Facility NEI ID
MIR
Incidence
Max HI
Organ
MIR
Max HI
Organ
Cancer Risk
480295025
1.02E-08
7.74E-07
1.69E-01
respiratory
1.05E-07
3.31 E-01
respiratory
10%
481136003
8.30E-08
5.78E-05
5.01 E-04
respiratory
1.11 E-07
5.40E-04
respiratory
75%
481215032
2.17E-08
1.94E-05
1.76E-02
respiratory
2.17E-08
1.76E-02
respiratory
100%
482015019
5.68E-09
2.53E-06
8.60E-04
respiratory
5.95E-09
8.71 E-04
respiratory
95%
482015020
1.23E-09
8.96E-07
3.31 E-06
respiratory
2.03E-08
2.05E-04
kidney
6%
483553000
3.28E-07
4.33E-05
2.47E-02
respiratory
1.99E-06
4.39E-01
respiratory
16%
483672020
3.09E-07
1.19E-05
2.97E-02
neurological
3.09E-07
2.97E-02
neurological
100%
484392009
1.13E-06
1.01E-04
1.45E-01
respiratory
1.16E-06
1.42E-01
respiratory
97%
484392010
1.79E-10
4.98E-08
1.78E-05
reproductive
1.80E-09
1.62E-04
neurological
10%
490115010
3.71 E-07
6.36E-05
3.07E-04
respiratory
3.41 E-07
2.89E-04
respiratory
100%
490115012
2.07E-08
4.44E-06
1.66E-03
respiratory
7.42E-07
6.80E-03
respiratory
3%
490355013
1.66E-07
6.15E-05
2.99E-03
respiratory
1.66E-07
2.99E-03
respiratory
100%
516505023
9.43E-09
1.87E-06
4.81 E-04
respiratory
3.72E-07
7.74E-03
kidney
3%
530615014
5.83E-11
2.98E-08
2.12E-03
reproductive
7.17E-12
2.12E-03
reproductive
100%
540572002
4.00E-07
9.54E-06
3.78E-03
respiratory
2.10E-06
5.44E-02
respiratory
19%
550876001
7.05E-07
8.68E-05
3.67E-03
liver
7.17E-07
3.64E-03
liver
98%
1 BOLD indicates a cancer risk great than 1 in a million or a noncancer risk greater than 1
3 of 3
-------
Table 2b - Maximum Predicted HEM-3 Chronic Risks
Allowable Emissions
Category Chronic Risk 1
Cancer
Cancer
Noncancer
Target
Facility NEI ID
MIR
Incidence
Max HI
Organ
01015223
2.11E-12
3.94E-10
2.46E-07
respiratory
01045482
5.57E-10
3.40E-08
1.49E-02
neurological
01073446
4.08E-10
1.07E-07
5.73E-05
neurological
01083198
1.54E-09
1.45E-07
6.74E-05
respiratory
2017365
4.33E-07
3.94E-05
7.75E-03
reproductive
02090348
7.22E-11
2.66E-09
1.97E-04
respiratory
02090529
5.20E-07
1.88E-05
4.50E-04
respiratory
02150683
1.20E-07
1.54E-07
9.89E-05
respiratory
04013510
0.00E+00
0.00E+00
5.94E-04
neurological
04013602
5.51 E-09
3.71 E-06
1.57E-05
neurological
04019300
1.52E-07
4.39E-05
3.54E-04
neurological
04019433
2.08E-07
9.91 E-05
2.67E-02
respiratory
4703758
2.09E-06
5.43E-04
1.10E-02
respiratory
05051409
1.09E-05
4.37E-04
4.51 E-02
respiratory
05113377
1.51E-07
6.15E-07
1.52E-01
respiratory
05143331
3.11E-10
1.62E-08
1.70E-05
neurological
06029545
6.43E-09
6.92E-07
6.74E-06
respiratory
06037429
2.55E-08
4.77E-05
3.22E-04
respiratory
06037490
1.97E-08
7.08E-06
1.80E-04
neurological
06059538
2.93E-08
1.52E-05
2.82E-03
neurological
06065520
2.80E-07
4.23E-05
8.20E-03
respiratory
06071540
3.11E-07
3.86E-06
3.04E-02
neurological
06073306
1.75E-09
1.03E-07
2.98E-03
respiratory
09001684
0.00E+00
0.00E+00
5.92E-07
developmental
09001688
0.00E+00
0.00E+00
7.45E-07
neurological
09001693
1.84E-07
7.04E-05
9.29E-03
respiratory
09003626
3.67E-08
2.37E-06
3.76E-03
respiratory
10003349
5.67E-08
1.14E-05
9.10E-05
neurological
010515017
6.48E-09
3.46E-07
7.65E-03
respiratory
010892023
4.94E-08
2.08E-05
3.66E-03
liver
12005313
5.32E-09
1.01 E-06
3.77E-03
respiratory
12009358
1.57E-09
9.89E-08
3.04E-05
respiratory
12023284
3.16E-07
4.04E-06
5.22E-03
respiratory
12031634
2.29E-07
3.06E-05
1.63E-03
respiratory
12061380
7.20E-07
4.76E-05
4.80E-03
respiratory
12083270
2.39E-10
1.43E-08
8.25E-07
respiratory
12099695
0.00E+00
0.00E+00
4.51E-10
developmental
13009224
1.13E-07
1.51 E-05
2.49E-02
respiratory
13063388
5.42E-07
3.63E-04
1.30E-02
respiratory
13067506
1.09E-06
9.74E-04
5.25E-03
respiratory
13153431
3.53E-06
3.36E-04
2.10E-01
respiratory
15003346
1.34E-08
1.65E-06
2.70E-04
respiratory
18097487
1.45E-08
2.77E-06
2.91 E-04
liver
19163356
1.33E-07
1.03E-05
1.22E-03
respiratory
20035486
3.79E-08
1.34E-06
8.02E-02
respiratory
20173193
1.37E-08
4.57E-07
1.09E-05
respiratory
20173453
1.05E-07
7.89E-06
2.62E-03
respiratory
24005458
5.14E-06
1.64E-03
1.43E-02
reproductive
24015282
1.98E-07
1.33E-05
1.47E-03
neurological
24017525
2.89E-10
4.13E-07
4.11 E-06
neurological
25009450
1.38E-10
2.50E-08
1.89E-05
neurological
25009477
1.73E-11
1.84E-09
2.54E-04
reproductive
26065644
2.37E-08
5.80E-06
3.93E-03
respiratory
27053389
1.72E-08
1.82E-05
2.43E-03
respiratory
28089367
7.86E-08
1.94E-06
1.44E-04
reproductive
29183329
1.14E-07
5.79E-06
1.76E-03
respiratory
29189332
1.68E-07
4.83E-05
3.38E-02
respiratory
29189513
3.98E-07
1.81E-04
1.23E-02
respiratory
33011652
3.00E-09
9.62E-07
2.03E-04
respiratory
36103518
0.00E+00
0.00E+00
4.24E-03
neurological
36103521
3.52E-09
6.74E-07
1.11 E-04
neurological
36111630
6.00E-08
5.39E-07
5.63E-03
neurological
37049483
6.80E-10
4.88E-08
1.40E-04
respiratory
37051340
5.69E-09
6.55E-07
1.03E-03
respiratory
1 of 3
-------
Table 2b - Maximum Predicted HEM-3 Chronic Risks
Allowable Emissions
Category Chronic Risk 1
Cancer
Cancer
Noncancer
Target
Facility NEI ID
MIR
Incidence
Max HI
Organ
37119600
1.02E-08
5.94E-06
2.79E-04
respiratory
37133455
5.55E-09
3.59E-07
2.68E-04
respiratory
39017343
1.56E-07
3.42E-05
1.32E-04
respiratory
39021672
4.79E-07
3.09E-05
1.04E-01
respiratory
39057430
1.99E-10
2.19E-08
5.19E-05
respiratory
39061524
3.11E-06
8.09E-04
4.14E-02
respiratory
39109419
9.36E-08
1.04E-06
6.12E-02
respiratory
39151479
5.55E-06
2.22E-04
1.09E-01
respiratory
40143460
2.24E-06
2.34E-04
8.23E-03
respiratory
40143494
9.12E-08
4.21 E-05
6.65E-03
respiratory
40143523
9.66E-06
3.24E-04
4.67E-01
kidney
42003603
1.06E-07
1.15E-05
2.79E-03
respiratory
42029689
0.00E+00
0.00E+00
2.36E-06
developmental
42045473
6.49E-08
4.12E-05
5.26E-03
respiratory
42055233
3.31E-11
1.18E-08
9.66E-07
neurological
42081660
2.09E-06
1.54E-04
8.15E-02
kidney
45045417
2.31 E-08
1.04E-05
4.70E-04
respiratory
45063428
1.34E-08
5.22E-07
2.43E-03
liver
48027213
1.67E-10
4.16E-08
3.42E-04
respiratory
48029468
3.67E-06
3.45E-04
1.07E-01
respiratory
48231376
9.25E-08
1.20E-05
5.52E-04
neurological
48439416
2.92E-07
1.85E-05
1.57E-01
respiratory
48439508
2.17E-11
2.08E-08
1.97E-06
reproductive
49003696
7.16E-08
8.30E-07
8.24E-03
respiratory
49011663
6.56E-06
7.57E-04
9.24E-03
respiratory
050272001
2.40E-07
1.56E-06
3.83E-03
neurological
51700334
7.43E-11
4.39E-08
6.19E-08
respiratory
51710293
8.47E-06
2.54E-03
2.88E-02
respiratory
51810292
1.86E-06
4.40E-04
4.99E-02
respiratory
53029438
9.66E-07
4.56E-05
9.66E-04
respiratory
53033383
2.39E-06
7.27E-04
1.32E-02
reproductive
53033384
2.72E-06
3.83E-04
2.65E-02
respiratory
53033406
8.91 E-08
1.20E-05
2.19E-04
neurological
53033532
4.18E-07
1.90E-04
1.33E-02
respiratory
53053447
5.30E-07
1.83E-04
1.63E-03
respiratory
53061398
6.06E-07
1.07E-03
1.03E-02
respiratory
54033246
2.32E-10
4.04E-08
1.93E-07
respiratory
54067418
1.21 E-08
1.45E-08
4.38E-04
neurological
55075288
5.05E-09
1.11E-07
4.29E-05
neurological
55079687
0.00E+00
0.00E+00
5.66E-09
neurological
060292024
4.32E-11
6.83E-09
2.00E-07
reproductive
060372022
8.37E-08
9.94E-06
2.40E-03
respiratory
060376004
6.72E-08
1.17E-04
5.60E-04
neurological
060812015
2.66E-08
1.35E-05
1.09E-02
respiratory
090032025
7.42E-11
3.75E-09
2.24E-06
neurological
090075021
1.39E-07
1.87E-06
1.04E-02
neurological
120862016
1.04E-07
9.59E-05
2.42E-04
neurological
130516005
9.98E-07
7.41 E-05
1.22E-02
respiratory
131276002
2.52E-08
8.92E-07
1.23E-04
reproductive
132332017
2.32E-06
5.00E-05
5.98E-02
neurological
181035004
1.91 E-08
1.64E-06
9.80E-03
respiratory
181412011
1.94E-07
2.22E-05
1.61 E-04
respiratory
201252005
1.70E-08
5.06E-07
5.28E-04
respiratory
201732006
3.10E-08
1.02E-05
3.86E-02
neurological
201732007
2.53E-06
3.92E-04
4.90E-02
respiratory
201732027
1.45E-05
5.35E-03
1.12E-01
neurological
280475024
5.72E-10
1.33E-07
5.28E-05
reproductive
370492008
7.35E-06
5.01 E-04
3.11E-02
respiratory
391335027
1.47E-08
6.19E-07
1.36E-03
neurological
401092026
1.56E-06
5.02E-04
8.60E-02
respiratory
401432003
5.82E-09
2.16E-06
5.40E-04
respiratory
401432013
1.19E-06
6.18E-05
8.75E-02
respiratory
401432014
5.46E-06
1.88E-04
3.35E-01
respiratory
401432021
2.82E-06
5.40E-04
2.30E-01
respiratory
2 of 3
-------
Table 2b - Maximum Predicted HEM-3 Chronic Risks
Allowable Emissions
Category Chronic Risk 1
Cancer
Cancer
Noncancer
Target
Facility NEI ID
MIR
Incidence
Max HI
Organ
480295025
1.00E-08
7.42E-07
1.73E-01
respiratory
481136003
7.67E-08
5.31 E-05
5.11E-04
respiratory
481215032
2.20E-08
1.97E-05
1.77E-02
respiratory
482015019
5.14E-09
2.32E-06
8.62E-04
respiratory
482015020
1.11 E-09
8.41 E-07
3.37E-06
respiratory
483553000
3.27E-07
4.31 E-05
2.47E-02
respiratory
483672020
3.15E-07
1.21 E-05
3.03E-02
neurological
484392009
1.13E-06
1.02E-04
1.45E-01
respiratory
484392010
1.18E-10
3.76E-08
1.78E-05
reproductive
490115010
3.78E-07
6.48E-05
3.13E-04
respiratory
490115012
2.07E-08
4.44E-06
1.66E-03
respiratory
490355013
1.66E-07
6.15E-05
2.99E-03
respiratory
516505023
9.44E-09
1.87E-06
4.81 E-04
respiratory
530615014
7.31E-12
3.74E-09
2.16E-03
reproductive
540572002
4.00E-07
9.54E-06
3.78E-03
respiratory
550876001
6.55E-07
8.19E-05
3.74E-03
liver
1 BOLD indicates a cancer risk great than 1 in a million or a noncancer risk greater than 1
3 of 3
-------
Table 3 - Maximum Predicted Acute Risks
Actual Emissions
Facility NEI ID
Pollutant
Maximum Hazard Quotient1
REL
AEGL1
AEGL2
ERPG1
ERPG2
01015223
Formaldehyde
2.65E-05
1.32E-06
8.57E-08
1.21 E-06
1.21E-07
01015223
Xylenes (mixed)
3.04E-06
1.20E-07
1.67E-08
0.00E+00
0.00E+00
01015223
Toluene
8.15E-07
4.02E-08
6.70E-09
1.59E-07
2.74 E-08
01015223
Ethyl benzene
0.00E+00
5.61 E-08
1.64E-09
0.00E+00
0.00E+00
01045482
Xylenes (mixed)
7.63E-06
3.00E-07
4.20E-08
0.00E+00
0.00E+00
01045482
Toluene
2.22E-06
1.09E-07
1.82 E-08
4.32E-07
7.46E-08
01045482
Ethyl benzene
0.00E+00
3.48E-07
1.02 E-08
0.00E+00
0.00E+00
01045482
Cumene
0.00E+00
9.67E-09
1.61E-09
0.00E+00
0.00E+00
01073446
Toluene
2.63E-04
1.30E-05
2.16E-06
5.12E-05
8.84 E-06
01073446
Xylenes (mixed)
3.03E-05
1.19E-06
1.67E-07
0.00E+00
0.00E+00
01073446
Ethyl benzene
0.00E+00
2.13E-07
6.22E-09
0.00E+00
0.00E+00
01083198
2,4-Toluene diisocyanate
0.00E+00
3.34E-05
7.93 E-06
6.59E-05
4.25E-06
01083198
Xylenes (mixed)
4.63E-06
1.82E-07
2.55E-08
0.00E+00
0.00E+00
01083198
Triethylamine
2.89E-06
0.00E+00
0.00E+00
0.00E+00
0.00E+00
01083198
Ethyl benzene
0.00E+00
7.12E-07
2.08E-08
0.00E+00
0.00E+00
01083198
Toluene
4.60E-07
2.27E-08
3.78E-09
8.95E-08
1.55E-08
01083198
Cumene
0.00E+00
2.15E-10
3.58E-11
0.00E+00
0.00E+00
2017365
Propyl cellosolve
7.14E-02
0.00E+00
0.00E+00
0.00E+00
0.00E+00
2017365
2-Butoxyethyl acetate
1.05E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
2017365
Formaldehyde
2.43E-04
1.21E-05
7.85E-07
1.11E-05
1.11 E-06
2017365
Xylenes (mixed)
7.25E-05
2.85E-06
3.99E-07
0.00E+00
0.00E+00
2017365
Arsenic compounds
3.82E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
2017365
Toluene
2.18E-05
1.07E-06
1.79E-07
4.24E-06
7.32 E-07
2017365
Mercury (elemental)
3.80E-06
0.00E+00
1.34 E-09
0.00E+00
1.14E-09
2017365
Ethyl benzene
0.00E+00
1.50E-07
4.37E-09
0.00E+00
0.00E+00
2017365
Methyl methacrylate
0.00E+00
1.23E-07
1.76 E-08
0.00E+00
0.00E+00
2017365
Triethylamine
4.48E-08
0.00E+00
0.00E+00
0.00E+00
0.00E+00
2017365
Methanol
7.92E-09
3.21 E-10
8.21 E-11
8.53E-10
1.71 E-10
2017365
Cumene
0.00E+00
7.26E-11
1.21E-11
0.00E+00
0.00E+00
02090348
Diethylene glycol monobutyl ethe
4.96E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
02090348
Methylene diphenyl diisocyanate
0.00E+00
0.00E+00
0.00E+00
0.00E+00
3.03E-05
02090348
Triethylamine
5.01 E-06
0.00E+00
0.00E+00
0.00E+00
0.00E+00
02090348
Xylenes (mixed)
1.63E-06
6.41 E-08
8.97E-09
0.00E+00
0.00E+00
02090348
Toluene
4.35E-07
2.14E-08
3.57E-09
8.47E-08
1.46E-08
02090348
Formaldehyde
1.40E-07
6.98E-09
4.52E-10
6.40E-09
6.40E-10
02090348
Ethyl benzene
0.00E+00
2.88E-08
8.39E-10
0.00E+00
0.00E+00
02090348
Cumene
0.00E+00
4.47E-09
7.46E-10
0.00E+00
0.00E+00
02090529
Diethylene glycol monobutyl ethe
1.42E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
02090529
2-Butoxyethyl acetate
1.32E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
02090529
Xylenes (mixed)
1.92E-05
7.54E-07
1.06E-07
0.00E+00
0.00E+00
02090529
Toluene
1.07E-05
5.26E-07
8.76 E-08
2.08E-06
3.58E-07
02090529
Methylene chloride
3.78E-06
7.66E-08
2.78E-08
5.29E-08
2.03E-08
02090529
Chlorobenzene
0.00E+00
3.50E-06
2.33E-07
0.00E+00
0.00E+00
02090529
Ethylene glycol methyl ether
3.10E-06
0.00E+00
0.00E+00
0.00E+00
0.00E+00
02090529
Methanol
1.09E-06
4.44E-08
1.13E-08
1.18E-07
2.36 E-08
02090529
Ethyl benzene
0.00E+00
5.67E-07
1.65E-08
0.00E+00
0.00E+00
02090529
n-Hexane
0.00E+00
0.00E+00
3.80E-08
0.00E+00
0.00E+00
02090529
Cumene
0.00E+00
2.78E-10
4.63E-11
0.00E+00
0.00E+00
02150683
Benzene
7.31 E-06
5.59E-08
3.65E-09
5.94E-08
1.98E-08
02150683
Methanol
2.71 E-06
1.10E-07
2.81 E-08
2.92E-07
5.84 E-08
02150683
Phenol
1.34E-06
1.34E-07
8.74 E-08
2.05E-07
4.10E-08
02150683
Toluene
1.11 E-06
5.47E-08
9.12E-09
2.16E-07
3.73E-08
1 of 27
-------
Table 3 - Maximum Predicted Acute Risks
Actual Emissions
Facility NEI ID
Pollutant
Maximum Hazard Quotient1
REL
AEGL1
AEGL2
ERPG1
ERPG2
02150683
Methylene chloride
1.25E-06
2.54E-08
9.22E-09
1.75E-08
6.73E-09
02150683
Xylenes (mixed)
9.45E-07
3.71 E-08
5.20E-09
0.00E+00
0.00E+00
02150683
Ethyl benzene
0.00E+00
2.43E-08
7.08E-10
0.00E+00
0.00E+00
02150683
Cumene
0.00E+00
8.73E-10
1.45E-10
0.00E+00
0.00E+00
04013510
Xylenes (mixed)
2.15E-03
8.44E-05
1.18E-05
0.00E+00
0.00E+00
04013510
Toluene
3.31 E-05
1.64E-06
2.73E-07
6.45E-06
1.11 E-06
04013602
Xylenes (mixed)
9.93E-05
3.90E-06
5.46E-07
0.00E+00
0.00E+00
04013602
Toluene
6.09E-05
3.01 E-06
5.01 E-07
1.19E-05
2.05 E-06
04013602
Hydrofluoric acid
6.86E-06
2.01 E-06
8.24E-08
1.03E-06
1.03E-07
04013602
Ethyl benzene
0.00E+00
5.98E-06
1.74 E-07
0.00E+00
0.00E+00
04013602
Methanol
3.80E-06
1.54E-07
3.94 E-08
4.10E-07
8.19E-08
04013602
Benzene
9.00E-07
6.89E-09
4.50E-10
7.32E-09
2.44E-09
04013602
n-Hexane
0.00E+00
0.00E+00
1.17E-08
0.00E+00
0.00E+00
04019300
Xylenes (mixed)
4.45E-04
1.75E-05
2.45E-06
0.00E+00
0.00E+00
04019300
Toluene
2.18E-04
1.07E-05
1.79 E-06
4.24E-05
7.32 E-06
04019300
Methanol
6.03E-05
2.45E-06
6.25E-07
6.49E-06
1.30 E-06
04019300
Ethyl benzene
0.00E+00
3.29E-05
9.61 E-07
0.00E+00
0.00E+00
04019433
Phenol
3.77E-04
3.77E-05
2.46E-05
5.75E-05
1.15E-05
04019433
Methylene chloride
3.13E-04
6.34E-06
2.30 E-06
4.38E-06
1.68E-06
04019433
Arsenic compounds
8.36E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
04019433
Butyl carbitol acetate
7.23E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
04019433
Xylenes (mixed)
3.37E-06
1.32E-07
1.85E-08
0.00E+00
0.00E+00
04019433
Toluene
1.39E-06
6.85E-08
1.14E-08
2.70E-07
4.67E-08
04019433
Ethyl benzene
0.00E+00
4.68E-07
1.36 E-08
0.00E+00
0.00E+00
04019433
Cumene
0.00E+00
1.43E-08
2.38E-09
0.00E+00
0.00E+00
4703758
Ethylene glycol ethyl ether
6.02E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
4703758
Diethylene glycol monoethyl ethe
3.75E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
4703758
Methylene diphenyl diisocyanate
0.00E+00
0.00E+00
0.00E+00
0.00E+00
2.03E-04
4703758
Toluene
1.46E-04
7.19E-06
1.20E-06
2.84E-05
4.90 E-06
4703758
Xylenes (mixed)
1.55E-04
6.11 E-06
8.55E-07
0.00E+00
0.00E+00
4703758
Methanol
1.32E-04
5.37E-06
1.37 E-06
1.43E-05
2.85 E-06
4703758
Propyl cellosolve
3.67E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
4703758
Ethyl benzene
0.00E+00
8.53E-06
2.49E-07
0.00E+00
0.00E+00
4703758
Phenol
3.03E-07
3.03E-08
1.98E-08
4.63E-08
9.26E-09
4703758
Cumene
0.00E+00
9.46E-09
1.58E-09
0.00E+00
0.00E+00
05051409
Ethylene glycol ethyl ether
7.94E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
05051409
Toluene
1.30E-03
6.40E-05
1.07E-05
2.53E-04
4.37E-05
05051409
Propyl cellosolve
1.12E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
05051409
Formaldehyde
7.63E-04
3.82E-05
2.47E-06
3.50E-05
3.50 E-06
05051409
Butyl carbitol acetate
8.10E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
05051409
Xylenes (mixed)
7.15E-04
2.81 E-05
3.93 E-06
0.00E+00
0.00E+00
05051409
Methylene diphenyl diisocyanate
0.00E+00
0.00E+00
0.00E+00
0.00E+00
2.71 E-04
05051409
2-Butoxyethyl acetate
1.25E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
05051409
Phenol
1.23E-05
1.23E-06
8.02 E-07
1.88E-06
3.76 E-07
05051409
Arsenic compounds
1.42E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
05051409
Methanol
5.25E-06
2.13E-07
5.45E-08
5.66E-07
1.13E-07
05051409
Ethyl benzene
0.00E+00
4.31 E-07
1.26E-08
0.00E+00
0.00E+00
05051409
Beryllium compounds
0.00E+00
0.00E+00
0.00E+00
0.00E+00
3.73E-09
05051409
Cumene
0.00E+00
9.41 E-11
1.57E-11
0.00E+00
0.00E+00
05113377
Ethylene glycol ethyl ether acetal
1.88E+00
0.00E+00
0.00E+00
0.00E+00
0.00E+00
05113377
Propyl cellosolve
1.54E-01
0.00E+00
0.00E+00
0.00E+00
0.00E+00
05113377
2-Butoxyethyl acetate
1.12E-01
0.00E+00
0.00E+00
0.00E+00
0.00E+00
2 of 27
-------
Table 3 - Maximum Predicted Acute Risks
Actual Emissions
Facility NEI ID
Pollutant
Maximum Hazard Quotient1
REL
AEGL1
AEGL2
ERPG1
ERPG2
05113377
Phenol
9.31 E-03
9.31 E-04
6.07 E-04
1.42E-03
2.84 E-04
05113377
Methylene chloride
8.67E-03
1.76E-04
6.39E-05
1.21 E-04
4.67E-05
05113377
Toluene
1.92E-03
9.46E-05
1.58E-05
3.74E-04
6.45E-05
05113377
Xylenes (mixed)
1.91 E-03
7.51 E-05
1.05E-05
0.00E+00
0.00E+00
05113377
Ethyl benzene
0.00E+00
6.75E-05
1.97 E-06
0.00E+00
0.00E+00
05113377
Methanol
2.51 E-05
1.02E-06
2.60E-07
2.70E-06
5.41 E-07
05143331
Formaldehyde
2.10E-05
1.05E-06
6.79E-08
9.62E-07
9.62E-08
05143331
Toluene
5.83E-06
2.88E-07
4.80E-08
1.14E-06
1.96 E-07
05143331
Xylenes (mixed)
4.71 E-06
1.85E-07
2.59E-08
0.00E+00
0.00E+00
05143331
Methanol
9.74E-07
3.95E-08
1.01E-08
1.05E-07
2.10E-08
05143331
Phenol
1.99E-07
1.99E-08
1.30E-08
3.04E-08
6.07E-09
05143331
Ethyl benzene
0.00E+00
7.17E-09
2.09E-10
0.00E+00
0.00E+00
06029545
Diethylene glycol monobutyl ethe
2.19E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
06029545
Triethylamine
8.72E-06
0.00E+00
0.00E+00
0.00E+00
0.00E+00
06029545
Xylenes (mixed)
6.53E-06
2.57E-07
3.59E-08
0.00E+00
0.00E+00
06029545
Toluene
3.65E-07
1.80E-08
3.00E-09
7.10E-08
1.23E-08
06029545
Ethyl benzene
0.00E+00
1.42E-07
4.13E-09
0.00E+00
0.00E+00
06029545
Cumene
0.00E+00
3.57E-11
5.95E-12
0.00E+00
0.00E+00
06037429
Xylenes (mixed)
1.48E-04
5.83E-06
8.16E-07
0.00E+00
0.00E+00
06037429
Toluene
8.85E-05
4.36E-06
7.27E-07
1.72E-05
2.98 E-06
06037429
Ethyl benzene
0.00E+00
1.17E-05
3.41 E-07
0.00E+00
0.00E+00
06037429
Methyl methacrylate
0.00E+00
2.32E-06
3.31 E-07
0.00E+00
0.00E+00
06037429
Methylene chloride
1.20E-06
2.43E-08
8.84E-09
1.68E-08
6.46E-09
06037429
Phenol
1.78E-07
1.78E-08
1.16E-08
2.71 E-08
5.42E-09
06037429
Methanol
4.56E-08
1.85E-09
4.73E-10
4.91 E-09
9.82E-10
06037490
Toluene
2.57E-04
1.27E-05
2.11 E-06
5.00E-05
8.63E-06
06037490
Xylenes (mixed)
1.35E-05
5.32E-07
7.44E-08
0.00E+00
0.00E+00
06037490
Triethylamine
1.38E-07
0.00E+00
0.00E+00
0.00E+00
0.00E+00
06037490
Ethyl benzene
0.00E+00
9.20E-08
2.68E-09
0.00E+00
0.00E+00
06037490
Cumene
0.00E+00
1.55E-08
2.58E-09
0.00E+00
0.00E+00
06059538
Tetrachloroethene
2.50E-04
2.09E-05
3.13E-06
7.37E-06
3.58 E-06
06065520
Xylenes (mixed)
2.88E-05
1.13E-06
1.58E-07
0.00E+00
0.00E+00
06065520
Toluene
1.47E-05
7.26E-07
1.21 E-07
2.87E-06
4.95E-07
06065520
Formaldehyde
7.09E-06
3.55E-07
2.29E-08
3.25E-07
3.25E-08
06065520
Ethyl benzene
0.00E+00
3.13E-06
9.13E-08
0.00E+00
0.00E+00
06065520
Vinyl acetate
0.00E+00
1.10E-07
4.17E-09
1.46E-07
1.01 E-08
06065520
Methanol
1.56E-08
6.33E-10
1.62E-10
1.68E-09
3.36E-10
06065520
Phenol
3.56E-09
3.56E-10
2.32E-10
5.43E-10
1.09E-10
06065520
1,4-Dioxane
5.71E-10
2.81 E-11
1.43E-12
0.00E+00
0.00E+00
06065520
Cumene
0.00E+00
6.09E-12
1.02E-12
0.00E+00
0.00E+00
06071540
Tetrachloroethene
4.27E-02
3.56E-03
5.34 E-04
1.26E-03
6.10E-04
06071540
Xylenes (mixed)
1.39E-03
5.46E-05
7.65E-06
0.00E+00
0.00E+00
06071540
Toluene
1.91E-04
9.42E-06
1.57 E-06
3.72E-05
6.42E-06
06071540
Ethyl benzene
0.00E+00
1.43E-09
4.17E-11
0.00E+00
0.00E+00
06073306
2,4-Toluene diisocyanate
0.00E+00
1.76E-05
4.17E-06
3.46E-05
2.24E-06
06073306
Toluene
1.54E-05
7.59E-07
1.27E-07
3.00E-06
5.18E-07
06073306
Dimethyl formamide
0.00E+00
0.00E+00
2.76 E-07
1.24E-05
2.48E-07
06073306
Methylene chloride
4.35E-06
8.82E-08
3.20E-08
6.08E-08
2.34 E-08
06073306
Xylenes (mixed)
4.07E-06
1.60E-07
2.24E-08
0.00E+00
0.00E+00
06073306
Methanol
2.15E-06
8.73E-08
2.23E-08
2.32E-07
4.64E-08
06073306
Hydrofluoric acid
6.77E-07
1.98E-07
8.13E-09
1.02E-07
1.02 E-08
06073306
Methyl methacrylate
0.00E+00
1.12E-07
1.60E-08
0.00E+00
0.00E+00
3 of 27
-------
Table 3 - Maximum Predicted Acute Risks
Actual Emissions
Facility NEI ID
Pollutant
Maximum Hazard Quotient1
REL
AEGL1
AEGL2
ERPG1
ERPG2
06073306
Ethyl benzene
0.00E+00
3.94E-08
1.15E-09
0.00E+00
0.00E+00
06073306
Styrene
2.11E-08
5.21 E-09
8.06E-10
2.11 E-09
4.03E-10
06073306
Phenol
3.39E-09
3.39E-10
2.21 E-10
5.17E-10
1.03E-10
06073306
n-Hexane
0.00E+00
0.00E+00
3.39E-09
0.00E+00
0.00E+00
09001684
Methanol
1.84E-05
7.46E-07
1.91E-07
1.98E-06
3.96 E-07
09001688
Toluene
1.30E-05
6.43E-07
1.07E-07
2.54E-06
4.39E-07
09001688
Methanol
2.80E-06
1.13E-07
2.90E-08
3.01 E-07
6.02 E-08
09001693
2-Butoxyethyl acetate
1.77E-02
0.00E+00
0.00E+00
0.00E+00
0.00E+00
09001693
Butyl carbitol acetate
1.19E-02
0.00E+00
0.00E+00
0.00E+00
0.00E+00
09001693
Phenol
2.62E-04
2.62E-05
1.71E-05
4.00E-05
8.00 E-06
09001693
Methylene chloride
3.37E-04
6.84E-06
2.48E-06
4.72E-06
1.82 E-06
09001693
Formaldehyde
2.87E-04
1.43E-05
9.27E-07
1.31E-05
1.31 E-06
09001693
Xylenes (mixed)
2.16E-04
8.48E-06
1.19E-06
0.00E+00
0.00E+00
09001693
Diethylene glycol monobutyl ethe
2.07E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
09001693
Toluene
1.40E-04
6.89E-06
1.15E-06
2.72E-05
4.70 E-06
09001693
Ethylene glycol ethyl ether acetal
1.38E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
09001693
Methanol
6.99E-06
2.84E-07
7.25E-08
7.53E-07
1.51 E-07
09001693
Ethylene glycol ethyl ether
1.10E-06
0.00E+00
0.00E+00
0.00E+00
0.00E+00
09001693
Ethyl benzene
0.00E+00
6.03E-07
1.76 E-08
0.00E+00
0.00E+00
09001693
Cumene
0.00E+00
2.42E-08
4.04 E-09
0.00E+00
0.00E+00
09001693
Methylene diphenyl diisocyanate
0.00E+00
0.00E+00
0.00E+00
0.00E+00
1.79E-08
09001693
n-Hexane
0.00E+00
0.00E+00
5.25E-11
0.00E+00
0.00E+00
09003626
2-Butoxyethyl acetate
2.79E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
09003626
Ethylene glycol ethyl ether
2.56E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
09003626
Butyl carbitol acetate
3.03E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
09003626
Ethylene glycol methyl ether
2.53E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
09003626
Methylene chloride
2.17E-05
4.39E-07
1.60E-07
3.03E-07
1.17E-07
09003626
Formaldehyde
1.43E-05
7.15E-07
4.63E-08
6.55E-07
6.55E-08
09003626
Phenol
1.05E-05
1.05E-06
6.83E-07
1.60E-06
3.20E-07
09003626
Xylenes (mixed)
1.07E-05
4.19E-07
5.86 E-08
0.00E+00
0.00E+00
09003626
Toluene
6.52E-06
3.22E-07
5.36 E-08
1.27E-06
2.19E-07
09003626
Methanol
1.68E-06
6.83E-08
1.74 E-08
1.81 E-07
3.62E-08
09003626
Ethyl benzene
0.00E+00
1.67E-07
4.86 E-09
0.00E+00
0.00E+00
09003626
Tetrachloroethene
1.08E-07
9.00E-09
1.35E-09
3.18E-09
1.54 E-09
09003626
Arsenic compounds
3.89E-08
0.00E+00
0.00E+00
0.00E+00
0.00E+00
09003626
Cumene
0.00E+00
2.34E-08
3.90E-09
0.00E+00
0.00E+00
09003626
Beryllium compounds
0.00E+00
0.00E+00
0.00E+00
0.00E+00
1.00E-11
10003349
Xylenes (mixed)
2.62E-05
1.03E-06
1.44E-07
0.00E+00
0.00E+00
10003349
Styrene
1.77E-06
4.38E-07
6.78E-08
1.77E-07
3.39E-08
10003349
Methanol
6.30E-07
2.55E-08
6.53E-09
6.78E-08
1.36 E-08
10003349
Ethyl benzene
0.00E+00
6.64E-07
1.94 E-08
0.00E+00
0.00E+00
10003349
Arsenic compounds
2.16E-07
0.00E+00
0.00E+00
0.00E+00
0.00E+00
10003349
Toluene
3.34E-08
1.65E-09
2.75E-10
6.51 E-09
1.12E-09
010515017
2-Butoxyethyl acetate
3.73E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
010515017
Methanol
4.26E-04
1.73E-05
4.41 E-06
4.58E-05
9.17E-06
010515017
Xylenes (mixed)
1.80E-04
7.06E-06
9.89E-07
0.00E+00
0.00E+00
010515017
Ethylene glycol ethyl ether acetal
8.47E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
010515017
Toluene
5.44E-05
2.69E-06
4.48E-07
1.06E-05
1.83 E-06
010515017
Methylene chloride
5.04E-06
1.02E-07
3.72 E-08
7.06E-08
2.71 E-08
010515017
Phenol
2.43E-06
2.43E-07
1.59E-07
3.72E-07
7.43E-08
010515017
Acetaldehyde
1.22E-06
7.08E-09
1.17E-09
3.19E-08
1.59E-09
010515017
Ethyl benzene
0.00E+00
5.49E-07
1.60E-08
0.00E+00
0.00E+00
4 of 27
-------
Table 3 - Maximum Predicted Acute Risks
Actual Emissions
Facility NEI ID
Pollutant
Maximum Hazard Quotient1
REL
AEGL1
AEGL2
ERPG1
ERPG2
010515017
Methylene diphenyl diisocyanate
0.00E+00
0.00E+00
0.00E+00
0.00E+00
3.63E-07
010515017
Ethyl acrylate
0.00E+00
4.04E-10
9.15E-11
3.35E-07
1.14 E-10
010515017
1,2-Epoxybutane
0.00E+00
8.99E-08
4.61 E-08
0.00E+00
0.00E+00
010515017
Cumene
0.00E+00
2.30E-09
3.84E-10
0.00E+00
0.00E+00
010892023
Phenol
1.73E-01
1.73E-02
1.13E-02
2.65E-02
5.29E-03
010892023
Methylene chloride
4.95E-02
1.00E-03
3.65E-04
6.93E-04
2.66E-04
010892023
Xylenes (mixed)
2.72E-04
1.07E-05
1.50 E-06
0.00E+00
0.00E+00
010892023
Ethyl benzene
0.00E+00
5.54E-06
1.62E-07
0.00E+00
0.00E+00
12005313
Butyl carbitol acetate
3.74E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
12005313
Diethylene glycol dimethyl ether
8.60E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
12005313
Methylene diphenyl diisocyanate
0.00E+00
0.00E+00
0.00E+00
0.00E+00
2.48E-04
12005313
Xylenes (mixed)
9.54E-05
3.75E-06
5.25E-07
0.00E+00
0.00E+00
12005313
Toluene
1.18E-05
5.85E-07
9.74 E-08
2.31 E-06
3.99E-07
12005313
Ethyl benzene
0.00E+00
1.18E-05
3.44E-07
0.00E+00
0.00E+00
12005313
Formaldehyde
1.03E-05
5.17E-07
3.34 E-08
4.74E-07
4.74 E-08
12005313
Triethylamine
3.11E-06
0.00E+00
0.00E+00
0.00E+00
0.00E+00
12005313
Cumene
0.00E+00
1.13E-08
1.88E-09
0.00E+00
0.00E+00
12009358
Phenol
2.71 E-06
2.71 E-07
1.76 E-07
4.13E-07
8.26E-08
12009358
Methylene chloride
2.17E-06
4.41 E-08
1.60E-08
3.04E-08
1.17E-08
12009358
Xylenes (mixed)
9.99E-07
3.92E-08
5.49E-09
0.00E+00
0.00E+00
12009358
Toluene
3.51 E-07
1.73E-08
2.88E-09
6.83E-08
1.18E-08
12009358
Methanol
2.67E-07
1.08E-08
2.77E-09
2.87E-08
5.75E-09
12009358
Methylene diphenyl diisocyanate
0.00E+00
0.00E+00
0.00E+00
0.00E+00
2.33E-07
12009358
Ethyl benzene
0.00E+00
5.71 E-08
1.67E-09
0.00E+00
0.00E+00
12009358
n-Hexane
0.00E+00
0.00E+00
4.79E-11
0.00E+00
0.00E+00
12023284
Glycol Ethers
7.97E-02
0.00E+00
0.00E+00
0.00E+00
0.00E+00
12023284
Toluene
3.45E-03
1.70E-04
2.84 E-05
6.72E-04
1.16E-04
12023284
Methylene chloride
1.32E-03
2.68E-05
9.75 E-06
1.85E-05
7.13E-06
12023284
Phenol
7.10E-04
7.10E-05
4.63E-05
1.08E-04
2.17E-05
12023284
Xylenes (mixed)
3.00E-04
1.18E-05
1.65E-06
0.00E+00
0.00E+00
12023284
Styrene
4.87E-05
1.20E-05
1.86 E-06
4.87E-06
9.31 E-07
12023284
Methanol
9.11 E-06
3.70E-07
9.45E-08
9.81 E-07
1.96 E-07
12023284
Ethyl benzene
0.00E+00
2.64E-06
7.69E-08
0.00E+00
0.00E+00
12023284
n-Hexane
0.00E+00
0.00E+00
1.01 E-07
0.00E+00
0.00E+00
12031634
Butyl carbitol acetate
2.13E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
12031634
Toluene
1.11E-04
5.50E-06
9.16E-07
2.17E-05
3.75 E-06
12031634
Formaldehyde
2.70E-05
1.35E-06
8.73E-08
1.24E-06
1.24E-07
12031634
Xylenes (mixed)
2.01 E-05
7.88E-07
1.10E-07
0.00E+00
0.00E+00
12031634
Phenol
1.07E-05
1.07E-06
6.98E-07
1.63E-06
3.27E-07
12031634
Methanol
2.56E-06
1.04E-07
2.66E-08
2.76E-07
5.52 E-08
12031634
Ethyl benzene
0.00E+00
6.26E-07
1.82 E-08
0.00E+00
0.00E+00
12031634
Methylene chloride
3.86E-07
7.84E-09
2.85E-09
5.41 E-09
2.08E-09
12031634
1,1,1-Trichloroethane
2.51 E-07
1.31 E-08
5.17E-09
8.98E-09
4.49E-09
12031634
Styrene
4.33E-08
1.07E-08
1.65E-09
4.33E-09
8.26E-10
12031634
n-Hexane
0.00E+00
0.00E+00
3.35E-08
0.00E+00
0.00E+00
12031634
Cumene
0.00E+00
2.19E-08
3.65E-09
0.00E+00
0.00E+00
12031634
Benzene
1.34E-08
1.02E-10
6.69E-12
1.09E-10
3.62E-11
12061380
Ethylene glycol ethyl ether
4.24E-02
0.00E+00
0.00E+00
0.00E+00
0.00E+00
12061380
2-Butoxyethyl acetate
2.39E-02
0.00E+00
0.00E+00
0.00E+00
0.00E+00
12061380
Toluene
9.15E-04
4.51 E-05
7.52 E-06
1.78E-04
3.08E-05
12061380
Formaldehyde
1.58E-04
7.90E-06
5.11 E-07
7.24E-06
7.24E-07
12061380
Methanol
1.86E-05
7.56E-07
1.93E-07
2.01 E-06
4.01 E-07
5 of 27
-------
Table 3 - Maximum Predicted Acute Risks
Actual Emissions
Facility NEI ID
Pollutant
Maximum Hazard Quotient1
REL
AEGL1
AEGL2
ERPG1
ERPG2
12061380
Ethyl benzene
0.00E+00
3.66E-06
1.07E-07
0.00E+00
0.00E+00
12061380
Arsenic compounds
1.24E-06
0.00E+00
0.00E+00
0.00E+00
0.00E+00
12061380
Cumene
0.00E+00
5.97E-09
9.96E-10
0.00E+00
0.00E+00
12083270
Formaldehyde
2.06E-05
1.03E-06
6.65E-08
9.42E-07
9.42E-08
12083270
Ethyl acrylate
0.00E+00
6.05E-09
1.37E-09
5.01 E-06
1.71 E-09
12083270
Xylenes (mixed)
3.11E-07
1.22E-08
1.71E-09
0.00E+00
0.00E+00
12083270
Toluene
2.44E-07
1.20E-08
2.01 E-09
4.75E-08
8.21 E-09
12083270
Ethyl benzene
0.00E+00
1.19E-08
3.46E-10
0.00E+00
0.00E+00
12083270
Cumene
0.00E+00
2.21 E-10
3.69E-11
0.00E+00
0.00E+00
12099695
Methanol
2.46E-06
9.99E-08
2.55E-08
2.65E-07
5.30E-08
13009224
Formaldehyde
5.87E-03
2.94E-04
1.90E-05
2.69E-04
2.69E-05
13009224
Toluene
1.98E-03
9.75E-05
1.62E-05
3.85E-04
6.65E-05
13009224
Xylenes (mixed)
1.72E-03
6.75E-05
9.46E-06
0.00E+00
0.00E+00
13009224
Epichlorohydrin
2.45E-04
1.45E-05
3.51 E-06
1.68E-05
4.20E-06
13009224
Ethyl benzene
0.00E+00
3.94E-05
1.15E-06
0.00E+00
0.00E+00
13009224
Methylene diphenyl diisocyanate
0.00E+00
0.00E+00
0.00E+00
0.00E+00
3.79E-05
13009224
Methanol
1.52E-05
6.17E-07
1.58E-07
1.64E-06
3.27E-07
13009224
Phenol
2.65E-06
2.65E-07
1.73E-07
4.04E-07
8.08E-08
13009224
Styrene
1.39E-06
3.44E-07
5.31 E-08
1.39E-07
2.66E-08
13009224
Arsenic compounds
1.72E-06
0.00E+00
0.00E+00
0.00E+00
0.00E+00
13009224
Beryllium compounds
0.00E+00
0.00E+00
0.00E+00
0.00E+00
1.98E-10
13063388
Butyl carbitol acetate
2.39E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
13063388
Diethylene glycol monobutyl ethe
2.38E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
13063388
Ethylene glycol ethyl ether
5.34E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
13063388
Ethylene glycol methyl ether
5.20E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
13063388
Formaldehyde
1.46E-04
7.32E-06
4.73E-07
6.71 E-06
6.71 E-07
13063388
Arsenic compounds
1.19E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
13063388
Toluene
7.40E-05
3.65E-06
6.09E-07
1.44E-05
2.49E-06
13063388
Xylenes (mixed)
8.60E-05
3.38E-06
4.73E-07
0.00E+00
0.00E+00
13063388
Ethylene glycol ethyl ether acetal
3.19E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
13063388
Phenol
1.88E-05
1.88E-06
1.23E-06
2.88E-06
5.75E-07
13063388
Methylene diphenyl diisocyanate
0.00E+00
0.00E+00
0.00E+00
0.00E+00
1.39E-05
13063388
Methylene chloride
9.67E-06
1.96E-07
7.13E-08
1.35E-07
5.21 E-08
13063388
1,2-Dimethoxyethane
7.12E-06
0.00E+00
0.00E+00
0.00E+00
0.00E+00
13063388
Styrene
4.79E-06
1.18E-06
1.83E-07
4.79E-07
9.14E-08
13063388
2,4-Toluene diisocyanate
0.00E+00
1.92E-06
4.55E-07
3.78E-06
2.44E-07
13063388
Methanol
4.93E-06
2.00E-07
5.12E-08
5.31 E-07
1.06 E-07
13063388
Carbitol acetate
5.33E-06
0.00E+00
0.00E+00
0.00E+00
0.00E+00
13063388
2-Butoxyethyl acetate
4.13E-06
0.00E+00
0.00E+00
0.00E+00
0.00E+00
13063388
2-(Hexyloxy)ethanol
3.24E-06
0.00E+00
0.00E+00
0.00E+00
0.00E+00
13063388
Ethyl benzene
0.00E+00
2.66E-06
7.76 E-08
0.00E+00
0.00E+00
13063388
Acrylic acid
1.97E-07
2.69E-07
8.45E-09
3.94E-07
7.88E-09
13063388
Benzene
7.54E-07
5.77E-09
3.77E-10
6.13E-09
2.04 E-09
13063388
Epichlorohydrin
4.17E-07
2.47E-08
5.96 E-09
2.85E-08
7.14E-09
13063388
Methyl methacrylate
0.00E+00
8.84E-08
1.26E-08
0.00E+00
0.00E+00
13063388
m-Xylene
8.74E-08
0.00E+00
0.00E+00
0.00E+00
0.00E+00
13063388
Cumene
0.00E+00
4.55E-08
7.58E-09
0.00E+00
0.00E+00
13063388
o-Xylene
3.49E-08
0.00E+00
0.00E+00
0.00E+00
0.00E+00
13063388
Chloroform
2.79E-08
0.00E+00
1.35E-11
0.00E+00
1.75E-11
13063388
n-Hexane
0.00E+00
0.00E+00
5.22E-09
0.00E+00
0.00E+00
13063388
Trichloroethylene
0.00E+00
1.68E-09
4.91 E-10
2.18E-09
4.37E-10
13063388
Carbon tetrachloride
3.19E-09
2.16E-11
5.05E-12
4.66E-11
9.61 E-12
6 of 27
-------
Table 3 - Maximum Predicted Acute Risks
Actual Emissions
Facility NEI ID
Pollutant
Maximum Hazard Quotient1
REL
AEGL1
AEGL2
ERPG1
ERPG2
13063388
Beryllium compounds
0.00E+00
0.00E+00
0.00E+00
0.00E+00
1.37E-09
13063388
Triethylamine
4.83E-10
0.00E+00
0.00E+00
0.00E+00
0.00E+00
13063388
Aniline
0.00E+00
7.60E-12
4.95E-12
0.00E+00
0.00E+00
13063388
1,4-Dioxane
4.06E-12
1.99E-13
1.00E-14
0.00E+00
0.00E+00
13063388
Propylene oxide
3.93E-12
7.20E-14
1.80E-14
1.01 E-13
2.10E-14
13063388
Ethylene oxide
0.00E+00
0.00E+00
1.50E-13
0.00E+00
1.35E-13
13067506
Propyl cellosolve
5.76E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
13067506
2-Butoxyethyl acetate
2.60E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
13067506
Diethylene glycol monobutyl ethe
1.47E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
13067506
Xylenes (mixed)
6.13E-04
2.41 E-05
3.37 E-06
0.00E+00
0.00E+00
13067506
Diethylene glycol monomethyl et
3.93E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
13067506
Butyl carbitol acetate
2.42E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
13067506
Toluene
1.00E-04
4.95E-06
8.26E-07
1.96E-05
3.38 E-06
13067506
Triethylamine
1.13E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
13067506
Ethylene glycol methyl ether
3.34E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
13067506
Methylene diphenyl diisocyanate
0.00E+00
0.00E+00
0.00E+00
0.00E+00
2.94 E-05
13067506
Arsenic compounds
1.55E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
13067506
Phenol
8.53E-06
8.53E-07
5.56 E-07
1.30E-06
2.60E-07
13067506
Methanol
3.64E-06
1.48E-07
3.77E-08
3.92E-07
7.84 E-08
13067506
Formaldehyde
2.33E-06
1.16E-07
7.54E-09
1.07E-07
1.07E-08
13067506
Ethyl benzene
0.00E+00
1.39E-06
4.05E-08
0.00E+00
0.00E+00
13067506
Carbitol acetate
1.37E-06
0.00E+00
0.00E+00
0.00E+00
0.00E+00
13067506
Methyl methacrylate
0.00E+00
2.05E-07
2.94 E-08
0.00E+00
0.00E+00
13067506
Propylene oxide
5.23E-08
9.53E-10
2.35E-10
1.35E-09
2.75E-10
13067506
Carbon tetrachloride
2.92E-08
1.98E-10
4.63E-11
4.27E-10
8.82E-11
13067506
n-Hexane
0.00E+00
0.00E+00
2.35E-08
0.00E+00
0.00E+00
13067506
Beryllium compounds
0.00E+00
0.00E+00
0.00E+00
0.00E+00
8.29E-09
13067506
Cumene
0.00E+00
6.40E-09
1.07E-09
0.00E+00
0.00E+00
13067506
p-Xylene
1.97E-09
0.00E+00
0.00E+00
0.00E+00
0.00E+00
13153431
Butyl carbitol acetate
5.01 E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
13153431
2,4-Toluene diisocyanate
0.00E+00
2.60E-04
6.17E-05
5.13E-04
3.31 E-05
13153431
Phenol
3.65E-04
3.65E-05
2.38E-05
5.57E-05
1.11 E-05
13153431
Methylene chloride
4.54E-04
9.21 E-06
3.34 E-06
6.35E-06
2.44E-06
13153431
2-Butoxyethyl acetate
3.89E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
13153431
Xylenes (mixed)
2.29E-04
9.01 E-06
1.26E-06
0.00E+00
0.00E+00
13153431
Toluene
8.99E-05
4.44E-06
7.40E-07
1.75E-05
3.03 E-06
13153431
Methanol
3.91 E-05
1.59E-06
4.05E-07
4.21 E-06
8.42E-07
13153431
Ethyl acrylate
0.00E+00
4.79E-08
1.09E-08
3.97E-05
1.36 E-08
13153431
Formaldehyde
2.96E-05
1.48E-06
9.58E-08
1.36E-06
1.36 E-07
13153431
Ethyl benzene
0.00E+00
2.28E-05
6.66E-07
0.00E+00
0.00E+00
13153431
Benzene
1.29E-06
9.85E-09
6.44E-10
1.05E-08
3.49E-09
13153431
Cumene
0.00E+00
1.63E-07
2.72 E-08
0.00E+00
0.00E+00
13153431
Ethylene oxide
0.00E+00
0.00E+00
2.01 E-08
0.00E+00
1.81 E-08
15003346
Toluene
2.38E-06
1.18E-07
1.96 E-08
4.64E-07
8.02 E-08
15003346
Xylenes (mixed)
6.14E-07
2.41 E-08
3.38E-09
0.00E+00
0.00E+00
15003346
Ethyl benzene
0.00E+00
4.82E-08
1.41E-09
0.00E+00
0.00E+00
18097487
Phenol
7.56E-04
7.56E-05
4.93E-05
1.15E-04
2.31 E-05
18097487
Methylene chloride
6.14E-04
1.24E-05
4.52 E-06
8.59E-06
3.30 E-06
18097487
Toluene
4.37E-05
2.16E-06
3.59E-07
8.51 E-06
1.47E-06
18097487
Formaldehyde
6.48E-06
3.24E-07
2.10E-08
2.97E-07
2.97E-08
18097487
1,4-Dioxane
2.06E-06
1.01 E-07
5.16E-09
0.00E+00
0.00E+00
18097487
Xylenes (mixed)
4.31 E-07
1.69E-08
2.37E-09
0.00E+00
0.00E+00
7 of 27
-------
Table 3 - Maximum Predicted Acute Risks
Actual Emissions
Facility NEI ID
Pollutant
Maximum Hazard Quotient1
REL
AEGL1
AEGL2
ERPG1
ERPG2
18097487
Methanol
2.85E-07
1.15E-08
2.95E-09
3.06E-08
6.13E-09
18097487
Ethyl benzene
0.00E+00
2.94E-09
8.58E-11
0.00E+00
0.00E+00
19163356
Toluene
1.41E-04
6.98E-06
1.16E-06
2.75E-05
4.76 E-06
19163356
Xylenes (mixed)
5.30E-06
2.08E-07
2.92 E-08
0.00E+00
0.00E+00
19163356
Methanol
3.51 E-06
1.42E-07
3.64E-08
3.78E-07
7.56 E-08
19163356
Formaldehyde
3.02E-06
1.51E-07
9.77E-09
1.38E-07
1.38E-08
19163356
n-Hexane
0.00E+00
0.00E+00
3.10E-07
0.00E+00
0.00E+00
19163356
Ethyl benzene
0.00E+00
5.56E-08
1.62E-09
0.00E+00
0.00E+00
19163356
Cumene
0.00E+00
8.07E-09
1.34E-09
0.00E+00
0.00E+00
20035486
Diethylene glycol monobutyl ethe
9.73E-02
0.00E+00
0.00E+00
0.00E+00
0.00E+00
20035486
2-Butoxyethyl acetate
1.85E-02
0.00E+00
0.00E+00
0.00E+00
0.00E+00
20035486
2,4-Toluene diisocyanate
0.00E+00
2.63E-03
6.24E-04
5.19E-03
3.35 E-04
20035486
Formaldehyde
3.15E-03
1.58E-04
1.02E-05
1.44E-04
1.44E-05
20035486
Xylenes (mixed)
3.10E-03
1.22E-04
1.71E-05
0.00E+00
0.00E+00
20035486
Acrylic acid
6.17E-04
8.41 E-04
2.64E-05
1.23E-03
2.47E-05
20035486
Toluene
1.03E-03
5.06E-05
8.43E-06
2.00E-04
3.45E-05
20035486
Ethylene glycol methyl ether
4.38E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
20035486
Phenol
1.66E-04
1.66E-05
1.08E-05
2.53E-05
5.06 E-06
20035486
Methanol
1.34E-04
5.42E-06
1.38 E-06
1.44E-05
2.88 E-06
20035486
Ethyl benzene
0.00E+00
9.24E-05
2.69E-06
0.00E+00
0.00E+00
20035486
Acrylonitrile
0.00E+00
1.59E-08
1.22E-09
7.21 E-09
2.06 E-09
20035486
n-Hexane
0.00E+00
0.00E+00
6.35E-11
0.00E+00
0.00E+00
20173193
Ethyl benzene
0.00E+00
1.32E-07
3.85E-09
0.00E+00
0.00E+00
20173193
Xylenes (mixed)
1.30E-07
5.10E-09
7.14E-10
0.00E+00
0.00E+00
20173193
Cumene
0.00E+00
1.62E-08
2.70E-09
0.00E+00
0.00E+00
20173453
2-Butoxyethyl acetate
1.72E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
20173453
Xylenes (mixed)
2.63E-04
1.03E-05
1.45E-06
0.00E+00
0.00E+00
20173453
Toluene
1.48E-04
7.31 E-06
1.22E-06
2.89E-05
4.99 E-06
20173453
Ethyl benzene
0.00E+00
2.55E-06
7.44E-08
0.00E+00
0.00E+00
20173453
Methyl methacrylate
0.00E+00
1.75E-06
2.50E-07
0.00E+00
0.00E+00
20173453
Methanol
1.01E-07
4.10E-09
1.05E-09
1.09E-08
2.18E-09
24005458
Ethylene glycol methyl ether
2.31 E-01
0.00E+00
0.00E+00
0.00E+00
0.00E+00
24005458
Propyl cellosolve
9.23E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
24005458
Dimethyl formamide
0.00E+00
0.00E+00
2.66E-05
1.20E-03
2.39E-05
24005458
Glycol ethers
7.73E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
24005458
Butyl carbitol acetate
7.29E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
24005458
Toluene
1.37E-04
6.78E-06
1.13E-06
2.68E-05
4.62E-06
24005458
Xylenes (mixed)
5.03E-05
1.98E-06
2.77E-07
0.00E+00
0.00E+00
24005458
Mercury (elemental)
6.40E-06
0.00E+00
2.26E-09
0.00E+00
1.92 E-09
24005458
Methanol
1.63E-06
6.62E-08
1.69E-08
1.76E-07
3.51 E-08
24005458
Methylene diphenyl diisocyanate
0.00E+00
0.00E+00
0.00E+00
0.00E+00
5.11E-07
24005458
Ethyl benzene
0.00E+00
2.74E-09
8.00E-11
0.00E+00
0.00E+00
24015282
Tetrachloroethene
2.99E-04
2.49E-05
3.74 E-06
8.80E-06
4.27E-06
24015282
Xylenes (mixed)
8.63E-06
3.39E-07
4.74 E-08
0.00E+00
0.00E+00
24015282
Toluene
4.15E-06
2.05E-07
3.41 E-08
8.08E-07
1.39E-07
24015282
Ethyl benzene
0.00E+00
7.31 E-08
2.13E-09
0.00E+00
0.00E+00
24015282
Cumene
0.00E+00
2.98E-10
4.97E-11
0.00E+00
0.00E+00
24017525
Xylenes (mixed)
5.66E-05
2.22E-06
3.11E-07
0.00E+00
0.00E+00
24017525
Ethyl benzene
0.00E+00
2.53E-06
7.37E-08
0.00E+00
0.00E+00
24017525
Toluene
1.40E-06
6.89E-08
1.15E-08
2.72E-07
4.70E-08
24017525
Cumene
0.00E+00
2.83E-07
4.72 E-08
0.00E+00
0.00E+00
25009450
Toluene
6.80E-06
3.36E-07
5.59E-08
1.33E-06
2.29E-07
8 of 27
-------
Table 3 - Maximum Predicted Acute Risks
Actual Emissions
Facility NEI ID
Pollutant
Maximum Hazard Quotient1
REL
AEGL1
AEGL2
ERPG1
ERPG2
25009450
Ethylene glycol methyl ether
5.05E-06
0.00E+00
0.00E+00
0.00E+00
0.00E+00
25009450
Diethylene glycol monobutyl ethe
4.26E-06
0.00E+00
0.00E+00
0.00E+00
0.00E+00
25009450
Xylenes (mixed)
1.06E-06
4.16E-08
5.82 E-09
0.00E+00
0.00E+00
25009450
Methylene chloride
4.77E-07
9.68E-09
3.51 E-09
6.68E-09
2.57E-09
25009450
Methanol
1.31E-08
5.32E-10
1.36E-10
1.41 E-09
2.82E-10
25009450
Ethyl benzene
0.00E+00
9.91 E-09
2.89E-10
0.00E+00
0.00E+00
25009450
n-Hexane
0.00E+00
0.00E+00
1.08E-09
0.00E+00
0.00E+00
25009477
Diethylene glycol monobutyl ethe
2.95E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
25009477
Glycol ethers
7.70E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
25009477
Toluene
1.03E-05
5.07E-07
8.45E-08
2.00E-06
3.46E-07
25009477
Methanol
1.01E-05
4.09E-07
1.05E-07
1.09E-06
2.17E-07
25009477
Formaldehyde
4.76E-07
2.38E-08
1.54 E-09
2.18E-08
2.18E-09
25009477
Xylenes (mixed)
2.34E-08
9.19E-10
1.29E-10
0.00E+00
0.00E+00
25009477
Ethyl benzene
0.00E+00
7.49E-10
2.19E-11
0.00E+00
0.00E+00
26065644
2,4-Toluene diisocyanate
0.00E+00
2.88E-04
6.84 E-05
5.68E-04
3.67E-05
26065644
Methanol
1.32E-04
5.36E-06
1.37 E-06
1.42E-05
2.84 E-06
26065644
Xylenes (mixed)
6.18E-05
2.43E-06
3.40E-07
0.00E+00
0.00E+00
26065644
Toluene
2.04E-05
1.01 E-06
1.68E-07
3.97E-06
6.85E-07
26065644
Ethyl benzene
0.00E+00
1.10E-05
3.21 E-07
0.00E+00
0.00E+00
26065644
Formaldehyde
1.40E-06
7.00E-08
4.53E-09
6.42E-08
6.42E-09
27053389
Diethylene glycol monobutyl ethe
2.77E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
27053389
Diethylene glycol monomethyl et
2.17E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
27053389
Ethylene glycol ethyl ether acetal
1.87E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
27053389
2-Butoxyethyl acetate
1.52E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
27053389
2,4-Toluene diisocyanate
0.00E+00
4.34E-06
1.03 E-06
8.56E-06
5.52 E-07
27053389
Xylenes (mixed)
1.04E-05
4.08E-07
5.71 E-08
0.00E+00
0.00E+00
27053389
Toluene
4.66E-06
2.30E-07
3.83E-08
9.07E-07
1.57E-07
27053389
Methylene diphenyl diisocyanate
0.00E+00
0.00E+00
0.00E+00
0.00E+00
1.15E-06
27053389
Methanol
2.06E-07
8.35E-09
2.13E-09
2.22E-08
4.43E-09
27053389
Phenol
1.62E-07
1.62E-08
1.06 E-08
2.48E-08
4.96 E-09
27053389
Ethyl benzene
0.00E+00
1.73E-07
5.05E-09
0.00E+00
0.00E+00
27053389
Styrene
8.74E-08
2.16E-08
3.34 E-09
8.74E-09
1.67E-09
27053389
Propylene oxide
9.66E-08
1.76E-09
4.34E-10
2.49E-09
5.07E-10
27053389
Formaldehyde
5.49E-08
2.75E-09
1.78E-10
2.52E-09
2.52E-10
27053389
Methyl methacrylate
0.00E+00
3.70E-08
5.29E-09
0.00E+00
0.00E+00
27053389
Acrylic acid
2.46E-09
3.36E-09
1.05E-10
4.92E-09
9.84E-11
27053389
n-Hexane
0.00E+00
0.00E+00
3.00E-09
0.00E+00
0.00E+00
27053389
Cumene
0.00E+00
9.17E-12
1.53E-12
0.00E+00
0.00E+00
28089367
Glycol Ethers
1.06E-01
0.00E+00
0.00E+00
0.00E+00
0.00E+00
28089367
Xylenes (mixed)
1.68E-03
6.61 E-05
9.25E-06
0.00E+00
0.00E+00
28089367
Toluene
9.24E-04
4.56E-05
7.59 E-06
1.80E-04
3.11 E-05
28089367
Ethyl benzene
0.00E+00
4.79E-05
1.40E-06
0.00E+00
0.00E+00
28089367
Methanol
8.92E-06
3.62E-07
9.25E-08
9.60E-07
1.92 E-07
29183329
Butyl carbitol acetate
9.22E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
29183329
Xylenes (mixed)
3.57E-04
1.40E-05
1.97 E-06
0.00E+00
0.00E+00
29183329
Glycol Ethers
7.81 E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
29183329
Methanol
2.02E-05
8.20E-07
2.09E-07
2.17E-06
4.35E-07
29183329
Toluene
1.29E-05
6.36E-07
1.06 E-07
2.51 E-06
4.34 E-07
29183329
Methylene diphenyl diisocyanate
0.00E+00
0.00E+00
0.00E+00
0.00E+00
1.58E-05
29183329
Carbitol acetate
5.34E-06
0.00E+00
0.00E+00
0.00E+00
0.00E+00
29183329
Phenol
3.61 E-06
3.61 E-07
2.35E-07
5.51 E-07
1.10E-07
29183329
Cumene
0.00E+00
3.06E-06
5.10E-07
0.00E+00
0.00E+00
9 of 27
-------
Table 3 - Maximum Predicted Acute Risks
Actual Emissions
Facility NEI ID
Pollutant
Maximum Hazard Quotient1
REL
AEGL1
AEGL2
ERPG1
ERPG2
29183329
Ethylene glycol methyl ether
3.52E-06
0.00E+00
0.00E+00
0.00E+00
0.00E+00
29183329
Propyl cellosolve
2.96E-06
0.00E+00
0.00E+00
0.00E+00
0.00E+00
29183329
p-Xylene
2.82E-06
0.00E+00
0.00E+00
0.00E+00
0.00E+00
29183329
Formaldehyde
1.51E-06
7.56E-08
4.89E-09
6.93E-08
6.93E-09
29183329
Ethylene glycol ethyl ether acetal
9.18E-07
0.00E+00
0.00E+00
0.00E+00
0.00E+00
29183329
Chlorobenzene
0.00E+00
3.23E-07
2.15E-08
0.00E+00
0.00E+00
29183329
Ethyl benzene
0.00E+00
2.00E-07
5.83E-09
0.00E+00
0.00E+00
29183329
Acrylic acid
1.85E-08
2.52E-08
7.92E-10
3.70E-08
7.39E-10
29183329
Ethylene glycol ethyl ether
5.95E-08
0.00E+00
0.00E+00
0.00E+00
0.00E+00
29183329
Ethyl acrylate
0.00E+00
3.78E-12
8.56E-13
3.13E-09
1.07E-12
29183329
Epichlorohydrin
2.48E-09
1.46E-10
3.54E-11
1.69E-10
4.23E-11
29183329
Benzene
9.06E-10
6.93E-12
4.53E-13
7.36E-12
2.45E-12
29183329
Arsenic compounds
5.60E-10
0.00E+00
0.00E+00
0.00E+00
0.00E+00
29183329
n-Hexane
0.00E+00
0.00E+00
3.60E-10
0.00E+00
0.00E+00
29183329
1,3-Butadiene
0.00E+00
1.70E-14
2.00E-15
1.18E-12
5.90E-14
29189332
2,4-Toluene diisocyanate
0.00E+00
6.06E-04
1.44E-04
1.19E-03
7.71 E-05
29189332
Propyl cellosolve
7.59E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
29189332
Butyl carbitol acetate
6.15E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
29189332
Xylenes (mixed)
3.73E-04
1.46E-05
2.05E-06
0.00E+00
0.00E+00
29189332
Toluene
2.76E-04
1.36E-05
2.27E-06
5.38E-05
9.30E-06
29189332
Methylene diphenyl diisocyanate
0.00E+00
0.00E+00
0.00E+00
0.00E+00
2.07 E-04
29189332
Methanol
1.39E-04
5.65E-06
1.44E-06
1.50E-05
3.00E-06
29189332
Diethylene glycol monobutyl ethe
1.64E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
29189332
Formaldehyde
8.21 E-05
4.11E-06
2.66E-07
3.77E-06
3.77E-07
29189332
Phenol
6.59E-05
6.59E-06
4.29E-06
1.01 E-05
2.01 E-06
29189332
Ethyl acrylate
0.00E+00
8.30E-08
1.88E-08
6.89E-05
2.35E-08
29189332
Methylene chloride
3.66E-05
7.43E-07
2.70E-07
5.12E-07
1.97E-07
29189332
Ethylene glycol methyl ether
2.16E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
29189332
Glycol Ethers
1.83E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
29189332
Ethylene glycol ethyl ether acetal
1.26E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
29189332
Diethylene glycol monoethyl ethe
1.04E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
29189332
Carbitol acetate
7.99E-06
0.00E+00
0.00E+00
0.00E+00
0.00E+00
29189332
Methyl methacrylate
0.00E+00
4.38E-06
6.26E-07
0.00E+00
0.00E+00
29189332
Cumene
0.00E+00
2.83E-06
4.72E-07
0.00E+00
0.00E+00
29189332
Acrylic acid
3.58E-07
4.88E-07
1.53E-08
7.16E-07
1.43E-08
29189332
Styrene
1.04E-06
2.56E-07
3.96E-08
1.04E-07
1.98E-08
29189332
Chlorobenzene
0.00E+00
1.34E-06
8.92E-08
0.00E+00
0.00E+00
29189332
Ethyl benzene
0.00E+00
1.36E-06
3.98E-08
0.00E+00
0.00E+00
29189332
Triethylamine
1.28E-06
0.00E+00
0.00E+00
0.00E+00
0.00E+00
29189332
Ethylene glycol ethyl ether
8.15E-07
0.00E+00
0.00E+00
0.00E+00
0.00E+00
29189332
p-Xylene
4.42E-07
0.00E+00
0.00E+00
0.00E+00
0.00E+00
29189332
1,2-Epoxybutane
0.00E+00
2.53E-07
1.29E-07
0.00E+00
0.00E+00
29189332
Ethylene oxide
0.00E+00
0.00E+00
1.21E-08
0.00E+00
1.09E-08
29189332
n-Hexane
0.00E+00
0.00E+00
4.32E-09
0.00E+00
0.00E+00
29189332
Benzene
1.87E-09
1.43E-11
9.36E-13
1.52E-11
5.07E-12
29189332
Epichlorohydrin
3.30E-10
1.95E-11
4.72E-12
2.26E-11
5.65E-12
29189332
Acrylonitrile
0.00E+00
3.70E-14
3.00E-15
1.70E-14
5.00E-15
29189513
Toluene
8.01 E-04
3.95E-05
6.59E-06
1.56E-04
2.69E-05
29189513
Diethylene glycol monobutyl ethe
5.27E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
29189513
Glycol Ethers
1.33E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
29189513
Methylene diphenyl diisocyanate
0.00E+00
0.00E+00
0.00E+00
0.00E+00
1.07 E-04
29189513
Methanol
2.51 E-05
1.02E-06
2.60E-07
2.70E-06
5.40E-07
10 of 27
-------
Table 3 - Maximum Predicted Acute Risks
Actual Emissions
Facility NEI ID
Pollutant
Maximum Hazard Quotient1
REL
AEGL1
AEGL2
ERPG1
ERPG2
29189513
Xylenes (mixed)
2.67E-05
1.05E-06
1.47E-07
0.00E+00
0.00E+00
29189513
Phenol
1.25E-05
1.25E-06
8.18E-07
1.92E-06
3.83E-07
29189513
Tetrachloroethene
1.15E-05
9.62E-07
1.44E-07
3.40E-07
1.65E-07
29189513
Methylene chloride
1.11 E-05
2.25E-07
8.16E-08
1.55E-07
5.96 E-08
29189513
Benzene
1.41E-06
1.08E-08
7.06E-10
1.15E-08
3.83E-09
29189513
Ethyl benzene
0.00E+00
5.39E-07
1.57E-08
0.00E+00
0.00E+00
29189513
n-Hexane
0.00E+00
0.00E+00
1.48E-07
0.00E+00
0.00E+00
33011652
Toluene
6.33E-05
3.12E-06
5.21 E-07
1.23E-05
2.13E-06
33011652
Xylenes (mixed)
2.11 E-05
8.30E-07
1.16E-07
0.00E+00
0.00E+00
33011652
Phenol
1.78E-06
1.78E-07
1.16E-07
2.72E-07
5.43E-08
33011652
Methylene chloride
1.84E-06
3.74E-08
1.36 E-08
2.58E-08
9.93E-09
33011652
Ethyl benzene
0.00E+00
1.29E-06
3.75E-08
0.00E+00
0.00E+00
33011652
Formaldehyde
7.25E-07
3.62E-08
2.34E-09
3.32E-08
3.32 E-09
33011652
Methanol
1.09E-08
4.41 E-10
1.13 E-10
1.17E-09
2.34E-10
33011652
Cumene
0.00E+00
6.77E-09
1.13E-09
0.00E+00
0.00E+00
33011652
Methylene diphenyl diisocyanate
0.00E+00
0.00E+00
0.00E+00
0.00E+00
3.63E-09
36103518
Xylenes (mixed)
1.76E-03
6.91 E-05
9.67E-06
0.00E+00
0.00E+00
36103518
Toluene
7.90E-04
3.90E-05
6.49E-06
1.54E-04
2.66E-05
36103521
Xylenes (mixed)
9.26E-05
3.64E-06
5.09E-07
0.00E+00
0.00E+00
36103521
Toluene
2.94E-05
1.45E-06
2.42E-07
5.73E-06
9.90E-07
36103521
Ethyl benzene
0.00E+00
1.59E-06
4.65E-08
0.00E+00
0.00E+00
36111630
Glycol Ethers
6.54E-02
0.00E+00
0.00E+00
0.00E+00
0.00E+00
36111630
Xylenes (mixed)
1.66E-03
6.52E-05
9.13E-06
0.00E+00
0.00E+00
36111630
Toluene
1.05E-03
5.19E-05
8.65E-06
2.05E-04
3.54 E-05
36111630
Formaldehyde
2.60E-04
1.30E-05
8.42E-07
1.19E-05
1.19E-06
36111630
Ethyl benzene
0.00E+00
8.74E-06
2.55E-07
0.00E+00
0.00E+00
37049483
Methanol
3.44E-05
1.40E-06
3.57E-07
3.71 E-06
7.41 E-07
37049483
Xylenes (mixed)
2.33E-05
9.15E-07
1.28E-07
0.00E+00
0.00E+00
37049483
Phenol
1.35E-05
1.35E-06
8.79E-07
2.06E-06
4.12E-07
37049483
Toluene
1.40E-05
6.92E-07
1.15E-07
2.73E-06
4.72 E-07
37049483
Methylene chloride
1.27E-05
2.58E-07
9.35E-08
1.78E-07
6.84 E-08
37049483
Methyl methacrylate
0.00E+00
6.22E-07
8.89E-08
0.00E+00
0.00E+00
37049483
Ethyl benzene
0.00E+00
3.86E-07
1.13E-08
0.00E+00
0.00E+00
37049483
Formaldehyde
2.94E-07
1.47E-08
9.50E-10
1.35E-08
1.35E-09
37049483
Benzene
7.51 E-08
5.75E-10
3.76E-11
6.10E-10
2.03E-10
37049483
Cumene
0.00E+00
1.62E-08
2.69E-09
0.00E+00
0.00E+00
37051340
Xylenes (mixed)
1.42E-05
5.56E-07
7.79E-08
0.00E+00
0.00E+00
37051340
Toluene
2.89E-06
1.43E-07
2.38E-08
5.63E-07
9.73E-08
37051340
Ethyl benzene
0.00E+00
7.44E-07
2.17E-08
0.00E+00
0.00E+00
37051340
Cumene
0.00E+00
4.54E-10
7.57E-11
0.00E+00
0.00E+00
37119600
Xylenes (mixed)
3.48E-04
1.37E-05
1.91E-06
0.00E+00
0.00E+00
37119600
Toluene
2.77E-04
1.36E-05
2.27E-06
5.39E-05
9.30 E-06
37119600
Formaldehyde
1.28E-04
6.39E-06
4.13E-07
5.86E-06
5.86 E-07
37119600
Phenol
4.22E-05
4.22E-06
2.75 E-06
6.44E-06
1.29E-06
37119600
Methylene diphenyl diisocyanate
0.00E+00
0.00E+00
0.00E+00
0.00E+00
2.85E-05
37119600
Ethyl benzene
0.00E+00
1.31 E-05
3.83E-07
0.00E+00
0.00E+00
37119600
Hydrofluoric acid
3.47E-06
1.02E-06
4.17E-08
5.21 E-07
5.21 E-08
37119600
Styrene
2.96E-06
7.31 E-07
1.13E-07
2.96E-07
5.65E-08
37119600
Methylene chloride
7.33E-07
1.49E-08
5.40E-09
1.03E-08
3.95E-09
37119600
Tetrachloroethene
5.13E-07
4.28E-08
6.41 E-09
1.51 E-08
7.33E-09
37119600
1,1,1-Trichloroethane
1.51E-07
7.89E-09
3.11E-09
5.40E-09
2.70E-09
37119600
Benzene
9.83E-08
7.52E-10
4.91 E-11
7.99E-10
2.66E-10
11 of 27
-------
Table 3 - Maximum Predicted Acute Risks
Actual Emissions
Facility NEI ID
Pollutant
Maximum Hazard Quotient1
REL
AEGL1
AEGL2
ERPG1
ERPG2
37119600
n-Hexane
0.00E+00
0.00E+00
1.62E-08
0.00E+00
0.00E+00
37119600
Arsenic compounds
6.05E-09
0.00E+00
0.00E+00
0.00E+00
0.00E+00
37119600
Methanol
2.55E-09
1.04E-10
2.65E-11
2.75E-10
5.49E-11
37133455
Toluene
4.25E-05
2.10E-06
3.50E-07
8.29E-06
1.43E-06
37133455
2,4-Toluene diisocyanate
0.00E+00
8.65E-06
2.05 E-06
1.71E-05
1.10E-06
37133455
Xylenes (mixed)
1.54E-05
6.06E-07
8.49E-08
0.00E+00
0.00E+00
37133455
Ethyl benzene
0.00E+00
9.06E-07
2.64E-08
0.00E+00
0.00E+00
37133455
Cumene
0.00E+00
7.84E-07
1.31E-07
0.00E+00
0.00E+00
37133455
Arsenic compounds
1.42E-07
0.00E+00
0.00E+00
0.00E+00
0.00E+00
37133455
Methanol
7.96E-09
3.23E-10
8.26E-11
8.57E-10
1.71E-10
37133455
Mercury (elemental)
4.39E-10
0.00E+00
1.55E-13
0.00E+00
1.32E-13
39017343
Ethylene glycol ethyl ether
2.88E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
39017343
Butyl carbitol acetate
1.19E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
39017343
2-Butoxyethyl acetate
8.02E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
39017343
Propyl cellosolve
6.13E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
39017343
Toluene
2.18E-06
1.07E-07
1.79E-08
4.24E-07
7.33E-08
39017343
Xylenes (mixed)
1.91E-06
7.51 E-08
1.05E-08
0.00E+00
0.00E+00
39017343
2,4-Toluene diisocyanate
0.00E+00
5.07E-07
1.20E-07
9.99E-07
6.45E-08
39017343
Methanol
1.27E-06
5.16E-08
1.32 E-08
1.37E-07
2.74 E-08
39017343
Ethyl benzene
0.00E+00
5.83E-08
1.70E-09
0.00E+00
0.00E+00
39017343
Arsenic compounds
2.42E-08
0.00E+00
0.00E+00
0.00E+00
0.00E+00
39021672
2-Butoxyethyl acetate
9.02E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
39021672
Xylenes (mixed)
5.50E-04
2.16E-05
3.02 E-06
0.00E+00
0.00E+00
39021672
2,4-Toluene diisocyanate
0.00E+00
1.11 E-04
2.64E-05
2.19E-04
1.42E-05
39021672
Formaldehyde
2.22E-04
1.11E-05
7.18E-07
1.02E-05
1.02 E-06
39021672
Toluene
1.71E-04
8.41 E-06
1.40E-06
3.32E-05
5.74 E-06
39021672
Diethylene glycol monoethyl ethe
1.86E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
39021672
Ethylene glycol methyl ether
8.73E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
39021672
Phenol
1.83E-05
1.83E-06
1.19E-06
2.79E-06
5.58E-07
39021672
Ethyl benzene
0.00E+00
2.00E-05
5.82E-07
0.00E+00
0.00E+00
39021672
Methylene chloride
1.52E-05
3.08E-07
1.12E-07
2.12E-07
8.16E-08
39021672
Epichlorohydrin
1.36E-06
8.02E-08
1.94 E-08
9.29E-08
2.32 E-08
39021672
Acrylic acid
1.25E-07
1.70E-07
5.34E-09
2.49E-07
4.99E-09
39021672
Cumene
0.00E+00
1.64E-07
2.74 E-08
0.00E+00
0.00E+00
39021672
Benzene
1.81E-07
1.38E-09
9.04E-11
1.47E-09
4.90E-10
39021672
Arsenic compounds
5.80E-08
0.00E+00
0.00E+00
0.00E+00
0.00E+00
39021672
n-Hexane
0.00E+00
0.00E+00
5.51 E-09
0.00E+00
0.00E+00
39021672
Beryllium compounds
0.00E+00
0.00E+00
0.00E+00
0.00E+00
1.88E-11
39057430
Glycol Ethers
1.10E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
39057430
Toluene
2.87E-06
1.42E-07
2.36 E-08
5.59E-07
9.65E-08
39057430
Xylenes (mixed)
2.00E-06
7.85E-08
1.10E-08
0.00E+00
0.00E+00
39057430
Methylene chloride
3.85E-07
7.82E-09
2.84 E-09
5.40E-09
2.08E-09
39057430
Ethyl benzene
0.00E+00
2.99E-08
8.74E-10
0.00E+00
0.00E+00
39057430
Methanol
1.60E-08
6.48E-10
1.66E-10
1.72E-09
3.44E-10
39057430
Propylene oxide
9.68E-09
1.77E-10
4.35E-11
2.50E-10
5.09E-11
39057430
Benzene
8.27E-09
6.33E-11
4.14E-12
6.72E-11
2.24E-11
39057430
Cumene
0.00E+00
7.16E-10
1.19 E-10
0.00E+00
0.00E+00
39057430
Acetaldehyde
2.02E-10
1.17E-12
1.94E-13
5.28E-12
2.64E-13
39061524
Diethylene glycol dimethyl ether
7.16E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
39061524
Toluene
1.96E-05
9.65E-07
1.61E-07
3.81 E-06
6.58E-07
39109419
Xylenes (mixed)
1.84E-06
7.25E-08
1.01 E-08
0.00E+00
0.00E+00
39109419
Ethyl benzene
0.00E+00
2.90E-08
8.45E-10
0.00E+00
0.00E+00
12 of 27
-------
Table 3 - Maximum Predicted Acute Risks
Actual Emissions
Facility NEI ID
Pollutant
Maximum Hazard Quotient1
REL
AEGL1
AEGL2
ERPG1
ERPG2
40143460
2-Butoxyethyl acetate
5.48E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
40143460
Butyl carbitol acetate
5.01 E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
40143460
Ethylene glycol ethyl ether acetal
4.41 E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
40143460
Ethylene glycol ethyl ether
3.75E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
40143460
Ethylene glycol methyl ether
1.57E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
40143460
Toluene
1.11 E-03
5.49E-05
9.14E-06
2.17E-04
3.74 E-05
40143460
Xylenes (mixed)
1.14E-03
4.50E-05
6.29E-06
0.00E+00
0.00E+00
40143460
Methylene diphenyl diisocyanate
0.00E+00
0.00E+00
0.00E+00
0.00E+00
7.63E-05
40143460
Ethyl benzene
0.00E+00
4.58E-05
1.34E-06
0.00E+00
0.00E+00
40143460
Formaldehyde
1.68E-05
8.41 E-07
5.44E-08
7.71 E-07
7.71 E-08
40143460
Methanol
9.91 E-07
4.02E-08
1.03E-08
1.07E-07
2.13E-08
40143494
Formaldehyde
4.26E-03
2.13E-04
1.38E-05
1.95E-04
1.95E-05
40143494
Phenol
3.63E-04
3.63E-05
2.37E-05
5.55E-05
1.11 E-05
40143494
2-Butoxyethyl acetate
2.01 E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
40143494
Toluene
1.19E-04
5.85E-06
9.75E-07
2.31 E-05
3.99E-06
40143494
Xylenes (mixed)
2.69E-05
1.06E-06
1.48E-07
0.00E+00
0.00E+00
40143494
Ethyl benzene
0.00E+00
7.88E-08
2.30E-09
0.00E+00
0.00E+00
40143494
Methylene diphenyl diisocyanate
0.00E+00
0.00E+00
0.00E+00
0.00E+00
6.97E-08
40143494
Cumene
0.00E+00
4.87E-09
8.12E-10
0.00E+00
0.00E+00
40143523
Ethylene glycol methyl ether acel
7.32E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
40143523
Xylenes (mixed)
6.69E-04
2.63E-05
3.68E-06
0.00E+00
0.00E+00
40143523
Toluene
3.17E-04
1.56E-05
2.60E-06
6.17E-05
1.07E-05
40143523
Phenol
3.93E-05
3.93E-06
2.56E-06
6.00E-06
1.20E-06
40143523
Ethyl benzene
0.00E+00
3.20E-05
9.35E-07
0.00E+00
0.00E+00
40143523
Methylene diphenyl diisocyanate
0.00E+00
0.00E+00
0.00E+00
0.00E+00
1.69E-05
40143523
Methanol
8.14E-06
3.30E-07
8.44E-08
8.76E-07
1.75E-07
40143523
Cumene
0.00E+00
1.84E-08
3.07E-09
0.00E+00
0.00E+00
42003603
Xylenes (mixed)
5.17E-05
2.03E-06
2.84 E-07
0.00E+00
0.00E+00
42003603
Toluene
2.79E-05
1.37E-06
2.29E-07
5.43E-06
9.37E-07
42003603
Methylene diphenyl diisocyanate
0.00E+00
0.00E+00
0.00E+00
0.00E+00
3.25E-05
42003603
Phenol
1.22E-05
1.22E-06
7.93E-07
1.86E-06
3.72 E-07
42003603
Formaldehyde
1.38E-05
6.92E-07
4.48E-08
6.34E-07
6.34 E-08
42003603
Styrene
1.82E-06
4.50E-07
6.96E-08
1.82E-07
3.48E-08
42003603
Ethyl benzene
0.00E+00
1.21E-06
3.54E-08
0.00E+00
0.00E+00
42003603
Arsenic compounds
8.85E-07
0.00E+00
0.00E+00
0.00E+00
0.00E+00
42003603
Methanol
5.74E-07
2.33E-08
5.95E-09
6.18E-08
1.24E-08
42003603
n-Hexane
0.00E+00
0.00E+00
3.23E-09
0.00E+00
0.00E+00
42003603
Cumene
0.00E+00
8.64E-10
1.44E-10
0.00E+00
0.00E+00
42045473
Propyl cellosolve
6.62E-02
0.00E+00
0.00E+00
0.00E+00
0.00E+00
42045473
2-Butoxyethyl acetate
8.57E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
42045473
Phenol
1.06E-03
1.06E-04
6.93E-05
1.62E-04
3.24E-05
42045473
Methylene chloride
1.20E-03
2.43E-05
8.82E-06
1.68E-05
6.45E-06
42045473
Toluene
4.05E-04
2.00E-05
3.33E-06
7.88E-05
1.36 E-05
42045473
Xylenes (mixed)
3.78E-04
1.49E-05
2.08E-06
0.00E+00
0.00E+00
42045473
Ethyl acrylate
0.00E+00
2.53E-07
5.73E-08
2.10E-04
7.16E-08
42045473
Diethylene glycol monobutyl ethe
1.61 E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
42045473
Butyl carbitol acetate
1.37E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
42045473
Methylene diphenyl diisocyanate
0.00E+00
0.00E+00
0.00E+00
0.00E+00
5.33E-05
42045473
Ethylene glycol methyl ether
4.27E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
42045473
Ethylene glycol ethyl ether acetal
2.25E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
42045473
Ethyl benzene
0.00E+00
1.42E-05
4.15E-07
0.00E+00
0.00E+00
42045473
Formaldehyde
1.11E-05
5.54E-07
3.59E-08
5.08E-07
5.08E-08
13 of 27
-------
Table 3 - Maximum Predicted Acute Risks
Actual Emissions
Facility NEI ID
Pollutant
Maximum Hazard Quotient1
REL
AEGL1
AEGL2
ERPG1
ERPG2
42045473
Methyl methacrylate
0.00E+00
5.91 E-06
8.44E-07
0.00E+00
0.00E+00
42045473
Methanol
5.32E-06
2.16E-07
5.52 E-08
5.73E-07
1.15E-07
42045473
Epichlorohydrin
4.16E-06
2.46E-07
5.94 E-08
2.84E-07
7.11 E-08
42045473
Acrylic acid
1.19E-07
1.62E-07
5.10E-09
2.38E-07
4.76E-09
42045473
Benzene
5.86E-08
4.48E-10
2.93E-11
4.76E-10
1.59E-10
42045473
Styrene
1.74E-08
4.29E-09
6.63E-10
1.74E-09
3.32E-10
42045473
n-Hexane
0.00E+00
0.00E+00
2.08E-08
0.00E+00
0.00E+00
42045473
Acetaldehyde
6.06E-09
3.52E-11
5.82E-12
1.58E-10
7.92E-12
42055233
Xylenes (mixed)
5.30E-05
2.08E-06
2.91 E-07
0.00E+00
0.00E+00
42055233
Toluene
1.04E-05
5.12E-07
8.54 E-08
2.02E-06
3.49E-07
42055233
Ethyl benzene
0.00E+00
1.15E-06
3.36 E-08
0.00E+00
0.00E+00
42055233
n-Hexane
0.00E+00
0.00E+00
2.69E-09
0.00E+00
0.00E+00
42081660
Diethylene glycol monobutyl ethe
9.25E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
42081660
Xylenes (mixed)
3.22E-04
1.27E-05
1.77 E-06
0.00E+00
0.00E+00
42081660
Toluene
1.98E-04
9.75E-06
1.63E-06
3.85E-05
6.65E-06
42081660
Ethyl benzene
0.00E+00
2.53E-05
7.37E-07
0.00E+00
0.00E+00
42081660
Methylene diphenyl diisocyanate
0.00E+00
0.00E+00
0.00E+00
0.00E+00
9.44E-06
45045417
Propyl cellosolve
5.77E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
45045417
Butyl carbitol acetate
2.77E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
45045417
2-Butoxyethyl acetate
1.74E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
45045417
Ethylene glycol ethyl ether acetal
1.64E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
45045417
Toluene
5.67E-05
2.80E-06
4.67E-07
1.10E-05
1.91 E-06
45045417
Xylenes (mixed)
1.50E-05
5.91 E-07
8.27E-08
0.00E+00
0.00E+00
45045417
Methylene diphenyl diisocyanate
0.00E+00
0.00E+00
0.00E+00
0.00E+00
1.07E-05
45045417
Ethylene glycol methyl ether
8.20E-06
0.00E+00
0.00E+00
0.00E+00
0.00E+00
45045417
Diethylene glycol monoethyl ethe
6.28E-06
0.00E+00
0.00E+00
0.00E+00
0.00E+00
45045417
Diethylene glycol monobutyl ethe
5.66E-06
0.00E+00
0.00E+00
0.00E+00
0.00E+00
45045417
Methanol
2.55E-06
1.04E-07
2.65E-08
2.75E-07
5.49E-08
45045417
Phenol
4.01 E-07
4.01 E-08
2.61 E-08
6.12E-08
1.22E-08
45045417
Formaldehyde
4.82E-07
2.41 E-08
1.56E-09
2.21 E-08
2.21 E-09
45045417
Ethyl benzene
0.00E+00
4.44E-07
1.30E-08
0.00E+00
0.00E+00
45045417
Benzene
4.74E-08
3.62E-10
2.37E-11
3.85E-10
1.28E-10
45045417
Methylene chloride
4.46E-08
9.05E-10
3.29E-10
6.25E-10
2.40E-10
45045417
Methyl methacrylate
0.00E+00
1.68E-08
2.40E-09
0.00E+00
0.00E+00
45045417
n-Hexane
0.00E+00
0.00E+00
1.11 E-08
0.00E+00
0.00E+00
45045417
Cumene
0.00E+00
2.15E-09
3.58E-10
0.00E+00
0.00E+00
45063428
Methylene chloride
4.93E-03
1.00E-04
3.63E-05
6.90E-05
2.65E-05
45063428
Phenol
2.93E-03
2.93E-04
1.91E-04
4.47E-04
8.94E-05
45063428
Xylenes (mixed)
4.63E-04
1.82E-05
2.55 E-06
0.00E+00
0.00E+00
45063428
Toluene
2.73E-04
1.35E-05
2.25E-06
5.32E-05
9.19E-06
48027213
Methanol
5.00E-06
2.03E-07
5.19E-08
5.39E-07
1.08E-07
48027213
Toluene
1.01E-06
4.98E-08
8.29E-09
1.96E-07
3.39E-08
48027213
Xylenes (mixed)
1.06E-06
4.15E-08
5.82E-09
0.00E+00
0.00E+00
48027213
Methylene chloride
1.02E-07
2.07E-09
7.52E-10
1.43E-09
5.49E-10
48027213
Ethyl benzene
0.00E+00
3.20E-08
9.35E-10
0.00E+00
0.00E+00
48029468
Ethyl acrylate
0.00E+00
1.93E-06
4.38E-07
1.60E-03
5.48E-07
48029468
Formaldehyde
1.42E-03
7.12E-05
4.60E-06
6.52E-05
6.52 E-06
48029468
Xylenes (mixed)
2.04E-04
8.00E-06
1.12E-06
0.00E+00
0.00E+00
48029468
Toluene
1.30E-04
6.39E-06
1.06 E-06
2.52E-05
4.36 E-06
48029468
2-Butoxyethyl acetate
8.39E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
48029468
Benzene
3.93E-05
3.01 E-07
1.97E-08
3.20E-07
1.07E-07
48029468
Methylene diphenyl diisocyanate
0.00E+00
0.00E+00
0.00E+00
0.00E+00
2.49E-05
14 of 27
-------
Table 3 - Maximum Predicted Acute Risks
Actual Emissions
Facility NEI ID
Pollutant
Maximum Hazard Quotient1
REL
AEGL1
AEGL2
ERPG1
ERPG2
48029468
Methanol
9.04E-06
3.67E-07
9.38E-08
9.74E-07
1.95E-07
48029468
Ethyl benzene
0.00E+00
8.48E-06
2.47E-07
0.00E+00
0.00E+00
48029468
Ethylene glycol methyl ether
6.75E-06
0.00E+00
0.00E+00
0.00E+00
0.00E+00
48029468
Phenol
3.06E-06
3.06E-07
1.99E-07
4.66E-07
9.33E-08
48029468
Styrene
1.41E-06
3.48E-07
5.37E-08
1.41 E-07
2.69E-08
48029468
Methyl methacrylate
0.00E+00
1.41E-06
2.01 E-07
0.00E+00
0.00E+00
48029468
Cumene
0.00E+00
2.73E-07
4.54E-08
0.00E+00
0.00E+00
48029468
p-Xylene
1.98E-07
0.00E+00
0.00E+00
0.00E+00
0.00E+00
48029468
Acrylonitrile
0.00E+00
1.41E-09
1.08E-10
6.40E-10
1.83E-10
48029468
n-Hexane
0.00E+00
0.00E+00
6.74E-10
0.00E+00
0.00E+00
48231376
Toluene
2.41 E-04
1.19E-05
1.98 E-06
4.69E-05
8.11 E-06
48231376
Xylenes (mixed)
1.98E-04
7.79E-06
1.09 E-06
0.00E+00
0.00E+00
48231376
Styrene
5.95E-05
1.47E-05
2.27E-06
5.95E-06
1.14E-06
48231376
Ethylene glycol methyl ether
2.16E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
48231376
2,4-Toluene diisocyanate
0.00E+00
3.98E-06
9.44E-07
7.84E-06
5.06 E-07
48231376
Methanol
1.56E-06
6.34E-08
1.62E-08
1.68E-07
3.36E-08
48231376
Ethyl benzene
0.00E+00
1.88E-07
5.47E-09
0.00E+00
0.00E+00
48231376
n-Hexane
0.00E+00
0.00E+00
7.90E-08
0.00E+00
0.00E+00
48439416
Butyl carbitol acetate
2.73E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
48439416
Methylene diphenyl diisocyanate
0.00E+00
0.00E+00
0.00E+00
0.00E+00
3.14E-04
48439416
Xylenes (mixed)
7.73E-05
3.03E-06
4.25E-07
0.00E+00
0.00E+00
48439416
Diethylene glycol dimethyl ether
4.71 E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
48439416
Ethylene glycol methyl ether
2.75E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
48439416
Toluene
1.97E-05
9.73E-07
1.62E-07
3.84E-06
6.63E-07
48439416
Dimethyl formamide
0.00E+00
0.00E+00
4.10E-07
1.85E-05
3.69E-07
48439416
Propyl cellosolve
1.02E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
48439416
Formaldehyde
6.34E-06
3.17E-07
2.05E-08
2.91 E-07
2.91 E-08
48439416
2-Butoxyethyl acetate
1.79E-06
0.00E+00
0.00E+00
0.00E+00
0.00E+00
48439416
Arsenic compounds
1.41E-06
0.00E+00
0.00E+00
0.00E+00
0.00E+00
48439416
p-Xylene
1.42E-07
0.00E+00
0.00E+00
0.00E+00
0.00E+00
48439416
Ethyl benzene
0.00E+00
1.30E-07
3.80E-09
0.00E+00
0.00E+00
48439416
Cumene
0.00E+00
3.43E-08
5.71 E-09
0.00E+00
0.00E+00
48439416
Methyl methacrylate
0.00E+00
1.58E-08
2.25E-09
0.00E+00
0.00E+00
48439416
Carbon tetrachloride
9.68E-09
6.57E-11
1.53E-11
1.41 E-10
2.92E-11
48439416
n-Hexane
0.00E+00
0.00E+00
2.68E-09
0.00E+00
0.00E+00
48439416
Aniline
0.00E+00
6.13E-10
4.00E-10
0.00E+00
0.00E+00
48439416
Beryllium compounds
0.00E+00
0.00E+00
0.00E+00
0.00E+00
2.02E-10
48439508
2-Butoxyethyl acetate
5.09E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
48439508
Tetrachloroethene
1.90E-06
1.58E-07
2.37E-08
5.58E-08
2.71 E-08
48439508
Toluene
1.05E-06
5.16E-08
8.60E-09
2.04E-07
3.52 E-08
48439508
Xylenes (mixed)
8.80E-07
3.46E-08
4.84 E-09
0.00E+00
0.00E+00
48439508
Phenol
5.49E-08
5.49E-09
3.58E-09
8.37E-09
1.67E-09
48439508
Ethyl benzene
0.00E+00
4.59E-08
1.34 E-09
0.00E+00
0.00E+00
48439508
Methanol
1.14E-08
4.61 E-10
1.18 E-10
1.22E-09
2.45E-10
49003696
1,1,1-Trichloroethane
1.88E-03
9.81 E-05
3.87E-05
6.71 E-05
3.36 E-05
49003696
Xylenes (mixed)
1.55E-03
6.08E-05
8.52 E-06
0.00E+00
0.00E+00
49003696
Methylene diphenyl diisocyanate
0.00E+00
0.00E+00
0.00E+00
0.00E+00
2.66E-04
49003696
Toluene
9.95E-05
4.91 E-06
8.18E-07
1.94E-05
3.35 E-06
49003696
Ethylene glycol ethyl ether
7.64E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
49003696
Ethyl benzene
0.00E+00
3.62E-05
1.06 E-06
0.00E+00
0.00E+00
49003696
Trichloroethylene
0.00E+00
4.76E-06
1.39 E-06
6.17E-06
1.23E-06
49003696
Tetrachloroethene
1.06E-05
8.84E-07
1.33E-07
3.12E-07
1.52 E-07
15 of 27
-------
Table 3 - Maximum Predicted Acute Risks
Actual Emissions
Facility NEI ID
Pollutant
Maximum Hazard Quotient1
REL
AEGL1
AEGL2
ERPG1
ERPG2
49003696
1,2-Epoxybutane
0.00E+00
4.54E-07
2.32E-07
0.00E+00
0.00E+00
49003696
Formaldehyde
5.14E-07
2.57E-08
1.66E-09
2.36E-08
2.36E-09
49003696
Cumene
0.00E+00
3.16E-07
5.27E-08
0.00E+00
0.00E+00
49003696
Methanol
5.05E-08
2.05E-09
5.24E-10
5.44E-09
1.09E-09
49003696
Hydrofluoric acid
2.06E-08
6.02E-09
2.47E-10
3.09E-09
3.09E-10
49003696
Arsenic compounds
3.75E-09
0.00E+00
0.00E+00
0.00E+00
0.00E+00
49011663
Propyl cellosolve
1.46E-02
0.00E+00
0.00E+00
0.00E+00
0.00E+00
49011663
Diethylene glycol dimethyl ether
9.01 E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
49011663
Butyl carbitol acetate
3.07E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
49011663
Formaldehyde
8.18E-04
4.09E-05
2.65E-06
3.75E-05
3.75 E-06
49011663
Phenol
4.92E-04
4.92E-05
3.21 E-05
7.51 E-05
1.50E-05
49011663
Methylene chloride
4.54E-04
9.22E-06
3.35 E-06
6.36E-06
2.45E-06
49011663
Diethylene glycol monobutyl ethe
3.99E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
49011663
Xylenes (mixed)
3.44E-04
1.35E-05
1.89 E-06
0.00E+00
0.00E+00
49011663
Toluene
2.61 E-04
1.29E-05
2.15E-06
5.09E-05
8.79 E-06
49011663
Methanol
1.41E-04
5.74E-06
1.47E-06
1.52E-05
3.05 E-06
49011663
Ethylene glycol ethyl ether
1.28E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
49011663
Ethylene glycol methyl ether
8.41 E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
49011663
Dimethyl formamide
0.00E+00
0.00E+00
1.59 E-06
7.15E-05
1.43E-06
49011663
Glycol Ethers
5.61 E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
49011663
2-(Hexyloxy)ethanol
4.78E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
49011663
Methylene diphenyl diisocyanate
0.00E+00
0.00E+00
0.00E+00
0.00E+00
3.00E-05
49011663
Diethylene glycol monomethyl et
2.21 E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
49011663
2-Butoxyethyl acetate
1.28E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
49011663
Acetaldehyde
6.61 E-06
3.84E-08
6.34E-09
1.73E-07
8.63E-09
49011663
1,2-Epoxybutane
0.00E+00
4.26E-06
2.18E-06
0.00E+00
0.00E+00
49011663
Ethylene glycol ethyl ether acetal
3.72E-06
0.00E+00
0.00E+00
0.00E+00
0.00E+00
49011663
Acrylonitrile
0.00E+00
2.16E-06
1.66E-07
9.84E-07
2.81 E-07
49011663
Ethyl benzene
0.00E+00
3.34E-06
9.74 E-08
0.00E+00
0.00E+00
49011663
Carbon tetrachloride
2.63E-06
1.78E-08
4.16E-09
3.84E-08
7.93E-09
49011663
Benzene
2.40E-06
1.83E-08
1.20E-09
1.95E-08
6.49E-09
49011663
Tetrachloroethene
1.27E-06
1.06E-07
1.59E-08
3.74E-08
1.82 E-08
49011663
Cumene
0.00E+00
7.92E-07
1.32E-07
0.00E+00
0.00E+00
49011663
Triethylamine
7.82E-07
0.00E+00
0.00E+00
0.00E+00
0.00E+00
49011663
Methyl methacrylate
0.00E+00
5.07E-07
7.25E-08
0.00E+00
0.00E+00
49011663
1,1,1-Trichloroethane
3.36E-07
1.76E-08
6.93E-09
1.20E-08
6.02E-09
49011663
Chlorobenzene
0.00E+00
3.10E-07
2.06 E-08
0.00E+00
0.00E+00
49011663
Styrene
1.99E-07
4.92E-08
7.60E-09
1.99E-08
3.80E-09
49011663
Acrylic acid
5.21 E-08
7.10E-08
2.23E-09
1.04E-07
2.08E-09
49011663
Aniline
0.00E+00
9.24E-08
6.03E-08
0.00E+00
0.00E+00
49011663
Trichloroethylene
0.00E+00
3.60E-08
1.05E-08
4.67E-08
9.34E-09
49011663
Ethyl acrylate
0.00E+00
9.00E-11
2.04E-11
7.46E-08
2.55E-11
49011663
n-Hexane
0.00E+00
0.00E+00
2.38E-08
0.00E+00
0.00E+00
49011663
m-Xylene
1.07E-08
0.00E+00
0.00E+00
0.00E+00
0.00E+00
49011663
Epichlorohydrin
8.77E-09
5.18E-10
1.25E-10
6.00E-10
1.50E-10
49011663
1,4-Dioxane
2.45E-10
1.20E-11
6.12E-13
0.00E+00
0.00E+00
49011663
Ethylene oxide
0.00E+00
0.00E+00
6.04E-13
0.00E+00
5.44E-13
050272001
Toluene
3.91 E-03
1.93E-04
3.22E-05
7.62E-04
1.32 E-04
050272001
Xylenes (mixed)
1.21 E-03
4.76E-05
6.67E-06
0.00E+00
0.00E+00
050272001
Methanol
1.62E-04
6.56E-06
1.68E-06
1.74E-05
3.48E-06
050272001
Ethyl benzene
0.00E+00
5.44E-05
1.59 E-06
0.00E+00
0.00E+00
050272001
n-Hexane
0.00E+00
0.00E+00
1.44E-06
0.00E+00
0.00E+00
16 of 27
-------
Table 3 - Maximum Predicted Acute Risks
Actual Emissions
Facility NEI ID
Pollutant
Maximum Hazard Quotient1
REL
AEGL1
AEGL2
ERPG1
ERPG2
51700334
Ethyl benzene
0.00E+00
2.24E-10
6.54E-12
0.00E+00
0.00E+00
51710293
2,4-Toluene diisocyanate
0.00E+00
1.01E-03
2.39E-04
1.99E-03
1.28E-04
51710293
Toluene
1.60E-03
7.87E-05
1.31 E-05
3.11E-04
5.37E-05
51710293
Xylenes (mixed)
1.56E-03
6.13E-05
8.58 E-06
0.00E+00
0.00E+00
51710293
Methylene chloride
1.46E-04
2.96E-06
1.07 E-06
2.04E-06
7.85E-07
51710293
Formaldehyde
8.61 E-05
4.30E-06
2.79E-07
3.95E-06
3.95E-07
51710293
Benzene
1.21E-05
9.28E-08
6.07E-09
9.86E-08
3.29E-08
51710293
Ethyl benzene
0.00E+00
5.92E-06
1.73E-07
0.00E+00
0.00E+00
51710293
Hydrofluoric acid
8.45E-07
2.47E-07
1.01E-08
1.27E-07
1.27E-08
51710293
Cumene
0.00E+00
1.93E-07
3.21 E-08
0.00E+00
0.00E+00
51810292
Methylene chloride
8.10E-04
1.64E-05
5.97 E-06
1.13E-05
4.36 E-06
51810292
Phenol
5.70E-04
5.70E-05
3.72 E-05
8.70E-05
1.74 E-05
51810292
Xylenes (mixed)
1.59E-04
6.24E-06
8.74E-07
0.00E+00
0.00E+00
51810292
Toluene
7.26E-05
3.58E-06
5.97E-07
1.41 E-05
2.44E-06
51810292
Methanol
3.71 E-05
1.51 E-06
3.85E-07
4.00E-06
8.00E-07
51810292
Methylene diphenyl diisocyanate
0.00E+00
0.00E+00
0.00E+00
0.00E+00
2.95 E-06
51810292
Ethyl benzene
0.00E+00
5.78E-07
1.69E-08
0.00E+00
0.00E+00
51810292
Cumene
0.00E+00
1.23E-08
2.05E-09
0.00E+00
0.00E+00
51810292
n-Hexane
0.00E+00
0.00E+00
3.85E-09
0.00E+00
0.00E+00
53029438
Phenol
6.69E-04
6.69E-05
4.36 E-05
1.02E-04
2.04 E-05
53029438
Methylene chloride
6.43E-04
1.31 E-05
4.74 E-06
9.00E-06
3.46E-06
53029438
Toluene
8.65E-05
4.27E-06
7.11E-07
1.68E-05
2.91 E-06
53029438
Benzene
1.10E-05
8.43E-08
5.52E-09
8.96E-08
2.99E-08
53029438
Ethyl benzene
0.00E+00
6.18E-06
1.80E-07
0.00E+00
0.00E+00
53029438
Xylenes (mixed)
8.13E-07
3.20E-08
4.47E-09
0.00E+00
0.00E+00
53029438
Cumene
0.00E+00
2.63E-07
4.39E-08
0.00E+00
0.00E+00
53029438
n-Hexane
0.00E+00
0.00E+00
4.57E-09
0.00E+00
0.00E+00
53033383
Diethylene glycol monobutyl ethe
2.39E-01
0.00E+00
0.00E+00
0.00E+00
0.00E+00
53033383
Toluene
1.25E-03
6.15E-05
1.03E-05
2.43E-04
4.20E-05
53033383
2-Butoxyethyl acetate
1.43E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
53033383
Xylenes (mixed)
7.65E-05
3.01 E-06
4.21 E-07
0.00E+00
0.00E+00
53033383
Methanol
2.74E-05
1.11 E-06
2.84E-07
2.95E-06
5.90E-07
53033383
Tetrachloroethene
4.08E-06
3.40E-07
5.10E-08
1.20E-07
5.83E-08
53033383
Methylene chloride
3.89E-06
7.89E-08
2.87E-08
5.44E-08
2.09E-08
53033383
Ethyl benzene
0.00E+00
2.67E-06
7.80E-08
0.00E+00
0.00E+00
53033383
Arsenic compounds
2.71 E-06
0.00E+00
0.00E+00
0.00E+00
0.00E+00
53033383
Cumene
0.00E+00
1.69E-08
2.81 E-09
0.00E+00
0.00E+00
53033383
Beryllium compounds
0.00E+00
0.00E+00
0.00E+00
0.00E+00
8.78E-10
53033384
Diethylene glycol monobutyl ethe
9.69E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
53033384
Xylenes (mixed)
1.49E-04
5.84E-06
8.18E-07
0.00E+00
0.00E+00
53033384
2,4-Toluene diisocyanate
0.00E+00
4.53E-05
1.07E-05
8.93E-05
5.76 E-06
53033384
2-Butoxyethyl acetate
9.86E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
53033384
Toluene
5.00E-05
2.47E-06
4.11 E-07
9.74E-06
1.68E-06
53033384
Phenol
1.08E-05
1.08E-06
7.06 E-07
1.65E-06
3.31 E-07
53033384
Methylene chloride
1.01 E-05
2.05E-07
7.44E-08
1.41 E-07
5.44E-08
53033384
Arsenic compounds
8.35E-06
0.00E+00
0.00E+00
0.00E+00
0.00E+00
53033384
Ethyl benzene
0.00E+00
5.77E-06
1.68E-07
0.00E+00
0.00E+00
53033384
Beryllium compounds
0.00E+00
0.00E+00
0.00E+00
0.00E+00
2.71 E-09
53033384
Cumene
0.00E+00
2.01E-10
3.36E-11
0.00E+00
0.00E+00
53033406
Xylenes (mixed)
1.45E-04
5.70E-06
7.99E-07
0.00E+00
0.00E+00
53033406
Toluene
1.58E-05
7.80E-07
1.30E-07
3.08E-06
5.32 E-07
53033406
Ethyl benzene
0.00E+00
4.84E-06
1.41 E-07
0.00E+00
0.00E+00
17 of 27
-------
Table 3 - Maximum Predicted Acute Risks
Actual Emissions
Facility NEI ID
Pollutant
Maximum Hazard Quotient1
REL
AEGL1
AEGL2
ERPG1
ERPG2
53033406
Methanol
3.56E-10
1.44E-11
3.69E-12
3.83E-11
7.66E-12
53033532
2-Butoxyethyl acetate
1.82E-02
0.00E+00
0.00E+00
0.00E+00
0.00E+00
53033532
Toluene
2.91 E-04
1.43E-05
2.39 E-06
5.66E-05
9.78 E-06
53033532
Xylenes (mixed)
1.23E-04
4.84E-06
6.77E-07
0.00E+00
0.00E+00
53033532
Methanol
6.02E-05
2.44E-06
6.24E-07
6.48E-06
1.30 E-06
53033532
Mercury (elemental)
1.59E-05
0.00E+00
5.63E-09
0.00E+00
4.78E-09
53033532
Methylene chloride
1.14E-05
2.32E-07
8.42E-08
1.60E-07
6.16E-08
53033532
Phenol
6.83E-06
6.83E-07
4.45E-07
1.04E-06
2.08E-07
53033532
Arsenic compounds
7.44E-06
0.00E+00
0.00E+00
0.00E+00
0.00E+00
53033532
Ethyl benzene
0.00E+00
4.70E-06
1.37E-07
0.00E+00
0.00E+00
53033532
2,4-Toluene diisocyanate
0.00E+00
8.74E-07
2.07E-07
1.72E-06
1.11 E-07
53033532
Formaldehyde
1.41E-06
7.05E-08
4.56 E-09
6.46E-08
6.46E-09
53033532
Benzene
9.55E-07
7.31 E-09
4.78E-10
7.76E-09
2.59E-09
53033532
Glycol Ethers
6.89E-07
0.00E+00
0.00E+00
0.00E+00
0.00E+00
53033532
Methylene diphenyl diisocyanate
0.00E+00
0.00E+00
0.00E+00
0.00E+00
4.71 E-07
53033532
Maleic anhydride
0.00E+00
0.00E+00
0.00E+00
2.08E-07
2.08E-08
53033532
Triethylamine
6.94E-08
0.00E+00
0.00E+00
0.00E+00
0.00E+00
53033532
Cumene
0.00E+00
5.62E-09
9.37E-10
0.00E+00
0.00E+00
53033532
Ethylene oxide
0.00E+00
0.00E+00
3.06E-10
0.00E+00
2.75E-10
53033532
Hydrofluoric acid
1.37E-11
4.00E-12
1.64E-13
2.05E-12
2.05E-13
53033532
n-Hexane
0.00E+00
0.00E+00
2.14E-13
0.00E+00
0.00E+00
53053447
Glycol ethers
9.30E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
53053447
Toluene
2.23E-04
1.10E-05
1.84 E-06
4.35E-05
7.51 E-06
53053447
Xylenes (mixed)
4.84E-05
1.90E-06
2.66E-07
0.00E+00
0.00E+00
53053447
2-Butoxyethyl acetate
2.52E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
53053447
Methanol
9.51 E-06
3.86E-07
9.86E-08
1.02E-06
2.05E-07
53053447
Ethyl benzene
0.00E+00
2.43E-06
7.07E-08
0.00E+00
0.00E+00
53053447
Phenol
5.84E-08
5.84E-09
3.81 E-09
8.92E-09
1.78E-09
53053447
Arsenic compounds
4.49E-08
0.00E+00
0.00E+00
0.00E+00
0.00E+00
53053447
Benzene
1.96E-08
1.50E-10
9.80E-12
1.59E-10
5.31 E-11
53053447
Beryllium compounds
0.00E+00
0.00E+00
0.00E+00
0.00E+00
1.31E-11
53061398
Diethylene glycol monobutyl ethe
4.26E-02
0.00E+00
0.00E+00
0.00E+00
0.00E+00
53061398
Propyl cellosolve
4.03E-02
0.00E+00
0.00E+00
0.00E+00
0.00E+00
53061398
Arsenic compounds
1.08E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
53061398
Toluene
5.76E-04
2.84E-05
4.74 E-06
1.12E-04
1.94 E-05
53061398
Styrene
4.23E-04
1.04E-04
1.61E-05
4.23E-05
8.07 E-06
53061398
2,4-Toluene diisocyanate
0.00E+00
1.20E-04
2.85E-05
2.37E-04
1.53E-05
53061398
Mercury (elemental)
1.06E-04
0.00E+00
3.75E-08
0.00E+00
3.19E-08
53061398
Xylenes (mixed)
5.87E-05
2.31 E-06
3.23E-07
0.00E+00
0.00E+00
53061398
Ethylene glycol ethyl ether acetal
4.53E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
53061398
Formaldehyde
3.98E-05
1.99E-06
1.29E-07
1.82E-06
1.82 E-07
53061398
Methylene chloride
3.93E-05
7.98E-07
2.90E-07
5.51 E-07
2.12E-07
53061398
Phenol
2.93E-05
2.93E-06
1.91 E-06
4.48E-06
8.96 E-07
53061398
Methanol
2.95E-05
1.20E-06
3.06E-07
3.18E-06
6.36 E-07
53061398
Epichlorohydrin
1.81E-05
1.07E-06
2.59E-07
1.24E-06
3.10E-07
53061398
Hydrofluoric acid
5.90E-06
1.73E-06
7.08E-08
8.84E-07
8.84E-08
53061398
Methylene diphenyl diisocyanate
0.00E+00
0.00E+00
0.00E+00
0.00E+00
5.23E-06
53061398
Trichloroethylene
0.00E+00
1.21 E-06
3.53E-07
1.57E-06
3.14E-07
53061398
Ethyl benzene
0.00E+00
1.73E-06
5.04E-08
0.00E+00
0.00E+00
53061398
Benzene
1.44E-06
1.10E-08
7.22E-10
1.17E-08
3.91 E-09
53061398
Tetrachloroethene
1.25E-06
1.04E-07
1.56E-08
3.68E-08
1.79E-08
53061398
Glycol Ethers
1.13E-06
0.00E+00
0.00E+00
0.00E+00
0.00E+00
18 of 27
-------
Table 3 - Maximum Predicted Acute Risks
Actual Emissions
Facility NEI ID
Pollutant
Maximum Hazard Quotient1
REL
AEGL1
AEGL2
ERPG1
ERPG2
53061398
Cumene
0.00E+00
3.01 E-07
5.01 E-08
0.00E+00
0.00E+00
53061398
Chlorobenzene
0.00E+00
2.42E-07
1.61 E-08
0.00E+00
0.00E+00
53061398
Acrylic acid
5.04E-08
6.87E-08
2.16E-09
1.01 E-07
2.01 E-09
53061398
Acetaldehyde
1.78E-07
1.03E-09
1.70E-10
4.64E-09
2.32E-10
53061398
Propylene oxide
1.71E-07
3.11E-09
7.67E-10
4.41 E-09
8.97E-10
53061398
Ethylene glycol ethyl ether
7.10E-08
0.00E+00
0.00E+00
0.00E+00
0.00E+00
53061398
Methyl methacrylate
0.00E+00
3.07E-08
4.39E-09
0.00E+00
0.00E+00
53061398
n-Hexane
0.00E+00
0.00E+00
2.05E-08
0.00E+00
0.00E+00
53061398
Ethyl acrylate
0.00E+00
1.95E-12
4.42E-13
1.62E-09
5.52E-13
53061398
Carbon tetrachloride
5.13E-10
3.48E-12
8.12E-13
7.50E-12
1.55E-12
53061398
1,4-Dioxane
1.31E-11
6.43E-13
3.30E-14
0.00E+00
0.00E+00
53061398
p-Xylene
2.12E-12
0.00E+00
0.00E+00
0.00E+00
0.00E+00
53061398
Acrylonitrile
0.00E+00
4.27E-13
3.30E-14
1.94E-13
5.60E-14
54067418
Toluene
3.35E-03
1.65E-04
2.75E-05
6.52E-04
1.13E-04
54067418
Formaldehyde
1.10E-03
5.48E-05
3.55 E-06
5.02E-05
5.02 E-06
54067418
Xylenes (mixed)
2.03E-04
7.97E-06
1.12E-06
0.00E+00
0.00E+00
54067418
Tetrachloroethene
7.09E-05
5.91 E-06
8.86 E-07
2.09E-06
1.01 E-06
54067418
Ethyl benzene
0.00E+00
8.85E-06
2.58E-07
0.00E+00
0.00E+00
55075288
Toluene
3.77E-05
1.86E-06
3.10E-07
7.34E-06
1.27E-06
55075288
Xylenes (mixed)
4.27E-05
1.68E-06
2.35E-07
0.00E+00
0.00E+00
55075288
Methanol
1.39E-05
5.64E-07
1.44E-07
1.50E-06
3.00E-07
55075288
Ethyl benzene
0.00E+00
1.11 E-06
3.25E-08
0.00E+00
0.00E+00
55075288
Arsenic compounds
1.17E-07
0.00E+00
0.00E+00
0.00E+00
0.00E+00
55075288
Cumene
0.00E+00
8.41 E-09
1.40E-09
0.00E+00
0.00E+00
55079687
Toluene
1.26E-07
6.20E-09
1.03E-09
2.45E-08
4.23E-09
060292024
Propyl cellosolve
1.84E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
060292024
Methylene chloride
7.96E-07
1.62E-08
5.87E-09
1.11 E-08
4.29E-09
060292024
Xylenes (mixed)
1.27E-07
5.01 E-09
7.01 E-10
0.00E+00
0.00E+00
060292024
Toluene
1.01E-07
4.99E-09
8.32E-10
1.97E-08
3.40E-09
060292024
Methanol
3.18E-08
1.29E-09
3.30E-10
3.43E-09
6.86E-10
060292024
Hydrofluoric acid
1.01E-08
2.96E-09
1.22E-10
1.52E-09
1.52E-10
060292024
Ethyl benzene
0.00E+00
2.50E-09
7.30E-11
0.00E+00
0.00E+00
060372022
Xylenes (mixed)
3.23E-04
1.27E-05
1.78 E-06
0.00E+00
0.00E+00
060372022
2,4-Toluene diisocyanate
0.00E+00
6.34E-05
1.50E-05
1.25E-04
8.07 E-06
060372022
Carbitol acetate
2.07E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
060372022
Toluene
1.29E-04
6.38E-06
1.06 E-06
2.52E-05
4.35 E-06
060372022
Formaldehyde
5.04E-05
2.52E-06
1.63E-07
2.31 E-06
2.31 E-07
060372022
Ethylene glycol methyl ether
3.14E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
060372022
Ethylene glycol ethyl ether acetal
1.50E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
060372022
2-Butoxyethyl acetate
1.10E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
060372022
Benzene
1.45E-06
1.11E-08
7.27E-10
1.18E-08
3.94 E-09
060372022
Styrene
1.03E-06
2.54E-07
3.92 E-08
1.03E-07
1.96 E-08
060372022
Methyl methacrylate
0.00E+00
6.40E-07
9.14E-08
0.00E+00
0.00E+00
060372022
1,3-Butadiene
0.00E+00
9.52E-09
1.19E-09
6.49E-07
3.25E-08
060372022
Methanol
4.69E-07
1.90E-08
4.86 E-09
5.05E-08
1.01 E-08
060372022
Methylene diphenyl diisocyanate
0.00E+00
0.00E+00
0.00E+00
0.00E+00
3.48E-07
060372022
Ethyl benzene
0.00E+00
2.65E-07
7.72 E-09
0.00E+00
0.00E+00
060372022
Triethylamine
2.44E-07
0.00E+00
0.00E+00
0.00E+00
0.00E+00
060372022
Chlorobenzene
0.00E+00
1.65E-07
1.10E-08
0.00E+00
0.00E+00
060372022
Acrylic acid
1.76E-08
2.40E-08
7.54E-10
3.52E-08
7.04E-10
060372022
Cumene
0.00E+00
2.11E-08
3.51 E-09
0.00E+00
0.00E+00
060372022
n-Hexane
0.00E+00
0.00E+00
4.59E-09
0.00E+00
0.00E+00
19 of 27
-------
Table 3 - Maximum Predicted Acute Risks
Actual Emissions
Facility NEI ID
Pollutant
Maximum Hazard Quotient1
REL
AEGL1
AEGL2
ERPG1
ERPG2
060376004
Xylenes (mixed)
2.22E-04
8.74E-06
1.22E-06
0.00E+00
0.00E+00
060376004
Toluene
1.40E-04
6.91 E-06
1.15E-06
2.73E-05
4.71 E-06
060376004
Styrene
8.38E-05
2.07E-05
3.20E-06
8.38E-06
1.60E-06
060376004
2-Butoxyethyl acetate
4.24E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
060376004
Diethylene glycol monobutyl ethe
3.63E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
060376004
Methylene chloride
8.09E-06
1.64E-07
5.96 E-08
1.13E-07
4.36 E-08
060376004
Methanol
2.45E-06
9.95E-08
2.54 E-08
2.64E-07
5.28E-08
060376004
Formaldehyde
1.47E-06
7.33E-08
4.74E-09
6.72E-08
6.72E-09
060376004
Trichloroethylene
0.00E+00
1.38E-07
4.02 E-08
1.79E-07
3.58E-08
060376004
Phenol
1.93E-07
1.93E-08
1.26E-08
2.95E-08
5.89E-09
060376004
n-Hexane
0.00E+00
0.00E+00
2.45E-07
0.00E+00
0.00E+00
060376004
Carbon tetrachloride
2.10E-07
1.42E-09
3.32E-10
3.06E-09
6.32E-10
060376004
Ethylene glycol ethyl ether acetal
2.04E-07
0.00E+00
0.00E+00
0.00E+00
0.00E+00
060376004
Ethyl benzene
0.00E+00
1.13E-07
3.29E-09
0.00E+00
0.00E+00
060376004
Acetaldehyde
1.08E-07
6.27E-10
1.04E-10
2.82E-09
1.41 E-10
060812015
Xylenes (mixed)
9.21 E-06
3.62E-07
5.07E-08
0.00E+00
0.00E+00
060812015
Toluene
1.82E-06
8.99E-08
1.50E-08
3.55E-07
6.13E-08
090032025
Toluene
1.52E-05
7.51 E-07
1.25E-07
2.96E-06
5.12E-07
090032025
Methanol
2.81 E-06
1.14E-07
2.92 E-08
3.03E-07
6.06 E-08
090032025
Xylenes (mixed)
4.06E-07
1.59E-08
2.23E-09
0.00E+00
0.00E+00
090032025
Ethyl benzene
0.00E+00
1.93E-08
5.64E-10
0.00E+00
0.00E+00
090075021
Xylenes (mixed)
7.18E-06
2.82E-07
3.95E-08
0.00E+00
0.00E+00
090075021
Trichloroethylene
0.00E+00
6.15E-07
1.79E-07
7.97E-07
1.59E-07
090075021
Toluene
7.51 E-07
3.71 E-08
6.18E-09
1.46E-07
2.53E-08
090075021
Ethyl benzene
0.00E+00
7.13E-08
2.08E-09
0.00E+00
0.00E+00
120862016
Toluene
8.66E-03
4.27E-04
7.12E-05
1.69E-03
2.91 E-04
120862016
Xylenes (mixed)
6.55E-04
2.57E-05
3.60E-06
0.00E+00
0.00E+00
120862016
Ethyl benzene
0.00E+00
2.45E-06
7.14E-08
0.00E+00
0.00E+00
130516005
2-Butoxyethyl acetate
1.42E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
130516005
Toluene
4.04E-04
2.00E-05
3.33 E-06
7.88E-05
1.36E-05
130516005
Diethylene glycol monoethyl ethe
2.25E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
130516005
Propyl cellosolve
1.19E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
130516005
Ethylene glycol ethyl ether acetal
1.00E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
130516005
Styrene
6.33E-05
1.56E-05
2.42E-06
6.33E-06
1.21 E-06
130516005
Xylenes (mixed)
7.76E-05
3.05E-06
4.27E-07
0.00E+00
0.00E+00
130516005
Methanol
1.53E-05
6.20E-07
1.58E-07
1.65E-06
3.29E-07
130516005
Ethylene glycol ethyl ether
8.96E-06
0.00E+00
0.00E+00
0.00E+00
0.00E+00
130516005
Phenol
5.63E-06
5.63E-07
3.67E-07
8.59E-07
1.72 E-07
130516005
Methylene chloride
3.60E-06
7.31 E-08
2.66E-08
5.05E-08
1.94 E-08
130516005
Ethyl benzene
0.00E+00
2.06E-06
6.00E-08
0.00E+00
0.00E+00
130516005
Diethylene glycol monobutyl ethe
4.03E-07
0.00E+00
0.00E+00
0.00E+00
0.00E+00
130516005
Formaldehyde
3.43E-07
1.72E-08
1.11E-09
1.57E-08
1.57E-09
130516005
n-Hexane
0.00E+00
0.00E+00
2.69E-07
0.00E+00
0.00E+00
130516005
Carbon tetrachloride
1.69E-07
1.15E-09
2.68E-10
2.47E-09
5.11 E-10
130516005
Cumene
0.00E+00
9.59E-08
1.60E-08
0.00E+00
0.00E+00
130516005
Benzene
3.35E-08
2.56E-10
1.67E-11
2.72E-10
9.06E-11
130516005
o-Xylene
2.65E-08
0.00E+00
0.00E+00
0.00E+00
0.00E+00
130516005
m-Xylene
2.65E-08
0.00E+00
0.00E+00
0.00E+00
0.00E+00
130516005
p-Xylene
2.65E-08
0.00E+00
0.00E+00
0.00E+00
0.00E+00
130516005
Acetaldehyde
7.97E-09
4.62E-11
7.64E-12
2.08E-10
1.04E-11
130516005
Methyl methacrylate
0.00E+00
6.03E-09
8.61 E-10
0.00E+00
0.00E+00
130516005
Propylene oxide
4.30E-10
7.84E-12
1.93E-12
1.11E-11
2.26E-12
20 of 27
-------
Table 3 - Maximum Predicted Acute Risks
Actual Emissions
Facility NEI ID
Pollutant
Maximum Hazard Quotient1
REL
AEGL1
AEGL2
ERPG1
ERPG2
130516005
Aniline
0.00E+00
9.32E-11
6.08E-11
0.00E+00
0.00E+00
131276002
2-Butoxyethyl acetate
1.27E-02
0.00E+00
0.00E+00
0.00E+00
0.00E+00
131276002
Toluene
1.92E-04
9.48E-06
1.58 E-06
3.74E-05
6.47E-06
131276002
Xylenes (mixed)
6.01 E-05
2.36E-06
3.30E-07
0.00E+00
0.00E+00
131276002
Styrene
4.14E-05
1.02E-05
1.58 E-06
4.14E-06
7.91 E-07
131276002
Ethylene glycol ethyl ether acetal
2.17E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
131276002
Ethyl benzene
0.00E+00
3.70E-06
1.08E-07
0.00E+00
0.00E+00
131276002
n-Hexane
0.00E+00
0.00E+00
1.60E-07
0.00E+00
0.00E+00
131276002
Cumene
0.00E+00
4.99E-10
8.32E-11
0.00E+00
0.00E+00
132332017
Toluene
1.79E-02
8.83E-04
1.47E-04
3.48E-03
6.02E-04
132332017
Xylenes (mixed)
4.71 E-03
1.85E-04
2.59E-05
0.00E+00
0.00E+00
132332017
Methanol
7.13E-04
2.89E-05
7.39 E-06
7.68E-05
1.54 E-05
132332017
Phenol
3.12E-04
3.12E-05
2.03E-05
4.76E-05
9.52 E-06
132332017
Ethyl benzene
0.00E+00
1.71E-04
5.00 E-06
0.00E+00
0.00E+00
132332017
2,4-Toluene diisocyanate
0.00E+00
4.85E-05
1.15E-05
9.56E-05
6.17E-06
132332017
Formaldehyde
1.34E-04
6.71 E-06
4.34E-07
6.15E-06
6.15E-07
132332017
Methylene diphenyl diisocyanate
0.00E+00
0.00E+00
0.00E+00
0.00E+00
5.58E-05
132332017
n-Hexane
0.00E+00
0.00E+00
3.43E-05
0.00E+00
0.00E+00
132332017
Tetrachloroethene
5.57E-06
4.64E-07
6.96 E-08
1.64E-07
7.95E-08
132332017
Chlorobenzene
0.00E+00
5.90E-06
3.93E-07
0.00E+00
0.00E+00
132332017
Ethyl acrylate
0.00E+00
6.08E-09
1.38E-09
5.05E-06
1.72E-09
132332017
Benzene
7.59E-07
5.80E-09
3.79E-10
6.16E-09
2.05E-09
181035004
Butyl carbitol acetate
1.52E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
181035004
Toluene
1.91E-06
9.40E-08
1.57E-08
3.71 E-07
6.41 E-08
181035004
Xylenes (mixed)
5.15E-07
2.02E-08
2.83E-09
0.00E+00
0.00E+00
181035004
Ethyl benzene
0.00E+00
3.91 E-08
1.14E-09
0.00E+00
0.00E+00
181412011
Xylenes (mixed)
2.12E-04
8.33E-06
1.17E-06
0.00E+00
0.00E+00
181412011
Toluene
2.83E-05
1.40E-06
2.33E-07
5.51 E-06
9.51 E-07
181412011
Phenol
5.65E-06
5.65E-07
3.68E-07
8.62E-07
1.72 E-07
181412011
Ethyl benzene
0.00E+00
9.15E-07
2.67E-08
0.00E+00
0.00E+00
201252005
Glycol Ethers
6.04E-02
0.00E+00
0.00E+00
0.00E+00
0.00E+00
201252005
Toluene
1.54E-04
7.58E-06
1.26E-06
2.99E-05
5.17E-06
201252005
Xylenes (mixed)
1.33E-04
5.21 E-06
7.30E-07
0.00E+00
0.00E+00
201252005
Ethyl benzene
0.00E+00
3.55E-06
1.03E-07
0.00E+00
0.00E+00
201252005
n-Hexane
0.00E+00
0.00E+00
1.48E-07
0.00E+00
0.00E+00
201732006
Ethylene glycol ethyl ether
1.63E-01
0.00E+00
0.00E+00
0.00E+00
0.00E+00
201732006
Glycol Ethers
9.05E-02
0.00E+00
0.00E+00
0.00E+00
0.00E+00
201732006
Formaldehyde
1.12E-03
5.58E-05
3.61 E-06
5.11 E-05
5.11 E-06
201732006
Styrene
4.94E-04
1.22E-04
1.89E-05
4.94E-05
9.43E-06
201732006
Toluene
2.38E-04
1.18E-05
1.96 E-06
4.64E-05
8.02 E-06
201732006
Methanol
1.15E-04
4.68E-06
1.20E-06
1.24E-05
2.48E-06
201732006
Xylenes (mixed)
1.03E-04
4.05E-06
5.66E-07
0.00E+00
0.00E+00
201732006
Methylene chloride
7.06E-05
1.43E-06
5.20E-07
9.88E-07
3.80E-07
201732006
2,4-Toluene diisocyanate
0.00E+00
1.68E-05
3.99 E-06
3.31 E-05
2.14E-06
201732006
Ethyl benzene
0.00E+00
4.38E-06
1.28E-07
0.00E+00
0.00E+00
201732006
Triethylamine
1.29E-06
0.00E+00
0.00E+00
0.00E+00
0.00E+00
201732006
n-Hexane
0.00E+00
0.00E+00
2.13E-07
0.00E+00
0.00E+00
201732006
Phenol
3.82E-09
3.82E-10
2.49E-10
5.83E-10
1.17 E-10
201732007
Glycol Ethers
3.40E-02
0.00E+00
0.00E+00
0.00E+00
0.00E+00
201732007
Toluene
9.07E-04
4.48E-05
7.46E-06
1.77E-04
3.05E-05
201732007
Methylene chloride
6.09E-04
1.24E-05
4.49E-06
8.53E-06
3.28E-06
201732007
Xylenes (mixed)
4.08E-04
1.60E-05
2.24E-06
0.00E+00
0.00E+00
21 of 27
-------
Table 3 - Maximum Predicted Acute Risks
Actual Emissions
Facility NEI ID
Pollutant
Maximum Hazard Quotient1
REL
AEGL1
AEGL2
ERPG1
ERPG2
201732007
Methylene diphenyl diisocyanate
0.00E+00
0.00E+00
0.00E+00
0.00E+00
3.17E-05
201732007
Styrene
1.59E-05
3.92E-06
6.06E-07
1.59E-06
3.03E-07
201732007
Ethyl benzene
0.00E+00
9.56E-06
2.79E-07
0.00E+00
0.00E+00
201732007
Methanol
7.62E-07
3.09E-08
7.90E-09
8.20E-08
1.64E-08
201732007
n-Hexane
0.00E+00
0.00E+00
3.72 E-08
0.00E+00
0.00E+00
201732027
2-Butoxyethyl acetate
4.21 E-02
0.00E+00
0.00E+00
0.00E+00
0.00E+00
201732027
Tetrachloroethene
1.04E-02
8.68E-04
1.30E-04
3.06E-04
1.49E-04
201732027
Arsenic compounds
8.76E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
201732027
Toluene
5.15E-03
2.54E-04
4.24E-05
1.00E-03
1.73E-04
201732027
Xylenes (mixed)
1.75E-03
6.89E-05
9.65E-06
0.00E+00
0.00E+00
201732027
Formaldehyde
1.60E-03
7.98E-05
5.16E-06
7.31 E-05
7.31 E-06
201732027
Benzene
4.85E-04
3.71 E-06
2.42E-07
3.94E-06
1.31 E-06
201732027
Glycol Ethers
1.79E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
201732027
Diethylene glycol monobutyl ethe
1.31E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
201732027
Ethyl benzene
0.00E+00
5.24E-05
1.53 E-06
0.00E+00
0.00E+00
201732027
Methanol
2.25E-05
9.15E-07
2.34E-07
2.43E-06
4.86 E-07
201732027
Cumene
0.00E+00
7.53E-08
1.25E-08
0.00E+00
0.00E+00
201732027
2,4-Toluene diisocyanate
0.00E+00
2.51 E-08
5.96 E-09
4.95E-08
3.20E-09
201732027
n-Hexane
0.00E+00
0.00E+00
2.92E-10
0.00E+00
0.00E+00
280475024
2-Butoxyethyl acetate
1.26E-02
0.00E+00
0.00E+00
0.00E+00
0.00E+00
280475024
Xylenes (mixed)
5.17E-05
2.03E-06
2.85E-07
0.00E+00
0.00E+00
280475024
Toluene
6.20E-06
3.06E-07
5.10E-08
1.21 E-06
2.08E-07
280475024
Ethyl benzene
0.00E+00
1.81 E-06
5.27E-08
0.00E+00
0.00E+00
280475024
Cumene
0.00E+00
1.25E-07
2.08E-08
0.00E+00
0.00E+00
370492008
Butyl carbitol acetate
1.64E-02
0.00E+00
0.00E+00
0.00E+00
0.00E+00
370492008
Diethylene glycol monoethyl ethe
8.02E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
370492008
Propyl cellosolve
6.97E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
370492008
2-Butoxyethyl acetate
4.82E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
370492008
Methylene chloride
2.44E-03
4.96E-05
1.80E-05
3.42E-05
1.32 E-05
370492008
Ethylene glycol ethyl ether acetal
1.03E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
370492008
Toluene
1.50E-04
7.39E-06
1.23E-06
2.92E-05
5.04 E-06
370492008
Methanol
1.57E-04
6.38E-06
1.63E-06
1.69E-05
3.39 E-06
370492008
Arsenic compounds
1.32E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
370492008
Xylenes (mixed)
1.24E-04
4.87E-06
6.81 E-07
0.00E+00
0.00E+00
370492008
Diethylene glycol dimethyl ether
9.75E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
370492008
2,4-Toluene diisocyanate
0.00E+00
2.92E-05
6.92 E-06
5.75E-05
3.71 E-06
370492008
Formaldehyde
8.34E-05
4.17E-06
2.70E-07
3.82E-06
3.82 E-07
370492008
Ethylene glycol ethyl ether
4.51 E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
370492008
Ethylene glycol methyl ether
2.04E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
370492008
Phenol
6.02E-06
6.02E-07
3.92 E-07
9.19E-07
1.84 E-07
370492008
Carbitol acetate
3.80E-06
0.00E+00
0.00E+00
0.00E+00
0.00E+00
370492008
Styrene
1.91E-06
4.73E-07
7.30E-08
1.91 E-07
3.65E-08
370492008
Ethyl benzene
0.00E+00
2.30E-06
6.71 E-08
0.00E+00
0.00E+00
370492008
Cumene
0.00E+00
1.43E-06
2.39E-07
0.00E+00
0.00E+00
370492008
Tetrachloroethene
1.42E-06
1.18E-07
1.77E-08
4.17E-08
2.03E-08
370492008
Methyl methacrylate
0.00E+00
4.90E-07
7.01 E-08
0.00E+00
0.00E+00
370492008
Propylene oxide
5.27E-07
9.61 E-09
2.37E-09
1.36E-08
2.77E-09
370492008
Acetaldehyde
1.44E-07
8.35E-10
1.38E-10
3.76E-09
1.88E-10
370492008
Acrylic acid
2.39E-08
3.26E-08
1.03E-09
4.95E-08
9.58E-10
370492008
Mercury (elemental)
5.94E-08
0.00E+00
2.10E-11
0.00E+00
1.78E-11
370492008
Hydrofluoric acid
2.77E-08
8.10E-09
3.32E-10
4.15E-09
4.15E-10
370492008
n-Hexane
0.00E+00
0.00E+00
3.21 E-08
0.00E+00
0.00E+00
22 of 27
-------
Table 3 - Maximum Predicted Acute Risks
Actual Emissions
Facility NEI ID
Pollutant
Maximum Hazard Quotient1
REL
AEGL1
AEGL2
ERPG1
ERPG2
370492008
Benzene
2.31 E-08
1.77E-10
1.16 E-11
1.88E-10
6.27E-11
370492008
Methylene diphenyl diisocyanate
0.00E+00
0.00E+00
0.00E+00
0.00E+00
1.31 E-08
370492008
Trichloroethylene
0.00E+00
3.67E-09
1.07E-09
4.76E-09
9.51 E-10
370492008
Epichlorohydrin
7.15E-09
4.22E-10
1.02E-10
4.89E-10
1.22E-10
370492008
1,3-Butadiene
0.00E+00
3.20E-11
3.99E-12
2.18E-09
1.09E-10
370492008
1,4-Dioxane
6.74E-10
3.32E-11
1.69E-12
0.00E+00
0.00E+00
370492008
Ethylene oxide
0.00E+00
0.00E+00
7.96E-11
0.00E+00
7.17E-11
370492008
Vinyl chloride
3.06E-11
8.62E-12
1.78E-12
4.24E-12
4.24E-13
391335027
Ethylene glycol ethyl ether acetal
2.08E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
391335027
Xylenes (mixed)
6.02E-04
2.37E-05
3.31 E-06
0.00E+00
0.00E+00
391335027
Toluene
1.49E-04
7.36E-06
1.23E-06
2.91 E-05
5.02 E-06
391335027
Ethyl benzene
0.00E+00
2.36E-06
6.88E-08
0.00E+00
0.00E+00
401092026
Butyl carbitol acetate
1.31E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
401092026
Methylene chloride
8.79E-04
1.78E-05
6.48E-06
1.23E-05
4.73 E-06
401092026
Phenol
6.39E-04
6.39E-05
4.17E-05
9.76E-05
1.95E-05
401092026
Toluene
2.92E-04
1.44E-05
2.40E-06
5.69E-05
9.83 E-06
401092026
Formaldehyde
1.94E-04
9.68E-06
6.26E-07
8.87E-06
8.87E-07
401092026
Ethylene glycol ethyl ether acetal
1.10E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
401092026
Xylenes (mixed)
8.13E-05
3.19E-06
4.47E-07
0.00E+00
0.00E+00
401092026
Ethyl acrylate
0.00E+00
6.92E-08
1.57E-08
5.74E-05
1.96 E-08
401092026
Hydrochloric acid
3.60E-06
2.80E-06
2.29E-07
1.68E-06
2.52E-07
401092026
Epichlorohydrin
5.83E-06
3.45E-07
8.34 E-08
3.99E-07
9.98E-08
401092026
Methylene diphenyl diisocyanate
0.00E+00
0.00E+00
0.00E+00
0.00E+00
5.12E-06
401092026
Methanol
1.11 E-06
4.52E-08
1.15E-08
1.20E-07
2.40E-08
401092026
Benzene
9.46E-07
7.24E-09
4.73E-10
7.69E-09
2.56E-09
401092026
Ethyl benzene
0.00E+00
5.19E-07
1.51 E-08
0.00E+00
0.00E+00
401092026
Cumene
0.00E+00
1.37E-07
2.28E-08
0.00E+00
0.00E+00
401092026
p-Xylene
5.59E-08
0.00E+00
0.00E+00
0.00E+00
0.00E+00
401092026
Ethylene oxide
0.00E+00
0.00E+00
1.47E-08
0.00E+00
1.33E-08
401432003
Toluene
7.65E-05
3.77E-06
6.29E-07
1.49E-05
2.57 E-06
401432003
Methanol
3.20E-05
1.30E-06
3.32E-07
3.44E-06
6.89E-07
401432003
Xylenes (mixed)
1.94E-05
7.64E-07
1.07E-07
0.00E+00
0.00E+00
401432003
Ethyl benzene
0.00E+00
5.12E-07
1.49E-08
0.00E+00
0.00E+00
401432013
Ethylene glycol ethyl ether
3.85E-01
0.00E+00
0.00E+00
0.00E+00
0.00E+00
401432013
2-Butoxyethyl acetate
2.43E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
401432013
Formaldehyde
1.73E-03
8.63E-05
5.58 E-06
7.91 E-05
7.91 E-06
401432013
Xylenes (mixed)
9.12E-04
3.58E-05
5.01 E-06
0.00E+00
0.00E+00
401432013
Toluene
1.58E-04
7.77E-06
1.30 E-06
3.07E-05
5.30 E-06
401432013
Methanol
1.69E-04
6.88E-06
1.76 E-06
1.82E-05
3.65E-06
401432013
2,4-Toluene diisocyanate
0.00E+00
3.07E-05
7.29E-06
6.06E-05
3.91 E-06
401432013
Methylene diphenyl diisocyanate
0.00E+00
0.00E+00
0.00E+00
0.00E+00
5.11 E-05
401432013
Ethyl benzene
0.00E+00
4.31 E-05
1.26E-06
0.00E+00
0.00E+00
401432014
Xylenes (mixed)
9.99E-04
3.92E-05
5.49E-06
0.00E+00
0.00E+00
401432014
Toluene
8.59E-05
4.24E-06
7.06E-07
1.67E-05
2.89 E-06
401432014
Ethyl benzene
0.00E+00
4.74E-05
1.38 E-06
0.00E+00
0.00E+00
401432014
Methylene diphenyl diisocyanate
0.00E+00
0.00E+00
0.00E+00
0.00E+00
4.60E-05
401432014
Phenol
4.57E-06
4.57E-07
2.98E-07
6.97E-07
1.39E-07
401432014
Arsenic compounds
5.10E-06
0.00E+00
0.00E+00
0.00E+00
0.00E+00
401432014
Methanol
9.46E-07
3.84E-08
9.82E-09
1.02E-07
2.04 E-08
401432014
Cumene
0.00E+00
4.77E-08
7.94E-09
0.00E+00
0.00E+00
401432014
Beryllium compounds
0.00E+00
0.00E+00
0.00E+00
0.00E+00
1.65E-09
401432021
Diethylene glycol monobutyl ethe
8.65E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
23 of 27
-------
Table 3 - Maximum Predicted Acute Risks
Actual Emissions
Facility NEI ID
Pollutant
Maximum Hazard Quotient1
REL
AEGL1
AEGL2
ERPG1
ERPG2
401432021
2-Butoxyethyl acetate
5.50E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
401432021
Diethylene glycol monomethyl et
1.25E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
401432021
Toluene
3.57E-04
1.76E-05
2.94 E-06
6.95E-05
1.20E-05
401432021
2,4-Toluene diisocyanate
0.00E+00
5.05E-05
1.20E-05
9.96E-05
6.43E-06
401432021
Xylenes (mixed)
5.52E-05
2.17E-06
3.04 E-07
0.00E+00
0.00E+00
401432021
Styrene
2.03E-05
5.01 E-06
7.75E-07
2.03E-06
3.87E-07
401432021
Methylene chloride
1.85E-05
3.75E-07
1.36 E-07
2.59E-07
9.95E-08
401432021
Methanol
5.93E-06
2.41 E-07
6.15E-08
6.38E-07
1.28E-07
401432021
1,1,1-Trichloroethane
4.65E-06
2.43E-07
9.58E-08
1.66E-07
8.32E-08
401432021
Methyl methacrylate
0.00E+00
3.91 E-06
5.58E-07
0.00E+00
0.00E+00
401432021
Ethylene glycol ethyl ether acetal
3.99E-06
0.00E+00
0.00E+00
0.00E+00
0.00E+00
401432021
Phenol
1.66E-06
1.66E-07
1.08E-07
2.53E-07
5.07E-08
401432021
Ethyl benzene
0.00E+00
2.16E-06
6.30E-08
0.00E+00
0.00E+00
401432021
Formaldehyde
1.46E-06
7.28E-08
4.71 E-09
6.68E-08
6.68E-09
401432021
Carbon tetrachloride
3.16E-07
2.15E-09
5.01 E-10
4.62E-09
9.54E-10
401432021
Benzene
1.14E-07
8.74E-10
5.72E-11
9.29E-10
3.10E-10
401432021
n-Hexane
0.00E+00
0.00E+00
1.09E-07
0.00E+00
0.00E+00
401432021
Tetrachloroethene
3.16E-08
2.63E-09
3.95E-10
9.30E-10
4.52E-10
401432021
Cumene
0.00E+00
3.08E-08
5.14E-09
0.00E+00
0.00E+00
401432021
1,2-Epoxybutane
0.00E+00
8.82E-09
4.52 E-09
0.00E+00
0.00E+00
401432021
Acrylic acid
2.49E-09
3.40E-09
1.07E-10
5.16E-09
9.97E-11
401432021
Epichlorohydrin
3.51E-10
2.08E-11
5.02E-12
2.40E-11
6.01 E-12
480295025
Formaldehyde
1.37E-04
6.85E-06
4.44E-07
6.28E-06
6.28E-07
480295025
Xylenes (mixed)
1.98E-05
7.77E-07
1.09E-07
0.00E+00
0.00E+00
480295025
Methanol
1.13E-05
4.59E-07
1.17E-07
1.22E-06
2.44E-07
480295025
Toluene
1.01E-05
5.00E-07
8.33E-08
1.97E-06
3.41 E-07
480295025
Methylene chloride
9.54E-06
1.94E-07
7.03E-08
1.34E-07
5.14E-08
480295025
Styrene
4.47E-06
1.10E-06
1.71 E-07
4.47E-07
8.53E-08
480295025
Cumene
0.00E+00
1.11 E-06
1.85E-07
0.00E+00
0.00E+00
480295025
Ethyl benzene
0.00E+00
8.74E-07
2.55E-08
0.00E+00
0.00E+00
480295025
Phenol
3.47E-07
3.47E-08
2.26E-08
5.29E-08
1.06E-08
480295025
n-Hexane
0.00E+00
0.00E+00
4.82 E-09
0.00E+00
0.00E+00
481136003
2-Butoxyethyl acetate
6.60E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
481136003
Phenol
1.60E-03
1.60E-04
1.04E-04
2.44E-04
4.87E-05
481136003
Methylene chloride
1.50E-03
3.05E-05
1.11E-05
2.11E-05
8.10E-06
481136003
Toluene
1.88E-04
9.26E-06
1.54 E-06
3.65E-05
6.31 E-06
481136003
Xylenes (mixed)
6.79E-05
2.67E-06
3.74 E-07
0.00E+00
0.00E+00
481136003
Ethylene glycol ethyl ether acetal
5.95E-06
0.00E+00
0.00E+00
0.00E+00
0.00E+00
481136003
Ethyl benzene
0.00E+00
3.29E-06
9.60E-08
0.00E+00
0.00E+00
481136003
Trichloroethylene
0.00E+00
2.69E-07
7.86E-08
3.49E-07
6.98E-08
481136003
n-Hexane
0.00E+00
0.00E+00
5.33E-08
0.00E+00
0.00E+00
481136003
Styrene
1.46E-08
3.60E-09
5.56E-10
1.46E-09
2.78E-10
481215032
Diethylene glycol monobutyl ethe
1.43E-02
0.00E+00
0.00E+00
0.00E+00
0.00E+00
481215032
Diethylene glycol monomethyl et
6.66E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
481215032
Xylenes (mixed)
6.70E-05
2.63E-06
3.69E-07
0.00E+00
0.00E+00
481215032
Toluene
1.79E-05
8.83E-07
1.47E-07
3.49E-06
6.02 E-07
481215032
Methylene chloride
2.02E-05
4.10E-07
1.49E-07
2.83E-07
1.09E-07
481215032
Ethyl benzene
0.00E+00
3.17E-06
9.24E-08
0.00E+00
0.00E+00
481215032
Methanol
2.39E-06
9.70E-08
2.48E-08
2.58E-07
5.15E-08
481215032
n-Hexane
0.00E+00
0.00E+00
6.08E-09
0.00E+00
0.00E+00
482015019
Methylene chloride
7.46E-05
1.51 E-06
5.50E-07
1.05E-06
4.02 E-07
482015019
Phenol
4.93E-05
4.93E-06
3.21 E-06
7.52E-06
1.50 E-06
24 of 27
-------
Table 3 - Maximum Predicted Acute Risks
Actual Emissions
Facility NEI ID
Pollutant
Maximum Hazard Quotient1
REL
AEGL1
AEGL2
ERPG1
ERPG2
482015019
2-Butoxyethyl acetate
3.18E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
482015019
Formaldehyde
1.76E-05
8.80E-07
5.69E-08
8.06E-07
8.06 E-08
482015019
Toluene
8.55E-06
4.22E-07
7.03E-08
1.66E-06
2.88E-07
482015019
Xylenes (mixed)
2.42E-06
9.51 E-08
1.33E-08
0.00E+00
0.00E+00
482015019
1,1,1-Trichloroethane
6.14E-07
3.21 E-08
1.26E-08
2.20E-08
1.10E-08
482015019
Arsenic compounds
8.89E-08
0.00E+00
0.00E+00
0.00E+00
0.00E+00
482015019
Ethyl benzene
0.00E+00
5.51 E-08
1.61E-09
0.00E+00
0.00E+00
482015019
Methanol
9.67E-09
3.92E-10
1.00E-10
1.04E-09
2.08E-10
482015019
Hydrofluoric acid
5.70E-10
1.67E-10
6.84E-12
8.55E-11
8.55E-12
482015020
Methylene chloride
4.36E-05
8.85E-07
3.21 E-07
6.11 E-07
2.35E-07
482015020
Xylenes (mixed)
3.06E-06
1.20E-07
1.68E-08
0.00E+00
0.00E+00
482015020
Toluene
6.78E-07
3.35E-08
5.58E-09
1.32E-07
2.28E-08
482015020
Methanol
3.38E-07
1.37E-08
3.51 E-09
3.64E-08
7.29E-09
482015020
Ethyl benzene
0.00E+00
8.83E-08
2.57E-09
0.00E+00
0.00E+00
482015020
Formaldehyde
6.03E-09
3.02E-10
1.95E-11
2.76E-10
2.76E-11
482015020
m-Xylene
3.04E-09
0.00E+00
0.00E+00
0.00E+00
0.00E+00
482015020
n-Hexane
0.00E+00
0.00E+00
1.02E-10
0.00E+00
0.00E+00
483553000
2-Butoxyethyl acetate
1.06E-02
0.00E+00
0.00E+00
0.00E+00
0.00E+00
483553000
Toluene
2.90E-04
1.43E-05
2.39 E-06
5.65E-05
9.77 E-06
483553000
Methylene chloride
3.15E-04
6.38E-06
2.32 E-06
4.40E-06
1.69E-06
483553000
Xylenes (mixed)
1.54E-04
6.04E-06
8.46E-07
0.00E+00
0.00E+00
483553000
Formaldehyde
1.91E-05
9.53E-07
6.17E-08
8.74E-07
8.74 E-08
483553000
Phenol
3.66E-06
3.66E-07
2.39E-07
5.59E-07
1.12E-07
483553000
Ethyl benzene
0.00E+00
3.64E-06
1.06 E-07
0.00E+00
0.00E+00
483553000
Methanol
8.82E-07
3.58E-08
9.15E-09
9.50E-08
1.90E-08
483553000
Cumene
0.00E+00
2.27E-07
3.79E-08
0.00E+00
0.00E+00
483553000
1,2-Epoxybutane
0.00E+00
7.35E-08
3.76 E-08
0.00E+00
0.00E+00
483553000
Hydrofluoric acid
4.10E-08
1.20E-08
4.92E-10
6.15E-09
6.15E-10
483553000
Methylene diphenyl diisocyanate
0.00E+00
0.00E+00
0.00E+00
0.00E+00
3.99E-08
483553000
n-Hexane
0.00E+00
0.00E+00
1.12E-08
0.00E+00
0.00E+00
483553000
Trichloroethylene
0.00E+00
2.99E-09
8.72E-10
3.88E-09
7.75E-10
483553000
Methyl methacrylate
0.00E+00
1.44E-11
2.06E-12
0.00E+00
0.00E+00
483553000
Benzene
8.30E-14
1.00E-15
0.00E+00
1.00E-15
0.00E+00
483672020
Tetrachloroethene
5.70E-03
4.75E-04
7.13E-05
1.68E-04
8.15E-05
484392009
Propyl cellosolve
4.49E-02
0.00E+00
0.00E+00
0.00E+00
0.00E+00
484392009
Butyl carbitol acetate
1.44E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
484392009
Methylene chloride
2.67E-04
5.41 E-06
1.96 E-06
3.73E-06
1.44E-06
484392009
2,4-Toluene diisocyanate
0.00E+00
4.91 E-05
1.16E-05
9.68E-05
6.25E-06
484392009
Toluene
5.32E-05
2.63E-06
4.38E-07
1.04E-05
1.79 E-06
484392009
Glycol ethers
4.95E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
484392009
Ethylene glycol ethyl ether
4.25E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
484392009
o-Xylene
2.92E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
484392009
Xylenes (mixed)
2.20E-05
8.64E-07
1.21 E-07
0.00E+00
0.00E+00
484392009
Ethyl acrylate
0.00E+00
2.44E-08
5.54 E-09
2.03E-05
6.93E-09
484392009
Formaldehyde
1.54E-05
7.68E-07
4.97E-08
7.04E-07
7.04 E-08
484392009
Triethylamine
7.08E-06
0.00E+00
0.00E+00
0.00E+00
0.00E+00
484392009
Diethylene glycol monobutyl ethe
6.28E-06
0.00E+00
0.00E+00
0.00E+00
0.00E+00
484392009
Ethyl benzene
0.00E+00
2.58E-06
7.53E-08
0.00E+00
0.00E+00
484392009
Benzene
1.27E-06
9.72E-09
6.36E-10
1.03E-08
3.44E-09
484392009
Methanol
1.72E-07
6.98E-09
1.78E-09
1.85E-08
3.70E-09
484392009
Cumene
0.00E+00
6.18E-08
1.03E-08
0.00E+00
0.00E+00
484392009
p-Xylene
1.87E-08
0.00E+00
0.00E+00
0.00E+00
0.00E+00
25 of 27
-------
Table 3 - Maximum Predicted Acute Risks
Actual Emissions
Facility NEI ID
Pollutant
Maximum Hazard Quotient1
REL
AEGL1
AEGL2
ERPG1
ERPG2
484392010
Propyl cellosolve
1.81E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
484392010
Toluene
6.19E-05
3.06E-06
5.09E-07
1.21 E-05
2.08 E-06
484392010
Methylene chloride
5.98E-06
1.21E-07
4.40E-08
8.37E-08
3.22E-08
484392010
Xylenes (mixed)
1.57E-07
6.19E-09
8.66E-10
0.00E+00
0.00E+00
484392010
Diethylene glycol monobutyl ethe
1.24E-08
0.00E+00
0.00E+00
0.00E+00
0.00E+00
484392010
Ethyl benzene
0.00E+00
4.04E-09
1.18 E-10
0.00E+00
0.00E+00
484392010
Cumene
0.00E+00
7.52E-10
1.25E-10
0.00E+00
0.00E+00
484392010
Ethyl acrylate
0.00E+00
9.55E-13
2.17E-13
7.92E-10
2.71E-13
484392010
Formaldehyde
5.90E-10
2.95E-11
1.91 E-12
2.71 E-11
2.71 E-12
484392010
Benzene
4.05E-11
3.10E-13
2.00E-14
3.29E-13
1.10 E-13
484392010
Methanol
5.01 E-12
2.03E-13
5.20E-14
5.40E-13
1.08E-13
484392010
Triethylamine
6.90E-14
0.00E+00
0.00E+00
0.00E+00
0.00E+00
490115010
Xylenes (mixed)
2.49E-05
9.78E-07
1.37E-07
0.00E+00
0.00E+00
490115010
Toluene
1.20E-06
5.91 E-08
9.86E-09
2.33E-07
4.03E-08
490115010
Ethyl benzene
0.00E+00
7.01 E-07
2.05E-08
0.00E+00
0.00E+00
490115012
Xylenes (mixed)
4.13E-05
1.62E-06
2.27E-07
0.00E+00
0.00E+00
490115012
Toluene
1.02E-05
5.05E-07
8.41 E-08
1.99E-06
3.44E-07
490115012
1,1,1-Trichloroethane
6.34E-06
3.32E-07
1.31 E-07
2.27E-07
1.13E-07
490115012
Ethyl benzene
0.00E+00
2.07E-06
6.05E-08
0.00E+00
0.00E+00
490355013
2,4-Toluene diisocyanate
0.00E+00
7.37E-05
1.75E-05
1.45E-04
9.38 E-06
490355013
Xylenes (mixed)
1.70E-04
6.69E-06
9.37E-07
0.00E+00
0.00E+00
490355013
Methylene diphenyl diisocyanate
0.00E+00
0.00E+00
0.00E+00
0.00E+00
1.63E-04
490355013
Toluene
1.06E-04
5.21 E-06
8.68E-07
2.06E-05
3.55 E-06
490355013
Methylene chloride
2.91 E-05
5.91 E-07
2.14E-07
4.07E-07
1.57E-07
490355013
1,1,1-Trichloroethane
2.02E-05
1.06E-06
4.17E-07
7.24E-07
3.62E-07
490355013
Ethyl benzene
0.00E+00
6.11 E-06
1.78E-07
0.00E+00
0.00E+00
490355013
Formaldehyde
4.83E-06
2.41 E-07
1.56 E-08
2.21 E-07
2.21 E-08
490355013
Methanol
1.49E-06
6.04E-08
1.54 E-08
1.60E-07
3.21 E-08
490355013
Benzene
5.14E-07
3.93E-09
2.57E-10
4.18E-09
1.39E-09
490355013
Cumene
0.00E+00
2.14E-07
3.56 E-08
0.00E+00
0.00E+00
490355013
1,3-Butadiene
0.00E+00
3.26E-09
4.07E-10
2.22E-07
1.11 E-08
490355013
n-Hexane
0.00E+00
0.00E+00
9.65E-09
0.00E+00
0.00E+00
516505023
Xylenes (mixed)
2.76E-05
1.08E-06
1.52 E-07
0.00E+00
0.00E+00
516505023
Toluene
1.10E-05
5.45E-07
9.09E-08
2.15E-06
3.72 E-07
516505023
Phenol
5.60E-07
5.60E-08
3.65E-08
8.54E-08
1.71 E-08
516505023
Methylene chloride
5.22E-07
1.06E-08
3.84E-09
7.30E-09
2.81 E-09
516505023
Ethyl benzene
0.00E+00
9.92E-08
2.89E-09
0.00E+00
0.00E+00
516505023
Triethylamine
3.26E-08
0.00E+00
0.00E+00
0.00E+00
0.00E+00
530615014
Butyl carbitol acetate
1.36E-02
0.00E+00
0.00E+00
0.00E+00
0.00E+00
530615014
Diethylene glycol monobutyl ethe
1.36E-02
0.00E+00
0.00E+00
0.00E+00
0.00E+00
530615014
Toluene
2.98E-04
1.47E-05
2.45E-06
5.79E-05
1.00E-05
530615014
Methylene chloride
1.91E-06
3.87E-08
1.41 E-08
2.67E-08
1.03E-08
530615014
Phenol
3.29E-07
3.29E-08
2.14E-08
5.02E-08
1.00E-08
540572002
Xylenes (mixed)
3.03E-04
1.19E-05
1.67E-06
0.00E+00
0.00E+00
540572002
Toluene
1.84E-04
9.10E-06
1.52 E-06
3.59E-05
6.20E-06
540572002
Formaldehyde
1.03E-04
5.17E-06
3.35E-07
4.74E-06
4.74 E-07
540572002
Diethylene glycol monomethyl et
1.36E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
540572002
Phenol
4.22E-06
4.22E-07
2.75E-07
6.45E-07
1.29E-07
540572002
Ethyl benzene
0.00E+00
4.51 E-06
1.31 E-07
0.00E+00
0.00E+00
540572002
Methanol
3.56E-06
1.44E-07
3.69E-08
3.83E-07
7.66E-08
540572002
Ethylene glycol ethyl ether
9.42E-07
0.00E+00
0.00E+00
0.00E+00
0.00E+00
540572002
Styrene
1.67E-08
4.13E-09
6.38E-10
1.67E-09
3.19E-10
26 of 27
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Table 3 - Maximum Predicted Acute Risks
Actual Emissions
Facility NEI ID
Pollutant
Maximum Hazard Quotient1
REL
AEGL1
AEGL2
ERPG1
ERPG2
540572002
Methylene diphenyl diisocyanate
0.00E+00
0.00E+00
0.00E+00
0.00E+00
1.78E-08
540572002
n-Hexane
0.00E+00
0.00E+00
3.50E-10
0.00E+00
0.00E+00
550876001
Phenol
1.67E-02
1.67E-03
1.09E-03
2.55E-03
5.09 E-04
550876001
2-Butoxyethyl acetate
7.30E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
550876001
Methylene chloride
6.94E-03
1.41 E-04
5.11E-05
9.71 E-05
3.74 E-05
550876001
Ethylene glycol ethyl ether acetal
2.70E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
550876001
Toluene
5.26E-04
2.60E-05
4.33 E-06
1.02E-04
1.77E-05
550876001
Xylenes (mixed)
5.34E-04
2.10E-05
2.94 E-06
0.00E+00
0.00E+00
550876001
Diethylene glycol monoethyl ethe
3.23E-04
0.00E+00
0.00E+00
0.00E+00
0.00E+00
550876001
Methanol
2.45E-04
9.94E-06
2.54 E-06
2.64E-05
5.28E-06
550876001
Styrene
3.44E-05
8.51 E-06
1.31 E-06
3.44E-06
6.57E-07
550876001
Diethylene glycol monobutyl ethe
3.65E-05
0.00E+00
0.00E+00
0.00E+00
0.00E+00
550876001
Ethyl benzene
0.00E+00
9.14E-06
2.67E-07
0.00E+00
0.00E+00
550876001
Ethylene glycol methyl ether
6.54E-06
0.00E+00
0.00E+00
0.00E+00
0.00E+00
550876001
Ethylene glycol ethyl ether
5.63E-06
0.00E+00
0.00E+00
0.00E+00
0.00E+00
550876001
Formaldehyde
4.33E-06
2.16E-07
1.40E-08
1.98E-07
1.98E-08
550876001
Glycol Ethers
4.50E-06
0.00E+00
0.00E+00
0.00E+00
0.00E+00
550876001
Propyl cellosolve
2.33E-06
0.00E+00
0.00E+00
0.00E+00
0.00E+00
550876001
Methyl methacrylate
0.00E+00
1.40E-06
2.00E-07
0.00E+00
0.00E+00
550876001
Acetaldehyde
8.31 E-07
4.82E-09
7.97E-10
2.17E-08
1.09E-09
550876001
Methylene diphenyl diisocyanate
0.00E+00
0.00E+00
0.00E+00
0.00E+00
6.85E-07
550876001
n-Hexane
0.00E+00
0.00E+00
4.73E-07
0.00E+00
0.00E+00
550876001
Carbon tetrachloride
1.49E-07
1.01E-09
2.36E-10
2.18E-09
4.50E-10
550876001
m-Xylene
6.48E-08
0.00E+00
0.00E+00
0.00E+00
0.00E+00
550876001
Propylene oxide
2.84E-08
5.18E-10
1.28E-10
7.34E-10
1.49E-10
550876001
Benzene
1.20E-08
9.21 E-11
6.02E-12
9.78E-11
3.26E-11
550876001
Cumene
0.00E+00
1.09E-09
1.81E-10
0.00E+00
0.00E+00
550876001
o-Xylene
1.22E-09
0.00E+00
0.00E+00
0.00E+00
0.00E+00
550876001
p-Xylene
1.22E-09
0.00E+00
0.00E+00
0.00E+00
0.00E+00
550876001
Aniline
0.00E+00
4.11 E-11
2.68E-11
0.00E+00
0.00E+00
201732006a
Glycol Ethers
1.02E-03
0.00E+00
0.00E+00
0.00E+00
0.00E+00
201732006a
Toluene
6.41 E-04
3.16E-05
5.27E-06
1.25E-04
2.16E-05
201732006a
Methanol
3.86E-04
1.56E-05
4.00 E-06
4.15E-05
8.31 E-06
201732006a
n-Hexane
0.00E+00
0.00E+00
1.77 E-06
0.00E+00
0.00E+00
201732006a
Phenol
3.17E-08
3.17E-09
2.06E-09
4.83E-09
9.66E-10
39021672a
Toluene
7.22E-08
3.56E-09
5.93E-10
1.41E-08
2.43E-09
39021672a
Benzene
1.13E-09
8.64E-12
5.65E-13
9.18E-12
3.06E-12
1 Some maximum acute impacts may be at onsite locations.
Note: BOLD indicates acute risks greater than 1
27 of 27
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Table 4 - Multipathway Screening Analysis Results
Actual Emissions
Facility NEI ID
Pollutant
Tier 11
Tier21
10892023
Cadmium
2.66E-01
Below Tier 1 Threshold
4019433
Cadmium
1.00E-01
Below Tier 1 Threshold
5051409
Cadmium
1.33E-06
Below Tier 1 Threshold
60372022
Cadmium
3.43E-05
Below Tier 1 Threshold
9001693
Cadmium
5.53E-07
Below Tier 1 Threshold
9003626
Cadmium
2.00E-09
Below Tier 1 Threshold
12005313
Cadmium
1.04E-05
Below Tier 1 Threshold
12023284
Cadmium
2.02E-05
Below Tier 1 Threshold
12031634
Cadmium
7.59E-04
Below Tier 1 Threshold
120862016
Cadmium
6.39E-03
Below Tier 1 Threshold
13009224
Cadmium
4.32E-08
Below Tier 1 Threshold
13063388
Cadmium
6.54E-05
Below Tier 1 Threshold
13067506
Cadmium
8.75E-03
Below Tier 1 Threshold
201732006
Cadmium
1.98E-03
Below Tier 1 Threshold
201732027
Cadmium
2.65E-02
Below Tier 1 Threshold
2017365
Mercury (methyl)
1.86E-03
Below Tier 1 Threshold
24005458
Mercury (methyl)
5.68E-03
Below Tier 1 Threshold
370492008
Cadmium
1.33E-01
Below Tier 1 Threshold
370492008
Mercury (methyl)
5.34E-06
Below Tier 1 Threshold
37119600
Cadmium
3.64E-06
Below Tier 1 Threshold
37133455
Mercury (methyl)
1.14E-07
Below Tier 1 Threshold
39021672
Cadmium
4.12E-04
Below Tier 1 Threshold
401432014
Cadmium
1.38E-06
Below Tier 1 Threshold
401432021
Cadmium
1.29E+00
Below Tier 1 Threshold
40143523
Cadmium
1.26E+00
Below Tier 1 Threshold
42003603
Cadmium
1.25E-04
Below Tier 1 Threshold
42081660
Cadmium
1.11 E+00
Below Tier 1 Threshold
484392009
Cadmium
1.12E-02
Below Tier 1 Threshold
484392010
Cadmium
1.55E-08
Below Tier 1 Threshold
49011663
Cadmium
4.15E-02
Below Tier 1 Threshold
53033383
Cadmium
3.51 E-06
Below Tier 1 Threshold
53033384
Cadmium
5.78E-06
Below Tier 1 Threshold
53033532
Cadmium
5.64E-03
Below Tier 1 Threshold
53033532
Mercury (methyl)
1.54E-02
Below Tier 1 Threshold
53053447
Cadmium
3.50E-08
Below Tier 1 Threshold
1 of 2
-------
Table 4 - Multipathway Screening Analysis Results
Actual Emissions
Facility NEI ID
Pollutant
Tier 11
Tier21
53061398
Cadmium
5.54E-03
Below Tier 1 Threshold
53061398
Mercury (methyl)
6.02E-01
Below Tier 1 Threshold
1 Numeric value indicates number of times facility level emissions above screening threshold.
2 of 2
-------
Appendix 8 Acute Impacts Refined Analysis
-------
Refined Acute Modeling Approach
Initial acute screening risk calculations were performed with the HEM-3
model. HEM-3 estimates acute (1-hour) impacts at both polar and census block
receptors. It is assumed for this short period of time that an exposed individual
could be located at any off-site location. The lack of readily available detailed
property boundary information for many of the facilities evaluated made it difficult
to determine whether receptors were on- or off-site. In the absence of such
information, the first ring of polar receptors was placed 100 meters from the plant
center for many facilities. However, these polar rings often transected on-site
locations, restricting public access to exposures at these levels and thereby
overestimating exposures.
The screening approach used by HEM-3 to estimate maximum 1-hour
exposures also likely overestimates. To estimate maximum 1 -hour
concentrations at each receptor, HEM-3 sums the maximum concentrations
attributed to each source, regardless of whether those maximum concentrations
occurred during the same hour. In other words, HEM-3 assumes that the
maximum impact from each source at each receptor occurs at the same time. In
actuality, maximum impacts from different sources may occur at different times.
This appendix includes the results of refined acute assessments
performed for facilities that exceeded short-term health benchmarks using the
HEM-3 approach. The refinements can address both areas of conservatism
described above. The simplest refinement is to plot the HEM-3 polar grid results
on aerial photographs of the facilities; This allows the assessment of off-site
locations that may be accessible to the public {e.g., roadways and public
buildings.). The attached figure present the estimated hazard quotient values
(the modeled 1-hour concentration of a pollutant divided by its short-term health
benchmark) at the polar receptors near the facilities.
-------
Table 1 - Maximum Predicted Acute Risks Greater than 1 (Refined Approach)
Facility NEI ID
Pollutant
Criteria
Baseline - Actuals
Refined Modeling Approach 1
HEM-3
(Screening)
Refined
Results
Petroleum Refining Source Sector
5113377
Ethylene glycol
ethyl ether acetate
REL
2
2
Max off-site HQ at centroid of census tract
(populated)
1 Indicates modeling technique used to refined estimates; see figures depicting off-site impacts
-------
Figure 1 -05113377
Acute Ethylene Glycol Ethyl Ether Acetate HQ (REL)
------- |