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Technical Support Document
EPA's Air Toxics Screening Assessment
2020 AirToxScreen TSD
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EPA-452/B-24-001
May 2024
Technical Support Document
EPA's Air Toxics Screening Assessment
2020 AirToxScreen TSD
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Health and Environmental Impacts Division
Research Triangle Park, NC
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Contents
Contents iv
Tables viii
Figures viii
Common Acronyms and Abbreviations x
1. Introduction 1
1.1. Overview 1
1.2. Purpose and Overview of AirToxScreen Steps 2
1.3. What AirToxScreen Is 4
1.4. Air Toxics Screening Assessment History 5
1.5. How EPA and State, Local and Tribal Air Agencies Use AirToxScreen Results 6
1.6. How AirToxScreen Results Should Not Be Used 7
1.7. The Risk Assessment Framework AirToxScreen Uses 8
1.8. The Scope of AirToxScreen 9
1.8.1. Sources of Air Toxic Emissions That AirToxScreen Addresses 10
1.8.2. Stressors that AirToxScreen Evaluates 11
1.8.3. Exposure Pathways, Routes and Time Frames for AirToxScreen 11
1.8.4. Receptors that AirToxScreen Characterizes 12
1.8.5. Endpoints and Measures: Results of AirToxScreen 12
1.9. Model Design 13
1.9.1. The Strengths and Limitations of the Model Design 15
2. Emissions 17
2.1. Sources of Emissions 17
2.1.1. Pollutants and Pollutant Groups 20
2.1.2. Emissions Categorization: NEI and AirToxScreen 24
2.1.3. Differences Between the current NEI and Emissions Used for AirToxScreen 26
2.1.4. Overview of Differences in Emissions for CMAQ and AERMOD 28
2.2. Preparation of Emissions Inputs for CMAQ 30
2.2.1. Sectors in the CMAQ AirToxScreen Platform 30
2.2.2. Fires and Biogenics 33
2.2.3. Speciation 34
2.2.4. Temporalization 34
2.2.5. Spatial Allocation 34
2.3. Preparation of Emissions Inputs for AERMOD 35
2.3.1. Source and Run Groups - Overview 35
2.3.2. Point Sources Excluding Airports 43
2.3.3. Airport Point Sources 47
2.3.4. Nonroad, On-road and Nonpoint - County-level Sources 50
2.3.5. Residential Wood Combustion 53
2.3.6. Oil and Gas 53
2.3.7. Agricultural Livestock 54
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2.3.8. Commercial Marine Vessels - County/Shape-level Sources 54
2.3.9. Urban/Rural Determination for All Emission Sources 56
3. Air Quality Modeling and Characterization 61
3.1. Modeling Overview 61
3.1.1. Photochemical Model Selection 61
3.1.2. Dispersion Model Selection 63
3.2. Meteorological Data 63
3.2.1. Meteorological Data Inside the Contiguous States 63
3.2.2. Meteorological Data Outside the Contiguous States 65
3.3. CMAQ Setup 66
3.3.1. Sources Modeled in CMAQ 66
3.3.2. Boundary and Initial Conditions 72
3.4. AERMOD Setup 74
3.4.1. Sources Modeled in AERMOD 74
3.4.2. Receptor Placement 74
3.4.3. Model Options 79
3.4.4. AERMOD Simulations 79
3.4.5. Post-processing of AERMOD Results 80
3.5. Hybrid Modeling 82
3.5.1. Overview 82
3.5.2. Treatment of Species 84
3.5.3. Ambient Monitoring Data Preparation 86
3.5.4. Model Performance Statistics 87
3.5.5. Hybrid Evaluation 89
3.5.6. Non-hybrid Evaluation 91
4. Estimating Exposures for Populations 94
4.1. Estimating Exposure Concentrations 94
4.2. About HAPEM 94
4.3. HAPEM Inputs and Application 96
4.3.1. Data on Ambient Air Concentrations 96
4.3.2. Population Demographic Data 96
4.3.3. Data on Population Activity 96
4.3.4. Microenvironmental Data 97
4.4. Exposure Factors 99
4.5. Evaluating Exposure Modeling 100
4.6. Summary 100
5. Characterizing Effects of Air Toxics 101
5.1. Toxicity Values and Their Use in AirToxScreen 101
5.2. Types of Toxicity Values 102
5.2.1. Cancer URE 102
5.2.2. Noncancer Chronic RfC 105
5.3. Data Sources for Toxicity Values 105
5.3.1. U.S. EPA Integrated Risk Information System 106
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5.3.2. U.S. Department of Health and Human Services, Agency for Toxic Substances and
Disease Registry 106
5.3.3. California Environmental Protection Agency Office of Environmental Health Hazard
Assessment 106
5.3.4. U.S. EPA Health Effects Assessment SummaryTables 106
5.3.5. World Health Organization International Agency for Research on Cancer 107
5.4. Additional Toxicity Decisions for Some Chemicals 107
5.4.1. Polycyclic Organic Matter 107
5.4.2. Glycol Ethers 108
5.4.3. Acrolein 108
5.4.4. Metals 108
5.4.5. Adjustment of Mutagen UREs to Account for Exposure During Childhood 109
5.4.6. Diesel Particulate Matter 110
5.5. Summary Ill
6. Characterizing Risks and Hazards in AirToxScreen 112
6.1. The Risk-characterization Questions AirToxScreen Addresses 112
6.2. How Cancer Risk Is Estimated 113
6.2.1. Individual Pollutant Risk 113
6.2.2. Multiple-pollutant Risks 113
6.3. How Noncancer Hazard is Estimated 114
6.3.1. Individual Pollutant Hazard 114
6.3.2. Multiple-pollutant Hazard 115
6.4. How Risk Estimates and Hazard Quotients Are Calculated for AirToxScreen at Block,
County and State Levels 116
6.4.1. Aggregation of Block-level Results to Larger Spatial Units 116
6.5. The Risk Characterization Results That AirToxScreen Reports 116
6.6. Summary 118
7. Variability and Uncertainty Associated with AirToxScreen 120
7.1. Introduction 120
7.2. How AirToxScreen Addresses Variability 120
7.2.1. Components of Variability 121
7.2.2. Quantifying Variability 122
7.2.3. How Variability Affects Interpretation of AirToxScreen Results 124
7.3. How AirToxScreen Addresses Uncertainty 124
7.3.1. Components of Uncertainty 125
7.3.2. Components of Uncertainty Included in AirToxScreen 126
7.4. Summary of Limitations in AirToxScreen 131
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8. References 134
Appendix A. Glossary A-l
Appendix B. Air Toxics Modeled in AirToxScreen B-l
Appendix C. Estimating Background Concentrations for AirToxScreen C-3
Appendix D. Model Evaluation Summaries D-l
Appendix E. Exposure Factors for AirToxScreen E-l
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Tables
Table 2-1. Summary of emissions sources in the 2019 NEI 18
Table 2-2. Pollutant groups 21
Table 2-3. PAH/POM pollutants group 21
Table 2-4. Metal speciation factors for NEI metal compounds 23
Table 2-5. Map of NEI data categories to AirToxScreen categories 25
Table 2-6. Release parameter defaults/changes to the FF10 inventory files for point sources 27
Table 2-7. Differences in spatial characterization of sources between CMAQ and AERMOD 29
Table 2-8. Platform sectors for the 2019 emissions modeling platform 31
Table 2-9. Run groups for AERMOD 36
Table 2-10. Resolution of the run groups modeled as gridded sources 37
Table 2-11. Non-gridded AERMOD run groups 37
Table 2-12. AERMOD gridded run groups 38
Table 2-13. AirToxScreen source groups 41
Table 2-14. Assignment of AERMOD source type for point sources 45
Table 2-15. Options for temporal variation specification in helper files 47
Table 2-16. Lead emissions (kg/yr) at SMO in 2019 by aircraft operation mode 49
Table 2-17. Airport emissions file format 49
Table 2-18. NEI SCCs covered in the CMV run group 54
Table 3-1. CMAQ HAPs 61
Table 3-2. Vertical layer structure for WRF and CMAQ (heights are layer top) 64
Table 3-3. WRF Options Used for the Alaska, Hawaii, and Puerto Rico/Virgin Islands domains 66
Table 3-4. Boundary conditions from 2019 remote concentration estimates 73
Table 3-5. CMAQ HAP boundary conditions applied as zero value 73
Table 3-9. List of hybrid HAPs evaluated 89
Table 4-1. Key differences between recent versions of HAPEM 95
Table 4-2. Microenvironments used in HAPEM modeling for AirToxScreen 99
Table 6-1. Criteria establishing AirToxScreen drivers and contributors of health effects for risk
characterization 117
Figures
Figure 1-1. AirToxScreen - basic steps 2
Figure 1-2. Detailed steps and approach used in AirToxScreen 4
Figure 1-3. The general air toxics risk assessment process 8
Figure 1-4. Conceptual model for AirToxScreen 10
Figure 1-5. The AirToxScreen risk assessment process and corresponding sections of this TSD 14
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Figure 2-1. Example fugitive source characterization: NEI length = 1897 feet, width = 680 feet and
angle = 22 46
Figure 2-2. Port shapes for Los Angeles and Long Beach, California 55
Figure 3-l(a). Map of the CMAQ modeling domain; the blue box denotes the 12 km CONUS modeling
domain 68
Figure 3-l(b). Map of the CMAQ modeling domain; the green box denotes the 9 km Alaska modeling
domain 69
Figure 3-l(c). Map of the CMAQ modeling domain; the red box denotes the 3 km Hawai'i modeling
domain 70
Figure 3-l(d). Map of the CMAQ modeling domain; the red box denotes the 3 km Puerto Rico/Virgin
Islands modeling domain 71
Figure 3-2. CBSAs exceeding 1 million people Error! Bookmark not defined.
Figure 3-3. Dense (left) and coarse (right) receptor grid layout in CMAQ Lambert Projection 76
Figure 3-4. Example grid cell with subgrid cells and census blocks 77
Figure 3-5. CMAQ domain with expanded cell showing hybrid receptors; colors indicate modeled
concentrations; dots in inset show locations of receptors within a grid cell 83
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Common Acronyms and Abbreviations
|jg/m3
AERMOD
ASPEN
ATSDR
CAA
CHAD
CAP
CMAQ
CONUS
EC
EPA
EGU
HAP
HAP EM
HEM
HI
HQ
IRIS
ISC
MOVES
NATA
NEI
NMIM
OAQPS
PAH
PM
PM2.5
PM10
POM
RfC
RTR
SCC
S/L/T
URE
WRF
microgram(s)/cubic meter
atmospheric dispersion model developed by the American Meteorological Society and the
U.S. Environmental Protection Agency's Regulatory Model Improvement Committee
Assessment System for Population Exposure Nationwide
Agency for Toxic Substances and Disease Registry
Clean Air Act
Consolidated Human Activity Database
criteria air pollutant
Community Multiscale Air Quality model
Contiguous United States (modeling domain for CMAQ)
exposure concentration
Environmental Protection Agency
electricity generating unit
hazardous air pollutant
Hazardous Air Pollutant Exposure Model
Human Exposure Model
hazard index
hazard quotient
Integrated Risk Information System
Industrial Source Complex
Motor Vehicle Emissions Simulator
National Air Toxics Assessment
National Emissions Inventory
National Mobile Inventory Model
Office of Air Quality Planning and Standards
polycyclic aromatic hydrocarbon
particulate matter
particulate matter less than 2.5 microns in diameter
particulate matter less than 10 microns in diameter
polycyclic organic matter
reference concentration
Risk and Technology Review
Source Classification Code
state, local or tribal agency
unit risk estimate
Weather Research Forecasting model
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1. Introduction
1.1. Overview
The Air Toxics Screening Assessment (AirToxScreen) is the U.S. Environmental Protection Agency's (EPA;
referred to throughout this document as "we") ongoing thorough evaluation of air toxics across the
United States. EPA developed AirToxScreen as a state-of-the-science tool to inform both national and
localized efforts to collect air toxics information, characterize emissions and help prioritize pollutants
and areas of interest for further study to gain a better understanding of risks. AirToxScreen is the
successor to the National Air Toxics Assessment, or NATA.
The goal of AirToxScreen is to identify those air toxics which are of greatest potential concern in terms
of contribution to population risk. Screening-level ambient and exposure concentrations and estimates
of risk and hazard for air toxics in each state have previously been generated at the census tract level,
but for the 2020 version of AirToxScreen, have been generated at the census block level.
This AirToxScreen Technical Support Document (TSD) describes the data and approaches EPA used to
conduct AirToxScreen, including descriptions of how we:
compiled emissions data and prepared them for use as model inputs (Section 2);
estimated ambient concentrations of air toxics (Section 3);
estimated exposures to air toxics for populations (Section 4);
selected toxicity values (Section 5);
characterized human-health risks and hazards (Section 6); and
addressed variability and uncertainty (Section 7).
References to additional documents are included (Section 8) to facilitate access to more detailed
technical information on the emissions inventories, dispersion modeling, photochemical modeling,
exposure modeling and toxicity values.
The TSD also includes the following appendixes:
Appendix A - a glossary of the key terms and their definitions
Appendix B - a list of air toxics included in AirToxScreen
Appendix C - procedures used to estimate AirToxScreen background concentrations
Appendix D - additional model evaluation summaries
Appendix E - documentation on HAPEM8 and its use in AirToxScreen
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We also provide a "Supplemental Data" folder with this document that contains the Microsoft® Access™
and Microsoft® Excel™ files referenced throughout this TSD.
This TSD satisfies basic documentation protocol expected of EPA products and provides a resource for
the technically oriented user community by summarizing the data sources, methods, models and
assumptions used in AirToxScreen. This document does not provide quantitative results for
AirToxScreen and thus presents no exposure or risk estimates. You can find results and other specific
information for AirToxScreen on the AirToxScreen website (https://www.epa.gov/AirToxScreen). Links
to previous assessments are also available on this site.
1.2. Purpose and Overview of AirToxScreen Steps
AirToxScreen follows several basic steps to produce the final assessment. These AirToxScreen steps are
depicted in Figure 1-1.
AirToxScreen Analytical Steps
Compile National
Emissions
Inventory
Estimate ambient
concentrations of
air toxics across
the U.S.
Estimate
population
exposures
Characterize
potential public
health risks from
inhalation
w ^
w ^
w ^
Figure 1-1. AirToxScreen - basic steps
The first and most time-consuming step in AirToxScreen is assembling the National Emissions Inventory
(NEI), a detailed, nationwide inventory of air toxics emissions. The NEI includes emissions from point,
nonpoint and mobile sources, as well as emissions from biogenic sources and fires. These source data
form the EPA's air emissions modeling platform and are the foundation of AirToxScreen's air quality
modeling. Section 2 of this document details the steps EPA used to construct this extensive database.
After preparing the NEI emissions and other needed data (e.g., meteorological data), we use these data
as inputs to two air quality models used to estimate ambient air concentrations of air toxics: the
American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD)
atmospheric dispersion model and the Community Multiscale Air Quality (CMAQ) photochemical model.
AERMOD is used for all AirToxScreen air toxics modeled, and CMAQ is used for a list of 52 air toxics that
are incorporated into CMAQ multipollutant version 5.4. CMAQ provides the overall mass, chemistry and
formation for hazardous air pollutants (HAPs) formed secondarily in the atmosphere (e.g.,
formaldehyde, acetaldehyde and acrolein), whereas AERMOD provides spatial granularity and more
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detailed source attribution. CMAQ also provides the biogenic and fire concentrations, as these sources
are not run in AERMOD. Section 3 of this document details the steps EPA used to model for
AirToxScreen.
For the HAPs modeled in both CMAQ and AERMOD, we combine the model-calculated annual average
concentrations using a hybrid approach. We next use these concentrations, along with the other
concentrations calculated by AERMOD, to prepare census block-level concentrations of all modeled air
toxics. Then, using the HAPEM8 exposure model, we account for human activity patterns and develop
exposure concentrations, or ECs. Finally, we estimate census block-level risks by applying health
benchmark data to the ECs. Sections 4 through 6 of this TSD detail these steps.
EPA's state, local and tribal (S/L/T) air agency partners play an integral role in AirToxScreen. First, S/L/T
specialists review early versions of NEI's source data for their area, working with local industry and other
emissions sources to develop and forward corrections to their area's emissions data. They also review
preliminary risk results during early stages of the AirToxScreen modeling process, which often reveals
other inaccuracies in the data. This review and feedback process helps ensure that AirToxScreen's input
data are as accurate as possible in the final version.
For the 2020 AirToxScreen, we modeled the 2020 NEI. For point sources, a draft version of the 2020 NEI
was modeled using AERMOD in the Summer of 2023, the results of which were used to prioritize review
of emissions that might contribute to high risk in the final results. Our S/L/T partners then reviewed the
point source emissions in the draft 2020 NEI (without airports and railyards) along with draft risk
estimates. We reviewed and incorporated S/L/T point source changes to emissions and release point
locations and parameters into the final 2020 NEI. The full AirToxScreen modeling process for all
emissions sectors using the hybrid approach (AERMOD and CMAQ) was performed. We then previewed
these final results with S/L/T agencies before releasing the 2020 AirToxScreen in 2024.
Figure 1-2 provides a more detailed flowchart showing the emissions sources used in the air quality
models and how the hybrid approach fits into the overall approach.
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Inventory
(NEI HAPs)
Point {with release
point info.), Nonpoint
& Mobile Sources
Biogenics & Fires
Emissions Processing
(SMOKE & Other Scripts)
Emissions Processing
(SMOKE)
Air Dispersion Modeling
(AERMOD)
Non-CMAQ
HAPS*
Add Background
Ambient
Monitoring Data
Photochemical Grid
Modeling (CMAQ)
Model
Performance
Evaluation
CMAQ
HAPS
Hybrid Approach
combine AERMOD and CMAQ
to predict ambient
concentrations at census blocks
Modeled
Ambient
concentrations
Inhalation
Exposure
(apply
exposure
ratios)
Chronic Risk
Characterization
includes all HAPs in AK/HI/PR/VI since not
part of CMAQ modeling domain
Figure 1-2. Detailed steps and approach used in AirToxScreen
1.3. What AirToxScreen Is
AirToxScreen is a first-pass, screening tool intended to evaluate the human-health risks posed by air
toxics across the United States. AirToxScreen provides screening-level estimates of the risk of cancer and
other potentially serious health effects from inhaling air toxics.
AirToxScreen uses emissions data compiled for a single year as inputs to air quality models. The models
use these source data along with meteorological data for the same year to estimate ambient air
concentrations of certain air toxics. EPA then combines these modeled concentrations with census data
and other information to calculate exposure concentrations of the air toxics. We also estimate cancer
risks and potential noncancer health effects associated with chronic inhalation exposure to the toxics.
EPA generates AirToxScreen results for each U.S. state at county and census block levels. We also
generate results for Puerto Rico, the Virgin Islands and the District of Columbia. These results help state,
local and tribal agencies prioritize air toxics, emission sources and locations of interest for further study.
They also help air agencies plan and implement national, regional and local efforts to reduce toxic air
pollution.
AirToxScreen provides a "snapshot" of outdoor air quality as it relates to air toxics. It also suggests the
risks to human health if air toxic emission levels were to remain the same as those estimated for the
assessment year. The estimates only reflect risks associated with chronic (assumed to be a 70 year
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lifetime) inhalation exposures to air toxics at the population level. Assumptions and methods we use to
complete the assessment limit the types of questions that AirToxScreen can answer reliably. You should
consider these limitations, described throughout this document and summarized in Section 7, when
interpreting the AirToxScreen results or when using them to address questions posed outside of
AirToxScreen.
AirToxScreen results can provide general answers to questions about emissions, ambient air
concentrations and exposures and risks across broad geographic areas (such as counties, states and the
nation) for the year modeled in the assessment.
AirToxScreen can answer questions such as the following:
Which air toxics pose the greatest potential risk of cancer or adverse noncancer effects across the
entire United States?
Which air toxics pose the greatest potential risk of cancer or adverse noncancer effects in certain
areas of the United States?
Which air toxics pose less, but still significant, potential risk of cancer or adverse noncancer effects
across the entire United States?
When risks from long-term inhalation exposures to all outdoor air toxics are considered together,
how many people could experience a lifetime cancer risk greater than levels of concern (e.g., 1-in-l
million or 100-in-l million)?
When considering potential adverse noncancer effects from long-term exposures to all outdoor air
toxics together for a given target organ or system, how many people could experience exposures
that exceed the reference levels intended to protect against those effects (i.e., a hazard quotient
greater than 1)?
1.4. Air Toxics Screening Assessment History
EPA's first national-scale air toxics study was the Cumulative Exposure Project (Caldwell et al. 1998). EPA
developed this project based on estimates of air toxics emissions present before the Clean Air Act (CAA)
was amended in 1990. The Cumulative Exposure Project estimated outdoor air toxics concentrations in
each contiguous-U.S. census tract.
For the first National Air Toxics Assessment (NATA), EPA enhanced the Cumulative Exposure Project
framework to include estimates of population exposure and health risk. The first NATA used a more
refined inventory of air toxics emissions developed for 1996, known at that time as the National Toxics
Inventory. EPA submitted this assessment for a technical peer review in January 2001 to a panel of EPA's
Science Advisory Board (EPA 2001b). The panel provided detailed comments later that year on the
validity of the overall approach, the elements of the assessment (including the data, models and
methods used) and the manner in which these components were integrated into a national-scale
assessment (EPA 2001a).
EPA incorporated many of the Science Advisory Board's suggestions into NATA and published the results
of that assessment in 2002. Since then, five versions of NATA have been completed - representative of
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air toxic emissions in 1999, 2002, 2005, 2011 and 2014, respectively - based on significant triennial
updates of the national emission inventories. In general, the scope of NATA progressively expanded with
subsequent versions, and some methods were refined and improved.
Beginning with emissions year 2017, NATA was succeeded by the AirToxScreen assessment, which use
the same basic methods as those used in the 2014 NATA. The goal of AirToxScreen was to build upon
the methodological foundation of NATA and to provide more frequent (i.e., annual) updates.
AirToxScreen for emission years 2017, 2018, and 2019 have been released with results at the census
tract level, consistent with NATA. As we strive to continue updating and improving our national scale
screening-level approach, the 2020 version of AirToxScreen incorporates the most significant update to
the air toxics screening assessment approach to date - a new receptor approach to provide results at
the census block level. This shift from census tracts to census blocks provides results at a more granular
geographic scale and helps to better answer the questions outlined in Section 1.3 and the goals outlined
below in Section 1.5.
1.5. How EPA and State, Local and Tribal Air Agencies Use AirToxScreen
Results
We designed AirToxScreen to help guide efforts to reduce toxic air pollution and to provide information
that can be used to further the already significant emissions reductions achieved in the United States
since 1990. EPA and S/L/T air agencies use AirToxScreen to identify those air toxics and source sectors
(e.g., point or mobile sources) having the highest exposures and health risks. The assessment results also
help us identify geographic patterns and ranges of risks across the country and across individual states
and county areas within states.
Specifically, we use AirToxScreen results to:
identify pollutants and industrial source categories of greatest concern;
improve understanding of health risks posed by airtoxics;
help set priorities for the collection of additional information;
set priorities for improving emission inventories;
expand and prioritize EPA's network of air toxics monitors;
support communities in designing their own local assessments;
enhance targeted risk-reduction activities; and
provide a multiple-pollutant modeling framework linking air toxics to the Criteria Pollutant Program
(EPA 2024a).
Similarly, S/L/T air agencies use AirToxScreen to:
prioritize pollutants and emission source types;
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identify places of interest for further study;
get a starting point for local assessments;
focus community efforts; and
inform monitoring programs.
1.6. How AirToxScreen Results Should Not Be Used
As described above, AirToxScreen is a screening-level assessment, designed to answer specific types of
questions. The underlying assumptions of AirToxScreen and its methods limit the range of questions
that can be answered reliably.
AirToxScreen results should not be used:
as a definitive means to pinpoint specific risk values within a censustract or census block;
to characterize or compare risks at local levels (such as between neighborhoods);
to characterize or compare risks between states;
as the sole basis for developing risk reduction plans or regulations;
as the sole basis for determining appropriate controls on specific sources or air toxics; or
as the sole basis to quantify benefits of reduced air toxic emissions.
The limitations of the assessment methods prevent AirToxScreen from serving as a stand-alone tool.
Furthermore, although EPA reports results currently at the census block or previously at the census tract
level in AirToxScreen, the average risk estimates are far more uncertain at these levels of spatial
resolution than at the county or state level. To analyze air toxics in smaller geographic areas, such as
within a suspected "hotspot," other tools such as site-specific monitoring and local-scale assessments
coupled with refined and localized data should be used.
These caveats are integral to the proper interpretation of AirToxScreen results. You should use
AirToxScreen results only to address those questions for which the assessment methods are suited. EPA
does not use AirToxScreen as the sole source of information leading to regulations or guiding the
enforcement of existing rules. Some of the methods used to conduct AirToxScreen are like those used in
air-related risk assessments conducted under the CAA mandate (such as residual risk assessments of
HAP emissions from point sources, or assessments of exposures to criteria air pollutants (CAPs) for
evaluations of National Ambient Air Quality Standards), AirToxScreen fundamentally differs from such
assessments in that it is not a regulatory program.
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1.7. The Risk Assessment Framework AirToxScreen Uses
In AirToxScreen we use methods consistent with the general risk assessment framework used
throughout EPA. This section overviews EPA's risk assessment framework and summarizes the
AirToxScreen process. Later sections detail the analytical components of this process.
EPA has published a series of guidelines (EPA 2024b) that establishes and explains the methods
recommended for assessing human-health risks from environmental pollution. This series makes
recommendations for carcinogen risk assessment, exposure assessment, chemical mixtures risk
assessment and other major EPA-wide risk assessments. EPA has also developed the three-volume Air
Toxics Risk Assessment (ATRA) Reference Library (EPA 2004a,b; EPA 2006a) as a reference for those
conducting air toxics risk assessments. This library details the fundamental principles of risk-based
assessment for air toxics, how to apply those principles in various settings, and strategies for reducing
risk at the local level. EPA's guidelines and methods are consistent with the National Research Council's
recommendations on conducting risk assessments (NRC 1983, 1994).
As described in more detail in these guidelines and documents, EPA's risk assessment process has three
phases (Figure 1-3), the second of which has two parts.
The first phase (problem formulation) comprises the initial planning and scoping activities and
definition of the problem, which results in the development of a conceptual model.
The second phase (analysis) includes two components:
~ Exposure assessment; and
~ Toxicity assessment.
The third phase is risk characterization, a synthesis of the outputs of the exposure and toxicity
assessments to characterize health risks for the scenario described in the initial phase.
Problem Formulation
II
Analysis
Toxicity Assessment
Exposure Assessment Hazard Identification
Dose-response Assessment
Risk Characterization
Figure 1-3. The general air toxics risk assessment process
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Source: Adapted from EPA (2004a).
An air toxics risk assessment starts with problem formulation. This first step begins with the systematic
planning and scoping needed before any analyses are begun. This planning process helps ensure that
the objectives of the assessment are met, resources are used efficiently, and the overall effort is
successful.
One important product of the problem formulation step is a conceptual model that describes how
releases of air toxics might pose risks to people. The conceptual model serves as a guide or "road map"
to the assessment. It defines the physical boundaries, potential sources, emitted air toxics, potentially
exposed populations, chemical fate and transport processes, expected routes of exposure and potential
health effects. The planning and scoping activities and problem formulation we conduct before carrying
out the analyses, are critical - they set the course for the assessment and inform EPA's decisions
regarding specific methods, models and data sources to use. The following section (1.8) describes the
conceptual model developed for AirToxScreen - the product of the first phase.
Meanwhile, the rest of this document is concerned primarily with describing the analysis phase of the
general air toxics risk assessment process (and specifically with describing the analyses conducted for
AirToxScreen). The analysis phase is the stage at which we use the risk assessment processes to evaluate
the problem at hand. Section 1.9 outlines the analytical steps, with detailed descriptions of each step
presented in later sections of this document.
1.8. The Scope of AirToxScreen
The national-scale assessment described in this document is consistent with EPA's definition of a
cumulative risk assessment, as stated in EPA's Framework for Cumulative Risk Assessment (EPA 2003, p.
6), as "an analysis, characterization, and possible quantification of the combined risks to health or the
environment from multiple agents or stressors." The Framework emphasizes that a conceptual model is
an important output of the problem formulation phase of a cumulative risk assessment. The conceptual
model defines the actual or predicted relationships among exposed individuals, populations or
ecosystems and the chemicals or stressors to which they might be exposed. Specifically, the conceptual
model lays out the sources, stressors, environmental media, routes of exposure, receptors and
endpoints (i.e., measures of effects) relevant to the problem or situation that is being evaluated. This
model takes the form of a written description and a visual representation of the relationships among
these components.
The conceptual model can sometimes include components that are not addressed specifically or
quantitatively by an assessment, but that are nevertheless important to consider.
Section 2.4 of the report for the 1996 NATA presented to EPA's Science Advisory Board for review (EPA
2001b) included a conceptual model. Some of the specifics included in that conceptual model have since
evolved as newer assessments have been completed (for example, the number of air toxics evaluated
has increased substantially since the 1996 NATA). The fundamental components included in NATA and
the relationships among them, however, have been generally consistent for all six NATAs and for
AirToxScreen. Moreover, the conceptual model described in this document is very similar to the one
presented in the documentation for the 1996 NATA.
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AirToxScreen is national in scope, covering the United States, Puerto Rico and the U.S. Virgin Islands. It
focuses on long-term inhalation exposures to air toxics In general, AirToxScreen is intended to provide
EPA with the best possible national-scale population-level estimates of exposure to and risks associated
with air toxics, considering data availability, technical capabilities and other potentially limiting factors.
The conceptual model for the AirToxScreen is presented in Figure 1-4. Each component included in the
model is described briefly in the sections that follow.
1.8.1. Sources of Air Toxic Emissions That AirToxScreen Addresses
Sources of primary air toxic emissions included in AirToxScreen (i.e., the AirToxScreen categories) are
point, nonpoint, mobile, biogenics and fires in the contiguous United States and Alaska, Hawaii, Puerto
Rico, and the U.S. Virgin Islands. Examples of point sources are large waste incinerators and factories.
Nonpoint sources include residential wood combustion (RWC), commercial cooking, and consumer and
commercial solvents. Mobile sources include vehicles found on roads and highways, such as cars and
trucks, and nonroad equipment, including lawn mowers and construction equipment. Nonroad sources
also include marine vessels, trains and aircraft.
Sources
Stressors
Pathways/
Media
Routes
Major
stationary
Nonpoint
Mobile
(on road &
nonroad)
Fires
Bio-
genics
Secondary
Background
in ambient
air
Indoor air
sources
Background in
other media
Clean Air Act HAPs
(plus DPM)
Outdoor air
microenvironments
Indoor air
microenvironments
In-vehicle
m icroen vi ron ments
Water
Food
Soil
Inhalation
Ingestion
Dermal
=1
General
Population
Receptors/Subpopulations
Male
~_
Female
Age
0-1
2-4
Age
5-15
Age
16-17
Age
18-64
65+
JC
Hispanic
White
African
American
Asian
American
T
Endpoints
(Specific noncancer
target organ or
system endpoints
shown)
Measures
Pollutant-specific, by tract,
and cumulative for cancer
risk (e.g., by cancer type,
weight of evidence) and
for respiratory hazard
index
Cancers
(leukemia, lung, others)
Respiratory
~=
Neurological
Blood (including
marrow & spleen)
Liver &
kidney
Cardio-
vascular
Suggestive Evidence of Carcinogenic Potential
Likely to be Carcinogenic to Humans
Carcinogenic to Humans
Distribution of
high-end cancer
risk estimates
Estimated percent of
population within specified
cancer risk ranges
Estimated
number of
cancer cases
Other'health
effects
Cardiovascular Hazard Quotient
Liver & Kidney Hazard Quotient
Blood Hazard Quotient
CNS Hazard Quotient
Respiratory System Hazard Index
Distribution of
estimated
values
(HQ or HI)
Estimated percent of
population within specified
ranges of quotient or
index values
Figure 1-4. Conceptual model for AirToxScreen
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Blue boxes indicate elements included in AirToxScreen; clear boxes indicate elements that could be included in future
assessments. In the "Sources" included here, "Major stationary" includes both major and area sources as defined for
regulatory purposes in the CAA. "Nonpoint" refers to smaller (and sometimes less discrete) sources that are typically
estimated on a top-down basis (e.g., by county). Additional explanation of source types included in AirToxScreen is presented
in Section 2. DPM refers to diesel particulate matter. HQ and HI refer to hazard quotient and hazard index, respectively.
AirToxScreen only considers outdoor sources of air toxics. It does not address indoor sources of toxics,
for example, those emitted from household chemicals. In addition, AirToxScreen background estimates
do not consider background air toxics from other media, such as water.
AirToxScreen presents results by both these broad source categories and by more detailed AirToxScreen
source groups. Details on this and other aspects of emission sources are presented in Section 2; details
on air quality modeling and characterization are presented in Section 3.
1.8.2. Stressors that AirToxScreen Evaluates
The stressors evaluated through AirToxScreen can include any of the 188 current HAPs defined in the
CAA and diesel PM. The set of air toxics included in AirToxScreen is determined by the emission and
toxicity data available at the time of the assessment. Diesel PM, an indicator of diesel exhaust, is
included in the set of stressors for AirToxScreen. The spreadsheet file "AirToxScreen_Pollutants.xlsx"
within the Supplemental Data folder accompanying this TSD lists the pollutants that AirToxScreen
assesses and provides more detailed information on the NEI and AirToxScreen pollutants. In Appendix B
of this document, Table B-l lists the CAA pollutants that are not included in AirToxScreen and the reason
for their omission.
1.8.3. Exposure Pathways, Routes and Time Frames for AirToxScreen
Exposure to air toxics from all sources is determined by multiple interactions among complex factors,
including the locations and nature of the emissions, the emission-release conditions, local meteorology,
locations of receptor populations, and the specific behaviors and physiology of individuals in those
populations. The combination of air toxics that people inhale, and the chemical interactions among
those air toxics, influence the risks associated with these exposures. This high level of complexity makes
aggregating risk across both substances and sources useful for depicting the magnitude of risks
associated with inhalation of air toxics.
The air quality modeling step of AirToxScreen includes evaluating the transport of emitted particles and
gases through the air to receptors. AirToxScreen modeling accounts for transformation of substances in
the atmosphere (also referred to as secondary formation) and losses of substances from the air by
deposition, where data are available and the modeling approach supports it. For air toxics with sufficient
ambient monitoring data, or with emissions data primarily due to point sources, we estimate
background concentrations. With fate and transport of emissions considered, and the presence of some
background concentrations, AirToxScreen estimates outdoor ambient concentrations across the nation.
AirToxScreen focuses on exposures due to inhalation of ambient air. Human receptors are modeled to
account for an individual's movement among microenvironments, such as residences, offices, schools,
exterior work sites and automobiles, where concentration levels can be quite different from general
outdoor concentrations. The exposure assessment estimates air concentrations for each substance
within each modeled microenvironment. The exposure assessment also accounts for human activities
that can affect the magnitude of exposure (e.g., exercising, sleeping). This component of AirToxScreen
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accounts for the difference between ambient outdoor concentrations and the exposure concentrations
(ECs; i.e., long-term-average concentrations to which people are exposed after accounting for human
activities).
To date, AirToxScreen has not estimated air toxic concentrations in water, soil or food associated with
deposition from air, or the bioaccumulation of air toxics in tissues. Similarly, AirToxScreen has not
estimated human exposures to chemicals via ingestion or dermal contact. EPA considers these pathways
important, but refined tools and data required to model multipathway concentrations and human
exposures on the national scale are not yet readily available for use for many air toxics.
AirToxScreen estimates average annual outdoor concentrations, which are used to develop long-term
inhalation exposures for each of the air toxics. For cancer and chronic (long-term) health effects, the
exposure is assumed to be continuous over a lifetime (i.e., 70 years for the purposes of this analysis).
Subchronic and acute (lasting less than 24 hours) exposures are not estimated in AirToxScreen because
the emissions database contains only annual-total emissions. If the emission inventories are later
expanded to cover short-term (e.g., hourly, daily) emission rates, we would consider incorporating
shorter exposure times into AirToxScreen.
1.8.4. Receptors that AirToxScreen Characterizes
AirToxScreen characterizes average risks to people belonging to distinct human subpopulations using
exposure factors estimated at census tract scales. The overall population is divided into cohorts based
on residential location, life stage (age) and daily activity pattern. A cohort is generally defined as a group
of people within a population assumed to have identical exposures during a specified exposure period.
Residential locations are specified according to U.S. Census tracts, which are geographic subdivisions of
counties that vary in size but typically contain about 4,000 residents each. Life stages are stratified into
six age groups: 0-1, 2-4, 5-15, 16-17, 18-64, and 65 and older. Daily-activity patterns specify time
spent in various microenvironments (e.g., indoors at home, in vehicles, outdoors) at various times of
day. For each combination of residential census tract and age, 30 sets of age-appropriate daily activity
patterns are selected to represent the range of exposure conditions for residents of the tract. A
population-weighted typical exposure estimate is calculated for each cohort, and this value is used to
estimate representative risks, as well as the range of risks, for a "typical" individual residing in that tract.
Risk results for individual cohorts are not included in AirToxScreen results.
AirToxScreen does not include non-human receptors (e.g., wildlife and native plants). The complexity of
the varied ecosystems across the vast area AirToxScreen covers precludes considering potential adverse
ecological impacts at this time. Local- and urban-scale assessments can be developed to include non-
human receptors, contingent on the availability of necessary resources, data and methodologies. We
currently, however, have no plans to include non-human receptors in AirToxScreen.
1.8.5. Endpoints and Measures: Results of AirToxScreen
AirToxScreen reports estimated cancer risks and noncancer hazards attributed to modeled sources. Key
measures of cancer risk developed for AirToxScreen include:
upper-bound estimated lifetime individual cancer risk; and
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estimated numbers of people within specified risk ranges (e.g., number of individuals with
estimated long-term cancer risk of 1-in-l million or greater or less than 100-in-l million).
For noncancer effects, the key measures presented in AirToxScreen are hazard indexes summed across
all air toxics modeled for the respiratory system. Other target organs and systems are also shown.
AirToxScreen characterizes cancer risk and potential noncancer effects based on estimates of inhalation
exposure concentrations determined at the census block level. This approach is used only to determine
geographic patterns of risks within counties, and not to pinpoint specific risk values for each census
block. We are reasonably confident that the patterns (i.e., relatively higher levels of risk within a county)
represent actual differences in overall average population risks within the county. We are less confident
that the assessment pinpoints the exact locations where higher risks exist, or that the assessment
captures the highest risks in a county. EPA provides the risk information at the census block level rather
than just the county level, however, because the county results are less informative (in that they show a
single risk number to represent each county). Information on variability of risk within each county would
be lost if block-level estimates were not provided. This approach is consistent with the purpose of
AirToxScreen, which is to provide a means to inform both national and more localized efforts to collect
air toxics information and to characterize emissions (e.g., to help prioritize air toxics and areas of
interest for more refined data collection such as emissions testing or monitoring). Nevertheless, the
assumptions made in allocating mobile and nonpoint source emissions within counties can result in
significant uncertainty in estimating risk levels, even though general spatial patterns are reasonably
accurate.
1.9. Model Design
Consistent with the general approach for air toxics risk assessment described in Section 1.7 and
illustrated in Figure 1-3, the analysis phase of AirToxScreen includes two main components: estimating
exposure and estimating toxicity. The outputs of these analyses are used in the third phase, risk
characterization, which produces health-risk estimates that can be used to inform research or risk
management. These two phases (analysis and risk characterization) represent the "core" of EPA's
assessment activities associated with AirToxScreen. This set of activities is referred to here as the
"AirToxScreen risk assessment process."
The AirToxScreen risk assessment process can be characterized by four main steps:
compiling the nationwide inventory of emissions from outdoorsources;
estimating nationwide ambient outdoor concentrations of the emitted air toxics;
estimating population exposures to these air toxics via inhalation; and
characterizing potential health risks associated with these inhalation exposures.
The fourth step (risk characterization) also requires that quantitative dose-response or other toxicity
values be identified for each air toxic included in the assessment. These values are taken from those
developed by other EPA and non-EPA programs. Although this step does not require a "new"
quantitative dose-response assessment to be conducted as part of AirToxScreen, it does require that we
make important scientific and policy decisions regarding the appropriate values to use in AirToxScreen.
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Because these decisions are critical to the risk results, the identification of appropriate dose-response
values is also described in this TSD in Section 5. The AirToxScreen risk assessment process is illustrated in
Figure 1-5. The development of the emission inventory, air quality modeling, inhalation exposure
modeling and risk characterization must be conducted sequentially - completing each step requires
outputs from the previous step, and toxicity values are required to carry out the risk-characterization
calculations. Cancer risks and the potential for noncancer health effects are estimated using available
information on health effects of air toxics, risk-assessment and risk-characterization guidelines, and
estimated population exposures.
Each of these five components is described briefly here and explained in detail in the remainder of this
document:
Section 2 explains the source types and air toxics included in the AirToxScreen emissions inventory.
It also describes the processes we carried out to prepare the emissions for the air quality models.
Section 3 discusses the models and procedures used to estimate ambient concentrations of air
toxics, with links and references to technical manuals and other detailed documentation for the
models used for AirToxScreen.
Compile Nationwide Identify Toxicity
Emission Inventory Values
(Section 2) (Section 5)
1
Conduct Air
National Air Quality Modeling
(Section 3)
Emissions
Inventory
Ambient
Concentrations
Model Inhalation
Exposures (Section 4)
1
Conduct Risk
Exposures Characterization
(Section 6)
T
Cancer Risks,
Chronic Noncancer
Figure 1-5. The AirToxScreen risk assessment process and corresponding sections of this TSD
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Section 4 explains the processes used to estimate population-level exposure to outdoor ambient
levels of air toxics, accounting for information on activities and other characteristics that can affect
inhalation exposures.
Section 5 discusses the dose-response values used for AirToxScreen, the sources from which these
values are obtained and assumptions made specific to AirToxScreen.
Section 6 provides the calculations used to estimate cancer risk and potential noncancer hazard.
Section 7 describes the uncertainties and limitations associated with the AirToxScreen process that
must be considered when interpreting AirToxScreen results.
As noted at the beginning of this section, this document is intended to serve as a resource
accompanying the most recent national-scale assessment. Accordingly, although the following sections
contain information on the AirToxScreen process that are generally applicable to all previous
assessments, references to specific technical processes and supporting details typically emphasize what
we did for the current version of AirToxScreen.
1.9.1. The Strengths and Limitations of the Model Design
EPA developed AirToxScreen to inform both national and localized efforts to characterize air toxics
emissions and health risks (e.g., prioritize air toxics or areas of interest for monitoring and community
assessments). Because of this targeted objective, tools other than AirToxScreen may be more
appropriate for assessing health risks outside the specific purpose of AirToxScreen (e.g., for evaluating
risks from either a broader or more specific perspective).
To further define and clarify what AirToxScreen should not be used for, this section contains
descriptions of some of the important data and results that are not included in AirToxScreen:
AirToxScreen does not include information that applies to specific locations. The assessment focuses
on variations in air concentration, exposure and risk among geographic areas such as census blocks,
counties and states. All questions asked, therefore, must focus on the variations among these
geographic areas (census blocks, counties, etc.). Moreover, as previously mentioned, results are far
more uncertain at the census block level than for larger geographic areas such as states or regions.
(Section 7 contains discussions on the higher uncertainty at small geographic scales such as census
blocks.)
AirToxScreen does not include data appropriate for addressing epidemiological questions such as
the relationship between cancer risks or noncancer health effects and proximity of residences to
point sources, roadways and other sources of air toxics emissions.
The results do not include impacts from sources in Canada or Mexico other than as general
background sources. Thus, the results for states bordering these countries do not comprehensively
reflect sources of transported emissions that could be significant.
AirToxScreen does not include results for individuals. Within a census block, all individuals are
assigned the same ambient air concentration, chosen to represent a typical ambient air
concentration. Similarly, the exposure assessment uses activity patterns that do not fully reflect the
actual variations among individuals.
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The results do not include exposures and risk from all compounds. For example, of the 181 air toxics
included in AirToxScreen, only 127 air toxics have been assigned dose-response values. In EPA's
judgment, the remaining air toxics do not have adequate data to quantitatively assess their impacts
on health. Therefore, they do not contribute to the aggregate cancer risk or target-organ-specific
hazard indexes estimated in AirToxScreen. Of note, the assessment does not quantify cancer risk
from diesel PM, although EPA has concluded that the general population is exposed to levels close
to or overlapping with levels that have been linked to increased cancer risk in epidemiology studies.
AirToxScreen, however, does quantify noncancer effects of diesel PM.
Other than lead, which is both a CAP and a HAP, the results do not include the air pollutants, known
as CAPs (particulate matter, ground-level ozone, carbon monoxide, sulfur oxides, nitrogen oxides),
for which the CAA requires EPA to set National Ambient Air Quality Standards (other than CAP
impacts on secondary formation of formaldehyde, acetaldehyde and acrolein).
The results do not reflect all pathways of potential exposure. The assessment includes risks only
from direct inhalation of the emitted air toxics compounds. It does not consider air toxics
compounds that may deposit onto soil, water and food and subsequently enter the body through
ingestion or skin contact.
The assessment results reflect exposure at outdoor, indoor and in-vehicle locations, but only to
compounds released into the outdoor air. The assessment does not include exposure to air toxics
emitted indoors, such as those from stoves, those that out-gas from building materials or those
from evaporative benzene emissions from cars in attached garages. The assessment also does not
consider toxics released directly to water and soil.
The assessment does not fully reflect variation in background ambient air concentrations.
Background ambient air concentrations are average values over broad geographic regions.
The assessment may not capture all sources that have episodic emissions (e.g., facilities with short-
term deviations in emissions resulting from startups, shutdowns, malfunctions and upsets). Where
available, episodic emission information is used (e.g., for electricity generating units). In the absence
of additional data, we assume emission rates are uniform throughout the year.
Short-term (acute) exposures and risks are not included in AirToxScreen.
Atmospheric transformation and losses from the air by deposition are not accounted for in
AirToxScreen air toxics that are not modeled in CMAQ.
The evaluations to date have not assessed ecological effects, given the complexity of the varied
ecosystems across the vast area covered by AirToxScreen.
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2. Emissions
The systematic compilation of a detailed, nationwide inventory of air toxics emissions is the first major
step in the AirToxScreen risk assessment process. This section contains descriptions of the emissions
used for AirToxScreen. Section 2.1 describes the emissions data sources and preparation of the
emissions used in AirToxScreen. Section 2.2 discusses the processing of emissions for input into the
CMAQ model, and Section 2.3 discusses the processing for input into the AERMOD model.
2.1. Sources of Emissions
AirToxScreen is intended to address
outdoor emissions of all hazardous air
pollutants (HAPs) and diesel particulate
matter (PM), together called "air toxics"
in this document. To model air toxics,
emissions of both air toxics and criteria
air pollutants (CAPs, including CAP
precursors such as ammonia and volatile
organic compounds) are used to address
the chemical interactions that occur
across all pollutants.
AirToxScreen combines modeling from
CMAQ and AERMOD for the contiguous
United States. CMAQ multipollutant
modeling addresses all sources in the National Emissions Inventory (NEI) for CAPs and about 52 air toxics
including diesel particulate matter. Emissions from outside the United States are represented by CMAQ
boundary conditions as discussed in Section 0. For the remaining "non-CMAQ" HAPs and non-CMAQ
parts of the modeling domain (i.e., Alaska, Hawaii, Puerto Rico and the U.S. Virgin Islands), only
AERMOD is used. For these pollutants and geographic regions, spatially uniform background
concentrations based on remote concentrations are added to the AERMOD-modeled data to represent
influences from transport and emissions outside the modeling domain (Section Error! Reference source n
ot found.)- AERMOD modeling addresses all pollutants covered by AirToxScreen and all anthropogenic
sources except prescribed and agricultural burning.
The main source of the emissions data for the CAPs and HAPs modeled for AirToxScreen is the National
Emissions Inventory, or NEI. The NEI is a comprehensive and detailed estimate of air emissions of CAPs
and HAPs from all air emissions sources in the United States, including the territories of Puerto Rico and
the U.S. Virgin Islands, and offshore sources and commercial marine vessels (CMVs) in federal waters. A
complete NEI, consisting of point stationary sources, nonpoint sources, mobile sources and fires, is
prepared every 3 years by EPA. It is based primarily upon emission estimates and emission model inputs
provided by S/L/T air agencies for sources in their jurisdictions, supplemented by data developed by
EPA. These data are submitted electronically to the Emissions Inventory System (EIS). CAPs are required
under EPA's Air Emissions Reporting Requirements (AERR). HAPs are submitted voluntarily. Lead is both
a HAP and a CAP, so it must be submitted under the AERR. Currently, states are required to report
facilities with lead emissions greater than or equal to 0.5 tons per year (TPY). In addition to CAPs and
HAPs, the NEI includes speciated particulate matter (PM) and diesel PM. The NEI diesel PM is computed
Sometimes "air toxics" and "HAPs" are used interchangeably.
In this document, however, "air toxics" refers to the HAPs that
EPA is required to control under Section 112 of the 1990 Clean
Air Act (EPA 2016a) plus diesel PM. The 1990 Clean Air Act
Amendments required EPA to control HAPs (EPA 2018) and
provided for revisions to be made to that list. Currently, the list
includes 188 HAPs. Diesel PM is not a HAP; however, it has
been included in AirToxScreen. Some evidence indicates that
localized high lifetime cancer risks are associated with
exposure to diesel PM. However, EPA currently does not have
sufficient evidence to develop a unit risk estimate for it.
Therefore, the potential adverse noncancer effects associated
with diesel PM are estimated in AirToxScreen (using an
Integrated Risk Information System reference concentration),
but its cancer risks are not.
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as the PM10 emissions for on-road and nonroad engines burning diesel or residual oil fuels. Although
stationary engines also can burn diesel fuel, only mobile source sectors are used for estimating diesel
PM emissions.
To build as complete an NEI as possible, EPA augments the S/L/T-submitted data using various sources
of information, including the Toxics Release Inventory (TRI), and applies HAP-to-CAP emission-factor
ratios to CAP emissions reported by S/L/T.
Table 2-1 contains a summary of the sources of emissions data in the NEI. More detailed information on
all data sources can be found in 2020 NEI documentation and the 2020 Emissions Modeling Platform
documentation.
Table 2-1. Summary of emissions sources in the 2020 NEI
Source
Description
Stationary
point
Most stationary point-source HAP data were submitted voluntarily by S/L/T.
For some point sources, EPA gap-filled HAPs. Sources of gap-filled HAPs include: TRI data and
augmentation using emission-factor ratios (of HAP to CAP) applied to S/L/T-reported CAP
emissions. This source group includes the following modeling platform sectors: Electric
Generating Units (ptegu), Point source oil and gas (pt_oilgas), and remaining non-EGU point
(Ptnonipm), with results of point airports and point railyards published separately.
Ptegu: 2020 NEI point source EGUs, replaced with hourly Continuous Emissions Monitoring
System (CEMS) values for NOX and S02, and the remaining pollutants temporally allocated
according to CEMS heat input where the units are matched to the NEI. Emissions for all sources
not matched to CEMS data come from 2020 NEI point inventory. Annual resolution for sources
not matched to CEMS data, hourly for CEMS sources. EGUs closed in 2020 are not part of the
inventory.
Pt_oilgas: 2020 NEI point sources that include oil and gas production emissions processes for
facilities with North American Industry Classification System (NAICS) codes related to Oil and Gas
Extraction, Natural Gas Distribution, Drilling Oil and Gas Wells, Support Activities for Oil and Gas
Operations, Pipeline Transportation of Crude Oil, and Pipeline Transportation of Natural Gas.
Includes U.S. offshore oil production.
Ptnonipm: All 2020 NEI point source records not matched to the airports, ptegu, or pt_oilgas
sectors. 2020 NEI rail yards, while in the ptnonipm modeling platform sector, are published
separately for AirToxScreen (see below).
Point airports
2020 NEI CAP and HAP emissions for aircraft operations including commercial, general aviation,
air taxis and military aircraft, auxiliary power units and ground support equipment computed by
the EPA for approximately 20,000 airports. Methods include the use of the Federal Aviation
Administration's (FAA's) Aviation Environmental Design Tool (AEDT) For further information, see
Section 3.2 of 2020 National Emissions Inventory Technical Support Document: Point Sources and
2020 National Emissions Inventory: Aviation Component.
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Source
Description
Point rail
yards
The 2020 NEI includes estimates compiled by the EPA for most rail yards in the US. Yard emissions
are associated with the operation of switcher engines at each yard. S/L/T data also provided by
California, the District of Columbia, Minnesota, Texas, and Washoe County Nevada. Switch yards
are reported as point sources to SCC 28500201. Some states report switch yards to nonpoint
(2285002010); however, EPA prefers that these emissions be reported as point sources and may
be retiring this SCC in the next NEI cvcle. Details for rail vards are documented in a report, "2020
National Emissions Inventory Locomotive Methodology", on the 2020 Supplemental data FTP site
under the nonpoint rail folder. S/L/Ts submitted point rail yard emissions were compared to EPA-
computed emissions to avoid double counting between the S/L/T and EPA emissions.
Stationary
nonpoint
Includes many different source stationary source categories that are generally too ubiquitous to
be inventoried as point sources and are therefore estimated at the county level. Examples of
these sources include residential heating, consumer and commercial product usage, commercial
cooking, oil and gas production, and industrial, commercial and institutional fuel combustion
(where not in the point inventory). Emission estimates for these are developed by EPA and/or
submitted bv S/L/T. For further information, see 2020NEITSD: Section 6 nonpoint Overview
Biogenics
The biogenic emissions for the 2020 National Emissions Inventory (NEI) were computed based on
2020 meteorology data from the Weather Research and Forecasting (WRF) model version 3.8
(WRFv3.8) and using the Biogenic Emission Inventory System, version 4 (BEIS4) model. Includes
VOC, NOx and three HAPs: formaldehyde, acetaldehyde and methanol. For further information,
see 2020 NEI TSD Section 8: Biogenics.
Locomotives
The 2020 locomotive sector includes railroad locomotives powered by diesel-electric engines. A
diesel-electric locomotive uses 2-stroke or 4-stroke diesel engines and an alternator or a
generator to produce the electricity required to power its traction motors. The locomotive source
category is further divided up into categories: Class 1 line haul, Class ll/lll line haul, Passenger, and
Commuter, and Yard. Railyard emissions are included as point sources as described above. The
locomotives sector includes data from SLT agency-provided emissions data, and an EPA dataset of
locomotive emissions. HAP emissions were estimated by applying speciation profiles to the VOC
or PM estimates. These "HAP fractions" were updated for 2017 NEI. These profiles are posted in
the workbook "2017Rail_HAP_AugmentationProfileAssignmentFactors_20200128.xlsx" on the
2017 Supplemental data FTP site. 12-3 HAP estimates were calculated at the yard and link level,
after the criteria emissions had been allocated. Where submitting agencies did not supply HAPs,
those estimates were also derived via this VOC/PM speciation method. For further information,
see 2020 NEI TSD Section 12. Locomotives.
CMVs
EPA's Commercial Marine Vessels estimates are computed using detailed satellite-based
automatic identification system (AIS) activity data from the US Coast Guard. The details of these
calculation are available in the documents "2020NEI C1C2 Documentation" and "2020 C3 Marine
Emissions Tool Documentation" on the 2020 Supplemental data FTP site under folder nonpoint.
CMV.
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Source
Description
On-road
Onroad mobile sources include emissions from motorized vehicles that normally operate on
public roadways. This includes passenger cars, motorcycles, minivans, sport-utility vehicles, light-
duty trucks, heavy-duty trucks, and buses. The sector includes emissions generated from parking
areas, emissions from short-duration idle during pickups/deliveries, emissions from vehicles when
they start, and emissions while the vehicles are moving. The sector also includes "hoteling"
emissions, which refers to the time spent idling in a diesel long-haul combination truck during
federally mandated rest periods of long-haul trips. Onroad emissions in the 2020 NEI are
comprised of emission estimates calculated based on version 3 of the MOVES model run with
State, Local, and Tribal (S/L/T)-submitted activity data and other MOVES inputs when provided,
except for California and tribes, for which the NEI includes submitted emissions. In cases where
S/L/T submitted data are not provided, EPA-developed default activity based on data from the
Federal Highway Administration (FHWA) and other data sources. EPA also developed default data
for all other inputs required by MOVES which are used where S/L/T data of sufficient quality are
not available. EPA added DIESEL-PM10 and DIESEL-PM25 for all diesel fuel SCCs, and they were
set equal to the PMio and PM2.5 emissions from these diesel SCCs. For more information, see
2020 NEI TSD Section 5 Onroad Mobile.
Nonroad,
excluding
airports,
locomotives
and CMVs
The mobile nonroad equipment data category includes all mobile source emissions that do not
operate on roads, excluding commercial marine vehicles, railways, and aircraft. The emissions
included in the NEI for this category are modeled using MOVES and cover nonroad equipment in
10 broad economic sectors: construction, agriculture, industrial, lawn & garden (commercial and
residential), commercial, logging, railroad support (excluding locomotives), recreational vehicles,
recreational marine (pleasure craft; excluding commercial marine vessels), and underground
mining. Nonroad equipment emissions were computed by running the MOVES3,l which
incorporates the NONROAD model. MOVES3 and its predecessor MOVES2014b incorporated
updated nonroad engine population growth rates, nonroad Tier 4 engine emission rates, and
sulfur levels of nonroad diesel fuels. MOVES3 was used for all states other than California, which
developed their own emissions using their own tools. For further information, see 2020NEI TSD:
Section 4 Nonroad Eauipment.
Fires
Wildfires and prescribed burns that occur during the inventory year are included as "non-point"
sources beginning with the 2020 NEI. Previous NEIs had wildland fires labeled as "events" sources.
Emissions from these fires, as well as agricultural fires, make up the National Fire Emissions
Inventory (NFEI). Estimated emissions from all these fire types in the 2020 NEI are calculated from
burned area data. Input data sets are collected from State/Local/Tribal (S/L/T) agencies and from
national agencies and organizations. Raw burned area data compiled from S/L/T agencies and
national data sources are cleaned and combined to produce a comprehensive burned area data
set. Emissions are then calculated using fire emission tools/models that rely on burned area as
well as fuel and climatological weather information. For more information, including HAP factors
used, see 2020NEI TSD, Section 7: Wild and Prescribed Fires and Field Burning. The resulting
emissions are compiled by date and location as day-specific emission estimates.
2.1.1. Pollutants and Pollutant Groups
AirToxScreen air quality modeling requires emissions of criteria air pollutants (CAPs) and their
precursors in addition to HAP and diesel PM. We also need to aggregate the emissions of certain NEI
pollutants to match the AirToxScreen pollutants. This section discusses the pollutants and pollutant
groups in the NEI used in AirToxScreen and the aggregation needed to generate the AirToxScreen
pollutants.
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In CMAQ, we model CAPs and precursors and about 52 air toxics, including diesel particulate matter.
Table 2-2 shows the specific air toxics used in CMAQ. For AERMOD, we model nearly all HAPs covered by
the NEl; those that are not modeled are due to lack of emissions or risk considerations. Table B-l in
Appendix B provides more detail about each HAP excluded. For AirToxScreen, we aggregated NEI
pollutants that can be reported as either a group or as specific individual pollutants belonging to the
group into pollutant groups for the AirToxScreen modeling and results. This aggregation was done in the
emissions modeling process, prior to the air quality modeling. Table 2-2 lists the groups. For example,
individual glycol ethers are grouped into the single AirToxScreen HAP "glycol ethers." The spreadsheet
file "AirToxScreen_Pollutants.xlsx" within the Supplemental Data folder accompanying this TSD shows
the individual HAPs and groups used in AERMOD and CMAQ. The following subsections give more details
about some specific AirToxScreen pollutant groups.
Table 2-2. Pollutant groups
Group
Chromium VI (Hexavalent)
Cresol cresylicacid (mixed isomers)
Cyanide compounds
Glycol ethers
Nickel compounds
PAHPOM
Polychlorinated biphenyls (aroclors)
Xylenes (mixed isomers)
2.1.1.1. Assignment of PAHs into PAH modeling groups
In AirToxScreen, Polycylic aromatic hydrocarbons and polycylic organic matter are represented by a
lumped group, "PAHPOM".
The individual compounds in the PAHPOM group have widely varying risks. As a result, we modeled the
PAHPOM in separate risk-based groups based on their unit risk estimate (URE). For AirToxScreen, we
summed the concentrations and risks across all PAHPOM risk groups. For the tabular emission
summaries on the AirToxScreen website (https://www.epa.gov/AirToxScreen), the individual PAHPOM
compounds (i.e., in Table 2-3) are provided.
The PAH groups are based on the groups established for AirToxScreen and are listed below along with
the individual PAHs assigned to each. Note that two pollutants representing polycylic organic matter in
the NEI are unspeciated: pollutant code 250 (PAH/POM-Unspecified) and pollutant code 130498292
(PAH, total). These are assigned to PAH_880E5.
Table 2-3. PAH/POM pollutants group
PAH Group
NEI Pollutant Code
NEI Pollutant Description
URE l/(|ig/m3)
PAH_000E0
120127
Anthracene
PAH_000E0
85018
Phenanthrene
PAH_000E0
129000
Pyrene
PAH_101E2
56495
3-Methylcholanthrene
0.01
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PAH Group
NEI Pollutant Code
NEI Pollutant Description
URE l/(|ig/m3)
PAH_114E1
57976
7,12-
Dimethylbenz[a] Anthracene
0.114
PAH_176E2
189640
Dibenzo[a,h]Pyrene
9.6E-03
PAH_176E2
189559
Dibenzo[a,i]Pyrene
9.6E-03
PAH_176E2
191300
Dibenzo[a,l]Pyrene
9.6E-03
BENZOAPYRNE
50328 (see Note 1)
Benzo[a]Pyrene
9.6E-04
PAH_176E3
192654
Dibenzo[a,e]Pyrene
9.6E-04
PAH_176E3
53703
Dibenzo[a,h]Anthracene
9.6E-04
PAH_176E3
194592
7H-Dibenzo[c,g]carbazole
9.6E-04
PAH_176E3
3697243
5-Methylchrysene
9.6E-04
PAH_176E3
41637905
Methylchrysene
9.6E-04
PAH_176E4
56553
Benz[a]Anthracene
9.6E-05
PAH_176E4
205992
Benzo[b]Fluoranthene
9.6E-05
PAH_176E4
205823
Benzo[j]fluoranthene
9.6E-05
PAH_176E4
226368
Dibenz[a,h]acridine
9.6E-05
PAH_176E4
224420
Dibenzo[a,j]Acridine
9.6E-05
PAH_176E4
193395
lndeno[l,2,3-c,d]Pyrene
9.6E-05
PAH_176E4
5522430
1-Nitropyrene
9.6E-05
PAH_176E5
207089
Benzo[k]Fluoranthene
9.6E-06
PAH_176E5
86748
Carbazole
9.6E-06
PAH_176E5
218019 (see Note 2)
Chrysene
9.6E-06
PAH_192E3
8007452
Coal Tar
9.9E-04
PAH_880E5
83329
Acenaphthene
4.8E-05
PAH_880E5
208968
Acenaphthylene
4.8E-05
PAH_880E5
203338
Benzo(a)Fluoranthene
4.8E-05
PAH_880E5
195197
Benzo(c)phenanthrene
4.8E-05
PAH_880E5
192972
Benzo[e]Pyrene
4.8E-05
PAH_880E5
203123
Benzo(g,h,i)Fluoranthene
4.8E-05
PAH_880E5
191242
Benzo[g,h,i,]Perylene
4.8E-05
PAH_880E5
56832736
Benzofluoranthenes
4.8E-05
PAH_880E5
91587
2-Chloronaphthalene
4.8E-05
PAH_880E5
284 (see Note 3)
Extractable Organic Matter
(EOM)
4.8E-05
PAH_880E5
206440
Fluoranthene
4.8E-05
PAH_880E5
86737
Fluorene
4.8E-05
PAH_880E5
779022
9-Methyl Anthracene
4.8E-05
PAH_880E5
26914181
Methylanthracene
4.8E-05
PAH_880E5
2422799
12-
Methylbenz(a)Anthracene
4.8E-05
PAH_880E5
65357699
Methylbenzopyrene
4.8E-05
PAH_880E5
90120
1-Methylnaphthalene
4.8E-05
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PAH Group
NEI Pollutant Code
NEI Pollutant Description
URE l/(|ig/m3)
PAH_880E5
91576
2-Methylnaphthalene
4.8E-05
PAH_880E5
832699
1-Methylphenanthrene
4.8E-05
PAH_880E5
2531842
2-Methylphenanthrene
4.8E-05
PAH_880E5
2381217
1-Methylpyrene
4.8E-05
PAH_880E5
130498292
PAH, total
4.8E-05
PAH_880E5
198550
Perylene
4.8E-05
PAH_880E5
250
PAH/POM - Unspecified
4.8E-05
PAH_880E5
N590
Polycyclic aromatic
compounds (includes 25
specific compounds)
4.8E-05
Note 1: Benzo[a]pyrene is the only PAHPOM with an RfC (2E-6 ng/m3). Therefore, chronic noncacer HI values for
the PAHPOM group are based solely on benzo[a]pyrene concentrations.
Note 2: Even though chrysene URE is 9.6E-7, put into 9.6E-6 group (there is no lower risk group other than 0 so this
is conservative).
Note 3: pollutant code retired in 2016
2.1.1.2. Metal groups
AirToxScreen includes metal compound
groups consistent with metal emissions
in the NEI. Metal emissions in the NEI
represent only the mass of the metal
with a few exceptions for specific
compounds of hexavalent chromium
(chromium VI) and nickel of known
composition. Prior to modeling, we
applied factors that convert the emissions of specific metal compounds to the portion of the compound
that is metal. Table 2-4 shows the HAPs that have metal speciation factors other than 1.
The three nickel compounds and three chromium VI compounds in the NEI are shown in the table below
with the corresponding adjustment factors to compute the emissions that account for just the metal
portion of the compound. Note that after applying the adjustments, the chromium VI compounds are
grouped into chromium VI and the nickel compounds are grouped into nickel. These are generally small
in mass compared to the metal-only pollutants (nickel and chromium VI) and are only present for
stationary sources.
Table 2-4. Metal speciation factors for NEI metal compounds
Description
AirToxScreen Pollutant Group
pollutant_cd (CAS)
Metal Speciation Factor *
Nickel oxide
NICKELCOMPOUNDS
1313991
0.7858
Chromium trioxide
CHROMIUM VI (HEXAVALENT)
1333820
0.52
Chromic acid (VI)
CHROMIUM VI (HEXAVALENT)
7738945
0.4406
* Metal speciation factor is the ratio of the molecular weight of the metallic element to the molecular weight of
the compound.
Example: Adjusting Emissions for Chromium VI Compounds
Chromic acid (VI) (H2Cr04> has a molecular weight of about
118.01. Chromium, with an atomic mass of 52, is the toxic
element of interest in this metal compound. Emissions reported
in NEI are therefore multiplied by 0.4406 (i.e., 52 / 118.01), and
the resulting emission rate is used in AirToxScreen modeling.
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2.1.1.3. Diesel PM
Diesel PM is neither a CAP nor HAP as defined by Section 112 of the CAA, however it was identified as a
mobile source air toxic in EPA's 2007 rule, "Control of Hazardous Air Pollutants From Mobile Sources
final rule" (EPA 2007a). Prior to the 2014 NEI, it was generated separately for AirToxScreen modeling
from the NEI and was not included as a separate NEI pollutant. However, starting with the 2014 NEI,
diesel PM emissions are included in the NEI, as discussed above. The NEI-generated diesel PM emissions
from the mobile-source, engine-exhaust PMio emissions were used for engines burning diesel or
residual-oil fuels. These sources include on-road, nonroad, point-airport-ground support equipment,
point-locomotives, nonpoint locomotives, and all PM from diesel or residual-oil-fueled nonpoint CMVs.
Diesel PM emissions were set equal to PMio emissions for these engines. Although stationary engines
also can burn diesel fuel, only mobile-related diesel engine SCCs were used.
2.1.1.4. Pollutant information file
The spreadsheet file "AirToxScreen_Pollutants.xlsx" within the Supplemental Data folder accompanying
this TSD includes a crosswalk that contains NEI pollutant codes/descriptions, AirToxScreen group
information, CMAQ names, metal adjustment factors, URE, RfC and target organ information.
2.1.2. Emissions Categorization: NEI and AirToxScreen
As explained on the NEI website, the NEI includes five data categories: point, nonpoint (formerly called
"stationary area"), nonroad mobile, on-road mobile, and events consisting of wild and prescribed fires.
NEI summaries are generally provided by sectors and tiers, which describe the type of emission source
(e.g., industrial processes - oil and gas production). Some sectors and tiers cut across data categories
since stationary sources are inventoried as both point and nonpoint. For example, the NEI sector "Fuel
Combustion - Commercial/Institutional - Oil" results from large institutions inventoried as point sources
(e.g., large universities with onsite steam plants) as well as commercial/institutional entities that are
small and ubiquitous in nature, so their emissions are inventoried as county sums.
AirToxScreen summaries are provided by AirToxScreen broad summary categories and by more detailed
source groups. The broad AirToxScreen summary categories are point, nonpoint, on-road, nonroad,
fires, biogenics and secondary. Some of these categories are named the same as the NEI data categories,
but they are not identical. For example, the NEI nonpoint category includes CMVs and locomotives,
while the AirToxScreen category does not. Similarly, the AirToxScreen nonroad category includes
airports, CMVs and locomotives, while the NEI category does not. Table 2-5 contains comparisons
between the NEI data categories and the AirToxScreen categories. "Secondary" is not included in
Table 2-5 since it not a primary emissions category covered in the NEI, but rather a result of atmospheric
chemistry from the modeled emissions of CAPS and HAPs.
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Table 2-5. Map of NEI data categories to AirToxScreen categories
NEI Data Category
AirToxScreen Category (Reflecting AirToxScreen Summary
Results)
Point
Point1
Emissions estimates for sources that are
individually inventoried and usually located at a
fixed, stationary location (although portable
sources such as some asphalt- or rock-crushing
operations are also included). Point sources include
large industrial facilities and electric power plants
but also increasingly include many smaller
industrial and commercial facilities, such as dry
cleaners and gas stations, that had traditionally
been included as nonpoint sources. The choice of
whether these smaller sources are estimated
individually and included as point sources or
inventoried as a nonpoint source aggregated to
county or tribal areas is determined by the
separate S/L/T air agency.
Same as NEI point except:
Excludes portable sources, which are not modeled in either
CMAQ or AERMOD because no geographic information
other than the state code is included.
AirToxScreen includes airports and railyards in the nonroad
sector.
Nonpoint
Nonpoint
Sources that individually are too small in
magnitude or too numerous to inventory as
individual point sources and that can often be
estimated more accurately as a single aggregate
source for a county or tribal area. Examples are
residential heating and consumer solvent use. Wild
fires, prescribed fires, and agricultural fires are
included., CMVs and locomotive emissions are
included. Agricultural activities (crops & livestock
dust, fertilizer application, and livestock waste) are
included. Biogenic emissions that come from
vegetation are also included.
Same as NEI nonpoint except:
• AirToxScreen includes wild fires, prescribed fires
and agricultural fires as FIRES.
• AirToxScreen includes CMV and line-haul
locomotives in Nonroad
• AirToxScreen has a separate category for biogenics
Onroad
Onroad
Emissions estimates for mobile sources, such as
cars, trucks and buses.
Same as NEI onroad.
Nonroad
Nonroad
Emissions estimates for nonroad equipment such
as lawn and garden equipment, agricultural,
construction, industrial and commercial equipment
and recreational equipment
Same as NEI nonroad, but the AirToxScreen nonroad also
includes CMVs, locomotives, aircraft engine emissions
occurring during LTOs, and the ground support equipment
and auxiliary power units associated with the aircraft.
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NEI Data Category
AirToxScreen Category (Reflecting AirToxScreen Summary
Results)
Event
Fires
Prescribed and wildfire emissions computed as
day- and location-specific events
Wildfires, prescribed burning and agricultural burning.
These are modeled in CMAQ but not AERMOD.
Wildfires and prescribed burning are generated via the
SMARTFIRE2 model at specific geographic coordinates for
each day, and are assigned to 12-km grid cells for input into
CMAQ.
Agricultural burning estimates are in the NEI as county-level
emissions, but EPA-derived data are developed as day- and
location-specific emissions, and S/L/T-submitted data are
county level but prepared for CMAQ as point sources with
day-specific emissions.
Biogenic Emissions
Emissions of formaldehyde, acetaldehyde and methanol
from vegetation (trees, plants and soils) computed from the
Biogenic Emission Inventory System within CMAQ. These
are gridded to 12-km cells and modeled in CMAQ but are
not modeled in AERMOD.
1ln results presented online for assessments for the 2002 and early NATA inventories, point sources were divided into major
sources and area sources; these were sometimes referred to as stationary sources. Major sources are defined in the CAA as
stationary sources that have the potential to emit either at least 10 TPY of a HAP or at least 25 TPY of any combination of H APs.
Area sources are stationary sources for which the locations are known but that emit at levels below the major source emissions
thresholds. This terminology is not used in AirToxScreen, and stationary-source emissions are referred to only as point-source
or nonpoint-source emissions. Point sources in the AirToxScreen results refer to those sources, including smaller sources, for
which a specific location for their emissions is identified by latitude and longitude descriptions, and nonpoint sources are those
stationary sources that are not point sources.
2.1.3. Differences Between the current NEI and Emissions Used for
AirToxScreen
Although 2020 NEI is the main basis of the emissions fed into the air quality models for AirToxScreen,
there were several differences between the 2020 NEI and emissions data used for the AirToxScreen
modeling.
State, local, and tribal agencies reviewed emissions information along with scaled preliminary risk
information, which were estimated from pollutant-specific preliminary draft AERMOD 2020 results. EPA
reviewed the comments and changes and incorporated the accepted changes into the modeling
platform.
Differences that result from differences in emissions processing (which reflect the specific role and
function of the resulting inventory within the context of the AirToxScreen risk assessment process) are
more accurately described as post-processing procedures rather than substantive changes.
Release parameter changes were made to the SMOKE flat file to fill in missing data or change out-of-
range stack parameters. Data reporters of point sources must provide height, diameter, temperature
and either velocity or flowrate, but they do not have to provide fugitive parameters.
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Prior to emissions processing, we default missing or out-of-range stack or fugitive parameters and
compute the velocity from the flowrate (if velocity is not provided). We chose to do the defaulting prior
to the emissions processing and include the defaulted parameters directly into the FF10 prior to input
into SMOKE. This is done for two reasons: 1) to provide better transparency in the FF10 files with
respect to the data used in the model, and 2) to ensure that emission inputs are consistent across CMAQ
and AERMOD models, since both use the FF10 as the starting point. The out-of-range parameters were
chosen to be consistent with the range checks used in the Emissions Inventory System (EIS). The fugitive
defaults were consistent with what has been used in previous NATAs.
Table 2-6 shows the changes made and why. Even though SMOKE does not use the fugitive release point
parameters, they are included in the table to make it complete.
Table 2-6. Release parameter defaults/changes to the FF10 inventory files for point sources
Field
Existing Value
New Value
Conditions/Notes1
For point sources with stack releases (ERPtype NOT equal to "1")
stkhgt
missing
use pstk2 or global defaults2
None
stkdiam
missing
use pstk2 or global defaults3
None
stkvel
missing
calculate from stkflow and
stkdiam if not missing;
otherwise reference by SCC
from pstk2 or global defaults3
vel = 4*stkflow/(pi*stkdiamA2)
If the flow and diam are missing such
that you cannot compute, use new
value based pstk or global defaults
stktemp
missing
use pstk2 or global defaults3
None
stkhgt
Outside
SMOKE range
use minimum value or
maximum value in feet
Less than 1 ft (0.3048 m) or greater than
1300 ft (396 m)
stkdiam
Outside
SMOKE range
use minimum value or
maximum value in ft
Less than 0.001 ft (0.0003048 m) or
greater than 300 ft (91.4 m)
stkvel
Outside
SMOKE range
use minimum value or
maximum value in ft/s
Less than 0.001 ft/s (0.0003048 m/s) or
greater than 1000 ft/s (304.8 m/s)
stktemp
Outside
SMOKE range
use minimum value or
maximum value in F
Less than -30 F (-34.4 C or 248.15 K) or
greater than 4000 F (2204.4 C or
2477.6 K)
For fugitive release points (not used in CMAQ)
fug_width_ydim
missing
32.808 ft
None
fug_length_xdim
missing
32.808 ft
None
fug_angle
missing
0
None
fug_height
missing
10 ft
fug_width_ydim and /or
fug_length_xdim are missing
fug_height
missing
0
WHEN fug_width_ydim and
fug_length_xdim are not missing and >
0
For coke ovens: any release point that emits coke oven emissions (pollutant code 140)-all pollutants at that
release point are changed to the below
stkhgt
<126 ft
126 ft
erptype NOT = "1"
fug_height
<126 ft
126 ft
erptype = "1"
fug_length_xdim
<50 ft
50 ft
erptype = "1"
fug_width_ydim
<50 ft
50 ft
erptype = "1"
Comments were put into the modeling file to indicate why a record was changed:
ERPVelCompute - velocity computed from the flowrate provided in the inventory
ERPHtRange - height in the inventory was out of range
ERPDiamRange - diameter in the inventory was out of range
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Field
Existing Value
New Value
Conditions/Notes1
ERPVelRange - velocity in the inventory or velocity calculated from the flowrate in the inventory was out of
range
ERPTempRange - temperature in the inventory was out of range
ERPFugHeightO - fugitive height in the inventory was set to 0 because the width and length were not missing
ERPFugMissing - fugitive height, length and width were missing or fugitive length and/or width were missing.
ERPCokeovenl26 - fugitive or stack height of release point emitting coke oven emissions was less than 126 ft
ERPCokeovenFug50 - fugitive length or width was less than 50 ft.
Pstk provides default stack parameters and is provided with other SMOKE ancillary files (ge_dat directory) on
the website. The pstk file is formatted: region_cd, see, stkhgt (m), stkdiam (m), stktemp (K) and stkvel (m/s)
Global defaults (converted to English): stkvel = 13.1234 ft; stktemp=72.05 F, stkdiam=0.6562 ft, stkhgt=9.8425
ft
Out-of-range values exist because the flow range checks in EIS allow some velocities to be above or below the
range, and we run the velocity check after computing the missing flowrates.
Other emissions processing changes include:
For on-road emissions, California provided emissions are adjusted in the modeling platform, and
adjusted model-ready files sum to annual totals from California, but have the temporal and spatial
patterns reflecting the highly resolved meteorology and SMOKEMOVES. The AirToxScreen inventory
also includes a more refined set of SCCs that includes road type to support spatial allocation of
county-level emissions to finer scales.
Sources with FIPS state-county codes ending in 777 (for example, in-flight lead and asphalt plants
that have no geographic coordinates) were removed from the inventory.
Nonpoint tribal data (FIPS beginning in 88) were not used in the modeling because spatial surrogates
were not available and possible double counting would introduce uncertainty.
Other miscellaneous changes included air toxic name conversions, placing individual air toxics into
groups, and similar transcription and phraseology conversions (e.g., to crosswalk the identity of an
emitted air toxic to a substance with a quantitative dose-response value).
2.1.4. Overview of Differences in Emissions for CMAQ and AERMOD
By design, there are differences in the sources of emissions used by CMAQ and AERMOD. Differences in
the emissions inputs to the two models are due to design differences in how the models are run. The
emissions input into AERMOD exclude AirToxScreen categories more appropriately addressed by CMAQ,
namely biogenics and three types of fires: wildfires, prescribed burning and agricultural-field burning.
Biogenic emissions are generated within CMAQ using the Biogenic Emission Inventory System (BEIS)
model with hourly meteorological inputs to generate hourly gridded (12 km x 12 km) emissions of
several photochemical-model species, including three HAPs: formaldehyde, acetaldehyde and methanol.
This category of emissions is routinely part of CMAQ runs and is more appropriately modeled in CMAQ
due to its broad spatial and refined temporal resolution and meteorological dependence. Wildfires and
prescribed and agricultural burning are also included in the CMAQ run but not in AERMOD because
CMAQ provides for in-line plume rise of fires to higher vertical layers based on the acres burned. These
algorithms are also used for agricultural burning, which is grouped with the other fires to allow us to
retain source attribution from the fire and biogenic CMAQ zero-out runs (although not between the
different fire types) discussed in Section 3.3.1.
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In addition to differences in the sources of emissions used for the two models, there are also differences
in the way we process or model the emissions. Emissions modeling transforms the emissions inventory
into the format needed by the air quality model and provides the source characterization. The emission
inventory primarily contains annual emissions represented as either point sources with locations
specified by latitude and longitude or county-level sources specified by FIPS code (county code). In the
inventory, large facilities are inventoried as point sources; while more ubiquitous sources, such as wood
stoves, solvent use, and cars and trucks, are inventoried at the county level.
Emissions modeling is performed in three main steps: spatial allocation, temporal allocation and
speciation. Spatial allocation provides the models with the horizontal characterization of the emissions
source. For example, emissions modeling provides AERMOD with locations for point sources and
locations and spatial extent of sources emitted over large or small areas, and it provides CMAQ with
gridded emissions. Vertical allocation of emissions is performed within the air quality models using stack
parameter information and other model inputs.
Temporal allocation produces hourly variation in emissions based on the monthly, day-of-week and/or
diurnal variation associated with the specific type of source.
Speciation takes the inventory pollutant and converts it to the pollutant used by the air quality model
(or in subsequent processes). For example, the compound nickel oxide is converted to nickel because
the risk information is only for the nickel portion of the compound. For AERMOD, sources also must be
characterized using parameters that may not be in the inventory, such as release heights (which are not
inventoried for mobile and nonpoint sources) and initial vertical dispersion (which is not in the inventory
for any source).
Emissions processing for CMAQ and AERMOD is described in Sections 2.2 and 2.3, respectively.
However, Section 2.2 contains only a summary since a separate technical support document is available
that describes the CMAQ emissions modeling in detail.
Table 2-7 summarizes the spatial allocation differences between the models. As seen in Table 2-7,
CMAQ uses 12-km horizontal resolution along with vertical resolution for point sources and fires,
whereas AERMOD spatial resolution depends on the source category. Both models use hourly emissions,
however CMAQ uses pollutant-specific hourly emissions whereas AERMOD uses the same hourly
variation for all sources in a "run group," as described in Section 2.3.
Table 2-7. Differences in spatial characterization of sources between CMAQ and AERMOD
Category
NEI Resolution
Spatial Approach for AERMOD
Spatial Approach for
CMAQ
Point (excluding airports)
Point
Point - vertical stack and fugitive based
on NEI information on emission-release
point
12-km (CONUS),
9-km (AK), and
3-km (HI, PR, and VI)
grid cells
Airports
Point
Point - runways & 10 m2 areas
consistent with NEI geographic
coordinates
12-km (CONUS),
9-km (AK), and
3-km (HI, PR, and VI) grid
cells
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Category
NEI Resolution
Spatial Approach for AERMOD
Spatial Approach for
CMAQ
Locomotives
Point (railyards)
and
County/Shape
Nonpoint - 12-km grid cells in the
CONUS domain, 9-km grid cells for AK;
3-km grid cells for HI, PR and VII
Point - point fugitives
12-km (CONUS),
9-km (AK), and
3-km (HI, PR, and VI) grid
cells
CMVs, ports and
underway
County/Shape
Shapes from the NEI; separate shapes
used for CMV at ports versus underway
12-km (CONUS),
9-km (AK), and
3-km (HI, PR, and VI) grid
cells
On-road, nonroad
equipment and other
nonpoint
County
12-km or 4-km grid cells, depending on
the category, in the CONUS domain; 9-
km grid cells for AK; 3-km grid cells for
HI, PR and VI
12-km (CONUS),
9-km (AK), and
3-km (HI, PR, and VI) grid
cells
Agricultural burning and
biogenic emissions
County
Not modeled
12-km (CONUS),
9-km (AK), and
3-km (HI, PR, and VI) grid
cells
Fires (prescribed and
wild)
Point
Not modeled
12-km (CONUS),
9-km (AK), and
3-km (HI, PR, and VI) grid
cells
2.2. Preparation of Emissions Inputs for CMAQ
EPA routinely prepares emissions for photochemical grid models such as CMAQ by developing an
emissions modeling platform, and the SMOKE modeling system is used as the primary emissions
modeling tool. An emissions modeling platform includes the emission inventories, the ancillary data files
(e.g., for speciation, temporal allocation and spatial allocation) and the approaches used to transform
inventories for use in air quality modeling.
The platform used for this study is described in depth in the technical support document found on the
emissions modeling platforms website. For 2020 information, please see in particular:
https://www.epa.gov/air-emissions-modeling/2020-emissions-modeling-platform-technical-support-
document. Emissions inputs and ancillary data for speciation and for temporal and spatial allocation are
available at the emissions modeling platform ftp site.
A summary of the platform's key features is below.
2.2.1. Sectors in the CMAQ AirToxScreen Platform
For the purposes of preparing the CMAQ model-ready emissions, the NEI is split into finer-grained
sectors used for emissions modeling. The significance of an emissions modeling or "platform sector" is
that the data are run through all the SMOKE programs independently from the other sectors except the
final merge program (Mrggrid). The sectors used for the AirToxScreen CMAQ platform are listed in
Table 2-8.
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Table 2-8. Platform sectors for the 2020 emissions modeling platform
Platform Sector:
abbreviation
NEI Data
Category
Description and Resolution of the Data Input to SMOKE
EGU units:
ptegu
Point
2020 NEI point source EGUs, replaced with hourly Continuous Emissions
Monitoring System (CEMS) values for NOX and S02, and the remaining
pollutants temporally allocated according to CEMS heat input where the units
are matched to the NEI. Emissions for all sources not matched to CEMS data
come from 2020 NEI point inventory. Annual resolution for sources not
matched to CEMS data, hourly for CEMS sources. EGUs closed in 2020 are not
part of the inventory.
Point source oil and
gas:
pt_oilgas
Point
2020 NEI point sources that include oil and gas production emissions
processes for facilities with North American Industry Classification System
(NAICS) codes related to Oil and Gas Extraction, Natural Gas Distribution,
Drilling Oil and Gas Wells, Support Activities for Oil and Gas Operations,
Pipeline Transportation of Crude Oil, and Pipeline Transportation of Natural
Gas. Includes U.S. offshore oil production.
Aircraft and ground
support equipment:
airports
Point
2020 NEI point source emissions from airports, including aircraft and airport
ground support emissions. Annual resolution.
Remaining non-EGU
point:
ptnonipm
Point
All 2020 NEI point source records not matched to the airports, ptegu, or
pt_oilgas sectors. Includes 2020 NEI rail yard emissions. Annual resolution.
Agricultural:
livestock
Nonpoint
2020 NEI nonpoint livestock emissions. Livestock includes ammonia and other
pollutants (except PM2.5). County and annual resolution.
Agricultural Fertilizer
Nonpoint
2020 agricultural fertilizer ammonia emissions computed inline within CMAQ.
Area fugitive dust:
afdust
Nonpoint
PM10 and PM2.5 fugitive dust sources from the 2020 NEI nonpoint inventory;
including building construction, road construction, agricultural dust, and
paved and unpaved road dust. The emissions modeling system applies a
transport fraction reduction and a zero-out based on 2020gridded hourly
meteorology (precipitation and snow/ice cover). Emissions are county and
annual resolution.
Biogenic:
be is
Nonpoint
Year 2020 emissions from biogenic sources. These were left out of the CMAQ-
ready merged emissions, in favor of inline biogenic emissions produced during
the CMAQ model run itself. Version 4 of the Biogenic Emissions Inventory
System (BEIS) was used with Version 6 of the Biogenic Emissions Landuse
Database (BELD6). Therefore, the biogenic emissions used here are similar to
the 2020 NEI biogenic emissions, but not exactly the same.
Category 1, 2 CMV:
cmv_clc2
Nonpoint
2020 NEI Category 1 (CI) and Category 2 (C2), commercial marine vessel
(CMV) emissions based on Automatic Identification System (AIS) data. Point
and hourly resolution.
Category 3 CMV:
cmv_c3
Nonpoint
2020 NEI Category 3 (C3) commercial marine vessel (CMV) emissions based on
AIS data. Point and hourly resolution.
locomotives:
rail
Nonpoint
Line haul rail locomotives emissions from 2020 NEI. County and annual
resolution.
Nonpoint source oil
and gas:
np_oilgas
Nonpoint
Nonpoint 2020 NEI sources from oil and gas-related processes. County and
annual resolution.
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Platform Sector:
abbreviation
NEI Data
Category
Description and Resolution of the Data Input to SMOKE
Residential Wood
Combustion:
Rwc
Nonpoint
2020 NEI nonpoint sources with residential wood combustion (RWC)
processes. County and annual resolution.
Solvents: np_solvents
Nonpoint
Emissions of solvents from the 2020 NEI (Seltzer, 2021). Includes household
cleaners, personal care products, adhesives, architectural and aerosol
coatings, printing inks, and pesticides. Annual and county resolution.
Remaining nonpoint:
Nonpt
Nonpoint
2020 NEI nonpoint sources not included in other platform sectors. County and
annual resolution.
Nonroad:
Nonroad
Nonroad
2020 NEI nonroad equipment emissions developed with MOVES3, including
the updates made to spatial apportionment that were developed with the
2016vl platform. MOVES3 was used for all states except California, which
submitted their own emissions for the 2020 NEI. County and monthly
resolution.
On-road:
On road
On-road
Onroad mobile source gasoline and diesel vehicles from parking lots and
moving vehicles from 2020 NEI. Includes the following emission processes:
exhaust, extended idle, auxiliary power units, evaporative, permeation,
refueling, vehicle starts, off network idling, long-haul truck hoteling, and brake
and tire wear. MOVES3 was run for 2020 to generate emission factors.
On-road California:
onroad_ca_adj
On-road
California-provided 2020 CAP and HAP (VOCs and metals) onroad mobile
source gasoline and diesel vehicles from parking lots and moving vehicles
based on Emission Factor (EMFAC), gridded and temporalized based on
outputs from MOVES3. Polycyclic aromatic hydrocarbon (PAH) emissions are
based on MOVES3.
Point source
agricultural fires:
ptagfire
Nonpoint
Agricultural fire sources for 2020 developed by EPA as point and day-specific
emissions. Only EPA-developed ag. fire data are used in this study, thus 2020
NEI state submissions are not included. Agricultural fires are in the nonpoint
data category of the NEI, but in the modeling platform, they are treated as
day-specific point sources. Updated HAP-augmentation factors were applied.
Point source
prescribed fires:
ptfire-rx
Events
Point source day-specific prescribed fires for 2020 NEI computed using
SMARTFIRE 2 and BlueSky Pipeline. The ptfire emissions were run as two
separate sectors: ptfire-rx (prescribed, including Flint Hills /grasslands) and
ptfire-wild.
Point source wildfires:
ptfire-wild
Point source day-specific wildfires for 2020 NEI computed using SMARTFIRE 2
and BlueSky Pipeline.
Non-US. fires:
ptfire_othna
N/A
Point source day-specific wildfires and agricultural fires outside of the U.S. for
2020. Canadian fires for May through December are provided by ECCC. All
other fire emissions, including Canadian emissions from January through April,
as well as Mexico, Caribbean, Central American, and other international fires,
are from v2.5 of the Fire INventory (FINN) from National Center for
Atmospheric Research (Wiedinmyer, C., 2023).
Canada Area Fugitive
dust sources not from
the NEI:
canada_afdust
N/A
Area fugitive dust sources from ECCC for 2020 with transport fraction and
snow/ice adjustments based on 2020 meteorological data. Annual and
province resolution.
Canada Point Fugitive
dust sources:
canada_ptdust
N/A
2020 point source fugitive dust sources from ECCC with transport fraction and
snow/ice adjustments based on 2020 meteorological data. Monthly and
province resolution.
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Platform Sector:
abbreviation
NEI Data
Category
Description and Resolution of the Data Input to SMOKE
Canada and Mexico
stationary point
sources:
canmex_point
N/A
Canada and Mexico point source emissions not included in other sectors.
Canada point sources for 2020 were provided by ECCC and Mexico point
source emissions for 2016 were provided by SEMARNAT. Mexico sources were
projected from 2019ge (EPA, 2023a) with COVID adjustments applied. Canada
monthly temporalization adjusted for COVID. Annual and monthly resolution.
Canada and Mexico
agricultural sources:
canmex_ag
Canada and Mexico agricultural emissions. Canada point sources for 2020
were provided by ECCC and Mexico emissions for 2016 were provided by
SEMARNAT and adjusted to 2019. COVID adjustments were not applied to the
ag sector. Annual resolution.
Canada low-level oil
and gas sources:
canada_og2D
2020 Canada emissions from upstream oil and gas. This sector contains the
portion of oil and gas emissions which are not subject to plume rise. The rest
of the 2020 Canada oil and gas emissions are in the canmex_point sector.
Provided by ECCC with COVID-adjusted monthly temporalization. Monthly
resolution.
Canada and Mexico
nonpoint and nonroad
sources: canmex_area
N/A
2020 Canada and Mexico nonpoint source emissions not included in other
sectors. Canada: ECCC provided a 2020 inventory and surrogates. Mexico:
applied COVID adjustments to 2019ge. Monthly temporalization adjusted for
COVID.
Canada on-road
sources:
Canada_onroad
N/A
Canada onroad emissions. 2020 Canada inventory provided by ECCC and
processed using updated surrogates. COVID impacts applied to monthly
profiles (not to annual totals). Province and monthly resolution.
Mexico on-road
sources:
Mexico_onroad
N/A
Mexico onroad emissions. 2020 MOVES-Mexico with COVID adjustments
applied. Municipio and monthly resolution.
2.2.2. Fires and Biogenics
To approximate how much fires and biogenic primary emissions contribute to overall air toxics
concentrations, we use a concept known as "zero-out" runs in CMAQ. This technique lets us split out the
individual impacts from these source categories in the AirToxScreen results.
CMAQ can compute biogenic emissions during a run, and it has options to take multiple sets of point-
source fire emission files as input. These features were used in this study to quantify the impacts of
biogenic and fire emissions by running CMAQ three times:
1. The base-case run used all fire and anthropogenic emissions with the option to generate biogenic
emissions turned on.
The biogenic zero-out run used all fire and anthropogenic emissions but with the option to generate
biogenic emissions turned off.
The fire zero-out run used all anthropogenic emissions had the option to generate biogenic emissions
turned on, but it excluded the input files for wild, prescribed, and anthropogenic fires.
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2.2.3. Speciation
The emissions modeling step for chemical speciation creates the "model species" needed by the air
quality model for a specific chemical mechanism. These model species are either individual chemical
compounds (i.e., "explicit species") or groups of species (i.e., "lumped species"). Model species are
created in the emissions modeling process by directly mapping emissions from the emission inventory to
the appropriate model species or by speciation of inventory species where a one-to-one match does not
exist. For example, VOCs are speciated into numerous VOC-related model species defined by the
chemical mechanism.
In the AirToxScreen modeling platform, all CMAQ species that are explicit AirToxScreen HAPs were
generated by directly mapping the NEI emissions for these HAPs; no HAPs in the United States were
generated through speciation of VOC or PM2.5 in the emissions modeling step.
More information on speciation approach and detailed tables of modeling species are available in
section 3.2 of the technical support document for the 2020 emissions modeling platform.
2.2.4. Temporalization
While the total emissions are important, the timing of the occurrence of emissions is also essential for
accurately simulating ozone, PM and other pollutant concentrations in the atmosphere. Temporal
allocation (i.e., temporalization) is the process of distributing aggregated emissions to a finer temporal
resolution, thereby converting annual emissions to hourly emissions as is required by CMAQ.
Temporalization takes these aggregated emissions and distributes them to the month, and then
distributes the monthly emissions to the day and the daily emissions to the hours of each day. This
process is typically done by applying temporal profiles to the inventories in this order: monthly, day of
the week, and diurnal, with monthly and day-of-week profiles applied only if the inventory is not already
at that level of detail.
Sector-specific information, including meteorological-based temporal allocation and detailed charts of
the temporal settings used for each sector in the emissions modeling platform can be found in section
3.3 of the technical support document. Ancillary data are available at the emissions modeling platform
ftp site.
2.2.5. Spatial Allocation
For CMAQ, all emissions were allocated to 12-km grid cells for the CONUS domain, 9-km grid cells for the
AK domain, and 3-km grid cells for HI domain and VI/PR domain. Sources with geographic coordinates
such as point sources and fires are mapped to the appropriate grid cell based on those coordinates.
Sectors with county-level resolution were allocated to 12-km (or 9-km/3-km) grid cells using spatial
surrogates, which are developed based on shapefiles of data with spatial patterns expected for the
emissions category.
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Spatial surrogates were assigned to emissions sources based on SCC. A detailed description of these
assignments can be found in section 3.4 of the emissions modeling platform technical support
document.
2.3. Preparation of Emissions Inputs for AERMOD
For AirToxScreen, we use the capability in SMOKE to produce helper files that can be further post-
processed to generate the source (SO) Pathway for AERMOD. The SO Pathway contains the source
location and parameter information used by AERMOD. Helper files provide information about source
location, release characteristics and temporal variability. They also provide source emissions. For
sources in the CONUS, helper files also provide the column and row of the 12-km meteorological grid
cell associated with the appropriate meteorological data (see also Section 3) to use for the emission
source. Helper file formats and information vary by the type of source. For example, some emissions
sources are point sources, some are gridded and some (like ports) are defined by polygon shapes.
Gridded sources use different grid resolutions depending on their location (e.g., all Alaska gridded
sources are 9 km2) and/or the type of source.
The SMOKE interface for AERMOD models each source using a unit emissions rate rather than actual
pollutant-specific emissions. The location, source parameter and temporal helper files join to produce
the SO Pathway for each source's AERMOD run. AERMOD provides source-specific concentrations per
unit emissions rate (also known as %/Q, abbreviated here as X/Q) that are not pollutant specific (but are
source specific). These concentrations are then combined with pollutant-specific emissions for each
source, using the emissions helper file from the emissions modeling process, to create the pollutant-
and source-specific concentrations as shown below.
X
Cpoiiutanti-" x Emissionspo||utant j
Because we are using a unit emission rate in AERMOD, we do not account for chemistry or deposition
that would be specific to any individual pollutant.
The format and content of the helper files vary by type of source, and we have chosen to organize the
creation of separate sets of helper files by "run group." A run group is a set of sources that use similar
source characterization methods or parameters. Run groups allow us to combine sources with similar
characteristics and run them in AERMOD together, even though they may have very different emissions.
The emissions from the specific sources in the run group can then be applied to the source group X/Qs
to create source-specific concentrations. We also combine the sources within the run group into source
groups, which is the source-level resolution of concentration and risk results for AirToxScreen.
In AirToxScreen, we allocate county-level emissions to grid cells for both CONUS and non-CONUS (i.e.,
Alaska, Hawaii, Puerto Rico and the Virgin Islands) sources; however, we use different grid cell
resolutions, as discussed below.
2.3.1. Source and Run Groups - Overview
Source and run groups are similar, but serve different functions in AirToxScreen. Source groups provide
source attribution for the AirToxScreen census block risk results.
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Run groups organize sources for modeling in AERMOD to minimize the number of separate model runs.
It would be resource prohibitive to run the thousands of different source classification codes separately
in AERMOD, so we grouped them into a manageable number of run groups that all have the same
spatial resolution, approach for temporal variation and release characteristics (height and sigma-z). Run
groups typically have multiple source groups, though two of them (OILGAS and RWC) have the same
source group as run group.
The inventory run groups are shown in Table 2-9. Those modeled in AERMOD as AREA sources using grid
cells (i.e., "AREA (gridcell)") were gridded from county level to grid cells using the same underlying
surrogate data (shape files) used in CMAQ.
Table 2-9. Run groups for AERMOD
Run Group
NEI Data
Category
AirToxScreen
Category
Resolution of
Inventory Prior to
Emissions Modeling
Modeled in
AERMOD as:
Point
Point
Point,
Nonroad1
Point
POINT, AREA
(fugitive)
Airport
Point
Nonroad
Point
POINT, AREA
(runway)
Onroad On-network LD (LDON)
Onroad
Onroad
County
AREA (gridcell)
Onroad Off-network LD (LDOFF)
Onroad
Onroad
County
AREA (gridcell)
Onroad On-network HD (HDON)
Onroad
Onroad
County
AREA (gridcell)
Onroad Off-network HD (HDOFF)
Onroad
Onroad
County
AREA (gridcell)
Onroad Off-network-hoteling (extended
idling and auxiliary power units) (HOTEL)
Onroad
Onroad
County
AREA (gridcell)
Onroad On-network LD (LDON)
Onroad
Onroad
County
AREA (gridcell)
Nonroad (NONRD)
Nonroad
Onroad
County
AREA (gridcell)
Nonpoint 10-meter release height (NPHI)
Nonpoint
Nonpoint
County
AREA (gridcell)
Nonpoint low-level release height (NPLO)
Nonpoint
Nonpoint,
Nonroad2
County
AREA (gridcell)
Nonpoint Oil and Gas (OILGAS)
Nonpoint
Nonpoint
County
AREA (gridcell)
Nonpoint Residential Wood Combustion
(RWC)
Nonpoint
Nonpoint
County
AREA (gridcell)
Nonpoint Agricultural Livestock (AG)
Nonpoint
Nonpoint
County
AREA (gridcell)
Nonpoint Oil and Gas (OILGAS)
Nonpoint
Nonpoint
County
AREA (gridcell)
Commercial Marine Vessels (CMV) Ports
Nonpoint
Nonroad
Shapes
POLYGON
Commercial Marine Vessels (CMV)
Underway
Nonpoint
Nonroad
Gridcell
AREA (gridcell)
1 Rail yards.
2Locomotives.
The run groups modeled as grid cells have different resolutions: 3 kilometers for all grid cells in Hawaii,
Puerto Rico and the Virgin Islands; 9 kilometers for all grid cells in Alaska, and either 12 or 4 kilometers
for grid cells in the CONUS, depending on the run group. These are summarized in Table 2-10.
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Table 2-10. Resolution of the run groups modeled as gridded sources
Run Group (resolution is the value of the
number in the run group abbreviation)
CONUS GRID:
12US1
Alaska
GRID: 9AK1
Hawaii
GRID = 3HI1
Puerto Rico &
Virgin Islands
GRID = 3PR1
Onroad On-network LD (LDON)
LDON4
LDON9AK
LDON3HI
LDON3PR
Onroad Off-network LD (LDOFF)
LDOFF12
LDOFF9AK
LDOFF3HI
LDOFF3PR
Onroad On-network HD (HDON)
HDON4
HDON9AK
HDON3HI
HDON3PR
Onroad Off-network HD (HDOFF)
HDOFF12
HDOFF9AK
HDOFF3HI
HDOFF3PR
Onroad Off-network-hoteling (extended idling
and auxiliary power units) (HOTEL)
HOTEL4
HOTEL9AK
N/A
N/A
Nonroad (NONRD)
NONRD12
NONRD9AK
NONRD3HI
NONRD3PR
Nonpoint 10-meter release height (NPHI)
NPHI12
NPHI9AK
NPHI3HI
NHI3PR
Nonpoint low-level release height (NPLO)
NPL012
NPL09AK
NPL03HI
NPL03PR
Nonpoint Oil and Gas (OILGAS)
OILGAS4
OILGAS9AK
OILGAS3HI
N/A
Nonpoint Residential Wood Combustion (RWC)
RWC12
RWC9AK
RWC3HI
N/A
Nonpoint Agricultural Livestock (AG)
AG 12
N/A
N/A
N/A
N/A means there are no emissions for this run group in this grid.
Table 2-11 and Table 2-12 describe the source characteristics of these run groups and how they relate to
the platform sectors used for CMAQ emissions modeling.
Table 2-11. Non-gridded AERMOD run groups
Run Group
NEI Category and
AirToxScreen
CMAQ Sector
AERMOD Modeling Features: Release
Height (RH; meters), Initial Vertical
Dispersion (az; meters) and Temporal
Approach
Description of Sources
POINT
NEI: point
Platform: ptegu,
pt_oilgas,
ptnonipm
Release parameters for individual release
points at each facility are taken from the 2017
NEI, with defaulting done for missing or out-
of-range parameters.
Spatial: Use specific geographic coordinates
for stacks ("POINT") and geographic
coordinates along with fugitive length and
width for fugitives ("AREA").
Temporal: Temporal profiles based on SCC
codes for ptnonipm; based on continuous
emissions monitoring data for ptegu.
All NEI point sources containing
AirToxScreen HAP emissions
except for facility source type 100
(airports).
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Run Group
NEI Category and
AirToxScreen
CMAQ Sector
AERMOD Modeling Features: Release
Height (RH; meters), Initial Vertical
Dispersion (ctz; meters) and Temporal
Approach
Description of Sources
AIRPORTS
NEI: point
Platform:
ptnonipm
RH=3 ctz =0
Spatial: For runway-area (line) sources:
length based on a database containing
runway endpoint coordinates; 50-m width
for the major airports, 25-m width for the
OTAQ-provided (smaller) airports. All facility
emissions (NEI) spread equally over the
runway(s).
For nonrunway sources^ 10-m-square area
centered on NEI coordinates.
Temporal: monthly/day-of-week/hourly
profiles. Different profiles for Alaska
seaplanes.
Airport Facilities in the point
inventory - all emissions where
facility source type code = 100
(airports). Includes seaplane ports
and heliports, Emissions used at
the facility unit's process release
point.
For these facilities all sub-facility
emissions (airplane landing and
takeoffs, ground support
equipment) are summed to the
facility level, and are treated the
same way with regards to spatial
and temporal allocation.
CMVP
NEI: Nonpoint
Platform:
cmv_cl2 and
cmv_c3
C1/C2 uses RH = 8.4 and ctz = RH/2.15. C3
uses RH = 20 and ctz = 40.7 based on CMAQ
vertical emissions.
Spatial- polygon shapes updated from the
NEI inventory shapes (there could be
multiple polygons per port).
Temporal: hourly data based on AIS activity
data
C1/C2 and C3 commercial marine
vessels at ports.
Table 2-12. AERMOD gridded run groups
Run Group
NEI Category and
AirToxScreen
CMAQ Sector
AERMOD Modeling Features: Release
Height (RH; meters), Initial Vertical
Dispersion (az; meters) and Temporal
Approach
Description of Sources
LD0N4
LD0N9AK
LD0N3HI
LD0N3PR
NEI: on-road
Platform: on-
road
RH = 1.3, ctz = 1.2, Resolution is 4 km for
CONUS, 9 km for AK and 3 km for HI, PR, VI.
Temporal: monthly temporal variation is
pollutant-specific and county-specific.
County-specific hourly profiles are the same
for all pollutants based on benzene hourly
emissions from SMOKE-MOVES (aggregate
only SCCs in this run group).
On-network light duty mobile
emissions such as passenger car
exhaust and light duty passenger
truck brake and tire wear.
Emissions derived from SMOKE-
MOVES. Includes refueling since
temporal profile and spatial
surrogate for on-network is a
better match for refueling than
off-network.
LD0FF12
LD0FF9AK
LD0FF3HI
LD0FF3PR
NEI: on-road
Platform: on-
road
RH = 0.5, ctz = 0.5, Resolution is 12 km for
CONUS, 9 km for AK and 3 km for HI, PR, VI.
Temporal: same approach as LDON4, but
aggregate only SCCs in the LDOFF run group
to compute hourly profiles.
Off-network light duty mobile
emissions such as passenger car
and passenger light truck start
emissions. Derived from SMOKE-
MOVES. Tailpipe height (no
turbulence).
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Run Group
NEI Category and
AirToxScreen
CMAQ Sector
AERMOD Modeling Features: Release
Height (RH; meters), Initial Vertical
Dispersion (ctz; meters) and Temporal
Approach
Description of Sources
HD0N12
HD0N9AK
HD0N3HI
HD0N3PR
NEI: on-road
Platform: on-
road
RH = 3.4, CTz = 3.2, Resolution is 4 km for
CONUS, 9 km for AK and 3 km for HI, PR, VI.
Temporal: monthly temporal variation is
pollutant-specific and county-specific.
County-specific hourly profiles based on
PM2.5 hourly emissions from SMOKE-MOVES
(aggregate only SCCs in this run group).
On-network heavy duty mobile
emissions such as running exhaust
from combination long haul and
short haul trucks and buses.
Includes brake and tire wear.
Derived from SMOKE-MOVES.
HD0FF12
HD0FF9AK
HD0FF3HI
HD0FF3PR
NEI: on-road
Platform: on-
road
RH = 3.4, CTz = 0.5, Resolution is 12 km for
CONUS, 9 km for AK and 3 km for HI, PR, VI.
Temporal: same approach as HDON run
groups, but use only SCCs in the HDOFF run
groups to compute hourly profiles.
Off-network heavy duty mobile
emissions such as start emissions
from combination long haul and
short haul trucks and buses:
Derived from SMOKE-MOVES.
Minimal dispersion for start
emissions. Tailpipe height (no
turbulence.
H0TEL4
H0TEL9AK
NEI: on-road
Platform: on-
road
RH = 3.4, CTz = 0.5, Resolution is 4 km for
CONUS, 9 km for AK and 3 km for HI, PR, VI.
Temporal: same approach as HDON run
groups, but use only SCCs in the HOTEL run
groups to compute hourly profiles.
Extended idling and auxiliary
power units (APU) that occur at
truck stops. Minimal dispersion for
hoteling (e.g., extended idling)
emissions. Tailpipe height (no
turbulence).
N0NRD12
N0NRD9AK
N0NRD3HI
N0NRD3PR
NEI: nonroad
Platform:
nonroad
RH = 2, CTz = 1, Resolution is 12 km for
CONUS, 9 km for AK and 3 km for HI, PR, VI.
Temporal: monthly temporal variation is
pollutant-specific and county-specific.
County-specific diurnal scalars computed
based on benzene hourly emissions for the
aggregate of all SCCs in this run group
summed across all days of the year.
Nonroad equipment such as lawn
mowers, turf equipment,
agriculture and construction
equipment, commercial
generators, power-washing
equipment, pleasure craft,
recreational off-road.
NPHI12
NPHI9AK
NPHI3HI
NPHI3PR
NEI: nonpoint
Platform: some
of nonpt
RH=10, CTz = 4.7, Resolution is 12 km for
CONUS, 9 km for AK and 3 km for HI, PR, VI
Temporal: uniform monthly/day-of-week.
Diurnal: Use SMOKE hourly profile 26 -
mostly daytime emissions-fuse qflag
HROFDY).
Industrial processes (e.g., chemical
plants, refineries, mines, metals);
fuel combustion - industrial,
commercial, institutional and
residential (except wood); waste
disposal.
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Run Group
NEI Category and
AirToxScreen
CMAQ Sector
AERMOD Modeling Features: Release
Height (RH; meters), Initial Vertical
Dispersion (ctz; meters) and Temporal
Approach
Description of Sources
NPL012
NPL09AK
NPL03L0
NPL03PR
NEI: nonpoint
Platform: some
ofnonpt
RH = 3.9, CTz = 3.6, Resolution is 12 km for
CONUS, 9 km for AK and 3 km for HI, PR, VI.
Temporal: same as NPHI run groups.
Solvents (consumer, commercial);
surface coating; commercial
cooking; locomotives, bulk
terminals, gas stations (stage 1);
miscellaneous non-industrial
(portable gas cans, auto repair
shops, structure fires, and
nonpoint mercury categories such
as human cremation, dental
amalgam).
0ILGAS4
0ILGAS9AK
0ILGAS3HI
NEI: nonpoint
Platform:
nP_oilgas
RH=10, CTz =4.7, Resolution is 4 km for
CONUS and 9 km for AK. There are no
emissions in HI, PR or VI.
Temporal: monthly profiles that align with
the county/scc temporal profiles used in
CMAQ v7.1 platform. Use qflag= MONTH
and generate county-specific run group
monthly profiles based on benzene
emissions aggregated over all SCCs within
each county.
Oil and gas sources reported in the
nonpoint NEI data category (i.e.,
county-level emissions).
RWC12
RWC9AK
RWC3HI
NEI: nonpoint
Platform: rwc
RH = 6.4, CTz = 3.2, Resolution is 12 km for
CONUS, 9 km for AK and 3 km for HI. There
are no emissions in PR or VI.
Temporal: hourly by grid cell based on
county-specific hourly emissions (created by
SMOKE using year-to-day factors derived
from meteorological data that are used for
many SCCs in this sector)-for all 8760 hours
in the year-based on benzene emissions
summed by hour and county across all SCCs
in the run group.
Fireplaces, woodstoves, hydronic
heaters used for residential
heating.
AG 12
NEI: nonpoint
Platform: ag
RH = 1, CTz = RH/2.15 = 0.465 m.
Resolution is 12 km. There are no emissions
in AK, HI, PR or VI.
Temporal: hourly profile based on ammonia
hourly emissions for the Ag emissions sector
(which includes both livestock and fertilizer).
Miscellaneous area sources;
agriculture production - livestock,
beef cattle - finishing operations,
dairy cattle - drylot/pasture dairy-
confinement; swine production -
operations; poultry production.
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Run Group
NEI Category and
AirToxScreen
CMAQ Sector
AERMOD Modeling Features: Release
Height (RH; meters), Initial Vertical
Dispersion (ctz; meters) and Temporal
Approach
Description of Sources
CMVU12
CMVU9AK
CMVU3HI
CMVU3PR
NEI: nonpoint
Platform:
cmv_cl2 and
cmv_c3
C1/C2 uses RH = 8.4 and ctz = RH/2.15. C3
uses RH = 20 and ctz = 40.7 based on CMAQ
vertical emissions.
Resolution is 12 km for CONUS, 9 km for AK
and 3 km for HI and PR/VI
Temporal: hourly by grid cell based on AIS
activity data
C1/C2 and C3 commercial marine
vessels underway
Each run group contains one or more source groups for purposes of presenting the AirToxScreen results.
There are no situations where a source group fits into multiple run groups. Table 2-13 lists these source
groups.
Table 2-13. AirToxScreen source groups
Source Group Name
AirToxScreen
Category
Source Group Description
Run Group
PT-StationaryPoint
POINT
All facilities in the POINT data category that
have geographic coordinates other than those
with facility type = 100 (which are in the
"Airport" source group) or facility type = 151
(which are in the "Railyard" source group)
POINT
OR-LightDuty-
OffNetwork-Gas
Onroad
On-road, light duty, nondiesel (i.e., gasoline &
ethanol blends) vehicles - off network
processes (e.g., "starts")
LDOFF
OR-LightDuty-
OffNetwork-Diesel
Onroad
On-road, light duty, diesel vehicles - off
network processes (e.g., "starts")
LDOFF
OR-HeavyDuty-
OffNetwork-Gas
Onroad
On-road, heavy duty, nondiesel vehicles - off
network processes (e.g., "starts")
HDOFF
OR-HeavyDuty-
OffNetwork-Diesel
Onroad
On-road, heavy duty, diesel vehicles - off
network processes (e.g., "starts")
HDOFF
OR-LightDuty-
OnNetwork-Gas
Onroad
On-road, light duty, nondiesel vehicles - on
network processes (e.g., running emissions)
LDON
OR-LightDuty-
OnNetwork-Diesel
Onroad
On-road, light duty, diesel vehicles - on
network processes (e.g., running emissions)
LDON
OR-HeavyDuty-
OnNetwork-Gas
Onroad
On-road, heavy duty, nondiesel vehicles - on-
network processes (e.g., "running")
HDON
OR-HeavyDuty-
OnNetwork-Diesel
Onroad
On-road, heavy duty, diesel vehicles - on-
network processes (e.g., "running")
HDON
OR-Refueling
Onroad
On-road refueling
LDON
OR-HeavyDuty-Hoteling
Onroad
On-road, heavy duty diesel vehicle extended
idling and auxiliary power units
HOTEL
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Source Group Name
AirToxScreen
Category
Source Group Description
Run Group
NR-Recreational-inc-
PleasureCraft
Nonroad
Off-road motorcycles, snow mobiles, golf
carts, outboard pleasure craft, personal
watercraft, inboard/sterndrive pleasure craft,
etc.
NONRD
NR-Construction
Nonroad
Paving equipment, plate compactors,
trenchers, tampers/rammers,
tractors/loaders/backhoes, cranes, signal
boards/light plants, etc.
NONRD
NR-
CommercialLawnGarden
Nonroad
Mowers, leaf blowers, turf equipment,
chippers/ stump grinders, tillers, chainsaws,
snow blowers, etc.
NONRD
NR-
ResidentialLawnGarden
Nonroad
Mowers, leaf blowers, tillers, chainsaws,
snow blowers, shredders, etc.
NONRD
NR-Agriculture
Nonroad
Agricultural tractors, combines, sprayers,
balers, tillers, irrigation sets, swathers, etc.
NONRD
NR-
Commercial Equipment
Nonroad
Generator sets, pressure washers, pumps,
hydropower units, etc.
NONRD
NR-AIIOther
Nonroad
Industrial Equipment (Forklifts,
sweepers/scrubbers, other oil field
equipment), logging equipment, railroad
maintenance, underground mining
equipment
NONRD
N R-CM V_C lC2_ports
Nonroad
Commercial marine vessel (CMV) emissions -
C1/C2 while at ports
NR-CMV Ports
NR-CMV_C3_ports
Nonroad
CMV emissions - C3 while at ports
NR-CMV Ports
NR-
CMV_ClC2C3_underway
Nonroad
CMV emissions - both C1/C2 and C3 marine -
while underway
NR-CMV
Underway
NR-Locomotives
Nonroad
Locomotive emissions
NPLO
NR-Point-Airports
Nonroad
Point source inventory where facility type =
100 "Airport"
AIRPORT
NR- Point-Railyards
Nonroad
Point source inventory where facility type =
151 "Rail Yard"
POINT
NP-industrial
Nonpoint
Nonpoint mining and quarrying, paved and
unpaved roads, food and kindred products,
mineral products, chemical manufacturing,
non-ferrous metals and nonpoint industrial
processes not elsewhere classified
NPHI
NP-CommercialCooking
Nonpoint
Commercial cooking (charbroiling, frying)
NPLO
NP-OilGas
Nonpoint
Oil and gas operations (pumps, dehydrators,
tanks, engines
OILGAS
NP-SolventsCoatings
Nonpoint
Degreasing, dry cleaning, consumer and
commercial solvents, industrial surface
coating, non-industrial surface coating
NPLO
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Source Group Name
AirToxScreen
Category
Source Group Description
Run Group
NP-StorageTransfer_
BulkTerminals_GasStage
1
Nonpoint
Bulk terminals, petroleum, organic and
inorganic chemical storage and transport -
has gas station emissions of only stage 1 (tank
refueling) not stage 2 (vehicle refueling)
NPLO
NP-
MiscellaneousNonindust
rial
Nonpoint
Poultry/livestock, laboratories, dental alloy,
motor vehicle fires, portable fuel containers,
residential charcoal grilling
NPLO
NP-
FuelCombustion_not_R
WC
Nonpoint
Residential, industrial, commercial and
institutional (ICI) fuel combustion, but
excludes residential wood combustion
NPHI
NP-
ResidentialWoodCombu
stionRWC
Nonpoint
Residential wood combustion - woodstoves,
fireplaces
RWC
NP-WasteDisposal
Nonpoint
Open burning, yard and household waste,
managed burning, slash (logging debris),
onsite incineration, waste water treatment,
composting, landfills, scrap materials
NPHI
NP- Agricultural Livestock
Nonpoint
Emissions from livestock waste (HAPs only)
AG
FIRE
FIRE
CMAQ category only: wildfires, prescribed
fires and agricultural burning; while the
modeled results are not separated by type of
fire, the tabular emissions summary breaks
out the three fire types separately
BIOGENICS
BIOGENICS
CMAQ category only: VOCs and particular
HAPs (formaldehyde, acetaldehyde and
methanol) emitted from vegetation (and NOX
from soils)
SECONDARY
SECONDARY
Not an emissions group: formed in the
atmosphere due to photochemical reactions
(CMAQ)
BACKGROUND
BACKGROUND
Not an emissions group: concentrations due
to ubiquitous nature of some HAPs with long
residence time or coming into the modeling
domain from outside the domain such as
carbon tetrachloride
The next section describes in more detail the development of emissions and characterization of the
emissions for the run groups by NEI data category.
2.3.2. Point Sources Excluding Airports
Point sources used in AERMOD are all sources in the point data category in the NEI that are in the United
States, Puerto Rico or Virgin Islands;1 have geographic coordinates present in the NEI and emit at least
one HAP. This section discusses the nonairport point sources; airports are discussed in Section 2.3.3.
1Offshore platforms (i.e., FIPS = 85; meaning federal waters) are excluded.
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The starting point for the emissions modeling for point sources is the SMOKE modeling file, or FF10
discussed in Section 2.1.3. The format of this point FF10 file is in the SMOKE User's Manual
https://www.cmascenter.Org/smoke/documentation/4.0/html/ch08s02s08.html#sect input ptinv fflO.
The SMOKE FF10 was split into the CMAQ platform sectors (see Table 2-8) ptnonipm, ptegu and
Pt_°Mgas, and the ptnonipm sector was further split out into airports and nonairports using the
FACILITY_SOURCE_TYPE field. The FACILITY_SOURCE_TYPE indicates the type of facility (where available)
and a value of "100" is used for airports (including large and small airports, heliports and seaplanes).
For purposes of AERMOD, the ptnonipm (after removal of airports) and pt_oilgas were run in the same
way and were not distinguished from one another. The ptegu sector was treated differently due to
different temporalization. The ptegu sector allowed for hourly variation at the unit level, whereas the
ptnonipm (including pt_oilgas) used the monthly, day-of-week and diurnal profiles used by SMOKE.
Within the run, each ptegu unit was temporalized using hourly emission values as discussed in
Section 2.3. Non-EGU units were modeled in AERMOD using temporal allocation factors derived from
the temporal profiles used in SMOKE for CMAQ. Many facilities included a mixture of EGU and non-EGU
processes. In such cases, all sources at a given facility were modeled in the same AERMOD run.2 This
ensured that ambient impacts were calculated for a consistent set of receptor locations for all sources at
the facility.
2.3.2.1. Point source characterization for AERMOD
In the SMOKE FF10 modeling file, a source is a unique combination of EIS process id and EIS release
point id. In AERMOD, we chose to group together sources with the same release point characteristics
(geographic coordinates, release point type and release point parameters) and temporal profile since
they will have the same X/Qs, and grouping them reduces the number of AERMOD runs needed. This is
because many processes use the same temporal profiles, so there are fewer unique combinations of
these parameters than process id/release point id combinations. The FF10 fields that must be unique
(when combined) are: FACILITYJD, MONTHLY temporal profile code, WEEKLY temporal profile code,
ALLDAY temporal profile, ERPTYPE (except that ERPTYPEs 3, 4 and 6 are treated as equivalent), STKHT,
STKVEL, STKTEMP, STKDIAM, FUG_HEIGHT, FUG_WIDTH_YDIM, FUG_LENGTH_XDIM, FUG_ANGLE,
LATITUDE and LONGITUDE. Sources within the same facility were run together in AERMOD. To assign the
proper meteorological data, each facility was assigned to a grid cell based on the geographic coordinates
of the release points.
Helper files were developed for point locations, point source parameters, area source parameters,
temporal parameters and emissions. Point locations were based on the latitude and longitudes in the
FF10 (the release point coordinates) and were converted to UTM coordinates. Point source parameters
used the release point type code (field name is ERPTYPE in the FF10 file) and associated stack or fugitive
parameter information from the FF10.
Table 2-14 lists the release point types and how each is assigned an AERMOD type.
2AERMOD temporalization is performed at the level of source IDs, so using different temporalization schemes at
one facility is possible.
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Table 2-14. Assignment of AERMOD source type for point sources
Emission Release Point
Type Code (NEI)
Emission Release Point Type
Description
AERMOD Source Type Code or Special
Adjustment
1
Fugitive
AREA
2
Vertical
POINT
3
Horizontal
POINTHOR
4
Goose neck
POINTHOR
5
Vertical with rain cap
POINTCAP
6
Downward-facing vent
POINTHOR
For AERMOD area sources, the following fields are used to characterize a fugitive release (units in
parentheses):
Latitude and longitude (decimal degrees)
FUG_HEIGHT- fugitive height (ft)
FUG_WIDTH_YDIM3 -fugitive width (east/west), (ft)
FUG_LENGTH_XDIM4 - fugitive length (north/south), (ft)
FUG_ANGLE- fugitive angle (degrees)
The NEI allows fugitive release angles of 0 to 89 degrees. While the fugitive release point latitude and
longitude are not specified to be any particular location in the area source in the emission inventory
system, due to the limits on the angle and the general conventions for specifying the source in AERMOD,
we interpret the release point as the follows: 1) the lat/lon is treated as the most western corner; 2) the
angle is measured clockwise from true (geodetic) north (not magnetic north); 3) length is the measure
along the side that would run in the north-south direction if the angle were 0 degrees and 4) width is the
measure along the side that would run in the east-west direction if the angle were 0 degrees.
As shown in Figure 2-1, the release point is at the push pin, the width is 680 feet, the length is 1897 feet,
and the angle is 22 degrees.
Geographic coordinates were converted to UTM, with all release points within the same facility assigned
to the same UTM zone. Release parameters are converted to meters in SMOKE as required by AERMOD.
The initial vertical dispersion (ctz) was set to FUG_HEIGHT/4.3 if the FUG_HEIGHT was greater than 10
meters, and 0 m if otherwise.
For AERMOD point sources, the following fields are used to characterize a stack release. The SMOKE
FF10 units are listed below; they are converted to metric (m, m/s, Kelvin) as required by AERMOD.
Latitude and longitude (decimal degrees)
STKHGT- height of stack (ft)
STKDIAM - diameter of stack (ft)
STKTEMP - temperature of stream exiting stack (°F)
STKVEL - velocity of stream exiting stack (ft/s)
3These SMOKEFF10 variables will be renamed to FUG_WIDTH_XDIM and FUG_LENGTH_YDIM to keep with the
convention of X as the east-west direction and Y as the north-south direction.
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Separate helper files are derived for AREA versus POINT AERMOD source types because the source
parameters required for these two types differ.
2.3.2.2. Point source temporalization
As in CMAQ point sources in the ptnonipm sector of the AirToxScreen CMAQ platform were modeled
differently in AERMOD from those in the ptegu sector with respect to the temporalization of the
emissions. The ptegu sectors were temporalized allowing for hourly variation at the unit level, whereas
the ptnonipm and pt_oilgas sectors used the monthly, day-of-week and diurnal profiles used by SMOKE
for the CMAQ platform. Within the run, each ptegu unit was temporalized using hourly emission values
as discussed below. Non-EGU units were modeled in AERMOD using temporal allocation factors derived
from the temporal profiles used in SMOKE for CMAQ. Many facilities included a mixture of EGU and non-
EGU processes. In such cases, all sources at a given facility were modeled in the same AERMOD run.4
This ensured that ambient impacts were calculated for a consistent set of receptor locations for ail
sources at the facility.
For non-EGUs, temporal helper files were prepared based on the temporal cross reference file for point
sources (PTREF) and the temporal profile files (TPRO) used by SMOKE (same data as are used for CMAQ).
The AERMOD scalars reflect diurnal, day-of-week and/or monthly variability and are determined from
4AERMOD temporalization is performed at the level of source IDs, so using different temporalization schemes at
one facility is possible.
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Figure 2-1. Example fugitive source characterization:
NEI length = 1897feet, width = 680 feet and angle = 22
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the SMOKE diurnal, weekly and monthly temporal profile values. Table 2-15 shows the temporal
variability options for non-EGUs.
Table 2-15. Options for temporal variation specification in helper files
Temporal
Variability
TPRO
Qflag (for
AERMOD)
Number of
Scalars
Notes and Calculation of Scalar Using
SMOKE TPRO Fractionsl
Uniform - every
hour of every day
emits the same
amount
Monthly profile code
262, daily profile
code 7, hourly profile
code 24
MONTH
12
AERMOD Scalar value is equal to 1/12 for
each of the 12 months (identical to profile
code 262)
Monthly Variation
only
Day-of-week profile
code 7, hourly profile
code 24
MONTH
12
AERMOD Scalar value month is monthfracm
Where monthfracm is monthly fraction
from the TPRO monthly profile assigned to
the source
Diurnal Variation
Only
Monthly profile code
262, day-of-week
profile code 7
HROFDAY
24
AERMOD Scalar value month is dayfracd
Where dayfracd is the diurnal fraction from
the TPRO diurnal profile assigned to the
source
Month and hour
of day type
variation in which
M-F is the same,
but Sat and
Sunday can be
different
Diurnal profile code
same for all
weekdays
MHRDOW
864
(=24hrs*3d
ay
types* 12
months)
AERMOD Scalar value =
monthfracm * dayfracd * hourfrach
Where monthfracm is the monthly fraction,
dayfracd is the day-of-week fraction and
hourfrach is the diurnal fraction for each
hour for weekdays, Saturday and Sunday
from the TPRO
5 x sum of weekday scalars + sum of
Saturday scalars + sum of Sunday
scalars = 1
Month and hour
of day variation
and every day of
the week could be
different
Diurnal profile code
varies by day-of-
week
MHRDOW7
2016
(=24 hrs*7
day types *
12 months)
AERMOD Scalar value =
monthfracm * dayfracd * hourfrach
Where monthfracm is the monthly fraction,
dayfracd is the day-of-week fraction and
hourfrach is the diurnal fraction for each
hour every day of the week from the TPRO
Sum of scalars is 1
For EGUs, hourly temporalization was used, based on continuous emission monitoring data for heat
input. The approach is the same as used in CMAQ and is described in more detail in the modeling
platform TSD. For AERMOD, separate facility-specific helper files were developed with the hourly scalars
for each hour of the year.
2.3.3. Airport Point Sources
Airports are in the SMOKE point FF10 file and can be identified by the FACIUTY_SOURCE_TYPE field
"100" (for an airport facility). We modeled airports as one of two types: runway line sources or small
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(10- by 10-meter) area sources. The runway airports are those for which we have geographic
coordinates for the runway endpoints from either the National Transportation Atlas Database or an
OTAQ-supplied database. The nonrunway airports have no runway information and were modeled as
small area sources. These include smaller airports, seaplanes and heliports. Although there may be
multiple processes at an airport (e.g., commercial aircraft emissions and ground support equipment
emissions), we did not treat them differently with respect to source characterization. Emissions at an
airport were summed and apportioned equally among the runways or to the small area source. Airports
have multiple sources in the AERMOD helper files only if the airport had more than one runway.
Runway information is not in the NEI. The NEI has a site latitude and longitude, and all release points
(fugitives) use the site coordinates. To get runway endpoints, we matched the NTAD/OTAQ databases
with the NEI emissions. Non-matches (NEI facilities with no match to NTAD/OTAQ based on airport
identifiers, or NEI facilities with coordinates inconsistent with the runway locations) were modeled as
area sources based on the NEI coordinates. Matches were done using the airport location id (typically a
3- or 4-character field), which is an alternate facility id in EIS and is the id used for the NTAD/OTAQ
airports. This process resulted in 7,384 nonrunway airports and 11,991 runway airports.
2.3.3.1. Airport characterization for AERMOD
For runway airports, an airport source is a runway at an airport facility; for nonrunway airports, an
airport source is a 10-by-10-meter area source at an airport facility. Runways are modeled as AERMOD
"LINE" sources, using the coordinates of the runway endpoints, which are assumed to be in the center of
the runway width. All runways are assumed to be 50 meters wide (NTAD-based runways) or 25 meters
wide (OTAQ-based runways) and have a release height and initial vertical dispersion of 3 meters. All
pollutant emissions are divided equally across all runways at the airport.
Airports without runway characterization are modeled as 10m by 10 m area sources with the same
release height and vertical dispersion as runway airports. The NEI airport coordinates are used as the
southwest corner of the area source and the angle is 0.
2.3.3.2. Airport temporalization
Airports used a single set of temporal profiles for all processes within the airport, since the emissions
from all processes are combined on the airport runways or as the small area sources.
All airports use the same set of monthly, day-of-week and diurnal profiles except for Alaska seaplanes.
See the Emissions Modeling Platform documentation for more information on temporalization of airport
emissions.
2.3.3.3. Airport emissions
Emissions are applied to X/Qs for airports differently than other point sources. Instead of multiplying the
source-based emissions to the X/Qs, we multiply the total facility emissions to a facility-aggregated X/Q,
which for runway airports is the aggregate across all runways. In creating the emissions inputs to
AERMOD, the unit emission rate, 1000 g/s, is the unit emission rate for the entire airport, not each
runway. To apportion the 1000 g/s to each runway, the 1000 g/s is divided by the number of runways.
AERMOD then outputs the annual average concentration for the entire airport, not each runway. The
annual average concentrations reflect the apportionment between the different runways. Because it can
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be assumed that each runway will get the same share of actual HAP emissions as the unit emission rate
based on the fraction variable, the specific HAP emissions for the entire airport can be used instead of
runway-specific emissions. This approach reduces the number of records in both the emissions helper
file used for X/Q and the AERMOD annual average concentration output file based on the unit emission
rate.
NEI Lead emissions near airports included lead emitted during the climb-out and approach modes,
which occur at altitude and are not included in AirToxScreen. To account for this, we adjusted the NEl-
specific lead5 emissions estimates used in AERMOD down by 50 percent, based on previous modeling
conducted at the Santa Monica (SMO) airport indicating that nearly 50 percent of emissions occurred in
these higher-altitude modes (see Table 2-16).
Table 2-16. Lead emissions (kg/yr) at SMO in 2008 by aircraft operation mode
Mode
Emissions (% of Total)
Taxi to runway
20.4 (17.6%)
Run-up
13.5 (11.4%)
Takeoff roll
10.0 (8.4%)
Climb-out
37.9 (32.7%)
Approach
17.9 (15.8%)
Landing
9.4 (7.9%)
Taxi to apron
9.5 (8.4%)
The emissions helper file format for runway and nonrunway airports is the same and is shown in
Table 2-17.
Table 2-17. Airport emissions file format
Field Name
Description
State abbrev
2-character abbrev. Use TB for tribe
Facility id
Identifer for facility (i.e., EIS ID)
Facility name
Facility name
FAC_SOURCE_TYPE
Code indicating facility type (from SMOKEFF)
pollutant name
Use SMOKE shortname
Emissions (tons)
Emissions from the SrcID multiplied by the Metal/CN speciation factor (column Al)
from the AirToxScreen_Pollutants.xslx)
For airports, for metals only, multiply metal pollutant emissions from
(2275050011=General Aviation /Piston; 2275060011=AirTaxi /Piston) by 0.5
5We designed the programs to reduce all metals from the following 2 SCCs: 2275050011=General Aviation /Piston
and 2275060011=Air Taxi /Piston by 50%; however, the only metal emissions from these 2 SCCs are lead, so only
lead was reduced.
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2.3.4. Nonroad, On-road and Nonpoint - County-level Sources
Nonroad, on-road and nonpoint (other than CMVs and locomotives) sources in the NEI are estimated at
the county level and use various levels of temporal variation. For AERMOD, we characterize these as
"AREAPOLY" sources, which provide an emission flux over a defined polygon shape. We also
characterize the temporal variability based on the various options available in AERMOD.
The main steps needed to prepare county-level sources for AERMOD are spatial allocation, assignment
of temporal scalar factors and assignment of release parameters. County-level sources must undergo
spatial allocation prior to being input into AERMOD. All county-level sources are allocated to finer
resolution using spatial surrogates. For CONUS domain sources, they are allocated to 4-km or 12-km
resolution, and the size is primarily based on our confidence in the surrogate data at fine resolution and
its representativeness of the source category for which it is being used. For non-CONUS county-level
sources, they are allocated to 9-km resolution (Alaska) or 3-km resolution (Hawaii, Puerto Rico and the
Virgin Islands). This method was first used in the 2014 NATA. Mostly, the same underlying surrogate
data are used for AERMOD as CMAQ and are discussed in detail in the technical support document of
the emissions modeling platform developed for CMAQ emissions. One difference is that the National
Land Cover (NLCD) data are not available for Hawaii, Puerto Rico and the Virgin Islands (i.e., grids 3HI
and 3PR), so an alternative land cover database, the Coastal Change Analysis Program (C-CAP) database
was used for the land cover categories. C-CAP does not have as many categories as the NLCD; low and
medium intensity land was not available and other land categories were substituted. For example, if the
surrogate called for open+low, then only open was used in the 3HI and 3PR grids. Another difference is
that CMAQ uses 12 km for all gridded emissions whereas AERMOD uses 4 km for some of the source
groups in the CONUS. As in CMAQ, the spatial surrogates are assigned according to SCC codes.
In addition to spatial allocation, the emissions are temporally allocated to capture the temporal
variability of emissions throughout the year, day and/or hour of day. Temporal allocation is done
because not all emissions are emitted uniformly throughout the year. Ideally each specific SCC (and in
some cases SCC and county) would be given a different temporal profile as is done when preparing
emissions for CMAQ. But because all sources in the same run group must have the same scalars, we
need temporal scalars that best represent the aggregation of the individual SCCs. In addition, these
scalars, apart from onroad and nonroad mobile run groups, do not vary by pollutant because AERMOD is
run at a unit emissions rate and the pollutant-specific emissions are applied after the model is run. The
scalars can vary by county. For the onroad and nonroad mobile categories, AERMOD preserves the
pollutant-specific monthly variation.
For each run group, SMOKE is used to produce AERMOD helper files that provide the locations and
coordinates of the gridded sources, the area source parameters, the temporal scalars and the emissions
by source and pollutant, broken out by run group.
The subsections below provide more information on how the emissions are developed and modeled in
AERMOD. Detailed information on the development of the emissions at county level is provided in the
national emissions inventory technical support document.
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2.3.4.1. On-road
On-road uses 5 run groups: LDON4, LDOFF12, HDON4, HDOFF12 and HOTEL4, as described in Table 2-12.
These run groups distinguish on-network (ON) versus off-network (OFF) sources, heavy-duty versus
light-duty sources, and hoteling (extended idling and auxiliary power units).
For characterization, the release height of LD ON vs OFF is different (1.3 m vs. 0.5 m) to account for
added dispersion from the vehicle wake. Sigma-z is also different (1.2 vs. 0.5) to account for more
dispersion on the roads. Off-network sources use minimal dispersion associated with start emissions (no
turbulence). HD on and off and hoteling (which is pertinent only to HD vehicles) use the same release
height based on average tailpipe height (3.4 m) but different dispersion. Sigma-z for on-network is 3.2
m, but for off-network and hoteling it is 0.5 m to account for less dispersion during idling.
On-network sources and hoteling use finer resolution (4 km vs. 12 km) in the CONUS domain than for
off-network. This is because we have more confidence in the spatial surrogates used to allocate the
county-level emissions. The on-network sources use AADT data, whereas the off-network emissions are
allocated to broad classes of land use data. Off-network exhaust and evaporative emissions from
passenger cars are allocated to the "all development" land category, which consists of the sum of the
following land categories: developed, open space; developed, low intensity; developed, medium
intensity; and developed, high intensity. This is land with greater than zero percent impervious surface.
For HI, PR and VI, which use C-CAP, there is no low or medium intensity, so it is the sum of open space
and high intensity. Hoteling uses a set of truck stops that was updated for the 2020 Modeling Platform.
The temporal resolution for onroad run groups is hourly across the year. Different profiles were
developed for off-network than on-network due to the types of mobile processes (running vs. starts, for
example) that occur. They are also different for heavy duty versus light duty because of the different
vehicle mixes (trucks vs. cars). For the modeling platform, more refined temporal profiles were
developed for the diurnal variation in vehicle miles travelled based on telematics data analyzed under
the CRC Project A-100 (ERG 2017).
In developing the hourly scalars for CONUS areas, we used the SMOKE-MOVES hourly emissions reports
by county and SCC for a key pollutant for each run group. We chose PM2.5 for HD run groups and
benzene for LD run groups. We then aggregated emissions across all SCCs in that run group. Hourly
emissions were converted to local time (considering daylight saving time where appropriate). SMOKE-
MOVES was run only for the CONUS. National averages were used for non-CONUS temporal profiles.
Because we run grid cells (and not counties) in AERMOD, we assigned each grid cell to a county for
purposes of assigning county-specific hourly emissions. Due to the unit emissions rate, we could not
account for differences across pollutants in temporal variation, which can occur due to different
temperature impacts on EFs that vary by pollutant, except at the monthly level. We imparted monthly
variation to on-road run groups by pollutant by running unit emissions rates by SEASON to get season-
specific X/Q values, and then by post-processing the X/Q by at the seasonal level (i.e., applying seasonal
emissions). This is further discussed in Section 3.4.
The temporal variability of on-road mobile sources varies by emissions process (e.g., running vs. idling),
vehicle type, road type and pollutant. Different pollutants can have different temporal variation because
some pollutants' emission factors depend on meteorology, which has diurnal variation.
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For non-CONUS areas, we used hourly scalars by run group that were developed by taking a national
average of the hourly scalars in the CONUS area.
2.3.4.2. Nonroad
Nonroad equipment, i.e., emissions other than planes, trains and ships that are generated by MOVES,
are put into a single run group. While there are differences in types of equipment and therefore
emissions characterization, for this national study we chose to use a more simplified approach by
characterizing equipment with the same spatial resolution, temporal profile and release characteristics.
The release characteristics are a release height of 2 meters and an initial vertical dispersion of 1 meter.
We chose a spatial resolution of 12 km for the CONUS domain; however, we used a variety of spatial
surrogates for different SCCs. For example, for agricultural nonroad SCCs such as combines, tractors and
balers, agricultural land from NLCD was used, whereas for pleasure craft, water (also from NLCD) was
used. For non-CONUS, we also used land use surrogates and allocated to grid cells.
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2.3.4.3. Nonpoint - NPHI12 and NPL012
Nonpoint stationary sources are broken out into several run groups: NPHI, NPLO, RWC and OILGAS.
NPHI and NPLO are very similar; they use the same temporal profile (diurnal profile 26 and uniform
weekday and monthly profiles. They are allocated to a 12-km spatial resolution (12 km for CONUS
sources and a 9- or 3-km resolution for non-CONUS). NPHI uses a release height of 10 meters and an
initial vertical dispersion of 4.7 meters, which is based on release height/2.15. NPLO uses a release
height of 3.9 meters and an initial vertical dispersion of 3.6 meters. Residential non-wood combustion is
in the NPHI12, which uses the same release height as industrial, commercial and institutional (ICI) fuel
combustion.
Other than locomotive emissions, the two nonpoint run groups are the same as the CMAQ "nonpt"
sector. The CMAQ "nonpt" sector excludes locomotive emissions, which are in the NPLO run group.
2.3.5. Residential Wood Combustion
This is a source group that has its own run group; i.e., the run group and source group are identical. It is
separate from other run groups because its temporalization, which depends on daily temperature, is
unique. This run group comprises wood stoves (indoor and outdoor) and fireplaces. The NEI SCC codes
are 2104008* (includes wood stoves and fireplaces, fire pits/chimineas and hydronic heaters) and
2104009000 (fire logs).
We used a 6.4 m release height and 3.2 m initial vertical dispersion. All SCCs in the source group used
the same spatial surrogate: NLCD Low Intensity Development.
The temporal approach uses temperature data to assign emissions to days of the year. This was done
using the "Gentpro" feature of SMOKE that was developed specifically for this sector, as the level of
residential wood combustion activity depends on the daily minimum temperature. We took the same
approach using 2020 meteorological data. Diurnal profiles based on device type were also applied. For
AERMOD, we took the SMOKE-generated hourly emissions by SCC and created the temporal scalars
using benzene emissions for all SCCs by hour of the year. Hourly scalars were computed by county as:
Scalarhour-i = 8760 * (Benzene)co unty/hour-i/ £all hour5,county(BdlZCIlc)
To assign grid cells to counties, we used the county having the most RWC HAP emissions.
Puerto Rico and the Virgin Islands do not have any emissions for this source group. For Alaska and
Hawaii, the hourly scalars were computed in the same way as the CONUS scalars. However, the
underlying data SMOKE used for generating the hourly benzene emissions were not based on day-
specific meteorological data. Instead, monthly temporal profiles for Alaska and Hawaii were calculated
from the national average of the 2020 meteorology-based profiles, and SMOKE SCC-to-profile mappings
were used for daily and diurnal variation.
2.3.6. Oil and Gas
This run group covers all nonpoint oil and gas sector emissions in the NEI. It contains only one source
group - the oil and gas source group. This allows a fine spatial allocation resolution of 4 km. This
resolution was chosen because the surrogates are based on well locations from a commercially available
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database called HPDI that compiles oil and gas data (such as individual well locations, production
information, drilling information and well completion data) from state databases - the same underlying
data used by the Oil and Gas tool during the creation of the NEI. Because of the different types of
activity data underlying the tool, there are numerous spatial surrogates applied to the various SCCs that
make up the nonpoint oil and gas category. The same source of data (HPDI) was used to develop
temporal profiles by county and SCC. This run group uses only monthly variation. County-specific
monthly scalars were computed from benzene monthly emissions across all SCCs from the run group in
the county (as shown below). If for a certain county there are no benzene emissions, then the sum of all
HAPs was used.
Benzene,
Scalan= — -x
#ofdayS| y Benzene;
of days;
Where:
Benzene, = the tons of benzene in month i for the county associated with the met grid cell
across all SCCs in OILGAS
# of days, = the number of days in month i
The release height chosen is the same as NPHI - 10 m height and 4.7 m for the initial vertical dispersion.
2.3.7. Agricultural Livestock
Like oil and gas, this run group contains just the one source group - agricultural livestock. We kept this
run group separate due to the different temporal nature of livestock emissions from other nonpoint run
groups and to be consistent with the temporal allocation used in CMAQ. In CMAQ, these emissions were
modeled in the "ag" sector and use a meteorologically based temporalization. For AERMOD, a separate
run group was created for consistent temporalization across the two models. For AERMOD, hourly
scalars were computed from hourly ammonia emissions. The release height chosen was 1 m, and 0.465
m for the initial vertical dispersion.
2.3.8. Commercial Marine Vessels - County/Shape-level Sources
This run group has the same sources as the cmv_clc2 and cmv_c3 CMAQ modeling sectors except that
for AERMOD, we include only sources in U.S. state or territory waters, whereas in CMAQ, ships in federal
waters were also included. The NEI inventory uses different SCCs to distinguish between ships that are
at ports (hoteling or maneuvering) versus ships that are moving along waterways, or "underway"
(cruising or in low speed zones), and uses different SCCs for diesel versus residual oil ships. The NEI
provides emissions by port or underway "shape," which are drawn to provide sub-county geographic
resolution of the cmv emissions. In all, there are four SCCs in this run group. Table 2-18 lists the SCCs in
the CMV run group.
Table 2-18. NEI SCCs covered in the CMV run group
SCC
Sector
Description: Mobile Sources Prefix for All
2280002100
cmv
Marine Vessels; Commercial; Diesel; Port
2280002200
cmv
Marine Vessels; Commercial; Diesel; Underway
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SCC
Sector
Description: Mobile Sources Prefix for All
2280003100
cmv
Marine Vessels, Commercial; Residual; Port emissions
2280003200
cmv
Marine Vessels, Commercial; Residual; Underway emissions
For AERMOD, each port source is characterized by a polygon shape with multiple vertices and SCC. The
polygon shape is based on the NEI shape. It is assumed that the emissions are homogeneous in each
polygon. Note that in the NEI, a single port may be characterized by multiple NEI polygon port shapes.
The polygon emissions for all polygons within a single port are proportional to the total port emissions
based on the shape's area compared to the total area of ali polygons for that port. The underway shapes
were the 12 km CMAQ grid cells for CONUS and 9 km grid cells for Alaska and 3 km grid cells for Hawaii
and Puerto Rico. The AirToxScreen underway polygons were simplified from the NEI underway shapes
such that multiple simplified polygons were constructed to represent complex NEI underway shapes. For
underway shapes, the emissions of the simplified shape are assumed proportional to the NEI shape
based on the simplified shape area. An example of a port shape is shown in Figure 2-2.
'no' , O £ Pacific Coast Hwy ® I I ®
^ \ XfiTwNGYOb W Pacific Coast Hwy" "= Q E Pacific Coast
.J I f ' TjH * ® 2 I | f ' /
. I „ I '1 I I
• ^ • j -
- * I J? t ^ ml ' VV;7lh 51 • I AF7l»lSl
I :
pW Pociftc Cojjt Mwy" |
¦
E Pacific Coast
i ^ '
,W7lh,Sl . "] . LE71
- _ r St
5 * 1 BNit-Parl $f , , $
F* tp*" f ie* ^
¦ J? Anaciti iN />
PI
Porl of Icny Bwch
\iiii
A»4
!;' a «*>, f 3m»*r>y.;
^ long Beach T
•rn -v> » _ec J
® «)V
**• t j
minaj Island? g -
oflosAnqthnCf ^ g, ' *
Port, oft Lony^Beoch
DNSF-Port
iony Beach Harbor
BN^F Port of
\onvBeoth
Fort MacArthur
Figure 2-2. Port shapes for Los Angeles and Long Beach, California
For AirToxScreen, release characteristics are different for C1/C2 vessels than for C3. For C1/C2, we used
a release height of 8.4 meters and an initial vertical dispersion of 3.907 meters. Category 3 vessels are
larger and different release parameters were used for consistency with CMAQ. In CMAQ, C3 vessels
were characterized as point sources so the model would compute plume rise. We developed the C3
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AERMOD release height and initial vertical dispersion to be consistent with the characterization in
CMAQ. We used the same release height, 20 m. For the initial vertical dispersion, we examined
summaries of plume rise of C3 from CMAQ; in particular, we looked at a SMOKE report of the annual
NOx vertical distribution by county for the CMAQ cmv_c3 sector. For every county, we found that 50
percent of the emissions were above layer 3 and 50 percent were below. The midpoint of layer 3 is 60.7
meters. Therefore, we computed sigma-z as 60.7-20 = 40.7 m.
2.3.9. Urban/Rural Determination for All Emission Sources
The urban/rural determination for 2020 AirToxScreen differed from that of previous AirToxScreen
assessments (EPA, 2022). For previous assessments, the urban/rural determination was based on
population and on a source-by-source basis. For 2020, a more wholistic approach was used in that the
urban/rural status of the CMAQ grid cells was determined and that any source within the grid cell
(including the gridded sources) would use the same urban/rural classification of the source's parent grid
cell. The urban/rural classification is based on methodology outlined in Section 7.2.1 of the Guideline on
Air Quality Models (EPA, 2023b). A source is considered urban if 50% or more of the area within 3 km of
the source is commercial, industrial, or high intensity residential land use areas or if the population
density in the same 3 km radius circle is 750 people/km2. To determine the land use criterion, the
AERSURFACE tool (EPA, 2020) was used with 2016 National Land Cover Data. AERSURFACE, normally
used to calculate albedo, Bowen ratio, and surface roughness for AERMET, gives a count of land use
types in a circle of user defined radius. The 2016 NLCD categories of interest were categories 23 and 24
(developed medium and developed high intensity, respectively). For the population density criterion,
the 2020 Census block populations were used with the census block centroid used to determine if the
block was within 3-km of the grid cell. The following methodology was used for the CONUS domain:
1. For nine points on or in a 12 km CMAQ grid cell (four corners, cell center, and locations between
each corner), AERSURFACE was run for a 3-km radius circle for each point (Figure 2-3).
Figure 2-3. Points used for urban/rural determination for 12 km CMAQ grid cells.
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2. For each of the nine points, the count of the number of categories 23 and 24 30x30 rn cells
within the 3-km radius circle was computed. Additionally, the number of cells where the
impervious land cover is < 50% of the cell is calculated and subtracted from the number of cells
with categories 23 and 24. For example, if there were 100 cells with categories 23 and 24 and
50 of those cells had 50% or less impervious coverage, then the final count is 50 cells. Given the
definitions of categories 23 and 24, there should not be any cells with impervious coverage <
50% but this was done as a double check.
3. For each of the nine points, calculate the total population of 2020 census blocks within the 3 km
radius circle for each point. The census block centroids were used as the locations to determine
if a block was within 3 km.
4. Calculate the population density of the 3-km area for each point.
5. Determine if each point is urban based on land use (50% of the area in the 3-km circle is
category 23 and 24) as well as if the point is urban based on population density. Each point has
an urban designation based on land use and an urban designation based on population.
6. For each grid cell, if three or more of the points is considered urban either by land use or
population density, then the grid ceil is considered urban. Otherwise, it is rural.
The result was 954 grid cells were considered urban (Figure 2-4).
Figure 2-4. Urban grid cells for the CONUS domain
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Because recent land use data was not available for Alaska, Hawaii, Puerto Rico, and Virgin Islands, only
population density was considered when determining the urban/rural status of the CMAQ grid cells for
those domains. The use of population density alone is adequate since it was found for the contiguous
US, the urban designation was mostly driven by the number of urban points based on population
density. Because population is currently not available for the Virgin Islands, the 3-km CMAQ cells over
the Virgin Islands are considered rural. When population data becomes available, the urban/rural
classification for the Virgin Islands CMAQ cells will be assessed. For Alaska, nine points are used as
described for the 12 km CMAQ cells over the contiguous US and an example layout is shown in
Figure 2-5. For Hawaii and Puerto Rico, five points were used, the four corners and the center
(Figure 2-6). For Alaska, if more than three points are considered urban based on population density,
the cell is considered urban. For Hawaii and Puerto Rico if at least one of the points is considered urban
based on population density, then the cell is considered urban. For Alaska, no cell is designated urban
and for Hawaii, 56 cells are designated as urban (Figure 2-7) and 84 cells are designated urban for
Puerto Rico (Figure 2-8).
Figure 2-5. Points used for urban/rural determination for 9 km Alaska CMAQ grid cells.
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Figure 2-6. Points used for urbon/rurol determination for 3 km Hawaiian and Puerto
Rico/Virgin Islands CMAQ grid cells.
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'
0 510 20 30 40
Kilometers
Figure 2-8. Urban grid cells for the Puerto Rico/Virgin Islands domain.
The input urban population needed by AERMOD is calculated for each of the urban cells in each domain
(US, Hawaii, and Puerto Rico). To calculate the populations, each census block centroid is assigned to a
CMAQ grid cell and each cell's total population is calculated. This is done for all CMAQ cells that contain
a census block centroid. To calculate the urban population for each urban cell, the population of that cell
and the surrounding eight cells are totaled, and that number represents the urban population for the
urban cell.
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3. Air Quality Modeling and Characterization
The AirToxScreen emission estimates described in Section 2 are used as inputs to EPA air quality models
to estimate ambient concentrations of emitted air toxics. An air quality model is a set of mathematical
equations that uses emissions, meteorological and other information to simulate the behavior and
movement of air toxics in the atmosphere. Air quality models estimate outdoor concentrations of air
toxics at specified locations. The modeling approach for the HAPs in AirToxScreen includes development
and application of a hybrid approach blending a chemical transport model (CMAQ) with a dispersion
model (AERMOD) to estimate ambient concentrations of about 50 of the more prevalent and higher risk
HAPs as described in Section 3.5. The HAPs modeled in the hybrid approach capture a vast majority of
the total risk nationally. Treatment for the remaining "non-hybrid" HAPs are described in Section 3.1.2.
3.1. Modeling Overview
3.1.1. Photochemical Model Selection
For AirToxScreen photochemical modeling, we used CMAQv5.4
(https://doi.org/10.5281/zenodo.7218076; https://www.epa.gov/cmaq; http://www.cmascenter.org.)
with the Carbon-Bond 6r5 (CB6r5-CMAQ) chemical mechanism, AER07 aerosol module with non-volatile
Primary Organic Aerosol (POA). CMAQ is a comprehensive, three-dimensional grid-based Eulerian air
quality model designed to simulate the formation and fate of gaseous and particulate species, including
ozone, oxidant precursors, primary and secondary PM concentrations, and sulfur and nitrogen
deposition over urban and regional spatial scales. CMAQ includes numerous science modules that
simulate the emission, production, decay, deposition and transport of organic and inorganic gas-phase
and pollutants in the atmosphere (Appel et al., 2018). While most compounds are grouped when model
chemistry is applied, the CB6r5-CMAQ chemical mechanism treats formaldehyde, acetaldehyde,
benzene, methanol, and naphthalene explicitly. In this version, xylene concentrations are aggregated
across the xylene isomers rather than separately generating concentrations for each isomer. For more
information on CMAQ, see https://www.epa.gov/air-research/communitv-multi-scale-air-qualitv-cmaq-
modeling-svstem-air-qualitv-management or http://www.cmascenter.org.
Table 3-1 lists HAPs included in the multipollutant version of CMAQv5.4 used for AirToxScreen.
Table 3-1. CMAQ HAPs
Air Toxic
CMAQ Species Name(s)
1,1,2,2-Tetrachloroethane
CL4_ETHANE
1,3-Butadiene
BUTADIENE13
1,3-Dichloropropene
DICL_PROPENE
l,4-Dichlorobenzene(p)
DICL_BENZENE
2,4-Toluene diisocyanate
TOL_DIIS
Acetaldehyde
ALD2, ALD2_PRIMARY
Acetonitrile
ACET_NITRILE
Acrolein
ACROLEIN, ACROLEIN_PRIMARY
Acrylic acid
ACRYACID
Acrylonitrile
ACRY_NITRILE
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Air Toxic
CMAQ Species Name(s)
Arsenic
AASI, A AS J, ASSK
Benzene (including benzene from gasoline)
BENZENE
Benzo-A-Pyrene
ABENAPYI, ABENAPYJ, BENAPY
Beryllium
ABEK, ABEI, ABEJ
Cadmium
ACDI, ACDJ, ACDK
Carbon tetrachloride
CARBONTET
Carbon tetrachloride (without background)
CARB_TET_NBC
Carbonyl sulfide
CARBSULFIDE
Chlorine
CL2
Chloroform
CHCL3
Chloroprene
CHLOROPRENE
Hexavalent Chromium Compounds
ACR_VIK, ACR_VIJ, ACR_VII
Trivalent Chromium Compounds
ACRJIIK, ACRJIII, ACRJIIJ
Diesel PM
ADE_ECI, ADE_ECJ, ADE_OCI, ADE_OCJ, ADE_S04J,
ADE_N03J, ADE_OTHRI, ADE_OTHRK, ADE_K
Ethyl benzene
ETHYLBENZENE
Ethylene dibromide (Dibromoethane)
BR2_C2_12
Ethylene dichloride (1,2-Dichloroethane)
CL2_C2_12
Ethylene oxide
ETOX
Formaldehyde
FORM, FORM_PRIMARY
Hexamethylene-l,6-diisocyanate
HEXMETH_DIS
Hexane
HEXANE
Hydrazine
HYDRAZINE
Hydrochloric acid
HCL
Lead Compounds
APBK, APBJ, APBI
Maleic anhydride
MAL_ANHYDRID
Manganese Compounds
AMN_HAPSK, AMN_HAPSJ, AMN_HAPSI
Mercury Compounds
HG, HGIIGAS, APHGI, APHGJ, APHGK
Methanol
MEOH
Methyl chloride (Chloromethane)
METHCHLORIDE
Methylene chloride (Dichloromethane)
CL2_ME
m-xylene, p-xylene, o-xylene and xylenes
(isomers and mixture)
XYLENE
Naphthalene
NAPHTHALENE
Nickel Compounds
ANIK, ANN, ANIJ
Polycyclic Organic Matter
PAH_OOOEO
Polycyclic Organic Matter
PAH_176E5
Polycyclic Organic Matter
PAH_880E5
Polycyclic Organic Matter
PAH_176E4
Polycyclic Organic Matter
PAH_176E3
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Air Toxic
CMAQ Species Name(s)
Polycyclic Organic Matter
PAH_192E3
Polycyclic Organic Matter
PAH_101E2
Polycyclic Organic Matter
PAH_176E2
Polycyclic Organic Matter
PAH_114E1
Propylene dichloride (1,2-Dichloropropane)
PROPYL_DICL
Quinoline
QUINOLINE
Styrene
STYRENE
Tetrachloroethylene (Perchloroethylene)
CL4_ETHE
Toluene
TOLU
Trichloroethylene
CL3_ETHE
Triethylamine
TRIETHYLAMIN
Vinyl chloride
CL_ETHE
3.1.2. Dispersion Model Selection
For AirToxScreen air dispersion modeling, we used AERMOD (Cimorelli et al. 2005), a steady-state plume
model that incorporates air dispersion based on planetary boundary layer turbulence structure and
scaling concepts. AERMOD is EPA's preferred near-field modeling system of emissions for distances up
to 50 km (EPA 2017). AERMOD version 22112 was used for all AirToxScreen run groups.
3.2. Meteorological Data
3.2.1. Meteorological Data Inside the Contiguous States
3.2.1.1. Input WRF Data
For use in all AirToxScreen modeling, we derived gridded meteorological data for the contiguous United
States (CONUS) from version 4.1.1 of the Weather Research and Forecasting Model (WRF), Advanced
Research WRF (ARW) core (Skamarock et al. 2008). The WRF Model is a state-of-the-science mesoscale
numerical weather prediction system developed for both operational forecasting and atmospheric
research applications (http://wrf-model.org/). The CONUS WRF simulation used the same 12-km CMAQ
map projection, a Lambert Conformal projection centered at coordinates (-97, 40) with true latitudes at
33 and 45 degrees north. The 12-km WRF domain consisted of 459 by 299 grid cells and 35 vertical
layers up to 50 millibars. The 12-km CONUS WRF model was initialized using the 12-km North
American Model (12NAM; Only, http://nomads.ncdc.noaa.gov/data.php; download from
ftp://nomads.ncdc.noaa.gov/NAM/analysis_only/) analysis product provided by National Climatic
Data Center (NCDC). Where 12NAM data was unavailable, the 40-km Eta Data Assimilation System
(EDAS) analysis (ds609.2) from the National Center for Atmospheric Research (NCAR) was used.
Analysis nudging for temperature, wind, and moisture was applied above the boundary layer only.
The model simulations were conducted continuously. The 'ipxwrf' program was used to initialize
deep soil moisture at the start of the run using a 10-day spin-up period. The 2020 WRF meteorology
simulated was based on 2011 National Land Cover Database (NLCD; National Land Cover Database
2011, http://www.mrlc.gov/nlcd2011.php). The WRF simulation included the physics options of the
Pleim-Xiu land surface model (LSM), Asymmetric Convective Model version 2 planetary boundary
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layer (PBL) scheme, Morrison double moment microphysics, Kain- Fritsch cumulus parameterization
scheme utilizing the moisture-advection trigger (Ma and Tan 2009) and the RRTMG long-wave and
shortwave radiation (LWR/SWR) scheme (Gilliam and Pleim, 2010). In addition, the Group for High
Resolution Sea Surface Temperatures (GHRSST; Stammer et al. 2003, analysis,
https://www.ghrsst.org/) 1-km SST data was used for SST information to provide more resolved
information compared to the more coarse data in the NAM analysis.
3.2.1.2. MCIP Processing for CMAQ
The 2020 WRF meteorological outputs were processed using the Meteorology-Chemistry Interface
Processor (MCIP) package (Otte and Pleim 2010), version 5.3.3, to derive the specific inputs to CMAQ:
horizontal wind components (i.e., speed and direction), temperature, moisture, vertical diffusion rates
and rainfall rates for each grid cell in each vertical layer. Table 3-2 shows the vertical layer structure
used in WRF and the CMAQ meteorological inputs.
Table 3-2. Vertical layer structure for WRF and CMAQ (heights are layer top)
WRF & CMAQ Layers
Sigma P
Approximate Height (m)
35
0.0000
17,556
34
0.0500
14,780
33
0.1000
12,822
32
0.1500
11,282
31
0.2000
10,002
30
0.2500
8,901
29
0.3000
7,932
28
0.3500
7,064
27
0.4000
6,275
26
0.4500
5,553
25
0.5000
4,885
24
0.5500
4,264
23
0.6000
3,683
22
0.6500
3,136
21
0.7000
2,619
20
0.7400
2,226
19
0.7700
1,941
18
0.8000
1,665
17
0.8200
1,485
16
0.8400
1,308
15
0.8600
1,134
14
0.8800
964
13
0.9000
797
12
0.9100
714
11
0.9200
632
10
0.9300
551
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WRF & CMAQ Layers
Sigma P
Approximate Height (m)
9
0.9400
470
8
0.9500
390
7
0.9600
311
6
0.9700
232
5
0.9800
154
4
0.9850
115
3
0.9900
77
2
0.9950
38
1
0.9975
19
0
1.0000
0
3.2.1.3. MMIF Processing for AERMOD
WRF output was processed through the Mesoscale Model Interface (MMIF) program (version 3.4.2) to
create AERMET-ready meteorological input data and processed in AERMET (version 22112). MMIF was
processed for AERMOD in accordance with EPA guidance, Guidance on the Use of the Mesoscale Model
Interface Program (MMIF) for AERMOD Applications (U.S. EPA 2023c). As options, we used the default
FLM layers (see MMIF User's Guide for details), TOP for vertical interpolation and MMIF-calculated
mixing heights. AERMET (version 22112) was run with the adjusted u* option to better represent
concentrations in AERMOD under low-wind stable conditions.
3.2.2. Meteorological Data Outside the Contiguous States
3.2.2.1. Input WRF Data
The 2020 gridded meteorological data covering areas outside the contiguous states, the 3-km Hawaii
and Puerto Rico/Virgin Islands and the 9-km Alaska domains, was derived from the publicly available
WRF version 4.1.1, ARW core (https://www2.mmm.ucar.edu/wrf/users/).
Grid Domain Specifications:
• Alaska with 9 km horizontal grid spacing (312 cells in the x-direction, 252 cells in the y-direction)
with 35 layers centered on 63° N and 155° W.
• Hawaii with 3 km horizontal grid spacing (225 cells in the x-direction, 201 cells in the y-direction)
with 35 layers centered on 21° N and 157° W
• Puerto Rico and Virgin Islands with 3 km horizontal grid spacing (150 cells in the x-direction, 150
cells in the y-direction) with 35 layers centered on 18 ° N and 66° W.
The WRF model was initialized using the 0.25-degree Global Forecast System (GFS) model analysis
(NCEP/NWS/NOAA/USDC, 2015, https://doi.org/10.5065/D65D8PWK). Analysis nudging for
temperature, wind, and moisture was applied above the boundary layer only. The model simulations
were conducted continuously. The 'ipxwrf' program was used to initialize deep soil moisture at the start
of the run using a 10-day spin-up period. The 2020 WRF meteorology simulated was based on IGBP-
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Modified MODIS 20-category Land Use data. The WRF simulation included the physics options indicated
in Table 3-3. In addition, the Group for High Resolution Sea Surface Temperatures (GHRSST; Stammer et
al. 2003, analysis, https://www.ghrsst.org/) 1-km SST data was used for SST information to provide more
resolved information compared to the coarser data in the NAM analysis.
Table 3-3. WRF Options Used for the Alaska, Hawaii, and Puerto Rico/Virgin Islands domains.
Option
Settings for ALASKA
Settings for HI & PR/VI
Cumulus
Kain-Fritsch (w/Trigger 1) w/
cu_rad_feedback = .true.
Kain-Fritsch (w/Trigger 1) w/
cu_rad_feedback = .true.
Precipitation
Hourly Incremental
Hourly Incremental
PBL
MYNN2
YSU
Sea Surface
Temperatures
GHRSST
GHRSST
LSM
MYNN
Noah
Microphysics
New Eta (Ferrier, mp_physics = 5)
New Eta (Ferrier, mp_physics = 5)
Longwave and
Shortwave
radiation
RRTMG
RRTMG
Model Top
50 mb
50 mb
3.2.2.2. MCIP Processing for CMAQ
The 2020 WRF meteorological outputs for the 3-km Hawaii and Puerto Rico/Virgin Islands and the 9-km
Alaska domains were processed using the Meteorology-Chemistry Interface Processor (MCIP) package
(Otte and Pleim 2010), version 5.3.3, to derive the specific inputs to CMAQ: horizontal wind components
(i.e., speed and direction), temperature, moisture, vertical diffusion rates and rainfall rates for each grid
cell in each vertical layer. Table 3-2 shows the vertical layer structure used in WRF and the CMAQ
meteorological inputs.
3.2.2.3. MMIF Processing for AERMOD
For meteorological data covering areas outside the contiguous states (Alaska, Hawaii, Puerto Rico and
the U.S. Virgin Islands), the same methodology used for the contiguous U.S. described in Section 3.2.1.3
was used.
3.3. CMAQ Setup
3.3.1. Sources Modeled in CMAQ
AirToxScreen CMAQ modeling included a base year run (primary and secondary annual average
concentrations) and "zero-out" runs for biogenics and fires (primary annual average concentrations).
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The annual simulations included a "ramp-up" period of 10 days to mitigate the effects of initial
concentrations. All 366 model days were used in the annual average levels of air toxics modeled. The
model extends vertically from the surface to 50 millibars (approximately 17,600 meters) using a sigma-
pressure coordinate system.
The CMAQ model runs were performed for four domains covering:
(1) Contiguous United States (CONUS), as shown in Figure 3-l(a). This single domain covers the
entire CONUS and large portions of Canada and Mexico using 12 km by 12 km horizontal
grid spacing.
(2) Alaska (Figure 3-l(b)) with 9 km by 9 km horizontal grid spacing.
(3) Hawaii (Figure 3-l(c)) with 3 km by 3 km horizontal grid spacing.
(4) Puerto Rico and Virgin Islands (Figure 3-l(d)) with 3 km by 3 km horizontal grid spacing.
Error! Reference source not found.4 provides some basic geographic information regarding the f
our CMAQ domains. Air quality conditions at the outer boundary of the four CMAQ domains
were taken from a global model (described in Section 3.3.2).
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Figure 3-1 (a). Map of the CMAQ modeling domain; the blue box denotes the 12 km CONUS
modeling domain.
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? ' V
x,
w*
i|8S 13
--
UZ
\
Y
/ X
I \
j'M/ JH
( j- Russia
xj
H
w"
United States
CA
• sUf
Wm
AK 9km Domain
x,y coordinate: -1107000,-1134000
cols, rows: 312,252
Figure 3-2(b). Map of the CMAQ modeling domain; the green box denotes the 9 km Alaska
modeling domain.
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a w
\
\ H
(V
HI3km domain
x,y origin: -391500, -346500
cols, rows: 225,201
Figure 3-3(c). Map of the CMAQ modeling domain; the red box denotes the 3 km Hawaii
modeling domain.
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\
Dominican
Republic
/ / """ _ British VirgiFi Island
I Puerto Rico
1 o j ^
Virgin Islands
PR3km domain
x,y origin: -274500,-202500
cols, rows: 150,150
Figure 3-4(d). Map of the CMAQ modeling domain; the red box denotes the 3 km Puerto
Rico/Virgin Islands modeling domain.
Table 3-4. Geographic information for the CMAQ modeling domains
CMAQ Modeling Domain Configurations
Domain
Map Projection
Grid
Resolution
Coordinate
Center
True Latitudes
Dimensions &
Vertical Extent
Continental US
Lambert Conformal
Projection
12 km
97 W, 40 N
33 and 45 N
459 x 299 x 35
Alaska
Lambert Conformal
Projection
9 km
63 N, 155 W
60 and 70 N
312x252x35
Hawaii
Lambert Conformal
Projection
3 km
21 N, 157 W
19 and 22 N
225x201x35
Puerto Rico/Virgin
Islands
Lambert Conformal
Projection
3 km
18 N, 66 W
17 and 19 N
150 x 150 x 35
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3.3.2. Boundary and Initial Conditions
The 2020 annual lateral boundary and initial species concentrations were provided using a global 3-D
GEOS-Chem vl4.0.1. GEOS-Chem is a 3-D model of atmospheric chemistry driven by meteorological
inputs from the Goddard Earth Observing System of the National Aeronautics and Space Administration
(NASA) Global Modeling Assimilation Office. GEOS-Chem was run using the standard (or default) options
and full atmospheric chemistry (https://geoschem.github.io/index.html). The GEOS-Chem simulation
was performed at 2 x 2.5-degree horizontal resolution with a 72-layer vertical structure (36 layers in
troposphere, hybrid terrain following coordinate). Simulation used full chemistry including an online
stratosphere, non-local planetary boundary layer, and simple secondary organic aerosols. The 2020
simulation required extending the methane inputs to the year 2020, updating lightning inputs, and other
parameters for 2020. Emissions included online Model of Emissions of Gases and Aerosols from Nature
(MEGAN) version 2.1 (Guenther et al. 2012), online DUST module, and online sea salt module. Global
Fire Emissions Database (GFED; https://www.globalfiredata.org/) were monthly mean. Anthropogenic
emissions included fugitive, combustion, and industrial dust (Philip et al. 2017). Marine emissions were
based on Community Emissions Data System (CEDS) version 2 including shipping vessels
(https://www.pnnl.gov/projects/ceds). Aircraft Emissions Inventory Code (AEIC; Simone et al. 2013)
monthly aircraft input data. In addition, CEDS and AEIC was scaled by Covid-19 adjustmeNt Factors fOR
eMissions (CONFORM) dataset (Doumbia et al. 2021). Meteorology used in this 2020 GEOS-Chem run
was from Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA2; GMAO
2015) meteorology at 2 x 2.5-degree. With the exception of input updates for 2020, these were the
default options and inputs distributed with vl4.0.1.
In addition to the standard chemistry, we also included Persistent Organic Pollutants (POPs) and
mercury from specialized applications of GEOS-Chem. The POPs model configuration (Friedman et al.
2014) in vl2.0.1 for 2018 applied to PHE, and PYR using 4x5 degree resolution. For mercury, we applied
the mercury model configuration (Selin et al., 2007) from a 2013 v9 simulation with 47 layers and 2x2.5-
degree resolution. The mercury concentrations were averaged to months and used as a climatology.
Benzo-a-pyrene was applied as a monthly value from previous 2018 boundary conditions data files.
Where CMAQ species were not available from either the standard chemistry, the POPs or mercury
simulation, then they were treated as climatology using values from previous multipollutant boundary
condition applications.
Because GEOS-Chem does not include all modeled HAPs, we also used remote concentration estimates
as nonvarying background (in space and time). These were computed based on data from the five NOAA
GMD sites: Cape Kumukahi, Hawaii (KUM); Mauna Loa, Hawaii (MLO); Niwot Ridge, Colorado (NWR);
Barrow, Alaska (BRW); and Alert, Canada (ALT) and the Trinidad Head Site (AGAGE). More information
on how these were derived is in Appendix C.
For the remaining CMAQ HAP BCs not provided by H-CMAQ nor estimated using remote concentrations
listed in Table 3-4, a value of zero was applied (shown in Table 3-5) due to a lack of data.
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Table 3-4. Boundary conditions from 2020 remote concentration estimates
Pollutant
RCEat298K and 1
atm (|ig/m3)
2020
RCE (pptv)
2020
Remote
Network
Location(s)
CMAQ HAP
Chloroform
0.075
15.3
AG AGE
Trinidad Head
X
Methyl chloride
(chloromethane)
1.155
559.7
NOAAGMD
KUM, MLO, NWR, BRW,
ALT
X
Carbon tetrachloride
0.487
77.5
NOAAGMD
KUM, MLO, NWR, BRW,
ALT
X
Methyl bromide
(bromomethane)
0.028
7.1
NOAAGMD
KUM, MLO, NWR, BRW,
ALT
Methyl chloroform (1,1,1-
trichloroethane)
0.007
1.2
NOAAGMD
KUM, MLO, NWR, BRW,
ALT
Dichloromethane
(methylene chloride)
0.238
68.5
NOAAGMD
KUM, MLO, NWR, BRW,
ALT
X
Tetrachloroethene
(perchloroethylene,
tetrachloroethylene)
0.011
1.7
NOAAGMD
KUM, MLO, NWR, BRW,
ALT
X
Table 3-5. CMAQ HAP boundary conditions applied as zero value
Air Toxic
2020 CMAQ Species Name(s)
1,1,2,2-Tetrachloroethane
CL4_ETHANE
1,3-Dichloropropene
DICL_PROPENE
l,4-Dichlorobenzene(p)
DICL_BENZENE
2,4-Toluene diisocyanate
TOL_DIIS
Acetonitrile
ACET_NITRILE
Acrylic acid
ACRYACID
Acrylonitrile
ACRY_NITRILE
Arsenic
AASI, AASJ, ASSK
Beryllium
ABEK, ABEI, ABEJ
Benzo-A-Pyrene
ABENAPYI, ABENAPYJ, BENAPY
Cadmium
ACDI, ACDJ, ACDK
Carbon tetrachloride
CARB_TET_NBC
Carbonyl sulfide
CARBSULFIDE
Chlorine
CL2
Chloroprene
CHLOROPRENE
Chromium Compounds
ACR_VIK, ACR_VIJ, ACR_VII
Chromium Compounds
ACRJIIK, ACRJIII, ACRJIIJ
Diesel PM*
ADE_ECI, ADE_ECJ, ADE_OCI, ADE_OCJ, ADE_S04J,
ADE_N03J, ADE_OTHRI, ADE_OTHRK, ADE_K
Ethyl benzene
ETHYLBENZENE
Ethylene dibromide (Dibromoethane)
BR2_C2_12
Ethylene dichloride (1,2-Dichloroethane)
CL2_C2_12
Ethylene oxide
ETOX
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Air Toxic
2020 CMAQ Species Name(s)
Hexamethylene-l,6-diisocyanate
HEXMETH_DIS
Hexane
HEXANE
Hydrazine
HYDRAZINE
Lead Compounds
APBK, APBJ, APBI
Maleic anhydride
MAL_ANHYDRID
Manganese Compounds
AMN_HAPSK, AMN_HAPSJ, AMN_HAPSI
Mercury Compounds
APHGI, APHGK
m-xylene, p-xylene, o-xylene and xylenes
XYLENE
(isomers and mixture)
Naphthalene
NAPHTHALENE
Nickel Compounds
ANIK, ANN, ANIJ
Polycyclic Organic Matter
PAH_000E0
Polycyclic Organic Matter
PAH_176E5
Polycyclic Organic Matter
PAH_880E5
Polycyclic Organic Matter
PAH_176E4
Polycyclic Organic Matter
PAH_176E3
Polycyclic Organic Matter
PAH_192E3
Polycyclic Organic Matter
PAH_101E2
Polycyclic Organic Matter
PAH_176E2
Polycyclic Organic Matter
PAH_114E1
Propylene dichloride (1,2-Dichloropropane)
PROPYL_DICL
Quinoline
QUINOLINE
Styrene
STYRENE
Trichloroethylene
CL3_ETHE
Triethylamine
TRIETHYLAMIN
Vinyl chloride
CL_ETHE
3.4. AERMOD Setup
3.4.1. Sources Modeled in AERMOD
AERMOD modeling comprised point, nonpoint, on-road and nonroad sources. We excluded fires
(agricultural burning, wildfires and prescribed fires) and biogenic emissions.
3.4.2. Receptor Placement
For the CONUS domain, we used the following receptors:
1. Equally spaced "gridded" receptors at 1 km resolution. This differs from previous editions of
AirToxScreen which used 1 km spacing in highly populated areas and 4 km in all other areas.
2. Populated 2020 census-block c receptors (discussed in Section 3.4.2.2)
3. Monitoring site receptors (discussed in Section 3.4.2.3)
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For non-CONUS areas (AK, HI, PR, VI), we used:
1. Equally spaced "gridded" receptors at 1 km resolution. In previous editions of AirToxScreen, Alaska
was modeled at 3 km resolution.
2. Populated 2020 census-block receptors
3. Monitoring site receptors
To facilitate the CMAQ/AERMOD hybrid modeling, we used these receptors to compute an AERMOD
average concentration corresponding to each CMAQ grid cell. These concentrations could then be used
in the hybrid equation. Gridded receptors were also used for averaging concentrations for the gridded
sources (nonpoint and mobile) and interpolating to block receptors and monitor receptors. Receptors at
the monitoring locations were used in the model evaluation.
3.4.2.1. Gridded receptors
We used gridded receptors throughout the CONUS area and the three non-CONUS areas. The purposes
of the gridded receptors were to provide a uniform grid in the CMAQ grid cells to adequately capture
near-field concentration gradients of sources in and surrounding the grid cells and to provide a grid used
to interpolate census block and monitor receptors post-modeling. New to 2020, the gridded receptors
were also used to calculate a gridded average concentration for the gridded source types (nonpoint and
mobile) during AERMOD post-processing. For all domains, a uniform resolution of 1 km was used for the
gridded receptors. This differed from previous editions of AirToxScreen where gridded receptor
resolution was based on the population of the 2013 Core Base Statistical Areas (CBSA). See EPA (2022)
for details on the previous receptor strategy. The updated strategy resulted in 144 receptors per 12-km
CMAQ grid cell in the CONUS domain, nine receptors in each 3-km CMAQ grid cell in Hawaii and Puerto
Rico/Virgin Islands, and 81 receptors in each 9-km CMAQ grid cell in Alaska.
Each gridded receptorrepresented the center of a subgrid cell within the CONUS 12-km CMAQ grid cells,
Hawaii and Puerto Rico/Virgin Islands 3-km CMAQ grid cells, and Alaska 9-km CMAQ grid cells
(Figure 3-5). These gridded receptors, plus populated block and monitor receptors when available
within a subgrid cell, were averaged (Figure 3-6). These subgrid-cell averages were then used to
calculate the overall AERMOD average within each grid cell, which was then used in the hybrid equation
that combines CMAQ and AERMOD results. Receptor elevations and hill heights were determined using
AERMAP (version 18081).
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HI PR/VI
CONUS
Alaska
Figure 3-5. CONUS, HawaiiPuerto Rico/Virgin Islands, and Alaskan receptor grid layouts in
CMAQ Lambert Projection
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+
• •
• •
•
• • •
• •*.. • •
• •
•
• •
• +
•
•
•
•
•
• •
•
•
*
• "•••• _
• • •.«••• *
• _ ••••••••
• • *
•• • • ... • • 2 . .
• • •
• •
• •
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•
•
•
• •
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• •
• • •
+#
*
•
•
•
+
•
•
•
•
•
•
•
•
+
•
•
+
•
•
•
.
• .
+
•
•
• Census block
^ Gridded receptor
Figure 3-6. Example grid cell with subgrid cells and census blocks
When performing the dispersion modeling for point sources, gridded receptors within 50 km of any
emission point at the facility were explicitly modeled in AERMOD. For airports, any gridded receptor
within 50 km of any point along a runway or within 50 km of any part of the 100-by-100 m area sources
was explicitly modeled. For gridded sources (all domains), any gridded receptor within a grid cell that
was within 50 km of the center of the gridded source was explicitly modeled. For ports and underway
sources, any gridded receptor that was within 50 km of a side of the polygon or within 50 km of the
center of the source was explicitly modeled.
3.4.2.2. Census-block receptors
The locations of census-block receptors were based on the 2020 U.S. Census. Prior to the 2020
AirToxScreen, the census block centroids were used to represent the census blocks in modeling.
Beginning with the 2020 AirToxScreen, census block concentrations, risks, and hazard quotients are
reported. With this new emphasis on the census block results, EPA developed a new receptor strategy
to represent the census blocks. Within each census block, a grid of receptors was created to calculate
concentrations throughout the census block, not just at the census block centroid. This new approach
would allow us to account for non-ambient air (i.e., receptors on facility property) and allow for more
spatially representative concentrations for each census block. An example would be large rural blocks
previously represented by the census block centroid but now the block has a grid to calculate more
representative concentrations for the block (Figure 3-7).
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Figure 3-7. Rural census block represented by census block centroid on left and grid of
receptors on right.
The grid resolution within each census block varied from 25 meters to 10 km at resolutions of 25 m, 50
m, 100 m, 250 m, 500 m, 1 km, 2 km, 5 km and 10 km, depending on the size of the census block. When
developing the resolution of a census block, the initial resolution was 100 m. If there were fewer than
five receptors in the block at 100 m resolution, the resolution would be increased to 50 m and
subsequently to 25 m if there were still fewer than five receptors in the block at 50 m resolution
Alternatively, if there were more than 50 receptors at 100 m resolution, then the resolution was
decreased to a coarser resolution (250 m, 500 m, etc.) until the number of gridded receptors was
between 4 and 50 receptors. If the number of receptors was less than four and the resolution was 25 m
or the census block was less than 500 x 500 m, then census block centroid was used to represent the
census block in modeling. When performing the dispersion modeling for point sources in all domains,
populated block receptors within 5 km of any emission point at the facility were explicitly modeled in
AERMOD. For airports in all domains, any populated block receptor within 5 km of any point along a
runway or 5 km from any point of the 100-by-100 m area sources was modeled. For gridded sources in
all domains, census blocks were not modeled; concentrations were assigned to the census block
receptors during the hybrid calculations. . For ports and underway sources in all domains, any block
receptor within 5 km of a side of the polygon or within 10 km of the center of the source was explicitly
modeled. Non-populated blocks were not modeled in AirToxScreen.
With the new census block receptor strategy, the probability that a receptor was located in non-ambient
air (inside the fence line of a facility) was more likely than when using the older census block centroid
receptor strategy. To check for ambient air and modeled receptors for a facility, port, or airport,
receptor locations were checked to see if they were within a specified distance of sources for a facility
or, when available, within a pre-determined property boundary of the facility. The use of property
boundaries or an ambient air distance were used to flag receptors that do not represent ambient
conditions for a particular census block and thus lead to a less representative block average
concentration. For the 2020 AirToxScreen, 137 facilities used a property boundary to determine if a
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receptor was located in ambient air relative to the facility6. If a receptor was located within the property
boundary it was considered non-ambient and flagged in the AERMOD input file. Facilities without a
property boundary used a specified distance to check receptor-source distances. The default distance
used to check was 30 m. Ten facilities used distances other than 30 m, with those distances ranging from
35 to 150 m. If a receptor was within the specified distance from any source at the facility, it was
considered non-ambient and flagged in the AERMOD input file. For airports and ports, property
boundaries were not used and the default 30 m distance was used to check for ambient air. For airports,
this distance was used to make sure no receptors on the runways for airports with runways. Treatment
of non-ambient receptors in post-processing is discussed in Section 3.4.5 below.
3.4.2.3. AERMOD receptors at monitoring sites
The Ambient Monitoring Archive for HAPs monitoring data sites was used for the model evaluation
(found in the Supplemental Data folder on the AirToxScreen website). Therefore, we obtained a unique
set of geographic coordinates for all monitors to include as receptors in the AERMOD modeling.
AERMAP (version 18081) was used to generate receptor elevations and hill heights for input into
AERMOD. The modeling distance criteria used for census blocks was used for the monitor receptors.
Unlike the census blocks, monitors were not checked for ambient air status.
3.4.3. Model Options
For all AERMOD runs, the FASTALL option was used to decrease model runtimes, especially for gridded,
airport, and CMV shapes. For all AERMOD runs excluding point and airport sources, the FLAT option was
used (terrain ignored).
For sources determined to be urban based on the parent CMAQ grid cell, the AERMOD urban option was
used with the population of the grid cell.
3.4.4. AERMOD Simulations
We ran AERMOD for each of the run groups described in Table 2-11. For the run groups that included
the gridded source types in the CONUS area, gridded sources were also used as the sources in the non-
CONUS areas. Receptor placement was as described in Section 3.4.2, and each source was assigned the
meteorological files corresponding to the WRF grid cells in which they were located for the gridded
sources.
For the point sources and airports, each facility or airport was run in its own AERMOD run. For the point
sources, each AERMOD source in the facility was its own AERMOD source group with the source group
name corresponding to the AERMOD source ID. For each airport, all emissions sources were assigned to
a total group (group ALL). Similarly, for the CMV runs and gridded sources, each CMV shape or grid cell
was run in its own AERMOD run. The CMV emissions sources in each AERMOD run were assigned to
groups based on the source. For underway emissions, a single group representing all three ship types
was used. For the port emissions, groups were assigned: 1) a group for CI and C2 and 2) a C3 group. For
the 12-km gridded sources and non-CONUS gridded sources, the source was assigned to source group
6 As new editions of AirToxScreen are released, the number of facilities with property boundaries will increase with
each edition.
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with the same name as the AERMOD source ID. For the 4-km gridded sources, all sources that shared
the same parent 12-km grid cell and thus the same meteorological data were run together in one
AERMOD run. Each 4-km source was given its own source group in the AERMOD output.
As discussed in Section 2.3 for the various run groups, different temporalization resolutions were used
(hourly, monthly, etc.). The scalars from the temporal helper files were used in conjunction with the unit
emission rate of 10,000 tons/year to develop hourly unit emissions for the modeled sources. For hourly
emissions, this was simply the product of the hourly scalar and 10,000 tons/year, then applying a
conversion factor of 251.99577 to yield an emission rate in g/s. If a source was an hourly source, the
resulting emission rate was divided by the area of the source to yield the correct units for AERMOD.
For sources using monthly emission factors, a weighting factor was calculated by multiplying each
monthly scalar by the number of days for the month. These products were summed across all 12
months. The hourly emission rate E for each month i was then calculated as:
Where EF, is the monthly scalar factor from the temporal helper file and 10,000 is the unit emission rate
in tons/year. If the source was an area source, £, was divided by the area of the source. Since each hour
for a particular month has the same emission rate, the AERMOD EMISFACT keyword was used to
represent the hourly emissions. The emission rate calculated above was used for the factor, and the
base emission rate on the SRCPARAM line was set to 1.0 g/s or 1.0 g/s/m2 for area sources. For
emissions that varied by hour of day only, the AERMOD EMISFACT HROFDY was used and the hourly
emission rate £, for hour i (1-24) was calculated as:
Where 366 represents the number of days per year.
Area sources' emission rates were divided by the area of the source.
For emissions that varied by month, day of the week, MHRDOW or MHRDOW7, and hour of day, the
month weighting factor was applied in the temporal helper file. The hourly emission rate applied in
AERMOD was calculated as the product of 10,000 tons/year, the emission factor, and the conversion
factor of 215.9957. As with the other emission factors, area sources' emission rates were divided by the
area of the source.
3.4.5. Post-processing of AERMOD Results
Post-processing of the AERMOD runs consisted of two steps: 1) interpolation of populated census blocks
and monitors from gridded receptors for point sources, airports and ports or 1) averaging of gridded
receptors within a CMAQgrid cell for the gridded sources, and 2) calculation of HAP-specific
concentrations. For all AirToxScreen run groups except the gridded sources, interpolation of
concentrations to census blocks and monitors occurred at blocks and monitors from 5 to 50 km from the
7215.9957 is the conversion factor from tons/hr to g/s.
24
E,=
10000 x EF,
x 251.9957
366
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source. Interpolation was done for each individual facility (point source, airport, CMV shape) or gridded
source. For the gridded source types, the gridded 1 km receptors were averaged within the CMAQ grid
cell and the averaging matched the resolution of the source type.
As noted in Section 3.4.2.2, we flagged census block receptors that were within facility property
boundaries or within the facility specific ambient air distance. During post-processing for a particular
facility, the concentration for any receptor deemed non-ambient is ignored for that facility unless one of
the following conditions are found:
• If census block is represented by the census block centroid, then the concentration is included
for the facility so that the census block has some representation for the facility,
• If a census block has multiple receptors and all receptors for the census block are non-ambient
one of them is selected to represent the census block for that facility.
Interpolation of census block receptors and monitors was done for the point sources, airports, and
ports. For a receptor located between four gridded receptors, the concentration at the receptor was
based on linear interpolation between the four gridded receptors. For receptors near the 50-km edge of
the modeling domain and thus without four receptors around it, the interpolated receptor's
concentration was the average of the nearest gridded receptors.
For nonmobile AirToxScreen run groups (point, airports, CMV, nonpoint, RWC, oil and gas, non-CONUS
nonroad) the HAP/source group-specific concentrations at each receptor were calculated by dividing the
HAP emissions (tons/year) for each source group at each AERMOD source by 10,000 tons/year.
For the mobile run groups, the HAP/source group-specific concentrations were calculated based on
monthly emissions. The AERMOD output for these run groups was average concentrations by month.
AERMOD also output the hours that had missing or calm meteorological data. Only hours that were
noncalm and not missing in the meteorological data were included in the averaging, consistent with the
calculation of long-term concentrations in AERMOD. The following methodology was used to calculate
the annual average HAP/source group specific concentrations at each receptor:
1. Multiply the concentration from the monthly output file by the number of valid hours for the
particular month based on review of the AERMOD errors file. Add the resulting product to a running
total of concentration for each month and modeled source group.
2. For each season, multiply the total calculated in step 1 and 2 by the ratio of the source group's
monthly HAP emissions (tons/month) to the modeled monthly emissions (based on 10,000
tons/year) listed in the AERMOD output file.
3. Add the result from step 3 to a running total across all months. Also, for each month, loop through
the months to determine the total number of valid hours for the year. If all meteorological data
hours were noncalm and not missing, this results in a total of 8,784 hours (2020 was a leap year).
The number may be less if calms and missing data are present in the meteorological data.
4. Divide running total concentration from step 4 by the running total of hours from step 4 to calculate
an annual average HAP/source group-specific concentration. This division of concentration by hours
is equivalent to how AERMOD calculates annual average concentrations in a simulation, a sum of
hourly concentrations divided by the number of noncalm and nonmissing hours.
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For 2020 AirToxScreen, we used a new approach for the gridded sources. In previous editions of
AirToxScreen, census block receptors and monitors were interpolated from the gridded receptors
modeled for these source types. This interpolation implies a certain level of precision in the spatial
allocation of emissions. Within the AERMOD modeling for these gridded sources, gridded receptors near
the center of the source have the highest concentrations within the grid cell and receptors near the
edge of the grid cell, especially the upwind directions, would have the lowest concentrations of the grid
cell. Interpolation to block receptors or monitors would then lead to the census blocks near the center
of the grid cell having the highest concentrations and the census blocks near the edges would have
lower concentrations. This concentration distribution would not necessarily be reflective of emissions
but more of the meteorology. Depending on the source type, the emissions are spread throughout the
CMAQgrid or in the case of certain CONUS onroad and oil & gas emissions, spread over 4 km cells. For
example, consider the commercial cooking nonpoint source emissions. The emissions are spread over
the 12 km grid cell and uniform throughout. Using the older interpolation method would lead to the
highest block concentrations near the center of the 12 km cell. However, there may be no actual
commercial cooking facilities near those blocks. To have a more representative gridded source
concentrations, we decided to average the gridded receptors' concentrations within each CMAQ grid
cell and that averaging matched the emissions resolution. Using the commercial cooking example once
more for a 12 km CMAQ cell, the 144 gridded receptors' commercial cooking concentrations were
averaged together for each HAP, resulting in a single commercial cooking value for each HAP. Similarly,
for Alaska, the 81 gridded receptors were averaged for the 9 km gridded sources, and so on for the 3 km
cells in Hawaii and Puerto Rico/Virgin Islands. For the 4 km gridded sources in the CONUS domain, the
receptors were averaged within 4 km mini-cells within the parent 12 km cell. So, for a particular 12 km
grid cell, there would be nine 4 km cell averages.
These gridded averages were then assigned to the block receptors and monitors during the hybrid
calculations.
Once HAP/source group-specific concentrations are calculated for each gridded receptor, block receptor
and monitor receptor, the resulting concentrations were output for input into the hybrid program.
3.5. Hybrid Modeling
The hybrid approach combines the annual concentration results from the AERMOD and CMAQ models
to compute ambient concentrations at census block receptors. The subsections below contain
discussions on the hybrid air modeling approach used for AirToxScreen for the CONUS.
3.5.1. Overview
For 58 of the most prevalent and highest risk air toxics (see Table 3-1), we used a hybrid air quality
modeling method combining the fine spatial scale and source attributions of AERMOD (Cimorelli et al.
2005; EPA 2023d) with the full treatment of chemistry and transport afforded by CMAQ. In this
application, AERMOD treated all species as chemically nonreactive. The emissions and meteorological
data sets used in CMAQ were processed further to generate AERMOD inputs consistent with CMAQ.
AERMOD receptor locations were based on the centroids of populated census blocks, monitoring-site
positions, and evenly distributed points within each horizontal CMAQgrid cell in the four domains (see
Figure 3-1 and Figure 3-8 for an example in the CONUS domain), resulting in at least 144, and sometimes
more than 10,000, receptors per cell and 6.5 million receptors nationwide in the CONUS domain.
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Figure 3-8. CMAQ domain with expanded cell showing hybrid receptors; colors indicate
modeled concentrations; dots in inset show locations of receptors within a grid cell
The equation below was used to calculate the annual average estimates of air concentrations at
receptor locations, which were constrained to CMAQ-grid-average values, with AERMOD providing
subgrid-scale spatial texture.
Where:
C = AERMODm
C
CMAQpnfb
AERMODrec
AERMODgridavs
CMAQsec
CMAQpfires
CM A Qpbiogenics
CMAQbackground
CMAQp
AERMOD,
+ CMAQcrr CMAQp
. + CMAQ,
PBIOGENICS
+ CMAQ
BACKGROUND
concentration at a receptor
concentration in CMAQ grid cell, contributed by primary emissions,
excluding fires and biogenics
concentration at AERMOD receptor
average of all AERMOD results within a CMAQ grid, calculated through
averaging the receptors in each of the nine 4-km cells in a CMAQ grid
cell, and then taking the average of the nine grid cells
contribution from atmospheric reactions in CMAQ grid cell
contribution from primary emissions of fires in CMAQ grid cell
contribution from primary emissions of biogenics in CMAQ grid cell
contribution from background in CMAQ grid cell for carbon
tetrachloride
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This hybrid approach, which builds on earlier area-specific applications to Philadelphia, PA (Isakov et al.
2007) and Detroit, Ml (Wesson et al. 2010), attempts to characterize nonreactive and reactive species
across multiple spatial scales. However, the blending of two different modeling platforms challenges
adherence to basic mass-conservation principles. To address this issue, CMAQ tracks primary and
secondary contributions by source type, enabling the AERMOD estimate at each receptor location to be
normalized to the CMAQ primary contribution. Anchoring concentration averages to CMAQ largely
retains mass conservation. The constraint to CMAQ average grid values imposed by the above equation
minimized possible redundancies and allows us to combine results from these two very different
models.
3.5.2. Treatment of Species
As noted above, we applied the hybrid model to 58 of the highest risk air toxics (shown in Table 3-1)
among the 181 air toxics included in AirToxScreen. CMAQ bases its treatment of atmospheric chemistry
on gas-phase reaction processes optimized to characterize ozone, linked with heterogeneous and
thermodynamic processes for PM formation. This structure allows us to model explicit chemical species.
For example, formaldehyde and acetaldehyde generate significant amounts of peroxy radicals, which
lead to enhanced ozone production and secondary PM formation. This demonstrates the multipollutant
linkages driven by atmospheric processes that CMAQ can simulate.
Chemical species not incorporated as explicit species in chemical mechanisms are added as nonreactive
tracers (e.g., several halogenates) or included in simple reaction schemes, such as 1,3-butadiene decay
and subsequent acrolein generation, decoupled from the chemical mechanism. The emissions mass of
several less reactive VOCs, such as the prevalent benzene, toluene and xylene species, are tracked as
nonreactive tracers. CMAQ treats these as lumped carbon bond species in its reaction calculations,
assuming that atmospheric chemistry minimally influences air concentrations. AERMOD, which treats all
pollutants as nonreactive, was applied to the remaining air toxics not incorporated within CMAQ.
The calculation of the AERMOD grid cell average, AERMODgridavg in the hybrid equation is a three-step
process:
1. Calculate a total AERMOD concentration at each gridded receptor, block receptor and monitor
receptor by reading the individual post-processed run group concentrations and adding to a running
total.
2. Calculate the average concentration at each subgrid cell centered on each gridded receptor in each
CMAQ grid cell (see Section 3.4.2.1 for details on gridded receptors and example subgrid cells). This
results in 144 subgrid-cell averages in each CMAQ cell in the CONUS domain, 81 subgrid cell
averages in Alaska, or 9 averages in the other non-CONUS domains.
3. Average the 9 or 144 averages in each grid cell to calculate an overall average for the grid cell,
AERMODgridavg-
After calculating AERMODgridavg the following steps are taken:
1. For secondary HAPs (acetaldehyde, formaldehyde and acrolein), calculate the secondary
concentration by subtracting the CMAQ primary concentration from the total CMAQ concentration
at each grid cell. For nonsecondary HAPs, secondary concentrations are zero.
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2. For fire and biogenic HAPs, calculate the primary concentration at each grid cell by subtracting the
no-fire/no-biogenic CMAQ concentration from the base CMAQ concentration, where base
concentration includes all sources. For nonfire and nonbiogenic HAPs, secondary concentrations are
zero.
3. At each grid cell, calculate the primary anthropogenic concentrations by subtracting primary fire and
primary secondary concentrations from the total primary concentration from the base CMAQ
results.
4. Divide the CMAQ primary concentration from step 3 by the AERMODgridavg and multiply by the
AERMOD total concentration at each receptor in the CMAQ grid cell. This is the primary hybrid
concentration.
5. To yield the individual source groups' hybrid concentrations (e.g., nonroad construction equipment),
multiply each receptor's hybrid primary concentration by the ratio of the receptor's source group
concentration to its total AERMOD concentration.
6. For carbon tetrachloride, two CMAQ species were used, one for which boundary conditions and
initial conditions were set to zero (CARBONTET_NOBC) and one with non-zero initial and boundary
conditions (CARBONTET). Then calculate background concentrations for carbon tetrachloride for
each CMAQ grid cell by subtracting CARBONTET_NOBC from CARBONTET. Set total primary
concentrations equal to the CARBONTET_NOBC concentrations.
7. Calculate total hybrid concentrations at each receptor by adding the total primary hybrid from step
4 with the secondary, fire and biogenic concentrations from steps 1 and 2.
8. For each census block, average across all the receptors for the block for each source group and total
concentration.
In the non-CONUS domains, the hybrid calculations shown above were not performed. Instead, the
hybrid concentration, C, is the total AERMOD concentration at each census block receptor and the
secondary, biogenics, fires, and appropriate background from CMAQ were added to the AERMOD
results.
For receptors outside the CMAQ domain in Alaska, the CMAQ domain average of fire, biogenic,
secondary, and carbon tetrachloride background were used.
For non-CMAQ HAPs in all areas, relevant background was added to the total AERMOD concentrations
for methyl bromide (0.027 ng/m3) and methyl chloroform (0.006 ng/m3). Background for the two HAPs
were based on the five NOAA GMD sites: Cape Kumukahi, Hawaii (KUM); Mauna Loa, Hawaii (MLO);
Niwot Ridge, Colo. (NWR); Barrow, Alaska (BRW); and Alert, Canada (ALT) and the Trinidad Head Site
(AGAGE). The hybrid program then output hybrid concentrations for each run group, including fires,
biogenics, secondary and total concentrations for each HAP. Separate files were created for gridded
receptors, block receptors and monitors by CMAQ domain. The program output the AERMOD results for
the non-CMAQ HAPs in a similar format.
For AirToxScreen, the hybrid ambient concentrations at the block level were used to estimate
exposures.
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An operational model performance evaluation of the HAPs simulated for the 2020 AirToxScreen was
conducted using the Ambient Monitoring Archive for HAPs for the year 2020
(https://www.epa.gov/amtic/amtic-air-toxics-data-ambient-monitoring-archive); more details can be
found in the Supplemental Data folder on the AirToxScreen website). The model evaluation included
both the hybrid air toxics and non-hybrid air toxics. The hybrid evaluation compared the HAPs for which
there are valid ambient data (i.e., completeness criteria protocol) to compare against CMAQ, AERMOD
and the hybrid model predictions. Likewise, the HAP non-hybrid evaluation used similar observational
completeness criteria constraints to compare against HAPs estimated by adding AERMOD to remote
ambient concentrations (where available) that are assumed to reflect background conditions.
Note that when pairing observed to model data, there are spatial-scale differences between CMAQ,
AERMOD and the hybrid model predictions. A CMAQ concentration represents a 12-km grid-cell volume-
averaged value. The AERMOD model concentration represents a specific point within the modeled
domain. The hybrid model concentration combines the AERMOD point concentration gradients with the
12-km CMAQ grid-cell volume average. The ambient observed measurements are made at specific
spatial locations (latitude/longitude). Several annual graphical presentations and statistics of model
performance were calculated and prepared. Graphical presentations include:
1. Box and whisker plots that show the distribution and the bias of the predicted and observed data,
and
2. Regional maps that show the mean bias and error calculated at individual monitoring sites and
3. Box and whisker plots which show the model-to-monitor ratios.
3.5.3. Ambient Monitoring Data Preparation
EPA has created annual average concentrations of the ambient monitoring data for the year 2020 using
data in the Ambient Monitoring Archive for HAPs. These data primarily come from AQS; however, they
also come from non-AQS sources (e.g., air agencies that do not submit some of their data to AQS, other
federal agencies that collect relevant data, special studies, etc.). All annual averages are in units of
micrograms per cubic meter (ng/m3) using local meteorological data when available or standard
conditions otherwise. An annual average is created for each unique pollutant/monitoring site/sampling
duration using the following procedures:
1. There must be at least 3 valid quarters of monitoring data for the year. A quarter is considered valid
if it contains at least 7 daily averages.
2. Hourly monitoring data are averaged to daily (and sub-hourly data are averaged to hourly) using the
following criteria translating to the ceiling of 75% completeness:
Sampling Duration Averaging To Minimum Count
5 MINUTES
HOURLY
9
10 MINUTES
HOURLY
5
15 MINUTES
HOURLY
3
30 MINUTES
HOURLY
2
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150 MINUTES
DAILY
8
90 MINUTES
DAILY
12
1 HOUR
DAILY
18
2 HOUR
DAILY
9
3 HOURS
DAILY
6
4 HOUR
DAILY
5
5 HOUR
DAILY
4
6 HOUR
DAILY
3
8 HOUR
DAILY
3
12 HOUR
DAILY
2
24 HOURS
DAILY
1
3. The median Regression on Order Statistic (ROS) is the annual average used, where the percentage of
data below the method detection limit (MDL) cannot exceed 80%.
4. Some pollutants were summed to better reflect the AirToxScreen modeled pollutant. This occurred
for PAH groups (individual PAHs belonging to the same PAH groups were summed), xylenes (m/p-
with o-xylene were summed), and 1,3-dichloropropylene (cis- and trans- were summed).
The data used in the model evaluation are provided in the Supplemental Data folder.
3.5.4. Model Performance Statistics
The Atmospheric Model Evaluation Tool (AMET) was used to conduct AirToxScreen HAP evaluation
(Appel et al., 2011). There are various statistical metrics used by the science community for model
performance evaluation. For a robust evaluation, the principal evaluation statistics used to evaluate
model performance are based on the following metrics: two-bias metrics (mean bias and normalized
mean bias); three-error metrics (mean error, normalized mean error and root mean square error) and
correlation coefficient.
Common Variables:
M = predicted concentration
O = observed concentration
X = predicted or observed concentration
o = standard deviation
I. Mean Bias, Mean Error and Root Mean Square Error (ng/m3)
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1 "
Mean Bias = ~/\{M - 0)
n j
1 "
Mean Error = "A \M - 0\
n j
Roof Mean Square Error ¦
i
£ (M-Of
n
Mean Bias (MB) quantifies the tendency of the model to overestimate or underestimate values, while
Mean Error (ME) and Root Mean Square Error (RMSE) measure the magnitude of the difference
between modeled and observe values regardless of whether the modeled values are higher or lower
than observations.
II. Normalized Mean Bias and Error (unitless)
S(M-O)
Normalized Mean Bias = —
n
1(0)
Normalized mean bias (NMB) is used as a normalization to facilitate a range of concentration
magnitudes. This statistic averages the difference (modeled minus observed) over the sum of observed
values. NMB is a useful model performance indicator because it avoids overinflating the observed range
of values, especially at low concentrations.
£\m-o\
Normalized Mean Error = —
n
I(O)
Normalized mean error (NME) is similar to NMB, where the performance statistic is used as a
normalization of the mean error. NME calculates the absolute value of the difference (model minus
observed) over the sum of observed values.
III. Correlation Coefficient (unitless)
Correlation =
1 y
(n-D2?
0-0
A
cr
(
M-M
W
cr
n
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Correlation coefficient (r) provides an indication of the strength of linear relationship and is signed
positive or negative based on the slope of the linear regression.
3.5.5. Hybrid Evaluation
An annual operational model performance evaluation for HAPs used in the hybrid model calculation was
conducted to estimate the ability of the hybrid model as well as to compare to the predictions from the
CMAQ and AERMOD modeling systems to replicate the 2020 HAP observed ambient concentrations.
Inclusion of all three model results is intended to demonstrate the merged attributes of the hybrid
model used for AirToxScreen. Statistical assessments of each model versus observed pairs were paired
in time and space and aggregated on an annual basis. Results from the hybrid evaluation are presented
in Appendix E. Table 3-6 provides a list of HAPs evaluated in the hybrid model performance evaluation
and the number of observed monitoring sites (based on completeness criteria of observations, found in
the model evaluations in the Supplemental Data folder on the AirToxScreen website). Error! Reference s
ource not found, shows the 2020 HAP monitoring locations.
Table 3-6. List of hybrid HAPs evaluated
Model Air Toxic
Measured Air Toxic
No. of Sites
Acetonitrile
ACET_NITRILE_24_HOURS
31
Acrolein
AC RO LE1 N_24_H 0 U RS
56
Acrylonitrile
ACRY_NITRILE_24_HOURS
9
Acetaldehyde
ALD2_24_HOU RS
73
Arsenic
ARSENIC_PM10_24_HOURS
40
ARSENIC_PM25_24_HOURS
173
Benzene
BENZENE_l_HOUR
15
BE NZE N E_24_HOU RS
181
BENZENE_5_MINUTES
7
BENZENE_3_HOURS
1
Beryllium
BERYLUUM_PM10_24_HOURS
32
Ethylene dibromide
BR2_C2_12_24_H OU RS
12
1,3-Butadiene
BUTADIENE13_l_HOUR
14
BUTADIENE13_24_HOURS
108
Benzo-a-pyrene
Ba P_P M 10_24_H 0 U RS
27
Cadmium
CADMIUM_PM10_24_HOURS
38
CADMIUM_PM25_24_HOURS
128
Carbon tetrachloride
CARBONTET_24_HOU RS
173
CARBONTET_l_HOUR
3
Carbonyl sulfide
CARBSULFIDE_5_MINUTES
7
Chloroform
C H C L3_24_H 0 U RS
153
Chloroprene
CHLOROPRENE_24_HOURS
6
Vinyl chloride
CL_ETHE_24_HOURS
24
Chlorine
CL2_PM2_5_24_HOURS
279
Ethylene dichloride
CL2_C2_12_24_HOURS
63
Methylene chloride
CL2_ME_24_HOURS
63
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Model Air Toxic
Measured Air Toxic
No. of Sites
CL2_ME_5_MINUTES
7
Trichloroethylene
CL3_ETHE_24_HOURS
66
1,1,2,2-Tetrachloroethane
CL4_ETHANE_24_HOURS
14
Tetrachloroethylene
CL4_ETHE_24_HOURS
137
CL4_ETHE_5_MINUTES
7
Chromium Compounds (only
hexavalent chromium was modeled)
CR_VI_PM10_24_HOURS
7
l,4-Dichlorobenzene(p)
DICL_BENZENE_24_HOURS
70
1,3-Dichloropropene
DICL_PROPENE_24_HOURS
5
Ethyl benzene
ETHYLBE NZE N E_l_HOU R
15
ETHYLBENZENE_24_HOURS
175
Ethylene Oxide
ETOX_24_HOURS
33
Formaldehyde
FORM_24_HOURS
73
Hexane
HEXANE_l_HOUR
15
HEXANE_24_HOURS
107
Lead Compounds
LEAD_PM10_24_HOURS
43
LEAD_PM25_24_HOURS
277
Manganese Compounds
MANGANESE_PM10_24_HOURS
42
MANGANESE_PM25_24_HOURS
277
Methyl chloride
METHCHLORIDE_24_HOURS
150
METHCHLORIDE_5_MINUTES
7
Naphthalene
NAPHTHALENE_24_HOURS
52
Nickel Compounds
NICKEL_PM10_24_HOURS
35
NICKEL_PM25_24_HOURS
277
Polycyclic Organic Matter
PAH_OOOEO_24_HOU RS
29
Polycyclic Organic Matter
PAH_176E4_24_HOURS
28
Polycyclic Organic Matter
PAH_176E5_24_HOURS
11
Polycyclic Organic Matter
PAH_880E5_24_HOU RS
29
Propylene dichloride
PROPYL_DICL_24_HOURS
22
Styrene
STYRENE_l_HOUR
14
STYRENE_24_HOURS
131
Toluene
TOLUENE_l_HOUR
4
TOLUENE_24_HOURS
131
Xylene
XYLENE_24_HOURS
128
XYLENE_l_HOUR
4
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Figure 3-8. 2020 monitoring locations for the hybrid HAPs evaluation
3.5.6. Non-hybrid Evaluation
An annual operational model performance evaluation for HAPs used in the non-hybrid model calculation
was conducted to estimate the ability of the AERMOD model to replicate the 2020 HAP observed
ambient concentrations. Statistical assessments of modeled results versus observed pairs were paired in
time and space and aggregated on an annual basis. Table 3-1 provides a list of HAPs evaluated in the
non-hybrid model performance evaluation and the number of pairs (based on completeness criteria of
observations, found in the model evaluations in the Supplemental Data folder on the AirToxScreen
website) used in the annual median. Figure 3-9 shows the 2020 non-hybrid HAP monitoring locations.
Results from the non-hybrid evaluation are presented in Appendix E
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Table 3-10. List of non-hybrid HAPs evaluated
Model Air Toxic
Measured Air Toxic
No. of Sites
Antimony
ANTIMONY_PM25_24_HOURS
130
ANTIMONY_PM10_24_HOURS
26
Benzyl Chloride (alpha-Chlorotoluene)
BENZYLCHLO_24_HOURS
3
Bromoform (Tribromomethane)
BROMOFORM_24_HOURS
34
Carbon disulfide
CARBNDISULF_24_HOURS
37
Chlorobenzene
CHLROBZNE_24_HOURS
52
Cobalt
COBALT_PM25_24_HOURS
127
COBALT_PM10_24_HOURS
29
Cumene (Isopropylbenzene)
CUMENE_24_HOURS
28
CUMENE_l_HOUR
14
CUMENE_3_HOURS
1
Ethyl Chloride (Chloroethane)
ETHYLCHLRD_24_HOURS
47
Ethylidene Dichloride (1,1-Dichloroethane)
ETHIDDICHLD_24_HOURS
20
Hexachloro-l,3-butadiene
H EXC H LR BT_24_H 0 U RS
11
Methyl Bromide (Bromomethane)
METHYLBROM_5_MINUTES
7
METHYLBROM_24_HOURS
104
Methyl Chloroform (1,1,1-Trichloroethane)
MTHYLCHLRF_5_MINUTES
6
MTHYLCHLRF_24_HOURS
47
MTHYLCHLRF_l_HOUR
3
Methyl Iodide (lodomethane)
MTHYLIODID_24_HOURS
5
Methyl Isobutyl Ketone (4-Methyl-2-
pentanone)
MIBK_24_HOURS
59
Methyl Methacrylate
M M ETAC RYLAT_24_HOU RS
13
Methyl tert-butyl ether
MTBE_24_HOURS
11
Propanal (Propionaldehyde)
PROPIONAL_24_HOURS
63
p-Dioxane (1,4-Dioxane)
P_DIOXANE_24_HOURS
6
Selenium
SELENIUM_PM25_24_HOURS
277
SELENIUM_PM10_24_HOURS
28
SELENIUM_TSP_24_HOURS
9
1,2,4-Trichlorobenzene
TRICBZ124_24_HOURS
24
2,2,4-Trimethylpentane
TRMEPN224_24_HOURS
68
TRMEPN224_l_HOUR
15
TRMEPN224_3_HOURS
1
Vinyl Acetate
VINYLACET_24_HOURS
21
Vinylidene Chloride (1,1-Dichloroethylene)
VINYLIDCLOR_24_HOURS
18
1,1,2-Trichloroethane
TRICLA112_24_HOU RS
22
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4. Estimating Exposures for Populations
Estimating inhalation exposure concentrations (ECs) is a critical step in determining potential health
risks. Ambient concentrations do not consider that people move through locations and
microenvironments where pollutant concentrations can differ. Different people have different daily
activities, spend different amounts of time engaged in those activities, and engage in those activities in
different locations. Most activities occur indoors (e.g., in homes, workplaces, schools and vehicles)
where pollutant concentrations can differ from those outdoors. Therefore, the average concentration of
a pollutant that people breathe can differ significantly from the ambient concentration at a fixed
outdoor location.
This section contains a discussion of how EPA estimated ECs for AirToxScreen. It begins with an overview
of the surrogate approach used that included new exposure modeling for some AirToxScreen pollutants
and applications of exposure-to-ambient concentration ratios for the remaining pollutants. This is
followed by a more detailed description of this approach, a summary of the user inputs and other data
required, and an overview of the quality-assurance measures included in estimating exposures. Further
details on the exposure calculations for AirToxScreen can be found in Appendix E.
4.1. Estimating Exposure Concentrations
For AirToxScreen, EPA used a combination of direct modeling and exposure factors to estimate
inhalation ECs for AirToxScreen. This approach used census block-level ambient concentrations
estimated with air quality models, as described in Section 3, and yielded census block-level exposure
concentration estimates that we used to determine potential health risks for AirToxScreen.
EPA used version 8 of the EPA Hazardous Air Pollutant Exposure Model (HAPEM8) to conduct direct
exposure modeling for AirToxScreen. HAPEM, described in detail in Section 4.2, is a screening-level
exposure model that estimates inhalation ECs corresponding to estimated ambient-pollutant
concentrations. We used HAPEM8 for a selected group of surrogate pollutants and source categories to
estimate census tract level exposure factors. Census tracts were used because most HAPEM inputs are
were available at the census tract level. For each surrogate pollutant and AirToxScreen category (i.e.,
point, nonpoint, on-road mobile and nonroad mobile), EPA calculated the ratio of EC to ambient
concentration (i.e., an exposure factor) for each census tract. Using each pollutant's chemical properties,
we then mapped each pollutant/category combination to the surrogate pollutants and source
categories. Per census block, we multiplied the ambient concentration of the pollutant by the
surrogate's exposure factor for the census tract containing the census block, resulting in estimated ECs.
Section 4.4 further describes this exposure-factor approach.
4.2. About HAPEM
Nearly two decades ago, EPA developed HAPEM for Mobile Sources (HAPEM-MS) to assess inhalation
exposure to air toxics from highway mobile sources. This version of HAPEM used carbon monoxide as a
tracer for highway mobile-source air toxic emissions. Today, HAPEM8 predicts inhalation ECs for a wide
range of air toxics using either modeled ambient concentrations or measured data (without regard to
source category), and the model no longer uses carbon monoxide as a tracer.
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Following other improvements, HAPEM version 4 and later (including HAPEM8) can estimate annual
average human-exposure levels nationwide at a spatial resolution as fine as the census tract level (EPA
2002b, EPA 2005c, EPA 2007b, EPA 20124). These changes make HAPEM8 suitable for regional and
national inhalation risk assessments such as AirToxScreen. The 1996 and 1999 NATAs estimated
Inhalation ECs using HAPEM4 and HAPEM5 respectively; the 2011 and 2014 NATAs as well as 2017-2019
AirToxScreens used HAPEM7 (EPA did not use HAPEM6 for NATA). 2020 AirToxScreen uses HAPEM8.
Table 4-1 outlines some key differences between these three HAPEM versions. A complete history of the
model can be found in the User's Guide for HAPEM8 (EPA 2024c).
Table 4-1. Key differences between recent versions of HAPEM
Characteristic
HAPEM5
HAPEM7
HAPEM8
Data source for population
demographics
1990 U.S. Census
2000 U.S. Census
2020 U.S. Census
Characterization of
microenvironmental factors
Probability distributions
Same as HAPEM5
Same as HAPEM7
Method for creation of
annual average activity
patterns from daily activity-
pattern data
Sampling a limited number
of daily diaries to
represent an individual's
range of activities,
accounting for
autocorrelation
Same as HAPEM5, except
now includes commuter-
status criterion
Same as HAPEM7
Interpretation of exposure-
concentration range for a
given cohort/tract
combination
Variability of annual ECs
across cohort/tract
members
Same as HAPEM5, except
now includes adjustments
based on proximity to
roadway
Same as HAPEM7
HAPEM uses a general approach of tracking representative individuals of specified demographic groups
as they move among indoor and outdoor microenvironments and between locations. As described in the
following section, personal-activity and commuting data, specific to a hypothetical person's
demographic groups, are used to determine the census tracts containing residential and work locations
and the microenvironments within each tract. Using stochastic sampling, the model estimates ECs by
selecting empirically based factors reflecting the relationship between ECs within each
microenvironment and the outdoor (ambient) air concentrations at that location.
To estimate long-term ECs for a hypothetical person, the pollutant concentrations in each
microenvironment visited are first combined into a daily-average concentration. The daily averages are
then combined with proper weighting for season and day type to calculate a long-term average. Finally,
the long-term averages are stratified by demographic group and census tract to create a distribution of
ECs for each stratum. The median of each distribution represents the best estimate of exposure for a
"typical" person of that demographic group in that census tract. In this case, "typical" does not refer to a
specific individual in the population or even the average over a group of individuals. Rather, this is a
hypothetical person living at the centroid of a census tract who engages in the usual activities (both
indoor and outdoor) for someone in that demographic group and census tract.
Additional technical information on HAPEM can be found in the User's Guide for HAPEM8 (EPA 2024c).
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4.3. HAPEM Inputs and Application
HAPEM requires four main types of information to estimate ECs: (1) ambient concentrations of air
toxics, (2) population data from the U.S. Census Bureau, (3) population-activity data and (4)
microenvironmental data. The subsections below discuss these inputs, along with descriptions of the
data used for AirToxScreen and related information on how EPA configured the model and applied it to
conduct direct exposure modeling.
4.3.1. Data on Ambient Air Concentrations
HAPEM typically uses annual average, diurnally distributed ambient air concentrations as input data.
These concentrations can be monitoring data or concentrations estimated using a dispersion model or
other air quality model.
For AirToxScreen, EPA estimated annual average ambient concentrations for each census tract using a
hybrid CMAQ-AERMOD approach discussed in Section 3.5 to calculate block-level averages, and then
used populating weighting to estimate tract-level averages. EPA stratified the air quality outputs for a
selected group of pollutants by one or more of the four principal AirToxScreen categories (i.e., point,
nonpoint, on-road mobile and nonroad mobile), using those results as surrogates for the remaining
pollutants not modeled in CMAQ-AERMOD. Thus, exposure-model results generated for AirToxScreen
can be summarized for each principal AirToxScreen category or any combination of those categories.
4.3.2. Population Demographic Data
HAPEM divides the exposed population into cohorts such that each person in the population is assigned
to one and only one cohort, and all the cohorts combined make up the entire population. A cohort is
defined as a group of people whose exposure is expected to differ from exposures of other cohorts due
to certain characteristics shared by the people within that cohort. For AirToxScreen, we specified
cohorts by residential census tract and age, with the population in each census tract divided into six age
groups: 0-1, 2-4, 5-15, 16-17, 18-64 and >65 years of age. These groups were developed using
demographic data derived from the 2010 U.S. Census. EPA aggregated the predicted inhalation ECs
across cohorts to estimate ECs for the general population.
4.3.3. Data on Population Activity
HAPEM uses two types of data to define activities for the modeled population: activity-pattern data
(specifying the frequency, location and duration of daily activities) and commuting-pattern data
(specifying the work tracts for people living in each home tract). HAPEM uses these data together to
place a hypothetical commuter in either the home or work tract and in a specific microenvironment at
each 3-hour time step (the time step used for AirToxScreen). The microenvironment assignments and
locations derived from these data are then used to calculate ECs (as explained in the next section).
Data on human activity patterns are used to determine the frequency and duration of exposure within
various microenvironments (such as indoors at home, in-vehicle and outdoors). Activity-pattern data are
taken from demographic surveys of individuals' daily activities that specify the sequence, duration and
locations of those activities. The default source of activity-pattern data used by HAPEM and for
AirToxScreen is EPA's Consolidated Human Activity Database (CHAD; EPA 2024d). To develop the version
of CHAD (version April 2020) used in AirToxScreen, data from 21 individual U.S. studies of human
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activities were combined into one complete data system containing over 45,000 person-days of activity-
pattern records. Because of design limitations in the studies from which it is derived, CHAD may not well
represent all demographic groups, particularly ethnic minorities and low-income populations. Also, the
activity-pattern data in CHAD is mostly limited to one- or two-day periods. Extrapolation of these short-
term records to the annual activity patterns needed for assessments of air toxics exposure introduces
some uncertainty into the analysis.
To address this extrapolation uncertainty, HAPEM uses a stochastic process to create simulated long-
term (multi-day) activity patterns from daily activity-pattern data that account for day-to-day
autocorrelation. These algorithms create annual average activity patterns from daily activity-pattern
data to better represent the variability among individuals within a cohort-tract combination. For each
day type and demographic group, daily-activity diaries were divided into three groups based on
similarity using a cluster analysis. To simulate the activities of an individual, one diary was selected from
each group for each day type, resulting in nine diaries in total. Then, for each day type, the sequence of
the selected diaries was determined according to the probability of transition from one cluster group to
another, as determined by analyses of the CHAD data. The simulation was repeated 30 times, resulting
in a set of 30 estimates of annual ECs for each demographic group in each census tract. Use of a limited
number of diaries and the transition probabilities is a way to account for day-to-day autocorrelation of
activities for an individual, so each exposure-concentration estimate represents an estimate for an
individual rather than an average for the group. Therefore, with this approach, the range represents the
variability of ECs across the group.
Commuting-pattern data, the second type of population activity data used in HAPEM, are derived for
each cohort from a U.S. Census database containing information on tract-to-tract commuting patterns.
These data specify the number of residents in each tract that work in that tract and every other census
tract (i.e., the population associated with each home-tract/work-tract pair) and the distance between
the centroids of the two tracts. An important limitation is that the commuting-pattern data included in
HAPEM do not account for the movement of school-age children who travel (or commute) to a school
located outside of their home tract.
4.3.4. Microenvironmental Data
A microenvironment is a three-dimensional space in which human contact with an environmental
pollutant occurs. In HAPEM, this space is treated as a well-characterized, relatively homogenous location
with respect to pollutant concentrations for a specified period. The inhalation exposure estimate is
determined by the sequence of microenvironments visited by the individual. The concentration in each
microenvironment is estimated by using the three microenvironmental factors listed below to adjust the
ambient-concentration estimate for the census tract where it is located:
a penetration factor that is an estimate of the ratio of the microenvironmental concentration to the
concurrent outdoor concentration in the immediate vicinity of the microenvironment; penetration
factors are pollutant-specific estimates that are derived from reported measurement studies;
a proximity factor that is an estimate of the ratio of the outdoor concentration in the immediate
vicinity of the microenvironment to the outdoor concentration represented by the ambient air
concentration input to the model; and
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an additive factor that accounts for emission sources within or near a particular microenvironment,
such as indoor emission sources. As noted below, the additive factor is not used for AirToxScreen.
The relationship between the estimated ECs, the input ambient concentrations and these three factors
is demonstrated by the equation below.
C(i,k,t) = CONC(i t) x PENk x PROXk + ADDk
Where:
Coxt) = EC predicted within census tract i and microenvironment k for time step t, in
units of ng/m3
CONC(i t) = ambient concentration for census tract i for time step t, in units of ng/m3
PENk = penetration factor for microenvironment k
PROXk = proximity factor for microenvironment k
ADDk = additive factor accounting for sources within microenvironment k, in units
of ng/m3
Stochastic processes can be used to select work tracts, ambient air concentrations and
microenvironmental factors. This important feature allows exposures to be characterized with
probability distributions rather than point estimates, which more accurately reflect the variability of
these components and simulate some of the variability found in measurement studies.
In HAPEM, the characteristics of each microenvironment are used to assign each microenvironment to
one of three groups: indoors, outdoors and in-vehicle. AirToxScreen uses the 18 microenvironments
shown in Table 4-2. The microenvironments in the indoor group were further classified as associated
with either residence or other buildings, while those in the outdoor group were categorized as either
near-road or away-from-road. Each group consists of microenvironments expected to have similar
penetration factors, thus allowing microenvironmental factors developed for one microenvironment to
be applied to other microenvironments in the same group. Within each census tract, HAPEM uses
estimates of the number of people living within each of three distance-from-road bins to stochastically
vary the proximity factor based on distance-from-road (i.e., proximity factors are higher for
microenvironments near major roadways, lower for microenvironments relatively far from major
roadways). The additive factor (ADDk) in the expression for EC, above, was set to zero for AirToxScreen
because indoor-source data are currently incomplete (recall that AirToxScreen covers only pollutants
derived from outdoor sources).
An important consideration is that data to support quantitative microenvironmental factors are not well
developed for many of the air toxic compounds and for most of the microenvironments, which
introduces uncertainty into the analysis of exposures. Section 7 contains a discussion on uncertainty and
variability regarding this and other issues for AirToxScreen.
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Table 4-2. Microenvironments used in HAPEM modeling for AirToxScreen
Indoors
Outdoors
In Vehicle
Residence
Near-road
Car/Truck
Residential
Motorcycle/Bicycle
Public Transit
Other Building
Outdoors, Near Roadway
Air Travel
Outdoors, Parking Garage
Bar/Restaurant
Outdoors, Service Station
Hospital
Residential Garage
Office
Waiting Outdoors for Public Transit
Public Access
Away-from-road
School
Ferryboat Outdoors
Waiting Inside for Public Transit
Other
4.4. Exposure Factors
HAPEM exposure modeling for AirToxScreen requires substantial time and resources for data collection
and processing, computing and model processing. Due to these requirements, EPA conducted HAPEM
modeling for AirToxScreen only for selected pollutants, which we present below along with how we
used them to estimate ECs for the remaining AirToxScreen pollutants.
Coke oven emissions (emitted by point sources and present in ambient air as either particulates or
gases) and diesel PM (modeled as particulates from nonpoint and mobile sources) were special
cases that EPA modeled as themselves in HAPEM and not used as surrogates for any other
pollutants not modeled in HAPEM.
Benzene and 1,3-butadiene are gas-phase pollutants emitted by many processes (and all four
principal AirToxScreen categories) in nearly all U.S. locations. EPA selected benzene as the surrogate
for all other gas-phase pollutants not modeled in HAPEM (EPA considers benzene modeling in
AirToxScreen to be more reliable than 1,3-butadiene modeling).
Unspeciated, generic PAHs ("PAH, total"), which are pollutants that can be present in either gas
phase or particulate phase in ambient air, are emitted by all four principal AirToxScreen categories
and from a wide variety of processes. EPA selected "PAH, total" as the surrogate for all other mixed-
phase pollutants not modeled by HAPEM.
Chromium (VI) is a highly toxic particulate-phase pollutant emitted by all four principal AirToxScreen
categories. EPA selected it as the surrogate for all other particulate pollutants not modeled in
HAPEM and emitted by point or nonpoint sources.
EPA selected nickel, a particulate-phase pollutant emitted by a variety of processes spread across
the United States, as the surrogate for all other particulate pollutants not modeled in HAPEM and
emitted by mobile sources.
Appendix E to this document contains further details on the application of HAPEM8 in the AirToxScreen
analysis. A spreadsheet file within the Supplemental Data folder accompanying this TSD spreadsheet
contains the tract-level exposure-to-ambient concentration ratios (i.e., exposure factors) for each
surrogate pollutant. (Access the AirToxScreen website at https://www.epa.gov/AirToxScreen.)
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Overall, the HAPEM exposure predictions are lower than the corresponding predicted air quality values.
This reduction likely results from the inability of many pollutants to penetrate efficiently into an indoor
environment. (Recall that indoor sources of air toxics are not included in AirToxScreen).
4.5. Evaluating Exposure Modeling
A model-performance evaluation can provide valuable information regarding model uncertainty when
using computer-simulation models of human exposures to pollutants. Also, a well-conducted evaluation
can greatly increase confidence in model results for a given application or use. One type of performance
evaluation uses measurements and environmental data as a benchmark to compare modeling
estimates. EPA has worked with the Mickey Leland Center (NUATRC 2011) on past assessments to help
identify new and independent sources of personal-monitoring data for use in comparison with the
AirToxScreen results.
Extensive peer review involving independent scientific and technical advice from scientists, engineers
and economists can be another valuable component of a model evaluation. In July 2000, HAPEM4
underwent external peer review by technical experts for both the microenvironmental factors used in
the model and the overall application of the model for use in air toxics screening assessments. A
discussion of several of the issues addressed by these reviews is included in Appendix A of the report for
the 1996 NATA presented to EPA's Science Advisory Board for review (EPA 2001b). In 2001, EPA's
Scientific Advisory Board reviewed the application of HAPEM4 as part of the 1996 NATA review (EPA
2001a). Although several limitations were identified in the current methodology, HAPEM4 was
acknowledged as an appropriate tool to help better understand the relationship of human exposures to
ambient-concentration levels. Since then, numerous studies have used and cited subsequent HAPEM
versions.
4.6. Summary
Estimating inhalation ECs is a critical step in determining potential health risks because ambient
concentrations do not account for movements of individuals among locations and
microenvironments where pollutant concentrations can differ.
We estimated inhalation exposure factors for each pollutant/source group/census tract for
AirToxScreen using the HAPEM8 model, which were then used to estimate exposure concentrations
at the census block level.
These ECs can be used to determine census block-level potential health risks.
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5. Characterizing Effects of Air Toxics
Exposure to air toxics is associated with increased incidence of cancer and a variety of adverse
noncancer health effects. The type and severity of effects depends on several factors, including the
identity and nature of the chemical to which an individual is exposed, the magnitude and duration of
exposure, and the unique behaviors and sensitivities of exposed individuals.
EPA uses a toxicity assessment to identify and
quantify the adverse health effects associated with
exposure to a chemical, following EPA risk
assessment methods. As indicated in Figure 1-3 of
this document and described in more detail in
Volume 1 of EPA's ATRA Reference Library (EPA
2004a), two processes constitute toxicity
assessment: hazard identification (during which the
specific adverse effects are identified that can be
causally linked with exposure to a given chemical)
and dose-response assessment (which
characterizes the quantitative relationship between
chemical dose or concentration and adverse
effects, that is, the hazard(s) identified in the first step).
Ultimately, the results of the toxicity assessment, referred to in this document as "toxicity values," are
used along with exposure estimates to characterize health risks for exposed populations (as described in
Section 6). Although the toxicity assessment is integral to the overall air toxics risk assessment, it is
usually done prior to the risk assessment. EPA has completed this toxicity assessment for many air toxics
and has made available the resulting toxicity information and dose-response values, which have
undergone extensive peer review.
This section explains how toxicity assessments are used in the AirToxScreen risk assessment process.
Specifically, the sections that follow provide an overview of the cancer and noncancer toxicity values
used in AirToxScreen and the primary sources of these values. They also describe several adjustments
and assumptions to toxicity values specific to the AirToxScreen risk assessment process.
5.1. Toxicity Values and Their Use in AirToxScreen
The toxicity values used in AirToxScreen are quantitative expressions used to estimate the likelihood of
adverse health effects given an estimated level and duration of exposure. These toxicity values are
based on the results of dose-response assessments, which estimate the relationship between the dose
and the frequency or prevalence of a response in a population or the probability of a response in any
individual. Because AirToxScreen is focused on long-term exposures, the toxicity values used in
AirToxScreen are based on the results of chronic dose-response studies when such data are available.
Chronic dose-response assessments can be used to help evaluate the specific 70-year-average (i.e.,
"lifetime") ECs associated with cancer prevalence rates, or, for noncancer effects, the concentrations at
which noncancer adverse health effects might occur given exposure over an extended time (possibly a
lifetime, but the time frame also can be shorter).
The phrase "dose-response" is used generally
throughout this document to refer to the
relationship between a level of a chemical and a
physical response. The values EPA uses for
inhalation, however, are derived for exposure
concentration, although with consideration of dose.
Consideration of the relationship between
exposure concentration, dose and dosimetry (how
the body handles a chemical once it is inhaled) is
inherent in the derivation of values. The term
"toxicity values" is used here to refer to the RfCs
and UREs used in inhalation risk assessment.
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The toxicity values that are combined with ECs to conduct the risk characterization in AirToxScreen are
based on the results of quantitative dose-response assessments. The actual values used, however, are
not strictly considered dose-response or concentration-response values. To estimate cancer risks in
AirToxScreen, EPA converted the results of cancer dose-response assessments for a given chemical to a
URE that incorporates certain exposure assumptions. This value can be multiplied by the 70-year-
average EC to obtain a lifetime cancer risk estimate. To evaluate the potential for noncancer adverse
health effects, we used chronic dose-response data to estimate a threshold that is the EC in air at which
adverse health effects are assumed to be unlikely (i.e., the RfC). These two types of values are described
in more detail in the following section.
The toxicity values used in AirToxScreen are consistent with those EPA's Office of Air Quality Planning
and Standards (OAQPS) has compiled for chronic inhalation exposures to air toxics. The full set of
toxicity values used for AirToxScreen (and their sources) can be accessed via the AirToxScreen
Supplemental Data file; see Appendix B for details. Sources of chronic dose-response assessments used
for AirToxScreen were prioritized according to OAQPS risk assessment guidelines and level of peer
review, as discussed below.
5.2. Types of Toxicity Values
Each toxicity value used in AirToxScreen is best described as an estimate within a range of possible
values appropriate for screening-level risk assessments. Note that the uncertainty in the dose-response
assessments and toxicity values that AirToxScreen relies on is to some extent one-sided, providing a
conservative (health-protective) estimate of risk. The "true" cancer risk and potential for adverse
noncancer impacts are believed to be lower than those estimated in this assessment, although the
possibility remains that they could be greater. Uncertainty in the derivation of the dose-response values
and in other aspects of the AirToxScreen process is discussed in Section 7.
5.2.1. Cancer URE
A cancer dose-response curve is used to demonstrate
the quantitative relationship between dose and the
likelihood of contracting cancer. If the dose-response
relationship is linear, the cancer response is assumed
to increase proportionally with the dose (which might
be expressed as an EC, an absorbed internal dose, a
dose to a specific organ or tissue, or other measure).
We have proposed that linear extrapolation of
carcinogenic risk in the low-dose region of the curve
is a reasonable approach for estimating risk at
relatively low exposures, such as those typically
experienced by the general population for air toxics (i.e., the true value of the risk is unknown, and could
be as low as zero). An upper-bound lifetime cancer risk represents a plausible upper limit to the true
probability that an individual will contract cancer due to exposure over a 70-year lifetime to a given
hazard (e.g., exposure to an air toxic).
For an inhalation risk assessment (and for AirToxScreen), a URE can be used to calculate the estimated
cancer risk from inhalation ECs. A URE is calculated by using dose-response information for a chemical
and developing a factor in the appropriate units that can be combined directly with ECs in air to
The URE is 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
(Hg/m3) in air. UREs are considered upper-
bound estimates, meaning they represent a
plausible upper limit to the true value. The
true risk is likely to be less, but could be
greater.
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estimate individual cancer risks, given certain assumptions regarding the exposure conditions.
Specifically, the URE represents the upper-bound of the excess cancer risk estimated to result from
continuous exposure to a concentration of 1 ng of a substance per m3 of air, over a 70-year lifetime and
assuming a daily inhalation rate of about 20 m3/day. The risk value is derived from the slope of the dose-
response curve as estimated using a linearized multistage statistical model in the low-dose portion of
the curve. The interpretation of the URE is as follows: If the URE is 3 x 10 s ng/m3, no more than three
excess cancer cases would develop per 1 million people if they were exposed daily for a lifetime to a
concentration of 1 ng/m3. To the extent that true dose-response relationships for some air toxics
compounds are not strictly linear, this assumption could result in overestimates of cancer risk. The
upper bound is not a true statistical confidence limit because the URE reflects unquantifiable
assumptions about effects at low doses. Thus, although the actual carcinogenic risk is likely to be lower
than what is reflected in the URE, it also might be higher.
The URE estimates the toxic potency of a chemical.
EPA's weight-of-evidence (WOE) descriptors provide
estimates of the level of certainty regarding a
chemical's carcinogenic potential. We evaluate three
broad categories of toxicological data to make a WOE
determination: (1) human data (primarily
epidemiological); (2) animal data (results of long-term
experimental animal bioassays) and (3) supporting
data, including a variety of short-term tests for
genotoxicity and other relevant properties,
pharmacokinetic and metabolic studies and structure-
activity relationships. We evaluate these data in
combination to characterize the extent to which they
support the hypothesis that an agent or chemical causes cancer in humans. The approach outlined in
EPA's Guidelines for Carcinogen Risk Assessment (EPA 2005a) considers available scientific information
regarding carcinogenicity. It provides a narrative approach to characterizing carcinogenicity rather than
assigning chemicals to specific categories (as was done previously by EPA according to the 1986
guidelines). To provide some measure of clarity and consistency in an otherwise free-form, narrative
characterization, standard descriptors are used as part of the hazard narrative to express the conclusion
regarding the WOE for carcinogenic-hazard potential. The five recommended standard hazard
descriptors are described below.
Carcinogenic to Humans: This descriptor indicates strong evidence of human carcinogenicity. It is
appropriate when the epidemiologic evidence of a causal association between human exposure and
cancer is convincing. Alternatively, this descriptor might be equally appropriate with a lesser weight of
epidemiologic evidence that is strengthened by other lines of evidence. It can be used when all the
following conditions are met: (1) evidence of an association between human exposure and either cancer
or the key precursor events of the agent's mode of action is strong but insufficient for a causal
association; (2) evidence of carcinogenicity in animals is extensive; (3) the mode(s) of carcinogenic
action and associated key precursor events have been identified in animals and (4) evidence is strong
that the key precursor events that precede the cancer response in animals are anticipated to occur in
humans and progress to tumors, based on available biological information.
Likely to Be Carcinogenic to Humans: This descriptor is appropriate when the WOE is adequate to
demonstrate carcinogenic potential to humans but does not reach the WOE for the descriptor
EPA's Weight of Evidence (WOE)
Descriptors
(EPA 2005a)
• Carcinogenic to humans
• Likely to be carcinogenic to humans
• Suggestive evidence of carcinogenic
potential
• Inadequate information to assess
carcinogenic potential
• Not likely to be carcinogenic to humans
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"Carcinogenic to Humans." Adequate evidence consistent with this descriptor covers a broad spectrum.
At one end of the spectrum is evidence for an association between human exposure to the agent and
cancer and strong experimental evidence of carcinogenicity in animals. At the other end, with no human
data, the weight of experimental evidence shows animal carcinogenicity by a mode or modes of action
that are relevant or assumed to be relevant to humans. The use of the term "likely" as a WOE descriptor
does not correspond to a quantifiable probability. Moreover, additional data, such as information on the
mode of action, might change the choice of descriptor for the illustrated examples.
Suggestive Evidence of Carcinogenic Potential: This descriptor is appropriate when the WOE is
suggestive of carcinogenicity; that is, a concern for potential carcinogenic effects in humans is raised,
but the data are judged insufficient for a stronger conclusion. This descriptor covers a spectrum of
evidence associated with varying levels of concern for carcinogenicity, ranging from a positive cancer
result in the only study on an agent to a single positive cancer result in an extensive database that
includes negative studies in other species. Depending on the extent of the database, additional studies
may or may not provide further insights.
Inadequate Information to Assess Carcinogenic Potential: This descriptor is appropriate when available
data are judged inadequate for applying one of the other descriptors. Additional studies generally would
be expected to provide further insights.
Not Likely to Be Carcinogenic to Humans: This descriptor is appropriate when the available data are
considered robust for deciding no basis for human hazard concern exists. In some instances, positive
results in experimental animals can occur when the evidence is strong and consistent that each mode of
action in experimental animals does not operate in humans. In other cases, there can be convincing
evidence in both humans and animals that the agent is not carcinogenic. A descriptor of "not likely"
applies only to the circumstances supported by the data. For example, an agent might be "Not Likely to
Be Carcinogenic" by one route but not necessarily by another. In those cases that have one or more
positive animal experiments but the results are judged to be not relevant to humans, the narrative
discusses why the results are not relevant. As with the "likely" descriptor, the term "not likely" here
does not correspond to a quantifiable probability.
Important to note is that these WOE categories express only a relative level of certainty that these
substances might cause cancer in humans. The categories do not specifically connote relative levels of
hazard or the degree of conservatism applied in developing a dose-response assessment. For example, a
substance with suggestive evidence of carcinogenic potential might impart a greater cancer risk to more
people than another substance that is carcinogenic to humans.
The process of developing UREs includes several important sources of uncertainty. Many of the air toxics
in AirToxScreen are classified as "likely" carcinogens. The term likely, as used in this instance, means
that data are insufficient to prove these substances definitively cause cancer in humans. That some are
not human carcinogens at environmentally relevant ECs is possible, and the true cancer risk associated
with these air toxics might be zero. UREs for most air toxics were developed from animal data using
health-protective methods to extrapolate to humans. Actual human responses may differ from those
predicted. For more information, see EPA's Guidelines for Carcinogen Risk Assessment (EPA 2005a).
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5.2.2. Noncancer Chronic RfC
The RfC is an estimate of a continuous inhalation
exposure that is thought to be without an appreciable
risk of adverse health effects over a lifetime. The
population considered when deriving RfCs includes
sensitive subgroups (i.e., children, asthmatics and the
elderly). The RfC is derived by reviewing a health-
effects database for a chemical and identifying the
most sensitive and relevant endpoint, along with the
principal study or studies demonstrating that endpoint. The value is calculated by dividing the no-
observed-adverse-effect level (or an analogous exposure level obtained with an alternate approach, e.g.,
a lowest-observed-adverse-effect level or a benchmark dose) by uncertainty factors reflecting the
limitations of the data used.
As with UREs for cancer risk assessment, the process of developing RfCs includes several important
sources of uncertainty, which span perhaps an order of magnitude. Uncertainty factors are intended to
account for (1) variation in sensitivity among the individuals in the population, (2) uncertainty in
extrapolating laboratory animal data to humans, (3) uncertainty in extrapolating from data obtained in a
study involving a less-than-lifetime exposure, (4) uncertainty in using lowest-observed-adverse-effect-
level or other data rather than no-observed-adverse-effect-level data and (5) inability of any single study
to address all possible adverse outcomes in humans adequately. Additionally, an adjustment factor is
sometimes applied to account for scientific uncertainties in the data or study design not explicitly
captured in the uncertainty factors (e.g., a statistically inadequate sample size or poor exposure
characterization). For more information, refer to EPA's Methods for Derivation of Inhalation Reference
Concentrations and Application of Inhalation Dosimetry (EPA 1994).
Unlike linear dose-response assessments for cancer, noncancer risks generally are not expressed as a
probability that an individual will experience an adverse effect. Instead, in an air toxics risk assessment,
the potential for noncancer effects in humans is typically quantified by calculating the ratio of the
inhalation EC to the RfC. This ratio is referred to as the hazard quotient (HQ). For a given air toxic,
exposures at or below the RfC (i.e., HQs are 1 or less) are not likely to be associated with adverse health
effects. As exposures increase above the RfC (i.e., HQs are greater than 1), the potential for adverse
effects also increases. The HQ, however, should not be interpreted as a probability of adverse effects.
Additional information is provided in the description of risk characterization for AirToxScreen in Section
6 of this document.
5.3. Data Sources for Toxicity Values
Information on dose-response assessments for evaluating chronic exposures for AirToxScreen was
obtained from multiple sources and prioritized according to OAQPS risk assessment guidelines and level
of peer review. Our approach for selecting appropriate toxicity values generally places greater weight on
the EPA-derived toxicity values than those from other agencies (listed below).
Additionally, the approach of favoring EPA values (when they exist) has been endorsed by EPA's Science
Advisory Board. This ensures the use of values most consistent with well-established and scientifically
based EPA science policy. A spreadsheet file "AirToxScreen_Pollutants.xlsx" within the Supplemental
The RfC is 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.
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Data folder accompanying this TSD contains the toxicity values for both cancer and noncancer chronic
effects used in AirToxScreen. Cancer effects are characterized according to the extent to which available
data support the hypothesis that a pollutant causes cancer in humans.
5.3.1. U.S. EPA Integrated Risk Information System
We disseminate dose-response assessment information in several forms, depending on the level of
internal review. The Integrated Risk Information System (IRIS) is an electronic database prepared and
maintained by EPA that contains information on human-health effects that could result from exposure
to various substances in the environment. These assessments have undergone external peer review and
subsequent revision, compliant with requirements EPA instituted in 1996 for the IRIS review process.
Externally peer-reviewed assessments under development for IRIS were given first consideration for
AirToxScreen. These assessments, which reflect the most recent available toxicity information and data
analysis, were used in some cases to supersede existing values on IRIS. Current IRIS values were used for
AirToxScreen when peer-reviewed IRIS values under development were not available.
5.3.2. U.S. Department of Health and Human Services, Agency for Toxic
Substances and Disease Registry
The Agency for Toxic Substances and Disease Registry (ATSDR) publishes minimal risk levels (MRLs) for
many substances based on health effects other than cancer. The MRL is defined as an estimate of
human exposure to a substance that is likely to be without an appreciable risk of adverse effects (other
than cancer) over a specified duration of exposure. For noncancer values in AirToxScreen, inhalation
MRLs were used when IRIS RfC values were not available or when the ATSDR value was based on more
recent, peer-reviewed data and analysis methods than the IRIS value (because the ATSDR concept,
definition and derivation are analogous to IRIS). ATSDR does not develop assessments based on
carcinogenicity. After internal and external review, MRLs are published in pollutant-specific
toxicological-profile documents. ATSDR regularly updates these toxicological-profile documents; they
are available at Toxic Substances Portal MRLs (ATSDR 2015).
5.3.3. California Environmental Protection Agency Office of Environmental
Health Hazard Assessment
California's Office of Environmental Health Hazard Assessment (OEHHA) develops UREs based on
carcinogenicity and reference exposure levels (RELs) based on health effects other than cancer. The REL
is defined as a concentration level at or below which no adverse health effects are anticipated. For
cancer and noncancer values in AirToxScreen, OEHHA UREs and inhalation RELs were used when their
derivation was determined to be consistent with the concepts and definitions of IRIS or ATSDR. OEHHA
dose-response information is available at Air Toxicology and Epidemiology (OEHHA 2016). Technical
support documents for assessing hot spots are available on the OEHHA website at Hot Spots Guidelines
(OEHHA 2015).
5.3.4. U.S. EPA Health Effects Assessment Summary Tables
The Health Effects Assessment Summary Tables (EPA 1997) are a comprehensive listing consisting
almost entirely of provisional UREs, RfCs and other risk assessment information of interest that various
EPA offices have developed. The assessments, which have never been submitted for EPA consensus,
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were last updated in 2001. AirToxScreen uses information from these tables only when no values from
the sources discussed in Sections 5.3.1 through 5.3.3 are available.
5.3.5. World Health Organization International Agency for Research on
Cancer
The International Agency for Research on Cancer of the World Health Organization (WHO) coordinates
and conducts research on cancer and provides information on related cancer research and
epidemiology. Although the agency does not develop quantitative dose-response values, it has
published a series of monographs (WHO 2018) on the carcinogenicity of a wide range of substances. The
following "degrees of evidence" published by the International Agency for Research on Cancer were
used when EPA WOE determinations were not available for a substance or are out of date (see the
AirToxScreen Glossary of Terms in Appendix A for definitions of each):
Group 1: Carcinogenic to humans;
Group 2A: Probably carcinogenic to humans;
Group 2B: Possibly carcinogenic to humans;
Group 3: Not classifiable as to human carcinogenicity; and
Group 4: Probably not carcinogenic to humans.
5.4. Additional Toxicity Decisions for Some Chemicals
After the dose-response information was prioritized, we made additional changes to some of the
chronic inhalation exposure values to address data gaps, increase accuracy and avoid underestimating
risk for AirToxScreen.
5.4.1. Polycyclic Organic Matter
A substantial proportion of polycyclic organic matter (POM) reported in the NEI was not speciated into
individual compounds. For example, some emissions of POM were reported in NEI as "PAH, total" or
"PAH/POM-Unspecified." In other cases, individual POM compounds were reported for which no
quantitative cancer dose-response value has been published in the sources used for AirToxScreen. As a
result, we made simplifying assumptions that characterize emissions reported as POM. This allows us to
quantitatively evaluate cancer risk for these species 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 were grouped into categories. EPA assigns dose-response values based on
the known or estimated toxicity for POM within each group.
For AirToxScreen, we divided unspeciated POM emissions into ten POM groups. We discuss the POM
groups used for the AirToxScreen in Section 2.1.1.1. We concluded that three PAHs - anthracene,
phenanthrene and pyrene - are not carcinogenic, and therefore no URE was assigned to these for
AirToxScreen. Details of the analysis that led to this conclusion can be found in the document entitled
Development of a Relative Potency Factor (RPF) Approach for Polycyclic Aromatic Hydrocarbon (PAH)
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Mixtures: In Support of Summary Information of the Integrated Risk Information System (IRIS) (EPA
2010a).
5.4.2. Glycol Ethers
Much of the emission-inventory information for the glycol ether category reported only the total mass
for the entire group without distinguishing among individual glycol ether compounds. In other cases,
emissions of individual glycol ether compounds that had not been assigned dose-response values were
reported. Individual glycol ether compounds vary substantially in toxicity. To avoid underestimating the
health hazard associated with glycol ethers, we protectively applied the RfC for ethylene glycol methyl
ether (the most toxic glycol ether for which an assessment exists) to glycol ether emissions of
unspecified composition.
5.4.3. Acrolein
EPA first derived an IRIS RfC for acrolein in 2003 (EPA 2003a). This value was based on a 1978 subchronic
rodent study that identified a lowest-observed-adverse-effect level (LOAEL) for nasal lesions (Feron et al.
1978). In 2008, the OEHHA derived a chronic reference exposure level for acrolein that was based on a
more recent subchronic rodent study. The newer study identified a no-observed-adverse-effect level
(NOAEL) for nasal lesions (OEHHA 2008; Dorman et al. 2008). Because both studies identified nasal
lesions as the critical effect and because the Dorman et al. (2008) study identified a NOAEL, we used the
more recent OEHHA REL for acrolein in AirToxScreen.
5.4.4. Metals
Several decisions made for AirToxScreen regarding the toxicity values used for metal compounds are
discussed in this section.
Chromium (VI) compounds. We used the IRIS RfC for particulate chromium (VI) instead of the RfC for
chromic acid mists and dissolved aerosols to avoid underestimating the health hazard associated with
these compounds. The RfC for particulate chromium (VI) is less than those RfCs for chromic acid mists
and dissolved aerosols.
Lead. We do not use the lead RfC to calculate noncancer adverse effects. Instead, we estimate ambient
concentrations which can be compared to the primary National Ambient Air Quality Standard (NAAQS)
for lead. The primary NAAQS for lead incorporates an ample margin of safety, to be protective of all
potential health effects for the most susceptible populations. The NAAQS, developed using the EPA
Integrated Exposure, Uptake, Biokinetic Model, was preferred over the RfC for noncancer adverse
effects because the NAAQS for lead was developed using more recent toxicity and dose-response
information on the noncancer adverse impacts of lead. The NAAQS for lead was set to protect the health
of the most susceptible children and other potentially at-risk populations against an array of adverse
health effects, most notably including neurological effects, particularly neurobehavioral and
neurocognitive effects (which are the effects to which children are most sensitive).
Nickel compounds. The cancer inhalation URE for most of the emissions of nickel compounds included
in AirToxScreen (including unspecified nickel emissions reported as "nickel compounds") was derived
from the IRIS URE for insoluble nickel compounds in crystalline form. Soluble nickel species, and
insoluble species in amorphous form, do not appear to produce genotoxic effects by the same toxic
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mode of action as insoluble crystalline nickel. Nickel speciation information for some of the largest
nickel-emitting sources, including oil and coal combustion, suggests that at least 35 percent of total
nickel emissions could be soluble compounds. The remaining insoluble nickel emissions, however, are
not well characterized. Consistent with this limited information, we conservatively assumed for
AirToxScreen that 65 percent of emitted nickel is insoluble and that all insoluble nickel is crystalline.
Because the nickel URE listed in IRIS is based on nickel subsulfide and represents pure insoluble
crystalline nickel, it was adjusted to reflect an assumption that 65 percent of the total mass of emitted
nickel might be carcinogenic. In cases where a chemical-specific URE was identified for a reported nickel
compound, it was used without adjustment. Furthermore, the MRL in Table 2 of the ATSDR is not
adjusted because the noncancer effects of nickel are not thought to be limited to the insoluble
crystalline form.
Manganese. We used the ATSDR MRL for manganese (Mn) as the chronic inhalation reference value in
AirToxScreen. There is an existing IRIS RfCfor Mn (EPA 1993), and ATSDR published an assessment of
Mn toxicity which includes a chronic inhalation reference value (i.e., an ATSDR Minimal Risk Level, MRL).
(ATSDR 2012). Both the 1993 IRIS RfC and the 2012 ATSDR MRL were based on the same study (Roels et
al. 1992); however, ATSDR used updated dose-response modeling methodology (benchmark dose
approach) and considered recent pharmacokinetic findings to support their MRL derivation. Because of
the updated methods, we determined that the ATSDR MRL is the appropriate noncancer health
reference value for manganese in AirToxScreen.
5.4.5. Adjustment of Mutagen UREs to Account for Exposure During
Childhood
For carcinogenic chemicals acting via a mutagenic mode of action (i.e., chemicals that cause cancer by
damaging genes), we recommend that estimated risks reflect the increased carcinogenicity of such
chemicals during childhood. This approach is explained in detail in the Supplemental Guidance for
Assessing Susceptibility from Early-Life Exposure to Carcinogens (EPA 2005b). Where available data do
not support a chemical-specific evaluation of differences between adults and children, the Supplemental
Guidance recommends adjusting for early-life exposures by increasing the carcinogenic potency by 10-
fold for children up to 2 years old and by 3-fold for children 2 to 15 years old. These adjustments have
the aggregate effects of increasing the estimated risk by about 60 percent (a 1.6-fold increase) for a
lifetime of constant inhalation exposure. EPA recommends making these default adjustments only for
carcinogens known to be mutagenic and for which data to evaluate adult and juvenile differences in
toxicity are not available.
For AirToxScreen, we adjusted the UREs for acrylamide, benzidine, chloroprene, coke oven emissions,
ethyl carbamate, ethylene oxide, methylene chloride, nitrosodimethylamine and PAHs upward by a
factor of 1.6 to account for the increased risk during childhood exposures. Although trichloroethylene is
carcinogenic by a mutagenic mode of action, the age-dependent adjustment factor for the URE only
applies to the portion of the slope factor reflecting risk of kidney cancer. For full lifetime exposure to a
constant level of trichloroethylene exposure, we adjusted the URE upward by a factor of 1.12 (rather
than 1.6 as discussed above). For more information on applying age-dependent adjustment factors in
cases where exposure varies over the lifetime, see Toxicological Review of Trichloroethylene (EPA 2011).
These nine air toxics were the only ones that met the adjustment criteria described above at the time of
this assessment. The overall lifetime adjustment was applied because a single, lifetime-average EC was
estimated for AirToxScreen rather than age-group-specific exposures. Note that the IRIS assessment
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contains vinyl chloride UREs for both exposure from birth and exposure during adulthood. However, the
overall vinyl chloride URE already includes exposure from birth. Thus, we used that value with no
additional factor applied.
5.4.6. Diesel Particulate Matter
Diesel PM (DPM) mass (expressed as jag DPM/m3) has historically been used as a surrogate measure of
exposure for whole diesel exhaust. Although uncertainty exists as to whether DPM is the most
appropriate parameter to correlate with human health effects, it is considered a reasonable choice until
more definitive information about the mechanisms of toxicity or mode(s) of action of diesel exhaust
becomes available.
In EPA's 2002 Diesel Health Assessment Document (Diesel HAD), exposure to diesel exhaust was
classified as likely to be carcinogenic to humans by inhalation from environmental exposures, in
accordance with the revised draft 1996/1999 EPA cancer guidelines. Several other agencies (National
Institute for Occupational Safety and Health, the International Agency for Research on Cancer, the
World Health Organization, California EPA and the U.S. Department of Health and Human Services) had
made similar hazard classifications prior to 2002. EPA also concluded in the 2002 Diesel HAD that it was
impossible to calculate a cancer unit risk for diesel exhaust due to limitations in the exposure data for
the occupational groups or the absence of a dose-response relationship.
In the absence of a cancer unit risk, the Diesel HAD sought to provide additional insight into the
significance of the diesel exhaust cancer hazard by estimating possible ranges of risk that might be
present in the population. An exploratory analysis was used to characterize a possible risk range, and
found that environmental risks from diesel exhaust exposure could plausibly range from a low of 10"5 to
as high of 10"3 for long-term exposures. Because of uncertainties, the analysis acknowledged that the
risks could be lower than 10"5, and a zero risk from diesel exhaust exposure was not ruled out.
Noncancer health effects of acute and chronic exposure to diesel exhaust emissions are also of concern
to EPA. The agency derived a diesel exhaust reference concentration (RfC) after considering four well-
conducted chronic rat inhalation studies showing adverse pulmonary effects. The RfC is 5 ng/m3 for
diesel exhaust measured as diesel particulate matter. This RfC does not consider allergenic effects such
as those associated with asthma, immunologic effects or the potential for cardiac effects. Emerging
evidence in 2002, discussed in the Diesel HAD, suggested that exposure to diesel exhaust could
exacerbate these effects, but the exposure-response data were lacking at that time to derive an RfC.
The Diesel HAD noted that the cancer and noncancer hazard conclusions applied to the general use of
diesel engines then on the market, and as cleaner engines replace a substantial number of existing ones,
the applicability of the conclusions would need to be reevaluated.
Several studies published since 2002 continue to report increased lung cancer with occupational
exposure to older engine diesel exhaust. Of note since 2011 are three new epidemiology studies that
examined lung cancer in occupational populations (for example, in truck drivers, underground nonmetal
miners and other diesel motor related occupations). These studies (Garshick et al. 2012; Silverman et al.
2012; Olsson et al. 2011) reported increasing risks with exposure to diesel exhaust, and positive
exposure-response relationships were also evident to varying degrees. These newer studies (along with
others that have appeared in the scientific literature) add to the evidence EPA evaluated in the 2002
Health Assessment Document and further reinforce the lung cancer hazard concern. The findings from
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these newer studies do not necessarily apply to newer technology diesel engines since the newer
engines have large reductions in the emission constituents.
In June 2012, the World Health Organization's International Agency for Research on Cancer (IARC)
evaluated the full range of cancer-related health-effects data for diesel engine exhaust (IARC 2014).
IARC concluded that diesel exhaust should be regarded as "carcinogenic to humans." This designation
was an update from its 1988 evaluation that considered the evidence to be indicative of a "probable
human carcinogen." IARC is a recognized international authority on the carcinogenic potential of
chemicals and other agents.
Also in 2012, EPA and industry asked the Health Effects Institute to convene a panel to review recently
published epidemiology studies of occupational exposures to diesel engine exhaust. The request was in
part to determine whether new studies could be used in a quantitative risk assessment (QRA) to
calculate a cancer URE. In a final report published at the end of 2015 (HEI 2015), the panel concluded
that newer studies made considerable progress toward addressing a number of major limitations in
previous epidemiologic studies of diesel engine exhaust. It further stated that, although uncertainties
still remain, these newer studies provide a useful basis for potentially conducting a QRA of diesel engine
exhaust exposures, but specifically to diesel engine exhaust from older diesel engines. Currently, there
are no ongoing activities at EPA related to conducting a QRA for diesel engine exhaust.
5.5. Summary
To evaluate the potential of a given air toxic to cause cancer and other adverse health effects, we
identified potential adverse effects that the substance causes and evaluated the specific ECs at
which these effects might occur.
The URE represents the upper-bound excess cancer risk estimated to result from continuous
exposure to a concentration of 1 ng of a substance per m3 of air over a 70-yearlifetime.
The RfC is an estimate of a continuous inhalation EC over a 70-year lifetime that is thought to be
without an appreciable risk of adverse effects. The population considered in the derivation of RfCs
includes sensitive subgroups (i.e., children, asthmatics and theelderly).
Dose-response-assessment information for chronic exposure was obtained from multiple sources
and prioritized according to conceptual consistency with OAQPS risk assessment guidelines and level
of peer review.
After considering dose-response information, EPA adjusted some chronic-toxicity values to increase
accuracy and to avoid underestimating risk.
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6. Characterizing Risks and Hazards in AirToxScreen
Risk characterization, the final step in our risk assessment process for air toxics, combines the
information from modeled exposure estimates with the dose-response assessment. The result is a
quantitative estimate of potential cancer risk and noncancer hazard associated with real-world exposure
to air toxics. The term "risk" implies a statistical probability of developing cancer over a lifetime.
Noncancer "risks," however, are not expressed as a statistical probability of developing a disease.
Rather, noncancer "hazards" are expressed as a ratio of an exposure concentration (EC) to a reference
concentration (RfC) associated with observable adverse health effects (i.e., a hazard quotient).
This section contains information on the risk characterization conducted for AirToxScreen. After a brief
overview of the risk-related questions that AirToxScreen is intended to address, the methods used to
characterize cancer risk and noncancer hazards for AirToxScreen are described. A discussion of the
quantitative results included in AirToxScreen follows this description.
6.1. The Risk-characterization Questions AirToxScreen Addresses
AirToxScreen risk characterization considers both cancer risk and the potential for noncancer effects
from inhalation of air toxics nationwide, in both urban and rural areas. The purpose of AirToxScreen is to
understand cancer risks and noncancer hazards to help EPA and others identify air toxics and source
categories of greatest potential concern and to set priorities for collecting additional data. The
assessment represents a "snapshot" in time for characterizing risks from exposure to air pollutants; it is
not designed to characterize risks sufficiently for regulatory action. The risk characterization for
AirToxScreen, which was limited to inhalation risk from outdoor sources, was designed to answer the
following questions:
Which air toxics pose the greatest potential risk of cancer or adverse noncancer effects acrossthe
entire United States?
Which air toxics pose the greatest potential risk of cancer or adverse noncancer effects in specific
areas of the United States?
Which air toxics pose less, but still significant, potential risk of cancer or adverse noncancer effects
across the entire United States?
When risks from inhalation exposures to all outdoor air toxics are considered in combination, how
many people could experience a lifetime cancer risk greater than levels of concern (such as 1-in-l
million or 100-in-l million)?
When potential adverse noncancer effects from long-term exposures to all outdoor air toxics are
considered in combination for a given target organ or system, how many people could experience
exposures that exceed the reference levels intended to protect against those effects (i.e., a hazard
quotient greater than 1)?
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6.2. How Cancer Risk Is Estimated
To estimate cancer risks in AirToxScreen, the results of cancer dose-response assessments for a given
chemical were converted to a unit risk estimate (URE). That URE was then multiplied by the estimated
inhalation exposure concentration to obtain an estimate of individual lifetime cancer risk. The approach
used in AirToxScreen for characterizing cancer risk is consistent with EPA's 2005 final Guidelines for
Carcinogen Risk Assessment (EPA 2005a). When used with the cancer UREs described in Section 5, the
approach is also consistent with EPA's Supplemental Guidance for Assessing Susceptibility from Early-
Life Exposure to Carcinogens (EPA 2005b).
6.2.1. Individual Pollutant Risk
In AirToxScreen, individual lifetime cancer risk associated with exposure to a single air pollutant was
estimated by multiplying an average estimated long-term exposure concentration by the corresponding
URE for that pollutant. Thus, the equation below estimates the probability of an individual developing
cancer over a lifetime from the exposure being analyzed due to a given inhalation exposure, over and
above that due to any other factors.
Risk = EC x URE
Where:
Risk = estimated incremental lifetime cancer risk for an individual due to exposure to a
specific air toxic, unitless (expressed as a probability)
EC = estimate of long-term inhalation exposure concentration for a specific air toxic, in
units of ng/m3
URE = the corresponding inhalation unit risk estimate for that air toxic, in units of
l/(Hg/m3)
Note that UREs are typically upper-bound estimates, so actual risks may be lower than predicted. Also,
the true value of the risk is unknown.
6.2.2. Multiple-pollutant Risks
EPA estimates individual lifetime cancer risks resulting from exposure to multiple air toxics by summing
the chronic cancer risk for each air toxic that can be quantified. This estimate of risk focuses on the
additional lifetime risk of cancer predicted from the exposure being analyzed, over and above that due
to any other factors. The following equation estimates the predicted cumulative individual cancer risk
from inhalation of multiple substances:
Risktot = Riski + Risk2 + ... + Riski
Where:
Risktot = total cumulative individual lifetime cancer risk, across/'substances
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Risk, = individual risk estimate for the /th substance
In AirToxScreen, the estimated ECs are not considered upper bounds. Rather, they represent central-
tendency estimates of ECs at the census block level. Because cancer slope factors are 95-percent upper-
confidence intervals (not "most probable estimates"), summing traditional risk levels can cause the
resulting sum to overestimate a 95-percent upper-confidence-level risk for a mixture.
In AirToxScreen, we assume that exposures to multiple carcinogens can be added together to estimate
risks. This approach has drawbacks: Effects from multiple chemicals may be greater or less than additive,
and statistical limitations exist. But this straightforward calculation is widely used to estimate
cumulative risks, especially in screening assessments like AirToxScreen.
Information on non-additive interactions is not readily available in a form that can be used for
AirToxScreen. Without this specific information, cancer risk from various chemicals is conservatively
assumed to be additive. Thus, the cancer risks from all air toxic compounds listed as carcinogenic or
likely carcinogenic to humans were summed to determine cumulative cancer risks for AirToxScreen.
More information on EPA's methods for conducting risk assessment of mixtures can be found in the
Framework for Cumulative Risk Assessment (EPA 2003b).
6.3. How Noncancer Hazard is Estimated
To evaluate the potential for noncancer adverse
health effects, EPA uses chronic dose-response data to
estimate a reference concentration (RfC) - the EC at
which adverse health effects are assumed to be
unlikely. (See Section 5.2.2 for more information on
noncancer RfCs.) Due to the wide variety of endpoints,
hazard-identification procedures for noncancer effects
have not been described as completely in EPA
guidance as those for carcinogens. But EPA has
published guidelines for assessing several specific types
of chronic noncancer effects (mutagenicity, developmental toxicity, neurotoxicity and reproductive
toxicity). These can be found at Products and Publications Relating to Risk Assessment Produced by the
Office of the Science Advisor (EPA 2016b). EPA has also published a framework for using studies of these
and other effects in inhalation risk assessment (EPA 1994).
6.3.1. Individual Pollutant Hazard
EPA estimated chronic noncancer hazards for AirToxScreen by dividing a chemical's estimated long-term
EC by the RfC for that chemical to yield a hazard quotient (HQ). The following equation estimates the
noncancer hazard due to a given inhalation exposure:
EC
HQ=—
RfC
Where:
HQ = the hazard quotient for an individual air toxic, unitless
EPA's Chronic Noncancer
Guidelines
• Mutagenicity (EPA 1986)
• Developmental Toxicity (EPA 1991)
• Neurotoxicity (EPA 1998)
• Reproductive Toxicity (EPA 1996)
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EC = estimate of long-term inhalation exposure concentration for a specific air toxic,
in units of mg/m3
RfC = the corresponding reference concentration for that air toxic, in units of mg/m3
An HQ value less than or equal to 1 indicates that the exposure is not likely to result in adverse
noncancer effects. An HQ value greater than 1, however, does not necessarily suggest a likelihood of
adverse health effects and cannot be interpreted to mean that adverse health effects are statistically
likely to occur. The statement is simply whether, and by how much, an EC exceeds the RfC, indicating
that a potential exists for adverse health effects.
6.3.2. Multiple-pollutant Hazard
We estimated chronic noncancer hazards for multiple air toxics by summing chronic noncancer HQs for
individual air toxics that cause similar adverse health effects. The result is a hazard index (HI).
Aggregation in this way produces a target-organ-specific HI, defined as a sum of HQs for individual air
toxics that affect the same organ or organ system. More information on chemical mixtures risk
assessment methods can be found in the EPA supplementary guidance for risk assessment of mixtures
(EPA 2000).
The following equation estimates the HI from inhalation of multiple substances:
HI = HQ! + HQ2 + ... +HQi
Where:
HI = the hazard index for chronic exposure to air toxics 1 through /', unitless
HQ, = the hazard quotient for the /th air toxic, where all i air toxics are assumedto
affect the same target organ or organ system, unitless
As with the HQ, an HI value less than or equal to 1 indicates that the exposure is not likely to result in
adverse noncancer effects. An HI value greater than 1, however, does not necessarily suggest a
likelihood of adverse health effects and cannot be interpreted as a statistical probability of adverse
effects occurring.
This equation assumes an additive effect from simultaneous exposures to several chemicals. Summing
HQs is inappropriate when effects from multiple chemicals are synergistic (greater than additive) or
antagonistic (less than additive). As is the case with cancer risk, quantitative information on non-additive
interactions resulting in noncancer hazards is not readily available; consequently, the noncancer HQs are
assumed to be additive for chemicals with the same target organ or organ system.
For AirToxScreen, we report His for 14 target organs or systems. Results indicate that respiratory
hazards are the primary noncancer hazards for inhalation exposures to the modeled chemicals.
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6.4. How Risk Estimates and Hazard Quotients Are Calculated for
AirToxScreenat Block, County and State Levels
The cancer risk and HQs for each modeled air toxic are estimated from ECs (not ambient concentrations)
by combining them with UREs and inhalation RfCs (or their equivalents). As described previously, the
modeling conducted for AirToxScreen resulted in ambient concentrations for each air toxic emitted by
modeled sources, with the level of spatial resolution varying by source type and the corresponding
modeling approach (see Section 3).
6.4.1. Aggregation of Block-level Results to Larger Spatial Units
Block-level ambient concentrations were aggregated up to the county, state and national levels using a
method that weights concentration according to the population within a region. For a county, for
example, a population-weighted ambient concentration was estimated by multiplying the block-level
concentrations by the population of each block, summing these population-weighted concentrations,
and dividing by the total county population encompassing all blocks to obtain a final population-
weighted, county-level concentration. The process for aggregating from the block to the county level
can be expressed using the following equation:
This same method was applied when aggregating up to the state or national level, using the appropriate
concentration and population values. AirToxScreen results include ambient concentrations, ECs, cancer
risks, and noncancer HQs at the block, county, state and national levels.
The ambient concentrations derived at the block level also were used to estimate ECs using either direct
exposure modeling with HAPEM or with the exposure factors derived from HAPEM modeling (i.e., ratios
of EC to estimated ambient concentration). (See Section 4 for a more thorough discussion of
AirToxScreen exposure modeling and estimates.) Because the exposure factors were applied at the tract
level, each census block was assigned the tract-level EC or exposure factor and then the census block-
level ECs are estimated.
6.5. The Risk Characterization Results That AirToxScreen Reports
AirToxScreen provides a snapshot of the outdoor air quality and the risks to human health that would
result if air toxic emission levels remain unchanged. The assessment was based on an inventory of air
toxics emissions from 2020. Individuals were assumed to spend their entire lifetimes exposed to these
air toxics. Therefore, we did not account for the reductions in emissions that have occurred since the
Conc countyk p
rur countyk
2 C°ncblockixP°blocki
Where:
P Opblocki
P Opcountyk
ConCcountyk
ConCblocki
population-weighted concentration for countyk
ambient concentration in block i (contained withincounty k)
population in block i (contained within countyk)
population in countyk
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year of the assessment, or those that might happen in the future due to regulations for mobile and
industrial sources.
The evaluation of national-scale results and comparison of risks among chemicals make it possible to
estimate which air toxics pose the greatest potential risk to human health in the United States.
AirToxScreen reports a summary of these findings. Cancer risks are presented as lifetime risks, meaning
the risk of developing cancer due to inhalation exposure to each air toxic compound over a normal
lifetime of 70 years. Noncancer hazards are presented in terms of the ratio between the exposure and
an RfC for inhalation exposures (i.e., the HQ). As described previously in this section, HQs are combined
across chemicals where a common target organ or system is expected to estimate HI (i.e., for
respiratory).
Using these quantitative results, AirToxScreen classifies certain pollutants as drivers or contributors at
the national or regional scale based on certain criteria. Table 6-1 contains the criteria for classifying the
air toxics included in AirToxScreen at the regional and national level. In general, drivers and contributors
were defined as air toxics showing a particular level of risk or hazard for some number of people
exposed.
For example, for a pollutant to be categorized in AirToxScreen as a cancer contributor at the national
level, the individual lifetime cancer risk for that pollutant must have been shown by the assessment to
be at least 1-in-l million and the number of people exposed to that pollutant must have been shown to
be at least 25 million. For a pollutant to be categorized in AirToxScreen as a regional driver of noncancer
health effects, the chronic hazard index for that pollutant must have been shown to exceed 1 and the
number of people exposed to that pollutant must have been shown to be at least 10,000.
AirToxScreen results indicate that most individuals' estimated risk is between 1-in-l million and 100-in-l
million, although a small number of localized areas show risks of higher than 100-in-l million. Although
individuals and communities may be concerned about these results, recall that AirToxScreen is not
designed to assess specific risk values at local levels. The results are best used as a tool to prioritize
pollutants, emissions sources and locations of interest for further investigation.
Table 6-1. Criteria establishing AirToxScreen drivers and contributors of health effects for risk
characterization
Risk-characterization Category
Criterion
(Criteria in both columns must be met)
Individual Health Risk or Hazard
Index Exceeds...
Minimum Number of People
Exposed (in millions) is...
Cancer Risk (value in first column represents individual lifetime cancer risk, in 1 million)a
National cancer driver
10
25
Regional cancer driver
(either set of criteria can be used)
10
1
100
0.01
National cancer contributor
1
25
Regional cancer contributor
1
1
Hazard Index (value in first column represents chronic hazard index for any organ/organ system)b
National noncancer driver
1
25
Regional noncancer driver
1
0.01
a Cancer risks are upper-bound lifetime cancer risks; that is, a plausible upper limit to the true probability that an individual will
contract cancer over a 70-year lifetime as a result of a given hazard (such as exposure to a toxic chemical). This risk can be
measured or estimated in numerical terms (e.g., one chance in a hundred).
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b Hazard index is the sum of the HQs for substances that affect the same target organ or organ system. Because different
pollutants can cause similar adverse health effects, combining HQs associated with different substances is often appropriate to
understand the potential health risks associated with aggregate exposures to multiple pollutants.
Furthermore, the risks estimated by the assessment do not consider indoor sources of air toxics or
ingestion exposure to any pollutants. Also, although AirToxScreen estimates cancer and noncancer risks
for numerous pollutants, additional chemicals might exist that are not identified or for which toxicity
information is unavailable. Therefore, these risk estimates represent only a subset of the total potential
cancer and noncancer risk associated with airtoxics.
Analytical results (including modeled ambient concentrations, exposure and risks) for each AirToxScreen
are also provided at the census block, county and state levels for those who wish to do their own
technical analyses using the most refined output available. In performing such analyses, users must be
extremely mindful of the purposes for which AirToxScreen was developed. AirToxScreen was developed
as a screening tool to inform both national and more localized efforts to collect air toxics information
and characterize emissions (e.g., prioritize pollutants or areas of interest for more refined data collection
such as monitoring). The results are most meaningful when viewed at the state or national level.
Nevertheless, reported spatial patterns within a county likely represent actual variations in overall
average population risks. Less likely, however, is that the assessment pinpoints the exact locations
where higher risks exist or that the assessment captures the highest risks in a county.
Using these results alone to draw conclusions about local concentrations and risk is inappropriate. This
assessment does not attempt to identify areas or populations that have significantly higher risks than
others. Rather, it focuses on characterizing geographic patterns and ranges of risk across the country. In
general, however, spending time in larger urban areas tends to pose greater risks than spending time in
smaller urban and rural areas because the emissions of air toxics tend to be higher and more
concentrated in areas with more people. This trend is not universal, however, and can vary from
pollutant to pollutant according to its sources. The trend also can be affected by exposures and risk from
non-inhalation and indoor sources of exposure.
Based on the AirToxScreen results, millions of people live in areas where air toxics pose potential health
concerns. Although air quality continues to improve, more needs to be done to meet the CAA's
requirements to reduce the potential exposure and risk from these chemicals. EPA will continue to
develop air toxic regulations and cost-effective pollution prevention and other control options to
address indoor and urban pollutant sources that significantly contribute to risk.
You can access all AirToxScreen results via the following website: https://www.epa.gov/AirToxScreen.
6.6. Summary
The purpose of AirToxScreen is to understand cancer risk and noncancer health effects to help EPA
and others identify pollutants and source categories of greatest potential concern and to set
priorities for collecting additional information to improve future assessments.
Cancer risk is expressed as a statistical probability that an individual will develop cancer. Cancer risks
were assumed to be additive across chemicals for AirToxScreen.
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Noncancer hazard is expressed as an HQ, which is the ratio of the EC to an RfC associated with
observable adverse effects. Noncancer hazards were assumed to be additive across chemicals that
effect the same target organ or organ system.
AirToxScreen estimates most individuals' cancer risk to be between 1-in-l million and 100-in-l
million, although a small number of localized areas show risk higher than 100-in-l million.
Air toxics data for AirToxScreen are presented at the national, state, county and census block levels.
The results are most meaningful when used to identify patterns of risk over larger areas. Using these
results in the absence of additional information to draw conclusions about local concentrations and
risk is inappropriate.
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7. Variability and Uncertainty Associated with AirToxScreen
7.1. Introduction
This section discusses the variability and uncertainty
associated with AirToxScreen. Clearly understanding
these two concepts - found in all broad-scale
assessments that rely on models and data - will
help you understand which questions AirToxScreen
can answer appropriately, and which it cannot.
As stated in Section 1, AirToxScreen results should
not be used for limited-scale or site-focused
applications. AirToxScreen results are intended to
characterize broad-scale risk to help identify those
air toxics and source types associated with the
highest exposures and posing the greatest potential
health risks. The results are intended to identify
geographic patterns and ranges of risks across the
country. To avoid over-interpretation and
misapplication of the results, users must first
understand the concepts of variability and uncertainty and then must recognize the role that these
elements play in AirToxScreen results.
Air toxic emissions, air concentrations and exposures are not the same throughout the United States,
and the risks associated with air toxics are not the same for all people. Some areas have higher
concentrations than others. At certain times, the concentration is higher at a given location than at
other times. The risks for some individuals are below the national average, while for others the risks are
above the national average. For these reasons, understanding how the ambient (outdoor) air
concentration, exposure and risks from air toxics vary throughout the United States is essential for
understanding AirToxScreen. This information comes from a process called variability analysis.
EPA seeks to protect health with reasonable confidence based on the best data available. Estimates of
air concentrations, exposures and risks, however, must always involve assumptions. Assumptions are
necessary to simplify the problem at hand. They make AirToxScreen possible given available information
and resources. That said, assumptions introduce uncertainties into AirToxScreen results because we can
never be fully confidence that the assumptions are entirely correct. Understanding the extent of these
uncertainties, the level of confidence that can be placed in statements related to the assessment, and
how this confidence affects the ability to make reasoned decisions is essential. This information comes
from a process called uncertainty analysis.
7.2. How AirToxScreen Addresses Variability
The AirToxScreen process focuses on the variation in ambient air concentrations, exposures and risks
across the United States, Puerto Rico and the U.S. Virgin Islands. Included, for example, are variations in
source locations and the amounts of pollutants these sources emit, variations in meteorological
conditions across the country, and variations in the daily activities of people. This section presents
Key Definitions for this Section
Variability represents the diversity or
heterogeneity in a population or parameter
(e.g., variation in heights of people). Variability
cannot be reduced by taking more (or better)
measurements; however, it can be accounted
for by a more detailed modeling approach
(e.g., modeling peoples' heights in terms of
age will reduce the unexplained variability due
to variation in heights).
Uncertainty refers to the lack of knowledge
regarding the actual values of model input
variables (parameter uncertainty) and of
physical systems (model uncertainty).
Uncertainty can be reduced through improved
measurements and improved model
formulation.
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information on the key components that drive variability in risks associated with air toxics and the
variability components that AirToxScreen addresses. A brief explanation is also provided on how
AirToxScreen results should be interpreted given variability.
7.2.1. Components of Variability
AirToxScreen results show how air concentrations, exposures and risks vary across broad areas of the
country. They do not fully characterize how concentration, exposure and risk vary among individuals,
except to the extent these individuals live in different geographic regions and are affected by the values
typical of a census block in that region. AirToxScreen results also do not fully characterize how ambient
air concentrations might vary temporally and they do not show how concentrations vary spatially within
a census block. The following list explains some of the components of variability that determine
differences in ambient air concentrations and individual risks.
Temporal. Sources do not emit pollutants at constant rates. Similarly, the meteorological conditions that
affect dispersion in the atmosphere vary over time. Thus, the ambient air concentration at a given
location can vary over time.
Geographic. The influence of pollutant emissions on a location's ambient concentration depends on the
degree of atmospheric dispersion of the emissions as they travel from the source to the location.
Dispersion depends on both meteorological conditions, which vary from place to place, and the travel
distance from the source. As a result, the ambient air concentration can vary greatly among different
locations. The AirToxScreen analysis accounts for some geographic variation by using available
meteorology data representative of the location and by modeling ambient concentrations for census
areas, but the spatial resolution of model predictions is limited.
Individual location. Two individuals might live at different locations within the same census block. The
ambient concentration estimated for the block is only an approximation of conditions at all locations in
the block. Different locations within that block may have different average ambient concentrations.
Therefore, exposures and risks may also vary.
Individual activity patterns. Two people may live at the same location but engage in different activities
(called an "activity pattern") during each day. Concentrations of substances indoors often differ from
concentrations outdoors. If one person spends more time indoors than the other person, the average air
concentration to which the two are exposed will differ, even though the ambient air concentration is the
same. Similarly, one person may spend more time in a car and be exposed to an air concentration that is
typical near roads. The net effect would be that the concentration of each pollutant in the air inhaled by
these two people would differ. In other words, their exposure differs.
In addition, the amount of outdoor pollution that penetrates into buildings and vehicles varies due to
differences in ventilation and structural integrity. Thus, two people who live in the same location and
spend the same amount of time indoors or in vehicles can still be exposed to different pollutant
concentrations.
Susceptibility. Two individuals may live at the same location and engage in the same activities, but one
person may be more susceptible than another. Susceptibility refers to the extent to which an individual
takes a pollutant into the body, transports it into an organ or tissue that might be adversely affected by
it, or develops an adverse effect.
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A more susceptible person may develop a higher concentration of a pollutant in his or her organs or
tissues, or have a higher chance of developing an adverse health effect, than another individual, even
when exposures for both individuals are the same. For example, people breathe at different rates; two
people breathing the same air may bring different amounts of a pollutant into their bodies. The amount
of a pollutant reaching an organ or tissue can also vary between individuals, even if both bring the same
amount into their lungs. The amount of time the pollutant remains in the body may also differ. Finally,
the innate sensitivity to the effect may vary, even at equal doses in the tissues. The net effect of these
factors is that either the dose of the pollutant delivered to the organs or tissues of the body or the level
of response (or both) can differ substantially between two people even if they are exposed to the same
pollutant concentrations.
The extent to which each factor described above influences variation in individual risk can depend on
the age, gender or ethnic group to which an individual belongs, as well as on that individual's lifestyle.
These groups comprise different receptor populations, or cohorts, and the exposures and risks can differ
among them.
7.2.2. Quantifying Variability
EPA conducts AirToxScreen to understand how ambient air concentrations, exposures and risks vary
geographically - not among specific individuals. EPA calculates the ambient air concentrations for each
specific, discrete location based on the emission sources and meteorological conditions affecting those
specific blocks. Some temporal variation is accounted for in AirToxScreen calculations. For example,
meteorological data used for air quality modeling is temporally dynamic. The air quality modeling
therefore captures important variations in ambient conditions on an hourly basis before the resulting
modeled ambient air concentrations are time averaged. The ambient concentration inputs to HAPEM
are stratified into eight 3-hour time blocks; HAPEM then calculates ECs for each 3-hour time block
before calculating an overall, long-term average EC. Although this approach to air quality and exposure
modeling considers some important temporal variations, these time-stratified model outputs are
averaged prior to the risk characterization step and are not included in the AirToxScreen results
reported by EPA.
The AirToxScreen concentrations and risks, however, do reflect a degree of geographic variation. The
smallest geographic area for which AirToxScreen results are reported is the census block. Although
results are reported at the census block level, average risk estimates are far more uncertain at this level
of spatial resolution than at the county or state level. Census blocks are the smallest geographic areas
that the Census Bureau uses. Blocks are bounded by visible or virtual features such as streets, streams,
and city or town boundaries. Census blocks are typically small in area and population; for example, in an
urban area, a census block might correspond to a block bounded by city streets. In remote areas,
however, census blocks might be large and irregular, comprising many square miles. Blocks typically
have approximately 50 residents.
Air concentrations are estimated in AirToxScreen at various levels of resolution depending upon the
source type modeled. Secondary formation, fires and biogenics (modeled in the CONUS) are at 12-km
grid cell resolution. Other sources use census block resolution, though the emissions for some sources
are at the grid cell level - these grid cell-level emissions originate from even broader geographic scales
(county and national level) and are less certain at these finer scales, as discussed below.
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Assessment results do not reflect variations in the susceptibility of people within a census block because
the focus is to compare typical exposures and risks in different blocks. As a result, individual exposures
or risks might differ in either direction. You should consider exposures or risks determined in
AirToxScreen as representative of the geographic area where an individual lives, but not as that
individual's personal risk.
Thus, the results of the AirToxScreen analysis do not allow for a comparison of ambient air
concentrations, exposures or risks between two individuals. They do, however, enable you to
understand the variation in typical values for these quantities among counties or states and to a lesser
degree among census blocks. For an individual, however, the values may differ from the typical value for
the county or state if that individual lives in a part of the area that has a higher or lower than typical
value, has an activity pattern that causes a higher or lower exposure than is typical, or is more (or less)
susceptible than a "typical" person used in this assessment.
For the purposes of estimating and reporting risk, EPA assumes that individuals within a census block
have the same exposure and risk. This assumption allows AirToxScreen users to examine variation in
individual exposure among census blocks, but not the variation within a census block. Activity patterns
are included for each of six cohorts defined by age. Even within a receptor population, some variability
in activity patterns among individuals is considered. Differences in susceptibility, however, are not
included in AirToxScreen. EPA took this approach for AirToxScreen for two primary reasons:
An overall purpose of AirToxScreen is to examine broad differences driven by geography.
AirToxScreen considers only geographic differences in pollutant concentration, exposure and risk.
The goal is to understand how these three factors differ among people living in different geographic
areas. EPA assesses these differences, as mentioned above, by tracking differences in air
concentration in different census blocks, producing differences in the typical pollutant
concentrations, exposures and risks in different blocks. Differences in susceptibility, however, can
produce differences in risk between two individuals in the same census block, and reporting on
these differences is not a purpose of AirToxScreen.
The variability in susceptibility is difficult to model at the national scale. Very limited information is
available on differences in susceptibility among individuals. Even if EPA were to choose to calculate
and report differences among individuals in a census block, scientifically reliable information
necessary to produce these calculations is not available for many of the pollutants. Given current
information, it may be possible to estimate variability in the rates at which people breathe, but this
is only a small part of the overall variation in susceptibility. EPA therefore has chosen not to
incorporate this source of variation between individuals.
Considering these limitations, EPA elected to incorporate differences in emissions and meteorology
(resulting in differences in ambient air concentration) and differences in location of typical individuals
(resulting in differences in exposure) among census blocks. Variation in activity patterns for different age
groups is reflected in the assessments to the degree that the age of residents varies by location.
Variability in susceptibility is not included for the reasons given above. We address temporal variation in
inputs by developing time-weighted averages of emissions characteristics, meteorological conditions
and ECs. Temporal variation in the estimated ambient air concentrations, however, is not reflected in
the results (only time-weighted annual averages are presented).
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7.2.3. How Variability Affects Interpretation of AirToxScreen Results
The AirToxScreen analysis illustrates how ambient air concentration, exposure and risk vary throughout
the United States. The assessment does not focus on the variation in exposure and risk among
individuals. It focuses on variation among well-defined geographic areas, such as counties or states,
based on calculations of ambient air concentration, exposure and risk in various census blocks. To a
lesser degree, variation among demographic groups is also addressed by AirToxScreen in that
differences in activity patterns are considered in modeling ECs using HAPEM. Risk results, however, are
not presented separately for individual demographic groups.
The information contained in the maps, charts and tables produced in AirToxScreen display predictions
of cancer risk and noncancer hazard. Cancer risk results include statements such as:
"X percent of the census blocks in a given area are characterized by a typical lifetime excess
cancer risk of less than R."
For this statement, if X is 25 percent and R is 1-in-l million, the result would be:
"25 percent of the census blocks are characterized by a typical risk of less than 1-in-l million."
This statement does not necessarily mean that 25 percent of individuals in the specified area have a
cancer risk of less than 1-in-l million. Some people in these census blocks would be expected to have a
risk above 1-in-l million. Although a person may live in a census block where the typical or average risk
is less than 1-in-l million, that person may live nearer the source than the average person in the census
block, may have an activity pattern that leads to greater exposure, or may be more susceptible. All these
factors could cause that individual to experience a risk above the typical value for that census block.
Conversely, the individual could have a lower risk by living farther from the source, having an activity
pattern that produces lower exposures, or being less susceptible.
The important point to remember when interpreting the maps and charts of the AirToxScreen analysis is
that they show variation among values of ambient air concentration, exposure or risk in census blocks or
larger areas such as counties. This presentation allows the user to identify geographic regions (counties
or states) where these values are higher or lower than the aggregated national average for all census
blocks. It does not allow users to identify individuals who have higher or lower values of ambient air
concentration, exposure or risk. Nevertheless, individuals with a high risk are more likely to be in
geographic regions characterized by a high risk than in those geographic regions characterized by a low
risk. The same can be said for exposure (i.e., individuals with a high exposure are more likely to be found
in geographic regions characterized by high exposure than in those regions characterized by low
exposure).
7.3. How AirToxScreen Addresses Uncertainty
No scientific statement (in risk assessment or other areas of science) can be made with complete
confidence. Risk estimates are always uncertain to some degree due to issues such as those discussed
below. To maintain transparency and openness in the presentation of risk results, the party conducting a
risk assessment must explain these uncertainties and how these uncertainties increase or decrease
confidence.
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The AirToxScreen analysis produces statements about variability in ambient air concentrations,
exposures and risks across geographic regions for typical individuals, as described in Section 7.2. In this
section, the discussion of uncertainty is intended to address the confidence with which these
statements regarding variability can be made. Note that uncertainty does not prevent EPA from making
a statement of risk, nor does it prevent EPA from taking reasonable actions. Uncertainty does require,
however, that the nature of the uncertainty, and the implications for decisions, be understood so the
degree of support for the statement can be correctly and properly interpreted.
7.3.1. Components of Uncertainty
Uncertainty arises from a variety of sources. To understand the sources of uncertainty affecting a risk
assessment, it is instructive to consider the process by which a study such as AirToxScreen is performed,
as described in the following sections.
Problem formulation. We must first define the problem to be addressed. For example, a question that
might help define the problem could include, "Is the occurrence of adverse human health effects
correlated with emissions from industrial facilities?" What the study is intended to address and how the
results will be used should be clear at the outset. This initial step in the analysis introduces problem-
formulation uncertainty. The purpose of AirToxScreen is described in Section 1 of this document, where
the question addressed in the assessment is defined as precisely as possible (e.g., that the study is
limited to estimates of health effects in human populations) and the reader is informed of the
limitations of the assessment. The issue of problem-formulation uncertainty is not considered further in
this document.
Defining the analysis components. This step describes what can influence the answer to the problem. In
AirToxScreen, the multiple influences include emissions from a variety of sources (e.g., mobile,
stationary, biogenic); atmospheric dispersion and chemistry; activity patterns for different cohorts; UREs
and RfCs and other considerations. Where the science is poorly developed, the factors that must be
included might not be clear. Resources may be limited, making the inclusion of all factors in the study
infeasible. This step in the analysis, which results in the conceptual model for the assessment,
introduces conceptual uncertainty. This issue is also addressed in the discussion of the limitations of
AirToxScreen in Section 1, where the aspects of the problem that are (and are not) included in the study
are addressed (e.g., that the study addresses inhalation of air toxics only). The issue of conceptual
uncertainty is not considered further here.
Selecting models. All risk assessments use models. The AirToxScreen analysis uses a series of
mathematical models. EPA uses models in AirToxScreen to produce the emissions inventory, to calculate
ambient air concentrations, to calculate exposures and to calculate risks (for cancer and noncancer
effects). All scientific models involve uncertainties because a model reduces a (potentially very complex)
set of chemical, biological, physical, social or other processes to manageable algorithms that can be
used to perform calculations and make forecasts. The simplifications inherent in the development of a
model introduce uncertainties.
Typically, more than one model is available to apply to a problem, and those models can produce
different results. Thus, uncertainty is introduced as to which model, and which model results, should be
used. As a simple example, AirToxScreen uses a linear statistical model to relate EC and cancer risk:
Cancer risk equals the exposure (air concentration) multiplied by a URE. Uncertainty analysis involves
asking a series of questions: Are we certain this linear relationship is correct? Could the relationship be
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quadratic (i.e., risk equals exposure multiplied by the square of the dose)? Could the relationship have a
threshold (i.e., no risk is apparent until the exposure becomes sufficiently large)? What are the
implications for estimates of risk if these different models are used? What are the implications for
decisions if a clear choice among the models cannot be made?
This step in the analysis introduces model uncertainty. Judging model uncertainty can be both
quantitative and qualitative. Qualitative issues involve the scientific plausibility of the model. Does the
model include all important processes? Does it explain the phenomenon (e.g., atmospheric dispersion)
well? Is the model well accepted in the scientific community - has it passed critical tests and been
subject to rigorous peer review?
Quantitative issues involve comparing model results against sets of data (although this also involves
issues of parameter uncertainty discussed in the next bullet). Does the model generally predict these
data accurately? Are the predictions accurate to within a factor of 2; a factor of 4? What is the effect of
any approximation methods used in the model?
Applying models. The models used in the AirToxScreen analysis require parameter inputs such as
emission rates, stack heights, fractions of time spent indoors and UREs. Although models describe
general relationships among properties of the real world (e.g., the linear relationship between exposure
and cancer risk), parameters quantify these properties for specific cases (e.g., the numerical value of the
URE for benzene). Parameters provide the numbers needed in the models. Various databases are
available from which we can estimate these parameters, and the methods used to collect the data and
to compile the databases introduce uncertainties. These factors all introduce parameter uncertainty.
Although parameter uncertainty has both quantitative and qualitative aspects, common practice is to
characterize this source of uncertainty quantitatively, with some qualitative caveats. For example,
parameter uncertainty might be characterized by a confidence interval, which states that the true value
of the parameter (such as the stack height for a facility) probably lies somewhere between 40 and 60
meters or that the stack height is "known to be within" a factor of 1.2, or that the stack height is
"accurate to within" 20 percent. Attached to this quantitative characterization of uncertainty will be a
qualitative caveat such as "the estimate of this uncertainty is based on measurements made in 1990 at
facilities similar to the one considered in this study, but a change in the design of stacks might have been
made since 1990." This qualitative statement provides some idea of the confidence with which the
quantitative assessment of uncertainty can be applied.
7.3.2. Components of Uncertainty Included in AirToxScreen
For this discussion, we have divided the uncertainties in
AirToxScreen into three sources, based on the three steps
leading from the estimate of emissions to the calculations
of risk. Uncertainty in ambient air concentrations is due to
uncertainty in the emissions estimates and in the air
quality models. Uncertainty in exposure is due to
uncertainty in activity patterns, locations of individuals
within a census block, and microenvironmental
concentrations as reflected in the exposure model. Finally, uncertainty in risk is due to uncertainty in the
shape of the relationship between exposure and effects, the URE and the RfC. These three sources of
uncertainty are discussed below.
AirToxScreen Components that Include
Uncertainty
• Ambient concentrations
• Exposure estimates
• Risk estimates
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Ambient air concentrations. Considering first the predictions of ambient air concentrations, the specific
sources of uncertainty derive from the parameters for the following: emissions, stack data, particle sizes
and reactivity, chemical speciation, terrain, boundary conditions, background concentration,
meteorology and model equations. These sources of uncertainty are discussed briefly in this section.
Emissions parameters, including emission rates and locations of sources, are taken from the NEI
database. The NEI is a composite of estimates produced by state and local regulatory agencies, industry
and EPA. Some of these data were further modified during the AirToxScreen review. We have not fully
assessed the quality of specific emission rates and locations in the NEI and resultant AirToxScreen
emissions (e.g., industrial emissions from a specific census block), although we have conducted reviews.
Some of the parameter values may be out of date, errors might have been introduced in transcribing
raw data to a computer file, and other data-quality issues may be present. Emission estimates use a
variety of methods such as emission factors, material balances, engineering judgment and source
testing. Some release point locations use an average facility location instead of the location of each
specific unit within the facility. Release point parameters may be defaulted for some situations. Fugitive
release parameters are not required and are defaulted where missing. In addition, TRI data does not
provide release-point parameters other than identifying sources as "stack" or "fugitive"; the release
parameters used historical defaults from previous inventories or new defaults.
Uncertainty also is inherent in the emission models used to develop inventory estimates. For example,
we estimate county-level air toxic emissions from nonroad equipment by applying fractions of toxic total
hydrocarbons to estimates of county-level hydrocarbons for gaseous air toxics and gas-phase PAHs and
fractions of toxic particulate matter to estimates of county-level particulate matter for particle-phase
PAHs. We use emission factors based on milligrams per mile for metals. The toxic fractions are derived
from speciation data, based on limited testing of a few equipment types. The estimates of county-level
total organic gases and particulates are derived from the EPA MOVES model. In MOVES, uncertainties
are associated with emission factors, activity and spatial-allocation surrogates. National-level emissions
for nonroad equipment in MOVES are allocated to the county level using surrogates, such as
construction costs adjusted for geographic construction material cost (to allocate emissions of
construction equipment) and employees in manufacturing (to allocate industrial equipment). Availability
of more specific local data on equipment populations and usage will result in more accurate inventory
estimates. For onroad sources, activity data either come from states or are allocated from states to
counties using surrogates. Different surrogates are used for different emission types and geographic
locations. These surrogates vary in quality from location to location.
For mobile and nonpoint sources, we typically allocate emissions rates from the county level to grid cells
through a surrogate such as traffic data, land cover or other land use types (such as truck stops for
heavy duty diesel idling emissions). This allocation introduces additional uncertainty because the data
on the surrogates also have uncertainty, and the correlations between the surrogates and the emissions
are imperfect.
The health effects of a pollutant depend on its chemical form when inhaled. For many sources, the
emissions reported to the NEI or NEI database itself do not include information on chemical speciation
of the pollutants of interest, but instead contains the total rate of pollutant emitted in all its forms. For
example, chromium obtained from TRI or from some states is reported as chromium and are speciated
into hexavalent and trivalent forms in an NEI augmentation step. We make assumptions about chemical
speciation based on values estimated to be representative at such sources, considering information on
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source type, typical feedstock materials, knowledge of the process involved, or other relevant factors.
Any one source, however, may have different values than the ones assumed.
The dispersion, or movement, of pollutants in the atmosphere is influenced by the topography of the
area surrounding a source, which is characterized by terrain parameters. Although CMAQ model
estimates consider topography, the AERMOD model estimates as implemented for AirToxScreen do not
in all cases. AERMOD estimates for point sources consider topography, but the estimates for the
emissions sources modeled as gridded area sources do not because considering topography in the
model requires a single source elevation, which is not always possible for large grid cell sources. Not
accounting for terrain introduces uncertainty into predictions of ambient air concentrations, particularly
in areas with hills or mountains.
Other sources of uncertainty in the modeling of ambient air concentrations are the values used for the
boundary conditions in CMAQ and the background concentration estimates added to AERMOD
concentrations for the non-CMAQ HAPs. These sources may include, for example, contributions from
long-range transport of compounds from other counties and states. For more details on background
concentrations, refer to the discussion in Section 3.
The model equations used in the air quality models represent another source of uncertainty. The
AERMOD dispersion model uses steady-state Gaussian equations, which make several assumptions that
simplify plume dispersion. The CMAQ model is more complex in its treatment of pollutant dispersion
and atmospheric dynamics than AERMOD; nevertheless, many assumptions underlie its Eulerian
approach to dispersion, which are outlined further in the science documentation for the CMAQ model.
While the hybrid approach of combining the CMAQ and AERMOD model output improves
AirToxScreen's treatment of chemistry and transport, there are uncertainties in the implementation.
The approach requires emissions and meteorological inputs to be consistent between the models. While
we treated emissions as consistently as possible, some simplifications were necessary. The main
difference was in the temporal treatment of the emissions. The temporal allocation used in AERMOD
was not exactly the same as in CMAQ for the county-level sources, though average profiles based on the
CMAQ temporal approach were developed for use in AERMOD. There were also differences in the
spatial treatment of CMVs, though for the large vessels, AERMOD source characterization parameters
were developed based on summaries of the CMAQ vertical distribution of emissions. In addition to
inconsistencies in model inputs, the hybrid approach uses an AERMOD grid cell average to normalize the
individual AERMOD concentrations within a grid cell. The AERMOD values are less representative of the
true AERMOD average in grid cells where there are fewer gridded receptors (i.e., nine gridded receptors
were used in less-populated areas).
To help characterize the aggregate uncertainty of the predictions of the air quality models, EPA
compared modeled concentrations to available monitoring data on ambient air quality. For each
monitor-pollutant combination, we compared the predicted annual average concentrations at the
monitor location to the sampled annual average concentrations. We present these comparisons in the
model evaluations found in the Supplemental Data folder on the AirToxScreen website.
Measured concentrations were taken from EPA's Ambient Monitoring Archive for HAPs. These data
primarily come from AQS; however, they also come from non-AQS sources (e.g., air agencies that do not
submit some of their data to AQS, other federal agencies that collect relevant data, special studies, etc.).
For AirToxScreen, the exact locations of the monitors were used for the model-to-monitor comparison,
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an approach that increases accuracy over previous assessments. For more details about the model-to-
monitor analyses for previous assessments, see Comparison of 1996 ASPEN Modeling System Results to
Monitored Concentrations (EPA 2002c), Comparison of 1999 Model-Predicted Concentrations to
Monitored Data (EPA 2006b), Comparison of 2002 Model-Predicted Concentrations to Monitored Data
(EPA 2009) and Comparison of 2005 Model-Predicted Concentrations to Monitored Data (EPA 2010b).
Discrepancies between model predictions and concentration measurements can be attributed to five
sources of uncertainty:
emission characterization (e.g., specification of source location, emission rates and release
characterization);
meteorological characterization (e.g., representativeness);
model formulation and methodology (e.g., characterization of dispersion, plume rise, deposition,
chemical reactivity);
monitoring; and
boundary conditions/background concentrations.
Underestimates for some pollutants could be a result of the following:
The NEI may be missing specific emission sources.
Emission rates may be underestimated or overestimated due to emission-estimation techniques
and/or spatial allocation of national estimates to county, and county estimates to grid cells.
The accuracy of the monitor averages is uncertain; the monitors, in turn, have their own sources of
uncertainty. Sampling and analytical uncertainty, measurement bias and temporal variational! can
cause the ambient concentrations to be inaccurate or imprecise representations of the true
atmospheric averages.
Exposure. Sources of uncertainty in the relationship between ambient air concentrations and ECs
include those associated with microenvironmental factors and activity patterns. HAPEM calculates the
EC in various microenvironments (e.g., indoors at home, in a car) based on inputs of predicted ambient
air concentrations and microenvironmental factors. The factors are characterized as probability
distributions to reflect the variability found in air toxics measurements more fully. For many air toxics,
the measurement studies needed to estimate microenvironmental factors are not available, so the
values used are based on measurement studies of similar compounds in similar situations. This practice
introduces uncertainty into the estimation of ECs for such compounds. In addition, even for air toxics
with measurement studies, the estimated microenvironmental factors have some uncertainty because
the number of such studies is limited. Furthermore, the uniform application of the microenvironmental
factors to all census tracts introduces uncertainty by not accounting for possible geographic differences
among tracts (e.g., different window-opening behavior, different levels of buildingintegrity).
The activity-pattern sequences for individuals used in HAPEM are based on the Consolidated Human
Activity Database (CHAD). As explained in Section 4.3.3 HAPEM algorithms consider the variability in
activity patterns among individuals within a cohort-tract combination. They do this largely by addressing
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correlation between subsequent activity patterns assumed to occur for each cohort-tract combination.
The representativeness of the daily diaries in CHAD is uncertain because they are a compilation of many
studies, including some older studies and some for which the data are based on non-random sampling.
It is also uncertain how well the model algorithms represent actual daily autocorrelation between types
of activity. This latter issue, however, pertains only to the variability of the ECs across the demographic
group and not the median EC, which is the concentration reported by AirToxScreen.
The commuting data used in HAPEM are based on an EPA analysis of information from a special study by
the U.S. Census Bureau (2020). HAPEM uses this information, reflecting 2020 data, in coordination with
the activity-pattern data to place an individual in either the home tract or work tract at each time step.
These data introduce some uncertainty because they simplify commuting patterns to a pair of home and
work census tracts and may not reflect certain details of some commutes (e.g., the additional census
tracts encountered by commuters who travel to non-adjacent tracts or more complex commuting
patterns that are not point to point). An additional important consideration is that the commuting-
pattern data included in HAPEM do not account for the movement of school-age children who travel (or
commute) to a school located outside the tracts in which they reside.
Risk. Concerning the predictions of risk, the specific sources of uncertainty in dose-response
relationships (in addition to those considered for ambient air concentration and exposure) are hazard
identification, dose-response models for carcinogens, UREs and RfCs.
One component of predicting risk is hazard identification. AirToxScreen's cancer-risk estimates assume
that a compound either is a carcinogen or produces a noncancer effect. We base this on the results of a
hazard-identification stage that assesses the evidence that an air toxic produces either cancer or a
noncancer effect. Because the evidence for either judgment is never unequivocal, a compound labeled
as a carcinogen or one deemed to produce noncancer effects may produce neither effect in humans.
This possibility introduces uncertainty into the calculation of risk - the risk could be zero. As the
evidence for the original conclusion (i.e., that the compound produces the effect) increases, this
uncertainty decreases.
AirToxScreen's cancer-risk estimates assume that the relationship between exposure and probability of
cancer is linear. In other words, the probability of developing cancer is assumed proportional to the
exposure (equal to the exposure multiplied by a URE). This type of dose-response model is used
routinely in regulatory risk assessment because it is believed to be conservative; that is, if the model is
incorrect, it is more likely to lead to an overestimate of risks than to an underestimate. Other
scientifically valid, biologically based models are available. These produce estimates of cancer risk that
differ from those obtained from the linear model. Uncertainty in risk estimates is therefore introduced
by the inability to justify completely the use of one model or the other (because each model has some
scientific support). An essential consideration is that this uncertainty is, to some extent, one-sided. In
other words, conservatism when uncertainty exists allows more confidence in the conclusion that true
risks are less than predicted than in the conclusion that risks are greater than predicted.
URE parameters have associated uncertainty. In some cases, the UREs are based on maximum-likelihood
estimates of the slope of the dose-response relationship derived from reliable data. In other cases, the
UREs are based on "upper-bound" estimates (i.e., the slope is not the best estimate, but is a
conservative value that is likely to lead to overestimates of risk) derived from less reliable data. For
some compounds, the UREs are derived from human-exposure studies, but for others they are from
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animal exposures. These considerations introduce uncertainty into the URE values, and the amount of
uncertainty varies among pollutants.
Another source of uncertainty in estimating risk derives from the values chosen for the RfC parameters
used to calculate an HQfor noncancer health risk. The RfC, which (like the URE) is based on limited
information, is uncertain; as a result, the value of HQ is uncertain. As is the case for UREs, the
uncertainty in the RfC is generally one-sided, and the risk is unlikely to be greater than predicted.
7.4. Summary of Limitations in AirToxScreen
EPA developed this assessment to inform both national and more localized efforts to collect information
and characterize or reduce air toxics emissions (e.g., to prioritize pollutants or areas of interest for
monitoring and community assessments). As described above, uncertainty and variability characterize
many of the elements in the assessment process for AirToxScreen, as in other assessments that derive
results from environmental data and modeling of environmental data. Because of this, EPA suggests
exercising caution when using the results of these assessments, as the overall quality and uncertainty of
each assessment vary from location to location and from pollutant to pollutant. More localized
assessments, using local-scale monitoring and modeling, are often needed to better characterize local-
level risks.
Recognizing the specific limitations in AirToxScreen results is critical to interpreting and using them
properly, including that the results:
apply to geographic areas, not specific locations;
do not include comprehensive impacts from sources in Canada or Mexico;
are restricted to the year to which the assessment pertains (because the assessment usesemissions
data only from that year);
do not reflect exposures and risk from all compounds;
do not reflect all pathways of exposure;
reflect only compounds released into the outdoor air;
do not fully capture variations in background ambient airconcentrations;
may underestimate or overestimate ambient air concentrations for some compounds due to spatial
uncertainties;
are based on default, or simplifying, assumptions where data are missing or of poor quality; and
may not accurately capture sources with episodic emissions or other uncertainties.
The results apply to geographic areas, not specific locations. The assessment focuses on variations in air
toxics concentrations, exposures and risks among areas such as census tracts, counties and states. All
questions asked, therefore, must focus on the variations among different areas. They cannot be used to
identify "hot spots" where concentrations, exposures or risks may be significantly higher than other
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locations. Furthermore, this type of modeling assessment cannot address the kinds of questions an
epidemiology study might, such as the relationship between cancer risks and proximity of residences to
point sources, roadways and other sources of pollutant emissions.
The results do not include comprehensive impacts from sources in Canada or Mexico. The AirToxScreen
results for states that border these countries do not thoroughly reflect these potentially significant
sources of transported emissions.
The results apply to groups, not to specific individuals. Within a census block, all individuals are assigned
the same ambient concentration, chosen to represent a typical concentration.
Similarly, the exposure assessment uses activity patterns that do not fully reflect variations among
individuals. As a result, the exposures and risks in a census block should be interpreted as typical values
rather than as means, medians or some other statistical average. The values are likely to be in the
midrange of values for all individuals in the census block.
The results for the current AirToxScreen are restricted to 2020 because the assessment used emissions
data from that year. Also, the assumption regarding emissions in the assessment is that the levels
remain constant throughout one's lifetime (the emissions are not today's levels, nor are they projected
levels). Emissions continue to decrease, however, as (1) mobile-source regulations are phased in over
time, (2) EPA-issued air toxics regulations for major industrial sources reach compliance due dates, (3)
state and industry initiatives to reduce air pollutants continue and (4) some facilities are closed or have
made process changes or other changes that have significantly reduced their emissions since 2020.
The results do not reflect exposures and risk from all compounds. Only 138 of the 181 air toxics (180
CAA HAPs plus diesel PM) modeled in AirToxScreen have dose-response values. The remaining air toxics
are not considered in the aggregate cancer risk or target-organ-specific hazard indexes. Of significance is
that the assessment does not quantify cancer risk from diesel PM, although EPA has classified diesel PM
as likely to be carcinogenic to humans by inhalation from environmental exposures. Currently, a URE for
diesel PM has not yet been derived; therefore, a quantitative estimate of the cancer risks was included
in AirToxScreen. An IRIS RfCfor diesel PM allows AirToxScreen to include a quantitative estimate of its
noncancer effects.
The results do not reflect all pathways of exposure. The assessment includes only risks from direct
inhalation of the emitted pollutants. It does not consider pollutants that might then deposit onto soil
and into water and food, and therefore enter the body through ingestion or skin contact. Consideration
of these routes of exposure could increase estimates of exposure and risk.
The assessment results reflect only compounds released into the outdoor air. The assessment does not
include exposure to pollutants produced indoors, such as from stoves or out-gassing from building
materials, or evaporative benzene emissions from cars in attached garages. For some compounds such
as formaldehyde, these indoor sources can contribute significantly to the total exposure for an
individual, even if only inhalation exposures are considered. In addition, the assessment does not
consider pollutants released directly to water and soil. It does include secondary formation, the
transformation of one pollutant into another in the atmosphere.
The assessment does not use CMAQ for all pollutants and hence may not appropriately estimate long-
range transport for these non-CMAQ pollutants. For pollutants not estimated in CMAQ, the assessment
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uses background ambient air concentrations based on remote concentration estimates, but these would
not account for variations due to regional transport of these pollutants.
AirToxScreen may underestimate or overestimate ambient air concentrations for some compounds in
some locations due to spatial uncertainty in mobile and nonpoint emissions, which are more uncertain
at finer geographic scales.
The assessment uses default, or simplifying, assumptions where data are missing or of poor quality. Data
for some variables used in the modeling for emissions and dispersion of pollutants (such as stack height
and facility location) may be unavailable or flawed. In such instances, these values are replaced by
default assumptions. For example, a stack height for a facility might be set equal to stack heights at
comparable facilities or the location of the release points within a facility might be placed at the center
of the facility. These substitutions introduce uncertainty into the final predictions of ambient
concentrations, exposures and risks.
AirToxScreen may not accurately capture sources with episodic emissions except for those with
continuous emissions monitoring (CEMS) data, which use hourly emissions. AirToxScreen also does not
include any short-term (a few days or weeks) deviations from a facility's typical emissions pattern, such
as during startups, shutdowns, malfunctions and upsets. AirToxScreen modeling uses temporal profiles
for sources without CEMS that would not capture non-routine emissions spikes.
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8. References
Akhtar, F., Henderson, B., Appel, W., Napelenok, S., Hutzell, B., Pye, H. and Foley, K. 2012. Multiyear
Boundary Conditions for CMAQ 5.0 from GEOS-Chem with Secondary Organic Aerosol
Extensions. 11th Annual Community Modeling and Analysis System Conference, Chapel Hill, NC,
October 2012.
Appel, K.W., Gilliam, R.C., Davis, N., Zubrow, A. and Howard, S.C. 2011. Overview of the Atmospheric
Model Evaluation Tool (AMET) vl.l for Evaluating Meteorological and Air Quality Models.
Environ. Modell. Softw., 26(4): 434-443. Available online at http://www.cmascenter.org/.
Appel, K.W., Napelenok, S., Hogrefe, C., Pouliot, G., Foley, K.M., Roselle, S.J., Pleim, J.E., Bash, J., Pye,
H.O.T., Heath, N., Murphy, B., Mathur, R. 2018. Overview and evaluation of the Community
Multiscale Air Quality Model (CMAQ) modeling system version 5.2. In Mensink C., Kallos G.
(eds), Air Pollution Modeling and its Application XXV. ITM 2016. Springer Proceedings in
Complexity. Springer, Cham. Available online at https://doi.org/10.1007/978-3-319-57645-9 11.
ATSDR. 2012. (Agency for Toxic Substances & Disease Registry). Toxicological Profile for Manganese.
Available online at http://www.atsdr.cdc.gov/toxprofiles/tp.asp?id=102&tid=23.
ATSDR. 2015. Toxic Substances Portal Minimal Risk Levels (MRLs) for Hazardous Substances. Available
online at https://www.atsdr.cdc.gov/mrls/index.html. Last updated 28 October 2015. Last
accessed 10 December 2015.
Bey, I., Jacob, D.J., Yantosca, R.M., Logan, J.A., Field, B.D., Fiore, A.M., Li, Q., Liu, H.Y., Mickley, L.J.,
Schultz, M.G. 2001. Global modeling of tropospheric chemistry with assimilated meteorology:
Model description and evaluation. Journal of Geophysical Research 106, 23073.
https://doi.org/10.1029/2001JD0008Q7.
Caldwell, J.C., Woodruff, T.J., Morello-Frosch, R. and Axelrad, D.A. 1998. Application of Health
Information to Hazardous Air Pollutants Modeled in EPA's Cumulative Exposure Project.
Toxicology and Industrial Health, 14(3): 429-454.
Cimorelli, A.J., Perry, S.G., Venkatram, A., Weil, J.C., Paine, R.J., Wilson, R.B., Lee, R.F., Peters, W.D. and
Brode, R.W. 2005. AERMOD: A Dispersion Model for Industrial Source Applications. Part I:
General Model Formulation and Boundary Layer Characterization. Journal of Applied
Meteorology, 44: 682-693.
Cook, R., Phillips, S., Houyoux, M., Dolwick, P., Mason, R., Yanca, C., Zawacki, M., Davidson, K., Michaels,
H., Harvey, C., Somers, J. and Luecken, D. 2011. Air Quality Impacts of Increased Use of Ethanol
under the United States' Energy Independence and Security Act. Atmospheric Environment, 45:
7714-7724.
Dorman D.C., Struve M.F., Wong B.A., Marshall M.W., Gross E.A., and Willson G.A., 2008. Respiratory
tract responses in male rats following subchronic acrolein inhalation. Inhal Toxicol 20(3): 205-16.
Doumbia, T., Granier, C., Elguindi, N., Bouarar, I., Darras, S., Brasseur, G., Gaubert, B., Liu, Y., Shi, X.,
Stavrakou, T., Tilmes, S., Lacey, F., Deroubaix, A., and Wang, T., 2021. Changes in global air
AirToxScreen 2020 Documentation
134
-------
pollutant emissions during the COVID-19 pandemic: a dataset for atmospheric modeling, Earth
Syst. Sci. Data, 13, 4191-4206, https://doi.org/10.5194/essd-13-4191-2021.
East, J.D., Henderson, B. H., Napelenok, S. L., Koplitz, S. N., Sarwar, G., Gilliam, R., Lenzen, A., Tong, D.,
Pierce, R. B., Garcia-Menendez, F., 2022. Inferring and evaluating satellite-based constraints on
NOx emissions estimates in air quality simulations. Atmospheric Chemistry and Physics, Vol 22,
issue 24, 15981-16001, https://doi.org/10.5194/acp-22-15981-2022.
EPA (U.S. Environmental Protection Agency). 1986. Guidelines for Mutagenicity Risk Assessment.
EPA/630/R-98/003. EPA, Washington, DC. Available online at
https://www.epa.gov/sites/default/files/2013-09/documents/mutagen2.pdf. Last accessed 10
December 2015.
EPA. 1991. Guidelines for Developmental Toxicity Risk Assessment. EPA/600/R-91/001. EPA,
Washington, DC. Available online at https://www.epa.gov/sites/default/files/2014-
11/documents/dev tox.pdf. Last accessed 26 October 2015.
EPA. 1993. Integrated Risk Information System Review of Manganese. Available online at
https://cfpub.epa.gov/ncea/iris2/chemicalLanding.cfmPsubstance nmbr=373.
EPA. 1994. Methods for Derivation of Inhalation Reference Concentrations and Application of Inhalation
Dosimetry. EPA/600/8-90/066F. EPA Office of Research and Development (ORD), Washington,
DC. Available online at https://www.epa.gov/sites/default/files/2014-
11/documents/rfc methodology.pdf. Last accessed 27 October 2015.
EPA. 1996. Guidelines for Reproductive Toxicity Risk Assessment. EPA/630/R-96/009. EPA, Washington,
DC. Available online at https://www.epa.gov/sites/default/files/2014-
11/documents/guidelines repro toxicitv.pdf. Last accessed 10 December 2015.
EPA. 1997. Health Effects Assessment Summary Tables (HEAST). EPA NCEA, Washington, DC. Available
online at http://cfpub.epa.gov/ncea/cfm/recordisplav.cfm?deid=2877.
EPA. 1998. Guidelines for Neurotoxicity Risk Assessment. EPA/630/R-97/0. EPA, Washington, DC.
Available online at https://www.epa.gov/sites/default/files/2014-
11/documents/neuro tox.pdf. Last accessed 10 December 2015.
EPA. 2000. Supplementary Guidance for Conducting Health Risk Assessment of Chemical Mixtures.
EPA/630/R-00/002. Risk Assessment Forum, Washington, DC. Available online at
http://ofmpub.epa.gov/eims/eimscomm.getfilePp download id=4486. Last accessed 8
December 2015.
EPA. 2001a. NATA - Evaluating the National-scale Air Toxics Assessment 1996 Data - An SAB Advisory.
EPA/SAB/EC/ADV-02/001. Science Advisory Board, Washington, DC. Available online at
http://archive.epa.gov/airtoxics/nata/web/pdf/sabreptl201.pdf. Last accessed 29 November
2015.
EPA. 2001b. National-scale Air Toxics Assessment for 1996. Draft for EPA Science Advisory Board Review:
January 18, 2001. EPA-453/R-01-003. EPA Office of Air Quality Planning and Standards (OAQPS),
AirToxScreen 2020 Documentation
135
-------
Research Triangle Park, NC. Available online at
http://archive.epa.gov/airtoxics/nata/web/html/sabrev.html. Last accessed 29 November 2015.
EPA. 2002a. Health Assessment Document for Diesel Engine Exhaust. EPA/600/8-90/057F. EPA ORD/
National Center for Environmental Assessment (NCEA), Washington, DC. Available online at
http://cfpub.epa.gov/ncea/cfm/recordisplav.cfmPdeich29060. Last accessed 4 December 2015.
EPA. 2002b. The HAPEM User's Guide Hazardous Air Pollutant Exposure Model, Version 4. EPA OAQPS,
Research Triangle Park, NC. Available online at
http://archive.epa.gov/airtoxics/nata/web/zip/hapem4guide4.zip. Last accessed 29 November
2015.
EPA. 2002c. Comparison of ASPEN Modeling System Results to Monitored Concentrations. EPA.
Available online at http://archive.epa.gov/airtoxics/nata/web/html/mtom pre.html. Last
updated 22 October 2015. Last accessed 30 November 2015.
EPA. 2003a. Integrated Risk Information System Review of Acrolein. Available online at
https://cfpub.epa.gov/ncea/iris2/chemicalLanding.cfmPsubstance nmbr=364.
EPA. 2003b. Framework for Cumulative Risk Assessment. EPA/630/P-02/001F. EPAORD/NCEA,
Washington, DC. Available online at https://www.epa.gov/sites/default/files/2014-
11/documents/frmwrk cum risk assmnt.pdf. Last accessed 26 October 2015.
EPA. 2004a. Air Toxics Risk Assessment Reference Library. Volume 1: Technical Resource Manual. EPA-
453/K-04-001A. EPA OAQPS, Research Triangle Park, NC. Available online at
https://www.epa.gov/sites/default/files/2013-08/documents/volume 1 reflibrarv.pdf. Last
accessed 2 December 2015.
EPA. 2004b. Air Toxics Risk Assessment Reference Library. Volume 2: Facility-Specific Assessment. EPA-
453/K-04-001B. EPA OAQPS, Research Triangle Park, NC. Available online
https://www.epa.gov/sites/default/files/2013-08/documents/volume 2 facilitvassess.pdf. Last
accessed 2 December 2015.
EPA. 2005a. Guidelines for Carcinogen Risk Assessment. EPA/630/P-03/001F. EPA, Washington, DC.
Available online at https://www.epa.gov/sites/default/files/2013-
09/documents/cancer guidelines final 3-25-05.pdfLast accessed 26 October 2015.
EPA. 2005b. Supplemental Guidance for Assessing Susceptibility from Early-Life Exposure to
Carcinogens. EPA/630/R-03/003F. EPA, Washington, DC. Available online at
https://www.epa.gov/sites/default/files/2013-09/documents/childrens supplement final.pdf.
Last accessed 4 December 2015.
EPA. 2005c. The HAPEM User's Guide Hazardous Air Pollutant Exposure Model, Version 5. EPA OAQPS,
Research Triangle Park, NC. Available online at http://www.epa.gov/fera/hazardous-air-
pollutant-exposure-model-hapem-users-guides. Last accessed 29 November 2015.
EPA. 2006a. Air Toxics Risk Assessment Reference Library. Volume 3: Community-Scale Assessment. EPA-
453/K-06-001C. EPA OAQPS, Research Triangle Park, NC. Available online at
AirToxScreen 2020 Documentation
136
-------
https://www.epa.gov/sites/default/files/2013-08/documents/volume 3 communitvassess.pdf.
Last accessed 2 December 2015.
EPA. 2006b. Comparison of 1999 Model-Predicted Concentrations to Monitored Data. EPA OAQPS.
Available online at http://archive.epa.gov/airtoxics/natal999/web/html/99compare.html. Last
updated 13 September 2015. Last accessed 30 November 2015.
EPA. 2007a. Control of Hazardous Air Pollutants from Mobile Sources Regulatory Impact Analysis.
EPA420-R-07-002. EPA Office of Transportation and Air Quality (OTAQ) Assessment and
Standards Division, Ann Arbor, Ml. Available online at
https://nepis.epa.gov/Exe/ZyPdf.cgi?Dockev=P1004LNN.PDF. Last accessed 16 July 2018.
EPA. 2007b. The HAPEM User's Guide Hazardous Air Pollutant Exposure Model, Version 6. EPA OAQPS,
Research Triangle Park, NC. Available online at https://www.epa.gov/fera/hazardous-air-
pollutant-exposure-model-hapem-users-guides. Last accessed 29 November 2015.
EPA. 2009. Comparison of 2002 Model-Predicted Concentrations to Monitored Data. EPA OAQPS.
Available online at http://archive.epa.gov/nata2002/web/html/compare.html. Last accessed 30
November 2015.
EPA. 2010a. Development of a Relative Potency Factor (RPF) Approach for Polycyclic Aromatic
Hydrocarbon (PAH) Mixtures: In Support of Summary Information of the Integrated Risk
Information System (IRIS) (External Review Draft). EPA/635/R-08/012A. EPA, Washington, DC.
Available online at http://cfpub.epa.gov/ncea/iris drafts/recordisplav.cfm?deid=194584. Last
accessed 19 October 2015.
EPA. 2010b. Results of the 2005 NATA Model-to-Monitor Comparison, Final Report. Prepared by Eastern
Research Group for EPA OAQPS. Available online at
https://www.epa.gov/sites/default/files/2021-
03/documents/dav3techregioommenmodeltomonitorcomparison.pdf. Last accessed 30
November 2015.
EPA. 2011. Toxicological Review of Trichloroethylene. EPA NCEA, Washington, DC. Available online at
https://cfpub.epa.gov/ncea/iris/search/index.cfm?kevword=trichloroethylene. Last updated 11
September 2011.
EPA. 2016a. Overview by Section of CAA. EPA OAR, Washington, DC. Available online at
https://www3.epa.gov/airtoxics/overview.html. Last updated 23 February 2016. Last accessed
23 May 2024.
EPA. 2016b. Products and Publications Relating to Risk Assessment Produced by the Office of the
Science Advisor (OSA). EPA OSA, Washington, DC. Available online at
http://www.epa.gov/osa/products-and-publications-relating-risk-assessment-produced-office-
science- advisor. Last updated 23 August 2016. Last accessed 16 August 2018.
EPA. 2017. Revisions to the Guideline on Air Quality Models: Enhancements to the AERMOD Dispersion
Modeling System and Incorporation of Approaches To Address Ozone and Fine Particulate
Matter. 82 Federal Register 10 (17 January 2017), pp. 5182-5231.
AirToxScreen 2020 Documentation
137
-------
EPA. 2018. Initial List of Hazardous Air Pollutants with Modifications. EPA Office of Air and Radiation
(OAR), Washington, DC. Available online at https://www.epa.gov/haps/initial-list-hazardous-air-
pollutants-modifications. Last updated 16 March 2017. Last accessed 18 June 2018.
EPA. 2020. AERSUFACE. Available online at: https://www.epa.gov/scram/air-qualitv-dispersion-
modeling-related-model-support-programs#aersurface. Last updated 29 February 2020. Last
accessed 23 May 2024.
EPA. 2022. Technical Support Document: EPA's Air Toxics Screening Assessment: 2018 AirToxScreen TSD.
EPA-454/B-22-002. August 2022. EPA, Research Triangle Park, NC.
EPA. 2023a. Technical Support Document (TSD): Preparation of Emissions Inventories for the 2020 North
American Emissions Modeling Platform. EPA-454/B-23-004. December 2023. Available at:
https://www.epa.gov/system/files/documents/2023-12/2020 emismod tsd dec2023 4.pdf.
EPA, 2023b. Guideline on Air Quality Models. 40 CFR Part 51 Appendix W.
EPA. 2023c. Guidance on the Use of the Mesoscale Model Interface Program (MMIF) for AERMOD
applications. EPA-454/B-24-006. October 2023. EPA, Research Triangle Park, NC.
EPA. 2023d. User's Guide for the AMS/EPA Regulatory Model - AERMOD. EPA-454/B-23-008. October
2023. EPA, Research Triangle Park, NC.
EPA. 2024a. Criteria Air Pollutants. EPA, Washington, DC. Available online at
https://www.epa.gov/criteria-air-pollutants. Last updated 2 May 2024. Last accessed 17 May
2024.
EPA. 2024b. Risk Assessment Guidance and Tools. EPA, Washington, DC. Available online at
https://www.epa.gov/risk/risk-assessment-guidance. Last updated 19 January 2024. Last
accessed 17 May 2024.
EPA. 2024c. The HAPEM User's Guide Hazardous Air Pollutant Exposure Model, Version 8. EPA OAQPS,
Research Triangle Park, NC. Available online at http://www.epa.gov/fera/hazardous-air-
pollutant-exposure-model-hapem-users-guides. Last accessed 17 May 2024.
EPA. 2024d. Consolidated Human Activity Database (CHAD). EPA, Washington, DC. Available online at
https://www.epa.gov/healthresearch/consolidated-human-activity-database-chad-use-human-
exposure-and-health-studies-and. Last updated 17 September 2023. Last accessed 17 May 2024.
EPA. 2024a. The HAPEM User's Guide Hazardous Air Pollutant Exposure Model, Version 8. EPA OAQPS,
Research Triangle Park, NC. Available online at http://www.epa.gov/fera/hazardous-air-
pollutant-exposure-model-hapem-users-guides. Last accessed 17 May 2024.
EPA. 2024b. Consolidated Human Activity Database (CHAD). EPA, Washington, DC. Available online at
https://www.epa.gov/healthresearch/consolidated-human-activity-database-chad-use-human-
exposure-and-health-studies-and. Last updated 17 September 2023. Last accessed 17 May 2024.
ERG. 2017. Improvement of Default Inputs for MOVES and SMOKE-MOVES: CRC Project A-100. February
28, 2017. Prepared for: The Coordinating Research Council. ERG No. 4020.00.001.001.
AirToxScreen 2020 Documentation
138
-------
Eyth, Alison, J. Vukovitch, M. Strum, P. Dolwick, G. Pouliot, C. Allen, J. Beidler, B. Baek and Z. Adeleman.
2016. "Development of 2011 Hemispheric Emissions for CMAQ," Presented at the 2016 CMAS
Conference, October 24, 2016, University of North Carolina, Chapel Hill.
Feron, V.J., Kryusse, A., Til H.P., et al. 1978. Repeated exposure to acrolein vapor: subacute studies in
hamsters, rats and rabbits. Toxicology 9:47-57.
Friedman, C.L., Y. Zhang, and N.E. Selin, 2014. Climate change and emissions impacts on atmospheric
PAH transport to the Arctic, Environ. Sci. Technol., 48 (1), 429-437, doi:10.1021/es403098w.
Garshick, E., Laden, F., Hard, J.E., Davis, M.E., Eisen, E.A. and Smith, T.J. 2012. Lung Cancer and
Elemental Carbon Exposure in Trucking Industry Workers. Environmental Health Perspectives,
120:1301-1306. Available online at http://dx.doi.org/10.1289/ehp.1204989. Last accessed 10
December 2015.
Gilliam, R.C., Pleim, J.E., 2010. Performance Assessment of New Land Surface and Planetary Boundary
Layer Physics in the WRF-ARW. Journal of Applied Meteorology and Climatology 49, 760-774.
Guenther, A.B., Jiang, X., Heald, C.L., Sakulyanontvittaya, T., Duhl, T., Emmons, L.K., and Wang, X, 2012.
The Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1): an
extended and updated framework for modeling biogenic emissions. GMD, Volume 5, Issue 6,
1471-1492.
Global Modeling and Assimilation Office (GMAO), 2015. lnst3_3d_asm_Cp; MERRA-2 IAU State
Meteorology Instantaneous 3-hourly (p-coord, 0.625x0.5L42), version 5.12.4, Greenbelt, MD,
USA: Goddard Space Flight Center (GSFC DAAC). Doi: 10.5067/VJAFPL1CSIV.
HEI (Health Effects Institute). 2015. Diesel Emissions and Lung Cancer: An Evaluation of Recent
Epidemiological Evidence for Quantitative Risk Assessment. HEI, Boston, MA. Available online at
https://www.healtheffects.org/publication/diesel-emissions-and-lung-cancer-evaluation-recent-
epidemiological-evidence-quantitative. Last updated 24 November 2015. Last accessed 10
December 2015.
Henderson, B.H., Akhtar, F., Pye, H.O.T., Napelenok, S.L. and Hutzell, W.T. 2014. A Database and Tool for
Boundary Conditions for Regional Air Quality Modeling: Description and Evaluation. Geosci.
Model Dev., 7:339-360.
Hoesly, R. M., Smith, S. J., Feng, L., Klimont, Z., Janssens-Maenhout, G., Pitkanen, T., Seibert, J. J., Vu, L.,
Andres, R. J., Bolt, R. M., Bond, T. C., Dawidowski, L., Kholod, N., Kurokawa, J.-I., Li, M., Liu, L., Lu,
Z., Moura, M. C. P., O'Rourke, P. R., and Zhang, Q., 2018: Historical (17502014) anthropogenic
emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS),
Geosci. Model Dev., 11, 369-408, doi: 10.5194/gmd-ll-369-2018.
IARC (International Agency for Research on Cancer). 2014. IARC Monographs on the Evaluation of
Carcinogenic Risks to Humans, Volume 105 (2014). Available online at
http://monographs.iarc.fr/ENG/Monographs/voll05/index.php. Last accessed 10 December
2015.
AirToxScreen 2020 Documentation
139
-------
Isakov, V., Irwin., J. and Ching, J.K. 2007. Using CMAQ for Exposure Modeling and Characterizing the Sub-
grid Variability for Exposure Estimates. Journal of Applied Meteorology and Climatology,
46:1354-1371.
Janssens-Maenhout, G., Dentener, F., Van Aardenne, J., Monni, S., Pagliari, V., Orlandini, L., Klimont, Z.,
Kurokawa, J., Akimoto, H., Ohara, T., others, 2012. EDGAR-HTAP: a harmonized gridded air
pollution emission dataset based on national inventories. European Commission Publications
Office, Ispra (Italy). JRC68434, EUR report No EUR 25, 299-2012.
Kang, D.; Willison, J.; Sarwar, G.; Madden, M.; Hogrefe, C.; Mathur, R.; Gantt, B. and Saiz-Lopez, A., 2021
(October). Improving the Characterization of Natural Emissions in CMAQ, Environmental
Manager, A&WMA.
Keller, C.A., M.S. Long, R.M. Yantosca, A.M. Da Silva, S. Pawson, and D.J. Jacob, 2014. HEMCO vl.0: A
versatile, ESMF-compliant component for calculating emissions in atmospheric models, Geosci.
Model Devel., 7, 1409-1417, doi:10.5194/gmd-7-1409-2014.
Luecken, D. J., Yarwood, G., and Hutzell, W. T, 2019. Multipollutant modeling of ozone, reactive nitrogen
and HAPs across the continental US with CMAQ-CB6, Atmos Environ, 201, 62-72,
10.1016/j.atmosenv.2018.11.060.
Ma, L-M. and Tan, Z-M, 2009. Improving the behavior of the Cumulus Parameterization for Tropical
Cyclone Prediction: Convection Trigger. Atmospheric Research 92 Issue 2, 190-211.
http://www.sciencedirect.com/science/article/pii/S0169809508002585.
Mathur, R., Xing, J., Gilliam, R., Sarwar, G., Hogrefe, C., Pleim, J., Pouliot, G., Roselle, S., Spero, T. L.,
Wong, D. C., and Young, J, 2017. Extending the Community Multiscale Air Quality (CMAQ)
modeling system to hemispheric scales: overview of process considerations and initial
applications, Atmos Chem Phys, 17, 12449-12474, 10.5194/acp-17-12449-2017.
National Centers for Environmental Prediction/National Weather Service/NOAA/U.S. Department of
Commerce. 2015, updated daily. NCEP GFS 0.25 Degree Global Forecast Grids Historical Archive.
Research Data Archive at the National Center for Atmospheric Research, Computational and
Information Systems Laboratory. https://doi.org/10.5065/D65D8PWK.
NRC (National Research Council). 1983. Risk Assessment in the Federal Government: Managing the
Process. Committee on the Institutional Means for Assessments of Risk to Public Health,
Commission on Life Sciences. NRC. National Academy Press, Washington, DC.
NRC. 1994. Science and Judgment in Risk Assessment. Committee on Risk Assessment of Hazardous Air
Pollutants, Board on Environmental Sciences and Technology, Commission on Life Sciences.
NRC. National Academy Press, Washington, DC.
NUATRC (Mickey Leland National Urban Air Toxics Research Center). 2011. Available online at
https://cfpub.epa.gov/ncer abstracts/index.cfm/fuseaction/outlinks.centers/center/136. Last
updated 30 December 2011. Last accessed 10 December 2015.
OEHHA (Office of Environmental Health Hazard Assessment, California). 2008. California Environmental
Protection Agency. Technical Support Document for the Derivation of Noncancer Reference
AirToxScreen 2020 Documentation
140
-------
Exposure Levels. Available online at
https://oehha.ca.gov/media/downloads/crnr/appendixdlfinal.pdf.
OEHHA. 2015. Hot Spots Guidelines. OEHHA, Sacramento, CA. Available online at
http://www.oehha.ca.gov/air/hot spots/index.html. Contents last updated 6 March 2015.
OEHHA. 2016. Air Toxicology and Epidemiology. OEHHA, Sacramento, CA. Available online at
http://www.oehha.ca.gov/air/allrels.html. Values last updated 28 June 2016.
Otte T.L. and Pleim, J.E. 2010. The Meteorology-Chemistry Interface Processor (MCIP) for the CMAQ
Modeling System: Updates through v3.4.1. Geoscientific Model Development, 3:243-256.
Philip, S., Martin, R.V., Snider, G., Weagle, C.L., van Donkelaar, A., Brauer, M., Henze, D.K., Klimont, Z.,
Venkataraman, C., Guttikunda, S.K., and Zhang, Q., April 2017. "Anthropogenic fugitive,
combustion and industrial dust is a significant, underrepresented fine particulate matter source
in global atmospheric models." Environmental Research Letters; Bristol, Vol. 12, Iss. 4.
Doi:10.1088/1748-9326/aa65a4.
Powers, J. G., Klemp, J. B., Skamarock, W. C., Davis, C. A., Dudhia, J., Gill, D. O., Coen, J. L., Gochis, D. J.,
Ahmadov, R., Peckham, S. E., Grell, G. A., Michalakes, J., Trahan, S., Benjamin, S. G., Alexander,
C. R., Dimego, G. J., Wang, W., Schwartz, C. S., Romine, G. S., Liu, Z. Q., Snyder, C., Chen, F.,
Barlage, M. J., Yu, W., and Duda, M. G., 2017. THE WEATHER RESEARCH AND FORECASTING
MODEL Overview, System Efforts, and Future Directions, B Am Meteorol Soc, 98, 1717-1737,
10.1175/Bams-D-15-00308.1.
Pye, H.O. and Pouliot, G.A. 2012. Modeling the Role of Alkanes, Polycyclic Aromatic Hydrocarbons, and
Their Oligomers in Secondary Organic Aerosol Formation. Environ. Sci. Technol, 46, 11, 6041-
6047.
Roels HA, Ghyselen P, Buchet JP, et al. 1992. Assessment of the permissible exposure level to
manganese in workers exposed to manganese dioxide dust. Br J Ind Med 49:25-34.
Selin, N.E., D.J. Jacob, R.J. Park, R.M. Yantosca, S. Strode, L. Jaegle, and D. Jaffe, 2007. Chemical cycling
and deposition of atmospheric mercury: Global constraints from observations, J. Geophys. Res.,
112, D02308, doi:10.1029/2006JD007450.
Silverman, D.T., Samanic, C.M., Lubin, J.H., Blair, A.E., Stewart, P.A., Vermeulen, R., Coble, J.B., Rothman,
N., Schleiff, P.L., Travis, W.D., Ziegler, R.G., Wacholder, S. and Attfield, M.D. 2012. The Diesel
Exhaust in Miners Study: A Nested Case-Control Study of Lung Cancer and Diesel Exhaust.
Available online at http://dx.doi.org/10.1093/inci/dis034. Last accessed 10 December 2015.
Simone, N.W., Stettler, M.E.J., Barrett, S.R.H., 2013. Rapid estimation of global civil aviation emissions
with uncertainty quantification, Transportation Research Part D: Transport and Environment,
Volume 25, 33-41, ISSN 1361-9209, https://doi.Org/10.1016/j.trd.2013.07.001.
Simpson, D., 2018. Soil N emissions for 2000-present. (D81.3.6.1.) [dataset],
Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Barker, D.M., Duda, M.G., Huang, X., Wang, W. and
Powers, J.G. 2008. A Description of the Advanced Research WRF Version 3. Available online at
AirToxScreen 2020 Documentation
141
-------
https://openskv.ucar.edU/islandora/obiect/technotes:500. Last updated 5 December 2014. Last
accessed 16 December 2015.
Stammer, D., F.J. Wentz, and C.L. Gentemann, 2003, Validation of Microwave Sea Surface Temperature
Measurements for Climate Purposes, J. Climate, 16, 73-87.
U.S. Census Bureau. 2020. Decennial Census of Population and Housing. Available online at
https://www.census.gov/programs-surveys/decennial-census/decade/2020/2020-census-
main.html. Last accessed 23 May 2024.
Wesson, K., Fann, N., Morris, M., Fox, T. and Hubbell, B. 2010. A Multi-pollutant, Risk-based Approach to
Air Quality Management: Case Study for Detroit. Air Pollut. Res., 1:296-304.
doi:10.5094/APR. 2010.037.
Wiedinmyer, C., Akagi, S. K., Yokelson, R. J., Emmons, L. K., Al-Saadi, J. A., Orlando, J. J., and Soja, A. J.,
2011. The Fire INventory from NCAR (FINN): a high-resolution global model to estimate the
emissions from open burning, Geosci. Model Dev., 4, 625-641, 10.5194/gmd-4-625-2011.
Xu, L., Pye, H. O. T., He, J., Chen, Y. L., Murphy, B. N., and Ng, N. L., 2018. Experimental and model
estimates of the contributions from biogenic monoterpenes and sesquiterpenes to secondary
organic aerosol in the southeastern United States, Atmos Chem Phys, 18, 12613-12637,
10.5194/acp-18-12613-2018.
Yarwood, G., J. Jung, G. Whitten, G. Heo, J. Mellberg and M. Estes. 2010: Updates to the Carbon Bond
Chemical Mechanismfor Version 6 (CB6). Presented at the 9th Annual CMAS Conference, Chapel
Hill, NC. Available at
https://www.cmascenter.org/conference/2010/abstracts/emerv updates carbon 2010.pdf.
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Appendix A. Glossary
"N"-in-1 million cancer risk:
A risk level of "N"-in-1 million implies that up to "N" people out of one million equally exposed people may contract
cancer if exposed continuously (24 hours per day) to the specific concentration over 70 years (an assumed lifetime).
For example, a 1 -in-1 million risk means that one person may develop cancer for every 1 million people exposed. A
10-in-1 million risk means 10 people may develop cancer per 1 million exposed, etc. These risks would be in addition
to cancer cases that would normally occur in one million unexposed people. Note that AirToxScreen looks
at lifetime cancer risks. This shouldn't be confused with or compared to annual cancer risk estimates. To compare an
annual cancer risk estimate with AirToxScreen results, multiply the annual estimate by 70 (or divide the lifetime risk
by 70).
Activity-pattern data:
Data that depict actual human physical activity, the location of the activity and the time of day it takes place. The
Hazardous Air Pollution Model (HAPEM) uses activity-pattern data from EPA's Comprehensive Human Activity
Database (CHAD).
Adverse health effect:
A change in body chemistry, body function or cell structure that could lead to disease or health problems.
Air Toxics Screening Assessment (AirToxScreen):
EPA's ongoing thorough review of air toxics in the United States. AirToxScreen results help scientists focus on
pollutants, emission sources and places that may need further study to better understand risks. AirToxScreen also
spurs improvements in what we know about U.S. air toxics. This includes expanding air toxics monitoring, improving
and updating emission inventories, improving air quality modeling, driving research on health effects and exposures
to both ambient and indoor air, and improving assessment tools.
AMS/EPA Regulatory Model (AERMOD):
EPA's preferred model to simulate near-field (i.e., within 50 km) dispersion of emissions. AERMOD models near-
surface (boundary-layer) air turbulence in simple and complex terrain. This allows AERMOD to simulate how
pollutants move and disperse in the air. It calculates pollutant concentrations from surface and elevated point, area,
line and volume sources at many discrete points (receptors).
Air toxics:
Pollutants known to cause or suspected of causing cancer or other serious health effects. Air toxics are also known
as toxic air pollutants or hazardous air pollutants.* Health concerns are linked to both short- and long-term exposures
to these pollutants. Many air toxics cause respiratory, neurological, immune or reproductive effects, particularly for
more susceptible or sensitive groups such as children. Five important air pollutants are not included in the list of air
toxics because the Clean Air Act addresses them separately as "criteria pollutants." These are particulate matter
(PM), nitrogen oxides (NOx), sulfur oxides (SOx), ozone and carbon monoxide. Lead is both a criteria pollutant and an
air toxic. Criteria pollutants are not addressed in AirToxScreen.
*Diesel particulate matter is not a hazardous air pollutant but is included in the AirToxScreen air toxics.
Ambient:
Surrounding, as in the surrounding environment. In AirToxScreen, ambient air refers to the outdoor air surrounding a
person through which pollutants can be carried. Therefore, the ambient concentrations estimated by AirToxScreen
are concentrations estimated in the outdoor environment. AirToxScreen also estimates exposure concentrations that
result when a person moves through various microenvironments, including the indoor environment.
Ambient air monitoring:
Process of collecting outdoor air samples to determine how much of an air pollutant is present at a location.
Monitoring is used to:
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• assess the extent of pollution;
• provide air pollution data to the public in a timely manner;
• help implement air quality goals or standards;
• evaluate whether emissions control strategies are effective;
• provide data on air quality trends;
• help evaluate air quality models or modeling results; and
• support research (for example, long-term studies of the health effects of air pollution).
State, local and tribal agencies often monitor near places where screening tools (such as AirToxScreen) suggest the
chance of high concentrations or risks.
Area and other sources:
Sources of air pollution that, by themselves, generally have lower emissions than "major sources" of air pollution (like
factories). Area sources are often too small or too widespread to be inventoried as individual sources. They include
facilities with air toxics emissions below the major source threshold as defined in the Clean Air Act (less than 10 tons
of a single toxic air pollutant or less than 25 tons of multiple toxic air pollutants emitted in any one year). Area sources
include smaller facilities, such as dry cleaners.
As a separate definition, area sources in air quality modeling refer to those modeled in two dimensions (with length
and width), as compared to point sources modeled at a single location.
Assessment System for Population Exposure Nationwide (ASPEN):
A computer model used to estimate toxic air pollutant concentrations. The ASPEN model includes:
• rate of pollutant release;
• location of and height from which the pollutants are released;
• wind speeds and directions from the meteorological stations nearest to release;
• breakdown of the pollutants in the atmosphere after release (i.e., reactive decay);
• settling of pollutants out of the atmosphere (i.e., deposition);
• transformation of one pollutant into another (i.e., secondary formation or decay).
Atmospheric transformation (secondary formation):
The process by which chemicals are transformed into other chemicals in the air (atmosphere). When a chemical is
transformed, the original pollutant no longer exists; it is replaced by one or more new chemicals. Compared to the
original chemical, the transformed chemical can have more, less or the same toxicity. Transformations and removal
processes affect both the fate of the chemical and how long it stays in the air, called its persistence. Persistence is
important because human exposure to a chemical depends on the length of time the chemical remains in the air. In
AirToxScreen, we use both "atmospheric transformation" and "secondary formation"; they mean the same thing.
Background concentrations:
The amount of a pollutant that exists in the air that does not come from a specific source. These pollutants may come
from a natural source or from distance sources. Background concentrations can explain pollutant concentrations
found even without recent human-caused emissions. In AirToxScreen, we add background concentrations to
AERMOD concentrations but not to CMAQ concentrations, which include background already. Most risk from
AirToxScreen background concentrations is from carbon tetrachloride, a common pollutant that has few emission
sources but is persistent due to its long half-life.
Biogenic emissions:
Biogenic emissions are emissions from natural sources, such as plants and trees. These sources emit formaldehyde,
acetaldehyde and methanol; formaldehyde and acetaldehyde are key risk drivers in AirToxScreen. Biogenic sources
also emit large amounts of other nonhazardous VOCs. We estimate biogenic emissions with a model that uses
vegetation and land use data with temperature and solar radiation data. In addition to being a primary source of air
toxics, compounds emitted by biogenic sources sometimes react with human-caused pollutants to form secondary
pollutants. The AirToxScreen biogenics source group includes only the primary emissions.
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Cancer Risk:
The probability of contracting cancer over the course of a lifetime, assuming continuous exposure (assumed in
AirToxScreen to be 70 years).
Carcinogen:
A chemical, physical or biological agent that can cause cancer.
Carcinogenicity:
Ability to produce cancer cells from normal cells.
Chemical Abstracts Service (CAS) number:
A unique number assigned to a chemical by the Chemical Abstracts Service, a service of the American Chemical
Society. The purpose is to make database searches easier, as chemicals often have many names.
Census blocks:
Statistical areas bounded by visible features such as roads, streams, and railroad tracks. Generally small in area. In a
city, a census block looks like a city block bounded on all sides by streets. Census blocks in suburban and rural areas
may be large, irregular, and bounded by a variety of features, such as roads, streams, and transmission lines. In
remote areas, census blocks may encompass hundreds of square miles. Census blocks are not delineated based on
population, and many census blocks do not have any population..
Census tracts:
Land areas defined by the U.S. Census Bureau. Tracts usually contain from 1,200 to 8,000 people, with most having
close to 4,000 people. Census tracts are usually smaller than 2 square miles in cities, but are much larger in rural
areas.
Cohort:
A group of people assumed to have identical exposures during a certain period. Using cohorts makes modeling
exposures of a large population easier to manage. In AirToxScreen, we divide the entire population into a set of
cohorts. Each person is assigned to one and only one cohort, and all the cohorts combined equal the entire
population.
Community Multiscale Air Quality (CMAQ) modeling system:
An air quality model used in AirToxScreen. CMAQ estimates how pollutants move and disperse in the air. It includes
the effect of atmospheric chemistry - how pollutants react in the air-a unique feature of the model. CMAQ's
structure allows it to calculate concentrations over a very large area, including many emission sources.
Concentration:
A way to describe how much of a pollutant is in the air. Concentration is usually shown as an amount, or mass, of
pollutant per certain volume of air. In AirToxScreen, most concentrations are in micrograms (|jg) of air pollutant per
cubic meter (m3) of air (a "box" of air one meter on each side).
Consolidated Human Activity Database (CHAD):
An in-depth EPA database of human activity. CHAD includes data from over 20 activity studies dating to 1982. It also
includes data from other assessments of human exposure, intake dose and risk.
Diesel particulate matter:
A mixture of particles that is part of diesel exhaust. EPA lists diesel exhaust as a mobile-source air toxic due to the
cancer and noncancer health effects linked to exposure to whole diesel exhaust. Diesel PM (expressed as grams
diesel PM/m3) has been used as a surrogate exposure measure for whole diesel exhaust.
Dispersion model:
A computerized set of equations that uses emissions and meteorological data to simulate how air pollutants behave
and move in the air. A dispersion model estimates outdoor concentrations of individual air pollutants at chosen
locations (called receptors).
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Emission Inventory System (EIS):
An EPA information system for collecting emission inventory data and generating emission inventories.
Emissions:
Pollutants released into the air.
Emission inventory:
A listing, by source, of the location and amount of air pollutants released into the air during some period (in
AirToxScreen, a single year).
Exposure assessment:
An exposure assessment is part of an air toxics risk assessment such as AirToxScreen. The assessment determines
(or estimates):
• how a person may be exposed to chemicals (for example, by breathing);
• how much of a chemical to which a person is likely to be exposed;
• how long and/or how often they will be exposed; and
• how many people are likely to be exposed.
HAP:
Hazardous air pollutant; another name for air toxics.
Hazard index (HI):
The sum of hazard quotients for toxics that affect the same target organ or organ system. Because different air toxics
can cause similar adverse health effects, combining hazard quotients from different toxics is often appropriate. As
with the hazard quotient, exposures below an HI of 1.0 likely will not result in adverse noncancer health effects over a
lifetime of exposure. An HI equal to or greater than 1.0, however, doesn't necessarily suggest a likelihood of adverse
effects.
Hazard quotient (HQ):
The ratio of the potential exposure to a substance and the level at which no adverse effects are expected (calculated
as the exposure divided by the appropriate chronic or acute value). A hazard quotient less than or equal to 1.0
indicates that adverse noncancer effects are not likely to occur, and thus can be considered to have negligible
hazard. For HQs greater than 1.0, the potential for adverse effects increases, but we do not know by how much.
Hazardous Air Pollutant Exposure Model (HAPEM):
A computer model designed to estimate inhalation exposure for specified population groups and air toxics. The model
uses census data, human-activity patterns, ambient air quality levels, and indoor/outdoor concentration relationships
to estimate an expected range of inhalation exposure concentrations for groups of people.
Human Exposure Model (HEM):
A computer model used primarily for conducting inhalation risk assessments for sources emitting air toxics to ambient
air.
Inhalation exposure:
Introducing air toxics (or other pollutants) into the body via breathing. Once inhaled, air toxics can be deposited in the
lungs, taken into the blood, or both.
Integrated Risk Information System (IRIS):
An EPA program that identifies and characterizes the health hazards of chemicals found in the environment. IRIS is
EPA's preferred source of toxicity information.
Lifetime cancer risk:
The probability of contracting cancer over the course of a lifetime (assumed to be 70 years for the purposes of
AirToxScreen).
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Major sources:
Defined by the Clean Air Act as those stationary facilities that emit or have the potential to emit 10 tons of any one
toxic air pollutant or 25 tons of more than one toxic air pollutant per year.
Median:
The middle value of a set of ordered values (i.e., half the numbers are less than or equal to the median value). A
median is the 50th percentile of the data.
Metropolitan statistical area (MSA):
A region with a relatively high population density at its core and close economic ties throughout the area. As defined
by the U.S. Census Bureau, an MSA must have at least one urban area of 50,000 or more inhabitants.
Microenvironment:
A small space in which human contact with a pollutant takes place. AirToxScreen models cohort activities in indoor,
outdoor and in-vehicle microenvironments:
1. Indoor locations:
• Residence
• Office
• Store
• School
• Restaurant
• Church
• Manufacturing facility
• Auditorium
• Healthcare facility
• Service station
• Other public building
• Garage
2. Outdoor locations:
• Parking lot/garage
• Near road
• Motorcycle
• Service station
• Construction site
• Residential grounds
• School
• Sports arena
• Park/golf course
3. In-vehicle locations:
• Car
• Bus
• Truck
• Train/subway
• Airplane
• Other
Microgram:
One-millionth of a gram. One gram is about one twenty-eighth of an ounce, or about the weight of a raisin or paper
clip.
Mobile source:
Air pollution sources that can move from place to place, like cars or trucks. Mobile sources are divided into two
categories: on-road and nonroad vehicles/engines.
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Monitoring:
See Ambient air monitoring.
Motor Vehicle Emission Simulator (MOVES):
An emissions modeling system that estimates emissions for mobile sources at the national, county, and project level
for criteria air pollutants, air toxics and greenhouse gases.
National Emissions Inventory (NEI):
A national database of air emissions data. EPA prepares NEI with input from many state and local air agencies, from
tribes and from industry. This database contains information on stationary and mobile sources that emit criteria air
pollutants and their precursors, as well as hazardous air pollutants. NEI includes estimates of annual emissions, by
source, of air pollutants in each area of the country. NEI includes emission estimates for all 50 states, the District of
Columbia, Puerto Rico and the U.S. Virgin Islands.
National Mobile Inventory Model (NMIM):
Computer tool containing EPA's NONROAD model for estimating county-level inventories of nonroad mobile
emissions.
Noncancer risks:
Risks associated with health effects other than cancer.
Nonroad mobile sources:
Mobile sources not used on roads and highways for transportation of passengers or freight. Nonroad sources include:
• aircraft;
• heavy equipment;
• locomotives;
• marine vessels;
• recreation vehicles (snowmobiles, all-terrain vehicles, etc.); and
• small engines and tools (lawnmowers, etc.).
On-road mobile sources:
Mobile sources used on roads and highways for transportation of passengers or freight. On-road sources include:
• passenger cars and trucks
• commercial trucks and buses; and
• motorcycles.
Percentile:
Any one of the points dividing a set of values into parts that each contain 1/100 of the values. For example, the 75th
percentile is a value such that 75 percent of the values are less than or equal to it.
Polycyclic organic matter (POM):
A broad class of compounds that includes polycyclic aromatic hydrocarbons. Polycyclic organic matter (POM)
compounds form mainly from combustion and are present in the air as particles. Sources of POM emissions include:
• vehicle exhaust;
• forest fires and wildfires;
• asphalt roads;
• coal;
• coal tar;
• coke ovens;
• agricultural burning;
• residential wood burning; and
• hazardous waste sites.
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Not all POM reported to EPA's National Emission Inventory is broken down by pollutant. So, we make some
simplifying assumptions to model and assess the risk from the different pollutants that make up polycyclic organic
matter.
Reference concentration (RfC):
An estimate of a continuous inhalation exposure unlikely to cause adverse health effects during a lifetime. This
estimate includes sensitive groups such as children, asthmatics and the elderly.
Risk:
The probability that adverse effects to human health or the environment will occur due to a given hazard (such as
exposure to a toxic chemical or mixture of toxic chemicals). We can measure or estimate some risks in numerical
terms (for example, one chance in a hundred).
Rural:
A county is considered "rural" if it does not contain a metropolitan statistical area with a population greater than
250,000 and the U.S. Census Bureau designates 50 percent or less of the population as "urban." Note that this
definition does not necessarily apply for any regulatory or implementation purpose. It is consistent with the definition
EPA used in the analyses to support the Integrated Urban Air Toxics Strategy.
Science Advisory Board (SAB):
A panel of scientists, engineers and economists who provide EPA with independent scientific and technical advice.
Secondary formation:
See "Atmospheric transformation (Secondary Formation)"
Secondary sources:
See "Atmospheric transformation (Secondary Formation)"
Sparse Matrix Operator Kernel Emissions (SMOKE):
A modeling system that processes emissions data for use in air quality models. It uses the Biogenic Emission
Inventory System (BEIS) to model biogenic emissions. It also has a feature to use MOVES emission factors, activity
data and meteorological data to compute hourly gridded on-road mobile emissions.
Stationary sources:
Sources of air emissions that do not move. Stationary sources include large industrial sources such as power plants
and refineries, smaller industrial and commercial sources such as dry cleaners, and residential sources such as
residential wood combustion and consumer products usage. Stationary sources may be "major" or "area" sources
based on definitions in the Clean Air Act. In AirToxScreen, we present sources as "point" and "nonpoint" rather than
"major" and "area" sources. "Point" and "nonpoint" reflect how we modeled each emission source. Some smaller
sources that are area sources in the inventory (based on the amount of their emissions) are modeled as point
sources because their location was identified with latitude and longitude coordinates.
Susceptibility:
The increased likelihood of an adverse effect. Susceptibility is often discussed in terms of relationship to a factor
describing a human population (for example, life stage, demographic feature or genetic trait).
Toxicity weighting:
A way to prioritize pollutant emissions based on risk. To calculate toxicity-weighted emissions, we multiply emissions
from a facility or source being assessed by a toxicity factor for each pollutant. Pollutants that are more harmful (for
the same emission rate) have a higher toxicity factor. By weighting the amount of a pollutant released to its toxicity,
we can compare relative risk from different pollutants in emission inventories. Toxicity weighting is very useful if the
number of pollutants is large, helping risk assessors focus on pollutants that contribute the most to risk.
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Typical:
Describes a hypothetical person living at a census tract centroid (a reference point usually but not always located at
the geographic center of a census tract) and doing the types of things (indoors and outdoors) that most people living
in that tract would do. To describe that person's risk, AirToxScreen divides the population into cohorts (groups
assumed to have the same exposures each day) based on where they live, how old they are and their daily activity
patterns. Fora census tract, we select age-appropriate activity patterns to model the range of exposure conditions for
residents of the tract. We can then calculate a population-weighted typical exposure estimate for each cohort. We use
this value to estimate risks for a "typical" individual residing in that tract.
Unit risk estimate (URE):
The upper-bound excess lifetime cancer risk estimated to result from continuous exposure to an air toxic at a
concentration of 1 microgram per cubic meter (|jg/m3) in air. You can interpret the URE as follows: If the URE = 3 x
10 s per |jg/m3, as many as three more people might be expected to develop cancer per one million people exposed
daily for a lifetime to 1 microgram (|jg) of the chemical in 1 cubic meter (m3) of air. UREs are considered upper-bound
estimates designed to keep us from underestimating risks. The true risk may be lower and is considered unlikely to
be higher. In AirToxScreen, we multiply the model-output concentrations for an air toxic by that pollutant's URE to
calculate exposure risks from that air toxic.
Upper-bound:
A likely upper limit to the true value of a quantity. This is usually not a true statistical confidence limit.
Upper-bound lifetime cancer risk:
A likely upper limit to the true probability that a person will contract cancer over a 70-year lifetime due to a given
hazard (such as exposure to a toxic chemical). This risk can be measured or estimated in numerical terms (for
example, one chance in a hundred).
Urban:
A county is considered "urban" if it either includes a metropolitan statistical area with a population greater than
250,000 or the U.S. Census Bureau designates more than 50 percent of the population as "urban." Note that this
definition does not necessarily apply for any regulatory or implementation purpose. It is consistent with the definition
EPA used in the analyses to support the Integrated Urban Air Toxics Strategy.
Volatile organic compounds (VOCs):
Chemicals emitted as gases from certain solids or liquids. VOCs are known for being common indoor air pollutants.
EPA regulates VOCs in the ambient air because some cause adverse health effects and because they can react with
other pollutants to form ozone and secondary air toxics. Cars and trucks, some industries, and even plants and trees
emit VOCs.
Weight-of-evidence for carcinogenicity (WOE):
A system used by the EPA for characterizing the extent to which available data support the hypothesis that an agent
causes cancer in humans. The approach, outlined in EPA's Guidelines for Carcinogen Risk Assessment (2005),
considers all scientific information in determining the WOE. Five standard descriptors are used as part of the WOE
narrative:
1. Carcinogenic to humans.
2. Likely to be carcinogenic to humans.
3. Suggestive evidence of carcinogenic potential.
4. Inadequate information to assess carcinogenic potential.
5. Not likely to be carcinogenic to humans.
Each of these descriptors is explained in its own glossary entry. You can read more details about WOE narratives in
the AirToxScreen Technical Support Document.
Carcinogenic to humans:
This descriptor indicates strong evidence of human carcinogenicity. It covers different combinations of evidence. This
descriptor is appropriate when there is convincing epidemiologic evidence of a link between human exposure and
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cancer. It might also be appropriate when a lesser weight of epidemiologic evidence is strengthened by other lines of
evidence.
This descriptor can be used when all of the following conditions are met:
• there is strong evidence of an association between human exposure and either cancer or the key precursor
events of the agent's mode of action, but not enough for a causal association;
• there is extensive evidence of carcinogenicity in animals;
• the mode(s) of carcinogenic action and associated key precursor events have been identified in animals;
and
• there is strong evidence that the key precursor events that precede the cancer response in animals are
anticipated to occur in humans and progress to tumors, based on available biological information.
Likely to be carcinogenic to humans:
This descriptor is appropriate when the weight of evidence is enough to show the potential to cause cancer in
humans but does not meet all conditions necessary to be called "carcinogenic to humans." Adequate evidence
consistent with this descriptor covers a broad spectrum. At one end of the spectrum is a plausible association
between human exposure to the agent and cancer and strong experimental evidence of carcinogenicity in animals. At
the other, with no human data, the weight of experimental evidence shows animal carcinogenicity by a mode or
modes of action that are relevant or assumed to be relevant to humans. The use of the term "likely" as a WOE
descriptor does not correspond to a quantifiable probability. Moreover, additional information, for example, on mode
of action, might change the choice of descriptor for the illustrated examples.
Suggestive evidence of carcinogenic potential:
This descriptor is appropriate when the weight of evidence suggests carcinogenicity, raising concern for potential
carcinogenic effects in human, but the data are judged insufficient for a stronger conclusion. This descriptor covers a
spectrum of evidence ranging from a positive cancer result in the only study on an agent to a single positive cancer
result in an extensive database that includes negative studies in other species. Depending on the extent of the
database, additional studies might or might not provide further insights.
Inadequate information to assess carcinogenic potential:
This descriptor is appropriate when available data are judged inadequate for applying one of the other descriptors.
Additional studies generally would be expected to provide further insights.
Not likely to be carcinogenic to humans:
This descriptor is appropriate when the available data are considered strong enough for deciding that there is no
basis for cancer concerns for humans. In some cases, there can be positive results in experimental animals, but the
evidence is strong and consistent that each mode of action does not operate in humans. In other cases, the evidence
can be convincing that the agent is not carcinogenic in humans or animals. "Not likely" applies only to the
circumstances supported by the data. For example, an agent might be "not likely to be carcinogenic" by one route but
not necessarily by another. In cases having positive animal experiment(s), but the results are judged not to be
relevant to humans, the narrative discusses why the results are not relevant.
Weather Research and Forecasting (WRF) model:
A mesoscale numerical weather-prediction system for atmospheric research and weather forecasting. It can generate
atmospheric conditions using real input data or idealized conditions.
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Appendix B. Air Toxics Modeled in AirToxScreen
A master pollutant list for AirToxScreen in spreadsheet format, "2018AirToxScreen_Pollutants.xlsx," is
provided in the Supplemental Data folder on the AirToxScreen website
(https://www.epa.gov/AirToxScreen). This file includes all air toxics modeled in AirToxScreen and indicates
the inventory types(s) reporting them. The file also includes the toxicity values used in AirToxScreen. The
names shown in this spreadsheet match the terminology used in the 1990 Clean Air Act (CAA)
Amendments; for example, the file lists "chromium compounds" but does not indicate which individual
compounds containing chromium were modeled, and it lists four forms of xylenes (o-, m-, p- and mixed
isomers), but these were grouped and modeled as a single entity. The file also contains indications about
whether cancer risks and chronic noncancer hazard quotients were estimated for each air toxic.
The spreadsheet also contains an air toxic names crosswalk and metal speciation factors used to conduct
the modeling of emissions. This crosswalk contains a link between lists of air toxic names in two data bases
used for AirToxScreen:
the names used in the National Emissions Inventory (NEI); and
the names used for AirToxScreen.
In addition, the file contains the speciation of metal chemicals based on their metal mass fractions. The
metal speciation factor was used to adjust modeled mass emissions prior to modeling and conducting risk
calculations because metal toxicity is usually evaluated relative to the amount of metal ion present rather
than the total mass of the metal compound. Most metal and cyanide compounds are reported in the NEI as
just the metal or cyanide parts; consequently, most fractions are 1, including the two cyanide compounds.
If the NEI data reporters did not adjust the emissions downward to account for just the metal part, a more
health-protective (higher risk) result would be obtained.
Table B-l contains the air toxics that were not modeled for AirToxScreen and why. Note that although
diesel PM was modeled for AirToxScreen and is included in the list of air toxics modeled, it is not
categorized as a HAP in the CAA. Diesel PM emissions were computed based on PMioemissions from on-
road and nonroad mobile sources burning diesel or residual fuels.
Note that NEI = National Emissions Inventory.
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Table B-l. Pollutants excluded from AirToxScreen
Pollutant
NEI Pollutant
Code(CAS
Number) a
Reason for Exclusion
In Previous
Assessments?
Chromium III
- In building the NEI, chromium VI is
emphasized, and chromium III may be
missing data.
n
2,3,7,8-Tetrachlorodibenzo-p-
dioxin
1746016
Dioxins and furans are not in the NEI due to
uncertainty in the completeness or accuracy
of the S/L/T agency data for this group of
pollutants. In addition, the most significant
exposure route for dioxin is ingestion, not
inhalation, so dioxin's relative contribution to
AirToxScreen's inhalation risk estimates likely
would not be large.
n
Other dioxins/furans
multiple
n
Radionuclides
Radionuclides are not in the NEI due to
uncertainty in the completeness or accuracy
of the S/L/T agency data for this group of
pollutants. In addition, the NEI currently is
not compatible with emissions reported in
units other than mass, and therefore suitable
emissions data have not been compiled for
these substances on a national scale.
n
DDE
72559
incorrectly
referred to in
the Section
112(b) list as
3547-04-4
This pollutant was not reported to the 2018
NEI.
n
Fine mineral fibers (including
rockwool, slagwool and fine
mineral fibers)
Fine mineral
fibers: 383
Rockwool: 617
Slagwool: 616
Rockwool has zero emissions in the 2017 NEI.
Slagwool and fine mineral fibers are excluded
from previous assessments.
n
Asbestos
1332214
Air concentrations of asbestos are often
measured in terms of numbers of fibers per
unit volume, but the NEI provides tons, which
cannot be converted.
n
Diazomethane
334883
This pollutant has 0 emissions in the 2017
NEI.
n
Beta-propiolactone
57578
This pollutant has 0 emissions in the 2017
NEI.
y
aln most cases, the NEI pollutant code is the same as the CAS number. In a few cases (e.g., coke oven emissions) a CAS number has
not been assigned, and NEI uses a unique pollutant code.
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Appendix C. Estimating Background Concentrations for AirToxScreen
This appendix contains the methods we used to estimate background concentrations for AirToxScreen.
Data
Overview
Boundary conditions are calculated for the following 14 pollutants: chloroform, methyl chloride
(chloromethane), benzene, carbon tetrachloride, methyl bromide (bromomethane), methyl chloroform
(1,1,1-trichloroethane), dichloromethane (methylene chloride), tetrachloroethene (perchloroethylene,
tetrachloroethylene), chromium VI, aresenic, chromium, lead, manganese, and nickel.
Air Quality Data
Air quality data from remote locations come from three sources. The Advanced Global Atmospheric Gases
Experiment (AGAGE) is a global network supported by several international institutions and organizations
that have monitored trace gases since 1978 (https://agage.mit.edu/). The National Oceanic and
Atmospheric Administration (NOAA) Global Monitoring Laboratory (GML) "conducts research that
addresses three major challenges: greenhouse gas and carbon cycle feedbacks, changes in clouds, aerosols,
and surface radiation, and recovery of stratospheric ozone" (https://gml.noaa.gov/). The Halocarbons And
other Trace Species (HATS) is a program within GML that quantifies "the distributions and magnitudes of
the sources and sinks for important ozone-depleting and greenhouse gases" (https://gml.noaa.gov/). Lastly,
The Interagency Monitoring of Protected Visual Environments (IMPROVE) network is a monitoring network
cooperative represented by several federal agencies and state organizations, along with some international
representation that focuses on visibility in Class I federal areas
(http://vista.cira.colostate.edu/lmprove/improve-program/). Below are the data sources and sites used for
each pollutant.
Pollutant Data Source Sites
Chloroform
AGAGE
THD
Methyl chloride (Chloromethane)
NOAA
KUM, MLO, NWR, BRW, ALT
Benzene
NOAA and IMPROVE
KUM, MLO, NWR, BRW, ALT
Carbon tetrachloride
NOAA
MLO, NWR, BRW, SMO, SPO
Methyl bromide (Bromomethane)
NOAA
KUM, MLO, NWR, BRW, ALT
Methyl chloroform (1,1,1-
Trichloroethane)
NOAA
ALT, SUM, BRW, MHD, THD, NWR, KUM, MLO,
SMO, CGO, PSA, SPO
Dichloromethane (Methylene chloride)
NOAA
KUM, MLO, NWR, BRW, ALT
Tetrachloroethene (Perchloroethylene,
Tetrachloroethylene)
NOAA
KUM, MLO, NWR, BRW, ALT
Chromium VI (PM2.5)
IMPROVE
DENA, KALM, PORE, REDW, TRCR, TUXE, HACR
Aresenic (PM2.5)
IMPROVE
DENA, KALM, PORE, REDW, TRCR, TUXE, HACR
Chromium (PM2.5)
IMPROVE
DENA, KALM, PORE, REDW, TRCR, TUXE, HACR
Lead (PM2.5)
IMPROVE
DENA, KALM, PORE, REDW, TRCR, TUXE, HACR
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Manganese (PM2.5)
IMPROVE
DENA, KALM, PORE, REDW, TRCR, TUXE, HACR
Nickel (PM2.5)
IMPROVE
DENA, KALM, PORE, REDW, TRCR, TUXE, HACR
Below is location information for each AGAGE and NOAA site.
Code
Name
Country
Lat.
Lon.
Elev. (m)
ALT
Alert, Nunavut
Canada
82.451
-62.507
185
BRW
Barrow Atmospheric Baseline Observatory
USA
71.323
-156.611
11
CGO
Cape Grim, Tasmania
Australia
-40.683
144.69
94
KUM
Cape Kumukahi, Hawaii
USA
19.561
-154.888
8
MHD
Mace Head, County Galway
Ireland
53.326
-9.899
5
MLO
Mauna Loa, Hawaii
USA
19.536
-155.576
3397
NWR
Niwot Ridge, Colorado
USA
40.053
-105.586
3523
PSA
Palmer Station, Antarctica
USA
-64.774
-64.053
10
SMO
Tutuila
USA
-14.247
-170.564
42
SPO
South Pole, Antarctica
USA
-89.98
-24.8
2810
SUM
Summit
Greenland
72.596
-38.422
3209.5
THD
Trinidad Head, California
USA
41.054
-124.151
107
Air Quality Data - AGAGE
The AGAGE air quality data came from the following URL:
Chloroform -
https://agage2.eas.gatech.edu/data archive/agage/gc-md/complete/california/
Air Quality Data - NOAA
The NOAA air quality data came from the following URLs:
Methyl chloride -
https://gml.noaa.gov/aftp/data/hats/solvents/CH3CCI3/flasks/GCMS/CH3CCL3 GCMS flask.txt
Benzene - Because benzene data are not posted publicly (https://gml.noaa.gov/hats/flask/flasks.html). US
benzene data are obtained from the latest version of the Ambient Monitoring Archive (AMA;
https://www.epa.gov/amtic/amtic-ambient-monitoring-archive-haps) under the link "All .Rda data files by
year". All US benzene data (AQS parameter code 45201) are obtained from the following NOAA sites:
NOAA Site Code
AMA ID
City
State
KUM
15-001-NKUM
Pahoa
HI
MLO
15-OOl-NMLO
Kaulapuu
HI
NWR
08-013-NNWR
Boulder
CO
BRW
02-185-NBRW
Barrow
AK
Benzene data from the NOAA site ALT are obtained from Steven Montzka (stephen.a.montzka@noaa.gov)
via the ERG contractor Regi Oommen (Regi.Oommen@erg.com).
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Carbon tetrachloride - https://gml.noaa.gov/aftp/hats/solvents/CCI4/insituGCs/CATS/hourly/
Methyl bromide - https://gml.noaa.gov/aftp/data/hats/methylhalides/ch3br/flasks/CH3BR GCMS flask.txt
Methyl chloroform -
https://gml.noaa.gov/aftp/data/hats/solvents/CH3CCI3/flasks/GCMS/CH3CCL3 GCMS flask.txt
Dichloromethane - https://gml.noaa.gov/aftp/data/hats/solvents/CH2CI2/flasks/ch2cl2 GCMS flask.txt
Tetrachloroethene - https://gml.noaa.gov/aftp/data/hats/solvents/C2CI4/flasks/pce GCMS flask.txt
Air Quality Data - IMPROVE
Login information is needed to access IMPROVE data (http://vista.cira.colostate.edu/lmprove/improve-
data/). However, IMPROVE data are obtained from the AMA (https://www.epa.gov/amtic/amtic-ambient-
monitoring-archive-haps) under the link "All .Rda data files by year". The tables below indicate the AQS site
codes and parameter codes pulled from the IMPROVE sites.
IMPROVE Site Code
AQS ID
City/Location
State
DENA
02-068-0003
Denali
AK
KALM
41-033-0010
Kalmiopsis
OR
PORE
06-041-0002
Sausalito
CA
REDW
06-015-0002
Redwood National Park
CA
TRCR
02-170-0011
Trapper Creek
AK
TUXE
02-122-9000
Tuxedni
AK
HACR
15-009-9001
Haleakala National Park
HI
Pollutant AQS Parameter Code
Chromium VI
14115
Arsenic
88103
Chromium
88112
Lead
88128
Manganese
88132
Nickel
88136
Meteorology Data
Pressure (determined from elevation) and temperature data are needed to convert the VOC air quality data
to local conditions. Some sites have collocated meteorology data, and some sites do not. For those that do
not, a nearby meteorology site is used. The speciated PM data collected from the IMPROVE sites are
already in local conditions and therefore, have no unit conversion. Meteorology data are collected for all
NOAA and AGAGE sites. The temperature data come from three different sources. The HATS sites BRW,
MLO, SMO, SPO, and SUM have collocated meteorology data. The remainder of the sites (except forTHD)
obtained meteorology data from NOAA's Global Historical Climatology Network daily (GHCNd). This
network is comprised of over 100,000 stations located across the globe monitoring a variety of different
meteorological parameters (https://www.ncei.noaa.gov/products/land-based-station/global-historical-
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climatology-network-daily). The AGAGE site THD had collocated meteorology data in the past; however,
data were obtained via email through NOAA and ERG contacts (see Appendix 2 and Appendix 3). Below
outlines which sites use which meteorology data sources.
Meteorology Data Source Site(s)
Collocated NOAA
BRW, MLO, SMO, SPO, SUM
GHCNd
KUM, NWR, ALT, MHD, CGO, PSA
Contact
THD
Meteorology Data - Collocated Meteorology Data
The collocated meteorology data (temperature at 2 meters) come from the following URLs (YYYY is the
four-digit year):
Site URL
BRW
https://gml.noaa.gov/aftD/data/meteoroloev/in-situ/brw/met brw insitu 1 obop hour YYYY.txt
MLO
https://gml.noaa.gov/aftp/data/meteorologv/in-situ/mlo/met mlo insitu 1 obop hour YYYY.txt
SMO
https://gml.noaa.gov/aftp/data/meteorologv/in-situ/smo/met smo insitu 1 obop hour YYYY.txt
SPO
https://gml.noaa.gov/aftp/data/meteorologv/in-situ/spo/met spo insitu 1 obop hour YYYY.txt
SUM
https://gml.noaa.gov/aftp/data/meteorologv/in-situ/sum/met sum insitu 1 obop hour YYYY.txt
Meteorology Data - GHCNd
Meteorology data (average daily temperature; field name "TAVG") come from the closest GHCNd station
(https://www.ncei.noaa.gov/pub/data/ghcn/dailv/bv station/) from the available inventory of stations
(https://www.ncei.noaa.gov/pub/data/ghcn/dailv/ghcnd-inventory.txt). As of this writing, the follow URLs
are used.
Site URL Start Yr End Yr Dist. (m)
ALT*
https://www.ncei.noaa.gov/pub/data/ghcn/da
Iv/bv station/CA002400305.csv.gz
2004
2023
6,033
BRW
https://www.ncei.noaa.gov/pub/data/ghcn/da
Iv/bv station/USW00027502.csv.gz
1945
2023
6,111
CGO*
https://www.ncei.noaa.gov/pub/data/ghcn/da
Iv/bv station/ASN00091245.csv.gz
1988
2023
22
HFM
https://www.ncei.noaa.gov/pub/data/ghcn/da
Iv/bv station/USW00094746.csv.gz
1998
2023
38,519
KUM*
https://www.ncei.noaa.gov/pub/data/ghcn/da
Iv/bv station/USW00021504.csv.gz
1998
2023
24,320
LEF
https://www.ncei.noaa.gov/pub/data/ghcn/da
Iv/bv station/USROOOOWGLI.csv.gz
1997
2021
30,271
MHD*
https://www.ncei.noaa.gov/pub/data/ghcn/da
Iv/bv station/EIM00003962.csv.gz
1973
2023
95,369
MLO
https://www.ncei.noaa.gov/pub/data/ghcn/da
Iv/bv station/USROOOOHPTA.csv.gz
1999
2021
16,567
NWR*
https://www.ncei.noaa.gov/pub/data/ghcn/da
Iv/bv station/USS0005J08S.csv.gz
1989
2023
2,605
PSA*
https://www.ncei.noaa.gov/pub/data/ghcn/da
Iv/bv station/AYM00089061.csv.gz
1976
2023
1,683
SMO
https://www.ncei.noaa.gov/pub/data/ghcn/da
Iv/bv station/AQW00061705.csv.gz
1945
2023
18,604
SPO
https://www.ncei.noaa.gov/pub/data/ghcn/da
Iv/bv station/AYW00090001.csv.gz
1994
2023
2,234
SUM
https://www.ncei.noaa.gov/pub/data/ghcn/da
Iv/bv station/GLE00146908.csv.gz
2004
2023
586,671
THD
https://www.ncei.noaa.gov/pub/data/ghcn/da
Iv/bv station/USROOOOCSHH.csv.gz
2001
2021
22,643
USH
https://www.ncei.noaa.gov/pub/data/ghcn/da
Iv/bv station/ARM00087938.csv.gz
1957
2023
1,113
*Sites where GHCNd meteorological data are used.
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Other Data Files
The elevation for each site is determined from
https://gml.noaa.gov/dv/site/gmdsites.php?program=all&projtable=l under the table "HATS Flask
Sampling". Data from this table are saved as NOAA_SiteMetalnfo.csv.
Methodology
Meteorology Data Averages
Collocated meteorology data. The collocated meteorology data are measured hourly. All hourly values are
averaged up to daily after removing null values for each site if there are a minimum of 18 non-null hours for
a given day. The temperature (°C) at 2 meters is selected for the temperature variable. Daily temperature is
then averaged up to monthly for each site.
GHCNd meteorology data. GHCNd meteorology data are measured daily, and average temperature data are
selected (tenths °C). Therefore, the variable "TAVG" is divided by 10 and is used as the temperature
variable (°C). Daily temperature is averaged up to monthly temperature for each site.
THD meteorology data. The variable "TemperatureC" is pulled for the sub-hourly temperature data from
THD. After subsetting only to values with the proper QC flag ("TemperatureC. QcDataDescriptor" equal to
"S"), an hourly average is calculated if there are at least nine sub-hourly values, and a daily value is
calculated if there are at least 18 hourly values. From there, daily values are averaged to monthly values.
See Appendix 2 and Appendix 3 for email correspondence regarding the THD data.
Air Quality Data Averages
NOAA and AGAGE monthly average. NOAA and AGAGE data have a sub-daily sampling duration with an
approximately weekly sampling frequency (with the exception of the in situ data for carbon tetrachloride).
The average of each pollutant/site is calculated for each month. A minimum number of two values is
needed to calculate a monthly average. All zeros are removed for chloroform before taking a monthly
average. The following equation is used to calculate the monthly average for site i, pollutant j, year k,
month I for M concentrations within a given month:
Converting units. Elevation and monthly temperature data are paired for every pollutant site/month
average. Each site/month average is converted from its original units (i.e., ppt) to ng/m3 standard
conditions and jig/m? local conditions using the following equations for site i, pollutant j, year k, and
month I:
Where MWj is the molecular weight of pollutant j (g/mol); 24.46 is the volume (L/mol) of gas at normal
temperature and pressure. Molecular weight is pulled from the Archive (from
AMA_POLLUTANT_CODES_DICTIONARY).
M
m=l
dijxi (l*g/m3 STD) =
dj,j,k,i (PPL) ,, MWj
1000 X 24.46
d; i k 1 (VVt)
dijxi fag/™- LC) = ' 1000— X
(24.46* 101325 * (tiM + 273)/(298 * p*))
MWj
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Where ti k i is the temperature (°C) for site i, year k, and month I; normal temperature and pressure is
defined as 101325 Pa (1 atm) and 298 K; pt is the pressure (Pa) for site i defined as
Pi = 101325 * (1 - 0.0000225577 * Elevt)5-25588
Where EleVi is the elevation (m) at site i.
The equation for pressure can be found here: https://www.engineeringtoolbox.com/air-altitude-pressure-
d 462.html. All three units (i.e., ppt, jig/m? standard conditions, and jig/m? local conditions) are retained
for annual site and overall averages.
Annual average. The annual average is taken for all three units for site i, pollutant j, year k for L monthly
averages using the following equation:
L
fc,j,k = ^ ^
1=1
Latitude-weighted averages. The final boundary condition for pollutant j and year k is calculated by
summing a latitude-weighted annual site-level averages for N sites using the following equation:
Gi b- —
Zi=1 cos I ¦J * niJ>k
J],k -w rnJHMi
Ji=icus ^ 180
Ei=lcosI
Where latt is the latitude for site i. The methodology for latitude-weightings have been described in
Montzka et al. (2011; 1999).
IMPROVE data. IMPROVE data are averaged differently than NOAAand AGAGE. Because of the large
percentage of non-detects in the IMPROVE data, the Method Detection Limit (MDL) is an upper bound and
used as a surrogate for ambient concentrations. Unlike NOAA and AGAGE, the boundary condition is
calculated more directly by averaging all the MDLs for all collected samples across the entire year for each
pollutant (with the exception of chromium VI, explained below). All IMPROVE data are collected in units of
jig/m} local conditions. No unit conversion or latitude-weighting is done.
Chromium VI. Chromium VI is calculated differently than the other IMPROVE parameters. The boundary
condition for chromium VI is calculated by weighting the boundary condition for chromium by a ratio using
the following equation:
h
n _ uChromiumVI,k : n
"Chromium VI,k ~ T * " Chromium,k
"Chromium TSP,k
Where Gchromium Vj k is the final boundary condition for chromium VI for year k, Gchromiurnk is the final
boundary condition for chromium for year k, bchromiumVi k is the annual average for chromium VI (TSP) LC
(AQS parameter code 14115) only from NATTS across all available sites for year k, and bchromiumTSp k is
the annual average for chromium (TSP) LC (AQS parameter code 14112) across all networks and across all
available sites for year k. With bchromiumVik and bchromiumTSpk, non-detects are treated as zeros and
are included in the calculation. All data are pulled from the AMA.
Results
Name ppt lig/m3 (STD) fig/m3 (LC)
Chloroform
15.27
0.0745
0.0774
Benzene
30.51
0.0974
0.0878
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Bromomethane
7.12
0.0276
0.0236
Chloromethane
559.74
1.1554
0.9824
Dichloromethane
68.51
0.2379
0.2043
Tetrachloroethylene
1.68
0.0114
0.0099
Carbon tetrachloride
77.46
0.4871
0.4074
Methyl chloroform
1.23
0.0067
0.0063
Arsenic (PM2.5)
—
—
2.152E-04
Chromium (PM2.5)
—
—
1.334E-04
Lead (PM2.5)
—
—
5.070E-04
Manganese (PM2.5)
—
—
2.492E-04
Nickel (PM2.5)
—
—
1.108E-04
Chromium VI
—
—
2.034E-07
Name % Below MDL
Arsenic (PM2.5)
98.9
Chromium (PM2.5)
86.8
Lead (PM2.5)
81.4
Manganese (PM2.5)
61.0
Nickel (PM2.5)
90.1
Citations
Montzka S.A., Butler J.H., Elkins J.W., Thompson T.M., Clarke A.D., and Lock L.T. (1999) Present and future
trends in the atmospheric burden of ozone-depleting halogens. Nature, 398, 690-694.
Montzka S.A., Dlugokencky E.J., and Butler J.H. (2011) Non-C02 greenhouse gases and climate change.
Nature, 476(7358), 43-50, doi: 10.1038/naturel0322, August 3.
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Appendix D. Model Evaluation Summaries
EPA performed model evaluations for other AirToxScreen pollutants. These evaluations, including
graphics, can be found in the Supplemental Data folder on the AirToxScreen website.
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Appendix E. Exposure Factors for AirToxScreen
The memorandum contained within this Appendix (below) describes in detail how EPA developed
exposure factors for each chemical assessed in AirToxScreen. These calculated exposure factors can be
found in the Supplemental Data folder accompanying this TSD.
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MEMORANDUM
To: Matt Woody, Rod Truesdell, and Michael Moeller
From: ICF: Minti Patel, Chris Holder, Aishwarya Javali, Jared Wang, Graham Glen, and
Melissa Polansky
Innovate! Inc.: David Yarnell, Ben Holloway, and Michael Blair
Date: December 4, 2023
Re: Updating the Hazardous Air Pollutant Exposure Model (HAPEM) for Use in the
2020 Air Toxics Screening Assessment (AirToxScreen)
ICF ("we") updated the default input files accompanying the Hazardous Air Pollution
Exposure Model (HAPEM), and we updated some of the HAPEM source code to
accommodate the new default files. The resulting new version of HAPEM (i.e., HAPEM8),
with its default files, simulates exposure concentrations for all populated census tracts
using 2020 census data, commuting data from the 2012-2016 and 2015-2020 American
Community Survey (ACS), and time-activity data from the April 2020 version of the U.S.
Environmental Protection Agency (EPA) Consolidated Human Activity Database (CHAD).
In this technical memorandum, we describe how we updated the default files and model
source code, including the quality-assurance (QA) steps we used and the format of the
final default files. HAPEM8 and its updated default files will be available for download as
EPA's latest, default version of HAPEM.1 We modeled exposure concentrations using
HAPEM8 for the 2020 Air Toxics Screening Assessment (AirToxScreen), as described in a
separate memorandum.2
1. Introduction to HAPEM and its Use in AirToxScreen
HAPEM is a model used by EPA to perform screening-level assessments of long-term
inhalation exposures to hazardous air pollutants (HAPs). Exposure concentrations output
1 We anticipate HAPEM8 and its User's Guide will be made available by EPA online in Winter 2023-2024. As of
April 26, 2021, HAPEM7 is available for download at https://www.epa.gov/fera/human-exposure-modeling-
hazardous-air-pollutant-exposure-model-hapem.
2 We describe the use of HAPEM8 in the 2020 AirToxScreen in the ICF Memorandum "HAPEM8 Modeling for the
2020 Air Toxics Screening Assessment (AirToxScreen)" dated December 4, 2023, to Matt Woody, Rod Truesdell,
and Michael Moeller of EPA's Office of Air Quality Planning and Standards.
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by HAPEM are stratified by location (i.e., U.S. census tract), age group, and the individual
source categories and HAPs being modeled. The model's default files cover all 50 states
in the US, the District of Columbia, Puerto Rico, and the U.S. Virgin Islands (USVI).
AirToxScreen uses HAPEM with these default files. Therefore, exposure concentrations
produced for the AirToxScreen have the same stratifications discussed above, though
AirToxScreen-specific post-processing includes accumulating exposure concentrations
into a lifetime period of exposure (defined as 70 years). AirToxScreen (the successor to
the National Air Toxics Assessment, or NATA) is a nationwide modeling assessment of air
concentrations, exposure concentrations, and potential human health cancer risks and
chronic hazards associated with exposure to HAP emissions from man-made and
naturally occurring sources. These results are spatially partitioned by various census
geographies. EPA models air concentrations using two air-concentration models:
AERMOD (the atmospheric dispersion model developed by the American Meteorological
Society and the EPA Regulatory Model Improvement Committee) and CMAQ (EPA's
Community Multiscale Air Quality model). Those modeled air concentrations are the "air
quality" inputs for HAPEM. AirToxScreen is not an enforcement tool to determine
compliance with various standards of emissions, air quality, or health impacts; rather, it is
a screening-level tool used to rank HAPs based on potential health impacts (nationally
and locally), estimate the numbers of people and demographics potentially subject to
health risks above levels of concern, identify gaps in data, and prioritize locations, source
categories, and HAPs to inform additional data collection and assessment.
Data on where people live and work, and otherwise how they spend their time, are critical
to the completeness of the exposure modeling conducted with HAPEM. The version of
HAPEM currently available for download (HAPEM7) uses census data from the year 2010
and activity patterns gleaned from the 2014 version of CHAD.3 We have updated the
default files used by HAPEM to reflect or approximate 2020 census data and the version
of CHAD available in April 2020. We also have updated HAPEM source code as necessary,
mostly to accommodate the sizes of the updated inputs.
2. Updating Census-based Data
a. Population File - "population_HAPEM8.txt"
The HAPEM default population input file ("population_HAPEM8.txt" in HAPEM8) provides
the number of people in each HAPEM age group residing in each tract in the 50 states
3 The content, functionality, and implementation of HAPEM7 are discussed in the HAPEM7 User's Guide, available
as of April 26, 2021 at https://www.epa.gov/fera/hazardous-air-pollutant-exposure-model-hapem-users-guides.
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plus the District of Columbia, Puerto Rico, and the USVI. The HAPEM default ages are
binned into six groups: 0-1, 2-4, 5-15,16-17,18-64, and 65 years and older.
HAPEM7: For the previous HAPEM model (HAPEM7), the population data were derived
from the 2010 census Summary File 1: Table PCT12 ("Sex by Age"), available separately for
males and females, and provided by each year of age. Population data for the USVI were
not available from Table PCT12, but they were available by querying the census American
FactFinder web page. For the purposes of HAPEM, the male and female data from Table
PCT12 were aggregated male+female and into the HAPEM age groups. The American
FactFinder USVI data were available by groups of ages which did not match the HAPEM
age groups. For the purposes of fitting the USVI age groups to the HAPEM age groups, it
was assumed that population counts were evenly distributed among the incremental
years represented in the USVI 0-4-year group (i.e., two fifths being 0-1 and three fifths
being 2-4 years old) and in the 15-17 group (i.e., one third being 15 and two thirds being
16-17 years old); all other USVI age groups (e.g., 5-9,10-14,18-19,...,62-64, 65-66,...,85 and
over) required no subdivision to fit into the HAPEM age groups.
HAPEM8: For HAPEM8, we used the 2020 census' Table PCT12 ("Sex by Single-year Age")
to update the HAPEM population file for all areas except the USVI. We obtained
population data for the USVI from the 2020 census' Table PCT1 ("Sex by Single Years of
Age", U.S. Virgin Islands). We summed the population information across the two sexes
and aggregated the single-age data into the six default HAPEM age groups.
i. Quality Assurance
We checked that the HAPEM8 default population file contained all the expected census
geographies (i.e., all the 2020 tracts) by comparing against the 2020 census gazetteer
tract file4 (and tigerweb.geo.census for the USVI). We created the file using Microsoft®
Excel™, where we cross-checked our processing formulas to ensure individual ages were
accurately summed into the HAPEM age groups. We also compared the grand total of
those binned population numbers to the grand total of the raw census data of individual
ages. Lastly, we compared the HAPEM8 population file against the HAPEM7 file to ensure
proper formatting.
ii. Content and Format
The HAPEM8 population data are contained in a fixed-width, space-delimited text file
with characteristics shown in Table 2. The file contains seven columns and a total of
85,427 rows of data (after two header rows). Each data row corresponds to a tract, where
4 As of August 2023 , the census gazetteer files are available at https://www.census.gov/geographies/reference-
files/time-series/geo/gazetteer-files.html.
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the first field identifies the tract using Census Federal Information Processing Series
(FIPS) coding5, and fields 2-7 contain population counts per age group. Population counts
are whole numbers (no commas separating thousands). The first header row labels the
fields, where the age-group columns are identified by the youngest age within the group
(i.e., B_00 for age group 0-1 years old, B_02 for age group 2-4, and so on). The second
header row serves an unknown purpose, but we retained it from the HAPEM7 population
file. In Figure 9 we show the first ten data rows of the population file. On the whole, the
HAPEM8 total tract populations range from 0 (for 617 tracts across 42 states and
territories, which is less than 1 percent off all tracts) to 37,892, with an average of 3,919.
The total population in this file is 334,822,301.
Table 2. Characteristics of the HAPEM8 Population File
Variable
Description
Character Start
Position on Data Row
Character Length on
Data Rowa
TRACT
Full census FIPS code for home tract
1
11
B_00
Total population ages 0-1 years
17
8
B_02
Total population ages 2-4
25
8
B_05
Total population ages 5-15
33
8
B_16
Total population ages 16-17
41
8
B_18
Total population ages 18-64
49
8
ID
CO
1
CD
Total population ages 65 and older
57
8
Note: FIPS = Census Federal Information Processing Series
a Any unused character space after a number and/or between fields consists of blank spaces.
5 The full tract identifier used by census consists of a 2-digit state code, a 3-digit county code, and a 6-digit tract
code, concatenated together to form an 11-digit code.
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TRACT
B 00
B 02
B 05
B 16
w
1
l->
CO
B 65
COM
COM
COM
COM
COM
COM
01001020100
30
67
252
56
1086
284
01001020200
33
78
283
77
1268
316
01001020300
60
109
471
91
1887
598
01001020400
81
137
596
CO
CO
2383
961
01001020501
75
115
625
138
2599
770
01001020502
84
144
569
105
2090
292
01001020503
81
120
542
104
2188
581
01001020600
77
141
642
101
2198
570
01001020700
91
161
491
86
2105
475
01001020801
44
104
487
131
1861
516
Figure 9. Excerpt from the HAPEM8 Population File
b. Commuting-flow File - "commute_flow_HAPEM8.txt"
In HAPEM, the tract where a person resides is their home tract, and the tract where a
person works is their work tract. Some people work within their home tract (i.e., the work
tract is the home tract); the remaining employed people work outside their home tract.
For the employed people in each home tract, the HAPEM default commuting-flow input
file ("commute_flow_HAPEM8.txt" in HAPEM8) provides the fraction of those people who
work within their home tract and the fraction that commute to work in each other tract.
For each home tract, the file contains only the tract(s) where residents of the home tract
work (i.e., there are no fractions of 0). These commuting data are provided for nearly all
the (home) tracts contained in the HAPEM population file, with exceptions noted in the
discussion below.
HAPEM7: For the previous HAPEM model (HAPEM7), the commuting-flow data were
derived from data provided by the U.S. Department of Transportation (DOT) Federal
Highway Administration (FHWA)—specifically, their Microsoft® Access™-based Census
Transportation Planning Products (CTPP) 2006-2010 file, based on 2006-2010 five-year
summary data from the ACS and commissioned by the American Association of State
Highway and Transportation Officials (AASHTO). This Access database contains
estimates of the total number of workers commuting within or between tracts.
HAPEM8: For the HAPEM8 commuting-flow file, we used the FHWA CTPP data based on
the 2012-2016 five-year summary data from the ACS (the most current available).6 The
6 As of August 2023, the 2012-2016 CTPP data are available at https://ctpp.transportation.org/2012-2016-5-vear-
ctPP/.
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data are available by state, and we downloaded the state files and concatenated them
into the overall commuting-flow file.
Because the 2012-2016 CTPP data uses geography (census tracts) for the 2010 census,
we mapped the data to 2020 census tracts using a 2020 relationship file made available
by the Census Bureau.7 The relationship file provides a one-to-many crosswalk from 2010
tracts to 2020 tracts, indicating the surface area of overlap between the two vintages of
tracts. We used proportion of total overlapping tract area (sum of land area and water
area) to redistribute the CTPP commuter data to the 2020 tracts.
To produce the commuter fractions, we divided the number of workers in each home-
tract/work-tract pair by the total number of workers residing in the home tract. We
calculated the distance between each home-tract/work-tract pair using the 2020
census coordinates of tract internal points (i.e., centroids), available from the 2020
census gazetteer.4 More specifically, we used the distGeo function in the geosphere
package of R8 to calculate the distance between the internal-point tract coordinates.
A small number of tracts were absent from the CTPP data as home tracts (totaling 920
tracts, 1 percent of all tracts; 469 of these were unpopulated, while 451 were populated).
HAPEM will model each missing tract as if all its employed residents work within the home
tract (i.e., for the purposes of HAPEM modeling, they essentially do not commute), so we
did not insert any data for these missing tracts. Additionally, the CTPP contained no data
on all 32 tracts in the USVI. To prevent the USVI from being conspicuously missing from
the commuting file, we inserted one record for each USVI tract, where work tract equals
home tract and the commute distance is 0 kilometer (km), which is how HAPEM would
model them if they remained missing from the file.
i. Quality Assurance
We ensured that the data downloaded from the CTPP website matched that obtained
from the CTPP online queries, by randomly checking four tracts from five different states.
We confirmed the numbers of home and work tracts at various stages of the analysis. We
also ensured the accuracy of the commuting fractions including the usage of the
relationship file to estimate flows between the 2020 census tracts, through a thorough
check of the calculations for Alaska. We ensured that the cumulative commuting fraction
equaled 1 for each home tract (with an allowance for very small rounding errors). We used
7 As of August 2023, the census relationship files are available at https://www.census.gov/geographies/reference-
files/time-series/geo/relationship-files. 2020. htm l#tract.
8 As of August 2023, the R geosphere package files are available at
https://www.rdocumentation.Org/packages/geosphere/versions/l.5-18.
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mapping software to check a small number of the commuting distances calculated by
the distGeo function.
ii. Content and Format
The HAPEM8 commuting-flow data are contained in a fixed-width, space-delimited text
file with characteristics shown in Table 3. The file contains five columns (the first being
empty) and a total of 6,004,343 rows of data with no header rows. Each data row
corresponds to a unique home-tract/work-tract pair, where the second and third fields
respectively contain the home and work tract identifiers using FIPS coding, and the fourth
and fifth fields respectively contain the commuting distance (in km) and the fraction of
workers commuting between the associated home and work tracts. Distance values are
presented to no more than two decimal places (i.e., hundredths of km, which is tens of
meters), while commuting fractions are presented to no more than eight decimal places.
In Figure 10 we show the first ten data rows of the commuting-flow file. On the whole, the
data show on average there are 71 work tracts per home tract, up to a maximum of 313
work tracts. In 351 home tracts (which is less than 1 percent of home tracts), all workers
worked within their home tract.
Table 3. Characteristics of the HAPEM8 Commuting-flow File
Character Start
Character
Field
Position on Data
Length on Data
Number
Description
Row
Rowa
1
Leading space in file
1
1
2
Full census FIPS code for home tract
2
11
3
Full census FIPS code for work tract
14
11
4
Distance in kilometers between home and work tract
26
8
5
Fraction of workers in the home tract commuting to
34
10
the work tract
Note: FIPS = Census Federal Information Processing Series
a Any unused character space after a number and/or between fields consists of blank spaces.
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01001020100
01001020100
O
o
o
0 . 03045067
01001020100
01001020803
6. 98
0 .00365408
01001020100
01001020200
1. 92
0.04263094
01001020100
01001020300
3 .12
0 .04872107
01001020100
01001020400
4.56
0 .01827040
01001020100
01001020501
7.51
0 . 07308161
01001020100
01001020502
7 . 07
0 . 02436054
01001020100
01001020503
6.25
0 . 03654080
01001020100
01001020600
o
CO
0 . 02436054
01001020100
01001020700
7.52
0.06090134
Figure 10. Excerpt from the HAPEM8 Commuting-flow File
Commuting distances greater than 120 km are assumed in HAPEM to be very atypical for
a daily commuter. As noted in the HAPEM User's Guide,1 during an earlier development
stage of HAPEM, commuting flows were examined as a function of distance. The analysis
revealed that commute flows generally decreased linearly in log space with increasing
distance, but at commute distances greater than about 100 km that trend flattened. This
suggested that those longer commutes likely did not occur daily. Since HAPEM is
designed to construct daily commutes for simulated workers, it would not be appropriate
for HAPEM to model daily commutes longer than about 120 km, and thus HAPEM ignores
these longer commutes in constructing the commute distance distributions for each
tract. Most home tracts have at least one work tract that is more than 120 km away; that
is, in approximately 64 percent of home tracts there is at least one person residing there
who commutes farther than 120 km. However, this affects only 3 percent of home-
tract/work-tract pairs. Ignoring these records with commuting distances greater than 120
km, the average tract-to-tract distance is 22.5 km (weighting all tract pairs equally, not
by numbers of people performing those commutes; that average is 42.1 km when
commuting distances greater than 120 km are included).
c. Commuting-time File -
"commute_time_HAPEM8.txt"
While the HAPEM commuting-flow file (see Section b) contains information on the
frequency distribution of commuting distances for workers in a given home tract, the
HAPEM commuting-time file ("commute_time_HAPEM8.txt" in HAPEM8) contains
information on the method of commuting (public versus private transit) and the average
commuting time per person. These commuting-time data are provided for all the tracts
contained in the HAPEM population file, though no commuting data were available for the
USVI, as discussed below.
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HAPEM7: For the previous HAPEM model (HAPEM7), the commuting-time file data were
derived for 2010 from the 2006-2010 five-year summary data from the ACS Tables
B08301 ("Means of Transportation to Work for Workers 16+ Years"), C08134 ("Means of
Transportation to Work by Travel Time to Work for Workers 16+ Years who Did Not Work
at Home"), and C08136 ("Aggregate Travel Time to Work (in Minutes) by Means of
Transportation to Work for Workers 16+ Years who Did Not Work at Home").
HAPEM8: For the HAPEM8 commuting-time file, relative to the HAPEM7 file, we identified
equivalent data for the year 2020 from other tables from the ACS 2016-2020 five-year
summary data, as detailed in the following paragraphs.
Table B08134 ("Means of Transportation to Work by Travel Time to Work for Workers 16+
Years who Did Not Work at Home") contains the numbers of people commuting to work,
irrespective of commuting time, for specific means of transit in broader groups than in
Table B08301 that was used for HAPEM7. We used Table B08134 to derive the proportion
of workers traveling by public transit (i.e., bus, trolley bus, streetcar, trolley car, subway,
elevated train, railroad, and ferryboat) and the proportion of commuters traveling by
private transit (i.e., car, truck, van, taxicab, motorcycle, bicycle, any other non-public
means except walking). People working from home (i.e., workers not commuting) were not
included in this dataset. We excluded people walking to work, which are cases where we
assume people work within their home tract and thus are not considered commuters for
the purposes of HAPEM exposure modeling. As such, the fractions of workers commuting
by public and private transit sum to 1, except a relatively small number of tracts
(approximately 1,064, or 1 percent of all tracts) where the survey recorded no commuting
activity.
ACS Table B08136 ("Aggregate Travel Time to Work (in Minutes) by Means of
Transportation to Work for Workers 16+ Years who Did Not Work at Home") contains
travel times to work by the same transit means as in Table B08134, summed across all
people who use those means. We divided these aggregate travel times by the
corresponding population counts from Table B08134, resulting in average per-person
travel times to work, by public transit and by private transit. We then multiplied the
average per-person travel times by two to derive the round-trip time used in HAPEM8
commuting-time file. Commuting times related to public transit include time spent
waiting at a bus or train stop, and commuting times (and population counts from Table
B08134) related to private transit include walking commuters; these times are included in
our calculations because they cannot be disaggregated from the total commuting time. If
the data derived from Table B08134 (used for the proportions of workers commuting by
public and private means) indicated that a tract had no commuters using public means,
then we set commuting times to 0 for public means; similarly, we set private commuting
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times to 0 if there were no private commuters, and we set both public and private
commuting times to 0 if there were no commuters at all.
Commuting data for the USVI were not available from the ACS, so we set all their workers
to work in their home tract (i.e., commute neither by public nor private transit, with
commuting times equal to 0). This is consistent with how we approached USVI data in the
commuting-flow file.
Aggregate commuting-time data also were unavailable from Table B08136 (either missing
entirely from the table, or present in the table but with flags [orvalue entries]
indicating a lack of reliable data) for 87 percent9 of tracts in areas outside the USVI. We
used county-average aggregate times for 68 percent of these missing tracts (i.e., for 32
percent of all tracts outside of the USVI) and state times for the remaining 32 percent of
missing tracts (i.e., for 5 percent of all tracts outside of the USVI). We divided those
county and state aggregate times by the county and state counts of commuters to
produce average, per-person, one-way commuting times, and we multiplied by two to
obtain round-trip times. We stratified these county and state averages by public- and
private-transit means.
For the State of Wyoming, although state time aggregates had a null value, values were
available for some counties. To derive a state-level aggregate, we summed values across
all counties. We used this to substitute as a state-level value in cases of missing tract-
and county-level aggregates in Wyoming.
i. Quality Assurance
We checked that the HAPEM8 default commuting-time file contained all the expected
census geographies (i.e., all the 2020 tracts) by comparing against the default population
file (see Section a). We spot-checked several very different tracts (e.g., rural Alaska, city
in Alaska, Queens County in New York City) to ensure that the ACS data pulled into our
Excel processing file matched the raw data displayed on the ACS website. We checked
each of our Excel processing formulas, including aggregations across census transit
types, the calculations of county- and state-average data, and the compilation of those
data into a complete set of tract data. We ensured that the public and private
commuting proportions summed to 1 for every record except the tracts with 0
commuters. For consistency, we confirmed that tracts with commuting workers (from the
HAPEM8 default commuting-fraction file, discussed later in Section d) had non-0
commuting-time values in the final file.
9 It was unclear why a large percentage of these data were missing or marked as insufficient.
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ii. Content and Format
The HAPEM8 commuting-time data are contained in a tab-delimited text file with
characteristics shown in Table 4. The file contains five columns and a total of 85,427 rows
of data with no header rows. Each row corresponds to a tract, where the first field
contains the tract identifier using census FIPS coding, the second and third fields
respectively contain the proportion of commuters who travel by public transit (excluding
taxicabs) and by private transit (including taxicabs), and the fourth and fifth fields
respectively contain the average round-trip times (in minutes) commuting to work by
public transit and by private transit. All values in fields 2-5 are displayed to four decimal
places. In Figure 11 we show the first ten data rows of the commuting-time file. On the
whole (except the USVI), the data show that 86 percent of commuters used private
transit, and all commuters in 40 percent of census tracts used private transit. The
conditional-average round-trip private-transit commute was 53 minutes (100 minutes
for public transit) (conditional averaging considers only non-zero values). This statistic
treats every tract equally, rather than weighting by commuting population, and it includes
county and state averages where we used them. The longest round-trip commuting times
in the data set are 163 minutes for private transit and 336 minutes for public transit.
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Table 4. Characteristics of the HAPEM8 Commuting-time File
Field Number Description
1 Full census FIPS code for home tract
2 Proportion of workers commuting outside of the home by public transit
3 Proportion of workers commuting outside of the home by private transit
4 Average round-trip commuting time for workers commuting outside of the home by
public transit
5 Average round-trip commuting time for workers commuting outside of the home by
private transit
Note: The position where table values begin and the number of characters per value are not relevant in a tab-
delimited format.
01001020100
0.0000
1.0000
0.0000
50
8320
01001020200
0.0000
1.0000
0.0000
50
8320
01001020300
0.0000
1.0000
0.0000
49
5615
01001020400
0.0377
0.9623
89.9083
50
8320
01001020501
0.0166
0.9834
89.9083
50
8320
01001020502
0.0000
1.0000
0.0000
50
8320
01001020503
0.0000
1.0000
0.0000
50
8320
01001020600
0.0000
1.0000
0.0000
50
8320
01001020700
0.0000
1.0000
0.0000
50
8320
01001020801
0.0000
1.0000
0.0000
50
8320
Figure 11. Excerpt from the HAPEM8 Commuting-time File
d. Commuting-fraction File -
"commute_fraction_HAPEM8.txt"
The HAPEM commuting-fraction file ("commute_fraction_HAPEM8.txt" in HAPEM8)
contains the fraction of workers in each tract who commute to work and the fraction who
do not commute, stratified by age group. Workers who walk to work are not included as
commuters for HAPEM8.
HAPEM7: The HAPEM7 commuting-fraction data were derived for 2010 from the 2006-
2010 five-year summary data from the ACS—specifically, ACS Table B23001 ("Sex by Age
by Employment Status for the Population 16 Years and Over") and ACS Table B08101
("Means of Transportation to Work by Age for Workers 16+ Years"). HAPEM7 included
Armed Forces members but did not include those walking to work.
HAPEM8: For the HAPEM8 commuting-fraction file, relative to the HAPEM7 file, we
identified equivalent data for 2020 from Table B08101 of the ACS 2016-2020 five-year
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summary data, not including those walking to work. Detailed calculation methods are
discussed in the following paragraphs.
ACS Table B08101 ("Means of Transportation to Work by Age for Workers 16+ Years")
contains the numbers of people per age group commuting to work by various means of
transit (e.g., "Total", "Car, truck, or van: Drove alone", "Car, truck, or van: Carpooled",
"Public Transportation (excluding taxicab)"). We used this table to derive 1) the numbers
of workers who commuted by means other than walking and 2) the number of people per
HAPEM age group who are workers. As we did in calculating the proportion of workers
commuting by public and private transit (see Section c), we excluded people walking to
work because they likely work within their home tract, and for simplicity we consider
them not to be commuters in HAPEM.
For each tract and HAPEM age group, we calculated the fraction of workers commuting as
(number of people aged 16+ years who commute to work other than by walking) +
(number of workers aged 16+ years). The fraction of workers not commuting is 1 minus the
above fraction.
Commuting data for the USVI were not available from the ACS, so we set data in the
commuting-fraction file such that all workers in the USVI work in their home tract (i.e., did
not commute). This is consistent with how we treated USVI data in the commuting-flow
and commuting-time files (see Sections b and c, respectively).
i. Quality Assurance
We performed systematic data processing using R. As a thorough check, we also
repeated the processing in Excel (by a separate person than who authored the R code),
finding that both methods of processing resulted in the same values. We checked that all
commuting-fraction numbers were between 0 and 1. We ensured that the fractions of
workers in each age group commuting and not commuting summed to 1 for every record.
We compared the HAPEM8 and HAPEM7 files to ensure proper layout.
ii. Content and Format
The HAPEM8 commuting-fraction data are contained in a tab-delimited text file with
characteristics shown in Table 5. The file contains five columns and a total of 85,427 rows
of data with no header rows. Each row corresponds to a tract, where the first field
contains the tract identifier using census FIPS coding, the second and third fields
respectively contain the fraction of workers aged 0-1 years who do not commute and
who do commute, and the remaining fields show the same data for each of the other five
HAPEM age groups. All values in fields 2-13 are displayed to four decimal places. Nobody
younger than 16 years is considered employed and a commuter, so all values for "does
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not commute to work" are 1 and all values for "commutes to work" are 0 for the first three
HAPEM age groups. In Figure 12 we show the first ten data rows of the commuting-fraction
file. On the whole (except the USVI), the data show that the average tract commuting
fraction is 0.80 (80 percent of workers commuting) for ages 16-17 years, 0.89 for ages
18-64 years, and 0.83 for 65+ years. This statistic treats every tract equally, rather than
weighting by commuting population.
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Table 5. Characteristics of the HAPEM8 Commuting-fraction File
10
13
12
11
7
3
4
6
2
8
9
5
Full census FIPS code for home tract
Proportion of age group 1 (ages 0-1 years) that does not commute to work
Proportion of age group 1 (ages 0-1) that commutes to work
Proportion of age group 2 (ages 2-4) that does not commute to work
Proportion of age group 2 (ages 2-4) that commutes to work
Proportion of age group 3 (ages 3-15) that does not commute to work
Proportion of age group 3 (ages 3-15) that commutes to work
Proportion of age group 4 (ages 16-17) that does not commute to work
Proportion of age group 4 (ages 16-17) that commutes to work
Proportion of age group 5 (ages 18-64) that does not commute to work
Proportion of age group 5 (ages 18-64) that commutes to work
Proportion of age group 6 (ages 65 and older) that does not commute to work
Proportion of age group 6 (ages 65 and older) that commutes to work
Note: The position where table values begin and the number of characters per value are not relevant in a tab-
delimited format.
01001020100 1.0000 0.0000 1.0000 0.0000 1.0000 0.0000 0.0000 1.0000 0.0101 0.9899
0.0000 1.0000
01001020200 1.0000 0.0000 1.0000 0.0000 1.0000 0.0000 0.0000 1.0000 0.0149 0.9851
0.0000 1.0000
01001020300 1.0000 0.0000 1.0000 0.0000 1.0000 0.0000 0.0909 0.9091 0.0334 0.9666
0.0000 1.0000
01001020400 1.0000 0.0000 1.0000 0.0000 1.0000 0.0000 0.0000 1.0000 0.0379 0.9621
0.1374 0.8626
01001020501 1.0000 0.0000 1.0000 0.0000 1.0000 0.0000 0.0000 1.0000 0.0589 0.9411
0.0000 1.0000
01001020502 1.0000 0.0000 1.0000 0.0000 1.0000 0.0000 0.0000 1.0000 0.0051 0.9949
1.0000 0.0000
01001020503 1.0000 0.0000 1.0000 0.0000 1.0000 0.0000 0.0000 1.0000 0.0896 0.9104
1.0000 0.0000
01001020600 1.0000 0.0000 1.0000 0.0000 1.0000 0.0000 0.0000 1.0000 0.0717 0.9283
0.1000 0.9000
01001020700 1.0000 0.0000 1.0000 0.0000 1.0000 0.0000 0.0000 1.0000 0.0178 0.9822
0.0000 1.0000
01001020801 1.0000 0.0000 1.0000 0.0000 1.0000 0.0000 0.1304 0.8696 0.0917 0.9083
0.0000 1.0000
Note: Contents wrap around due to space constrictions in this figure.
Figure 12. Excerpt from the HAPEM8 Commuting-fraction File
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e. Distance-to-road File -
"proximity_road_HAPEM8.txt"
The HAPEM distance-to-road file ("proximity_road_HAPEM8.txt" in HAPEM8) contains
information on the fraction of a tract's residents that live within each of three categories
of distance from a major roadway, by age group. These distances are 0-75 m, greater
than 75 m up to 200 m, and greater than 200 m. The file contains these data for all tracts
in the HAPEM8 population file. We conducted the proximity assessment at the level of
census blocks and stratified by age and sex, and then we aggregated the block-level
results up to the tract level and stratified only by age group.
We used block-level geographies from the 2020 Census TIGER/Line Shapefiles10 for all
areas except the USVI. We used block-level population data from the 2020 census' Table
P12 ("Sex by Age for Selected Age Categories"). For the USVI, we used 2020 tract-level
geographies and population data from the 2020 census' Table PCT1 ("Sex by Single Years
of Age", U.S. Virgin Islands). We downloaded the geometries and demographic data
separately before joining them into a single table in a PostGIS server.
We compiled roadway location data from the 2022 Census TIGER/Line "All Roads" U.S.
roadway layer. We considered the three roadway types shown in Table © to be major
roads for the purposes of evaluating enhanced pollutant exposure to people living near
heavy-use roads, assuming that other features such as traffic circles, cul-de-sacs, local
or neighborhood roads, rural roads, and city streets do not meet the definition.
Table 6. Types of "Major" Roads Included in the Roadway-proximity Assessment
Roadway Type Definition
Primary Road Generally divided, limited-access highways within the interstate highway
system or under state management, and distinguished by the presence of
interchanges. Accessible by ramps and may include some toll highways.
Ramp Allows controlled access from adjacent roads onto a limited-access highway,
often in the form of a cloverleaf interchange.
Secondary Road Main arteries, usually in the U.S., state, or county highway systems. Have one or
more lanes of traffic in each direction, may or may not be divided, and usually
have at-grade intersections with many other roads and driveways.
We used Postgres software utilizing PostGIS to perform the steps noted below for the
roadway-proximity geospatial analyses.
10 As of August 2023, the U.S. Census TIGER/Line data available at https://www.census.gov/geo/maps-
data/data/tiger-line.html.
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1. We created 75- and 200-m buffers around all major roadways. We clipped these
buffers at the boundaries of census blocks, such that no buffer crossed a block
boundary.
2. We assumed uniform population across blocks and used area analysis to calculate
the ratio of each block area within each buffer. For each block, we calculated the
fraction of the area that was within the 75-m buffer, the fraction that was within the
200-m buffer (subtracting the 75-m portion to create results for the 75-to-200-m
distance), and the fraction that was outside the 200-m buffer. We calculated the
ratios of (block area that fell within each of the major-roadway buffers) divided by
(total block area).
3. For each block and buffer, we multiplied the ratio from Step 2 above by the block
population count per gender and age group. These are the numbers of people
residing 0-75 m, greater than 75 m up to 200 m, and greater than 200 m of a major
roadway, at the block level and stratified by sex and age.
4. We aggregated the data from Step 3 above to the tract level and summed together
the male and female data. We then divided the population counts within the major-
roadway buffers by the total tract population, stratified by each of the six HAPEM
age groups. The result for each age group is the fraction of residents who live within
each of the three distance buffers of a major roadway.
i. Quality Assurance
We implemented several layers of QA with multiple staff members at different stages of
the processing. A major focus was on calculations performed in Step 4 above (i.e., the
final steps of processing population data and aggregating to the tract level). We reviewed
the block-level population data to ensure they were complete, and we reviewed our
processed block-level results to ensure they included all blocks nationwide.
We checked that the major-roadway buffer ratios from Step 3 summed to 1 for every
block (and in Step 4 summed to 1 for every tract). In this process, we implemented post-
processing algorithms to remove rounding errors so that fractions summed to 1 where
appropriate (when processed at 4 decimal places).
We spot-checked that the processed tract population data summed to the correct
state-total populations and summed correctly across age groups. We also noted that we
should not always expect the fraction of tract area within the individual major-roadway
buffers to equal the fraction of tract population within the buffers. This is because we
performed the assessment at the block level and then aggregated to the tract level,
where each block has a unique population density that makes aggregated populations
unequal to aggregated areas.
We also discovered that HAPEM8 throws an error if any age group in a tract has all its
population living within 75 m of a major roadway. This happened with a single tract, and
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we worked around the model error by setting 99.98% (0.9998) living in that buffer, with
0.01% (0.0001) living in the second buffer and again 0.01% living in the third buffer.
ii. Content and Format
The HAPEM8 distance-to-road data are contained in a tab-delimited text file with
characteristics shown in Table 7. The file contains 22 columns and a total of 85,427 rows of
data with no header rows. Each row corresponds to a tract, where the first field contains
the tract identifier using census FIPS coding, fields 2-4 contain the fractions of tract area
within each of the three roadway buffers, and the remaining fields show similar data for
the fractions of people in each HAPEM age group who reside within those buffers. All
values in fields 2-22 are displayed to four decimal places. The population fractions in
tracts with 0 residents are shown as 0 values. In Figure 13 we show the first ten data rows
of the distance-to-road file.
Table 7. Characteristics of the HAPEM8 Distance-to-road File
Field
Number Description
1 Full census FIPS code for home tract
2 Proportion of tract area located 0-75 m from major roadway
3 Proportion of tract area located beyond 75 m of major roadway, up to 200 m
4 Proportion of tract area located beyond 200 m of major roadway
5 Proportion of age group 1 (ages 0-1 years) residing 0-75 m from major roadway
6 Proportion of age group 1 (ages 0-1) residing > 75 m of major roadway, up to 200 m
7 Proportion of age group 1 (ages 0-1) residing > 200 m of major roadway
8 Proportion of age group 2 (ages 2-4) residing 0-75 m from major roadway
9 Proportion of age group 2 (ages 2-4) residing > 75 m of major roadway, up to 200 m
10 Proportion of age group 2 (ages 2-4) residing > 200 m of major roadway
11 Proportion of age group 3 (ages 5-15) residing 0-75 m from major roadway
12 Proportion of age group 3 (ages 5-15) residing > 75 m of major roadway, up to 200 m
13 Proportion of age group 3 (ages 5-15) residing > 200 m of major roadway
14 Proportion of age group 4 (ages 16-17) residing 0-75 m from major roadway
15 Proportion of age group 4 (ages 16-17) residing > 75 m of major roadway, up to 200 m
16 Proportion of age group 4 (ages 16-17) residing > 200 m of major roadway
17 Proportion of age group 5 (ages 18-64) residing 0-75 m from major roadway
18 Proportion of age group 5 (ages 18-64) residing > 75 m of major roadway, up to 200 m
19 Proportion of age group 5 (ages 18-64) residing > 200 m of major roadway
20 Proportion of age group 6 (ages 65 and older) residing 0-75 m from major roadway
21 Proportion of age group 6 (ages 65 and older) residing > 75 m of major roadway, up to 200 m
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Field
Number Description
22 Proportion of age group 6 (ages 65 and older) residing > 200 m of major roadway
Note: The position where table values begin and the number of characters per value are not relevant in a tab-
delimited format.
01001020100 0.0601 0.0865 0.8534 0.0778 0.1077 0.8145 0.0778 0.1077 0.8145 0.0512
0.0889 0.8599 0.0903 0.1279 0.7818 0.0412 0.0743 0.8845 0.0607 0.0998 0.8395
01001020200 0.0526 0.0644 0.8830 0.0562 0.0716 0.8722 0.0562 0.0716 0.8722 0.0421
0.0552 0.9027 0.0543 0.0742 0.8715 0.0586 0.1170 0.8244 0.0525 0.0752 0.8723
01001020300 0.0740 0.1116 0.8144 0.0598 0.0978 0.8424 0.0598 0.0978 0.8424 0.0403
0.0752 0.8845 0.0572 0.0898 0.8530 0.0501 0.0951 0.8548 0.0937 0.1450 0.7613
01001020400 0.1151 0.1740 0.7109 0.1024 0.1859 0.7117 0.1024 0.1859 0.7117 0.1000
0.2404 0.6596 0.1250 0.2122 0.6628 0.1107 0.1902 0.6991 0.1082 0.2026 0.6892
01001020501 0.0800 0.1126 0.8074 0.0322 0.0527 0.9151 0.0322 0.0527 0.9151 0.0240
0.0453 0.9307 0.0378 0.0791 0.8831 0.0305 0.0568 0.9127 0.0235 0.0460 0.9305
01001020502 0.0000 0.0000 1.0000 0.0000 0.0000 1.0000 0.0000 0.0000 1.0000 0.0000
0.0000 1.0000 0.0000 0.0000 1.0000 0.0000 0.0000 1.0000 0.0000 0.0000 1.0000
01001020503 0.0563 0.0932 0.8505 0.0185 0.0312 0.9503 0.0185 0.0312 0.9503 0.0294
0.0496 0.9210 0.0181 0.0305 0.9514 0.0303 0.0512 0.9185 0.0519 0.0874 0.8607
01001020600 0.1428 0.2165 0.6407 0.1410 0.2209 0.6381 0.1410 0.2209 0.6381 0.1288
0.2146 0.6566 0.0941 0.1996 0.7063 0.1301 0.2333 0.6366 0.1211 0.2206 0.6583
01001020700 0.0524 0.0783 0.8693 0.0544 0.0994 0.8462 0.0544 0.0994 0.8462 0.0490
0.1127 0.8383 0.0523 0.1090 0.8387 0.0596 0.1172 0.8232 0.0648 0.1203 0.8149
01001020801 0.0152 0.0248 0.9600 0.0733 0.0347 0.8920 0.0733 0.0347 0.8920 0.0298
0.0320 0.9382 0.0403 0.0548 0.9049 0.0422 0.0493 0.9085 0.0693 0.0669 0.8638
Note: Contents wrap around due to space constrictions in this figure.
Figure 13. Excerpt from the HAPEM8 Distance-to-road File
3. Updating Activity Files - "durhw_HAPEM8.txt",
"cluster_HAPEM8.txt", and
"clustertrans_HAPEM8.txt"
We updated the HAPEM activity file ("durhw_HAPEM8.txt" in HAPEM8) to reflect the most
recent version of CHAD as of April 2020. This version of CHAD has nearly four times the
number of activity diaries as the version used for HAPEM7. Accordingly, we also updated
the HAPEM cluster file ("cluster_HAPEM8.txt" in HAPEM8) and the HAPEM cluster-
transition file ("clustertrans_HAPEM8.txt" in HAPEM8).
Starting with HAPEM5, we analyzed CHAD data to create longitudinal activity patterns
using Markov chains. In HAPEM8, we refit the Markov chain model to the most recent
CHAD to include more activity-pattern studies and, thus, more daily activity patterns.
The data analysis groups the daily patterns into one, two, or three activity categories (or
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"clusters") of similar activity patterns for each of 36 combinations of type of day (the
three day types of HAPEM: summer weekday, non-summer weekday, and weekend), age
(the six age groups discussed in this memo), and commuter type (two types: commutes
or does not commute). Whether one, two, or three activity clusters are assigned to a day-
age-commuter combination depends on the availability of CHAD data. For HAPEM8,17
day-age-commuter combinations were assigned three clusters, 2 were assigned two
clusters, and 17 were assigned one cluster. We defined clusters based on similar times
spent in five broad microenvironments (i.e., indoors residence, indoors other, outdoors
near-roadway, outdoor other, and in-vehicle).
In HAPEM, for each day-age-commuter combination, one daily activity pattern is
randomly selected from all the CHAD data that correspond to that combination. The
starting activity category (i.e., for the first day) is selected according to the relative
frequencies of each category. The activity category for the second day is selected
according to the transition probabilities from the starting category. Transition
probabilities are the relative frequencies of each activity category when the same
subject was in the starting category on the first day and the given activity category on
the next day. The activity category for the third day is selected according to the
transition probabilities from the second day's category. This is repeated for all days in the
day type, producing a sequence of daily activity categories. For a given simulated person,
each day is assigned an activity pattern representative of the day's activity category.
Once a particular activity pattern is selected as representative of an activity category,
that pattern is always used for that category for that simulated person. Further details on
the cluster and cluster-transition approach can be found in Appendix A of the HAPEM7
User's Guide (a 2015 memorandum from ICF to EPA's Ted Palma and Terri Hollingsworth).
For HAPEM8, we also forced our analysis of CHAD to consider children in the first three
age groups (through age 15 years) to never be commuters (even if CHAD has them
"working"). This was to better comply with the census-based commuting data (discussed
in this memo) where workers start at age 16 years. We had to create "dummy" records in
the cluster-transition file for commuting children, since HAPEM8 expects these records
to be present in the file even though they are never used by the model. The result is that
the "clustrans_HAPEM8.txt" output file has 36 data records, one for each combination of
the 6 demographic groups, 3 day types, and 2 commuting categories, and the 9
categories of commuting children under 16 years old are dummy records.
f. Quality Assurance
We ensured that each CHAD record was represented in the activity file and formatted
appropriately, including the proper sets of columns for each day-age-commuter
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combination. We ensured that the same CHAD records were represented in the cluster
file and formatted appropriately. We ensured that the cluster-transition file contained
the correct combinations of day-age-commuter and was formatted appropriately.
The HAPEM8 activity and cluster files both contain 178,621 records with the same CHADID
on the same record in both files. This was a change for HAPEM8 that allowed us to
simplify HAPEM8 algorithms (counterbalanced by more complex code to develop the
activity files). By having matching CHADIDs, all diaries are available for use in HAPEM8 and
will remain available for future CHAD updates.
g. Content and Format
The HAPEM8 activity data are contained in a fixed-width, space-delimited text file with
characteristics shown in Table 8. The file contains 878 columns and a total of 178,621 rows
of data with one header row. Each row corresponds to a person-day of activity in CHAD,
where the first field contains an identifier for the record, the next 12 fields can be used
together to describe the study respondent, and the remaining fields contain duration
values for how long the subject spends in each microenvironment, for each hour of a day,
and at work versus at home. All values in fields 15-878 are displayed as whole numbers
(i.e., whole minutes). In Figure 14 we show the header and first data row of the HAPEM8
activity file. This record is from a white non-Hispanic female from an unspecified county
in California. She was a child between 1 and 2 years old (unemployed and non-
commuting). This record was from 16 June 1989, which was a summer weekday. She spent
most of her day indoors at home, except in the afternoon when she was outdoors for 1
hour total, in a vehicle for 40 minutes total, and in some other indoor location for 35
minutes total.
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Table 8. Characteristics of the HAPEM8 Activity File
Variable
Number Variable Description
Character
Start Character
Position on Length on
Data Row Data Row8
CHADID
ID of event in CHAD
10
5
6
10
11
12
13
14
ZIP
ST
COU
SEX
RACE
WORK
YEAR
MN
DY
AGE
G
DT
CT
ZIP code of subject's residence
2-character FIPS code of state where event took
place
3-character FIPS code of county where event took
place
Gender of subject (1=female, 2=male, 9=unknown)
Race of subject (1=white non-Hispanic, 2=black non-
Hispanic, 3=Hispanic any race, 4=Asian or other non-
Hispanic, 9=unknown)
Employment status of subject ("Y"=employed,
"N"=unemployed, "X"=missing)
Year when the event took place
Month when the event took place
Day of month when event took place
Age of subject (presented to two decimal places)
HAPEM8 age group (1-6)
Type of day when the event took place (1=summer
weekday, 2=non-summer weekday, 3=weekend)
Commuter status of subject (1=does not commute,
2=commutes)
11 6
17 3
20 4
24 4
28 5
33 5
38 5 or 6,
depending
on the
next field
Field length varies such
that the last digit of
each month entry lines
up
Field length varies such
that the last digit of
each day entry lines up
51
57
60
63
15-878 No header text Duration of event (minutes). There are 864 of these Field lengths vary such
fields, cycling through each of the 18 that the last digit of
microenvironments, 24 hours of the day, and 2 each duration entry lines
commute types. The values are sequenced so that the up down the file
18 microenvironment durations for the first hour in the
home location come first, followed by the 18
microenvironment durations for the second hour in
the home location, and so on, until all the 432 values
for the home location are specified. These are
followed by the 432 values for the work location.
1. a Any unused character space before a number or character and/or between fields
consists of blank spaces.
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CHADID
ZIP
ST
cou
SEX
RACE
WORK YEAR
MN DY
AGE
G
DT CT
CAC 0116 6A
93277
06
000
1
1
N
1989
6 16
1. 67
1
1
1
60
0
0
0 0
0 0
0
0
0
0
0
0
0
0
0
0
0
60
0
0
0
0
0
0
0
0 0
0
0
0
0
0
0
0
0
60
0
0
0
0
0
0
0
0
0
0
0 0
0
0
0
0
0
60
0
0
0
0
0
0
0
0
0
0
0
0
0
0 0
0
0
60
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0 60
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
60
0
0 0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
60
0
0
0
0
0 0
0
0
0
0
0
0
0
0
0
0
0
60
0
0
0
0
0
0
0
0 0
0
0
0
0
0
0
0
0
60
0
0
0
0
0
0
0
0
0
0
0 0
0
0
0
0
0
60
0
0
0
0
0
0
0
0
0
0
0
0
0
0 0
0
0
60
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0 60
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
60
0
0 0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
30
0
0
0
0
0 0
0
0
0
0
0
0
0
0
0
0
30
30
0
0
0
0
0
0
0
0 0
0
0
0
0
0
0
0
30
60
0
0
0
0
0
0
0
0
0
0
0 0
0
0
0
0
0
45
0
0
0
0
0
15
0
0
0
0
0
0
0
0 0
0
0
60
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0 0
0
0
0
35
0
25
0
0
0
0
0
0
0
0
0
0
0
60
0
0 0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
60
0
0
0
0
0 0
0
0
0
0
0
0
0
0
0
0
0
60
0
0
0
0
0
0
0
0 0
0
0
0
0
0
0
0
0
60
0
0
0
0
0
0
0
0
0
0
0 0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0 0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0 0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0 0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0 0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0 0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0 0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0 0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0 0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0 0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0 0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0 0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0 0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0 0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0 0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0 0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0 0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0 0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0 0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0 0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0 0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Note: Contents wrap around due to space constrictions in this figure.
Figure 14. Excerpt from the HAPEM8 Activity File
The HAPEM8 activity-cluster data are contained in a fixed-width, space-delimited text
file with characteristics shown in Table 9. The file contains six columns and a data row
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corresponding to each data row in the activity file, plus one header row. The first field
contains an identifier for the record, the next three fields together identify the age-day-
commuter combination of the event, and the final two fields respectively identify the
cluster number of the event and the number of clusters that exist for all records
corresponding to the age-day-commuter combination. In Figure 15 we show the first ten
data rows of the HAPEM8 cluster file, indicating all are in the first age group, on summer
weekdays, for non-commuters, and all are in the first cluster and all belong to only one
cluster.
Table 9. Characteristics of the HAPEM8 Cluster File
Variable
Number
Variable Description
Character
Start Position
on Data Row
Character
Length on
Data Row8
1
g
HAPEM8 age group (1-6)
1
5
2
dt
Type of day when the event took place (1=summer
weekday, 2=non-summer weekday, 3=weekend)
6
5
3
ct
Commuting status of subject (1=does not commute,
2=commutes)
11
5
4
chadid
ID of event in CHAD
16
12
5
clus
Cluster category of event
28
5
6
nclus
Number of clusters for the corresponding combination of
g, dt, and ct
33
1
2. a Any unused character space before a number or character and/or between fields consists of blank
spaces.
g
dt
ct
chadid
clus
nclus
l
1
1
CAC 0116 6A
1
1
l
1
1
C AC 01251A
1
1
l
1
1
C AC 0148 9A
1
1
l
1
1
C AC 015 6 2A
1
1
l
1
1
CAC 015 6 8A
1
1
l
1
1
C AC 0180 9A
1
1
l
1
1
CAC 0183 OA
1
1
l
1
1
CAC 019 8 2A
1
1
l
1
1
CAC02 03 6A
1
1
l
1
1
CAC 0 213 2A
1
1
Figure 15. Excerpt from the HAPEM8 Cluster File
The HAPEM8 activity-cluster-transition data are contained in a fixed-width, space-
delimited text file with characteristics shown in Table 10. The file contains 16 columns, with
a data row corresponding to each age-day-commuter combination, plus a header row.
The first three fields identify the age group, day type, and commuter status, while the
fourth field identifies the number of clusters that exist for that age-day-commuter
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combination, fields 5-7 contain the cumulative fractions of the combination within each
cluster, and the remaining fields identify the cumulative transition probabilities of all
possible combinations of the subject's cluster number on day X and the subject's cluster
number on day X+1. In Figure 16 we show the age group #1 data rows of the HAPEM8
cluster-transition file. Several of the day-commuter combinations shown in the excerpt
(day-type 1 with both commuting status, day-type 2 with commuting, and day-type 3
with commuting) are all in cluster #1. For day-type 2 non-commuting, about 49% of
diaries are in cluster #1, 89% are in cluster #2, and all are in cluster #3. Logically there is a
100% probability of a cluster #1, 2, or 3 diary transitioning to a cluster #1, 2, or 3 diary (i.e.,
values of 1.00000). There are relatively high probabilities of a cluster #1 diary
transitioning to a cluster #1 diary (0.60714) or a cluster #2 diary transitioning to a cluster
#1 or 2 diary (0.86207). There are relatively low probabilities of a cluster #2 or #3 diary
transitioning to a cluster #1 diary (0.13793 and 0.11111, respectively) or a cluster #3 diary
transitioning to a cluster #1 or 2 diary (0.33333). For day-type 3 non-commuting, about
55% (0.54806) of diaries are in cluster #1, 89% (0.88666) are in cluster #2, and all are in
cluster #3 (1.00000). Logically there is a 100% probability of a cluster #1, 2, or 3 diary
transitioning to a cluster #1, 2, or 3 diary (i.e., values of 1.00000). There are relatively high
probabilities of a cluster #1 diary transitioning to a cluster #1 diary (0.90000), a cluster
#2 diary transitioning to a cluster #1 or 2 diary (0.62500), or a cluster #3 diary
transitioning to a cluster #1 or 2 diary (0.75000), with a 50% (0.50000) probability of a
cluster #3 diary transitioning to a cluster #1 diary. There is a relatively low probability of a
cluster #2 diary transitioning to a cluster #1 diary (0.37500).
Table 10. Characteristics of the HAPEM8 Cluster-transition File
Character
Character
Variable
Start Position
Length on
on Data Row
Data Rowb
1 g HAPEM8 age group (1-6) 1 4
2 dt Type of day when the event took place (1=summer 5 4
weekday, 2=non-summer weekday, 3=weekend)
3 ct Commuting status of subject (1=does not commute, 9 4
2=commutes)
4 nclus Number of clusters for the corresponding combination 13 3
of g, dt, and ct
5 clustl Cumulative fraction of g/dt in cluster #1 16 8
6 clust2 Cumulative fraction of g/dt in clusters #1-2 24 8
7 clust3 Cumulative fraction of g/dt in clusters #1-3 32 8
8 prob11 Cumulative transition probability from cluster #1 to #1 40 8
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Variable
Number Variable Description8
Character
Start Position
on Data Row
Character
Length on
Data Rowb
9
prob12
Cumulative transition probability from cluster #1 to
clusters #1-2
48
8
10
prob13
Cumulative transition probability from cluster #1 to
clusters #1-3
56
8
11
prob21
Cumulative transition probability from cluster #2 to #1
64
8
12
prob22
Cumulative transition probability from cluster #2 to
clusters #1-2
72
8
13
prob23
Cumulative transition probability from cluster #2 to
clusters #1-3
80
8
14
prob31
Cumulative transition probability from cluster #3 to #1
88
8
15
prob32
Cumulative transition probability from cluster #3 to
clusters #1-2
96
8
16
prob33
Cumulative transition probability from cluster #3 to
clusters #1-3
104
7
a For the cluster* fields, if nclus = 1 then clust2 and clust3 = 0 in the file; similarly, if nclus = 2 then
clust3 = 0. The same is true for the prob* fields (if nclus = 1 then profc>12, prob13, prob21, prob22,
prob23, prob31, prob32, and prob33 = 0, and if nclus= 2 then prob13, prob23, prob31, prob32, and
prob33 = 0).
3. b Any unused character space before a number or character and/or between fields
consists of blank spaces.
g dt ct nclus clustl clust2 clust3 probll probl2 probl3 prob21 prob22
prob23 prob31 prob32 prob33
1111 1.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000
0.00000 0.00000 0.00000 0.00000
112 1 1.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000
0.00000 0.00000 0.00000 0.00000
12 13 0.48625 0.88567 1.00000 0.60714 1.00000 1.00000 0.13793 0.86207
1.00000 0.11111 0.33333 1.00000
12 2 1 1.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000
0.00000 0.00000 0.00000 0.00000
13 13 0.54806 0.88666 1.00000 0.90000 1.00000 1.00000 0.37500 0.62500
1.00000 0.50000 0.75000 1.00000
13 2 1 1.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000
0.00000 0.00000 0.00000 0.00000
Note: Contents wrap around due to space constrictions in this figure.
Figure 16. Excerpt from the HAPEM8 Cluster-transition File
AirToxScreen 2020 Documentation
E-27
-------
3. Updating Source Code
We made several modifications to various source-code modules for HAPEM8. Most
modifications were minor and functioned either to ensure proper execution from the
command line or to ensure that data-array dimensions were large enough to
accommodate the revised default model input data discussed in this memorandum. The
changes to the "durav" module were more significant. We describe below the specific
changes we made to the specific modules.
"durav_HAPEM8.f90" (compiled into an executable named "durav_HAPEM8.exe"):
Simplified code since updated activity input files already were sorted consistently.
The number of code lines now is reduced by more than half.
"indexpop_HAPEM8.f90" (compiled into an executable named "indexpop_HAPEM8.exe"):
No changes.
"commute_HAPEM8.f90" (compiled into an executable named
"commute_HAPEM8.exe"):
Increased seven array bounds from 80000 to 99000.
Updated the status for two files to eliminate a compiler warning.
"airqual_HAPEM8.f90" (compiled into an executable named "airqual_HAPEM8.exe"):
No changes.
"hapem_HAPEM8.f90" (compiled into an executable named "hapem_HAPEM8.exe"):
Broke down one large seven-dimensional array into six six-dimensional arrays, one for
each demographic group.
Revised the reading of the commuting database file to six times (for six demographic
groups) rather than one time overall.
AirToxScreen 2020 Documentation
E-28
-------
Table E-l. HAPs Assessed in AirToxScreen, with their HAPEM8 HAP Phases and Surrogate Chemical
Assignments
Boiling Point
AirToxScreen
Matching Chemical
Modeled in HAPEMd
Walue
(°C)a
source"
Phase0
Phase0
PP
NNP
MM-ORMM-NR
Acetaldehyde
20
CDC
G
ACETALD
G
Benz
Benz
Benz
Benz
Acetamide
165
CDC
G
ACETAMIDE
G
Benz
Benz
Benz
Benz
Acetonitrile
82
CDC
G
ACETONIT
G
Benz
Benz
Benz
Benz
Acetophenone
202
NIH
G
ACETOPHEN
G
Benz
Benz
Benz
Benz
Acrolein
53
CDC
G
ACROLEI
G
Benz
Benz
Benz
Benz
Acrylic acid
141
CDC
G
ACRYLCACID
G
Benz
Benz
Benz
Benz
Acrylamide
175
CDC
G
ACRYLMID
G
Benz
Benz
Benz
Benz
Acrylonitrile
77
CDC
G
ACRYLONITRL
G
Benz
Benz
Benz
Benz
2-Acetylaminofluorene
400 P
CS
G/P
ACTYALFLUR2
G/P
PAH
PAH
PAH
PAH
Allyl chloride
45
CDC
G
ALLYLCHLORD
G
Benz
Benz
Benz
Benz
4-Aminobiphenyl
302
CDC
G/P
AMNOBIPNYL4
G/P
PAH
PAH
PAH
PAH
Aniline
184
CDC
G
ANILINE
G
Benz
Benz
Benz
Benz
Antimony
1,587
CDC
P
ANTIMONY
P
Cr6
Cr6
Ni
Ni
Arsenic
612
CDC
P
ARSENIC
P
Cr6
Cr6
Ni
Ni
Benzene
80
CDC
G
BENZENE
G
Benz
Benz
Benz
Benz
Benzidine
400
CDC
G/P
BENZIDINE
G/P
PAH
PAH
PAH
PAH
Benzotrich loride
221
NIH
G
BENZOTRICHL
G
Benz
Benz
Benz
Benz
Benzyl chloride
179
NIH
G
BENZYLCHLO
G
Benz
Benz
Benz
Benz
BERYLLIUM
2,500
NIH
P
BERYLLIUM
P
Cr6
Cr6
Ni
Ni
Biphenyl
256
NIH
G
BIPHENYL
G
Benz
Benz
Benz
Benz
Bis(chloromethyl)ether
106
CDC
G
BISCHLROME
G
Benz
Benz
Benz
Benz
Bromoform
149
CDC
G
BROMOFORM
G
Benz
Benz
Benz
Benz
Bis(2-ethylhexyl)phthalate
386
CDC
G/P
BS2TYLHXYLP
G/P
PAH
PAH
PAH
PAH
beta-Propiolactone
162
httoV/www.cdc.aov/niosh/
G
BT-PRPLACTN
G
Benz
Benz
Benz
Benz
docs/81-
123/Ddfs/0528.Ddf
1,3-Butadiene
138
NIH
G
BUTADIE
G
Buta
Buta
Buta
Buta
Cadmium
765
CDC
P
CADMIUM
P
Cr6
Cr6
Ni
Ni
Calcium cyanamide
>2,444
CDC
P
CALCIUMCYA
P
Cr6
Cr6
Ni
Ni
Captan
314
CS
G/P
CAPTAN
G/P
PAH
PAH
PAH
PAH
Carbaryl
315
CS
G/P
CARBARYL
G/P
PAH
PAH
PAH
PAH
Carbon disulfide
47
CDC
G
CARBNDISULF
G
Benz
Benz
Benz
Benz
Carbonyl sulfide
-50
NIH
G
CARBNYLSUL
G
Benz
Benz
Benz
Benz
Carbon tetrachloride
77
CDC
G
CARBONTET
G
Benz
Benz
Benz
Benz
Catechol
245
CDC
G
CATECHOL
G
Benz
Benz
Benz
Benz
Chloroform
62
CDC
G
CHCL3
G
Benz
Benz
Benz
Benz
Chloroacetic acid
106
CDC
G
CHLACETACD
G
Benz
Benz
Benz
Benz
2-Chloroacetophenone
244
CDC
G
CHLACETPH2
G
Benz
Benz
Benz
Benz
Chloramben
312
CS
G/P
CHLORAMBEN
G/P
PAH
PAH
PAH
PAH
Chlordane
175
NIH
G
CHLORDANE
G
Benz
Benz
Benz
Benz
Chlorine
-33
CDC
G
CHLORINE
G
Benz
Benz
Benz
Benz
Chloroprene
59
CDC
G
CHLOROPRENE
G
Benz
Benz
Benz
Benz
Chlorobenzilate
146
NIH
G
CHLROBZLAT
G
Benz
Benz
Benz
Benz
Chlorobenzene
132
CDC
G
CHLROBZNE
G
Benz
Benz
Benz
Benz
Chloromethyl methyl ether
59
CDC
G
CHLROMME
G
Benz
Benz
Benz
Benz
Chromium (VI)
2,642
CDC
P
CHROMHEX
P
Cr6
Cr6
Ni
Ni
Chromium Trioxide
250
CDC
G/P
AirToxScreen 2020 Documentation
E-29
-------
Chem.
AirToxScreen
Name
Boiling Point
Matching Chemical
Modeled in HAPEMd
Walue
(°C)a
Source13
Phase0
Phase0
PP
NNP
MM-ORMM-NR
Chromium III
2,672
httD://books.aooale.com/b
ooks?id=SFD30BvPBhoC
&Da=PA123&ba=PA123
&da=chromium+lll+rneltin
a+Doint&source=bl&ots=u
3HliDrKMv&sia=dlSMKFL
5z0sVI0z8Z4NhlsFHaaE
&hl=en&sa=X&ei=4nklVP
LvJ4LS8AGbiYD4DA&ve
d=0CFkQ6AEwCQ#v=on
eDaae&a=chromium%20l I
l% 20melti na% 20ooi nt&f=f
alse
P
CHROMTRI
P
Cr6
Cr6
Ni
Ni
Ethylene dichloride
83
CDC
G
CL2 C2 12
G
Benz
Benz
Benz
Benz
T rich loroethy lene
87
CDC
G
CL3 ETHE
G
Benz
Benz
Benz
Benz
Cobalt
3,100
CDC
P
COBALT
P
Cr6
Cr6
Ni
Ni
Coke oven emissions
V
CDC
G/P
COKEOVEN
G/P
Coke
Coke
Coke
Coke
Cresol/Cresylic Acid (Mixed Isomers)
202
CDC
G
CRESOLS
G
Benz
Benz
Benz
Benz
m-Cresol
202
CDC
G
o-Cresol
191
CDC
G
p-Cresol
202
CDC
G
Cumene
152
CDC
G
CUMENE
G
Benz
Benz
Benz
Benz
Cyanide
V
CDC
G/P
CYANIDE
G
Benz
Benz
Benz
Benz
Hydrogen cyanide
26
CDC
G
2,4-Dichlorophenoxy Acetic Acid
345 P
CS
G/P
D24SALTS
G/P
PAH
PAH
PAH
PAH
Diethyl sulfate
210
NIH
G
DETHLSULFAT
G
Benz
Benz
Benz
Benz
Diazomethane
-23
CDC
G
DIAZOMETHAN
G
Benz
Benz
Benz
Benz
Dibenzofuran
287
NIH
G/P
DIBENZOFUR
G/P
PAH
PAH
PAH
PAH
1,2-Dibromo-3-chloropropane
196
CDC
G
DIBRM3CHLPR
G
Benz
Benz
Benz
Benz
Dibutyl Phthalate
340
CDC
G/P
DIBUTYLPHTH
G/P
PAH
PAH
PAH
PAH
3,3'-Dichlorobenzidine
400
NIH
G/P
DICHLBZD33P
G/P
PAH
PAH
PAH
PAH
Dichlorvos
140 at 40
mmHG
NIH
G
DICHLORVOS
G
Benz
Benz
Benz
Benz
1,4-Dichlorobenzene
173
CDC
G
DICHLRBNZN
G
Benz
Benz
Benz
Benz
Dichloroethyl ether
177
CDC
G
DICHLROETET
G
Benz
Benz
Benz
Benz
1,3-Dichloropropene
108
NIH
G
DICLPR013
G
Benz
Benz
Benz
Benz
Diesel PM
V
httDV/www.eoa.aov/reaior
1 /eco/ai rtox/diesel htm I
P
DIESEL_PM10
P
DPM
DPM
DPM
DPM
Diethanolamine
268
NIH
G/P
DIETHNLAMIN
G/P
PAH
PAH
PAH
PAH
Dimethylcarbamoyl chloride
167
NIH
G
DIMTHYCARB
G
Benz
Benz
Benz
Benz
N,N-Dimethylformamide
153
CDC
G
DIMTHYFORM
G
Benz
Benz
Benz
Benz
4-Dimethylaminoazobenzene
371
CS
G/P
DIMTHYLAMAZ
G/P
PAH
PAH
PAH
PAH
Dimethyl phthalate
284
NIH
G/P
DIMTHYLPHTH
G/P
PAH
PAH
PAH
PAH
Dimethyl sulfate
188
NIH
G
DIMTHYLSULF
G
Benz
Benz
Benz
Benz
3,3'-Dimethylbenzidine
300
CDC
G/P
DIMTYLBZ33P
G/P
PAH
PAH
PAH
PAH
1,1-Dimethyl Hydrazine
64
CDC
G
DIMTYLHYD11
G
Benz
Benz
Benz
Benz
3,3'-Dimethoxybenzidine
391
CS
G/P
DIMTYOXB33P
G/P
PAH
PAH
PAH
PAH
2,4-Dinitrotoluene
300
NIH
G/P
DINITOTOL24
G/P
PAH
PAH
PAH
PAH
4,6-Dinitro-o-cresol
312
CDC
G/P
DINITROCRES
G/P
PAH
PAH
PAH
PAH
2,4-Dinitrophenol
312
CS
G/P
DINITROPH24
G/P
PAH
PAH
PAH
PAH
1,2-Diphenylhydrazine
293
NIH
G/P
DIPYLHYZ12
G/P
PAH
PAH
PAH
PAH
N,N-dimethylaniline
192
CDC
G
DMTYLANILNN
G
Benz
Benz
Benz
Benz
Epichlorohydrin
118
NIH
G
EPICHLORO
G
Benz
Benz
Benz
Benz
1,2-Epoxybutane
63
NIH
G
EPOXYBUTA12
G
Benz
Benz
Benz
Benz
Ethylene dibromide
131
CDC
G
ETHDIBROM
G
Benz
Benz
Benz
Benz
Ethyleneimine
56
CDC
G
ETHENIMINE
G
Benz
Benz
Benz
Benz
Ethylene glycol
197
CDC
G
ETHGLYCOL
G
Benz
Benz
Benz
Benz
AirToxScreen 2020 Documentation
E-30
-------
Boiling Point
AirToxScreen
Matching Chemical
Name
Walue
(°C)a
Source13
Phase0
Name
Phase0
PP
NNP
MM-ORMM-NR
Ethylidene dichloride
-17
CDC
G
ETHIDDICHLD
G
Benz
Benz
Benz
Benz
Ethylene thiourea
230
CDC
G
ETHTHUREA
G
Benz
Benz
Benz
Benz
Ethyl acrylate
99
CDC
G
ETHYLACRYL
G
Benz
Benz
Benz
Benz
Ethyl benzene
136
CDC
G
ETHYLBENZ
G
Benz
Benz
Benz
Benz
Ethyl carbamate
185
NIH
G
ETHYLCARBA
G
Benz
Benz
Benz
Benz
Ethyl chloride
-139
NIH
G
ETHYLCHLRD
G
Benz
Benz
Benz
Benz
Ethylene oxide
11
CDC
G
ETOX
G
Benz
Benz
Benz
Benz
Formaldehyde
-21
CDC
G
FORMALD
G
Benz
Benz
Benz
Benz
1,2-Dimethoxyethane
82
NIH
G
GLYCOLETHR
G
Benz
Benz
Benz
Benz
2-(Hexyloxy)ethanol
258
NIH
G
2-Butoxyethyl acetate
192
CDC
G
2-Propoxyethyl Acetate
184
CS
G
Butyl carbitol acetate
245
NIH
G
Carbitol acetate
219
NIH
G
Cellosolve Acetate
145
CDC
G
Cellosolve Solvent
124
CDC
G
Diethylene glycol diethyl ether
189 E
CS
G
Diethylene glycol dimethyl ether
161 E
CS
G
Diethylene glycol ethyl methyl ether
168 P
CS
G
Diethylene glycol monobutyl ether
230
NIH
G
Diethylene glycol monoethyl ether
196
NIH
G
Diethylene glycol monomethyl ether
194 E
CS
G
Ethoxytriglycol
256
NIH
G
Ethylene glycol methyl ether
124
CDC
G
Ethylene Glycol Monomethyl Ether Acetate
145
CDC
G
Ethylene glycol mono-sec-butyl ether
192
CS
G
Glycol ethers
120-240
httoV/msdssearch.dow.co
G
m/PublishedLiteratureDO
WCOM/dh 012d/0901b8
038012d976. odf?f i leoat h
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110-
00977.Ddf&fromPaae=Ge
tDoc
Methoxytriglycol
249
httoV/msdssearch.dow.co
G
m/PublishedLiteratureDO
WCOM/dh 012d/0901b8
038012d976. odf?fi leoat h
=oxvsolvents/Ddfs/norea/
110-
00977.Ddf&fromPaae=Ge
tDoc
N-Hexyl carbitol
260 E
CS
G
Phenyl cellosolve
245 E
CS
G
Propyl cellosolve
150
httoV/msdssearch.dow.co
G
n/PublishedLiteratureDO
WCOM/dh 012d/0901b8
038012d976. odf?fi leoat h
=oxvsolvents/odfs/norea/
110-
00977.odf&fromPaae=Ge
tDoc
Triethylene Glycol Dimethyl Ether
215 E
CS
G
Triglycol monobutyl ether
278
NIH
G/P
Hydrochloric acid
-85
CDC
G
HCL
G
Benz
Benz
Benz
Benz
Heptachlor
392
CS
G/P
HEPTACHLOR
G/P
PAH
PAH
PAH
PAH
Hexamethylene Diisocyanate
212
NIH
G
HEXAMTHLE
G
Benz
Benz
Benz
Benz
Hexane
69
CDC
G
HEXANE
G
Benz
Benz
Benz
Benz
AirToxScreen 2020 Documentation
E-31
-------
Chem.
AirToxScreen
Name
Boiling Point
Name
Matching Chemical
Modeled in HAPEMd
Walue
(°C)a
Source13
Phase0
Phase0
PP
NNP
MM-ORMM-NR
Hexachloroethane
187
CDC
G
HEXCHLETHN
G
Benz
Benz
Benz
Benz
Hexachlorocyclopentadiene
238
CDC
G
HEXCHLPNTD
G
Benz
Benz
Benz
Benz
Hexachlorobutadiene
215
CDC
G
HEXCHLRBT
G
Benz
Benz
Benz
Benz
Hexachlorobenzene
325
NIH
G/P
HEXCHLROBZ
G/P
PAH
PAH
PAH
PAH
Hexamethylphosphoramide
233
NIH
G
HEXMTHPHO
G
Benz
Benz
Benz
Benz
Hydrogen Fluoride
19
CDC
G
HF
G
Benz
Benz
Benz
Benz
Mercury
356
CDC
G/P
HGSUM
G/P
PAH
PAH
PAH
PAH
1,2,3,4,5,6-Hexachlorocydohexane
323
CDC
G/P
HXCCL123456
G/P
PAH
PAH
PAH
PAH
Hydrazine
113
CDC
G
HYDRAZINE
G
Benz
Benz
Benz
Benz
Hydroquinone
285
CDC
G/P
HYDROQUIN
G/P
PAH
PAH
PAH
PAH
Isophorone
215
CDC
G
ISOPHORONE
G
Benz
Benz
Benz
Benz
LEAD
1,740
CDC
P
LEAD
P
Cr6
Cr6
Ni
Ni
Maleic anhydride
202
CDC
G
MALANHYD
G
Benz
Benz
Benz
Benz
Manganese
1,962
CDC
P
MANGANESE
P
Cr6
Cr6
Ni
Ni
4,4'-Methylenebis(2-Chloraniline)
209
NIH
G
MB2CLRAN44P
G
Benz
Benz
Benz
Benz
Methylene chloride
39
CDC
G
MECL
G
Benz
Benz
Benz
Benz
Methanol
64
CDC
G
METHANOL
G
Benz
Benz
Benz
Benz
Methoxychlor
89
NIH
G
METHOXYCHL
G
Benz
Benz
Benz
Benz
Methyl bromide
3
CDC
G
METHYLBROM
G
Benz
Benz
Benz
Benz
Methyl isobutyl ketone
116
CDC
G
MIBK
G
Benz
Benz
Benz
Benz
Fine Mineral Fibers
NA
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P
MINFIB
P
Cr6
Cr6
Ni
Ni
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Communications/united s
tates/oroduct promotional
materials/finished asset
s/usa-mineral-wool-300a-
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Methyl methacrylate
101
CDC
G
MMETACRYLAT
G
Benz
Benz
Benz
Benz
Methyl tert-butyl ether
55
NIH
G
MTBE
G
Benz
Benz
Benz
Benz
4,4'-Methylenedianiline
397
CDC
G/P
MTHYDIAN44P
G/P
PAH
PAH
PAH
PAH
Methyl chloride
-98
CDC
G
MTHYLCHLRD
G
Benz
Benz
Benz
Benz
Methyl Chloroform
74
CDC
G
MTHYLCHLRF
G
Benz
Benz
Benz
Benz
4,4'-Methylenediphenyl Diisocyanate
313
CS
G/P
MTHYLDPNLDS
G/P
PAH
PAH
PAH
PAH
Methylhydrazine
88
CDC
G
MTHYLHYZIN
G
Benz
Benz
Benz
Benz
Methyl iodide
43
CDC
G
MTHYLIODIDE
G
Benz
Benz
Benz
Benz
Methyl isocyanate
39
CDC
G
MTHYLISOCY
G
Benz
Benz
Benz
Benz
Naphthalene
260
CDC
G
NAPHTH
G
Benz
Benz
Benz
Benz
Nickel
2,913
CDC
P
NICKEL
P
Cr6
Cr6
Ni
Ni
Nickel oxide
1,955
NIH
P
Nickel refinery dust
2,730
httoY/www.cdc.aov/niosh/
P
docs/81-
123/Ddfs/0445.Ddf
4-Nitrobiphenyl
340
CDC
G/P
NITROBIPHL4
G/P
PAH
PAH
PAH
PAH
Nitrobenzene
211
CDC
G
NITROBNZNE
G
Benz
Benz
Benz
Benz
4-Nitrophenol
279
NIH
G/P
NITROPHENL4
G/P
PAH
PAH
PAH
PAH
2-Nitropropane
121
CDC
G
NITROPROPA2
G
Benz
Benz
Benz
Benz
N-Nitrosodimethylamine
152
CDC
G
NNITROSDIM
G
Benz
Benz
Benz
Benz
N-Nitrosomorpholine
224
NIH
G
NNITROSMPH
G
Benz
Benz
Benz
Benz
N-Nitroso-n-methylurea
164 P
CS
G
NNITROSURE
G
Benz
Benz
Benz
Benz
o-Anisidine
225
CDC
G
o-Anisidine
G
Benz
Benz
Benz
Benz
o-Toluidine
200
CDC
G
O-TOLUIDINE
G
Benz
Benz
Benz
Benz
Anthracene
342
NIH
G/P
PAH_000E0
G/P
PAH
PAH
PAH
PAH
Phenanthrene
340
NIH
G/P
Pyrene
404
NIH
P
3-Methylcholanthrene
178
httDV/www.soeclab.com/c
G
PAH J 01E2
G
Benz
Benz
Benz
Benz
omDound/c50328.htm
AirToxScreen 2020 Documentation
E-32
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Chem.
AirToxScreen
Name
Boiling Point
Matching Chemical
Modeled in HAPEMd
Walue
(°C)a
Source13
Phase0
Phase0
PP
NNP
MM-ORMM-NR
7,12-Dimethylbenz[a]anthracene
122
httD://www.siamaaldrich.c
om/cataloa/Droduct/suoel
co/442425?lana=en&reai
on=US
G
PAHJ14E1
G
Benz
Benz
Benz
Benz
Dibenzo[a,h]pyrene
308 E
CS
G/P
PAHJ76E2
P
Cr6
Cr6
Ni
Ni
Dibenzo[a,i]pyrene
552 P
cs
P
Dibenzo[a,l]pyrene
552 P
CS
P
5-Methylchrysene
449 P
cs
P
PAHJ76E3
P
Cr6
Cr6
Ni
Ni
7H-Dibenzo[c,g]carbazole
544 P
cs
P
Benzo[a]pyrene
360
httD://www.sDeclab.com/c
omDound/c50328.htm
G/P
Coal tar
>250
htto ://www. i nchem .ora/do
cuments/icsc/icsc/eicsl 41
5.htm
G/P
Dibenzo[a,e]pyrene
552 P
CS
P
Methylchrysene
449 P
CS
P
1 -Nitropyrene
445 P
cs
P
PAHJ76E4
P
Cr6
Cr6
Ni
Ni
Benz[a]anthracene
438
NIH
P
Benzo[b]fluoranthene
4,665 P
CS
P
Benzo[j]fluoranthene
480 E
CS
P
Dibenz[a,h]acridine
534 P
CS
P
Dibenzo[a,j]Acridine
534 P
CS
P
lndeno[1,2,3-c,d]pyrene
530
httD://www.sDeclab.com/c
omoound/cl 93395.htm
P
Benzo[k]fluoranthene
480
httD://www.sDeclab.com/c
omDound/c207089.htm
P
PAHJ76E5
P
Cr6
Cr6
Ni
Ni
Carbazole
355
httoV/www.siamaaldrich.c
om/cataloa/Droduct/siama
fc5132?lana=en&reaion=
US
G/P
Chrysene
448
httD://www.sDeclab.com/c
omDound/c218019.htm
P
Dibenzo[a,h]anthracene
262
httoV/www.siamaaldrich.c
om/cataloa/Droduct/suoel
co/48574?lana=en&reaio
n=US
G/P
PAHJ92E3
G/P
PAH
PAH
PAH
PAH
12-Methylbenz(a)anthracene
410 P
CS
P
PAH_880E5
G/P
PAH
PAH
PAH
PAH
1 -Methylnaphthalene
240
NIH
G
1-Methylphenanthrene
359
htto ://www. natu re. nos .aov
/hazardssafetv/toxic/ohen
1met.pdf
G/P
1 -Methylpyrene
372
httD://www.chemicalbook.
com/ChemicalProductPro
sertv EN CB7421679 ht
71
G/P
2-Chloronaphthalene
256
httD://www.chemicalbook.
com/ChemicalProductPro
sertv EN CB8854627.ht
71
G
2-Methylnaphthalene
241
httD://www.sDeclab.com/c
omDound/c91576.htm
G
2-Methylphenanthrene
339
CS
G/P
9-Methyl Anthracene
196
CS
G
Acenaphthene
279
NIH
G/P
Acenaphthylene
265
NIH
G/P
Benzo(a)fluoranthene
295
NIH
G/P
Benzo(c)phenanthrene
430 P
CS
P
Benzo(g,h,i)fluoranthene
406 P
CS
P
Benzo[e]pyrene
465 P
CS
P
AirToxScreen 2020 Documentation
E-33
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Chem.
AirToxScreen
Name
Boiling Point
Name
Matching Chemical
Modeled in HAPEMd
Walue
(°C)a
Source13
Phase0
Phase0
PP
NNP
MM-ORMM-NR
Benzo[g,h,i,]Perylene
550
NIH
P
Benzofluoranthenes
406 P
CS
P
Extractable Organic Matter (EOM)
NA
NA
NA
Fluoranthene
384
NIH
G/P
Fluorene
295
NIH
G/P
Methylanthracene
360 P
CS
G/P
Methylbenzopyrene
479 P
CS
G/P
PAH, total
240-400
httD://www.eDa.aov/rea3h
G/P
wmd/bf-
1 r/rea i o nal/anal vt i cal/sem i-
volatile.htm
Perylene
276
httD://www.siamaaldrich.c
G/P
om/cataloa/Droduct/aldric
h/394475?lana=en&reaio
n=US
Parathion
375
CDC
G/P
PARATHION
G/P
PAH
PAH
PAH
PAH
2,4,4'-Trichlorobiphenyl (PCB-28)
164
CS
G
PCB
G/P
PAH
PAH
PAH
PAH
4,4'-Dichlorobiphenyl (PCB-15)
144
CS
G
Decachlorobiphenyl (PCB-209)
460 P
CS
P
Heptachlorobiphenyl
415 P
CS
P
Hexachlorobiphenyl
396 P
CS
G/P
Pentachlorobiphenyl
365 E
CS
G/P
Polychlorinated biphenyls
365 E
CS
G/P
Tetrachlorobiphenyl
360 P
CS
G/P
p-Dioxane
101
CDC
G
P-DIOXANE
G
Benz
Benz
Benz
Benz
Tetrachloroethylene
121
CDC
G
PERC
G
Benz
Benz
Benz
Benz
Phenol
182
CDC
G
PHENOL
G
Benz
Benz
Benz
Benz
p-Phenylenediamine
267
CDC
G/P
PHNYLNDIAMI
G/P
PAH
PAH
PAH
PAH
Phosgene
8
CDC
G
PHOSGENE
G
Benz
Benz
Benz
Benz
Phosphine
88
CDC
G
PHOSPHINE
G
Benz
Benz
Benz
Benz
Phosphorus
280
CDC
G/P
PHOSPHORS
G/P
PAH
PAH
PAH
PAH
Phthalic anhydride
295
CDC
G/P
PHTHANHYDR
G/P
PAH
PAH
PAH
PAH
Pentachloronitrobenzene
328
NIH
G/P
PNTCHLNBZ
G/P
PAH
PAH
PAH
PAH
Pentachlorophenol
309
CDC
G/P
PNTCLPHENOL
G/P
PAH
PAH
PAH
PAH
Propylene dichloride
97
CDC
G
PROPDICLR
G
Benz
Benz
Benz
Benz
Propionaldehyde
48
NIH
G
PROPIONAL
G
Benz
Benz
Benz
Benz
1,3-Propanesultone
180
NIH
G
PROPNESLT13
G
Benz
Benz
Benz
Benz
Propoxur
D
CDC
NA
PROPOXUR
NA
EF=1
EF=1
EF=1
EF=1
Propylene oxide
34
CDC
G
PROPYLENEOX
G
Benz
Benz
Benz
Benz
1,2-Propylenimine
66
NIH
G
PROPYLENIMI
G
Benz
Benz
Benz
Benz
Quinoline
238
NIH
G
QUINOLINE
G
Benz
Benz
Benz
Benz
Quinone
S
CDC
NA
QUINONE
NA
EF=1
EF=1
EF=1
EF=1
Selenium
685
CDC
P
SELENIUM
P
Cr6
Cr6
Ni
Ni
Styrene
145
CDC
G
STYRENE
G
Benz
Benz
Benz
Benz
Styrene oxide
194
httoV/www.siamaaldrich.c
G
STYROXIDE
G
Benz
Benz
Benz
Benz
om/cataloa/Droduct/aldric
h/s5006?lana=en&reaion
=US
Titanium tetrachloride
136
httD://www.siamaaldrich.c
G
TITATETRA
G
Benz
Benz
Benz
Benz
om/cataloa/Droduct/aldric
h/697079?lana=en&reaio
n=US
2,4-Toluene diisocyanate
251
NIH
G
TOL_DIIS
G
Benz
Benz
Benz
Benz
Toluene-2,4-Diamine
292
CDC
G/P
TOLDIAM24
G/P
PAH
PAH
PAH
PAH
Toluene
111
CDC
G
TOLUENE
G
Benz
Benz
Benz
Benz
Toxaphene
D
CDC
NA
TOXAPHENE
NA
EF=1
EF=1
EF=1
EF=1
2,4,5-Trichlorophenol
247
NIH
G
TRCLPHNL245
G
Benz
Benz
Benz
Benz
AirToxScreen 2020 Documentation E-34
-------
Walue
(°C)a
Boiling Point
Source13
AirToxScreen
Matching Chemical
Modeled in HAPEMd
Phase0 PP NNP MM-ORMM-NR
2,4,6-Trichlorophenol
246
NIH
G
TRCLPHNL246
G
Benz
Benz
Benz
Benz
1,2,4-Trichlorobenzene
213
CDC
G
TRICBZ124
G
Benz
Benz
Benz
Benz
1,1,2-Trichloroethane
114
CDC
G
TRICLA112
G
Benz
Benz
Benz
Benz
Triethylamine
89
CDC
G
TRIETHLAMN
G
Benz
Benz
Benz
Benz
Trifluralin
140
httD://www.sDeclab.com/c
G
TRIFLURALIN
G
Benz
Benz
Benz
Benz
omDound/c1582098.htm
2,2,4-Trimethylpentane
400-480
Source: Adapted from the "Classification of Inorganic Pollutants" table at EPA's
Volatile Organic Compound web page (available as of 2 February 2023 at
https://www.epa. aov/indoor-air-aualitv-iaa/technical-overview-volatile-oraanic-
compounds). as adapted from: World Health Organization, 1989. "Indoor air
quality: organic pollutants." Report on a WHO Meeting, Berlin, 23-27 August
1987. Euro Reports and Studies 111. Copenhagen, World Health
Organization Regional Office for Europe.
AirToxScreen 2020 Documentation
E-35
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Table E-3. HAPs Modeled in HAPEM8 for AirToxScreen
HAPEM8
Modeled in HAPEM
3b
AirToxScreen HAP
HAP Phase3
P
NP
M-OR
M-NR
Benzene
G
y
y
y
y
1,3-butadiene
G
y
y
y
y
Coke Oven Emissions
G/P
y
PAH, Total
(polycyclic aromatic hydrocarbons; aggregate mass
of unspecified congeners)
G/P
y
y
y
y
Chromium (VI)
(compounds of hexavalent chromium)
P
y
y
DPM
(diesel particulate matter)
P
y
y
y
Nickel
P
y
y
Note: For chromium (VI), DPM, and nickel, EPA modeled air concentrations corresponding
to emissions from all four source types, but per EPA direction we only generated
exposure concentrations for the subset of source types shown in the table. See
discussion at the bottom of Section Error! Reference source not found..
a G=gaseous; G/P=gaseous or particulate depending on conditions; P=particulate.
b P =point; NP=non-point; M-OR=mobile on-road; M-NR=mobile non-road.
Table E-4. Truncations Applied to Exposure Factors for AirToxScreen
Source Type
HAP
Point
Non-point
Mobile On-road
Mobile Non-road
Total (Aggregate of all
Source Types)
Benzene
1.00
0.89
1.09
0.89
0.97
1,3-butadiene
0.99
0.92
1.18
0.92
1.03
Coke oven emissions
0.87
NA
NA
NA
0.87
PAH, total
0.70
0.66
0.83
0.66
0.72
Chromium (VI)
0.60
0.94
NA
NA
0.45
Diesel PM
NA
1.27
0.60
0.48
0.53
Nickel
NA
NA
0.56
0.48
0.52
AirToxScreen 2020 Documentation E-36
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United States
Office of Air Quality Planning and
Publication No. EPA-452/B-
Environmental Protection
Standards
24-001
Agency
Health and Environmental Impacts
May 2024
Division
Research Triangle Park, NC
------- |